Excluding the poor: the
impact of public expenditure
on child malnutrition in Peru
GISSELE GAJATE-GARRIDO
IFPRI
APRIL 2011
Research question
Why is the urban-rural gap in child malnutrition
increasing in Peru despite government efforts to
improve the provision of public services?
 To answer this question I need to analyze the causal
impact of total regional public expenditure on early
childhood nutritional outcomes.
Introduction: 1/ 4
Summary of findings
 Public expenditure has no effect on child nutrition in rural
areas. This statement is true regardless of the type of
expenditure analyzed.
 These areas are shown to have lower levels and worst quality of
public services.
 In urban areas, there is a positive and significant impact of
public spending on children’s nutritional outcomes.
 Over 70% of the increase in malnutrition disparities between
rural and urban areas can be explained by the impact of public
expenditure.
Introduction: 2/4
Summary of findings
 Spending in health and sanitation is the most important
channel through which public expenditure impacts child
health, followed by transportation and education.
 Spending in nutritional interventions do not impact child
well-being.
 Yet, in cities, despite the availability of effective public
goods and services, the worse off children do not benefit
from public good provision. These are the children
belonging to poor indigenous households and are the ones
with the lowest birth weights.
Introduction: 3/4
Contributions to the literature
 I use a panel of children  I control for unobserved
heterogeneity.
 I provide exogenous variation to correct for the
potential endogeneity of regional public expenditure.
 The variation comes from the level of natural
resource royalties assigned to each region = natural
resource endowments in each region × world prices.
 In contrast to health input prices this instrument is
proven not to alter individual regional migration.
Introduction: 4/4
Outline
 Introduction
 Child Nutrition and Public spending
 Data
 Identification Strategy
 Specification
 Results
 Robustness Checks
 Discussion
Motivation
 Thirty percent of children less than 5 years old
endure chronic malnutrition in Peru .
 Malnourishment has a negative impact on children’s:
 Cognitive development (Glewwe et. al. 2001, Walker et. al.
2005)
 Human capital formation (Glewwe et. al., 1995, Maluccio et.
al. 2006).
 It may hamper a country's economic development
(Thomas and Strauss 1998).
Child Nutrition : 1/2
Motivation
 Between 2002 and 2006, per capita government
expenditure in Peru increased by 34%.
 Almost all the most important spending
categories increased even more: education by
35%, poverty alleviation programs by 16%, health
and sanitary infrastructure by 38% and transport
by 40%.
 Yet total the urban rural gap in child
malnutrition increased from 26% to 32% during
this time.
Child Nutrition : 2/2
The impact of public spending
 Malnutrition in developing countries is caused by food
deprivation and infectious diseases which are related to
poverty, lack of access to markets, deficient
transportation, inadequate health and sanitary
infrastructure and low levels of education (Wolfe et al.
1982, Alderman et al. 2001, Escobal et al. 2005, Mwabu
2008, Aguiar et al. 2007).
 Public goods and services could directly affect a child’s
nutritional status by providing access to health facilities,
better sanitary infrastructure or nutritional programs
(Valdivia 2004, Thomas et al. 1996, Frankenberg et al.
2005).
Public Spending: 1/4
What determines the efficacy of public spending?
1.
Quantity and quality
of public goods and
services
Supply constraints
2. Possession of key
assets that could affect
demand and efficient
use of public services
Demand constraints
Public Spending: 2/4
Supply Constraints
 There exist differences in the quality and
availability of public services between urban and
rural areas (Valdivia 2004). Van de Poel et al.
(2007) found that Peru had the largest
urban/rural gap in nutritional levels of 47
developing countries.
 In rural areas delivery of public goods is poorly
monitored. Lack of accountability  high rates of
absenteeism, irregular working hours.
Public Spending: 3/4
Demand Constraints
 The ability of a household to take advantage of
public goods and services is not only determined
by the availability (the actual supply) and the
quality of these.
