Using data from several countries, including Cote d'Ivoire, India, Pakistan, Taiwan, and Thailand, this book analyzes household survey data from developing countries and illustrates how such data can be used to cast light on a range of short-term and long-term policy issues.
Have gaps in health outcomes between the poor and better off grown? Are they larger in one country than another? Are health sector subsidies more equally distributed in some countries than others? Are health care payments more progressive in one health care financing system than another? What are catastrophic payments and how can they be measured? How far do health care payments impoverish households? Answering questions such as these requires quantitative analysis. This in turn depends on a clear understanding of how to measure key variables in the analysis, such as health outcomes, health expenditures, need, and living standards. It also requires set quantitative methods for measuring inequality and inequity, progressivity, catastrophic expenditures, poverty impact, and so on. This book provides an overview of the key issues that arise in the measurement of health variables and living standards, outlines and explains essential tools and methods for distributional analysis, and, using worked examples, shows how these tools and methods can be applied in the health sector. The book seeks to provide the reader with both a solid grasp of the principles underpinning distributional analysis, while at the same time offering hands-on guidance on how to move from principles to practice.
This investigation proposes a conceptual framework for measurement necessary for an analysis of household finance and economic development. The authors build on and, where appropriate, modify corporate financial accounts to create balance sheets, income statements, and statements of cash flows for households in developing countries, using an integrated household survey. The authors also illustrate how to apply the accounts to an analysis of household finance that includes productivity of household enterprises, capital structure, liquidity, financing, and portfolio management. The conceptualization of this analysis has important implications for measurement, questionnaire design, the modeling of household decisions, and the analysis of panel data.
A comprehensive, one-stop guide to identifying, reducing, and managing nonresponse in household surveys Nonresponse and its impact on the sample selection mechanism of a survey is a common problem that often arises while collecting survey data. Handbook of Nonresponse in Household Surveys is a complete guide to handling the nonresponse problem, outlining statistical methods and techniques for improving response rates and correcting response data. The authors begin with an introduction to the nonresponse problem along with basic concepts and definitions. Subsequent chapters present current theories and methods that enable survey researchers to skillfully account for nonresponse in their research. Exploring the latest developments in the field, the book also features: An introduction to the R-indicator as an indicator of survey quality Discussion of the different causes of nonresponse Extensive treatment of the selection and use of auxiliary information Best practices for re-approaching nonrespondents An overview of advanced nonresponse correction techniques Coverage of adaptive survey design Throughout the book, the treatment of each topic is presented in a uniform fashion. Following an introduction, each chapter presents the key theories and formulas underlying the topic and then illustrates common applications. Discussion concludes with a summary of the main concepts as well as a glossary of key terms and a set of exercises that allows readers to test their comprehension of the presented material. Examples using real survey data are provided, and a related website features additional data sets, which can be easily analyzed using Stata® or SPSS® software. Handbook of Nonresponse in Household Surveys is an essential reference for survey researchers working in the fields of business, economics, government, and the social sciences who gather, analyze, and draw results from data. It is also a suitable supplement for courses on survey methods at the upper-undergraduate and graduate levels.
Many economic and social surveys are designed as panel studies, which provide important data for describing social changes and testing causal relations between social phenomena. This textbook shows how to manage, describe, and model these kinds of data. It presents models for continuous and categorical dependent variables, focusing either on the level of these variables at different points in time or on their change over time. It covers fixed and random effects models, models for change scores and event history models. All statistical methods are explained in an application-centered style using research examples from scholarly journals, which can be replicated by the reader through data provided on the accompanying website. As all models are compared to each other, it provides valuable assistance with choosing the right model in applied research. The textbook is directed at master and doctoral students as well as applied researchers in the social sciences, psychology, business administration and economics. Readers should be familiar with linear regression and have a good understanding of ordinary least squares estimation.
Development researchers face many challenges in producing robust and persuasive analyses, often within a short time-frame. This edited volume tackles these challenges head-on, using examples from other fields to provide practical guidance to research producers and users.
Two decades after its original publication, The Analysis of Household Surveys is reissued with a new preface by its author, Sir Angus Deaton, recipient of the 2015 Nobel Prize in Economic Sciences. This classic work remains relevant to anyone with a serious interest in using household survey data to shed light on policy issues. The book reviews the analysis of household survey data, including the construction of household surveys, the econometric tools useful for such analysis, and a range of problems in development policy for which this survey analysis can be applied. Chapter 1 describes the features of survey design that need to be understood in order to undertake appropriate analysis. Chapter 2 discusses the general econometric and statistical issues that arise when using survey data for estimation and inference. Chapter 3 covers the use of survey data to measure welfare, poverty, and distribution. Chapter 4 focuses on the use of household budget data to explore patterns of household demand. Chapter 5 discusses price reform, its effects on equity and efficiency, and how to measure them. Chapter 6 addresses the role of household consumption and saving in economic development. The book includes an appendix providing code and programs using STATA, which can serve as a template for users' own analysis.
