Context and Targeting Analysis for Guatemala* Final Report

International Food Policy Research Institute

April 2019

Report elaborated by Manuel A. Hernandez, Francisco Ceballos, Rosamaria Dasso, Maribel Elias, and Braulio Britos

* This report is made possible by the support of the American people through the United States Agency for International Development (USAID). The contents are the responsibility of the authors and do not necessarily reflect the views of the International Food Policy Research Institute (IFPRI), USAID or the United States Government. All correspondence should be directed to Manuel A. Hernandez at [email protected]. 1

Executive Summary

▪ The overall goal of the study is to collect and analyze multiple national, regional, and local information to help identify high-priority municipalities for the development of the GFSS Interagency Country Plan 2018-2022 and guide coordinated investment by multiple US Government agencies to improve food security, nutrition, and poverty in Guatemala. ▪ The poverty rate in Guatemala is still high compared to other countries in the region (59.3% according to the last survey of living conditions ENCOVI of 2014) and has further increased in recent years both in rural and urban areas. The level of chronic malnutrition or stunting rate, a measure of long-term nutritional deprivation, has, in turn, decreased to some extent in the past years (from 54.5% in 2002 to 46.5% in 2014/15 according to the maternal and child health surveys ENSMI), but it is still among the highest in the region and higher than other countries in the world with a similar level of economic development than Guatemala. The wasting rate, a measure of short-term acute malnutrition or hunger, has remained very low (0.7% according to the ENSMI 2014/15). ▪ A correlation analysis at the municipality level indicates that the main factors associated with the level of chronic malnutrition in the country are extreme poverty and risk of frosts. These correlation patterns confirm the high association between malnutrition and poverty, i.e. municipalities with high levels of stunting are also municipalities with a high prevalence of poor people. Similarly, areas with a high risk of frosts, such as the Western Highlands, are also areas with a high level of stunting. Other indicators moderately associated with the level of chronic malnutrition are precarious employment, deficit of basic grains, and road accessibility. ▪ The study focuses on the departments prioritized by USAID and USDA, as well as by the National Strategy to Reduce Chronic Malnutrition (ENPDC). These include the departments of San Marcos, Quetzaltenango, Totonicapán, Huehuetenango, Quiche, Sololá, Alta Verapaz, and Chiquimula, which comprise a total of 163 municipalities (out of 340) and are generally the areas with the highest level of poverty and stunting in the country. ▪ The classification and ranking of municipalities in the departments of interest is based on economic criteria such as production potential and efficiency in the use of resources, which combined with the current food security and nutrition status of a municipality permits to better identify high-priority level areas and their characteristics, as opposed to other classification methods such as poverty maps or simple clustering analysis. ▪ To estimate the representative agricultural production potential of an area and its efficiency level, the analysis incorporates different market, socioeconomic, biophysical, and accessibility factors, which explain great part of the heterogeneity in rural Guatemala. This requires the combination of different data sources, including household surveys, census data, and detailed geographic and climate data. The use of advanced econometric methods in the estimations further permits to weight these different factors relying exclusively on economic theory and empirical evidence. ▪ The resulting agricultural production potential and efficiency level of a municipality is then separately combined with different food security and nutrition indicators of the municipality, including the level of chronic malnutrition, poverty, vulnerability to climate change, and vulnerability to food insecurity, to derive the corresponding rankings. The level of chronic malnutrition is based on the last National Height Census of 2015, while the level of poverty is based on the last Census of Population and Housing of 2002. The measure of vulnerability to climate change is based on a recent study by Bouroncle et al. (2017) that accounts for the degree of exposure (temperature variation), sensitivity (crop area variation), and adaptation of the population in an area.

The measure of vulnerability to food insecurity is based on an index derived by SESAN-MAGA (2012) that accounts for vulnerability in food security and nutrition, climatic risks, and the response capacity of the government. ▪ The reported results include the full list of municipalities ranked based on their estimated potential and level of efficiency, both nationally and in the eight prioritized departments, as well as the list of municipalities in the eight departments classified based on their potential, efficiency, and current situation in terms of chronic malnutrition, poverty, vulnerability to climate change, and vulnerability to food insecurity. Accompanying maps are included to better appreciate the results and the spatial distribution of the different categorized municipalities in the targeted areas. ▪ The study also provides a detailed profile of the categorized areas and their vulnerable and at-risk populations. The profile is based on different food security and nutrition indicators as well as demographic and socioeconomic characteristics, which have been compiled from multiple available data sources. An interactive platform has further been developed to provide a comprehensive profile for each of the prioritized municipalities. ▪ The reported ranking and classification of municipalities should be viewed as a first assessment in the design of policies for rural development and food security and nutrition in the areas of interest, which could eventually be complemented with more detailed information at the local level (if available) and interviews with key local informants (if applicable). Some of the prioritized departments were, for example, also visited to further discuss and corroborate part of the results of the analysis with the Department Directors of MAGA and their technical teams.

Acronyms

ENCOVI – Encuesta Nacional de Condiciones de Vida

ENPDC – Estrategia Nacional para la Prevención de la Desnutrición Crónica

ENSMI – Encuesta Nacional de Salud Materno Infantil

GDP pc PPP – Gross domestic product per capita in purchase power parity terms

GFSS – United States Government Global Food Security Strategy

HAZ – Height-for-Age Z-score

HWZ – Height-for-Weight Z-score

INE – Instituto Nacional de Estadística

IVISAN – Índice de Vulnerabilidad a la Seguridad Alimentaria y Nutricional

MAGA – Ministerio de Agricultura, Ganadería y Alimentación

SESAN – Secretaría de Seguridad Alimentaria y Nutricional

USAID – United States Agency for International Development

USDA – United States Department of Agriculture

WAZ – Weight-for-Age Z-score

1 Introduction

The objective of this study is to collect and synthesize available national, regional, and local information about the current context in Guatemala related to malnutrition, hunger, and poverty, which will be used to inform the GFSS country team’s development of their five-year (2018-2022) GFSS Interagency Country Plan in their geographic Zone of Influence (ZOI) and focused populations. In particular, the study intends to help identify high-priority municipalities for the development of the GFSS Interagency Country Plan, which in turn will guide coordinated investment by multiple US Government agencies to improve food security, nutrition, and poverty in Guatemala. The idea is to establish a ranking of municipalities compatible with investment criteria in terms of potential for rural (agricultural) development of the municipality and its current food security and nutrition situation (poverty, chronic malnutrition, vulnerability, and risks), and to provide a detailed profile of the prioritized areas and their vulnerable populations. The study focuses on the Departments prioritized by USAID, USDA and by the New National Strategy to Reduce Chronic Malnutrition (ENPDC). These include the following eight departments, which comprise a total of 163 municipalities: Table 1. Prioritized departments Department Prioritized by # Municipalities San Marcos USAID, USDA 33 Quetzaltenango USAID, USDA 21 Totonicapán USAID, USDA 30 Huehuetenango USAID, USDA & ENPDC 8 Quiche USAID, USDA & ENPDC 24 Sololá USDA 19 Alta Verapaz ENPDC 17 Chiquimula ENPDC 11

In contrast to other classification methods such as poverty maps or simple clustering analysis, the context and targeting analysis described below permits to rank and classify the areas of interest based on economic criteria such as production potential and efficiency in the use of resources, which combined with the food security and nutrition status of the area permits to better identify high-priority areas and their characteristics. As shown in Figure 1 below, the analysis incorporates different market, socioeconomic, biophysical, and accessibility indicators that explain great part of the heterogeneity in rural households in Guatemala and that should be included in the design of any policies for rural development and food security and nutrition. Further, the weights of these different indicators in the analysis rely exclusively on economic theory and empirical evidence using advanced econometric methods in the estimations. The combination of the current food security and nutrition status of an area with its production potential and level of efficiency permits a more detailed diagnostic of the actual needs of an area and the potential solutions to overcome them, as summarized in Figure 2. For example, we can identify two areas (Zone A and B) with high levels of chronic malnutrition, high poverty rates, low accessibility, and high vulnerability or risks. If one of the areas has in addition a low production potential (i.e. Zone B), then interventions such as conditional cash transfers, safety net programs, and nutrition programs should be prioritized, at least in the short run; and, since the area shows low potential for agricultural development, these should be complemented with non-agricultural development programs to aid long-term economic growth. On the other hand, if the area exhibits a high production potential (i.e.

Zone A), then rural (agricultural) development programs should be implemented combined (if necessary) with short-term nutrition programs.

Market Economic criteria Conditions Socioeconomic Production potential Characteristics Efficiency level Biophysical Conditions RANKING (Climate, altitude, + + steepness, soil quality) Poverty PROFILES

Accessibility Malnutrition (Transport costs) Vulnerability/Risk

Weights assigned based on economic theory and empirical evidence Diagnostic on Food Security & Nutrition

Figure 1. Why perform a typology analysis?

Differentiated rural Conditional cash development programs transfers, safety net based on local needs & programs & nutrition nutrition programs programs

Profile of areas to identify potential bottle necks

Production potential and Area of high Area of low efficiency level based on Typology potential & high potential & high market conditions; average inefficiency average inefficieny socioeconomic; biophysical; and accessibility

High chronic malnutrition; Diagnostic on Area of high food & Area of high food & High poverty; Food Security & nutrition nutrition Low accessibility; Nutrition vulnerability vulnerability Adverse biophysical conditions Zone A Zone B

Figure 2. How can a typology analysis contribute for policy design?

The remainder of the report is organized as follows. Section 2 details the methodology of the study, including the estimation method, data used, and subsequent steps followed to categorize and rank the municipalities in the areas of interest. Section 3 describes the current state and past evolution of different poverty, malnutrition, and hunger 6 indicators in Guatemala as a whole and in the prioritized departments, and evaluates potential factors that correlate with the level of malnutrition (stunting) across different municipalities. Section 4 presents the results of the geographic targeting analysis, including the list of municipalities ranked based on their estimated potential and level of efficiency, both nationally and in the eight focalized departments, as well as the list of municipalities in the eight departments classified based on their potential, efficiency, and current situation in terms of chronic malnutrition, poverty, vulnerability to climate change, and vulnerability to food insecurity. Accompanying maps are included to allow for a better interpretation of the results and the spatial distribution of the different categorized municipalities in the targeted areas. Section 5 provides a detailed profile of the categorized areas based on different food security and nutrition indicators and demographic and socioeconomic characteristics of their population, including their vulnerable and at-risk populations. A brief description of the interactive platform developed with a full profile of each prioritized municipality is also provided. Section 6 concludes.

2 Methodology

This section details the methodology used to calculate the agricultural productive potential and efficiency level for every rural micro-region in Guatemala. The objective is to estimate the productive potential and efficiency level for each municipality in the country and, in particular, in the eight prioritized departments.1 The methodology is similar to the one used by the World Bank to estimate poverty maps, in which information from household surveys is combined with census data to exploit the richness of the former and the representativeness of the latter. The agricultural census data (CENAGRO, for its name in Spanish) permits, for example, to determine the socioeconomic characteristics of producers from rural areas in Guatemala, which combined with multiple survey data and detailed geographic data, permits to obtain estimates for the productive potential and efficiency level of each municipality.

Step 1: Estimation of the stochastic profit frontier function The stochastic profit frontier function is defined as:

휋푖푗 = 푓(푃푖푗, 푊푖푗) exp( 푣푖푗 − 푢푖푗(푍푖, 퐺푗, 퐴푗)) (1) where:

• πij denotes the profit of producer i in zone j. • Pij is the price vector that the producer faces • Wij is the price vector of the inputs that the producer faces • vij is an stochastic error with two tails which is identically and independently distributed with 2 푁(0, 휎휈 ), and is independent from uij. • uij non-negative random variable linked to production inefficiencies which is independently + 2 distributed following a semi-normal distribution, 푁 (0, 휎푢푖푗) • Zi is the vector of socio-economic characteristics and fixed factors of the producer's farm • Gj is the vector of bio-physical conditions in zone j 2 • Aj is the cost (time) to access markets faced by producers in zone j.

We use household-level information from the ENCOVI-2014 to estimate πij, Pij, Wij,, Zi in equation (1). We measure profits (πij) by the net producer income after subtracting different types of costs (labor, transport, storage, etc.).

Product and input prices (Pij, Wij) are calculated as described in the following chart:

1 Refer to Maruyama and Torero (2011) for a previous typology of rural micro regions in Guatemala following a similar stochastic profit frontier approach. 2 See Table A1 in Appendix A for more details of each variable. 8

Median prices calculation

1. We obtain the implicit price for every product from each producer. 2. We aggregate the implicit prices in five groups: 1) Fruits and vegetables, (2) Industrial crops, (3) Cereals, (4) Legumes y (5) Tubers.

3. We calculate the median price for each group. 4. If there is no price information at the municipality level, we impute the median prices of the corresponding department or region. We follow this process until every municipality in the sample has a price vector for each group of goods.

Input prices are calculated in the same way (skipping step 2).

3 Market access conditions Aj are available at the level of the primary sample unit (PSU). These conditions (i.e. time taken to the nearest town of 20,000 people) are calculated using a model of accessibility that is described in Appendix D using information from multiple sources (MAGA, the National Commission of Electrical Energy,

ENCOVI, INE, and the Central Bank). Bio-physical conditions Gj, which are detailed below, were provided by MAGA. The information for land use is available at the PSU-level. The rest of conditions (altitude, slope, land quality, weather) are measured at the municipality level. There is enough data to find a representative relationship between market conditions (prices) faced by producers, their socio-economic characteristics, bio-physical and accessibility conditions of the area, and profits and actual efficiency. Appendix Table A2 and Table A3 present the Maximum Likelihood estimates of equation (1). In Table A2, we show the estimated profits (πij), which depend directly on median prices of products and inputs at the municipality level faced by producer i in municipality j (as well as the inefficiency of each producer). In addition, we include the cross-product of prices to allow for potential complementarities across products.

In Table A3, we report the estimated variance of uij, which roughly measures the inefficiency level of each producer. It is assumed that the variance of the inefficiency is heteroskedastic (varies across producers) and depends on the socio-economic characteristics of each producer, and the bio-physical conditions that he faces. These same characteristics also affect profits through the inefficiency level.

Step 2: Prediction of the productive potential and efficiency level Once we obtain the parameters from Step 1, we can predict the values for the productive potential and efficiency for every municipality. For the estimation of the productive potential we use the (median) price vectors of the municipality, as in the following linear relationship

3 Market access conditions faced by producers are measured at the PSU (Primary Sample Unit) since GPS data is only available at that level. This means that in the estimation all producers within a single PSU have the same market access conditions. 9

휋푝푟푒푑푖푐푡푒푑,푗 = 훾1푃푗 + 훾2푊푗 (2)

where 훾1 and 훾2 are the estimated parameters (see Appendix Table A2) related to the median prices of products (푃푗) and inputs(푊푗), respectively. In this estimation, we have as many observations as municipalities. To estimate the efficiency level, given that the median values of socio-economic characteristics and fixed factors

(Zi) are not representative at the municipality level (ENCOVI is not representative at this level), we use information from the agricultural census (CENAGRO) of 2003 to calculate these median values of vector Zi. Market access (Aj) is calculated for the centroid of each municipality. For the biophysical conditions, we use municipality-level information on altitude, slope, weather, and land quality from MAGA. Finally, since it is possible to have more than one type of land use within a municipality, we predict efficiency levels for all potential land uses within every municipality.4

4 Since we have 5 categories for land use (see Table A1), we generate 5 values of efficiency for each municipality. 10

INPUTS FOR ESTIMATION PROCESS (ESTIMATION) OUTPUT (OF THE ESTIMATION)

Frontier inputs: price of products (P), of inputs (W), profits (π). Econometric model Assignment of weights of stochastic profit to inputs based on Efficiency inputs: land, frontier function economic theory and STEP 1: value of assets, socio- ESTIMATION empirical evidence economic characteristics (Z), bio-physical conditions (G), STEP 2: market access (A). PREDICTION AT THE NOTE: P, W, Z from ENCOVI at the MUNICIPALITY LEVEL

household level; G and A from MAGA and other sources at the PSU OUTPUT OF THE INPUTS FOR THE or municipality level. PREDICTION PREDICTION

Productive potential at the Estimation Output

municipality level and Frontier inputs: price of efficiency based on socio- products (P) and inputs (W) economic characteristics, Efficiency inputs: land, biophysical and market value of assets, socio- access conditions according economic characteristics (Z), to areas within the biophysical conditions (G), municipality market access (A).

Assignment of weights NOTE: Medians of P and W from ENCOVI at the municipality level; Median of Z from Productive potential and CENAGRO at the municipality level; G and A

efficiency for each from MAGA and other sources according to MUNICIPALITY areas within the municipality

Figure 3. Summary of the methodology

The formula used to obtain the efficiency level is the following:

1−Φ(휎∗−휇∗푗푔/휎∗) 1 2 퐸푓푓𝑖푐𝑖푒푛푐푦푗푔 = { } 푒푥푝 (−휇∗푗푔 + 휎∗ ) (3) 1−Φ(−휇∗푗푔/휎∗) 2

11 for each municipality j and every potential land use g, where  is the cumulative normal distribution and

2 휖푗휎푢푗푔 휎푢푗푔 휎푣 휇∗푗푔 = − 2 2 푎푛푑 휎∗ = 2 2 휎푢푗푔 + 휎푣 휎푢푗푔 + 휎푣

2 The variance of the inefficiency component is 휎푢푗푔 and depends on the socio-economic characteristics and the biophysical and market access conditions (recall it is heteroskedastic). The variance of the stochastic 2 component, 휎푣 , is constant and was estimated in Step 1 (see Appendix Table A2), and 휖푗 is the prediction error of the potential profit 휖푗 = 휋표푏푠푒푟푣푒푑,푗 − 휋푝푟푒푑푖푐푡푒푑,푗. The generated information is then given to a team of geographers, who clean the data base and extract the actual types of land use in each municipality. The mapping then takes place. To obtain the unique values of efficiency (and potential) for every municipality (Map B), and not for every municipality and land use (Map A), we calculate the weighted average of all efficiencies in each municipality, using weights proportional to the area (denoted by hjg in the next figure) that each land use occupies within each municipality.

Map A Map B

Effic. 1a, Effic.1b, Pot.1 Weighted average of Pot.1 efficiencies Municipality 1 Municipality 1 effic. effic. 1 , Pot.1 2a, efic. 2c, Pot.2 effic.1c, Pot.1 Pot.2 Municipality 2

푒푓𝑖1 = (푒푓𝑖1푎 ∗ ℎ1푎) + effic. 2 , Pot.2 Municipality 2 (푒푓𝑖1푏 ∗ ℎ1푏) + (푒푓𝑖1푐 ∗ ℎ1푐 ) effi. 2b, Pot.2

Figure 4. Calculation of a municipality's efficiency

3 Country context

This section describes the current situation and past evolution of a number of development indicators related to poverty, hunger, and malnutrition in Guatemala. We first discuss trends over the past few decades for each of these indicators at the national level, after which we place the situation of the country both in the context of Latin America and the Caribbean and more broadly in the world. We further analyze the relative situation of the 8 prioritized departments within the general context of Guatemala. We focus on four measures: (i) Poverty and extreme poverty rates, or the fraction of the population living under the national poverty and extreme poverty lines determined by INE; (ii) Stunting rate, or the fraction of children under 5 years old with low height-for-age z-score (HAZ), a measure of long-term nutritional deprivation; (iii) Underweight rate, or the fraction of children under 5 years old with low weight-for-age z-score (WAZ), a measure combining short- and long-term nutritional deficiency, commonly used since weight is easier to measure than height; and (iv) Wasting rate, or the fraction of children under 5 years old with low weight-for-height z-score (WHZ), a measure of short-term acute malnutrition or hunger.5 The section also discusses the topic of seasonality in nutrition deficiencies and ends with a correlation analysis evaluating potential factors related with the level of malnutrition (stunting) across different municipalities in the country.

