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University of Chicago Press
THe Economics of School Choice / Edition 74

THe Economics of School Choice / Edition 74


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ISBN-13: 9780226355337
Publisher: University of Chicago Press
Publication date: 04/28/2003
Series: National Bureau of Economic Research Conference Report Series
Edition description: 1
Pages: 368
Product dimensions: 6.00(w) x 9.00(h) x 1.10(d)

About the Author

Caroline M. Hoxby is a professor of economics at Harvard University and director of the Economics of Education Program at the National Bureau of Economic Research.

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The Economics of School Choice

By National Bureau of Economic Research.

University of Chicago Press

Copyright © 2003 National Bureau of Economic Research.
All right reserved.

ISBN: 0226355330

Does Public School Competition Affect Teacher Quality?

Eric A. Hanushek and Steven G. Rivkin

Vouchers, charter schools, and other forms of choice have been promoted as a way to improve public schooling, but the justification for that position is largely based on theoretical ideas. Until quite recently there was little evidence on public school responsiveness to competition from private schools, other public school districts, or charter schools, and empirical research remains quite thin. Under most conceivable scenarios of expanded choice, even with private school vouchers, the public school system will still remain the primary supplier of schooling. Therefore, it is important to know what might happen to quality and outcomes in the remaining public schools. This research is designed to provide insights about that from an analysis of how public schools respond to competition from other public schools.

The empirical analysis has two major components. First, estimates of average school quality differences in metropolitan areas across Texas are compared to the amount of public school competition in each. At least for the largest metropolitan areas, the degree of competition is positively related toperformance of the public schools. Second, the narrower impact of metropolitan area competition on teacher quality is investigated. Because teacher quality has been identified as one of the most important determinants of student outcomes, it is logical to believe that the effects of competition on hiring, retention, monitoring, and other personnel practices would be one of the most important aspects of any force toward improving public school quality. The results, although far from conclusive, suggest that competition raises teacher quality and improves the overall quality of education.

Prior to the analysis of Texas public schools we briefly consider the various margins of competition for public schools. Although many simply assume that expanded availability of alternatives will lead to higher public school quality, the institutional structure of public schools raises some questions about the strength of any response.

1.1 The Margins of Competition

Competition for public schools may emanate from a variety of sources. Neighborhood selection places families in particular public school districts and specific school catchment areas within districts. Families also choose whether to opt out of the public schools and send their children to parochial or other private school alternatives. Although these choices have operated for a long time, recent policy innovations have expanded competition within the public school sector. The ability to attend school in neighboring districts, charter schools, and private schools with public funding enhances choice and potentially imposes additional competitive pressures on public schools.

Most of the attention to private schools has concentrated on student performance in Catholic schools. The literature on Catholic school performance is summarized in Neal (1998) and Grogger and Neal (2000). The evidence has generally indicated that Catholic schools on average outperform public schools. This superiority seems clearest in urban settings, where disadvantaged students face fewer options than others.

Our main interest, however, centers on the reactions of public schools to the private sector. In an important article about the impact of private schools on schools in the public sector, Hoxby (1994) demonstrates that public schools in areas that have larger concentrations of Catholic schools perform better than those facing less private competition. This analysis provides the first consistent evidence suggesting that public schools react to outside competition.

The most important element of competition comes from other public schools. Specifically, households can choose the specific jurisdiction and school district, a la Tiebout (1956), by their choice of residential location. Although adjustment is costly, these choices permit individuals to seek high-quality schools if they wish. Residential location decisions are of course complicated, involving job locations, availability of various kinds of housing, school costs and quality, and availability of other governmental services. Nonetheless, given choice opportunities plus voting responses, this model suggests pressure on schools and districts to alter their behavior; competitive alternatives that lead families to choose other schools would yield downward pressure on housing prices and perhaps even an enrollment decline.

The ensuing public pressures might be expected to lead administrators and teachers to respond. For example, job performance may affect a super-intendent's ability to move to another district or a principal's autonomy or ability to remain in a school. Better performance by teachers may make the school more attractive to other high-quality teachers, thereby improving working conditions.

Offsetting forces may, nonetheless, mute any competitive pressures. The current structure of many school systems including tenure for teachers and administrators likely lessens the impact of competitive forces. Institutionally, district survival is virtually guaranteed under plausible changes in the competitive environment.

