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\pard \qc\sl0 
{\plain }{\plain \b \par
}\pard \qc\sl0 
{\plain \b \par
}{\plain \b \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\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 Kenneth J. Meier\par
}{\plain \f1 John Bohte\par
}{\plain \f1 J. L. Polinard\par
}{\plain \f1 Robert D. Wrinkle\par
}{\plain \f1 \par
}{\plain \f1 \par
}{\plain \f1 Report 6.   July 1999\par
}{\plain \f1 \par
}{\plain \f1 \par
}\pard \sl0 
{\plain \f1 For further information contact:\par
}{\plain \f1 \tab \par
}{\plain \f1 \tab Kenneth J. Meier, Department of Political Science, Texas A&M University\par
}{\plain \f1 \tab 409-845-4232  kmeier@polisci.tamu.edu\par
}{\plain \f1 \par
}{\plain \f1 \tab John Bohte, Department of Political Science, Texas A&M University,\par
}{\plain \f1 \tab 409-845- 2327      }{\field{\fldinst  GOTOBUTTON BM_1_ {\plain \f1 }}{\fldrslt }}{\plain \f1 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 956-381-3341 Polinard@panam.edu\par
}{\plain \f1 \par
}{\plain \f1 \tab Robert D. Wrinkle, Department of Political Science, University of Texas-Pan \tab American, 
956-381-3341  rdwe116@panam1.panam.edu\par
}{\plain \f1 \tab \par
}{\plain \f1 \tab See the Texas Educational Excellence Project at http://people.tamu.edu/~kmeier/teep/\par
}{\plain \f1 \par
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\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 Minority student pass rates in Texas on the TAAS exam consistently have lagged behind those 
for Anglo students. Recent trends in Latino test scores, however, show some slight gains. From 1995 to 
1998, the statewide pass rate for Latino students on the TAAS has improved from 46.1% to 61.9%, 
compared to a rate of change for non-minority students of 74.8% to 87.9%.  This narrowing of the gap 
in Latino/Anglo pass rates, while modest,  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 technique of analysis used by the Texas Educational Excellence Project is that of multiple 
regression. This analytical tool 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 what is 
generally know as 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 The Texas school districts included in the study are those with at least 1000 students, which 
have no more than 90 percent Anglo students and at least 10 percent Latino students.  In other words, 
we focus on multiethnic districts.  This restriction results in a total number of 303.  Most of the data 
used in the analysis comes from the Texas Educational Agency and the rest from the U. S. Bureau of 
the Census, School District Data File.\par
}{\plain \f1 \par
}{\plain \f1 \tab As noted above, the statewide results showed a slight narrowing of tbe gap between Latino and 
Anglo students on TAAS pass rates.  For our multiethnic districts, the 1995 gap was 47.6 percent 
Latino vs. 71.3 percent Anglo while by 1998 the gap was 69.2 percent Latino vs. 86.6 percent Anglo.  
In three years, the Latino-Anglo gap narrowed from 24.7 points to 17.4 points, a considerable 
reduction. \par
}{\plain \f1 \par
}\pard \qc\sl0 
{\plain \b\f1 Dependent Variable: Student Performance}{\plain \f1 \par
}\pard \sl0 
{\plain \f1 \par
}{\plain \f1 \tab Several states use annual standardized tests to assess achievement at the basic skills level, and, 
often, require a certain level of proficiency on the test as a graduation requirement.  While basic skills 
are not the only educational focus of public schools, they are a crucial element and offer one measure of 
performance. 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 all of 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 \b\f1 Independent Variables}{\plain \f1 \par
}\pard \sl0 
{\plain \f1 \par
}{\plain \f1 \tab We establish an education production function by including a variety of factors known to 
influence educational performance.  These variables are culled from the education literature and are 
frequently used in education production functions. Our variables can be divided into two sets of 
independent variables. 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 question of what relationship exists between expenditures and educational 
outcomes is one of the most contested questions in all of educational policy. Hanushek (1986; 1989; 
1996) contends that there is no consistent relationship between money and student outcomes.  Although 
this finding has been challenged by others (Hedges and Greenwald 1996), it remains the conventional 
wisdom.  In recent longitudinal studies, however, Murray (1995), Evans, Murray and Schwab (1997), 
and Murray, Evans and Schwab (1995) found that districts that increased expenditures had improved 
performance afterward. Bohte (1999) found that expenditures in Texas 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.\par
}{\plain \f1 \par
}{\plain \f1 \tab We use three distinct expenditure variables: 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.  The presence of 
more experienced teachers should have a positive effect on student performance.  In this sense, teacher 
experience is an important resource variable. Our first variable is a measure of average teacher 
experience (in years) for each district. To further measure teacher attributes, we also include the 
percentage of non-certified teachers in each district.  Our expectation is that this relationship should be 
negative.\par
}{\plain \f1 \par
}{\plain \b\f1 3. Policy Variables.}{\plain \f1  Education policies are specific policies adopted to influence student performance. 
