Beatrice Lorge Rogers, James Wirth, Kathy Macías, Parke ... · Figure 1: Range and Number of...

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

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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

Table 3.3: Country Descriptive Statistics on Malnutrition 10

Figure

Figure 1: Range and Number of Malnourished Children In Panama (based on HAZ) by Province 16

List of Maps

Map 1 Provinces and Zones, Panama 6 Map 2 Prevalence of Chronic Malnutrition, Non-Indigenous,

Standardized Categories 11 Map 3 Prevalence of Chronic Malnutrition, Indigenous, Standardized Categories 11 Map 4 Prevalence of Chronic Malnutrition Based on Small

Area Estimation, by Dominio 12 Map 5 Prevalence of Chronic Malnutrition, Non-Indigenous, Based on Small Area Estimation, By District 13 Map 6 Prevalence of Chronic Malnutrition, Indigenous, Based on

Small Area Estimation, by District 14 Map 7 Number of Chronically Malnourished Children, Non- Indigenous Areas, Based on Small Area Estimation, by District 14 Map 8 Number of Chronically Malnourished Children, Indigenous Areas, Based on Small Area Estimation, by District 15 Map 9 Discordance of Poverty and Malnutrition, Non-Indigenous Areas 18 Map 10 Discordance of Poverty and Malnutrition, Indigenous Areas 19

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

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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 sub-provincial 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).

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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 non-indigenous 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).

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http://iresearch.worldbank.org/PovMap/index.htm

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 census6, 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.

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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 at what level

Province, Sampling Domain, Urban/Rural

Number of households in survey

6,363

Number of children (aged 1-5 years) in survey

1,955

Number of children (aged 1-5 years) in Census

248,731

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

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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.

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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.

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http://www.mef.gob.pa/

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.

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Table 3.1: Comparison of Malnutrition Prevalence and Mean HAZ of Small Area Estimation and Survey

Estimates: Panama Malnutrition Prevalence Mean HAZ National Model** Dominio N Hunger Mapping

Results *Survey

Estimates Hunger Mapping

Results *Survey

Estimates 0.128 0.113 -0.515 -0.380 1 Panama City 123

(0.023) (0.132) 0.179 0.195 -0.851 -0.763 2 District of Panama 106

(0.035) (0.168) 0.108 0.039 -0.493 -0.283 3 San Miguelito 61

(0.024) (0.150) 0.181 0.169 -0.845 -0.721 4 Panama Oeste 189

(0.028) (0.125) 0.223 0.210 -0.985 -1.040 5 Panama Este 143

(0.039) (0.160) 0.168 0.279 -0.757 -1.212 6 Bocas del Toro 296

(0.037) (0.197) 0.251 0.238 -1.196 -1.006 7 Cocle 96

(0.041) (0.139) 0.161 0.137 -0.779 -0.702 8 Colon 119

(0.031) (0.138) 0.193 0.115 -0.868 -0.592 9 Chiriqui 156

(0.032) (0.145) 0.203 0.151 -1.003 -0.820 10 Darien 112

(0.053) (0.206) 0.174 0.134 -0.882 -0.674 11 Herrera 66

(0.034) (0.145) 0.175 0.094 -0.900 -0.275 12 Los Santos 47

(0.035) (0.145) 0.175 0.253 -1.007 -1.032 13 Veraguas 105

(0.037) (0.146) 0.670 0.629 -2.466 -2.297

(N=1955) (K=82) (R2=0.2940)

14 Indigenous Areas 336 (0.042) (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

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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 Malnutrition Prevalence HAZ WHZ WAZ

Mean a 37.92 10.21 21.48 Median 24.73 8.40 18.11 IQR 19.00-58.71 2.42 – 5.32 11.23 – 31.23 Minimum 10.56 1.06 4.10 Maximum 92.48 42.59 64.86 National Prevalence (mean prev, weighted) 24.32 10.20 14.26 a 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.

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Map 2: Panama, Non-Indigenous, Standardized Categories

Map 3: Panama, Indigenous Areas, Standardized Categories

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These maps demonstrate the sharp differences in estimated prevalence for the non-indigenous 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 non-indigenous 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

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Map 5

Map 6

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Note the pronounced difference in the quartile boundaries for the indigenous and non-indigenous 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

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Map 8:

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Range of prevelance estimates

0255075100

Percent of children

Number of Malnourished Children

0 2500 5000 7500 10000

Panama City

District ofPanama

District of SanMiguelito

PanamaOeste

Panama Este

Bocas delToro

Coclé

Colón

Chiriqui

Darien

Herrera

Los Santos

Veraguas

ComarcaKuna Yala

ComarcaEmbére

ComarcaNgöbe Bungle

IndigenousSegments

12440

Number of children

Figure 1 Range and Number of Malnourished Children In Panama (based on HAZ) by Province

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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 non-indigenous 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 non-indigenous 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.

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http://www.ipgh.org/

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

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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

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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 region-wide 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.

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6. References Atlas Universal y de Panamá 2006. Panamá:.Promotora Educativa S.A. Benson, T. 2006. Insights from poverty maps for development and food relief program targeting. International Food Policy Research Institute. Food Consumption and Nutrition Division Discussion Paper 205. Benson, T.; J.Chamberlin; I.Rhinehart, 2005. A Investigation of the Spatial Determinants of the Local Prevalence of Poverty in Rural Malawi. Food Policy 30:5-6 Oct/Dec 2005, 532-50. Bermudez, Odilia 2006. Situación Nutricional, Patrón de Consumo y Acceso a Alimentos: Informe Final de Consultoría. Panama: Min. de Economia y Finanzas, Dirección de Políticas Sociales, April. Demombynes, G.; C.Elbers; J. Lanjouw; P.Lanjouw; J.Mistiaen; B.Ozler, 2002. Producing an Improved Geographic Profile of Poverty: Methodology and Evidence from Three Developing Countries. WIDER Discussion Paper 2002-39. Elbers, C.; J.O.Lanjouw; P.Lanjouw, 2002; Micro Level Estimation of Welfare. Washington DC World Bank Policy Research Paper WPS2911, October.

