On our honor, we have neither given nor received any unacknowledged aid on this paper. – Anh Tran & Keifer Winn

ECON-304 – Health: A Social Science Exploration

An Empirical Analysis of Community Feeding Program and Their Educational Benefitsin Rural Ghana

Anh Tran & Keifer Winn5/17/2014

Table of Contents

I. Introduction……………………………………………………..Pg. 2

A. Relevance……………………………………………...…Pg. 2

B. Background on Community Feeding Programs in Ghana……………………………………….Pg. 4

II. Theoretical Framework…………………………………………Pg. 6

III. Literature Review…...................................................................Pg. 8

IV. Data & Empirical Strategy…………………………………….Pg. 11

V. Results…………………………………………………………..Pg. 14

VI. Discussion……………………………………………………..Pg. 15

VII. Conclusion…………………………………………………….Pg. 17

VIII. Appendix……………………………………………………..Pg. 19

IX. References……………………………………………………..Pg. 21

1

I. Introduction

The correlation between poverty and nutrition carries heavy

implications for Ghana’s population and public policy. With a

sizable portion of its population impoverished, it behooves the

Ghanaian government to combat malnutrition. Community feeding

programs are examples of the country’s attempt to improve

nutrition – which in turn allows for higher educational

attainment and increases potential economic output.

Utilizing data collected by the Ghana Living Standards

Survey (GLSS), we examine the correlation between community

feeding program participation with educational attainment and

achievement. Does this program incur benefits on educational

achievement? If there are positive relationships between

participation in community feeding programs with education, to

what degree do these programs help? Community feeding programs

also combat malnutrition, which fosters secondary benefits. A

healthier population would be greatly beneficial for the Ghanaian

government: good health would lead to higher potential economic

output, in turn allowing for more tax revenues. These tax

revenues could be applied to education and generate more 2

community feeding programs – each would help the country’s

economic growth and its population’s well-being.

Relevance

According to the World Bank, over thirty percent of Ghana’s

population lives below the poverty line (“Ghana”). By

comparison, the United States’ poverty rate is almost three times

smaller at eleven percent. Poverty distribution is not equal

throughout the country. Rural inhabitants bear the brunt of

poverty’s detriments, while women—who are expected to generate

income and raise the family—suffer more than male counterparts

(“Ghana: Homegrown” 8). Ghana’s high poverty level has further

implications on nutrition.

Ghana’s poor are further disproportionately burdened with

malnutrition. Approximately forty-one percent of children under

five years old are malnourished (Poel 1). The severe

malnutrition seen in Ghana’s children carries equally severe

repercussions: malnourished children are expected to develop at

slower rates; their economic productivity diminishes; morbidity

and mortality rates increase (2). Indeed, fifty-four percent of

3

all infant deaths in Ghana were related to malnutrition in the

past year! Malnutrition poses a vicious issue for the

development of Ghana, as it is closely related to other problems

such as low educational attainment and low productivity.

Together, these factors created a self-perpetuating cycle of

poverty to people in rural Ghana. Therefore, eliminating hunger

and undernourishment should be prioritized public policy goal in

any developing country.

In this research paper, we attempt to evaluate the

interrelation between health and education in developing country.

More specifically, we look at community feeding programs in Ghana

and try to assess whether participation in such programs would

have any significant effect on educational attainment and

achievements. Since Ghana has been one of the pioneer countries

in school feeding initiatives, a program where the children are

provided meals for free by attending school, rigorous evaluation

of such pilot program is crucial before considering

implementation at a larger scale (Afoakwa 1). Theory has

suggested that participation in such programs is positively

correlated with educational attainment and achievement through 4

two main mechanisms. First of all, sufficient nutrition would

boost cognitive ability, increasing the chance for higher

academic achievements. Second, the free meals at school can serve

as an economic incentive for students to go to class more

regularly (Lawson 11). These relations will be tested in our

research. The result can be used to assess the effectiveness of

this school feeding intervention on education outcomes of people.

