This project is funded by the European Union under the 7th Research Framework Programme (theme SSH) Grant agreement nr 290752. The views expressed in this press release do not necessarily reflect the views of the European Commission.
Working Paper n° 50
Poverty and Female Homicide in Mexican
Municipalities: A Bayesian Spatio-Temporal
Analysis
Miguel Flores, Corey S. Sparks
Tecnologico de Monterrey, University of Texas
at San Antonio
Title: Poverty and Female Homicide in Mexican Municipalities: A Bayesian Spatio-Temporal Analysis Authors: Miguel Flores1 and Corey S. Sparks2
1. Escuela de Gobierno y Transformacion Publica, Tecnologico de Monterrey 2. Department of Demography, The University of Texas at San Antonio
Introduction
Mexico, with a 1.964.375 km² area, is not only one of the most territorial extensive countries of Latin America, but also one of the most populated, with approximately 112 million inhabitants according to the 2010 Population Census. The country is also one of the most unequal, as in 2014 approximately 46% of the population lived in poverty conditions (CONEVAL, 2015). In the same way, it is one of the most violent. In recent years, Mexico has experienced a notable increase in criminal activity, which has impacted every sector of society. The upsurge of violence is recognized as a problem of social and public health, a serious social epidemic that manifests itself not only in the number of deaths or mortality rates, but also in the quantity, intensity, and variety of ways in which violence manifested. This phenomenon has gone on to have detrimental effects on individual and collective life, in the deterioration of quality of life and the health conditions of the population.
Gender Based Violence and Poverty
The most condemnable expression of gender inequality is, without a doubt, violence against women. This type of violence is a structural and a historic fact in our country, present in practically all areas of women’s lives, either as daughters, sisters, girlfriends, wives, workers, or retired (Castro and Riquer, 2012). Inequality and discrimination are faced by women in society, politics, and the labor market. Women often face violence of physical, sexual or psychological nature in different spheres of our society. In Mexico, 41.2% of women of 15 years and older, married or with a partner, have suffered some type of violence by their partner, and in the case of divorced or separated women, this percentage reaches 72%. Domestic violence against women is, sadly, an extended fact in our society. This type of violence has raised in an unprecedented way in our country over the last decade (Navarro et al. 2013).
Studies have shown a narrow relationship between poverty and violence. On one side, it has been shown that poverty constitutes a risk factor for the appearance of physical violence at home. On the other side, poverty is a consequence of violence; that is, violence impoverishes and slows down the economic development, since: (a) the attention of social violence and domestic violence consequences causes expenses in police and judicial systems, as well as provision of social services which, in conjunction, compromises resources that could be destined to more productive activities, and (b) in the specific case of the women who suffer domestic violence, they are less productive in their workplace, which is a direct loss to national production (Buvinic and Morrison, 2005). In addition, empirical evidence has been gathered showing that poverty and the factors associated with it, together with certain environmental conditions such as violence caused by drug trafficking in some areas of the country, and sociocultural patterns that reproduce machismo, have created a breeding ground for the rise in violence against women to be intensified in some areas of the country (Aranda, 2014).
Furthermore, the most extreme case of violence against women is homicide. This type of violence is also known as gender-based, committed against women expressly because they are women, and has both sociopolitical as well as a public health implications with damaging effects on the lives of all women, their social environment, and the whole society. (Vives-Cases et al. 2015). Within gender-based types of violence, there are also sub-types identified as partner-family related homicide, and homicide related to other interpersonal conflicts defined by the nature of the relationship between the perpetrator and the victim. Among the later, an infamous term, “Femicide”, is becoming recognized worldwide as the ultimate manifestation of violence against women at all ages. This may take place at
home, in the workplace, in the community, and in their relations with the state; violence that is intrinsically linked to deeply entrenched gender inequality and discrimination, economic disempowerment, and aggressive or machismo masculinity. Femicide represents backlash against women who are empowered, for instance by wage employment, or have moved away from traditional female roles. These are deaths that cause no political stir and no stutter in the rhythm of the region's neo-liberal economy because, overwhelmingly, state authorities fail to investigate them, and the perpetrators go unpunished (Prieto-Carron et al. 2007)1.
