THREE ESSAYS ON TEACHER LABOR MARKETS IN THAILANDyw658sz0538/Pumsaran... · 07/04/2009 · teacher colleges. Using data from Thailand Labor Force Survey from 1985 to 2005, I find

THREE ESSAYS ON TEACHER LABOR MARKETS

IN THAILAND

A DISSERTATION

SUBMITTED TO THE SCHOOL OF EDUCATION

AND THE COMMITTEE ON GRADUATE STUDIES

OF STANFORD UNIVERSITY

IN PARTIAL FULFILLMENT OF THE REQUIREMENTS

FOR THE DEGREE OF

DOCTOR OF PHILOSOPHY

Pumsaran Tongliemnak

August 2010

http://creativecommons.org/licenses/by/3.0/us/

This dissertation is online at: http://purl.stanford.edu/yw658sz0538

© 2010 by Pumsaran Tongliemnak. All Rights Reserved.

Re-distributed by Stanford University under license with the author.

This work is licensed under a Creative Commons Attribution-3.0 United States License.

ii



http://purl.stanford.edu/yw658sz0538

I certify that I have read this dissertation and that, in my opinion, it is fully adequatein scope and quality as a dissertation for the degree of Doctor of Philosophy.

Martin Carnoy, Primary Adviser


Eric Bettinger


Linda Darling-Hammond


Francisco Ramirez

Approved for the Stanford University Committee on Graduate Studies.

Patricia J. Gumport, Vice Provost Graduate Education

This signature page was generated electronically upon submission of this dissertation in electronic format. An original signed hard copy of the signature page is on file inUniversity Archives.

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ABSTRACT

The first essay of this dissertation examines the role of teacher characteristics

in schools on student outcomes using datasets from TIMSS 1999 and TIMSS 2007

international tests. Taking an advantage that students have to take both mathematics

and science subjects from different teachers, I use the method of First Difference (FD)

analysis in order to remove the potential biases between teacher attributes and

unobserved student characteristics. The findings show some contradictory outcomes

between the FD analysis and ordinary least squares (OLS) analysis. From the FD

analysis, I find that students who study with female teachers and teachers trained in

faculties of education in relevant fields such as mathematics education and science

education have higher test scores than those studying with male teachers and teachers

who were trained in faculties of sciences. This differs with the findings from the OLS

showing that in TIMSS 1999, students who have male teachers score higher. Teacher

experience is also positively correlated with student achievement but is only

significant in the FD analysis. Teaching certificates do not appear to be positively

related to student achievement. Overall, the FD outcomes seem to be more consistent

across the years. Nevertheless, the differences between the FD and OLS results may

stem from the small sample size of teachers in the TIMSS.

The second essay looks into the problem of recruitment of well-qualified high

school and college graduates to work as primary and secondary school teachers. In

analyzing the problems of recruitment and retention of teachers, I compare teacher

salaries and benefits vis-à-vis other mathematics and science-oriented professions

v

namely medical professions, engineers, accountants, scientists and nurses. In addition,

I compare incomes between people who graduate from teacher colleges and non-

teacher colleges. Using data from Thailand Labor Force Survey from 1985 to 2005, I

find that after controlling for relevant characteristics, teachers are the most poorly paid

of all professions, including nurses. They also have the lowest pay among public

sector employees. The difference in terms of an opportunity cost between male and

female teachers is also striking. In general, male teachers forgo greater potential

earnings than female teachers because they have more career options with better pay.

By comparing only among the graduates from teacher colleges, I find that male

graduates earn more than their peers if they chose other occupations whereas female

graduates earn less if they make other choices. Results from the wage decomposition

analysis also support the hypothesis that teachers are systematically under-

compensated compared to those in other mathematics and science oriented

occupations.

The third essay looks at the reasons teachers choose part-time jobs, the type of

jobs they choose, and the amount of income they receive from these jobs, as well as

factors influencing these decisions. The dataset used in this essay was collected by the

Faculty of Economics, Chulalongkorn University in 2006, and consists of 573 teachers

from 260 schools in 8 provinces. I find that approximately 20-25% of Thai teachers

participated in moonlighting activities. The majority of them have part-time jobs

including tutoring, selling food and other products, and farming. Low salaries and high

level of indebtedness are the most important factors associated with the increased

likelihood of having a part-time job. Overall, teachers who have part-time jobs receive

vi

lower official salaries but end up having higher incomes than those who do not

moonlight. Young teachers and teachers who teach at the preprimary levels are more

likely to moonlight than others. The economic status of teachers, as measured by

salary or their level of indebtedness, influences their decision to moonlight in selling

and farming. However, economic status does not correlate significantly with their

decision to tutor as their part-time job. This suggests that increasing teacher salary

alone would not reduce the problem of teachers engaging in tutoring to supplement

their income.

vii

ACKNOWLEDGEMENTS

This dissertation could not have been completed without the help of many individuals.

First and foremost, I would like to express heartfelt gratitude to my primary advisor

Martin Carnoy for being the main source of consistent support in my pursuit of

doctoral education. I still remember vividly the incredible excitement I felt when he

called to inform me that I was admitted to the Economics of Education PhD program.

Martin has been the driving force behind my studies, and has always been there for me

during important academic and non-academic events in my life. Without his constant

motivation, it undoubtedly would have taken me longer to finish this thesis. His

support has been invaluable, especially during the last month of painstakingly revising

a thesis while suffering from what I like to think of as “post-defense syndrome,” when

putting pen to paper proved to be particularly challenging. My life would undoubtedly

have taken a different turn had I not met Martin. As his student, I will always look him

up and hope to emulate his scholastic accomplishment.

I also would like to thank my dissertation reading committee: Eric Bettinger,

Linda Darling Hammond and Francisco Ramirez. I consider myself very fortunate to

have had a committee consisting of top scholars from such diverse specialties. Despite

their busy schedules, my committee always made time to read my thesis drafts. Eric

gave several suggestions necessary to advance one of the chapters into appropriate

academic journal form. Linda pushed me to think more deeply about the policy

viii

implications of my work. Finally, Chiqui also made a number of useful suggestions

from the theoretical perspective of the sociology of education.

For the dataset used in my thesis I would like to thank several individuals and

institutions. The National Center for Education Statistics (NCES), Institute of

Education Sciences and US Department of Education sponsored my training on the

use of PISA, TIMSS, PIRLS datasets.

Thailand‟s National Statistics Office provided the Labor Force Survey.

Voraprapa Nakavachara shared her useful codebooks and do-files with me, making

the Labor Force Survey data considerably easier to navigate. Further, the UC-UTCC

Research Center at the University of the Thai Chamber of Commerce provided an

online tool to facilitate the analysis of Thailand‟s Labor Force Survey.

Professor Kitti Limskul and the Faculty of Economics at Chulalongkorn

University allowed me to use their Thailand Teacher Survey dataset . It is one of the

best designed surveys of Thai teachers I have ever seen. My thanks also goes to Saksit

Thananittayaudom in the Faculty of Economics at Chulalongkorn who facilitated my

contact with the staff at this institution.

I would also like to thank Margaret Irving for her patience and generosity in

reading and editing several of my drafts and always providing valuable comments. She

is one of the people who worked hard to improve my thesis. Kathy Vesom also has

been there for advice and support and helped with editing from the early stages right

through the latest version. I have always been able to rely on her for prompt and

helpful feedback.

ix

In addition to my committee members who were directly involved with my

dissertation, I received considerable help from many individuals in the Economics

Department where I did most of my coursework at Stanford. I thank Avner Greif,

Gavin Wright, Luigi Pistaferri, Giacomo De Giorgi, Ran Abramitzky and Petra Moser

for their guidance and inspiration during my coursework years. I also benefited from

comments by participants at various seminars where I presented parts of this

dissertation. I thank Richard Murnane, Gerald Fry, Christopher Wheeler and Hans

Wagemaker for providing helpful comments to help improve my work.

I owe a great intellectual debt to the late Professor Somboon Siriprachai of the

Faculty of Economics, Thammasat University who passed away almost two years ago.

He was my greatest mentor, who introduced me to the socio-political aspects of

economics, was always a source of encouragement, and recognized the potential in me

from early on.

Several organizations and individuals provided financial assistance while I

pursued my graduate education in the USA. I am grateful for financial support from

the Royal Thai Government. I also thank the staff at the Office of Educational Affairs,

Royal Thai Embassy in Washington D.C (OEADC) for helping me to deal with a

plethora of paperwork.

At Stanford, I appreciate the generous financial support I received from the

Stanford Graduate Fellowship as a „Burt and Deedee McMurtry Fellow‟. The Bechtel

International Center also provided free housing for me and my wife at the best

location on the Stanford campus for two years in exchange for working there as

x

resident host, a position which gave me great opportunities to get involved in various

campus activities that I would not have experienced had I not worked there. The staff

at the I-Center were unfailingly friendly and supportive throughout our stay.

In the Bay Area, I am fortunate to have lived close to many relatives from

Thailand. I thank Jua Rattanapan and his wife Taneerat for their kindness and support.

My special thanks to Jurasri Katzka for her generosity in letting me stay at her house

in Sunnyvale during my first and last year at Stanford. Her benevolence considerably

eased my financial burden as a graduate student.

There are many professors and fellow students at SUSE who have helped me

during my coursework years or at research seminars. In particular, I benefited from the

teaching and useful comments on my work by Susanna Loeb, Sean Reardon and

Edward Hartel. I am also grateful to have been surrounded by talented and caring

fellow students. Allison Atteberry, Frank Adamson, Rekha Balu, Nii Addy, Daphna

Bassok and Joe Robinson provided excellent advice at different stages of my thesis.

Tara Beteille and Rie Kijima deserved special thanks for helping prepare me for the

oral defense.

I would like to thank friends from the University of Chicago, Krislert

Samphantharak, Weerachart Kilenthong and Anan Pawasutipaisit, who were always a

source of lively conversation and willingly offered help whenever I had questions. The

community of Thai students at Stanford also made my time here particularly

memorable. I enjoyed socializing with Thai students especially during the years when

xi

I was living on campus. Several people in the Thai community at Stanford I now count

among my lifelong friends.

Sermsak Lolak and his family‟s presence at Stanford reminded me of my days

at a medical school in Thailand. I enjoyed the company of several doctor friends who

live in the Bay area. Sirirat Ularntinon and Pichai Ittasakul always cheered me up,

especially over the last month of revision when I was particularly stressed out. I was

remarkably lucky to have the support of three friends who happened to be practicing

psychiatrists! Keiko Funahashi also provided friendship and moral support. Napat

Boonsaeng is like my good dhamma brother who never once hesitated to agree to

drive for three hours for an excursion to faraway monasteries. I enjoyed his company

in many worldly and spiritual activities.

On the spiritual side, I know that I was only able to cope with the burden and

stress of writing a thesis due to the guidance provided by several of my Buddhist

teachers. I was fortunate that the Abhayagiri Buddhist Monastery is only a few hours

away from the Stanford campus. I enjoyed the dhamma teaching by the abbots, Ajahn

Pasanno and Ajahn Amaro. Buddhanusorn Temple in nearby Fremont also provides a

venue for traditional Buddhist activities. Moreover, the sermons by the venerable

Ajahn Chah, Ajahn Dtun, Ajahn Jayasaro,Ajahn Pramote Pamojjo and other monks

always provided a source of hope and consolation. Knowing that when I return to

Thailand I will be in close proximity to many great Ajahns has helped me to cope with

the anxiety of going back to a country in the throes of ongoing turmoil and political

crisis.

xii

Finally, I would like to thank my family. My parents, Surapong and Suwanee

Thongliamnak have always given me their complete and unconditioned love and

moral support during my many years away from them. Although they are all happy

that I had the chance to come to the US, I missed many opportunities to share fully

with them the sorrows and joys they experienced at home. I promise to make it up to

them when I go back.

Lastly, and most importantly, I am deeply grateful to my wife, Prowpannarai

Mallikamarl Tongliemnak, for always being there for me during my long years in the

US. She would undoubtedly have been better off living in Thailand but she

courageously decided to forgo her life there to stay with me here. Life as the spouse of

graduate student in foreign country is tough emotionally, financially and in many other

ways. I am sure she would have been a much better PhD student than I am, given her

remarkably successful year as a Stanford Master‟s student, but I wonder if I could

match her excellence as a spouse with all of the unrelenting love, patience and

encouragement she has given so selflessly over the years. Because of this, I dedicate

this thesis to her.

xiii

TABLE OF CONTENTS

CHAPTER 1:THE PREMISE AND BACKGROUND OF THE STUDY ............................... 1

1.1 PROBLEM STATEMENT ............................................................................................................ 2

1.2 LITERATURE REVIEW ............................................................................................................. 6

1.2.1 On the Correlation of Teachers’ Characteristics with Student Achievement ................. 6

1.2.2 On the Distribution of Teachers ...................................................................................... 9

1.2.3 On Teacher’s Income and Other Economic Factors ..................................................... 11

1.2.4 On Teacher Moonlighting ............................................................................................. 13

1.2.5 On Thailand’s Teacher Characteristics and Educational Outcomes............................ 15

1.2.6 Summary of the Literature and the Contribution of the Dissertation ........................... 16

1.3 RESEARCH QUESTIONS.......................................................................................................... 17

1.3.1 Main Hypothesis of the Study ........................................................................................ 17

1.3.2 Specific Research Questions ......................................................................................... 18

CHAPTER 2: TEACHER’S EDUCATIONAL BACKGROUND AND STUDENT

ACHIEVEMENT IN THAILAND ............................................................................................ 20

2.1 INTRODUCTION ..................................................................................................................... 20

2.2 BACKGROUND AND DATA .................................................................................................... 22

2.2.1 The Making of a Thai Teacher ...................................................................................... 22

2.2.2 Current Teachers Issues in Thailand ............................................................................ 24

2.2.3 Data ............................................................................................................................... 26

2.2.4 Overview of Thailand’s Participation in TIMSS1995,1999,2007 ................................. 28

2.2.5 Evidence of School Quality Clustering and Teacher Sorting ........................................ 30

2.3 METHODOLOGY .................................................................................................................... 32

2.3.1 Standard OLS Model ..................................................................................................... 33

2.3.2 First Difference ............................................................................................................. 35

2.3.3 Possible Difference Between First Difference and OLS ............................................... 39

2.4 RESULTS ............................................................................................................................... 40

2.4.1 Descriptive Statistics ..................................................................................................... 40

2.4.2 OLS Results ................................................................................................................... 43

2.4.2.1 Outcomes of TIMSS 1999 .................................................................................... 43


2.4.2.3 Comparison of the OLS Results .......................................................................... 45

2.4.3 First Difference Results ................................................................................................. 46



2.4.3.3 Comparison of the First Difference Results ........................................................ 49

2.4.4 Comparison between OLS and First Difference Outcomes .......................................... 50

APPENDIX A: QUANTILE REGRESSION ........................................................................................ 71

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CHAPTER 3: PAY DIFFERENCES BETWEEN MATHEMATICS-SCIENCE

TEACHERS AND OTHER MATHEMATICS-SCIENCE ORIENTED OCCUPATIONS

3.1 INTRODUCTION ..................................................................................................................... 75

3.2 RELATED LITERATURE .......................................................................................................... 78

3.3 BACKGROUND AND DATA ..................................................................................................... 79

3.3.1.Background of Thai Teachers

3.3.1.1 Socioeconomic Status of Thai Teachers: A Historical Perspective .................. 79

3.3.1.2 Salary Structure of Teachers in Thailand ......................................................... 83

3.3.1.3 Feminization of the Teaching Profession in Thailand ...................................... 84

3.3.1.4 Role of Thai Government in Recruiting Math-Science Teachers ...................... 85

3.3.2.Data

3.3.3.Descriptive Statistics………………………………………………………………………….87

3.3.3.1 Comparison Between Teachers and Other Occupations .................................. 87

3.3.3.2 Age-Earning Profiles of Teachers and Other Occupations .............................. 94

3.4 METHODOLOGY .................................................................................................................... 94

3.4.1 Mincer Earnings Function ......................................................................................... 95

3.4.2 Oaxaca-Binder Wage Decomposition ........................................................................ 97

3.5 RESULTS ............................................................................................................................... 99

3.5.1 Mincer Earnings Results

3.5.1.1 Comparison among Academic-track graduates .............................................. 101

3.5.1.2 Comparison among Teacher’s College graduates .......................................... 103

3.5.1.3 Determinants of Earnings of Teacher and Non-Teachers ............................... 104

3.5.2 Oaxaca-Binder Results ............................................................................................. 106

3.6 DISCUSSIONS ...................................................................................................................... 108

CHAPTER 4: TEACHER EARNINGS AND TEACHER MOONLIGHTING .................. 130

4.1 INTRODUCTION ................................................................................................................... 130

4.2 BACKGROUND AND DATA ................................................................................................... 131

4.2.1.Background of Teacher Moonlighting in Thailand…………………………………..…131

4.2.2.Data…………………………………………………………………………………………...132

4.2.3.Descriptive Statistics

4.2.3.1 Thai Teacher’s Background from the Survey .................................................. 133

4.2.3.2 The Distribution of Thai Teachers Across Schools ......................................... 136

4.2.3.3 Overview of Teacher Moonlighting ................................................................. 137

4.3 METHODOLOGY.................................................................................................................. 138

4.3.1 Probit Model on Teacher Participation in Part-Time Job ………..………………..…138

4.3.2 Wage Earnings Regressions……...……………………………………….……………….139

4.4 RESULTS ..............................................................................................................................141

4.4.1 Probit Estimation Results …..………………………………………………………………141

4.4.2.Earning Functions Results………………………………………………….……..……..…142

4.5 DISCUSSIONS ...................................................................................................................... 145

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CHAPTER 5: CONCLUSIONS AND POLICY IMPLICATIONS ...................................... 162

5.1 TEACHER’S EDUCATIONAL BACKGROUND AND STUDENT ACHIEVEMENT ......................... 164

5.2 PAY DIFFERENCES BETWEEN MATH-SCIENCE TEACHERS AND OTHER MATH-SCIENCE

ORIENTED OCCUPATIONS ......................................................................................................... 166

5.3 TEACHER EARNINGS AND TEACHER MOONLIGHTING ......................................................... 170

BIBLIOGRAPHY ..................................................................................................................... 173

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LIST OF TABLES

TABLE 2-1: THAILAND’S EDUCATIONAL STATISTICS FOR TIMSS 1995,1999,2007..................... 59

TABLE 2-2: DISTRIBUTION OF STUDENT AND SCHOOL AT DIFFERENT LEVEL OF MATH SCORES 60

TABLE 2-3: NUMBER OF THAI TEACHERS BY EDUCATIONAL LEVEL ......................................... 61

TABLE 2-4: NUMBER OF STUDENT AND TEACHER BY AVERAGE SCHOOL SES AND TEACHER’S

EDUCATIONAL BACKGROUND, TIMSS 1999 ........................................................... 61

TABLE 2-5: NUMBER OF STUDENT AND TEACHER BY AVERAGE SCHOOL SES AND TEACHER’S

EDUCATIONAL BACKGROUND ,TIMSS 2007 .......................................................... 61

TABLE 2-6: DESCRIPTIVE STATISTICS OF TEACHERS’ CHARACTERISTICS BY STUDENT’S SES . 61

TABLE 2-7: OUTCOMES OF OLS REGRESSION FROM TIMSS 1999 ............................................... 63

TABLE 2-8: OLS REGRESSION OF TEACHER CHARACTERISTICS BY STUDENT SES, 1999 ........... 64

TABLE 2-9: OUTCOMES OF OLS REGRESSION FROM TIMSS 2007 ............................................... 65

TABLE 2-10: OLS REGRESSION OF TEACHER CHARACTERISTICS BY STUDENT SES, 2007 ........... 66

TABLE 2-11: DESCRIPTIVE STATISTICS OF FIRST DIFFERENCE ANALYSIS , TIMSS 2007 .............. 67

TABLE 2-12: DESCRIPTIVE STATISTICS OF FIRSTIFFERENCE ANALYSIS , TIMSS 1999 ................. 67

TABLE 2-13: OUTCOMES OF OLS REGRESSION FOR SCIENCE TEACHERS ..................................... 68

TABLE 2-14: FIRST DIFFERENCE (FD) OUTCOMES BY SES OF STUDENTS , TIMSS 1999 ............... 69

TABLE 2-15: FIRST DIFFERENCE (FD) OUTCOMES BY SES OF STUDENTS , TIMSS 2007 ............... 70

TABLE 2-16: QUANTILE REGRESSION OUTCOMES , TIMSS 1999.................................................. 73

TABLE 2-17: QUANTILE REGRESSION OUTCOMES , TIMSS 2007.................................................. 74

TABLE 3-1: TEACHER LABOR FORCE BY URBANICITY IN 2005 ................................................ 114

TABLE 3-2: LABOR FORCE CHARACTERISTICS BY OCCUPATION 2005 .................................... 115

TABLE 3-3: DESCRIPTIVE STATISTICS OF THAI LABOR FORCE IN 2005.................................... 116

TABLE 3-4: OLS OUTPUTS OF THAI LABOR FORCE .................................................................. 117

TABLE 3-5: REGRESSIONS BY SUB-SAMPLE OF OCCUPATIONS 1985 ....................................... 119





TABLE 3-10: REGRESSION OUTPUTS OF GRADUATES OF ACADEMIC TRACK: 1985-2005 ......... 124

TABLE 3-11: REGRESSION OUTPUTS OF GRADUATES OF ALL-TRACKS: 1985-2005 ................. 126

TABLE 3-12: REGRESSION OUTPUTS OF GRADUATES OF TEACHER’S COLLEGES: 1985-2005 ... 128

xvii

TABLE 3-13: THE SUMMARY OF OAXACA-BINDER WAGE DECOMPOSITION ANALYSIS............. 129

TABLE 4-1: DESCRIPTIVE STATISTICS : THAILAND TEACHER SURVEY ................................... 148

TABLE 4-2: DESCRIPTIVE STATISTICS: TEACHER AND PART-TIME JOB ................................... 149

TABLE 4-3: DISTRIBUTION OF THAI TEACHERS ...................................................................... 149

TABLE 4-4: PERCENTAGE OF TEACHERS BY EDUCATIONAL BACKGROUND ........................... 149

TABLE 4-5: EDUCATIONAL BACKGROUND OF MATHEMATICS TEACHERS BY MAJOR ............ 150

TABLE 4-6: EDUCATIONAL BACKGROUND OF SCIENCE TEACHERS BY MAJOR ....................... 151

TABLE 4-7: CHARACTERISTICS OF MOONLIGHTING AND NON-MOONLIGHTING TEACHERS ... 152

TABLE 4-8: PERCENTAGE OF MOONLIGHTING TEACHERS BY TEACHER’S BACKGROUND ...... 152

TABLE 4-9: TEACHER INCOME BY SUBJECT TAUGHT ............................................................. 153

TABLE 4-10: TEACHER HAVING PART-TIME JOB AND HOURS WORKED BY SUBJECT ............... 155

TABLE 4-11: REGRESSION OUTPUTS BY TEACHERS AND THEIR ECONOMIC ASPECTS ............. 155

TABLE 4-12: OLS REGRESSION OF TEACHER CHARACTERISTICS ON TEACHER’S EARNING ..... 156

TABLE 4-13: OLS REGRESSION ON TEACHER’S EARNING BY SUB-GROUP OF TEACHERS ......... 158

TABLE 4-14: PROBIT REGRESSION OUTCOMES OF PROBABILITY TO MOONLIGHT .................... 160

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LIST OF FIGURES

FIGURE 2-1: DISTRIBUTION OF TIMSS MATH SCORES, 1995-2007 .............................................. 53

FIGURE 2-2: DISTRIBUTION OF TIMSS SCIENCEFIGURE 2-3 SCORES, 1995-2007 ........................ 53

FIGURE 2-3: DISTRIBUTION OF TIMSS MATH SCORE BY LEVEL OF TEACHER EDUCATION,2007..54

FIGURE 2-4: DISTRIBUTION OF TIMSS MATH SCORES BY TEACHER'S EDUCATIONAL

BACKGROUND,1999 ......................................................................................... …. 54

FIGURE 2-5: DISTRIBUTION OF TIMSS MATH SCORES BY TEACHER'S EDUCATIONAL

BACKGROUND,2007 ............................................................................................. 55

FIGURE 2-6: DISTRIBUTION OF TIMSS MATH SCORES BY TEACHING CERTIFICATE,1999 ........... 55

FIGURE 2-7: PERCENTAGE OF TEACHER WITH M.A. AND STUDENT TEST SCORES,2007 ............. 56

FIGURE 2-8: PERCENTAGE OF TEACHER WITHOUT B.A. AND STUDENT TEST SCORES,2007 ....... 56

FIGURE 2-9: DISTRIBUTION OF STUDENT OF IN-FIELD TEACHERS AND TEST SCORE;1999,2007 57

FIGURE 2-10:DISTRIBUTION OF STUDENT BY PARENT’S EDUCATION AND TEST SCORES;

1999,2007 ............................................................................................................. 57

FIGURE 2-11: TEACHERS’ EDUCATIONAL BACKGROUND BY SES OF STUDENT, TIMSS 1999. ...... 58

FIGURE 2-12: TEACHERS’ EDUCATIONAL BACKGROUND BY SES OF STUDENT, TIMSS 2007..…. 58

FIGURE 2-13:QUANTILE REGRESSION OF STUDENT MATH SCORES, TIMSS 1999.. ...................…72

FIGURE 2-14:QUANTILE REGRESSION OF STUDENT MATH SCORES, TIMSS 2007……….....……72

FIGURE 3-1: EARNINGS OF MATH-SCIENCE OCCUPATIONS FROM 1985-2005 .......................... 110

FIGURE 3-2: EARNINGS OF MATH-SCIENCE OCCUPATIONS FROM 1985-2005: MALE ............... 111

FIGURE 3-3: EARNINGS OF MATH-SCIENCE OCCUPATIONS FROM 1985-2005: FEMALE….... 111

FIGURE 3-4: MONTHLY EARNINGS BY PUBLIC-PRIVATE CATEGORY, 2005 .............................. 112

FIGURE 3-5: EARNINGS DIFFERENCE BETWEEN TEACHER AND OTHER OCCUPATIONS,

1985-2005 ........................................................................................................... 112

FIGURE 3-6: AGE-EARNING PROFILE, 2005: MALE ................................................................... 113

FIGURE 3-7: AGE-EARNING PROFILE ,2005: FEMALE ............................................................... 113

FIGURE 4-1: DISTRIBUTION OF TEACHER’S INCOME BY MOONLIGHTING STATUS ................... 147

FIGURE 4-2: DISTRIBUTION OF TEACHER’S INCOME BY MOONLIGHTING JOBS ........................ 147

1

CHAPTER 1

THE PREMISE AND BACKGROUND OF THE

STUDY

Secondary education in Thailand has been plagued both by problems of

inequality of student outcomes and students‘ declining academic competency as

measured by international tests. It is believed that the main factors responsible for

these problems are declining teacher quality and the unequal distribution of qualified

teachers among students with different social backgrounds. The quality of new

entrants into teacher training institutions may have decreased over the years as a result

of the changing economic structure of the country. More able students appear to be

increasingly choosing to enroll in other careers. Lower economic returns to teaching

could also affect the distribution of able teachers into hard-to-staff schools in rural or

low socioeconomic urban areas. Relatively low teacher incomes also contribute to

teachers taking part-time jobs—a practice that could eventually create adverse effects

on the learning of students.

In this dissertation, I will analyze several important aspects of teacher labor

markets in Thailand. This dissertation aims to be a collection of empirical essays that

focus on the value of teacher resources for learning and the factors that may influence

the quality of those resources and their distribution across different groups of students.

2

Specifically, the three essays will analyze the relation of teacher quality to student

achievement, teachers‘ salaries and their part-time work, and the distribution of

teachers across various teaching conditions.

The first essay will be an analysis of an education production function relating

student achievement to various teacher attributes, with special attention to the relation

of student achievement to teacher educational backgrounds and its distribution across

student socioeconomic status and students‘ ability distribution. The second essay

focuses on the economic incentives that may influence the decision of high school and

college graduates to become teachers. These incentives include salaries and other

benefits for teachers compared to those in alternative occupations. The third essay

focuses on the issue of teacher moonlighting and its prevalence among teachers in

Thailand

1.1 PROBLEM STATEMENT

Education is one of the most important components of national policy in

Thailand. As a developing country that has moved from an agriculture-based economy

to a predominantly industrial and service economy, Thailand has focused on

increasing the quality of education in order to prepare a suitable and reasonably

competitive labor force for an increasingly knowledge-based global economy

(ONEC,2007). The government has substantially improved key educational indicators,

such as the average number of years of education and the net enrollment rates in

3

secondary schooling.1 However, the quality of education as measured by student

achievement in mathematics and science is relatively low by international standards

compared to other countries (Martin et al 1996; 2000; 2008) and appears to have

decreased over time even though other school and family factors have improved. This

weakness in mathematics and science could be affecting the quality of the labor force

in the long run.

Despite the better education of teachers in terms of their formal level of

education; i.e., certification and advanced professional training

(ONEC:1995,1999,2007), there is evidence of declining teacher quality in Thailand as

measured by lower university entrance examination scores of students entering

faculties of education, declining relative teacher salaries, and the fading popularity of

the profession (Fry,1999). In turn, this could be influencing the quality of education

since teachers are probably an important factor influencing student learning. The

evolving economy of Thailand could be one of the main contributors to the decline in

teacher quality. Before Thailand entered its status as one of the Newly Industrialized

Countries (NICs) in the 1980s, teaching was a highly regarded occupation and a

desirable career choice for high-ability students who would benefit from government

scholarships with secure lifetime jobs and comfortable working conditions

(Watson,1980). But this appears to have changed.

1 1 During 1999-2003, the average years of education in the population increased from 9.4 to 9.8 years,

and net enrollment rates in secondary schooling, from 85 to 92% (Source: Thailand Education

Statistics, Ministry of Education, 2006)

4

The change in economic structure has resulted in a change in career

opportunities and preferences for secondary school students when choosing university

majors. Demand and compensation in the private sector increased for engineers,

scientists, and lawyers. On the other hand, teaching and other public services have

become relatively low paying occupations, generating less interest in the younger

generation.

This dissertation will be divided into three separate yet interconnected essays

that revolve around measures of teacher quality and their distribution with respect to

socioeconomic status of students and, in turn, the factors determining teacher quality,

such as the economic return to teachers and their engagement in moonlighting

activities.

The first essay will examine the role of teacher characteristics in schooling on

student outcomes by analyzing international TIMSS test data to estimate the relation

of factors such as socioeconomic status of students, quality of schools, and attributes

of teachers to student achievement in Thailand. This is an overly simplistic approach

to understanding influences on student learning, but little education production

function research has been done for Thailand in the past. My interest is both in

understanding the distribution of teacher attributes and whether teacher attributes are

correlated with student achievements and their socioeconomic backgrounds.

The second essay will look into the problem of recruitment of well-qualified

high school and college graduates to work as primary and secondary school teachers.

In analyzing the problems of recruitment and retention of teachers, I compare teacher

5

salaries and benefits vis-à-vis other professions. And since Thailand has many teacher

training institutions of varying prestige and quality, it is important to analyze the

payoff to graduates from different institutions. Further, payoffs to various occupations

may have changed over time.

Since many Thai teachers receive additional incomes by engaging in

moonlighting activities, in the third essay, I analyze the reasons teachers choose part-

time jobs, the type of jobs they choose and the amount of income they receive from

these jobs, as well as the factors that influence these decisions. These findings are

possible with the availability of a survey of Thai teachers that focuses on their

economic conditions and labor market behavior. With this new dataset, I will also

analyze teacher movements once they enter the profession, how teachers choose their

school locations, and as in the case of the international TIMSS test data, how they are

distributed in different socioeconomic areas of the country. This is important because

student achievement is unequal across different regions of Thailand. For instance, the

poor Northeastern region has the lowest student achievements in the country. This

might result from lack to access to education or lack of ―teacher quality.‖ Using this

dataset, I will be able to go into greater detail than possible with the TIMSS to test

whether there is a pattern of teacher attributes distribution into the different regions

and types of schools according to student socioeconomic background.

6

1.2. LITERATURE REVIEW

The literature related to this dissertation can be classified into four groups: 1)

various characteristics of teacher that are correlated with student achievement; 2)

factors determining the supply of teachers; 3) the relative income of teachers; and 4)

teacher moonlighting and its possible implications for student outcomes.

1.2.1. On the Correlation of Various Teachers’ Characteristics with Student

Achievement

There is a great deal of existing research examining the effect of teacher

attributes on student outcomes. The relevant teacher attributes include teacher‘s

experience, their educational background, their certification and training, their ability,

as proxied by test scores, and the quality of the school from which they graduated.

Teacher quality: An Overview

The Coleman Report (Coleman et al, 1966) represented the first major attempt

to address the question of teacher quality in the literature. Estimating an education

production function, the report found no significant relationship between school

factors or teacher attributes and student outcomes. The authors argued instead that

student socioeconomic status is the most influential determinant of student

performance. However, subsequent studies have found in contrast that teacher quality

plays an important role in student outcomes (Hanushek, 1971; Murnane, 1975;

Murnane & Phillips, 1981;. Sanders & Rivers, 1996; Goldhaber, Brewer & Anderson,

1999; Rivkin et al 2002 ; Sanders, 1998; Rockoff, 2004; Hanushek, Rivkin and Kain,

7

2005; Boyd, Grossman, Lankford, Loeb and Wyckoff, 2006; Aaronson, Barrow and

Sander,2007; Clotfelter, Ladd and Vigdor,2007 ; Kane, Rockoff and Staiger, 2008,

Boyd et al, 2009)

Evidence from a large scale panel data study of students in Texas found a

significant relationship between teacher quality and average increase in student

achievement in mathematics and reading (Hanushek, Rivkin and Kain, 2005). The

Texas results suggest that a one standard deviation increase in teacher quality at the

grade level will increase student test scores by 10% of a standard deviation (ibid, 418).

Using data of Chicago public schools, Aaronson, Barrow and Sander (2007) show that

a one standard deviation improvement in teacher quality in math is associated with a

gain of 10-20% on the average math test score.

Teacher experience Much empirical research argues that teacher experience is among

the most important factors contributing to student achievement (Murnane &

Phillips,1981; Hanushek 1986; Ferguson, 1991; Furguson & Ladd, 1996; Hanushek,

Rivkin and Kain,2005) This result is unsurprising. Studies found that first year

teachers produce student achievement gains significantly less than teachers who have

10-15 years of teaching experience (Rockoff, 2004; Hanushek, Rivkin and Kain, 2005;

Kane, Rockoff & Staiger, 2008). The impact of experience might be less significant

once a teacher has taught for more than a few years. Ferguson and Ladd (1996) found

that teacher experience does not affect the achievement of students beyond five years

of teaching. In addition, Ehrenberg and Brewer (1994) found that the percentage of

teachers in school with ten or more years of experience does not have statistically

8

significant effect on student probability of dropping out of high school or student

achievement gains. However, Murnane & Phillips (1981) found that teacher

experience has a positive effect on a primary school teacher‘s ability to raise the test

scores of low-income African-American students during the first seven years of

teaching.

Teacher education: Teacher education encompasses several areas, including the

number of years spent in school, the individual‘s college major, and the quality of

their education. In a survey of the literature, Hanushek (1997) reported that none of

the 40 reviewed studies using value-added estimation found a statistically significant

positive effect of teacher education on student performance. However, this result may

not hold outside the U.S., as other countries may be subject to greater variability in

teacher education level (Hanushek,1995).

A number of studies have explored the relationship between student test scores

and the educational qualifications of teachers such as certification, and whether they

hold a bachelors or masters degree in mathematics (Goldhaber & Brewer, 1997;

Darling-Hammond, 2000; Guyton & Farokhi, 1987; Monk, 1994; Monk & King,

1994; Boyd et al, 2006; Clotfelter, Ladd and Vigdor, 2007; Goldhaber, 2007, Boyd et

al, 2008). Goldhaber & Brewer (2000) found that teachers with subject-specific

training outperform those without subject matter preparation in their students‘

mathematics scores. Researchers have also attempted to use quality of institution as

proxy for the quality of training and found the positive relationship between quality of

9

training and student achievement (Ballou & Podgursky,1997; Ehrenberg & Brewer,

1994 ; Boyd, et al,2005; Boyd et al, 2008).