Background on Community Feeding in Ghana

Ghana’s community feeding programs are constructed to

simultaneously advance domestic food production, combat the

country’s pervasive levels of malnourishment and increase

educational attainment (via higher enrolment, attendance, and

retention).1 In 2005 the country implemented the Ghana Home

Grown School Feeding Programme (GSFP) which uses locally grown

foods to feed children in rural areas who attend public primary

schools (Afoakwa 1). Small-scale farmers are responsible for

ninety percent of Ghana’s food production – a statistic that

underscores their importance to the country’s agricultural

1 See visual representation in Figure 1 of Appendix

5

production and GSFP’s potential to secure income for a sizeable

portion of Ghanaians (“Ghana: Homegrown” 10).

Utilizing local ingredients in this program furthers the

country’s agricultural production – a significant source of

revenue for many rural Ghanaians – and effectively ensures income

stability for rural households (Afoakwa 1). In areas where GSFP

is implemented, farms grow – on average – 1 to 2 acres due to

increased demand (“Ghana: Homegrown” 28). In total, GSFP created

US $147 million for small-scale farmers who contributed to the

program between 2005 and 2010 (32).

Community feeding programs also aim to reduce malnourishment

and hunger among rural schoolchildren in order to achieve higher

levels of health. In 2007 the GSFP program fed 447,527 students

and plans to extend coverage beyond one million pupils in the

future (“Ghana: Homegrown” 16). The program allows for one hot

meal to be served to students for $0.32 per child per day (31).

Each meal is created according to the Food and Agriculture

Organization’s nutritional requirements for children and consists

of a combination of: maize, beans, rice, fish, yams, meat, eggs,

6

vegetables, and fruits (31). However, despite these government

meals’ nutritional balance, one meal per day is not sufficient

enough to increase a child’s health under the typical conditions

in rural Ghana. In order to reconcile this ‘health’ gap, GSFP is

supplemented by other government sponsored activities to create

more robust program against poverty’s detrimental health effects.

Students are also educated on malaria and HIV/AIDS prevention,

provided with de-worming tablets, and taught best-practices in

sanitation (16).

The final goal of Ghana’s community feeding program is

increased educational attainment – as measured by enrolment,

attendance rates, and retention levels. The program targets

those schools which are most beleaguered according to these

measurements; schools exhibiting the highest drop-out rates and

lowest levels of literacy were assigned highest priority for

participation in GSFP (“Ghana: Homegrown” 17). These programs

are decidedly successful in achieving this last goal. On

average, participating schools undergo 20.3 percent growth in

enrolment while non-participants witness 2.8 percent growth;

further, retention increases by ten percent when community 7

feeding programs are present (47). While more Ghanaian children

are exposed to education, the effects on cognitive ability may

only be witnessed in the future (seen in literacy rates, etc.).

The costs associated with GSFP is split evenly between the

Ghanaian government and the Government of the Netherlands

(“Ghana: Homegrown” 29). While the food provided to schools is

sold lower than market value – a result of no middle-man – and

more cost-effective than the alternative, providing this food is

still a burden for the Ghanaian government. The costs are only

exacerbated by the program’s rapid expansion. The Ghanaian

government has recently been forced to expand the program beyond

the initial budget and use funds allocated for other programs

(30). While the program is certainly beneficial in the long-run,

the benefits are minimal in the short-term – questioning the

program’s sustainability.

II. Theoretical Framework

The conceptual framework associated with community feeding

programs is relatively straightforward and each aspect can be

explained with a simple economic framework. The educational

8

benefits associated with school feeding programs are manifest in

three forms: increased enrolment, short-term alleviation of

hunger, and improved nutritional status (Lawson 11).2 Providing

a regular meal for financially struggling families lowers the

opportunity costs of attending school; when children are able to

attend school, parents do not have to provide as much nutrition

and are more likely to forego the costs associated with the lost

domestic labor (such as retrieving water, jobs associated with

sustenance farming, or other daily tasks). These lowered

opportunity costs therefore allow the most impoverished children

to attend school and increase enrolment rates.

The other educational components relating to community

feeding programs – namely short-term hunger levels and

nutritional status – are explained by the meals themselves. By

providing an extra meal for students, the government creates an

environment where students are more apt to effectively ‘consume’

education (Lawson 12). Lower levels of short-term hunger and

higher nutritional status both lead to higher student focus and

allows for improved cognitive skills (12). Further, a more

2 See visual representation in Figure 2 of Appendix

9

balanced diet strengthens the immune system and prevents illness

from diminishing a student’s time spent in class learning.

