oh, won't you stay: a multilevel analysis of the difficulties in retaining qualified teachers

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Oh, Won't You Stay: AMultilevel Analysis of theDifficulties in RetainingQualified TeachersKatharine Omenn Strunk & Joseph Paul RobinsonPublished online: 15 Dec 2009.

To cite this article: Katharine Omenn Strunk & Joseph Paul Robinson (2006) Oh, Won'tYou Stay: A Multilevel Analysis of the Difficulties in Retaining Qualified Teachers,Peabody Journal of Education, 81:4, 65-94, DOI: 10.1207/s15327930pje8104_4

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PEABODY JOURNAL OF EDUCATION, 81(4), 65–94Copyright © 2006, Lawrence Erlbaum Associates, Inc.

Oh, Won’t You Stay: A MultilevelAnalysis of the Difficulties in RetainingQualified Teachers

Katharine Omenn Strunk and Joseph Paul RobinsonStanford University School of EducationStanford University

The No Child Left Behind Act of 2001 requires that every classroom bestaffed with a “qualified teacher.” A growing literature is focusing on whatcauses teachers to leave their jobs and/or the teaching occupation, ratherthan solely on factors influencing teacher recruitment. This article usesnationally representative data from the 1999–2000 Schools and StaffingSurvey to address teacher attrition in a multilevel analytic framework,accounting for the clustering of teachers within schools within states.Drawing from a theoretical framework rooted in occupational wage theoryand social identity theory, we find teachers are more likely to leave if (a) they are specialized instructors (especially foreign language); (b) theyhave a probationary teaching certificate; (c) they are less experienced; (d) the racial composition of the students is heavily minority; (e) the students’racial composition is less matched to their own race/ethnicity; and, forteachers of some races, (f) the teaching staff’s racial composition is morematched to their own race/ethnicity.

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We contributed equally to this product. It was truly a collaborative effort. We thankAnthony Bryk and Sean Reardon for their valuable assistance, and Jennifer King Rice for herthoughtful comments. All remaining errors are our own.

Correspondence should be sent to Katharine Omenn Strunk, 507 CERAS, 520 GalvezMall, Stanford University, Stanford, CA 94305. E-mail: [email protected]

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The No Child Left Behind Act of 2001 requires that every classroom bestaffed with a “qualified teacher.” As districts struggle to meet this man-date, widespread attention has focused on the shortage of qualifiedteachers to staff the nation’s elementary and secondary classrooms.Discussions about the causes of this teacher shortage have lead to the cre-ation and assessment of policies and programs designed to address thisproblem. The majority of attention has focused on the recruitment of newand qualified personnel: Programs such as Teach for America, the NewYork City Teaching Fellows, and the Teaching Opportunity Program,among others, are attempting to entice intelligent and capable profession-als into teaching, even if only for a short while. However, enhanced teacherrecruitment efforts may be only one element of a solution for the lack of qualified teachers in the nation’s classrooms. The U.S. Department of Education found that almost 7% of teachers left teaching between the1994 and 1995 school years, suggesting that teacher attrition substantiallyaffects the supply of skilled teachers (U.S. Department of Education,National Center for Educational Statistics, 1997). This finding may indicatethat expanded efforts to retain qualified teachers as well as to recruit themmay be in order. As such, a growing literature is focusing on what causesteachers to leave their jobs and/or the teaching occupation, rather thansolely on factors influencing teacher recruitment.

To date, much of this research has focused on what sorts of teachercharacteristics lead to teachers’ decisions to leave their jobs. However,little is known about whether the causes of teacher attrition can be linkedto individual teacher characteristics, or rather to larger school attributesor district-wide traits.1 The larger organizational context can provideadditional information on an individual teacher’s choice to leave his orher job. Moreover, linking teacher attrition to specific types of schoolsand/or districts can provide valuable insight into the equity issues thatmay emerge if teachers are more likely to leave certain schools than others. We add to the existing literature on teacher attrition by examin-ing the larger school context, allowing for variation to occur at both theteacher and the school levels, and controlling for state-specific effects.

The next section of this article briefly outlines the methodological andtheoretical rationales on which our study is based. We then briefly describethe relevant extant work on teacher quit and transfer decisions, provid-ing an empirical platform from which we begin our study. We then dis-cuss our data, obtained from the 1999–2000 Schools and Staffing Survey

K. O. Strunk and J. P. Robinson

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1A notable exception is found in the work of Richard Ingersoll (2001) of the University of Pennsylvania, who also seeks to contextualize individual teacher choice within schoolsand districts. Neil Theobald (1990) also examined the role played by specific school districtcharacteristics on teacher turnover.

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(SASS) and Teacher Follow up Survey (TFS) from the National Center forEducation Statistics, and our use of the hierarchical general linear model-ing (HGLM) methodology with a binary outcome. Next, we review ouranalyses and findings. In the final section, we summarize this researchand conclude.

Conceptual Framework

Methodological Rationale

Sociologists, economists, and policymakers interested in education andlabor markets are questioning what causes teachers to leave their jobsand/or teaching in general and what can be done to stem the flow ofteachers out of schools. The factors causing teacher attrition can bebroadly categorized into four levels: teacher characteristics, school attrib-utes, district traits, and the larger state context. Although the body of liter-ature examining teacher attrition traditionally has focused on teachercharacteristics, more recent work has broadened the scope to considersome school- and district-level characteristics that may affect teachers’decisions to leave their schools. However, very little work has looked atteacher attrition as an individual teacher decision nested within the largerschool and district contexts, and even less work has considered the nest-ing of schools and districts within states. This is a crucial distinction:Teachers are not spread randomly about the country, or even within astate or district. Rather, teachers are nested within schools, which in turnare grouped within districts, and then states. Failure to account for thisnested structure can lead to the misestimation of standard errors in tradi-tional regression analyses. Moreover, using a hierarchical model allowsvariables measured at one level to differentially affect relations occurringat another level, as well as allowing for variables at multiple levels to sep-arately predict the outcome. To reap the benefits of using a nested struc-ture, we use HGLM with a binary outcome (Raudenbush & Bryk, 2002).

Theoretical Rationale

The theoretical underpinning of our study is based in standard laboreconomics and social theory and relies on the understanding that teachersare, like other professionals, rational actors who make choices about theircareers and lives in accordance with their own preferences for wages,working conditions, and other unobservable factors. Teachers with agiven level of ability and in a given market have a specific choice set ofoccupations presented to them, and they select the occupation that best

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satisfies their preferences. Professionals choose to be teachers becausethey believe that the combination of wages, working conditions, andunobservable factors they will receive from teaching will yield greaterbenefits than they would receive from another occupation.

Some of these unobservable factors and working conditions may haveto do with teachers’ social identities and how comfortable they are teach-ing in a specific sociocultural context. Tajfel (1982) defined social identity as“that part of the individuals’ self-concept which derives from their knowl-edge of their membership of a social group (or groups) together with thevalue and emotional significance of that membership” (p. 24). To theextent that teachers of a given racial/ethnic group identify with their ownracial/ethnic group, they may prefer to work in schools where the studentand/or teaching staff reflects their own identity. Mismatch between theteacher’s race/ethnicity and that of the students and other staff is pre-dicted to result in a greater likelihood of attrition.

Central to teachers’ job selections, then, is the concept of opportunitywage, which refers to the wages and working conditions (including socio-cultural context) teachers would obtain from an alternative occupation(Hanushek, Kain, & Rivkin, 2002; Murnane & Olsen, 1989a, 1989b, 1990).Teachers of a given quality, experience level, certification type, and subjectspecialty yield a given payout (monetary as well as nonpecuniary). Ifanother occupation (or the choice of no occupation at all) offers them ahigher total payout, they will leave teaching in favor of the alternativewith the highest payout.

