Cross-National Transitions in a Multivariate Matching Context
Klevmarken, Lupton, and Stafford 345
plans, individual retirement accounts, or privately held assets, across all age groups should lead to an increase in quintile mobility, given heterogeneous portfolio compo-
sitions combined with varying rates of return. Nevertheless, these channels for in- creased mobility are likely to be most prevalent and increasing for older age groups.
Indeed, we find that while the Shorrocks’ index has fallen for the youngest age group younger than 40 from 0.535 to 0.512, the index has increased by 11.7 percent for
households aged 40 to 65 and by 10.9 percent for households older than age 65.
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Given this rise in the heterogeneous volatility of wealth evolution for those older than age 65, proposed policy measures to increase individual access and hence exposure to
the corporate equities market by way of individualized Social Security accounts, while increasing mean returns, is likely to also further increase rate of return hetero-
geneity and hence wealth inequality.
Although age and other demographic differences may be important, the largest difference between Sweden and the United States is in the initial wealth distribution.
As shown in Table 3, the United States wealth distribution is much more spread out than the Swedish distribution. The result is that the quintiles from which we are
measuring mobility are larger and hence a larger absolute change in wealth is re- quired in order to change quintiles. It is therefore difficult to compare mobility in
the two countries. Given the larger wealth dispersion, it is possible for the United States to have larger absolute wealth changes but still have less rank mobility.
In the next section, we standardize the initial wealth distribution between Sweden and the United States and recompute the transition tables. We also outline a method
for standardizing or matching on multiple variables. We apply this matching method to the HUS-PSID data to see if there is differential wealth mobility beyond
that related to the cross-sectional dispersion and factors such as race, age, marital status, and longer run family income.