The Impact of Free Secondary Education: Experimental Evidence from … · The Impact of Free...

Duflo, Dupas, Kremer Returns to Secondary Education: Ghana March 2017

The Impact of Free Secondary Education:Experimental Evidence from Ghana

Ghana Education Evidence Summit

March, 2017

Esther Duflo Pascaline Dupas Michael Kremer

MIT Stanford Harvard


Introduction

• With growth of primary education, increased calls for free secondary education, internationally (it is one of the SDG) and in Ghana

• Part of the campaign platform of Ghana’s new government

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

• Others less optimistic • Secondary education is expensive

• Will students learn?

• Will they get jobs?


This Study

• 2,064 students admitted to particular secondary school and major, but did not enroll in term 1 (mostly due to lack of fund)

• 682 received offer of 4-year scholarships to attend day secondary school

• Examine impact of free secondary education holding admission criteria constant. Relevant policy.


II. Background & Study Design


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


This Study

• 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

• Scholarship covers the full tuition and fees for a day student for 4 years• Average Cost = GHX 1920 (~US$480)


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)• 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 15% stragglers) (8-year impacts)• Average age at time of survey: 25• 97% survey rate • Focus on this data for labor market outcomes; questions are

better


III. Impact on Educational Attainment and Learning


1. Impacts on Educational Attainment

•Scholarship increased educational attainment –although close to half of the students who did not win the lottery eventually enrolled

•Winners of the lottery were 30 percentage points (50%) more likely to enroll in SHS

• Spent 1.26 more years in secondary education than non-winners

•Effects similar across genders, majors, cells


0%

20%

40%

60%

80%

100%

Sh

are

enr

olle

d in

SH

S

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

1. Impacts on SHS Enrollment


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 (3.09 – 1.83 = 1.26)

• Free secondary education would pay for 6-15 years of schooling per each additional year • Lower bound assumption: leads 25% of students not passing

JHS exam to pass • Upper bound assumption : No effect on JHS pass rate

• Estimated cost of scholarship: $7,600 per additional graduate


2. Learning Outcomes Total Standardized Score

0

-0.175

0.183

0.151

0.021

0.28

-0.2

-0.1

0

0.1

0.2

0.3

0.4

All*** Female*** Male

Combined

Stan

dar

diz

ed

sco

re o

n c

ogn

itiv

e t

est

s (2

01

3)

Control

Treatment


By category of high school

0.19

0.49

0.09

0.24

-0.22

-0.05

-.4

-.2

0.2

.4.6

Sta

nd

ard

ize

d S

core

on

Cog

niti

ve S

kills

Te

st

Cat A/B (n=95), pval=.163Cat C (n=1,286), pval=.01***

Cat D (n=603), pval=.066*

Control Treatment


Tertiary Education

•Gaps of multiple years between SHS and tertiary education are not uncommon in Ghana, so may not be able to observe the full long run-effect of scholarships yet on tertiary.

•As of now, only 9.1% of the control group ever enrolled in tertiary education. The scholarship increased tertiary enrollment by 3.0 percentage points (33%)

•Big increase, but still way below expectations


3. Ever enrolled in tertiary education (2016)

0.091

0.075

0.107

0.1210.124

0.118

0

0.02

0.04

0.06

0.08

0.1

0.12

0.14

All** Female** Male

Combined

Eve

r En

rolle

d in

Te

rtia

ry E

du

cati

on

(2

01

6)

Control

Treatment


3. Ever enrolled in tertiary education (2016)

0.11

0.095

0.126

0.077

0.06

0.095

0.163

0.188

0.139

0.092

0.079

0.104

0

0.02

0.04

0.06

0.08

0.1

0.12

0.14

0.16

0.18

0.2

All** Female** Male All Female Male

Academic Vocational

Eve

r En

rolle

d in

Te

rtia

ry E

du

cati

on

(2

01

6)

Control

Treatment


V. Impacts on Marriage, Fertility and Health Behavior


Marriage and Fertility Effects

•At age 25, women are 9.1 percentage points (26%) less likely to have ever lived with a partner,

•10.7 percentage points (18%) less likely to have ever been pregnant,

•11.5 percentage points (18%) less likely to have had an unwanted pregnancy , and

•Have had .217 (27%) fewer children. • The effects are seen across majors

•Effects for men are not significant


4. Marriage and Fertility

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

All*** Female*** Male All*** Female*** Male

Ever lived with partner (2016) Ever pregnant/had a pregnant partner (2016)

Control

Treatment


4. Marriage and Fertility

0.519

0.814

0.212

0.389

0.597

0.182

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

Nu

mb

er

of

child

ren

eve

r h

ad (

20

16

)

