ࡱ> qspG ubjbjَ qq]      .^^(  = Pc P$Nl ^l l l    l hl  2, S#  l THE BEST SCHOOL DISTRICTS IN TEXAS FOR AFRICAN AMERICAN STUDENTS 1996-1999 A REPORT OF THE TEXAS EDUCATIONAL EXCELLENCE PROJECT Number 9 May 2000 For further information contact: http://www-bushschool.tamu.edu/kmeier/teep 979-862-8847 or in South Texas Robert D. Wrinkle, Department of Political Science, University of Texas, Pan American, 956-381-3341 rdwe116@panam1.panam.edu The Texas Educational Excellence Project (TEEP) is a joint program of the George Bush School of Public Service and the Department of Political Science at Texas A&M University. It also has research associates at the University of Texas-Pan American and Oakland University. TEEP seeks to apply scholarly research to educational policy issues in order to make recommendations for greater quality and equity in Texas school systems. The Best School Districts in Texas for African American Students 1996-99 The large gains for all students on the TAAS exam over the past five years slowed dramatically in 1999. African American students continued to close the gap with Anglo students--64 percent passed in 1999 vs. 87.9 percent-but the gap still remains substantial. Statewide averages, however, mask some impressive gains by individual school districts. The Texas Educational Excellence Project believes the first step in improving black tests scores is to identify school districts that do a better job of educating black students. Programs and policies in these districts can then be used by other districts to improve performance. The Linden-Kildare Independent School District provides one such example. TAAS pass rates for black students in Linden-Kildare have improved from 54.3 percent in 1996 to 95.2 percent in 1999. This dramatic improvement has resulted from a variety of early intervention techniques at lower grade levels to make sure students acquire necessary skills. Small class sizes along with the use of Title I teachers to address reading and similar problems form part of the Linden-Kildare integrated program. While their program does not target black students per se, by focusing on the educational needs of all students, they get excellent results. Linden-Kildare is a relatively small school, and their approach might not be directly transferable to large urban school districts. Many urban schools, however, also get dramatic improvements. Waco ISD adopted a program that eliminated social promotions, instituted individual diagnostics, year round tutorials, and mandatory summer school for students who fail the TAAS. The Waco pass rate for black students has increased from 33.6 in 1996 to 67.2 in 1999. The Texas Educational Excellence Project uses a technique of analysis known as multiple regression to identify school districts that do a better job of educating black students. This analytical tool makes it possible to develop generalizations about the overall performance of Texas school districts in how well they educate black students, while also providing information that can be used to make comparisons across individual school districts. Our model is based on what is generally know as an education production function where student performance (defined as black pass rates on the TAAS) is a function of inputs into the educational process, such as operating expenditures, student-teacher ratios, and various educational policies. Estimation of this production function results in predictions about how well districts are expected to do, given the level of inputs available to them. Based on the results of the production function model, we compare how well districts actually perform to how well the statistical model predicts they should perform based on their inputs. The difference, if any, between the actual results and the predictions indicates how well districts are doing in educating black students. An Education Production Function School districts are organizations; they receive inputs (resources and students) from their environment and produce outputs (educated students among others). A vast literature has designated a variety of education production functions whereby the outputs of school systems can be evaluated relative to their inputs (Burtless 1996; Smith 1995; Hanushek 1986; 1989; 1996). Our