Joe Pacha and Norm Durflinger are both with the Educational Administration and Foundations Department at Illinois State University. Sherrilyn Billger is with ISU’s Department of Economics, and Frank Beck is with ISU’s Department of Sociology.
In the 1940’s, Illinois had more than 11,000 school districts, most of which were one-room schools. Since then, district consolidations and school closures have whittled the number to 866 as of July 1, 2010.
Little research exists about the predictors and outcomes of school closure. Research attempting to do so used only part of the measures for a limited time. If in-depth analyses of the causes and consequences of school closure have been studied, it has been on a case-by-case basis.
As Illinois State University researchers, we worked together three years gathering data from 1972 to 2005 for a study of Illinois school closure to answer the following questions:
• What are the demographic, economic and educational causes of school closure?
• What trends lead up to a closure decision and which are most important?
• What are the demographic, economic and educational impacts resulting from school closure? Are these effects immediate or do they manifest over time?
• Under what circumstances does the closure of a school bring about demographic, economic and educational benefits for a county, district or community?
We wanted to understand the relative size and importance of these forces, expecting that multiple factors are important and at work. We also wanted to understand the issues in order to help school boards and administrators better understand what is involved as they make difficult decisions concerning their schools.
We believe that our analysis confirms that school closure is not tied to the two most often cited issues — money and enrollment. Other issues and factors come into play in the decision making process and are important for both legislators and school leaders to understand.
In previous issues of The Illinois School Board Journal, we looked at elementary (September/October 2010) and junior high (November/December 2010) closures. In this issue, we turn to high schools.
Predictors over time
Graphs 1 to 4 on the next page represent the significant predictors of high school closure over time: the education fund; per pupil operating expenditures; enrollment; and equalized assessed valuation (EAV). These graphs represent a 10-year history of each of these four variables just prior to the closure of high schools. It is important to remember that this is an aggregate of all the closed schools. The bottom line of the graph represents “time” and starts at 10 years and ends with closure at zero.
So what do these charts tell us about closing or not closing high schools?
Graph 1, the education fund, shows a steady increase over the first five to six years prior to the time of the school closure. An interesting phenomenon happens about four years before closure: the education fund starts a rapid decline. This mirrors a similar decline in enrollment, and it becomes apparent that with declining enrollment and the continued increases in the resources needed (per pupil operating expenses) that the community is unable to provide the support needed.
Graph 2, per pupil operating expenditures, shows a steady increase throughout the 10-year period. What is striking about this is that as enrollment falls, the cost to operate the school continues to increase at a very steady rate. This gap between enrollment and costs to operate demonstrates one of the major factors in school closure.
Graph 3, school enrollment, demonstrates why this is one of the main predictors of school closure. When declining enrollment is combined with increasing operating expenditures per pupil and decreasing education fund expenditures, the formula for school closure is high. Conversely, when enrollment increases, operating expenditures per pupil decrease, the education fund stabilizes and the health of the high school becomes more stable.
Graph 4, equalized assessed valuation, demonstrates a rising value that ends up quite a bit higher over the 10-year period before school closure. When compared to high schools that remained open, this amount is significantly lower and highly connected to the inability of the school to raise funds in the same manner as the open schools.
Additional predictors
Not all predictors can be shown over time, so to what degree do they affect closure overall? The analysis of the data suggests 28 variable predictors shown in Tables 1 and 2. These tables provide an overview of the predictors and their values in helping understand their relationship to high school closure.
Table 1 addresses the “educational/school” predictors; Table 2 the “community” predictors.
To read the tables, use the following formula: “Increasing (insert the variable name) (insert the column designation) the likelihood of closure.” The first variable would read: “Increasing enrollment significantly decreases the likelihood of closure.” The second variable would read: “Increasing EAV per pupil is neutral toward the likelihood of school closure.”
Individually, what do these variables mean and how can we better understand them in the context of the whole?
First, several variables have no influence on high school closure. These variables are designated as “neutral.”
Two variables increase the likelihood of closure: district tax rate and percentage of the community with high school diplomas.
• An increase in the district tax rate increases the likelihood of school closure. EAV plays an important role in schools and those at a disadvantage are high schools where EAV does not generate the same amount of revenue as high schools that remained open. Even though Graph 4 indicates a steady increase in EAV, the values are not the same as those of open schools, thus forcing increased tax rates to try to generate the same amount of funding. Continued increases in the tax rate in a community often results in the community being unable to continue to support the tax burden.
