As corruption has gained more and more attention in general, and as its effects upon people have been explored and found to be very negative, the moral view of corruption has become more negative over time, and the tolerance for it much lower. This is reflected in the different laws and conventions that have been made against it. As such, I hypothesized that corruption would have a more negative effect as the public opinion of corruption has changed. I interact corruption with a time dummy (all country-years on and after 2000 coded 1, all before coded 0), to see if there is a significantly different effect after the year 2000, which should capture any change in the effect over time.83
Much to my surprise the coefficient for the interaction term in estimation one (1) is not significant. As we understand by now, the fixed effects estimation estimates the within country
83 The use of a dummy variable here is justified on theoretical grounds. The conventions and laws are enacted right before and right after 2000, and as such, it is a natural cut off point. A continuous year variable would not have been able to capture the variance in accordance to the theoretical argument.
103
104 variance over time. Seeing as for this hypothesis time is of interest, I would have expected this to be significant, if there is any effect. The dummy variable for year is highly significant, but it is a constitutive term, and as such is not to be directly interpreted. Market potential and trade is significant at the five percent level, with the expected positive signs. The high mean VIF score arises from the collinearity between constitutive terms and the interaction variable. This could easily be fixed by removing the constitutive terms, but this is a serious error in interaction models, according to the interaction literature. Once the interaction is dropped, the VIF values drop, and only trade remains above the “threshold” of 10.
In estimation two, using random effects, there is no real change. The interaction term is insignificant. Given that it was not significant in the fixed effects estimation this comes as no surprise. There is no between variation in the year variable (naturally), so for there to be a significantly different effect once adding the between variance was not expected
In estimation three, using the within and between transformation on the interaction variable, neither the within nor the between component is significant. This tells us that the insignificant result in the previous estimations is not due to competing coefficients, which I discussed in previous models, but simply that neither the within effect nor the between effect is significant for FDI inflows. This once again also nicely illustrates that indeed, the fixed effects estimator is very good if you want to measure the within effect of a variable. The findings in this model would imply that there is no significant change in the effect of corruption after the year 2000 upon FDI inflows. Interestingly, the VIF value increases significantly here. This is because once the within and the between components are separated, they are individually much more collinear with the corruption variable and the year dummy variable respectively. As stated above, there is no technical or easy solution to this, and it is recommended to simply leave the multicollinearity as it is because it does reflect reality.84 Once again, when the interaction term is dropped, the only variable that is above the “threshold” of 10 is trade.85
84 The fact that the between component of the interaction is highly collinear with corruption only reflects that corruption has relatively little variation over time within countries, but much between countries. The within component of the interaction is highly collinear with the variable for year, which is natural because one the constitutive part is a dummy of that variable.
85 In addition, the Collin command in STATA shows no issues of multicollinearity once the interaction term is excluded (appendix 9.2).
105 In the fourth estimation, using the Prais-Winsten approximated transformation, the results change in an interesting way. Now the within component is significant, albeit at the ten percent level, and positive.86 This would indicate that after the year 2000 becoming less corrupt increases FDI more than becoming less corrupt before the year 2000. Specifically, in countries after the year 2000, a one unit increase in the CPI (meaning less corruption) increases FDI inflow with 5.29 percent. This supports hypothesis 8, however, the significance level is low, and technically below the limit for this thesis, the five percent level. However, the grinter graph of the marginal effect indicates that the effect is substantial, and that it is also statistically significant (using 95 percent confidence intervals) (see appendix 9.10). The results also indicate that AR-1, autocorrelation or non-stationarity issues caused the within effect of corruption to be non-significant, which is interesting in itself, and very believable as these are issues that occur in predominantly longitudinal data. This shows us clearly how important it is to account for econometric issues.
In the fifth estimation the significance level for the within effect of the interaction term increases to the five percent level. The size of the coefficient also increases some in size. This increases the robustness of the results.
Theoretical interpretation:
A significant positive coefficient for the within effect of the interaction term indicates that after the year 2000, the effect of corruption is different from before the year 2000. Becoming less corrupt attracts more FDI inflow now, than it did for the time period before 2000 and vice versa.
This then, in light of the theory and framework, can be explained as hypothesized. As the knowledge of the costs and unfairness of corruption has spread through the populations of the world, and governments have become less tolerant and more judgmental towards corruption by implementing laws and conventions, the potential costs in terms of risk and uncertainty has increased over time. The cost-benefit calculation of investing in a corrupt country and engaging
86 Due to the high amount of observations in this model (1858), significance at the 10 percent level must be interpreted with much caution and skepticism.
106 in corruption is then potentially very different today, than it were in 1995, and then presumably, before that as well.87 This supports hypothesis 8.