ࡱ> G pbjbjَ (p]=PkP$ X^< R# THE BEST SCHOOL DISTRICTS IN TEXAS tc \l1 "THE BEST SCHOOL DISTRICTS IN TEXAS FOR LATINO STUDENTS 1996-1999 Robert D. Wrinkle and Nick A. Theobald A REPORT OF THE TEXAS EDUCATIONAL EXCELLENCE PROJECT NUMBER 10 JULY 2000 For further information, contact: http:www-bushschool.tamu.edu/kmeier/teep 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 1996-1999 The education of minority students is of primary concern for education leaders and policy-makers in Texas. Latino students continue to lag behind Anglo students in the states fundamental measurement of basic skillsthe TAAS. In 1999, 70.1 percent of Latino students passed the TAAS, compared with 89.7% for Anglo students. This compares to scores of 54.2% for Latinos and 79.8% for Anglos in 1996. 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. The Los Fresnos Consolidated school district is an example of one such exemplary district. In 1999, 84.15% of Latino students in Los Fresnos passed the TAAS, an improvement of almost ten percent over the 1996 pass rate of 74.9%. The Los Fresnos district attributes much of their success to the way the teachers, staff and parents work together. A special focus has been on the continual development of an aligned curricula for the entire district. Los Fresnos is a relatively small school district. As such, many of their programs and approaches might not immediately transfer to other, larger districts. However, Brazosport, a much larger district, also has an impressive record of educating Latino students. Many of their programs are not targeted specifically at Latino students, but rather at all students. By focusing on improvements for all students, the Brazosport ISD contributes to the education of minority students as well. 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 very 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 districts 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 1996-1999 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 Hanusheks 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 years 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 1996-1999. 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, many of the independent variables are powerful predictors of Latino student performance. Eight 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 and percentage of Latinos with at least a high school education. Percentage of poor students, noncertified teachers and the percentage of Latino poverty in the school district are negatively related to performance. No other independent variable achieved statistical significance. 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 teacher salaries by about $2,000 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 Anahuac. In 1999, they were predicted to have a Latino pass rate of 62.61, while their actual pass rate of 77.93 was a 15.32% improvement. 