One of the other main objectives for community feeding

programs is to make participants healthier. Utilizing an

economic framework, health is a facet of human capital:

investment in health induces greater individual utility by

providing a better life or greater longevity (Becker 380).

Further, investment in health is complemented by schooling.

Another year of education is correlated with higher survival

rates and physical well-being: educated individuals make better-

informed decisions, consume more nutritional diets, and are taken

care of by ‘better’ doctors (390). This relationship also

operates in the opposite direction. When one is in better

health, education is a viable option for her. This relationship

can be visually represented with the production function:

10

Figure 1 – Production Function Comparing Health, Education, &

Economic Output

The associations with community feeding programs, education,

and health are closely intertwined. Health status and education

status are complementary; as one increases, the probability of

the other is more likely to increase as well. This relationship

makes community feeding programs a prime option in Ghana’s fight

against poverty.

III. Literature Review

Much literature surrounds the effects of malnutrition. A

bevy of articles were used to guide this paper’s research and

11

proved immensely helpful. Each article provides a different

perspective on the topic. Poel et al clearly define the

determinants of malnourishment, Maluccio et al examine the long-

term educational effects associated with proper and improper

nutrition, while Vermeersch and Kremer analyze community feeding

programs’ causal effects on education for young students.

Poel et al examine the determinants of malnutrition in

Ghana. They find a positive relationship between child

malnutrition and age. As the child grew older, the likelihood of

malnutrition increases – this is likely because of the increased

demand for more nourishment. The authors also confirm

breastfeeding as a determinant of child malnutrition: a mother

who relies on breastfeeding her child at older ages may not be

able to purchase other types of nutrition (Poel 8). This

phenomenon can also be explained by the child disliking other

types of foods, and therefore consumes a less balanced diet.

Poel also concludes that mother’s education has a negative

effect on malnutrition (Poel 8). In other words, the higher the

mother’s education level, the less likely her child will be

12

malnourished. Even if a mother only has primary education, the

child is fifteen percent less likely to suffer from inadequate

diet (ceteris paribus). The theoretical framework supporting

this claim is that educated mothers will know best practices for

keeping their children healthy, and therefore are more likely to

allocate household resources for their child’s optimal benefit

(9). Income also plays a role in malnutrition; holding all other

variables constant, the richest twenty percent of Ghanaians are

eighteen percent less likely to raise malnourished children (9).

However, the presence of running water and a flush-style toilet

did not have statistically significant results.

Maluccio et al examine childhood health’s effects on

education in Guatemala. By giving random villages food

supplements, they conduct a controlled experiment to determine

the causal effects of health and nutrition on adults’ educational

outcomes. In order to achieve this measurement, the authors

examine random health interventions from birth until 36 months

old (Malaccio 3). They then track these individuals’ cognitive

ability in the long-term, with some tests conducted over twenty-

five years later. 13

Their research concludes that both men and women experience

higher cognitive ability when adequate nutrition is provided in

the developing stages of life; however, there are differences

according to gender. Women in the treatment group were more

likely to achieve higher grade attainment – most graduated

primary school and some went on to secondary school, while men

did not experience any significant boost in this regard (Malaccio

4). Further, those women given better nutrition throughout their

developing stages were more effective in school and received

better grades than females in the control group. Both men and

women, however, did experience statistically significant higher

levels of literacy: on average, the treated had fourteen percent

higher literacy rates (37). Further, both men and women in the

treatment group progressed through education more quickly and

reached educational ‘benchmarks’ faster.

Vermeersch and Kremer also study the effects of good health

on educational attainment by utilizing a controlled experiment in

Kenya. Specifically, they chose twenty-five randomly selected

preschools to ‘treat’ with subsidized school meals (and thus

allowed households to allocate funds to other resources and made 14

the child healthier) while the other twenty-five received meals

at the same rate. They found that attendance for the treatment

group was thirty percent higher than the control (Vermeersch 33).

