Incompatibility of trends in multi-year estimates from the American Community Survey

TL;DR

This paper demonstrates that multi-year estimates from the American Community Survey are inherently incompatible when comparing different period lengths, due to their different time spans and biases, and proposes a weighted averaging method to improve comparability.

Contribution

The paper introduces a simple, nonparametric weighted averaging method to reduce bias when comparing ACS multi-year estimates of different durations.

Findings

Weighted averages can improve comparability of different MYEs

Comparison of MYEs can lead to spurious trend conclusions

Method requires only polynomial algebra and short data spans

Abstract

The American Community Survey (ACS) provides one-year (1y), three-year (3y) and five-year (5y) multi-year estimates (MYEs) of various demographic and economic variables for each "community", although the 1y and 3y may not be available for communities with a small population. These survey estimates are not truly measuring the same quantities, since they each cover different time spans. Using some simplistic models, we demonstrate that comparing different period-length MYEs results in spurious conclusions about trend movements. A simple method utilizing weighted averages is presented that reduces the bias inherent in comparing trends of different MYEs. These weighted averages are nonparametric, require only a short span of data, and are designed to preserve polynomial characteristics of the time series that are relevant for trends. The basic method, which only requires polynomial algebra, is outlined and applied to ACS data. In some cases there is an improvement to comparability, although a final verdict must await additional ACS data. We draw the conclusion that MYE data is not comparable across different periods.