What matters for teacher retention in public schools? Comparing neighborhood,
school and family effects
Samantha Haussmann
(Departamento de Demografia e Cedeplar/UFMG)
Raquel Zanatta Coutinho (Departamento de Demografia e Cedeplar/UFMG)
Maria Teresa G. Alves (
Departamento de Ciências Aplicadas à Educação e GAME/
UFMG)
Flavia Xavier (
Departamento de Ciências Aplicadas à Educação e GAME/
UFMG)
Abstract
The relationship between teacher’s characteristics and academic performance is well established. Teacher’s regularity is one of the most important components. In Belo Horizonte (Brazil), where this study takes places, the allocation of students in public schools respects a boundary based on physical address of the household and proximity to the school building. Teacher’s assignment, on the other hand, is based on personal choice and position availability. As schools are strongly segregated, they tend to reflect the socioeconomic characteristics of the areas they are located in. In this project we investigate whether teacher retention is associated with those characteristics, controlled by school features and family variables. Neighborhood variables come from the 2010 Census. School and family variables come from the Educational Census and Basic
Education Evaluation System (Sistema de Avaliação da Educação Básica – Saeb), respectively.
Using regression models and spatial autocorrelations, we find that neighborhood conditions are as important for teacher retention as students’ socioeconomic status and school effects.
Keywords: education, teacher retention, family effects, neighborhood effects, school effects INTRODUCTION
Educational studies in the field of demography, economics and sociology have investigated how teacher’s characteristics, such as qualification and practices, can contribute for students’ academic performance (HANUSHECK, RIVKIN, 2004; RUTTER et al, 2008; FONTANIVE; KLEIN, 2010; LADD, 2008; MORICONI, 2012; GUIMARÃES, 2013). Although studies diverge on the size and importance of the effects, the most efficient teachers can indeed contribute for student´s success.
Moreover, as literature has shown, teachers and students are not randomly distributed across schools. In Belo Horizonte - Brazil, where this study takes places, the allocation of students in public schools respects a geographic boundary based on physical address of the household and proximity to the school building. Teacher’s school allocation, on the other hand, is based on personal choice and availability of open positions, which generates an ever-changing flow of education employees among schools in the same municipality. Important enough for this research is the fact that salaries remain the same, so they are not the motivation for this change.
One of the most important attribute of an efficient teacher is his/her regularity. In Brazil, educational system is highly segregated between urban and rural schools, public and private and by neighborhoods, where it tends to reflect the socioeconomic characteristics of the area (STOCO; ALMEIDA, 2011; KOSLINSKI; ALVES, 2012; KOSLINSKI; ALVES; LANGE, 2013). Different violence rates and school reputation might also be important push and pull factors for teacher´s motivation to move.
Taking teachers as a central object in the process of learning, in this study we aim to investigate if teacher retention, measured by the proportion of faculty that remains in the same school in an interval of 5 years, is associated with certain characteristics of the school neighborhood, controlled by school features and student´s family background.
Our hypothesis is that more qualified and experienced teachers will be more likely to work in better areas (richer neighborhoods with schools that present better educational outcomes and reputation) and to remain in those schools. Likewise, we expect that schools located in
disadvantaged neighborhoods will present worse rates of teacher retention (BOYD et al, 2011; FLORES, 2008; KOSLINSKI; ALVES, 2012).
RESEARCH QUESTIONS
1) Is there an association between school neighborhood characteristics and teacher retention for 2015, controlling for school and family characteristics?
2) Is there a spatial correlation between teacher retention among different school neighborhoods?
DATA AND METHODS
We will use information from several data sources to compile a database in which the unit of analysis is Middle and Elementary public schools (grades 1 through 9), which are managed by the municipality. Independent variables, sources of data and references are summarized in Box 1 in the Appendix.
Belo Horizonte Urbanization and Housing Department (PBH-Urbel) provided us maps containing school districts and school locations, so that each school was placed under their appropriate Census Weight Area (CWA)1. In five of them, there is no Middle and Elementary public school managed by the municipality. On the other hand, it is common to find more than one school inside the same CWA, so those schools share the same characteristics for what we call school
neighborhood variables. The variables for the CWA - school neighborhood - come from 2010
Census (Censo Demográfico, 2010). We allow those to serve as a second level in our hierarchical models.
Each school also has its own school variables. They come from the Educational Census (Censo Escolar, 2015). One of those school variables is the Teacher Retention Index (INEP, 2015), which measures the proportion of teachers that has remained in the same school in the last 5 years Each school also has a mean value for the students’ Socioeconomic Status (SES). This Index was compiled by Alves, Soares, Xavier (2014) to represent the average of socioeconomic conditions of the student body. We call it family variable because they refer to the immediate conditions of the households in which the students live (parental education and household appliances). The responses come from self-administered questionnaires filled out by students when they complete the Basic Education Evaluation System (Saeb, 2015).
