{\rtf1\ansi \deflang1033\deff0{\fonttbl
{\f0\fnil \fcharset0 \fprq2 Times New Roman;}{\f1\fnil \fcharset0 \fprq2 Courier New;}{\f2\fnil \fcharset0 \fprq2 Courier 10cpi;}}{\colortbl
\red0\green0\blue0;}
{\stylesheet{\fs20 \snext0 Normal;}
{\*\cs1 \additive footnote ref;}
{\*\cs2 \additive Default Para;}
{\*\cs3 \additive endnote text;}
{\*\cs4 \additive endnote refe;}
{\*\cs5 \additive footnote tex;}
{\*\cs6 \additive toc 1;}
{\*\cs7 \additive toc 2;}
{\*\cs8 \additive toc 3;}
{\*\cs9 \additive toc 4;}
{\*\cs10 \additive toc 5;}
{\*\cs11 \additive toc 6;}
{\*\cs12 \additive toc 7;}
{\*\cs13 \additive toc 8;}
{\*\cs14 \additive toc 9;}
{\*\cs15 \additive index 1;}
{\*\cs16 \additive index 2;}
{\*\cs17 \additive toa heading;}
{\*\cs18 \additive caption;}
{\*\cs19 \additive _Equation Ca;}
{\*\cs20 \additive 1;}
}\margl1440\margr1440\ftnbj\ftnrestart\aftnnar\revisions \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\tx0\tx720\tx1440\tx2160\tx2880\tx3600\tx4320\tx5040\tx5760\tx6480\tx7200\tx7920\tx8640\tx9360 
{\plain \f1 \par
}\pard \sl0\tx0\tx720\tx1440\tx2160\tx2880\tx3600\tx4320\tx5040\tx5760\tx6480\tx7200\tx7920\tx8640\tx9360 
{\plain \f1 \par
}{\plain \f1 \par
}{\plain \f1 \par
}{\plain \f1 \par
}{\plain \f1 \par
}{\plain \f1 \par
}\pard \qc\sl0\tx0\tx720\tx1440\tx2160\tx2880\tx3600\tx4320\tx5040\tx5760\tx6480\tx7200\tx7920\tx8640\tx9360 
{\plain \f1 BLACK STUDENT IMPROVEMENTS ON THE TAAS EXAM\par
}\pard \sl0\tx0\tx720\tx1440\tx2160\tx2880\tx3600\tx4320\tx5040\tx5760\tx6480\tx7200\tx7920\tx8640\tx9360 
{\plain \f1 \par
}{\plain \f1 \par
}{\plain \f1 \par
}{\plain \f1 \par
}{\plain \f1 \par
}{\plain \f1 \par
}{\plain \f1 \par
}{\plain \f1 \par
}\pard \qc\sl0\tx0\tx720\tx1440\tx2160\tx2880\tx3600\tx4320\tx5040\tx5760\tx6480\tx7200\tx7920\tx8640\tx9360 
{\plain \f1 A REPORT OF THE\par
}\pard \sl0\tx0\tx720\tx1440\tx2160\tx2880\tx3600\tx4320\tx5040\tx5760\tx6480\tx7200\tx7920\tx8640\tx9360 
{\plain \f1 \par
}\pard \qc\sl0\tx0\tx720\tx1440\tx2160\tx2880\tx3600\tx4320\tx5040\tx5760\tx6480\tx7200\tx7920\tx8640\tx9360 
{\plain \f1 TEXAS EDUCATIONAL EXCELLENCE PROJECT\par
}\pard \sl0\tx0\tx720\tx1440\tx2160\tx2880\tx3600\tx4320\tx5040\tx5760\tx6480\tx7200\tx7920\tx8640\tx9360 
{\plain \f1 \par
}{\plain \f1 \par
}{\plain \f1 \par
}{\plain \f1 \par
}{\plain \f1 \par
}{\plain \f1 \par
}{\plain \f1 \par
}\pard \qc\sl0\tx0\tx720\tx1440\tx2160\tx2880\tx3600\tx4320\tx5040\tx5760\tx6480\tx7200\tx7920\tx8640\tx9360 
{\plain \f1 Kenneth J. Meier\par
}{\plain \f1 Robert D. Wrinkle\par
}{\plain \f1 J.L. Polinard\par
