teacher tradeoffs.aerj

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JournalAmerican Educational Research

DOI: 10.3102/00028312083295992009; 46; 690 originally published online Jan 23, 2009;Am Educ Res J

Eileen Lai HorngConditions and Student Demographics

Teacher Tradeoffs: Disentangling Teachers’ Preferences for Working

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Teacher Tradeoffs: Disentangling Teachers’Preferences for Working Conditions and

Student Demographics

Eileen Lai Horng

Stanford University

One of the greatest differences in resources across schools in California comes from an inequitable distribution of teachers. This study identifies reasons forthis sorting of teachers by surveying 531 teachers in a California elementary

school district. The surveys ask the teachers to make choices between variousworkplace characteristics. With this information, the study disentangles stu-dent demographics from other characteristics of teaching jobs that are ame-

nable to policy influences. It finds that teachers identify workingconditions—particularly, school facilities, administrative support, and class

sizes—and salaries as significantly more important than student character-istics when selecting a school in which to work.

K EYWORDS: equity, teacher preferences, teacher retention

One of the greatest differences in resources across schools in California

comes from an inequitable distribution of teachers. Poor, non-White,and low-achieving students are far more likely to attend difficult-to-staffschools, characterized by high rates of teacher attrition and limited teacherapplicant pools (Carey, 2004; Darling-Hammond, 2004; Education Trust–

West, 2005; Esch et al., 2005). When teachers choose among teaching jobs,they are at least in part expressing their preferences for different job condi-tions. Schools with less favorable conditions have greater difficulty recruitingand retaining teachers and consequently have higher rates of teacher turn-over. This study seeks to identify the characteristics of schools that teachersfavor in their choice of teaching jobs.

EILEEN L AI HORNG is a research associate at the Institute for Research onEducation Policy & Practice, Stanford University, 520 Galvez Mall, Stanford, CA94305; e-mail: [email protected]. Her research focuses on the career paths ofteachers and principals, district policies that affect the distribution of humanresources across schools, and the impact of educator characteristics and mobilitypatterns on student outcomes

American Educational Research Journal

September 2009, Vol. 46, No. 3, pp. 690–717

DOI: 10.3102/0002831208329599

© 2009 AERA. http://aerj.aera.net


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Other studies have found that teachers tend to avoid schools servinglarge concentrations of low-income, minority, and low-performing students(see S. J. Carroll, Reichardt, Guarino, & Mejia, 2000; Hanushek, Kain, &

Rivkin. 2004; Lankford, Loeb, & Wyckoff, 2002; Scafidi, Sjoquist, &Stinebrickner, 2005). However, these studies are limited to analyses ofobserved teacher mobility, so they are unable to determine if teachers aremoving from one school to another due to student characteristics or highlycorrelated working conditions. For example, while Hanushek et al. (2004)speculated that their observed teacher preferences for student ethnicity mayactually have been proxies for school working conditions, they admitted,“our analysis does not permit disentangling the various potential aspects of

working conditions” (p. 351).

This study uses a conjoint analysis methodology to ask teachers to tradeoff student demographics, salaries, and working conditions to begin to disen-tangle the influence each has on teachers’ decisions of where to teach. It sug-gests that due to the confluence of negative conditions at schools servinglow-income, minority, and low-achieving students, variation in teacher attri-tion across schools at least in part reflects teachers’ preferences for workingconditions and not solely students. Furthermore, this study suggests that some

working conditions are significantly more important to teachers than studentdemographics and salary when they choose a school in which to work.

Background: Teacher Turnover and Preferences of Teachers

Across the United States, approximately half a million teachers leavetheir schools each year. Only 16% of this teacher attrition at the school levelcan be attributed to retirement. The remaining 84% of teacher turnover isdue to teachers transferring between schools and teachers leaving the pro-fession entirely (Alliance for Excellent Education, 2008). Recent literature hasbegun to investigate the complexities of teacher turnover, making importantdistinctions such as teacher attrition versus migration, within-district versus

between-district transfers, and teachers leaving permanently versus thoseleaving and later returning (see DeAngelis & Presley, 2007; S. M. Johnson,Berg, & Donaldson, 2005). However, whether teachers are chronically leav-ing a school to transfer to another school or district, to leave teaching per-manently or temporarily, or to retire, the effects on that individual school arethe same. The situation is further exacerbated when the school has difficultyattracting teachers to fill the vacancies.

The labor economics theory of utility maximization provides a usefulframework for understanding and investigating the movement and distributionof teachers across schools (Boyd, Lankford, Loeb, & Wyckoff, 2004; Loeb &Reininger, 2004; Stinebrickner, 2001). Utility maximization assumes that peoplemake decisions in order to maximize their utility or happiness. Different peo-ple have different opportunities and prefer different things, leading them tomake different choices, whether they can articulate those preferences or not.People’s opportunities and preferences are not static but rather change with


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resources and materials for students, lower teacher salaries, and feweropportunities for teachers to participate in school-wide decision making(T. G. Carroll, Fulton, Abercrombie, & Yoon, 2004; Darling-Hammond, 2003;

Hirsch & Emerick, 2006; Ingersoll, Quinn, & Bobbitt, 1997; Oakes, 2002;Schneider, 2004; Wyckoff, Boyd, Lankford, & Loeb, 2003).

Because school working conditions and student characteristics are sohighly correlated, teachers may be choosing to not work with low-incomestudents, low-performing students, and students of color because of the poor

working conditions at the schools which these students attend. Consequently,the relationship between teacher turnover and student characteristics, at leastin part, may be a spurious one, driven by teachers’ preferences for favorable

working conditions rather than their aversions to teaching certain kinds of

students. Hanushek et al. (2004) hypothesized about the differential attritionof teachers from non-White schools: “If the results capture teacher prefer-ences for student race or ethnicity, then districts possess few policy options.But, we might speculate that these estimates at least partially proxy for moregeneral working conditions” (p. 351). By avoiding unattractive working con-ditions, teachers may inadvertently—rather than purposefully—be avoidinglow-income students, low-performing students, and students of color.

There is evidence that teachers care about school working conditionsand might be motivated to stay at a school that they would otherwise leave(or select a school they would otherwise avoid) if the working conditions

were improved. Specifically, studies have found the following working con-ditions1 to be important to teachers and likely to impact the distribution ofteachers among schools:

• Salary (see Imazeki, 2005; Kirby, Naftel, & Berends, 1999; Lankford et al.,2002; Mont & Rees, 1996; Murnane, Singer, & Willett, 1989; Rickman &Parker, 1990; Theobald & Gritz, 1996)

• Class size (see Allen, 2005; Chambers & Fowler, 1995; Hanushek & Luque,2000; Lankford et al., 2002; Mont & Rees, 1996)

• Administrative support (see Allen, 2005; Darling-Hammond, 2002; Farkas, Johnson, & Foleno, 2000; Hirsch & Emerick, 2006; Ingersoll, 2003; S. M. Johnson & Birkeland, 2003; MetLife, 2001; Sclan, 1993)

• School facilities (see Buckley, Schneider, & Shang, 2005; Darling-Hammond,2002; Earthman, 2002; Hirsch & Emerick, 2006; Public Education Network,2003)

• Commute time (see Tennessee Advisory Commission on IntergovernmentalRelations, 2000)

• Input on school-wide decisions (see Allen, 2005; Chapman & Hutcheson,1982; Hare & Heap, 2001; Hirsch & Emerick, 2006; Howard, 2003; Ingersoll,2002; National Education Association, 2003; Sclan, 1993)

• Resources for students (see Hirsch & Emerick, 2006; National Education Association, 2003; Theobald & Gritz, 1996)

There is at least one study which suggests that teacher attrition is related tostudent demographics as well as working conditions and salaries. Loeb,Darling-Hammond, and Luczak (2005) linked California teacher survey data




