teacher sorting and the flight of urban schools

8/9/2019 Teacher Sorting and the Flight of Urban Schools

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EducationalEvaluationandPolicyAnalysisSpring2002, Vol. 24, No. 1, pp . 37-62

Teacher Sorting and the Plight of Urban Schools:A Descriptive Analysis

Hamilton LankfordUniversityat Albany, SUNY

Susanna LoebStanford University

James WyckoffUniversity atAlbany, SUNYThis paper uses rich new data on New York State teachersto: determine how much variation n theaverageattributesof teachers exists across schools, identif, schools that have the least-qualifiedteachers, assesswhether the distributionhas changedover time, and determineho w the distributionof teachersis impacted by attritionand transfer, as well as by the job matches between teachersandschools at the startof careers.Our resultsshow striking differences in the qualificationsof teachersacrossschools. Urbanschools, in particular,have lesser-qualified eachers;andNew York City standsout among urban areas.Low-income, low-achieving and non-white students, particularly hose inurbanareas, ind themselves in classeswith many of the least skilledteachers. Salaryvariationrarelycompensates or the apparentdifficulties of teaching n urban settingsand, in some cases, contributesto the disparities.Keywords: teachersorting, urban schools

At the federal level and in every state, policy-makers are struggling to address the low academicachievement of many K- 12 students and the gapsin achievement among income and racial-ethnicgroups of students. Concern over low student per-formance has a long history, however, it has takenon recent urgency in an era marked by court casesthat focus on adequacy, by dramatic increasesin achievement information, and by widespreadcalls for accountability. Recent research has em-phasized the link between teachers and studentoutcomes.' Yet, even with increases in spendingequity within states (Evans, Murray and Schwab,2001), substantial differences remain acrossschools in the qualifications of teachers (Betts,Rueben and Danenberg, 2000). Disparities in

know little about the extent of teacher sorting orits correlates.

An understanding of how schools differ in thequalifications of their teachers and the mecha-nisms driving these differences is useful for de-signing effective policies that address inequitiesor inadequacies in instructional resources. In thispaper we use New York State as a case study: todetermine how much variation in the average at-tributes of teachers currently exists across schools,to identify which schools have the least quali-fied teachers, to place the current situation in con-text by assessing whether the distribution hasgotten more or less dispersed over time, and todetermine how the distribution of teachers is im-pacted by attrition an d transfer of teachers, as


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Lankford, Loeb, and Wyckoffschools at the start of teaching careers. We alsoexamine differences in teaching salaries acrossschools to provide evidence of how salary differ-entials are currently contributing to or alleviatinginequities in teacher resources.Unlike most previous studies of teacher labormarkets, we focus on the distribution of teachersacross schools and not solely on the averagecharacteristics of the teacher workforce. Our datacomes from administrative records in New YorkState that allow us to follow all teachers in thestate over the past 15 years. The breadth of thedata (all teachers in all schools) allows analyticalflexibility not possible with smaller datasets. Forexample, we can compare the sorting of teachersthat occurs overall in the state to the sorting be-tween schools within the same district and thusseparate out district sorting from school sort-ing. We also have a large enough sample to lookonly at new teachers and thus distinguish cur-rent sorting mechanisms from historical sorting.The length of the data allows us to assess whetherthe current inequities in teacher quality acrossschools is a recent phenomenon or one that haspersisted. The data is richer in its descriptionsof teachers than other administrative datasets usedto date, including teachers' test scores and under-graduate institution. It also allows us to matchteachers to characteristics of the schools in whichthey teach in a way that most national longitudinalsurveys, such as High School and Beyond or theNational Longitudinal Survey of Youth, do not.Our results show striking differences in thequalifications of teachers across schools. Urbanschools, in particular, have lesser-qualified teach-ers, and New York City stands out among urbanareas. Low-income, low-achieving and nonwhitestudents, particularly those in urban areas, findthemselves in classes with many of the leastskilled teachers. Finally, we find that salary vari-ation rarely compensates for the apparent diffi-culties of teaching in urban settings and, in somecases, contributes to the inequities in teacherresources across schools.In what follows, we start with a discussion ofpotential causes of teacher sorting across schools.The third section introduces our data and meth-ods. The fourth section describes the variation inteacher qualifications and characteristics acrossschools. It looks at how schools in different lo-

how these relationships have changed over timein addition to, exploring the impact of teacherattrition and transfers on the qualifications ofteachers in different schools. The fifth section de-scribes the variation in teacher salaries across thestate; and the final section concludes with a dis-cussion of policy implications and directions forcontinued research.

BackgroundThis paper describes the sorting of teachersacross schools and does no t test hypotheses forwhy this sorting occurs. Yet, in order to place theresults in context, it is worth considering whyschools might differ in the characteristics of theirteachers. Research in this area is very limited, bu tfour plausible explanations emerge. First, differ-

ences may be driven simply by differences in thepreferences of residents. That is, one school maystrive to hire one type of teacher and another maystrive to hire a different type of teacher. Even ifboth schools are choosing from the same pool ofpotential teachers, they will end up with teachingstaffs that differ systematically. As an example,schools with a high percent of minority studentsmay benefit from having teachers with similarracial and ethnic backgrounds. These teachersmay have attended lower ranked undergraduateinstitutions and may score lower on teacher examsthan other teachers of similar quality. If this werethe case then we may see teachers with poorerqualifications as measured by test score and schoolranking in schools with a high percent of minor-ity students even though the teachers in theseschools are not necessarily of lower quality. Weknow of no studies that systematically examinethis issue.A second plausible explanation for variation inaverage teacher qualifications across schools isthat districts may differ, no t in their preferencesbut in the efficiency of their hiring practices. In-efficiencies in hiring will lead to systematic dif-ference in teachers across districts. Districts witheffective hiring (aggressive recruiting, spring joboffers, etc ... .) will end up with higher-qualityteaching staffs even though they are initiallyfaced with the same pool of potential teachers. Fewstudies have explored district-hiring practices,

though Pflaum & Abramson (1990) and Ballouand Podgursky (1997) do provide evidence that


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TeacherSorting and the Plightof UrbanSchoolsThird, within districts, schools vary in the

political power they exert, which may lead to dif-ferences in teacher qualifications. For example,schools with strong parental input may not ac-cept low-quality teachers. Bridges (1996) foundthat when parents and students complained aboutpoor teachers the teachers were likely to be trans-ferred to schools with high student transfer rates,large numbers of students receiving free or re-duced price lunches, and large numbers of minor-ity students.While efficient hiring and district assignmentmay contribute to the disparities observed in thedata, teacher preferences are likely to be particu-larly influential. Teachers differ fundamentallyfrom other school resources. Unlike textbooks,computers, and facilities, teachers have prefer-ences about whether to teach, what to teach, andwhere to teach. Potential teachers prefer one typeof district to another; and within districts, theyprefer one school to another. There has been muchdiscussion about the role that compensation playsin the ability of schools to attract and retain high-quality teachers. A large body of literature suggeststhat teachers respond to wages. As a group, thesestudies show that individuals are more likely tochoose to teach when starting teacher wages arehigh relative to wages in other occupations (Baughand Stone, 1982; Brewer, 1996; Dolton 1990,1993; Dolton and van der Klaaw, 1999; Doltonand Makepeace, 1993; Hanushek and Pace, 1995;Manski, 1987; Mont and Rees, 1996; Mumane,Singer & Willett, 1989; Rickman and Parker,1990; Stinebrickner, 1998, 1999,2000; Theobald,1990; Theobald and Gritz, 1996). Baugh andStone (1982), fo r example, find that teachers are atleast as responsive to wages in their decision toquit teaching, as are workers in other occupations. 2Teachers are more likely to quit when they workin districts with lower wages.Salaries are one element of teaching jobs thatare likely to impact sorting, bu t nonpecuniary jobcharacteristics appear important as well. Thesecharacteristics may include class size, prepara-tion time, facilities, or characteristics of the stu-dent body, among other things. As an example,class size reduction in California resulted in anincrease in demand for teachers across the state.Teachers in schools with low-achieving studentschose to move to higher achieving schools, leav-

Danenberg, 2000; Bohrnstedt and Stecher, 1999).Similarly, in Texas, Hanushek, Kain and Rivkin(1999) found teachers moving to schools withhigh-achieving students and, in New York City,Lankford (1999) found experienced teachers mov-ing to high socioeconomic status schools whenpositions became available.

