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EXPLORING THE NEXUS BETWEEN

URBAN DEVELOPMENT AND MIGRATION:

A CASE STUDY IN THE REPUBLIC OF SERBIA

XLV Annual Scientific Conference:

Cities and Regions in the Digital Era and the Challenge of the Transition Towards the Circular Economy

Turin (Italy), September 4-6, 2024

Authors: Milanović Zbiljić Sandra¹*, Stanković J. Jelena¹, Marjanović Ivana¹

¹Faculty of Economics, University of Niš, Trg kralja Aleksandra Ujedinitelja 11, 18000 Niš, Srbija

*sandra.milanovic@eknfak.ni.ac.rs

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Presentation content:

Intoduction
Urbanization and Migration: A Global Perspective
Urban Development and Migration in the Republic of Serbia
Data overview
Methodology and model development
Results
Conclusion, policy implications and further research recommendations

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Introduction

The process of urban development often serves as both a magnet and a catalyst for migration.
Rapid urbanization establishes appealing urban hubs that attract migrants seeking employment, education, and improved living standards.
Internal migration, particularly from rural to urban areas, is widely recognized as the primary driver of urbanization (Menashe‐Oren& Bocquier, 2021).
Therefore, objective of this study is twofold:

(a) to develop the urban development index for the Republic of Serbia; and
(b) to investigate the correlation between levels of urban development and migration patterns to determine whether heightened urban development in the Republic of Serbia corresponds to an increased population influx.
Data from official sources are analyzed to determine the urban development index by employing a two-stage multi-criteria approach.

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Urbanization and Migration: A Global Perspective (1)

Urban development is a multifaceted and dynamic process that shapes the physical, economic, and social landscapes of cities and metropolitan areas. It involves the expansion and transformation of urban spaces through human activities, which significantly impact the environment (Yigitcanlar & Teriman, 2015) causing infrastructure development, economic growth, and social change.

As cities evolve, they become hubs of activity, attracting businesses, residents, and institutions that drive further growth and development. Therefore they are observed as "the most dramatic manifestation of human activities on the environment" (Yigitcanlar & Teriman, 2015, p. 341).

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Urbanization and Migration: A Global Perspective (2)

Generally, ubran development is being influenced by various factors, including economic, environment and social development.

Economic development includes aspects such as enhancing labor force mobility, increasing urban productivity, and facilitating commercial growth and residential development.
Environmental development focuses on the reducing air pollution and greenhouse gas emissions, promoting urban renewal, and shaping land use patterns.
Social development encompasses the impacts on public health, behavior, personal identity, life experience, and safety, as well as the equality for all inhabitants (Lin et al., 2022).

Overall, urban development is not just about the physical expansion of cities but also about fostering economic prosperity, social equity, and environmental sustainability.

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Urbanization and Migration: A Global Perspective (3)

Migration according to the neoclassical economic theory is an economic decision made by individuals seeking to maximize their income and improve their living conditions (Vogler & Rotte, 2000).
Migration according to the push-pull theory is observed through "push" factors (e.g., unemployment, conflict) that drive people away from their home country, while "pull" factors (e.g., job opportunities, safety) attract them to a new location (Vogler & Rotte, 2000).
Migrations: internal/external.

The research revealed substantial variation in the incidence of internal migration. For example, measured over five years, migration intensities varied from highs exceeding 50 percent in New Zealand and South Korea to lows of less than 6 percent in Egypt and India. Measured over a single year, the differential was no less striking, with the migration intensity in Iceland almost 20 times the level recorded in Macedonia (Bell et al., 2015).

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Urban Development and Migration in the Republic of Serbia (1)

Over the past thirty years, Serbia's statehood, state organization, administrative and physical aspects, as well as its geopolitical position and orientation, have all seen significant changes.

Major cities' previous settlement development, agglomeration policy, and spatial organization have changed (Racz, 2023).

Serbia's cities are a diverse network of settlements with varying demographic makeup, levels of economic growth, and functional capacities that arise from distinct development predispositions within certain geographic and socio-historical contexts (Krunić et al., 2021).

Serbia is divided into NUTS regions where Northern Serbia is one of the NUTS1 macro regions, constituted by the NUTS2 regions of Vojvodina and Belgrade. The other macro region is Southern Serbia, covering the two NUTS2 regions of Šumadija and Western Serbia, and Southern and Eastern Serbia.

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Urban Development and Migration in the Republic of Serbia (2)

Internal migrations in the Republic of Serbia, 2023.

During the year 2023, 145,843 persons changed their place of residence, i.e. permanently moved from one place (settlement) of the Republic of Serbia to another. The average age of persons who changed their place of residence is 36.1 years (36.7 years for men and 35.6 years for women).

