Assessing the bias due to non-coverage of residential movers in the German microcensus panel: an evaluation using data from the socio-economic panel

The German Microcensus (MC) is a large scale rotating panel survey over three years. The MC is attractive for longitudinal analysis over the entire participation duration because of the mandatory participation and the very high case numbers (about 200 thousand respondents). However, as a consequence of the area sampling that is used for the MC , residential mobility is not covered and consequently statistical information at the new residence is lacking in theMCsample. This raises the question whether longitudinal analyses, like transitions between labour market states, are biased and how different methods perform that promise to reduce such a bias. Based on data of the German Socio-Economic Panel (SOEP), which covers residential mobility, we analysed the effects of missing data of residential movers by the estimation of labour force flows. By comparing the results from the complete SOEP sample and the results from the SOEP, restricted to the non-movers, we concluded that the non-coverage of the residential movers can not be ignored in Rubins sense. With respect to correction methods we analysed weighting by inverse mobility scores and loglinear models for partially observed contingency tables. Our results indicate that weighting by inverse mobility scores reduces the bias to about 60 percent whereas the official longitudinal weights obtained by calibration result in a bias reduction of about 80 percent. The estimation of loglinear models for nonignorable nonresponse leads to very unstable results. --Panel survey,labour market analysis,residential mobility,non-coverage bias,log-linear modelling,inverse probability weighting

Dealing with Incomplete Household Panel Data in Inequality Research

Population surveys around the world face the problem of declining cooperation and participation rates of respondents. Not only can item nonresponse and unit nonresponse impair important outcome measures for inequality research such as total household disposable income; there is also a further case of missingness confronting household panel surveys that potentially biases results. The approach commonly used in such surveys of interviewing all adult household members and aggregating their individual incomes to yield a final outcome measure for welfare analyses often suffers from partial unit non-response (PUNR), i.e., the non-response of at least one unit, or member, of an otherwise participating household. In these cases, the aggregate income of all household members lacks at least one individual's income. These processes are typically not random and require appropriate correction. Using data from more than twenty waves of the German Socio-Economic Panel (SOEP) we evaluate four different strategies to deal with this phenomenon: (a) Ignorance, i.e., assuming the missing individual's income to be zero. (b) Adjustment of the equivalence scale to account for differences in household size and composition. (c) Elimination of all households observed to suffer PUNR, and re-weighting of households observed to be at risk of but not affected by PUNR. (d) Longitudinal imputation of the missing income components. The aim of this paper is to show how the choice of technique affects substantive results in the inequality research. We find indications of substantial bias on income inequality and poverty as well as on income mobility. These findings are obviously even more important in cross-national comparative analyses if the data providers in the individual countries deal differently with PUNR in the underlying data.Household Panel Surveys, Partial Unit Non-Response, Inequality, Mobility, Imputation, SOEP

Dealing with Incomplete Household Panel Data in Inequality Research

Population surveys around the world face the problem of declining cooperation and participation rates of respondents. Not only can item nonresponse and unit nonresponse impair important outcome measures for inequality research such as total household disposable income; there is also a further case of missingness confronting household panel surveys that potentially biases results. The approach commonly used in such surveys of interviewing all adult household members and aggregating their individual incomes to yield a final outcome measure for welfare analyses often suffers from partial unit non-response (PUNR), i.e., the non-response of at least one unit, or member, of an otherwise participating household. In these cases, the aggregate income of all household members lacks at least one individual's income. These processes are typically not random and require appropriate correction. Using data from more than twenty waves of the German Socio-Economic Panel (SOEP) we evaluate four different strategies to deal with this phenomenon: (a) Ignorance, i.e., assuming the missing individual's income to be zero. (b) Adjustment of the equivalence scale to account for differences in household size and composition. (c) Elimination of all households observed to suffer PUNR, and re-weighting of households observed to be at risk of but not affected by PUNR. (d) Longitudinal imputation of the missing income components. The aim of this paper is to show how the choice of technique affects substantive results in the inequality research. We find indications of substantial bias on income inequality and poverty as well as on income mobility. These findings are obviously even more important in cross-national comparative analyses if the data providers in the individual countries deal differently with PUNR in the underlying data.Household Panel Surveys, Partial Unit Non-Response, Inequality, Mobility, Imputation, SOEP

