Make or Buy Urban Public Transport Services: A Rational Choice?

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y a high degree of demand uncertainty are more likely to be operated by external providers but who do not bear commercial risks. The contractual diculties associated with outsourcing in the presence of uncertainty 13 are mitigated by the type of contracts chosen to govern service provision as the preferred contractual options when demand is uncertain are cost plus and gross cost contracts. At last, we nd that the length of the network (Size i ) impacts positively on the likelihood of contracting out, consistently with the results obtained by Levin and Tadelis (2009). This suggests that cities with large networks, although enjoying sucient economies of scale to produce the service in-house, prefer to benet from their better access to private suppliers. Conversely, while small networks may lack the scale for in-house provision, their operation is substantially less likely to be privatized because of their low attractiveness to private operators. risks and 0 otherwise. Then we regressed this variable on Uncertainty i. Results of our estimates are available on request; a particularly interesting result is the following: Commercial risk = −0.001 ∗ Uncertainty + 2.055 ∗∗∗ P olitics − 0.176 (Pseudo R 2 = 0.18). 13 In particular, the risks of contract renegotiation. 18
Table 3: Determinants of organizational choices Contracting diculties & economies of scale Ordered Logit Multinomial Logit Ordered Logit Multinomial Logit (1) (2) (3) (4) Direct Semi-public Direct Semi-public provision contracting provision contracting Complexityi -1.059 1.236 0.727 -0.972 1.133 0.692 (0.338)*** (0.449)*** (0.540) (0.335)*** (0.453)** (0.547) Citiesi -0.030 0.034 0.033 (0.018)* (0.026) (0.024) Uncertaintyi 0.0003 -0.0007 -0.0002 0.0003 -0.0007 -0.0002 (0.0001)* (0.449)** (0.0002) (0.0002)* (0.0003)** (0.0002) Sizei 0.002 -0.005 -0.0004 0.003 -0.005 -0.002 (0.001)* (0.449)*** (0.002) (0.001)** (0.002)** (0.002) Non-monetary factors Unemploymenti 0.091 0.001 -0.250 0.102 0.002 -0.267 (0.084) (0.114) (0.123)** (0.085) (0.117) (0.127)** Inequalityi -0.315 0.297 0.385 -0.336 0.310 0.411 (0.149)** (0.195) + (0.211)* (0.152)** (0.199) + (0.216)* T axesi -31.149 52.329 30.861 -29.018 49.028 28.947 (10.301)*** (16.853)*** (13.265)** (10.392)*** (17.159)*** (13.631)** Regional choicesi 2.000 -1.912 -3.338 2.142 -2.084 -3.443 (0.867)** (1.312) + (1.197)*** (0.876)** (1.331) + (1.210)*** W ater contractsi 0.163 -0.108 -0.311 0.142 -0.099 -0.249 (0.112) + (0.161) (0.158)** (0.113) (0.161) (0.160)* P oliticsi -0.067 0.306 -0.610 -0.086 0.312 -0.510 (0.410) (0.593) (0.551 (0.412) (0.594) (0.556) Ad hoc inter-cities arrangementi -0.068 -0.829 1.020 0.087 -1.001 0.878 (0.421) (0.772) (0.558)* (0.439) (0.813) (0.579) + T rend 0.016 0.168 -0.224 0.033 0.158 -0.249 (0.079) (0.121) (0.110)** (0.081) (0.123) (0.116)** Constant -340.972 448.822 -321.203 497.978 (242.779) (221.170)** (246.700) (233.937)** Log − L -111.590 -98.037 -110.206 -96.624 LRχ 2 (K − 1) 34.13 61.23 36.89 64.06 P seudo R 2 0.1326 0.2380 0.1434 0.2489 P rob ≻ χ 2 0.0003 0.0000 0.0002 0.0000 N 154 154 154 154 Note : standard errors in parentheses; + p ≺ 0.15; *p ≺ 0.10; **p ≺ 0.05; ***p ≺ 0.01. 19
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Page 3 and 4: are largely protected from competit
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Page 9 and 10: services in city i in 2006. The alt
Page 11 and 12: Economies of scale. In addition to
Page 13 and 14: Figure 3: Geographical repartition
Page 15 and 16: Table 1: Checklist of our variables
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Page 23 and 24: 6 Conclusions Our objective in this
Page 25 and 26: Fraquelli, Giovanni, Massimilano Pi
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