National trends Figure 5 shows the evolution of national poverty and extreme poverty rates since 2000, as reported by the representative national household surveys (ENCOVI). The figure distinguishes between extreme poverty (individuals living under the extreme poverty line, which captures basic subsistence levels) and moderate poverty (individuals under the poverty line but above the extreme poverty line). Total poverty levels are high, with the overall poverty rate in 2014 at almost 60%, indicating that around 3 out of 5 individuals in the country live under the poverty line. The extreme poverty rate in 2014 was 23.4%, comprising around one third of the poor. Despite an initial decrease in the beginning of the 21st century, poverty levels have increased slightly over the past decade and a half, from 56.4% to 59.3%. A worrying aspect of this overall trend, however, is that it seems to be due to an increase in the rates of extreme poverty, which increased by around 50% (almost 8 percentage points) between 2000 and 2014. Figure 6 explores these same indicators but differentiating between urban and rural areas. As it is well-known, rural areas show a much higher prevalence of poverty than urban areas (almost twice the urban levels). For rural areas, the trends at the national level remain, with rural extreme poverty increasing and moderate poverty decreasing, with an overall upward trend in the total poverty level. Interestingly, poverty in urban areas shows a sharp increase since 2000 of around 15 percentage points, a phenomenon likely led by rising rural-to-urban migration and higher cost- of-living in urban areas, probably accompanied by an insufficient increase in urban labor opportunities. In the same period, and in contrast to the rural trends, both moderate and extreme urban poverty levels showed an increase. Figure 7 depicts the dynamics of stunting rates (HAZ) over almost three decades, both at the national (solid line) and rural-urban levels (bars). The data come from the nationally representative ENSMI surveys on maternal and child health and indicate stunting rates in children under 5 years old. Overall, stunting is high, with around half of

5 The z-scores are internationally-standardized indicators for nutritional deficiency based on the WHO methodology. Under this methodology, an individual is considered as malnourished under any of these measures when the respective z-score is below -2. 13 the children under 5 showing chronic malnutrition, with considerably higher rates in rural than in urban areas. Nevertheless, stunting has been declining since 1987, by almost 16 percentage points nationally (from 62.2 to 46.5 percent). In urban areas, however, while stunting has declined since the beginning of the period under consideration, this indicator has shown signs of increase in the past decade, from 33.4 percent in 2008-09 to 34.6 percent in 2014- 15.

80% 70% 59.3 60% 56.4 51.2 53.7 50% 40% 35.9 40.7 35.9 40.4 30% 20% 10% 23.4 15.7 15.3 13.3 0% 2000 2006 2011* 2014

Extreme poverty Poverty

Figure 5. Poverty and extreme poverty ― National rates between 2000 and 2014

80% 74.5 76.1 70.5 71.4 70% 60% 40.8 50% 50.7 46.1 50.2 42.1 40% 35.0 30.2 27.3 30% 30.9 20% 24.9 29.9 24.5 35.3 10% 23.8 24.4 21.2 11.2 0% 2.8 5.3 5.1 Urban Rural Urban Rural Urban Rural Urban Rural 2000 2006 2011* 2014

Extreme poverty Poverty

Figure 6. Poverty and extreme poverty ― Urban and rural rates between 2000 and 2014

Figure 8 and Figure 9 show the trends for prevalence of underweight (WAZ) and wasting (WHZ) in children under 5-years old. The improvement in these malnutrition indicators is much more dramatic over the same period. Nationally, underweight rates fell in more than half, from 26.9 percent in 1987 to 12.6 percent in 2014-15. The decrease is seen in both rural and urban areas, though the latter exhibit a slight increase in the past decade, in line with the trends in stunting and poverty described above. In terms of wasting, the figure indicates that short-term

14 acute malnutrition (as measured in this particular fashion) has practically disappeared, at levels of under 1 percent in 2014-15, both in rural and urban areas.6

60 62.2 50 55.2 53.2 54.5 40 48.2 46.5

10 50.4 66.8 39.9 62.4 38.8 61.6 41.4 60.8 33.4 56.8 34.6 53.0 0 1987 1995 1998-99 2002 2008-09 2014-15

Urban areas Rural areas All

Figure 7. Stunting (HAZ<-2) ― National and urban/rural areas rates between 1987 and 2015

40 26.9 30 20.9 19.5 17.5 20 12.7 12.6

10 21.0 29.2 13.3 24.5 11.8 24.0 12.0 20.3 8.0 15.5 9.5 14.3 0 1987 1995 1998-99 2002 2008-09 2014-15

Urban areas Rural areas All

Figure 8. Underweight (WAZ<-2) ― National and urban/rural areas rates between 1987 and 2015

Regional and international comparison This subsection compares the current poverty and nutrition situation of Guatemala’s relative to other countries in the Latin American & Caribbean region and, more generally, across the world. We focus on the four indicators presented above and contrast these with a country’s GDP per capita in purchase power parity (PPP) terms, a standard measure of overall economic development adjusted for differences in cost-of-living across countries. All indicators

6 It is important to clarify that this is just an internationally-comparable standardized measure for acute malnutrition. In practice, severe short-term undernourishment is still an issue in the country, particularly in relation to seasonal hunger and income and food availability shocks (see Section 3.4 below). 15 are obtained from the World Bank’s World Development Indicators (WDI) and correspond to the latest available measure for each country within the past 10 years. We present several scatterplots distinguishing between Guatemala (blue diamond), other countries in the region (orange triangles), and the rest of the world (grey circles). The underlying idea behind these comparisons is that countries with similar levels of GDP pc in PPP terms should find themselves at similar stages of the development process and, as such, should share similarities in terms of their poverty and nutrition situation. Large disparities in these comparisons would be indicative of idiosyncratic factors affecting a country’s development path.

10 3.9 3.0 2.3 2.2 1.6 0.7 2.1 2.4 3.4 4.2 2.0 3.6 1.7 2.4 1.4 1.7 0.8 0.7 0 1987 1995 1998-99 2002 2008-09 2014-15

Urban areas Rural areas All

Figure 9. Wasting (WHZ<-2) ― National and urban/rural areas rates between 1987 and 2015

Figure 10 shows this comparison for poverty (using national poverty lines). At 59.3 percent in 2014, Guatemala shows one of the highest poverty rates in the world. In the region, only Honduras has a higher rate (62.8 in 2014), with Haiti and Mexico are closely behind (58.5 in 2012 and 53.2 in 2014, respectively). When compared against countries with a similar level of development (as captured by the PPP GDP pc), Guatemala is well above most other countries in its category.

Figure 10. Poverty rate and GDP pc (PPP) ― Relative to Region and World

Figure 11. Stunting rate and GDP pc (PPP) ― Relative to Region and World

A similar exercise is shown in Figure 11 for stunting. In this case, the contrast is even more dramatic, where Guatemala not only exhibits one of the highest stunting rates in the world but it is also well above other countries in the region (including Honduras and Haiti, with lower GDP pc and similar poverty rates) and other countries with similar levels of development. It is interesting to note, however, that this contrast does not seem to be associated to the country’s level of inequality alone. Figure A1 in Appendix A compares the GINI Index for Guatemala to that of other countries in the region, indicating that, while high, the level of inequality measured by this index is comparable to that of other LAC countries. Finally, Figure A2 shows that Guatemala’s underweight rate is still above most other countries in the region (probably due to the very high levels of long-term nutritional deficiency, which affect the underweight status of children), though at levels much more comparable to other countries around the world with similar PPP GDP pc. Figure A3 and Figure A4 show the situation for the prevalence of wasting and overweight. In terms of these indicators, Guatemala is well at par or below its benchmarks, both regionally and globally.

Prioritized departments Next, we shift the focus to the heterogeneous development levels of the departments within Guatemala, particularly on the 8 departments prioritized by USAID, USDA and the ENPDC (see Table 1). Figure 12 shows the poverty rate for each of the 22 departments in Guatemala, ordered from lower to higher. The bars in blue indicate the prioritized departments. It follows that prioritization is indeed aligned with those departments most affected by poverty, with Alta Verapaz and Sololá showing the highest levels, with more than 4 out of 5 people being under the poverty line. In this regard, however, Quetzaltenango and San Marcos show poverty levels closer to the average of the country, at around 60 percent. Figure A5 shows the same exercise for the case of the extreme poverty rate. Here, Alta Verapaz is even more of an outlier, with more than half of its population under the extreme poverty line (i.e. under subsistence levels). We also compare the relative position of the 8 prioritized departments in terms of stunting, underweight, and overweight rates. Figure 13 shows that these departments are again within the top half of departments with the highest stunting rates in the country. However, the pattern does not correspond exactly to that found in terms of poverty, with the salient exception of Alta Verapaz, which shows the highest rates of poverty and extreme poverty but exhibits a much more moderate prevalence of stunting (at 50 percent) compared to other departments (with

Totonicapán being the highest at 70 percent). Figure A6 shows a similar pattern in terms of the rate of children under five identified as underweighted, with most of the prioritized departments showing the highest rates and roughly following the same ranking as in the case of stunting. Figure A7 suggests that undernourishment is not generally coupled with overweight problems, since most of the prioritized departments are at or below the national average.

90 80.9 83.1 77.5 80 73.8 74.7 70.6 70 60.2 60 56.0 50 40 30

POVERTY RATE(%) POVERTY 20 10 0

Figure 12. Poverty rate ― Prioritized departments

90 80 67.7 68.7 70.0 70 65.6 60 54.8 55.6 48.8 50.0 50 40 30

20 STUNTING STUNTING RATE (%) 10 0

Figure 13. Stunting rate ― Prioritized departments

Seasonality The issue of seasonality is also important, particularly when examining hunger and malnutrition patterns among the most vulnerable population groups.7 Agricultural production and the related demand for agricultural labor in high value crops have a marked seasonal component, which can be aggravated by weather shocks and pest and diseases (most importantly, roya in coffee). Lower staple crop availability at certain times of the year results in price fluctuations that can also have a large effect on net food consumer households. Moreover, rural populations with

7 For more information, see SESAN Seasonal Hunger Reports 2016 and 2017. 18 limited health service availability and poor sanitation practices are subject to vector-borne and infectious diseases that have a seasonal (mostly weather-related) component. Short-term nutrition deprivations may translate to long-term nutritional deficiencies. For instance, the nutritional literature discusses the issue of ‘critical period programming,’ or the idea of environmental conditions in a certain sensitive period of life (e.g. pregnancy, post-lactating period, etc.) having long-run, irreversible effects (see, for instance, Maccini and Yang, 2009). While the literature is inconclusive about the exact period in the life of a child that is most critical, the evidence is clear in that temporary shocks to food availability can induce long-term effects in stunting (e.g., Glewwe and King, 2001; Hoddinott and Kinsey, 2001). In this context, putting in place mechanisms to identify food shortage crises and safety nets to mitigate their impact could be an important policy objective. To illustrate the extent of seasonality and its effect on nutrition, we rely on a detailed database on the number of acute malnutrition cases reported per week.8 Figure 14 shows the weekly evolution of the number of cases at the national level since 2015. It can be clearly seen that the months from May to August show a marked increase in acute malnutrition. Following the February-April harvest of basic grains, this is a period of dwindling food reserves, and coincides with a low demand for hired agricultural labor and the hot, rainy season. Figure 15 in turn reports the evolution of weekly number of cases during 2016 in the 8 prioritized departments. While the figure does not allow for a cross-department comparison since the number of cases is not adjusted by the total number of children at risk, it does allow to observe the incidence of seasonality at a more local level. The figure shows that seasonality is an issue across all prioritized departments, and particularly marked in the case of Huehuetenango, Quetzaltenango, and Sololá, together with Chiquimula starting in June, following geographical differences in agricultural seasons.

600

CASES 500 OF

400

UMBER

N - 300

200 ALNUTRITION

M 100

CUTE A 0 Jan Feb Mar Apr May Jun Jul Ago Sep Oct Nov Dec 2015 2016 2017

Figure 14. Number of reported cases of acute malnutrition per week ― National level, 2015-2017

A final piece of evidence for the effects of seasonality on nutritional outcomes is presented in Figure 16, where we show the average height-for-age z-score (HAZ, related to stunting) for children born across different months of the year. The source for these data are the 2012-14 monitoring surveys around the impact evaluation for the Plan del Pacto Hambre Cero national strategy against malnutrition, which are representative at the level of the 176

8 This database is compiled by SESAN and also includes statistics on acute-malnutrition related mortality. It is available at http://www.siinsan.gob.gt/SemanasSalaSituacional. 19 municipalities with the highest stunting rates in the Censo Nacional de Talla 2008. The pattern in the figure indicates an intriguing relationship between the month a child is born and his level of HAZ, where children born in the month of January are considerably worse-off in nutritional terms (i.e. lower HAZ) than children born later in the year.9 Such a pattern suggests that short-term nutritional deprivation over certain critical periods over the growth stage of young children (or even before their birth through reduced nutritional intakes by their mothers) can translate into long-term impacts. Nonetheless, further research is needed in this regard to confirm these observed relationships and establish the causal mechanisms behind them.

CASES 50 OF

UMBER N - 30

20 ALNUTRITION

M 10 CUTE

A 0 Jan Feb Mar Apr May Jun Jul Ago Sep Oct Nov Dec Chiquimula Huehuetenango Quetzaltenango Quiché Sololá Totonicapán

Figure 15. Number of reported cases of acute malnutrition per week ― Prioritized departments, 2016

-2.50 -2.45 -2.40 -2.40

-2.35 -2.32 -2.32 -2.30 -2.29 -2.30 -2.27 -2.26 -2.26 -2.25 -2.24 -2.24 -2.25 -2.23

-2.20 HAZ SCORE HAZ -2.15 -2.10 -2.05 -2.00 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Figure 16. Relationship between Height-for-Age (HAZ) Z-score and month of birth ― Prioritized departments, 2016

9 Importantly, these differences are not related to a difference in the number of children born in different months or to geographic or socioeconomic factors. Moreover, the pattern described holds for rural areas but not for urban areas, which is further indicative of the link with agricultural seasonality. 20

Factors associated with chronic malnutrition Lastly, this subsection examines the correlation between the level of chronic malnutrition (stunting) and different indicators linked to vulnerability on food security and nutrition. The objective of this exercise is to determine which major food security and nutrition drivers are more strongly associated with the level of chronic malnutrition in a municipality. We consider the 10 indicators at the municipality level included in the Indice de Vulnerabilidad a la Inseguridad Alimentaria y Nutricional (IVISAN) proposed by SESAN-MAGA (2012). These include five indicators directly related to food availability, access, and consumption: staple grains shortage, extreme poverty, employment insecurity, illiteracy, and sanitation infrastructure; three indicators related to climatic risks: risk of frost, drought, and flooding; and two indicators related to the response capacity to adverse events: road index (quality) and state density.10 We also use the reported stunting rate for each municipality from the last National Height Census (Ministerio de Educacion-SESAN-INE, 2015). We consider in the exercise all 340 municipalities in the country.

Availability Deficit of Grains Risk of Frost

Extreme poverty Risk of Drought

Access Precarious Risk of Flooding Stability / Employment Stunting Resilience

Analphabetism Road Index Consumption Sanitation State Density

Figure 17. Stunting and indicators associated to food security and nutrition

The analysis consists in deriving the partial correlation between the stunting rate and each of these ten indicators following a multivariate linear regression approach. Since several of these indicators are correlated between each other, the idea is to derive the “direct” or “net” correlation between the stunting rate and each variable after accounting for the potential influence (effect) of all the other variables on both the stunting rate and the variable of interest. The steps followed to derive these “net” correlations are detailed in Appendix C.

Figure 18 presents the resulting standardized coefficients of partial correlations, which can be interpreted as the “net” or “direct” change (in standard deviations) in the stunting rate associated with one standard deviation change in the corresponding indicators. The two indicators that are more correlated with the level of stunting in an area are extreme poverty (partial correlation of 0.438) and risk of frost (partial correlation of 0.406). These results confirm the high association between malnutrition and poverty, i.e. municipalities with high levels of stunting are also municipalities with a high prevalence of poor people. Similarly, areas with a high risk of frosts are also areas with a high level of stunting; this is in line with a high prevalence of stunting in municipalities in the Western Highlands, which are areas more prone to face frosts. Other indicators moderately associated with the level of chronic malnutrition are the index of precarious employment (0.298), deficit of basic grains (0.228) and road index (0.201).

10 All variables were compiled by SESAN-MAGA in 2012; the level of analphabetism was then updated in 2014. 21

Overall, while we cannot establish causality, these correlations suggest the importance of taking into account these five indicators when trying to understand the prevalence of stunting across municipalities in the country.

0.60 0.50 0.438* 0.406* 0.40 0.298* 0.30 0.228* 0.201* 0.20 0.061 0.10 0.026 -0.077 -0.074 0.00 -0.005 -0.10

-0.20

Road Index Road

Risk of Frost Riskof

Analphabetism

Risk of Drought Riskof Employment

Sanitation Index Sanitation

ExtremePoverty

Risk of Flooding Riskof

Deficit of Grains Deficitof Index of Precarious Indexof StateDensity Index

*Significant at 5% level.

Figure 18. Standardized coefficients of partial correlation between the stunting rate and indicators associated to food security and nutrition

4 Geographic targeting

Results: Building the typology for Guatemala In Table B1 of Appendix B we display the full list of municipalities in Guatemala, sorted by their estimated productive potential and inefficiency level, while in Table B2 we report the ranking for the subset of 163 municipalities in the 8 prioritized districts. As indicated above, these rankings are based on the representative farmer in a given municipality, considering the average market, socioeconomic, biophysical, and accessibility conditions in the municipality. Table B3 reports, in turn, the ranking of municipalities based on their accessibility level (time, measured in hours, to the closest locality of 20,000 people or more) for the 8 prioritized departments. In order to better understand the geographic differences in productive potential and inefficiencies throughout the country, Map 1 shows the results based on the 9 categories shown in Table 2. The categories arise from splitting the estimated potential and inefficiency variables into terciles, i.e. high, medium and low, and combining these two dimensions.

Table 2. Categories of productive potential and inefficiency level

Potential Inefficiency High Medium Low High Medium Low

Each color in the table is associated with a category in Map 1.11 Municipalities with high potential are shown in green shades, municipalities with medium potential in brown shades, and those with low potential in red shades. The intensity of each shade decreases as inefficiency becomes smaller. Map 1 itself represents a tool for the design of policies aiming to develop rural areas. In areas with low productive potential, regardless of their efficiency level, agricultural activities do have a comparative advantage and thus it is preferable to explore alternative policies for rural development. In contrast, areas with medium or high potential can benefit from increased agricultural development and, taking into account the efficiency level of each municipality, interventions can be tailored to address each area’s specific bottlenecks. For instance, predominantly green areas are located in departments such as Jutiapa, Petén, Santa Rosa, San Marcos and Retalhuleu. On the other hand, departments such as Izabal, Alta Verapaz, Chiquimula, Suchitepequez and others have municipalities with mostly low productive potential (red areas).

11 The following categories are not considered in this classification: no data, regional limit, district limit, protected area, lake. The category "no data" includes the department of Guatemala, which is mostly urban. 23

Map 1. Combining production potential and efficiency (9 categories). National Level

Map 2, in turn, repeats the same exercise but considering only the municipalities in the 8 prioritized departments. The relative categorization, however, is now done within this group: i.e. the municipalities in the eight departments are divided into terciles to determine areas with high, medium, and low potential and efficiency. Note that since this is a relative analysis, the map at the national level does not necessarily coincide with the map for the eight departments, as the former is relative to all municipalities in the country while the latter is relative to only the 163 municipalities in the 8 focalized departments.12 San Marcos is in this case the department with the highest proportion of areas with high potential (green shades). This indicates that among the 8 prioritized departments, San Marcos is better suited for agricultural activities and, hence where more agricultural development should be promoted, including activities that promote the development of agricultural corridors across contiguous municipalities that show a high production potential. On the other hand, Alta Verapaz is the department with the biggest proportion of

12 For example, a municipality of high/medium potential relative to other municipalities within the eight prioritized departments could be of low potential when considering all other municipalities in the country. 24 areas with low potential and low inefficiency, so in these municipalities the priority should probably focus around social safety-net-like programs, at least in the short run, as well as in the promotion of non-agricultural development activities.

Map 2. Combining production potential and efficiency (9 categories). Eight prioritized departments

The typology of micro-regions can also be combined with other information of interest such as chronic malnutrition, poverty and vulnerability in order to both provide a more detailed diagnosis of the needs of the different municipalities and to help prioritize investments. Table 3 describes the classifications used, arising from combining the estimated levels of potential and efficiency of the different areas with their corresponding levels of chronic malnutrition (or other indicators of interest). In this regard, if the ultimate goal is to reduce malnutrition, the areas of focus should be those in the left section of the table (at least in the short run). These areas, in turn, could represent areas of low agricultural potential, i.e. critical areas (red section), or areas with medium or high agricultural potential, i.e. high priority areas (dark green section). Hence, the type of intervention required to alleviate malnutrition in each of the categories would be quite different. Alternatively, the methodology allows us to identify 25 areas with low levels of malnutrition (right section of the table). If these were also areas with low agricultural potential, this would suggest a low priority for these areas in terms of further developing their agricultural sector; but if these were areas with a medium or high potential, these could be areas for further agricultural development in the medium or long term (considering the reduction of chronic malnutrition as a primary policy objective). Table 3. Example of 7-typology categories based on production potential, efficiency level and chronic malnutrition

Chronic malnutrition High Medium Low Potential High Medium Low High Medium Low High Medium Low efficiency efficiency efficiency efficiency efficiency efficiency efficiency efficiency efficiency High High Low priority areas Medium priority areas with High priority areas performance with agricultural Medium agricultural opportunities areas opportunities Medium priority areas without Low Critical areas Low priority areas agricultural opportunities

Map 3 through Map 6, show the 7-group typology for the eight prioritized departments based on different prioritization goals: • Map 3: Production potential, efficiency level, and chronic malnutrition (based on National Height Census of 2015). • Map 4: Production potential, efficiency level, and poverty rate (based on Population and Housing Census of 2002). • Map 5: Production potential, efficiency level, and agricultural vulnerability to climate change (based on index constructed by Bouroncle et al., 2017). • Map 6: Production potential, efficiency level, and vulnerability index to food and nutrition insecurity (based on IVISAN, index constructed by SESAN-MAGA, 2012). In Table B4 through Table B7 we report the corresponding list of municipalities by category resulting from Map 3 through Map 6. Depending on the variable being targeted, the priority of the classification would vary. For instance, a high proportion of municipalities in Huehuetenango are regarded as high priority when we take chronic malnutrition (Map 3) into account, but not when we consider poverty rates (Map 4), where Alta Verapaz arises as the department with the highest priority. This result draws from the fact that while Huehuetenango has higher levels of chronic malnutrition, Alta Verapaz has higher levels of poverty. Similar conclusions can be reached for Map 5 and Map 6.

