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The Analysis of Household Surveys: A Microeconometric Approach to Development Policy |
INTRODUCTION
[Table of Contents] [Purpose] [Policy and data] [Structure and Outline]
The collection of household survey data in developing countries is hardly a new phenomenon. The National Sample Survey Organization in India has been collecting such data on a regular basis since the 1940s, and there are many other countries with long-running and well-established surveys. Until recently, however, the handling and processing of large microeconomic data sets was both cumbersome and expensive, so that survey data were not widely used beyond the production of the original survey reports. In the last ten or fifteen years, the availability of cheap and convenient microcomputers has changed both the collection and analysis of household survey data. Calculations that could be done only on multimillion-dollar mainframes in 1980—and then with some difficulty—are now routinely carried out on cheap laptop computers. These same machines can be carried into the field and used to record and edit data as they are provided by the respondents. As a result, survey data are becoming available in a more timely fashion, months rather than years after the end of the survey; freshly collected data are much more useful for policy exercises than are those that are many years old. At the same time, analysts have become more interested in exploring ways in which survey data can be used to inform and to improve the policy process. Such explorations run from the tabulations and graphical presentation of levels of living to more basic research on household behavior.
This book is about the analysis of household survey data from developing countries and about how such data can be used to cast light on a range of policy issues. Much of the analysis works with household budget data, collected from income and expenditure surveys, though I shall occasionally address topics that require wider information. I shall use data from several different economies to illustrate the analysis, drawing examples of policy issues from economies as diverse as Côte d’Ivoire, India, Pakistan, South Africa, Taiwan (China), and Thailand. I shall be concerned with methodology as well as substance, and one of the aims of the book is to bring together the relevant statistical and econometric methods that are useful for building the bridge between data and policy. The book is not intended as a manual for the analysis of survey data—it is hardly possible to reduce policy research to a formula—but it does provide a number of illustrations of what can be done, with fairly detailed explanations of how to do it. Nor can a "how-to" book provide a comprehensive review of all the development topics that have been addressed with household survey data; that purpose has already been largely met by the microeconomic survey papers in the three volumes of the Handbook of Development Economics. Instead, I have focused on topics on which I have worked myself, in the hope that the lack of coverage will be compensated for by the detailed knowledge that can only come from having carried out the empirical research. The restriction to my own work also enables me to provide the relevant computer code for almost all of the empirical results and graphics, something that could hardly be combined with the broad coverage of a genuine survey. The Appendix gives code and programs using stata; in my experience, this is the most convenient package for working with data from household surveys. The programs are not a package; users will have to substitute their own data sets and will need sufficient basic knowledge of stata to adapt the code. Nevertheless, the programs provide a template for generating results similar to those presented and discussed here. I have also tried to keep the programs simple, sometimes at the expense of efficiency or elegance, so that it should not be too difficult to translate the logic into other packages.
I hope that the material will be of interest to development practitioners, in the World Bank and elsewhere, as well as to a more academic audience of students of economic development. The material in the first two chapters is also designed to help readers interpret applied econometric work based on survey data. But the audience that I most want to reach is that of researchers in developing countries. Statistical offices, research institutes, and universities in developing countries are now much less constrained by computation than they were only a few years ago, and the calculations described here can be done on personal computers using readily available and relatively inexpensive software. I have also tried to keep the technical presentation at a relatively modest level. I take for granted most of what would be familiar from a basic course in econometrics, but I devote a good deal of space to expositions of useful techniques—such as nonparametric density and regression estimation, or the bootstrap—that are neither widely taught in elementary econometrics courses nor described in standard texts. Nevertheless, there are points where there is an inevitable conflict between simplicity, on the one hand, and clarity and precision, on the other. When necessary I have "starred" those sections or subsections in which the content is either necessarily technical or is of interest only to those who wish to try to replicate the analysis. Occasional "technical notes," usually starred, are shorter digressions that can readily be skipped at a first reading.
Household surveys provide a rich source of data on economic behavior and its links to policy. They provide information at the level of the individual household about many variables that are either set or influenced by policy, such as prices, transfers, or the provision of schools and clinics. They also collect data on outcomes that we care about and that are affected by the policy variables, such as levels of nutrition, expenditure patterns, educational attainments, earnings, and health. Many important research questions concern the link between the instruments of policy and the outcome variables: the rate of return to government-provided schooling, the effectiveness of various types of clinics, the equity and efficiency effects of transfers and taxes, and the nutritional benefits of food subsidies. Because household surveys document these links, they are the obvious data bases for this sort of policy research, for evaluating the welfare benefits of public programs. Of course, associations in the data establish neither causality nor the magnitude of the effects. The data from household surveys do not come from controlled experiments in which the effects of a "treatment" can be unambiguously and convincingly determined.