In order to address the research questions, our analyses contain four steps. We first describe the dependent variable (teacher retention) and each of the independent variables used, testing for correlations at the school and neighborhood level. Then, to minimize the number of variables, we run Principal Component Analysis to create seven different indexes: school neighborhood (economics, demographic, socio-environmental), school (organization, teacher quality, student quality). We changed the direction of the sign accordingly, so that a higher value would mean a higher vulnerability in that context. We used Student’s SES to test for Family effects. Following, we run Pearson Correlation and linear regression models for each of the indexes to evaluate the existence of neighborhood, school and family effects on teacher retention. Future steps will include multi-level models to take into account the fact that schools are nested into CWAs2. Lastly, we use Local Indicator of Spatial Association (LISA) (ANSELIN, 1994; RAMOS, 2002) and Spatial Autocorrelation (Global Moran I) (MORAN, 1947; ANSELIN, 1994; BAILEY & CATRELL, 1996) to verify whether Teacher Retention is spatially dependent, suggesting that the mean values of retention of a certain area might be associated with the mean values of retention of its surrounding areas, indicating the existence of an environmental/geographic component that might drive the intention to change to a new school.
RESULTS
Our universe of public schools is composed of 176 unique institutions, located in 62 different neighborhoods (CWA). Figure 1 shows the spatial distribution teacher retention, which has a mean value of 3.29 (min 2.00, max 4.22, sd 0.44).
1
Belo Horizonte is the capital of the state of Minas Gerais and has over 2.5 million inhabitants spread in a geographic area of 331.401km2. For Census purpose, the city is divided into 67 Census Weight Areas.
2
Though a single-variable random-effects specification nesting schools in CWA is significant, the advantage of adding one level is small. Future steps will improve the model fit to account for this dependency.
At the school level (not shown), teacher retention is positively associated with the mean of the students’ results in proficiency exams (Pearson correlation of 0.23 for grades 1 through 9). It is negatively associated with age/grade distortion rate (-0.22) and school dropout (-0.16). Contrarily to expected, it is positively associated with the mean number of students per classroom (0.32).
At the neighborhood level (not shown), it is positively associated with population density (0.25), with proportion with trash collection (0.28), and negatively associated with old age dependency ratio 0.18), proportion inappropriate wall 0.18) and proportion without bathroom (-0.19). Contrarily to expected, it is negatively associated with mean income per capita (-0.18).
Figure 1: Categories of Teacher Retention for Belo Horizonte Census Weight Areas (CWA), 2015
Source: 2010 Brazilian Census and 2015 Educational Census.
We create seven different indexes which can be visualized in the Appendix: school neighborhood (economics, demographic, educational, socio-environment) and school (organization, student quality, teacher quality). We changed the direction of the sign accordingly, so that a higher value would mean a higher vulnerability. We also included the variable Student SES as a proxy for Family effects.
We ran linear regression models of Teacher Retention on each of these Indexes and on Student SES (univariate column), plus a full model containing all of them (Table 1).
Table 1: Linear regression models of Teacher Retention on each of the Indexes of neighborhood, school and Family (Univariate) and with all indexes (Full Model). Belo Horizonte public schools, Brazil, 2015, N=176
Index Null Univariate Full model
Nei gh bo rh ood Demographic 0.051** 0.129*** Economic 0.023 -0.074 Educational -0.002 0.028 Socio-environment -0.129*** -0.092*** Sc ho ol Organization -0,043** -0.063*** Student quality -0, 055*** -0.037 Teaching quality -0,001*** 0.001
Family Student SES 0.046*** 0.054***
Constant 3.29*** 0.404
Note: Higher index values represent higher vulnerability, with exception of Student SES (the higher, the better). *** 0,01 statistical significance; **0,05 statistical significance; *0,1 statistical significance. Source: 2010 Brasilian Census, 2015 Educational Census and 2015 Basic Education Evaluation System.
At the univariate model, teaching retention is associated with the Demographic Index. That means, retention seems to be higher in areas with higher population density and higher concentration of working age population (18 to 64) when compared to old age (65+). Because areas with high density could be well developed (high buildings) or under developed (favelas), this coefficient requires further exploration. Demographic index remains strong in the full model, after controlling for the other features.
