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Journal of Urban Design

ISSN: 1357-4809 (Print) 1469-9664 (Online) Journal homepage: http://www.tandfonline.com/loi/cjud20

Evaluating the local socio-economic impact ofredevelopments using shift-share analysis: a casestudy of destination redevelopments in Las Vegas(1990–2010)

Joseph J. Danko III & Dean M. Hanink

To cite this article: Joseph J. Danko III & Dean M. Hanink (2017) Evaluating the local socio-economic impact of redevelopments using shift-share analysis: a case study of destinationredevelopments in Las Vegas (1990–2010), Journal of Urban Design, 22:3, 347-369, DOI:10.1080/13574809.2017.1281733

To link to this article: http://dx.doi.org/10.1080/13574809.2017.1281733

Published online: 25 Jan 2017.

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Journal of urban Design, 2017Vol. 22, no. 3, 347–369http://dx.doi.org/10.1080/13574809.2017.1281733

Evaluating the local socio-economic impact of redevelopments using shift-share analysis: a case study of destination redevelopments in Las Vegas (1990–2010)

Joseph J. Danko III and Dean M. Hanink

Department of geography, university of Connecticut, storrs, CT, usa

ABSTRACTMonitoring neighbourhood change associated with a redevelopment is important for policy-makers, business leaders and residents. It helps evaluate public policy and changes in the needs of residents and businesses. However, using raw data (e.g. census data) to track such changes can be problematic. It does not allow one to distinguish between trends attributable to macro- and micro-scale processes. This paper demonstrates how a novel neighbourhood-level, GIS-based spatial approach using shift-share analysis can help resolve this issue. To illustrate its utility, this technique is used to examine the local socio-economic impact of destination redevelopments in Las Vegas between 1990 and 2010.

Introduction

Neighbourhood change has been and remains one of the most analyzed topics in urban research. A lot of interest in neighbourhood change emerged as a reaction to the deplorable living conditions in the rapidly industrializing cities of the nineteenth and early twentieth centuries (Engels 1845; Hoyt 1939). Changes in the (particularly racial and economic) com-position of neighbourhoods due to inner-city slum clearance practices and large-scale urban renewal efforts became a much debated topic during the interwar period (Mumford 1958; Jacobs 1961; Ward 1998). The post-industrial urban exodus of the late twentieth century and the back to the city movement of the early twenty-first century has sparked and continues to inspire new research and discussions about the assumptions of neighbourhood change and urban revitalization strategies, with foundations in neo-liberalism, place promotion, gentrification, New Urbanism and the creative class (Whyte 1988; Harvey 1989; Ward 1998; Friedman, Lin, and Krawitz 2002; Peck and Tickell 2002; Florida 2003; Lee, Slater, and Wyly 2008; Mele 2013; Delmelle 2016).

Understanding how and why the demographic composition of a neighbourhood changes is an important task for numerous individuals, including public officials, business leaders and residents. Redevelopment projects can be expected to change the built environment of a neighbourhood in such a way that real estate rents and prices are pushed upward,

© 2017 informa uK limited, trading as Taylor & francis group

CONTACT Joseph J. Danko joseph.danko@uconn.edu

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348 J. J. DANKO AND D. M. HANINK

yielding associated demographic change in the same way as urban renewal projects did in the past and currently in the more general processes of gentrification. Monitoring such neighbourhood change helps evaluate the effectiveness of public policy regarding economic and community development. It also helps track changes in the needs and well-being of local residents and businesses.

One of the most challenging tasks of evaluating neighbourhood change is assessing the degree to which a specific local factor, such as a new single or series of redevelopments, affects the change in the demographic composition of a particular neighbourhood. Part of the difficulty of attributing a specific level of neighbourhood change to a particular type of site is the fact that trends in raw data (e.g. statistics measured by the United States Census) are influenced by multiple macro- and micro-scale processes. For example, suppose the wealthy population rises in a hypothetical downtown neighbourhood where numerous sports stadiums have been built in the past decade. Analyzing the change in this population subset using census data would not indicate whether this trend is primarily driven by a macro-scale process (e.g. an economic boom or a decrease in the municipal property tax for the entire city) or a local factor (e.g. the proximity to sporting events or the increase in the amenities and attractions nearby the stadiums). As a result, interpretations about the relationship between the increase in the wealthy residents in this neighbourhood and sport stadium developments from such raw data sources could be misleading, and these percep-tions might cause public and private entities to make costly mistakes based on such assumptions.

