Outline: Synthesis Paper - Friedman School of Nutrition Science and

Transcription

Mapping Hunger in Panama:
A Report on Mapping Malnutrition Prevalence
Beatrice Lorge Rogers, James Wirth, Kathy Macías, Parke Wilde
Gerald J and Dorothy R Friedman School of Nutrition
Science and Policy, Tufts University
Boston, Massachusetts USA
March 2007
Acknowledgments
This report is the result of a collaboration between the World Food Programme, Office
for Latin America and the Caribbean, and the Friedman School of Nutrition Science and
Policy, Tufts University, Boston. The authors deeply appreciate the advice and support
of our project officers, Judith Thimke, Carlos Acosta, and Mahadevan Ramachandran.
We received helpful advice on the PovMap method from Qinghua Zhao and Peter
Lanjouw of the World Bank. We appreciate the responsiveness of individuals
responsible for management of the data sets with which we worked: Roberto González
Batista and Edith de Kowalczyk Arosemena of the Ministerio de Economía y Finanzas
(MEF). We received many responses from other individuals in these offices; their help is
acknowledged even though we cannot identify all of them by name. Patrick Florance at
Tufts University provided help in implementing GIS analysis. We also received support
from the offices of the World Food Program: Xinia Soto and Lilibeth Herrera at
WFP/LAC in Panama, for which we are grateful.
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Table of Contents
1.
Introduction................................................................................................................. 1
1.2
Goals of the Project............................................................................................. 4
2. Data............................................................................................................................. 4
2.1
General considerations........................................................................................ 4
2.2
Data Used in the Present Analysis ...................................................................... 5
3. Results......................................................................................................................... 8
3.1
Chronic Malnutrition Prevalence Rates: Standardized Categories................... 10
3.2
Malnutrition Prevalence by Quartiles ............................................................... 12
3.3
Malnutrition Prevalence: Numbers of Children Affected................................. 14
3.4
Relationship of Poverty Prevalence to Malnutrition Prevalence ...................... 17
4. Discussion................................................................................................................. 19
5. Next steps.................................................................................................................. 20
6. References................................................................................................................. 22
Appendix........................................................................................................................... 25
Technical Appendix Tables .......................................................................................... TA 1
iii
List of Tables
Table 2.1.
Characteristics of Data Sets Used in the Analysis
5
Table 3.1:
Comparison of Malnutrition Prevalence and Mean HAZ
based on Small Area Estimation and Survey Estimates
9
Country Descriptive Statistics on Malnutrition
10
Table 3.3:
Figure
Figure 1:
Range and Number of Malnourished Children In Panama
(based on HAZ) by Province
16
List of Maps
Map 1
Map 2
Map 3
Map 4
Map 5
Map 6
Map 7
Map 8
Map 9
Map 10
Provinces and Zones, Panama
Prevalence of Chronic Malnutrition, Non-Indigenous,
Standardized Categories
Prevalence of Chronic Malnutrition, Indigenous,
Standardized Categories
Prevalence of Chronic Malnutrition Based on Small
Area Estimation, by Dominio
Prevalence of Chronic Malnutrition, Non-Indigenous,
Based on Small Area Estimation, By District
Prevalence of Chronic Malnutrition, Indigenous, Based on
Small Area Estimation, by District
Number of Chronically Malnourished Children, NonIndigenous Areas, Based on Small Area Estimation, by
District
Number of Chronically Malnourished Children, Indigenous
Areas, Based on Small Area Estimation, by District
Discordance of Poverty and Malnutrition, Non-Indigenous Areas
Discordance of Poverty and Malnutrition, Indigenous Areas
Appendix Tables
Table A1
Data Requirements for the Prediction of Malnutrition
Table A2
Variables Used in the Analysis of Malnutrition, Panama
Technical Appendix
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Mapping Hunger:
A Report on Mapping Malnutrition Prevalence in the
Dominican Republic, Ecuador, and Panama
Beatrice Lorge Rogers, James Wirth, Kathy Macías, Parke Wilde
Gerald J and Dorothy R Friedman School of Nutrition
Science and Policy, Tufts University
Boston, Massachusetts USA
March, 2007
1.
Introduction
The government of Panama has made a commitment to reducing the prevalence of
malnutrition in the country, and has implemented a series of programs to address various
aspects of malnutrition including supplementary feeding programs for pregnant and
lactating women and their children (PAC – Programa de Alimentación Complementaria),
school feeding, and various micronutrient supplementation programs. Recently, a
program of conditional cash transfers was implemented, targeted to the poorest
corregimientos, and a cash transfer program, the bono familiar was started as a pilot. Of
course, reducing poverty and hunger is the first Millennium Development Goal (UN
2001), where “hunger” is commonly defined in terms of nutritional status, which in turn
is measured as children’s anthropometric status.
Panama has seen a deterioration in the nutrition situation between the most recent
Encuesta de Niveles de Vida (ENV) conducted in 2003, and the previous survey of 1997.
Growth retardation, or chronic malnutrition, rose nationally from 14% in 1997 to 21% in
2003. There was an alarming increase in the prevalence of growth retardation among
children less than six months of age, from 4% in 1997 to 13% in 2003. Increases in
chronic malnutrition prevalence between the two surveys were observed in urban and
rural areas, and among the extreme poor, poor, and non-poor, though of course the
prevalences are much higher among the poor and extreme poor, the rural, and especially
the indigenous populations. At the same time, there was a small (statistically significant)
increase in the percentage of preschool children at risk of overweight, or actually
overweight – from 21% to 22% at risk, and from 3.7% to 4.1% overweight (Valdez and
Castro de Barba 2006).
The observation of a worsening situation with respect to chronic malnutrition resulted in
the initiation of changes in the design and targeting of supplementary feeding programs
to emphasize expanded services to the districts with the highest prevalence of extreme
poverty (most of which are in indigenous areas), and the priority districts, and in the
1
initiation of the conditional cash transfer and bono familiar food stamp programs. 1 The
bono familiar was initiated as a pilot, starting in Santa Fé and Mironó, and expanded to a
few other indigenous areas in 2006. All these programs are seen as an integral part of the
government’s strategic plan to combat extreme poverty (Gabinete Social 2006).
An important step toward realizing the goal of reducing malnutrition (here interpreted to
refer to undernutrition, not nutritional excess) is to identify the places where the problem
is the most severe. Localizing malnutrition makes it possible to understand better the
underlying causes of the problem in different places, and to target resources appropriately
to the areas in which they will make the most difference. In addition, mapping provides a
powerful tool for visualization of the nature of the nutrition problem in a country. Maps,
because they are intuitively interpretable, can be useful for evidence based advocacy
purposes.
In this study, the outcome of interest is childhood malnutrition, measured in terms of
anthropometric status (height-for-age) of children less than five years of age. A child
falling below negative two standard deviations of the mean HAZ is considered
malnourished. 2
Nutrition surveys typically collect information representative at the Province level, but
previous studies have found significant variation in nutritional outcomes among smaller
administrative units within provinces (Fujii, 2003; Larrea et al. 2005; Benson 2006). The
present study used the technique of Small Area Estimation (SAE) to analyze data from
three countries: Ecuador, Panama, and the Dominican Republic, in order to produce subprovincial estimates of child malnutrition.
1.1
Analytic Approach: Small Area Estimation
The technique of Small Area Estimation (SAE) makes it possible to use sample survey
data, combined with a national census, to develop malnutrition prevalence estimates at
highly disaggregated levels. The approach of Small Area Estimation is to identify a
1
The districts with extreme poverty are: Kankintú, Müna, Besiko, Nole Duima, Mironó, Kusapín, Comarca
Kuna Yala, Ñürum, Cémaco, and Sambú. The Priority districts are Chiriquí Grande, Bocas de Toro, Las
Palmas, Donoso, Cañazas, Santa Fé, Chepigana, Chirmán, Tolé, La Pintada, Changuinola, Olá, Pinogana,
Chagres, San Francisco, Soná, Penonomé, La Mesa, Calobre, and Renacimiento.
2
Each anthropometric indicator has a different interpretation. Low height-for-age (HAZ), or stunting, is an
indicator of chronic malnutrition, the result of long-term food insufficiency, often combined with other
conditions (low birth weight, frequent illness). Low weight-for-height is a measure of thinness; if severe, it
indicates wasting or acute malnutrition: a lack of food in the short term (also often combined with current
illnesses like diarrhea and infection). Global malnutrition is measured by weight-for-age (WAZ). A child
can be malnourished by this criterion if s/he is stunted or wasted; either condition would result in a child
having low weight-for-age. These indicators are calculated with reference to standardized growth curves
for children under age five. It is widely recognized that the growth trajectory of healthy, well-nourished
children is similar among populations, irrespective of nationality or ethnicity, so that international standards
are appropriate to assess nutritional status at the population level across countries (WHO 1995; 2006). The
surveys used in this study made use of the NCHS/CDC/WHO anthropometric standards for growth
(Waterlow et al 1977). New standards were published in 2006 (WHO 2006) that might slightly alter the
distribution of malnutrition prevalence reported here (deOnis et al 2006).
2
sample survey representative of the national population that contains information on the
outcome of interest – in this case, malnutrition. A predictive model is developed based
on the information contained in the survey, using variables that are also present in the
national census. The parameters derived from that predictive model are then applied to
the census data, producing estimates of malnutrition for every geographic unit in the
census. The level of disaggregation is constrained only by the desired level of precision:
the smaller the unit, the less precise the estimate (Hentschel et al 2000).
We used a program developed by the World Bank for estimating poverty prevalence, and
adapted it for use with nutrition indicators. 3 The program performs both steps of the
process: it first performs the regression analysis using a statistically representative
household sample survey, and then applies the results to census data, producing percent
prevalence estimates at any level of geographic disaggregation. The PovMap program
uses a statistical technique called bootstrapping to estimate a standard error around each
estimate from the census data; this makes it possible to assess the precision of the
estimate produced, construct confidence intervals, and determine whether two geographic
units are statistically significantly different from each other. 4
The present report provides estimates of malnutrition prevalence for children under age
five in Panama. The key indicator of malnutrition in these countries, as in most of Latin
America, is low height-for-age (HAZ), or stunting. Wasting is quite uncommon in most
Latin American settings, including Panama.
