An empirical total survey error decomposition using data combination

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11640%2F21%3A00545022" target="_blank" >RIV/00216208:11640/21:00545022 - isvavai.cz</a>

Alternative codes found

RIV/67985998:_____/21:00553109

Result on the web

<a href="https://doi.org/10.1016/j.jeconom.2020.03.026" target="_blank" >https://doi.org/10.1016/j.jeconom.2020.03.026</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.jeconom.2020.03.026" target="_blank" >10.1016/j.jeconom.2020.03.026</a>

Result language

angličtina

Original language name

An empirical total survey error decomposition using data combination

Original language description

Survey error is known to be pervasive and to bias even simple, but important, estimates of means, rates, and totals, such as the poverty and the unemployment rate. In order to summarize and analyze the extent, sources, and consequences of survey error, we define empirical counterparts of key components of the Total Survey Error Framework that can be estimated using data combination. Specifically, we estimate total survey error and decompose it into three high level sources of error: generalized coverage error, item non-response error and measurement error. We further decompose these sources into lower level sources such as failure to report a positive amount and errors in amounts conditional on reporting a positive value. For errors in dollars paid by two large government transfer programs, we use administrative records on the universe of program payments in New York State linked to three major household surveys to estimate the error components previously defined. We find that total survey error is large and varies in its size and composition, but measurement error is always by far the largest source of error. Our application shows that data combination makes it possible to routinely measure total survey error and its components. Our results allow survey producers to assess error reduction strategies and survey users to mitigate the consequences of survey errors or gauge the reliability of their conclusions.

Czech name

—

Czech description

—

Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

50202 - Applied Economics, Econometrics

Project

<a href="/en/project/GA20-27317S" target="_blank" >GA20-27317S: The Nature and Implications of Survey Error: Evidence From Linked Administrative Data</a><br>

Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2021

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Journal of Econometrics

ISSN

0304-4076

e-ISSN

1872-6895

Volume of the periodical

224

Issue of the periodical within the volume

2

Country of publishing house

CH - SWITZERLAND

Number of pages

20

Pages from-to

286-305

UT code for WoS article

000689638900003

EID of the result in the Scopus database

2-s2.0-85098185548