Shift-share analysis provides a means of avoiding such misinterpretations of neighbour-hood change statistics. It is a technique used for decomposing change in order to identify contributions made by macro- and micro-scale processes (Isard 1960). Shift-share analysis was originally introduced in economic and regional geography literature as a technique for evaluating industrial competitiveness (Dunn 1960). Specifically, it was used to subdivide total employment change in a particular industry and region into three components: the amount of change due to trends in total employment at the national scale (i.e. ‘national growth effect’); the amount of change due to trends in employment in a given industry at the national scale (i.e. ‘industrial mix effect’); and the amount of change in employment attributed to regional factors or the competitive advantage of that region (i.e. ‘local effect’ or ‘competitive effect’). Since its inception, shift-share analysis has undergone a number of extensions and improvements (Barff and Knight 1988; Firgo and Fritz 2016). This technique has also been applied to numerous other applications beyond employment, such as demog-raphy (Franklin and Plane 2004; Franklin 2014) and tourism (Sirakaya, Choi, and Var 2002). This paper will demonstrate that shift-share analysis can also be especially useful in urban research focused on analyzing neighbourhood demographic change.

This paper will not just demonstrate another ordinary application of shift-share analysis to a new set of data. Rather, it presents a novel approach due to its spatial specification and its usefulness for resolving the aforementioned difficult issue in neighbourhood change research. Specifically, it shows how the local effect component of shift-share analysis can be utilized to isolate the change in a given variable due to local factors. Other statistical methods, such as t-tests or regression models, can subsequently be applied to quantify the degree to which a specific type of site, such as a type of redevelopment (e.g. waterfront revitalizations), particular forms of urban design (e.g. flagship architecture), or other factors (e.g. crime

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JOURNAL OF URBAN DESIGN 349

vis-à-vis the broken windows theory [Wilson and Kelling 1982]), affects change in neigh-bourhood characteristics.

The goal of the present research is to illustrate this approach on a test dataset in order to show its relative simplicity in terms of computation, data requirements and statistical interpretation for future neighbourhood change research. The test dataset used in this research centres on one of the most popular and expensive types of urban redevelopments in the world, destination redevelopments (i.e. revitalization projects involved in the arts, entertainment and recreation industries), and the socio-economic characteristics of nearby neighbourhoods in Las Vegas (Judd and Fainstein 1999; Campo and Ryan 2008; Grodach 2008; Santos and Mildner 2010). Such projects can have impacts at many scales, from the regional, to the metropolitan, to the local, depending upon their size and scope. This paper concentrates on identifying local impacts. Specifically, the study utilizes a variation of the local effect of shift-share analysis developed by Arcelus (1984) and statistical t-tests to eval-uate the local socio-economic impact of destination redevelopments in Las Vegas between 1990 and 2010.

Destination redevelopments fit into a class of urban redevelopment projects that Robertson (1995) called special activity generators. Such facilities are designed to draw a large number of visitors, especially from outside the metropolitan area. Their impacts are expected to be region-wide in many cases, with an expectation of employment and revenue growth being generated for the region. They are also expected to have important neigh-bourhood impacts, with growth in employment, business formation and intensive real estate development in areas in close proximity to the redevelopment project (Chapin 2004). Beyond sports stadiums, there appears to be little actual evaluation of the direct local, neighbour-hood-scale, impact of special activity generators. For example, Markusen and Gudwa (2010) describe a number of evaluations of urban cultural destinations with flagship facilities designed by leading architects, but they are focused on economic-base style visitor impacts, rather than local spillover effects. Such spillover effects have been analyzed in the case of sports stadiums, as reviewed by Ahlfeldt and Maennig (2010). They found that the spatial extent of spillover effects vary, depending upon the stadium and the method of assessment, from about one-half to three miles. The spillover effect of interest in the stadium analyses is typically with respect to real estate prices. Different forms of hedonic regressions are used, including those with direct spatial terms and differences-in-differences specifications that have a time component.

The analysis in this paper differs in two basic ways from the analyses reported in the related literature. First, the analysis is focused on demographic change rather than changes in real estate prices, business formation or tourist spending. Second, a spatial shift-share analysis is used rather than the regression analysis that is commonly used in the related literature. A straightforward hedonic analysis would be inappropriate to the interest here in demographic change as opposed to real estate price impacts, but a differences-in-differences regression model could be used. However, the shift-share analysis here simplifies the analysis by avoiding some of the specification problems common to differences-in-differences anal-ysis (Bertrand, Duflo, and Mullainathan 2004) and by making it possible to isolate local, or neighbourhood, effects in demographic change.