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 was the Los Fresnos Consolidated School District with a score of 17.41, followed by Pittsburg with a 15.88 score. The top 35 districts are shown in Table 2. The first column is the numerical score over the 1996-1999 period by which the districts are ranked. The second column is the 1999 score and the third column is the average pass rate for Latino students for the 1996-1999 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 1996 through 1999 there may be districts that have improved greatly over the last year that are not ranked well. The twenty five best districts for 1999 are listed in Table 3. There are a few districts that seem to have made great strides in the last year, such as Burnet Consolidated which ranks 3rd for 1999, but only 52nd over the four year period. The Willis school district ranked 23rd in 1999 compared to ranking 152nd for the four year period. This is a result of the district showing a 10.90% improvement over the 1999 expected pass rate compared to performing 0.48% below 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 Brazosport, with a 1996-1999 score of 13.70, followed by Aldine (11.41) and Mission Consolidated (10.25). 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 districts 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). This paper focuses on those school districts that have done an exemplary job of educating Latino students. By recognizing districts which have a better than expected level of performance on the TAAS, a set of role models for other districts has been identified. 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. 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. TABLE 1: LATINO EDUCATIONAL PRODUCTION FUNCTION Variable Coefficient Standard Error Low Income -.0511 .0171 Gifted .2124 .0626 Attendance 3.6555 .2930 Teacher Salary K .4360 .1705 Class size -.0200 .2352 Temporary Teacher Certification -.2162 .0713 Teacher Experience .2242 .1687 State Aid .0281 .0125 High School Education .0988 .0297 %Poverty Background -.0731 .0248 Per Pupil Instructional .0013 .0010 R2 (adj)= .55 F= 102.80 significance of F < .000 Table 2. 35 Best Districts for Latino Students SCORE 99 SCORE AVERAGE 1 Los Fresnos Consolidated 17.41 15.00 84.15 2 Pittsburg 15.88 15.69 75.50 3 Anahuac 15.32 16.85 77.93 4 Mount Vernon 14.26 13.09 80.50 5 South Texas 14.09 8.26 90.93 6 Ferris 13.79 14.55 76.43 7 Brazosport 13.70 10.58 83.10 8 San Benito Consolidated 13.24 9.02 74.20 9 TulosoMidway 12.43 12.34 73.20 10 White Settlement 12.25 6.06 78.20 11 Terrell 11.93 14.54 76.73 12 Del Valle 11.72 11.56 69.85 13 Aldine 11.41 8.25 74.93 14 Texas City 11.32 