Essays on Economics of Education by Javier Alfonso Luque Submitted in Partial Fulfillment of the Requirements for the Degree Doctor of Philosophy Supervised by Professor Eric A. Hanushek Department of Economics The College Arts and Sciences University of Rochester Rochester, New York 2003 UMI Number: 3102286 UML UMI Microform 3102286 Copyright 2003 by ProQuest Information and Learning Company. All rights reserved. This microform edition is protected against unauthorized copying under Title 17, United States Code.
ProQuest Information and Learning Company 300 North Zeeb Road P. Box 1346 Ann Arbor, Mi 48106-1346 Curriculum Vitae The author was born in Lima, Peru on September 13%, 1970. He attended the Pontificia Universidad Catolica del Peru from 1988 to 1993, and graduated with a Bachelor of Arts degree in 1993. He came to the University of Rochester in the fall of 1996 and began graduate studies in Economics.
He received the Banco Central de Reserva del Peru Graduate Studies Fellowship between 1996 and 1998, and a Graduate Studies Fellowship from the University of Rochester between 1998 and 2000. He pursued his research in Economics of Education under the direction of Professor Eric Hanushek and received the Master of Arts degree in 2000. iti Abstract Three different aspects of Economics of Education are analyzed. Chapter One focuses on the international eviderice on resources and education outcomes.
Chapter Two analyzes education preduction functions, controlling explicitly for teachers’ characteristics, and presents an evaluation of the Teach for America Program. Chapter Three covers the determinants of teachers’ salaries, assessing the impacts that varying class sizes have on them, and how certification credentials are valued. in Chapter One, Economics of Education is faced from the international perspective. The data from the Third International and Science Study provides a way of comparing performance in different schooling systems.
The results from analyses of educational production functions within a range of developed and developing countries show general problems with the efficiency of resource usage similar to those found previously in the United States. These effects do not appear to be dictated by variations related to income level of the country or level of resources in the schools. Neither do they appear to be determined by school policies that involve compensatory application of resources. The conventional view that school resources are relatively more important in poor countries also fails to be supported.
In Chapter Two, education production functions that take into account the unobservable characteristics of teachers are employed -to estimate the effect of class, iv teacher and school characteristics on student outcomes. An evaluation of the TFA program is also performed. Panel information on students, teachers and schools for the Houston School District between 1996 and 2000 is employed in the analysis. The analysis finds: smaller class size effects than those observed in the literature and a positive effect of the TFA, among other results.
In Chapter Three, the determinants of teachers’ salaries are assessed. The framework for the analysis is hedonic regression. Instrumental variables are employed.to correct for possible sources of biases. Data for teachers and schools coming from the National Center for Education and Statistics is employed in the analysis.
The results show a positive relationship between class size and teacher salaries. Table of Contents Introduction "_—. 1 Chapter | Education production functions and the international evidence. 7 IHoc co ri ii atiidẢđdÔỔÔỔÔỔ.Data and methodoÌOBV.
cuc HH no TH nh vn hy ky vu 16 1.1 Education production Ñimcflons. ch nh rên 26 1. Selection an compensafOry DOÏÌICI€S. Family and sChOOÏÌS.
HH HQ nh khien 38 I59o vn a. 49 Chapter 2 Assessing teacher effects with.an evaluation of the Teach for America Program.cccceeecece eeeeeeeeeecedeeeeesseeneeaeeaneaeanstseen ss 52 2.2 Review of the literature.0 c cece eee ecsseceeeeeaeeseenenes 56 2. Non-experimental evaluafion.cc se cvy 59 "SN. Teacher charact€riSHCS.
Teach for America Program. con HH ng ng nà 65 “ "m›. Fixed effects resulfs. cv vn vs 87 2.
Teacher fixed effects and direct measures of - f€aCheT QUAÏÏẨY. cu TS TH nh vn ve 88 2.5 Teacher quality and mobility patterns. 93 Chapter 3 Assessing the impact of class sizes and teacher requirements 18:0:. cuc HH VẤ HH ki Đo nh nề 95 3.cu vn Kế HH.1 Empirical SỈTAf€BV.2 ° ieee ccececneceeceee sess ease sceeeeeeeaseeeneeseoes 107 3.
eee cece cece cece eset scene ne eee ees ceeneneeteneneeenens 111 3. ccc cece cece nace eeeeeueeeeteneeneeeenseeereenssnes 120 vi [2701145100 Pa. ¬ 123 Appendices vii List of Tables Table Title Table 1.1 Alternative estimates of the impact of resourcen on international math and science performance across COUTTICS. cuc seo [5 Table 1.2 Distribution of estimated production function parameter across Countries and AGe (2113 ng.3 Distribution of Teacher qualification and Estimated Effect on Student QufCOIm€S.- HT nh Hi Tà ngà kg Tay 25 Table 1.4 Distribution of estimated family background parameters across countries and age STOPUS.
cece cece ec ec eee eeeeeeneeeasaeeeseaeeninaees 30 Table 1.5 Sign and statistical significance of alternative estimates of class effects allowing for compensatory placement, age 9 cohort.6 Sign and statistical significance of alternative estimates of class effects allowing for compensatory placement, age 13 cohort.7 Additions to explanatory power of school inputs, age 9 cohort.8 Addtition to explanatory power of school inputs, age 13 cohort.9 Estimated Class Size Effects Compared to Impact of Disadvantaged Background, age 9 cohOoF.10 Estimated Class Size Effects Compared to Impact of Disadvantaged Background, age 13 cohorf. cv ke se 45 Table.11 Differences in family background slopes (age 13-age 9) for pooled sampÌ@$.- TH HH K nh nh nà kh nà 48 Table 2.1 Education production functions (Total Sample).2 Education production functions (Teachers with less than 2 years D0824 ii) 10 nh.3 Đistribution of Teacher Fixed efects. con nen se 82 Table 2.4 Average Teacher Fixed effects according to mobility patterns.1 Estimated effect of on teacher salaries of selected variables.2 Estimated effect of class size on salaries by r€glOPS.3 Estimated effect of class size by metropolitan area vn.4 Selected determinants of teacher turn OV€T. vn se 119 Table 3.5 Predictor of difficulties finding †eaCher.