}\pard \sl0\tx0\tx720\tx1440\tx2160\tx2880\tx3600\tx4320\tx5040\tx5760\tx6480\tx7200\tx7920\tx8640\tx9360 
{\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 \par
}{\plain \f1 \par
}{\plain \f1 \par
}{\plain \f1 \par
}{\plain \f1 For further information contact:\par
}{\plain \f1 \par
}\pard \li720\sl0\tx-720\tx0\tx720\tx1440\tx2160\tx2880\tx3600\tx4320\tx5040\tx5760\tx6480\tx7200\tx7920\tx8640 
{\plain \f1 Kenneth J. Meier, Department of Political Science, Texas A&M 
University, 409-845-4232 kmeier@polisci.tamu.edu\par
}\pard \sl0\tx0\tx720\tx1440\tx2160\tx2880\tx3600\tx4320\tx5040\tx5760\tx6480\tx7200\tx7920\tx8640\tx9360 
{\plain \f1 or\par
}\pard \li720\sl0\tx-720\tx0\tx720\tx1440\tx2160\tx2880\tx3600\tx4320\tx5040\tx5760\tx6480\tx7200\tx7920\tx8640 
{\plain \f1 Robert D. Wrinkle, Department of Political Science, 
University of Texas-Pan American, 956-381-3341 
rwrinkle@panam.edu\par
}\pard \sl0\tx0\tx720\tx1440\tx2160\tx2880\tx3600\tx4320\tx5040\tx5760\tx6480\tx7200\tx7920\tx8640\tx9360 
{\plain \f1 \par
}\pard \qc\sl480\tx0\tx720\tx1440\tx2160\tx2880\tx3600\tx4320\tx5040\tx5760\tx6480\tx7200\tx7920\tx8640\tx9360 
{\plain \f1 }{\plain \b\f1 Black Student Improvements on the TAAS Exam}{\plain \f1 \par
}\pard \sl480\tx0\tx720\tx1440\tx2160\tx2880\tx3600\tx4320\tx5040\tx5760\tx6480\tx7200\tx7920\tx8640\tx9360 
{\plain \f1 \tab Between 1992 and 1997 the pass rate for black students on 
the TAAS exam increased by approximately 30 percentage points.  
Despite these gains, the pass rate for black students in 1997 was 
only 55.7 percent trailing the pass rate for white students (84.9 
percent) by a substantial margin.  The first step in improving 
black tests scores is to identify school districts that do a 
better job of educating black students.\par
}{\plain \f1 \tab The Texas Educational Excellence Project used a statistical 
technique called multiple regression to identify the 25 best 
school districts for black students.  The approach controls for 
the number of economically disadvantaged students, black poverty, 
the education level of black families, school finances, class 
size, teacher experience, teacher qualifications and other 
factors that might affect student performance.  Comparing school 
systems requires that some adjustment be made in how difficult 
the task is that each district faces.  The accepted way to do 
this is with an education production function.  The study then 
compared how well school districts actually did to how well the 
statistical model predicted that they would do.  The difference 
indicates how well the district does in teaching black students.  
