Uganda SBF Baseline Report 7-31-06

7/30/2019 Uganda SBF Baseline Report 7-31-06

1/40

SECOND DRAFT

July 31, 2006

by

Daniel O. Gilligan

The International Food Policy Research Institute

Sarah Adelman

University of Maryland, College Park

Kim Lehrer

University of British Columbia

An Evaluation of Alternative School-Based Feeding Programs

in Northern Uganda:

Report on the Baseline Survey


2/40

TABLE OF CONTENTS

ACKNOWLEDGEMENTS ............................................................................................ ii

1. Introduction ................................................................................................................. 1

2. Evaluation Design ....................................................................................................... 22.1 Motivation and Objectives .................................................................................... 22.2 Identification Strategy ........................................................................................... 32.3 Description of the Interventions............................................................................ 4

3. Sample Design ............................................................................................................ 63.1 Sample Location and Defining Sampling Units ................................................... 63.2 Sample Selection ................................................................................................... 63.3 Estimates of Statistical Power ............................................................................... 9

4. The Baseline Survey ................................................................................................. 144.1 Data Collection and Survey Instruments ............................................................ 16

5. How Well Did the Randomization Do? A Comparison of the Distribution ofOutcomes and Explanatory Variables Across Treatment Groups ........................... 225.1 Demographics Variables ..................................................................................... 225.2 Education ............................................................................................................ 225.3 Anthropometry .................................................................................................... 245.4 Iron Status ........................................................................................................... 245.5 Morbidity ............................................................................................................ 25

6. Conclusion ................................................................................................................ 25

References ...................................................................................................................... 36


3/40

ii

ACKNOWLEDGEMENTS

We would like to thank a number of people at the World Food Programme (WFP) officesin Uganda for assistance with the baseline survey. First, our appreciation goes toPurnima Kashyap and Ken Davies at WFP-Kampala for their willingness to experiment

with alternative school-based feeding modalities and for considerable material and othersupport in conducting the baseline survey. In particular, we acknowledge generousprovision of transportation for the survey teams and funding for military escorts to theinternally displaced peoples (IDP) camps from the WFP-Kampala office. MartinMuwaga at the WFP-Kampala office was also extremely helpful in the preparation ofactivities for the baseline survey. We also thank Gilbert Buzu at the WFP-Lira Districtoffice, and Stella Ogalo and Bai Sankoh at the WFP-Pader District office for theirsupport.

The data collection for the baseline survey was managed by Professor Joseph Konde-Luleat the Institute of Public Health at Makerere University. We are grateful to him for his

efforts in getting the survey completed on time and within budget. We are also extremelygrateful to Moses Odeke and Okello Jaspher, who together managed the daily operationsof the field teams. They managed the undertaking with a great deal of professionalismand with a focus on data quality. Thanks too to the team leaders and enumerators whohelped make the data collection a success. Special thanks also goes to Dr. VictoriaMukasa, who trained the health data collection team on the collection of hemoglobin datausing the Hemocue and on the collection of anthropometry data. We greatly appreciateher kindness and expertise. We also thank everyone involved in the data entry, includingDr. Sheba Gitta, Godfrey, Tony, Berna and their teams.

We are grateful to Dr. Rose Nassali-Lukwago, Director of the Education StandardsAgency (ESA) in the Ministry of Education and Sports, for securing ESAs involvementin preparation of the school achievement tests used in the baseline survey. Thanks toJoyce Othieno at ESA and her team for their work in designing the tests.


4/40

1. Introduction

School-based feeding programs use in-school meals or take-home rations linked to schoolattendance to attract children to school in order to improve school enrollment andattendance. When the food is provided during the school day, these food-for-education

(FFE) programs may have an additional impact on learning and cognitive developmentby improving attention spans and increasing learning efficiency. More controversial isthe notion that school-based feeding programs may also improve the nutritional status ofschool aged populations (World Bank, 2006). Many studies have addressed some of thepossible benefits of school-based feeding programs, but few have undertaken acomprehensive evaluation of the impact and cost-effectiveness of alternative programs atmeeting the major education and nutrition objectives.

The World Food Programme (WFP) and the World Bank are currently funding a series ofsuch comprehensive evaluations in three countries: Uganda, Burkina Faso and Laos. Allthree country studies will compare impacts of in-school feeding programs (SFP) to take-

home rations (THR) conditional on primary school attendance. They will also comparethe impacts of each of these programs to outcomes in a control group. In Uganda, theevaluation study has an experimental design, with primary schools randomly assigned toone of these three treatment groups for the duration of the study. The Uganda study istaking advantage of an expansion of WFPs school-based feeding operations into parts ofNorthern Uganda to conduct the study. WFP will manage and fund the SFP program, itscurrent modality for school-based feeding in Uganda, as well as the THR program, on anexperimental basis. Data collection includes a baseline survey of households and schoolsconducted from October-December 2005, prior to the start of the programs, and aresurvey planned for April 2007, after the programs have operated for at least one year.This prospective, randomized design will enable causal inference of the impact of theseprograms on education and nutrition objectives.

In addition to these evaluation design features, the Uganda study uses a sample drawnentirely from households living in Internally Displaced Peoples (IDP) camps in Paderand Lira districts in Northern Uganda, the site of a 20 year insurgency by the LordsResistance Army (LRA). The LRA is opposed to the Ugandan government andperiodically engages in fighting with Ugandan military, but the LRAs mission and goalsare vague and poorly articulated. Their primary tactics have involved terrorizingcommunities in the four districts of Northern Uganda through abductions of children forrecruitment into the LRA and through periodic gruesome attacks on civilians. TheUnited Nations Childrens Fund (UNICEF) estimates that 25,000 children have beenabducted by the LRA since 1986, nearly half of these since 2002 (USAID, 2006). Since2000, attacks on civilians have forced people to seek relative safety by congregating intoIDP camps, first in Kitgum and Gulu districts, which were the worst affected, and later inPader and then Lira. The United States government estimates that more than 1.5 millionUgandans have been displaced by the LRA and now live in these crowded camps(USAID, 2006). Estimates by WFP indicate that up to 80 percent of the population ofPader district and 50 percent of the population of Lira district were living in IDP campsin 2004. This context adds an additional dimension to the school-based feeding Uganda


5/40

2

study. The research in Uganda will provide evidence on the impacts of school-basedfeeding programs in this crisis setting, in which schooling for most children wasinterrupted during the transition to the IDP camps, schools have often struggled forresources and to retain teachers, and children are less in demand as a source of laborbecause agricultural activity is vastly reduced in the camps.

The purpose of this report is to describe the evaluation study design, explain the sampledesign, and introduce the baseline survey data. The review of the baseline data willinvestigate how successful the random assignment of treatments to IDP camps was atproviding statistically similar samples across treatment groups by comparing distributionsof key outcome and control variables across the two treatments and the control group.

2. Evaluation Design

2.1 Motivation and Objectives

The motivation and objectives of the school-based feeding study are described in detail inthe proposal for funding of the resurvey submitted to the World Banks ResearchCommittee (Alderman et al, 2006). Here, we provide a summary.

The evaluation study has the following objectives:

(i) quantify the effect of SFP and THR, both absolute (over a control group) andrelative (to each other), on education outcomes including primary schoolenrollment, age at entry, attendance, dropout rates, and grade repetition,differentiating effects by gender

(ii) measure the absolute and relative impact of each program on nutrition-relatedoutcomes including anthropometry and iron status

(iii) assess the absolute and relative impact of each program on learningperformance and, if possible, cognitive development through the combinedeffect of improved attendance and reductions in short-term hunger during theschool day

(iv) determine the relative cost-effectiveness of these two modalities of fooddelivery at achieving the outcomes described in (i)-(iii)

There is considerable research on the effects of school-based feeding programs on asubset of these outcomes.1 In particular, experimental evaluation studies in Jamaica(Grantham-McGregor, Chang and Walker, 1998; Powell et al., 1998) and in Kenya(Grillenberger et al., 2003; Whaley et al., 2003) provide evidence of education benefits ofschool meals and of limited benefits for nutritional status.

Despite this evidence, a number of important questions remain unanswered. Forexample, no study we are aware of estimates the effect of a school-based feeding

1 See Ravallion and Wodon (2000); Ahmed and del Ninno (2002); and Jacoby (2002), for some examples.See also Caldes and Ahmed (2004) for a review.