Map 3. 7-groups typology (based on production potential, efficiency level and chronic malnutrition). Eight prioritized departments.

Map 4. 7-groups typology (based on production potential, efficiency level and poverty rate). Eight prioritized departments.

Map 5. 7-groups typology (based on production potential, efficiency level and agricultural vulnerability). Eight prioritized departments.

Map 6. 7-groups typology (based on production potential, efficiency level and vulnerability index to food insecurity and nutrition- IVISAN). Eight prioritized departments.

Complementing the results of the typology with qualitative assessments from key MAGA informants in selected departments In order to cross-check the results of the typology analysis, we randomly selected three out of the eight prioritized departments to conduct interviews with the Department Directors of MAGA and their technical teams. The selected departments were: Chiquimula (East) and San Marcos and Quetzaltenango (West). In each of these departments we performed a similar exercise. First, we explained the main objectives of the study and the methodology used. Then, we asked each department’s director and their technical team to separately rank the municipalities in terms of their agricultural production potential and the level of economic development of the municipality. The agricultural production potential comprised in this case the quality of land, other biophysical conditions and accessibility, while the level of development comprised market factors and socioeconomic characteristics of the municipality.13 Lastly, we showed them the ranking that we obtained using the methodology and discussed with them the main differences between the rankings (when applicable). In Appendix E, we present the rankings resulting from this exercise with MAGA. Additionally, we asked for the main products that could be promoted among the municipalities ranked from the first position to the third position. From the interviews, we obtained the following conclusions. First, the combination of the two rankings (agricultural productive potential and economic development) was not too different from the ranking obtained with the typology (particularly in the extremes). Second, when local authorities think about “productive potential”, they also take into account the extension of the land available for production (regardless of its quality), the volume of production and the number of farmers. This is slightly different from the approach used in the typology analysis, where the ranking is based on the representative producer in the municipality (not the total number of producers) while controlling for the average characteristics in the area in terms of market factors, socioeconomic characteristics, biophysical conditions and accessibility. Third, at the time we were making the ranking with the team, other variables such as social conflicts appeared to be important. For instance, in San Marcos there is a tension between two municipalities: Ixchiguan and Tajumulco. When considering the production ranking, these municipalities occupied the first places (from the High Plateau), while in terms of economic development these municipalities were ranked at the bottom. In addition, we asked about specific products that could be promoted among the municipalities ranked among the top, as well as about bottlenecks and possible solutions to improve agricultural productivity in these areas. The products identified with the highest potential widely differed within (and across) departments; in Chiquimula, for example, from coffee and livestock in Esquipulas to beans, maize, and melon in Ipala, and coffee and green vegetables in Camotan. Similarly, the main bottlenecks and potential solutions to address them significantly varied across departments. In the case of Chiquimula, the authorities indicated that the expansion of irrigation systems is critical, while in San Marcos they indicated the promotion of value chains for high-value crops and in Quetzaltenango the introduction of new agricultural technologies, territorial ordering, and access to credit. This qualitative exercise helped to enrich the quantitative analysis and demonstrates the importance of engaging with local authorities when designing municipality-specific policies to promote agricultural development.

13 Asking for two separate rankings in terms of agricultural production potential and economic development also facilitated the exercise for MAGA representatives as it is harder to take into account all potential factors together when trying to derive a single ranking following a qualitative approach (as opposed to the quantitative approach implemented in the typology analysis where an econometric model is estimated). 31

5 Demographic targeting and at-risk populations

This section provides a characterization of the different categories of municipalities identified in the analysis from from the previous section. In particular, given a level of prioritization (for instance, the incidence of malnutrition as represented by the stunting rate), in the previous section we identified different groups of municipalities according to their potential for agricultural development and their priority with respect to a dimension of interest. We report here the average features of the municipalities in each of these resulting categories and compare them across categories. The comparison is made across a number of key indicators relating to socio-economic characteristics, demographic factors, exposure and overall vulnerability indices to different shocks, and other important determinants of food security and nutritional wellbeing. Table A4 details all the indicators used in the analysis together with their respective sources. The main objective of such an exercise is to identify the principal dimensions along which the groups differ, which can prove useful when determining overall strategies to foster agricultural development and to assess the appropriateness of alternative approaches. Certainly, a more detailed assessment can then be achieved when examining the characteristics of each municipality in particular, as discussed below. Table 4 shows the average profile of the groups of municipalities resulting from combining the information on agricultural efficiency and potential with that on stunting rates at the municipality level. For each prioritization category (along the table’s columns), it shows the average level for each characteristic of interest (along the table’s rows). In the case of those characteristics represented by a unit-less metric, such as the vulnerability indices or the variables capturing the exposure to different climatic risks, the resulting averages are labelled as low, mid, and high to indicate if their values belong to the lower, middle, or higher tercile of the distribution of the index across the municipalities in the prioritized departments. Figure 19, in turn, focuses on a subset of key demographic and human development indicators across three groups of interest from the 7-group typology: critical areas (i.e. low agricultural potential and high stunting rate), high-priority areas (i.e. high/medium agricultural potential and high stunting rate), and high-performance areas (i.e. high/medium agricultural potential and low stunting rate). First, these groups can differ across a number of demographic characteristics such as illiteracy rates, years of education of the household head, and their ethnic composition. In this respect, prioritized areas seem to count with higher illiteracy rates, lower levels of education, and a much higher proportion of indigenous population than high- performance areas. These elements are important to consider when designing and implementing rural development programs in those areas, i.e. development programs that could have worked in high-performance areas will not necessarily work in high-priority areas and vice versa. Moreover, the differences shown in the table and figure with respect to water and sanitation infrastructure and other geographic features such as risk of different climatic hazards point out the importance of approaching development interventions from a broad perspective that takes into account the different needs of the communities and other existing conditions in order for them to succeed. Finally, the data reveals that it is essential to take into account the scale of operation of agricultural units when promoting agricultural-led development, as the prioritized areas show much smaller landholding sizes than those in high- performance areas. Table A5, Table A6, and Table A7 present the same exercise for the case of the three other alternative dimensions along which to prioritize investments, including poverty, vulnerability to climate change, and vulnerability to food and nutritional security. Overall, the results are similar in many respects to those found above along the poverty dimension and point to common patterns to take into account when selecting and designing interventions.

Table 4. Characterization of municipalities according to their prioritization in terms of stunting Medium Medium Low priority Low High High Critical priority priority Indicator areas priority priority performance areas areas with areas with without areas areas areas agric. opps. agric. opps. agric. opps. Poverty rate (%) 86.1 76.1 53.1 87.4 80.1 55.2 61.8 Extreme poverty rate (%) 35.0 28.4 12.4 38.9 29.4 13.7 17.1 Stunting rate (%) 62.5 47.9 27.7 63.2 46.5 31.5 27.8 Vulnerability to climate change (index) Low (0.61) Low (0.62) Mid. (0.65) Mid. (0.66) Mid. (0.66) Low (0.62) Mid. (0.67) Vulnerability to food and nutritional security (index) Mid. (0.87) Mid. (0.62) Low (0.24) Mid. (0.93) Mid. (0.75) Low (0.30) Low (0.28) Staple grains shortage (%) 58.8 65.1 61.0 56.0 54.9 62.5 52.2 Work insecurity (%) 74.5 70.5 63.8 77.4 74.4 66.0 69.4 Illiteracy rate (%) 20.6 20.1 18.5 26.6 20.4 15.5 15.0 Sanitation infrastructure (%) 18.7 24.0 38.9 21.1 22.6 35.5 26.0 Risk of frost (index) Mid. (0.27) Mid. (0.28) Mid. (0.22) Mid. (0.31) Mid. (0.33) High (0.43) Mid. (0.07) Risk of drought (index) Mid. (0.66) Mid. (0.65) Mid. (0.67) Low (0.56) Low (0.59) Mid. (0.69) Low (0.51) Risk of floods (index) High (0.03) High (0.06) High (0.06) High (0.05) High (0.08) High (0.03) High (0.19) Potential impact of climate change (index) Low (0.54) Low (0.59) Mid. (0.65) Mid. (0.65) Mid. (0.66) Low (0.58) Mid. (0.69) Area suited for Arabica coffee vulnerable to CC (%) 22.5 31.5 34.5 17.5 21.6 23.7 38.9 Accessibility to locality of more than 20,000 inhabitants (hours) 1.36 1.19 1.31 1.74 1.58 1.07 0.94 Adaptive capacity to climate change (index) Mid. (0.68) Mid. (0.66) Mid. (0.66) Mid. (0.67) Mid. (0.66) Mid. (0.65) Low (0.64) Total population 33,865 28,034 37,080 23,752 34,272 23,014 28,306 Indigenous population (%) 88.8 84.1 61.6 90.4 69.2 42.7 20.0 Household head education (years) 2.3 3.3 4.6 2.8 3.5 4.6 3.8 Dependency ratio (%) 43.4 41.3 37.9 45.6 42.6 39.2 41.5 Landholding size (hectares) 1.3 2.1 3.5 1.6 2.5 1.1 7.0

Stunting rate (%)

Vulnerability to climate Poverty rate (%) change (index)

Vulnerability to food Landholding size and nutritional security (hectares) (index)

Indigenous population Sanitation (%) infrastructure (%)

Critical areas High priority areas High performance areas

Figure 19. Key indicators across three groups of interest from prioritization in terms of stunting

Interactive platform An online interactive platform has also been developed that provides the user the possibility to explore the above Maps and resulting municipality categories in more detail and provides a comprehensive socio-economic, productive, and risk and vulnerability profile of each of the prioritized municipalities. Figure A8 in Appendix A shows a screenshot of this platform. This platform is intended to further help in the design of rural development policies for each specific municipality. As noted above, municipalities categorized as of high-priority share common patterns, which, in turn, differ with municipalities categorized as critical or as of high-performance; yet, within the high-priority group of municipalities there are still characteristics unique to each municipality that should also be taken into account when designing policies to address bottlenecks that limit agricultural development on a case by case basis. For instance, high-priority areas identified in Table 4 above seem to show a low education level and high illiteracy rates (comparable to critical areas); however, among this group there are still municipalities with a relatively higher education level where agricultural extension programs could eventually work more easily than in other high-priority municipalities with lower than the average education levels. Certainly, this information could also be complemented with local information obtained from key informants in each area.

6 Conclusions

This study has gathered and analyzed multiple national, regional, and local information to identify high-priority municipalities among the eight prioritized departments for the development of the GFSS Interagency Country Plan 2018-2022, which in turn will guide coordinated investment by multiple US Government agencies to improve food security, nutrition, and poverty in Guatemala. The study establishes a ranking of the 163 municipalities compatible with investment criteria in terms of the agricultural production potential and efficiency in the use of resources of an area combined with its current food security and nutrition situation in terms of chronic malnutrition, poverty, vulnerability and risks. The analysis incorporates different market, socioeconomic, biophysical, and accessibility indicators that explain great part of the heterogeneity in rural Guatemala. The study further develops an extended profile of the municipalities and categorized areas and their vulnerable populations, including an interactive online platform. A few remarks are worth noting. First, the resulting ranking and classification of municipalities should be viewed as a first assessment in the design of policies for rural development and food security and nutrition in the prioritized areas that could then be complemented with more detailed information at the local level (if applicable). The analysis uses the best available and most recent disaggregated data at the municipality level, but there are some factors that cannot be included due to a lack of detailed information; for instance, an index of social tension (or trust) or an index for private sector development, which could probably be locally assessed for a selected (and smaller) number of municipalities, e.g. municipalities ranked as “high-priority”. Second, recall that the analysis approximates the production potential and level of efficiency of a representative farmer in a municipality given, in turn, the average market, socioeconomic, biophysical, and accessibility conditions in that municipality. Hence, while a municipality might not show a high potential for agricultural development in general, it could be the case that it could have some potential for a specific product, as well as for the development of non-agricultural related activities. This would certainly require a more in-depth assessment on a case by case basis; and in this regard the detailed profile provided for each municipality on the interactive platform could provide a more accurate first approximation on this matter. Third, the municipalities in the prioritized departments have been separately classified depending on their production potential, efficiency level, and a third targeting variable: chronic malnutrition, poverty, vulnerability to food security and nutrition, or agricultural vulnerability to climate change. Certainly, the classification of municipalities could alternatively be based on a combination of these different targeting variables (or on other targeting indicators), depending on the ultimate goals pursuing. Finally, the prioritization tool developed should be viewed as a dynamic process that could be easily extended or updated in the medium- or long-term as more data become available.

7 References

Bouroncle, C., P. Imbach, B. Rodríguez-Sánchez, C. Medellín, A. Martinez-Valle, and P. Läderach, 2017. "Mapping Climate Change Adaptive Capacity and Vulnerability of Smallholder Agricultural Livelihoods in Central America: Ranking and Descriptive Approaches to Support Adaptation Strategies," Climatic Change 141(1): 123- 137. Bunn, C., F. Castro, and M. Lundy, 2017. "Los Impactos del Cambio Climático para el Café en Centro América," 23vo Simposio Latinoamericano de Caficultura, San Pedro Sula, Honduras. Glewwe, P., H.G. Jacoby, and E.M. King, 2001. “Early Childhood Nutrition and Academic Achievement: A Longitudinal Analysis,” Journal of Public Economics 81(3), pp. 345–68. Hoddinott, J., and B. Kinsey, 2001. “Child Growth in the Time of Drought,” Oxford Bulletin of Economics and Statistics 63(4), pp. 409–36. Maruyama, E., and Torero, M. 2011. A typology to identify the different types of rural micro regions in terms of their characteristics and development constraints and options in Guatemala1. Mimeo, IFPRI, Washington DC. Ministerio de Educacion-SESAN-INE, 2015. “IV Censo Nacional de Talla en Escolares.” Maccini, S., and Yang, D., 2009. “Under the Weather: Health, Schooling, and Economic Consequences of Early- Life Rainfall,” American Economic Review 99(3), pp.1006-1026. SESAN, 2016. “Plan de Respuesta para la Atención del Hambre Estacional 2016,” available at http://www.sesan.gob.gt/wordpress/wp-content/uploads/2017/07/ESTRATEGIA-HAMBRE-ESTACIONAL- 2016-1.pdf. SESAN, 2017. “Plan de Respuesta para la Atención del Hambre Estacional 2017,” available at http://www.sesan.gob.gt/wordpress/wp-content/uploads/2017/07/Hambre-estacional-2017.pdf. SESAN-MAGA, 2012. “Priorización de municipios a través del índice de vulnerabilidad a la inseguridad alimentaria y nutricional de la población de Guatemala (IVISAN)”.

Appendix A. Supplementary Figures and Tables

Figure A1. GINI Index and GDP pc (PPP) ― Relative to Region and World

Figure A2. Underweight rate and GDP pc (PPP) ― Relative to Region and World

Figure A3. Wasting rate and GDP pc (PPP) ― Relative to Region and World

Figure A4. Overweight rate and GDP pc (PPP) ― Relative to Region and World

90 80 70 60 53.6 50 39.941.141.141.8 40 28.6 30 22.0 20 16.7

10 EXTREME POVERTY RATE(%) POVERTYEXTREME 0

Figure A5. Extreme poverty rate ― Prioritized departments

90 80 70 60 50 40

30 21.4 18.018.519.2 20 15.215.5 10.212.1

UNDERWEIGHT RATE (%) 10 0

Figure A6. Underweight rate ― Prioritized departments

90 80 70 60 50 40 30 20 OVERWEIGHT RATE(%) OVERWEIGHT 10 3.1 3.9 4.6 4.6 4.6 4.8 4.9 4.9 0

Figure A7. Overweight rate ― Prioritized departments

Table A1. Description of Variables Variables Source Prices of agricultural products (P) ENCOVI, 2014 Input prices (W) Socieconomic characeristics and Fixed inputs (Z): • Size of the plot and capital (value of productive assests), literacy status of the ENCOVI, 2014. producer, number of household members who worked in the farm, if the

producer belongs to a group of farmers, if the producer received technical assistance, own land and formal access to credit. CENAGRO, 2003. Biophysical data (G): Land use, 5 categories: MAGA (2015) • Areas not available for agriculture, Crops, Forest and other related uses, Humid interior areas, and water. Biophysical data (G): Weather, 23 categories: • Warm humid, Warm Semi-dry, Warm Sub-humid, Very Warm humid, Very Warm Sub-humid, Semi-Warm humid, Semi-Warm Semi-dry, Semi-Warm Sub-humid, Semi-Warm Very humid, Semi-cold humid, Semi-cold Very humid, Semi cold Sub-humid, Mild extremely humid, Mild humid, Mild Very- humid, Mild Sub-humid, Cold humid, Cold Very humid, Very Warm Semi-dry, Mild Semi-dry, Cold Sub-humid, Semi-cold, extremely humid. MAGA (2015) Altitude, 5 categories: • 0-500, 500-1000, 1000-1500, 1500-2000, 2000 or more. Slope, 3 categories: • Intensification with medium practices of soil conservation, Intensification with strong practices of soil conservation, Diversification with strong practices of soil conservation. Land quality, 4 categories: • High, Medium, Low, and Very Low. MAGA; Comisión Nacional Access to Markets (A): access to the closest locality with at least 20,000 people (in de Energía Eléctrica; hours) ENCOVI 2014; INE; Banco Central 39

Table A2. Estimation Results. Frontier estimation

Frontier estimation (dependent lnprofit) lnprice1 0.133 (0.315) lnprice2 0.158 (0.360) lnprice4 -5.333*** (1.046) lnprice5 1.190** (0.558) lnw_price 0.538 (0.331) lnprice1_lnprice1 -0.0709 (0.0696) lnprice1_lnprice2 0.0992 (0.103) lnprice1_lnprice4 -0.560** (0.223) lnprice1_lnprice5 0.594*** (0.165) lnprice2_lnprice2 0.294*** (0.0643) lnprice2_lnprice4 -0.853*** (0.245) lnprice2_lnprice5 0.299** (0.150) lnprice4_lnprice4 2.556*** (0.408) lnprice4_lnprice5 -1.353*** (0.389) lnprice5_lnprice5 0.620*** (0.147) lnprice1_lnw_price -0.0559 (0.115) lnprice2_lnw_price -0.238*** (0.0853) lnprice4_lnw_price -0.244 (0.214) lnprice5_lnw_price -0.220 (0.142) lnw_price_lnw_price 0.0317 (0.0475) Constant 11.18*** (0.756) lnsig2v -0.440*** (0.0408) Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1 Note: Price are (1) Fruits and vegetables, (2) Industrial crops, (3) Cereals, (4) Staples, (5) Tubers, W are wages.

