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1 POVERTY AND S TUDENT ACHIEVEMENT : THE APPLICATION OF COMPENSATORY PRACTICES TO EDUCATION FUNDING IN THE STATE OF FLORIDA By JEREMY ALLEN MOORE A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY UNIVERSITY OF FLORIDA 2011
2 2011 Jeremy Allen Moore
3 To My dad who instilled in me faith, curiosity, self efficacy and a genuine love of learning, and my m om, whose soft hear t and kindness are unparalleled To Diane who taught me the art of organization, among many things, and to Tiffany, whose love, optimism and encouragement have been an unwavering support
4 ACKNOWLEDGMENTS I want to thank God for granting me the abilities and opportunities to receive a high quality education, and for filling me with a desire for life long learning. The support of my family throughout my life has been fundamental to successes I have experienced. My dad shaped me with love and discipline as a child, and through both nature and nurture, imparted to me some of his creativity and aptitude to understand th ings and do things. He is an interesting, talented and delightful man and I am proud to call him my father. My Mother always been inspiring to me. She is selfless in a way that is beyond compare. My step mother Diane t aught me as a 4 th grade student, and then simply never stopped. I would not have succeeded in schools or in life without her support, guidance, and efforts on my behalf. I would like to thank my wife Tiffany. I only succeeded in completing this disserta tion by her grace. She never once voiced her discontent that my time was taken for research and writing, even though we were planning our wedding during the final stages of this process. She is loving, understanding and supportive, and her brilliant positi ve energy is contagious. I love her very much. We are optimistic about our future abroad, and are excited to start a new chapter of our lives together. I want to mention my family in Georgia. Traci, Paul, Nick and Jena are a testimony to the fact that family is truly the greatest priority. I admire their faith, and enjoy all the times we spend together; I wish there were more of them. Best wishes to my sister Gen and her new marriage, Brent who will be graduating from high school soon, and Bruce who unc onditionally loves and cares for my mother
5 I would like to acknowledge some friends that have played a significant role in my life and have helped to shape me into who I am as a person. There is no doubt that I learned from the family values, ambitious n ature, and class of the Collins family. As a childhood friend, Paul and his family were excellent role models for me and I love them to this day. Dr. Gio Valiante has been an influential friend since we met at the University of Florida during our undergrad years. Our philosophical dialogue (and our meaningless banter) is something that I cherish. We understand each other at a deeper level, and experience life in much of the same ways. Charlie and Krissy Sternberg are the greatest of friends and I miss them often. Everyone loves their fun I have been blessed with many great personal friends in recent years. All of these people mean more to me than they really know. Other friends have played a significa nt role in my professional life as an educator Robert, Don, Pete, Molly, the Olympia crew, Polly and the Blankner folks, were all involved in my development as a school administrator We had a unique synergy of good people who knew how to work hard and pl ay hard, we helped each other grow in many ways, and we will always be friends because of it. My committee chair, Dr. R. Craig Wood, has been an instrumental part of this long journey. I would like to thank him for the time and effort he provided in helpi ng me reach my goal. His incredible wealth of knowledge in finance and law has been invaluable in accomplishing this task. His knowledge of English grammar was of great assistance, and his humorous demeanor is endearing. I would also like to thank my other committee members, Dr. James Doud, Dr. David Honeyman, and Dr. David Miller for the time they spent to help stretch my thinking and refine my research.
6 This doctoral program has been a significant part of my life for many years. The experience has facili tated my professional growth, and caused me to reflect on my life, priorities, and ambitions. I want to than k all of the members of the doctoral cohort who I began the program with. We spent countless hours together in classes and studies, and shared in ea person as a result of my time with these colleagues. And now it is time for post dissertation mental serenity.
7 TABLE OF CONTENTS page ACKNOWLEDGMENTS ................................ ................................ ................................ .. 4 LIST OF TABLES ................................ ................................ ................................ ............ 9 LIST OF FIGURES ................................ ................................ ................................ ........ 10 ABSTRACT ................................ ................................ ................................ ................... 11 CHAPTER 1 INTRODUCTION ................................ ................................ ................................ .... 13 Statement of Purpose ................................ ................................ ............................. 23 Significance of the Study ................................ ................................ ........................ 24 Limitations and Delimitations ................................ ................................ .................. 24 Summary ................................ ................................ ................................ ................ 25 2 REVIEW OF LITERATURE ................................ ................................ .................... 26 Poverty Review ................................ ................................ ................................ ....... 26 Children, Poverty, and Achievement ................................ ................................ ....... 34 America and Poverty ................................ ................................ ............................... 40 Education Fi nance Research ................................ ................................ .................. 51 State Education Funding ................................ ................................ ......................... 61 State Funding Formulas ................................ ................................ .......................... 63 Adequacy, Standards, and Poverty ................................ ................................ ........ 71 Florida Education Finance and Accountability ................................ ........................ 78 Summary ................................ ................................ ................................ ................ 87 3 METHODOLOGY ................................ ................................ ................................ .... 93 Data ................................ ................................ ................................ ........................ 93 Methodology A ................................ ................................ ................................ ........ 97 Compensatory Education Practices ................................ ................................ ........ 99 Methodology B ................................ ................................ ................................ ...... 102 Methodology C ................................ ................................ ................................ ...... 104 Summary ................................ ................................ ................................ .............. 107 4 RESULTS ................................ ................................ ................................ ............. 121 Descriptive Statistics ................................ ................................ ............................. 121 Poverty and Student Achievement Correlations ................................ ................... 125 Poverty Weight Applied to the FEFP ................................ ................................ ..... 127 Adequacy Study Applied to the FEFP ................................ ................................ ... 128
8 Summary ................................ ................................ ................................ .............. 130 5 CONCLUSIONS AND RECOMMENDATIONS ................................ ..................... 133 Findings ................................ ................................ ................................ ................ 133 Research Question 1 ................................ ................................ ...................... 134 Research Question 2 ................................ ................................ ...................... 135 Research Question 3 ................................ ................................ ...................... 135 Research Que stion 4 ................................ ................................ ...................... 136 Conclusions ................................ ................................ ................................ .......... 137 Recommendations for Future Research ................................ ............................... 141 REFERENCES ................................ ................................ ................................ ............ 143 Books and Articles ................................ ................................ ................................ 143 Legal Citati ons ................................ ................................ ................................ ...... 151 Reports, Studies, Resources, and Presentations ................................ ................. 151 United States and Florida Documents ................................ ................................ .. 154 BIOGRAPHICAL SKETCH ................................ ................................ .......................... 159
9 LIST OF TABLES Table page 2 1 2011 Health and Human Services Poverty Guidelines ................................ ....... 88 2 2 U.S. Children Living in Poor and Low Income Families, 2009 ............................ 88 2 3 U.S. NAEP Scores ................................ ................................ .............................. 89 2 4 U.S. TIMMS Scores, 2003 ................................ ................................ .................. 89 2 5 U.S. TIMMS Scores, 2007 ................................ ................................ .................. 90 2 6 State Education Finance and Poverty Figures ................................ .................... 90 2 7 Expenditures Per Pupil for Public Elementary and Secondary Education (Constant 2 007 2008 dollars) ................................ ................................ ............. 92 3 1 Poverty based Funding 2011 ................................ ................................ ............ 108 3 2 Florida FRPL and FCAT Data, 2003 2009 ................................ ....................... 109 4 1 Florida FRPL Eligibility Percentages ................................ ................................ 131 4 2 Percentage of Students Scoring at Level 3 or higher on FCAT Reading .......... 131 4 3 Percentage of Students Scoring at Level 3 or higher on FCAT Math ............... 131 4 4 Correlations between FCAT Scores and FRPL Percentages ........................... 132 4 5 Poverty Weights Applied to the FEFP ................................ .............................. 132
10 LIST OF FIGURES Figure page 3 1 Pearson r Correlation Formula ................................ ................................ ........ 120
11 Abstract of Dissertation Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy POVERTY AND STUDEN T ACHIEVEMENT: THE APPLICATION OF COMPENSATORY PRACTICES TO EDUCATION FUNDING IN THE STATE OF FLORIDA By Jeremy Allen Moore December 2011 Chair: R. Craig Wood Co Chair: David S. Honeyman Major: Educational Leadership It is commonly argued that the burdens of poverty have a negative effect on student achievement in schools. Historically in the United States, efforts have been made to address the ills of poverty and its impact in classrooms, and supplementary funding has been legislated for students from low socio economic ba ckgrounds. It is debatable whether monetary increases can be directly associated to improvements in student achievement. Measuring the degrees to which poverty and achievement are associated can be crucial information for policymakers, and a review of the finance program, provides basis for research. This study determined if t here were associations between Free and Reduced Price L unch (FRPL) eligibility percentages and studen t testing results from the Florida Comprehensive Assessment Test (FCAT), displaying a relationship between poverty and achievement in Florida schools. In addition, this study reviewed compensatory practices of the fifty state legislatures and employed a th eoretical poverty weight for the funding system in Florida. Furthermore, the poverty weights utilized in a contemporary
12 adequacy study were initiated into the Florida Education Finance Program (FEFP). This study correlated poverty and student achievement i n Florida schools, and presented the funding implications of applying funding formula. This study was successful in quantifying correlations between poverty and student achievement in Florida by utilizi ng FRPL as a proxy for poverty and FCAT as an indicator of student achievement. Correlation results ranging from 0.761 to 0.855 demonstrated strong associations between these variables. Over the span of years observed, as poverty levels increased in Flor ida schools, 76 percent to 86 percent of the corresponding student achievement scores decreased. These connections provided measured relationships between poverty and student achievement. Compensatory practices of state legislatures and a recent adequacy s tudy were the basis for establishing theoretical poverty weights. The fiscal implications of applying these weights to the FEFP were concluded. The additional cost of operationalizing the poverty weight of 0.193 in the FEFP was calculated at $975,311,823. The poverty weight applied in this scenario increased the total education funding in 2010 2011 by 5.39 percent. The additional funding generated in the FEFP by employing the poverty weight of .25 was $1,267,259,596, and the increase in funding displayed b y including the poverty weight of .40 was $2,043,470,009. The .25 poverty weight increased overall education funding in Florida by 7 percent, and the .40 poverty weight increased funding by 11.3 percent. This study was successful in quantifying correlation s between poverty and student achievement in Florida, and demonstrated the fiscal consequences of education finance program.
13 CHAPTER 1 INTRODUCTION The immense field of education finance is rife with cogent issues, often controversial, addressing everything from the smallest school budget to panoptic programs and international affairs. Financing of public elementary and secondary education reaches out into countless aspects of society, and in some way impacts the lives of almost every citizen. It is easy to understand why educators, economists, politicians and theorists around the world debate and altercat e over money and schooling. The relationships between money, funding systems, poverty, and achievement create a host of problems that sometimes lack plausible definitive solutions. Analyzing these various relationships establishes a foundation for this stu dy. Poverty is a perpetuating social issue that has undeniable associations to the indicators that researchers use to distinguish levels of economic disadvantage. In edu cation finance research, the most commonly utilized indicator for poverty is the percentage of students receiving free or reduced price lunches in accordance with the National School Lunch Program (NSLP). 1 There are limitations in utilizing Free and 1 Analysis of Horizontal and Vertical Equity in the Public Schools of Tennessee, 1994 Journal of Education Finance 32, no. 3 (2007): 328 351; Carolyn A. Receiving Their Fair Share of Federal Education Funds? School Level Title I Funding in New York, Los Journal of Education Finance 33, no. 2 (2007): 130 146; G. Kennedy Greene, Luis A. Huerta, an Journal of Education Finance 33, no. 1 (2007): 49 68; Kern t Risk Children: An Econometric Application of Research Journal of Education Finance 33, no. 1 (2006): 297 Journal of Education Finance 31, no. 3 (2006): 221 237; Bruce From Theoretical Journal of Education Finance 30, no. 3 (2005): 259 287.
14 Reduce d Priced Lunch (FRPL) percentages for research, including data reporting difficulties, families who choose not to participate in the NSLP, student stigmas, and FRPL fraud that may skew research to some degree. Despite some limitations, FRPL data has been d emonstrably effective in education finance and child poverty research. This indicator is utilized in this study to determine varying percentages of poverty among Florida schools. There is a substantial body of literature denoting numerous factors which pl ace students at risk of academic failure, and it is widely held that poverty is a pivotal factor having the potential to negatively affect the achievement of students. The relationship between money and achievement is highly researched and contested, resul ting in volumes of valuable studies, yet delivering little clarity or conclusive findings that practitioners and policymakers hold to be incontrovertible. Several of these studies were in 1966, which many interpreted to claim that resources and schools have little effect on student achievement. 2 Numerous academic works of a similar nature have been conducted subsequently, many refuting this assertion and others substantiating it. 3 These studies analysis of these production 2 James Coleman, Ernst Campbell, Carol Hobson, James McPartland, Alexander Mood, Frederic Weinfeld, and Robert York, Equality of Educational Opportunity Washington, DC: U.S. Government Printing Office (1966). 3 See e.g. works supporting Coleman Report: Christopher Jencks, Inequality: A Reassessment of the Effect of Family and Schooling in America ( Educational Researcher 18, no. 4 (1989): 45 51. The American Economic Review 67, no. 4 (1977): 639 652; Larry V. Hedges, Richard D. Laine and Rob Analysis of Studies of the Effects of Differential School Inputs Educational Researcher 23, no. 3 (199 4): 5 14.
15 function research studies displays patterns and generalizations that serve as a basis f or theory. 4 An investigation of American poverty, its effects on student achievement, and production function research are vital elements of this examination. It is evident that public schools cannot exist without some level of resources and funding, but t he relationship between money and student achievement is an enduring conundrum. There is not a direct correlation between dollars spent and achievement results for students. 5 Money is absolutely necessary to operate a public school with the purpose of deve loping active productive citizens and increasing human capital. Student achievement realized in a school setting requires an investment. Increasing student achievement for students who come from a background of poverty requires even more of an investment. It is undeniable that the education of poor students necessitates greater expenditures than the education of other students to reach a given academic standard. 6 Educators, policymakers, economists, and taxpayers want to determine the most productive and ef ficient investment that will yield adequate levels of student achievement. What is the right amount of money to devote to educational purposes in order to obtain adequate results? What compensatory measures are appropriate for students in poverty? Answers vary. A review of public school funding and related research can elucidate some issues. 4 Analysis of Educational Researcher 23, no. 3 (1994): 5 14. 5 Eric Hanushek, "Throwing Mo ney at Schools," Journal of Policy Analysis and Management 1, no. 1 (1981): 19 41. 6 Vern Brimley and Rulon Garfield, Financing Education in a Climate of Change, 8 th ed. (Boston: Allyn & Bacon, 2002).
16 The expenditures for K 12 public education in the United States have increased considerably in the last century, yet it is debatable whether the outcomes justify the noted escalation in funding. Public education is the single largest area of state and local government spending in the United States, accounting for approximately one fifth of direct state and local government expenditures in 1996. 7 Education expenditures, adjusted for inflation, increased from $2 billion in 1890 to almost $190 billion in 1990. 8 Real per pupil expenditures increased by nearly 70 percent in the 1960s, increased approximately 22 percent in the 1970s, and increased more than 48 percent in the 1980s. 9 In 2000, public elementary and secondary schools were funded a total of $396.5 billion. These funds derived from federal (7 percent), state (51 percent) and local (42 percent) sources. Teacher salaries accounted for about 53 percent of spending f or school districts. When districts received additional funds, about 40 percent was allocated for class size reductions and 10 percent spent on increased teacher salaries. 10 Other monies were utilized for support services, building operation and maintenance administration, transportation, and other support functions. Recent data indicates that $476.8 billion was spent on public elementary and secondary education in the United States during the year 2007. Sixty one percent of these dollars were expenditures on 7 ublic School District Funding in the United Public Administration Review 62, no. 1 (2002): 63 72. 8 Eric A. Hanushek, Making Schools Work: Improving Performance and Controlling Costs (Washington D.C.: The Brookings Instit ution, 1994). 9 Association. New Orleans, LA. 10 hat Does the Education Dollar Buy?: Relationships of Staffing, Staff Characteristics, and Staff Salaries to State Per
17 instruction, 5.3 percent on student support services, 4.9 percent on instructional support staff, 2.0 percent on general administration, 5.6 percent on school administration, 9.8 percent on operation and maintenance, 4.2 percent on student transportatio n, 3.3 percent on other support services, 3.8 percent on food services, and 0.2 percent on enterprise operations. 11 The persisting pattern of resource allocation has been cited as a possible cause for the ambiguous link between funds and outcomes. 12 Educati on dollars, on an inflation adjusted, per pupil basis, are about three times higher today than they were forty years ago. Much of these dollars have been utilized to add professional staff in the areas of art, music and physical education, as well as addin g large numbers of instructional aides to school staffs. 13 From 1985 to 2007, the inflation adjusted per pupil expenditure for public elementary and secondary education rose by almost $4,000. A study published by the Economic Policy Institute asserts that r eports of longitudinal inflation adjusted spending numbers are overstated based on the fact that purchases made by public schools are different than those typically made under the Consumer Price Index (CPI) used to measure inflation. 14 Long term national en rollment trends indicate large increases in students identified as having disabilities that require expensive special education services. These services account for a significant portion of 11 National Center for Education Statistics, Revenues and Expenditures for Pub lic Elementary and Secondary Education: School Year 2006 07 (Fiscal Year 2007). 12 Financial Model for Determining School Journal of Education Fin ance 20 (1994): 66 87. 13 Allen Odden and Carolyn Busch, Financing Schools for High Performance: Strategies for Improving the Use of Educational Resources (San Francisco: Jossey Bass, 1998). 14 Richard Rothstein and Karen Hawley Miles, Where's The Money Gone? Changes in the Level and Composition of Education Spending Economic Policy Institute, 1995.
18 the increases in education expenditures. 15 Many different analyses of education expenditures exist, but it is certain that the gross amount of resources dedicated to education has increased over time. Significant amounts of money are utilized to educate students in the United States. Funding comparisons concerning the United States and other developed countries who are members of the Organization for Economic Cooperation and Development (OECD) can provide another perspective. In 2007, the United States ranked 5 th out of 30 nations in the amount of educational expenditur e per student. When including poverty and disability as factors into the comparison, the Uni ted States ranked thirteenth The United States ranked 22 nd out of 30 OECD nations for fiscal effort per $1,000 of Gross Domestic Product. 16 public schools in the United States can be viewed from the simple perspective of expenditures per student, or it can be viewed from the more complex and accurate vantage point that takes into account the heterogeneity of the student population, the diversi ty of education needs, and the tax to education cannot be determined accurately without consideration of all four of these 17 The United States educates co nsiderably more students who come from a background of poverty than most other OECD countries. The complexity of needs of U.S. students is different than other developed nations. 15 Richard Rothstein and Karen Hawley Miles, Where's The Money Gone? Changes in the Level and Composition of Education Spending Economic Policy Instit ute, 1995. 16 National Center for Education Statistics, Condition of Education 2007, http://nces.ed.gov/pubs2007/2007064.pdf 17 d Starves Public
19 Despite the significant monies allocated to educational purposes in the Unite d States, positive results in student achievement are not assured. The Program for International Student Assessment (PISA) is a system of international assessments that measures literacy, mathematics, and science of 15 year old students. PISA is sponsored by OECD. In 2006, students in the United States had an average score of 489 on the combined science scale, lower than the OECD average score of 500. The average U.S. score in mathematics was 474, lower than the OECD average of 498. 18 These scores do not ref lect differences in student populations among nations, including the factor of poverty. The Scholastic Assessment Test (SAT) is a commonly used, and debated, measure for comparisons of student academics. The average per pupil expenditure from 1968 1993 inc reased from approximately $3,000 to nearly $6,000, while the average SAT score decreased from 960 to nearly 900 during the same period. 19 This drop may be accounted for by considering the significant increase in the numbers of students taking the exam over time, including more low and average achieving students, coupled with a significant increase in percentage of low income and minority test takers. 20 Another method of analyzing student achievement is longitudinal review of NAEP data in reading, writing, mat h and science. Student performance in these subject areas did not increase at the same rate as the 22 percent rate of increase 18 Highlights From PISA 2006: Performance of U.S. 15 Year Old Students in Science and Mathematics Literacy in an International Context, nces.ed.gov/pubs2008/2008016.pdf 19 National Center for Education Statistics, Digest of Education Statistics, http://nces.ed.gov/pubsearch/pubsinfo.asp?pubid=94115 20 National Center for Education Statistics, Digest of Education Statistics, http://nces.ed.gov/pubs2007/minoritytrends/ind_3_1 4.asp
20 in spending during the 1970s, nor the 48 percent increase during the 1980s. 21 These data reinforce the fact that an increase in spending does not proportionally correlate with an increase in student achievement results. Due to absence of this parallel relationship, research on the association between money and achievement has been prolific. In the past two decades, education finance research has focused on the topic of adequacy. Different approaches emerged as researchers continued to address the question of how much money is necessary for an adequate education. The Professional Jud gment approach, the Evidence Based approach, the Successful Schools approach, and the Statistical Analysis approach have been utilized to determine education adequacy. 22 Few studies utilize all four approaches in determining results. 23 The results of these adequacy studies have been particularly significant to the outcomes of education litigations across the United States and have far reaching implications. 24 Various adequacy studies include a focus on compensatory elements to address students in poverty. The se compensatory practices vary, yet it is recognized that additional resources are a necessity for students from low income families. Unique differences in states have led to variations in taxation and school funding mechanisms. Under The Tenth Amendment o f The United States Constitution, 21 National Center for Education Statistics, Digest of Education Statistics, http://nces.ed.gov/pubsearch/pubsinfo.asp?pubid=94115 22 Odden, Allan, and L. O. Picus, 2000. School finance: A Policy Perspective. New York: McGraw Hill. 23 R. Craig Wood and Educational Considerations 35, no. 4 (2007): 51 55. 24 David C. Thompson, R. Craig Wood, and Faith E. Crampton, Money and Schools (New York: Eye on Education, 2008).
21 education is affirmed as a state responsibility. 25 Federal interest and involvement is extensive and influential, yet each state legislature maintains direct control, including responsibilities for funding schools. Each leg islature approaches education finance in a different manner, and historically there have been vast disparities and inequities in resource allocations within and among states. 26 In the early 1900s, education finance researchers asserted the belief that all children of a state were equal and were entitled to equal advantages. This concept addressed the vast funding disproportions within states, began to shift control from locally taxed districts to state control, and eventually initiated the concept of state aid formulas. 27 Multiple researchers contributed, adding a variety of complexities to basic state formulas in attempts to move closer toward finance equity and address specific state needs. Moving beyond horizontal fairness and considering all children as equals, state legislatures have created vertical adjustments to basic formulas, accounting for district and student differences that require variations in funding. 28 The majority of state legislatures incorporate multiple classifications of vertical adjust ments in formulas in order to address the needs of different student populations. This type of need equalization creates the basis for compensatory education, bilingual education and special education supports, and sets the foundation 25 U.S. CONST. amend. X 26 Formula: A 10 Journal of Education Finance 29, no. 4 (2004): 315 335; R oss Rubenstein, Journal of Education Finance 26, no. 2 (2000): 187 f or Journal of Education Finance 30, no. 4 (2005): 382 398; National Conference of State Legislatures, "The Search for Equity in School Funding," An Education Partners Working Paper, Denver (1996): 23 pages. 27 Elwood P. Cubberly, School Fun ds and Their Apportionment (New York: Columbia Teachers College, 1906). 28 David C. Thompson, R. Craig Wood, and Faith E. Crampton, Money and Schools (New York: Eye on Education, 2008).
