One of my colleagues in the economics department at SJSU, Tom Means, is the former mayor of Mountain View. He tells some interesting stories about how a lack of economic understanding on the part of some urban planners leads to poor decision making.
One of his examples concerns the so-called "cost of community service" studies, which try to calculate the increase in public expenditures the city will incur with different levels of development. Some of the methods used confuse average with marginal costs, and thus result in high-density projects being assigned too high of costs.
For example, say the average cost of providing city parks is $100 per resident. If 100 new residents move in due to new development, some cost of community service methods would say this development will cause the city to incur $10,000 in additional costs for park provision. And of course, a higher-density development, say one with 200 residents, will impose twice as many costs.
This is despite the fact that, to a large extent, park provision is a fixed cost, and does not depend that much on the number of users. In other words, the marginal cost of a new resident is likely very low, at least with respect to parks, but also with respect to a variety of other services. For example, emergency ambulance service provision also has a fixed-cost component.
Another good example of how an urban planning method results in a bias against high-density and infill development is that many cities use a measure known as "level of service" or LOS, to determine the traffic impact fees they charge developers. This creates a bias against infill development due to the "last-in" problem. (See 4:30 of this video for a more detailed explanation of this problem.)
In short, new development in higher density areas causes more congestion (measured by vehicle throughput) than development in low density areas. Thus developers pay higher impact fees for infill development, even though development in higher density areas may in fact facilitate transit and other forms of non-automotive transportation (which in turn may actually lead to higher person throughput, even while vehicle throughput declines.)
The state of California has recently passed SB 743 which attempts to address the bias introduced by the LOS method. Melanie Curry has summarized and explained the implications of this legislation here and here.
Both of these examples relate to the research Ralph McLaughlin have been doing on Benefit-Cost Analysis models used by transportation planners. The specifics differ in each of the situations, but what they have in common is they all represent situations where deficiencies in planning methods lead to a bias against higher-density, urban development. I plan to circulate our manuscript for this project later this summer.
One of his examples concerns the so-called "cost of community service" studies, which try to calculate the increase in public expenditures the city will incur with different levels of development. Some of the methods used confuse average with marginal costs, and thus result in high-density projects being assigned too high of costs.
For example, say the average cost of providing city parks is $100 per resident. If 100 new residents move in due to new development, some cost of community service methods would say this development will cause the city to incur $10,000 in additional costs for park provision. And of course, a higher-density development, say one with 200 residents, will impose twice as many costs.
This is despite the fact that, to a large extent, park provision is a fixed cost, and does not depend that much on the number of users. In other words, the marginal cost of a new resident is likely very low, at least with respect to parks, but also with respect to a variety of other services. For example, emergency ambulance service provision also has a fixed-cost component.
Another good example of how an urban planning method results in a bias against high-density and infill development is that many cities use a measure known as "level of service" or LOS, to determine the traffic impact fees they charge developers. This creates a bias against infill development due to the "last-in" problem. (See 4:30 of this video for a more detailed explanation of this problem.)
In short, new development in higher density areas causes more congestion (measured by vehicle throughput) than development in low density areas. Thus developers pay higher impact fees for infill development, even though development in higher density areas may in fact facilitate transit and other forms of non-automotive transportation (which in turn may actually lead to higher person throughput, even while vehicle throughput declines.)
The state of California has recently passed SB 743 which attempts to address the bias introduced by the LOS method. Melanie Curry has summarized and explained the implications of this legislation here and here.
Both of these examples relate to the research Ralph McLaughlin have been doing on Benefit-Cost Analysis models used by transportation planners. The specifics differ in each of the situations, but what they have in common is they all represent situations where deficiencies in planning methods lead to a bias against higher-density, urban development. I plan to circulate our manuscript for this project later this summer.