Two such policies deal with the 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; Bohte 1999). Our first policy variable is the student-teacher 
ratio in each district.  We expect this variable to have a negative relationship to student pass rates. 
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 (from zero to more than thirty percent).  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, especially in Texas, vary widely in terms of environmental or background 
characteristics. To ensure that we are comparing apples to apples, controls must be included for 
various district background characteristics. Using of controls 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 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 1995 through 1998.  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 most of the variables, 
with the exception of class size, per pupil instructional funds and teacher experience, are significant 
predictors of average district Latino pass rates.  \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 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 Los Fresnos Consolidated School District should have an average Latino 
student pass rate of 58.8% from 1995 to 1998. The Los Fresnos actual pass rate of 76.9% represents 
an 17.1% 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 +18.3%, followed by Los Fresnos with a rating of +17.1% and Pittsburgh with a rating 
of +15.37%.  \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 Latino student pass 
rate for 1998. The fourth column is the average pass rate for Latino students from 1995 to 1998 in the 
district.  For example, Aldine had a 1995-1998 score of 11.28 to rank tenth.  The 1998 score for 
Aldine was 11.49 and the 1995-1998 average Latino student pass rate on the TAAS was 69.15 
percent. \par
}{\plain \f1 \par
}{\plain \f1 \tab Our ranking is based on the average scores for 1995 through 1998.  Consequently, it may not 
recognize districts where dramatic improvements have been made recently. For example, Brazosport 
improved a great deal between 1995 and 1998.   Brazosport also ranks seventh in Table 3, the ranking 
of the twenty five best school districts for Latinos in 1998.  This compares to Brazosport\'92s ranking of  
thirteenth out of the more than 300 districts examined for the entire four year period.\par
}{\plain \f1 \par
}{\plain \f1 \tab The Appendix Table 1 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 303 districts in the 
study.\par
}{\plain \f1 \par
}{\plain \f1 \tab Given the rate of improvement in Latino TAAS scores over the past few 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 continue to close the gap between Latino and Anglo student 
performance in Texas.\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 continue to lag behind those for Anglo students.  
Although Latino students have closed this gap somewhat over the past few years, a substantial 
difference remains.  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 From our discussion, visits and talks with various school district personnel, we continue to 
believe 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
}{\plain \f1 \pard\page \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 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 Bothe, John, 1999.  "Teacher Salaries, Class Size, and Student Performance." College Station, TX: 
Texas Educational Excellence Project: Report 4 (January).\par
}\pard \sl0 
{\plain \f1 \par
}\pard \fi-1440\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
}\pard \fi-720\li720\sl0 
{\plain \f1 Evans, William N., Sheila E. Murray, and Robert M. Schwab.  1997.  "Schoolhouses, Courthouses, 
and Statehouses After }{\plain \i\f1 Serrano}{\plain \f1 ." }{\plain \i\f1 Journal of Policy Analysis and 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.}{\plain \f1 \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 \sl0 
{\plain \f1 \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 \b\f1 \par
}\pard \fi-720\li720\sl0 
{\plain \b\f1 }{\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
}\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 \f3 \par
}\pard \sl0 
\pard\page 
\pard \qc\sl0 
{\plain \f3 }{\plain \b\f3 TABLE 1: LATINO EDUCATIONAL PRODUCTION FUNCTION\par
}\pard \sl0 
{\plain \b\f3 \par
}{\plain \b\ul\f3 Variable           Coefficient           Standard Error}{\plain \ul\f3 \par
}{\plain \f3 \par
}{\plain \f3 Low Income         -.0632                     .0173 \par
}{\plain \f3 \par
}{\plain \f3 Gifted              .2576                     .0659\par
}{\plain \f3 \par
}{\plain \f3 Attendance         3.1286                     .2883\par
}{\plain \f3 \par
}{\plain \f3 Teacher Salary K    .7522                     .1684 \par
}{\plain \f3 \par
