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Participation and coverage

The CFP has long been integrated with health interventions [5]; in fact, the distribution of food is conditional on participation in regular health examinations at NHS health centres.

TABLE 2. Participation in the complementary feeding programme by age-group

  Systematica Sporadicb Nonec
<2 yr 87.6 7.7 4.7
2-5 yr 79.7 8.9 11.5
Total 81.5 8.6 9.9

a. Collection of milk-cereal three or four times during the period of reference. with no interval between collections exceeding 30 days.
b. Collection of milk-cereal once or twice during the reference period, or three or four times but at intervals exceeding 0 days.
c. Children whose parents never collected the milk-cereal.

Data obtained from the survey indicated that an estimated 87.4% of the sample regularly attended health examinations and thus were entitled to the free milk-cereal. The corresponding systematic rate of participation in the CFP was 81.5%; another 8.6% of the beneficiaries were only occasional participants in the milk-cereal programme, and probably also only occasionally participated in health examinations. Thus, total participation in the CFP by the children attending the NHS at the time of the survey reached 90.1%. Systematic participation was higher in the children 12-23 months old than in those 2-5 years old, with smaller differences between both groups with regard to partial participation (table 2). Non-participants tended to be concentrated more in the older (11.5%) than in the younger group (4.7%).


Socio-economic characteristics of participants and non-participants

Other things being equal, it seems appropriate to hypothesize that participation in the CFP is inversely related to income and wealth, given that the implicit income transfer of the programme is relatively more important for low-income than for high-income households. Several household characteristics related to income and wealth tend to confirm this. Other traits included in the study do not invalidate the hypothesis, but are not shown in the tables since there was no systematic difference between participants and non-participants.

By pooling monthly monetary income and monetary transfers of household members and expressing this in per capita terms, higher systematic participation among the lower-income deciles can be observed. On average, participation reached 84% in deciles 1-4 versus 71% in deciles 6-9 and zero in the highest income decile. On the other hand, non-participants represented 8% of the potential beneficiaries in the four lowest income deciles, a proportion that increased to 20% among the four highest income deciles.

Table 3 shows that differences between participants and non-participants with respect to where and how they lived and the availability of housing services favoured the latter. Conditions for partial participants generally lay midway between these categories; this group is therefore deleted from the remainder of the descriptive analysis.

Other factors that better characterize participants and non-participants are age, education, marital status, sex of household head, and working status and occupation of parents.

TABLE 3. Housing conditions and appliances according to degree of participation in the CFP

  Degree of participation
Systematic Partial None
Poorest 73.9 53.2 60.8
Not so poor 26.1 46.9 39.2
Owns house 33.1 32.9 39.2
Low-quality construction
Walls 30.7 23.4 18.9
Ceiling 7.6 3.1 6.8
Floors 5.3 7.8 2.7
Water and sewage
Tap water outside house 4.5 3.1 1.3
No sewage connection 8.2 0.0 5.4
Absence of:
Shower or tub 16.0 9.4 8.1
Kitchen 27.6 20.3 17.6
Sink 36.6 28.1 21.6
Radio 75.1 84.4 81.1
Stove 53.3 65.6 62.2
Television 84.5 92.2 89.2
Washing machine 35.0 39.1 48.6
Refrigerator 44.9 50.0 62.2
Car 6.6 12.5 12.2
Crowding (personsper bedroom) 3.3 3.0 2.9

Values are percentages, except as otherwise indicated. Deegres of participation are as defined for table 2.

TABLE 4. Individual characteristics of parents by degree of participation in the CFP

  Degree of participation
  Systematic None
Age (years)    
<20 4.8 2.7
21-40 88.6 73.5
>41 6.1 9.5
Formal education (years)    
None 2.0 0
1-3 4.2 9.6
4-8 45.1 35.6
>8 48.7 54.8
Marital status    
Single 10.9 9.5
Consensural union 14.7 13.5
Married 66.8 66.2
Separated/widowed 7.1 10.8
Head of household 7.1 5.5
Formal education (years)    
None 1.4 5.3
1-3 4.1 7.0
4-8 35.1 29.8
>8 59.4 57.9
Resides in house 82.0 79.7

Values are percentages.