This increased attendance and enrolment posed negative issues by

overcrowding the classroom. Teacher to pupil ratios were

increased by such a degree that teaching quality was greatly

diminished (15). Further, the time spent eating meals displaced

the time spent teaching – effectively decreasing teaching time by

fifteen percent (15). Subsidized meals also led to higher test

scores for the control group – an almost direct indication of

health’s effects on education attainment. On average, students

in the treatment group scored a standard deviation above students

in the control on oral and written tests (32).

The nutritional effects of community feeding programs in the

Kenyan treatment schools are underwhelming. Only boys

experienced higher weight, but did not exhibit any changes in

height (Vermeersch 1). Girls experienced neither changes in

weight nor height (1). The lack of evidence for physical

improvements leads the authors to believe that increased

15

attendance – not the nutritional value supplied by school meals –

is responsible for higher test scores.

Community feeding programs were crafted with the intent to

help those most in need. Those who are the most undernourished

need the chance to increase nutrient intake and either allocate

resources to other tasks or use it to supplement nourishment.

Literature shows that increases in nourishment leads to benefits

in education – further, higher education levels has benefits on

health as well. However, our research does advance beyond the

current literature’s bounds. While Poel et al’s work looks at

the causes of malnutrition and strongly links the illness with

poverty, our work looks to the remedies via community feeding

programs. However, we do build off the control variables

presented by the authors. Unlike Maluccio – who looks at the

long-term cognitive effects on supplemental nourishment on

children less than three years old – we examine the short-term

effects of providing supplemental nourishment for children under

twelve. Lastly, our research is very similar to Vermeersch and

Kremer, but instead we apply the similar framework to Ghana’s

school system.16

IV. Data & Empirical Strategy

The data source for this research paper will be based on

GLSS5, Ghana Living Standards Survey fifth round, dating from

September of 20053 to September of 2006. It is the national

survey used to collect data on demographics, education, health

and other household characteristics of Ghana. The dataset is a

thorough collection of household demographics as well as health

and education patterns. Another notable feature of this dataset

is that it is used to prepare Poverty Analysis Report of Ghana.

Therefore, we trust the consistency and relevance in the

measurements in the context of Ghana.

Based on the available data, we built the following model to

evaluate the relation between participation in community feeding

program and school attendance in rural Ghana:

Attendance = β1Community Feeding + β2Household size + β3Household income

+ β4Household infrastructure + β5Mother’s edu + β6Gender + β7Region

+ β8Ethnicity + β9Food Expenditure + β10Distance to school + ε

3 The Ghana Home Grown School Feeding Program was implemented in early 2005. Therefore these results measure only the very beginning of GSFP’s impacts. However, many similar community feeding programs were instituted within Ghana around this time.

17

To estimate school attendance, our data is based on

respondents’ answers to two questions in the survey: (i) “have

you ever attended any school?” (ii) if you do, “have you attended

any school over the last 12 months?” A binary variable is created

to represent whether the individuals attended school in the

previous 12 months of the survey. Another binary variable is

generated to capture the status of community feeding program

participation of each individuals in the survey. To control for

factors that have potential to affect school attendance, three

set of variables are considered: household characteristics,

geographic and cultural factors, and functional controls.

The ‘household characteristics’ variable is measured by

household size, household income, household basic infrastructure,

mother’s education, and individual’s gender. Since many Ghanaians

have large families, measuring household size will shed light

school attendance rates. Because school-age children in Ghana

are more likely to play a role in family support such as baby

sitting or field labor (in comparison with children in developed

societies) they are less likely to attend school. Thus, the size

of the family is thought to have negative effect on attendance 18

rate of primary and secondary school students in Ghana. The

household basic infrastructure is proxy by whether the household

has a pipeline water system or not. This variable effectively

captures some public infrastructure provided to the household.

Moreover, water supply, as opposed to other public goods such as

electricity provision, prove to be a more rigorous variable since

the literature suggests water retrieval from natural resources is

responsible for crowding out much of the time for going to

school.

The model further controls for ‘household characteristics’

with the inclusion of mother’s education. As the literature

suggests, a more educated mother would more likely to encourage

her children to go to school (Becker 390). The survey contained

data on mother’s highest education qualification. To evaluate

effects of different levels of mother’s education, we stratify

mother’s education into two main categories: “some education” and

“high education.” While the former represents mothers who have

completed their highest education at any level lower than high

school, the latter embodies those who have obtained any

qualification from high school and above or any vocational 19

certification. Another binary variable is created to control for

gender as we expected to see discrepancy in school attendance

between male and female students.