9.04 75.82 15 Point Isabel 11.24 10.11 73.95 16 McGregor 10.93 9.75 80.18 17 Coleman 10.55 17.93 74.90 18 Mission Consolidated 10.25 7.03 77.00 19 ColumbiaBrazoria 10.04 9.11 77.02 20 MonahansWickettPyo 9.95 11.71 75.43 21 Angleton 9.53 12.74 79.78 22 Ysleta 9.51 9.55 73.77 23 Alvarado 9.20 7.69 71.05 24 Pearland 9.01 7.95 79.65 25 La Feria 8.84 10.89 75.85 26 Crowley 8.78 6.43 81.50 27 Kaufman 8.36 9.83 69.28 28 Plainview 8.35 9.06 70.60 29 Royse City 8.34 12.16 75.30 30 Mexia 8.30 11.05 71.00 31 Bishop Consolidated 8.25 15.94 72.78 32 Edna 8.17 1.58 72.93 33 Hidalgo 8.16 8.34 70.78 34 Breckenridge 8.07 9.42 71.45 35 Weslaco 8.00 5.86 73.68 Table 3. The Best Districts in 1999 1 Coleman 17.93 2 Anahuac 16.85 3 Burnet Consolidated 16.03 4 Bishop Consolidated 15.94 5 Pittsburg 15.69 6 Los Fresnos Consolidated 15.00 7 Merkel 14.60 8 Ferris 14.55 9 Terrell 14.54 10 Mount Vernon 13.09 11 Crane 12.92 12 Angleton 12.74 13 TulosoMidway 12.34 14 Royse City 12.16 15 Calhoun County 11.97 16 La Marque 11.75 17 MonahansWickettPyo 11.71 18 Hillsboro 11.63 19 Del Valle 11.56 20 Mexia 11.05 21 La Feria 10.89 22 Brazosport 10.58 23 Willis 10.30 24 El Campo 10.26 25 Point Isabel 10.11 Table 4. The Best Large School Districts Rank Name Score 99 Score Average 1 Brazosport 13.70 10.58 83.10 2 Aldine 11.41 8.25 74.93 3 Mission Consolidated 10.25 7.03 77.00 4 Ysleta 9.51 9.55 73.77 5 Weslaco 8.00 5.86 73.68 6 Goose Creek 5.83 6.23 68.22 7 Harlingen 5.70 4.36 72.60 8 McAllen 5.61 2.67 71.88 9 Eagle Pass 5.59 7.08 66.20 10 Alvin 5.11 7.13 68.77 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 Name Score 99 Score Average 185 Abilene 1.57 0.05 64.22 293 Alamo Heights 8.94 7.51 66.82 13 Aldine 11.41 8.25 74.93 81 Alice 3.75 5.91 63.60 242 Alief 4.50 7.87 63.13 154 Alpine 0.30 0.80 67.88 23 Alvarado 9.20 7.69 71.05 66 Alvin 5.11 7.13 68.77 96 Amarillo 3.05 2.29 65.55 3 Anahuac 15.32 16.85 77.93 74 Andrews 4.29 2.57 70.47 21 Angleton 9.53 12.74 79.78 73 Aransas County 4.49 1.73 67.88 241 Aransas Pass 4.39 1.16 56.05 285 Arlington 8.00 7.55 61.92 299 Austin 9.96 13.56 51.83 141 Ballinger 1.04 6.28 68.50 198 Bandera 2.13 3.76 65.10 199 Bastrop 2.15 0.50 59.53 58 Bay City 5.63 2.25 65.90 128 Beeville 1.46 0.39 64.40 203 Bellville 2.37 2.98 61.35 142 Belton 1.00 4.44 67.25 124 Big Spring 1.65 2.39 64.05 143 Birdville 0.99 1.89 72.88 31 Bishop Consolidated 8.25 15.94 72.78 170 Boerne 0.56 0.21 67.53 103 Borger 2.68 1.35 67.40 62 Brady 5.33 5.86 69.05 7 Brazosport 13.70 10.58 83.10 34 Breckenridge 8.07 9.42 71.45 144 Bridgeport 0.93 1.75 67.25 259 Brooks 6.02 0.30 54.55 206 Brownfield 2.60 3.18 58.08 189 Brownsville 1.84 1.53 61.70 127 Brownwood 1.48 3.39 64.50 255 Bryan 5.71 2.98 59.20 52 Burnet Consolidated 6.10 16.03 69.20 46 Calallen 6.69 4.84 76.63 92 Caldwell 3.14 0.60 68.55 