In recent years, there has been a great deal of interest in social experiments, including the use of household survey data to evaluate the results of social experiments. Nevertheless, experiments are not always possible, and real experiments usually deviate from the ideal in ways that present their own difficulties of interpretation. In some cases, good luck, inspiration, and hard work throw up circumstances or data that allow a clear evaluation of policy effects in the absence of controlled experiments; these "quasi" or "natural" experiments have been the source of important findings as well as of some controversy. Even without such solutions, it seems as if it ought to be possible to use standard survey data to say something about the policy effects in which we are interested. A good starting point is to recognize that this will not always be the case. Many policy questions are not readily answerable at all, often because they are not well or sharply enough posed, and even when an answer is available in principle, there is no reason to suppose that it can be inferred from the data that happen to be at hand. Only when this is appreciated is there much chance of progress, or even of a realistic evaluation of what can be accomplished by empirical analysis.
Much of the empirical microeconomic literature in development uses econometric and statistical methodology to overcome the nonexperimental nature of data. A typical study would begin with a structural model of the process at hand, for example, of the effects on individual health of opening a new clinic. Integral to the model are statistical assumptions that bridge the gap between theory and data and so permit both the estimation of the parameters of the model and the subsequent interpretation of the data in terms of the theory. I have no difficulty with this approach in principle, but often find it hard to defend in practice. The statistical and economic assumptions are often arbitrary and frequently implausible. The econometric technique can be complex, so that transparency and easy replicability are lost. It becomes difficult to tell whether the results are genuine features of the data or are consequences of the supporting assumptions. In spite of these problems, I shall spend a good deal of space in Chapter 2 discussing the variety of econometric technique that is available for dealing with nonexperimental data. An understanding of these matters is necessary in order to interpret the literature, and it is important to know the circumstances in which technical fixes are useful.
Most of the analysis in this book follows a different approach which recognizes that structural modeling is unlikely to give convincing and clean answers to the policy questions with which we are concerned. Rather than starting with the theory, I more often begin with the data and then try to find elementary procedures for describing them in a way that illuminates some aspect of theory or policy. Rather than use the theory to summarize the data through a set of structural parameters, it is sometimes more useful to present features of the data, often through simple descriptive statistics, or through graphical presentations of densities or regression functions, and then to think about whether these features tell us anything useful about the process whereby they were generated. There is no simple prescription for this kind of work. It requires a good deal of thought to try to tease out implications from the theory that can be readily checked against the data. It also requires creative data presentation and processing, so as to create useful and interesting stylized facts. But in the end, I believe that we make more progress, not by pretending to estimate structural parameters, but by asking whether our theories and their policy implications are consistent with well-chosen stylized facts. Such facts also provide convenient summaries of the data that serve as a background to discussions of policy. I hope that the examples in this book will make the case that such an approach can be useful, even if its aims are relatively modest.
Household budget surveys collect information on who buys what goods and services and how much they spend on them. Information on how poor people spend their money has been used to describe poverty and to build the case for social reform since the end of the eighteenth century, and household surveys remain the basis for documenting poverty in developing countries today. When surveys are carried out on a regular basis, they can be used to monitor the welfare of various groups in society and to keep track of who benefits and who loses from development. Large-scale national surveys allow a good deal of disaggregation and allow us to look beyond means to other features of distributions, distinguishing households by occupational, regional, sectoral, and income groups.
In most poor countries, a large fraction of government revenue is raised by indirect taxes on goods and services, and many countries subsidize the prices of commodities such as basic foodstuffs. Household expenditure surveys, by revealing who buys each good and how much they spend, tell us who pays taxes and who benefits from subsidies. They thus yield a reckoning of the gainers and losers from a proposed changes in taxes and subsidies. When data are collected on the use of services provided by the state, such as health and education, we also discover who benefits from government expenditures, so that survey data can be used to assess policy reform and the effectiveness of government taxation and expenditure.
Data from household surveys are also a base for research, for testing theories about household behavior, and for discovering how people respond to changes in the economic environment in which they live. Some recent surveys, particularly the World Bank’s Living Standards Measurement Surveys, have attempted to collect data on a wide range of household characteristics and activities, from fertility and physical measurement of weights and heights to all types of economic transactions. Such data allow us to examine all the activities of the household and to trace the behavioral links between economic events and individual welfare.
This book follows the progression of the previous three paragraphs, from data description through to behavioral analysis. Chapters 1 and 2 are preliminary to the main purpose and are concerned with the collection of household survey data, with survey design, and with its consequences for analysis. Chapter 1 is not meant to provide a guide to constructing surveys in developing countries, but rather to describe those 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; the techniques discussed here are used throughout the rest of the book, but I also attempt to be more general, covering methods that are useful in applications not explicitly considered. This is not a textbook of econometrics; these two chapters are designed for readers with a basic knowledge of econometrics who want some preparation for working with household survey data particularly, but not exclusively, from developing countries.