6/40

3

program on school participation (enrollment and attendance) for all school-age childrenin a population. It is more common for studies to estimate either the enrollment effectalone or the attendance effect of programs for children already enrolled in school. Thisstudy will directly measure the impacts of each program on school attendance forchildren living in the catchment area of the school.

Also, very little is known about the relative effectiveness of alternative modalities ofoperating school-based feeding programs. The side-by-side comparison of theeffectiveness of SFP and THR in this study should be revealing. The SFP program mayhave greater impacts on attendance, by providing direct incentives to children to come toschool for food, and on learning achievement by providing children with food during theschool day while they are learning. The THR program, on the other hand, may be moreeffective because it is more easily targeted to individual households within a school andis simpler to manage logistically. Important related questions concern how the relativebenefits of SFP and THR differ by school quality, student age, and householdcomposition. We know of only one study, by Tan, Lane and Lassibille (1999), that

compares the relative effectiveness of different modalities of improving schoolparticipation and learning from the same sample. Using an experimental design, thatstudy finds that school meals were less effective in reducing dropout rates and improvinglearning than programs that provide learning materials or parent-teacher partnerships.However, the study does not compare alternative modalities of food-for-educationprograms and does not measure program effects on attendance or nutrition.

In addition, most studies provide only indirect evidence on the effects of school-basedfeeding programs on household members other than the targeted student (Ahmed and delNinno, 2002, is an exception). This issue is particularly important when considering therelative benefits of in-school meals to take-home rations. Our research in NorthernUganda collects detailed information on the activities, consumption and health status ofother household members, including the targeted childs siblings and mother, to informthe effects of these programs on intrahousehold resource allocation and on the welfare ofentire families participating in the programs.

Finally, little is known about the effectiveness of school-based feeding interventions forprotecting education and nutrition investments during a violent conflict or other crisis andfollowing involuntary resettlement. This context provides a unique environment to studythe returns to school-based feeding programs following disruptions in schooling, wheredemand for child labor is low, and where many children have suffered psychologicaltrauma. The results of this study should be revealing for the potential benefits of suchprograms for the millions of people displaced by conflict throughout Africa andelsewhere.

2.2 Identification Strategy

The evaluation uses an experimental, randomized, prospective design. A prospectivestudy collects data before and after the interventions begin in order to observe changes inoutcome variables during the period of the interventions. The experimental design was


7/40

4

achieved by randomly assigning the interventions (SFP, THR or control) to IDP camps,which serve as the catchment area for primary schools in most cases. This design enablescalculation of difference-in-differences impact estimates of a treatment as simply theaverage before-and-after change in the outcome, Y, for individuals or households in anintervention group, T, minus the comparable average change in the outcome for the

control group (or alternative treatment group), C,

(1) CCTT YYYYE 0101 ,

where 0 indicates pre-treatment observation and 1 is post-treatment.

The random assignment of IDP camps into the treatment and control groups makes itpossible to place a causal interpretation on estimated impacts because, on average for alarge enough sample, observed differences in outcomes between any two groups ofcamps must be due to the interventions and not toselection effects.

2Selection effects are

caused by characteristics of the IDP camps or households that are correlated with the

outcomes of interest and with the probability of receiving the intervention. Selectioneffects lead to bias in estimates of program impact. Typically there are two causes ofselection effects: (i) targeting of the program to communities (here, IDP camps) based onfactors affecting the outcome, and (ii) actions by the community or the household thataffect participation in the program, either through lobbying the government ororganization providing the treatment, or through the households decision to participate.Random assignment of IDP camps to the interventions eliminates potential bias fromprogram targeting or lobbying, but household selection effects and bias from samplingerror may still exist. These can be controlled for, in part, by estimating impactsconditional on a set of pretreatment control variables,X0, as,

(2) 00101 |XYYYYE CCTT .

The resurvey of households, learning centers and IDP camps is scheduled forMarch/April 2007, roughly 17 months after the baseline survey. This will allow the SFPand THR interventions to operate during the 2006 school year and into the first term of2007.

2.3 Description of the Interventions

WFPs School Feeding Program (SFP) provides a free fortified mid-morning snack andlunch to all students enrolled in schools operating their program. The snack consists of a

porridge made from micronutrient fortified corn-soya-blend (CSB), sugar, and water.The lunch consists mainly of hotposho (maize meal) and beans, sometimes substitutedwith cassava or sorghum millet or complemented with vegetables and fruit from schoolgardens. The lunch also includes vegetable oil and salt. The combined meals provideroughly 1049 kcals of energy, 32.6 gm protein, and 24.9 gm fat at a cost of US$ 0.17 per

2 Heckman, Ichimura and Todd (1997) describe how randomizing program access identifies causal impactsof program participation.


8/40

5

child per school day. The ration also meets two thirds of the childs daily vitamin andmineral requirements, including 99 percent of iron requirements.

In order to qualify for the SFP, schools are required to meet facility requirementsincluding the presence of cooking facilities, latrines, and a basic hand washing facility.

The government and WFP (through its food-for-assets program) work with schools toprovide sources of safe drinking water. The food-for-assets program sometimes providesresources for building teacher housing in conjunction with the SFP. Families withchildren in the SFP are required to contribute fuel wood and a fee of USH 200 (roughly0.10 $US) per month toward the pay of the cooks. According to WFP, there is no limit tothe number of school age children from a household that can receive school-basedfeeding.

The rations provided in the take-home rations (THR) program are equal in size andcomposition to the food received by SFP beneficiaries. These rations are provided toTHR beneficiary households once per month. THR beneficiary households receive a

THR ration for each primary-school age child that is enrolled and attends school at least85 percent of the time. Details on how school attendance would be monitored for theTHR program were not available at the time of the baseline survey. Complementaryinfrastructure such as school kitchens and water storage tanks are not provided orrequired in THR camps as they are in camps receiving the SFP. However, access to theseservices is only available to SFP beneficiaries at school. The distribution of the quality ofsanitary, cooking and water facilities outside of school should be similar in SFP and THRcamps. We test for this below.

An important characteristic of the IDP camp setting for this study is that all campresidents in Pader and Lira districts receive a general monthly food ration from WFP.The size of these monthly rations is adjusted for household size, but not for the agecomposition of household members.3 These general food rations are delivered separatelyfrom the THR rations. In areas where other sources of food and income are available,WFP provides a fraction of the full monthly ration. In Pader, residents of all campsreceive a 75 percent ration. In Lira most camps receive a 50 percent ration, though somereceive a 25 percent ration. The pattern of distribution of general food rations by WFPsuggests that access to alternative income and food sources is significantly more limitedin Pader than in Lira. The composition of the food rations is similar to that of the schoolfeeding ration: maize meal, beans, corn soya blend and oil. One implication of thegeneral food ration is that the food provided by the interventions is an exact substitute forwhat is typically served in the home. This suggests that in SFP and THR camps, the FFEration increases the amount of food available to the household, but not the type.

3 A full ration for a household with five members, for example, is considered sufficient to meet all dailyfood needs for such a household with a typical composition, such as two adults and three children.


9/40

6

3. Sample Design

3.1 Sample Location and Defining Sampling Units

The sample was drawn from IDP camps in Pader and Lira districts. Although WFP

operates school meal programs in villages in at least 13 other districts in Uganda withmore than 400,000 students receiving meals, the use of a prospective evaluation designrequired conducting a baseline survey before the initiation of any program in sitesincluded in the study. Though a large multi-year expansion of school feeding to severalother districts had been expected when this study was being planned, donors did notsupport the expansion. However, WFP decided to conduct a smaller expansion ofschool-based feeding into Pader and Lira districts in early 2006 using its own funds,which created the opportunity to conduct the evaluation. The programs were introducedonly in IDP camps in these districts because living conditions were generally worseinside the camps, though primary school enrollment and attendance rates may not havebeen lower inside the camps than outside. Also, WFP already had a presence in the

camps because it provides them with general food rations.

Primary schools in the IDP camps, called learning centers (LCs), are an agglomerationof students and staff displaced from their home primary schools in their villages of origin.In addition, if a local primary school existed in the area in which the camp was formed,this host school is also embedded in the learning center with the displaced schools. Insome cases, the classes of the original schools are preserved within the LCs, though it ismore common for students from different displaced schools to be intermingled in classesin an LC, in part due to teacher shortages. Consultations with WFP district staff fromLira and Pader indicated that most IDP camps contained only one LC at the primaryschool level and that in most cases nearly all students in the LC would be residents of thecamp. Based on this information, it was decided that camps would serve as the clustersfor the sample and that recently-collected camp census data could be used to draw thehousehold sample.