100 Calhoun County 2.69 11.97 65.70 110 Cameron 2.30 5.30 64.90 263 Canutillo 6.34 7.89 54.83 179 Carrizo Springs Cons 1.22 1.09 58.17 194 CarrolltonFarmers Br 2.00 1.70 68.30 197 Castleberry 2.11 4.24 61.78 161 Cedar Hill 0.18 10.64 72.20 122 Center 1.75 8.27 63.13 157 Channelview 0.06 4.54 66.68 42 Childress 7.12 3.47 70.53 93 Clear Creek 3.11 3.86 75.38 256 Cleburne 5.83 6.60 60.20 309 Cleveland 14.96 15.24 43.50 107 Clint 2.46 2.47 64.78 17 Coleman 10.55 17.93 74.90 237 Colorado 4.15 5.01 61.33 19 ColumbiaBrazoria 10.04 9.11 77.02 227 Columbus 3.47 6.70 62.70 211 Comal 2.78 3.55 63.63 191 Comanche 1.92 7.85 67.25 71 Connally 4.62 0.31 70.30 269 Conroe 6.80 6.42 59.67 65 Copperas Cove 5.16 3.55 74.15 131 Corpus Christi 1.29 0.10 66.47 137 CorriganCamden 1.12 3.32 63.83 207 Corsicana 2.64 0.98 59.83 304 Cotulla 11.49 11.94 47.45 54 Crane 6.04 12.92 74.95 26 Crowley 8.78 6.43 81.50 252 Cureo 5.21 5.17 59.70 149 CypressFairbanks 0.53 1.30 71.43 41 Dalhart 7.22 6.49 71.55 265 Dallas 6.49 11.85 56.55 214 Decatur 3.00 1.33 62.15 209 Deer Park 2.73 0.66 66.78 12 Del Valle 11.72 11.56 69.85 261 Denton 6.19 4.35 59.33 106 Denver City 2.56 3.93 71.72 246 Devine 4.82 3.52 63.38 201 Diboll 2.34 4.53 59.75 289 Dickinson 8.71 7.99 50.35 125 Dilley 1.56 1.00 63.85 250 Dimmitt 5.15 8.06 58.05 192 Donna 1.92 6.07 57.15 305 Dublin 11.59 8.02 50.50 72 Dumas 4.56 5.96 64.80 136 Duncanville 1.16 1.19 70.45 60 Eagle Pass 5.59 7.08 66.20 123 Eagle MtSaginaw 1.71 5.90 70.85 216 East Central 3.03 4.22 66.38 48 Eastland 6.52 7.77 72.47 109 Ector County 2.38 3.13 60.95 83 EdcouchElsa 3.48 1.25 70.32 146 Edgewood 0.67 4.43 58.15 77 Edinburg 3.99 1.59 67.28 32 Edna 8.17 1.58 72.93 223 El Paso 3.40 8.22 61.40 40 El Campo 7.24 10.26 72.13 213 Elgin 2.96 0.80 60.92 165 Ennis 0.32 0.43 64.70 63 Everman 5.24 4.14 73.35 276 Fabens 7.45 6.24 55.80 6 Ferris 13.79 14.55 76.43 134 Floresville 1.23 3.38 62.95 86 Flower Bluff 3.31 2.86 72.00 306 Floydada 11.92 5.78 49.60 268 Fort Bend 6.67 5.91 64.40 275 Fort Worth 7.44 4.14 52.63 298 Fredericksburg 9.94 13.33 56.10 64 Freer 5.19 8.09 71.88 37 Frenship 7.67 9.85 73.93 159 Friona 0.05 4.91 65.20 102 Frisco 2.68 3.05 68.90 282 Ft Sam Houston 7.70 7.48 73.27 78 Ft. Stockton 3.98 0.17 63.80 182 Gainesville 1.47 4.41 63.13 98 Galena Park 2.89 9.32 65.47 61 Galveston 5.33 9.71 63.53 114 Garland 2.09 0.32 69.45 79 Gatesville 3.91 6.13 72.70 240 George West 4.32 0.05 63.38 297 Georgetown 9.72 17.63 60.90 174 Giddings 0.81 2.17 66.63 243 Glen Rose 4.53 11.60 66.38 43 Goliad 7.07 2.11 72.82 272 Gonzales 7.15 6.52 54.70 56 Goose Creek 5.83 6.23 68.22 200 Graham 2.25 5.57 64.03 147 Grand Prairie 0.64 2.86 67.47 236 Greenville 4.11 3.52 58.72 278 Greenwood 7.54 5.75 62.55 91 GregoryPortland 3.15 1.60 75.28 105 Groesbeck 2.61 0.83 66.97 258 Harlandale 