Political factors and efficiency in the responsible production of municipal solid waste services: A dynamic DEA with a network structure approach

The production of municipal solid waste (MSW) is driven by socioeconomic development and population growth. In this context, public policymakers, mainly in developing countries, are increasingly focused on ensuring sustainable production and consumption patterns through sustainable management and efficient use of resources. Because of this, we seek to contribute to the effectiveness of public policies and the role of managers in achieving the global Sustainable Development Goals 3, 6, 11, and 12. This study examines the relevance of political and contextual factors on the dynamic efficiency of the network structure of municipal solid waste services in Brazil. We used Dynamic Data Envelopment Analysis with Structure Network to calculate the overall efficiency and specific efficiency of the two proposed divisions: operational and economic. We then analyzed the effects of years in political cycles (pre-election, election year, post-election) and contextual factors (ideology, GDP, density, transfers, political size, and geography) on MSW efficiency using Generalized Estimating Equation. The dataset included longitudinal panel data from 218 MSW from 2012 to 2022. Our results showed an average efficiency score of 57.5 %, with only six MSW positioned on the efficiency frontier. Evidence of stagnation in the sector's productivity was also revealed, undermining the goals and objectives of public policies. The results suggest that the years within political cycles significantly impact sector operator efficiency before, during, and after the electoral period. Specifically, lower levels of efficiency are observed during election years, suggesting potential opportunistic behavior throughout most periods of the electoral cycle. The main contribution of this work lies in its analysis of the different periods of a political cycle, and role of capital and ideological politics in intertemporal efficiency across two divisional networks.