Mothers of children participating in the CFP were younger on average and had less formal education than non-participants (table 4). A rather small proportion of them in both groups were heads of households, although this sample may have had a higher proportion of female family heads because some of them (e.g., single mothers) lived with their parents or other relatives, where other women might head the households. No significant differences between the groups were perceived in terms of marital status, females as heads of household, or the position of substitute mother. A similar proportion of mothers in both groups participated in the labour market, mainly in domestic service (table 5). There are, however, indications that working mothers participating in the CFP held lower-status jobs. For example, proportionally more of them held blue-collar jobs and worked in the minimum employment programmes (PEM/POJH), whereas a relatively larger number of non-participants were either white-collar workers or self-employed.

It is surprising to find that fathers' education in terms of years of formal training was on average lower among the non-participants, which is the opposite of what was found for mothers. The proportion of fathers in the labour force, as well as type of occupation, did not differ between groups, except that 5% of the participants were in the PEM/POJH, whereas none of the non-participants were.

TABLE 5 Working status and occupation of parents by degree of participation in the CFP

  Degree of participation
Systematic None
Employed 27.1 25.0
Domestic service 36.0 33.3
Blue-collar 13.4 4.8
White-collar 19.5 23.8
Self-employed 15.9 23.8
PEM/POJHa 15.2 4.8
Employed 85.3 83.1
Blue-collar 44.1 46.9
White-collar 29.7 30.6
Self-employed 20.5 20.4
PEM/POJHa 4.7 0.0
Armed forces 0.9 2.0

Values are percentages
a. Subsidized employment programmes.


Demand for the programme

Accessibility of the health centres

Given that a prerequisite for access to free food is regular participation in preventive health examinations, we assessed the accessibility of health centres in terms of both monetary and time costs.

Health clinics were generally well located; in fact, around 70% of the households were within walking distance of them and thus did not have to pay for transportation. Those who did use transportation generally spent no more than 100 pesos (US$0.40) monthly, about 1%-1.5% of the average family income. In addition, mothers spent less than an hour going back and forth to the health clinic.

The picture changes substantially when mothers reported the cost of time spent waiting for health services. The majority of non-participants (58%) reported waiting more than three hours to have their child examined; this figure was 44% for participants. A much lower direct time cost was reported for obtaining the free food at the health centres: 76% had to wait less than half an hour each time.

Thus, the opportunity cost of time required to participate in preventive health was too high for many mothers compared with the perceived value of the health and food transfer obtained from the NHS. This hypothesis was confirmed by mothers who reported that lack of time was the main reason they did not take their child for regular health examinations at the clinics.

The other reason given was the reported "poor quality of service" provided at the clinics. This should not come as a surprise, given the fact that non-participant households were on average economically better off and probably had more access to private health insurance. Workers in the formal market are required by law to withdraw a fraction of their wages/ salaries for private health insurance for themselves and their families.

When mothers were asked where they sought health services when their children were sick, 40% of non-participants specified a private physician, compared to only 16% of participants. Only 6.6% of the non-participants took their child to the health centre, compared to 58% of participants.


Acceptability of the milk-cereal

The demand for milk-cereal can be expected to depend on, among other factors, the perceived quality of the product, in terms of both taste and nutritional value.

With respect to general acceptance, over one-third of non-participant households said either that the children did not like the product or that it made them sick. The remainder argued basically that the implicit income transfer was low: for example, health services were perceived to be of low quality, the child was said not to need it, or the time cost involved was too high. The explicit cost of participation, as suggested earlier, was clearly not a constraining factor and was seldom mentioned. Thus, an association was detected between the acceptability of the product and the degree of participation in the programme.


Participation in the CFP and calorie intake

Government food-distribution programmes are aimed at bringing about changes in the food consumption of vulnerable households and/or of individual members within these households. The CFP is a targeted programme oriented toward increasing consumption to improve the nutrition of poor households, particularly those with members of at-risk groups, such as infants, preschool-age children, and pregnant and lactating women.

In the case of milk-cereal, the family may divide the product between the child and other members or share it with other households. They may also substitute milk-cereal for food that would otherwise have been allocated to the child. Thus, the actual impact of the CFP on the calorie intake and nutrition status of the beneficiaries is not known a priori. Information obtained from the 24-hour-recall surveys allowed for the assessment of average family and individual calorie consumption and composition of the diet. Calorie intakes were contrasted with calorie requirements using FAD/WHO 1985 standards

TABLE 6. Intra-household leakage of milk-cereal: consumption by family size, income quintile, and age-group

  Familiesa Users by age-group ( % )
Quintile N % <6 yr 6-15 yr > 15yr
Small familiesb
1 37 55.2 87.7 8.3 4.0
2 47 60.3 92.2 4.9 3.0
3 32 50.7 96.1 0 3.9
4,5 16 45.7 90.4 9.6 0
Total 136 56. 0      
Large familiesc
1 44 65.7 90.1 7.5 2.4
2 24 52.1 86.9 11.4 1.6
3 22 48.8 94.5 2.9 2.5
4,5 8 42.1 90.6 9.5 0
Total 98 55.4      

a. Households that actually consumed milk-cereal on the days of the surveys.
b. Five or fewer members.
c. More than five members.

