{\rtf1\ansi \deflang1033\deff0{\fonttbl {\f0\fnil \fcharset0 \fprq2 Courier 10cpi;}{\f1\fnil \fcharset0 \fprq2 CG Times;}{\f2\fnil \fcharset0 \fprq2 Courier 12cpi;}}{\colortbl \red0\green0\blue0;} {\stylesheet{\fs20 \snext0 Normal;} {\*\cs1 \additive\f1 footnote ref;} {\*\cs2 \additive\f1 Default Para;} }{\info{\title }{\author Preferred Customer}{\operator Preferred Customer} } \margl1440\margr1440\ftnbj\ftnrestart\aftnnar \sectd \sbknone\endnhere {\*\pnseclvl1\pndec\pnstart1{\pntxta .}} {\*\pnseclvl2\pnlcltr\pnstart1{\pntxta .}} {\*\pnseclvl3\pnlcrm\pnstart1{\pntxta .}} {\*\pnseclvl4\pndec\pnstart1{\pntxtb (}{\pntxta )}} {\*\pnseclvl5\pnlcltr\pnstart1{\pntxtb (}{\pntxta )}} {\*\pnseclvl6\pnlcrm\pnstart1{\pntxtb (}{\pntxta )}} {\*\pnseclvl7\pndec\pnstart1{\pntxta .}} {\*\pnseclvl8\pnlcltr\pnstart1{\pntxta .}} {\*\pnseclvl9\pnlcrm\pnstart1} \pard \qc\sl0 {\plain \f1 }{\plain \b\f1 \par }{\plain \b\f1 \par }{\plain \b\f1 \par }{\plain \b\f1\fs32 LATINO STUDENT IMPROVEMENTS ON THE TAAS EXAM}{\plain \f1\fs32 \par }{\plain \f1 \par }{\plain \f1 \par }{\plain \f1 \par }{\plain \f1 \par }{\plain \f1 \par }{\plain \f1 \par }{\plain \f1 \par }{\plain \f1 \par }{\plain \f1 \par }{\plain \f1\fs28 A Report of the Texas Educational Excellence Project\par }{\plain \f1\fs28 \par }{\plain \f1\fs28 Texas A&M University\par }{\plain \f1\fs28 \par }{\plain \f1\fs28 University of Texas-Pan American\par }{\plain \f1 \par }{\plain \f1 \par }{\plain \f1 \par }{\plain \f1 John Bohte\par }{\plain \f1 J. L. Polinard\par }{\plain \f1 Kenneth J. Meier\par }{\plain \f1 Robert D. Wrinkle\par }{\plain \f1 \par }{\plain \f1 \par }{\plain \f1 \par }{\plain \f1 \par }{\plain \f1 \par }{\plain \f1 \par }\pard \sl0 {\plain \f1 For further information contact:\par }{\plain \f1 \par }{\plain \f1 \tab John Bohte, Department of Political Science, Texas A&M University,\par }{\plain \f1 \tab 409-845- 2327 johnny@polisci.tamu.edu\par }{\plain \f1 \par }{\plain \f1 \tab J. L. Polinard, Department of Political Science, University of Texas-Pan American\par }{\plain \f1 \tab 965-381-3341 Polinard@panam.edu\par }{\plain \f1 \par }{\plain \f1 or visit us on the world wide web at\par }{\plain \f1 \tab http://people.tamu.edu/~kmeier/\par }{\plain \f1 \par }\sect \sectd \pgnrestart\pgndec\pgnx720\pgny720\footery2160\endnhere {\footer \pard \sl-240 {\plain \f1 \par }\pard \qc\sl0\pvpara\posyt\phmrg\absw9360 \chpgn \par \pard {\plain \f1 \par }} {\*\pnseclvl1\pndec\pnstart1{\pntxta .}} {\*\pnseclvl2\pnlcltr\pnstart1{\pntxta .}} {\*\pnseclvl3\pnlcrm\pnstart1{\pntxta .}} {\*\pnseclvl4\pndec\pnstart1{\pntxtb (}{\pntxta )}} {\*\pnseclvl5\pnlcltr\pnstart1{\pntxtb (}{\pntxta )}} {\*\pnseclvl6\pnlcrm\pnstart1{\pntxtb (}{\pntxta )}} {\*\pnseclvl7\pndec\pnstart1{\pntxta .}} {\*\pnseclvl8\pnlcltr\pnstart1{\pntxta .}} {\*\pnseclvl9\pnlcrm\pnstart1} \pard \qc\sl0 {\plain \f1 LATINO STUDENT IMPROVEMENTS ON THE TAAS EXAM\par }\pard \sl0 {\plain \f1 \par }{\plain \f1 \tab \par }{\plain \f1 \tab The pass rates for Latino students in Texas on the TAAS exam have lagged behind those for Anglo students. Recent trends in Latino test scores are encouraging, however. From 1991 to 1997, the statewide pass rate for Latino students on the TAAS has improved from 41.5% to 61.9%, compared to a rate of change for non-minority students of 68.9% to 84.9%. This improvement in Latino pass rates is notable, but much more progress is needed. One of the major goals of the Texas Educational Excellence Project is identifying those school districts that have made significant strides in improving the performance of Latino students on the TAAS exam. By identifying exemplary districts, we hope to provide the public and policy makers with information that will inform future policy making efforts aimed at improving Latino education in Texas. \par }{\plain \f1 \par }{\plain \f1 \tab The approach of the Texas Educational Excellence Project is to use a statistical technique, multiple regression analysis, as a tool for identifying the top school districts in Texas for Latino students. Multiple regression analysis makes it possible to develop generalizations about the overall performance of Texas school districts in educating Latino students, while also providing information that can be used to make comparisons across individual school districts. Our model is based on an education \'93production function\'94 where student performance (defined as Latino 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}{\plain \i\f1 actually}{\plain \f1 perform to how well the statistical model }{\plain \i\f1 predicts}{\plain \f1 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 Latino students.\par }{\plain \f1 \par }{\plain \f1 \tab Our units of analysis are 350 Texas school districts with at least 1000 students. To make sure that the districts are performing relatively similar functions, we further limit the analysis to districts with some but no more than 90 percent Anglo students and at least 10 percent Latino students, that is, multiethnic districts. This reduces our total number of districts to 262. Our data come from two basic sources: The Texas Educational Agency and the U. S. Bureau of the Census, School District Data File.