An Analysis of the Impact of Political Economy on the Dynamics of Urbanization in the Period 1922-1979

Extended Abstract: 
Introduction
Urbanization in Iran has undergone rapid and dramatic changes, and different forms of settlement and social life have always existed in urban, rural, and nomadic areas. transformations in rural, nomadic, and urban communities, and their social foundations and forces play a crucial role in shaping the urban form, urbanization, and its relationship with urbanism. Ideologies and economic and political processes throughout history have directly influenced the social-spatial structures of society, including urban settlements. In fact, cities are human spaces that effectively reflect the impacts of policymakers' decisions on spatial structures and urban classes. Therefore, they provide valuable locations for studying the historical evolution of a society. Based on this premise, the political economy of the city aims to examine the relationship between these historical transformations and their secondary processes and structures within urban space.
Studying the population dynamics and characteristics of urbanization in a historical context in any country can provide a scientific analytical foundation for understanding its economic, social, and political transformations. It can also serve as a useful guide to determine the country's position in the global economic system and the changes that have occurred in this role over time. Thus, given the importance and necessity of this subject, this study attempts to answer the following question:
The main issue and question addressed in this research, which encompasses its nature, is the study of the impact of political economy on the urbanization trends in Iran in the time period from 1921 to 1978. The research question can be formulated as follows: How has the political economy influenced the mechanisms of urbanization in Iran? The study aims to provide answers to the ambiguities surrounding this research question.
 
MethodologyThe present study is applied in terms of its objective and descriptive-analytical in terms of data collection. It focuses on examining the impact of political economy on urbanization trends from 1921 to 1978. Data gathering was conducted through two methods: documentary (library) research. After reviewing the theoretical foundations and research literature, a series of indicators and criteria were extracted. A quantitative approach was used to analyze the comprehensive data covering all of Iran in the time period from 1921 to 1978, considering the statistics and figures related to the number of cities. The analysis of research variables was performed using the statistical method of "J-statistic" and analyzed using the SPSS software. The J-statistic is calculated by multiplying the number of observations by a chi-square distribution with degrees of freedom equal to the difference between the number of instrumental variables and the number of estimated coefficients.
 
Results and discussion
According to the results of the null hypothesis test of Moran's test, the presence of spatial effects in the model of urban population growth in the country's provinces is confirmed at a high level of significance. Additionally, based on the Lagrange coefficient (LM) test statistics between the two models, namely the spatial lag and spatial error models, the spatial error model is selected as the most appropriate model. According to the results, at a significance level of 5%, the spatial lag model is rejected in comparison to both the spatial error and fixed effects models. Considering that the Lagrange coefficient test results also indicate the superiority of the spatial error model over the spatial lag model, the spatial error model will be used in estimating the model of population growth in the country's cities. Furthermore, the results indicate a statistically significant coefficient of spatial autocorrelation (λ) at a high level, affirming the existence of spatial dependence in the disturbance components of the population growth model and the number of cities in the country.
The coefficient of spatial autocorrelation indicates to what extent the urban population growth in one province is influenced by shocks occurring in the population growth of other provinces in the country.Among the economic variables, poverty index, unemployment rate, and specialization index have been identified as the most influential economic factors on urban population growth in the country's provinces. The poverty index, as a control variable, has a negative effect on urban population growth and is statistically significant at a higher level. This means that cities with higher poverty rates have lower population growth. This suggests that poverty and deprivation are among the key drivers of migration flows.
In other words, provinces with higher unemployment rates (lower employment rates) have experienced higher migration rates to developed urban areas and consequently have had lower population growth. This result supports Michael Todaro's theory, which states that individuals are generally motivated to migrate in search of employment or better job opportunities. According to Todaro's theory, job search is one of the main reasons for migration. On the other hand, cities (or provinces) that have higher levels of development in infrastructure have shown less inclination for migration because individuals usually migrate in search of employment or better job opportunities.Therefore, provinces (cities) with a higher share of injected budget and infrastructure and public services have lower migration rates and have experienced higher population growth rates.
 
Conclusion
Among the economic indicators, variables such as poverty, unemployment rate, and specialization were identified as the most influential factors on urban population growth. In terms of social indicators, the migration rate, general fertility rate, and active population were considered significant factors. However, according to the results, the infrastructure index (road network) included in the model did not have a significant effect. Additionally, the poverty index had a negative impact on urban population growth, which was statistically significant at a higher level. However, the Gini coefficient as a measure of inequality did not have a significant effect on urban population growth. It should be noted that the presence of spatial dependence in the model of urban population growth was confirmed in this study, but the causes of spatial dependence and the variables contributing to it were not discussed or examined.
 
Funding
There is no funding support.
 
Authors’ Contribution
Authors contributed equally to the conceptualization and writing of the article. All of the authors approved thecontent of the manuscript and agreed on all aspects of the work declaration of competing interest none.
 
Conflict of Interest
Authors declared no conflict of interest.
 
Acknowledgments
 We are grateful to all the scientific consultants of this paper.