Chapter 4 Empirical Results
4.1 Statistical Analyses
Because my dependent variable is coded as dichotomous, the main models are estimated using probit. I also estimated logit models for robustness checks. The predicted probabilities were generated using CLARIFY (Tomz, Wittenberg, & King 1999; King, Tomz, &
Wittenberg 2000). My data has a pooled cross-sectional structure for the years 2010 to 2019.
The results from the models on the perception of corruption can be seen in Table 3.
As expected, in Model 1 and Model 3, the total number of CICIG investigations has a positive and statistically significant effect on the perception of corruption among Guatemalans. It indicates that the government’s anti-corruption effort is an important determinant of perception of corruption in the country from its begin in 2008 to the end in 2018. In the Model 2 and Model 4, I decompose the total number of CICIG investigations into two variables. Both coefficients for the number of charged investigations and the number of uncharged investigations are positive and statistically significant. This finding suggests that the number of people identified with corruption with sentences or without them increases the likelihood that an individual perceives corruption in the country. More importantly, the results demonstrate that the number of charged investigations has a more significant coefficient than that for the number of uncharged investigations. This finding suggests that the number of charged investigations makes citizens more likely to perceive corruption.
Table 3 also demonstrates exciting findings for the control variables. Among Guatemalans, older generations are more likely to perceive corruption. Also, those that consider that the country’s national economic performances are negative are more likely to increase the likelihood of perceived corruption. However, the coefficient of egotropic economic outlook does not reach statistical significance. Education attainment has a positive and statistically significant coefficient for the perception of corruption. Specifically, higher educated individuals tend to perceive corruption. This finding suggests that highly educated individuals
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have more access to information about corruption, so they are more likely to perceive corruption.
My results show whether individual life in a rural or urban area does not matter for the likelihood of the perception of corruption. Contrary to expectations, the presidential approval or disapproval do not increase perception. There are also puzzling results regarding political ideology and the frequency of reading news. For instance, both leftist and rightist leaning individuals are less likely to perceive corruption than those who do not have a clear political ideology. Individuals that follow news every day, those that follow news every week, and those that rarely follow news have a higher likelihood of perceiving corruption than those who never follow news.
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Table 3 Probit and Logit Models on Perception of Corruption in Guatemala
Variables Model 1
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Pseudo R squared 0.0469 0.0472 0.0469 0.0471
Observations 6,041 6,041 6,041 6,041
Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1
In Table 4, I calculate the predicted probability of key variables in Model 1 to analyze an individual’s propensity further to perceive corruption. If all the variables are held at their means, a Guatemalan citizen has 79.19% of the probability of perceived corruption. Different situations can increase or decrease the percentage of the probability of perceived corruption.
When fewer individuals are under CICIG’s investigations (at one standard deviation lower), the probability of an individual perceiving corruption is 75.85%. However, when there are more individuals under CICIG’s investigations (at one standard deviation higher), the probability of perceiving corruption increases by 6.4%.
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Table 4 Predicted Probabilities of Corruption Perception for Model 1
Situation %Probability of perceiving corruption
All at mean 79.19%
Note 1: Fewer= the number of individuals under CICIG investigation at 1 standard deviation lower Note 2: More= the number of individuals under CICIG investigation at 1 standard deviation higher Note 3: Lower educated=education at 1 standard deviation lower
Note 4: Higher educated=education at 1 standard deviation higher
Holding all the variables at their means, education attainment resulted to be an important determinant. The breach between those that are highly educated and low educated is significantly large. Higher educated individuals are more likely to perceive corruption (84.73%). However, those that are lower educated are considerably less likely to perceive corruption (72.63%) at the time others do.
Some of the interesting scenarios are related to the sociotropic evaluation variable. Those that consider the national economy is going good have a 74.65% chance of perceiving corruption.
In contrast, having a negative sociotropic evaluation increases the probability of 80.44%, holding all the variables at their means. This shows how the perception of corruption is affected by the economic outlook. Another situation is the difference founded by ideology
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preferences. The predicted probabilities suggest that left-leaning has less probability of perceiving corruption (75.29%) compare to those in the right-leaning (77.77%) and center (81.11%).
Finally, the simulation results show that a female respondent has less probability (77.71%) than male respondents (80.64%) to perceive corruption. Simulations using key variables of Model 1 different situations can create a difference in the likelihood that an individual perceives corruption. Overall, Table 3 and Table 4 demonstrate that the presence of the government’s anti-corruption efforts, education attainment, and economic outlook serve as important determinants for explaining the likelihood of corruption perception in Guatemala.