 It is also related to the capability and motivation
of this household to take full advantage of these
goods and services. E.g.: language barriers,
income barriers, child resilience, child gender.
Public Spending: 4/4
Data
 Individual Longitudinal Data
 The Young Lives Study (YLS - An International Study of
Childhood Poverty) is a recently generated data set that tracks
the lives of 2052 children growing up in Peru over 15 years (1st
round: June 2002, 2nd round: Oct. 2006-Aug. 2007).
 Information is available from birth until around the children’s
6th birthday .
 Macro Level Data
 National Household Surveys: representative at regional level.
 Public expenditure data from the Ministry of Economics and
Finance.
Data: 1/3
Main variables
 Dependent variable: nutritional status: height-for-age z-
scores  long run indicator of the nutritional status of
children.
Percentage of children malnourished in the sample:
• 1st round 17% (Urban areas 11%, Rural 29%)
• 2nd round 32% (Urban areas 20%, Rural 58%)
 Independent variable: total public expenditure =
investment + current expenditure. Most important
spending categories: education (35%), poverty alleviation
programs (27%), health and sanitary infrastructure (14%)
and transport (9%).
Data: 2/3
Descriptive statistics for urban and rural
Round 1
Variable
Round 2
Urban
Rural
Urban
Rural
Probability of living in the area
65%
35%
69%
31%
Probability of being malnourished
11%
29%
20%
58%
Maternal education level
2.18
1.07
2.38
1.21
Household size
5.58
5.96
5.25
6.08
Household Wealth score
1.14
-2.10
1.07
-2.42
General Physician in locality
80%
27%
78%
32%
Pediatrician/Gynecologist
50%
4%
39%
3%
Midwife in locality
88%
50%
79%
52%
Public Health Center
80%
21%
99%
92%
Disease prevention Campaigns
93%
78%
99%
95%
Highlands
39%
72%
36%
77%
Population
6058
2416
21246
1769
Source: Young Lives Study Round 1 and 2
Identification Strategy
 I use the level of Mining Royalties assigned to each region
each year (not spent) as a source of exogenous variation.
 Royalties assigned to each region had vary greatly
geographically and have increased dramatically across
years, due to the increase in the price of minerals. Thus
providing sufficient variation for identification.
 Royalties are arguably unrelated to children’s nutritional
outcomes, provided their expansion is uncorrelated with
changes in net per capita income, migration patterns and
health status.
Identification Strategy: 1/2
Exogeneity tests
 I verify that increases in royalties do not expand local economic
activity by running a pooled OLS regression (with data from
2002 and 2006) of net per capita income on the level of royalties
assigned to the region.
 I test that the level of royalties is not related to the general
population health level by running a Probit regression of health
status on the amount of royalties assigned to the region.
 Finally, I run a Probit regression of migration status on the
royalty level assigned regionally.
 In all cases royalties do not have a statistically significant impact
on the dependent variable.
Identification Strategy: 2/2
Specification
 1st stage:
Pjt   j  t  1 X jt   2 Z jt   jt
R
Pjt : level of public expenditure in period t and region j
Zjt : level of mining royalties assigned in period t to region j
 2nd stage:
Nijtc  i   j  t  Pjt  1 X ijt   2 X ijt  3 X ijt   4 X t  5 X t  ijt
c
M
H
C
R
Ncijt : height-per-age z-score level
i, j and t : individual, regional and year fixed effects, respectively
Xct, XMt, XHt and XCt : child, parental, household and community characteristics, respectively
XRjt : other regional characteristics that vary over time
 I use a first difference approach and cluster the standard errors at
the region-year level to account for any variation within region
and year. I also include regional and year fixed effects.
Specification: 1/1
Pooled and Panel Data Estimation
Public spending has
no effect on child
nutrition in rural
areas.
In urban areas, there
is a positive and
significant impact of
public spending on
children’s nutritional
outcomes.