Teachers with varying experience levels, certification types, educationlevels, and subject specialties have different nonteaching options avail-able to them. As such, we include these measures as indicators of teachercharacteristics that may affect their decisions to stay or leave. We controlfor teacher gender and race/ethnicity because male and female teachersand teachers of different racial and ethnic backgrounds may face differentopportunity sets as well as have different preferences. Teacher salaries are key components of opportunity wage, and we include teacher-levelmeasures of their total school-related earnings as well as district-levelmeasures of district-set salary schedules. To account for the working con-ditions faced by a teacher, we include at the school level school enroll-ment; school type (elementary, secondary, and combined elementary andsecondary); the poverty level of the school; and the location of the schoolin an urban, suburban, or rural environment. To assess if teachers con-sider the racial/ethnic composition of the school in their decisions to stayor leave, we include two measures of racial/ethnic school composition:that of the students and of the faculty. To test whether teachers of a given


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race or ethnicity are more likely to stay teaching in a school in which thereare larger proportions of students and/or teachers of his or her ownracial/ethnic group, we include interaction effects between student andteacher racial and ethnic composition and teacher race/ethnicity.

A Brief Review of the Literature

Although literature on teacher attrition runs the gamut from descriptive,qualitative studies to complicated quantitative analyses of very specificrationales and attributes, we focus on the quantitative work to provide a baseline for comparison with our own results using HGLM. We also con-centrate on the predictor attributes that we use in our own research: predic-tors that may affect the way teachers weight the utility they receive fromworking as a teacher in a given school and district as opposed to in anotherschool/district or in an alternate profession.

Teacher Characteristics

As we discussed in the preceding section, the existing literature hasmainly focused on what attributes of the individual teacher significantlypredict teacher turnover. Because we are interested in parsing out teachers’utility functions and possible opportunity wages, we focus on what teachercharacteristics may make teachers most attractive to or attracted to alternateemployers. We concentrate on teacher “quality,” subject specialty, gender,race/ethnicity, and salary.

Teacher quality. Many studies find that the more “qualified” teachersare more likely to leave their schools or the profession (Boyd, Lankford,Loeb, & Wyckoff, 2005; Hanushek et al., 2002; Ingersoll, 2001; Murnane &Olsen, 1989a, 1989b, 1990; Podgursky, Monroe, & Watson , 2004; Rees,1991). Many of these studies define quality as teacher achievement on stan-dardized tests and find that teachers who scored higher on such exams areless likely to remain in teaching. Because SASS and the TFS are not linked toteacher test scores, we rely on three (very) loose proxies of teacher quality:certification type, higher degree attainment, and experience. Extantresearch on the relationship between certification type and turnover isminimal at best, although Boyd, Grossman, Loeb, Lankford, and Wycoffare working on a study of New York teachers, using certification type as apredictor of teacher attrition. However, a great deal of attention has beenpaid to teacher experience. We summarize some of the main findings next.


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Experience. Previous literature has found that teaching experiencepredicts various measures of teacher “quality.” More specifically, teachersare found to be most efficacious and “successful” after the “probationaryperiod,” which typically lasts 2 or 3 years (until a teacher receives tenure).However, after a certain point, usually at about 7 to 10 years, it is hypothe-sized that teacher “burnout” occurs, and teachers become relatively lessproficient (Hanushek, 1972).

Experience in and of itself has proven a strong predictor of teachers’quit decisions (Hanushek et al., 2002; Ingersoll, 2001; Murnane & Olsen,1989a, 1989b; Rees, 1991). Fifteen percent of teachers in Murnane andOlsen’s (1989a) Michigan sample stopped teaching after 1 year, as opposedto a 9% quit rate after 2 years, and 8% after 3 years. Attrition was less than3% per year among teachers with 8 or more years of experience. Hanusheket al. (2002) found a similar trend in recent Texas data. This research pointsto a U-shaped curve of teacher experience and quits: Younger teachershave a higher rate of turnover, which declines as teachers hit middleage/experience level, and then rises again as teachers near retirement.

Subject specialty. Researchers have found that a teacher’s subject specialtysignificantly predicts the probability that he or she will quit or transfer (Boe,Bobbit, & Cook, 1997; Grissmer & Kirby, 1992; Ingersoll, 2001; Murnane &Olsen, 1989a, 1989b, 1990; Rumberger, 1987). Overall, research shows thatchemistry and physics teachers are predicted to teach an average of 2 yearsless than high school teachers with other subject specialties (Murnane &Olsen, 1989a, 1989b, 1990). Ingersoll (2001) also found that special educa-tion teachers are more likely to depart than other teachers. Such findingsbolster the opportunity wage theory underlying our study.

Because certain teachers, such as chemistry, physics, and special educa-tion teachers, may be able to earn relatively high wages outside of theteaching profession compared to other teachers, the nonpecuniary bene-fits they gain from teaching must be disproportionately high to enticethem to stay in teaching. As such, in order to retain science and specialeducation teachers, the wages and nonpecuniary benefits associated withteaching for these specialized instructors may need to be greater thanthose for teachers with fewer or less attractive nonteaching options.

Gender. There are conflicting findings concerning which gender has agreater likelihood of quitting or transferring. Some recent research hasfound that male teachers are more likely to quit or transfer than are femaleteachers (Boyd et al., 2005; Ingersoll, 2001). However, Rees (1991) found thatmale and female teachers exhibit similar quit behavior before they get mar-ried, but women become much more likely to quit after they are married.


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This may be explained by a possible interaction between age and gender,which posits that women are more likely to quit when they near childbear-ing age or have children (Murnane & Olsen, 1989a, 1989b, 1990).

School and District Characteristics

We focus on teacher salary and working conditions because we areconcerned with those job characteristics over which teachers seek to maxi-mize their utility from work.

Salary. It is no surprise that teachers respond positively to increasedsalaries: The higher the salary, the lower the likelihood that a teacher willquit or transfer (Baugh & Stone, 1982; Boyd et al., 2005; Hanushek et al.,2002; Ingersoll, 2001; Manski, 1987; Murnane & Olsen, 1989a, 1989b, 1990;Rees, 1991; Stinebrickner, 1998). Murnane and Olsen (1989a, 1989b, 1990)provided proof of this trend from Michigan, Colorado, and North Carolina,whereas Hanushek et al. (2002), Boyd et al. (2005), and Rees (1991) foundevidence in Texas and New York. Ingersoll (2001), in his analysis of the1999–91 SASS, found evidence of this on a national level.

Moreover, extant work provides a basis for our opportunity wagetheory: Brewer (1996) found that increases in other local districts’ salariesrelative to wages in a given district are positively associated with teacherquit behavior in that given district. Imazeki (2004) and Gritz and Theobald(1996) found a similar result in Wisconsin and Washington, respectively.

Working conditions. If our opportunity wage hypothesis holds, teacherswill, at least partially, base their quit decisions on their schools’ workingconditions. Working conditions, however, are not easily measurable, asmany factors that make up the working environment may be unobserved.We attempt to capture quantifiable aspects of working environments,including measures of the racial makeup of the student body and teachingstaff, poverty, and the urban environment of a school.

