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Analyzing Demographic Source Data: Evaluating Accuracy and Criteria Revealed

This article explores the process of analyzing demographic source data, including the importance of evaluating accuracy and the criteria used to assess data sources. It includes examples of popular data sources and highlights the differences between public and private sources.

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Analyzing Demographic Source Data: Evaluating Accuracy and Criteria Revealed

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  1. Question Everything:Analyzing Demographic Source Data Lynn Wombold Kyle R. Cassal

  2. Analyzing Demographic Source Data • Introduction: Data Development • Annual demographic updates a variety of data sources • Similarities • Reflect change in the population and its characteristics • Flawed • Differences • Criteria used to evaluate source data • Examples of our reviews

  3. Analyzing Demographic Source Data • Workshop prompted by our data users • Data users’ dilemma: Compared to software, data more difficult to evaluate • Software: • Does it include the functionality needed? • Does it work? • Data: • Does it include the variables needed? • Is it accurate?

  4. Analyzing Demographic Source Data Evaluating accuracy requires a yardstick reference to other data • Public data sources: summary data • Well known, cited • Readily accessible • Well documented • Geographic limitations • Dated • Similar universe to demographic updates • Private data sources: record-based • Less familiar, also cited • Limited access, by license only • Limited documented • Geographically unlimited • Current • Not calibrated against public sources

  5. Analyzing Demographic Source Data • To use a data source effectively understand what it can/cannot do • Basic criteria • Purpose: Why is the data collected, estimated or marketed? • Data Coverage: What population is included? • Timing: How often is the data updated? • Geographic Coverage: What areas are included? • Examples to illustrate what these criteria reveal what we have learned

  6. Purpose of the Data Kyle R. Cassal

  7. Analyzing Demographic Source Data Example 1: Net worth Household wealth = assets – debts • SIPP / Survey of Income and Program Participation, US Census Bureau • SIPP data collection: income, employment, health insurance and participation in govt. assistance programs • Longitudinal, multi-panel survey • SCF / Survey of Consumer Finances, Federal Reserve Board • SCF data collection: investment, savings, pension coverage, business ownership, use of financial institutions and markets, and debt • Triennial statistical survey

  8. Analyzing Demographic Source Data Example 1: Net worth • Data collected by sample surveys • Both sources subject to budget restraints on sample size • Remedy oversampling target households • SIPP: low income households • SCF: wealthy households • Impact:

  9. Analyzing Demographic Source Data Example 2: Per capita income Aggregate income / population • US Census Bureau / American Community Survey • ACS data collection: Detailed information on the American population and workforce • Continual Survey • Bureau of Economic Analysis, Regional Economic Accounts • BEA data collection: Estimates of gross domestic product (GDP) for the US, including regional estimates of GDP and personal income for states and local areas • Administrative Data

  10. Analyzing Demographic Source Data Example 2: Per capita income Aggregate income / population

  11. Analyzing Demographic Source Data Example 2: Per capita income Aggregate income / population • Impact:

  12. Data Coverage Lynn Wombold

  13. Analyzing Demographic Source Data Example 3: Employment • Comparing two series’ from one source, Bureau of Labor Statistics • Quarterly Census of Employment & Wages (QCEW) • Local Area Unemployment Statistics (LAUS) • Origin of the data • QCEW covered employment & wages reported by employers • LAUS labor force estimates, unemployment and employment • Current Population Survey (US Census Bureau) • ACS (US Census Bureau) • State unemployment insurance • Current employment statistics (CES, BLS)

  14. Analyzing Demographic Source Data Example 3: Employment • Population coverage • QCEW • Includes full- and part-time workers, temp workers, multiple job holders (more than once) • Excludes self-employed, proprietors, domestic workers, unpaid family workers • LAUS • Includes workers for pay plus unpaid family workers (15+ hours/week) • Multiple job holders counted once • Geographic coverage similar • QCEW: US, states, counties, metro areas • LAUS: US, states, counties, metro/micro areas, select cities

  15. Analyzing Demographic Source Data • Example 3: Employment • Differences between two series’ from BLS result in different estimates of employment • QCEW: employment by place of work • LAUS: employment by place of residence • Impact current estimates (September 2015) • QCEW 140,442,220 • LAUS 148,980,000

  16. Analyzing Demographic Source Data Example 4: Housing units • Comparing two [public] sources • US Postal Service • US Census Bureau • Purpose of the data • USPS delivering US mail Delivery statistics • US Census Bureau tracking the housing inventory Decennial census counts, annual estimates of housing units and characteristics

  17. Analyzing Demographic Source Data Example 4: Housing units • Data collection/estimation US Postal Service • Counts of deliverable residential addresses • Active counts • Possible counts • P.O. Box counts • Administrative records • Coverage/Timing US Postal Service • Carrier routes • Monthly totals

