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5. Considering the Gap between High- and Low- Performing Schools
- Georgetown University Press
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99 C H A P TE R FIVE Considering the Gap between High- and LowPerforming Schools SINCE THE PASSAGE OF THE NO CHILD LEFT BEHIND ACT, there has been close scrutiny of the achievement gap and the needs of the lowest-performing schools. In this context, mayoral control raises questions of equity. Though in the previous chapter we examined the effects of mayoral control on overall district achievement, we did not look at where those gains are coming from. Does mayoral control contribute to, or help to reduce, achievement inequality in school districts? That is the question we explore in this chapter. We examine the nature of the achievement gap between the topand bottom-performing schools in each school district in our sample, using statistical analysis to compare mayor- and nonmayor-led districts. In examining these issues, we hope to better explain the redistributive aspects of mayoral control. The likelihood of redistribution within an integrated governance framework is open for theoretical debate. On one hand, integrated governance offers mayors an opportunity to reallocate resources to the schools most in need. In cities that experience a shrinking revenue base, mayors can institute fiscal discipline, prioritize resources in terms of needs, and improve efficiency in service delivery. On the other hand, electoral incentives and competition with other school systems may lead the mayor to invest more heavily in schools that serve high-achieving students. Mayors may see high-achieving schools as a better tool for targeting likely voters or recruiting middle-class families to the city. In our empirical assessment in this chapter, we weigh the evidence in support of both theories, and we find that on balance mayorcontrolled systems are simultaneously associated with wider achievement gaps but not with declining performance in the bottom quarter. Put another way, 100 CHAPTER FIVE all schools in these districts appear to be gaining, but those at the top of the distribution are gaining at faster rates. We examine the data in several ways to arrive at this conclusion. First, we examine the performance of schools in the lowest 10th and 25th percentiles from 1999 to 2003. We track school performance in both mayor- and nonmayor -led districts. Second, we examine the ratio of high-performing (top 25 percent) schools to low-performing (bottom 25 percent) schools. We employ fixed-effects regression models to compare across districts and specify the effects of mayoral control on this inequality ratio. Third, we conclude the chapter with a discussion of the implications of these findings in designing urban education policy. TRACKING THE LOWEST-PERFORMING SCHOOLS AS A COHORT For more than a decade, both scholarly and popular works have brought to light the continued inequality in America’s public schools. Some scholars, such as Mintrop (2004), have argued that to close the achievement gap, inputs must complement outcome-based accountability systems. Mayors in charge of urban school systems have the ability to redirect funds to instructional purposes and potentially develop new funding sources. Such new resources may allow for improved performance in the lowest-performing schools. To see if this is the case, we examine the performance of the cohort of schools that was lowest performing in 1999, the start of our five-year window of achievement data analysis.1 For each district in the sample, we examined the distribution of school performance in 1999. We identified the schools that fell in the lowest 10 percent in 1999, and also marked schools in the lowest 25 percent in 1999.2 We then tracked these cohorts of low-performing schools over the period, through 2003, to see if their performance remained stagnant or if they improved. We examined elementary and middle schools separately. We did not examine high school cohorts because most districts do not have enough high schools to constitute (at the school level) a large enough cohort of low-performing schools. Performing this cohort analysis allows us to address the questions: Do these schools end up in the same spot where they started? Or do they make genuine progress during this period? But without student-level data, it needs to be noted that this analysis cannot directly address what is happening to the lowest-performing students. We do not know, for instance, if students in the lowest-performing schools are staying in those schools or transferring elsewhere. At the end of the chapter, we discuss again the [18.190.28.78] Project MUSE (2024-04-17 21:44 GMT) Considering the Gap between High- and Low-Performing...