The empirical analysis of Borland and Howsen (1992) and its extension and refinement in Hoxby (2000) investigate public school responses to Tiebout forces using the concentration of students in school districts within metropolitan areas as a measure of competition. Borland and Howsen find that metropolitan areas with less public school competition have lower school quality. Noting, however, that the existing distribution of families across districts reflects endogenous reactions to school quality, Hoxby pursues alternative strategies to identify the causal impact of concentrations. She finds that consideration of endogeneity increases the estimated impact of competition on the performance of schools. Our analysis builds on these specifications of public school competition.

The general consideration of Tiebout competition, however, leaves many questions open. For example, it is not obvious how to define the "competitive market." Although the district is the fundamental operating and decision-making unit in most states, districts themselves can be very large and heterogeneous. This heterogeneity could lead to competition, and responses, that are more local in nature--say, at the school rather than the district level. For example, Black (1999) and Weimer and Wolkoff (2001) suggest that school quality differences are capitalized into housing prices at the individual school rather than the district level. This ambiguity motivates our use of alternative measures of the level of competition.

Much recent attention has focused on more radical forms of competition such as vouchers or charter schools. Again, whereas most debate focuses on the performance of these alternatives, our interest is the reaction of public schools to these competitive alternatives. With the exception of Hoxby (chap. 8 in this volume), however, little consideration has been given to the actions of public schools.

1.2 The Importance of Teacher Quality

The difficulty of identifying and measuring school quality constitutes a serious obstacle to learning more about the effects of competition. A substantial body of work on the determinants of student achievement has failed to yield any simple descriptions of the key school and teacher factors. Although class size and other variables may significantly affect outcomes for specific populations and grades, financial measures (spending per pupil and teacher salaries) and real resources (teacher experience and degrees, class size, facilities, and administration) do not appear to capture much of the overall variation in school or teacher quality (Hanushek 1986, 1997).

On the other hand, schools and teachers have been shown to be dramatically different in their effects on students. A variety of researchers have looked at variations among teachers in a fixed effect framework and have found large differences in teacher performance (see, e.g., Hanushek 1971, 1992; Murnane 1975; Armor et al. 1976; Murnane and Phillips 1981). The general approach has been to estimate value added achievement models and to assess whether or not performance gains differ systematically across teachers. It is important to note that value added models control for differences in entering achievement and thus remove a number of potential sources of bias, including differences in past performance and school factors, individual ability, and so forth. In every instance of such estimation, large differences have been found. Of particular significance for the work here, these differences have generally been weakly related to the common measures of teachers and classrooms found in the more traditional econometric estimation.

These analyses have not, however, conclusively identified the impacts of different teachers. Because parents frequently set out to choose not just specific schools but also specific teachers within schools, the makeup of individual classrooms may not be random. This possibility is compounded by two other influences. First, teachers and principals also enter into a selection process that matches individual teachers with groupings of children. Second, if the composition of the other children in the classroom is impor-tant--that is, if there are important peer group effects on achievement--the gains in an individual classroom will partially reflect the characteristics of the children and not just the teacher assigned to the classroom. These considerations suggest a possibility that classroom outcome differences reflect more than just variations in teacher quality.

A recent paper by Rivkin, Hanushek, and Kain (2001) uses matched panel data for individual students and schools to estimate differences in teacher quality that are not contaminated by other factors. Because that work forms the basis for the investigation here, it is useful to understand the exact nature of it. The authors use a value added model that compares the pattern of school average gains in achievement for three successive cohorts as they progress through grades five and six. The value added model, by conditioning on prior achievement, eliminates unmeasured family and school factors that affect the level of beginning achievement for a grade and permits concentration on just the flow of educational inputs over the specific grade. The analysis then introduces fixed effects for individual schools and for specific grades in each school, allowing for effects of stable student ability and background differences, of overall quality of schools, and of the effectiveness of continuing curricular and programmatic elements for individual grades. This basic modeling provides what is essentially a prediction of achievement growth for individuals based on each one's past performance and specific schooling circumstances. The central consideration, then, is how much changes in teachers affect the observed patterns of student achievement growth within each school.

This analysis shows that cohort differences in school average gains rise significantly as teacher turnover increases. By controlling for other potentially confounding influences, the methodology generates a lower bound estimate of the variance in teacher quality based on within-school differences in test score gains among the cohorts.

