1. Data Cleaning and Imputation Imputation done on economic variables (assets, income, consumption, financial transfers, health expenses), education, self-reported health, being depressed last month, general limitations of activity, ADL, IADL, numeracy, self-reported reading skills, making ends meet, number of children and grandchildren, urban or rural, maybe body mass index, grip strength Outliers: look at distribution (1 th, 5 th, 50 th, 95 th, 99 th percentile, relation to the median)‏

2.  Periodicity (monthly vs annual): consumption, pensions, pensions, occupational pensions)‏  Check range of values for categorical variables  Currency mix-up, relevant for euro countries (easier to detect for countries with high value of exchange rate, local currency/euro)‏  Consistency of info between ownership and brackets  Change in values between W1 and W2: should not be great for home, IRA's, life insurance, mortgage, pensions, consumption, long term insurance payment

3. Use info from related variables -> judgment call  Last year's income vs last payment this year  Health expenses and illnesses  Look at sequentially asked variables (4 questions for numeracy)‏  Body mass index (height and weight)‏  House acquisition and having received a bequest Assume that everything can go wrong, but be conservative about changes Notify MEA and the imputation group about any deviations from the standard questionnaire Wave 1?

4. Do the changes at a country levels for cases that are reasonably clear. Put missing if you think the value is definitely wrong but can't get the “correct” one. Preserve original values before changing Create a flag variable with remarks about each variable Please contact me via e-mail for questions/problems (cdimitri@unisa.it)‏cdimitri@unisa.it Discuss issues with country team leaders