The prevalence of stunting at the national level based on our SAE results is 24.3% for
Panama, and of wasting is 10.2%, higher than in most Latin American countries. Note
though, as we shall see below, these figures vary between the indigenous and nonindigenous populations. The precision of the malnutrition estimates falls as the
prevalence falls, and this is reflected in the model fit, or R2 of the regressions used to
develop the predictions in PovMap, and in the standard errors of the estimates
(Demombynes et al 2002). It stands to reason that in countries and regions with a high
prevalence of malnutrition, environmental and socioeconomic causes predominate; as
3
At the writing of this report, PovMap v. 2.0 (beta version) can be downloaded free at the World Bank
Website: http://iresearch.worldbank.org/PovMap/index.htm. See Zhao 2005
4
SAE estimates the outcome variable for each child in a community according to the following equation:
1)
Y = β0 +β1W + β2 X + β3 Z + u,
where X, W, and Z are vectors of individual, household, and community characteristics respectively. The
term u represents the disturbance or error term, which may be decomposed into two parts as follows:
2)
u ci = η c + εci ,
where η c is the variance accounted for by the community, and εci is the variance accounted for at the
individual levelTo produce an estimate of the variance around the estimate of the individual’s nutritional
outcome, the computer repeats the regression a given number of times, sampling randomly from the
variances around the parameter estimates (βs) and the error terms (η and ε). The variances are derived from
the regression model estimated using the survey data. The approach is described in a number of papers that
explain the underlying statistics and give examples of applications to estimation of the prevalence of
poverty (Elbers, Lanjouw, and Lanjouw, 2003, 2001; Zhao, 2005; Hentschel et al. 2000; Demombynes et
al. 2002) and malnutrition (e.g., Fujii 2003; 2005; Larrea 2005; Gilligan et al. 2003; Haslett and Jones
2005).
3
conditions improve in a country, the remaining malnutrition may be due in large part to
idiosyncratic characteristics of a child’s household and caretaker – factors that cannot be
included in the present model because such information is not included in the census.
1.2
Goals of the Project
The goal of the present project was to apply the PovMap method to the estimation of
malnutrition prevalence in three countries, including Panama. The purpose was to see
whether the method, developed for poverty estimation, would produce estimates of
malnutrition prevalence that were consistent with previous survey results, and then to use
the technique to estimate the prevalence of malnutrition at a more disaggregated level. A
second purpose of this project was to develop clear guidance to others wishing to adapt
the PovMap method to the estimation of malnutrition prevalence. A companion to this
report provides a manual for the application of this method (Rogers et al. 2007). 5
2.
Data
2.1
General considerations
Causal models of malnutrition depend on having information on the child, his household,
and the community in which he lives. A wide literature on the causes and the factors
associated with malnutrition provides guidance on the types of information that could be
used to predict the nutritional status of a child (UNICEF 1990, 1991; Smith and Haddad
2000). Appendix Table A1 shows examples of the key variables and the level
(individual, household, community) at which they are measured. The SAE process
depends on having enough variables that are comparable in both the survey and the
census. One challenge of using the SAE method is to find suitable proxies for variables
that would ideally be included in a predictive model.
The survey and the census must contain similar information on the individual child and
on the household. Additional variables can be added to the predictive model, which
describe the location in which the child lives. Every segment represented in the survey
will also be represented in the census 6 , so that information on the segment or the
community in which the segment is located can be derived from the census and added to
the survey data.
In addition to information on individual children and their households and communities,
secondary sources can provide institutional and geographic information that contributes
to a more accurate prediction of malnutrition prevalence, such as access to health and
5
A complete report on the results of all three country studies is available from the authors or from
WFP/LAC.
6
Not every sample survey uses the census sampling frame. In such cases, it may not be possible to match
census segments with survey segments (see, for example, Simler 2006). In such cases, information can be
calculated at the smallest unit for which the survey and sample segments can be matched – the community,
the district. In all the countries in the present study, survey and census segments could be matched. Since
official boundaries of administrative units may change, a key step in preparation for analysis is to ensure
that the same boundaries are used for these units in both the census and the survey.
4
schooling services, and coverage by social programs; land use (percentage of land in
agriculture, forests, swamp, and other uses), climate (rainfall, history of flooding and
drought), elevation and slope. (Appendix Table A2 shows the variables used in the
analysis, with their sources.)
Merging data from diverse sources poses challenges, as geographic units of institutional
and geographic data must correspond to the units of the survey and the census. Data sets,
both administrative and geographic, cannot be merged until the definitions of
administrative levels are made consistent. Further, the survey and the census must have
been implemented fairly close to each other in time, and there should not have been any
major social or economic disruptions between the survey and the census that would be
likely to change the situation with regard to nutrition, food security, or poverty.
2.2
Data Used in the Present Analysis
Table 2.1 summarizes the characteristics of the core data sets used.
Table 2.1 Data Sets Used in the Analysis
Name of Survey
ENV
Encuesta de Niveles
de Vida
Year of Survey
2003
Survey representative Province, Sampling
at what level
Domain, Urban/Rural
Number of
6,363
households in survey
Number of children
1,955
(aged 1-5 years) in
survey
Number of children
248,731
(aged 1-5 years) in
Census
Year of Census
2000
The data sets used for the Panama analysis were the Encuesta de Niveles de Vida (ENV),
implemented in 2003, and the National Census, implemented in 2000. The ENV was a
Living Standards Measurement Survey supported by the World Bank, with a sample of
8,000 households, a relatively small sample for such a survey. Geographic information
was obtained from a variety of publicly available sources. Appendix Table A2 shows the
variables included in the Panama analysis along with their sources.
The first administrative level below national is the Province; there are 9 provinces, 3
indigenous areas, called comarcas, with the status of provinces, and two comarcas with
the status of corregimiento (Atlas 2006). The second administrative unit is the District,
of which there are 75, and the third administrative unit is the corregimiento. There were
593 corregimientos in Panama at the time of the Census; these are the corregimientos and
5
their boundaries included in the present analysis. 7 The ENV collected information to be
representative of fourteen domains, or “dominios”. These domains correspond
approximately to the country’s nine provinces, except for the province of Panama, which
was divided into five sampling domains: Panama City, the rest of the District of Panama,
the District of San Miguelito, and Panama Oeste and Panama Este. The survey was
representative of these domains.
Map 1: Panama Provinces
Indigenous areas constituted a single domain. In addition to the three comarcas, whose
population is largely or entirely indigenous, there are five provinces (Bocas del Toro,
Chiriquí, Darien, Panama, Veraguas) that have significant indigenous populations. In
these provinces, if a segment had a population that was greater than 50% indigenous by
self-report, that segment was included in the domain Indigenous Areas; otherwise, it was
included in the province in which it was located geographically. Thus, all the domains
for which ENV results are reported correspond to a Province or a defined contiguous
geographic area within a province, except for the domain “Indigenous Areas”, which is
geographically dispersed.
In order to apply the SAE method and compare our resulting estimates to the results from
the ENV, we classified every census segment according to its percentage indigenous
7
New corregimientos have been formed since then; as of 2006 there are 621 corregimientos in Panama.
6
population, and placed all the segments with more than 50% indigenous individuals into
the “Indigenous Areas” domain. 8
This division affects the presentation of our results in the form of maps. Our estimates
were at the district level, but some districts have two separate estimates, one for the
indigenous portion, and one for the non-indigenous portion. Thus, estimates are presented
separately for the indigenous and non-indigenous domains. This separation allows us to
see clearly the dramatic differences in malnutrition prevalence between the indigenous
and non-indigenous areas of the country.
There was an important limitation in the Panama census data set. Age of the child is a
key variable for predicting nutritional status. Typically, an infant grows close to his
recommended growth trajectory for the first six months of life (that period for which
breastfeeding is sufficient for growth), and then begins to drop below it. Malnutrition
rates increase for children up to the age of about 24 months, and then stabilize or even
drop slightly up to age five. For this reason, malnutrition rates for children under age five
should be measured starting at six months; including children younger than this will
underestimate malnutrition, since younger infants have not yet fallen off their growth
path. In the Panama census, only age in completed years is available, even for infants.
The predictive model was thus estimated using age in completed years. We did not
include children below 12 months in the model. We had the choice of including children
below six months, or excluding children 6 to 11 months. We concluded that including
infants 0 to 6 months would produce misleadingly low estimates of malnutrition
prevalence.
2.4
Level of Disaggregation of the Estimates
We initially performed our estimates at the level of the corregimiento, but found that due
to small population sizes and quite imprecise estimates at this level, the district-level
estimates were more reliable.
It might be possible to improve the precision of the estimates, and thus permit estimation
at the level of the corregimiento by modifying the model. Some specific steps that might
be taken are: remove from the model any variables whose coefficients are non-significant
and have high standard errors; introduce interaction terms that would modify the effects
of certain key variables according to child’s age or selected geographic characteristics. 9
Another possibility is to perform the original survey regressions in a statistical program
that allows the use of a robust regression technique to identify multivariate outliers; these
cases could be removed or down-weighted prior to conducting the estimation using
PovMap.
8
In ENV, indigenous segments are those with greater than 50% indigenous population (see
http://www.mef.gob.pa/ ). To assure comparability between our estimates and the survey results, we
recalculated the percentage of indigenous population in the survey segments.
9
PovMap offers some options for testing variations on the predictive (Beta) model. It also offers a test to
determine whether the model is overfitted to the specific data set being used.
7
3.
Results
Table 3.1 shows malnutrition prevalence (based on HAZ), and average HAZ, comparing
the estimates derived from SAE with the same measures derived from the survey data
alone. These figures provide an initial sense of the plausibility of the SAE estimates.
These results are reassuring: in general, the SAE procedure, estimated at the district level
and aggregated to the level of province, produces estimates that are within 2 SE’s of the
survey-derived figures.
8
Table 3.1: Comparison of Malnutrition Prevalence and Mean HAZ of Small Area Estimation and Survey
Estimates: Panama
Malnutrition Prevalence
Mean HAZ
National
Hunger Mapping
*Survey
Hunger Mapping
*Survey
Dominio
N
Model**
Results
Estimates
Results
Estimates
123
0.128
0.113
-0.515
-0.380
(N=1955)
1 Panama City
(K=82)
(0.023)
(0.132)
(R2=0.2940)
0.179
0.195
-0.851
-0.763
2 District of Panama 106
(0.035)
3 San Miguelito
0.108
61
(0.168)
0.039
(0.024)
4 Panama Oeste
0.181
189
0.223
143
0.169
0.168
296
0.210
0.251
96
0.279
0.161
119
0.238
0.193
156
0.137
0.203
112
0.115
0.174
66
0.151
0.175
47
0.134
0.175
105
0.094
336
0.670
(0.042)
-0.779
-0.702
-0.868
-0.592
-1.003
-0.820
-0.882
-0.674
-0.900
-0.275
(0.145)
0.253
(0.037)
14 Indigenous Areas
-1.006
(0.145)
(0.035)
13 Veraguas
-1.196
(0.206)
(0.034)
12 Los Santos
-1.212
(0.145)
(0.053)
11 Herrera
-0.757
(0.138)
(0.032)
10 Darien
-1.040
(0.139)
(0.031)
9 Chiriqui
-0.985
(0.197)
(0.041)
8 Colon
-0.721
(0.160)
(0.037)
7 Cocle
-0.845
(0.125)
(0.039)
6 Bocas del Toro
-0.283
(0.150)
(0.028)
5 Panama Este
-0.493
-1.007
-1.032
(0.146)
0.629
-2.466
-2.297
(0.132)
Note: Hunger mapping results are based up predictions of a national-level model calculated in PovMap 2.0
* Survey estimates of malnutrition prevalence and mean HAZ scores are based on children 1 - 5 years of age with
HAZ = ± 5. Prevalences weighted using the inverse of the household sampling fraction (varname=factor)
** Hunger mapping results calculated using "cluster locational effect"
( ) Standard Errors Displayed in Parentheses
9
Table 3.2 shows the mean, median and range of prevalence of malnutrition according to
the three malnutrition indicators: HAZ, WHZ and WAZ as estimated by SAE, to show
how these estimates vary. Many of the maps shown below describe malnutrition
prevalence in terms of the distribution within the country, that is, by national-level
quartile. This table, and the map that follows, show information about the absolute
prevalence
Table 3.2. Malnutrition Indicators for Panama: Prevalence Percentages
Mean a
Median
IQR
Minimum
Maximum
National Prevalence
(mean prev, weighted)
a
HAZ
Malnutrition Prevalence
WHZ
WAZ
37.92
24.73
19.00-58.71
10.56
92.48
10.21
8.40
2.42 – 5.32
1.06
42.59
21.48
18.11
11.23 – 31.23
4.10
64.86
24.32
10.20
14.26
Simple mean of the small areas, based on small area estimates at the distrito level.
