CORRESPONDENCE - WS-1 OPPOSITION

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does not change. Since one is only changing the house they live in due to the logit error and the moving costs, we can identify the fixed cost of moving. If people move houses a lot within a zipcode, moving costs must be low. If they do it rarely, moving costs must be high. Equation (9) gives the regression: Yt =—YO,M+Xi Yt = InPt(SIj,0,Tn) Pt (jlj, o, Tom) which identifies the fixed moving cost parameter cpo,M . Note that there is only one log difference instead of two since the households begin in the same state. We also need the variable moving cost parameter, Od,M. Consider two mature households of equal tenure T., both living in non -rent controlled housing, one living in neighborhood j and the other in neighborhood j'. Suppose they immediately move to either neighborhood j* or j**. Both of these are choices constitute immediate renewals. Therefore, Equation (9) gives the specification: JJ'j*,i** _ Wd,M (dj,j* — dYJ-) — Pd,M (dj,j** — dj',j") +Xj,j',j*,j*. Yl,;.:*. = In Pt(j*9,01T,,)�-1n�Pt(j**j,0,T-)� 9.j ,J ,J Pt U* �j" 0,'r ,) Pt (j** lj', 0, Tiro) Intuitively, this compares the relative probabilities of moving to j* vs j** depending on whether one starts in j or j'. If j is very close to j*, but far from j**, then the difference in moving costs between the moves in large. However, if j' is equidistant between the two, the moving costs between the two locations are the same. The relationship between these differences in distances and differences in migration probabilities identifies the marginal cost of moving with respect to distance. Using similar considerations, one can estimate the interaction term parameter �oT M. The equation is detailed in the appendix. As one would expect, the equations for young households are very similar to the ones described above, but the probability of transitioning to a mature household must be taken 31