3.2 Sample Selection

The number and list of camps that could be sampled took into account WFPs limitedbudget for the expansion, its allocation of the budget across districts and its prioritizationof camps within districts. The budget allowed for adding 74,000 students to eitherschool-based feeding program in both districts based on the most recent availableschoolenrollment data, which were from May 2005 in Lira and from 2004 in Pader.4 WFPallocated 63.5 percent of this budget, or 47,000 students, to Pader and the remainder of27,000 students to Lira. Pader, which had 56.6 percent of students in the two districtsbased on enrollment, was given slight priority in this allocation because of the relativeseverity of the insurgency there.

4 Implicit in the use of current enrollment data by WFP was some projected growth in enrollment if theprograms were effective.


10/40

7

WFPs existing in-school feeding program (SFP) in other districts relied entirely onregional targeting of needy schools, with all students in selected schools receiving freemeals. Despite the greater ease of targeting take-home rations to individual householdswithin schools, WFP chose to keep consistency across programs and apply this school-level targeting principle to the experimental THR program as well. As a result, all

students in learning centers selected for either program in Pader and Lira would receivethat program.

Rather than sample LCs for the programs from all IDP camps in the two districts, WFPand the governments District Education Officers preferred to provide a list of targetedLCs in each district from which the three treatments could be randomly allocated. Withineach district, LCs were ranked and grouped into two levels of priority. WFP districtoffice staff decided on the criteria for these rankings. In Pader, LCs were targeted onremoteness and lack of accessibility to income-generating opportunities (e.g., land andbusiness opportunities). In Lira, LCs were prioritized based on the intensity of theinsurgency in the area, so priority camps tended to reside in areas where all households

had been displaced. These targeting criteria reflect the importance WFP placed onpoverty and crisis impacts over education and nutrition objectives. Based on thesecriteria, learning centers were added to the high priority list in each district until theprogram enrollment targets of 47,000 students in Pader and 27,000 in Lira were met.This led to 12 high priority LCs in Pader and 11 high priority LCs in Lira. For thepurposes of the evaluation, additional LCs were added to these lists from the lowerpriority group of schools in each district to provide enough LCs for selection of a controlgroup. In Pader, the top five priority LCs from the low priority list were added. In Lira,WFP staff placed no ranking on the LCs within the low priority group, so five LCs wereselected from this group at random with probability proportional to size. After this initialselection of 33 learning centers, we discovered that three of the LCs selected in Paderactually reside within one large learning center in Kalongo camp. This reduced thenumber of LCs and camps to 31. Based on this targeted approach to LC selection, theIDP camps hosting these 31 LCs represent the population for the evaluation study.

The next stage of sampling involved random treatment assignment. After stratifying LCsby district, they were assigned to the treatment groups (SFP, THR and control) usingblock randomization. This involved selecting LCs in groups of three, with one LCrandomly assigned to each treatment from within each group. This ensures as equal adistribution of LCs across treatments as possible. In Lira, LCs were ordered by prioritygroup and by county in the high priority group; groups of three were taken starting fromthe top of the list. In Pader, LCs were ordered by priority and then by size beforeselecting groups of three. This additional ranking by size ensured that the Pader sampledid not end up with one treatment group with a small number of very large clusters.

After the initial assignment of LCs to treatments, we learned that two LCs in the Lirasample, Ogur and Ogur Central, were in the same camp. Moreover, Ogur had beenselected to receive SFP and Ogur Central was assigned to the control group. Maintainingthe validity of these assignments by preventing crossover of Ogur Central students toOgur to receive school meals would be impossible in a crowded camp like Ogur. Since


11/40

8

Ogur LC, with an enrollment of 3078 students, is almost five times larger than OgurCentral LC, we placed both of these LCs from Ogur camp in the SFP group. Anothercamp, Alanyi, was selected with probability proportional to size to replace Ogur Centralin the control group in order to maintain the original number of clusters.

Table 1 presents the IDP camps in each treatment group by district with enrollment datafrom the District Education Officer for the learning center(s) in that camp.5 There are 11IDP camps in the SFP group, and 10 each in the THR and control groups. A strikingfeature of the LCs in these camps is that they are large. Average LC enrollment is 4431students. Within treatment groups, SFP and THR are smaller, with average enrollment of3718 and 3515, respectively. Control group mean enrollment is larger primarily becausethe two largest LCs were selected into the control group by chance. Like mostenrollment figures in Uganda, these are probably overstated because, since theintroduction of the Universal Primary Education (UPE) program, the governmentprovides funding to schools based on enrollment via capitation grants. This providesschools with an incentive to overstate enrollment.

Figure 1 shows a map of IDP camps in Lira district, with treatment group identified inhandwriting. Figure 2 provides a crude map of all IDP camps in the sample for Paderdistrict. Figure 3 provides a map of many, but not all, IDP camps in the Pader sample,with a better representation of administrative areas.

Household samples were selected from each camp using data from a recent revalidationof IDP camp resident lists conducted by WFP in Lira district and by World Vision onbehalf of WFP in Pader. Camp revalidations allow WFP to maintain current and accuraterecords on residency in the camp, for the purpose of general food distribution. Theserevalidation exercises were completed in June 2005, and provided the equivalent of acamp census for each IDP camp. Data collected include household head name;household members names, gender and ages; and block name or number of thehouseholds location in the camp. These blocks identify neighborhoods within thecamps demarcated by roads or physical boundaries.

From the camp revalidation lists, the household sample was selected at random fromamong households with children between the ages of 6-17.6 Random sampling wasstratified by block, where the fraction of the camp sample drawn from each block wasproportional to that blocks share of households with children aged 6-17. Block-levelstratification is desirable because in many camps blocks were formed as householdsarrived at the camp. This meant that residents within a block could share common villageof origin and timing of arrival in the camp. Other characteristics such as ethnicity mayalso be more highly correlated within blocks.

5 In most cases, LCs have the same name as the camp in which they reside, though there are exceptions.Also, Aloi Camp contains two LCs, which were grouped for sampling.6 The age range 6-17 was chosen to identify the sample based on evidence from the 1999 Uganda NationalHousehold Survey (UNHS) that primary school enrollment rates for children up to age 17 remained high.


12/40

9

Preliminary estimates of statistical power indicated that 40 households per camp shouldbe selected to identify impacts on primary school attendance. Thus, the primary samplefor each camp included 40 households. A secondary or alternate sample of 10 additionalhouseholds was also selected in each camp to provide alternates in case households in theprimary sample could not be found. This turned out to be important because the security

situation in Pader and Lira made it time consuming and expensive to visit each camp, asdescribed in the section below on data collection. As a result, most of the householdsurvey data had to be collected on the first day a camp was visited. In some cases, thehousehold survey team of roughly 31 enumerators could not all find a household tointerview in the primary sample of 40 households. If the primary sample of householdswas exhausted during a camp visit either through enumeration or non-response,households from the alternate list were interviewed. Common reasons for a sampledhouseholds absence include seeking medical attention or working in the fields. In somecases, the household did not have any children age 6-17 living at home at the time of theinterview or the household did not exist. Survey staff were told by camp administratorsthat these ghost households were sometimes created by camp residents attempting to

obtain additional rations from the general food distribution.

3.3 Estimates of Statistical Power

Using baseline survey data, we analyzed the statistical power of the sample to identify theimpact of the treatments, using school attendance rates as the outcome variable because itis one of the major education outcomes of this study.7 We sought to determine theminimum detectable effect size that gives an 80 percent chance (the powerof the test) ofrejecting the null hypothesis of zero change in the attendance rate as a result of receivinga treatment at the 0.05 level of significance. We constructed an attendance measure forprimary-school-age children (age 6-12) unconditional on school enrollment as thebroadest measure of school participation. For children enrolled in school, the baselinehousehold questionnaire captures recent attendance using two questions. One questionasked how many days a childs school was open in the past seven days. Another questionasked how many of these days the child attended school. Attendance rates for theenrolled sub-sample was calculated as the fraction of days in which the school was openthat the child attended school. To this sample, we appended the sample of children whowere not enrolled or attending school, and assigned them the value of zero for thisvariable. The attendance rate is then the simple average of this attendance variable overthe entire sample of 6-12 year olds. Using this definition, the baseline self-reportedschool attendance rate for 6-12 year olds was 78.3%. Accounting for stratification ondistricts (Pader or Lira) and clustering at the camp level, the estimated 95% confidenceinterval on this proportion is (.764, .802). The baseline survey includes just over 29households interviewed per camp on average. Using the Optimal Design (OD) software,

7 With the abolition of primary school fees under the Government of Ugandas UPE program in 1997,primary school enrollment rates increased dramatically (Appleton, 2001), but attendance improved moremodestly. From the Uganda National Household Survey, Deininger (2003) estimates the primary schoolattendance rate for 6-12 year olds in 1999 is 73.5 percent. As a result, increasing school attendance is thecurrent frontier for improving school participation in Uganda.