• An increase in the percentage of those with high school diplomas in the community increases the likelihood of school closure. This study was a statistical analysis of vast amounts of data and information, but statistics do not explain why this variable would behave the way it does. More research needs to be done in this area, but it could be tied in with high school graduate parents expecting more opportunities for their students than the school can provide with declining enrollment.
Four variables, if increased, can decrease the likelihood of closure. Of these four predictors, one appears to be under the control of the school leadership. Knowing and understanding this variable is important, because influencing it is within the realm of the school board and administration.
• An increase in enrollment will significantly decrease the likelihood of closure. Many would say that this variable is not in the “control” of the school; how can schools be held accountable for the number of students who attend? But many factors can help attract students to a school. How students are treated, the quality of the education they receive, school activities offered, preparation for post-secondary education — all of these factors are within the control of school leadership. But even more important than attracting students is keeping them in school. A high dropout rate signals a need to look at retaining students and meeting their needs. Schools can’t do it alone. The community must help with this variable by providing opportunities for families that will attract them to live and stay in the community.
• An increase in the school’s poverty rate decreases the likelihood of closure. While this seems to be counter-intuitive, increases in the poverty rate also bring additional funding from state and federal sources. By offering additional programs and help to students in need, schools decrease the likelihood of closure.
• An increase in median home values in the community decreases the likelihood of closure. An increase in the median home values does several things. First, it increases the EAV and makes more funding available for the school. Second, increased values means homes are in demand, which usually means people are moving into the community. Finally, increasing home values demonstrate community vitality and present the community as healthy and stable.
• Location in an urban setting decreases the likelihood of school closure. Therefore the converse is true also: location in a rural setting increases the likelihood of closure. The more rural the high school, the more likely that high school will close. This variable, by its definition, signifies less population (and fewer enrollments) and often lower EAV.
What does it all mean?
What conclusions can be drawn from this? Breaking them into three parts will facilitate a better understanding of the overall ramifications.
First, when comparing closed and open high schools without taking into account any other similarities or differences, the following are important:
• Schools that closed were more rural and had a higher percentage of people with high school degrees.
• Schools that remained open had higher enrollments, greater percentages of poverty rates, higher median home values and were in more urban areas.
Second, when looking at closed high schools alone, the following findings are important:
• A steep downward trend in enrollment precedes closure by four to six years.
• A downward trend in the education fund seems to precede closure by three to five years.
• A pronounced upward trend in per pupil operating expenditures precedes closure decisions, but drops off two to three years before closure.
Third, when comparing open and closed high schools that are similar on all other characteristics measured, the findings are:
• Larger enrollments decrease the probability of closure significantly.
• Community factors, such as household income and unemployment, are not strongly related to the probability of closure.
• Having a larger percentage of the community with a high school diploma increases the likelihood of closure.
• Test scores and academic achievement do not seem to influence high school closure.
• When all school and community level effects are compared within levels of “ruralness,” consistently schools in the smallest and most isolated of rural counties (i.e. counties with no single community larger than 2,500 residents) have the highest probability of closure. This is compared to schools in other rural counties with larger communities all the way up to and including schools in metropolitan counties.
When school board members and administrators compare the above findings to their own school(s) they may find one or two characteristics of a school that closed. Does that mean that their school is doomed to close?
Careful analysis of all of the predictors, not just one or two, must be made. However, one or two factors can be a warning sign of things to come. Taking a careful look at all of the factors together can help give school leaders the answers they need and show them where to concentrate their efforts.
For more information
If individual schools would like a quick determination of the probability of closure, please contact the authors. By providing some basic data this can be done in a short amount of time. In-depth analysis of the probability of school closure will take additional time and research but can be accomplished for a nominal fee to cover expenses.
For additional information concerning individual school closure predicted probabilities contact:
Frank Beck — fdbeck@ilstu.edu or 309-438-7770
Sherrilyn Billger — smbillg@ilstu.edu or 309-438-8720
Norm Durflinger — nddurfl@ilstu.edu or 309-438-8989
Joseph Pacha — jpacha@ilstu.edu or 309-438-8575
References
Alan J. DeYoung, The Life and Death of a Rural American High School: Farewell Little Kanawha, New York: Garland, 1995
David R. Reynonds, There Goes the Neighborhood: Rural School Consolidation at the Grass Roots in Early Twentieth-Century Iowa, Iowa City, Iowa: University of Iowa Press 1999