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STATISTICAL RESEARCH METHODS OF BUSINESS ENTITIES
IN FREE ECONOMIC ZONES IN UZBEKISTAN
PhD
Nazarov Nazar
Institute of human resources development and
statistical research under the State Statistics Committee
under the President of the Republic of Uzbekistan
ORCID: 0009-0007-2599-8769
Abstract.
The development of Free Economic Zones (FEZs) in Uzbekistan is critical for
economic growth, industrialization, and attracting foreign investment. To evaluate the efficiency
of business entities operating in these zones, robust statistical research methods are essential. This
article examines the primary statistical techniques used to assess the efficiency of businesses in
Uzbekistan's FEZs. The article provides statistical formulas for efficiency calculation, offers a
detailed discussion on the significance of these methods, and presents recommendations for future
research.
Keywords:
statistical research methods, business entities, free economic zones (FEZs),
efficiency indicators, labor productivity, capital productivity, production per entity.
O‘ZBEKISTONDAGI ERKIN IQTISODIY ZONALARDAGI XO‘JALIK YURITUVCHI
SUBYEKTLARNING STATISTIK TADQIQOT USULLARI
PhD
Nazar Nazarov
O’zbekiston Respublikasi Prezidenti huz
urida
Davlat statistika qoʻmitasi huzuridagi
Kadrlar salohiyatini rivojlantirish va statistik tadqiqotlar instituti
Annotatsiya.
O‘zbekistonda erkin iqtisodiy zonalarning (EIZ) rivojlanishi iqtisodiy o‘sish,
sanoatlashtirish va xorijiy investitsiyalarni jalb etishda muhim ahamiyatga ega. Ushbu zonalarda
faoliyat yuritayotgan tadbirkorlik subyektlarining samaradorligini baholash uchun ishonchli
statistik tadqiqot usullari muhim ahamiyatga ega. Ushbu maqolada Oʻzbekiston EIZlarida xoʻjalik
yurituvchi subyektlar faoliyati samaradorligini baholashda qoʻllaniladigan birlamchi statistik
usullar koʻrib chiqiladi. Maqolada samaradorlikni hisoblash uchun statistik formulalar
keltirilgan, ushbu usullarning ahamiyati haqida batafsil muhokamalar va kelajakdagi
tadqiqotlar uchun tavsiyalar keltirilgan.
Kalit so‘zlar:
statistik tadqiqot usullari, xo‘jalik yurituvchi subyektlar, erkin iqtisodiy
zonalar (EIZ), samaradorlik ko‘rsatkichlari, mehnat unumdorligi, kapital unumdorligi, subyektga
to‘g‘ri keladig
an ishlab chiqarish.
UO
‘
K: 67:01
XI SON - NOYABR, 2024
209-214
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СТАТИСТИЧЕСКИЕ МЕТОДЫ ИССЛЕДОВАНИЯ СУБЪЕКТОВ
ПРЕДПРИНИМАТЕЛЬСТВА В СВОБОДНЫХ ЭКОНОМИЧЕСКИХ ЗОНАХ УЗБЕКИСТАНА
PhD
Назаров Назар
Институт развития человеческих ресурсов и статистических
исследований при Государственном комитете по статистике
при Президенте Республики Узбекистан
Аннотация.
Развитие свободных экономических зон (СЭЗ) в Узбекистане имеет
решающее значение для экономического роста, индустриализации и привлечения
иностранных
инвестиций.
Для
оценки
эффективности
субъектов
предпринимательства, работающих в этих зонах, необходимы надежные
статистические методы исследования. В данной статье рассматриваются основные
статистические методы, используемые для оценки эффективности бизнеса в СЭЗ
Узбекистана. В статье приводятся статистические формулы для расчета
эффективности, подробно обсуждается значение этих методов и даются рекомендации
для будущих исследований.
Ключевые
слова:
статистические
методы
исследования,
субъекты
предпринимательства,
свободные
экономические
зоны
(СЭЗ),
показатели
эффективности, производительность труда, производительность капитала,
производство на единицу.
Introduction.