22 for per pupil funding weights. 29 Every state legislature accounts for these issues differently in the established funding formula, a detail that will be further explicated. Socio economic inequalities in relation with compensatory education practices, funding formulas, and per pupil weights are central to this study. The Florida Legislature is responsible for the adequate provision and appropriate allocation of state education funding. 30 In 1973, the state enacted the Florida Education ee to each student in the Florida public education system the availability of programs and services appropriate to his or her educational needs which are substantially equal to those available to any similar student notwithstanding geographic differences a nd varying local economic factors." 31 One of the fundamental aspects of this funding formula is that it is based upon the number of individual students participating in a particular educational program. Varying funding weights, also known as cost factors, a re associated with the educational programs. Funding increases based on district necessities and demographic or economic factors are provided to determine the total funding for each district. The cost factors recognized in the FEFP are categorized into fou r groups: Basic Programs; Exceptional Student Programs; English for Speakers of Other Languages; and Vocational Education Programs. 32 There are some compensatory elements that are incorporated in the 29 David C. Thompson, R. Craig Wood, and Faith E. Crampton, Money and Sc hools (New York: Eye on Education, 2008). 30 F.S. § 1000.01(3) 31 F.S. § 236.012(1) 32 F.S. § 1011.62(c)
23 Florida education provision system, yet poverty is not a cost factor that is recognized when determining per pupil funding. The Florida Comprehensive Assessment Test (FCAT), taken by students in grades three through ten, is the primary tool for determining standardized academic achievement of students in the sta te of Florida. The FCAT is the foundation of the state wide accountability and assessment program designed to meet the requirements delineated by the federal No Child Left Behind (NCLB) act. 33 Student scores range from Level 1 to Level 5 in multiple subject areas, with scores at Level 3 or above indicating that the student is meeting standards in that particular area. Students who score below a Level 3 are required to have a Progress Monitoring Plan and receive intensive remediation in the subject. 34 The FCAT is utilized in this study as the measurement instrument for student achievement. Statement of Purpose This study determined if there were associations between FRPL percentages and student achievement indicators, displaying a relationship between poverty and achievement in Florida schools. In addition, this study reviewed compensatory practices of the fifty state legislatures and employed a theoretical poverty weight for the state of Florida. Furthermore, the poverty weights utilized in a contemporary adeq uacy study were initiated into the FEFP. This study correlated poverty and student achievement in Florida schools, and presented the funding implications of initiating theoretical 33 20 U.S.C. § 6301 34 F.S. § 1003.4156, F.S. § 1003.428, and F.S. § 1008.25 (5)
24 compensatory practices into the FEFP. The study addresses the following rese arch questions: 1. What are relationships between FRPL percentages and FCAT achievement data in Florida schools? 2. What are relationships between FRPL percentages and FCAT achievement data in Florida elementary schools? 3. What are the fiscal implications of initi ating a theoretical poverty weight into the FEFP? 4. What are the fiscal implications of employing the compensatory practices of a contemporary adequacy study into the FEFP? Significance of the Study While it may be widely accepted that poverty and student ac hie vement are associated, there were no comprehensive published correlations of poverty and student achievement by the state of Florida. This study displayed the statistical relationships between these variables. State legislatures utilize d various compens atory measures in funding formulas in order to provide additional funding for students in poverty. The state of Florida did not include poverty based compensatory measures in the FEFP. In this study, compensatory elements from other states and components o f a contemporary adequacy study were applied to the FEFP in order to demonstrate the financial effects of theoretical poverty weights in Florida education finance. Limitations and Delimitations In this study, FRPL percentages for all Florida public schools excluding charter schools and lab schools, were utilized in poverty achievement correlations. In addition, FRPL percentages from public elementary schools, excluding all other schools, were utilized for additional analysis. The data associated with FRPL program enrollment s have limitations, b ut it is the most widely utilized proxy for poverty in education
25 research. 35 Percentages of students scoring at Level 3 or above in Reading, and students scoring at Level 3 or above in Math on the FCAT were the achieve ment data utilized for correlation purposes. Utilizing FCAT as an indicator of student achievement may also have limitations, but it is the primary assessment dictated by the state of Florida to measure student achievement in accordance to NCLB. 36 Contrasti ng compensatory practices of the fifty state legislatures, as well as elements revealed in modern adequacy studies, were the foundations for determining theoretical poverty weights. Legislatures alter funding formulas for numerous reasons, and a current cl earinghouse of funding formula changes and details did not exist. Education adequacy studies have inherent limitations. Summary Understanding poverty and its effects can be very complex. A review of poverty definitions and statistics, and the implic ations of poverty on children and achievement were critical to this study. Historical context and the policies of federal and state governments in addressing poverty were discussed in order to provide perspective. Examinations of education finance, state f unding formulas, and adequacy study methodologies were essential to creating a foundation. Issues of poverty and student achievement and the interconnectedness to education finance are the heart of this study. 35 Risk Children: An Econometric Application of Research 36 F.S. § 1008.22
26 CHAPTER 2 REVIEW OF LITERATURE Poverty, th e associations between money and student achievement, and compensatory practices are fundamental issues in education finance discussions. Equity and adequacy issues and the formulae utilized in state funding systems are essential considerations. Student in equalities in relationship to the variances of state education systems and expenditures create a platform for inquiry. An analysis of Florida educational funding methodologies juxtaposed with the compensatory policies of other states provides a basis for r esearch in the matter of education finance and children in poverty. The effects of poverty on children are of central importance to education and society. Historically, poverty has crippled students in classrooms and created traditions of deficiency, infer iority, and substandard living. 37 Wide ranging definitions and perspectives of poverty exist, yet its negative impact on education and performance is indisputable. The issue at the core of numerous education debates is the correlation of money and student achievement and the associated fi scal and political realities. Compensatory education practices are prevalent in the United States, varying considerably from state to state, employing different processes and approaches to address the weighty issues of poverty and education. Poverty Revie w A universal understanding and review of poverty provides perspective and definition for further analysis within education and state contexts. Poverty can be subjective to some degree, therefore multiple definitions and poverty concepts exist. 37 Teachers College Record 10 8, no. 6 (2005): 949 995.
27 These conce pts create a basis for understanding and comparison. For many Americans, the term poverty suggests destitution and lack of ability to meet basic needs. The majority of participant responses to a poverty survey focused poverty definitions on hunger or lack of food, homelessness, and not being able to meet basic needs. 38 The American poor can also be described as those who, for reasons beyond their control, cannot help themselves. 39 There is a notable association between levels of education and poverty. Poverty 40 Absolute poverty is a set standard which is consistent for all persons in all countries, cultures and levels of civilization. A measure of absolute poverty q uantifies the number of people below a given threshold that is independent of time and place. This type of measure is not affected by income distribution because it is a consistent established level. The rationale behind the concept of absolute poverty is that it sets a standard for all persons across the world in order to make meaningful comparisons. It is not altered by time period or location. A disadvantage of absolute poverty measurement is that the amount of specific resources required for survival is not consistent in different places and times. 41 Different amounts of heat and types of shelter are required for human survival in Greenland versus Tahiti. An example of an absolute measurement would be the percentage of the population eating less food than 38 Catholic Campaign for Human Development, Poverty Pulse Low Income Survey Wave IV January 2004, http://www.usccb.org/cchd/PP4FINAL.PDF 39 Michael Harrington, The Other America: Poverty in the United States New York: Scribner (1997). 40 Pete Alcock, Understanding Poverty (UK: Palgrave MacMillan, 2006). 41 Ibid.
28 is required to sustain the human body. Absolute poverty is a condition characterized by severe deprivation of basic human needs, including food, safe drinking water, sanitation facilities, health, shelter, education and information. It depends not only on income but also on access to services. 42 Absolute poverty is further defined as the absence of any two of eight basic needs. Basic needs are, Food: Body Mass Index must be abov e 16; Safe drinking water: Water must not come from solely rivers and ponds, and must be available nearby (less than 15 minutes' walk each way); Sanitation facilities: Toilets or latrines mus t be accessible in or near the home; Health: Treatment must be received for serious illnesses and pregnancy; Shelter: Homes must have fewer than four people living in each room. Floors must not be made of dirt, mud, or clay; Education: Everyone must atten d school or otherwise learn to read; Information: Everyone must have access to newspapers, radios, televisions, computers, or telephones at home; Access to services: a complete panoply of education, health, legal, social, and financial services. 43 A person who does not have a body mass index above 16 is considered to be severely deprived of food, a person who does not have access to treatment for serious illness is considered to be severely deprived of health care, and a person who has not attended school an d cannot read is considered severely deprived of education. A person who is severely deprived in any two of the described categories is considered to be in absolute 42 Shailen Nandy Christina Pantazis Simon Pemberton and Peter Townsend Child Poverty in the Developing World (Studies in Poverty, Inequality, and Social Exclusion) (UK: Policy Press, 2003). 43 Ibid.
29 poverty. Absolute poverty is a state of wretched, extreme poverty in which basic necessitie s of life may not be met; a lack of subsistence. 44 Relative poverty is based on the comparison between the standard of living for those who are poor and the standard of living for the other members in the society who are not poor. This comparison is focused on a specific group or population and is reliant on a given threshold. 45 Generally, people in relative poverty are considered to earn resources sufficient to meet basic needs, yet earn significantly less than the majority of the population being considered A particular group of persons in the United States described as being in a state of relative poverty may have an extremely higher standard of living compared to a group of persons in relative poverty in Zimbabwe, yet both Relative poverty measurements can take into account various locations, cultures and times in order to establish a meaningful comparison. Relative poverty comparisons can elicit questionable results, part icularly in small sample sizes. 46 For example, if the median annual income of an affluent neighborhood were $1 million, then a household that earns $100,000 could be considered poor on the relative poverty scale. Similar situations may occur on the other en d of the scale, indicating that a group of people with an extremely low income who lack basic food, shelter, water, and healthcare and are not considered poor on the relative scale because they have more resources than the other members of the population w ho are in even greater destitution. Income distribution 44 Jeffrey Sachs, The End of Poverty: Economic Possibilities in Our Time (New York: Penguin, 2005). 45 Alcock, Understanding Poverty 46 Bradley Schiller, The Economics of Poverty and Discrimination (New Jersey: Prentice Hall, 1998).
30 affects relative poverty measurements. If there were more equal distribution of wealth, then relative poverty would be reduced. Relative poverty elucidates inequalities and disparities of various memb ers of given populations. 47 The World Bank a group of five international organizations that are responsible for assisting countries in economic development and elimination of poverty, indicates that there were 1.4 billion persons living on less than $1.25 per day (the international poverty line) in the developing world. 48 In 19 79, Peter Townsend, a British sociologist, developed an accepted definition of poverty that combines several measures: "Individuals, families and groups in the population can be said to be in poverty when they lack the resources to obtain the type of diet, participation in the activities and have the living conditions and the amenities which are customary, or at least widely encouraged or approved in the societies to which they belong. Their resources are so seriously below those commanded by the average fa mily that they are in effect excluded from the ordinary living patterns, customs, and activities." 49 The various definitions and related poverty concepts are essential to consider for the purposes of measuring poverty in any context. Measuring poverty is a practice involving several definitions and standards for the basis of compiling data and making comparisons and evaluations. The Federal set a mount of income that a family needs for food, clothing, transportation, shelter, and other necessities in order to establish an adequate standard of living in the United States. The FPL, established annually by the Census Bureau, is determined by the total 47 Bradley Schiller, The Economics of Poverty and Discrimination (New Jersey: Prentice Hall, 1998). 48 Global Purchasing Power Parities and Real Expenditures: 2005 International Comparison Program, http://siteresources.worldbank.org/ICPINT/Resources/icp final.pdf 49 Peter Towns end, Poverty in the United Kingdom (London: Allen Lane, 1979).
31 cost of all fundamental resources that an average adult consumes in one year, and varies according to age, family size, and other factors. In 1963 1964, Molly Orshansky of the Social Security Administration developed poverty thresholds based on the amount of money for food that was required for various sized families to survive. She estimate for required food, adjusted for rural and urban environments. This became k nown as the Orshansky Index, and it is still utilized today as the foundation for poverty measure. 50 The FPL is utilized to calculate all official poverty population statistics, including the number of Americans in poverty each year. The thresholds are dete rmined by using a forty eight cell matrix and are rounded to the nearest dollar. They are also adjusted each year according to inflation and are reported annually by the U.S. Census 51 These guidelines (see Table 2 1) are such as Head Start, National School Lunch, and Food Stamp programs. The guidelines are determined each year by the Department of Health and Human Services. Guidelines vary by family size and there are different figures for the contiguous forty eight states, Hawaii, and Alaska. Utilization of the poverty threshold, without regard to other factors, can be problematic when analyzing poverty and making policy decisions. One problem with the who fall at the bottom of the spectrum of poverty and those who are very close to the 50 Michael Katz, The Undeserving Poor: From the War on Poverty to the War on Welfare (New York: Pantheon, 1990). 51 Table 2 1.
32 poverty line. Also, a poverty thresh old is purely quantitative, and it is difficult to ascertain numbers based measures for social factors such as education and health. In addition, charitable contributions and antipoverty programs (Earned Income Tax Credit, Food Stamps, etc.) are not counte d as income, and income is the only source recognized in determining the United States poverty line. 52 Many factors are important in poverty policy decisions. Poverty thresholds, poverty guidelines, and other poverty measures provide a vehicle for statistic al analysis and evaluation of poverty conditions in various settings. These statistics and analyses provide further perspective into poverty as an issue affecting society and education. The U.S. Census Bureau displayed poverty statistics for the United Sta tes. The official poverty rate for the United States was 14.3 percent in 2009, up from 13.2 percent in 2008. In 2009, 43.6 million people in the United States were in poverty, including Whites (9.4 percent), Blacks (25.8 percent), Hispanics (25.3 percent), and Asians (12.5 percent). The first year these types of poverty comparisons were available was 1959. The poverty rate in 2009 was 8.1 percent lower than in 1959, yet the highest since 1994. In 2009, there were 15.5 million (20.7 percent) children under t he age of 18 in poverty, and there were 24.7 million (12.9 percent) of people aged 18 64 in poverty. 53 The United Nations has a global development network named the United Nations Development Programme (UNDP) that publishes an annual Human Development Repor t. The group uses a Human Poverty Index to assess the development of industrialized nations. This index is based on the probability at birth of 52 John Hills, Inequality and the State (Oxford: Oxford University Press, 2004). 53 U.S. Census Bureau. Income, Poverty, and Health Insurance Coverage in the United States: 2009, http://www.census.gov/prod/2010pubs/p60 238.pdf
33 not surviving to age sixty, people lacking functional literacy skills, long term unemployment, and population be low 50 percent of median income. According to this index, the United States ranked 12 th out of ranked countries. 54 Poverty is a significant concern in the United States. 55 Some poverty trends and statistics help to illustrate the historical undulating path of poverty in the United States. In the 1950s, the overall poverty rate for people in the United States was 22 percent. In the 1960s, the poverty rate declined significantly to 12.1 percent, and increased somewhat to 12.5 percent by 1971. It began to decr ease and fell to a rate of 11.1 percent in 1973, yet increased to 12.3 percent in 1975. It steadily increased to reach 15.2 percent in 1983, remained around 13 percent in the 1980s, but increased back up to 15.1 percent by 1993. As the rate dropped, 11.3 p ercent of Americans were poor in 2000, but by 2006 the poverty rate increased again to 12.3 percent. 56 In 2009, 43.6 million people were poor, up from 39.8 million in 2008 and 37.3 million in 2007. The nation's official poverty rate in 2009 was 14.3 percen t, up from 13.2 percent in 2008 the second statistically significant annual increase in the poverty rate since 2004. 57 This continuous course of increasing and decreasing poverty rates is data that adds to the picture of American poverty. 54 Human Development Report (2003). Millennium Development Goals: A Co mpact Among Nations to End Human Poverty, http://hdr.undp.org/en/media/hdr03_complete.pdf 55 Ibid. 56 Census Bureau data contained in Current Population Reports, Series P 60 Nos. 124, 140, 145, 149, 154, 157, 161, 166, 168, 174, 180, 185, 207, 210, 214, 219, 222, 226, 229, 231, and 233. 57 U.S. Census Bureau, 2010 Annual Social and Economic Supplement (ASEC) http://www.census.gov/hhes/www/poverty/about/overview/index.html
34 Children, Povert y, and Achievement There are substantial numbers of children living in poverty in the United States resulting in a significant burden on the system of public education. The National Center for Children in Poverty (NCCP) at Columbia University indicated tha t 11.9 million (17 percent) children in the United States lived in poverty in 2004, and 26.8 million (38 percent) lived in low income families. See Table 2 2. Low income families were considered to be those with incomes less than 200 percent of poverty lev el family income. In 2007, 13.2 million (19 percent) children lived in poverty, and 28.8 million (40 percent) families were considered to be low income. 58 The number of children living in poverty increased by 33 percent between the years 2000 and 2009. Ther e were 3.8 million more children living in poverty in 2009 than in 2000. 59 In 2009, 21 percent (15.3 million) children lived in families that are poor, and 9 percent (6.8 million) lived in families that experience extreme poverty. 60 Through calculation of a Gini coefficient, The Luxembourg Income Study indicated that the United States ranked twenty ninth out of thirty developed countries in the percentage of children who fall below the designated poverty line. When utilizing a median income of 60 percent, Mex ico is the only developed nation with higher child poverty rates, and the United States falls well behind several European nations. 61 One of the problems facing public schools in the United States is the daunting task of educating such great numbers of chi ldren in poverty. 58 National Center for Child ren in Poverty, November 2008 Report. Low Income Children in the United States: National and State Trend Data, 1997 2007, http://www.nccp.org/publications/pdf/text_851.pdf 59 ?, National Center for Children in Poverty, 2011. http://www.nccp.org/publications/pdf/text_1001.pdf 60 Ibid. 61 Luxembourg Income Study Key Figures, http://www.lisproject.org/key figures/key figures.htm
35 Children living in poverty have less achievement promoting advantages in their lives than their more affluent peers. Poor children more often come from families with low levels of educational attainment and have less assistance with thei r schooling. 62 In 2007, 83 percent of children whose parents had less than a high school diploma lived in low income families, and over half of children whose parents earned only a high school degree are low income as well. 63 Poor children live in homes with less reading material and less access to computers and internet. 64 Low income children are more likely to come from homes with extended or multiple families and have parents who are more often unemployed, underemployed, or work multiple jobs in order to ea rn a minimum income. 65 Poor students are more likely to have less access to health care, experience unattended health issues, and have school attendance concerns. Because children from poverty have less access to health care, they are more likely to be sick er longer. 66 Children from low income families exhibit more anti social behavior, violence, cruelty, dishonesty, and non cooperative and disobedient behavior than their middle and upper class peers. 67 62 National Center for Children in Poverty, November 2008 Report. Low Income Children in the United States: National and State Trend Data, 1997 2007, http://www.nccp.org/publications/pdf/text_851.pdf 63 Ibid. 64 Ibid. 65 e of Washington D.C.: U.S. Department of Commerce. 66 Current Popula tion Survey, 2000, 2001and 2002 Annual Social and Economic Supplements. Washington D.C.: U.S. Department of Commerce. 67 Pedro Carneiro and James Heckman, Human Capital Policy. Presented at the 2002 Alvin Hansen Seminar, Harvard University, Cambridge, MA.
36 The absence of human capital that includes the cultural c apital of access to a history of success, parents with degrees, homes with books and computers, friends who want to move up in society, and opportunities to travel increases the enormous dependence of poor children on public education. The greater the co ncentration of poor students at a school, the more severe poverty is and the greater the correlation between family income and student achievement is. Unlike children who have access to learning capital in the home and the community, economically disadvant aged children depend completely on public education. 68 The culture and structure of families in poverty is substantially different than the culture of other families, and a pre existing education deficiency coupled with lack of advantages continues the cyc le of poverty in America. Poverty levels remain the most reliable data source for predicting the academic failure of children. 69 Student achievement, high school drop out rates, and other indicators are associated with poverty levels. Students from low inco me families are six times more likely to drop out of school than students from more advantaged backgrounds. 70 The National Assessment of Educational Progress (NAEP) is administered periodically in a number of academic subjects with two major goals: to asses s student performance reflecting current educational practices and to measure change in student performance reliably over time. The NAEP is governed by the National Assessment Governing Board. According to the NAEP, the achievement gap in average math scor es between 9 year olds in high and low poverty schools was 22 points in 1996, down from a 28 point gap in 1992 and a 24 point gap in 1994. A ten 68 Journal of Education Finance 31, no. 3 (2006): 221 237. 69 Educating At Risk Children: One Hundred First Yea rbook of the National Society for the Study of Education, Part II, ed. S. Stringfield and D. Land (Chicago: University of Chicago Press, 2002): 1 28. 70 of Education), http://nces.ed.gov/nationsreportcard/
37 point difference on NAEP is approximately equal to one grade level. The average math achievement for 9 year old s in high poverty schools fell more than 2 grade levels behind the performance levels in low poverty schools. The achievement gap in reading between 9 year olds in high and low poverty schools was 38 points in 1996. Although this was down from a 40 point g ap in 1992, it represents a 3 to 4 grade level gap in student performance. 71 The NAEP defines three achievement levels: Basic, Proficient, and Advanced. Students score above, at, or below each level. In 2003, 55 percent of 4 th graders and 43 percent of 8 th graders who were eligible for Free or Reduced Price Lunch (FRPL) scored below the Basic level in reading achievement on the NAEP, compared to 24 percent of 4 th graders and 19 percent of 8 th graders who were not eligible for FRPL. 38 percent of 4 th graders and 52 percent of 8 th graders who were eligible for FRPL scored below the Basic level in mathematics achievement on the NAEP, compared to 12 percent of 4 th graders and 21 percent of 8 th graders who were ineligible. 72 In 2005, the NAEP revealed corroborating disparity data in the area of mathematics. 4 th grade students who were eligible for FRPL scored 230 out of 500. Peers in the same or similar schools who were ineligible scored 245 on the same exam. 4 th grade students eligible for FRPL who were attending s chools with more than 75 percent of students in poverty scored 219. Students not eligible for FRPL who were attending schools with less than 10 percent of students in poverty scored 256. 73 This represents a disparity gap of 37 points. Results from the 2005 and 2007 NAEP displays 71 School Poverty and Academic Performance: NAEP Achievement in High Poverty Schools A Special Evaluation Report for the National Assessment of Title I, www.ed.gov/pubs/schoolpoverty 72 National Center for of Education), http://nces.ed.gov/nationsreportcard/ 73 Ibid.
38 the continuing achievement gap associated to poverty. See Table 2 3. The range for NAEP Reading and Math tests is 0 500 and the range for Writing and Science tests was 0 300. All numbers indicated average scale scores. The Trends in International Mathematics and Science Study (TIMMS) provided data on the mathematics and science achievement of United States 4 th grade and 8 th grade students compared to that of students in other countries. TIMMS is conducted by the International Association for the Evaluation of Educational Achievement (IEA), an international organization of national research institutions and governmental resea rch agencies. 74 Table 2 4 displays the average test scores for students in schools with different levels of poverty. A clear pattern emerged: As poverty in the schools increased, the test scores from students in those schools decreased. These data represen ted a reliable and compelling achievement gap between children in low income families and their more affluent peers. It is clear that, on average, students who are eligible for FRPL do not achieve as high as their peers who do not experience the burdens of poverty. It is also clear that, on average, students who attend schools with higher percentages of students in poverty do not achieve as high as students who attend schools with lower percentages of students in poverty. Poverty continues to be the most co nsistent indicator of academic failure, and concentration of poverty at the district level, individual school level and classroom level exacerbates the issue. Concentration of poverty intensifies the negative effects on academic achievement. All children, both poor and non poor, achieve at an average 74 International Association for the Evaluation of Educational Achievement, http://www.iea.nl/timss2011.html
39 lower level in schools with a high concentration of poverty. 75 Schools that serve large populations of poor students are highly likely to have lower average achievement levels than other schools. 76 A high concent ration of students in poverty has negative effects on students, teachers, and the school, extending beyond the effect of the poverty of the individual student. 77 The degree of poverty that children experience, coupled with the concentration of low income ch ildren in a school or classroom have a proliferating linear relationship with cost per pupil, where the cost per pupil rises with the percentage of low income children. 78 The amount of resources required to adequately instruct students depends on both the concentration of low income children and the degree of poverty experienced by these children. Concentrated student poverty is attributable to dynamic factors, in respo nse to longstanding conditions and public policies. These factors include patterns of urban and rural economic decline, residential suburbanization, municipal school district boundaries, and the vestiges of de jure racial segregation, and more recently, re segregation in the public schools. While concentrated poverty is a predominant feature across the landscape of public education in the 50 states, the patterns and characteristics in each state vary. 79 75 William Taylor and Diane Piche. 1990. A Report on Shortchanging Children: The Impact of Fiscal Inequity on the Education of Students at Risk Washington, DC: Congress of the U.S., House Committee on Education and Labor. 76 Robert Slavin, Nancy Karwiet, an d Nancy Madden, Effective Programs for Children at Risk (Boston: Allyn and Bacon, 1989). 77 Laura Lippman, Shelley Burns, Edith McArthur, (1996 ). Urban Schools: The Challenge of Location and Poverty Washington, DC: U.S. Department of Education, Office of Educational Research and Improvement 78 Kern Alexander and Richard Salmon, Public School Finance (Boston: Allyn & Bacon, 1995). 79 September 2010, http://www. schoolfundingfairness.org/National_Report_Card.pdf
40 In a recent study of Texas elementary schools, the rate of poverty among students was by far the most important variable explaining differences in achievement. A high percentage of economically disadvantaged students in the school corresponded to low academic levels. 80 or poor children than for children from families with average total income. The closer the family income is to the median or average for the United States, the closer the cost should be to the average expenditure to educate the child from a regular, averag 81 Poverty is a monumental obstacle to education attainment and more resources are required to adequately educate children who come from a background of poverty. America and Poverty America has been deemed a land of opportunity l argely because of the perception that appropriate motivations and actions may be sufficient to provide a life outside of Book The Epic of America 82 The American Dream is a bel ief in the idea that America provides a freedom that enables citizens to reach their goals of a good life through hard work. It is based on ideas of meritocracy rather than a system of class structure and its meaning has evolved through the course of natio nal history. For some Americans, its meaning is held in greater opportunities for material prosperity to be gained relative to the opportunities in other countries. For others, its meaning lies in the freedom of 80 81 Risk Children: An Econometric Application of Research Journal of Education Finance 31, no. 3 (2006): 297 319. 82 James T. Adams, The Epic of America ( Boston: Little, Brown & Company, 1931).
41 choices to be made without prejudice and per secution based on religion, class, race or gender. For many, the American Dream is a guarantee of an education that is provided for all children, allowing for career opportunities and hope for a good life, free from poverty. Education markedly increases th e odds of upward mobility. 83 Education has long been viewed as the vehicle to transport persons from a life of poverty to acceptable conditions, supporting a foundation for compulsory education in the United States. It is widely believed that using public m oney to provide education will benefit society at large by generating increased wealth, improved employment opportunities, and reductions in social problems. 84 While numerous economists and social scientists focus on wealth disparity and debate whether Amer ica deserves this positive reputation, the American Dream is part of our national culture, and education is an integral part of this optimism. The positivity surrounding America as a nation designed for upward mobility is greatly contested. Compared with those of other developing countries, U.S. poverty rates are extraordinarily high, as are the odds of remaining in poverty intergenerationally. No longer do immigrants from Europe want to come to America; the social and economic policies of their country ar e superior, their public schools are better funded, and their overall standard of living is higher. The weight of poverty in America places a heavy burden on public schools as the most important agents of poverty remediation. The results of this burden are manifested in a prevailing view that the public schools are failed institutions. 85 83 Lisa A. Keister, Wealth in America: Trends in Wealth and Inequality (Cambridge: Cambridge University Press, 2000): 64. 84 Martin Carnoy (1994). Education and Productivity. The International Encyclopedia of Educatio n (Vol. 3, pp. 1690 1695). New York: Elsevier Science. 85 Journal of Education Finance 33 no. 2 (2007): 203 220.
