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

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.

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

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.

Another look at the Regression Discontinuity Design

The attractiveness of the Regression Discontinuity Design (RDD) rests on its similarity to an experimental design. On the other hand, it is of limited applicability since rarely assignment to the treatment is based on known pre-program measures. Besides, it only allows to identify the mean impact on a very specific sub-population. Here we show that the RDD generalizes to the instances in which eligibility is established on a pre-program measure and eligible individuals are allowed to self-select into the program. This set-up is also convenient to test the validity of conventional non-experimental estimators of the mean impact.program evaluation, second control group, specification tests

Connections and Performance in Bankers' Turnover: Better Wed over the Mixen than over the Moor

In this paper we study top executive turnover in Italian Banks over the period 1993-2001. We relate the probability of survival of top executives (Presidents, CEOs and General Managers) to bank performance and the manager’s local connections, controlling for (observable and unobservable) bank and manager characteristics by exploiting longitudinal information on bank-manager appointments. We measure the extent? of managers’ local connections by the distance between the province of the bank’s headquarters and the manager’s province of birth. We show that top managers tend to be local in the sense that the distribution of this distance is heavily skewed towards zero. On the basis of this evidence, we address two questions. First, we investigate whether connections affect the duration of the appointment at the bank. Second, we ask whether connections entrench managers at the expense of the bank’s performance. We find that connections generally increase the probabilities of managerssurviving at their banks, and that the positive effect of performance on tenure (as amply documented by the executive turnover literature) disappears once connections are taken into account. On the other hand, we provide evidence against the hypothesis that managerial connections contain information valuable for enhancing a bank’s performance. In particular, we find that highly connected boards cause the shorter survival of banks, and that those who benefit from connections are top managers themselves (mostly Presidents and General Managers). This suggests that connections may be collusion devices with which to maintain and share rents.connections, executive turnover, commercial and cooperative banks

What do we learn from recall consumption data?

In this paper we use two complementary Italian data sources (the 1995 Istat and Bank of Italy household surveys) to generate household-specific non-durable expenditure in the Bank of Italy sample that contains relatively high-quality income data. We show that food expenditure data are of comparable quality and informational content across the two surveys, once heaping, rounding and time averaging are properly accounted for. We therefore depart from standard practice and rely on the estimation of an inverse Engel curve on Istat data to impute non-durable expenditure to Bank of Italy observations, and show how these estimates can be used to analyse consumption age profiles conditional on demographics. Our key result is that predictions based on a standard set of demographic and socioeconomic indicators are quite different from predictions that also condition on simulated food consumption, in the sense that their age profile is less in line with the implications of the standard consumer intertemporal optimization problem.recall errors, heaping and rounding, multiple imputations and consumption

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.