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Understanding Teacher Distribution Cross-Nationally: Recent Empirical Evidence


by Amita Chudgar & Thomas F. Luschei - November 02, 2016

Background/Context: There are several important limitations of the existing international literature on the distribution of teachers across students, schools, and regions. Most importantly, the coverage of low-income countries is somewhat uneven, particularly in Africa.

Research Questions: We examine the question of who teaches marginalized children across more than 20 countries in Asia, Latin America and the Caribbean, and sub-Saharan Africa.

Research Design: We employed descriptive analysis of four cross-national datasets to describe the distribution of teachers across students and schools. We first identified students in the top and bottom levels of key marginalization measures, including achievement scores, belongings in the home, parental education, and language. We then calculated separate means of the key teacher characteristics for students at the top and bottom levels of marginalization to identify gaps in teacher attributes across more and less advantaged students. We also examined gaps in teacher attributes across schools at the top and bottom levels of school infrastructure, average test score performance, size, climate, and location.

Findings/Results: Our results provide systematic and persuasive cross-national evidence of an inequitable distribution of teachers across schools and students.

Conclusions/Recommendations: Our analysis highlights the inequities in education systems that governments may be perpetuating either advertently or inadvertently through teacher recruitment and deployment practices. This underscores the need for governments to continue evaluating their teacher placement policies that may exacerbate such inequities. This work also highlights the importance of simple descriptive data analyses that government agencies may undertake to assess and evaluate the patterns of teacher distribution within their own systems.



INTRODUCTION


In this research note we report the results of an analysis of four large-scale cross-national datasets to explore who teaches marginalized children across more than 20 education systems in Asia, Latin America and the Caribbean, and sub-Saharan Africa. This investigation is derived from a larger UNICEF-funded investigation that aimed to understand teacher distribution patterns cross-nationally (Chudgar & Luschei, 2013). Given the centrality of teachers to ensure and improve learning (e.g., Rivkin, Hanushek, & Kain, 2005), the global shortage of teachers, and the relatively high costs of teacher salaries, understanding the profile of teachers of marginalized children is critical. Nationally, such a profile serves as a gauge of the personnel and financial resources allocated to high-need children and can support the exploration of widespread patterns of low learning levels. Cross-nationally, similarities and distinctions in teacher allocation patterns can provide insights into the policy contexts that create or exacerbate educational inequities and underperformance.


This study is situated in the backdrop of a global shortage of qualified teachers. Since the beginning of the 21st century, steady successes in enrolling millions of previously unenrolled children into formal schooling have also brought new challenges. Many lower-income countries struggle with scant resources as they attempt to staff growing numbers of classrooms and ensure that children who enroll in school also learn. But teacher salaries are a significant expense in most education budgets, and the supply of qualified youth to serve as teachers is limited in many of these countries. According to UNESCO’s 2010 EFA Global Monitoring Report entitled “Reaching the Marginalized,” global teacher shortages have led to severe negative consequences for marginalized children (UNESCO, 2010). Specifically, due to “teacher quality gaps” in many countries, marginalized children often have less access to qualified teachers than other students. While the equity or distributional dimension of teacher shortage is acknowledged, it is relatively understudied, as the brief review of the literature below indicates. This review sets the stage for our analysis, in which we seek to understand cross-nationally who teaches marginalized children across three world regions.


Before proceeding with the review, we discuss briefly the notion of marginalization that guides this work. This understanding has been informed by the 2010 EFA report we mention above, as well as by the accompanying Deprivation and Marginalization in Education (DME) Dataset (UNESCO, 2010a). Our conceptualization of marginalization encompasses at least three dimensions: social origin, economic status, and geographic location. Of course, a child may face marginalization along more than one dimension, so these dimensions are neither exclusive nor exhaustive. For the purposes of the work we describe here, we operationalize these dimensions through a child’s academic performance, home background, rural-urban location, and school conditions. As an example, a low-performing child from a family with low socioeconomic status and less educated parents who do not speak the language of the test at home would be considered marginalized. Similarly, a child attending a small rural school with fewer infrastructures than average would also be considered marginalized.