Missing Income Data in the German SOEP: Incidence, Imputation and its Impact on the Income Distribution

This paper deals with the question of selectivity of missing data on income questions in large panel surveys due to item-non-response and with imputation as one alternative strategy to cope with this issue. In contrast to cross-section surveys, the imputation of missing values in panel data can profit from longitudinal information which is available for the very same observation units from other points in time. The "row-and-column imputation procedure" developed by Little & Su (1989) considers longitudinal as well as cross-sectional information in the imputation process. This procedure is applied to the German Socio-Economic Panel study (SOEP) when deriving annual income variables, complemented by purely cross-sectional techniques. Based on the SOEP, our empirical work starts with a description of the overall incidence of imputation and its relevance given by imputed income as a percentage share of the total income mass: e.g. while 21 % of all observations have at least one missing income component of their pre-tax post-transfer income, 9 % of the overall income mass is imputed. However, this picture varies considerably for more recent sub-samples of the panel survey. Secondly, we analyze the respective impact of imputation on the personal distribution of income as well as on results of income mobility. When comparing income inequality measures based only on truly observed information to those derived from all (i.e., observed and imputed) observations, we find an increase in inequality due to imputation and this effect appears to be relevant in both tails of the distribution, although somewhat more prominent among higher incomes. Longitudinal analyses show firstly a positive correlation of item-non-response on income data over time, but also provide evidence of item-non-response as being a predictor of subsequent unit-non-response. Applying various income mobility indicators there is a robust picture about income mobility being understated using truly observed information only. Finally, multivariate models show that survey-related factors (number of interviews, interview mode) as well as indicators for variability in income receipt (due to increased complexity of household structure and income composition) are significantly correlated with item-non-response. In conclusion, our empirical results based on the German SOEP indicate the selectivity of item-non-response on income questions in social surveys and push the necessity for adequate imputation.Item-Non-Response, Imputation, Income Inequality

Measuring Income in Household Panel Surveys for Germany: A Comparison of EU-SILC and SOEP

Empirical analyses of economic inequality, poverty, and mobility in Germany are, to an increas-ing extent, using microdata from the German Federal Statistical Office's contribution to the European Union Statistics on Income and Living Conditions (EU-SILC) as well as data from the German Socio-Economic Panel (SOEP). In addition to their significance for national reporting, the EU-SILC data are of great international significance for comparative EU-wide measurement, description, and analysis in support of the European Commission's stated objective of fighting poverty and reducing social inequality through the European social cohesion process. It is therefore crucial to assess the quality of the German contribution to EU-SILC, particularly in view of evidence in the literature of methodological problems in this still relatively young survey with respect to the representation of specific social groups and the distri-bution of key educational characteristics that can have a considerable impact on the degree and structure of inequality and poverty (see Hauser 2008, Causa et al. 2009, Nolan et al. 2009). While previous papers have critically examined the German EU-SILC contribution in comparison to the cross-sectional data from the German Survey of Income and Expenditure (EVS), the present paper compares EU-SILC-based results about income trends, inequality, and mobility with results based on SOEP, a widely used alternate panel survey of private households in Germany. The - in some cases severe - differences identified are discussed in the context of the surveying and interviewing methods, post-data-collection treatment of the micro-data as well as sample characteristics of the two studies, all of which exert a major influence on the substantive results and thus on the core findings regarding the social situation of Germany in EU-wide comparison.Inequality, poverty, mobility, household panel, EU-SILC, SOEP

Item Non-response and Imputation of Annual Labor Income in Panel Surveys from a Cross-National Perspective