dependent variable is the school districts pass rate for black students on the TAAS exam. Texas requires all school districts to administer exams to students in several grades on an annual basis. We make no claim that results on TAAS exams account for all of the overall learning experience of black students. Student performance is a multi-dimensional concept that can be measured in variety of different ways. However, pass rates on TAAS exams do measure whether students are picking up basic academic skills from grade to grade. Our dependent variable, therefore, focuses primarily on how well districts perform in teaching black students basic skills, and should not be construed as an overall measure of black student learning. The independent variables fall into four general types--environmental constraints, financial resources, teacher qualifications, and district policies. Environmental constraints are factors that restrict agency performance; in the case of education the key constraint is how difficult/easy it is to educate students. In the context of educational policy, poverty is a serious constraint on student performance. The measures of constraint are the percent of poor students (defined as those eligible for free school lunches) and the percentage of black families that live in poverty. We also measure the educational level of blacks in the school district using the percentage of blacks in the school district over age 25 with at least a high-school diploma. The education variable should be positively related to student performance and the other two measures should be negatively related to black pass rates. Financial resources are the basic raw materials of any organization's attempt to meet its goals. Three measures of financial resources are included--per student instructional funds, average teacher's salary, and percent of funds received via state aid. These represent total resources devoted to education, the attractiveness of teaching positions in a competitive marketplace, and state efforts to overcome the unequal distribution of local financial resources. The relationship between expenditures and educational outcomes is one of the most contested questions in all of educational policy. Hanushek (1986; 1989; 1996) contends that there is no consistent relationship between money and student outcomes. Although this finding has been challenged by others (Hedges and Greenwald 1996), it remains the conventional wisdom. In recent longitudinal studies, however, Murray (1995), Evans, Murray and Schwab (1997), and Murray, Evans and Schwab (1995) found that districts that increased expenditures had improved performance afterward. Bohte (1999) found that expenditures were correlated with higher test scores even when controlling for the previous year's test scores. We consider expenditures a critical variable for inclusion in the model. All relationships should be positive. The two teacher qualification measures (or lack thereof) are the percent of teachers who hold a temporary certification in a subject specialty (as opposed to a permanent certification) and the average number of years of teacher experience. The relationship for non-certification should be negative, while the expectation is that more experienced teachers will lead to higher student outcomes. Finally, the education production function contains three policy measures--the percentage of students taking gifted classes, class size, and student attendance (percent attending on an average day). Performance should be positively related to gifted classes and attendance and negatively related to class size. Texas has a large number of school districts; many are very small or deal with a homogeneous student body. In an effort to use a set of organizations relatively similar in the task that they perform, we have restricted our analysis to school districts with a least 1000 students and at least 10 percent black students. These restrictions resulted in a total of 170 districts in the study. The data analysis is a pooled time series with data from the years 1995 through 1998. In any pooled time series one needs to control for serial correlation resulting from any trend in the variables over time. A series of dummy variables are introduced to achieve this. The