On the Determinants of Educational Corruption: The Case of Ukraine

Abstract

This article utilizes a unique data set to examine the relationship between a group of potential explanatory variables and educational corruption in Ukraine. Our corruption controls include bribing on exams, on term papers, for credit, and for university admission. We use a robust nonparametric approach in order to estimate the probability of bribing across the four different categories. This approach is shown to be robust to a variety of different types of endogeneity often encountered under commonly assumed parametric specifications. Our main findings indicate that corruption perceptions, past bribing behavior, and the perceived criminality of bribery are significant factors for all four categories of bribery. From a policy perspective, we argue that when bribe control enforcement is difficult, anti‐corruption education programs targeting social perceptions of corruption could be appropriate. (JEL K42, J16, C14)

Why farm households have differences in corruption experiences? Evidences from Bangladesh

“…Differences in the willingness to pay bribe among different professional groups are results of the differences in perceptions about corruption among the groups (Čábelková and Hanousek 2004). Student's perception about honesty of their educational institution and positive thinking about their future career potentiality can influence their involvement in bribery (Shaw 2009). Trust network may substitute bribe through implicit quid pro quo, and people with trust network are less likely to pay bribe (Hunt 2004).…”

“…Bribery and corruption are used as synonyms in the literature. A common tendency of the literature (Hauk and Sáez 1999;Swamy et al 2001;Torgler and Valev 2006;Shaw 2009) is to begin with corruption in the title and then to focus mostly on bribery. However, the underlying reasons, forms, and consequences of bribery and other forms of corruption (e.g.…”

Anti-Corruption Education at Technical University

“…Main corruption risks in educational system of Ukraine have been investigated by Osipan, 2007;Shaw et al, 2014;Ivashchuk & Radzikhovska, 2016;Denisova-Schmidt & Prytula, 2017.…”

Household Income and Relationships with Different Power Entities as Determinants of Corruption

“…These factors include income, education, location, gender, marital status, profession, attitude and perception towards corruption, trust network, city size, etc. (Anik, Bauer, & Alam, 2013;Čábelková & Hanousek, 2004; Herrera, Razafindrakoto, & Rouband, 2007;Hunt, 2004;Mocan, 2008;Shaw, 2009;Swamy et al, 2001;Torgler & Valev, 2006 …”

“…These factors include income, education, location, gender, marital status, profession, attitude and perception towards corruption, trust network, city size, etc. (Anik, Bauer, & Alam, 2013;Čábelková & Hanousek, 2004;Herrera, Razafindrakoto, & Rouband, 2007;Hunt, 2004;Mocan, 2008;Shaw, 2009;Swamy et al, 2001;Torgler & Valev, 2006) across households based on differences in socio-economic characteristics, assuming that a specific type of household is equally vulnerable to corruption in all sectors may be inappropriate. Furthermore, household level characteristics may influence households' probability of experiencing a particular form of corruption (Anik et al, 2013), and a particular service may be more prone to a specific form of corruption.…”