Although each of these hypotheses about thesorting of teachers is plausible, in fact, we knowvery little about sorting or the causal relation-ships that lead to sorting. In this paper we pro-vide an empirical foundation on which to buildmodels of teacher and school district behavior.

Data andMethodsNew York State faces trends in teacher demand

similar to those faced across the United States.About 38% of New York's current teachers willhave retired or reached age 55 within the next fiveyears. Policies of accountability and class size re-duction are also increasing demand for teachersin the State. New York serves as a good examplefor examining the teacher workforce because ofthese trends and because it comprises a diversepopulation and a range of schools across whichteachers can demonstrate their preferences.Our database links seven administrative data-sets and various other information characterizingdistricts, communities, and local labor markets.It includes information for every teacher andadministrator employed in a New York publicschool at any time from 1984-85 through 1999-2000. The core data comes from the PersonnelMaster File (PMF), part of the Basic EducationData System of the New York State EducationDepartment. In a typical year there are approxi-mately 180,000 teachers identified in the PMF.We have linked these annual records throughtime, yielding detailed data characterizing thecareer history of each individual. Several otherdatabases that contain a range of informationabout the qualifications of prospective and actualteachers, as well as the environments in whichthese individuals make career decisions, sub-stantially enrich this core data.3The heart of our analysis relies on having mea-sures of teacher skills. Teaching is a complicatedendeavor; it is difficult to assess how well individ-uals perform in the classroom. A few studies quan-tify the quality of teachers by the contributions they


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Lankford, Loeb, and Wyckoff(2000), Sanders and Horn (1994) and Sanders andRivers (1996) for examples). This approach hasadvantages and disadvantages both conceptuallyand in practice.4 Researchers more commonlyemploy measures of individuals' attributes asproxies for teacher quality (D eAngelis, 1999, forexample). Many of these measures are quite weak:whether or not the teacher holds a master's degree,whether or not the teacher is certified, and yearsof teaching experience. They have not consis-tently been found to influence student learning(Hanushek, 1986 & 1997). However, studies withricher detail on teachers, such as teacher test scoresor the quality of teachers' undergraduate institu-tion, have often found effects on student outcomes(Ehrenberg and Brewer, 1994; Ferguson, 1991;Ferguson and Ladd, 1996). Thus, the quality ofdata is important. In their analysis of who choosesto teach, Hanushek and Pace (1995) employ teach-ers' reading, vocabulary, and mathematics testscores (administered as part of the HSB survey),arguing that it is plausible "that 'smarter' teacherswith higher achievement of their ow n couldperform better in the classroom." Ballou andPodgursky (1997) make a similar point and pro-vide a summary of the literature that speaks to therelationship between the strength of academicbackground and teacher effectiveness. Their analy-sis of teacher pay and quality employs collegeselectivity, academic major, undergraduate GPA,and SA T scores as indicators of quality.We employ measures similar to those usedby Hanushek and Pace and discussed by Ballouand Podgursky. We identify the institutions fromwhich individual teachers earned their under-graduate degrees from the NYS Teacher Certifi-cation Database (TCERT) and combine it withthe Barron's ranking of college selectivity toconstruct variables measuring the selectivity ofthe college from which each teacher graduated.We draw information regarding the teacher cer-tification exam scores of individual teachers andwhether they passed on their first attempts fromthe NYS Teacher Certification Exam History File(EHF). In order to assess the distribution of teach-ers across the schools, we create multiple mea-sures of average teacher characteristics at theschool level. These measures include:

* The percent of eachers with no prior teachingexperience

* The percent not certified in any current as-signment* The percent certified in all current assign-ments* The percent of exam takers who failed theNTE General Knowledge Exam or the NYS TCELiberal Arts and Science Exam on their firstattempt* The percent who attended Barron's CollegeGuide most competitive and highly competitiveschools* And the percent who attended competitive,less competitive, or least-competitive schoolsThese are a subset of the measures we have avail-able but they are illustrative of the trends weobserved in all our teacher attribute measures.To simplify the discussion we also create a com-posite measure using principal components analy-sis that combines a number of these characteristics.Appendix B describes the components of thismeasure. It has a reliability of 0.86 and explains52% of the variation in its component measures.The measure has a mean of zero and a standarddeviation of one, indicating that a one unit changein the composite corresponds to a one standarddeviation change.5

The UnevenDistributionof Teachers:To start,we examine all teachers in the New York Statesystem in 1999-2000. We create school levelaverages of the teacher measures described aboveand examine the distribution of these measuresacross the schools in the state. We then look at thecorrelation among the various measures to seewhether schools with less qualified teachers onone dimension also tend to have lesser-qualifiedteachers on other dimensions. Finally we de-compose the variance in teacher qualifications tosee how much variation exists between regions orlabor markets in the state, how much betweendistricts within regions, and how much betweenschools within districts.Parsing the variation into within-district,between-district-within-labor-market, and be-tween-labor market can be useful for assessingthe likely causes for the disparities. Fo r example,if most of the variation in teacher qualificationsexists among (not within) regional labor markets,then characteristics that vary substantially acrossregions such as the availability of alternative oc-cupations for teachers or the cultural attitudes of


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TeacherSorting and the Plightof UrbanSchoolsbetween-districts-within-labor-markets, then dis-trict policies such as hiring practices or salaryschedules may play a key role, as may other districtcharacteristics. If, thirdly, there is much variationamong schools within the same district, this fo-cuses attention on school-level differences such asthe non-pecuniary attributes of schools that affectteachers' decisions about where to teach.Correlateswith TeacherQualifications:Whilethe above analysis describes the extent of sorting,it does no t reveal the characteristics of schoolswith systematically greater-or-lesser qualifiedteachers. We next look across several dimensionsincluding urbanicity, student race, student poverty,student English proficiency, and student perfor-mance on state assessment exams. In order to sep-arate urban, suburban and rural differences fromdifferences in student characteristics, we look atdifferences in teacher qualifications across schoolswithin each of the large urban areas in New YorkState, as well as across the State as a whole.Changes in the DistributionOver Time: Sofar the analysis has concentrated on a single year,1999-2000. We next explore time trends in order

to see whether this year is an anomaly, whether itis part of a long run trend of change, or whethersimilar variation in teacher qualifications acrossschools has characterized the teacher labor marketovertime. We look at the stock of teachers in eachyear as well as the characteristics of teachersentering the workforce. The full teacher samplegives a picture of the inequities across schools,while the new teacher sample shows how these in-equities are impacted by changes on the margin. Asingle year of new teachers may no t substantiallyimpact differences across schools but can provideinformation on the mechanisms driving teachers'choices of schools and districts.