The second set of variables controlled for are ‘geographic

and cultural factors’. A factor variable is created to control

for the fixed effects of regions (ten in total) while another one

is generated to control for different ethnicities in Ghana.

Lastly a set of variables were created to control two

‘functional factors’. Specifically, these variables are food

expenditure and distance to the nearest school. It is established

in the literature that distance to the nearest school is a strong

indicator of attendance behaviors and food expenditure

supplements this information by illustrating indirect effects on

school attendance (Maluccio 23). We believe that food expenditure

is strongly correlated with food price, controlling for family

size. Thus, the higher the food expenditure is, the more likely

school-aged children would attend school where school feeding

programs are available. Therefore, the inclusion of this factor

20

would help improve the accuracy of our evaluation on the effect

of community feeding on school attendance per se.

Our estimation is based on two primary methods: regression

model and “treatment-control” model. While our regression

analysis is fairly standard, it is also subdivided into both OLS

model and binomial probit model in order to assess the relation

between community feeding and school attendance under different

statistical methods. In the “treatment-control” model,

participation in any community feeding program is considered as a

“treatment” while non-participation is placed as “controls.” The

means of school attendance in the treatment group is compared

with it in the control group in order to shed light on

educational effect of community feeding programs. The validity of

this method depends on the underlying assumption that community

feeding programs in Ghana are implemented on a randomized basis.

Justification on the use of this method will be further

elaborated in the discussion section.

V. Results

21

The regression results are presented in the table 1. While,

the objects of observations for model (1) and (2) are primary

school students (age less than or equal to 12), model (3) and (4)

exhibit results on junior secondary school students (age of 12 to

16). The regional and ethnic factors are accounted for in the

analysis but not included in table 1 for presentational

purposes.4

Table 1—Regression Results, OLS and Probit Models

(1) (2) (3) (4)Model OLS Probit OLS ProbitVARIABLES School Attendance

Comm. feeding 0.086*** 0.120*** 0.099*** 0.136***[0.033] [0.033] [0.033] [0.033]

HH size -0.012*** -0.013*** -0.011** -0.013***[0.004] [0.005] [0.004] [0.005]

Mother some educ. 0.134*** 0.231*** 0.131*** 0.212***[0.033] [0.031] [0.033] [0.031]

Mother high educ. 0.132*** 0.248*** 0.126*** 0.221***[0.048] [0.045] [0.048] [0.046]

Food expenditure 0.000* 0.000* 0.000 0.000[0.000] [0.000] [0.000] [0.000]

Dist. to nearest primary school

-0.015*** -0.030***

[0.003] [0.007]Dist. to nearest junior sec. school

-0.013*** -0.023***

[0.003] [0.003]Male -0.041 -0.048* -0.037 -0.044

[0.025] [0.027] [0.025] [0.027]Agri. income -0.000 -0.000 -0.000 -0.000

[0.000] [0.000] [0.000] [0.000]Piped water 0.127** 0.165*** 0.119** 0.145**

[0.059] [0.059] [0.059] [0.060]Constant 0.314* 0.425**

4 For full regression results, see Figure 3 in Appendix

22

[0.190] [0.181]

Observations 1,393 1,419 1,396 1,422R-squared 0.186 0.190Standard errors in brackets— *** p<0.01, ** p<0.05, * p<0.1

The result of “treatment-control” model is presented in

table 2. Column (1) and (3) are results of “unmatched” and ATT

models, respectively, for school attendance of primary school

students in rural area. Similarly, column (2) and (4) are results

on school attendance of junior secondary school students.