basic production function is shown in table 1. Several variables are powerful predictors of black student pass rate. These include expenditure, background, and policy variables. Teacher salaries are strongly and positively related to the black student pass rate, as is the percentage of black adults age 25 and older with at least a high school education. Attendance also is strongly and positively related to the black student pass rate. The greater the percentage of low-income students in the district, the lower the black student pass rate. No other variable achieved statistical significance. The results of this model allow us to compare school districts as to how well they do above (or below) expectations. As an illustration, the model predicted that the Houston Independent School District would have an average black pass rate of 53.48% from 1996-99. Houstons actual pass rate of 59.83 represents a 6.35 percentage point improvement over this standard. Based on this method, the top ranked school district for black students in Texas was Pittsburg with a rating of +22.0% followed closely by Linden-Kildare with a +21.41 score and Ferris with a +20.60 score. The top 35 districts are shown in table 2. The first column is the numerical score on which the districts are ranked. The second column is the average pass rate for black students from 1996 to 1999 and the third column is the ranking score for 1999 only. These thirty-five districts represent a variety of different types of school districts located throughout the state. Table 3 reports the 25 best districts for black students in 1999. A comparison of this table with Table 2 gives some indication of relative movement among the rankings of school districts. Linden-Kildares performance in 1999 is striking in magnitude. Recent gains are likely the result of the benefits of policies adopted earlier so these are the districts that are likely to continue to be rated highly in future studies. Although our top 35 includes districts of all sizes, large districts often cannot change as rapidly as small districts simply because so many students are involved. Table 4 presents the top ten large districts (those with 15,000 or more students). Aldine, Goose Greek, Galena Park and Garland top this list of large districts. The table in the Appendix gives an alphabetical listing of all of the school districts examined in this study, along with their scores. Any person interested in a specific school district can examine the Appendix to locate that district and identify the score and rank. Conclusion This study has identified those school districts in Texas that performed better than expected on the pass rate for black students. These districts should serve as role models for other districts in Texas. The districts have a wide variety of programs for early diagnosis, coordination of curriculum, and parental involvement. If effective programs and performances from these districts are identified, then they can be transferred to other districts with an overall benefit to black students. Although this study only examines exemplary districts, that should not detract from the relatively low pass rate for black students in Texas. A great deal of additional improvement is needed in these districts as well as other districts to close the test gap between black and Anglo students. Substantial progress has been made in the last few years; a great distance remains to be covered. Improving educational opportunities for all Texas children requires a long-term commitment to education. Problems develope over a period of decades; solutions require both time and hard work. Table 1. The Education Production Function Dependent Variable = % of black students passing the TAAS 1996-99 Independent Variable Coefficient Standard Error Low Income Students -.0639 .0325 Gifted Classes .1535 .1041 Attendance 3.2679 .4979 Teacher Salaries (000) 0.7800 .3093 Class Size -.1927 .3814 NonCertified Teachers .0438 .1251 Teacher Experience .0190 .2924 State Funding Percent .0206 .0205 Instructional Funding Per Student .0012 .0017 Black Education (25+) .2058 .0656 Black Poverty -.0504 .0431 R-Square .52 Omitted are coefficients for individual year dummy variables. Table 2. Top Ranked Districts for Educating Black Students 1996-99 