13/40

10

these parameters give a minimum detectable effect size of 10 percentage points (to 88.3%in the resurvey) at a power of .805.

Several factors suggest that these estimates are conservative, and that actual power maybe larger or, equivalently, that minimum detectable effect size may be smaller. First,

these self-reported attendance rates are probably inflated. Attendance data collectedduring unannounced visits by survey enumerators, which are being conducted in theperiod after the baseline survey and once the treatments are in place, will likely showlower control group attendance. This provides more room for the SFP or THR programsto have an effect on attendance rates. Second, these estimates were based on attendanceat any time of day, but students in grades 3-7 are expected to remain in school in theafternoon for nearly three hours after lunch. Attendance in Ugandan primary schools istypically recorded separately for the morning and afternoon given the common practiceof students leaving school grounds during the lunch break when no school meals programis in place. Afternoons is when attendance suffers most, and we may observe larger gainsin afternoon attendance (and associated learning) particularly in SFP. Third, these

estimates assume a two-tailed test of the hypothesis of equal school attendance ratesacross two treatment arms. For measuring impacts of SFP or THR relative to the controlgroup, a one-tailed hypothesis that school-based feeding will increase attendance isappropriate. For these tests, the minimum detectable effect size would be smaller.Nonetheless, in comparing the relative impact of SFP to THR, the two-tailed test wouldstill be required because we have no strong prior assumption on which program will havea larger impact. Fourth, beyond the cost-effectiveness and impact studies, much of theresearch using these data will involve regression analysis in which the randomization willprovide identification of access to the program (as much of IFPRIs research onPROGRESA has done). For these studies, power is not a concern.

If no significant differences in impact between the SFP and THR treatments are found, itmay still be possible to identify differences in cost-effectiveness of the two interventions.The THR program may be much less costly to operate because of the absence of schoolbased activities to provide a kitchen, and prepare and serve food in a minimally healthyenvironment each day. Schools in the SFP program incur considerable capital costs inpreparing the school for the program and labor costs to provide the meals. Moreover, theTHR program could, in principle, be targeted. To the degree that the costs of a FFEintervention can be concentrated on a sub-group that responds better to the intervention,the overall cost effectiveness will improve.

In addition, other techniques could be used to improve the power of hypothesis tests atthe analysis stage. For example, controlling for cluster- or individual-level covariatesthat are correlated with the outcome variable when estimating impacts in cluster-randomized trials can dramatically improve power (Raudenbush, 1997). That is, ratherthan estimate the average difference-in-differences impact on Y as (1) above, power isimproved by estimating the conditional impact as in (2). Covariates that perform best inthis regard are those that better explain variation in the outcome between clusters thanwithin clusters. A number of plausibly exogenous variables describing school qualityfrom the school surveys collected during the baseline could be used, including school


14/40

11

infrastructure, qualifications and gender of the head teacher or principal, and availabilityof teaching materials. Moreover, we argue that the average impact of school-basedfeeding conditional on school quality variables is a better measure of impact for thepurpose of this study than the unconditional one.


15/40

12

Table 1: IDP Camps by Treatment Group and Pre-Program Learning Center Enrollment

In-school feeding (SFP) Take-home rations (THR) Control

WFP Pre-

ProgramEnrollment

PADER1

Lira Palwo 7337 Kalongo 10522 Patongo 16553 Adilang 3506 Amyel 5779 Geregere 2847Wol 2674 Corner Kilak 1387 Omiya Pacwa 2792

Puranga 8432 Arum 1274 Lagute 6039Atanga 1657 Omot 3831 Pajule 4305

Subtotal 23606 22793 32536 46399

LIRA2

Amugu 1656 Alebtong 5253 Apala 5766Okwang 3135 Corner Adwari 2414 Aliwang 675Abako 620 Abia 2577 Alanyi 3244Barr 3892 Orit 277 Aloi 13334Ogur 3710 Agweng 2220 Aromo 5384Orum 4278

Subtotal 17291 12741 28403 30032

PADER & LIRA 40897 35534 60939 76,431

N schools 11 10 10

Mean school enroll 3718 3553 6094NOTES:1Pader enrollment data are from 2004.2Lira enrollment data are from May 2005.


16/40

13

Figure 1: Map of Sample Camps, Lira Distirct


17/40

14

Figure 2: Map of Sample Camps, Pader Distirct

KEY:

1. Kilak Corner2. Arum3. Patongo4. Adilang5. Amyel6. Pajule7. Laguti8. Atanga9. Kalongo10.Wol11.Omiya Pachwa12.Puranga13.Omot14.Lira Palwo


18/40

15

Figure 3: Map of IDP Camps, Pader Distirct

Source: EU-Acholi Programme, Office of the Prime Minister


19/40

16

4. The Baseline Survey

4.1 Data Collection and Survey Instruments

Data collection for the baseline survey was a collaboration between IFPRI and the

Institute of Public Health at Makerere University in Kampala. All data were collectedbetween October 7 and December 17, 2005. The survey teams consisted of 31 householdsurvey enumerators, 6 nurses trained as health survey enumerators, 7 team leaders and 2fieldwork managers.

Table 2 lists the survey instruments and type of data collected with each instrument. Themost time-consuming data collection was for the household and health surveys. Thehousehold survey gave particular attention to data collection concerning entry into theIDP camp and the challenges of life in the camp. The baseline education and educationhistory modules are also very detailed.

Hemoglobin status was determined on site using blood samples taken by finger prickwith hemoglobin measurement obtained from a Hemocue analyzer. This blood dataprotocol and the entire study received approval from the ethics review board at theNational Council for Science and Technology in Uganda. Ethics approval was alsoreceived by the review board at IFPRI. Overall, non-response for blood data collectionwas not a significant problem.

Two separate groups of achievement tests were developed by the Uganda EducationStandards Agency (a testing branch of the Ministry of Education and Sports) appropriatefor grade P2 (lower primary) and grade P5 (upper primary) students. These tests weredeveloped in consultation with senior teachers from Pader and Lira districts to ensuretheir relevance and were pretested using students from those districts not in the study.The lower primary exams were administered to children in the baseline sample enrolledin grades P2 and P3, as well as to children in the sample age 7-9 who were not enrolled inschool. The upper primary exams were administered to children enrolled in grades 5 and6 and to non-enrolled children age 10-12.

Training of the enumeration team and pre-tests of the survey instruments was conductedfrom September 7-October 7, 2005. In addition, the survey team held a one dayintroductory meeting with camp leaders from all 31 camps in the study to inform themabout the purpose of the study and the methods of data collection. In Lira, the householdand health data collection was conducted from October 7-November 5 in 13 of the 16camps. The remaining three Lira camps were visited from December 3-6. In Pader,household and health data collection took place from November 11-25. Data for theother instruments, including camp questionnaires, learning center questionnaires, pricelists, and achievement tests, were collected from December 5-17. In addition, thecollection of attendance data via unannounced visits by an enumerator hired in each campis ongoing.