\tab }{\plain \b\f1 An Education Production Function \par
}\sect \sectd \sbknone\pgnx6120\pgny15120\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 \sl480\tx0\tx720\tx1440\tx2160\tx2880\tx3600\tx4320\tx5040\tx5760\tx6480\tx7200\tx7920\tx8640\tx9360 
{\plain \f1 \tab School districts are organizations; they receive inputs 
(resources and students) from their environment and produce 
outputs (educated students among others).  A vast literature has 
}{\plain \f1 designated a variety of education production functions whereby 
the outputs of school systems can be evaluated relative to their 
inputs (Burtless 1996; Smith 1995; Hanushek 1986; 1989; 1996).  \tab Our dependent variable is the school districts pass rate for 
black students on the TAAS exam.  Texas requires all school 
districts to administer exams to students in several grades on an 
annual basis.  \par
}{\plain \f1 \tab The independent variables fall into four general types--environmental constraints, financial resources, teacher 
qualifications, and district policies.  Environmental constraints 
are factors that restrict agency performance; in the case of 
education the key constraint is how difficult/easy it is to 
educate students.  The measures of constraint, all correlated 
with poverty, are the percent of poor students (defined as those 
eligible for free school lunches), the percentage of blacks over 
age 25 with a least a high school diploma, and the percentage of 
black families that live in poverty.  The education variable 
should be positively related to student performance and the other 
two measures should be negatively related to black pass rates.\par
}{\plain \f1 \tab Financial resources are the basic raw materials of any 
organization's attempt to meet its goals.  Three measures of 
financial resources are included--per student instructional 
funds, average teacher's salary, and percent of funds received 
via state aid.  These represent total resources devoted to 
education, the attractiveness of teaching positions in a 
competitive market place, and state efforts to overcome the 
unequal distribution of local financial resources.  All 
relationships should be positive.\par
}{\plain \f1 \tab The two teacher qualification measures (or lack thereof) are 
the percent of teachers who hold a temporary certification in a 
subject specialty (as opposed to a permanent certification) and 
the average number of years of teacher experience.  The 
relationship for noncertification should be negative, but the 
predicted relationship for experience is ambiguous (see Meier, 
Gill and Waller 1997).  \par
}{\plain \f1 \tab Finally, the education production function contains three 
policy measures--the percentage of students taking gifted 
classes, class size, and student attendance (percent attending on 
an average day).  Performance should be positively related to 
gifted classes and attendance and negatively related to class 
size.\pard \sl480\tx0\tx720\tx1440\tx2160\tx2880\tx3600\tx4320\tx5040\tx5760\tx6480\tx7200\tx7920\tx8640\tx9360 
}{\plain \*\cs1\f1 }{\plain \*\cs1\super\f1 \par
}{\plain \*\cs1\f1 \tab Texas has a large number of school districts; many are very 
small or deal with a homogeneous student body.  In an effort to 
use a set of organizations relatively similar in the task that 
they perform, we have restricted our analysis to school districts 
with a least 1000 students and at least 10 percent black 
students.\par
}{\plain \*\cs1\f1 \tab The data analysis is a pooled time series with data from the 
years 1991 through 1997.  In any pooled time series one needs to 
control for serial correlation resulting from any trend in the 
variables over time.  A series of dummy variables are introduced 
to achieve this.\par
}{\plain \*\cs1\f1 \tab The basic production function is shown in table 1.  Since 
many of the individual variables are highly correlated with each 
other and since we are not concerned with the relationships 
between these variables and black pass rates, we will not 
interpret these coefficients.  Our objective is merely to use the 
education production function to predict how well each school 
should do based on the types of inputs that the school district 
has.  We then compare how well a district should do to how well 
