Policy-critical, micro-level statistical data are often unavailable at the desired level of disaggregation. We present a Bayesian methodology for “downscaling” aggregated count data to the micro level, using an outside statistical sample. Our procedure combines numerical simulation with exact calculation of combinatorial probabilities. We motivate our approach with an application estimating the number of farms in a region, using count totals at higher levels of aggregation. In a simulation analysis over varying population sizes, we demonstrate both robustness to sampling variability and outperformance relative to maximum likelihood. Spatial considerations, implementation of “informative” priors, non-spatial classification problems, and best practices are discussed.

Hedonic modeling of Oklahoma cow auction data is used to determine the market value of bred cow characteristics. We use Agricultural Marketing Service data that let us consider more years and more lots of cattle than is typical for a cattle hedonic study. The greatest price premiums were for black, late-gestating cows, categorized as high quality by market reporters and weighing between 1,600 and 1,700 lb. Previous research on optimal cow size finds much smaller-size cows are optimal, and our research finds that larger cows receive a lower price per pound but still receive a substantially higher price per head.