Working Paper: NBER ID: w20098
Authors: Charles F. Manski
Abstract: Federal statistical agencies in the United States and analogous agencies elsewhere commonly report official economic statistics as point estimates, without accompanying measures of error. Users of the statistics may incorrectly view them as error-free or may incorrectly conjecture error magnitudes. This paper discusses strategies to mitigate misinterpretation of official statistics by communicating uncertainty to the public. Sampling error can be measured using established statistical principles. The challenge is to satisfactorily measure the various forms of non-sampling error. I find it useful to distinguish transitory statistical uncertainty, permanent statistical uncertainty, and conceptual uncertainty. I illustrate how each arises as the Bureau of Economic Analysis periodically revises GDP estimates, the Census Bureau generates household income statistics from surveys with non-response, and the Bureau of Labor Statistics seasonally adjusts employment statistics.
Keywords: No keywords provided
JEL Codes: C82; E01; I32
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| lack of reported uncertainty in GDP estimates (E01) | policymakers make decisions under false impression of accuracy (D78) |
| lack of reported uncertainty in GDP estimates (E01) | inappropriate monetary policies (E64) |
| revisions to GDP estimates (E01) | affect economic decisions (F69) |
| nonresponse in surveys (C83) | introduces permanent statistical uncertainty (C46) |
| reporting practices (point estimates without uncertainty) (C46) | public and policymaker perceptions of economic conditions (E66) |
| improved communication of uncertainty (D80) | enhanced decision quality (D91) |