3.1
Chronic Malnutrition Prevalence Rates: Standardized Categories
The following two maps show prevalence of stunting in Panama, with separate estimates
for indigenous areas.). These maps show malnutrition prevalence by HAZ, using
common categories, rather than the relative measure of quartiles.
10
Map 2: Panama, Non-Indigenous, Standardized Categories
Map 3: Panama, Indigenous Areas, Standardized Categories
11
These maps demonstrate the sharp differences in estimated prevalence for the nonindigenous and indigenous areas. Among the non-indigenous districts, not one has a
prevalence rate above 40 percent. Among the Indigenous areas, only three have
prevalence rates below 40 percent, and none is below 20 percent. The two have the
appearance of completely different countries.
In the discussion that follows, results are presented, largely in terms of quartiles of
malnutrition prevalence and numbers of children affected. The quartiles emphasize how
areas within the countries vary with respect to each other, not with respect to an absolute
level. The quartile boundaries vary widely between the indigenous and the nonindigenous areas, and in some cases, the quartile spans a very wide range. It is important
to pay attention to these details in interpreting the results that follow.
3.2
Malnutrition Prevalence by Quartiles
The next three maps demonstrate the value of disaggregating malnutrition estimates.
Map 4 shows the prevalence estimates based on SAE at the level of the dominio. The
next two maps show malnutrition prevalence estimates at the district level. Two maps are
shown for the district level estimates: one for the non-indigenous areas, and one for the
indigenous areas. These maps clearly demonstrate the degree of variability among
districts within a given province or domain.
Map 4
12
Map 5
Map 6
13
Note the pronounced difference in the quartile boundaries for the indigenous and nonindigenous districts. Among the indigenous, the very lowest quartile starts at 35 percent
prevalence, and reaches to 56 percent. Among the non-indigenous areas, a prevalence of
35 percent puts the district squarely in the worst, highest prevalence quartile. Thus if
national quartile boundaries were used rather than quartiles calculated separately for the
indigenous and non-indigenous districts, the indigenous area map would be entirely in the
highest quartile, while all the non-indigenous districts would all be in the lower three.
3.3
Malnutrition Prevalence: Numbers of Children Affected
The next set of maps show that while prevalence is clearly far worse among the
indigenous areas, the actual numbers of children affected by malnutrition are higher in
the non-indigenous areas, reflecting the fact that the indigenous population as a whole
represents only 11.3 percent of the population of Panama (Bermudez 2006). Given the
small percentage of the population that is indigenous, it is striking that fully 39 percent of
the 60,522 chronically malnourished children in Panama are living in the indigenous
areas of the country. Taken together, these maps show that the indigenous are a relatively
small but highly vulnerable population, concentrated in rural areas; among the indigenous
districts, those closer to the cities (Panama and Colón) have lower rates of malnutrition
than those areas that are further away from these population centers.
Map 7
14
Map 8:
15
Figure 1
Range and Number of Malnourished Children In Panama (based on HAZ) by Province
Range of prevelance estim ates
Num ber of Malnourished Children
Indigenous
Segments
Comarca
Ngöbe Bungle
12440
Comarca
Embére
Comarca
Kuna Yala
Veraguas
Los Santos
Herrera
Darien
Chiriqui
Colón
Coclé
Bocas del
Toro
Panama Este
Panama
Oeste
District of San
Miguelito
District of
Panama
Panama City
100
75
50
25
0
0
Percent of children
2500
5000
7500
Number of children
16
10000
The maps and graph above show once again how different conclusions about targeting
would be drawn based on percent prevalence and based on absolute numbers. The five
domains that represent the province of Panama show widely varying prevalence rates, but
all are relatively low; yet together, these areas account for over 16,000 malnourished
children or about 25 percent of the national total of 60,522. The indigenous segments
and the indigenous comarca of Ngöbe Buglé show the highest prevalence and also
account for the largest number of malnourished children: close to 20,000, or about a third
of the total. Based on these results, we can calculate the consequences of targeting
interventions based on prevalence alone, or based on numbers of children affected. The
ten districts with the highest number of malnourished children account for 27,958
children, or 46 percent of all malnourished children. If the districts with the highest
prevalence were targeted (with prevalence calculated nationally, without separating
Indigenous from non-Indigenous), 18,030 children or about 30% of the total would be
reached. Once again, we do not wish to suggest that areas of high prevalence and small
numbers be ignored; rather, we point out the importance of considering both numbers and
prevalence in the design of nutrition interventions.
3.4
Relationship of Poverty Prevalence to Malnutrition Prevalence
Poverty is closely related to malnutrition, and low purchasing power is a key predictor of
malnutrition in virtually all studies. Still, one of the striking results of this analysis is that
poverty and malnutrition rates show a great deal of divergence. Poverty is not a perfect
predictor of the presence of malnutrition, because there are multiple factors besides
poverty that affect children’s nutritional status. Maps 8 and 9 show graphically, for nonindigenous and indigenous areas, the degree of discordance at the district level between
quartile of poverty and quartile of malnutrition (by HAZ). For these maps, both
malnutrition quartile and poverty quartile were calculated separately for the nonindigenous areas and the indigenous areas. Poverty estimates were produced using SAE
by Panama’s Instituto Panamericano de Geografía e Historia (IPGH) 10 at the District
level, The maps show districts that are in the same quartile of poverty and hunger; those
that differ by only one quartile, and those that differ by two or more (that is, a district in
the lowest quartile of poverty but the third or fourth quartile of malnutrition prevalence,
for example). The pink colors indicate that malnutrition estimates are higher than
poverty; in the blue areas, poverty is higher. The darker colors indicate greater
divergence. It is striking how few areas show agreement between the poverty and the
malnutrition quartiles of prevalence, and how many diverge by two or more quartiles.
This is particularly noteworthy given that both the poverty estimates and the malnutrition
estimates are based on the same survey with census data.
The discordance between estimates of malnutrition and poverty is unambiguous. For
non-indigenous areas, there are 38 districts in which poverty and malnutrition quartiles
disagree. Of these, 25 have higher malnutrition, and 13 have higher poverty. For
10
Estimates from Panama’s IPGH poverty mapping project, conducted by the Ministerio de Economía y
Finances, were constructed using the same data sources as the Tufts malnutrition mapping study, the 2003
ENV and 2000 Census.
17
indigenous areas, there are 27 districts in which poverty and malnutrition quartiles
disagree. Of these, 11 have higher malnutrition, and 16 have higher poverty. There are a
total of 7,672 children in districts that are in the lowest quartile of malnutrition, but not of
poverty: 113 in indigenous areas and 7,559 in non indigenous areas. If a program were
targeted to the two highest quartiles of poverty for both non-indigenous and indigenous
areas without considering malnutrition, 20 districts that show the two highest quartiles of
malnutrition would be excluded, resulting in 6,007 malnourished children potentially
being missed, or 10.2% of total malnourished. These results show the added value that is
gained by estimating malnutrition prevalence rather than relying on poverty as a proxy
for malnutrition.
Map 9: Discordance of Poverty and Malnutrition, Non-Indigenous Areas
18
Map 10: Discordance of Poverty and Malnutrition, Indigenous Areas
4.
Discussion
Small Area Estimation has enormous potential to guide policy decisions to address
malnutrition. Nutrition surveys cannot generally be disaggregated below the level of
province, and we have seen that there is wide variation in malnutrition prevalence within
provinces. With the information provided by these estimates, much better targeting is
possible.
A key result of the analysis is the demonstration that areas of high malnutrition
prevalence are not always those where the greatest numbers of malnourished children are
living. These results suggest establishing targeting mechanisms and priorities based on a
dual strategy: one based on locating the large number of malnourished children living in
relatively more affluent urban areas, and another based on reaching those areas that have
very high prevalence, many of them in more remote and rural locations. Such a dual
19
strategy is critical in Panama because the prevalence rates for malnutrition are
exceedingly high among the indigenous population, and this population tends to located
disproportionately in rural, relatively remote areas. Although their numbers are small,
the very high proportion of children affected by malnutrition means that targeting of
indigenous populations should be a high priority.
Malnutrition is in many ways a more complex and less predictable phenomenon than
poverty, subject to the influence of more unobservable (in a survey setting) factors
relating to child and caretaker characteristics and family environment. For this reason, it
would build confidence in the application of SAE techniques to estimating malnutrition
prevalence if a body of empirical work were produced that confirmed their accuracy with
on-the-ground verification. The criterion for accuracy is correct classification: if areas
are divided into highest prevalence, high, medium, and low, does the measurement of
malnutrition on the ground preserve these same classifications?
We hoped to provide even more disaggregated estimates of malnutrition prevalence, but
these estimates were generally too imprecise for us to have confidence in these results. It
might well be possible to improve precision by modifying the model, eliminating
imprecisely estimated parameters and judiciously adding interaction terms, and by
eliminating multivariate outliers through robust regression implemented prior to running
the data in PovMap. It would be worth making this effort particularly for those districts
that comprise large populations: the urban districts of the province of Panama. At the
same time, it may well be that for most policy purposes, the first sub-provincial level is
sufficient for most purposes given the administrative structure in place in these countries.
Once a geographic area is identified as high-risk, on-the-ground assessment will be
needed to determine the nature of the intervention to be designed and implemented.
The power of the SAE technique is that it allows conclusions to be drawn about
conditions on the ground at a level of detail that would be impossible if these conclusions
had to be based on primary data collection. Once a model is developed, SAE could
presumably be used also to track changes in the local situation, and thus probable
changes in the prevalence of malnutrition. Recall, though, that the disaggregated
prevalence estimates are based on having census data. Typically, censuses are repeated
at best at ten-year intervals, while surveys may be repeated every five or six years. A
new survey would make it possible to explore whether the underlying factors associated
with malnutrition in the country had changed, so that the direction or strength of the
association of a particular condition or characteristic with nutritional status was altered.
But it is the census that provides the information about the distribution of those
conditions, for example, notable changes in housing quality, sanitation, access to public
services or to roads and markets, that would permit re-estimation of malnutrition
prevalence.