Over 70% of the
increase in
malnutrition
disparities between
rural and urban
areas can be
explained by the
impact of total public
expenditure.
Impact of public expenditure on a child’s
nutritional level for the whole sample, rural
and urban areas
Z-score height for age
Panel
OLS
Pooled
IV
Panel
IV
Panel
Rural
IV
Panel
Urban IV
Total Public Spending
-0.062
0.149
0.086
-0.052
0.294
(0.048)
(0.110)
(0.098)
(0.051)
(0.093)***
Other Controls
YES
YES
YES
YES
YES
Number of observations
1,877
3,836
1,877
676
1,201
R-squared
0.353
0.328
Dependent Variable:
Robust standard errors clustered by region-year in parentheses, * significant at 10%; ** significant at 5%;
*** significant at 1%. They include regional, individual and time fixed effects, as well as child’s age and
health status, maternal education, # of children under 5 years of age in the household, drinking water
availability, household size, wealth score, public programs in the community, population size, belonging to
the highlands, being an urban area, a poverty index and the level of regional debt payments..
Source: Young Lives Study Round 1 and 2, ENAHO 2002, 2006 and Ministry of Economic and Finance (MEF) Budgetary Expenditure for 2000 – 2006.
Panel Data Estimation
The most important
channel through
which public
expenditure impacts
child health is health
and sanitation
efforts, followed by
transport and
education.
Spending in
nutritional
interventions do not
impact child wellbeing.
Note: each
coefficient represents
an independent
regression.
Impact of public expenditure by government
sector on a child’s nutritional level
Dependent Variable:
Z-score height for age
Whole
Sample
Rural
Sample
Urban
Sample
0.89
-0.476
3.802
(1.091)
(0.480)
(1.509)**
0.192
-0.134
0.513
(0.198)
(0.142)
(0.133)***
0.413
-0.187
4.618
(0.579)
(0.183)
(6.909)
1.284
-6.879
1.813
(1.260)
(40.510)
(0.460)***
Other Controls
YES
YES
YES
Number of observations
1,877
676
1,201
Total Public Spending in Health and
Sanitary
Total Public Spending in Education
Total Public Spending in Poverty
Alleviation
Total Public Spending in Transportation
Robust standard errors clustered by region-year in parentheses, * significant at 10%; ** significant at
5%; *** significant at 1%. They include regional fixed effects, individual fixed effects and time fixed
effects and all the previous controls. Spending measured in 100 million soles. The instrument for all the
IV estimations is the amount of natural resources royalties assigned to the regions in each fiscal year.
Source: Young Lives Study Round 1 and 2, ENAHO 2002, 2006 and Ministry of Economic and Finance (MEF) Budgetary Expenditure for 2000 – 2006.
Probability of accessing a health professional, program
or facility in your locality when public spending increases
Dependent Variable: Availability
of health inputs in the locality
General Physician
Pediatrician/Gynecologist
Midwife
Disease prevention
Child growth controls
Public Health Center
Panel IV
Rural
Urban
-0.092
0.438
(0.063)
(0.082)***
-0.06
0.325
(0.027)**
(0.115)***
-0.143
0.205
(0.120)
(0.096)**
-0.039
0.166
(0.072)
(0.098)*
-0.029
0.166
(0.079)
(0.098)*
0.171
0.02
(0.098)*
(0.094)
Note:
Each
coefficient
represents an
independent
regression.
Robust S.E. clustered by region-year in parentheses, * significant at 10%; ** significant at 5%; *** significant at 1%. Controls include regional and time fixed
effects, household wealth score, number of people living in the locality, average educational level in the region, highlands, poverty index and debt level.
Source: Young Lives Study Round 1 and 2, ENAHO 2002, 2006 and Ministry of Economic and Finance (MEF) Budgetary Expenditure for 2000 – 2006.
IV Estimation
Increases in public
spending generate
worse health
outcomes in rural
areas, while
improvement in
urban ones.
