Alan Hernández-Solano

This study analyzes how market failures alter the magnitude and distribution of economic effects of climate change on rural households in the South-Southeast of Mexico. To this end, we develop a Computable General Equilibrium (CGE) model that explicitly incorporates failures in the maize market. The model simulates two exogenous shocks—a reduction in maize yields and an increase in its price—and compares two counterfactual scenarios: with and without market failures. The results show that these imperfections increase the economic cost of climate change, raising the decline in regional real income from 0.76% to 2.75% and significantly modifying its distributional effects. In particular, producers with holdings of up to 2 hectares shift from a 0.57% income gain under perfectly integrated markets to a 5.45% loss when the market failure is incorporated. These findings show that assumptions about how markets function affect both estimated climate costs and the identification of vulnerable households, underscoring the importance of distributional considerations for sustainable rural development. Adaptation strategies should account for subsistence producers’ limited market participation and reliance on self-consumption rather than relying exclusively on price-based incentives. Targeted improvements in credit access, input markets, and extension services warrant evaluation as potential responses, although their effectiveness is not directly assessed by the model.

Agroecology offers a sustainable solution to the growing demand for food by mitigating environmental degradation caused by conventional agricultural practices. However, evidence on the impacts of public policies promoting agroecology remains limited. This study provides a comparative analysis of differences associated with the Estrategia de Acompañamiento Técnico (EAT), part of the Producción para el Bienestar (PpB) program, on the agroecological transition of producers in two municipalities in Mexico. The EAT promotes this transition through training and technical assistance, while PpB provides direct financial support. The analysis compared three groups of production units (EAT and PpB, only PpB, and without support), using soil quality indicators (pH, compaction, electrical conductivity, redox potential, and organic matter) and an assessment of agroecological status. The comparative analysis indicates that production units participating in the EAT tend to exhibit higher values in some of the agroecological transition indicators analyzed. Implementation challenges were identified, including labor shortages, logistical difficulties, limited understanding of agroecological benefits, and parallel policies promoting chemical fertilizers that counteract EAT's objectives. The study is relevant because it expands the evidence on the effects of public policies in agroecology, providing recommendations to enhance EAT, guide large-scale evaluations, and support the implementation of similar initiatives in other contexts.

Poverty in Mexico is measured biennially through socioeconomic surveys identifying people with income deficiencies and six social dimensions. This approach faces significant limitations: high costs, operational difficulties in regions with violence, and restricted capacity for timely policy responses, as evidenced during COVID-19. Emerging methods based on satellite imagery and remote sensing expand poverty analysis scope and efficiency. However, implementation faces challenges from scarce primary data, particularly satellite imagery and field measurements essential for predictive model training. This article develops a multispectral database for Mexico designed to facilitate training and validation of advanced machine learning algorithms for poverty prediction. This contribution provides a methodological tool for designing robust and scalable approaches while reducing reliance on traditional socio-economic survey methods. Spanish-language metadata / Metadatos en españolTítulo en español: Creación de un conjunto de datos multiespectrales a partir de imágenes satelitales para la predicción de la pobreza a nivel municipal en México Resumen: En México, la pobreza se mide cada dos años mediante encuestas socioeconómicas que identifican a las personas con ingresos insuficientes y tienen en cuenta seis dimensiones sociales. Este enfoque presenta importantes limitaciones: altos costos, dificultades operativas en regiones afectadas por la violencia y una capacidad limitada para dar respuestas políticas oportunas, como quedó demostrado durante la pandemia de COVID-19. Los métodos emergentes basados en imágenes satelitales y teledetección amplían el alcance y la eficacia del análisis de la pobreza. Sin embargo, su implementación se enfrenta a dificultades derivadas de la escasez de datos primarios, en particular de imágenes satelitales y mediciones sobre el terreno, que son esenciales para el entrenamiento de los modelos predictivos. Este artículo presenta una base de datos multiespectral para México diseñada para facilitar el entrenamiento y la validación de algoritmos avanzados de aprendizaje automático para la predicción de la pobreza. Esta contribución ofrece una herramienta metodológica para diseñar enfoques robustos y escalables, al tiempo que reduce la dependencia de los métodos tradicionales de encuestas socioeconómicas. Palabras Claves: Conjunto de datos, pobreza, imágenes satelitales, aprendizaje automático Smart citations: https://scite.ai/reports/10.61467/2007.1558.2026.v17i3.1283Dimensions.Open Alex.

