are resolved. For the rest of the paper, we will only use data from the 19 lawyers with at least one trial and one non-trial outcome. We will do this because some of our models, like the one we present below, compare outcomes of lawsuits that go to trial to outcomes of lawsuits that do not go to trial for the same lawyer. Lawyers who do not have at least one lawsuit that goes to trial and at least one that does not go to trial contribute very little to these estimations. The inclusion of these lawyers, however, would require the estimation of many more parameters in a non-linear model. As we mentioned earlier, the inclusion of these lawyers would require the estimation of an additional 30 lawyer effects while only adding an additional 85 observations.

Consider now the following model: where the subscript i denotes the case and the subscript l denotes the lawyer. The dependent variable posil is a dummy variable equal to one if the worker recovers a positive award. If the case ended in a trial ruling, the dummy will be equal to one if the worker was awarded a positive amount at trial and if this award was in fact collected. If the lawsuit did not end in a trial ruling, then posil is simply a dummy variable for whether the case was settled (all settlements are for positive amounts) as opposed to being dropped. We consider two observable characteristics: gender and tenure, and allow the effects of these variables to be different for trial and non-trial outcomes. We also allow trials to have a different intercept than do lawsuits that do not end as trials.

We estimate the parameter αl for each lawyer, which among other things captures the differences in settlement probabilities across lawyers, controlling for gender and tenure and conditional on the case not going to trial. Note also that γ, through the parameter αl, also affects the probability of recovering a positive amount at trial. Since the two parameters γ and αl enter multiplicatively in the last term of the equation, we estimate this model with non-linear least squares.<sup>8</sup> The first column of table 4 presents the results of the estimation of equation 3. The first result to point out is that the estimate of β2 is 0.04 and statistically significant at the 0.01 level. This result tells us that, conditional on going to trial, workers with high tenure tend to collective positive awards. We also see that the estimate of γ is -1.06 and is significant at the 0.05 level. This means that lawyers who tend to settle cases that do not go to trial (ones with high values for αl) tend not to collect positive awards for cases that go to trial.

We therefore see that cases that go to trial are more likely to end with the worker collecting something when the worker was employed for a long time at the firm and when the worker’s lawyer drops a high fraction of cases that do not go to trial. One simple explanation for these results is that these types of

> 8 Some cases are grouped together in the same lawsuit. We calculate the standard errors of our estimated parameters allowing for arbitrary heteroscedasticity and allowing for an arbitrary correlation of the error terms among cases grouped together in the same lawsuit.

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