The estimation of "pure" teacher quality differences reveals that the variation in teacher quality within schools (i.e., ignoring all variation across schools) is large in Texas elementary schools. One standard deviation of teacher quality--for example, moving from the median to the 84th percentile of the teacher quality distribution--increases the annual growth of student achievement by at least 0.11 standard deviations, and probably by substantially more. This magnitude implies, for example, that having such an 84th percentile teacher for five years in a row rather than a 50th percentile teacher would be sufficient to eliminate the average performance gap between poor students (those eligible for free or reduced lunch) and nonpoor students.

Evidence on the importance of teacher quality forms the basis for a major segment of the empirical analysis here. Specifically, if the degree of local competition is important, it should be possible to detect its impact on teacher quality by examining performance variation along with the amount of local competition across the state of Texas. In particular, it would be surprising for competition to exert a substantial effect on students without influencing the quality of teaching, and investigating these effects provides information about the mechanism behind any observed impacts of competition.

1.3 Empirical Analysis

We investigate how varying amounts of public school competition in the classic Tiebout sense affect student performance and the hiring of teachers. It is important to note that our efforts are not general. We leave aside many of the issues discussed previously and in the other papers of this volume about possible details and dimensions of competition and concentrate entirely on issues of academic performance across broadly competitive areas. Nonetheless, the importance of this topic for individual labor market outcomes and for the politics of schools justifies the choice.

The empirical work exploits the rich data set on student performance of the University of Texas at Dallas Texas Schools Project. Because Texas is a large and varied state, a wide range of local circumstances is presented. Indeed, there are twenty-seven separate metropolitan statistical areas (MSAs) in Texas. These areas, described in table 1.1, vary considerably in size and ability to mount effective competition across districts. The basic Tiebout model assumes a wide variety of jurisdictional choices such that people can choose among alternative public service provision while retaining flexibility in housing quality and commuting choices. Clearly, the smaller MSAs of Texas offer limited effective choice in all dimensions, so it will be interesting to contrast results across the various areas of the state.

We employ the Texas Schools Project data first to estimate overall quality differences between MSAs and to compare these results with the degree of public school competition. Following that, we investigate whether or not competition raises the quality of teaching.

As suggested by the previous discussions, this analysis is best thought of as a reduced-form investigation. We do not observe the underlying decisionmaking by school officials; nor do we have detailed and precise measures of the competition facing individual schools and districts. Instead we use aggregate indicators of potential competition from public schools and concentrate on whether or not there are systematic patterns to student outcomes.

1.3.1 The Texas Database

The data used in this paper come from the data development activity of the UTD Texas Schools Project. Its extensive data on student performance are compiled for all public school students in Texas, allowing us to use the universe of students in the analyses. We use fourth-, fifth-, and sixth-grade data for three cohorts of students: fourth-graders in 1993, 1994, and 1995. Each cohort contributes two years of test score gains. Students who switch public schools within the state of Texas can be followed just as those who remain in the same school or district, a characteristic we use in our analysis.

The Texas Assessment of Academic Skills (TAAS), which is administered each spring, is a criterion-referenced test used to evaluate student mastery of grade-specific subject matter. We focus on test results for mathematics, the subject most closely linked with future labor market outcomes. We transform all test results into standardized scores with a mean of zero and variance equal to one. The bottom 1 percent of test scores and the top and bottom 1 percent of test score gains are trimmed from the sample in order to reduce measurement error. Participants in bilingual or special education programs are also excluded from the sample because of the difficulty in measuring school and teacher characteristics for these students.

The empirical analysis considers only students attending public school in one of the twenty-seven MSAs in Texas (identified in table 1.1). A substantial majority of all Texas public school students attend schools in one of these MSAs. Each MSA is defined as a separate education market, and measures of competition are constructed for each. The analysis is restricted to MSAs because of the difficulty of defining school markets for rural communities. Below we discuss potential problems associated with defining education markets in this way.

1.3.2 Competition and School Quality

How will public school competition affect the provision of education? Although Tiebout-type forces would be expected to raise the efficiency of schooling, it is not clear that more competition will necessarily result in higher school quality. If wealth differences or other factors related to school financing lead to more resources in areas with less competition, the efficiency effects of competition could be offset by resource differences. Therefore we consider differences in both school quality and school efficiency across metropolitan areas.

