Attrition adjustment for panel waves

The panel-facing nonresponse step: it adjusts for attrition (nonresponse between waves) when building a longitudinal weight, reweighting the units that stayed in to also represent those that dropped out, among the units that remain eligible. It is a thin wrapper over step_nonresponse() – so the theory stays visible in the recipe – that uses the same estimators but under a name that reads correctly in a panel cascade and with the panel conventions: the covariates should come from a wave where the unit was observed (e.g. the first period), and it does NOT absorb eligibility (out-of-scope and unknown-eligibility go in step_drop_ineligible() / step_unknown_eligibility()).

Usage

step_attrition(
  spec,
  respondent,
  method = c("propensity", "rhg", "weighting_class", "calibration"),
  formula = NULL,
  by = NULL,
  engine = c("logit", "tree", "forest", "boost"),
  num_classes = 5L,
  id = NULL
)

Arguments

spec

a weighting_spec.

respondent

an unquoted 0/1 column or logical condition, TRUE for the units that responded in the wave being adjusted (e.g. disp_T2 == "R").

method

attrition estimator: "propensity" (individual 1/phi, the SLID/ECLAC response-propensity weighting; default), "rhg" (response homogeneity groups: propensity stratified into num_classes classes), "weighting_class" (design-variable cells), or "calibration" (Sarndal-Lundstrom).

formula

model formula for "propensity"/"rhg", in covariates observed for responders and nonrespondents (from a wave where the unit was seen).

by

adjustment cells for "weighting_class".

engine

propensity engine ("logit"/"tree"/"forest"/"boost").

num_classes

number of propensity classes for "rhg".

id

optional stable step id.

Value

the weighting_spec with the attrition step appended.

Details

The attrition adjustment prescribed by ECLAC's household-survey manual (ch. XVI) and used by Statistics Canada's SLID (Naud 2002; LaRoche 2003) is response-propensity weighting (method = "propensity", i.e. inverse of the estimated response probability; Little 1986; Rosenbaum 1987) – not an ECLAC invention. method = "rhg" is the response-homogeneity-group variant (propensity stratified into num_classes groups, then the class-mean rate), which stabilises the weights.

Two ECLAC prescriptions are not implemented, and the difference matters when they apply. The manual (ch. XVI, sec. B.1.b) allows two fallback imputations for units with no auxiliary information at all: a nonrespondent whose rotation panel does not overlap (impute the overall effective response rate as its phi), and a newly incorporated nonrespondent (impute the adjusted expansion factor of its household). Here a covariate that is NA for an eligible unit is an error, not an imputation – an NA propensity would let that nonrespondent survive the adjustment silently, which is the failure the error exists to prevent, and the package will not silently substitute a value of its own for a missing input.

So when the error fires, the fix belongs in the data, and either ECLAC route is available to you there: impute the missing covariates before the step (the manual's own fallback is the overall effective response rate, i.e. a constant, which is what a model with no covariates for those units amounts to), give a newly incorporated nonrespondent its household's adjusted factor, or restrict formula to a covariate set observed for every eligible unit. What the package will not do is choose one of those for you and leave no trace in the recipe. Multi-wave retention chaining (decomposing Pr(in at T3) into Pr(reach T2) x Pr(T2 to T3)) is likewise absent: this step adjusts one transition at a time, which is what ch. XVI specifies for two consecutive periods.

See also

step_nonresponse(), step_panel_overlap(), step_drop_ineligible()

Examples

wide <- panel_merge(
  list(T1 = subset(panel_ine, ola == 1), T2 = subset(panel_ine, ola == 2)),
  by = c("id_hogar", "nper"), require = "all")
weighting_spec(wide, base_weights = w_base_T1) |>
  step_panel_overlap(prob = 5 / 6) |>
  step_drop_ineligible(disp_T2 == "OS", reason = "left the target population") |>
  step_attrition(respondent = disp_T2 == "R", method = "propensity",
                 formula = ~ edad_T1 + sexo_T1 + region_T1) |>
  prep()
#> 
#> == Weighting specification (weightflow) ==
#> Data    : 1717 cases
#> Base wts: w_base_T1
#> Steps   :
#>   1. panel overlap  [panel_overlap_1]
#>   2. drop ineligible (left the target population)  [drop_ineligible_1]
#>   3. attrition (propensity)  [nonresponse_1]
#> Status  : estimated (prep)
#> 
#> Stage summary:
#>                         stage n_active sum_wts cv_wts deff_kish n_eff
#>                          base     1717  245498  0.300     1.090  1575
#>    stage_1_step_panel_overlap     1717  294597  0.300     1.090  1575
#>  stage_2_step_drop_ineligible     1633  279699  0.302     1.091  1496
#>        stage_3_step_attrition     1464  279688  0.303     1.092  1341
#> 
#> deff_kish = 1 + CV^2 (Kish design effect from unequal weighting);
#> n_eff = n_active / deff_kish. Both worsen with each adjustment and
#> improve with trimming.
#>