\par }{\plain \f1 \par }\pard \qc\sl0 {\plain \f1 }{\plain \b\f1 Dependent Variable: Student Performance}{\plain \f1 \par }\pard \sl0 {\plain \f1 \par }{\plain \f1 \tab The state of Texas requires students in certain grades to take standardized TAAS tests every year. The percentage of Latino students in each district who pass these tests is the dependent variable in our analysis. We do not claim that results on TAAS exams account for the overall learning experience of Latino students. Student performance is a multi-dimensional concept that can be measured in variety of different ways. However, pass rates on TAAS exams }{\plain \b\f1 do}{\plain \f1 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 Latino students basic skills, and should not be construed as an overall measure of Latino student learning. \par }\pard \qc\sl0 {\plain \f1 }{\plain \b\f1 Independent Variables}{\plain \f1 \par }\pard \sl0 {\plain \f1 \par }{\plain \f1 \tab A variety of factors known to influence education performance are included in our production function. All the independent variables are culled from the education literature and are frequently used in education production functions. Essentially, we use two sets of independent variables in our analysis. The first set of variables includes resource input and educational policy variables. The second set of variables includes measures that control for differences in environmental characteristics across school districts. \par }{\plain \f1 \par }{\plain \b\f1 Resource and Policy Variables}{\plain \f1 \par }{\plain \f1 \tab \par }{\plain \b\f1 1.}{\plain \f1 }{\plain \b\f1 Expenditures}{\plain \f1 . The relationship between expenditures and educational outcomes is one of the most contested relationships in educational policy. Examining a wealth of studies, 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.\par }{\plain \f1 \par }{\plain \f1 \tab Our expenditure variables include per pupil operating expenditures, teacher salaries, and the percentage of district money from state funds. Per pupil operating expenditures are used in preference to total per pupil spending because many Texas districts spend lavishly on non-operating activities. Education is personnel intensive, and most spending pays salaries of teachers and other staff. Higher salaries are perceived in economic theory as a way to attract better qualified persons to a profession (Hanushek and Pace 1995). Finally, state aid can be used to compensate for inequities in local tax bases. Although Texas is not known for redistributive educational policies and has a long history in court on this issue (}{\plain \i\f1 San Antonio Independent School District v. Rodriquez}{\plain \f1 , 1973; }{\plain \i\f1 Edgewood Independent School District v. Kirby}{\plain \f1 , 1987; See also Texas Research League 1986; Accountable Cost Advisory Committee 1986; Weiher 1988), greater funds from state governments can compensate for a meager local tax base. The relationships between these expenditure variables and district Latino pass rates should be positive - i.e., more financial resources should lead to better performance on TAAS exams. \par }{\plain \f1 \par }{\plain \b\f1 2. Teacher Attributes.}{\plain \f1 Teachers are a crucial force in shaping student performance. As a profession based on life-long learning, there should be some advantage in having teachers with adequate experience, especially in multiracial districts. The presence of more experienced teachers should have a positive effect on student performance. In this sense, teacher experience can be viewed as an important resource variable. Our first variable is a measure of average teacher experience (in years) for each district. To further tap into the concept of experience, we also include the percentage of non-certified teachers in each district. Our expectation is that this relationship should be negative.\par }{\plain \b\f1 3. Policy Variables.