20/40

17

Table 2: Survey Instruments in the Baseline Survey

Survey Instrument Topics and respondentHousehold survey Household demographics, housing conditions, sanitation, water

sources, camp details, employment, agricultural activities, assets,

WFP and other aid, credit, non-food consumption, foodconsumption, education, health status (children and mothers) andknowledge, healthcare providers, child activities, mother/primarycaregiver activities, social capital, shocks, parenting assessment,GPS location of householdRespondent: household head or spouse

Health survey Immumization history, other health card data, anthropometry(weight, height), hemoglobin status (collected by Hemocue);covering female respondent and all children under age 15Respondent: female head of household or primary caregiver andchildren up to age 16 (for physical measurement only)

Camp questionnaire Camp formation, camp demographics, infrastructure and services,camp access, main activities and income sources of camp residents,camp financing and government/aid agency/NGO support; campadministration and decision making, security and shocksRespondent: Formal camp leader or other camp administrator

Learning center questionnaire GPS location, learning center characteristics and rules for gradepromotion, personnel characteristics, physical infrastructure,teaching materials, examination performance, school fees andfinance, school management and decision making, school feedingRespondent: Head teacher or other learning center administrator

Price list Prices for food consumption itemsRespondent: retail sellers in local market

Achievement tests Literacy exam for lower primary (grades P2/P3 or age 7-9)Numeracy exam for lower primary (grades P2/P3 or age 7-9)Literacy exam for upper primary (grades P5/P6 or age 10-12)Numeracy exam for upper primary (grades P5/P6 or age 10-12)General knowledge exam for upper primary (grades P5/P6 or age10-12)Respondent: school age child

Learning center attendance

records

Class attendance records for 3rd term 2005 only (beginning in

September) were collected on each sampled student, when possibleRespondent: learning center records

Unannounced attendance visits Morning and afternoon attendance collected by unannounced visitstwice per month starting in April 2006Respondent: child age 6-17


21/40

18

The enumeration teams generally resided in Lira town or Pader town, traveling to an IDPcamp for enumeration each morning, and returning to the district town each evening forsecurity reasons. These trips often took 1-2 hours each way. Enumeration teams traveledto all camps in Lira and Pader with military escort. These escorts typically included twoCIVICON military trucks with roughly ten Ugandan Defense Force (UPDF) soldiers in

each truck. The escorts were needed to protect the enumeration teams as they traveled tothe camps each day because LRA attacks are common on the roads that connect thecamps. LRA attacks inside the camps are rarer, but do occur. Upon reaching the campfor enumeration, the soldiers stayed with their trucks while the enumerators were free tomove about the camps. The appearance of military escorts is common in the IDP camps.Although we do not believe these escorts would have biased households responses in thesurvey, any bias that exists should be evenly distributed across treatment and controlgroups. Moreover, to the extent this bias is fixed over time, it will drop out of impactestimates in difference-in-difference estimates after the resurvey.

Under these conditions, nearly all of the household and health survey data had to be

collected in one day at each camp. At most camp visits, there were 31 householdenumerators present and only enough time for household survey enumerators to completeone household questionnaire. In some cases, questionnaires that could not be completedon the first visit were completed during a follow-up visit, such as those in whichachievement tests were administered. As a result, only slightly more than 29 householdswere interviewed per camp, well below the target of 40.

Table 3 summarizes the data collected by camp. Data were collected on 903 households.This is well below the intended sample of 1240, which reflects the difficult conditions fordata collection and the inability of the survey team to revisit camps for later enumeration.The number of respondents found was generally higher in Pader camps, in part becausethe difficult security situation there and lack of access to job opportunities effectivelyrestricts Pader residents movements. Many Lira camps, on the other hand, are in saferareas with easy access to nearby towns. As a result, many residents of camps in Lirahave some source of income outside the camps.

In general, fewer health questionnaires were collected than household questionnaires.This is due in part to logistical difficulties of getting children and their mothers to acentral location in the camp for physical measurement and blood collection.

Table 3 also shows that seven camps in Pader and two camps in Lira have more than oneprimary school learning center in operation. Table 4 names the learning centers and thetotal number of host and displaced primary schools in each learning center. By thedesign of the evaluation, all learning centers within a camp will receive the sametreatment. In some cases, one of these learning centers is the original host school for thatarea, and the displaced schools are combined into a second learning center. A commonviewpoint held by school headmasters was that, in this situation, the LC made up ofdisplaced schools was likely to receive more services from the government or reliefagencies. The potential for this difference in schooling conditions at LCs within the samecamp will be controlled for in constructing impact estimates, since the survey includes


22/40

19

considerable information on school infrastructure and services, and it is known which LCeach child attends.

Finally, the summary of achievement test data collected shows that it was difficult tomotivate students to take the exams because they were given in the period just after

school had completed for the year. Some households had already left the camps forholidays. Still there are enough achievement test results to create difference-in-differenceestimates of program impact on learning achievement for a subsample of the children.This will also serve to inform whether single-difference estimates based only on (a morecomplete set of) test scores collected during the resurvey provide reliable estimates withlow bias.


23/40

20

Table 3: Data Collected by IDP Camp

IDP Camp Questionnnaire Achievement Tests

Household HealthLearningCenter

GeneralProficiency

Numeracy Literacy

PADER

Adilang 30 25 2 1 6 6Amyel 30 26 1 0 0 0

Arum 30 28 1 0 7 7Atanga 27 22 2 3 7 7Corner Kilak 31 28 2 3 7 7Geregere 30 28 1 2 6 6Kalongo 44 36 3 1 3 3Lagute 29 28 1 0 4 4Lira Palwo 41 36 1 3 13 13Omiya Pacwa 33 26 1 1 4 4Omot 25 24 2 2 4 4

Pajule 29 25 2 1 4 4Patongo 30 25 2 2 12 12Puranga 28 22 1 3 10 10Wol 32 29 1 3 7 7

Subtotal 469 408 23 25 94 94

LIRA

Abia 34 34 1 2 7 8Agweng 28 27 1 1 8 9Alanyi 30 23 1 0 2 2Alebtong 29 29 1 1 5 5Aliwang 32 31 1 0 10 10

Aloi Rhino/High/Corner 29 24 2 3 4 4Amugu 29 25 1 3 9 8Apala 34 30 0 1 2 2

Abako 30 25 1 3 6 8Barr 29 25 1 0 0 0Adwari 31 23 1 0 4 5Ogur 28 21 3 1 2 2Aromo 8 4 1 0 0 0Okwang 17 11 1 0 0 0Orit 27 24 1 0 14 14Orum 19 9 1 1 7 7

Subtotal 434 365 18 16 80 84

TOTAL 903 773 41 41 174 178


24/40

21

Table 4: Number of Primary Schools, by Learning Center

CAMP LEARNING CENTER

NUMBER OF

SCHOOLS

ADILANG ADILANG KULAKA 2ADILANG LALAL 8

AMYEL AMYEL 5ARUM ARUM LEARNING CENTRE 3ATANGA LACEKOCOT 1

ATANGA 3GEREGERE GEREGERE 3KALONGO ST.CHARLES KALONGO 5

NIMARO P/S 4ST PETER ANYANG 8

CORNER KILAK PADER ONGANY P7 2CORNER KILAK P/S 2

LAGUTE LAGUTI 4LIRA PALWO LIRA PALWO 8

OMIYA PACWA OMIYA PACWA 5OMOT OMOT LEARNING CENTER 7

PAJULE PAPAA 7PAJULE P7 3

PATONGO PATONGO P7 8PATONGO P 7 4

PURANGA PURANGA LEARNING CENTRE 8WOL WOL KICO 7ABIA ABIA P/S 3AGWENG AGWENG P/S 4ALEBTONG ALEBTONG P/S 7ALOI ALOI S.S 8

ALOI HIGH P/S 6AMUGU AMUGU P/S 6APALA APALA 4ABAKO ABAKO P7 3BARR BARR P/S 4ADWARI CORNER ADWAR 3OGUR OKWALO AMARA 2

OGUR PS 3OGUR CENTRAL 2

AROMO OKETKWER 8OKWANG OKWANG P/S 8ORIT ORIT P/S 3

ORUM ORUM P/S 8ALIWANG ALIWANG P/S 4

ALANYI ALANYI 4


25/40

22

5. How Well Did the Randomization Do? A Comparison of the Distribution of

Outcomes and Explanatory Variables Across Treatment Groups

The purpose of random assignment of treatments across clusters in an experimentalevaluation design is to assure that there are no systematic differences between treatment

groups before the interventions. However, even with randomized treatment assignment,differences in the distribution of baseline outcome and control variables can arise inmoderate sized samples due to sampling error. If such differences are found, it isimportant to control for them when estimating impacts, either by conditioning on thecontrol variables or using difference-in-differences impact estimates to remove thebaseline difference in mean outcomes.

This section investigates whether the distribution of key outcome and control variablesare different across treatment groups. The hypothesis of equal distributions is testedusing a simple t-test for equality of means for continuous variables and a t-test of equalproportions for binary variables.8 We first investigate the equality of distributions of

household demographics variables. We then present tests for various education andnutrition outcomes and related control variables. In the estimates presented here,treatment 1 is the SFP program, treatment 2 is THR, and treatment 3 is the control group.

