The Analysis of Household

SurveysA Microeconometric Approach

to Development Policy

Winner of the 2015Nobel Prize in Economics

Angus Deaton

Reissue Editionwith a New Preface

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The AnAlysis of

household

surveys

The AnAlysis of

household

surveys

A Microeconometric Approach

to Development Policy

Reissue Edition with a New Preface

Angus Deaton

© 2018 International Bank for Reconstruction and Development / The World Bank1818 H Street NW, Washington, DC 20433Telephone: 202-473-1000; Internet: www.worldbank.org

Some rights reserved

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References in this publication to “Taiwan,” “Republic of China,” and “Taiwan (China)” refer to the region “Taiwan, China.” References to “Hong Kong” refer to the region “Hong Kong SAR, China.”

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Attribution—Please cite the work as follows: Deaton, Angus. 2018. The Analysis of Household Surveys: A Microeconometric Approach to Development Policy. Reissue Edition with a New Preface. Washington, DC: World Bank. doi:10.1596/ 978-1-4648-1331-3. License: Creative Commons Attribution CC BY 3.0 IGO

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ISBN (paper): 978-1-4648-1331-3ISBN (electronic): 978-1-4648-1352-8DOI: 10.1596/ 978-1-4648-1331-3

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v

Contents

Preface xi

Introduction 1Purpose and intended audience 1Policy and data: methodological issues 2Structure and outline 4

Chapter 1: The design and content of household surveys 71.1 Survey design 9

Survey frames and coverage 10Strata and clusters 12Unequal selection probabilities, weights, and

inflation factors 15Sample design in theory and practice 17Panel data 18

1.2 The content and quality of survey data 22Individuals and households 23Reporting periods 24Measuring consumption 26Measuring income 29

1.3 The Living Standards Surveys 32A brief history 32Design features of LSMS surveys 34What have we learned? 35

1.4 Descriptive statistics from survey data 40Finite populations and superpopulations 40The sampling variance of the mean 43Using weights and inflation factors 44Sampling variation of probability-weighted estimators 49

References in this publication to “Taiwan,” “Republic of China,” and “Taiwan (China)” refer to the region, “Taiwan, China.” References to “Hong Kong” refer to the region, “Hong Kong SAR, China.”

vi THE ANALYSIS Of HOUSEHOLD SURvEYS

Stratification 49Two-stage sampling and clusters 51A superpopulation approach to clustering 56Illustrative calculations for Pakistan 57The bootstrap 58

1.5 Guide to further reading 61

Chapter 2: Econometric issues for survey data 632.1 Survey design and regressions 66

Weighting in regressions 67Recommendations for practice 71

2.2 The econometrics of clustered samples 73The economics of clusters in developing countries 73Estimating regressions from clustered samples 74

2.3 Heteroskedasticity and quantile regressions 78Heteroskedasticity in regression analysis 79Quantile regressions 80Calculating quantile regressions 83Heteroskedasticity and limited dependent

variable models 85Robust estimation of censored regression models 89Radical approaches to censored regressions 91

2.4 Structure and regression in nonexperimental data 92Simultaneity, feedback, and unobserved heterogeneity 93Example 1. Prices and quantities in local markets 93Example 2. Farm size and farm productivity 95Example 3. The evaluation of projects 97Example 4. Simultaneity and lags: nutrition

and productivity 98Measurement error 99Selectivity issues 101

2.5 Panel data 105Dealing with heterogeneity: difference- and

within-estimation 106Panel data and measurement error 108Lagged dependent variables and exogeneity in

panel data 1102.6 Instrumental variables 111

Policy evaluation and natural experiments 112Econometric issues for instrumental variables 115

2.7 Using a time-series of cross-sections 116Cohort data: an example 117Cohort data versus panel data 120Panel data from successive cross sections 121Decompositions by age, cohort, and year 123

Contents vii

2.8 two issues in statistical inference 127Parameter transformations: the delta method 128Sample size and hypothesis tests 129

