Geography and culture matter for malnutrition in Bolivia

Geography and culture matter for malnutrition

in Bolivia

Rolando Morales*, Ana Marıa Aguilar, Alvaro Calzadilla

Ciess-Econometrica, Bolivia

Abstract

The prevalence of health problems and malnutrition in Bolivia is exceptionally high, even in

comparison to other underdeveloped countries. This study analyzes the relationship between a two

measures of child health—height-for-age and weight-for-age z-scores—and a set of physical and

cultural determinants of child nutrition, including mother’s characteristics, household assets and

access to public services. The ultimate aim is to identify the most important determinants of child

health and to measure the relative impact of each factor on the height and weight z-scores. A

sequential strategy was adopted in order to estimate a two-equation linear model with correlated error

terms. A major finding points to geographical and cultural variables as main causes of nutritional

status and highlights the role of mother’s anthropometrical characteristics. This study uses data on

over 3000 children gathered from a Demographic and Health Survey (DHS).

# 2004 Elsevier B.V. All rights reserved.

JEL classification: O12; I12; I38

Keywords: Child malnutrition; Height; Weight; Child health; SUR estimation

1. Introduction

The purpose of this study is to identify the principal determinants of child health in

Bolivia. The indicators used to measure child health are the height-for-age z-score and the

weight-for-age z-score of children less than 36 months of age. There are no standard

references for other indicators of malnutrition.

http://www.elsevier.com/locate/ehb

Economics and Human Biology 2 (2004) 373–389

* Corresponding author.

E-mail address: [email protected] (R. Morales).

1570-677X/$ – see front matter # 2004 Elsevier B.V. All rights reserved.

doi:10.1016/j.ehb.2004.10.007

Among the determinants of child health, we consider physical and cultural context,

mother’s social and anthropometric characteristics, household assets and access to public

services. We introduced sets of variables belonging to each of the described categories

in a stepwise approach. In the first steps, OLS models were estimated for height and

weight z-scores. Thereafter, we applied an algorithm for simultaneous estimation of both

equations, considering that they are correlated.

1.1. Malnutrition in Bolivia

Although malnutrition is a well-known problem in Bolivia, its complex causality is not

fully understood. Data provided by Demographic and Health Surveys (DHS) conducted in

1989, 1994, and 1998 indicate a downward trend in the percentage of undernourished

children. Unfortunately, this downward tendency for child malnutrition has been

accompanied by increases in certain underprivileged areas. For example, in the Department

of Potosi, the percentage of children under 2 standard deviation for height-for-age

increased from 33.2% in 1994 to 49.2% in 1998. Table 1 describes the evolution of z-scores

of height-for-age, weight-for-age and height-for-weight between 1994 and 1998; it also

shows the differences in nutritional status between males and females, and urban and rural

children.

The 1998 DHS shows that more than 25% of children are below �2 z-score for height-

for-age, 50% of them had a z-score lower than �1.15 and half fall between �2.09 and

�0.27. Very few children have a height-for-age score over zero.

The same survey shows that 10% of children are below �2 standard deviations for

weight-for-age, 50% have a z-score lower than �0.53 and 50% fall between �1.28 and

0.28. Nearly 40% of children had a weight-for-age z-score over zero and only 1.5% of

children had a weight-for-age z-score over 2 standard deviations. These data illustrate that

malnutrition affects child height more than weight.

1.2. Geographical and Ethnic Characteristics

Bolivia is located between the Tropic of Cancer (23.45N latitude) and the Tropic of

Capricorn (23.45S latitude), and is classified as a tropical country even though a

significant portion of its territory is not tropical. The population is overwhelmingly

concentrated in high and cold areas with low agricultural productivity. Bolivia is the only

country in the Americas where indigenous peoples (Aimaras, Quechuas and other ethnic

R. Morales et al. / Economics and Human Biology 2 (2004) 373–389374

Table 1

Percentage of children two standard deviations below the median (z-scores: children between 3 and 35 months old)

Characteristics Height-for-age Weight-for-age Weight-for-height

1994 1998 1994 1998 1994 1998

Male 28.2 27.1 16.5 9.9 5.5 2.1

Female 28.3 24.0 15.2 9.0 3.2 1.4

Urban 20.9 18.3 11.6 6.1 3.3 1.3

Rural 36.6 35.6 20.4 14.1 5.6 2.4

Source: Instituto Nacional de Estadıstica, Bolivia.

groups) continue to represent the majority of the population. In addition, the pattern of

human settlement shows a high concentration in a few main cities and dispersion in

rural areas. Generally, towns distant from the main cities have insufficient community

facilities.

