As the issue of rising income inequality in the United States has become a forefront issue in our society, so have the problems within our educational system which, at least in part, drive the phenomenon. While many agree that our schools and our educational system need improvement, there is no clear resolution as to the best method. School choice has long been a potential reform strategy and one promoted by many politicians. It may be argued that, by introducing a market-based system into our educational system, efficiency is increased through competition and better student-school matches. Additionally, school choice could provide greater equality of opportunity and give alternatives to families and students who may not be able to afford to move to a different residential area and/or attend a private school. Lastly, merit pay and other staffing differences (which traditional public school incentive structures lack) could encourage teachers to achieve better or different results. Podgursky states, “regulatory freedom, small size of wage-setting units and competitive market environment make pay and personnel practices more market and performance-based in private and charter schools” (Podgursky, 2006). This behavior supposedly allows charter and private schools to attract more qualified teachers than public schools, thereby disadvantaging public school students. On the other hand, opponents of charter schools make the arguments which follow. First, school choice negatively impacts the low-performing public schools by taking away high-performers and funding, as well as hurting overall enrollment numbers. Secondly, charter schools are risky, unproven and are essentially a gamble with the lives of children and taxpayer dollars. Thirdly, charter schools undermine the ideal of equal and excellent education for all by creating elitist academies which exacerbate the issue of inequality in schools.
In accordance with the rising popularity of school choice as an educational reform option, charter schools have quickly grown in number. Since the day Minnesota passed the nation’s first charter school law in 1991 to today in 2018, the number of charter schools in the United States has grown from one school in St. Paul with 35 students to nearly 7,000 schools enrolling close to 3.2 million students (National Alliance for Public Charter Schools). This rapid growth of charter schools as a percentage of our public school educational system has resulted in charter schools becoming a focus of much research.
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The question of whether or not charter schools are significantly more effective than traditional public schools at raising student achievement has been examined in multiple studies. Past research hoping to answer the question of charter effectiveness found that charter attendance through a lottery-based admission policies to an urban charter had a positive impact on student performance, but that the results for nonurban charters were mixed (Angrist et al.,2013; Hoxby and Murarka, 2009; Dobbie and Fryer, 2011; and Gleason et al., 2010). This apparent success of urban charters above that of TPS in urban areas is despite evidence indicating students at urban charters are typical of the overall urban student population and thus effectiveness of charters above other urban schools is not driven by the type of student who attends (Angrist, 2013). Angrist (2013) further investigates school-level factors, which could explain the success of some urban charters over other, to find that urban and lottery-sample charter effectiveness can be explained by adherence to a No Excuses approach to urban education. No Excuses schools advocate discipline, traditional skills and selective teacher hiring (Angrist, 2013). Despite these positive findings regarding urban charter schools and the impact these schools have on student achievement, there is also some research which points to the damage to a student if a student is enrolled in a new charter school. Studies have found that a student enrolled in a new charter school will actually experience a negative impact on their growth, but as years pass and faculty experience grows, this impact declines (Bifulco & Bulkley, 2008). This accumulation of experience enhancing teacher quality could make a difference in charter school effectiveness, but charter schools have also been found to have significantly higher turnover rates than TPS due to variables such as age, perception of workload and union presence (Stuit & Smith, 2010; Torres, 2016). Furthermore, the teachers who possess the strongest academic backgrounds are the most likely to leave as opposed to their less academic peers (Stuit and Smith, 2010).
In this paper, I hope to examine the degree to which teacher quality, as defined by value-added score as well as certification and experience, differs between traditional public schools (TPS) and charter schools within the Philadelphia area. Value-added scores have been commonly used as a measure of teacher effectiveness and quality in research completed over the past few years (Goldhaber, 2018; Hanushek et al., 2005; Sass & Harris, 2012; Koedel & Betts, 2011; Chetty et al., 2014) and used as a measure of teacher evaluation. With this came a debate over whether or not these value-add scores were a good measure of the impact of a teacher or biased by student sorting and teaching to the test, Chetty (2014) analyzed these value-added models and found they worked well in isolating a teacher’s impact on student achievement from other factors
The reason for examining teacher quality as opposed to another input (such as students or funding) is as follows: research has consistently demonstrated that teacher quality has an outsized impact on student outcomes. A 2000 study by Darling-Hammond on teacher quality and student achievement analyzed data from all fifty states in a survey of policies, state case study analyses and the 1993-94 School and Staffing Surveys and National Assessment of Educational Progress to see how teacher qualifications and other school inputs related to overall student achievement. The study finds through quantitative analysis that measures of teacher preparation and certification had the strongest correlations with student achievement in both mathematics and readings. This correlation was the strongest both before and after controlling for student poverty and language status (Darling-Hammond, 2000). More recently, in 2010, a study on statewide end-of-course tests in North Carolina was conducted to examine the relation of teacher credentials to student achievement. Evidence was found that teacher qualifications impacts student achievement in systematic ways. (Clotfelter, 2010). Interestingly enough, despite the above findings where urban charters are found to be more effective than urban TPS, teacher qualifications through licensure is generally higher in TPS. Traditional public schools must hire fully licensed teachers as per the No Child Left Behind Act. Private schools and charters can, and typically do, hire uncertified teachers. Ninety-three percent of teachers in TPS hold regular state licenses as opposed to the approximately seventy-one percent in charter schools and the fifty-eight percent in private schools (Podgursky, 2006).