5.1 Demographics Variables

Tables 5a and 5b present results for demographics variables. In Table 5a, the tests do notreject equality of means of household size, number of children under age 6, number ofprimary school age children (age 6-12), and number of all school age children (age 6-17).In Table 5b, we cannot reject equality of mean of the number of a childs living parentsor share of children who are orphaned from both parents across the treatment groups.However, the average age of children under 18 in the control group sample is slightlylower than in the SFP or THR treatment groups, with a p-value on the t-test of 0.083.

5.2 Education

Table 6 presents differences in education outcomes and related control variables atbaseline across the three treatments. Here, we find evidence of differences across thetreatment groups for several variables. For example, the average number of times astudent repeated any grade is significantly higher in the THR group than in the other twogroups. This average is also fairly high, at 0.614 in the full sample of children enrolled ingrades P1-P7. Travel times to school are significantly lower for children residing in THRcamps than for children in the other two groups. However, these travel times are low onaverage, at just over 7 minutes for children in THR camps and 10-12 minutes in the othercamps. Moreover, average distances to learning centers are less than half a mile and arenot significantly different across camps. These data on travel to school reflect the small,crowded nature of IDP camps.

8 The t-tests for equality of means presented here are based on an assumption of homogenous varianceacross groups.


26/40

23

Attendance variables show mixed results on equality of distributions. Although wecannot reject equality of times school was not attended in terms 1 and 2, average days perweek that school was attended during weeks when the student attended school areslightly, but significantly, lower in THR learning centers. However, better attendancemeasures are available in the data. In SFP camps, LCs were open significantly more days

on average in the past 7 days than in other camps, and enrolled students in SFP campsreport significantly more days attending school in the past 7 days. The combined effecton attendance is measured by the share of school days in which the enrolled childattended school in the past 7 days. For this measure too, students in camps selected toreceive the SFP intervention starting in 2006 have significantly higher attendance(92.5%) than their counterparts in THR camps or the control group (both around 89%).Although these attendance rates are conditional on enrollment, they are surprisingly high.As a further test of this important difference in baseline attendance rates, we conducted aKolmogorov-Smirnov test, a non-parametric test of the equality of the distributionfunctions across the treatment groups, for this attendance variable. That test also rejectsequality of the distribution of attendance rates between SFP camps and each of the THR

and control groups, at p-values of 0.060 and 0.020, respectively.

Another attendance measure is the share of school days in which children in grades P3-P7return to school after lunchtime. Table 6 shows that students often return home atlunchtime. Anecdotal evidence from camp visits indicates that afternoons are a timewhen attendance is quite low, as hungry students are less motivated to return to school.However, self-reported afternoon attendance for students enrolled in grades P3-P7remains high over the last week, with attendance rates around 85% and no significantdifferences across treatment groups. Although P3-P7 students in camps selected for theSFP program are significantly less likely to return home for lunch, their afternoonattendance rates are not significantly different than students in other groups.

The data on self-reported school enrollment status show high enrollment rates overall.Average gross primary school enrollment, the total number of children enrolled inprimary school as a share of the number of primary school age (6-12) children in thesample, is over 90.0 percent in the SFP and THR groups and 87.9 percent in the controlgroup. Differences in means across groups are not significant. The average netenrollment rate, the number of primary school age children enrolled as a share of thenumber of primary school age children in the sample, is just over 86.0 percent in the SFPand THR groups and 83.2 percent in the control group. Again, differences in meansacross groups for this important outcome variable are not significant.

During the 2005 school year, NGOs other than WFP operated school feeding programs inthe camps for some period. The t-tests show that the proportion of students receivingschool feeding at any time during the school year is significantly higher in THR camps(0.218) than in SFP (0.161) or control (0.138) camps. Also, the number of months thatschool feeding was received is significantly higher in THR camps, though it remains lowat 1.35 months. During the week when baseline data were collected, there was littleevidence of school feeding, with proportions receiving any school feeding in the past 7days at 10.5 percent in the control group, 3.3% in the SFP group and 1.4% in the THR


27/40

24

group. Differences between these averages were significant for each pairing of treatmentgroups.

5.3 Anthropometry

Tables 7a-7d present tests of equality of means for anthropometry variables. The resultsshow no significant differences in mean anthropometric status across treatment groupsfor adults, for children age 13-17, or for children age 5-9. Children age 10-12 in thecontrol group have significantly lower mean height-for-age z-score (HAZ) than theircounterparts in the other treatment groups. More troubling, however, are severaldifferences in anthropometry for children age 0-4 (Table 7d). Young children in thegroup selected to receive SFP have significantly lower anthropometric status than thesame cohort assigned to the other treatments for measures including weight-for-age z-score (WAZ), underweight prevalence, HAZ, and stunting prevalence. Young children inthe SFP group also have significantly lower weight-for-height z-score than those in theTHR group.

The construction these anthropometric measures in the baseline data suffered fromincomplete age data for children. The health questionnaire included questions about eachsubjects' birth day, month, and year. However, sufficient data to calculate a child's agein months, which is necessary for height-for-age (children under 18) and weight- for-age(children under 10) calculations, was obtained for only 44 percent of the sample underthe age of 18. Age in months was also calculated for an additional 39 children under 2based on information reported in the household questionnaire regarding the number ofmonths that the child has lived in the household and the number of months that the childwas breastfed. Age in months for the remaining children was calculated as 12 times thechild's age in years. This approach (as well as using months breastfed or months living inthe household) provides a lower-bound for the child's age in months, which will tend tooverstate HAZ and WAZ estimates. Given that Ugandan children are more prone tounder-nutrition than obesity, overstating z-scores provides a more conservative estimateof the prevalence of stunting and underweight. The missing age data will be collectedduring the resurvey by enumerators trained to probe for this type of information.

5.4 Iron Status

As shown in Table 8a, blood iron levels as measured by hemoglobin collected by thefinger prick method are significantly lower for adults in the control group than for thosein the SFP group. The same holds for children age 13-17. For children age 5-9, those inthe control group have significantly lower hemoglobin levels than their cohort in theTHR group. Hemoglobin levels are also significantly higher for children age 0-4 in theTHR group than in the SFP group.

Table 8b shows that prevalence of anemia in the sample is rather high, particularlyamong children age 0-9. For those in the 5-9 age group, the control group hassignificantly higher anemia prevalence than either intervention group. For 0-4 year olds,


28/40

25

anemia is significantly lower in the THR group than in the SFP group, where it reachesalmost 70 percent.

5.5 Morbidity

Table 9 presents selected results on equality of means on morbidity variables acrosstreatment groups. For the five variables shown, there is no significant difference inmeans across groups, except that average number of days sick is significantly higher forchildren under age 18 in the control group.

6. Conclusion

Overall, there are significant differences in means of several variables across interventiongroups in these tables. Apparently, random assignment of treatments has not achievedstatistically comparable baseline values across several dimensions of outcome and control

variables. The most important difference in distributions identified include (i)significantly higher mean baseline attendance rates in SFP camps, and (ii) significantlydifferent mean baseline anthropometric status for 0-4 year olds across treatment groups.As with other differences in baseline outcomes, these can be controlled for in impactestimates after the resurvey by estimating treatment effects as difference-in-differences.Similarly, baseline differences in key control variables can also be controlled for whenestimating impacts.


29/40

26

Table 5a: Tests for Equality of Means for Household Demographics

Unit of analysis: household

Mean Subpop. Estimate Std. Err. Obs Ho: treat=t1 Ho: treat=t2

hhsize: Number of people in household P>|t| P>|t|treat==1 5.850993 0.1113875 302

treat==2 5.841751 0.1085171 297 0.9527

treat==3 5.996416 0.1194286 279 0.3736 0.3378

hhsize05: Number of children under 6

treat==1 1.231788 0.0603479 302

treat==2 1.306397 0.0578747 297 0.3733

treat==3 1.362007 0.0657802 279 0.1449 0.5251

hhsize612: Number of children age 6-12

treat==1 1.956954 .0582504 302

treat==2 1.86532 .0594409 297 0.2713

treat==3 1.853047 .067651 279 0.2429 0.8913

hhsize617: Number of children age 6-17

treat==1 2.668874 0.0773382 302

treat==2 2.555556 0.074979 297 0.2939

treat==3 2.573477 0.0782633 279 0.3874 0.8688

Table 5b: Tests for Equality of Means for Household Demographics

Unit of analysis: children under 18


Average number of child's living biological parents P>|t| P>|t|

treat==1 1.460102 0.0230118 1178

treat==2 1.475153 0.0226383 1147 0.6413

treat==3 1.465392 0.0237735 1098 0.8730 0.7662Percentage of orphans (both parents)

treat==1 0.114601 0.0092823 1178

treat==2 0.0985179 0.0088007 1147 0.2093treat==3 0.0938069 0.0088002 1098 0.1053 0.7053Average age of children under 18

treat==1 8.048387 0.1310032 1178

treat==2 7.855275 0.1325102 1147 0.3003

treat==3 7.716758 0.1392154 1098 0.0827* 0.471* Significant at the 10% level; **Significant at the 5% level; ***Significant at the 1% level.


