ANALYSIS OF SOCIAL AND ECONOMIC DISPARITIES IN
CROSS-BORDER AREA
ROMANIA – REPUBLIC OF MOLDOVA
Burlacu (Nazare) Luminita
“Al.I. Cuza” University
Iasi, Romania
lumina_strategii@yahoo.com
Abstract
The main reason of this article is to realize a statistic analysis of regional disparities having
in mind the indicators which reflect the existing socio – economical situation, in the crossborder area Romania – Republic of Moldova. Data which was used in the analysis has been
obtained from the National Romanian Institute of Statistics and National Office of Statistics
from the Republic of Moldova for the year 2008. For the statistical indicators, analysis was
used "Sigma 3 Rule". In this matter, regarding the correlations that exist between the
analyzed variables, we obtained a group of Romanian counties and districts from the
Republic of Moldova. Identifying and quantifying the existing differences between the
Romanian counties Botosani, Iasi, Vaslui and Galati in Romania and all the districts in the
Republic of Moldova may contribute to the set up of a specific strategy for the development of
the cross-border area Romania – Republic of Moldova.
Keywords
regional disparities, cross-border area, 3 Sigma Rule
1.
INTRODUCTION
The integration of Romania in the European Union implied the alignment of our
country to the politics of economic and social development applied to the other
member states of the union. This fact also determined the change or the economic
and diplomatic relations between Romania, member state of the EU and the
Republic of Moldova, as a country outside the community area. At the same time,
EU has imposed on Romania a set of supplementary measures of border security,
having as a consequence a reduction of traffic on the border with the Republic of
Moldova. Although Romania and the Republic of Moldova share some of the
history, official language and culture, in the economic field there are major
differences, especially in the rhythm of economic development. In order to set up
some strategies to reduce the existent differences between territorial administrative
units in the cross-border region Romania – Republic of Moldova, as a requirement
of the proper development, several data describing the present situation are
necessary. The present case study start from the hypothesis that between the
counties of Botosani, Iasi, Vaslui and Galati in Romania and all the districts in the
Republic of Moldova, comprised in the cross-border area Romania – Republic of
Moldova, there are significant differences from the point of view of the level of
economic and social development. The synthetic expression of the level of economic
and social development will be made by two latent variables (two main
components): The economic development of the cross-border area Romania –
Republic of Moldova and The social development of the cross-border area Romania
– Republic of Moldova. In this case we apply the method of Principal Component
Analysis, having in view a set of indications, whose values were registered in
official statistics: the National Institute of Statistics – the Directions of the Counties
of: Botosani, Iasi, Vaslui and Galati, as well as the National Office of Statistics of
the Republic of Moldova.
Based on the two main components extracted by the Principal Component Analysis
method, we evaluate the existent disparities in the cross-border area Romania –
Republic of Moldova, on the level of economic and social development using the
rule "3 Sigma", applied to the dispersion diagram built in the plan of the two main
coordinates. For each county, or district, the factorial scores were assessed,
considering the two factorial axes. Analysing the dispersion diagram marked by rule
"3 Sigma", we may notice that at the level of the cross-border area Romania –
Republic of Moldova there are significant differences, among which disparities and
outliers. Data processing was done by the statistic program SPSS.
2. EXTRACTING THE REPRESENTATIVE FACTORS OF
THE OF ECONOMIC AND SOCIAL DEVELOPMENT
USING THE METHOD OF PRINCIPAL COMPONENT
ANALYSIS
After the processing, with SPSS program, by the method of Principal Component
Analysis of the data representing values of the variables reflecting the economic and
social status of the counties of Botoşani, Iaşi, Vaslui and Galaţi in Romania, and
respectively of the 34 districts (including the municipalities of Bălţi and Chşinău) in
the Republic of Moldova and UTA Găgăuzia, we got results on the statistic
variables, average level and the standard deviation calculated on each variable, the
correlation matrix, the value of the statistic  and KMO statistic, the variance of
the variables, the own values and the variance explained by each factorial axis, the
coordinates of the variables on the factorial axes and graphic representations.
