Kaplan:2016fe (Non-durable Consumption and Housing Net Worth in the Great Recession: Evidence from Easily Accessible Data.)

In 2013; Mian, Rao, Sufi used proprietary data on the US housing market (obtained from Core Logic) and personal consumption expenditures (obtained from master card) from 2006-2009 to estimate that the elasticity of consumption expenditures to changes in the housing share of household net worth.

This paper replicates the main results from Mian, Rao, and Sufi using data that is more easily accessed by economists in academia.

Data

The authors proxy the housing data from Core Logic, using housing data from Zillow. The data is freely downloadable from the Zillow website.

Instead of the mastercard data, the authors use data from the Kilts Neilsen scanner retail survey. This survey includes weekly price and quantity levels for sales at the bar code level for about 40,000 US stores from 2006-2009 (the panel is still ongoing and currently runs to 2014). KMV estimate that this subset is approximately 40% of aggregate US consumption on non-durables.

Replication

Using the Core logic and mastercard data to, MRS report an elasticity of consumption expenditures to changes in the housing share of household net worth between 0.33 and 0.36 (depending on the controls in the regression and regression technique – OLS vs IV2SLS).

Using the Zillow and Kilts Neilsen data, KVM report an elasticity between 0.24 and 0.36.

The similarity of these findings despite the very different data sets is encouraging.

New contributions

In addition to replicating the results from MRS, KMV have 3 main findings:

1. They show that the interaction between the fall in local house prices and the size of initial leverage is not statistically significant, after controlling for the direct impact of house price changes.
2. They separate the price and quantity components in the fall in consumption expenditure during the great recession. They construct a proxy measure of the quantity of household expenditure by aggregating quantity sold from all stores at the product level, and then multiplying by an average price for the product. When this is used as the dependent variable in the regression, elasticities are approximately 20% lower.
3. They use the Diary Survey of the Consumer Expenditure Survey to estimate the elasticity of total non-durable goods and survives to the counter part found in the Kilts Nielsen dataset. They obtain an elasticity between 0.7 and 0.9, meaning that their estimated consumption to household share of wealth elasticity should be lowered by approximately 20% when applied to all non-durable goods and services.