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Feb 2018Returns to Secondary Education: GhanaDuflo, Dupas, Kremer

DEC Lecture Series, World Bank

The Impact of Free Secondary Education:Experimental Evidence from Ghana

Gratefully acknowledging funding from NIH, NSF, PCD, J-PAL PPE Initiative, the IGC, 3ie, Nike Foundation+ the work of many wonderful surveyors and research assistants over the past 10 years

EstherDuflo

PascalineDupas

MichaelKremer

MIT Stanford Harvard


Introduction• With growth of primary education, increased calls for

free secondary education, internationally (it is one of the SDG)

• Many anticipate large economic and social impacts, especially for girls

• Others less optimistic • Secondary education is expensive precisely• Will students learn? • Is the curriculum adapted for a terminal secondary degree or

really a preparation for tertiary? (Goldin, 1999) • Will they get jobs?


Introduction: Current Evidence

• Plenty of evidence on primary, but little high-quality evidence on impact of secondary education in developing countries

• Evidence from developed countries not necessarily relevant.• Developing countries have vastly greater levels of education than

developed countries had at comparable income levels (Pritchett, 2001)

• Quality of education in developing countries may be very different (Hanushek and Woessman, 2012)

• Ozier (2016) RD study based on Kenya’s secondary school admission test

• Delayed fertility onset for women• Limited labor market outcomes, but increased formal employment for

men• “Marginal” candidates who just got in secondary

Duflo, Dupas, Kremer Returns to Secondary Education: Ghana


This Study• Ghana. Ongoing longitudinal study started in 2008

• Sampled 2,064 students admitted to particular secondary school and major, but did not enroll (mostly due to lack of fund)

• 682 (randomly selected) received 4-year scholarships to attend day secondary school

• Examine impact of opportunity of free secondary education holding admission criteria and other means to finance school constant.

• Relevant policy, in Ghana and elsewhere.


RoadmapI. IntroductionII. Background and DataIII. Impact on Secondary Education

• Impacts on SHS Participation & Completion• Learning Outcomes

IV. Impacts on Fertility, Marriage and Health BehaviorV. Earnings, Tertiary education VI. Conclusion


Background and Data


Ghana’s Education System• 95% primary school enrollment; 75% JHS enrollment• 70% of JHS students take JHS finishing exam

• 60% of test takers pass• 80% of those who pass enroll in SHS (Ajayi 2014)

• Tuition for day (non-boarding) SHS students in 2011: 500 Ghana cedis (Per capita GDP in 2011: 2400 Ghana cedis)

• Only 700 SHS nationwide (compared to 9000 JHS)• Girls 6 p.p. (20%) less likely to reach SHS

• Some who do not enroll in SHS enroll in (much smaller) TVI (technical vocational institute) system


Experimental Design• 2,064 students sampled from 177 SHS across 5 regions

of rural southern Ghana. • admitted into a particular Senior High School (SHS) and major

based on JHS exam score• not enrolled in any SHS as of December 2008• some girls who graduated from JHS in 2007

• 682 randomly selected to receive 4-year SHS scholarship • Stratified by district, SHS, JHS, gender and year took JHS exam

• Scholarship covers the full tuition and fees for a day student for 4 years

• Average Cost = GHX 1920 (~US$480)


Timing and randomization • May 2008: Wrote JHS exit exam• July 2008: Graduated from JHS• August 2008: Admitted into SHS, placed into specific

major• September 2008: School year 2008-2009 starts• December 2008: Sampled• January 2009: Randomized into treatment or control

• July 2012: Eligible to graduate from SHS• September 2013: Earliest can enroll in tertiary if

graduated from SHS in July 2012


Senior High School Curriculum• All students take English, Math, Science, Social Studies, but

also admitted to a major• 40% of sample admitted to academic majors

• General Arts (includes French, more social science)• General Science (advanced math, chemistry, physics, bio)

• 60% in vocationally-oriented majors: Home Economics, Visual Arts, Agriculture, Technology, and Business