Rank District Score TAAS 99 Score 1 Pittsburg 22.01 76.30 22.11 2 LindenKildare 21.41 79.82 29.62 3 Ferris 20.60 74.90 19.35 4 Hooks 17.38 72.22 22.56 5 Atlanta 16.15 74.98 22.36 6 Sweeny 15.60 75.50 18.01 7 Del Valle 15.54 66.07 15.83 8 Connally 13.94 73.23 10.54 9 Texas City 12.34 66.85 9.44 10 McGregor 12.33 73.85 7.85 11 Kountze 10.73 63.33 5.56 12 Tatum 10.64 66.82 13.97 13 Angleton 10.62 72.20 17.93 14 LibertyEylau 10.31 66.75 13.02 15 Sulpher Springs 9.62 68.82 4.80 16 Stafford MSD 9.50 74.28 7.63 17 Aldine 9.14 66.78 6.32 18 La Grange 8.89 63.75 2.37 19 Goose Creek 8.58 62.63 6.89 20 WilmerHutchins 8.36 57.75 3.17 21 Terrell 8.27 66.22 9.80 22 New Boston 8.00 69.30 5.09 23 El Campo 7.61 63.55 17.21 24 Newton 7.46 58.95 12.99 25 ColumbiaBrazoria 7.28 64.05 11.82 26 Galena Park 7.19 66.15 8.66 27 Vernon 7.17 61.67 11.29 28 DaingerfieldLone St 7.10 64.72 2.34 29 Sabine 7.08 66.28 6.52 30 Garland 6.96 67.88 6.29 31 Bay City 6.94 59.63 3.25 32 Grand Prairie 6.45 67.97 1.88 33 Houston 6.35 59.83 2.16 34 Lamar Consolidated 6.33 61.60 10.12 35 Denison 5.87 63.80 11.96 Table 3. The Best Districts in 1999 Rank District 1999 Score 1 LindenKildare 29.62 2 Hooks 22.56 3 Atlanta 22.36 4 Pittsburg 22.11 5 Ferris 19.35 6 Sweeny 18.01 7 Angleton 17.93 8 El Campo 17.21 9 Del Valle 15.83 10 Tatum 13.97 11 Rice Consolidated 13.29 12 LibertyEylau 13.02 13 Newton 12.99 14 Hillsboro 12.16 15 Denison 11.96 16 ColumbiaBrazoria 11.82 17 Vernon 11.29 18 Connally 10.54 19 Lamar Consolidated 10.12 20 Rockdale 10.03 21 Terrell 9.80 22 Taylor 9.76 23 Texas City 9.44 24 Waco 9.26 25 Galena Park 8.66 Table 4. The Best Large School Districts Enrollment 15,000+ Score TAAS 99 Score 1 Aldine 9.14 66.78 6.32 2 Goose Creek 8.58 62.63 6.89 3 Galena Park 7.19 66.15 8.66 4 Garland 6.96 67.88 6.29 5 Grand Prairie 6.45 67.97 1.88 6 Houston 6.35 59.83 2.16 7 Wichita Falls 5.28 63.28 0.90 8 Amarillo 4.18 57.13 4.52 9 Beaumont 3.90 57.88 2.16 10 Abilene 3.81 62.30 4.27 References Bothe, John, 1999. "Class Size, Teacher Salaries and Student Performance." College Station, TX: Texas Educational Excellence Project. Burtless, Gary. 1996. Does Money Matter? The Effect of School Resources on Student Achievement and Adult Success. Washington, D.C.: Brookings Institution. Hanushek, Eric A. 1986. "The Economics of Schooling: Production and Efficiency in Public Schools." Journal of Economic Literature 24:1141-77. Hanushek, Eric A. 1989. "The Impact of Differential Expenditures on School Performance." Educational Researcher 23 (4): 45-65. Hanushek, Eric A. 1996. "School Resources and Student Performance." In Does Money Matter? The Effect of School Resources on Student Achievement and Adult Success, Gary Burtless, ed. Washington, D.C.: Brookings Institution. Hedges, Larry V. and Rob Greenwald. 1996. "Have Times Changed? The Relation between School Resources and Student Performance." In Does Money Matter? The Effect of School Resources on Student Achievement and Adult Success, ed. Gary Burtless. Washington: Brookings. Murray, Sheila E. 1995. "Two Essays on the Distribution of Education Resources and Outcomes." PhD. diss. Department of Economics, University of Maryland. Murray, Sheila E., William N. Evans and Robert M. Schwab. 1995. "Money Matters After All: Evidence From Panel Data on the Effects of School Resources." University of Kentucky and University of Maryland working paper: The Martin School. Smith, Kevin B. 1995. "Policy, Markets, and Bureaucracy: Reexamining School Choice." Journal of Politics 56 (May), 475-491. Appendix. Scores for All Districts Rank District Score TAAS 1999 Score 48 Abilene 3.81 62.30 4.27 17 Aldine 9.14 66.78 6.32 100 Alief 2.77 61.10 3.46 45 Amarillo 4.18 57.13 4.52 40 Anahuac 5.33 60.28 6.48 13 Angleton 10.62 72.20 17.93 137 Arlington 7.56 56.45 7.08 152 Athens 11.25 43.78 9.58 5 Atlanta 16.15 74.98 22.36 139 Austin 7.98 44.83 12.31 66 Bastrop 1.33 53.92 5.10 31 Bay City 6.94 59.63 3.25 47 Beaumont 3.90 57.88 2.16 148 Bellville 10.15 45.38 7.90 151 Brenham 11.23 44.00 11.06 114 Bryan 4.40 49.92 0.07 62 Caldwell 1.88 58.03 6.58 112 Cameron 4.24 50.40 2.64 155 Carthage 12.26 45.38 9.10 127 Cedar Hill 5.70 60.80 14.34 58 Center 2.53 54.22 0.13 52 Channelview 3.41 64.45 3.01 158 Chapel Hill 15.04 39.03 12.05 138 Clarksville 7.87 48.78 