The Effect of Private Tutoring Expenditures on Academic Performance of Middle School Students: Evidence from the Korea Education Longitudinal Study

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1 The Effect of Private Tutoring Expenditures on Academic Performance of Middle School Students: Evidence from the Korea Education Longitudinal Study Deockhyun Ryu Changhui Kang Department of Economics Chung-Ang University June 2009

2 Motivation Controversies over the effectiveness of educational expenditures 1. Public school expenditures Positive: Greenwald et al. (1996); Card and Krueger (1996,JEP); Krueger (2003,EJ); Banerjee et al. (2007) Negligible: Hanushek (1986,JEL; 1997,EEPA; 2003,EJ); Betts (1996); Leuven et al. (2007)

3 Motivation Controversies over the effectiveness of educational expenditures 1. Public school expenditures Positive: Greenwald et al. (1996); Card and Krueger (1996,JEP); Krueger (2003,EJ); Banerjee et al. (2007) Negligible: Hanushek (1986,JEL; 1997,EEPA; 2003,EJ); Betts (1996); Leuven et al. (2007) 2. Private school attendance Positive: Evans and Schwab (1995,QJE); Neal (1997,JOLE) Negligible: Goldhaber (1996); Figlio and Stone (1999); Altonji et al. (2005,JPE)

4 Motivation Controversies over the effectiveness of educational expenditures 1. Public school expenditures Positive: Greenwald et al. (1996); Card and Krueger (1996,JEP); Krueger (2003,EJ); Banerjee et al. (2007) Negligible: Hanushek (1986,JEL; 1997,EEPA; 2003,EJ); Betts (1996); Leuven et al. (2007) 2. Private school attendance Positive: Evans and Schwab (1995,QJE); Neal (1997,JOLE) Negligible: Goldhaber (1996); Figlio and Stone (1999); Altonji et al. (2005,JPE) 3. Private tutoring Positive: Dang (2007); Dang and Rogers (2008); Ono (2007) Negligible: Briggs (2001); Gurun and Millimet (2008); Kang (2007)

5 Obstacles in an Empirical Analysis Conventional specification y it = β 0 + y it 1 β 1 + s it β 2 + X it β 3 + α i + u it (1)

6 Obstacles in an Empirical Analysis Conventional specification y it = β 0 + y it 1 β 1 + s it β 2 + X it β 3 + α i + u it (1) Empirical challenges due to endogeneity of private tutoring: Cov(s it, α i + u it ) 0

7 Four empirical methods 1. Instrumental variable (IV) methods First-born indicator (F i ) as an IV for s it. A key assumption : Cov(F i, α i + u it ) = 0.

8 Four empirical methods 1. Instrumental variable (IV) methods First-born indicator (F i ) as an IV for s it. A key assumption : Cov(F i, α i + u it ) = First-difference (FD) methods Treat α i as a fixed constant. A key assumption : Cov(s it s it 1, u it u it 1 ) = 0.

9 Four empirical methods 1. Instrumental variable (IV) methods First-born indicator (F i ) as an IV for s it. A key assumption : Cov(F i, α i + u it ) = First-difference (FD) methods Treat α i as a fixed constant. A key assumption : Cov(s it s it 1, u it u it 1 ) = Propensity-score matching (M) methods Matching a treated unit with a similar untreated unit(s). Conditional Independence Assumption (CIA) : y 0 T W.

10 Four empirical methods 1. Instrumental variable (IV) methods First-born indicator (F i ) as an IV for s it. A key assumption : Cov(F i, α i + u it ) = First-difference (FD) methods Treat α i as a fixed constant. A key assumption : Cov(s it s it 1, u it u it 1 ) = Propensity-score matching (M) methods Matching a treated unit with a similar untreated unit(s). Conditional Independence Assumption (CIA) : y 0 T W. 4. Nonparametric bounding (NB) methods Calculate estimated bounds of ATE instead of point estimates. Key assumptions: Monotone Treatment Response (MTR) and Monotone Treatment Selection (MTS)

11 Propensity-score matching methods Let t T = {0, 1, 2} be a treatment indicator.

12 Propensity-score matching methods Let t T = {0, 1, 2} be a treatment indicator. Average treatment effect on the treated (ATT): θ m,l 0 = E(y m y l T = m) = E(y m T = m) E(y l T = m)

