Improving population estimates with the use of dummy variables

Donald Pursell

Demography, 1970, vol. 7, issue 1, 87-91

Abstract: The ratio correlation method of estimating county population may be improved with the use of dummy variables and stratification to represent county “type.” Unsatisfactory results were derived when the ratio correlation technique was applied to West Virginia counties using only such conventional symptomatic indicators as births, deaths, non-agricultural employment, and automobile registrations, probably due to the relatively unique absolute population decrease 1950–1960 that occurred in many counties. To improve the predictive power of the technique: counties were stratified along the lines recently suggested by Rosenberg; dummy variables were introduced to represent such county characteristics as region or rate of development; and stratification and dummy variables were combined in a single test. The average 1960 estimating error for the 55 counties was reduced from 4.6 to 2.3 percent when dummy variables and stratification were used to supplement the conventional indicators. Copyright Population Association of America 1970

Date: 1970
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (7)

Downloads: (external link)
http://hdl.handle.net/10.2307/2060025 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:demogr:v:7:y:1970:i:1:p:87-91

Ordering information: This journal article can be ordered from
http://www.springer.com/economics/journal/13524

DOI: 10.2307/2060025

Access Statistics for this article

Demography is currently edited by John D. Iceland, Stephen A. Matthews and Jenny Van Hook

More articles in Demography from Springer, Population Association of America (PAA)
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().