10.96 149 Cleveland 10.56 37.47 11.08 153 Cold SpringOakhurst 11.32 35.78 10.99 146 College Station 10.00 52.20 12.03 25 ColumbiaBrazoria 7.28 64.05 11.82 105 Columbus 3.34 55.70 7.23 69 Commerce 1.19 57.38 8.13 8 Connally 13.94 73.23 10.54 57 Copperas Cove 2.99 63.85 5.29 63 CorriganCamden 1.87 54.72 0.85 122 Corsicana 5.55 47.05 5.58 145 Crockett 9.82 40.60 15.63 82 Crosby 0.14 61.10 3.64 37 Crowley 5.69 72.60 3.53 97 Cureo 2.27 52.60 6.03 28 DaingerfieldLone St 7.10 64.72 2.34 109 Dallas 3.88 51.03 6.47 7 Del Valle 15.54 66.07 15.83 35 Denison 5.87 63.80 11.96 89 Denton 0.86 57.20 5.84 94 DeSoto 1.62 64.88 0.14 61 Diboll 2.08 55.13 8.55 131 Dickinson 6.67 44.35 9.81 96 Duncanville 1.93 60.58 0.54 79 East Central 0.14 62.13 1.86 51 Edna 3.43 58.67 4.67 23 El Campo 7.61 63.55 17.21 103 Elgin 3.23 50.45 2.97 120 Ennis 5.48 51.80 8.45 81 Everman 0.04 59.00 3.87 121 Fairfield 5.52 53.20 7.80 3 Ferris 20.60 74.90 19.35 116 Fort Worth 5.18 47.60 4.73 73 Fort Bend 0.52 63.95 2.34 124 Ft Sam Houston 5.62 68.02 2.64 26 Galena Park 7.19 66.15 8.66 59 Galveston 2.22 50.08 3.05 30 Garland 6.96 67.88 6.29 104 Giddings 3.31 54.42 3.62 130 Gilmer 6.57 49.72 2.91 87 Gladewater 0.61 55.00 5.37 143 Gonzales 8.60 43.97 8.02 19 Goose Creek 8.58 62.63 6.89 32 Grand Prairie 6.45 67.97 1.88 144 Greenville 9.71 42.60 6.85 134 Groesbeck 7.31 49.10 3.13 46 Hallettsville 4.05 63.47 2.84 76 HardinJefferson 0.26 56.53 1.13 74 Hearne 0.47 49.75 1.63 161 Hempstead 18.76 37.65 26.28 117 Henderson 5.21 50.28 2.64 56 Hillsboro 3.03 55.80 12.16 159 Hitchcock 15.80 40.17 19.89 4 Hooks 17.38 72.22 22.56 33 Houston 6.35 59.83 2.16 133 Huntsville 7.25 49.22 4.33 54 Irving 3.18 66.38 4.50 125 Jacksonville 5.64 45.42 3.27 55 Jasper 3.12 57.33 0.01 49 Jefferson 3.78 57.92 5.63 75 Judson 0.26 65.07 2.60 85 Kilgore 0.43 53.65 1.45 78 Killeen 0.15 62.90 0.24 39 Kirbyville 5.41 59.25 2.30 92 Klein 1.35 64.55 7.64 11 Kountze 10.73 63.33 5.56 84 La Vega 0.35 55.00 2.84 72 La Marque 0.73 55.65 7.11 18 La Grange 8.89 63.75 2.37 34 Lamar Consolidated 6.33 61.60 10.12 126 Lancaster 5.69 53.53 4.52 99 Liberty 2.63 54.53 3.52 14 LibertyEylau 10.31 66.75 13.02 2 LindenKildare 21.41 79.82 29.62 106 Livingston 3.42 48.72 5.19 36 Longview 5.70 60.55 7.95 108 Lubbock 3.82 52.72 2.37 95 Lufkin 1.76 54.17 2.17 141 Madisonville 8.34 43.95 8.86 88 Manor 0.79 53.17 9.93 140 Marlin 8.08 43.85 8.80 98 Marshall 2.56 51.85 1.37 10 McGregor 12.33 73.85 7.85 70 Mesquite 0.96 60.72 3.40 43 Mexia 4.89 61.58 4.32 113 Midland 4.36 49.10 6.35 160 Mineola 18.62 42.42 14.49 77 Mount Pleasant 0.25 56.45 3.03 115 Nacognoches 4.73 51.95 1.80 110 Navasota 3.91 47.25 2.04 22 New Boston 8.00 69.30 5.09 24 Newton 7.46 58.95 12.99 50 North Forest 3.67 58.15 12.29 132 Palestine 6.98 48.03 6.95 68 Paris 1.21 58.20 2.74 67 Pflugerville 1.28 66.18 0.80 1 Pittsburg 22.01 76.30 22.11 80 Port Arthur 0.08 47.58 3.19 42 Queen City 5.26 59.88 0.09 83 Randolph Field 0.31 75.05 9.23 38 Rice Consolidated 5.50 55.90 13.29 135 Richardson 7.51 57.83 9.63 53 Rockdale 3.36 60.65 10.03 147 Royal 10.14 40.92 4.72 123 Rusk 5.62 48.33 5.68 29 Sabine 7.08 66.28 6.52 154 San Augustine 11.80 45.05 12.04 156 San Antonio 12.84 43.00 13.83 136 Sealy 7.54 51.55 8.92 101 Sheldon 2.84 58.38 1.95 65 Shepherd 1.81 54.90 7.17 107 Sherman 3.78 53.70 4.50 93 Silsbee 1.56 52.55 5.14 157 Smithville 13.77 43.08 1.82 118 Spring 5.35 60.88 4.50 16 Stafford MSD 9.50 74.28 7.63 15 Sulpher Springs 9.62 68.82 4.80 6 Sweeny 15.60 75.50 18.01 12 Tatum 10.64 66.82 13.97 71 Taylor 0.88 55.78 9.76 111 Teague 4.14 58.08 4.70 142 Temple 8.50 48.88 5.26 21 Terrell 8.27 66.22 9.80 128 Texarkana 5.75 47.88 5.82 9 Texas City 12.34 66.85 9.44 129 Tyler 5.90 52.90 4.52 90 Van Vleck 1.18 58.55 5.38 27 Vernon 7.17 61.67 11.29 64 Waco 1.87 49.90 9.26 150 Waller 10.97 47.97 14.04 119 Waxahachie 5.45 53.20 6.45 60 West Oso 2.13 52.03 0.49 86 West OrangeCove 0.44 52.85 2.50 91 Westwood 1.29 56.10 5.39 44 Whorton 4.60 57.92 0.63 41 Wichita Falls 5.28 63.28 0.90 20 WilmerHutchins 8.36 57.75 3.17 102 Yoakum 3.02 51.22 1.79 PAGE 2 PAGE 14  XU?Hs{3U// 4"4#4N4447788X8@AA)AD E!EaEGGG{HHIdIIIIXJJJvKKLLMMMNN*N,NnNrusuxuyuzu{uuuuuuuumHnH jU0J6OJQJ6>*0J>*OJQJ 0JOJQJ0J 6OJQJOJQJ 5OJQJ5I  ./XYZ[\]op H  ./XYZ[\]op&'[TU}  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