Gender of Teachers: Past research suggests that students instructed by female

teachers perform better than those instructed by men. It is not clear why teacher

gender exhibits a relationship with student achievement. However, the relationship

between gender of teachers and students whom they teach seem to play some role on

student achievement as the interaction between teacher and student gender is shown to

be significant in many studies (Goldhaber & Brewer, 1997: Goldhaber, Brewer &

Anderson, 1999: Hanushek, 1971; Harbison & Hanushek, 1992; Dee, 2007).

1.2.2 On the Distribution of Teachers

Teachers respond to both pecuniary and non-pecuniary incentives in their

decision in where to teach. There is a great deal of research analyzing the teacher

distribution or the sorting of teacher to different socioeconomic background of schools

(Lankford, Loeb and Wyckoff, 2002; Clotfelter, Ladd & Vigdor, 2006; Bonesronning,

Falch & Strom, 2005; Peske & Haycock, 2006). The dominant factors contributing to

teacher distribution include wages, working conditions, the socioeconomic status of

schools and students, and school location. Key contributions are outlined below.

Wages: The literature suggests that teachers are more likely to teach at schools or in

districts that pay higher starting wages (Dolton & Van der Klaaw, 1999 ; Rickman &

Parker, 1990). Also, the salaries of alternative occupations play an important role

when college graduates make a decision to enter teaching profession.

10

Working conditions: Other non-monetary factors that influence the teacher‘s

distribution are working conditions such as student attributes, school facilities, class

size, school safety and other socioeconomic characteristics of schools (Loeb and Page,

2000). The differences across schools in these non-monetary factors are particularly

important in Thailand since teachers‘ official salaries are standardized by the

government under the centrally unified salary scheme.

Many researchers suggest that teachers are attracted to schools with higher-

achieving and higher socioeconomic-status students (Hanushek, Rivkin & Kain, 2005;

Betts, Reuben & Dannenberg, 2000). A survey of California schools found that

schools with larger class sizes and less appealing working conditions have greater

problems with teacher turnover and vacancies (Loeb, Darling-Hammond & Luczak,

2005). The movement of high quality teachers to more advantaged schools occurs in

tandem with the demands of parents at such schools to seek out high quality teachers

(Clotfelter, Ladd & Vigdor, 2007)

Location: Boyd et al (2005) present evidence to suggest that teachers prefer to teach

close to where they grew up and in districts that are similar to those they attended in

high school. For example, the authors found that 61% of teachers in New York State

started teaching in a school within 15 miles of their high schools (Boyd, Lankford,

Loeb & Wyckoff, 2005).

11

1.2.3 On Teacher’s Income and Other Economic Factors

The economic return to teaching is an important consideration in attracting people

to the profession. The quality of high school graduates who enter teacher training

institutions in Thailand has been declining as a course of study, and choosing

programs that lead to more highly remunerated job opportunities have expanded in

tandem with the country‘s growing economy. In addition to attracting a lower quality

pool of teachers, the relatively low salaries of teachers also exacerbate problems such

as low motivation of in-service teachers, teacher moonlighting, and teachers leaving

the profession. The literature review in this section will deal with teacher salaries

relative to other professions, the determinants of teacher salaries, and teacher

moonlighting.

Teacher compensation and student achievement. Allegretto, Corcoran & Mishel

(2004) provide a useful summary of the literature on teacher wages and education

quality. There is evidence that financial compensation of teachers increased the quality

of education (Larry, 2005; Clotfelter, Glennie, Ladd and Vigdor, 2008). On the

contrary, Hanushek (2005) finds no association between the salary and the

performance of teachers. For international comparison, Carnoy et al (2009) find that

students from countries with a lower Gini coefficient and a low salary difference

between teachers and scientists perform better in mathematics than students from

countries with greater salary differences and more unequal income distribution.

12

Teacher salaries relative to other professions. The issue of the salaries paid to

teachers relative to those in other occupations has been a topic of considerable interest

to policymakers due to its potential impact on teacher recruitment. There is much

debate as to how teacher salaries rank relative to the salaries of those in other

occupations, and various attempts to settle this question have produced conflicting

outcomes. Much of this work draws on initiatives by the International Labour

Organization (ILO) (ILO, 1991; Carnoy &ILO, 1996).

In most countries, teachers are employed in the public sector, and teacher

salaries are therefore determined by the government of that country, contingent upon

the proportion of national and sometimes state budgets allocated to education. As a

result, teacher pay depends on the capacity of governments to pay, as well as the

policies adopted by the education ministry, and on the capacity and willingness of

families to pay for education, either through taxes or direct payment to schools.

As previously stated, the primary topic of interest to scholars is how the

salaries of teachers rank against those of other occupations. The results appear to

differ across countries, depending on the empirical methodology employed. In most

studies, the authors controlled for differences in years of education, gender and

experience. More contentious issues include whether an analysis of teacher salaries

should account for the fact that teachers work fewer hours than those in most other

occupations, and in many countries receive 2-3 months of summer vacation that do not

exist in other occupations.

13

In a study of Cote d‘Ivoire, Komenan and Grootaert (1988) found that teachers

are relatively overpaid. Conversely Asadullah (2005) suggested that teachers in

Bangladesh are relatively underpaid. Similarly, Henke et al (2000) found that teachers‘

salaries are low compared to other college graduates who pursue alternative

occupations and most closely resemble those of social workers, ministers and clerical

staff.

In a study of Argentina, Vegas, Pritchett and Experton (1999) used 1997

household survey data to show that teacher salaries vary across cities. In a World Bank

study, Liang (1999) analyzed 12 Latin American countries‘ teacher salaries by using

household surveys conducted in 1995 and 1996, and adjusting from yearly to hourly

income. He found that teacher salaries appear to be higher than those of non-teachers

with the same human capital characteristics. Other studies on the relative salaries of

teacher in Latin America include those by Piras and Savedoff (1998), Psacharopoulos

(1987), and Psacharopoulos, Valenzuela and Arends (1996), Carnoy & DeAngelis

(2000), and Carnoy & Welmond (1998).

1.2.4 On Teacher Moonlighting

Moonlighting is defined as the act of having a secondary job in addition to a

primary job. One possible reason for moonlighting may be that workers need to hold

multiple jobs in order to subsist in an underpaid occupation (Divocky, 1978; Guthrie,

1969; Turner, 1962; Wisniewski & Hilty, 1987). US data suggests that teaching is

among the professions with the highest rate of moonlighting (Divocky, 1978).

14

Moonlighting among teachers has been viewed as an indicator of teacher

dissatisfaction and the low status of the career, potentially producing detrimental

effects for the education of children, and possibly leading to their exit from the

profession. For example, Henderson, Darby & Maddux (1982) studied the extent of

teacher moonlighting in Texas public schools. They found that 22% of teachers in the

sample were moonlighters, with most of these holding menial, low-paying secondary

jobs. 46% of the teachers in the survey said the low wages paid by teaching was their

main reason for moonlighting.

Raffel & Groff (1990) used data from a sample of teachers in Delaware and

found that a majority of teachers were willing to moonlight, and would have continued

to moonlight even if their salaries were increased to replace moonlighting income.

From a study of 312 K-12 teachers, Betts & Paterson (2006) found that the pattern in

prevalence, pay and type of moonlighting activity among teachers is quite diverse.

Teacher moonlight especially tutoring afterschool class has been criticized for

its adverse impact on students in developing countries (Bray 2003, 2005:

Jayachandran,2008). Teachers have incentive not to teach fully in regular classrooms.

In some countries such as Hong Kong, Singapore, teachers are prohibited by the

government from taking tutoring as a part-time job (ibid).

Despite criticism of teacher moonlighting, the same activities in certain

professions are not viewed as negatively. For instance, university professors or

physicians who have part-time jobs are viewed as extending their practice and

knowledge, which in turn brings greater experience and may have positive effects on

15

their primary jobs (Allard, 1982; Howsam, 1985; Langway, 1978; Linnell, 1982). This

may similarly apply to teachers who work part-time in certain types of jobs such as

private tutoring or teaching evening programs. Therefore, a pertinent question is

whether the impact of moonlighting on student learning depends on the type of jobs

that teachers do.

1.2.5 On Thailand’s Teacher Characteristics and Educational Outcomes

Teacher characteristics may affect students differently depending on social,

economic and cultural context. Estimated relations in one social and cultural context

may not apply to others. In the case of Thailand, there has been limited research done

on the effect of teacher attributes on students. In a World Bank report on Thai

education, Lockheed (1987) analyzed the effect of school and teacher background

characteristics on the mathematics achievement of 8th

grade students. She found that

students of male teachers perform less well on tests than students of female teachers.

However, the impact of teacher qualifications in Lockheed‘s study was inconsistent

across rural and urban schools. In urban schools, children appeared to learn less with

more qualified mathematics teachers, while teacher qualification had no significant

effect in rural schools.

Concerning educational interventions, Raudenbush et al (1993) have studied

the effect of two programs, ‗on-the-job learning‘ and ‗regular classroom supervision,‘

on the quality of instruction provided by Thai teachers and on student achievement.

They used data from 400 primary schools and 4,000 teachers in 1988 to compare the

effects of these interventions. The authors found that classroom supervision has a large

16

positive significant effect on student achievement, while there appears to be no

significant relationship between in-service training and the quality of instruction or

student test scores.

1.2.6 Summary of the Literature and the Contribution of the Dissertation

From the review of literature, we can see that much of the past research has

focused on the various determinants of student achievement or on the distribution of

teacher resources. However, these works do not bring together the relation between

teacher characteristics and student outcomes with the salary structure of teachers

compared to other professions. Also, not many studies have focused on the

distribution of teacher characteristics across different socioeconomic groups of

students in relation to the relative salary and non-pecuniary factors associated with

teaching in different locations with different groups of pupils. Moreover, the lack of

empirical research on Thailand‘s teacher labor market does not provide much insight

into Thai teachers in terms of their contributions and economic status. My dissertation

is an attempt to fill that void in the literature, in addition to contributing to the

literature on aspects of Thailand‘s teachers.

17

1.3 RESEARCH QUESTIONS

The main foci of the dissertation are to explore the effects and directions of the

relationships between the distribution of teacher characteristics, student

socioeconomic status and student academic outcomes in different contexts in

Thailand. In addition, the economic aspects of teachers such as their incomes and their

outside school part-time jobs are to be investigated.

1.3.1 Main Hypotheses of the Study

Three main hypotheses will be examined in this dissertation.

H1: Teacher skills are unequally distributed across the social class and ability of

students in Thailand; i.e., higher socioeconomic class students or students who achieve

higher test scores are likely to be taught by more qualified teachers.

H2: Teachers are paid relatively low relative salaries in Thailand, and there is a

shortage of highly skilled teachers, particularly in low-income urban and rural areas.

This shortage is increasing as teacher relative salaries fall over time. The shortage

impacts lower SES students more than higher SES students.

H3: Thai teachers respond to moonlighting opportunities according to economic

incentives, such as their official salaries and other sources of incomes. And teachers in

rural areas tend to have part-time jobs unrelated to education more than those in urban

areas. This trend has impacted low-income students more than students from high SES

backgrounds.

18

1.3.2 Specific Research Questions

Chapter 2: The Relation of Teacher characteristics to Student Achievement

1. Which teacher attributes are related to student achievement in Thailand?

2. What is the distribution of teacher characteristics among different types of

schools, based on average student characteristics such as student academic

achievement and socioeconomic background?

3. How are the teacher educational backgrounds related to student socioeconomic

status and ability distribution?

Chapter 3: Teacher salaries in Thailand

1. What are the factors determining the salaries of Thai teachers?

2. What are the differences between salaries of teachers and salaries of competing

occupations?

3. How do salaries of teachers change over time with economic changes and how

have teachers salaries compared with salaries of other occupations over time?

4. What are the relationships between the changing economic situation and the

return to occupations for students in teacher training institutions other types of

tertiary education? What are the returns to teaching of university graduates by

their genders and academic backgrounds?

19

Chapter 4: Teacher moonlighting and teacher distribution

1. Which factors most influence the decision of teachers to take additional part-

time jobs?

2. What are the factors associated with the distribution of teachers among various

settings, such as location, type of school and socioeconomic level of the

school?

3. How do teachers select the type of school and its location? What is the

distribution of Thai teacher characteristics across regions/provinces?

20

CHAPTER 2

TEACHER’S EDUCATIONAL BACKGROUND

AND STUDENT ACHIEVEMENT IN THAILAND

2.1 INTRODUCTION

There is increasing agreement that teacher‘s educational backgrounds and

experience are important to the learning of their students. Teacher‘s educational

backgrounds such as the subjects they had studied, the quality of schools or

curriculum in which they had been trained, the level of education that they obtained,

all appear to be related to the learning of students. Such preparation is the foundation

of how teachers convey their knowledge of the subjects to the classrooms. The

importance of teacher‘s educational background and experience should be more

prominent in complicated subjects such as mathematics and sciences that require more

advanced knowledge of the subjects. Teacher‘s lack of adequate preparation in these

subjects could jeopardize how much their students learn.

In this chapter, I try to understand the impact of teacher quality in general and

their educational backgrounds in particular on the achievement of students in

Thailand. There are several reasons why this issue is important and worth

investigating. Firstly, teachers in Thailand are hardly homogeneous in terms of their

training quality as there are more than 100 institutions that feed teachers into the Thai

teacher labor market. Also, in Thailand the mathematics and science teachers could be

21

educated from either the faculty of science or faculty of education. Each places

different emphasis on and approaches to the disciplines. Secondly, the problem of out-

of-field teachers in mathematics and science is one of the most imminent educational

problems in Thailand. A large percentage of Thai students, especially those in the rural

areas, studies mathematics and science with teachers who do not have relevant training

in the subjects. Thirdly, Thailand has been experiencing a trend in the past decade that

in-service teachers have to acquire advanced education such as a master‘s degree or a

doctorate degree in order to be successful in their career.

Despite many concerns regarding teacher‘s qualities in terms of their

educational background, little is known about the link between such qualities and

student learning in Thailand. There is no prior study that links the educational

background of teachers, for instance, whether they are trained in the faculty of

education or faculty of mathematics/science, whether they have the relevant training in

the subject matter, and whether they have bachelor‘s degrees or advanced degrees to

the learning of students.

Some related previous research on the relationship of teacher attributes to

student achievement was conducted as part of a World Bank study in the 1980s

(Lockheed, 1987) that used a dataset from the International Educational Achievement

(IEA) survey of 8th

grade students in mathematics and found that students in urban

schools achieved at lower levels with more qualified teachers. The effect was the

opposite to that of students in rural schools.

The most comprehensive survey of out-of-field study in Thailand was

conducted by Siribanpitak & Boonyananta (2007) as part of the ―6 Nations Survey of

22

Teachers‖(Ingersoll, 2007). It found that a quarter of science and mathematics teachers

in Thailand do not have relevant educational training in the subjects they teach. The

situation is much worse in small schools in rural areas. However, the study only

reports the summary statistics and does not distinguish between in-field teachers who

were graduates of faculties of science and education.

In sum, the qualifications of teachers, such as their level of education and

whether they have a teaching certificate, are the main measures of teacher quality I use

in this chapter. However, student achievement also correlates with other

characteristics of teachers available in the data, such as the teacher‘s classroom

practice, teacher experience, and in addition may correlate with school characteristics.

Therefore, in addition to teacher‘s educational background, I will also analyze the

relation of some of these factors to student achievement.

2.2 BACKGROUND AND DATA

2.2.1 The Making of a Thai Teacher:

In Thailand, students choose their college majors upon entering universities via

the national university entrance examination or the direct entrance exams administered

by each university department. Students who wish to be a teacher need to enter one of

more than 80 faculties of education throughout the country, which include the faculties

of education of 41 Rajabhat Universities (formerly teachers‘ colleges). Students who

graduate from universities with non-education majors such as science, arts, and social

sciences, can also be teachers in those subjects if they have teaching certificates.

23

After the 1999 National Education Act, starting in 2002, the requirement for

completion from the faculties of education was extended from 4 to 5 years, with the

last year devoted to teaching practice in real schools. Aspiring teachers who do not

have a B.A. in education from a faculty of education have an option to enroll in a one

year teacher training program in order to be a qualified teacher. Upon graduation from

teacher training institutes, graduates who wish to be a full-time teacher have to take an

exam offered by one of 175 educational districts throughout the country and get a job

depending on the availability of the position in that district. Teachers of mathematics

and sciences may enter a faculty of education and major in the field of ‗mathematics

education‘ or ‗science education‘ (with subfields in physics, biology, chemistry,

computer, etc.) with a focus on content knowledge and pedagogy, including teacher

values and etiquette in the context of Thai culture. Another route for mathematics and

sciences teachers is through the faculties of sciences to obtain a B.A. in sciences

(mathematics, physics, biology, chemistry, etc).

The instructional effectiveness of teachers who graduate from the faculties of

sciences and teachers who graduate from faculties of education majoring in

science/education may differ. In general, students who enter faculties of education to

earn a B.A. degree are less mathematics-science oriented than students who enter the

faculties of sciences. Teachers who graduate from faculties of science may be better

prepared at the general and high level content knowledge of mathematics/science but

have less training in pedagogy or how to teach in a classroom. On the other hand,

teachers who are trained in faculties of education may have less mathematics/science

24

knowledge outside of the curriculum taught at the designated levels but could be better

trained in pedagogy and how to conduct a classroom effectively.

2.2.2 Current Teachers Issues in Thailand

Currently, there are several education issues regarding the quality of Thai

teachers, notably the problems of out-of-field teachers, the implementation of teaching

certificates in 2002 and the increasing popularity of graduate education among

teachers.

1) Out of field teachers. In Thailand, the problem of out-of-field teachers is

related to the shortage of teachers trained in mathematics, science, and language,

necessitating the allocation of teachers who are trained in other disciplines to teach

these subjects. Siribanpitak and Boonyananta‘s (2007) study found that one quarter of

mathematics teachers in Thailand do not have an educational background in

mathematics.

The problem of out-of-field may also be shared unequally between schools

with different socioeconomic background students and teachers at different grades of

school. Schools in urban areas have more in-field teachers than schools in rural areas.

The preference for teachers to work in urban areas leads to the problem of well-

qualified teachers teaching in urban, well-endowed schools and leaving the out-of-

field teachers to teach in rural areas. Also, mathematics teachers who teach at higher

grade levels tend to be in-field more than those teaching at the lower levels because

higher grade levels require more advanced knowledge of mathematics and science.

25

2) Teaching certificates. Teaching certificates have been a much discussed

initiative among Thai educational policy makers as a tool to help improve the quality

of Thai teachers. Although the proposal of teaching certificates in Thailand dates back

to 1945, their implementation only occurred after the 1999 National Education Act.

By 2002, teaching certificates were a requirement for new teachers and were given to

in-service teachers entering the profession prior to the enactment who applied for

them. Currently teaching certificates are valid for 5 years and are renewable. In order

to obtain a teaching certificate, teachers need to fulfill the requirement of coursework

related to pedagogy and content knowledge.

In general, teaching certificates are given to teachers upon graduation from

acknowledged teacher-training programs. They are not bestowed by any specific

examination of content or pedagogical knowledge. Therefore, the main role of a

teacher certificate in Thailand is to control that teachers study at government-approved

institutions and that institutions must adhere to the standards in order to be eligible for

issuing teaching certification. In some cases, in-service teachers who still do not have

certificates can take part-time classes in the evening or weekend in order to fulfill the

requirement of their certification.

3) Teacher academic preparation. Overall, Thailand does not have a serious

problem in terms of the lack of formal academic degree for teachers. At the

elementary school level, Thailand has a high proportion of teachers who complete the

bachelor‘s degree compared to other Asian countries (Ingersoll, 2007). Instead of the

under education of teachers, Thailand has seen a new surge of ‗degree inflation‘ in the

labor force (Tang Juang, 2007). There has been an increase in terms of enrollment at

26

the graduate level in the general population and among teachers in the past 15 years.

From 1994 to 2002, the enrollment in B.A., M.A., and Ph.D degrees increased 3.5, 17

and 157 times respectively (ibid, 25). From Table 2-3, the percentage of teacher who

holds a master‘s degree has increased from 3% to 10 % from 1996 to 2005. One of the

reasons for the expansion of graduate programs is that the 1999 National Education

Act supports greater autonomy for public universities, and with such autonomy,

universities have begun new graduate programs to attract students and gain greater

prestige. Another major stimulus is the change in government regulations toward

university financial administration as part of a loan condition from the Asian

Development Bank (ADB) during the 1997 Asian economic crisis. Moreover, in 2004

there was a conversion of Rajabhat and Rajamongala Institutions—the former

teachers‘ colleges—into universities, thus allowing for the creation of many new

graduate programs. The drive for financial autonomy leads the universities to open

such programs in order to generate more income for self-sustenance. Nowadays, in-

service teachers have various options to enroll in M.A. or Ph.D. programs, such as

weekend or evening programs catering to people with full-time jobs.

2.2.3 Data

This analysis uses data from the Trend in International Mathematics and

Sciences (TIMSS), an international assessment conducted by the International

Association for the Evaluation of Educational Achievement (IEA) of 4th

and 8th

grade

students in mathematics and sciences. Thailand participated in TIMSS in 1995, 1999

and 2007. The advantages of using the TIMSS dataset are that a) TIMSS is one of a

27

few datasets available in Thailand which provides rich information on the relationship

between teacher characteristics and student achievement. In case of Thailand, 8th

grade

students were surveyed in these three rounds of TIMSS. b) The three rounds of TIMSS

from 1995 to 2007 allow a comparison of changes in teacher characteristics and

student achievement over time, especially after the enactment of the 1999 National

Educational Act, which introduced many policy changes.

The TIMSS data were constructed by a three-level sampling: school,

classroom, and student, with assigned weights at each level of sampling. In the case

of Thailand, about 150 schools were randomly selected from all secondary schools in

the country, and one 8th

grade classroom was randomly selected from each selected

school. Detailed background information was gathered through separate student,

teacher and school principal questionnaires. The student background questionnaires

provide information about family background and students‘ study practices at school

and home. The teacher questionnaires give information about their educational

background, experience and classroom practice. The school-level data provides

information about school resources, school locations and on what basis schools admit

students.

Limitation of TIMSS

Despite the incredibly vast information that the TIMSS has given to

educational researchers, it is a cross-sectional data survey. Researchers should be

cautious when using the dataset to make any causal claim on the relationship between

variables of interest. The example of the bias in the case of the relationship between

28

teacher attributes and student achievement is that teachers are not likely distributed

randomly across schools. They may be sorted to certain type of schools by the

school‘s attributes. Moreover, student achievement reflects a cumulative learning

process over time and most students in Thai schools are assigned to a new set of

teachers every academic year. Therefore, it may not be correct to relate the

achievement of a student at single point in time to their current teachers without

considering the contribution of students‘ previous teachers.

Finally, the TIMSS is designed to measure the student achievement using a

random sample of schools and classrooms within school in each country. As the

TIMSS does not have the primary purpose to evaluate the influence of teachers on

student achievement, teachers are not randomly selected with regard to their different

qualifications. Therefore, the analysis of the relationship between teacher background

and student achievement is beset by shortcomings in terms of the survey design.

2.2.4 Overview of Thailand’s participation in TIMSS 1995,1999 and 2007

Table 2-1 shows statistics from TIMSS 1995, 1999 and 2007, the three surveys

in which Thailand has participated. In this 12-year interval we can observe some

notable changes in Thailand‘s demographics and educational statistics. The total

population of the country increased from 58 to 63.4 million (9%) and the number of

school-aged children and in-service teachers have increased from 1,297,000 to

1,500,000 (15.6%) and from 650,000 to 800,000 (23%) respectively.

The quality of teachers as measured by their level of education and

mathematics-related majors has improved over the years. The majority of Thai

mathematics teachers have obtained at least bachelor‘s degrees (96.74%, 97.2%, 99.05

29

% from 1995-2007). A noteworthy improvement is the ratio of mathematics teachers

who have master‘s degrees. This has increased from 3% in 1995 and 1999 to 12% in

2007. The percentage of teachers with certification also increased (90 to 97%) from

1999 to 2007 (Table 2-6).

Students‘ background characteristics in terms of parental education and

educational resources have also improved significantly over the years. From Table 2-

1 , percentages of parents who obtain secondary and university degrees as their highest

levels of education have increased between 1995 and 2007 from 14% to 40% and 9%

to 12% respectively. Students also possess more computers at home in 2007 than they

had in 1995 and 1999 (4% in 1995, 8% in 1999 and 47% in 2007). This is due to the

Thai population‘s greater access to secondary and tertiary education over the years and

the much lower prices of personal computers in the past decade.

However, despite the seemingly better quality of teachers and better family

background of students, Thai students have fared worse in each round of TIMSS from

1995 to 2007. The declining international mathematics achievement of Thai students

is demonstrated both in terms of average scale scores and the percentage of students

who scored in the higher international percentiles. Table 2-1 shows that the average

scale scores of TIMSS in mathematics of Thai students have gone down from 577 in

1995 to 467 in 1999 to 441 in 2007. Although the test scores from the three rounds

might not be perfectly comparable considering there are different participating

countries in each round, the numbers still indicate a downward trend in terms of the

competitiveness of Thai students in mathematics achievement. Moreover, according to

30

the TIMSS mathematics report (Martin et al, 1996; 2000;2008) as appeared in Table

2-2, the percentages of Thai students who score in top 10th, 25th, 50th and 75th

percentiles have declined in each round (from 7% to 3% in top 10th, 54% to 34% in

top 50th and 85% to 66% in top 75th).

2.2.5 Evidence of School Quality Clustering and Teacher Sorting

Clustering of high performing schools and student SES.

The distribution of test scores across schools within Thailand can be analyzed

from the TIMSS data. The high scoring students are clustered in fewer schools from

TIMSS 1995 to TIMSS 2007. Table 2-2 shows that 116 schools (79%) in 1995, 100

schools (67%) in 1999 and 84 schools (56%) in 2007 have more than 10 students who

scored above the 50th

percentile of the TIMSS mathematics score. The same pattern

obtains for the number of schools where at least 10 students score above the 75th

percentile; 54 schools (37%), 45 schools (30%) and 38 schools (25%) from TIMSS

1995,1999 and 2007 respectively.

In terms of the distribution of qualified teachers as measured by their

educational background, there are more in-field mathematics teachers in 2007 than in

1999. The distribution of test scores for students who were taught by in-field teachers

(Figure 2-4 & 2-5). Figure 2-9 shows that the percentage of students who were taught

by in-field teachers and scored above 50th

and 75th

percentiles have increased slightly

from 1999 to 2007.

The inequality of distribution of student test scores according to their

socioeconomic status as measured by parental education is also evident. From Figure

2-10, in 1995, 4.26 percent of students who scored above the 50th

percentile were from

31

parents with at least one university degree. The percentage increased to 7.85% and

11.08% in 1999 and 2007 respectively. On the other hand, the ratio of students who

score above 50th

percentile who have parents with secondary education increase more

slowly. The difference between students with parents who have university and

secondary education is sharper for students scoring above 75th

percentile. In sum,

students from families with higher parental education seem to constitute a larger

proportion of top scorers over the years or it could just be a result of the greater

expansion of tertiary education among Thai population.

Teacher Sorting

There is evidence of inequality in terms of teacher distribution among the

different socioeconomic status of students in Thailand. Table 2-6 shows the

distribution of teacher attributes, such as their experience and educational

backgrounds, across schools based on the socioeconomic status (SES) of students.

Schools with students of higher average SES tend to attract teachers with higher

average years of experience compared to schools with lower average student SES.

Another notable relationship is that there is a higher percentage of teachers who have a

B.A. in Mathematics or M.A./Ph.D for schools with higher average student SES.

Overall, the data shows that there is a relationship between teacher quality as

measured by their educational background and experience and student SES. However,

this cross sectional dataset does not allow us to explore the nature of this relationship,

that is, whether better quality teachers move to schools with higher SES students or

high SES families choose schools with better quality teachers.

32

2.3 METHODOLOGY

I estimate student achievement as a function of four sets of variables: teacher‘s

socio-human capital background, teacher‘s classroom practice, student‘s

socioeconomic background, student‘s educational practice and the school‘s academic

environment.

TIMSS uses a stratified sampling design within country and has varying

sampling probabilities for different students (Martin and Kelly, 1997). I thus use

weighted least squares (WLS) estimation with sampling probabilities as weights. The

method of WLS ensures that the proportional contribution to the parameter estimates

of each stratum in the sample is the same for the whole population (DuMouchel and

Duncan, 1983; Wooldridge, 2001). The achievement data in TIMSS is comprised of 5

different scores based on a probability distribution. In order to deal with the 5

plausible values of mathematics achievement, I take advantage of the recently released

STATA command (Macdonald, 2008) in which all 5 plausible values are jointly

considered in the calculation of coefficients and standard errors of the variables of

interest.

I will use the method of the First-Difference (FD) or Math-paired estimates as

the primary analysis and compare the outcome with the traditional ordinary least

squares (OLS) estimation. In Appendix A, I will also use the Quantile Regression

approach as a supplementary analysis to analyze how teacher characteristics are

correlated to student achievement across the ability distribution of student.

33

2.3.1 Standard OLS model

The OLS model is a simple form of education production function of student

outcome as a function of various teacher, student and school characteristics.

1 2 3 4 5 6ics cs cs ics ics ics s cs icsY TeachSES TeachEduP StuSES StuEduP StuOPT School (1)

Variables of interest: From equation (1), the dependent variable icsY

is the TIMSS

mathematics test score of student i from classroom c and school s. The independent

variables represent the vectors of teacher, student and school attributes. is a

vector of teacher‘s socioeconomic background, is a vector of teacher

classroom practice, is a vector of student‘s socioeconomic background,

is a vector of student‘s educational practice, is a vector of

student‘s opportunity to learn (OTL) and is a vector of school environment.

The error terms at school and student levels are represented by and .

The variables that are used as proxies for student‘s socioeconomic background

and demographics are student gender, age, parental education, number of book at

home, and whether students have to work for wages. The variables that are used as

proxies for student‘s educational practice are their hours of studying mathematics

outside classroom, their level of confidence in mathematics and their attitude toward

mathematics.

The variables chosen to represent teacher‘s socio-human capital background

are gender, years of teaching, the number of books at home, whether they obtained a

TeachSES

TeachEduP

StuSES

StuEduP StuOPT

School

34

B.A. in mathematics from faculty of science or a B.A. in mathematics education from

faculty of education, whether teaching is their first career choice and if they would like

to change the career. The variables for teacher‘s classroom practice/attitude are the

frequency with which teachers check students‘ homework, the emphasis placed on

problem solving and homework, hours of preparation for a class, the influence the

teacher has on the choice of textbook or mathematics curriculum content and the

percentage of their working time on teaching mathematics.

The variables for school environment are class size, the school‘s educational

resources, school safety, whether students are selected on the basis of academic

achievement and the level of peer pressure among students.

Potential bias of OLS

Interpreting the estimated parameters as representing a causal effect of

teacher characteristics on student performance is potentially problematic, since the

variation in teacher attributes such as experience and educational background may not

be exogenous to the variation in student achievement. A number of plausible

explanations exist for the endogeneity of these variables. High quality teachers may be

sorted to schools with a better academic environment where parents are of high

socioeconomic status and students of high ability. Another likely scenario is that

parents or students may apply to schools with different levels of teacher quality

according to their ability as measured by performance on school entrance

examinations (as is the case in most Thai secondary schools). Table 2-6 shows that by

several measures, teachers with better qualifications typically work in schools with

1 2,

35

higher proportions of advantaged students as measured by their home educational

resources. Teachers with a mathematics degree and more experience are more likely to

be found in schools with a higher proportion of students who have high home

educational resources. With this nonrandom sorting of teachers to schools, the

coefficient estimates represent a mixture of true ‗teacher effects‘ and the

‗sorting effect‘. In order to get the most accurate estimates, we need a strategy to

identify the effect of teachers that represent exclusively exogenous variation in teacher

characteristics

2.3.2 First Difference (or Match-Paired)

I will use the method of the first-difference (FD) estimates or Match-Paired

analysis to correct for bias in the relationship between teacher characteristics and

student achievement. Since the TIMSS data is cross-sectional and teachers may not be

randomly assigned to teach the classroom, certain characteristics of teachers may

correlate with student characteristics that are also related to their test score. For

instance, teachers who graduate with certain academic backgrounds such as from

faculties of science or those with teaching certificates might be more likely to be

assigned or to choose to teach in schools where the majority of students has high

socioeconomic status and better academic environments at home. In this case, teacher

sorting may confound the impact of academic background in science or teaching

certificate on student achievement, and the coefficients of teacher attributes are likely

biased if we use the traditional OLS analysis.

1 2,

36

There is an advantage in analyzing the TIMSS in that each student must take

examinations in both mathematics and science and the results can be linked to the

particular teacher in mathematics and science classes. In the case of mathematics and

science, the aptitudes of students in these two subjects are highly correlated as

demonstrated by the high test score correlation between math and science in both

rounds of TIMSS (0.78 and 0.85 in 1999 and 2007). As a result, I assume that there is

a consistency in the student‘s pattern of performance between these two subjects, a

trend that we can make use of.

Similarly to fixed effect or panel data analysis where student test scores,

teacher attributes, and other classroom characteristics have changed over time while

characteristics such as their home environment and socioeconomic status remain fixed,

the first-difference estimates view the difference between mathematics and science

scores of each student comparable to the difference between two points in time. It

differences out the unobservable characteristics of students that may be correlated with

their test scores and their likelihood of being assigned to teachers with certain non-

random characteristics. It can address the concern that the prior links between teacher

characteristics and student achievement partly reflect the nonrandom sorting of

students on teachers. Therefore, the estimates of teacher attributes are likely less

biased.

For this specification, the educational outcome of a student is a function of

observed student, teacher and school characteristics. The model I use is largely

similar to the work of Dee & Cohodes (2008) which is also derived from the studies of

37

return to schooling using match-pairs data from twin pairs by Ashenfelter & Krueger

(1994), Ashenfelter & Rouse (1998), and Rouse (1999).

The educational production function models are examined as follows

0 1 1mic mc i i c c micY Z X C S ……(2)

0 1 1sic sc i i c c sicY Z X C S ……(3)

Variables of interest: The dependent variable micY is the TIMSS score of student (i) of

class (c) for Mathematics (m), sicY is the TIMSS score of student (i) of class (c) for

Science (s). ( ) is a vector of mathematics (science) teacher characteristics

including teacher experience, their educational background, whether they possess

graduate diploma or teaching certificate. is a vector of observable student

characteristics such as their SES background, time spent on homework. is student‘s

unobservable attributes such as innate ability, motivation, the family involvement in

education, cC and

cS are unobservable classroom and school characteristics such as

peer effect, school quality. ic is a random error term.

It is very likely that the sorting of teacher is not random in that certain teacher

characteristics are correlated to the factors such as student ability or school‘s

environment ( ( , ) 0ic iE Z ; ( , ) 0ic cE Z S ). Therefore, the traditional OLS regression

on (2) or (3) can yield the biased estimation of teacher‘s attributes on student

achievement.

mcZscZ

iX

i

38

Using the First-Difference (FD) estimator by differencing equation (2) and (3),

we can identify the effect of teacher qualification on student mathematics achievement

by eliminating the potential source of bias in case the teacher characteristics are

correlated with student ability or school factors.

1( ) ( )mic sic mc sc mic sicY Y Z Z ……(4)

The effects of teacher characteristics such as ‗gender‘, ‗education‘ and ‗experience‘

may have different effects on mathematics and science education of students. In order

to deal with this issue, I will present the results of the interaction between subject

(mathematics & science) and teacher characteristics. I will also examine the effects of

teacher characteristics in sub-samples of students and schools, dividing groups on

variables such as gender, socioeconomic status of students and household location.

