Measuring the effect of shocks on inequality: It’s all about the data

To identify shocks in VARs using short-run sign or exclusion restrictions, the highest-frequency data possible is usually preferred. For income inequality, there is tension between high frequency and high quality. Annual datasets that survey large numbers of people provide high-quality estimates of income. Higher frequency surveys generally provide a sparser sampling of individual income. Some previous studies have used the higher frequency data, presumably to match the frequency necessary to identify the shock. Using data obtained from the higher frequency, lower respondent surveys might result in misleading conclusions. We combine the two surveys and construct a set of quarterly-frequency income quantiles that are scaled to the annual data but fluctuate according to the high-frequency survey. We show that using these data yields different economic conclusions than simply using the raw high-frequency income series. In particular, we show in two simple applications to technology shocks and house price shocks that one obtains different conclusions about the permanence and/or the direction of the responses of income inequality.