}{\plain \f1 Education policies are adopted to influence student performance. Two such policies deal with student learning environment--class size and gifted classes. Although many studies indicate that only major changes in class size are effective, schools with smaller class sizes should have an advantage at the margins (see Pate-Bain et al. 1992; Nye et al. 1992; Hedges and Greenwald 1996; Hanushek 1996, 54). Our first policy variable is the student-teacher ratio in each district. Gifted classes are generally viewed as venues for providing the best education that a school system can offer (See DeHaan 1963). The number of students enrolled in gifted classes varies greatly across school districts in Texas (0 to 31%). Greater access to gifted classes should result in better student performance. To summarize, class size should have a negative relationship to exam performance, while the availability of gifted classes should be positively related to exam performance. \par }{\plain \f1 \par }{\plain \b\f1 Control Variables}{\plain \f1 \par }{\plain \f1 \par }{\plain \f1 \tab School districts vary widely in terms of environmental or background characteristics. To ensure that we are comparing apples to applies, controls must be included for various district background characteristics. Controlling for district background characteristics is also a crucial step that facilitates comparisons of findings across different school districts. \par }{\plain \f1 \par }{\plain \f1 \tab Our first control variable measures district poverty. In the context of educational policy, poverty is a serious constraint on student performance. Poverty not only means students lack access to learning tools in the home (computers, educational toys, etc.) but is also correlated with a less stable and less supportive home environment (e.g., single parent households, high rates of teen pregnancy, and low educational expectations; Necochea and Cune 1996; Fuller et. al. 1996). Our first measure of poverty is the percent of students in each district that qualify for free or reduced-price meals in school lunch programs. As the percentage of students in poverty rises, district pass rates on TAAS exams should decline. Our second poverty measure was the percentage of Hispanic families in the school district with incomes below the poverty level.\par }{\plain \f1 \par }{\plain \f1 \tab The home educational background of Latino students is the third control variable used in the analysis. We use the percentage of Latino adults, age 25 and older with at least a high school education. Generally, minority students who come from districts in which there are large numbers of adult Latinos with strong educational backgrounds tend to perform at higher levels than students who come from districts where there are lesser numbers of educated Latinos (Meier and Stewart 1991). The relationship between percentage of high school educated Latinos and Latino pass rates should be positive.\par }{\plain \f1 \par }{\plain \f1 \tab The fourth control variable is the percentage foreign-born residents per district. Our concern here is the performance of Latino students, and one means of controlling for recent immigration is to use the percentage foreign born citizens in each district. Our expectation is that there should be a negative relationship between percent foreign-born citizens and Latino pass rates.\par }{\plain \f1 \par }{\plain \f1 \tab Our final control variable is student attendance, measured as percentage average daily attendance. Crucial to learning is the idea that students attend class. Our expectation is that the relationship between attendance and student performance should be positive. \par }{\plain \f1 \par }\pard \qc\sl0 {\plain \f1 }{\plain \b\f1 Findings}{\plain \f1 \par }\pard \sl0 {\plain \f1 \par }{\plain \f1 \tab Our production function is based on a pooled-time series analysis of educational inputs and average Latino pass rates using data from the years 1991 through 1997. As any time series tends toward serial correlation, we include a series of dummy variables to control for any serial correlation.\par }{\plain \f1 \par }{\plain \f1 \tab The basic production function is found in Table 1. The results show that all variables, with the exception of class size, are significant predictors of average district Latino pass rates. However, the percentage of foreign born residents variable is not in the expected direction. Our expectation was that this variable would be negatively related to Latino student performance. However, the variable is strongly and}{\plain \b\f1 positively}{\plain \f1 related to Latino student performance. That is, when one controls for poverty, the family educational background of Latinos and various other controls, percentage foreign born has a positive influence on Latino student performance. This finding comports with other research which suggests that first generation immigrant students tend to be [relatively] high achieving (Oropesa and Landale 1997).\par }{\plain \f1 \par }{\plain \f1 \tab Our other variables perform about as expected. Student attendance is strongly related to high performance as are teacher experience, teacher salaries and state aid. Pass rates tend to be depressed in districts with high numbers of uncertified teachers, high district poverty levels, and high percentages of students from low income families. Essentially, these results are very similar to previous research on minority student achievement (see Meier and Stewart, 1991; Polinard, Wrinkle, and Meier, 1995).