I'm working on regression in Stata, in which I'm taking income as being dependent on sex, education and tenure. Tenure in my data is the number of years spent in the current job. The regression itself doesn't matter really, be it an OLS, quantile or whatever.
I have panel data of 15 waves (or years). My respondents have changed jobs through time and I have calculated tenure to be the time of the last wave minus the time at which they last changed jobs. This means tenure data for each respondent is only available from the time they last changed jobs onwards. So if I have data for 2000-2014 and someone changed jobs in 2010, tenure is then available for 2010-2013 and before 2010 would be coded missing.
Whenever I include tenure in my model, the number of observations used in the model drops dramatically. I assume this is due to listwise deletion, as Stata removes observations for which no tenure data are available. In the example above, this would mean observations of years 2000-2009 would be ignored.
Should I therefore code the years 2000-2009 as 0 instead of missing? In that case, Stata would take it into account in the regression. However, what would that mean for coefficients, standard errors etc, e.g., the regression results? Would they be biased or be completely wrong?