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WP DefaultsCJ4]@4 Heading 1$a$5CJ(^@( Default Para(_@!( Footnote Re1"`@1" Hypertext"a@A" Hyperlink*b@R* Body TextCJ(c@a( footnote ref(d@q( InitialStyleŠźÎ˙˙˙˙instruction in the district. Teachers, staff and parents work together to implement early intervention programs. In recent years, a special focus has been on the continual development of an aligned curricula for the entire district. This approach has allowed students and teachers to continue to develop and focus on successful programs and strategies. Obviously, the Los Fresnos approach works. Bangs and Los Fresnos are relatively small school districts. As such, many of their programs and approaches might not immediately transfer to other, larger districts. However, Ysleta, a much larger district, also continues to have an impressive record of educating Latino students. In 2001, for example, the Ysleta district has a Latino student pass rate of 83.9%. What makes this more impressive is that more than half of Ysleta’s students live below the poverty line. Ysleta’s Teacher Laptop Initiative helps to give teachers the necessary tools they need to educate the districts children. Ysleta also just began a program to assure that all students have access to computers, both in school and at home. This strong commitment towards their teachers and students helps Yselta rank high academically among urban districts. The analytical technique used by the Texas Educational Excellence Project to identify exemplary performing districts is multiple regression analysis. Simply comparing pass rates ignores other factors which influence performance, and many of these factors are variables in which schools have little or no control over. Multiple regression analysis allows us to assess the impact of certain policy and resource related variables while controlling for other variables. By the use of this analytical technique, TEEP can develop ratings of overall performance in educating Latino students by Texas school districts given certain levels of resources, which then allows us to make more valid comparisons across individual school districts. The model used in this analysis is based on what the literature identifies as an “educational production function.” A large literature has been developed which designates various education production functions to evaluate the outputs of schools to their inputs (Burtless 1996; Smith 1995; Hanushek, 1986; 1989; 1996). In this function, performance (here identified as Latino pass rates on the TAAS) is a function of various inputs into the process of educating students. These inputs include the district’s level of operating expenditures, percent of low-income students, the poverty level of the district, level of education of Latinos in the district, and various educational policies of the district. The prediction of how well the district should perform in educating Latino students is a result of the estimation of the established production function. Thus, with the results of the estimation, we can compare how well districts actually perform to how well the model predicts they will perform given a certain level of resources. This difference of actual to predicted is the measure of how well the districts are doing in educating Latino students. In other words, those districts that actually perform better than predicted, are those districts that are doing a superior job of educating Latino students. The 1998-2001 Education Production Function The dependent variable in our production function is the school district pass rate for Latino students. Each year, all Texas school districts administer the TAAS exam to students in a variety of grades. The district average for all grades is our dependent variable. Obviously, it would be egregious to claim that this variable adequately captures the entire range of learning for Latino students. However, it is a measure of how well students do in acquiring basic skills. Thus, by rating school districts on this measure, we have a measure of how well the district does in teaching basic skills to Latino students. We make no claims that this is an overall measure of Latino student learning. Our independent variables are of four distinct types: school district policies, measures of teacher quality, financial resources available to the district, and environmental constraints. The school district policies include class size, attendance rates, and percentage of students enrolled in gifted classes. We expect performance to be negatively