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350 J. J. DANKO AND D. M. HANINK

Data

This project uses US Census data (1990–2010), American Community Survey (ACS) data (2009–2013), and locations of redevelopments built between 1990 and 2010 as recorded and verified by the Las Vegas Economic and Urban Development Department. US Census and ACS data are collected from the database housed in the National Historical Geographic Information System of the Minnesota Population Center at the University of Minnesota (MPC 2011). Census and ACS variables considered in this analysis are those often associated with neighbourhood change, including population, White-alone population, Black-alone popu-lation, Hispanic population regardless of race, population by age, population by educational attainment, population by employment status, households by income, housing units and vacant housing units (Table 1; Figure 1). For the population by educational attainment, population by employment and households by income variables, 2009–2013 ACS data were used to replace data that were not available in the 2010 Census. Both census block groups and census blocks were used as units of observation in the analysis. Because they are based on survey rather than complete enumeration, it is important to note that the ACS data are subject to sampling error that may be considerable at such local scales.

This paper focuses on destination redevelopments, defined as redevelopment projects connected to the arts, entertainment and recreation industries as per the 2012 North American Industry Classification System (United States Census Bureau 2013). The name is based on the rationale that these sites are constructed with the hope of spurring an urban renaissance and increasing interurban competitiveness by creating special attractions. It is anticipated that individuals will make trips into these neighbourhoods to

Table 1. Description of las Vegas characteristics used in the present study.

1990 2000 2010 2009–2013Total population 258,295 478,434 583,756 –White population 202,549 334,230 362,264 –black population 29,529 49,570 64,858 –Hispanic population 32,369 112,962 183,859 –Children (under 18 years

old)64,461 124,055 149,755 –

Young adults (18‒29 years old)

51,887 79,375 93,886 –

adults (30‒44 years old) 64,813 115,915 126,884 –Middle-aged adults

(45‒64 years old)50,602 103,792 143,188 –

seniors (65 years old and over)

26,532 55,297 70,043 –

Housing units 109,670 190,724 243,701 –Vacant housing units 9,935 13,974 32,012 –lower-income households

(< $50,000)71,733 90,235 – 91,463

Middle-income households ($50,000‒$100,000)

20,327 45,676 – 51,031

upper-income households (> $100,000)

6,497 33,605 – 52,761

Population with at least a bachelor’s degree

22,564 56,989 – 81,778

Population without a bachelor’s degree

146,160 256,216 – 302,985

employed civilians 131,001 214,301 – 260,019unemployed civilians 9,297 16,176 – 38,472

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JOURNAL OF URBAN DESIGN 351

visit these places and/or will specifically relocate to these neighbourhoods in order to live, work and/or start a business due to the presence of these sites and associated amenities. Destination redevelopments are a staple of neoliberal redevelopment strategies, based on theories of place promotion and attracting members of the creative class in order to improve the quality of urban life and to increase economic productivity of often

Figure 1. spatial distribution of total population, Hispanic population, senior population and lower income households (i.e., earning under $50,000) in las Vegas over time (1990–2010).

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352 J. J. DANKO AND D. M. HANINK

deprived, inner-city neighbourhoods (Harvey 1989; Ward 1998; Judd and Fainstein 1999; Florida 2003; Campo and Ryan 2008; Santos and Mildner 2010). Examples of destination redevelopments include the construction of new or renovation of existing theatres/concert halls, sports stadiums, museums, casinos and parks. Destination redevelopments in Las Vegas are identified from a large list of revitalization projects that were completed between 1990 and 2010 (Figure 2). This list has been compiled from a variety of databases maintained by local organizations in Las Vegas and Clark County (most notably, the Economic and Urban Development Department in Las Vegas). According to this list, there were 102 redevelopments completed during this time period and 14 of these projects were destination redevelopments (Figure 3).

There are several reasons for choosing to study the socio-economic impact of destination redevelopments in Las Vegas between 1990 and 2010. Not only has Las Vegas established a legacy as one of the biggest entertainment capitals of the world, but it has made (and continues to make) substantial investments of public and private funds into destination redevelopments (Congressional Quarterly Staff 2009). Public officials in Las Vegas rely on this approach to increase interurban competitive standing of the city and to prevent and even combat the decline of its downtown and older neighbourhoods (City of Las Vegas 2000). Las Vegas is also an interesting case study for a shift-share application because such a decomposition technique helps meet the statistical challenge of isolating the impact of destination redevelopments on neighbourhood change given the existing stock of these sites in the city.

