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Table 16 shows that in rounds 2 and 3, students attending treatment schools were significantly less likely to receive a full breakfast. This is potentially a serious concern. Are parents of treatment schools withholding a full breakfast because they know the child will receive a substantial lunch? If this is indeed the case, then the training offered by the school feeding program needs to stress the importance of an adequate breakfast, even when a full lunch is to be offered at noon.

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Map 1: Region 1 Agricultural Employment Intensity

Source: Bureau of Statistics, Guyana and Ministry of Agriculture. 2006. Map 2: Region 7 Agricultural Employment Intensity Regions | and 7 are characterized by high poverty levels as shown in Figure 1. Although regions 1 and 7 contain only 2.8 and 2.2 percent of the country’s population respectively, more than half of their populations are below the poverty threshold. Both regions also have a large proportion of children who do not attend school or attend irregularly or who leave school with an incomplete education®. Low rates of access to education have reinforced the vicious circle of poverty in the last years, particularly in regions 1, 7, 8 and 9. The SFP provides poor households in remote rural areas with an incentive to send their children to school and save the cost of a daily meal, which in some cases represents a considerable proportion of disposable income.

Source: Own estimates using Household Budget Survey data 2006 Figure 1: Poverty in Guyana, by Region

Map 3: Region 1 Location of Treatment and Control Schools

Note: Kokerite annex is now considered part of Kokerite school. The total number of schools adds up to 64. No schools were surveyed in Cluster 3. Schools that began school feeding after Round 3 were kept in the control group. Source: SFP

Source: Own estimations using Guyana IE surveys, 2007-2009 Table 2 gives the number of schools and students included in treatment and comparison groups by region. Details of the number of questionnaires and data forms achieved at each survey round are shown in Table 3.

Table 4: Perceived impact of school feeding — attendance Figure 3 shows mean school-level attendance by treatment and control groups for each school year covered by the impact evaluation. Between round | and round 3 (school years 2006-07 and 2008-09) mean attendance fell marginally in the control groups, but rose in the treatment schools. Figure 3 illustrates the school-level differences between comparison groups ir attendance rates, which can certainly bias the impact estimators. However, both groups show similar trends. School attendance is an important outcome indicator to assess the impact of a school feeding program. We obtained both school-level and individual-level attendance data. We also asked head teachers and class teachers whether they had noticed any change in attendance since school feeding had started (Table 4). Most respondents agreed that school feeding was associated with increased attendance, although head teachers in Round 3 had a relatively lower rate of positive perception’.

Source: Own estimations using Guyana IE surveys, 2007-2009 Table 5: Attendance in two weeks preceding survey day

Source: Own estimations using Guyana IE surveys, 2007-2009

Source: Own estimations using Guyana IE surveys, 2007-2009 Table 6: Reasons for absence in two weeks preceding survey day

Table 7a: Guyana’s health indicators for coastal and hinterland regions The descriptive statistics on stunting and wasting indicators based on Guyana’s Community- Based School Feeding Program surveys reiterate findings of earlier studies: high levels of stunting and a low prevalence of wasting (less than 3%). The results show that even at baseline, the prevalence of stunting (height-for-age less than -2 z-scores) was slightly higher in control schools (20%) than in intervention schools (17%). By the third round of the evaluation (2009), the prevalence of stunting had fallen by nearly three percentage points among intervention children, and risen by more than three percentage points among the children attending control schools. The differences in the prevalence of stunting between the two groups had thus risen from four percentage points in 2007 to ten in 2009. Table 7a highlights differences between coastal and hinterland regions in key health and nutrition indicators.

Table 7: Nutritional status of students * See Annex 2 (Nutrition Notes) for an explanation of these indicators Not stunted / wasted: > -1.0 s.d.; At risk of stunting/wasting:-1.0 to -2.0 s.d.; Stunted / wasted:-2.0 to -3.0 s.d; Severely stunted / wasted: < -3.0 s.d.

Source: Own estimations using Guyana IE surveys, 2007-2009 Figure 6: Mean height for age z-scores by group and by survey round

Source: Own estimations using Guyana IE surveys, 2007-2009 Figure 7: Stunted and severely stunted children - Mean height for age z-scores by group at survey rounds 1 and 2 The difference between control and treatment schools in relation to stunting continued in rounds 2 and 3. However, Figure 6 shows that while the mean height-for-age fell slightly for children attending control schools, it rose among children attending treatment schools. Figure 7 illustrates the differences of the school feeding on children who were stunted or severely stunted at Round 1. As found in all other studies (see footnotes 3 and 4), the prevalence of wasting in Amerindian communities is lower than in the coastal communities of Guyana.

