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Beschreibung:
The computation of cross sectional weights in household panels is challenging because household compositions change over time. Sampling probabilities of new household entrants are generally not known and assigning them zero weight is not satisfying. Two common approaches to cross sectional weighting address this issue: (1) "shared weights" and (2) modeling or estimating unobserved sampling probabilities based on person-level characteristics. We survey how several well-known national household panels address cross sectional weights for different groups of respondents (including immigrants and births) and in different situations (including household mergers and splits). We show that for certain estimated sampling probabilities the modeling approach gives the same weights as fair shares, the most common of the shared weights approaches. Rather than abandoning the shared weights approach when orphan respondents (respondents in households without sampling weights) exist, we propose a hybrid approach; estimating sampling weights of newly orphan respondents only.