The evidence on gender violence and associated factors is relatively large, but not enough in the Mexican context, which has been the focus of attention around the world because of the high rate of homicides in recent years (Blanco and Villa, 2008; Avila et al, 2014; Fernández de Juan, 2014, Rodriguez, 2014). These factors associated with violence are not exclusively related to the socioeconomic status, such as poverty or low education levels, but might include cultural ones as some social and cultural patterns of domination towards the female gender are found deeply rooted in Mexican society and are reproduced that often translates into violence (Delgado et al, 2015; Vizcarra, 2008). In this sense, the patriarchal culture, coupled with factors of drug violence and negative socioeconomic conditions, makes this mixture of phenomena affect females more intensely (Cerva, 2014). Likewise, other contextual factors such as alcoholism and other addictions that have been associated with violence against women (Jaen et al, 2015).
Empirical studies and official statistics that measure gender issues have shown that female gender is a determinant factor of vulnerability, precarious employment, difficulty in accessing the justice system, education, among other welfare indicators (Gonzalez and Galletti, 2015; Vizcarra, 2008). It has also been shown that inequality conditions are still prevalent, both in accessing goods and services and other elements of well-being in which female gender is the most affected, for example, in health coverage, even though women need more of these services when compared to men, the costs associated with these services are highly unequal between the two genders (Moctezuma, Narro and Orozco, 2014: 133).
Although the information on gender violence has a more distant roots, it has been during the past decade where studies associated with this phenomenon have had a considerable increase, mainly because of the femicides in the border areas of northern Mexico (Aranda, 2014). Some acts of violence against women and femicide have been associated with the establishment of maquiladoras, mainly in the US border states given that such establishments primarily concerned female labor for its efficiency and cheap price (Ravelo and Sanchez, 2006; Sanchez, Ravelo and Melgoza, 2015). In this regard, there have been documented cases of violence towards female workers of maquiladoras since many locate in outlying areas of the city involving long trips exposing themselves to violence which in many cases end up in femicides (Aziz, 2008; Vega, 2012).
There are important theoretical reasons to believe that homicides both cluster together in space and diffuse through communities in a contagion-like process. Investigations of spatial or temporal diffusion have generally analyzed total homicide, or focused on specific types of homicide to the exclusion of others. Missing from the literature is an investigation of the spatiotemporal diffusion of homicide and whether these patterns differ in important ways. Homicide is a complex crime, involving multiple interactions between actors, and is often the unintended result of other criminal offenses. These
1For purposes of the present study, one must recognize that no distinction on the type of motivation of female homicides is made. This is because no detailed information on the motivation or the perpetrator is available from official sources in Mexico. Nonetheless, as it is discussed in the next section, counts of homicides by gender at the municipal level allow fulfilling the objectives of the present research.
precipitating interactions characterize the etiology of specific homicide types, and serve as clues to whether particular homicide types are susceptible to processes of contagion (Zeoli et al 2015).
Data
This study uses poverty data produced by the Consejo Nacional de Evaluacion de la Politica de Desarrollo Social (CONEVAL) for each Mexican municipality for the years 1990, 2000, and 2010. The methodology followed by CONEVAL consists of estimating poverty based on income levels, which in turn defines three alternative measures of poverty: food-based poverty, capabilities-based poverty, and assets-based poverty (which are equivalent to extreme poverty, poverty, and moderate poverty). A household is considered food poor if its members’ income falls below the lowest income necessary to afford a minimum basket of food. A household is considered to be in capabilities-based poverty if its members cannot afford to cover their basic expenses of food, health, and education, according to an officially defined basket. Finally, a household is considered to be in assets-based poverty if its members cannot cover their expenses of food, health, education, dressing, home, and public transportation (Rodriguez-Oreggia et al. 2013).
The analysis uses food-based poverty for two main reasons. First, this measurement has been considered as a case of extreme poverty (Wilson and Silva 2013). It highlights the prevalence of extremely poor households across Mexico. Second, when the other two definitions are contrasted they basically show the same spatial distribution across Mexican municipalities. A poverty mapping for each of the periods under analysis are shown in Figure 1. As observed, poverty is characterized by substantial variations across regions, states, municipalities, and rural-urban communities. More importantly, for the purposes of this work, it does not seem to be randomly distributed across the geography. Its distribution is likely to show spatial variations and clustering of high poverty incidence in specific regions of the country and if the case, it leads to statistical significance in the identification of spatial clusters or spatial poverty traps.