Table A3. Estimation results Inefficiency determinants

Inefficiency determinants (lnsig2u) Land size -0.631 (1.748) Land size squared 0.00311 (0.0628) Value of productive assets 0.0468 (0.0622) Value of productive assets*land -0.336*** (0.0765) Educational level of the producer: none or pre-primary -0.883 (1.304) Educational level of the producer: none or pre-primary*land 1.240 (1.663) Educational level of the producer: primary 0.0204 (1.287) Educational level of the producer: primary*land -0.137 (1.656) Educational level of the producer: secondary 0.159 (1.306) Educational level of the producer: secondary*land -0.0670 (1.684) Literacy status of the producer -0.854*** (0.251) Literacy status of the producer*land 1.149*** (0.272) Number of household members who work in the farm -0.156 (0.125) Number of household members who work in the farm*land -0.0216 (0.124) Belong to a group of farmers 0.942 (2.666) Belong to a group of farmers*land -0.881 (2.523) Received technical assistance 0.408 (0.493) Received technical assistance*land -0.522 (0.841) Own land 0.192 (0.169) Own land*land -0.776*** (0.196) Formal access to credit 0.786 (0.801) Formal access to credit*land -2.824* (1.539) Formal access to credit*ownland -0.232 (0.915) Formal access to credit*ownland*land 1.138 (1.767) access to the closest city with at least 20000 people (in hours) -0.0401 (0.108) access to the closest city with at least 20000 people (in hours)*land -0.0105 (0.0901) Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1 41

Table A3. Estimation results Inefficiency determinants (cont.) Determinants of the inefficiency (lnsig2u) Weather 1: Warm humid -9.967** (5.023) Weather 2: Warm Semi-dry -11.75** (4.946) Weather 3: Warm Sub-humid -22.32 (14.90) Weather 4: Very Warm humid -11.13** (4.916) Weather 5: Very Warm Sub-humid -10.63** (4.900) Weather 6: Semi-Warm humid -10.94** (4.900) Weather 7: Semi-Warm Semi-dry -21.69* (12.64) Weather 8: Semi-Warm Sub-humid -13.51*** (4.908) Weather 9: Semi-Warm Very humid -11.58** (4.865) Weather 10: Semi-cold humid -12.62*** (4.890) Weather 11: Semi-cold Very humid 73.75 (214.7) Weather 12: Semi cold Sub-humid -17.60 (11.07) Weather 13: Mild extremely humid -11.49** (4.839) Weather 14: Mild humid -11.65** (4.856) Weather 15: Mild Very-humid -10.69** (4.893) Weather 16: Mild Sub-humid -8.374 (6.140) Weather 17: Cold humid -10.77** (4.853) Weather 18: Cold Very humid -11.20** (4.785) Weather 19: Very Warm Semi-dry -11.65** (4.838) Weather 20: Mild Semi-dry -27.15 (20.91) Weather 21: Cold Sub-humid -11.78** (5.697) Weather 22: Semi-cold, extremly humid -12.94** (5.671) Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

Table A3. Estimation results Inefficiency determinants (cont.) Determinants of the inefficiency (lnsig2u) Altitude 1 (0 -500) -0.483 (1.158) Altitude 2 (500-1000) 0.507 (1.035) Altitude 3 (1000-1500) 0.880 (0.849) Altitude 4 (1500-2000) -0.387 (0.473) Quality 1: High agricultural potential 0.000789 (0.260) Quality 2: Medium agricultural potential -0.0230 (0.174) Quality 3: Low agricultural potential 0.0723 (0.154) Slope 1: Intensification with medium practices of soil conservation 0.00784 (0.00523) Slope 2: Intensification with strong practices of soil conservation -0.0223 (0.0204) Slope 3: Diversification with strong practices of soil conservation 0.0120 (0.0106) Land use 1: Areas not available for agriculture -0.0233 (0.150) Land use 2: Crops -0.0502 (0.143) Land use 3: Forests and other related uses 0.177 (0.160) Land use 4: Humid interior areas -0.768** (0.321) Constant 13.80*** (4.992) Observations 2,910 Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

Table A4. Indicators of interest for 7-group typology profiles Variable Source Observations Main Indicators of Interest Poverty rate (%) Censo de Población 2002 Extreme poverty rate (%) Censo de Población 2002 Stunting rate (%) Censo de Talla 2015 Vulnerability to climate change (index) Bouroncle et al. 2017 Vulnerability to food and nutritional security (index) IVISAN 2012

Food and Nutritional Security Staple grains shortage (%) IVISAN 2012 Employment insecurity (%) IVISAN 2012 Illiteracy rate (%) CONALFA 2015 Sanitation infrastructure (%) IVISAN 2012

Social and Environmental Threats Risk of frost (index) IVISAN 2012 Risk of drought (index) IVISAN 2012 Risk of floods (index) IVISAN 2012 Potential impact of climate change (index) Bouroncle et al. 2017 Area suited for Arabica coffee vulnerable to CC (%) Bunn et al. 2017

Response Capacity Accessibility to locality of more than 20,000 inhab. (hours) IFPRI Adaptive capacity to climate change (index) Bouroncle et al. 2017

Socioeconomic Characteristics and Other Variables Total population Censo de Población 2002 Indigenous population (%) Censo de Población 2002 When a given municipality is not available, its Household head education (years) ENCOVI 2014 value is set at the departamental average. When a given municipality is not available, its Dependency ratio (%) ENCOVI 2014 value is set at the departamental average. Landholding size (hectares) Censo Agropecuario 2003 Most important crop (quintals and % of total) Censo Agropecuario 2003 Second most important crop (quintals and % of total) Censo Agropecuario 2003 Third most important crop (quintals and % of total) Censo Agropecuario 2003

Table A5. Characterization of municipalities according to their prioritization in terms of poverty Medium Low priority Medium Low High priority High Critical areas priority Indicator priority priority areas with performance areas without areas with areas areas agric. areas agric. agric. opps. opps. opps. Poverty rate (%) 89.8 80.1 52.0 90.3 80.3 54.9 50.1 Extreme poverty rate (%) 43.4 26.9 10.7 43.6 27.6 11.4 8.7 Stunting rate (%) 58.0 52.8 33.3 57.1 47.6 36.1 24.2 Vulnerability to climate change (index) Mid. (0.65) Low (0.60) Low (0.62) Mid. (0.68) Mid. (0.65) Low (0.61) Mid. (0.65) Vulnerability to food and nutritional security (index) High (0.94) Mid. (0.72) Low (0.19) High (0.95) Mid. (0.72) Low (0.34) Low (0.09) Staple grains shortage (%) 58.1 65.6 60.9 52.1 58.6 62.5 49.8 Work insecurity (%) 76.2 72.2 62.4 78.0 73.8 67.1 66.8 Illiteracy rate (%) 25.7 18.4 16.1 25.2 21.5 15.8 12.8 Sanitation infrastructure (%) 16.8 21.8 38.9 17.8 22.7 36.1 31.7 Risk of frost (index) Mid. (0.16) Mid. (0.30) Mid. (0.31) Mid. (0.26) Mid. (0.28) High (0.48) Mid. (0.07) Risk of drought (index) Low (0.57) Mid. (0.69) High (0.71) Low (0.53) Low (0.56) Mid. (0.67) Mid. (0.62) Risk of floods (index) High (0.04) High (0.02) High (0.09) High (0.06) High (0.09) High (0.03) High (0.20) Potential impact of climate change (index) Mid. (0.62) Low (0.55) Low (0.57) Mid. (0.68) Mid. (0.66) Low (0.57) Mid. (0.66) Area suited for Arabica coffee vulnerable to CC (%) 29.8 22.5 35.3 25.7 17.4 21.9 40.9 Accessibility to locality of more than 20,000 inhab. 1.65 1.20 1.03 1.75 1.56 1.05 0.79 (hours) Adaptive capacity to climate change (index) High (0.68) Mid. (0.65) Mid. (0.67) Mid. (0.68) Low (0.65) Mid. (0.65) Low (0.63) Total population 29,111 24,765 44,163 33,120 24,859 23,728 27,411 Indigenous population (%) 91.0 88.9 59.9 88.6 60.8 47.0 13.8 Household head education (years) 2.6 2.4 4.9 2.9 3.4 4.5 4.0 Dependency ratio (%) 43.1 42.0 38.6 46.7 42.4 39.3 39.1 Landholding size (hectares) 2.4 1.2 2.8 2.9 3.3 0.9 5.3

Table A6. Characterization of municipalities according to their prioritization in terms of vulnerability to food and nutritional security Medium Low priority Medium Low High priority High Critical areas priority Indicator priority priority areas with performance areas without areas with areas areas agric. areas agric. agric. opps. opps. opps. Poverty rate (%) 88.5 80.6 52.7 86.0 81.3 54.7 57.6 Extreme poverty rate (%) 41.7 28.4 10.7 38.5 31.0 11.9 12.7 Stunting rate (%) 54.9 55.2 33.6 57.6 47.6 35.1 27.2 Vulnerability to climate change (index) Mid. (0.65) Low (0.61) Low (0.60) High (0.68) Mid. (0.65) Low (0.60) Mid. (0.66) Vulnerability to food and nutritional security (index) High (0.98) Mid. (0.72) Low (0.15) High (0.98) Mid. (0.78) Low (0.15) Low (0.15) Staple grains shortage (%) 61.7 61.9 61.6 56.1 57.7 58.8 50.2 Work insecurity (%) 77.1 71.8 62.0 78.8 73.5 64.4 68.3 Illiteracy rate (%) 28.6 17.3 14.6 28.6 20.3 10.6 13.6 Sanitation infrastructure (%) 18.2 21.1 38.2 25.6 21.3 28.6 27.0 Risk of frost (index) Mid. (0.22) Mid. (0.26) Mid. (0.29) Mid. (0.32) Mid. (0.27) High (0.52) Mid. (0.06) Risk of drought (index) Mid. (0.63) Mid. (0.62) High (0.73) Low (0.55) Low (0.57) Mid. (0.70) Low (0.56) Risk of floods (index) High (0.02) High (0.06) High (0.07) High (0.09) High (0.05) High (0.03) High (0.19) Potential impact of climate change (index) Mid. (0.63) Low (0.56) Low (0.55) Mid. (0.69) Mid. (0.65) Low (0.54) Mid. (0.68) Area suited for Arabica coffee vulnerable to CC (%) 27.0 25.1 35.0 21.9 20.2 20.7 39.1 Accessibility to locality of more than 20,000 inhab. 1.56 1.29 1.01 1.78 1.60 0.91 0.84 (hours) Adaptive capacity to climate change (index) Mid. (0.67) Mid. (0.67) Mid. (0.66) Mid. (0.68) Mid. (0.65) Mid. (0.65) Low (0.64) Total population 23,756 38,568 33,884 27,915 29,903 22,703 28,416 Indigenous population (%) 95.8 84.3 60.4 92.2 61.6 41.6 18.6 Household head education (years) 2.6 2.6 4.7 3.0 3.4 4.6 3.7 Dependency ratio (%) 43.1 41.6 39.1 44.8 44.0 39.1 40.0 Landholding size (hectares) 2.1 1.6 2.7 1.9 2.8 0.9 6.8

Table A7. Characterization of municipalities according to their prioritization in terms of agricultural vulnerability to climate change Medium Low priority Medium Low High priority High Critical areas priority Indicator priority priority areas with performance areas without areas with areas areas agric. areas agric. agric. opps. opps. opps. Poverty rate (%) 77.9 83.1 73.7 82.1 77.0 68.4 62.1 Extreme poverty rate (%) 31.5 35.3 24.6 34.4 27.9 21.8 18.5 Stunting rate (%) 46.8 53.1 52.3 45.7 49.6 43.2 35.8 Vulnerability to climate change (index) High (0.74) Mid. (0.66) Low (0.53) High (0.72) Mid. (0.66) Low (0.58) Low (0.57) Vulnerability to food and nutritional security (index) Mid. (0.69) Mid. (0.80) Mid. (0.56) Mid. (0.71) Mid. (0.68) Mid. (0.53) Low (0.39) Staple grains shortage (%) 51.4 63.2 61.9 48.2 58.1 59.5 62.8 Work insecurity (%) 74.7 74.4 67.3 76.5 74.0 68.9 65.5 Illiteracy rate (%) 23.5 18.5 19.3 23.4 21.6 16.3 12.9 Sanitation infrastructure (%) 19.8 24.7 27.4 18.2 23.2 28.1 34.4 Risk of frost (index) Mid. (0.05) Mid. (0.28) High (0.38) Mid. (0.13) Mid. (0.33) High (0.43) Mid. (0.07) Risk of drought (index) Low (0.52) Mid. (0.60) High (0.76) Low (0.52) Low (0.53) Mid. (0.67) High (0.72) Risk of floods (index) High (0.11) Mid. (0.00) High (0.04) High (0.11) High (0.09) High (0.01) High (0.12) Potential impact of climate change (index) High (0.80) Mid. (0.66) Low (0.39) High (0.76) Mid. (0.67) Low (0.50) Low (0.52) Area suited for Arabica coffee vulnerable to CC (%) 48.5 24.1 23.0 36.5 20.6 12.1 15.2 Accessibility to locality of more than 20,000 inhab. (hours) 1.79 1.58 0.79 1.83 1.37 1.15 1.01 Adaptive capacity to climate change (index) Mid. (0.68) Mid. (0.66) Mid. (0.66) High (0.68) Mid. (0.65) Mid. (0.65) Low (0.61) Total population 41,977 21,374 34,006 30,824 29,501 21,366 31,954 Indigenous population (%) 56.7 80.3 92.4 61.8 57.4 64.5 49.0 Household head education (years) 2.6 3.0 3.5 2.8 3.5 4.0 4.1 Dependency ratio (%) 41.0 42.7 41.2 44.5 43.7 40.5 40.6 Landholding size (hectares) 4.1 2.1 0.9 4.6 2.8 0.9 4.1

Figure A8. Online interactive platform

Appendix B. Rankings and Categories of Municipalities

Table B1. National ranking of municipalities based on production potential and efficiency level

municipality ranking ranking code Departamento Municipio potential inefficiency 1710 Petén Sayaxché 1 302 1007 Suchitepéquez San Lorenzo 2 198 2211 Jutiapa Comapa 3 126 1315 Huehuetenango Todos Santos Cuchumatán 4 123 1712 Petén Poptún 5 314 412 Chimaltenango San Pedro Yepocapa 6 262 1201 San Marcos San Marcos 7 47 1101 Retalhuleu Retalhuleu 8 283 2217 Jutiapa Quesada 9 164 1707 Petén Santa Ana 10 280 1613 Alta Verapaz Chisec 11 237 920 Quetzaltenango Coatepeque 12 292 2206 Jutiapa Yupiltepeque 13 166 1103 Retalhuleu Santa Cruz Muluá 14 304 1108 Retalhuleu Nuevo San Carlos 15 311 1104 Retalhuleu San Martín Zapotitlán 16 216 1107 Retalhuleu Champerico 17 317 1706 Petén San Francisco 18 284 1703 Petén San Benito 19 319 1702 Petén San José 20 291 1714 Petén El Chal 21 275 1713 Petén Las Cruces 22 299 1705 Petén La Libertad 23 298 606 Santa Rosa Oratorio 24 297 208 El Progreso San Antonio La Paz 25 226 1420 Quiché Ixcán 26 233 1209 San Marcos Tajumulco 27 105 1207 San Marcos Tacaná 28 54 610 Santa Rosa Santa María Ixhuatán 29 196 1204 San Marcos Comitancillo 30 15 2205 Jutiapa Asunción Mita 31 266 1607 Alta Verapaz Panzós 32 151 2212 Jutiapa Jalpatagua 33 199 1102 Retalhuleu San Sebastián 34 224 2213 Jutiapa Conguaco 35 187 1105 Retalhuleu San Felipe 36 252 201 El Progreso Guastatoya 37 267 1109 Retalhuleu El Asintal 38 168 206 El Progreso Sansare 39 254 49 municipality ranking ranking code Departamento Municipio potential inefficiency 2214 Jutiapa Moyuta 40 218 1212 San Marcos Nuevo Progreso 41 156 1106 Retalhuleu San Andrés Villa Seca 42 323 2209 Jutiapa El Adelanto 43 121 2216 Jutiapa San José Acatempa 44 145 2208 Jutiapa Jerez 45 188 2210 Jutiapa Zapotitlán 46 169 2203 Jutiapa Santa Catarina Mita 47 202 1208 San Marcos Sibinal 48 97 608 Santa Rosa Chiquimulilla 49 300 612 Santa Rosa Santa Cruz Naranjo 50 228 605 Santa Rosa San Rafael Las Flores 51 211 607 Santa Rosa San Juan Tecuaco 52 155 1704 Petén San Andrés 53 320 1216 San Marcos Catarina 54 147 513 Escuintla Nueva Concepción 55 305 1709 Petén San Luis 56 272 601 Santa Rosa Cuilapa 57 242 2105 Jalapa San Carlos Alzatate 58 131 1901 Zacapa Zacapa 59 185 2215 Jutiapa Pasaco 60 244 1325 Huehuetenango San Sebastián Coatán 61 49 1304 Huehuetenango Cuilco 62 140 614 Santa Rosa Nueva Santa Rosa 63 159 1225 San Marcos San Cristóbal Cucho 64 18 1226 San Marcos Sipacapa 65 173 1221 San Marcos La Reforma 66 282 1230 San Marcos La Blanca 67 204 1203 San Marcos San Antonio Sacatepéquez 68 10 1227 San Marcos Esquipulas Palo Gordo 69 37 1214 San Marcos San José El Rodeo 70 318 1219 San Marcos San Pablo 71 222 1211 San Marcos San Rafael Pie De La Cuesta 72 241 1224 San Marcos San José Ojetenán 73 12 1501 Baja Verapaz Salamá 74 217 1229 San Marcos San Lorenzo 75 83 1302 Huehuetenango Chiantla 76 129 1805 Izabal Los Amates 77 213

50 municipality ranking ranking code Departamento Municipio potential inefficiency 1701 Petén Flores 78 316 1218 San Marcos Ocós 79 212 1711 Petén Melchor De Mencos 80 295 1205 San Marcos San Miguel Ixtahuacán 81 120 609 Santa Rosa Taxisco 82 279 611 Santa Rosa Guazacapán 83 270 1604 Alta Verapaz Tactic 84 133 706 Sololá Santa Catarina Ixtahuacán 85 24 1416 Quiché Sacapulas 86 6 1318 Huehuetenango San Mateo Ixtatán 87 64 2204 Jutiapa Agua Blanca 88 234 1202 San Marcos San Pedro Sacatepéquez 89 4 1217 San Marcos Ayutla (Tecún Umán) 90 265 1213 San Marcos El Tumbador 91 239 1206 San Marcos Concepción Tutuapa 92 75 1222 San Marcos Pajapita 93 263 2103 Jalapa San Luis Jilotepeque 94 128 1421 Quiché Pachalún 95 149 2201 Jutiapa Jutiapa 96 177 2202 Jutiapa El Progreso 97 220 924 Quetzaltenango Palestina De Los Altos 98 51 2104 Jalapa San Manuel Chaparrón 99 276 1708 Petén Dolores 100 309 2008 Chiquimula Concepción Las Minas 101 248 602 Santa Rosa Barberena 102 208 921 Quetzaltenango Génova Costa Cuca 103 214 1210 San Marcos Tejutla 104 52 1004 Suchitepéquez San Bernardino 105 132 1316 Huehuetenango San Juan Atitán 106 82 604 Santa Rosa Casillas 107 179 204 El Progreso San Cristóbal Acasaguastlán 108 321 1903 Zacapa Río Hondo 109 281 603 Santa Rosa Santa Rosa De Lima 110 195 1228 San Marcos Río Blanco 111 107 1615 Alta Verapaz Fray Bartolomé De Las Casas 112 229 503 Escuintla La Democracia 113 293 514 Escuintla Sipacate 114 273 509 Escuintla San José 115 308 511 Escuintla Palín 116 258 512 Escuintla San Vicente Pacaya 117 235 510 Escuintla Iztapa 118 287

51 municipality ranking ranking code Departamento Municipio potential inefficiency 2009 Chiquimula Quezaltepeque 119 119 1507 Baja Verapaz San Jerónimo 120 223 2102 Jalapa San Pedro Pinula 121 130 309 Sacatepéquez Santa Lucía Milpas Altas 122 79 501 Escuintla Escuintla 123 289 502 Escuintla Santa Lucía Cotzumalguapa 124 288 1313 Huehuetenango San Miguel Acatán 125 43 2207 Jutiapa Atescatempa 126 184 202 El Progreso Morazán 127 306 203 El Progreso San Agustín Acasaguastlán 128 192 506 Escuintla Tiquisate 129 274 1327 Huehuetenango Aguacatán 130 66 1331 Huehuetenango Santa Ana Huista 131 240 1314 Huehuetenango San Rafael La Independencia 132 58 1322 Huehuetenango Concepción Huista 133 125 1323 Huehuetenango San Juan Ixcoy 134 134 1306 Huehuetenango San Pedro Nectá 135 122 1333 Huehuetenango Petatan 136 117 1329 Huehuetenango San Gaspar Ixchil 137 57 1310 Huehuetenango Santa Barbara 138 124 1332 Huehuetenango San Pedro Nectá 139 115 1317 Huehuetenango Santa Eulalia 140 61 1303 Huehuetenango Malacatancito 141 247 1330 Huehuetenango Santiago Chimaltenango 142 67 1321 Huehuetenango Tectitán 143 72 1328 Huehuetenango San Rafael Petzal 144 56 407 Chimaltenango Patzún 145 111 1617 Alta Verapaz Chisec 146 260 1602 Alta Verapaz Santa Cruz Verapaz 147 190 1616 Alta Verapaz La Tinta 148 146 1605 Alta Verapaz Tamahú 149 163 1614 Alta Verapaz Chahal 150 259 205 El Progreso El Jícaro 151 278 1312 Huehuetenango La Democracia 152 141 505 Escuintla Masagua 153 294 613 Santa Rosa Pueblo Nuevo Viñas 154 255 1608 Alta Verapaz Senahú 155 174 1309 Huehuetenango San Ildefonso Ixtahuacán 156 86 508 Escuintla Guanagazapa 157 322 1508 Baja Verapaz Purulhá 158 153 507 Escuintla La Gomera 159 286 415 Chimaltenango Zaragoza 160 81