Bolivia’s geography varies widely, from tropical in the lowlands to glacial in the highest

parts of the Andes. Temperatures depend primarily on elevation and show little seasonal

variation. The adverse geographical environment aggravates the problems of income

generation and domestic food production. Another major obstacle to accessing basic

services and markets in the main cities is inadequate and costly transportation caused by

the country’s rocky terrain and scattered population. In addition, Andean inhabitants are all

too familiar with food crises; recurrent cycles of drought, frost and hail affect crops and

frequently kill livestock.

1.3. The data source

The DHS survey of 1998 is the primary source of information used in this research.

Physical measurements (height and weight) were taken of children under 3 years of age. A

second source of data is the geographical data base elaborated by Ciess-Econometrica,

which provided useful information at the municipal level. A third source is the information

about community services and facilities elaborated by UDAPE and the Viceministerio de

Participacion Popular at the municipal level. The height and weight data are converted into

z-scores.

The main effort to identify variables relating to child health has been focused on

variables that can explain the gap between the reference and the observed distribution of

the z-scores (for height or for weight). While a large volume of data is available, it fails to

provide information about the existence of specific nutritional programs, access to food,

regional feeding/eating practices, household consumption and prices.

Table 2 shows descriptive statistics concerning the variables identified as the most

important determinants of child nutrition and its taxonomy.

R. Morales et al. / Economics and Human Biology 2 (2004) 373–389 375

Table 2

Descriptive statistics of the determinants of child nutrition

Group of variables Variables Signification Mean Std. dev. Minimum Maximum

Control variable Ch_age Age in months 17.54 10.21 0.00 35.00

(Ch_age)3 Cubic of child age

divided by 1000

10.96 12.77 0.00 42.88

Context variables Quechua Quechua speaking 0.18 0.39 0.00 1.00

Altitude Altitude (3000 m) 2.26 1.44 0.13 4.00

Mother’s characteristics Mo_height Mother height z-score �2.08 0.92 �4.11 1.94

Mo_edu Mother’s years of

education

6.25 4.69 0.00 19.00

Household assets and access

to public services

Floor Covered floor 0.56 0.50 0.00 1.00

Refrigerator Possesses refrigerator 0.23 0.42 0.00 1.00

Drainage Public sewer system 0.24 0.43 0.00 1.00

2. Modeling child health

A bidimensional measure of child health, hY1,Y2i, has been adopted, where Y1 is the

z-score for height and Y2 the z-score for weight for children under 3 years old. These two

variables, hY1,Y2i, are related to the set of variables listed in Table 2.

We have adopted a sequential strategy to identify the set of variables related to our

bidimensional measure of child health. In the first five steps of this sequence, we have

applied ordinary least squares (OLS) to estimate each equation related to height and to

weight z-scores. In the sixth step, we have assumed the general framework under the

umbrella of a simultaneous equation system with the SUR option.

3. The model

We have elaborated six models from the simplest to the most complex. Each new model

contains the variables included in the previous one. Each step in building the model takes

variables in the same order as presented in Table 2.

3.1. Control variables

Most studies aimed at identifying factors that explain the anthropometric differences

between the observed population and the reference population have shed light on a

systematic age effect (Thomas et al., 1992; Gibson, 2002; Barooah, 2002). This effect was

also found in our data. The outcome of the estimation by OLS corresponding to the first

step in the model building process is shown in Appendix Table A.1. The independent

variables in this model are the child age and the cubic of the child age divided by 1000. At

the end of this paper, Table 6 shows that at each step in the process of developing the model,

the coefficient estimates related to age (for height and weight) have only small variations.