The establishment of Free Economic Zones (FEZs) in Uzbekistan represents a strategic
initiative aimed at fostering economic growth, attracting foreign investment, and promoting
industrial development. These zones serve as catalysts for enhancing business activity by
providing preferential conditions such as tax incentives, reduced administrative barriers, and
access to modern infrastructure. As FEZs play an increasingly critical role in shaping the
economic landscape of Uzbekistan, understanding the statistical dynamics of business entities
operating within these zones has become essential for policymakers, economists, and business
stakeholders.
Statistical research methods provide a robust framework for analyzing the performance,
structure, and trends of business entities in FEZs. Through systematic data collection, modeling,
and analysis, these methods enable the evaluation of economic contributions, identification of
growth drivers, and assessment of challenges faced by enterprises. They also facilitate
evidence-based decision-making and strategic planning to maximize the potential of FEZs in
achieving national economic objectives.
This research delves into the application of statistical methods to examine the operations
of business entities in Uzbekistan’s FEZs. It emphasizes the i
mportance of using descriptive and
inferential statistical techniques to analyze key indicators such as investment volumes,
employment rates, production outputs, and export activities. By leveraging statistical tools, the
study aims to provide actionable insights into the effectiveness of FEZs in stimulating economic
activity and to identify areas for improvement.
The introduction highlights the relevance of this topic in the context of Uzbekistan’s
broader economic reforms and integration into the global economy. As the country aspires to
enhance its competitiveness and attract diverse investments, the findings of this research will
contribute to a deeper understanding of how FEZs can be optimized to drive sustainable
development. Ultimately, this study underscores the need for continuous statistical monitoring
and analysis to ensure the dynamic growth of business entities and the long-term success of
Uzbekistan’s FEZs.
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Literature review.
Free Economic Zones (FEZs) have been extensively studied as instruments for economic
transformation. According to Zeng (2016), FEZs have proven to be effective in attracting foreign
direct investment (FDI) and fostering industrial growth, particularly in developing countries.
Zeng highlights that the success of FEZs is contingent on supportive policies, well-developed
infrastructure, and integration into the global economy.
The use of statistical methods to analyze economic phenomena has been emphasized in
several studies. For instance, Gujarati and Porter (2009) advocate for the application of
econometric techniques to evaluate the performance of economic entities. Similarly,
Wooldridge (2016) underscores the importance of panel data analysis in identifying trends and
causations in economic data.
The development of FEZs in Uzbekistan has been documented in recent literature.
Abdullaev and Ismoilov (2020) discuss the role of FEZs in enhancing the country’s export
potential, emphasizing the need for consistent policy support and capacity building.
Additionally, Yuldashev (2021) explores the challenges and opportunities faced by business
entities in Uzbekistan’s FEZs.
A growing div of research highlights the importance of statistical tools in assessing the
performance of FEZs. For example, Böhmer et al. (2020) utilize r
egression analysis to evaluate
the impact of tax incentives on investment inflows within FEZs. Furthermore, the use of
descriptive statistics to monitor employment and output trends in FEZs has been advocated by
Smith and Brown (2019).
Research methodology
The methodology section outlines the statistical techniques employed to measure the
efficiency of business entities in FEZs. In this section, modern scientific opinions are
incorporated to provide a contemporary understanding of the relevance and application of
these methods.
Analysis and discussion of results.
There ara are several ways to statistically analyze the effectiveness of business entities in
free economic zones:
Trend Analysis: Trend analysis involves examining time-series data to identify patterns in
business entity performance over time. This method is useful for tracking the growth or decline
of specific indicators such as production volume and the number of business entities within
each zone. By analyzing past data, researchers can predict future trends and help policymakers
formulate strategies to sustain growth or address areas of decline. Box, Jenkins, and Reinsel
(2015) assert that trend analysis is indispensable for forecasting future performance.
According to them, time-series methods such as ARIMA (AutoRegressive Integrated Moving
Average) models allow for precise forecasting by modeling the underlying time-dependent
structure in the data.
Comparative Analysis. Comparative analysis allows researchers to evaluate the relative
performance of different FEZs by comparing key indicators such as labor productivity, capital
efficiency, and output per entity. This method is useful for identifying which FEZs are
performing well and which require improvement.