42 In 1962, the wealth of the richest one percent of U.S. households was approximately 125 times greater than that of the typical household. By 2004, it was 190 times. 86 In 200 7, the richest 1 percent of U.S. households owned 34.3 percent of the nation's private wealth, more than the combined wealth of the bottom 90 percent. 87 It is argued that the positive effects of redistributed wealth from government policies have little effe ct in opposition to the negative effects of government policies that exacerbate wealth disparity. 88 Persons in a state of poverty in the United States have less upward mobility than in most other developed nations. 89 Those at the bottom are relegated to an e conomic exile that is exacerbated either by direct government action that elevates the prospects of the wealthy or by government inaction that fails to overcome market forces. 90 Children unfortunate enough to be born into the lowest quartile of wealth have 91 The United States has become an increasingly more polarized and static society; on e in which children have become comparatively more disadvantaged. 92 86 Mishel Lawrence, Jared Ber nstein, and Sylvia Allegretto, The State of Working America 2006/2007 (Ithaca, N.Y.: ILR Press, 2007). 87 Ibid. 88 Stephen Holmes and Cass Sunstein, The Cost of Rights: Why Liberty Depends on Taxes (New York: W.W. Norton, 2000): 29. 89 A.B. Krueger (Cambridge, MA: MIT Press, 2003): 11. 90 Michael Walzer, Spheres of Justice: A Defense of Pluralism and Equality (New York: Boise, 1983). 91 Elizabeth H. Peters, Review of Economics and Statistics 24 (1992): 456 466. 92 A.B. Krueger (Cambridge, MA: MIT Press, 2003): 11.
43 Poverty has a significant history in the United States as a focal policy issue for politicians and Presidents. In the 1930s, President Franklin Delano Roosevelt established a group of socia giving relief to impoverished people and creating new financial systems to rebound from the Great Depression. In the 1960s President Lyndon B. Johnson initiated a set of eliminating racial prejudice and poverty. On January 8, 1964 President Johnson and attention was focused on the effects of p overty on educational productivity. 93 This war on poverty focused more federal resources into various compensatory programs. The intended purpose of compensatory education arose as an additional investment in human capital for low income students to compen sate for higher levels in human capital afforded to more advantaged populations. 94 United States Congress passed the Economic Opportunity Act, and established the Office of Economic Opportunity (OEO) which served to facilitate these new programs. 95 Criticism of these welfare proponent programs increased in the 1980s and 1990s and the OEO was dismantled, yet some programs such as Head Start and Job Corps still exist. 96 Research since the 1960s supports an assertion from advocates of The Great Society: Children living in poverty require additional assistance to succeed in school. 93 Mark Carnes and Jason Garraty, The American Nation, 13 ed. (New York: Longman, 2007). 94 Education and Urban Society 7, no. 3 (1975): 303 333. 95 Carnes and Garraty, The American Nation 96 Ibid.
44 Schools serving large numbers of poor children necessitate additional resources to meet their needs and bring them to parity with their more aff luent peers. 97 Poverty remains an important political issue for Americans today, and political parties and politicians operate under various ideals to address the American poor. It has long been recognized that deficiencies in society and families impact ch ildren and their education. Government action in response to these deficiencies has taken various forms over the decades. Children from poverty may benefit from government programs that help mitigate the hindering effects of living in a less advantaged hom e. Although the responsibility of education is relegated to individual state legislatures by the 10 th Amendment of the U.S. Constitution, 98 federal interest and intervention continues to be significant. While the majority of education related programs are m anaged by state systems, several federal programs exist in order to help address the negative effects of poverty on children. The Head Start program was established in 1965 99 and remains as the longest running social program created to intervene in the cy cle of poverty in the United States. The Office of Head Start (OHS), 100 Administration for Children and Families (ACF) 101 97 School Level Title I Funding in New York, Los Angeles Journal of Education Finance 33, no. 2 (2007): 130 146. 98 U.S. CONST. amend. X 99 Infants and Young Children 18, no. 1 (2005): 16 24. 100 U.S. Department of Health and Human S ervices, Administration for Children and Families, Office of Head Start. http://www.acf.hhs.gov/programs/ohs/index.html 101 U.S. Department of Health and Human Services, Administration for Children and Families, http://www.acf.hhs.gov/index.html
45 and United States Department of Health and Human Services (HHS) 102 administer the national Head Start program and publish this mission state national program that promotes school readiness by enhancing the social and cognitive development of children through the provision of educational, health, nutritional, social 103 T he Head Start program has enrolled over 25 million children since it began in 1965. 104 In 2007, there were 908,412 children enrolled in the Head Start program and the government appropriation for 2007 was $6,877,975,000. The average cost per child enrolled w as $7,326. 105 The program engages parents in their children's learning and helps them to make progress toward literacy and education goals. Children from birth to age five from families with low income (in accordance with the Federal Poverty Guidelines) 106 are eligible for Head Start services. Children in poverty have a disadvantage entering school because they are often unprepared relative to their more affluent peers. Children enrolled in Head Start exhibited positive effects in cognitive, social emotional an d health domains that were measured. 107 Head Start provides necessary resources allowing for parent and child preparation for education. Child preparation efforts result in higher success rates in schooling for children and can help to break the cycle of pov erty. 102 U.S. Department of Health and Human Services, http://www.hhs.gov/ 103 U.S. Department of Health and Human Services, Administration for Children and Families, Office of Head Start http://www.acf.hhs.gov/programs/ohs/about/index.html 104 U.S. Department of Heal th and Human Services, Administration for Children and Families, Office of Head Start http://www.acf.hhs.gov/programs/ohs/about/index.html 105 Ibid. 106 Table 2 1. 107 U.S. Department of Health and Human Services, Administration for Children and Families, Office of Head Start http://www.acf.hhs.gov/programs/ohs/about/index.html
46 Another program aimed at mitigating the effects of poverty on the public education system is the Federal Title I program. According to the United States Department of ied to challenging State academic standards in order to reinforce and amplify efforts to 108 As the largest single federal investment in schooling, Title I of the No Child Left Behind (NCLB) Act provided over $13.9 billion in compensatory assistance in 2008. Title I funds reach 12.5 million students in preschool through high school, enrolled in p ublic and private schools. 109 Approximately two thirds of public schools receive some amount of Title I funds. The United States Department of Education appropriates Title I funds by multiplying the state average per pupil expenditure by 40 percent of the ch ildren in each district over five years old living below the federal poverty guidelines. 110 Federal legislation allows school districts to determine minimum levels of eligible pupils necessary to receive Title I funds, but directs funds to be distributed to schools with less than 35 percent eligible pupils. 111 The legislation also requires that districts fund schools with more than 75 percent eligible pupils, in rank order from highest to lowest. 112 Not all school districts utilize Title I funds in the same manne r, but Title I legislation requires that the majority of the funds must be distributed at the school level. State Education 108 U.S. Department of Education, Office of Elementary and Secondary Education, http://www2.ed. gov/programs/titleiparta/index.html 109 U.S. Department of Education, Office of Elementary and Secondary Education, http://www2.ed.gov/programs/titleiparta/index.html 110 Ibid. 111 20 U.S.C. § 1113(b)(A) 112 20 U.S.C. § 1113(a)(3)(c)(A)
47 departments can take only 1 percent of the total Title I allocation for administrative purposes; 99 percent must pass to school dist ricts. 113 Public schools with poverty rates above 40 percent can utilize Title I funds, in conjunction with state and local funds, to operate a school wide program to improve instruction for the whole school. 114 Schools with poverty rates below 40 percent, or those choosing not to operate a school wide program, can offer a "targeted assistance program" that identifies students who are at risk of not meeting state standards, and designs an instructional program to meet the needs of those students. 115 Both school w ide and targeted assistance programs are based on effective means of improving student achievement and include strategies to support parental involvement. 116 The Title I program is successful in providing some additional resources and services to less advant aged students across the country, yet the effects of poverty on education are still prevalent. 117 The National School Lunch Program (NSLP) is a federal meal program operating in public and non profit private schools and residential child care institutions. The NSLP was established in 1946 as a result of the National School Lunch Act. 118 In 1947, the NSLP cost was $70 million; in 2007 the cost was $8.7 billion. The program provided free or reduced price lunches to more than 30.5 million (nearly 42 percent) chil dren each 113 20 U.S.C. § 1004(a)(1 ) 114 20 U.S.C. § 1114(a)(1) 115 20 U.S.C. § 1115(a) 116 117 Ibid. 118 U.S. Department of Agriculture, Food and Nutrition Service, National School Lunch Program, http://www.fns.usda.gov/cnd/lunch/aboutlunch/NSLPFactSheet.pdf
48 school day in 2007. 119 In 1998, Congress expanded the National School Lunch Program to include reimbursement for snacks served to children in educational afterschool programs. 120 At the state level, the NSLP is typically administered by state educati on agencies, which operate the program through agreements with school food authorities. 121 School districts and independent schools that choose to take part in the lunch program receive cash subsidies and donated commodities from the United States Department of Agriculture (USDA) for each meal they serve. 122 In return, schools must offer free or reduced price lunches to eligible children, and they must serve lunches that meet Federal requirements. 123 From July 1, 2010 through June 30, 2011, children from families with incomes at or below 130 percent of the federal poverty level were eligible to receive free meals. Families with incomes between 130 percent and 185 percent of the federal poverty level were eligible to receive reduced price meals. 124 Students can be ch arged no more than 40 cents for a reduced price meal. 125 For the period July 1, 2008, through June 30, 2009, 130 percent of the poverty level was $27,560 for a family of four; 185 percent was $39,220 for a family of four. 126 NSLP enrollment data serves as one of the best sources of data on low income students in the entire nation. As such, 119 Ibid. 120 7 U.S.C. § 210.7 121 7 U.S.C. § 210.3 122 7 U.S.C. § 210.4 123 7 U.S.C. § 210.10 124 U.S. Department of Agriculture, Food and Nutri tion Service, National School Lunch Program, http://www.fns.usda.gov/cnd/lunch/aboutlunch/NSLPFactSheet.pdf 125 Ibid. 126 USDA Food and Nutrition Service, Direct Certification in the National School Lunch Program: State Implementation Progress Report to Congre ss, Special Nutrition Programs Report No. CN 08 DC, December 2008, http://www.fns.usda.gov/ora/menu/published/CNP/FILES/DirectCert08.pdf
49 data are also used to determine funding for a variety of federal and state programs that target children and families in poverty. NCLB legislation requires each state to hold public schools accountable for the achievement of their students, with additional layers of accountability for low income students. 127 The NCLB law recognizes the percentage of students enrolled in the NSLP as a primary indicator of school poverty. The loc al educational agency shall us e the same measure of poverty, which measure shall be the number of children ages 5 through 17 in poverty counted in the most recent census d ata approved by the Secretary, the number of children eligible for free an d reduced p riced lunches under the Richard B. Russell National School Lun ch Act, the number of children in families receiving assistance under the S tate program funded under part A of title IV of the Social Security Act, or t he number of children eligible to receive medical assistance under the Me dicaid program, or a composite of such indicators, with respect to all school attendance areas in the local educational agency (A) to identify eligible school attendance areas; (B) to determine the ranking of each area; and (C) to determine allocations under subsection (c). 128 The most commonly accepted method of determining the incidence of children with greater educational needs as a result of low income is participation in the NSLP a nd free or reduced price lunch eligibility. 129 Some have suggested that a significant challenge to FRPL data is the fact that some states or school districts do not report these data correctly. In addition, there are few procedures within the construct of th e program to prevent fraud or misreporting, and federal documentation of household income is not required. FRPL is often underreported at the middle school and high 127 20 U.S.C. § 1001 128 20 U.S.C. § 113(a)(5) 129 Risk Children: An Econometric Application of Research Based Cost Differentials
50 school levels due to student perception that it is a stigma to be considered poor by their peers. 130 Large discrepancies and reporting swings in some U.S. school districts is another area of concern. 131 These inconsistencies have caused funding difficulties and queries of fraudulent activity. 132 Despite limitations and concerns, FRPL data remains the most widely utilized poverty data in education research. While some legislatures and researchers utilize U.S. Census Bureau data, and poverty rates vary considerably, the most prevalent source utilized as a poverty proxy in education research is FRPL data State K 12 public education systems face the challenge of educating extraordinarily high numbers of students in poverty. Using the U.S. Census schools is 16%. Nine states have chi ld poverty rates of over 20%, with Mississippi at 26% and Louisiana and Washington, D.C. at 25%. While the Census poverty rate differentiates above and below poverty at 100% of the federal poverty level (approximately $20,000 for a family of four), it is m ore common in education to assess poverty levels using eligibility for the federal free and reduced price lunch (FRL) program. The threshold for this program is 185% of the federal poverty level, or approximately $37,000 for a family of four. When poverty rates are expressed in this commonly used metric for student poverty, the national rate is 41%. Eleven states have average FRL rates over 50%, with Mississippi (68%) and New Mexico (61%) topping the ystem, the student poverty rate is 50%, with more than 3 million children qualifying for federal free and reduced price lunch. 133 The NSLP is another Federal program working to provide compensatory assistance to disadvantaged children, and is utilized as a q uantifier for school poverty. 130 School Journal of School Health 79, no. 10 (2009): 485 94; Teachers College Record New York Times March 1, 2008. 131 Education Next 10, no. 1 (2010): 67 71 132 Ibid. 133 September 2010, http://www.schoolfundingfairness.org/National_Report_Card.pdf
51 The role of the Federal government in providing assistance in education is noteworthy, and it is useful to contextualize poverty issues, yet this involvement is not successful in eliminating the achievement gap between poor s tudents and their peers. The United States Department of Education lists sixty two federal education programs, descriptions, and details that are managed by the Office of Elementary and Secondary Education (OESE). 134 Current Federal assistance programs do no t have the ability to rapidly change the economic status and family situation of its students, and poverty remains a substantial barrier in educational attainment and success. State education finance systems are the fundamental instrument for provision of equitable and adequate educational resources for all students. Analyzing these systems is critical to considerations of compensatory practices and addressing poverty as an education impediment. Education Finance Research Research in education finance has led to studies of historical significance. The Coleman Report 135 was the result of a commission established by Congress in 1966 to study resources and the educational opportunities available to minority children. The authors of the study concluded that the largest determinants of student achievement are the educational backgrounds and aspirations of other students in the school. They 134 U.S. Department of Education, Office of Elementary and Secondary Education http://www.ed.gov/about/offices/list/oese/programs.html 135 James Coleman, Ernst Campbell, Carol Hobson, James McPartland, Alexander Mood, Frederic Weinfeld, and Robert York. Equality of Educational Opportunity (Washington, DC: U.S. Government Printing Office, 1966).
52 independent of his background and general s 136 From these assertions a popular perception arose that money is not a significant determinant in the schooling of students from low socio economic or minority backgrounds. Reports conveyed that money does not matter in education. 137 Subsequen t studies countered the Coleman concerning the issue of money and its association to student achievement. 138 There is a perception with some policymakers that there is litt le correlation between education spending and schooling results. 139 This belief is consistently challenged, continuing the research between money and student performance. After many years of research, the relationship between school resources and student ac hievement remains a controversial subject. Researchers have spent decades investigating the relationship between educational expenditures and student achievement use a basic co nceptual model that defines achievement as a function of school resources, student ability, student socioeconomic background, and other school 140 In 1975, chapters by Levin and Rothenberg in the Handbook for Evaluation Research were the first to advocate in prominent 136 Ibid. 137 Ibid. 138 Matters: Why Arguments to the 139 Eric Hanushek, "The Impact of Differential Expenditures on School Performance," Educational Researcher 18, no. 4 (1989): 45 51. 140 Educational Expenditures Relate to Student Achievement: Insights from Texas Journal of Educational Finance 24, no. 3 (1999): 281 302.
53 educational literature, the use of economic evaluation in educational decision making. 141 Education cost studies utilized by researchers vary in design. In recent years as these cost analyses and adequacy studies have become the centerpiece of education finance litigation, having significant influence on state education budgetary decisions, th e reliability and validity of these analyses have been examined and scrutinized. In some situations these studies are held as the gold standard and other times they are viewed as pure alchemy. 142 Reviewing various approaches and differences reveals some diff iculties in the relationships between education finance and student performance. Average Expenditure studies and Resource Cost Models were used extensively in the 1980s to measure educational services. The first goal of these studies was to identify necess ary resources used to provide a particular set of services. 143 Prices of resources vary from district to district according to level and intensity of services. As the issue of adequacy came to the forefront, these studies became more tailored in the 1990s, a nd several approaches emerged. The Professional Judgment approach, the Evidence Based approach, the Successful Schools approach, and the Statistical Analysis approach have been utilized to determine education adequacy. 144 The Professional Judgment approach utilizes a focus group of educators, policymakers, stakeholders to prescribe a basket of services and resources necessary to provide an adequate education. A calculation of the cost of these services is 141 Milbrey W. McLaughlin, Evaluation and Education: At a Quarter Century (Chicago: University of Ch icago Press, 1991). 142 Journal of Education Finance 32, no. 2 (2006): 170 201. 143 Robert Berne and Leanna Stiefel, The Measurement of Equity in School Finance: Conceptual, Methodological, and Empirical Dimensions (Baltimore MD: The John Hopkins University Press, 1984). 144
54 established. This approach recognizes recommendation s from a panel of experts in order to decide the vital components of an adequate education. 145 The panel advises on what inputs (teachers, resources, programs, etc.) are necessary to meet the stated educational standard. This approach can result in the creat ion of a hypothetical prototype school that is ideally equipped. A primary concern of the professional judgment approach is the fact that there are significant discrepancies between various expert panels. 146 Another problem is that it relies on judgment rath er than specific research indicating linkage between programs and student achievement. Also, some models do not distinguish between recommended strategies for typical schools and schools with low socio economic or special populations. 147 This approach is lim ited in design, application, and generalizability, yet the majority of studies rely on this methodology. Eric Hanushek referred to the Professional Judgment Approach as an 148 Many of these Professional Judgment studies are presented as scientifically based analyses, but objective research examination of this methodology reveals that they are guided by opinions. 149 145 John Chambers and J. T. Parrish. 1994. State level Edu cation Finance, in Cost analysis for education decisions Advances in educational productivity v 4, W.S. Barnett ed. Greenwich, CT: JAI Press. 146 William Duncombe, John Ruggiero, and John Yinger. 1996. Alternative Approaches to Holding Schools Accountable, in Holding Schools Accountable, H.F. Ladd ed. Washington, D.C.: The Brookings Institute. 147 Educational Policy 17 (2003): 586 612. 148 Eric Hanushek. The Institution, Stanford University, October 2005). 149 Heuristic Examination of the Professional Educational Considerations 35, no. 4 (2007): 51 55
55 In the Evidence Based approach there is a requirement of a specific empirical research basis for recommended resource configurations and provisions. This approach is built around the concept of determining costs of multiple educational strategies that are considered to be the most successful in supporting stu dent achievement. 150 This approach incorporates a variety of researched educational concepts rather than one intervention. The majority of these strategies are virtually impossible to cost out, and generalizability is questionable. Evidence Based studies may assert research basis, but standards for acceptable research involved in this approach are indefinite. 151 The Successful Schools approach is initiated with an examination of the standardized student achievement results of schools in a given district or sta te. standard are utilized to determine adequate expenditure levels. 152 In a modified version of the Successful Schools approach, the Successful Districts approach identif ies districts that have been successful in educating students to the state level of proficiency, and uses the weighted averages of expenditures to establish a base level of funding adequacy. Outlier districts, those that spend significantly above or below the average, may be removed from the analysis. 153 The approach is based on the concept that school districts spending less than this specified amount would reach this newly determined adequate level of funding. Another modified version of the Successful 150 Ibid. 151 Ibid. 152 153 Ibid.
56 Scho ols approach, The Improvement Model, identifies districts that have experienced the most significant achievement gains over a period of time. The average expenditures of these districts, removing outliers, are analyzed to determine an adequate spending lev el. 154 One flaw in these approaches is that the estimate is based on a highly select sample. The programs and implementation costs may differ considerably in an altered setting. 155 Socio economics plays a significant role in implementation costs. Hanushek crit icized these methods because schools meeting the standards are mostly comprised of more advantaged student populations. He indicated that they spend more than necessary to meet the standards because of local support and emphasis on education. 156 Statistical Analysis models have been research tools utilized to establish relationships between money and student achievement. Production Function models and Cost Function models are statistical analyses that create regression equations utilizing multiple variables to establish a curve of best fit. The education Production Function model has been utilized to determine which quantities and qualities of educational resources are positively correlated with a specific set of student outcomes. These studies can also deter mine which quantities and qualities of educational resources are more or less effective in school districts with different types of students or across various types of districts. This model identifies the possible outcomes that can 154 James Peyser and Robert Costrell Education Next, Spring 2004, pp. 23 29, http://media.hoover.org/documents/ednext20042_22.pdf 155 Thomas Downes. What is adequate? Operationalizing the concept of adequacy forNew York. February 2004. Prepared for the EFRC Symposium on School Finance and Organizational Structure in New York State, http://www.bos.frb.org/economic/nesg /papers/Downes.pdf 156 Institution, Stanford University, October 2005).
57 be achieved with a provi ded combination of inputs. Given a quantity of available inputs, it is possible to calculate the maximum output that can be achieved. In a simple model, available resources for education are the inputs, student achievement is the outcome, and schooling is the process that translates inputs to outcomes. Poverty is a variable that can be included in Production Function models. The complexity of the schooling process and the number of inputs that can impact outcomes makes application of Production Function res earch questionable in education contexts. 157 It is challenging to determine the most efficient method of this process, and equally difficult for policymakers to agree upon desired levels of educational outcomes. Eric Hanushek has published production fun ction research. In one meta analysis study, Hanushek analyzed 187 various regression equations from other studies with consideration of seven different inputs: 1) Teacher/pupil ratio; 2) Teacher education; 3) Teacher experience; 4) Teacher salary; 5) Per p upil expenditure; 6) Administrative inputs; and 7) Facilities. 158 It is generally accepted that smaller classes leads to higher student achievement, yet out of 152 studies considered by Hanushek, only 14 presented that lower pupil/teacher ratios were positiv ely correlated to student achievement. Only 8 out of 113 studies indicated that the level of teacher education is significant to an increase in student performance. More positive correlations were discovered with teacher experience and teacher salary varia bles. Hanushek attributed this to other factors rather than the individual variable itself. Hanushek stated similar findings concerning per pupil expenditures and administrative inputs. Hanushek also 157 for Education Statistics, Selected papers in School Finance, 1995. 158 Educational Researcher 18 (1989): 45 65
58 determined that there is little correlation between faci lities and student performance. 159 reasons. Hedges, Laine, and Greenwald reviewed the same studies that Hanushek utilized in the aforementioned analysis, but used different statistical procedures other persuasive in showing that, with the possible exception of facilities, there is evidence of statistically reliable relations between educational resource inputs and school outcomes, and there is much more evidence of positive relations than of negative relations between resourc 160 Other arguments cite reasons for misinterpretation of results of hypothesis testing, inclusion of confounded data elements, and choice of performance measure. 161 According to Hedges, Laine, and positively related to student outcomes, with effect sizes large enough to suggest that moderate increases in spending may be associated with significant increases in achievement." 162 Due to the preponderance of mixed results, some researchers have deduced that models other than the production function model may be more appropriate 159 160 Analysis of Educational Researcher 23 (1994): 5 14. 161 Ibid. 162 Robert Greenwald, Larry V. Hedges, and Richard D. Laine, "The Effect of School Resources on Student Achievement," Review of Educational Research 66 (1996): 361 409.
59 when dealing with this issue in education. 163 Other models exe rcise a different approach to provide analyses of this issue. Another type of statistical analysis, the Cost Function model, employs a regression analysis with expenditure per pupil as the dependent variable, and district and student characteristics, as we ll as desired performance levels, as the independent variables. The result produces an adequate expenditure per pupil for the average district, and then, for all other districts. This model is used to determine a cost estimate for students to achieve a des ired set of educational outcomes, and to estimate how those costs differ in districts with varying characteristics that serve students with varying needs. 164 The cost of students achieving a state standard could be more in an urban district than in a suburba n or more affluent district. It is possible to apply this model across all districts to come up with the minimum cost to educate students to an established, adequate standard. The Cost Function is an extension of the Production Function in which the goal i s to directly estimate, in a single model, the costs of achieving desirable outcomes, and to estimate the cost of associated inputs. Most outcome measures in Cost Function studies have been narrowly specified, including primary measures of student achievem ent in core subject areas. 165 Education Cost Functions can provide evidence on the spending implications of student needs, including poverty. Some results of this model suggest funding levels for inner city districts of two to three times 163 Funding Equity: A M Journal of Education Finance 20, no. 1 (1994): 21 46; David Educational Evaluation and Policy Analysis 14 (1992): 307 33 Input to a Fish? Problems with the Production Education Policy Analysis 1, no. 12 (1993). 164 165 Ibid.