2.9 Guide to further reading 131

Chapter 3: Welfare, poverty, and distribution 1333.1 Living standards, inequality, and poverty 134

Social welfare 134Inequality and social welfare 136Measures of inequality 138Poverty and social welfare 140The construction of poverty lines 141Measures of poverty 144The choice of the individual welfare measure 148Example 1. Inequality and poverty over time

in Côte d’Ivoire 151Example 2: Inequality and poverty by race in

South Africa 156Exploring the welfare distribution: inequality 157Lorenz curves and inequality in South Africa and

Côte d’Ivoire 160Stochastic dominance 162Exploring the welfare distribution: poverty 164

3.2 nonparametric methods for estimating densities 169Estimating univariate densities: histograms 170Estimating univariate densities: kernel estimators 171Estimating univariate densities: examples 175Extensions and alternatives 176Estimating bivariate densities: examples 180

3.3 Analyzing the distributional effects of policy 182Rice prices and distribution in Thailand 182The distributional effects of price changes: theory 183Implementing the formulas: the production and

consumption of rice 187Nonparametric regression analysis 191Nonparametric regressions for rice in Thailand 194Bias in kernel regression: locally weighted regression 197The distributional effects of the social pension in

South Africa 2003.4 Guide to further reading 202

Chapter 4: Nutrition, children, and intrahousehold allocation 2044.1 the demand for food and nutrition 206

Welfare measures: economic or nutritional? 206Nutrition and productivity 210

viii The AnAlysis of household surveys

The expenditure elasticity of nutrition: background 211Evidence from India and Pakistan 213Regression functions and regression slopes

for Maharashtra 216Allowing for household structure 219The effect of measurement errors 221

4.2 intra-household allocation and gender bias 223Gender bias in intrahousehold allocation 224A theoretical digression 225Adults, children, and gender 229Empirical evidence from India 231Boys versus girls in rural Maharashtra: methodology 234Standard errors for outlay equivalent ratios 235Boys versus girls in rural Maharashtra: results 237Côte d’lvoire, Thailand, Bangladesh, and Taiwan (China) 238

4.3 equivalence scales: theory and practice 241Equivalence scales, welfare, and poverty 243The relevance of household expenditure data 244Cost-of-living indices, consumers’ surplus, and

utility theory 245Calculating the welfare effect of price 246Equivalence scales, the cost of children, and utility theory 247The underidentification of equivalence scales 248Engel’s method 251Rothbarth’s method 255Other models of equivalence scales 260Economies of scale within the household 262Utility theory and the identification of economies of scale 268

4.4 Guide to further reading 269

Chapter 5: Looking at price and tax reform 2715.1 The theory of price and tax reform for

developing countries 273Tax reform 273Generalizations using shadow prices 277Evaluation of nonbehavioral terms 278Alternative approaches to measuring

behavioral responses 2795.2 The analysis of spatial price variation 283

Regional price data 283Household price data 283Unit values and the choice of quality 288Measurement error in unit values 292

5.3 Modeling the choice of quality and quantity 293A stripped-down model of demand and unit values 294

Contents ix

Modeling quality 296Estimating the stripped-down model 299An example from Côte d’lvoire 302Functional form 303Quality, quantity, and welfare: cross-price effects 306Cross-price effects: estimation 311Completing the system 314

5.4 empirical results for India and Pakistan 315Preparatory analysis 316The first-stage estimates 316Price responses: the second-stage estimates for Pakistan 317Price estimates and taste variation, Maharashtra 320

5.5 Looking at price and tax reform 323Shadow taxes and subsidies in Pakistan 324Shadow taxes and subsidies in India 325Adapting the price reform formulas 326Equity and efficiency in price reform in Pakistan 328Equity and efficiency in price-reform in India 330

5.6 Price reform: parametric and nonparametric analysis 3325.7 Guide to further reading 334

Chapter 6: Saving and consumption smoothing 3356.1 Life-cycle interpretations of saving 337