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to district salary and staffing pattern data and found that teacher salaries and working conditions are strong and significant predictors of teacher turnover.Furthermore, in regression models, the estimated effect of student character-

istics on teacher turnover is significantly reduced when district salarylevels and teachers’ ratings of working conditions—including class sizes,facilities, and availability of textbooks—are taken into account. According toDarling-Hammond (2002),

The frequently observed flight of teachers from schools serving low-income and minority students is at least in part a function of thedegree to which many of those schools also exhibit poor workingconditions rather than solely attributable to the characteristics of thestudents or communities themselves. From a policy perspective thisis good news, since it points to remediable factors—i.e., the avail-ability of materials, class sizes, high-quality leadership, and profes-sional learning opportunities—that can be altered by policy to shapethe availability of teachers to all students. (p. 64)

Methods: Conjoint Analysis of Teachers’ Preferences

To explore these dynamics empirically, I utilize a conjoint analysismethodology.2 Predominantly used for marketing research, this methodol-ogy asks respondents to make difficult tradeoffs to understand their relative

preferences. The basic assumption of this method is that the choices individuals make can be predicted by understanding how they make tradeoffs.The individual may not be able to clearly articulate what he or she valuesand why, but these values may be revealed by the choices the individualmakes among entities that have characteristics which vary in systematic ways(Findikaki-Tsamourtzi & Dajani, 1981). Conjoint analysis allows inferencesto be made about respondents’ value systems by providing them with a setof options to choose from and examining the tradeoffs they make.

When people are asked how important different characteristics are to

them using traditional surveys with a rating scale, they may rate all charac-teristics as, say, “very important.” But the reality of budget constraints andlimited resources means difficult tradeoffs need to be made and some factorsmust become less important than others. Conjoint analysis has been used toassess the relative influence of different job features in people’s decision-making processes (see Chonko & Griffin, 1983; Fischer, 1976; Ford, Huber, & Gustafson, 1972; Kienast, MacLachlan, & McAlister, 1983; Ritchie & Beardsley, 1978; Wittink, Krishnamurthi, & Nutter, 1982). However, I amunaware of any other research that has utilized conjoint analysis to investi-gate the job choice preferences of teachers.

I asked teachers in the Crystal Springs School District3 to complete a Web-based survey in which they reported their preferences for the following10 workplace characteristics: salary, class size, administrative support, inputon school-wide decisions, commute time, resources for students, school facil-ities, student performance, student ethnicity, and student socioeconomic


https://www.researchgate.net/publication/13037876_Trade-off_analysis_finds_the_best_reward_combinations?el=1_x_8&enrichId=rgreq-d22b53fe-b0a8-48ae-94bb-5515f85f7d9d&enrichSource=Y292ZXJQYWdlOzI1MDE4NTA4ODtBUzoxMDIzOTU0NzIyNTI5MzJAMTQwMTQyNDM5MTQ2MQ==https://www.researchgate.net/publication/248557485_Multidimensional_Utility_Models_for_Risky_and_Riskless_Choice?el=1_x_8&enrichId=rgreq-d22b53fe-b0a8-48ae-94bb-5515f85f7d9d&enrichSource=Y292ZXJQYWdlOzI1MDE4NTA4ODtBUzoxMDIzOTU0NzIyNTI5MzJAMTQwMTQyNDM5MTQ2MQ==https://www.researchgate.net/publication/222593656_Predicting_Job_Choices_with_Models_that_Contain_Subjective_Probability_Judgments_An_Empirical_Comparison_of_Five_Models?el=1_x_8&enrichId=rgreq-d22b53fe-b0a8-48ae-94bb-5515f85f7d9d&enrichSource=Y292ZXJQYWdlOzI1MDE4NTA4ODtBUzoxMDIzOTU0NzIyNTI5MzJAMTQwMTQyNDM5MTQ2MQ==https://www.researchgate.net/publication/222593656_Predicting_Job_Choices_with_Models_that_Contain_Subjective_Probability_Judgments_An_Empirical_Comparison_of_Five_Models?el=1_x_8&enrichId=rgreq-d22b53fe-b0a8-48ae-94bb-5515f85f7d9d&enrichSource=Y292ZXJQYWdlOzI1MDE4NTA4ODtBUzoxMDIzOTU0NzIyNTI5MzJAMTQwMTQyNDM5MTQ2MQ==https://www.researchgate.net/publication/230243811_Employing_conjoint_analysis_in_making_compensation_decisions?el=1_x_8&enrichId=rgreq-d22b53fe-b0a8-48ae-94bb-5515f85f7d9d&enrichSource=Y292ZXJQYWdlOzI1MDE4NTA4ODtBUzoxMDIzOTU0NzIyNTI5MzJAMTQwMTQyNDM5MTQ2MQ==https://www.researchgate.net/publication/227872117_A_MARKET_RESEARCH_APPROACH_TO_DETERMINING_LOCAL_LABOR_MARKET_AVAILABILITY_FOR_NON-MANAGEMENT_JOBS?el=1_x_8&enrichId=rgreq-d22b53fe-b0a8-48ae-94bb-5515f85f7d9d&enrichSource=Y292ZXJQYWdlOzI1MDE4NTA4ODtBUzoxMDIzOTU0NzIyNTI5MzJAMTQwMTQyNDM5MTQ2MQ==https://www.researchgate.net/publication/227872117_A_MARKET_RESEARCH_APPROACH_TO_DETERMINING_LOCAL_LABOR_MARKET_AVAILABILITY_FOR_NON-MANAGEMENT_JOBS?el=1_x_8&enrichId=rgreq-d22b53fe-b0a8-48ae-94bb-5515f85f7d9d&enrichSource=Y292ZXJQYWdlOzI1MDE4NTA4ODtBUzoxMDIzOTU0NzIyNTI5MzJAMTQwMTQyNDM5MTQ2MQ==https://www.researchgate.net/publication/24099436_Comparing_Derived_Importance_Weights_Across_Attributes?el=1_x_8&enrichId=rgreq-d22b53fe-b0a8-48ae-94bb-5515f85f7d9d&enrichSource=Y292ZXJQYWdlOzI1MDE4NTA4ODtBUzoxMDIzOTU0NzIyNTI5MzJAMTQwMTQyNDM5MTQ2MQ==http://aer.sagepub.com/http://aer.sagepub.com/https://www.researchgate.net/publication/24099436_Comparing_Derived_Importance_Weights_Across_Attributes?el=1_x_8&enrichId=rgreq-d22b53fe-b0a8-48ae-94bb-5515f85f7d9d&enrichSource=Y292ZXJQYWdlOzI1MDE4NTA4ODtBUzoxMDIzOTU0NzIyNTI5MzJAMTQwMTQyNDM5MTQ2MQ==https://www.researchgate.net/publication/227872117_A_MARKET_RESEARCH_APPROACH_TO_DETERMINING_LOCAL_LABOR_MARKET_AVAILABILITY_FOR_NON-MANAGEMENT_JOBS?el=1_x_8&enrichId=rgreq-d22b53fe-b0a8-48ae-94bb-5515f85f7d9d&enrichSource=Y292ZXJQYWdlOzI1MDE4NTA4ODtBUzoxMDIzOTU0NzIyNTI5MzJAMTQwMTQyNDM5MTQ2MQ==https://www.researchgate.net/publication/227872117_A_MARKET_RESEARCH_APPROACH_TO_DETERMINING_LOCAL_LABOR_MARKET_AVAILABILITY_FOR_NON-MANAGEMENT_JOBS?el=1_x_8&enrichId=rgreq-d22b53fe-b0a8-48ae-94bb-5515f85f7d9d&enrichSource=Y292ZXJQYWdlOzI1MDE4NTA4ODtBUzoxMDIzOTU0NzIyNTI5MzJAMTQwMTQyNDM5MTQ2MQ==https://www.researchgate.net/publication/13037876_Trade-off_analysis_finds_the_best_reward_combinations?el=1_x_8&enrichId=rgreq-d22b53fe-b0a8-48ae-94bb-5515f85f7d9d&enrichSource=Y292ZXJQYWdlOzI1MDE4NTA4ODtBUzoxMDIzOTU0NzIyNTI5MzJAMTQwMTQyNDM5MTQ2MQ==https://www.researchgate.net/publication/248557485_Multidimensional_Utility_Models_for_Risky_and_Riskless_Choice?el=1_x_8&enrichId=rgreq-d22b53fe-b0a8-48ae-94bb-5515f85f7d9d&enrichSource=Y292ZXJQYWdlOzI1MDE4NTA4ODtBUzoxMDIzOTU0NzIyNTI5MzJAMTQwMTQyNDM5MTQ2MQ==https://www.researchgate.net/publication/222593656_Predicting_Job_Choices_with_Models_that_Contain_Subjective_Probability_Judgments_An_Empirical_Comparison_of_Five_Models?el=1_x_8&enrichId=rgreq-d22b53fe-b0a8-48ae-94bb-5515f85f7d9d&enrichSource=Y292ZXJQYWdlOzI1MDE4NTA4ODtBUzoxMDIzOTU0NzIyNTI5MzJAMTQwMTQyNDM5MTQ2MQ==https://www.researchgate.net/publication/222593656_Predicting_Job_Choices_with_Models_that_Contain_Subjective_Probability_Judgments_An_Empirical_Comparison_of_Five_Models?el=1_x_8&enrichId=rgreq-d22b53fe-b0a8-48ae-94bb-5515f85f7d9d&enrichSource=Y292ZXJQYWdlOzI1MDE4NTA4ODtBUzoxMDIzOTU0NzIyNTI5MzJAMTQwMTQyNDM5MTQ2MQ==https://www.researchgate.net/publication/230243811_Employing_conjoint_analysis_in_making_compensation_decisions?el=1_x_8&enrichId=rgreq-d22b53fe-b0a8-48ae-94bb-5515f85f7d9d&enrichSource=Y292ZXJQYWdlOzI1MDE4NTA4ODtBUzoxMDIzOTU0NzIyNTI5MzJAMTQwMTQyNDM5MTQ2MQ==http://aer.sagepub.com/