The Impact of Attrition andTransfers:Teach-ers make choices that impact equity, not onlywhen they choose their first teaching job, butalso, when they decide to quit or transfer. One ofthe most interesting aspects of our data is theability to follow the career choices that teachersmake. In this part of the analysis, we follow co-horts of teachers examining the choices theseteachers make about whether to remain in teach-ing in New York and whether to remain in thesame school or district. For those who quit or trans-fer, we compare their attributes to those of theircolleagues who stay and we explore the educa-tional environments they leave and move to. Thisanalysis shows whether quit and transfer behav-iors impact the distribution of teacher qualifica-tions across schools.

TeacherSalaries:Ultimately we are interestedin determining causes of the distribution of teach-ers that we see. Teacher salaries are evident pol-icy tools for influencing this distribution. In thefinal part of the analysis, we look at salary differ-ences across schools to determine whether thesedifferences are likely to be adding to the dispari-ties that we see or reducing additional inequitiesthat would exist if salaries were the same acrossschools. We run similar analyses as those fo rteacher characteristics, including decompositions,correlations with school and student characteris-tics, and changes over time.

The TeacherWorkforceThe Uneven Distribution of Teachers: Byalmost any measure, the qualifications of New

York's teachers are unevenly distributed acrossschools. This is true across a wide range of teacherquality attributes. Table I shows the 10th, 50thand 90th percentiles for a variety of measures of

TABLE ISchool Quantiles or New York State TeacherAttributes, 2000

PercentileTeacher Quality Attribute 10th Median 90thOverall teacher quality factor -2.974 0.469 2.093% With no teaching experience 0.000 0.067 0.176%BA degree or less 0.029 0.125 0.262%Not certified in any assignment 0.000 0.038 0.243%Permanent certification in all assignments 0.449 0.731 0.889%Fail General Knowledge or Liberal Arts Exam 0.000 0.077 0.308%BA from most competitive college 0.000 0.088 0.234


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Lankford, Loeb, and Wyckoffteacher qualifications across all schools in theState. The differences across schools are striking.Many schools have no teacher who is new, isteaching ou t of their certification area, failed acertification exam on their first attempt, or whograduated from the least competitive undergrad-uate colleges (10th percentile or below). On theother hand, many other schools (90th percentileor above) have a substantial portion of teacherswho are brand new teachers (18%), who are onlyteaching courses for which they are not certified(24%), or who failed a certification exam on theirfirst attempt (about one third). In some schoolsless than half of the teachers are permanentlycertified in all of the courses they teach while inother schools this figure is nearly 90%. Clearly,the qualifications of teachers are not evenly dis-tributed across schools.Correlations Among Multiple Measures ofTeacher Quality: Even though it is feasible thatsome schools have less skilled teachers asmeasured in on e dimension while others haveless skilled teachers as measured in anotherdimension, this is generally no t the case. Theschool-level teacher attribute measures are highlycorrelated as shown in Table 2.Schools that have low quality teachers as mea-sured by one attribute are more likely to have lowquality teachers based on all other measures. Fo rexample, schools with high proportions of teacherswho failed exams are more likely to have teachersfrom less competitive colleges (correlations ofapproximately 0.45) and schools with a high pro-portion of teachers who are not certified to teachany of the courses that they currently teach aremuch more likely to have teachers who graduatedfrom the less competitive colleges (correlation of.40). The teacher quality factor is highly corre-lated with all the individual teacher attributes.Thus, New York's schools are subject to substan-tial systematic sorting of teachers based on theirqualifications. Understanding the geography overwhich teacher sorting takes place provides insightsinto the factors that contribute to sorting.

Decompositionof the Variation: For the pur-pose of this analysis we roughly characterize ninelabor markets in the state consisting of six indi-vidual Metropolitan Statistical Areas (MSAs)and three remaining rural areas (Boyd, Lankford,Loeb, and Wyckoff (2001) provides support for6

Fo r the composite factor we find that approxi-mately 25% the variation is between regions inthe state; 40% is between districts within re-gions; and 35% is among schools within districts.Although, this partition differs across some ofthe component measures, there is remarkableconsistency in the finding that most of the vari-ance in teacher qualifications occurs either be-tween districts within regions or among schoolswithin districts. This result becomes particularlystriking when we control for differences betweenthe New York City region and the rest of thestate. 7 When the New York City region is removedfrom the analysis, in Table 4, only two percent ofthe variation in the composite teacher qualityindex lies between regions.'The results in Table 4 indicate that most of thevariance in the qualifications of teachers resultsfrom either differences between districts withinregions or differences between schools withindistricts.9 While the New York City region differsfrom the rest of the state in the qualifications ofits teachers, there is very little difference acrossthe other regions. The substantial sorting ofteachers within districts suggests that nonpecu-niary characteristics of the schools may beplaying an important role in teachers' choicesof where to teach. The sorting that occurs acrossdistricts within regions suggests that districtpolicies, including hiring practices, salaries anddistrict-wide nonpecuniary attributes of jobs mayalso be important. Since teacher qualificationsdiffer widely across New York, and most of thesedifferences exist within regions, we explore intra-region sorting in greater detail.

Correlateswith Teacher Qualifications: norder to characterize the sorting process we lookat differences in teacher measures: (a) across labormarkets, (b) between urban and suburban areaswithin each metropolitan area, (c) across studentsof different race, (d) across students in differentincome groups, (e) across students with differ-en t English proficiency, and (f) between high-performing and low-performing schools. Again,the results are striking, if predictable. Substan-tially less qualified teachers teach poor, minoritystudents in urban areas.

LaborMarkets: As suggested by the decom-position, there are few differences across regionsin the qualifications of teachers. Figure 1shows


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Teacher Sortingand the Plight of UrbanSchools

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Lankford, Loeb, and WyckoffTABLE 3VarianceDecomposition

Between district Between schoolTeacher Quality Attribute Between region within region within districtOverall teacher quality factor 0.25 0.40 0.35% With no teaching experience 0.02 0.25 0.72% BA degree or less 0.02 0.35 0.63% Not certified in any assignment 0.17 0.47 0.36%Permanent cert. in all assignments 0.14 0.42 0.44%Fail General Knowledge or Liberal Arts Exam 0.16 0.38 0.45%BA from most competitive college 0.14 0.35 0.51%BA from least competitive college 0.41 0.28 0.31

tion to this general pattern is that the New YorkCity Region has fewer qualified teachers than theother regions in the state. This is true at the 10th,50th, and 90th percentile. However, the differ-ences at the 10th percentile are by far the great-est. There are a group of schools in the New YorkCity region that have substantially less skilledteachers than those found in the rest of the state.

Urbanicity:For each of the urban labor marketsin New York State, Table 5 shows the distributionof school-level teacher attributes for the urban andsuburban schools separately. With the exceptionof the Utica-Rome region, urban schools haveteachers with lesser qualifications. For example, inten percent of urban schools in the Buffalo regionone third of the teachers had failed the liberal artsexam, whereas in suburban schools, only one fifthfailed the exam. Similar trends are evident acrossmultiple measures and across the multiple metro-politan areas. Again, the results for the New YorkCity region are most striking. Ten percent of NewYork City urban schools have an average teacherquality measure that is five standard deviationslower than the state average. The table also showsimportant differences in teacher qualifications

within urban districts. In every urban district, theschools at the 10th percentile are qualitatively dif-ferent from those at the 90th percentile.

