Mediciones de magnitudes ópticas

In the following, we estimate an OLS model to capture the effect of each period on each screen condi- tional on the structural screens. We assume that the periods, which indicate collusion or competition, directly and causally affect the distribution of the bids and therefore the value of the screens. We use the number of bids in a tender and the value of the contract as structural screens. We reasonably assume that the number of bids in a tender positively affects the competition. The more firms that submit bids in a tender, the fiercer is the competition. The same yields for a contract with higher value. Firms compete fiercer for contracts with higher value because they earn a higher income. Since the structural screens positively affect the competition, both should positively affect the co- efficient of variation as the skewness statistic and negatively affect the percentage difference, the kurtosis statistic and the relative distance.

contract t. ”precartel”, ”year98”, ”cartel” and ”postcartel” denote the dummy variables for the pre- cartel period, the year 1998, the cartel period and the post-cartel period, respectively. Note that i is the subscript for each screen and t is the subscript for contracts.

For the coefficient of variation and the relative distance, we find that all estimated coefficients of each period are significant in table 3.7. For the kurtosis statistic and the skewness statistic, the estimated coefficients of 1998 are insignificant. All coefficients are insignificant for the percentage difference in the first and second lowest bids. Moreover, the estimated coefficients for the number of bids in a tender indicate opposite results from the hypothesis of competition for the coefficient of variation, the kurtosis statistic and the skewness statistic. Solely for the relative distance, we find that the number of bids in a tender negatively affects the value of the relative distance in the direction of a more competitive behavior when the number of bids in a tender rises. However, that estimated coefficient is only significant at 10%. Moreover, the value of the contract is significant solely for the coefficient of variation and indicates that the coefficient of variation decreases when the value of the contract increases. The result, therefore, indicates that competition softens when the value of the contract tendered rises.

As shown in the implementation of the simple screens, the statistical tests for all screens, except for the skewness statistic, indicate that 1998 does not significantly differ from the post-cartel period. We test this prediction again for all screens in the OLS estimation. We find again in table 3.8 that the estimated coefficients of 1998 do not differ from those of the post-cartel period for all screens, except for the skewness statistic. In summary, the tests again confirm the similarity between 1998 and the post-cartel period.

In a last step, we want to capture only the impact of collusion on the screens. Therefore, we exclude from the sample all observations from the pre-cartel period and we only use observations from the year 1998, the cartel period and the post-cartel period. We estimate the following OLS models with White robust standard deviation for each screen.

screenit= β0+ β1nbrbidst+ β2minbidst+ β3cartel + t (3.7)

Where ”nbrbids” and ”minbids” denote the number of bids in a tender t and the value of a contract t, receptively. ”cartel” denote the dummy variable for t the cartel period. Note that i is the

Table 3.7: Estimation of OLS models for each screen

Endog. Var. cv rd kurto skew diffperc

Nbrbids -0.13* -0.17* 0.14** -0.06** 0.34 (0.078) (0.094) (0.064) (0.028) (0.553) Minbids -0.71*** -0.18 -0.07 0.02 0.51 (0.166) (0.221) (0.166) (0.061) (1.122) Pre-cartel 6.49*** 4.96*** 1.33** -0.70** 2.02 (0.857) (1.178) (0.653) (0.288) (5.309) Year98 11.34*** 2.83*** -0.79 0.08 2.05 (1.108) (1.002) (0.782) (0.343) (5.643) Cartel 5.18*** 5.42*** 1.84*** -0.75*** 1.61 (0.684) (0.853) (0.508) (0.218) (5.507) Post-cartel 10.33*** 2.06*** -1.00* 0.56** 3.17 (1.013) (0.732) (0.570) (0.240) (4.406) N 334 321 290 321 334 R2 0.77 0.49 0.51 0.48 0.21

Note: ”Endog. Var.”, ”cv”, ”rd”, ”kurto”, ”skew” and ”diffperc” denote the endogenous variable in the OLS model, the coefficient of variation, the kurtosis statistic, the skewness statitistic and the percentage difference in the first and second lowest bids, respectively. ”nbrbids”, ”minbids”, ”cartel”, ”post-cartel”, ”pre-cartel”, ”year98”, ”N” and ”R2” denote the number of bids in a tender, the value of a contract, the cartel period (from January 1999 to April 2005), the post-cartel period (from April 2005 to the end of 2006), the pre-cartel period (from 1995 to 1997) and the year 1998, the number of observations and the adjusted R squared, respectively.

Table 3.8: Statistical tests for the post-cartel period against year 1998 cv rd kurto skew diffperc

Table 3.9: Estimation of OLS models for the bid-rigging effect of each screen

Endog. Var. cv rd kurto skew diffperc

Intercept 10.49 1.92** -0.94 0.26 2.01 (1.021) (0.839) (0.663) (0.274) (5.643) Nbrbids -0.09 -0.11 0.14* -0.04 0.41 (0.092) (0.093) (0.072) (0.030) (0.640) Minbids -0.71*** -0.06 0.02 -0.01 0.66 (0.175) (0.231) (0.179) (0.063) (1.206) Cartel -5.57*** 3.00*** 2.71*** -1.05*** -1.02 (0.594) (0.337) (0.327) (0.145) (0.945) N 259 246 217 246 334 R2 0.45 0.17 0.25 0.18 0.21

Note: ”Endog. Var.”, ”cv”, ”rd”, ”kurto”, ”skew” and ”diffperc” denote the endogenous variable in the OLS model, the coefficient of variation, the kurtosis statistic, the skewness statitistic and the percentage difference in the first and second lowest bids, respectively. ”nbrbids”, ”minbids”, ”cartel”, ”N” and ”R2” denote the number of bids in a tender, the value of a contract, the cartel period (from January 1999 to April 2005), the number of observations and the adjusted R squared, respectively.

potential bid-rigging conspiracies. For instance, if the coefficient of variation decreases by approx- imately 5% in a market, it could potentially indicate a bid-rigging conspiracy. In any case, such a result should suggest that deeper investigations are needed. Likewise, if the comparison of similar markets shows such differences, as observed for the cartel period, one should investigate the possi- bility of bid-rigging conspiracies deeper.

A similar interpretation can be made for the other screens, except for the percentage difference in the first and second lowest bids. Since the Ticino cartel is certainly one of the severest uncovered bid-rigging cartels known in Switzerland, we suggest that the estimated coefficients for the cartel period should be considered conservative thresholds and benchmarks. Finding smaller differences or a weaker evolution in the screens would of course not indicate the absence of potential bid-rigging issues (seeImhof et al., 2017, for detecting incomplete bid-rigging cartels, i.e., when firms do not rig

all contracts or/and when only a subset of firms participate in the bid-rigging cartel).

Finally, most structural screens in the estimated equations in table 3.9 are insignificant. Only the number of bids in a tender is significant for the kurtosis statistic, but it positively affects the kurtosis statistic excluding a competitive effect due to more bids in a tender. Finally, the value of a contract is significant only for the coefficient of variation and excludes fiercer competition for a contract with