Advances and Applications in Statistics © 2014 Pushpa Publishing House, Allahabad, India Published Online: January 2015 Available online at http://www.pphmj.com/journals/adas.htm Volume 43, Number 2, 2014, Pages 91-105 Received: April 8, 2014; Revised: September 23, 2014; Accepted: September 27, 2014 2010 Mathematics Subject Classification: 62J05, 62P25, 62-07. Keywords and phrases: bivariate treatment effect, bivariate probit, multivariate Heckman model, sample selection. MEASURING BIVARIATE AVERAGE TREATMENT EFFECT Patrick Franco Alves and Gustavo T. L. da Costa Department of Economics Brasília University-UnB Brasília, Brazil e-mail: patrickfrancoalves@yahoo.com.br Brazilian Institute of Geography and Statistics-IBGE Rio de Janeiro, Brazil e-mail: gustavo.costa@ibge.gov.br Abstract We present the methodology for the measurement of the average treatment effects under a bivariate selection mechanism. The formulation of the bivariate average treatment effect comes from the multivariate sample-selection model, where the bivariate normal distribution is necessary in order to derive the bivariate inverse Mills ratio. Under this approach there are seven different average treatment effects. An application case is done using a cross-section data for Brazilian industrial firms. It is shown that this methodology can be easily used in any bivariate self-selection mechanism case since there is not an intricate computational solution for the problem.