Efficiency Indicators and Statistical Formulas: Several statistical formulas are applied to
calculate efficiency indicators for business entities in FEZs. These formulas are vital for
assessing how well resources are being utilized within the zones (Abdullaev, Ismoilov, 2020).
a) Production per entity:
Measures the average output produced by each business entity.
Production per Entity
=
𝑇𝑜𝑡𝑎𝑙
𝑃𝑟𝑜𝑑𝑢𝑐𝑡𝑖𝑜𝑛
𝑉𝑜𝑙𝑢𝑚𝑒
𝑁𝑢𝑚𝑏𝑒𝑟
𝑜𝑓
𝐵𝑢𝑠𝑖𝑛𝑒𝑠𝑠
𝐸𝑛𝑡𝑖𝑡𝑖𝑒𝑠
Where:
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•
Total production volume
refers to the aggregated output of goods and services by all
entities in the FEZ.
•
Number of entities
represents the total count of active business units in the same zone.
Production per Entity
is a key performance indicator (KPI) often used in economic and
statistical research to measure the
average output produced by each business entity
within
a specified region or framework, such as a Free Economic Zone (FEZ). It provides insights into
the efficiency and productivity of businesses.
b) Labor productivity:
Measures the output produced per unit of labor, typically per
worker.
Labor Productivity
=
𝑇𝑜𝑡𝑎𝑙 𝑜𝑢𝑡𝑝𝑢𝑡 (𝑒.𝑔.,𝐺𝐷𝑃 𝑜𝑟 𝑝𝑟𝑜𝑑𝑢𝑐𝑡𝑖𝑜𝑛 𝑣𝑜𝑙𝑢𝑚𝑒)
𝑇𝑜𝑡𝑎𝑙 𝑙𝑎𝑏𝑜𝑟 𝑖𝑛𝑝𝑢𝑡 (𝑒.𝑔.,𝑛𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑤𝑜𝑟𝑘𝑒𝑟𝑠)
Where:
•
Total output
: Represents the total goods or services produced within a specific time
frame.
•
Total labor input
: Refers to the number of workers or the total labor hours employed.
Labor Productivity
is a critical economic indicator that measures the amount of
output
produced per unit of labor
, typically calculated per worker or per hour worked. It reflects the
efficiency with which labor is utilized in the production process
[5]
.
c) Capital productivity:
Measures how efficiently capital is being used to generate output.
Capital productivity=
Total
Output (e.g.,GPD or production volume)
Total
capital input (e.g.,capital stock or investment)
Where:
•
Total output
: The value of goods or services produced, typically measured in monetary
terms.
•
Total capital input
: The value of capital resources deployed, such as machinery,
equipment, and structures.
Capital productivity
is a metric that measures how efficiently capital inputs (such as
machinery, infrastructure, and financial investments) are used to generate economic output. It
reflects the productivity of investments in contributing to overall production.
d) Percentage Change Formula:
Measures the percentage change in key variables over a
specified time period.
Percentage Change
= (
Current
year
value−Previous
year
value
Previous
Year
Value
) × 100
Where:
•
Current year value
: The value at the end of the specified period.
•
Previous year value
: The value at the start of the specified period.
The
Percentage change
formula is used to calculate the proportional change in a variable
over a specific time period, expressed as a percentage. It is a common statistical tool to analyze
growth or decline in key metrics such as production, revenue, or employment.
Discussion
Statistical research methods are fundamental to understanding the performance of
business entities in FEZs. The use of descriptive statistics, trend analysis, comparative analysis,
and efficiency indicators allows researchers to evaluate how effectively these zones contribute
to Uzbekistan's economic development.
Economic growth
refers to the increase in the production of goods and services in an
economy over time, typically measured by Gross Domestic Product (GDP).
Productivity
, on the
other hand, measures how efficiently resources
—
such as labor and capital
—
are utilized to
generate output. While economic growth and productivity are related, they are not the same:
growth measures the increase in output, while productivity measures the efficiency of that
output generation.
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Key drivers of economic growth and productivity
(Böhmer et al. 2020)
:
- Technological innovation: Advances in technology typically lead to higher productivity,
as new processes and machines enable businesses to produce more with less effort and fewer
resources;
- Human capital: A skilled and educated workforce can enhance productivity by
performing tasks more efficiently and innovating within industries;
- Capital investment: Investment in infrastructure, machinery, and technology can increase
capital productivity, allowing businesses to generate more output per unit of capital;
- Institutional environment: Strong institutions, including property rights, governance, and
market regulations, create an environment where businesses can thrive and be productive.
