Alberto Cavallo Presentation
PPPs and Exchange Rates
Evidence from Online Data in Seven Countries
Alberto Cavallo
MIT and NBER
March 2015
Agenda
- motivation
- Data
- RERs
- Shock Adjustments(half-lives, persistence comparisons)
- Relative prices vs E shocks
Conclusions
- RERs shock adjustment is much faster than previously recorded ➜ months, not years
-
- micro data necessary to compare relative price levels
- Tradable, perfectly matched goods
- Deviations of RER from normal levels increase pressure to adujust either Relative Prices or E
- Adjustment margin and speeds vary by country
- Argentina, Chaina, Australia ➜ mostly prices
- Brazil, South Africa, UK ➜ mostly E
Motivation

Motivation

Motivation

Online Data and the Billion Prices Project
- Academic Project at Mit to collect data from retailers that post prices online.
- Started in 2008, joint with Roberto Rigobon (MIT)
- Objective: inflation measurement and macro/international research
- We collect daily data from hundreds of large retailers, for all goods sold, in 50 counteries.

How does Data Scraping work
- Every day, our software downloada a public webpage, analyses its HTML code, extract price data, and stores it in a database

Alternatinve Data Sources for PPPs

Are Online Price Indices match CPIs
- Our Online Price Indices match CPIs ➜ implies price change are similar

But for PPP comparisons, price levels are key.
Are Price Levels Different?
- Cavallo (2015) ➜ simultaneous random sampling of online and offline prices using barcode-scanning app and crowdsourced workers

Are Price Levels Different?
- Cavallo (2015) ➜ simultaneous random sampling of online and offline prices using barcode-scanning app and crowdsourced workers

PPP Series with Online Data

Methodology

A Country-level RER For tradable goods

Methodology

Methodology

Methodology

Methodology

Methodology

Methodology

Aggregate Results

Aggregate Results

Aggregate Results
- Most RERs appear to fluctuate around certain levels (relative PPP)
- Are these levels reasonable
- Comparison to ICP

Rates of Convergence and Half-lives of RERs Shocks
- There is much faster mean reversion in these RERs than in the literature

- Why faster adjustment?
- Micro date more volatile ➜ control for sale events, stockouts
- Only tradable goods
- Identical Goods across country ➜ perfect matching
Co-movement in Relative prices and E
- Deviation of RER from normal level increase pressure to adjust

- Adjustment may come through relative prices or the exchange rate
- Magnitude persistence of each shock could matter
- Implies that E co-moves with Pus/PLc the PPP exchange rate
Argentina

Brazil

UK

Australia

China

South Africa

A Vector Error Correction Model

Error Correction Model

Extension
- We can further:
- Â Restrict the analysis to periods when the deviation is big (RERs over a given threshold)
- Speeds of adjustment increase
- Distinguish between devation in one sector/product vs deviation multiple sectors/products
- Single-sector Deviation (eg gas prices) tend to be corrected via prices, while multi-sector deviations leads to mecro adjustments via the nominal exchange rate
Conclusions
- RERs shock adjustment is much faster than previously recorded months, not years
- Micro-date necessary to compare relative price levels
- Tradable perfectly matched goods
- Deviation of RER from normal levels increase pressure to adjust either relative price or E
- Adjustment margin and speeds vary by country
- Argentina, China, Australia mostly Prices
- Brazil, South Africa, UK mostly E
Using CPIs:Relative Prices and E

Devation in Many Products
