Latviešu valodas versija nav pieejama
Pierre Lamarche
- 8 January 2020
- WORKING PAPER SERIES - No. 2357Details
- Abstract
- This paper studies the heterogeneity of the marginal propensity to consume out of wealth (MPC) both across and within countries. We estimate the MPC based on a cross-country harmonized household level dataset which combines surveys on wealth, income and consumption. We use panel regressions and an instrumental variable approach. First, our panel-based MPC estimates are very similar to those obtained on aggregate data and show substantial heterogeneity across countries. The wealth effect is coming both from housing and financial assets, while the main asset channel varies between countries. Second, the MPC is higher for low-wealth households, whatever the country. Third, we find some asymmetries across countries regarding the reaction to losses versus gains. Fourth, higher MPC is obtained for the two main consumption expenditure categories. Fifth, we find evidences that housing prices shock decreases consumption inequality while financial wealth shocks have a limited effect on consumption inequality.
- JEL Code
- D12 : Microeconomics→Household Behavior and Family Economics→Consumer Economics: Empirical Analysis
E21 : Macroeconomics and Monetary Economics→Consumption, Saving, Production, Investment, Labor Markets, and Informal Economy→Consumption, Saving, Wealth
C21 : Mathematical and Quantitative Methods→Single Equation Models, Single Variables→Cross-Sectional Models, Spatial Models, Treatment Effect Models, Quantile Regressions - Network
- Household Finance and Consumption Network (HFCN)
- 23 May 2017
- STATISTICS PAPER SERIES - No. 22Details
- Abstract
- In this paper, we estimate consumption in the first wave of the Eurosystem Household Finance and Consumption Survey for a subset of countries that account for around 85% of the aggregate final consumption expenditure of households in the euro area. For this purpose we use the methodology described by Browning et al. (2003), taking advantage of the few questions on consumption asked to households participating in the survey and information on consumption collected in the Household Budget Surveys. Using also the framework developed for statistical matching, we give assessments of the uncertainty related to this kind of estimation. We find that the quality of estimation varies greatly across countries and in general is sensitive to the Conditional Independence Assumption implicitly made through this exercise. At any rate, estimations of consumption (provided throughout this paper) should be used with caution, bearing in mind that they rely on strong assumptions.
- JEL Code
- D120 : Microeconomics→Household Behavior and Family Economics→Consumer Economics: Empirical Analysis
D140 : Microeconomics→Household Behavior and Family Economics→Household Saving; Personal Finance
D310 : Microeconomics→Distribution→Personal Income, Wealth, and Their Distributions - Network
- Household Finance and Consumption Network (HFCN)
Annexes - 25 June 2015
- WORKING PAPER SERIES - No. 1817Details
- Abstract
- This paper studies the heterogeneity of the marginal propensity to consume out of wealth using French household surveys. We find decreasing marginal propensity to consume out of wealth across the wealth distribution for all net wealth components. The marginal propensity to consume out of financial assets tends to be higher compared with the effect of housing assets, except in the top of the wealth distribution. Consumption is less sensitive to the value of the main residence than to other housing assets. We also investigate the heterogeneity arising from indebtedness and from the role of housing assets as collateral.
- JEL Code
- D12 : Microeconomics→Household Behavior and Family Economics→Consumer Economics: Empirical Analysis
E21 : Macroeconomics and Monetary Economics→Consumption, Saving, Production, Investment, Labor Markets, and Informal Economy→Consumption, Saving, Wealth
C21 : Mathematical and Quantitative Methods→Single Equation Models, Single Variables→Cross-Sectional Models, Spatial Models, Treatment Effect Models, Quantile Regressions