A clear pattern is observed; the South exhibits the highest concentration of higher poverty rates. This pattern appears to persist across periods; where Southern states such as Chiapas, Yucatan, Oaxaca, and Guerrero are repeatedly among the poorest. Most of the observed areas with high poverty are concentrated in rural communities. These settlements have long-standing historical inherited processes of economic and social exclusion, lacking of basic services such as drinking water, roads, and health care; and they have suffered from hunger, malnutrition, and preventable disease (Diaz-Cayeros et al. 2016).
The information for homicides comes from the vital statistics of the Instituto Nacional de Estadistica y Geografia (INEGI). These data consider all types of female homicides (ICD-10: X85-Y09) that occurred during the years 1990, 2000, and 2010. Even though we analyzed other databases that are currently available in Mexico, the final set of results is based on female homicide data from the official vital statistics reported by INEGI. This is the only source of information that allows comparability across periods and to a lesser extent, undercounts of homicides deaths. The population at risk, or total population count for each municipality, comes from the corresponding Population Census 1990, 200 and 2010.
Female homicide rates experienced a considerable increase, particularly in 2010, which almost doubled its levels reported in 2000, from 2.57 to 4.21 per 100,000 inhabitants. One must note that during such periods, the country witnessed a dramatic increase in violence levels where the most violent scenarios arose in areas with a high level of trafficking activities and with a long-standing presence of drug-trafficking organizations (DTOs or cartels). However, and as discussed in the result section, the distribution of homicides across municipalities have persistently been higher in some regions of the country, indicates distinctive geographic patterns that may also cast evidence to a persistent formation of geographic clustering.
Hypotheses
Based on the associations between poverty and violence towards women, we hypothesize that in areas of Mexico with higher poverty rates, and in places that are more remotely located (farther from cities), female homicide mortality rates will be higher. We furthermore expect that in areas that have seen large amounts of drug-related violence over recent decades will also shows easily observable geographic clusters of female homicide.
Methods
A Bayesian spatio-temporal modeling strategy is used for all analysis. Model specification
All statistical models follow Bayesian model specifications. In the Bayesian modeling paradigm, all model parameters are considered to be random variables and are given a prior distribution. All inference about these parameters is made from the posterior distribution of the parameters, given the observed data and the information given in the priors. This is generally referred to as Bayes Theorem, and typically stated as:
Where p(θ|y) is the posterior distribution of the model parameter of interest, p(y|θ) is the model likelihood function, here defined as a Poisson likelihood, and p(θ) is the prior distribution for the parameters in the model. Inference for all parameters is done via their posterior distribution, which can be used to derive mean values, quantiles or other descriptive statistics.
All outcomes are analyzed using hierarchical modeling specifications. All predictors are measured at the municipio level. For all models, we follow a similar generalized linear mixed model (GLMM) structure. We assume a Negative binomial likelihood for the distribution of female deaths. The relative risk (θ) is parameterized as a spatio-temporal structured GLMM :
(θ)=log( E)+exp(β
0+ xk'βk+ τ∗Year+ui+vi)
where E is the expected number of female homicides in each muncipio. E is estimated by applying the time-specific national female homicide rate to the yearly female population of each municipio. The ratio y/E is commonly called the standardized mortality ratio. The linear predictor of the GLMM is formed with overall mean β0 , fixed covariate effects (βk) of municipio-level predictors (xk),
municpio level i.i.d. random effect (uj), municipio level spatially structured random effect (vj), and a
fixed time effect measured by τ. The terms, uj and vj, form a random effect specification referred to as the Besag, York and Mollie model (BYM), common in spatial epidemiology (Besag, York, & Mollie, 1991; Lawson, 2013). Prior distributions are specified for the fixed effects (β) and the municpio level random effects (uj and vj). In the BYM model, β0, is given a uniform prior, because of a sum to zero
constraint places upon the municipio level random effects (uj and vj). uj is given a non-informative Gaussian prior, with a high variance, and vj is given a conditionally autoregressive Gaussian prior, with form:
v ∼ N
(
u¯j, τ/n)
, which states that the municipio level random effect is the spatially weighted average of its spatially adjacent municipio neighbors, with some variance to be estimated and given a prior distribution. The variances of uj and vj are specified in terms of their precisions, which is common in Bayesian
modeling, with the precision being the inverse of the variance: τ = 1/σ2, such that low precisions equal high variances. Each precision was given a vague inverse Gamma (.5, .0005) distribution prior. To identify neighbors for each municipio, a Queen contiguity based neighbor rule is used.
