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This appendix contains additional results on using scanner data to estimate inflation rates at the household level. There are three sections. Section 1 shows cross-sectional distributions of Fisher and Paasche inflation rates. Section 2 shows the evolution over time of measures of dispersion of Fisher and Paasche inflation rates. Section 3 shows cross-sectional distributions of two-year inflation rates measured with Fisher and Paasche indexes.

We use scanner data to estimate inflation rates at the household level. Households' inflation rates are remarkably heterogeneous, with an interquartile range of 6.2 to 9.0 percentage points on an annual basis. Most of the heterogeneity comes not from variation in broadly defined consumption bundles but from variation in prices paid for the same types of goods - a source of variation that previous research has not measured. The entire distribution of household inflation rates shifts in parallel with aggregate inflation. Deviations from aggregate inflation exhibit only slightly negative serial correlation within each household over time, implying that the difference between a household's price level and the aggregate price level is persistent. Together, the large cross-sectional dispersion and low serial correlation of household-level inflation rates mean that almost all of the variability in a household's inflation rate over time comes from variability in household-level prices relative to average prices for the same goods, not from variability in the aggregate inflation rate. We provide a characterization of the stochastic process for household inflation that can be used to calibrate models of household decisions.

We show how to use panel data on household consumption to directly estimate households’ risk preferences. Specifically, we measure heterogeneity in risk aversion among households in Thai villages using a full risk-sharing model, which we then test allowing for this heterogeneity. There is substantial, statistically significant heterogeneity in estimated risk preferences. Full insurance cannot be rejected. As the risk sharing, as-if-complete-markets theory might predict, estimated risk preferences are unrelated to wealth or other characteristics. The heterogeneity matters for policy: Although the average household would benefit from eliminating village-level risk, less-risk-averse households who are paid to absorb that risk would be worse off by several percent of household consumption.

We use a model of optimal portfolio choice to measure heterogeneity in risk aversion among households in Thai villages. There is substantial heterogeneity in risk preferences, positively correlated in most villages with alternative estimates based on a full risk-sharing model.

We show how to use panel data on household consumption to directly estimate households’ risk preferences. Specifically, we measure heterogeneity in risk aversion among households in Thai villages using a full risk-sharing model, which we then test allowing for this heterogeneity. There is substantial, statistically significant heterogeneity in estimated risk preferences. Full insurance cannot be rejected. As the risk sharing, as-if-complete-markets theory might predict, estimated risk preferences are unrelated to wealth or other characteristics. The heterogeneity matters for policy: Although the average household would benefit from eliminating village-level risk, less-risk-averse households who are paid to absorb that risk would be worse off by several percent of household consumption.

This paper studies household beliefs during the recent US housing boom. To characterize the heterogeneity in households’ views about housing and the economy, we perform a cluster analysis on survey responses at different stages of the boom. The estimation always finds a small cluster of households who believe it is a good time to buy a house because house prices will rise further. The size of this “momentum” cluster doubled towards the end of the boom. We also provide a simple search model of the housing market to show how a small number of optimistic investors can have a large effect on prices without buying a large share of the housing stock.

This paper is an extension of Monika Piazzesi's and Martin Schneider's work while they were in the Research Department of the Federal Reserve Bank of Minneapolis.