Moreover, public
expenditure is
increasing the level of
child health inputs
used only in urban
areas.
Probably poor quality
services are not
generating interest in
the rural population
decreasing the
potential demand.
Impact of public expenditure on a child
health outcomes and inputs by area
Dependent Variable: Child
health outcomes and inputs
Prenatal care intensity
Tuberculosis vaccine
Low birth weight
Serious illness
Panel IV
Rural
Urban
-0.020
0.118
(0.064)
(0.053)**
0.001
0.014
(0.002)
(0.007)*
0.092
-0.025
(0.017)***
(0.016)
0.051
-0.112
(0.029)*
(0.049)**
Robust standard errors clustered by region-year in parentheses, * significant at 10%; ** significant at
5%; *** significant at 1%. They include regional fixed effects, individual fixed effects and time fixed
effects and all the previous controls.
Source: Young Lives Study Round 1 and 2, ENAHO 2002, 2006 and Ministry of Economic and Finance (MEF) Budgetary Expenditure for 2000 – 2006.
Public spending impact on child nutrition by
personal characteristics in urban areas
0.35
0.3
Coefficient
0.25
0.2
0.262
***
0.267
***
0.294
***
0.314
***
0.334
***
0.348
***
Male
Not poor
Urban
0.15
Not indigenous
Female
0.1
Higher BW
0.05
0
Child/Household characteristics
In cities,
despite the
availability of
effective
public goods,
the worse off
children
(poor,
indigenous
and with the
lowest birth
weights) do
not benefit
from public
good
provision.
Source: Young Lives Study Round 1 and 2, ENAHO 2002, 2006 and Ministry of Economic and Finance (MEF) Budgetary Expenditure for 2000 – 2006.
Robustness checks
 Tests for selection bias due to attrition (4.39%) reveal
almost no statistically significant differences. The only
significant differences of means are for mother’s height and
probability of having an immunization program in your
community.
 According to the first stage regression parameters the
instrument is significant at a 1% level for all specifications
and it displays a positive and non immaterial effect on the
level of total public expenditure.
 For all the estimations the F statistic (at a 5% significance
level) ensured that the maximal bias of the IV estimator
relative to OLS was no bigger than 5%. This fits the
definition of a strong instrument according to Stock, Wright
and Yogo (2002).
Robustness checks: 1/1
Discussion
 This paper offers new evidence of the differential impact of public
expenditure on child health outcomes in a developing country.
 In contrast to previous work:
 It uses longitudinal micro data  it controls for unobserved heterogeneity
 It exploits variation in natural resource royalties not related to child
nutrition except through public spending  it corrects for the endogeneity
of regional public expenditure.
 This study concludes that policy makers need to account for both
supply and demand constraints to public service provision and
access.
 This will improve the effectiveness of government expenditure
and will help break the cycle of poverty and malnutrition.
Discussion: 1/1
Evolution of World Mineral Prices: 2002-2006
Note: Copper, zinc, lead and tin are measured in US$/lb, gold and silver are measured in US$/Oz.tr. Oil is measured in US$/bbl. and
natural gas in US$ per 1000 feet3. Silver and natural gas are measured on the right vertical axis.
Source: London Metal Exchange and London Bullion Market Association and NGA, annual reports, Oil Patch Research Group Back
Back
Impact of royalties on net income per capita
Dependent Variable: Net income per capita
Natural Resources Royalties
Model 1
Model 2
Model 3
Model 4
-2.99
(30.42)
Next Year's Natural Resources Royalties
18.28
(14.53)
Average future Natural Resources Royalties
14.65
(10.84)
Two years from now Nat. Res. Royalties
11.43
(8.24)
Community poverty line
Dummy for year=2002
Total Public Spending
R-squared
26.25
26.16
26.17
26.19
(3.03)***
(3.04)***
(3.04)***
(3.03)***
-2,382
-2,254
-2,234
-2,230
(194.14)***
(178.09)***
(185.81)***
(189.22)***
5.33
4.93
4.90
4.91
(0.42)***
(0.42)***
(0.41)***
(0.39)***
0.23
0.23
0.23
0.23
Robust standard errors in parentheses, * significant at 10%; ** significant at 5%; *** significant at 1%. Standard errors are clustered by region
and year. Geographical Regions: 25. Observations: 171,903 Note: Royalties are measured in 10 million soles and net per capita income in soles
per year. Source: ENAHO 2002, 2006 and Ministry of Economic and Finance Expenditure Statistics for 2002 - 2008.