Student Characteristics:As we try to betterunderstand teacher sorting, we look at the extentto which the qualifications of teachers are sortedwith respect to the racial, economic, and lan-guage attributes of students. We find that non-white students experience less skilled teachersthan white students, poor students experienceless skilled teachers than nonpoor students, andstudents with limited English proficiency experi-ence less skilled teachers than non-LEP students.Table 6 summarizes the results for race andpoverty status.' 0 For New York State, 17% ofnonwhite students have teachers who are not cer-tified to teach any of their current teaching as-signments, compared with four percent for whitestudents. Twenty-one percent of nonwhite stu-dents' teachers have failed either the GeneralKnowledge or Liberal Arts and Science certifi-cation exam, compared with seven percent forwhite students. Poor students also experienceless skilled teachers. For example, on average28% of teachers of poor students have failed the

TABLE 4Analysis of VarianceExcluding New York City Region

Between district Between schoolTeacher Quality Attribute Between region within region within districtOverall teacher quality factor 0.02 0.32 0.66%With no teaching experience 0.01 0.27 0.73%BA degree or less 0.03 0.32 0.65%Not certified in any assignment 0.03 0.40 0.57%Permanent cert. in all assignments 0.02 0.32 0.66%Fail General Knowledge or Liberal Arts Exam 0.01 0.25 0.75%BA from most competitive college


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Teacher Sortingand the Plightof UrbanSchools3-

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FIGURE 1. The distributionof composite teacherquality by region.Note. Buffalo reflects the schools in the Buffalo MSA outside of the Buffalo City School District. The Buffalo CitySchool District has a certification program that differs from that in use in the remainder of the State and thereforecertification data is not comparable and the composite measure could not be computed. The standard error of themean for the state is .034. The standard errors of the means for the regions are .065, .074, .048, .073, .064, .103,.067, .064, .066 respectively. Alb =Albany, Sch = Schenectady.

certification exams, compared with 20% for non-poor students. IUrban areas differ from rural areas and regionsdiffer among themselves in the characteristics oftheir students. In order to assess whether the dif-ferences in teacher qualifications across groups ofstudents is a result of these students living in dif-ferent regions or urban areas, we look across stu-dent groups in four out of five of the state's largesturban centers.'2 Table 6 presents the results. Again,we see that nonwhite students experience teacherswith lesser qualifications on a number of measures.In Yonkers, Rochester and Syracuse white stu-dents attend schools with teachers with .20 to .35standard deviations higher in skills than nonwhitestudents as measured by our factor, and nonpoorstudents attend schools with teachers with .20 to

School District are substantially greater. In NewYork City, 21% of nonwhite students have teach-ers who are not certified in any subject taught,compared to 15% of white students. Twenty sixpercent of nonwhite students have teachers whofailed either the General Knowledge or LiberalArts and Science certification exam, compared to16% of white students. Similarly, poor studentshave lower quality teachers than nonpoor students;22% of poor students have teachers who are notcertified in any subject taught, compared to 17% ofnonpoor students; and 30%of poor students haveteachers who failed the certification exam, com-pared to 21% of nonpoor students. In general thedifferences between the qualifications of teachersteaching LEP and non-LEP students in all urbanareas is much smaller than the differences for race

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Teacher Sortingand the Plightof Urban SchoolsTABLE 6Teacher Attributesfor the Average Studentwith Given Characteristics

Overall Not Failed Gen B.A. fromteacher No certified Know or leastquality teaching in any Liberal Arts competitiveRegion factor experience subject taught Exam college

New York StateNonwhite -1.484 0.099 0.166 0.212 0.214White 0.847 0.067 0.040 0.071 0.102Poor -2.393 0.118 0.207 0.279 0.250Nonpoor -1.223 0.098 0.159 0.202 0.239

New York City SDNonwhite -2.183 0.109 0.212 0.256 0.247White -0.726 0.078 0.150 0.161 0.254Poor -2.562 0.120 0.215 0.296 0.268Nonpoor -1.341 0.100 0.167 0.212 0.258

Yonkers City SDNonwhite -0.733 0.081 0.043 0.188 0.205White -0.531 0.075 0.045 0.180 0.200Poor -1.273 0.098 0.039 0.208 0.221Nonpoor -0.995 0.094 0.031 0.203 0.211

Rochester City SDNonwhite -0.302 0.105 0.148 0.107 0.103White 0.051 0.089 0.147 0.099 0.107Poor -0.418 0.108 0.173 0.120 0.097Nonpoor -0.221 0.111 0.171 0.111 0.096Syracuse City SDNonwhite 1.029 0.080 0.058 0.100 0.045

White 1.254 0.063 0.054 0.095 0.043Poor 0.970 0.081 0.056 0.109 0.046Nonpoor 1.194 0.069 0.046 0.103 0.040Note. All differences between Nonwhites and Whites and between Poor and Nonpoor are significant at the p < .01 level exceptfor those in italics.

across the cities as well. Syracuse students, for ex-ample, are taught by more qualified teachers thanthose in the other urban settings to the extent thatnonwhite students in Syracuse have teachers withhigher average quality scores than white studentsin the other cities.In summary, lesser-qualified teachers teachpoor, nonwhite students. Much of these differ-ences are due to differences in average character-istics of teachers across districts, not within urbandistricts; but differences among schools withinurban districts are important as well. The NewYork City school district, in particular, exhibitslarge differences among student groups in thequalifications of their teachers. Note, this analy-sis only assess differences in the average charac-

Lower performing students are also more likelyto be in schools with less-skilled teachers. We donot have individual-level test scores, yet we dohave test scores at the school level. We partitionschools by the percent of their students that per-formed at the lowest level on the 4th and 8th gradeEnglish Language Arts exam."3 The results aresimilar if we use mathematics exams instead(the correlations between the two scores are ap-proximately 0.9). Table 7 shows that schools inwhich more than 20% of students are scoring atthe lowest level have consistently less qualifiedteachers than the other schools. Fo r example, 35%had failed their General Knowledge or LiberalArts and Science exam, compared to nine per-cent in schools in which none of the students had


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Lankford, Loeb, and WyckoffTABLE 7AverageSchool Attributes of Teachers by Student Test Score-4thGradeELA Level 1,2000

Percent of Students in Level 14th Grade EL ATeacher Quality Attribute 0 0% to 20% level for each of themean teacher attributes: -p


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Teacher Sorting and the Plightof UrbanSchoolsstable in both New York City and its suburbs from1985 through 2000. We note a slight upward trendduring our time period from 23.3 percent to 25.3%in the urban districts and 15.4% to 16% in the sub-urban districts. This same pattern occurs in othermetropolitan regions for this variable. Figure 2does show a steady increase in the portion of teach-ers who failed either the NTE General Knowledgeor the NYSTCE Liberal Arts and Science certifi-cation exams in New York City, though some ofthis change may be due to changes in the test overtime. Similar analyses of the other metropolitanareas in the state suggest little or no change in theaverage characteristics of teachers.The difference between suburban and urbanareas has also remained relatively constant overtime. Urban schools have consistently employedless qualified teachers than their suburban coun-terparts. We also examine changes over time fornew teachers and again find little change. For ex-ample, in 1985 13.9% of new teachers had re-ceived their BA degree from a least competitivecollege; in 2000 this number was 13.8%. In gen-eral, the qualifications of teachers and the sortingof teachers appear stable over time.

The Impact of Attrition and Transfers: Newteachers impact the disparities between schoolsby their choice of first teaching job. Transfers ofteachers between schools and attrition of teachersfrom the New York State system may also impactequity if there are systematic patterns in the teach-ers who leave or transfer and in the schools theyleave or move to. In this section we follow thecohort of teachers who were hired to their firstteaching jobs in 1993 over the next five years oftheir careers.' 4 Fewer than 40% of teachers withno prior teaching experience hired in 1993 re-mained in the same school in which they begantheir careers by 1998. Most of those leaving these

schools left public school teaching in New YorkState.' 5 Table 8 illustrates some differences incareer paths of teachers. We look separately atdifferences in patterns between urban and sub-urban schools in New York City, urban and sub-urban schools in other large metropolitan areas(Buffalo, Rochester, and Syracuse), schools inother metropolitan areas, and schools in ruralareas.'6

The teacher turnover rate tends to be higher inurban schools, particularly those in the large urbanareas. In urban districts in the New York CityRegion, for example, 38% of teachers were in thesame school five years later, compared to 46% insuburban schools. In the other large metropolitanareas the corresponding numbers were 29% and43% for urban and suburban schools respectively.

