do private tutoring expenditures raise academic performance? evidence from middle school students in...

Do Private Tutoring Expenditures RaiseAcademic Performance? Evidence from MiddleSchool Students in South Korea*

Deockhyun Ryu and Changhui Kang

Received 16 March 2011; Accepted 8 August 2012

To shed light on the effectiveness of educational inputs for student outcomes, thispaper examines the effect of private tutoring expenditures on the academic perfor-mance of middle school students in South Korea. To address endogeneity, thepaper uses instrumental variables, first-difference, propensity-score matching andnonparametric bounding methods. We apply these methods to a panel dataset fromSouth Korea, the Korea Education Longitudinal Study. The results show that thetrue effect of private tutoring remains, at most, modest. Instrumental variables(first-difference) estimates suggest that a 10-percent increase in expenditure raisesa test score by 0.03 standard deviations or 1.1 percent (0.002 standard deviations or0.08 percent). Matching estimates imply that the same amount of increase inexpenditure leads to a 0.33 to 0.72 percent higher average test score. The tightestbounds of the effect of tutoring reveal that a 10-percent increase in expenditureimproves the test score by a low of 0 to a high of 2.01 percent, while statistical testsfail to rule out zero effects. The modest effects of private tutoring found in thepresent study are comparable to the effects of public school expenditures on testscores and earnings estimated in previous studies.

Keywords: private tutoring, test scores, instrumental variables, matching,nonparametric bounds, South Korea.

JEL classification codes: I20, C30.

doi: 10.1111/asej.12002

I. Introduction

Does money matter for student academic performance? In the Western world, thisquestion is usually addressed by looking at the impacts of public school expen-ditures (Hanushek, 2003; Krueger, 2003) or private school attendance (Altonjiet al., 2005) on student educational outcomes. Despite decades of research on thesubject, however, there is no definite consensus. Recently, a new branch ofresearch is beginning to emerge on the subject. A small but growing literature

*Kang (corresponding author): Department of Economics, Chung-Ang University, 221 Heukseok-Dong Dongjak-Gu, Seoul 156-756, South Korea. Email: [email protected]. Ryu: same address asKang. Email: [email protected]. We thank an anonymous referee and seminar participants at KoreaUniversity, Sogang University and the 2010 Korea Labor Economic Association Conference for helpfulcomments and suggestions. This paper draws in part on a research project (CR 2010-12) on ‘Economicanalysis on private tutoring in Korea’ undertaken for the Korean Educational Development Institute.

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Asian Economic Journal 2013, Vol. 27 No. 1, 59–83 59

© 2013 The AuthorsAsian Economic Journal © 2013 East Asian Economic Association and Wiley Publishing Asia Pty Ltd

investigates the impacts of private tutoring on student outcomes.1 While itremains to be seen whether and how such literature sheds light on the importanceof monetary educational inputs for student outcomes, existing studies show con-flicting evidence on the causal impacts of private tutoring. Dang (2007) and Ono(2007) find strong effects of private tutoring; in contrast, Briggs (2001), Gurunand Millimet (2008) and Kang (2007) document negligible impacts of tutoringand coaching on education outcomes.

Although informative for furthering our understanding of the effectiveness ofeducational investments, recent studies on private tutoring face at least twolimitations. First, studies are often based on questionable empirical methods todetermine the causal effects of private tutoring. They either rely on instrumentalvariables (IV), which are potentially correlated with the outcome, or they fail toexplicitly control for the endogeneity of private tutoring (Briggs, 2001; Dang,2007; Ono, 2007). Second, the measures of educational outcomes used in thesestudies are usually indirect and too crude to enable a deep understanding of thecausal impacts of private tutoring.2

This paper contributes to the literature by examining the causal effects ofprivate tutoring expenditures on the academic performance of middle schoolstudents in South Korea. South Korea offers an interesting case for analysis. InSouth Korea, there exist widespread and large-scale markets for private tutoringoutside the public education system, with substantial private tutoring expendi-tures by parents.3 Given the large size of private tutoring markets and theirpotential impacts on public education, many, including parents and educationalpolicy-makers, are concerned about the effectiveness of private tutoring inimproving student academic performance. From a broader perspective, an exami-nation of the effect of private tutoring serves to illuminate the impacts of mon-etary educational inputs on student outcomes.

Using a dataset on private tutoring in South Korea, the current study examinesthe effectiveness of private tutoring expenditures for improving academic perfor-mance. As well as exploring the impacts of educational inputs by looking into

1 Here the focus is on private supplementary instruction of academic subjects that involves financialtransactions outside the formal school system. Such private tutoring is often observed in manycountries where the public education system is poorly equipped or the existing system fails to satisfyhighly motivated parents. While private tutoring is most prominent in East Asian societies, such asJapan, Hong Kong, Taiwan and South Korea, studies report the presence of private tutoring in a widerange of countries from Egypt to Kenya, India, Romania, Canada and the UK (Bray, 1999).2 Dang (2007) employs self-reported academic ranking (poor, average, good and excellent) inschools; Ono (2007) examines the attending college’s mean score of entrance examinations; Gurunand Millimet (2008) consider whether students intend to attend a university. In contrast, Briggs (2001)and Kang (2007) rely on test scores alone to measure educational outputs: SAT and ACT scores in theUSA and the College Scholastic Ability Test in South Korea, respectively.3 The Ministry of Education (2007) shows that, for all income groups, private tutoring expenses areabout 9 percent of total income for households with school-age children. At the national level, totalhousehold expenditures on private tutoring in 2009 amount to 2.6 percent of the national GDP and55.8 percent of the national annual budget for public education (Statistics Korea, Media Briefing, 23February 2010).

ASIAN ECONOMIC JOURNAL 60


private tutoring, this paper aims to make additional contributions to the literaturein the following two dimensions. First, the current study employs a more directmeasure of the educational outcome and more detailed information on privatetutoring expenditures than earlier studies on private tutoring. For empirical analy-sis, we rely on longitudinal information on test scores for each of three primaryacademic subjects (Korean, English and mathematics) and tutoring expendituresfor each subject.4

Second, to overcome endogeneity of tutoring expenditures in estimations, thecurrent paper employs four different empirical methods that are frequently used inthe recent treatment effects literature to draw causal estimates: an instrumentalvariable (IV) method, a first-difference (FD) method, a propensity-score matchingmethod and a nonparametric bounding method.

We apply four empirical methods to a panel dataset from South Korea, the KoreaEducation Longitudinal Study (KELS), which has detailed longitudinal informa-tion on private tutoring expenditures and test scores of three primary subjects(Korean, English and mathematics) for students in grades 7 to 9. A common findingfrom the four empirical methods is that the true effect of private tutoring remains,at most, modest. IV (FD) estimates suggest that a 10-percent increase in expendi-ture raises the average overall score by 0.03 standard deviations (SD) or 1.1 percent(0.002 SD or 0.08 percent); matching estimates imply that the same amount ofincrease in expenditure leads to a 0.05 to 0.72 percent higher average test score;the tightest bounds of the effect of tutoring reveal that a 10-percent increase inexpenditure improves the average test score by a minimum of 0 to a maximum of2.01 percent, while statistical tests fail to rule out zero effects of tutoring. Ourcurrent findings for the effect of private tutoring for middle school studentsgenerally agree with the results of Kang (2007) for high school students in grade 12.There is no compelling evidence that causal impacts of private tutoring are strongand differ according to the grade level of the student in South Korea.