2
2.1 Descriptive Statistics
The average level and the standard deviation calculated for each variable are
presented in table 2 (output of Descriptive statistics), based on the data from the
table 1 (Database for analysis).
Table 1. Database for analysis
The variables included in this analysis were: total number of enterprises (intrepr),
total number of employees (angajati), turnover expressed in million Euro (C.A),
average salary expressed in Euro (sal.med), total number of unemployed (someri),
number of doctors on 10.000 inhabitants (medici) and total population (pop.tot).
Table 2. Descriptive Statistics
Descriptive Statistics
total number of enterprises
total number of employees
turnover expressed in
million Euro
average salary expressed in
Euro
total number of
unemployed
number of doctors on
10.000 inhabitants
total population
Mean
2107,0513
23069,2821
Std. Deviation
5427,51902
56728,39268
Analysis
N
39
39
601,0736
1679,86903
39
130,4054
56,02184
39
1905,5641
4682,80996
39
17,3385
5,75703
39
150799,5385 192914,23468
39
Source: Processed with SPSS program, based on the data from the table 1
In table 2 we can notice that the analysed variable registers standard deviations
significant to the average levels, suggesting that there are major differences between
the values of the variables for different statistic units.
2.2 Statistics test
Statistics
2
2
and statistics KMO
is used to test the independence hypothesis of the variables. "To this
end, we formulate the following hypotheses: H0 – the independence hypothesis (the
matrix of correlations is a unit matrix); H1 – the dependence hypothesis"1, by which
we admit that between the variables there are statistic connections.
Table 3. Calculated value of the statistics test 
KMO and Bartlett's Test
2
Kaiser-Meyer-Olkin Measure of Sampling Adequacy.
,728
Bartlett's Test of
Sphericity
Approx. Chi-Square
474,040
df
21
Sig.
,000
Source: Processed with SPSS program, based on the data from the table 1
Pintilescu, C., Analiză Statistică Multivariată, Editura Universităţii „Al. I. Cuza”
Iaşi, 2007, p. 56.
1
In tabel 3 – output KMO and Bartlett's Test is given by the calculated value of the
statistics test  = 474,040, as well as the value of the probability associated to the
calculated statistics test Sig. = 0,000. For a value of Sig. <0,05 associated to the
2
calculated value of the statistics test  , we reject hypothesis H0 and accept
hypothesis H1. We can guarantee with a 95% probability that between the statistical
variables there are significant connections and that the matrix of the correlations is
not a unit matrix. Also, in the output in table 3 we present the value KMO = 0,728
>0,5, supporting the idea that between the variable there are significant statistical
connections and that Principal Component Analysis can be applied in this case.
2
2.3 Own values
The own values ‫ ג‬k, associated to each factorial axis and variant explained by each
factorial axis (Total Variance Explained output) are presented in Table 4.
Table 4. Own values and variance explained by each factorial axis
Total Variance Explained
Extraction Sums of Squared
Initial Eigenvalues
Loadings
% of
Cumulative
% of
Cumulative
Total Variance
%
Total Variance
%
1
5,090
72,714
72,714 5,090
72,714
72,714
2
1,001
14,302
87,015 1,001
14,302
87,015
3
,607
8,677
95,693
4
,246
3,516
99,209
5
,029
,421
99,630
6
,024
,337
99,967
7
,002
,033
100,000
Extraction Method: Principal Component Analysis.
Source: Processed with SPSS program, based on the data from the table 1
Component
In Total Variance Explained output in table 4, the own values of the matrix of
correlations are:
‫ ג‬1 = 5,090; ‫ ג‬2 = 1,001; ‫ ג‬3 = 0,607; ‫ ג‬4 = 0,246; ‫ ג‬5 = 0,029; ‫ ג‬6 = 0,024; ‫ ג‬7 =
0,002.