}{\plain \f3 Class size         -.1128                     .2332\par
}{\plain \f3 \par
}{\plain \f3 Teacher \par
}{\plain \f3 Certification      -.1823                     .0735\par
}{\plain \f3 \par
}{\plain \f3 Teacher \par
}{\plain \f3 Experience          .0159                     .1728\par
}{\plain \f3 \par
}{\plain \f3 State Aid           .0364                     .0126\par
}{\plain \f3 \par
}{\plain \f3 High School \par
}{\plain \f3 Education           .1009                     .0304\par
}{\plain \f3 \par
}{\plain \f3 %Poverty \par
}{\plain \f3 Background         -.0890                     .0256\par
}{\plain \f3 \par
}{\plain \f3 Per Pupil \par
}{\plain \f3 Instructional       .0010                     .0010\par
}{\plain \f3 \par
}{\plain \f3 \par
}{\plain \ul\f3                                                      }{\plain \f3 \par
}{\plain \f3 \par
}{\plain \f3 R2 (adj)= .62\par
}{\plain \f3 \par
}{\plain \f3 F= 149.04\par
}{\plain \f3 \par
}{\plain \f3 significance of F < .000\par
}{\plain \f3 \par
}{\plain \f3 \par
}{\plain \f3 \par
}\pard\page 
\pard \qc\sl0 
{\plain \f3 }{\plain \b\f3 Table 2. 25 Best Districts for Latino Students\par
}{\plain \b\ul\f3 \par
}{\plain \b\ul\f3 Rank  Name               Score   98 Score  Average }{\plain \b\f3           }{\plain \f3 \par
}{\plain \f3 \par
}{\plain \f3   1 South Texas          18.30     11.59     88.38\par
}{\plain \f3   2 Los Fresnos Con      17.09     18.95     76.93\par
}{\plain \f3   3 Pittsburgh           15.37     15.24     68.35\par
}{\plain \f3   4 White Settlement     14.87      9.68     74.78\par
}{\plain \f3   5 Anahuac              14.18     15.28     70.35\par
}{\plain \f3 \par
}{\plain \f3   6 Mount Vernon         14.06      9.02     75.03\par
}{\plain \f3   7 San Benito Cons      13.36     16.76     67.38\par
}{\plain \f3   8 Ferris               13.33     10.80     70.45\par
}{\plain \f3   9 Mission Cons         13.01      9.82     72.70\par
}{\plain \f3  10 Aldine               11.28     11.49     69.15\par
}{\plain \f3 \par
}{\plain \f3  11 Del Valle            11.17     17.44     60.95\par
}{\plain \f3  12 Texas City           11.16      6.19     69.60\par
}{\plain \f3  13 Brazosport           10.95     14.02     75.30\par
}{\plain \f3  14 Alvarado             10.63     11.57     64.93\par
}{\plain \f3  15 Columbia\_Brazoria     9.39      6.87     70.43\par
}{\plain \f3 \par
}{\plain \f3  16 Pecos\_Barstow\_T       9.22      8.68     62.92\par
}{\plain \f3  17 Tuloso\_Midway         9.15     10.86     65.38\par
}{\plain \f3  18 Ysleta                9.06     10.98     66.43\par
}{\plain \f3  19 Hidalgo               9.01      8.39     62.70\par
}{\plain \f3  20 Childress             8.97      1.00     66.05\par
}{\plain \f3 \par
}{\plain \f3  21 Tatum                 8.90     \_0.06     69.90\par
}{\plain \f3  22 Pearland              8.85      9.27     74.30\par
}{\plain \f3  23 Jim Hogg County       8.84      8.88     68.10\par
}{\plain \f3  24 Edna                  8.67     10.54     67.80\par
}{\plain \f3  25 Mexia                 8.66      0.67     65.10\par
}{\plain \f3 \par
}{\plain \f3 }\pard\page 
\pard \qc\sl0 
{\plain \f3 }{\plain \b\f3 Table 3. 25 Best Districts for Latinos 1998}{\plain \f3 \par
}\pard \sl0 
{\plain \f3 \par
}{\plain \ul\f3 Rank Name              98 Score}{\plain \f3 \par
}{\plain \f3   1 Los Fresnos Con      18.95\par
}{\plain \f3   2 McGregor             17.51\par
}{\plain \f3   3 Del Valle            17.44\par
}{\plain \f3   4 San Benito Cons      16.76\par
}{\plain \f3   5 Anahuac              15.28\par
}{\plain \f3 \par
}{\plain \f3   6 Pittsburgh           15.24\par
}{\plain \f3   7 Brazosport           14.02\par
}{\plain \f3   8 Point Isabel         11.64\par
}{\plain \f3   9 South Texas          11.59\par
}{\plain \f3  10 Alvarado             11.57\par
}{\plain \f3 \par
}{\plain \f3  11 Lytle                11.55\par
}{\plain \f3  12 Aldine               11.49\par
}{\plain \f3  13 Ysleta               10.98\par
}{\plain \f3  14 Tuloso\_Midway        10.86\par
}{\plain \f3  15 Burnet Cons          10.85\par
}{\plain \f3 \par
}{\plain \f3  16 Ferris               10.80\par
}{\plain \f3  17 El Campo             10.66\par
}{\plain \f3  18 Coleman              10.61\par
}{\plain \f3  19 Edna                 10.54\par
}{\plain \f3  20 Terrell              10.43\par
}{\plain \f3 \par
}{\plain \f3  21 West Oso             10.32\par
}{\plain \f3  22 Frenship              9.90\par
}{\plain \f3  23 Mission Cons          9.82\par
}{\plain \f3  24 White Settlement      9.68\par
}{\plain \f3  25 Monahans\_Wicket       9.46\par
}{\plain \f3 \par
}{\plain \f3 }\pard\page 
\pard \qc\sl0 
{\plain \f3 Appendix Table 1. Scores for All Districts\par
}\pard \sl0 
{\plain \ul\f3 Rank  Name                       Score      98 Score  Average    }{\plain \f3   \par