13 Propensity-score matching methods Let t T = {0, 1, 2} be a treatment indicator. Average treatment effect on the treated (ATT): θ m,l 0 = E(y m y l T = m) = E(y m T = m) E(y l T = m) Constructing counterfactuals:

14 Propensity-score matching methods Let t T = {0, 1, 2} be a treatment indicator. Average treatment effect on the treated (ATT): θ m,l 0 = E(y m y l T = m) = E(y m T = m) E(y l T = m) Constructing counterfactuals: 1. Matching under CIA: θ m,l 0 = E(y m y l T = m, W ) = E(y m T = m, W ) E(y l T = m, W ) = E(y m T = m, W ) E(y l T = l, W )

15 Propensity-score matching methods Let t T = {0, 1, 2} be a treatment indicator. Average treatment effect on the treated (ATT): θ m,l 0 = E(y m y l T = m) = E(y m T = m) E(y l T = m) Constructing counterfactuals: 1. Matching under CIA: θ m,l 0 = E(y m y l T = m, W ) = E(y m T = m, W ) E(y l T = m, W ) = E(y m T = m, W ) E(y l T = l, W ) 2. Calculate the propensity score P m ml (w) = P m ml (T = m T = l or T = m, W = w).

16 Propensity-score matching methods Let t T = {0, 1, 2} be a treatment indicator. Average treatment effect on the treated (ATT): θ m,l 0 = E(y m y l T = m) = E(y m T = m) E(y l T = m) Constructing counterfactuals: 1. Matching under CIA: θ m,l 0 = E(y m y l T = m, W ) = E(y m T = m, W ) E(y l T = m, W ) = E(y m T = m, W ) E(y l T = l, W ) 2. Calculate the propensity score P m ml (w) = P m ml (T = m T = l or T = m, W = w). 3. Propensity-score matching: E(y l T = m) = E[E(y l P m ml (W ), T = l) T = m]

17 Nonparametric bounding methods 1. Let t T = {0, 1, 2} be a treatment indicator.

18 Nonparametric bounding methods 1. Let t T = {0, 1, 2} be a treatment indicator. 2. Average causal effects: E[y i (1) y i (0)], E[y i (2) y i (1)], and E[y i (2) y i (0)]

19 Nonparametric bounding methods 1. Let t T = {0, 1, 2} be a treatment indicator. 2. Average causal effects: E[y i (1) y i (0)], E[y i (2) y i (1)], and E[y i (2) y i (0)] 3. Decompose E[y(t)] by E[y(t)] = E[y z = t]pr(z = t) + E[y(t) z t]pr(z t)

20 Nonparametric bounding methods 1. Let t T = {0, 1, 2} be a treatment indicator. 2. Average causal effects: E[y i (1) y i (0)], E[y i (2) y i (1)], and E[y i (2) y i (0)] 3. Decompose E[y(t)] by E[y(t)] = E[y z = t]pr(z = t) + E[y(t) z t]pr(z t) 4. Given y(t) [K 0, K 1 ], worst-case (WC) bounds of E[y(t)] E[y z = t]pr(z = t) + K 0 Pr(z t) E[y(t)] E[y z = t]pr(z = t) + K 1 Pr(z t)

21 Nonparametric bounding methods 1. Let t T = {0, 1, 2} be a treatment indicator. 2. Average causal effects: E[y i (1) y i (0)], E[y i (2) y i (1)], and E[y i (2) y i (0)] 3. Decompose E[y(t)] by E[y(t)] = E[y z = t]pr(z = t) + E[y(t) z t]pr(z t) 4. Given y(t) [K 0, K 1 ], worst-case (WC) bounds of E[y(t)] E[y z = t]pr(z = t) + K 0 Pr(z t) E[y(t)] E[y z = t]pr(z = t) + K 1 Pr(z t) 5. UB of E[y(t m )] E[y(t l )] = UB of E[y(t m )] LB of E[y(t l )] LB of E[y(t m )] E[y(t l )] = LB of E[y(t m )] UB of E[y(t l )]

22 Nonparametric bounding methods 1. MTR bounds of E[y(t)] Monotone treatment response (MTR): t l < t m y(t l ) y(t m )

23 Nonparametric bounding methods 1. MTR bounds of E[y(t)] Monotone treatment response (MTR): t l < t m y(t l ) y(t m ) E[y z t]pr(z t) + K 0 Pr(z > t) E[y(t)] E[y z t]pr(z t) + K 1 Pr(z < t)

