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object(Timber\Post)#3742 (44) { ["ImageClass"]=> string(12) "Timber\Image" ["PostClass"]=> string(11) "Timber\Post" ["TermClass"]=> string(11) "Timber\Term" ["object_type"]=> string(4) "post" ["custom"]=> array(5) { ["_wp_attached_file"]=> string(12) "R_512MWR.pdf" ["wpmf_size"]=> string(6) "174102" ["wpmf_filetype"]=> string(3) "pdf" ["wpmf_order"]=> string(1) "0" ["searchwp_content"]=> string(16758) "Funding Formulas for California Schools IV An Analysis of Governor Brown’s Weighted Pupil Funding Formula, May Budget Revision May 2012 Heather Rose, Jon Sonstelie, and Margaret Weston Supported with funding from The Silver Giving Foundation and the Stuart Foundation http://www.ppic.org /main/home.asp Funding Formulas for California Schools IV 2 Summary In his 2012– 13 budget, Governor Brown proposed a new system for allocating state revenue among California school districts. In May the governor r evised his proposal. Using the PPIC School Finance Model ( available at www.ppic.org/main/dataSet.asp?i=1229 ), in this update we show how these proposals would guide the allocation of additional re venue among school districts. Under both proposals, school districts educating high percentages of disadvantaged students would receive the largest revenue gains. Compared to the original proposal, the revised proposal provides less funding for disadvantag ed students and substantially reduces differences in gains among districts. Contents Summary 2 Tables 4 Governor Brown’s May Proposal 5 Modeling the Proposal 6 Conclusion 8 About the Authors 9 Acknowledgments 9 The PPIC School Finance Model can be found in PPIC's Data Depot http://www.ppic.org /main/home.asp Funding Formulas for California Schools IV 4 Tables 1 Average change in funding from 2010 –11 to revenue in weighted pupil funding simulation, dollars per student 6 2 The distribution of revenue changes across districts 7 http://www.ppic.org /main/home.asp Funding Formulas for California Schools IV 5 Governor Brown’s May Proposal In his 2012–13 budget released in January, Governor Brown proposed a new system for allocating state revenue among California school districts, a proposal we analyzed in a previous paper. 1 In the May revision of his budget, the governor also revised his school fi nance proposal. This paper updates our previous analysis to account for those changes. The governor proposes to replace the current system with a weighted pupil funding (WPF) formula. The revised proposal addresses many important questions, including the t ransition from the current system to the new system and accompanying modifications to the state’s accountability system. As in our previous analysis, however, our focus is how the WPF formula would change the allocation of revenue among school districts. I n this regard, the revised proposal makes three important changes:  Weights for disadvantaged students. With a weighted pupil funding formula, the revenue a district receives equals a base rate multiplied by a sum of student weights in the district. In the governor’s original proposal, students who are disadvantaged (English learners or students who qualify for free or reduced- price lunch) had a weight of 1.37. This weight increases as disadvantaged students become more than 50 percent of students, the concentration factor. All other students had a weight of 1.0. The revised proposal reduces the weight for disadvantaged students from 1.37 to 1.20. The concentration factor remains, but with the lower weight.  Weights for grade level. In the original proposal, students who were not disadvantaged had a weight of 1.0, regardless of their grade. Under the revised proposal, student weights vary by grade level. Using students in grade 4 though 6 as the base with a weight of 1.0, the grade- level weights are as follows: Grade level Student weight K–3 1.1078 4–6 1.0000 7–8 1.0298 9–12 1.1931 In determining weights for a district, the formula multiplies grade- level weights by weights for disadvantage. For example, a disadvantaged student in grade 3 has a weight of 1.33 (1.11 x 1.20).  Add -ons for Targeted Instructional Improvement Block Grant (TIIBG) and Home -to -School Transportation (HST). The original proposal consolidated these two programs into the WPF formula. The revised proposal elim inates the two programs but adds the revenue that districts now receive in the two programs to the revenue from the WPF formula. A district would receive its formula revenue, plus the revenue it now receives from those two programs. In 2010 –11, these progr ams totaled $1.3 billion, 4 percent of all state K –12 funding. 