More Schooling Is Not Always Better: Evidence from an Instrumental - - PowerPoint PPT Presentation

More Schooling Is Not Always Better: Evidence from an Instrumental Variables Approach to Educational Reform in Vietnam

Phu Viet Le Fulbright Economics Teaching Program August 19, 2015

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Table of contents

• 1. History of education reforms in Vietnam and estimates of

returns to education

• 2. Estimating the return to education
• 3. Using instrumental variables to estimate the causal effect of

education on earnings in Vietnam

• 4. Results and robust checks
• 5. Conclusions and policy implications

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History of education reforms in Vietnam and estimates of returns to education

In the 20th century alone, Vietnam undertook three education reforms in 1950, 1956, and the last one in 1980s, after each major political upheaval:

◮ The 1950 education reform: reduced from 11 years under a

French colonial system to 9 years, including three levels corresponding to 4, 3, 2 years.

◮ The 1956 education reform: shifted from a 9-year and 12-year

system in the North and the temporarily liberated region to a 10-year system like that in the Soviet Union, with each level taking 4, 3, and 3 years.

◮ The third education reform in 1980s: changed from the

10-year system to 12-year system like that in North America which was already implemented in the South.

◮ Obstacles in assessing quality due to lack of criteria and/or

data.

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The third education reform in 1980s

◮ Context: two concurrent

education systems existed in the North and the South of the 17th parallel after the unification in

• 1975. The North followed

communist-oriented 10-year education system, while the South still kept its North American 12-year system.

◮ The third education reform

unified these two systems under a newly formed general education system throughout the country which takes 12 years to complete.

◮ In the new education system,

primary, lower secondary and upper secondary school takes 5, 4, and 3 years, respectively.

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Timeline of the third education reform

Breakdown of grades (left) and transition time (right). Source: VHLSS

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Characteristics of the third education reform

◮ The third education reform included a series of small

educational renovations rather than a quick adoption of a new system, unlike Doi Moi, the most dramatic economic reform in 1986. It took many years, from 1979 to 1996. Reforms on curriculum and textbooks were even much more cautious.

◮ Primary education remained free as it was aimed for

• universalization. However, tuitions were required for secondary
• schools. Increasing private contributions and semi-public

schools were allowed. Irregular expenses and supplementary teaching were widespread.

◮ Lower secondary schools required 33 weeks, which was shorter

than that in other comparable countries.

◮ The teaching method was teacher-centered, less interactive,

which encouraged rote-learning.

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Data sources and samples

◮ Four rounds of Vietnam Household’s Living Standard Surveys

(VHLSS) in 2004, 06, 08, and 10.

◮ The focus is a group with the highest obtained education level

not exceeding a high-school diploma, expectedly the most affected group by the additional schooling year than college or university graduates. This group may also have the highest return to schooling.

◮ Restricted to non-farm wage earners, who were at least 20

years old, and not currently enrolled in any school.

◮ Other important reasons for excluding those with higher

educational levels.

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Descriptive summaries of the data

North of the 17th parallel Mean and standard deviation (SD)∗

Variable 2004 2006 2008 2010 Age 35.05 35.07 35.71 35.95 SD (10.98) (11.43) (11.13) (11.24) Annual Wage (x1000) 7,840.39 10,230.59 14,512.24 22,366.23 SD (6,173.53) (7,742.54) (11,392.26) (14,105.42) EDUC 9.82 9.93 9.96 9.76 SD (2.07) (2.02) (2.05) (2.42) EXP 7.67 7.68 7.50 — SD (7.56) (7.79) (7.18) Observations 2,074 2,191 2,261 2,307 ⋆ EXP: actual years of experience at the time of survey, unlike a standard Mincer approach which assumes EXP = Age − EDUC − b. b is the age of compulsory education. Why? job switching is prevalent, and experience may be unrelated to current work. ⋆ The official exchange rate during this period increased from USD/VND 15,746 in 2004 to 18,613 in 2010.

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Impact of years of birth on the average number of years in school∗ 2004 2006 2008 2010

∗Point estimates and 95% confidence intervals, using regressions

• n year dummies.