}{\plain 161 Abilene                      \_0.59         0.33        58.80\par
}{\plain 289 Alamo Heights                \_9.28       \_12.91        61.85\par
}{\plain  10 Aldine                       11.28        11.49        69.15\par
}{\plain 112 Alice                         2.47         5.40        56.72\par
}{\plain 250 Alief                        \_5.99        \_5.25        57.08\par
}{\plain 131 Alpine                        0.98        \_0.82        62.22\par
}{\plain  14 Alvarado                     10.63        11.57        64.93\par
}{\plain  77 Alvin                         4.10         5.89        62.15\par
}{\plain  95 Amarillo                      3.16         1.20        59.90\par
}{\plain   5 Anahuac                      14.18        15.28        70.35\par
}{\plain  38 Andrews                       7.23         5.39        66.60\par
}{\plain  46 Angleton                      6.48         9.33        71.18\par
}{\plain  88 Aransas County                3.45         8.04        59.67\par
}{\plain 260 Aransas Pass                 \_6.66        \_5.47        48.97\par
}{\plain 286 Arlington                    \_9.16       \_10.10        55.95\par
}{\plain 259 Athens                       \_6.52        \_6.66        47.83\par
}{\plain 273 Austin                       \_7.53        \_8.50        47.22\par
}{\plain 122 Ballinger                     1.53        \_2.60        61.50\par
}{\plain 202 Bandera                      \_2.55         4.24        56.05\par
}{\plain 192 Bastrop                      \_1.95         0.55        53.10\par
}{\plain  61 Bay City                      4.81         8.50        58.95\par
}{\plain  89 Beeville                      3.35         4.65        58.38\par
}{\plain 160 Belton                       \_0.49         2.95        59.35\par
}{\plain  80 Big Spring                    3.92         2.79        59.40\par
}{\plain  97 Bishop Consolidated           3.10         6.85        62.28\par
}{\plain 172 Boerne                       \_0.84         0.45        60.88\par
}{\plain  75 Borger                        4.12         1.18        63.55\par
}{\plain  68 Brady                         4.46         7.18        62.92\par
}{\plain  13 Brazosport                   10.95        14.02        75.30\par
}{\plain  27 Breckenridge                  8.37         8.16        65.47\par
}{\plain 118 Bridgeport                    2.13         0.36        60.80\par
}{\plain 213 Brooks                       \_3.65        \_2.94        48.38\par
}{\plain 255 Brownfield                   \_6.35        \_6.16        50.67\par
}{\plain 180 Brownsville                  \_1.21         2.57        54.92\par
}{\plain  78 Brownwood                     4.08         4.64        59.58\par
}{\plain 278 Bryan                        \_7.86        \_3.51        51.72\par
}{\plain 150 Burnet Consolidated          \_0.08        10.85        57.55\par
}{\plain  29 Calallen                      8.31         6.03        71.60\par
}{\plain  50 Caldwell                      5.84         4.42        65.10\par
}{\plain 156 Calhoun County               \_0.25        \_0.12        55.13\par
}{\plain 135 Cameron                       0.92         5.11        57.65\par
}{\plain 252 Canutillo                    \_6.02        \_5.57        49.75\par
}{\plain 178 Carrizo Springs Cons         \_1.11        \_4.50        53.40\par
}{\plain 190 CarrolltonFarmers Br         \_1.94        \_5.06        63.67\par
}{\plain 136 Castleberry                   0.91        \_8.74        57.92\par
}{\plain 102 Cedar Hill                    2.88        \_6.94        68.47\par
}{\plain 108 Channelview                   2.75        \_1.88        63.95\par
}{\plain  20 Childress                     8.97         1.00        66.05\par
}{\plain  93 Clear Creek                   3.18         0.56        70.55\par
}{\plain 219 Cleburne                     \_3.96        \_8.97        55.50\par
}{\plain 301 Cleveland                   \_13.49       \_15.30        37.90\par
}{\plain  65 Clint                         4.60         5.13        58.65\par
}{\plain  33 Coleman                       7.87        10.61        67.05\par
}{\plain 214 Colorado                     \_3.68        \_1.21        55.10\par
}{\plain  15 Columbia\_Brazoria             9.39         6.87        70.43\par
}{\plain 218 Columbus                     \_3.95        \_3.18        55.58\par
}{\plain 199 Comal                        \_2.43         0.74        58.00\par
}{\plain 227 Comanche                     \_4.30        \_3.73        58.25\par
}{\plain  47 Connally                      6.40         5.09        66.38\par
}{\plain 265 Conroe                       \_6.81        \_6.72        55.00\par
}{\plain  55 Copperas Cove                 5.45         7.70        68.05\par