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status. The selection of these 10 characteristics as well as the specific levelsfor these characteristics (such as “$4,000 additional salary” or “15 students in

your class”) was determined after consultation with teachers, school and

district administrators, and researchers who are familiar with this field. Thecharacteristics were then refined with two pilot studies—for example, therange in salary levels was expanded (from $0–$6,000 to $0–$8,000) toprovide a more dramatic range for respondents to weigh against the othercharacteristics.

The Crystal Springs School District is a large elementary school districtin Southern California. In 2003–2004, the district had 40 elementary schoolsand over 1000 teachers, with over 90% holding full teaching credentials. Thedistrict serves a diverse student population including more than 25,000 stu-

dents. In 2003–2004, the student population was 64.3% Latino or Hispanic,17.0% White (not Hispanic), 8.5% Filipino, 4.9% African American, 3.6%

Asian, 0.9% Pacific Islander, and 0.4% American Indian or Alaskan Native. Additionally, about half of the students in the district were eligible for freeor reduced-price lunch, and more than one third of the students were Englishlanguage learners.

During the 2003–2004 academic year, I contacted each of the 40 princi-pals in the district, 37 of whom agreed to have teachers in their school par-ticipate. Ultimately, 1,018 teachers in these 37 schools were invited tocomplete the Web-based, conjoint analysis survey. Of these 1,018 teachers,547 responded, representing a 53.7% response rate. This sample represents49.3% of all the full-time classroom teachers in the district. Incomplete sur-

veys, surveys completed by uncertified teachers, surveys with inconsistentpatterns, and surveys completed in under 8 minutes were removed.Ultimately, results from 531 surveys were used for the analyses. The sampleof teachers underrepresents beginning teachers (i.e., teachers in their first 2

years of teaching) but is otherwise representative of teachers in the districtin terms of demographics. For example, 83.4% of the sample is female com-pared to 83.6% of the population, and 24.1% of the sample is Latino or

Hispanic compared to 25.8% of the population.I used this adaptive conjoint analysis survey to examine the tradeoffs

teachers would make among the 10 workplace characteristics. The premiseof conjoint analysis is that it is possible to make inferences about individuals’

value systems by examining their hypothetical choices among a set of options(Green & Srinivasan, 1990).

Conjoint analysis differs from the two traditional methods for estimatingteachers’ preferences for teaching conditions. The first asks people abouttheir preferences through surveys. Generally, these surveys use a “composi-

tional” approach in which respondents rate each characteristic separately.For example, a teacher might be given a list of teaching job characteristicsand asked to indicate how important each is using a Likert-type scale.Conjoint analysis, on the other hand, uses a “decompositional” approach in

which respondents are asked to judge sets of characteristics (i.e., profiles).The respondents’ preferences for these profiles are then analyzed to estimate


https://www.researchgate.net/publication/235356399_Conjoint_Analysis_in_Marketing_New_Developments_With_Implications_for_Research_and_Practice?el=1_x_8&enrichId=rgreq-d22b53fe-b0a8-48ae-94bb-5515f85f7d9d&enrichSource=Y292ZXJQYWdlOzI1MDE4NTA4ODtBUzoxMDIzOTU0NzIyNTI5MzJAMTQwMTQyNDM5MTQ2MQ==http://aer.sagepub.com/http://aer.sagepub.com/https://www.researchgate.net/publication/235356399_Conjoint_Analysis_in_Marketing_New_Developments_With_Implications_for_Research_and_Practice?el=1_x_8&enrichId=rgreq-d22b53fe-b0a8-48ae-94bb-5515f85f7d9d&enrichSource=Y292ZXJQYWdlOzI1MDE4NTA4ODtBUzoxMDIzOTU0NzIyNTI5MzJAMTQwMTQyNDM5MTQ2MQ==http://aer.sagepub.com/


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how important each of the individual workplace characteristics is to them. As a simplified example, a teacher may be presented with Job A (high salaryand unsafe facilities) and Job B (low salary and safe facilities) and asked to

rate which she would prefer and to what degree. If the teacher responds thatshe prefers Job A much more than Job B, one can infer that salary is moreimportant to this teacher than school facilities. Rather than asking teachersto state how important salary is to them, the importance of salary is derivedby analyzing teachers’ preferences for different work profiles. As teachersare presented with varying profiles, more nuanced estimations of impor-tance can be made. This decompositional approach has been demonstratedto be more reliable and accurate than a compositional approach (Green &

Wind, 1981; R. B. Johnson, 1995).

The second traditional method uses data on employees’ (in this caseteachers’) transfer and quit behaviors to estimate preferences for job charac-teristics. The studies of teacher turnover in California, Texas, New York, andGeorgia described above are examples of these. As discussed previously,these studies are limited in that they are unable to disentangle the effects ofdifferent school characteristics that are highly correlated in actual schools(such as working conditions and student demographics). Additionally, thesestudies have substantial data requirements—needing to follow teachers formany years and needing adequate measures of school characteristics. In fact,researchers rarely have access to this data, and, as examples, these studieshave very little information about schools other than their student composi-tion. Conjoint analysis simulates choices instead of observing choices andthus can assess the importance of a range of job characteristics. Additionally,because this method asks teachers to trade off hypothetical teaching jobsrather than actual ones, confounding external factors such as district budgetcuts, changes in wages of nonteaching jobs in the local labor market, and

varying local teacher union bargaining power are removed. Teachers areasked to compare hypothetical job profiles, assuming all else is equal.

