Housing in Retirement: A Cross-Country Analysis

by Makoto Nakajima, Federal Reserve Bank of Philadelphia and Irina Telyukova, University of California, San Diego

The extent to which retirees save or dissave late in life, and the reasons why they do so, have been the subject of a debate in the lifecycle consumption and saving literature for some time. While the literature typically focuses on retirees’ net worth, and debates whether retirees save for precautionary or bequest reasons, we document that the pattern of dissaving among retirees varies dramatically depending on whether they own a home. Thus, it is important to focus on housing separately from non-housing assets, and to account for high home ownership rates late in life, if one wants to understand the nature and reasons for saving in retirement.

In this project, we will use cross-country data on fifteen developed economies to (a) document the patterns of (dis)saving among retirees in housing versus non-housing assets, and (b) explain the differences in these cross-country patterns. In particular, we will evaluate how much risk of large out-of-pocket medical expenses, the extent of public pension provision, and the magnitude of the social safety net for the elderly each impact the lifecycle patterns of home ownership, housing and financial asset accumulation, as well as borrowing, in retirement, and what they imply for the relative importance of precautionary versus bequest motives. We will do so by using a rich life-cycle model of saving and home ownership in retirement, to evaluate quantitatively the extent to which cross-country variation in policy and risk features account for the differences in observed saving behavior across countries.

The data we will use are the Health and Retirement Study (HRS/AHEAD) for the U.S., the English Longitudinal Study of Ageing (ELSA) , and the Survey of Health, Ageing and Retirement in Europe (SHARE) for thirteen developed countries in Europe. An exceptional feature of these data sets is that they are all modeled after, and harmonized with, the HRS, which makes cross-country comparison natural. In these data, we will document life-cycle patterns of financial and housing assets, home ownership rates, and debt rates, among retirees in each country. We will also document the extent to which retirees are covered by health insurance, what their health condition is, how high their out-of-pocket medical expenses are, how well they are covered by social safety nets, public pensions, and the like. Importantly, we will characterize the full wealth and income distribution among retirees in each country, which will be an key input into the model.

We will then use these variations in the data to identify the impact of each important feature of the environment and public policy on retiree saving decisions, by first calibrating the model to U.S. data, then varying the relevant policy and preference variables to quantify how much these changes account for the observed changes in retiree saving behavior.

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