Confirming what many observers believe,teachers beginning their careers in New YorkCity urban schools are far more likely to leavepublic school teaching in New York State thanare other teachers in the state. Thirty-five percentof New York City urban teachers leave the sys-tem. No other area has separations that exceed29%. However, in contrast to what many wouldpredict, New York City has the lowest inter-district transfer rate of any area. This may bedue to more schools per district in New YorkCity however, when school and district transfersare combined, New York City urban schools stillhave the lowest transfer rate. This would suggestthat while the City does lose a large number ofteachers to the suburbs, in percentage terms it isno t extraordinary. 17

Given that teachers have been shown to gainsubstantial skills over the first few years of teach-ing (Rivkin, Hanushek, and Kain, 2000), highexit rates and the resulting churning of teacherson the front end of the experience distribution

TABLE 8Dispositionof 1993 New Teachersas of 1998, by Region

New York Buffalo, Rochester, OtherCity region Syracuse regions MSA regions Rural New York

Urban Suburban Urban Suburban Urban Suburban regions StateSame school 38.3** 45.6 29.2** 43.0 39.2 40.2 42.7 40.2Different school 20.5** 15.3 29.6** 20.1 24.9** 13.4 12.2 18.4Different district 6.3** 14.4 12.6 14.8 13.3* 22.0 18.5 11.3Not in NYS system 35.0** 24.8 28.6* 22.1 22.7 24.4 26.5 30.1


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Lankford, Loeb, and Wyckoffmay impact the quality of education that studentsreceive. Additionally, if more qualified teachersgenerally transfer or leave the system, leavingbehind their less qualified colleagues, the nega-tive impact of this turnover may increase. Thisappears to be the case. Table 9 shows that NewYork State teachers who began their careers in1993 and transfer to a different district or quitteaching have stronger qualifications than thosewho remain in the same district. Teachers trans-ferring to a different district are half as likely tohave failed either the NTE General Knowledgeor NYSTCE Liberal Arts and Science certifica-tion exam. They are 35% more likely to have re-ceived their BA from a highly or most competi-tive college and they are about half as likely tohave received their BA from the least competi-tive colleges. Those who leave teaching in NewYork public schools altogether are somewhatless likely to have failed the certification exams,60% more likely to have received their BA froma most or highly competitive college, and some-what less likely to have graduated from the leastcompetitive college. In contrast, there is little dif-ference in the qualifications of teachers who re-main in the same schools and those that transferto other schools in the same district. This simi-larity may be due to district or union rules thatdictate which teachers may transfer.Comparable results for New York City teachersgenerally show larger differences for those trans-ferring or quitting. Those transferring to anotherdistrict have failed the certification exams halfas often as those remaining in the same school.They are twice as likely to have attended a mostor highly competitive college, and about half aslikely to have attended the least com petitive col-lege. New York City teachers who leave teachingin New York State are also more qualified thanthose who remain. While the leaving behavior

of teachers is consistent with the hypothesisthat the opportunity cost for the bes t teachers isgreatest, it suggests the importance of policiesthat seek to retain, as well as to attract, highlyskilled teachers.What is the nature of the school environmentsthat these leavers exit and what environmentsare they drawn to? There are systemic patterns,particularly when teachers move across districtboundaries. Table 10 illustrates this dynamicfor teachers in the 1993 cohort who transfer dur-ing the next five years. Teachers generally leaveschools where the proportion of poor and non-white students is about 75% to 100% greater thanit is in the schools to which they transfer and theirclasses are about two students smaller. Perhapsmost interestingly, their salaries are between 4 and15% greater in their new district than they wouldhave been had they remained in their original dis-trict.' 5 We see that this systematic sorting is notas strong for transfers within districts, though stillevident. Receiving schools have on average fourpercentage points fewer poor students and twopercentage points fewer nonwhite students.For the cohort of teachers in the New YorkCity region (see Table 11), the differences be-tween the teaching environments before and aftertransferring are even greater than for the state asa whole. Across districts, sending schools havethree times higher the proportion of poor studentsand two times higher the proportion of nonwhitestudents than receiving schools. Salary differen-tials are particularly interesting; teachers in theNew York City region who transfer to other dis-tricts receive between a 12 an d 22% salary in-crease. Again, we see some similar trends in theintradistrict transfers bu t the size of the differ-ence is much smaller. In general, the results pro-vide evidence that moves impact the quality andcomposition of the teaching workforce. Teachers

TABLE 9Attributes of New York State Beginning Teachers in 1993 by Their1998 Employment Status

Same Different Different Not inTeacher Attribute school school district NYS system%Fail General Knowledge or Liberal Arts Exam 0.142 0.158 0.072** 0.125%BA from most competitive college 0.116 0.112 0. 157** 0.191**%BA from least competitive college 0.157 0.169 0.087** 0.145%With master degree when hired 0.391 0.364- 0.382** 0.389


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TeacherSortingand the Plightof UrbanSchoolsTABLE 10Attributes of Sending andReceiving Schoolsfor 1993 Teachers in New York State Who Transferredby 1998

Within District Between DistrictSending Receiving Sending ReceivingSchool & District Attribute school school Difference school school Difference

Proportion students poor 0.549 0.506 -0.043- 0.381 0.192 -0.189**Proportion students LEP 0.110 0.108 -0.002 0.062 0.034 -0.028*Proportion students nonwhite 0.639 0.621 -0.018 0.404 0.231 -0.173**Class size 24.0 24.2 0.2 23.5 21.7 -1.8*Salary schedule na na na $33,237 $34,535 $1,298**Actual salary na na na $31,685 $36,482 $4,798**Note. Statistical significance refers to differences between sending and receiving schools for the mean of each teacher attribute:-p


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Lankford, Loeb, and Wyckoffteachers, schools may need to pay more to attractand retain equally qualified teachers.While less variation in salary exists within re-gions, this variation nonetheless appears to belarge enough to impact teacher sorting.20 To helpassess whether these differences are likely to becontributing to teacher sorting, Figure 3 plots the10th, 50th, and 90th percentile starting salary foreach region of the state. It shows that approxi-mately ten percent of districts have starting wageslower than $30,000, while another ten percenthave starting wages higher than $41,000.21 TheNew York City metropolitan area has the highestoverall starting salaries. Within regions, the dif-ference in starting salaries between districts atthe 90th percentile and those at the 10th percentileranges from $3,937 in the Rochester region to$10,687 in the New York City region. These dif-ferences are economically substantial and maybe contributing to sorting among districts withina region.2 2

35,000-30,000-

J 25,000-1- 20,000-aj

10,000.5,0000,000

Changesover time: Salary schedules changeover time. For each of the metropolitan area re-gions in New York State we calculated averagestarting salaries in each year fo r urban districtsan d for suburban districts separately using theConsumer Price Index (CPI), to normalize overtime. We find that starting salaries in most NewYork urban and suburban districts in 2000 do notexceed and are often less than what they were in1970 in real terms. This finding is consistent withEhrenberg, Brewer, Gamoran, and Wilms (2001).Salaries tended to decrease in the 1970s and early1980s, increase in the later 1980s an d remainrelatively constant during the 1990s.