Potential Bias in the First-Difference Approach

Even though the First-Difference (FD) approach can address the problem of

nonrandom sorting of students or teachers to a certain extent, bias could still exist in

the case that students perform well in one subject relative to another. For instance, a

student with a high mathematics score but low science score may have happened to

have a mathematics teacher who graduated from a faculty of education and a science

teacher who graduated from a faculty of science, but the student may just be good at

mathematics and not very good (or interested in) science regardless of his or her

teachers. In this case, the effect of a teacher characteristic such as having graduated

from a faculty of education would be overestimated. Since we do not know the

39

student‘s academic history, we cannot control for previous test scores in the same

subject with other teachers.

However, if the pattern of correlation between mathematics and science scores

is consistent across the student sample or if the average student test scores in

mathematics and science are highly correlated then this problem should be less of a

concern. In the case of TIMSS 1999 and TIMSS 2007, the correlations between Thai

students‘ mathematics and science test scores are very high at 0.78 and 0.85 in 1999

and 2007 respectively. Therefore, the threat of this bias seems to be less.

2.3.3 Possible Difference between First Difference and OLS

There are several plausible explanations for the observed differences between

the OLS and FD estimates. First, it is possible that the relationship between teacher

background and student achievement is subject-specific and differs across

mathematics and science. Different methods of training in each subject may result in

different levels of effectiveness in pedagogy. As a consequence, the results from the

OLS estimation, which focuses only on mathematics, may differ from the FD

estimates, which relate to both mathematics and science teachers.

Second, in the FD analysis many observable factors that may influence student

test scores such as classroom characteristics or family factors are eliminated via

differencing. This should not pose any problem if these factors tend to impact both

mathematics and science achievement similarly. However, if these factors influence

40

student learning in mathematics and science differently, not taking these factors into

account may produce omitted variable bias.

2.4 RESULTS

2.4.1 Descriptive Statistics:

Table 2-6 presents the unweighted descriptive statistics from samples of Thai

students and teachers who participated in TIMSS 1999 and TIMSS 2007. In addition

to the statistics of the total samples, I analyze the descriptive statistics of each

socioeconomic status quartile of students. In so doing, I constructed an index of

student‘s SES. Students are divided into 4 subgroups according to their SES from the

lowest quartile (Quartile1) to the highest quartile (Quartile4).

Following are summaries of relevant descriptive statistics. Over time, the test

scores of Thai students have decreased at every SES level from 1999 to 2007. We can

see from Table 2-6 that the SES of student is highly correlated with student test score.

Students from higher SES have higher mathematics scores in both 1999 and 2007.

Regarding teacher‘s gender, 70% of Thai students studied mathematics with

female teachers in 1999 and the percentage has decreased to 65% in 2007.

Interestingly, the proportion of students who study with female teachers has increased

at every level of SES of student in both years. This suggests that female teachers may

sort themselves to higher SES schools perhaps by their better teaching exam scores

that enable them to choose high SES schools first. Alternatively, male teachers may be

less concerned about where they teach. So they are more likely to end up teaching at

lower SES schools.

41

The average experience of teachers has increased from 13 to 15.5 years in

1999 to 2007. For both years, the average experience of teachers is higher as the SES

of students increases. This provides some evidence of Thai teacher sorting to schools

of varying quality by teacher experience, assuming that the average SES of students in

the school is related to school quality.

On teaching certification, the percentage of students who study under teachers

with a teaching certificate has increased from 89% to 97% from 1999 to 2007. From

Table 2-6, the teaching certificate doesn‘t seem to be associated with the SES of

students. This is because prior to the 1999 National Educational Act, teachers were not

required to have a teaching certificate but from 2002, teachers are required by law to

obtain one except for some in-service teachers who started working before the

enactment.

The 1999 National Educational Act also introduced a requirement that new

teachers must have bachelor‘s degrees. In TIMSS 1999, there were about 2.7% of

students studying with teachers who did not have bachelor‘s degrees. While in the

TIMSS 2007, there were no teachers who possessed less than B.A. We can see the

new trend of Thai teachers seeking master‘s or Ph.D. diplomas after the new

autonomy of public universities and the elevation of status of former teachers‘

colleges to universities in 2004. The percentage of mathematics teachers who have

M.A./Ph.D. doubled from 6% to 12% in the eight years between these two rounds of

TIMSS.

The ratio of teachers having graduate diplomas was higher at higher SES

groups in 1999. In 2007, the difference in the proportion of teachers having graduate

42

diplomas was more prominent than in 1999 between students in the 1st and 2

nd SES

quartiles and students in the 3rd

and 4th

quartiles (10 % versus 14%).

In terms of teachers‘ educational backgrounds, the percentages of mathematics

teachers with different academic backgrounds are surprisingly similar over time. The

majority of 8th

grade mathematics teachers in Thailand have a B.A. in mathematics.

The percentages of teachers with a B.A. in mathematics in the TIMSS 1999 and the

TIMSS 2007 are 68% and 69% respectively. Mathematics teachers who have a B.A. in

education and a B.A. in other fields are about 3% and 2% in both years while the

percentage of teachers who have a B.A. in science or science education decreased in

2007. There are some interesting patterns in teacher educational background. Students

of higher SES have a higher proportion of teachers who have B.A. in mathematics

while they have the lowest proportion of teacher with a B.A. in mathematics

education, education and other fields in both years. This suggests there is a sorting of

teachers who have training in mathematics (from science faculties) to schools with

higher SES.

Overall, from the TIMSS 1999 to the TIMSS 2007, Thai students were

increasingly likely to study with teachers with teaching certificate, graduate degrees

and greater experience. The proportion of male teachers also increased over the years,

although students from higher SES groups tended to have a higher proportion of

female teachers. Despite all these changes, the proportion of students studying with

infield/out-of-field teacher remained the same over the 8 years.

43

2.4.2 OLS Results

2.4.2.1 Outcomes of TIMSS 1999

Table 2-7 shows that students‘ mathematics performance is negatively

correlated with having a female teacher. Students who study with a female teacher

earn 12 points less than those studying with a male teacher ,other things being equal

(Table 2-7, Model4). This correlation is consistently negative in all quartiles of the

SES distribution. Teacher‘s experience is positively associated with 1.7 points of

student mathematics score, however, the relationship is not significant for students

across the SES distribution. It suggests that students in higher SES benefit more from

having experienced teachers. On the education level of teachers, Table 2-7 shows that

students who study with teachers without bachelor‘s degrees and those who have

master‘s degree have higher mathematics scores compared to students who have a

B.A. But after controlling for other teachers‘, schools‘ and students‘ characteristics

(Table 2-7, model3-4) only the coefficient for teachers who do not have a B.A.

remains statistically significant. Teachers with B.A in Science have the strongest

correlation with student test score, following by teachers with a B.A. in mathematics

and a B.A. in ‗other‘ fields of preparation respectively. The magnitude of the

relationships is consistent across SES groups of student but the level of significance

diminishes at the lowest student SES (Table 2-8). The OLS results suggest that even

though the great majority of Thai teachers who are teaching in middle school are

trained in mathematics and do have a B.A. degree, only preparation in a science

department seems to be ―better‖ in terms of relation of the type of preparation to

student performance.

44

2.4.2.2 Outcomes of TIMSS 2007

Table 2-9 shows that students who have a female teacher have a higher score

than those with a male teacher. The female coefficient is statistically significant and

robust across SES groups of students especially the highest SES one (Table 2-10).

This is the opposite result from the regression estimates using TIMSS 1999 data. For

teacher‘s experience, Table 2-9 shows that 1 year of additional experience of teacher

is positively associated with 1.4 additional points of student mathematic score.

However, the relationship between teacher‘s experience and test score is only

significant in the highest SES group. In TIMSS 2007, Thailand had only teachers with

a bachelor‘s degree and higher (M.A./Ph.D.) in the sample. The OLS model (Table 2-

9) shows that students who study with teachers who have master‘s degrees have higher

mathematics scores compared to students whose teachers have a B.A. by 22 points.

From Table 2-10, the positive coefficients are consistent across SES groups but the

significance disappears at highest SES group. Teaching certificate is negatively and

significantly correlated with student test score in all our regression models (Table 2-9,

Table 2-10).

For teacher‘s educational background, teachers with a B.A. in science have the

highest correlation with student achievement, following by teachers with a B.A. in

mathematics and mathematics education respectively. However, these relationships

are not consistent across student SES groups. With a teacher who has a B.A. in

mathematics as a reference, Table 2-10 shows that students in the lower SES groups

(Quartile 1-3) are better off if they have a teacher who has B.A. in science while

students in the highest SES group (Quartile 4) are better off if they have a teacher with

45

B.A. in mathematics and students in this group will be much worse off academically if

they have a teacher with B.A. in education or mathematics education.

2.4.2.3 Comparison of the OLS results from TIMSS 1999 and TIMSS 2007

The notable difference between the TIMSS 1999 and 2007 results is the

change in the relation of teacher‘s gender to students‘ mathematics test score. Having

a male teacher is positively related to students‘ mathematics performance for TIMSS

1999 but the opposite is true for TIMSS 2007. The teacher experience seems to play a

significant role in both rounds of TIMSS; that is, having a teacher with more

experience is positively correlated with higher student test score but the strength of the

relationship seems to diminish for students in the lower SES groups. The relationship

between a master‘s degree and test score is positive but not quite significant in the

TIMSS 1999, but it is very significant across the board in TIMSS2007. This could be

the consequence of a trend of Thai teachers acquiring master‘s degrees in the past

decade and those teachers increasingly more likely to work in higher scoring schools.

Regarding teacher‘s educational background, teachers with their degree in science or

science education have positive and significant relationship with test scores of students

in both TIMSS 1999 and 2007 while ones with backgrounds in education and

mathematics education are negatively correlated with student test score.

46

2.4.3 First Difference Results

2.4.3.1 Outcomes from First Differences: TIMSS 1999

Using first differences to estimate students‘ ―net achievement‖ in mathematics

in the TIMSS 1999, I find significant and positive coefficients for female teachers,

teacher experience, and for certain aspects of teacher education, some surprising.

From Table 2-14, students who study with female teachers score on average

about three points higher in mathematics than those studying with male teachers. The

relationship of having female teacher on student achievement is stronger for students

in the lowest SES quartiles (Quartiles 1 and 2), where students of female teachers

score around 5.2-5.6 points higher than those studying with male teachers.

Teacher experience also exhibits a significant relationship with student

achievement in mathematics. Students who study with a teacher who has 1 more year

of teaching experience score 0.2 points higher on the TIMSS mathematics test. The

relationship is more significant with lower p-value for those in higher SES quartiles

(Quartile 3 and 4) where teacher experience is associated with 0.5-0.8 points of


Teacher education also appears to play an important role in mathematics

achievement. Surprisingly, students in classes where the teacher does not have a B.A.

perform significantly better than those in classes where teachers have this degree. This

relationship is significant for students across the entire SES distribution. A student

who studies with a teacher without a B.A. scores about 17 points higher and the gap is

greater for those who belong to the highest SES group where they have 23 points

higher than students who have teacher with a B.A. However, we have to remember

47

that there are few teachers without a BA degree teaching in Thai schools (only 3% in

the TIMSS sample) and those without a BA degree in the teaching force are bound to

be quite unusual. The trend is significant at every level of student SES. Student

achievement is also positively associated with being in a class where the teacher holds

a graduate degree, relative to classrooms where teachers hold only a B.A.

qualification. This relationship is only significant for students at higher SES levels (

Quartile 3-4).

Students who have teacher with graduate education score 4.6 points higher

than those who have teacher with just B.A. However, the impact of teacher‘s graduate

degree on student achievement is still relatively small compared to the impact of a

teacher without B.A.

The First Difference results also suggest that students with teachers who have

graduated from faculties of education in majors such as mathematics, science

education, or education have higher test scores in mathematics than students who have

teachers holding degrees from other faculties, including faculties of science. These

relationships hold for students at all SES levels.

The coefficient for teachers holding teaching certificates is positive but does

not have any significant relationship with the achievement of students either in overall

or at each SES distribution of student.

48

2.4.3.2 Outcomes from First Differences: TIMSS 2007

As with the TIMSS 1999 data, the estimates using TIMSS 2007 data support

the results that students who study with female teachers score higher in mathematics

than those studying with male teachers. Table 2-15 shows that students who study

with female teachers score about 6.2 points higher than those studying with male

teachers. The relationship is consistent across the lower and middle SES groups

(Quartile1-3) but ceases to be significant for students in the highest SES bracket.

Also as in the 1999 data, teachers who graduate from faculties of education

and specialize in mathematics and science are associated with higher student

mathematics performance. Students with such teachers score about 15-19 points

higher in mathematics across SES groups than do students with teachers who graduate

from faculties of science with mathematics or science degrees. Students in classrooms

with teachers who graduated from other faculties also score higher than those studying

with teachers who graduated from faculties of science and who studied in mathematics

or sciences departments.

For the most part, whether teachers have a teaching certificate is positively

related to student mathematics scores, but the relationship is not statistically

significant except for students in the 3rd

SES quartile. Further, in contrast to the

outcomes from TIMSS 1999, other teacher attributes such as experience and graduate

degrees do not exhibit a significant relationship with student mathematics

achievement.

49

2.4.3.3 Comparison Between 1999 and 2007 First Difference (FD) outcomes

Estimates using the first difference approach show that after removing both

observable and unobservable student and classroom fixed effects, there is a positive

association between female teachers and student test scores in both 1999 and 2007.

However, the female teacher effect is not statistically significant for students in the

highest SES quartile. Teacher experience also demonstrates a positive relationship

with student test scores, but is only significant in TIMSS 1999. Notably, this

relationship is negative for students in the lowest SES quartile in both years.

The impact of teacher education on student test scores differs between TIMSS

years. Being in a classroom where a teacher holds less than a bachelor‘s degree is

positively related to student mathematics test score in 1999 for all SES groups.

Furthermore, having a teacher who holds a masters degree is also positive but more

significant for students in higher SES brackets in this year. The level of teacher‘s

education is not significant in 2007. The FD analysis also shows that a teaching

certificate is positively related to student test scores but is not significant in both years.

Two unexpected findings emerge from this analysis, namely a negative but not

statistically significant relationship between the test scores of low SES students and

teacher experience, and a positive relationship between the achievement of students

and teachers who do not have a B.A. One possible explanation for the latter is that in

some high-scoring schools, students are assigned to capable teachers-in-training who

are in their last year of college training, have little teaching experience and have not

yet received a teaching certificate. An explanation for the former may be that in many

50

low-scoring schools, the more experienced teachers who teach there may be teaching

in such schools because they cannot get jobs in schools with better working

conditions, or they may simply be burnt out.

2.4.4 Comparison between OLS and First Difference outcomes

From Table 2-7 and 2-8, we see that the OLS outcomes show that students who

have male teachers score higher in the TIMSS 1999 data, while students of female

teachers have higher mathematics scores in TIMSS 2007 (Table 2-9, Table 2-10).

However, the FD outcomes for both TIMSS rounds constantly suggest that students of

female teachers do better in mathematics than those of male teachers. Both OLS and

FD from Table 2-14 and 2-15 , on average, find evidence of a positive relationship

between teacher experience and student achievement in both rounds of TIMSS,

however, only the FD analysis finds negative but not significant relationships for

students in the lowest SES of students. There is little evidence of a positive

relationship between student test scores and being in a classroom where the teacher

holds a teaching certificate; indeed, the OLS analysis of TIMSS 2007 suggests a

negative association.

Being taught by a teacher without a B.A. exhibits a positive relationship with

student achievement for TIMSS 1999 in both the OLS and FD analyses. Having a

teacher with an M.A. is similarly positively related to student achievement in both

cases but only significant in the case of FD. The TIMSS 2007 data suggests slightly

different results: OLS shows that students who have teachers with an M.A. score

51

significantly higher than their peers taught by teachers without a graduate degree,

while FD finds the opposite results, although the latter are not significant.

OLS and FD suggest some contradictory outcomes with respect to the

relationship between a teacher‘s educational background and student achievement.

The FD results show that students who have teachers trained in faculties of education

in the relevant fields (mathematics education for mathematics teachers or science

education for science teachers) have higher test scores across all student SES groups.

This positive correlation across SES groups includes teachers with a B.A. in education

for TIMSS 1999. Students who have teachers trained in other fields also have higher

test scores in both years. The positive correlation between test scores and having an

‗other fields‘ teacher in the FD analysis is not much different from the results in the

OLS.

In order to understand the difference between the outcomes from the FD and

the OLS, unpacking correlations between teacher background and student

achievement may shed some light on this issue. From Table 2-13, in TIMSS 1999,

students who have science teachers with a B.A. in mathematics have the highest

science test scores, followed by students with science teachers with B.A. in science. In

the same year, students who have mathematics teachers with B.A.‘s in sciences have

the highest mathematics test scores followed by students with mathematics teachers

who hold B.A‘s in mathematics. In the TIMSS 2007 data, students who have science

teachers with B.A.‘s in science education from faculty of education have the highest

science test scores while students with mathematics teachers who have B.A.‘s in

science again have the highest test scores in mathematics.

52

There is a reason to believe that the FD method yields more accurate

estimation than the traditional OLS. While the outcomes of the OLS estimation are

less consistent between mathematics and science subjects and between the two rounds

of TIMSS, the outcomes from the FD seem to be more consistent across the years. The

consistency of the relationship between teacher‘s background and student achievement

in the FD suggests the possibility that after removing unobservable characteristics of

students and classroom, the true correlation between teacher background and student

achievement seems to be reasonably more accurate as the outcomes are quite

consistent over the years.

53

Figure 2-1

Figure 2-2

0

.00

2.0

04

.00

6

Den

sity

200 400 600 800Math score

TIMSS 2007

TIMSS 1999

TIMSS 1995

Thailand: TIMSS 1995-2007

Distribution of TIMSS Math Score

0

.00

2.0

04

.00

6.0

08

Den

sity

200 400 600 800Math score

TIMSS 2007

TIMSS 1999

TIMSS 1995

Thailand: TIMSS 1995-2007

Distribution of TIMSS Science Score

54

Figure 2-3

Note: In TIMSS 2007, there is no teacher without Bachelor’s degree.

Figure 2-4

0

.00

2.0

04

.00

6

Den

sity

200 400 600 800Math Test Score

Bachelor's Degree

MA/PHD

kernel = epanechnikov, bandwidth = 13.21

by Level of Teacher Education

Distribution of Math Test Score (TIMSS 2007)

0

.00

2.0

04

.00

6

Den

sity


Math

Math Education

Science/Science Education

Education

Others


TIMSS 1999

by Teacher Educational Background


55

Figure 2-5

Figure 2-6

0

.00

2.0

04

.00

6.0

08

Den

sity


Math

Math Education

Science/Science Education

Education

Others


TIMSS 2007

by Teacher Educational Background


0

.00

1.0

02

.00

3.0

04

.00

5

Den

sity


No Teaching Certificate

Have Teaching Certificate


by Teaching Certificate


56

Figure 2-7

Figure 2-8

Algeria

ArmeniaAustralia

Bahrain

Bosnia and Herzegovina

Botswana

Bulgaria

Chinese Taipei

Colombia

Cyprus

Czech Republic

Egypt

El Salvador

England

Georgia

Ghana

Hong Kong

IndonisiaIran

Israel

Italy

Japan

Jordan

Korea

Kuwait

Lebanon

Lithuania

Malaysia

Malta

Morocco

Norway

OmanPalestinian

Qatar

Romania

Russia

Saudi Arabia

ScotlandSerbia

Singapore

SloveniaSweden

Thailand

Tunisia

Turkey

Ukraine

USA

300

400

500

600

Sta

nda

rdiz

ed

Ma

th S

core

0 20 40 60 80 100Percentage of Teacher with degree beyond BA

TIMSS 2007

Teacher Education and Math score

Algeria

ArmeniaAustralia

Bahrain

Bosnia and Herzegovina

Botswana

Bulgaria

Chinese Taipei

Colombia

Cyprus

Czech Republic

Egypt

El Salvador

England

Georgia

Ghana

Hong Kong

IndonisiaIran

Israel

Italy

Japan

Jordan

Korea

Kuwait

Lebanon

Lithuania

Malaysia

Malta

Morocco

Norway

OmanPalestinian

Qatar

Romania

Russia

Saudi Arabia

Scotland Serbia

Singapore

SloveniaSweden

Thailand

Tunisia

Turkey

Ukraine

USA

300

400

500

600

Sta

nda

rdiz

ed

Ma

th S

core

0 20 40 60 80 100Percentage of Teacher without BA

TIMSS 2007

Teacher Educational background and Math score

57

Figure 2-9

Distribution of Student of in-field teachers and their test scores, TIMSS 1999 and 2007

Figure 2-10

Distribution of Student by their Parent’s education and student test score

34.0437.5

17.38 19.64

1999 2007

Percentage of students scoring above 50th percentilePercentage of students scoring above 75th percentile

4.26

7.85

11.08

4.86 5.25

7.94

1995 1999 2007

Percentage of students who scored above 50th percentile

by highest education level of parents

Finish UniversityFinish Secondary

2.98

5.86

8.24

2.893.38

3.94

1995 1999 2007

Percentage of students who scored above 75th percentile

by highest education level of parents

Finish University

Finish Secondary

58

Figure 2-11: Percentage of Teachers’ educational background by SES of student, TIMSS

1999

Figure 2-12 : Percentage of Teachers’ educational background by SES of student, TIMSS

2007

Notes: The socioeconomic status (SES) of student is created using the parent’s education and

number of books at home. Quartile 1(Q1) and Quartile 4 (Q4) represent the lowest and highest

SES levels respectively.

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Q1 Q2 Q3 Q4

Other

Edu

Sci

Math_Edu

Math

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Quartile 1 (Lowest SES)

Quartile2 Quartile3 Quartile4 (Highest SES)

Others

Education

Science

Math Edu

Math

59

Table 2-1 : Thailand’s Educational Statistics over 3 rounds of TIMSS

Education-related Variables Year

1995 1999 2007

Country Characteristics

Population (millions) 58.02 60.6 63.4

Percentage of Urban population 31.9 32.3 33

Percentage of Secondary school student 37 39 42

GNP Per capita (PPP adjusted) 6,870 6,490 7,440

Education Spending per capita (US Dollar) 206 340 415

Education spending (% of GNP) 3 4.8 4

Number of Secondary students (thousand) 1297 1,434 1,500

Number of Secondary teachers (thousand) 650 750 800

DATA from TIMSS

Math Achievement

Thailand Mean Math score 522 (5.7) 467 (5.1) 441 (5.0)

Percent of Thai students achieving international level

top 10% 7 (1.2) 4 (0.8) 3 (0.8)

top 25% 23 (2.6) 16 (1.8) 12 (0.7)

top 50% 54 (2.7) 44 (2.6) 34 (2.2)

top 75% 85 (1.4) 81 (1.6) 66 (2.0)

International Average 513 (0.3) 487 (0.7) 500 (0)

Number of participating students 5833 5732 5412

Number of participating Math teachers 147 150 150

Number of participating schools 147 150 150

Number of Participating countries 45 41 59 Student’s Socioeconomic background 1.Parents Education

University 9 (1.4) 9 (0.9) 12 (1.1)

Secondary School 14 (1.4) 13 (0.8) 40 (0.2)

Primary school 73 (2.6) 40 (1.3) 26 (1.6)

not finished primary school - 30 (1.5) -

2.Have Computer at home 4 (0.9) 8 (0.7) 46.58(0.7)

*Standard errors are in parenthesis Source: Data are collected from TIMSS International Mathematics report (1995,1999,2007) and Thailand Education Statistics (1995,1999,2007)

60

Table 2-2 : Distribution of Student and School at different level of TIMSS Math Scores

25th Percentile

50th Percentile

75th Percentile

95th Percentile

Percent Percent Percent Percent /S.E /S.E /S.E /S.E

1995 1.TIMSS Math scores 449.7 [5.1 ] 498.1 [ 5.4] 547.04 [5.2 ] 615.47 [ 4.8] 2.Number of students ( Total = 5833) 4374 (0.75) 2917 (0.50) 1458 (0.25) 293 (0.05) 3. Number of Schools with 10 students score higher than 25th..95th percentiles

143 (0.97) 116 (0.79) 54 (0.37) 7 (0.05)

4. Number of Schools with total average score higher than 25th..95th percentiles (Total = 147)

126 (0.86) 67 (0.46) 20 (0.14) 3 (0.02)

1999

1. TIMSS Math scores 414.98 [5.4 ] 465.6 [5.2 ] 526.88 [ 5.6] 610.614 [ 5.3]

2. Number of students (Total = 5732) 4299 (0.75) 2866 (0.50) 1433 (0.25) 287 (0.05)

3. Number of Schools with 10 students score higher than 25th..95th percentiles

144 (0.96) 100 (0.67) 45 (0.30) 12 (0.08)


123 (0.82) 69 (0.46) 23 (0.15) 2 (0.01)

2007

1. TIMSS Math scores 392.62 [ 4.5] 447.4 [5.1 ] 507.55 [ 5.3] 597.63 [ 4.8] 2.Number of students (Total = 5412) 4059 (0.75) 2706 (0.50) 1353 (0.25) 271 (0.05) 3. Number of Schools with 10 students score higher than 25th..95th percentiles

133 (0.89) 84 (0.56) 38 (0.25) 10 (0.07)


122 (0.81) 67 (0.45) 23 (0.15) 2 (0.01)

*Standard errors of TIMSS scores are in bracket, Percentages are in parenthesis

Source: Author’s calculation from TIMSS 1995, 1999, 2007 datasets

61

Teachers are from all level of education and all types of pre-tertiary schools in both public and private

institutions; Source: Thailand Educational Statistics Yearbook 1996,2003,2005

Table 2-4 : Number of Student and Teacher by average School SES and Teacher’s

educational background, TIMSS 1999

School By SES Math Math Edu Science Education Others

Quartile 1 (Lowest SES) Student 658 0 191 66 284

Teacher 20 0 6 2 9

Quartile2 Student 753 28 65 64 397

Teacher 21 1 2 2 11

Quartile3 Student 1,136 0 41 0 270

Teacher 27 0 1 0 7

Quartile4 (Highest SES) Student 1,144 0 177 0 120

Teacher 26 0 4 0 3

Total Student 3,691 28 474 130 1,071

Teacher 94 1 13 4 30

Table 2-5 : Number of Student and Teacher by average School SES and Teacher’s

educational background, TIMSS 2007

School By SES Math Math Edu Science Education Others

Quartile 1 (Lowest SES) Student 1,032 98 69 50 300

Teacher 26 2 1 1 7


Teacher 22 4 2 2 8


Teacher 23 1 2 1 9

Quartile4 (Highest SES) Student 877 17 34 25 118

Teacher 32 0 2 1 4

Total Student 3761 248 247 176 980

Teacher 103 7 7 5 28

Note: The SES of school is calculated by averaging the SES of students in the school.

Source: Author’s calculation from TIMSS 1999, 2007 datasets

Table 2-3 :Number of Thai Teachers by Educational Level

Teacher Education Level 1996 2003 2005

Below B.A. 110,964 (18.26%) 54,059 (8.6%) 39,748 (6.16%)

Bachelor’s degree 476,282 (78.4%) 475,442 (75.5%) 492,145 (76.33%)

Master+ PhD degree 20,289 (3.33%) 63,656 (10.11%) 52,425 (8.13%)

Total teachers 607,535 (100%) 629,625 (100%) 644,687 (100%)

61

Table 2-6 : Descriptive Statistics of Teachers’ Characteristics by Student’s SES from TIMSS 1999 and TIMSS 2007

Full Sample SES Quarter 1 SES Quarter 2 Obs Mean S.D Obs Mean S.D Obs Mean S.D

TIMSS 1999

Math score 5732 471.827 (80.55) 1210 446.826 (73.91) 2062 460.842 (75.00)

Teacher Sex (Female=1) 5688 0.700 (0.46) 1199 0.661 (0.47) 2049 0.689 (0.46)

Teacher Experience (year) 5616 12.930 (9.14) 1175 10.198 (8.50) 2019 12.321 (8.92)

Have Teaching Certificate (yes=1) 5664 0.888 (0.31) 1187 0.893 (0.31) 2037 0.887 (0.32)

Below BA (yes=1) 5629 0.027 (0.16) 1177 0.019 (0.14) 2032 0.025 (0.16)

BA (yes=1) 5629 0.915 (0.28) 1177 0.933 (0.25) 2032 0.921 (0.27)

Have MA/PhD degree (yes=1) 5629 0.058 (0.23) 1177 0.048 (0.21) 2032 0.054 (0.23)

Teacher’s Education Background (yes=1, no =0)

Math 5394 0.684 (0.46) 1164 0.656 (0.48) 1968 0.658 (0.47)

Math Education 5394 0.005 (0.07) 1164 0.005 (0.07) 1968 0.009 (0.09)

Science/Science Edu 5394 0.088 (0.28) 1164 0.096 (0.30) 1968 0.085 (0.28)

Education 5394 0.024 (0.15) 1164 0.032 (0.18) 1968 0.028 (0.17)

Others 5394 0.199 (0.40) 1164 0.210 (0.41) 1968 0.220 (0.41)

TIMSS 2007 Math score 5412 452.761 (81.29) 1549 424.325 (68.10) 1377 442.713 (73.23) Teacher Sex (Female=1) 5373 0.644 (0.48) 1520 0.582 (0.49) 1367 0.644 (0.48) Teacher Experience (year) 5412 15.527 (10.40) 1549 14.737 (10.18) 1377 15.319 (10.32) Have Teaching Certificate ( yes=1) 5412 0.970 (0.17) 1549 0.966 (0.18) 1377 0.975 (0.16) Have MA/PhD degree* ( yes=1) 5373 0.123 (0.33) 1520 0.109 (0.31) 1367 0.104 (0.31) Teacher’s Education Background ( yes=1, no =0) Math 5412 0.695 (0.46) 1549 0.666 (0.47) 1377 0.655 (0.48) Math Education 5412 0.046 (0.21) 1549 0.063 (0.24) 1377 0.053 (0.22) Science/Science Edu 5412 0.046 (0.21) 1549 0.045 (0.21) 1377 0.052 (0.22) Education 5412 0.033 (0.18) 1549 0.032 (0.18) 1377 0.040 (0.20) Others 5412 0.181 (0.39) 1549 0.194 (0.40) 1377 0.200 (0.40)

*In 2007 Math teachers only have B.A. and above Standard Deviation in parenthesis. Quartile 1(Q1) and Quartile 4 (Q4) represent the lowest and highest SES levels respectively.

62

Full Sample SES Quarter 3 SES Quarter 4

Obs Mean S.D Obs Mean S.D Obs Mean S.D

TIMSS 1999

Math score 5732 471.827 (80.55) 1583 489.612 (80.27) 877 500.046 (86.73)

Teacher Sex (Female=1) 5688 0.700 (0.46) 1574 0.726 (0.45) 866 0.734 (0.44)

Teacher Experience (year) 5616 12.930 (9.14) 1561 14.301 (8.91) 861 15.598 (9.71)

Have Teaching Certificate (yes=1) 5664 0.888 (0.31) 1570 0.887 (0.32) 870 0.889 (0.31)

Below BA (yes=1) 5629 0.027 (0.16) 1555 0.029 (0.17) 865 0.040 (0.20)

BA (yes=1) 5629 0.915 (0.28) 1555 0.911 (0.28) 865 0.883 (0.32)

Have MA/PhD degree (yes=1) 5629 0.058 (0.23) 1555 0.060 (0.24) 865 0.076 (0.27)

Teacher’s Education Background (yes=1, no=0)

Math 5394 0.684 (0.46) 1472 0.716 (0.45) 790 0.732 (0.44)

Math Education 5394 0.005 (0.07) 1472 0.003 (0.05) 790 0.001 (0.04)

Science/Science Edu 5394 0.088 (0.28) 1472 0.084 (0.28) 790 0.090 (0.29)

Education 5394 0.024 (0.15) 1472 0.017 (0.13) 790 0.015 (0.12)

Others 5394 0.199 (0.40) 1472 0.180 (0.38) 790 0.162 (0.37)

TIMSS 2007 Math score 5412 452.761 (81.29) 1415 445.748 (74.69) 1071 516.074 (84.03) Teacher Sex (Female=1) 5373 0.644 (0.48) 1415 0.658 (0.47) 1071 0.711 (0.45) Teacher Experience (year) 5412 15.527 (10.40) 1415 15.646 (10.33) 1071 16.781 (10.79) Have Teaching Certificate(yes=1) 5412 0.970 (0.17) 1415 0.976 (0.15) 1071 0.962 (0.19) Have MA/PhD degree* (yes=1) 5373 0.123 (0.33) 1415 0.137 (0.34) 1071 0.149 (0.36) Teacher’s Education Background(yes=1, no=0) Math 5412 0.695 (0.46) 1415 0.671 (0.47) 1071 0.819 (0.39) Math Education 5412 0.046 (0.21) 1415 0.042 (0.20) 1071 0.016 (0.13) Science/Science Edu 5412 0.046 (0.21) 1415 0.052 (0.22) 1071 0.032 (0.18) Education 5412 0.033 (0.18) 1415 0.033 (0.18) 1071 0.023 (0.15) Others 5412 0.181 (0.39) 1415 0.202 (0.40) 1071 0.110 (0.31)

*In 2007 Math teachers only have B.A. and above Standard Deviation in parenthesis. Quartile 1(Q1) and Quartile 4 (Q4) represent the lowest and highest SES levels respectively.