related to class size. Larger classes should reduce student performance on the TAAS. The other two measures should be positively related to student performance. Measures of teacher quality include teacher certification (measured as the percent of district teachers who only have a temporary certificate to teach in their area) and the average years of teacher experience. We expect that more experienced teachers will have a positive effect on student performance, while the percentage of noncertified teachers should be negatively related to performance. We consider financial resources to be among the most important ingredients that school districts have to influence student performance. However, the relationship between financial resources and student performance is a controversial one among educational researchers. Hanushek, in a variety of works (1986; 1989; 1996) finds no consistent relationship between money and student performance. For some time this finding has been the conventional wisdom for educational policy researchers. Lately, however, a number of researchers have qualified Hanushek’s position. For example, in recent longitudinal studies, Murray (1995), Evans, Murray and Schwab (1997) and Murray, Evans and Schwab (1995) reported that districts that increased expenditures had improved student performance. A 1999 study by Bohte found that expenditures were correlated with higher test scores in Texas, even when controlling for the previous year’s test scores. We use three measures of financial resources: instructional funds per pupil; the average teacher salary for the district and percent of school district funds received from the state. These measures capture a variety of monetary influences, specific resources devoted to teaching, the ability to compete for teachers in the market as well as state efforts to overcome local inadequacies in financial resources. It is our expectation that all relationships will be positive. Environmental constraints are factors in the district that impede student performance. Even though schools cannot alter these factors, it is important to control for these factors when assessing the performance of schools. Among constraints included in our model are the percentage of Latino families living in poverty in the district, the percentage of poor students in the district (measured by the percentage eligible for free school lunches) and the percentage of Latinos age 25 and above in the district with at least a high school education. This education variable should be positively related to performance and the other two should be negatively related. Poverty is an especially constraining factor which schools have no control over. Yet, certain districts are better at addressing the needs of students living in poverty and decreasing the negative effects that it has on student performance. The Data Our analysis is limited to school districts above a certain size (1000 students) and Latino student population (10%). We do this because Texas has a very large number of school districts that are either very small or deal with a homogeneous population. The analysis is a pooled time series of data from 1998-2001. Analytically, all time series need to control for serial correlation that results from trends in the data. We introduce a series of dummy variable to control for serial correlation. The production function equation is shown in Table 1. As can be seen in the table, with one exception, all of the independent variables are powerful predictors of Latino student performance. Ten of the 11 variables are statistically significant. These include all three environmental constraints, school district policies, teacher qualifications and financial resources. These coefficients indicate the amount of change in the dependent variable, Latino pass rates, that is related to a one unit change in the independent variable. Student attendance is strongly and positively related to student performance, as are teacher salaries, percent of gifted students, amount of state aid, instructional funds per student, higher average years of teacher experience and percentage of Latinos with at least a high school education. Percentage of poor students, higher rates of non-certified teachers and the percentage of Latino poverty in the school district are negatively related to performance. It is important to note that since schools have little, or in the case of the environmental constraints, no control over the levels of these variables, it would be difficult to greatly improve scores