5.85 5.02 58.78 57 Harlingen 5.70 4.36 72.60 284 Hayes Consolidated 7.89 8.58 59.60 167 Hearne 0.39 1.88 61.40 300 Hempstead 10.42 6.24 50.88 121 Hereford 1.78 5.49 64.90 33 Hidalgo 8.16 8.34 70.78 36 Hillsboro 7.99 11.63 66.90 291 Hitchcock 8.85 12.09 54.53 226 Hondo 3.47 0.02 58.70 193 Houston 1.98 5.44 58.28 68 Hudson 4.95 7.74 70.25 247 Huntsville 4.93 1.14 61.55 235 Ingleside 4.02 3.54 62.42 270 Ingram 6.85 5.13 61.65 132 Irving 1.28 2.78 68.43 307 Jacksonville 12.52 12.77 46.33 45 Jim Hogg County 6.86 4.28 74.38 224 Jourdanton 3.42 6.78 64.10 210 Judson 2.76 5.45 69.02 116 Katy 2.01 1.72 76.45 27 Kaufman 8.36 9.83 69.28 273 Kenedy 7.21 4.15 52.47 290 Kermit 8.76 12.99 53.45 75 Kerrville 4.16 6.10 69.82 172 Killeen 0.70 2.04 70.00 155 Kingsville 0.13 4.78 64.48 233 Klein 3.87 9.05 70.32 90 La Marque 3.23 11.75 66.93 281 La Vernia 7.61 13.89 61.55 158 La Joya 0.01 0.16 57.97 115 La Porte 2.07 1.44 71.43 178 La Grange 1.20 0.98 62.63 171 La Vega 0.69 4.72 61.35 25 La Feria 8.84 10.89 75.85 173 Lake Worth 0.76 2.29 55.63 112 Lamar Consolidated 2.19 3.98 66.20 217 Lamesa 3.05 0.37 55.63 120 Lampasas 1.79 1.26 66.70 248 Lancaster 5.04 7.20 59.58 262 Laredo 6.20 8.77 59.55 218 Leander 3.19 4.79 66.38 222 Levelland 3.38 0.65 62.67 204 Liberty 2.44 1.67 61.63 44 Littlefield 7.02 7.21 66.70 111 Lockhart 2.26 1.78 64.80 1 Los Fresnos Consolid 17.41 15.00 84.15 196 Lubbock 2.07 1.10 63.15 95 LubbockCooper 3.10 7.37 71.22 260 Lufkin 6.14 6.70 57.22 249 Luling 5.11 8.82 56.03 187 Lyford 1.83 3.17 60.83 104 Lytle 2.66 3.98 66.63 183 Madisonville 1.48 0.43 62.65 245 Manor 4.71 13.55 56.92 89 Mansfield 3.24 4.58 72.65 230 Marble Falls 3.78 4.81 58.65 190 Marlin 1.86 8.68 58.72 283 Mathis 7.88 5.77 49.83 59 McAllen 5.61 2.67 71.88 16 McGregor 10.93 9.75 80.18 280 McKinney 7.55 4.66 54.85 221 Medina Valley 3.35 9.49 62.53 184 Mercedes 1.52 0.68 65.00 51 Merkel 6.11 14.60 74.32 145 Mesquite 0.84 2.18 68.20 30 Mexia 8.30 11.05 71.00 267 Midland 6.51 6.04 55.50 308 Midlothian 13.88 6.85 57.70 50 Mineola 6.14 7.96 71.75 160 Mineral Wells 0.17 0.88 61.10 18 Mission Consolidated 10.25 7.03 77.00 20 MonahansWickettPyo 9.95 11.71 75.43 4 Mount Vernon 14.26 13.09 80.50 205 Mount Pleasant 2.54 11.76 58.83 168 Muleshoe 0.47 5.71 62.10 295 Nacognoches 9.08 4.57 54.72 234 Navasota 3.97 2.70 56.90 140 Needville 1.05 3.62 70.72 220 New Braunfels 3.29 7.71 64.30 129 Newton 1.46 3.73 65.98 164 North East 0.29 1.73 71.03 87 North Forest 3.29 8.16 63.95 219 Northside 3.29 4.93 67.00 138 OdemEdroy 1.10 0.91 68.85 232 Orange Grove 3.87 0.15 63.78 176 Palacios 1.16 0.79 68.32 238 Palestine 4.23 1.71 58.05 49 Pampa 6.23 1.90 72.18 148 Pasadena 0.59 1.71 64.97 24 Pearland 9.01 7.95 79.65 38 PecosBarstowToyah 7.66 3.12 67.72 135 Perryton 1.20 3.75 68.25 162 Pflugerville 0.19 2.13 73.28 84 