The Food Energy Intake (FEI) method is among the most widely used approaches for establishing income poverty lines, particularly in low- and middle-income countries where it is often used as a baseline to define official poverty lines. This method links poverty measurement to the consumption of calories and other essential nutrients, identifying households that lack the resources necessary to meet minimum nutritional requirements. However, due to data limitations, the construction of poverty lines based on the FEI method relies on a series of assumptions that are often left to the discretion of specialists, without adequate discussion regarding their appropriateness or potential implications. These assumptions can significantly influence the resulting poverty lines and, consequently, the poverty estimates derived from them. In this study, we focus on the Mexican official method of poverty measurement to explore an often overlooked step in poverty measurement: the treatment of extreme values in nutrient availability estimates constructed using household survey data. While this may initially appear to be a minor technical consideration, we demonstrate that the criteria used to identify and potentially exclude outlying observations exert a substantial influence on poverty metrics, which reveals weak fulfillment of the FEI assumptions, as well as important limitations in the expenditure data. This underscores the critical importance of adopting transparent, rigorous, and consistent methodologies for managing outliers, alongside a careful evaluation of other assumptions that are commonly overlooked during the poverty line construction process. We assess the sensitivity of poverty estimates in Mexico employing twelve different outlier-handling methodologies. For each method, we compute the corresponding poverty lines and poverty rates. Our findings reveal substantial variation in extreme poverty rates—ranging from 10.7% to 26.7%—solely attributable to the criteria adopted for the treatment of extreme values. Our analysis suggests that such sensitivity may originate in the decision to use income, rather than expenditure, as the underlying welfare measure in the implementation of the FEI method. We conclude that addressing the treatment of outliers is essential for ensuring consistent and reliable poverty measures, but a thorough revision of the method adopted by the Mexican government to measure poverty is essential.

El programa Producción para el Bienestar (PpB) beneficia a 1.8 millones de pequeños y medianos productores agrícolas en México mediante transferencias anuales de entre $6,000 y $24,000 pesos, con el objetivo de fomentar la productividad agrícola y la autosuficiencia alimentaria. Este estudio realiza un análisis exploratorio del impacto del programa en la seguridad alimentaria utilizando datos de la ENIGH 2022 y el método de pareamiento por puntaje de propensión. Aunque no es posible descartar la existencia de sesgos de selección que subestimen los efectos obtenidos, los resultados sugieren que los hogares beneficiarios presentan un mayor gasto en alimentos básicos como leche y frutas frescas. No obstante, se observa una mayor incidencia de experiencias severas de inseguridad alimentaria y de dieta poco variada. Estos hallazgos evidencian que las transferencias no son suficientes para compensar las desventajas estructurales que enfrentan los beneficiarios de PpB. Se recomienda rediseñar el programa para mejorar su impacto en la disponibilidad y diversidad alimentaria en comunidades rurales.

Este libro contribuye al debate nacional sobre la medición del bienestar al proponer una forma más precisa y sensible de comprender la pobreza en un país tan diverso como México. Parte de una premisa fundamental: las líneas de pobreza definidas a escala nacional no reflejan adecuadamente las diferencias en precios, patrones de consumo y condiciones de vida que influyen directamente en el bienestar de las personas. Con ello en mente, la obra revisa críticamente cómo la literatura ha incorporado las diferencias territoriales en la medición de la pobreza —desde el uso de precios diferenciados hasta la construcción de canastas regionales— y analiza, de forma complementaria, los enfoques de regionalización, las metodologías para delimitar regiones y las principales propuestas desarrolladas para el caso mexicano. Este marco teórico permite desarrollar una metodología de medición de la pobreza desde una perspectiva regional, tomando como referencia la metodología oficial utilizada en México. La propuesta se aplica a tres esquemas de regionalización, lo que permite estimar canastas, líneas de pobreza y métricas de incidencia, intensidad y severidad, al tiempo que se evalúa la robustez de los resultados y de la aproximación metodológica. Más que presentar conclusiones definitivas, el libro identifica áreas de mejora en la medición oficial y los desafíos de adoptar un enfoque territorial. Aunque la regionalización ofrece un potencial claro, su implementación requiere evidencia sólida y procesos institucionales transparentes y participativos. Bajo estas condiciones, las estimaciones regionales pueden convertirse en una herramienta sólida para orientar con mayor precisión las políticas públicas.

