Shining a Light on Purchasing Power Parities

47 Pages Posted: 20 Mar 2018 Last revised: 23 Mar 2023

See all articles by Maxim Pinkovskiy

Maxim Pinkovskiy

Federal Reserve Bank of New York

Xavier Sala-i-Martin

Columbia University, Graduate School of Arts and Sciences, Department of Economics

Date Written: March 2018

Abstract

Nighttime lights data are a measure of economic activity whose error is plausibly independent of the measurement errors of most conventional indicators. Therefore, we can use nighttime lights as an independent benchmark to assess existing measures of economic activity (Pinkovskiy and Sala-i-Martin (2016)). We employ this insight to generate three findings in the study of PPP-adjusted estimates of GDP around the world between 1992 and 2010. First, we find that while market exchange rates described poor economies better than did PPP-adjusted estimates in the late 1990s (Dowrick and Akmal 2008; Almas 2012), this pattern has disappeared by the 2010s. Second, we also find that estimates of PPPs have been steadily improving from one price survey round to the next, including during the controversial 2005 and 2011 rounds. Third, we leverage this fact to assess whether it is optimal to measure relative prices as close as possible to the year of interest or to use the latest available relative price data and discard the rest, and provide a theoretical framework in which the latter may be optimal. Using data from the Penn World Tables, we find that, indeed, it is optimal to only use the latest price data, and hence, to revise existing PPP-adjusted estimates whenever a new price survey is released.

Suggested Citation

Pinkovskiy, Maxim and Sala-i-Martin, Francesc Xavier, Shining a Light on Purchasing Power Parities (March 2018). NBER Working Paper No. w24419, Available at SSRN: https://ssrn.com/abstract=3143344

Maxim Pinkovskiy (Contact Author)

Federal Reserve Bank of New York ( email )

Francesc Xavier Sala-i-Martin

Columbia University, Graduate School of Arts and Sciences, Department of Economics ( email )

420 W. 118th Street
New York, NY 10027
United States
212-854-7055 (Phone)

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