Score contribution per author:
α: calibrated so average coauthorship-adjusted count equals average raw count
For linear panel data models with fixed effects and sample selection, we correct for selectivity bias in the within-group estimator. The proposed procedure is equivalent to a pooled weighted least squares based on pairwise differences augmented with correction terms. A computationally affordable method of estimating nuisance temporal correlation parameters in the selection equation errors is also proposed. Analytic standard errors are derived for the multi-step estimator. Our method is easier to implement and performs well in comparison to the minimum distance approach according to simulations.