Projecting Local Survey Response in a Changing Demographic Landscape: A Case Study of the Census in New York City

Abstract

Abstract Low rates of survey response are a problem that survey organizations increasingly experience across the world. Understanding local socio-demographic reasons for low response, and projecting future response, can inform targeted outreach and messaging to communities. Using the US Census as a case study, we study mail response behavior both cross-sectionally and over time. The cross-sectional part of this study (2010) helps inform the selection of explanatory variables for a two-panel difference model (2000–2010). In turn, the two-panel difference model offers an in-depth understanding of which demographic indicators shape response across time. Increases in response between 2000 and 2010 were found to be associated with growth in the elderly and in the Dominican populations, while growth in blacks, renters, and those with low educational attainment were associated with decreases in response. Coefficients from the two-panel difference model are then applied to demographic changes from 2008–2012 to 2013–2017, to project response rates for the most recent period. While much of our findings are in line with prior survey research, we demonstrate the importance of projecting response behavior using a local perspective, as national-level results do not always apply to smaller jurisdictions. In a diverse country such as the United States, a hyper-diverse city such as New York often carves its own path. To our knowledge, this is the first paper that connects shifts in socio-demographic neighborhood characteristics with changes in response rates. As racial and ethnic diversity increases in cities across western societies, our methodology allows jurisdictions to project response rates using shifts in socio-demographic neighborhood characteristics that are tied to self-response. Such a tailored approach can be replicated across different kinds of surveys, countries, and time points, which will allow for the identification of local factors that drive changes in survey response and help establish a precisely customized outreach plan.