Table 2—Treatment Effect Results

Model (1) (2) (3) (4)Unmatched ATT

VARIABLES School Attendance

Treated 0.170*** 0.176*** 0.096** 0.092**[0.031] [0.031]

T-Stat 5.47 5.69 2.17 2.07Constant 0.392*** 0.392***

[0.015] [0.015]

Observations 1,419 1,422 1419 1422R-squared 0.021 0.022

Standard errors in brackets*** p<0.01, ** p<0.05, * p<0.1

VI. Discussion

Overall, the regression results (seen in Table 1) suggest a

positive correlation between community feeding program and school

23

attendance across primary school and junior secondary school in

rural area of Ghana. In the OLS estimation, participation in

community feeding program is associated with 8.6 percent and 9.9

percent increase in the likelihood of attending school in the

previous 12 months for primary and junior secondary school

students, respectively. Similarly, the probit model showed that

participation in community feeding program is correlated with 12

percent and 13.6 percent increase in the likelihood of attending

school in the previous 12 months for primary and junior secondary

school students, respectively.5 These results are significant at

1 percent level. Among other control variables, mother’s

education at both low and high categories, household size,

distance to nearest school and household piped water supply

exhibited strong correlation with school attendance in the

expected signs. The fact that we have only a small set of

variables but obtained R-squared of 18 percent—19 percent in the

OLS models showed that our selected variables are relevant.

5 It should be noted that the results from the OLS and Probit models cannot bedirectly compared with one another. Since they do not have the same number of observations the results cannot be directly applied: they have dissimilar samples, each with unique observations. However, bearing these discrepancies perpetually in mind, one can more confidently state that a relation between community feeding programs and school attendance exists.

24

The results in the “treatment-effect” model (Table 2) also

indicate a strong relation between participation in community

feeding program and school attendance across different

educational levels in rural area of Ghana. Our following

discussion will focus on the ATT model (Average Treatment-on-the-

Treated) since it is a more rigorous estimation of treatment

effects than the unmatched model. The treatment group exhibited a

9.6 percent and 9.2 percent higher in the likelihood of attending

school in the previous 12 months for primary and junior secondary

school, respectively. These results are significant at 5 percent

level.

Since community feeding programs across Ghana are not

enacted on a randomized basis, the discrepancy between treatment

and control does not necessarily indicate causality from

treatment. There are reasons to believe that sample bias played a

role in these “treatment-control” differences. There might be

sample bias that overestimates our results. For example, if

government officials purposefully chose more advanced areas to

roll out the programs, a positive difference is merely a result

of such selection bias than any treatment effect. Since there are25

political motivations for government officials to pursue a biased

programs assignment, it is possible that the observed discrepancy

is overestimated.

Another reason why our observations may be overestimated is

because community feeding programs are geared toward more rural

and disadvantaged areas. In order to promote school attendance

and improve overall equality in the country, the programs might

actually be implemented in places with initially lower school

attendance. If that happens to be the case, then our observed

difference is further overestimated. In the scope of this paper,

since we do not have resources to further investigate the actual

implementation of community feeding programs across Ghana and as

different assumptions lead to opposite conclusions, we leave the

interpretation of the “treatment-control” magnitudes for future

scholars.

VII. Conclusion

There is one main policy implication that stems from our

results: more funding for education. There is a clear

relationship between community feeding programs and attendance

26

rates. While these efforts are laudable, there is still more

work to be done. One study noted that the enrollment in schools

with community feeding programs increased by 20.3% while those

schools without only witnessed 2.3% increases in enrollment

(“Ghana: Home” 47). These drastic enrollment increases need to

be reciprocated with increased numbers of teachers; there should

be at least ten percent more teachers in schools with community

feeding programs. Without more teachers, the quality of

education will decrease, leading to decreasing marginal returns

on GSFP.

Further investment in education facilities would be

immensely effective for teachers and students. Many schools

where GSFP was enacted did not have dining halls (“Ghana:

Homegrown” 31). For this reason, teachers are required to

conduct meals within the classroom. The time consumed by

preparation and cleanup lessens the amount of time spent

teaching. Lower teacher-student ratios and decreased teaching

time adversely affects student’s academic quality, undermining

Ghana’s efforts to combat poverty’s effects.

27

A third negative effect created by the community feeding

program requires government intervention. Students who receive

meals at school from GSFP or other programs typically receive

fewer meals at home (“Ghana: Homegrown” 53). Despite the fact

that GSFP is created in order to boost local economies and

decrease hunger, the fact remains that much of rural Ghanaians

are impoverished. Families who are guaranteed one meal per child

often find it more beneficial to invest in other goods. This

undermines the GSFP’s intent and reduces the program’s benefits.