LITERATURE ON TEACHER DISTRIBUTION AND ITS LIMITATIONS


While international research on teacher distribution is limited, robust and growing literature on teacher sorting in the United States has found consistently inequitable patterns in disadvantaged children’s access to higher-quality teachers, both nationally and in several states (Bacolod, 2007; Clotfelter, Ladd, & Vigdor, 2005, 2006). Influential studies—like that of Lankford, Loeb, and Wyckoff (2002), which found that low-income, minority, and academically struggling students in New York state have disproportionately low access to well educated, skilled, and experienced teachers—have been central in defining these conversations. In an analysis of a nationally representative sample of teachers, Hill (2007) found that teachers with greater content-specific knowledge in mathematics are more likely to work with economically advantaged students. More recently, in a study of all school districts in the state of Washington, Goldhaber, Lavery, and Theobald (2015) found that across elementary, middle, and high school levels, several measures of teacher quality (experience, licensure exam scores, and value-added impact on student achievement) are distributed inequitably across most categories of student disadvantage, including eligibility for free/reduced price lunch, underrepresented minorities, and lower achievement scores.


Internationally, the evidence on teacher distribution is much more limited, particularly in developing countries. A few international studies stand out as exceptions. In an analysis of the 2003 eighth grade Trends in International Mathematics and Science Study (TIMSS) data, Akiba, LeTendre, and Scribner (2007) found large cross-national differences in access of economically disadvantaged children to qualified teachers, as measured by teachers’ education, experience, and certification. In many countries, the authors identified opportunity gaps in low-income students’ access to qualified teachers. Yet they also found a few exceptions, like Japan and South Korea, where less advantaged children have similar or even greater access to qualified teachers than others. In contrast, evidence from Latin America suggests inequitable patterns in poor and rural children’s access to more educated and experienced teachers. There is also some evidence from Latin America that the teachers of children in low-income rural areas are more likely to be male than the teachers of more advantaged or urban children (Luschei, 2012).


Although each of the above studies helps to shed light on the teacher distribution question internationally and cross-nationally, there are several important limitations of the existing international literature. The coverage of low-income countries is somewhat uneven in this work. For instance, the African continent has been conspicuously absent from cross-national analysis to date. This is an important oversight, as we know that the problems of teacher shortage are most acute in sub-Saharan Africa (Gagnon & Legault, 2015). A significant reason for the uneven coverage in the literature is the data that have been relied on for past work. Although there are several regional and cross-national databases that may be used for such investigations, the majority of the existing studies are limited to a few more widely used sources. In preparing our cross-national analysis of teacher distribution, we perceived this to be an important gap in the literature that could be addressed by bringing together a wide range of diverse databases.


DATA: IDENTIFYING AND PREPARING THE DATA


Our analysis had a few important requirements that guided our search for the ideal datasets. We wanted to ensure a fair representation of countries from Asia, Latin America and the Caribbean, and sub-Saharan Africa. In addition, it was important to have access to information on teachers and a range of teacher attributes. We needed to know where and whom these teachers were teaching, so we also required datasets with information on the students of these teachers and on schools where these teachers taught. The data also needed to link students to their teachers.


The most suitable datasets for this work are standard cross-national test performance datasets, such as the TIMSS. In addition to TIMSS, there are many cross-national datasets that can be immensely valuable for a range of analyses that may not have been the primary purpose of the data collection effort. Student performance data from these datasets generally provide information on students’ home backgrounds (allowing us to gauge their marginalized status), teacher attributes, and school backgrounds. With appropriate sample weights, such data can be used to generate profiles of teachers of a representative group of students, as we intended. Four datasets fulfilled all of these requirements and provided a fair representation of countries across the world regions. We used the 2007 TIMSS data from Hong Kong (SAR), Indonesia, Malaysia, Mongolia, Taipei, and Thailand (IEA, 2008); the 2005/2006 SERCE data for Brazil, Chile, Colombia, Cuba, Guatemala, Mexico, Peru, and Uruguay (LLECE, 2006); the SACMEQ-II (2000-2004) data from Botswana, Kenya, Malawi, Mozambique, Namibia, South Africa, and Tanzania (SACMEQ, 2004); and the PASEC data collected between 2003 and 2005 for Benin and Guinea (PASEC, 2005). While it was not possible to find data from these countries and systems gathered in the same year, we felt that capturing the timeframe between 2000-2007 was a reasonable compromise. The datasets focused on children in late primary to post-primary grades: grade 8 for TIMSS countries, grade 6 for SERCE and SACMEQ countries, and grade 5 for PASEC countries.


