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