Urban-Suburbandifferences: In 1970 in everymajor metropolitan region, salaries paid to urbanteachers either matched or exceeded those paid tosuburban teachers. In most of these regions, thispattern continued through 2000. In Buffalo andSyracuse, for example, there has been little dif-ference over time between suburban and urban

C+"I

FIGURE 3. The distributionof startingsalaryoveralland within regionsof New York State.

SP1;11_k*

0I

p el",1$11$-04


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salaries either for starting teachers or more expe-rienced teachers. They remain almost identicaltoday. In Rochester urban salaries have beenhigher on average than suburban salaries thoughthis difference has diminished in recent years,especially for new teachers. In 2000 in Rochester,there was essentially no difference in averagestarting salaries between urban and suburbandistricts. Teachers with 20 years of experienceearned, on average, approximately $5,000 morein urban schools than in suburban schools. Thisdifference may or may not be enough to compen-sate for difficulties of teaching in urban schools.The pattern in the New York City region is quitedifferent. Over the 1970-2000 period, New YorkCity urban salaries at both the entry level and theveteran level fell substantially behind their sub-urban counterparts (see Figure 4a).23 In 2000,starting salaries fo r novice New York Cityschool district teachers with a master's degreewere about 15% lower than those for comparablesuburban teachers; those for veteran teacherswere more than 25% less than their suburbancounterparts.Wages andStudentCharacteristics: imilar toteacher qualifications, salaries are not randomly

50,000

40,000

V 30,000

ts

20,000

10,000

01970

TeacherSortingand the Plight of UrbanSchoolsdistributed across districts. Table 12 shows howsalaries differ fo r teachers of students with differ-ent demographic characteristics across our metro-politan regions. In most instances, teachers ofnonwhite, poor, or low-achieving students receiveroughly the same starting salaries, as do teachersof white, nonpoor, an d non-low-achieving stu-dents. The New York City region is an interestingexception. Starting teachers of nonwhite and poorstudents in this region receive about $2,800 lessthan starting teachers for white and nonpoor stu-dents receive. Starting teachers of low-performingstudents typically earn about $1,700 less thanteachers of non-low-performing students. Otherexceptions include the Syracuse and Utica-Romeregions where poor and nonwhite students haveteachers with lower starting salaries. In general, itappears that wage differentials do not compensatefo r the potential nonpecuniary effects of teachingpoor, nonwhite, or low-achieving students and insome instances salary differences further exasper-ate this situation.

Discussionand ConclusionsWe draw the following primary conclusionsfrom ou r analysis:

Suburbs

New York City

1975 1979 1983 1987 1991 1995 1999Year

I


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Lankford, Loeb, and Wyckoff80,000 -70,000 -60,000 -

e~cn

50,000 -40,000 -30,000 -20,00010,000 -

01970

Suburbs

New York City

1975 1979 1983 1987 1991 1995 1999Year

FIGURE 4b. Estimated realsalariesor teacherswith MA and 20 years experience, New York CityMetropolitanArea, 1970-2000.

* Teachers are systematically sorted acrossschools and districts such that some schools em-ploy substantially more qualified teachers thanothers do.* Differences in the qualifications of teachers inNew York State occurs primarily between schoolswithin districts and between districts within re-gions, not across regions.

* The exception to the result that there is lit-tle difference in average teacher characteristicsacross regions is for the New York City region,which on average employs substantially less qual-ified teachers.

* Nonwhite, poor, and low performing students,particularly those in urban areas, attend schoolswith less qualified teachers.

TABLE 12Average Salariesor Teacherswith MA andNo Experience Teaching Students with VariousAttributes byRegion, 2000 Nonwhite White Poor Nonpoor Low Non-lowMetropolitan Region students students students students achieving achievingAlbany (Sch./Troy) $33,071 $32,358 $32,300 $32,642 $32,625 $32,535Buffalo $32,492 $32,260 $32,602 $32,226 $32,193 $32,207New York City $35,953 $38,816 $35,570 $38,502 $35,575 $37,311Rochester $32,037 $31,727 $31,680 $31,946 $31,755 $31,923Syracuse $33,372 $34,535 $33,800 $34,662 $33,795 $34,608Utica/Rome $26,528 $28,347 $27,458 $28,349 $27,698 $28,200Mid-Hudson $35,856 $35,093 $35,078 $35,610 $35,359 $35,544Southern Tier $30,063 $30,193 $30,164 $30,294 $30,482 $30,358North Country $31,903 $31,315 $31,372 $31,393 $31,351 $31,470New York State $35,564 $34,785 $34,774 $35,676 $34,960 $35,343


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Teacher Sorting and the Plightof UrbanSchools* Although there may have been some increasein disparities in average teacher qualifications inrecent years, similar differences have existed forat least the last 15 years.* Transfer and quit behavior of teachers is con-sistent with the hypothesis that more qualifiedteachers seize opportunities to leave difficult work-

ing conditions and move to more appealing envi-ronments. Teachers are more likely to leave poor,urban schools and those who leave are likely tohave greater skills than those who stay.* The current salary structure for teachers likelydoes no t alleviate the inequitable distribution ofteachers and may well make it worse.From a policy perspective, urban schools con-front an enormous challenge. As the analysisabove has shown, urban schools systematically

receive less qualified teachers than their suburbancounterparts and many of the dynamics work tothe disadvantage urban students. Not coinciden-tally, these schools are most in need of teacherswho are able to increase the performance of stu-dents achieving at the lowest levels. In New YorkState, urban students are four times more likelythan their suburban peers to perform below basicproficiency. 2 4 Throughout the United States,nonurban students are 50% more likely to per-form at a basic proficiency level than their urbanpeers. In high poverty settings, urban studentsreach basic proficiency half as often as theirnonurban peers.25Recruitment and retention ofhigh-quality teach-ers have become a popular policy strategy for im-proving the performance of low-achieving stu-dents. Some states have now adopted incentivesto attract teachers specifically to low-performingschools. For example, the states of Massachusettsand New York have adopted "signing bonus" poli-cies. However, many policies aimed at attractingteachers do not target specific schools. For exam-ple, California recently enacted a tax credit of up to50% of the tax that would otherwise be imposed ona teacher's salary and Oklahoma recently boostedits teacher salaries by $3,000 across the board.These nontargeted policies are unlikely to impactthe dramatic disparities in teacher qualificationsacross schools.Moreover, this is a difficult time to hire morehigh quality teachers. The baby boom generationof teachers is reaching retirement age and the baby

tionally, policies such as increased educationalstandards, accountability, and class size reductionincrease the demand for highly skilled teachers.All of these trends may well have the adverseeffect of reducing the qualifications of teachersavailable to low-performing urban schools as thehigher skilled teachers move to openings in lowerpoverty, suburban districts.Policies that aim to improve the achievement oflow-performing students but ignore teacher labormarket dynamics are unlikely to impact the sort-ing of teachers that appears to strongly disadvan-tage poor, urban students. This analysis providesa foundation for further work by documenting theextent and nature of teacher sorting. However,there is much to learn about the behavior of indi-viduals interested in teaching and the schools thatemploy them. What factors are most important asindividuals choose to become teachers and decidein which school to teach? Can changes in the struc-ture of salaries alter the current sorting of teach-ers? Universally, what policies can be employedto attract and retain high-quality teachers in low-performing schools? Effective policy proposalsmay well depend on research to provide the an-swers to these questions.