I use adaptive conjoint analysis. The term adaptive refers to the fact that

it uses an Internet-based survey that is customized for each respondent. Thesurvey reacts to the teacher’s prior responses and adjusts subsequent ques-tions to challenge the teacher to make more difficult trade offs. This maxi-mizes the information gathered and minimizes the survey length (Green, Krieger, & Agarwal, 1991; Huber, Wittink, Fiedler, & Miller, 1993; SawtoothSoftware, n.d.).

Results: The Importance of Different Workplace Characteristics

The analysis of the survey data is divided into three stages. First, char-acteristic level utility values and characteristic importance scores are calcu-lated for each respondent and averaged across the sample of respondents.Second, respondent subgroups are compared. Finally, the teachers’ prefer-ences for hypothetical job profiles are predicted by calculating the overallutility of different job profiles.


https://www.researchgate.net/publication/276958242_New_Way_to_Measure_Consumers'_Judgements?el=1_x_8&enrichId=rgreq-d22b53fe-b0a8-48ae-94bb-5515f85f7d9d&enrichSource=Y292ZXJQYWdlOzI1MDE4NTA4ODtBUzoxMDIzOTU0NzIyNTI5MzJAMTQwMTQyNDM5MTQ2MQ==https://www.researchgate.net/publication/276958242_New_Way_to_Measure_Consumers'_Judgements?el=1_x_8&enrichId=rgreq-d22b53fe-b0a8-48ae-94bb-5515f85f7d9d&enrichSource=Y292ZXJQYWdlOzI1MDE4NTA4ODtBUzoxMDIzOTU0NzIyNTI5MzJAMTQwMTQyNDM5MTQ2MQ==https://www.researchgate.net/publication/249721130_Estimating_an_Evaluation_Utilization_Model_Using_Conjoint_Measurement_and_Analysis?el=1_x_8&enrichId=rgreq-d22b53fe-b0a8-48ae-94bb-5515f85f7d9d&enrichSource=Y292ZXJQYWdlOzI1MDE4NTA4ODtBUzoxMDIzOTU0NzIyNTI5MzJAMTQwMTQyNDM5MTQ2MQ==https://www.researchgate.net/publication/245194763_Adaptive_Conjoint_Analysis_Some_Caveats_and_Suggestions?el=1_x_8&enrichId=rgreq-d22b53fe-b0a8-48ae-94bb-5515f85f7d9d&enrichSource=Y292ZXJQYWdlOzI1MDE4NTA4ODtBUzoxMDIzOTU0NzIyNTI5MzJAMTQwMTQyNDM5MTQ2MQ==https://www.researchgate.net/publication/245194763_Adaptive_Conjoint_Analysis_Some_Caveats_and_Suggestions?el=1_x_8&enrichId=rgreq-d22b53fe-b0a8-48ae-94bb-5515f85f7d9d&enrichSource=Y292ZXJQYWdlOzI1MDE4NTA4ODtBUzoxMDIzOTU0NzIyNTI5MzJAMTQwMTQyNDM5MTQ2MQ==https://www.researchgate.net/publication/271675768_The_Effectiveness_of_Alternative_Preference_Elicitation_Procedures_in_Predicting_Choice_Journal_of_Marketing_Research_30_105-114?el=1_x_8&enrichId=rgreq-d22b53fe-b0a8-48ae-94bb-5515f85f7d9d&enrichSource=Y292ZXJQYWdlOzI1MDE4NTA4ODtBUzoxMDIzOTU0NzIyNTI5MzJAMTQwMTQyNDM5MTQ2MQ==http://aer.sagepub.com/http://aer.sagepub.com/https://www.researchgate.net/publication/276958242_New_Way_to_Measure_Consumers'_Judgements?el=1_x_8&enrichId=rgreq-d22b53fe-b0a8-48ae-94bb-5515f85f7d9d&enrichSource=Y292ZXJQYWdlOzI1MDE4NTA4ODtBUzoxMDIzOTU0NzIyNTI5MzJAMTQwMTQyNDM5MTQ2MQ==https://www.researchgate.net/publication/276958242_New_Way_to_Measure_Consumers'_Judgements?el=1_x_8&enrichId=rgreq-d22b53fe-b0a8-48ae-94bb-5515f85f7d9d&enrichSource=Y292ZXJQYWdlOzI1MDE4NTA4ODtBUzoxMDIzOTU0NzIyNTI5MzJAMTQwMTQyNDM5MTQ2MQ==https://www.researchgate.net/publication/271675768_The_Effectiveness_of_Alternative_Preference_Elicitation_Procedures_in_Predicting_Choice_Journal_of_Marketing_Research_30_105-114?el=1_x_8&enrichId=rgreq-d22b53fe-b0a8-48ae-94bb-5515f85f7d9d&enrichSource=Y292ZXJQYWdlOzI1MDE4NTA4ODtBUzoxMDIzOTU0NzIyNTI5MzJAMTQwMTQyNDM5MTQ2MQ==https://www.researchgate.net/publication/249721130_Estimating_an_Evaluation_Utilization_Model_Using_Conjoint_Measurement_and_Analysis?el=1_x_8&enrichId=rgreq-d22b53fe-b0a8-48ae-94bb-5515f85f7d9d&enrichSource=Y292ZXJQYWdlOzI1MDE4NTA4ODtBUzoxMDIzOTU0NzIyNTI5MzJAMTQwMTQyNDM5MTQ2MQ==https://www.researchgate.net/publication/245194763_Adaptive_Conjoint_Analysis_Some_Caveats_and_Suggestions?el=1_x_8&enrichId=rgreq-d22b53fe-b0a8-48ae-94bb-5515f85f7d9d&enrichSource=Y292ZXJQYWdlOzI1MDE4NTA4ODtBUzoxMDIzOTU0NzIyNTI5MzJAMTQwMTQyNDM5MTQ2MQ==https://www.researchgate.net/publication/245194763_Adaptive_Conjoint_Analysis_Some_Caveats_and_Suggestions?el=1_x_8&enrichId=rgreq-d22b53fe-b0a8-48ae-94bb-5515f85f7d9d&enrichSource=Y292ZXJQYWdlOzI1MDE4NTA4ODtBUzoxMDIzOTU0NzIyNTI5MzJAMTQwMTQyNDM5MTQ2MQ==http://aer.sagepub.com/


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Average Utility Values and Importance Scoresof Workplace Characteristics

Utility values represent the desirability of the characteristic levels

(i.e., their relative “worth”) and are computed with the Sawtooth Software Adaptive Conjoint Analysis program using ordinary least squares regression.The average utility values reported in Figure 1 demonstrate the teachers’preferences for the workplace characteristic levels in this study. Conjointutility values are interval data because they are scaled to an arbitrary additiveconstant within each characteristic. Therefore, the utility values of levelsbetween characteristics (e.g., $4,000 additional salary vs. 20 students in yourclass) cannot be directly compared. However, the utility values of levels

within a characteristic can be compared. For example, $0 additional salary

has an average utility value of –55.42, while $4,000 additional salary has anaverage utility value of +6.70 and $8,000 additional salary per year has anaverage utility value of +48.71—indicating that, on average, respondentsprefer higher salaries to lower ones.

Importance scores, on the other hand, can be compared across char-acteristics. Importance scores characterize the relative importance of each

–55.42

6.70

48.71

–74.12

32.89

41.23

–67.21

7.63

59.57

–43.17

6.25

36.92

–60.96

12.34

48.62

–50.52

50.52

–69.50

69.50

–11.99

3.538.46

–12.09

14.82

–2.73

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18.68

–5.47

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–80.00

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Attribute Levels

A v e r a g e U t i l i t y V a l u e s s

Salary Class

Size

Administrative

Support

Input on

Decisions

Commute

Time

Student

Resources

Facilities Student

Ethnicity

Student

API

Student

SES

Figure 1. Average utility values for each level of the workplace characteristic

variables.




