Andrea Neri

Measurement Error in the Bank of Italy's Survey of Household Income and Wealth

This paper is aimed at evaluating the incidence of measurement error in the Bank of Italys Survey of Household Income and Wealth (SHIW). In the case of time-invariant variables, we assess the degree of inconsistency of answers given by panel households in subsequent survey waves. For quantities that vary with time, we estimate the incidence of measurement error by decomposing observed variability into true dynamics and error-induced noise. We apply the Heise model or the latent Markov model, depending on whether the data are continuous or categorical. We also present regression models that explain the error-generating process. Our results are relevant to researchers who use SHIW data for economic analysis, but also to data producers involved in similar income and wealth surveys. The methods we describe and test can be employed in a number of contexts to gain better understanding of data-related problems and plans for survey improvement.

To misreport or not to report? The measurement of household financial wealth

The objective of the paper is to adjust for the bias due to unit non-response and measurement error in survey estimates of total household financial wealth. Sample surveys are a useful source of information on household wealth. Yet, survey estimates are affected by non-sampling errors. In particular, in the case of household wealth, unit non-response and measurement error can severely bias the estimates. Using the Italian Survey on Household Income and Wealth (SHIW), we exploit the available auxiliary information in order to assess the magnitude of this bias. We find evidence that for this kind of survey, non-sampling errors are a major issue, possibly more serious than sampling errors. Moreover, in the case of SHIW the potential bias due to measurement error seems to outweigh that induced by non-response.

Statistical Matching and Uncertainty Analysis in Combining Household Income and Expenditure Data

Among the goals of statistical matching, a very important one is the estimation of the joint distribution of variables not jointly observed in a sample survey but separately available from independent sample surveys. The absence of joint information on the variables of interest leads to uncertainty about the data generating model since the available sample information is unable to discriminate among a set of plausible joint distributions. In the present paper a short review of the concept of uncertainty in statistical matching under logical constraints, as well as how to measure uncertainty for continuous variables is presented. The notion of matching error is related to an appropriate measure of uncertainty and a criterion of selecting matching variables by choosing the variables minimizing such an uncertainty measure is introduced. Finally, a method to choose a plausible joint distribution for the variables of interest via iterative proportional fitting algorithm is described. The proposed methodology is then applied to household income and expenditure data when extra sample information regarding the average propensity to consume is available. This leads to a reconstructed complete dataset where each record includes measures on income and expenditure.

Income and wealth sample estimates consistent with macro aggregates: some experiments

The Bank of Italyi?½s Survey of Household Income and Wealth (SHIW) is widely used to study the economic behavior of Italian households. Like most similar surveys, the SHIW is biased downward in its estimates by the lesser propensity of wealthy families to participate and by the tendency to underreport income and wealth. This work assesses the various techniques for correct the bias, applying them to the period 1995-2012. Calibration techniques, which produce estimates consistent with the macro-economic information available from other sources, are also employed.