Source: Own estimations using Guyana IE surveys, 2007-2009 Figure 9: Mean participation scores by survey round and group

Source: Own estimations using Guyana IE surveys, 2007-2009 Figure 10: Mean disconnect scores by survey round and group

Source: Own estimations using Guyana IE surveys, 2007-2009 fable 12: Participation of parents in school feeding-related activities

Figure 11: Food Crises - Safety Nets in Low and Middle Income Countries (number of programs) In Guyana, Regions | and 7 are rural areas where many families depend on local agricultural markets to preserve their purchase power on food consumption. Other adjacent regions within Guyana contribute to meeting the agricultural and food needs of regions 1 and 7, but subsistence farming is common. In Guyana, food deficit and dietary requirements rise and fall, respectively, in the period where food prices increases took place (see Figure 12)°.

Source: FAO, 2009; Guyana Market Corporation, 2010. Figure 12: Food Deficit and Average Dietary Requirement in Guyana

Table 14: Guyana SFP Costs vs. India’s Day Meal Program

Source: Own estimations using Guyana IE surveys, 2007-2009 Table 15: Food groups and scores by survey round and by group

Source: Own estimations using Guyana IE surveys, 2007-2009 Figure 14: Mean diet diversity scores by survey round and group

Figure 15: Mean food frequency scores by survey round and group The treatment group began at baseline with higher food frequency and diet diversity scores, in line with the higher wealth indicators noted in Section 5.2. Food prices rose substantially in Guyana in the period between rounds | and 2. Both the figures and the table show that while food frequency and diet diversity fell between rounds 1 and 2, they fell more sharply in the control group. By Round 3, the gap between the groups had increased substantially. These findings suggest that the school feeding program was able to at least partially cushion the schools in the treatment group against the increase in food prices.

Source: Own estimations using Guyana IE surveys, 2007-2009 Figure 16: Treatment and Control (T/C) Ratios of Frequency of Food Consumption

Source: Own estimations using Guyana IE surveys, 2007-2009 Figure 17: Estimation of SFP impacts on height-for-age z-scores

Source: Own estimations using Guyana IE surveys, 2007-2009 Figure 18: Treatment and Control Groups Students’ Height Comparison (centimeters)

Source: Own estimations using Guyana IE surveys, 2007-2009 Figure 19: Treatment and Control Groups Students’ Weight Comparison (kilograms. Nevertheless, the estimated models for at-risk children of wasting (Annex 4) show that SFP increases the BMI from 1.07 (0.704/0.65 16) to 1.34 (0.874/0.65) standard deviations compared to non-participants. The average treatment effect for the complete sample of children that reported BMI was insignificant, but it was significant for the subgroup of children that are at higher risk of wasting, according to the WHO cutoffs.

Source: Own estimations using Guyana IE surveys, 2007-2009 Table 17: School Enrollment in 2007 and 2009 Enrollment at baseline was higher for control schools. More schools were incorporated in the treatment group in 2008 and 2009 making the total number of children enrolled in treatment schools higher than in control schools. The changes in enrollment can be confounding because comparison groups were dissimilar at baseline. This is suggestive of sample selection in the outcome variable and would lead to biased program impact estimates.

Source: Own estimations using Guyana IE surveys, 2007-2009 Figure 20a shows the model estimations in the number of students enrolled in SFP schools compared to non-SFP schools. The overestimation of impacts, given by selectivity present in SFP participation variable that affect the enrollment outcomes, was predominantly biased towards girls in the predictions. This is because the program had a higher effect on increasing girls’ enrollment. On average, the program creates 196 newly enrolled girls compared to an average of 161 newly enrolled boys. The predictions in enrollment by region are shown in Figure 20. These includes the estimations of the mean enrollment after running the two models one with sample selection correction and the other with simple fixed-effects regression. Selectivity is present predominantly in Region | and total enrollment had higher predicted values for Region 7. Primary enrollment in Region | is higher than Region 7. A marginal increase in enrollment in Region 7 relative to Region | will be better captured in the model because of the higher enrollment gap existing in Region 7 between primary-age population and primary enrollment.

