Do Solar Lamps Help Children Study?
Contrary Evidence from a Pilot Study in Uganda
Chishio Furukawa†
First Draft: July 2012
This Draft: January 2013
Abstract
Over half a billion children lack adequate lighting and rely on dim,
smoky, and dangerous kerosene-based lighting for their evening studies.
This paper examines if the brighter, clean, safe, and zero-marginal-cost
light of solar lamps enhances children’s learning outcomes. In a randomized field experiment, unexpectedly, solar lamps lowered test scores
by 5 points. Given that solar lamps increased reported study time by
approximately 30 minutes, this may be due to flickering from lack of full
charge that lowered their productivity. The nationwide learning assessment survey suggests that solar lamps likely have insignificant effect on
educational attainment.
JEL Classifications: I25, O14, Q55
*Special thanks to Andrew Foster for his guidance in economic analysis. I also thank Sriniketh Nagavarapu
and David Weil for inspiring this study, and Daniel Prinz for detailed feedback. Hisashi Noma at the Institute
for Statistical Mathematics advised statistical analysis. Uwezo Uganda generously shared their data from
Learning Assessment Surveys. The Brown International Scholars Program 2011-12; and the Barbara Anton
Internship Grant from the Pembroke Center for Teaching and Research on Women; and the Center of
Environmental Studies at Brown University provided funding for this research. I am indebted to Ruth
Nanteza, Abdulrazaq Nassir, John Ssebayigga, Miho Shinke, and Abdulwahid Ngobya, for their assistance
in the fieldwork in Kyannamukaaka, and above all, to the office of Barefoot Power Uganda Ltd., Dirk Kam,
Benard Kalyango, Frank Yiga, Joyce Demucci, Annet Nalumansi, and Francis Ejuku, who provided guidance
and supplied the solar lamps. Without their support and co-operation from students, parents, and teachers
of the Kyannamukaaka community, this research could not have been completed. All errors are mine.
† Brown
University Class of 2012.5, Sc.B. Applied Mathematics-Economics and A.B. Environmental Studies;
Email: [email protected]
1
1
Introduction
Lack of adequate lighting critically undermines children’s ability to study in
the evening and consequently inhibits their performance in the classrooms. A
large fraction of studies on educational policy interventions has focused on improving school environments and student attendance, but not on the household
study environment. Previous research has examined, among other things, the
impact of merit scholarship (Kremer et al. 2009) [1], textbooks (Glewwe et al.
2009) [2], teacher attendance (Glewwe et al. 2003) [3], midday meals (Vermeersch and Kremer 2004, McEwan 2010) [4, 5] deworming (Kremer and Miguel
2001), eyeglasses provision (Glewwe et al. 2006) [6], and information on the
benefits of schooling (Jensen 2010) [7]. This paper examines the educational
benefits of introducing solar panel-charged LED lamps for non-electrified rural
households.
The Lighting Africa program of the World Bank and the International
Finance Cooperation as well as various solar light enterprises advocate for
the provision of solar lamps. They believe that the bright and clean light
can enhance learning of children who use kerosene-based lighting for their
homework. Today, approximately 800 million children1 lack access to reliable
lighting and rely on dim kerosene candles as their main source of lighting,
which inhibits their study through dimness, indoor air pollution, fire risks,
and high marginal cost of usage that makes parents unwilling to provide ample
kerosene. An abundance of qualitative evidence suggests that solar lamps can
be a potential solution to improving the poor household study environments
prevalent in Africa and South Asia where electricity is scarce and unreliable.
Using experimental and observational evidence from Uganda, however, this
paper finds no support for this hypothesis. A pilot randomized field experiment with 204 participants (including siblings) conducted over a period of
1
International Energy Agency [8] reports that there are approximately 2 billion people
who do not have access to reliable energy. Given the population composition in developing
countries where those with age under 15 has about 40 percent of the entire population, a
back-of-the-envelop would give 800 million. Since not all the children go to schools, the
number of children who actually study in the evening would be smaller than 800 million.
2
5 months with the most affordable model of solar lamp finds not only no
evidence for improvement but also modest evidence for solar lamps lowering
children’s test scores. Point estimates are approximately -5 for both English
and mathematics and -16 when specified to students in the top quantile at
the baseline. In this way, the magnitude is much larger for girls, consistent
with results from other studies that girls’ learning achievements depend on
their study and efforts more than boys’, and for the students with originally
high performance, consistent with the explanation that solar lamps affected
those who study hard. An even more perplexing result is that children with
solar lamps report an average study time of 30 minutes longer per day than
those left with kerosene – while the estimate is much lower than theirs, also
consistent with previous field studies reporting longer study time for children
who received solar lightings. There are three possible explanations for these
contradictory results: (i) significant reporting bias of study time as children
do not have a watch or clock at home; (ii) decrease in productivity of study
due to the flickering of light when the recharge is done inadequately and the
batteries are low; and (iii) additional factors, such as intra-household dynamics that inhibit children from using the lamp that may have led to decrease in
test scores. The nationally representative Uwezo Uganda Learning Assessment
Survey also suggests that children with solar lamps improved their literacy and
numeracy only at similar speed as those with kerosene, failing to support the
notion that solar lamps can help children study.
Nevertheless, these results are far from implying that solar lamps cannot
help in an ideal condition. This study has a number of limitations, including
relatively small sample size and short observation time. Furthermore, the solar
lamps used in this experiment have already been phased out and replaced by
those with brighter LED bulbs and batteries of higher quality. These results,
however, do imply three important lessons: first, merely providing solar lamps
without thorough training of users for recharge and maintenance is highly
likely to be ineffective; second, market distribution has likely been contributing to positive selection where only those who are careful and can learn how
to use solar lamps actually adopt the technology, implying that scaling up
3
may eventually face a limit; and third, field experiments, in addition to wellcontrolled laboratory experiments, and measurement of actual outputs (test
scores), in addition to inputs (study time), are necessary in order to make
accurate prediction of robustness and effectiveness of new technologies.
This paper proceeds as follows: section 2 will discuss the background and
review previous literatures; section 3 will explain the details of randomized
evaluation and the learning assessment data; section 4 will present the results from the randomized evaluationand the learning assessment survey; and
section 5 will include some concluding remarks.
2
2.1
Lighting and Learning
Lighting Sources in Rural Uganda
According to the Uwezo Uganda Learning Assessment Survey, over 70 percent
of Ugandan households use simple wick kerosene candles, which are the least
costly source of lighting in the short-run, but provide only around one lumen
of light. In contrast, 2 percent use solar lighting, which requires relatively high
capital investment, but provide brighter light of 15 lumens for some options.
Electricity users remain as few as 5 percent, and many of them do not have
access to a reliable source. (Appendix Table 1)
There are three important pathways through which the use of kerosene
candles can limit students’ study at home. First, the dimness of light can
make children inefficient and less focused on their work. Second, serious air
pollution emitted from kerosene candles harms children’s visionary and air
quality-related health. Fire risks posed by kerosene candles discourage children
from continuing to study. In a related study (Furukawa 2012) [9], I find that
the health and safety consequences of using kerosene candles are substantial.
Third, the high marginal cost of burning kerosene makes parents unwilling to
provide enough kerosene for children to continue their studies. In a survey of
children, “no more kerosene left” (45.74 percent) and “parents told to stop”
(21.28 percent) were the second and the third most common reasons for going
4
to bed without completing their homework, after the top reason, being “too
tired” (46.81 percent)2 .