Back
Impact of royalties on the probability of being ill in
the last 4 weeks
Dependent Variable: Health status
Average future Natural Resources Royalties
Model
1
Model
2
Model
3
Model
4
Model
5
0.001
(0.001)
Next Year's Natural Resources Royalties
0.001
(0.001)
Natural Resources Royalties Current Year
0.003
(0.002)
Lagged Natural Resources Royalties
0.004
(0.005)
Double Lagged Nat. Res. Royalties
0.003
(0.008)
Other Controls
YES
YES
YES
YES
YES
Robust standard errors in parentheses, * significant at 10%; ** significant at 5%; *** significant at 1%. Standard errors are clustered by region
and year. Geographical Regions: 25. Observations: 171,903. Marginal effects calculated at the means. Controls: Community poverty line, total
public spending, year fixed effects and regional fixed effects.
Note: Royalties are measured in 10 million soles and net per capita income in soles per year.
Source: ENAHO 2002, 2006 and Ministry of Economic and Finance Expenditure Statistics for 2000 - 2008.
Back
Impact of royalties on the probability of migrating
to another region
Dependent Var. Probability of migrating
Average future Natural Resources Royalties
Model 1
Model 2
Model 3
Model 4
0.001
(0.010)
Next Year's Natural Resources Royalties
0.004
(0.012)
Natural Resources Royalties
0.026
(0.021)
Lagged Natural Resources Royalties
0.041
(0.026)
Double Lagged Nat. Res. Royalties
Poverty Index
Total Public Spending
Model 5
-0.126
(0.085)
0.000
(0.000)
-0.127
(0.085)
0.000
(0.000)
-0.140
(0.084)*
0.000
(0.000)
-0.139
(0.085)*
0.000
(0.000)
Robust standard errors in parentheses, * significant at 10%; ** significant at 5%; *** significant at 1%. Standard errors are clustered by
region. Geographical Regions: 23. Observations: 1,962. Marginal effects calculated at the means
Note: Public Spending and Royalties are measured in 100 million soles.
Source: Young Lives Study Round 1 and 2, Ministry of Economic and Finance Expenditure Statistics for 2002 and 2006.
0.034
(0.041)
-0.138
(0.085)
0.000
(0.000)
Back
First stage estimation: Impact of royalties on public expenditure
for the whole sample, rural and urban areas
Dependent Variable: Public Spending
Natural Resources Royalties
Pooled IV Panel IV
Panel
Rural IV
Panel
Urban IV
2.067
2.125
2.883
1.347
(0.047)***
(0.069)***
(0.175)***
(0.053)***
Other Controls
YES
YES
YES
YES
Number of observations
3,836
1,877
676
1,201
Partial R-squared of excluded instrument
0.332
0.340
0.298
0.353
F-test for weak identification
1929
946
271
635
Robust standard errors clustered by region-year in parentheses, * significant at 10%; ** significant at 5%; *** significant at 1%.
They include regional fixed effects and time fixed effects. Spending measured in 100 million soles.
The independent variable is the amount of natural resources royalties assigned to the regions in each fiscal year.
Source: Young Lives Study Round 1 and 2, ENAHO 2002, 2006 and MEF Budgetary Expenditure for 2000 - 2006.
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