\par }{\plain \f1 \par }{\plain \f1 \tab As noted above both Anglo and Latino pass rates have improved statewide. For all of the school districts in the model, there was an average improvement of 21.3% in the pass rate for Latino students over the seven year period. This rate of improvement resulted in an average 1997 Latino pass rate of 63.0% for the districts in the model. Over this time period, the Anglo pass rate improved 17.0% for an average pass rate of 83.0%. The average gap between Anglo and Latino pass rates for the multiethnic districts in the study is now 20%.\par }{\plain \f1 \par }{\plain \f1 \tab Results from this education production function make it possible to identify Texas school districts that excel in teaching basic reading and mathematics skills to Latino students. For example, our model predicts that the Mission Consolidated School District should have an average Latino student pass rate of 45.3% from 1991 to 1997. Mission\'92s actual pass rate of 56.8% represents an 11.5% improvement over this standard. The same logic is used in evaluating the entire sample of Texas school districts. The top school district for Latino students in Texas is the South Texas district, with a rating of +22.6%, followed by Mission with a rating of +11.6% and Merkel with a rating of +11.3%.\par }{\plain \f1 \par }{\plain \f1 \tab The South Texas school district is somewhat unique and may not be comparable to other districts. The South Texas district is a district that overlays several other school districts and operates magnet schools. As a result, its student body is different from that of most other districts. This qualification should not be taken to imply that South Texas is not an exceptional school district. South Texas produces excellent results for both Anglos and Latinos and has done so for an extended period of time.\par }{\plain \f1 \par }{\plain \f1 \tab The top 25 districts for Latino students are shown in table 2. The first column of that table is the numerical score on which the districts are ranked. The second column is the average pass rate for Latino students from 1991 to 1997 in the district, and the third column is the Latino student pass rate for 1997.\par }{\plain \f1 \par }{\plain \f1 \tab Our numerical score ranking is based on the average scores for 1991 through 1997. Consequently, it may not recognize districts where dramatic improvements have been made recently. For example, the Point Isabel district did not make the list of the top twenty-five Latino districts. However, Point Isabel has improved its Latino pass rate by 26 points from 1995 to 1997 and gained close to 20 points on our rating measurement. Schools that have made large improvements during this time period are quite likely to appear in our ratings in future years.\par }{\plain \f1 \par }{\plain \f1 \tab Table 3 is the alphabetical listing of all of the districts in the study. For each district we report these same scores as noted above as well as its rank among the 262 districts in the study.\par }{\plain \f1 \par }{\plain \f1 \tab Given the rate of improvement in Latino TAAS scores over the past seven years and the leadership provided by the high performing Latino districts, we expect that, over the course of the next seven years, these multiethnic districts will have an additional average improvement of more than twenty percent in the Latino TAAS pass rate.\par }{\plain \f1 \par }\pard \qc\sl0 {\plain \f1 }{\plain \b\f1 Conclusion}{\plain \f1 \par }\pard \sl0 {\plain \f1 \par }{\plain \f1 \tab TAAS scores for Latino students in Texas lag behind those for Anglo students. Although Latino students have closed this gap somewhat over the past seven years, the difference remains substantial. This study identified school districts in Texas who have done a good job of educating Latino students after adjusting for resources, backgrounds and the type of students. The districts that we identify are those that are performing well above expectations. These are the districts that educators should look to for successful programs.\par }{\plain \f1 \par }{\plain \f1 \tab Our interaction with various school districts convinces us that there are no miracles in education, for Latino students or any other types of students. Only well designed programs that are consistently applied over long periods of time produce payoffs. If the top 25 districts have anything in common, it is that, hard work over a long period of time.\par }\pard\page \pard \sl0 {\plain \f1 \par }{\plain \f1 \par }\pard \qc\sl0 {\plain \f1 }{\plain \b\f1 The Texas Educational Excellence Project}{\plain \f1 \par }\pard \sl0 {\plain \f1 \par }{\plain \f1 \tab The Texas Educational Excellence Project (TEEP) is a joint program of the political science departments 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.}{\plain \b\f1 \par }\pard\page \pard \qc\sl0 {\plain \b\f1 }{\plain \f1 }{\plain \b\f1 References}{\plain \f1 \par }\pard \fi-720\li720\sl0 {\plain \f1 \par }\pard \fi-720\li720\sl0 {\plain \f1 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.