Source: https://publikacije.stat.gov.rs/G2024/HtmlL/G20241189.html (Accessed on June 10, 2024)

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Data Overview

Indicator	Definition
Institutions for Pre-School Children Number of Facilities Per Capita	The number of pre-school institutions or facilities (such as kindergartens) available per capita, indicating access to early childhood education
Regular Primary Schools Per Capita	The number of primary schools available per capita, reflecting the accessibility of primary education in the population
Number of Kilometres of Modern Roads per 1,000 Inhabitants	The total length of modern, paved roads (in kilometres) per 1,000 inhabitants, indicating the quality and accessibility of transportation infrastructure
% of Women in Committees of the Assembly of Municipalities and Cities	The percentage of women who hold positions in the decision-making committees within municipal and city assemblies. This variable reflects gender representation in local governance
% Without School Education	The proportion of the population aged 15 and older who have never attended formal schooling or received any form of education
% Higher Education	The proportion of the population aged 15 and older who have higher education
Number of Births per 1,000 Inhabitants	The annual number of live births per 1,000 people in the population, reflecting the birth rate

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Data Overview

Indicator	Definition
Life Expectancy	The average number of years a newborn is expected to live, assuming that current mortality rates at each age will remain constant throughout their lifetime
Number of Inhabitants Per Doctor	The average number of people served by a single medical doctor, indicating access to healthcare services
Number of Employees per 1,000 Inhabitants	The total number of employed individuals per 1,000 residents, indicating the employment rate within the population
Average Earnings Excluding Taxes and Contributions, Per Employee	The average gross salary per employee before deducting taxes and mandatory social contributions, representing the income level of employees
Income and Budget Receipts Per Capita	The average amount of income and budget revenues per person, representing the economic resources available per capita
Number of Legally Convicted Adults per 1,000 Inhabitants	The number of adults who have been legally convicted of a crime per 1,000 inhabitants, reflecting the crime rate and legal outcomes within the population
Tourists per 1,000 Inhabitants	The number of tourists visiting the area per 1,000 residents, reflecting the area's attractiveness to visitors and the potential impact of tourism on the local economy

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Methodology and model development

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Methodology and model development

Weight Determination: CRITIC Method:

The CRITIC (Criteria Importance Through Intercriteria Correlation) method was employed to determine the weights of each indicator. This method assigns weights based on the variability and conflict among the criteria:

Variability is captured through the standard deviation, reflecting the diversity in each indicator.
Conflict is captured by analysing the correlation between indicators, ensuring that the weights reflect both the uniqueness and significance of each criterion in the overall analysis.

Aggregation Process: TOPSIS Method:

TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) was used to aggregate the weighted indicators into the final index. This method ranks municipalities by calculating their relative closeness to an ideal solution, which represents the best possible performance across all indicators. The TOPSIS method ensures that the final ranking reflects both the strengths and weaknesses of each municipality in relation to the others.

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Criteria weights

% of Women in Committees of the Assembly of Municipalities	% Without School Education	% High Education	Number of Births per 1,000 Inhabitants	Life Expectancy of Live Births	Number of Employees per 1,000 Inhabitants	Average Earnings Excluding Taxes and Contributions, Per Employee
0.058	0.0846	0.061	0.095	0.044	0.0371	0.059
Institutions for Pre-School Children Number of Facilities Per Capita	Regular Primary Schools Per Capita	Number of Inhabitants Per Doctor	Income and Budget Receipts Per Capita	Tourists per 1,000 Inhabitants	Number of Kilometres of Modern Roads per 1,000 Inhabitants	Number of Legally Convicted Adults per 1,000 Inhabitants
0.057	0.051	0.129	0.055	0.070	0.071	0.127

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Correlation Between Urban Development and Migration

The weak negative correlation indicates that, on average, higher levels of urban development are associated with slightly lower net migration flows. However, since the correlation is both small and statistically insignificant, it implies that this relationship is not strong or consistent across the municipalities studied.

This suggests that urban development, as captured by the SMUDI, might not be a major determinant of migration patterns in Serbia.

The insignificant correlation suggests that other factors not included in the SMUDI might be more influential in driving migration patterns.

The varied conditions across the 166 municipalities could lead to different migration behaviors that are not uniformly captured by the composite index.

Correlations
		SMUDI	NetMigrationRate
SMUDI	Pearson Correlation	1	-.108
	Sig. (2-tailed)		.164
	N	166	166
NetMigrationRate	Pearson Correlation	-.108	1
	Sig. (2-tailed)	.164
	N	166	166

The weak and insignificant correlation suggests that policymakers should not rely solely on improving urban development to influence migration patterns. Instead, targeted policies addressing specific migration drivers might be necessary.

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Conclusion

The Composite Index is a powerful tool for policymakers, urban planners, and stakeholders. It distils complex and varied data into a single metric that can be used to:

Compare the level of urban development across different municipalities.
Identify strengths and weaknesses in various dimensions of urban development.
Guide resource allocation and policy interventions aimed at enhancing urban development.

Policy recommendations: Since the emergence of the term „smart urban development“, urban development is centred around people and community (Dijck, Poell, & Waal, 2018). This development has the potential to enhance direct collaboration between various citizen groups, public authorities, and political actors (Falco & Kleinhans, 2018).
Further research development: Include additional data into the model such as quality of life, affordable housing, ecological aspects of urban development and similar.

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This research is part of the 101059994 – UR-DATA - HORIZON-WIDERA-2021-ACCESS-02 project, funded by the European Union. Views and opinions expressed are however those of the authors only and do not necessarily reflect those of the European Union or the European Research Executive Agency. Neither the European Union nor the European Research Executive Agency can be held responsible for them.

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