Elbers C., Lanjouw J. and Lanjouw P. 2003. “Micro-level estimation of poverty and inequality”, Econometrica, 71, 355-364.

Elbers, C., J.O.Lanjouw, P. Lanjouw, 2004. Imputed Welfare Estimates in Regression Analysis. Washington DC: World Bank Policy Research Paper WPS 3294, April. Engle, P.; P.Menon; L.Haddad, 2001. Care and Nutrition: Concepts and Measurement. Washington DC: International Food Policy Research Institute. Fujii, T. 2003. Micro-Level Estimation of the Prevalence of Stunting and Underweight Among Children in Cambodia. Report to Ministry of Health, Royal Government of Cambodia (preliminary report). UN World Food Programme, March (mimeo). Fujii, T., 2005. Micro-level Estimation of Child Malnutrition Indicators and Its Application in Cambodia. Washington, DC: World Bank, Policy Research Working Paper WPS3662, July. Gabinete Social 2006. Sistema de Protección Social. Resumen de trabajo del Ministerio de Desarrollo Social. Panamá. Gilligan, D.; A. Veiga; M.H.D.Benicio; C.A.Monteiro, 2003. An Evaluation of Geographic Targeting in Bolsa Alimentação in Brazil: Report Submitted to the

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Government of Brazil. Washington DC: International Food Policy Research Institute, April. Haslett, S., Jones, G. 2005. Small area estimation using surveys and censuses: some practical and statistical issues. Statistics in Transition. 7(3), 541 – 555. Haslett, S.; G.Jones; with D. Parajuli, forthcoming. Small Area Estimation of Poverty, Caloric Intake, and Malnutrition in Nepal. Kathmandu: Government of Nepal, Central Bureau of Statistics. World Food Programme and World Bank. Hentschel, J.; J.O.Lanjouw; P.Lanjouw; J. Poggi, 2000. Combining Census and Survey Data to Trace the Spatial Dimensions of Poverty. World Bank Economic Review 14:1, (January) 147-165. Larrea, C., 2005. Poverty, Food Poverty, and Malnutrition Regression Models for Ecuador. Taken from the EcuaMapAlimentaria website on August, 18, 2006. http://www.ecuamapalimentaria.info/ deOnis, M.; A.W.Onyango; E.Borghi; C.Garze; H.Yang, 2006. Comparison of the World Health Organization (WHO) Child Growth Standards and the National center for Health Statistics/WHO international growth reference: implications for child health programmes. Public Health Nutrition 9:7, 942-947. Rogers, BL.; J.Wirth; P.Wilde; K.Macías, 2007 Introduction to the Estimation of Malnutrition Prevalence by Small Area Estimation using the PovMap Program. Boston, MA: Tufts University Friedman Nutrition School; Report submitted to World Food Programme/LAC, Panama, February 2007. Simler, K. 2006. Nutrition Mapping in Tanzania: An Exploratory Analysis. Washington DC: International Food Policy Research Institute, Food Consumption and Nutrition Division Discussion Paper #204, March. Smith, L.; L.Haddad, 2000. Overcoming Child Malnutrition in Developing Countries: Past Achievements and Future Choices. Washington DC: International Food Policy Research Institute. Agriculture, Food and Environment Discussion Paper #30. UNICEF (United Nations Children’s Fund), 1991. Strategy for improved nutrition for children and women in developing countries. UNICEF Policy Review. New York: UNICEF. United Nations 2001. Road Map Towards the Implementation of the United Nations Millennium Declaration: Report of the Secreary General. A/56/326, 6 September 2001 Valdés, V.E.; R.Castro de Barba 2006. Hacia La Erradicación de la Desnutrición Infantile en Centroamérica y Panamá. Anexo A: Diagnóstico de la Desnutrición Infantil

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en el País y Los Instrumentos para Combatirla. Panamá, República de Panamá, December. Waterlow, J.C.; R.Buzina; W.Keller; JM Lane; MZ Nichaman; JM Tanner. 1977. The presentation and use of height and weight data for comparing the nutritional status of groups of children under the age of 10 years. Bulletin of the World Health Organization 55: 489-498 WHO (World Health Organization) WHO Child Growth Standards: Length/Height-for-age, Weight-for-age, Weight-for-length, Weight-for-height and Body Mass Index-for age Methods and Development. Geneva: World Health Organization 2006 WHO (World Health Organization) 1995. Physical Status: The Use and Interpretation of Anthropometry. Geneva: WHO. Zhao, Q., 2005. User Manual for PovMap 1.1a. Development Research Group. From the World Bank website, August 12, 2006. http://iresearch.worldbank.org/PovMap/index.htm

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http://iresearch.worldbank.org/PovMap/index.htm

Appendix Table A1 Data Requirements for the Prediction of Malnutrition Table A2 Variables Used in the Analysis of Malnutrition, Panama

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Table A1: Data Requirements for the Prediction of Malnutrition

Level

Variable

Possible Source

Individual Age in months Census, Survey Gender Census, Survey Birth Order Survey; rare in census Food consumption Rare in survey; never in census Illness Survey, not census

Household Household size Census, survey Number of children under 5 yrs of age Census, survey Number of adult females Census, survey Number of persons per room - crowding Census, survey

Education of child’s mother Survey; usually no link to mother in

census Education levels of adult household members Census, survey

Economic status, wealth – ownership of key consumption goods

Census, survey

Food consumption: Adequacy Diversity

Rarely if ever available in survey;

never in census Sources – purchase, home production, etc.