Age profiles of consumption 339Consumption and saving by cohorts 342Estimating a life-cycle model for Taiwan (China) 345

6.2 short-term consumption smoothing and permanent income 350

Saving and weather variability 351Saving as a predictor of income change? 354

6.3 Models of saving for poor households 357The basic model of intertemporal choice 357Special cases: the permanent income and

life-cycle models 359Further analysis of the basic model:

precautionary saving 361Restrictions on borrowing 363Borrowing restrictions and the empirical evidence 369

6.4 social insurance and consumption 372Consumption insurance in theory 375Empirical evidence on consumption insurance 376

6.5 saving, consumption, and inequality 383Consumption, permanent income, and inequality 383Inequality and age: empirical evidence 386Aging and inequality 390

x THE ANALYSIS Of HOUSEHOLD SURvEYS

6.6 Household saving and policy: a tentative review 393Motives, consequences, and policy 394Saving and growth 395Determinants of saving 397

6.7 Guide to further reading 399

Code appendix 401

Bibliography 439

Subject index 463

Author index 474

xi

Preface

It is a pleasure to write a new preface to The Analysis of Household Surveys on its twentieth birthday. It would be better still if this were a preface to a new edi-tion, and I hope that one day I shall write it. In the meantime, I hope that this Preface might be a guide to a new reader, by labeling those parts of the book that seem still relevant, as well as those that would be leading candidates for revision or updating.

The origins of the book go back to the early days of the Living Standards Measurement Study (LSMS), which was set up in the World Bank around 1980. As its name suggests, the original idea was to promote household surveys that would enable the better measurement of poverty and of living standards around the world, something that was difficult to do with the data then avail-able. As time went on, and people came and went, the LSMS surveys evolved into multi-purpose tools that would permit not only measurement, but also analysis, permitting a better understanding of how people’s lives work, what makes them tick, and why they are as well off or as poorly off as they are. Hence “the analysis” of household surveys, not just the measurement of income, or consumption, or wellbeing. In the original conception, there were to be a series of volumes on different specific topics, with this volume being more general, though with examples of topics that could be covered using the approach.

Today, it is hard even to remember how relatively uncommon the analysis of individual household records was. Although there had been important very early studies, including the 1955 book on family budgets by Sigbert Prais and Hendrik Houthakker—which legend has it was the first economic analysis to use an electronic computer—and although micro analysis figured in some of the early textbooks, most economists were trained in econometric methods that explicitly or implicitly focused on aggregate time series. So there was a lot of important and survey-relevant material that remained uncovered. Students who had completed their econometrics courses and turned to household sur-vey data found much that puzzled them.

Perhaps the most obvious gap in standard economics training was then (as it largely still is today) the topic of survey design, and how survey design should (if at all) be incorporated into analysis. Survey data come with “weights,” related

xii THE ANALYSIS Of HOUSEHOLD SURvEYS

to survey design (for example, they might be the reciprocals of the probabilities of selection), and many generations of economists have had to wonder for themselves what should be done with them. Of course, this is standard material for survey design statisticians, but those who design surveys do not work in the same way economists do and sometimes have different ways of thinking and different objectives. So, when I started writing the book, I wanted to try to understand these issues better for myself, so I went back to the survey litera-ture, and wrote up what I found. Chapter 1 is still one of the few treatments of survey design from an economics perspective. One important change is that, at around the time I was writing, STATA introduced a full suite of survey-design-based econometric software, so it is no longer necessary for analysts to do what I had to do, and write my own code.

Thinking about survey design forces the analyst to confront the difference between sample and population, and to think seriously about the population to which the analysis is supposed to apply. for survey samplers, the aim is often the estimation of some characteristic of a finite population, such as the median consumption of Indian families in 2015, something that could be known with certainty from a census, a complete listing of the population. Is this the right way of thinking about a regression analysis? Or should we consider some pos-sible super population, of which the current population is but one possible realization. These issues are important, and are rarely considered. As an exam-ple, even today papers in economics and in health routinely publish standard errors of means calculated from complete enumerations, such as mortality rates.

