Water Security and Common Pool Resource Management
Cooperman, Alicia, Alexandra R. McLarty, and Brigitte Seim. 2021. "Understanding uptake of community groundwater monitoring in rural Brazil." Proceedings of the National Academy of Sciences 118 (29). https://doi.org/10.1073/pnas.2015174118
Resource monitoring is often cited as important for effective common pool resources management. In practice, not all monitoring interventions are successful, particularly when the resource, such as groundwater, is challenging to monitor and measure. We conducted a field experiment on groundwater monitoring in Ceará, Brazil, where communities are increasingly reliant on groundwater yet do not engage in monitoring. Despite careful implementation, uptake of monitoring within the 80 treatment communities was low. To unpack this low uptake, we conduct multimethods exploratory research. We find that uptake is less likely in communities facing high coordination costs, either within the community leadership or across the broader community. Uptake is also less likely when there are physical barriers to monitoring, when there are more substitutes for groundwater, and when there is lower variability in water availability. Our findings can inform future monitoring interventions in similar contexts worldwide.
Slough, Tara, et al. 2021. "Adoption of community monitoring improves common pool resource management across contexts." Proceedings of the National Academy of Sciences 118 (29).
Pervasive overuse and degradation of common pool resources (CPRs) is a global concern. To sustainably manage CPRs, effective governance institutions are essential. A large literature has developed to describe the institutional design features employed by communities that successfully manage their CPRs. Yet, these designs remain far from universally adopted. We focus on one prominent institutional design feature, community monitoring, and ask whether nongovernmental organizations or governments can facilitate its adoption and whether adoption of monitoring affects CPR use. To answer these questions, we implemented randomized controlled trials in six countries. The harmonized trials randomly assigned the introduction of community monitoring to 400 communities, with data collection in an additional 347 control communities. Most of the 400 communities adopted regular monitoring practices over the course of a year. In a meta-analysis of the experimental results from the six sites, we find that the community monitoring reduced CPR use and increased user satisfaction and knowledge by modest amounts. Our findings demonstrate that community monitoring can improve CPR management in disparate contexts, even when monitoring is externally initiated rather than homegrown. These findings provide guidance for the design of future programs and policies intended to develop monitoring capabilities in communities. Furthermore, our harmonized, multisite trial provides sustainability science with a new way to study the complexity of socioecological systems and builds generalizable insights about how to improve CPR management.
Natural Disasters, Climate Change, and Covid-19
Objective. We investigate the impact of a global health crisis on political behavior. Specifically, we assess the impact of Covid-19 incidence rates, and the impact of temporal and spatial proximity to the crisis, on voter turnout in 2020 Brazilian municipal elections. Methods. We use Ordinary Least Squares and Spatial Durbin Error models to evaluate sub-national variation in municipal-level Covid-19 incidence and voter turnout. We include controls for political, economic, health, and state context. Results. Ceteris paribus, increasing deaths in the month leading up to the election from 0.01 to 1 per 1,000 people is associated with a 5 percentage point lower turnout; higher cases and deaths earlier in the pandemic are generally associated with greater turnout. Covid-19 incidence rates in nearby municipalities affect local turnout in the same directions. Conclusion. Higher Covid-19 incidence near the time of the election decreases voter turnout, while incidence farther from the election increases voter turnout.
Constantino, Sara, Alicia Cooperman, and Thiago Moreira. 2021. "Voting in a Global Pandemic: Assessing Dueling Influences of Covid-19 on Turnout." Social Science Quarterly: 1-26. https://doi.org/10.1111/ssqu.13038
Emergency spending is often exempt from campaign period restrictions and procurement guidelines, making it attractive for opportunistic politicians, but natural disasters are seen as outside political business cycles. However, droughts are frequent but challenging to measure, so politicians can leverage discretion for electoral gain. This paper analyzes electoral cycles, term limits, and partisan targeting around municipal drought declaration in Northeast Brazil. Two sources of exogeneity (rainfall shocks, electoral calendar) isolate the effect of non-climatic factors on drought declarations. I find that drought declarations, which trigger relief, are more likely in mayoral election years. Incumbents are more likely to win re-election if they declare a drought in the election year, during below or above average rainfall. The results are consistent with interviews suggesting voters reward competent mayors and mayors trade relief for votes. This study highlights the interaction between distributive and environmental politics, which has increasing consequences due to climate change.
Cooperman, Alicia. "(Un)Natural Disasters: Electoral Cycles in Disaster Relief." Comparative Political Studies. Forthcoming.
Cooperman, Alicia Dailey. 2017. “Randomization Inference with Rainfall Data: Using Historical Weather Patterns for Variance Estimation.” Political Analysis 25(3): 277-88. https://doi.org/10.1017/pan.2017.17
Many recent papers in political science and economics use rainfall as a strategy to facilitate causal inference. Rainfall shocks are as-if randomly assigned, but the assignment of rainfall by county is highly correlated across space. Since clustered assignment does not occur within well-defined boundaries, it is challenging to estimate the variance of the effect of rainfall on political outcomes. I propose using randomization inference with historical weather patterns from 73 years as potential randomizations. I replicate the influential work on rainfall and voter turnout in presidential elections in the United States by Gomez, Hansford, and Krause (2007 and compare the estimated average treatment effect (ATE) to a sampling distribution of estimates under the sharp null hypothesis of no effect. The alternate randomizations are random draws from national rainfall patterns on election and would-be election days, which preserve the clustering in treatment assignment and eliminate the need to simulate weather patterns or make assumptions about unit boundaries for clustering. I find that the effect of rainfall on turnout is subject to greater sampling variability than previously estimated using conventional standard errors.