Transport lecture11

Economics 190/290 Lecture 11: 

Economics 190/290 Lecture 11 Transportation Economics: Analysis of Demand II

II Discrete Choice: 

II Discrete Choice Suppose that consumers n=1,…,N are choosing over a discrete number of items j=1,…,J (e.g. modes of transport) As a researcher, we do not exactly know consumer tastes -- tastes are “random” across consumers How should we model utility and demand?

Conditional utility:: 

Conditional utility: Conditional on choosing alternative j, utility is: Xj are the characteristics of good j Sn is characteristics of the decision maker is (unknown) parameters of utility is “random” utility from consuming item j is “systematic” utility

Systematic portion of utility:: 

Systematic portion of utility: For example, utility obtained from item j: where, cjn = costs of transport, b1<0 wn = wage of consumer n tj = time taken in travel, b2<0 and b3 >< 0

Random portion of utility:: 

Random portion of utility: Prob. that consumer n chooses item i: This will depend on the probability distribution for

Random portion of utility - graph: 

Random portion of utility - graph

Probit distribution:: 

Probit distribution: For example, suppose there are only two items i=1,2, and that the utility difference is normally distributed Denote the density function for the standard normal distribution as where is the cumulative standard normal distribution

Probit distribution (cont’d):: 

Probit distribution (cont’d): Then, the probability of choosing item 1 is: where is the cumulative standard normal distribution

Estimation of Probit:: 

Estimation of Probit: Suppose that consumer 1,…N1 choose travel mode 1, and consumers N1+1,…,N choose travel mode 2. Then the log-likelihood is: This is maximized over the choice of b

Estimation of Probit (cont’d):: 

Estimation of Probit (cont’d): Example, from a sample of 280 urban commuters choosing between two modes of transportation in Chicago: (1) auto and (2) transit We use data on mode characteristics (time to travel, cost, distance), and also the income of consumers. The estimated systematic utility function is:

Estimation of Probit (cont’d):: 

Estimation of Probit (cont’d): where, DT=1 for transit, =0 for auto tj=time in travel, Distj=distance, cj=cost of travel Incn=income, wn=wage

Demand Curve:: 

Demand Curve: Expected demand for mode 1 as: So, we obtain downward sloping demand!

Demand Curve (cont’d): 

Demand Curve (cont’d) Q: Can we get this demand curve from some “aggregate” consumer? (Answer later)

Value of Time:: 

Value of Time: We can also compute the value of time as: so that time spent in commuting is valued at 41% of the individual’s wage. Note: with more than two alternative, Probit becomes difficult to estimate, so consider...

Multinomial Logit distribution:: 

Multinomial Logit distribution: Suppose that the j=1,…,J random terms are distributed as “extreme value”: With m=1, the probability of choosing item i is:

Estimating the Logit:: 

Estimating the Logit: Suppose that consumer n chooses the item j*. The log-likelihood function is: This is maximized over the choice of b

Estimation of Logit (cont’d):: 

Estimation of Logit (cont’d): Example, from a sample of San Francisco commuters choosing between: (1) auto alone, (2) bus + auto access, (3) bus + walking access, (4) carpool (this was before BART) The estimated systematic utility function is:

Estimation of Logit (cont’d):: 

Estimation of Logit (cont’d): where, Dj=1 for mode j, =0 otherwise tj=in-vehicle time in travel, tjout =out of vehicle travel time cj=cost of travel wn=wage of traveler

Demand Curve:: 

Demand Curve: Expected demand for mode i is: So, Again, we obtain downward sloping demand! Slope of demand will be smaller for the mode of transport with higher-wage travelers.

Demand Curve (cont’d): 

Demand Curve (cont’d) Q: Can we get this demand curve from some “aggregate” consumer? (Answer later)

Value of Time:: 

Value of Time: We can also compute the value of time as: so that time spent in commuting is valued at 49% of the wage, but time spent out of the vehicle is valued at (or higher) than the wage!

Problem with the Logit:: 

Problem with the Logit: The relative probability of choosing items i and k is: So Pi/Pk is independent of other alternatives This is the “irrelevance of independent alternatives” (IIA) property E.g., the “red bus, blue bus” problem

Red bus-blue bus problem:: 

Red bus-blue bus problem: Suppose the relative probability of choosing the bus over a car is 2:1 (this is the “odds ratio”). Initially only a red bus is available. Then another bus comes available, which is blue, but otherwise identical to the red bus. It seems sensible that the relative probability of choosing any bus over a car is 2:1, so the relative probability of choosing each bus over the car should be 1:1

Red bus-blue bus problem (cont’d):: 

Red bus-blue bus problem (cont’d): But for the logit, the relative probability Pi/Pk is unchanged when a new alternative become available, so the red bus still has 2:1 odds ratio over a car. Therefore, the blue bus also has a 2:1 odds of being chosen (as it is identical to the red). So the relative probability of taking any bus over a car becomes 4:1 when the blue bus is added.

Red bus-blue bus problem (cont’d):: 

Red bus-blue bus problem (cont’d): Why does this problem with the logit arise? Because we have assumed that the random terms in utility are independent! Contrast with the probit, where we assumed that the difference was normally distributed, so that these errors are correlated (but probit is hard to estimate for more than two modes of transport) Solution: use nested logit

Nested Logit: e.g. Vacation Choice: 

Nested Logit: e.g. Vacation Choice Each type of choice is modeled as logit

Typical Elasticities (Table 2-2): 

Typical Elasticities (Table 2-2)

Typical Elasticities (Table 2-2, cont’d): 

Typical Elasticities (Table 2-2, cont’d)

Value Time Elasticities (Table 2-3): 

Value Time Elasticities (Table 2-3)

Utility Function for Logit: 

Utility Function for Logit Suppose that utility is: where Vj are coefficients, and set s=1

The consumer problem:: 

The consumer problem: Max U(X) subject to: Leads to the demands, which is the same as the logit probabilities!

The consumer problem (cont’d):: 

The consumer problem (cont’d): Substitute demands into utility function, to obtain “indirect utility”: So this is a valid measure of overall welfare in the logit model. It can be shown that,