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characteristic (i.e., how much influence the characteristic has on the respondent’schoices compared to the other characteristics). Importance scores are calcu-lated by calculating the range in the characteristic’s utility values (i.e., subtract-ing the utility of the least-preferred level from the utility of the most-preferred

level) and adjusting those ranges so that the sum of the 10 characteristic impor-tance scores is 100. The following equations demonstrate how importancescores are calculated if only three characteristics are considered.

I1 = (U1 / U1+U2+U3) × 100%I2 = (U2 / U1+U2+U3) × 100%I3 = (U3 / U1+U2+U3) × 100%

where I1 through I3 are importance scores for three characteristics of a

teaching job and U1 through U3 are the utility value ranges of three char-acteristics (i.e., highest utility value minus lowest utility value for eachcharacteristic).

The average importance scores reported in Figure 2 demonstrate therespondents’ preferences for the workplace characteristics.4 Since the impor-tance scores of the 10 characteristics totals 100, if the characteristics wereequally preferred by the respondents, the importance score of each charac-teristic would be 10. Upon examination of the importance scores, it is evi-dent that the 10 characteristics are not equally preferred. Further, two-tailed,

paired-samples t tests comparing each characteristic importance with everyother characteristic importance (see Table 1) reveal that every characteristicimportance is significantly different from every other characteristic impor-tance at the .05 level, with the exception of two pairs: administrative supportand class size and student performance and student socioeconomic status.

Figure 2. Average importance scores for the workplace characteristic variables.




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T a b l e 1

t V a l u e s o f T w o - T a i l e d , P a i r

w i s e t T e s t s f o r I m p o r t a n c e

S c o r e s o f W o r k p l a c e C h a r a c t e r i s t i c s V a r i a b l e s

A d m i n i s t r a t i v e

C l a s s

C o m m u t e

R e s o u r c e s

I n p u t o n

S t u d e n t

S

t u d e n t

F a c i l i t i e s

S u p p o r t

S i z e

T i m e

S a l a r y

f o r S t u d e n t s

D e c i s i o n s

S E S

P e r f o r m a n c e

A d m i n i s t r a t i v e s u p p o r t

– 3 . 9

1 * *

C l a s s s i z e

– 3 . 9

1 * *

– 0 . 1

6

C o m m u t e t i m e

– 8 . 5

2 * *

3 . 8

4 * *

3 . 9 8 * *

S a l a r y

– 1 0 . 7 0 * *

– 6 . 3

0 * *

– 6 . 5

8 * *

– 3 . 2 2 *

*

R e s o u r c e s f o r s t u d e n t s

– 1 6 . 7 5 * *

1 0 . 8

8 * *

9 . 7 9 * *

6 . 1 0 * *

2 . 6 1 * *

I n p u t o n d e c i s i o

n s

– 1 9 . 5 0 * *

1 7 . 1

1 * *

1 3 . 9

5 * *

– 1 1 . 1 8 * *

6 . 6 8 * *

– 5 . 4

1 * *

S t u d e n t S E S

2 6 . 4

0 * *

2 3 . 7

0 * *

2 2 . 6

0 * *

1 9 . 8 0 *

*

1 5 . 0

0 * *

1 4 . 9

4 * *

9 . 5 5 * *

S t u d e n t p e r f o r m a n c e

2 8 . 0

2 * *

2 3 . 3

0 * *

2 3 . 9

2 * *

1 8 . 9 5 *

*

1 5 . 2 7 * *

1 5 . 6

1 * *

9 . 3 1 * *

– 0 . 1 7

S t u d e n t e t h n i c i t y

2 9 . 7

2 * *

2 7 . 2

4 * *

2 4 . 8

0 * *

2 2 . 2 8 *

*

1 7 . 2

3 * *

1 7 . 8 7 * *

1 2 . 5

6 * *

– 3 . 2

1 * *

2 . 4 3 *

N o t e .

S E S = s

o c i o e c o n o m i c s t a t u s .

* p < .

0 5 .

* * p < . 0

1 .




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Comparing Subgroups of Teachers

The survey included 10 respondent background questions by whichteachers are sorted into subgroups: gender, ethnicity, age, education, socio-economic status growing up, whether part of the first generation in theirfamily to go to college, number of children under 18, years of teachingexperience, number of students, and satisfaction with current teachingassignment. Table 2 demonstrates the respondent subgroups and samplesize of each.5

Initially, a one-way analysis of variance (ANOVA) is performed for eachcombination of respondent background variable and workplace characteris-tic. Table 3 displays the F ratios of the one-way ANOVAs. Given that thisanalysis involves 100 bivariate comparisons that are not strictly independent,a conservative level of significance ( p


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T a b l e 3

F R a t i o s f o r O n e - W a y A N O V A s f o r E a c h C o m b i n a t i o n o f R e s p o n d e n t B a c k g r o u n d V a r i a b l e

a n d W o r k p l a c e C h a r a c t e r i s

t i c

N u m b e r

F i r s t

S E S

N u m b e r

o f

G e n e r a t i o n

G r o w i n g

o f

T e a c h i n g

A g e

G e n d e r E t h n i c i t y

C h i l d r e n

E d u c a t i o

n

t o C o l l e g e

U p

S t u d e n t s

E x p e r i e n c e S a

t i s f a c t i o n

F a c i l i t i e s

5 . 6 0 * *

2 4 . 9

2 * *

2 . 5 7

0 . 8 4

3 . 8 3

0 . 0 0

1 . 9 0

1 . 9 3

2 . 4 8

3 . 8 9 *

A d m i n i s t r a t i v e s u p p o r t

3 . 0 8 *

0 . 2 5

2 . 7 7

3 . 9 0 *

0 . 0 7

0 . 0 3

2 . 4 5

6 . 5 6 *

4 . 0 5 *

1 . 5 7

C l a s s s i z e

4 . 5 7 * *

6 . 3 8 *

3 . 4 4 *

0 . 0 1

0 . 1 2

0 . 8 3

0 . 2 1

3 7 . 6

4 * *

3 . 2 7 *

0 . 1 7

C o m m u t e t i m e

0 . 4 0

1 . 5 9

4 . 9 0 * *

1 . 1 5

0 . 1 7

0 . 9 6

3 . 7 0 *

0 . 4 4

0 . 5 0

1 . 0 9

S a l a r y

4 . 3 3 * *

3 . 7 4

0 . 4 2

0 . 4 9

1 5 . 1 3 *

*

1 . 7 8

0 . 5 4

2 . 8 5

8 . 4 9 * *

9 . 9 7 * *

R e s o u r c e s f o r s t u d e n t s

0 . 2 3

1 . 7 7

3 . 4 6 *

0 . 0 1

2 . 6 0

3 . 9 7 *

1 . 0 1

4 . 1 9 *

0 . 1 4

1 . 2 2

I n p u t o n d e c i s i o

n s

0 . 4 8

0 . 0 0

0 . 1 8

0 . 2 4

1 1 . 6 0 *

*

0 . 4 1

1 . 5 2

0 . 5 6

2 . 4 4

3 . 4 2 *

S t u d e n t S E S

2 . 5 0

5 . 5 3 *

0 . 5 6

0 . 3 7

0 . 1 0

0 . 6 4

0 . 7 2

0 . 9 6

2 . 2 0

0 . 8 7

S t u d e n t p e r f o r m

a n c e

4 . 5 5 * *

0 . 4 2

0 . 0 4

0 . 0 1

1 . 2 4

0 . 0 9

0 . 5 3

0 . 8 5

7 . 5 6 * *

0 . 5 3

S t u d e n t e t h n i c i t y

2 . 2 0

8 . 0 5 * * 1

1 . 2 2 * *

1 . 1 1

0 . 0 9

2 . 9 8

1 . 5 5

3 . 1 1

2 . 8 6

2 . 3 0

N o t e .