5.
Next steps
A number of institutions have committed themselves to promoting the use of SAE
for hunger mapping in Latin America, including the World Bank, the World Food
20
Programme, and national governments. Our experience suggests that this is both feasible
and potentially valuable as a means of understanding the nature of the nutrition problem
in different areas of a country, improving the targeting of resources, and advocating for
attention to be given to problems of hunger and malnutrition. If this is to be a regionwide effort, then institutionalizing the capacity to implement the analysis, specifically
with respect to nutrition, and to apply it to mapping the results should be a high priority.
We have developed a manual for the implementation of PovMap for estimating
malnutrition prevalence (Rogers et al., 2007). Plans are under way to develop intensive
course modules for the purpose. Key decisions include not only the content and structure
of the training, but also the best place within the government or the research and
academic communities to institutionalize the capacity for the analysis.
A second effort that is already in process is the modification of PovMap to adapt it to the
specialized needs of malnutrition estimation: specifically, to adjust for the multiple layers
of clustering, and also to allow for negative values of the outcome variable. This will
produce estimates that are more statistically defensible, with standard errors that are a
more accurate reflection of the precision of the estimate. Whether this improves the
results in the sense that it changes the classification of the areas is an empirical question
to investigate.
Field-testing of the prevalence estimates produced by the SAE technique should be a high
priority. Promotion of the use of SAE and hunger mapping is based on the confidence
that the estimates produced are valid and accurate. Up to now, there have been few field
studies that would verify the SAE approach, and more of these have related to poverty
than to malnutrition.
We recommend harmonizing data collection efforts within countries. Often, different
government agencies are responsible for different data collection efforts, but there is no
reason why they could not consider the possibilities for improving the consistency of
these efforts. A series of workshops among the various institutions responsible for
national surveys, that would bring together the responsible people in a country to
consider the possibilities, might start a productive process that, in the longer term, would
improve the usefulness of all the data collection efforts.
21
6.
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Government of Brazil. Washington DC: International Food Policy Research Institute,
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24
Appendix
Table A1
Data Requirements for the Prediction of Malnutrition
Table A2
Variables Used in the Analysis of Malnutrition, Panama
25
Table A1: Data Requirements for the Prediction of Malnutrition
Level
Variable
Individual
Age in months
Gender
Birth Order
Food consumption
Illness
Household
Household size
Number of children under 5 yrs of age
Number of adult females
Number of persons per room - crowding
Census, Survey
Census, Survey
Survey; rare in census
Rare in survey; never in census
Survey, not census
Education of child’s mother
Education levels of adult household members
Economic status, wealth – ownership of key
consumption goods
Food consumption:
Adequacy
Diversity
Sources – purchase, home production, etc.
Quality of housing
Household water source
Household sanitation: latrine, garbage disposal
Electricity, fuel, telephone
Income, total, by source, earner
Livelihoods: income sources, earners
Female/Male household head
Ethnicity of members
Location: urban/rural
Household food insecurity
Community/Cluster
Economic Inequality
Marketing infrastructure:
Access to roads
Transportation infrastructure
Volatility of prices
Services:
Access to health services
Access to/enrollment in school
Local livelihoods:
Dependence on agriculture
Unemployment
Remittances
Distance in kilometers to urban centers, markets
Ethnic diversity
Province/Region
Possible Source
Land type, quality, land uses
Climate:Rainfall; Droughts; Floods
Topography Elevation, slope
26
Census, survey
Census, survey
Census, survey
Census, survey
Survey; usually no link to mother in
census
Census, survey
Census, survey
Rarely if ever available in survey;
never in census
Census, Survey
Census, Survey
Census, Survey
Census, survey
Rarely collected in survey or census
Limited information
Census, Survey
Usually available census and survey
Census, survey
Rarely collected in survey or census
Can be computed from hh assets
GIS
GIS
Secondary sources; rarely avail.
Government sources
Variable data, often not consistent
between survey and census
GIS
Census
GIS
GIS
GIS
Table A2: Variables Used in the Analysis of Malnutrition, Panama
Level
Variable
Source
Individual Child
Nutrition status
Age
Gender
ENV
Census/ENV
Census/ENV
Household
Household size
Number of children under five yrs of age
Number of adult females
Number of persons per room - crowding
% of employed household members
Female/Male household head
Marital status of household head
Education status of household head
Education level of adult household members
Per capita household salary
Household sanitation: methods of garbage
disposal, access to toilet facilities
Electricity, cooking fuel, telephone
Housing type and building materials
Household water source and access
Census/ENV
Census/ENV
Census/ENV
Census/ENV
Census/ENV
Census/ENV
Census/ENV
Census/ENV
Census/ENV
Community/Segmento
Education level of household heads
Sanitation services: water, septic, garbage
Household services – electricity, fuel, telephone
Ethnicity of household heads
Segment is urban or rural
Access to all-year roads
Distance to urban center
Topography
Land type
Floods since 1998
Population density
a
The Earth Resources Observation and Science system, part of the US Geological Survey
b
The Earth Resources Observation and Science system, part of the US Geological Survey
c
Comisión Centroamericana de Ambiente y Desarrollo, http://www.ccad.ws
d
Sistema de Inventario de Desastres
Region/Corregimiento
27
Census/ENV
Census/ENV
Census/ENV
Census/ENV
Census
Census
Census
Census
Census
CCAD
GFK Macon
EROS System, USGSb
CCADc
Desinventard
Census/WFP
Technical Appendix Tables
Table 1.
District Level Prevalence Percentage and Number of Children
Affected by Chronic Malnutrition
2
Table 2.
Variable Names and Descriptions
5
Table 3.
Regression Results (Beta Model)
10
Table 4.
Census and Survey Variables and Recoding
12
Table 5.
Comparison of Survey and Census Descriptive Statistics
27
Technical Appendix
1
Table 1. District Prevalence and Number of Children Affected (HAZ) : Non-Indigenous and
Indigenous Segments
District Name
Bocas del Toro
BOCAS DEL TORO
CHANGUINOLA
CHIRIQUÍ GRANDE
Coclé
AGUADULCE (CAB)
ANTÓN
LA PINTADA
NATÁ
OLÁ
PENONOMÉ
Colón
COLÓN
CHAGRES
DONOSO
PORTOBELO
SANTA ISABEL
Chiriquí
ALANJE
BARÚ
BOQUERÓN
BOQUETE
BUGABA
DAVID
DOLEGA
GUALACA
REMEDIOS
RENACIMIENTO
SAN FÉLIX
SAN LORENZO
TOLÉ
Darién
CHEPIGANA
PINOGANA
Herrera
CHITRÉ
LAS MINAS
LOS POZOS
OCÚ
PARITA
PESÉ
SANTA MARÍA
Los Santos
GUARARÉ
Technical Appendix
ID
Prevalence
Standard
Total
Error
Children
NON-INDIGENOUS SEGMENTS WITHIN DISTRICTS
101
102
103
23.38
14.53
24.57
7.45
3.30
6.38
346
2966
436
81
431
107
201
202
203
204
205
206
14.42
28.22
24.61
20.37
30.58
28.54
3.12
5.01
5.55
4.23
6.42
5.05
3005
4363
2464
1590
562
7359
433
1231
606
324
172
2100
301
302
303
304
305
13.67
34.24
25.98
20.62
20.12
3.33
6.16
6.94
6.09
4.54
16258
1118
1345
793
310
2222
383
349
164
62
401
402
403
404
405
406
407
408
409
410
411
412
413
22.92
20.37
17.42
24.21
21.22
12.72
13.29
22.16
22.42
39.77
17.15
19.59
32.10
5.41
3.90
4.16
7.20
3.86
3.07
3.54
4.19
5.47
8.02
4.83
4.51
5.67
1230
5297
1018
898
5424
8929
1376
757
285
1633
337
432
802
282
1079
177
217
1151
1136
183
168
64
649
58
85
257
501
502
19.93
21.23
4.74
6.22
2116
1061
422
225
601
602
603
604
605
606
607
12.83
32.16
21.85
18.16
16.49
14.88
16.57
3.25
6.53
4.99
3.86
4.36
3.65
4.16
2948
795
642
1308
611
935
556
378
256
140
238
101
139
92
701
15.76
4.40
576
91
2
Malnourished
Children
Indigenous Segments
District Name
LAS TABLAS
LOS SANTOS
MACARACAS
PEDASÍ
POCRÍ
TONOSÍ
Panamá
ARRAIJÁN
BALBOA
CAPIRA
CHAME
CHEPO
CHIMÁN
LA CHORRERA
PANAMÁ (CIUDAD)
PANAMA (Resto del
Distrito)
SAN CARLOS
SAN MIGUELITO
TABOGA
Veraguas
ATALAYA
CALOBRE
CAÑAZAS
LA MESA
LAS PALMAS
MONTIJO
RÍO DE JESÚS
SAN FRANCISCO
SANTA FÉ
SANTIAGO
SONÁ
District Name
Bocas del Toro
BOCAS DEL TORO
CHANGUINOLA
CHIRIQUÍ GRANDE
Colón
COLÓN
DONOSO
SANTA ISABEL
Chiriquí
ALANJE
BARÚ
Technical Appendix
ID
Prevalence
Standard
Error
Total
Children
Malnourished
Children
702
703
704
705
706
707
15.16
14.25
21.60
24.31
19.03
23.93
3.24
3.77
5.11
6.17
4.80
4.92
1404
1497
687
230
180
802
213
213
148
56
34
192
801
802
803
804
805
806
807
808
12.93
17.63
30.26
18.88
22.69
21.84
18.81
13.00
3.26
5.71
5.63
4.79
4.24
5.07
3.06
2.49
13148
228
3460
1654
2884
343
10623
25033
1700
40
1047
312
654
75
1998
3254
808
809
810
811
18.18
26.37
11.06
21.14
4.14
5.46
2.35
8.39
26924
1438
22370
76
4895
379
2474
16
901
902
903
904
905
906
907
908
909
910
911
ID
14.24
3.87
649
92
15.96
5.04
1186
189
19.71
6.61
1596
315
24.86
6.72
1089
271
30.65
5.64
1723
528
15.70
4.78
1120
176
19.10
5.93
401
77
23.31
7.31
949
221
17.14
5.15
1248
214
10.56
2.97
5434
574
19.21
4.87
2469
474
INDIGENOUS SEGEMENTS WITHIN DISTRICTS
Prevalence
Standard
Total Malnourished
Error
Children
Children
101
102
103
75.84
57.54
74.33
20.87
5.90
6.45
869
5673
594
659
3264
442
301
303
305
50.90
53.85
58.50
9.42
11.37
13.11
145
20
22
74
11
13
401
402
64.20
62.07
8.88
7.94
124
491
80
305
3
Indigenous Segments
District Name
BOQUERÓN
BOQUETE
BUGABA
DAVID
DOLEGA
GUALACA
REMEDIOS
RENACIMIENTO
SAN FÉLIX
SAN LORENZO
TOLÉ
Darién
CHEPIGANA
PINOGANA
Panamá
ARRAIJÁN
CHEPO
CHIMÁN
LA CHORRERA
PANAMÁ
SAN CARLOS
SAN MIGUELITO
Veraguas
CAÑAZAS
LAS PALMAS
SANTA FÉ
SANTIAGO
Comarca Kuna Yala
COMARCA KUNA YALA
Comarca Emberá
CÉMACO
SAMBÚ
Comarca Ngöbe Buglé
BESIKO
MIRONÓ
MÜNA
NOLE DUIMA
ÑÜRÜM
KANKINTÚ
KUSAPÍN
Technical Appendix
ID
Prevalence
Standard
Error
Total
Children
Malnourished