30/40

27

Table 6:Tests for Equality of Means for Education Variables

Unit of analysis: household members age 5 and older currently enrolled or attending school


Number of times any class was repeated P>|t| P>|t|treat==1 0.5983264 0.0315903 717

treat==2 0.6781437 0.0366292 668 0.0978*

treat==3 0.5736926 0.0366621 631 0.6089 0.0442**

Age started primary

treat==1 7.140865 0.0547484 717

treat==2 7.106767 0.058234 665 0.6695

treat==3 7.1584 0.0599854 625 0.8288 0.5370

Distance from house to school/learning center

treat==1 .4368044 .0372463 287

treat==2 .3897764 .0525214 275 0.4625

treat==3 .4113252 .0520327 261 0.6867 0.7710

Time to travel to school

treat==1 11.79763 .9412798 288

treat==2 7.345273 .4748500 275 0.0000***

treat==3 10.42146 .9188817 261 0.2977 0.0027***

Avg days per week attended school during weeks when attending

treat==1 4.879433 .0217482 705

treat==2 4.767372 .031354 662 0.0031***

treat==3 4.831731 .0269967 624 0.1651 0.1220

Complete weeks not attended during term I

treat==1 0.2952646 0.0383568 718

treat==2 0.3655589 0.0451883 662 0.2335

treat==3 0.3413078 0.0452299 627 0.4347 0.7047

Complete weeks not attended during term II

treat==1 0.2642559 0.0455647 719

treat==2 0.2181818 0.0308738 660 0.4109

treat==3 0.2694805 0.0399049 616 0.9324 0.3058

Days school open in past 7

treat==1 4.555398 0.0418751 704

treat==2 4.384733 0.0413672 655 0.0039**

treat==3 4.364078 0.0500476 618 0.0032** 0.7493

Days attended school in past 7, conditional on enrollment

treat==1 4.203438 0.0529324 698


31/40

28

treat==2 3.885496 0.0549701 655 0.0000**

treat==3 3.868379 0.0608156 623 0.0000** 0.8343

Share of school days attended in past 7 for enrolled students

treat==1 .9253394 .0079382 663

treat==2 .8892141 .0095463 632 0.0036***

treat==3 .8874503 .0099626 587 0.0027*** 0.8983

Days brought food to school in past 7

treat==1 0.0723589 0.0166629 691

treat==2 0.0402477 0.0128642 646 0.1311

treat==3 0.0795455 0.0219846 616 0.7920 0.1190

Days came home for lunchtime in the past 7

treat==1 3.399417 0.0791863 686

treat==2 3.35085 0.0743000 647 0.6556

treat==3 3.122951 0.0798357 610 0.0144** 0.0366**

Share of school days in past 7 in which student came home at lunchtime (grades P3-P7)

treat==1 .6733333 .0227256 360

treat==2 .7533333 .0222026 320 0.0124**

treat==3 .7240446 .0303533 314 0.1751 0.4351

Days attended school after lunchtime in past 7 (grades P3-P7)

treat==1 3.969697 .0876993 363

treat==2 3.694969 .089163 318 0.0289**

treat==3 3.663551 .0932738 321 0.0171** 0.8078

Share of school days attended school after lunchtime in past 7 (grades P3-P7 only)treat==1 .8573182 .0161916 353

treat==2 .8565817 .0166444 314 0.9748

treat==3 .8476035 .0172524 306 0.6817 0.7081

Net enrollment rate, ages 6-12

treat==1 0.8651877 0.0141082 540

treat==2 0.862963 0.0147985 586 0.9133

treat==3 0.8323699 0.0163965 519 0.1276 0.1657

Gross enrollment rate, ages 6-12

treat==1 0.9013733 0.010535 801treat==2 0.9002695 0.0110001 742 0.9422

treat==3 0.8788301 0.0121783 718 0.1597 0.1910

Days ate anything before school in past 7

treat==1 1.148905 0.0727652 685

treat==2 1.1849 0.0766341 649 0.7333

treat==3 1.055921 0.0753992 608 0.3757 0.2312


32/40

29

Days spent money at school in past 7

treat==1 0.0952381 0.0221704 693

treat==2 0.0492308 0.0168958 650 0.1023

treat==3 0.0805921 0.021903 608 0.6402 0.2536

Number of textbooks

treat==1 1.390756 0.068661 714

treat==2 1.234328 0.0674994 670 0.1050

treat==3 1.383758 0.0734196 628 0.9445 0.1336

Amount paid for supplies

treat==1 2423.262 108.4298 705

treat==2 2437.774 105.4348 638 0.9239

treat==3 1811.872 71.75421 594 0.0000*** 0.0000***

Proportion receiving school feeding at all this year

treat==1 0.1611111 0.0137009 720

treat==2 0.2175857 0.0159284 671 0.0071***treat==3 0.1376582 0.0137051 632 0.2284 0.0002***

Proportion receiving school feeding in the past 7 days

treat==1 0.0328103 0.0067282 701

treat==2 0.0138675 0.0045903 649 0.0223**

treat==3 0.1053485 0.0123594 617 0.0000*** 0.0000***

Number of months received school feeding this year

treat==1 .7007092 .067328 705treat==2 1.354863 .5504883 658 0.2228

treat==3 .6416938 .0706122 614 0.5460 0.2143

* Significant at the 10% level; **Significant at the 5% level; ***Significant at the 1% level.


33/40

30

Table 7a:Tests for Equality of Means for Anthropometry Variables,Ages 13-17 and 18 & Up