52 municipality ranking ranking code Departamento Municipio potential inefficiency 304 Sacatepéquez Sumpango 161 70 1603 Alta Verapaz San Cristóbal Verapaz 162 144 504 Escuintla Siquinalá 163 312 1905 Zacapa Teculután 164 290 1902 Zacapa Estanzuela 165 285 1911 Zacapa San Jorge 166 221 1906 Zacapa Usumatlán 167 269 1907 Zacapa Cabañas 168 245 1505 Baja Verapaz Granados 169 231 916 Quetzaltenango Zunil 170 80 910 Quetzaltenango San Mateo 171 32 903 Quetzaltenango Olintepeque 172 19 911 Quetzaltenango Concepción Chiquirichapa 173 31 919 Quetzaltenango El Palmar 174 253 913 Quetzaltenango Almolonga 175 8 906 Quetzaltenango Cabricán 176 100 918 Quetzaltenango San Francisco La Unión 177 22 902 Quetzaltenango Salcajá 178 13 922 Quetzaltenango Flores Costa Cuca 179 203 908 Quetzaltenango San Miguel Siguilá 180 14 1403 Quiché Chinique 181 78 1417 Quiché San Bartolomé Jocotenango 182 55 1419 Quiché Chicamán 183 160 1609 Alta Verapaz San Pedro Carchá 184 152 1412 Quiché Joyabaj 185 106 1410 Quiché Cunén 186 94 1311 Huehuetenango La Libertad 187 175 1904 Zacapa Gualán 188 268 1405 Quiché Chajul 189 162 1301 Huehuetenango Huehuetenango 190 227 207 El Progreso Sanarate 191 243 1801 Izabal Puerto Barrios 192 296 1610 Alta Verapaz San Juan Chamelco 193 102 2106 Jalapa Monjas 194 250 1305 Huehuetenango Nentón 195 182 1909 Zacapa La Unión 196 176 1223 San Marcos Ixchiguán 197 76 1308 Huehuetenango San Pedro Soloma 198 17 1014 Suchitepéquez Patulul 199 277 709 Sololá San Andrés Semetabaj 200 101 713 Sololá San Lucas Tolimán 201 137

53 municipality ranking ranking code Departamento Municipio potential inefficiency 2002 Chiquimula San José La Arada 202 197 909 Quetzaltenango San Juan Ostuncalco 203 44 2003 Chiquimula San Juan La Ermita 204 91 1804 Izabal Morales 205 230 1409 Quiché San Pedro Jocopilas 206 93 1418 Quiché Canilla 207 77 1402 Quiché Chiché 208 71 1307 Huehuetenango Jacaltenango 209 170 1411 Quiché San Juan Cotzal 210 62 1910 Zacapa Huité 211 127 1001 Suchitepéquez Mazatenango 212 264 1021 Suchitepéquez San Jose La Maquina 213 257 1005 Suchitepéquez San José El Idolo 214 307 1003 Suchitepéquez San Francisco Zapotitlán 215 315 1015 Suchitepéquez Santa Barbara 216 271 1009 Suchitepéquez San Pablo Jocopilas 217 114 1016 Suchitepéquez San Juan Bautista 218 249 1011 Suchitepéquez San Miguel Panam 219 201 1018 Suchitepéquez Zunilito 220 215 1019 Suchitepéquez Pueblo Nuevo 221 200 1020 Suchitepéquez Río Bravo 222 301 1012 Suchitepéquez San Gabriel 223 113 1017 Suchitepéquez Santo Tomás La Unión 224 158 905 Quetzaltenango Sibiliá 225 171 1215 San Marcos Malacatán 226 118 904 Quetzaltenango San Carlos Sija 227 68 1220 San Marcos El Quetzal 228 261 2006 Chiquimula Olopa 229 143 705 Sololá Nahualá 230 29 1612 Alta Verapaz Santa María Cahabón 231 191 1611 Alta Verapaz Lanquín 232 194 1413 Quiché Nebaj 233 110 714 Sololá Santa Cruz La Laguna 234 50 708 Sololá Concepción 235 46 715 Sololá San Pablo La Laguna 236 33 710 Sololá Panajachel 237 310 703 Sololá Santa María Visitación 238 104 719 Sololá Santiago Atitlán 239 96 711 Sololá Santa Catarina Palopó 240 30 702 Sololá San José Chacayá 241 161 718 Sololá San Pedro La Laguna 242 206 716 Sololá San Marcos La Laguna 243 25

54 municipality ranking ranking code Departamento Municipio potential inefficiency 1408 Quiché San Antonio Ilotenango 244 74 1319 Huehuetenango Colotenango 245 109 2001 Chiquimula Chiquimula 246 172 2011 Chiquimula Ipala 247 207 1502 Baja Verapaz San Miguel Chicaj 248 157 401 Chimaltenango Chimaltenango 249 95 1908 Zacapa San Diego 250 232 2004 Chiquimula Jocotán 251 40 2107 Jalapa Mataquescuintla 252 167 312 Sacatepéquez Ciudad Vieja 253 148 303 Sacatepéquez Pastores 254 135 311 Sacatepéquez Santa María De Jesús 255 89 305 Sacatepéquez Santo Domingo Xenacoj 256 28 316 Sacatepéquez Santa Catarina Barahona 257 23 310 Sacatepéquez Magdalena Milpas Altas 258 65 314 Sacatepéquez Alotenango 259 210 308 Sacatepéquez San Lucas Sacatepéquez 260 178 307 Sacatepéquez San Bartolomé Milpas Altas 261 48 313 Sacatepéquez San Miguel Dueñas 262 219 707 Sololá Santa Clara La Laguna 263 41 915 Quetzaltenango Huitán 264 5 917 Quetzaltenango Colomba Costa Cuca 265 313 1401 Quiché Santa Cruz Del Quiché 266 34 1404 Quiché Zacualpa 267 59 1320 Huehuetenango San Sebastián Huehuetenango 268 42 1414 Quiché San Andrés Sajcabajá 269 112 901 Quetzaltenango Quetzaltenango 270 20 1407 Quiché Patzité 271 16 1506 Baja Verapaz Santa Cruz El Chol 272 225 1601 Alta Verapaz Cobán 273 193 2101 Jalapa Jalapa 274 183 1504 Baja Verapaz Cubulco 275 136 1415 Quiché San Miguel Uspantán 276 116 907 Quetzaltenango Cajolá 277 36 2010 Chiquimula San Jacinto 278 150 1802 Izabal Livingston 279 246 1606 Alta Verapaz San Miguel Tucurú 280 181 2005 Chiquimula Camotán 281 139 409 Chimaltenango Patzicía 282 88 701 Sololá Sololá 283 38 1803 Izabal El Estor 284 189 914 Quetzaltenango Cantel 285 1 717 Sololá San Juan La Laguna 286 39 923 Quetzaltenango La Esperanza 287 11 1503 Baja Verapaz Rabinal 288 63 2007 Chiquimula Esquipulas 289 209 55 1326 Huehuetenango Santa Cruz Barillas 290 165 municipality ranking ranking code Departamento Municipio potential inefficiency 1002 Suchitepéquez Cuyotenango 291 251 912 Quetzaltenango San Martín Sacatepéquez 292 90 403 Chimaltenango San Martín Jilotepeque 293 142 807 Totonicapán Santa Lucía La Reforma 294 108 1010 Suchitepéquez San Antonio Suchitepéquez 295 205 1006 Suchitepéquez Santo Domingo Suchitepéquez 296 256 410 Chimaltenango Santa Cruz Balanyá 297 69 402 Chimaltenango San José Poaquil 298 103 413 Chimaltenango San Andrés Itzapa 299 87 414 Chimaltenango Parramos 300 85 416 Chimaltenango El Tejar 301 60 1013 Suchitepéquez Chicacao 302 186 704 Sololá Santa Lucia Utatlán 303 26 1324 Huehuetenango San Antonio Huista 304 138 806 Totonicapán Santa María Chiquimula 305 7 315 Sacatepéquez San Antonio Aguas Calientes 306 92 804 Totonicapán San Andrés Xecul 307 9 712 Sololá San Antonio Palopó 308 27 405 Chimaltenango Santa Apolonia 309 73 802 Totonicapán San Cristóbal Totonicapán 310 3 301 Sacatepéquez Antigua 311 238 302 Sacatepéquez Jocotenango 312 180 1406 Quiché Santo Tomás Chichicastenango 313 35 404 Chimaltenango San Juan Comalapa 314 84 408 Chimaltenango San Miguel Pochuta 315 303 808 Totonicapán San Bartolo Aguas Calientes 316 154 803 Totonicapán San Francisco El Alto 317 21 1008 Suchitepéquez Samayac 318 99 805 Totonicapán Momostenango 319 53 306 Sacatepéquez Santiago Sacatepéquez 320 45 406 Chimaltenango Tecpán Guatemala 321 98 801 Totonicapán Totonicapán 322 2 411 Chimaltenango Acatenango 323 236

Table B2. Ranking of municipalities in eight prioritized departments based on production potential and efficiency level municipality ranking ranking code Departamento Municipio potential inefficiency 1315 Huehuetenango Todos Santos Cuchumatán 1 97 1201 San Marcos San Marcos 2 44 1613 Alta Verapaz Chisec 3 147 920 Quetzaltenango Coatepeque 4 160 1420 Quiché Ixcán 5 146 1209 San Marcos Tajumulco 6 83 1207 San Marcos Tacaná 7 50 1204 San Marcos Comitancillo 8 15 1607 Alta Verapaz Panzós 9 114 1212 San Marcos Nuevo Progreso 10 117 1208 San Marcos Sibinal 11 78 1216 San Marcos Catarina 12 111 1325 Huehuetenango San Sebastián Coatán 13 45 1304 Huehuetenango Cuilco 14 106 1203 San Marcos San Antonio Sacatepéquez 19 10 1224 San Marcos San José Ojetenán 24 12 1225 San Marcos San Cristóbal Cucho 15 18 1227 San Marcos Esquipulas Palo Gordo 20 35 1226 San Marcos Sipacapa 16 126 1230 San Marcos La Blanca 18 137 1219 San Marcos San Pablo 22 143 1211 San Marcos San Rafael Pie De La Cuesta 23 150 1221 San Marcos La Reforma 17 159 1214 San Marcos San José El Rodeo 21 163 1229 San Marcos San Lorenzo 25 71 1302 Huehuetenango Chiantla 26 100 1218 San Marcos Ocós 27 141 1205 San Marcos San Miguel Ixtahuacán 28 95 1604 Alta Verapaz Tactic 29 101 706 Sololá Santa Catarina Ixtahuacán 30 23 1416 Quiché Sacapulas 31 6 1318 Huehuetenango San Mateo Ixtatán 32 58 1202 San Marcos San Pedro Sacatepéquez 33 4 1206 San Marcos Concepción Tutuapa 36 65 1213 San Marcos El Tumbador 35 148 1222 San Marcos Pajapita 37 157 1217 San Marcos Ayutla (Tecún Umán) 34 158 1421 Quiché Pachalún 38 112 924 Quetzaltenango Palestina De Los Altos 39 47

57 municipality ranking ranking code Departamento Municipio potential inefficiency 2008 Chiquimula Concepción Las Minas 40 152 921 Quetzaltenango Génova Costa Cuca 41 142 1210 San Marcos Tejutla 42 48 1316 Huehuetenango San Juan Atitán 43 70 1228 San Marcos Río Blanco 44 85 1615 Alta Verapaz Fray Bartolomé De Las Casas 45 145 2009 Chiquimula Quezaltepeque 46 94 1313 Huehuetenango San Miguel Acatán 47 41 1328 Huehuetenango San Rafael Petzal 62 52 1329 Huehuetenango San Gaspar Ixchil 55 53 1314 Huehuetenango San Rafael La Independencia 50 54 1317 Huehuetenango Santa Eulalia 58 56 1327 Huehuetenango Aguacatán 48 59 1330 Huehuetenango Santiago Chimaltenango 60 60 1321 Huehuetenango Tectitán 61 63 1332 Huehuetenango San Pedro Nectá 57 90 1333 Huehuetenango Petatan 54 92 1306 Huehuetenango San Pedro Nectá 53 96 1310 Huehuetenango Santa Barbara 56 98 1322 Huehuetenango Concepción Huista 51 99 1323 Huehuetenango San Juan Ixcoy 52 102 1331 Huehuetenango Santa Ana Huista 49 149 1303 Huehuetenango Malacatancito 59 151 1616 Alta Verapaz La Tinta 65 110 1605 Alta Verapaz Tamahú 66 121 1602 Alta Verapaz Santa Cruz Verapaz 64 131 1614 Alta Verapaz Chahal 67 154 1617 Alta Verapaz Chisec 63 155 1312 Huehuetenango La Democracia 68 107 1608 Alta Verapaz Senahú 69 127 1309 Huehuetenango San Ildefonso Ixtahuacán 70 72 1603 Alta Verapaz San Cristóbal Verapaz 71 109 913 Quetzaltenango Almolonga 77 8 902 Quetzaltenango Salcajá 80 13 908 Quetzaltenango San Miguel Siguilá 82 14 903 Quetzaltenango Olintepeque 74 19 918 Quetzaltenango San Francisco La Unión 79 22 911 Quetzaltenango Concepción Chiquirichapa 75 29 910 Quetzaltenango San Mateo 73 30 916 Quetzaltenango Zunil 72 69 906 Quetzaltenango Cabricán 78 79 922 Quetzaltenango Flores Costa Cuca 81 136 919 Quetzaltenango El Palmar 76 153 1417 Quiché San Bartolomé Jocotenango 84 51 1403 Quiché Chinique 83 68 1419 Quiché Chicamán 85 118 1609 Alta Verapaz San58 Pedro Carchá 86 115 1412 Quiché Joyabaj 87 84 municipality ranking ranking code Departamento Municipio potential inefficiency 1410 Quiché Cunén 88 76 1311 Huehuetenango La Libertad 89 128 1405 Quiché Chajul 90 120 1301 Huehuetenango Huehuetenango 91 144 1610 Alta Verapaz San Juan Chamelco 92 81 1305 Huehuetenango Nentón 93 130 1223 San Marcos Ixchiguán 94 66 1308 Huehuetenango San Pedro Soloma 95 17 709 Sololá San Andrés Semetabaj 96 80 713 Sololá San Lucas Tolimán 97 103 2002 Chiquimula San José La Arada 98 135 909 Quetzaltenango San Juan Ostuncalco 99 42 2003 Chiquimula San Juan La Ermita 100 74 1402 Quiché Chiché 103 62 1418 Quiché Canilla 102 67 1409 Quiché San Pedro Jocopilas 101 75 1307 Huehuetenango Jacaltenango 104 123 1411 Quiché San Juan Cotzal 105 57 905 Quetzaltenango Sibiliá 106 124 1215 San Marcos Malacatán 107 93 904 Quetzaltenango San Carlos Sija 108 61 1220 San Marcos El Quetzal 109 156 2006 Chiquimula Olopa 110 108 705 Sololá Nahualá 111 27 1612 Alta Verapaz Santa María Cahabón 112 132 1611 Alta Verapaz Lanquín 113 134 1413 Quiché Nebaj 114 88 716 Sololá San Marcos La Laguna 124 24 711 Sololá Santa Catarina Palopó 121 28 715 Sololá San Pablo La Laguna 117 31 708 Sololá Concepción 116 43 714 Sololá Santa Cruz La Laguna 115 46 719 Sololá Santiago Atitlán 120 77 703 Sololá Santa María Visitación 119 82 702 Sololá San José Chacayá 122 119 718 Sololá San Pedro La Laguna 123 138 710 Sololá Panajachel 118 161 1408 Quiché San Antonio Ilotenango 125 64 1319 Huehuetenango Colotenango 126 87 2001 Chiquimula Chiquimula 127 125 2011 Chiquimula Ipala 128 139 2004 Chiquimula Jocotán 129 38 707 Sololá Santa Clara La Laguna 130 39 915 Quetzaltenango Huitán 131 5 917 Quetzaltenango Colomba Costa Cuca 132 162 59 municipality ranking ranking code Departamento Municipio potential inefficiency 1401 Quiché Santa Cruz Del Quiché 133 32 1404 Quiché Zacualpa 134 55 1320 Huehuetenango San Sebastián Huehuetenango 135 40 1414 Quiché San Andrés Sajcabajá 136 89 901 Quetzaltenango Quetzaltenango 137 20 1407 Quiché Patzité 138 16 1601 Alta Verapaz Cobán 139 133 1415 Quiché San Miguel Uspantán 140 91 907 Quetzaltenango Cajolá 141 34 2010 Chiquimula San Jacinto 142 113 1606 Alta Verapaz San Miguel Tucurú 143 129 2005 Chiquimula Camotán 144 105 701 Sololá Sololá 145 36 914 Quetzaltenango Cantel 146 1 717 Sololá San Juan La Laguna 147 37 923 Quetzaltenango La Esperanza 148 11 2007 Chiquimula Esquipulas 149 140 1326 Huehuetenango Santa Cruz Barillas 150 122 912 Quetzaltenango San Martín Sacatepéquez 151 73 807 Totonicapán Santa Lucía La Reforma 152 86 704 Sololá Santa Lucia Utatlán 153 25 1324 Huehuetenango San Antonio Huista 154 104 806 Totonicapán Santa María Chiquimula 155 7 804 Totonicapán San Andrés Xecul 156 9 712 Sololá San Antonio Palopó 157 26 802 Totonicapán San Cristóbal Totonicapán 158 3 1406 Quiché Santo Tomás Chichicastenango 159 33 808 Totonicapán San Bartolo Aguas Calientes 160 116 803 Totonicapán San Francisco El Alto 161 21 805 Totonicapán Momostenango 162 49 801 Totonicapán Totonicapán 163 2

Table B3. Ranking of municipalities in eight prioritized departments based on accessibility municipality ranking code Departamento Municipio accessibility 902 Quetzaltenango Salcajá 1 802 Totonicapán San Cristóbal Totonicapán 2 923 Quetzaltenango La Esperanza 3 804 Totonicapán San Andrés Xecul 4 1216 San Marcos Catarina 5 803 Totonicapán San Francisco El Alto 6 1217 San Marcos Ayutla (Tecún Umán) 7 1218 San Marcos Ocós 8 922 Quetzaltenango Flores Costa Cuca 9 1407 Quiché Patzité 10 903 Quetzaltenango Olintepeque 11 702 Sololá San José Chacayá 12 907 Quetzaltenango Cajolá 13 918 Quetzaltenango San Francisco La Unión 14 710 Sololá Panajachel 15 910 Quetzaltenango San Mateo 16 921 Quetzaltenango Génova Costa Cuca 17 1229 San Marcos San Lorenzo 18 1214 San Marcos San José El Rodeo 19 1215 San Marcos Malacatán 20 905 Quetzaltenango Sibiliá 21 911 Quetzaltenango Concepción Chiquirichapa 22 1402 Quiché Chiché 23 1222 San Marcos Pajapita 24 1301 Huehuetenango Huehuetenango 25 1202 San Marcos San Pedro Sacatepéquez 26 1314 Huehuetenango San Rafael La Independencia 27 924 Quetzaltenango Palestina De Los Altos 28 1408 Quiché San Antonio Ilotenango 29 1203 San Marcos San Antonio Sacatepéquez 30 1401 Quiché Santa Cruz Del Quiché 31 1210 San Marcos Tejutla 32 913 Quetzaltenango Almolonga 33 908 Quetzaltenango San Miguel Siguilá 34 701 Sololá Sololá 35 901 Quetzaltenango Quetzaltenango 36 805 Totonicapán Momostenango 37 806 Totonicapán Santa María Chiquimula 38 920 Quetzaltenango Coatepeque 39 807 Totonicapán Santa Lucía La Reforma 40 704 Sololá Santa Lucia Utatlán 41 712 Sololá San Antonio Palopó 42 2003 Chiquimula San Juan La Ermita 43 709 Sololá San Andrés Semetabaj 44 2009 Chiquimula Quezaltepeque 45 1228 San Marcos Río Blanco 46 1331 Huehuetenango Santa Ana Huista 47 1205 San Marcos San Miguel Ixtahuacán 48 1230 San Marcos La Blanca 49 2011 Chiquimula Ipala 50