Quality of housing Census, Survey Household water source Census, Survey Household sanitation: latrine, garbage disposal Census, Survey Electricity, fuel, telephone Census, survey Income, total, by source, earner Rarely collected in survey or census Livelihoods: income sources, earners Limited information Female/Male household head Census, Survey Ethnicity of members Usually available census and survey Location: urban/rural Census, survey Household food insecurity Rarely collected in survey or census

Community/Cluster Economic Inequality Can be computed from hh assets Marketing infrastructure: Access to roads GIS

GIS Transportation infrastructure Volatility of prices Secondary sources; rarely avail.

Services: Access to health services Access to/enrollment in school

Government sources

Variable data, often not consistent between survey and census

Local livelihoods: Dependence on agriculture Unemployment

Remittances Distance in kilometers to urban centers, markets GIS Ethnic diversity Census

Province/Region Land type, quality, land uses GIS Climate:Rainfall; Droughts; Floods GIS

Topography - Elevation, slope

GIS

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Table A2: Variables Used in the Analysis of Malnutrition, Panama

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

Level

Variable

Source

Individual Child Nutrition status ENV Age Census/ENV Gender Census/ENV

Household Household size Census/ENV Number of children under five yrs of age Census/ENV Number of adult females Census/ENV Number of persons per room - crowding Census/ENV % of employed household members Census/ENV Female/Male household head Census/ENV Marital status of household head Census/ENV Education status of household head Census/ENV Education level of adult household members Census/ENV Per capita household salary

Household sanitation: methods of garbage disposal, access to toilet facilities

Census/ENV

Electricity, cooking fuel, telephone Census/ENV Housing type and building materials Census/ENV Household water source and access Census/ENV

Community/Segmento Education level of household heads Census Sanitation services: water, septic, garbage Census Household services – electricity, fuel, telephone Census Ethnicity of household heads Census Segment is urban or rural Census

Region/Corregimiento Access to all-year roads CCAD Distance to urban center GFK Macon

EROS System, USGSb Topography CCADc Land type

Desinventard Floods since 1998 Population density Census/WFP

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http://www.ccad.ws/

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 ID Prevalence Standard

ErrorTotal

Children Malnourished

Children NON-INDIGENOUS SEGMENTS WITHIN DISTRICTS

Bocas del Toro BOCAS DEL TORO 101 23.38 7.45 346 81 CHANGUINOLA 102 14.53 3.30 2966 431 CHIRIQUÍ GRANDE 103 24.57 6.38 436 107 Coclé AGUADULCE (CAB) 201 14.42 3.12 3005 433 ANTÓN 202 28.22 5.01 4363 1231 LA PINTADA 203 24.61 5.55 2464 606 NATÁ 204 20.37 4.23 1590 324 OLÁ 205 30.58 6.42 562 172 PENONOMÉ 206 28.54 5.05 7359 2100 Colón COLÓN 301 13.67 3.33 16258 2222 CHAGRES 302 34.24 6.16 1118 383 DONOSO 303 25.98 6.94 1345 349 PORTOBELO 304 20.62 6.09 793 164 SANTA ISABEL 305 20.12 4.54 310 62 Chiriquí ALANJE 401 22.92 5.41 1230 282 BARÚ 402 20.37 3.90 5297 1079 BOQUERÓN 403 17.42 4.16 1018 177 BOQUETE 404 24.21 7.20 898 217 BUGABA 405 21.22 3.86 5424 1151 DAVID 406 12.72 3.07 8929 1136 DOLEGA 407 13.29 3.54 1376 183 GUALACA 408 22.16 4.19 757 168 REMEDIOS 409 22.42 5.47 285 64 RENACIMIENTO 410 39.77 8.02 1633 649 SAN FÉLIX 411 17.15 4.83 337 58 SAN LORENZO 412 19.59 4.51 432 85 TOLÉ 413 32.10 5.67 802 257 Darién CHEPIGANA 501 19.93 4.74 2116 422 PINOGANA 502 21.23 6.22 1061 225 Herrera CHITRÉ 601 12.83 3.25 2948 378 LAS MINAS 602 32.16 6.53 795 256 LOS POZOS 603 21.85 4.99 642 140 OCÚ 604 18.16 3.86 1308 238 PARITA 605 16.49 4.36 611 101 PESÉ 606 14.88 3.65 935 139 SANTA MARÍA 607 16.57 4.16 556 92 Los Santos GUARARÉ 701 15.76 4.40 576 91



ErrorTotal


Children LAS TABLAS 702 15.16 3.24 1404 213 LOS SANTOS 703 14.25 3.77 1497 213 MACARACAS 704 21.60 5.11 687 148 PEDASÍ 705 24.31 6.17 230 56 POCRÍ 706 19.03 4.80 180 34 TONOSÍ 707 23.93 4.92 802 192 Panamá ARRAIJÁN 801 12.93 3.26 13148 1700 BALBOA 802 17.63 5.71 228 40 CAPIRA 803 30.26 5.63 3460 1047 CHAME 804 18.88 4.79 1654 312 CHEPO 805 22.69 4.24 2884 654 CHIMÁN 806 21.84 5.07 343 75 LA CHORRERA 807 18.81 3.06 10623 1998 PANAMÁ (CIUDAD) 808 13.00 2.49 25033 3254 PANAMA (Resto del Distrito) 808 18.18 4.14 26924 4895 SAN CARLOS 809 26.37 5.46 1438 379 SAN MIGUELITO 810 11.06 2.35 22370 2474 TABOGA 811 21.14 8.39 76 16 Veraguas ATALAYA 901 14.24 3.87 649 92 CALOBRE 902 15.96 5.04 1186 189 CAÑAZAS 903 19.71 6.61 1596 315 LA MESA 904 24.86 6.72 1089 271 LAS PALMAS 905 30.65 5.64 1723 528 MONTIJO 906 15.70 4.78 1120 176 RÍO DE JESÚS 907 19.10 5.93 401 77 SAN FRANCISCO 908 23.31 7.31 949 221 SANTA FÉ 909 17.14 5.15 1248 214 SANTIAGO 910 10.56 2.97 5434 574 SONÁ 911 19.21 4.87 2469 474 INDIGENOUS SEGEMENTS WITHIN DISTRICTS District Name ID Prevalence Standard