Children
403
404
405
406
407
408
409
410
411
412
413
57.57
73.29
86.20
56.41
72.25
76.25
73.22
92.48
76.40
69.09
77.29
12.03
19.54
7.24
9.94
17.55
21.13
9.43
6.50
7.86
7.63
4.80
28
7
51
109
8
4
52
22
57
94
418
16
5
44
61
6
3
38
20
44
65
323
501
502
52.01
60.72
7.48
8.18
1130
502
588
305
801
805
806
807
808
809
810
55.84
73.81
47.21
38.50
61.24
77.00
70.61
7.19
7.03
7.77
26.24
7.13
25.82
9.15
785
673
229
6
576
3
95
438
497
108
2
353
2
67
903
905
909
910
35.21
65.58
57.15
47.75
9.72
6.53
8.87
16.36
169
201
316
12
60
132
181
6
1001
59.35
9.89
3688
2189
1101
1102
54.12
45.45
8.97
10.10
881
261
477
119
1201
1202
1203
1204
1205
1206
1207
72.59
82.94
82.84
86.87
74.14
64.48
61.35
5.78
4.40
4.18
4.05
4.95
6.56
7.08
2516
1516
4335
1420
1576
3055
2272
1826
1257
3591
1234
1168
1970
1394
4
Table 2: Variable Names and Descriptions
Variable Name
Description
Individual-Level Variables
haz
Height for age Z-score
waz
Weight for age Z-score
whz
Weight for Heigth Z-score
hombre
Child sex (Male = 1, Female=0)
age1223
Child aged 12 -23 months (Yes = 1, No = 0)
age2435
Child aged 24 - 35 months (Yes = 1, No = 0)
age3659
Child aged 36 - 69 months (Yes = 1, No = 0)
Household-Level Variables
fheadhh
Head of household is female (Yes =1, No =0)
Number of kids aged 0 to 59 months in the
num_kids059
household
Number of adult females aged 15 to 64 years
in the household
num_adfem
num_tot
Number of total members in the household
Ratio of members in the vivienda to rooms in
crowding
the vivienda
Ratio of employed members of household to
total members
depratio
Head of household is either 'casado' or 'unido'
mari_uni
(Yes = 1, No = 0)
Head of household is separated from
'marriage'/'union' or 'divorced (Yes = 1, No =
mari_sep
0)
Head of household is 'widow(er)' (Yes = 1, No
= 0)
mari_viudo
Head of household is 'single' (Yes = 1, No =
mari_soltero
0)
Head of household has no formal education or
ed_nonenr
No Response(Yes = 1, No = 0)
Head of household has completed preschool
or hase some primary education (Yes = 1, No
ed_presp
= 0)
Head of household has completed primary
ed_prime
school education (Yes = 1, No = 0)
Head of household has some secondary
ed_ssec
Head of household has completed secondary
ed_csec
Head of household has completed vocational
ed_cvoc
education (Yes = 1, No = 0)
Head of household has more advanced
ed_suped
(university +) education (Yes = 1, No = 0)
Highest level of education of adult female (15
hifemed
- 64) within the household
Highest level of education of adult female (15
hifemedsq
- 64) within the household squared (quadratic)
Highest level of education of adult male (15 himaled
64) within the household
Technical Appendix
5
Type a
Source
C
C
C
D
D
D
D
ENV
ENV
ENV
Census/ENV
Census/ENV
Census/ENV
Census/ENV
D
Census/ENV
C
Census/ENV
C
C
Census/ENV
Census/ENV
C
Census/ENV
C
Census/ENV
D
Census/ENV
D
Census/ENV
D
Census/ENV
D
Census/ENV
D
Census/ENV
D
Census/ENV
D
Census/ENV
D
Census/ENV
D
Census/ENV
D
Census/ENV
D
Census/ENV
C
Census/ENV
C
Census/ENV
C
Census/ENV
Base
Case b
*
*
*
Variable Name
Description
Highest level of education of adult male (15 64) within the household squared (quadratic)
Number of children (6-11yrs) in the household
priminhh
attending primary school
Number of children (6-11yrs) in the household
not attending primary school
primouthh
Number of children (12-17yrs) in the
secinhh
household attending secondary school
Number of children (12-17yrs) in the
secouthh
household not attending secondary school
sal_percap
Per capita total salary of the household
salfem_percap
Per capita salary of females of the household
Household connected to Community Septic
System (1=Yes, 0=Other Sanitation types)
san_alcan
Household has Septic Tank (1=Yes, 0=Other
san_tanq
Sanitation types)
Household has Latrine or hole in ground
san_hueco
(1=Yes, 0=Other Sanitation types)
Household has no Sanitation Service (1=Yes,
0=Other Sanitation types)
san_notien
Household members have exclusive use of
Sanitation (1=Yes, 0=Other Sanitation User
types)
usosolo
Household members have Shared and No
Response of Sanitation (1=Yes, 0=Exclusive
usocompnr
use of sanitation)
Cooking Fuel of Gas/Electricity (1=Household
use Gas/Electricity, 0=Other Cooking Fuel
types)
com_gaselec
Cooking Fuel of Wood/Charcoal
(1=Household use Wood/Charcoal, 0=Other
com_lencar
Cooking Fuel types)
Cooking Fuel when HH does not cook
com_nc
(1=Does not cook, 0=Other Cooking Fuels)
Dummy if household has a phone or cellular
telecell
phone (1=Yes, 0=No)
Vivienda-Level Variables
Vivienda type of Individual homes (1=Casa
viv_casa
Individual, 0=Other Vivienda types)
Vivienda type of Improvised Housing
viv_improv
(1=Improvisada, 0=Other Vivienda types)
Vivienda type for Apartments
(1=Apartamento, 0=Other Vivienda types)
viv_apartam
Vivienda type for Room in a Dwelling
(1=Cuarto en Casa de vecinidad, 0=Other
viv_cuarto
Vivienda types)
Roof type of Concrete and Tile (1=tile or
t_contej
concrete, 0=Other roof types)
himaledsq
Technical Appendix
6
Type a
Source
C
Census/ENV
C
Census/ENV
C
Census/ENV
C
Census/ENV
C
C
C
Census/ENV
Census/ENV
Census/ENV
D
Census/ENV
D
Census/ENV
D
Census/ENV
D
Census/ENV
D
Census/ENV
D
Census/ENV
D
Census/ENV
D
Census/ENV
Base
Case b
*
*
*
D
D
Census/ENV
D
Census/ENV
D
Census/ENV
D
Census/ENV
D
Census/ENV
D
Census/ENV
*
Variable Name
Description
t_fibmema
t_pajpen
p_bloq
p_madera
p_quinad
p_metal
p_cana
p_sinotr
pisoconc
pisomade
pisotier
piso_otronr
acuedpoz
aguapozrio
aguadent
aguafuer
elecmuncom
elecplant
elecotronr
basserv
bastira
basentq
basotronr
Technical Appendix
Roof type of Fiber, Metal, or Wood (1=fiber
(tejalit, panalit) or metal or wood, 0=Other roof
types)
Roof type of Straw (1=straw (paja or penca),
0=Other roof types)
Walls made of Brick (1=Yes, 0=Other Wall
types)
Walls made of Wood (1=Yes, 0=Other Wall
types)
Walls made of Adobe (1=Yes, 0=Other Wall
types)
Walls made of Metal (1=Yes, 0=Other Wall
types)
Walls made of Straw, i.e. Caña, Paja, Penca
(1=Yes , 0=Other Wall types)
Walls made of Other (1=Without Wall, Other,
No Response, 0=Other Wall types)
Floor made of Concrete, i.e. Tile, Brick, Stone
(1=Yes, 0=Other Floor types)
Floor made of Wood (1=Yes, 0=Other Floor
types)
Floor made of Dirt (1=Yes, 0=Other Floor
types)
Floor made of Other (1=Other materials and
no response, 0=Other Floor types)
Vivienda receives water from community
water system (1=Yes, 0=Other Water types)
Vivienda receives water from river or well
(1=Yes, 0=Other Water types)
Vivienda has water inside the dwelling
(1=Yes, 0=Other Water Location types)
Vivienda has water outside of Dwelling
(1=Yes, 0=Other Water Location types)
Vivienda connected to community/public
electricity grid (1=Yes, 0=Other Electricity
types)
Vivienda produces own electricity with
generator (1=Yes, 0=Other Electricity types)
Vivienda recieves electricity from other
method/no response (1=Yes, 0=Other
Electricity types)
Garbage removed by collection service
(1=Yes, 0=Other Garbage Elimination)
Garbage thrown in nearby lots or under patio
(1=Yes, 0=Other Garbage Elimination)
Garbage thrown into river/stream (1=Yes,
0=Other Garbage Elimination)
Garbage burned/Burried (1=Yes, 0=Other
Garbage Elimination)
7
Type a
Source
D
Census/ENV
D
Census/ENV
D
Census/ENV
D
Census/ENV
D
Census/ENV
D
Census/ENV
D
Census/ENV
D
Census/ENV
D
Census/ENV
D
Census/ENV
D
Census/ENV
D
Census/ENV
D
Census/ENV
D
Census/ENV
D
Census/ENV
D
Census/ENV
D
Census/ENV
D
Census/ENV
D
Census/ENV
D
Census/ENV
D
Census/ENV
D
Census/ENV
D
Census/ENV
Base
Case b
*
*
*
*
*
*
*
Variable Name
Description
Segmento-Level Variables
Percent of households in upm/segmento
seg_ed_prime
whose heads have completed primary school
whose heads have completed secondary
seg_ed_csec
school
whose heads have completed vocational
seg_ed_cvoc
school
whose heads have completed superior
seg_ed_suped
education
Percent of households in upm/segmento that
have community water source
seg_acuedpoz
seg_aguadent
have access to water inside their dwelling
seg_basserv
have garbage collection service
seg_elecmuncom
have electricity from the community
have sanitation linked to community septic
seg_san_alcan
system
seg_usosolo
exclusively use their toilet facilities
seg_com_gaselec
whose cooking fuel is either gas or electricity
have a telephone
seg_telefono
seg_cell
have a cellular telephone
seg_indig
have an indigenous household head
urban
Type of segment (urban=1, rural=0)
Corregimiento-Level Variables
Population density (population/area in km2) of
corregimiento
corr_popdens
Distance (m) from border of Corregimiento to
db_20t100k
nearest city w/ pop 20-100k
Distance (m) from border of Corregimiento to
db_100pk
nearest city w/ pop 100+k
elevmean
Mean elevation (meters) of Corregimiento
elevrang
Elevation range (meters) in Corregimiento
% of Corregimiento that is Bosques
siempreverdes y semisiempreverdes de
latifoliadas
per_bsvd
% of Corregimiento that is Sistemas
per_agpec
agropecuarios
per_bsdl
semideciduos de latifoliadas
Technical Appendix
8
Type a
Source
C
Census
C
Census
C
Census
C
Census
C
Census
C
Census
C
Census
C
Census
C
Census
C
Census
C
Census
C
Census
C
Census
C
D
Census
Census/ENV
C
WFP
C
GFK MACON
C
C
C
GFK MACON
EROS
EROS
C
CCAD
C
CCAD
C
CCAD
Base
Case b
*
Variable Name
Description
Type a
Source
per_agua
C
CCAD
C
CCAD
C
CCAD
C
CCAD
C
Desinventar
C
Census/ENV
C
C
ENV
Census
per_mang
per_oth
per_road
num_flood
Model Variables
dcorregc
sweight
cweight
% of Corregimiento that is Cuerpos de agua
manglares
% of Corregimiento that is Pantanos y
humedales, Areas con escasa vegetacion,
Sistemas productivos acuaticos
(camaroneras, salineras), Sabanas, Bosques
deciduos de latifoliadas, Paramos, Urbano
% of Corregimiento within 5 km of an all year
road
# of floods (inundaciones or Marejedas) since
1998 per District
Unique ID 9 digits long - Dominio (2),
Province (2), District (2), Corregimiento (2),
Indigenous Segs (1)
Survey weight, equal to the ENV expansion
factor variable divided by 100 (factor/100)
Census weight, a constant equal to one
a
Base Case refers to the variable that is omitted from the model to
avoid overspecification (i.e. the "dummy variable trap")
b
D=Dichotomous (Dummy) Variable, C=Continuous Variable
Technical Appendix
9
Base
Case b
Table 3. Regression results
Dependent Variable = Height for Age Z-score
Std.
