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Adjust Monetary Variables for Inflation and PPP

apply_ppp_adjustment.Rd

Applies price adjustments to a monetary variable in a list of LIS/LWS datasets using LIS-provided deflators. Adjustments can be made for domestic inflation (CPI), purchasing power parity (PPP), or both (lisppp).

Usage

apply_ppp_adjustment(data_list, var_name, database, transformation = "lisppp")

Arguments

data_list

A named list of data frames, from LIS or LWS microdata.

var_name

A string. The name of the monetary variable to be adjusted.

database

A string, either "lis" or "lws", indicating the source database. Required for determining how to handle the deflators and income reference years.

transformation

A string specifying the type of adjustment:

  • "lisppp" (default): Adjusts by CPI and by PPP.

  • "cpi": Adjusts only for domestic inflation (CPI).

  • "ppp": Adjusts only for purchasing power parity (PPP), for cross-country comparability.

Value

A list of data frames, with the specified variable adjusted based on the chosen transformation.

Details

For LWS datasets and income variables, the function accounts for discrepancies between survey years and income reference years. It merges the appropriate deflator tables before applying the requested adjustment.

Important: When using "ppp" or "lisppp" transformations, the monetary values are converted out of their original currency. These adjustments are intended to support cross-country comparability, but the result is no longer expressed in national currency units.

Examples

if (FALSE) { # \dontrun{ 
library(lissyrtools)

# --- Example 1: CPI Adjustment (Domestic Inflation, Italy) ---

it_data <- lissyuse("it", vars = "dhi", from = 2010)
run_weighted_mean(it_data, var_name = "dhi")  # Nominal income

it_data_cpi <- apply_ppp_adjustment(it_data, "dhi", database = "lis", transformation = "cpi")
run_weighted_mean(it_data_cpi, var_name = "dhi")  # Real income (CPI-adjusted, base year = 2017)


# --- Example 2: PPP Adjustment Across Countries (France, Poland, US, UK, Mexico in 2016) ---
multi_2016 <- lissyuse(c("fr16", "pl16", "us16", "uk16", "mx16"), vars = "dhi")
run_weighted_mean(multi_2016, var_name = "dhi")  # Nominal

multi_2016_ppp <- apply_ppp_adjustment(multi_2016, "dhi", database = "lis", transformation = "ppp")
run_weighted_mean(multi_2016_ppp, var_name = "dhi")  # PPP-adjusted


# --- Example 3: LIS PPP (Across Time and Countries: Canada & Mexico, 2015–2020) ---
can_mex <- lissyuse(c("ca15", "ca18", "ca20", "mx16", "mx18", "mx20"), vars = "dhi")
run_weighted_mean(can_mex, var_name = "dhi")  # Nominal

can_mex_lisppp <- apply_ppp_adjustment(can_mex, "dhi", database = "lis", transformation = "lisppp")
run_weighted_mean(can_mex_lisppp, var_name = "dhi")  # Fully deflated (real + PPP)


# --- Example 4: Reference Year Differences in Same Survey Year (Germany 2017, LIS vs LWS) ---
lis_de17 <- lissyuse("de17", vars = "dhi")
lws_de17 <- lissyuse("de17", vars = c("dhi", "dnw"), lws = TRUE)

apply_ppp_adjustment(lis_de17, "dhi", database = "lis", transformation = "lisppp")[[1]] %>% dplyr::summarise(mean_cpi_lis = mean(cpi)) 
apply_ppp_adjustment(lws_de17, "dhi", database = "lws", transformation = "lisppp")[[1]] %>% dplyr::summarise(mean_cpi_lis = mean(cpi))

# Even with the same survey year (DE17), different income reference years are accounted for automatically.
} # }