Untangling Community (County) Data: Valuable Resources for CD Practitioners Bo Beaulieu Purdue Center for Regional Development September 2013

Information sources that already exists either in published or unpublished format. Data collected by someone else that are being “re- used” by others In contrast, “primary data” are those that you have collected first hand Secondary data can be either qualitative or quantitative in nature What are Secondary Data?

 t3Evs t3Evs Understanding Key Typologies

Are they the same? Urban-Rural Metropolitan-Nonmetropolitan The urban-rural typology is based on the size of a Census-recognized town or city. The metropolitan-nonmetropolitan classification focuses on the county as the unit of analysis.

Defining Urban  Urban: All territory, population, and housing units located in an Urbanized Area (UA) or an Urban Cluster (UC).  UAs generally involve a nucleus of 50,000 or more people that may or may not contain any cities of 50,000+. UCs represent areas of at least 2,500 but less than 50,000 persons.

Defining Rural  Rural: All territory, population, and housing units located outside an Urbanized Area (UA) and Urbanized Cluster (UC) not designated as urban. It typically represents open country and settlements with fewer than 2,500 residents.

Defining metropolitan area Central counties with one or more UAs of 50,000 or more residents and outlying counties that are economically tied to the central counties (i.e., 25% of workers living in the outlying counties commute to the central counties, or 25% or more of the employment in the outlying counties are made up of commuters from the central counties).

Defining Nonmetro  Micropolitan Area: Any nonmetropolitan county with an urban cluster of at least 10,000 but not more than 49,999 persons. An outlying county is included if commuting to the central micropolitan county for employment is 25% or more, or if 25% or more of the employment in the outlying county is made up of commuters from the central county.  Noncore Area: Any nonmetro county not meeting the micropolitan designation. Contains no city, town, or urban cluster of at least 10,000 people. Includes open countryside.

The ERS Urban-Rural Continuum Codes METROPOLITAN COUNTIES 1 Counties in metro areas, 1 million + population 2 Counties in metro areas of 250, million population 3 Counties in metro areas of fewer than 250,000 population NONMETROPOLITAN COUNTIES 4 Urban population of 20,000+, adjacent to a metro area 5 Urban population of 20,000+, not adjacent to a metro area 6 Urban population of 2,500-19,999, adjacent to a metro area 7 Urban population of 2,500-19,999, not adj. to metro area 8 Completely rural or fewer than 2,500 population, adjacent to a metro area 9 Completely rural or fewer than 2,500 population, not adjacent to a metro area

Finding the Codes on the ERS Web Site Core-Based Statistical Areas population/rural-classifications/what-is-rural.aspx

Understanding the Good, the Bad, and the Ugly about Secondary Data

They already exists!! Less expensive and less time consuming way to gather information Allows you to quickly get a handle on current and emerging issues The Good...

Saves you the trouble of launching a more costly primary data collection effort May yield more accurate data than what you would obtain through primary data collection efforts (large vs. small samples) Can help to fine tune the focus of your primary data efforts, including your audience The Good...

Inconsistencies in definitions Data may be inaccurate or incomplete; biased Potential problems with “reliability” Data are usually only indirect measures of the issues you are interested in Data can be old Interpretation of the data must be done with caution The Bad... Or the Ugly !

What is the source of the data? Does it cover the correct geographical location? Does it provide data on the audience you’re interested in? Does it deal with the issue/topic you want to focus on? Does it represent current data? Are the available for the same time period? Are definitions of the variables you’re interested in the same over time? Key questions you should ask

Some Good Sources of Data Federal Government Regional Organizations State Agencies Local Government Others Trade associations Private sector

U.S. Census Bureau Main Portal  Census Bureau A-Z Subjects  Census of Agriculture  State and Metropolitan Area Data Book  American Community Survey  Key Federal Data Resources

Economic Research Service  Bureau of Economic Analysis  step=1&isuri=1 step=1&isuri=1 Key Federal Data Resources

Secondary Data Variables Of Relevance to Community/Economic Development POPULATION Population Size Population Composition Population Distribution Migration Patterns POPULATION Population Size Population Composition Population Distribution Migration Patterns ECONOMIC Employment Status Income and Earnings Poverty Status Businesses/Firms Labor Force Composition Current and Future Jobs EDUCATION Attainment School Enrollment Dropout Status Performance Assessments SOCIAL Health and Nutrition Status Health Care Resources Crime Rates Housing Food Assistance Enrollment Child Care Access/Enrollment LOCAL GOVERNMENT Revenues Expenditures

Data analysis options: Cross-sectional -- look at data at one point in time Comparative -- examine the data in your county relative to other counties of interest Longitudinal – focus on how the data change over a longer time period Okay, I’ve Found those Data. Now What?

Conditions that the data describe The direction of change The intensity of change How your county/community compares to other similar counties/communities The overall picture that the data paint about your county/community What to Look For

Quick Quiz Are the following capturing cross-sectional, comparative, and/or longitudinal information?

Educational Status Lower Among Rural Minorities

Poverty Status Among Minorities

Percent of Workers Employed in Creative Occupations in 2000

Employment Composition in the Nonmetro U.S., Source: Bureau of Economic Analysis, Regional Economic Information Systems (in percent)

Review the data What information did you find most interesting? What specific data would you want to communicate to county leaders and/or your Extension advisory committee? Your Turn