\par }\pard \sl0 {\plain \f1 \par }\pard \fi-720\li720\sl0 {\plain \f1 Chubb, John and Terry Moe. 1990. }{\plain \i\f1 Politics, Markets and America's Schools}{\plain \f1 . Washington: Brookings.\par }\pard \sl0 {\plain \f1 \par }\pard \fi-720\li720\sl0 {\plain \f1 DeHaan, Robert F. 1963. }{\plain \i\f1 Accelerated Learning Programs}{\plain \f1 . Washington: Center for Applied Research in Education, Inc.\par }\pard \fi-720\li720\sl0 {\plain \f1 \par }\pard \fi-720\li720\sl0 {\plain \f1 }{\plain \i\f1 Edgewood Independent School District v. Kirby}{\plain \f1 . Texas SupCt, No. C-8353, (1989).\par }\pard \sl0 {\plain \f1 \par }{\plain \f1 Evans, William N., Sheila E. Murray, and Robert M. Schwab. 1997. "Schoolhouses,\par }{\plain \f1 \tab Courthouses, and Statehouses After }{\plain \i\f1 Serrano}{\plain \f1 ." }{\plain \i\f1 Journal of Policy Analysis and\par }{\plain \i\f1 \tab Management}{\plain \f1 16 (Winter), 10-31.\par }\pard \fi-720\li720\sl0 {\plain \f1 \par }\pard \fi-720\li720\sl0 {\plain \f1 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?" }{\plain \i\f1 Teachers College Record}{\plain \f1 97 (Spring):400-418.\par }\pard \sl0 {\plain \f1 \par }\pard \fi-720\li720\sl0 {\plain \f1 Hanushek, Eric A. and Richard R. Pace. 1995. "Who Chooses to Teach (and Why)?" }{\plain \i\f1 Economics of Education Review}{\plain \f1 14 (June):107-117.\par }\pard \sl0 {\plain \f1 \par }\pard \fi-720\li720\sl0 {\plain \f1 Hanushek, Eric A. 1986. "The Economics of Schooling: Production and Efficiency in Public Schools." }{\plain \i\f1 Journal of Economic Literature}{\plain \f1 24 (September):1141-1177.\par }\pard \sl0 {\plain \f1 \par }\pard \fi-720\li720\sl0 {\plain \f1 Hanushek, Eric A. 1996. "School Resources and Student Performance." In }{\plain \i\f1 Does Money Matter? The Effect of School Resources on Student Achievement and Adult Success,}{\plain \f1 ed. Gary Burtless. Washington: Brookings.\par }\pard \sl0 {\plain \f1 \par }\pard \fi-720\li720\sl0 {\plain \f1 Hanushek, Eric A. 1989. "Expenditures, Efficiency, and Equity in Education: The Federal Government's Role." }{\plain \i\f1 American Economic Review}{\plain \f1 79 (May):46-51.\par }\pard \sl0 {\plain \f1 \par }\pard \fi-720\li720\sl0 {\plain \f1 Hedges, Larry V. and Rob Greenwald. 1996. "Have Times Changed? The Relation between School Resources and Student Performance." In }{\plain \i\f1 Does Money Matter? The Effect of School Resources on Student Achievement and Adult Success,}{\plain \f1 ed. Gary Burtless. Washington: Brookings.\par }\pard \sl0 {\plain \f1 \par }\pard \fi-720\li720\sl0 {\plain \f1 Lasswell, Harold. 1936. }{\plain \i\f1 Politics: Who Gets What, When, How?}{\plain \f1 New York: McGraw Hill.\par }\pard \sl0 {\plain \f1 \par }\pard \fi-720\li720\sl0 {\plain \f1 Lipsky, Michael. 1980. }{\plain \i\f1 Street Level Bureaucracy}{\plain \f1 . New York: Russell Sage Foundation.\par }\pard \fi-720\li720\sl0 {\plain \f1 Long, Norton. 1952. "Bureaucracy and Constitutionalism." }{\plain \i\f1 American Political Science Review}{\plain \f1 46 (September), 808-818.\par }\pard \fi-720\li720\sl0 {\plain \f1 \par }\pard \fi-720\li720\sl0 {\plain \f1 Meier, Kenneth J. and Joseph Stewart, Jr. 1991. }{\plain \i\f1 The Politics of Hispanic Education}{\plain \f1 . Albany: SUNY Press.\par }\pard \sl0 {\plain \f1 \par }\pard \fi-720\li720\sl0 {\plain \f1 Murray, Sheila E. 1995. "Two Essays on the Distribution of Education Resources and Outcomes." PhD. diss. Department of Economics, University of Maryland.\par }\pard \sl0 {\plain \f1 \par }\pard \fi-720\li720\sl0 {\plain \f1 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.\par }\pard \fi-720\li720\sl0 {\plain \f1 \par }\pard \fi-720\li720\sl0 {\plain \f1 Necochea, Juan and Zullmara Cune. 1996. "A Case Study of Within District School Funding Inequities." }{\plain \i\f1 Equity & Excellence in Education}{\plain \f1 29 (September): 69-77.\par }\pard \sl0 {\plain \f1 \par }\pard \fi-720\li720\sl0 {\plain \f1 Nye, Barbara A., Jayne Boyd-Zacharias, B. Dewayne Fulton, and Mark P. Wallenhorst. 1992. "Smaller Classes Really are Better." }{\plain \i\f1 American School Board Journal }{\plain \f1 179 (May): 31-33.\par }\pard \sl0 {\plain \f1 \par }{\plain \f1 Oropesa, R. S. and Nancy S. Landale. 1997. \'93Immigrant Legacies.\'94 }{\plain \i\f1 Social Science Quarterly}{\plain \f1 \tab 78:399-416.\par }{\plain \f1 \par }\pard \fi-720\li720\sl0 {\plain \f1 Pate-Bain, Helen, C.M. Achilles, Jayne Boyd-Zacharias, and Bernard McKenna. 1992. "Class Size Does Make a Difference." }{\plain \i\f1 Phi Delta Kappan}{\plain \f1 74 (November): 253-56.\par }\pard \sl0 {\plain \f1 \par }\pard \fi-720\li720\sl0 {\plain \f1 Polinard, J. L., Robert D. Wrinkle and Kenneth J. Meier. 1995. \'93The Influence of Educational and Political Resources on Minority Students\'92 Success,\'94 }{\plain \i\f1 Journal of Negro Education}{\plain \f1 64: 463-474.