Figure 2. location of redevelopments in las Vegas built between 1990 and 2010.

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JOURNAL OF URBAN DESIGN 353

Methods

As previously indicated, the approach of quantifying the local socio-economic impact of destination redevelopments used in this paper is a two-step process. The first step is to isolate the amount of socio-economic change near destination redevelopments in Las Vegas that is due to local factors, as calculated by the local effect component of shift-share analysis. The second step is to determine the extent to which destination redevelopments play a role in these changes using statistical t-tests. The statistical t-tests are used for two comparisons. The first set of t-tests are used to examine how the socio-economic characteristics of the neighbourhoods near destination redevelopments are changing compared to randomly sampled locations in Las Vegas. This set will indicate the local socio-economic impact of the destination redevelopments in Las Vegas. The second set of t-tests is used to examine how the socio-economic characteristics of neighbourhoods change near destination redevelop-ment compared to other types of redevelopments. This set will indicate the advantage or disadvantage of destination redevelopments compared to other types of redevelopments.

Neighbourhood (i.e. the unit of analysis) is defined in this research as the number of census units within a one-mile (i.e. approximately 1.6 km) radius of the destination redevel-opments, randomly sampled locations and other types of redevelopments. The types of census units are the smallest ones that exist for the variables under consideration. Census blocks are used for the following variables: population, population by race and Hispanic

Figure 3. spatial distribution of destination redevelopments (black dots) and other types of redevelopments (white dots) in las Vegas over time (1990–2010).

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354 J. J. DANKO AND D. M. HANINK

origin. For the economic variables, the units are census block groups. Although the distance is arbitrary, the logic behind the decision to use a one-mile buffer for this analysis is that this distance is small enough to examine change in the immediate vicinity of the sites yet large enough to capture residential areas even along primarily commercial-zoned streets. A sen-sitivity analysis was not conducted, so the results should be treated as specific rather than general.

Conventional shift-share analysis is a commonly used method of allocating growth in a region to three separate components: national, sectoral and competitive (Knudsen 2000). In summary form, using employment as an example, shift-share is computed as:

where Eirt is the employment in sector i in place r at time t, N is Eirt-1 times the national growth rate (%) in total employment, S is Eirt-1 times the national growth rate in total employment in sector i after subtracting the national growth rate, and C is simply (Eirt‒Eirt-1)‒(N+S). The competitive effect C is the growth (or decline) in employment that can be attributed to conditions in place r, controlling for national and sectoral effects.

The version of local effect component of shift-share analysis used to quantify the amount of socio-economic change was developed by Arcelus (1984), who recommends disaggre-gating the competitive local effect component into two subcomponents. The first subcom-ponent identifies the portion of the competitive effect that is due to the change in the statistical population of a given variable, which is referred to as the regional effect (R). For example, if one were applying shift-share analysis to the change in population under 18 years old in a particular city, the regional effect would illustrate the extent to which the population under 18 years old in the given city changes due to the change in the overall population of that city. The second subcomponent identifies the portion of the competitive effect that is due to the change in the statistical sample, or cohort, of the same variable, which is referred to as the regional cohort mix effect (RM). Continuing the example, the regional mix effect would illustrate the extent to which the population under 18 years old in the given city changes to a particular process related to the population under 18 years old in that city.

The sum of the regional effect and the regional cohort mix effect components is equal to the competitive effect component. The regional effect and regional cohort mix effect are calculated using the following:

where Rri the regional effect for cohort i in region r; RMr

i the regional cohort mix effect for

cohort i in region r; Pri is the regional statistical population in cohort i in the first period; pn

is the population change rate between the two periods at the reference area; pni is the pop-

ulation change rate in cohort i between the two periods at the reference area; pr is the population change rate in region r between the two periods; pr

i is the population change

rate in cohort i in region r between the two periods; and HPri is homothetic statistical

(1)Eirt− E

irt-1= N + S + C

(2)Rri= HPr

i∗ ( pr − pn) +

(

Pri− HPr

i

)

∗ ( pr − pn)

(3)RMri= Her

i∗ [

(

pri− pr

)

−(

pni− pn

)

] +(

Pri− HPr

i

)

∗ [(

pri− pr

)

−(

pni− pn

)