Table A.1

Estimation with the control variable

Dependent

variables

N observations Parameters Root mean

square error

Square multiple

correlation

Fisher-stat Probability

Ch_height 3099 2 1.22 0.12 214.94 0.00

Ch_weight 3099 2 1.04 0.16 289.72 0.00

Equation Coefficients Std. err. t-Student P > t [95% Confidence

interval]

Ch_height

Ch_age �0.10 0.01 �18.61 0.00 �0.11 �0.09

Ch_age3000 0.06 0.00 13.50 0.00 0.05 0.07

_cons �0.05 0.06 �0.87 0.38 �0.17 0.06

Ch_weight

Ch_age �0.11 0.00 �22.79 0.00 �0.12 �0.10

Ch_age3000 0.07 0.00 17.89 0.00 0.06 0.07

_cons 0.65 0.05 12.77 0.00 0.55 0.75

In Appendix Table A.1, the R2 coefficients show that 12.19% of the total variation of the

z-score for height is explained by the age effect and 15.77% of the variation of the z-score

of weight is explained by this variable. All the estimates are significant. We could also

investigate the existence of a specific gender effect. If we add a gender variable to the

previous model, the coefficient estimates are not significant.

Fig. 1 shows a negative trend between birth and 20–24 months, and an improvement

thereafter. As expected, weight precedes height slightly in both the decreasing and

increasing sides of the curve.

3.2. Context variables

The circumstance variables for child-bearing are related to cultural and geographical

context and to the anthropometrical characteristics of their mothers.

3.2.1. Cultural context

Language is one way to identify culture. In Bolivia, in addition to Spanish, there are two

principal indigenous cultures: Quechua and Aimara.

Good childcare practices are, in many cases, behaviors acquired informally. This means

that culture is important and that verbal communication has great relevance. In Bolivia,

Spanish is the institutional’’ language.1 Women who speak only native languages tend to

close around the family and seek help from elder relatives, who often provide inadequate

advice. Table 3 shows the mean of the child’s z-scores for height and weight according to

the mother’s language. It is therefore possible to see the difference between the

anthropometric indicators in each one of these cultures. In a previous regression exercise

(not reported here), we included the Aimara and Quechua languages as dummy variables.

Both variables were statistically significant; however, the Aimara coefficients became less

significant when other mother-related variables were introduced, particularly the mother’s

education. Compared to their Spanish and Aimara peers, Quechua mothers have the worst


Fig. 1. The age effect on nutrition.

1 The majority of health workers use Spanish to address and instruct mothers. This is an important

disadvantage for all non-Spanish-speaking mothers.

child health indicator. These differences are related to the education of mothers. When we

introduce anthropometric characteristics and education of mothers in the regression, the

Aimara effect loses its significance, as opposed to the Quechua effect which, even in the

presence of other variables, continues to be significant. We further develop this point in the

section related to mother’s characteristics.

It is not clear why Quechua children have worse nutritional indicators than other

children.2 It is generally accepted3 that genetics have minimal influence on the nutritional

status of children under 3 years old. Therefore, differences in nutritional indicators seem to

be highly correlated to cultural patterns of childcare. In this case, however, despite the

existence of several studies pointing out that Quechua-speaking families (after controlling

for other factors) have worse nutritional indicators than others, no studies have aimed at

identifying factors that could explain these differences. In Appendix Table A.2, the

coefficient estimate for this variable (Quechua) is high in both equations. Nevertheless, it


Table 3

Height and weight child z-scores (means) by mother’s language

Language Ch_height Ch_weight

Spanish �1.03 �0.38

Aimara �1.56 �0.58

Quechua �1.75 �0.87

Table A.2

Adding Quechua culture

Dependent

variables


square error

Square multiple

correlation


Ch_height 3099 3 1.18 0.17 213.58 0.00

Ch_weight 3099 3 1.02 0.19 242.00 0.00


interval]

Ch_height

Ch_age �0.10 0.01 �19.58 0.00 �0.11 �0.09

Ch_age3000 0.06 0.00 14.17 0.00 0.05 0.07

Quechua �0.74 0.05 �13.61 0.00 �0.85 �0.64

_cons 0.11 0.06 1.95 0.05 0.00 0.23

Ch_weight

Ch_age �0.11 0.00 �23.58 0.00 �0.12 �0.10

Ch_age3000 0.07 0.00 18.46 0.00 0.06 0.07

Quechua �0.53 0.05 �11.12 0.00 �0.62 �0.43

_cons 0.76 0.05 15.05 0.00 0.66 0.86

2 For example, if both have common conditions of life, it is not clear why Aimara children’s nutrition

indicators are better than Quechua children’s. This was also observed by Miller (1985) and Greksa (1984). They

found that, controlling all other factors, indigenous children have worse indicators than Hispanic children. They

did not look for an explanation to this phenomenon. More research is needed to identify the causes of bad nutrition

indicators among indigenous children.3 See, for example, Habicht et al. (1974).

