Presentation on theme: "Nonresponse bias in studies of residential mobility Elizabeth Washbrook, Paul Clarke and Fiona Steele University of Bristol Research Methods Festival,"— Presentation transcript:
Nonresponse bias in studies of residential mobility Elizabeth Washbrook, Paul Clarke and Fiona Steele University of Bristol Research Methods Festival, 3 July 2012
The problem of panel nonresponse Household survey panel data permits social scientists to analyse a wide range of issues that cannot be addressed with cross-sectional data But the value of panel data is potentially undermined by nonresponse (dropout or intermittent missingness) – Smaller sample sizes reduce the efficiency of estimates – More seriously, selective nonresponse can lead to biased estimates – those who remain in the sample become untypical of the population as a whole
Residential mobility application The study of residential mobility/migration is at the core of studies of demography and the life course – how do different groups change their housing or location in response to changing circumstances? Nonresponse issues are rarely considered in the substantive literature on mobility, yet there are reasons to think it might be even more of a problem here than in other applications. Moving house (the outcome of interest) is often cited as a key reason why people drop out of panel surveys → movers who remain are not typical of movers as a whole – PSID 1968-1989 had a 51% attrition rate. Fitzgerald et al. (JHR 1998) provide data showing at least 20% of attritors were lost following a move
Residential mobility in the BHPS BHPS is representative sample of 5500 households in 1991, interviewed annually (18 waves of data on over 10,000 individuals). Sample of men 20-59, living in England or Wales in year t-1, from Waves 6-18 (1996-2008) – Full-time students and retirees excluded – Focus on men avoids the ‘double-counting’ problem in which sample individuals move together as a couple 4,724 individuals contributing 33,347 person-year observations (mean 7.1)
Residential mobility in the BHPS Outcome =1 if individual moved to a different residence within the same region between t-1 and t (longer distance moves coded 0) – The majority of moves are local (85% in this sample) – Motivations for short- and long-distance moves tend to the quite different: long-distance moves are more job-related while short-distance moves are more housing-related Outcome observed for 94.5% of observations, among which mobility rate is 9.6%. 38% of sample individuals are known to have moved at least once, 16% more than once. 36% drop out of the panel at least once, 6% re-enter at a later wave
Exclusion restrictions Outcome instrument – Log average sale price of properties in region of residence over 12 months prior to t-1, deflated by RPI. From Land Registry data (only available for England and Wales from 1995 onwards). – Expect that high house prices will deter mobility, but will have no independent effect on response, conditional on year and region fixed effects. Response instrument – Sample membership status. Original 1991 sample adult (OSM; omitted), 65%; ECHP joiner in 1997, 4%; Celtic booster sample joiner in 1999, 14%; parent of OSMs child, 9%; original 1991 sample child, 8%. TSMs dropped. – Survey-related variables are often used as instruments in this context (e.g. Cappellari and Jenkins 2008). The rationale is that stronger survey attachment will have been fostered among OSMs than among later joiners or those involved only because of family ties.
Results I. Nonignorability and IV parameters Value of γ implies moving reduces the expected response probability from 0.95 to 0.55.
Conclusions Estimates of some predictors of moving house in the BHPS differ depending on whether or not attrition bias is accounted for in the analysis – The positive effect of unemployment is markedly larger than suggested by MAR estimates – The positive effect of economic inactivity (p<.1) is insignificant in the MAR estimates – Higher qualifications are no longer significantly associated with greater mobility when non-response is accounted for The direction of the changes implies that effects are underestimated for covariates negatively associated with response and overestimated for those positively associated with response
Conclusions Both the DD and BP models reject ignorability of non-response. Corrections made by the two models are in the same direction, but larger in the former case. The log likelihood suggests the DD model is a slightly better fit. Next steps: simulation studies to explore the effect of including exclusion restrictions of varying strengths when the error distribution is mis-specified The potentially causal nature of the relationship between mobility and nonresponse implies that it is particularly important to consider the issue in studies of mobility, and provides an a priori reason for favouring a DD-type response mechanism. There are other examples where the DD model may be more appropriate, e.g. studies modelling poor health as the outcome