• Supplementary fees for vocational majors, general science• Differing preparation for tertiary, labor market• Over 20% of academic admits switch to vocational, 30% of

vocational admits switch to academic major• Implies we may understate impact of differences by major


Timeline of Surveys• Fall 2008: Baseline Survey, distributed cell phones to improve follow up rate

• Spring 2013: In-person Follow-up Survey (5-year impacts)• 96.3% survey rate • Include cognitive testing questions based on PISA

• Spring 2015: follow-up survey by phone (home tracking for 15% stragglers) (7-year impacts)

• Spring 2016: follow-up survey by phone (home tracking for 10% stragglers) (8-year impacts)

• 96.4% survey rate • Improves labor market survey questions compared to 2015

• Summer 2017: Follow up survey by phone (home tracking for 8% stragglers) (9-year impacts)

• Average age at time of survey: 26• 95.4% survey rate • Further improves labor market surveys (but also have comparable data to 2016)


Baseline: Beliefs about Education• Sample believed there were high returns to SHS

• 276% increase in earnings if complete SHS

• Government employment is seen as the pathway to these high returns

• Over 70% believed SHS would lead to government employment or employment in a profession dominated by government employees (teacher or nurse) by age 25


Impact on Secondary Education and Learning

1. Impact on secondary school participation2. Impact of knowledge/skills (2013)


1. Impact of Scholarship on SHS Enrollment

0%20

%40

%60

%80

%10

0%Sh

are

enro

lled

in S

HS

Term 1----------

Term 2---2008/09----

Term 3----------

Term 1----------

Term 2---2009/10----

Term 3----------

Term 1------2010

Term 2/11-------

Term 32011/12

Treatment: BECE'08 Boys BECE'08 Girls BECE'07 Girls

Control: BECE'08 Boys BECE'08 Girls BECE'07 Girls


Impacts on SHS Participation & Completion

Feb 2018Returns to Secondary Education: GhanaDuflo, Dupas, Kremer18

What does it mean for the debate on free secondary education?

• Ghana: Protracted political debates around this question in the past three elections (December 2008, December 2012, December 2016)

• Proponents focus on benefits• Opponents focus on costs

• Many “inframarginals” – people who would have paid on their own.

• Dec 2016: Pro-Free SHS camp won election• Sep. 2017: free SHS launched

• GoG estimates that the policy led 90,000 additional children to enroll this fall

• But debate goes on, with opposition asking where the money is going to come from

• Our study: quantifies these costs and benefits• Many domains


Fiscal Cost of Free SHS policy

• Scholarship winners = 3.09 years in SHS; non-scholarship winners = 1.83

• Scholarship paid for 3.09 years of education per 1.26 additional years in our sample.

• Cost of free education: Upper bound: no effect of scholarship on JHS pass rate

• Assume 80% of qualified students complete SHS regardless, other 20% behave like our sample

• Free SHS requires paying for 15 years of schooling for each additional year of attainment

• If promise of free secondary education leads 25% of students not passing exam to pass

• Free SHS requires paying for ~6 years of schooling for each additional year of SHS attainment

• Important margin: only 40% of those who start JHS pass final exam


2. Learning Outcomes


2. Learning Outcomes

All Female Male(1) (2) (3)

Standardized score, Reading test (2013) Treatment effect 0.147 0.151 0.136 Standard error (0.046)*** (0.064)** (0.064)** Comparison mean -0.000 -0.096 0.100 p-value on equality of effects (2)= (3): .875Standardized score, Math test (2013) Treatment effect 0.129 0.170 0.078 Standard error (0.047)*** (0.065)*** (0.065) Comparison mean -0.000 -0.191 0.199 p-value on equality of effects (2)= (3): .316


2. Total Standardized Score: By gender/cohort


2. By gender/major


2. By Quartile of JHS exit exam score


2. By School Category


2. Learning Outcomes: Other MeasuresAll Female Male(1) (2) (3)

National political knowledge standardized score (2013) Treatment effect 0.098 0.120 0.062 Standard error (0.047)** (0.064)* (0.064) Comparison mean 0.000 -0.239 0.250