ErrorTotal


Children Bocas del Toro BOCAS DEL TORO 101 75.84 20.87 869 659 CHANGUINOLA 102 57.54 5.90 5673 3264 CHIRIQUÍ GRANDE 103 74.33 6.45 594 442 Colón COLÓN 301 50.90 9.42 145 74 DONOSO 303 53.85 11.37 20 11 SANTA ISABEL 305 58.50 13.11 22 13 Chiriquí ALANJE 401 64.20 8.88 124 80 BARÚ 402 62.07 7.94 491 305



ErrorTotal


Children BOQUERÓN 403 57.57 12.03 28 16 BOQUETE 404 73.29 19.54 7 5 BUGABA 405 86.20 7.24 51 44 DAVID 406 56.41 9.94 109 61 DOLEGA 407 72.25 17.55 8 6 GUALACA 408 76.25 21.13 4 3 REMEDIOS 409 73.22 9.43 52 38 RENACIMIENTO 410 92.48 6.50 22 20 SAN FÉLIX 411 76.40 7.86 57 44 SAN LORENZO 412 69.09 7.63 94 65 TOLÉ 413 77.29 4.80 418 323 Darién CHEPIGANA 501 52.01 7.48 1130 588 PINOGANA 502 60.72 8.18 502 305 Panamá ARRAIJÁN 801 55.84 7.19 785 438 CHEPO 805 73.81 7.03 673 497 CHIMÁN 806 47.21 7.77 229 108 LA CHORRERA 807 38.50 26.24 6 2 PANAMÁ 808 61.24 7.13 576 353 SAN CARLOS 809 77.00 25.82 3 2 SAN MIGUELITO 810 70.61 9.15 95 67 Veraguas CAÑAZAS 903 35.21 9.72 169 60 LAS PALMAS 905 65.58 6.53 201 132 SANTA FÉ 909 57.15 8.87 316 181 SANTIAGO 910 47.75 16.36 12 6 Comarca Kuna Yala COMARCA KUNA YALA 1001 59.35 9.89 3688 2189 Comarca Emberá CÉMACO 1101 54.12 8.97 881 477 SAMBÚ 1102 45.45 10.10 261 119 Comarca Ngöbe Buglé BESIKO 1201 72.59 5.78 2516 1826 MIRONÓ 1202 82.94 4.40 1516 1257 MÜNA 1203 82.84 4.18 4335 3591 NOLE DUIMA 1204 86.87 4.05 1420 1234 ÑÜRÜM 1205 74.14 4.95 1576 1168 KANKINTÚ 1206 64.48 6.56 3055 1970 KUSAPÍN 1207 61.35 7.08 2272 1394


Table 2: Variable Names and Descriptions Variable Name Description Type a Source Base

Case b

Individual-Level Variables haz Height for age Z-score C ENV waz Weight for age Z-score C ENV whz Weight for Heigth Z-score C ENV hombre Child sex (Male = 1, Female=0) D Census/ENV age1223 Child aged 12 -23 months (Yes = 1, No = 0) D Census/ENV age2435 Child aged 24 - 35 months (Yes = 1, No = 0) D Census/ENV age3659 Child aged 36 - 69 months (Yes = 1, No = 0) D Census/ENV * Household-Level Variables fheadhh Head of household is female (Yes =1, No =0) D Census/ENV

num_kids059 Number of kids aged 0 to 59 months in the household C Census/ENV

num_adfem Number of adult females aged 15 to 64 years in the household C Census/ENV

num_tot Number of total members in the household C Census/ENV

crowding Ratio of members in the vivienda to rooms in the vivienda C Census/ENV

depratio Ratio of employed members of household to total members C Census/ENV

mari_uni Head of household is either 'casado' or 'unido' (Yes = 1, No = 0) D Census/ENV * Head of household is separated from 'marriage'/'union' or 'divorced (Yes = 1, No = 0) D Census/ENV mari_sep Head of household is 'widow(er)' (Yes = 1, No = 0) D Census/ENV mari_viudo Head of household is 'single' (Yes = 1, No = 0) D

Census/ENV mari_soltero

ed_nonenr Head of household has no formal education or No Response(Yes = 1, No = 0) D Census/ENV *

ed_presp

Head of household has completed preschool or hase some primary education (Yes = 1, No = 0) D Census/ENV

ed_prime Head of household has completed primary school education (Yes = 1, No = 0) D Census/ENV

ed_ssec Head of household has some secondary school education (Yes = 1, No = 0) D Census/ENV

ed_csec Head of household has completed secondary school education (Yes = 1, No = 0) D Census/ENV

ed_cvoc Head of household has completed vocational education (Yes = 1, No = 0) D Census/ENV

ed_suped Head of household has more advanced (university +) education (Yes = 1, No = 0) D Census/ENV

hifemed Highest level of education of adult female (15 - 64) within the household C Census/ENV

hifemedsq Highest level of education of adult female (15 - 64) within the household squared (quadratic) C Census/ENV

himaled Highest level of education of adult male (15 - 64) within the household C Census/ENV