School racial and ethnic composition. Research indicates that teachers aremore likely to quit or transfer schools if the student body at their school islargely minority. This has been shown to be true in the states of New York,Texas, Georgia, and Washington (Boyd et al., 2005; Hanushek et al., 2002;Scafidi, Stinebrickner, & Sjoquist, 2003; Theobald, 1990). In fact, Hanusheket al. found that teacher mobility is more strongly related to student racialcomposition than to salary, so much so that a school with 10% more Blackstudents would require 10% higher salaries to neutralize the increasedprobability of teacher attrition. In Georgia, Scafidi et al. found that Black


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students make up approximately 37% of the student population in schoolsin which teachers stay. This is not that different from the proportion of Black students in schools from which teachers leave, but remain in district(39%), but it is significantly different from the average proportion of Blackstudents (47%) in schools from which teachers change districts entirely(Scafidi et al., 2003).

Matched teacher–student racial composition. Not only are teachers morelikely to leave high-minority schools, but research indicates that nonminor-ity teachers are more likely than minority teachers to leave schools withhigh proportions of minority students (Boyd et al., 2005; Hanushek et al.,2002; Scafidi et al., 2003). Boyd et al. (2005) found evidence that Whiteteachers are twice as likely transfer out of schools that are largely non-White and to leave teaching entirely, relative to when the student body islargely White. Scafidi et al.’s (2003) findings from Georgia support thisresult. Contrary to White teachers, however, both studies indicate thatBlack teachers do not react strongly to the student body racial makeup. Infact, there is evidence that minority teachers stay longer, on average, thando White teachers employed by the same district (Hanushek et al., 2002).Higher rates of minority enrollment increase the probability that Whiteteachers exit a school, whereas increases in percentage Black and percent-age Hispanic tend to reduce the probability of transitions for Black teachers(Hanushek et al., 2002; Scafidi et al., 2003). Dworkin (1980) found thatWhite teachers in an urban district in Texas reported “seriously consideringleaving the field of education” more often than non-White teachers if theywere assigned to work in a school where the majority student racial groupwas different from their specified preference. Imazeki (2004) found thatBlack teachers in Wisconsin similarly favor schools with higher proportionsof Black students. This research indicates that White teachers view higherproportion minority schools as less preferable, whereas Black teachers mayconsider them more preferable.

There are numerous rationales that might explain this differential affectof a minority student population on minority and nonminority teachers’quit decisions. It may be that the working conditions implied by a heavilyminority school are less preferable to Whites than to Black teachers becauseminority prevalence is in and of itself a deterrent to White teachers.Alternatively, race may proxy for other unmeasured school factors, such assafety or school resources. Or it may not be a function of racial bias or proxy,but rather a desire to be in familiar settings and to reside in an area close tohome (Boyd et al., 2005; Hanushek et al., 2002). Moreover, this finding mayresult from teachers feeling more comfortable with students similar tothemselves. Social identity theory holds that individuals seek to maintain


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positive self-images associated with their (racial) identity group and thatone mechanism for doing so is minimizing potentially image-damaging sit-uations by avoiding interactions with members of other groups and byseeking out interactions with one’s own group members (Garcia & Crocker,2004; Tajfel, 1982). In an employment context, individuals may chooseemployment opportunities where they can serve and work side by sidewith people of their own race/ethnicity.

Empirical support for social identity theory in the workplace exists inresearch that has found higher job satisfaction and lower turnover amongworkers of homogeneous characteristics (Jackson, 1991). Within a school,racial heterogeneity of staff can facilitate tension and job dissatisfaction.Bryk and Schneider (2002) offered a case study of a Chicago elementaryschool whose racially divided Hispanic and Anglo staff mistrust eachother in part because of racial identity issues. Mueller, Finley, Iverson, andPrice (1999) found that White teachers were more likely to leave a schoolwhen they experienced racial mismatch, echoing the findings of previousresearch.

Furthermore, teachers from a racial/ethnic background different fromthat of their students may experience a “cultural shock,” resulting inlower levels of job satisfaction (Gottlieb, 1964). This may be especially truefor White teachers of higher socioeconomic status assigned to minority,urban schools (Dworkin, 1980). These feelings of mistrust, social identitydifferences, or cultural shock may all lead to job dissatisfaction, lessenedcommitment to the school, and increased turnover.

Poverty. Researchers have found that teacher turnover is higher inlower income schools. Ingersoll (2001), using a national sample, foundthat high-poverty public schools have moderately higher rates of teacherturnover. Scafidi, Sjoquist, and Stinebrickner (2003) found that teachers inGeorgia are more likely to change schools if they begin their teachingcareers in schools with lower income students. However, they believe thatpoverty may be associated with high proportions of minority students,indicating that teachers are more likely to leave poor schools, which arealso the schools with high levels of minority students.

Severe policy implications stem from the higher turnover rates in high-poverty and high-minority schools.2 These correlations imply that thelowest achieving and most disadvantaged students are more likely to


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2Extant research also finds a relationship between student achievement and teacher attri-tion, such that teachers are more likely to leave schools with lower student achievement(Boyd et al., 2005; Hanushek et al., 2002; Scafidi et al., 2003). Student achievement is likely animportant working condition, but one that we cannot test in this analysis.

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have teachers new to the school and to the profession. If less experiencedteachers are, on average, less proficient and/or effective, then the associationamong student achievement, income, and proportion minority indicatesthat these disadvantaged students are more likely to have lower qualityteachers. Moreover, because the turnover rate in these schools is high, thehigher quality teachers who do stay in teaching are more likely to leave theselower performing, lower income, and higher minority schools, causing acycle that matches lower quality teachers with the neediest children.

Urban environment. Research findings are mixed as to how teacherturnover is related to a school’s urban environment. Rees (1991), usingurban location as a proxy for a school’s working environment, found thatteachers who work in urban districts have a higher probability of leavingtheir jobs. He attributed this finding to the more difficult working environ-ments of inner city schools. However, Ingersoll (2001) found that urbanpublic schools do not have particularly high rates of teacher turnover.

The research to date points to many important factors in teachers’ quit(or transfer) decisions. However, there is still much to be determined aboutwhat causes teachers to leave schools and how to keep teachers in the field.Many of the relevant studies focus on a single state, limiting the feasibleinferences to a larger national population. Although single-state studies canoften dig deeper, with more detailed information about teachers and theirdecisions and backgrounds (see Hanushek et al., 2002, and Boyd et al., 2005,for excellent state-level examples of Texas and New York, respectively), anational sample provides a broader look at teachers’ quit decisions.

Although Ingersoll (2001) used the 1990–91 SASS and the additionalTFS to look at a national sample of teachers and their quit decisions, ourstudy departs from his work and previous state-specific analyses in anumber of ways. First, as we described in brief earlier and describe inmore detail later, we use HGLM analysis to account for the hierarchicalstructure of teachers in schools and of schools within states. We also focuson teachers’ monetary and nonpecuniary working conditions in additionto teacher characteristics that may contribute to their quit decision.3 In


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3Ingersoll (2001) used a multilevel logistic regression framework that accounts for thenonrandom clustering of teachers within schools. To address teacher turnover when morethan one teacher observation comes from the same school, we consider the use of hierarchi-cal modeling, such that teachers from the same school in the SASS data set are nested withinthat school. This adjusts for correlated error terms of teachers within the same school andallows us to measure the variation in turnover across schools more accurately. Similarly, wefurther account for the nested structure of schools within states by including a third, state,level into our model.

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addition, we examine how the type of teacher certification (emergency,probationary, provisional, temporary, or regular), and whether a teacher iscertified in his or her main teaching field, affects teacher leave decisions. Wedo so in part to help assess policies and programs geared to solving theteacher recruitment problem (e.g., the New York City Teaching Fellows andTeach for America) that provide alternative certifications to teachers to get“good” teachers into schools quickly.

