Mexican Municipalities Research Articles

Patterns of Spatial Distribution of Migration and Poverty in Mexican Municipalities: A Bayesian Spatial Analysis

Se utilizaron dos modelos gaussianos latentes para medir los efectos de la pobreza sobre la distribución espacial de las tasas de migración municipal durante el período 2015-2020. Para ello, se estimó el saldo migratorio neto a partir de la diferencia entre el número de inmigrantes y emigrantes en áreas geográficas pequeñas, con el objetivo de comprobar la hipótesis de que las personas en situación de pobreza permanecen inmóviles. El saldo migratorio en municipios pobres es significativamente menor al observado en municipios no pobres. Los resultados muestran que un aumento de un punto porcentual en la pobreza municipal incrementaría 1.3 puntos la tasa de migración municipal, sugiriendo que municipios de zonas metropolitanas como la Ciudad de México, Monterrey o Guadalajara, cuyo acceso al empleo y/o a la vivienda es mayor, tienen tasas de inmigración más altas que el promedio nacional.

Impact of an enhanced sobriety checkpoints programme and publicity campaign on motor vehicle collisions, injuries and deaths in Leon, MX: a synthetic control study

ObjectiveDrunk driving is a major cause of road traffic injuries and deaths in Latin America. We evaluated the impact of a drunk driving intervention in Leon, Mexico on road traffic...

Beyond the immediate effects of income inequality on homicide rates: A reply to Daly's critique

This study responds to Martin Daly's critique of our 2022 study on the correlation between income inequality and homicide rates in Mexican municipalities. Our updated analysis incorporates both immediate and lagged effects of income inequality, revealing significant non-linear relationships between past inequality and current homicide rates. We find that higher levels of past inequality interact with present inequality to increase homicide rates, particularly among currently average and highly unequal municipalities –not so much in less unequal municipalities. These findings support Daly's argument that economic inequality's influence on violent behavior accumulates over time, highlighting the need for a time dimension in homicide rate models. Our study emphasizes the importance of considering historical economic conditions when addressing socio-economic determinants of homicide, aiming to contribute constructively to ongoing attempts to effectively reduce homicidal violence.

Fuelling Organised Crime: the Mexican War on Drugs and Oil Theft

Abstract We show that the Mexican war on drugs pushed drug cartels into large-scale oil theft. We propose a simple model in which government crackdowns on one criminal sector induce criminal organisations to invest in a new sector. When entering the new sector, challenger organisations with a residual share of the market in the traditional sector may leapfrog incumbent organisations. We bring the model to the data using detailed information on drug cartel presence, oil pipelines and illegal oil taps across Mexican municipalities. In line with the model predictions, municipalities with oil pipelines witnessed a greater increase in cartel presence than municipalities without pipelines after the crackdown on drugs, and the effect is driven by challenger criminal groups. Within the subset of municipalities with oil pipelines, we observe more illegal oil taps where the political party in favour of anti-drug trafficking policy won local elections by a small margin. Because of specialisation in different criminal sectors, municipalities with pipelines did not witness a surge in violence, but they did experience a decline in socioeconomic conditions.

The giving tree: impacts of deforestation on birth outcomes in Mexico

ABSTRACT Deforestation adversely impacts human health through mechanisms such as increased air pollution, shifts in disease vector distributions, and changes in local climate. Although the influence of deforestation on human health is recognized, its effects on infant health outcomes have been insufficiently studied. This paper examines the effects of deforestation on infant health by analysing birth certificate data from 2011 to 2021 across Mexican municipalities, combined with satellite-derived annual deforestation rates. We show that increased deforestation leads to decreased birth weight, higher rates of low birth weight (LBW), and reduced Apgar scores. Specifically, an increase of one standard deviation in deforestation results in a reduction of average birth weight by 19 grams, an increase in the probability of LBW by 0.43% points, and an increase in the probability of a low Apgar score by 4.7% points. These effects are consistently observed even after controlling for different definitions of forest cover, excluding data during the COVID-19 pandemic, and limiting the analysis to municipalities with at least 2% forest coverage. This study provides critical evidence that deforestation directly impairs newborn health in Mexico, highlighting a significant and underappreciated environmental cost with long-term implications for human capital.