A second important issue is precisely how to define the relevant competition. The importance of district administrators in allocating funds, determining curriculum, hiring teachers, and making a variety of other decisions suggests that much if not most of the effects of competition should operate at the district level. However, anecdotal evidence on school choice provides strong support for the notion that parents actively choose among schools within urban and large suburban school districts, consistent with the view that principals and teachers exert substantial influence on the quality of education. This anecdotal information is reinforced by the aforementioned research on housing capitalization.

We treat the basis for competition as an empirical question. In the estimation, we conduct parallel analyses where competition is measured on the basis of the concentration of students both in schools and separately in districts.

Although Hoxby (2000) provides the empirical context within which to place this study of school efficiency, the methodology employed here is much closer to the work by Abowd, Kramarz, and Margolis (1999) on interindustry wage differences. Just as interindustry wage differences reflect both worker heterogeneity and industry factors, interschool or district differences in student performance reflect both student heterogeneity and school factors. However, a comparison of wage differences for a worker who switches industries or of achievement differences for a student who switches schools effectively eliminates problems introduced by the heterogeneity of workers or students. In this way the availability of matched panel data facilitates the identification of sector effects.

Equation (1) describes a value added model of learning for student i in grade g in MSA m at time t:

(1) Achievementigmt = familyi familyigt MSAm errorigmt,

where the change in achievement in grade g equals test score in grade g minus test score in grade g - 1.

The overall strategy concentrates on estimation of metropolitan area fixed effects (MSAm) for each of the twenty-seven MSAs in Texas. Importantly, this model removes all fixed family, individual, and other influences on learning (familyi) as well as time-varying changes (familyigt) in family income, community type (urban or suburban), specific year effects, and the effect of moving prior to the school year (students may or may not move prior to fifth grade).

In this model of student fixed effects in achievement gains, the MSA quality fixed effects are identified by students who switch metropolitan areas. These twenty-seven MSA fixed effects provide an index of average school quality for the set of metropolitan areas. Although most of the variation in school quality likely occurs within an MSA, such variation is ignored because of the focus on competition differences among MSAs. Importantly, by removing student fixed effects in achievement gains, this approach effectively eliminates much of the confounding influences of student heterogeneity present in analyses based on cross-sectional data.

Nevertheless, we do not believe that students switch districts at random, and changes in circumstances not captured by the student fixed effects may dictate the characteristics of the destination school as well as affecting student performance. For example, families who experience job loss or divorce may relocate to inferior districts, whereas families who experience economic improvements may tend to relocate to better districts. If the limited number of time-varying covariates does not account for such changes in family circumstances, the estimates of metropolitan area school quality will reflect both true quality differences and differences in family circumstances.

However, even if the rankings of metropolitan area average quality are contaminated, regressions of these rankings on the degree of competition may still provide consistent estimates of competition effects as long as the omitted student and family effects are not related to the degree of competition. The fact that mobility across regions is most importantly linked to job relocations and less to seeking specific schools or other amenities certainly mitigates any problems resulting from nonrandom mobility (Hanushek, Kain, and Rivkin 2001a). On the other hand, other factors, including school resources, that may be correlated with the measures of competition may confound the estimated effects of competition on school quality and, more importantly, on school efficiency. We do include average class size as a proxy for school efficiency. Although average class size captures at least a portion of any difference in resources, there is a good chance that influences of confounding factors remain.

Two other important issues more specific to the study of school competition are the measurement of the degree of competition and the identification of separate public school markets. Following general analyses of market structure, we calculate a Herfindahl index based alternatively on the concentration of students by district and by school across the metropolitan areas. As Hoxby (2000) points out, the Herfindahl index is itself endogenously determined by the location decisions of families. Any movement of families into better districts within a metropolitan area will change the value of the Herfindahl index, raising it if families concentrate in larger districts and lowering it if families move to smaller districts, as would be the case with urban flight. In essence, the Herfindahl index reflects both the initial administrative structure of schools and districts as well as within-metropolitan area variation in school or district quality. Only the former provides a good source of variation, and that is the source of variation Hoxby attempts to isolate with her instrumental variable approach, which deals with the endogeneity of school districts. We do not have available instruments, so the second source of variation may introduce bias of an indeterminate direction.

The identification of the relevant education market (i.e., defining the appropriate set of schools from which parents choose) also presents a difficult task. It is certainly the case that a number of families who work in an MSA choose to live outside the MSA, and thus measuring school competition using the census definitions of MSAs almost certainly introduces some measurement error in the calculation of the Herfindahl index that would tend to bias downward the estimated effects of competition.