Data and Methodology

Data

We use the SASS and its supplement, the TFS, for the 1999–2000 schoolyear (the TFS, then, is from the 2000–01 school year). The National Centerfor Education Statistics collects this nationally representative sample of teachers, principals, schools, and districts from a random sample ofschools stratified by state, sector, and school level. Teachers within theseschools are randomly sampled and linked to their school identificationnumber. In this way, we are able to link principal- and school-administra-tor-reported school characteristics to individual teachers. Districts, as well,are linked to their schools, such that we can link together teachers, schools,and districts. The TFS is given to the same schools the following year, andall those in the original teacher sample who had left from their jobs wereasked to answer questions about their departures. We weight our sampleto account for the complex stratified sampling structure used in the SASSand the TFS. Our sample contains 28,885 teachers in 6,481 schools.

Methodology

As previously stated, our review of the existing research on teacherturnover has failed to produce any studies using hierarchical modelingtechniques. However, it is methodologically incorrect to treat teacherswithin the same school as independent observations in a logistic regres-sion model for the simple fact that these teachers are subjected to the sameschool-based working conditions, many of which may be unobservableand/or unmeasurable and therefore not easily accounted for in a stan-dard ordinary least squares regression analysis. Many previous studiesused fixed-effects regression frameworks to attempt to account for thenested structure of the data.4 The use of HLM also provides an advantage


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4The use of fixed effects alone does not entirely account for the nested nature of the data.One must adjust the standard errors of the estimated coefficients by treating schools as

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over fixed-effects regression analysis because HLM enables us to examineschool-level correlates while still accounting for the clustered structure ofstudents within schools and states.

Outcome variable and model choice. We use an HGLM to analyze thebinary outcome of whether a teacher left the school. Level 1 is the individualteacher level, and Level 2 is the school level. Additional district-level predic-tors have been added to the Level 2 model because not enough variationexisted within districts in the SASS data set to warrant the inclusion of aseparate district level in the model. This is likely less a function of an actuallack of between-district variation and more likely a result of the SASS datasample, which has relatively few school observations within each district.

The outcome variable, LEAVE, indicates whether the teacher left theschool in which he or she taught the previous year (LEAVE = 1) or not(LEAVE = 0). Note that we recode SASS data on teacher attrition from acategorical variable to a dichotomous outcome variable. This implies thatwe group together teachers who leave their school and/or district withteachers who actually quit the teaching profession. In addition, we makeno distinction between teachers who voluntarily left the school and thosewho left involuntarily.

All Level 1 and Level 2 predictors have been centered around theirgroup means. This allows us to examine the probability of quitting of theaverage teacher within each school j and each state k. We can thus inter-pret the grand intercept as the probability that the average teacher withineach school will leave.

Fixed effects. In modeling teacher turnover, we assume that only theintercept term varies randomly. That is, we assume that individual teachersexhibit variation in their decisions to leave their schools. Likewise, weassume that some portion of turnover can be modeled at the school leveland that the remaining proportion of variance left unexplained varies ran-domly across schools. In our final model, the Level 2 error term for theintercept is highly significant, confirming our assumption. Level 1 error isheteroskedastic and must be treated as random.

Furthermore, we assume that slope coefficients (i.e., nonintercept coef-ficients) do not vary randomly at any level. For example, we assume thatthe effect of having an advanced degree on a teacher’s decision to leavedoes not vary across individuals, schools, or states. Therefore, we fix the


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clusters in which the teachers are grouped (Bertrand, Duflo, & Mullainathan, 2004). Withoutaccounting for this clustering, even fixed effects analyses will produce deflated standarderrors, resulting in an increase in the likelihood of Type I errors (i.e., incorrect rejection of thenull hypothesis).

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slope coefficients at both Level 2 (school) and Level 3 (state). We do treatthe slope coefficients on the teacher race variables differently, however.We model the relationships between teacher race/ethnicity and the prob-ability of quitting as a function of the racial composition of their respec-tive schools in terms of student composition and faculty composition. Westill constrain these slope equations such that their error terms are fixed,reflecting our assumption that the relationship between race and outcomedoes not vary randomly in different schools.

We include no state-level predictors of attrition. Rather, we account forthe nesting of schools within states to account for state characteristics thatmay influence teachers’ likelihoods of attrition, such as statewide policies.We constrain all nonintercept error terms at Level 3 to equal zero. TheLevel 3 intercept error term is highly significant in each of our models,therefore confirming that variation exists across states in teacher turnoverand warranting the inclusion of this third (state) level.

Descriptive Statistics

We present the relevant Level 1 and Level 2 descriptive statistics inTables 1 and 2, respectively.

Variance, Reliability, and Plausible Values

Table 3 shows the proportion of variance at each level for the uncondi-tional, unconditional Level 2, and final models. We see that approximately17% of the variation in the probability of attrition is within schools,whereas the far majority, 81%, is between schools. Only 2% of the variationcan be found between states. From our unconditional unit-specific model(with no Level 1 or Level 2 predictors), we see that there is substantialbetween-school variability in the probability that a teacher leaves his orher school. The between-school variation suggests that the probability ofteacher turnover varies substantially from school to school and that mostschools are likely to face some yearly turnover. Some schools within a stateface virtually no teacher attrition, whereas others see turnover as high as77%. However, we do not explain a significant amount of the variation inteacher attrition with the inclusion of our teacher- and school-level predic-tors. Also worthy of note is that the reliability of our models remains high,at around .64 consistently throughout our model development.5


77

5Constructing a 95% plausible value interval, we can ascertain how variable schools arein their turnover rates, where the coefficient estimate on attrition in the unconditional model(i.e., γ00) is –2.28 and τ00 = 3.12.

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78

Table 1Level 1 Descriptive Statistics

Variable N M SD Min Max

Teacher left (dichotomous 28,885 .14 .35 .00 1.00outcome variable)Teacher is a male 28,885 .33 .47 .00 1.00Teacher has a master’s or doctoral degree 28,885 .43 .50 .00 1.00

Years of experience categories0 to 2 28,885 .11 .31 .00 1.003 to 4 28,885 .09 .29 .00 1.005 to 10 28,885 .22 .41 .00 1.0011 to 20 28,885 .26 .44 .00 1.0021 or more 28,885 .32 .47 .00 1.00

Subject area of main assignmentGeneral pre-K/elementary 28,885 .24 .42 .00 1.00Math/science 28,885 .18 .38 .00 1.00English 28,885 .12 .33 .00 1.00Social science 28,885 .08 .27 .00 1.00Special education 28,885 .11 .31 .00 1.00Foreign language 28,885 .03 .18 .00 1.00ESL/bilingual education 28,885 .01 .11 .00 1.00Vocational education 28,885 .07 .25 .00 1.00Other 28,885 0.17 .37 .00 1.00

Certified in main field 28,885 .95 .22 .00 1.00Certification type in main assignment

Regular 28,885 .88 .32 .00 1.00Probationary 28,885 .03 .16 .00 1.00Provisional 28,885 .02 .15 .00 1.00Temporary 28,885 .01 .11 .00 1.00Emergency 28,885 .00 .07 .00 1.00

Race of teacherAmerican Indian 28,885 .03 .16 .00 1.00White 28,885 .83 .37 .00 1.00Asian/Pacific Islander 28,885 .03 .16 .00 1.00Black 28,885 .07 .25 .00 1.00Hispanic 28,885 .05 .21 .00 1.00

Interaction terms: Experience Categories × Male

0 to 2 28,885 .03 .18 .00 1.003 to 4 28,885 .03 .18 .00 1.005 to 10 28,885 .07 .26 .00 1.0011 to 20 28,885 .07 .26 .00 1.0021 or more 28,885 .11 .32 .00 1.00

Note. Min = minimum; Max = maximum; ESL = English as a second language.

