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Generalized boosting, a non-parametric modeling technique, was used to estimate the propensity scores.
Specifically, we first estimate a Probit model to estimate the propensity of hiring returnee managers.
The first step in estimating the treatment effect is to estimate the propensity score.
This sub-section presents the result of the probit regression model, which was used to estimate the propensity score for matching the off-farm participants with non-participants.
This sub-section presents the result of the logit regression model, which was used to estimate the propensity score for matching the cooperative members with nonmembers.
Finally, I estimate the propensity score by year and 2-digit NACE industry to control for common aggregated demand and supply shocks.
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With the updating rule, we could estimate the propensities θ ik.
First, we estimate the propensities of residues or pairs in contact and incorporate them as input features for prediction.
The results of the bivariate probit model estimating the propensity scores are given in Table 6.
After implementing the logit model for cooperative membership, we estimated the propensity scores.
There are however two ways of incorporating sampling weights when estimating the propensity score.
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