]

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JOURNAL OF URBAN DESIGN 355

population for cohort i in region r, i.e. the regional structure of a given variable if it were identical to the structure of the same variable at the reference area), which is calculated using the following:

where Pr the total employment in the region r, Pni is the population in cohort i for the refer-

ence area, and Pn is the total national population, all for the first period.For this research, the reference area is Las Vegas and the region is the one-mile buffer

around the given sites (i.e. destination redevelopments, randomly sampled sites and other types of redevelopments). For the following variables, the regional effect component is used to illustrate local socio-economic change: total population and housing units. The regional cohort mix effect component is used for the remaining variables to illustrate local socio-eco-nomic change, including White population, Black population, Hispanic population; popula-tion by age (i.e. grouped into four categories: children [under 18 years old], young adults [18‒29 years old], adults [29‒44 years old]), middle-aged adults [45‒64 years old] and seniors [65 years old and over]); population by educational attainment (i.e. population without a bachelor’s degree and population with at least a bachelor’s degree); population by employ-ment status (i.e. employed civilians and unemployed civilians); households by income (i.e. grouped into three categories: lower-income households [under $50,000], middle-income households [$50,000‒100,000] and upper-income households [over $100,000]); and vacant housing units. For the population by educational attainment and population by employment status variables, census block groups and 2009–2013 ACS data were used instead of census blocks due to the fact that these variables were not collected as a part of the 2010 census and released at the census block level.

Results

Results from the shift-share analyses and t-tests are summarized in a series of Tables, which show the local socio-economic impact of the destination redevelopments and the advantage of destination redevelopments in driving such socio-economic neighbourhood changes compared to other types of redevelopments. The destination developments built in the 1990s potentially affected socio-economic neighbourhood changes in three time periods: 1990–2000, 2000–2010 and 1990–2010. The destination redevelopments built in the 2000s potentially affected socio-economic neighbourhood changes during the same decade. One can also combine all of the destination redevelopments built during the 1990s and 2000s in order to assess how these sites affected socio-economic neighbourhood change during the 2000s.

For the destination redevelopments built in the 1990s, a comparison of the changes in the local component of the shift-share analysis of the socio-economic characteristics of the census units within one mile of these redevelopments and randomly sampled sites between 1990 and 2000 indicates a variety of local socio-economic effects of destination redevelop-ments during the 1990s (Table 2). The effects of destination redevelopments during this time period are implied by the variables that have statistically significant (α = 0.05) differ-ences between the changes in local component of shift-share analysis near destination

(4)HPri = Pr

∗Pni

Pn

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356 J. J. DANKO AND D. M. HANINK

Tabl

e 2.

 T-t

ests

resu

lts c

ompa

ring

the

aver

age

soci

o-ec

onom

ic c

hang

e be

twee

n 19

90 a

nd 2

000

in ra

w d

ata

and

the

loca

l com

pone

nt o

f shi

ft-s

hare

ana

lysi

s fo

r are

as

with

in o

ne m

ile o

f des

tinat

ion

rede

velo

pmen

ts b

uilt

in th

e 19

90s a

nd ra

ndom

ly sa

mpl

ed si

tes i

n la

s Veg

as.

Raw

dat

aLo

cal c

ompo

nent

of s

hift

-sha

re a

naly

sis

Des

tinat

ion

rede

-ve

lopm

ents

(n =

4)

Rand

om s

ampl

es

(n =

100

)p-

valu

e (α

= 0

.05)

Des

tinat

ion

rede

vel-

opm

ents

(n =

4)

Rand

om s

ampl

es

(n =

100

)p-

valu

e (α

= 0

.05)

Cens

us b

lock

sTo

tal p

opul

atio

n3,

773

5,52

40.

149

−13

,337

524

0.00

0W

hite

pop

ulat

ion

−2,

502

2,25

20.

000

−2,

310

−1,

520

0.29

0bl

ack

popu

latio

n37

351

70.

522

332

328

0.99

1H

ispa

nic

popu

latio

n5,

245

1,99

40.

083

−2,

019

534

0.00

7Ch

ildre

n (u

nder

18

year

s ol

d)1,

474

1,56

80.

819

495

145

0.05

3

Youn

g ad

ults

(18‒

29 y

ears

ol

d)37

775

20.

234

952

210

0.00

1

adul

ts (3

0‒44

yea

rs o

ld)

1,22

51,

248

0.92

660

4−

910.