The rest of the paper is organized as follows. Section II discusses the relatedliterature. Section III outlines the empirical strategy of the paper. Data are dis-cussed in Section IV and the empirical results are revealed in Section V. SectionVI concludes.

II. Related Literature

The impact of monetary educational expenditure on student academic performanceis one of the most controversial issues in educational research. Papers thatsummarize the debate offer conflicting views on the gains from educationalinvestments.

4 Using data from the Korean Education and Employment Panel (KEEP) that has information onoverall tutoring expenditures and average test scores of Korean, English and mathematics subjects,Kang (2007) shows that the true effect of private tutoring on total scores of college entrance tests forhigh school students (grade 12) remains, at best, modest in Korea. Given his results on the impact oftutoring on 12th-graders, we extend his study by focusing on middle school students, in grades 7 to 9,because the impacts of tutoring can vary by grade level.

DO TUTORING EXPENDITURES RAISE PERFORMANCE? 61


On the one hand, there is a long list of research on the effectiveness of publicschool expenditures, but inconclusive results have been drawn. For example,Card and Krueger (1996), Guryan (2001) and Krueger (2003) present evidencefor the effectiveness of public school expenditures. In contrast, Betts (1996),Hanushek (2003) and Leuven et al. (2007) cast doubt on the conclusion of theseresearchers and suggest several factors that can explain discrepancies in conclu-sions (e.g. Betts, 1996). Recent studies based on natural experiments or random-ization in developing countries continue to reveal conflicting evidence on theeffectiveness of public school inputs (Banerjee et al., 2007; Glewwe et al., 2007;Leuven et al., 2007).

On the other hand, studies focusing on private schools (e.g. Catholic schools)seem to agree no more about the impacts of educational inputs. While Evans andSchwab (1995) and Neal (1997) show educational benefits of attending Catholichigh school, Altonji et al. (2005) and Goldhaber (1996) find no significant gaps intest scores between public and private schools. Importantly, many studies onprivate and Catholic schooling seem to suffer from a lack of reliable exogenousvariation for identifying the causal effect. Altonji et al. (2005) argue that twofrequently used instrumental variables, religious affiliation of the parents andgeographical proximity of Catholic schools, are not a useful source of identificationof Catholic school effects. Research drawing on private school voucher experi-ments in the USA and some Latin American countries reveals recent evidence onthe impacts of private school attendance. McEwan (2004) states that although thereis evidence suggestive of positive impacts of small-scale vouchers targeted to somedemographic groups (e.g. poor African American) on education outcomes, existingevidence is not yet sufficient to support large-scale voucher programs.

As a third line of research in which the current study engages, a small butgrowing literature investigates the impacts of private tutoring on students. Whilemore research is warranted to draw a firm conclusion, existing studies showrather ambiguous evidence on the causal impacts of private tutoring. Dang (2007)investigates the effect of private tutoring in Vietnam; Ono (2007) explores privatetutoring in Japan. These studies usually find a strong effect of private tutoring onstudent performance. In contrast, Briggs (2001), Gurun and Millimet (2008) andKang (2007) examine the impact of private tutoring in the USA, Turkey andSouth Korea, respectively, finding negligible effects of coaching and tutoring onstudent educational outcomes. As argued earlier, these studies are usually basedon questionable empirical methods for drawing causal estimates. Given the needto accumulate evidence on the true impacts of private tutoring, the current paperaims to make a contribution by examining better data and using more reliableempirical methods.

III. Empirical Framework

For empirical analysis we consider a value-added model of educational produc-tion function expressed by:



y y S X uit it it it i it= + + + + +−β β β β α0 1 1 2 3 , (1)

where yit is the Z-score of student i (i = 1, . . . , N) in year t (t = 2006, 2007) thatis normalized from the raw test score (Yit) to have mean zero and variance one ineach grade sample; yit-1 is a measure of i’s pre-determined academic capabilitythat attempts to control for i’s unobserved and unmeasured characteristics (e.g.cognitive abilities, motivation and perseverance); sit is a logarized value of theaverage monthly expenditures on i’s tutoring in year t (Sit);5 Xit is a vector of i’spersonal and family backgrounds as well as school characteristics at t; ai is i’sunobserved and unmeasured characteristics that are left uncontrolled by yit-1; anduit is the random error term.

Provided that OLS estimates for b2 can be biased due to the endogeneity of sit,we employ four methods that can address the endogeneity problem in the absenceof randomization for private tutoring. First, the IV method follows an idea ofBlack et al. (2005, p. 695), using an indicator of whether a student is first-born inthe family (Fi) as an IV for sit. The rationale behind this idea is that a student’sbirth order is, no doubt, determined by nature, while parents usually invest morein the first-born’s education than in the later-born’s. Given that Cov(Fi, sit) isusually positive, as also empirically found in the current study, a key assumptionfor causal estimation in this method is that Cov(Fi, ai + uit) is equal to zero.Although Fi per se is given exogenously and there are studies showing littleimpact of birth order on a child’s educational outcomes (Retherford and Sewell,1991; Rodgers et al., 2000), such an assumption may be too strong due topotentially non-zero Cov(Fi,ai), while we may suppose Cov(Fi, uit) = 0. Even if itis difficult to assume Cov(Fi,ai) = 0, the literature suggests that there is a smallerrisk in supposing that Cov(Fi,ai) is positive rather than negative for the followingtwo reasons.

First, papers that report strong, if any, birth order effects usually show negativerather than positive effects of birth order on intelligence (Black et al., 2007;Zajonc 1976). Namely, intelligence of older siblings is either as high or higherthan that of younger siblings on average. It is a well-established empirical regu-larity that a child’s high intelligence leads to high academic performance inschool. Second, previous empirical studies show that parents favor the first-bornover the later-born with respect to educational investments in general (Blacket al., 2005). To the extent that parents favor the first-born in monetary educa-tional investments, they will tend to support the same child more over othereducational dimensions as well, say, by providing better emotional and non-financial supports for the first-born. Provided that Cov(Fi, ai + uit) is likely to bepositive, we can infer that our two-stage least squares (2SLS) estimates for b2 will

5 To deal with zero spending in the log transformation, a value of 10 is added to every student’s rawvalue of tutoring expenditure. The value of 10 is used because it is the smallest accounting unitreported in the survey (KRW10 000). Whether a smaller value (e.g. 1) is added to every expenditureor the level of raw values is employed rather than the log, the results are qualitatively similar.



overstate the causal effects of private tutoring on test scores rather than understatethem. If we find that 2SLS estimates for b2 fail to be substantially different fromzero, for instance, we conclude that private tutoring does not lead to a substantialimprovement in a student’s academic performance.

As a second method of estimation, we employ an FD method, removing astudent’s time-invariant characteristics by differencing within i. As long as non-zero Cov(Fi,ai) primarily yields biased estimates for b2, first-differencing is amethod that can address an endogeneity problem in a longitudinal context. To theextent that Cov(sit - sit-1, uit - uit-1) = 0, which is more justifiable than Cov(sit,ai + uit) = 0 or Cov(Fi, ai + uit) = 0, FD estimates for b2 can deliver causal estimatesfor the effect of private tutoring. Because Equation (1) is a dynamic panel datamodel, we rely on Arellano and Bond (1991) for the estimation.