Until today, there have been few studies that have examined the impact of
solar lamp provision on the hours of study and found positive impacts. Given
these results, the World Bank Lighting Africa program suggests “facilities can
extend to other potential grants/funds aimed at supporting the health, education, etc. impact of solar portable lights.” Agoramoorthy and Hsu (2009)
report that by using a 5-7 W panel that costs 87.5USD they were able to
increase the average number of study hours per night from 1.47 to 2.71. [10]
Although they claim that “this increase in study hours has had a positive influence on students’ performance at school,” they do not present any evidence
of such an improvement. This study furthers these previous studies, first, by
using 1W3 panels costing about 15USD, which is more affordable and scalable,
and second, by collecting the students’ test scores.
2.2
What Improves Learning Outcomes
What are the most effective ways to improve children’s learning outcomes?
This is the central question that a number of education economists have been
asking in the past decade, both in the context of developing and developed
countries. There are three important pieces of evidence especially relevant to
analyzing the impact of solar lamps on children’s educational outcomes:
First, there is some evidence confirming the causal impact of increased
study time on school achievement. The correlation between the two is not
entirely driven by omitting variables such as attitude and ability, but there
is likely a significant causality. Using the probability of being paired with a
roommate with video games as an instrument, Stinebrickner and Stinebrickner
(2007) estimate a large impact of study time and efforts on students’ achievement. [11] Although this paper is concerned with upper primary school stuThis survey asked up to three reasons that they stopped studying without completing
homework.
3
The study only used 0.5W panels in the first month. However, given that rainy season
could be a serious concern for solar lamps’ function, the researchers provided 1W panel later
on.
2
5
dents, it is likely that students’ study plays a significant role in determining
their school learning outcomes.
Second, girls’ achievement is generally more dependent on their study environments and incentives than boys’. In a certification reward randomized
trial in Israel, Angrist and Lavy (2009) find substantial impact for girls, but
not for boys. [12] This result is consistent with the finding of Anderson (2008)
that an early childhood intervention had a substantial impact for girls in both
short- and long-term, but no effect for boys for long-term. [13] By comparing
the magnitude of effect for girls and boys, this paper aims to further examine
if the effect of introducing solar lamps reflects such gender differences.
Third, providing higher incentives is generally more effective than providing
better study environments in improving children’s learning outcomes. Some
of the most successful programs in improving children’s learning outcomes
are merit scholarships (Kremer et al. 2004) [1] and financial incentives for
inputs (Fryer 2010) [14]. On the other hand, some other programs such as
providing textbooks or midday meals showed positive impact on achievement
only among the subgroup that were more capable of harnessing such resources;
only the top quarter improved their test scores from provision of textbooks,
and only schools with experienced teachers improved their test scores from
midday meals (Vermeersch and Kremer 2004). [4] At the same time, McEwan
(2010) found no evidence of improvement in test scores for midday meals
provision using a regression discontinuity approach. [5] Deworming program in
Kenya substantially increased children’s school participation, but had a limited
impact on their achievements(Miguel and Kremer 2001). [15] Considering
these pieces of evidence, ex ante, it can be expected that provision of solar
lamp may actually have limited impact on children’s achievement.
6
3
Data
This paper combines experimental and observational data from Uganda. The
experimental data is from a 5-month pilot randomized field experiment by
Barefoot Power Uganda Ltd 4 in seven primary schools in Kyannamukaaka,
rural Uganda. The observational data is from the Uwezo Uganda Learning
Assessment Survey 2011.
3.1
Randomized Evaluation in rural Uganda, 2011
This pilot randomized evaluation was designed to assess if the most affordable
solar lamps can enhance children’s study efforts and increase their test scores
in relatively high maintenance services5 . Although it would have been ideal to
use a brighter lighting source in order to test the maximum possible benefit
of introducing the technology as a pilot study, the $10 solar lamp was chosen
because it is more scalable and suitable given the funding for this study.
3.1.1
Participants
The research team recruited 155 upper primary school students between grades
5 and 7. Since this trial also assessed the impact on children’s health, it targeted those who reported some respiratory symptoms, like coughing, chest
pain, and difficulty breathing during the baseline survey. As a pilot experiment, we were not able to conduct any power calculations that would reach
statistical significance. However, to increase the sample size and draw implications for the intra-household dynamics of solar lamp usage, the research
team also collected test scores from siblings of students reported (29 additional
observations).
Barefoot Power Uganda Ltd. is a solar lamp enterprise that designs and distributes
solar lighting in Uganda. Its major products include the Firefly series, which has various
degrees of lighting, and the larger-scale PowaPack series. The company received Lighting
Africa 2010 Outstanding Product Award. (http://barefootpoweruganda.com/)
5
During the resurveys, the surveyors asked if the lamps were functioning well. If the
student said no, they replaced the students’ lamps with the properly functioning lamps.
4
7
The additional inclusion criteria included day school students, the usage
of kerosene candles as a main lighting source, parents’ participation in the
baseline survey, absence of elder siblings in the study group to avoid withinhousehold spillover effect. (Appendix Figure 1) There was no previous study,
thus, power calculation could not be conducted.
3.1.2
Interventions
This research used Firefly 56 , which has 5 LED bulbs that collectively provide 15 lumens. Through a public lottery of a coin toss at each school, the
team distributed the lamps to half of the participants (73). One of the research
team members was officially trained by the Barefoot Power Uganda Microfranchise Training Program and learned how to teach the users how to charge the
lamps. The research team provided training regarding how to use the lamps;
demonstrating to put it on the roof or on the ground so that sunrays can hit
perpendicularly and conveying to be careful of relatively vulnerable parts such
as the button and the neck. Realizing that a 0.5W panel may not be sufficient
for adequate recharge during the rainy season, the research team supplied 1W
panels a month after the intervention. For ethical consideration, students in
the control group were also given solar lamps in December upon the conclusion
of the study. Since reselling was a potential threat that increases dropouts, the
participating schools kept the panels at school for the first two weeks so that
children would have to bring back the lamps every morning for recharge. As
panels and lamps were given separately, reselling did not become a large concern. To further encourage compliance, the research team replaced the lamps
with any faultiness every month after the intervention. About 20 lamps were
replaced in the first month, but after introducing the 1W panels, this came to
no more than 10 lamps.
6
Firefly 5 is the cheapest option at only $10.
8
3.1.3
Assessments
This research aimed to assess children’s effort level, proxied by reported study
time, and their actual school achievement, proxied by test scores. Students’
test scores were reported by school teachers, and their siblings’ test scores were
reported by the students themselves, as some siblings were in different schools.
There were four periods of test scores reported: the 1st semester’s final exam
as baseline, the 2nd semester’s final that was 2 months after the intervention,
the 3rd semester’s mid-term, which only some schools had, and the end-ofacademic year final that grade 7 pupils took at the national level, and grade 5
and grade 7 pupils took at each of their schools. Since the nation-wide exam
was marked on a scale of 10, it was multiplied by 10 to make the range of test
scores consistent.
3.1.4
Limitations: Threats to Internal Validity
There are five major threats to the internal validity of this research: validity
of randomization, lack of full recharge, sensitization, differential dropouts, and
income effects. These factors altogether may have underestimated the efficacy
and benefits in the ideal situation of solar lamp provision.
