Children’ Malnutrition and Mother’s Education in Peru ∗

Ignacio Garron Vedia †

Shreyo Mallik ‡

Irina Valenzuela §

March 11, 2014

1 Introduction

Malnutrition in the first few years of life has important consequences on the future physical, in-tellectual, emotional and social capacity of children; and it may be seen as an indicator of thelevel of development of a country [6]. In Peru, according to the Demographic Household Survey(DHS) 2012, 18.1% of children under 5 years old suffers from chronic malnutrition1 (low height toage) compared to the 23.8% in 2009, with a higher incidence on rural areas (31.9%) than urbanareas (10.5%). Even though, the proportion of malnourished children in rural and urban areashas decreased from 14, 2% and 40.3% in 2009 to 10.5% and 31.9% in 2012, respectively, the gapbetween them is still very large. In particular, in Peru, rural children are most at risk of beingmalnourished, the same way that children from poor households [1].

Children nutrition status is often related to poverty, however, there are some countries ,such asIndia, that despite its impressive growth rate and poverty decline, it still has a worst performancein nutrition than some Subsahara countries 2. Deaton and Dreze (2009) [5] find a new puzzle ofnutrition while analyzing Indian data—is the bad performance (decline in average calories intake)due to changes of physical activity or improvements in health environment? This evidence mayimply that we may look at other intrinsic factors such as education, access to adequate water andsanitation, cultural practices and institutions besides income inequality [7]. Particularly, nutritionis heavily important in unborn babies and young children, as this may have long-term effect ontheir future life. That is, a higher probability of earning more money each year of their lives [3].

An important factor that determines children’s nutrition is the mother’s education. Accordingto Glewwe (1999) [8], the mechanisms from which education of the mother affects the child’s healthis by: (i) direct acquisition of basic health knowledge in school; and (ii) Literacy and numeracyskills acquired on schools, that helps mothers to increase their health’s knowledge and to enhancetheir ability on treatment children with some sort of illness. The author finds that mother’s healthknowledge (measures as knowledge about vaccination, infection treatment, diarrhea and potablewater) alone seems to be a main skill for raising child health. The study precises that schooling

∗Group Project developed for Development Economics - Barcelona Graduate School of Economics.†ignacio.garron@barcelonagse.eu‡shreyo.mallik@barcelonagse.eu§irina.valenzuela@barcelonagse.eu1Defined as Height/age < -2 standard deviations respect to the World Health Organization.2From a sample of 23 countries of sub-Saharan Africa for which they compared the data, only Eritrea is showing

a worst results than India [5].

1

contributes indirectly to the mother’s health knowledge by providing her the literacy and numericalskills needed to acquire it outside school.

Alcazar et. Al (2011) [2] analyze the factors that explains children’s malnutrition with anemphasis on mother’s knowledge of nutrition. The authors state that the knowledge of the mothermay come from different sources such as health centers, friends, family and mass media. Also, thefood that the mother provides to her child, her hygiene practice and the importance that gives tothe health controls are the reflection of the mother’s nutrition and childcare knowledge. They foundthat in fact mother’s nutritional knowledge (measured as the comparison between the height/ageor weight at birth of the child vs. the mother’s perception of it) has a positive impact on child’shealth (measured as height per age).

The aim of this work is to analyze the correlation between nutrition status of children under5 years old and mother’s education using the DHS of Peru for 2012. We use five indicators forassessing children nutrition status: i) whether the child is underweight, ii) stunted, iii) wastedor iv) anemic, and v) an aggregate index based on the previous indicators; and five indicators forevaluating the mother’s education: formal education, hygiene knowledge and three others proxiesof mother’s knowledge of nutrition. According to our results, the set of indicators of mother’seducation is negatively correlated with children’s malnutrition, with a stronger correlation in ruralareas.