Exports and Income Inequality in Mexico

What is the effect of exports on local income inequality in developing countries? To respond to this question, we combine data on exports with a panel of welfare indicators for 2000 Mexican municipalities, and implement an instrumental variable approach to address endogeneity concerns. Our results show that a 10 per cent increase in the exports per worker reduces local income inequality, measured by the Gini coefficient, by 0.17 points (using a 0 to 100 scale). This is driven by income growth among households at the bottom of the income distribution. We also find that although exports do raise the total amount of labour incomes at the municipal level, average labour incomes do not change. This occurs because municipalities with growing exports also experience an increase in employment and working-age population driven by inflows of returning migrants and lower emigration outflows. Remittances also decline in response to rising exports.

A high density of ultra-processed food, alcohol & tobacco retail stores, and social inequalities are associated with higher mortality rates of non-communicable diseases in Mexican adults: 2005 to 2021.

Non-communicable diseases (NCDs) are the leading causes of mortality in Mexico. Factors contributing to NCDs-related deaths may vary across small geographic areas such as municipalities. We aimed to predict municipal-level factors associated with NCD mortality in Mexican adults from 2005 to 2021 using the small-area analysis (SSA) approach. We gathered data on population sociodemographic, access to healthcare services, and mortality records at the municipal-level from census and public institutions from 2005 to 2021. We identified municipal predictors of NCDs mortality rates (MR) using negative binomial regression models. A total of 584,052 observations of Mexican adults were analyzed. The national expected NCDs MR per 100,000 inhabitants was 210.7 (95%CI: 196.1-226.7) in 2005 and increased to 322.4 (95%CI: 300.3-346.4) by 2021. Predictors of NCDs mortality (quintile 5 vs. quintile 1) included; indigeneity (IRR = 1.15, 95%CI: 1.12-1.19), poverty (IRR = 1.14, 95%CI: 1.13-1.15), affiliation with Mexican Social Security Institute (IRR = 1.11, 95%CI: 1.09-1.14), households with television (IRR = 1.14, 95%CI: 1.11-1.17), and high density of ultra-processed food, alcohol & tobacco retail stores (IRR = 1.15, 95%CI: 1.13-1.17). The greatest increases in MR were observed in municipalities from Oaxaca (>200% increments). There was an overall increase in NCDs MR from 2005 to 2021, with a significant geographic variation among Mexican municipalities. The results of this study highlight the importance of identifying priority areas in the country that urgently require public policies focused on local factors associated with deaths from NCDs, such as the regulation of the ultra-processed food, alcohol & tobacco retail stores, and efforts to reduce social inequalities.

Shifts in property crime patterns during the COVID-19 pandemic in Mexico

Abstract Objectives To estimate the dynamic effects of the COVID-19 pandemic on property crime in Mexico. We do so for a longer time horizon than the existing literature and for two types of property crime: on-line and on-site. Methods A retrospective ecological analysis during the COVID-19 pandemic using an event study methodology. This paper uses administrative data from Mexico’s National Public Security System from January 2017 to December 2022. Our data is structured as a monthly series covering all 2457 Mexican municipalities. Results We find heterogeneity in the pandemic’s impact by type of property crime and a dynamic, permanent effect for both types of property crime. Namely, our results point to a drop and quick recovery of fraud—property crime mostly committed on-line—, which in the medium run surpasses pre-pandemic levels by 44%. Further, our results point to a significant drop in robbery, theft, domestic burglary, and motor vehicle theft—all property crimes committed on-site—, with a slight recovery after the end of the national lockdown, even though rates for these crimes never reached pre-pandemic levels. Conclusions The 2020 pandemic caused a permanent shift in property crime from on-site to on-line spaces. Potential mechanisms point to disruptions in mobility in retail and recreational areas, residential zones, and workplaces as important mediating factors.

A multi-glimpse deep learning architecture to estimate socioeconomic census metrics in the context of extreme scope variance

Convolutional Neural Networks (CNNs) are leveraged for a wide range of satellite imagery information extraction tasks. However, for tasks which seek to estimate aggregated information across highly variable geographic extents, existing techniques are subject to critical limitations. We engage with a specific case study exploring this challenge: estimating census variables across 2358 Mexican municipalities, which range in scope from 2.21 km2 (˜74,000 30 m pixels) to 72,417.9 km2 (millions of pixels). Building on recent literature which has illustrated the capability of deep learning to extract socioeconomic information from satellite imagery, we specifically seek to establish baseline metrics of error that might be expected when estimating a range of census variables based on coarse-resolution (Landsat) satellite imagery alone. For each of 52 variables, we implement a multi-glimpse recurrent attention model, in which we parametrically determine subsets of each municipality to sample across iterative steps. Results of a five-fold validation indicate that nearly half of the tested variables (22) can be estimated with r2 values greater than 0.75. Results suggest considerable promise for the use of satellite imagery to estimate socioeconomic factors in both historic time periods for which surveys were not conducted, as well as contemporary inaccessible regions.