In the same spirit, Chapter 2 was designed as a bridge from what is taught in an econometrics course and what applied economists will find when they confront microeconomic data. for several years, I used this chapter to teach a course at Princeton that helped prepare applied students in labor and develop-ment. When econometrics is taught by specialists, which has the huge advan-tage of providing an overarching statistical framework, it is often useful to work through some of the nitty-gritty issues of practice that are not conceptually interesting, but can make the difference between convincing and unconvinc-ing results. Sometimes this takes the form of warnings, that technical fixes rarely fix anything by themselves, and that techniques, such as panel data methods, which can work magic in ideal conditions, can be undermined by imperfections of various kinds, particularly measurement error.

If I were rewriting Chapter 2 today, I would be even more skeptical. As I taught the material over the years, it became clear that many of the uses of instrumental variables and natural experiments that had seemed so compel-ling at first lost a good deal of their luster with time. One problem is the reliance of instrumental variables on exclusion restrictions. The orthogonality of instru-ments to the error term requires that they be uncorrelated with omitted vari-ables so that, when we are interested in the effect of x on y, and z is an instrument, then z can only affect y through its effect on x, and not through any other mechanism. This often seems plausible when an instrument is first

PREfACE xiii

proposed, but over time, other researchers, or other facts, can make the story much less plausible. There is no general rule here, and some of the studies using natural experiments and instruments have worn well, but that is more the exception than the rule.

Twenty years later, I now find myself very much more skeptical about instru-ments in almost any situation. I should also note a mea culpa: the late Tony Atkinson, in his pre-publication review of the book, had noted that instrumen-tal variables hardly ever worked. I should have paid more attention to his views.

Natural experiments are a form of instrumental variables. They often give a clean answer, eliminating effects that otherwise would cloud the analysis. Yet the “clean” answer is not always the answer that we want for policy or under-standing. This is one aspect of the familiar trade-off between internal and external validity; a natural experiment is like a laboratory experiment where many factors are held constant, but where we have little idea whether the effect will be replicated in settings that may be more relevant for policy. Of  course, a good laboratory experiment tries to isolate some fundamental mechanism that will always be present, even when modified, but such experi-ments require more theory and background knowledge than is usually present in natural experiments in economics. Occasionally such experiments look more like anecdotes than analysis.

If I were writing the book today, there would be a new chapter on random-ized controlled trials (RCTs).1 Two decades ago I, like most economists, thought that if only we could do RCTs, life would be straightforward; we could dis-cover the laws of behavior, understand poverty, and eliminate it. And indeed, one of the major developments in applied microeconomics in the last 20 years has been the widespread use of RCTs, particularly in economic development.

As always, the practice has brought useful experience with its complement of successes and failures. RCTs often yield new insights and unexpected find-ings. Yet they also have more problems than we anticipated, both in theory and practice. They are not magic tools, any more than panel data or instrumental variables were magic tools. Indeed, once upon a time, economists thought of linear regression as a magic tool, and the history of econometrics teaching has been one in which the great enthusiasm of the early days gave way to a sadder catalog of regression diseases and diagnostics. The same is happening and will continue to happen with RCTs although for sure, and like regressions, they are likely to remain useful tools. But statistical inference with RCTs is much more difficult than it at first appears, causality can rarely be firmly established, the  influence of omitted variables is not magically erased by randomization, and the lack of blinding—usually impossible in economics—can undermine estimation in the same way that failure of exclusion restrictions undermines

1. for readers interested in what such a chapter would look like, a good account can be found in my 2018 paper with Nancy Cartwright in Social Science and Medicine, vol. 210, pp. 2–21: “Understanding and Misunderstanding Randomized Controlled Trials,” https://doi.org/10.1016/j .socscimed.2017.12.005.

xiv THE ANALYSIS Of HOUSEHOLD SURvEYS

instrumental variable estimation. And, as is widely understood, but often for-gotten, the fact that some mechanism works in one place is no guarantee that it works anywhere else. RCTs cannot, by themselves, support a program of unconditional discovery of “what works.”