(2)= (3): .521International political knowledge standardized score (2013) Treatment effect 0.067 0.005 0.104 Standard error (0.047) (0.059) (0.059)* Comparison mean 0.000 -0.402 0.419

(2)= (3): .240Knows how to use the internet (2015) Treatment effect 0.066 0.088 0.034 Standard error (0.023)*** (0.030)*** (0.030) Comparison mean 0.593 0.417 0.775

(2)= (3): .201Knows how to use the internet (2016) Treatment effect 0.039 0.047 0.018 Standard error (0.022)* (0.030) (0.029) Comparison mean 0.639 0.474 0.811

(2)= (3): .481


Other outcomes


Has WhatsApp account (2017) Treatment effect 0.030 0.080 -0.027 Standard error (0.024) (0.033)** (0.033) Comparison mean 0.572 0.489 0.659 p-value on equality of effects (2)= (3): .021**Has Facebook account (2017) Treatment effect 0.053 0.077 0.016 Standard error (0.023)** (0.032)** (0.032) Comparison mean 0.553 0.408 0.706 p-value on equality of effects (2)= (3): .171


Returns to a year of secondary education: Comparing OLS and IV

• OLS estimate: estimate in the control group. • IV estimate: use scholarship as instrument for years of

education. • Caveats:

• Direct impact of getting scholarship on outcome:• Financial [positive and negative]• Self confidence • Psychological incentive effects.

• A few children in the control group go to technical institute: • To the extent quality is lower, returns to education are under-

estimated.

• Interpretation:• IV is the estimate for compliers: potentially those with

highest return (if they are credit constrained).


2. Learning Outcomes: OLS vs. IV

All

OLS IV

(1) (2)

Total standardized score (2013)

Effect of year of education 0.213 0.124

Standard error (0.014)*** (0.034)***

p-value on equality of effects OLS=IV: .014***


2. Learning Outcomes: OLS vs. IV

Total Standardized Score (2013)

CombinedAll Female Male

OLS IV OLS IV OLS IV(1) (2) (3) (4) (5) (6)

Secondary Education (Lower Bound)Effect of year of education 0.213 0.124 0.253 0.155 0.160 0.088

Standard error (0.014)*** (0.034)*** (0.019)*** (0.048)*** (0.016)*** (0.047)*p-value on equality of effects (1)=(2): .014*** (3)=(4): .058* (5)=(6): .158

Secondary Education + TVI (Upper Bound) Effect of year of education 0.211 0.135 0.246 0.162 0.157 0.102

Standard error (0.014)*** (0.037)*** (0.015)*** (0.050)*** (0.016)*** (0.054)*p-value on equality of effects (1)=(2): .055* (3)=(4): .111 (5)=(6): .339


Putting Machine Learning to the test• OLS appears to over-estimate. Can causal effect be

recovered by controlling for the very rich set of control variables that we do have?

• Since there are many many potential control variables, we apply the Double Machine Learning method proposed in Chernozhukov et al. (2017)

• Briefly, the idea is similar to partialing out the effect of the slew of unobservables from both schooling and test scores.

• We also do the same thing, weighing the observations by the heterogeneity in treatment effect in the first stage [to get the effect on people who observationally look like the compliers]


For test scores, ML does well.

0

0.05

0.1

0.15

0.2

0.25

OLS (no cont.)* OLS* IV* DoubleML* DoubleML(Weighted)*

Total Standardized Score


Impacts on Fertility, Marriage and Health Behavior


Delayed fertility (and marriage) for Women

0.120.16

0.23

0.29

0.48

0.59

0.64

0.7

0.110.15

0.220.26

0.4

0.50.54

0.63

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

2009 2010 2011 2012 2013*** 2015*** 2016*** 2017**

Ever pregnant

Control Treatment


(Reported) fertility for Men is also impacted but effects are much more modest

0.010.02

0.03

0.06

0.11

0.21

0.25

0.33

00.02

0.03

0.06

0.09

0.16

0.23

0.33

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

2009 2010 2011 2012 2013 2015 2016 2017

Ever had a pregnant partner

Control Treatment


Ever pregnant


Living Arrangements


Marriage and Fertility (2017)Combined


Ever lived with partner(2016)Treatment effect -0.063 -0.091 -0.027Standard error (0.020)*** (0.028)*** (0.028)Comparison mean 0.241 0.344 0.134p-value on equality of effects AF=AM=VF=VM: .419 F=M: .106