The Ghanaian government is a cash-strapped entity to say the

least. Future foreign investment needs to be received in order

for Ghana to ensure high-quality education and combat the

negative health effects associated with poverty. The Netherlands

have done well to pave the road for a brighter Ghanaian future,

but other developed nations need to contribute for Ghana’s

citizens to reach their healthy destinations.

28

VIII. Appendix

29

Figure 1 – Developmental Objectives and Expected Outputs of the Ghana SchoolFeeding Program Source: Afoakwa, 2

Figure 2 –Relationship betweenCommunity Feeding

Programs and Potential

Educational Effects– Source: Lawson,

12

30

_cons .4247728 .1812282 2.34 0.019 .0692497 .7802959 90 .032518 .1817528 0.18 0.858 -.3240341 .3890701 82 -.1161761 .3592571 -0.32 0.746 -.8209461 .5885938 81 -.0492981 .204575 -0.24 0.810 -.4506216 .3520253 75 .4623303 .4887851 0.95 0.344 -.4965403 1.421201 73 .1066585 .18836 0.57 0.571 -.2628553 .4761723 72 -.1005979 .2232383 -0.45 0.652 -.538534 .3373382 71 -.1913998 .2142656 -0.89 0.372 -.6117338 .2289342 69 -.0009031 .2190486 -0.00 0.997 -.43062 .4288138 67 -.0802657 .2191431 -0.37 0.714 -.5101681 .3496366 66 -.3700154 .3186694 -1.16 0.246 -.9951627 .2551319 65 -.0453526 .1844359 -0.25 0.806 -.4071682 .3164631 64 -.0998409 .1809909 -0.55 0.581 -.4548985 .2552167 63 -.1622048 .1778145 -0.91 0.362 -.511031 .1866214 62 -.084966 .1750095 -0.49 0.627 -.4282895 .2583576 61 -.1207329 .198629 -0.61 0.543 -.5103917 .268926 57 -.1808448 .2408542 -0.75 0.453 -.6533387 .2916491 53 -.1845083 .1740123 -1.06 0.289 -.5258757 .1568591 52 .1502195 .1854482 0.81 0.418 -.213582 .5140211 51 .1007402 .1879197 0.54 0.592 -.2679098 .4693902 47 .205518 .1850446 1.11 0.267 -.1574919 .5685279 45 .0507739 .1854529 0.27 0.784 -.313037 .4145847 44 -.1976295 .2665364 -0.74 0.459 -.7205053 .3252463 43 -.2003099 .3151156 -0.64 0.525 -.8184856 .4178657 42 -.15951 .3675833 -0.43 0.664 -.8806138 .5615938 41 .1877937 .2888332 0.65 0.516 -.3788228 .7544101 30 -.1265682 .1715325 -0.74 0.461 -.4630708 .2099344 22 -.2471754 .4889551 -0.51 0.613 -1.206379 .7120286 21 -.014128 .1836543 -0.08 0.939 -.3744104 .3461543 18 .059646 .2085016 0.29 0.775 -.3493804 .4686724 17 .0435163 .2027278 0.21 0.830 -.3541835 .441216 16 -.0272838 .2065556 -0.13 0.895 -.4324928 .3779252 15 .2580105 .2257988 1.14 0.253 -.1849487 .7009697 14 -.011454 .1729916 -0.07 0.947 -.350819 .327911 12 .351671 .2207282 1.59 0.111 -.0813409 .7846829 11 -.3679491 .2853085 -1.29 0.197 -.9276508 .1917527 10 -.0556883 .168721 -0.33 0.741 -.3866755 .2752989 9 .1927204 .226438 0.85 0.395 -.2514927 .6369336 8 .0133125 .1668715 0.08 0.936 -.3140465 .3406715 7 .0231688 .2760537 0.08 0.933 -.5183775 .5647151 6 .2205384 .2232854 0.99 0.323 -.2174901 .6585668 4 .0517855 .2051121 0.25 0.801 -.3505917 .4541627 3 -.0501053 .2472657 -0.20 0.839 -.535177 .4349663 s1q13 10 .0363002 .1054534 0.34 0.731 -.1705722 .2431726 9 .0280055 .0977561 0.29 0.775 -.1637667 .2197777 8 .0239902 .0916078 0.26 0.793 -.1557207 .2037012 7 .0516733 .0891254 0.58 0.562 -.1231678 .2265144 6 .1594944 .0796027 2.00 0.045 .0033343 .3156544 5 -.0206561 .0890355 -0.23 0.817 -.1953209 .1540088 4 .1456303 .0913157 1.59 0.111 -.0335075 .3247682 3 .0781562 .1435973 0.54 0.586 -.2035449 .3598572 2 .1573997 .0852852 1.85 0.065 -.009908 .3247074 region dwater_q10 .1191172 .0587007 2.03 0.043 .0039616 .2342729 agri1c -4.98e-10 5.96e-10 -0.84 0.403 -1.67e-09 6.71e-10 male -.0370959 .025099 -1.48 0.140 -.0863338 .012142jssschooldist -.0131753 .0026837 -4.91 0.000 -.01844 -.0079105 fexpendc 2.92e-09 2.12e-09 1.38 0.169 -1.24e-09 7.09e-09 m_highedu .1259166 .0478631 2.63 0.009 .0320215 .2198117 m_someeduc .1306683 .0333238 3.92 0.000 .0652955 .1960412 hhsize -.0111883 .0044417 -2.52 0.012 -.0199019 -.0024748 comfeed .0993792 .032668 3.04 0.002 .035293 .1634653 s_attend12 Coef. Std. Err. t P>|t| [95% Conf. Interval]

