Using survey weights with ihsMW

For researchers targeting population-representative estimates, accounting for the complex survey design of the Malawi Integrated Household Survey (IHS) is non-negotiable. The ihsMW package abstracts the tedious configuration of these designs, integrating directly with existing R infrastructure to provide statistically sound representations natively.

1. Why weights matter

The IHS utilizes a stratified two-stage cluster sample design explicitly mapped to represent the national population, rural/urban disparities, and specific geographical districts simultaneously.

Unweighted means are NOT nationally representative. Computing a simple average across an unweighted data.frame yields biased estimates skewed by disproportionate sampling allocations inherent to rural versus urban tracks.

“Overall, the IHS5 sample design is a stratified two-stage sample… Therefore, it is imperative that the survey weights be used when making national-level or regional-level estimates.” — National Statistical Office (2020), IHS5 Basic Information Document, Sampling Section.

2. The IHS_survey() function

To bridge the gap between raw data downloads and proper statistical weights seamlessly, ihsMW provides the IHS_survey() function. This wrapper extracts the intended indicators alongside their underlying survey dimensions dynamically.

library(ihsMW)

# Automatically intercept consumption variables and inject structural weighting
svy <- IHS_survey("rexp_cat01", round = "IHS5")

# The output natively masks as a tbl_svy allowing tidy-eval manipulation
class(svy)
#> [1] "tbl_svy"     "svydesign2"  "svydesign"

3. Computing weighted estimates

Once instantiated, the survey object behaves exactly as a standard complex environment gracefully tracking variances internally.

If you prefer classic structural base approximations using the survey package:

library(survey)

# Compute the statistically accurate, nationally representative average
svymean(~rexp_cat01, design = svy, na.rm = TRUE)

# Segment the nationally representative consumption by explicit strata
svyby(~rexp_cat01, ~stratum, svy, svymean, na.rm = TRUE)

Alternatively, leverage the srvyr package bridging dplyr pipelines into weighted topologies intuitively:

library(srvyr)

# Tidy-style summaries mapping the underlying survey dimensions natively
svy |>
  group_by(stratum) |>
  summarise(mean_cons = survey_mean(rexp_cat01, na.rm = TRUE))

4. Multi-round weighted analysis

Because population sizes, spatial mapping coordinates, and primary sampling units fundamentally shift globally between cross-sectional spans, survey objects targeting discrete rounds should NOT be intelligently pooled naively under a unified statistical architecture without intensive independent reweighting operations.

Instead, when querying multiple rounds natively, IHS_survey() protects inferences iteratively yielding a distinct instantiated named list structure explicitly encapsulating the unique configurations cleanly.

# Requesting pooled objects targets isolated arrays preserving isolated bounds
svy_list <- IHS_survey("rexp_cat01", round = c("IHS4", "IHS5"))

# Apply functional iteration computing the unique representation safely
lapply(svy_list, function(s) {
  survey::svymean(~rexp_cat01, design = s, na.rm = TRUE)
})

5. Weight variables per round

ihsMW relies on a hard-coded internal mapping dictating the string topologies targeted during object instantiation dynamically.

Round	`weight_var`	`strata_var`	`cluster_var`
IHS1	`wght`	`stratum`	`ea_id`
IHS2	`hh_wgt`	`stratum`	`ea_code`
IHS3	`hh_wgt`	`stratum`	`ea_id`
IHS4	`hh_wgt`	`stratum`	`ea_id`
IHS5	`hhweight`	`stratum`	`ea_id`

Crucial Warning: These mappings are assumed static but should always be independently verified by the researcher. You must actively cross-reference these fields against the explicit World Bank Microdata Library BIDs natively avoiding invalid proxy mappings implicitly.

6. Common mistakes

Researchers approaching the package frequently implement standard code workflows resulting in structurally invalid endpoints natively:

Using raw IHS() outcomes: Extracting variables exclusively natively bypassing survey targets avoids structural weights natively skewing estimates entirely inappropriately. Always utilize IHS_survey() if inference depends on it.
Naive pooling across bounds: Extracting IHS(round = "all") into a static .dta equivalent and independently wrapping it natively into a single global .svydesign() fundamentally crashes cluster targets natively. Always retain separate round lists securely.
Forgetting the clustering attributes natively: Ignoring the structural requirement utilizing standard survey::svydesign() directly without capturing the nest = TRUE flag fundamentally masks variance clusters inflating assumed structural confidences artificially.