Ever pregnant/had a pregnant partner (2016)Treatment effect -0.071 -0.107 -0.023Standard error (0.024)*** (0.032)*** (0.031)Comparison mean 0.403 0.582 0.213p-value on equality of effects AF=AM=VF=VM: .164 F=M: .061*

Number of children ever had (2016)Treatment effect -0.130 -0.217 -0.030Standard error (0.040)*** (0.054)*** (0.054)Comparison mean 0.519 0.814 0.212p-value on equality of effects AF=AM=VF=VM: .051* F=M: .014**

Had unwanted first pregnancy (full sample) (2016)Treatment effect -0.071 -0.115 -0.019Standard error (0.024)*** (0.032)*** (0.031)Comparison mean 0.375 0.566 0.181p-value on equality of effects AF=AM=VF=VM: .108 F=M: .032**


Marriage and Fertility• Timing of results suggests it’s unlikely to be an

“incarceration effect”• Potential mechanisms:

• (1) Increase in the opportunity cost of bearing and raising children (Becker, 1991)

• Evidence of increased earnings, tertiary education• (2) Education may shape/change preferences for children

• for females academic admits, scholarship reduced desired fertility by age 50 by 0.23 children (15.7% decrease)

• No evidence for female vocational admits• (3) Ability to make better choices thanks to better decoding

of information (Rosenzweig and Schultz, 1989)• Health behavior


Health BehaviorAll Female Male(1) (2) (3)

Desired fertil ity: # of children by age 50 (2013) Treatment effect -0.029 -0.043 -0.015 Standard error (0.049) (0.070) (0.070) Comparison mean 3.629 3.639 3.619 p-value on equality of effects (2)= (3): .779Index of risky sexual behavior(safe--> risky)(2013) Treatment effect -0.040 0.001 -0.075 Standard error (0.029) (0.040) (0.040)* Comparison mean 0.000 0.096 -0.099 p-value on equality of effects (2)= (3): .178Index of STI risk exposure (2013) Treatment effect -0.058 -0.040 -0.071 Standard error (0.028)** (0.039) (0.039)* Comparison mean -0.000 0.092 -0.096 p-value on equality of effects (2)= (3): .573Preventative health behavior (3 questions) (2013) Treatment effect 0.109 0.118 0.104 standard error (0.037)*** (0.052)** (0.052)** Comparison mean 1.624 1.691 1.555 p-value on equality of effects (2)= (3): .841


Marriage and Fertility : ML vs OLS (2017)

-0.18

-0.16

-0.14

-0.12

-0.1

-0.08

-0.06

-0.04

-0.02

0OLS (no cont.)* OLS* IV* DoubleML*

DoubleML(Weighted)*

Number of Children Ever Had

2017 2016


Labor Market Impacts


Macro Context During Study Period

• Rapid growth ends in 2012, induces fiscal retrenchment:

• Nursing and Teacher Training programs allowances and quotas removed in 2014

• Common for participants to wait two years before getting admission to tertiary education due to quotas

• Government hiring freeze in 2015

• SHS length shortened from 4 to 3 years in 2009/2010• Study participants graduated in a double cohort with

students who enrolled a year later


Labor Market Effects• Bleak labor market:

• 2016: only 46% of women and 69% of men in control group had positive earnings in last month.