Social determinants of hypertensive disorders of pregnancy trends in Mexican municipalities

ObjectivesTo analyze the hypertensive disorders of pregnancy (HDP) trends in the 2000–2020 period and its association with social determinants of health (SDH) in Mexican municipalities. Study designThis was a retrospective longitudinal study analyzing data from routine information systems on 21,493,076 hospital discharges due to maternal causes and SDH data of 2,439–2,450 municipalities between 2000 and 2020. Main outcomes measuresWe used joinpoint regression models to analyze HDP trends and hybrid negative binomial regression models to examine the association between SDH and HDP rates at intra- and inter-municipality levels. ResultsHDP rate increased from 0.54 in 2000 to 2.42 cases per 100 live births in 2020 (annual average percent change of 7.7 %, 95 % CI 6.6–8.9). Municipalities with higher marginalization index had higher HDP rates (IRR 1.12, 95 % CI 1.01–1.24; 1.36, 95 % CI 1.25–1.47; 1.30, 95 % CI 1.23–1.37, and 1.06, 95 % CI 1.00–1.12, in 2000–2004, 2005–2009, 2010–2014, 2015–2020, respectively). Municipal increases in the percentage of population with high schooling were associated with decreases in HDP rates in 2000–2009. Meanwhile, the role of socioeconomic status at the state level was less consistent, yet the HDP rates were lower in most socially advantaged regions. ConclusionDuring 21 years, HDP rates increased overall in Mexican municipalities, suggesting an increased maternal morbidity burden due to HDP. The association of municipal marginalization and socioeconomic level with HDP trends indicates a need to ensure equitable interventions to improve maternal health in Mexican municipalities.

Cardiovascular Diseases Mortality in Mexican Municipalities: A Spatio-Temporal Approach

Cardiovascular Diseases Mortality in Mexican Municipalities: A Spatio-Temporal Approach

Socioeconomic structure of yellow corn producers in two Mexican municipalities as reference sources

Yellow corn has gained strategic importance for the current government of Mexico due to the enormous amount of thisgrain that is imported, which in turn represents a large flight of capital due to the costs paid abroad to purchase it. Underthese premises, work is being done to obtain a characterization of the different productive units of yellow corn in theirsocioeconomic structure, which allows the generation of an intervention protocol at the three levels of government, in thosemunicipalities where it is possible to plant yellow corn, with native seeds and agroecological fertilizers. The purpose is topromote the development of rural culture, in every sense, and at the same time represents greater income for producers dueto the monetary resource that will no longer go abroad and will remain in the production units. To achieve these objectives,a municipality that has planted yellow corn for several years is taken as a reference point: Ocuituco, Morelos. And anothermunicipality that planted corn in large areas and stopped doing so: Calpulalpan, Tlaxcala.

Drivers of urban biodiversity in Mexico and joint risks from future urban expansion, climate change, and urban heat island effect.

Urbanization is a phenomenon where humans concentrate in high densities and consume more per capita energy than in rural areas, imposing high pressures on biodiversity and ecosystem services. Although Mexico is recognized as a megadiverse country and there is an understanding of ecological and evolutionary processes underlying this high diversity, only some efforts have been devoted to understanding how urban biodiversity has been shaped. Here, we compiled a set of socioeconomic and ecological variables to explore macroecological patterns in urban biodiversity across Mexican municipalities. Specifically, we tested the species-area relationships (SAR) between rural and urban areas across municipalities and evaluated the relative role of different socioeconomic and ecological variables driving urban species richness for terrestrial vertebrates. Finally, we explored the exposure of Mexican municipalities to future urban expansion, the urban heat island (UHI) effect, and climate change. Urban and rural settlements show differences in the shape of SAR models. We found that urban area, size of the network of urban protected areas, the number of ecoregions, and GDP explained the urban total species richness relatively well. Mexican cities in the northeast region may be at a higher risk than others. Based on our analyses, policymakers should identify priority urban conservation sites in cities with high species richness and low urbanization development. These actions would alleviate future urban biodiversity loss in these growing cities.

Beyond direct exposure to violence: effects of living in disordered and violent communities on psychological distress in young Mexican people.

