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Big Data in the U.S. Consumer Price Index: Experiences & Plans Crystal Konny, Brendan Williams, and David Friedman Federal Economic Statistical Advisory Committee Meeting - June 14, 2019 1 U.S. B UREAU OF L ABOR S TATISTICS bls.gov

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Big Data in the U.S. Consumer Price Index:

Experiences & Plans

Crystal Konny, Brendan Williams, and David Friedman Federal Economic Statistical Advisory Committee Meeting - June 14, 2019

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Potential Benefits

 Transaction prices  Larger sample sizes  Reduced collection costs  Reduced or eliminated respondent burden  Data descriptiveness  Real-time expenditures and weights

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Challenges

 Methodological

Product life cycle, representativeness, data descriptiveness

 Operational

Data lag, continuity, quality verification Geographic structure System design

 Legal, Policy, and Budgetary

Contracting for data, webscraping agreements, confidentiality concerns

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Alternative Data

 Data not collected through traditional field

collection procedures by BLS staff

(traditional = in-store/on-phone/manually on website)

 Three main categories:

Corporate Secondary Source Web/Mobile app scraping data

 Decade of explorations & pilot projects –

transition into production

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General steps for Alt Data Projects

 Determine what to pursue  Evaluate options  Evaluate selected source (definition, coverage,

ther quality dimensions)

 Evaluate data quality over predefined time  Methods to test  Evaluate results  Transition to production?

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Criteria for use in production (to date)

 As good or better than current pricing

methodology

 Does improvement in index justify any

additional costs – cost effective?

 In general, is it a good fit for CPI?  Use of short-term solution while continuing to

research longer-term improvements

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Corporate Data

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CorpX

20 40 60 80 100 Oct-14 Jan-15 Apr-15 Jul-15 Oct-15 Jan-16 Apr-16 Jul-16 Cosmetics

Misc. Goods

Jewelry Dishes

Misc. Household

Men’s suits or blazers Women's tops, skirts, and suits Women’s outerwear

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Impact of Incorporating CorpX

96 98 100 102 104 106 108 Jul-16 Oct-16 Jan-17 Apr-17 Jul-17 Oct-17 Jan-18 Apr-18 Jul-18 Oct-18 Apparel CPI Apparel CPI + Transaction Data

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CorpY

 February 2012 refused to initiate new

prescription drug sample

 March 2015 agreement to supply data

corporately

 May 2015 first use in index

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CorpY

CorpY In-store Item Selection Probability Proportional to Size (PPS)

ver the past year nationally by sales

excluding lowest 10% of transactions PPS based on price of the last 20 prescriptions sold Geography National Outlet Specific Price Average price of at least 100 transactions Single price Insurance prices Mostly cash prices National price Outlet specific price Per pill price Per prescription price Patent Loss Unit prices averaged across brand and generic Based on analyst monitoring of patents for an NDC Data Frequency Bimonthly odd collection Monthly and bimonthly

dd/even collection

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Secondary Source Data

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Hospitals and Physicians’ Services

 Relative Importance 4.04%; response rate for

Medical Care is 48.1%

 4,116 price quotes  Cash price overrepresented  High respondent burden  High collection costs  Difficult collection methodology

Researching use of medical claims datasets

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New Vehicle Observations

100,000 200,000 300,000 400,000 500,000

CPI JDPower

Number of Observations/Month

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Model Year Price Indexes

80 85 90 95 100 105 Jan-08 Jan-09 Jan-10 Jan-11 Jan-12 Jan-13 Jan-14 Jan-15 2009 2010 2011 2012 2013 2014 2015

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Experimental Index for New Vehicles Untaxed)

90 95 100 105 110 115 200712 200812 200912 201012 201112 201212 201312 201412 201512 201612 201712 201812 Exp New Vehicles (Untaxed) Official New Vehicles (Taxed)

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Web/Mobile app scraping data

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Crowd Sourced Motor Fuels

Regular Unleaded Gasoline

90 95 100 105 110 115 120 Nov-17 Dec-17 Jan-18 Feb-18 Mar-18 Apr-18 May-18 Jun-18 Jul-18 Aug-18 Sep-18 Oct-18 CPI GasBuddy Data

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Plans

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Establishing Priorities

 Relative importance of the item  Number of quotes replaced  Cost of collection relative to cost of alternative

data

 Respondent relationship with BLS  Concentration of respondents in the sample  Ease of implementation  Accuracy issues in the current index…

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In the works

Item RI # quotes concen tration issues prior ity Source

f data

% sample Gasoline (all types) 4.344 3,778 M L H scrape 100 Other motor fuels 0.094 830 M L H scrape 90 New vehicles 3.695 1,900 L H H sec 100 Physicians' services 1.728 1,993 L H H sec 75 Hospital services 2.312 2,123 L H H sec 85 Cable and satellite television service 1.501 1,906 H H H sec 95 Wireless telephone services 1.693 1,279 H H H sec 98 Land-line telephone services 0.572 874 H H H sec 95 Internet services & electronic info providers 0.780 773 H H H sec 95

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In pursuit

RI # quotes concent ration issues priority Source

f data Experience

% sample

Prescription drugs 1.316 4,641 H H H corp some Limited service meals and snacks 2.542 2,808 M L M corp pursue Delivery services 0.014 231 H L corp pursue Airline fares 0.683 1,745 H L M scrape, corp research Used cars and trucks 2.329 4,537 H H H sec Prod, seek 100 Postage 0.094 230 H L sec prod Leased cars and trucks 0.655 265 L H M sec research 100 Electricity 2.655 1,406 M M H seek Utility (piped) gas service 0.747 1,404 M M H seek Rent and OER 31.548 seek

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Conclusions

 Significant portion of the CPI based on

alternative data within 5 years

 Substantial R&D on methodology needed  Alternative data introduced

incrementally alongside monthly publication

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Questions for FESAC

 Do you have any reactions to the general

criteria CPI has used to date for determining fitness for use? Are we missing anything, etc.?

 Do our criteria for establishing priorities in

moving forward make sense to you?

 Any advice for meeting the methodological

challenges BLS faces with some of the alternative data sources?

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Contact Information

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Brendan Williams

Senior Economist Branch of Consumer Prices Williams.Brendan@bls.gov

Crystal Konny

Branch Chief Branch of Consumer Prices Konny.crystal@bls.gov

David Friedman

Associate Commissioner Prices and Living Conditions Friedman.david@bls.gov