1. Validity of Randomization: Table 17 checks the extent to which
randomization was valid with respect to various underlying household characteristics. It confirms that the treatment and control group have jointly
insignificant differences (F-statistic=0.91) from one another. A few variables
that show statistically significant differences are uniform ownership, cooking
time, and number of children under 18 living together. However, these do not
show any systematic biases.
2. Lack of Full Recharge: When the solar lamps could not be charged
fully, they shed light only for short periods of time (47 percent report a shortage of light) and experience flickering problems (45 percent report flickering
of light). In these times, some children in the treatment group decided to
use kerosene candles again. There was a rainy season between October and
7
This table is exactly the same as a related paper (Furukawa 2012).
9
December, which may have further inhibited the recharge process.
3. Attenuation (Sensitization among kerosene candle users): After
hearing that kerosene candles were not good for health, some households in the
control group purchased electric torches or kerosene lanterns, which provide
better lighting. There were 24 students who reported that they purchased
torches. This leads to underestimation of positive results and overestimation
of negative results.
4. Attrition: There were a few cases of dropouts from the study. One student in the control group had her best friend who was in the treatment group,
and thus, they studied together using solar lighting. One student had his lamp
stolen. Nevertheless, they were included as the Intention-to-Treat estimation
would be more appropriate than the Treatment-on-Treated estimation.
5. Siblings’ Selection Bias: Since siblings’ test scores depended on
students’ ability to recall their siblings’ test scores, there is a significant bias
where siblings’ average test scores were 34.58 point higher than the students
themselves. (t=20.7) This difference may be due to reporting or recalling bias.
Nevertheless, inclusion or exclusion of the siblings’ test scores did not alter the
regression results.
3.2
Uwezo Uganda Learning Assessment Survey 2011
Uwezo Uganda conducts nationwide learning assessment surveys in literacy
and numeracy every year since 2009, in order to evaluate the extent to which
children are actually learning. They designed primary school level Mathematics and English tests, and collected data from approximately 48,000 households
in 2011. The survey contains a wealth index, which includes asset information
and basic living conditions, and most importantly, lighting sources.
One limitation is that the survey does not ask the type of solar lamps each
household uses. Therefore, it is difficult to examine if the solar lamps they
use were the most affordable type or rather the expensive type that has larger
panels and brighter light than the most affordable options.
10
4
Results
4.1
4.1.1
Randomized Evaluation
Test Scores
Table 2 presents the summary regression results with respect to each time
period as in Equation (1). The control variables are age, gender, class, sibling
dummy, and imputed log of household expenditure8 .
T estScoreit = α0 + α1 T RTit + α2 Xit + εit
(1)
Since the trial was randomized, the test score before the solar lamp distribution is not statistically significantly different. Nevertheless, it is important
to note that there was a large difference of (-3.7) when the control variables
were added. This raises a concern of failure to randomize; the treatment was
weakly correlated with the underlying characteristics so that the point estimate also changed. This is due to a small sample size. However, for average
test scores both with and without controls, the solar lamp group performs
lower and lower over time, making the difference in test scores of final exam
for 3rd semester statistically significant at 90 percent and at 95 percent with
control variables.
The magnitude of α1 is -6.5 on a scale of 100, which should be considered
practically significant. The magnitude of impact is larger for girls, which is
consistent with the previous findings that girls’ test scores depend on their
study more than boys’. For girls’ scores of mathematics, the point estimate
was -9.7 points, significant at the 95% confidence level. The results are not
driven entirely by Mathematics or entirely by English. This is also consistent
with a questionnaire that children find lack of lighting as a contributing factor
inhibiting their study for both subjects. Given that the difference becomes
larger over time, regressions with a difference-in-difference specification as in
Using the data from Uganda National Household Survey 2005/06, the income level was
computed as follows:
log(household expenditure) = 0.5679545×TVradio + 1.106305×bicycle +
0.3776105×motorcycle + 0.4172422×phone + 11.45439
8
11
Equation (2) can identify the statistical significance of the trend:
T estScoreit = β0 + β1 T RTi + β2 T RTi ∗ timet + β3 Xi + ft + εit
(2)
Here, the time fixed effect is appropriate because the test scores depend on
overall difficulty of tests that vary from time to time. The coefficient of interest
is β2 . Control variables (Xi ) are included in order to reduce the standard error.
They include age, class, gender, imputed log household expenditure,9 and a
sibling dummy. The time variable is omitted due to multicollinearity. Missing
values are dummied out.
Table 3 shows that there are both statistically significant and insignificant
(but consistently negative) coefficients depending on the exact specification.
With all specifications, the β2 remains relatively constant. The p-values from
the Hausman test were 0.003 for time fixed effect in Mathematics and the average, indicating that the estimation from time random effect is inconsistent.
Therefore, I report the specifications that give consistent estimators, excluding time random effect. The β2 is about -0.9 to -1.1, which means that, on
average, the test scores declined by about 1 point as the students with solar
lamps took the next test. After the study period, this accumulated to about
5 points in decline. The standard deviation changes with various specifications: random effect and clustering at the individual levels have coefficients
9
Since it was impossible to directly ask the wealth level, the questionnaire included some
asset information that can help approximate the expenditure. Using the Uganda National
Household Survey, I constructed the income variable with the following formula:
Log(household expenditure)=-0.196×bednets+0.172×radioorTV+0.632×mobilephone
+0.466×bicycle+0.0584×averagenumberofmeals+0.744×othertypesofstoves
+1.456×improvedfirewoodstove+0.293×improvedcharcoalstove+0.39×traditionalmetalstove
+0.611×gravityflowscheme+0.36×river,stream,lake,pond+0.15×protectedwell/spring
+0.142×borehole-0.845×privatelyconnectedpipeline+0.568×cement1.148×earthandcowdung
+0.215×othertypesofwall-0.156×burntbrickswithcement+0.0973×burntbrickswithmud
+0.346×un-burntbricks-0.341×mudandpoles+-0.72×thatchandstraw+0.208×ironsheetroof
-0.0355×asbestos+1.188×tiles-0.0606×othertypesofroof-0.0115×everychildhasblanket+11.64
Here, I choose log expenditure because it is (i) less subject to seasonal variation compared
to income and (ii) log makes the distribution normal, which is less subject to extreme values
than log normal.
12
significant at 90 percent. The sub-group analysis shows that boys experienced
minimal effect whereas girls lost points considerably: β2 for girls is about -1.4.
This variation is similar to the other studies discussed in the introduction.
In addition, Mathematics has β1 of around -0.8 and English has β2 of about
-1.0. These are not significantly different from one another, but just by cutting
the threshold value, English has a few specifications that make its β2 , at 90
percent, significant. Given that many students reported that they feel a lack
of lighting would affect their mathematics more, this result is not consistent.
However, some may say that reading would require more bright light than
thinking about mathematics problems.
4.1.2
Reported Study Time
Another outcome of interest was study time: previously, it was reported that
solar lamps can allow children to study for a longer time because of brighter
light, lack of health and fire risks, and lack of concern for kerosene running
out.
Table 3 confirms this result: students with solar lamps, on average, reported 0.43 hours longer study time (p<0.01). This holds with school fixed
effect. Furthermore, this increase was more significant for girls (0.52 hours
with p=0.008) than for boys (0.34 hours with p=0.102), and more significant
for the participants themselves than for their siblings.
4.1.3
Sub-group Analysis for Top Quintile Students
A study on the impact of textbooks (Glewwe et al. 2007) conducts a sub-group
analysis for top quintile students and finds significant impact among them.[2]
They argue that this is because those who can study from English textbooks
can benefit from textbook provision. They do not find such impact among
others. In a similar way, it is helpful to check if the top students actually
performed especially lower compared to other students.