by simply increasing or decreasing the levels of these variables. For example, districts would need to increase average teacher salaries by about $3,000 a year to increase pass rates by one percent. Most districts could not afford such a large increase in salaries. Yet, certain districts seem better at utilizing the resources they have available. By comparing the expected pass rate with the actual pass rate, we can identify those schools that make the most of their resources. To illustrate this analysis, consider the case of East Chambers. For the period of 1998-2001, they were predicted to have a Latino pass rate of 69.72, while their actual average pass rate was 84.43; meaning that 14.71% more Hispanic students passed the TAAS than predicted. These results allow us to compare school districts as to how well they perform relative to expectations. Based on this method, the top rated school district for Latino students in Texas over the 1998-2001 period was the Los Fresnos Consolidated School District with a score of 16.35, followed by Bangs with a 16.19 score. The top 25 districts are shown in Table 2. The first column is the average pass rate for Latino students for the 1998-2001 period. The second column is the numerical score (the percent above or below the predicted pass rate) over the 1998-2001 period by which the districts are ranked. The third column is the score for the 2000 period. The top-ranked districts represent a wide spectrum of Texas school districts. Some are quite large, others very small. Some are from border areas, while others are from large metropolitan areas. In short, these districts are widely representative of all Texas school districts. Since our ranking is based on the average scores for 1998 through 2001 there may be districts that have improved greatly over the last year that are not ranked well. The twenty five best districts for 2000 are listed in Table 3. There are a few districts that seem to have made great strides in the last year, such as Jacksboro which ranks first for 2000, but only 49th. over the four year period. The Ballinger school district ranked twelfth in 2000 compared to ranking 114th. for the four year period. This is a result of the district showing a 11.86% improvement over the 2000 expected pass rate compared to performing just 2.99% above the expected pass rate for the four year period. This one-year performance, if continued, will greatly improve these districts overall rating in coming years. Many relatively small school districts can more rapidly move up (or down) our rankings. It is more difficult for larger school districts to make rapid relative changes, as the number of students involved is so large. In order to more clearly identify well performing large districts, we have displayed the larger school districts (those above 10,000 student population) in Table 4. The format of Table 4 is the same as that of Table 2. The top-rated large school district is Aldine, with a 1998-2001 score of 10.80, followed by Ysleta (10.29) and Goose Creek (6.55). These districts consistently rank among the higher-performing large districts in the state. We provide an appendix in which all of the school districts covered in this study are listed alphabetically, along with their scores. Any person interested in a specific school district’s rating and ranking may find that information in the appendix. Conclusion This report is one of the continuing studies of Texas school districts by the Texas Educational Excellence Project (TEEP). A major focus of the project is to identify those school districts that have done an exemplary job of educating Latino students. The analysis of those districts that have a better than expected level of performance on the TAAS, identifies a set of role models for other districts. While these districts do not all share a common set of programs and/or curricula, many of their programs and activities may be identified and transferred to other districts. All persons interested in the education of minority students in the state should have an interest in the identification and support of exemplary programs. The identification of these high-performing districts should not be construed to indicate that all is well in the education of Latino students in Texas. Latinos continue to lag behind Anglos in terms of TAAS pass rates, and lead them in dropouts. While progress is being made, much more needs to be done. Educators and policy-makers cannot afford to rest on their laurels. The education of minority students is an evolving and necessary policy focus for the state. References Accountable Cost Advisory Committee. 