NYSASBO staff are constantly working to improve the link between research, policy, and practice in school finance. That’s why NYSASBO research staff attended the Association for Education Finance and Policy’s (AEFP) annual conference last week in Denver, CO.
The theme of the conference was “The Perils of Research Irrelevance: Balancing Data Use Against Privacy Concerns.” Many of you may remember the 2012 legislative battle over a contract that had been signed with data company InBloom, followed by the company’s fall after nearly every one of its clients pulled out amid growing privacy concerns. InBloom offered 400 fields of data for each student, and parents were generally not okay with that much information floating around about their children, no matter what entity held the key to the data.
Having been at the Legislature when the InBloom battle occurred, I found it interesting now to be among researchers discussing the topic of student privacy. We heard from a parent who was leading a coalition to protect privacy, and many of the research-field attendees were just not having it. On the one hand, parents might argue that data about their student should only be collected if it is definitely to the student’s personal benefit, but on the other hand, researchers might argue that everything they do is for the student’s benefit, even if parents can’t see that.
With this privacy theme in mind, we attended sessions where researchers presented their papers that offered new or updated ideas about school finance and other topics. It occurred to me that many of these papers used quantitative data but were lacking in the area of real-world knowledge. Researchers would make comments like, “Wow, what an impressive data set,” and “Great choice of a statistical model,” but there wasn’t as much focused questions like, “Why might this trend be occurring?” and “What are the practical implications of this research?”
For example, two papers examined the impact of New York State’s Campaign for Fiscal Equity case on equity in school funding. The researchers wanted to see if court-mandated reforms were an effective way to equalize aid. However, I think I disheartened them when I asked how their research accounted for the fact that the reform was never fully implemented, as our Foundation Aid formula was frozen and aid was cut, and we still have $4.8 billion left in unfunded aid. Given this result, it seems that studying the impact of court-mandated reforms is not a simple matter.
While some researchers don’t like to let policy implications affect their work, others invite policy experts to lead formal discussions about their papers. NYSASBO was chosen as a discussant for a paper that proposed a new constitutional cost methodology for New York State Foundation Aid. While the paper offered solutions that one researcher described as a “lawyer’s dream,” it needed feedback from a policy perspective. As a leader in New York’s education policy field, NYSASBO was well poised to identify areas that might come against obstacles in the Legislature, and to identify unintended consequences of this proposed cost methodology.
As I continued to think about the topic of research and student privacy, I wondered how school business officials weigh quantitative data vs. practicality and unintended consequences in their work. While it takes a lot of research to identify the most efficient ways to spend and save, often you have to weigh the political and practical implications of what you are doing against the educational benefits.
Papers presented at the conference concerned, for example, various methods for assessing student eligibility for reduced price lunch programs and cost-adjusting measures of student poverty to better assess the additional cost of educating economically disadvantaged students. What areas of your work do you think would be most helpful to researchers, and what research have you found to be most helpful to you in your business decisions? I’m interested in hearing your feedback in the comments.
A number of great papers were presented at the conference. Here I list some of the most relevant ones. If you’re interested in reading any of these, email me at kshannon@nysasbo.org and I will email them to you:
DAVID S. KNIGHT, University of Texas at El Paso. Who Bears the Cost of District Funding Cuts? Equity Implications of Teacher Layoffs. KATHARINE O. STRUNK, University of Southern California. (Data types used: Administrative data obtained from state or school system)
CHRISTOPHER A. CANDELARIA, Stanford University. The Sensitivity of Causal Estimates from Court-Ordered Finance Reform on Spending and Graduation Rates. KENNETH A. SHORES, Stanford University. (Data types used: Data collected by government agency (e.g. NCES, BLS), Publicly available data)
RAND QUINN, University of Pennsylvania. Does Statewide Finance Reform and Federal Fiscal Stimulus Reduce District Spending Disparities? Evidence from Pennsylvania. MATTHEW P. STEINBERG, University of Pennslvania, CAMERON ANGLUM, University of Pennsylvania. (Data types used: Administrative data obtained from state or school system, Data collected by government agency (e.g. NCES, BLS))
ROBERT G. CRONINGER, University of Maryland - College Park. Alternative Indicators of Low-Income Students, School Funding Formulas and the Community Eligibility Provision of the Healthy Hungry-Free Kids Act. JENNIFER KING RICE, University of Maryland - College Park, LAURA CHECOVICH, University of Maryland - College Park. (Data types used: Administrative data obtained from state or school system)
BRUCE BAKER, Rutgers University. Improving School Finance Equity through Cost-Adjusted Poverty Measures. LORI TAYLOR, Texas A&M University, JESSE LEVIN, American Institutes for Research, JAY CHAMBERS, American Institutes for Research. (Data types used: Administrative data obtained from state or school system, Other type of data)
MARGUERITE ROZA, Georgetown University. The Pension Equity Gap: How Publicly-funded Retirement Systems Shortchange High Minority Schools. (Data types used: Administrative data obtained from state or school system, Other type of data)