24 Nonparametric bounding methods 1. MTR bounds of E[y(t)] Monotone treatment response (MTR): t l < t m y(t l ) y(t m ) E[y z t]pr(z t) + K 0 Pr(z > t) E[y(t)] E[y z t]pr(z t) + K 1 Pr(z < t) 2. MTS bounds of E[y(t)] Monotone treatment selection (MTS): t l < t m E[y(t) z = t l ] E[y(t) z = t m ]

25 Nonparametric bounding methods 1. MTR bounds of E[y(t)] Monotone treatment response (MTR): t l < t m y(t l ) y(t m ) E[y z t]pr(z t) + K 0 Pr(z > t) E[y(t)] E[y z t]pr(z t) + K 1 Pr(z < t) 2. MTS bounds of E[y(t)] Monotone treatment selection (MTS): t l < t m E[y(t) z = t l ] E[y(t) z = t m ] E[y z = t]pr(z t) + K 0 Pr(z < t) E[y(t)] E[y z = t]pr(z t) + K 1 Pr(z > t)

26 Nonparametric bounding methods 1. MTR+MTS bounds of E[y(t)] h<t E(y z = h)pr(z = h) + E(y z = t)pr(z t) E[y(t)] E(y z = h)pr(z = h) + E(y z = t)pr(z t) h>t

27 Nonparametric bounding methods 1. MTR+MTS bounds of E[y(t)] h<t E(y z = h)pr(z = h) + E(y z = t)pr(z t) E[y(t)] E(y z = h)pr(z = h) + E(y z = t)pr(z t) h>t 2. MIV+MTR+MTS bounds of E[y(t)] Monotone IV (MIV): u 1 < u 2 E[y(t) υ = u 1 ] E[y(t) υ = u 2 ]

28 Nonparametric bounding methods 1. MTR+MTS bounds of E[y(t)] h<t E(y z = h)pr(z = h) + E(y z = t)pr(z t) E[y(t)] E(y z = h)pr(z = h) + E(y z = t)pr(z t) h>t 2. MIV+MTR+MTS bounds of E[y(t)] Monotone IV (MIV): u 1 < u 2 E[y(t) υ = u 1 ] E[y(t) υ = u 2 ] MIV+MTR+MTS bounds of E[y(t)]

29 Data Korea Education Longitudinal Study (KELS),

30 Data Korea Education Longitudinal Study (KELS), A nation-wide sample of 6,908 students in grade 7.

31 Data Korea Education Longitudinal Study (KELS), A nation-wide sample of 6,908 students in grade 7. Main variables 1. Outcome: Korean, English and math subject scores. Both individual subject and average scores are standardized into Z scores. 2. Tutoring expenditures: monthly expenditures for individual subjects and three subjects as a whole.

32 Table: Descriptive Statistics of the Sample Total sample First-born Difference Variables N Mean S.D. Mean S.D. Mean S.E. T-value Average score Test score of Korean Test score of English Test score of math Total expenditures Any tutoring (Yes=1) Expenditures for Korean Tutoring for Korean Expenditures for English Tutoring for English Expenditures for math Tutoring for math Prior average score Prior score of Korean Prior score of English Prior score of math Hours of self-study Male (Yes=1) Number of children Handicap (Yes=1) First-born (Yes=1)

33 Table: OLS, 2SLS and FD Estimates: Average Scores Dep variable: Ln(Tutoring Expenditure) Normalized average score Method: OLS 2SLS First-difference (1) (2) (3) (4) Ln(Tutoring (0.005)** (0.086)** (0.002)** expenditures) [0.214] [1.096] [0.075] First-born child (0.032)** Self-study (0.017)** (0.008)** (0.031)** (0.030)** Prior avg score (0.003)** (0.001)** (0.003) Male (0.036)** (0.016)** (0.026)** No. of children (0.024)** (0.011) (0.014)* Handicapped (0.079) (0.036) (0.040) Intact family (0.058)** (0.026) (0.035) Parents avg age (0.004) (0.002)** (0.002)** Parents avg edu (0.008)** (0.004)** (0.005)* Have religion (0.031)** (0.014)* (0.020)** Ln(income) (0.029)** (0.014) (0.058)* Year (0.028)** (0.013) (0.016) Intercept (0.294)** (0.129)** (0.251) School Yes Yes Yes characteristics F (IV excluded from the 2nd stage) R-square Number of sample 8,631 8,631 8,631 7,126

