1. A discussion of Comparing register and survey wealth data ( F. Johansson and A. Klevmarken) & The Impact of Methodological Decisions around Imputation and the Choice of the Aggregation Unit (J. Frick, M.Grabka and E. Siermiska) by Romina Gambacorta

2. Summary Main findings of the papers and comments; link between the two papers and the LWS project and suggestions for future research

3. Paper 1 - Johansson and Klevmarken – Register vs survey data The assessment of measurement errors. data sets: the Swedish register data (LINDA) and two surveys UU-RAND and SHARE_SE (sub samples of the Swedish population register 50+). register data for financial assets as true values. Evaluation of register error for housing values.  In general, both the register error and the survey error are negatively correlated to the true values (rich under- report, poor over-report)  for (very) rich people there is no evidence for underreporting but for selective non-response (with UU- RAND)

4. Paper 1 - Johansson and Klevmarken – measurement errors and estimations Consequences of measurement errors  bias in the estimations: when wealth is the dependent variable because the explanatory variables (age, gender, schooling, health) are negatively related to wealth survey error; when wealth is the explanatory variable (for example in explaining bank holdings also with other variables), due to both the large variance of the errors in the explanatory variable and to the presence of correlation of the true value with the measurement error.

5. Questions and comments to paper1 Try to give explanation to some of the findings! On average estimated register values for houses are greater than the true market values  adjustment factor accounts only for sold houses and not for other characteristics that can affect the house prices. Different results about underreport and non-response for the very rich  due to the differences between the two surveys? Under-report and non-response are higher with UU_RAND survey (telephone interview, information only about the household and the spouse) …

6. Questions and comments to paper1 … !  be careful in generalizing results: non-response pattern and survey participation greatly vary among countries and among surveys. For what concerns the consequences of measurement errors on the estimates: all the findings are based on the assumption that register errors are uncorrelated with survey errors. How is this assumption motivated? Do results hold when it is removed?

7. Paper 2 – Frick Grabka Siermiska Imputation and aggregation units The impact of imputation and of the choice of aggregation unit to wealth and inequality estimates using the German Socio-Economic Panel survey. Methods Probit models for the probability of measurement errors (inconsistency  editing and item non-response  imputation) and for the state of ownership; Multiple imputation methods: for each missing value more than one value (5) provided maximum-likelihood based Heckman selection model controlling for sample selection adding randomly errors they obtain multiple imputation.

8. Paper 2 – Frick Grabka Siermiska Imputation and aggregation units Main results: selectivity in the state of ownership and measurement errors are related to age, gender, education and employment status; imputation increases both the value of wealth and share of wealth holders but with different effects for different kind of assets and in most cases reduces inequality; the choice of the unit of aggregation affects wealth and inequality measures  inequality is reduced by the within household redistribution effect: women and young profit more from within household redistribution.

9. Questions and comments to paper2 … about the method: instruments when modelling the probability of measurement errors used to correct the probability of ownership. (EX. interviewer experience: related to the measurement error but not to the ownership of the asset) more details about: the inconsistency rule adopted to locate the value that needs editing (extremely large-low values or under-reporting model?) the correction rule adopted to edit these items. In particular do the authors validate the effects of editing on survey estimation?

10. Comparison between single and multiple imputation methods and between deterministic and random imputation rules. Can the two effects be distinguished?

11. Questions and comments to paper2 … about the results: Individual data: how much of the results about young and gender gap can be due respectively to the selectivity in the state of ownership in the survey and to the labour market in German? household’s per capita data refers more to individual well-being  differences shown can be seen as a positive effect on individual welfare of within household redistribution

12. LWS and suggestions for future research LWS data imports heterogeneous measurement errors and non-response from each data set Need analysis for more countries which methods to resolve these problems? Which effects? use Johansson and Klevmarken methodology to asses the reduction in estimation bias achieved by the use of instrumental variables? (Problems in retrieving instruments…) Imputation methods analysed by Frick et al. can induce measurement errors in the data: can we use Johansson and Klevmarken methodology to assess the impact of these errors on the estimates?