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The middle class in Latin America and the Caribbean has been a central focus of policy debates in the region since the COVID-19 pandemic began. To identify and track vulnerable and middle-class populations accurately, it is necessary to update the upper and lower bounds for the middle class using 2017 purchasing power parity exchange rates. This paper contributes with a two-step methodology for updating these thresholds. The method indicates that updating the USD 13 lower-bound line in 2011 purchasing power parity dollars to 2017 purchasing power parity dollars results in a vulnerability line of USD 14. The study also finds an upper bound of USD 81 per person per day in 2017 purchasing power parity, compared with USD 70 in 2011 purchasing power parity. These thresholds are robust to a variety of assumptions and methodologies. The results of this study indicate that the proportion of the population in Latin America and the Caribbean classified as middle class increased from 36.3 percent in 2011 to 37.2 percent in 2017. However, there were no significant changes in the characteristics of this group.

Multiple imputation (Statistics)

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Blood alcohol --- Drinking and traffic accidents --- Traffic fatalities. --- Multiple imputation (Statistics) --- Estimation theory. --- Mathematical models.

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Forest surveys --- Natural resources surveys --- Multiple imputation (Statistics) --- Missing observations (Statistics) --- Databases --- Management. --- United States.

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This book brings together a collection of articles on statistical methods relating to missing data analysis, including multiple imputation, propensity scores, instrumental variables, and Bayesian inference. Covering new research topics and real-world examples which do not feature in many standard texts. The book is dedicated to Professor Don Rubin (Harvard). Don Rubin has made fundamental contributions to the study of missing data. Key features of the book include: Comprehensive coverage of an imporant area for both research and applications. Adopts a pragmatic approach to describing a wide range of intermediate and advanced statistical techniques. Covers key topics such as multiple imputation, propensity scores, instrumental variables and Bayesian inference. Includes a number of applications from the social and health sciences. Edited and authored by highly respected researchers in the area.

Mathematical statistics --- Bayesian statistical decision theory. --- Missing observations (Statistics) --- Missing observations (Statistics). --- Bayesian statistical decision theory --- Data, Missing (Statistics) --- Missing data (Statistics) --- Missing values (Statistics) --- Observations, Missing (Statistics) --- Values, Missing (Statistics) --- Estimation theory --- Multivariate analysis --- Multiple imputation (Statistics) --- Bayes' solution --- Bayesian analysis --- Statistical decision

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Matched sampling is often used to help assess the causal effect of some exposure or intervention, typically when randomized experiments are not available or cannot be conducted. This book presents a selection of Donald B. Rubin's research articles on matched sampling, from the early 1970s, when the author was one of the major researchers involved in establishing the field, to recent contributions to this now extremely active area. The articles include fundamental theoretical studies that have become classics, important extensions, and real applications that range from breast cancer treatments to tobacco litigation to studies of criminal tendencies. They are organized into seven parts, each with an introduction by the author that provides historical and personal context and discusses the relevance of the work today. A concluding essay offers advice to investigators designing observational studies. The book provides an accessible introduction to the study of matched sampling and will be an indispensable reference for students and researchers.

Mathematical statistics --- Statistical matching --- Sampling (Statistics) --- wiskundige statistiek --- Concatenation, File (Statistics) --- Data fusion (Statistics) --- Data matching (Statistics) --- Data merging (Statistics) --- File concatenation (Statistics) --- Fusion, Data (Statistics) --- Imputation, Mass (Statistics) --- Mass imputation (Statistics) --- Matching, Data (Statistics) --- Matching, Statistical --- Merging, Data (Statistics) --- Microsimulation modeling (Statistics) --- Modeling, Microsimulation (Statistics) --- Random sampling --- Statistics of sampling --- Statistics --- Statistical matching.