• 2017: 47% and 70%, so almost no change! • When we include back wages, shadow wages (from working

with parents) and/or in-kind income we get 63% and 85% respectively


What are they up to in 2017?All Female Male(1) (2) (3)

Enrolled in formal study/ training (2017) Treatment effect 0.022 0.046 -0.003 Standard error (0.014) (0.019)** (0.019) Comparison mean 0.081 0.068 0.096 p-value on equality of effects (2)= (3): .069*Wage worker (2017) Treatment effect 0.049 0.042 0.048 Standard error (0.022)** (0.031) (0.030) Comparison mean 0.282 0.212 0.356 p-value on equality of effects (2)= (3): .888Actively searching for a job (2017) Treatment effect 0.092 0.111 0.069 Standard error (0.021)*** (0.030)*** (0.030)** Comparison mean 0.249 0.203 0.297 p-value on equality of effects (2)= (3): .328Min. training for your job: SHS (2017) Treatment effect 0.068 0.058 0.076 Standard error (0.017)*** (0.024)** (0.023)*** Comparison mean 0.121 0.102 0.142 p-value on equality of effects (2)= (3): .582Public sector wage employee (2017) Treatment effect 0.032 0.041 0.023 Standard error (0.009)*** (0.012)*** (0.012)* Comparison mean 0.024 0.019 0.028 p-value on equality of effects (2)= (3): .286


Big differences by Gender/MajorFemale Male Female Male

(5) (6) (8) (9)Enrolled in formal study/ training (2017) Treatment effect 0.080 0.015 0.022 -0.019 Standard error (0.031)** (0.031) (0.026) (0.025) Comparison mean 0.081 0.093 0.061 0.100 p-value on equality of effects (5)= (6): .137 (8)= (9): .251Wage worker (2017) Treatment effect 0.090 -0.007 0.016 0.096 Standard error (0.049)* (0.049) (0.041) (0.040)** Comparison mean 0.223 0.351 0.213 0.358 p-value on equality of effects (5)= (6): .164 (8)= (9): .161Actively searching for a job (2017) Treatment effect 0.104 0.061 0.110 0.077 Standard error (0.049)** (0.048) (0.040)*** (0.039)** Comparison mean 0.194 0.331 0.199 0.275 p-value on equality of effects (5)= (6): .529 (8)= (9): .563Min. training for your job: SHS (2017) Treatment effect 0.026 0.099 0.085 0.063 Standard error (0.038) (0.038)*** (0.032)*** (0.031)** Comparison mean 0.129 0.109 0.088 0.164 p-value on equality of effects (5)= (6): .171 (8)= (9): .619Public sector wage employee (2017) Treatment effect 0.033 0.010 0.051 0.034 Standard error (0.020) (0.020) (0.017)*** (0.016)** Comparison mean 0.018 0.036 0.021 0.022 p-value on equality of effects (5)= (6): .422 (8)= (9): .470

Academic Major Admits Vocational Major Admits


Labor market outcome: earnings (2016)


Labor market outcome: earnings (2017)


Labor Market Outcomes: Combined Earnings (2017)


Labor Market Effects: Discussion• Vocational Admit Males have very high return

• Credit Constraints?

• Academic Females• Consistent results across years: more likely to be wage employed• Big gains in tertiary• In 2016: wide range of possible outcomes for those not in school• In 2017: start seeing positive effect (including delayed wages)

• Vocational Female: large effects in 2016 which have gone back to zero.

• Academic Males• Current data suggests poor return, even for those not in school-

Why?


Labor Market Outcomes: Discussion• Despite the fact that these youth are 26, they are still

not settled in the labor market1. Many are still intending to go back to school (tertiary); they

take effective investment in this direction (extra classes, applications)

2. They are still looking for a job, even when they don’t have one.

3. Those effects (taking steps to go to tertiary, looking for jobs) are even stronger for secondary school graduates.


Schooling preparation


Labor Market Outcomes OLS vs. IV• OLS estimates consistently lower than the IV estimates

• Machine learning does nothing to “fix” it

• Consistent with (at least) two hypotheses• Financially constrained students have higher returns to

education.• Partly captured by observables, but not much.