1 Heather Rose, Jon Sonstelie, and Margaret Weston, “Funding Formulas III: An Analysis of Governor Brown’s Weighted Pupil Fundi ng Formula” ( Public Policy Institute of California, 2012). http://www.ppic.org /main/home.asp Funding Formulas for California Schools IV 6 Modeling the Proposal The original proposal envisioned phasing in the new formula over six years, with the base rate rising to $6,990 per student when the formula is fully implemented in 2017 –18. We simulated the fully implemented formula and compared the revenue that districts would receive with what they actually received in 2010 –11. To simulate the revised proposal, we used the same demographic and economic assumptions as in the original simulations but applied the new weights for grade level and disadvantage, as well as included the TIIBG and HST add -ons. We chose the base rate to make our simulations with this revised proposal comparable to the original simulation. In particular, we chos e the base rate so that the aggregate revenue received by all districts in the new simulation equals the aggregate revenue they received in the original simulation. 2 Table 1 shows the average gains districts would receive under the new proposal relative to their current funding. For the sake of comparison, it also shows the gains districts would have received under the original proposal. Districts are categorized by type and by percentage of students who are disadvantaged. For each category, the table reports the average gain in revenue per pupil in moving from the allocation in 2010 –11 to the allocation districts received in our simulation. The first column for each district type is the average gain under the original proposal. The second column is the average gain under the revised proposal. For example, unified districts with fewer than 20 percent of students who are disadvantaged would gain an average of $1,229 per student under the original proposal. Those districts would gain an average of $1,881 under the revised proposal. TABLE 1 Average change in funding from 2010– 11 to revenue in weighted pupil funding simulation, dollars per student Disadvantaged students (%) Unified districts Elementary districts High school districts Original proposal Revised proposal Original proposal Revised proposal Original proposal Revised proposal 0–20 $1,229 $1,881 $1,241 $1,585 $631 $1,516 20–40 1,576 2,034 1,956 2,083 621 1,717 40–60 2,073 2,278 2,199 2,158 1,134 2,168 60–80 3,124 2,996 3,542 2,946 1,998 2,457 80–100 3,963 3,520 5,287 3,923 3,369 3,391 SOURCE: Authors’ calculations based on PPIC School Finance Model. Compared to the actual revenue that districts received in 2010–11, the revised proposal has two important features. First, it allocates relatively more additional revenue to districts with high percentages of disadvantaged students. Second, for any level o f student disadvantage, high school districts receive less additional revenue than other districts. The original proposal also shared these two features; the revisions have merely lessened the differences in revenue gains among districts. Because of the lo wer weight for disadvantaged students in the revised proposal, districts with high percentages of such students would 2 The base rate t hat equalizes revenue for those two proposals is $7,014 per pupil. The revised proposal would first restore the revenue limit deficit factor before fully implementing the new formula. In our simulation, only 36 districts with a total of 16,232 student s had undeficited revenue limits in excess of their WPF entitlement. We ignored this constraint in our simulation. http://www.ppic.org /main/home.asp Funding Formulas for California Schools IV 7 receive smaller revenue increases in the revised proposal than they received in the original proposal. Districts with few disadvantaged st udents would receive relatively larger increases. Because of the grade- level weights in the revised proposal, high school districts would receive relatively larger increases in the revised proposal than in the original proposal. Their revenue gains are sti ll smaller than for other districts, but the revised proposal has reduced the differences. Overall, the May revisions to the governor’s proposal have maintained differences in revenue gains among districts but reduced their magnitude. This reduction is cle arly visible in Table 2. Under the original proposal, 39.9 percent of students attended districts with revenue gains between 30 and 50 percent. Under the revised proposal, 60.1 percent of students attend such districts. 3 TABLE 2 The distribution of revenue changes across districts Districts Students Students (%) Original proposal Revised proposal Original proposal Revised proposal Original proposal Revised proposal less than -10 45 38 61,851 35,793 1.1 0.6 -10–0 51 48 80,749 81,215 1.4 1.4 0–10 38 19 143,834 38,677 2.6 0.7 10–20 64 28 344,654 75,381 6.2 1.3 20–30 152 114 925,465 430,246 16.5 7.7 30–40 165 272 1,502,210 1,680,142 26.8 30.0 40–50 89 142 731,469 1,706,652 13.1 30.5 50–60 68 109 518,398 830,423 9.3 14.8 60–70 58 90 419,109 564,910 7.5 10.1 70–80 56 21 393,665 160,321 7.0 2.9 80–90 48 0 233,660 0 4.2 0 90–100 40 0 206,356 0 3.7 0 100–110 7 0 42,338 0 0.8 0 SOURCE: Authors’ calculations based on PPIC S chool Finance Model. In reporting our simulation results, we have focused on average gains by district type and by level of student disadvantage. In “ School District Revenue Changes under Governor Brown’s May Proposal ,” a spreadsheet available on the PPIC website , we report gains for individual districts. Revenue gains may differ for districts of similar characteristics because they received different revenue per pupil under the current system. Similarly, under the gov ernor’s revised proposal, revenue per pupil can differ for similar districts because of the revenue add- ons. For both TIIBG and HST, revenue per pupil is not evenly distributed across districts. These differences are rooted in the complex history of Califo rnia’s school finance system and are not apparent in the averages reported in Table 1. However, they are apparent in the spreadsheet . 