Scraped Dat a and St icky Prices

Transcription

1 Scraped Dat a and St icky Prices Albert o Cavallo MIT & NBER Banque de France, December 2015

2 Motivation St icky prices are a fundament al element of t he monet ary transmission mechanism in many macro models Last decade saw an explosion of empirical research thanks to increased access to scanner and CPI micro-data Many puzzling stylized facts. Micro prices are too flexible Many tiny price changes unimodal distributions of the size of price changes. Flat or downward sloping hazard rates ( newer prices have higher probabilities of changing than older prices) T hese fact s are influencing t he way st icky prices are modeled: Eg, Midrigan (2011), Woodford (2009), Alvarez and Lippi (2014)

3 This paper I argue that measurement errors in CPI and scanner data create spurious small changes which alter some key stylized facts. I use scraped online prices, which are free from: Weekly averages (in scanner dat a) Imputation for temporary missing prices (in CPI data) Effect s on st ylized fact s: Increase the duration (stickiness) of price changes Eliminate tiny changes alters the shape of distribution Produce initially upward-sloping hazard functions

4 Part of a Research Agenda 1. Introduce Scraped Data as a new source of micro prices Advantages and disadvantages Are t he st ylized-facts different? Are online prices special? Today 2. Multiple sectors and countries bet t er understand sources of heterogeneity and determinants of stickiness 3. Publish live stickiness statistics policy applications

5 Scraping Online Prices We use specialized web-scraping software Every day, a robot downloads a public webpage, analyses its HTML code, extract price data, and stores it in a database

6 Compared to other micro-price data sources

7 Data in this Paper Largest supermarket in five countries (Mkt shares 28% Arg, 15% Bra, 27% Chile, 30% Colombia)

8 Measurement Error in Scanner Data Scanner Data Unit values Eichembaum et al (2014) T ime averages Campbell and Eden (2014) Modern scanner datasets (eg. Nielsen s) have weekly average prices $11 $10 Week 1 Week 2 Week 3 increases the number and reduces the size of price changes

9 Effect on Implied Durations Prices are stickier than in scanner data

10 Effect on the Size of Changes The distribution of the size of change is bi-modal, with little mass near zero percent

11 Effect on Hazard Functions The hazard rate is the probability of a price change conditional on time since the last change

12 Measurement Error in CPI data CPI micro data has imputed prices for temporarily missing observations (Klenow Krystov report 7% in US CPI data) In t he US, BLS uses cell-relative imputation missing price imputed from the average change in price of similar goods.

13 Effect on Durations I ran a simulation of cell-relative imputation on a monthly version of the online data

14 Effect on t he Size of Changes [Compare to US CPI results]

15 Effect on t he Size of Changes CR Imputation results are very similar to those in Klenow and Kryst ov (2008) with US CPI data. Share of price changes below thresholds (absolute value)

16 Effect on Hazard Rates

17 Summary of Results The lack of time averages and price imputations in online data changes the stylized facts on price changes Increases duration Fewer small changes bi-modal distributions Hump-shaped and spiked hazard functions In the 2 nd part of the paper I use the cross-country data to: Provide more evidence of bi-modal distribution and hump-shaped hazards [Details] Show that in inflation is correlated with the relative frequency of increases and decreases [Details]

18 But wait...maybe online prices are special? Online sales are still only about 10% of retail sales in developed countries! Papers with online data, such as Ellison and Ellison (2009), Llunnemann and Wintr (2011), Gorodnichenko and Talavera (2014), find that online prices tend to change more frequent ly and with smaller sizes than offline prices Key to distinguish between different type of online prices

19 Which Online Prices? Price Aggregators or Shopbots Offline Prices Online Prices Online Marketplaces Online-only Retailers I st udy ret ailers t he sell both online and offline ( multi-channel ). For t hese t ype of ret ailers, online and offline prices are very similar.

20 Validation in this paper

21 More generally Cavallo (2015) Are Online and Offline Prices Similar? Large-scale comparison of online and offline prices collected simultaneously in 50 retailers in 10 countries. Using crowdsourcing + mobile app Key questions: Are offline goods also available online? Are t he price levels similar? Identical ~70% of the time Are price changes similar in timing, frequency, and size? No Yes ~ 85% of the time Yes

22 Conclusions Online scraped prices are a unique source of data to study st ickiness. Similarities to offline prices (transactions) Free from t ime averages and imputations impact on stylized fact s Available in multiple countries and sectors no differences in methods, time periods, treatment, etc. Real-time, no delays policy applications Micro data is messy we need to be aware and control for errors/ biases New data collection opportunities scraped online prices are just one example.

23 Data collection Quot e from Griliches (1985), on the uneasy alliance between economists and data: New data collection technologies allow us to obtain data cust omized t o fit research needs

24 T he size of price changes

25 Smoothed Hazard Functions

26 Survival Bias

27 Cross-Country Evidence Only the relative frequency of increases/ decreases (freq+/freq-) is correlated with inflation

28 Online-Offline Data Collection Collect prices for: a random set of goods at the website and a random physical store of a given retailer at t he same t ime (7-day window) Initial goal: 10 countries, 5-10 ret ailers in each Ret ailers must meet t hree condit ions: 1. Be a t op 20 largest ret ailer by market share 2. Sell both online and offline 3. Possible for us to link online and offline products (via UPC or product id)

29 List of Retailers Retailers (July 2015)

30 Offline Data Collection Crowdsourcing + mobile phones Android App apps/details?id=com.mit.bpp Freelancers are hired to do a simple task: Version 1: Use the app to scan and price 10 random offline products in any physical store Version 2: Same as above, and ret urn every week (4 in t ot al) t o price the same products. Pay: from 15 to 30 cents per price scanned Crowdsourcing minimizes time in store (ot herwise kicked out )

31 The BPP App Every day we process and consolidate the offline data We then use the barcode ids to check prices online using customized web scrapers Baseline results allow the online price to be collected up to 7 days later For groceries, online and offline zipcodes may not coincide

32 Validation Exercise Prices collected online and offline are very similar in most countries Levels About 70% are identical Changes different timing, but same frequency and mean size Why explains the 30% that is different? Data errors: offline collection errors, imperfect matching, zip code differences Sales (location specific) Un-synchronized time series there is anticipation in online data