\par }\pard \sl0 {\plain \f1 \par }\pard \fi-720\li720\sl0 {\plain \f1 }{\plain \i\f1 San Antonio Independent School District v. Rodriquez}{\plain \f1 . 411 U.S. 1 (1973).\par }\pard \fi-720\li720\sl0 {\plain \f1 \par }\pard \fi-720\li720\sl0 {\plain \f1 Smith, Kevin B. and Kenneth J. Meier. 1995. }{\plain \i\f1 The Case Against School Choice}{\plain \f1 . Armonk, NY: M.E. Sharpe.\par }\pard \fi-720\li720\sl0 {\plain \f1 \par }\pard \fi-720\li720\sl0 {\plain \f1 Texas Research League. 1986. "Bench Marks for 1986-87 School District Budgets in Texas." Austin, TX: Texas Research League.\par }\pard \fi-720\li720\sl0 {\plain \f1 \par }\pard \fi-720\li720\sl0 {\plain \f1 Weiher, Gregory R. 1988. "Why Redistribution Doesn't Work: State Educational Reform Policy and Governmental Decentralization in Texas." }{\plain \i\f1 American Politics Quarterly}{\plain \f1 16 (April): 193-210.}{\plain \par }\pard \sl0 \pard\page \pard \qc\sl0 {\plain }{\plain \b TABLE 1: LATINO EDUCATIONAL PRODUCTION FUNCTION\par }\pard \sl0 {\plain \b \par }{\plain \b \par }{\plain \b\ul Variable Coefficient Standard Error}{\plain \par }{\plain \par }{\plain Low Income -.131 .014\par }{\plain \par }{\plain Gifted .156 .052\par }{\plain \par }{\plain Attendance 1.95 .211\par }{\plain \par }{\plain Teacher Salary .0004 .0001\par }{\plain \par }{\plain Class size -.126 .182}{\plain \f2\fs20 }{\plain \super\f2\fs20 *}{\plain \par }{\plain \par }{\plain Teacher \par }{\plain Certification -.237 .056\par }{\plain \par }{\plain Teacher \par }{\plain Experience .344 .144\par }{\plain \par }{\plain State Aid .063 .010\par }{\plain \par }{\plain High School \par }{\plain Education 12.26 2.42\par }{\plain \par }{\plain %Poverty \par }{\plain Background -10.346 2.10\par }{\plain \par }{\plain %Foreign \par }{\plain Born 20.430 3.27\par }{\plain \par }{\plain Per Pupil \par }{\plain Operating .002 .0004\par }{\plain \par }{\plain Intercept -165.933 21.497\par }{\plain \par }{\plain \ul }{\plain \par }{\plain \par }{\plain R}{\plain \super 2}{\plain (adj)= .71\par }{\plain \par }{\plain F= 272.89\par }{\plain \par }{\plain significance of F < .000\par }{\plain }{\plain \super *}{\plain Not significant\par }{\plain \par }\pard\page \pard \qc\sl0 {\plain }{\plain \b Table 2. The Top 25 Texas School Districts for Latino Students}{\plain \par }\pard \sl0 {\plain \par }{\plain \ul Rank District Score Average TAAS 1997 TAAS }{\plain \par }{\plain \par }{\plain 1 South Texas 22.6 85.3 92.4\par }{\plain 2 Mission Consolidated 11.5 56.8 76.0\par }{\plain 3 Merkel 11.3 55.5 73.3\par }{\plain 4 Los Fresnos Consolid 10.2 54.5 82.2\par }{\plain 5 Coleman 10.0 55.6 76.4\par }{\plain \par }{\plain 6 Ballinger 9.8 55.9 64.6\par }{\plain 7 Ferris 9.8 51.2 75.9\par }{\plain 8 Aldine 9.7 53.8 76.0\par }{\plain 9 Childress 9.3 50.3 72.1\par }{\plain 10 Mount Pleasant 9.1 49.8 59.7\par }{\plain \par }{\plain 11 San Benito Consolida 8.2 49.8 73.0\par }{\plain 12 Freer 8.1 54.0 67.1\par }{\plain 13 Brazosport 7.7 56.2 81.8\par }{\plain 14 Troy 7.6 56.5 74.7\par }{\plain 15 Columbia\_Brazoria 7.3 55.1 78.4\par }{\plain \par }{\plain 16 Texas City 7.3 53.4 75.4\par }{\plain 17 La Feria 7.2 51.6 76.5\par }{\plain 18 Jim Hogg County 7.1 53.8 72.3\par }{\plain 19 Garland 6.9 55.5 66.6\par }{\plain 20 Calallen 6.8 56.5 76.4\par }{\plain \par }{\plain 21 Kaufman 6.7 47.7 67.5\par }{\plain 22 McAllen 6.7 54.6 69.6\par }{\plain 23 Tuloso\_Midway 6.6 50.6 73.9\par }{\plain 24 Vernon 6.4 49.0 63.1\par }{\plain 25 La Grange 6.4 50.5 64.7\par }{\plain \par }{\plain Average for 262 \par }{\plain Districts in Study 0.0 44.7 62.8 \par }{\plain \par }{\plain \par }{\plain \par }{\plain \par }{\plain \par }\pard\page \pard \sl0 {\plain }{\plain \b Table 3. Scores for all Texas School Districts}{\plain \par }{\plain \par }{\plain \ul Rank District Score Average TAAS 1997 TAAS }{\plain \par }{\plain 107 Abilene 1.0 47.5 62.6\par }{\plain 243 Alamo Heights \_7.1 50.1 62.1\par }{\plain 8 Aldine 9.7 53.8 76.0\par }{\plain 42 Alice 4.5 46.7 62.4\par }{\plain 229 Alief \_6.0 46.8 62.6\par }{\plain 96 Alpine 1.6 49.2 65.9\par }{\plain 86 Alvin 2.3 48.1 66.4\par }{\plain 71 Amarillo 2.9 46.1 64.5\par }{\plain 41 Anahuac 4.7 48.0 71.1\par }{\plain 72 Andrews 2.9 51.0 69.4\par }{\plain 46 Angleton 4.3 53.3 78.9\par }{\plain 70 Aransas County 2.9 44.8 71.3\par }{\plain 141 Aransas Pass \_0.8 40.0 55.9\par }{\plain 234 Austin \_6.2 38.5 51.1\par }{\plain 6 Ballinger 9.8 55.9 64.6\par }{\plain 245 Bandera \_7.1 37.9 68.0\par }{\plain 200 Bastrop \_3.4 39.9 56.2\par }{\plain 43 Bay City 4.5 44.7 65.3\par }{\plain 37 Beeville 5.1 45.6 61.4\par }{\plain 131 Belton \_0.2 44.9 64.9\par }{\plain 27 Big Spring 6.1 47.5 64.6\par }{\plain 53 Bishop Consolidated 4.0 49.7 68.6\par }{\plain 211 Boerne \_4.0 43.7 66.0\par }{\plain 30 Borger 5.7 51.6 71.0\par }{\plain 103 Brady 1.3 46.6 67.3\par }{\plain 13 Brazosport 7.7 56.2 81.8\par }{\plain 93 Breckenridge 1.8 43.6 64.9\par }{\plain 105 Bridgeport 1.1 46.5 66.2\par }{\plain 151 Brooks \_1.1 39.7 52.0\par }{\plain 208 Brownfield \_3.9 36.8 56.9\par }{\plain 188 Brownsville \_2.8 41.4 59.5\par }{\plain 119 Brownwood 0.3 44.3 62.3\par }{\plain 242 Bryan \_6.9 37.6 59.1\par }{\plain 193 Burnet Consolidated \_3.0 41.2 58.0\par }{\plain 20 Calallen 6.8 56.5 76.4\par }{\plain 95 Caldwell 1.6 46.9 68.5\par }{\plain 203 Calhoun County \_3.5 39.8 62.4\par }{\plain 102 Cameron 1.4 44.1 64.5\par }{\plain 260 Canutillo \_11.6 36.3 50.6\par }{\plain 91 Carrizo Springs Cons 1.9 41.1 55.7\par }{\plain 121 Carrollton Farmers Br 0.2 51.1 65.7\par }{\plain 98 Castleberry 1.5 46.3 61.1\par }{\plain 129 Channelview \_0.2 48.2 66.8\par }{\plain 9 Childress 9.3 50.3 72.1\par }{\plain 189 Cleburne \_2.9 39.6 56.8\par }{\plain 253 Cleveland \_8.7 31.5 43.1\par }{\plain 146 Clint \_0.9 42.7 63.1\par }{\plain 5 Coleman 10.0 55.6 76.4\par }{\plain 164 Colorado \_1.7 43.3 55.7\par }{\plain 15 Columbia\_Brazoria 7.3 55.1 78.4\par }{\plain 130 Columbus \_0.2 44.5 57.5\par }{\plain 187 Comal \_2.8 44.7 60.1\par }{\plain 183 Comanche \_2.6 44.1 56.4\par }{\plain 45 Connally 4.3 51.0 74.0\par }{\plain 74 Corpus Christi 2.8 49.4 64.3\par }{\plain 61 Corrigan\_Camden 3.6 46.1 53.3\par }{\plain 258 Cotulla \_11.1 28.6 46.0\par }{\plain 196 Crane \_3.2 46.2 68.9\par }{\plain 171 Cuero \_2.0 40.8 58.6\par }{\plain 87 Cypress\_Fairbanks 2.3 55.4 67.2\par }{\plain 117 Dalhart 0.3 44.1 67.8\par }{\plain 109 Dallas 0.9 41.1 53.5\par }{\plain 175 Decatur \_2.2 42.3 52.4\par }{\plain 190 Deer Park \_2.9 48.4 64.0\par }{\plain 79 Del Valle 2.6 43.5 68.2\par }{\plain 238 Denton \_6.8 41.2 59.4\par }{\plain 108 Denver City 0.9 52.4 70.5\par }{\plain 221 Devine \_5.1 41.2 64.9\par }{\plain 52 Diboll 4.1 45.3 62.3\par }{\plain 248 Dickinson \_7.7 33.8 44.9\par }{\plain 77 Dilley 2.7 40.5 63.7\par }{\plain 201 Dimmitt \_3.4 40.0 52.6\par }{\plain 147 Donna \_0.9 37.1 57.3\par }{\plain 232 Dublin \_6.0 32.4 44.8\par }{\plain 118 Dumas 0.3 42.7 61.0\par }{\plain 134 Eagle Pass \_0.3 42.3 63.9\par }{\plain 214 East Central \_4.2 44.4 65.3\par }{\plain 90 Ector County 1.9 42.1 60.1\par }{\plain 33 Edcouch\_Elsa 5.6 47.0 67.8\par }{\plain 165 Edgewood \_1.8 35.8 54.6\par }{\plain 58 Edinburg 3.8 47.6 65.7\par }{\plain 38 Edna 5.1 49.8 75.7\par }{\plain 136 El Campo \_0.5 44.2 72.7\par }{\plain 173 El Paso \_2.1 44.0 60.1\par }{\plain 218 Elgin \_4.9 39.7 57.8\par }{\plain 132 Ennis \_0.3 44.2 64.3\par }{\plain 28 Everman 6.1 52.5 67.9\par }{\plain 217 Fabens \_4.8 37.5 53.6\par }{\plain 7 Ferris 9.8 51.2 75.9\par }{\plain 111 Floresville 0.8 42.5 59.4\par }{\plain 63 Flower Bluff 3.5 52.9 71.1\par }{\plain 224 Floydada \_5.4 34.9 45.1\par }{\plain 247 Fort Bend \_7.5 46.2 63.8\par }{\plain 228 Fort Worth \_5.9 36.2 48.9\par }{\plain 251 Fredericksburg \_8.6 37.9 56.2\par }{\plain 12 Freer 8.1 54.0 67.1\par }{\plain 49 Frenship 4.3 50.0 70.0\par }{\plain 82 Friona 2.5 47.2 61.1\par }{\plain 54 Frisco 4.0 49.3 60.2\par }{\plain 186 Ft Sam Houston \_2.7 61.7 68.6\par }{\plain 100 Ft Stockton 1.4 45.2 63.9\par }{\plain 197 Galena Park \_3.3 42.6 62.1\par }{\plain 106 Galveston 1.1 44.7 60.8\par }{\plain 19 Garland 6.9 55.5 66.6\par }{\plain 158 George West \_1.4 43.8 65.4\par }{\plain 154 Georgetown \_1.3 50.0 60.6\par }{\plain 219 Giddings \_4.9 41.1 64.1\par }{\plain 237 Glen Rose \_6.7 44.6 66.0\par }{\plain 32 Goliad 5.6 51.0 77.2\par }{\plain 137 Gonzales \_0.6 42.0 53.3\par }{\plain 84 Goose Creek 2.4 47.6 67.7\par }{\plain 148 Grand Prairie \_1.0 47.7 63.6\par }{\plain 120 Greenwood 0.3 48.1 56.3\par }{\plain 59 Gregory\_Portland 3.8 53.9 76.8\par }{\plain 254 Harlandale \_8.7 35.9 56.9\par }{\plain 40 Harlingen Cons. 4.9 50.8 72.3\par }{\plain 205 Hayes Consolidated \_3.6 45.9 55.8\par }{\plain 152 Hearne \_1.2 43.4 60.0\par }{\plain 262 Hempstead \_13.6 27.0 55.1\par }{\plain 122 Hereford 0.1 41.2 61.1\par }{\plain 140 Hidalgo \_0.7 40.7 67.9\par }{\plain 48 Hillsboro 4.3 41.7 70.3\par }{\plain 250 Hitchcock \_8.3 36.2 44.9\par }{\plain 143 Hondo \_0.9 41.1 57.4\par }{\plain 177 Houston \_2.4 40.3 58.1\par }{\plain 249 Huntsville \_8.1 39.9 57.3\par }{\plain 76 Ingleside 2.7 47.3 60.9\par }{\plain 174 Ingram \_2.2 45.0 57.0\par }{\plain 44 Irving 4.4 50.7 64.7\par }{\plain 18 Jim Hogg County 7.1 53.8 72.3\par }{\plain 65 Jourdanton 3.4 47.8 61.2\par }{\plain 104 Judson 1.2 53.6 69.7\par }{\plain 21 Kaufman 6.7 47.7 67.5\par }{\plain 135 Kenedy \_0.5 40.3 49.5\par }{\plain 244 Kermit \_7.1 37.6 55.0\par }{\plain 113 Kerrville 0.6 46.2 72.6\par }{\plain 110 Killeen 0.8 51.4 70.1\par }{\plain 116 Kingsville 0.3 45.4 60.6\par }{\plain 17 La Feria 7.2 51.6 76.5\par }{\plain 25 La Grange 6.4 50.5 64.7\par }{\plain 142 La Joya \_0.9 37.2 54.6\par }{\plain 252 La Vernia \_8.7 40.6 57.4\par }{\plain 144 La Porte \_0.9 51.5 70.2\par }{\plain 128 La Vega \_0.1 41.5 56.8\par }{\plain 163 Lake Worth \_1.6 36.1 51.5\par }{\plain 184 Lamar