3 Of the 86 districts receiving less revenue under the revised proposal than in the status quo, 59 are basic aid districts, whi ch are districts with more property tax revenue that their entitlement. Under the WPF program, these districts would continue to have their own pro perty tax revenue but they would lose categorical revenue. http://www.ppic.org /main/home.asp Funding Formulas for California Schools IV 8 Conclusion The revisions to the governor’s proposal have reduced differences among districts in how new revenue would be allocated. In the original proposal, high school districts received relatively smaller increases in revenue than other districts. The grade- level weights in the new proposal have lowered that difference. For a given level of student disadvantage, under the new proposal high school districts would receive similar revenue increases as other districts receive. The revised proposal also distributes additional revenue more evenly among districts of the same type. The original proposal channeled proportionally more revenue to districts with high percentages of disadvantaged students. This is still true with the revised proposal, but the differences are sm aller among districts with different levels of student disadvantage. http://www.ppic.org /main/home.asp Funding Formulas for California Schools IV 9 About the Authors Heather Rose is an adjunct policy fellow at the Public Policy Institute of California (PPIC) and an associate professor at the School of Education at the University of California, Davis. She specializes in the economics of education and school finance. Her recent projects include a study of affirmative action policies at the college level and an investigation of how high school curriculum affects the test score gap b etween white and minority students. She has also studied the effects of high -school curriculum on students’ subsequent earnings. She holds a B.A. in economics from the University of California, Berkeley, and an M.A. and Ph.D. in economics from the Universi ty of California, San Diego. Jon Sonstelie is an adjunct policy fellow at PPIC and professor of economics at the University of California, Santa Barbara. His research interests include several areas in public finance and urban economics, including the effect of public school quality on private school enrollment, the incidence of the property tax, the demand for public school spending, the economics of rationing by waiting, and the effect of transportation innovations on residential locations. He was previou sly a research fellow at Resources for the Future. He holds a B.A. from Washington State University and a Ph.D. from Northwestern University. Margaret Weston is a policy associate at PPIC’s Sacramento Center, where her work focuses on K –12 school finance. Before joining PPIC, she taught high school English and drama in the Baltimore City Public Schools through Teach For America. She holds an M.A in teaching from Johns Hopkins University and an M.A. in public policy from the University of Michigan. Acknowl edgments We thank Carol Bingham and Heather Carlson from the California Department of Education for providing much of the data used in the simulation model. We thank our advisory group, consisting of Carol Bingham and Elizabeth Dearstyne from the Departmen t of Education, Chris Ferguson and Nicolas Schweizer from the Department of Finance, and Edgar Cabral and Rachel Ehlers from the Legislative Analyst’s Office, for their advice and recommendations as we developed and refined the model for 2010 –11. PUBLIC POLICY INSTITUTE OF CALIFORNIA Board of Directors Gary K. Hart, Chair Former State Senator and Secretary of Education State of California Mark Baldassare President and CEO Public Policy Institute of California Ruben Barrales President and CEO San Diego Regional Chamber of Commerce María Blanco Vice President, Civic Engagement California Community Foundation Brigitte Bren Chief Executive Officer International Strategic Planning, Inc. Robert M. Hertzberg Partner Mayer Brown, LLP Walter B. Hewlett Chair, Board of Directors William and Flora Hewlett Foundation Donna Luc as Chief Executive Officer Lucas Public Affairs David Mas Masumoto Author and Farmer Steven A. Merksamer Senior Partner Nielsen, Merksamer, Parrinello, Gross & Leoni, LLP