The third method of estimation is a propensity-score matching method, whichis popular in recent microeconometric evaluation literature (Heckman et al.,1999; Smith and Todd, 2005).6 For subsequent use, let us discretize the level oftutoring expenditures and define Ti as a treatment indicator that is equal to zero ifthe average monthly expenditure on tutoring (Si) is equal to zero; one if it isgreater than zero but less than or equal to H1; and two if it is greater than H1. Inthe empirical analysis below, we set H1 equal to KRW200 000 (US$195.3) foraverage overall tutoring expenditures for three subjects, KRW30 000 (US$29.3)for average expenditures for Korean alone, and KRW90 000 (US$87.9) foraverage expenditures for each of English and mathematics alone.7 Each studentreceives treatment t ∈ T = {0,1,2}. Because there are three discrete levels oftutoring expenditures in the current estimation, we rely on Lechner (2001), whohas developed general propensity-score matching methods for more than twomutually exclusive treatments. What follows draws heavily on Larsson (2003)and Lechner (2001).

Given three different levels of expenditures or treatments ({0,1,2}), we denotepotential outcomes by {y0, y1, y2}. For each student, only one outcome can beobservable in the data and the others are counterfactuals. Our evaluation problemis to estimate the average impact of treatment m compared to treatment l forcombinations of m, l ∈ {0,1,2} (m > l). More formally, the outcome of interest isqml = E(ym - yl|T = m). Here, qml is the multiple-treatment version of the averagetreatment effect on the treated (ATT), which denotes the average treatment effectof treatment m relative to treatment l for participants in treatment m. To the extentthat E(ym|T = m) is easily constructed from the data, matching methods attempt toconstruct the unobservable counterfactual E(yl|T = m) under assumptions. A keyassumption employed in the matching literature is the conditional independence

6 In the matching method as well as the IV method and a bounding method presented shortly, wetreat the data as pooled cross-sectional data.7 Such thresholds are arbitrary. As robustness checks, we construct two alternative Ti’s by employ-ing different values as a new threshold between 1 and 2 of Ti. The results based on such thresholds arequalitatively similar to those reported in the current paper. The alternative results are available uponrequest.



assumption (CIA): y0, y1, y2 � T|W ≡ (X, yt-1), with � meaning independence. Inthe context of the current paper, the CIA states that for a student who shows thesame observable characteristics (W ≡ (X, yt-1)), a level of private tutoring expen-ditures is independent of the potential test score.8 Under the CIA, a matchingmethod estimates qml by:

ˆ ˆ ( ) ˆ ( )

ˆ ( ) ˆ { ˆ ( , )

θ ml m l

mW

l

E y T m E y T m

E y T m E E y T l W T m

= = − =

= = − = =

| |

| | | }}.

Extending Rosenbaum and Rubin (1983) into the multiple treatments frame-work, Lechner (2001) shows that it is not necessary for matching to condition onmultidimensional W but only to condition on the participation probability condi-tional on W (the propensity score). Hence,

ˆ ( ) ˆ [ ˆ ( ( ), ) ],( )

||E y T m E E y P w T l T ml

P

l m ml

wm ml| | |= = = =

where Pm|ml(w) = Pm|ml(T = m|T = l or T = m, W = w) ∈ (0, 1). We employ amatching protocol for the estimation of qml suggested in Lechner (2001, Table 1;it is described in the appendix).

The matching literature argues that matching estimators in general have lowbias if the data include a rich set of variables related to treatment assignment(Smith and Todd, 2005). While a pre-tutoring level of i’s performance (yt-1),which is likely to be a good proxy for a student’s pre-treatment characteristics, isincluded in W, there is some concern as to whether a set of variables in W offerssufficiently rich information on treatment assignment. With the current data, weappear to have no good way to address such a concern. Nonetheless, it does notseem inappropriate to suppose that a matching method yields a version of theestimates for the causal impact of private tutoring under an assumption that differsfrom the three other estimation methods. If different versions of the estimatedimpact under different assumptions are found within a close range, we may viewsuch estimates as reassuring.

Given the longitudinal structure of the current data, there is an alternativemethod of estimating qml by matching: a difference-in-differences (DD) matchingstrategy (Smith and Todd, 2005). This is a different way of taking advantage ofthe same amount of information in the data. The DD matching estimates qml by

ˆ ˆ ( ) ˆ{( ) ( ) }θDDMml

tm

tl

t tm

tl

tl

tl

tE y y T m E y y y y T m= − = = − − − =

=− −| |1 1

ˆ{( ) } ˆ { ˆ ( ,( )

| ( )|E y y T m E E y y P Tt

mtl

t P tl

tl

xm ml

txm ml− = − − =− −1 1| | ll T mt) },| =

8 Among the variables in W, we exclude the variable ‘hours of self-study’ because its level is likelyto be jointly determined with T.



where Pm|ml(x) = Pm|ml(T = m|T = l or T = m, X = x) ∈ (0, 1) and the generalized CIAassumes that ( ), ( ), ( )y y y y y y T Xt t t t t t t t

01

0 11

1 21

2− − −− − − � | . In Subsection V.2, wereport two sets of matching results. One set is those of the conventional matching;the other is those of the DD matching.

Along with estimates for ATT, we report the average treatment effect oftreatment m relative to treatment l for a participant drawn randomly from thepopulation (ATE). According to Lechner (2001), the ATE is calculated asfollows:

ˆ ˆ ( ) ( ) .γ Nml m l

j

E y y T j P T j= − = ⋅ =⎡⎣ ⎤⎦=

∑ |0

2

Such an ATE is comparable to the average treatment effects drawn from thebounding method presented below.

The fourth method of estimation is a nonparametric bounding method that hasrecently received attention in empirical analysis. The goal is to calculate the lowerand upper bounds of the average treatment effect given a few assumptions. For thefollowing presentation of the method, we draw on Gonzalez (2005), Kang (2007)and Manski and Pepper (2000), among others.

Let us define the response function yi(·):T → Y, which maps treatments intooutcomes. The realized outcome y ≡ y(z) is the level of y for a student whoactually receives treatment z. The latent outcome y(t) (t � z) describes what levelof performance the student would have achieved had he or she received treatmentt.

To set up bounds for the treatment effects, we first decompose E[y(t)] by

E y t E y z t Pr z t E y t z t Pr z t[ ( )] [ ] ( ) [ ( ) ] ( ).= = = + ≠ ≠| | (2)

To make bounds analysis feasible, let us suppose that y is bounded by [K0, K1].9

Because the unobservable counterfactual E[y(t)|z � t] is bounded by [K0, K1], wehave the worst-case (WC) bounds of E[y(t)] given by

E y z t Pr z t K Pr z t E y tE y z t Pr z t K Pr z t

[ ] ( ) ( ) [ ( )][ | ] ( ) (

| = = + ≠ ≤≤ = = + ≠

0

1 )).(3)

To further tighten the bounds of E[y(t)], assumptions are introduced belowindividually as well as jointly. The first assumption is monotone treatmentresponse (MTR), which is specified as follows:10

9 In fact, specific values of K0 and K1 make no difference in our reported results, because the currentpaper examines the MTR + MTS and MIV + MTR + MTS bounds alone, which are not functions ofK0 and K1. Estimated bounds based on other assumptions are available upon request.10 This assumption is drawn from a theory that there will be non-negative impacts of increasededucational spending on a student’s academic performance. A majority of empirical studiessupport the validity of such an assumption. Although the magnitude of positive effects of educational



t t y t y tl m l m< → ≤( ) ( ). (4)

Another assumption to be employed is monotone treatment selection (MTS),which is specified by:11

t t E y t z t E y t z tl m l m< → = ≤ =[ ( ) ] [ ( ) ].| | (5)