Variable Names
Coefficient Err.
_intercept_
9.562
0.431
AGE1223
-0.048
0.071
AGE2435
0.036
0.071
AGUAFUER
-0.080
0.165
AGUAPOZRIO
-0.122
0.186
BASENTQ
0.117
0.109
BASOTRONR
0.020
0.227
BASTIRA
0.235
0.151
COM_LENCAR
0.146
0.138
COM_NC
1.352
0.619
CORR_POPDENS
0.016
0.007
CROWDING
-0.012
0.018
DB_100PK
0.000
0.000
DB_20T100K
0.000
0.000
DEPRATIO
-0.224
0.222
ED_CSEC
-0.311
0.452
ED_CVOC
0.107
0.382
ED_PRESP
0.129
0.217
ED_PRIME
0.138
0.241
ED_SSEC
-0.215
0.304
ED_SUPED
-0.920
1.025
ELECOTRONR
-0.175
0.165
ELECPLANT
-0.141
0.123
ELEVMEAN
0.000
0.000
ELEVRANG
0.000
0.000
FHEADHH
0.163
0.103
HIFEMED
-0.187
0.217
HIFEMEDSQ
0.042
0.037
HIMALED
-0.015
0.063
HIMALEDSQ
0.001
0.009
HOMBRE
-0.120
0.058
MARI_SEP
-0.032
0.121
MARI_SOLTERO
-0.020
0.181
MARI_VIUDO
0.052
0.165
NUM_ADFEM
0.029
0.051
NUM_FLOOD
-0.005
0.002
NUM_KIDS059
-0.128
0.050
NUM_TOT
0.007
0.026
PER_AGPEC
0.000
0.001
PER_AGUA
-0.012
0.014
PER_BSDL
0.001
0.003
PER_MANG
0.005
0.005
PER_OTH
0.001
0.002
PER_ROAD
-0.002
0.002
PISOMADE
0.029
0.163
Technical Appendix
10
t
22.172
-0.670
0.507
-0.484
-0.657
1.068
0.086
1.556
1.057
2.185
2.305
-0.678
-0.586
-0.022
-1.008
-0.688
0.281
0.596
0.571
-0.709
-0.898
-1.061
-1.141
-0.689
1.431
1.582
-0.862
1.132
-0.247
0.072
-2.058
-0.267
-0.111
0.316
0.571
-1.989
-2.563
0.271
0.254
-0.905
0.499
1.064
0.692
-1.131
0.180
|Prob|>t
0.000
0.503
0.612
0.628
0.511
0.286
0.931
0.120
0.290
0.029
0.021
0.498
0.558
0.982
0.313
0.492
0.779
0.551
0.568
0.479
0.370
0.289
0.254
0.491
0.152
0.114
0.389
0.258
0.805
0.943
0.040
0.789
0.911
0.752
0.568
0.047
0.010
0.786
0.800
0.366
0.618
0.287
0.489
0.258
0.857
Table 3. Regression results
Dependent Variable = Height for Age Z-score
Std.
Variable Names
Coefficient Err.
PISOTIER
-0.261
0.132
PISO_OTRONR
-0.003
0.275
PRIMINHH
-0.061
0.043
PRIMOUTHH
-0.120
0.095
P_CANA
-0.200
0.191
P_MADERA
-0.222
0.122
P_METAL
-0.182
0.194
P_QUINAD
-0.172
0.194
P_SINOTR
-0.517
0.285
SALFEM_PERCAP
0.000
0.001
SAL_PERCAP
0.001
0.000
SAN_ALCAN
-0.062
0.146
SAN_NOTIEN
0.093
0.142
SAN_TANQ
0.186
0.095
SECINHH
-0.145
0.054
SECOUTHH
-0.164
0.072
SEG_ACUEDPOZ
0.000
0.002
SEG_AGUADENT
0.000
0.002
SEG_BASSERV
-0.003
0.002
SEG_CELL
-0.005
0.003
SEG_COM_GASELEC
0.005
0.002
SEG_ED_CSEC
0.002
0.003
SEG_ED_CVOC
0.005
0.008
SEG_ED_PRIME
-0.007
0.002
SEG_ED_SUPED
0.000
0.004
SEG_ELECMUNCOM
0.000
0.002
SEG_INDIG
-0.008
0.002
SEG_SAN_ALCAN
0.000
0.002
SEG_TELEFONO
0.003
0.002
SEG_USOSOLO
0.002
0.002
TELECELL
0.113
0.076
T_CONTEJ
0.042
0.145
T_PAJPEN
0.145
0.159
URBAN
0.045
0.106
USOCOMPNR
-0.160
0.096
VIV_APARTAM
-0.028
0.158
VIV_CUARTO
-0.018
0.174
VIV_IMPROV
-0.568
0.327
N
K
R-Square
Adjusted R-Square
Technical Appendix
1955
82
0.294
0.263
11
t
-1.969
-0.009
-1.417
-1.263
-1.047
-1.817
-0.935
-0.885
-1.812
-0.501
1.372
-0.423
0.657
1.966
-2.682
-2.288
0.272
0.093
-1.871
-1.698
1.954
0.466
0.617
-3.062
0.124
0.066
-4.869
-0.121
1.461
0.993
1.478
0.292
0.909
0.428
-1.666
-0.176
-0.103
-1.738
|Prob|>t
0.049
0.993
0.157
0.207
0.295
0.069
0.350
0.376
0.070
0.616
0.170
0.673
0.512
0.049
0.007
0.022
0.785
0.926
0.061
0.090
0.051
0.641
0.538
0.002
0.902
0.947
0.000
0.904
0.144
0.321
0.140
0.770
0.363
0.669
0.096
0.861
0.918
0.082
Table 4: Census and Survey Variables and Recoding
Variable Group
Panama Census (c)
ENV Survey (s)
Matched Variables
Variable names in italics
Individual
Sex
Age
Parentesco
P02_SEXO
1. Hombre
2. Mujer
P03_EDAD
Age in years
P003
1. Hombre
2. Mujer
agemo
Age in months
P01_JEFE
p002
1. Jefe
2. Conyuge del Jefe
3. Hijo/a
4. Nuera o Yerno
5. Nieto o Bisnieto
6. Padre o Madre del Jefe
7. Suegro/a
8. Otro Pariente
9. No Pariente
Technical Appendix
1. Jefe (a)
2. Esposa(o) o companera(o)
3. Hijo/a
4. Yerno/Nuera
5. Nieto/a
6. P/Madre
7. Suegro/a
8. Hermano/a
9. Cunado/a
10. Otro Pariente
11. Empleado/a Domestico/a
12. Pensionista/Huesped
13. Otro no pariente
12
hombre
(c) (s) Hombre
age1223 – 1 year old
age2435 – 2 years old
age3659 – 3 -5 years old
jefe
(c) (s) jefe
Esposa
(c) Conyuge del Jefe
(s) Esposo(a) o Companero(a)
hijo
(c) (s) hijo(a)
bloodrel
(c) Nuera o Yerno, Nieto o
Bisnieto, Suegro/a, Padre o
Madre, Otro Pariente
(s) Yerno/Nuera, Nieto/a,
P/Madre, Suegro/a,
Hermano/a, Cunado/a, Otro
Pariente
nobldrel
(c) No Pariente
Variable Group
Panama Census (c)
ENV Survey (s)
Matched Variables
(s) Empleado/a Domestico/a,
Pensionista/huseped, Otro, no
pariente
Household
Female Headed Household
Total persons in household
Number of Adult Females
(age15-64) in household
Number of Children (aged 059 months) in household
Crowding
Female Headed Household if:
1) Self report as head and female
2) No male head is present, oldest female spouse is household
head
3) No head or spouse of head is identified, head is oldest
member 15-64 (HH is female headed if this person is
female)
4) If no head or spouse or member 15-64, oldest member is
head (HH is female headed if this person is female)
fheadhh
H22_TPER
Integer 1 – 37
Calculated using age
(P03_EDAD) and sex
(P02_SEXO)
Calculated using age
(P03_EDAD)
H22_TPER/V05_NCUA
Total members/ rooms
miembros
Integer 1 – 23
Calculated using age (edadmo)
and sex (P003)
num_tot
Calculated using age (edadmo)
num_kids059
miembros/cuartos
Total members/ rooms
crowding
The ENV data had housing units
Technical Appendix
13
num_adfem
Variable Group
Panama Census (c)
ENV Survey (s)
Matched Variables
Dependency Ratio
(# of non-working household
members/total members)
Marital Status of Household
Head
Calculated using P14_TRAB
and H22_TPER
with no rooms, which would
make the cuartos variable have
a value of zero. In order to
avoid dividing by zero, we
changed these zero values to
one room because an analysis of
all these cases showed that they
were “choza/rancho” with no
walls.