}{\plain 144 Corpus Christi                0.30         3.31        60.75\par
}{\plain 110 Corrigan\_Camden               2.62         0.44        60.00\par
}{\plain 183 Corsicana                    \_1.37        \_2.24        54.40\par
}{\plain 298 Cotulla                     \_12.59       \_14.74        41.92\par
}{\plain  81 Crane                         3.82         4.28        65.53\par
}{\plain 258 Cureo                        \_6.46        \_4.25        51.40\par
}{\plain 139 Cypress\_Fairbanks             0.74        \_2.48        66.72\par
}{\plain  36 Dalhart                       7.66         8.53        64.68\par
}{\plain 222 Dallas                       \_4.06        \_7.90        52.40\par
}{\plain 184 Decatur                      \_1.54        \_6.78        56.65\par
}{\plain 235 Deer Park                    \_4.84        \_4.61        59.75\par
}{\plain  11 Del Valle                    11.17        17.44        60.95\par
}{\plain 275 Denton                       \_7.59        \_7.91        52.63\par
}{\plain  64 Denver City                   4.61         2.96        68.38\par
}{\plain 224 Devine                       \_4.11        \_6.35        56.42\par
}{\plain 239 Diboll                       \_5.13       \_11.11        51.88\par
}{\plain 282 Dickinson                    \_8.47        \_8.39        43.95\par
}{\plain  79 Dilley                        4.07         0.69        58.90\par
}{\plain 203 Dimmitt                      \_2.77        \_0.71        54.05\par
}{\plain 147 Donna                        \_0.05        \_1.63        51.15\par
}{\plain 300 Dublin                      \_12.80       \_10.15        41.15\par
}{\plain  82 Dumas                         3.80         6.82        58.38\par
}{\plain 154 Duncanville                  \_0.23         1.89        64.50\par
}{\plain  90 Eagle Mt\_Saginaw              3.30         4.61        66.63\par
}{\plain  71 Eagle Pass                    4.31         7.43        59.22\par
}{\plain 242 East Central                 \_5.29        \_3.51        58.70\par
}{\plain  30 Eastland                      8.31         8.71        66.97\par
}{\plain  59 Ector County                  4.96         5.04        57.53\par
}{\plain  60 Edcouch\_Elsa                  4.96         4.17        64.85\par
}{\plain 158 Edgewood                     \_0.44        \_0.19        49.92\par
}{\plain  58 Edinburg                      5.10         5.86        61.83\par
}{\plain  24 Edna                          8.67        10.54        67.80\par
}{\plain 176 El Paso                      \_1.02        \_1.50        56.38\par
}{\plain  85 El Campo                      3.69        10.66        62.67\par
}{\plain 238 Elgin                        \_5.13        \_3.12        52.67\par
}{\plain 148 Ennis                        \_0.05        \_4.39        58.80\par
}{\plain  51 Everman                       5.84         8.84        67.63\par
}{\plain 269 Fabens                       \_6.99        \_7.97        49.08\par
}{\plain   8 Ferris                       13.33        10.80        70.45\par
}{\plain 155 Floresville                  \_0.24         0.78        55.35\par
}{\plain 100 Flower Bluff                  3.00         6.77        65.38\par
}{\plain 293 Floydada                    \_11.44       \_17.04        43.08\par
}{\plain 280 Fort Bend                    \_8.10        \_7.36        57.80\par
}{\plain 279 Fort Worth                   \_7.92        \_8.18        46.58\par
}{\plain 253 Fredericksburg               \_6.21        \_6.66        51.17\par
}{\plain  49 Freer                         6.31         7.37        64.18\par
}{\plain  34 Frenship                      7.72         9.90        67.40\par
}{\plain 191 Friona                       \_1.94         2.42        57.45\par
}{\plain  86 Frisco                        3.61         0.70        62.92\par
}{\plain 231 Ft Sam Houston               \_4.50        \_2.98        71.25\par
}{\plain  96 Ft. Stockton                  3.14         6.14        57.85\par
}{\plain  73 Gainesville                   4.23        \_4.89        62.30\par
}{\plain 182 Galena Park                  \_1.37         2.89        56.25\par
}{\plain  91 Galveston                     3.27         9.12        54.78\par
}{\plain 104 Garland                       2.86        \_0.72        65.60\par
}{\plain 247 George West                  \_5.87        \_7.28        54.78\par
}{\plain 233 Georgetown                   \_4.63        \_7.96        61.22\par
}{\plain 167 Giddings                     \_0.72        \_1.77        59.80\par
}{\plain 171 Glen Rose                    \_0.83         2.72        61.90\par
}{\plain  42 Goliad                        6.72         4.43        67.02\par
