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NYC 9/11 Public Portal Document

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Naj^/(1-B) of its net (generalized) , income (after rent and I commuting) , on shopping trips to destination 1 and a share B of its i income on rent and commuting. Because of the restriction (1'), these shares sum to one and exhaust the household's available 3 income. The household’s derived demand for the number of shopping trips to zone 1 via mode n (given workplace i and the residential S choice (j,k,m)) is then given by:

——------ z for each {l,n} (5) ^ijln !

As apparent from equation (5), the number of shopping trips (l,n) decreases as each such shopping trip becomes more costly (i.e., as g£j]_n increases), generating a substitution effect in favor of other (l,n). An income effect occurs as the household becomes poorer due to increases in housing rent or commuting cost /• which decrease the household’s purchasing power available for shopping trips. A problem remains in the specification of the J utility function. Note that the expenditure shares (a^^’s) are 1- J , specific. Hence, there are too many parameters. To reduce the number of parameters and make their estimation manageable, a well- established notion is adapted from a tradition of retail trade I models developed in the 1960s [Lakshmanan and Hansen (1965) and Huff (1964)]. The notion is that purchasing power expended at a ) particular shopping zone is proportional to a suitable function of that zone’s shopping floor space. For purposes of NYREG, the expenditure shares are specified by the following function:

(6)

I Ep;exp(K,)

Here, Fj^ is the aggregate shopping floor space available in the 1th shopping zone and o is a parameter to be estimated. The exponent Kj^ measures attributes of the zone other than its floor space which play a role in attracting shopping expenditures. It is reasonable to expect that w > 0. This reflects an agglomeration (or size) effect in favor of larger shopping zones. An interpretation might be that a shopping concentration confers external benefits of comparison shopping and variety. With u > 0, the model hypothesizes that shoppers spend more money per square foot of shopping space in larger shopping zones than they do in smaller ones. Seemingly, we have not economized on parameters because we have replaced the Qj^’s with the new Ky’s. However, as we shall see, the Kj^’s are easy to calibrate, while the o helps

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