The summary statistics suggests that 52.5 point is the approximate quantile
cut-off point. Table 3 presents the result of regression above and below the
13
52.5 points in the baseline. This analysis finds that the top quintile group has
a coefficient of -16.1 points while the others have coefficient of -0.9. To confirm,
at the baseline, there was no difference between the two groups. This result is
consistent with the explanation that solar lamps affect children’s performance
through their study – the large drop in test scores was driven by the top
students who study, not by students who do not study. It is also possible that,
depending on how the tests are designed and curved, it may be easier for top
students to lose the points.
4.1.4
Discussion
It is perplexing that while reported study time increased, children felt encouraged to study, and children’s health moderately improved, their test scores
went down. A few potential explanations are:
a. Reporting Bias in Study Time
Although test scores are not subject to reporting bias because they are
given from the school teachers, study time may be subject to reporting bias.
Those who received solar lamps may have felt that they were studying more
or were motivated to report longer study times out of a sense of gratitude. In
fact, many households did not have a watch or a clock at home, thus, it was
difficult for the students to know the precise length of their study.
b. Decrease in Study Productivity
If the study time indeed increased but the test scores decreased, then it
must be the case that their study productivity dropped substantially. What
could explain such a drop in productivity?
• One significant concern of solar lighting is inappropriate recharge, which
leads to the flickering of light or a shortage of lighting time. To recharge
the battery fully, the panels must be exposed to the sun throughout the
day at a right angle. Although the researchers informed the students how
to recharge, this was particularly difficult for households to implement.
Consequently, 47 percent of those who received solar lamps reported
that they experienced flickering and 48 percent reported some shortage
14
of lighting. When the solar lamps stopped working, 34.7 percent said
they gave up on their work and 48.6 percent said they switched back
to kerosene candles. Nevertheless, if the households did not prepare the
kerosene, then the children would be forced to stop. When the solar
lamps start flickering, 64.6 percent said that they kept studying even
under such light, 6.25 percent said that they stopped studying, and 29.17
percent said that they switched to another lighting source.
• Another possible explanation is that the children actually did not get to
use the solar lamps due to their parents’ or other siblings’ usage. Since
children generally have weak negotiation power within the households,
other members who want to use the solar panel may have used them for
themselves.
It is also important to note that 96 percent of the students who participated in
this study and received the solar lamps reported that their quality of study improved after receiving the lamps. This critical contradiction between the qualitative and quantitative evidence suggests that the test score results are either
largely subject to the error or that the direct question from the researchers
regarding the benefit of intervention is often unreliable due to reporting biases
out of the participants’ sense of gratitude.
4.1.5
Empirical Tests for Hypotheses
Which of these explanations match the data? The first hypothesis of reporting bias is difficult to test, but is likely to be true given that 48 percent of
those who received solar lamps said that they experienced some shortages of
lighting. Although positive, the correlation between study time and test score
is not statistically significant. (Appendix Table5) The second hypothesis may
contribute to the decline in test scores because 47 percent report that they
experienced flickering, but it may still not be the driving force. The third hypothesis is unlikely to be very significant because children seem to have been
using the solar lamps for themselves. In a follow-up questionnaire, 77 percent
15
of the students reported that they had no conflict using the solar lamps with
other siblings and only 19 percent said they did.
All in all, none of these three explanations seem to match the data perfectly:
perhaps it is a combination of these factors and some noise due to the relatively
small sample size that caused these results.
Lastly, I would like to note the possibility that this study may have underestimated the negative impact of solar lamps on education due to the experimental effect: evidence suggests that those who received solar lamps felt
encouraged to study10 whereas 61 percent (34/55) of those who were left with
kerosene felt discouraged to study.11 Overall, the direction of bias is ambiguous
because attenuation could have led to an overestimation of negative results,
but encouragement effect could have led to their underestimation.
4.2
Learning Assessment Survey
It is helpful to check if the results of the randomized trial are consistent with
the observational data.
4.2.1
Endogenous Regression
Table 4 presents the endogenous regression results of test scores on the choice
of lighting sources as in Equation (3):
T estScoreij = γ0 + γ1 Lightij + γ2 Xij + εij
(3)
Compared to kerosene candle group, electricity, kerosene lantern, and solar
lamp group perform significantly higher, even after controlling for underlying
features. At the same time, the magnitude of coefficient drops as more and
more control variables are added. this is only an endogenous regression with
significant omitted variable bias, such as preference for education and access
Among 55 students with solar who were surveyed, all of them answered that they were
“very encouraged” to study when asked “Do you feel encouraged to study?”
11
In contrast, 17 percent (7/41) of those who received the solar lamps reported that they
felt discouraged to study.
10
16
to the market; no causation can be claimed from this analysis. Nevertheless,
the observation that kerosene lanterns’ coefficients are generally and slightly
higher than the solars’ was unexpected. Since the exact types of solar lamps
and kerosene lanterns are not reported, it is impossible to make a comparison
between the two in a precise way.
4.2.2
Cross Sectional Endogenous Analysis
One way to check if this difference in learning outcome comes from the pathway
of interest – differential productivity due to provision of brighter, safer, and
healthier light with zero marginal cost – is to see whether the improvement
in test scores is faster for those with solar lamps as compared to those with
kerosene candles. Figure 2 presents the change in average test scores over age.
While this largely depends on test design, if everything is held constant and
solar lamps do help children study, then there should be a steeper increase in
test scores over age.
Nevertheless, this is not what is observed. The difference in learning attainment is observed just from age 6 (Primary 1) and the difference remains more
or less unchanged until age 10. Note that the questions were set at the level
of primary 2. Therefore, beyond age 9 or 10, the improvement in children’s
learning outcome is no longer reflected in an increase in test scores. In this
way, focusing on the age range of 6 to 9 or 10 would likely give more accurate
analysis. It is likely that solar lamps is no better improvement than kerosene
lanterns, and even compared to kerosene candles, the impact is practically
insignicant.
Here, the primary concern is that a significant selection bias that may
have led to the overestimation of the effect: choice of better lighting is most
likely positively correlated with the ability to learn faster. This is analogous to
other studis that found positive impact with observational data, but no impact
with experimental ones. (Kremer 2003) [16] Even with this omitted variable
that would give positive selection bias, there does not seem to be any positive
impact. Therefore, it is likely that the effect of solar lamps is insignificant.
17
4.2.3
Discussion on External Validity of the Randmoized Evaluation
A valid instrument for the adoption of solar lamps is, in fact, very difficult to
find. Possible candidates are geographic variation of (i) rain and (ii) insolation. Rainfall fails to satisfy the instrument validity because it affects solar
lamp adoption in two main ways: it discourages solar lamp adoption because
panels can become spoiled after continuous exposure to the rain, and it also
encourages adoption because it produces higher seasonal income, which allows
households to purchase them. Moreover, these factors contribute to solar lamp
adoption and function in the long-run, which no longer have a significant idiosyncratic variation like they do in the short-term, and thus, be correlated
with the underlying factors such as agricultural productivity, income, and socioeconomic well-being. Insolation, intuitively, may seem to affect children’s
learning outcome only through the adoption and functionality of solar lamps.
However, again, insolation is correlated with rainfall, making the exogeniety
condition unsatisfied.