63

Table 2-7 : Outcomes of OLS Regression from TIMSS 1999 Dependent Variable = TIMSS 1999 Math score

Teacher Characteristics

Model1 Model2* Model3* Model4*

Female Teacher (yes=1) -3.914 -7.084** -10.496*** -12.370***

(2.427) (2.441) (2.505) (2.393)

Experience 3.110*** 2.981*** 1.583** 1.735***

(0.473) (0.517) (0.519) (0.493)

Experience squared -0.047** -0.035 -0.023 -0.040*

(0.017) (0.019) (0.019) (0.018)

Have Certificate (yes=1) 21.445*** 8.619* 5.01 6.562

(3.552) (3.645) (3.636) (3.473)

Level of Education (base = BA)

Below BA 105.924*** 61.817*** 56.949*** 44.524***

(11.216) (11.372) (11.409) (10.829)

MA 22.903*** 3.928 5.86 3.273

(4.558) (4.834) (4.961) (4.708)

Education Background (base = BA in Math)

Math Education -71.883*** -61.915*** -73.275*** -57.738***

(14.559) (15.495) (15.539) (14.731)

Sciences/Science Edu 23.323*** 21.529*** 27.371*** 24.898***

(3.813) (3.917) (4.051) (3.812)

Education -23.479** -37.119*** -22.752** -10.17

(7.189) (7.194) (7.221) (6.944)

Others -21.165*** -16.054*** -20.215*** -20.090***

(2.851) (2.902) (2.876) (2.748)

R-squared 0.1 0.19 0.261 0.357

N 5188 4750 4710 4508

* Model 2 includes teacher classroom practice variables. Model 3 includes teacher classroom practice, teacher characteristics, school characteristic variables. Model 4 includes teacher classroom practice, school characteristics and student characteristic variables. Robust standard errors in parentheses, *** p<0.01, ** p<0.05, * p<0.1

64

Table 2-8 : OLS Regression of Teacher Characteristics by Quartile of Student SES TIMSS 1999

Dependent Variable = TIMSS 1999 Math score


SES of Student (Q4 = highest)

Q1 Q2 Q3 Q4

Female Teacher (yes=1) -8.385 -14.2*** -17.97*** -7.213

(5.295) (4.011) (4.758) (7.134)

Experience 0.58 1.147 1.301** 1.798**

(1.033) (0.809) (1.071) (1.465)

Experience squared -0.014 -0.025 -0.014 -0.037

(0.037) (0.029) (0.039) (0.052)

Have Certificate (yes=1) 13.454 8.979 -3.721 7.464

(7.405) (5.623) (7.096) (10.631)

Level of Education (base = BA)

Below BA 38.801 71.781*** 29.181 39.257

(46.246) (17.974) (18.206) (26.222)

MA -11.307 0.548 1.148 12.823

(12.612) (8.453) (9.078) (11.663)


Math Education -58.896 -49.397* -65.469 -59.808

(31.599) (20.706) (36.156) (68.874)

Sciences/Science Edu 11.535 25.547*** 31.647*** 30.797**

(8.291) (6.494) (7.611) (10.599)

Education 0.266 3.234 -31.559* -33.582

(15.828) (10.726) (15.244) (21.175)

Others -31.5*** -20.835*** -13.918* -27.26**

(5.837) (4.504) (5.696) (8.860)

R-squared 0.335 0.323 0.362 0.511

N 913 1660 1266 669

The regressions include teacher classroom practice, school characteristics and student characteristic variables. Robust standard errors in parentheses, *** p<0.01, ** p<0.05, * p<0.1

65

Model 2 includes teacher classroom practice variables. Model 3 includes teacher classroom practice, teacher characteristics, school characteristic variables. Model 4 includes teacher classroom practice, school characteristics and student characteristic variables. Robust Standard errors in parentheses, *** p<0.01, ** p<0.05, * p<0.1

Table 2-9 : Outcomes of OLS Regression from TIMSS 2007 Dependent Variable = TIMSS 2007 Math score

Teacher Characteristics Model1* Model2* Model3* Model4*

Teacher Sex (Female =1) 23.910*** 18.510*** 17.869*** 14.593***

(2.284) (2.445) (2.565) (2.441)

Experience 1.354** 1.616** 1.620*** 1.379**

(0.489) (0.496) (0.491) (0.482)

Experience squared -0.028* -0.044** -0.045** -0.042**

(0.014) (0.014) (0.014) (0.014)

Have Certificate (Yes=1) -66.607*** -68.208*** -82.826*** -70.273***

(7.007) (6.420) (7.192) (7.232)

Education Level (base = B.A)

Master's degree 24.747*** 29.081*** 21.966*** 21.387***

(3.563) (3.557) (3.683) (3.422)

Educational Background (base = Math)

Math Education -38.788*** -34.494*** -15.150** -16.428**

(3.856) (4.097) (5.235) (5.019)

Science/Science Edu 11.303* 13.337** 28.508*** 29.656***

(4.568) (4.282) (4.469) (4.399)

Education -22.774*** -16.142** -11.880* -7.338

(4.604) (5.025) (5.532) (5.470)

Others -13.694*** -7.272* 2.38 0.54

(2.864) (2.912) (3.145) (3.005)

R-Squared 0.058 0.112 0.273 0.35

N 5373 5287 4636 4636

66

Table 2-10 : Outcomes of OLS Regression of Teacher Characteristics by Quartile of Student SES TIMSS 2007

Dependent Variable = TIMSS 2007 Math score

SES of Student (Q4 = highest)

Q1 Q2 Q3 Q4

Teacher Sex (Female =1) 12.903** 14.192** 13.842** 27.864***

(4.517) (4.867) (4.779) (7.352)

Experience 1.577 1.848 0.885 3.579*

(0.964) (1.010) (0.970) (1.456)

Experience squared -0.037 -0.061* -0.029 -0.117**

(0.028) (0.029) (0.028) (0.043)

Have Certificate (Yes=1) -47.865* -110.472*** -68.802*** -79.709***

(20.109) (17.026) (12.880) (16.729)

Education Level (base = B.A.)

Master's degree 17.117* 19.021** 15.919* 11.548

(7.933) (7.123) (6.433) (11.315)

Education Background (base = Math)

Math Education -13.43 -6.898 -3.967 -59.491*

(8.902) (9.454) (10.072) (25.801)

Science 19.933* 42.646*** 28.552*** -8.853

(8.772) (7.777) (8.181) (13.744)

Education 4.427 -3.463 -12.597 -78.872***

(9.775) (9.735) (11.175) (21.470)

Others -5.366 11.247 -1.05 15.291

(5.993) (6.051) (5.574) (10.259)

R-Squared 0.184 0.268 0.264 0.415

N 1233 1185 1246 972

Notes: The regressions include teacher classroom practice, school characteristics and student characteristic Robust standard errors in parentheses, *** p<0.01, ** p<0.05, * p<0.1

67

Table 2-11 : Descriptive Statistics of First Difference Analysis , TIMSS 2007

Variables

Science Math Difference

N Mean S.D N Mean S.D Mean S.D

Student Test Score 5412 476.9 (78.88) 5412 452.76 (81.29) 24.237 (42.42)

Sex of Teacher (Female =1) 5412 0.691 (0.46) 5373 0.644 (0.48) 0.053 (0.64)

Experience 5412 14.313 (9.78) 5412 15.527 (10.40) -1.214 (12.28)

Teaching Certificate(Yes =1) 5344 0.990 (0.10) 5412 0.970 (0.17) 0.020 (0.20)

Education Level (M.A.=1) 5375 0.121 (0.33) 5373 0.123 (0.33) -0.002 (0.42)

Education background

Infield 5412 0.424 (0.49) 5412 0.695 (0.46) -0.271 (0.68)

Infield and Education 5412 0.238 (0.43) 5412 0.046 (0.21) 0.192 (0.47)

Science/Math 5412 0.006 (0.08) 5412 0.046 (0.21) -0.039 (0.22)

Education 5412 0.043 (0.20) 5412 0.033 (0.18) 0.011 (0.28)

Others 5412 0.288 (0.45) 5412 0.181 (0.39) 0.107 (0.60)

Table 2-12 : Descriptive Statistics of First Difference Analysis , TIMSS 1999

Variables

Science Math Difference

N Mean S.D N Mean S.D Mean S.D

Student Test Score 5732 485.411 (65.06) 5732 471.827 (80.55) 13.584 (42.74)

Sex of Teacher (Female =1) 5732 0.878 (0.33) 5732 0.695 (0.46) 0.183 (0.39)

Experience 5622 12.781 (8.64) 5616 12.930 (9.14) -0.107 (9.65)

Teaching Certificate (Yes =1) 5732 0.827 (0.38) 5732 0.878 (0.33) -0.051 (0.49)

Education Level (yes =1,no=0)

Below B.A. 5699 0.024 (0.15) 5629 0.027 (0.16) -0.003 (0.23)

B.A. 5699 0.911 (0.29) 5629 0.915 (0.28) -0.005 (0.40)

M.A./Ph.D 5699 0.066 (0.25) 5629 0.058 (0.23) 0.009 (0.33)

Education background (yes=1, no=0)

Infield 5512 0.237 (0.43) 5394 0.684 (0.46) -0.450 (0.60)

Infield and Education 5512 0.411 (0.49) 5394 0.005 (0.07) 0.388 (0.50)

Science/Math 5512 0.028 (0.17) 5394 0.088 (0.28) -0.055 (0.31)

Education 5512 0.092 (0.29) 5394 0.024 (0.15) 0.073 (0.34)

Others 5512 0.232 (0.42) 5394 0.199 (0.40) 0.044 (0.58)

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Table 2-13 : Outcomes of OLS Regression for Science Teachers

Dependent Variable = TIMSS 1999,2007 Science score

VARIABLES TIMSS 1999 TIMSS 2007

Sex of Teacher (Female = 1) 7.32*** 9.91***

(1.77) (2.40)

Experience 1.91*** -3.96***

(0.14) (0.46)

Experience Squared -0.02*** 0.12***

(0.00) (0.01)

Have Teaching Certificate (Yes=1) 3.87* 27.56***

(2.27) (10.59)


Below B.A. -9.42 NA

(6.40)

MA/PhD -11.95*** 23.10***

(3.53) (3.40) Education Background (base = Science)

Science Education -5.99*** 11.61***

(2.20) (2.83)

Math/Math Education 2.33 -79.14***

(5.34) (13.60)

Education -28.46*** -17.54***

(3.31) (5.44)

Others -36.78*** 5.44**

(2.47) (2.69)

Constant 483.58*** 457.95***

(4.22) (10.96)

Observations 5475 5307

R-squared 0.10 0.04

Notes: The regressions include teacher classroom practice, school characteristics and student characteristic. Robust standard errors in parentheses, *** p<0.01, ** p<0.05, * p<0.1

69

Table 2-14 : First Difference (FD) Outcomes by SES Quartile of Students , TIMSS 1999 Dependent Variable = Difference between Math-Science scores in TIMSS 1999

VARIABLES Full SES of Student (Q4 = highest)

Q1 Q2 Q3 Q4

Sex of Teacher (Female=1) 2.95*** 5.23*** 5.60*** -0.35 3.27

(0.66) (1.42) (1.11) (1.20) (2.10)

Experience 0.20** -0.20 0.01 0.50*** 0.80***

(0.09) (0.21) (0.15) (0.18) (0.27)

Experience Squared -0.00** 0.00 -0.00 -0.01** -0.01***

(0.00) (0.00) (0.00) (0.00) (0.00)

Have Teaching Certificate (Yes=1) 1.68 0.21 -0.58 2.09 5.52

(1.27) (2.98) (2.11) (2.20) (3.43)


Below B.A. 16.92*** 17.95** 14.27*** 15.65*** 22.81***

(2.79) (8.13) (5.27) (4.37) (6.23)

M.A./Ph.D 4.62** -1.97 2.11 9.86*** 10.14**

(1.89) (5.15) (3.32) (3.16) (4.40)

Education background (base = infield)

Infield and Education 13.61*** 15.67*** 15.30*** 9.69*** 9.36***

(1.09) (2.46) (1.84) (2.00) (2.87)

Science/Math 8.17*** 0.70 3.26 12.53*** 12.72***

(2.09) (4.97) (3.89) (3.60) (4.73)

Education 12.46*** 15.74*** 14.26*** 6.65* 9.64*

(1.86) (3.87) (2.95) (3.85) (5.32)

Others 5.24*** 3.19 4.46** 5.95*** 5.11

(1.18) (2.97) (1.91) (2.12) (3.23)

Observations 5036 992 1841 1387 736

R-squared 0.06 0.09 0.09 0.04 0.06

Notes: Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

70

Table 2-15 : First Difference (FD) Outcomes by SES Quartile of Students , TIMSS 2007 Dependent Variable = Difference between Math-Science scores in TIMSS 2007

VARIABLES Full SES of Student (Q4 = highest)

Q1 Q2 Q3 Q4

Sex of Teacher (Female=1) 6.15*** 4.21* 9.78*** 8.95*** 1.19

(1.01) (2.24) (2.42) (2.55) (2.53)

Experience 0.05 -0.11 0.45 0.06 0.08

(0.18) (0.41) (0.43) (0.44) (0.43)

Experience Squared -0.01** -0.00 -0.03* -0.02 -0.01

(0.01) (0.01) (0.01) (0.01) (0.01)

Have Teaching Certificate 5.83 -2.54 -2.58 21.60*** 8.48

(Yes=1) (3.79) (11.57) (8.99) (7.64) (8.21)


M.A./Ph.D -0.97 -2.55 -3.10 1.25 2.38

(1.59) (4.65) (3.24) (4.17) (3.43)

Education background (base = infield)

Infield and Education 15.36*** 18.09*** 14.00*** 18.88*** 15.29***

(1.31) (3.23) (2.68) (3.01) (3.23)

Science/Math -19.22*** 0.57 -9.81* -30.84*** -19.55***

(2.95) (7.26) (5.43) (6.29) (7.47)

Education 2.95 -4.82 8.30 2.47 -7.72

(2.58) (4.42) (6.25) (6.92) (8.03)

Others 8.97*** 9.08*** 8.76*** 8.29*** 7.55***

(1.15) (2.68) (2.59) (2.94) (2.76)

Observations 5268 938 988 850 914

R-squared 0.05 0.05 0.07 0.12 0.04

Notes: Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

71

Appendix A

Quantile Regression

The Quantile Regression (QR) is a type of regression analysis that does not use

the method of least squares (LS), which estimate the conditional mean of the explained

variable at certain values of explanatory variables. Instead, the QR estimates the

conditional median or other quantiles of explained variables (for a more detailed

discussion see (Koenker, 2005)). It allows the analysis of the impact of regressors on both

the location and scale parameters of the model. The approach is semiparametric in that it

avoids assumptions about the parametric distribution of errors, This makes it suitable for

heteroskedastic data. It provides information about the relationship between the

achievement outcome ( ) or SES of student and regressors at the test score. Currently,

there is a large literature using the QR approach for labor market analysis (Arias et

al,2001; Buchinsky,1994; Buchinsky, 1997; Chamberlain,1994; Fitzenberger, 1999)

icsT

72

Figure 2-13 :

Figure 2-14 :

73

Table 2-16 : Quantile Regression Outcomes , TIMSS 1999 , Dependent Variable is TIMSS 1999 Math score


Quantiles

0.1 0.2 0.3 0.4 0.5 0.6 0.7 08 0.9

Female Teacher (yes=1) -5.311 -7.079 -9.594*** -11.63*** -13.780*** -12.790*** -12.223*** -9.815** -9.644*

(3.990) (3.891) (2.535) (3.069) (2.625) (3.398) (2.469) (3.226) (4.099)

Experience 1.865* 1.525 1.888*** 1.802** 1.715** 1.614* 1.542** 1.787* 1.552

(0.797) (0.791) (0.527) (0.645) (0.558) (0.729) (0.536) (0.703) (0.869)

Experience squared -0.058* -0.044 -0.058** -0.057* -0.056** -0.055* -0.043* -0.047 -0.031

(0.028) (0.028) (0.019) (0.023) (0.020) (0.026) (0.019) (0.025) (0.031)

Have Certificate (yes=1) 8.73 11.120* 13.435*** 15.078** 12.715** 12.111* 11.142** 10.092* 1.302

(5.641) (5.654) (3.764) (4.603) (3.958) (5.154) (3.781) (4.952) (6.399)

Below BA 57.874*** 53.538** 47.245*** 45.790** 44.072*** 38.423* 29.703* 26.48 13.707

(17.422) (17.715) (11.847) (14.320) (12.338) (16.043) (11.778) (14.208) (18.957)

MA 8.955 12.481 7.599 13.534* 12.179* 16.628* 14.619** 11.239 17.188

(6.989) (6.961) (4.651) (5.733) (5.009) (6.658) (4.973) (6.688) (8.933)


Math Education -47.976* -62.015* -53.59*** -64.90*** -62.385*** -46.673* -46.749** -55.138** -49.426*

(23.440) (24.284) (16.206) (19.276) (16.726) (21.254) (15.666) (20.063) (23.995)

Sciences/Science Edu 26.863*** 26.94*** 31.068*** 29.927*** 30.192*** 30.042*** 29.268*** 24.834*** 27.081***

(6.305) (6.243) (4.136) (5.048) (4.409) (5.767) (4.265) (5.664) (7.107)

Education 22.694 7.056 1.685 -1.394 -2.782 -7.612 -8.591 -14.897 -1.712

(11.958) (11.680) (7.627) (9.165) (7.831) (10.056) (7.225) (9.150) (11.827)

Others -17.9*** -18.1*** -20.80*** -21.26*** -19.6*** -21.9*** -19.018*** -20.09*** -22.749***

(4.680) (4.557) (3.037) (3.676) (3.132) (4.021) (2.893) (3.785) (4.803)

Psudo R-squared 0.173 0.176 0.183 0.189 0.196 0.205 0.21 0.22 0.22

N 4508 4508 4508 4508 4508 4508 4508 4508 4508

The regressions include teacher classroom practice, school characteristics and student characteristic variables. Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

74

Table 2-17 : : Quantile Regression Outcomes , TIMSS 2007, Dependent Variable is TIMSS 2007 Math score


Quantiles

0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9

Teacher Sex (Female=1) 14.123*** 14.564*** 15.998*** 17.148*** 18.082*** 21.978*** 13.442*** 13.545*** 10.798*

(3.063) (3.105) (3.131) (3.003) (2.817) (2.996) (3.054) (3.662) (4.782)

Experience 3.079*** 2.773*** 2.116*** 1.665** 1.043 0.772 1.083 1.053 0.814

(0.564) (0.581) (0.603) (0.590) (0.564) (0.615) (0.642) (0.800) (1.064)

Experience_squared -0.087*** -0.085*** -0.072*** -0.055** -0.034* -0.025 -0.036 -0.033 -0.016

(0.016) (0.017) (0.018) (0.017) (0.016) (0.018) (0.019) (0.023) (0.031)

Educational Level (base = B.A.)

Master's degree 2.797 6.682 13.299** 17.509*** 21.899*** 24.518*** 26.446*** 30.676*** 28.833***

(4.095) (4.165) (4.352) (4.253) (4.067) (4.427) (4.607) (5.649) (7.528)

Educational Background (base=Math)

Math_Edu -11.609 -15.525* -7.944 -9.234 -12.726* -14.968* -18.883** -19.475* -29.417*

(6.608) (6.796) (7.044) (6.794) (6.486) (6.978) (7.231) (8.874) (11.758)

Science 41.621*** 37.651*** 39.489*** 39.571*** 31.452*** 27.903*** 24.131*** 20.976** 18.859

(5.871) (5.932) (6.152) (5.915) (5.625) (6.080) (6.222) (7.411) (9.668)

Education 23.005*** 9.18 0.565 -7.613 -4.969 -9.109 -8.704 -13.418 -3.391

(6.782) (7.165) (7.488) (7.270) (6.965) (7.597) (7.856) (9.533) (12.468)

Others 7.342 3.889 1.551 0.145 -1.115 -2.996 -5.352 -8.153 -8.388

(3.778) (3.786) (3.943) (3.820) (3.628) (3.932) (4.071) (4.947) (6.523) Have Certificate (Yes=1) -80.914*** -78.312*** -63.838*** -76.850*** -73.502*** -63.732*** -69.212*** -68.331*** -62.355***

(10.938) (10.488) (10.977) (10.660) (10.053) (10.889) (11.190) (13.707) (17.461)

Psudo R-Squared 0.167 0.18 0.19 0.2 0.2 0.21 0.22 0.22 0.25

N 4636 4636 4636 4636 4636 4636 4636 4636 4636

The regressions include teacher classroom practice, school characteristics and student characteristic variables. Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

75

CHAPTER3

PAY DIFFERENCES BETWEEN MATHEMATICS-

SCIENCE TEACHERS AND OTHER

MATHEMATICS-SCIENCE ORIENTED

OCCUPATIONS: EVIDENCE FROM THAILAND

3.1 INTRODUCTION

In this chapter, I compare the salaries of mathematics and science teachers with

those of selected mathematics and science-oriented occupations. This comparison is

important since a central explanation for the poor performance of Thai students in

mathematics and science, both internationally (Martin et al 1996; 2000; 2008) and

domestically (The Nation 2009, 2010), is a shortage of high quality mathematics and

science teachers. Many researchers observe that a large earnings gap between

mathematics and science teachers and alternative occupations dissuade students who

have high ability in mathematics and science from entering the teaching profession as

they would rather choose other occupations that generate higher economic returns

(Carnoy et al, 2009; Corcoran, Evans, and Schwab, 2004).

76

In order to study how differences in salaries might affect individuals‘ career

choices, I compare the income of mathematics and science teachers with those of

mathematics and science oriented occupations such as engineers, scientists, medical

professionals and accountants. These fields require high levels of mathematics and/or

science proficiency in order to pass the requisite university entrance examination or in

order to graduate. I also include nurses in the comparison group. While the study and

practice of nursing does not require that individuals are highly skilled in mathematics

or science, it bears considerable resemblance to the teaching profession in terms of the

high degree of feminization of the profession and its predominantly public sector

orientation with a comparable salary scale.

The assumption that the failure to recruit high ability teachers as a reason for

low student achievement is arguably controversial. Critics may contend that there are a

host of alternative explanations for lower student achievement, such as the nature of

the mathematics curriculum (Schmidt et al, 2001) and deficiencies in the home

learning environment. Proponents of the important role of teacher training may also

believe that a well-designed training curriculum can transform teacher trainees with a

mediocre academic background or in-service teachers into highly capable mathematics

and science teachers (Stigler, Lee, Lucker & Stevenson, 1982; Westbury, 1992;

Grossman & Thompson, 2004; Remillard, 2000; Smith & Star, 2007)

The idea of comparing earnings of teachers and other occupations is often met

with methodological challenges. Among them is the issue of selection bias in terms of

the intrinsic ability of teacher and other professions, since in Thailand students are

77

admitted to study teacher, science, engineering, etc. based on their entrance

examination scores. In order to minimize the ability bias among individual workers, I

also compare the salaries of teachers and non-teachers who both graduated from

teacher‘s training institutions

The main purpose of this chapter is to find the difference in the structural pay

between occupations. The pay difference often depends on other factors such as the

structure of the country‘s economy, government policies and the demand and supply

of the labor force.

Currently available data in Thailand do not allow us to verify whether the

relatively low level of teacher salary compared to other math-science occupations

directly affects the learning of students in math-science subjects. However, the data

from the Labor Force Survey (LFS) of Thailand‘s National Statistics Office does

allow us to compare between the monetary incentives of mathematics and science

teachers and alternative occupations in order to answer the following questions:

1. How large was the pay gap between mathematics and science teachers and

those in alternative occupations over the twenty years from 1985 to 2005?

2. What are the determinants of teacher incomes and how do these compare to

factors influencing incomes in other occupations?

3. How large are the pay gaps between teachers and non-teachers among the

graduates of four-year universities in general and among the graduates of

teachers‘ colleges in particular?

78

4. What does the decomposition of teacher wages tell us about the most important

factors in teacher wages over time compared to the factors influencing wages

in other professions?

3.2 RELATED LITERATURE

There is a large body of literature on teacher pay that can be divided into three

broad categories. The first attempts to isolate the effect of teacher pay on teacher

quality or student achievement were to compare teachers with varying salaries within

the same school district or country on characteristics related to teacher ―quality‖ or

―performance‖ (as measured by student outcomes). There is no clear consensus among

researchers in this area. While Larry (2005) finds that monetary incentives can

improve teacher quality, Hanushek (2005) finds no association between the salary and

the performance of teachers. Allegretto, Corcoran & Mishel (2004) provide a useful

summary of the literature on teacher wages and associated quality. Though the results

are mixed, they argue in their review that a positive association between teacher

quality measures and teacher pay appears to be more pronounced in the long run than

in the short run.

The second category of analyses compares the salaries of teachers and those in

other occupations in order to determine whether teachers are relatively overpaid or

underpaid. Although this approach is popular among education researchers, it is often

misleading because the outcomes are likely to reflect the difference between teachers

and the average income in a society, including the income in low paying occupation

79

such as farmers or unskilled laborers. Studies are also largely unable to account for

variability in the opportunity costs of teachers when they forgo alternative career

options. Notably, mathematics and science teachers face a higher opportunity cost than

teachers in social sciences or humanities because they are subject to higher forgone

earnings from choosing teaching over better-paid jobs requiring a similar skill set.

The third category of analyses examines the relationship between relative

teacher pay and student achievement. This research has tentatively found that the

achievement of students is higher in countries where teachers are paid relatively well,

or where inequality of income is low. Carnoy et al (2009) find that countries with a

lower Gini coefficient and a low salary difference between male teachers and

scientists perform better in terms of student achievement in international mathematics

tests. Carnoy et al (2008) also suggest that a high degree of income equality between

teachers and other high-skilled occupations in Cuba contributes to the country‘s high

achievement in mathematics.


3.3.1 Background of Thai Teachers

3.3.1.1 Socioeconomic Status (SES) of Thai Teachers: A Historical Perspective

The SES of teachers in Thailand has changed in parallel with the history of the

country. From the thirteenth through to the middle of the nineteenth century, education

was centered around Buddhist temples throughout the country, with monks acting as

teachers. As monks are restricted in their contact with females by religious rules,

80

education at the temples was provided exclusively for boys in subjects such as

reading, writing, arithmetic and religion. The teacher-monks were highly respected in

the community for performing this role, but did not gain any monetary return from

teaching.

Thailand entered a period of modernization during the reign of King

Chulalongkorn (Rama V) in the late nineteenth century. Several new administrative

departments were founded by the King, including the Ministry of Education, which

was established in 1892. During this period, the government established teacher

training institutions in order to produce laymen teachers in accordance with the

modern education system. The first national education act in 1921 mandated that all

boys and girls were required by law to attend primary schools for four years, and thus

an expanding teacher labor force was necessary. By the end of 1930s, the process of

replacing monks with government-trained lay teachers had accelerated to full capacity

(Keyes, 1991).

In order to encourage the production of teachers in this early period,

scholarships were offered to provincial students who agreed to go back and teach in

their home districts upon completing their training. A number of prestigious King‘s

scholarships were also granted to promising young students to study abroad, mostly in

England, with a requirement to return to teach in Thailand (Sumawong, 1973). As a

result, a large number of bright young people chose to enter the teaching profession.

Despite the selectivity and the respectable status of teachers in these early

years, teaching had never been considered a high paying occupation. As of the early

81

1930s, teacher salaries were little higher than peasant incomes (Keyes, 1991).

However, low pay was offset by teachers‘ status as government officials, which at the

time was considered a prestigious career with lifetime stability.

Another significant change came during the 1960s when Thailand received

support from the United States to develop the infrastructure of the country in an

attempt to counter the influence of communist ideology during the Vietnam War. The

first National Economic and Social Development Plan was drafted in 1961. Mass

education was seen as central to modern development and as a tool to spread the

ideology of ―Nation, Religion and Monarchy‖ the three pillars of Thailand as a nation.

In order to produce a large number of teachers, the government created more than 30

new teachers‘ colleges throughout the country during 1960-1970.

Once Thailand produced a large number of teaching graduates, teaching

gradually came to be seen as a less high status occupation. In particular, those who

were trained in the less prestigious teachers‘ colleges qualified for the same benefits

and status as those who graduated from the more prominent teacher training

universities in Bangkok. The supply of teachers overall also increased exponentially

such that Thailand became a country with an excess supply of teachers. This excess

supply persists today.

Since 1984, the teachers‘ colleges began to expand their academic programs to

include other disciplines such as sciences and liberal arts. All teachers‘ colleges were

renamed as Rajabhat Institutes in 1992 and later elevated to Rajabhat Universities in

2004.

82

Currently, teachers are not generally regarded as having high social status in

Thai society. This is in part a function of the modest economic returns to teaching and

in part due to their perceived intellect. Among the college-educated population,

students who choose to train as teachers are viewed as enrolling in teacher training

colleges or Rajabhat Universities due to a failure to gain acceptance into the

respectable government universities (Jukping, 2008).

Among departments in prestigious government universities, schools of

education tend to have among the lowest entrance examination scores. Many students

enroll in training programs as a means to obtain a university education and with no

intent to work as schoolteachers following graduation. The example of Chulalongkorn

University, considered to be one of the most prestigious universities in Thailand,

illustrates this point. Its school of education is considered one of the best in terms of

the qualification of faculty and students. Nonetheless, many of its students list other

departments as their first or second preference for admission, but obtain acceptance

into the faculty of education instead due to their low average entrance score. Nearly 80

percent of its graduates do not take up teaching as a profession, largely as a result of

the failure of teacher salaries to keep pace with the cost of living as Thailand continues

to develop economically (Somwang,2010). In practice, many in-service teachers in

Thailand come from substandard institutions such as the former teacher colleges.

83

3.3.1.2 Salary Structure of Teachers in Thailand

The majority of teachers in Thailand work in the public sector and are subject

to the salary scheme and benefits of other government officials. The starting salary of

teachers in public institutions is determined by their educational qualifications when

first entering the teaching profession. The annual incremental increase in their salary

depends on their performance, professional service and higher educational

qualifications acquired while working in the profession. After a teacher‘s retirement at

60 years old, that person is entitled to a state pension.

Besides monetary benefits, public school teachers are provided with some non-

pecuniary benefits such as free medical care for themselves and their family members

and subsidized education for their children. They also receive low-cost loans to

purchase houses, motorcycles and cars. In some rural areas, free or subsidized housing

and a hardship allowance are provided.

As of 2010, the starting salaries for teachers with bachelor‘s and master‘s

degrees are 7,940 and 8,990 bahts respectively. The salaries can be increased with

years of work to 30,000-50,000 baht. Besides salaries from the government, teachers

are entitled to special allowances of 3,500-15,600 baths when they have worked for a

number of years.2

2 Office of the Teacher Civil Service and Educational Personnel Commission

http://203.146.15.33/webtcs/ (retrieved July 2010)

http://203.146.15.33/webtcs/

84

Apart from some high quality private schools in Bangkok and major provinces,

on average, teachers who teach in private schools receive lower salaries and fewer

benefits than public school teachers. The terms of benefits for private school

employment vary from school to school.

3.3.1.3 Feminization of the Teaching Profession in Thailand

Teaching in many countries is a predominantly female occupation. In Western

countries such as the U.S., Canada and across Europe, the feminization of the teaching

profession began as far back as the late nineteenth century (Rothman,1978;

Harrigan,1992;Harrigan 1998). However, as girls in Thailand were not enrolled in

formal education until 1921, the number of male and female teachers was

approximately equal as late as the 1930s-1940s (Jukping, 2008). However, once

Thailand entered the phase of US-sponsored development in the 1960s during the

Vietnam War, the demand for professional and semi-professional male workers began

to expand in other sectors such as industry and services. The ratio of women relative

to men in teaching therefore began to increase (Educational Planning Office, 1966).

Moreover, during the 1960s, Thai women were encouraged to engage in productive

activities. Teaching was seen as a central mechanism to achieve this objective, and the

highest rate of growth of the female teaching force took place during this period

(Watson, 1974). The ratio of female teachers has increased over the years. From the

labor force survey data, the ratio has increased from approximately 40% in 1950s to

70% in 1990s and 80% in 2000s.

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3.3.1.4 Role of Thai Government in Recruiting Math-Science Teachers

The Thai government has been aware of the obstacles in recruiting highly

capable students to work as mathematics and science teachers since they are more

inclined to choose other careers with better job prospects and higher incomes. As a

result, the Thai government has started a program called the ―Program for Promotion

of Science and Mathematics Teacher‘s Production‖ in 1998. It gives scholarships to

highly capable high school graduates to study in approved faculties of sciences in

fields such as Mathematics, Physics, Chemistry, Biology and Computer Science plus 1

year scholarship for teacher‘s training program.

In 2005, the program had changed its policy to only sponsoring college

graduates from faculties of sciences to a one-year teacher‘s training program. The

recipients of the scholarship have a privilege that they do not need to take an exam for

teaching positions and receive guaranteed salaries of 10,000 baht for 2 years. They

also have a chance to receive scholarships to continue their education at the master‘s

and doctorate levels. Currently, the program has produced 3,210 mathematics and

science teachers serving schools throughout the country.

3.3.2 Data

The datasets used in this analysis are drawn from the National Labor Force

Survey of Thailand from 1985 to 2005. The Thailand Labor Force Survey (LFS) has

been conducted by the Thai National Statistical Office (NSO) since 1963. The primary

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purpose of the LFS is to assess the labor force characteristics of the Thai population.

The first versions were presented in the form of aggregate and tabulated statistics,

however raw data which can be analyzed using statistical software are available from

1985 until the present.

The LFS uses the two-stage sampling methodology in the survey process.

Thailand is comprised of 76 provinces, and each province is divided into municipal

areas and non-municipal areas. The primary sampling units in the first stage are blocks

for municipal areas and villages for non-municipal areas. Fifteen households are

chosen from each municipal area and twelve households are chosen for each non-

municipal area.

The survey collects information regarding individuals‘ employment situation

and demographic background. Prior to 2001, individuals aged 13 and older were

classified according to whether they were in the labor force or not. In 2001, the NSO

re-defined the labor force population as anyone aged 15 or older. The total labor force

is composed of the current labor force and seasonally inactive laborers with the current

labor force constituted by both employed and unemployed individuals. Before 1994,

the sample size was approximately 84,000 people. From 1994-2000, the sample size

was increased to approximately 170,000 people.

The populations of interest in this analysis are the labor force who work as

mathematics-science teachers and other mathematics-science oriented occupations

such as engineers, scientists, physicians, dentists, accountants, economists and nurses.

Since there is no category of teacher by subjects, but only by their levels (pre-primary,

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primary, secondary school teachers), mathematics and science teachers are put on the

same salary scale as teachers of other subjects. I therefore use the salaries of secondary

school teachers in general as a proxy for the salaries of mathematics and science

teachers.

The data in the LFS also provide information on the educational background of

teacher and non-teacher population in Thailand whether they attain their tertiary

education from teachers‘ colleges, technical institutions or academic-tracked

universities. This allows for the comparison of the incomes of teachers who graduate

with a bachelor‘s degree from teacher training colleges and teachers who graduate

from four-year universities in the academic track. It also allows for comparing the

incomes of individuals who graduate from teacher colleges but choose to pursue other

careers against those who pursue a teaching career.

3.3.3 Descriptive Statistics

Thailand has undergone a lot of economic and social changes over the past 20

years. In the early 1980s, the country had entered industrialization with a large export

sector, which led to high economic growth. In general, the salary levels of the private

sector respond to the change in supply and demand of the labor market much more

accurately and quickly than the salary levels of the public sector set by the central

government. During the past 20 years from 1985 to 2005, we can see the greater

earnings gap between the private and public sectors from the late 1980s until the late

1990s when Thailand was struck by the Asian financial crisis of 1997.

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Prior to the crisis, the salaries of the occupations such as engineering, medicine

and accounting in the private sector had risen significantly compared to occupations in

the public sector. The trend of salaries in the public sector shows a modest but steady

increase. However, the economic crisis of 1997 sharply reduced salaries in the private

sector compared to the salaries of public sector, which were relatively stable. For 4-5

years after the 1997 economic crisis, the gap between public and private salaries began

to rise again.

Another important trend of earnings during this 20-year period is that the earnings

gap between male and female workers has been closing. Nakavachara (2010) suggests

that this is due to females‘ increasing educational attainment. In the following

sections, I will present several variations of descriptive analyses of the comparisons

between the earnings of teachers and other occupations.

3.3.3.1 Comparison between teachers and other occupations

In order to compare the earnings between teachers and other occupations over

the years. I calculate the average incomes of each occupation by year adjusting them

with the Consumer Price Index (CPI) to 2000 prices. The comparison is done by

gender of the labor force, under the assumption that there is a difference in terms of

preference and opportunity cost between males and females in certain occupations.

I compare the earnings between occupations in several ways. Besides the main

comparison between teachers and five mathematics-science oriented occupations, I

compare teachers and non-teacher university-educated groups. Since most of the

secondary school teachers in Thailand have their bachelor‘s degree, the gap between

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the earning of teachers and other graduates gives a good picture of the economic

situation of the profession. Moreover, I compare the earnings between people who

graduated from teachers‘ colleges and work as teachers and those who work in other

occupations. Generally, the graduates of teachers‘ colleges have lower SES and

academic ability than the graduates from 4-years-government universities. Therefore,

it makes sense to compare the earning gap between teachers and non-teachers of the

sub-population of teachers‘ college graduates. In addition, I compare the earnings by

sector of work and teachers and non-teachers in the general population. Finally, I

compare the age-earning profiles of teachers and the mathematics-science oriented

occupations every 5 years from 1985 to 2005.

a) Earning gap between teacher and the non-teacher college-educated population

From the trend between male and female teachers and other professionals in

the labor force, we can show the trend that male teachers or male graduates of

teachers‘ colleges receive much lower average salaries than other males in the labor

force with bachelor‘s degrees.

On the other hand, although female teachers also receive, on average, less

salary than the college-educated female population, the gap is relatively much smaller

than for males. During the 1997 economic crisis, as the average earnings of the

college-educated female population generally declined, the average salary of female

teachers became higher than average salary of the rest of the higher educated female

population. But after the crisis, from 2002 on, average college-graduate salaries

recovered and again came to exceed the salaries of female teachers.

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b) Earning gaps between teacher and other mathematics-science oriented

occupations

When comparing salaries of teachers with some mathematics-science oriented

occupations such as engineers, physicians/dentists, accountants/economists and

nurses, we see from Figure 3-1 that the gap between teacher salaries and other

occupations (except nurses) has also widened gradually from 1985. Teachers have

much lower average salaries compared to engineers and physicians. The gap became

smaller after the 1997 economic crisis when incomes of engineers and economists

dropped sharply. From 2001 on, salaries of engineers, physicians, economists and

accountants have significantly risen again. The difference between teacher salaries and

each of the 5 occupations is shown in Figure 3-5.