CHAD ALDEMAN, Bellwether Education Partners. Pension Inequities Within Cities and Across States. LESLIE KAN, Bellwether Education Partners. (Data types used: Administrative data obtained from state or school system)
THOMAS DOWNES, Tufts University. Why Has The Growth of User Fees as a Source of Local Education Revenues Been So Limited?. KIERAN KILLEEN, University of Vermont. (Data types used: Administrative data obtained from state or school system, Data collected by government agency (e.g. NCES, BLS), Publicly available data)
ROSS MILTON, Cornell University. Crowd-out of Private Contributions to Local Public Goods: Evidence from School Tax Referenda. (Data types used: Administrative data obtained from state or school system, Data collected by government agency (e.g. NCES, BLS), Publicly available data)
CORBIN LEONARD MILLER, Cornell University. Availability of School Resources, District Expenditure, and School Quality: Evidence from a Regression Discontinuity of School Property Tax Elections. JASON COOK, Cornell University. (Data types used: Administrative data obtained from state or school system, Data collected by government agency (e.g. NCES, BLS), Self-collected data, Publicly available data)
MICHAEL S. HAYES, Rutgers University-Camden. Effects of School District Income Taxes on Property Values: An Unintended Consequence. PHUONG NGUYEN-HOANG, University of Iowa. (Data types used: Self-collected data, Publicly available data)
JANE ARNOLD LINCOVE, Tulane University. How Do Schools Pay Teachers When There is No Union Contract? Evidence from New Orleans. NATHAN BARRETT, Tulane University, KATHARINE O. STRUNK, University of Southern California. (Data types used: Administrative data obtained from state or school system, Other type of data)
JAMES COWAN, American Institutes for Research. School Counselors and Student Outcomes in High School. DAN GOLDHABER, American Institutes for Research and University of Washington Bothell. (Data types used: Administrative data obtained from state or school system, Publicly available data)
LUCY SORENSEN, Duke University. Outside the Classroom: Evidence on Non-Instructional Spending and Student Outcomes. (Data types used: Administrative data obtained from state or school system, Other type of data)
WALKER SWAIN, Vanderbilt University. School-Based Benefits of School-Based Health Services: Evidence from the Non-Urban Districts of Tennessee. (Data types used: Administrative data obtained from state or school system, Other type of data)
BRADLEY D. MARIANNO, University of Southern California. Negotiating the Great Recession: How Teacher Collective Bargaining Agreements Change in Times of Financial Duress. KATHARINE O. STRUNK, University of Southern California. (Data types used: Data collected by government agency (e.g. NCES, BLS), Self-collected data, Publicly available data)
STEPHANE LAVERTU, Ohio State University. The Impact of Local Tax Referenda on School District Administration and Student Achievement. VLADIMIR KOGAN, Ohio State University, ZACHARY PESKOWITZ, Emory University. (Data types used: Administrative data obtained from state or school system, Data collected by government agency (e.g. NCES, BLS), Self-collected data, Publicly available data)
JAMES V. SHULS, University of Missouri - St. Louis. Examining Inequities in Teacher Pension Benefits. (Data types used: Administrative data obtained from state or school system, Other type of data)
MARTIN F. LUEKEN, Friedman Foundation. Determinants of Cashing Out: A Behavioral Analysis of Refund Claimants and Annuitants in the Illinois Teachers’ Retirement System. MICHAEL PODGURSKY, University of Missouri. (Data types used: Administrative data obtained from state or school system, Other type of data)
DON BOYD, The Rockefeller Institute of Government. The Interplay between Retirement Plan Funding Policies, Contribution Volatility, and Funding Risk. YIMENG YIN, The Rockefeller Institute of Government. (Data types used: Administrative data obtained from state or school system, Other type of data)
ROBERT M. COSTRELL, University of Arkansas. The Simple Analytics of the "80 Percent" Rule for Pension Funding, and the Policy of High Assumed Returns. (Data types used: Administrative data obtained from state or school system, Other type of data)
MIKE CONLIN, Michigan State University. School Board Elections: Candidacy Decision, Incumbency Advantage, Retrospective Voting and Candidate Characteristics. BRIAN SWETS, Michigan State University. (Data types used: Administrative data obtained from state or school system)
RICHARD S. L. BLISSETT, Vanderbilt University. Disentangling the Personal Agenda: Identity and School Board Members’ Perceptions of Problems and Solutions. THOMAS L. ALSBURY, Seattle Pacific University. (Data types used: Data collected by government agency (e.g. NCES, BLS), Self-collected data, Other type of data)
AMANDA WARCO, Georgetown University. State Education Spending: How Much State Education Spending is Student-based?. MARGUERITE ROZA, Georgetown University. (Data types used: Data collected by government agency (e.g. NCES, BLS), Publicly available data)
LORI L. TAYLOR, Texas A&M University. On the Allocative Efficiency of Small School Districts. SHAWNA GROSSKOPF, Oregon State University, KATHY J. HAYES, Southern Methodist University. (Data types used: Administrative data obtained from state or school system, Data collected by government agency (e.g. NCES, BLS), Publicly available data)
DANIEL B. JONES, University of South Carolina. Governors Matter: Partisan Affiliation and State Education Spending. ANDREW HILL, University of South Carolina. (Data types used: Data collected by government agency (e.g. NCES, BLS), Publicly available data)
JOSEPH WHITLEY, Oregon State University. The Effect of Fiscal Stress Labeling on Agenda Setting Behavior, Voter Turnout, and School Tax Referenda Election Outcomes: Evidence From Ohio. PAUL N. THOMPSON, Oregon State University. (Data types used: Administrative data obtained from state or school system, Data collected by government agency (e.g. NCES, BLS), Self-collected data, Other type of data, Publicly available data)