Kim Polese Chairman ClearStreet, Inc. Thomas C. Sutton Retired Chairman and CEO Paci fic Life Insurance Company The Public Policy Institute of California is dedicated to informing and improving public policy in California through independent, objective, nonpartisan research on major economic, social, and political issues. The institute’s goal is to raise public awar eness and to give elected representatives and other decisionmakers a more informed basis for developing policies and programs. The institute’s research focuses on the underlying forces shaping California’s future, cutting across a wide range of public poli cy concerns, including economic development, education, environment and resources, governance, population, public finance, and social and health policy. PPIC is a private operating foundation. It does not take or support positions on any ballot measures or on any local, state, or federal legislation, nor does it endorse, support, or oppose any political parties or candidates for public office. PPIC was established in 1994 with an endowment from William R. Hewlett. Mark Baldassare is President and Chief Executive Officer of PPIC. Gary K. Hart is Chair of the Board of Directors. Short sections of text, not to exceed three paragraphs, may be quoted without written permission provided that full attribution is given to t he source. Research publications reflect the views of the authors and do not necessarily reflect the views of the staff, officers, or Board of Directors of the Public Policy Institute of California. Copyright © 2012 Public Policy Institute of California All rights reserved. San Francisco, CA PUBLIC POLICY INSTITUTE OF CALIFORNIA 500 Washington Street, Suite 600 San Francisco, California 94111 phone: 415.291.4400 fax: 415.291.4401 www.ppic.org PPIC SACRAMENTO CENT ER Senator Office Building 1121 L Street, Suite 801 Sacramento, California 95814 phone: 916.440.1120 fax: 916.440.1121" } ["___content":protected]=> string(102) "

R 512MWR

" ["_permalink":protected]=> string(167) "https://www.ppic.org/publication/funding-formulas-for-california-schools-iv-an-analysis-of-governor-browns-weighted-pupil-funding-formula-may-budget-revision/r_512mwr/" ["_next":protected]=> array(0) { } ["_prev":protected]=> array(0) { } ["_css_class":protected]=> NULL ["id"]=> int(8836) ["ID"]=> int(8836) ["post_author"]=> string(1) "1" ["post_content"]=> string(0) "" ["post_date"]=> string(19) "2017-05-20 02:41:18" ["post_excerpt"]=> string(0) "" ["post_parent"]=> int(4218) ["post_status"]=> string(7) "inherit" ["post_title"]=> string(8) "R 512MWR" ["post_type"]=> string(10) "attachment" ["slug"]=> string(8) "r_512mwr" ["__type":protected]=> NULL ["_wp_attached_file"]=> string(12) "R_512MWR.pdf" ["wpmf_size"]=> string(6) "174102" ["wpmf_filetype"]=> string(3) "pdf" ["wpmf_order"]=> string(1) "0" ["searchwp_content"]=> string(16758) "Funding Formulas for California Schools IV An Analysis of Governor Brown’s Weighted Pupil Funding Formula, May Budget Revision May 2012 Heather Rose, Jon Sonstelie, and Margaret Weston Supported with funding from The Silver Giving Foundation and the Stuart Foundation http://www.ppic.org /main/home.asp Funding Formulas for California Schools IV 2 Summary In his 2012– 13 budget, Governor Brown proposed a new system for allocating state revenue among California school districts. In May the governor r evised his proposal. Using the PPIC School Finance Model ( available at www.ppic.org/main/dataSet.asp?i=1229 ), in this update we show how these proposals would guide the allocation of additional re venue among school districts. Under both proposals, school districts educating high percentages of disadvantaged students would receive the largest revenue gains. Compared to the original proposal, the revised proposal provides less funding for disadvantag ed students and substantially reduces differences in gains among districts. Contents Summary 2 Tables 4 Governor Brown’s May Proposal 5 Modeling the Proposal 6 Conclusion 8 About the Authors 9 Acknowledgments 9 The PPIC School Finance Model can be found in PPIC's Data Depot http://www.ppic.org /main/home.asp Funding Formulas for California Schools IV 4 Tables 1 Average change in funding from 2010 –11 to revenue in weighted pupil funding simulation, dollars per student 6 2 The distribution of revenue changes across districts 7 http://www.ppic.org /main/home.asp Funding Formulas for California Schools IV 5 Governor Brown’s May Proposal In his 2012–13 budget released in January, Governor Brown proposed a new system for allocating state revenue among California school districts, a proposal we analyzed in a previous paper. 