it actually does.  This difference is our measure of overall 
quality, how much better or worse a district does than would be 
expected.\par
}{\plain \*\cs1\f1 \tab As an illustration, the model predicted that the Aldine 
school district would have an average black pass rate of 38% from 
1991-97.  Aldine\'92s actual pass rate of 44.3 represents a 6.3% 
improvement over this standard.  Based on this method, the top 
ranked school district for black students in Texas was Ferris 
with a rating of +15.2% followed closely by Hooks with 15.1%.\par
}{\plain \*\cs1\f1 The top 25 districts are shown in table 2.  The first column is 
the numerical score on which the districts are ranked.  The 
second column is the average pass rate for black students from 
1991 to 1997 in this district, and the third column is the black 
student pass rate for 1997.\par
}\pard \qc\sl480\tx0\tx720\tx1440\tx2160\tx2880\tx3600\tx4320\tx5040\tx5760\tx6480\tx7200\tx7920\tx8640\tx9360 
{\plain \*\cs1\f1 }{\plain \*\cs1\b\f1 Conclusion}{\plain \*\cs1\f1 \par
}\pard \sl480\tx0\tx720\tx1440\tx2160\tx2880\tx3600\tx4320\tx5040\tx5760\tx6480\tx7200\tx7920\tx8640\tx9360 
{\plain \*\cs1\f1 \tab This study identified those school district in Texas that 
performed better than expected on the pass rate for black 
students.  These districts should serve as role models for other 
districts in Texas.  What is needed is an examination of the 
programs in these districts to see what they do that other 
district do not do.  If such programs are identified, then they 
could be transfer to other districts with an overall benefit to 
black students.\par
}{\plain \*\cs1\f1 \tab Although this study examined exemplary districts, that 
should not detract from the relatively low pass rate for black 
students in Texas.  A great deal of additional improvement is 
needed in these districts as well as other districts to close the 
test gap between black and Anglo students.  Substantial progress 
has been made since 1991; a great distance remains to be 
traveled.\par
}{\plain \*\cs1\f1 \par
}{\plain \*\cs1\f1 \par
}{\plain \*\cs1\f1 \tab The Texas Educational Excellence Project (TEEP) is a joint 
program of 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.\par
}{\plain \*\cs1\f1 \par
}\pard \tx0\tx720\tx1440\tx2160\tx2880\tx3600\tx4320\tx5040\tx5760\tx6480\tx7200\tx7920\tx8640\tx9360
{\plain \*\cs1\f1 }{\plain \*\cs1\b\f1 \tab Table 1. The Education Production Function\par
}{\plain \*\cs1\f1 \par
}\pard \qc\tx0\tx720\tx1440\tx2160\tx2880\tx3600\tx4320\tx5040\tx5760\tx6480\tx7200\tx7920\tx8640\tx9360 
{\plain \*\cs1\f1 Dependent Variable = % of black students passing the TAAS\par
}\pard \tx0\tx720\tx1440\tx2160\tx2880\tx3600\tx4320\tx5040\tx5760\tx6480\tx7200\tx7920\tx8640\tx9360
{\plain \*\cs1\f1 \par
}{\plain \*\cs1\f1 }{\plain \*\cs1\ul\f1 Independent Variable              Coefficient      Standard Error\par
}{\plain \*\cs1\f1 \par
}{\plain \*\cs1\f1 Low Income Students                -.0900             .0226\par
}{\plain \*\cs1\f1 \par
}{\plain \*\cs1\f1 Gifted Classes                      .0425             .0731\par
}{\plain \*\cs1\f1 \par
}{\plain \*\cs1\f1 Attendance                         1.5624             .2792 \par
}{\plain \*\cs1\f1 \par
}{\plain \*\cs1\f1 Teacher Salaries (000)              .8220             .2104\par
}{\plain \*\cs1\f1 \par
}{\plain \*\cs1\f1 Class Size                         -.2581             .2627\par
}{\plain \*\cs1\f1 \par
}{\plain \*\cs1\f1 NonCertified Teachers              -.0749            -.0823\par
}{\plain \*\cs1\f1 \par
}{\plain \*\cs1\f1 Teacher Experience                 -.0393             .1879\par
}{\plain \*\cs1\f1 \par
}{\plain \*\cs1\f1 State Funding Percent               .0393             .0143\par
}{\plain \*\cs1\f1 \par
}{\plain \*\cs1\f1 Instructional Funding Per Student   .0025             .0012\par
}{\plain \*\cs1\f1 \par
}{\plain \*\cs1\f1 Black Education (25+)               .1973             .0425\par
}{\plain \*\cs1\f1 \par
}{\plain \*\cs1\f1 Black Poverty                      -.0936             .0284\par
}{\plain \*\cs1\f1 \par
}{\plain \*\cs1\f1 \par
}{\plain \*\cs1\f1 R-Square           .67\par
}{\plain \*\cs1\f1 \par
}{\plain \*\cs1\f1 Omitted are coefficients for individual year dummy variables.      