Figures 1.1 and 1.2 plot the metropolitan area school quality fixed effects against the Herfindahl index, the measure of competition. The estimates of school quality are obtained from student fixed effect regressions of achievement gain on twenty-seven metropolitan area dummy variables and controls for free lunch eligibility, community type, and whether the student moved prior to the grade. The five largest metropolitan areas are specifically identified in the figures. Figure 1.1 measures competition by the concentration of students into school districts, whereas figure 1.2 measures competition by the concentration of students into schools, implicitly permitting competition to occur both within and across districts. The overall patterns presented in figures 1.1 and 1.2 do not reveal a strong positive relationship between competition at either the district or school level and school quality. Rather, the scatter of points moves roughly along a horizontal line regardless of whether competition is measured at the school or district level. Not surprisingly, the coefficient on Herfindahl index from a regression of the metropolitan area fixed effect on the Herfindahl index is small and not significantly different from zero regardless of whether competition is measured at the school or district level (table 1.2). Note that competition varies far less when measured at the school level, because any dominance of large districts is ignored.

In contrast to the lack of an overall positive relationship between competition and school quality, the school fixed effects for the five largest metropolitan areas suggest the presence of a positive relationship between school quality and competition: the ordering of Dallas, Houston, San Antonio, Fort Worth, and Austin according to school quality exactly matches the ordering by competition regardless of how competition is measured. This is confirmed by the regression results in table 1.2 that allow for separate slope coefficients for the five largest MSAs. Although there is little or no evidence that competition at the school or district level is significantly related to school quality for the smaller MSAs, the competition effect is positive and significant at the 1 percent level for the five largest metropolitan areas. Because some of the smaller MSAs in Texas actually get quite small and offer far fewer choices of districts (see table 1.1), it would not be surprising if the incentive effects of competition were much weaker in comparison to the effects in the large MSAs. Effective competition may require a minimum range of housing and public service quality (Tiebout 1956).

Regardless of MSA size, however, competition should have its sharpest effects on reducing inefficiencies in resource use and education production. In a coarse effort to isolate competition effects on efficiency, the first-stage regressions underlying figures 1.3 and 1.4 include average class size as a control for resource differences. Not surprisingly, given the strong evidence that class size and other resource differences explain little of the total variation in school quality, the inclusion of class size has little impact on either the observed patterns in the figures or the Herfindahl index coefficients (see table 1.3).

All in all, the figures and regression results suggest that competition improves school quality in larger areas with substantial numbers of school and district choices. However, a sample size of twenty-seven with only five very large MSAs is quite small, and there may simply not be enough variation to identify more precisely the effect of competition on average school quality and efficiency. Moreover, although the matched panel data remove many of the most obvious sources of bias, the limited number of time-varying characteristics may fail to control for all confounding family and student influences.

1.3.3 Competition and Teacher Quality

The suggestive results for the effects of competition on overall quality leave uncertainty about the strength of the Tiebout forces. This portion of the empirical analysis investigates a much narrower question with a methodology that likely does a far better job of controlling for confounding influences on student outcomes. Although the quality of teaching is only one of many possible determinants of school quality, evidence in Rivkin, Hanushek, and Kain (2001) strongly suggests that it is the most important factor. Consequently, it would be highly unlikely that competition would exert a strong effect on school quality without affecting the quality of teachers.

At first glance the problem might appear to be quite simple: More competitive areas should lead schools to hire better teachers as measured by teacher education, experience, test scores, and other observable characteristics. However, two issues complicate any simple analysis: (a) evidence overwhelmingly shows that observable characteristics explain little of the variation in teacher quality in terms of student performance (see Hanushek 1986, 1997); and (b) competition could lead schools to raise teacher quality per dollar spent but not the level of quality, and it is quite difficult to account for cross-sectional differences in the price of teacher quality.


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Table of Contents

1.Does Public School Competition Affect Teacher Quality?
2.Can School Choice and School Accountability Sucessfully Coexist?
3.The Role of Special Education in School Choice
4.School Vouchers: Results from Randomized Experiments
5.Introducing School Choice into Multidistrict Public School Systems
6.School Vouchers as a Redistributive Device: An Analysis of Three Alternative Systems
7.Neighborhood Schools, Choice and the Distribution of Educational Benefits
8.School Choice and School Productivity: Could School Choice Be a Tide that Lifts All Boats?
Author Index
Subject Index

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