Score contribution per author:
α: calibrated so average coauthorship-adjusted count equals average raw count
Misclassification of the dependent variable in binary choice models can result in inconsistency of the parameter estimates. I estimate probit models that treat misclassification probabilities as estimable parameters for three labour market outcomes: formal sector employment, pension contribution and job change. I use Living Standards Measurement Study (LSMS) data from Nicaragua, Peru, Brazil, Guatemala and Panama. I find that there is a significant misclassification in 11 of the 16 cases that I investigate. If misclassification is present but is ignored, estimates of the probit parameters and their SEs are biased toward zero. In most cases, predicted probabilities of the outcomes are significantly affected by misclassification of the dependent variable. Even a moderate degree of misclassification can have substantial effects on the estimated parameters and on many of the predictions.