Measurement and Cross-Country Differences

Table 10 shows the results of this exercise first on

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Unformatted text preview: on are being made. Table 10 shows the results of this exercise. First, on using the SIPP sample, pooling both sons and daughters together, and splitting the sample by the median level of net worth (approximately $65,000 in 1984 dollars) the results point to a sharp difference between those below the median and those above. The IGE is 0.458 for those with lower than median net worth, but only 0.274 for those above the median level. Though the difference looks large, one could not reject the null hypothesis of equality at the 5% significance level. The second set of results compares those at or below the first quartile of net worth with those at the top quartile. In this case the difference is even more dramatic and is statistically significant at the 5% level. In fact, for the top quartile the IGE appears to be close to 0. Indeed, the permanent income model would predict this result if income is uncorrelated with ability. Similar attempts were slightly less conclusive using SER data for fathers’ earnings, as the bottom half of table 10 shows. Whereas estimates for the low end of the net-worth distribution were similar to that found using the SIPP, the estimates for those with high net worth were significantly 43 See Mulligan (1997, p. 247). Source: Mazumder (2005) TABLE 10.—INTERGENERATIONAL ELASTICITY BY LEVEL OF NET WORTH Elasticity (Standard Error) N Overall High Net Low Net Worth Worth Diff. t-Stat. SIPP Results Father earnings Log avg. 84–85 Low is median High is median Father earnings Log avg. 84–85 Low is 25th percentile High is 75th percentile 0.369 (0.069) 1514 0.274 (0.184) 757 0.458 (0.112) 757 0.184 0.855 (0.215) 0.044 (0.135) 380 0.465 (0.122) 379 0.508 2.795 (0.182) SER Results Father earnings Log Avg. 79–85 Low is median High is median Father earnings Log Avg. 79–85 Low is 25th percentile High is 75th percentile 0.480 (0.068) 2,186 0.304 (0.110) 1,093 0.465 (0.090) 1,093 0.160 1.130 (0.142) 0.205 (0.113) 547 0.515 (0.130) 547 0.310 1.799 (0.172) Dependent variable is log averag...
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This document was uploaded on 02/26/2014 for the course ECON 560 at The University of British Columbia.

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