decreases after the introduction of other variables in the model (Table 6). The inclusion of

this variable allows us to explain the height and weight z-scores of 17.15 and 19.00%,

respectively. Quechua is a dummy variable. Appendix Table A.2 shows that belonging to

Quechua culture results in a height z-score 0.74 points below the height mean and a weight

z-score 0.53 points below the weight mean.

3.2.2. Physical context

Geography is widely accepted to be one of the most important factors underlying

Bolivian development. However, there is still insufficient knowledge to explain: the

relative importance of these variables in the Bolivian developmental process, especially

health and nutrition compared to other economic and political variables; the relationship

between geography and human settlement patterns; the capacity of geographic variables to

explain nutritional disparities; and the possibility that regional nutritional disparities will

diminish in the future.

People whose livelihood depends on agriculture are influenced by annual fluctuations of

rainfall and temperature. This is particularly true in rural areas without irrigation facilities.

Climatic seasonality affects the growing and harvesting of crops, as well as the scheduling

and intensity of both agricultural and off-farm labor.

Some papers analyzing the high prevalence of malnutrition in Bolivia have

hypothesized that factors such as high altitude, low oxygen concentration (hypoxia)

and/or genetics have a negative influence on children’s growth.4 This argument was based

on anthropometric surveys which demonstrated that growth at high altitudes lagged behind

international growth standards, even when dietary surveys indicated sufficient nutrients. At

present, there is an ongoing debate about whether or not to accept high altitude as a factor in

malnutrition. A number of papers show that good health-care practices can result in normal

patterns of growth and development in children despite high altitude. However, our model

suggests that altitude does in fact have an impact on nutrition.

A well-documented volume on high altitude (Heath and Reid, 1995) points out that

altitude causes a reduction in barometric pressure, oxygen concentration and humidity,

while also increasing coldness, and solar, ultraviolet and cosmic radiation. These factors

establish a complex process of adaptation that includes most bodily systems. Basal

metabolism increases with altitude and coldness, and higher amounts of iron and energy

are required. Other symptoms like anorexia, low food ingestion, and high protein loss only

appear in circumstances of acute altitude change.

The term high altitude has no precise scientific definition. The authors defined it as

3000 m or more, based on the signs and symptoms suffered by lowlanders when ascending

mountains. The acclimatization and/or adaptation processes are complex. In addition to

low oxygen concentration with low atmospheric pressure, a variety of factors influence the

life of highlanders, such as genetic background, diet and chronic infections.

One example is the varied findings of studies on children living at high altitudes

worldwide. Most of them show that altitude plays a relative role in defining the growth

patterns of young children in comparison to other factors such as well-being and

socioeconomic conditions.


4 Frisancho and Beker (1970); Greksa (1986) (1984); see also Miller (1991).

Additionally, high altitude may increase the age of menarche and decrease the rate of

fertility, both important factors in preventing early pregnancies and increasing inter-

gestational gaps. Despite this, the weight of high-altitude newborns is described as lower

than that of those born at lower altitudes. This is explained by the reduction of maternal

oxygen transport, fetal hypoxia and limited fetal growth.

In our model-building process, altitude has a specific effect on children’s height and

weight in all its steps. Altitude is defined here as 3000 m or above. The estimate coefficient

for this variable in the equation for height z-score is �0.16 and in the equation for weight z-

score is �0.07. This means, for example, that the children in the Altiplano (which has an

altitude of 4000 m) risk having z-scores 0.48 and 0.28 for height and weight, respectively,

lower than children living at sea level. Furthermore, its coefficient estimates vary only

slightly (Table 6) when other variables are introduced into the model. All the coefficients

are significant at 0.0000 level.

In Appendix Table A.3, the R2 coefficients show that after adding altitude, 20.21% of the

z-score variation for height and 19.75% of the z-score variation for weight are explained by

the independent variables of the model.