Within these datasets we identified a range of variables, based on the literature, that reasonably measured different attributes of teachers.1 These variables ranged from standard demographic variables, such as  teacher age, sex, and experience, to variables that indicated the extent of teacher preparation, such as education and training. The datasets offered some qualitative information about the teachers as well, such as their sense of preparedness to perform their jobs (TIMSS), their satisfaction with their current positions and desire for reassignment (SERCE), their absence in the previous month and desire to remain in the profession (PASEC), and the relative importance of their living and working situations (SACMEQ). SACMEQ also offered teacher test scores, a unique feature of these data. We similarly selected variables to identify marginalized children and varying school circumstances. We settled on some standard home background variables, including family socioeconomic status (SES), parental education level, whether the family spoke the test language at home, and if the child was high performing or not. At the school level, we used variables like school size, school infrastructure, location, and various principal reports on school environment.


Important considerations in our variable selection were relevance to the study and reasonably uniform availability across the study countries. A key limitation of this work is that several important variables were not available in the data. For instance, children with disabilities are a key marginalized population (for example, see UNESCO, 2010, p. 181), but they are not identified in any of these large data collection efforts.


METHODS: ANALYZING FOUR CROSS-NATIONAL DATABASES


Our analytical technique was informed by research on teacher sorting in the United States, in particular Lankford et al.’s (2002) study of nearly all teachers in New York State. We first identified students in the top and bottom levels of key marginalization measures, including achievement scores, belongings in the home, parental education, and language. We then calculated separate means of the key teacher characteristics for students at the top and bottom levels of marginalization to identify gaps in teacher attributes across more and less advantaged students. For example, in Botswana we found that average teacher experience for students in the top wealth decile was close to 13 years, whereas average teacher experience for students from families in the bottom wealth decile was close to 9 years. In Brazil, the averages for these two groups were 15 and 12 years respectively. In Indonesia, teacher experience for the top and bottom wealth groups was 17 and 12 years, respectively. The data reveal that in each of these countries, more experienced teachers teach children from wealthier families. Since our goal was not to compare the precise magnitude of gaps across countries, but rather the general direction of these gaps, this approach to calculating means worked even when the teacher variables were measured somewhat differently in different datasets. We also examined gaps in teacher attributes across schools at the top and bottom levels of school infrastructure, average test score performance, size, climate, and location.


We identified a gap as equity dampening if teachers of more advantaged children were more educated, better trained, or more satisfied compared to other teachers. For teacher age and experience we identified a gap as equity dampening if the teachers of more affluent children were more experienced and older. This decision was guided by the common practice of paying teachers by experience and seniority. Even if experience itself may have a nonlinear relationship with student performance, it does have a positive linear relationship with teacher salary. In other words, funds follow teachers with more experience. Our decision regarding teacher gender was more difficult. Although research on the impact of teacher gender is limited, the few studies available suggest either a positive impact of female teachers or interactions between teacher and student gender (e.g., Dee, 2007). Given this limited evidence base, we chose to identify situations where greater proportions of male teachers worked with disadvantaged children as inequitable. To correct for sampling procedures and to ensure that results were nationally representative of students in each country, we used sample weights when available.


RESULTS: PATTERNS OF TEACHER DISTRIBUTION FROM CROSS-NATIONAL DATA ANALYSIS


Tables 1-4 provide summary results from our analysis of 23 education systems (nine from sub-Saharan Africa, six from Asia, and eight from Latin America and the Caribbean) (Chudgar & Luschei, 2013). Instead of combining four datasets and three regions in one table, we present the results separately to account for nuances in our data. The columns refer to teacher attributes such as age, experience, education, and training, as well as  a host of qualitative information available on teachers in the data that we noted earlier. The rows refer to student or school attributes across which the teacher attribute gaps were calculated. In general, we compared teacher attributes of high- and low- performing students, high- and low-SES students, students with mothers who had high and low levels of education, and students who did and did not speak the test language at home. We similarly compared teacher attributes across large and small schools, urban and rural schools, schools with high and low levels of infrastructure and resources, and positive and less positive climate.

