Living Standards Analytics

Introduction.- Graphical exploratory methods.- Sample size issues.- Beyond linear regression.- Adjustment for spatial correlation.- The issue of causality.- Non-homogeneity/mixtures.- Bayesian analysis.- Grouping methods.- Panel data issues.- Measures of poverty and inequality.- Bootstrap.- Fuzzy methods for poverty measures.- Combining data sets.

Dominique Haughton (Ph.D. MIT 1983) is Professor of Mathematical Sciences at Bentley University in Waltham, Massachusetts, near Boston, and Affiliated Researcher at the Université Toulouse 1, France.  Her major areas of interest are applied statistics, statistics and marketing, the analysis of living standards surveys, data mining, and model selection. She is the editor-in-chief of Case Studies in Business, Industry and Government Statistics (CSBIGS), and has published over fifty articles in scholarly journals, including The American Statistician, Annals of Statistics, Sankhya, Communications in Statistics, and Statistica Sinica. Dominique is a Fellow of the American Statistical Association.

Jonathan Haughton (Ph.D. Harvard 1983) is Professor of Economics at Suffolk University, and Senior Economist at the Beacon Hill Institute for Public Policy, both in Boston. A specialist in the areas of economic development, international trade, and taxation, and a prize-winning teacher, he has lectured, taught, or conducted research in over a score of countries on five continents. His Handbook on Poverty and Inequality (with Shahidur Khandker) was published by the World Bank in 2009, his articles have appeared in over 30 scholarly journals, and he has written numerous book chapters and over a hundred reports.

Overall, the book is highly accessible and nicely produced. The authors characterise it as 'a gateway book', and I think that, for researchers in policy analysis and household survey work who learnt their trade some time ago, this is an apt description: The book provides an excellent introduction to some of the more recent developments. I shall certainly recommend it to colleagues in the public policy domain...It includes traditional staples such as linear regression and sampling, but also more recent and advanced tools such as the use of directed acyclic graphs in modelling causality, Kohonen networks to group data, Bayesian approaches, propensity score matching, and survival models. It also places considerable emphasis on the power of modern graphical methods - with the consequence that the book has some very attractive colour diagrams, such as bubble plots and cartograms, which certainly demonstrate the power of modern tools.
International Statistical Review, 81, 2, Review by David J. Hand