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Finding County-Based Data from Hidden Sources

Finding County-Based Data from Hidden Sources. Lisa Neidert Population Studies Center University of Michigan. Three Problems. Produce county-based data from summary data Not all counties represented Produce county-based data from microdata County identifiers are not in microdata

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Finding County-Based Data from Hidden Sources

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  1. Finding County-Based Data from Hidden Sources Lisa Neidert Population Studies Center University of Michigan

  2. Three Problems • Produce county-based data from summary data • Not all counties represented • Produce county-based data from microdata • County identifiers are not in microdata • Produce county-based data from microdata • County identifier in data • Some county populations are too small for reliable data

  3. American Community Survey (ACS) • Replacement for the census long-form questionnaire • 3,000,000 households a year • County-level data every year Not quite

  4. ACS Products Schedule

  5. Distribution of US counties by size

  6. Statistics based on ACS 1-year data: Unit is county

  7. Statistics based on ACS 3-year data: Unit is county

  8. What are PUMAs? • Public Use Microdata areas • Combination of population geographies that sum to at least 100,000 population. • In rural areas, several counties will form a PUMA. In an urban area, a county will be subdivided into multiple PUMAs. • PUMAs do not cross state boundaries • Smallest geography available in the microdata.

  9. Statistics based on ACS 3-year data:Unit is PUMA

  10. Convert PUMA-based statistics to county-based statistics

  11. PUMA-based statistic

  12. Converted to county-based statistic

  13. Example based on microdata • Previous example used a table from summary data • Distribution of the baby boom population • Microdata allows user-generated table • Distribution of earning equality among couples

  14. Where do couples have egalitarian earnings profiles? • Micro-data step

  15. Where do couples have egalitarian earnings profiles? • Micro-data step • Produce PUMA-specific results

  16. Where do couples have egalitarian earnings profiles? • Micro-data step • Produce PUMA-specific results • Convert PUMA-based results to county-based using cross-walk

  17. What about microdata with county identifiers? • Identifiers on Natality Detail files • 1968-1988 | all counties identified • 1989-2005 | only counties > 100,000 • 2006+ | no state or county identifiers • Distribution of births by county (1988) • <100 | 512 counties • <500 | 1,998 counties • <1000 | 2,498 counties • Some extreme cases • Loving county, TX 2 births • Hinsdale county, CO 3 births • Petroleum county, MT 3 births

  18. Solution • Cumulate small population counties by PUMA • Calculate Fertility measures • Total Fertility Rate • Timing of fertility events • Non-marital childbearing • Use cross-walk to assign PUMA characteristic to counties

  19. Finished Product

  20. Future Directions • Cautionary • Pseudo-county data • Small population-based statistics • County population may be incorrect weight • Web-based tool (PUMA to County) • Input PUMA-based table • Output County-based table • GIS ready • Include indicator for multi-county PUMAs

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