NotesWe would like to thank Donald Boyd and SteveRivkin, as well as seminar participants at the Ameri-can Education Research Association and the PublicPolicy Institute of California, for their helpful com-ments. We would also like to thank Ginger Cook,Miguel Socias, and Istvan Vanyolos for research as-sistance and Ron Danforth, Edith Hunsberger andCharles Mackey of the New York State Departmentof Education for assistance with the data. We aregrateful to the Smith Richardson Foundation, the Of-fice of Educational Research and Improvement, U.S.Department of Education, and the New York StateDepartment of Education for financial support. Theydo not necessarily support the views expressed in thispaper. All errors are attributable to the authors. Ques-tions should be addressed to Susanna Loeb atsloeb @ tanford.edu.' Rivkin, Hanushek, and Kain (2000) attribute atleast seven percent of the total variance in test-scoregains to differences in teachers and they argue that thisis a lower bound. Sanders and Rivers (1996) find thatthe difference between attending classes taught byhigh-quality teachers (highest quartile grouping) andattending classes taught low-quality teachers (lowest


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Lankford, Loeb, and Wyckoffstudent achievement. They also find residual effects ofteachers in latter years. That is, having a high qualityteacher in grade three increases learning no t only ingrade three bu t also in grades four and five.

2 These findings may appear to be contradictory toqualitative studies (such as Berliner, 1987; Feistritzer,1992; Murphy, 1987; and Wise, Darling-Hammondand Praskac, 1987) which tend to find that ideology andthe value individuals place on education for societyare important in decisions about whether and where toteach. However, because individuals' answers to ques-tions may not reflect their actions, factors less empha-sized by respondents, such as wages and job stability,may still be relatively important to teachers.

I See Appendix A for a description of the adminis-trative datasets that we have linked together for thisanalysis.4Value-added measures have the benefit that, ulti-mately, we care about how education affects studentlearning. They are direct measures of student learning.If we can design tests that measure the learning we care

about, then we can define teacher quality in terms of thislearning. However, very few school systems have thetype of data that would support the implementation ofa value-added assessment system. Moreover, becauseteacher assignments to students are not random, we mayconfound assignment with teacher quality. Value-addedquality also cannot be measured for potential teachersor for first-year teachers since individuals must teach forseveral years before value-added can be estimated, andthese measures are likely to be volatile for novice teach-ers because practice and effectiveness typically changesubstantially over the first few years of teaching (Rivkin,Hanushek, and Kain, 2000). More fundamentally,value-added teacher quality measures place a heavyburden on tests to capture the contributions of teachingthat we care about, not only overall, but at each levelof academic achievement.

IOur measures of teacher qualifications reflect theperformance of individual teachers and the attributesof the colleges and universities they attended. In addi-tion to the measures presented, we also know: indi-vidual teacher certification exam scores and whetherthe individual passed each of three component tests inthe general battery as well as scores on the content spe-cialty tests; whether the individual is certified to teacheach of the courses they teach; their tenure status; theireducation level; and their experience teaching. Fo reach of the higher educational institutions they at-tended we know: the identity of the college, the distri-bution of its math and verbal SA T scores, its rankingin the Barron's College Guide, and its admissionsand attendance rate. There is remarkable consistencyamong most of the measures. Tables C I and C2 in Ap-pendix C provide a sense of this. The factor that we use

robustness of our results and found that the choice offactor made little difference.6 The MSAs are defined by the Office of Budget and

Management and used by the US Census Bureau. Theurban regions are Albany-Schenectady-Troy (includingAlbany, Montgomery, Rensselaer, Saratoga, Schenec-tady, Schoharie), Buffalo-Niagara Falls (including Erieand Niagra counties), New York City (includingPutnam, Rockland, Westchester Nassau, and Suffolkcounties), Rochester (including Genesee, Livingston,Monroe, Ontario, Orleans, Wayne counties), Syracuse(including Cayuga, Madison, Onondaga, Oswego), andUtica-Rome (including Herkimer and Oneida coun-ties). Th e rural regions are Mid-Hudson (includingColumbia, Delaware, Dutchess, Greene, Orange, Ot-sego, Sullivan, and Ulster counties), North Country(including Clinton, Essex, Franklin, Fulton, Hamilton,Jefferson, Lewis, St. Lawrence, Warren, Washingtoncounties) and the Southern Tier (including Allegany,Broome, Cattaraugus, Chautauqua, Chemung, Che-nango, Schuyler, Seneca, Tioga, Tompkins, Steuben,Wyoming, Yates counties).

I Note that we calculated these figures with eachsub-district of New York City counted as a separatedistrict. If these were combined into a single districtthen the percent of variation within districts would begreater.

8 We ran the decomposition for elementary schoolsalone in addition to running it for all schools and foundlittle difference in the variance decomposition. Resultsavailable.

9Th e proportion of the variance that is within ver-sus between districts differs across regions. In theAlbany region, 74 percent is within districts; in theBuffalo region, 73 percent; in the New York City re-gion, 42 percent; in the Nassau-Suffolk region, 56 per-cent; in the Rochester region, 76 percent; in the Syra-cuse region, 66 percent; in the Utica-Rome region,71 percent; in the Hudson region, 61 percent; in theSouthern Tier region, 68 percent; and in the NorthernCountry region, 54 percent.

10Poverty status is more accurately reported forstudents in kindergarten through sixth grade. Be-cause of this, we only include schools that have somethese grades in the poor-nonpoor comparison. Therace comparisons are estimated over the full se t ofschools.

" LE P students also receive less qualified teacherswhen compared to non-LEP students, although the dif-ferences are not as great as those comparing non-whites to whites and poor to non-poor. These resultsare no t included in the tables.

12Buffalo is omitted from this table, as there is notcomparable information about certification and as a


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TeacherSortingand the Plightof UrbanSchoolscations, Buffalo has differences similar to those ofRochester.

13 New York's student achievement data for 4th and8th grade English Language Arts and Math place eachstudent's test results in one of four performance lev-els. The school data indicate the number of students ineach level. To examine low-performing students weemployed the portion of the students tested whoseresults place them in the lowest performance group,Level 1.Level I for 4th grade EL A is described by theNew York State Education Department as, "These stu-dents have serious academic deficiencies. They showno evidence of any proficiency in one or more of theelementary standards and incomplete proficiency in allthree standards."

14 We have performed this analysis with cohortsstarting in different years and over different durations.Th e results show the same patterns and generally dif-fe r in predictable ways. For example, when a longerperiod is employed we find increases in transfers andresignations.

15 A teacher is determined to be outside the NewYork system when they are not observed in the data fortwo consecutive years. Our analysis ends in 1998 inorder to follow teachers until 2000.16When viewed from the perspective of predomi-nant teaching assignment, there appears to be remark-able consistency in the career paths of teachers duringthe early part of their careers. Elementary teachers areless likely to transfer to a different district or leave the

system than their peers teaching middle and high schoolsubjects. Among middle and high school teachers,transfer and exit behavior is very similar.12 We are unable to determine how many of what

appear to be teachers leaving the system are in facttransfers to schools in other states, (e.g., suburban loca-tions in New Jersey and Connecticut).

18We have estimated salary differences in two waysthat likely provide upper and lower bounds on thesedifferences. In the first method we employ estimatesof the salary schedules in both districts and use theindividual's actual education and experience to deter-mine what they made in their new district and whatthey would have made in their old district. The calcu-lation in the new district assumes district experience isone, regardless of how much experience they had intheir prior district(s). In many cases this will be anunderestimate as teachers are frequently given experi-ence credit when they enter a district. The alternativemethod examines the difference in what they were ac-

tually paid in their new district and what they receivedin the prior year in their previous district. This is likelyan overestimate because teachers may have received araise if they had remained in their old district.19We found similar results nationally using metro-

politan areas in the 1993-94 Schools and StaffingSurvey. Between 67 and 76% of variation in startingsalaries across districts could be attributed to averagedifferences between metro areas. Half of this acrossmetro area variance was explained by differences inthe salaries of non-teaching college graduates. Metro-politan areas with higher paying alternative opportu-nities for teachers pay teachers more.