• The underestimate is mainly driven by non participation—possibly in turn by the inability to match the tertiary result. Those who are more likely to go to school are more likely to go to tertiary and they are thus less likely to work.


Comparing OLS, ML and IV (2016)


Comparing OLS, ML and IV (2017)


Satisfaction• Skeptics warn that gap between expectations and

reality could create dissatisfaction

• We find: • No indication of greater depression/unhappiness in general

(7 questions mental health index)• Women are more satisfied with finances

• Concentrated among vocational admits• But more dissatisfied with life in general (academic admits)• Employed men less satisfied with their job

• Satisfaction questions hard to interpret (ambition may have been affected by education)


Conclusion• Scholarships increased secondary school completion rates by 30 percentage

points• Secondary education leads to significant gains on cognitive scores• Secondary education delays fertility and marriage; enables healthier behaviors • Treatment effects for women are greater on a number of dimensions

• Learning, fertility and marriage, tertiary enrollment, wage employment

• Labor market outcomes are mixed• Many of the youth are still not settled in a job. Large fraction are not in school but not

working, or still doing on the job search• Male Vocational majors experience improved labor market outcomes• Female Academic majors increase tertiary enrollment and we start seeing positive effect

in 2017• Male Academic majors do not see significant benefits by age 26 –fell way short of sky-

high expectation at baseline• Consistent with a limited number of tertiary spots/government jobs and little cognitive gains for them.

• The youth seem to be looking for a ”real” job and are not willing to settle for less.

• Education accentuates this search: more likely to search, more likely to apply to tertiary, more likely to be dissatisfied with their current job


Additional Slides & Tables


ReferencesCesur, R., & Mocan, N. H. (2013). Does secular education impact religiosity, electoral participation and the propensity to vote for Islamic parties? Evidence from an education reform in a Muslim country. Working Paper

Becker, Gary S. (1991). “An Economic Analysis of Fertility,” Democratic and Economic Change in Developed Countries, Gary S. Becker, ed., Princeton: Princeton University Press.

Duflo, E. (2004). The medium run effects of educational expansion: Evidence from a large school construction program in Indonesia. Journal of Development Economics, 74(1), 163-197.

Goldin, Claudia (1999). “America’s graduation from high school: The evolution and spread of secondary schooling in the twentieth century.” The Journal of Economic History, 58(02), 345-374

Gulesci, Selim and Erik Meyersson (2015). “ ‘For the Love of the Republic’: Education, Secularism, and Empowerment.” Working Paper.

Hanushek, E. A., & Woessmann, L. (2012). Do better schools lead to more growth? Cognitive skills, economic outcomes, and causation. Journal of Economic Growth, 17(4), 267-321.

Heckman, J. J. (1991). Randomization and social policy evaluation.

Keats, Anthony (2014). “Women’s Schooling, Fertility, and Child Health Outcomes: Evidence from Uganda’s Free Primary Education Program.” Mimeo, Wesleyan University.

Krueger, A. B., & Malečková, J. (2003). Education, poverty and terrorism: Is there a causal connection?. The Journal of Economic Perspectives, 17(4), 119-144.

Mocan, N. H., & Cannonier, C. (2012). Empowering women through education: Evidence from Sierra Leone. NBER Working Paper No. w18016/

Osili, Una Okonkwo & Bridget Long (2008). “Does Female Schooling Reduce Fertility? Evidence from Nigeria.” Journal of Development Economics 87(1):57-75

Ozier, Owen (2016). “The Impact of Secondary Schooling: A Regression Discontinuity Analysis.” Journal of Human Resources, forthcoming

Prichett, Lant (2001). Where has all the education gone? The World Bank Review, 15(3), 367-391

Rosenzweig, Mark and T. Paul Schultz (1989). “Schooling, information, and nonmarket productivity: Contraceptive Use and its effectiveness.” International Economic Review 30: 457-477