1986. "Accountable Cost Study and Recommendations of the Accountable Cost Advisory Committee to the State Board of Education." Austin, TX: Texas Education Agency. Chubb, John and Terry Moe. 1990. Politics, Markets and America's Schools. Washington: Brookings. DeHaan, Robert F. 1963. Accelerated Learning Programs. Washington: Center for Applied Research in Education, Inc. Edgewood Independent School District v. Kirby. Texas SupCt, No. C-8353, (1989). Evans, William N., Sheila E. Murray, and Robert M. Schwab. 1997. "Schoolhouses, Courthouses, and Statehouses After Serrano." Journal of Policy Analysis and Management 16 (Winter), 10-31. Fuller, Bruce, Costanza Eggers-Pierola, Susan D. Holloway, Xiaoyam Liang and Marylee F. Rambaud. 1996. "Rich Culture, Poor Markets: Why do Latino Parents Forego Preschooling?" Teachers College Record 97 (Spring):400-418. Hanushek, Eric A. and Richard R. Pace. 1995. "Who Chooses to Teach (and Why)?" Economics of Education Review 14 (June):107-117. Hanushek, Eric A. 1986. "The Economics of Schooling: Production and Efficiency in Public Schools." Journal of Economic Literature 24 (September):1141-1177. Hanushek, Eric A. 1996. "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. Hanushek, Eric A. 1989. "Expenditures, Efficiency, and Equity in Education: The Federal Government's Role." American Economic Review 79 (May):46-51. 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. Lasswell, Harold. 1936. Politics: Who Gets What, When, How? New York: McGraw Hill. Lipsky, Michael. 1980. Street Level Bureaucracy. New York: Russell Sage Foundation. Long, Norton. 1952. "Bureaucracy and Constitutionalism." American Political Science Review 46 (September), 808-818. Meier, Kenneth J. and Joseph Stewart, Jr. 1991. The Politics of Hispanic Education. Albany: SUNY Press. 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. Necochea, Juan and Zullmara Cune. 1996. "A Case Study of Within District School Funding Inequities." Equity & Excellence in Education 29 (September): 69-77. Nye, Barbara A., Jayne Boyd-Zacharias, B. Dewayne Fulton, and Mark P. Wallenhorst. 1992. "Smaller Classes Really are Better." American School Board Journal 179 (May): 31-33. Oropesa, R. S. and Nancy S. Landale. 1997. “Immigrant Legacies.” Social Science Quarterly 78:399-416. Pate-Bain, Helen, C.M. Achilles, Jayne Boyd-Zacharias, and Bernard McKenna. 1992. "Class Size Does Make a Difference." Phi Delta Kappan 74 (November): 253-56. Polinard, J. L., Robert D. Wrinkle and Kenneth J. Meier. 1995. “The Influence of Educational and Political Resources on Minority Students’ Success,” Journal of Negro Education 64: 463-474. San Antonio Independent School District v. Rodriquez. 411 U.S. 1 (1973). Smith, Kevin B. and Kenneth J. Meier. 1995. The Case Against School Choice. Armonk, NY: M.E. Sharpe. Texas Research League. 1986. "Bench Marks for 1986-87 School District Budgets in Texas." Austin, TX: Texas Research League. Weiher, Gregory R. 1988. "Why Redistribution Doesn't Work: State Educational Reform Policy and Governmental Decentralization in Texas." American Politics Quarterly 16 (April): 193-210. TABLE 1: LATINO EDUCATIONAL PRODUCTION FUNCTION Variable Coefficient Standard Error Low Income -.0723 .0125 Gifted .0901 .0544 Attendance 3.1345 .2666 Teacher Salary K .0631 .1415 Class size .5091 .2137 Teacher Non certification -.1812 .0573 Teacher Experience .6684 .1223 State Aid .0532 .0106 High School Education .1238 .0171 %Poverty Background -.0629 .0149 Per Pupil Instructional K 4.2960 .8575 R2 = .32 F= 64.82 Table 2. The Forty Best Districts for Latinos 1998-2001 Rank District Score TAAS 2001 Score 1 Bangs 15.06 90.30 8.70 2 Los Fresnos Consolid 14.49 89.32 11.81 3 Brazosport 14.10 90.27 13.66 4 Del Valle 13.86 77.00 13.75 5 Grand-Saline 13.36 85.65 14.15 6 Point Isabel 13.21 82.18 14.20 7 Valley-View 12.96 84.57 11.69 8 Angleton 12.92 90.63 12.11 9 Pittsburgh 12.19 81.75 10.43 10 Mount Vernon 11.79 86.90 12.23 11 Burnet Consolidated 11.36 83.65 8.21 12 Monahans-Wickett-Pyo 11.25 86.63 11.39 13 San Benito Consolida 11.07 81.10 10.81 14 McGregor 10.98 87.60 6.93 15 Ferris 10.56 82.88 5.80 16 Coleman 10.38 83.85 1.92 17 Rosebud-Lott 10.35 85.88 5.02 18 Hidalgo 9.89 81.43 17.11 19 La Marque 9.71 80.45 9.56 20 South Texas 9.43 93.98 5.67 21 Tuloso-Midway 9.43 81.57 7.93 22 Aldine 9.28 81.38 7.23 23 Ysleta 9.20 81.65 7.12 24 Columbia-Brazoria 9.06 84.40 11.86 25 La Feria 8.98 83.43 4.89 26 El Campo 8.77 83.57 5.38 