PharrSan JuanAlamo 3.37 2.93 67.78 133 Pine Tree 1.27 2.11 65.60 2 Pittsburg 15.88 15.69 75.50 28 Plainview 8.35 9.06 70.60 212 Pleasanton 2.83 2.40 60.20 15 Point Isabel 11.24 10.11 73.95 126 Port Arthur 1.52 2.13 57.15 76 Post 4.01 5.61 70.02 296 Poteet 9.23 7.43 54.42 188 Presidio 1.84 3.13 54.63 108 Randolph Field 2.43 3.96 83.43 151 Raymondville 0.43 6.42 60.95 166 Reagan County 0.32 2.19 68.00 288 Red Oak 8.71 18.48 62.35 53 Rice Consolidated 6.05 8.80 65.72 279 Richardson 7.55 6.35 62.65 139 Rio Hondo 1.08 4.03 66.30 55 Robinson 5.83 7.37 76.77 80 Robstown 3.85 5.04 64.77 215 Rockdale 3.00 1.70 62.25 177 Roosevelt 1.17 4.76 64.00 202 Round Rock 2.35 4.42 70.25 195 Royal 2.04 0.32 55.10 29 Royse City 8.34 12.16 75.30 303 San Antonio 11.27 10.43 52.03 8 San Benito Consolida 13.24 9.02 74.20 225 San Angelo 3.43 6.57 62.13 292 San Diego 8.87 7.31 50.47 99 San Marcos 2.78 6.47 64.73 310 San Elizario 16.78 20.82 43.80 82 San FelipeDel Rio C 3.66 2.18 65.60 254 Santa Rosa 5.38 3.58 55.42 274 SchertzCiboloU. Ci 7.25 7.46 61.10 150 Sealy 0.52 3.15 66.20 264 Seguin 6.39 8.54 57.55 186 Seminole 1.75 1.70 65.93 208 Shallowater 2.67 1.72 64.38 67 Sharyland 5.07 3.89 73.40 277 Sheldon 7.46 14.18 60.35 156 Sinton 0.08 1.92 63.78 244 Slaton 4.59 1.59 59.15 294 Smithville 9.04 3.19 54.38 101 Snyder 2.69 5.06 68.18 88 Socorro 3.25 0.93 68.10 266 Somerset 6.50 0.75 57.63 286 Sonora 8.03 0.18 60.83 231 South San Antonio 3.80 2.54 63.38 5 South Texas 14.09 8.26 90.93 302 Southside 11.01 8.57 46.90 228 Southwest 3.57 1.40 57.58 239 Spring 4.26 3.69 69.22 229 Spring Branch 3.76 6.22 60.10 251 Stafford MSD 5.16 0.04 64.13 119 Stephenville 1.93 3.20 70.65 39 Sweeny 7.25 8.51 75.75 175 Taft 1.16 2.65 57.45 47 Tatum 6.58 1.62 72.18 163 Taylor 0.21 6.40 64.13 311 Teague 21.98 8.81 45.85 287 Temple 8.40 9.00 58.13 11 Terrell 11.93 14.54 76.73 14 Texas City 11.32 9.04 75.82 152 Troy 0.39 3.52 69.35 69 Tulia 4.83 1.85 67.90 9 TulosoMidway 12.43 12.34 73.20 271 Tyler 6.93 10.32 57.42 85 United 3.35 4.23 64.28 257 Uvalde Consolidated 5.83 5.55 55.47 181 Van Vleck 1.43 4.63 68.90 97 Vernon 2.93 2.81 67.45 94 Victoria 3.10 2.74 65.28 180 Waco 1.29 2.93 57.13 301 Waller 10.72 12.72 51.05 118 Waxahachie 1.98 2.49 68.70 35 Weslaco 8.00 5.86 73.68 113 West Oso 2.10 0.32 61.70 10 White Settlement 12.25 6.06 78.20 117 Whorton 2.00 0.07 64.13 70 Wichita Falls 4.80 3.40 70.50 169 Willis 0.48 10.30 61.40 153 WilmerHutchins 0.39 7.82 57.45 253 Yoakum 5.29 1.33 61.90 22 Ysleta 9.51 9.55 73.77 130 Zapata 1.41 0.44 62.17 *+UVWx ^dhqb&&..////44440525S5U57:B:K>M>~>>>?AtAAAABJJNNNNNbQQQ-RRRS4SSS TdTT(UUJV0J60J6OJQJ >*OJQJ5>*OJQJ 5OJQJCJH*6CJ5CJCJ0JB*5 jUL*WXvwxy$1$1$1$*WXvwxy>?R a b OP234abKLNO!!!f#g#&&&&((,,,F1G1s3t366:9;96:7:B:C:w<x<y<L>M>}>~>>>>>b>?R a b OP234ab1$$$1$1$bKLNO!!!f#g#&&&&((,,,F1G1s3t3661$1$$1$6:9;96:7:B:C:w<x<y<L>M>}>~>>>>>!?"