Consolidated \_2.7 43.5 64.2\par }{\plain 192 Lamesa \_3.0 36.2 55.2\par }{\plain 126 Lampasas \_0.1 43.8 68.8\par }{\plain 170 Laredo \_1.9 45.0 61.1\par }{\plain 145 Levelland \_0.9 43.7 59.0\par }{\plain 39 Littlefield 5.1 45.4 65.1\par }{\plain 66 Lockhart 3.2 44.4 63.5\par }{\plain 4 Los Fresnos Consolid 10.2 54.5 82.2\par }{\plain 169 Lubbock \_1.9 43.1 60.4\par }{\plain 127 Lubbock\_Cooper \_0.1 45.9 65.3\par }{\plain 204 Lufkin \_3.6 38.6 54.9\par }{\plain 81 Luling 2.6 43.4 58.3\par }{\plain 80 Lyford 2.6 45.5 59.3\par }{\plain 227 Lytle \_5.5 36.9 65.2\par }{\plain 50 Manor 4.3 44.2 59.0\par }{\plain 178 Marble Falls \_2.4 40.2 62.2\par }{\plain 149 Marlin \_1.0 40.9 58.3\par }{\plain 194 Mathis \_3.1 32.9 47.2\par }{\plain 22 McAllen 6.7 54.6 69.6\par }{\plain 123 McGregor 0.0 49.3 76.7\par }{\plain 255 McKinney \_9.7 34.4 56.9\par }{\plain 172 Medina Valley \_2.1 42.6 64.6\par }{\plain 47 Mercedes 4.3 48.6 64.1\par }{\plain 3 Merkel 11.3 55.5 73.3\par }{\plain 199 Midland \_3.3 38.7 51.8\par }{\plain 160 Mineral Wells \_1.5 41.1 54.0\par }{\plain 2 Mission Consolidated 11.5 56.8 76.0\par }{\plain 60 Monahans\_Wickett\_Pyo 3.6 49.7 74.2\par }{\plain 10 Mount Pleasant 9.1 49.8 59.7\par }{\plain 222 Muleshoe \_5.3 38.5 56.4\par }{\plain 206 Navasota \_3.7 37.4 56.7\par }{\plain 67 Needville 3.1 51.0 66.8\par }{\plain 150 New Braunfels \_1.0 44.9 64.5\par }{\plain 64 Newton 3.5 45.2 68.0\par }{\plain 124 North East \_0.0 52.8 70.1\par }{\plain 125 Northside [Bexar] \_0.1 50.0 66.0\par }{\plain 51 Odem\_Edroy 4.2 50.9 70.1\par }{\plain 88 Orange Grove 2.0 46.4 60.7\par }{\plain 138 Palacios \_0.6 49.2 68.3\par }{\plain 235 Palestine \_6.6 37.1 53.7\par }{\plain 35 Pampa 5.4 50.3 74.0\par }{\plain 223 Pasadena \_5.3 41.6 61.5\par }{\plain 56 Pearland 4.0 56.2 80.0\par }{\plain 139 Pecos\_Barstow\_Toyah \_0.7 43.6 68.9\par }{\plain 55 Perryton 4.0 49.6 66.5\par }{\plain 85 Pflugerville 2.3 57.1 72.0\par }{\plain 78 Pharr\_San Juan\_Alamo 2.6 46.8 63.8\par }{\plain 34 Plainview 5.5 49.2 69.6\par }{\plain 176 Pleasanton \_2.3 39.5 58.1\par }{\plain 94 Point Isabel 1.7 44.7 71.9\par }{\plain 133 Post \_0.3 43.3 70.4\par }{\plain 233 Poteet \_6.1 34.7 52.0\par }{\plain 231 Presidio \_6.0 34.9 45.3\par }{\plain 153 Raymondville \_1.3 41.4 58.2\par }{\plain 191 Reagan County \_2.9 48.6 69.6\par }{\plain 185 Rice Consolidated \_2.7 39.5 61.8\par }{\plain 114 Rio Hondo 0.5 44.2 65.3\par }{\plain 92 Robstown 1.8 39.8 62.3\par }{\plain 168 Rockdale \_1.9 41.6 58.5\par }{\plain 209 Roosevelt \_3.9 41.7 68.1\par }{\plain 97 Round Rock 1.6 55.1 69.0\par }{\plain 210 Royal \_3.9 34.5 50.0\par }{\plain 73 Royse City 2.9 50.5 74.7\par }{\plain 180 San Marcos \_2.5 41.4 62.0\par }{\plain 11 San Benito Consolida 8.2 49.8 73.0\par }{\plain 155 San Diego \_1.4 38.1 46.8\par }{\plain 256 San Antonio \_9.7 35.1 48.5\par }{\plain 57 San Felipe\_Del Rio C 3.9 47.2 64.2\par }{\plain 261 San Elizario \_12.7 23.8 36.2\par }{\plain 202 San Angelo \_3.5 42.9 62.0\par }{\plain 226 Santa Rosa \_5.5 38.0 52.8\par }{\plain 225 Schertz\_Cibolo\_U. Ci \_5.4 42.0 59.1\par }{\plain 212 Sealy \_4.1 43.0 67.1\par }{\plain 162 Seguin \_1.6 42.6 55.9\par }{\plain 156 Seminole \_1.4 46.7 67.5\par }{\plain 166 Shallowater \_1.8 43.3 57.9\par }{\plain 68 Sharyland 3.0 50.1 70.2\par }{\plain 115 Sheldon 0.3 47.9 62.7\par }{\plain 159 Sinton \_1.5 42.1 61.0\par }{\plain 181 Slaton \_2.5 40.1 57.3\par }{\plain 259 Smithville \_11.5 33.8 50.8\par }{\plain 36 Snyder 5.4 49.2 63.9\par }{\plain 75 Socorro 2.7 49.1 65.7\par }{\plain 230 Somerset \_6.0 32.4 54.6\par }{\plain 198 Sonora \_3.3 47.6 53.1\par }{\plain 195 South San Antonio \_3.1 42.3 62.1\par }{\plain 1 South Texas 22.6 85.3 92.4\par }{\plain 257 Southside \_10.4 29.1 42.3\par }{\plain 246 Southwest \_7.5 33.3 53.8\par }{\plain 241 Spring \_6.9 47.8 67.0\par }{\plain 220 Spring Branch \_4.9 44.1 59.9\par }{\plain 207 Stafford MSD \_3.8 45.7 62.9\par }{\plain 83 Sweeny 2.4 52.7 71.6\par }{\plain 215 Taft \_4.5 35.8 56.1\par }{\plain 182 Taylor \_2.6 42.3 62.0\par }{\plain 216 Temple \_4.6 42.4 55.2\par }{\plain 16 Texas City 7.3 53.4 75.4\par }{\plain 14 Troy 7.6 56.5 74.7\par }{\plain 29 Tulia 6.0 49.7 65.5\par }{\plain 23 Tuloso\_Midway 6.6 50.6 73.9\par }{\plain 161 Tyler \_1.6 41.9 56.1\par }{\plain 62 United 3.5 46.0 62.7\par }{\plain 239 Uvalde Consolidated \_6.8 34.1 54.2\par }{\plain 112 Van Vleck 0.6 48.9 70.0\par }{\plain 24 Vernon 6.4 49.0 63.1\par }{\plain 89 Victoria 1.9 43.7 63.7\par }{\plain 167 Waco \_1.9 36.8 51.2\par }{\plain 236 Waller \_6.7 34.2 51.4\par }{\plain 99 Waxahachie 1.4 46.8 67.8\par }{\plain 31 Weslaco 5.7 48.5 74.2\par }{\plain 213 West Oso \_4.1 37.0 58.5\par }{\plain 179 Wharton \_2.4 40.3 64.2\par }{\plain 101 Wichita Falls 1.4 47.2 68.4\par }{\plain 26 Wilmer\_Hutchins 6.2 45.5 63.2\par }{\plain 240 Yoakum \_6.9 39.4 53.2\par }{\plain 69 Ysleta 2.9 49.4 71.9\par }{\plain 157 Zapata \_1.4 39.3 66.0\par }}