27 Alvin 8.70 79.73 9.83 28 Barbers-Hill 8.64 85.78 4.95 29 Eagle Pass 8.53 75.70 6.08 30 Bishop Consolidated 8.49 84.40 7.19 31 Alvarado 8.37 77.18 6.83 32 Galena Park 8.23 77.30 9.19 33 Frenship 8.11 84.18 6.34 34 Eastland 8.09 82.82 6.81 35 Dumas 7.98 76.18 7.98 36 Hillsboro 7.88 74.45 6.01 37 Sweeny 7.68 86.35 9.07 38 Galveston 7.65 73.05 6.14 39 Anahuac 7.51 79.63 2.38 40 Calhoun County 7.46 80.68 9.49 Table 3. The Best Districts in 2001 1 Hidalgo 17.11 2 Hereford 14.83 3 Point Isabel 14.20 4 Grand-Saline 14.15 5 Del Valle 13.75 6 Brazosport 13.66 7 Mount Vernon 12.23 8 Angleton 12.11 9 Columbia-Brazoria 11.86 10 Los Fresnos Consolid 11.81 11 Mexia 11.80 12 Orange Grove 11.79 13 Merkel 11.74 14 Valley-View 11.69 15 Monahans-Wickett-Pyo 11.39 16 Denver City 11.24 17 San Benito Consolida 10.81 18 Ballinger 10.45 19 Pittsburgh 10.43 20 Alvin 9.83 21 La Joya 9.77 22 Groesbeck 9.67 23 La Marque 9.56 24 Calhoun County 9.49 25 Galena Park 9.19 Table 4. The Best Large Districts for Latinos Enrollment 15,000+ 1 Aldine 9.28 81.38 7.23 2 Ysleta 9.20 81.65 7.12 3 Galena Park 8.23 77.30 9.19 4 Goose Creek 6.67 77.20 7.65 5 McAllen 6.11 78.80 6.05 6 Harlingen 5.53 80.03 3.90 7 La Joya 5.39 68.72 9.77 8 Edinburg 3.95 73.70 2.94 9 Pharr-San Juan-Alamo 3.93 76.18 3.46 10 United 3.52 71.63 2.65 Appendix A. All Districts in the Study 164 Abilene 0.75 75.35 3.86 310 Alamo Heights -6.39 77.25 -4.53 22 Aldine 9.28 81.38 7.23 54 Alice 6.62 71.32 8.18 323 Alief -7.60 67.75 -10.24 204 Alpine -1.50 76.05 -0.41 31 Alvarado 8.37 77.18 6.83 27 Alvin 8.70 79.73 9.83 170 Amarillo 0.65 71.75 0.63 39 Anahuac 7.51 79.63 2.38 82 Andrews 4.64 78.72 3.88 8 Angleton 12.92 90.63 12.11 315 Aransas Pass -6.56 65.47 -2.67 76 Aransas County 5.12 76.82 3.66 332 Arlington -7.96 67.68 -7.86 325 Athens -7.69 62.67 -5.41 337 Austin -8.41 59.72 -5.34 81 Ballinger 4.79 81.97 10.45 219 Bandera -1.85 73.18 -9.68 1 Bangs 15.06 90.30 8.70 28 Barbers-Hill 8.64 85.78 4.95 154 Bastrop 1.03 71.15 -1.06 60 Bay City 6.17 74.80 7.01 127 Beeville 2.12 74.52 1.30 143 Bellville 1.59 74.82 3.23 100 Belton 3.49 78.78 3.58 176 Big Spring 0.36 69.88 -0.05 30 Bishop Consolidated 8.49 84.40 7.19 134 Bloomington 1.92 71.22 1.32 132 Boerne 1.98 78.00 2.43 157 Borger 0.98 74.22 5.81 362 Boyd -11.76 59.10 -2.46 137 Brady 1.82 77.23 0.20 3 Brazosport 14.10 90.27 13.66 61 Breckenridge 6.12 77.85 2.35 346 Brenham -9.33 64.02 -7.62 189 Bridgeport -0.52 73.57 3.78 181 Brooks 0.16 67.43 5.13 276 Brownfield -4.40 66.07 -7.96 221 Brownsville -1.92 70.45 -3.19 199 Brownwood -1.07 72.05 1.10 233 Bryan -2.46 68.88 -0.40 11 Burnet Consolidated 11.36 83.65 8.21 79 Calallen 4.95 83.72 2.94 109 Caldwell 3.00 77.65 1.76 40 Calhoun County 7.46 80.68 9.49 125 Cameron 2.38 75.22 -2.17 288 Canutillo -5.09 65.85 -0.79 78 Canyon 5.06 83.13 7.31 300 Carrizo Springs Cons -5.57 65.88 -4.00 224 CarrolltonFarmers Br -2.10 73.95 -1.66 347 Castleberry -9.34 62.85 -13.17 326 Cedar Hill -7.70 73.43 -7.05 355 Celina -10.33 67.03 -5.97 358 Center -10.95 61.17 -18.21 237 Channelview -2.51 71.07 -1.17 364 Chapel Hill -12.36 57.22 -11.67 162 Childress 0.84 75.93 8.99 178 Clear Creek 0.28 80.50 -1.94 309 Cleburne -6.26 66.65 -6.40 370 Cleveland -17.01 51.15 -20.92 229 Clifton -2.35 . -0.91 149 Clint 1.28 71.48 0.86 16 Coleman 10.38 83.85 1.92 172 College Station 0.44 81.65 -2.70 211 Colorado -1.62 74.20 -3.74 24 Columbia-Brazoria 9.06 84.40 11.86 304 Columbus -5.81 71.50 -7.84 163 Comal 0.77 75.60 2.97 107 Comanche 3.26 78.75 4.34 155 Comfort 1.02 73.88 6.83 297 Community -5.48 63.40 -0.36 188 Connally -0.44 75.82 -4.65 291 Conroe -5.15 69.77 -1.94 111 Copperas Cove 2.85 81.68 2.06 182 Corpus Christi -0.01 73.72 -1.11 239 Corrigan-Camden -2.57 69.78 -0.55 264 Corsicana -3.84 66.97 -5.76 365 Cotulla -12.46 57.35 -7.96 75 Crane 5.12 82.68 2.35 320 Crockett -7.14 61.83 -14.58 202 Crosby -1.35 75.05 -0.50 51 Crowley 6.69 87.90 4.75 307 Crystal-City -6.21 59.20 -5.11 250 Cureo -3.04 72.95 0.81 201 Cypress-Fairbanks -1.34 77.60 -2.95 120 Dalhart 2.51 76.28 -4.54 357 Dallas -10.76 59.58 -10.78 198 Dayton -1.06 69.15 0.58 257 Decatur -3.46 71.38 -2.27 194 Deer Park -0.80 77.30 0.83 4 Del Valle 13.86 77.00 13.75 301 Denton -5.61 65.88 -6.12 50 Denver City 6.86 83.22 11.24 112 DeSoto 2.79 79.82 0.49 294 Devine -5.25 70.13 -5.96 191 Diboll -0.62 69.63 0.90 283 Dickinson -4.79 62.55 -2.33 167 Dilley 0.70 70.80 -1.13 216 Dimmitt -1.77 69.03 5.54 316 Donna -6.89 59.67 -9.02 305 Dublin -5.96 64.60 0.32 35 Dumas 7.98 76.18 7.98 159 Duncanville 0.88 75.63 -2.33 29 Eagle Pass 8.53 75.70 6.08 208 Eagle