 

Table 1. Sub-Saharan Africa, SACMEQ Data: Gaps in access to teacher attributes, by school and student attributes: Total number of countries (out of 7) where the sign associated with teacher attribute is negative

Teacher attribute

Age

Experience

Male

Education

Training

Test Score

Living Situation Importance

Work Situation Importance

Resources Importance

Student attribute

 

 

 

 

 

 

 

 

 

High-Low Math Score

7

7

7

4

6

5

2

5

4

High-Low Wealth

 

5

5

6

6

5

5

3

4

6

High-Low SES

 

4

5

6

5

5

4

4

5

4

High-Low Mother's Education

5

5

6

4

5

5

4

4

6

Test Language Spoken-Not Spoken

3

3

6

5

5

5

2

5

3

School attribute

 

 

 

 

 

 

 

 

 

Large-Small school

 

4

4

5

4

6

3

2

5

4

Positive-Negative School Condition

3

4

4

4

6

5

5

5

5

High-Low School Resources

5

6

5

3

5

5

4

6

6

High-Low Teacher Performance

4

4

0

4

4

6

3

3

4

Positive-Negative Student Behavior

5

4

4

4

4

3

4

5

4

Positive-Negative Teacher Behavior

1

2

3

5

3

4

5

5

5

Notes

1. Each cell represents the number of countries where a particular teacher attribute (column title) was distributed negatively by a given student or school attribute (row title). For instance, the first cell in this table indicates that in all 7 countries in the SACMEQ data, teachers of lower performing students were younger than the teachers of higher performing students.

2. Figures that are bold and underlined indicate that the given teacher attribute was negatively distributed for a given student or school attribute in more than half the countries

 

Table 2. Sub-Saharan Africa, PASEC data: Gaps in access to teacher attributes, by school and student attributes: Total number of countries (out of 2) where the sign associated with teacher attribute is negative

Teacher attribute

Age

Experience

Male

Education

Training

Contract Teacher

Other Job

Absence

Wants to Remain in School

Wants to Remain in Profession

Student attribute

 

 

 

 

 

 

 

 

 

 

High-Low Math Score

1

1

0

1

1

0

1

2

0

1

High-Low Mother's Education

2

1

2

2

1

1

2

1

2

1

Test Language Spoken-Not Spoken

1

1

0

1

1

0

0

2

1

1

High-Low Lifestyle

 

2

1

1

1

0

1

1

2

2

0

School attribute

 

 

 

 

 

 

 

 

 

 

Large-Small School

 

2

2

1

0

0

2

0

2

1

1

High-Low School Neighborhood Infrastructure

1

1

0

0

1

0

0

1

2

0

High-Low School Supplies

0

1

1

0

1

1

2

1

0

2

High-Low Parent Involvement in School

2

2

1

0

1

2

1

2

0

2

Urban-Rural School

 

2

2

2

1

2

2

2

1

1

1

Note. Each cell represents the number of countries where a particular teacher attribute (column title) was distributed negatively by a given student or school attribute (row title). For instance, the first cell in this table indicates that in 1 of the 2 countries in the PASEC data, teachers of lower performing students were younger than the teachers of higher performing students.


Table 3. Latin America and the Caribbean, SERCE data: Gaps in access to teacher attributes, by school and student attributes: Total number of countries (out of 8) where the sign associated with teacher attribute is negative

Teacher attribute

Age

Experience

Male

Education

Training

Permanent

Other Job

Satisfaction

Wants Reassignment

Student attribute

 

 

 

 

 

 

 

 

 

High-Low Math Score

3

5

8

6

7

1

2

8

7

High-Low SES

 

4

6

6

6

4

5

2

8

8

High-Low Mother's Education

3

5

8

6

7

1

2

8

7

Test Language Spoken-Not Spoken

4

6

6

6

4

5

2

8

8

School attribute

 

 

 

 

 

 

 

 

 

Large-Small School

 

4

5

7

7

3

3

3

7

6

High-Low School Resources

5

6

7

6

3

4

1

5

8

Positive-Negative School Climate

2

4

5

5

2

2

2

8

6

Positive-Negative Principal Perception

6

6

3

4

5

5

2

8

7

Urban-Rural School

 

5

5

7

6

4

4

0

3

7

Notes

1. Each cell represents the number of countries where a particular teacher attribute (column title) was distributed negatively by a given student or school attribute (row title). For instance, the first cell in this table indicates that in 3 out of 8 countries in the SERCE data, teachers of lower performing students were younger than the teachers of higher performing students.

