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Models of Urban Income Inequality and Heterogeneity.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Models of Urban Income Inequality and Heterogeneity./
作者:	Kudko, Ievgenii.
面頁冊數:	1 online resource (147 pages)
附註:	Source: Dissertation Abstracts International, Volume: 79-03(E), Section: A.
Contained By:	Dissertation Abstracts International79-03A(E).
標題:	Economics. -
電子資源:	click for full text (PQDT)
ISBN:	9780355309799

Models of Urban Income Inequality and Heterogeneity.
Kudko, Ievgenii.

Models of Urban Income Inequality and Heterogeneity. - 1 online resource (147 pages)

Source: Dissertation Abstracts International, Volume: 79-03(E), Section: A.

Thesis (Ph.D.)--State University of New York at Buffalo, 2017.

Includes bibliographical references

Chapter 1 of this thesis explores the effect of increasing income inequality on the rates of urban sprawl and income segregation. Namely, we are interested in studying how an increase in income inequality would affect the size of an urban structure and what what would happen to residential segregation by income: would there be a higher level of residential mixing between the rich and the poor or would they form more segregated communities. We used a mixed logit model from random utility theory and built a theoretical framework to examine how these three phenomena are related. We found that the effect of rising income inequality on urban sprawl and income segregation depends on the preference characteristics of workers in an urban area.f First, cities with either high values of elasticity of choice probability with respect to commuting times or a higher degree of preference heterogeneity among residents will have less geographic sprawl and a lesser degree of income segregation when income inequality rises. High values of elasticity of choice probability with respect to commuting times are likely to be observed in cities with a wide range of public transportation alternatives. In other words, it is likely that those cities with better public transportation infrastructure expand less and suffer from less income segregation than cities dominated by a single mode of transportation like driving. Second, depending on the initial residential location of the rich, an increase in income inequality may either aggravate an existing level of income segregation between the city and the suburbs, or invert this trend. In this chapter we discuss two simulation scenarios that show both outcomes.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9780355309799Subjects--Topical Terms:

555568
Economics.
Index Terms--Genre/Form:

554714
Electronic books.

Models of Urban Income Inequality and Heterogeneity.
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500 $a Source: Dissertation Abstracts International, Volume: 79-03(E), Section: A.
500 $a Adviser: Alex Anas.
502 $a Thesis (Ph.D.)--State University of New York at Buffalo, 2017.
504 $a Includes bibliographical references
520 $a Chapter 1 of this thesis explores the effect of increasing income inequality on the rates of urban sprawl and income segregation. Namely, we are interested in studying how an increase in income inequality would affect the size of an urban structure and what what would happen to residential segregation by income: would there be a higher level of residential mixing between the rich and the poor or would they form more segregated communities. We used a mixed logit model from random utility theory and built a theoretical framework to examine how these three phenomena are related. We found that the effect of rising income inequality on urban sprawl and income segregation depends on the preference characteristics of workers in an urban area.f First, cities with either high values of elasticity of choice probability with respect to commuting times or a higher degree of preference heterogeneity among residents will have less geographic sprawl and a lesser degree of income segregation when income inequality rises. High values of elasticity of choice probability with respect to commuting times are likely to be observed in cities with a wide range of public transportation alternatives. In other words, it is likely that those cities with better public transportation infrastructure expand less and suffer from less income segregation than cities dominated by a single mode of transportation like driving. Second, depending on the initial residential location of the rich, an increase in income inequality may either aggravate an existing level of income segregation between the city and the suburbs, or invert this trend. In this chapter we discuss two simulation scenarios that show both outcomes.
520 $a Chapter 2 recognizes the importance of income inequality in pricing traffic congestion. In reality, drivers deviate from each other in their values of time. Since the value of time depends on workers' income, those with higher earnings will bear heavier losses from traffic congestion. While the urban literature generally recognizes the importance of the value of time in the theory of congestion pricing, there are few papers that quantify the difference in welfare benefits between income-attentive Pigouvian pricing and the quasi-Pigouvian tolling, which is levied under an assumption of the average value of time.
520 $a We find a superiority of an income-attentive tolling scheme over the quasi-Pigouvian tolling. Our results suggest that tolling traffic congestion under the assumption of a constant value of time results in a loss of about 2.04 percent of welfare on a per capita basis compared to the first-best road pricing. Furthermore, income-attentive tolling reduces the level of residential income segregation between the city and the suburb by 2.12 percent more than the quasi-Pigouvian tolling. This may be considered as an additional source of benefit if one believes that residential segregation by income serves no good for society.
520 $a On the other hand, income attentive tolling leads to a lower reduction in the average travel time and the annual per capita travel delay. This happens because a more congested central city has a lower average income than a less congested suburb. Levying a tax that recognizes these income differences leads to a lower congestion toll for the city drivers and a higher congestion toll for the suburban drivers. However, since the lion's share of the traffic congestion occurs on the city roads, a reduction in the average travel delay will be smaller when implementing such an income-attentive pricing scheme. Higher values of either the value of time or the exponent of the BPR congestion function only enhance all aforementioned results. At the same time, a higher level of the idiosyncratic tastes over the population of workers could decrease a bulk of welfare superiority obtained by income attentive tolling. This could also happen when the earnings of the central city drivers decline, raising an income gap between the city and the suburb.
520 $a The objective of Chapter 3 is to shed light on how income inequality affects residential segregation. Specifically, we are asking whether there is a racial cause underlying residential segregation, or whether it is primarily motivated by disparities in income and socio-economic status. To conduct this analysis, we build a multinomial logit model from discrete choice theory and examine residential location decisions of individual households that reside in the Greater Los Angeles Area, taking their places of work as given. We picked Greater Los Angeles as our study area due to its diversity, which allows our analysis to recognize the multiracial composition of the United States. The resulting parameter estimates describe how households of various personal characteristics consisting of ethnicity, age, education and income level value socio-demographic and economic characteristics of the neighborhoods that they live in. (Abstract shortened by ProQuest.).
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
650 4 $a Economics. $3 555568
655 7 $a Electronic books. $2 local $3 554714
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710 2 $a State University of New York at Buffalo. $b Economics. $3 1189416
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