Mt-Saginaw -1.56 73.28 -6.57 45 Early 7.15 92.32 7.76 71 East-Chambers 5.36 79.07 -16.01 286 East Central -4.87 72.20 -7.09 34 Eastland 8.09 82.82 6.81 215 Ector County -1.72 66.38 -2.63 131 Edcouch-Elsa 2.01 75.88 2.01 92 Edgewood 3.95 70.60 3.63 93 Edinburg 3.95 73.70 2.94 139 Edna 1.72 76.45 -0.20 308 El Paso -6.26 66.75 -6.64 26 El Campo 8.77 83.57 5.38 282 Elgin -4.76 67.30 -4.76 225 Ennis -2.13 72.93 -1.25 66 Everman 5.87 81.38 5.26 290 Fabens -5.10 64.35 -0.08 366 Fairfield -12.59 61.20 -13.35 293 Farmersville -5.19 74.50 0.09 15 Ferris 10.56 82.88 5.80 146 Floresville 1.48 72.53 0.78 105 Flower Bluff 3.34 80.40 3.26 354 Floydada -10.12 61.35 -7.62 220 Fort Worth -1.88 64.63 2.36 311 Fort Bend -6.45 72.43 -7.25 312 Fredericksburg -6.48 68.22 -2.71 59 Freer 6.31 80.13 2.17 33 Frenship 8.11 84.18 6.34 110 Friona 2.92 77.70 3.22 89 Frisco 4.26 77.63 7.78 313 Ft Sam Houston -6.53 85.25 -4.93 218 Ft. Stockton -1.82 69.88 -5.90 328 Gainesville -7.78 65.25 -9.30 32 Galena Park 8.23 77.30 9.19 38 Galveston 7.65 73.05 6.14 203 Garland -1.46 73.22 -2.37 91 Gatesville 3.99 80.80 7.04 193 George West -0.77 76.15 2.78 361 Georgetown -11.38 65.88 -9.46 179 Giddings 0.18 76.68 1.82 268 Glen Rose -3.96 75.95 -0.55 363 Godley -12.19 59.95 -9.74 85 Goliad 4.54 80.05 6.41 360 Gonzales -11.17 60.13 -10.13 53 Goose Creek 6.67 77.20 7.65 113 Graham 2.70 78.78 3.78 77 Granbury 5.10 78.10 5.61 5 Grand-Saline 13.36 85.65 14.15 247 Grand Prairie -2.92 71.55 -3.58 284 Grand-View -4.82 75.93 0.01 73 Grape-Creek 5.24 76.82 2.68 295 Greenville -5.31 65.00 1.47 251 Greenwood -3.18 76.40 3.79 116 Gregory-Portland 2.62 82.82 2.50 101 Groesbeck 3.45 76.25 9.67 168 Harlandale 0.67 71.65 4.25 69 Harlingen 5.53 80.03 3.90 273 Hayes Consolidated -4.22 70.00 -2.00 343 Hearne -9.04 63.00 -13.45 352 Hempstead -10.07 64.00 -8.27 353 Henderson -10.11 64.65 -13.45 46 Hereford 7.02 78.63 14.83 18 Hidalgo 9.89 81.43 17.11 36 Hillsboro 7.88 74.45 6.01 359 Hitchcock -11.04 61.40 -8.53 222 Hondo -1.93 68.07 -0.23 217 Houston -1.79 65.98 -0.70 147 Hudson 1.45 77.28 -3.14 287 Humble -4.94 75.68 -6.04 246 Huntsville -2.91 72.30 -1.76 245 Hurst-Euless-Bedford -2.89 77.88 -5.57 230 Hutto -2.35 75.68 1.92 214 Ingleside -1.72 71.50 3.15 331 Ingram -7.91 67.72 -11.03 169 Irving 0.66 75.05 -1.46 41 Jacksboro 7.46 84.35 8.30 368 Jacksonville -13.87 53.15 -16.89 43 Jim Hogg County 7.36 82.20 7.29 260 Jourdanton -3.65 70.93 -0.24 275 Judson -4.36 73.30 -4.98 136 Karnes-City 1.83 74.65 6.72 196 Katy -0.94 81.70 -2.03 49 Kaufman 6.92 78.05 5.66 119 Kennedale 2.52 78.52 2.83 348 Kermit -9.70 60.03 -1.58 84 Kerrville 4.54 78.05 5.86 314 Kilgore -6.56 64.60 -11.45 232 Killeen -2.44 76.75 -5.07 102 Kingsville 3.45 75.63 5.43 329 Klein -7.79 75.20 -7.65 74 La Vega 5.17 74.07 4.69 70 La Joya 5.39 68.72 9.77 192 La Grange -0.65 72.13 -2.45 19 La Marque 9.71 80.45 9.56 25 La Feria 8.98 83.43 4.89 185 La Porte -0.09 78.07 4.86 274 La Vernia -4.22 76.38 -3.84 240 Lake Worth -2.57 61.88 -11.21 223 Lake-Travis -1.99 78.60 -4.52 135 Lake-Dallas 1.87 79.55 -2.34 138 Lamar Consolidated 1.73 74.85 0.42 319 Lamesa -7.11 61.03 -6.02 262 Lampasas -3.77 71.63 -11.34 272 Lancaster -4.18 65.10 -0.47 345 Laredo -9.33 65.53 -8.07 205 Leander -1.51 75.38 1.30 200 Levelland -1.17 72.63 -0.10 253 Lewisville -3.23 77.78 -5.45 306 Liberty -6.01 67.50 -11.50 56 Liberty-Hill 6.48 83.97 3.29 161 Little-Elm 0.85 68.63 -2.71 104 Littlefield 3.40 73.75 2.04 367 Livingston -13.77 60.58 -8.12 158 Llano 0.91 79.40 2.97 166 Lockhart 0.71 71.88 1.90 115 Longview 2.65 71.60 -1.44 2 Los Fresnos Consolid 14.49 89.32 11.81 108 Lubbock-Cooper 3.23 81.97 0.86 180 Lubbock 0.17 72.90 0.82 255 Lufkin -3.28 68.63 0.42 334 Luling -7.97 63.70 -4.35 266 Lyford -3.89 67.05 -3.04 80 Lytle 4.95 74.13 1.17 254 Madisonville -3.26 71.05 1.44 330 Magnolia -7.79 66.02 -11.02 356 Manor -10.44 58.60 -16.75 106 Mansfield 3.29 82.05 -1.94 259 Marble Falls -3.56 68.70 7.48 96 Marion 3.78 82.63 5.16 339 Marlin -8.60 61.38 -17.75 171 Marshall 0.65 72.57 -2.91 271 Mathis -4.15 61.70 -1.28 62 McAllen 6.11 78.80 6.05 14 McGregor 10.98 87.60 6.93 270 McKinney -3.98 66.70 6.02 322 Medina Valley -7.53 65.63 -12.29 261 Mercedes -3.71 70.90 -6.15 65 Merkel 5.88 82.65 11.74 174 Mesquite 0.42 76.03 -2.59 68 Mexia 5.60 79.52 11.80 296 Midland -5.47 65.30 -4.64 318 Midlothian -6.97 72.60 -1.57 129 Mineola 2.05 76.28 1.56 126 Mineral Wells 2.14 72.47 5.71 64 Mission Consolidated 6.01 80.95 2.54 12 Monahans-Wickett-Pyo 11.25 86.63 11.39 10 Mount Vernon 11.79 86.90 12.23 341 Mount Pleasant -8.84 59.83 -9.41 121 