The first two factorial axes explain 87,015% of the total variance (table 4, column
Cumulative %). Based on Kaiser criterion, we extract only the components with a
proper value which equals or is greater than 1, meaning that, in our case, the number
of factorial axes to be explained will be given by the two axes.
2.4 The Coordinates of the variables
The coordinates of the variables on the factorial axes show the value of the ratios of
correlations between variables and the respective factorial axis. The values in table 5
Component Matrix show the position of the variables on factorial axes. For example,
the variable total number of enterprises has a positive coordinate (0,864) on the first
factorial axis and a negative coordinate (- 0,466) on the second factorial axis.
Increased values (near the value 1) of the coordinate of the variable on the factorial
axes show that those variables are strongly correlated with the respective factorial
axis.
Table 5 Coordinates of the variables
Rotated Component Matrix(a)
Component
total number of employees
total number of enterprises
turnover expressed in million Euro
number of doctors for 10.000 inhabitants
1
,900
,864
,837
2
,351
,466
,486
,757
,015
total number of unemployed
average salary expressed in Euro
total population
,138
,920
,301
,866
,683
,715
Extraction Method: Principal Component Analysis.
Rotation Method: Varimax with Kaiser Normalization.
a Rotation converged in 3 iterations.
Source: Processed with SPSS program, based on the data from the table 1
The coordinates of the variables on the factorial axes represent the ratios of the
linear equation of the variable connections. For the data in table 5, the first two
factorial axes are new variables defined by the linear combination of the initial
variables in the form of:
C1 = 0,900·employees + 0,864·enterprises + 0,837·C.A + 0,757·doctors
C2 = 0,920·unemployed + 0,866·average salary + 0,715·total population
In accordance with the variables explaining the two factorial axes, the first factorial
axis represents The Economic Development, and the second factorial axis represents
The Social Development.
3. THE RULE OF THE "3 Sigma"
3.1 The rule of the “3 Sigma"
“We have X a random, discontinuous or continuous variable, with the average M(x)
= m and the variance (dispersion) V(X) = σ². According to Cebâşev’s inequality, we
get: with a probability of at least 8/9, the random variable X takes values within the
interval (m − 3σ, m + 3σ). ). [Nenciu, 1986]. The random variable can follow any
type of distribution.”2
3.2 Localization intervals and types of differences/disparities identified in the
cross-border area Romania-Republic of Moldavia
Identifying types of differences/disparities (regarding the economical-social level of
development) between counties and districts that belong to the cross-border area
Romania-Republic of Moldavia, may be facilitated by marking the “3 Sigma” on the
factorial axes, the main components being centered variables. Depending on the
variation intervals in which the statistical units analyzed are being situated, we may
consider different types of cross-border differences/disparities, shown in the table
below:
Table 6. Localization intervals and types of cross-border differences/disparities
Localization intervals in the rule
system
"3 Sigma"
I1 = (-σ, σ)
I2 = (-2σ, σ)U(σ, 2σ)
I3 = (-3σ, -2σ)U(2σ, 3σ)
I4 = (-∞, -3σ)U(3σ, +∞)
Type of cross-border
differences/disparities
Relative similarities (small differences)
Big differences
Disparities (great differences)
Outlayers
The typology of differences/disparities, identified in the plane of the two factorial
axes, marked in standard deviation units through a network of parallel lines,
according to the values of the “3 Sigma” may be explained by correlating different
criteria like: the direction of the factorial axe, the distance between the points (units)
and the origin and the cosine of the angles between the position vectors.
4. IDENTIFYING CROSS-BORDER DIFFERENCES USING
THE RULE OF THE “3 Sigma”
4.1 The localization of counties/districts regarding the factorial axes of
economical-social development, at the level of the year 2008
The graphic representation of statistical units in the plane of factorial axes of Ci1
and Ci2 coordinates (through the Principal Component Analysis method), is given in
Figure 1.
