Estimating the Intergenerational Elasticity and Rank Association in the US : Overcoming the Current Limitations of Tax Data

Ideal estimates of the intergenerational elasticity (IGE) in income require a large panel of income data covering the entire working lifetimes for two generations. Previous studies have demonstrated that using short panels and covering only certain portions of the lifecycle can lead to considerable bias. I address these biases by using the PSID and constructing long time averages centered at age 40 in both generations. I find that the IGE in family income in the U.S. is likely greater than 0.6 suggesting a relatively low rate of intergenerational mobility in the U.S. I find similar sized estimates for the IGE in labor income. These estimates support the prior findings of Mazumder (2005a, b) and are also similar to comparable estimates reported by Mitnik et al. (2015). In contrast, a recent influential study by Chetty et al. (2014) using tax data that begins in 1996, estimates the IGE in family income for the U.S. to be just 0.344 implying a much higher rate of intergenerational mobility. I demonstrate that despite the seeming advantages of extremely large samples of administrative tax data, the age structure and limited panel dimension of the data used by Chetty et al. leads to considerable downward bias in estimating the IGE. I further demonstrate that the sensitivity checks in Chetty et al. regarding the age at which children's income is measured, and the length of the time average of parent income used to estimate the IGE, are also flawed due to these data limitations. There are also concerns that tax data, unlike survey data, may not adequately reflect all sources of family income. Estimates of the rank-rank slope, Chetty et al.'s preferred estimator, are more robust to the limitations of the tax data but are also downward biased and modestly overstate mobility. However, Chetty et al.'s main findings of sizable geographic differences within the U.S. in rank mobility, are unlikely to be affected by these biases. I conclude that researchers should continue to use both the IGE and rank based measures depending on their preferred concept of mobility. It also important for researchers to have adequate coverage of key portions of the lifecycle and to consider the possible drawbacks of using administrative data