121 viii List of Figures Figure Title Figure 1.1 International Math and Sclence sCOTES OVeT LÏm€.2 lì! St coi g6 66ẼẺ.3 Class size coefficient and GDP per capita (9 years oÌđ).4 Class size coefficient and GDP per capifa (13 years old).5 Class size coefficient and Class Size (9 years oÌd).6 Class size coefficient and Class Size (13 years oÌd).1 Distributton ofteacher fixed effects (Total Sample).2 Distribution of teacher fixed effect (less than 2 years of experience).86 ix List of Appendices Appendix Table Al TIMSS Descriptive statistics for Age 9 cohorf. 129 Appendix Table A2 TIMSS Descriptive statistics for Age 13 cohort. H ky và 130 Appendix Table A3 Selected Coefficient Estimates by Country and Age Cohort.131 Appendix B _ Description Data from Houston School District CHSD).133 Appendix Table Bl Sample characteristics HSD oo. cee eeeec eee ee eee ee ens ¬- .135 Appendix Table B2 Education production functions: Adding last year score on right-hand side (Total Sample).
136 Appendix Table B3 Education production functions: Adding last year score on right-hand side (Teachers with less than 2 years of experience). ¬ ceed et eR eE ESA SEE ENR eS eel 37 Appendix Table B4 Education production functions: Allowing non linearities in Class sizes (Total G003 mm. 138 Appendix C] Description Data from SASS. 2n ru uk 139 Appendix Table C1 Description of sample.
eck cee eee eens teseauecebesecuneenes 141 Appendix Table C2 Basic descriptive statisticts by region and So Øg©ogTraphiC ÌOCALÏOD cv n2 ng ng nh nh TY nh net 142 Appendix C2 Requirements for certification. Appendix Table C4 Percentage of districts requiring full certification by state. 145 Intreduction The emphasis on the human capital policy, which has become the hub of government prograrns around the worid, is accepted as a natural and enlightened approach of policy. The importance of human capital for the distribution of economic success and ultimately for the growth of national economies comes from its relationship with individual productivity and earnings.
The central focus is on how systematic policy actions of governments affect the performance of students, Most of the research attention has actually gone to the relevance of resources as a policy tool. Chapter One focuses on the resources allocated to schools in an internabonal context. Previous research for the United States, summarized by Hanushek (1986, 1997), shows that resources devoted to schools are not consistently related to student outcomes. However, the question arises is does this result change once we allow for different institutional settings and different levels of resource allocation to schools.
The literature on international evidence is sparce and, in addition, studies are hard to compare since information tends to be country specific and study specific. Although —when comparisons are available~ there seems to be slightly stronger relationships between resources and outcomes at the international level than in the United States. The primary objective of Chapter One is to provide a consistent set of estimates for production functions from a sample of developing and developed countries. This analysis has been made possible by recent international testing and data collection, which provides scores on common examinations across countries.
‘Building upon the testing and surveys of the Third International Mathematics and Science Study (TIMSS), we consider specifically how families and schools contribute te within country variations in student performance. Among other school inputs, we assess the impact of class sizes and teacher training on student outcomes. Special attention is devoted to the possible bias introduced by compensation policies within schools. We then go beyond the standard production function estimation to assess whether schooling systems in different countries work to narrow or widen - performance differences, giving that students have similar opportunities.
The analyses of educational production functions show similar results with those found previously for the United States. Furthermore, the results do not appear to be sensitive to variations in the income level of the country or the level of resources. in the schools. Nor do they appear to be determined by school policies that involve a compensatory application of resources.
The conventional view that school resources _ are relatively more important in poor countries also fails to be supported. tư In Chapter Two, the impact of teacher characteristics on student outcomes is explored in detail. Previous research has established that variations in teacher quality ia a principal cause of variation in student outcomes. A standard education production approach is employed with a focus on possible sources of biases present in previous literature.
Biases usually come from a number of unobservable characteristics in students, teachers and schools. ‘The uncertainty derived from the unobservable characteristics of students, teachers and schools makes evaluation m education a difficult task. Households choose te locate themselves in different sections of metropolitan areas frequently due to a particular school or school district (Ticbout (1956)). Within schools we can expect that students will be sorted into classrooms and programs, based upon the teacher quality and classroom characteristics.
Unobservable teacher characteristics include effort and innate ability, which are not directly observable by the researcher or the policy maker, but that may have a direct impact on student outcomes. Nevertheless, it is important to find measures of teacher quality that will allow us to compare teachers, and make evaluations of the different teacher training programs or the characteristics of teachers. In this study we use a database which allows us to characterize such effects with extraordinary precision. The characteristics of panel data and teacher-school and student-teacher matches allow us to overcome many of the standard problems found 4 in education literature (related to unobservable characteristics).
estimation strategy consists of the value added approach with specific fixed effects. In this study we use data from the Houston School District, in Houston, Texas. This data allows us to follow students, teachers and schools on a direct matching. We have extensive information.
At the same time, the data allows estimation using | - teacher and school specific effects, controlling for possible sources of non-random sorting.