1. www.bea.gov Big Data in the National Accounts Experience in the United States Brent Moulton Advisory Expert Group on National Accounts Washington, DC 9 September 2014

2. www.bea.gov 2 What are big data? ▪ Wikipedia: “Any collection of data sets so large and complex that it becomes difficult to process using… traditional data processing applications.” ▪ IBM: “Every day we create 2.5 quintillion bytes of data… This data comes from everywhere… This is big data.” ▪ Forbes: “12 big data definitions: what’s yours?”  # 11 – “The belief that the more data you have, the more insights and answers will arise automatically from the pool”  # 12 – “A new attitude… that combining data from multiple sources could lead to better decisions.”

3. www.bea.gov Big data and official statistics ▪ Statistical agencies as producers of big data  Consistency in format and presentation  Catalogued in common, machine-readable format  Accessible in bulk  Desirable to make government data available on a single platform ▪ Big data as source data for national accounts  Administrative data, especially micro-data  Data from private sources  Web scraping 3

4. www.bea.gov Concerns about using big data ▪ Do the concepts match those needed for national accounts? ▪ How representative are the data?  Selection biases ▪ Is it possible to fill the gaps in coverage? ▪ Do the data provide consistent time series and classifications? ▪ How timely are the data? ▪ How cost effective? 4

5. www.bea.gov Defined-benefit pension funds ▪ For the SNA’s new treatment of defined- benefit pensions, BEA found it useful to work with administrative micro-data filed by pension funds  “Form 5500” data from Pension Benefit Guaranty Corporation  ~ 45,000 records per year covering 98% of private pension funds  BEA had to edit data to remove data errors and anomalies 5

6. www.bea.gov Private source data for early estimates ▪ For “advance” GDP estimate (release about 30 days after the end of the quarter), official monthly/quarterly indicators are not always available ▪ Examples of private source data used by BEA:  Ward’s/JD Powers/Polk (auto sales/price/registrations)  American Petroleum Institute (oil drilling)  Air Transport Association of America (airlines)  Variety magazine (motion picture admissions)  Smith Travel Research (hotels and motels)  Investment Company Institute (mutual fund sales) 6

7. www.bea.gov Health care satellite account ▪ Schultze Commission (At What Price? 2002) recommended that health care price indexes should be based on cost of treating a specific diagnosis ▪ BEA is preparing a health care satellite care ( http://www.bea.gov/national/health_care_satellite_account.htm ) http://www.bea.gov/national/health_care_satellite_account.htm  One approach uses insurance claims data for several million insured individuals  Claims grouped in disease episodes  Allows comparison of change in cost for treating particular diseases 7

8. www.bea.gov 8 Local area tracking system ▪ Used by BEA’s regional accounts staff for independent data on regional economies ▪ Used to vet official statistics before publishing ▪ Types of data  Employment data: largest employers, principal industries, recent layoffs  Natural events affecting the economy  Local real estate and financial trends ▪ Automated using web scraping methods  Identifying key word searches  Archiving relevant articles

9. www.bea.gov BEA research on depreciation ▪ Identifying depreciation in the presence of obsolescence is a long-standing issue ▪ BEA research on motor vehicle depreciation proposes to address this problem using data on “build dates,” which can differ from model years ▪ Data scraping – VIN-level data from decodethis.com combined with auction data from NADA and data from other auto websites ▪ Goal is improved estimates of depreciation 9

10. www.bea.gov Conclusions ▪ Big data will become increasingly important ▪ Priority to improving data quality, filling gaps, and keeping up with changing economy ▪ Big data especially useful for research projects ▪ Big data may allow for more timely or higher frequency estimates ▪ Attention must continue to be paid to traditional data quality issues 10