A distinctive trend of teacher salary from Figure 3-2 and Figure 3-3 is that it

gradually increases every year during the economic boom of early 1990s and remained

so even after the 1997 economic crisis. The possible explanation is that teacher

salaries are determined by the central government and are rather immune to economic

fluctuations compared to the salaries of workers in the private sector. Another

interesting trend is that teacher salaries are very similar in both amount and direction

with the salaries of nurses. Teachers and nurses are both predominantly female

occupations (67% female for teacher, 97% for nurses in 2005) and the majority of the

teaching and nursing labor force is in the public sector (90% for teachers, 95% for

nurses in 2005).

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When considering the trend separately by gender, the earnings gap between

male teachers and male engineers, physicians/dentists and accountants are larger than

the gap between female teachers and females in the respective occupations. The gap

between female teachers and nurses is quite small. The gap between male teachers and

male nurses varies more, so that, overall, male teachers earn more except during 1990-

1995 when male nurses seem to have had higher average earnings.

Table 3-2 shows descriptive statistics for the 6 occupations in this study. We

see that teachers‘ average salary is about 50 percent of that in the medical profession,

62 percent of engineers‘ salaries but not very different from scientists‘ salaries. With

respect to educational backgrounds, more than 90% of the labor force in these

mathematics-science oriented occupations (85% for nurses) finished university

education.

c) Earnings among graduates of teachers‘ colleges

The problem of selection bias from a difference in ability between teachers‘

colleges and other mathematics-science oriented occupations is a concern when

comparing the earnings gap between teaching and other careers. In general, people

who enter teacher training programs have lower entrance examination test scores.

Therefore, in order to compare the earnings of teachers and non-teachers who

supposedly have the same level of academic ability, I compare the earnings of those in

the labor force who have graduated from teachers‘ colleges and chose to work as

teachers and those who chose to work in other professions.

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Many in Thailand‘s labor force have received their degrees from one of the

many teacher training institutions in Thailand. Male graduates who take up a teaching

job have less average income than their male counterpart teacher training college

graduates who work in other occupations. The opposite is the case for the female

graduates of teachers‘ colleges. The female graduates who take teaching jobs earn

more than their peers who take other jobs, although the gap is quite small. The

economic crisis of 1997 made the earnings of both male and female graduates who

work in other jobs decline sharply relative to teacher salaries, which remained more or

less constant.

d) Earning between teachers and the labor force as a whole:

When comparing the gap between teachers and the labor force aged 17-60

years old regardless of education level. Thai teachers earn much more than average

Thai workers. However, the gap between female teachers and female workers as a

whole is larger than for males.

Overall, these trends of earnings between teachers and other occupations

suggest that the opportunity cost for Thai male teachers is higher than that for Thai

female teachers since male teachers are likely to earn more if they choose other jobs

than are female teachers. In both the overall male population and the population of

male teachers‘ college graduates, the earning gap between male teachers and almost

all 5 other mathematics-science oriented occupations are significantly larger than the

gap for females. The gaps between male teachers and non-teachers are also larger

when comparing only graduates of teachers‘ colleges. Teachers earn significantly

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more than the average member of the labor force in Thailand but the gap is smaller for

males than females.

e) Earnings gap between sectors of work

There are four main sectors of work in Thailand: private, public, state enterprise,

and self-employed (including family business, business owner) sectors. For most

occupations, working in the private sector is associated with higher salary but may

have less career security, whereas working in the public sector means a lower salary

but has more job security. A public sector worker is unlikely to be laid off once getting

employed.

Workers in state-enterprises earn significantly more than public sector workers

and still have a high degree of job security. Figure 3-4 shows the breakdown of public-

private salaries among the six occupations. When only considering the average salary

in the public sector, the difference between teachers‘ salaries and the salary in other

mathematics-science occupations is much reduced. From Figure 3-4, the official

government‘s average salaries for engineers, scientists, accountants and nurses are

quite low compared to their peers in the private sector. It seems that the high average

earnings of the engineering and medical professionals are mainly the result of high

salaries in the private sector. Interestingly, in contrast to other professions, teachers in

public school receive substantially higher pay on average than teachers who work in

private schools.

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3.3.3.2 Age-Earning Profiles of Teachers and Other Occupations

Figure 3-6 and Figure 3-7 show the age-earning profiles of teacher and other

professions by gender. The trends of the age-earning profiles go in the same direction

as the average yearly returns of occupations in Figure 3-1. The age-earning profiles of

teachers and nurses are quite similar, while the earnings of engineers and doctors are

higher than teachers in every age group.

3.4 METHODOLOGY

In order to estimate the effect on earnings of being a teacher vis-à-vis other

occupations, I employ two strategies: the Mincer wage-earnings function and the wage

decomposition analysis. The data used in this section is from the nationally

represented Thailand‘s Labor Force Survey (LFS) starting from 1985 until 2005. I use

the first and the fourth quarter of each year. The dataset consists of data on the

characteristics of labor forces in Thailand, such as their income, work-related

characteristics, and their SES.

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3.4.1 Mincer Earnings Function

The first approach is to use the Mincer earnings function (Mincer, 1974) to find the

effects of different mathematics-science oriented occupations such as Teachers,

Scientists, Engineers, Medical Doctors/Dentists, Nurses, Economists /Accountants on

the earnings of workers in the labor market controlling for other characteristics of the

workers.

2

0 1 2 3 4 5 6 7log( )i iY Occ Age Age Sex Edu Location Typework ..(1)

Variables of Interests: The dependent variable (Y ) is the total monthly earnings of

teachers and the other 5 mathematics-science oriented occupations. The total monthly

earnings include salaries, fringe benefits, other incomes and bonus. The reason I

choose monthly instead of hourly wages is because monthly income is a better

indicator of compensation in Thailand as it is used as a criterion for many economic

decisions such as whether you are qualified for credit cards, bank loans, or

government benefits. Although monthly earnings is subject to bias from not

accounting for hours of work, it is offset by the reported additional income that people

work part-time. Hours of work is also considered a characteristic of the job rather than

an individual choice. Moreover, monthly wages reflect more accurate survey data in

Thailand as people tend to report their hours of work incorrectly, especially for many

workers who have to continue to work outside their official working hours.

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The independent variables include dummy variables for occupation ( Occ : teacher,

scientist, engineer, doctor, accountant, nurse), age ( Age), age squared ( 2Age ), sex.

LOCATION is a vector of the characteristics of location such as the urbanicity, the

gross provincial province (GPP) and the income inequality of the province where

individuals work. EDU is a dummy variable of highest level of education (primary,

secondary, diploma, college) and Typework is a dummy variable of sector of work

(government, state enterprise, private sector, self-employed). The regressions are

computed separately for men and women.

Potential Bias of Earnings Function

The OLS estimation of the wage premium may be biased because wages are

observed only for participants in the labor market and individuals may self-select into

wage employment on the basis of some unobservable factors such as ability, which

also affect wages. In that case, the OLS estimate of wage premium will be biased. In

order to correct for this problem, I will only use the observations of individuals who

receive bachelor‘s degrees in academic-track or those who went to four-year

universities. I leave out the observations of individuals who graduated from teachers‘

colleges.

In addition to find the wage gap between teachers and other mathematics-

science oriented occupations, I compare the earnings between just teachers and non-

teachers among the graduates from teachers‘ colleges using the following wage

equation:

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2

0 1 2 3 4 5 6 7log( )i iY Teacher Age Age Sex Edu Location Typework …(2)

Where Teacher is a dummy variable of being a teacher. The other variables are

same as in expression (1).

3.4.2 Oaxaca-Binder Wage Decomposition

The OLS has the limitation that earning differences are only expressed as the

intercept of occupational dummy variables. However, variations in the slope

coefficients are not exploited. By using the decomposition approach (Blinder,1973;

Oaxaca,1973) between two groups of workers (teachers and non-teachers), in the

similar approach to Nakavachara (2010) who segregates male-female wages in Thai

labor market, I can examine how much the difference between mean earnings of these

two group can be explained by labor characteristics and how much is unexplainable by

differences in labor characteristics. The explainable part is sometimes called ‗the

endowment gap‘ while the latter part is called ‗the discrimination gap‘ or ‗the residual

gap‘.

Oaxaca Model:

Earning equations of non-teachers (NT) and teachers (T)

T T T TY b x e ………….. (2)

NT NT NT NTY b x e ………….. (3)

TY is the monthly earnings of teacher, NTY is the monthly earnings of non-teacher,

Tb and NTb are coefficients of observable characteristics or the ‗wage structures‘ of

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teacher and non-teacher respectively, x is the vector of observable attributes such as

education, sex, age, age-squared, location and employment status.

Following Oaxaca (1973), the wage differential between the groups can be derived as

follows

NT T NT T NT NT T TY Y Y b x b x …….. (4)

By this expression, a reference wage structure must be chosen. The reference wage

structure ( b ) is a pay mechanism under the assumption that everyone faces the same

pay mechanism regardless of their occupations. In this case, I will use the pooled wage

structure as a reference structure (the expressions herein are derived from

Nakavachara, 2010, p.p.10-11)

( ) ( )NT T NT T NT T NT T NT TY Y Y x x b x b b

NT Tb x bx ……..… (5)

The first term and the second term on the right-hand side of equation (5)

denote the endowment gap and the residual gap. The endowment gap is explained by

the differences in individual characteristics between the two groups weighted by the

coefficients estimated for non-teachers in the income equation. The residual gap is

explained by the differences in the market returns to the occupation of teachers and

non-teachers.

Following this approach, one can examine how much of the average wage-gap

between teachers and non-teachers can be explained by difference in the productive

characteristics of teachers and non-teachers and how much is not explained by the

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differences in characteristics (the residual component). If the residual component is

substantial, then one may argue that there is a ‗premium‘ in the earnings of a particular

occupation, that is not due to the superior (or inferior) human capital characteristics

associated with that occupation.

Potential Bias of Oxaca-Binder Wage Decomposition Analysis

The decomposition approach to earning differentials is sensitive to omitted

variable bias and model misspecification. Further we may face an occupational

selection problem (selection into teaching and non-teaching occupations). If selection

into teaching and non-teaching is endogenous, selectivity corrected earnings functions

should be used in an amended decomposition framework as proposed by Neuman and

Oaxaca (2004).

3.5 RESULTS

3.5.1. Mincer Earnings results

Table 3-4 reports OLS estimates of Mincer Earnings regressions of teachers

and mathematics-science oriented occupations from (2). The wage premium is

represented by the coefficients of occupation dummy variables using the pooled

sample of teachers and the five mathematics-science oriented occupations. In the full

model (column 1) all related variables are used in the regression: the age, urbanity,

occupation and educational level all have significant effects on the monthly earnings.

Using income of accountants/economists as a reference variable, the earnings of

teachers are the lowest among the six occupations. College education gives the highest

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return in terms of additional earnings compared to the lower levels (lower secondary,

secondary, diploma). Workers in the private sector earn significantly higher incomes

even when controlling for human capital characteristics.

When we consider only the sub-sample of teachers (Table 3-4, column 2), age

and higher education have positive effects on teacher earnings. Teachers in the urban

areas and those who work in public schools have significantly higher estimated

earnings.

I also run the OLS for the sub-samples of male, female, workers in public and

private sectors, and workers in urban and rural areas. There are some interesting

results from this set of regressions. For instance, when considering only workers in

the public sector, teachers still have the lowest earnings among the six occupations,

but the gap is small. Physicians and nurses are the highest paid among the public

servants while engineers are paid rather low salaries in the public sector. And despite

the unified salary system across urban/rural, teachers and other government officials

tend to earn more in urban areas. This suggests that teachers/government officials who

work in urban areas might receive extra income from a part-time job or other labor

market opportunities or simply because there are more higher paying government

official positions in urban than in rural areas.

Regarding the type of employment, higher age and being in an urban area have

different effects for the public and private sector. Age plays a much larger and

significant role in the public sector. This makes sense because salaries in the public

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system depend very much on the number of years of work while that is less the case in

the private sector.

3.5.1.1 Comparison between Teachers-Non Teachers among graduates of

Academic-track Universities

Table 3-10 reports the regression outcomes of 5 rounds from 1985 to 2005.

The purpose is to compare the earnings gap between teachers and other five

mathematics-science oriented occupations controlling for other factors such as their

age, sector of work and workplace location. In order to remove bias from teachers‘

educational background, the teachers who graduated from teachers‘ colleges will be

removed from the observations so that we can directly compare graduates of

universities who work as teachers and as other professions.

An interesting outcome for all years is that controlling for age, the urbanicity

of the workplace, marital status, and the sector of work, teachers earn less than all five

occupations including nurses, but the impacts and the significant level varies.

Following are the results of the regression analysis.

Male engineers earn more than teachers from 1985, 1990, 1995 at an

increasing rate but the gap has decreased in 2000 and 2005. Female engineers also

earn much more than female teachers but the gap fell sharply in 2000 and 2005. The

economic crisis of 1997 is the likely reason that the earnings gap decreased between

engineers and teachers.

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For medical professions, both males and females increasingly earn more than

teachers each round. The noteworthy difference from other professions is that the

earnings of medical professions did not decrease after the economic crisis.

Scientists in general earn more than teachers. However, the significance only

appears in year 1990 for female and in year 2005 for both genders. This also reflects

problems of low pay for scientists in Thailand. Both male and female accountants-

economists earn significantly more than teachers in every year except for males in

1990. The gaps seem to be stable over time.

The pay of nurses is most closely related with teachers. Female nurses earn

significantly more than female teachers in every year from 1985, but the earning gap

has not been large. Male nurses earn more than teachers as well, but it is only

significant in 2005. Both male and female nurses earn significantly more than

teachers in 2005.

In sum, teachers earn significantly less than all 6 mathematics-science oriented

occupations. The highest earning group consists of engineers and medical

professionals, followed by accountants/economists and scientists. Nurses and teachers

are in the lowest-earning group. This is evidence that the graduates of the four-year

universities in Thailand have to forgo a large portion of their earnings if they pursue

teaching careers instead of other high-paying mathematics and science related

professions.

The impacts of other factors on the earnings of university graduates are as

follows. Male workers who work in the public sector earn significantly more than

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those in private sector. However, female workers in the public sector only earned

significantly more than in the private sector in 2005. And from 1995, the earnings of

graduates in state enterprise sectors are significantly higher than public and private

sector earnings for both males and females.

3.5.1.2 Comparison between Teachers-Non Teachers Among Graduates of

Teachers‘ colleges:

Another way of limiting the educational bias between teachers and non-

teachers is to compare the earnings of the graduates of teachers‘ colleges who chose to

be a teacher and those who work in other occupations.

The results are as follows: from Table 3-12, male graduates of teachers‘

colleges who chose to be teachers earn less than those who chose to work in other

professions in all 5 years of my analysis. The gap between male teachers and non-

teachers has increased from 1985 until 1995 before it decreased in 2000, possibly

because of the 1997 economic crisis. The gap widened again in 2005.

On the other hand, the female graduates of teachers‘ colleges earn more than

non-teachers. The gap has not changed much over the year except after the economic

crisis in 2000 when the earnings of female teachers was much larger than non-

teachers. The effect of being a female teacher in 2005 stayed about the same as before

the 1997 economic crisis.

The results from the regression estimates support the trend in the earnings of

teachers and non-teachers (Figure 3-2, 3-3) in the general population. It confirms that

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the opportunity cost for males to enter the teaching profession is much higher than for

females even if we only control for the labor force graduating from teachers‘ colleges.

Other factors associated with the earnings of teachers‘ college alumni include age,

sector of work and location of a teacher. The older graduates or those with more

working experience earn more than the younger ones. Both male and female graduates

earn more if they work in a municipal area. The graduates of teachers‘ colleges who

work in the provinces with high gross provincial product (GPP) earn more, while

those working in provinces with more inequality of income (or high Gini-coefficients)

earn less.

In conclusion, other things being equal, teachers earn less than other

occupations among graduates of academic-track university graduates for both males

and females. However, when comparing only among graduates of teachers‘ colleges,

male graduates of teachers‘ colleges earn significantly more if they choose other

occupations instead of teaching. On the other hand, female graduates of teachers‘

colleges earn significantly more if they work as teachers. These relationships have

been statistically significant from 1985 to 2005.

3.5.1.3 Determinants of Earnings of Teacher and other Mathematics-Science

oriented occupations

The outcomes from Table 3-5 to Table 3-9 present the factors associating with

earning in five mathematics-science oriented occupation including teachers, and the

rest of the labor force in 5 rounds from 1985 to 2005.

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For the Thai labor force overall, being male has a positive association with

higher earnings. When considering each occupation separately, male workers earn

more than female workers in accounting in 1990 and 2005, and in engineering and

medicine in 2000 and 2005. The only occupation in which females earn more than

males every year is nursing, but the gaps are only significant in 1985 and 2000.

The sector of work seems to matter. Individuals earn most if they work in the

‗public enterprise‘ sector following by the ‗public sector‘ and least in the ‗private

sector‘. A single person earns more than those who are married or widowed. Workers

in the provinces with high income (high GPP) and greater inequality of income (high

Gini-coefficient) have positive and negative association with earnings, respectively.

These associations are constant and statistically significant from 1985 to 2005.

Being a teacher in the public sector is associated with higher income. Other

professions such as engineers, medical professionals and accountants earn more if they

are in the private sector. The interesting trend is the change in the earnings of nurses.

In 1985 and 1990, nurses earned more if they worked in the public sector; however,

from 1995 onward they earned more in the private sector.

Teaching is the only occupation among the mathematics-science oriented

occupations where working in an urban or municipal area is significantly and

positively associated with earnings in every year during this period. This is quite

perplexing since as a government official, teachers should receive similar salaries

whether they are in municipal or non-municipal areas. The possible explanation is that

a teacher in an urban area has a higher probability of receiving extra income from

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moonlighting activities. Nurses and accountants also earned more in urban areas from

1995 to 2005.

3.5.2 Oaxaca-Binder Results

Table 3-13 presents the results from the Blinder-Oaxaca decomposition

analysis. The regression coefficients underlying the decomposition are derived from

the same OLS earnings function as reported in Table 3-4. The main comparison is

between the average monthly earnings of teachers and the average monthly earnings

of workers in the 5 mathematics-science oriented occupations (engineers, scientists,

physicians, accountants, nurses). Since the average income of non-teachers is higher

than teachers (from Table 2), I decompose the Non-teacher (NT) over the teacher (T)

mean earnings gaps. The incomes are adjusted by the Consumer Price Index (CPI) to

constant year 2000 earnings.

The result in Table 3-13 shows the analysis for 2005, 2000, 1990 and 1985.

Panel A of the table summarizes the predicted means of non-teacher and teacher

monthly earnings, and the difference between non-teacher and teacher earnings. From

the Table, the magnitude of the gap in terms of monthly earnings was 1620.51 baht in

1985, 1918.22 in 1990, 2700.16 in 1995, 2401.78 in 2000 and 3489.33 in 2005. We

can see that the earning gap between earnings of non-teachers and teachers had been

widening from 1985 to 1995. After the 1997 Asian economic crisis, the gap was

smaller in 2000 and in 2005 it sharply increased again. This trend reflects the same

magnitude and direction with those in Figure 3-2 and 3-3.

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The Blinder-Oaxaca decomposition allows us to estimate the portion of the

total gap that is accounted for by the explainable characteristics and the unexplained

portion. The magnitude of the explained portion of the monthly earnings was -3093 (-

145.8%) in 1985, -3477 (-128%) in 1995 and -3832 (-109.8%) in 2005. Interestingly,

the negative values in the explained portion mean that if observed characteristics such

as age, sex, education and type of employment were the only factors used in

determining earnings and they were compensated for at the same rate for teachers and

non-teachers, then we should observe higher earnings for teachers compared to non-

teachers. In other words, teachers are systemically under-compensated or have

negative wage premiums compared to workers in these 6-non-teaching fields. From

the outcomes, the unexplained gaps are larger than the explained gap in an opposite

direction, which means that there are other unobservable characteristics with large

effect that make non-teachers receive higher pay.

We can analyze further in detailed decomposition (Part B of the Table 3-13)

that separates the explained portion of the gap into each of the observable

characteristics. For example, the negative explained gaps were heavily influenced by

the age variables (56-91%). The effect of university education increasingly plays a

role in non-teacher income from 1995-2005, whereas the effect of obtaining a

secondary school degree negatively increases over time. This means that people who

only have secondary degrees earn less in 2005 than in previous years if they work in

non-teaching fields. The gender of workers also plays a negative role for non-teachers,

which means that females earn more as teachers, but the effect is small (3.37%).

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3.6 DISCUSSIONS

My analysis of the wage data for teachers and some selected mathematics-

science oriented occupations (engineers, physicians/dentists, accountants/economists,

nurses) yields the results that on average, a teacher‘s pay is significantly lower than

other mathematics-science occupations and tied closely to the pay of nurses. However,

when we control for factors such as the level of education, sector of work, sex, age and

location of work, teaching becomes the lowest paying job and even falls below the pay

of nurses. Also, teaching is the lowest-paying job among individuals who work in the

public sector, other characteristics being equal.

The earnings gaps between teachers and other occupations had increased over

the years until the gap was reduced during the 1997 Asian Economic Crisis and

shortly after. Recently, the gaps have been expanding continuously since 2002,

making teaching a less desirable job for individuals who have the ability to choose

from several career choices.

Gender plays a large role in determining the opportunity cost when one

chooses to work as a teacher or in other occupations. Thai men have higher

opportunity costs than Thai women when entering the teaching profession. In other

words, men could earn more than women if they choose careers in other mathematics-

science oriented occupations. This is the case even among graduates of teachers‘

colleges. Male graduates of teachers‘ colleges earn more than their peers if they work

in other jobs whereas female graduates of teachers‘ colleges earn less if they choose

jobs other than teaching.

109

When we compare teachers and non-teachers, the decomposition technique

shows that if we account only for observable characteristics, teachers would receive

higher average salaries than if they worked in non-teaching jobs. Therefore, as a

profession, teaching is systematically under-compensated. However, the greater effect

of unobservable factors outweighs the observable characteristics, which results in

lower incomes for teachers. The unexplainable factor may come from the market‘s

different demand for labor in other occupations.

The Oaxaca outcome makes some sense because, on the surface, Thai teachers

are quite well qualified in terms of their educational degree obtained. However, the

data cannot indicate the innate ability of the workforce or the bias associated with

people self-selecting themselves into more secure but less well-paid careers. In reality,

even though individuals obtain the same level of education, the ability of Thai students

who enter the faculties of medicine, engineering, sciences, nurse, accounting or

education are quite different from the start.

110

Figure 3-1: Earnings of math-science occupations from 1985-2005

Source: Thailand Labor Force Survey 1985-2005 (Quarter 1). Earning is adjusted by Consumer Price Index (CPI) of Year 2000. The population

shown in this figure includes both male and female.

0

5000

10000

15000

20000

25000

30000

35000

40000

Engineers (CPI)

Physicians,Dentists (CPI)

Nurses (CPI)

Economist-Accountant (CPI)

Primary School teacher (CPI adjusted)

Secondary School teacher (CPI adjusted)

111

Figure 3-2: Earnings of math-science occupations from 1985-2005: Male

Source: Thailand Labor Force Survey 1985- 2005 (Quarter1), National Statistical Office, Thailand

: The monthly earning is adjusted with Consumer Price Index (CPI) Thailand, year 2000

Figure 3-3: Earnings of math-science occupations from 1985-2005: Female

Source: Thailand Labor Force Survey 1985- 2005 (Quarter1), National Statistical Office, Thailand

: The monthly earning is adjusted with Consumer Price Index (CPI) Thailand, year 2000

0

5000

10000

15000

20000

25000

30000

35000

Engineers

Physicians-Dentists

Nurses

Economist-Accountant

Teachers

0

5000

10000

15000

20000

25000

30000

35000

Engineers

Physicians-Dentists

Nurses

Economist-Accountant

Teachers

112

Baht/month

Figure3-4: Monthly earnings by public-private category, 2005

Source: Thailand Labor Force Survey 2005 (Quarter1), National Statistical Office, Thailand

Figure 3-5: Difference between teacher and other occupations, 1985-2005

Source: Thailand Labor Force Survey 1985-2005 (Quarter1), National Statistical Office, Thailand

0

10000

20000

30000

40000

50000

60000

70000

Government sector

Private Sector

All

-5000

0

5000

10000

15000

20000

25000

teacher-engineer

teacher-physicians/dentists

teacher/nurse

teacher-accountant/economist

113

Figure 3-6: Male: Age-Earning Profile (2005)


Figure 3-7: Female: Age-Earning Profile (2005)


0

10000

20000

30000

40000

50000

60000

Engineer

Medical Profession

Nurse

Econ/Accountant

Teachers

Overall

BA-Holder

0

10000

20000

30000

40000

50000

60000

70000

80000

90000

Total Population

Teachers

Engineer

Medical

Nurse

Econ/ACC

BA holders

114


Table 3-1: Teachers in Thailand’s labor force by Urbanicity in 2005

Teacher Jobs

Income Hours of work Age Sex (female =1)

Urban Rural Urban Rural Urban Rural Urban Rural

Secondary School Teacher

Mean 16318.9 14155.48

34.7725 34.958 41.704 39.27 0.6400 0.54128

S.D 8731.754 10358.21

8.289542 9.9001 9.4253 9.111 0.4802 0.49944

N 1011 216

1011 216 1014 218 1014 218

Primary School Teacher

Mean 17155.89 15537.89

34.045 34.254 44.033 41.85 0.7161 0.64319

S.D 7752.484 7441.14 7.7400 7.7215 7.7674 8.494 0.4509 0.47962

N 1491 424 1492 424 1501 426 1501 426

Pre-Primary School Teacher

Mean 12035.66 13241.65 33.949 35.27 39.514 38.75 0.9285 0.9375

S.D 6855.197 14941.68

7.79755 6.980 8.9991 9.147 0.2584 0.24462

N 138 47 139 47 140 48 140 48

Total Mean 16546.54 14946.15 34.32503 34.545 42.880 40.82 0.6979 0.63150

S.D 8176.558 9129.726 7.963074 8.4197 8.6275 8.823 0.4592 0.48274

N 2647 687 2649 687 2662 692 2662 692

115

Table 3-2: Descriptive Statistics: labor force characteristics by Occupation 2005

Variables Teacher Engineer Scientist Physician/

Dentist Nurse Accountant/ Economist

Income Mean 17206.39 27658.73 19822.31 33661.33 17097.79 21727.4

(Baht/month) S.D 7521.922 19694.91 9855.693 21987.55 6946.711 13480.44

Sex (female=1) Mean 0.6698322 0.070922 0.4615385 0.4108527 0.961194 0.6790123

S.D 0.4703478 0.2576096 0.5188745 0.4939067 0.1932765 0.4683037

Age Mean 42.5059 35.76232 37.51613 37.44203 35.99585 38.78729

S.D 8.603905 9.650056 9.312611 9.505993 8.689312 9.215581

Hours of Work Mean

S.D

Education

Primary Mean 0.0011171 0.0057971 0 0.0036232 0.0027682 0

S.D 0.0334076 0.076028 0 0.0601929 0.0525587 0 Lower Secondary Mean 0.0022343 0.0057971 0 0 0 0.0110497

S.D 0.0472191 0.076028 0 0 0 0.10468

Secondary Mean 0.0111714 0.0231884 0 0.0072464 0.0152249 0.019337

S.D 0.1051112 0.1507201 0 0.0849707 0.1224888 0.1378972

Diploma Mean 0.0167571 0.0202899 0 0 0.1314879 0.019337

S.D 0.1283703 0.1411948 0 0 0.3380502 0.1378972

Bachelor's Mean 0.9663262 0.9391304 1 0.9855072 0.8484429 0.9475138

S.D 0.1804025 0.2394382 0 0.1197274 0.3587152 0.223314 Urban (urban=1) Mean 0.7920523 0.8376812 0.9032258 0.9601449 0.8871972 0.8453039

S.D 0.4058716 0.3692786 0.3005372 0.195974 0.3164611 0.3621153

Employer

Public sector Mean 0.9044047 0.2405797 0.5806452 0.75 0.9564014 0.441989

S.D 0.2940589 0.4280563 0.5016103 0.4337993 0.204271 0.4973107 State Enterprise Mean 0.0001596 0.1304348 0.0645161 0 0.0020761 0.121547

S.D 0.012633 0.3372703 0.249731 0 0.0455329 0.3272141

Private sector Mean 0.0951165 0.6028986 0.3548387 0.1956522 0.0373702 0.3977901

S.D 0.2933992 0.490008 0.4863735 0.3974225 0.189733 0.4901191

N 6266 345 31 276 1445 362


116

Table 3-3: Descriptive Statistics Thai Labor Force in 2005

N Mean S.D

Earning (baht/month) 8725 18232.03 9806.215

Sex (female=1,male=0) 8725 0.6876462 0.4635075

Age 8725 40.82888 9.136237

Occupation (dummy)

Teachers 8725 0.7181662 0.4499186

Accountants 8725 0.04149 0.199432

Nurse 8725 0.165616 0.3717569

Physician/Dentist 8725 0.0316332 0.1750317

Scientist 8725 0.003553 0.0595045

Engineer 8725 0.0395415 0.1948907

Education (dummy)

Primary School 8725 0.0016046 0.0400274

Lower Secondary 8725 0.0022923 0.0478254

Secondary 8725 0.0124928 0.1110774

Diploma 8725 0.0354155 0.1848381

University 8725 0.9456734 0.2266741

Urban (urban=1) 8725 0.8175358 0.3862488

Employment

Government 8725 0.8615473 0.3453943

state enterprise 8725 0.0108883 0.1037831 private sector 8725 0.1222923 0.3276418


117

Table3- 4: OLS outputs of Thai Labor Force Analysis

Dependent variable = Monthly Earning (in Baht)

Variables Full Teacher Male Female Government Private Municipal Non-Municipal

Sex ( male=1) -4.2 -26.43 107.29 413.59 -283.26 1,077.95**

(254.03) (186.22) (190.34) (1399.46) (292.86) (492.16)

Age 353.57*** 221.95*** 382.72*** 466.01* 454.05*** 2.84 364.72*** 353.19*

(102.32) (85.78) (104.00) (237.60) (80.23) (447.06) (116.22) (212.38)

Age_square 3.35*** 4.33*** 2.55** 3.11 1.62 7.06 3.31** 3.04

(1.27) (1.05) (1.30) (2.89) (0.99) (5.73) (1.43) (2.67)

Municipal =1 (rural=0) 799.60*** 653.58*** 557.59* 1,257.94** 1,062.32*** -255.69

(283.93) (217.24) (300.12) (594.64) (216.24) (1370.48)

Occupations (base = accountants/economists)

Teachers -6,020.62*** -7,208.94*** -3,254.60** -1,355.57** -14,039.0*** -6,253.08*** -5,112.61***

(633.73) (647.92) (1430.37) (642.35) (1722.20) (723.43) (1285.16)

Nurse -1,884.75*** -3,343.70*** 1,298.89 1,908.71*** -1,896.47 -1,787.30** -2,633.36*

(698.53) (692.24) (2323.40) (672.14) (3578.81) (791.89) (1476.34)

Physician/Dentist 12,016.62*** 10,786.96*** 14,200.88*** 10,093.96*** 35,857.33*** 11,711.84*** 14,227.75***

(906.81) (1122.49) (1729.75) (822.02) (3299.65) (991.27) (3018.97)

Scientist -1,000.78 -2,510.90 1,584.77 2,185.77 -1,435.00 -723.67 -4,923.30

(2055.91) (2527.10) (3602.76) (1700.15) (7544.93) (2211.26) (6295.89)

Engineer 8,974.52*** 15,032.19*** 8,473.13*** -551.25 10,459.30*** 7,886.71*** 12,838.29***

(863.14) (2211.09) (1546.22) (1105.66) (2233.91) (990.42) (1728.47)

Education Level (base= more than BA)

Primary School -17,650.10*** -23,150.6*** -3,171.39 -29,529.9*** 506.52 -43,113.9*** -17,407.9*** 0

(4144.48) (3436.94) (5473.54) (6747.78) (3807.83) (11906.39) (4397.85) (0.00)

Lower Secondary -19,073.31*** -22,888.4*** -10,677.44** -20,911.8*** -104.02 -48,241.7*** -19,768.8*** 697.51

118

Robust standard errors in parentheses, *** p<0.01, ** p<0.05, * p<0.1

(3959.61) (3437.55) (5194.24) (6511.20) (3459.50) (12881.02) (4274.93) (7631.57)

Secondary -13,172.55*** -16,335.8*** -2,871.70 -19,103.2*** 5,595.46* -41,785.6*** -14,256.2*** 8,072.57

(3329.26) (2581.60) (4398.66) (5460.11) (3015.35) (9692.90) (3484.50) (6408.62)

Diploma -11,294.32*** -15,191.0*** -1,974.05 -14,504.1*** 5,848.11** -39,831.4*** -11,038.8*** 5,948.99

(3219.40) (2512.65) (4276.12) (5290.17) (2908.31) (9879.15) (3312.34) (6364.71)

University -8,698.46*** -14,215.8*** 991.39 -13,340.2*** 8,182.09*** -38,257.9*** -8,475.07*** 9,251.77

(3172.86) (2439.24) (4242.33) (5122.95) (2880.30) (9263.97) (3257.67) (6209.65)

Employment Sector (base = government)

state enterprise 2,074.97 -10,453.8** -427.4 7,120.44*** 2,514.91* -1,780.52

(1309.61) (4855.33) (1619.11) (2272.12) (1397.27) (4539.74)

private sector 1,961.42*** -3,495.01*** -611.54 7,295.59*** 2,211.63*** 1,109.40

(388.98) (318.57) (406.73) (841.52) (451.95) (731.77)

Constant 8,826.42** 12,649.19*** 1,003.17 5,547.88 -13,544.9*** 51,589.53*** 9,378.91** -9,991.96

(3772.85) (2908.68) (4739.87) (7003.42) (3371.78) (11521.15) (3989.31) (7791.61)

Observations 4207 3135 2897 1310 3682 492 3427 780

R-squared 0.44 0.55 0.46 0.43 0.51 0.57 0.43 0.47

119


Table 3-5 : Regressions by sub-sample of occupations 1985 Dependent variable = Monthly Earning (in Baht)

Variables Engineer Scientist Medicine Nurse Accountant Teachers Others

SEX 0.00 0.00 1,842.56 -734.64* -555.21 103.94 407.80***

(0.00) (0.00) (1220.77) (419.61) (688.33) (118.69) (53.66)

AGE -1,569.70* 0.00 -428.48 190.07** 660.45* 339.08*** 198.86***

(792.00) (0.00) (714.13) (95.30) (383.78) (54.87) (13.80)

AGE Squared 35.76*** 12.94 8.84 -0.29 -5.26 -3.10*** -1.89***

(11.21) (0.00) (9.66) (1.29) (5.17) (0.70) (0.18)

Sector of work (base=Public)

Private 8,665.90* 0.00 4,788*** -61.78 4,195.83*** -1,588*** -1,608.2***

(4082.45) (0.00) (1241.97) (435.61) (1557.87) (171.26) (68.46)

State Enterprise 7,203.67 0.00 0.00 0.00 5,428.45*** 0.00 2,373.79***

(4142.70) (0.00) (0.00) (0.00) (1715.46) (0.00) (126.98)

Urban Area (Yes=1) 0.00 0.00 -5.59 272.86 864.06 620.45*** 1,451.45***

(0.00) (0.00) (974.06) (237.50) (1555.06) (123.12) (51.99)

Marital Status (base=Single)

Married 1,576.01 0.00 2,376.12* 51.36 266.12 -199.46 -332.31***

(2164.85) (0.00) (1265.96) (294.99) (835.76) (133.49) (68.55)

Widowed 0.00 0.00 2,269.99 -277.37 -681.61 -485.56 -1,170.5***

(0.00) (0.00) (2667.39) (599.31) (3915.31) (479.75) (210.50)

Divorced 0.00 0.00 0.00 -1,029.60 0.00 286.27 -724.99***

(0.00) (0.00) (0.00) (641.73) (0.00) (357.65) (244.31)

Separated 0.00 0.00 0.00 0.00 0.00 -1,288.4** -1,287***

(0.00) (0.00) (0.00) (0.00) (0.00) (534.17) (200.10)