Past research has not only identified teacher quality to be the most important determinant on student achievement, but it has also shown that disadvantaged students are much more likely to have low-quality teachers – as measured by degrees, experience and advanced credentials (Clotfelter, Ladd & Vigdor, 2005; Kalogrides & Loeb, 2013; Lankford, Loeb, & Wyckoff, 2002). To see a specific example of inequality in distribution, one can look to Lankford’s study on New York schools. Using data to determine differences in teachers across schools, Lankford found that urban schools have less-qualified teachers. This inequity of suburban versus urban disproportionately impacts low-income, non-white students (Lankford et al., 2002). Kalogrides, Loeb and Beteille (2013) focus within one district on student and teacher assignments to find that disadvantaged students (minorities and low-income) were more likely to have a novice teacher assigned. This phenomenon is known as a “teacher quality gap.” Recent work by Goldhaber (2018), has continued to add on to this field of study through examining teacher quality using value-added scores.
Despite the wealth of research regarding the importance of teacher quality and research on teacher quality gaps in traditional public schools, not much is written on whether or not the size of teacher quality gaps differs between TPS and charter schools. Ozek (2018) examined this in a working paper analyzing teacher value-add in Florida schools to find that teachers working in above-averge poverty charter schools have significantly higher value-added scores as compared to TPS teachers in similar conditions. Secondarily, Ozek finds that cross-sector differences such as experience and educational attainment do not appear to explain the gaps in teacher effectiveness, but find returns to experience are higher for teachers in the charter school system and considerable differences in teacher support and influence on policies and practices.
Research Question & Data
By examining the presence of teacher quality gaps in charter schools relative to traditional public schools, I hope to address an old question through a new lens. Although there is a general consensus regarding the role of teacher quality in determining student outcomes, research analyzing charter school teachers or differences between teacher quality levels between charter schools and traditional public schools is lacking. This is primarily due to different data reporting requirements as well as the smaller number of charter schools in each district as compared to traditional public schools (Ozek, 2018). Despite this issue at the nationwide level, Pennsylvania provides the necessary data to examine teacher quality gaps for both charter and traditional public schools in Philadelphia. Hence, my research question will examine the degree to which teacher quality gaps exist in Philadelphia District schools and the difference existing between charter and TPS. For my analysis, I will be compiling data provided by the Pennsylvania Department of Education. Information on the makeup of the student body by race and the percentage qualifying for free or reduced lunch is public for each individual school. For teacher data, my primary focus will be on the teacher value-added score, but as part of a secondary analysis, I will also examine average years of experience and certification.
- Student Race: Proportion of disadvantaged students is defined as percentage of population consisting of underrepresented minorities (URM)
- Student Socioeconomic Status: Proportion of disadvantaged students is defined as percentage of population qualifying for free or reduced price lunch (FRL)
- Years of Experience: Low-quality teachers are defined as those with 5 years or fewer of teaching experience as used in a previous teacher quality gap study performed by Goldhaber (2018). I will also sensitize these results by examining if there is a change when one defines low-quality as one and two years of experience.
- Licensure: Low-quality teachers are defined as those not meeting full licensure requirements. While I would ideally be able to analyze the percentage of teachers at a school who scored in the lowest quartile of their licensure exam (as used in other studies), that information is not public for the Philadelphia school district. Instead, I am able to use the percentage of fully certified teachers per school.
- Pennsylvania Value-Added Score: PVAAS provides information measuring growth (per year) and projecting future achievement. Thus, if a student is already underperforming, a teacher is still able to see if the underperformance level is less than that before the year he or she taught the student (indicating a positive impact). Using these scores as a measure of teacher quality is supported by Chetty (2014). PVAAS has denied me access to teacher-specific scores, but school-level data is public. It is not appropriate to compare the growth measure values from these individual reports to each other as the different standard errors are unaccounted for. As a result, the department provided an average growth index for each district and school in PA.
My hope is that this research will be of interest to politicians and academics involved with education reform as well as the general public who sees the debate over education reform as a pertinent issue in society. More specifically, I would like to utilize the study of teacher quality gaps as a way to examine not just the differences in effectiveness between charter and TPS, but also the level of equality offered by each. If charter schools truly do offer a more equal educational opportunity to students (as defined by the gap between advantaged and disadvantaged students), then the presence of a teacher quality gap in charter schools must be significantly smaller than that of a TPS.
My methodology for calculating teacher quality gaps in each school will mirror the approach used by Clotfelter et al (2005), Goldhaber et al (2018) and Ozek et al. (2018). For a given measure of teacher quality, I will find the number of schools in the district by defined school type which qualify as having on average a “low-quality” teacher population. Similarly, for a certain measure of student disadvantage (URM or FRL), I will find the proportion of schools which service a majority disadvantaged population. Using this information, I can calculate the school-level exposure rate of disadvantaged students to low-quality teachers per each defined disadvantaged segment of students and each defined measure of teacher quality. Secondarily, I will utilize this same approach to calculate the exposure rate of advantaged students’ exposure to “low-quality” teachers for per each defined disadvantaged segment of students and each defined measure of teacher quality. The teacher quality gap is found to be the difference between the two average school-level exposure rates. The calculated gaps found per school and between charter and public schools can then be compared and tested for significance.
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