From multiple modes for surveys to multiple data sources for estimates

Constance F. CitroNote 1


Users, funders and providers of official statistics want estimates that are “wider, deeper, quicker, better, cheaper” (channeling Tim Holt, former head of the UK Office for National Statistics), to which I would add “more relevant” and “less burdensome”. Since World War II, we have relied heavily on the probability sample survey as the best we could do - and that best being very good - to meet these goals for estimates of household income and unemployment, self-reported health status, time use, crime victimization, business activity, commodity flows, consumer and business expenditures, et al. Faced with secularly declining unit and item response rates and evidence of reporting error, we have responded in many ways, including the use of multiple survey modes, more sophisticated weighting and imputation methods, adaptive design, cognitive testing of survey items, and other means to maintain data quality. For statistics on the business sector, in order to reduce burden and costs, we long ago moved away from relying solely on surveys to produce needed estimates, but, to date, we have not done that for household surveys, at least not in the United States. I argue that we can and must move from a paradigm of producing the best estimates possible from a survey to that of producing the best possible estimates to meet user needs from multiple data sources. Such sources include administrative records and, increasingly, transaction and Internet-based data. I provide two examples - household income and plumbing facilities - to illustrate my thesis. I suggest ways to inculcate a culture of official statistics that focuses on the end result of relevant, timely, accurate and cost-effective statistics and treats surveys, along with other data sources, as means to that end.

Key Words:

Surveys; Administrative records; Total error; Big data; Income; Housing.

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