The software R (R Development Core Team, 2015)and the R package R-INLA (T G Martins, Simpson, Lindgren, & Rue, 2013; Rue, Martino, & Chopin, 2009) were used to prepare data for analysis and parameter estimation. To estimate the Bayesian models, the Integrated Nested Laplace Approximation, or INLA, approach is used in lieu of Markov Chain Monte Carlo. INLA is a recently developed, computationally simpler method for fitting Bayesian models, compared to traditional Markov Chain Monte Carlo (MCMC) approaches. INLA fits models that are classified as latent Gaussian models, which are applicable in many settings (Martino & Rue, 2010). In general, INLA fits a general form of additive models that are composed of traditional “fixed” regression effects, and combinations of random effects. As this model is often parameterized as a Bayesian one, we are interested in the posterior marginal distributions of all the model parameters. Rue and Martino (2007) show that the posterior marginal for the random effects (x) in such models can be approximated via numerical
integration (Rue et al., 2009; Schrödle & Held, 2011). The posterior distribution of the hyperparameters
(θ) of the model can also be approximated as a Gaussian approximation of the posterior and using the
sampling-based estimate of the posterior, it is arrived at numerically. This method of fitting the spatio-temporal models specified above has been presented by numerous authors (Blangiardo, Cameletti, Baio, & Rue, 2013; Lindgren & Rue, 2015; Thiago G. Martins, Simpson, Lindgren, & Rue, 2013; Schrodle & Held, 2011; Schrödle & Held, 2011), with comparable results to MCMC. To summarize the posterior distributions of the model parameters, posterior means and 95% credible intervals are
calculated.
One of the goals of this analysis is to identify areas where female homicide risk is clustered. To identify clusters of risk Bayesian exceedence probabilities are used (Lawson, 2013). An exceedence probability is:
Pr(θ>θ *)
, where θ* is some critical level of risk that is specified. Here, the exceedence probability of the female homicide mortality rate being higher than 1.25 times the expect value (25% increase in risk) is used. These exceedence probabilities will allow the “significance” of the cluster to be mapped. When the probability is high, then there is a statistically important difference between the risk in the identified area, and that the area represents a spatial cluster of elevated mortality.
Results
Figures 1 – 3 show the time-specific exceedence probabilities for each municipio in Mexico. There are several persistent areas of spatial clustering in the northwest of the country (states of Chihuahua, Durango and Sinaloa), as well as the southwest in states of Oaxaca, Michoacan, Gurrero and Mexico state. Many municipios within these states show high levels of clustering (exceedence probabilities of excess risk greater than .95) and these clusters are stable over time. Associated with the exceedence probabilities, the relative risks are also mapped in Figures 4-6. The municipios in the darkest blue have estimated relative risk between 1.5 and 8 times the national average for female homicide mortality.
Figure 1. Exceedence probabilities for Relative risk > 1.25, 1990
Figure 2. Exceedence probabilities for Relative risk > 1.25, 2000
Figure 4. Posterior fitted values of the Relative risk, 1990
Figure 6. Posterior fitted values of the Relative risk, 2010
Table 1 shows the result of the regression models. These models include terms that test for the overall impact of poverty on the relative risk of female homicide as well as interaction terms between poverty and distance to a city of 100,000 persons or more.
Table 1. Results from spatio-temporal regression model of female homicide risk in Mexican municipios.
The models indicate that, the poverty by distance to city interactions show significant terms, suggesting that in poorer municipios that are also far from urban areas, the risk of homicide is higher for women. Two effects offset the risk, age at marriage and proportion of the population that is married. We also see that there is a large variance term for the spatial random effects and the spatial effects in the model account for 97% of the variance in the random effect terms.
Discussion
In this paper, we review literature on the issue of domestic violence towards women within Mexico. Specifically, we focus on our analysis on how poverty interacts with remotness of communities to increase risk of homicide for women in Mexico over time. We find support for our research hypotheses that significant geographic clustering of female homicide exists in Mexico, and that the spatial patterns are persistent over time. Related to the risk of mortality, poverty shows significant associations,
indicating support for our other hypothesis that poverty and female homicide are related, net of other controls, and that these effects are especially strong in remote areas with high poverty rates.