Constant 14,308.08 -3,065.11 8,006.80 -1,434.28 -13,390** -3,554*** -1,851.1***

(14825.53) (0.00) (11828.58) (1556.01) (6553.69) (999.92) (242.60)

Observations 18 2 22 79 96 255 9155

R-squared 0.90 1.00 0.83 0.76 0.32 0.63 0.32

120


Variables Engineer Medicine Nurse Accountant Teachers Others

SEX -5,033.96 1,397.55 440.69 1,349.08** 16.41 509.22***

(6180.84) (1677.79) (613.07) (677.62) (312.25) (76.05)

AGE -550.80 871.71 544.41*** 634.25* 817.02*** 243.40***

(1817.22) (796.35) (129.88) (341.91) (128.65) (18.93)

AGE Squared 21.69 -6.99 -4.96*** -5.65 -8.33*** -2.33***

(24.29) (9.38) (1.75) (4.78) (1.54) (0.24)


Private 15,796.23*** 3,030.40 -131.51 1,829.67* -955.36** -2,441.62***

(4314.07) (2269.34) (571.52) (951.64) (402.54) (101.20)

State Enterprise 7,668.65* 0.00 2,092.12* 1,246.05 0.00 3,147.87***

(4371.82) (0.00) (1130.70) (1200.59) (0.00) (186.56)

Urban Area (Yes=1) -2,399.08 -3,472.94 -68.48 -606.87 777.12** 2,025.65***

(4526.79) (2880.84) (309.18) (963.52) (318.52) (73.86)


Married -4,374.28 2,124.64 -39.58 1,507.22* 37.13 -415.73***

(4388.70) (3138.28) (307.04) (816.87) (398.72) (97.89)

Widowed -1,233.02 7,222.72 1,124.46 0.00 -1,309.51 -1,430.5***

(7690.93) (5352.33) (854.34) (0.00) (1292.45) (275.43)

Divorced 0.00 0.00 -764.48 12,297.32*** -1,097.55 -875.96**

(0.00) (0.00) (1165.01) (3937.88) (1499.63) (370.59)

Separated 0.00 0.00 0.00 4,139.26 -378.22 -1,306.5***

(0.00) (0.00) (0.00) (3560.03) (1866.77) (288.20)

Constant 7,146.70 -10,898.32 -6,067.5*** -7,705.14 -12,082*** -1,596.5***

(32474.81) (14627.75) (2226.02) (5971.35) (2529.91) (342.67)

Observations 45 29 165 231 295 13232

R-squared 0.36 0.45 0.51 0.27 0.33 0.22


121



SEX -2,164.86 2,027.97 -146.11 -335.29 125.45 444.05***

(5588.45) (4355.97) (1133.24) (620.28) (189.58) (61.31)

AGE 2,894.75* 2,091.30 464.84*** 780.26** 898.74*** 265.07***

(1543.31) (1688.07) (167.53) (336.72) (73.39) (15.63)

AGE Squared -28.18 -15.26 -2.54 -3.29 -7.86*** -2.31***

(20.56) (20.52) (2.23) (4.67) (0.87) (0.20)


Private 23,561.33*** 29,628.85*** 1,931.97* 5,449.02*** -4,608*** -4,234.2***

(3173.63) (9199.94) (1105.93) (797.10) (337.46) (76.08)

State Enterprise 12,187.58*** 0.00 0.00 5,959.53*** 0.00 5,206.65***

(3787.41) (0.00) (0.00) (923.95) (0.00) (159.87)

Urban Area (Yes=1) 5,505.89 -4,185.03 720.91* 984.74 690.76*** 2,515.95***

(3340.49) (5030.07) (408.74) (664.27) (185.24) (59.52)


Married 1,644.88 -4,299.15 118.16 948.70 -317.86 -683.16***

(3111.51) (4766.73) (422.69) (650.85) (241.56) (77.45)

Widowed 0.00 -52,779.4*** -1,853.26 4,311.64 -272.48 -2,533.6***

(0.00) (18061.91) (1644.77) (5862.79) (659.79) (208.18)

Divorced 0.00 0.00 -557.19 -1,936.27 -356.84 -1,439.8***

(0.00) (0.00) (2430.60) (4142.14) (836.69) (281.64)

Separated 0.00 0.00 136.59 0.00 -1,446.76* -1,864.3***

(0.00) (0.00) (1905.93) (0.00) (838.52) (226.96)

Constant -57,886.15** -30,099.16 -2,227.44 -14,035.2** -11,147*** 487.19*

(28255.51) (31411.28) (2923.64) (5886.37) (1501.12) (285.09)

Observations 67 69 524 439 858 31985

R-squared 0.55 0.34 0.24 0.38 0.52 0.28


122



SEX 5,470.03** 4,908.53** -1,384.2*** 397.16 -266.36 666.85***

(2742.76) (2397.35) (510.24) (432.84) (189.54) (46.29)

AGE 2,175.38** 1,706.93 840.24*** -105.06 42.77 253.67***

(887.85) (1131.64) (77.13) (203.79) (87.34) (12.35)

AGE Squared -17.72 -15.41 -5.87*** 10.09*** 5.65*** -1.77***

(11.15) (13.91) (1.03) (2.76) (1.08) (0.16)


Private 17,357.31*** 19,065.93*** 3,126.16*** 3,200.35*** -4,476.1*** -5,549.2***

(2308.51) (4452.17) (472.17) (597.42) (294.27) (57.08)

State Enterprise 9,630.18*** -12,684.78 907.79 4,939.13*** 0.00 6,681.92***

(2600.57) (15897.12) (2673.98) (639.55) (0.00) (130.72)

Urban Area (Yes=1) 3,945.39* 8,604.61*** 718.24*** 936.50** 861.26*** 2,521.08***

(2081.46) (3166.90) (187.25) (436.96) (184.63) (47.03)

GINI-Coefficient 13,601.25 19,049.31 7.37 6,363.64** 604.68 -3,024.43***

(13577.35) (19183.91) (1375.67) (3026.79) (1527.19) (349.55)

Provincial GPP 0.30** -0.39** 0.05*** 0.33*** 0.04 0.24***

(0.13) (0.18) (0.02) (0.03) (0.02) (0.00)


Married 1,265.45 4,855.63* 208.55 2,416.53*** 66.45 -190.62***

(2354.86) (2782.89) (208.94) (435.70) (243.04) (58.45)

Widowed 0.00 0.00 -737.90 -2,079.17 -571.48 -2,210.9***

(0.00) (0.00) (726.84) (2678.03) (647.44) (149.19)

Divorced -759.39 0.00 -1,262.91 2,646.86 -609.48 -1,093.3***

(9691.23) (0.00) (802.79) (1959.46) (749.75) (179.77)

Separated 2,823.01 0.00 -1,685.97* -1,170.57 -1,256.06 -1,741.2***

(13824.94) (0.00) (892.72) (1854.18) (1060.03) (164.57)

Constant -57,624.0*** -28,767.08 -9,512.2*** -5,969.49 2,573.17 -275.93

(15732.65) (22873.90) (1473.29) (3765.95) (1785.57) (267.42)

Observations 277 222 1912 1799 2874 88385

R-squared 0.35 0.32 0.50 0.35 0.48 0.28


123


Variables Engineer Scientist Medicine Nurse Accountant Teachers Others

SEX 1,261.67 2,653.20 4,708.23* -875.67 3,689.62*** 217.35 956.56***

(4286.37) (5936.93) (2566.68) (1022.80) (1379.11) (446.37) (53.32)

AGE 2,446.96*** 1,470.65 224.77 1,082.43*** 1,277.88** 1,092.11*** 410.23***

(802.93) (3242.96) (993.99) (159.86) (618.06) (135.45) (12.76)

AGE Squared -15.56 -6.84 7.89 -6.53*** -6.16 -7.01*** -4.00***

(9.72) (43.20) (11.77) (2.06) (7.84) (1.62) (0.16)


Private 22,113.2*** 13,633.09** 27,339*** 2,499.2*** 12,136.7*** -2,956*** -4,399***

(2307.55) (6601.72) (3530.64) (838.96) (1333.76) (571.08) (71.57)

State Enterprise 17,098.4*** 36,550.*** 0.00 15,241.9** 11,681*** 0.00 9,842***

(3125.45) (10363.99) (0.00) (6163.57) (1988.48) (0.00) (173.52)

Self-employed 0.00 0.00 0.00 0.00 0.00 0.00 -2,830.83

(0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (5598.61) Urban Area (Yes=1) 1,633.00 1,233.97 -5,677.96 1,092.48** 3,855.74** 2,345.1*** 2,463.6***

(2346.54) (11584.99) (5029.15) (519.68) (1721.19) (550.80) (54.69)


Married 4,778.07** 4,285.04 -2,442.63 -891.55** -747.66 -139.21 -753.1***

(2368.46) (7207.77) (3332.61) (354.44) (1415.11) (529.06) (67.44)

Widowed 2,928.63 0.00 10,051.67 1,410.04 -11,219.88* -821.78 -3,276***

(17456.12) (0.00) (13441.80) (1424.03) (6030.13) (1579.07) (163.76)

Divorced 0.00 -3,368.85 8,795.66 -998.78 7,212.14 -923.84 -2,163***

(0.00) (16982.41) (17999.00) (1081.32) (5996.52) (1403.38) (193.84)

Separated 142.75 0.00 -86,698*** -5,692*** -6,998.11 -2,517.11 -2,414***

(10157.24) (0.00) (21959.90) (1896.67) (5956.26) (2161.68) (179.41)

Constant -57,950.*** -30,441.99 16,195.87 -11,755*** -26,495.4** -16,894*** 324.65

(16196.66) (58481.46) (18872.93) (3000.36) (11598.94) (2695.99) (254.73)

Observations 383 34 228 1522 381 1333 92757

R-squared 0.38 0.50 0.34 0.38 0.41 0.37 0.17


124

Table 3-10 : Regression Outputs of graduates of University (Academic Track): 1985-2005 Dependent variable = Monthly Earning (in Baht)

VARIABLES 1985 1990 1995 2000 2005

Female Male Female Male Female Male Female Male Female Male

AGE 530.51*** 501.55** 624.67*** 1,028.11*** 829.20*** 1,830.05*** 497.16*** 429.32** 1,105.56*** 1,839.09***

(109.50) (228.64) (113.50) (279.18) (106.59) (253.45) (75.90) (200.06) (128.54) (302.83)

AGE_SQR -5.19*** -3.77 -5.73*** -10.34*** -6.28*** -18.13*** 0.04 1.49 -6.26*** -13.11***

(1.45) (2.91) (1.48) (3.39) (1.37) (3.02) (1.00) (2.46) (1.62) (3.58) Occupation (base= Teacher)

Engineer 0 6,855.9*** 16,954.4*** 9,707.81*** 23,853.1*** 11,928.2*** 6,808.55*** 11,508.2*** 9,950.83*** 10,709.7***

0 (1188.12) (2032.32) (1207.3) (2424.85) (1221.43) (1054.65) (811.33) (2021.09) (1186.59)

Scientist 0 3,066.78 6,136.87*** 1,794.92 0 5,148.67 1,256.61 0 10,434.0*** 8,209.95**

0 (2378.08) (1712.37) (3253.31) 0 (5110.61) (1837.7) 0 (1850.65) (3979.8)

Medicine 2,197.55*** 3,671*** 4,399.54*** 6,222.90*** 6,746.18*** 13,101.8*** 11,612.5*** 18,454.4*** 19,518.5*** 22,046***

(729.57) (927.35) (879.81) (1430.96) (1061.94) (1127.64) (637.46) (877.54) (884.67) (1437.55)

Nurse 723.61** 73.21 567.78* 1,711.22 1,799.75*** 2,218.23 1,792.65*** 997.4 5,309.67*** 5,855.58**

(289.85) (1480.91) (331.07) (2154.48) (322.62) (1983.96) (198.4) (1352.33) (372.39) (2505.53)

Accountant,Economist 3,220.36*** 1,760.80* 2,298.08*** 3,931.60*** 3,054.34*** 1,196.42 2,166.54*** 1,534.63* 4,525.97*** 5,827.72***

(406.52 (1028.12 (422.15 (1056.78 (496.83 (1185.26 (289.16 (801.79 (606.94 (1579.26


Private -444.28 837.13 14.06 1,986.11** 620.70 5,502.17*** 397.30 3,470.14*** 2,805.04*** 10,348.9***

(352.24) (897.13) (370.52) (964.10) (473.19) (1057.29) (268.92) (705.47) (511.96) (1125.71)

State Enterprise 127.71 2,575.01** 2,194.35*** -220.68 3,256.58*** 4,595.51*** 2,322.86*** 4,232.96*** 9,177.29*** 11,723.3***

(757.45) (1294.74) (756.62) (1305.76) (798.24) (1413.92) (418.96) (944.44) (1404.62) (2184.51)

Urban Area (Yes=1) 323.85 775.56 573.14* -815.28 853.49*** 354.87 1,090.46*** 2,047.19*** 1,420.22*** 2,084.34*

(271.93) (613.68) (322.08) (780.25) (285.82) (615.91) (168.32) (451.93) (472.85) (1132.00)


Married 19.86 873.59 390.61 188.74 279.39 -883.17 445.51** 1,751.99*** -410.93 619.97

(261.19) (629.23) (322.68) (923.89) (299.45) (856.27) (186.16) (613.32) (357.96) (1144.58)

Widowed 58.03 73.81 395.05 539.53 -1,718.19* 545.28 -779.50 -4,721.68 -82.92 -1,854.43

(747.91) (3475.05) (1134.15) (2671.15) (1019.05) (3758.17) (588.12) (4070.86) (1191.07) (7428.96)

Divorced -8.10 0.00 1,931.24 7,738.65 -1,005.33 -3,269.26 -1,259.18* 3,429.15 -512.17 -618.99

(661.11) (0.00) (1192.82) (5657.45) (1294.93) (5172.76) (668.07) (2537.59) (1027.41) (5323.04)

Separated 22.81 -3,389.59 -663.39 8,161.93 -1,380.59 -1,879.71 -1,862.85** 4,184.53 -4,761.97*** -8,356.76

125

(1881.21) (2452.14) (1684.86) (5620.87) (1346.51) (4261.48) (744.84) (5667.66) (1597.50) (6105.91)

Constant -7,648.84*** -9,265.6** -9,080.61*** -16,849.5*** -11,815.8*** -31,247.4*** -6,637.32*** -8,462.49** -18,828.8*** -38,590.4***

(1918.04) (4076.95) (2080.05) (5228.30) (2011.04) (4951.31) (1381.79) (3786.14) (2442.92) (6022.97)

Observations 295 177 481 290 1331 628 4951 2142 2699 1182

R-squared 0.47 0.53 0.41 0.35 0.32 0.41 0.38 0.36 0.39 0.39


126

Table 3-11 : Regression Outputs of graduates of University (All Tracks-Academic, Teacher, Technical):1985-2005 Dependent variable = Monthly Earning (in Baht)

1985 1990 1995 2000 2005

VARIABLES Female Male Female Male Female Male Female Male Female Male

AGE 419.33*** 485.57*** 548.98*** 741.30*** 771.03*** 1,448.91*** 502.13*** 558.54*** 928.82*** 1,496.06***

(53.78) (129.97) (77.09) (155.66) (73.51) (148.07) (49.20) (120.84) (69.09) (176.18)

AGE Squared -3.84*** -3.89** -4.61*** -6.91*** -5.61*** -13.76*** -0.29 -0.61 -3.41*** -9.33***

(0.71) (1.65) (0.98) (1.88) (0.92) (1.76) (0.63) (1.46) (0.84) (2.08)

Occupation (base= Teacher)

Engineer 0.00 8,246.3*** 17,320*** 10,018.9*** 25,418.9*** 13,330.7*** 8,317.9*** 12,630*** 12,399.7*** 13,067.7***

(0.00) (755.07) (1790.64) (779.61) (2097.11) (803.04) (895.37) (580.04) (1635.61) (739.24)

Scientist 0.00 2,959.67 6,346.3*** 2,509.93 0.00 5,069.54 1,977.33 0.00 10,130.4*** 8,537.37***

(0.00) (1855.29) (1486.04) (2303.46) (0.00) (3781.01) (1576.63) (0.00) (1373.23) (2799.11)

Medicine 2,807.8*** 3,730.8*** 4,498*** 5,427.43*** 6,616*** 13,039.7*** 11,900*** 18,810*** 19,273.8*** 21,614.0***

(498.03) (690.93) (764.74) (940.92) (914.04) (797.87) (538.92) (653.30) (694.44) (934.63)

Nurse 500.10*** -139.89 532.54** 1,379.77 1,608.18*** 1,873.30 1,991.5*** 959.23 4,428.03*** 4,600.41***

(178.15) (1160.32) (237.88) (1505.61) (227.61) (1447.00) (139.84) (1028.34) (212.37) (1736.69)

Accountant,Economist 3,067.6*** 1,899*** 2,641.1*** 4,489.58*** 3,554.65*** 3,176.51*** 3,424.8*** 3,054*** 5,078.24*** 6,158.94***

(242.07) (614.26) (307.10) (648.71) (341.32) (700.72) (208.71) (555.38) (441.90) (1030.91)

Sector of work (base=Public

Private -704.34*** 230.62 -444.05* 817.63 -1,047.4*** 2,346.81*** -1,311*** 1,623.2*** -1,284.0*** 4,997.90***

(184.59) (490.61) (250.54) (581.07) (305.72) (595.99) (182.86) (463.22) (301.76) (660.36)

State Enterprise -146.50 3,869.9*** 2,000.7*** -775.97 2,395.73*** 2,407.49*** 1,283.2*** 2,810.8*** 7,478.73*** 8,553.20***

(487.82) (845.08) (636.61) (875.40) (636.16) (910.58) (338.73) (681.77) (1138.68) (1501.68)

Area (Urban = 1) 237.80** 357.41 324.10* -190.95 458.08*** 261.66 815.00*** 1,231.9*** 586.01** 895.39*

(117.46) (288.79) (182.37) (353.77) (170.60) (300.50) (107.94) (250.19) (228.42) (486.73)


Married 92.87 488.15 240.66 358.10 234.64 -353.91 355.52*** 1,136.7*** -111.85 374.40

(126.07) (344.43) (198.28) (476.48) (195.28) (478.17) (129.03) (379.37) (207.01) (619.42)

Widowed 213.77 -1,034.07 435.09 991.04 -837.78 -474.39 -56.03 -308.32 -489.24 -102.73

(370.72) (2700.47) (567.86) (1697.10) (517.15) (2089.40) (320.84) (2073.95) (523.45) (2754.67)

127


Divorced 41.24 471.42 538.16 7,327.68* 1,182.62** -1,268.37 -349.17 2,525.75* -442.86 -81.95

(401.57) (2596.86) (674.51) (4017.29) (583.10) (1678.21) (349.77) (1462.53) (520.87) (2348.48)

Separated -268.67 -2,051.42 -832.12 2,302.77 -1,147.69* -616.83 -1,592*** 58.22 -3,398.9*** -4,814.81

(703.68) (1540.71) (906.18) (2326.23) (688.77) (1667.57) (457.05) (1735.10) (820.84) (3047.07)

Constant -5,354 *** -7,962*** -7,541*** -11,489*** -10,177*** -23,409*** -6,337*** -9,321*** -14,761*** -28,974***

(966.00) (2376.52) (1450.39) (2964.95) (1424.22) (2921.70) (925.93) (2338.66) (1362.46) (3517.98)

Observations 698 423 980 612 2667 1368 8489 3983 6177 2747

R-squared 0.50 0.55 0.37 0.41 0.32 0.40 0.44 0.38 0.47 0.41

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Table 3-12 : Regression Outputs of graduate of Teacher’s Colleges : 1985-2005 Year 1985 1990 1995 2000 2005 Male Female Male Female Male Female Male Female Male Female

Age 225.80*** 269.40*** 482.44*** 240.37** 982.23*** 712.59*** 312.38*** 211.86*** 917.85*** 753.26*** (67.99) (52.55) (167.65) (111.56) (206.00) (130.71) (86.96) (50.77) (152.66) (68.60) Age squared -1.86** -2.03*** -3.55* -0.94 -7.09*** -3.86** 2.49** 3.01*** -1.92 -0.97 (0.83) (0.69) (2.02) (1.41) (2.37) (1.60) (1.02) (0.62) (1.75) (0.82) Working as Teacher (Yes=1,No=0) -724.8*** 2.11** -1,174.3*** 397.24** -2,391.8*** 396.50*** -1,279.5*** 990.61*** -4,716.19*** 359.73*** (193.66) (170.05) (368.47) (210.18) (440.13) (291.81) (183.85) (117.52) (309.63) (218.96) Sector of Work (leftout = Public) Private Sector -1,818 *** -1,371.6*** 5.01 -2,370.9*** -1,440.73* -1,619.22*** -1,729*** -3,337*** -4,130.53*** -3,531.81*** (342.86) (229.07) (566.79) (303.42) (799.02) (448.08) (331.05) (171.77) (561.58) (289.82) State Enterprise 0.00 0.00 2,115.56* 1,330.49* 3,709.54*** 7,213.18* 1,262.85** 3,584.37*** 6,322.48*** 8,264.60*** (0.00) (0.00) (1083.30) (728.60) (1283.07) (3688.33) (629.06) (770.65) (1187.55) (1399.45) Urban Area ( Yes=1) 495.50*** 268.78** 302.14 311.37* 713.87* -17.83 386.04** 523.19*** 509.04 600.28*** (168.39) (106.11) (320.27) (175.43) (396.38) (258.21) (173.74) (107.47) (355.03) (221.44) Marital Status (leftout = single) Married 2.15 145.65 703.54 -87.02 209.53 -933.56*** 1,047.48*** 222.97 885.38* -247.42 (237.10) (108.74) (509.67) (209.25) (683.96) (323.10) (304.37) (142.19) (528.25) (220.14) Widowed 738.71 -534.21 -750.06 -492.11 3,477.84 -1,350.23** 2,776.11** -40.59 1,541.04 -554.06 (693.34) (327.97) (1852.44) (607.43) (2796.44) (684.57) (1114.54) (290.20) (1828.51) (489.46) Divorced 303.49 -188.83 5,765.81** -702.21 -1,179.92 111.04 1,082.20 -127.60 699.78 -835.62 (616.18) (378.70) (2688.80) (661.41) (2430.51) (1053.96) (1137.05) (296.51) (1779.36) (514.51) Separated 0.00 86.03 434.34 650.21 2,976.07 -720.93 -152.20 -854.02* -2,027.57 -2,907.82*** (0.00) (539.14) (2469.90) (668.28) (2803.76) (1089.68) (976.51) (441.01) (1977.52) (814.68) Provincial GPP 0.02 0.01 0.02 0.01 -0.05 0.04* 0.02 0.10*** 0.13*** 0.06*** (0.01) (0.01) (0.03) (0.02) (0.04) (0.02) (0.02) (0.01) (0.04) (0.02) Provincial GINI-coefficient -3,060*** 2,170.92*** -430.62 -805.35 -5,385.09* 262.96 -2,632.72* -307.68 4,271.41 4,631.53*** (850.63) (549.01) (1845.94) (1029.21) (2911.08) (1805.45) (1412.62) (861.72) (2676.78) (1546.01) Constant 101.67 -3,524.4*** -6,186.48* -1,258.07 -11,258.3** -10,056*** -2,138.18 -1,873.16* -16,283.9*** -14,212.7*** (1250.20) (924.70) (3361.34) (2138.37) (4372.16) (2614.90) (1836.91) (1041.72) (3272.09) (1511.74)

Observations 224 240 240 276 573 853 2998 5098 2590 4519 R-squared 0.55 0.80 0.44 0.58 0.40 0.47 0.51 0.58 0.50 0.56

Standard errors in parentheses (*** p<0.01, ** p<0.05, * p<0.1)

129

Table 3-13: The summary of Oaxaca-Binder wage decomposition analysis

2005 2000 1995 1990 1985

A.Predicted earnings (baht/month) effect percent effect percent effect percent effect percent Effect Percent

Non-teachers 19762.11 17989.23 17573.33 14874.56 12876.83

Teachers 16272.78 15587.45 14873.17 12956.34 1256.32

Total Difference (T) 3489.334 100% 2401.78 100% 2700.16 100% 1918.22 100% 1620.51 100%

B.Detailed Decomposition: Explainable characteristics

Age Age-Squared

Age -1956.51 -56.0712 -1833.45 -76.3371 -1756.48 -65.05096 -1748.22 -91.13767 -1657.98 -78.187794

Age_square -1872.31 -53.6582 -1769.34 -73.6679 -1678.45 -62.161131 -1644.32 -85.72117 -1567.45 -73.918538

Sex (female=1) -117.854 -3.37755 -104.456 -4.34911 -110.423 -4.0894984 -103.691 -5.405583 -105.354 -4.9683331

Education

Primary School -27.8303 -0.79758 -23.974 -0.99818 -21.355 -0.7908791 -19.574 -1.02042 17.654 0.8325356

Lower Secondary -60.063 -1.72133 -55.694 -2.31886 -52.856 -1.9575136 -47.632 -2.483134 -40.353 -1.9029856

Secondary -64.5309 -1.84938 -60.483 -2.51826 -30.847 -1.1424138 -50.254 -2.61986 -53.967 -2.545001

Diploma -822.739 -23.5787 -804.34 -33.4893 -769.37 -28.493497 -604.37 -31.5064 -604.34 -28.499748

University 574.2577 16.45752 546.32 22.74646 478.03 17.703766 504.31 26.2905 485.34 22.88789

Area (municipal = 1) 72.17708 2.068506 68.476 2.851052 60.376 2.2360156 63.942 3.3334 57.283 2.7013784

State Enterprise 100.6718 2.885129 112.73 4.693602 94.384 3.4954966 103.934 5.41825 90.463 4.2660964

private sector 341.9599 9.800148 315.73 13.14567 309.476 11.461395 314.83 16.4126 285.9 13.482606

Explained (E) -3832.77 -109.843 -3608.48 -150.242 -3477.52 -128.78941 -3231.05 -168.44 -3092.8 -145.85171

Unexplained (U) 7322.11 210% 6009.26 250% 6177.68 228.78% 5149.27 268.40% 5213.31 245.85%

Total Difference (T) 3489.334 100% 2401.78 100% 2700.16 100% 1918.22 100% 1620.51 100%

130

CHAPTER 4

TEACHER EARNINGS AND TEACHER

MOONLIGHTING: EVIDENCE FROM THAILAND

4.1 INTRODUCTION

In this chapter, I will look mainly into the issue of teacher moonlighting in Thailand. I

use a detailed survey of teachers in Thailand initiated recently by the Office of the

Education Council, Ministry of Education. Firstly, I will examine the determinants of

earnings for teachers with regard to their moonlighting activities. Secondly, I will analyze

the probability that teachers engage in moonlighting jobs based on their observable

characteristics.

Since this dataset has details on other aspects of teachers, such as their location and

the majors and institutions where teachers attended during their undergraduate and

graduate education, I will also use the data to reexamine the determinants of teachers‘

earnings in the same manner I did in Chapter 3. The data is also useful for comparison

with the distribution of teachers based on their educational background in addition to

what I did in Chapter 2.

131


4.2.1 Background of Teacher Moonlighting in Thailand

Thai teachers‘ relatively low incomes engenders several important problems, such

as teachers‘ debt. Teachers‘ debt is well-recognized by the Thai government and has led

to several policies for debt relief. One way teachers respond to their low incomes is to

work in additional jobs besides their primary teaching job.

Thai society does not view teachers who moonlight or work in part-time jobs as

something unusual. Teachers are often seen spending their time after school in jobs such

as tutoring privately, selling food and other products such as insurance policies,

cosmetics and other direct-marketing items. Oftentimes, the activities occur in the

vicinity of school causing complaints from other teachers or students. Since people know

that teaching is a low paying job, society does not view negatively teachers who try to

make ends meet by moonlighting. So far, the Ministry of Education does not have strict

laws prohibiting teachers from moonlighting. This is in contrast to some countries such as

Hong Kong and Singapore where teachers are banned from tutoring in after-school (Bray

2003,2005).

Despite the prevalence of moonlighting activities among teachers, there is not

much empirical evidence on teachers‘ part-time jobs in Thailand. A related research is on

the phenomenon of tutoring schools in Thailand. Sinlarat et al (2002) show from their

survey data that the number of registered tutoring schools in Thailand has increased

exponentially from 302 schools in 1985 to more than 1,500 schools in 2005. The survey

also found that more than half of teachers in the tutoring business earn more than 50% of

132

their regular salaries. However, the estimate is likely to be biased downward. Many

tutoring schools are unregistered as they operate on a small scale or at students‘ or

teachers‘ houses.

4.2.2 Data

The dataset used in this section was collected in 2006 by the Faculty of

Economics, Chulalongkorn University under the research project ―Education Resources

of Thailand‖ funded by Office of the Education Council, Ministry of Education Thailand.

The full survey consisted of questionnaires of 573 teachers, 310 principals, 260 schools

and 3,102 parents from 8 provinces on various issues about the education resources.

The survey methodology was run by clustering schools into urban, rural and sub-

urban areas. Schools were clustered into 4 levels (extra-large, large, medium and small)

by their size and by education level (primary and secondary). The selection of provinces

from each region was done by a stratified sampling method. Eight provinces from five

regions from a total of 76 provinces in Thailand were selected. The provinces in the

survey were Bangkok (central region), Nakorn Pathom (central region), Rayong (central

region), Chiang Mai (northern region), Chiang Rai (northern region), Ubonrajchatani

(northeastern region), Sri Saket (northeastern region), Nakorn Sri Thammarat (southern

region). The 260 schools in the study were selected from 5,109 schools in these 8

provinces3.

The survey consisted of three questionnaires for teachers, principals and parents

asking detailed questions regarding the following:

3 In 2005, there were 32,364 schools in Thailand

133

1. Background characteristics and working conditions of teachers and principals,

such as their education, experience, salaries and other income, debt, assets and their

workload.

2. Background characteristics of parents, such as their occupation, income,

socioeconomic status, expenditure/income of household, expenditure for education of

their children, their expectation/satisfaction of the children and the worthiness of the

educational investment in their children.

3. General aspects of schools, e.g. location, educational quality as assessed by the

governmental assessment agency in 2006, number of classrooms, teachers, buildings,

school resources, budget, expenditures and so on.

4.2.3 Descriptive Statistics

4.2.3.1 Thai Teachers’ Background from the Survey

Table 4-1 shows that there were 573 teachers in the survey, 288 (50.3%) of them

are primary school teachers and 285 (49.7%) are secondary school teachers—122

(21.3%) of the teachers surveyed teach mathematics, 176 (30.7%) teach Thai, 88 (15.4%)

teach science, 58 (10.1%) teach social science, 56 (9.8%) teach English and the rest teach

subjects such as career education, physical education, fine arts and dance.

Almost has all the teachers surveyed have bachelor‘s degrees from universities

and 17% of them have a master‘s degree. Among those with university education, almost

70% of them have a bachelor‘s degree in education while 7% have a bachelor‘s degree in

science, 2% in liberal arts and 16% in other majors.

134

I take advantage of the information on the institutions from which teachers

graduated and divide those institutions into five categories: 1) teachers‘ colleges in

Bangkok;4 2) teachers‘ colleges outside Bankgok; 3) open universities;

5 4) prestigious

four-year government universities; and 5) other non-prestigious four-year universities.

Most teachers in the survey graduated from teachers‘ colleges throughout the

country (51.3%) and teachers‘ colleges in Bangkok (18.5%) following by open

universities (11.7%). Only a small percentage (4.9%) of them graduated from prestigious

four-year government universities.

Let‘s consider only the secondary school mathematics and science teachers in this

sample to compare their educational backgrounds with the data we have on teachers from

the TIMSS in Chapter 2. From Table 4-5 and Table 4-6, of 42 secondary school

mathematics teachers in this sample, 33 (76%) majored in mathematics in a faculty of

education and three (7%) majored in mathematics in a faculty of science. As for the

secondary school science teachers, 28 of 50 in the sample (56%) graduated from a faculty

of education and 21 (42%) graduated from a faculty of science.

4 As of 2004, all the teachers‘ colleges in Thailand have been upgraded to Rajabhat Universities.

For the purpose of my analysis, I put teachers who graduated from teachers‘ colleges, Rajabhat

Institutes (upgraded from teachers‘ colleges starting from 2001) and Rajabhat Universities in the

category of graduates from teachers‘ colleges.

5 There are 2 open universities in Thailand : Ramkhamhaeng University and Sukhothai

Thammathirat University where any person wishing to enroll can apply without having to take an

entrance examination. In most programs, students are supported by distant learning facilities and

not required to attend classes. At the end of a semester, they need to take exam for each class and

pass in order to earn credit. The tuition fees of open universities are much cheaper than other

universities.

135

Besides being able to group teachers into those graduating from faculties of

sciences and faculties of education, data from this survey tells us the type of institution

they graduated from. Table 4-4 suggests that even among mathematics and science

teachers who graduated from faculties of science, most of them graduated from faculties

of science in teachers‘ colleges or former teachers‘ colleges which are not known for

their high-quality. For instance, 67% of mathematics teachers with a B.A. from a faculty

of science graduated from a teachers‘ college. Likewise, 95% of science teachers with a

B.A. from a faculty of science graduated from science departments in teachers‘ colleges.

The rest attended a faculty of education in teachers‘ colleges in Bangkok and other

provinces.

In sum, this dataset points out that most in-service teachers in Thailand graduated

from one of the many teacher-training institutions. Even those teaching in subjects such

as mathematics and science also attended a department of science in one of the teachers‘

colleges. It supports the claim that most teachers in Thailand were educated in what are

considered lower quality institutions (Somwang, 2010).

The proportion of teachers who graduated from a faculty of science is higher in

the TIMSS 2007 survey. About 70% of students sampled in the TIMSS studied with

teachers who reported having completed a bachelor‘s degree in mathematics. The

percentage of teachers who graduated with a degree from a faculty of science in the Thai

sample discussed in this section is much lower, about 7%. The disparity may result from

schools with better resources and teacher quality being surveyed in the TIMSS. It could

also be a result of teachers‘ misrepresenting their education background when they filled

out the TIMSS survey; e.g., teachers who graduated with mathematics major from a

136

faculty of education reported that they have a mathematics degree rather than a

mathematics education or education background.

4.2.3.2 The Distribution of Thai Teachers Across Schools

Data on the workplace location and the undergraduate institution of teachers

provide an approximate picture of teacher distribution in Thailand. It suggests that

teaching is a highly localized occupation. From the survey, Table 4-3 shows that more

than half of the teachers in many provinces graduated from the teachers‘ colleges in the

same or nearby provinces.

The ratio of teachers who graduated from institutions in the same province or

region are high in northern or northeastern provinces such as in Chiang Rai (80.6%) ,

Chiang Mai (67.74%) and Ubon Rajjathani (71.24%). In most cases, teachers who did not

attend institutions in their provinces or regions had studied at more renowned teacher

training institutions in Bangkok. A small percentage of teachers graduated from a region

outside their current locations or Bangkok (around 5% in the southern, 6% in the

northern, 10% in the northeastern and 20% in the central regions)

These numbers seem to be close to the findings on teachers in New York State,

where 61% of teachers teach in schools located close to their hometowns. If they grew up

in an urban school district, the proportion is as high as 88% (Boyd, Lankford, Loeb &

Wyckoff, 2005). Although we do not have details on the hometown of teacher in the Thai

sample, it is very likely that teachers entered teachers‘ colleges in or near their

hometowns, since teachers‘ colleges were established primarily to serve the local

communities.

137

4.2.3.3 An Overview of Teacher Moonlighting from the Survey

In Table 4-2, 4-7, 4-8, I analyze and compare the characteristics of teachers who

moonlight and those who do not moonlight. Table 4-2 shows that 24% of teachers in the

survey have part-time jobs. Table 4-7 shows that the teachers who moonlight have a

lower average salary and higher household expenditure than the non-moonlighting

teachers. However, moonlighting teachers have higher final incomes. The average age

and experience of teachers who moonlight are lower than the non-moonlighting. Both

types have about the same male/female ratio. The distributions of teachers‘ incomes by

their moonlighting status and their type of jobs are shown in Figure 4-1 and Figure 4-2.