}{\plain 240 Gonzales                     \_5.15        \_6.27        50.42\par
}{\plain  57 Goose Creek                   5.11         4.41        61.50\par
}{\plain 221 Graham                       \_4.05        \_0.90        57.25\par
}{\plain 141 Grand Prairie                 0.57        \_2.05        62.53\par
}{\plain 209 Greenville                   \_3.41        \_6.11        53.15\par
}{\plain 267 Greenwood                    \_6.84        \_7.98        56.80\par
}{\plain 107 Gregory\_Portland              2.81         2.36        68.13\par
}{\plain  56 Groesbeck                     5.45        \_1.47        64.50\par
}{\plain 268 Harlandale                   \_6.99        \_2.81        50.70\par
}{\plain  69 Harlingen                     4.34         6.55        65.93\par
}{\plain 262 Hayes Consolidated           \_6.69        \_3.38        54.63\par
}{\plain 169 Hearne                       \_0.77        \_2.61        56.10\par
}{\plain 296 Hempstead                   \_12.38       \_10.86        41.70\par
}{\plain 127 Hereford                      1.19         2.17        57.00\par
}{\plain  19 Hidalgo                       9.01         8.39        62.70\par
}{\plain 117 Hillsboro                     2.19         3.24        54.38\par
}{\plain 284 Hitchcock                    \_8.79        \_8.34        48.33\par
}{\plain 223 Hondo                        \_4.09        \_3.45        51.58\par
}{\plain 195 Houston                      \_2.14        \_0.83        52.97\par
}{\plain 115 Hudson                        2.25         0.12        62.97\par
}{\plain 244 Huntsville                   \_5.45        \_6.04        54.53\par
}{\plain 228 Ingleside                    \_4.34        \_6.74        55.20\par
}{\plain 264 Ingram                       \_6.78        \_2.97        53.92\par
}{\plain 123 Irving                        1.43        \_0.01        63.03\par
}{\plain 294 Jacksonville                \_11.47       \_11.80        41.65\par
}{\plain  23 Jim Hogg County               8.84         8.88        68.10\par
}{\plain 153 Jourdanton                   \_0.23        \_3.74        59.33\par
}{\plain 193 Judson                       \_1.98        \_2.55        64.63\par
}{\plain 114 Katy                          2.28         0.76        71.63\par
}{\plain  32 Kaufman                       7.87         6.54        62.83\par
}{\plain 271 Kenedy                       \_7.34       \_11.28        47.05\par
}{\plain 274 Kermit                       \_7.56        \_9.31        48.38\par
}{\plain 120 Kerrville                     1.75         2.04        62.03\par
}{\plain 194 Killeen                      \_2.04         0.36        62.97\par
}{\plain 173 Kingsville                   \_0.87         1.66        57.92\par
}{\plain 186 Klein                        \_1.56        \_6.39        67.53\par
}{\plain 134 La Porte                      0.93        \_1.32        65.22\par
}{\plain  35 La Feria                      7.69         8.79        67.80\par
}{\plain 200 La Vega                      \_2.45         4.02        52.55\par
}{\plain 113 La Joya                       2.36         0.61        52.55\par
}{\plain 145 La Grange                     0.19        \_2.36        57.60\par
}{\plain 288 La Vernia                    \_9.26         5.92        56.30\par
}{\plain 159 Lake Worth                   \_0.48         3.23        49.83\par
}{\plain 162 Lamar Consolidated           \_0.60         1.38        58.65\par
}{\plain 217 Lamesa                       \_3.89        \_7.87        50.72\par
}{\plain 109 Lampasas                      2.73         6.80        60.92\par
}{\plain 216 Lancaster                    \_3.88        \_8.29        56.85\par
}{\plain 230 Laredo                       \_4.46        \_9.89        55.70\par
}{\plain 211 Leander                      \_3.48         0.09        59.88\par
}{\plain 188 Levelland                    \_1.65        \_1.75        56.88\par
}{\plain 220 Liberty                      \_4.01        \_1.47        53.80\par
}{\plain 101 Littlefield                   2.94         1.28        59.60\par
}{\plain 111 Lockhart                      2.50         0.70        58.78\par
}{\plain   2 Los Fresnos Consolid         17.09        18.95        76.93\par
}{\plain 197 Lubbock                      \_2.17        \_1.70        57.35\par
}{\plain  84 Lubbock\_Cooper                3.73         2.56        63.95\par
}{\plain 257 Lufkin                       \_6.40        \_4.78        50.58\par
}{\plain 196 Luling                       \_2.15       \_12.10        53.45\par
}{\plain 187 Lyford                       \_1.61        \_0.99        53.80\par
}{\plain 146 Lytle                         0.17        11.55        55.90\par
}{\plain 137 Madisonville                  0.88        \_3.55        58.15\par
