Synthetic rotating- and pure-panel datasets — panel

Four small, reproducible household-panel datasets that ship with weightflow to test and illustrate the panel tools. They share one structure and differ only in the rotation system, so the same code runs on a pure panel and on each rotating design. All are in long format: one row per person and per wave the person is in sample. Continuing units keep the same household_id / person_id across waves, so they link the panel; the household is the natural cluster and c(household_id, person_no) the person-level key.

Usage

panel_puro

panel_cl

panel_ine

panel_us

Format

A data.frame with one row per person-wave and the columns:

household_id: household id, persistent across waves (the panel link / cluster).
person_id, person_no: person id and person-number within household; c(household_id, person_no) is the person key.
stratum, psu: design stratum and primary sampling unit (PSU nested in stratum, at least two PSUs per stratum), for the coordinated bootstrap / jackknife.
region, sex, age: covariates usable as estimation domains.
wave: wave (month) index.
rotation_group, month_in_sample: rotation group and order-in-sample.
pw: design (base) weight.
disposition: between-wave disposition: "R", "NR", "OS", "UNK".
lf_status: labour status within the working-age population, a factor with levels "emp" / "unemp" / "inact"; NA for non-respondents. This is the status argument of step_cre() (the previous-wave composite auxiliary).
employed, unemployed: employed / unemployed indicators (labour force; NA if not "R" or not in the labour force).
income: labour income (NA if not "R").

Details

The between-wave disposition disposition follows the four-state taxonomy the longitudinal cascade needs: "R" responded, "NR" eligible nonresponse (reweight), "OS" out of scope – left the target population between waves, so the household exits permanently and is not reweighted – and "UNK" unknown eligibility. The variables of interest (employed, unemployed, income) are observed only when disposition == "R" (and, for the labour-force items, when the person is in the labour force), otherwise NA; they repeat across waves for continuing persons, with within-person persistence, so net change and gross flows are meaningful.

The datasets differ only in the rotation calendar (which waves each rotation group is in sample), giving different overlap structures:

panel_puro: Pure panel, no rotation: every unit is followed across all 4 waves; the sample shrinks only through attrition. Style of EU-SILC / SLID pure panels.
panel_cl: Chile ENE, 2-2-2 (in-out-in): 3 waves, consecutive overlap ~1/2, and units that return (in sample in waves 1 and 3 but not 2). The real ENE has no public rotation group; rotation_group is included for teaching, but the panel also links through the persistent ids alone.
panel_ine: INE Uruguay ECH / StatCan LFS, 6-month rotation: 6 groups in sample each wave, one sixth rotating out per wave, so consecutive overlap ~5/6.
panel_us: US CPS, 4-8-4: 3 waves, consecutive overlap ~3/4, with a cohort that leaves and returns after the 8-month gap.

Examples

# rotation structure of the 6-month panel
panel_design(panel_ine, unit = c("household_id", "person_no"), wave = "wave",
             rotation_group = "rotation_group", pattern = "6")
#> <weightflow panel design>
#>   waves      : 3 (1, 2, 3)
#>   unit       : household_id + person_no
#>   rotation   : rotation_group  pattern: 6
#>   units      : 2783 (linked in >=2 waves: 2063, 74%)
#>   overlap (row wave retained in column wave):
#>   1    2    3   
#> 1 1.00 0.83 0.64
#> 2 0.83 1.00 0.81
#> 3 0.65 0.82 1.00
#>   Pr(panel selection), adjacent : 0.833, 0.833  (full combination: 0.667)
#>   overlap implied by pattern    : 0.83 0.67   (lag 1 2)
#>   pattern                       : 6 group(s) in sample, cycle 6, useful lags 1, 2, 3, 4, 5
# coordinated change of the unemployment rate between two waves
t1 <- subset(panel_ine, wave == 1 & disposition == "R")
t2 <- subset(panel_ine, wave == 2 & disposition == "R")
wb <- wave_bootstrap(
  list(T1 = weighting_spec(t1, base_weights = pw),
       T2 = weighting_spec(t2, base_weights = pw)),
  replicates = 100, strata = "stratum", psu = "psu", seed = 1, progress = FALSE)
change_mean(wb, "unemployed")
#> <weightflow net change>
#>   T1 -> T2
#>   change     : -0.0149512   SE 0.010056
#>   95% CI    : [-0.0346606, 0.00475807]
#>   V1 0.0001126 | V2 0.0001035 | Cov 5.75e-05 | rho 0.533   (levels)
#>   V = 0.0001011  vs  V1+V2 = 0.0002161  (deff_change 0.468: overlap saved 53%)