61 municipality ranking code Departamento Municipio accessibility 1406 Quiché Santo Tomás Chichicastenango 51 904 Quetzaltenango San Carlos Sija 52 1211 San Marcos San Rafael Pie De La Cuesta 53 711 Sololá Santa Catarina Palopó 54 1329 Huehuetenango San Gaspar Ixchil 55 801 Totonicapán Totonicapán 56 1403 Quiché Chinique 57 1324 Huehuetenango San Antonio Huista 58 1204 San Marcos Comitancillo 59 2007 Chiquimula Esquipulas 60 914 Quetzaltenango Cantel 61 808 Totonicapán San Bartolo Aguas Calientes 62 2001 Chiquimula Chiquimula 63 1421 Quiché Pachalún 64 1224 San Marcos San José Ojetenán 65 708 Sololá Concepción 66 1412 Quiché Joyabaj 67 1303 Huehuetenango Malacatancito 68 917 Quetzaltenango Colomba Costa Cuca 69 1223 San Marcos Ixchiguán 70 1310 Huehuetenango Santa Barbara 71 1206 San Marcos Concepción Tutuapa 72 915 Quetzaltenango Huitán 73 1212 San Marcos Nuevo Progreso 74 1602 Alta Verapaz Santa Cruz Verapaz 75 1319 Huehuetenango Colotenango 76 707 Sololá Santa Clara La Laguna 77 2005 Chiquimula Camotán 78 2006 Chiquimula Olopa 79 2002 Chiquimula San José La Arada 80 2010 Chiquimula San Jacinto 81 1313 Huehuetenango San Miguel Acatán 82 1409 Quiché San Pedro Jocopilas 83 1333 Huehuetenango Petatan 84 1404 Quiché Zacualpa 85 1604 Alta Verapaz Tactic 86 1332 Huehuetenango San Pedro Nectá 87 1610 Alta Verapaz San Juan Chamelco 88 1226 San Marcos Sipacapa 89 1312 Huehuetenango La Democracia 90 1418 Quiché Canilla 91 1316 Huehuetenango San Juan Atitán 92 1225 San Marcos San Cristóbal Cucho 93 1320 Huehuetenango San Sebastián Huehuetenango 94 1322 Huehuetenango Concepción Huista 95 906 Quetzaltenango Cabricán 96 1327 Huehuetenango Aguacatán 97 1213 San Marcos El Tumbador 98 2008 Chiquimula 62Concepción Las Minas 99 1603 Alta Verapaz San Cristóbal Verapaz 100 municipality ranking code Departamento Municipio accessibility 1411 Quiché San Juan Cotzal 101 1220 San Marcos El Quetzal 102 713 Sololá San Lucas Tolimán 103 1416 Quiché Sacapulas 104 1615 Alta Verapaz Fray Bartolomé De Las Casas 105 1221 San Marcos La Reforma 106 1308 Huehuetenango San Pedro Soloma 107 1420 Quiché Ixcán 108 1328 Huehuetenango San Rafael Petzal 109 1617 Alta Verapaz Chisec 110 1410 Quiché Cunén 111 1307 Huehuetenango Jacaltenango 112 1302 Huehuetenango Chiantla 113 919 Quetzaltenango El Palmar 114 705 Sololá Nahualá 115 1613 Alta Verapaz Chisec 116 1325 Huehuetenango San Sebastián Coatán 117 715 Sololá San Pablo La Laguna 118 717 Sololá San Juan La Laguna 119 714 Sololá Santa Cruz La Laguna 120 1309 Huehuetenango San Ildefonso Ixtahuacán 121 1315 Huehuetenango Todos Santos Cuchumatán 122 1414 Quiché San Andrés Sajcabajá 123 1330 Huehuetenango Santiago Chimaltenango 124 1611 Alta Verapaz Lanquín 125 716 Sololá San Marcos La Laguna 126 1321 Huehuetenango Tectitán 127 1207 San Marcos Tacaná 128 2004 Chiquimula Jocotán 129 706 Sololá Santa Catarina Ixtahuacán 130 1306 Huehuetenango San Pedro Nectá 131 1608 Alta Verapaz Senahú 132 1305 Huehuetenango Nentón 133 1219 San Marcos San Pablo 134 1605 Alta Verapaz Tamahú 135 719 Sololá Santiago Atitlán 136 1417 Quiché San Bartolomé Jocotenango 137 1609 Alta Verapaz San Pedro Carchá 138 1227 San Marcos Esquipulas Palo Gordo 139 703 Sololá Santa María Visitación 140 912 Quetzaltenango San Martín Sacatepéquez 141 909 Quetzaltenango San Juan Ostuncalco 142 1612 Alta Verapaz Santa María Cahabón 143 1606 Alta Verapaz San Miguel Tucurú 144 916 Quetzaltenango Zunil 145 1601 Alta Verapaz Cobán 146 1208 San Marcos Sibinal 147 1201 San Marcos San Marcos 148 1209 San Marcos Tajumulco 149 1614 Alta Verapaz Chahal 150 1318 Huehuetenango San Mateo Ixtatán 151 1326 Huehuetenango Santa Cruz Barillas 152 718 Sololá San Pedro La Laguna 153 1317 Huehuetenango Santa Eulalia 154 1419 Quiché Chicamán 155 1311 Huehuetenango La Libertad 156 1304 Huehuetenango Cuilco 157 1413 Quiché Nebaj 158 1415 Quiché San Miguel Uspantán 159 1607 Alta Verapaz Panzós 160 1323 Huehuetenango San Juan Ixcoy 161 1616 Alta Verapaz La Tinta 162 1405 Quiché Chajul 163

Table B4. 7-group typology based on production potential, efficiency level and chronic malnutrition municipality code Departamento Municipio category 1315 Huehuetenango Todos Santos Cuchumatán Alta prioridad 1209 San Marcos Tajumulco Alta prioridad 1204 San Marcos Comitancillo Alta prioridad 1208 San Marcos Sibinal Alta prioridad 1325 Huehuetenango San Sebastián Coatán Alta prioridad 1604 Alta Verapaz Tactic Alta prioridad 706 Sololá Santa Catarina Ixtahuacán Alta prioridad 1416 Quiché Sacapulas Alta prioridad 1318 Huehuetenango San Mateo Ixtatán Alta prioridad 1206 San Marcos Concepción Tutuapa Alta prioridad 924 Quetzaltenango Palestina De Los Altos Alta prioridad 1316 Huehuetenango San Juan Atitán Alta prioridad 1313 Huehuetenango San Miguel Acatán Alta prioridad 1306 Huehuetenango San Pedro Nectá Alta prioridad 1310 Huehuetenango Santa Barbara Alta prioridad 1314 Huehuetenango San Rafael La Independencia Alta prioridad 1317 Huehuetenango Santa Eulalia Alta prioridad 1322 Huehuetenango Concepción Huista Alta prioridad 1323 Huehuetenango San Juan Ixcoy Alta prioridad 1329 Huehuetenango San Gaspar Ixchil Alta prioridad 1330 Huehuetenango Santiago Chimaltenango Alta prioridad 1333 Huehuetenango Petatan Alta prioridad 1605 Alta Verapaz Tamahú Alta prioridad 1309 Huehuetenango San Ildefonso Ixtahuacán Alta prioridad 1603 Alta Verapaz San Cristóbal Verapaz Alta prioridad 906 Quetzaltenango Cabricán Alta prioridad 1417 Quiché San Bartolomé Jocotenango Alta prioridad 1419 Quiché Chicamán Alta prioridad 1410 Quiché Cunén Alta prioridad 1405 Quiché Chajul Alta prioridad 1305 Huehuetenango Nentón Alta prioridad 1402 Quiché Chiché Alta prioridad 1409 Quiché San Pedro Jocopilas Alta prioridad 1411 Quiché San Juan Cotzal Alta prioridad

64 municipality code Departamento Municipio category 1613 Alta Verapaz Chisec Alto rendimiento 920 Quetzaltenango Coatepeque Alto rendimiento 1212 San Marcos Nuevo Progreso Alto rendimiento 1216 San Marcos Catarina Alto rendimiento 1211 San Marcos San Rafael Pie De La Cuesta Alto rendimiento 1214 San Marcos San José El Rodeo Alto rendimiento 1219 San Marcos San Pablo Alto rendimiento 1221 San Marcos La Reforma Alto rendimiento 1230 San Marcos La Blanca Alto rendimiento 1218 San Marcos Ocós Alto rendimiento 1217 San Marcos Ayutla (Tecún Umán) Alto rendimiento 1222 San Marcos Pajapita Alto rendimiento 1421 Quiché Pachalún Alto rendimiento 2008 Chiquimula Concepción Las Minas Alto rendimiento 921 Quetzaltenango Génova Costa Cuca Alto rendimiento 1615 Alta Verapaz Fray Bartolomé De Las Casas Alto rendimiento 1303 Huehuetenango Malacatancito Alto rendimiento 1331 Huehuetenango Santa Ana Huista Alto rendimiento 919 Quetzaltenango El Palmar Alto rendimiento 922 Quetzaltenango Flores Costa Cuca Alto rendimiento 1301 Huehuetenango Huehuetenango Alto rendimiento 2002 Chiquimula San José La Arada Alto rendimiento 905 Quetzaltenango Sibiliá Alto rendimiento 703 Sololá Santa María Visitación Baja Prioridad 710 Sololá Panajachel Baja Prioridad 718 Sololá San Pedro La Laguna Baja Prioridad 719 Sololá Santiago Atitlán Baja Prioridad 2011 Chiquimula Ipala Baja Prioridad 917 Quetzaltenango Colomba Costa Cuca Baja Prioridad 1414 Quiché San Andrés Sajcabajá Baja Prioridad 901 Quetzaltenango Quetzaltenango Baja Prioridad 1601 Alta Verapaz Cobán Baja Prioridad 2010 Chiquimula San Jacinto Baja Prioridad 914 Quetzaltenango Cantel Baja Prioridad 923 Quetzaltenango La Esperanza Baja Prioridad 2007 Chiquimula Esquipulas Baja Prioridad 704 Sololá Santa Lucia Utatlán Baja Prioridad

65 municipality code Departamento Municipio category 1201 San Marcos San Marcos Baja prioridad con oport. agrícolas 1203 San Marcos San Antonio Sacatepéquez Baja prioridad con oport. agrícolas 1225 San Marcos San Cristóbal Cucho Baja prioridad con oport. agrícolas 1227 San Marcos Esquipulas Palo Gordo Baja prioridad con oport. agrícolas 1202 San Marcos San Pedro Sacatepéquez Baja prioridad con oport. agrícolas 1228 San Marcos Río Blanco Baja prioridad con oport. agrícolas 2009 Chiquimula Quezaltepeque Baja prioridad con oport. agrícolas 1332 Huehuetenango San Pedro Nectá Baja prioridad con oport. agrícolas 1312 Huehuetenango La Democracia Baja prioridad con oport. agrícolas 902 Quetzaltenango Salcajá Baja prioridad con oport. agrícolas 903 Quetzaltenango Olintepeque Baja prioridad con oport. agrícolas 910 Quetzaltenango San Mateo Baja prioridad con oport. agrícolas 916 Quetzaltenango Zunil Baja prioridad con oport. agrícolas 918 Quetzaltenango San Francisco La Unión Baja prioridad con oport. agrícolas 713 Sololá San Lucas Tolimán Baja prioridad con oport. agrícolas 1418 Quiché Canilla Baja prioridad con oport. agrícolas 1215 San Marcos Malacatán Baja prioridad con oport. agrícolas 904 Quetzaltenango San Carlos Sija Baja prioridad con oport. agrícolas 1420 Quiché Ixcán Prioridad media con oport. agrícolas 1207 San Marcos Tacaná Prioridad media con oport. agrícolas 1607 Alta Verapaz Panzós Prioridad media con oport. agrícolas 1304 Huehuetenango Cuilco Prioridad media con oport. agrícolas 1224 San Marcos San José Ojetenán Prioridad media con oport. agrícolas 1226 San Marcos Sipacapa Prioridad media con oport. agrícolas 1229 San Marcos San Lorenzo Prioridad media con oport. agrícolas 1302 Huehuetenango Chiantla Prioridad media con oport. agrícolas 1205 San Marcos San Miguel Ixtahuacán Prioridad media con oport. agrícolas 1213 San Marcos El Tumbador Prioridad media con oport. agrícolas 1210 San Marcos Tejutla Prioridad media con oport. agrícolas 1321 Huehuetenango Tectitán Prioridad media con oport. agrícolas 1327 Huehuetenango Aguacatán Prioridad media con oport. agrícolas 1328 Huehuetenango San Rafael Petzal Prioridad media con oport. agrícolas 1602 Alta Verapaz Santa Cruz Verapaz Prioridad media con oport. agrícolas 1614 Alta Verapaz Chahal Prioridad media con oport. agrícolas 1616 Alta Verapaz La Tinta Prioridad media con oport. agrícolas 1617 Alta Verapaz Chisec Prioridad media con oport. agrícolas 1608 Alta Verapaz Senahú Prioridad media con oport. agrícolas 908 Quetzaltenango San Miguel Siguilá Prioridad media con oport. agrícolas 911 Quetzaltenango Concepción Chiquirichapa Prioridad media con oport. agrícolas 913 Quetzaltenango Almolonga Prioridad media con oport. agrícolas 1403 Quiché Chinique Prioridad media con oport. agrícolas 1609 Alta Verapaz San Pedro Carchá Prioridad media con oport. agrícolas 1412 Quiché Joyabaj Prioridad media con oport. agrícolas 1311 Huehuetenango La Libertad Prioridad media con oport. agrícolas 1610 Alta Verapaz San Juan Chamelco Prioridad media con oport. agrícolas 1223 San Marcos Ixchiguán Prioridad media con oport. agrícolas 1308 Huehuetenango San Pedro Soloma Prioridad media con oport. agrícolas 709 Sololá San Andrés Semetabaj Prioridad media con oport. agrícolas 909 Quetzaltenango San Juan Ostuncalco Prioridad media con oport. agrícolas 66 municipality code Departamento Municipio category 2003 Chiquimula San Juan La Ermita Prioridad media con oport. agrícolas 1307 Huehuetenango Jacaltenango Prioridad media con oport. agrícolas 1220 San Marcos El Quetzal Prioridad media sin oport. agrícolas 1612 Alta Verapaz Santa María Cahabón Prioridad media sin oport. agrícolas 1611 Alta Verapaz Lanquín Prioridad media sin oport. agrícolas 702 Sololá San José Chacayá Prioridad media sin oport. agrícolas 711 Sololá Santa Catarina Palopó Prioridad media sin oport. agrícolas 714 Sololá Santa Cruz La Laguna Prioridad media sin oport. agrícolas 716 Sololá San Marcos La Laguna Prioridad media sin oport. agrícolas 2001 Chiquimula Chiquimula Prioridad media sin oport. agrícolas 707 Sololá Santa Clara La Laguna Prioridad media sin oport. agrícolas 1401 Quiché Santa Cruz Del Quiché Prioridad media sin oport. agrícolas 1404 Quiché Zacualpa Prioridad media sin oport. agrícolas 907 Quetzaltenango Cajolá Prioridad media sin oport. agrícolas 1606 Alta Verapaz San Miguel Tucurú Prioridad media sin oport. agrícolas 701 Sololá Sololá Prioridad media sin oport. agrícolas 717 Sololá San Juan La Laguna Prioridad media sin oport. agrícolas 912 Quetzaltenango San Martín Sacatepéquez Prioridad media sin oport. agrícolas 1324 Huehuetenango San Antonio Huista Prioridad media sin oport. agrícolas 712 Sololá San Antonio Palopó Prioridad media sin oport. agrícolas 802 Totonicapán San Cristóbal Totonicapán Prioridad media sin oport. agrícolas 803 Totonicapán San Francisco El Alto Prioridad media sin oport. agrícolas 801 Totonicapán Totonicapán Prioridad media sin oport. agrícolas 2006 Chiquimula Olopa Zonas Críticas 705 Sololá Nahualá Zonas Críticas 1413 Quiché Nebaj Zonas Críticas 708 Sololá Concepción Zonas Críticas 715 Sololá San Pablo La Laguna Zonas Críticas 1408 Quiché San Antonio Ilotenango Zonas Críticas 1319 Huehuetenango Colotenango Zonas Críticas 2004 Chiquimula Jocotán Zonas Críticas 915 Quetzaltenango Huitán Zonas Críticas 1320 Huehuetenango San Sebastián Huehuetenango Zonas Críticas 1407 Quiché Patzité Zonas Críticas 1415 Quiché San Miguel Uspantán Zonas Críticas 2005 Chiquimula Camotán Zonas Críticas 1326 Huehuetenango Santa Cruz Barillas Zonas Críticas 807 Totonicapán Santa Lucía La Reforma Zonas Críticas 806 Totonicapán Santa María Chiquimula Zonas Críticas 804 Totonicapán San Andrés Xecul Zonas Críticas 1406 Quiché Santo Tomás Chichicastenango Zonas Críticas 808 Totonicapán San Bartolo Aguas Calientes Zonas Críticas 805 Totonicapán Momostenango Zonas Críticas

Table B5. 7-group typology based on production potential, efficiency level and poverty rate municipality code Departamento Municipio category 1315 Huehuetenango Todos Santos Cuchumatán Alta prioridad 1613 Alta Verapaz Chisec Alta prioridad 1420 Quiché Ixcán Alta prioridad 1209 San Marcos Tajumulco Alta prioridad 1204 San Marcos Comitancillo Alta prioridad 1607 Alta Verapaz Panzós Alta prioridad 1208 San Marcos Sibinal Alta prioridad 1325 Huehuetenango San Sebastián Coatán Alta prioridad 1224 San Marcos San José Ojetenán Alta prioridad 1205 San Marcos San Miguel Ixtahuacán Alta prioridad 706 Sololá Santa Catarina Ixtahuacán Alta prioridad 1318 Huehuetenango San Mateo Ixtatán Alta prioridad 1206 San Marcos Concepción Tutuapa Alta prioridad 1316 Huehuetenango San Juan Atitán Alta prioridad 1615 Alta Verapaz Fray Bartolomé De Las Casas Alta prioridad 1313 Huehuetenango San Miguel Acatán Alta prioridad 1306 Huehuetenango San Pedro Nectá Alta prioridad 1310 Huehuetenango Santa Barbara Alta prioridad 1314 Huehuetenango San Rafael La Independencia Alta prioridad 1317 Huehuetenango Santa Eulalia Alta prioridad 1328 Huehuetenango San Rafael Petzal Alta prioridad 1329 Huehuetenango San Gaspar Ixchil Alta prioridad 1332 Huehuetenango San Pedro Nectá Alta prioridad 1605 Alta Verapaz Tamahú Alta prioridad 1614 Alta Verapaz Chahal Alta prioridad 1617 Alta Verapaz Chisec Alta prioridad 1608 Alta Verapaz Senahú Alta prioridad 1309 Huehuetenango San Ildefonso Ixtahuacán Alta prioridad 1417 Quiché San Bartolomé Jocotenango Alta prioridad 1419 Quiché Chicamán Alta prioridad 1609 Alta Verapaz San Pedro Carchá Alta prioridad 1412 Quiché Joyabaj Alta prioridad 1410 Quiché Cunén Alta prioridad 1405 Quiché Chajul Alta prioridad 1223 San Marcos Ixchiguán Alta prioridad 1402 Quiché Chiché Alta prioridad 1409 Quiché San Pedro Jocopilas Alta prioridad

