The irony is further heightened because it has long been understood that regression analysis of spatially distributed variables can lead to incorrect statistical inference (a result of inefficient or biased parameter estimates) when spatial autocorrelation exists and when model specifications fail to incorporate proper corrections for such spatial effects (Cliff & Ord, 1973). To our knowledge, however, no existing statistical investigation into the spatial distribution of poverty has adopted spatial econometric methods.This paper uses spatial econometric methods to re-examine place and family effects on child poverty. Specifically, we demonstrate how regression models can be tested for spatial effects, evaluate the results of failing to account for these effects in models of child poverty in the US, and correct for such effects in more properly specified models. We accomplish this by revisiting an article, published in this journal, that explores the determinants of geographic variability in county-level child poverty rates (Friedman & Lichter, 1998).