3.3. Mother’s characteristics

3.3.1. Mother’s anthropometrics

Some authors (Barrera, 1990) have suggested that the contribution of maternal

education to child health may be overestimated if maternal physical measures (such as

height and body mass index) are not controlled.

To a certain degree, maternal height results from the mother’s personal nutrition history

and the conditions of her life since birth. Small maternal size reflects a history of


Table A.3

Adding altitude

Dependent

variables


square error

Square multiple

correlation


Ch_height 3099 4 1.16 0.20 195.88 0.00

Ch_weight 3099 4 1.02 0.20 190.39 0.00


interval]

Ch_height

Ch_age �0.10 0.01 �19.89 0.00 �0.11 �0.09

Ch_age3000 0.06 0.00 14.33 0.00 0.05 0.07

Quechua �0.62 0.05 �11.33 0.00 �0.73 �0.51

Altitude �0.16 0.01 �10.88 0.00 �0.19 �0.13

_cons 0.45 0.07 6.93 0.00 0.33 0.58

Ch_weight

Ch_age �0.11 0.00 �23.66 0.00 �0.12 �0.10

Ch_age3000 0.07 0.00 18.49 0.00 0.06 0.07

Quechua �0.47 0.05 �9.82 0.00 �0.57 �0.38

Altitude �0.07 0.01 �5.39 0.00 �0.10 �0.04

_cons 0.91 0.06 15.86 0.00 0.80 1.02

deprivation that is directly associated with the reproduction of small progeny. However, the

impact of small maternal size goes beyond the outcome of pregnancy. It often limits a

woman’s capacity for work and leads to the growth of a stunted child with learning

disabilities. Data shows that a significant proportion of Bolivian women are relatively

short: half of them are below �2 standard deviations for height.

In appendix Table A.4 shows the results of the new model with the addition of the

mother’s height z-score. The coefficients for the mother’s height z-score are positive in both

equations: 0.34 in the first and 0.21 in the second. This shows the intergenerational effects

of the mother’s anthropometrics. Nevertheless, it is worth noting that if the height z-score

of the mother is �2.0, the height z-score for the child will diminish only by 0.68. All

coefficients are statistically significant, and the R2 coefficients increase in a significant way.

Now, 25.84% of the variation of the height z-score for the height and 22.57% of the

variation of the weight z-score are explained by variables of the model. The coefficients of

age and altitude change only a little.

To understand the effect of Aimara culture on nutrition, we have introduced a dummy

variable Aimara in the regression (after adding the mother’s height) obtains the level of

significance reported in Table 4.

As this table shows, the Aimara language is significant in the first equation but not in the

second. The overall test for both equations (not reported here) shows that, in this stage of

the model, the variable Aimara is still significant. Nevertheless, when we introduce

variables related to mother’s education, this variable loses significance (see below).


Table A.4

Adding mother’s height z-score

Dependent

variables


square error

Square multiple

correlation


Ch_height 3099 5 1.12 0.26 215.56 0.00

Ch_weight 3099 5 1.00 0.23 180.27 0.00


interval]

Ch_height

Ch_age �0.10 0.01 �20.06 0.00 �0.11 �0.09

Ch_age3000 0.06 0.00 14.15 0.00 0.05 0.06

Quechua �0.50 0.05 �9.45 0.00 �0.61 �0.40

Altitude �0.13 0.01 �8.78 0.00 �0.15 �0.10

Mo_height 0.34 0.02 15.33 0.00 0.30 0.39

_cons 1.05 0.07 14.16 0.00 0.91 1.20

Ch_weight

Ch_age �0.11 0.00 �23.68 0.00 �0.11 �0.10

Ch_age3000 0.07 0.00 18.32 0.00 0.06 0.07

Quechua �0.40 0.05 �8.38 0.00 �0.49 �0.31

Altitude �0.05 0.01 �3.77 0.00 �0.07 �0.02

Mo_height 0.21 0.02 10.60 0.00 0.17 0.25

_cons 1.28 0.07 19.30 0.00 1.15 1.41

3.3.2. Mother’s education

Formal education enables women to use the environment more fully to their own

advantage.5 It is also a potential source of higher income and empowerment. The impact on

child care may start with communication in Spanish, making better use of facilities and

understanding child care directions more thoroughly. A literate mother may take more

advantage of programs of mass communication, such as educational posters and leaflets

used by health institutions. Knowledge about nutrition becomes more relevant as an input

to child nutritional status (Webb and Block, 2003). In Bolivia, according to the DHS, one-

third of mothers have no more than 3 years of schooling and less than 10% have completed

secondary school.