60 average expenditure levels, making it politically problematic. 166 Not every child identified as living in poverty or qualifying for subsidized lunch needs specific, supplemental educational programs, or services. Cost Function models often include a measure of the numbers of s tudents in poverty in a given district as a broad socioeconomic proxy rather than a measure of individual programming needs or services. These broad scoped financial additions (sometimes incorporated in the form of reduced class sizes and teacher quality) often correlate with differences in student outcomes for impoverished students. 167 Cost Functions can also be utilized to develop more traditional adjustments in funding for student needs weighted pupil counts. 168 Cost Functions can be useful to generate a c ost index for each school district in a given state, and indices denote the relative cost of producing desired outcomes in each district. A plethora of factors affect school finance research in confounding ways. Given numerous approaches to determining edu cation adequacy, the convoluted relationship between money and student achievement is still being examined. It has been reasonably established that simply providing additional funding, or ment. Yet, it is also accepted that money and resources can be a significant force for student learning. In various studies, targeting increased spending on specific reforms and interventions 166 Willia 2002. Washington, DC: NCES, U.S. Department of Education, p. 141. 167 168 Journal of Education Fina nce 29, no. 3 (2004): 195 221.
61 has been effective in increasing student achievement. 169 Education al leaders and policymakers continue the endeavor to intensify the effects of focused funding on increasing student achievement. There are a multitude of reform ideas and initiatives in states across the nation. Hanushek asserts that incentive based progra ms, including merit based pay and school choice options, are also possible solutions to increased performance. 170 The specific manner in which money is spent at districts and schools, and quality program implementation are concentrations for research that ca n provide more answers to the money achievement relationship puzzle. Various approaches and statistical analyses that clearly explicate the relationship between money and student achievement are still debated and are beyond universal acceptance. Studies fo r further understanding continue. State Education Funding Education is a monumental responsibility that each state must prudently manage. State Legislatures approach this responsibility with varying systems, laws, policies and strategies. Provision of res ources for educational purposes is one area in which state legislatures differ substantially. Some education resources are provided by the federal 169 Barbara A. Wasik and Robert E. Slavin, "Preventing Early Reading Failure With One To One Tutoring: A Review of Five Programs," Reading Research Quarterly 28, no. 2, (1993): 178 200; Joseph R. Jenkins, and Kathleen Pool, "E ffects of Tutoring in Phonological and Early Reading Skills on Students at Journal of Learning Disabilities 33, no. 4, (2000): 579 590; Frederick Mosteller Richard J. Light, and Jason A. Sachs, "Sustained Inquiry in Education: Lessons from Skill Grouping and Class Size," Harvard Education Review 66, no. 4 (1996): 797 842; U.S. Department of Based Assessment of the Scientific Research Literature on Reading and Its Implications for Reading Instruction," Report of the National Reading Panel 2000, http://www.nichd.nih.gov/publications/nrp/smallbook.cfm; Frances A. Campbell et. al., "Early Childh ood Education: Young Adult Outcomes From the Abecedarian Project," Applied Developmental Science 6, no. 1 (2002): 42 57; Craig T. Ramey, Frances A. Campbell, and Clancy Blair, "Enhancing the Life Course for High Risk Children: Results from the Abecedarian Project," in Social Programs That Work edited by Jonathan Crane (Russell Sage Foundation, 1998), pp. 163 183. 170 Economic Journal 113, (2003): F64 F98
62 government and significant funds are generated by local means, but in most states the majority of resources p rovided to public schools come from state revenues. The share of state funds is generated by state sales taxes and income taxes, while property taxes generate the local share of funding for schools. Levels of funding and specific policies concerning taxati on for education have continually been controversial. The conflicts and politics vary in each state, resulting in significant differences in levels of funding for education. In addition, the percentage of families and children living in poverty varies from state to state, thus the financial burden of educating poor students varies as state legislatures and schools try to compensa te for societal issues. Table 2 6 displays education revenue and expenditure data in conjunction with the poverty rate and FRPL el igibility percentage for each state. The average amount of money spent on each public school student in 2006 ranges from $5,464 in Utah to $14,954 in New Jersey. 171 These numbers do not provide all perspectives and comparisons. Analogous to comparing the Uni ted States to other OECD countries, consideration of state revenues, wealth in each state and the different types of needs that exist for the children in each state can present a much different picture about the overall effort of the state in terms of educ ation funding. Distribution of these funds is a serious conundrum in fiscal and political discussions. There are several different variations of formulas that state legislatures utilize in order to proffer funding to districts and schools. There are differ ences in formulas which present contrasting state philosophies about addressing poverty and other student need factors related to education. 172 Considerations of wealth 171 Table 2 7 172 David C. Thompson, R. Craig Wood, and Faith E. Crampton, Money and Schools (New York: Eye on Education, 2008).
63 disparities and disproportionalities in fiscal provisions have been fundamental concerns. Issues of equity and adequacy are key elements in funding systems. These systems are the point of contention in courts, and they are the vehicle for providing equitable and adequate educational resources to students across the nation. 173 State Funding Form ulas A review of state funding formulas, origins, history, and distinctions provides context for study. In the early 1900s, as legislatures began considering resource allocation practices, researchers understood that wealthy districts with high property va lues generated more taxes for schools than neighboring, less advantaged counterparts. Elwood Cubberley asserted that all children of the state were equal and entitled to equal advantages, spawning a basis for state aid programs for school districts. 174 The w ork of Harlan Updegraff added ideas for variable equalization and reward for tax effort, and George Strayer and Robert Haig initiated the concept of a minimal educational program offering. Paul Mort continued in 1924 by defining weighted pupil expenditures arguing that educational program costs necessary for equality will differ according to influential variables. 175 Even at the inception of funding formulas, wealth disparity and disproportionalities were strongly evident in education finance. Based on the work of education finance theorists, state aid formulas were designed around multiple concerns for funding fairness. According to the general philosophy during those times, state formulas were supposed to attend to the unique needs of each district, addres sing variables that cause inequalities, and should have 173 Thompson, Wood, and Crampton, Money and Schools 174 Thompson, Wood, and Crampton, Money and Schools 175 Ibid.
64 applied universally to all districts in the state. State legislatures implemented formulas of different types based on varying philosophies specific to each state. 176 A Flat Grant is a set amount of mo ney based on numbers of pupils, provided to districts without any acknowledgement of local district funding capabilities and contributions. Localities had the option to supplement the grant with local revenue. Although district wealth disparity remained un changed under this model, several legislatures utilized this method until the 1970s when education finance litigation began to emerge. Equalization Grants work to match resources inversely according to local district capabilities. Under this model, the pri mary concern is often whether legislatures set taxation amounts or whether this decision is left to the local district. Foundation Plans, a type of Equalization Grant, have been the most utilized equalization formulas. Foundation Plans require that distric ts provide a minimum educational program, provide means for additional local spending, and are inversely proportional to district funding ability. Foundation Plans provide for an established minimal education for all students, despite variations in wealth. Resource Accessibility Plans, another type of Equalization Grant, also equalize revenues, but allow for even more local contribution and variability as long as revenue is not the reason for the variability. These plans include components of district power equalization, guaranteed tax base, and guaranteed tax yield. These vehicles of equalization operate under the philosophy that the ability to raise money from local taxes should be equalized, but the decision as to how much money should be raised is left t o the local district. It is based on the wealth neutrality principle the quality 176 Ibid.
65 of the state as a whole. 177 Many legislatures utilize multitier grants that have components of several different types of plans. A common combination includes Flat Grants or Foundations with some form of percentage equalization. Under a Full State Funding Grant, legislatures provide all resources for student education with no local con tribution. All local differences in taxes and spending are eliminated, and there is no local fiscal control. Hawaii is the only state utilizing this plan with a single school district. 178 There are multiple formulas that can be utilized by states as the prim ary funding mechanism for education. Different legislatures choose different methods based on multiple factors and political realities that are unique to the given state, but all previous descriptions of formulae are only part of the funding programs. Recognizing the tremendous variations of the sociopolitical structures of states supports the reality that each state educational system functions uniquely. Student need factors are addressed in a host of different ways. Some approaches place varying degre es of control on the amount of local funding that may be utilized. Some approaches ensure minimum funding for specific goals. Each approach has further distinctive qualities, yet debates exist concerning the actual funding impact of the different approache s compared to the political climate of the state. 179 Funding formulas, in addition to legislative and judicial decisions ultimately determine true allocations. Funding formulas, although varied, are designed to moderate issues of wealth inequality at a macro level, redistributing funds across districts within a state, with a goal of 177 Thompson, Wood, and Crampton, Money and Schools 178 Ibid. 179 National Tax Journal 47, no. 1 (1994): 89 110.
66 horizontal equity. The need equalizations and vertical equity systems within state funding plans are evidence of even more contrast and variance among states. Foundations of basic state formulas focus on horizontal equity, ensuring that funding formulas make equal provisions for all children. There are other layers and elements of funding plans that are crucial adjustments to these basic formulas. The concept of need equalization f ocuses on the fact there are significant differences in children that require more action beyond horizontal equity. Compensatory education programs aimed at poverty, bilingual education programs, and special education programs are central to many vertical equity considerations as they address the different educational needs of different children. 180 Compensatory education programs attempt to redress socioeconomic issues that burden students in the classroom, causing many students to necessitate additional res ources to be educated. Bilingual education programs, the necessity for this type of need equalization and the associated additional funding vary considerably among states. Special education programs, also varying from state to state, have been a focal poin t and area of intense litigation when it comes to need equalization programs. Some special education dollars are designated in federal legislation such as the Individuals with Disabilities Education Act (IDEA), enacted by Congress in 1990, and disputes wil l continue as special education students require additional monies. The majority of need equalization that occurs is in the form of per pupil weights and additional flat grants. 181 State legislatures enact some form of need 180 Thompson, Wood, and Crampton, Money and Schools 181 Ibid.
67 equalization funding with a basic formula in order to address these fundamental differences in children and their education. Every State Department of Education in the United States operates a unique funding system in order to distribute resources to each LEA (Local Education Authority). 182 Multifaceted systems are amalgamated in order to meet the political, legislative and educational requirements of the state. These systems are altered periodically in each state based on fiscal climates, legislative action s, and societal and political realities. 183 There is not a public clearinghouse that retains up to date state education finance system changes and current descriptions. In a 2009 publication, Verstegen asserted, It has been over 10 years since information h as been available for all 50 states related to state financing policies and programs for public elementary and secondary education. The most recent 50 state finance survey was conducted by the National Center for Education Statistics in 1997 98. Prior to t hat release, the Education Commission of the States disseminated a state finance survey in 1990. 184 Integrated into these state finance systems, compensatory education policies and apportionments change. The state poverty weights reported in education resea rch are incongruent over time due to the perpetual changes within state finance systems. Individual State Departments of Education may or may not publish current descriptions of finance formulas, compensatory practices and poverty weights. 182 Ibid. 183 Downes 184 State Survey of School Finance Policies And Journal of Education Finance 34, no. 3 (2009): 213 225.
68 The methods by which state legislatures make vertical equity adjustments may be as diverse as the children that benefit. Although there are similar components, the funding system of every state differs from its counterpart. State poverty based education funding programs have varied significantly in terms of size, focus, and method of funding. Some legislatures have adjusted the basic state aid formula to address poverty concerns, while others supply separate categorical grants, providing supplemental distributions that a ugment the basic state aid. Some legislatures utilize both approaches. In 2002, thirty eight state legislatures distributed some education funds on the basis of poverty. Twenty state legislatures based the distribution of poverty based funding on FRPL, and ten state legislatures determined this funding based on free lunch eligibility only. Six state legislatures used the U.S. Census Bureau data to determine poverty aid for education, three states used the number of children receiving Temporary Assistance Fo r Needy Families (TANF) (formerly known as compensatory program funding. 185 Acknowledging concentration of poverty to be a factor, twenty state legislatures relegated some or all lo w income funding to school districts with poverty rates above a specified threshold, and fifteen states varied the amount of supplementary funding per poor student, providing larger amounts of funds for school districts with higher poverty rates. 186 Legislat ures provided different parameters as to how funds had to be utilized. Stipulations, restrictions and areas of focus varied. There are multiple factors combined with poverty that attribute to decline in 185 Based Education Funding: A Survey of Current Programs and Options for 186 Ibid.
69 student achievement. In 2002, thirteen states combine d poverty measure with other student risk factors such as ethnicity, limited English proficiency, single parent households, and mobility rates. 187 The poverty based funding efforts of states varied as well. The per pupil poverty based funding effort ranged f rom $111 in Arkansas to $5,199 in Massachusetts, noting that some states provided no poverty based monies at all. 188 Among the states that did provide poverty based funding, the amount of additional funding ranged from 1.9 percent to 58.7 percent of the aver age per student allocation. Thirty eight states provided some level of poverty based funding and eleven states provided poverty based funding that exceeded 25 percent of the average per pupil funding level. 189 Based on 2007 data, another study reported diffe rences in compensatory practices based on poverty and other at risk factors. Currently, 34 states fund students that are low income, a proxy for being at risk of dropping out of school, or funding is based on students in need of remediation. In Kentucky, t he eligibility criterion is based on students eligible for the federal free lunch program in Michigan, it is free breakfast, lunch or milk per pupils. In Nebraska, a progressive percentage are multiplied by students qualified for free lunches/milk, or chil dren under 19 years of age living in a household with adjusted gross income less than $15,000, whichever is greater. In Iowa, eligibility is based partially on both free and reduced lunch (F&R) count in addition to budget enrollment of the school district Texas supports students eligible for F&R lunch and pupils who are pregnant. New York provides state support for students who are at risk for not meeting learning standards. Likewise, South Carolina provides funding for students who fail to meet statewide standards in reading, writing, and math, or who do not meet first grade readiness test standards. Weights vary but range between 1.0 (an additional 100%) in Minnesota for free lunch recipients, to 5% in Mississippi. Most states provide an additional 25% i n funding for low income students and target eligibility on either federal free or reduced price lunch status or both. Connecticut provides an 187 Ibid. 188 Ibid. 189 Base d Education Funding: A Survey of Current Programs and Options for
70 additional 25%, Georgia, 31%; Hawaii, 10%; Louisiana, 19%; Maine, 20%; Michigan, 11.5%; Minnesota, 100% for free lunch recipients and 50% for reduced lunch recipients; Missouri, 25%; Oregon, 25%; South Carolina, 26%; and Texas and Vermont, 25%. 190 The different methods state legislatures employ to increase vertical equity yield varying results. A 2010 report presented a National Report Card on Fair School Funding In this report, opportunity by providing a sufficient level of funding distributed to districts within the 191 The Report Card consisted of four separate but interrelated fairness measures: Funding Level, Funding Distribution, Effort and Coverage. Statistical analyses and other evaluations were conducted in each measure as state funding systems were ranked. Fourteen states had progressive funding systems, providing greater funding to high poverty districts than to low poverty district s. The most progressive funding systems were in Utah, New Jersey and Minnesota. 192 Twenty states had regressive funding systems, providing high poverty districts with less state and local revenue than low poverty districts, though the pattern was nonsystemat ic in fourteen of those states. Alabama, Illinois, New Hampshire, New York, Pennsylvania, and Texas displayed noticeably regressive funding patterns. 193 Six states were positioned relatively well on all four measures, 190 State Survey of School Finance Policies And Programs: An O Journal of Education Finance 34, no. 3 (2009): 213 Journal of Education Finance 34, no. 3 (2009): 213 225. 191 September 2010, http:// www.schoolfundingfairness.org/National_Report_Card.pdf 192 Ibid. 193 Ibid.
71 receiving Cs or higher on Effort and Fun ding Distribution, and ranked in the top half in Funding Level and Coverage. These states were Connecticut, Iowa, Massachusetts, New Jersey, Vermont, and Wyoming. 194 Four states earned below average ratings on each of the four measures: Illinois, Louisiana, Missouri, and North Carolina. These were low effort, regressive states receiving Ds or Fs on both indicators, and ranked below average in terms of Funding Level and Coverage. 195 This report on state education funding is another example of researchers analyzi ng compensatory practices of state legislatures. Adequacy, Standards, and Poverty Education policies that shifted focus from horizontal equity (equal distribution of resources for all students) to vertical equity (equitable distribution of resources with r ecognition of student need factors) have been developments in the Twentieth Century. 196 In the 1990s, policy focus in education finance experienced another paradigm change. During the 1990s, two primary factors shifted the focus of school finance to adequacy The first was whether differences in dollars per pupil produced substantive differences in educational opportunities or student learning based environment, had to link dolla rs to results student achievement. 197 Standards based education has continually increased in recent years. A test for school finance policy is whether it provides sufficient, adequate funding for districts and schools to implement 194 Ibid. 195 Ibid. 196 Thompson, Wood, and Crampton, Money and Schools 197 Odden, Allan, and L. O. Picus, 2000. School finance: A Policy Perspective. New York: McGraw Hill.
72 educational programs to educate students to a specified, measurable level or standard. Adequacy can be explained as a level of resources sufficient to achieve defined educational results. 198 Some courts utilize adequacy definitions that consider the likelihood that an education wil l prepare a student to be a contributing member of society. 199 Adequacy is unlike equity because it emphasizes outputs over inputs and shifts the concentration away from equal provisions for all students. 200 Determining adequacy requires attaching fiscal amoun ts to programs and implementation schemes. available funds and politics than what is necessary to achieve targeted student outcomes. 201 Clearer, more measurable objectives ha ve emerged in the era of standards, and in some cases revenues, allocations, and expenditures are related to these objectives. Adequacy discussions consider the quantity of resources necessary for students to reach a defined standard. It is certain that ad ditional resources are required for students from poverty to reach a given educational standard. 202 In its current condition, education is controlled, measured and dominated by standards. Mandates from No Child Left Behind 203 (NCLB) legislation have momentous 198 Educational Policy 17, no. 5 (2003): 586 612. 199 Theor Educational Policy 11, no. 3 (1997): 330 352. 200 Educational Policy 8, no. 4 (1994): 376 394. 201 Lawrence Picus School Business Affairs 65, no. 1 (1999): 27 31. 202 Finance Systems That Support Standards Based Journal of Education Finance 27, no. 3 (2002): 749 81. 203 20 U.S.C. § 6319
73 impacts on education systems and practices. Student retentions, employee bonuses, school choice and school reorganizations are some outcomes of mandates from the standards era. 204 Legislatures have established standards for education and student achievement and the accountability movement is now deep rooted in education systems across the nation. Funding for the legislative mandates associated with standards and accountability is not superfluous or universal, and school districts analyze the manner in which they utilize funds in order to determine if mandates can be met given the level of provided resources. Resource allocation is being reviewed in education institutions in an attempt to do more with less and meet legislative requirements. State legislative s tandards are the core of school curricula and are the driving force of assessment and evaluation. While changes have been required by law throughout areas of education in order to meet the standards based policies, changes in funding models have not experi enced the same degree of transformation. 205 Formulas were created on the premise of providing the minimum education needed to function and contribute in the industrial era. Since then, society, teaching, learning and nearly every educational system has chan ged. Claims commissioned by teachers unions and school board associations report that an additional $85 to $150 billion is necessary for schools to meet NCLB performance goals. 206 Funding systems should provide adequate resources for schools to meet legislat ive mandates. Additional resources are necessary for students in poverty to reach legislatively mandated standards. Academic standards, 204 20 U.S.C. § 6143, 20 U.S.C. § 6154 205 Systems That Support Standards Based 206 Education Next ( Spring 2004): 23 29, http://media.hoover.org/documents/ednext20042_22.pdf
74 educational adequacy, and accountability have become the mantra of public education. Inadequate performance on standardi zed tests can mean retention for elementary school students. In 2008, twenty three states required passage of an exit exam for high school graduation. 207 Despite standards, state legislatures have been slow to activate funding mechanisms specifically designe d to serve students in meeting those standards. Many formulas are still based on a design of wealth equity rather than a design of adequacy for legislated standards. 208 Despite indisputable evidence that socioeconomic elements, including poverty, play a cri tical role in the achievement of students, not all state legislatures utilize poverty as a student need factor into funding formulas. Adjustments are often made on political decisions rather than evidence. 209 public schools has been based on a politically determined amount of money available for state education aid without an analysis of educational needs and on local ability to raise money through property taxes. As a result, school revenues are the result of political 210 Pupil need weights are common in state formulas, yet they can be based more on political and budgetary considerations rather than careful analysis of the cost impacts of student needs. State legislatures may significantly underestimate the influence of poverty on 207 Edweek Re port, Diploma Counts 2008. EPE Research Center, Washington DC, http://www.edweek.org/media/ew/dc/2008/DC08_Press_FULL_FINAL.pdf 208 Journal of Education Finance 32, no. 3 (2007): 304 327. 209 Journal of Education Finance 32, no. 3 (2007): 304 327. 210 National Access Network, Teachers College, Columbia University, http://www.schoolfunding.info/policy/CostingOut/costingout.php3
75 cost. 211 Results of some funding studies may be biased toward plaintiffs or defendants in order to serve client needs due to political and economic factors. 212 A New York Cost Function study determined that students in poverty cost districts twice as much as a regular student to reach a given performance level. 213 An econometric analysis concluded that state school funding formulas generally underfund programs design ed to educate poor children. This study also indicates a general per pupil cost of 2.59 for at risk students. 214 A state formula adequacy study in Massachusetts recommended that students from poverty required a weighting of 3.0 where the percentage of low in come students in a school was 50 percent or higher. 215 In a study conducted for the state of Wisconsin utilizing a resource cost approach, the conclusion indicated that the funding of at risk students would require a cost differential of 3.4. 216 Another study suggests that a cost relationship to degrees of poverty is applicable to education funding. In this situation, per pupil weighting for students with median family income and above are weighted at 1.0, students eligible for FRPL are weighted at 2.0, students eligible for free lunch are weighted at 3.0, and students at the Orshansky Poverty Level and below are 211 and Pupil Need Weights in Schoo 212 213 Fowler Jr. (ed.), Developing in School Finance:2001 2002. Washington, DC: NCES, U.S. Department of Education, p. 141. 214 Guarantee the Provision of Adequate Educat ion to Low Finance. Washington, DC: National Center for Education Statistics. 215 Charlo ttesville, VA: Deborah A. Verstegen Associates. 216 Presentation at the American Education Research Association Annual Conference, Session 5.53, Seattle WA.
76 weighted 4.0. 217 There is no evidence that the gradations of poverty are so precisely symmetric with costs. There is evidence that costs r ise with concentrations and density in the spatial distribution of poverty. School finance literature has repeatedly utilized a 20 percent weight estimate for additional poverty program costs based on the historical pattern of Title I funding. 218 This prevai ling cost estimate is based on how much the federal government was willing to spend on Title I allocations in 1987, 219 not on actual, existing program costs. 220 Legislatures have utilized this unfounded 20 percent weight in the absence of definitive research o r due to political considerations. 221 222 demonstrates that categorical weights for poverty and at risk and non English language students are far less than what is ne eded if we are serious about all students performing 223 When recommending a cost differential for low income students in 217 A martya Sen, Inequality Reexamined (Oxford: Clarendon, 1995). 218 Helping Children Left Behind: State Aid and the Pursuit of Educa tional Equity Cambridge, MA: MIT Press, p. 106. 219 Education Evaluation and Policy Analysis 11, no. 1 (1989): 47 60. 220 l Issues in State Aid Helping Children Left Behind: State Aid and the Pursuit of Educational Equity Cambridge, MA: MIT Press, p. 106. 221 Ibid. 222 presented at the Annual Meeting of the American Education Finance Association (Orlando, FL, 2003): 10. 223 Cost of an Adequate Education in Maryland i n 1999 Contracted study for the Maryland Commission on Education Finance, Equity, and Excellence, 2001; Education in Kansas in 2000 Suitable Education in Montana in 2001 2002 Using th the Montana School Board Association, 2002. (This study was an advocacy piece).
77 Wyoming, Guthrie indicated that the actual cost of these programs was not well documented. 224 He then advised borrowing th e Kentucky add on rate (15 percent) for children qualifying for free lunch if the population exceeded 50 percent of the overall enrollment. 225 In absence of definitive research, Augenblick recommended in an adequacy study that Kansas utilize relative costs o bserved in other states. 226 In a commission responsible for recommendations for school reform in Maryland, Augenblick and Meyers used a Professional Judgment approach to determine costs. The weight recommended for low income children was 1.39 times the base allocation. 227 In their analysis of poverty based weights, Duncombe and Lukemeyer indicated that cost differentials commonly utilized by states, approximately 25 percent, are likely significantly underestimating the supplemental resources required to support at risk students in meeting state mandated standards. 228 The poverty based weights currently utilized in state funding formulas are not consistent, and may not be established on the basis of actual costs of educating students in poverty. Achievement results indicate that students from poverty are not meeting standards. Studies assert various increases in poverty weights and compensatory measures are recommended for low income students to meet legislatively mandated standards. 224 James Guthrie, Gerald Hayward, and James R. Smith. 1997. A Proposal Cost Based Block Grant Model for Wyoming School Finance San Francisco: Management Analysis and Planning Associates. 225 Ibid. 226 Suitable Education in Kansas in 2000 ver: Augenblick & Myers, Inc. 227 Commission on Education Finance, Equity and Excellence. 2002. Final Report. Annapolis, MD. 228 William Duncombe and Anna Lukemeyer. 2002. Estimating the Cost of Educational Adequacy: A Comparison of Approaches. Presented at the American Education Finance Association Annual Conference in Albuquerque, NM.
78 Florida Education Finance and Ac countability The doctrine of sovereign limits is in effect when determining where the responsibility of education lies. The United States Constitution does not explicate the role of federal government in education, leaving schooling as a state responsibil ity. There are twelve Articles in the Florida State Constitution; Article IX is dedicated to education. Section 1 of this Article reads, The education of children is a fundamental value of the people of the State of Florida. It is, therefore, a paramount duty of the state to make adequate provision for the education of all children residing within its borders. Adequate provision shall be made by law for a uniform, efficient, safe, secure, and high quality system of free public schools that allows students to obtain a high quality education and for the establishment, maintenance, and operation of institutions of higher learning and other public education programs that the needs of the people may require. 229 Education in Florida is a fundamental value and a par amount duty of the state. This duty is carried out in numerous education systems, managed at various levels. After significant incidents during the Civil Rights Movement, including Brown v. Board of Education 230 in the 1950s, there was a national movement o f education commitment to funding equalization, reform efforts were enacted as a legislative mandate in 1973. 231 A new system for funding public elementary and secondary schools eme rged and the Florida Education Finance Program (FEFP) 232 replaced the Minimum Foundations Program established in the 1940s. This reform shifted significant monies 229 Florida Const. art. 9, § 1 230 Brown v. Board of Education, 347 US 483 (1954). 231 Chapter 73 345, L.O.F. (1973) 232 Ibid.
