The Billion Prices Project Using Online Prices for Inflation and Research
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1 The Billion Prices Project Using Online Prices for Inflation and Research Alberto Cavallo MIT & NBER MFM Conference - NYU January 2016
2 Big Data in Macro and International Quote from Griliches (AER 1985) on the uneasy alliance between economists and data: Big Data A revolution in data collection technologies Data collected to fit our research purposes
3 Do we need better data for research? Source: Cavallo (2015) Scraped Data and Sticky Prices, NBER WP 21490
4 Do we need better data for inflation measurement? An extreme case: Argentina since 2007 Source: Cavallo, Cruces, Perez-Truglia (2016) Learning From Potentially-Biased Statistics, BPEA.
5 The Billion Prices Project at MIT Sloan Academic initiative to collect and use online price data for measurement and research applications Use web scraping to collect prices from large multi-channel retailers Daily data since 2008 from hundreds of retailers in 50 countries. Our Research is focused on Inflation Measurement & Forecasting Pricing Dynamics (Stickiness) Real Exchange Rate and PPP
6 What is Scraped Online Data? Collected from public websites using web-scraping software A robot periodically downloads a public webpage, analyses its HTML code, extract price data, and stores it in a database
7 Advantages and Disadvantages Online Data Advantages Cheap to collect Frequency (daily) Granularity All product details (brands, size, anything shown online) All goods and varieties available for sale (census) New goods automatically sampled Easier to compare internationally Disadvantages Not all categories of goods and services are online (not yet) Fewer retailers and locations than CPI Short time series relative to CPI No quantities (present in Scanner Data) Online and Offline prices may behave differently?
8 BPP and Daily Inflation Measurement In 2008 à daily price index for Argentina In 2010 à daily price index for the US on the BPP website Since 2011, PriceStats has been publishing daily inflation indices in 22 countries in real-time (3-day lag) Use scraping technology Connect to thousands of online retailers every day Find individual items Store key item information in a database Calculate real-time Indexes Date Item Price Description
9 Argentina Source: Cavallo & Rigobon (2015) The Billion Prices Project.
10 Argentina Source: Cavallo & Rigobon (2015) The Billion Prices Project.
11 US Price Index July 2014 January 2015 January 2011 May /16/ /20/2008 Source: Cavallo & Rigobon (2015) The Billion Prices Project.
12 US Annual Inflation Source: Cavallo & Rigobon (2015) The Billion Prices Project.
13 US Monthly Inflation Source: Cavallo & Rigobon (2015) The Billion Prices Project.
14 Developing vs Developed Countries Source: Cavallo & Rigobon (2015) The Billion Prices Project.
15 Sectors vs Global Aggregates Source: Cavallo & Rigobon (2015) The Billion Prices Project.
16 Differences with CPI : Quality Adjustments Many complex techniques applied in CPI methods, such as hedonic quality adjustments, are needed because the data has inherent limitations Online data has big data advantages: uncensored spells (automatically included at introduction) all varieties/models on display Source: Cavallo & Rigobon (2015) The Billion Prices Project.
17 Differences with CPI : Quality Adjustments Simple indices can approximate the level and trend of CPI inflation in hedonic-adjusted categories (as suggested in Silver & Heravi (99), Aizcorbe, Corrado & Doms (2003)) Source: Cavallo & Rigobon (2015) The Billion Prices Project.
18 Are Online and Offline Prices Similar? Simultaneous collection of online and offline prices in 50 large retailers in 10 countries Results: 70% of price levels are identical (see details) + Android App apps/details?id=com.mit.bpp Lowest in food/drugstores and highest in electronics/apparel. Similar frequency and size of price changes Source: Cavallo (2016) Are Online and Offline Prices Similar? Evidence from Multi-Channel Retailers.
19 International Relative Prices Classic questions with lots of puzzles Law of One Price and market segmentation PPP puzzle à slow mean reversion of RERs Low exchange rate pass-through Huge data limitations: CPIs à no levels, different baskets, products, and methods Big Mac indices à only 1 good! World Bank ICP à low frequency (5 years)
20 International Relative Prices We use online prices to improve (i) coverage of countries, (ii) quality of matches, (iv) quantity of goods, and (iii) frequency of observations Main challenge: matching product ids across countries QJE 2014 : RERs for goods sold by Apple, IKEA, H&M, Zara, and other global retailers à RER = 1 in currency unions but not in pegs or floats. Source: Cavallo, Neiman, & Rigobon (2014) Currency Unions, Product Introductions, and the Real Exchange Rate. Quarterly Journal of Economics.