An extensive body of literature supports the notion that a mother’s education is a

determinant of children’s nutritional status (Behrman and Wolfe, 1984; Thomas, Strauss

and Henriques, 1991; Gibson, 2001; Borooah, 2002,6 etc.). However, there is some

disagreement about its importance in relation to other determinants. Haddad et al. (2002),

on the basis of DHS studies for 16 countries, found that parental education is a positive and

significant determinant of the weight-for-age indicator in only slightly more than one-third

of cases. This finding contradicts the conventional wisdom that gives mother’s education

priority in the list of determinants of children’s nutritional status (see also Stifel et al.,

1999).

Duncan Thomas (1994) used household surveys from the United States, Brazil and

Ghana to show that a mother’s education has a greater effect on her daughter’s height, and

that a father’s education has a greater impact on his son’s height. The Bolivian DHS study,

however, does not support this finding.

In appendix Table A.5 shows the estimation outcome of the new model with the addition

of mother’s education. This table shows that a one-year increase in mother’s education

increases the height z-score by 0.06 and the weight z-score by 0.04. All coefficients in the

table are statistically significant at 0.0000. In addition, the table shows that the R2

coefficients increase by a significant amount. 29.05% of the total variation of the height z-

score and 24.57% of the variation of the weight z-score are explained by the variables of the

model.

We can observe in Table 6 that the coefficients of age and altitude change only slightly

with the addition of the new variables. However, the coefficient of Quechua language

shows a significant change. Adding to this model the dummy variable Aimara, all the other


Table 4

Effect of Aimara language on regression Appendix Table A.4

Equation Coef. Std. Err. T P > t

Ch_height Aimara �34.4483 9.4008 �3.6600 0.0000

Ch_weight Aimara �13.0373 8.3030 �1.5700 0.1160

Note: non-significant parameters are in bold.

5 See, for example, Barrera (1990); Behrman (2000), and Thomas (1994).6 Barooah (2002), in line with Basu and Foster (1998), employs a wider concept of literacy, pointing out the

important influence a literate person has on an illiterate mother.

variables are still significant. However, the Aimara language is no longer significant in any

of the equations (Table 5).

This is an important issue because it shows that the low nutritional levels in the Aimara

population are due to lack of mother’s education rather than simply being Aimara. In

contrast, being Quechua is still significant for explaining malnutrition.

3.4. Household assets and access to public services

As previously mentioned, the DHS survey does not have information about household

expenditures or assets. Lacking income and assets data, whether the dwelling’s floor is

covered or uncovered is considered an acceptable indicator of the household’s permanent

income.7 Possession of a refrigerator is also associated with income.


Table 5

Effect of Aimara language on regression Appendix Table A.5

Equation Coef. Std. err. T P > t

Ch_height Aimara �11.4211 9.4485 �1.2100 0.2270

Ch_weight Aimara 2.8020 8.5192 0.3300 0.7420

Table A.5

Adding mother’s education

Dependent

variables


square error

Square multiple

correlation


Ch_height 3099 6 1.10 0.29 211.04 0.00

Ch_weight 3099 6 0.99 0.25 167.84 0.00


interval]

Ch_height

Ch_age �0.10 0.00 �20.45 0.00 �0.11 �0.09

Ch_age3000 0.06 0.00 14.47 0.00 0.05 0.06

Quechua �0.26 0.06 �4.68 0.00 �0.37 �0.15

Altitude �0.15 0.01 �10.35 0.00 �0.17 �0.12

Mo_height 0.28 0.02 12.68 0.00 0.24 0.33

Mo_edu 0.06 0.00 11.83 0.00 0.05 0.06

_cons 0.58 0.08 7.01 0.00 0.42 0.74

Ch_weight

Ch_age �0.11 0.00 �23.94 0.00 �0.11 �0.10

Ch_age3000 0.07 0.00 18.56 0.00 0.06 0.07

Quechua �0.23 0.05 �4.61 0.00 �0.33 �0.13

Altitude �0.06 0.01 �4.90 0.00 �0.09 �0.04

Mo_height 0.17 0.02 8.48 0.00 0.13 0.21

Mo_edu 0.04 0.00 9.06 0.00 0.03 0.05

_cons 0.96 0.07 12.79 0.00 0.81 1.10

7 The 2000 Mecovi Data shows that among households with a covered floor, 62% are not extremely poor,

whereas in households with an uncovered floor, 62% are extremely poor.