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Rate of return to an additional schooling year in various countries∗

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Estimating the return to education

Relying on Mincer (1974)’s study: logYi = logY0 + β1 × EDUCi + β2 × EXPi + β3 × EXP2

i + εi

⋆ Dependent variable: logYi is the logarithm of income. ⋆ Explanatory variables:

◮ EDUCi is the total number of years in school, could be

transformed in educational degrees obtained: completing 12 years in the new education system corresponds to a high-school diploma etc.

◮ EXPi is the years of experience. ◮ May include other explanatory variables representing

demographic and sectoral factors.

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Estimating the return to education (2)

logYi = logY0 + β1 × EDUCi + β2 × EXPi + β3 × EXP2

i + εi

Meaning of the coefficients:

◮ β1: the average return to an extra year of schooling. For

example, β1 = 0.1 then each year of schooling will raise income by about 10%.

◮ Experience has a nonlinear impact on income as represented

by an inverted U-shaped function (β2 > 0 and β3 < 0). Experience may be more important for young workers but less important for more senior people. The marginal return to experience is calculated as {β2 + 2β3 × EXPi}. The optimal number of years of experience is {EXP∗ = − β2

2β3 }, which is

the peak of the experience-income parabola. This was about 26 years in a study in Eastern European countries and in Vietnam as well.

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Problems in estimating the return to education

⋆ Two major problems: omitted variables bias, and measurement errors.

◮ Omitted variables: personal abilities affect the number of

years in school and earnings. If personal abilities were not accounted for, ordinary-least-squares estimates may be biased due to correlations between the dependent variables (income) and the residuals.

◮ Measurement errors: it is difficult to measure education time

due to different criteria of ”what is education?”. Reported estimates are about 10-15% less than the actual numbers (Angrist and Krueger, Card). Then, OLS estimates are also biased.

◮ Other problems including the endogeneity of education and

functional forms of the return to education.

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Omitted variables bias problem

logYi = logY0+β1×EDUCi+β2×EXPi+β3×EXP2

i +γ×Abilityi+εi

⋆ Ability represents unobserved individual characteristics such as

• intelligence. Then, following Griliches, 1977:

E[b] = [X ′X]−1X ′Y = [X ′X]−1X ′[Xβ + γ × Ability + ε] = [X ′X]−1X ′Xβ

• =β

+ γ[X ′X]−1X ′Ability

• =γ Cov[Ability,EDUC]

Var[EDUC]

+ [X ′X]−1X ′ε

• =0

⇒ E[b1] = β1 + γ Cov[Ability, EDU] Var[EDUC] ⋆ Due to an expected positive correlation between individual ability and education (barring some special cases such as Bill Gates or Steve Job!), the estimated return to education may be exaggerated (thus suffered from an upward bias).

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Measurement errors problem

⋆ Incorrect measures of education may also skew the least-square

• estimates. In this case, we might estimate a return to EDUC∗

instead of the actual level of education EDUC: EDUC = EDUC∗ + errors logYi = logY0+β1×EDUCi+β2×EXPi+β3×EXP2

i +εi − β1 × errorsi

• composite residuals

⋆ Violation of a Gauss-Markov assumption on no correlation between the explanatory variable EDUC and the residual: Cov[EDUC∗ + errors, ε − β1 × errors] = β1σ2

e = 0

⇒ biased coefficient. ⋆ In the presence of measurement errors, the estimator of the return to education is: E[b1] = β1 Var[EDUC] Var[EDUC] + Var[errors] ⇒ Downward bias (attenuation bias) of the actual value. Magnitude depends on the signal-to-noise ratio.

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Functional form of the return to education: linear or nonlinear?

logYi = logY0 + β1 × EDUCi + β2 × EXPi + β3 × EXP2

i + εi

⋆ If the above function is correct (and conditions for least-square estimation hold) then the return to education β1 is BLUE. ⋆ Some studies used a second order polynomial of education β1 × EDUCi + β2 × EDUC 2

i in the same way as the experience

variable. ⋆ How to know which functional form is correct? ⇒ Non-parametric local regressions may help, without any condition on the functional form.