20 This is true nationally as well. Using the Schoolsand Staffing Surveys (1993-94) we found that althoughmost of the variation w as no t between districts withinthe same region, the variation that did exist withinregions was economically important. For example,in Pittsburgh, PA, the metro area in our sample with thelargest variation across districts (only MSAs for whichat least 20 districts were represented in SASS), thelowest starting salary was $18,500 while the highestwas $34,554. Chicago also showed substantial differ-ences across districts ranging from $19,891 to $31,621.The salaries for more experienced teachers showedeven greater variation within regions. In Chicago therewas a $36,978 difference in wages for teachers with20 years of experience and a Masters degree betweenthe lowest and highest paying district. Only Dallas,Huston and Tulsa showed ranges of less than $10,000and even there the differences across districts werelarge enough to be economically important.

21 These data are from the 1998-1999 academic year.22 As a check on the magnitude of salary differencesacross districts we looked at the distribution of salariesfor teachers with 20 years of experience. The variationacross districts is even larger for experienced teachers.Approximately ten percent of districts have salarieslower than $43,500 for these teachers, while anotherten percent have starting wages higher than $74,900.23 We normalized all salaries over time using the

Consumer Price Index for July of the relevant year. Noadjustments have been made to account for differencesin costs across places at a point in time.24 These statistics are based on results for the 1999-

2000 English Language Arts exam administered to4th graders. Similar results hold for the 4th grade mathand 8th grade math and ELA exams.25 Education Week, "QualityCounts '98: The UrbanChallenge," January 8, 1998 Bethesda, MD


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Lankford, Loeb, and WyckoffAppendix A

TABLE AlWorkforce Database

Certification and School andPersonnel data exam data SUNY student data district dataUniverse: All public school All individuals taking All SUNY applicants All public schools

teachers, superin- certification exams (including non-teachers) and districtstendents, princi-pals, and other staff

Elements: -Salary -Scores on each taking -High school attended -Enrollment-Course subject of NT E and NYSTCE -High school courses -Student poverty

and grade (general knowledge, -High school GP A (free and reduced-Class size pedagogy, and con- -SAT exam scores lunch counts)-Experience (dis- tent specialty) exams -College attended and -Enrollment by

trict and other) -College of under- dates race-Years of educa- graduate and graduate -Intended college major -Limited English

tion and degrees -Actual college major proficiencydegree attainment -Degrees earned -College GP A -Student test-Age -Zip code of residence -Degrees earned results

-Gender when certified -Dropout rates-Race -District wealth

-District salaryschedule

-Support staff andaides

Time period: 1969-70to 1984-85 to 1999-00 1989-90 to 1999-00 1969-70to1999-00 1999-00

Source: New York State New York State Edu- The State University of New York StateEducation cation Department New York EducationDepartment Department

Appendix BTABLE B IThe CompositeMeasureof Teacher QualityComponent: Scoring-Coefficient1. Percent of teachers with less than or equal to three years of experience2. Percent of teachers with tenure3. Percent of teachers with more than a B A degree4. Percent of teachers certified in all courses taught5. Percent of teachers from less-competitive or non-competitive colleges6. Average teacher score on the NTE communication skills exam7. Average teacher score on the NTE general knowledge exam8. Average teacher score on the NTE professional knowledge exam

-0.364490.360320.315760.39435

-0.275780.375380.346010.38134

Eigenvalue: 4.17 (52.14% of variation)Cronbach's alpha (reliability): 0.8641


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Teacher Sorting and the Plightof UrbanSchools45040 0350300

a)0.

2502001501001505 I I 1l 1111 40 Il-

BinFIGURE B1. Histogramoffactor.

Appendix C: Use of Individual and College Level Measures Proxies for Additional MeasuresTABLE ClUse of TeacherFailureon GeneralKnowledge Exam as Proxyfor Results of Other TeacherExams, 1999-00

Fail Either General Knowledge or LAST ExamExam Mean Standard Deviation No YesScore on General Knowledge Exam 660.1 11.6 663.4 641.7Score on Communication Skills Exam 662.2 10.9 664.8 647.6Score on Professional Knowledge Exam 659.9 10.7 662.3 646.7Score on Liberal Arts and Science Exam 250.8 28.9 260.8 204.8Score on Elementary Knowledge Exam 249.6 24.9 256.7 219.0Score on Secondary Knowledge Exam 251.9 24.9 257.2 211.9Fail General Knowledge Exam 0.150 0.357 0.000 0.987Fail Communication Skills Exam 0.113 0.316 0.040 0.523Fail Professional Knowledge Exam 0.090 0.286 0.033 0.407Fail Liberal Arts and Science Exam 0.149 0.357 0.000 0.839Fail Elementary Knowledge Exam 0.118 0.323 0.036 0.470Fail Secondary Knowledge Exam 0.111 0.315 0.045 0.610


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Lankford, Loeb, and WyckoffTABLE C2Use of Barron s Rankings of UndergraduateCollege as Proxyfor Other Measuresof College Quality, Weighted byNumber of NYS Public School TeachersReceiving BachelorsDegrees FromEachInstitution, 1999-00 Teachers

Colleges by Barron's RankingsOther Measure of College Quality 1 2 3 4SAT Scores

Verbal 25th percentileVerbal 75th percentileMath 25th percentileMath 75th percentileProportion of students with scoresInstitution did not report scores

Grade Point AverageProportion mean GPA 2.0 to 2.99Mean GPA > = 3.0

New York State Certification ExamsGeneral ExamsMean Liberal Arts and Science (LAST)Portion passing LASTMean elementaryPortion passing elementaryMean secondaryPortion passing secondaryContent Specialty ExamsMean elementaryPortion passing elementaryMean EnglishPortion passing EnglishMean mathPortion passing math

AdmissionProportion of applicants admittedProportion of admitted who attendAdmission information missingAttendance information missingOpen admission policy

5576595946890.910.010.100.91

2690.02264

0.02264

0.02273

0.022590.012570.1

0.60.290.020.020

515619504608

0.750.050.190.79

2590.07254

0.07255

0.06262

0.04251

0.062450.2

0.750.380.130.130

456553463561

0.790.13

429541420532

0.720.4

0.390.54

0.520.33

2520.122510.09252

0.1

2360.3237

0.25239

0.24258

0.07245

0.12310.33

2430.2235

0.272150.45

0.710.350.180.180

0.850.410.420.420.08

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Lankford, Loeb, and WyckoffStinebrickner, T. R. (1999). Estimation of a durationmodel in the presence of missing data. Review of

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AuthorsHAMILTON LANKFORD is Associate Pro-

fessor, Department of Economics, University atAlbany, SUNY, BAl lA Albany, NY 12222. Hespecializes in the economics of education.

SUSANNA LOEB is Assistant Professor,School of Education, Stanford University, 224CEBS 520 Galvez Mall, Stanford, CA 94305.She specializes in the economics of education.

JAMES WYCKOFF is Associate Professor,Nelson Rockefeller College of Public Affairsand Policy, University at Albany SUNY, Milne213A 135 Western Avenue Albany, NY 12222.He specializes in state and local public econom-ics and the economics of education.

Manuscript received August 2, 2001Revision received January 4, 2002Accepted January 8, 2002


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