1 In the May revision of his budget, the governor also revised his school fi nance proposal. This paper updates our previous analysis to account for those changes. The governor proposes to replace the current system with a weighted pupil funding (WPF) formula. The revised proposal addresses many important questions, including the t ransition from the current system to the new system and accompanying modifications to the state’s accountability system. As in our previous analysis, however, our focus is how the WPF formula would change the allocation of revenue among school districts. I n this regard, the revised proposal makes three important changes:  Weights for disadvantaged students. With a weighted pupil funding formula, the revenue a district receives equals a base rate multiplied by a sum of student weights in the district. In the governor’s original proposal, students who are disadvantaged (English learners or students who qualify for free or reduced- price lunch) had a weight of 1.37. This weight increases as disadvantaged students become more than 50 percent of students, the concentration factor. All other students had a weight of 1.0. The revised proposal reduces the weight for disadvantaged students from 1.37 to 1.20. The concentration factor remains, but with the lower weight.  Weights for grade level. In the original proposal, students who were not disadvantaged had a weight of 1.0, regardless of their grade. Under the revised proposal, student weights vary by grade level. Using students in grade 4 though 6 as the base with a weight of 1.0, the grade- level weights are as follows: Grade level Student weight K–3 1.1078 4–6 1.0000 7–8 1.0298 9–12 1.1931 In determining weights for a district, the formula multiplies grade- level weights by weights for disadvantage. For example, a disadvantaged student in grade 3 has a weight of 1.33 (1.11 x 1.20).  Add -ons for Targeted Instructional Improvement Block Grant (TIIBG) and Home -to -School Transportation (HST). The original proposal consolidated these two programs into the WPF formula. The revised proposal elim inates the two programs but adds the revenue that districts now receive in the two programs to the revenue from the WPF formula. A district would receive its formula revenue, plus the revenue it now receives from those two programs. In 2010 –11, these progr ams totaled $1.3 billion, 4 percent of all state K –12 funding. 1 Heather Rose, Jon Sonstelie, and Margaret Weston, “Funding Formulas III: An Analysis of Governor Brown’s Weighted Pupil Fundi ng Formula” ( Public Policy Institute of California, 2012). http://www.ppic.org /main/home.asp Funding Formulas for California Schools IV 6 Modeling the Proposal The original proposal envisioned phasing in the new formula over six years, with the base rate rising to $6,990 per student when the formula is fully implemented in 2017 –18. We simulated the fully implemented formula and compared the revenue that districts would receive with what they actually received in 2010 –11. To simulate the revised proposal, we used the same demographic and economic assumptions as in the original simulations but applied the new weights for grade level and disadvantage, as well as included the TIIBG and HST add -ons. We chose the base rate to make our simulations with this revised proposal comparable to the original simulation. In particular, we chos e the base rate so that the aggregate revenue received by all districts in the new simulation equals the aggregate revenue they received in the original simulation. 2 Table 1 shows the average gains districts would receive under the new proposal relative to their current funding. For the sake of comparison, it also shows the gains districts would have received under the original proposal. Districts are categorized by type and by percentage of students who are disadvantaged. For each category, the table reports the average gain in revenue per pupil in moving from the allocation in 2010 –11 to the allocation districts received in our simulation. The first column for each district type is the average gain under the original proposal. The second column is the average gain under the revised proposal. For example, unified districts with fewer than 20 percent of students who are disadvantaged would gain an average of $1,229 per student under the original proposal. Those districts would gain an average of $1,881 under the revised proposal. TABLE 1 Average change in funding from 2010– 11 to revenue in weighted pupil funding simulation, dollars per student Disadvantaged students (%) Unified districts Elementary districts High school districts Original proposal Revised proposal Original proposal Revised proposal Original proposal Revised proposal 0–20 $1,229 $1,881 $1,241 $1,585 $631 $1,516 20–40 1,576 2,034 1,956 2,083 621 1,717 40–60 2,073 2,278 2,199 2,158 1,134 2,168 60–80 3,124 2,996 3,542 2,946 1,998 2,457 80–100 3,963 3,520 5,287 3,923 3,369 3,391 SOURCE: Authors’ calculations based on PPIC School Finance Model. Compared to the actual revenue that districts received in 2010–11, the revised proposal has two important features. First, it allocates relatively more additional revenue to districts with high percentages of disadvantaged students. Second, for any level o f student disadvantage, high school districts receive less additional revenue than other districts. The original proposal also shared these