S E S = s

o c i o e c o n o m i c s t a t u s .

* p < .

0 5 .

* * p < . 0

1 .




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hoc tests to determine specifically which respondent subgroups differ signifi-cantly from others and in which direction.

The statistically significant differences in workplace characteristic aver-

age importance scores between respondent subgroups are as follows. Classsize is significantly more important to teachers 41 to 50 years old and thoseover 50 years old (compared to teachers 30 or under), female teachers (com-pared to male teachers), and teachers who currently have 20 or less students(compared to those with more than 20 students). Salary is significantly moreimportant to teachers 30 years old or younger (compared to those 41 to 50and those over 50), teachers with only a Bachelor’s degree (compared toteachers with a higher education degree), teachers in their first 5 years ofteaching (compared to those with 6 to 10 years of experience and those who

have taught more than 10 years), and teachers who are not “very satisfied” with their current teaching assignment (compared to those who are “verysatisfied”). Clean and safe school facilities are significantly more importantto female teachers (compared to male teachers) and teachers over the ageof 50 (compared to those 30 or under and those 41 to 50). Being able tofrequently provide input on school-wide decision making is significantlymore important to teachers with a master’s, PhD, and/or EdD degree (com-pared to teachers without a higher education degree). Commute time issignificantly more important to Caucasian teachers (compared to Latino/a orHispanic teachers). Student performance is significantly more important toteachers age 41 to 50 and those over 50 (compared to teachers 30 or under)and teachers with more than 10 years of experience (compared to teachers

with 5 or less years of experience and teachers who have taught between6 and 10 years). Finally, student ethnicity is significantly more important tomale teachers (compared to female teachers).

Next, 10-way ANOVAs are conducted to examine the differences in workplace characteristic importance scores by teacher subgroups, afteraccounting for the other 9 respondent background variables. For example,males and females are compared in their average facilities importance scores,

controlling for the other 9 respondent background variables. While this 10-way ANOVA model for school facilities is significant overall, only 11.7% (8.7%adjusted) of the variance of the facilities importance score can be accountedfor by the combination of the 10 respondent background variables. Similarly,the models for administrative support, class size, salary, input on school-widedecisions, and student ethnicity are significant, but the proportion of the vari-ance in each of the characteristics’ importance scores that can be accountedfor by the combination of the 10 respondent background variables is minimal(ranging from 6.2% to 10.6%). The models for the remaining workplace

characteristics (commute time, resources for students, student socioeconomicstatus, and student performance) are not significant.Finally, a multivariate analysis of variance (MANOVA) is performed to

generate a model which includes 9 of the workplace characteristics as crite-rion variables and all 10 respondent background variables as predictor

variables.6 Table 4 demonstrates the results of the multivariate model, by




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predictor variable (i.e., respondent background) and by criterion variable(i.e., workplace characteristic). With the use of Wilks’s Lambda criterion,statistically significant differences are found among the respondent sub-groups of gender, ethnicity, education, number of students, and satisfaction

with current teaching assignment on the combined workplace characteris-tics. However, the strength of relationship between each of the predictor

variables and the combined criterion variables is moderate, with the partial

effect size index, η2

, of each ranging from .01 to .07. No statistically signifi-cant differences are found among the subgroups of the other predictor vari-ables (age, socioeconomic status when growing up, whether part of the firstgeneration in their family to go to college, number of children under 18, and

years of teaching experience). Table 4 also demonstrates the results of themultivariate model by criterion variable. The model is significant for facilities,

Table 4

Results of MANOVA by Predictor Variable and by Criterion Variable

Wilks’s Hypothesis Error PartialPredictor Variable Lambda F df df Significance η2

Age 0.92 1.34 27 1329 .117 0.03Gender 0.94 3.44** 9 455 .000 0.06Ethnicity 0.94 1.66* 18 910 .041 0.03Number of children 0.98 0.98 9 455 .454 0.02Education 0.95 2.68** 9 455 .005 0.05First generation to college 0.99 0.48 9 455 .887 0.01SES growing up 0.96 1.04 18 910 .416 0.02Number of student 0.93 3.81** 9 455 .000 0.07

Teaching experience 0.96 1.05 18 910 .401 0.02Satisfaction 0.91 2.32** 18 910 .001 0.04

Note. SES = socioeconomic status.* p


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administrative support, class size, salary, and student ethnicity importancescores. The partial η2 of each is moderate to strong (ranging from 0.04 to0.12), indicating an average association between the set of 10 respondent

background variables and each of these workplace characteristics. No statisti-cally significant main effect is found for the other four criterion variables inthe model (commute time, resources for students, student performance, andstudent socioeconomic status).

Models of Teachers’ Preferences

Since the utility values are computed using ordinary least squares regres-sion, they can be interpreted as regression betas. The respondents’ rating for

a job profile is the dependent variable, and the workplace characteristics arethe independent variables. The overall utility of a given job profile for arespondent is the sum of the utility values of the specific characteristic levels.To calculate the average overall utility for a specific job profile for all therespondents, the overall utility (i.e., sum of the characteristic level utility

values) is calculated for each respondent and averaged across all respon-dents. The overall utilities of hypothetical job profiles are used to predict theteachers’ preferences and sorting patterns if they had these profiles aschoices. The model of a job profile overall utility for a respondent can berepresented with the following equation. Note that one level is dropped for

each attribute because of the linear dependency of attribute levels (becausethe sum of their utility values is 0).

Y = b1($4,000) + b2($8,000) + b3(20 students) + b4(15 students)+ b5 (average admin. support) + b6(very good admin. support)+ b7(occasional input) + b8(frequent input)+ b9(30-minute commute) + b10(5-minute commute)+ b11(enough resources) + b12(clean and safe facilities)+ b13(API 5) + b14(API 8) + b15(50% Latino/AA)

+ b16(95% Latino/AA) + b17(middle-income students)+ b18(low-income students) + constant + error

where Y is the respondent’s preference for a job profile (i.e., overall utility)and b1 through b18 are utility values for the characteristic levels.

An overall utility value, therefore, is a numeric value that reflects thejudgments, impressions, or evaluations that teachers form of job profiles

when they take all given workplace characteristic information into account.The greater the overall utility, the more preferred the job is to the sample of

teachers, on average. There are hundreds of possible combinations of char-acteristics to create hypothetical job profiles. One example is a job where ateacher would have 20 students, average administrative support, occasionalinput in school-wide decision making, enough resources for students, a30-minute commute from home, clean and safe facilities, and an additional$4,000 in annual salary in a school where the student population is 95%




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Latino or African American, mostly low income, and the academic achievementis represented by an API rank of 2. This job profile would have an averageoverall utility of +164.65 for the sample of teachers, which is only meaning-

ful when compared to another job profile. For example, a job with identical working conditions but at a school with a different student population—50%Latino or African American, mostly middle income, and academic achieve-ment represented by an API rank of 8—has an average overall utility of+227.79, indicating that this job is more attractive to the teachers, on average,than the previous hypothetical job. The implications of teachers’ preferencesfor different hypothetical job profiles will be considered later.

Limitations of the Study

The advantage of asking teachers to trade off hypothetical job profilesis that teachers’ preferences across a range of options can be observed ratherthan being limited by the jobs that actually exist. However, it is necessary tonote that there are also some important limitations to this study.

First, the theoretical framework underlying this study assumes thatteachers act as rational decision makers interested in maximizing their “utility”

when choosing a teaching job. Inasmuch as the framework’s assumptionsare not valid, the ensuing conclusions may not be as well.