Source: Own estimations using Guyana IE surveys, 2007-2009 Figure 20a: Estimated SFP impact on Enrollment by Gender, 2007-2009 4.3.3 Students’ attendance

Source: Own estimations using Guyana IE surveys, 2007-2009 ‘igure 21a: Average School Attendance and Academic Performance, 2007-2009

Table 17b: Propensity Score Matching Results

Note: Dependent Variable of the pscore model Dummy of Program Participation. Several control variables added at the individual, school and household levels. See annex for detail on propensity score (probabilistic) regression results. Source: Author’s estimates GY SFP Surveys Figure 21b: Propensity Scores for Treated and Untreated (common support) for all individuals with at least one score reported

Source: Own estimations using Guyana IE surveys, 2007-2009 The food frequency score has a mean in 2008 of 7.3 and rose to 7.9 in 2009. A higher food frequency score implies that the household consumed more food products rich in nutrients at a higher frequency on average per week. Figure 22 shows the impact coefficients from sample selection correction regressions~. The food frequency score is the outcome variable’. The coefficients show that compared to 2007 SFP increased the food frequency score on average 2.2 points in 2008 and 1.3 in 2009. The standard deviations from the food frequency scores for 2008 and 2009 are fairly similar, 3.7 and 3.8 respectively.

source: Uwn estimations using Guyana Lh surveys, 24UU0/-2U0U7F Another important outcome is diet diversity. Using the diet diversity score (defined in section 4.2.2.2.) it is possible to qualitatively assess how the diversity score changes based on household characteristics. This is important because school feeding programs are considered important

Note: Extended family types have small sample size. Means and Standard Deviations Reported Source: Own estimations using Guyana IE surveys, 2007-2009 Table 17c: Diet Diversity Scores and Households’ Characteristics

Figure 24b: Partially employed, with SFP Source: Own estimations using Guyana IE surveys, 2007-2009

Source: Own estimations using Poverty Figures (BOS); Guyana Central Bank data and GMC Prices. Table 13 shows the estimation of poverty rate and population at risk of falling into poverty before and after the food prices crisis between treatment and control areas. Using the national poverty rates in rural areas and assuming that the program would operate in all regions, rural areas without the program would have 6 percent more poverty prior to the food price crisis.

Source: Own estimations using Guyana IE surveys, 2007-2009 Table 18: Schools — enrollment

Schools had single grade classes, multigrade classes or a mix of the two. Of the 64 schools surveyed, twelve had single grade classes only, 31 had multigrade classes only and 21 had both types of classes. Table 19 provides data on the classes covered by the survey, using the class teacher questionnaire. Median enrolment was higher in single grade classes.

Table 20: Student profiles A significantly larger proportion of students in the treatment group lived in single parent homes: 20.6 percent in the treatment group as compared to 12.6 percent in the control group. We examined whether single parent children differed significantly from those living with both parents with regard to key outcome indicators, in both treatment and control groups. There was no significant difference in school attendance, but children living with their mothers only were less likely to be stunted than all other children, and children living with their father alone were more likely to obtain higher mathematics scores in the National Assessments.

Source: Own estimations using Guyana LE surveys, 2007-2009 Table 21: Profile of parents and their households, by survey round

Source: Own estimations using Guyana IE surveys, 2007-2009 Table 24: Amenities and possessions: parents’ and students’ responses (Round 3 only) Table 24 summarizes information for households’ amenities and possessions in Round 3 only. While information on household possessions was obtained at each survey round, the rest of the information given in Table 24 was obtained only in Round 3. Full details of household possessions for each survey round are given in Table A5-6 (Annex 5).

Source: Own estimations using Guyana IE surveys, 2007-2009 Graph A3-1 Stunting Distribution SFP Students 2007-2009 (Height-for-Age) It is important to appreciate that what matters with stunting is not the fact of being short, but rather the failure to achieve one’s full growth potential, and why this failure has occurred.

Source: Own estimations using Guyana IE surveys, 2007-2009 Short-term hunger

Source: Own estimations using Guyana IE surveys, 2007-2009 The food frequency questionnaire for the impact evaluation was restricted to a selected number of foods. The choice of foods was based on deficiencies identified anecdotally and from earlier studies.*> The questionnaire did not ask about the frequency of consumption of staples, mainly cassava in these communities. Studies on Amerindian diets are limited, and it is important to recall that food consumption patterns differ from tribe to tribe and according to the ecology of the area.

Graph A4-2 Distributions of Normalized Scores vs. Health Indicators Source: Author’s estimation using pscore command Stata v.10. with SFP Survey data # Although the scores could match at higher propensities, the results are enough to correct for observed biases in SFP participation, particularly those resulting from the student education and health outcomes.

Source: Author’s estimation using pscore command Stata v.10. with SFP Survey data

Source: Own estimations based on Guyana’s SFP IE Surveys Graph A4-3 Relationship between Delay in SFP Implementation and Attendance Rates

Source: Own estimations based on Guyana’s SFP IE Surveys Graph A4-4 Relationship between Delay in SFP Implementation and Total Standard Z-scores (Academic) Sample selection correction included this variable to increase accuracy in the observable attributes that may contribute to increase sample selection bias. In addition, other regional, school and cluster unobserved attributes were included in the selection equation to improve the accuracy in the estimations. Although the results reported are consistent, they should be interpreted with caution because sample selectivity will always be present in non-random assignments. However, the models allow approximate a fairly accurate estimate of SFP’s impacts.

