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Human Right to Water and Household Water Conservation in Contaminated Colombian Municipalities: A Machine Learning
Linda Carolina Henao-Rodríguez1, Jenny Paola Lis-Gutiérrez1, Melissa Lis-Gutiérrez2
1Escuela de Negocios, Fundación Universitaria Konrad Lorenz, Bogotá, Colombia.
Abstract:
In Colombia, water is usually physically available, but in some municipalities it is not safe for human consumption, which demonstrates that there is a persistent risk due to poor water quality for public health. In this line, previous research has identified demographic, psychosocial, and infrastructural determinants of water conservation in households, in contexts of scarcity and high contamination, leaving relevant analytical gaps in environments where the risk is determined by water quality and not by restrictions in the supply of the water resource. Only a small number of studies examine cases in which the quantity of water is sufficient in situations where the quality is compromised. In addition, the evidence in these contexts remains limited and scattered. This study examines conservation practices in thirteen Colombian municipalities with IRCA above 80 and focuses on how different factors interact when contamination, and not scarcity, shapes household decisions. The article addresses two main objectives. The first is to identify the socioeconomic, perceptual, and structural factors that predict the adoption of conservation practices across increasing levels of complexity. The second is to estimate the causal effect of perceived contamination on the probability of adopting these practices. The analysis is based on data from 4,246 households from the 2024 Quality of Life Survey (ECV, DANE). The dependent variable is an ordinal variable that captures the degree of complexity (associated with greater technical and financial requirements) of water conservation practices. Methodologically, the study combines cumulative threshold logistic models with L1 regularization to select the relevant variables in the prediction of the dependent variable. In addition, it applies a Double Machine Learning framework with AIPW estimation and cross-fitting to achieve causal identification under flexible control of confounding variables, of the effect of the perception of water contamination on the conservation practices of this resource. The results indicate that socioeconomic and educational capacity drives low-complexity saving and conservation practices, while persistent environmental degradation produces a saturation effect that restricts progress toward the adoption of intermediate-complexity practices. At the highest levels of complexity, the level of schooling and economic resources become decisive, evidencing that high-complexity conservation is less likely in low-income households and in the Afro-descendant population. DML-AIPW estimates show that perceived water contamination causally reduces the probability of conservation at the three levels of the dependent variable, with average treatment effects of -16.3, -8.1, and -5.6 percentage points. The estimates remain robust to alternative treatment definitions and propensity score trimming. The findings support the implementation of targeted equipment subsidies for the lowest socioeconomic stratum, the design of ethnically differentiated interventions, and the strengthening of communication strategies that promote the comprehensive adoption of conservation and raise awareness among the population about the harmful effects of contamination of the water resource.
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