68 municipality code Departamento Municipio category 920 Quetzaltenango Coatepeque Alto rendimiento 1216 San Marcos Catarina Alto rendimiento 1211 San Marcos San Rafael Pie De La Cuesta Alto rendimiento 1230 San Marcos La Blanca Alto rendimiento 1218 San Marcos Ocós Alto rendimiento 1217 San Marcos Ayutla (Tecún Umán) Alto rendimiento 1222 San Marcos Pajapita Alto rendimiento 1421 Quiché Pachalún Alto rendimiento 2008 Chiquimula Concepción Las Minas Alto rendimiento 1331 Huehuetenango Santa Ana Huista Alto rendimiento 922 Quetzaltenango Flores Costa Cuca Alto rendimiento 1301 Huehuetenango Huehuetenango Alto rendimiento 2002 Chiquimula San José La Arada Alto rendimiento 1307 Huehuetenango Jacaltenango Alto rendimiento 905 Quetzaltenango Sibiliá Alto rendimiento 703 Sololá Santa María Visitación Baja Prioridad 710 Sololá Panajachel Baja Prioridad 718 Sololá San Pedro La Laguna Baja Prioridad 2001 Chiquimula Chiquimula Baja Prioridad 2011 Chiquimula Ipala Baja Prioridad 917 Quetzaltenango Colomba Costa Cuca Baja Prioridad 1401 Quiché Santa Cruz Del Quiché Baja Prioridad 901 Quetzaltenango Quetzaltenango Baja Prioridad 1601 Alta Verapaz Cobán Baja Prioridad 2010 Chiquimula San Jacinto Baja Prioridad 914 Quetzaltenango Cantel Baja Prioridad 923 Quetzaltenango La Esperanza Baja Prioridad 2007 Chiquimula Esquipulas Baja Prioridad 704 Sololá Santa Lucia Utatlán Baja Prioridad 1324 Huehuetenango San Antonio Huista Baja Prioridad 802 Totonicapán San Cristóbal Totonicapán Baja Prioridad 803 Totonicapán San Francisco El Alto Baja Prioridad 801 Totonicapán Totonicapán Baja Prioridad 1201 San Marcos San Marcos Baja prioridad con oport. agrícolas 1203 San Marcos San Antonio Sacatepéquez Baja prioridad con oport. agrícolas 1225 San Marcos San Cristóbal Cucho Baja prioridad con oport. agrícolas 1227 San Marcos Esquipulas Palo Gordo Baja prioridad con oport. agrícolas 1604 Alta Verapaz Tactic Baja prioridad con oport. agrícolas 1202 San Marcos San Pedro Sacatepéquez Baja prioridad con oport. agrícolas 1210 San Marcos Tejutla Baja prioridad con oport. agrícolas 1228 San Marcos Río Blanco Baja prioridad con oport. agrícolas 2009 Chiquimula Quezaltepeque Baja prioridad con oport. agrícolas 1312 Huehuetenango La Democracia Baja prioridad con oport. agrícolas 902 Quetzaltenango Salcajá Baja prioridad con oport. agrícolas 903 Quetzaltenango Olintepeque Baja prioridad con oport. agrícolas 910 Quetzaltenango San Mateo Baja prioridad con oport. agrícolas 913 Quetzaltenango Almolonga Baja prioridad con oport. agrícolas 916 Quetzaltenango Zunil Baja prioridad con oport. agrícolas 918 Quetzaltenango San Francisco La Unión Baja prioridad con oport. agrícolas 1308 Huehuetenango San Pedro Soloma Baja prioridad con oport. agrícolas 709 Sololá San Andrés Semetabaj Baja prioridad con oport. agrícolas 909 Quetzaltenango San Juan Ostuncalco69 Baja prioridad con oport. agrícolas 2003 Chiquimula San Juan La Ermita Baja prioridad con oport. agrícolas

1215 San Marcos Malacatán Baja prioridad con oport. agrícolas 904 Quetzaltenango San Carlos Sija Baja prioridad con oport. agrícolas municipality code Departamento Municipio category 1207 San Marcos Tacaná Prioridad media con oport. agrícolas 1212 San Marcos Nuevo Progreso Prioridad media con oport. agrícolas 1304 Huehuetenango Cuilco Prioridad media con oport. agrícolas 1214 San Marcos San José El Rodeo Prioridad media con oport. agrícolas 1219 San Marcos San Pablo Prioridad media con oport. agrícolas 1221 San Marcos La Reforma Prioridad media con oport. agrícolas 1226 San Marcos Sipacapa Prioridad media con oport. agrícolas 1229 San Marcos San Lorenzo Prioridad media con oport. agrícolas 1302 Huehuetenango Chiantla Prioridad media con oport. agrícolas 1416 Quiché Sacapulas Prioridad media con oport. agrícolas 1213 San Marcos El Tumbador Prioridad media con oport. agrícolas 924 Quetzaltenango Palestina De Los Altos Prioridad media con oport. agrícolas 921 Quetzaltenango Génova Costa Cuca Prioridad media con oport. agrícolas 1303 Huehuetenango Malacatancito Prioridad media con oport. agrícolas 1321 Huehuetenango Tectitán Prioridad media con oport. agrícolas 1322 Huehuetenango Concepción Huista Prioridad media con oport. agrícolas 1323 Huehuetenango San Juan Ixcoy Prioridad media con oport. agrícolas 1327 Huehuetenango Aguacatán Prioridad media con oport. agrícolas 1330 Huehuetenango Santiago Chimaltenango Prioridad media con oport. agrícolas 1333 Huehuetenango Petatan Prioridad media con oport. agrícolas 1602 Alta Verapaz Santa Cruz Verapaz Prioridad media con oport. agrícolas 1616 Alta Verapaz La Tinta Prioridad media con oport. agrícolas 1603 Alta Verapaz San Cristóbal Verapaz Prioridad media con oport. agrícolas 906 Quetzaltenango Cabricán Prioridad media con oport. agrícolas 908 Quetzaltenango San Miguel Siguilá Prioridad media con oport. agrícolas 911 Quetzaltenango Concepción Chiquirichapa Prioridad media con oport. agrícolas 919 Quetzaltenango El Palmar Prioridad media con oport. agrícolas 1403 Quiché Chinique Prioridad media con oport. agrícolas 1311 Huehuetenango La Libertad Prioridad media con oport. agrícolas 1610 Alta Verapaz San Juan Chamelco Prioridad media con oport. agrícolas 1305 Huehuetenango Nentón Prioridad media con oport. agrícolas 713 Sololá San Lucas Tolimán Prioridad media con oport. agrícolas 1418 Quiché Canilla Prioridad media con oport. agrícolas 1411 Quiché San Juan Cotzal Prioridad media con oport. agrícolas

70 municipality code Departamento Municipio category 1220 San Marcos El Quetzal Prioridad media sin oport. agrícolas 2006 Chiquimula Olopa Prioridad media sin oport. agrícolas 702 Sololá San José Chacayá Prioridad media sin oport. agrícolas 708 Sololá Concepción Prioridad media sin oport. agrícolas 711 Sololá Santa Catarina Palopó Prioridad media sin oport. agrícolas 715 Sololá San Pablo La Laguna Prioridad media sin oport. agrícolas 716 Sololá San Marcos La Laguna Prioridad media sin oport. agrícolas 719 Sololá Santiago Atitlán Prioridad media sin oport. agrícolas 2004 Chiquimula Jocotán Prioridad media sin oport. agrícolas 707 Sololá Santa Clara La Laguna Prioridad media sin oport. agrícolas 915 Quetzaltenango Huitán Prioridad media sin oport. agrícolas 1404 Quiché Zacualpa Prioridad media sin oport. agrícolas 907 Quetzaltenango Cajolá Prioridad media sin oport. agrícolas 701 Sololá Sololá Prioridad media sin oport. agrícolas 717 Sololá San Juan La Laguna Prioridad media sin oport. agrícolas 912 Quetzaltenango San Martín Sacatepéquez Prioridad media sin oport. agrícolas 804 Totonicapán San Andrés Xecul Prioridad media sin oport. agrícolas 1406 Quiché Santo Tomás Chichicastenango Prioridad media sin oport. agrícolas 808 Totonicapán San Bartolo Aguas Calientes Prioridad media sin oport. agrícolas 805 Totonicapán Momostenango Prioridad media sin oport. agrícolas 705 Sololá Nahualá Zonas Críticas 1612 Alta Verapaz Santa María Cahabón Zonas Críticas 1611 Alta Verapaz Lanquín Zonas Críticas 1413 Quiché Nebaj Zonas Críticas 714 Sololá Santa Cruz La Laguna Zonas Críticas 1408 Quiché San Antonio Ilotenango Zonas Críticas 1319 Huehuetenango Colotenango Zonas Críticas 1320 Huehuetenango San Sebastián Huehuetenango Zonas Críticas 1414 Quiché San Andrés Sajcabajá Zonas Críticas 1407 Quiché Patzité Zonas Críticas 1415 Quiché San Miguel Uspantán Zonas Críticas 1606 Alta Verapaz San Miguel Tucurú Zonas Críticas 2005 Chiquimula Camotán Zonas Críticas 1326 Huehuetenango Santa Cruz Barillas Zonas Críticas 807 Totonicapán Santa Lucía La Reforma Zonas Críticas 806 Totonicapán Santa María Chiquimula Zonas Críticas 712 Sololá San Antonio Palopó Zonas Críticas

Table B6. 7-group typology based on production potential, efficiency level and agricultural vulnerability to climate change municipality code Departamento Municipio category 1315 Huehuetenango Todos Santos Cuchumatán Alta prioridad 1613 Alta Verapaz Chisec Alta prioridad 1420 Quiché Ixcán Alta prioridad 1209 San Marcos Tajumulco Alta prioridad 1607 Alta Verapaz Panzós Alta prioridad 1212 San Marcos Nuevo Progreso Alta prioridad 1325 Huehuetenango San Sebastián Coatán Alta prioridad 1214 San Marcos San José El Rodeo Alta prioridad 1224 San Marcos San José Ojetenán Alta prioridad 1318 Huehuetenango San Mateo Ixtatán Alta prioridad 1213 San Marcos El Tumbador Alta prioridad 1421 Quiché Pachalún Alta prioridad 2008 Chiquimula Concepción Las Minas Alta prioridad 921 Quetzaltenango Génova Costa Cuca Alta prioridad 1316 Huehuetenango San Juan Atitán Alta prioridad 1615 Alta Verapaz Fray Bartolomé De Las Casas Alta prioridad 2009 Chiquimula Quezaltepeque Alta prioridad 1329 Huehuetenango San Gaspar Ixchil Alta prioridad 1331 Huehuetenango Santa Ana Huista Alta prioridad 1614 Alta Verapaz Chahal Alta prioridad 1616 Alta Verapaz La Tinta Alta prioridad 1312 Huehuetenango La Democracia Alta prioridad 1608 Alta Verapaz Senahú Alta prioridad 1309 Huehuetenango San Ildefonso Ixtahuacán Alta prioridad 1417 Quiché San Bartolomé Jocotenango Alta prioridad 1419 Quiché Chicamán Alta prioridad 1609 Alta Verapaz San Pedro Carchá Alta prioridad 1311 Huehuetenango La Libertad Alta prioridad 1405 Quiché Chajul Alta prioridad 1305 Huehuetenango Nentón Alta prioridad 2003 Chiquimula San Juan La Ermita Alta prioridad 1418 Quiché Canilla Alta prioridad 1226 San Marcos Sipacapa Alto rendimiento 1217 San Marcos Ayutla (Tecún Umán) Alto rendimiento 1303 Huehuetenango Malacatancito Alto rendimiento 1602 Alta Verapaz Santa Cruz Verapaz Alto rendimiento 1301 Huehuetenango Huehuetenango Alto rendimiento 1307 Huehuetenango Jacaltenango Alto rendimiento

72 municipality code Departamento Municipio category 702 Sololá San José Chacayá Baja Prioridad 703 Sololá Santa María Visitación Baja Prioridad 708 Sololá Concepción Baja Prioridad 1408 Quiché San Antonio Ilotenango Baja Prioridad 2001 Chiquimula Chiquimula Baja Prioridad 707 Sololá Santa Clara La Laguna Baja Prioridad 1401 Quiché Santa Cruz Del Quiché Baja Prioridad 1407 Quiché Patzité Baja Prioridad 907 Quetzaltenango Cajolá Baja Prioridad 701 Sololá Sololá Baja Prioridad 914 Quetzaltenango Cantel Baja Prioridad 807 Totonicapán Santa Lucía La Reforma Baja Prioridad 704 Sololá Santa Lucia Utatlán Baja Prioridad 806 Totonicapán Santa María Chiquimula Baja Prioridad 804 Totonicapán San Andrés Xecul Baja Prioridad 712 Sololá San Antonio Palopó Baja Prioridad 802 Totonicapán San Cristóbal Totonicapán Baja Prioridad 1406 Quiché Santo Tomás Chichicastenango Baja Prioridad 808 Totonicapán San Bartolo Aguas Calientes Baja Prioridad 805 Totonicapán Momostenango Baja Prioridad 801 Totonicapán Totonicapán Baja Prioridad 1201 San Marcos San Marcos Baja prioridad con oport. agrícolas 1203 San Marcos San Antonio Sacatepéquez Baja prioridad con oport. agrícolas 1225 San Marcos San Cristóbal Cucho Baja prioridad con oport. agrícolas 1227 San Marcos Esquipulas Palo Gordo Baja prioridad con oport. agrícolas 1205 San Marcos San Miguel Ixtahuacán Baja prioridad con oport. agrícolas 1604 Alta Verapaz Tactic Baja prioridad con oport. agrícolas 1202 San Marcos San Pedro Sacatepéquez Baja prioridad con oport. agrícolas 1228 San Marcos Río Blanco Baja prioridad con oport. agrícolas 1306 Huehuetenango San Pedro Nectá Baja prioridad con oport. agrícolas 1321 Huehuetenango Tectitán Baja prioridad con oport. agrícolas 1327 Huehuetenango Aguacatán Baja prioridad con oport. agrícolas 1328 Huehuetenango San Rafael Petzal Baja prioridad con oport. agrícolas 902 Quetzaltenango Salcajá Baja prioridad con oport. agrícolas 906 Quetzaltenango Cabricán Baja prioridad con oport. agrícolas 911 Quetzaltenango Concepción Chiquirichapa Baja prioridad con oport. agrícolas 918 Quetzaltenango San Francisco La Unión Baja prioridad con oport. agrícolas 1403 Quiché Chinique Baja prioridad con oport. agrícolas 1610 Alta Verapaz San Juan Chamelco Baja prioridad con oport. agrícolas 1308 Huehuetenango San Pedro Soloma Baja prioridad con oport. agrícolas 709 Sololá San Andrés Semetabaj Baja prioridad con oport. agrícolas 1402 Quiché Chiché Baja prioridad con oport. agrícolas 1409 Quiché San Pedro Jocopilas Baja prioridad con oport. agrícolas 904 Quetzaltenango San Carlos Sija Baja prioridad con oport. agrícolas

73 municipality code Departamento Municipio category 920 Quetzaltenango Coatepeque Prioridad media con oport. agrícolas 1207 San Marcos Tacaná Prioridad media con oport. agrícolas 1204 San Marcos Comitancillo Prioridad media con oport. agrícolas 1208 San Marcos Sibinal Prioridad media con oport. agrícolas 1216 San Marcos Catarina Prioridad media con oport. agrícolas 1304 Huehuetenango Cuilco Prioridad media con oport. agrícolas 1211 San Marcos San Rafael Pie De La Cuesta Prioridad media con oport. agrícolas 1219 San Marcos San Pablo Prioridad media con oport. agrícolas 1221 San Marcos La Reforma Prioridad media con oport. agrícolas 1229 San Marcos San Lorenzo Prioridad media con oport. agrícolas 1302 Huehuetenango Chiantla Prioridad media con oport. agrícolas 1218 San Marcos Ocós Prioridad media con oport. agrícolas 706 Sololá Santa Catarina Ixtahuacán Prioridad media con oport. agrícolas 1416 Quiché Sacapulas Prioridad media con oport. agrícolas 1206 San Marcos Concepción Tutuapa Prioridad media con oport. agrícolas 1222 San Marcos Pajapita Prioridad media con oport. agrícolas 924 Quetzaltenango Palestina De Los Altos Prioridad media con oport. agrícolas 1210 San Marcos Tejutla Prioridad media con oport. agrícolas 1313 Huehuetenango San Miguel Acatán Prioridad media con oport. agrícolas 1310 Huehuetenango Santa Barbara Prioridad media con oport. agrícolas 1314 Huehuetenango San Rafael La Independencia Prioridad media con oport. agrícolas 1317 Huehuetenango Santa Eulalia Prioridad media con oport. agrícolas 1322 Huehuetenango Concepción Huista Prioridad media con oport. agrícolas 1323 Huehuetenango San Juan Ixcoy Prioridad media con oport. agrícolas 1330 Huehuetenango Santiago Chimaltenango Prioridad media con oport. agrícolas 1333 Huehuetenango Petatan Prioridad media con oport. agrícolas 1605 Alta Verapaz Tamahú Prioridad media con oport. agrícolas 1603 Alta Verapaz San Cristóbal Verapaz Prioridad media con oport. agrícolas 919 Quetzaltenango El Palmar Prioridad media con oport. agrícolas 922 Quetzaltenango Flores Costa Cuca Prioridad media con oport. agrícolas 1412 Quiché Joyabaj Prioridad media con oport. agrícolas 1410 Quiché Cunén Prioridad media con oport. agrícolas 1223 San Marcos Ixchiguán Prioridad media con oport. agrícolas 713 Sololá San Lucas Tolimán Prioridad media con oport. agrícolas 2002 Chiquimula San José La Arada Prioridad media con oport. agrícolas 909 Quetzaltenango San Juan Ostuncalco Prioridad media con oport. agrícolas 1411 Quiché San Juan Cotzal Prioridad media con oport. agrícolas 905 Quetzaltenango Sibiliá Prioridad media con oport. agrícolas 1215 San Marcos Malacatán Prioridad media con oport. agrícolas

74 municipality code Departamento Municipio category 1413 Quiché Nebaj Prioridad media sin oport. agrícolas 714 Sololá Santa Cruz La Laguna Prioridad media sin oport. agrícolas 1319 Huehuetenango Colotenango Prioridad media sin oport. agrícolas 915 Quetzaltenango Huitán Prioridad media sin oport. agrícolas 917 Quetzaltenango Colomba Costa Cuca Prioridad media sin oport. agrícolas 1404 Quiché Zacualpa Prioridad media sin oport. agrícolas 1320 Huehuetenango San Sebastián Huehuetenango Prioridad media sin oport. agrícolas 717 Sololá San Juan La Laguna Prioridad media sin oport. agrícolas 912 Quetzaltenango San Martín Sacatepéquez Prioridad media sin oport. agrícolas 1324 Huehuetenango San Antonio Huista Prioridad media sin oport. agrícolas 1220 San Marcos El Quetzal Zonas Críticas 2006 Chiquimula Olopa Zonas Críticas 705 Sololá Nahualá Zonas Críticas 1612 Alta Verapaz Santa María Cahabón Zonas Críticas 1611 Alta Verapaz Lanquín Zonas Críticas 2011 Chiquimula Ipala Zonas Críticas 2004 Chiquimula Jocotán Zonas Críticas 1601 Alta Verapaz Cobán Zonas Críticas 1415 Quiché San Miguel Uspantán Zonas Críticas 2010 Chiquimula San Jacinto Zonas Críticas 1606 Alta Verapaz San Miguel Tucurú Zonas Críticas 2005 Chiquimula Camotán Zonas Críticas 2007 Chiquimula Esquipulas Zonas Críticas 1326 Huehuetenango Santa Cruz Barillas Zonas Críticas 1230 San Marcos La Blanca no data 1332 Huehuetenango San Pedro Nectá no data 1617 Alta Verapaz Chisec no data 903 Quetzaltenango Olintepeque no data 908 Quetzaltenango San Miguel Siguilá no data 910 Quetzaltenango San Mateo no data 913 Quetzaltenango Almolonga no data 916 Quetzaltenango Zunil no data 710 Sololá Panajachel no data 711 Sololá Santa Catarina Palopó no data 715 Sololá San Pablo La Laguna no data 716 Sololá San Marcos La Laguna no data 718 Sololá San Pedro La Laguna no data 719 Sololá Santiago Atitlán no data 1414 Quiché San Andrés Sajcabajá no data 901 Quetzaltenango Quetzaltenango no data 923 Quetzaltenango La Esperanza no data 803 Totonicapán San Francisco El Alto no data

Table B7. 7-group typology based on production potential, efficiency level and vulnerability index to food insecurity and nutrition (IVISAN) municipality code Departamento Municipio category 1315 Huehuetenango Todos Santos Cuchumatán Alta prioridad 1613 Alta Verapaz Chisec Alta prioridad 1209 San Marcos Tajumulco Alta prioridad 1204 San Marcos Comitancillo Alta prioridad 1607 Alta Verapaz Panzós Alta prioridad 1325 Huehuetenango San Sebastián Coatán Alta prioridad 1224 San Marcos San José Ojetenán Alta prioridad 706 Sololá Santa Catarina Ixtahuacán Alta prioridad 1416 Quiché Sacapulas Alta prioridad 1318 Huehuetenango San Mateo Ixtatán Alta prioridad 1206 San Marcos Concepción Tutuapa Alta prioridad 1316 Huehuetenango San Juan Atitán Alta prioridad 1313 Huehuetenango San Miguel Acatán Alta prioridad 1310 Huehuetenango Santa Barbara Alta prioridad 1314 Huehuetenango San Rafael La Independencia Alta prioridad 1317 Huehuetenango Santa Eulalia Alta prioridad 1322 Huehuetenango Concepción Huista Alta prioridad 1323 Huehuetenango San Juan Ixcoy Alta prioridad 1327 Huehuetenango Aguacatán Alta prioridad 1329 Huehuetenango San Gaspar Ixchil Alta prioridad 1330 Huehuetenango Santiago Chimaltenango Alta prioridad 1332 Huehuetenango San Pedro Nectá Alta prioridad 1333 Huehuetenango Petatan Alta prioridad 1605 Alta Verapaz Tamahú Alta prioridad 1616 Alta Verapaz La Tinta Alta prioridad 1617 Alta Verapaz Chisec Alta prioridad 1608 Alta Verapaz Senahú Alta prioridad 1309 Huehuetenango San Ildefonso Ixtahuacán Alta prioridad 911 Quetzaltenango Concepción Chiquirichapa Alta prioridad 913 Quetzaltenango Almolonga Alta prioridad 916 Quetzaltenango Zunil Alta prioridad 1417 Quiché San Bartolomé Jocotenango Alta prioridad 1412 Quiché Joyabaj Alta prioridad 1405 Quiché Chajul Alta prioridad 1223 San Marcos Ixchiguán Alta prioridad 1409 Quiché San Pedro Jocopilas Alta prioridad 1411 Quiché San Juan Cotzal Alta prioridad

