Scientific research results in pandemic conditions (COVID-19)
36
4. Establishment of a Sharia Council at the center of Islamic civilization.
In contrast to the norm of profit-interest rate in the Islamic economy, a
more efficient and rational mechanism of resource allocation is a system
that counteracts many negative trends of the modern economy
(monopolization, the sharp gap between rich and poor, financial crises, etc.).
It also deserves serious study.
References:
1.
Ahmad, M. Abu-Alkheil (2012) "Ethical Banking and Finance: A
Theoretical and Empirical Framework for the Cross-Country and Inter-bank
Analysis of Efficiency, Productivity, and Financial Performance" Ltd.
Germany
2.
Bekkin R.I., 2009, Islamic economic model and modernity. Marjani
Publishers, Moscow
3.
Citibank annual report 2018, Citi Research, Reuters, SNL Research
4.
Chapra M.U What is Islamic Economics?-Jeddah 2001, p-33
5.
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8.
Hurriyat Khudoykulova, Chief expert of the State Committee of the
Republic of Uzbekistan on statistics Freelance Researcher (PhD) at
Tashkent State University of Economics
DISTRIBUTION OF DISTRICTS AND CITIES OF THE REPUBLIC OF
UZBEKISTAN BY LEVEL OF DEVELOPMENT
H. Khudoykulova
Abstract:
The socio-economic development of the districts and cities
included in primary administrative-territorial units of the Republic of
Uzbekistan was evaluated on the basis of the results of the evaluation using
the composite index based on the optimal combination of socio-economic
indicators selected by the method of the principal component analysis
(PCA)and the second administrative territorial units were distributed
according to the levels ofsocio-economic development.
Keywords:socio-economic development; Principal component analysis
(PCA), Composite Index.
JEL classification: C01, R12, R58
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37
INTRODUCTION
To ensure the effectiveness of regional policy and the rapid socio-
economic development of the regions, first of all, it is necessary to assess the
current socio-economic situation in the regions.
Targeted use of political and financial means provided by the state for
socio-economic development of the regions and in order to achieve their
goals, they must study the situation in the region in detail, identify resources
and reserves in the region, and, most importantly, have reliable information
about the development opportunities and prospects of the region. Assessing
the development potential of the regions means identifying a system of
appropriate indicators and assessing the current state of socio-economic
development in the region using the identified indicators. This task requires
qualified statistical analysis.
The chief present-day problem of socio-economic development, in
geographical-economic terms, is growing spatial inequality when viewed in
a regional approach. In the recent years regional disparities have become of
great interest to geographical and economic sciences, as manifested by a
fast-growing number of publications on the subject.
MATERIALS AND METHOD OF ANALYSIS
200 districts and cities of 14 primary administrative-territorial units of
the Republic of Uzbekistan (Republic of Karakalpakstan, 12 regions,
Tashkent city) were analyzed using Principle Component Analysis (PCA)
based on eleven economic and six social indicators, extracted from the
database of the State Committee of the Republic of Uzbekistan on Statistics
for 2017-2018.
Principal component analysis (PCA) is a mathematical procedure that
uses an orthogonal transformation to convert a set of observations of
possibly correlated variables into a set of values of linearly uncorrelated
variables called principal components (Davis, 1986)
The following formula was used to calculate the composition index
according to the main components and component index value:
Composite index = principal components variance contribution rate*
principal component coefficients.
Through the cumulative normal distribution, the composite indices are
normalised by values from 0 to 1 each.
Based on the Jenks method, inter-district differences were divided into
four categories of regions – low developed, medium developed, high
developed, and very high developed regions.
The level of development of Uzbekistan in terms of socio-economic
development was assessed using the composite index method. A high value
of the composite index represents a high level of development and a low
Scientific research results in pandemic conditions (COVID-19)
38
value represents a low level of development. The calculation process was
performed in a special statistical analysis program SPSS.
RESULTS AND ANALYSIS
In order to verify the adequacy of data for a factorial analysis (specially
PCA), the Barlett’s test of sphericity (to test the null hypothesis that the
variables in the correlation matrix of the populationare uncorrelated), and
the indicator MSA (MeAndijan State University named after Z.M.Boburre of
Sampling Adequacy) of Kaiser-Meyer-Olkin (to evaluate in which degree
each variable may be predicted by all the other variables) were used. The
results obtained by data processing with SPSS are presented in Table 1.
The significance level associated to Barlett’s test of sphericity,
Sig 0.000,
is smaller than 0.05(conventional value), which means the null hypothesis
of variables’ uncorrelation is rejected.
Therefore one can conclude that the considered variables are adequate
for a PCA. The value of the indicator MSA of KMO (0.73), greater than 0.5,
also indicate the suitability of the considered data for factor analysis
(Richarme, 2001).
