| Mobile Phone Ownership in DHS7 (IND 2019-21) [message #29015] |
Wed, 10 April 2024 11:21  |
researcher_dhs
Messages: 3
Registered: April 2024
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Member |
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I am currently completing a project that calculates household and women's phone ownership estimates by demographic characteristics (wealth index, gender, rural/urban etc.) across several countries' DHS surveys.
While running the standard code in R for calculating women's phone ownership (I provide the code/function below), I noticed that there is a high rate of NAs in the India data, higher than any other country's (in my dataset spanning 43 country-year surveys). If I run the standard code, I get a low phone ownership rate of approximately 8%. If I omit all NAs (84% of the data!), I still get phone ownership = 53.9% among women, which is higher than the rate stated in the official DHS report and in StatCompiler (50.9%)
Is there a reason for this?
Function for calculating women's phone ownership:
analyze_dhs_wm <- function(df_name) {
df <- get(df_name, envir = .GlobalEnv)
country_code <- substr(df_name, 1, 3)
if (!"v169a" %in% names(df)) {
# Handle the case where v169a does not exist in the dataframe
phone_ownership_weighted <- NA
phone_ownership_percent <- NA
na_percentage <- NA
} else {
# Proceed if v169a exists
phone_ownership_weighted <- sum(df$v169a * (df$v005 / 1e6), na.rm = TRUE) / sum(df$v005 / 1e6, na.rm = TRUE)
phone_ownership_percent <- phone_ownership_weighted * 100
na_percentage <- mean(is.na(df$v169a)) * 100
}
return(data.frame(country_code = country_code,
type = "DHS_wm",
phone_ownership_weighted = phone_ownership_weighted,
phone_ownership_percent = phone_ownership_percent,
na_percentage = na_percentage))
}
results_dhs_wm <- do.call(rbind, lapply(dhs_wm_data_frames, analyze_dhs_wm))
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