?V?W?????? @$$1$1$>!?"?V?W?????? @ @@G@H@|@}@@@@@@@ A=A>A?AuAvAAAAAAAAAAABB[BBBCSCTCCCDLDDDDEEEEEEF>F|FFFF7GuGGG/H0HnHHH(IfIgIII!J_JJJJJJJJJK8K^KKKKKKLCLDLjLd @ @@G@H@|@}@@@@@@@ A=A>A?AuAvAAAAAAAAAAAB$BB[BBBCSCTCCCDLDDDDEEEEEEF>F|FFFF7GuGGGG/H0HnHHH(IfIgIII!J_JJJJJJJJJK8K^KKKKKKL$LCLDLjLLLLMM)MOMuMMMMMN4NZNNNNNNNNNNNNjLLLLMM)MOMuMMMMMN4NZNNNNNNNNNNNNNNNO=OzOOO1PnPPP%QbQcQdQeQfQgQhQiQjQkQlQmQnQoQpQqQrQsQtQuQvQwQxQyQzQ{Q|Q}Q~QQQQQQQRRRRASBSSSTTUUJVKV7W8WWWWWXZXXXYRYdNNNNO=OzOOO1PnPPP%QbQcQdQeQfQgQhQiQjQkQlQmQnQoQpQqQ$qQrQsQtQuQvQwQxQyQzQ{Q|Q}Q~QQQQQQQRRR ,p@ P !$0 ,p@ P !$RRASBSSSTTUUJVKV7W8WWWWWXZX$0 ,p@ P !$0 ,p@ P !$ ,p@ P !$JVKV8WWWWWWXnp >*OJQJOJQJ 5OJQJ0J60J5 ZXXXYRYYY ZJZZZ[B[[[[:\x\\\2]p]]]*^h^^ ,p@ P !$RYYY ZJZZZ[B[[[[:\x\\\2]p]]]*^h^^^"_`___`X```aPaaa bHbbbc@c~ccc8dvddd0eneee(fffff g^ggghVhhhiNiiijFjjjk>k|kkk6ltlll.mlmmm&ndnnno\ooopTpppqLqqqe^^"_`___`X```aPaaa bHbbbc@c~ccc8dvddd ,p@ P !$d0eneee(fffff g^ggghVhhhiNiiijFjjjk>k ,p@ P !$>k|kkk6ltlll.mlmmm&ndnnno\ooopTpppqLqq ,p@ P !$qqrDrrrrewxRxxx yJyyyzBzzzz:{x{{{2|p|||*}h}}}"~ ,p@ P !$"~`~~~XP̀ Hā@~8v0n ,p@ P !$n(f ^چV҇NʈF‰>| ,p@ P !$>|6t.l&d\؎TЏLȐD<z4r,j$bޕZ֖RΗ JƘB:x2p*h"`ܝXԞP̟ HĠ@~8ve6t.l&d\؎TЏLȐ ,p@ P !$D<z4r,j$bޕZ֖R ,p@ P !$RΗ JƘB:x2p*h"` ,p@ P !$ܝXԞP̟ HĠ@~8v0no ,p@ P !$0nopop1$ ,p@ P !$+0P/ =!"#$%+0P/ =!"#$%. 00P/ =!"#$%. 00P/ =!"#$%. 00P/ =!"#$%. 00P/ =!"#$%+0P/ =!"#$%+0P/ =!"#$%+0P/ =!"#$%+0P/ =!"#$%+0P/ =!"#$%+0P/ =!"#$% [4@4Normal1$CJhmH nH 0@0 Heading 1$@&5<A@<Default Paragraph Font4&@4Footnote Reference,O, Hypertext 5>*B* (U@( Hyperlink>*B**B@"* Body TextCJ4O14 footnote ref CJOJQJ4(y8M:=FJMSp-ZGtJVpRab6 @BGLNqQRZX^d>kqw"~nRopSUVWYZ[\^_`bdefgijkmnopr>jLRYq>pTX]chlq")f m   @H[c ci'(++,,(-1-00s4}444z=}=%>,>_>h>>>????@@@@ AAAABBBBBC;CACyCCCCCCrDyDDDDD,E1EEE%F1FcFjFFFGGbGkGGGGGnHvHHHHHHH II-IAI}IIIIIIII;J@JK K@KFKKKKKMMNNNNNNBOJOOOkPsPPPPPQQQQRRRR}RRTTUUUUNVUVVVWWFWMWX X>XEX|XXXY6Y?YtYzYYYlZrZZZ&[0[d[p[\(\]]T]\]]]L^U^^^^^_____`aa,b1bjbrbbbccd#dddReYeNfSfg gBgHgggghii*j/jhjoj"k&kkkllXl_lllmmmmHnVnnnoo@oFoppqq(r.rt!tVt`tuuFvOvvv|wwwwxx|xxxlysyyy&z/z!{'{_{e{{{W|[|||L}R}}}D~K~~~~ <D~rxbhނZ`փ܃RW΄؄ ƅڅɆ:C|py*2"*`s܊ԋ݋ čЍv~,23<$,bhڒV\ғݓʔДÖ.4lp\aTZȜҜ ILǝz4:r ntheobald.\\FIDELITY\www\kmeier\teep\reports\teep010.doc ntheobald*C:\TEMP\AutoRecovery save of report010.asd ntheobald0\\FIDELITY\www\kmeier\teep\reports\report010.doc@\\DEMOCRACY\RM_2133Ne01:winspoolHP LaserJet 5Si/5Si MX PS\\DEMOCRACY\RM_2133W odXLetterPRIV''''0 \\DEMOCRACY\RM_2133W odXLetterPRIV''''0 .88pP@GTimes New Roman5Symbol3& Arial71Courier"@ hUIfbIfSIfwC$ r"THE BEST SCHOOL DISTRICTS IN TEXAS ntheobald ntheobaldOh+'0 $ @ L X dpx#THE BEST SCHOOL DISTRICTS IN TEXASsHE  ntheobaldCHthe Normal.dotH ntheobaldH3heMicrosoft Word 8.0T@@pP#@. 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