Table 10.

Determinants of Mobility: Child Age, Inverse Probability of Treatment Weighted Estimation (IPTW)

	(1)	(2)	(3)	(4)	(5)
Oldest kid 12 or 13	0.0090** (0.0023)		0.0095** (0.0023)
Youngest kid 18 or 19		0.0110** (0.0026)	0.0116** (0.0026)
Number of kids in high school				−0.0075** (0.0014)
At least one kid in high school					−0.0122** (0.0018)
Nb. of Observations	62,181	62,181	62,181	62,181	62,181
Nb. of Job Spells	3,316	3,316	3,316	3,316	3,316
Nb. of Scientists	2,977	2,977	2,977	2,977	2,977

Notes: The dependent variable is a binary variable that takes on a value one in the year we observe the elite scientist moving to a new academic position located within 50 miles. Distant movers (scientists moving more than 50 miles away) are excluded from this analysis. Estimation is by logit and marginal effects are reported. We weight individuals by the inverse probability of observing children information we model the probability of observing children information in our sample, as explained by gender, a full suite of birth year dummies, and degree dummies. Then in our regression predicting mobility,. All specifications include individual productivity and peer variables, as well as full age, vintage category, and year fixed effects. Robust standard errors are in parentheses, clustered at the individual level. See Section III for a full description of how the sample and variables were constructed.

⁺ p < 0.10

* p < 0.05

^**p < 0.01.