79 from the local school district level to the state level and put into action a new form of fundi vehicle for funding for education in Florida. The FEFP is designed to guarantee each student in the Florida public education system the availability of programs and services ap propriate to his or her educational needs that are substantially equal to those available to any similar student, notwithstanding geographic differences and varying local economic factors. 233 In order to provide equalization of educational opportunity, the F EFP acknowledges variations in local property tax bases, variations in costs of living, and variations in education program costs based on multiple factors. 234 FEFP funds are established by multiplying the number of full time equivalent (FTE) students in eac h of the funded education programs by cost factors to obtain weighted FTE (WFTE) students. 235 WFTE students are then multiplied by a Base Student Allocation and by a District Cost Differential in the major calculation to determine the Base Funding from state and local FEFP funds. 236 Other cost adjustments, including ESE Guaranteed Allocation, Supplemental Academic Instruction, Safe Schools Program, and eight other factors, are added to the Base Funding to comprise the Gross State and Local FEFP dollars. From th is, the Required Local Effort is subtracted, Adjustments are added, District Discretionary Lottery Funds 233 Ibid. 234 Florida Department of Education, 2010 11 Funding for Florida School Districts, http://www.fldoe.org/fefp/pdf/fefpdist.pdf. 235 F.S. § 1011.61(1)(a) 236 F.S. § 1011.62(1)(c)
80 are added, and Categorical Program Funds are added to finalize monies for the Total State Finance Program. 237 A key characteristic of the FEFP is that it establishes financial support for education based upon the individual student participating in a particular program rather than upon the number of classrooms or teachers. Program cost factors serve to ensure that each program receives an equitable share of funds in relation to its relative cost per student. There are various cost factors that the legislature includes in the FEFP to determine WFTE. 238 The 2010 2011 program cost factors included in the FEFP to determine WFTE were: students in kindergarten, g rades 1, 2, and 3 were weighted programs were weighted 4.935, students in English for Speaker s of Other Languages programs were weighted 1.147, and students in programs for Career Education were weighted 1.035. 239 Other student need factors are not included by the legislature in the FEFP to determine WFTE. 240 There is no poverty based weight that esta blishes a WFTE for low equitable distribution of funds for education. Education revenues and expenditures vary from state to state and district to district. School districts receive fun ding from state, federal and local sources. In 2006 237 F.S. §1011.62(5), F.S. §1011.62(6); F.S. §1011.62(7); F.S. §1011.62(8); F.S. §1011.62(9); F.S. §1011.62(10); F.S. §1012.225; F.S. §1006.28 1006.43; F.S. §1011.68. 238 F.S. § 1011.61(1)(a) 239 Florida Department of Education, 2010 11 Funding for Florida School Districts, http://www. fldoe.org/fefp /pdf/fefpdist.pdf 240 Ibid.
81 2007, Florida school districts received 40.63 percent of education funding from state sources, 50.47 percent from local sources (including the Required Local Effort portion of the FEFP), and 8.90 percent from federal sources. 241 In 2008 2009, the state contribution was $9,007,286,039 and the Required Local Effort was $8,267,476,367. 242 Local revenue for education funding was derived almost entirely from property taxes. Every scho ixty seven school districts must levy the necessary millage for the Required Local Effort from property taxes. State revenue for education is predominantly raised by the state sales tax. 243 Education expenditures differ between every state and every school d istrict. A credit to the FEFP and equity in education spending, Florida ranked 9 th in the nation in the difference in per pupil spending levels at the 95th and 5th percentiles. In many states, the spending among districts varies substantially. With a Coeff icient of Variation of 0.074, Florida had the second least disparity of all states in spending across school districts. 244 Compared to other states, Florida continually ranks low concerning the amount of money it spends on education. In 2008, Florida sp ent $ 7,539 per pupil, ranking thirty ninth in education spending compared to the U.S. average of $8,973. Florida spent 3 percent of its re sources on education, ranking forty second in the nation. Ranking forty sixth in this udents were educated in school districts that had a 241 Financial Profiles of Florida School Districts, 2006 2007 Financial Data Statistical Report, Florida Department of Education, School Business Services, Office of Funding and Financial Reporting, http://www.fldoe.org/fefp/pdf/07 08profiles.pdf 242 Ibid. 243 Florida Department of Education, 2010 11 Funding for Florida School Districts, http://www.fldoe.org/fefp /pdf/fefpdist.pdf 244 Quality Counts 2008 State Highlights Report, Florida
82 per pupil expenditure meeting or exceeding the national average. 245 Since 2000, the Florida Legislature reduced its portion of K 12 public education spending, shifting more costs to local districts. State s upport for schools grew at an average rate of 3 percent a year since 2000, while local funding for K 12 education grew at an average of more than 9 percent a year. For every $1,000 of residents' personal income Florida spent $33.51, compared with the U. S. average of $43.34, ranking fiftieth out of fifty states. 246 In regards to Florida education funding relative to other states, conservatives may declare efficiency and liberals may claim inadequacy; sources of education funding and schooling expenditures ar e political matters of contention. Considerations of efficiency, inefficiency, standards, achievement, legislative mandates, and adequacy are key elements in education finance debates. The complex dynamics of state funding formulas and legislative power i n conjunction with funding advocates and judicial review provide for an intricate political arena for education funding. Litigation efforts related to education have been substantial in states across the nation. Over the decades, claims have been filed in forty five states, basing suits on different premises. Lawsuits have generally followed three claims: education as a fundamental right, the equal protection of laws, and the education articles of state constitutions. 247 The state of Florida has encountered a lesser degree of educational funding conflict in comparison to some of its counterparts. The FEFP has been challenged in court, yet has not experienced significant alteration by judicial nged at the 245 Ibid. 246 Ibid. 247 Thompson, Wood, and Crampton, Money and Schools
83 Supreme Court level in Coalition for Adequacy and Fairness in School Funding v. Chiles 248 of judicial 249 This type of judgment, citing avoidance of judicial intrusion, has not been categorically corroborated in all similar court cases. In Coalition v. Chiles Justice Overton 250 The language used in Florida legislation asserts that it is its paramo unt duty to provide an adequate and high quality education for all students. Adequacy has not been easily determined. At the request of state legislators or private advocacy organizations, numerous consultants have been hired to conduct cost studies in ord er to determine the amount of school funding necessary to provide an adequate educational opportunity to all students. 251 A study of this kind has not been commissioned in Florida. ation accountability plan. The Elementary and Secondary Education Act (ESEA), reauthorized as the No Child Left Behind Act of 2001, 252 is the primary federal legislation affecting education from kindergarten through high school. NCLB is built on four princip les: accountability for student achievement, increased school choice, greater 248 Coalition for Advocacy v. Chiles 680 So. 2d 400 (Fla. 1996). 249 Ibid. 250 Coalition for Advocacy v. Chiles 680 So. 2d 400 (Fla. 1996). 251 Lori Benton, Examination and Application of the Education Adequacy Models and Studies to the State of Florida (PhD diss., University of Florida, 2008). 252 20 U.S.C. § 6301
84 local control and flexibility, and emphasis on research based educational practice. 253 NCLB is a driving force behind the standards based education movement in the United States. T his movement is focused on establishing high standards and setting measurable goals in efforts to improve student performance. NCLB requires all state legislatures to administer and report annual academic assessment results based on standards established b y each state. 254 Poverty is an explicit factor in the federal Adequately Yearly Progress (AYP) accountability measure, part of NCLB. 255 The FCAT, students in grades 3 through 11 in Florida. 256 State standards in reading, mathematics, writing, and science are assessed on this exam. Students take the reading and math sections of the FCAT exam every year (grades 3 through 10), complete the writing section in fourth, eighth, and tenth grades, and complete the science section in fifth, eighth, and eleventh grades. The FCAT, and the grades at which various tests are taken, is subject to review and change on an annual basis. Achievement Levels range from 1 (lowest) to 5 (highest) for all s ubjects tested. Students scoring at Level 3 on a subject. 257 Performance results have a range of significant consequences for students, schools, and districts, including pecunia ry implications in various forms. 258 253 Florida Department of Education, NCLB overview, http://www.ed.gov/nclb/overview/intro/edpicks.jhtml 254 20 U.S.C. § 6301 255 20 U.S.C. § 6161 256 F.S. § 1008.22 257 Florida Department of Education, FCAT overview, http://www.fldoe.org/faq/default.asp?Dept=202& ID=660#Q660 258 F.S. § 1008.33
85 Achievement results on the FCAT indicate that students from poverty do not perform as well as their more advantaged peers on the standardized exam. Students in districts with a higher concentration of poverty experienced lower scores than students in districts with lower poverty rates. 259 School grades in Florida, ranging from A to F, are derived exclusively from FCAT performance data. In 1999, 96 percent of high poverty schools earned school grades of C, D or F. Ninety five percent of F schools were high poverty schools, 1 percent (8 schools) were A schools, and 3 percent were B schools. 260 Department of Education, scoring at level 3 or above can be con score, indicating that a student is on or above grade level. 261 In 2006, the mean percentage of students who passed the FCAT in the five school districts with the highest concentration of poverty was 26.8 percent. 262 The mean percentage of students who passed the FCAT in the five school districts with the lowest concentration of poverty was 50.4 percent. 263 The five lowest performing school districts reported a mean rate of poverty of 26.8 percent, while the five highest performing school d istricts reported a mean rate of poverty of 14.3 percent. Students in all school districts with poverty rates 259 September 2010, http://www.schoolfundingfairness.org/National_Report_Card.pdf 260 Florida Legislature Office of Program Policy A nalysis and Government Accountability, Florida Actions Should Improve Student Performance in High Poverty Schools. Report 00 07 (2000), http://www.oppaga.state.fl.us/reports/pdf/0007rpt.p df 261 Florida Department of Education, FCAT overview, http://www.fldoe.org/faq/default.asp?Dept=202&ID=660#Q660 262 Concentration of Child Poverty in Comparison to Florida Comprehensive Assessment Test (FCAT) Performance, http://sitemaker.umich.edu/670gispolicybrief/files/670gispolicybriefformat.pdf 263 Ibid.
86 below 14 percent met and generally far exceeded the state average. 264 School recognition funds are issued by the state on the basis of student FCAT performance. In 2007, the twenty two districts with the lowest poverty levels averaged $63 per student in bonus money compared to $31 per student in the twenty two districts with the highest poverty levels. 265 Children from families with a high socioeconomic status perform notably better than their impoverished peers, resulting in an increase in funding for low poverty districts. The numbers of students in Florida who qualify for the FSLP constitute a substantial portion of the overall enrollment. In 1999, 4 were eligible for FRPL, rising to 46.0 percent in 2005. 266 There were 1,214,732 (45.8 percent) children who qualified for FRPL in 2008, and in 2009, 49.57 percent of students qualified. 267 As the fourth most populous state, Fl price lunch figures are comparable with the three most populous states, Texas (48.76 percent), California (51.67 percent), and New York (44.74 percent). 268 Eligibility for he ten year numerical increase during the last ten years amounts to 388,235 students (1,092,525 in 2001 02 vs. 1,480,760 in 2010 11), a cumulative increase of 35.54 percent in ten years. Fifty two districts reported 50 percent or more of their enrollment e ligible for FRPL in 2010 11, as 264 Ibid. 265 Charles J. Morris and the Okaloosa Citizens Alliance, http://www.oca1787.org/documents/flrecognition.pdf 266 Florida Department of Education, Education Information and Accountability Services, 2008 Series 2009 03F, www.fldoe.org/eias/eiaspubs/word/ frp lunch .rtf 267 Ibid. 268 Florida Department of Education, Education Information and Accountability Services, February 2011, Series 2011 19D, http://www.fldoe.org/eias/eiaspubs/
87 compared to only twenty nine districts ten years prior. 269 In 2011, St. Johns County had the lowest percentage of students eligible for free/reduced price lunch (22.06 percent), and Gadsden County had the highest percentage (8 1.70 percent). 270 Large numbers of low income students create a burden for educators and policymakers in Florida as they work to compensate for poverty issues. Summary This study reviewed poverty and quantitative measurements of poverty, focusing on FRPL as the preferred measure of poverty in education contexts in the United States. An investigation concerning the associations between poverty and student achievement, and a review of the federal role in addressing poverty concerns in schools were also condu cted. This study analyzed education funding, the relationship between money and achievement, and education finance research. An examination of the disparities in state education funding and compensatory practices was significant. Researching adequacy and a ccountability in education were also of central importance. This study focused on Florida and its education funding systems. The methodology utilized for this stu dy was discussed in Chapter 3. 269 Ibid. 270 Ibid.
88 Table 2 1. 2011 Health and Human Services Poverty Gui delines Persons in Family or Household 48 Contiguous States and D.C. Alaska Hawaii 1 $ 10,890 $ 13,000 $ 12,540 2 14,710 18,380 16,930 3 18,530 23,160 21,320 4 22,350 27,940 25,710 5 26,170 32,720 30,100 6 29,990 37,500 34,490 7 33,810 42,280 38,880 8 37,630 47,060 43,270 For each additional person, add 3,820 4,780 4,390 Source: Federal Register Vol. 76, No. 13, January 20, 2011, pp. 3637 3638 Table 2 2. U.S. Children Living in Poor and Low Income Families, 2009 Age Poor Low Income Under 3 3.1 million 5.9 million Under 6 6.1 million 11.7 million 6 to 11 5 million 10.3 million 12 to 17 4.2 million 9.3 million Under 18 15.3 million 31.3 million Source: Basic Facts a bout Low Income Chi l dren 2009, National Center for Children in Poverty, http://www.nccp.org/topics/childpoverty.html
89 Table 2 3. U.S. NAEP Scores Students FRPL eligibility 2007 Grade 4 Reading 2007 Grade 8 Reading 2007 Grade 4 Math 2007 Grade 8 Math Less than 10 percent 240 280 256 300 11 25 percent 231 272 248 292 26 50 percent 223 263 242 282 51 75 percent 212 253 234 271 More than 75 percent 200 241 222 259 Student FRPL Eligibility 2007 Grade 8 Writing 2007 Grade 12 Writing 2005 Grade 4 Science 2005 Grade 8 Science Eligible 141 138 135 130 Not Eligible 164 157 162 159 (Washington, DC: U.S. Department of Education), http://nces.ed.gov/nationsreportcard/ Table 2 4. U.S. TIMMS Scores 2003 Students in school eligible for FRPL Grade 4 Math Grade 4 Science Grade 8 Math Grade 8 Science 10 percent or less 567 579 547 571 11 25 percent 543 567 531 554 26 50 percent 533 551 505 529 51 75 percent 500 519 480 504 More than 75 percent 471 480 444 461 U.S. Average 518 536 504 527 International Average 495 489 466 473 Source: Highlights From the Third International Mathematics and Science Study (TIMSS) 2003, http://nces.ed.gov/pubs2005/2005005.pdf
90 Table 2 5. U.S. TIMMS Scores 2007 Students in school eligible for FRPL Grade 4 Math Grade 4 Science Grade 8 Math Grade 8 Science 10 percent or less 583 590 557 572 11 25 percent 553 567 543 559 26 50 percent 537 550 514 528 51 75 percent 510 520 482 495 More than 75 percent 479 477 465 466 U.S. Average 529 539 508 520 International Average 500 500 500 500 Source: Highlights f rom the T rends in International Mathematics and Science Study (TIMSS) 2007. http://nces.ed.gov/pubs2009/2009001.pdf Table 2 6. State Education Finance and Poverty Figures State 2 005 2006 Per Pupil Expenditure Public Elementary and Secondary 2006 Percentage of Revenue Public Elementary and Secondary 2005 2007 Average Poverty Rate Percentage 2005 2006 Stu dents Eligible for Free/Redu ce Price Lunch 2008 2009 Stu dents Eligible for Free/Redu ce Price Lunch Alabama 7,683 7,683 55.9 15.2 51.7% 52.2% Alaska 11,476 58.7 8.8 31.4% 34.1% Arizona 6,515 48.4 14.7 45.0% 47.4% Arkansas 8,030 56.8 15.1 52.9% 57.1% California 8,301 59.3 12.7 47.6% 51.7% Colorado 8,166 42.5 10.3 33.1% 35.4% Connecticut 13,072 38.5 8.7 26.5% 29.9% Delaware 11,621 63.2 9.3 36.1% 39.1% Dist. Columbia 13,752 NA 19.2 53.4% 67.1% Florida 7,812 39.5 11.7 45.8% 49.6% Georgia 8,595 44.4 13.5 49.8% 53.0% Hawaii 9,876 89.9 8.4 41.0% 41.7% Idaho 6,469 56.2 9.8 37.8% 39.7% Illinois 9,113 29.6 10.7 37.2% 39.3% Indiana 8,929 49.1 11.7 36.1% 41.8% Iowa 8,355 45.6 10.2 31.9% 34.0% Kansas 8,644 54.6 12.3 38.7% 42.9% Kentucky 7,668 57.3 15.7 49.5% 51.6%
91 Table 2 6. Continued Louisiana 8,486 43.4 17.1 61.2% 64.9% Maine 10,841 42.4 11.2 33.8% 37.0% Maryland 10,909 39.2 9 31.6% 34.7% Massachusett s 12,564 47 11.1 28.2% 30.7% Michigan 9,577 59.3 12 35.0% 41.1% Minnesota 9,159 71.2 8.5 30.3% 32.4% Mississippi 7,173 51 21.1 69.5% 68.3% Missouri 8,273 33.5 11.9 39.1% 38.7% Montana 8,626 46.2 13.4 34.5% 36.7% Nebraska 9,324 31.9 9.9 34.7% 38.4% Nevada 7,177 25.9 10 41.3% 39.0% New Hampshire 10,396 39.2 5.6 17.1% 20.5% New Jersey 14,954 42.3 8.1 26.8% 30.0% New Mexico 8,354 71.2 16.3 55.7% 61.4% New York 14,615 42.5 14.4 44.8% 44.7% North Carolina 7,396 62.5 14.1 42.6% 33.2% North Dakota 8,728 36.2 10.6 29.6% 31.6% Ohio 9,692 43.7 12.4 32.5% 36.4% Oklahoma 6,941 53.3 14.7 54.5% 56.1% Oregon 8,645 50.4 12.2 41.8% 44.5% Pennsylvania 10,723 35.4 11 31.4% 33.3% Rhode Island 12,609 41.1 10.7 34.9% 39.3% South Carolina 8,120 45.2 13.4 51.5% 52.5% South Dakota 7,775 33 10.7 32.0% 34.7% Tennessee 7,004 42.5 14.8 47.1% 49.9% Texas 7,480 33.8 16.4 48.2% 48.8% Utah 5,464 55.1 9.4 32.3% 31.2% Vermont 12,805 85.6 8.4 26.4% 28.9% Virginia 9,445 39.6 8.8 31.1% 33.0% Washington 7,984 60.8 9.4 36.5% 38.2% West Virginia 9,440 59.8 15.2 49.1% 50.0% Wisconsin 9,993 52.3 10.4 29.3% 33.5% Wyoming 11,437 44.1 10.5 31.6% 31.0% Source: National Center for Education Statistics, U.S. Census Bureau, and Florida Department of Education Expenditure, Revenue and FRPL data from National Center for Education Statistics, Common Core of Data, http://nces.ed.gov/ccd/bat/ Poverty rate data from U.S. Bureau of the Census, Poverty in the United States: 2002 Series P60 222 ; Income, Poverty, and Health Insurance Coverage in the United States, 2003 Series P60 226 ; Income, Poverty, and Health Insurance Coverage in the United States, 2004 Series P60 229 ; Income, Poverty, and Health Insurance Coverage in the United States, 2005 Series P60 231 ; Income, Poverty, and Health Insurance Coverage in the United States, 2006, Series P60 233 ; and Income, Poverty, and Health Insurance Coverage in the United States, 2007, Series P60 235 Florida Department of Education, Education Information and Accountability Services, February 2011, Series 2011 19D, http:/ /www.fldoe.org/eias/eiaspubs/
92 Table 2 7. Expenditures Per Pupil for Public Elementary and Secondary Education (Constant 2007 2008 dollars) Year Florida U.S. 1998 $ 8093 $ 8738 1999 8290 9023 2000 7982 9246 2001 8004 9556 2002 7935 9812 2003 8047 10009 2004 8269 10125 2005 8538 10288 2006 8911 10433 2007 9391 10720 Source: National Center for Education Statistics, Expenditure per pupil in fall enrollment in public elementary and secondary schools, by state or jurisdiction through 2006 07, http://nces.ed.gov/programs/digest/d09/tables/dt09_185.asp
93 CHAPTER 3 METHODOLOGY The purposes of this study were to analyze the relationship between poverty and student achievement in the state of Florida, and to determine the financial implications of theoretical poverty weights activated in the FEFP. In this research, poverty was measured in terms of eligibility percentages and students qualifying for the FRPL program, and student achievement was denoted by FCAT results. Descriptive and correlation statistics were used to quantify t he association of these data. E xamination s of compensatory elements of state education finance programs were utilized in conjunction with recent adequacy studies in order to construe theoretical poverty weights that could be exercised in Florida. These theoretical fiscal adjustments were applied to the education finance program in Florida in order to deduce the financial consequences. The study addressed the following research questions: 1. What were the relationships between FRPL percentages and FCAT achievement data in Florida schools? 2. What were the relationships between FRPL percentages and FCAT achievement data in Florida elementary schools? 3. What were the fiscal implications of initiating a theoretical poverty weight into the FEFP? 4. What were the fiscal implications of employing compensatory practices of a contemporary adequacy study in the FEFP? Data There were sixty seven counties in the state of Florida, each constituting a separate public school district. Within every s chool district, a percentage of students were eligible to receive free or reduced price lunch through the federal NSLP. 271 271 U.S. Department of Agriculture, Food and Nutrition Service, Na tional School Lunch Program. http://www.fns.usda.gov/cnd/lunch/aboutlunch/NSLPFactSheet.pdf
94 Student eligibility for this program was determined by the federal poverty guidelines. For this study, these data were used as the meas ure of poverty. FRPL data from school districts were annually reported to the Florida Department of Education (FDOE), and were organized by Education Information and Accountability Services (EIAS) in the FDOE. 272 Past reports that were not publicly published were obtained from EIAS in order to assemble longitudinal FRPL data for each school district. The FRPL percentages for each Florida district were compiled from 2004 to 2009, matching the years of available relevant FCAT data. 273 Additional FRPL information was utilized for more detailed an alysis in the year 2009. FRPL has often been underreported at the middle school and high school levels due to student perception that it is a stigma to be considered poor by the ir peers. FRPL data reporting has been the mos t accurate at the elementary school level, rather than middle and high school levels. 274 In this study, disaggregated FRPL data regarding students in grades three, four, and five, in all Florida school districts were utilized. The FDOE did not calculate and report FRPL eligibility in each district for individual grade levels in terms of percentages, yet individual grade level FRPL eligibility total numbers and individual grade level total student membership numbers were available. 275 FRPL percentages for each g rade level (third, fourth, and fifth) for each district were calculated by dividing the number of 272 Florida Department of Education, Education Information and Accountability Services, http://www.fldoe.org/eias/eiaspubs/default.asp 273 Table 3.2. 274 Donka Mirtcheva School Journal of School Health 79, no. 10 (2009): 485 94; Evidence about Tracking: Critiquing the New Repor Teachers College Record New York Times March 1, 2008. 275 Table 3 2.
95 students eligible for FRPL in each grade level by the total membership of each grade level. These calculations were completed for each Florida school district 276 The FCAT was achievement. 277 FCAT scores were the measure of student achievement utilized in this study. The FDOE upheld minimum requirements for FCAT administration, and requirements were consistent for all Florida school districts. FCAT Reading tests were administered to students in grades 3 10, FCAT Math tests were administered to students in grades 3 10, and FCAT Scienc e tests were administered to fifth, eighth, and tenth grade students. Very few students were exempt from taking the FCAT. In 2009, 14 percent of all public school student membership in Florida was served under IDEA. 278 Based on the student Individual Educa tion Plans (IEP), only 1 percent of these students with significant cognitive disabilities were exempt from FCAT testing. A relatively small number of English Language Learners (ELL) were also exempt. If an ELL had been receiving services in an English for Speakers of Other Languages (ESOL) program operated in accordance with an approved district Limited English Proficient (LEP) plan for one year or less, then the ELL was exempt from the FCAT Writing and Reading administrations. 279 LEP committee must have determined that this exemption was appropriate. 276 Ibid. 277 § 1008.22, F.S. 278 Florida Department of Education Bureau of E xceptional Education and Student Services, http://www.fldoe.org/ese/pdf/b 621.pdf 279 Florida Department of Education Bureau of Exceptional Education and Student Services, http://www.fldoe.org/ese/pdf/b 621.pdf
96 Student FCAT scores in 2009 ranged from one to five, with a score of 1 being the lowest and 5 being the highest. A score of 3 or higher was considered to be proficient on Reading, Mat proficient in grade level skills. Students scoring levels 4 and 5 are considered to have 280 In this study, a score of 3 or higher was demonstrative of adequate student achieve ment. The FDOE reported standardized test scores in various forms and categories. In this study, the percentages of students scoring at level 3 or higher on FCAT Reading in each Florida school district were utilized. The percentages of students scoring at level 3 or higher on FCAT Math in each district were also utilized. These data were assembled from the FDOE for each year from 2004 to 2009. 281 See Table 3 2. Although FCAT administration was taking place prior to 2004, testing policies and data collection methods differed. For the purpose of consistency, years 2004 to 2009 testing results were analyzed in this study. Additional analysis was conducted utilizing 2009 FCAT scores from elementary students. In this study, 2009 third grade FCAT Reading and third grade FCAT Math scores from all districts were utilized, 2009 fourth grade FCAT Reading and FCAT Math scores from all districts were utilized, and 2009 fifth grade FCAT Reading and FCAT Math scores from all districts were utilized. This study focused on pe rcentages of students scoring at level 3 or higher on FCAT exams in correlation to FRPL percentages in each school district. These 2009 achievement data 280 Guide to FCAT and FCAT 2.0, Accommodations for Students with Disabilities http://www.fldoe.org/ese/pdf/fcatteam.pdf 281 Table 3 2.