2 Data Base

The data used in the present work correspond to the Demographic Household Survey (DHS) ofPeru carried out by the National Institute of Statistics (INEI in Spanish). The period of analysis is2012, because since the DHS 2009 the sample has not only been extended, but also updated basedon the 2007 Population Census. The sample of DHS 2012 corresponds to a total number of 28.376households, from which 27.488 were interviewed (a 99% rate of response) and it was obtained atotal of 23.888 interviews with eligible woman of 15 and 49 years old (97.3% rate of response). Thissample is a probabislistic one selected in two stages: conglomerates were selected in the first stageand households in the second one. It is worth to mention that this DHS has not a sub-sample ofpanels on households, but just it has at conglomerate level.

3 Estimations

3.1 Variables for Assessing Malnutrition in Children

We construct five indicators that are used to asses malnutrition in children [9]: i) stunting, ii) un-derweight, iii) anemic, iv) wasting and v) index of aggregate nutrition status. There are measuredas follow 3:

1. Stunted children: It is a dummy variable that takes value of 1 if height-for-age < -2 standarddeviations (SD) of the WHO Child Growth Standards median for children below 5 yearsof age, and 0 otherwise. It reflects the cumulative effects of nutritional deprivation and/orrecurrent infections since and even before birth[9].

3The observations we used from the DHS to construct the previous indicators correspond to children whose motherslive in the household and to children who slept the night before of the interview’s day.

2

2. Underweight children: It is a dummy variable that takes value of 1 if weight-for-age < -2 SD ofthe WHO Child Growth Standards median for children below 5 years of age, and 0 otherwise.According to WHO (2010) [9], underweight children may have an increase of mortality risk.

3. Anemic children: It is a dummy variable that takes value of 1 whether the children under 5years old has a moderate or severe anemia, and 0 otherwise. This indicator is constructedbased on the DHSs variable Anemia Level, which classifies the hemoglobin level, adjusted byaltitude, into: severe, moderate, mild and not anemic. Iron-deficiency anemia is associatedwith increased risk for child mortality and negative consequences on the cognitive and physicaldevelopment of children and on physical performance[9].

4. Wasted children below 5 years of age: It is a dummy variable that takes value of 1 if Weightfor height < -2 SD of the WHO Child Growth Standards median for children below 5 yearsof age, and 0 otherwise. It reflects an acute under-nutrition, usually as a consequence of apoor diet and/or a high incidence of infectious diseases such as diarrhea[9].

5. Index of aggregate nutrition status: It takes the value of 1 if the child has at least one of thefour previous nutrition status, 0 otherwise.

3.2 Variables for Assessing Mother’s Education

We consider a set of indicators or proxies to the mother’s education, in particular to the mother’sknowledge of child nutrition:

1. Formal education of the mother: As Alcazar et. Al (2011) [2] stated, the education of themother may affect the nutrition status of the children through the skills and habits acquired inthe formal schooling. We use as indicator for mother’s formal education a dummy variable thattakes value of 1 if the mother has more than primary education, and 0 otherwise. Regardingthis last variable, Alcazar et. al (2011) [2] find a pattern in which higher percentage of womenwithout nutritional knowledge possesses primary education in Peru.

2. Hygiene practice of the mother: It takes value of 1 if the mother has at least two of thefollowing: washes her hands after using the bathroom, after changing diapers, before preparingfood, before eating and/or before feeding the child; and 0 otherwise. In order to constructthis variable, we create an index in which each of the previous hygiene practice has a equalweight, then we establish as a cut-off the value of 0.4. A index value greater or equal than 0.4,reflects that the mother follows (knows) at least two (out of five) hygiene practice. Accordingto Alcazar et. al (2011) [2], the mother’s hygiene knowledge (and its consequent hygienepractice) may have an effect on the incidence of diarrhea and fever in children, which cancause important losses of nutrients and salts, affecting in that way the children nutritionstatus.