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Using nonparametric local regression to approximate the return to education

◮ First stage, filter out residual variations ˜

Y due to other factors not education from the earning equation: logYi = logY0 + β1 × EDUCi + β2 × EXPi + β3 × EXP2

i + ˜

Yi Then, ˜ Y supposedly contains only variations due to education (and other uncontrolled factors).

◮ Second stage, estimate the residual earning function on the

education level: ˜ Yi = g(EDUCi) + εi g(.) is a polynomial of arbitrary order.

◮ Due to a nonparametric nature, we use graphical

representation of the estimated result. This method is also called LOESS or LOWESS (locally weighted scatter-plot smoothing).

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Evidence of non-linear return to education∗

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How to estimate the return to education in the presence of

• mitted variables bias and measurement errors?

If these problems are not accounted for, OLS estimates are biased with unknown directions.

◮ With regard to individual ability, including an IQ score or test scores as a

proxy for unobserved individual characteristics. However, most dataset do not have this information.

◮ Diff-in-Diff using repeated observations in a panel dataset, then factors

that do not change over time such as individual ability will be discarded. However, this method is restricted to only individuals working and studying at the same time, which is often groups with low educational attainments and a high return to education (selection bias).

◮ Another approach is to use data of twins. Twins are assumed identical in

every aspect, family, genetics, and ability. Thus difference in earnings may be due to education.

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How to estimate the return to education in the presence of

• mitted variables bias and measurement errors? (2)

Using instrumental variables that are correlated with education levels yet have no influence on earnings.

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How to estimate the return to education in the presence of

• mitted variables bias and measurement errors? (3)

Some famous instruments including:

◮ The month and quarter of birth may be a good instrument for education

• levels. In the US, students are required to attend school to a legally

required age based on the date of birth. Then, those born in the early quarters may be allowed to leave school earlier, thus having a shorter time in school. The date of birth unlikely has an impact on individual income. Vietnam does not have a similar regulation on school attendance, yet those born in the very first days of a calendar year (say Jan 1st) may be allowed to start school with those born in the previous year. Then DoB (say born in the first week of January) can be used as an instrument for education levels.

◮ The education level of spouse can be an instrument because marriages

• ften occur among couples of similar education levels. Or the education

level of parents can serve as an instrument because well-educated parents

• ften afford greater education to their children. The education level of

relatives expectedly does not affect a person’s earning.

◮ The distance to school, or an education policy which randomly affects

education in a natural experiment setting without an impact on earnings.

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Summary of the first part

◮ The average return to education in Vietnam is comparable

with that in other developing countries.

◮ The return to education varies nonlinearly, with the highest

return for primary and upper secondary school, and lowest for lower secondary school.

◮ Within the same level then the length of time does not

seem to affect income. For example, for those completed a high-school diploma then it does not matter if he/she did it in 10 or 12 years. Similarly, those completed lower secondary school, whether in 8 or 9 years it does not matter to earnings. ⇒ The value of the degree is very evident.

◮ Next, we will quantify the effect of staying in school for

• ne more year on earnings.

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Using instrumental variables to estimate the causal effect

• f studying grade 9th on earnings in Vietnam

◮ Common questions: Why do we have to use IV? To simplify

the matter, why don’t we use a dummy variable representing those with and without the 9th grade in an ordinary least square regression?

◮ The problem is that we don’t know who took grade 9th. In the

questionnaire, we only know who complete all three educational levels, whether 10 or 12 years. However, some had to repeat a class, so adding one more year of school. Some enrolled early, so avoiding the 9th grade, while others enrolled late and had to study one more year.

◮ Even if we knew, taking the 9th grade is also endogenous and

thus cannot be used as an explanatory variable.

