🤖 AI Summary
This study addresses the persistent spatial autocorrelation in residuals from the U.S. Census Bureau’s Low Response Score (LRS) model after ordinary least squares regression, which undermines predictive accuracy. Leveraging 2010 data from 71,076 census tracts—including mail nonresponse rates and 25 predictors—the authors systematically compare a suite of spatial autoregressive models. They find that spatial dependence arises primarily from the error term rather than global endogenous lags. Employing queen-contiguity spatial weights, they evaluate the spatial error model (SEM/SDEM), spatial Durbin model (SDM), and spatial lag model, complemented by spatial block cross-validation for robust generalization assessment. Results indicate that the SDEM achieves optimal performance while preserving interpretability, revealing that local neighborhood demographic characteristics influence response behavior through spatial spillover effects. Findings prove robust to alternative weight specifications and heteroskedasticity.
📝 Abstract
The U.S.\ Census Bureau's Low Response Score (LRS) is a central planning instrument for identifying places likely to require additional self-response outreach and nonresponse follow-up. The published LRS is intentionally interpretable: it is built from tract-level covariates using an ordinary least squares specification. That transparency, however, leaves open an important question for official statistics: how much spatial structure remains after the own-tract covariates have done their work, and what form does that structure take? Using the observed 2010 Census mail non-return rate for 71,076 U.S. census tracts and the twenty-five Erdman--Bates LRS predictors, this paper compares the full spatial autoregressive model family under queen-contiguity weights and validates the leading candidates with both random and spatial-block cross-validation. OLS leaves strong residual spatial autocorrelation ($I=0.399$). Formal diagnostics and model comparisons indicate that the remaining dependence is primarily error-type rather than a global endogenous lag process. Although the spatial Durbin model minimizes in-sample AIC, spatial-block validation reverses that ranking: the error-family models (SEM/SDEM) generalize best, while the AIC-best SDM is weakest out of sample. The SDEM provides an interpretable middle ground, absorbing residual spatial dependence while representing neighborhood demographic effects as local spillovers. Robustness checks show that these conclusions are invariant to the weights definition and are not an artifact of tract-size-driven heteroskedasticity. The results suggest that LRS-style response models should be evaluated with spatial validation, not only in-sample fit, and that local neighborhood context can be operationally meaningful without invoking a global response-contagion mechanism.