}{\plain 152 Manor                        \_0.22        \_4.74        54.70\par
}{\plain  99 Mansfield                     3.02         6.64        66.45\par
}{\plain 241 Marble Falls                 \_5.24        \_8.05        50.97\par
}{\plain 164 Marlin                       \_0.62         3.77        53.40\par
}{\plain 281 Mathis                       \_8.19        \_7.21        43.55\par
}{\plain  37 McAllen                       7.31         6.41        66.30\par
}{\plain  31 McGregor                      7.88        17.51        71.45\par
}{\plain 283 McKinney                     \_8.73       \_11.62        48.60\par
}{\plain 149 Medina Valley                \_0.07        \_1.24        58.58\par
}{\plain 168 Mercedes                     \_0.76        \_2.20        59.00\par
}{\plain  54 Merkel                        5.67        \_2.05        65.50\par
}{\plain 132 Mesquite                      0.98        \_0.79        63.55\par
}{\plain  25 Mexia                         8.66         0.67        65.10\par
}{\plain 261 Midland                      \_6.68        \_6.70        49.10\par
}{\plain 166 Mineral Wells                \_0.72        \_4.67        53.92\par
}{\plain   9 Mission Consolidated         13.01         9.82        72.70\par
}{\plain  28 Monahans\_Wickett\_Pyo          8.35         9.46        67.72\par
}{\plain  70 Mount Pleasant                4.32        \_6.74        59.70\par
}{\plain   6 Mount Vernon                 14.06         9.02        75.03\par
}{\plain 201 Muleshoe                     \_2.53        \_0.36        54.85\par
}{\plain 290 Nacognoches                  \_9.35       \_10.45        47.72\par
}{\plain 248 Navasota                     \_5.91        \_6.61        49.50\par
}{\plain 165 Needville                    \_0.71        \_1.81        63.65\par
}{\plain 179 New Braunfels                \_1.12         0.79        59.55\par
}{\plain  62 Newton                        4.75         2.53        61.88\par
}{\plain  44 North Forest                  6.63         8.28        61.40\par
}{\plain 163 North East                   \_0.61        \_2.52        65.78\par
}{\plain 198 Northside                    \_2.34        \_2.33        61.67\par
}{\plain 126 Odem\_Edroy                    1.34         2.98        62.25\par
}{\plain 206 Orange Grove                 \_3.27        \_4.18        56.75\par
}{\plain 189 Palacios                     \_1.73        \_1.25        63.63\par
}{\plain 254 Palestine                    \_6.21        \_2.40        50.17\par
}{\plain  52 Pampa                         5.79         3.45        67.00\par
}{\plain 185 Pasadena                     \_1.54         1.74        56.55\par
}{\plain  22 Pearland                      8.85         9.27        74.30\par
}{\plain  16 Pecos\_Barstow\_Toyah           9.22         8.68        62.92\par
}{\plain 129 Perryton                      1.07         2.78        61.60\par
}{\plain 138 Pflugerville                  0.85        \_3.15        69.15\par
}{\plain  92 Pharr\_San Juan\_Alamo          3.21         4.85        60.97\par
}{\plain   3 Pittsburgh                   15.37        15.24        68.35\par
}{\plain  40 Plainview                     7.02         5.08        63.75\par
}{\plain 174 Pleasanton                   \_0.88         2.47        54.30\par
}{\plain  26 Point Isabel                  8.55        11.64        65.20\par
}{\plain 130 Port Arthur                   1.04        \_0.68        51.10\par
}{\plain 105 Post                          2.85        \_1.45        62.38\par
}{\plain 291 Poteet                       \_9.38        \_4.55        46.28\par
}{\plain 125 Presidio                      1.36         6.30        51.17\par
}{\plain  63 Randolph Field                4.74         1.86        79.23\par
}{\plain 181 Raymondville                 \_1.36        \_2.17        54.15\par
}{\plain 175 Reagan County                \_0.97        \_4.09        63.22\par
}{\plain 236 Red Oak                      \_4.97        \_5.29        60.03\par
}{\plain 124 Rice Consolidated             1.37         8.75        55.42\par
}{\plain 256 Richardson                   \_6.36        \_7.10        58.33\par
}{\plain 140 Rio Hondo                     0.62         3.05        59.17\par
}{\plain  39 Robinson                      7.06         5.01        71.90\par
}{\plain 103 Robstown                      2.88         6.93        55.92\par
}{\plain 263 Rockdale                     \_6.74        \_4.25        53.08\par
}{\plain 170 Roosevelt                    \_0.81        \_1.38        59.15\par
}{\plain 177 Round Rock                   \_1.02        \_3.22        65.88\par
}{\plain 151 Royal                        \_0.22        \_2.42        48.13\par
}{\plain  43 Royse City                    6.67         7.26        67.35\par