Table 1. KMO and Bartlett's Test
Kaiser-Meyer-Olkin MeAndijan State University
named after Z.M.Boburre of Sampling Adequacy
0,73
Barlett’s test of sphericity
Approx. Chi-Square
1824,2
Df.
136
Sig.
0,0
Source: Author’s calculations
Note that, since Tahskent city-the capital of Uzbekistan presents very
different characteristics of economic development compared to other
administrative-territorial units, it requires an individual analysis of these
features, and it is not included in further analysis.
Table 2 represented the varimax rotated factor structure and majority
of the variables under study have been appropriately focused on the
structure exposes by this factor matrix. Five factors meet not only the
eigenvalue criterion, but also the variance proportion criterion. In social
sciences, the lowest limit of acceptability is 60 percent of variance accounted
by obtained factors (Hair, Anderson and Tahtam,1987). This solution
accounts for 71,6 percent of total variance.
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39
Table 2. Rotated Component Matrix
Input Variables
Factors
1
2
3
4
5
Industrial products per capita
(thousand soums)
0,091
0,779
-0,02
0,158
0,354
Agricultural, forestry and fishery
products per capita (thousand
soums)
0,115
0,221
0,072
0,92
0,02
Investments in fixed assets per
capita (thousand soums)
-0,009
0,031
-0,058 0,953
0,03
Foreign investment and loans
per capita (thousand soums)
0,272
0,435
0,078
0,083
0,709
Construction works per capita
(thousand soums)
0,006
-0,06
0,103
0,016
0,821
Housing construction, sq.m. (per
1000 people)
0,683
0,509
0,042
0,085
0,064
Retail
trade
per
capita
(thousand soums)
0,731
0,318
0,044
0,079
0,044
Services per capita (thousand
soums)
-0,209
-0,275
0,627
-0,066 0,336
Exports per capita (in US
dollars)
0,036
0,736
-0,074 0,023
-
0,182
Total number of operating
enterprises and organizations,
units
0,865
-0,073
-0,357 -0,04
-
0,041
Total number of operating small
enterprises and micro-firms,
units
0,831
-0,132
-0,352 -0,043 -0,04
Enrollment of the population
aged
3-6
in
preschool
education,%
0,448
0,619
0,181
0,164
0,026
Enrollment of students in one
shift in secondary schools,%
-0,024
-0,107
0,869
-0,044
-
0,012
Number of hospital beds (per
10,000 people)
0,817
0,134
0,014
0,032
0,11
Natural population growth rate,
person
0,218
-0,358
-0,699 -0,103
-
0,075
Unemployment rate,%
-0,477
-0,425
0,04
-0,219
-
0,155
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40
Number of people leaving the
country
for
permanent
residence, person
0,404
0,276
0,389
0,119
-
0,339
Eigenvalue
5,01
2,813
1,701
1,404
1,243
Proportion
of
Accounted
Variance
29,469 16,548 10,008 8,262
7,311
Source: Author’s calculations
A composition index was generated based on the optimal combination
of indicators selected by PCA of 189 districts and cities(excluded 11 districts
of Tashkent city). Using the natural intervals (Jenks) method, individual
economic development, social development, and socio-economic
development were divided into four development categories(Table 3).
Table 3. Distribution of districts and cities of the Republic of Uzbekistan
by level of development (excluding districts of Tashkent city)
Development
categories
Development type
Economic
Social
Socio-economic
Low
117
97
134
Medium
50
63
32
High
15
16
13
Very high
7
13
10
Source: Author’s calculations
From the results of the analysis it can be concluded that out of 189
districts and cities of the Republic of Uzbekistan,according to the level of
economic development, 7 regions are the most developed, 15 regions are
highly developed, 50 regions are moderately developed and 117 regions are
underdeveloped;
According to the level of social development, 13 regions are the most
developed, 16 regions are highly developed, 63 regions are moderately
developed and 97 regions are underdeveloped;
According to the level of socio-economic development, 10 regions are
the most developed, 13 regions are highly developed, 32 regions are
moderately developed and 134 regions are underdeveloped.
CONCLUSION
Socio-economic development is a multidimensional process that cannot
be fully assessed by a single indicator. This requires the construction of a
composite index of socio-economic development based on an optimal
combination of different development indicators. The analysis used PCA
method of multidimensional statistical analysis to analyze the socio-
economic development of the regions as a whole. This compositional index
Scientific research results in pandemic conditions (COVID-19)
41
of socio-economic development can serve as an information system that
monitors the pace of development of regions and the potential for the use of
funds spent on the development of the region. In order for development
potential to be continuously analyzed and compared in the decision-making
process, these indices should be calculated on a regular basis over a set
period of time.
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