Jaba, E., Evaluarea Statistică a Dezvoltării Economico – Sociale, Editura Junimea,
Iaşi, 2007, p.60
2
Figure 1 - Representation of statistical units on the first two factorial axes in relation
with the level of Economical Development (Factor score 1) and Social Development
(Factor score 2)
Values Ci1 and Ci2 represent factorial scores, meaning the composite value for
every county/district, corresponding to every main component. They are calculated
for every county/district, starting from the matrix of factorial scores coefficients, on
the basis of the equation of regression for main components in which the variables
with specific value are replaced for every county/district.
The square of the distance from the point (ai) to the origin is equal to the sum of the
square values of main components, calculated for the i unit, meaning:
d²( ai, 0) = ∑C²ik, where C ik (in our case k=2) represent the coordinates of points
in the plane of main coordinates that are centered variables, of ‫ ג‬k variance and
uncorrelated between them
By studying Figure 1, we see that some differences and disparities in counties and
districts concerning the level of economical-social development. They are
graphically presented through the distance of each point – county/district to the
origin, as well as through their localization regarding the two factorial axes, defined
as structural elements of economical-social development, identified through the
ACP method: The Economic Development and The Social Development.
The four counties from Romania: Botoşani, Iaşi, Vaslui, Galaţi, as well as Chisinau
and Bălţi districts from The Republic of Moldavia are localized at relatively large
distances from the origin of the axes, in opposite ways of the two axes, with very
different values of the angles’ cosines, these showing major cross-border
differences, with disparities values.
4.2 Building the typological diagram of the counties in the plane of the “3
Sigma”
The graphic representation of counties/districts in relation with the two main
components, extracted through the Principal Component Analysis method, The
Economic Development and The Social Development allows establishing a
location-typology of counties/districts in the cross-border area Romania – Republic
of Moldavia for the year 2008 (figure 2).
The first factorial axis, corresponding to the main component C1, defined by the
variables: employees; employers; C.A; medics, rate counties/districts depending on
their level of economic development, and the second factorial axis, defined by the
variables: unemployed ; medium salaries; total population rate counties/districts
depending on their level of social development.
Figure 2. Typology of counties/districts from the cross-border area Romania Republic of Moldav regarding the level of economical-social level of development
in the year 2008
In the diagram from figure 2 we can notice that the statistical units: Galaţi, Iaşi,
Bălţi, Edineţ and Chişinău, including UTA Găgăuzia have positive values on the
first factorial axis, different from most of the districts in the Republic of Moldova
and Vaslui, Botoşani which have negative values on the first factorial axis. Thus, the
first factorial axis shows two relatively homogenous groups of statistical units
(having positive and respectively negative values on the first factorial axis).
Moreover, the second factorial axis divides statistical units (counties/districts) in two
relatively homogenous groups: that have positive values: the four counties from
Romania: Botosani, Iasi, Vaslui, Galati and that have negative values: all the
districts from The Republic of Moldavia, including UTA Găgăuzia. The biggest
differences are shown between Vaslui and Chişinău, respectively between Chisinău,
and the rest of the districts of the Republic of Moldova.
In the level of the "3 Sigma" we identify 4 intervals, defined previously in figure 2.
The disparities of the 4 counties, and respectively of the districts in the Republic of
Moldova on all 4 intervals show the existence of major interregional disparities.
Also, we can notice different intra-group dispersion degrees.
In order to present the characteristics of the interregional disparities we
simultaneously consider: the direction of the factorial axes, the distance from the
origin and the co-sinus of the angles between the position vectors. We identify 3
distinct groups of counties/districts and two isolated cases. Their positioning shows
the existence of differences between counties and districts that compose the crossborder area of Romania – Republic of Moldavia, as well as the presence of some
outliers, in relation with the considered variables.
