Abstract

Cities strive to become more efficient, provide better services, and create a high-quality environment for their citizens. Over the last two decades, information and communication technologies (ICT) have become one of the primary means of achieving such progress. Beyond ICT adoption, cities have become increasingly aware of the vast amount of data they generate. Data has emerged as a key production factor in modern economies. In line with the ambition to become smart cities, data openness and urban data platforms represent crucial instruments for advancing digital government and improving governance efficiency (Yan et al., 2025). Consequently, the implementation of data platforms is often among the first strategic steps undertaken. In this context, urban data platforms are expected to reduce information costs, facilitate coordination among stakeholders, and enable data-driven decision-making processes. While urban data platforms are often discussed holistically, their development is connected to a multitude of preceding phases that must be completed before deployment. These include the establishment of appropriate technical and economic infrastructures. As a result, disparities between cities are widening in subtle ways, as those capable of effectively leveraging data gain structural advantages. Cities with stronger resource capacities can achieve greater innovation performance, thereby improving overall urban quality and competitiveness. Beyond these more immediate effects, Huang and Cheng (2024) identify several indirect impacts of data-driven industries. Their findings suggest that such industries not only contribute to economic growth but also foster AI entrepreneurship. However, this raises the question of whether urban data platforms meaningfully contribute to these macroeconomic objectives, making them not merely a nice-to-have but a need-to-have instrument for successful smart cities. The objective of this study is to examine whether Urban Data Platforms (UDPs) generate measurable and statistically significant macroeconomic effects. If such effects can be identified, this would provide evidence supporting the scaling of initial grassroots or lighthouse projects to broader policy implementation. To investigate this question, we rely on secondary data obtained from the European Commission’s Eurostat database and the Kohesio project platform. We collected annual data on poverty rates, unemployment, and GDP covering the period from 2015 to 2024. A potentially suitable method for conducting this analysis is the staggered Difference-in-Differences approach, which is particularly appropriate in contexts where treatment adoption occurs at different points in time. In our setting, cities adopted Urban Data Platforms between 2016 and 2020, implying staggered treatment timing. The average treatment effect is therefore estimated while accounting for variation in treatment timing across cities. This reflects the structural and potentially long-term nature of investments in digital governance infrastructure.

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