Control Treatment


4. Health Behavior

0

0.096

-0.099

-0.052

0.082

-0.183-0.2

-0.15

-0.1

-0.05

0

0.05

0.1

0.15

Ind

ex o

f ri

sky

sexu

al b

eh

avio

r (s

afe

-->r

isky

) (2

01

3)

Control Treatment


4. Health Behavior

1.624

1.691

1.555

1.74

1.815

1.669

1.4

1.45

1.5

1.55

1.6

1.65

1.7

1.75

1.8

1.85

Pre

ven

tati

ve h

eal

th b

eh

avir

o in

dex

(2

01

3)

Control Treatment


VI. 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 2013• 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—whole sample

• Bleak labor market: only 44% of women and 68% of men in control group had positive earnings in last month. • Recall negative labor market shocks during study period.

• 2.5 p.p. more students in treatment group in formal study/training • Mostly enrolled in tertiary, but also includes a few students still

enrolled in secondary education• No difference among vocational admits• 6.8 p.p for academic women • 3.6 p.p for academic men.

•Despite this in the whole sample, scholarship winners have better labor market outcomes (on extensive margin)


Key Effects: Full Sample

•5.5 percentage points (~10%) more likely to have any earnings in the past month

•Winning a scholarship increased inverse hyperbolic sine of Ghana cedi earnings by .308 (driven by fewer zeros)

•No evidence of intensive margin gains on log earnings if positive, hours conditional on work, earnings per hour


5. Labor Market Outcomes: Earnings

3.214

2.413

4.054

3.522

2.796

4.231

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

Inve

rse

hyp

erb

olic

sin

e e

arn

ings

(2

01

6)

Control Treatment



5.066

4.792

5.251

5.047

4.841

5.187

4.5

4.6

4.7

4.8

4.9

5

5.1

5.2

5.3

Log

ear

nin

gs la

st m

on

th if

po

siti

ve (

20

16

)

Control Treatment



0.556

0.441

0.679

0.611

0.504

0.718

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

Po

siti

ve e

arn

ings

(2

01

6)

Control Treatment


5. Vocational Admits : Earnings

•For vocational admits, interpretation straightforward because no effect on formal study/training

•Scholarships increase inverse hyperbolic sine earnings by .505

• Increase absolute earnings by 25.9 GHX (19% )

•Scholarship recipients 8.8 percent points (16%) more likely to have any earnings (on a base of 56%)

•Cannot reject similar effects by gender


5. Vocational Admits: Earnings

3.263

2.484

4.059

3.768

2.982

4.541

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

5

Inve

rse

hyp

erb

olic

sin

e e

arn

ings

(2

01

6)

Control Treatment



0.564

0.452

0.6790.652

0.539

0.764

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

Po

siti

ve e

arn

ings

(2

01

6)

Control Treatment



5.074

4.812

5.25

5.08

4.824

5.255

4.5

4.6

4.7

4.8

4.9

5

5.1

5.2

5.3

Log

ear

nin

gs la

st m

on

th if

po

siti

ve (

20

16

)

Control Treatment


Labor Market Outcomes: Academic Admits

• Too early to draw strong conclusions: some effect on tertiary education

• However, well-defined outcomes at date of survey:• “Positive earning or in school” +4 pp whole sample

(insignificant); +8.7pp for female (significant) -2% for males (insignificant)

• Result to date suggest effects on earnings, hours, and participation are all small and insignificantly different from zero, and significantly smaller than for vocational admits.


5. Earnings (Academic Majors, Full Sample)

3.143

2.313

4.047

3.162

2.526

3.778

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

Inve

rse

hyp

erb

olic

sin

e e

arn

ings

(2

01

6)

Control Treatment


5. Earnings (Academic Majors, Full Sample)

0.545

0.424

0.678

0.552

0.452

0.65

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

Po

siti

ve e

arn

ings

(2

01

6)

Control Treatment


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

• Labor market outcomes depend on SHS major • Vocational majors experience improved labor market

outcomes• Female Academic majors increase tertiary enrollment• Male Academic majors do not see significant benefits by age

25 –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.


Conclusion

• Treatment effects for women are greater on a number of dimensions • Learning, tertiary enrollment, fertility and marriage, labor

market outcomes

• Results may change over time: • Those in tertiary school will graduate and enter labor market

(for example those who went to teacher college and got a diploma got their posting this fall)

• Employment rates will likely increase in rest of sample

• Possible effect on children of secondary school graduates

• Plan to continue tracking the population for as long as possible.

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