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 ID: 435478
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Slide1
Nonresponse bias in studies of residential mobility
Elizabeth
Washbrook
, Paul Clarke and Fiona Steele
University of Bristol
Research Methods Festival, 3 July 2012Slide2
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 Slide3
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 moveSlide4
A standard model for mobilitySlide5
Modelling responseSlide6
The direct dependence (DD) modelSlide7
An alternative response modelSlide8
Maximum likelihood estimationSlide9
Maximum likelihood estimationSlide10
Exclusion restrictionsSlide11
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)Slide12
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 waveSlide13
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.Slide14
Results I. Nonignorability
and IV parameters
Value of
γ
implies moving reduces the expected response probability from 0.95
to 0.55.Slide15
Results II. Covariates of interestSlide16
Results III. Response equationSlide17
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 responseSlide18
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