Calculated using P701 and
miembros
P04_ESTC
p301
1. Unido/a
2. Separado/a de matrimonio
3. Separado/a de union
4. Casado/a
5. Divorciado/a
6. Viudo/a
7. Soltero/a
8. Menor de 15 anos
Technical Appendix
1. Unido(a)
2. Casado(a)
3. Separado(a) de matrimonio
4. Separado(a) de unión
5. Divorciado(a)
6. Viudo(a)
7. Soltero(a)
9. NR
14
depratio
mari_uni
(c) (s) Unido, Casado
mari_sep
(c) (s) Separado de matrimonio,
Separado de union,
Divorciado
mari_viudo
(c) (s) Viudo(a)
mari_soltero
(c) (s) Soltero(a)
mari_nr
(c) Menor de 15 anos
(s) NR
Variable Group
Panama Census (c)
ENV Survey (s)
Matched Variables
Education of Household
Head*
p546a – NivelAprobado
P11_EDUC
0. NA
1. preescolar
3. ensenanza especial
11-15. primaria 1-5
16. primaria completa
19. primaria ND
21 – 23. vocacional 1-3
29. vocacional ND
31 – 34. secundaria 1-4
35. secundaria 35?
36. secundaria completa
39. secundaria ND
41 – 43. superior no
universitaria 1-3
49. superior no universitaria
ND
51 – 56. universitaria 1-6+
59. universitaria ND
61. postgrado
69. postgrado ND
71-72. maestria 1-2
79. mestria ND
81- 84. doctarado 1-4
Technical Appendix
0. Ninguno
1. Preescolar
2. Primaria
3. 1er. Ciclo
4. 2do. Ciclo
5. Vocacional o Profesional y
Técnica
6.Universitaria
7. No Universitaria
8. Postgrado / Maestría /
Doctorado
99. NR
p548 – Certificado
1 Certificado de Primaria
2 Certificado de Vocacional o
Profesional y Técnico
3 Certificado de 1er. Ciclo
4 Diploma de 2do. Ciclo
5 Diploma de Educación
Superior No Universitaria
6 Técnico
15
ed_nonenr
(c) NA, ND
(s) Ninguno
ed_presp
(c) Prescolar, Ensenaza especial
(s) Prescolar, Primaria (no
certificado)
ed_prime
(c) Primaria completa
(s) Primaria (certificado)
ed_ssec
(c) Values 31- 35, 39
(s) 1er. Ciclo, 2do. Ciclo (no
certificado)
ed_csec
(c) Secundaria Completa
(s) 2do. Ciclo (certificado)
ed_cvoc
(c) Vocacional – Value 23.
(s) Vocacional o Profensional y
Técnica (certificado)
ed_suped
(c) Values 41 - 89
(s) Universitaria, No Universitaria,
Postgrado/Maestría/Doctorado
Variable Group
Panama Census (c)
ENV Survey (s)
Matched Variables
89. doctorado ND
99. ND
7 Licenciatura
8 Postgrado, Maestría,
doctorado
9 Otro
99 NR
To make the variables in the last
column, for each level of
schooling (preschool, primary,
secondary, vocational, and
superior), we assigned a person
to have completed that level of
schooling if they responded as
having completed that level.
Otherwise, all other responses
were considered as having
“some” level of that education.
For example, a person had
“some primary education”, if
s/he was coded as
P11_EDUC=11-15, 19. For
vocational, having reached the
final year of vocational
schooling (P11_EDUC=23)
was considered completed
Technical Appendix
16
To make variables in the last
column, we combined
information from the ENV
variables listed above. A person
was considered to have
completed a level of education
if s/he had a certificate from that
level. However, if they replied
as having attended a certain
level of schooling (p546) but
did not have a certificate, s/he
has some level of that
schooling. For example, a
person had some primary
education if s/he responded to
having attended primary school
(p546a=2) but did not obtain a
certificate (p547=2). All values
of superior education and
Variable Group
Panama Census (c)
ENV Survey (s)
Matched Variables
vocational education.
Highest Education for Any
Female 15 - 64 in Household
Calculated using ed_*variables,
P02_SEXO, and P03_EDAD
Female 15 - 64 in Household
Squared
Male 15 - 64 in Household
Calculated quadratic term of
hifemed
Technical Appendix
beyond were collapsed into one
category – superior education.
Calculated using ed_* variables, hifemed
0 – no female 15 -64 present
P003 and agemo
1 – no education/ No Response
2 – pre-school
3 – some primary
4 – primary
5 – some secondary
6 – secondary
7 – some vocational
8 – superior and beyond
Calculated quadratic term of
hifemedsq
hifemed
Calculated using ed_*variables,
P02_SEXO, and P03_EDAD
Calculated using ed_* variables, himaled
P003 and agemo
0 – no Male 15 -64 present
1 – no education/ No Response
2 – pre-school
3 – some primary
4 – primary
5 – some secondary
6 – secondary
7 – some vocational
8 – superior and beyond
17
Variable Group
Panama Census (c)
ENV Survey (s)
Matched Variables
Male 15 - 64 in Household
Squared
Enrollment
Per Capita Salary
Per Capita Female Salary
Dwelling
Dwelling (vivienda) Type
Technical Appendix
Calculate quadratic term by
squaring himaled
Calculate quadratic term by
squaring himaled
himaledsq
Calculated using P10_ESCU
and P03_EDAD
Calculated using P520 and
P004
priminhh - # of Primary Age
Children in School
For each household, we totaled
the number of primary school
aged children (5-11 years old)
attending or not attending
school. If a household had no
children of in that age range, the
values for priminhh and
primouthh would both be zero.
The same procedure was
followed for secondary school
aged children (12-17 years old).
Calculated using P23A_SUEL
and H22_TPER
Calculated using P23A_SUEL,
H22_TPER and P02_SEXO
Calculated using p729 and
miembros
Calculated using p729,
miembros and P003
sal_percap
V01_TIPO
v01 – TipoViv
viv_casa
(c) Ind. Permanente, Ind.
18
primouthh - # of Primary Age
Children Not in School
secinhh - # of Secondary Age
Children in School
secouthh - # of Secondary Age
Children Not in School
salfem_percap
Variable Group
Panama Census (c)
ENV Survey (s)
Matched Variables
1. Ind. Permanente
2. Ind. Semipermanente
3. Improvisada
4. Apartamiento
5. Cuarto en casa vecindad
6. Local no destinado
7. Damnificado
8. Indigente
9. Hogar particular colectivo
10. Asilos
11. Barcos
12. Carceles cuarteles colonia
penal
13. Conventos y otras
viviendas
14. Galeras casa barracas
15. Hospitales clinicas
sanatorios
16. Hoteles pensiones y casa
de hosped
17. Internados
18. Reformatorios
19. Otras
20. Retenes
21. Empadronamiento previo
Technical Appendix
19
1. Casa Individual
2. Choza o Rancho
3. Apartamento
4. Cuarto en Casa de
Vecindad
5. Improvisada
6. Otro
9. NR
Semipermanente
(s) Casa Individual
viv_improv
(c) (s) Improvisada
viv_apartam
(c) (s) Apartamento
viv_cuarto
(c) (s) Cuarto en Casa de
Vecindad
viv_otronr
(c) Values 6 – 23
(s) Values 6 and 9
Variable Group
Panama Census (c)
ENV Survey (s)
Matched Variables
Material del Techo
22. Y mas con datos de viv
23. Y mas sin datos de viv
V07_TECHO
v03
Material de las Paredes
0. No aplica
1. Concreto (cemento)
2. Teja
3. Tejalit panalit techolit
4. Metal (zinc aluminio etc.)
5. Madera protegida
6. Paja o penca
7. Otros materials
V06_PARE
1. Concreto / cemento
2. Teja
3. Fibra-cemento
4. Metal
5. Madera
6. Paja o penca
7. Otros materiales
9. NR
v02
0. No Aplica
1. Bloque ladrillo pedra
concreto
2. Madera (tablas troza)
3. Quincha adobe
4. Metal (zinc, aluminio etc.)
5. Paja, penca, cana, palos
6. Otros materials
7. Sin paredes
Technical Appendix
1. Bloque, ladrillo, etc.
2. Madera
3. Quincha / adobe
4. Metal
5. Caña, paja, penca, palos
6. Sin paredes
7. Otros materiales
9. NR
20
t_contej
(c) (s) Concreto, Cemento, Teja
t_fibmema
(c) Tejalit, Panalit, Techolit,
Metal, Madera
(s) Fibra-Cemento, Metal, Madera
t_pajpen
(c) (s) Paja o Penca
t_otronr
(c) (s) Otros Materiales, NR, NA
p_bloq
(c) Bloque, Ladrillo, Pedra,
Concreto
(s) Bloque, Ladrillo, etc.
p_madera
(c) (s) Madera (tables troza)
p_quinad
(c) (s) Quincha, Adobe
p_metal
(c) (s) Metal (zinc, aluminio etc.)