97 and federal lunch program eligibility data were fundamental in quantifying the relationship between pov erty and student achievement in Florida elementary schools. Methodology A Descriptive and correlation statistics were employed to analyze FRPL data and FCAT data in order to study the relationship of these variables. The descriptive statistics depicted c entral tendencies of the variables, and the correlation statistics evidenced relationships between poverty and student achievement. The mean, median, mode, and range of FRPL percentages for Florida school districts were examined for years 2004 to 2009. The mean, median, mode, and range of FCAT Reading scores for Florida school districts, and the mean, median, mode, and range of FCAT Math scores for Florida school districts were examined for years 2004 to 2009. Descriptive statistics focused on FRPL percenta ges as well as student FCAT scores of level 3 or higher, and were utilized to reveal central tendencies. 282 Correlation is the measure of relation between two or more variables. 283 The variables in this study include d FCAT scores (a student achievement indica tor), and FRPL percentages (a poverty indicator). Linear correlations, Pearson r were calculated to determine the extent to which the two variables were proportional to each other. 284 The formula that was applied to calculate correlation values was the cova riance of the two variables divided by the product of the standard deviations. A standard formula was used to calculate Pearson r where r = correlation coefficient, N = number of value in 282 Statsoft, Electronic Statistics Textbook, http://www.statsoft.com/textbook/basic statistics/ 283 Statsoft, Electronic Stati stics Textbook, http:// www.statsoft.com/textbook/basic statistics/#Correlations 284 Statsoft, Electronic Statistics Textbook, http:// www.statsoft.com/textbook/basic statistics/#Correlations
98 285 In this study, there were sixty seven school districts reporting FRPL data and FCAT scores, and in this context, a sample size of sixty seven districts (re presenting 2,635,276 students) provided acceptable level s of correlation significance. Higher numbers associated with one variable were, in general, paired with lower numbers associated with the second variable, indicating a negative relationship. 286 The FRP L percentages for all districts were correlated with the FCAT Reading scores for all districts. The FRPL percentages were also correlated with the FCAT Math scores for all districts, resulting in two correlation coefficients for each school district for th e years 2004 to 2009. Multiple additional correlation coefficients were calculated for the year 2009. FRPL percentages in grades 3, 4, and 5 were correlated with FCAT Reading and Math scores of level 3 or higher for students in grades 3, 4, and 5. FRPL pe rcentages in grades 3, 4, and 5 were correlated with the FCAT Reading and Math scores of level 3 or higher for third, fourth, and fifth grade FRPL students in the district. FRPL percentages in grades 3, 4, and 5 were correlated with FCAT Reading and Math s cores of level 3 or higher for third, fourth, and fifth grade students not eligible for FRPL. These specific correlations quantified relationships between poverty and student achievement in Florida elementary schools. 285 Figure 3 1. Source: Statistics Solutions, http://www.statisticssolutions.com/methods chapter/statistical tests/correlation pearson kendall spearman/ 286 Statsoft, Electronic Statistics Textbook, http:// www.statsoft.com/textbook/basic statistics/#Correlations
99 Compensatory Education Practices In t was defined as a provision of supplemental resources based on state utilization of FRPL eligibility or another measurable poverty indicator. In some states, h ave include d provision of supplemental resources based on other factors, including, but not limited to, students with disabilities, ELL, pregnant students, students from a family household with one parent, level of education of guardians, and students perf orming below desired proficiency levels on standardized exams. 287 There have been perpetual changes occurring in state finance systems, predominantly as a result of political forces and education litigation. The lack of accessibility of clear, comprehensive and current state finance formula information, and the changes that occur in state finance systems were considered in this study. In a 2009 report of education finance program descriptions, Verstegen stated, It has been over 10 years since information h as been available for all 50 states related to state financing policies and programs for public elementary and secondary education. The most recent 50 state finance survey was conducted by the National Center for Education Statistics in 1997 98. Prior to t hat release, the Education Commission of the States disseminated a state finance survey in 1990. 288 This Verstegen study included at risk factors other than poverty (i.e. low standardized test scores and student drop out rates) in its analysis of the compen satory elements in state programs. The analysis in this dissertation focused exclusively on poverty and did not include other at risk factors. 287 Indiana Complexity Index, § 20 43 5, I.C. 288 Deborah A. Verstegen State Survey of School Finance Policies And Journal of Education Finance 34, no. 3 (2009): 213 Journal of Education Finance 34, no. 3 (2009): 213 225.
100 In this study, research concerning state education finance policies was utilized to review state compensatory pr actices and poverty weights. 289 Information obtained from individual State Departments of Education websites concerning education finance systems was also utilized to determine recent compensatory practices and poverty weights. In addition, pertinent results from recent state education adequacy studies were utilized to conduct this study. 290 Review of state formulas indicated that compensatory education practices fell into three basic categories: 1) legislatures that actuated a poverty weight directly into the foundation formula; 2) legislatures that proffered supplemental resources through funding schemes that did not involve an explicit poverty weight in the foundation formula; and 3) legislatures that made no compensatory provisions for poor students. 291 See Ta ble 3 1. There were eleven state legislatures that operationalized an explicit poverty weight into the foundation formula, twenty four state legislatures allocated additional resources to school districts based on student poverty data through methods other than the foundation formula, and fifteen state legislatures did not provide any compensatory resources for students in poverty. 292 289 Deborah A. Verstegen and Teresa S State Survey of School Finance Policies And Journal of Education Finance 34, no. 3 (2009): 213 Poverty 002, Center on Budget and Policy Priorities, Washington DC; King Peabody Journal of Education 85, no. 1 (2010): 4 15; State Education Finan ce and Governance Profiles from Peabody Journal of Education (2010), were utilized for Alabama, Arkansas, Delaware, Florida, Georgia, Kentucky, Louisiana, Maryland Mississippi, North Carolina, Oklahoma, South Carolina, Tennessee, Texas, Virginia, and West Virginia. 290 Educational Adequacy Litigation in the American South: 1973 Peabody Journal of Education 85, no. 1 (2010): 16 31; Lori Benton, Examination and Application of the Education Adequacy Models and Studies to the State of Florida (PhD diss., University of Florida, 2008). 291 Table 3 1 292 Ibid.
10 1 The poverty weights initiated in the foundation formulas of various states ranged from 0.05 to .25, providing supplements to b ase level funding. 293 The average poverty weight utilized in foundation formulas was 0.193. In states not utilizing poverty weights in foundation formulas, the method for determining additional allowances and compensatory grants were unique to each individu al state. Compensatory resources in these states took various forms, including provisions of additional teacher allocations and allowances for programmatic resources based on multifarious formulas. In Arkansas, Colorado, Kansas, Minnesota, and New Hampshir e, the additional funding was weighted progressively based on concentration of poverty. When poverty concentration rates reached specific levels, associated weighting increased. In Colorado, Kansas, Kentucky, Mississippi, and Nebraska, supplementary funds were allocated for students qualifying for Free Lunch, but not for Reduced Lunch. In Vermont, Connecticut, and Oregon, measures other than FRPL were utilized to distribute poverty based supplements. Vermont utilized Food Stamp qualifications, Connecticut u tilized Temporary Family Assistance eligibility, and Oregon utilized Census data. The systems and policies legislatures employ have a significant impact on levels of funding. Statutory poverty weights provide some indication of the relative level of commit ment to poverty based funding. There are, however, numerous factors that can affect the amount of money those weights ultimately generate for low income children. One factor is the poverty measure being used. For example, a funding program that applies a 2 5 percent poverty weight to children eligible for the free and reduced price lunch program, below 185 percent of the poverty line, will generate substantially more funding than a program that applies a 25 percent poverty weight only to 293 Ibid.
102 children eligible fo r the free lunch program, below 130 percent of the poverty line. 294 Legislatures have utilize d noted, the real value of a statutory poverty weight is significantly affected by both the 295 Stat e foundation funding levels varied in terms of size, relative to overall edu cation spending. This has affect ed the true impact of poverty weights that are activated in foundation formulas. Methodology B Compensatory education program data were operati onalized as a theoretical poverty weight activated in the FEFP. In this study, compensatory policies of the fifty United States were reviewed. There were state legislatures that calculated a poverty weight explicitly into the foundation formula. 296 The avera ge poverty weight (0.193) utilized by these legislatures was activated as a theoretical poverty weight in the 2010 2011 FEFP. This theoretical weight applied to the percentage of all Florida public school students in all grades (PK 12) who qualified for th e federal FRPL program in 2010. The FEFP determined funding quantities by multiplying the number of full time equivalent (FTE) students in each of the funded education programs by cost factors to obtain W eighted FTE (WFTE) students. WFTE students were then multiplied by a Base Student Allocation and by a District Cost Differential in the major calculation to 294 Based Education Funding: A Survey of Current Programs and Options for n DC (2002). 295 Ibid. 296 Table 3 1
103 determine the Base Funding from state and local FEFP funds. 297 serve d to a ssure that each program received its equitable share of funds in relation to 298 Cost factors in the FEFP were weights for specific students or programs. In 2010, the added weights for Exceptional Student Education, English for Speakers of Other Languages, and Career Education p rograms ranged from .031 to 3.935 in the FEFP. 299 There was no poverty based weight that esta blished a WFTE for low income students included in the FEFP. The poverty weights calculated in the foundation formulas of other states ranged from 0.05 to .25, provi ding supplements to base level funding for students in poverty. The average poverty weight utilized in foundation formulas was 0.193. In this study, this average poverty weight was applied to the FEFP in the same manner as the other program cost factors i n order to establish a WFTE for low income students. This poverty based WFTE was added to the WFTE associated with the other program cost factors. The total WFTE was multiplied by the Base Student Allocation, and was multiplied by the District Cost Differe ntial, in order to determine Base Funding. Other supplements that were added to Base Funding included DJJ Supplement, Declining Enrollment Supplement, Sparsity Supplement, Discretionary Contribution, 0.748 Mills Discretionary Compression, 0.25 Additional M ills Discretionary Compression, Safe Schools, Reading Program, Supplemental Academic Instruction, ESE Guaranteed Allocation, Merit Award Program allocation, Instructional Materials, Teacher Lead, Student Transportation, State Fiscal Stabilization Funds All ocation, and 297 Table 3 1 298 The Funding for Florida School Districts Statistical Report, 2010 1011 http://www.fldoe.org/fefp/pdf/fefpdist.pdf 299 Ibid.
104 Minimum Guarantee. These additions were included individually based on district characteristics and necessary qualifications for various supplements. Supplement quantities varied based on district profiles. The supplements added to the Base Fu nding provided the Gross State and Local FEFP. Required Local Effort Taxes were subtracted to determine the Net State FEFP. Lottery and School Recognition and Class Size reduction Funding were added to determine the Total State Funding. The financial impli cations of integrating the theoretical poverty weight (0.193) were calculated leaving all other features of the FEFP the same. Methodology C The true cost of an adequate education is a problematical number to conclude. There is no single standard that appl ies across states as an absolute cost of an adequate education, yet numerous methods have been utilized by various state legislatures and advocacy groups in efforts to calculate the cost of an adequate education. 300 ed by state legislatures while in others it has been undertaken by Governors, state education agencies, or coalitions of educators. In some cases, cost analysis has been 301 There are four approaches that have been utilize d to conduct adequacy studies, including the Professional Judgment approach, the Evidence Based approach, the Successful Schools approach, and the Statistical Analysis approach. 302 Some studies 300 Journal of Educational Finance 30, no. 3 (2005): 259. 301 ducation in North Dakota in 2002 2. 302 R. Craig Wood and Anthony R. Rolle Educational Considerations 35, no. 4 (2007): 51 55
105 have utilized multiple approaches in the same analysis in effort s to increase the validity of the results. Very few adequacy studies have completed a comprehensive design that includes all four methodologies. Contemporary studies conducted in 2007 for Rhode Island and Montana by R.C. Wood and Associates utilized all fo ur methodologies, and were considered for this study. 303 Compensatory practices of the Rhode Island study were utilized in this dissertation rather than the compensatory recommendations described in the Montana s tudy because Rhode Island was more similar to Florida in regards to population density and relevant school demographics. 304 In 2010, Rhode Island had approximate ly one thousand eighteen persons per square mile, Montana had approximately seven persons per square mile, and Florida had three hundred fifty one persons per square mile. 305 The compensatory practices indicated in the Rhode Island study were applied as theoretical compensatory inclusions to the 2010 2011 FEFP. The Rhode Island study indicated that the results from all four approaches could be util ized by legislators in making policy decisions, and all results provided valuable information, but the results from Successful Schools approach could be the most useful. Thus, for the state of Rhode Island, given the present status and validity of educati on finance research it is recommended that the successful schools model and/or the cost function approach would be the most fruitful for the state of Rhode Island. Of these two models, the successful schools model and the cost function program, if one mode l were chosen, the successful 303 R.C. Wood and Associates, State of Rhode Island Education Adequacy Study, Final Report (2007). 304 305 Data calculated from NCES Common Core Data and U. S. Census Bureau, http://quickfacts.census.gov
106 schools model, if carefully designed and crafted, would have the greatest probability of yielding the most useful model. 306 While other approaches accounted for poverty in various ways, the Successful Schools approach in the Rho de Island study used two poverty different weights, similar to explicit poverty weights in other states, in its recommendations. In order to account for differences in student demographics and poverty factors, the researchers utilized he weighting mechanism. The first discount rate assumes that those students eligible for the federal free and reduced lunch program and English language learners cost 25 percent more to educate, resulting in a 25 percent discount rate for free and reduced lunch students. This percentage for free and reduced lunch students was based on an analysis of additional funding provided by states across the country. While it must be noted that variation existed among states in the additional percentage of funding pro vided for free and reduced lunch students, and some states also took into account concentration of poverty, the 25 percent additional funding was the most commonly used, educati on finance. 307 The discount rate of 25 percent was applied as an explicit poverty weight to the FEFP in the same manner that the average poverty weight was applied in Methodology B in order to determine the funding implications. For the second discount rate we assumed that students eligible for the free and reduced lunch program and English language learners cost 40 percent more to educate. This percentage was based on a variety of research that proposes that the current standard of practice (i.e. 25 percen t) underestimates the additional costs for such students. 308 306 R.C. Wood and Associates, State of Rhode Island Education Adequacy Study, Final Report (2007). 307 Ibid. 308 Ibid.
107 The discount rate of 40 percent was applied as an explicit poverty weight to the FEFP in the same manner that the average poverty weight was applied in Methodology B in order to determine the fundi ng implications. In the Rhode Island study, the discount rate of 25 percent was also utilized in the Statistical Analysis approach. The Professional Judgment approach methodology an explicit poverty measure, and in the Evidence utilized for analysis. The compensatory practices demonstrated in the Successful Schools approach and Statistical Analysis approach explicitly targeted poverty, rath er than other at risk factors, and were utilized for this study. Summary This chapter presented the design utilized for this research study. Data concerning the variables of poverty and student achievement were described. Methodologies of correlating the se data in multiple contexts were described. Review of State Department education finance policies was discussed, and compensatory practices examined. The methodology for applying a theoretical poverty weight of 0.193, based on the average poverty weight d irectly active in other foundation formulas, as a methodologies for applying poverty weights of 0.25 and 0.40 respectively, based on practices in a c ontemporary adequacy study, as compensatory element s education finance program were described. Results were reported in chapter 4.
108 Table 3 1. Poverty based Funding 2011 State Additional Funding Based on Poverty Foundation Formula Explicit Weight Alabama Yes Alaska No Arizona No Arkansas Yes California Yes Colorado Yes Connecticut Yes 0.25 Delaware No Florida No Georgia No Hawaii Yes 0.1 Idaho No Illinois Yes Indiana Yes Iowa Yes Kansas Yes Kentucky Yes 0.15 Louisiana Yes 0.22 Maine Yes 0.15 Maryland Yes Massachusetts Yes Michigan Yes Minnesota Yes Mississippi Yes 0.05 Missouri Yes 0.25 Montana No Nebraska Yes Nevada No New Hampshire Yes New Jersey Yes New Mexico No New York Yes North Carolina Yes North Dakota No Ohio Yes Oklahoma Yes 0.25
109 Table 3 1. Continued Oregon Yes 0.25 Pennsylvania Yes Rhode Island No South Carolina Yes South Dakota No Tennessee Yes Texas Yes 0.2 Utah No Vermont Yes 0.25 Virginia Yes Washington Yes West Virginia No Wisconsin Yes Wyoming No Source State Survey of School Journal of Education Finance 34, no. 3 (2009): 213 Based Education Funding: A Survey of Priorities, Washington DC; King Cotton's Lasti ng Peabody Journal of Education 85, no. 1 (2010): 4 15; State Education Finance and Governance Profiles from Peabody Journal of Education (2010), were utilized for Alabama, Arkansas, Delaware, Florida, Georgia, Kentucky, Louisiana, Maryland Mississippi, North Carolina, Oklahoma, South Carolina, Tennessee, Texas, Virginia, and West Virginia. Table 3 2. Florida FRPL and FCAT Data, 2003 2009 2003 4 Florida District Total Membership Total Eligi ble Percent Eligible FCAT 3+ % R ea d ing FCAT 3+% Math ALACHUA 29,422 13,787 18.5 63 66 BAKER 4,605 1,830 25.4 54 61 BAY 26,693 12,265 26.3 66 68 BRADFORD 3,897 2,375 28.4 56 56 BREVARD 73,856 19,409 29.1 72 78 BROWARD 272,785 107,765 30.5 58 66 CALHOUN 2,224 1,112 31.8 67 74 CHARLOTTE 18,273 5,994 32.1 65 73 CITRUS 15,510 6,615 32.7 63 65 CLAY 31,368 7,954 32.8 65 71 COLLIER 40,144 20,149 34.2 58 65