BMI of adults, 18 years and older P>|t| P>|t|treat==1 20.72644 0.2405329 223

treat==2 20.55646 0.1560786 242 0.5481

treat==3 20.52394 0.1773212 221 0.4996 0.8903

BMI of children age 13-17

treat==1 18.04825 0.2266688 101

treat==2 18.20177 0.210685 112 0.6210

treat==3 18.11544 0.3100638 95 0.8606 0.8143

HAZ of children age 13-17

treat==1 -1.204135 0.1247813 99

treat==2 -1.052661 0.1073921 111 0.3576

treat==3 -1.012505 0.140237 94 0.3092 0.8183

Stunting prevalence of children age 13-17

treat==1 0.2800000 0.0449732 100

treat==2 0.2142857 0.0388355 112 0.2691

treat==3 0.2210526 0.0426431 95 0.3454 0.9069


Table 7b:Tests for Equality of Means for Anthropometry Variables, Ages 10-12


BMI of children age 10-12 P>|t| P>|t|

treat==1 16.31616 0.1943295 141

treat==2 15.97321 0.1350478 148 0.1461

treat==3 15.96805 0.1788197 110 0.2011 0.9813


treat==1 -0.885568 0.1179133 139

treat==2 -0.7442055 0.109937 146 0.3818

treat==3 -1.100726 0.1204393 109 0.2097 0.0315**


treat==1 0.1901408 0.0329716 142

treat==2 0.1418919 0.0287185 148 0.2706

treat==3 0.2162162 0.0391222 111 0.6096 0.1191



34/40

31

Table 7c:Tests for Equality of Means for Anthropometry Variables, Ages 5-9


WAZ of children age 5-9 P>|t| P>|t|

treat==1 -0.6239953 0.0726746 277treat==2 -0.6857217 0.0694796 306 0.5401

treat==3 -0.7478222 0.0748611 255 0.2367 0.5445

Underweight prevalence of children age 5-9

treat==1 0.1191336 0.0194756 277

treat==2 0.1503268 0.0204429 306 0.2726

treat==3 0.1490196 0.0223137 255 0.3121 0.9656


treat==1 -0.5066683 0.0943376 266

treat==2 -0.4872634 0.0887985 298 0.8811

treat==3 -0.5718312 0.0965438 245 0.6302 0.5205


treat==1 0.1578947 0.0223715 266

treat==2 0.1778523 0.0221649 298 0.5281

treat==3 0.1632653 0.023628 245 0.8691 0.6542



35/40

32

Table 7d:Tests for Equality of Means for Anthropometry Variables, Ages 0-4


WAZ of children age 0-4 P>|t| P>|t|treat==1 -1.081841 0.1320734 172

treat==2 -0.7275721 0.0976538 221 0.0284**

treat==3 -0.6370463 0.1185274 214 0.0128** 0.5553

Underweight prevalence of children age 0-4

treat==1 0.3023256 0.0350475 172

treat==2 0.1809955 0.0259202 221 0.0048***

treat==3 0.2009346 0.0274138 214 0.0215** 0.5977


treat==1 -1.268619 0.1395119 149

treat==2 -0.6540274 0.1208303 191 0.0010***

treat==3 -0.6378803 0.1307229 190 0.0012*** 0.9279


treat==1 0.3355705 0.0387198 149

treat==2 0.2146597 0.029737 191 0.0124**

treat==3 0.2315789 0.0306325 190 0.0338** 0.6926

WHZ of children age 0-4

treat==1 -0.4516923 0.1051032 161

treat==2 -0.216035 0.0955808 202 0.0994*

treat==3 -0.2400673 0.1023086 203 0.1549 0.8640

Wasting prevalence of children age 0-4

treat==1 0.1180124 0.0254487 161

treat==2 0.1039604 0.0214934 202 0.672

treat==3 0.1182266 0.0226815 203 0.995 0.6488



36/40

33

Table 8a:Hemoglobin Status


ADULT P>|t| P>|t|

treat==1 12.44541 0.0997808 229treat==2 12.28714 0.102768 241 0.2713

treat==3 12.16622 0.1023584 222 0.0519* 0.4067

Ages 13-17

treat==1 12.55532 0.1465925 94

treat==2 12.39417 0.146276 103 0.4406

treat==3 12.15402 0.1666194 87 0.0728* 0.2807

Ages 10-12

treat==1 12.06547 0.1087487 139

treat==2 11.91429 0.1281092 147 0.3732

treat==3 11.94151 0.1309813 106 0.4660 0.8851

Ages 5-9

treat==1 11.45 0.0916515 273

treat==2 11.52606 0.0806523 307 0.5325

treat==3 11.31996 0.0819739 254 0.2941 0.0763*

Ages 0-4

treat==1 9.722081 0.1603692 173

treat==2 10.39721 0.1208012 215 0.0007***

treat==3 10.63773 0.5239429 220 0.1336 0.6589



37/40

34

Table 8b:Anemia Prevalence


ADULT P>|t| P>|t|treat==1 0.3231441 0.0309105 229

treat==2 0.3651452 0.0310198 241 0.3393

treat==3 0.3603604 0.0322283 222 0.4058 0.915

Ages 13-17

treat==1 0.3617021 0.0495679 94

treat==2 0.3786408 0.0478017 103 0.807

treat==3 0.4252874 0.0530132 87 0.3842 0.5157

Ages 10-12

treat==1 0.3453237 0.0403363 139

treat==2 0.4217687 0.0407386 147 0.1854

treat==3 0.3773585 0.0470891 106 0.6063 0.4794

Ages 5-9

treat==1 0.4688645 0.030208 273

treat==2 0.4723127 0.0284978 307 0.934

treat==3 0.5472441 0.031238 254 0.0724* 0.0775*

Ages 0-4

treat==1 0.699422 0.034866 173

treat==2 0.5953488 0.0334799 215 0.0336**

treat==3 0.6545455 0.032065 220 0.3473 0.2031



38/40

35

Table 9:Morbidity


average number of sick days, children < 18 P>|t| P>|t|

treat==1 2.465157 0.1301974 1148treat==2 2.700089 0.1450221 1127 0.2279

treat==3 2.796673 0.1440189 1082 0.0873* 0.6369

percentage of children with a disability

treat==1 0.0391645 0.0057237 1149

treat==2 0.0274823 0.0048684 1128 0.1208

treat==3 0.0341644 0.0055206 1083 0.5305 0.3632

percentage of children with a fever in the past 30 days

treat==1 0.2877071 0.0133687 1147

treat==2 0.2812777 0.0133953 1127 0.7342

treat==3 0.2812211 0.0136765 1081 0.7347 0.9976

percentage of children with a cough in the past 30 days

treat==1 0.2934498 0.0134586 1145

treat==2 0.2947462 0.0136073 1123 0.946

treat==3 0.312963 0.014112 1080 0.317 0.3529

percentage of children with diarrhea in the past 30 days

treat==1 0.1474695 0.0104756 1146

treat==2 0.1532977 0.0107573 1122 0.698

treat==3 0.1609621 0.011179 1081 0.3782 0.6213


39/40

36

References

Ahmed, Akhter. and Carlo del Ninno. 2002. The Food for Education Program inBangladesh: An Evaluation of its Impact on Educational Attainment and FoodSecurity. FCND Discussion Paper No. 138. International Food Policy Research

Institute, Washington, DC.

Alderman, Harold, Damien de Walque, Jed Friedman, and Daniel O. Gilligan. 2006.Research Proposal: An Evaluation of Alternative Food for EducationApproaches, submitted to the World Bank.

Appleton, Simon. 2001. Education, Incomes and Poverty in Uganda in the 1990s.Mimeo. Center for Research in Economic Development and International Trade(CREDIT) Research Paper no. 01/22, University of Nottingham.

Caldes, Natalia and Akhter Ahmed. 2004. Food for Education: A Review of ProgramImpacts. Mimeo. International Food Policy Research Institute, Washington, DC.

Grantham-McGregor, S. M., Chang, S., and Walker, S. P. 1998. Evaluation of SchoolFeeding Programs: Some Jamaican Examples.American Journal of ClinicalNutrition, 67(4), 785S-789S.

Grillenberger, Monika, Charlotte G. Neumann, Suzanne P. Murphy, Nimrod O. Bwibo,Pieter vant Veer, Joseph G.A.J. Hautvast, and Clive E. West. 2003. FoodSupplements Have a Positive Impact on Weight Gain and the Addition of AnimalSource Foods Increases Lean Body Mass of Kenyan Schoolchildren.Journal ofNutrition 133(11): 3957S-3964S.

Heckman, J.J., H. Ichimura, and P.E. Todd. 1997. Matching as an EconometricEvaluation Estimator: Evidence from Evaluating a Job Training Program.Review of Economic Studies 64:605-654.

Jacoby, Hanan G. 2002. Is There an Intrahousehold 'Flypaper Effect'? Evidence from aSchool Feeding Programme.Economic Journal112, no. 476:196-221.

Powell, Christine, A., Susan P. Walker, Susan M. Chang and Sally M. Grantham-McGregor. 1998. Nutrition and Education: a Randomized trial of the Effects ofBreakfast in Rural Primary School Children.American Journal of ClinicalNutrition 68: 873-879.

Raudenbush, Stephen W. 1997. Statistical Analysis and Optimal Design of ClusterRandomized Trials.Psychological Methods 2(2): 173-185.

Ravallion, Martin and Quentin Wodon. 2000. Does Child Labour Displace Schooling?Evidence on Behavioural Responses to an Enrollment Subsidy.EconomicJournal110, no. 462:158-175.


40/40

Tan, Jee-Peng, Julia Lane, and Gerard Lassibille. 1999. Student Outcomes in PhilippineElementary Schools: An Evaluation of Four Experiments. World Bank EconomicReview 13, no. 3:493-508.

USAID. 2005. Uganda Complex Emergency Situation Report No. 1 (FY 2005). January5. Washington, DC.

USAID. 2006. Uganda Complex Emergency Situation Report No. 2 (FY 2006). April 26.Washington, DC.

Whaley, Shannon E., Marian Sigman, Charlotte Neumann, Nimrod Bwibo, DonaldGuthrie, Robert E. Weiss, Susan Alber and Suzanne P. Murphy. 2003. TheImpact of Dietary Intervention on the Cognitive Development of Kenyan SchoolChildren.Journal of Nutrition. 133(11): 3965S-3971S.

World Bank. 2006.Repositioning Nutrition as Central to Development. Washington, DC.

Documents

Uganda SBF Baseline Report 7-31-06