Does this result indicate that the distribution of solar lamps actually hinders children’s study? The probable answer is no, because households typically
stopped using solar lamps after observing that they did not improve the quality of lighting. The experimental effect of encouraging these children to keep
using the solar lamp may have played a significant role in making them study
even under the flickering light of solar lamps.
Researchers conducted a resurvey in June 2012, six months after the study
was complete. Many students changed schools, and response rate was 47 percent. Among them, only 33 percent kept using the solar lamps and over 60
percent returned to using kerosene candles. After active checking of usage
stopped and maintenance service became unavailable, many children were unable to keep using the solar lamps. Nevertheless, 56 percent said that the
household was planning to purchase another solar lamp, 80 percent said they
would be willing to pay for repair, and 84 percent said that the lamps met
their expectation. Therefore, despite the limitations that the solar lamps may
have had, households still seem to be willing to invest in the new technology.
18
Furthermore, there is a sign that the users are able to learn how to recharge the
lamps appropriately over time: all of those who were resurveyed said they put
theri panels either on the roof or on the ground while there used to be those
who hang them on the wall (Figure 4 in Appendix), which is an inefficient way
to recharge the batteries.
5
Conclusion
Solar lamps are seen as some of the most robust alternative energy sources for
bringing modern lighting to developing countries and thereby improving the
educational environment, health, and safety of rural households. However, this
study shows that benefits of solar lamps are mixed: while health and safety
show evidence of improvement, children’s learning outcomes show no such
evidence; in fact, those who received the solar lamps performed, on average,
5 points lower compared to those who kept using kerosene candles after five
months. The exact reason for this unexpected result remains unclear; it may
be an error due to relatively small sample size (n=204), shortage or flickering
of light that made the lamps provide unstable light due to lack of full recharge,
or intra-household mechanisms that prevented children from using the lamps.
Currently, the World Bank Lighting Africa program rewards Outstanding
Product Award to the lamps that were identified as reliable and user-friendly.
Although the lamps used in this project came from the same series of solar
lamps that received the award, they still experienced signicant challenges in
the field. This study highlights the importance of long-term field evaluation of
durability of the products. Since the result largely contradicts anecdotal evidence, it is critical to examine whether the same results hold for future trials.
Although the technologies themselves may appear to provide clear benefits,
technologies combined with inappropriate usage may inhibit such benefits or,
even diminish the quality of life. Given the importance and potential of this
technology, future efforts and research should explore possible solutions such
as (i) local maintenance mechanisms that households can rely on; (ii) thorough
users training programs that reinforce appropriate usages; and (iii) reconsid19
eration of the balance between the brightness of LED bulbs and the size of
solar panels to ensure the long-term functioning.
20
References
[1] Michael Kremer, Rebecca T. Edward Miguel M. Edward Miguel: Incentives to Learn. In: The Review of Economics and Statistics 91 (2009),
S. 437–456
[2] Paul Glewwe, Michael K. ; Moulin, Sylvie: Many Children Left
Behind? Textbooks and Test Scores in Kenyal. In: American Economic
Journal: Applied Economics 1 (2009), S. 112–35)
[3] Paul Glewwe, Nauman I. ; Kremer, Michael: Teacher Incentives. In:
NBER Working Paper (2003)
[4] Vermeersch, Christel ; Kremer, Michael: School Meals, Educational
Achievement and School Competition: Evidence from a Randomized
Evaluation. In: World Bank Policy Research Working Paper (2004)
[5] McEwan, Patrick: The Impact of School Meals on Education Outcomes:
Discontinuity Evidence from Chile. (2011)
[6] Paul Glewwe, Meng Z. Albert Park P. Albert Park: The impact of
eyeglasses on the academic performance of primary school students: Evidence from a randomized trial in rural China. In: Working Paper (2006)
[7] Jensen, Robert: The (Perceived) Returns to Education and the Demand
for Schooling. In: The Quarterly Journal of Economics 125 (2010), S.
515–548
[8] International Energy Agency: Energy for all: Financing access for
the poor. (2011)
[9] Author: Health and Safety Benefits of Replacing Kerosene Candles by
Solar Lamps: Evidence from Uganda. In: Brown University Manuscript
(2012)
[10] Agoramoorthy ; Hsu: Lighting the Lives of the Impoverished in India’s Rural and Tribal Drylands. In: Human Ecology 37 (2009), S. 513–517
21
[11] Stinebrickner, Todd ; Stinebrickner, Ralph: The Causal Effect of
Studying on Academic Performance. In: NBER Working Paper (2007)
[12] Angrist, Joshua ; Lavy, Victorr: The Effects of High Stakes High
School Achievement Awards: Evidence from a Randomized Trial. In:
The American Economic Review 99 (2009), S. 301–331
[13] Anderson, Michael: Multiple Inference and Gender Differences in the
Effects of Early Intervention: A Reevaluation of the Abecedarian, Perry
Preschool, and Early Training Projectsl. In: Journal of the American
Statistical Association 103 (2008), S. 1481–95
[14] Fryer, Roland: Financial incentives and student achievement: Evidence
from randomized trials. In: The NBER Working Paper (2010)
[15] Kremer, Michael ; Miguel, Edward: Worms: Education and Health
Externalities in Kenya. In: The Poverty Action Lab Paper (2001)
[16] Kremer, Michael: Randomized Evaluations of Educational Programs
in Developing Countries: Some Lessons. In: The American Economic
Review 93 (2003), S. 102–106
22
A. Figures and Tables
Figure 1: Difference in Average Test Scores
23
Figure 2: Uwezo Uganda Learning Assessment Survey Change in Literacy and
Numeracy
24
Table 1. Baseline Descriptive Statistics
Household characteristics
Household head is father
Household head is farmer
Household head's years of education
Main dwelling is permanent
Num. of kerosene candles at home
Num. children below 18 living together
Num. older children living together
Cookstove if 3-stone stove
Num. rooms in main dwelling
Each child owns blanket at home
Each member has at least two clothes
Floor made of cement
Wall made of bricks
Water comes from unclean sources
Treatment
Mean
Observations
(1)
(2)
.5
74
(.5034)
.8108
74
(.3943)
6.8571
70
(3.0988)
.5
74
(.5034)
1.9444
72
(.6897)
5.2394
71
(2.728)
2.4058
69
(2.6696)
.527
74
(.5027)
3.9054
74
(1.2405)
.5811
74
(.4967)
.5405
74
(.5018)
.2838
74
(.4539)
.6757
74
(.4713)
.5921
76
(.4947)
Control
Difference
Mean
Observations
Mean
(3)
(4)
(5)
.5072
-0.007
69
(.5036)
(0.084)
.8551
-0.044
69
(.3546)
(0.063)
6.0484
0.809
62
(3.0911)
(0.540)
.5072
-0.007
69
(.5036)
(0.084)
1.9032
0.041
62
(.6945)
(0.120)
4.3676
0.872**
68
(2.4671)
(0.441)
2.2698
0.136
63
(2.1191)
(0.418)
.6377
-0.111
69
(.4842)
(0.083)
4.0294
-0.124
68
(1.486)
(0.231)
.6029
-0.022
68
(.4929)
(0.083)
.6029
-0.062
68
(.4929)
(0.084)
.2754
0.008
69
(.45)
(0.076)
.6957
-0.020
69
(.4635)
(0.078)
.5443
0.048
79
(.5012)
(0.080)
Note: robust standard errors in parenthesis.
There are few missing values due to missing entries in the survey.
"Water comes from unclean sources" means that water sources are either unprotected well or river.