Background: Household food insecurity (HFI) increased in Latin America by 9% between 2019 and 2020. Scant evidence shows who was unable to recover from the COVID-19 pandemic. Our aim was to use a Machine Learning (ML) approach to identify consistent and influential predictors of persistent moderate or severe HFI over 2 years.Methods: We use a three-wave longitudinal telephone survey with a probabilistic sample representative of the Mexican population. With a response rate of 51.3 and 60.8% for the second and third waves, the final sample size consisted of 1,074 individuals. The primary outcome was persistent HFI, i.e., respondents who reported moderate or severe HFI in 2021 and 2022. Twelve income-related predictors were measured in 2020, including baseline HFI. We employed 6 supervised ML algorithms to cross-validate findings in models, examined its precision with 4 standard performance indicators to assess precision, and used SHAP values (Shapley Additive exPlanations) to identify influential predictors in each model.Results: Prevalence of persistent moderate/severe HFI in 2021 and 2022 was 8.8%. Models with only a HFI 2020 baseline measure were used as a reference for comparisons; they had an accuracy of 0.79, a Cohen’s Kappa of 0.57, a sensitivity of 0.68, and a specificity of 0.88. When HFI was substituted by the suite of socioeconomic indicators, accuracy ranged from 0.70 to 0.84, Cohen’s Kappa from 0.40 to 0.67, sensitivity from 0.86 to 0.90, and specificity from 0.75 to 0.82. The best performing models included baseline HFI and socioeconomic indicators; they had an accuracy between 0.81 and 0.92, a Cohen’s Kappa between 0.61 and 0.85, a sensitivity from 0.74 to 0.95, and a specificity from 0.85 to 0.92. Influential and consistent predictors across the algorithms were baseline HFI, socioeconomic status (SES), adoption of financial coping strategies, and receiving government support.Discussion: Persistent HFI can be a relevant indicator to identify households that are less responsive to food security policies. These households should be prioritized for innovative government support and monitored to assess changes. Forecasting systems of HFI can be improved with longitudinal designs including baseline measures of HFI and socioeconomic predictors.

Approaches to the estimation of shadow prices generally assume that all but one market function correctly. However, multiple market failures are common in developing countries. We present a theoretical model and an empirical strategy to estimate the shadow price of a subsistence good in an economy where labor markets fail. Our results show that: 1) among subsistence producers, the shadow price of this good must be greater than or equal to the market price, and equal to it for surplus growers; and 2) current methods create biases when the otherwise-perfect-markets assumption is violated. The propositions are tested using a representative survey for rural Mexico. We find that the shadow wage is below that of the market (MXN $93.2/day vs. MXN $132.3/day), and that the shadow price for subsistence corn is over ten times greater than its market price (MXN $32.37/kg vs. MXN $3.19/kg). Unbiased shadow price estimates for subsistence goods help to overcome the limitations of current income poverty measures: their overestimation of the purchasing power of subsistence households and their underestimation of the value of subsistence goods. In rural Mexico, current practice underestimates the population in food poverty by 2%; an additional 9% has income above the poverty line yet fail to meet the utilization dimension of food security.

In this study, we explore how to use household expenditures and income surveys (HEIS) to provide replicable and comparable measures of nutrients availability at the population level. Our method formalizes the common practice in the literature and consists of three steps: identification of relevant food categories, pairing of food contents food groups in HEIS data, and calculation of the typical amount of nutrients by food group. We illustrate the usage of the method with Mexican data and provide a publicly available data set to readily convert food purchases into six nutrients: calories, proteins, vitamins A and C, iron, and zinc. We perform a descriptive analysis of the evolution of nutrients intake among Mexican households between 2008 and 2020, considering differences by income level. Our results reflect the effect of the COVID-19 pandemic on nutrient availability in Mexican households, mainly driven by a substantial reduction in the expenditure in food consumed away from home, although for most nutrients the trend was stable over most of the period.

We perform a systematic review of the literature on the association between income, employment, and urban poverty from a multidisciplinary perspective. Our results, derived from the analysis of 243 articles, confirm the significant role of employment in the urban poor’s lives, highlighting several factors that constrain their ability to improve their labour market outcomes: lack of access to public transport, geographical segregation, labour informality, among others. Furthermore, the paper finds different strategies used by the poor to promote their inclusion in their city’s economy. We found a major bias towards research focused on advanced economies, stressing the need for development studies dealing with the specific challenges of developing economies.