About 55% of the households reported actual consumption of milk-cereal, which was below the proportion of households (80%) participating in the programme (table 6). These facts do not necessarily imply any inconsistency, as households stated that on average the product lasted less than 20 days per month. The possibility that the product was being given away was also examined; 9% of participant households admitted that they did not actually consume the product but gave it away (7%), sold it (1.3%), or threw it away (0.8%). To analyse leakages of milk-cereal within the household, these three groups were classified according to income quintiles and average family size.

As shown in table 6, the proportion of households that consumed milk-cereal decreased in the high-income quintiles, particularly among large households, corroborating earlier findings. Actual consumption according to age-group shows that the product was very well targeted: over 87% was consumed by preschoolers in all income groups, regardless of family size. The 9% leakage took place mainly to school-age children (7%), particularly in the case of large households, and only 2% was consumed by adults.

For the children in the study, the CFP product was the third most important source of calories in all income groups after bread and other cereals, providing between 9% and 11% of the total calories consumed (between 132 and 171 kcal). A comparison by income quintile of children who consumed milk-cereal and those who did not shows that in three of the five quintiles average total calorie consumption was slightly higher for the former group. A test of whether the milk-cereal had a positive net impact on calorie intake can be performed only in the context of multivariate analysis to control for other intervening factors. The results of this analysis are presented below.

To understand the importance of milk-cereal in the calorie intake of preschoolers, three categories of children are compared: CFP 1, children participating systematically in the programme who reported consumption of the product in the food-consumption survey; CFP 11, those participating but who were not consuming milk-cereal at the time of the survey; and no CFP, those who did not participate in the CFP.

Average calorie intake from milk consumption (including milk-cereal) was greatest among CFP I children, particularly those in the poorest quintile. Consumption of types of milk other than milk-cereal was lower among CFP I children. Milk consumption by the CFP II group was lower on average, implying that when households ran out of milk-cereal, there was not full compensation in terms of purchased milk. This was particularly noticeable in the lowest income group, probably due to income constraints. As a consequence, average calorie intake was lower.


Calorie consumption and adequacy

Calorie adequacy, which is calorie intake expressed as a proportion of calorie requirements, increased with household per capita income (tab]e 7). The chi-square test indicates a strong statistical association between level of income and degree of calorie adequacy.

Moreover, in all income groups, food consumption provided on average 90% or more of the calorie requirements, which is a very good record for low-income households in a developing country. Nevertheless, unless the distribution within each income group is also considered, the average level of adequacy may be misleading. In fact, table 8 shows that in the first three income groups, an important fraction of children consumed less than 80% of their requirements. This is counterweighted by the fact that another fraction of the preschoolers within the same income groups had calorie intake above 100% of requirements.

TABLE 7. Relationship between degree of calorie adequacy and income quintile of children under study (N =413)

Quantile   Calorie adequacya Average calorie
<80% 80% -90% 90% - 100% > 100% adequacy
N (N= 124) (N= 51) (N= 56) (N= 182) (%)
1 131 38.9 15.3 11.5 34.4 90.0
2 121 28.9 12.4 16.6 42.1 96.2
3 106 29.2 7.5 10.5 52.9 103.1
4,5 55 12.7 14.5 18.2 54.6 134.0

c2 = 21.110 with 9 df. a = 0.012.
a. Percentages of children consuming the percentage of their calorie requirements indicated in the column heading.