Table 1. Baseline Descriptive Statistics continued
Individual characteristics
Gender (male)
Grade (either P5, P6, or P7)
Age
Wear uniform
Wear shoes
Have registered lunch at school
Use bed net last night
Sleeping with kerosene candles on
Did homework in the past 7 days
Time students cooked
Experienced illness or injury in 30 days
Num. days suffered from illness or injury
Difference
Treatment
Control
Mean
Observations
Mean
Observations
Mean
(1)
(2)
(3)
(4)
(5)
0.3947
.4937
-0.099
76
79
(0.492)
(.5032)
(0.080)
5.7237
5.8101
-0.086
76
79
(.7411)
(.7524)
(0.120)
12.942
13.1452
-0.203
69
62
(1.5707)
(1.7727)
(0.294)
.7027
.5588
0.144*
74
68
(.4602)
(.5002)
(0.081)
.2568
.2941
-0.037
74
68
(.4398)
(.459)
(0.076)
.6757
.6029
0.073
74
68
(.4713)
(.4929)
(0.081)
0.257
0.25
0.007
74
68
(0.525)
(0.436)
(0.081)
.1831
.0984
0.085
71
61
(.3895)
(.3003)
(0.060)
.662
.6613
0.001
71
62
(.4764)
(.4771)
(0.083)
1.9571
2.803
-0.846**
70
66
(1.7729)
(2.4001)
(0.364)
.7397
.8182
-0.078
73
66
(.4418)
(.3887)
(0.070)
3.5139
4.8462
-1.332
72
65
(3.3651)
(5.8769)
(0.830)
F-Statistics: Difference Jointly Significant=0.91
p -value=0.59
-4.045
(4.595)
74
0.011
-8.159*
(4.442)
66
0.315
-1.124
(4.779)
70
0.001
-5.377
(3.680)
64
0.512
-5.187
(4.011)
84
0.020
-8.549**
(3.582)
76
0.318
1.357
(8.364)
32
0.001
2.294
(7.996)
28
0.467
-3.322
-2.004
(3.060) (4.696)
201
97
0.006
0.002
-7.505*** -0.457
(2.677) (3.987)
179
85
0.349
0.443
-5.399
(4.448)
74
0.019
-9.579**
(4.690)
66
0.266
-5.450* -1.501
(3.153) (3.267)
176
169
0.017
0.001
-9.107*** -4.697*
(2.878) (2.541)
159
151
0.310
0.422
-0.111
(3.028)
201
0.000
-2.393
(2.371)
179
0.451
-1.038
(4.463)
117
0.000
-3.547
(3.663)
103
0.454
Observations
R-squared
(iii) Girls
A. TRT
Observations
R-squared
B. TRT
-3.407
(6.920)
34
0.007
-1.296
(6.202)
31
0.510
-5.859*
0.815
(3.064) (3.309)
176
168
0.021
0.000
-7.349*** -2.679
(2.677) (2.788)
159
150
0.375
0.355
-0.089
-2.227
-3.583 -8.641** 0.531
-3.415
-3.423 -7.578* -0.585
(4.310) (4.230) (5.560) (4.214) (4.480) (4.443) (5.711) (4.345) (4.708)
Observations
99
117
65
102
98
117
65
102
99
R-squared
0.000
0.002
0.007
0.041
0.000
0.005
0.006
0.030
0.000
B. TRT
-2.738
-5.672
-4.002
-7.133* -2.183
-7.796* -1.710
-9.440** -3.243
(3.534) (3.487) (4.427) (3.763) (4.251) (4.051) (5.475) (4.227) (3.421)
Observations
87
103
57
93
86
103
57
93
87
R-squared
0.438
0.453
0.572
0.417
0.302
0.377
0.487
0.334
0.488
Notes: (i) Robust standard errors in parentheses: *** p<0.01, ** p<0.05, * p<0.1
(ii) Row A presents regression estimates without controls and row B presents one with controls.
(iii) Sample size for 3rd mid-term is smaller than others because not all schools had the mid-term arranged.
-2.889
(3.642)
84
0.007
-5.155*
(2.949)
76
0.443
-2.650
(4.017)
70
0.006
-6.484*
(3.245)
64
0.506
-3.011
(4.333)
99
0.005
-3.189
(3.222)
88
0.529
-0.590
(3.864)
84
0.000
-1.762
(3.163)
76
0.455
-1.717
(2.871)
201
0.002
-4.949**
(2.259)
179
0.444
-0.394
(3.050)
169
0.000
-3.744
(2.318)
151
0.450
-4.177
(4.101)
70
0.013
-7.591*
(4.002)
64
0.330
Observations
R-squared
(ii) Boys
A. TRT
Observations
R-squared
B. TRT
(i) Boys and girls
A. TRT
-2.432
(5.583)
73
0.003
-6.648
(5.144)
65
0.297
-6.181*
(3.473)
175
0.018
-5.534*
(3.103)
158
0.367
-3.744 -9.704**
(6.101) (4.720)
65
102
0.006
0.041
-6.294
-4.826
(4.735) (4.230)
57
93
0.501
0.424
-6.786
(7.219)
33
0.025
-5.435
(7.205)
30
0.413
-3.818
(4.709)
98
0.007
-6.948*
(3.642)
87
0.463
Average
English
Mathematics
1st Final 2nd Final 3rd Mid 3rd Final 1st Final 2nd Final 3rd Mid 3rd Final 1st Final 2nd Final 3rd Mid 3rd Final
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
(10)
(11)
(12)
Table 2. Summary of Test Scores
English
Average Score
Average Score
All
Boys
Girls
Study Time (hrs)
TRT*time
(1)
-1.088
(0.868)
-0.967
(0.947)
-1.154
(0.911)
-0.361
(1.175)
-1.559
(1.212)
-2.874**
(1.165)
-0.0657
(0.768)
TRT*time
(2)
-0.894
(0.758)
-0.763
(0.859)
-1.054
(0.834)
-0.0833
(1.065)
-1.478
(1.036)
-2.606**
(1.135)
0.0734
(0.781)
TRT*time
(6)
-0.894*
(0.535)
-0.763
(0.678)
-1.054*
(0.572)
-0.0833
(0.879)
-1.478**
(0.684)
-2.606***
(0.734)
0.0734
(0.658)
0.414***
(0.147)
TRT
(7)
Regression Model
OLS
OLS
Indiv RE
Indiv RE
Time FE Indiv cluster
OLS
Control Variables
No
Yes
No
Yes
Yes
Yes
No
Notes: (i) Robust standard errors in parentheses for columns (1)-(5), (7)-(8), and clustered standard errors in parentheses for column (6)
*** p<0.01, ** p<0.05, * p<0.1
(ii) More details of regressions available in appendices.