001

Mid

dle-

aged

adu

lts

(45‒

64 y

ears

old

)1,

170

1,24

80.

704

−47

5−

910.

001

seni

ors (

65 y

ears

old

and

ov

er)

−47

462

10.

000

−1,

665

−12

0.00

0

Hou

sing

uni

ts30

2,01

20.

000

−7,

448

184

0.00

0Va

cant

hou

sing

uni

ts33

104

0.52

443

8−

150

0.00

0

Cens

us b

lock

gr

oups

low

er-in

com

e ho

useh

olds

(<

$50

,000

)−

1,16

577

70.

000

4,36

724

10.

000

Mid

dle-

inco

me

hous

e-ho

lds (

$50,

000

‒ $1

00,0

00)

926

1,28

50.

004

1,48

292

20.

000

upp

er-in

com

e ho

useh

olds

(>

$10

0,00

0)27

564

30.

000

−15

1−

110.

473

Popu

latio

n w

ith a

t lea

st a

ba

chel

or’s

degr

ee2

1,22

20.

000

−1,

211

−17

60.

000

Popu

latio

n w

ithou

t a

bach

elor

’s de

gree

2,40

13,

718

0.02

72,

226

197

0.00

0

empl

oyed

civ

ilian

s−

973

2,95

70.

000

−59

5−

850.

037

une

mpl

oyed

civ

ilian

s65

126

20.

076

529

850.

048

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JOURNAL OF URBAN DESIGN 357

redevelopment and randomly sampled sites. As such, the results of the shift-share analyses and t-tests suggest, other things being equal, that destination redevelopments built in the 1990s had a mixed effect on the compositional change of their neighbourhoods (i.e. census units within a one-mile buffer) between 1990 and 2000. The positive effects were that these destination redevelopments attracted young adults (i.e. individuals between 18 and 29 years old), adults (i.e. individuals between 30 and 44 years old), middle-income households (i.e. households earning between $50,000 and $100,000 in income), and population with at least a bachelor’s degree in their neighbourhoods during this decade. The negative effects, on the other hand, were that the neighbourhoods around these redevelopments declined in terms of the total population, Hispanic population, middle-aged adults (i.e. individuals between 45 and 65 years old), seniors (i.e. population 65 years old and over), total housing units and employed civilians, and experienced an increase in vacancies, lower-income house-holds (i.e. households earning less than $50,000 in income), population without a bachelor’s degree and unemployed civilians. In general, it appears that the destination developments in Las Vegas generated changes commonly associated with gentrification and other neolib-eral redevelopments, such as a decline of total and non-White populations (i.e. perhaps related to displacement) and an increase in lower-income households (Judd and Fainstein 1999; Lee, Slater, and Wyly 2008; Santos and Mildner 2010; Mele 2013).

Comparing the socio-economic neighbourhood effects of the destination redevelop-ments built in the 1990s and the other types of redevelopments built in the 1990s over the period of 1990 to 2000, the shift-share analyses and t-tests indicate that destinations rede-velopments in large part did not exhibit any advantages over other types of redevelopments in terms of generating positive neighbourhood change (Table 3). In fact, the only statistically significant difference between destination redevelopments and other types of redevelop-ments relates to middle-income households. Specifically, although destination redevelop-ments attracted middle-income households in their neighbourhoods during this time period, they were less effective at doing so compared to other types of redevelopments on average.

Examining the socio-economic neighbourhood changes between 2000 and 2010 spurred by destination redevelopments built in the 1990s compared to randomly sampled sites reveals slightly different, although still mixed, results regarding the lagged local socio-eco-nomic impact of these projects (Table 4). Positive effects include destination redevelopments attracting middle-aged adults, middle-income households, population with at least a bach-elor’s degree and employed civilians, as well as causing a decline in the number of unem-ployed civilians. Negative effects are that destination redevelopments are causing their neighbourhoods to decline in terms of total population, Hispanic population, children, sen-iors, housing units and upper-income households, and to increase in terms of lower-income households and population without a bachelor’s degree. In addition, the neighbourhoods near these sites are experiencing a decline in their White population, but not as much as is occurring in other Las Vegas neighbourhoods.

Compared to other redevelopments projects built in 1990 during this same time period, the results of the shift-share analyses and t-tests indicates that destination redevelopments built in the 1990s exhibit one statistically significant advantage in terms of spurring a positive neighbourhood change between 2000 and 2010 (Table 5). On average, destination redevel-opments attracted more employed civilians between 2000 and 2010 compared to other types of redevelopments built in the 1990s. This suggests that the advantage of the

Dow

nloa

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by [

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ares

Uni

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ity]

at 2

1:43

05

Nov

embe

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17

358 J. J. DANKO AND D. M. HANINK

Tabl

e 3.

