Examining the Nature and Magnitude of Intra-District Resource Disparities in New York State School Districts Larry Miller and Ross Rubenstein Maxwell School of Citizenship and Public Affairs Syracuse University

Agenda Background Research questions Sample, data and methods Quantitative analysis and results Qualitative analysis and results Policy discussion and recommendations

Previous Research 1.Inter-district distributions hide substantial variations across schools –Disparities across schools within districts sometimes larger than cross-district disparities 2.Possible “quantity/quality” trade-off Often higher spending, more teachers but less experience, education, licensure, lower salaries in high-need schools 3.Disparities may result from teacher distribution policies e.g., seniority transfer rights, salary schedule, position allocations 4.Research has examined only large districts 5.Relatively little is known about how districts do (or should) distribute resources to school sites

How Many NYS Districts Are “At- Risk*” of Intra-District Disparities? 233 Districts at risk (lower-bound est.) –Enrollment > 1,700 –Poverty > 10 percent –20 percent of this group receives CFE funds –1,049,863 students (59% of state) 12 Districts at risk (upper-bound est.) –Enrollment > 8,000 –Poverty > 60 percent –75 Percent of this group receives CFE funds –205,047 students (11% of state) * Excludes NYC

Research Questions Is intra-district equity just a big city problem? What is the nature and magnitude of intra-district disparities? What policies and/or mechanisms are responsible for the resources patterns we find?

Mixed Method Approach Quantitative Analysis –Univariate, bivariate and regression analysis –Describe intra-district resource allocation patterns Semi-structured interviews –Understand intra-district resource allocation processes –Identify policies related to quantitative findings

Data Sources New York State –IMF, PMF, state report cards, Chapter 655 reports School Districts –IEP counts, school websites No state-level school expenditure data available –Must be collected district-by-district

Summary Statistics District Name District ADistrict BDistrict CDistrict D Dependency Status Dependent District Independent District Regional Code Big Four District Downstate Suburb Downstate Small City Needs to ResourcesHigh Need Total Enrollment 33,858 21,715 17,158 10,249 Percent of Teachers w/Masters14%18%54%29% Schools in District Schools in Sample (elementary only) Total Per Pupil Expenditures $ 14,180 $ 13,033 $ 12,863 $ 14,334 NYS Aid as Share of PPE 63%65%63%52%

Findings: Teacher Qualifications and Student Poverty 1.Students who attend high poverty schools are taught by teachers: –With lower salaries (two districts) –Who are less likely to hold a temporary or permanent teaching certificate (two districts), and –Who have fewer years of teaching experience (two districts) –These relationships encompass all four districts 2.No systematic relationships between pupil- teacher ratio and student poverty in all four districts

Teacher Qualifications and Limited English Proficiency Students in schools with higher percentages of LEP students are: –More likely to attend schools with higher percentages of uncertified teachers (one district) –More likely to be in schools with lower pupil- teacher ratios (two districts) One district has higher ratio

Teacher Qualifications and Special Education 1.Schools with more students in special education have: –More certified teachers (one district) –Fewer temporarily certified teachers (one district) –Lower pupil-teacher ratios (two districts) 2.But these extra resources may not be free: –In one district with lower pupil-teacher ratios, we also find lower salaries and less experience

Teacher Qualifications and Academic Achievement 1.Only one district exhibited a significant relationship between teacher qualifications and academic achievement. Similar to special education, there appears to be a tradeoff taking place: –Schools with higher test scores have more experienced, higher paid teachers but higher pupil-teacher ratios 2.Remaining three districts showed no relationship between teacher quality and academic achievement

Summary of Quantitative Findings 1.In all four districts, higher proportions of poor students are related to lower observable teacher qualifications (certification and/or experience) 2.There sometimes appears to be a tradeoff taking place for special education students and, in one district, students with higher test scores: –Special education students have more teachers but not necessarily teachers with higher qualifications –Students with higher test scores have fewer teachers but with higher observable qualifications

Interview Data 14 interviews: –District A 1 district official interviewed in person –District B 5 district officials interviewed in person, 1 former district official interviewed in person –District C 2 district officials interviewed in a conference call –District D 3 district officials interviewed in a conference call –New York State 2 State policy makers interviewed in person

Findings: Resource Allocation Mechanisms Average class size –The single most important allocation mechanism Fund-based budgeting –General funds vs. special revenue funds Historical precedent –Once a school is allocated a resource, it’s hard to take it away Ad-hoc mechanisms –Program placement, administrative capacity, objectives of grantors

Resource Allocation and Teacher, Student and School Characteristics Teacher credentials –Not considered by any district in our sample –Mixed preferences for transfer privileges Support in smaller districts but not larger districts Student characteristics –2 districts claim to support poor academic achievement with additional resources* –2 districts claim to support poverty and LEP students with additional resources* * Did not find evidence of these patterns in quantitative data

Other Factors Influencing the Distribution of Resources Finance department organizational structure –3 out of 4 districts in our sample operate separate fund- based finance departments Political influence –Special revenue funds appear to be more susceptible to top-down and lateral political influence –General revenue funds appear to receive more school- based political pressure –Bigger districts face more organized political influence while smaller district officials felt little outside political pressure Transparency –Of the 129 schools and 83,000 students covered in our sample, none of the districts create or publish school- based budgets Previous effort discontinued in one district

Three Key Findings 1.Not just a big city problem 2.District-specific factors appear to be responsible for at least some of the inequalities between schools 3.Districts tend to focus on resources they can more readily control (class sizes, number of teachers) rather than those they can’t (teacher qualifications)

Policy Recommendations Promote greater district-level control over the distribution of teachers Continue to monitor student performance and increase accountability for school performance Reduce fragmentation in budgeting systems and move towards and “all funds” budgeting approach Improve accounting systems and reporting of financial information at the school level

Parking Lot

Summary Statistics, by School

Significant Bivariate Correlations, by District

WLS Regression Results, District A

WLS Regression Results, District B