Muleshoe 2.44 75.75 8.12 338 Nacognoches -8.44 63.60 -6.34 122 Natalia 2.44 74.68 -5.16 213 Navasota -1.64 67.05 -1.08 256 Needville -3.35 77.00 -5.44 244 New Braunfels -2.89 72.32 -1.99 226 New-Caney -2.19 70.65 2.07 148 Newton 1.43 73.50 1.94 263 North Forest -3.78 64.47 -6.72 195 North East -0.86 77.82 -0.06 236 Northside -2.51 74.60 -3.06 184 Odem-Edroy -0.06 76.38 -5.51 87 Orange Grove 4.32 79.10 11.79 173 Palacios 0.43 79.10 4.59 298 Palestine -5.48 66.15 -8.29 117 Pampa 2.58 75.80 -0.59 142 Pasadena 1.60 75.63 1.47 52 Pearland 6.69 87.07 5.60 133 Pecos-Barstow-Toyah 1.97 71.02 0.04 141 Perryton 1.70 75.60 2.06 210 Pflugerville -1.60 79.80 -0.82 94 Pharr-San Juan-Alamo 3.93 76.18 3.46 333 Pilot-Point -7.96 66.98 -6.28 187 Pine Tree -0.36 71.73 3.13 9 Pittsburgh 12.19 81.75 10.43 44 Plainview 7.35 77.43 6.29 340 Plano -8.79 74.10 -8.66 160 Pleasanton 0.86 71.77 4.72 6 Point Isabel 13.21 82.18 14.20 150 Port Arthur 1.28 64.85 4.89 350 Poteet -9.96 63.17 -11.35 177 Presidio 0.33 61.80 5.17 241 Princeton -2.74 66.88 2.42 234 Progreso -2.46 65.52 2.62 212 Randolph Field -1.64 91.07 -6.38 86 Raymondville 4.41 73.50 8.33 349 Red Oak -9.93 69.15 -9.08 88 Rice Consolidated 4.26 73.65 -0.74 335 Richardson -8.00 69.48 -7.57 249 Rio-Grande-City -3.01 63.60 -2.41 67 Rio Hondo 5.73 79.05 5.99 97 Robinson 3.70 85.50 2.74 47 Robstown 6.92 75.15 5.19 269 Rockdale -3.97 72.03 -2.63 209 Rockwall -1.58 74.70 -5.81 227 Roma -2.21 62.38 -5.88 183 Roosevelt -0.05 74.82 3.61 17 Rosebud-Lott 10.35 85.88 5.02 242 Round Rock -2.83 77.20 -2.39 235 Royal -2.49 67.55 -1.39 83 Royse City 4.56 77.30 4.76 190 San Felipe-Del Rio C -0.53 72.32 -2.26 369 San Elizario -14.59 55.33 -6.01 278 San Angelo -4.47 69.65 -4.74 327 San Diego -7.76 58.10 -15.55 13 San Benito Consolida 11.07 81.10 10.81 58 San Marcos 6.40 76.43 7.42 344 San Antonio -9.05 63.45 -7.93 289 Sanger -5.09 70.75 -10.05 267 Santa Rosa -3.93 68.32 1.62 281 Santa-Fe -4.74 71.80 -2.41 285 Schertz-Cibolo-U. Ci -4.85 72.30 -2.47 165 Sealy 0.72 75.10 -1.04 302 Seguin -5.72 65.30 -3.30 123 Seminole 2.43 77.28 5.49 197 Shallowater -0.94 76.00 2.96 57 Sharyland 6.46 81.65 7.86 342 Sheldon -8.86 66.40 -4.87 336 Sherman -8.23 63.58 -1.10 128 Sinton 2.07 72.55 2.76 277 Slaton -4.44 70.05 -0.89 299 Smithville -5.56 64.10 -3.70 118 Snyder 2.56 77.88 1.93 175 Somerset 0.37 72.20 1.23 20 South Texas 9.43 93.98 5.67 231 South San Antonio -2.40 73.40 -2.22 303 Southside -5.78 59.80 -0.19 144 Southwest 1.51 70.57 3.36 258 Spring Branch -3.51 67.70 -0.67 243 Spring -2.87 76.57 -5.43 292 Stafford MSD -5.19 71.80 -8.70 156 Stephenville 1.01 79.65 -3.08 95 Sulpher Springs 3.82 78.32 2.37 37 Sweeny 7.68 86.35 9.07 153 Taft 1.16 71.63 2.49 228 Tatum -2.31 71.55 -2.62 206 Taylor -1.53 72.63 -5.86 324 Teague -7.64 70.45 6.82 321 Temple -7.33 67.45 -5.55 42 Terrell 7.46 78.43 1.76 55 Texas City 6.54 79.63 4.49 140 Tomball 1.71 76.35 2.82 238 Troy -2.54 75.60 -1.04 151 Tulia 1.25 75.20 0.63 21 Tuloso-Midway 9.43 81.57 7.93 317 Tyler -6.94 65.45 -3.03 99 United 3.52 71.63 2.65 248 Uvalde Consolidated -3.00 66.13 0.74 7 Valley-View 12.96 84.57 11.69 265 Venus -3.86 66.43 -6.09 145 Vernon 1.49 75.35 2.30 130 Victoria 2.02 74.55 2.01 114 Waco 2.69 71.07 5.08 351 Waller -10.04 59.47 -10.48 207 Waxahachie -1.55 75.80 -3.51 279 Weatherford -4.52 70.60 2.55 48 Weslaco 6.92 81.40 5.71 90 West Oso 4.10 72.80 2.26 98 West-McLennan 3.66 77.98 8.30 124 White Settlement 2.39 76.80 -0.78 72 Whorton 5.28 75.30 4.42 103 Wichita Falls 3.41 77.72 2.14 63 Willis 6.08 75.45 7.38 252 Wilmer-Hutchins -3.21 57.63 -3.36 152 Wylie-Collin 1.22 77.68 1.80 280 Yoakum -4.70 75.80 -8.46 23 Ysleta 9.20 81.65 7.12 186 Zapata -0.24 65.57 -6.94 )JZ[‰Ą^   ]e„Œ×ÝáęˆÜn,v,”4œ4°4ś4Ź5ł5Š@•@LEMENEOEYEGFnFĄFžFüF)GĂGĘGÍG÷G˝HÔH=IZIÔIňIWJ˛JMKfKýKXLžLÁLóL MnMMŰMýMP&P˝PŰP2QJQĐQŕQŽR¨RˇRëR/SMSoTŠTŸTŇTűřňűňűäűňűŢűŢűŢűŢűŢűňűňűŢűŢűŢűňűřűřňűŢűŢűŢűŢűŢűŢűŢűŢűŢűŢűŢűŢűŢűŢűŢűŢűŢűŢűŢűŢűŢűŢűŢűÔ5CJOJQJnH 6CJnH0J5>*B*CJnHph˙ 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BEST SCHOOL DISTRICTS IN TEXAS Nick Theobald Nick Theobald1^Î1 Ë1lÎ1$Ë1bË1˘Ë1ôË1nÎ1Ě1rÎ1Ě12Ě1ˆĚ1ĆĚ1ČĚ1‚Í1„Í1†Í1*@ 0ˆÍ1,@ 0#@ 0#@1Č#@1Ä1˛'@1Ä1Ć'@1"Â0ę'@1"ž1f^@1*ž18ž1>ž18_@1@ž1l_@1Jž1`ž1Č_@1†ž0b@1šž1vg@1îž1Xh@1xż1Šż1Žj@1’ż1ža8_@a@žal_@aJža`ž€˜0€€˜0€€˜0€€˜0€€˜0€€˜0€€˜0€€˜0€€˜0€€˜0€€˜0€€˜0€€˜0€€˜0€€˜0€€˜0€€˜0€€˜0€€˜0€€˜0€€˜0€€˜0€€˜0€€˜0€€˜0€€˜0€€˜0€€˜0€€˜aČ_@a†ž`b@ašžavg@aîžaXh@axżaŠżaŽj@a’żahń|•­ĽŔŔČÎŇ×Ű˙˙Unknown Nick Theobald|„ ! ­ ´ w ~ ž Ľ B H Č Î ; C j r Ü ä ć ě ž¤žĆŮá#iq~ˆ{ € K1R15599Ž@”@6A;A!B)B1B8BMBTBUBZB_BfBjBqBłBżBđBřBtC|CDDÁDÉDăDëDgFoF‰F‘FŕFćF}HH˘IŞIľI˝IžIÂI`JiJŠJ•JóJúJKKŠK“KßKĺKţKLěMňMŞRąR˛RşRăRíR UUQUZUˇV˝V/W5WŠWŻW&X+X ZZÉ\Ó\Y]`]a]i]y]~]^^W^`^Ü^ŕ^"_(_``BaFa|a„apcucYjcjťlĂlfnwn¤sŹs€v…vâxčxi}q}×~Ţ~łť_ˆiˆ/Œ4Œöúp‘v‘­‘˛‘'’-’U“[“†””ě–ó–ô–ü–Ü—â—cœhœ!