TABLE 8. Nutrition status by income quintile (percentages)

Nutrition status Quintile All
(N = 775)
(N = 251)
(N = 232)
(N = 178)
4 and 5
(N = 113)
At biomedical riska 15.5 12.1 11.2 6.2 12.1
Wight for height
Obese 4.0 6.0 6.2 8.0 5.7
Overweight 19.0 19.0 16.9 20.4 18.7
Normal 72.2 71.6 72.5 67.3 71.4
  moderately 4.0 3.4 3.9 4.4 3.9
  severely 0.8 0.0 0.6 0.0 0.3
Total 100.0 100.0 100.0 100.0 100.0
Weight for age
Obese 8.7 10.8 7.9 14.1 9.9
Overweight 13.9 15.9 19.1 23.9 17.2
Normal 58.3 59.1 58.4 53.1 57.8
  low and moderately 18.3 14.2 13.5 8.8 14.6
  severely 0.8 0.0 1.1 0.0 0.5
Total 100.0 100.0 100.0 100.0 100.0
Height for age
Above normal 3.2 6.0 5.6 8.8 5.4
Normal 65.5 66.4 75.3 72.6 69.0
Below normal 31.3 27.6 19.1 18.6 25.5
Total 100.0 100.0 100.0 100.0 100.0

a. Insufficient monthly increase in weight according to the standard of the Ministry of Health.

TABLE 9. Relationship between nutrition status (weight for age) and calorie adequacy

  Nutrition status Total
Calorie adequacy Under-nourished Normal Over-weight Obese
< 80% 28 71 18 6 123
80%-90% 12 30 5 3 50
90%-100% 8 33 9 6 56
>100% 41 111 19 11 182
Total 89 245 51 26 411

c2 = 5.845 with 9 df. a = 0.755

TABLE 10. Relationship between nutrition status (weight for height) and calorie adequacy

  Nutrition status  
Calorie adequacy Under-nourished Normal Overweight and obese Total
<80% 8 89 26 123
80%-90% 2 37 11 50
90%-100% 2 35 19 56
> 100% 9 131 42 182
Total 21 292 98 411

c2 = 4.429 with 6 df. a = 0.619.

Nutrition status of the children under study

One of the objectives of the study was to determine whether participation in the CFP programme contributes to reducing the rate of undernutrition by increasing calorie intake and thus improving the calorie adequacy of low-income children.

As shown in table 8,4.2% of the children were undernourished according to weight for height, 15. 1% according to age, and 25.5% according to height for age. In this analysis, children with measurements less than 80% of the standard are considered to be undernourished when using weight for height or weight for age, and less than 90% in the case of height for age [5]. Within the group of undernourished, the proportion suffering from severe undernutrition was less than 1% using either weight-for-height or weight-forage indices (0.3% and 0.5% respectively).

As expected, the proportion of undernourished children as measured by height for age or weight for age decreased as income increased. In the latter case. the percentage of children 1-5 years old who were undernourished declined steadily from 19. 1% in quintile 1 to 8.8% in quintiles 4 and 5; in terms of height for age, this proportion decreased from 31.3% to 18.6%. By contrast, the proportion of overweight, obese, and above-height children increased with income, which is a general trend in developing economies. But in terms of weight for height' no differences were seen in rates of undernutrition among income strata. That indicator of short-term undernutrition shows that there were not many severe cases, and that the remaining moderate undernutrition levels could be explained by other associated factors such as morbidity, rather than by income.

To determine whether the inverse relationship between undernutrition rates measured by weight for age and per capita household income is mediated by increased calorie adequacy, table 9 was constructed. In fact, the chi-square test indicates that the hypothesis of a statistical association between nutrition status and degree of calorie adequacy is unfounded, in spite of the fact that both improved with income. The chi-square test also indicates that no statistical association existed between calorie adequacy and short-term undernutrition as measured by weight for height (table 10).

These findings bring into question the appropriateness of using calorie adequacy as a proxy for nutrition status. Economic theory suggests that the impact of family income on the nutrition status of children probably operates through a number of interrelated factors, all of which contribute to child development. In fact, the study shows that as income increased, not only did calorie adequacy improve but diversity in the diet increased, as did availability of housing services and equipment, demand for health services, and education level of the parents, all of which enhance family health and nutrition.

As suggested by Schiff and Valdes [6], it is the combined effect of all these factors that results in lower rates of malnutrition. Apparently, in the context of a middle-income country such as Chile, calorie adequacy is not one of the main constraints for improving the health and nutrition of preschoolers.

TABLE 11. Quantity and value of food distributed per child per month by the CFP by nutrition status and age

  Milk-cereal Rice Powdered soup Total value ($Ch)
kg $Ch kg $Ch kg $Ch
1-2 years 1 302 0 0 302    
2-5 years 1 302 0 0 302    
At-risk and undernourished
1-2 years 2 604 2 172 3 468 1,244
2-5 yeras 2 604 3 258 0   862

The prices per kilogram of product are 302 pesos ($CH) for milk-cereal, $CH 86 for rice, and &CH 156 for powdered soup.