}{\plain 133 San Marcos                    0.98         3.35        56.80\par
}{\plain  67 San Felipe\_Del Rio C          4.54         1.53        59.20\par
}{\plain 297 San Antonio                 \_12.40       \_10.85        44.85\par
}{\plain   7 San Benito Consolida         13.36        16.76        67.38\par
}{\plain 302 San Elizario                \_14.41       \_19.14        38.47\par
}{\plain 205 San Angelo                   \_3.08        \_1.12        56.35\par
}{\plain 266 San Diego                    \_6.84        \_5.56        44.22\par
}{\plain 287 Santa Rosa                   \_9.20        \_6.31        48.47\par
}{\plain 276 Schertz\_Cibolo\_U. Ci         \_7.81        \_7.38        54.08\par
}{\plain 212 Sealy                        \_3.60         0.22        57.70\par
}{\plain 229 Seguin                       \_4.35        \_5.21        52.85\par
}{\plain 204 Seminole                     \_2.88        \_1.31        58.10\par
}{\plain 225 Shallowater                  \_4.19        \_4.84        55.50\par
}{\plain 106 Sharyland                     2.84         6.09        65.13\par
}{\plain 208 Sheldon                      \_3.36        \_6.83        58.30\par
}{\plain 157 Sinton                       \_0.40         0.86        56.80\par
}{\plain 249 Slaton                       \_5.93        \_5.53        51.50\par
}{\plain 299 Smithville                  \_12.61        \_5.25        45.40\par
}{\plain  87 Snyder                        3.46         4.43        62.03\par
}{\plain  94 Socorro                       3.18         4.25        62.03\par
}{\plain 285 Somerset                     \_8.99        \_9.31        46.97\par
}{\plain 272 Sonora                       \_7.46        \_4.06        54.25\par
}{\plain   1 South Texas                  18.30        11.59        88.38\par
}{\plain 234 South San Antonio            \_4.79        \_3.92        55.42\par
}{\plain 295 Southside                   \_12.20       \_10.98        39.40\par
}{\plain 251 Southwest                    \_6.02         0.56        48.95\par
}{\plain 245 Spring                       \_5.54        \_3.06        63.20\par
}{\plain 215 Spring Branch                \_3.74        \_3.59        55.83\par
}{\plain 232 Stafford MSD                 \_4.62        \_6.51        59.00\par
}{\plain  83 Stephenville                  3.76         5.22        65.05\par
}{\plain  53 Sweeny                        5.67         7.27        68.55\par
}{\plain 226 Taft                         \_4.21         2.05        49.13\par
}{\plain  21 Tatum                         8.90        \_0.06        69.90\par
}{\plain 210 Taylor                       \_3.43        \_0.58        54.50\par
}{\plain 303 Teague                      \_21.63       \_22.25        39.15\par
}{\plain 277 Temple                       \_7.82        \_8.08        53.30\par
}{\plain  45 Terrell                       6.57        10.43        66.05\par
}{\plain  12 Texas City                   11.16         6.19        69.60\par
}{\plain  66 Troy                          4.55        \_3.16        68.20\par
}{\plain  41 Tulia                         6.74         4.39        64.15\par
}{\plain  17 Tuloso\_Midway                 9.15        10.86        65.38\par
}{\plain 243 Tyler                        \_5.31        \_6.01        52.42\par
}{\plain  76 United                        4.11         3.57        59.20\par
}{\plain 246 Uvalde Consolidated          \_5.72        \_4.13        48.40\par
}{\plain 142 Van Vleck                     0.56        \_5.10        64.45\par
}{\plain  72 Vernon                        4.30         0.48        62.20\par
}{\plain  98 Victoria                      3.07         3.08        57.75\par
}{\plain 207 Waco                         \_3.34         2.42        47.50\par
}{\plain 292 Waller                      \_10.81        \_9.04        44.30\par
}{\plain 143 Waxahachie                    0.37        \_2.39        61.45\par
}{\plain  48 Weslaco                       6.38         9.04        65.57\par
}{\plain 128 West Oso                      1.08        10.32        54.25\par
}{\plain   4 White Settlement             14.87         9.68        74.78\par
}{\plain 121 Wharton                       1.60         4.34        57.90\par
}{\plain  74 Wichita Falls                 4.17         5.09        63.65\par
}{\plain 237 Willis                       \_4.99         0.88        51.80\par
}{\plain 119 Wilmer\_Hutchins               2.12         7.83        54.05\par
}{\plain 270 Yoakum                       \_7.02        \_3.38        53.22\par
}{\plain  18 Ysleta                        9.06        10.98        66.43\par
}{\plain 116 Zapata                        2.23         1.94        55.85\par
}{\plain \f3 \par
}{\plain \f3 \par
}}