A qualidade educacional como determinante de cidades inteligentes brasileiras: uma análise de dados em painel

Abstract

In developing countries like Brazil, education is one of the main pillars of socioeconomic progress, playing a fundamental role in training public managers, qualifying the population, and reducing social and educational inequalities. Investigating how education can drive the smartness of cities is essential for creating more efficient, sustainable, and inclusive urban environments. Although its importance is widely recognized, the impact of quality education on the development of Brazilian smart cities is still an underexplored field. In this context, this research investigates how educational quality relates to the development of smart cities. The study employs General Systems Theory to analyze cities as complex, dynamic, and multidimensional systems and uses a panel data econometric model to empirically test this relationship. The model utilizes cross-sectional data (from 183 cities) and temporal data (from 2015 to 2023) to analyze the relationship between variables from different urban dimensions and the development of smart cities, considering the smart city ranking score as the dependent variable and educational quality as the variable of interest. To enhance robustness and reduce bias in the analysis, control variables were included in the model. First, control variables derived from the seven urban dimensions previously identified in the literature were used: natural environment, urban infrastructure, economy, people, governance, organizations, and technology & innovation. Then, to measure educational quality, eight education indicators were used as proxies: Enem score, average number of students per class, average daily class hours, age-grade distortion rate, approval rate, dropout rate, adequacy of teacher training, and regularity of the teaching staff. The results show that even when creating multiple panel models and adding new control variables, educational quality remains positive and significant for most quality indicators: a 1% increase in Enem scores indicates a 1.13% increase in city ranking scores; a 1% reduction in the age-grade distortion rate suggests a 0.106% increase in scores; while a 1% increase in school approval rates can raise city scores by 0.34%. The variables for the average number of students per class and average daily class hours, although significant in the initial models, lost significance as new variables were added. The variables for adequacy of teacher training and regularity of the teaching staff were not significant. Regarding the variables associated with urban dimensions, the results indicate that an increase in the number of doctors and a reduction in robbery-related homicides positively impact city development. The findings highlight the complexity of the interaction between education and urban development and indicate that the higher the educational quality, the greater the chances of a city developing as a smart space. The results also suggest that well-structured public policies should comprehensively address multiple dimensions of educational quality in urban development strategies. This study contributes to theory by exploring the effect of educational quality on the smart city index in Brazil. In terms of managerial contributions, this research provides important results for the creation of public policies focused on educational indicators. Future studies may expand the analysis by incorporating a greater number of educational and urban indicators. It is also suggested to investigate the long-term effects of quality education on urban development, particularly regarding the impact of new public policies on smart cities.