Relevance & Research Question:
Item nonresponse (INR) is a common phenomenon in surveys and particularly income questions are characterized by higher INR rates. INR not only reduces the sample size but might also produce non-ignorable missing data, i.e. when INR differs systematically between non responders and responders with regard to the variables of interest. In this case it is as problematic for sample representativeness as total respondent dropout. As cross-national comparisons of income using micro data have become more attractive in recent years, it is important to assess the extent to which such comparisons are meaningful. This paper examines determinants and mechanisms underlying INR on the income question in different countries.
Methods & Data:
The analysis is based on the WageIndicator survey in 2011 and 2012. The WageIndicator is a unique database for the abovementioned purpose, since it offers a large number of observations in a large number of countries. To study the comparability across countries, first cross-national INR rates are described by differentiating also core socio-demographic characteristics. Second, multi-level analysis is applied to examine determinants of the probability of INR of the wage measures by simultaneously controlling for differences within and between countries.
Results
The study enhances knowledge by offering insights into cross-national INR differences and introducing country-level explanations for differences in INR. As data collection will be completed by end of the year, the paper will present fresh results unavailable so far.
Added Value
So far, research has revealed that INR is related to age and education and that INR on income is concentrated in the lower income tail. A limitation of this research is that most studies have focused on mainly exploring within-country differences. Only with a better understanding of the underlying determinants and mechanisms of INR, tools and techniques can be developed to reduce INR and thereby improving data quality.