A practical comparison of the bivariate probit and linear IV estimators

This paper presents asymptotic theory and Monte-Carlo simulations comparing maximum-likelihood bivariate probit and linear instrumental variables estimators of treatment effects in models with a binary endogenous treatment and binary outcome. The three main contributions of the paper are (a) clarifying the relationship between the Average Treatment Effect obtained in the bivariate probit model and the Local Average Treatment Effect estimated through linear IV; (b) comparing the mean-square error and the actual size and power of tests based on these estimators across a wide range of parameter values relative to the existing literature; and (c) assessing the performance of misspecification tests for bivariate probit models. The authors recommend two changes to common practices: bootstrapped confidence intervals for both estimators, and a score test to check goodness of fit for the bivariate probit model.Scientific Research&Science Parks,Science Education,Statistical&Mathematical Sciences,Econometrics,Educational Technology and Distance Education

The impact of diabetes on employment in Mexico

This study explores the impact of diabetes on employment in Mexico using data from the Mexican Family Life Survey (MxFLS) (2005), taking into account the possible endogeneity of diabetes via an instrumental variable estimation strategy. We find that diabetes significantly decreases employment probabilities for men by about 10 percentage points (

Sharp IV bounds on average treatment effects on the treated and other populations under endogeneity and noncompliance

In the presence of an endogenous binary treatment and a valid binary instru- ment, causal effects are point identified only for the subpopulation of compliers, given that the treatment is monotone in the instrument. With the exception of the entire population, causal inference for further subpopulations has been widely ignored in econometrics. We invoke treatment monotonicity and/or dominance assumptions to derive sharp bounds on the average treatment effects on the treated, as well as on other groups. Furthermore, we use our methods to assess the educational impact of a school voucher program in Colombia and discuss testable implications of our assump- tions

Mother’s health after baby’s birth: does delivery method matter? : Does the delivery method matter?

The dramatic increase in the utilization of caesarean section has raised concerns on its impact on public expenditure and health. While the financial costs associated with this surgical procedure are well recognized, less is known on the intangible health costs borne by mothers and their families. We contribute to the debate by investigating the effect of unplanned caesarean deliveries on mothers’ mental health in the first nine months after the delivery. Differently from previous studies, we account for the unobserved heterogeneity due to the fact that mothers who give birth through an unplanned caesarean delivery may be different than mothers who give birth with a natural delivery. Identification is achieved exploiting exogenous variation in the position of the baby in the womb at the time of delivery while controlling for hospital unobserved factors. We find that mothers having an unplanned caesarean section are at higher risk of developing postnatal depression and this result is robust to alternative specifications

Reliability of the LaCoste-Romberg Surface Ship Gravity Meter S-9 during cruise 60-H-13 of the Texas A. & M. Research Vessel "Hidalgo"

Due to the character of the original source materials and the nature of batch digitization, quality control issues may be present in this document. Please report any quality issues you encounter to [email protected], referencing the URI of the item.Not availabl

Maximum likelihood and two-step estimation of an ordered-probit selection model

We discuss the estimation of a regression model with an ordered-probit selection rule. We have written a Stata command, oheckman, that computes two-step and full-information maximum-likelihood estimates of this model. Using Monte Carlo simulations, we compare the performances of these estimators under various conditions

Semiparametric bounds on treatment effects

We present a variety of semiparametric models that produce bounds on the average causal effect of a binary treatment on a binary outcome. The semiparametric assumptions exploit variation in observable covariates to narrow the bounds. In our main model, the outcome is determined by a generalized linear model, but the treatment may be arbitrarily endogenous. Our bounding strategy does not require the existence of an instrument, but incorporating an instrument narrows the bounds. The bounds are further improved by combining the semiparametric model with the joint threshold-crossing assumption of Shaikh and Vytlacil (2005).Program evaluation Selection bias Threshold-crossing model Linear index Binary response Discrete endogenous variable