In many papers, access to running water appears to be an important factor related to

child health. In our study, access to sewage is more important than access to running water.

Nevertheless, access to sewage generally means access to running water.

Appendix Table A.6 shows the outcome of the model’s estimates adding the variables

floor, refrigerator, and drainage. Given that the new variables introduced in the model are

dichotomous, the difference in the child height z-score between a household with a covered

floor and refrigerator and one with neither is 0.76. The difference in the weight z-score is

0.61. Therefore, these variables are important factors explaining child health.

Given that the data about height and weight are from the same children, we can expect

correlation among the equations explaining both variables. The application of a SUR

algorithm for the simultaneous estimation of both equations provides very similar

estimators than those of the OLS estimation, but with smaller standard errors. Appendix

Table A.6 shows the results of the new and final model with the SUR assumption. The

correlation between residuals is 0.5403 and is significant at the 0.0000 level.

It is possible to see in Appendix Table A.6 that all coefficients are statistically

significant at 0.01 (at least). In addition, this table shows that the R2 coefficients have


Table A.6

Adding household assets and public services

Dependent

variables


square error

Square multiple

correlation


Ch_height 3099 9 1.07 0.32 1446.45 0.00

Ch_weight 3099 9 0.97 0.27 1138.01 0.00


interval]

Ch_height

Ch_age �0.10 0.00 �21.29 0.00 �0.11 �0.09

Ch_age3000 0.06 0.00 14.98 0.00 0.05 0.07

Quechua �0.18 0.06 �3.20 0.00 �0.29 �0.07

Altitude �0.16 0.02 �10.64 0.00 �0.19 �0.13

Mo_height 0.27 0.02 12.02 0.00 0.22 0.31

Mo_edu 0.02 0.01 4.63 0.00 0.01 0.04

Floor 0.28 0.05 5.85 0.00 0.19 0.37

Refrigerator 0.34 0.06 6.05 0.00 0.23 0.45

Drainage 0.14 0.06 2.59 0.01 0.03 0.25

_cons 0.50 0.08 6.11 0.00 0.34 0.66

Ch_weight

Ch_age �0.11 0.00 �24.69 0.00 �0.12 �0.10

Ch_age3000 0.07 0.00 19.07 0.00 0.06 0.07

Quechua �0.15 0.05 �3.04 0.00 �0.25 �0.05

Altitude �0.08 0.01 �6.01 0.00 �0.11 �0.06

Mo_height 0.16 0.02 7.94 0.00 0.12 0.20

Mo_edu 0.01 0.00 2.89 0.00 0.00 0.02

Floor 0.25 0.04 5.68 0.00 0.16 0.33

Refrigerator 0.19 0.05 3.81 0.00 0.09 0.29

Drainage 0.17 0.05 3.43 0.00 0.07 0.27

_cons 0.91 0.07 12.13 0.00 0.76 1.05

R.

Mo

rales

eta

l./Eco

no

mics

an

dH

um

an

Bio

log

y2

(20

04

)3

73

–3

89

38

5

Table 6

Estimation summary

Square multiple

correlation

Model 1 Model 2 Model 3 Model 4 Model 5 Model 6

Control

variable (age)

