Errors in survey reports of consumption expenditures

This paper considers data quality issues for the analysis of consumption inequality exploiting two complementary datasets from the Consumer Expenditure Survey for the United States. The Interview sample follows survey households over four calendar quarters and consists of retrospectively asked information about monthly expenditures on durable and non-durable goods. The Diary sample interviews household for two consecutive weeks and includes detailed information about frequently purchased items (food, personal cares and household supplies). Each survey has its own questionnaire and sample. Information from one sample is exploited as an instrument for the other sample to derive a correction for the measurement error affecting observed measures of consumption inequality. Implications of our ?ndings are used as a test for the permanent income hypothesis.Consumption Inequality; Measurement Error; Permanent Income Hypothesis

Treatment effect estimation with covariate measurement error

This paper investigates the effect that covariate measurement error has on a conventional treatment effect analysis built on an unconfoundedness restriction that embodies conditional independence restrictions in which there is conditioning on error free covariates. The approach uses small parameter asymptotic methods to obtain the approximate generic effects of measurement error. The approximations can be estimated using data on observed outcomes, the treatment indicator and error contaminated covariates providing an indication of the nature and size of measurement error effects. The approximations can be used in a sensitivity analysis to probe the potential effects of measurement error on the evaluation of treatment effects

Errors in Survey Reports of Consumption Expenditures

This paper considers data quality issues to analyze the pattern of consumption inequality in the 1990s exploiting two complementary datasets from the US Consumer Expenditure Survey. The Interview sample follows survey households over four calendar quarters and consists of retrospectively asked information about monthly expenditures on durable and non-durable goods. The Diary sample interviews household for two consecutive weeks, and it includes detailed information about frequently purchased items (food, personal cares and household supplies). Each survey has its own questionnaire and sample. We exploit information from one sample as an instrument for the other to derive a correction for the measurement error affecting observed measures of consumption. We produce some evidence of non-classical measurement error affecting the aggregate measure of consumption both for diary and recall based data; we also show the implications of our findings to test for the Permanent income hypothesis.

Treatment effect estimation with covariate measurement error

This paper investigates the effect that covariate measurement error has on a conventional treatment effect analysis built on an unconfoundedness restriction that embodies conditional independence restrictions in which there is conditioning on error free covariates. The approach uses small parameter asymptotic methods to obtain the approximate generic effects of measurement error. The approximations can be estimated using data on observed outcomes, the treatment indicator and error contaminated covariates providing an indication of the nature and size of measurement error effects. The approximations can be used in a sensitivity analysis to probe the potential effects of measurement error on the evaluation of treatment effects.

What really happened to consumption inequality in the US?

This paper considers data quality issues for the analysis of consumption inequality exploiting two complementary datasets from the Consumer Expenditure Survey for the United States. The Interview sample follows survey households over four calendar quarters and consists of retrospectively collected information about monthly expenditures on durable and non-durable goods. The Diary sample interviews household for two consecutive weeks and includes detailed information about frequently purchased items (food, personal cares and household supplies). Most reliable information from each sample is exploited to derive a correction for the measurement error affecting observed measures of consumption inequality in the two surveys. We find that consumption inequality, as measured by the standard deviation of log non-durable consumption, has increased by roughly 5% points during the 1990s

The Impact of Measurement Error on Evaluation Methods Based on Strong Ignorability

When selection bias can purely be attributed to observables, several estimators have been discussed in the literature to estimate the average effect of a binary treatment or policy on a scalar outcome. Identification typically exploits the unconfoundedness of the treatment, which is verified if the participation status is independent of potential outcomes conditional on observable covariates. Assuming unconfoundedness, the average effect of the treatment can be estimated by matching, differencing within subpopulation averages of treated and untreated units, or by propensity score methods under an additional condition on the support of the covariates exploited. The latter condition, together with unconfoundedness, makes participation into the treatment group strongly ignorable, as defined by Rosenbaum and Rubin (1983). This paper derives conditions for identification and estimation of treatment effects when observable covariates relevant to unconfoundedness are measured with error. An expression for the measurement error bias is derived, and conditions are discussed for this to be zero. A bias correction procedure is also presented, which uses non-parametric estimates of functionals of the distribution of observed covariates.potential outcomes, small sigma asymptotics, treatment effects

Survey Instruments and the Reports of Consumption Expenditures: Evidence from the Consumer Expenditure Surveys

This paper provides evidence on the relevance of the collection mode for the analysis of consumption data for the United States using complementary data sets from the Consumer Expenditure Surveys (CEX). We first show that population figures from consumption reports obtained with diaries markedly differ from those obtained using recall data. We then exploit multiple measurements of food expenditure available in the CEX to identify the effects of the collection mode on important features of the distribution of consumption (not just its mean). Finally, we show how to purge the expenditure measurements from most of the effects of the collection mode and thus obtain an improved measure of consumption that combines information from multiple reports in the CEX. The paper concludes by suggesting some guidelines for empirical research that have important implications for the measurement of inequality and well being.Consumption, Data Collection Methods, Rank Invariance

Food and cash transfers: evidence from Colombia

We study food Engel curves among the poor population targeted by a conditional cash transfer programme in Colombia. After controlling for the endogeneity of total expenditure and for the (unobserved) variability of prices across villages, the best fit is provided by a log-linear specification. Our estimates imply that an increase in total expenditure by 10% would lead to a decrease of 1% in the share of food. However, quasi-experimental estimates of the impact of the programme on total and food consumption show that the share of food increases, suggesting that the programme has more complex impacts than increasing household income. In particular, our results are not inconsistent with the hypothesis that the programme, targeted to women, could increase their bargaining power and induce a more than proportional increase in food consumption

Another look at the regression discontinuity design

The attractiveness of the Regression Discontinuity Design (RDD) in its sharp formulation rests on close similarities with a formal experimental design. On the other hand, it is of limited applicability since rarely individuals are assigned to the treatment group on the basis of a pre-program measure observable to the analyst. Besides, it only allows to identify the mean impact of the program for a very specific sub-population of individuals. In this paper we show that the sharp RDD straightforwardly generalizes to the instances in which the eligibility for the program is established with respect to an observable pre-program measure with eligible individuals self-selecting into the treatment group according to an unknown process. This set-up also turns out very convenient to define a specification test on conventional non-experimental estimators of the program effect needed to identify the mean impact away from the threshold for eligibility. Data requirements are made explicit.

Why is consumption more log normal than income? Gibrat’s Law revisited

Significant departures from log normality are observed in income data, in violation of Gibrat’s law. We identify a new empirical regularity, which is that the distribution of consumption expenditures across households is, within cohorts, closer to log normal than the distribution of income. We explain these empirical results by showing that the logic of Gibrat’s law applies not to total income, but to permanent income and to maginal utility. These findings have important implications for welfare and inequality measurement, aggregation, and econometric model analysis