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– May 10, 2016
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AIER Employment Destinations Index Methodology

To calculate the EDI, we use data from the 2005-14 waves of the American Community Survey, downloaded from the Integrated Public Use Microdata Series hosted by the University of Minnesota.

Our outcome of interest is the proportion of each MSA’s population that are in-migrants aged 22-35. We use a combination of variables spanning the economic, demographic and amenity or quality of life characteristics of each MSA to predict this proportion. All variables are calculated from the ACS and use the provided sample weights.

The demographic variables are the total MSA population, a diversity index and the proportion of the MSA’s residents aged 22 or older who have a bachelor’s degree.

The economic variables are the unemployment rate, labor force participation rate, mean income for workers age 22-35 and the median rent paid by individuals 22-35. For rent as well as income, we exclude students from the calculations.

The quality of life variables are the proportion of workers who walk, bike or take public transportation to work, the number of people who work in bars and restaurants per 1000 residents, the number of people who work in arts and entertainment per 1000 residents.

For the regression, we match the city characteristics for year t to the in-migration rates in year t+1, which represent migration between year t and t+1. Thus we have nine usable waves of data: matching the 2005-13 city characteristics to the 2006-14 migration estimates. We take logs of both dependent and independent variables. We denote the logged independent variables for MSA j at time t as Xjt, and the log of in-migration between t and t+1 as yjt. Thus our estimating equation is:

yjt=βXjtjt

We obtain estimates of by OLS and use these coefficients with 2014 city data from ACS to predict log of in-migration to each city between 2014 and 2015. We order these predicted in-migrations from greatest to least to rank cities for the destination index.

Our unit of observation for this study is Metropolitan Statistical Areas (MSAs).  We used the U.S. Office of Management and Budget, 2013 definitions of MSAs. We divided the MSAs into four size categories based on population in 2013. The population data are from U.S. Census Bureau, American Community Survey, 2013.

 

Table 1: MSA Size

 

Classification

Total population

Major Metros

<= 2.5 million

Midsize Metros

1 million <=> 2.5 million

Small Metros

250,000 <=> 1 million

Smallest Metros

> 250,000

 

 

Dependent variable:
Young College Migrants – Percent of MSA population 22-35 years old, with BA degree or higher, not currently in school, who moved to the MSA between 2013 and 2014.
Source: U.S. Census Bureau, American Community Survey, 2005-2014.

Independent variables
Demographic variables                 

Diversity – One minus the squared percent of MSA population identifying as one of the following race categories, White, Black, Asian / Pacific Islander, Native American, Hispanic, Other.
Source: American Community Survey, 2005-2014.

Young adults with a BA – Percent of MSA population, individuals age 22 and over with a BA degree or higher.
Source: American Community Survey, 2005-2014.

Quality of Life variables
City Accessibility – Percentage of commuters in MSA that walk, bike, or take public transportation to work.
Source: U.S. Census Bureau, American Community Survey, 2005-2014.

Bars and restaurants – Number of employees at bars and restaurants per 1,000 MSA residents.
Source: U.S. Census Bureau, American Community Survey, 2005-2014.

Arts and entertainment – Number of employees in performing arts, spectator sports, and related venues, museums, historical sights, and similar institutions per 1,000 MSA residents.
Source: U.S. Census Bureau, American Community Survey, 2005-2014.

Economic variables
Labor Force Participation – Percentage of MSA population in the labor force.
Source: U.S. Census Bureau, American Community Survey, 2005-2014

Rent – Median rent, including utilities, for individuals age 22-35 with a BA degree or higher, not in school.
Source: U.S. Census Bureau, American Community Survey. 2005-2014.

Earnings – Average annual wages and salaries for individuals 22-35 years old, with a BA degree or higher, not currently in school.
Source: U.S. Census Bureau, American Community Survey, 2005-2014.

Unemployment – Percentage of MSA labor force that is unemployed.
Source: U.S. Census Bureau, American Community Survey, 2005-2014


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