While it specifies a source of endogeneity when conventional OLS methods areused to examine the impacts of educational investments, the MTS assumption canmake an important contribution to tightening the bounds of the true effect incombination with MTR. Joining MTS with MTR, we can obtain the MTR + MTSbounds of E[y(t)] given by

E y z h Pr z h E y z t Pr z t

E y t E y z h Pr z h

h t( ) ( ) ( ) ( )

[ ( )] ( ) (

| |

|

= = + = ≥

≤ ≤ = =<∑

)) ( ) ( ).+ = ≤>∑ E y z t Pr z t

h t|

(6)

One can further tighten the preceding MTR + MTS bounds, if there is an IV uthat satisfies mean independence: E[y(t)|u = u1] = E[y(t)|u = u2] where u1 � u2.Under mean independence, the expected test score of students with u = u1 is equalto that of students with u = u2 for any given level of private spending. In practice,however, finding such an IV is extremely difficult. As an alternative, Manski andPepper (2000) propose a monotone IV that satisfies mean monotonicity: E[y(t)|u = u1] � E[y(t)|u = u2] if u1 < u2. Under mean monotonicity, it is sufficient thatthe expected test score of students with u = u1 is less than or equal to that ofstudents with u = u2. We use a first-born indicator Fi as such a monotone IV.Namely, we suppose that for a given level of tutoring expenditure first-bornstudents (Fi = 1), on average, perform as well or better than students who arelater-born in the family (Fi = 0).

Combining MIV with MTR + MTS, the MIV + MTR + MTS bounds of E[y(t)]are given by

spending on students’ performance varies, it is rare that studies find strong negative impacts ofmonetary educational investments (see Hanushek (2003); an exception is a study by Leuven et al.(2007)).11 This assumption supposes that sorting into treatment is not exogenous but monotone in thesense that the average latent outcome y(t) is greater for those students whose parents spend a largeamount of money on private tutoring (z = tm) than for those whose parents spend a small amount(z = tl, tl < tm). For instance, high income parents are more likely to spend a large amount of moneyon private tutoring for their child than low-income parents, while children of high income parentstend to be more academically able and smarter than those of low-income parents (see e.g. Havemanand Wolfe, 1995).



Pr F u E y F u z h Pr z h F u

E y F

u u h tu F( ) sup ( , ) ( )

(

= ⋅ = = = =⎡⎣{+ =

≤ <∈ ∑∑ 1 1 1| |

| uu z t Pr z t F u E y t

Pr F u E y F u zu u

1 1

22

, ) ( ) [ ( )]

( ) inf ( ,

= ≥ = ⎤⎦} ≤

≤ = ⋅ = =≥

|

| hh Pr z h F u

E y F u z t Pr z t F u

h tu F) ( )

( , ) ( ) .

= =⎡⎣{+ = = ≤ = ⎤⎦}

>∈ ∑∑ |

| |

2

2 2

(7)

We calculate conditional expectations, E[y(t)|·], nonparametrically by relyingon a local linear regression (Fan, 1992) in which the control variable is a logarizedvalue of i’s family income and ˆ[ ( ) ]E y t |⋅ is evaluated at its mean value. In sectionV, we report estimated bounds under MTR + MTS and MIV + MTR + MTSassumptions alone, suppressing those under other assumptions for a terse presen-tation. Given the bounds of E[y(t)] under varying assumptions, the lower bound(LB) of an average treatment effect (ATE), E[y(tm)-y(tl)] (tm > tl), is calculated bythe difference between the lower bound of E[y(tm)] and the upper bound ofE[y(tl)]; the upper bound (UB) of ATE is obtained by the difference between theupper bound of E[y(tm)] and the lower bound of E[y(tl)]. Along with the bounds ofATE, bootstrap 5th and 95th percentiles of the lower and upper bound arecalculated, respectively. The interval between these percentiles shows a conser-vative 90-percent confidence interval for the true effect of private tutoring. Thenumber of the bootstrap samples is 50.

IV. The Data: The Korea Education Longitudinal Study

IV.1 Description of the main sample

For empirical analysis, the current study employs the KELS. The KELS is anannual longitudinal survey that has been conducted since 2005 by the KoreaEducational Development Institute (KEDI), a government-funded research insti-tute. The basic structure of KELS follows the National Educational LongitudinalStudies (NELS:88 and ELS:2002) of the USA.

The beginning cohort of KELS consists of 6908 students in grade 7, the firstyear of middle school in South Korea, in 2005. The sample of the students andschools is drawn by a stratification method to reflect the national population of703 914 seventh graders in 2929 middle schools. More specifically, at first, 150schools are selected nationwide in consideration of the regional distribution ofschools and students. In each school, 50 students are drawn at random, while allstudents are drawn if the school is attended by fewer than 50 students. Each of thesampled students is administered a series of personal, family and school-relatedquestionnaires. In addition, students’ homeroom teachers, school principals andparents are separately surveyed to collect background information on the sampledstudent.



In each wave of KELS, student academic performance is measured by achieve-ment tests for three subjects: Korean, English and mathematics. The test score ofeach subject is scaled from 0 (lowest) to 100 (highest). In the subsequent analysisthe raw score of each subject is normalized to have mean zero and variance one.In addition, we calculate a simple average of the three subject scores, or twosubject scores if only two are available. This average score is also normalized tohave mean zero and variance one.

Another important feature of the KELS data is the availability of detailedinformation on a student’s private tutoring experience and tutoring expendituresby parents, and the sibling composition from the parent questionnaire. It enablesus to construct the main explanatory variables and the (monotone) instrumentalvariable of this study. As regards private tutoring for a student, the parents areasked to report monthly average expenditures on private tutoring for each subjectof Korean, English and mathematics during the survey year. Such expendituresare reported not only for each subject, but for each type of tutoring method, suchas tutoring in hakwons (private tutoring institutions), tutoring by individual tutorsand tutoring via the Internet. Our measure of private tutoring expenditures is anoverall sum of expenditures for all such methods. In the analysis we use eithertotal expenditures on three subjects as a whole, or expenditures on each subject.

For the current study, we employ the first three waves (years 2005 to 2007) ofKELS. Because the test scores of the first wave are employed as a measure of astudent’s pre-determined quality (yt-1), we use test scores and information on thestudents collected in waves 2 and 3 more extensively. In the raw samples, thereare 6538 and 6310 valid test scores of the students in waves 2 and 3, respectively.After removing observations that have missing values for the variables used inregressions as well as observations of an only child in the family, we secure a totalof 8631 valid observations from 4949 individual students for further analysis. Wedraw 50.4 percent of the observations from wave 2, and the remainder from wave3. Descriptive statistics of the analysis sample are documented in Table 1.

IV.2 Descriptive statistics

The mean (SD) raw score for Korean, English and mathematics is 59.6 (19.6),56.4 (25.5) and 52.5 (25.4), respectively. The mean (SD) of the average raw scoreof the three subjects is 56.2 (20.7). Here, differences in the number of observa-tions across subjects are due to slightly different degrees of score availability.Because the average score is calculated on the basis of two or three subject scores,the number of observations is the largest for the average score, while it is smallerfor the individual subject scores. The same is true for tutoring expenditures andprior scores (yt-1) in the table.

If we compare test scores by birth order, the mean scores of Korean, Englishand mathematics among the first-born significantly exceed those of the later-bornstudents. The mean of the average raw score is also significantly greater for the



Tab

le1

Des

crip

tive

stat

isti

csof

the

sam

ple

Var

iabl

esT

otal

sam

ple

(1)

Fir

st-b

orn

(2)

Lat

er-b

orn

Diff

eren

ce[(

1)-

(2)]

NM

ean

SDM

ean

SDM

ean

SDM

ean

SEt-

valu

e

Ave

rage

scor

eof

thre

ete

sts

8631

56.2

20.7

58.9

20.7

53.9

20.4

5.00

40.