References
Ávila Burgos, Leticia, Valdez Santiago, Rosario, Barroso Quiab Abigail, Híjar, Martha, Rojas, Rosalba y Aurora del Río Zolezzi (2014), Prevalencia y factores asociados a violencia de pareja en usuarias de servicios públicos de salud en México: un análisis comparativo. RIC, vol. 66, núm. 1, pp. 45-58. Azis Nassif, Alberto (2012), Violencia y destrucción en una periferia urbana. El caso de Ciudad Juárez, México. Gestión y Política Pública, Volumen temático, pp. 227-268.
Besag, J., York, J., & Mollie, A. (1991). Bayesian image restoration, with two applications in spatial statistics. Annals of the Institute of Statistical Mathetmatics, 43, 1–59.
Blanco, Lorenzo and Sandra Villa (2008), Sources of Crime in the State of Veracruz: The Role of Female Labor Force Participation and Wage Inequality. Feminists Economics, vol.14, no. 13, pp. 51-75.
Blangiardo, M., Cameletti, M., Baio, G., & Rue, H. (2013). Spatial and spatio-temporal models with R-INLA. Spatial and Spatio-Temporal Epidemiology. http://doi.org/10.1016/j.sste.2013.07.003
Buvinic, M., Morrison, A., and Orlando, M. (2005). Violencia, crimen y desarrollo social en America Latina y el Caribe. Papeles de Poblacion, 11(43): 167-214.
Cerva Cerna, Daniela (2014), Participación política y violencia de género en México, Nueva Época, año LIX, núm. 222, pp. 117140.
Castro, r. and Riquer, F. (2012). Claroscuros en el conocimiento de la violence contra las mujeres, En Retratos de la Violencia Contra las Mujeres. Analisis de resultados de la Encuesta Nacional sobre la Dinamica de las Relaciones de los Hogares 2011, INEGI-INMUJERES, Cuadernos de Trabajo, 35.
CONEVAL (2015). Medicion de la Pobreza en Mexico y en las entidades Federativas 2014. Consejo Nacional de Evaluacion de la Politica de Desarrollo Social. Accessed:
Delgado Alvarez, Carmen, Estrada Aranda, Benito and José Alfredo López Huerto (2015), Gender and cultural effects on perception of psychological violence in the partner. Psicothema, vol. 27, no. 4, pp. 381-387.
Diaz-Cayeros, A. Estevez, F. and Magaloni, B. (2016). The Political Logic of Poverty Relief: Electoral Strategies and Social Policy in Mexico. Cambridge University Press.
Diem, C., and Pizarro, J. (2010). Social structure and family homicides. Journal of Family Violence, 25: 521-532.
Fernández de Juan, Teresa (2014), La educación sexual y de género vs el maltrato en la pareja.
Escenario sobre la violencia en jóvenes de Baja California. Estudios Fronterizos, nueva época, vol. 15, núm. 30, pp. 73-96.
González, M.G., Galletti, H.G. (2015). Intersecciones entre Violencia de Género, Pobreza y Acceso a la Justicia: El Caso de la Ciudad de La Plata. Oñati Socio-legal Series [online], 5 (2), 520-546. Available from: http://ssrn.com/abstract=2611592
Jaen Cortés, Claudia Ivethe, Rivera Aragón, Sofía y Elga Filipa Amorín de Castro (2015), Violencia de pareja en mujeres: prevalencia y factores asociados. Acta de investigación Psicológica, vol. 5, núm. 3, pp. 2224- 2239.
Lawson, A. B. (2013). Bayesian Disease Mapping: Hierarchical Modeling in Spatial Epidemiology,
Second Edition. Boca Raton: CRC Press.
Lindgren, F., & Rue, H. (2015). Bayesian Spatial Modelling with R-INLA. Journal of Statistical
Software, 63(19), 1–25. Retrieved from <Go to ISI>://WOS:000349847600001
Martino, S., & Rue, H. (2010). Case studies in Bayesian computation using INLA. In P. Mantovan & P. Secchi (Eds.), Complex Data Modeling and Computationally Intensive Statistical Methods (pp. 99– 114). Milan: Springer-Verlag.