Rate of Return of SHS for vocational majors

• Costs:• Includes add’l SHS fees paid, add’l school costs and foregone

earnings per scholarship winner from respondents’ last term, adjusted for inflation and number of terms in school

• 1.19 yrs of add’l SHS fees cost approximately GHX 140/yr in 2016 GHX• Winners paid GHX 160/yr more in add’l school costs• Foregone earnings:

• Benefits:• Scholarship winners earn add’l GHX 31.6/mo (GHX 378.76/yr)• Assume benefits persist through 30-yr working career

• Overall 16% return on SHS• 12% if assumed that winners didn’t begin working until 2016• SHS education worth GHX 4,936 (3% discount rate), up to GHX

3,200 (5% discount rate)

2008-09 2009-10 2010-11 2011-12

GHX 264 GHX 192 GHX 110 GHX 12


3. Tertiary education: ApplicationsAcademic Major Admits Vocational Major Admits

All Female Male All Female Male(4) (5) (6) (7) (8) (9)

Plans to continue to tertiary (2013)Treatment effect 0.267 0.289 0.240 0.241 0.282 0.197Standard error (0.038)*** (0.054)*** (0.053)*** (0.032)*** (0.045)*** (0.044)***Comparison mean 0.450 0.388 0.516 0.426 0.350 0.503p-value on equality of effects (5)=(6): .520 (4)=(7): .595 (8)=(9): .180

Sat for WASSCE exam (2015)Treatment effect 0.295 0.286 0.301 0.263 0.288 0.237Standard error (0.038)*** (0.053)*** (0.053)*** (0.031)*** (0.045)*** (0.043)***Comparison mean 0.449 0.418 0.482 0.423 0.352 0.494p-value on equality of effects (5)=(6): .839 (4)=(7): .516 (8)=(9): .416

Applied for tertiary education (2015)Treatment effect 0.094 0.131 0.059 0.073 0.100 0.047standard error (0.030)*** (0.043)*** (0.043) (0.025)*** (0.036)*** (0.035)Comparison mean 0.177 0.167 0.188 0.153 0.117 0.189p-value on equality of effects (5)=(6): .237 (4)=(7): .595 (8)=(9): .300

if applied: number of programs applied to (2015)Treatment effect -0.037 0.119 -0.155 -0.097 -0.108 -0.074standard error (0.169) (0.235) (0.241) (0.150) (0.232) (0.197)Comparison mean 1.681 1.457 1.896 1.653 1.556 1.712p-value on equality of effects (5)=(6): .416 (4)=(7): .792 (8)=(9): .911

Admitted to a tertiary program (2015)Treatment effect 0.024 0.055 -0.008 0.035 0.047 0.023standard error (0.023) (0.033)* (0.032) (0.019)* (0.027)* (0.027)Comparison mean 0.096 0.073 0.122 0.073 0.055 0.090p-value on equality of effects (5)=(6): .173 (4)=(7): .713 (8)=(9): .532


3. Tertiary education: Years of Enrollment

Academic Major Admits Vocational Major AdmitsAll Female Male All Female Male(4) (5) (6) (7) (8) (9)

Years spent attending tertiary education (2016)Treatment effect 0.084 0.134 0.031 0.058 0.036 0.076Standard error (0.045)* (0.064)** (0.064) (0.037) (0.054) (0.052)Comparison mean 0.192 0.147 0.241 0.112 0.100 0.124p-value on equality of effects (5)=(6): .252 (4)=(7): .652 (8)=(9): .593

Total years of education to date (2016)Treatment effect 1.367 1.421 1.290 1.167 1.200 1.113Standard error (0.152)*** (0.214)*** (0.212)*** (0.126)*** (0.179)*** (0.173)***Comparison mean 11.251 11.029 11.490 11.117 10.766 11.471p-value on equality of effects (5)=(6): .664 (4)=(7): .311 (8)=(9): .728


4. Marriage and FertilityAcademic Major Admits Vocational Major Admits

All Female Male All Female Male(4) (5) (6) (7) (8) (9)