2. Figures that are bold and underlined indicate that the given teacher attribute was negatively distributed for a given student or school attribute in more than half the countries

 

Table 4. Asia, TIMSS data: Gaps in access to teacher attributes, by school and student attributes: Total number of countries (out of 6) where the sign associated with teacher attribute is negative

Teacher attribute

 

Age

Experience

Male

Education

Training

Preparedness

Safety

School Climate

Work Condition

Student attribute

 

 

 

 

 

 

 

 

 

High-Low Math Score

4

5

5

4

3

6

5

6

2

High-Low SES

 

5

5

4

2

3

6

5

6

2

High-Low Mother's Education

5

5

6

6

3

6

5

6

3

Test Language Spoken-Not Spoken

2

3

4

5

2

4

3

4

2

School attribute

 

 

 

 

 

 

 

 

 

Large-Small School

 

6

5

4

2

3

5

3

5

2

Low-High Student Absences

3

4

3

3

4

5

0

1

2

Positive-Negative Climate

3

4

5

3

3

5

4

6

5

High-Low Teacher Success

4

4

4

3

4

5

4

6

5

Notes

1. Each cell represents the number of countries where a particular teacher attribute (column title) was distributed negatively by a given student or school attribute (row title). For instance, the first cell in this table indicates that in 4 out of 6 countries in the TIMSS data, teachers of lower performing students were younger than the teachers of higher performing students.

2. Figures that are bold and underlined indicate that the given teacher attribute was negatively distributed for a given student or school attribute in more than half the countries.


The tables reveal compelling evidence that in the majority (50 to 70%) of the countries we studied, teacher attributes are distributed unevenly. The data reveal a systematic pattern of teacher distribution in terms of teacher demographics, teacher qualifications, and teacher satisfaction with their work environments. However, there are some important distinctions in the findings depending on which category of marginalization we consider. For instance, comparing teacher attributes across students is generally more informative than comparisons across schools.


In 50 to 75% of the countries we studied, teachers who teach low-SES and low-performing children, children of less educated parents, and children who speak languages other than the test language at home tend to be disproportionately male, younger, and less experienced compared to other teachers. This demographic inequality is strikingly consistent across a range of definitions of marginalization and a range of diverse education systems across the globe. It is worth noting that this pattern of teacher demographics is also similar to the attributes of contract teachers in developing countries identified elsewhere (e.g., Chudgar, 2015). In most education systems, teacher salaries are tied with teacher experience and seniority (OECD, 2005). The unequal distribution of experienced teachers means not just that the most marginalized have access to the newest and least seasoned teachers, it also implies that financial resources are being directed in favor of more affluent children and schools across the three regions we studied. The pattern of teacher gender is equally striking, and unexpectedly consistent. Luschei (2012) noted similar patterns in Mexico, but we are not aware of any large-scale cross-national systematic data that shed light on this phenomenon. Teachers serve as role models for their students, especially in remote locations where children might be exposed to few educated, professional adults. An uneven exposure to female teachers is likely to have implications for both male and female children, as it shapes their perspectives on women’s role in society and the labor market. Importantly, the presence of female teachers may encourage parents in many countries to continue to send their female children to school beyond a certain age by allaying their fears of gender-based violence (UNESCO, 2015).


In terms of the distribution of teacher education and training, Asian countries exhibit more equitable patterns, owing perhaps to generally higher levels of education and training in these countries (Kang & Hong, 2008). But here once again the uneven preparation of teachers of marginalized children is evident. Interestingly, in the African data, where we have information on teachers’ test scores, and in the Asian data, where we know  teachers’ self-reported preparedness, this unevenness is most consistent and pronounced. This indicates a true chasm in teacher skills along a range of attributes when comparing teachers of children who are marginalized to those who are more advantaged. Once again, to the extent that teacher salary policies compensate teacher education and training, these data also indicate an uneven and inequitable distribution of resources.