21 We use machine learning to classify ids. The models train on language specific, hand-categorized items. International Relative Prices Nowà Much broader set of closely-matched goods sold by largest retailers in each country A Big Mac index with hundreds of products (e.g. coffee, decaf, ground ) and thousands of varieties (brands, sizes, and retailers) 50 thousand individually matched items 300 narrowly defined products Food, fuel, and electronics Sector and Country-Level Indices Daily frequency 7 countries
22 International Relative Prices We find faster mean reversion in RERs than observed with CPI-based RERs Australia vs US Argentina vs US Source: Cavallo & Rigobon (2015) The Billion Prices Project.
23 International Relative Prices Driven by faster co-movement of relative prices and nominal exchange rates Australia vs US Argentina vs US Source: Cavallo & Rigobon (2015) The Billion Prices Project.
24 Conclusions Online data can be a reliable source of information for inflation measurement Cheaper, faster, provides anticipation Big Data characteristics can simplify measurement Online prices increase the frequency and quality of micro price data available for research Re-evaluate old empirical puzzles or address questions that could not be answered before Big Data à opportunity to get involved in the grubby task of data collection, creating datasets that fit our specific research needs.
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26 Extra Slides
27 Each Data Source has Advantages and Disadvantages CPI Data Purpose: measure inflation Advantages Representative sample carefully-chosen goods many retailers and locations Long Time Series Collection of posted prices in stores Disadvantages Very costly to collect and access Low frequency (monthly) Limited number of goods and varieties Some unit values and imputed prices Difficult international comparisons
28 Each Data Source has Advantages and Disadvantages Scanner Data Purpose: marketing analytics (eg. brand market shares) Advantages Transaction data Contains quantities and sometimes costs Granularity Some product details for all goods sold Frequency (weekly) Disadvantages Limited coverage (supermarkets) High cost to collect/acquire Data characteristics vary greatly depending on provider, location, time period, etc. Hard to compare internationally Unit values and time-averages (eg: prices are often calculated as sales/quantity in a week)
29 Price Stickiness I use online data to show that many of the previous findings in the literature were driven by measurement biases in CPI and scanner data: Weekly average prices in scanner data Cell-relative imputation for temporary missing prices in CPI data Effects: Reduce the duration (stickiness) of price changes Create spurious small changes à altering the shape of distribution Cause downward sloping hazard functions Source: Cavallo (2015) Scraped Data and Sticky Prices, NBER Working Paper No.21490
30 Distribution of the Size of Price Changes The distribution is bimodal, with little mass near 0% à more consistent with adjustment/menu cost models Source: Cavallo (2015) Scraped Data and Sticky Prices, NBER Working Paper No.21490
31 Advantages of Online Data to Measure Stickiness Free from measurement bias High frequency Posted prices All products Uncensored price spells Available in multiple countries à no differences in methods, time periods or type of data. Real-time data, with no delays à policy applications
32 Anticipation in Online Prices Online prices tend to anticipate changes in CPI inflation trends. More than just quicker access to data Online prices tend to react faster to shocks. Why? Lower adjustment (or menu) costs Online shoppers may be less sensitive to price changes More intense and transparent competition We can study the link between online data and CPIs using simple VARs. VAR regressions with Δ CPI on the LHS and lags of Δ CPI and ΔOPI on the RHS (monthly data) Impulse responses show the impact of a 1% shock in online series (OPI) on future CPI (reflecting additional information not contained in lagged CPI)
33 Impulse Response USA Cummulative IRF - 1% Shock to Online Aggregate Inflation Source: PriceStats Data until March 2014 Month
34 Online vs Offline Prices Are online prices representative? No Online sales are still only about 10% of retail sales in developed countries Yes The `online store` is effectively the largest store for most retailers. Eg: Walmart has 4759 stores in the US. The median store has 0.02% of sales. The `online store` has 8% of sales Even if online transactions are rare, the online price can be a good proxy for the offline price\ Close matching in price indices Cavallo (2015) : simultaneous data collection for 50 large retailers in 10 countries shows they tend to have either identical online and offline price levels.
35 The BPP App Every day we process and consolidate the offline data List of Senders Files We then use the barcode id to check prices online
36 Are Online and Offline Prices Similar?
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