3. Mother’s knowledge of children’s nutrition and health acquired through health centers: We usetwo indicators as proxies: prenatal and postnatal checkup. According to Alcazar et. al (2011)[2], one of the main source of nutritional knowledge is the Health centers. In those checkups,the mothers may receive information on the importance of breastfeeding, advice on feedingpractice, among others.

• Prenatal control: It takes value of 1 if the mother has at least one prenatal chekup andwas diagnosed by a doctor, nurse, obstetrician or technical nurse; and 0 otherwise. This

3

indicator was constructed based on the information of the last child (due to the availableinformation).

• Postnatal control: It takes value of 1 if the mother has at least one postnatal chekup andwas diagnosed at a medical center (public/private); and 0 otherwise. This indicator wasconstructed based on the information of the last child (due to the available information).

4. Mother’s nutritional knowledge: This indicator is constructed based on the comparison be-tween the perception of the mother on the size of her last child at birth4 [10] [1], and whetherthe child is stunted or not (objective measure: height/age of the child). This indicator takesthe value of 1 if the perception of the mother matches with the variable stunted, and 0 oth-erwise. We consider that a better indicator could be the one that compares the mother’sperception of the size of her child at birth with the actual child’s height at the time he/shewas born, however, we assume that the stunted condition at birth may remain in the followingyears. In order to reduce the gap between the birth moment and the actual age of the child,we only use the information of the last child of the mother to construct this indicator.

3.3 Regressions

In order to assess the correlation between the indicators of children’s nutrition and mother’s ed-ucation, we perform univariate regressions5 of each variable of children’s nutrition on: i) formaleducation of the mother, ii) mother’s hygene practice, iii) pre-natal and post-natal chekup, and iv)mother’s knowledge of nutrition. However, we are not going to include in the analysis two of thechildren’s nutrition indicators: wasted and underweight, due to their low incidence on the childrenpopulation (0.67% and 3,4% of children under 5 years old are classified as wasted and underweight,respectively, according to the DHS 2012). Even though such indicators are not explicitly analyzed,they are included in the aggregate nutrition indicator.

Regarding that the children’s nutrition indicators are binary outcome variables, we could usea logit or probit estimation, which does not ignore the discreteness of the outcome variable andconstrain the predicted probabilities into (0,1). However, as stated by Cameron and Trivedi (2005)[4], an OLS estimation (which assumes a Linear Probability Model) is still an useful explanatorytool, moreover in the case of regressors containing dummy variables, the LPM is completely general.Besides, as our objective is not to analyze any causation but the correlation among our set ofindicators, an OLS estimation is a good approximation and a good guide to see which variablesare statistically significant. It is worth to mention that we incorporated some clustering correctionto the standard errors due to the fact that our observations is at individual (child) level, in whichsome observation (child) may have the same mother.

According to our results for 2012 [see Table 1], we observe that all of our five indicators ofmother’s education have a statistically significant coefficients (at 5% significance level), and ex-pected sign, in the regressions of the aggregate children’s nutrition status and stunted indicators.That is, mothers who possesses higher education than primary, has hygiene’s knowledge (and prac-tice), attends at least one pre and post-natal checkup (as indicator of acquiring some nutritionalknowledge through health centers) and recognizes the nutrition status of her child (as indicator ofnutritional knowledge) are negatively correlated with the malnutrition status of the child. In the

4Alcazar et. al (2011) [1] use a similar variable. In particular, they argue that the mothers nutritional knowledgecan be measured by comparing the mothers perception on the height/age (or alternatively weight of the born child)of her child with the objective measure of it. This means that a mother that has some knowledge about child’snutrition then she should be able to say whether her child has a standard height (or weight).

5The regressions are performed with Ordinary Least Squares with cluster by the household identification.

4

anemic regression, it turns out that just mother’s formal education and recognizing the nutritionstatus of the child have a statistically significant correlation with the anemic status of the child.