  \par
}{\plain \*\cs1\f1 \par
}{\plain \*\cs1\f1 \pard\page \par
}\pard \qc\tx0\tx720\tx1440\tx2160\tx2880\tx3600\tx4320\tx5040\tx5760\tx6480\tx7200\tx7920\tx8640\tx9360 
{\plain \*\cs1\f1 }{\plain \*\cs1\b\f1 Table 2. The Top 25 Texas School Districts for Black Students}{\plain \*\cs1\f1 \par
}\pard \tx0\tx720\tx1440\tx2160\tx2880\tx3600\tx4320\tx5040\tx5760\tx6480\tx7200\tx7920\tx8640\tx9360
{\plain \*\cs1\f1 \par
}{\plain \*\cs1\f1 }{\plain \*\cs1\ul\f1 District             Score     Average TAAS     1997 TAAS}{\plain \*\cs1\f1 \par
}{\plain \*\cs1\f1 \par
}{\plain \*\cs1\f1  1. Ferris            15.2          48.8           74.5\par
}{\plain \*\cs1\f1  2. Hooks             15.1          51.3           65.0\par
}{\plain \*\cs1\f1  3. Sulphur Springs   13.3          50.9           69.5\par
}{\plain \*\cs1\f1  4. Tatum             10.3          46.6           61.1\par
}{\plain \*\cs1\f1  5. Sweeny            10.2          50.0           74.6\par
}{\plain \*\cs1\f1 \par
}{\plain \*\cs1\f1  6. Diboll            10.0          38.5           49.1\par
}{\plain \*\cs1\f1  7. Pittsburg          9.8          45.1           73.6\par
}{\plain \*\cs1\f1  8. Garland            9.8          50.8           65.0\par
}{\plain \*\cs1\f1  9. New Boston         9.7          48.8           70.3\par
}{\plain \*\cs1\f1 10. Linden-Kildare     9.5          41.1           83.5\par
}{\plain \*\cs1\f1 \par
}{\plain \*\cs1\f1 11. Daingerfield-Lone\par
}{\plain \*\cs1\f1      Star              9.3          46.1           65.3\par
}{\plain \*\cs1\f1 12. Connally           9.1          50.0           73.4\par
}{\plain \*\cs1\f1 13. McGregor           8.1          47.8           75.4\par
}{\plain \*\cs1\f1 14. Wilmer-Hutchins    7.3          41.7           64.0\par
}{\plain \*\cs1\f1 15. Hallettsville      7.1          42.9           61.7\par
}{\plain \*\cs1\f1 \par
}{\plain \*\cs1\f1 16. Liberty Eylau      7.0          43.7           64.3\par
}{\plain \*\cs1\f1 17. Manor              6.8          40.2           61.0\par
}{\plain \*\cs1\f1 18. Aldine             6.3          44.3           67.4\par
}{\plain \*\cs1\f1 19. Del Valle          6.2          41.8           63.5\par
}{\plain \*\cs1\f1 20. Woodville          6.2          39.7           60.8\par
}{\plain \*\cs1\f1 \par
}{\plain \*\cs1\f1 21. Hardin-Jefferson   5.9          39.7           57.7\par
}{\plain \*\cs1\f1 22. Edna               5.9          40.6           63.9\par
}{\plain \*\cs1\f1 23. Texas City         5.7          42.0           65.4\par
}{\plain \*\cs1\f1 24. Randolph Field     5.4          59.1           79.4\par
}{\plain \*\cs1\f1 25. Houston            5.4          41.0           59.5\par
}{\plain \*\cs1\f1 \par
}{\plain \*\cs1\f1 STATE AVERAGE          0.0          36.5           54.9            
\par
}{\plain \*\cs1\f1 \par
}\pard \qc\tx0\tx720\tx1440\tx2160\tx2880\tx3600\tx4320\tx5040\tx5760\tx6480\tx7200\tx7920\tx8640\tx9360 
{\plain \f1 \par
}\pard\page 
\pard \qc\tx0\tx720\tx1440\tx2160\tx2880\tx3600\tx4320\tx5040\tx5760\tx6480\tx7200\tx7920\tx8640\tx9360 
{\plain \*\cs1\f1 }{\plain \*\cs1\b\f1 References}{\plain \*\cs1\f1 \par
}\pard \tx0\tx720\tx1440\tx2160\tx2880\tx3600\tx4320\tx5040\tx5760\tx6480\tx7200\tx7920\tx8640\tx9360
{\plain \*\cs1\f1 \par
}\pard \fi-720\li720\tx-720\tx0\tx720\tx1440\tx2160\tx2880\tx3600\tx4320\tx5040\tx5760\tx6480\tx7200\tx7920\tx8640 
{\plain \*\cs1\f1 Burtless, Gary. 1996. }{\plain \*\cs1\f1 }{\plain \*\cs1\i\f1 Does Money Matter? The Effect of School 
Resources on Student Achievement and Adult Success}{\plain \*\cs1\f1 . 
