essays on economics of education

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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

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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

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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

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Introduction Chapter | Chapter 2 Chapter 3 Table of Contents "_— 1 Education production functions and the international evidence : 7 IHoc co ri ii atiidẢđdƠỔƠỔƠỔ 7 I€¡cii6o 1n - 8 1.3.Data and methodoÌOBV cuc HH no TH nh vn hy ky vu 16 1.3.1 Education production Đimcflons - 20 non c4 edd

1.4.1 Dimishing TetUTDS ch nh rên 26

1.4.2 Selection an compensafOry DỌÌICI€S cà 31 1.4.3 Family and sChOỌÌS HH HQ nh khien 38 I59o vn a.- 4 49

Assessing teacher effects with.an evaluation of the Teach for

America Program 0.0 cccccesceceeeeeceeeccececeeeeeteteneceeateseesenerses 52

2.1 Introduction cccceee cece eeeeeeeeeecedeeeeesseeneeaeeaneaeanstseen ss 52 2.2 Review of the literature 0.0.0 c cece eee ecsseceeeeeaeeseenenes 56 2.2.1 Experimental approach se se 357 2.2.2 Non-experimental evaluafion -.-.«-.cc se cvy 59 "SN '(( 59 2.2.4 Teacher charact€riSHCS cu nh, 61 2.2.5 Teacher certIfiCaLIOH ch chớp 62 2.2.6 Teach for America Program co sec se 64 2.3 Methodology con HH ng ng nà 65 “ "m›.` 5 3 72 ' ` 75 2.5.1 Fixed effects resulfs 8Ï 2.5.2 TPA evaluation " 83 2.5.3 Teacher cducation cv vn vs 87 2.5.4 Teacher fixed effects and direct measures of - f€aCheT QUẠÏẨY cu TS TH nh vn ve 88 2.5.5 Teacher quality and mobility patterns 89 "“"“® c.'ứA 93 Assessing the impact of class sizes and teacher requirements 18:0: >0/-. Ea 95 3.1 ÍntroduCHOn cuc HH VẤ HH ki Đo nh nề 95 3.2 — Previous research cu vn Kế HH 98 3.3 it 201020009 nHH‹ưd(4 103

3.3.1 Empirical SỈTAf€BV cv Thy 103

3.3.2 ° ieee ccececneceeceee sess ease sceeeeeeeaseeeneeseoes 107 3.4 Results 0.0 eee cece cece cece eset scene ne eee ees ceeneneeteneneeenens 111

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Table Table 1.1 Table 1.2 Table 1.3 Table 1.4 Table 1.5 Table 1.6 Table 1.7 Table 1.8 Table 1.9 Table 1.10 Table 1.11 Table 2.1 Table 2.2 Table 2.3 Table 2.4 Table 2.5 Table 3.1 Table 3.2 Table 3.3 Table 3.4 Table 3.5 vii List of Tables Title

Alternative estimates of the impact of resourcen on international

math and science performance across COUTTICS cuc seo [5

Distribution of estimated production function parameter across

Countries and AGe (2113 ng rr44Ầ 23

Distribution of Teacher qualification and Estimated Effect on

Student QufCOIm€S - HT nh Hi Tà ngà kg Tay 25

Distribution of estimated family background parameters across

countries and age STOPUS 0.0 cece cece ec ec eee eeeeeeneeeasaeeeseaeeninaees 30

Sign and statistical significance of alternative estimates of class

effects allowing for compensatory placement, age 9 cohort 36

Sign and statistical significance of alternative estimates of class

effects allowing for compensatory placement, age 13 cohort 37 Additions to explanatory power of school inputs, age 9 cohort 40 Addtition to explanatory power of school inputs, age 13 cohort 4I Estimated Class Size Effects Compared to Impact of

Disadvantaged Background, age 9 cohOoF co nen 44

Estimated Class Size Effects Compared to Impact of

Disadvantaged Background, age 13 cohorf cv ke se 45 Differences in family background slopes (age 13-age 9)

for pooled sampÌ@$ - TH HH K nh nh nà kh nà 48 Education production functions (Total Sample) 77 Education production functions (Teachers with less than 2 years

D0824 ii) 10 nh Ữ5Ả 80 Đistribution of Teacher Fixed efects con nen se 82 Average Teacher Fixed effects according to mobility patterns 90

\/13191)1A 81319 + —|-NaaadđdđdaaiidỎỐẮẦẢẮỒ 92

Estimated effect of on teacher salaries of selected variables 113 Estimated effect of class size on salaries by r€glOPS 116