Chapter 3 makes the move toward substantive analysis and discusses the use of survey data to measure welfare, poverty, and distribution. I review the theoretical underpinnings of the various measures of social welfare, inequality, and poverty and show how they can be given empirical content from survey data, with illustrations from the Ivorian and South African Living Standards Surveys. I highlight a number of techniques for data analysis that have proved useful in policy discussions, with particular emphasis on graphical methods for displaying large amounts of data. These methods can be used to investigate the distribution of income, inequality, and poverty and to examine changes in the levels of living of various groups over time. The chapter also shows how it is possible to use the data to examine the distributional consequences of price changes directly, without having to construct econometric models. These methods are applied to an analysis of the effects of rice price policy on the distribution of real income in Thailand.
Chapter 4 discusses the use of household budget data to explore patterns of household demand. I take up the traditional topic of Engel curve analysis in developing countries, looking in particular at the demand for food and nutrition. For many people, nutritional issues are at the heart of poverty questions in developing countries, and Engel curve analysis from survey data allows us to measure the relationship between the elimination of hunger and malnutrition and more general economic development, as captured by increases in real disposable income. This chapter also addresses the closely related question of how goods are allocated within the household and the extent to which it is possible to use household data to cast light on the topic. One of the main issues of interest is how different members of the household are treated, especially whether boys are favored over girls. Analyses of the effects of household composition on demand patterns can perhaps shed some light on this, as well as on the old but vexed question of measuring the "costs" of children. In most surveys, larger households have more income and more expenditure, but they also have less income or expenditure on a per capita basis. Does this mean that large households are poorer on average or that small households are poorer on average? The answer depends on whether there are economies of scale to large households—whether two people need twice as much as one—and whether children, who are relatively plentiful in larger households, need less money to meet their needs than do adults. This chapter discusses the extent to which the survey data can be used to approach these questions.
Chapter 5 is about price reform, its effects on equity and efficiency, and how to measure them. Because surveys provide direct information on how much is consumed of each taxed or subsidized good, it is straightforward to calculate the first-round effects of price changes, both on revenue and on the distribution of real income. What are much harder to assess are the behavioral responses to price changes, the degree to which the demand for the good is affected by the change in price, and the extent to which revenues and expenditures from taxes and subsidies on other goods are affected. The chapter discusses methods for estimating price responses using the spatial price variation that is typically quite pronounced in developing countries. These methods are sensitive enough to detect differences in price responses between goods and to establish important cross-price effects between goods, effects that are often large enough to substantially change the conclusions of a policy reform exercise. Reducing a subsidy on one staple food has very different consequences for revenue and for nutrition, depending on whether or not there is a closely substitutable food that is also subsidized or taxed.
Chapter 6 is concerned with the role of household consumption and saving in economic development. Household saving is a major component and determinant of saving in most developing countries, and many economists see saving as the wellspring of economic growth, so that encouraging saving becomes a crucial component of a policy for growth. Others take the view that saving rates respond passively to economic growth, the roots of which must be sought elsewhere. Survey data can be used to explore these alternative views of the relationship between saving and growth, as well as to examine the role that saving plays in protecting living standards against fluctuations in income. The analysis of survey evidence on household saving, although fraught with difficulty, is beginning to change the way that we think about household saving in poor economies.
I have benefited from the comments of many people who have given generously of their time to try to improve my exposition, to make substantive suggestions, and in a few cases, to persuade me of the error of my ways. In addition to the referees, I should like to thank—without implicating any of them—Martha Ainsworth, Harold Alderman, Tony Atkinson, Dwayne Benjamin, Tim Besley, Martin Browning, Kees Burger, Lisa Cameron, David Card, Anne Case, Ken Chay, John Dinardo, Jean Drèze, Eric Edmonds, Mark Gersovitz, Paul Glewwe, Margaret Grosh, Bo Honoré, Susan Horton, Hanan Jacoby, Emmanuel Jimenez, Alan Krueger, Doug Miller, Juan Muñoz, Meade Over, Anna Paulson, Menno Pradhan, Gillian Paull, James Powell, Martin Ravallion, Jeremy Rudd, Jim Smith, T. N. Srinivasan, David Strömberg, Duncan Thomas, and Galina Voronov. I owe special thanks to Julie Nelson, whose comments and corrections helped shape Chapter 5, and to Christina Paxson, who is the coauthor of much of the work reported here. Some of the work reported here was supported by grants from the National Institute of Aging and from the John D. and Catherine T. MacArthur Foundation. The book was written for the Policy Research Department of the World Bank.
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