110 Table 3 2. Continued COLUMBIA 9,780 5,296 34.3 58 55 DAD E 371,773 233,200 35.2 49 54 DESOTO 4,975 3,076 36.4 50 55 DIXIE 2,169 1,399 37.9 52 50 DUVAL 129,545 63,723 38.6 55 56 ESCAMBIA 43,981 24,910 39.3 56 56 FLAGLER 8,562 2,925 39.5 65 67 FRANKLIN 1,347 841 39.7 49 51 GADSDEN 6,946 5,237 39.8 34 37 GILCHRIST 2,833 1,516 40.2 67 78 GLADES 1,012 676 40.6 50 44 GULF 2,150 1,013 41.7 63 66 HAMILTON 2,057 1,485 42.6 40 41 HARDEE 5,221 3,653 43.9 54 63 HENDRY 7,658 5,200 45 50 56 HERNANDO 19,587 8,606 45.9 58 60 HIGHLANDS 11,660 6,796 46.2 58 61 HILLSBOROUGH 181,776 86,754 46.8 59 66 HOLMES 3,383 1,921 46.9 58 65 INDIAN RIVER 16,622 6,618 46.9 59 63 JACKSON 7,183 3,721 47.1 62 65 JEFFERSON 1,489 1,073 47.7 34 38 LAFAYETTE 1,035 540 49.2 60 68 LAKE 33,988 13,105 50 60 64 LEE 66,429 29,921 50.2 61 64 LEON 32,194 11,344 50.2 68 72 LEVY 6,208 3,451 50.7 59 60 LIBERTY 1,405 659 51.2 63 67 MADISON 3,245 1,975 51.8 42 38 MANATEE 40,243 16,160 52.2 61 63 MARION 40,362 20,483 52.2 58 65 MARTIN 17,773 5,660 52.3 71 77 MONROE 9,123 3,323 53.5 65 69 NASSAU 10,544 3,388 54.1 66 69 OKALOOSA 31,006 8,820 54.2 73 79 OKEECHOBEE 7,275 3,946 54.2 53 60 ORANGE 165,881 67,405 54.5 56 61 OSCEOLA 43,906 22,955 54.8 54 55 PALM BEACH 170,217 71,056 55.6 58 66 PASCO 57,497 26,560 56.6 60 61 PINELLAS 114,466 43,421 56.8 61 62 POLK 84,104 45,838 57.1 54 57 PUTNAM 12,237 7,268 58.3 53 57 ST. JOHNS 23,170 4,278 59.4 72 74 ST. LUCIE 32,791 17,117 60.9 55 57
111 Table 3.2 Continued SANTA ROSA 24,427 7,450 60.9 74 77 SARASOTA 39,519 12,942 61.8 68 73 SEMINOLE 64,857 18,888 62.4 69 75 SUMTER 6,857 3,756 62.7 59 66 SUWANNEE 5,857 2,938 64.5 53 55 TAYLOR 3,560 1,925 66.8 57 55 UNION 2,171 1,015 67.9 58 58 VOLUSIA 64,058 25,152 70 65 68 WAKULLA 4,728 1,622 72.1 68 69 WALTON 6,522 3,342 72.2 64 63 WASHINGTON 3,425 1,957 75.4 58 63 2004 5 Florida District Total Membership Total Eligible Percent Eligible FCAT 3+ % R ea d ing FCAT 3+ % Math ALACHUA 29,104 13,466 24.1 64 68 BAKER 4,773 2,114 27.7 53 60 BAY 27,014 12,534 29.4 67 70 BRADFORD 3,814 2,240 30.7 55 58 BREVARD 74,370 22,952 30.7 73 80 BROWARD 273,334 110,124 30.9 59 68 CALHOUN 2,307 1,216 33.9 69 75 CHARLOTTE 17,421 7,769 34.9 67 76 CITRUS 15,652 6,820 34.9 63 68 CLAY 32,403 7,817 36.9 67 73 COLLIER 41,948 18,798 37.2 59 68 COLUMBIA 9,881 5,343 37.6 59 57 DAD E 366,125 232,793 37.6 52 59 DESOTO 4,921 2,955 37.7 51 59 DIXIE 2,143 1,391 40.3 52 55 DUVAL 127,461 64,152 40.7 59 61 ESCAMBIA 43,710 26,726 41.4 55 57 FLAGLER 9,694 2,975 43.4 64 68 FRANKLIN 1,371 838 43.6 51 53 GADSDEN 6,651 4,978 44.2 36 43 GILCHRIST 2,850 1,415 44.3 72 80 GLADES 1,237 827 44.4 48 50 GULF 2,177 1,040 44.6 62 67 HAMILTON 2,003 1,197 44.8 40 44 HARDEE 5,147 3,418 44.9 51 62 HENDRY 7,593 4,925 45.8 49 56 HERNANDO 20,595 9,240 46.3 60 64 HIGHLANDS 12,021 7,199 46.4 58 65 HILLSBOROUGH 188,661 94,093 47.8 59 67
112 Table 3 2. Continued. HOLMES 3,389 1,926 48 56 62 INDIAN RIVER 17,073 7,546 48.2 64 69 JACKSON 7,354 4,016 49.1 61 67 JEFFERSON 1,374 992 49.1 37 36 LAFAYETTE 1,058 560 49.7 58 67 LAKE 35,958 14,617 49.9 61 66 LEE 70,852 31,490 50 62 67 LEON 31,881 11,757 50.2 69 74 LEVY 6,244 3,419 50.3 56 60 LIBERTY 1,392 683 52 65 66 MADISON 3,180 2,023 52.7 45 41 MANATEE 41,066 17,805 52.9 61 65 MARION 41,103 22,129 53.1 58 66 MARTIN 17,859 6,057 53.2 73 80 MONROE 8,624 3,212 53.8 67 72 NASSAU 10,705 3,733 54.1 65 73 OKALOOSA 31,065 9,146 54.6 75 81 OKEECHOBEE 7,345 3,901 54.7 54 62 ORANGE 172,504 86,214 54.8 60 66 OSCEOLA 47,321 25,182 55.1 55 57 PALM BEACH 174,102 65,489 55.3 61 69 PASCO 60,614 27,754 56.8 60 62 PINELLAS 112,809 46,644 57.8 62 67 POLK 86,367 43,317 58.7 55 59 PUTNAM 12,431 8,097 59.8 53 59 ST. JOHNS 24,320 13,400 59.9 73 78 ST. LUCIE 34,794 16,754 60.1 57 60 SANTA ROSA 25,020 7,672 61.1 74 79 SARASOTA 41,160 15,457 61.1 67 73 SEMINOLE 66,333 18,357 63.6 70 76 SUMTER 7,144 3,907 63.6 60 69 SUWANNEE 5,776 3,006 64.9 54 56 TAYLOR 3,487 2,015 64.9 58 58 UNION 2,200 1,055 65.1 61 65 VOLUSIA 65,020 24,490 66.4 65 70 WAKULLA 4,848 1,691 66.9 68 76 WALTON 6,547 3,214 72.2 63 64 WASHINGTON 3,490 1,929 74.9 60 66 2005 6 Florida District Total Membership Total Eligible Percent Eligible FCAT 3+ % R ea d ing FCAT 3+ % Math ALACHUA 29,109 13,518 17.8 66 69 BAKER 4,903 2,096 24.7 54 61 BAY 27,614 12,630 26.5 69 72
113 Table 3 2. Continued BRADFORD 3,779 2,000 28.2 59 57 BREVARD 75,207 22,584 28.8 74 81 BROWARD 271,564 112,599 30 64 72 CALHOUN 2,274 1,178 30.5 70 76 CHARLOTTE 17,901 7,065 32.3 69 75 CITRUS 15,812 6,707 33.6 66 71 CLAY 34,167 8,427 35 69 75 COLLIER 43,288 17,858 35.4 62 68 COLUMBIA 10,188 5,488 36.6 61 58 DAD E 362,050 221,229 39.4 58 62 DESOTO 5,019 2,979 39.4 52 58 DIXIE 2,238 1,535 39.5 56 59 DUVAL 126,648 52,709 40.3 60 62 ESCAMBIA 43,458 26,846 41.3 59 59 FLAGLER 11,049 3,864 41.5 66 68 FRANKLIN 1,350 826 41.6 54 56 GADSDEN 6,515 5,225 41.7 39 43 GILCHRIST 2,892 1,445 41.9 75 83 GLADES 1,272 775 41.9 55 58 GULF 2,179 980 42.4 63 68 HAMILTON 2,006 1,124 42.8 42 42 HARDEE 4,967 3,177 43.5 54 64 HENDRY 7,572 5,277 43.5 52 60 HERNANDO 21,707 9,490 43.7 63 67 HIGHLANDS 12,128 6,950 44.2 59 65 HILLSBOROUGH 193,669 95,726 44.3 62 68 HOLMES 3,439 1,958 45 61 61 INDIAN RIVER 17,233 6,788 45.7 64 69 JACKSON 7,455 3,931 46.3 63 70 JEFFERSON 1,230 855 46.4 44 44 LAFAYETTE 1,080 560 47.5 59 64 LAKE 38,058 15,932 47.6 63 68 LEE 75,610 33,503 49.4 64 68 LEON 32,319 11,838 50 71 75 LEVY 6,256 3,490 51.8 60 66 LIBERTY 1,471 700 51.9 68 64 MADISON 3,032 2,231 52.2 50 43 MANATEE 42,348 18,433 52.5 63 67 MARION 42,017 22,059 52.6 62 69 MARTIN 18,150 4,815 52.7 73 80 MONROE 8,594 3,383 52.9 70 74 NASSAU 10,866 3,652 52.9 68 74 OKALOOSA 30,999 8,743 52.9 76 81 OKEECHOBEE 7,329 3,826 53.9 57 64 ORANGE 175,593 81,245 54.4 63 66
114 Table 3 2. Continued OSCEOLA 49,772 27,340 54.9 58 59 PALM BEACH 174,861 72,947 55.8 63 69 PASCO 62,766 27,283 56 63 63 PINELLAS 112,150 45,217 56.9 63 68 POLK 89,423 51,575 57.1 57 59 PUTNAM 12,268 8,055 57.3 56 59 ST. JOHNS 25,757 4,592 57.7 76 79 ST. LUCIE 36,189 19,022 59.4 58 60 SANTA ROSA 25,188 8,144 60.9 75 80 SARASOTA 41,884 12,081 61.1 71 75 SEMINOLE 67,508 20,566 61.2 73 78 SUMTER 7,416 3,919 61.8 63 70 SUWANNEE 5,954 3,147 64 56 58 TAYLOR 3,378 1,930 65.7 61 60 UNION 2,290 1,013 68.6 64 65 VOLUSIA 65,599 27,459 69.5 65 69 WAKULLA 4,914 1,741 69.7 69 76 WALTON 6,896 3,276 73.6 67 68 WASHINGTON 3,560 1,937 80.2 63 67 2005 6 Florida District Total Membership Total Eligible Percent Eligible FCAT 3+ % R ea d ing FCAT 3+ % Math ALACHUA 29,109 13,518 17.8 66 69 BAKER 4,903 2,096 24.7 54 61 BAY 27,614 12,630 26.5 69 72 BRADFORD 3,779 2,000 28.2 59 57 BREVARD 75,207 22,584 28.8 74 81 BROWARD 271,564 112,599 30 64 72 CALHOUN 2,274 1,178 30.5 70 76 CHARLOTTE 17,901 7,065 32.3 69 75 CITRUS 15,812 6,707 33.6 66 71 CLAY 34,167 8,427 35 69 75 COLLIER 43,288 17,858 35.4 62 68 COLUMBIA 10,188 5,488 36.6 61 58 DAD E 362,050 221,229 39.4 58 62 DESOTO 5,019 2,979 39.4 52 58 DIXIE 2,238 1,535 39.5 56 59 DUVAL 126,648 52,709 40.3 60 62 ESCAMBIA 43,458 26,846 41.3 59 59 FLAGLER 11,049 3,864 41.5 66 68 FRANKLIN 1,350 826 41.6 54 56 GADSDEN 6,515 5,225 41.7 39 43 GILCHRIST 2,892 1,445 41.9 75 83
115 Table 3 2. Continued GLADES 1,272 775 41.9 55 58 GULF 2,179 980 42.4 63 68 HAMILTON 2,006 1,124 42.8 42 42 HARDEE 4,967 3,177 43.5 54 64 HENDRY 7,572 5,277 43.5 52 60 HERNANDO 21,707 9,490 43.7 63 67 HIGHLANDS 12,128 6,950 44.2 59 65 HILLSBOROUGH 193,669 95,726 44.3 62 68 HOLMES 3,439 1,958 45 61 61 INDIAN RIVER 17,233 6,788 45.7 64 69 JACKSON 7,455 3,931 46.3 63 70 JEFFERSON 1,230 855 46.4 44 44 LAFAYETTE 1,080 560 47.5 59 64 LAKE 38,058 15,932 47.6 63 68 LEE 75,610 33,503 49.4 64 68 LEON 32,319 11,838 50 71 75 LEVY 6,256 3,490 51.8 60 66 LIBERTY 1,471 700 51.9 68 64 MADISON 3,032 2,231 52.2 50 43 MANATEE 42,348 18,433 52.5 63 67 MARION 42,017 22,059 52.6 62 69 MARTIN 18,150 4,815 52.7 73 80 MONROE 8,594 3,383 52.9 70 74 NASSAU 10,866 3,652 52.9 68 74 OKALOOSA 30,999 8,743 52.9 76 81 OKEECHOBEE 7,329 3,826 53.9 57 64 ORANGE 175,593 81,245 54.4 63 66 OSCEOLA 49,772 27,340 54.9 58 59 PALM BEACH 174,861 72,947 55.8 63 69 PASCO 62,766 27,283 56 63 63 PINELLAS 112,150 45,217 56.9 63 68 POLK 89,423 51,575 57.1 57 59 PUTNAM 12,268 8,055 57.3 56 59 ST. JOHNS 25,757 4,592 57.7 76 79 ST. LUCIE 36,189 19,022 59.4 58 60 SANTA ROSA 25,188 8,144 60.9 75 80 SARASOTA 41,884 12,081 61.1 71 75 SEMINOLE 67,508 20,566 61.2 73 78 SUMTER 7,416 3,919 61.8 63 70 SUWANNEE 5,954 3,147 64 56 58 TAYLOR 3,378 1,930 65.7 61 60 UNION 2,290 1,013 68.6 64 65 VOLUSIA 65,599 27,459 69.5 65 69 WAKULLA 4,914 1,741 69.7 69 76 WALTON 6,896 3,276 73.6 67 68
116 Table 3 2. Continued WASHINGTON 3,560 1,937 80.2 63 67 2006 7 Florida District Total Membership Total Eligible Percent Eligible FCAT 3+ % R ea d ing FCAT 3+ % Math ALACHUA 29,005 12,848 44.3 67 70 BAKER 4,975 2,065 41.5 59 65 BAY 27,007 11,924 44.2 70 74 BRADFORD 3,683 1,954 53.1 57 59 BREVARD 74,807 22,218 29.7 76 82 BROWARD 262,811 108,909 41.4 63 72 CALHOUN 2,227 1,141 51.2 68 78 CHARLOTTE 17,894 7,752 43.3 70 77 CITRUS 16,087 6,648 41.3 67 73 CLAY 35,723 8,916 25.0 70 76 COLLIER 43,186 18,703 43.3 63 68 COLUMBIA 10,179 5,482 53.9 63 63 DAD E 353,831 208,795 59.0 57 63 DESOTO 5,001 3,040 60.8 52 59 DIXIE 2,241 1,504 67.1 60 66 DUVAL 125,180 51,802 41.4 61 63 ESCAMBIA 42,709 24,105 56.4 59 61 FLAGLER 12,130 4,411 36.4 66 67 FRANKLIN 1,324 829 62.6 57 56 GADSDEN 6,648 5,304 79.8 38 43 GILCHRIST 2,889 1,488 51.5 77 85 GLADES 1,257 781 62.1 54 64 GULF 2,193 988 45.1 61 66 HAMILTON 2,036 1,347 66.2 43 43 HARDEE 5,037 3,083 61.2 56 64 HENDRY 7,463 4,556 61.0 53 61 HERNANDO 22,450 9,707 43.2 64 68 HIGHLANDS 12,457 6,794 54.5 60 67 HILLSBOROUGH 193,546 94,279 48.7 62 69 HOLMES 3,384 1,887 55.8 63 66 INDIAN RIVER 17,613 6,757 38.4 65 69 JACKSON 7,382 3,649 49.4 63 72 JEFFERSON 1,220 928 76.1 46 54 LAFAYETTE 1,074 564 52.5 60 73 LAKE 39,623 16,333 41.2 63 70 LEE 78,984 33,612 42.6 65 69 LEON 32,404 11,596 35.8 70 75 LEVY 6,260 3,614 57.7 60 69 LIBERTY 1,477 722 48.9 66 64 MADISON 2,935 2,141 72.9 51 47
117 Table 3 2. Continued MANATEE 42,315 18,117 42.8 63 66 MARION 42,569 22,249 52.3 62 69 MARTIN 18,245 5,262 28.8 75 82 MONROE 8,377 3,014 36.0 70 75 NASSAU 10,940 3,565 32.6 69 77 OKALOOSA 30,277 8,762 28.9 77 83 OKEECHOBEE 7,288 3,883 53.3 58 67 ORANGE 175,238 82,831 47.3 64 68 OSCEOLA 51,888 28,326 54.6 58 60 PALM BEACH 171,683 70,991 41.4 64 72 PASCO 64,680 27,543 42.6 64 65 PINELLAS 110,006 44,530 40.5 65 70 POLK 92,809 53,213 57.3 58 62 PUTNAM 12,103 8,087 66.8 57 62 ST. JOHNS 26,971 4,746 17.6 77 80 ST. LUCIE 38,799 20,370 52.5 57 59 SANTA ROSA 25,393 7,985 31.4 76 80 SARASOTA 42,190 15,020 35.6 73 77 SEMINOLE 66,344 20,339 30.7 74 80 SUMTER 7,434 3,942 53.0 65 72 SUWANNEE 5,981 3,151 52.7 57 60 TAYLOR 3,420 1,992 58.2 64 67 UNION 2,265 967 42.7 66 68 VOLUSIA 65,867 27,660 42.0 66 69 WAKULLA 5,050 1,782 35.3 72 76 WALTON 6,699 3,010 44.9 68 70 WASHINGTON 3,557 1,951 54.8 61 67 2007 8 Florida District Total Membership Total Eligible Percent Eligible FCAT 3+ % R ea d ing FCAT 3+ % Math ALACHUA 28,373 12,612 44.5 69 72 BAKER 4,958 2,140 43.2 62 71 BAY 26,236 11,855 45.2 73 78 BRADFORD 3,576 1,967 55.0 60 65 BREVARD 74,371 22,689 30.5 78 84 BROWARD 258,895 115,063 44.4 66 75 CALHOUN 2,229 1,127 50.6 74 82 CHARLOTTE 17,799 8,333 46.8 72 79 CITRUS 16,174 6,763 41.8 72 79 CLAY 36,125 9,083 25.1 73 79 COLLIER 42,721 18,914 44.3 65 70 COLUMBIA 10,134 5,465 53.9 66 69 DAD E 348,113 206,578 59.3 60 67
118 Table 3.2 Continued DESOTO 5,012 3,321 66.3 57 66 DIXIE 2,190 1,470 67.1 62 72 DUVAL 124,775 48,632 39.0 63 66 ESCAMBIA 41,855 24,895 59.5 62 66 FLAGLER 12,774 4,555 35.7 69 73 FRANKLIN 1,246 690 55.4 58 61 GADSDEN 6,516 4,636 71.1 40 50 GILCHRIST 2,889 1,374 47.6 80 86 GLADES 1,365 530 38.8 61 69 GULF 2,171 966 44.5 69 71 HAMILTON 2,018 1,433 71.0 46 46 HARDEE 5,014 3,184 63.5 59 71 HENDRY 7,308 4,500 61.6 55 68 HERNANDO 22,836 10,248 44.9 67 72 HIGHLANDS 12,445 7,434 59.7 62 68 HILLSBOROUGH 193,116 92,986 48.2 64 71 HOLMES 3,430 1,998 58.3 65 72 INDIAN RIVER 17,646 7,234 41.0 68 73 JACKSON 7,363 3,892 52.9 66 75 JEFFERSON 1,154 884 76.6 45 55 LAFAYETTE 1,089 549 50.4 62 74 LAKE 40,710 16,734 41.1 66 72 LEE 80,541 37,188 46.2 67 71 LEON 32,471 10,550 32.5 72 77 LEVY 6,228 3,829 61.5 65 71 LIBERTY 1,513 674 44.5 64 69 MADISON 2,783 1,999 71.8 49 49 MANATEE 42,524 18,926 44.5 65 70 MARION 42,565 22,184 52.1 65 72 MARTIN 18,109 4,195 23.2 77 83 MONROE 8,363 2,781 33.3 74 80 NASSAU 11,079 3,717 33.5 73 79 OKALOOSA 29,568 8,715 29.5 79 84 OKEECHOBEE 7,037 3,887 55.2 60 70 ORANGE 174,136 82,454 47.4 66 71 OSCEOLA 52,742 33,235 63.0 61 64 PALM BEACH 170,844 70,314 41.2 66 75 PASCO 66,313 27,712 41.8 68 69 PINELLAS 107,895 44,008 40.8 66 72 POLK 94,164 47,449 50.4 60 65 PUTNAM 11,808 7,978 67.6 59 66 ST. JOHNS 27,867 5,626 20.2 80 83 ST. LUCIE 40,347 21,781 54.0 60 64 SANTA ROSA 25,711 7,853 30.5 78 83 SARASOTA 42,013 14,758 35.1 75 81
119 Table 3.2 Continued SEMINOLE 65,355 20,629 31.6 76 82 SUMTER 7,518 3,984 53.0 70 76 SUWANNEE 6,005 3,299 54.9 59 64 TAYLOR 3,389 2,039 60.2 67 71 UNION 2,296 1,181 51.4 63 68 VOLUSIA 64,570 27,446 42.5 68 72 WAKULLA 5,178 1,956 37.8 74 78 WALTON 6,967 3,216 46.2 69 77 WASHINGTON 3,590 1,964 54.7 62 68 2008 9 Florida District Total Membership Total Eligible Percent Eligible FCAT 3+ % R ea d ing FCAT 3+ % Math ALACHUA 27,665 12,791 46.2 69 72 BAKER 5,065 2,322 45.8 64 74 BAY 25,958 12,360 47.6 74 77 BRADFORD 3,403 1,896 55.7 60 66 BREVARD 73,098 25,150 34.4 78 84 BROWARD 256,355 122,597 47.8 67 76 CALHOUN 2,246 1,237 55.1 76 81 CHARLOTTE 17,370 8,765 50.5 72 79 CITRUS 16,032 7,525 46.9 70 77 CLAY 35,997 10,617 29.5 74 79 COLLIER 42,534 21,654 50.9 67 72 COLUMBIA 10,058 5,786 57.5 67 70 DAD E 345,525 219,118 63.4 62 69 DESOTO 4,952 3,349 67.6 59 69 DIXIE 2,119 1,474 69.6 67 71 DUVAL 122,610 56,064 45.7 63 68 ESCAMBIA 40,924 24,046 58.8 65 68 FLAGLER 12,890 6,167 47.8 69 72 FRANKLIN 1,281 888 69.3 63 68 GADSDEN 6,417 5,729 89.3 44 54 GILCHRIST 2,750 1,465 53.3 82 84 GLADES 1,388 690 49.7 64 74 GULF 2,050 934 45.6 70 75 HAMILTON 1,953 1,286 65.9 45 46 HARDEE 5,108 3,543 69.4 60 71 HENDRY 7,038 4,937 70.2 58 68 HERNANDO 22,739 11,508 50.6 68 73 HIGHLANDS 12,280 7,784 63.4 63 70 HILLSBOROUGH 191,975 98,943 51.5 65 72 HOLMES 3,399 2,022 59.5 65 70 INDIAN RIVER 17,606 8,048 45.7 70 74
120 Table 3.2 Continued JACKSON 7,319 4,102 56.1 68 74 JEFFERSON 1,106 832 75.2 46 48 LAFAYETTE 1,119 596 53.3 65 75 LAKE 41,010 18,367 44.8 67 71 LEE 79,434 41,464 52.2 70 73 LEON 32,521 12,859 39.5 71 76 LEVY 6,024 3,799 63.1 65 72 LIBERTY 1,484 788 53.1 65 67 MADISON 2,715 2,049 75.5 53 48 MANATEE 42,583 19,918 46.8 66 69 MARION 42,618 23,383 54.9 66 72 MARTIN 18,067 6,030 33.4 77 84 MONROE 8,278 2,968 35.9 75 81 NASSAU 10,982 3,953 36.0 73 79 OKALOOSA 29,126 9,349 32.1 80 84 OKEECHOBEE 7,004 4,235 60.5 62 72 ORANGE 172,276 83,640 48.6 67 72 OSCEOLA 51,937 33,815 65.1 62 66 PALM BEACH 170,756 75,360 44.1 68 77 PASCO 66,784 30,720 46.0 69 70 PINELLAS 106,066 46,102 43.5 67 72 POLK 94,725 54,665 57.7 61 66 PUTNAM 11,493 8,033 69.9 61 67 ST. JOHNS 29,024 5,491 18.9 81 85 ST. LUCIE 38,839 22,413 57.7 60 65 SANTA ROSA 25,397 8,896 35.0 79 83 SARASOTA 41,070 16,784 40.9 76 81 SEMINOLE 64,928 22,350 34.4 77 82 SUMTER 7,650 4,159 54.4 73 78 SUWANNEE 5,978 3,607 60.3 61 64 TAYLOR 3,299 2,039 61.8 65 69 UNION 2,315 1,238 53.5 69 72 VOLUSIA 63,166 29,463 46.6 69 72 WAKULLA 5,264 2,169 41.2 75 78 WALTON 7,000 3,468 49.5 72 76 WASHINGTON 3,534 2,061 58.3 68 71 Figure 3 1. Pearson r Correlation Formula
121 CHAPTER 4 RESULTS The purposes of this study were to analyze the relationship between poverty and student achievement in the state of Florida, and to determine the financial implications of theoretical poverty weights and compensatory practices activated in the FEFP. To provide context for this study, a review of education finance literature was conducted. Poverty, its influence in Ameri can education, its impact on student achievement, its role in state education funding systems, and its effects on education finance were highlighted in this review. Compensatory practices and variations of poverty weights were analyzed. The methodology uti lized to correlate student achievement in Florida and poverty was described in Chapter 3. The methodology utilized to determine fiscal impacts of activating different theoretical poverty weights in the FEFP was also described. The questions specifically ad dressed for this study were: 1. What were the relationships between FRPL percentages and FCAT achievement data in Florida schools? 2. What were the relationships between FRPL percentages and FCAT achievement data in Florida elementary schools? 3. What were the fis cal implications of initiating a theoretical poverty weight into the FEFP? 4. What were the fiscal implications of employing compensatory practices of a contemporary adequacy study in the FEFP? Descriptive Statistics Numerous data regarding poverty and stude nt achievement in Florida were compiled and analyzed in this study. Descriptive and correlation statistics were utilized in the analysis of FRPL data and FCAT data in order to study relationships of these variab les. The descriptive statistics displayed cen tral tendencies of the variables, and
122 the correlation statistics evidenced relationships between poverty and stu dent achievement. There were sixty seven school districts in Florida utilized for the data sample. During the years of 2004 2009, these sixty se ven districts represented an average of 2,635,276 students. Descriptive statistics, including the mean, median, mode, and range of FRPL percentages for Florida school districts were examined for years 2004 to 2009. See Table 4 1. The mean, median, mode, and range of FCAT Reading scores (Table 4 2) for Florida school districts, and the mean, median, mode, and range of FCAT Math scores (Table 4 3) for Florida school districts were examined for years 2004 to 2009. Further statistics focused on elementary school FRPL and FCAT data in 2009. Descriptive statistics focused on FRPL percentages as well as student FCAT scores of level 3 or higher, and were utilized to reveal central tendencies. 309 During the years of 2004 to 2009, the lowest mean percentage of all Florida students qualifying for FRPL was 45.4 percent, occurring in 2004 and 2007, and the highest mean percentage of all Florida students qualifying for FRPL was 49.6 percent in 2009. 310 The mean percentage of third grade students qua lifying for FRPL in 2009 was 60.1 percent, the mean for fourth grade students was 58.4 percent, and the mean for fifth grade students was 56.5 percent. The FRPL eligibility percentages of elementary students were higher than overall averages that include m iddle and high school students. The lowest reported FRPL percentage in the sample was 17.6 percent in 2007 309 Statsoft Electronic Statistics Textbook, http://www.statsoft.com/textbook/basic statistics/ 310 Table 4 1
123 percent of third grade students in 2009 in Gadsden County. The rang e of wealth disparity according to FRPL data was lowest in 2005 with a range of 50.8 percent. The most substantial range of wealth disparity according to FRPL data was 70.3 percent in 2009. During the years of 2004 to 2009, the lowest mean percentage of all Florida students scoring at level 3 or higher on FCAT Reading was 58.9 percent, occurring in 2004, and the highest mean percentage of all Florida students scoring at level 3 or higher on FCAT Reading was 67 percent in 2009. 311 The mean percentage of thir d grade students scoring at level 3 or higher on FCAT Reading in 2009 was 73.7 percent, the fourth grade average was 74.4 percent, and the fifth grade mean was 70.3 percent. In 2009, elementary students scored higher on FCAT Reading tests than middle and h igh school students combined. During the years studied, the lowest reported average FCAT Reading score was 34 percent in Gadsden and Jefferson Counties in 2004. The highest average FCAT Reading score was 82 percent in Gilchrist County in 2009. In 2009, 66. 4 percent of third grade students eligible for FRPL scored 3 or higher on FCAT Reading, and 85.3 percent of third grade students who were not eligible for FRPL scored 3 or higher. 312 In 2009, 66.6 percent of fourth grade students eligible for FRPL scored 3 o r higher on FCAT Reading, and 85.6 percent of students who were not eligible for FRPL scored 3 or higher. 313 In 2009, 61.4 percent of fifth grade students 311 Table 4 2 312 Ibid. 313 Table 4 2.
124 eligible for FRPL scored 3 or higher on FCAT Reading, and 82.3 percent of fifth grade students who were not eligible for FRPL scored 3 or higher. 314 During the years of 2004 to 2009, the lowest mean percentage of all Florida students scoring at level 3 or higher on FCAT Math was 62.3 percent, occurring in 2004, and the highest mean percentage of all Florida students scoring at level 3 or higher on FCAT Math was 72.1 in 2009. 315 The mean percentage of third grade students scoring at level 3 or higher on FCAT Math in 2009 was 79.2 percent, the fourth grade average was 74.6 percent, and the fifth grade mean was 59 .6 percent. During the years studied, the lowest reported average FCAT Math score was 36 percent in Jefferson County in 2005. The highest average FCAT Math score was 86 percent in Gilchrist County in 2009. In 2009, 72.8 percent of third grade students elig ible for FRPL scored 3 or higher on FCAT Math, and 89.1 percent of third grade students who were not eligible for FRPL scored 3 or higher. 316 In 2009, 67.6 percent of fourth grade students eligible for FRPL scored 3 or higher on FCAT Math, and 84.7 percent o f fourth grade students who were not eligible for FRPL scored 3 or higher. 317 In 2009, 49.6 percent of fifth grade students eligible for FRPL scored 3 or higher on FCAT Math, and 72.9 percent of fifth grade students who were not eligible for FRPL scored 3 or higher. 318 314 Ibid. 315 Table 4 3 316 Ibid. 317 Ibid. 318 Table 4 3.