444

11.2

6T

est

scor

eof

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ean

8592

59.6

19.6

62.0

19.3

57.6

19.7

4.36

00.

423

10.3

0T

est

scor

eof

Eng

lish

8610

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25.5

59.6

25.5

53.8

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5.72

20.

550

10.4

1T

est

scor

eof

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h86

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.225

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0.54

79.

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ring

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ndit

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000)

8631

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922

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8.66

Any

tuto

ring

(Yes

=1)

8631

0.67

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0.73

30.

442

0.62

70.

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70.

010

10.5

8T

utor

ing

expe

ndit

ures

for

Kor

ean

7833

32.1

860

.22

35.4

59.8

29.6

60.5

5.78

31.

366

4.23

Tut

orin

gfo

rK

orea

n(Y

es=

1)78

330.

423

0.49

40.

472

0.49

90.

382

0.48

60.

090

0.01

18.

07T

utor

ing

expe

ndit

ures

for

Eng

lish

8041

77.8

810

1.97

88.5

103.

769

.199

.719

.456

2.27

58.

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utor

ing

for

Eng

lish

(Yes

=1)

8041

0.65

80.

475

0.72

20.

448

0.60

40.

489

0.11

80.

011

11.1

9T

utor

ing

expe

ndit

ures

for

mat

h81

0678

.97

107.

9688

.510

5.9

71.0

109.

017

.562

2.40

07.

32T

utor

ing

for

mat

h(Y

es=

1)81

060.

658

0.47

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721

0.44

90.

605

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90.

116

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111

.02

Pri

orav

erag

esc

ore

8631

0.06

80.

992

0.19

80.

990

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400.

980

0.23

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5P

rior

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eof

Kor

ean

8592

0.07

00.

980

0.17

50.

962

-0.0

170.

986

0.19

20.

021

9.11

Pri

orsc

ore

ofE

ngli

sh86

240.

058

0.99

40.

184

1.00

0-0

.045

0.97

80.

229

0.02

110

.71

Pri

orsc

ore

ofm

ath

8604

0.05

30.

998

0.16

20.

990

-0.0

370.

996

0.19

90.

022

9.25

Hou

rsof

self

-stu

dy86

315.

562

5.14

16.

043

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05.

167

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876

0.11

17.

91M

ale

(Yes

=1)

8631

0.50

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500

0.48

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500

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10.

500

-0.0

410.

011

-3.7

5N

umbe

rof

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dren

8631

2.33

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628

2.21

60.

505

2.42

60.

700

-0.2

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013

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61H

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(Yes

=1)

8631

0.03

40.

182

0.03

70.

189

0.03

20.

176

0.00

50.

004

1.22

Fir

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(Yes

=1)

8631

0.45

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498

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000

Inta

ctfa

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y(Y

es=

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913

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64.

28P

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8631

42.3

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Fam

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me

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3137

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43.1

3057

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urve

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07(Y

es=

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ool

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60.

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8631

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es=

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n(gr

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)86

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56

Not

e:S

D,

stan

dard

devi

atio

n.



first-born (58.9) than for the later-born (53.9). Yet, it is not clear whether thesedifferences between the two groups are causally created by variations in tutoringexpenditures.

As for private tutoring expenditures, parents spend more on tutoring for thefirst-born than for the later-born. While the overall average monthly spending onprivate tutoring is around KRW175 900 (approximately $171.7), the averagespending for the first-born (KRW198 400) is 26 percent greater than that for thelater-born (KRW157 400). The gap is significantly different from zero. Theproportion of those who have received private tutoring (those with positiveaverage monthly spending) is also far higher among the first-born (73.3 percent)than among the later-born (62.7 percent).

When tutoring experiences are subdivided into each of the three individualsubjects, English and mathematics are primary subjects of private tutoring.While parents spend a monthly average of KRW32 180 on Korean tutoringon average, they expend more than twice as much for each of English(KRW77 880) and mathematics (KRW78 970) tutoring.12 For each subject,parents expend a significantly greater amount on tutoring for the first-born thanfor the later-born.

First-born students have a pre-determined quality significantly greater than thelater-born. If we proxy a student’s pre-determined quality by the test score of theyear before, the mean of the average score as well as that of individual subjectscores are significantly higher for the first-born than for the later-born. The meanof the raw average score of the three subject is 65.20 (0.198 in normalizedZ-score) for the first-born and 56.83 (-0.040 in Z-score) for the later-born. Thepattern that the first-born have a better pre-determined quality than the later-bornremains the same if the mean of each subject score is employed. In addition,weekly hours of self-study excluding private tutoring hours are greater for thefirst-born than for the later-born.

If we examine other variables, the first-born tend to have background charac-teristics more favorable for academic performance than the later-born. Forexample, the first-born enjoys a smaller sibling size in the family and greateraverage education level and income of the parents than the later-born. Thepreceding comparisons of variables between the first-born and the later-born castdoubt on the validity of the exogenous IV assumption; rather, it supports thevalidity of the mean monotonicity assumption. To the extent that meanmonotonicity holds, we can draw some useful information regarding the causaleffects of private tutoring expenditures from the bounding estimations as well asfrom 2SLS estimations.

12 Monthly expenditures on private tutoring obtained from the KELS data are comparable to thenational monthly average expenditures reported by the Ministry of Education (2007). According tothe Ministry, the national average tutoring expenditures for Korean, English and mathematicsare KRW29 000, KRW76 000 and KRW73 000, respectively, for middle school students in SouthKorea.



V. Estimation Results

V.1 Ordinary least squares, two-stage least squares and first-differenceresults

Table 2 presents OLS, 2SLS and FD estimation results when we employ normal-ized average scores of the three subjects for yit and total expenditures on threesubjects for sit. Corresponding estimates based on test scores and expenditures ofeach subject are reported in Table 3. In both tables, the figures in square bracketsunder each of the estimates for b2 show the percent change in test score due to a10-percent increase in tutoring expenditure, which is evaluated at the mean of theraw test score.

From Table 2, the OLS estimate for b2 in column (2) suggests that the asso-ciation between the tutoring expenditure and normalized average test score ispositive but quite small in magnitude, although it is statistically significantlydifferent from zero. A 10-percent greater overall expenditure on private tutoringis related to no more than a 0.006 SD higher test score. Such a magnitude impliesthat a 10-percent greater expenditure is associated with only a 0.214-percenthigher test score. Such an association, however, may not be consistent and causaldue to the endogeneity of sit. Depending on the value of Cov(sit, ai + uit), the OLSestimate may be biased either upward or downward.

Other estimates in column (2) show the expected signs. For example, hours ofself-study, average test scores of the previous year, parents’ average educationand average age and no-religion are positively related to a student’s performance;female students have higher scores than male students. Family configuration(intact family as opposed to single or divorced parents), number of children, beinghandicapped and family income, however, fail to show strong associations withacademic performance.

The first-stage results of the 2SLS regression of tutoring expenditures on the IVand explanatory variables are presented in column (1) of Table 2. As expected,being first-born significantly increases private tutoring expenditures for a student.First-born students receive, on average, 19.1-percent greater expenditure on tutor-ing than later-born students. Such an amount is significantly different from zero.According to Stock et al. (2002), Fi is a strong IV for tutoring expenditures for astudent, because the F-statistic for the IV (36.17) greatly exceeds proposedthresholds of weak IV (e.g. 16.38).