Using data on annual individual labor income from three representative panel datasets (German SOEP, British BHPS, Australian HILDA) we investigate a) the selectivity of item non-response (INR) and b) the impact of imputation as a prominent post-survey means to cope with this type of measurement error on prototypical analyses (earnings inequality, mobility and wage regressions) in a cross-national setting. Given the considerable variation of INR across surveys as well as the varying degree of selectivity build into the missing process, there is substantive and methodological interest in an improved harmonization of (income) data production as well as of imputation strategies across surveys. All three panels make use of longitudinal information in their respective imputation procedures, however, there are marked differences in the implementation. Firstly, although the probability of INR is quantitatively similar across countries, our empirical investigation identifies cross-country differences with respect to the factors driving INR: survey-related aspects as well as indicators accounting for variability and complexity of labor income composition appear to be relevant. Secondly, longitudinal analyses yield a positive correlation of INR on labor income data over time and provide evidence of INR being a pre-dictor of subsequent unit-non-response, thus supporting the "cooperation continuum" hy-pothesis in all three panels. Thirdly, applying various mobility indicators there is a robust picture about earnings mobility being significantly understated using information from completely observed cases only. Finally, regression results for wage equations based on observed ("complete case analysis") vs. all cases and controlling for imputation status, indicate that individuals with imputed incomes, ceteris paribus, earn significantly above average in SOEP and HILDA, while this relationship is negative using BHPS data. However, once applying the very same imputation procedure used for HILDA and SOEP, namely the "row-and-column-imputation" approach suggested by Little & Su (1989), also to BHPS-data, this result is reversed, i.e., individuals in the BHPS whose income has been imputed earn above average as well. In our view, the reduction in cross-national variation resulting from sensitivity to the choice of imputation approaches underscores the importance of investing more in the improved cross-national harmonization of imputation techniques.Item non-response, imputation, income inequality, income mobility, panel data, SOEP, BHPS, HILDA

Item Non-response and Imputation of Annual Labor Income in Panel Surveys from a Cross-National Perspective

Using data on annual individual labor income from three representative panel datasets (German SOEP, British BHPS, Australian HILDA) we investigate a) the selectivity of item non-response (INR) and b) the impact of imputation as a prominent post-survey means to cope with this type of measurement error on prototypical analyses (earnings inequality, mobility and wage regressions) in a cross-national setting. Given the considerable variation of INR across surveys as well as the varying degree of selectivity build into the missing process, there is substantive and methodological interest in an improved harmonization of (income) data production as well as of imputation strategies across surveys. All three panels make use of longitudinal information in their respective imputation procedures, however, there are marked differences in the implementation. Firstly, although the probability of INR is quantitatively similar across countries, our empirical investigation identifies cross-country differences with respect to the factors driving INR: survey-related aspects as well as indicators accounting for variability and complexity of labor income composition appear to be relevant. Secondly, longitudinal analyses yield a positive correlation of INR on labor income data over time and provide evidence of INR being a predictor of subsequent unit-non-response, thus supporting the "cooperation continuum" hypothesis in all three panels. Thirdly, applying various mobility indicators there is a robust picture about earnings mobility being significantly understated using information from completely observed cases only. Finally, regression results for wage equations based on observed ("complete case analysis") vs. all cases and controlling for imputation status, indicate that individuals with imputed incomes, ceteris paribus, earn significantly above average in SOEP and HILDA, while this relationship is negative using BHPS data. However, once applying the very same imputation procedure used for HILDA and SOEP, namely the "row-and-columnimputation" approach suggested by Little & Su (1989), also to BHPS-data, this result is reversed, i.e., individuals in the BHPS whose income has been imputed earn above average as well. In our view, the reduction in crossnational variation resulting from sensitivity to the choice of imputation approaches underscores the importance of investing more in the improved cross-national harmonization of imputation techniques.Item non-response, imputation, income inequality, income mobility, panel data, SOEP, BHPS, HILDA

Internet mobility survey sampling biases in measuring frequency of use of transport modes

We develop a quantitative analysis of the biases that arise when measuring trip frequencies for a general population through an online survey instrument. Data from a national official survey in Italy, concerning both mobility behaviors and skills in using computers and internet, have been deployed to assess differences in mobility levels between those that can answer a computer/internet survey and those that cannot. Positive correlations were found between ability in using ICT tools and trip frequencies. These latter are about 15% to 150% higher for the "ICT literate", according to the travel means under consideration. A Heckman sample selection model showed us that these biases have different explanations. People knowing how to use internet are different from the others in they car driving behavior due to a range of self-related factors. Conversely, public transport patterns of use are more similar between the two groups: the observed bias is mainly due to the fact of using internet in itself, which could for example lead to a more active lifestyle. Such distinction is of practical interest because it can help defining a method to correct these biases. According to our results, the overestimation of public transport frequency of use of an internet survey could be corrected by looking at the internet diffusion in the population. On the contrary, corrections for car driving frequencies are more complex and should be based on differences in attitudinal and personal characteristics between internet survey respondents and the remainder of the populatio