Total 342.670487 1395 .245641926 Root MSE = .45602 Adj R-squared = 0.1534 Residual 277.620948 1335 .207955767 R-squared = 0.1898 Model 65.0495388 60 1.08415898 Prob > F = 0.0000 F( 60, 1335) = 5.21 Source SS df MS Number of obs = 1396

IX. References

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as Icon for Africa.” University of Ghana, Legon. N.D. URL:http://www.gcnf.org/

library/Ghana-School-Feeding-Programme-Overview-and-Progress.pdf

Becker, Gary. “Health as Human Capital: Synthesis and Extensions.” Oxford Economic Papers

2007 Nov. 59(3): 379-410. Doi: 10.1093/oep/gpm020

Behrman, Jere R. “The Impact of Health and Nutrition on Education.” The World Bank

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"GDP (current US$)." Data. N.p., n.d. Web. 3 May 2014. http://data.worldbank.org/indicator/

NY.GDP.MKTP.CD

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newsroom/wfp207421.pdf

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"Ghana: Poverty Past, Present and Future." Poverty Analysis. N.p., n.d. Web. 4 May 2014.

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ntentMDK:20204450~menuPK:435735~pagePK:148956~piPK:216618~theSitePK:4303\

67~isCURL:Y,00.html

Lawson, Ty M. Impact of School Feeding Programs on Educational, Nutritional, and

Agricultural Development Goals: A Systematic Review of Literature. Diss. Michigan

State University, 2012. Ann Arbor: MSU, 2012. URL: http://ageconsearch.umn.edu/

bitstream/142466/2/2012LawsonPlanB.pdf

Maluccio, John A., John Hoddinott, Jere R. Behrman, Reynaldo Martorell, Agnes R.

Quisumbing, Aryeh D. Stein. “The Impact of an Experimental Nutritional Intervention in

Childhood on Education among Guatemalan Adults.” International Food Policy

Research Institute 2006 June. URL: http://www.ifpri.org/sites/default/

files/publications/fcndp207.pdf

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Poel, Ellen Van de, Ahmad Reza Hosseinpoor, Caroline Jehu-Appiah,Jeanette Vega, and Niko

Speybroeck. “Malnutrition and the Disproportional Burden onthe Poor: The Case of

Ghana.” Int J Equity Health. 2007 Nov. 6(21): 1-12. Doi: 10.1186/1475-9276-6-21

Vermeersch, Christel and Kremer, Michael. “School Meals, Educational Achievement, and

School Competition: Evidence from a Randomized Evaluation.” (November 2004).

World Bank Policy Research Working Paper No. 3523. Availableat SSRN:

http://ssrn.com/abstract=667881

33