All
2nd-4th Qtl Average Score
1st Quintile Average Score
Mathematics
All
Coefficient estimated for:
Population Dependent Variable
All
Average Score
Table 3. Regression Retults
TRT*time TRT*time TRT*time
(3)
(4)
(5)
-0.962**
-0.885*
-0.880
(0.487)
(0.512)
(0.736)
-0.892
-0.758
-0.754
(0.590)
(0.627)
(0.822)
-1.067*
-1.047*
-1.044
(0.560)
(0.588)
(0.821)
-0.334
-0.0831
-0.0810
(0.742)
(0.771)
(1.036)
-1.368**
-1.460**
-1.451
(0.652)
(0.690)
(1.020)
-2.457***
-2.542***
-2.643**
(0.904)
(0.900)
(1.238)
-0.0603
0.0725
0.0822
(0.553)
(0.589)
(0.721)
OLS
Yes
0.498***
(0.164)
TRT
(8)
School FE
Yes
0.565***
(0.173)
TRT
(8)
FE
(8)
0.603***
(0.0300)
0.223***
(0.0411)
0.300***
(0.0165)
0.00292
(0.0395)
0.142***
(0.00783)
0.149***
(0.00233)
0.393***
(0.00236)
0.00144
(0.0113)
-2.227***
(0.104)
Observations
87,730
86,681
86,681
86,681
88,424
87,384
87,384
87,384
R-squared
0.025
0.452
0.456
0.013
0.454
0.456
Number of district
79
79
79
79
Notes: (i) Robust standard errors in parentheses, *** p<0.01, ** p<0.05, * p<0.1
(ii) The baseline lighting source is kerosene candle.
(iii) Household expenditure was calculated using the ownership of assets, such as TV/radio, bicycle, motorcycle, and phone.
(iv) The unit of random effect and fixed effect is district.
Table 4. Learning Assessment Survey Endogenous Regression
English
Mathematics
OLS
OLS
RE
FE
OLS
OLS
RE
(1)
(2)
(3)
(4)
(5)
(6)
(7)
Electricity
1.253***
1.056***
0.749***
0.736***
1.014***
0.836***
0.618***
(0.0310)
(0.0295)
(0.0247)
(0.0248)
(0.0340)
(0.0319)
(0.0298)
Solar lamps
0.354***
0.241***
0.221***
0.221***
0.369***
0.240***
0.224***
(0.0471)
(0.0361)
(0.0340)
(0.0340)
(0.0550)
(0.0424)
(0.0411)
Kerosene lantern
0.442***
0.358***
0.314***
0.313***
0.408***
0.316***
0.301***
(0.0182)
(0.0143)
(0.0137)
(0.0137)
(0.0212)
(0.0164)
(0.0165)
Other lights
-0.171***
-0.0574**
0.0412
0.0472
-0.347***
-0.229***
-0.0225
(0.0357)
(0.0289)
(0.0324)
(0.0327)
(0.0441)
(0.0362)
(0.0388)
Log(hh expenditure)
0.126***
0.140***
0.140***
0.143***
0.143***
(0.00652)
(0.00647)
(0.00648)
(0.00769)
(0.00780)
Class
0.166***
0.162***
0.161***
0.153***
0.149***
(0.0176)
(0.00193)
(0.00193)
(0.0172)
(0.00233)
Age
0.283***
0.287***
0.287***
0.389***
0.393***
(0.0104)
(0.00195)
(0.00195)
(0.0101)
(0.00235)
Male
0.0542*** 0.0506*** 0.0504***
0.00292
0.00160
(0.00982)
(0.00933)
(0.00933)
(0.0116)
(0.0113)
Constant
2.887***
-2.410***
-2.565***
-2.597***
4.295***
-2.223***
-2.213***
(0.00721)
(0.112)
(0.0901)
(0.0857)
(0.00884)
(0.123)
(0.106)
B. Appendix
B1 Correlation between Test Scores and Reported Study
Time
Although this analysis can only get at correlation, it is important to check
how noisy the reported study time may be. Appendix Table 5 presents the
regression as in Equation (4):
T estScorei = δ0 + δ1 StudyT imei + δ2 Xi + εi
(4)
The reported study time is positively correlated with test scores, but not
statistically significant at 90 percent. Furthermore, adding more controls decreases the coefficient, indicating that omitted variable bias is certainly significant. This shows that, although study time may be somewhat noisy, it is
likely still reflective of true study time.
30
Figure 3: (A) A child studying by a kerosene candle / (B) A child studying
by a solar lamp
31
Figure 4: Improper charging practice of solar lamps
32
Flow chart of recruitment, eligibility, intervention, dropouts, and inclusion of siblings’ test scores in
the randomized evaluation
Initial baseline survey at 7 primary schools
in Kyannamukaaka: 563 students
Exclusion: 408 students



Eligible: 155 students





Use kerosene candles
Report some AQRH symptoms
Not have siblings who are also in
the study group
Parents came to survey
Not a boarding student
Treatment (solar lamps): 76 students


Control (kerosene candles): 79 students
Drop-outs: 7 students
Drop-outs: 3 students


Lighting sources
AQRH symptoms
Have siblings who
are also in the
study group
Parents’ absence
Do not go to
boarding section


Solar lamp stolen
Move-out
Treatment (solar lamps): 73 students
Tired of study
Move-out
Control (kerosene candles): 72 students
＋
＋
26 siblings’ test scores
23 siblings’ test scores
Appendix Table 1. Lighting Sources in Uganda (2011)
Individual level
Household level
Frequency Percentage Frequency Percentage
(1)
(2)
(3)
(4)
Electricity
12,419
4.82
2,357
5.22
Solar
5,266
2.04
891
1.97
Generator
618
0.24
105
0.23
Lantern
38,244
14.84
6,610
14.64
Tadoba/candle
189,068
73.39
33,137
73.4
Other
12,022
4.67
2,048
4.54
Total
257,637
45,148
Note: (i) Generator is classified under electricity in the regression.
(ii) Data are from Uwezo Uganda Learning Assessment Survey 2011.
-0.394
(3.050)
38.44***
(2.172)
169
0.000
-3.744
(2.318)
-2.369***
(0.684)
-2.249
(2.281)
1.642
(1.597)
0.151
(2.168)
28.12***
(3.116)
53.63*
(28.00)
-1.717
(2.871)
38.89***
(2.126)
201
0.002
-4.949**
(2.259)
-2.015***
(0.654)
-2.362
(2.198)
1.693
(1.583)
1.714
(2.107)
29.98***
(3.117)
30.55
(26.86)
-3.011
(4.333)
45.07***
(3.400)
99
0.005
-3.189
(3.222)
-1.817*
(1.041)
-3.016
(3.242)
1.090
(3.347)
-1.782
(2.976)
30.07***
(3.393)
71.34*
(38.99)
-5.859*
(3.064)
49.98***
(2.155)
176
0.021
-7.349***
(2.677)
-1.846**
(0.792)
-4.603*
(2.633)
12.67***
(1.908)
-0.0451
(2.382)
21.97***
(3.257)
-0.987
(30.78)
0.815
(3.309)
38.31***
(2.302)
168
0.000
-2.679
(2.788)
-2.470***
(0.791)
-3.560
(2.688)
2.457
(1.997)
-0.860
(2.573)
26.87***
(3.493)
63.73*
(33.53)
-3.322
(3.060)
40.57***
(2.262)
201
0.006
-7.505***
(2.677)
-2.742***
(0.775)
-2.826
(2.632)
2.448
(1.910)
0.851
(2.308)
27.45***
(3.444)
49.64*
(27.89)
-2.004
(4.696)
44.14***
(3.537)
97
0.002
-0.457
(3.987)
-2.250**
(1.117)
1.424
(4.140)
5.306
(3.989)
-1.310
(3.606)
29.04***
(4.070)
45.28
(50.66)
-5.450*
(3.153)
48.05***
(2.309)
176
0.017
-9.107***
(2.878)
-2.238***
(0.810)
-5.983**
(2.890)
9.867***
(2.215)
-2.157
(2.332)
21.95***
(3.574)
45.98
(29.08)
-1.501
(3.267)
38.47***
(2.370)
169
0.001
-4.697*
(2.541)
-2.228***
(0.783)
-0.875
(2.604)
0.850
(1.798)
1.151
(2.268)
29.43***
(3.512)
42.88
(29.09)
Observations
151
179
88
159
150
179
85
159
151
R-squared
0.450
0.444
0.529
0.375
0.355
0.349
0.443
0.310
0.422
Notes: (i) Robust standard errors in parentheses: *** p<0.01, ** p<0.05, * p<0.1
(ii) Row A presents regression estimates without controls and row B presents one with controls.