p_cana
(c) (s) Caña, Paja, Penca, Palos
p_sinotr
Variable Group
Panama Census (c)
ENV Survey (s)
Matched Variables
Material del Piso
v04 – Piso
V08_PISO
0. No Aplica
1. Pavimentado
2. Madera
3. Tierra
4. Otro
Tenancy
1. Concreto / cemento
2. Mosaico, ladrillo, granito,
mármol
3. Madera
4. Tierra / arena
5. Otros materiales
9. NR
V04_TENE
v05
1. Propia totalmente pagada
2. Propia hipotecada
3. Alquilada
4. Cedida o prestada
5. Ocupantes de hecho
9. NR
0. No Aplica
1. hiptecada
2. alquilada
3. propia
4. cedida
5. condenada
6. otra
7. no declarada
Technical Appendix
21
(c) (s) Otros Materiales, Sin Paredes,
NR, NA
pisoconc
(c) Pavimentado
(s) Concreto/cemento, mosaico,
ladrillo, granito, marmol
pisomade
(c) (s) Madera
pisotier
(c) Tierra
(s) Tierra / Arena
piso_otronr
(c) No Aplica, Otro
(s) Otros Materiales, NR
ten_prop
(c) (s) (Propia) totalmenta pagada
ten_hipo
(c) (s) (Propia) hipotecada
ten_alqu
(c) (s) Alquilada
ten_ced
(c) Cedida
(s) Cedida o prestada
ten_otro
(c) Condenada, Otro
Variable Group
Panama Census (c)
ENV Survey (s)
Matched Variables
Water Source
Location of Water
Technical Appendix
(s) Ocupantes de hecho
ten_nr
(c) No Aplica, No Declarado
(s) NR
v19 – AguaBeber
V09_AGUA
acuedpoz
(c) (s) Acueducto Publico,
0. No aplica
1. Acueducto público
Acueducto de la Comunidad,
1. acueducto publico del idaan
2. Acueducto de la comunidad
Acueducto Particular, Pozo
2. acueducto publico de la
3. Acueducto particular
Sanitario
comunidad
4. Pozo sanitario
aguapozrio
3. acueducto particular
5. Pozo brocal no protegido
(c) Brocal no protegido, agua
4. pozo sanitario
6. Río, vertiente, quebrada,
lluvia, pozo superficial, rio o
5. brocal no protegido
lluvia
quebrada, carro cisterna
6. agua lluvia
7. Otro
(s) Pozo brocal no protegido, Rio,
7. pozo superficial
9. NR
vertiente, quebrada, lluvia, otro
8. rio o quebrada
agua_nr
9. carro cisterna
(c) No Aplica
10. otro
(s) NR
V10_AGIN
v22
aguadent
(c) Dentro
0. no aplica
1. Dentro de la vivienda
(s) Dento de la vivienda, En el
1. Dentro
2. En el patio de la vivienda
patio de la vivienda, Dentro de
2. fuera
3. Dentro de la vivienda y el
la vivienda y el patio
patio
aguafuer
4. Fuera de la vivienda y del
(c) Fuera
22
Variable Group
Panama Census (c)
ENV Survey (s)
Matched Variables
patio
9. NR
Eliminacion de la Basura
V14_BASU
v33
0. no aplica
1. carro recolector publico
2. carro recolector privado
3. terreno baldio
4. rio quebrada o mar
5. incineracion o quema
6. entierro
7. otra forma
Electricity
1. Servicio de vehículos o
carro del Municipio
2. Servicio de vehículos
particulares
3. La botan a otros lotes
4. La botan o tiran dentro del
patio
5. La botan o tiran al río,
quebrada o mar
6. La queman
7. La entierran
8. Otro
9. NR
V12_LUZ
v35
0. no aplica
1. electrico publico
Technical Appendix
1. Eléctrico Público
2. Eléctrico de la Comunidad
23
(s) Fuera de la vivienda y del patio
Agulocnr
(c) No Aplica
(s) NR
basserv
(c) Carro recolector
publico/privado
(s) Servicio de vehiculos o carro
del Municipio, Servicio de
vehiculos particulares
bastira
(c) Rio quebrada o mar
(s) La botan a otros lotes o tiran
dentro del patio, la botan o
tiran al rio, quebrada, o mar
basentq
(c) Incineracion or quema, entierro
(s) La queman, La entierran
basotronr – otro, NR
(c) Otra forma
(s) Otro, NR
elecmuncom
(c) Electrico Publico, Electrico de
la comunidad
(s) Electrico Publico, Electrico de
Variable Group
Panama Census (c)
ENV Survey (s)
Matched Variables
2. electrico de la comunidad
3. electrico propio (planta)
4. querosin/diesel
5. gas
6. otro
Servicio Sanitorio
3. Electricidad del Municipio
4. Electricidad Propia
5. Electricidad de particulares
6. Querosín o diesel, gas
7. Otro
9. NR
H16_SANI
v29
0. no aplica
1. de hueco o letrina
2. conectado a alcantarillado
3. conectado a tanque septico
4. no tiene
Uso del Sanitorio
1. conectado a alcantarillado
sanitorio
2. conectado a tanque septico
3. de hueco o letrina
4. no tiene
9. NR
H17_SUSO
v32
0. no aplica
Technical Appendix
1. Sólo del hogar
24
la comunidad, Electricidad del
municipio,
elecplant
(c) Electricidad Propia (planta),
querosin/diesel
(s) Electricidad Propia,
Electricidad de particulares,
Querosin o diesel, gas
elecotronr
(c) (s) No Aplica (NR), Otro
san_alcan
(c) (s) Conectado a alacantarillado
sanitorio
san_tanq
(c) (s) Conectado a tanque septico
san_hueco
(c) (s) De Hueco o Letrina
san_notien
(c) (s) no tiene
san_nr
(c) No aplica
(s) NR
usosolo
(c) Exclusivo Hogar
(s) Solo del hogar
Variable Group
Panama Census (c)
ENV Survey (s)
Matched Variables
1. exclusivo Hogar
2. compartido con otros
hogares
Cooking Fuel (combustible)
Phone
(Land & Cellular)
2. Compartido con otros
hogares
3. Compartido con otras
viviendas
9. NR
H18_COMB
v38
0. no aplica
1. gas
2. lena
3. carbon
4. querosin
5. electricidad
6. no cocina
H19_CTRES
0. No Aplica
1. Si
2. No
9. No declarado
H19_DTCEL
1. Gas
2. Leña
3. Electricidad
4. No cocina
5. Otro
9. NR
V40a1
1. Sí
2. No
9. NR
v40a2
0. No Aplica
1. Si
Technical Appendix
1. Sí
2. No
25
usocomp
(c) Compartido con otros hogares
(s) Compartido con otras hogares
o viviendas
uso_nr
(c) No Aplica
(s) NR
com_gaselec
(c) (s) Gas, Electricidad
com_lencar
(c) Leña, Carbon, Querosin
(s) Leña, Otro
com_nc
(c) No Aplica, No Cocina
(s) NR
telecell
(c) (s) Si
Variable Group
Panama Census (c)
ENV Survey (s)
Matched Variables
2. No
9. No declarado
Technical Appendix
9. NR
26
Table 5: Comparison of Survey and Census Descriptive Statistics
Survey (ENV)
Variable Names
Mean
SD
Range
Mean
Individual-Level Variables
hombre
0.514
0.500
0, 1
0.511
age1223
0.247
0.431
0, 1
0.233
age2435*
0.233
0.423
0, 1
0.256
age3659
0.520
0.500
0, 1
0.511
Household-Level Variables
fheadhh*
0.237
0.425
0, 1
0.195
num_kids059*
1.838
1.145
1, 8
1.730
num_adfem*
1.646
1.032
0, 7
1.601
num_tot*
6.900
3.827
2, 23
6.187
crowding*
3.223
2.970
0.31, 23
3.007
depratio*
0.711
0.161
0.14, 1
0.269
mari_uni
0.837
0.369
0, 1
0.846
mari_sep
0.097
mari_viudo
0.036
mari_soltero
0.029
ed_nonenr*
0.159
ed_presp
0.161
ed_prime*
0.238
ed_ssec
0.208
ed_csec*
0.122
ed_cvoc*
0.008
ed_suped
0.104
hifemed*
4.161
hifemedsq*
21.487
himaled*
3.540
himaledsq*
18.141
priminhh*
0.968
primouthh
0.099
secinhh*
0.433
secouthh*
0.202
sal_percap*
53.093
salfem_percap*
17.795
san_alcan
0.197
san_tanq*
0.213
san_hueco*
0.422
san_notien*
0.168
usosolo*
0.697
usocompnr*
0.303
com_gaselec*
0.691
com_lencar*
0.305
com_nc*
0.005
telecell*
0.363
Vivienda-Level Variables
viv_casa*
0.900
viv_improv*
0.006
Census
SD
Range
0.500
0.423
0.436
0.500
0, 1
0, 1
0, 1
0, 1
0.396
0.938
0.985
3.035
2.541
0.168
0.361
0, 1
1, 13
0, 12
2, 37
0.2, 32
0, 0.9
0, 1
0.296
0.187
0.167
0.366
0.367
0.426
0.406
0.328
0.090
0.305
2.043
17.921
2.369
17.972
1.110
0.414
0.709
0.518
101.055
51.319
0.398
0.410
0.494
0.374
0.460
0.460
0.462
0.460
0.068
0.481
0, 1
0, 1
0, 1
0, 1
0, 1
0, 1
0, 1
0, 1
0, 1
0, 1
0, 8
0, 64
0, 8
0, 64
0, 6
0, 5
0, 4
0, 3
0, 1667
0, 806
0, 1
0, 1
0, 1
0, 1
0, 1
0, 1
0, 1
0, 1
0, 1
0, 1
0.089
0.035
0.030
0.107
0.163
0.259
0.195
0.139
0.023
0.107
4.456
23.628
4.023
21.267
0.800
0.084
0.395
0.171
70.718
23.473
0.204
0.179
0.484
0.132
0.736
0.264
0.729
0.269
0.002
0.340
0.285
0.184
0.171
0.310
0.370
0.438
0.397
0.346
0.150
0.309
1.942
17.939
2.255
18.356
0.994
0.377
0.722
0.491
137.890
65.927
0.403
0.383
0.500
0.339
0.441
0.441
0.444
0.443
0.040
0.474
0, 1
0, 1
0, 1
0, 1
0, 1
0, 1
0, 1
0, 1
0, 1
0, 1
0, 8
0, 64
0, 8
0, 64
0, 11
0, 10
0, 8
0, 8
0, 3833
0, 3500
0, 1
0, 1
0, 1
0, 1
0, 1
0, 1
0, 1
0, 1
0, 1
0, 1
0.300
0.078
0, 1
0, 1
0.877
0.016
0.329
0.127
0, 1
0, 1
A1
Table 5: Comparison of Survey and Census Descriptive Statistics
Survey (ENV)
Census
Variable Names
Mean
SD
Range
Mean
SD
Range
viv_apartam*
0.051
0.219
0, 1
0.061
0.238
0, 1
viv_cuarto*
0.038
0.192
0, 1
0.047
0.211
0, 1
t_contej*
0.042
0.201
0, 1
0.068
0.252
0, 1
t_fibmema
0.823
0.382
0, 1
0.810
0.393
0, 1
t_pajpen
0.135
0.342
0, 1
0.121
0.327
0, 1
p_bloq*
0.576
0.494
0, 1
0.657
0.475
0, 1
p_madera*
0.227
0.419
0, 1
0.144
0.351
0, 1
p_quinad*
0.037
0.188
0, 1
0.048
0.213
0, 1
p_metal
0.030
0.170
0, 1
0.034
0.182
0, 1
p_cana
0.108
0.311
0, 1
0.100
0.300
0, 1
p_sinotr
0.021
0.145
0, 1
0.017
0.130
0, 1
pisoconc*
0.677
0.468
0, 1
0.714
0.452
0, 1
pisomade*
0.100
0.300
0, 1
0.064
0.244
0, 1
pisotier
0.195
0.397
0, 1
0.204
0.403
0, 1
piso_otronr*
0.028
0.164
0, 1
0.018
0.134
0, 1
acuedpoz
0.836
0.370
0, 1
0.851
0.356
0, 1
aguapozrio
0.164
0.370
0, 1
0.149
0.356
0, 1
aguadent*
0.799
0.400
0, 1
0.516
0.500
0, 1
aguafuer*
0.201
0.400
0, 1
0.484
0.500
0, 1
elecmuncom*
0.575
0.494
0, 1
0.708
0.455
0, 1
elecplant*
0.391
0.488
0, 1
0.276
0.447
0, 1
elecotronr*
0.033
0.179
0, 1
0.016
0.125
0, 1
basserv*
0.447
0.497
0, 1
0.483
0.500
0, 1
bastira*
0.185
0.389
0, 1
0.153
0.360
0, 1
basentq
0.339
0.474
0, 1
0.350
0.477
0, 1
basotronr*
0.029
0.168
0, 1
0.014
0.119
0, 1
* Survey and Census means significantly different at p<0.05 (two-sampled t-test w/ equal
variances)
Survey N = 1,955 (based on haz dataset)
Census N = 248,731
A2

Outline: Synthesis Paper - Friedman School of Nutrition Science and

Transcription

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