Case b

himaledsq Highest level of education of adult male (15 - 64) within the household squared (quadratic) C Census/ENV

priminhh Number of children (6-11yrs) in the household attending primary school C Census/ENV

primouthh Number of children (6-11yrs) in the household not attending primary school C Census/ENV

secinhh Number of children (12-17yrs) in the household attending secondary school C Census/ENV

secouthh Number of children (12-17yrs) in the household not attending secondary school C Census/ENV

sal_percap Per capita total salary of the household C Census/ENV salfem_percap Per capita salary of females of the household C Census/ENV

san_alcan Household connected to Community Septic System (1=Yes, 0=Other Sanitation types) D Census/ENV

san_tanq Household has Septic Tank (1=Yes, 0=Other Sanitation types) D Census/ENV

san_hueco Household has Latrine or hole in ground (1=Yes, 0=Other Sanitation types) D Census/ENV *

san_notien Household has no Sanitation Service (1=Yes, 0=Other Sanitation types) D Census/ENV

usosolo

Household members have exclusive use of Sanitation (1=Yes, 0=Other Sanitation User types) D Census/ENV *

usocompnr

Household members have Shared and No Response of Sanitation (1=Yes, 0=Exclusive use of sanitation) D Census/ENV

com_gaselec

Cooking Fuel of Gas/Electricity (1=Household use Gas/Electricity, 0=Other Cooking Fuel types) D Census/ENV *

com_lencar

Cooking Fuel of Wood/Charcoal (1=Household use Wood/Charcoal, 0=Other Cooking Fuel types) D Census/ENV

com_nc Cooking Fuel when HH does not cook (1=Does not cook, 0=Other Cooking Fuels) D

telecell Dummy if household has a phone or cellular phone (1=Yes, 0=No) D Census/ENV

Vivienda-Level Variables

viv_casa Vivienda type of Individual homes (1=Casa Individual, 0=Other Vivienda types) D Census/ENV *

viv_improv Vivienda type of Improvised Housing (1=Improvisada, 0=Other Vivienda types) D Census/ENV

viv_apartam Vivienda type for Apartments (1=Apartamento, 0=Other Vivienda types) D Census/ENV

viv_cuarto

Vivienda type for Room in a Dwelling (1=Cuarto en Casa de vecinidad, 0=Other Vivienda types) D Census/ENV

t_contej Roof type of Concrete and Tile (1=tile or concrete, 0=Other roof types) D Census/ENV



Case b

t_fibmema

Roof type of Fiber, Metal, or Wood (1=fiber (tejalit, panalit) or metal or wood, 0=Other roof types) D Census/ENV *

t_pajpen Roof type of Straw (1=straw (paja or penca), 0=Other roof types) D Census/ENV

p_bloq Walls made of Brick (1=Yes, 0=Other Wall types) D Census/ENV *

p_madera Walls made of Wood (1=Yes, 0=Other Wall types) D Census/ENV

p_quinad Walls made of Adobe (1=Yes, 0=Other Wall types) D Census/ENV

p_metal Walls made of Metal (1=Yes, 0=Other Wall types) D Census/ENV

p_cana Walls made of Straw, i.e. Caña, Paja, Penca (1=Yes , 0=Other Wall types) D Census/ENV

p_sinotr Walls made of Other (1=Without Wall, Other, No Response, 0=Other Wall types) D Census/ENV

pisoconc Floor made of Concrete, i.e. Tile, Brick, Stone (1=Yes, 0=Other Floor types) D Census/ENV *

pisomade Floor made of Wood (1=Yes, 0=Other Floor types) D Census/ENV

pisotier Floor made of Dirt (1=Yes, 0=Other Floor types) D Census/ENV

piso_otronr Floor made of Other (1=Other materials and no response, 0=Other Floor types) D Census/ENV

acuedpoz Vivienda receives water from community water system (1=Yes, 0=Other Water types) D Census/ENV *

aguapozrio Vivienda receives water from river or well (1=Yes, 0=Other Water types) D Census/ENV

aguadent Vivienda has water inside the dwelling (1=Yes, 0=Other Water Location types) D Census/ENV *

aguafuer Vivienda has water outside of Dwelling (1=Yes, 0=Other Water Location types) D Census/ENV

elecmuncom

Vivienda connected to community/public electricity grid (1=Yes, 0=Other Electricity types) D Census/ENV *

elecplant Vivienda produces own electricity with generator (1=Yes, 0=Other Electricity types) D Census/ENV

elecotronr

Vivienda recieves electricity from other method/no response (1=Yes, 0=Other Electricity types) D Census/ENV

basserv Garbage removed by collection service (1=Yes, 0=Other Garbage Elimination) D Census/ENV *

bastira Garbage thrown in nearby lots or under patio (1=Yes, 0=Other Garbage Elimination) D Census/ENV

basentq Garbage thrown into river/stream (1=Yes, 0=Other Garbage Elimination) D Census/ENV

basotronr Garbage burned/Burried (1=Yes, 0=Other Garbage Elimination) D Census/ENV



Case b

Segmento-Level Variables

seg_ed_prime Percent of households in upm/segmento whose heads have completed primary school C Census

seg_ed_csec

Percent of households in upm/segmento whose heads have completed secondary school C Census

seg_ed_cvoc

Percent of households in upm/segmento whose heads have completed vocational school C Census

seg_ed_suped

Percent of households in upm/segmento whose heads have completed superior education C Census

seg_acuedpoz Percent of households in upm/segmento that have community water source C Census

seg_aguadent Percent of households in upm/segmento that have access to water inside their dwelling C Census

seg_basserv Percent of households in upm/segmento that have garbage collection service C Census

seg_elecmuncom Percent of households in upm/segmento that have electricity from the community C Census

seg_san_alcan

Percent of households in upm/segmento that have sanitation linked to community septic system C Census

seg_usosolo Percent of households in upm/segmento that exclusively use their toilet facilities C Census

seg_com_gaselec Percent of households in upm/segmento whose cooking fuel is either gas or electricity C Census

seg_telefono Percent of households in upm/segmento that have a telephone C Census

seg_cell Percent of households in upm/segmento that have a cellular telephone C Census

seg_indig Percent of households in upm/segmento that have an indigenous household head C Census

urban Type of segment (urban=1, rural=0) D Census/ENV Corregimiento-Level Variables

corr_popdens Population density (population/area in km2) of corregimiento C WFP

db_20t100k Distance (m) from border of Corregimiento to nearest city w/ pop 20-100k C GFK MACON

db_100pk Distance (m) from border of Corregimiento to nearest city w/ pop 100+k C GFK MACON

elevmean Mean elevation (meters) of Corregimiento C EROS elevrang Elevation range (meters) in Corregimiento C EROS

per_bsvd

% of Corregimiento that is Bosques siempreverdes y semisiempreverdes de latifoliadas C CCAD *

per_agpec % of Corregimiento that is Sistemas agropecuarios C CCAD

per_bsdl % of Corregimiento that is Bosques semideciduos de latifoliadas C CCAD



Case b

per_agua % of Corregimiento that is Cuerpos de agua C CCAD

per_mang % of Corregimiento that is Bosques manglares C CCAD

per_oth

% of Corregimiento that is Pantanos y humedales, Areas con escasa vegetacion, Sistemas productivos acuaticos (camaroneras, salineras), Sabanas, Bosques deciduos de latifoliadas, Paramos, Urbano C CCAD

per_road % of Corregimiento within 5 km of an all year road C CCAD

num_flood # of floods (inundaciones or Marejedas) since 1998 per District C Desinventar