The 2SLS estimates for Equation (1) are shown in column (3) of Table 2. Theestimate for b2 suggests that a 10-percent increase in expenditure enhances astudent’s performance by 0.03 SD. Evaluated at the mean value of the test score,such an estimate implies that a 10-percent increase in expenditure raises theaverage test score by 1.10 percent. Although the estimate is significantly differentfrom zero, the magnitude of the effect of private tutoring does not seem large.Such a magnitude is much smaller than the amount of improvement in the testscore (2.8 to 3.6 percent) due to a 10-percent increase in per-pupil expenditure



Tab

le2

Ord

inar

yle

ast

squa

res

(OL

S),t

wo-

stag

ele

ast

squa

res

(2SL

S)an

dfir

st-d

iffe

renc

ees

tim

ates

ofth

eef

fect

oftu

tori

ngex

pend

itur

eson

test

scor

es:

Ave

rage

scor

esof

the

thre

esu

bjec

ts

Dep

ende

ntva

riab

le:

Ln(

Tut

orin

gex

pend

itur

e)N

orm

aliz

edav

erag

esc

ore

Est

imat

ion

met

hod:

OL

S2S

LS

Fir

st-d

iffer

ence

(1)

(2)

(3)

(4)

Ln(

Tut

orin

gex

pend

itur

es)

0.05

8(0

.005

)**

0.29

8(0

.086

)**

0.02

0(0

.002

)**

[0.2

14]

[1.0

96]

[0.0

75]

Fir

st-b

orn

chil

d0.

191

(0.0

32)*

*P

rior

aver

age

scor

e0.

331

(0.0

17)*

*0.

719

(0.0

08)*

*0.

638

(0.0

31)*

*0.

156

(0.0

30)*

*H

ours

ofse

lf-s

tudy

0.02

9(0

.003

)**

0.01

0(0

.001

)**

0.00

3(0

.003

)M

ale

0.20

8(0

.036

)**

-0.1

13(0

.016

)**

-0.1

63(0

.026

)**

Num

ber

ofch

ildr

en-0

.102

(0.0

24)*

*0.

003

(0.0

11)

0.03

4(0

.014

)*H

andi

capp

ed0.

081

(0.0

79)

-0.0

36(0

.036

)-0

.063

(0.0

40)

Inta

ctfa

mil

y0.

220

(0.0

58)*

*0.

038

(0.0

26)

-0.0

08(0

.035

)P

aren

ts’

aver

age

age

0.00

1(0

.004

)0.

004

(0.0

02)*

*0.

006

(0.0

02)*

*P

aren

ts’

aver

age

educ

atio

n0.

034

(0.0

08)*

*0.

018

(0.0

04)*

*0.

010

(0.0

05)*

Hav

ere

ligi

on0.

166

(0.0

31)*

*-0

.032

(0.0

14)*

-0.0

66(0

.020

)**

Ln(

Fam

ily

inco

me)

0.66

2(0

.029

)**

-0.0

01(0

.014

)-0

.143

(0.0

58)*

Sur

vey

year

2007

0.07

2(0

.028

)**

0.00

1(0

.013

)-0

.018

(0.0

16)

Inte

rcep

t-2

.975

(0.2

94)*

*-0

.606

(0.1

29)*

*-0

.136

(0.2

51)

Sch

ool

char

acte

rist

ics

Yes

Yes

Yes

F(I

Vex

clud

edfr

omth

e2n

dst

age)

36.1

7R

20.

276

0.63

3N

umbe

rof

sam

ple

8631

8631

8631

7126

Not

es:

Sta

ndar

der

rors

are

repo

rted

inpa

rent

hese

s.*

and

**in

dica

teth

atth

ees

tim

ate

issi

gnifi

cant

atth

e0.

05an

d0.

01le

vels

,res

pect

ivel

y.T

henu

mbe

rsin

squa

rebr

acke

tsar

epe

rcen

tch

ange

sin

test

scor

edu

eto

a10

-per

cent

incr

ease

inex

pend

itur

eth

atar

eev

alua

ted

atm

ean

valu

es.

IV,

inst

rum

enta

lva

riab

les.



Tab

le3

Ord

inar

yle

ast

squa

res

(OL

S),t

wo-

stag

ele

ast

squa

res

(2SL

S)an

dfir

st-d

iffe

renc

ees

tim

ates

ofth

eef

fect

oftu

tori

ngex

pend

itur

es:

indi

vidu

alsu

bjec

ts

Dep

ende

ntva

riab

le:

Ln(

Tut

orin

gex

pend

itur

e)N

orm

aliz

edte

stsc

ore

Est

imat

ion

met

hod:

OL

S2S

LS

Fir

st-d

iffer

ence

(1)

(2)

(3)

(4)

Pan

elA

:K

orea

n

Ln(

Tut

orin

gex

pend

itur

es)

0.02

6(0

.009

)**

0.84

5(0

.243

)**

0.02

7(0

.003

)**

[0.0

97]

[3.1

12]

[0.1

00]

Fir

st-b

orn

chil

d0.

114

(0.0

25)*

*F

(IV

)20

.91

R2

0.09

80.

418

Num

ber

ofsa

mpl

e7,

763

7,76

37,

763

6,39

8P

anel

B:

Eng

lish

Ln(

Tut

orin

gex

pend

itur

es)

0.08

4(0

.007

)**

0.34

3(0

.108

)**

0.02

3(0

.002

)**

[0.3

08]

[1.2

62]

[0.0

84]

Fir

st-b

orn

chil

d0.

162

(0.0

26)*

*F

(IV

)39

.76

R2

0.29

70.

570

Num

ber

ofsa

mpl

e8,

011

8,01

18,

011

6,62

9P

anel

C:

Mat

hem

atic

s

Ln(

Tut

orin

gex

pend

itur

es)

0.11

4(0

.008

)**

0.38

9(0

.119

)**

0.01

9(0

.002

)**

[0.4

18]

[1.4

33]

[0.0

69]

Fir

st-b

orn

chil

d0.

161

(0.0

26)*

*F

(IV

)39

.15

R2

0.29

70.

481

Num

ber

ofsa

mpl

e8,

052

8,05

28,

052

6,66

7

Not

es:

Oth

erex

plan

anto

ryva

riab

les,

who

sees

tim

ates

are

supp

ress

edhe

re,

are

sam

eas

inT

able

2.S

tand

ard

erro

rsar

ere

port

edin

pare

nthe

ses.

*an

d**

indi

cate

that

the

esti

mat

eis

sign

ifica

ntat

the

0.05

and

0.01

leve

ls,r

espe

ctiv

ely.

The

num

bers

insq

uare

brac

kets

are

perc

ent

chan

ges

inte

stsc

ore

due

toa

10-p

erce

ntin

crea

sein

expe

ndit

ure

that

are

eval

uate

dat

mea

nva

lues

.IV

,in

stru

men

tal

vari

able

s.



suggested by Krueger (2003). It is more analogous to the effect sizes suggested byGuryan (2001) in terms of test scores (0.77 to 1.15 percent), and by Card andKrueger (1996) in terms of labor market earnings (0.7 to 1.1 percent). Further-more, to the extent that there exists a potentially positive rather than negativecorrelation between Fi and ai, as is implied by the mean monotonicity assump-tion, our 2SLS estimate is more likely to even overstate the true effect of privatetutoring rather than understate it.