4.3 Interpretation of the groups
In order to interpret the groups identified, we take notice of the criteria established
before and the position of the counties regarding the two factorial axes. The
localization of the counties regarding the level of similarity according to economic
development and social development for year 2008 reveals a high degree of
dispersion of the counties/districts from the bordering area Romania – Republic of
Moldavia. We consider the first group the one composed from the majority of
districts in The Republic of Moldavia, including UTA Găgăuzia, situated within the
interval I1 = (-σ, σ), of 2 σ length, connected around the origin of the factorial axes
(center of gravity). This group is defined by similarities, meaning small differences
regarding the level of economical-social development, yet we see a high level of
dispersion within the group, where the districts like: Edineţ and UTA Găgăuzia
record positive factorial scores on the first factorial axis that defines secial
development (number of unemployed people, average salary, total population). The
first group also consists of the districts: Cimişlia, Leova, Ungheni, Străşeni, which
record negative factorial scores for the first and the second factorial axis as well.
The second group, composed of Bălţi district, located within the interval of I2 = (2σ, σ)U(σ, 2σ) shows an average difference of this district compared to the majority
of districts within The Republic of Moldavia and has a positive factorial score for
the first factorial axis (economic development) and a negative factorial score for the
second factorial axe.
Within the 3rd localization interval I3 = (-3σ, -2σ)U(2σ, 3σ), the following counties
are included: Iaşi, Galaţi and Botoşani, between which there are big differences with
the aspect of disparities in the plane of one or both coordinate axes. In this group,
Iaşi and Galaţi are counties with a positive degree of development from economical
and social point of views and Botosani county has a positive degree of economical
development and a negative degree of social development.
The 4th interval of localization I4 = (-∞, -3σ)U(3σ, +∞) includes Vaslui county and
Chisinau district, which record very big differences, outliers, with values greater
than 3 σ, regarding the origin of the factorial axes. Vaslui county and Chisinau
district are atypical, because they record great differences between the level of
economical and social development.
4.
CONCLUSIONS
Compared with other methods of classification (for example hierarchical
classification), classification on the basis of the “3 Sigma” rule has some
advantages: reduces the number of variables and offers a numerical measure –
standard units of deviation, useful for identifying types of differences between the
statistical units analyzed. In this paper, the “3 Sigma” rule was applied in the plane
of factorial axes resulted from the Principal Component Analysis method over some
indicators that reveal the economical and social development of counties and
districts that belong to the cross-border area of Romania – Republic of Moldavia for
the year 2008.
By applying the Principal Component Analysis method, two latent variables were
extracted: The Economical Development and The Social Development, which
concentrate the initial variable ensemble. On the basis of factorial scores calculated
for every county/district, a diagram of dispersion for studied statistical units was
created. Four localization intervals were identified, in which were included the
counties and districts that belong to the cross-border area of Romania – Republic of
Moldavia, this allowing us to affirm that within the analyzed area we have
similarities and differences that are greater between the territorial administrative
units, from the points of view of economical and social development. In the purpose
of minimizing the differences that presently exist between the districts and counties
from the cross-border area of Romania – Republic of Moldavia, it is necessary
strategy of local development that would mobilize the local communities towards
harnessing the local resources and drawing other external resources.
References
[1] Bonciu, F., Investiţiile străine directe şi noua ordine economică mondială,
Editura Universitară, Bucureşti, 2009;
[2] Jaba, E., Evaluarea Statistică a Dezvoltării Economico – Sociale, Editura
Junimea, Iaşi, 2007;
[3] Jaba, E., Statistica, Ediţia a treia, Editura Economică, Bucureşti, 2002;
[4] Jaba, E., Grama, A, Analiza statistică cu SPSS sub Window, Editura Polirom,
Iaşi, 2004;
[5] Pintilescu, C., Analiză Statistică Multivariată, Editura Universităţii „Al. I. Cuza”
Iaşi, 2007;
[6] Roscovan, M., Jurnal de tranzitie, Tipografia Reclama, Chişinău, 2007.
Web pages:
http://www.iasi.insse.ro/main.php
http://www.vaslui.insse.ro/main.php
http://www.botosani.insse.ro/main.php
http://www.galati.insse.ro/main.php
http://www.statistica.md/