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Divergence From Previous Research:Race and Determinants of Leaving

The most noticeable difference in the model specification between theextant literature and this research is the treatment of race. Previousresearch using one-level logistic regression analyses finds that racial simi-larity between students and teachers is a contributing factor to a teacher’sdecision to leave a school. We test if this common finding is observed in amultilevel model.

In addition, we examine another type of racial similarity: that of theteacher’s race to the percentage of school staff of the same race. We know


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Table 2Level 2 Descriptive Statistics

Variable N M SD Min Max

School attributesSchool type (grade-level organization)

Elementary 6,481 .50 .50 .00 1.00Secondary 6,481 .42 .49 .00 1.00Combined (spans elem. & sec. grades) 6,481 .08 .27 .00 1.00Total school enrollment (natural log) 6,481 6.14 .90 1.61 8.59

Percentage of students in school who areHispanic 6,481 9.79 18.94 .00 100.00White 6,481 67.98 32.66 .00 100.00Black 6,481 15.26 25.54 .00 100.00American Indian 6,481 4.18 15.01 .00 100.00Asian/Pacific Islander 6,481 2.78 9.53 .00 100.00

Percentage of teachers in school who areHispanic 6,481 3.88 12.21 .00 100.00White 6,481 85.11 23.44 .00 100.00Black 6,481 8.02 18.05 .00 100.00American Indian 6,481 1.50 7.38 .00 100.00Asian/Pacific Islander 6,481 1.50 8.51 .00 100.00Percentage of school receiving free lunch 6,481 40.08 27.63 .00 100.00

Urbanicity categoriesUrban 6,481 .23 .42 .00 1.00Suburban 6,481 .37 .48 .00 1.00Rural 6,481 .40 .49 .00 1.00

District attributesDistrict BA + no experience (natural log) 6,481 10.16 .14 9.70 10.71District MA + 20 years’ experience(natural log) 6,481 10.67 .20 9.88 11.33

Note. Min = minimum; Max = maximum; elem. = elementary; sec. = secondary; BA =bachelor of arts; MA = master of arts.

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of no extant teacher attrition research that specifically looked at thesepotential determinants of turnover using nationally representative data.Likewise, no research has adjusted for the correlated errors of teacherswithin the same school when looking at the turnover effects of staff com-position, thus producing potentially incorrect estimates. On the basis ofextant research on social identity theory, we hypothesize that a greaterpercentage of a school’s teaching staff of the same race as the individualteacher will have a negative effect on the probability of leaving. That is,we believe teachers will be more likely to remain employed in a schoolwhere there are relatively more teachers of their own race. Such an envi-ronment may be perceived by the teachers as more sympathetic to theirneeds as teachers and employees. Furthermore, a higher percentage of aparticular race among the teaching staff suggests that the surroundingcommunities are inhabited by members of that race. Therefore, a strongercommunity may exist for the teacher in the school and in the neighbor-hood. This combination is likely to positively influence teacher retention.

We are concerned, however, that high correlations may exist betweenthe percentage of a school’s teachers of a particular race and the percent-age of students of the same race. We examine the correlation matrix ofpercentage of teachers of a given race and the percentage of students of agiven race in Table 4.

From this correlation matrix, we see that a measure of the percentage ofstudents of a particular race is not necessarily the same as a measure ofthe percentage of teachers of a given race. However, some of the correla-tions are indeed high. Notably, the correlation between the percentage ofschool student enrollment that is Black and the percentage of teachers inthe school who are Black is .8111, and the correlation for the Asian/PacificIslander percentages is .7889. Therefore, we ran models that look at therace indicators (percentage of student enrollment and percentage ofteachers at a school) together and separately to avoid troubles arising


80

Table 3Proportion of Variance at Each Level, Reliability, and 95% Plausible Value Interval

Unconditional Unconditional FinalModel Level 2 Model

Proportion of variance within schools 17% 16% 17%Proportion of variance between schools 81% 82% 82%Proportion of variance between states 2% 2% 2%Plausible values (of probability of leaving) (.003, .766) (.004, .764) (.004, .785)Level 1 reliability .639 .643 .635Level 2 reliability .682 .685 .675

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from collinearity. These models examine the percentage of teachers of thesame racial category and the percentage of students of the same race foreach Level 1 race category. We also examine the effects of the percentageof teachers and students of different race/ethnicities on the quit probabili-ties for each Level 1 race category. In our final model, we include mea-sures of both student and teacher similarity in predicting Hispanic andAmerican Indian teachers’ probabilities of leaving, but only measures ofstudent similarity when predicting Black teachers’ probabilities of leav-ing and measures of teacher similarity when predicting Asian/PacificIslander teachers’ quit likelihoods.

Findings

The final model estimates the probability of attrition for the averageteacher in a school j to be .102. First we explore the findings of our finalmodel as they relate to our opportunity wage hypotheses, and then weexplore findings relevant to our social identity hypotheses.

Opportunity Wage Theory

Table 5 shows HGLM coefficients from our analyses of our opportunitywage hypotheses. We find mixed evidence in support of our opportunitywage theory.

Subject specialty. Although we did not find evidence in support of ourhypotheses that math/science teachers will have higher likelihoods of attri-tion given their market value outside of education, we do see that foreignlanguage teachers have significantly higher probabilities of quitting. This


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Table 4Correlation Matrix of Race, by Percentages of Students and Percentages of Teachers

% of School’s Student Enrollment Who Are% of School’s Teachers Who Are White Black Hispanic Asian/PI Am. Ind.

White .7087 –.5163 –.2997 –.2752 –.1170Black –.5361 .8111 –.0114 –.0396 –.0984Hispanic –.2999 –.0657 .5822 .0291 –.0374Asian/PI –.2178 –.0477 .0280 .7889 –.0285Am. Ind. –.2065 –.0752 –.0534 –.0241 .6455

Note. Bold values indicate the own-race correlations. PI = Pacific Islander; Am. Ind. =American Indian.

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may reflect a high value for foreign language ability in an increasinglyglobal economy outside of education. As expected, we do see lower likeli-hoods of attrition for English and social science teachers. Vocational educa-tion teachers also exhibit lower probabilities of quitting. This may againresult from fewer higher utility nonteaching opportunities. It is interestingthat, when we control for teachers’ certifications in their main subject fields,


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Table 5Hierarchical General Linear Model of Teacher Attrition as a Function of Teacher, School, and DistrictAttributes: Exploring the Opportunity Wage Theory

No Main Includes Certified Two-Level FinalVariable Field Variable (1) in Main Field (2) Model (3) Model (4)

Teacher characteristicsSubject specialty

Math/science –0.287*** –0.321*** –0.074 –0.321***English language arts –0.333** –0.359*** –0.072 –0.356**Social science –0.490*** –0.505**** –0.141 –0.50****Special education 0.198 0.172 0.249* 0.171Foreign language 0.449* 0.418* 0.522** 0.417*Bilingual/ESL –0.473 –0.497 0.094 –0.506Vocational education –0.282* –0.303* –0.027 –0.302*Other –0.254 –0.278 –0.029 –0.277

Certification typeProbationary 0.544* 0.573* 0.099 0.579*Provisional –0.175 –0.130 –0.166 –0.129Temporary 0.321 0.368 0.933*** 0.370Emergency –0.422 –0.348 0.041 –0.348