A weak effect of private tutoring is also found in the FD estimation. Theestimate in column (4) suggests that a 10-percent increase in expenditure leads tonothing but a 0.002 SD (or 0.075 percent) higher average test score. In sum, the2SLS and FD estimates suggest small (or modest at best) effects of privatetutoring on academic performance. Our current findings for the effect of privatetutoring for middle school students are in line with the results that Kang (2007)finds for high school students in grade 12.

If we disaggregate tutoring expenditures and test scores by subject, the patternsof weak effects of private tutoring change little. In Table 3, 2SLS estimatessuggest that a 10-percent increase in expenditure raises each test score of Korean,English and mathematics by 3.11, 1.26 and 1.43 percent, respectively. Asexplained earlier, such estimated effects are likely to be overestimates rather thanunderestimates of the effect of private tutoring. While the 2SLS estimate of b2 forKorean implies a non-negligible effect of tutoring, the FD estimate suggests anegligible effect. Similar patterns hold for English and mathematics subjects. TheFD estimates suggest that a 10-percent rise in expenditure increases each testscore of Korean, English and mathematics by a mere 0.10, 0.08, and 0.07 percent,respectively. Moreover, such FD estimates are fairly precise, as shown by thesmall standard errors of the estimates.13

V.2 Results of the matching method

Table 4 shows the estimation results of the matching method for the average scoreof the three subjects as well as for each subject score.14 Panel A reports theestimates of ordinary matching; Panel B those of DD matching. The estimates ofaverage treatment effects on the treated ( θN

m l, , ATT) are reported in columns (1)to (3); average treatment effects ( ˆ ,γ N

m l, ATE) are in columns (4) to (6). Theelasticities of ATT and ATE that show the percent change in test score due to a

13 While we use the monthly tutoring expenditures as a measure of tutoring intensity, one may becurious as to whether the results can change if hours of private tutoring are used as an alternativemeasure. If the latter are used as a measure of tutoring intensity, however, primary results are notaffected qualitatively. The 2SLS (FD) estimates suggest that a 10-percent increase in tutoring hoursraises the average test score by 0.85 (0.11) percent, the Korean test score by 2.79 (0.08) percent, theEnglish test score by 0.77 (0.08) percent, and the mathematics test score by 1.11 (0.22) percent. Fullsets of the estimates are available upon request.14 A STATA software called PSMATCH2 that was developed by Leuven and Sianesi (2003) isemployed for matching estimations.



Tab

le4

Ord

inar

yan

ddi

ffer

ence

-in

diff

eren

ces

mat

chin

ges

tim

ates

ofth

eef

fect

oftu

tori

ngex

pend

itur

es

Out

com

eva

riab

les

Ave

rage

trea

tmen

tef

fect

son

the

trea

ted

Ave

rage

trea

tmen

tef

fect

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10-percent increase in expenditure are below the ATT and ATE estimates, respec-tively. The elasticity for ATT (and that for ATE analogously) is calculated usingthe following formula:

10 ×× × =

× =

ˆ ˆ ( )ˆ ( )

,,θN

m li

mli

SD E S t m

S E Y t m

|

|(8)

where SD is a standard deviation of the raw test score before normalization(Y) (20.7 for overall average, 19.6 for Korean, 25.5 for English and 25.4for mathematics); θN

m l, is an estimated ATT of E[ym-yl|T = m]; andS E S t m E S t lml

i i≡ = − =ˆ ( ) ˆ ( )| | (m > l; m,l = 0,1,2). Because different treatmentlevels represent different expenditure levels, elasticities rather than ATT or ATEestimates are a better measure of comparison. We will focus on the elasticitiesof ATE estimates rather than those of ATT estimates, because the former arecomparable to those obtained in the bounding analysis.

Using the ordinary matching method, we also fail to find compelling evidencethat an increase in tutoring expenditure yields strong positive causal impacts onthe test scores. As regards the average score of the three subjects, the elasticitiesimply that a 10-percent increase in tutoring expenditure raises the average testscore by 0.33 to 0.72 percent, depending on where the effect is evaluated. Whentutoring expenditures and test scores are disaggregated by subject, similar pat-terns emerge. While the estimates are less precise, a 10-percent increase inexpenditure for Korean tutoring raises the Korean test score by 0.17 to 0.28percent. English and mathematics tutoring seem to be a bit more effective but theeffect sizes are analogous to the overall effect. A 10-percent increase in expen-diture for English (mathematics) tutoring enhances the test score by 0.30 to 0.88(0.91 to 1.31) percent. These estimates are quite precise as shown by smallstandard errors of the estimates.

The DD matching estimates in Panel B also reveal weak impacts of privatetutoring. A 10-percent increase in total tutoring expenditure increases the averagetest score by 0.05 to 0.22 percent. A 10-percent increase in tutoring expendituresfor Korean, English and mathematics raises the subject test score by 0.001 to 0.08,negative 0.09 to 0.25, and 0.22 to 0.52 percent, respectively. Taken as a whole,the matching estimates suggest that causal impacts of private tutoring on the testscores are modest.

V.3 Results of the bounds analysis

The estimated bounds of ATE of tutoring expenditures are presented in Table 5.In order to gain perspective, in the right-most two columns of the table, weconvert the upper bound estimate and its bootstrap 95th percentile into elasticitiesrepresenting a percent change in test score due to a 10-percent increase inexpenditure. The elasticity is calculated using Equation (8).



As for the bounding results for the total expenditures and average score ofthe three subjects shown in Panel A, it is difficult to conclude that privatetutoring strongly improves a student’s academic performance, whichever ATEsare employed for interpretations. MTR + MTS upper bounds suggest that a10-percent increase in expenditure raises a student’s test score at most by a 1.63to 2.14, depending on where the effect is evaluated. Because the lower bounds fail

Table 5 Estimated bounds of the effect of tutoring expenditures

LB UB LB UB UB UB5th percentile 95th percentile 95th percentile

Panel A: Average score for the three subjectsMTR + MTS bounds Elasticity

E[y(1) - y(0)] 0.000 0.501 0.000 0.568 1.820 2.063E[y(2) - y(1)] 0.000 0.367 0.000 0.448 1.630 1.989E[y(2) - y(0)] 0.000 0.667 0.000 0.741 2.139 2.376

MIV + MTR + MTS bounds ElasticityE[y(1) - y(0)] 0.000 0.482 0.000 0.553 1.749 2.008E[y(2 - y(1)] 0.000 0.346 0.000 0.429 1.535 1.905E[y(2) - y(0)] 0.000 0.627 0.000 0.723 2.010 2.317Panel B: Test score for Korean

MTR + MTS bounds ElasticityE[y(1) - y(0)] 0.000 0.055 0.000 0.119 0.179 0.386E[y(2) - y(1)] 0.000 0.077 0.000 0.153 0.312 0.616E[y(2) - y(0)] 0.000 0.093 0.000 0.172 0.294 0.543

MIV + MTR + MTS bounds ElasticityE[y(1) - y(0)] 0.000 0.048 0.000 0.111 0.156 0.361E[y(2) - y(1)] 0.000 0.073 0.000 0.141 0.294 0.570E[y(2) - y(0)] 0.000 0.084 0.000 0.148 0.267 0.469Panel C: Test score for English


MIV + MTR + MTS bounds ElasticityE[y(1) - y(0)] 0.000 0.484 0.000 0.561 2.153 2.498E[y(2) - y(1)] 0.000 0.381 0.000 0.458 1.956 2.350E[y(2) - y(0)] 0.000 0.654 0.000 0.741 2.476 2.807Panel D: Test score for Mathematics


MIV + MTR + MTS bounds ElasticityE[y(1) - y(0)] 0.000 0.500 0.000 0.567 2.380 2.697E[y(2) - y(1)] 0.000 0.422 0.000 0.497 2.275 2.680E[y(2) - y(0)] 0.000 0.696 0.000 0.760 2.791 3.047

Note: LB, lower bound; UB, upper bound.



to be significantly greater than zero, however, the estimated bounds do not ruleout zero causal effects, suggesting that the true effect of private tutoring isunlikely to be substantial.