(iii) Sample size for 3rd mid-term is smaller than others because not all schools had the mid-term arranged.
Constant
Siblings dummy
Log (hh expenditure)
Class
Male
Age
Observations
R-squared
B. TRT
Constant
A. TRT
179
0.451
-0.111
(3.028)
37.21***
(2.262)
201
0.000
-2.393
(2.371)
-1.289*
(0.693)
-1.899
(2.300)
0.938
(1.594)
2.576
(2.413)
32.51***
(3.269)
11.46
(32.58)
87
0.463
-3.818
(4.709)
46.67***
(3.595)
98
0.007
-6.948*
(3.642)
-2.112*
(1.244)
-5.547
(3.889)
-2.118
(3.968)
-1.873
(3.220)
29.92***
(3.901)
97.77**
(42.23)
158
0.367
-6.181*
(3.473)
51.91***
(2.347)
175
0.018
-5.534*
(3.103)
-1.458
(0.924)
-3.157
(3.068)
15.48***
(2.169)
2.044
(3.031)
21.96***
(3.465)
-47.73
(41.11)
Average
English
Mathematics
1st Final 2nd Final 3rd Mid 3rd Final 1st Final 2nd Final 3rd Mid 3rd Final 1st Final 2nd Final 3rd Mid 3rd Final
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
(10)
(11)
(12)
Appendix Table 2. Summary of Test Scores for Both Boys and Girls Combined
Observations
812
696
812
696
696
696
696
696
696
812
696
R-squared
0.471
0.665
0.637
0.471
0.665
Number of groups
131
131
131
131
Number of individual identifiers
203
174
Number of time
4
Notes: (i) Robust standard errors in parentheses for columns (1)-(9), and clustered standard errors in parentheses for columns (10) amd (11)
(ii) Coefficient on time variable is dropped in Column (5) due to multicollinearity with the Time Fixed Effect.
Indiv
Time
Mixed
Mixed
Mixed
Mixed
Indiv
Indiv
OLS
OLS
RE
FE
Effect
Indiv
Time
Indiv/Time Cluster Cluster
(1)
(2)
(4)
(5)
(6)
(7)
(8)
(9)
(10)
(11)
TRT
0.440
-2.039
-2.065
-2.083
-1.291
-0.908
-1.291
-0.908
0.440
-2.039
(2.359) (1.957)
(2.132) (1.987) (2.211) (2.201) (2.211)
(2.201)
(2.605) (2.129)
Time
2.364*** 1.910***
1.887***
1.909*** 1.907*** 1.909*** 1.907*** 2.364*** 1.910***
(0.630) (0.544)
(0.376)
(0.419) (0.419) (0.419)
(0.419)
(0.342) (0.392)
TRT*Time
-1.088
-0.894
-0.885* -0.880
-0.893
-0.892
-0.893
-0.892
-1.088** -0.894*
(0.868) (0.758)
(0.512) (0.736) (0.570) (0.570) (0.570)
(0.570)
(0.516) (0.535)
Age
-1.258***
-1.248** -1.241*** -0.999* -0.984* -0.999*
-0.984*
-1.258**
(0.346)
(0.581) (0.357) (0.584) (0.583) (0.584)
(0.583)
(0.586)
Class
3.680***
3.626** 3.590*** 3.905*** 4.139*** 3.905*** 4.139***
3.680***
(0.851)
(1.416) (0.876) (1.357) (1.348) (1.357)
(1.348)
(1.161)
Male
-2.881***
-2.903 -2.917*** -2.357
-2.221
-2.357
-2.221
-2.881*
(1.081)
(1.821) (1.118) (1.835) (1.821) (1.835)
(1.821)
(1.693)
Siblings dummy
25.03***
25.18*** 25.28*** 25.81*** 25.83*** 25.81*** 25.83***
25.03***
(1.602)
(2.087) (1.321) (1.166) (1.165) (1.166)
(1.165)
(2.904)
Log(hh expenditure)
0.344
0.336
0.331
0.569
0.395
0.569
0.395
0.344
(0.911)
(1.346) (0.825) (1.406) (1.382) (1.406)
(1.382)
(1.558)
Missing
-42.13*** -36.28*** -35.52*** -35.60*** -35.14*** -36.24*** -36.18*** -36.24*** -36.18*** -42.13*** -36.28***
(0.858) (1.004) (1.033) (1.090) (1.618) (1.181) (1.179) (1.181)
(1.179)
(1.435) (1.472)
Constant
37.24*** 24.80** 36.38*** 24.96 29.27*** 16.73
16.82
16.73
16.82
37.24*** 24.80
(1.752) (11.87) (1.734) (18.04) (11.05) (18.68) (18.24) (18.68)
(18.24)
(2.053) (19.44)
Indiv
RE
(3)
0.185
(2.440)
2.122***
(0.347)
-0.962**
(0.487)
Appendix Table 3. Regression of Average Test Scores for All Students
Appendix Table 4. Regression on Study Time (hours)
Dependent Variable:
Regression Form:
TRT
Age
Class
Male
Log(hh expenditure)
Constant
Observations
R-squared
Number of school
Study Time at Completion of Study
OLS
OLS
School-RE School-FE
(1)
(2)
(3)
(4)
0.414***
0.498***
0.498***
0.565***
(0.147)
(0.164)
(0.164)
(0.173)
0.0414
0.0414
0.0530
(0.0496)
(0.0574)
(0.0581)
0.220
0.220
0.188
(0.181)
(0.169)
(0.178)
-0.113
-0.113
-0.0917
(0.173)
(0.166)
(0.179)
0.0608
0.0608
0.0518
(0.129)
(0.123)
(0.131)
1.419***
-1.084
-1.084
-0.991
(0.0871)
(1.553)
(1.623)
(1.848)
61
53
53
53
0.120
0.207
0.222
4
4
Notes: (i) Robust standard errors in parentheses: *** p<0.01, ** p<0.05, * p<0.1
(ii) There are only four schools reported due to data limitation.
Study Time at Baseline
OLS
OLS
(5)
(6)
0.024
0.050
(0.131)
(0.143)
0.028
(0.049)
-0.019
(0.103)
0.046
(0.156)
0.083
(0.112)
1.410***
0.105
(0.083)
(1.526)
129
117
0
0.012
Appendix Table 5. Correlation between Test
Scores and Reported Study Time
OLS
OLS
(1)
(2)
Hours of Study
0.553
2.832
(3.089)
(3.509)
Age
-1.017
(1.114)
Class
3.800
(3.921)
Male
1.584
(3.316)
Log(hh expenditure)
0.733
(3.341)
Constant
23.78***
3.087
(5.108)
(39.72)
Observations
47
39
R-squared
0.001
0.073
Notes: (i) Robust standard errors in parentheses: *** p<0.01, ** p<0.05, * p<0.1
(ii) The dependent variable is the average test score of 1st Final.