Certification in main field –0.287** –0.150 –0.286**Advanced degree 0.171 0.176 0.174** 0.176Male –0.020 –0.024 0.007 –0.024Experience

0–2 years 0.851**** 0.809**** 0.734**** 0.809****3–4 years 0.847**** 0.823**** 0.786**** 0.824****5–10 years 0.433**** 0.434**** 0.428**** 0.433****> 21 years 0.047 0.051 0.115 0.052

Ln Salary –0.265 –0.262 –0.460** –0.262School and district characteristics

UrbanicityUrban 0.034 0.034 –0.022 0.083Rural –0.130 –0.130 –0.177* –0.139

Salary scheduleLn BA + no experience –1.215* –1.220* –0.385 –1.113*Ln MA + 20 years exp. –0.022 –0.019 –0.521* 0.022

Note. These models also control for teacher race/ethnicity, student and teacher racialcomposition, school size, school level, and percentage of students approved for thefree/reduced-price lunch program. ESL = English as a second language; Ln = natural log; BA = bachelor of arts; MA = master of arts.

*p < .10. **p < .05. ***p < .01. ****p < .001.

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all teachers become less likely to leave. This may be because obtaininga certification in a main subject field indicates a higher investment inteaching.

Gender. A possible explanation for the differential probability ofteachers of different subject specialties leaving their schools and/or theprofession may be found in the gender makeup of different specialties.For example, 91% of general elementary school teachers in our weightedsample are female, compared to 53% of math teachers and 77% of foreignlanguage teachers. Only 40% of social science teachers are female. Clearly,gender makeup varies significantly across subject specialties. However,we found no evidence that men are more likely to leave teaching thanwomen. Instead, we see that men may be less likely to quit. However, thecoefficient on the gender variable is not at all significant, indicating thatwe cannot draw any conclusions regarding males’ differential probabili-ties of leaving teaching from this sample. Rees (1991) suggested that, asteachers age, men and women face differing probabilities of leaving. Thisis predicated on the idea that women will leave the workforce for chil-drearing and homemaking. To test this finding in a multilevel framework,we ran a model with age and an interaction of age with gender. However,we found no significant effect of age or the interaction term. In addition,we tested for an interaction effect of gender with each experience cate-gory. Again, we found no significance to the interaction terms.

Experience. Teachers with more experience teaching in a district havea greater investment in that district as well as lower opportunity wages inanother district or profession. As such, we would expect teachers withmore experience to be less likely to leave their jobs. Table 6 shows this tobe the case. In accordance with most of the recent literature (see, e.g.,Boyd et al., 2005; Hanushek et al., 2002; Murnane & Olsen, 1989a), wefound that experience is not linearly related to the probability of leaving a teaching job. Using categorical variables of experience levels, we foundresults similar to the existing literature, in that teachers with zero to 2 yearsand 3 to 4 years of experience are substantially more likely to leave theirteaching jobs. The calculated probability of attrition for teachers with zeroto 2 years of experience is .174.

Teachers with 3 to 4 years of experience have a .177 calculated probabil-ity of leaving, which is highly significant and substantially greater thanthe teacher with average experience, or compared to the reference groupwith 11 to 20 years of teaching experience. Although still significantly dif-ferent from the average, we see that teachers with 5 to 10 years of experi-ence start to taper off in their probability to leaving, with a calculated


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probability of .120. Teachers with 21 or more years experience displayednonsignificantly higher likelihoods than the average.

Certification type. NCLB and other policies that aim to staff schoolswith regularly credentialed teachers often cite a belief that nonregularlycertified teachers will have a higher likelihood of quitting. Although thisanalysis makes no claim about the quality of teachers of different certifica-tion types, it does suggest that teachers with emergency certifications areno more likely to leave their education jobs than the teacher with the aver-age of all certification categories, all else held constant. The only certifica-tion type that is significantly different from the average is probationarycertification. Teachers in this category have a calculated probability ofattrition at .179, ceteris paribus, compared to teachers with regular certifica-tions, who have a quit probability of .100.

Advanced degree. We did not find that teachers with an advanced(master’s or doctoral) degree are significantly more likely to leave theirteaching jobs. However, the direction of the coefficient does suggest thatteachers with advanced degrees are more likely to quit, with a p value of.105. This finding may seem counterintuitive given that most policymakersstrive to get the most “qualified” teachers in the classroom, and they oftenuse credentials such as master’s degrees as proxies for quality. However,this finding aligns with an opportunity cost theory that individuals withcredentials that are valued outside of teaching and can attract high wageselsewhere are more likely to leave their current employment as a teacher.

School urban location. We did not find significant effects of school loca-tion on teacher attrition. This is surprising, given that we would expectteachers in rural schools to be less likely to leave their jobs, as in manyrural areas qualified teachers may not have plentiful alternate occupa-tional choices. If this is the case, teaching may be the most attractivechoice, whereas in suburban and urban areas, individuals who are quali-fied to teach may also be qualified to perform other local jobs.

Salary. As expected, an increase in schedule-set salary for teachers hasan observed effect of reducing the likelihood of teacher attrition. Themore teachers make in a given district, the greater their utility from teach-ing and the less likely they are to stray. It is appears that the base salaryoffered to teachers—the salary for a teacher with a bachelor’s degree andno experience—matters more for quit decisions than does the salary of experienced teachers—teachers with master’s degrees and 20 years ofexperience. Moreover, once we control for these district-set salary variables,


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any individual effect of individual salary on teacher attrition disappears.This is not surprising, given that salaries are set at the district level, making individual salary once controlling for district salary schedulesinsignificant.

Our final model also controls for school type (secondary and combinedelementary and secondary) and school size. We did not find significanteffects of school type on teachers’ likelihood of attrition, but we do seethat teachers are significantly less likely to leave larger schools. This maybe due to larger schools having more support staff, such that teachers donot assume administrative tasks as they are likely to do in smaller schools.

Specification check. Of great interest here is Model 3, which depicts ourfinal model, without including a third level to account for state fixedeffects. A good deal of extant literature has focused on single-state analy-ses of teacher attrition, and the limited work performed on a nationalsample has not accounted for the nested structure of schools within states(Boyd, Lankford, Loeb, & Wyckoff, 2002, 2005; Hanushek et al., 2002;Ingersoll, 2001; Murnane & Olsen, 1989a, 1989b). Our work shows theinherent importance of accounting for such state effects: Model 3 com-pares our findings without including a third state level to our final model,4, with the state level at Level 3. We see markedly different results whenwe do not control for inherent differences between states, results thatseem more in line with much of the previous literature and with tradi-tional opportunity wage theory hypotheses. When failing to account forthe nested structure of schools within states, we see an increase in the like-lihood of attrition for special education and foreign language teachers andno significant effects for teachers with other subject specialties. Model 3also shows an increased quit probability for teachers with an advanceddegree, as well as significant results for individual salaries over and abovedistrict-set salary points. Although still not significant, the direction of thecoefficient on gender switches to positive, and we see decreased probabil-ity of attrition for rural teachers. Model 3 also indicates that teachers withtemporary certifications have higher likelihoods of attrition, althoughthere is no effect of other certification types. The reliability estimate of thistwo-level model, at .473, is also much lower than that of the three-levelmodel. Clearly, including a state level in models of teacher attrition isimportant and worthy of further exploration.