A comprehensive framework for both reduction of nonresponse andpostsurvey adjustment for nonresponse This book provides guidance and support for survey statisticianswho need to develop models for postsurvey adjustment fornonresponse, and for survey designers and practitioners attemptingto reduce unit nonresponse in household interview surveys. Itpresents the results of an eight-year research program that hasassembled an unprecedented data set on respondents andnonrespondents from several major household surveys in the UnitedStates. Within a comprehensive conceptual framework of influences onnonresponse, the authors investigate every aspect of surveycooperation, from the influences of household characteristics andsocial and environmental factors to the interaction betweeninterviewers and householders and the design of the surveyitself. Nonresponse in Household Interview Surveys: * Provides a theoretical framework for understanding and studyinghousehold survey nonresponse * Empirically explores the individual and combined influences ofseveral factors on nonresponse * Presents chapter introductions, summaries, and discussions onpractical implications to clarify concepts and theories * Supplies extensive references for further study and inquiry Nonresponse in Household Interview Surveys is an important resourcefor professionals and students in survey methodology/researchmethods as well as those who use survey methods or data inbusiness, government, and academia. It addresses issues critical todealing with nonresponse in surveys, reducing nonresponse duringsurvey data collection, and constructing statistical compensationsfor the effects of nonresponse on key survey estimates.
The measurement of food consumption and expenditure is a fundamental component of any analysis of poverty and food security, and hence the importance and timeliness of devoting attention to the topic cannot be overemphasized as the international development community confronts the challenges of monitoring progress in implementing the 2030 Agenda for Sustainable Development. In 2014, the International Household Survey Network published a desk review of the reliability and relevance of survey questions as included in 100 household surveys from low- and middle-income countries. The report was presented in March 2014 at the forty-fifth session of the United Nations Statistical Commission (UNSC), in a seminar organized by the Inter-Agency and Expert Group on Food Security, Agricultural and Rural Statistics (IAEG-AG). The assessment painted a bleak picture in terms of heterogeneity in survey design and overall relevance and reliability of the data being collected. On the positive side, it pointed to many areas in which even marginal changes to survey and questionnaire design could lead to a significant increase in reliability and consequently, great improvements in measurement accuracy. The report, which sparked a lot of interest from development partners and UNSC member countries, prompted IAEG-AG to pursue this area of work with the ultimate objective of developing, validating, and promoting scalable standards for the measurement of food consumption in household surveys. The work started with an expert workshop that took place in Rome in November 2014. Successive versions of the guidelines were drafted and discussed at various IAEG-AG meetings, and in another expert workshop organized in November 2016 in Rome. The guidelines were put together by a joint FAO-World Bank team, with inputs and comments received from representatives of national statistical offices, international organizations, survey practitioners, academics, and experts in different disciplines (statistics, economics, nutrition, food security, and analysis). A list of the main contributors is included in the acknowledgment section. In December 2017 a draft of the guidelines was circulated to 148 National Statistical Offices from low- to high-income countries for comments. The document was revised following that consultation and submitted to UNSC, which endorsed it at its forty-ninth session in March 2018 (under item 3(j) of the agenda, agricultural and rural statistics. The version presented here reflects what was endorsed by the Commission, edited for language. The process received support from the Global Strategy for Agricultural and Rural Statistics. The document is intended to be a reference document for National Statistical Offices, survey practitioners, and national and international agencies designing household surveys that involve the collection of food consumption and expenditure data.
A complete guide to carrying out complex survey analysis using R As survey analysis continues to serve as a core component of sociological research, researchers are increasingly relying upon data gathered from complex surveys to carry out traditional analyses. Complex Surveys is a practical guide to the analysis of this kind of data using R, the freely available and downloadable statistical programming language. As creator of the specific survey package for R, the author provides the ultimate presentation of how to successfully use the software for analyzing data from complex surveys while also utilizing the most current data from health and social sciences studies to demonstrate the application of survey research methods in these fields. The book begins with coverage of basic tools and topics within survey analysis such as simple and stratified sampling, cluster sampling, linear regression, and categorical data regression. Subsequent chapters delve into more technical aspects of complex survey analysis, including post-stratification, two-phase sampling, missing data, and causal inference. Throughout the book, an emphasis is placed on graphics, regression modeling, and two-phase designs. In addition, the author supplies a unique discussion of epidemiological two-phase designs as well as probability-weighting for causal inference. All of the book's examples and figures are generated using R, and a related Web site provides the R code that allows readers to reproduce the presented content. Each chapter concludes with exercises that vary in level of complexity, and detailed appendices outline additional mathematical and computational descriptions to assist readers with comparing results from various software systems. Complex Surveys is an excellent book for courses on sampling and complex surveys at the upper-undergraduate and graduate levels. It is also a practical reference guide for applied statisticians and practitioners in the social and health sciences who use statistics in their everyday work.