ࡱ> @ `jbjb EhhZl       $llllPDl Abq q q @@@@@@@,B D^ A q q q q q A   q   @ .    q @  a  aE ll aa|/ A AaE Ea  THE BEST SCHOOL DISTRICTS IN TEXAS FOR AFRICAN AMERICAN STUDENTS 1999-2002 A REPORT OF THE TEXAS EDUCATIONAL EXCELLENCE PROJECT Number 20 June 2003 Kenneth J. Meier Nick Theobald Alisa Hicklin Texas A&M University Robert D. Wrinkle J. L. Polinard University of Texas-Pan American For further information contact: http://teep.tamu.edu 979-458-0104 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 Departments of Political Science at Texas A&M University and the University of Texas-Pan American. 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 1998-2002 Texas minority students continue to make impressive gains on the statewide TAAS exam. The results of the 2002 TAAS exam indicate that scores for African American students continue to close the gap with Anglo students. In 1996, only 46.9 percent of African American students passed the TAAS compared to 79.8 percent of Anglo students. In 2002, 77.2 percent of African American students passed all tests compared to 92.5 percent of Anglo students. However, while African American students have made impressive gains over the past five years, the gap still remains substantial. Statewide averages, however, mask some impressive performance 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 Atlanta Independent School District provides one such example. TAAS pass rates for black students in Atlanta have improved from 54.8 percent in 1996 to 92.7 percent in 2002. This dramatic improvement has resulted from a variety of efforts by school district leaders and teachers to identify effective programs and ensure district-wide implementation. Programs include early intervention programs implemented at lower grade levels to ensure students acquire fundamental skills. Atlanta has also instituted multiple programs aimed at providing additional help to low-performing students, through general after school programs and specialized TAAS workshops. The Atlanta district is a relatively small district, and their approach might not be directly transferable to large urban school districts. Many large districts, such as Waco ISD, also get dramatic improvements. Six years ago Waco ISD implemented a controversial no pass no promotion rule for 3rd and 8th graders. The debate was recently revisited with the implementation of the tougher TAKS test. Since Waco ISD has steadily improved their ranking for educating African Americans since implementing this policy, moving up to 3rd for large districts this year, the school boards decision to continue with their tough standards seems well placed (Culp, 2003). 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 the overall learning experience of black students. Student performance is a multi-dimensional concept that can be measured in a variety of 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 education 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 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 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 performance. 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 tasks they perform, we have restricted our analysis to school districts with at least 1000 students and at least 10 percent black students. These restrictions resulted in a total of 159 districts in the study. The data analysis is a pooled time series with data from the years 1999 through 2002. 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 control. The basic production function is shown in table 1. Several variables are powerful predictors of the black student pass rate. These include background and policy variables. The black student pass rate is strongly influenced by the percentage of black adults age 25 and older with at least a high school education. Attendance is also 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 New Boston Independent School District would have an average black pass rate of 72.83% from 1999-2002. New Bostons actual pass rate of 88.05% represents a 15.22 percentage point improvement over this standard. Based on this method, the top ranked school district for black students in Texas was Atlanta with a rating of +22.23% followed closely by Ferris with a +18.23 score and Hooks with a +18.22 score. The top forty 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 1999 to 2002 and the third column is the ranking score for 2002 only. These forty 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 2002 only. Tatum ISD and Kountze ISD lead the districts with high pass rates for 2002. Likewise, the Cuero Independent School Districts performance in 2001 is striking in magnitude, moving from a 1999-2002 average of 5.52 to 16.43 for 2002 only, a gain of over 10 percentage points on our score ranking. 