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Deciding to Quit: A Duration Model of Retirement in Congress
Unformatted Document Text:  The final step was the inclusion of the experience variable and the interaction between experience and position. Since in a duration model one is not just predicting the retirement but the time to retirement it is inappropriate to include the amount of time served in the House as an independent variable in equation. It would be predicting itself. The inappropriateness of doing this was pointed out by Box-Steffensmeier et al . (2003). However, in the same paper they also point out the usefulness of time interaction terms in Cox proportional hazard models. This is exactly what the variable of interest, career ceilings, represents. It is operationalized as position multiplied by time served in the House. Box-Steffensmeier et al. endorse this type of interaction as a way around the major assumption of Cox proportional hazard models, which is that the effects of the variables are proportional. By interacting a covariate with time one can allow the effect on the hazard of that covariate to vary monotonically over time. This is exactly what we want in this case. We are not interested in the effects of one’s position on the likelihood of retirement but whether the relationship between one’s position in the House and the likelihood of retirement changes over the course of one’s career. Theriault predicts that as time increases position will be a greater predictor of retirement. III - Results These variables are put together in a Cox proportional hazard model. This model has advantages that other duration models do not. Most importantly it allows the baseline hazard to vary. It does not assume a particular distribution for the hazard. This is a significant advantage over more constraining parametric models. The results of the model are shown below in Table 1. (The exact method was used to deal with ties.)

Authors: Sempolinski, Joseph.
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The final step was the inclusion of the experience variable and the interaction
between experience and position. Since in a duration model one is not just predicting the
retirement but the time to retirement it is inappropriate to include the amount of time
served in the House as an independent variable in equation. It would be predicting itself.
The inappropriateness of doing this was pointed out by Box-Steffensmeier et al . (2003).
However, in the same paper they also point out the usefulness of time interaction terms in
Cox proportional hazard models. This is exactly what the variable of interest, career
ceilings, represents. It is operationalized as position multiplied by time served in the
House. Box-Steffensmeier et al. endorse this type of interaction as a way around the
major assumption of Cox proportional hazard models, which is that the effects of the
variables are proportional. By interacting a covariate with time one can allow the effect
on the hazard of that covariate to vary monotonically over time. This is exactly what we
want in this case. We are not interested in the effects of one’s position on the likelihood
of retirement but whether the relationship between one’s position in the House and the
likelihood of retirement changes over the course of one’s career. Theriault predicts that as
time increases position will be a greater predictor of retirement.
III - Results
These variables are put together in a Cox proportional hazard model. This model
has advantages that other duration models do not. Most importantly it allows the baseline
hazard to vary. It does not assume a particular distribution for the hazard. This is a
significant advantage over more constraining parametric models. The results of the model
are shown below in Table 1. (The exact method was used to deal with ties.)


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