Model Variables

dcorregc

Unique ID 9 digits long - Dominio (2), Province (2), District (2), Corregimiento (2), Indigenous Segs (1) C Census/ENV

sweight Survey weight, equal to the ENV expansion factor variable divided by 100 (factor/100) C ENV

cweight Census weight, a constant equal to one C Census 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


Table 3. Regression results Dependent Variable = Height for Age Z-score

Variable Names CoefficientStd. Err. t |Prob|>t

_intercept_ 9.562 0.431 22.172 0.000AGE1223 -0.048 0.071 -0.670 0.503AGE2435 0.036 0.071 0.507 0.612AGUAFUER -0.080 0.165 -0.484 0.628AGUAPOZRIO -0.122 0.186 -0.657 0.511BASENTQ 0.117 0.109 1.068 0.286BASOTRONR 0.020 0.227 0.086 0.931BASTIRA 0.235 0.151 1.556 0.120COM_LENCAR 0.146 0.138 1.057 0.290COM_NC 1.352 0.619 2.185 0.029CORR_POPDENS 0.016 0.007 2.305 0.021CROWDING -0.012 0.018 -0.678 0.498DB_100PK 0.000 0.000 -0.586 0.558DB_20T100K 0.000 0.000 -0.022 0.982DEPRATIO -0.224 0.222 -1.008 0.313ED_CSEC -0.311 0.452 -0.688 0.492ED_CVOC 0.107 0.382 0.281 0.779ED_PRESP 0.129 0.217 0.596 0.551ED_PRIME 0.138 0.241 0.571 0.568ED_SSEC -0.215 0.304 -0.709 0.479ED_SUPED -0.920 1.025 -0.898 0.370ELECOTRONR -0.175 0.165 -1.061 0.289ELECPLANT -0.141 0.123 -1.141 0.254ELEVMEAN 0.000 0.000 -0.689 0.491ELEVRANG 0.000 0.000 1.431 0.152FHEADHH 0.163 0.103 1.582 0.114HIFEMED -0.187 0.217 -0.862 0.389HIFEMEDSQ 0.042 0.037 1.132 0.258HIMALED -0.015 0.063 -0.247 0.805HIMALEDSQ 0.001 0.009 0.072 0.943HOMBRE -0.120 0.058 -2.058 0.040MARI_SEP -0.032 0.121 -0.267 0.789MARI_SOLTERO -0.020 0.181 -0.111 0.911MARI_VIUDO 0.052 0.165 0.316 0.752NUM_ADFEM 0.029 0.051 0.571 0.568NUM_FLOOD -0.005 0.002 -1.989 0.047NUM_KIDS059 -0.128 0.050 -2.563 0.010NUM_TOT 0.007 0.026 0.271 0.786PER_AGPEC 0.000 0.001 0.254 0.800PER_AGUA -0.012 0.014 -0.905 0.366PER_BSDL 0.001 0.003 0.499 0.618PER_MANG 0.005 0.005 1.064 0.287PER_OTH 0.001 0.002 0.692 0.489PER_ROAD -0.002 0.002 -1.131 0.258PISOMADE 0.029 0.163 0.180 0.857



Table 3. Regression results Dependent Variable = Height for Age Z-score

Variable Names CoefficientStd. Err. t |Prob|>t

PISOTIER -0.261 0.132 -1.969 0.049PISO_OTRONR -0.003 0.275 -0.009 0.993PRIMINHH -0.061 0.043 -1.417 0.157PRIMOUTHH -0.120 0.095 -1.263 0.207P_CANA -0.200 0.191 -1.047 0.295P_MADERA -0.222 0.122 -1.817 0.069P_METAL -0.182 0.194 -0.935 0.350P_QUINAD -0.172 0.194 -0.885 0.376P_SINOTR -0.517 0.285 -1.812 0.070SALFEM_PERCAP 0.000 0.001 -0.501 0.616SAL_PERCAP 0.001 0.000 1.372 0.170SAN_ALCAN -0.062 0.146 -0.423 0.673SAN_NOTIEN 0.093 0.142 0.657 0.512SAN_TANQ 0.186 0.095 1.966 0.049SECINHH -0.145 0.054 -2.682 0.007SECOUTHH -0.164 0.072 -2.288 0.022SEG_ACUEDPOZ 0.000 0.002 0.272 0.785SEG_AGUADENT 0.000 0.002 0.093 0.926SEG_BASSERV -0.003 0.002 -1.871 0.061SEG_CELL -0.005 0.003 -1.698 0.090SEG_COM_GASELEC 0.005 0.002 1.954 0.051SEG_ED_CSEC 0.002 0.003 0.466 0.641SEG_ED_CVOC 0.005 0.008 0.617 0.538SEG_ED_PRIME -0.007 0.002 -3.062 0.002SEG_ED_SUPED 0.000 0.004 0.124 0.902SEG_ELECMUNCOM 0.000 0.002 0.066 0.947SEG_INDIG -0.008 0.002 -4.869 0.000SEG_SAN_ALCAN 0.000 0.002 -0.121 0.904SEG_TELEFONO 0.003 0.002 1.461 0.144SEG_USOSOLO 0.002 0.002 0.993 0.321TELECELL 0.113 0.076 1.478 0.140T_CONTEJ 0.042 0.145 0.292 0.770T_PAJPEN 0.145 0.159 0.909 0.363URBAN 0.045 0.106 0.428 0.669USOCOMPNR -0.160 0.096 -1.666 0.096VIV_APARTAM -0.028 0.158 -0.176 0.861VIV_CUARTO -0.018 0.174 -0.103 0.918VIV_IMPROV -0.568 0.327 -1.738 0.082 N 1955 K 82 R-Square 0.294 Adjusted R-Square 0.263