34 Table: OLS, 2SLS and FD Estimates: Individual Subjects Dependent variable: Ln(Tutoring Expenditure) Normalized test score Estimation method: OLS 2SLS First-difference (1) (2) (3) (4) Panel A: Korean Ln(Tutoring (0.009)** (0.243)** (0.003)** expenditures) [0.097] [3.112] [0.100] First-born child (0.025)** F (IV) Panel B: English Ln(Tutoring (0.007)** (0.108)** (0.002)** expenditures) [0.308] [1.262] [0.084] First-born child (0.026)** F (IV) Panel B: Mathematics Ln(Tutoring (0.008)** (0.119)** (0.002)** expenditures) [0.418] [1.433] [0.069] First-born child (0.026)** F (IV) 39.15

35 Table: Matching Estimates of the Effect of Tutoring Expenditures Average Treatment Effects on the Treated (ATT) Average Treatment Effects (ATE) Outcome variables θ 1,0 θ 2,1 θ 2,0 γ 1,0 γ 2,1 γ 2,0 N N N N N N (1) (2) (3) (4) (5) (6) A. Average score of 0.148** 0.077* 0.194** 0.162** 0.074* 0.223** the three subjects (0.031) (0.033) (0.040) (0.027) (0.029) (0.032) Elasticity: B. Test score of Korean (0.057) (0.052) (0.033) (0.041) (0.045) (0.027) Elasticity: C. Test score of 0.180** 0.110** 0.255** 0.184** 0.059* 0.232** English (0.032) (0.038) (0.040) (0.026) (0.029) (0.034) Elasticity: D. Test score of 0.182** 0.216** 0.288** 0.218** 0.168** 0.328** Mathematics (0.036) (0.039) (0.047) (0.028) (0.030) (0.037) Elasticity:

36 Table: Estimated means of Ê[y(t)] Sample Means: Whole Korean English Math E[y(0)] E[y(1)] E[y(2)]

37 Table: Estimated Bounds of the Effect Panel A: Average score of the three subjects LB UB LB UB UB UB 5 pctile 95 pctile 95 pctile MTR+MTS Bounds Elasticity E[y(1) y(0)] E[y(2) y(1)] E[y(2) y(0)] MIV+MTR+MTS Bounds Elasticity E[y(1) y(0)] E[y(2) y(1)] E[y(2) y(0)] Panel B: Test score of Korean MTR+MTS Bounds Elasticity E[y(1) y(0)] E[y(2) y(1)] E[y(2) y(0)] MIV+MTR+MTS Bounds Elasticity E[y(1) y(0)] E[y(2) y(1)] E[y(2) y(0)]

38 Table: Estimated Bounds of the Effect Panel C: Test score of English LB UB LB UB UB UB 5 pctile 95 pctile 95 pctile MTR+MTS Bounds Elasticity E[y(1) y(0)] E[y(2) y(1)] E[y(2) y(0)] MIV+MTR+MTS Bounds Elasticity E[y(1) y(0)] E[y(2) y(1)] E[y(2) y(0)] Panel D: Test score of Mathematics MTR+MTS Bounds Elasticity E[y(1) y(0)] E[y(2) y(1)] E[y(2) y(0)] MIV+MTR+MTS Bounds Elasticity E[y(1) y(0)] E[y(2) y(1)] E[y(2) y(0)]

39 Potential Explanations 1. Monetary educational investments may not always matter. Although controversial, evidence that public school resources are not an important determinant of outcomes. Public mismanagement due to bureaucracies and unionization.

40 Potential Explanations 1. Monetary educational investments may not always matter. Although controversial, evidence that public school resources are not an important determinant of outcomes. Public mismanagement due to bureaucracies and unionization. 2. Tutoring may crowd out self-study.

41 Potential Explanations 1. Monetary educational investments may not always matter. Although controversial, evidence that public school resources are not an important determinant of outcomes. Public mismanagement due to bureaucracies and unionization. 2. Tutoring may crowd out self-study. 3. Peer pressure in parents peer group Cultural factors in parenting (e.g., sleeping arrangement of infants). Prevalence of tutoring among parents peers.

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