As expected, socio-environmental characteristics of the neighborhood are negatively correlated with the Teacher Retention factor. This index may serve as a proxy for socially disadvantage areas, such as favelas. These poor areas have higher odds of lacking appropriate sewage services and trash collection, of having smaller dwelling where inhabitants need to share rooms and present worse levels of urban Quality of Life Index, which includes higher homicide rates, fewer recreation areas and higher risks for landslides and flooding, among others. At the full model, the coefficient remains negative and strongly significant.
Also, at the neighborhood level, the economic and educational profiles of the inhabitants who live in the school´s CWA shown no statistical significance for teacher retention.
At the school level, school poor organization (poor infra-structure and high number of students per classroom, among others) is negatively associated with retention. The more precarious is the conditions of the school, the more a teacher would want to leave. This coefficient remains significant after controlling for the other indexes.
Still at the school level, student quality index, which includes dropout rates, repetition rates and age/grade distortion rates, is negatively related with teacher retention, as expected, meaning that schools with high rates of dropout, retention and age/grade distortion are less capable of retaining their teachers for more than five years. On the other hand, the mean of school’s teacher’s features does not show any statistical significance.
Lastly, student SES, proxy for their families’ background, is positively associated with Teacher Retention. It means that Teachers tend to stay longer in schools where the student body socioeconomic conditions are good. This effect stays even after controlling for the other variables.
Figure 2 shows the results of the Global Moran I for Teacher Retention plotted against the coefficient of Teacher Retention of its neighboring areas. The test shows a positive correlation (0.19) meaning: there is some spatial dependency between the two, which can also be seen in Figure 3, that contains the Local Indicator of Spatial Association (LISA) for the CWA areas. Thereby, it is possible to identify clusters with significant local spatial autocorrelation by type of association.
Figure 2 and 3: Global Moran-I Distribution and Local Indicator of Spatial Association (LISA) for Teacher Retention Index by Belo Horizonte Census Weight Areas (CWA), 2015
In conclusion, even after controlling for school and family effects that are directly related to teachers’ permanence in school, the neighborhood conditions of the school matter for teacher retention. Furthermore, there is an important effect of students’ SES in teacher retention, indicating a strong correlation between the student family and household conditions and teacher retention. So, a teacher is influenced by the environment surrounding the school and the student and what happens behind those walls might ultimately impact decision to change workplaces.
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APPENDIX
Box 1 – Independent variables
Index Variable Database Reference
N e ighbo rhood v a ri a bl e s Economics
% of people receiving Bolsa Familia
(conditional cash transfer program) Demographic Census
Gonçalves, Menicucci, Amaral,
2017 Socio-environment % of non-white in household Demographic Census
BOYD et al. 2011
Demographic Population density Demographic Census
Demographic Young dependency ratio Demographic Census
Ipea, 2014
Demographic Old dependency ratio Demographic Census
Economic Mean per capita income Demographic Census
Socio-environment % household with inappropriate wall Demographic Census Economic Unemployment rate (10 and older) Demographic Census
Educational Illiteracy rate Demographic Census
Socio-environment Density of residents per room Demographic Census
Stoco, Almeida, 2011 Social-environment % Absent of sewage services Demographic Census
Social-environment % absent of trash collection Demographic Census Social-environment % households without bathroom Demographic Census Economic % people with informal jobs Demographic Census Educational
Age/grade distortion rate Demographic Census Rigotti, Cerqueira, 2004 Social-environment Index of urban quality of Life (IQVu) Town Hall PBH, 2014
Family variabl es
Family SES Index of Students Socioeconomic Status (parental education, occupation, income, household items, among others)
Saeb Alves, Soares, Xavier, 2014 S c hoo l V a ri a b le s
Organization Index of Infra-structure (library, kitchen,
computer lab, among others) Educational Census Alves, Xavier, 2017
Organization School system Educational Census
Boyd, et al, 2011 Teaching quality Teacher sex ratio Educational Census
Organization School level offered Educational Census
Gonçalves, Faustino, Costa, 2013 Student quality Age/grade distortion rate Educational Census
Organization Mean number of students per classroom Educational Census
Organization Index of management complexity Educational Census INEP, 2014 Organization Availability of Education for Young People
and Adults program (EJA) Educational Census
Carvalho, Santos, 2014 Student quality Índice de Desenvolvimento da Educação
Básica (IDEB)
Educational Census /
Saeb Alves, Soares, 2013 Student quality
Grade repetition rate Educational Census Ferrão, Fernandes, 2003 Student quality
Dropout rate Educational Census Ferrão, Fernandes,
2003 Teaching quality % of teachers with university degree in
their area of teaching Educational Census Marioni, 2014 Organization Performance in proficiency exams (Aneb e
