A statistical approach to address the problem of heaping in self-reported income data.

Autor/inn/en	Zinn, Sabine; Würbach, Ariane
Titel	A statistical approach to address the problem of heaping in self-reported income data.
Quelle	In: Journal of applied statistics, 43 (2016) 4, S. 682-703 PDF als Volltext Link als defekt meldenVerfügbarkeit
Beigaben	Literaturangaben
Sprache	englisch
Dokumenttyp	online; gedruckt; Zeitschriftenaufsatz
ISSN	0266-4763; 1360-0532
DOI	10.1080/02664763.2015.1077372
Schlagwörter	Fallstudie; Selbsteinschätzung; Einkommen; Fehleranalyse; Fallstudie; Selbsteinschätzung; Fehleranalyse; Einkommen; NEPS (National Educational Panel Study)
Abstract	Self-reported income information particularly suffers from an intentional coarsening of the data, which is called heaping or rounding. If it does not occur completely at random - which is usually the case - heaping and rounding have detrimental effects on the results of statistical analysis. Conventional statistical methods do not consider this kind of reporting bias, and thus might produce invalid inference. We describe a novel statistical modeling approach that allows us to deal with self-reported heaped income data in an adequate and flexible way. We suggest modeling heaping mechanisms and the true underlying model in combination. To describe the true net income distribution, we use the zero-inflated log-normal distribution. Heaping points are identified from the data by applying a heuristic procedure comparing a hypothetical income distribution and the empirical one. To determine heaping behavior, we employ two distinct models: either we assume piecewise constant heaping probabilities, or heaping probabilities are considered to increase steadily with proximity to a heaping point. We validate our approach by some examples. To illustrate the capacity of the proposed method, we conduct a case study using income data from the German National Educational Panel Study. (Orig.).
Erfasst von	DIPF \| Leibniz-Institut für Bildungsforschung und Bildungsinformation, Frankfurt am Main
Update	2020/2