Martins, T. G., Simpson, D., Lindgren, F., & Rue, H. (2013). Bayesian computing with INLA: New features. Computational Statistics & Data Analysis, 67, 68–83. http://doi.org/Doi
10.1016/J.Csda.2013.04.014
Martins, T. G., Simpson, D., Lindgren, F., & Rue, H. (2013). Bayesian computing with INLA: New features. Computational Statistics and Data Analysis, 67(2009), 68–83.
http://doi.org/10.1016/j.csda.2013.04.014
Moctezuma Navarro, David, Narro Robles, José y Lourdes Orozco Hernández (2014), La mujer en México: inequidad, pobreza y violencia. Nueva Época, Año LIX, núm. 220, pp. 117-146.
Navarro, D., Narro, J., and Hernandez, L. (2014). La mujer en Mexico: inequidad, pobreza y violencia. Revista Mexicana de Ciencias Politicas y Sociales, 220: 117-146.
Prieto-Carron, Thomson, M., and Macdonald, M. (2007). No More Killings! Women Respond to Feminicides in Central America. Gender and Development, 15(1) 25-40.
R Development Core Team. (2015). R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing. Retrieved from www.r-project.org
Ravelo Blancas, Patricia, Sánchez Díaz, Sergio (2006), Resistencia individual y colectiva ante la violencia de género. La experiencia de las obreras de las maquiladoras de Ciudad Juárez Revista de Estudios de Género. La Ventana, núm. 24, pp. 380-404.
Rodríguez Luna, Ricardo (2014), La política de prevención de violencia de género en México: revisión de las acciones orientadas a los hombres. Alegatos, núm.88, pp. 691-710.
Rodriguez-Oreggia, E., De La Fuente, A. De La Torre, R, and Moreno, H. (2013). Natural Disasters, Human Development and Poverty at the Municipal Level in Mexico. Journal of Development Studies, 49(3): 442-455.
Rue, H., Martino, S., & Chopin, N. (2009). Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations. Journal of the Royal Statistical Society. Series B:
Statistical Methodology, 71, 319–392. http://doi.org/10.1111/j.1467-9868.2008.00700.x
Sánchez Díaz, Sergio G.; Ravelo Blanca, Patricia; Melgoza Valdivia, Javier (2015),Violencia en la ciudad, en el trabajo maquilador y la subjetividad de obreras y obreros en Ciudad Juárez. El Cotidiano, núm. 191, mayo-junio, pp. 87-96.
Schrodle, B., & Held, L. (2011). A primer on disease mapping and ecological regression using INLA.
Computational Statistics, 26(2), 241–258. http://doi.org/Doi 10.1007/S00180-010-0208-2
Schrödle, B., & Held, L. (2011). Spatio-temporal disease mapping using INLA. Environmetrics, 22(6), 725–734. http://doi.org/10.1002/env.1065
Vives-Cases, C., Goicolea, I., Hernandez, A., Sanz-Barbero, A., Constanza, A., Schrottle, M., and Stoeckl, H. (2016). Expert Opinions on Improving Feminicide Data Collection across Europe: A Concept Mapping Study. PLoS ONE 11(2): e0148364. doi:10.1371/journal.
Vega Briones, Germán (2012), Expresiones de violencia de género en la frontera norte de México: el caso de Ciudad Juárez. Norteamérica, vol. 7, núm. 2, pp. 253-284.
Vizcarra Bordi, Ivonne (2008), Entre las Desigualdades de género: un lugar para las mujeres pobres en la seguridad alimentaria y el combate al hambre. Argumentos, Nueva Epoca, Vol. 21, núm. 57, pp. 141-170.
Wilson, Ch., and Silva, G. (2013). Mexico´s Latest Poverty Stats. Mexico Institute, Wilson Center. Available at: https://www.wilsoncenter.org/article/mexicos-latest-poverty-stats
Zeoli, A., Grady, S., Pizarro, J., and Melde, Ch. (2015). Modeling the Movement of Homicide by Type to Inform Public Health Prevention Efforts. American Journal of Public Health, 15(10): 2035-2041.
CONEVAL (2015). Medicion de la Pobreza en Mexico y en las entidades Federativas 2014. Consejo Nacional de Evaluacion de la Politica de Desarrollo Social. Accessed:
Scientific Coordinator : Xavier Oudin (oudin@ird.pdr.fr) Project Manager : Delia Visan (delia.visan@ird.fr)
Find more on www.nopoor.eu Visit us on Facebook, Twitter and LinkedIn