Interestingly, in all of our regressions (in particular, the ones of aggregate nutrition and stuntedindicators), we find that the variable nutritional knowledge and formal education of the motherhave the highest correlation with the nutrition status of the children compared with the otherindicators.

By regressing by area of residence (urban and rural), we find that in general, the coefficients arehigher in rural areas than in the urban ones [see Appendix: Table 3]. Besides, in the urban area,the indicator of hygiene practice is not longer statistically significant in any regression, and thepre and postnatal checkup are not significant in the regression of the aggregate index and anemicstatus of the child. It seems that in the urban area the relevant indicators related to the nutritionstatus of children are the formal education of the mother and her nutritional knowledge.

In addition, in order to evaluate whether our correlation relationships hold after controlling forsome other variables [see Appendix: Table 2]; we find that three indicators of mothers education(whether the mother possess more than primary education, hygiene practice and mothers nutritionalknowledge), are still statistically significant at 5% level and with the expected sign (negative) forthe stunted and aggregate nutrition index regressions [see Appendix: Table 3].

Malnutrition Index Stunted Anemic

(1) (2) (3) (4) (5) (1) (2) (3) (4) (5) (1) (2) (3) (4) (5)

(1) Mother’s higher than primary -0.21*** -0.22*** -0.03***(2) Hygene practice of the mother (Moderate) -0.07*** -0.08*** -0.00(3) Pre-natal chekup of the last child -0.12*** -0.17*** -0.00(4) Post-natal chekup of the last child -0.17*** -0.19*** -0.02(5) Mother’s perception of the size at birth -0.29*** -0.30*** -0.03***cons 0.43*** 0.36*** 0.42*** 0.45*** 0.51*** 0.34*** 0.27*** 0.37*** 0.38*** 0.42*** 0.16*** 0.14*** 0.14*** 0.15*** 0.16***

F 389.71 20.32 13.29 81.53 625.48 480.47 25.88 25.70 112.18 746.86 15.62 0.03 0.03 1.88 11.66r2 0.05 0.00 0.00 0.01 0.08 0.07 0.00 0.00 0.02 0.11 0.00 0.00 0.00 0.00 0.00N 9308 9305 9352 9220 9196 9190 9187 9215 9083 9181 8231 8228 8258 8141 8189

∗(p < 0.10), ∗ ∗ (p < 0.05),∗ ∗ ∗(p < 0.01)

Table 1: Univariate Regression for 2012: Children Nutrition Status vs. Mother’s Education

4 Conclusions

The present work has the objective to evaluate the correlation between the nutrition status ofchildren and mother’s education. We use 3 indicators of the nutrition status of children and 5indicators for the mother’s education. According to our results, it is showed that the differentvariables we use to measure the level of education (in particular nutritional knowledge) of themother are negatively correlated with the malnutrition status of the child, such correlations arestronger in rural areas. In particular, the variables of formal education (measured as whether themother possesses higher education than primary) and nutrition knowledge (as measured by themothers ability of recognizing the nutrition status of her child at birth), even after controlling forother variables, are significantly correlated.

Even though, the focus of our work is not on finding any causal relationship, we may believe, alsobased on previous papers, that the mother’s knowledge, regarding how to provide proper nutritionaland health care to her child, may be important on the final nutritional outcome of the child. Inorder to perform a further analysis on the causality relationship between these variables, it is worthto realize that, even though some controls were included in the regression of children’s nutritionstatus on mother’s education, there may some endogeneity problems. For instance, Alcazar et.Al (2011) [2] mention that mother’s nutrition knowledge may be affected by the perception of thenutritional status of her child, meaning that mothers with a malnourished child tend to get moreinformation about it. In such a case, it may be needed to use instrumental variables.

5

References

[1] Aguiar, C., Rosenfeld, J., Stevens, B., Thanasombat, S., and Masud, H. An analysisof malnutrition programming and policies in peru. Documento preparado para el InternationalEconomic Development Program, University of Michigan, Abril (2007).