Washington, D.C.: Brookings Institution.\par
}\pard \tx0\tx720\tx1440\tx2160\tx2880\tx3600\tx4320\tx5040\tx5760\tx6480\tx7200\tx7920\tx8640\tx9360
{\plain \*\cs1\f1 \par
}\pard \fi-720\li720\tx-720\tx0\tx720\tx1440\tx2160\tx2880\tx3600\tx4320\tx5040\tx5760\tx6480\tx7200\tx7920\tx8640 
{\plain \*\cs1\f1 Hanushek, Eric A. 1986. "The Economics of Schooling: Production 
and Efficiency in Public Schools." }{\plain \*\cs1\f1 }{\plain \*\cs1\i\f1 Journal of Economic 
Literature}{\plain \*\cs1\f1  24:1141-77.\par
}\pard \tx0\tx720\tx1440\tx2160\tx2880\tx3600\tx4320\tx5040\tx5760\tx6480\tx7200\tx7920\tx8640\tx9360
{\plain \*\cs1\f1 \par
}\pard \fi-720\li720\tx-720\tx0\tx720\tx1440\tx2160\tx2880\tx3600\tx4320\tx5040\tx5760\tx6480\tx7200\tx7920\tx8640 
{\plain \*\cs1\f1 Hanushek, Eric A. 1989. "The Impact of Differential Expenditures 
on School Performance." }{\plain \*\cs1\f1 }{\plain \*\cs1\i\f1 Educational Researcher}{\plain \*\cs1\f1  23 (4): 45-65.\par
}\pard \tx0\tx720\tx1440\tx2160\tx2880\tx3600\tx4320\tx5040\tx5760\tx6480\tx7200\tx7920\tx8640\tx9360
{\plain \*\cs1\f1 \par
}\pard \fi-720\li720\tx-720\tx0\tx720\tx1440\tx2160\tx2880\tx3600\tx4320\tx5040\tx5760\tx6480\tx7200\tx7920\tx8640 
{\plain \*\cs1\f1 Hanushek, Eric A.  1996. "School Resources and Student 
Performance." In }{\plain \*\cs1\f1 }{\plain \*\cs1\i\f1 Does Money Matter? The Effect of School 
Resources on Student Achievement and Adult Success}{\plain \*\cs1\f1 , Gary 
Burtless, ed. Washington, D.C.: Brookings Institution.\par
}\pard \tx0\tx720\tx1440\tx2160\tx2880\tx3600\tx4320\tx5040\tx5760\tx6480\tx7200\tx7920\tx8640\tx9360
{\plain \*\cs1\f1 \par
}\pard \fi-720\li720\tx-720\tx0\tx720\tx1440\tx2160\tx2880\tx3600\tx4320\tx5040\tx5760\tx6480\tx7200\tx7920\tx8640 
{\plain \*\cs1\f1 Meier, Kenneth J., Jeff Gill and George Waller.  1997.  "Optimal 
Performance vs. Risk Aversion: An Application of 
Substantively Weighted Least Squares" presented at the 
Fourth National Public Management Research Conference, 
Atlanta, Georgia, October 30-November 1, 1997.  \par
}\pard \tx0\tx720\tx1440\tx2160\tx2880\tx3600\tx4320\tx5040\tx5760\tx6480\tx7200\tx7920\tx8640\tx9360
{\plain \*\cs1\f1 \par
}\pard \fi-720\li720\tx-720\tx0\tx720\tx1440\tx2160\tx2880\tx3600\tx4320\tx5040\tx5760\tx6480\tx7200\tx7920\tx8640 
{\plain \*\cs1\f1 Smith, Kevin B.  1995.  "Policy, Markets, and Bureaucracy: 
Reexamining School Choice."  }{\plain \*\cs1\f1 }{\plain \*\cs1\i\f1 Journal of Politics}{\plain \*\cs1\f1  56 (May), 
475-491.\par
}\pard \tx0\tx720\tx1440\tx2160\tx2880\tx3600\tx4320\tx5040\tx5760\tx6480\tx7200\tx7920\tx8640\tx9360
{\plain \*\cs1\f1 \par
}}