Skip to contents

Create "Average Price Table"

Source: R/valid.R
valid_apt.Rd

valid_apt() in OECDsppps creates the "Average Price Table" by calculating: the

  • Number of observations - Number of observations by group as specified by group_by()

  • Average- Average price based on item-level price quotes by group as specified by group_by()

  • Maximum- Highest price based on item-level price quotes by group as specified by group_by()

  • Minimum - Lowest price based on item-level price quotes by group as specified by group_by()

  • Standard deviation - Standard deviation based on item-level price quotes by group as specified by group_by()

  • mMx-min ratio test and Coefficient of variation test - see Details for more information All item-level price quotes that do not pass the two tests are flagged in columns Max-min ratio FLAG andCoefficient of variation FLAG, respectively; see World Bank (2013), ICP (2021) and European Union/OECD et al. (2024) .

Usage

valid_apt(data, value = "Reference quantity price")

Arguments

data

A data frame or tibble containing at least one column with individual item-level price quotes.

value

Column containing the individual item-level price quotes, which should be based on the "reference quantity price"; see Details for more information.

Details

Reference quantity price: Scales the observed price to the quantity that should be surveyed. It is defined as: \[Reference~quantity~price = \frac{Observed~price}{Observed~quantity} \times Reference~quantity\] whenever the measurement unit of observed quantity is identical to the measurement unit of the reference quantity.

Max-min ratio test: The ratio between the maximal and minimal observed price for product \(j\), \(p_j\). Products where the maximal observed price is more than twice as big as the minimum are flagged in Max-min ratio FLAG: \[max-min~ratio = max(p_j)/min(p_j)\]

Coefficient-of-variation test: The standard deviation \(\sigma_{p_j}\) of product \(j\)'s price \(p_j\) expressed as a percentage of the average price for the product, \(\mu_{p_j}\). Products with a coefficient of variation greater than 20% will be flagged in Coefficient of variation FLAG: \[coefficient-to-variation: \sigma_{p_j} / \mu_{p_j}\]

Using the "Average Price Table" for additional validation: In addition to the raw data validation, valid_apt() can be used to check for outliers in the household expenditure share as well as price estimates from the CPD regression model, in which cases the input argument value takes either the reported item-level household expenditure shares, or sPPP estimates, respectively.

References

European Union/OECD, Hearne D, Bailey D (2024). Eurostat-OECD Methodological Manual on Purchasing Power Parities (2023 Edition), volume 12(1). OECD Publishing, Paris. doi:10.2785/384854 , https://doi.org/10.1080/21681376.2025.2475115.

ICP (2021). “A Guide to the Compilation of Subnational Purchasing Power Parities (PPPs).” International Comparison Program. https://thedocs.worldbank.org/en/doc/5064f2288436664bc8f9811c8a5b8c55-0050022021/original/Guide-Subnational-PPPs.pdf.

World Bank (2013). Measuring the Real Size of the World Economy: The Framework, Methodology, and Results of the International Comparison Program — ICP. World Bank. doi:10.1596/978-0-8213-9728-2 .

Examples

suppressPackageStartupMessages(library(dplyr))
library(OECDsppps)
uk_cpi |>
  select(Year, Region, `Product code`, `Reference quantity price`) |>
  group_by(Year, Region, `Product code`) |>
  valid_apt(value = "Reference quantity price") |>
  head(n = 2) |>
  t()
#>                               [,1]            [,2]           
#> Year                          "2018"          "2018"         
#> Region                        "East Midlands" "East Midlands"
#> Product code                  "210111"        "410518"       
#> Number of observations        "258"           "192"          
#> Average price of product      " 1.078527"     "20.285417"    
#> Maximum price of product      " 1.3"          "33.6"         
#> Minimum price of product      "0.99"          "8.00"         
#> Standard deviation            "0.08018559"    "7.81303085"   
#> Max-min ratio                 "1.313131"      "4.200000"     
#> Coefficient of variation      "0.07434731"    "0.38515506"   
#> Max-min ratio FLAG            "FALSE"         "TRUE"         
#> Coefficient of variation FLAG "FALSE"         "TRUE"