[2] Alcazar, L., Marini, A., and Walker, I. El rol de las percepciones y los conocimientosde las madres en el estado nutricional de sus ninos. Capitulos de Libros 1 (2011), 15–83.

[3] Banerjee, A., Banerjee, A. V., and Duflo, E. Poor economics: a radical rethinking ofthe way to fight global poverty. PublicAffairs, 2011.

[4] Cameron, A. C., and Trivedi, P. K. Microeconometrics: methods and applications. Cam-bridge university press, 2005.

[5] Deaton, A., and Dreze, J. Food and nutrition in india: facts and interpretations. Economicand political weekly (2009), 42–65.

[6] Demografica, E. de salud familiar. Instituto Nacional de Estadıstica e Informatica. Lima,Peru. (2012).

[7] Gajate-Garrido, G. Excluding the rural population: the impact of public expenditure onchild malnutrition in peru. The World Bank Economic Review (2013), lht036.

[8] Glewwe, P. Why does mother’s schooling raise child health in developing countries? evidencefrom morocco. Journal of human resources (1999), 124–159.

[9] Organization, W. H. Interpretation guide.

[10] Yamano, T., Alderman, H., and Christiaensen, L. Child growth, shocks, and food aidin rural ethiopia. World Bank Policy Research Working Paper, 3128 (2003).

6

A Control Variables

1. Mother’s Ethnicity: It is an indicator of whether the mother speaks in native language athome or not. It takes value of 1 if the mothers native language is quechua, aymara or anyother native language; and 0 otherwise.

2. Total Number of Children in the Household: It is the sum of the number of male and femalechildren in the household. Lesser the number of children of a mother, more resouces couldshe allocate per child and better nourished is the child.

3. Whether the Household is a part of Cash Conditional Transfer Program JUNTOS, which givesincentives to improve health and education performance of children: It takes value of 1 if thehousehold is a Cash Transfer Program beneficiary; and 0 otherwise. Such CCT program givesincentives to improve health and education performance of children.

4. Nutritional Status of the Mother (whether she is anemic or not): It takes the value 1 if themother is moderate or severe anemic, and 0 otherwise. This condition is likely to be presentin her pregnancy stage, affecting her child nutrition. Also, if the mother is anemic, it mightbe physically difficult for her to take proper care of her children.

5. Access to Potable Water: It takes value of 1 whether the household has access to piped water:inside dwelling, outside the dwelling but within the building or public tap; and 0, otherwise.

6. Access to Safe Water: In addition to the potability of the water used, other factors such aswhether the water has been boiled, treated with bleach/chlorine or solar disinfection countsin when the issue of the nutrition of the children in the household matter.

7. Access to Toilet Facility: It takes value of 1 whether the household has access to a restroom(inside or outside the dwelling) with a toilet connected to a public sewerage system or to aseptic tank; and 0, otherwise. The type of toilet used by the household might be a potentialfactor in the vulnerability of children to infections diseases and its consequent impact on theirnutrition.

8. Access to Basic Sanitation: In addition to using a inside/outside toilet, using a Ventilatedlatrine or a septic well involves in contributing significantly towards the nutritional status ofthe children in the household.

9. Poverty Status of the Household: It takes value of 1 whether the household is classifiedas extreme poor or poor, 0 otherwise. It is worth to mention that this dummy variablewas constructed based on the variable HV270, which classifies the household according toits socioeconomic level. This classification was made based on the assets of the surveyedhouseholds: households availability of goods and services, and the housing’s characteristics(e.g. whether the household has access to electricity, tv, radio, etc).

10. Children’s Health Insurance: It takes value of 1 if the child has a health insurance, and 0otherwise. If the child possess a health insurance, then it would be more likely for the childto be treated with proper medical facilities when he is ill.