Ever lived with partner(2016)Treatment effect -0.059 -0.093 -0.014 -0.063 -0.088 -0.031Standard error (0.033)* (0.045)** (0.044) (0.027)** (0.037)** (0.036)Comparison mean 0.231 0.314 0.141 0.247 0.369 0.126p-value on equality of effects (5)=(6): .209 (4)=(7): .932 (8)=(9): .267

Ever pregnant/had a pregnant partner (2016)Treatment effect -0.085 -0.134 -0.020 -0.072 -0.112 -0.020Standard error (0.038)** (0.050)*** (0.050) (0.031)** (0.042)*** (0.041)Comparison mean 0.393 0.529 0.245 0.404 0.621 0.185p-value on equality of effects (5)=(6): .107 (4)=(7): .799 (8)=(9): .117

Number of children ever had (2016)Treatment effect -0.142 -0.209 -0.051 -0.131 -0.249 -0.001Standard error (0.065)** (0.086)** (0.087) (0.054)** (0.072)*** (0.070)Comparison mean 0.500 0.731 0.252 0.530 0.883 0.178

p-value on equality of effects (5)=(6): .196 (4)=(7): .898 (8)=(9): .013**Had unwanted first pregnancy (full sample) (2016)

Treatment effect -0.067 -0.118 -0.004 -0.081 -0.130 -0.022Standard error (0.038)* (0.050)** (0.050) (0.032)** (0.043)*** (0.040)Comparison mean 0.371 0.523 0.211 0.369 0.595 0.153p-value on equality of effects (5)=(6): .106 (4)=(7): .768 (8)=(9): .065*

Desired fertility: # of children by age 50 (2013)Treatment effect -0.156 -0.233 -0.082 0.017 -0.015 0.048Standard error (0.083)* (0.118)** (0.117) (0.069) (0.099) (0.097)Comparison mean 3.656 3.659 3.652 3.620 3.652 3.588p-value on equality of effects (5)=(6): .362 (4)=(7): .109 (8)=(9): .648


4. Health BehaviorAcademic Major Admits Vocational Major Admits

All Female Male All Female Male

(4) (5) (6) (7) (8) (9)

Index of risky sexual behavior(safe-->risky)(2013)

Treatment effect -0.064 -0.059 -0.058 -0.066 -0.008 -0.114

Standard error (0.047) (0.066) (0.065) (0.039)* (0.056) (0.054)**

Comparison mean 0.023 0.120 -0.080 -0.013 0.082 -0.109

p-value on equality of effects (5)=(6): .991 (4)=(7): .973 (8)=(9): .175

Index of STI risk exposure (2013)

Treatment effect -0.123 -0.142 -0.094 -0.054 -0.022 -0.078

Standard error (0.047)*** (0.065)** (0.065) (0.039) (0.055) (0.054)

Comparison mean 0.044 0.142 -0.060 -0.019 0.072 -0.111


Preventative health behavior (3 questions) (2013)

Treatment effect 0.170 0.179 0.174 0.073 0.081 0.070

Standard error (0.061)*** (0.086)** (0.085)** (0.051) (0.073) (0.071)

Comparison mean 1.621 1.703 1.534 1.613 1.674 1.551



3. Tertiary education: Marginals vs. Inframarginals

• Gap more pronounced for vocational admits: • 16.2% continuation to tertiary among inframarginal

vocational admits; 5.4% among marginals• 22.4% vs. 16.4% respectively for academic admits

• No marginal/inframarginal gap for females• 17.9% continuation among infra-marginal females; 18.7%

among marginals; • 20.4% vs. 4.2% for males• Gender discrimination within the household? • Violation of assumption that no effect on propensity that

female infra-marginal SHS graduates go on to tertiary? (maybe scholarship increases effort, reduces interruption of school?)


School bill


3. Tertiary education: Plans vs success

DEC Lecture Series, World Bankpubdocs.worldbank.org/en/194861518548502794/DEC-Lecture... · Duflo,...

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