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 25 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). Galena Park, Aldine, and Waco 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 TAAS pass rate for black students. These districts can 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. Not all of the districts use the same approach, indicating that success can be attained in a multiplicity of ways. 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 over-all TAAS 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 develop over a period of decades; solutions require both time and hard work. Table 1. Education Production Function Dependent Variable = African American Pass Rate On the TAAS exam Percent Low Income-0.116(3.82)** Percent Gifted0.083(0.82)Attendance1.920(3.59)** Average Teacher Salary K0.285(1.18) Class Size-0.052(0.13) Non-Certified Teachers-0.003(0.03) Teacher Experience0.348(1.55) State Aid-0.009(0.45) Instructional Expenditures0.001(0.94) High School Education14.754(2.37)* % Poverty Background-7.898(1.79) Constant-133.160(2.48)* Observations 636 R-squared 0.33 Absolute value of t statistics in parentheses * significant at 5%; ** significant at 1% Table 2. Top 40 districts RankDistrictScoreTAAS2002 Score1Atlanta20.2391.0516.452Ferris18.2384.1010.863Hooks18.2287.6215.994Angleton16.0392.0013.955Pittsburg15.6383.604.736New Boston15.2288.0515.427Sweeny13.8189.188.418El Campo13.4782.7814.949Newton12.8878.7011.9010Galena Park12.3382.9711.7011Hillsboro11.9877.388.0012Del Valle11.4377.1012.8813Tatum11.4280.5717.3914Columbia-Brazoria11.3082.8810.1215Denison10.8882.4010.8716Sulphur Springs10.7383.3014.3017Kountze10.4677.8016.7618McGregor9.6182.326.4519Rice Cons9.4174.556.4820Aldine9.2378.208.6821Woodville7.4474.359.5122Queen City7.0374.0713.0923Waco6.8170.905.3724Groesbeck6.7176.307.9525Bay City6.7172.9710.6826La Marque6.5975.939.1227Mansfield6.4984.202.2728Lamar Cons6.2675.973.9029Longview6.1974.655.6030Abilene6.0477.977.8731Commerce5.9173.978.4632Goose Creek Cons5.6975.453.3633Shepherd5.5370.207.1034Cuero5.5274.4316.4335Wharton5.5073.783.9836Texas City5.4976.232.6437Daingerfield-Lone Star5.4675.727.1538Terrell5.1173.323.7239Everman4.9876.154.9140Mexia4.9573.286.76 Table 3. Top 25 for 2002 RankDistrictScoreTAAS2002 Score1Tatum11.4280.5717.392Kountze10.4677.8016.763Atlanta20.2391.0516.454Cuero5.5274.4316.435Hooks18.2287.6215.996New Boston15.2288.0515.427El Campo13.4782.7814.948Sulphur Springs10.7383.3014.309Angleton16.0392.0013.9510Queen City7.0374.0713.0911Del Valle11.4377.1012.8812Newton12.8878.7011.9013Galena Park12.3382.9711.7014Denison10.8882.4010.8715Ferris18.2384.1010.8616Bay City6.7172.9710.6817Columbia-Brazoria11.3082.8810.1218Woodville7.4474.359.5119La Marque6.5975.939.1220Aldine9.2378.208.6821Commerce5.9173.978.4622Sweeny13.8189.188.4123Hillsboro11.9877.388.0024Groesbeck6.7176.307.9525Abilene6.0477.977.87 Table 4. Top 10 Large Districts (15,000 + Students) RankDistrictScoreTAAS2002 Score1Galena Park12.3382.9711.702Aldine9.2378.208.683Waco6.8170.905.374Mansfield6.4984.202.275Lamar Cons6.2675.973.906Abilene6.0477.977.877Goose Creek Cons5.6975.453.368Garland4.2177.851.429Houston3.4270.437.2810Irving3.0577.451.25 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. Culp, Cindy V. 2003. Waco Trustees Grade Promotion Policy Will Increase Number of Students Retained? Waco Tribune. http://www.wacotrib.com/news/newsfd/auto/feed/news/2003/04/10/1049951906.00353.1168.7802.html 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 Scools RankDistrictScoreTAAS2002 Score30Abilene6.0477.977.8720Aldine9.2378.208.6899Alief-2.4970.88-4.1053Amarillo2.1569.400.9780Anahuac-0.4869.40-5.544Angleton16.0392.0013.95113Arlington-4.2671.12-3.14147Athens-9.3361.12-5.671Atlanta20.2391.0516.4568Bastrop0.7967.97-6.4525Bay