Figure 4 - uploaded by Alex Krumer
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The figure shows the average nationalistic bias of compatriot judges from different countries relative to the country's CPI score. The estimates are based on subsample estimations of model (1) without judge-per-season fixed effects for the performances of all ski jumpers from the respective countries. The dashed line depicts the linear relationship between the size of bias and the CPI score.
Source publication
Ski jumping competitions involve subjective evaluations by judges from different countries. This may lead to nationalistic bias, according to which judges assign higher scores to their compatriots. To test this claim empirically, we exploit within-performance variation of scores from all World Cup, World Championship, and Olympic Games competitions...
Contexts in source publication
Context 1
... Figure 4, we demonstrate a negative relationship between the nationalistic bias and the countries' CPI score for the performances of all ski jumpers from the respective countries. In other words, the higher the CPI (less corrupt country), the lower the nationalistic bias. ...
Context 2
... put this result into perspective, an increase in one standard deviation in CPI reduces the nationalistic bias by 0.03 style points, which is 10% of the within-jump standard deviation of style points' evaluation. In Column 2, we exclude South Korea as an extreme outlier (see Figure 4) and three countries (Sweden, Slovakia, and Romania) that only had few ski jumper performance observations (see Appendix A for more details). Our results are robust to exclusion of these countries. ...
Context 3
... Games, but was also the only country that hosted Olympic Games in our data, it is possible that our findings on the relationship between the CPI and nationalistic bias are driven by hosting the Olympic Games and not by Russia per se. To obviate this concern, we remove the data of the Olympic Games and perform analyses similar to those in Figures 3 and 4 and Table 8. The results presented in Appendixes E-G show a very similar pattern. ...
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