Estimated effect of class size by metropolitan area

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Figure 1.5 Figure 1.6 Figure 2.1

Figure 2.2

Class size coefficient and Class Size (9 years oÌd) 32

Class size coefficient and Class Size (13 years oÌd) 33

Distributton ofteacher fixed effects (Total Sample) 85

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Appendix B _ Appendix Table Bl Appendix Table B2 Appendix Table B3 Appendix Table B4 Appendix C] Appendix Table C1 Appendix Table C2 Appendix C2 Appendix Table C4

Description Data from Houston School District CHSD) 133 Sample characteristics HSD oo cee eeeec eee ee eee ee ens ¬- 135

Education production functions: Adding last year score

on right-hand side (Total Sample) 136

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 Education production functions: Allowing non linearities in Class sizes (Total G003 mm 138

Description Data from SASS 2n ru uk 139 Description of sample eck cee eee eens teseauecebesecuneenes 141

Basic descriptive statisticts by region and So

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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

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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

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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

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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 At the same time, the data allows us to isolate specific teacher effects, construct their distribution, and compare the distribution between different sets of

teachers

A special emphasis is placed on the Teach For America (TFA) program TFA allows college graduates to teach without fulfilling the traditional requirements These teachers usually teach in inner city schools This program has been subject to

considerable criticism, given that TFA teachers have very little experience once they start teaching

The results show that resources measured as class size are not related to

student outcomes, despite the fact that the data for this study comes from an inner city school district The variance in teacher fixed effects, a measure of teacher quality

which comes directly from student outcomes, was big Direct measures of teacher

quality, such as teachers results on certification tests, showed a positive but small

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we focus on the impact of class sizes The analysis is based on the Theory of Wage Differentials Teacher salaries, at least partially, will represent the market equilibrium | value of inputs, such as class sizes

The effects of changes in class size have been the focus of recent intense policy discussions Almost all of ‘the attention has been related | to student

performance The related research has tried to quantify the effects on student

outcomes and to interpret them from a policy perspective While controversy over the magnitude of such effects and the costs of change continues, the range of differences

is narrowing, and the options are becoming clearer The existing research, however,

has generally neglected the overall effects of smaller class sizes on the teachers _ When considered, this literature tends to concentrate on teacher behaviors —

classroom management, time on task, and the like ~ but these are largely details of the

process by which achievement gains are realized Most importantiy, the analysis

ignores the interactions of class size with overall teacher satisfaction and its -

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feedback effects could significantly reduce the costs of lowering class sizes On the other hand, if the policies simply show a systematic but small quantitative relationship, the feedback through employment factors would not enter significantly | inte the policy debate

This research project focuses on the determinants of teacher salaries Roughly

speaking, we find that increasing the class size by one student increases teacher salary

up to around 1.0 percent This effect is found to be statistically sỉ gnificant in some but not all of the empirical specifications and for some but not all points on the salary schedule At the same time, other factors have a stronger influence on district salaries For example, we find that teachers facing a higher number of minorities

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The empirical literature on human capital has concentrated on the private returns to the quantity of schooling obtained by individuals Standard Mincer formulation shows how investment can be translated into observed differences across individuals (Mincer (1970, 1974)) The relationship between years of schooling and earnings has been studied deeply and in general provides evidence of the importance of schooling

However, this simple approach does not take into consideration the quality differences that appear in schooling The variable “years of schooling” does not

reflect the quality of schooling that an individual had during his life Test scores have

been introduced to allow for the measurement of quality differences in schooling, The

standard approach, studied by the literature, is that different quality of outputs in the

education is related to different inputs

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Given that the quality of schooling seems to be important, it is necessary to investigate which factors affect student quality But at the same time, it is also

necessary to i nvestigate how these factors respond to specific country characteristics in this study we are able to match data fram different countries and investigate how

the student outcomes respond to changes in institutional settings and wealth

(measured by GDP) In addition, we present initial investigations on bow public education systems achieve their role of giving people equal opportunities (ie., reduce the inbound jnequality)

1.2 General Context

An important issue throughout the discussion of school quality has been the

relationship between outcome measures of quality (earnings, test scores, and the like)

and the resources devoted to schools This issue is twofold First, because direct

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resources as a policy tool On that score the US evidence has been reasonably clear