Social Identity Theory: Racial and Ethnic School Composition

As noted earlier, researchers have found that teachers may be morelikely to leave their jobs if their schools have high percentages of minority


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students. This finding is echoed in our analysis, as we see that teachers aremore likely to quit as the percentages of Black and American Indianstudents increase in their schools.

We posit that this apparent repulsion from minority-heavy schoolsmay be due less to racial bias or unpleasant working conditions associ-ated with heavily minority communities and may stem more from issuesof racial similarity and comfort. Although racial similarity betweenteachers and students has been an area of interest among educationalresearchers, our review of the literature finds that researchers havefocused exclusively on the teacher–student racial similarity componentand have not examined whether the percentage of the teaching staff of thesame race/ethnicity as the teacher has a significant influence on ateacher’s decision to remain in his or her current job.

Table 6 highlights the differences when considering teacher attrition as aresult of student and faculty race/ethnicity. The final model uses only themost significant predictors for each specific race category. We examine tworacial similarity measures: (a) the percentage of students in a school who areof the same race or ethnicity as the individual teacher and (b) the percentageof school faculty who are of the same race or ethnicity as the teacher.

Model 1 in Table 6 shows our findings when we examine how teachers’race and ethnicity and the racial/ethnic composition of the school are sepa-rately associated with teachers’ likelihood of attrition, controlling for thepercent of students approved for free and reduced price lunch. We see thatthe average teacher in a given school is significantly more likely to leave ifthere are higher percentages of minority students, and less likely to leave if there are more American Indian teachers. However, only one teacherrace variable is a significant predictor of teacher attrition: American Indianteachers seem significantly more likely to quit than a White teacher.

Because of the potential collinearity problems mentioned earlier,Models 2 and 3 explore student race/ethnicity composition and teacherrace/ethnicity composition separately, including interaction effects thatcapture the likelihood of a teacher of a given race/ethnicity exiting theschool if there are more students and teachers of the same race or ethnicityin that school. We see that student racial composition of the school has astronger and more significant power to predict the likelihood of the averageteacher leaving a given school. Teachers are more likely to leave a school ifthere is a higher proportion of minority students, but less likely to leave ifthere is a higher proportion of American Indian teachers. In accordancewith the predictions of social identity theory, we see that Black teachersare less likely to quit if they teach in schools with higher proportions ofBlack students or teachers. Contrary to what social identity theory would


86

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87

Tabl

e 6

Rac

ial C

ompo

siti

on E

ffect

s on

Tea

cher

Att

riti

on: E

xplo

ring

the

Soci

al Id

enti

ty T

heor

y H

ypot

hese

s

Inte

ract

ions

:N

o R

ace

Inte

ract

ion:

In

tera

ctio

n:

Stud

ent a

nd

Inte

ract

ions

: Fi

nal

Var

iabl

eIn

tera

ctio

ns (1

)St

uden

t Rac

e (2

)Te

ache

r R

ace

(3)

Teac

her

(4)

No

FRP

L(5

)M

odel

(6)

Scho

ol c

hara

cter

isti

cs%

FR

PL0.

005*

**0.

005*

**0.

007*

***

0.00

5***

0.00

5***

% s

tud

ents

Am

. Ind

.0.

008*

**0.

004*

0.00

8***

0.01

0***

*0.

006*

*%

stu

den

ts A

sian

/PI

0.02

0***

*0.

018*

**0.

021*

**0.

020*

***

% s

tud

ents

Bla

ck0.

006*

0.00

6***

0.00

6*0.

009*

**0.

005*

**%

stu

den

ts H

ispa

nic

0.00

5*0.

005*

*0.

005*

0.00

8***

*0.

004

% te

ache

rs A

m. I

nd.

–0.0

13**

**–0

.009

****

–0.0

13**

**–0

.012

****

–0.0

10**

**%

teac

hers

Asi

an/

PI–0

.011

0.00

1–0

.010

–0.0

090.

001

% te

ache

rs B

lack

–0.0

000.

000

–0.0

00–0

.000

% te

ache

rs H

ispa

nic

0.00

20.

003

0.00

10.

002

0.00

2Te

ache

r ch

arac

teri

stic

sA

m. I

nd.

0.43

7**

0.45

8**

0.45

9**

0.46

0**

0.46

0**

0.46

1**

% s

tud

ents

Am

. Ind

.–0

.002

0.00

10.

001

0.00

1%

teac

hers

Am

. Ind

.–0

.013

–0.0

14–0

.014

–0.0

14A

sian

/PI

–0.1

92–0

.273

–0.3

54–0

.333

–0.3

23–0

.399

% s

tud

ents

Asi

an/

PI0.

011*

**–0

.000

–0.0

00%

teac

hers

Asi

an/

PI0.

036*

*0.

037

**0.

037

**0.

036

**B

lack

–0.1

200.

282

0.16

60.

297

0.29

90.

292

% s

tud

ents

Bla

ck–0

.017

**–0

.015

–0.0

15–0

.017

**%

teac

hers

Bla

ck–0

.017

**–0

.003

–0.0

03H

ispa

nic

–0.1

24–0

.102

–0.2

65–0

.140

–0.1

37–0

.136

% s

tud

ents

His

pani

c–0

.001

–0.0

10**

–0.0

10**

–0.0

10**

% te

ache

rs H

ispa

nic

0.01

40.

025*

*0.

025*

*0.

025*

*

Not

e.Th

ese

mod

els

also

con

trol

for

sch

ool

size

, sc

hool

lev

el,

and

all

vari

able

s in

clud

ed i

n Ta

ble

5. F

RPL

= fr

ee o

r re

duce

d-pr

ice

lunc

h;

PI =

Pac

ific

Isla

nder

; Am

. Ind

. = A

mer

ican

Indi

an.

*p<

.10.

**p

< .0

5. **

*p<

.01.

****

p<

.001

.

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predict, Asian/Pacific Islander teachers are more likely to leave if theyteach in schools with higher proportions of Asian/Pacific Islanderstudents or teachers.

When we include all race/ethnicity composition variables as bothdirect effects on teacher attrition and as interactions with individualteacher race/ethnicity (Model 4), we see that Hispanic teachers seem lesslikely to leave if they teach in a school with a higher proportion ofHispanic students, but are more likely to leave if they teach in a schoolwith a higher proportion of Hispanic teachers.

Our final model (Model 6) does not include a measure of the percent-age of students in a school who are Asian/Pacific Islander or the percent-age of teachers in a school who are Black. We dropped these variablesbecause we see in Models 1 through 4 that including both faculty andstudent composition for Asian/Pacific Islanders and Black teachersresults in issues of multicollinearity and skews our results. Our finalmodel provides strong support for the social identity hypotheses:Although teachers on average are more likely to leave a school if there arehigher proportions of Black and American Indian students enrolled, wesee that Black and Hispanic teachers are less likely to exit a school if thereare higher proportions of Black and Hispanic (respectively) studentsenrolled. However, we find a curious result in that Hispanic andAsian/Pacific Islander teachers are more likely to leave a school if there isa higher proportion of Hispanic and Asian/Pacific Islander (respectively)teachers. This finding may result from the varied races and ethnicitiescaptured in both the “Hispanic” and “Asian/Pacific Islander” groups inthe SASS. We are likely capturing multiple different races and ethnicitieswith these broad categories. Further analysis with narrower categories isin order.

Because we see disturbing direct effects of racial/ethnic school compo-sition on teacher quit behavior, we attempt to disentangle the effects ofadverse working conditions from minority school composition by includ-ing a control for the percentage of students who have been approved forthe free and reduced-price lunch program. This variable is consistentlypositive and significant across all of our models, indicating that teachersare more likely to leave schools in which there are more poor students.However, our racial composition findings are robust to the inclusion ofthis poverty measure. This indicates that there are likely other causesbeyond simply poor working conditions that lead teachers to exit highlyminority schools and districts.