Second, the model developed by this study is hypothetical in nature. It

is used to predict how teachers would choose between different job profiles.In actuality, teachers may make different choices. Since teachers are cur-rently not being offered the choices proposed in this study, there is not a

way to test the goodness of fit of the model with actual data.Third, the teachers’ self-reports may be unreliable. For example, a respon-

dent may not have been willing to report that he or she would rather not teachminority students, if given the choice.7 Additionally, different respondents mayhave interpreted the terminology of the survey differently—for example, “admin-istrative support” is likely to mean different things to different teachers.

Fourth, the sample of teachers was limited to one district of conve-nience due to the challenge of acquiring district and school permissions tocontact individual teachers and invite them to complete the survey. Therelatively small sample size may pose degrees of freedom problems—con-sequently conservative levels of significance are used for analyses.

Additionally, these teachers likely made assumptions based upon their expe-riences in this district which may have influenced their responses. For exam-ple, this district is very close to the U.S.–Mexico border. Therefore, theseteachers are familiar with large concentrations of Latino/a and Hispanic stu-dents. When the survey asked these teachers to consider a school where“95% of the students are African American or Latino,” they most likely envi-sioned schools where most of the students are Latino rather than African

American. Teachers from other school districts may respond differently tothe survey based upon their unique experiences and assumptions. Another




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major limitation is this is an elementary school district. The implications ofthis study’s findings can only be generalized to a larger population to theextent that the preferences of the teachers in this one elementary school

district are representative of the preferences of teachers in other districts. Thefollowing discussion assumes that teachers throughout California have simi-lar preferences for workplace conditions as the teachers surveyed in thisstudy which may not be the case.

Discussion: New Insights Into Teacher Preferences

Having Clean and Safe Facilities, Very Good Administrative Support,and Small Class Sizes Are Very Important to Teachers

At the individual teacher level, there are a myriad of unmeasurable factors which may persuade a teacher to select one school over another. At the aggre-gate level, however, teachers are more likely to choose a school and less likelyto leave if teaching conditions are favorable (Loeb & Reininger, 2004). Anexamination of the average characteristic level utility values (see Figure 1)reveals what teachers in this study, on average, consider to be favorable teach-ing conditions. Respondents, on average, prefer higher salaries to lower ones,smaller class sizes to larger ones, very good administrative support to poorsupport, frequently giving input on school-wide decisions to rarely giving

input, shorter commute times to longer ones, having enough resources forstudents to not having enough, facilities that are clean and safe to ones thatare not, and higher performing students to lower performing ones. Additionally,on average, teachers in this study prefer schools where half of the students areLatino or African American and where most of the students are from middle-income families. Overall, the sample of teachers slightly prefers high-minorityschools (where 95% of the students are Latino or African American) to low-minority ones (where only 5% of the students are Latino or African American)and high-poverty schools (where most of the students are from low-incomefamilies) to low-poverty ones (where most of the students are from high-in-come families). This suggests that teachers may not have an aversion to teach-ing low-income or minority students, as hypothesized by researchers whohave examined teacher transfer patterns. Other factors, such as working con-ditions, may be driving teachers’ preferences for schools instead.

The average importance scores (see Figure 2) reveal the preferences therespondents have for the 10 workplace characteristics in this study. The averageimportance scores suggest that some working conditions may strongly influ-ence teachers’ decisions of where to teach. Of the 10 characteristics, schoolfacilities, administrative support, and class size are the 3 most important to the

sample of teachers. Importance scores are ratio data, so a characteristic with animportance score of 20 is twice as important as one with an importance scoreof 10. These data suggest that, on average, school facilities are more than twiceas important as each of the 3 student demographic variables when teachersselect among schools. Similarly, administrative support and class size are almosttwice as important as each of the student-body characteristics.8


https://www.researchgate.net/publication/234740526_Public_Policy_and_Teacher_Labor_Markets_What_We_Know_and_Why_It_Matters?el=1_x_8&enrichId=rgreq-d22b53fe-b0a8-48ae-94bb-5515f85f7d9d&enrichSource=Y292ZXJQYWdlOzI1MDE4NTA4ODtBUzoxMDIzOTU0NzIyNTI5MzJAMTQwMTQyNDM5MTQ2MQ==http://aer.sagepub.com/http://aer.sagepub.com/https://www.researchgate.net/publication/234740526_Public_Policy_and_Teacher_Labor_Markets_What_We_Know_and_Why_It_Matters?el=1_x_8&enrichId=rgreq-d22b53fe-b0a8-48ae-94bb-5515f85f7d9d&enrichSource=Y292ZXJQYWdlOzI1MDE4NTA4ODtBUzoxMDIzOTU0NzIyNTI5MzJAMTQwMTQyNDM5MTQ2MQ==http://aer.sagepub.com/


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The t tests comparing the characteristic importance scores (see Table 1)indicate that having clean and safe school facilities (vs. not clean and safeones) is statistically significantly more important to the sample of teachers than

each of the other workplace characteristics included in this study. Additionally,each of the working condition characteristics—specifically, clean and safefacilities (vs. not), very good administrative support (vs. poor), class size of 15students (vs. 33), one-way commute time of 5 minutes (vs. 60 minutes),enough textbooks, materials, and technology (vs. not enough), and frequentinput on school-wide decisions (vs. rare)—is statistically significantly moreimportant to the respondents, on average, than each of the student demo-graphic variables. This further suggests that teachers’ decisions of where toteach are more likely to be driven by their preferences for favorable working

conditions than by their reluctance to teach certain kinds of students.

Salary Is Not as Important to Teachers as Working ConditionsBut More Important Than Student-Body Characteristics

Salary is ranked fifth of the 10 workplace characteristics by averageimportance score (see Figure 2). Salary is, on average, significantly less impor-tant than some working conditions (see Table 1). On average, the differencebetween $0 versus $8,000 in additional annual salary is not as important tothese teachers as the differences between clean and safe versus not clean and

safe facilities, very good versus poor administrative support, 15 versus 33students in a class, and a one-way commute time of 5 versus 60 minutes.

The findings also suggest that, on average, school facilities are 30% moreimportant than salary (see Figure 2). Since importance scores depend on thespecific characteristic levels chosen, more accurately, the difference betweenfacilities that are clean and safe versus facilities that are not clean and safe is, onaverage, 30% more important to the respondents than the difference between $0additional salary per year versus an $8,000 increase in annual pay. Another wayto interpret this finding is that if the teachers sampled had to choose between

working at a school that was clean and safe or to receive an $8,000 annual salaryincrease, on average, they would choose the former over the latter.However, the results also reveal that receiving an additional $8,000 in

salary annually is significantly more important to the teachers than studentethnicity, performance, or socioeconomic status (with the defined ranges).This suggests that teacher transfer patterns among schools with differentstudent demographics may be altered with financial incentives such asincreased annual pay—however, these incentives may not be as effective asimproving school working conditions.

Working Conditions Are More Powerful Determinants of Where TeachersChoose to Work Than Student Demographics

This study provides evidence to support the hypothesis that student char-acteristics serve as proxies for working conditions when teachers choose a




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school. As previously described, studies conducted by S. J. Carroll et al. (2000), Lankford et al. (2002), Hanushek et al. (2004), and Scafidi et al. (2005) havefound that teacher transfer patterns are highly correlated with student charac-teristics, with teachers tending to move away from poor, low-performing, andminority students. While previous studies have been unable to disentanglestudent characteristics and working conditions to determine if the characteris-

tics of students themselves directly affected teachers’ decisions to migrate orserved as proxies for working conditions in the schools, this study is able todisconnect the two by asking teachers to make hypothetical tradeoffs.

