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Citation Information

Type Report
Title Risk and vulnerability in Guatemala: a quantitative and qualitative assessment
Publication (Day/Month/Year) 2004
URL http://econpapers.repec.org/RePEc:wbk:hdnspu:30154
This study combines quantitative data from the Living Standards Measurement Study and qualitative information from an in-depth qualitative study of poverty and exclusion conducted in 10 villages in Guatemala. Both data sources were designed to capture issues related to vulnerability, risks, and risk management. The quantitative survey included a risks and shocks module, in which households were asked to report if they had experienced a shock during the previous 12 months, using precoded questions for 28 economic, natural, social/political, and life-cycle shocks. These shocks were classified ex ante into covariant and idiosyncratic shocks. Households also reported: (1) whether these shocks triggered a reduction or loss of their income or wealth; (2) the main strategy that they used to cope with their welfare loss; (3) if they had succeeded in reversing the reduction or loss in their welfare by the time of the survey, and (4) the estimated time that had elapsed until successful resolution of the situation. Information on covariant shocks was also collected from the community questionnaire at the survey cluster level. The vulnerability assessment includes several types of analysis of shocks and their impact, including (1) factor analysis to understand the correlation structure or\"bunching\"of shocks; (2) a multivariate logistic model to examine the association between a household's characteristics and location and the probability that it reports a shock or incurs wealth and income losses due to the shock and the probability that it has recovered from the negative impact of the shock by the time of the interview; (3) nonparametric density estimation to estimate the counterfactual density of consumption or income; (4) multiple regression analysis to estimate the cost of shocks; (5) propensity score matching to estimate the cost of shocks; and (6) multiple regression analysis toestimate vulnerability to consumption poverty.

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