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Two proposed instruments for taking grade 9th

◮ The first instrument is the year of birth, for a subset of data

in the North of the 17th parallel: D1 =

• 1

1972 ≤ year of birth ≤ 1978

• therwise

◮ The second instrument is the interaction between the status

• f the 17th parallel and the year of birth, for the full country

dataset∗: N =

• 1

for Provinces north of the 17th parallel line

• therwise

D2 = D1 × N

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Assessing the condition for valid instrumental variables

◮ Random assignment of the instruments:

◮ No one knew the exact timing of the reform in order to

manipulate his or her time in school, or was able to migrate across the 17th parallel in order to avoid or take the 9th grade.

◮ There is also no reason for anyone to move from one region to

another just to avoid/take the 9th grade.

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Assessing the condition for valid instrumental variables

◮ (Exclusion restriction): Taking either of the two education

systems (prior or after reform) has no direct influence on earnings.

◮ May be acceptable when the curriculum had not changed.

This is true for those born in the overlapping years between the two education systems, when new textbooks had not been

• introduced. The only change is the extra year in school.

People affected are those born in between 1972-78 who started the 9th grade in 1990.

◮ This condition may not hold for those born well after the

reform years when there was sufficient time to change the academic program.

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Two-staged least squares (2SLS) with instrumental variables

EDUCi = α0 + α1D + α2Expi + α3Exp2

i + Xiα4 + ηi

logYi = logY0 + β1 × EDUC i + β2Expi + β3Exp2

i + Xiβ4 + εi ◮ First stage, estimate the impact of the reform on the number

• f years in school. D is the instrument; X is a vector of

explanatory variables, and ηi is the residual, supposedly iid normally distributed.

◮ Second stage, estimate the impact of education on earnings

via the instrument EDUC i in a conventional Mincerian equation.

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Main estimation result

First-stage regression of education levels on the instruments

⋆ Column [1] corresponds to a subset of data in the North of the 17th parallel. Column [2] corresponds to the full country dataset. ⋆ [*], [**], [***] represents coefficients being statistically significant at 90, 95, and 99% confidence levels. ⋆ The sample includes wage earners with age in between 20-70 years old.

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Discussions

First-stage regression is as expected:

◮ People born during the reform years in the North has a shorter

time in school by about 0.35-75 years, statistically significant in three out of four samples (2006, 2008, v 2010).

◮ Difference between the North and the South is stark: the

average schooling years in the North is significantly longer, about 1-2 years, than in the South, despite having a shorter education system. The difference increased once the North transitioned to a 12-year education system.

◮ This reflects the attitude towards education of Northern

people.

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Discussions

Second-stage regression of earnings on education attainments

⋆ Column [1] corresponds to a subset of data in the North of the 17th parallel. Column [2] corresponds to the full country dataset. ⋆ [*], [**], [***] represents coefficients being statistically significant at 90, 95, and 99% confidence levels. ⋆ The sample includes wage earners with age in between 20-70 years old.

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Discussions

Second-stage regression is surprising!

◮ The years of schooling is statistically insignificant, or even

negative and insignificant ⇒ more schooling does not raise earnings, or worse yet, reduce earnings.

◮ This result contradicts with least-square estimates assuming a

constant (linear) return to schooling, but in a total agreement with non-parametric local regressions. Why?

◮ A linear model assumes a constant return to an extra year (or

the average return), irrespective of grades completed.

◮ Nonparametric regressions show that studying grade 9th does

not raise earnings.

◮ The IV estimate of the impact of grade 9th on earnings is the

locally average treatment effect (LATE), only applied to those who were affected by the introduction of the reform, not all

• bservations in the data. Further, IV-LATE is unrelated to the

average return.

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Robust checks

⋆ Restricting to central provinces, within 400 kilometers of the 17th parallel. ⋆ Those provinces were expected to be similar in most socio-economic condition. ⋆ Avoid the influence of two major economic engines in Hanoi and HCMC.

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Robust checks

First-stage regression of education levels on the instruments

⋆ The sample includes provinces in the central of Vietnam, namely Thanh Ha, Ngh An, H Tnh, Qung Bnh in the North of the 17 parallel, and Qung Tr, Tha Thin Hu, Nng, Qung Nam, Qung Ngi, Bnh nh, Ph Yn, and Khnh Ha in the South of the 17 parallel. Central highland province Gia Lai v Kon Tum were not used. ⋆ Column [1] corresponds to a subset of data in the North of the 17th parallel. Column [2] corresponds to the all central provinces.

