The increasing availability of high resolution geocoded data on key outcomes (e.g. incomes) and potential drivers of these outcomes (e.g. program rollout, or variation in environmental factors) has prompted analysis of their relationship at finer and finer spatial scales. Our proposed work asks: what are the implications for analysis and inference when using high-resolution data with different error structures? In particular, when the hi-resolution nature of the data allows the researcher choice over the level of aggregation at which the analysis is conducted, some types of measurement error could counterintuitively favor a more aggregate level of analysis.
To date, little attention has been given to issues of aggregation and measurement error in spatial econometric analysis, and how they affect estimation bias and efficiency. The proposed research will contribute to the literature in two ways:(1) we will develop “rules of thumb” for understanding the optimal level of spatial aggregation for a given analysis, based on available knowledge of the measurement error, and (2) we would develop tools for improving inference when using high-resolution gridded or point data, including proper construction of standard errors. Our goal is to provide other researchers actionable tools and guidance.