Table 4: Census and Survey Variables and Recoding

Variable Group

Panama Census (c)

ENV Survey (s)

Matched Variables

Variable names in italics

Individual

Sex P02_SEXO 1. Hombre 2. Mujer

P003 1. Hombre 2. Mujer

hombre (c) (s) Hombre

Age P03_EDAD Age in years

agemo Age in months

age1223 – 1 year old age2435 – 2 years old age3659 – 3 -5 years old

Parentesco P01_JEFE

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

p002

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

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 fheadhh 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)

Total persons in household H22_TPER Integer 1 – 37

miembros Integer 1 – 23

num_tot

Number of Adult Females (age15-64) in household

Calculated using age (P03_EDAD) and sex (P02_SEXO)

Calculated using age (edadmo) and sex (P003)

num_adfem

Number of Children (aged 0-59 months) in household

Calculated using age (P03_EDAD)

Calculated using age (edadmo) num_kids059

Crowding H22_TPER/V05_NCUA Total members/ rooms

miembros/cuartos Total members/ rooms The ENV data had housing units

crowding



Variable Group

Panama Census (c)

ENV Survey (s)

Matched Variables


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.

Dependency Ratio (# of non-working household

members/total members)

Calculated using P14_TRAB and H22_TPER

Calculated using P701 and miembros

depratio

Marital Status of Household Head

P04_ESTC

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

p301 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

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*

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

p546a – NivelAprobado

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

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

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

7 Licenciatura 8 Postgrado, Maestría, doctorado 9 Otro 99 NR 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. beyond were collapsed into one

category – superior education. Highest Education for Any

Female 15 - 64 in Household Calculated using ed_*variables, P02_SEXO, and P03_EDAD

Calculated using ed_* variables, P003 and agemo

hifemed 0 – no female 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

Highest Education for Any Female 15 - 64 in Household

Squared

Calculated quadratic term of hifemed

Calculated quadratic term of hifemed

hifemedsq

Highest Education for Any Male 15 - 64 in Household

Calculated using ed_*variables, P02_SEXO, and P03_EDAD

Calculated using ed_* variables, P003 and agemo

himaled 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



Variable Group

Panama Census (c)

ENV Survey (s)

Matched Variables


Highest Education for Any Male 15 - 64 in Household

Squared

Calculate quadratic term by squaring himaled

Calculate quadratic term by squaring himaled

himaledsq

Enrollment Calculated using P10_ESCU and P03_EDAD 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 P520 and P004

priminhh - # of Primary Age Children in School primouthh - # of Primary Age Children Not in School secinhh - # of Secondary Age Children in School secouthh - # of Secondary Age Children Not in School

Per Capita Salary Calculated using P23A_SUEL and H22_TPER

Calculated using p729 and miembros

sal_percap

Per Capita Female Salary Calculated using P23A_SUEL, H22_TPER and P02_SEXO

Calculated using p729, miembros and P003

salfem_percap

Dwelling Dwelling (vivienda) Type V01_TIPO

v01 – TipoViv

viv_casa (c) Ind. Permanente, Ind.



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

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


22. Y mas con datos de viv 23. Y mas sin datos de viv

Material del Techo V07_TECHO

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

v03

1. Concreto / cemento 2. Teja 3. Fibra-cemento 4. Metal 5. Madera 6. Paja o penca 7. Otros materiales 9. NR

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

Material de las Paredes V06_PARE

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

v02

1. Bloque, ladrillo, etc. 2. Madera 3. Quincha / adobe 4. Metal 5. Caña, paja, penca, palos 6. Sin paredes 7. Otros materiales 9. NR

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


(c) (s) Otros Materiales, Sin Paredes, NR, NA

Material del Piso V08_PISO

0. No Aplica 1. Pavimentado 2. Madera 3. Tierra 4. Otro

v04 – Piso

1. Concreto / cemento 2. Mosaico, ladrillo, granito,

mármol 3. Madera 4. Tierra / arena 5. Otros materiales 9. NR

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

Tenancy V04_TENE

0. No Aplica 1. hiptecada 2. alquilada 3. propia 4. cedida 5. condenada 6. otra 7. no declarada

v05

1. Propia totalmente pagada 2. Propia hipotecada 3. Alquilada 4. Cedida o prestada 5. Ocupantes de hecho 9. 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


(s) Ocupantes de hecho ten_nr (c) No Aplica, No Declarado (s) NR

Water Source V09_AGUA

0. No aplica 1. acueducto publico del idaan 2. acueducto publico de la

comunidad 3. acueducto particular 4. pozo sanitario 5. brocal no protegido 6. agua lluvia 7. pozo superficial 8. rio o quebrada 9. carro cisterna 10. otro

v19 – AguaBeber

1. Acueducto público 2. Acueducto de la comunidad 3. Acueducto particular 4. Pozo sanitario 5. Pozo brocal no protegido 6. Río, vertiente, quebrada,

lluvia 7. Otro 9. NR

acuedpoz (c) (s) Acueducto Publico, Acueducto de la Comunidad, Acueducto Particular, Pozo Sanitario aguapozrio (c) Brocal no protegido, agua lluvia, pozo superficial, rio o quebrada, carro cisterna (s) Pozo brocal no protegido, Rio, vertiente, quebrada, lluvia, otro agua_nr (c) No Aplica (s) NR

