Who Is At Risk of Migrating? Developing Synthetic Populations to Produce Efficient Domestic Migration Rates Using the American Community Survey

Urban Science

Resseger, Matthew¹  Kim, Christina²  Granberry, Phillip³ 

[1]Author to whom correspondence should be addressed

[2]Department of Economics, University of Massachusetts Boston, Boston, MA 02125, USA

[3]Research Division, Boston Planning and Development Agency, Boston, MA 02201, USA

关键词: cohort-component method;    gross migration;    net migration;    intercensal population projection;    Boston demographics;   

DOI  :  10.3390/urbansci2030080

学科分类：社会科学、人文和艺术（综合）

来源: mdpi

【 摘 要 】

Success in producing a population projection predominately depends on the accuracy of its migration rates. In developing an interregional, cohort-component projection methodology for the U.S. city of Boston, Massachusetts, we created an innovative approach for producing domestic migration rates with synthetic populations using 1-year, American Community Survey (ACS), and Public Use Microdata Samples (PUMS). Domestic in- and out-migration rates for Boston used 2007–2014 ACS data and developed synthetic Boston and United States populations to serve as denominators for calculating these rates. To assess the reliability of these rates, we compared the means and standard deviations of eight years of these rates (2007–2014) with synthetic populations by single-year ages for females and males to rates produced from two ACS samples using the same migration data in the numerator but the prior year’s age data in the denominator. We also compared results of population projections for 2015 using these different migration rates to several 2015 U.S. Census Bureau population estimates for Boston. Results suggested our preferred rates with synthetic populations using one ACS sample for each year’s migration rates were more efficient than alternative rates using two ACS samples. Projections using these rates with synthetic populations more accurately projected Boston’s 2015 population than an alternative model with rates using the prior year’s age data.

【 授权许可】

CC BY