Teachers’ qualitative responses to their work environments are somewhat more varied across countries, but even for these variables teachers of marginalized children more consistently express dissatisfaction with their working conditions and a desire for reassignment. These patterns are especially consistent in Latin American and Caribbean countries but are also present in the Asian countries where teachers of marginalized children report unsafe school environments and negative school climates.


IMPLICATIONS FOR RESEARCH AND POLICY


Our cross-country analysis of teacher distribution, which used four large-scale secondary datasets and analyzed over 20 education systems across three world regions, identified two types of differences in teachers across less and more advantaged children: gaps in teacher qualifications and gaps in teacher demographics. While gaps in teacher training, education, and qualifications may be unsurprising, this study highlights a noteworthy cross-national consistency in these patterns. Another important contribution of this descriptive work is the identification of persistent demographic gaps across several different categories of marginalization. The disproportionate allocation of younger, less-experienced, male teachers in marginalized situations appears to be a fairly common and concerning occurrence in many education systems. These findings give rise to a large empirical research agenda, including an effort to understand why these patterns arise so consistently across so many diverse education systems. From a policy perspective, our analysis highlights the advertent or inadvertent inequities in education systems that governments may be perpetuating through teacher recruitment and deployment practices. This underscores the need for governments to continue evaluating their teacher placement policies that may exacerbate such inequities. This work also highlights the importance of simple descriptive data analyses that government agencies may undertake to assess and evaluate the patterns of teacher distribution within their systems. Finally, our findings regarding the dissatisfaction of teachers in marginalized environments underscore the importance of recognizing the preferences of teachers to live and work in safe locations with daily amenities that teachers in the developed world often take for granted.


Acknowledgement


The authors would like to acknowledge UNICEF, New York for financial support for the investigation from which this study is derived. We also thank our current and former graduate students for their excellent research assistance: Rebecca Devereaux, Loris Fagioli, and Giselle Navarro at Claremont Graduate University, and Madhur Chandra, Ben Creed, James Pippin, and Jutaro Sakamoto at Michigan State University.


Note


1. It is important to note that we refrain from using the phrase “teacher quality” in this manuscript to describe the focus of our work. We are aware that defining teacher quality is a complex task and measuring it even more so. We limit our attention here to standard teacher attributes that are commonly used in placement, promotion, and salary decisions.


References


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Cite This Article as: Teachers College Record, Date Published: November 02, 2016
https://www.tcrecord.org ID Number: 21712, Date Accessed: 10/20/2021 10:20:43 PM

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About the Author
  • Amita Chudgar
    Michigan State University
    E-mail Author
    AMITA CHUDGAR is an associate professor at Michigan State University’s College of Education. Her long-term interests as a scholar focus on ensuring that children and adults in resource-constrained environments have equal access to high-quality learning opportunities irrespective of their backgrounds. Her recent publications include Teacher Distribution in Developing Countries: Teachers of Marginalized Students in India, Mexico, and Tanzania, published by Palgrave Macmillan (2016, with Thomas F. Luschei) and “How Are Private School Enrolment Patterns Changing Across Indian Districts with a Growth in Private School Availability?” in the Oxford Review of Education (2016, with Benjamin Creed).
  • Thomas Luschei
    Claremont Graduate University
    E-mail Author
    THOMAS F. LUSCHEI is an associate professor in the School of Educational Studies at Claremont Graduate University. His research uses an international and comparative perspective to study the impact and availability of educational resources—particularly high-quality teachers—among economically disadvantaged children. His recent publications include Teacher Distribution in Developing Countries: Teachers of Marginalized Students in India, Mexico, and Tanzania, published by Palgrave Macmillan (2016, with Amita Chudgar) and “A Vanishing Rural School Advantage? Changing Urban/Rural Student Achievement Differences in Latin America and the Caribbean,” in the Comparative Education Review (2016, with Loris P. Fagioli).
 
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