Location of Water V10_AGIN

0. no aplica 1. Dentro 2. fuera

v22

1. Dentro de la vivienda 2. En el patio de la vivienda 3. Dentro de la vivienda y el

patio 4. Fuera de la vivienda y del

aguadent (c) Dentro (s) Dento de la vivienda, En el patio de la vivienda, Dentro de la vivienda y el patio aguafuer (c) Fuera



Variable Group

Panama Census (c)

ENV Survey (s)

Matched Variables


patio

9. NR (s) Fuera de la vivienda y del patio Agulocnr (c) No Aplica (s) NR

Eliminacion de la Basura V14_BASU

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

v33

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

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

Electricity V12_LUZ

0. no aplica 1. electrico publico

v35

1. Eléctrico Público 2. Eléctrico de la Comunidad

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

3. Electricidad del Municipio 4. Electricidad Propia 5. Electricidad de particulares 6. Querosín o diesel, gas 7. Otro 9. NR

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

Servicio Sanitorio H16_SANI

0. no aplica 1. de hueco o letrina 2. conectado a alcantarillado 3. conectado a tanque septico 4. no tiene

v29

1. conectado a alcantarillado sanitorio

2. conectado a tanque septico 3. de hueco o letrina 4. no tiene 9. NR

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

Uso del Sanitorio

H17_SUSO

0. no aplica

v32

1. Sólo del hogar

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

2. Compartido con otros hogares

3. Compartido con otras viviendas

9. NR

usocomp (c) Compartido con otros hogares (s) Compartido con otras hogares o viviendas uso_nr (c) No Aplica (s) NR

Cooking Fuel (combustible) H18_COMB

0. no aplica 1. gas 2. lena 3. carbon 4. querosin 5. electricidad 6. no cocina

v38

1. Gas 2. Leña 3. Electricidad 4. No cocina 5. Otro 9. 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

Phone (Land & Cellular)

H19_CTRES 0. No Aplica 1. Si 2. No 9. No declarado

H19_DTCEL

0. No Aplica 1. Si

V40a1 1. Sí 2. No 9. NR

v40a2

1. Sí 2. No

telecell (c) (s) Si



Variable Group

Panama Census (c)

Matched Variables


26

ENV Survey (s)

2. No 9. No declarado

9. NR

Technical Appendix

Table 5: Comparison of Survey and Census Descriptive Statistics Survey (ENV) Census Variable Names Mean SD Range Mean SD Range Individual-Level Variables hombre 0.514 0.500 0, 1 0.511 0.500 0, 1 age1223 0.247 0.431 0, 1 0.233 0.423 0, 1 age2435* 0.233 0.423 0, 1 0.256 0.436 0, 1 age3659 0.520 0.500 0, 1 0.511 0.500 0, 1 Household-Level Variables fheadhh* 0.237 0.425 0, 1 0.195 0.396 0, 1 num_kids059* 1.838 1.145 1, 8 1.730 0.938 1, 13 num_adfem* 1.646 1.032 0, 7 1.601 0.985 0, 12 num_tot* 6.900 3.827 2, 23 6.187 3.035 2, 37 crowding* 3.223 2.970 0.31, 23 3.007 2.541 0.2, 32 depratio* 0.711 0.161 0.14, 1 0.269 0.168 0, 0.9 mari_uni 0.837 0.369 0, 1 0.846 0.361 0, 1

mari_sep 0.097 0.296

0, 1 0.089 0.285 0, 1 mari_viudo 0.036 0.187 0, 1 0.035 0.184 0, 1 mari_soltero 0.029 0.167 0, 1 0.030 0.171 0, 1 ed_nonenr* 0.159 0.366 0, 1 0.107 0.310 0, 1 ed_presp 0.161 0.367 0, 1 0.163 0.370 0, 1 ed_prime* 0.238 0.426 0, 1 0.259 0.438 0, 1 ed_ssec 0.208 0.406 0, 1 0.195 0.397 0, 1 ed_csec* 0.122 0.328 0, 1 0.139 0.346 0, 1 ed_cvoc* 0.008 0.090 0, 1 0.023 0.150 0, 1 ed_suped 0.104 0.305 0, 1 0.107 0.309 0, 1 hifemed* 4.161 2.043 0, 8 4.456 1.942 0, 8 hifemedsq* 21.487 17.921 0, 64 23.628 17.939 0, 64 himaled* 3.540 2.369 0, 8 4.023 2.255 0, 8 himaledsq* 18.141 17.972 0, 64 21.267 18.356 0, 64 priminhh* 0.968 1.110 0, 6 0.800 0.994 0, 11 primouthh 0.099 0.414 0, 5 0.084 0.377 0, 10 secinhh* 0.433 0.709 0, 4 0.395 0.722 0, 8 secouthh* 0.202 0.518 0, 3 0.171 0.491 0, 8 sal_percap* 53.093 101.055 0, 1667 70.718 137.890 0, 3833 salfem_percap* 17.795 51.319 0, 806 23.473 65.927 0, 3500 san_alcan 0.197 0.398 0, 1 0.204 0.403 0, 1 san_tanq* 0.213 0.410 0, 1 0.179 0.383 0, 1 san_hueco* 0.422 0.494 0, 1 0.484 0.500 0, 1 san_notien* 0.168 0.374 0, 1 0.132 0.339 0, 1 usosolo* 0.697 0.460 0, 1 0.736 0.441 0, 1 usocompnr* 0.303 0.460 0, 1 0.264 0.441 0, 1 com_gaselec* 0.691 0.462 0, 1 0.729 0.444 0, 1 com_lencar* 0.305 0.460 0, 1 0.269 0.443 0, 1 com_nc* 0.005 0.068 0, 1 0.002 0.040 0, 1 telecell* 0.363 0.481 0, 1 0.340 0.474 0, 1 Vivienda-Level Variables viv_casa* 0.900 0.300 0, 1 0.877 0.329 0, 1 viv_improv* 0.006 0.078 0, 1 0.016 0.127 0, 1

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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

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