City6.7172.9710.6860Beaumont1.6370.47-0.67144Bellville-8.6362.40-11.82142Brenham-8.3660.83-2.5866Bryan1.0666.70-2.10105Caldwell-3.3465.575.8792Cameron-1.5866.82-8.15154Carthage-11.5860.15-8.67140Cedar Hill-8.1170.82-5.8890Center-1.3765.10-5.3447Channelview3.0076.183.86155Chapel Hill-12.0757.58-11.21110Clarksville-3.8163.120.64143Cleveland-8.5354.92-6.46121Coldspring-Oakhurst Cons-5.4155.73-0.68150College Station-10.3666.20-9.6214Columbia-Brazoria11.3082.8810.12148Columbus-9.5563.77-12.0831Commerce5.9173.978.4652Connally2.1875.15-4.3248Copperas Cove2.6677.45-0.52116Corrigan-Camden-4.5458.972.59102Corsicana-3.1963.604.87153Crockett-11.2153.25-7.1987Crosby-0.9372.800.8158Crowley1.7582.55-3.1134Cuero5.5274.4316.4337Daingerfield-Lone Star5.4675.727.15129Dallas-6.2063.42-4.2761Dayton1.4568.100.3212Del Valle11.4377.1012.8815Denison10.8882.4010.8791Denton-1.5170.002.0083Desoto-0.6576.65-5.0243Diboll4.4267.95-0.28130Dickinson-6.2161.38-3.0285Duncanville-0.9074.600.0277East Central-0.1172.971.00120East Chambers-5.4062.35-12.07107Edna-3.4364.88-2.488El Campo13.4782.7814.94141Elgin-8.3258.65-11.6984Ennis-0.8669.684.8639Everman4.9876.154.91132Fairfield-6.5865.58-7.732Ferris18.2384.1010.8695Fort Bend-2.2576.28-2.80103Fort Worth-3.2763.10-0.7010Galena Park12.3382.9711.7055Galveston1.9166.881.7944Garland4.2177.851.42101Giddings-2.9966.97-5.26100Gilmer-2.7466.43-2.47119Gladewater-5.2963.57-1.64118Gonzales-4.6960.502.5232Goose Creek Cons5.6975.453.3662Grand Prairie1.3674.12-1.55135Greenville-6.9759.22-5.6224Groesbeck6.7176.307.9571Hallettsville0.5674.43-2.3075Hardin-Jefferson0.2171.680.94152Hearne-11.0352.72-20.81159Hempstead-17.4352.35-16.46111Henderson-4.1164.431.4711Hillsboro11.9877.388.00157Hitchcock-14.4454.62-17.863Hooks18.2287.6215.9945Houston3.4270.437.2882Huntsville-0.5969.471.9346Irving3.0577.451.25131Jacksonville-6.5558.62-2.8876Jasper-0.0867.88-0.9256Jefferson1.8669.90-1.7497Judson-2.3872.55-4.6293Kilgore-1.8765.93-3.1488Killeen-0.9473.10-3.65124Klein-5.5273.88-3.1117Kountze10.4677.8016.7654La Grange1.9571.90-3.8026La Marque6.5975.939.1265La Vega1.2869.03-5.2528Lamar Cons6.2675.973.90125Lancaster-5.5765.40-7.9186Liberty-0.9171.000.5341Liberty-Eylau4.7773.603.6598Livingston-2.4564.33-0.5629Longview6.1974.655.6079Lubbock-0.2668.85-0.9442Lufkin4.6974.207.70123Madisonville Cons-5.4961.25-0.98106Malakoff-3.4264.45-0.11146Manor-9.2658.55-13.5127Mansfield6.4984.202.27156Marlin-12.9051.68-15.0864Marshall1.2970.354.1218McGregor9.6182.326.4549Mesquite2.5376.80-0.4340Mexia4.9573.286.76127Midland-5.6363.58-1.48158Mineola-17.2256.00-5.5173Mount Pleasant0.4267.93-0.45112Nacogdoches-4.2464.07-9.71108Navasota-3.6261.92-4.236New Boston15.2288.0515.429Newton12.8878.7011.90115North Forest-4.4361.17-3.34133Palestine-6.8961.75-5.9774Paris0.2169.45-0.4372Pflugerville0.5077.430.305Pittsburg15.6383.604.7370Port Arthur0.6964.22-0.5322Queen City7.0374.0713.0919Rice Cons9.4174.556.48137Richardson-7.1370.68-5.2663Rockdale1.3372.072.2296Royal-2.3565.80-0.29139Rusk-7.9659.80-11.0151Sabine2.3374.43-1.34151San Antonio-10.5056.55-10.68149San Augustine-9.7759.28-4.27126Sealy-5.5966.50-11.97104Sheldon-3.3171.03-2.0533Shepherd5.5370.207.10114Sherman-4.4266.35-1.4750Silsbee2.3471.822.68117Smithville-4.6064.38-9.4994Spring-2.1974.300.3957Stafford MSD1.8279.970.3416Sulphur Springs10.7383.3014.307Sweeny13.8189.188.4113Tatum11.4280.5717.3969Taylor0.7967.88-4.95134Teague-6.9670.08-18.77109Temple-3.7865.62-2.9438Terrell5.1173.323.7281Texarkana-0.5866.505.5736Texas City5.4976.232.64128Trinity-5.9655.50-8.9389Tyler-1.3670.502.1123Waco6.8170.905.37145Waller-8.9563.10-4.27136Waxahachie-7.0265.70-2.0259West Orange-Cove Cons1.7068.323.2678West Oso-0.1264.45-3.99138Westwood-7.4065.55-2.3835Wharton5.5073.783.9867Wichita Falls0.9771.680.53122Wilmer-Hutchins-5.4657.30-0.2821Woodville7.4474.359.51  PAGE 7 QY %'bkVxml0n0446899F9G9b9c9n9o9999999999999999999::::8:9:B:C:T:U:^:_::::PJ5 5OJQJ6CJOJQJnHCJOJQJnHCJH*OJQJnHB*CJOJQJph5CJOJQJnHCJOJQJnHI'(QRSTUVhi$a$ ) p@ P !$ ) p@ P !$ HP !$Y ^ _ NOUVxymn`a^$_$%%%'&'(())++5.6./11334444669949E9F9G9H9[9b9c9d9m9n9o9~999999999999999999999999999999 :::  ` ./01Rg}~Y $da$ ) p@ P !$$a$ ) p@ P !$ ^ _ NOUVxymn`a^$_$%%%'$a$`$da$%'&'(())++5.6./11334444669949E9$If $If]$a$`E9F9G9H9[9b9c9d9m9n9p]0I$$Ifl40Y  4 la$IfI$$Ifl40Y g!  4 la $If^ 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