The results suggest that the observed teacher transfer patterns are morelikely the consequence of teachers avoiding undesirable school environmentsrather than particular groups of students. In other words, working condi-tions, not student-body characteristics, are more powerful determinants of

where teachers choose to work. Figure 3 displays the overall utilities of fourhypothetical job profiles. Teachers’ responses to these hypothetical jobssuggest quite a lot about their decision making and, therefore, about the

distribution of teachers across schools.The model represented in Figure 3 predicts the respondents’ preferences

if they are presented with four different hypothetical job profiles. Since studentcharacteristics and school facility quality are highly correlated, many currentlyexisting teaching jobs fit Profiles A or B:

–77.70

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–100.00

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–60.00

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School A

Students:

95% AfricanAmerican or

Latino; mostlylow-income

Facilities:

NOT clean andsafe

School B

Students:


Latino; mostlymiddle-income

Facilities:

clean and safe

School C

Students:


Latino; mostlylow-income

Facilities:

clean and safe

School D

Students:

5% AfricanAmerican orLatino; mostlymiddle-income

Facilities:

NOT cleanand safe

Figure 3. Overall utilities of four hypothetical job profiles.


https://www.researchgate.net/publication/235183788_The_Distribution_of_Teachers_Among_California's_School_Districts_and_Schools?el=1_x_8&enrichId=rgreq-d22b53fe-b0a8-48ae-94bb-5515f85f7d9d&enrichSource=Y292ZXJQYWdlOzI1MDE4NTA4ODtBUzoxMDIzOTU0NzIyNTI5MzJAMTQwMTQyNDM5MTQ2MQ==https://www.researchgate.net/publication/247700402_Teacher_Sorting_and_the_Plight_of_Urban_Schools_A_Descriptive_Analysis?el=1_x_8&enrichId=rgreq-d22b53fe-b0a8-48ae-94bb-5515f85f7d9d&enrichSource=Y292ZXJQYWdlOzI1MDE4NTA4ODtBUzoxMDIzOTU0NzIyNTI5MzJAMTQwMTQyNDM5MTQ2MQ==https://www.researchgate.net/publication/247700402_Teacher_Sorting_and_the_Plight_of_Urban_Schools_A_Descriptive_Analysis?el=1_x_8&enrichId=rgreq-d22b53fe-b0a8-48ae-94bb-5515f85f7d9d&enrichSource=Y292ZXJQYWdlOzI1MDE4NTA4ODtBUzoxMDIzOTU0NzIyNTI5MzJAMTQwMTQyNDM5MTQ2MQ==https://www.researchgate.net/publication/280689603_Why_public_schools_lose_teachers?el=1_x_8&enrichId=rgreq-d22b53fe-b0a8-48ae-94bb-5515f85f7d9d&enrichSource=Y292ZXJQYWdlOzI1MDE4NTA4ODtBUzoxMDIzOTU0NzIyNTI5MzJAMTQwMTQyNDM5MTQ2MQ==http://aer.sagepub.com/http://aer.sagepub.com/https://www.researchgate.net/publication/280689603_Why_public_schools_lose_teachers?el=1_x_8&enrichId=rgreq-d22b53fe-b0a8-48ae-94bb-5515f85f7d9d&enrichSource=Y292ZXJQYWdlOzI1MDE4NTA4ODtBUzoxMDIzOTU0NzIyNTI5MzJAMTQwMTQyNDM5MTQ2MQ==https://www.researchgate.net/publication/235183788_The_Distribution_of_Teachers_Among_California's_School_Districts_and_Schools?el=1_x_8&enrichId=rgreq-d22b53fe-b0a8-48ae-94bb-5515f85f7d9d&enrichSource=Y292ZXJQYWdlOzI1MDE4NTA4ODtBUzoxMDIzOTU0NzIyNTI5MzJAMTQwMTQyNDM5MTQ2MQ==https://www.researchgate.net/publication/247700402_Teacher_Sorting_and_the_Plight_of_Urban_Schools_A_Descriptive_Analysis?el=1_x_8&enrichId=rgreq-d22b53fe-b0a8-48ae-94bb-5515f85f7d9d&enrichSource=Y292ZXJQYWdlOzI1MDE4NTA4ODtBUzoxMDIzOTU0NzIyNTI5MzJAMTQwMTQyNDM5MTQ2MQ==https://www.researchgate.net/publication/247700402_Teacher_Sorting_and_the_Plight_of_Urban_Schools_A_Descriptive_Analysis?el=1_x_8&enrichId=rgreq-d22b53fe-b0a8-48ae-94bb-5515f85f7d9d&enrichSource=Y292ZXJQYWdlOzI1MDE4NTA4ODtBUzoxMDIzOTU0NzIyNTI5MzJAMTQwMTQyNDM5MTQ2MQ==http://aer.sagepub.com/


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• Profile A: most of the students are low income, 95% of the students are African American or Latino, and facilities are not clean and safe.

• Profile B: most of the students are middle income, 5% of the students are

African American or Latino, and facilities are clean and safe.

If only these two jobs are compared, this study predicts that theteachers would greatly prefer Profile B (with an overall utility of +76.09) toProfile A (with an overall utility of –77.70). This would account for theteacher transfer patterns that other researchers have observed—namely,

when teachers transfer from one school to another, they tend to move awayfrom schools serving large concentrations of low-income students of colorto ones which do not (in this case, from A to B). One might then presumethat some schools are hard to staff because teachers do not want to teachpoor and minority students.

Importantly, the data from this study allow insights into the desirabilityfor jobs that do not currently exist in numbers as large as Profiles A and B,such as Profiles C and D:

• Profile C: most of the students are low income, 95% of the students are African American or Latino, and facilities are clean and safe.

• Profile D: most of the students are middle income, 5% of the students are African American or Latino, and the facilities are not clean and safe.

The overall utilities of Profile C (+61.30) and Profile D (–62.91) indicatethat good school facilities are a much more powerful incentive for teachingin a school than the demographic composition of the students who attendthe school. Therefore, previously documented teacher mobility patterns aremore likely due to teachers moving away from poor working conditions,such as unclean and unsafe facilities, than to teachers moving away fromlow-income and non-White students.

Different Kinds of Teachers Have Slightly Different Preferences for Workplace Characteristics, but Generally Teachers’ Preferences Are More Similar Than They Are Different

Respondent subgroup comparison analyses indicate that different sub-groups of teachers have slightly different preferences for the workplace char-acteristics in this study. For example, Figure 4 displays the average importancescores disaggregated by gender for each of the characteristics and suggeststhat clean and safe facilities may be more important to female teachers thanmale teachers. While there are some statistically significant differences in pref-

erences among different subgroups of teachers, the 10 teacher background variables account for very little (ranging from 6.2% to 11.7%) of the varianceof each of the characteristic importance scores, indicating that teachers in thesesubgroups are more similar than they are different. In other words, teachers’background and experience do not have a significant impact on which aspectsof their teaching environment they believe to be the most important to them




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when deciding where to work. Generally, the three most important workplacecharacteristics to all subgroups of teachers in this study are school facilities,administrative support, and class size.

Latino Teachers Favor Teaching Low-Income Students, Students of Color, andLow-Performing Students More Than Other Teachers

While the preferences of different subgroups of teachers for the work-place characteristics in this study do not vary much, their preferences forlevels within the characteristics do suggest some noteworthy differences. Ofparticular interest are the preferences of subgroups of teachers for differentstudent-body characteristics. As described previously, hard-to-staff schoolstend to have large concentrations of low-income students, students of color,

and low-performing students. In order to find teachers who are willing toremain for the long term at these schools, school districts could target theirrecruitment efforts on those teachers who prefer to teach these studentscompared to other teachers, regardless of the salary and working conditions.The results of this study indicate that different subgroups of teachers valuestudent demographic characteristic levels differently. Table 5 presents the

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