Table A4-7 SFP impacts on enrollment 2007-2009

Table A4-9 SFP Impacts on Stunting 2007-2009

Table A4-11 SFP Impacts Using Parents Data 2008: Food Frequency

Standard errors in Parenthesis * Significance at 10 percent. ** Significance at 5 percent. *** Significance at 1 percent. Source: Own estimations based on Guyana’s SFP IE Surveys Table A4-13 Math Scores Regressions with Fixed Effects and Sample Selection

Table A4-18 Food Frequency Regressions from Graph 22 Diff 2007-2008/2007-2009

Conceptual Framework for SFP role as a Safety Net Mechanism Source: Adapted from Colombo (2010). Linking CGE and Microsimulation Models: A Comparison of Different Approaches. International Journal of Microsimulation (2010) 3(1) 72-91

Figures in Guyanese Dollars (2005) Source: Guyana Poverty Assessment (1994), and BOS 2009. *Estimated Table A4-19 Poverty Line Thresholds in Guyana

The estimations of price differences between treatment and control areas for the period 2000 to 2010 is shown in graph A4-20 (for proteins and vegetables). All food groups showed price stability and small differences in the period previous to the food price crisis in both treatment and control areas. During and after the period of food price increases (mid-2007) the price differences for all food groups become higher. When the price difference is negative it indicates lower price levels for treatment areas. treatment and control areas, it is feasible that, with the incentive to provide additional meals with the SFP, local market production and food frequency-intake stability could lead to lower price levels in treatment areas. Graph A4-4 shows that the simulations produced an increase in the gap of comparison group food prices due to the assumptions made and the presence of the SFP.

Source: Own estimations using Guyana Central Bank data and Guyana Market Corporation Prices. Graph A4-21 Price Differences (Treatment-Control) by Food Groups (2000-2010)

The final step is to estimate actual poverty changes. To do so, we need the total poor population distributed by area (urban, rural) and the proportion of population distributed by region. In addition, poverty rates for 2009 were estimated using a conservative rate of population increase and poverty changes. Table A4-2 shows the total number of poor people and poverty rates in Figure A4-1 Steps for Estimation of Price Shocks and Poverty Changes

Table A4-22 Total poor population by area and Poverty Rates 2006 and 2009 The predictions based on the conservative scenarios using the baseline population of the Census 2002. The population growth is declining in Guyana in recent years therefore the 2009 estimates in Table A4-22 show an actual decrease in rural population and nationally. But overall there is an increase in both extreme and moderate poverty in rural areas, whereas urban areas experience a decline over the baseline and simulated year. Graph A4-23 shows the UN Population predictions for Guyana from 2005 to 2050 under three fertility scenarios. This shows the tendency of population reduction in this country. 2006 and 2009. For 2009, the difference in prices was used to change the poverty rate threshold*’. In consequence it could be estimated the difference in the number of people that fall below the poverty threshold according to the price shock differential in treatment and control areas. In other words, without the program food prices increases would have caused an upward shift in the poverty threshold and consequently in the number of poor people.

Regions 1, 7, 8 and 9 rank highest among the regions with access to public services and facilities such as schools, hospitals, electricity, water and roads. The EDMI is a marginality index built at the NDC level. The higher the score the least access to services and facilities by the community.

Graph A4-24 EDMI by NDC and Region in Guyana The simple estimations of the changes in poverty due to food price changes relied on severa. assumptions, which are enlisted below:

Note: Only treatment schools with data and grant awards included Source: SFP Data Table A4-24 SFP Costs

(*) Round 1 categories for academic and professional qualifications are not comparable to those of Rounds 2 and 3 — see text. Table A5 — 1: Head teachers’ profile

Table A5 — 2: Class teachers’ profile “' The designation of teachers is as follows: Senior Assistant-a certified teacher with 7 years of experience; Senior- a certified teacher with 5 years of experience; Assistant-a certified teacher; Temporary Qualified-teacher that has met the basic requirements but is not trained; Temporary Unqualified-a teacher who has not met the basic requirements and is not trained; Pupil-an untrained teacher under 17 years of age; Teacher Aide- an untrained teacher who assists teachers; Acting- an untrained teacher employed while a qualified teacher is found.

Figure 16 – uploaded by Angela Demas

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