Income transfer effected by the CFP

The free distribution of milk-cereal and other foods by the CFP is a means of transferring income to poor households. Table 11 shows the type, quantity, and value of food distributed by the CFP according to the age and nutrition status of children. Products such as rice and powdered milk are available in the market and thus have a market price that can be used to assess the implicit income transfer. This is not the case with either the milk-cereal mix or the infant soup (sopa-puré) which are not commercially available; the values used to estimate income transfer per child are therefore based on prices paid by the programme to manufacturers. (These values probably underestimate the income transfer to beneficiaries because the unit cost per kilogram does not include a retail margin. In addition, if we assume a one-to-one rate of substitution for powdered milk and cereal-milk, the value of the latter would be higher.)

The income transfer to households with healthy children (from a nutrition standpoint) was one-fourth of that given to at-risk or malnourished children in the case of 1-2-year-olds. This ratio increased to 35% in older children.

The relative importance of the income transfer provided by the CFP decreased with income: in the lowest income decile, the value of the CFP products represented 3.2% of household income if the child was healthy but could be as much as 13.2% or 9.2% if the child was undernourished or at risk, depending on age (1-2 and 2-5 years of age respectively). The relative value of the income transfer was less than 1% of household income for quintiles 3-5 for healthy children but slightly higher for at-risk or undernourished children. Thus, the CFP programme is effective in redistributing income to low-income households.

Regression analysis

Estimates of both the probability of participation in the CFP milk-cereal programme and of preschoolers, calorie intake are derived below.

Estimation of the demand for CFP products

Given that a very high proportion of the children under study did receive milk-cereal from the CFP programme (91%), the statistical analysis seeks to identify factors that negatively affected the probability of participation. According to the results from the Probit estimates (table 12), both factors, low acceptability of the milk-cereal by either the mother or the child, and long waiting time for health examinations (over two hours), were associated with reduced likelihood of participation. These results are in accordance with the earlier description of the characteristics of participants and non-participants. The probability of participation was also higher when the child was the oldest or only child. Other factors such as income decile, age of the child, age and education of the mother, and household size and composition were not statistically significant in explaining the demand for the programme within the sample of children attending the National Health Service, and therefore were not included in the final version of the model.

TABLE 12. Probit equation for the likelihood of participation in the CFP milk-cereal programme (children 1-5 years old)

Explanatory variable    
Constant 0.566** (3.799)
Low acceptability (dummy) -0.280* (-1.683)
Waiting time for health examinations (dummy)a 0.252* (1.601)
Parity (dummy)b 0.249* (1.633)
Sample size 718  
Log likelihood -199.2  
c2 8.53  
Degree of freedom 3  
Significance level 0.0362  

a. This variable takes the value of 1 when the waiting time for health examinations is less than two hours.
b. This variable takes the value of 1 if the child is the oldest or the only child.

* Significant at the 0.1 level using a two-tailed test.
** Significant at the 0.01 level using a two-tailed test.

Estimation of calorie intake of children under study

The model used to evaluate the impact of the CFP on the calorie intake of children is based on the Engel function, which measures the degree of association between changes in food consumption and changes in income and other variables. Under the assumption that prices are constant in a cross-sectional sample, the regression equations consider the following explanatory variables: logarithm of monthly per capita household expenditure (as a proxy for income); age and sex of the child; household composition; education of household head; status of participation of the child in existing on-site feeding programmes or, alternatively, the number of calories supplied by on-site feeding programmes; and predicted likelihood of participation in the CFP. The rationale for including observed participation in on-site feeding programmes as an independent variable for predicting the caloric intake of children is that the decision to participate in such programmes is basically made more by the programme administrators than by the household.

Table 13 presents the regression coefficients from two alternative models for explaining the calorie intake of children. These results show that the calorie intake of preschoolers increases with income, education of the household head, age of the child, and participation in on-site feeding programmes. The inclusion of the variable age squared" allows for the expected non-linear association between age and calorie intake. On the other hand, the inclusion of an explanatory variable for predicted participation in the CFP (based on regression results presented in table 12) appears to result in slightly lower calorie intake. The regression coefficients for this variable indicate that an increase of the probability of participation in the CFP from 0.7 to 0.8 implied a negligible reduction in daily calorie intake of 65 calories.

These results indicate that the provision of free milk-cereal by the CFP programme released household resources otherwise spent on food so that they could be used for purchasing complementary health and nutrition inputs. This income transfer occurred in such a way that it had no positive implications in terms of calorie intake.

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