Research Article
Measuring HS in Small, Vulnerable Municipalities: A Quantitative Approach
José Rafael Tovar Cuevas
1, *
,
Juan David Díaz Mutis
1, 2
,
Sandra Balanta Cobo
3
and Luis Miguel Tovar Cuevas
3
|
1 School of Statistics, Faculty of Engineering, University of the Valley, Santiago de Cali, Valle del Cuaca, Colombia |
2 Departmental Secretary of the Health, Santiago de Cali, Colombia |
3 Department of Social and Economic Sciences, Pontifical Javeriana University Santiago de Cali, Valle del Cauca, Colombia |
* Corresponding author |
Abstract:
This article presents a methodological proposal for formulating a Human Security Index (HSI), including information from institutional sources and the inhabitants' perception of security. The developed methodology uses quantitative methods to evaluate HS (Human Security) in small municipalities with large rural areas affected by the confluence of different social and economic problems. Given the security conditions in the area, it was impossible to use a random sampling mechanism. Therefore, the data collected have a sample size that cannot be considered significant enough to make inferences using a frequentist statistics approach. The method to construct the index is illustrated using Miranda's data, a Colombian municipality exposed to decades of armed conflict. With the answers given by 55 interviewees to questions related to the armed conflict such as presence-absence reminders and retained values of violent events, a proposal of 36 indices was made, and two of them were selected for the study, following some statistical criteria. In the construction of one of these selected indices, we used information from binary variables and, for the other index, we used information from count data. The values obtained by both indices for the municipality of Miranda were, respectively, 46.4 and 35.8. According to HS experts, both values can be considered moderate levels in the perception of insecurity by residents of the municipality.
Keywords: Bayes Theorem; Human Security; Index; Latent Variable; Principal Component Analysis