two features; the revisions have merely lessened the differences in revenue gains among districts. Because of the lo wer weight for disadvantaged students in the revised proposal, districts with high percentages of such students would 2 The base rate t hat equalizes revenue for those two proposals is $7,014 per pupil. The revised proposal would first restore the revenue limit deficit factor before fully implementing the new formula. In our simulation, only 36 districts with a total of 16,232 student s had undeficited revenue limits in excess of their WPF entitlement. We ignored this constraint in our simulation. http://www.ppic.org /main/home.asp Funding Formulas for California Schools IV 7 receive smaller revenue increases in the revised proposal than they received in the original proposal. Districts with few disadvantaged st udents would receive relatively larger increases. Because of the grade- level weights in the revised proposal, high school districts would receive relatively larger increases in the revised proposal than in the original proposal. Their revenue gains are sti ll smaller than for other districts, but the revised proposal has reduced the differences. Overall, the May revisions to the governor’s proposal have maintained differences in revenue gains among districts but reduced their magnitude. This reduction is cle arly visible in Table 2. Under the original proposal, 39.9 percent of students attended districts with revenue gains between 30 and 50 percent. Under the revised proposal, 60.1 percent of students attend such districts. 3 TABLE 2 The distribution of revenue changes across districts Districts Students Students (%) Original proposal Revised proposal Original proposal Revised proposal Original proposal Revised proposal less than -10 45 38 61,851 35,793 1.1 0.6 -10–0 51 48 80,749 81,215 1.4 1.4 0–10 38 19 143,834 38,677 2.6 0.7 10–20 64 28 344,654 75,381 6.2 1.3 20–30 152 114 925,465 430,246 16.5 7.7 30–40 165 272 1,502,210 1,680,142 26.8 30.0 40–50 89 142 731,469 1,706,652 13.1 30.5 50–60 68 109 518,398 830,423 9.3 14.8 60–70 58 90 419,109 564,910 7.5 10.1 70–80 56 21 393,665 160,321 7.0 2.9 80–90 48 0 233,660 0 4.2 0 90–100 40 0 206,356 0 3.7 0 100–110 7 0 42,338 0 0.8 0 SOURCE: Authors’ calculations based on PPIC S chool Finance Model. In reporting our simulation results, we have focused on average gains by district type and by level of student disadvantage. In “ School District Revenue Changes under Governor Brown’s May Proposal ,” a spreadsheet available on the PPIC website , we report gains for individual districts. Revenue gains may differ for districts of similar characteristics because they received different revenue per pupil under the current system. Similarly, under the gov ernor’s revised proposal, revenue per pupil can differ for similar districts because of the revenue add- ons. For both TIIBG and HST, revenue per pupil is not evenly distributed across districts. These differences are rooted in the complex history of Califo rnia’s school finance system and are not apparent in the averages reported in Table 1. However, they are apparent in the spreadsheet . 3 Of the 86 districts receiving less revenue under the revised proposal than in the status quo, 59 are basic aid districts, whi ch are districts with more property tax revenue that their entitlement. Under the WPF program, these districts would continue to have their own pro perty tax revenue but they would lose categorical revenue. http://www.ppic.org /main/home.asp Funding Formulas for California Schools IV 8 Conclusion The revisions to the governor’s proposal have reduced differences among districts in how new revenue would be allocated. In the original proposal, high school districts received relatively smaller increases in revenue than other districts. The grade- level weights in the new proposal have lowered that difference. For a given level of student disadvantage, under the new proposal high school districts would receive similar revenue increases as other districts receive. The revised proposal also distributes additional revenue more evenly among districts of the same type. The original proposal channeled proportionally more revenue to districts with high percentages of disadvantaged students. This is still true with the revised proposal, but the differences are sm aller among districts with different levels of student disadvantage. http://www.ppic.org /main/home.asp Funding Formulas for California Schools IV 9 About the Authors Heather Rose is an adjunct policy fellow at the Public Policy Institute of California (PPIC) and an associate professor at the School of Education at the University of California, Davis. She specializes in the economics of education and school finance. Her recent projects include a study of affirmative action policies at the college level and an investigation of how high school curriculum affects the test score gap b etween white and minority students. She has also studied the effects of high -school curriculum on students’ subsequent earnings. She holds a B.A. in economics from the University of California, Berkeley, and an M.A. and Ph.D. in economics from the Universi ty of California, San Diego. Jon Sonstelie is an adjunct policy fellow at PPIC and professor of economics at the University of California, Santa Barbara. His research interests include several areas in public finance and urban economics, including the effect of public school quality on private school enrollment, the incidence of the property tax, the demand for public school spending, the economics of rationing by waiting, and the effect of transportation innovations on residential locations. He was previou sly a research fellow at Resources for the Future. He holds a B.A. from Washington State University and a Ph.D. from Northwestern University. Margaret Weston is a policy associate at PPIC’s Sacramento Center, where her work focuses on K –12 school finance. Before joining PPIC, she taught high school English and drama in the Baltimore City Public Schools through Teach For America. She holds an M.A in teaching from Johns Hopkins University and an M.A. in public policy from the University of Michigan. Acknowl edgments We thank Carol Bingham and Heather Carlson from the California Department of Education for providing much of the data used in the simulation model. We thank our advisory group, consisting of Carol Bingham and Elizabeth Dearstyne from the Departmen t of Education, Chris Ferguson and Nicolas Schweizer from the Department of Finance, and Edgar Cabral and Rachel Ehlers from the Legislative Analyst’s Office, for their advice and recommendations as we developed and refined the model for 2010 –11. PUBLIC POLICY INSTITUTE OF CALIFORNIA Board of Directors Gary K. Hart, Chair Former State Senator and Secretary of Education State of California Mark Baldassare President and CEO Public Policy Institute of California Ruben Barrales President and CEO San Diego Regional Chamber of Commerce María Blanco Vice President, Civic Engagement California Community Foundation Brigitte Bren Chief Executive Officer International Strategic Planning, Inc. Robert M. Hertzberg Partner Mayer Brown, LLP Walter B. Hewlett Chair, Board of Directors William and Flora Hewlett Foundation Donna Luc as Chief Executive Officer Lucas Public Affairs David Mas Masumoto Author and Farmer Steven A. Merksamer Senior Partner Nielsen, Merksamer, Parrinello, Gross & Leoni, LLP Kim Polese Chairman ClearStreet, Inc. Thomas C. Sutton Retired Chairman and CEO Paci fic Life Insurance Company The Public Policy Institute of California is dedicated to informing and improving public policy in California through independent, objective, nonpartisan research on major economic, social, and political issues. The institute’s goal is to raise public awar eness and to give elected representatives and other decisionmakers a more informed basis for developing policies and programs. The institute’s research focuses on the underlying forces shaping California’s future, cutting across a wide range of public poli cy concerns, including economic development, education, environment and resources, governance, population, public finance, and social and health policy. PPIC is a private operating foundation. It does not take or support positions on any ballot measures or on any local, state, or federal legislation, nor does it endorse, support, or oppose any political parties or candidates for public office. PPIC was established in 1994 with an endowment from William R. Hewlett. Mark Baldassare is President and Chief Executive Officer of PPIC. Gary K. Hart is Chair of the Board of Directors. Short sections of text, not to exceed three paragraphs, may be quoted without written permission provided that full attribution is given to t he source. Research publications reflect the views of the authors and do not necessarily reflect the views of the staff, officers, or Board of Directors of the Public Policy Institute of California. Copyright © 2012 Public Policy Institute of California All rights reserved. San Francisco, CA PUBLIC POLICY INSTITUTE OF CALIFORNIA 500 Washington Street, Suite 600 San Francisco, California 94111 phone: 415.291.4400 fax: 415.291.4401 www.ppic.org PPIC SACRAMENTO CENT ER Senator Office Building 1121 L Street, Suite 801 Sacramento, California 95814 phone: 916.440.1120 fax: 916.440.1121" ["post_date_gmt"]=> string(19) "2017-05-20 09:41:18" ["comment_status"]=> string(4) "open" ["ping_status"]=> string(6) "closed" ["post_password"]=> string(0) "" ["post_name"]=> string(8) "r_512mwr" ["to_ping"]=> string(0) "" ["pinged"]=> string(0) "" ["post_modified"]=> string(19) "2017-05-20 02:41:18" ["post_modified_gmt"]=> string(19) "2017-05-20 09:41:18" ["post_content_filtered"]=> string(0) "" ["guid"]=> string(50) "http://148.62.4.17/wp-content/uploads/R_512MWR.pdf" ["menu_order"]=> int(0) ["post_mime_type"]=> string(15) "application/pdf" ["comment_count"]=> string(1) "0" ["filter"]=> string(3) "raw" ["status"]=> string(7) "inherit" ["attachment_authors"]=> bool(false) }