Table 4-8 shows that teachers who graduate from different types of universities

have about the same likelihood of moonlighting, about 20 percent. Teachers who teach

different courses also have about the same probability of moonlighting, except that

mathematics and science teachers are somewhat more likely to moonlight, while physical

education, Thai and social science teachers are somewhat less likely to moonlight.

Teachers from different regions are about as likely to moonlight as well.

Teachers in the southern provinces have the lowest and teachers from the northern region

have the highest ratio of moonlighting teachers.

Overall, we can estimate the percentage of moonlighting teachers to be between

17-25% of total teacher population. Critics may argue that the percentage of

moonlighting teachers may be actually be lower than reported because some teachers

might not want to report such activities for tax reasons or they may feel negative or

embarrassed by having to moonlight. In the case of Thai teachers, the tax reason should

138

be negligible since there is hardly an incident of the tax evasion charge from the

government agency for the middle income population. The main reason that teachers may

under-report moonlighting activities is that they may not feel comfortable telling others

about it. But since the proportion of moonlighting teachers is quite consistent across

provinces and regions, it would not be unreasonable to take these data at face value.

4.3 METHODOLOGY

I will do this in two parts: first I will estimate the probability that teachers participate in

part-time work outside school using probit analysis; then I will estimate the earnings

functions of government officials and teachers using the traditional wage earnings

regression.

4.3.1. Probit Model On Teacher Participation In Part-Time Jobs

In this section, I will examine the probability that teachers engage in

moonlighting activities based on their observable characteristics. Also there are several

job options for teachers in terms of the types of part-time work they do, and those too

may vary according to teachers‘ characteristics.

In the first model, I use a standard binary outcome model: a probit, to analyze the

factors associated with whether or not teachers participate in part-time work. In the next

part, I analyze the three most popular moonlighting activities: tutoring, selling

food/product and farming. The reason I use binary model instead of multinomial model is

because the categories of part-time jobs teachers choose are not necessary mutually

exclusive in nature since teachers may choose to do more than one jobs at the same time.

139

Therefore, I use a binary probit model for each of the three most popular part-time jobs.

The formal probit model is presented in the expression below. The results are presented

in Table 14.

Pr( 1 ) ( )y X X ( )

X

z dz

…(1)

Variable of Interest: Pr( 1 )y X is the probability that a teacher has a part-time job (y =

1 if has part-time job, y=0 if no part-time job). X is vector of regressors which affect the

probability of a teacher taking a moonlighting job, is the cumulative distribution

function (cdf) of the standard normal distribution.

4.3.2 Wage Earnings Regression

Since the majority of Thai teachers are government officials, the structure of

teachers‘ salaries are uniformly set across the country. The important factors which affect

the salaries of government officials include years of work, educational credentials and the

positions they hold. However, the variations in terms of income of government officials

depend on non-salary earnings such as fringe benefits, special allowance and

moonlighting income. These factors vary by the different circumstances teachers face.

For instance, teachers who teach main subjects for university entrance examination

(mathematics, science, English) have more opportunity to moonlight as a private tutor

than teachers of other subjects. Teachers who live in urban areas might have better access

to labor market opportunities than those in rural areas. With this concern in mind, we

need to analyze the determinants of official salaries and total earnings of teacher

separately.

140

Earnings Regressions

0 1 2 3log( )officialY TeachEDU SchoolWork TeachSES ...(2)

Total Earning 0 1 2 3 4log( )Y Moonlight TeachEDU SchoolWork TeachSES ...(3)

officialY is the official salary per month both in kind and in cash that teachers receive ,

totalY is the total earning per month from all sources that teachers report , TeachEDU is a

vector of teacher education (their level of education, college majors, quality of their

undergraduate institution) , SchoolWork is a vector of teachers‘ school related work, e.g.

what subject and level they teach, Moonlight is a dummy variable for the type of part-

time job teachers engage in outside school. TeachSES is a vector of teachers‘

socioeconomic status (sex, experience, school location, marital status).

141

4.4. RESULTS

4.4.1 Probit Estimation Results

Table 4-14 reports the results of probit estimates designed to estimate which Thai

teachers are likely to have part-time jobs or moonlight in different kinds of jobs. I include

the characteristics that are expected to influence the likelihood of teachers taking part-

time jobs, such as teachers‘ gender, experience, type of undergraduate institutions and the

majors in which they graduated, the subjects and grade levels that they teach, teachers‘

official income, and whether they are in debt.

Some teachers‘ characteristics are significantly related to the likelihood of

moonlighting. The coefficient for experience suggests that a teacher who has one more

year of work experience is two percentage points less likely of having a part-time job. A

teacher who teaches at pre-primary school level is also more likely to moonlight than an

upper secondary teacher. Teachers who teach Thai and social studies have about 14% and

10% less probability having part-time jobs than mathematics teachers. Teachers who

have a B.A. other than in education, science, or humanities are 10% less likely to

moonlight than those with a B.A. in education. Teachers who have debt are 11 percent

more likely to have a part-time job than those without debt. The higher the salary of

teachers of the more likely they are to moonlight but the estimated difference is very

small.

Considering the probability of teachers who go into tutoring as their part-time job,

males are 6 percentage point less likely than females to tutor and more experienced

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teachers less likely to tutor than less experienced teachers. Teachers at the low-primary

level are 13% more likely to tutor than teachers at the upper secondary level, though this

is barely significant, at the 10% level. Teachers of mathematics have a higher probability

of tutoring than teachers of Thai, social studies and science by 12%, 10% and 7%,

respectively. Teachers who have a B.A. in humanities are 7% less likely to tutor than

those with B.A. in education.

Only a few characteristics are significantly related to the probability of teachers

moonlighting as sellers of food and other products. Teachers who have debt are 3% more

likely to engage in selling than those who do not, and those in the Northeastern region are

3% less likely to sell food/products than teachers in the Southern province. Teachers at

lower secondary level are 3% less likely of selling than those in the upper secondary

level, but this is only significant at a 10% level.

Another popular moonlighting job for teachers is agriculture or farming. Among

the many teacher characteristics included in the regression, being male and being a

teacher at pre-primary grade level are the only two significant determinants of the

probability of moonlighting in agriculture. Males are 9% more likely to moonlight in

farming, while pre-primary schoolteachers are 92% more likely to moonlight as farmers

than those who teach at the upper secondary school level.

4.4.2 Earning Functions Results

Table 4-12 presents the outcomes of the OLS estimates of Thai teachers‘

earnings. The first column (Model 1) represents the official salary and benefits as a

dependent variable. It shows that the official income of a teacher is statistically and

143

positively related to their years of working. Other things being equal, a teacher who has

one more year of experience earns about 1,200 baht per month more. Teachers seem to

earn less if they graduated from institutions other than teachers‘ colleges but it is only

statistically significant in case they graduated from open universities where they earn

about 950 baht per month less than those graduating from teachers‘ colleges

The level of grade taught also has effect on the official salaries. A teacher who

teaches at pre-primary and lower primary levels has earns 4,200 and 2,600 baht less than

teachers in the upper secondary level. Other characteristics such as sex of teacher,

subjects they teach, their undergraduate major, whether they have a master‘s degree, their

marital status and the region of their schools do not have significant relation to their

official salaries and benefits.

In the second (Model2) and third (Model3) columns of Table 4-12, I use total

earnings of teachers including income from moonlighting as a dependent variable. In

Model 3, the official salary and benefits of teachers are controlled for.

From Model 2, teacher experience is positively related to total income while

having graduated from open universities and the non-prestigious four-year colleges are

negatively associated with their earnings. Teachers of pre-primary school also earn less

income than other teachers in other grade levels. Comparing with teachers who do not

have a part-time job, teachers who have part-time jobs in tutoring or renting apartment or

lending money earn significantly more—about 4,000 and 15,000 baht respectively.

Model 3 shows that when the official salary variable is controlled for, teacher

experience, and their undergraduate institutions still are significantly associated with total

144

income of teachers. One more year of work experience is related to 200 baht more in total

earnings. And teachers who graduated from open universities and non-prestigious four-

year colleges earn about 900 and 1,500 baht less than those graduating from teachers‘

colleges. Teachers working in the northern provinces earn more and those in the central

provinces tend to earn less compared to teachers in the southern region. Teachers who

have part-time jobs renting apartments or lending money earn significantly higher

incomes (about 19,900 baht per month) than teachers who do not moonlight. So do

teachers who tutor and sell food/products or farm. They earn about 5,000, 4,000 and

2,500 baht more than those without part-time jobs.

Table 4-13 shows the regression outcomes of teachers‘ characteristics on earning

by subgroup of teachers: those who moonlight and those who do not. Experience plays a

significant role in the incomes of teachers from both groups. One more year of teacher

experience of non-moonlighting and moonlighting teachers is associated with about

1,250 and 150 baht more per month, respectively.

The type of undergraduate institution has a different relationship to earnings for

teachers in the two groups. Graduating from open universities and non-prestigious four-

year colleges are associated with lower earnings of non-moonlighting teachers while

graduating from teachers‘ colleges in Bangkok has a positive but marginally significant

relationship to earnings for moonlighting teaches.

The level of grade taught only influences the earnings of non-moonlighting

teachers: they earn significantly less if they teach at the pre-primary school level.

145

Apart from teachers‘ experience, their undergraduate institution and the grade

level taught, no other teacher characteristics are significantly related to non-moonlighting

teachers‘ income. However, for moonlighting teachers, some variables, such as whether

they teach physical education or have a bachelor‘s degree in humanities and are from

northern provinces are positively related to earnings yet are only significant at a 10

percent level. Among moonlighting teachers, those who work in renting apartments or

lending money earn significantly higher than other teachers.

4.5 DISCUSSIONS

In conclusion, the outcomes from previous analyses of teacher moonlighting find

that around 20-25% of Thai teachers participate in moonlighting activities. Teachers, who

are younger, have debts and teach in early grade levels have greater probability of

participating in overall moonlighting activities. The tutoring job has high association with

teachers who are female, younger and teach at lower primary grade levels (grade 1-3).

Tutoring teachers are not ones likely to have debt, whereas teachers who moonlight in

sales are. Male teachers and those teaching at pre-primary level are more likely to do

farming in their part-time.

Some findings are not very surprising. It is quite logical that younger teachers

tend to moonlight more and having debt is highly associated with the decision to

moonlight. Teachers who teach popular subjects such as English, mathematics and

science are highly associated with the probability of tutoring although teaching English is

not significant. This is also predictable as these subjects are relatively hard, and for many

146

students, require more tutoring in addition to regular classroom teaching. Also, they are

pertinent to entrance examinations at high school and university levels.

The policy implications from these outcomes are as follows. Since teacher‘s debt

is highly significant and associated with the moonlighting activities, the government‘s

debt relief program will reduce the likelihood of teacher having part-time jobs in overall

especially in sales. However, the debt relief will not affect the prevalence of teacher‘s

tutoring activities as they are motivated by other factors.

Despite the evidence of teacher‘s moonlighting in Thailand, we cannot

empirically relate the moonlighting of teachers to the learning of students. It is reasonable

to view that teachers who moonlight have adverse effect on classroom teaching as they

need to put their efforts on additional activities. However, it may not be the case for every

moonlighting teacher. Critics may argue that teachers who moonlight in tutoring are more

experienced at teaching to the test than teachers who just teach from regular, official

textbooks. The new dataset is needed in order to investigate this relationship between

moonlighting and student learning.

The current pay structure of teacher also does not encourage teachers who

graduate from high-quality universities to join the teaching profession. Other things being

equal, teachers are still much better off if they graduate from one of the teachers‘

colleges. If we need to recruit a better crop of teachers from the more prestigious

institutions, the shift in policy on structural pay must be made in order to compensate

more to teachers based on their educational backgrounds in terms of the quality of their

programs or institutions.

147

Figure 4-1: Distribution of Teacher’s Income by Moonlighting Status

Figure 4-2: Distribution of Teacher’s Income by Moonlighting Jobs

0

.00

00

1.0

000

2.0

000

3.0

000

4D

en

sity

0 20000 40000 60000 80000 100000Teacher Income (Baht)

Original Salary of Non-Moonlighting Teacher

Original Salary of Moonlighting Teacher

Total Income of Moonlighting Teachers


by Teacher's Moonlighting Status

Income of Teachers

0

.00

00

2.0

000

4.0

000

6D

en

sity

0 20000 40000 60000 80000 100000Teacher Income (Baht)

Tutoring

Selling

Farming

Renting/Lending (Apartment/Money)

Others


by Teacher's Moonlighting Jobs

Income of Moonlighting Teachers

148

Source: Thailand Teacher Survey,2006 by Faculty of Economics, Chulalongkorn University

Table 4-1 : Descriptive Statistics : Thailand Teacher Survey

N n Mean S.D

Sex 573 138 0.24 0.43 male=1, female =0

Experience 568 568 18.71 9.80 years of working as teacher Subject taught Thai 573 176 0.31 0.46 Teach this subject = 1, not teach this subject = 0 Math 573 122 0.21 0.41 Teach this subject = 1, not teach this subject = 0 Science 573 88 0.15 0.36 Teach this subject = 1, not teach this subject = 0 English 573 56 0.10 0.30 Teach this subject = 1, not teach this subject = 0 Social Studies 573 58 0.10 0.30 Teach this subject = 1, not teach this subject = 0 Career-related education 573 25 0.04 0.20 Teach this subject = 1, not teach this subject = 0 Physical education 573 18 0.03 0.17 Teach this subject = 1, not teach this subject = 0 Others 573 30 0.05 0.22 Teach this subject = 1, not teach this subject = 0 Grade taught

Pre-Primary 572 16 0.03 0.17 Teach this grade = 1, not teach this grade =0

Lower Primary 572 81 0.14 0.35 Teach this grade = 1, not teach this grade =0

Upper Primary 572 191 0.33 0.47 Teach this grade = 1, not teach this grade =0

Lower Secondary 572 123 0.22 0.41 Teach this grade = 1, not teach this grade =0

Upper Secondary 572 161 0.28 0.45 Teach this grade = 1, not teach this grade =0

Teacher academic institution Teacher’s college in Bangkok 501 106 0.21 0.41 Graduate from T.C. in Bangkok = 1

Teacher’s college (Others) 501 294 0.58 0.49 Graduate from T.C.from other provinces = 1

Open Universities 501 67 0.13 0.34 Graduate from Open Universities = 1 Prestigious 4 years colleges 501 28 0.06 0.23 Graduate from prestigious colleges = 1

Other 4 years colleges 501 6 0.02 0.11 Graduate from other colleges = 1 Teacher academic degree

Master 572 100 0.17 0.38 Master's as Highest education =

BA 572 402 0.82 0.38 Bachelor's as Highest education = 1

Diploma 572 3 0.00 0.15 Diploma as Higher education =

School location Chiangrai 573 35 0.06 0.24 School located in province = 1, otherwise =0 Chiangmai 573 80 0.14 0.35 School located in province = 1, otherwise =0 Ubon Rajchatani 573 76 0.13 0.34 School located in province = 1, otherwise =0 Srisaket 573 77 0.13 0.34 School located in province = 1, otherwise =0 Nakorn Pathom 573 81 0.14 0.35 School located in province = 1, otherwise =0

Bangkok 573 74 0.13 0.34 School located in Bangkok = 1, otherwise =0 Rayong 573 78 0.14 0.34 School located in province = 1, otherwise =0 Nakorn Si Thammarat 573 72 0.13 0.33 School located in province = 1, otherwise =0 Teacher BA majors

BA in Education 573 233 0.41 0.49 Have BA in this field = 1, otherwise =0 BA in Science 573 42 0.07 0.26 Have BA in this field = 1, otherwise =0 BA in Educational studies 573 152 0.27 0.44 Have BA in this field = 1, otherwise =0 BA in Humanities 573 43 0.08 0.26 Have BA in this field = 1, otherwise =0 BA in Liberal Arts 573 10 0.02 0.13 Have BA in this field = 1, otherwise =0

BA in others 573 93 0.16 0.37 Have BA in this field = 1, otherwise =0

149


Table 4-3: Distribution of Teachers

Region Province

Percentage of teachers who attended colleges in

Same Province Same Region Same Region and

Bangkok

Northern 1.Chiangrai 58.06 80.65 96.78

2.Chiangmai 50 67.74 93.55

Northeastern 3.Ubon Rajchatani 52.11 62 91.58

4.Srisaket 2.74 71.24 86.31

Central 5.Nakorn Pathom 34.21 38.16 80.27

6.Rayong 0 29.41 77.93

Bangkok 7.Bangkok 83.95 NA NA

Southern 8.Nakorn Si Thammarat 37.4 53.11 95.31

Table 4-4 :Percentage of Teachers by their Faculties and Educational Background

N Percent Math/Science Majors

Undergraduate Institution

Teacher College

4 year universities

Math Teachers (N=52)

Faculty

Education 44 84.61% 75% 75% 9%

Science 5 9.62% 60% 66.70% 20%

Others 3 5.77% 0% 40% 33.30% Science Teachers (N=34)

Faculty

Education 12 35.3% 83.30% 75% 16.70%

Science 22 64.7% 100% 95% 4.70%


Table 4-2 : Descriptive Statistics : Teacher and Part-time job Teacher Characteristics N n Mean S.D

Have part-time Job 573 129 0.23 0.42 Have part-time job = 1, otherwise = 0 Teaching as part-time job 129 77 0.54 0.50 Teaching for part-time job =1, otherwise =0 Selling /restaurant 129 24 0.19 0.39 Selling for part-time job =1, otherwise =0 Agriculture/farming 129 22 0.17 0.38 Farming for part-time job =1, otherwise =0 Renting/Lending 129 6 0.01 0.21 Others for part-time job =1, otherwise =0 Income from part-time 105 7,531.90 7,442.50 Monthly income from part-time job Have debts 568 446 .78 .41 Have debt = 1, don’t have debt = 0

150

Table 4-5 : Educational background of Mathematics teachers by Major

Number Percentage

Bachelor’s degrees Faculty of EDUCATION (N=104) Mathematics 53 0.4491525

Primary education 15 0.1271186

Early childhood education 6 0.0508475

Social studies/sociology/social science 5 0.0423729

Sciences 5 0.0423729

Psychology, counseling 3 0.0254237

Education Management 3 0.0254237

Evaluation & Measure 3 0.0254237

Thai 2 0.0169492

Physical education 2 0.0169492

English 1 0.0084746

Home economics 1 0.0084746

Education Business 1 0.0084746

Agriculture 1 0.0084746

Faculty of Sciences (N=7)

Math 3 0.0254237

Biology 2 0.0169492

General sciences 1 0.0084746

Animal science 1 0.0084746

Other faculties (N=7)

Humanities 2 0.0169492

Engineering 2 0.0169492

Business Administration 1 0.0084746

Social Sciences 1 0.0084746

Arts 1 0.0084746

Total 118 100

Master's Degree Programs

Faculty of Education (N=12)

Math/Teaching Math 4 0.285714

Educational Management/Admin 4 0.285714

research methodology/statistics 2 0.142857

Curriculum development 1 0.071429

Primary education 1 0.071429

Faculty of Science (N=2)

Math/Teaching Math 2 0.142857

Total 14 100

151


Table 4-6 : Educational background of Science teachers by Major

Number Percentage

Faculty of EDUCATION (N=50) Biology 2 0.04 Thai 1 0.02 Psychology, counseling 2 0.04 Primary education 4 0.08 English 3 0.06 Sciences 26 0.52 Early childhood education 1 0.02 Physics 5 0.1 Chemistry 2 0.04 Agriculture 2 0.04 Industrial arts/Industry 1 0.02 Special education 1 0.02 Total 50 1 Faculty of Sciences (N=24) Physics 8 0.3333333 Biology 7 0.2916667 General sciences 7 0.2916667 Chemistry 2 0.0833333 Total 24 1 Other Faculties (N=3) Engineering 1 0.33 Medicine 1 0.33 Others 1 0.33 Total 3 1 Master’s Degrees (N=25) Education 16 0.64 Science 7 0.28 Education Management 1 0.04 Social Sciences 1 0.04 Total 25 1

152

Table 4-7 :Characteristics of Moonlighting and Non-Moonlighting teachers

Non-Moonlighting (N=453) Moonlighting (N=113) Mean SD N Mean SD N

Experience 19.23 9.734035 449 16.6 10.13835 112 Sex (Male =1, Female=0) 0.2428256 0.4292646 453 0.2212389 0.41693 113 Age 43.3 8.395978 448 41.3 9.042922 111 Official Salary 23209.91 9763.753 441 21754.37 11182.46 113 Total Income 23400.37 9822.745 440 28255.8 14471.04 111 Have Debt (Yes=1, No=0) 0.76 0.4272326 451 0.88 0.3230181 111 Household Expenditure 33043.45 15629.42 297 34330.88 17884.49 74 Satisfy with the salary received (Yes = 1, No=0) 0.6407 0.4803 453 0.534 0.5 113

Table 4-8 :Percentage of Moonlighting and Moonlighting Teachers by teacher’s background

Non-moonlighting Moonlighting

Undergraduate Institutions

Teacher's colleges in Bangkok 79.81 20.19

Teacher's colleges in other provinces 79.31 20.69

Open Universities 81.82 18.18

Prestigious 4 years government university 82.14 17.86

Other 4 years university 83.33 16.67

Undergraduate Fields

Education 75.97 24.03

Science 78.57 21.43

Humanities 86.05 13.95

Liberal Arts 90 10

Others 79.57 20.43

Subject Taught

Thai 85.63 14.37

Math 74.38 25.62

Science 74.42 25.58

English 81.82 18.18

Social Science 84.21 15.79

Career-Related Education 76 24

Physical Education 88.9 11.1

Others 73.33 26.67

Region

1. Bangkok 80 20

2. Central 80.39 19.61

3. Northern 75.95 24.05

4. North Eastern 81.42 18.58

5. Southern 82.05 17.95

153

Table 4-9 : Teacher Income by Subject Taught (Income in Bath/Month)

Official Income Final Income

Salary in cash

Salary in cash & kind

No Part-time job Tutoring

Others jobs

Desired Salary

Thai Mean 25069.71 25195.62 25303.97 29569.23 29510.91 34614.29

S.D 9786.692 9785.935 9783.655 9400.687 18411.52 44415.32

N 137 137 136 13 22 42

Math Mean 21978.2 22062.54 21942.94 27371.54 25780 27709.68

S.D 9167.898 9198.757 9188.096 13657.81 6182.062 13830.39

N 83 83 82 26 8 31

Science Mean 21364.24 21433.94 22134.77 31010.38 27288.33 28638.18

S.D 10753.98 10664.77 10737.99 22012.13 17939.6 15691.64

N 66 66 65 13 6 22

English Mean 22077.95 22088.97 22116.15 24069.15 23400 28801.18

S.D 9140.921 9133.141 9696.501 11897.65 . 14348.68

N 39 39 39 13 1 17

social studies Mean 24530.23 24790.65 25392.57 24333.33 28795 31461.54

S.D 8125.776 8341.598 8763.532 1527.525 10539.09 12991.12

N 48 48 47 3 6 13

Career-related education Mean 18418.24 18418.24 18418.24 26333.33 36250 31000

S.D 10535.1 10535.1 10535.1 11930.35 11086.78 18520.26

N 17 17 17 3 4 3

Physical Education Mean 23073.57 23073.57 23330.71 24690 20833.33 30000

S.D 8382.832 8382.832 8531.096 . 8251.263 18027.76

N 14 14 14 1 3 3

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Arts Mean 29800 32600 32600 .

S.D 7696.908 11908.98 11908.98 .

N 4 4 4 0

counseling Mean 23606.67 23606.67 23606.67 27500 40000

S.D 6981.103 6981.103 6981.103 . .

N 9 9 9 1 1

drama arts Mean 26690 26690 26690 .

S.D . . . .

N 1 1 1 0

Others Mean 16963.33 16963.33 17296.67 18480 31820 14700

S.D 9088.933 9088.933 8997.288 . . 5403.702

N 9 9 9 1 1 5

Total Mean 23078.3 23202.37 23405.11 27154.99 28154.72 30204.09

S.D 9630.669 9701.619 9803.682 13926.79 14502.76 27248.92

N 427 427 423 75 53 137 Source: Thailand Teacher Survey,2006 by Faculty of Economics, Chulalongkorn University

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Table 4-10 : Number of teacher having part-time job and hours worked by subject

Total

Part-time

Job

Tutoring

Part-time Ave hours tutoring/week

Thai 176 37 13 5.23

Math 122 35 27 5.87

Science 88 19 13 7.12

English 56 15 14 3.71

social studies 58 9 3 3

Career-related education 25 7 3 8.33

Physical Education 18 4 1 7

Others 11 2 1 5

Computer 2 2 2 5.5

Skills 1 1 .

Arts 4 0 .

Counseling 10 1 .

drama arts 1 0 .

Total 572 132 77 5.558442

TABLE 4-11 : Work status of teachers and their economic aspects

Part-time job by type Salary

Total

Income

Total

Expenditure

Have debt

(yes=1) Savings

a) Tutor Mean 21,566.63 27,017.01 21,942.90 0.867647 5295.794

N=70 S.D 11209.28 13697.65 12696.91 0.341394 8717.211

b) Sale food/products Mean 19,707.50 26,142.61 24,895.83 0.958333 686.087

N=25 S.D 10356.65 10176.12 14521.81 0.204124 15098.65

c) Farming

/agriculture Mean 23,500.45 29,161.36 27,262.73 1 1898.636

N=23 S.D 7619.605 18175.32 16416.9 0 8944.918

d) Renting/Lending Mean 26,588 26,788 26,040 1 3435

N=6 S.D 15663.68 16023.63 11670.82 0 2024.409

e) Other jobs Mean 24,358.33 28,741.67 24,000 1 3741.667

N=8 S.D 19,188.34 17,763.68 16,792.86 0 5162.21

Total Mean 22914.4 24348.16 20437.64 0.785211 3970.229

N= 572 S.D 10095.9 11089.67 11581.17 0.411038 9465.699

Salary, Total Income, Total Expenditure, Savings are in baht per month


156

TABLE 4-12 : OLS Regression of Teacher Characteristics on Teacher’s Earning Dependent Variable = log of Income (Bath/Month)

Teacher Characteristics Dependent Variable

Model 1 Model 2 Model 3

Official Salary Total Income Total Income

Sex (Male=1,Female=0) -0.01 -0.01 0.00

(0.03) (0.04) (0.03)

Experience 0.09*** 0.08*** 0.02***

(0.01) (0.01) (0.01)

Experience Squared -0.00*** -0.00*** -0.00***

(0.00) (0.00) (0.00)

Undergrad Institution

Open University -0.09* -0.14** -0.10**

(0.05) (0.06) (0.04)

Prestigious 4 years colleges -0.01 -0.05 -0.04

(0.14) (0.17) (0.11)

other 4 years colleges -0.06 -0.16** -0.12**

(0.07) (0.08) (0.05)

Teacher's college (Bangkok) 0.02 0.04 0

(0.04) (0.05) (0.03)

Have Master's degree (Yes=1) -0.03 -0.01 0.04

(0.05) (0.06) (0.04)

Level of Class taught (base = upper secondary)

Pre-Primary -0.28*** -0.24** -0.05

(0.09) (0.11) (0.07)

Lower Primary -0.15*** -0.09 0.03

(0.05) (0.06) (0.04)

Upper Primary -0.06 -0.04 0.01

(0.04) (0.05) (0.03)

Lower Secondary -0.04 -0.05 0

(0.04) (0.05) (0.03)

Class Taught (base = Math)

Thai 0.01 0 0

(0.04) (0.05) (0.03)

Science -0.07 -0.04 0

(0.05) (0.06) (0.04)

English -0.00 -0.1 -0.07

(0.05) (0.07) (0.04)

Social Studies 0.02 0.03 0.02

(0.05) (0.07) (0.04)

Career-related education -0.12 -0.04 0.04

(0.08) (0.09) (0.06)

P.E. -0.06 0.01 0.06

(0.09) (0.11) (0.07)

Others -0.08 -0.09 -0.03

(0.07) (0.09) (0.06)

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Undergraduate Majors (base = B.A. In Education)

BA in Science 0.07 0.04 0

(0.06) (0.07) (0.05)

BA in EDU Studies 0.02 0.03 0.02

(0.04) (0.05) (0.03)

BA in Humanities -0.02 -0.02 -0.04

(0.05) (0.07) (0.04)

BA in Liberal Arts 0.03 0.04 0.05

(0.12) (0.14) (0.09)

BA in others 0.03 0.03 -0.01

(0.05) (0.07) (0.04)

Region (base = South)

Central -0.01 -0.05 -0.06*

(0.04) (0.05) (0.03)

North -0.04 -0.01 0.05*

(0.04) (0.05) (0.03)

Northeast -0.02 -0.01 0.01

(0.05) (0.06) (0.04)

Marrital Status (base = Single)

Married 0.04 0.03 -0.01

(0.04) (0.04) (0.03)

Divorced/Separated -0.07 -0.06 0.03

(0.07) (0.08) (0.05)

Type of Part-time job (base = no part-time job/Selling)

Tutoring 0.26*** 0.18***

(0.05) (0.03)

Selling Food/Product 0.11 0.18***

(0.09) (0.06)

Agriculture/Farming 0.12 0.12**

(0.09) (0.06)

Renting/Lending 0.40** 0.49***

(0.18) (0.11)

Others -0.02 -0.22

(0.27) (0.16)

Official Salary including benefits 0.00***

(0.00)

Constant 8.94*** 9.03*** 8.74***

(0.07) (0.08) (0.05)

Observations 483 477 477

R-squared 0.7 0.61 0.83


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TABLE 4-13 : OLS Regression on Teacher’s Earning by Sub-group of Teachers Dependent Variable = log of Income (Bath/Month)

Teacher Characteristics Total Income Total Income

Non-Moonlighting Teacher Moonlighting Teacher

Sex (Male=1,Female=0) -0.01 0.05 (0.045) (0.09)

Experience 0.08*** 0.04**

(0.007) (0.02)

Experience Squared -0.00*** -0.00**

(0.00) (0.00) Undergrad Institution Open University -0.19*** -0.10 (0.063) (0.12) Prestigious 4 years colleges -0.12 0.27 (0.174) (0.39)

other 4 years colleges -0.24*** -0.06

(0.082) (0.23)

Teacher's college (Bangkok) -0.02 0.17*

(0.05) (0.10) Have Master's degree (Yes=1) 0.02 -0.01 (0.061) (0.11)

Level of Class taught (base = upper secondary)

Pre-Primary -0.36*** 0.16 (0.129) (0.21) Lower Primary -0.07 0.12 (0.065) (0.12) Upper Primary -0.02 -0.03 (0.049) (0.10) Lower Secondary -0.03 -0.08 (0.051) (0.13)

Class Taught (base = Math) Thai 0 -0.00 (0.053) (0.10) Science -0.04 -0.15 (0.064) (0.13) English -0.07 -0.18 (0.073) (0.12) Social Studies 0.06 0.01 (0.069) (0.13) Career-related education -0.11 0.25 (0.098) (0.19) P.E. -0.05 0.42*

(0.111) (0.23)

Others -0.09 -0.16

(0.092) (0.18)

Undergraduate Majors (base = B.A. In Education) BA in Science 0.02 0.20 (0.074) (0.17)

BA in EDU Studies 0.04 0.07

(0.052) (0.10)

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BA in Humanities -0.05 0.33* (0.067) (0.19)

BA in Liberal Arts 0.05 0.00 (0.133) (0.00) BA in others 0.06 -0.12

(0.069) (0.14)


Central -0.03 -0.03 (0.055) (0.10) North -0.04 0.16* (0.049) (0.09) Northeast -0.01 -0.02 (0.059) (0.12) Marrital Status (base = Single) Married 0.02 0.04

(0.047) (0.08)

Divorced/Separated -0.06 0.06 (0.085) (0.17) Type of Part-time job (base = Sale of Food/Products) Tutoring 0.08 (0.10)

Agriculture/Farming -0.07

(0.12) Renting/Lending 0.57*** (0.21)

Others -0.18 (0.24) Official Salary including benefits 0.00*** (0.00) Constant 9.03*** 8.81*** (0.088) (0.17)

Observations 367 110 R-squared 0.63 0.79


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TABLE 4-14 : Predicting Moonlighting, PROBIT REGRESSION Dependent Variable = 1 if teachers a.have part-time job b.tutoring c.selling d.farming

Teacher Characteristics Mean dY/dX

a b c d

Moonlighting Tutoring Selling Farming

Sex (Male=1,Female=0) 0.2408 0.02 -0.06** -0.00 0.09*

(0.508) (-1.970) (-0.106) (3.017)

Experience 18.7072 -0.02** -0.01** 0.00 0.01

(-2.159) (-2.185) (0.131) (1.689)

Experience Squared 446.7951 0.00 0.00 -0.00 -0.00

(1.115) (0.552) (-0.222) (-1.941)

Undergrad Institution

Open University 0.1337 -0.00 -0.01 0.04 0.01

(-0.005) (-0.277) (1.076) (0.327)

Prestigious 4 years colleges 0.0120 -0.04 0.43

(-0.205) (1.662)

other 4 years colleges 0.0559 -0.02 -0.03

(-0.236) (-0.429)

Teacher's college (Bangkok) 0.2116 0.02 0.05 0.02 -0.02

(0.453) (1.149) (0.810) (-1.658)

Have Master's degree (Yes=1) 0.1748 0.00 0.10 -0.02

(0.007) (1.897) (-0.707) Level of Class taught (base = upper secondary)

Pre-Primary 0.0280 0.40*** -0.01 0.01 0.92***

(2.828) (-0.101) (0.225) (2.811)

Lower Primary 0.1416 0.21** 0.13* -0.02 0.26

(2.717) (2.073) (-0.822) (1.669)

Upper Primary 0.3339 0.01 0.05 -0.03 0.10

(0.118) (1.202) (-1.339) (1.331)

Lower Secondary 0.2150 -0.03 -0.01 -0.03* 0.09

(-0.459) (-0.349) (-1.387) (0.995)

Class Taught (base = Math)

Thai 0.0977 -0.14*** -0.12*** 0.03 0.02

(-2.821) (-3.483) (1.117) (0.863)

Science 0.1012 -0.02 -0.07** -0.01 -0.01

(-0.367) (-1.829) (-0.313) (-0.602)

English 0.0436 -0.07 0.01

(-1.160) (0.277)

Social Studies 0.0314 -0.10** -0.10*** 0.05 0.09

(-1.629) (-2.437) (1.074) (1.826)

Career-related education 0.0524 0.01 -0.05 0.02

(0.075) (-0.941) (0.407)

P.E. 0.0733 -0.08 -0.06 0.20 0.34

(-0.690) (-0.926) (1.485) (1.796)

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Others 0.2653 -0.00 -0.06 -0.00 0.01

(-0.022) (-1.151) (-0.031) (0.193) Undergraduate Majors (base = B.A. In Education)

BA in Science 0.0733 -0.01 0.07

(-0.155) (1.055)

BA in EDU Studies 0.2164 0.03 0.04 -0.00 0.00

(0.482) (0.898) (-0.220) (0.057)

BA in Humanities 0.0750 0.01 -0.07** 0.01 0.01

(0.109) (-1.403) (0.310) (0.259)

BA in others 0.1623 -0.10* 0.03 -0.02 -0.02

(-1.430) (0.499) (-0.728) (-1.091)


Central 0.2164 0.03 0.03 0.00 0.02

(0.537) (0.732) (0.029) (0.800)

North 0.2932 -0.03 -0.00 -0.02 -0.03

(-0.606) (-0.077) (-0.902) (-1.596)

Northeast 0.1414 0.02 0.04 -0.03** 0.03

(0.328) (0.853) (-1.545) (1.134)

Marrital Status (base = Single)

Married 0.6597 -0.01 0.04 0.00 -0.01

(-0.132) (1.117) (0.190) (-0.358)

Divorced/Separated 0.0681 0.04 0.10 0.01

(0.426) (1.255) (0.281)

Official Salary including benefits 22914.4 0.00** 0.00** -0.00 -0.00

(2.256) (2.297) (-1.375) (-0.356)

Have Debt (Yes=1) 0.7852 0.11*** 0.04 0.03**

(2.320) (1.097) (1.620)

Constant -0.888* -1.005* -1.604* -4.429**

(-2.453) (-2.482) (-2.325) (-2.985)

Observations 468 468 362 235

Pseudo R-squared 0.1 0.17 0.13 0.35

Outcomes (dY/dX) are change in the probability for a one-unit change in the independent variable at the mean. Z-stat in parenthesis Astericks indicate coefficients with p-values less than 0.01(***),0.05(**) or 0.10(*)

162

CHAPTER 5

CONCLUSIONS AND POLICY IMPLICATIONS

The final chapter of this dissertation will discuss findings from Chapter 2-4

and related policy implications. Overall, the dissertation has as its purpose analyzing

teacher labor markets in Thailand with a particular focus on mathematics and science

teachers. My analysis encompassed several aspects of teacher labor markets, including

the difficulties inherent in the production of quality teachers and the recruitment of

high ability individuals into the profession, the earnings gap between teachers and

competing occupations, and the problem of teacher moonlighting. These aspects of

teachers and their employment may also relate to the classroom learning of students. I

therefore attempt to identify those characteristics of mathematics and science teachers

that are most closely associated with the achievement of Thai students. In order to

better understand the composition of the teacher labor market, I then analyze the

earnings gap between teachers and six mathematics-science oriented occupations

(engineers, medical professionals, scientist, accountants, economists and nurses.) My

analysis concludes with a consideration of the probability of teachers having a part-

time job.

