Students are also asked to describe their sentiment toward bribing. In particular, if a student believes that a teacher accepting a bribe is a "criminal" or "a bribe-taker," then bribery is categorized as a crime.
Thus we include a dummy for student employment status in an attempt to capture an income effect on bribing (Mocan 2008).
A word of caution at this point: This variable could have severe measurement bias because the gold medal could result either from hard work or from bribing. Thus the interpretation of the effect of this variable depends upon what it actually measures.
Finally, we investigate the relationship between bribing in secondary and bribing in tertiary education.
In Table 2 we report the observed difference in bribing frequencies across the four different types of bribes for each of the explanatory variables considered.
Our main objective is to estimate the probability of bribing across each of the four types of bribes we observe in the data set: bribing on exams, for entrance, for credit, and on term papers.
Under the above framework we can allow feedback between perceptions and bribing with feedback parameters [[beta].sub.12] and [[beta].sub.21].
[Z.sub.1] contains other control variables of interest such as past bribing behavior or gender.
For example, bribing for entrance could increase the probability of bribing on an exam.
If the misclassification probabilities are zero, then the marginal effects derived by Equation (11) represent the true marginal effect of X on actual bribing behavior ([[??].sub.1]).
Table 3 reports the results for bribing on exams for the nonparametric and probit specifications.
First, we show that A can prevent B from invading by bribing only legislators in [0, [v.sup.-1](0)] and paying them [a.sup.*](z) = v(0) - v(z).
This implies that A cannot prevent an invasion by B without bribing legislators outside the [0, [v.sup.-1](0)] range.
Let b equal the cost to B of bribing the [m.sup.th] cheapest legislator in A's coalition.