  18. Analyzing Demographic Source Data Example 4: Housing units • Data collection/estimation US Census Bureau • Census of Housing • Independent estimates of housing units • Surveys • American Community Survey (ACS) ˗Manufactured Homes Survey • Current Population Survey (CPS) ˗ Rental Housing Finance Survey (RHFS) • Housing Vacancy Survey (CPS/HVS) ˗Survey of Construction (SOC) • American Housing Survey (AHS)˗Building Permit Survey • Survey of Market Absorption of Apartments (SOMA) Sponsored by HUD

  19. Analyzing Demographic Source Data Example 4: Housing units • Coverage/Timing US Census Bureau • Census of Housing • Occupancy, tenure, mortgage status • US to block level • Decennial • Independent estimates: total housing units • States and counties • Annual • Surveys • Detailed housing characteristics: US, regions and divisions (select metros) • Biennial to monthly, except ACS ACS: 12- or 60-month average, depending upon population size

  20. Analyzing Demographic Source Data Example 4: Housing units • Purpose of the data • US Census Bureau enumerating population, housing inventory • USPS delivering US mail • Different goals different definitions of housing units • US Census Bureau: living quarters • Occupied housing: • Direct access from outside or common hallway • Living separately from others in building • Vacant housing • USPS: addresses • Active • Possible

  21. Analyzing Demographic Source Data Example 4: Housing units How do different definitions affect data coverage? • Occupied units or households • US Census Bureau: living quarters • Direct access from outside or common hallway Including boats, RVs, unconventional housing with no address • Living separately from others in building Separate rooms, but not separate addresses • USPS: active addresses • Business vs. residential use Definitions differ • Active addresses for group quarters Not households

  22. Analyzing Demographic Source Data How do different definitions affect data? • Vacant units: USPS • Postal carriers identify vacant addresses as possibles or “No Stats” • Possibles • Change of address • Vacant for 90+ days • No Stats file • Units under construction • RR addresses vacant for 90+ days • Addresses “not likely to be active for some time” • Demolitions • Vacant units: US Census Bureau • Identifies vacancies included • Available for sale/rent • Sold/rented, not occupied yet • Available for seasonal, recreational use • For migrant workers (farming) • Includes units under construction if closed to elements • Exclude vacant units • Open to elements • Condemned, boarded up • Temporarily vacant

  23. Analyzing Demographic Source Data Differences in scope/coverage • Housing units: US Census Bureau • Counts and estimates of living quarters • Occupied or vacant • Characteristics • Geographic coverage: • Census: comprehensive • Annual sources (estimates) vary • Annual • Limited data • Limited geography • Address counts: US Postal Service • Delivery statistics • Active or possible addresses • Residential or business use • Geographic coverage: • Postal carrier routes • County summaries • Monthly (no time series) • Available to public, for a fee • Licensed annually

  24. Analyzing Demographic Source Data Example 4: Housing units • Compared two [public] sources • US Census Bureau: housing units • US Postal Service: delivery statistics • Differences • Similarity Both sources based upon address lists • Census MAF/TIGER • Delivery statistics address totals

  25. Analyzing Demographic Source Data Private Data Sources • Similarity to census data or delivery statistics: address list base • Difference: availability • Public data sources are summarized or redacted to remove any personal identification. • Census Bureau’s MAF/TIGER NA • USPS address totals summarized delivery statistics • Private, licensed databases include addresses

  26. Analyzing Demographic Source Data • Private data attributes: • Geographically unlimited • Limited access, by license only • Less familiar • Limited documentation • Current • Not calibrated against public sources • Private data analysis same principles • Purpose • Coverage • Timing • Calibration

  27. Question Everything:

  28. Timing Kyle R. Cassal

  29. Natural Disasters • Indiana Tornadoes (3/2/2011) • Joplin, MI Tornado (5/22/2011) • Bastrop, TX (9/4/2011) • Waldo, CO Fire (6/28/2012) • Hurricane Sandy (10/31/2012) • Moore, OK tornado (5/20/2013) • Black Forest, CO Fire (6/4/2013) • Butte, CA Fire (9/9/2015) • Valley, CA Fire (9/12/2015)

  30. Geographic Coverage Kyle R. Cassal

  31. Analyzing Demographic Source Data • Geographic sources • US Census Bureau: TIGER • Private sources • Data types • Street networks • Address ranges • Boundaries • Census statistical areas (block groups, tracts) • Political areas (states, counties, places, CSDs, CDs, CBSAs) • Areas of interest (ZIP Codes)

  32. Multiple Block Group Boundaries

  33. Multiple Block Group Boundaries

  34. Las Vegas, ZIP Code 89138

  35. Palm Beach, ZIP Code 33496

  36. Question Everything! Analyzing Demographic Source Data • No perfect databases, public or private • Effective use of the data understanding each source • Purpose of the data: why it’s collected, estimated, marketed • Data Coverage: what populations are included • Timing: how often the source is updated • Geographic coverage: what areas are included

  37. Question Everything! Analyzing Demographic Source Data For more information about Esri data: http://doc.arcgis.com/en/esri-demographics/

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