The association between community violence and mental health has been studied by reports of individual experiences, particularly in adolescents and youths, but little is known about the effect of living in disordered and violent communities. This study aims to determine the possible relation between living in disordered and violent community environments and psychological distress in Mexican adolescents and youths regardless of their individual experience of victimization and to assess the potential modifying effect of sex and age on this association. Data come from a cross-sectional survey with a representative sample of adolescents and youths living in Mexican municipalities, including 39,639 participants aged from 12 to 29 years. Disordered and violent community environments were assessed using reports from a secondary sample of adults who lived in the same communities as participants. Using exploratory factor analysis, three contextual variables related to disordered and violent community environment were created: social disorder, vandalism, and criminality. Multilevel linear regression models with random intercept were estimated. Adolescents and youths who lived in environments with higher social disorder had more psychological distress. Men in environments with greater vandalism had a higher level of psychological distress. Unexpectedly, women from communities with higher levels of crime had fewer symptoms. It is necessary to address the violence that exists in these communities, creating strategies that reduce not only crime, but also the social disorder and vandalism that could contribute to developing negative effects on mental health.

Analysis of the swine movement network in Mexico: A perspective for disease prevention and control.

Pig farming in Mexico is critical to the economy and food supply. Mexico has achieved advancements in swine health and established an electronic database that records swine movements (Sistema Nacional de Avisos de Movilización, SNAM). In this study, we characterized swine movement patterns in México between 2017 and 2019 to identify specific areas and periods that require concentrated efforts for effective epidemiological surveillance and disease control. We employed a Social Network Analysis (SNA) methodology to comprehensively describe and analyze the intricate patterns of pig movement. In addition, we sought to integrate swine population density into the analysis. We used metrics to characterize the network structure and identify the most critical nodes in the movement network. Cohesion metrics were used to identify commercial communities characterized by a high level of interconnectivity in swine movements between groups of nodes. Of a cumulative count of 662,255 pig shipments, 95.9% were attributed to slaughterhouse shipments. We observed that 54% of all Mexican municipalities were part of the network; however, the density of the movement network was less than 0.14%. We identified four Swine Production Centers in Mexico with high interconnectivity in the movement network. We detected moderate positive correlations (ρ ≥0.4 and <0.6, p < 0.001) between node metrics and swine population indicators, whereas the number of commercial swine facilities showed weak correlations with the node metrics. We identified six large, geographically clustered commercial communities that aligned with the Swine Production Centers. This study provides a comprehensive overview of swine movement patterns in Mexico and their close association with swine production centers, which play a dual role as producers and traders within the swine industry of Mexico. Our research offers valuable insights for policymakers in developing disease prevention and control strategies.

Proximal determinants of suboptimal early child development during the first three years of life in socially deprived Mexican contexts.

Assessing the status and determinants of early child development (ECD) requires accurate and regularly updated measurements. Yet, little information has been published on the subject in low- and middle-income countries, particularly regarding the proximal determinants of childhood development in contexts of high social marginalization. This article analyzes the factors that favor or mitigate suboptimal ECD outcomes in Mexico. A cross-sectional study was conducted using recently collected data for 918 children aged 0-38 months from socially marginalized communities in 23 Mexican municipalities. The ECD outcomes of the children were estimated based on indicators of chronic undernutrition and neurodevelopment (normal, lagging and at risk of delay). The distribution of outcomes was described across the ECD proximal determinants analyzed, including the co-occurrence of chronic undernutrition and suboptimal neurodevelopment. Covariate-adjusted prevalence of the ECD outcomes and co-occurrences were calculated as post-estimations from a multiple multinomial logistic regression. The prevalence of chronic undernutrition was 23.5%; 45.9% of children were classified with neurodevelopmental lag, and 11% at risk of neurodevelopmental delay. The prevalence of stunting co-occurring with suboptimal neurodevelopment came to 15.4%. The results of the multinomial logistic regression model indicated that early gestational age, low birth weight, a low household socioeconomic level, being male and having numerous siblings were all associated with the co-occurrence of chronic undernutrition and suboptimal child neurodevelopment. This study identified important predictors of child development in the first three years of life, specifically in two of its principal indicators: nutritional and neurodevelopmental status. Most of the predictors observed can be improved by means of social programs and interventions. Trial registration: ClinicalTrials.gov ID: NCT04210362.