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Robust checks

Second-stage regression of earnings on education attainments

⋆ The sample includes provinces in the central of Vietnam, namely Thanh Ha, Ngh An, H Tnh, Qung Bnh in the North of the 17 parallel, and Qung Tr, Tha Thin Hu, Nng, Qung Nam, Qung Ngi, Bnh nh, Ph Yn, and Khnh Ha in the South of the 17 parallel. Central highland province Gia Lai v Kon Tum were not used. ⋆ Column [1] corresponds to a subset of data in the North of the 17th parallel. Column [2] corresponds to the all central provinces.

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Validity of the instruments

Tests of validity of the instruments Tests of weak instruments and under-identification all reject the null of no correlation between the instruments and the endogenous variables in all samples, except 2004 data.

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The nature of IV-LATE estimates of the return to education∗

Instrumental Variables - Local Average Treatment Effects: plimβIV = E[βi ∗ ∆Si] E[∆Si] βi is the return to education, ∆Si is the extra schooling obtained by person i as a result of the reform.

◮ The estimate is only relevant to those affected by the reform.

People dropped out before the reform initiated weren’t affected ⇒ high internal validity but low external validity.

◮ Those completed grade 9th were affected the most and thus

had the largest influence on the estimate of βIV through the weight ∆Si. Those dropped out while studying grade 9th affected the estimate by a lesser degree.

◮ This estimate is unrelated to the population’s average return

to education as derived from the original Mincer’s study.

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Why Zero Returns to 9th Grade in Vietnam?

◮ The IV-LATE estimate only applies to those affected by the

reform, that is those who had completed at least 8 years of

• schooling. Those people would have a much lower return to

an extra year in school than those with only 3-4 years of schooling.

◮ The IV estimates in other countries showing a positive and

large return to education (sometimes up to 20%) are due to the purposive placement of the education program in poor and less educated regions. In contrast, the last education reform in Vietnam applied to those with already a high education level.

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Why Zero Returns to 9th Grade in Vietnam? (2)

Other reasons:

◮ A rigid wage structure (wage grids) for government’s

employees.

◮ A slow pace of education reform due to political reasons leads

to a virtually unchanged curriculum in the first few years of implementation.

◮ The signaling value of the degree (“a sheepskin effect”) is

more important than the quality of education.

◮ Pischke and von Wachter (2005): for the case of Germany,

apprenticeship training could make up for a loss of schooling time.

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Conclusions

◮ Earnings increase nonlinearly with the years in school. ◮ The value of the degrees is very evident. Length in school

does not affect earnings for each education level obtained.

◮ Three reasons explain why earnings do not increase as per the

classical Mincer’s equation: rigid wage structure; ineffective schooling that leads to a low productivity; and lack of a mechanism to signal schooling quality.

◮ As a consequence, fake degrees/fake learning cannot be

avoided!

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Policy implications

◮ Reduce schooling time or increase the integration between

subjects.

◮ Move to performance-based salary instead of a rigid wage

structure.

◮ Remove the completion exam at the end of an academic year.

Lower secondary school completion exam was removed

• recently. Next target would be upper secondary school

completion exam?

◮ The optimal education policy would be one that targets the

poor/disadvantage who likely have the highest marginal

• return. In Vietnam, that would be towns/communes far from

big city, in mountainous areas, or the ethnic minorities.

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Cautions and limitations

◮ The return to education in this study singularly focuses on

personal income, not the rate of return for the society as a

• whole. The personal return to the 9th grade might be zero,

but not for the whole country. The latter could be either positive or negative, more work is needed.

◮ No general equilibrium effects considered: increasing time in

school reduces labor supply, thus raising equilibrium wages (if labor market is competitive).

◮ Other effects not considered such as health and longevity,

crime effects.

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