Quechua Quechua

and altitude

Mother’s

height z-score

Mother’s

education

Assets and public

services, SUR model

Ch_talla 0.12 0.17 0.20 0.26 0.29 0.32

Ch_peso 0.16 0.19 0.20 0.23 0.25 0.27

Ch_talla

Ch_age �0.10 �0.10 �0.10 �0.10 �0.10 �0.10

Ch_age3000 0.06 0.06 0.06 0.06 0.06 0.06

Quechua �0.74 �0.62 �0.50 �0.26 �0.18

Altitude �0.16 �0.13 �0.15 �0.16

Mo_height 0.34 0.28 0.27

Mo_edu 0.06 0.02

Floor 0.28

Refrigerator 0.34

Drainage 0.14

_cons �0.05 0.11 0.45 1.05 0.58 0.50

Ch_peso

Ch_age �0.11 �0.11 �0.11 �0.11 �0.11 �0.11

Ch_age3000 0.07 0.07 0.07 0.07 0.07 0.07

Quechua �0.53 �0.47 �0.40 �0.23 �0.15

Altitude �0.07 �0.05 �0.06 �0.08

Mo_height 0.21 0.17 0.16

Mo_edu 0.04 0.01

Floor 0.25

Refrigerator 0.19

Drainage 0.17

_cons 0.65 0.76 0.91 1.28 0.96 0.91

increased again. 31.82% of the total variation of the height z-score and 26.86% of the

variation of the weight z-score are explained by the model’s variables.

As one can see, it is possible to reject the hypothesis that some of the coefficients in both

equations are nil. This is an important result because it shows that the variables in the final

model are meaningful to the children’s health.

3.5. Summary of different models

In the six models developed, all the coefficients are significant at 0.01 level. In Table 6, it

is clear that: (a) the coefficient estimates of age and the cubic of age in both equations vary

slightly with the introduction of new variables in the model, suggesting that these variables

have a strong lineal independence from the other variables in the model; (b) we can make a

similar observation for altitude; (c) the introduction of mother’s height, skills and assets

changes the estimation of the cultural variable Quechua, but the magnitude of the change

was not enough to loose signification (which was not the case with the Aimara culture); (d)

the importance of mother’s height diminishes when we account for skills; (e) the

importance of the mother’s education decreases when assets and residence are taken into

account.


Table 7

Tests of equality of the coefficients in both equations

Are the estimates equal? Ch_height–Ch_weight = 0

Chi(1) Prob > Chi(1)

Ch_age 1.35 0.2448 Yes

(Ch_age)3 6.21 0.0127 Yes

Quechua 0.24 0.6209 Yes

Altitude 32.16 0.0000

Mo_height 27.68 0.0000

Mo_edu 4.85 0.0277 Yes

Floor 0.60 0.4367 Yes

Refrigerator 8.03 0.0046

Drainage 0.32 0.5716 Yes

_cons 28.31 0.0000

Fig. 2. Partial correlations with height and weight z-scores controlling all other variables.

Table 7 provides a surprising result: six out of nine variables in both equations do not

have statistically different coefficients. This means, for example, that belonging to

Quechua culture has a similar effect in the height z-score as in the weight z-score. That is

also the case for the mother’s education, and for households with a covered floor and access

to public services. Concerning variables that do not have similar effects, we can observe

that mother’s height is more important for height than for weight and that altitude

influences height than weight.

Finally, Fig. 2 shows the partial correlation of health indicators for each one of the sets

of variables defined in Table 2, controlling the other sets. All partial correlations are

significant at the 0.0000 level.

4. Conclusion

This study indicates that the number of stunted and underweight children in Bolivia

remains high. To identify the factors explaining this observation, we have tested a set of

regression models.

Factors related to child health were classified as control variables, context variables,

mother’s characteristics, household assets and access to public services.

Equations for height and weight z-scores were estimated, adding in each step the

following variables: (1) children’s age, (2) altitude and mother’s language, (3) mother’s

height and mother’s education, (4) household assets, consisting of having a covered floor,

possession of a refrigerator, and public drainage through the public sewer. To complete the

analysis, two simultaneous equation models were estimated. The model considered

belongs to the family of seemingly unrelated regression (SUR) models, with two equations:

Y1 to explain height scores and Y2 to explain weight scores.

The main finding of the study is that altitude and Quechua culture both influence child

health. Finally, we emphasize the need for more investigations of the physical process in

which these factors affect children’s nutrition.

Acknowledgments

The authors would like to thank IADB Research Network for financial support and Jere

Behrman and Emmanuel Skoufias for useful comments that helped improve earlier

versions of this paper.

Appendix A

See Tables A.1–A.6.

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Geography and culture matter for malnutrition in Bolivia