How clientelism undermines state capacity: Evidence from Mexican municipalities

Does clientelism perpetuate the weak state capacity that characterizes many young democracies? Prior work explains that clientelistic parties skew public spending to private goods and under-supply public goods. Building on these insights, this article argues that clientelism creates a bureaucratic trap. Governments that rely on clientelism invest in labor-intensive, low-skilled bureaucracies that can design and implement relatively more straightforward distributive policies. Although such bureaucracies are useful to win some elections, they lack the administrative capacity to sustain economic and human development. Empirically, the article examines the wage structure of municipal bureaucracies as a proxy for the personnel’s education and skills in Mexico between 2012 and 2018. During this period, turnover in the party in power in municipalities was frequent, a situation that also allows investigating how resilient the bureaucratic trap is to increased competition. The results show that all political parties invest in labor-intensive, low-skilled bureaucracies. However, the bureaucratic trap has a different grip on the Institutional Revolutionary Party (PRI), a quintessential clientelistic party, compared to other parties. After an electoral turnover, PRI’s bureaucracies have a larger proportion of low-wage personnel compared to the bureaucracies of other parties. Moreover, after an electoral turnover, the PRI allocates more resources to social assistance, subsidies, and internal transfers that are more conducive to clientelism. The overall size of the bureaucracy and the total wage bill are not affected in the same way, suggesting that there is indeed a trade-off between hiring lower and higher skilled employees. While prior work has proposed other clientelism-induced negative equilibria, this article offers a more direct path from clientelism to state capacity. The results help explain why more fiscal resources, political competition, and demand-side strategies to fight vote buying are insufficient and underscore the importance of civil service reform to tame clientelism.

The geographic visualization of financial inclusion and exclusion in Mexican municipalities

In this paper, we present a gridded population cartogram illustrating a recently constructed financial inclusion index for Mexico 2020 (Dircio-Palacios-Macedo et al., 2023). Its importance relies in that it allows us to identify the municipalities with different levels of financial inclusion in a geographical disaggregated manner, which can be used for the ultimate goal of relevant policy analysis.

Direct and indirect effects of age on dengue severity: The mediating role of secondary infection.

Severe dengue occurrence has been attributed to increasing age and different dengue virus (DENV) serotypes that cause secondary infections and immune-enhancing phenomena. Therefore, we examined if the effect of age on dengue severity was mediated by infectivity status while controlling for sex and region. Further, we assessed the spatial clustering of dengue severity for individuals with primary and secondary infection across Mexican municipalities. Health data from 2012 to 2017 was retrieved from Mexico's Ministry of Health. A mediation analysis was performed using multiple logistic regression models based on a directed acyclic graph. The models were explored for the direct effect of age on dengue severity and its indirect impact through secondary infection. In addition, severe dengue clusters were determined in some Northeastern and Southeastern municipalities through spatial analysis. We observed a nonlinear trend between age and severe dengue. There was a downward trend of severe dengue for individuals between 0 and 10 years and an upward trend above 10 years. The effect of age on dengue severity was no longer significant for individuals between 10 and 60 years after introducing infectivity status into the model. The mediating role of infectivity status in the causal model was 17%. Clustering of severe dengue among individuals with primary infection in the Northeastern region may point to the high prevalence of DENV-3 in the region. Public health efforts may prevent secondary infection among infants and the aged. In addition, there should be a further investigation into the effect of DENV-3 in individuals with primary disease.

The potential impact of wildfire smoke on COVID-19 cumulative deaths in the San Diego-Tijuana border region

2020 broke records for the most active fire year on the West Coast, resulting in the worst air quality observed in decades. Concurrently, the public health threat of COVID-19 caused over 1 million deaths in the United States (US) and Mexico in 2020 and 2021. Due to the effect of air pollution on respiratory diseases, wildfire-specific particulate matter is a hypothesized driver of COVID-19 severity and death. Capitalizing on wildfire smoke that hit the San Diego-Tijuana border region in September 2020, we applied synthetic control methods to explore its potential differential role in affecting COVID-19 mortality on both sides of the border. Daily data on COVID-19 cumulative deaths for US counties were obtained from the CDC COVID tracker and data for Mexican municipalities was obtained from the Mexican Secretary of Health. Counties and municipalities with wildfire smoke exposure were identified using the National Oceanic and Atmospheric Administration hazard mapping smoke product (HMS); a day where 90% of the area was covered by smoke was considered exposed for the main analyses. Unexposed counties/municipalities were considered as potential controls. The San Diego-Tijuana border region was covered by dense smoke by the 7th of September; 707 COVID-19 deaths had occurred in San Diego and 1367 in Tijuana. While a slight increase in cumulative mortality was observed in San Diego, no change was found in Tijuana; neither estimate indicated a strong precise effect of wildfire smoke on COVID-19 mortality. We hope this study will serve as an illustration of how border contexts can be used to investigate differential vulnerability to wildfire smoke for infectious diseases. Examining the interactive effect of COVID-19 and smoke can help in recognizing the implications of these dual health risks which will be increasingly important as wildfires become more frequent and severe in the context of climate change.