In the introduction to the book, I speculate about what can be done in the absence of experiments. While I am now more skeptical about what can be done with experiments, I continue to believe in what I wrote then. The trick is to use the data to tell us something that we didn’t know before, and that can help us change our minds, or see things differently. Sometimes this can be done from simple descriptive statistics; that Indian per capita calorie consump-tion was falling during a period of rapid economic growth was an important finding in and of itself, and suggested a whole program of enquiry. That almost half of all children in India were severely malnourished was a finding that then Prime Minister Manmohan Singh described as “a national shame.” Such straightforward descriptions can have huge effects on policy, as can correla-tions and regressions.

Causal testing can also be supported in the same way through what philoso-phers call the “hypothetico deductive method.” Given a hypothesis or an idea, the key is to develop and work it to the point where its implications can be transparently checked on the data. Sometimes, this will take an RCT, but if we are clever and diligent enough, it can often be tested by looking at old data in new ways, from a mean, a median, or a cross-tabulation. The analytical work goes into the drawing out of implications from the hypothesis, not into deriv-ing complex econometric methods or technical fixes.

Chapters 3 through 6 are more substantive, though all use econometric methods to generate their findings. All of them could be updated with new results, and in some cases (such as the model of quality choice in Chapter 5), I  would modify or be more skeptical about the underlying assumptions. But I  believe that these chapters—as well as the STATA code that comes at the end—serve their original purpose of providing worked examples of the sort of analysis that survey data are suited to.

The book has been widely used since 1997, it is frequently cited, and it has been downloaded from the Bank’s website nearly 34,000 times. I am delighted that it will now be back in print for those who would like to have a “real” copy on their “real” desktops.

Angus DeatonPrinceton, December 2018

References in this publication to “Taiwan,” “Republic of China,” and “Taiwan (China)” refer to the region, “Taiwan, China.” References to “Hong Kong” refer to the region, “Hong Kong SAR, China.”

References in this publication to “Taiwan,” “Republic of China,” and “Taiwan (China)” refer to the region, “Taiwan, China.” References to “Hong Kong” refer to the region, “Hong Kong SAR, China.”

References in this publication to “Taiwan,” “Republic of China,” and “Taiwan (China)” refer to the region, “Taiwan, China.” References to “Hong Kong” refer to the region, “Hong Kong SAR, China.”

References in this publication to “Taiwan,” “Republic of China,” and “Taiwan (China)” refer to the region, “Taiwan, China.” References to “Hong Kong” refer to the region, “Hong Kong SAR, China.”

References in this publication to “Taiwan,” “Republic of China,” and “Taiwan (China)” refer to the region, “Taiwan, China.” References to “Hong Kong” refer to the region, “Hong Kong SAR, China.”

References in this publication to “Taiwan,” “Republic of China,” and “Taiwan (China)” refer to the region, “Taiwan, China.” References to “Hong Kong” refer to the region, “Hong Kong SAR, China.”

References in this publication to “Taiwan,” “Republic of China,” and “Taiwan (China)” refer to the region, “Taiwan, China.” References to “Hong Kong” refer to the region, “Hong Kong SAR, China.”

on different data sets and different machines. The code listed here is available on the following site:

http://www.worldbank.org/householdsurveys

References in this publication to “Taiwan,” “Republic of China,” and “Taiwan (China)” refer to the region, “Taiwan, China.” References to “Hong Kong” refer to the region, “Hong Kong SAR, China.”

References in this publication to “Taiwan,” “Republic of China,” and “Taiwan (China)” refer to the region, “Taiwan, China.” References to “Hong Kong” refer to the region, “Hong Kong SAR, China.”

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.

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