125 Poverty and Student Achievement Correlations In this study, poverty in Florida schools and school districts was determined by the percentages of student eligibility in the FRPL program. Student achievement in Florida was determined by the result s of standardized test scores on the FCAT. The FRPL percentages for each Florida district were compiled from 2004 to 2009 and matched to the available relevant FCAT data from those years, and analysis was conducted using Pearson r correlation. The FRPL per centages for all districts were correlated with the FCAT Reading scores for all districts. The FRPL percentages were also correlated with the FCAT Math scores for all districts, resulting in two correlation coefficients for each school district for the yea rs 2004 to 2009. See Table 4 4. Poverty data in Florida elementary schools are more accurate than poverty data in middle and high schools. 319 Elementary school FRPL information was utilized for more detailed analysis in the year 2009, and additional correlat ion coefficients were calculated for the year 2009 in regards to third, fourth and fifth grade students. All correlation coefficients between FCAT Reading scores and FRPL percentages indicated a negative relationship, and all correlation coefficients betw een FCAT Math scores and FRPL percentages also indicated a negative relationship. Higher numbers associated with one variable were, in general, paired with lower numbers associated with the second variable, indicating this negative correlation. 320 When inclu ding all students, the relationship with t he strongest association was the 319 l Lunch Program: Importance of School Journal of School Health 79, no. 10 (2009): 485 94; Teachers College Record New York Times March 1, 2008. 320 Statsoft, Electronic Statistics Textbook, http:// www.st atsoft.com/textbook/basic statistics/#Correlations
126 correlation between FCAT Reading scores and FRPL percentages in 2007. 321 This correlation coefficient was 0.855. The relationship with the weakest association was the correlation bet ween FCAT Math scores and FRPL percentages in 2008. This correlation coefficient was 0.761. All associations were relevant, yet quantitative relationships were weaker when analyzing the subset of Florida elementa ry schools. The strongest correlation of th ese 2009 elementary school analyses was the relationship between fourth grade FCAT Reading scores and associated FRPL percentages. The coefficient w as 0.785. The weakest correlation of these 2009 elementary school analyses was the relationship between fou rth grade FCAT Math scores and associated FRPL percentages. The coefficient was 0.586. Although FRPL percentages were higher in Florida elementary schools relative to average FRPL percentages in all schools, the scores in elementary schools were notably h igher as well. The correlations between poverty and student achievement in Florida elementary schools we re on average not as strong as the correlations recognized when including all schools. Overall, FCAT Readin g scores had stronger correlations with FRP L than FCAT Math scores. In general, FRPL eligibility percentages were higher in elementary schools than they were in the whole district populations, and average FCAT scores in both reading and math were notably higher as well. The average correlation coef ficient for FCAT Reading and FRPL percentages was 0.826, and the average correlation coefficient for FCAT Math and FRPL percentages was 0.782, both indicating a recognizable and substantial relationship between poverty and student achievement. By analyzi ng these data it cannot be determined that poverty causes lower student 321 Table 4 4
127 achievement, as other variables impact this relationship, but the data unquestionably displays a significant association between poverty and student achievement levels in Florida. Pove rty Weight Applied to the FEFP T heoretical poverty weight s established by reviewing policies of state legislatures and practices utilized in a contemporary adequacy study, were applied to the FEFP in order to determine funding implications. In 2010, the added weights for Exceptional Student Education, English for Speakers of Other Languages, and Career Education programs ranged from .031 to 3.935 in the FEFP 322 There was no pover ty based weight that established a WFTE for low income students included in the FEFP. The poverty weights calculated in the foundation formulas of other states ranged from 0.05 to .25, providing supplements to base level funding for students in poverty. T he average poverty weight utilized in foundation formulas was 0.193. This poverty weight was applied to the FEFP in the same manner as the other program cost factors in the FEFP in order to establish a WFTE for low income students. According to the Secon d Calculation of the 2010 2011 FEFP, the Unweighted FTE state total was 2,645,079.41. 323 In 2009, there were 53.47 percent of students who were eligible for FRPL. 324 Multiplying the Unweighted FTE by the percentage of students eligible for FRPL resulted in an Unweighted FTE of 1,414,323.96. Multiplying 322 The Funding for Florida School Districts Statistical Report, 2010 1011 http://www.fldoe.org/fefp/pdf/fefpdist.pdf 323 The Funding for Florida School Districts Statistical Report, 2010 1011 http://www.fldoe.org/fefp/pdf/fefpdist.pdf 324 Florida Department of Education, Education Information and Accountability Services, 2010, Series 2011 03D, http://www.fldoe.org/eias/eiaspubs/pdf/frplunch0910.pdf
128 this Unweighted FTE by a theoretical program cost factor or poverty weight of 0.193 resulted in a WFTE of 272,964.52. Prior to calculat ing a poverty weight, the 2010 2011 Funded Weighted FTE was 2,852,181.12. Aft er adding the additional WFTE from the applied poverty weight, the new Funded Weighted FTE was 3,125,145.64. The Base Student Allocation (BSA) of $3,623.76 was multiplied by the new Funded Weighted FTE with a result of $ 11,324,777,764.41. In the 2010 2011 FEFP, when the District Cost Differential was applied to the BSA times Funded Weighted FTE, the result was a Base Funding of 0.99936 of the BSA times Funded Weighted FTE. This percentage was utilized in the theoretical calculation to yield a new total Base Funding of $11,317,529,906.64. All other individualized district additions remained the same, and all other factors, including the lottery and school recognition funds, class size reduction funds, state fiscal stabilization funds, and local funding were a dded. The Total State, Local, and Federal Funding established in the 2010 2011 FEFP was $18,082,453,785. After the application of a theoretical poverty weight of 0.193, the Total State, Local, and Federal Funding established was $19,057,765,608. The additi onal funding generated from the poverty weight was $975,311,823. See Table 4 5. Adequacy Study Applied to the FEFP Compensatory elements indicated in an education adequacy study conducted for the state of Rhode Island in 2007 were applied as theoretical c ompensatory inclusions to the 2010 2011 FEFP. These inclusions were operationalized in the FEFP as explicit poverty weights in the same manner as the theoretical poverty weight in the previous context. The poverty weight from the last context was 0.193, ba sed on an average of the weights utilized by other state legislatures. Two discount rates were utilized as compensatory elements in the Successful Schools approach in the Rhode Island
129 adequacy study. B ased on analyses of additional funding provided by st ates across the country recommended a rate of 25 percent In addition, the researchers revealed that a variety of research indicated that the current standard of practice (i.e. 25 percent) underestimated the additional costs for students in poverty, therefore they recommended another rate of 40 percent. The researchers completed calculations and scenarios for both compensatory rates. The discount rate of 25 percent utilized in the Rhode Island adequacy study was emplo yed as a poverty weight that was applied directly into the foundation formula of the FEFP. According to the Second Calculation of the 2010 2011 FEFP, the Unweighted FTE state total was 2,645,079.41. 325 In 2009, there were 53.47 percent of students eligible f or FRPL. 326 Multiplying the Unweighted FTE by the percentage of students eligible for FRPL resulted in an Unweighted FTE of 1,414,323.96. Multiplying this Unweighted FTE by a theoretical program cost factor of 0.25 resulted in a WFTE of 353,580.99. Prior to calculating a poverty weight, the Funded Weighted FTE was 2,852,181.12. After adding the additional WFTE from the applied poverty weight, the new Funded Weighted FTE was 3,205,762.11. The BSA of $3,623.76 was multiplied by the new Funded Weighted FTE with a result of $ 11,616,912,503.73. After adjusting for the District Cost Differential, the new total Base Funding was $11,609,477,679.73. All other elements and additions of the FEFP remained congruent to the 2010 2011 FEFP calculations. After the application of a poverty weight of 0.25, the Total State, Local, and 325 The Funding for Florida School Districts Stati stical Report, 2010 1011 http://www.fldoe.org/fefp/pdf/fefpdist.pdf 326 Florida Department of Education, Education Information and Accountability Services, 2010, Series 2011 03D, http://www.fldoe.org/eias/eiaspubs/pdf/frplunch0910.pdf
130 Federal Funding established was $19,349,713,381. The additional funding generated from the poverty weight was $1,267,259,596. The discount rate of 40 percent recommended in the Rhode Island adequac y study was employed as a poverty weight that was applied directly into the foundation formula of the FEFP. The Unweighted FTE associated with students eligible for FRPL was 1,414,323.96. Multiplying this Unweighted FTE by a theoretical program cost factor of 0.40 resulted in a WFTE of 565 729.58. Prior to calculating a poverty weight, the Funded Weighted FTE was $ 2,852,181.12. After adding the additional WFTE from the applied poverty weight, the new Funded Weighted FTE was $ 3,417,910.70. The BSA of $3,623. 76 was multiplied by the new Funded Weighted FTE with a result of $ 12,385,688,092.73. After adjusting for the District Cost Differential, the new total Base Funding was $12,377,786,147.58. All other elements and additions of the FEFP remained congruent to the 2010 2011 FEFP calculations. After the application of a poverty weight of 0.40, the Total State, Local, and Federal Funding established was $20,125,923,794. The additional funding generated from the poverty weight was $2,043,470,009. See Table 4 5. Sum mary This chapter presented the statistical relationships between student achievement and poverty in Florida schools. Utilizing FRPL as a proxy for poverty and FCAT as an indicator of student achievement, relationships between these two variables were rev ealed. Compensatory elements from policy and research were employed as explicit poverty weights in the FEFP. This chapter exhibited fiscal implications of applying theoretical poverty weights to the FEFP. Conclusions and recommendations were discussed in C hapter 5.
131 Table 4 1. Florida FRPL Eligibility Percentages Year Mean Median Mode Range 2004 45.4 47.1 54 56.9 2005 46.8 49.1 44 50.8 2006 45.9 46.3 52 62.4 2007 45.4 45.1 41 62.2 2008 45.8 45.2 44 51.6 2009 49.6 50.5 46 70.3 Grade 3 (2009) 60.1 58.3 58, 59 68.8 Grade 4 (2009) 58.4 56.1 53 70.1 Grade 5 (2009) 56.5 54.4 50, 52 68.9 Table 4 2. Percentage of Students Scoring at Le vel 3 or higher on FCAT Reading Year Mean Median Mode Range 2004 58.9 58 58 40 2005 59.7 60 59, 60, 61 39 2006 62.3 63 63 37 2007 63.1 63 63 39 2008 65.6 66 66 40 2009 67 67 65, 67 38 Grade 3 (2009) 73.7 72 72 37 Grade 4 (2009) 74.4 74 70 36 Grade 5 (2009) 70.3 69.5 72 35 Table 4 3. Percentage of Students Scoring at Level 3 or higher on FCAT Math Year Mean Median Mode Range 2004 62.3 63 66 42 2005 64.7 65 67 45 2006 66.1 67 68 41 2007 67.9 68 69 42 2008 71.5 71 71, 72 40 2009 72.1 72 72 39 Grade 3 (2009) 79.2 79 80 43 Grade 4 (2009) 74.6 75 73 35 Grade 5 (2009) 59.6 59 59 39
132 Table 4 4. Correlations between FCAT Scores and FRPL Percentages Year FCAT Reading Scores and FRPL FCAT Math Scores and FRPL 2004 0.835 0.792 2005 0.803 0.777 2006 0.827 0.803 2007 0.855 0.789 2008 0.824 0.761 2009 0.813 0.767 Grade 3 (2009) 0.729 0.594 Grade 4 (2009) 0.785 0.586 Grade 5 (2009) 0.757 0.633 Table 4 5. Poverty Weights Applied to the FEFP FEFP Formula Component 2010 2011 FEFP Methodology A Methodology B Methodology C Unweighted FTE 2,645,079.41 2,645,079.41 2,645,079.41 2,645,079.41 Poverty Weight 0.193 0.25 0.4 Funded Weighted FTE 2,852,181.12 3,125,145.64 3,205,762.11 3,417,910.70 Base Student Allocation $ 3,623.76 $ 3,623.76 $ 3,623.76 $ 3,623.76 Total Base Funding $ 10,342,218,083 $ 11,317,529,906 $ 11,609,477,679 $ 12,377,786,147 Total Funding $ 18,082,453,785 $ 19,057,765,608 $ 19,349,713,381 $ 20,125,923,794 Increase in Funding $ 975,311,823 $ 1,267,259,596 $ 2,043,470,009 Percent Increase 5.39 7 11.3
133 CHAPTER 5 CONCLUSIONS AND RECOMMENDATIONS The purpose of this study was to explicate correlations between poverty and student achievement in Florida, and to apply the compensatory practices evident in education finance research to the education funding system in Florida. This study determined if t here were associations between FRPL percentages and student achievement indicators, displaying a relationship between poverty and achievement in Florida schools. In addition, this study reviewed compensatory elements of the fifty state legislatures and emp loyed a theoretical poverty weight for the state of Florida. Furthermore, the poverty weights recommended in a contemporary adequacy study were initiated into the FEFP. In chapter 4, the results of the data analysis and research were presented. This chapte r provided a summary of the findings, conclusions, and implications for future research. The study addressed the following research questions: 1. What are relationships between FRPL percentages and FCAT achievement data in Florida schools? 2. What are relations hips between FRPL percentages and FCAT achievement data in Florida elementary schools? 3. What are the fiscal implications of initiating a theoretical poverty weight into the FEFP? 4. What are the fiscal implications of employing the compensatory practices of a contemporary adequacy study into the FEFP? Findings A primary focus of this study was to ascertain the quantitative association between poverty and student achi Due to social factors that affect data sampling in poverty in middle and high schools, 327 the study 327 Donka Mirtcheva and School Journal of School Health 79, no. 10 (2009): 485 94;
134 separated elementary school data and quantified the poverty achievement relationship in this subset. Introducing a poverty weight based on education finance research into d substantial changes in funding. Applying the compensatory elements of a contemporary adequacy study also displayed significant changes to funding levels. The results of the research questions are summarized below. Research Question 1 Findings of this s tudy revealed the extent to which poverty and student achievement indicators were associated in Florida. In Florida schools, poverty was most commonly measured by percentages of students eligible to receive free or reduced price lunch, 328 and results from FC AT Reading and FCAT Math testing were the primary data utilized to determine levels of student achievement. 329 A statistical analysis of poverty and achievement data established empirical relationships for these dynamics. Data from a six year time period, 20 04 2009, were analyzed. Correlations between poverty and reading were stronger than correlations between poverty and math. The empirical relationships between poverty and student achievement ranged from 0.761 to 0.855 over the six year period. Thi s e vidence illustrated that school districts that had higher percentages of students eligible for FRPL, more often had lower student achievement results in the state of Florida. Evidence about Tracking: Critiquing the New Report fro Teachers College Record New York Times March 1, 2008. 328 Risk Chil dren: An Econometric Application of Research Journal of Education Finance 33, no. 1 (2006): 297 319. 329 F.S. § 1008.22
135 Research Question 2 As students have moved into middle and high schools, ne gative social stigmas have become attached to accepting free and reduced price lunches in schools and fewer students have been enrolled in the NSLP program. 330 FRPL enrollment has been the primary indicator of poverty, and the number s of students in poverty that have been underreported are unknown. In 2009, average FRPL percentages were higher in Florida elementary schools (58.36 percent) relative to average FRPL percentages in all Florida public schools (49.62 percent). This study separated the elementary ( grades 3, 4, 5) poverty achievement correlations in order to isolate a subset that was less affected by the underreported variable. In 2009, the FCAT scores were higher in elementary schools relative to the average scores of all schools. The correlations b etween FRPL and FCAT Reading scores in grades 3, 4, and 5 ranged from 0.728 to 0.784, and the correlations between FRPL and FCAT Math scores in grades 3, 4, and 5 ranged from 0.586 to 0.633. Due to the notably higher FCAT scores demonstrated in elem entary grades, the poverty achievement correlatio ns were overall weaker in the elementary subset than the correlations displayed in all schools combined. Research Question 3 In 2010, thirty five legislatures recognized poverty as a factor that affects stud ent achievement and apportioned funding increases for students in poverty. 331 The Florida 330 Donka Mirtcheva School Journal of School Health 79, no. 10 (2009): 485 94; Evidence about Tracking: Critiquing the New Repor Teachers College Record New York Times March 1, 2008. 331 Table 3 1
136 legislature did not. 332 The political priorities have been diss imilar among state legislatures; so too have the compensatory practices related to education funding greatl y varied. The poverty weights calculated in foundation funding formulas ranged from 0.05 to .25, 333 allocating supplements to base level funding for students in poverty. The average poverty weight utilized in foundation formulas was 0.193. This poverty weig ht was applied to the 2010 2011 FEFP in the same manner as the other program cost factors in the FEFP in order to establish supplementary funding for low income students in Florida. The additional funding that would have been generated for poor students fr om the theoretical poverty weight was $975,311,823. 334 Research Question 4 Numerous adequacy studies have been conducted in the United States in recent decades, making attempts to establish the quantity of fiscal resources necessary to provide an adequate e ducation. 335 There are four approaches that have been regularly utilized to complete adequacy studies, including the Professional Judgment approach, the Evidence Based approach, the Successful Schools approach, and the Statistical Analysis approach, yet very few studies have employ ed all four approaches to comprehensively substantiate study conclusions. The adequacy study conducted in 2007 for Rhode Island by R.C. Wood and Associates utilized all four methodologies. 336 332 Ibid. 333 Ibid. 334 Table 4 5 335 Deborah Verstegen Journal of Education Finance 32, no. 3 (2007): 304 327. 336 R.C. Wood and Associates, State of Rhode Island Education Adequacy Study, Final Report (2007).
137 The compensatory practices indicated in t he Rhode Island study were applied as theoretical poverty weights to the 2010 2011 FEFP. The poverty weights of 25 percent and 40 percent described in the study were theoretically applied and calculated in the same manner as the other program cost factors in the FEFP. The additional funding generated from the 25 percent poverty weight was $1,267,259,596, and the additional funding generated from the 40 percent poverty weight was $2,043,470,009. Conclusions It is commonly argued that the burdens of povert y have a negative effect on student success in schooling. Historically in the United States, efforts have been made to address the ills of poverty and its impact in classrooms, and supplementary funding has been legislated for poor students. 337 It is debatab le whether monetary increases can be directly associated to improvements in student achievement. 338 Determining the degrees to which poverty and achievement are associated can be crucial information for policymakers, and a review of the compensatory practice s of state legislatures, Children who grow up in an environment surrounded by affluence have more access to resources and extensive exposure to diverse experiences compared to their impoverish ed peers. Parents with lower levels of formal education completion are more often unemployed, and provide less assistance to their children in regards to 337 T able 3 1. 338 Christopher Jencks, Inequality: A Reassessment of the Effect of Family and Schooling in America ( Educational Researcher 18, no. 4 (1989): 45 51.
138 schooling. 339 Students from poor families have less contact with reading materials and less access to co mputers, 340 and they have lower levels of school attendance. 341 The lack of learning capital present in the lives of poor children has a negative impact on academic achievement, 342 and concentrated poverty in schools exacerbates the issue. 343 Schools comprised of large populations of poor students are more likely to have lower average achievement levels than other schools. 344 Resources intended to compensate for poverty have been allocated from multiple sources. The federal government has recognized challenges assoc iated with poverty and schools and has enacted programs to address the concerns. Head Start, Title I, and NSLP are some federal programs aimed at mitigating the effects of poverty in schools. 345 At the state level, numerous legislatures have activated educat ion funding 339 National Center for Children in Poverty, November 2008 Report. Low Income Children in the United States: National and State Trend Data, 1997 2007, http://www.nccp.org/publications/p df/text_851.pdf Washington D.C.: U.S. Department of Commerce. 340 National Center for Children in Poverty, November 2008 Report. Low Income Children in the United States: National and State Trend Data, 1997 2007, http://www.nccp.org/publications/pdf/text_851.pdf 341 Current Population Survey, 2000, 2001and 2002 Annual Social and Economic Supplements. Washington D.C.: U.S. Department of Commerce. 342 Educating At Risk Children: One Hundred First Yearbook of the National Society for the Study of Education, Part II, ed. S. Stringfield a nd D. Land (Chicago: University of Chicago Press, 2002): 1 28. 343 Laura Lippman, Shelley Burns, Edith McArthur, (1996 ). Urban Schools: The Challenge of Location and Poverty Washington, DC: U.S. Department of Education, Office of Educational Research and Im provement, Kern Alexander and Richard Salmon, Public School Finance (Boston: Allyn & Bacon, 1995). 344 Robert Slavin, Nancy Karwiet, and Nancy Madden, Effective Programs for Children at Risk (Boston: Allyn and Bacon, 1989). 345 U.S. Department of Health and Human Services, Administration for Children and Families, Office of Head Start http://www.acf.hhs.gov/programs/ohs/about/index.html ; U.S. Department of Education, Office
139 formulas that provide additional fiscal resources for students in poverty. 346 There has been considerable debate over the impact of money on student achievement, and there is no parallel and consistent correlation between amounts of money spent a nd levels of educational success. 347 The measure of fiscal resources necessary to ensure that children achieve an adequate educational standard is the basis of numerous adequacy studies. This study was successful in quantifying correlations between poverty a nd student achievement in Florida by utilizing FRPL as a proxy for poverty and FCAT as an indicator of student achievement. Correlation results ranging from 0.761 to 0.855 demonstrated strong associations between these variables. 348 Over the span of years observed, as poverty levels increased in Florida schools, 76 percent to 86 percent of the corresponding student achievement scores decreased. These connections provided measured relationships between poverty and student achievement. Reviewing the compensa tory policies evident in education funding mechanisms across the nation provided foundation for analysis. Thirty five state legislatures employed a type of compensatory provision into the funding formula for education. The average poverty weight that was a pplied directly to foundation formulas by state legislatures was theoretically applied to the FEFP to determine the fiscal consequence. of Elementary and Secondary Education, http://www2.ed.gov/programs/titleiparta/index.html ; U.S. Department of Agriculture, Food and Nutrition Service, National School Lunch Program, http://www.fns.usda.gov/cnd/lunch/aboutlunch/NSLPFactSheet.pdf 346 Table 4 5 347 Analysis of Studies of the Effects of Differenti Educational Researcher 23, no. 3 (1994): 5 14. 348 Table 4 4
140 This study did not analyze the politics involved in establishing the individual program cost factors in the FEFP, and no conclusion or recommendation asserts that the theoretical poverty weight be utilized. The additional cost of including the average poverty weight in the FEFP was calculated at $975,311,823. 349 The poverty weight applied in this scenario increased the total education funding in 2010 2011 by 5.39 percent. Adequacy studies have often include d compensatory recommendations related to students in poverty. The 2007 Rhode Island adequacy study indicated that compensatory practices across the country validated a poverty weight of 25 percent. 350 The second rate utilized in the study (40 percent) was ba sed on a variety of research that proposed that the current standard of practice (25 percent) underestimated the additional costs for students in poverty. 351 The recommendations and research conducted from the Rhode Island adequacy study were theoretically a pplied to the FEFP. Every adequacy study is unique to the state that it applies to, and no conclusion or recommendation asserts that the exact poverty weights recommended in the Rhode Island be utilized in Florida. The additional funding generated in the F EFP by employing the poverty weight of 25 percent was $1,267,259,596, and the increase in funding displayed from the poverty weight of 40 percent was $2,043,470,009. 352 The 25 percent poverty weight increased overall education funding in Florida by 7 percent and the 40 percent poverty weight increased funding by 11.3 percent. 349 Table 4 5 350 R.C. Wood and Associates, State of Rhode Island Education Adequacy Study, Final Report (2007). 351 Ibid. 352 Table 4 5
141 It is certain that students experiencing poverty exhibit lower levels of achievement and require more resources to be educated to an established educational standard. Some legislatures including Florida, do not specifically account for poverty in the process of distributing education funding. A review of education finance research and adequacy studies displays compensatory practices across the nation. Applying these practices to Florid implications. As a matter of public policy, vertical equity is a crucial element of education funding in the state of Florida. Ideally, increased funding for poor students could be realized by the init iation of a poverty weight into the FEFP by the Florida legislature. Even if no new monies were introduced into the overall education funding system, it is recommended that a very modest poverty program cost factor be introduced into the FEFP. This may req uire temporary decreases in funding for other students, but the finance program would be improved, and a catalyst for more equitable and improved funding for poor students would be in place. Recommendations for Future Research In accordance with NCLB, all state legislatures conduct standardized testing to measure student achievement. 353 In addition, each legislature operates a specific measure of poverty in its schools. Future research could extend beyond Florida, revealing the achievement poverty correlatio ns in all other states. Differences in testing and poverty measures could make comparisons between states problematic, but 353 F.S. § 1008.22
142 individual state correlations would provide data concerning the relationships between poverty and student achievement. This study uti lized the compensatory recommendations from the 2007 Rhode Island adequacy study. This adequacy study was examined because it is a comprehensive and contemporary study that utilized all four methodologies of determining education adequacy. Two of the metho dologies, the Successful Schools Model and the Advanced Statistical/Cost Function Model, recommended a poverty weight of 25 percent, and the Successful Schools Model also recommended a poverty weight of 40 percent. Other adequacy studies conducted have uti lized different compensatory recommendations regarding resource allocation to students in poverty. Various recommendations from all other adequacy studies could be theoretically applied to the FEFP to determine the funding implications. The current economi c climate had a noteable impact on this study. As FRPL percentages vary with changing economic conditions, new opportunities for study become evident. What are the effects of the recession on education funding and student achievement in Florida ? Are poor s tudents underserved?
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159 BIOGRAPHICAL SKETCH Jeremy Allen Moore was born in Atlanta, Georgia, and grew up in the towns of Punxsutawney, Pennsylvania and Ormond Beach, Florida. He completed high school studies at Seabreeze High in Daytona Beach in 1993. Jeremy continued his education at the University of Florida and has always been an avid Gator fan. He gradua ted with a Bachelor of Arts in e ducation in 1997, earned a Master of Education degree in 1998, and completed a Specialist degree in educational leadership in 2002. In December of 2011, Jeremy received a Doctor of Philosophy degree in educational leadership from the University of Florida. Working in schools has been a passion and a successful career for Jeremy. He has been a teacher at the elementary and middle school levels, coached high school soccer, and worked as a school administrator. He served as an assistant principal at Maitland Middle School and at Olympia High School near Orlando, Florida In 2006, Jeremy began as the P rincipal of Brookshire Elementary School in Winter Park, Florida, and after four years, moved to Grand Cayman to serve as the Secondary School Principal of the Cayman International School. Jeremy was fortunate to meet Tiffan y Pascuzzi and they married in April of 2011. They enjoy living in Grand Cayman, plan to have children, and are excited about where Director at various international schools around the globe, serving as an executive school leader and superintendent in large urban school districts, writing, consulting, and teaching at the university level.