Understanding the dynamics of Thailand‘s economic structure over the past 20

years serves as a useful starting point for understanding the problems and concerns of

Thai teachers. The modernization and industrialization of the country in the early

163

1980s has expanded job opportunities in the industrial and service sectors while the

earnings gap between public sector professions including teaching and other

occupations in the private sector has been widening. In relative terms, teaching has

become a low paying occupation. Furthermore, it has gradually lost its social status as

a highly respectable job due in part to a change in social values, and in part to the

perception that teacher training is for students who are not admitted into other college

majors. As a result, the teaching profession has failed to recruit and retain highly

capable individuals. This issue has been particularly salient in the case of mathematics

and science teachers, where individuals with the requisite skills to teach mathematics

or science at the secondary school level may face a number of more attractive career

options. The low incomes of teachers and the debts they incur as a result often

necessitates that they secure supplementary income by moonlighting in part-time jobs

in order to make ends meet. The resultant high workload of teachers who have part-

time jobs might diminish their efforts in the classroom. These conditions are likely to

negatively impact Thai students in their efforts to learn mathematics and science, and

provide a possible explanation for the declining scores of Thai students on both

national and international tests over the years.

It is important to bear in mind that the data that I use in Chapters 2-4, TIMSS,

the Thailand Labor Force Survey, and the Thailand Teacher Survey - are cross-

sectional. The regression analysis using these datasets may be subject to certain biases,

such as self-selection. Although various techniques are employed in order to correct

for such biases, one should exercise caution in drawing causal inferences on the

relationship between factors such as teacher characteristics and outcomes such as

164

student achievement, individual earnings, or the probability of a teacher having a part-

time job.

5.1 Teacher‘s Educational Background and Student Achievement

In Chapter 2, I analyzed the education production function and identified

several consistent relationships between teacher characteristics and student

achievement in mathematics. A key dimension of this analysis is the relationship

between a teacher‘s educational background and student achievement; specifically the

effectiveness of teachers who graduate from a faculty of education versus those from a

faculty of science, and whether a teaching certificate and a masters degree in teaching

matter.

The outcomes from TIMSS 1999 and TIMSS 2007 using ordinary first-

difference (FD) analysis suggest that mathematics teachers who were educated in a

faculty of education in the major fields (i.e. who obtained a B.A. in science education

or mathematics education) tend to be associated with higher mathematics achievement

among their students than teachers who graduated from a faculty of science (those

who obtained a B.A. in mathematics or sciences). It should be noted that the findings

do contradict the results of the ordinary least squares (OLS) regressions in which

teachers who graduated from faculty of science tend to do better than those graduating

from a faculty of education. There are several reasons that can explain the differences

in these two methods, such as the bias from the non-random selection of teachers in

the survey or bias from the sorting of teacher education background to schools of

different qualities.

165

The level of education attained by a teacher is also an important issue. The

results in Chapter 2 show that there is a positive relationship between teachers with

master‘s degree and student achievement for both FD and OLS analyses. There is a

surprising result where in 1999 a teacher who did not have a Bachelor‘s degree has

more impact on student achievement than a teacher with a B.A. degree; however, in

2007 only a teacher with a master‘s degree remains significantly associated with


The estimated results in Chapter 2 have a number of policy implications, since

the importance of teacher characteristics such as educational background, certification

and the nature of graduate education have been widely debated among Thai

policymakers. The findings of this study provide conflicting views with the policy

held by the Ministry of Education in Thailand, namely that teachers who are trained in

a faculty of science are better equipped to effectively teach students in mathematics

and science than those graduating from a faculty of education.

This assumption forms the basis for an initiative implemented in 1998 by the

Thai government: the ―Program for the Promotion of the Production of Science and

Mathematics Teachers.‖ This program aimed to recruit highly skilled individuals to

become mathematics and science secondary school teachers, providing scholarships to

high performing high school graduates to study sciences in approved faculties of

sciences in fields such as mathematics, physics, chemistry, biology and computers.

Following an additional teacher-training year, they were placed in secondary schools

throughout the country.

166

The results of my analysis suggest that in addition to the focus of the

government to recruit graduates from faculties of sciences, the effort should also be

directed to recruiting high ability students to enroll in the mathematics or science

education programs in the faculties of education or to give incentive for the graduates

of the programs to go into teaching. It is possible that mathematics and science

teachers who are trained in the subject matter plus the pedagogy from the faculty of

education can deliver the lessons more effectively than teachers who only have

relevant knowledge but do not have enough training in classroom teaching.

Other policy implications relate to teaching certificates and teachers‘ graduate

education. My results do not support the claim that a teaching certificate is important

to improving student test scores in mathematics.

5.2 Pay Differences Between Mathematics-Science Teachers and Other Mathematics-

Science Oriented Occupations

In Chapter 3, I compared the earnings of teachers with individuals in other

mathematics and science oriented occupations, namely medical professionals,

engineers, accountants, scientists and nurses.

From the descriptive data, it appears that on average engineers and medical

professionals are most highly remunerated, whereas teachers and nurses earn

comparable salaries. However, controlling for key covariates I find that teachers are

most poorly paid relative to all other occupations including nurses. Moreover, teachers

have the lowest paying jobs among all public sector employees, controlling for

relevant characteristics.

167

The earnings gap between teachers and other occupations widened from 1985

until the 1997 economic crisis, during which the earnings of other occupations were

stagnant or decreased. After 2003, the earnings gap between teachers and other

occupations once again began to widen. This is in part a consequence of the fact that

the majority of teachers operate in the public sector, whereas a high proportion of the

workforce for many other occupations is often employed in the private sector. For

most occupations in Thailand (with the exception of teaching) private sector pay is

higher than in the public sector.

It is also important to consider findings related to the opportunity cost faced by

teachers. Among the general population, male teachers have more career options with

better pay than female teachers and forgo greater potential earnings than females if

they choose careers as teachers. Even when comparing only among the graduates from

teachers‘ colleges in an attempt to control for ability bias, male graduates of teachers‘

colleges earn more than their peers if they chose other occupations while female

graduates who choose not to teach earn less.

A wage decomposition to compare the earnings of teachers and other

mathematics and science related occupations based on observed characteristics such as

age, sex, education and type of employment suggests that teachers should have earned

more than what the system currently pays them. Teachers are systematically under-

compensated (compared to workers in six other mathematics and science oriented

occupations. There are many unobserved factors such as market demand of labor

force, differences in skills and abilities of workers that contribute to the negative wage

premium experienced by teachers.

168

When only comparing the earnings of individuals with bachelors‘ degrees from

academic-track universities (those graduating from faculties of education, science and

other departments, excluding teacher colleges), the gaps between teachers and other

occupations among university graduates persist. Engineers and medical doctors earn

significantly higher than teachers (about 58% and 94% respectively) while scientists

and accountants earn moderately higher at around 11% and 28%respectively.

The analysis in this chapter has important policy implications. First, the

government should be aware of the higher opportunity cost faced by mathematics and

science teachers as compared to teachers of other subjects. The current single-salary

scheme for teachers across all subjects should be reviewed. Although it may not be

politically feasible to set a different salary scale for mathematics and science teachers,

it may be possible to consider providing special allowances to this group.

Thai policymakers have been concerned with the problem of low teacher

salaries for some time. One problem is that there is less flexibility in public sector

salaries, which are unable to keep pace with changes in the private sector. However,

providing greater benefits in terms of allowances or improvements in teachers‘ welfare

may alleviate the problem.

Several policies have been debated to address the issues of how to attract and

retain teachers in this low salary environment. I have already discussed the initiative to

attract highly capable individuals into mathematics and science teaching by providing

teacher training scholarships to graduates of faculties of science who are then placed

in secondary schools with a moderate salary of 10,000 baht per month.

169

A new government initiative based on the idea of increasing monetary

incentives for teachers is called ―A New Breed of Teacher.‖ Under this scheme, the

government guarantees scholarships to a number of high school students to study in

selected faculties of education and to third and fourth year university students from

faculties of education with the requirement that they must work as teachers upon

graduation. The ―New Breed of Teacher‖ program aims at all prospective teachers,

regardless of subject taught.

However, this initiative is controversial in the teacher community, as the new

crop of teachers will receive 15,000 baht in addition to their regular salaries. There is

resistance to this aspect of the scheme from the Office of the National Budget and

from in-service teachers who consider a policy that only increases the salary of a new

group of teachers while neglecting the rest to be unfair practice. Currently, the law is

still in the process of being passed.

While this attempt by the government to raise new teacher salaries and recruit

highly capable students is well-intentioned, it faces considerable opposition on the

basis of fairness. Moreover, it is unclear whether the number of new teachers produced

under this system would be sufficient to meet Thailand‘s needs.

My analysis suggests that providing monetary incentives such as scholarships

and increased salaries is a good start in the process of attracting bright young students

to the teaching profession. In the long run, the quality of the teaching force could be

upgraded significantly. In addition, the government should also focus on differences in

the earnings gap between mathematics and science teachers and other occupations.

The opportunity cost faced by mathematics and science teachers relative to individuals

170

in occupations requiring similar skills is significantly greater than the opportunity cost

faced by teachers in other subjects. Therefore, the salaries of mathematics and science

teachers should be adjusted accordingly in order to recruit and retain highly capable

people to teach these subjects. Yet, as I suggest above, this policy would also likely

face considerable opposition from current teachers.

5.3 Teacher Earnings and Teacher Moonlighting

In Chapter 4, I examined the extent of teachers‘ part-time work using a survey

of nearly 600 teachers from eight provinces. I found that around 20-25% of Thai

teachers participated in moonlighting activities. The majority of them have part-time

jobs including tutoring, selling food and other products, farming, acting as a landlord,

lending money and various other services.

The most important factors associated with increased likelihood of teachers

having a part-time job are low salaries and a high level of indebtedness. Overall,

teachers who have part-time jobs receive lower official salaries but end up having

higher incomes than those who do not moonlight. Moreover, younger teachers and

teachers who teach at the pre-primary and primary levels have higher probabilities of

moonlighting than others.

These findings contribute to the thinking about policy regarding the

moonlighting of teachers in a number of ways. Focusing on the issue of teacher

tutoring, I find that having debt is unrelated to the likelihood of teachers taking on a

tutoring position. In addition, the fact that tutoring is most prevalent among teachers of

lower primary school (grades 1-3) runs contrary to the belief that tutoring in Thailand

171

is more common among students at higher grade levels. Younger, female teachers and

those who teach math, science and English have higher probabilities of moonlighting

through tutoring than those teaching other subjects.

Teachers who have high levels of debt are most likely to moonlight by selling

food and other products. This form of part-time work is also more common among

teachers from schools in the southern provinces. Since Thailand is an agricultural

country, many teachers in rural areas continue to work on farms in addition to their

full-time jobs. The teacher characteristics that are most associated with farm work are

being male and teaching at the pre-primary school level.

There is currently no research on the effect of teacher moonlighting on student

learning. However, it is likely that spending considerable time and effort on a part-

time job might negatively affect the quality of education provided by a teacher in the

classroom. This assumption, of course, depends on the type of part-time job and how

much time is dedicated to it.

From my research results in Chapter 4, the likelihood of moonlighting is

associated with teacher salaries and debt status. There are no firm rules against the

practice of moonlighting by Thai teachers, as it is impossible to restrict teachers from

working outside their regular hours. However, if policymakers believe that

moonlighting activities are detrimental to the quality of classroom teaching and seek

to curb such practices, they might consider initiating policies that increase teacher

income and welfare. However, this may not be effective for teachers who moonlight in

tutoring since the results from Chapter 3 suggest no significant relationship between

the economic status of teachers and the likelihood of tutoring.

172

Moonlighting also has wider implications for the ethics of the teaching

profession, as it often generates conflicts of interest. For instance, teachers who teach

part-time may use materials from their tutoring classes for school examinations. In a

similar vein, teachers may not appropriately cover the syllabus in a regular classroom

but will instead conduct a more thorough review of material at their after school

business in order to create incentives for students to enroll in their tutoring classes. In

the case of teachers who sell food or products, many target their students or parents in

the vicinity of the school. Some teachers may even sell candies or snacks to their

students in the classroom.

This points to the need for the establishment of a set of ethical standards or a

board of overseers that monitors and limits the extent to which these conflicts of

interest arise. In the case of tutoring, teachers may be advised against using the

materials from their tutoring class in official schools exams, or be carefully monitored

to ensure that they are fully engaged in their regular classes. Teachers may also be

prohibited from selling food and other products to students in the school district.

173

BIBLIOGRAPHY

Aaronson, D., Barrow, L., & Sander, W. (2007). Teachers and student achievement in

the Chicago Public Schools. Journal of Labor Economics, 25, 95–135.

Allard, S.(1982). A summary of institutional policies affecting outside and offload

employment for faculty at Maryland public higher education institutions. Annapolis,

MD: State Board for Higher Education

Arias, O., Hallock,K. and Sosa-Escudero, W.(2001). Individual Heterogeneity in

the Returns to Schooling: Instrumental Variables Quantile Regression Using Twins

Data, Empirical Economics, 26(1), 7–40.

Asadullah, M.N., (2005). Pay differences between teachers and other occupations:

Some empirical evidence from Bangladesh. Journal of Asian Economics, 17(6), 1044-

1065.

Ashenfelter, O., & Rouse, C. (1998). Income, schooling, and ability: Evidence from a

new sample of identical twins. Quarterly Journal of Economics, 113(1), 253-284.

Ashenfelter, O., & Krueger, A.(1994). Estimates of the Economic Returns to

Schooling from a New Sample of Twins. American Economic Review, 84(5),1157–73.

Ballou, D., & Podgursky, M. (1997). Teacher Pay and Teacher Quality. Kalamazoo,

Mich.: W.E. Upjohn Institute.

Ballou, D., & Podgursky, M. (1997b). Recruiting smarter teachers. Journal of Human

Resources, 30(2), 326–338.

Betts, J., Rueben, K., & Danenberg, K. (2000). Equal resources, equal outcomes? The

distribution of school resources and student achievement in California. San Francisco:

Public Policy Institute of California.

Betts, S.C. & W. Paterson.(2006). The Decision to Moonlight or Quit: Incorporating

Multiple Jobholding Into a Model of Turnover. Journal of Organizational Culture,

Communications and Conflict, 10(1)

Blinder, A. (1973). Wage discrimination: Reduced form and structural estimates.

Journal of Human Resources, 8, 436–455.

Bonesrønning, H., Falch, T., & Strøm, B. (2005). Teacher sorting, teacher quality, and

student composition. European Economic Review, 49, 457–83.

http://econpapers.repec.org/article/eeeasieco/

174

Boyd, D., P. Grossman, H. Lankford, S. Loeb, and J. Wyckoff. (2006). How changes

in entry requirements alter the teacher workforce and affect student achievement.

Education Finance and Policy, 1(2), 176-216.

Boyd D., Grossman, P., Lankford, H., Loeb, S., & Wyckoff, J. (2009). Teacher

Preparation and Student Achievement. Education Evaluation and Policy Analysis,

31(4), 416-440.

Boyd, D., H. Lankford, S. Loeb, and J. Wyckoff. (2005). The Draw of Home: How

Teachers' Preferences for Proximity Disadvantage Urban Schools. Journal of Policy

Analysis and Management , 24(1), 113-132.

Boyd D., Lankford, H., Rockoff, J., Loeb, S., & Wyckoff, J. (2008). The Narrowing

Gap in New York City Teacher Qualifications and its Implications for Student

Achievement in High-Poverty Schools. Journal of Policy Analysis and Management.

27(4), 793-818

Bray, M. (2003): Adverse Effects of Private Supplementary Tutoring, Report,

UNESCO, International Institute for Educational Planning.

Bray, M. (2005) Private Supplementary Tutoring: Comparative Perspectives on

Patterns and Implications, Mimeo, University of Hong Kong.

Buchinsky, M. (1994). Changes in U.S.Wage Structure 1963–1987: An Application of

Quantile Regression. Econometrica,62(2), 405–58.

Carnoy, M. & ILO. (1996). Impact of structural adjustment on the employment and

training of teachers: report for discussion at the Joint Meeting on the Impact of

Structural Adjustment on Educational Personnel, Geneva, ILO Office (ed). Geneva:

International Labour Office.

Carnoy, M., A.K.Gove., J.H.Marshall (2007) Cuba's Academic Advantage: Why

Students in Cuba Do Better in School. Stanford CA: Stanford University Press.

Carnoy, M. & DeAngelis, K. (2000). Does ability influence individual earnings, and if

so, by how much?. Stanford School of Education (mimeo)

Carnoy, M., T.Beteille., I. Brodziak., P. Loyalka., T. Luschei (2009).Teacher

Education and Development Study in Mathematics (TEDS-M): Do Countries Paying

Teachers Higher Relative Salaries Have Higher Student Mathematics Achievement?.

International Association for the Evaluation of Educational Achievement

Carnoy, M. & Welmond, M. (1998). Do teachers get paid too much? A worldwide

comparison of teacher pay. Stanford University (mimeo)

http://www.stanford.edu/~sloeb/Narrowing.pdf




175

Chamberlain, G.(1994) Quantile Regression,Censoring and the Structure of Wages, in

Christopher Sims (ed), Advances in Econometrics. New York: Elsevier, 171–209.

Clotfelter, Charles T., Ladd, H.F. &Vigdor, J.L. (2006). Teacher-Student Matching

and the Assessment of Teacher Effectiveness. Journal of Human Resources, 41(4),

778-820.

Clotfelter, C.T., Elizabeth, G., Ladd, H. & Vigdor, J.L. (2007). Teacher credentials

and student achievement: Longitudinal analysis with student fixed effects. Economics

of Education Review. 26, 673-82.

Clotfelter, C.T., Elizabeth, G., Ladd, H. & Vigdor, J.L. (2008). Would Higher Salaries

Keep Teachers in High Poverty Schools? Evidence from a Policy Intervention in

North Carolina. Journal of Public Economics, 92(5), 1352-1370.

Coleman, J.S. et al. (1996). Equality of educational opportunity. Washington, DC:

National Center for Educational Statistics.

Corcoran, S., Evans, W.N. & Schwab, R.S. Changing Labor Market Opportunities for

Women and the Quality of Teachers 1957-2000. American Economic Review Papers

and Proceedings, May 2004, 94(2), 23-235.

Dee, T. & Cohodes, S. (2008). Out-of-Field Teaching and Student Achievement:

Evidence from ‗Matched-Pairs‘ Comparisons. Public Finance Review 36(1), 7-32.

Dee, T.S.(2007). Teachers and the Gender Gaps in Student Achievement,

Journal of Human Resources 42(3),528-554.

Divocky, D. (1978a). Moonlighting: Occupational habit or benefit? Learning,

November, 40-45.

Divocky, D. (1978b). Moonlighters in Profile. Learning, November, 42-43.

Dolton, P. J. & van der Klaauw, W. (1999). The Turnover of Teachers: A Competing.

Risks Explanation. Review of Economics and Statistics, 81(3), 543–52.

DuMouchel, W.H. & Duncan, G. J. (1983). Using Sample Survey Weights in Multiple

Regression Analysis of Stratified Samples, Journal of the American Statistical

Association, 535-43.

176

Ehrenberg, R. G., & Brewer, D. J. (1994). Do School and Teacher Characteristics

Matter? Evidence from High School and Beyond. Economics of Education Review,

13(1), 1–17.

Ferguson, R. and Ladd, H. (1996). How and Why Money Matters: An Analysis of

Alabama Schools. In H.F. Ladd, (ed.) Holding School Accountable. Washington, DC:

Brookings Institution, 265-98.

Fitzenberger, B. (1999). Wages and Employment Across Skill Groups. Heidelberg:

Physica-Verlag.

Foondun, A. R. (2002) The Issue of Private Tuition: An Analysis of the Practice in

Mauritius and Selected South-East Asian Countries, International Review of

Education, 48(6), 485–515.

Fry, G. (1999). Teaching personnel strategy in Thailand: A review and

recommendations. Bangkok: UNESCO.

Goldhaber, D. D., & Brewer, D. J. (2000). Does Teacher Certification Matter? High

School Teacher Certification Status and Student Achievement. Educational

Evaluation and Policy Analysis, 22(2), 129–145.

Goldhaber, D. D., & Brewer, D. J. , & Anderson, D.J.(1999). A three-way error

components analysis of educational productivity. Education Economics, 7 (3), 199-

208

Goldhaber, D. D., & Brewer, D. J.(1997). Why don‘t schools and teachers seem to

matter? Assessing the impact of unobservables on educational productivity. Journal of

Human Resources, 32(3), 505-523

Goldhaber, D.D. (2007) Everyone‘s Doing It, But What Does Teacher Testing Tell Us About

Teacher Effectiveness?, Journal of Human Resources, 42(4), 765-794.

Government of Thailand (1995). Education Statistics in Brief. Bangkok

Grossman, P., & Thompson, C. (2004). Curriculum materials: Scaffolds for new

teacher learning? Center for the Study of Teaching and Policy, University of

Washington, Seattle, WA: University of Washington. Center for the Study of

Teaching and Policy.

Guthrie, H. W. (1969). Teachers in the moonlight. Monthly Labor Review, 92(2), 28-

31.

177

Guyton, E. & Farokhi, E. (1987). Relationships among academic performance, basic

skills, subject matter knowledge, and teaching skills of teacher education graduates.

Journal of Teacher Education, 38, 37-42

Hammond, L. D. (2000). Teacher Quality and Student Achievement. Educational

Policy Analysis Archives, 8(1), 1-50.

Hanushek, E.A. (1971). Teacher characteristics and gains in student achievement:

Estimation using micro data. American Economic Review, 61 (2), 280-288

Hanushek, E. A. & Pace, R. R. (1995). Who Chooses to Teach (and Why)? Economics

of Education Review,14(2), 101–117.

Hanushek, E.A. (1995). Education production functions. In M.Carnoy (ed.),

International encyclopedia of economics of education, 2nd

ed, Tarrytown, NY:

Pergamon, 277-282.

Hanushek, E.A. (1997). Assessing the Effects of School Resources on Student

Performance: An Update. Educational Evaluation and Policy Analysis. 19(2): 141-64.

Hanushek E.A., J.A. Kain, and S.G. Rivkin. (2005). Teachers, Schools, and Academic

Achievement. Econometrica. Vol. 73 (March): 417-58.

Harbison, R.W & Hanushek, E.A. (1992). Educational performance of the poor:

Lessons from Rural Northeast Brazil. New York: Oxford University Press.

Harrigan, P.(1992). The Development of a Corps of Public School Teachers in

Canada, 1870-1980. History of Education Quarterly, 32(4),483-521

Harrigan, P.(1998). Women Teachers and the Schooling of Girls in France: Recent

Historiographical Trends. French Historical Studies, 21(4), 593-610.

Henderson, D.L., Darby, C.E., Maddux, C.D. (1982) Moonlighting, salary, moral, and

the approaching teacher shortage: A follow-up study. Texas State Teachers

Association.

Henke, R.R., Chen, X., & Geis, S. (2000). Progress Through the Teacher Pipeline:

1992-93 College Graduates and Elementary/Secondary School Teaching as of 1997,

Washington DC: U.S.Department of Education. National Center for Education

Statistics.

178

Howsam,R.B. (1985). Academic consulting in colleges and universities. Enclaves of

intellectual and moral integrity ( A Report to the Coordinating Board). Austin, TX:

Texas Collegeand University System.

Jayachandran, S. (2008) ―Incentives to Teach Badly? After-School Tutoring in

Developing Countries,‖ working paper.

Komenan, A., Grootaert, C.,(1988),Teacher-nonteacher pay differences in Cote

d'Ivoire, No 12, Policy Research Working Paper Series, The World Bank.

ILO. (1991). Teachers in developing countries: A survey of employment conditions.

Geneva: International Labour Office (ILO).

Ingersoll, R.M., (2007). A Comparative Study of Teacher Preparation and

Qualifications in Six Nations, Consortium for Policy Research in Education.

University of Pennsylvania.

Jukping, S. (2008) Mae Khru Khong Chaat: A study of women teachers of Thai

hilltribe children, Ph.D.Dissertation, The University of Iowa.

Kane, T. J., Rockoff, J. E., & Staiger, D. O. (2008). What does certification tell us

about teacher effectiveness? Evidence from New York City. Economics of Education

Review, 27(6), 615-631.

Keyes, C.F., (1991) The proposed world of the school: Thai villagers' entry into a

bureaucratic state system. in Keyes, C.F.,(ed) Reshaping Local Worlds: Formal

Education and Cultural Change in Rural Southeast Asia .New Haven: Yale Southeast

Asia Studies

Koenker, R. (2005): Quantile Regression. Cambridge University Press

Komenan, A. & Grootaert, C. (1990) Pay differences between teachers and other

occupations: Some empirical evidence from cote d'ivoire, Economics of Education

Review, 9(3), 209-217.

Lankford, M., Loeb, S., & Wyckoff, J. (2002). Teacher Sorting and the Plight of

Urban Schools. A Descriptive Analysis. Educational Evaluation and Policy Analysis,

24(1), 37–62.

Langway, L. (1978, July) Too much moonlighting? Newsweek, p.84.

http://econpapers.repec.org/RePEc:wbk:wbrwps:12

http://econpapers.repec.org/RePEc:wbk:wbrwps:12

http://www.eric.ed.gov/ERICWebPortal/search/simpleSearch.jsp?_pageLabel=ERICSearchResult&_urlType=action&newSearch=true&ERICExtSearch_SearchType_0=au&ERICExtSearch_SearchValue_0=%22Ingersoll+Richard+M.%22

179

Liang, X. (1999). Teacher pay in 12 Latin American countries: How does teacher pay

compare to other professions? LCSHD Paper Series No. 49. Washington, D.C.: The

World Bank.

Linnell, R.H.(ed). (1982). Dollars and scholars: An inquiry into the impact of faculty

income upon the function and future of the academy. Los Angeles, CA: University of

Southern California Press.

Lockheed, M.E (1987). School and Classroom Effects on Student Learning Gain: The

Case of Thailand. World Bank Discussion Paper (Report No. EDT 98).

Loeb, S., Darling-Hammond, L., & Luczak, J. ( 2005). How teaching conditions

predict teacher turnover in California schools. Peabody Journal of Education, 80(3),

44-70.

Loeb, S., & Page, M. E. (2000). Examining the Link Between Teacher Wages and

Student Outcomes: The Importance of Alternative Labor Market Opportunities and

Non-Pecuniary Variation. The Review of Economics and Statistics, 82(3), 393–408.

Lankford, H., Loeb, S., & Wyckoff, J. (2002). Teacher sorting and the plight of urban

schools: A descriptive analysis. Educational Evaluation and Policy Analysis, 24, 37–

62.

Macdonald, K. (2008), PV: Stata module to perform estimation with plausible values,

Statistical Software Components, Boston College Department of Economics,

Martin, M. O., & Kelly, D. L. (1997). Technical report volume II: Implementation and

analysis. Chestnut Hill, MA: TIMSS International Study Center.

Martin, M. O., Mullis, I. V. S., & Foy, P. (2008). TIMSS 2007: International

Mathematics Report: Findings from IEA’s Trends in International Mathematics and

Science Study at the Fourth and Eighth Grade. Chestnut Hill, MA: Boston College.

Martin, M., Mullis, I., Beaton, A., Gonzalez, E., Kelly, D., & Smith, T. (1996).

Mathematics and Science Achievement in the Final Years of Secondary School: IEA's

Third International Mathematics and Science Report. Chestnut Hill, MA: Boston

College.

Martin, M., Mullis, I., Gonzalez, E., Gregory, K,D.,Garden,K.M., & Smith, T. (2000).

TIMSS 1999 International Mathematics Report. Chestnut Hill, MA: Boston College.

Mincer, J. (1974). Schooling, Experience, and Earnings. New York: National Bureau

of Economic Research. Columbia University Press.

180

Monk, D. H. (1994). Subject Area Preparation of Secondary Mathematics and Science

Teachers and Student Achievement. Economics of Education Review. Vol. 13(2): 125-

45.

Monk, D. H. and King, J. A. (1994). Multi-Level Teacher Resource Effects on Pupil

Performance in Secondary Mathematics and Science: The Role of Teacher Subject

Matter Preparation in Ehrenberg, R. G. (ed.) Contemporary Policy Issues: Choices

and Consequences in Education. Ithaca, NY: ILR Press: 29-58.

Monk, D. H. (1994). Subject Area Preparation of Secondary Mathematics and Science

Teachers and Student Achievement. Economics of Education Review, 13(2), 125–145.

Murnane, R.J.(1975). The impact of school resources on the learning of inner city

children. Cambridge, MA: Balinger Publishing Company

Murnane, R. J., & Phillips, B. R. (1981). What Do Effective Teachers of Inner-city

Children Have in Common? Social Science Research, 10, 83–100.

Nakavachara, V, (2010), Superior female education: Explaining the gender earnings

gap trend in Thailand, Journal of Asian Economics, 21(2), 198-218.

National Statistics Office of Thailand. (2003a). Report of the Labor Force

Survey,Whole Kingdom, Quarter 2 (April – June 2003). Bangkok, Thailand.

National Statistics Office of Thailand. (2003a). Report of the Labor Force Survey,

Whole Kingdom, Quarter 4 (October – December 2003). Bangkok, Thailand.

Neuman, S., & Oaxaca, R. (2004). Wage decompositions with selectivity corrected

wage equations: A methodological note. Journal of Economic Inequality, 2(1), 3–10.

Oaxaca, R. L. (1973). Male-female wage differentials in urban labour market.

International Economic Review, 9, 693–709.

Office of the Teacher Civil Service and Educational Personnel Commission

http://203.146.15.33/webtcs/ (retrieved July 2010)

Office of the National Education Commission (1995). Education in Thailand 1995.

Office of the Prime Minister, Bangkok.





http://203.146.15.33/webtcs/

181

Peske, H.G. & Haycock, K. (2006). Teaching equality: How poor and minority

students are shortchanged on teacher quality. Washington D.C.

Piras, C. & Savedoff, W.D. (1998). How Much Do Teachers Earn?. Office

of the Chief Economist, Working Paper Series WP-375

Psacharopoulos, G., Valenzuela, J. & Arends, M. (1996). Teacher salary in Latin

America: A review. Economics of Education Review, 15(4), 401-406.

Psacharopoulos, G. (1987). Are teachers overpaid? Some evidence from Brazil.

Teaching & Teacher Education, 3, 315-318

Raffel, J.A., & Groff, L.R.(1990). Shedding light on the dark side of teacher

moonlighting. Educational Evaluation and Policy Analysis.12 (4), 403-414.

Raudenbush, S.W., Eamsukkawat, S., Di-Ibor, I., Kamali, M. & W.Taoklam

(1993).On-the-Job Improvements in Teacher Competence: Policy Options and Their

Effects on Teaching and Learning in Thailand. Educational Evaluation and Policy

Analysis.15(3), 279-297.

Remillard, J. T. (2000). Can curriculum materials support teachers' learning? Two

fourth-grade teachers' use of a new mathematics text. Elementary School Journal,100,

331-350.

Rickman, B.D & Parker, C.D. (1990). Alternative Wages and Teacher Mobility: A

Human Capital Approach. Economics of Education Review, 9(1), 73-79.

Rockoff, J. (2004). The impact of individual teachers on student achievement:

Evidence from panel data. American Economic Review, 94, 247–252.

Rothman, S. (1978). Women's proper place. New York: Basic Books

Sanders, W. L., & Rivers, J. C. (1996). Research project report: Cumulative and

residual effects of teachers on future student academic achievement. Knoxville, TN:

University of Tennessee Value-Added Research and Assessment Center.

Schmidt, W.H., McKnight, C.C. (2001). Why Schools Matter?: A Cross-National

Comparison of Curriculum and Learning. Jossey-Bass: San Francisco, Calif.

Siribanpitak, P. & Siriporn Boonyananta. (2007). The Qualifications of the Teaching

Force in Thailand in Ingersoll, Richard M (ed), A Comparative Study of Teacher

Preparation and Qualifications in Six Nations Consortium for Policy Research in

Education. University of Pennsylvania.

http://www.eric.ed.gov/ERICWebPortal/recordDetail?accno=EJ406860

http://www.eric.ed.gov/ERICWebPortal/recordDetail?accno=EJ406860

http://www.eric.ed.gov/ERICWebPortal/search/simpleSearch.jsp?_pageLabel=ERICSearchResult&_urlType=action&newSearch=true&ERICExtSearch_SearchType_0=au&ERICExtSearch_SearchValue_0=%22Ingersoll+Richard+M.%22

182

Smith, J. P. & Star, J. R. (2007). Expanding the notion of impact of K-12 Standards-

based mathematics and reform calculus programs. Journal for Research in

Mathematics Education, 38, 3-34.

Stigler, J. W., Lee, S.-Y., Lucker, G. W. & Stevenson, H. W. (1982). Curriculum and

achievement in mathematics: A study of elementary school children in Japan, Taiwan,

and the United States. Child Development, 74, 315-322.

Somwung Pitiyanuwat (2010) Interview in Bangkok Post. January 19th

2010.

Sumawong, N. (1973). Teacher Education in Thailand. In A.Tapinkae&L.Setti

(eds),Education in Thailand: Some Thai Perspectives. Washington D.C: US

Government Printing Office.

Tangchuang, P. (2007). Is there an Inflation of Doctoral Degree Programs? CELS

Working Paper no. 4. Centre for Education and Labour Studies (CELS) Faculty of

Education, Chiang Mai University.

The Nation.(2009). ―Grade-12 students perform poorly across the board in all main

subjects‖ Published on April 7,2009.

The Nation. (2010). ―Student achieve low grades in ONET exams‖ Published on April

2, 2010.

Turner, E. (1962). Moonlight over the chalkboard. NEA Journal, 51(April), 29-30.

Vegas, E., Pritchett, L., & Experton, W. (1999). Attracting and retaining qualified

teachers in Argentina: Impact of the structure and level of compensation, LCSHD

Paper Series No. 38. Washington D.C.: The World Bank.

Watson,J. (1974).Primary Education in Thailand: Plans,Problems and Possibilities.

Comparative Education, 10(1),35-47

Watson, K. (1980). Educational development in Thailand. Singapore: Heinemann

Asia.

Westbury, I. (1992). Comparing American and Japanese achievement: Is the United

States really an underachiever? Educational Researcher, 21(5), 18-24.

Wisniewski, R., & Hilty, E. (1987). Moonlighting: A disturbing tradition in

education. Unpublished paper presented at the annual meeting of the American

Educational Research Association, Washington, D.C.

Wooldrige, J.M.(2001) Econometric Analysis of Cross Section and Panel Data.

Cambridge MA: MIT University Press.

183

Wright, S. P., Horn, S. P., & Sanders, W. L. (1997). Teacher and Classroom Context

Effects on Student Achievement: Implications for Teacher Evaluation. Journal of

Personnel Evaluation in Education, 11, 57–67.

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