Correlates of Rural School Closure, USDA 2006-2009     

CREES Grant Program, United State Department of Agriculture.

Download the project's synopsis and goals.

Synopsis

Research Questions:

• What are the demographic, economic, and educational predictors of rural   school closure?
• What are the demographic, economic, and educational impacts of rural school closure?  When do these impacts manifest themselves, if ever?
• Under what circumstances are the demographic, economic, and educational changes brought on by the closure or reorganization beneficial to a county, district, or community?

Goals of this Research:

The overarching goals of this project include:

• Identifying the demographic, economic, and educational factors that lead to school closure.
• Identifying the magnitude of impact that school closure or consolidation has on counties, districts, and local communities. 
• Creating a statistical model that can be used as a tool to guide state   policymakers and local school leaders as they investigate consolidation and   school closures for their community schools.

To accomplish this, the team will create a data set spanning 35 years including information from federal, state, and local data sources such as the Census Bureau, the University of Illinois Extension Office, Bureau of Labor   Statistics, Illinois State Board of Education Report Cards, and others.  Factors included in the analysis will include: population demographics; economic factors   such as unemployment rates, wages, and property values; school variables such as   expenditures, drop-out, and graduation rates; and ecological factors such as community type (e.g., urban or rural), and distance to an urban area.

Using   the project’s statistical model as a tool, policymakers and school leaders will be able to compare their districts’ characteristics and trends against the predictors of school closure to assess whether or not their district is heading   toward closure or consolidation. In addition, from this tool policymakers and school leaders will be able to learn about the possible impact of school closure and consolidation on the involved communities by entering their data into the   statistical model that bases its prediction of impacts on data collected from communities who have already experienced school closures and/or consolidation.

Research Team

Frank Beck, Sherrilyn Billger, Norman Durflinger, Joseph Pacha, and Lynne Curry