The inclusion of our measure of student poverty in a school demon-strates the importance of including such a measure in attrition models.Model 5 examines our full interactions model without the inclusion of a


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control for poverty. We see that all of the direct minority compositioneffects gain in both magnitude and significance when we do not includethe percentage of students approved for free and reduced-price lunch. Afailure to include such a poverty measure would result in omitted vari-able bias, leading to a misestimation of the racial composition effects.

Calculated Probabilities for theLikelihood of Attrition, by Race/Ethnicity

For ease of comparison across racial/ethnic groups, we calculated thelikelihood of the average teacher of each race exiting a school. We also calculated the likelihood of all other teachers exiting the same school.Because our analyses took into account school and state effects, the inter-cept term of each model reflects the likelihood of the average teacher leav-ing. To calculate the probability of a Black teacher leaving, for instance,we must subtract the proportions of all other teachers contributing to theintercept term. Thus, a Black teacher’s probability of exiting, all else heldconstant at the mean, is

where γ000 is the grand intercept, γBlack is the coefficient on the teacher beingBlack, G is the total number of non-Black race groups (not includingWhites, which is the omitted category in the model), and γg is the coeffi-cient on being a teacher of racial group g.

A non-Black teacher’s probability of leaving the same average school is

Table 7 presents these calculated probabilities, based on the final model,for each racial group. For instance, take two teachers who are identical (atthe average) on all characteristics and in the same average school. Let oneteacher be Black and the other non-Black. The non-Black teacher has aprobability of .091 of leaving the school, and the otherwise-identical Blackteacher has a .119 probability of leaving, a nonsignificant difference. Ifthese two teachers were situated in a school with a student body com-posed of 25.5% more Black students (an increase of 1 standard deviation),the non-Black teacher’s probability of leaving increases significantly, to.105. However, the Black teacher’s probability of leaving decreases signifi-cantly, from .119 to .084. This is a prime example of social identity theory.


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Contrary to our initial expectations, we found that an increase in the per-centage of teachers of one’s own race increases the likelihood of attrition forboth Asian and Hispanic teachers. For example, moving from the averagepercentage of Asian teachers to 1 standard deviation above the averageincreases the likelihood of attrition for an Asian teacher, from .067 to .091,yet non-Asian teachers’ predicted attrition is unaffected by the difference inthe percentage of Asian teachers at the school. Similarly, Hispanic teacherattrition is predicted to increase from .081 at the average to .105 when thepercentage of Hispanic teachers increases by 1 standard deviation. This is incontrast to the response of Hispanic teachers when the percentage ofHispanic students increases to 1 standard deviation above average. In thatcase, their predicted probability of attrition decreases from .081 to .068.

If these teachers strongly identify with their racial/ethnic group, thensocial identity theory would predict greater attachment to schools withhigher concentrations of individuals of one’s own racial/ethnic group,


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Table 7Calculated Probabilities of Racial Similarity Changes in the Final Model

Calculated Probability, Holding All Else Constant

ComparisonProbability, by to Teachers

Fixed Effect Teacher Race of Other Races

School and district characteristicsIntercept (i.e., teacher of “average” race

and average characteristics)a .093**** NATeacher characteristics

Race of teacherAm. Ind.b .137** .092

+1 SD in % of school’s students who are Am. Ind.b .140 .100**+1 SD in % of school’s teachers who are Am. Ind.b .126 .086****

Asian/PIb .067 .094+1 SD in % of school’s teachers who are Asian/PIb .091** .093

Blackb .119 .091+1 SD in % of school’s students who are Blackb .084** .105**

Hispanicb .081 .093+1 SD in % of school’s students who are Hispanicb .068** .100+1 SD in % of school’s teachers who are Hispanicb .105*** .094+1 SD in both % students and % teachers Hispanic .089 .100

Note. Am. Ind. = American Indian; PI = Pacific Islander.aThe term has a random effect. bThe variable has been centered around its group mean.*p < .10. **p < .05. ***p < .01. ****p < .001.

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ceteris paribus. These findings are not necessarily inconsistent with thistheory. It is well accepted that teachers’ work is unlike that of most profes-sionals in that teachers interact with their students more often than withtheir colleagues. Teachers may derive greater satisfaction from educatingstudents who reflect their own racial identity. Yet a staff whose racial com-position reflects one’s own racial identity may proxy for other characteris-tics undesirable to the teacher and thus does not necessarily reflectdistaste for colleagues of one’s own racial/ethnic group. Moreover, withina broad racial category like “Hispanic” or “Asian,” many subgroups exist.Howard (2000) provided a review of the literature on how these subgroupidentities (e.g., Cuban American, Mexican American, within the largerHispanic group) may be more pervasive than loyalty to the group as awhole. Perhaps the subgroup delineation is more salient for teachers inregard to their loyalty to colleagues than to their loyalty to future genera-tions (i.e., their students) of the group as a whole.

Conclusion

As researchers continue to consider how to attract qualified personnelinto education fields, they also have recently begun to devote sizableattention to the issue of teacher retention. It is not enough to know whatbrings individuals into teaching. We must learn what types of individualcharacteristics, school attributes, and district traits are effective at retain-ing the existing supply of teachers in their current positions. In this article,we build on existing research by modeling teacher turnover in a multi-level context, recognizing that teachers are nested within schools andstates. Using HGLM for the binary outcome of teacher attrition, our meth-ods of modeling turnover correct for the correlated error terms of teacherswithin any given school and state. Previous research ignoring the struc-ture of teachers within schools and states may have produced biased esti-mates and incorrect standard errors.

Combining the theories of opportunity wages and social identity in ourconceptual framework, we found that teachers who likely have greateropportunities outside the public school system are more likely to leavetheir teaching jobs, whereas teachers with more invested in their teachingposition are less likely to leave.

Our results also suggest that researchers reconsider their conceptualnotions of racial similarity as a determinant of attrition. We found that thepercentage of students of any given race is not equivalent to the percentageof teaching staff of the same race. Researchers’ decisions regarding which


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racial similarity measure to include in a model will affect their conceptionsand representations of how teacher attrition is determined, which may inturn influence any resulting policy recommendations.

Our analysis presents a novel application of hierarchical modeling tech-niques to research on teacher attrition, and we hope we have encouragedfuture research in this vein. However, we must stipulate some caveats of ouranalyses. Our determination of attrition is whether the teacher is teaching inthe same school the following year. Therefore, it may not have been theteacher’s decision to leave the school. Rather, their employment may havebeen terminated by the employer, because of layoffs or firings. Also, teachersare more likely to be fired in their first 2 to 3 years, before achieving tenure,which may account for a portion of the observed high odds ratio for teacherswith 0 to 2 years of experience. Moreover, our analysis does not look atteacher migration. We simply recoded the SASS data set such that “movers”and “leavers” both count as attrition, as opposed to “stayers.” However, asIngersoll (2001) noted, in the organization-level literature on teacherturnover migration is as important as attrition because it has the same effecton students, administrators, and other teachers within the school the teacherleaves. It does not matter to them whether the teacher has simply moved toanother school within the district, or whether he or she has left the systementirely—they still lose that teacher from their school. This is one possibleavenue of future research on teacher retention, which we hope researcherswill examine in a multilevel analysis to properly address the nesting ofteachers within schools within states.

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oh, won't you stay: a multilevel analysis of the difficulties in retaining qualified teachers

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