URBAN FREIGHT DISTRIBUTION POLICIES: JOINT ACCOUNTING OF NON-LINEAR ATTRIBUTE EFFECTS AND DISCRETE MIXTURE HETEROGENEITY 1 Valerio Gatta* and Edoardo Marcucci* * DIPES/CREI, University of Roma Tre "Transport, Spatial Organization and Sustainable Economic Development” - Venice - September 18-20, 2013 XV Conference of the Italian Association of Transport Economics and Logistics

Outline 2  Research goals  Survey and Data description  Main results  Conclusions

Research goals 3  Urban Freight Transport (UFT). Main agents: retailers, transport providers, own account  Policy makers are interested in knowing, before implementing a given policy, the most likely reactions  One-size-fit-all policies are usually implemented with mixed results  Study context  Lack of appropriate data (elicitation costs & low interest of agents ► in-depth investigation of transport providers’ preferences  Policy makers usually evaluate policies assuming linear effects on agent’s utility for attribute variations.  Not only inter-agent but also intra-agent heterogeneity  Joint analysis of heterogeneity & non-linear effects  Contributions to UFT literature

Survey and data description (I) 4  Stated Ranking Exercise in Rome’s Limited Traffic Zone  Volvo Research Foundation (2009), “Innovative solutions to freight distribution in the complex large urban area of Rome”  Project  Advancement from stakeholder consultation to final attribute selection criteria  Attribute definition  Levels and ranges selection  Progressive design differentiation by agent-type with updated priors (efficient design, 3+1 waves)  Main steps

Survey and data description (II) 5  Attribute levels and ranges  Example of a ranking task Attribute Number of levels Level and range of attribute (Status Quo underscored) Loading/unloading bays (LUB)3400, 800, 1200 Probability of free l/u bays (PLUBF)310%, 20%, 30% Entrance fees (EF)5200€, 400€, 600€, 800€, 1000€ Policy 1Policy 2Status Quo Loading/Unloading bays Probability to find L/U bays free20%10% Entrance fee1000 €200 €600 € Policy ranking 

Survey and data description (III) 6  66 units  Total number of observations: 1128  The sample of transport providers Transport provider agent distribution by main freight sector 1)Food (fresh, hotels, restaurants) 2)Personal and house hygiene (pharmaceuticals, watches) 3)Stationery (paper, toys, books, CDs) 4)House accessories (computers, dish-washer) 5)Services (flowers, animal food) 6)Clothing (cloth, leather) 7)Construction (cement, chemicals) 8)Cargo (general cargo)

Models estimated 7  M1 - Multinomial logit model with linear effects (attributes linear and normalized)  M2 – Multinomial logit model with non-linear effects (effects coding)  M3 – Latent class model with linear effects (the same specification as in M1)  M4 – Latent class model with non-linear effects (the same specification as in M2)  Comparison between models through WTP measures (confidence intervals based on Delta method)  Discrete choice models

Main results (I) 8  Model fit: adj.Rho 2 =  Coefficients statistically significant, with the expected sign  Tariff plays the lion part in explaining preferences  SQ adversion  M1 – MNL, linear effect, attributes linear and normalized VariableCoefficientSt. Err. t-stat LUB PLUBF EF ASC_Alt ASC_Alt

Main results (II) 9  Better fit (adj.Rho 2 = 0.281)  All the reported coefficients are statistically significant  PLUBF is linear  M2- MNLwith non-linear effects (effects coding) VariableCoefficientSt. Err. t-stat LUB LUB PLUBF EF EF EF EF ASC_Alt ASC_Alt

Main results (II) 10  M2 - MNLwith non-linear effects (effects coding)  In line with prospect theory (Kahneman & Tversky, 1979)

Main results (III) 11  Better fit (adj.Rho 2 = 0.377) – almost equal class membership probabilities  Class 1 comprises more price-sensitive agents  Agents in Class 2 are more interested in LUB and PLUBF  M3 - LC with linear effects (same specification as in M1) VariableCoeff. t-statCoeff.t-stat LUB PLUBF EF ASC_Alt ASC_Alt CLASS 1 CLASS 2 Estimated latent class probabilities: Class1 = 0.50; Class 2 = 0.50

Main results (IV) 12  Better fit (adj.Rho 2 = 0.423), C1 price sensitive; C2 Bays sensitive  The same considerations of M3 apply here  M4 - LC with non-linear effects (same specification as in M2 CLASS 1 CLASS 2 Estimated latent class probabilities: Class1 = 0.50; Class 2 = 0.5 VariableCoeff. t-statCoeff.t-stat LUB LUB PLUBF EF EF EF EF ASC_Alt ASC_Alt Discriminant socio-economic variables to explain class membership (CART model): Number of customers (145) Number of deliveries per day(4,5)

Main results (V) 13  WTP comparison M1M2M3M4 Policy Class 1Class 2Class 1Class l/u bays -95 (± 9) -113 (± 16) -48 (± 9) -246 (± 16) -52 (± 13) -487 (± 145) l/u bays -191 (± 18) -142 (± 17) -96 (± 18) -492 (± 33) -62 (± 12) -676 (± 210) + 20 units of prob. bays free -149 (± 20) -143 (± 16) -36 (± 22) -517 (± 34) -57 (± 15) -750 (± 218)  Impact of Non-linear effects: M1 vs M2 Overall efficiency loss for P1, P2, P3 = 18€, 49€, 6€  Impact of Heterogeneity: M1 vs M3 Overall efficiency loss for P1, P2, P3 = 198€, 396€, 481€  Impact of joint Heterogeneity &Non-linear effects: M1 vs M4 Overall efficiency loss for P1, P2, P3 = 435€, 614€, 693€ P1 P2 P3

Conclusion 14  The results obtained are relevant both from a theoretical as well as practical and policy-oriented perspective  The paper represents a first attempt at bridging the gap between theory, applied research and data needs  Relevant biases could characterize the results obtained if non- linearity & heterogeneity are not duly accounted for  There is a need for a sophisticated agent-specific model treatment to implement well-tailored and effective policies.  Final remarks  Similar investigation on retailers and own-account  Dealing with: i) interactive choice models; ii) Bayesian estimation methods; iii) sample size increment  Future research

Thanks for your attention! 15  Questions?  Questions? Questions?