An empirical measurement of the option for the poor

This study links both census and religious service data, aggregating them at significant geographical levels. This makes it possible to test (1) if there is empirical evidence of the Catholic Church prioritizing the pastoral service to the poorest population of Mexico, and (2) if the results at diff...

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Bibliographic Details
Main Author: Esparza Ochoa, Juan Carlos
Format: Others
Language:English
Published: 2013
Subjects:
Online Access:http://hdl.handle.net/2152/ETD-UT-2012-12-6641
Description
Summary:This study links both census and religious service data, aggregating them at significant geographical levels. This makes it possible to test (1) if there is empirical evidence of the Catholic Church prioritizing the pastoral service to the poorest population of Mexico, and (2) if the results at different levels of analysis are consistent. To answer these questions, I will introduce the analysis by an overview of the research and the conceptualization of poverty and the way the Catholic Church has faced this social condition, particularly in Latin America and Mexico. Following the overview, the research design is presented specifying research questions, hypotheses, data, and the procedures followed to process and analyze such data. In my analysis I will present the geographical distribution of five dimensions of poverty in Mexico (deprivation of material goods, lack of running water, limited access to health services, illiteracy, and ethnicity) and the main indicator of pastoral services offered by the Catholic Church (number of parishes). Data from different sources will be linked and aggregated at different geographical levels through statistical and GIS platforms. Two main innovative tools to achieve this are the Areas of Direct Pastoral Influence (ADPI) and the Maximum Historically Consistent Geographical Units (MxHCGUs). These resources help to distribute and link socio-demographic and pastoral data. ADPIs facilitate focusing on the detailed relationships whereas MxHCGUs can be re-aggregated to higher-level units of analysis. The analysis includes descriptive geo-statistical tools to identify geographic patterns and test for spatial autocorrelation. Negative binomial regressions test the correlation of poverty and pastoral services at different levels of aggregation of the data. Besides identifying the levels and dimensions of poverty where there is empirical evidence of the priorities of pastoral service, I address the consistency of the different geographical aggregations and explain the differences. I emphasize the analysis of the levels of geographical aggregation directly relevant to the organizational structure of the Catholic Church: the ecclesiastical circumscriptions and the parishes. I will explain in detail the characteristics of both administrative-territorial levels and their importance in order to understand the provision of pastoral care. Although former sociological studies have never considered these levels in the study of poverty, they are the very units of aggregation used by the Catholic Church in pastoral strategies and decisions. Therefore, these are the most pertinent levels of analysis for a study about the priorities of pastoral services. It should be noted that the main limitation of this research is the lack of longitudinal data that would be necessary to test causality. However, this study links these kinds of data for the first time and there is no source of more complete information: the data presented here are actually the basis for the official maps of the Mexican Catholic Church. Therefore this means a major advance in this kind of research. On the one hand, the dataset that I put together sets the basic structure to organize historical censuses and ecclesiastical data; on the other hand, although the results are limited to cross-sectional data, this exploratory step is crucial for my broader research agenda because this study will evaluate basic procedures that will enable the later incorporation and analysis of longitudinal data from more than 120 years. === text