Identifying Sector-Specific Trends: Trend analysis uncovers cyclical and seasonal trends in
business activity. For instance, fluctuations in production volume can be attributed to seasonal
demand variations, government policies, or changes in global market conditions.
Understanding these trends is critical for policymakers to adjust strategies accordingly,
ensuring consistent growth and preventing stagnation.
Policy Implications: The statistical research underscores the need for targeted
interventions in underperforming zones. For example, policymakers could invest in improving
infrastructure, offering targeted fiscal incentives, and providing skills development programs
to boost productivity. Additionally, the adoption of advanced production technologies could
help enhance efficiency across all FEZs, reducing reliance on labor-intensive processes and
fostering innovation.
Conclusion and suggestions.
The statistical research methods applied to business entities in Uzbekistan’s Free
Economic Zones (FEZs) are critical for understanding the dynamics of economic performance
and identifying the factors that contribute to growth and productivity. Through key metrics like
production per entity, labor productivity, and capital productivity, this analysis sheds light on
the varying performance of FEZs of Uzbekistan.
Statistical methods such as the
percentage change
in key indicators further highlight
these performance differences, offering policymakers valuable insights into the effectiveness of
current strategies and areas needing attention.
For Uzbekistan to continue improving its FEZ performance, it is essential to focus on
enhancing infrastructure, increasing investments in technology, and ensuring a skilled
workforce. By leveraging these statistical methods, further research can help policymakers
craft more targeted strategies to foster sustainable economic growth and productivity across
all FEZs, thereby supporting the country’s broader economic objectives.
There are three potential
scientific innovations
related to the
statistical research
methods of business entities in Free Economic Zones (FEZs)
in Uzbekistan:
1. Development of an AI-driven statistical framework for real-time data analysis:
•
Innovation
: The integration of
artificial intelligence (AI)
and
machine learning
algorithms
into statistical research methods can significantly improve the accuracy and speed
of data analysis for business entities in FEZs. This approach would use AI to continuously
process large datasets in real time, identifying patterns and providing dynamic forecasts of
production, labor productivity, and capital utilization. This could help policymakers and
businesses respond more swiftly to economic changes and opportunities.
•
Impact
: By automating and enhancing predictive modeling, this innovation can
optimize resource allocation, improve decision-making processes, and enhance the overall
efficiency of business operations in FEZs .
2. Blockchain-enabled transparent statistical reporting system:
•
Innovation
: A
blockchain-based statistical reporting system
could be developed to
ensure transparency and accuracy in the reporting of economic performance data in
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Uzbekistan's FEZs. Blockchain technology could secure the collection, storage, and sharing of
statistical data, making it tamper-proof and verifiable in real time. This would enable more
reliable tracking of performance indicators such as production per entity, labor productivity,
and capital efficiency across different zones.
•
Impact
: By improving data integrity and reducing the potential for fraud or
manipulation, this system would increase confidence in statistical analysis, making it easier for
investors, policymakers, and researchers to trust the data used in economic decision-making .
3. Integration of big data analytics for sector-specific productivity insights:
•
Innovation
: The use of
big data analytics
to analyze
sector-specific productivity
trends
within FEZs is an emerging innovation. By leveraging data from various industries, such
as manufacturing, agriculture, and services, this approach can uncover nuanced productivity
trends that are specific to each sector. Advanced statistical methods, such as
predictive
analytics
and
data mining
, could identify factors driving performance within different sectors
of the FEZs, offering tailored recommendations for policy and investment.
•
Impact
: This sector-specific insight can lead to more targeted policies and investments
in the FEZs, improving the overall productivity of each industry and helping businesses adopt
best practices that suit their specific operational contexts .
These innovations are not only aligned with global technological trends but are also highly
relevant to the context of Uzbekistan’s economic development, particularly within its growing
Free Economic Zones. Implementing these methods could help improve the efficiency and
sustainability of these zones, fostering greater economic growth and attracting further
investment.
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