Abstract
Scholars are increasingly assessing the impact of conservation interventions at national and regional scales with robust causal inference methods designed to emulate randomized control trials (quasi-experimental methods). Although spatial and temporal data to measure habitat loss and gain with remote sensing tools are increasingly available, data to measure spatially explicit poverty and human well-being at a high resolution are far less available. Bridging this data gap is essential to assess the social outcomes of conservation actions at scale and improve understanding of socioenvironmental synergies and trade-offs. We reviewed the kinds of socioeconomic data that are publicly available to measure the effects of conservation interventions on poverty and well-being, including national census data, representative household surveys funded by international organizations, surveys collected for individual research programs, and high-resolution gridded poverty and well-being data sets. We considered 4 challenges in the use of these data sets: consistency and availability of indicators and metrics across regions and countries, availability of data at appropriate temporal and spatial resolutions, and technical considerations associated with data available in different formats. Potential workarounds to these challenges include analytical methods to help resolve data mismatches and the use of emerging data products.