Total Bias in Income Surveys when Nonresponse and Measurement Errors are Correlated

IF 1.6 4区 数学 Q2 SOCIAL SCIENCES, MATHEMATICAL METHODS
Andrea Neri, Eleonora Porreca
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Abstract

Abstract Household surveys on income might suffer from quality limitations mainly due to the difficulty of enrolling households (unit nonresponse) and retrieving correct information during the interview (measurement error [ME]). These errors are likely to be correlated because of latent factors, such as the threat of disclosing personal information, the perceived sensitivity of the topic, or social desirability. For survey organizations, assessing the interplay of these errors and their impact on the accuracy and precision of inferences derived from their data is crucial. In this article, we propose to use a standard sample selection model within a total survey error framework to deal with the case of correlated nonresponse error (NR) and ME in estimating average household income. We use it to study the correlation between the two errors, quantify the ME component due to this correlation, and evaluate ME among nonrespondents. Using the Italian Survey on Income and Wealth linked with administrative income data from tax returns, we find a positive correlation between the two errors and that households at the extremes of the income distribution mainly cause this association. Our results show that ME contributes more to the total error than unit nonresponse and that it would be larger in absence of the correlation between the two errors. Finally, efforts to reduce nonresponse rates are worthwhile only for nonrespondents in the lowest estimated response propensity group. If these households participate, the bias decreases because of the reduction in NR that offsets the increase in ME.
无反应与测量误差相关时收入调查的总偏差
摘要家庭收入调查存在质量限制的主要原因是难以纳入住户(单位无响应)和难以在访谈中获取正确信息(测量误差[ME])。由于潜在的因素,如泄露个人信息的威胁、话题的感知敏感性或社会可取性,这些错误很可能是相关的。对于调查组织来说,评估这些错误的相互作用及其对从其数据中得出的推断的准确性和精度的影响至关重要。在本文中,我们建议在总调查误差框架内使用标准样本选择模型来处理在估计平均家庭收入时相关的非响应误差(NR)和ME的情况。我们用它来研究两种误差之间的相关性,量化由于这种相关性而产生的ME成分,并评估非受访者的ME。利用意大利收入和财富调查与来自纳税申报单的行政收入数据相关联,我们发现这两个错误之间存在正相关关系,而收入分配极端的家庭主要导致了这种关联。结果表明,ME对总误差的贡献大于单位非响应,并且在两种误差之间没有相关性的情况下,ME对总误差的贡献更大。最后,努力减少无回复率是值得的,只有在最低估计的反应倾向组的无回复率。如果这些家庭参与,由于NR的减少抵消了ME的增加,因此偏差会减少。
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来源期刊
CiteScore
4.30
自引率
9.50%
发文量
40
期刊介绍: The Journal of Survey Statistics and Methodology, sponsored by AAPOR and the American Statistical Association, began publishing in 2013. Its objective is to publish cutting edge scholarly articles on statistical and methodological issues for sample surveys, censuses, administrative record systems, and other related data. It aims to be the flagship journal for research on survey statistics and methodology. Topics of interest include survey sample design, statistical inference, nonresponse, measurement error, the effects of modes of data collection, paradata and responsive survey design, combining data from multiple sources, record linkage, disclosure limitation, and other issues in survey statistics and methodology. The journal publishes both theoretical and applied papers, provided the theory is motivated by an important applied problem and the applied papers report on research that contributes generalizable knowledge to the field. Review papers are also welcomed. Papers on a broad range of surveys are encouraged, including (but not limited to) surveys concerning business, economics, marketing research, social science, environment, epidemiology, biostatistics and official statistics. The journal has three sections. The Survey Statistics section presents papers on innovative sampling procedures, imputation, weighting, measures of uncertainty, small area inference, new methods of analysis, and other statistical issues related to surveys. The Survey Methodology section presents papers that focus on methodological research, including methodological experiments, methods of data collection and use of paradata. The Applications section contains papers involving innovative applications of methods and providing practical contributions and guidance, and/or significant new findings.
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