ŃĺĹžĐžŸ Ÿ|Ÿ…Ÿ˝ŸĆŸę ó ,Ą1Ą˘ ˘ŕ˘ĺ˘eŚmŚđŞřŞŽŤˇŤ#­%­‹­‘­ŽŽBŽKޞ˛î´ô´ ¸¸ž¸Ĺ¸,ş2ş˘şŤşŮ Ú Ü ä í ú aa5)9)˘)­)Ç)Ě)ç)ň)**ř*+Š-´- .P.^/k/˝/Â/í/ď/ţ/01#14191G1R1Z1_1ł2´2Ľ4Ś45595:5E5G5k5w5ž5Ÿ5ł5ľ56666O6S6ý8999!9,9.929ĄşŤş˙˙ Nick TheobaldJNickfitX:Users:theobald:Documents:Microsoft User Data:Word Work File A_934 Nick TheobaldJNickfitX:Users:theobald:Documents:Microsoft User Data:Word Work File A_934 Nick TheobaldJNickfitX:Users:theobald:Documents:Microsoft User Data:Word Work File A_934 Nick TheobaldHNickfitX:Users:theobald:Documents:Microsoft User Data:Word Work File A_5 Nick TheobaldHNickfitX:Users:theobald:Documents:Microsoft User Data:Word Work File A_5 Nick TheobaldHNickfitX:Users:theobald:Documents:Microsoft User Data:Word Work File A_5 Nick TheobaldHNickfitX:Users:theobald:Documents:Microsoft User Data:Word Work File A_5 Nick TheobaldAData:Documents:Grunt_Work:Meier:kmeier:teep:reports:report018.doc Nick TheobaldAData:Documents:Grunt_Work:Meier:kmeier:teep:reports:report018.doc Nick TheobaldAData:Documents:Grunt_Work:Meier:kmeier:teep:reports:report018.docĄş˘ş¨şŤş:‘$:‘$:‘$:‘$:‘$Ź$Ź CDbcduvˆ‰ŽŸ Ą˛łŘŮńňóô+,?ź˝uvwx şťTUž Ÿ   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spending by $250 per studentlarge increase instructional expenditures, especially large districtsDel Valle ISD63.147713.86Bangs ISD15.06Los Fresnos14.49nty five best districts can measure and improve their own performance. 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Meier Robert D. Wrinkle and Nick A. Theobald A REPORT OF THE TEXAS EDUCATIONAL EXCELLENCE PROJECT NUMBER 18 SEPTEMBER 15 For further information, contact: http://teep.tamu.edu 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. The project 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 Latino Students 1998-2001 The education of minority students is of primary concern for education leaders and policy-makers in Texas. While Latino students have made impressive gains in the last decade, they continue to lag behind Anglo students in the state’s fundamental measurement of basic skills, the TAAS. In 1991 41.5 percent of Latino students passed the TAAS, compared with 68.9% for Anglo students, a gap of 27.4 percentage points. Ten years later, Latino students had reduced to deficit to 14.8 percentage points, scoring an average pass rate of 75.6% in 2001 compared to the average Anglo pass rate that year of 90.4%. Obviously, Latino students are narrowing the gap. However, these overall gains at the state level, while impressive, are not equally distributed across all districts. Some Latino school districts have made even more impressive gains while others have fallen behind. It is the aim of the Texas Educational Excellence Project to identify school districts that do a better job of educating Latino students. The programs and policies used by the exemplary districts then may be used as a standard by which other districts can measure and improve their own performance. Bangs ISD is an example of one such exemplary district. In 2001, 89.1% of Latino students in Bangs the TAAS. This high pass rate for Latino students helps the district achieve the highest score in our ranking system. Los Fresnos Consolidated is another example of an exemplary district, ranking in the top five for the past five years. 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Second is a dedicated faculty at each campus that expects every student to be successful regardless of ethnicity. Third is a close-knit student body that values education and is proud to be part of an exemplary campus. district s ESL programs, faculty and staff holding high expectations for all students. 0°Đ/ °ŕ=!° "° # $ %° contribute to their success;ocampus,sy,t districts for 2001Hildago118Orange Grove1877914.329.289.20Galena Park8.23 0°Đ/ °ŕ=!° "° # $ %°also recently  students1 Draft Ä4Ć6Ć8ĆŠÍŒÍŽÍvÎĐýýűőőűűű„Đ`„Đ passedBilly Rankin, Bang s superintendent, credits three factors that contribute to their success:  One is a strong ESL program at each campus. Second is a dedicated faculty at each campus that expects every student to be successful regardless of ethnicity. Third is a close-knit student body that values education and is proud to be part of an exemplary campus. district s ESL programs, faculty and staff holding high expectations for all students. 0°Đ/ °ŕ=!° "° # $ %° contribute to their success;ocampus,sy,t