 T-t

ests

resu

lts c

ompa

ring

the

aver

age

soci

o-ec

onom

ic c

hang

e be

twee

n 19

90 a

nd 2

000

in ra

w d

ata

and

the

loca

l com

pone

nt o

f shi

ft-s

hare

ana

lysi

s fo

r are

as

with

in o

ne m

ile o

f des

tinat

ion

rede

velo

pmen

ts a

nd o

ther

type

s of r

edev

elop

men

ts b

uilt

in th

e 19

90s i

n la

s Veg

as.

Raw

dat

aLo

cal c

ompo

nent

of s

hift

-sha

re a

naly

sis

Des

tinat

ion

rede

-ve

lopm

ents

(n =

4)

Oth

er ty

pes

of re

de-

velo

pmen

ts (n

= 1

6)p-

valu

e (α

= `0

.05)

Des

tinat

ion

rede

vel-

opm

ents

(n =

4)

Oth

er ty

pes

of

rede

velo

pmen

ts

(n =

16)

p-va

lue

(α =

0.0

5)Ce

nsus

bl

ocks

Tota

l pop

ulat

ion

3,77

32,

984

0.45

7−

13,3

37−

14,0

400.

617

Whi

te p

opul

atio

n−

2,50

2−

2,61

40.

829

−2,

310

−1,

922

0.63

0bl

ack

popu

latio

n37

346

60.

671

332

491

0.65

8H

ispa

nic

popu

latio

n5,

245

4,34

00.

556

−2,

019

−2,

145

0.84

5Ch

ildre

n (u

nder

18

year

s old

)1,

474

1,21

50.

553

495

350

0.39

6Yo

ung

adul

ts (1

8‒29

yea

rs o

ld)

377

166

0.49

695

286

80.

582

adul

ts (3

0‒44

yea

rs o

ld)

1,22

598

70.

342

604

583

0.83

9M

iddl

e-ag

ed a

dults

(4

5‒64

yea

rs o

ld)

1,17

085

80.

132

−47

5−

642

0.25

4

seni

ors (

65 y

ears

old

and

ove

r)−

474

−23

50.

033

−16

65−

1,34

60.

137

Hou

sing

uni

ts30

151

0.57

8−

7,44

8−

7,17

70.

733

Vaca

nt h

ousi

ng u

nits

3398

0.58

043

844

00.

985

Cens

us

bloc

k gr

oups

low

er-in

com

e ho

useh

olds

(<

$50,

000)

−1,

165

−1,

079

0.71

84,

367

3,90

10.

232

Mid

dle-

inco

me

hous

ehol

ds

($50

,000‒$

100,

000)

926

1,01

60.

170

1,48

21,

711

0.04

1

upp

er-in

com

e ho

useh

olds

(>

$100

,000

)27

527

30.

966

−15

1−

682

0.05

1

Popu

latio

n w

ith a

t lea

st a

ba

chel

or’s

degr

ee2

820.

301

−1,

211

−1,

415

0.18

8

Popu

latio

n w

ithou

t a b

ache

lor’s

de

gree

2,40

11,

729

0.19

42,

226

2,00

30.

231

empl

oyed

civ

ilian

s−

973

−67

70.

364

−59

5−

212

0.06

7u

nem

ploy

ed c

ivili

ans

651

262

0.06

152

915

70.

066

Dow

nloa

ded

by [

Tar

biat

Mod

ares

Uni

vers

ity]

at 2

1:43

05

Nov

embe

r 20

17

JOURNAL OF URBAN DESIGN 359

Tabl

e 4.

 T-t

ests

resu

lts c

ompa

ring

the

aver

age

soci

o-ec

onom

ic c

hang

e be

twee

n 20

00 a

nd 2

010

in ra

w d

ata

and

the

loca

l com

pone

nt o

f shi

ft-s

hare

ana

lysi

s fo

r are

as

with

in o

ne m

ile o

f des

tinat

ion

rede

velo

pmen

ts b

uilt

in th

e 19

90s a

nd ra

ndom

ly sa

mpl

ed si

tes i

n la

s Veg

as.