76 municipality code Departamento Municipio category 920 Quetzaltenango Coatepeque Alto rendimiento 1420 Quiché Ixcán Alto rendimiento 1216 San Marcos Catarina Alto rendimiento 1211 San Marcos San Rafael Pie De La Cuesta Alto rendimiento 1214 San Marcos San José El Rodeo Alto rendimiento 1230 San Marcos La Blanca Alto rendimiento 1218 San Marcos Ocós Alto rendimiento 1213 San Marcos El Tumbador Alto rendimiento 1217 San Marcos Ayutla (Tecún Umán) Alto rendimiento 1222 San Marcos Pajapita Alto rendimiento 1421 Quiché Pachalún Alto rendimiento 2008 Chiquimula Concepción Las Minas Alto rendimiento 921 Quetzaltenango Génova Costa Cuca Alto rendimiento 1303 Huehuetenango Malacatancito Alto rendimiento 1331 Huehuetenango Santa Ana Huista Alto rendimiento 922 Quetzaltenango Flores Costa Cuca Alto rendimiento 1301 Huehuetenango Huehuetenango Alto rendimiento 2002 Chiquimula San José La Arada Alto rendimiento 1307 Huehuetenango Jacaltenango Alto rendimiento 905 Quetzaltenango Sibiliá Alto rendimiento 702 Sololá San José Chacayá Baja Prioridad 703 Sololá Santa María Visitación Baja Prioridad 710 Sololá Panajachel Baja Prioridad 716 Sololá San Marcos La Laguna Baja Prioridad 718 Sololá San Pedro La Laguna Baja Prioridad 2001 Chiquimula Chiquimula Baja Prioridad 2011 Chiquimula Ipala Baja Prioridad 917 Quetzaltenango Colomba Costa Cuca Baja Prioridad 1401 Quiché Santa Cruz Del Quiché Baja Prioridad 901 Quetzaltenango Quetzaltenango Baja Prioridad 2010 Chiquimula San Jacinto Baja Prioridad 914 Quetzaltenango Cantel Baja Prioridad 923 Quetzaltenango La Esperanza Baja Prioridad 2007 Chiquimula Esquipulas Baja Prioridad 704 Sololá Santa Lucia Utatlán Baja Prioridad 1324 Huehuetenango San Antonio Huista Baja Prioridad 802 Totonicapán San Cristóbal Totonicapán Baja Prioridad 801 Totonicapán Totonicapán Baja Prioridad

77 municipality code Departamento Municipio category 1201 San Marcos San Marcos Baja prioridad con oport. agrícolas 1203 San Marcos San Antonio Sacatepéquez Baja prioridad con oport. agrícolas 1227 San Marcos Esquipulas Palo Gordo Baja prioridad con oport. agrícolas 1202 San Marcos San Pedro Sacatepéquez Baja prioridad con oport. agrícolas 1210 San Marcos Tejutla Baja prioridad con oport. agrícolas 1228 San Marcos Río Blanco Baja prioridad con oport. agrícolas 2009 Chiquimula Quezaltepeque Baja prioridad con oport. agrícolas 902 Quetzaltenango Salcajá Baja prioridad con oport. agrícolas 903 Quetzaltenango Olintepeque Baja prioridad con oport. agrícolas 906 Quetzaltenango Cabricán Baja prioridad con oport. agrícolas 910 Quetzaltenango San Mateo Baja prioridad con oport. agrícolas 918 Quetzaltenango San Francisco La Unión Baja prioridad con oport. agrícolas 709 Sololá San Andrés Semetabaj Baja prioridad con oport. agrícolas 713 Sololá San Lucas Tolimán Baja prioridad con oport. agrícolas 2003 Chiquimula San Juan La Ermita Baja prioridad con oport. agrícolas 1215 San Marcos Malacatán Baja prioridad con oport. agrícolas 904 Quetzaltenango San Carlos Sija Baja prioridad con oport. agrícolas 1207 San Marcos Tacaná Prioridad media con oport. agrícolas 1212 San Marcos Nuevo Progreso Prioridad media con oport. agrícolas 1208 San Marcos Sibinal Prioridad media con oport. agrícolas 1304 Huehuetenango Cuilco Prioridad media con oport. agrícolas 1219 San Marcos San Pablo Prioridad media con oport. agrícolas 1221 San Marcos La Reforma Prioridad media con oport. agrícolas 1225 San Marcos San Cristóbal Cucho Prioridad media con oport. agrícolas 1226 San Marcos Sipacapa Prioridad media con oport. agrícolas 1229 San Marcos San Lorenzo Prioridad media con oport. agrícolas 1302 Huehuetenango Chiantla Prioridad media con oport. agrícolas 1205 San Marcos San Miguel Ixtahuacán Prioridad media con oport. agrícolas 1604 Alta Verapaz Tactic Prioridad media con oport. agrícolas 924 Quetzaltenango Palestina De Los Altos Prioridad media con oport. agrícolas 1615 Alta Verapaz Fray Bartolomé De Las Casas Prioridad media con oport. agrícolas 1306 Huehuetenango San Pedro Nectá Prioridad media con oport. agrícolas 1321 Huehuetenango Tectitán Prioridad media con oport. agrícolas 1328 Huehuetenango San Rafael Petzal Prioridad media con oport. agrícolas 1602 Alta Verapaz Santa Cruz Verapaz Prioridad media con oport. agrícolas 1614 Alta Verapaz Chahal Prioridad media con oport. agrícolas 1312 Huehuetenango La Democracia Prioridad media con oport. agrícolas 1603 Alta Verapaz San Cristóbal Verapaz Prioridad media con oport. agrícolas 908 Quetzaltenango San Miguel Siguilá Prioridad media con oport. agrícolas 919 Quetzaltenango El Palmar Prioridad media con oport. agrícolas 1403 Quiché Chinique Prioridad media con oport. agrícolas 1419 Quiché Chicamán Prioridad media con oport. agrícolas 1609 Alta Verapaz San Pedro Carchá Prioridad media con oport. agrícolas 1410 Quiché Cunén Prioridad media con oport. agrícolas 1311 Huehuetenango La Libertad Prioridad media con oport. agrícolas 1610 Alta Verapaz San Juan Chamelco Prioridad media con oport. agrícolas 1305 Huehuetenango Nentón Prioridad media con oport. agrícolas 1308 Huehuetenango San Pedro Soloma Prioridad media con oport. agrícolas 909 Quetzaltenango San Juan Ostuncalco Prioridad media con oport. agrícolas 1402 Quiché Chiché Prioridad media con oport. agrícolas 1418 Quiché Canilla Prioridad media con oport. agrícolas

78 municipality code Departamento Municipio category 1220 San Marcos El Quetzal Prioridad media sin oport. agrícolas 2006 Chiquimula Olopa Prioridad media sin oport. agrícolas 1413 Quiché Nebaj Prioridad media sin oport. agrícolas 711 Sololá Santa Catarina Palopó Prioridad media sin oport. agrícolas 715 Sololá San Pablo La Laguna Prioridad media sin oport. agrícolas 2004 Chiquimula Jocotán Prioridad media sin oport. agrícolas 707 Sololá Santa Clara La Laguna Prioridad media sin oport. agrícolas 915 Quetzaltenango Huitán Prioridad media sin oport. agrícolas 1407 Quiché Patzité Prioridad media sin oport. agrícolas 1601 Alta Verapaz Cobán Prioridad media sin oport. agrícolas 2005 Chiquimula Camotán Prioridad media sin oport. agrícolas 701 Sololá Sololá Prioridad media sin oport. agrícolas 717 Sololá San Juan La Laguna Prioridad media sin oport. agrícolas 1326 Huehuetenango Santa Cruz Barillas Prioridad media sin oport. agrícolas 804 Totonicapán San Andrés Xecul Prioridad media sin oport. agrícolas 712 Sololá San Antonio Palopó Prioridad media sin oport. agrícolas 1406 Quiché Santo Tomás Chichicastenango Prioridad media sin oport. agrícolas 808 Totonicapán San Bartolo Aguas Calientes Prioridad media sin oport. agrícolas 803 Totonicapán San Francisco El Alto Prioridad media sin oport. agrícolas 805 Totonicapán Momostenango Prioridad media sin oport. agrícolas 705 Sololá Nahualá Zonas Críticas 1612 Alta Verapaz Santa María Cahabón Zonas Críticas 1611 Alta Verapaz Lanquín Zonas Críticas 708 Sololá Concepción Zonas Críticas 714 Sololá Santa Cruz La Laguna Zonas Críticas 719 Sololá Santiago Atitlán Zonas Críticas 1408 Quiché San Antonio Ilotenango Zonas Críticas 1319 Huehuetenango Colotenango Zonas Críticas 1404 Quiché Zacualpa Zonas Críticas 1320 Huehuetenango San Sebastián Huehuetenango Zonas Críticas 1414 Quiché San Andrés Sajcabajá Zonas Críticas 1415 Quiché San Miguel Uspantán Zonas Críticas 907 Quetzaltenango Cajolá Zonas Críticas 1606 Alta Verapaz San Miguel Tucurú Zonas Críticas 912 Quetzaltenango San Martín Sacatepéquez Zonas Críticas 807 Totonicapán Santa Lucía La Reforma Zonas Críticas 806 Totonicapán Santa María Chiquimula Zonas Críticas

Appendix C. Partial correlation analysis at the municipality level between stunting rate and indicators of food security and nutrition

This appendix details the procedure followed to derive the “direct” or “net” correlation between the stunting rate and each of the following ten indicators of food security and nutrition. The exercise is performed at the municipality level and the working sample includes all 340 municipalities in Guatemala.

Table B8. Food security and nutrition indicators considered

Availability, Access and Consumption Climatic Risks Response Capacity 1. Deficit of basic grains (VAR1) 6. Risk of frost (VAR6) 9. Road index (VAR9) 2. Extreme poverty (VAR2) 7. Risk of drought (VAR7) 10. Index of state density (VAR10) 3. Precarious employment (VAR3) 8. Risk of flooding (VAR8) 4. Analphabetism (VAR4) 5. Index of sanitation (VAR5)

In particular, the next four steps are followed.

1. Step 1: The variables are first standardized for comparability purposes across indicators (as the unit of measure differs across variables). The standardization of each variable consists in subtracting the corresponding sample mean and dividing it by the standard deviation.

2. Step 2: Obtain the residual (RESD1) of the linear regression of stunting rate (DES) on all indicators except deficit of grains (VAR1). That is, the following equation is estimated

10 ˆ ˆ (B1) DES = a + biVAR i + e  obtain a y b , i=2 where a is a constant term and e is an estimation error. The residual of the estimation (RESD1) is given by,

10 ˆ ˆ (B2) RESD1 = DES − a − biVAR i . i=2

Note that by construction none of the nine indicators (VAR2-VAR10) included in the regression equation (B1) are correlated with the residual term RESD1. More specifically, the effect of each of the nine indicators on the stunting rate has been isolated.

3. Step 3: Obtain the residual (RESV1) of the linear regression of deficit of grains (VAR1) on all the remaining nine indicators (VAR2-VAR10).

10 ˆ ˆ (B3) VAR1 = c + diVAR i + u  obtain c y d , i=2

where c is a constant term and u is an estimation error. The residual of the estimation (RESV1) is given by,

10 ˆ ˆ (B4) RESV1 = VAR1− c −  diVAR i . i=2

Again, by construction, none of the nine indicators (VAR2-VAR10) considered in regression equation (B3) are correlated with the residual term RESV1. Similar to Step 2, the effect of each of the nine indicators on the deficit of grains has been isolated.

4. Step 4: Calculate the Spearman correlation between the residual terms obtained in Step 2 (RESD1) and Step 3 (RESV1). This correlation is the standardized partial correlation coefficient between the stunting rate (DES) and the deficit of grains (VAR1) and captures the “direct” or “net” correlation between these two variables after accounting for the effect of all the other indicators (VAR2-VAR10) on the two variables.

The same process is repeated for the remaining variables.

Appendix D. Accessibility model

The accessibility model involves spatial analysis using a geographic information system (GIS) and a combination of spatial variables that influence the movement of people. The analysis assumes that people travels via highways, major roads, or walkways (where these exist), and around facilities near their homes (that is, schools, healthcare centers, or even a local public telephone). This model simulates the time it takes a person to reach the nearest facility using the fastest available method and route of travel.

Figure D1. Friction image

The accessibility analysis is developed on a raster format, which means that the entire area of analysis is first converted into a grid of cells measuring 20 by 20 meters. Each cell is assigned a “friction” value based on characteristics of slope, roads, and barriers, which allows each cell to be allotted a given value for the time it would be required to traverse it in order to reach the nearest facility.

Figure D2. Friction surface Figure D3. Shortest and least cost routes

Figure D2 represents geography in a cost matrix. A value of 1 indicates the ‘cheapest’ way to travel (e.g. flat terrain), and it goes up as the slope gets higher (presence of mountains). Also, the presence of barriers (in this case rivers) represent the highest values. In this case the friction surface recognizes the river as a more difficult barrier to cross than the mountain. We illustrate this with an example. In Figure D2, the purple route would be considered the shortest route if distance were the only thing that mattered, but the green one would be considered as the least cost route. In this regard, the accessibility model will pick the best option to move based not only on distance but also considering slope, roads, and rivers.

In practice, the scenario would be similar to the picture below (Figure D4), where the chosen path is the one that goes along the valley, avoiding the river and the steep areas.

Figure D4. Routes

The first variable used in this analysis is transportation infrastructure or roads. First, each type of road is assigned an average travel speed, and the corresponding cell is assigned a crossing time in seconds:

In addition to this classification according to fixed speed, additional roads are classified as dirt road tracks and walking trails. A slope variable is used to calculate walking speeds. The walking velocity is drawn from Tobler (1993), and comprises three categories corresponding to navigation by horseback/dirt road, by footpath, and off–footpath/no roads. The following calculations result, where S is slope:

Walking velocity on footpath = [6 × exp(−3.5 × abs(S + 0.05))]

Mode of transportation Average speed (kms per hour) Dirt road tracks (Walking velocity on footpath) × 1.25 Walking trails (Walking velocity on footpath) No roads (Walking velocity on footpath) × 0.6

The third and final variable used in this model corresponds to the presence of natural barriers –rivers in particular, which prevent people from traveling a straight line if there is no bridge. Cells corresponding to areas with a river and no bridge are assigned a high travel time to deter travel along that way. Once the friction model is built and each cell has been allocated a travel time value, cost-weighted distance algorithms are run over the resulting raster surface, calculating the accumulated time required to travel each particular route and choosing the one that is least time-consuming (in terms of the time costs outlined above). The resulting time taken from every point are shown graphically in the map below. 83

Figure D5. Time to cities

Appendix E. Interviews with Key Informants from MAGA in Selected Departments

a) Chiquimula

In Chiquimula, we talked with Ing. Hector Antonio Guerra and his technical team. In the next table, we present the ranking made by them.

Table E1. Ranking by MAGA - Chiquimula

Ranking Productive Ranking Economic Development Ranking 1 Esquipulas Concepcion 2 Ipala San Jose Larada 3 Camotan Ipala 4 Chiquimula Quetzaltepeque 5 Concepcion Esquipulas 6 Olopa Chiquimula 7 Quetzaltepeque San Jacinto 8 San Jacinto San Juan 9 San Juan Ermita Olopa 10 San Jose Larada Camotan 11 Jocotan Jocotan

While Esquipulas is regarded as the most productive municipality in Chiquimula, it occupied the fifth place in terms of economic development. The most important product in Esquipulas is coffee. The authorities also estimated that the production of livestock is one of the main drivers of Esquipula’s economy. In the case of Ipala, the most important products are beans, maize, and melon. They also considered the production of livestock with certain potential. Lastly, Camotán’s main products are coffee and green vegetables.

In terms of social conflicts, the authorities mentioned the case of Jocotán, where there were severe conflicts with authorities caused by discrepancies regarding hydroelectrical issues. They also mentioned the civil conflict between the departments of Zacapa and Chiquimula, which has resulted in many deaths.

When we asked them about the possible solutions to improve productive potential in the region, they mentioned two main alternatives: the establishment of new irrigation systems and the reactivation of traditional irrigation (Example: Grinder).

b) San Marcos

In San Marcos, we met with Ing. Gilben Escobar and his team. The following table presents the ranking made by them. In this case, local authorities built the ranking by first organizing the districts in geographic zones. For instance, they made first the ranking of the Coast, then continued with Boca Coast, and so forth. The comparison is relative to each zone.

Table E2. Ranking by MAGA – San Marcos

Zone Ranking Productive Ranking Economic Development Ranking 1 Ayutla Catarina 2 La Blanca Pajapita 3 Catarina Malacatan Coast 4 Ocos Ocos 5 Malacatan La Blanca 6 Pajapita Ayutla 1 Tumbador Tumbador 2 San Pablo San Rafael 3 San Rafael Pie de la Cuesta San Pablo Boca Coast 4 La Reforma San Jose El Rodeo 5 El Quetzal La Reforma 6 Nuevo Progreso El Quetzal 7 San Jose El Rodeo Nuevo Progreso 1 San Pedro Sacatepequez San Pedro Sacatepequez 2 San Antonio Sacatepequez San Marcos 3 Esquipulas Palo Gordo San Antonio Sacatepequez Valley 4 San Cristobal Cucho Esquipulas Palo Gordo 5 San Marcos San Cristobal Cucho 6 San Lorenzo San Lorenzo 7 Rio Blanco Rio Blanco 1 Tajumulco Tejutla 2 Ixchiguan Tacana 3 Tejutla San Miguel 4 Concepcion Tatuapa Sibinal High 5 Tacana San Jose Ojetenan plateau 6 Sibinal Sipacapa 7 Comitancillo Comitancillo 8 San Miguel Ixtahuacan Concepcion Tatuapa 9 San Jose Ojetenan Ixchiguan 10 Sipacapa Tajumulco

The main products of Ayutla are African palm, banana, rice and watermelon. The main products of La Blanca are maize, banana, African palm and sesame. Catarina holds substantial livestock. Municipalities in Boca Costa have coffee as their main product, while it is potato in the High Plateau.

In terms of social conflicts, drugs are the main problem in the department and its borders. For instance, Ixchiguan and Tajumulco have bordering and water problems. On the other hand, San Miguel and Iscapaca are municipalities with mining issues. There are other municipalities with currently have some energy issues.

In terms of potential solutions, authorities mentioned promoting value chains for high-value crops, improving prices for agricultural products, prioritizing the plantation of some crops according to the potential of the land, and implementing production programs. Another mechanism that was mentioned was working with the youth and offering technical assistance. c) Quetzaltenango

In Quetzaltenango, we met with Ing. Jose Daniel Tiscoj and his team. In the next table, we present the ranking results.

Table E3. Ranking by MAGA - Quetzaltenango Ranking Productive Ranking Economic Development Ranking 1 Coatepeque Quetzaltenango 2 Génova Coatepeque 3 Colomba Salcajá 4 Flores La Esperanza 5 El Palmar San Mateo 6 San Martín Sacatepéquez San Juan Ostuncalco 7 Zunil Colomba 8 San Juan Ostuncalco Génova 9 Concepción Chiquirichapa Flores Costa Cuca 10 Almolonga Cantel 11 Palestina de los Altos Almolonga 12 Quetzaltenango Zunil 13 San Carlos Sija Olintepeque 14 Salcajá San Carlos Sija 15 Sibilia Sibilia 16 Cantel Concepción Chiquirichapa 17 Olintepeque San Martín Sacatepéquez 18 San Miguel Sigüilá Palestina de los Altos 19 Cabricán San Francisco La Unión 20 Huitán El Palmar 21 San Francisco La Unión San Miguel Sigüilá 22 Cajolá Cabricán 23 La Esperanza Cajolá 24 San Mateo Huitán

The main products in Coatepeque are oilcloth, African palm, banana, pineapple, livestock, coffee, maize, sesame, and green vegetables. The main products in Genova are oilcloth, pineapple, livestock, tropical fruits, maize, sesame, green vegetables, and coffee. Lastly, the main products in Colomba are coffee, maize, green vegetable, and livestock.

In terms of conflicts, Genova and Coatepeque are currently facing issues around water. Coatepeque also has energy issues. In other municipalities like San Juan Ostuncalco, Almolonga, and Cajola, the population is distrustful, and they are in constant social tension. 87

As potential solutions, the authorities mentioned the introduction of new agricultural technologies, territorial ordering, and access to credit.