The resources devoted to schools are not closely or consistently related to student

si outcomes, While there has been some controversy over this analysis, only a minority of studies have found significant and positive relationships between resources and _ performance.“

The general structure of the production function estimation, designed to pinpoint causality has focused on a model such as:

Eq 1.1

0, = SF Py Sys A) + 2,

Where Oj, is the performance of student i at time f, Fy is a vector of family inputs —

- cumulative for student ? at time t, Py denotes cumulative peer inputs for student fat

time ¢, Sz denotes cumulative school inputs for student é at time #, A; denotes innate

ability of student i, and vy denotes a stochastic term

The proxy question ~i.e whether resources are an indicator of quality differences

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This stronger relationship couid simply reflect a positive relationship between

“resources and other factors which might arise if wealthier parents ‘on average both

contribute more directly to performance and put more resources into their schools in

the growth setting, there is no direct evidence of the proxy relationship Resources

tend to give incorrect signs and to be poor proxies (Hanushek and Kimko (2000))

Empirical work on quality in an international setting has, however, been even rarer than in the United States Few international data sets have information on outcomes

and resources, although- when available- there seems to be slightly stronger relationships between resources and outcomes in the production function setting of

equation 1.1 (Heyneman and Loxley (1983), Hanushek (1995), Vignoles et al (20060)) When this data is available, it has been difficult to summarize because the

data sets tend to be very specialized and to be very different across studies In

addition, little is known about the value of proxy relationship across countries

? Por discussions of the basic result from estimation of the effects of resources, see Hanushek (1986, 1997) For a discussion of the controversies, see Hedgeds, Laine and Greenwald (1994), Greenwald,

Hedges and Laine {1996} and Hanushek (1996) , ,

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Such comparative analysis has been largely precluded in the past, although some

work does exist Perhaps the largest and most influential study is Heyneman and

Loxley (1983) They analyze data from the second International Mathematics and Science Study along with other country specific tests Their primary conclusion is that resource vatiations appear to be more closely related to student performance in developing countries than in the developed ones

To put the resource issue into perspective, it is perhaps most useful to begin with

aggregate differences across countries The comparison of cognitive achievement

across countries capitalizes on seven voluntary international tests of student

achievement in mathematics and science that were conducted over the past three

decades Two were conducted by the International Association for the Evaluation of Educational Achievement (IEA) and five were administered by the International

Assessment of Educational Progress (LAEP)*, The IEA, since its establishment in 1959, has had a long and unique role in developing comparative education research for almost all aspects of primary and secondary education On the other hand, the TAEP, started in 1988, builds on the statistical techniques and procedures developed

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The concentration on mathematics and science corresponds to the theoretical

‘emphasis on the importance of research and development (R&D) activities as the source of growth (e.g Romer (1990)) Able students with good understanding of

mathematics and science form a pool of future engineers and scientists At least for the United States, Bishop (1992) provides separate confirmation of the importance of mathematics in determining individual productivity and income Additionally, while

some test information exists for other subjects, it cannot be compared readily with the

mathematics and science scores and therefore is not used here

An overview of the testing results is best seen from Figure 1.1 This figure shows countries results on each of the math and science tests from the early 1960s through the Third Internationa! Mathematics and Science Study in 1995, In the figure, all of

the scores in each year are normalized to a world mean of 50 (see Hanushek and Kim (1995); Hanushek and Kimko (2060)) While a different array of countries has

participated in the test, some sense of the overall pattern can be seen from the figure

There is an aggregate tendency for East Asian countries to perform better and for developing countries to score worse Nonetheless, the performance of individual

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through 1990, as demonstrated by Hanushek and Kimko (2000) Specially, there is no

pattern to scores and resources, at least after controlling for differences in families

over time Table 1.1 reproduces the estimated resource effects on achievement for a

sample including ail country-years of test data that also had complete input data Of the three separate resource ‘measures ~expenditure per pupil, proportion of GDP

devoted to public education, and pupil-teacher ratio in primary schools, all three go in the wrong direction

There are good reasons to be cautious about these results, however, since the

simplified production estimates do not measure any organizational or structural differences in the school systems of the various countries These factors — if important and if correlated with resources- wil bias the estimated coefficients Since these

estimates might be biased because they can mask substantiai within-country variation,

we go on to consider variations in scores for individual countries Similar estimation

has been conducted: for the TIMSS international testing by Wobmann (2000) He

combines the microleve! TIMSS information with data about characteristics of the

overall system —centralization, private school options, unionization, and the like- and