7

B Tables

HC70 Ht/A Standard deviations (according to WHO)HC71 Wt/A Standard deviations (according to WHO)HV103 Slept last nightHV025 Type of place of residenceM18 Size of child at birthM2A Prenatal: doctorM2B Prenatal: nurse/midwifeM2C Prenatal: obstetricianM2D Prenatal: health specialist (Technical Nurse)M2E Prenatal: health workerM73 Where was the baby checked for the first timeV133 Education in single yearsS490AA Do you wash your hands after using the bathroom ?S490AB Do you wash your hands after changing diapers ?S490AC Do you wash your hands before preparing food ?S490AD Do you wash your hands before serving food ?S490AE Do you wash your hands before eating ?S490AF Do you wash your hands before feeding the child ?HA1 Women’s age in yearsV131 EthnicityV202 Sons at homeV203 Daughters at homeV467D Getting medical help for self: distance to healS484 Affiliated or incorporated into the program JunHA57 Anemia level

Table 2: Variables used in the regression with their respective codes in the DHS

8

Mal

nutr

itio

nIn

dex

Stu

nte

dU

nder

wei

ght

Wast

edA

nem

ic

RU

SR

US

RU

SR

US

RU

S

(1)

Mot

her

’shig

her

than

pri

mar

y-0

.11*

**-0

.13*

**-0

.16*

**-0

.14*

**

-0.1

3***

-0.1

7**

*-0

.03***

-0.0

2***

-0.0

3***

-0.0

0-0

.00

-0.0

00.

01-0

.04*

**-0

.03*

**(2

)H

ygi

ene

pra

ctic

eof

the

mot

her

(Moder

ate)

-0.0

6***

0.01

-0.0

3**

-0.0

5**

-0.0

1-0

.03*

*0.0

0-0

.01

-0.0

00.

00

0.01

0.00

-0.0

20.0

20.0

0(3

)P

re-n

atal

chec

kup

ofth

ela

stch

ild

-0.0

2-0

.04

-0.0

30.

01

-0.0

9**

-0.0

30.0

4**

-0.0

30.0

2-0

.01

0.0

1-0

.00

-0.0

20.0

3-0

.00

(4)

Pos

t-nat

alch

eckup

ofth

ela

stch

ild

-0.0

3-0

.06

-0.0

7***

-0.0

4**

-0.0

6**

-0.0

8***

-0.0

3**

0.0

1-0

.02***

0.00

0.01

0.00

0.00

0.0

1-0

.01

(5)

Mot

her

’sp

erce

pti

onof

the

size

atbir

th-0

.28*

**-0

.22*

**-0

.26*

**-0

.30*

**

-0.2

3***

-0.2

7**

*-0

.05***

-0.0

3***

-0.0

4***

-0.0

0*

-0.0

1**

-0.0

1***

-0.0

3**

-0.0

1-0

.03**

*co

ns

0.73

***

0.60

***

0.70

***

0.6

3***

0.5

6***

0.6

3***

0.0

8***

0.0

9***

0.1

0***

0.0

2**

-0.0

10.0

10.

21**

*0.1

0*0.

18*

**

F79

.67

58.5

320

0.69

119.

61

136.9

3355.7

914.2

211.3

136.3

21.

13

1.53

1.94

1.42

2.5

45.

32r2

0.10

0.07

0.11

0.14

0.1

20.

16

0.0

20.0

10.0

20.

00

0.00

0.00

0.00

0.0

00.

00N

3779

5282

9061

3771

527

59046

3771

5275

9046

3771

527

590

46338

246

87

8069

∗(p<

0.1

0),∗∗

(p<

0.05

),∗∗∗(p<

0.0

1)U

=U

rban

,R

=R

ura

lan

dS

=A

llsa

mple

Tab

le3:

Reg

ress

ion

wit

hco

ntr

olva

riab

les

for

2012

:C

hil

dre

nN

utr

itio

nS

tatu

svs.

Moth

er’s

Ed

uca

tion

9