Manski and Pepper (2000, p.1004) suggest an informal method to check thevalidity of the joint MTR + MTS hypothesis. Under MTR + MTS, it should besatisfied that E[y|z = u] is a weakly increasing function of u; namely,

′ ≤ ⇒ = ′ = ′ = ′

≤ = ′ ≤ =

u u E y z u E y u z u

E y u z u E y u z uMTR MTS

[ ] [ ( ) ]

[ ( ) ] [ ( )

| |

| | ]] [ ]= =E y z u|(9)

To examine the validity of the joint MTR + MTS hypothesis, in Table 6 wepresent sample means of ˆ[ ( )]E y 0 , ˆ[ ( )]E y 1 and ˆ[ ( )]E y 2 that are calculatednonparametrically, as explained in Section III. In each of the whole and individualsubject samples, ˆ[ ]E y z u| = is increasing with u; hence, it seems unlikely thateither MTS or MTR is violated.

The tightest MIV + MTR + MTS bounds in Panel A draw a largely similarpicture that private tutoring does not raise a student’s performance substantially.The upper bounds suggest that a 10-percent increase in expenditure improvesaverage test score by, at most, 1.54 to 2.01 percent. The lower bounds, however,fail to rule out zero effects of private tutoring.15

If we examine the bounding results for the expenditure and the test score ofeach individual subject in Panels B to D of Table 5, the MIV + MTR + MTSbounds also fail to reveal compelling evidence that private tutoring is stronglyeffective for raising a student’s performance. While the upper bounds of the effectof private tutoring drawn for Korean show negligible impacts of private tutoring,those upper bounds obtained for each of English and mathematics present non-negligible effects. At the largest, a 10-percent increase in expenditure raises anEnglish test score by a 1.96 to 2.48 percent, and a mathematics test score by a

15 Employing a different dataset for South Korea (the Korean Education and Employment Panel,KEEP), Kang (2007) implements a similar bounding method to examine the causal effect of privatetutoring expenditures on test scores. While lower limits of the MIV + MTR + MTS bounds fail toexceed zero, their upper limits suggest that a 10-percent increase in spending raises a student’s testscore by 0.53 to 0.76 percent (Table 6).

Table 6 Estimated means of E y t[ ( )]

Means: Sample

Whole Korean English Math

E[y(0)] -0.392 -0.012 -0.396 -0.392E[y(1)] 0.107 0.096 0.095 0.096E[y(2)] 0.481 0.190 0.497 0.484



2.28 to 2.79 percent. Nonetheless, the lower MIV + MTR + MTS bounds fail tosignificantly exclude zero effects of tutoring. Hence, a conservative interpretationwould be that there is a lack of evidence that private tutoring strongly enhancesa student’s academic performance of Korean, English and mathematics alike.

To summarize the preceding estimation results, causal impacts of privatetutoring on student academic performance seem to be modest at best. If we take2SLS, FD and matching estimates as point estimates of the causal impacts ofprivate tutoring, a 10-percent increase in expenditure raises the overall averagescore by 0.08 to 1.10 percent, a Korean subject score by 0.10 to 3.11 percent, anEnglish subject score by 0.08 to 1.26 percent, and a mathematics subject score by0.07 to 1.43 percent. Moreover, such ranges of the effect of private tutoring ingeneral remain within the bounds of the ATE estimated by the nonparametricbounding method.

VI. Concluding Remarks

In order to shed light on the effectiveness of educational inputs for studentoutcomes, the present paper examines a relatively unexplored dimension ofeducational inputs: private tutoring expenditures. The paper employs IV, FD,propensity-score matching and nonparametric bounding methods. Using thesemethods we show that the true effect of private tutoring remains modest at best.IV (FD) estimates suggest that a 10-percent increase in expenditure raises theaverage overall score by 0.03 SD or 1.10-percent (0.002 SD or 0.08 percent);matching estimates imply that the same amount of increase in expenditure leadsto a 0.05 to 0.72 percent higher average test score; the tightest bounds of the trueeffect of tutoring reveal that a 10-percent increase in expenditure improves theaverage test score by a low of 0 to a high of 2.01 percent, while statistical tests failto rule out zero effects of tutoring. The modest impacts of private tutoring foundin the present study are comparable to the effects of public school expenditures ontest scores and earnings estimated by previous studies. In addition, our currentfindings for the effect of private tutoring for middle school students concur withthe results that Kang (2007) finds for high school students in grade 12. There isno compelling evidence that causal impacts of private tutoring are strong anddiffer according to the grade level of the student.

Kang (2007) proposes two potential explanations for modest impacts of privatetutoring in South Korea. We believe they also apply to the current context. First,overall quality of teachers in the private tutoring sector may be responsible for thesmall effects of private expenditures. In Korea, full-time public school teachersare tenured up to 62 years of age and enjoy the same employment benefits asgovernment officials. In contrast, contracts of instructors in private tutoring insti-tutions (hakwons) are usually short-term in nature and fairly unstable, as in othersmall private firms. This will cause teachers’ quality in the private sector to beworse than that in the public sector.



Second, peer pressure among parents may explain the lack of the effect. Whenprivate tutoring is a norm in parents’ peer groups, the decision to invest inchildren’s tutoring may be based on a subjective/cultural belief about the effec-tiveness of private tutoring, or the concern about being viewed by peers asneglectful of their children’s education. If decisions regarding tutoring are basedon peer pressure, the small effects of private tutoring are not surprising.

In addition, we speculate that private tutoring reduces a student’s enjoyment oflearning and motivation for self-study, thereby crowding out the hours of self-study. To the extent that students taking private tutoring reduce and offset theeffort to self-study, the total amount of learning effort that yields the educationoutcome remains unchanged. In such a case, the effect of private tutoring is likelyto be marginal.

Although further research is warranted to investigate the causes of our empiri-cal results, the second and third potential explanations of the results warn thatmoney spent on private tutoring might be an unproductive educational investmentin Korea. If private tutoring does not yield a large improvement in outcome butsimply leads to an arms-race equilibrium, a government intervention might becalled for to weaken competition for higher private tutoring expenditures. In fact,since the early 2000s, the Korean Government has introduced several measures tocurb private tutoring expenditures, such as a direct control of prices charged byhakwons and a restriction of daily operation hours. Opponents against suchmeasures argue that they are excessive interventions by the government in theprivate market. Under such circumstances, an evaluation of the arguments offeredby both sides requires solid empirical evidence on the true impacts of privatetutoring. The empirical results of this paper provide a useful base for suchresearch.

Although this paper suggests a few potential explanations for the weak impactsof private tutoring in South Korea, searching for empirical evidence for suchsuggestions as well as for alternative explanations will be a useful undertaking forfuture research. In addition, while we find weak effects of private tutoring inKorea, further research into whether similar monetary educational investmentsraise student educational outcomes in different countries and contexts will beinformative.

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