Private corruption

• Grant number: 16/03267-6
• Support Opportunities: Regular Research Grants
• Start date: June 01, 2016
• End date: May 31, 2018
• Field of knowledge: Applied Social Sciences - Administration - Business Administration
• Principal Investigator: Paulo Roberto Arvate
• Grantee: Paulo Roberto Arvate
• Host Institution: Escola de Administração de Empresas de São Paulo (EAESP). Fundação Getúlio Vargas (FGV). São Paulo , SP, Brazil
• Associated researchers: Chen Yen-Tsang

Abstract

Despite the serious consequences of private corruption both to the firms and host countries, (Argandoña, 2006; Dridi, 2013; Halter & de Arruda, 2009; Ogrean, Herciu, & Bela_cu, 2008; Zhang, 2012), the study of this phenomenon is still recent and limited (Gopinath, 2008). Althoug it is a international issue, developing countries are specially affected (Gopinath, 2008). The understanding on this subject is still limited - not only because of its complexity, but also because of the lack on a systemic model of the phenomenon, because of the non-trivial relationships between variables and because of the endogeneity (Calderón & Álvarez-Arce, 2006). This project proposes a transnational study questioning: 1) the influence of the host country's corruption on its private environment corruption, comparing Brazil to other country with distinct corruption levels, 2) The influence of prior exposure to ethical norms on corruption practice predisposition and 3) The tradeoffs faced when opting to engage or not in a corrupt practice in the private context. The present project will use a random experimental design that will make it possible to overcome both endogeneity and reverse causality (Antonakis, Bendahan, Jacquart, & Lalive, 2014) and to stablish causal relationships, contributing to the area through consistent exploration of this phenomenon. For analysis, it will be used the scenario technique for manipulating a variable, Conjoint Analysis (Discrete Choice Analysis) to analyze the respondents priority order when opting to behave or not corruptly. The results will be analyzed with ANOVA and conditional logistic regression. (AU)