Raw

dat

aLo

cal c

ompo

nent

of s

hift

-sha

re a

naly

sis

Des

tinat

ion

rede

vel-

opm

ents

(n =

4)

Rand

om

sam

ples

(n

= 1

00)

p-va

lue

(α =

0.0

5)D

estin

atio

n re

deve

lop-

men

ts (n

= 4

)Ra

ndom

sam

ples

(n

= 1

00)

p-va

lue

(α =

0.0

5)Ce

nsus

bl

ocks

Tota

l pop

ulat

ion

−1,

715

3,02

60.

000

−6,

965

518

0.00

0W

hite

pop

ulat

ion

−2,

130

367

0.00

0−

325

−1,

088

0.00

1bl

ack

popu

latio

n12

260

0.17

5−

38−

400.

989

His

pani

c po

pula

tion

455

1,85

50.

000

−2,

680

517

0.01

6Ch

ildre

n (u

nder

18

year

s ol

d)−

704

741

0.00

0−

287

−55

0.02

5

Youn

g ad

ults

(18‒

29 y

ears

ol

d)13

91,

107

0.00

063

974

20.

406

adul

ts (3

0‒44

yea

rs o

ld)

−1,

250

1,05

20.

000

727

0.77

9M

iddl

e-ag

ed a

dults

(4

5‒64

yea

rs o

ld)

1,07

61,

052

0.92

660

727

0.01

4

seni

ors (

65 y

ears

old

and

ov

er)

−32

540

30.

000

−26

211

40.

000

Hou

sing

uni

ts−

303

1,47

60.

000

−3,

122

226

0.00

0Va

cant

hou

sing

uni

ts99

045

20.

022

−23

6−

660.

337

Cens

us

bloc

k gr

oups

low

er-in

com

e ho

useh

olds

(<

$50

,000

)−

2,40

154

0.00

080

3−

800.

001

Mid

dle-

inco

me

hous

ehol

ds

($50

,000‒$

100,

000)

293

702

0.00

661

9−

540.

000

upp

er-in

com

e ho

useh

olds

(>

$10

0,00

0)78

1,02

40.

000

−26

328

0.01

1

Popu

latio

n w

ith a

t lea

st a

ba

chel

or’s

degr

ee14

11,

177

0.00

095

−88

0.00

7

Popu

latio

n w

ithou

t a

bach

elor

’s de

gree

−4,

310

2,14

10.

000

608

820.

000

empl

oyed

civ

ilian

s−

1,73

92,

657

0.00

095

021

40.

004

une

mpl

oyed

civ

ilian

s−

249

615

0.00

0−

2,40

6−

239

0.01

0

Dow

nloa

ded

by [

Tar

biat

Mod

ares

Uni

vers

ity]

at 2

1:43

05

Nov

embe

r 20

17

360 J. J. DANKO AND D. M. HANINK

Tabl

e 5.

 T-t

ests

resu

lts c

ompa

ring

the

aver

age

soci

o-ec

onom

ic c

hang

e be

twee

n 20

00 a

nd 2

010

in ra

w d

ata

and

the

loca

l com

pone

nt o

f shi

ft-s

hare

ana

lysi

s fo

r are

as

with

in o

ne m

ile o

f des

tinat

ion

rede

velo

pmen

ts a

nd o

ther

type

s of r

edev

elop

men

ts b

uilt

in th

e 19

90s i

n la

s Veg

as.

Raw

dat

aLo

cal c

ompo

nent

of s

hift

-sha

re a

naly

sis

Des

tinat

ion

rede

velo

pmen

ts

(n =

4)

Oth

er ty

pes

of

rede

velo

pmen

ts

(n =

16)

p-va

lue

(α =

0.0

5)

Des

tinat

ion

rede

velo

pmen

ts

(n =

4)

Oth

er ty

pes

of

rede

velo

pmen

ts

(n =

16)

p-va

lue

(α =

0.0

5)Ce

nsus

blo

cks

Tota

l pop

ulat

ion

−1,

715

−1,

898

0.44

5−

6,96

5−

6,95

30.

983

Whi

te p

opul

atio

n−

2,13

0−

2,49

40.

172

−32

5−

457

0.48

0bl

ack

popu

latio

n12

−65

0.73

3−

38−

930.

806

His

pani

c po

pula

tion

455

645

0.50

8−

2,68

0−

1,96

00.

365

Child

ren

(und

er 1

8 ye

ars o

ld)

−70

4−

695

0.91

4−

287

−24

80.

625

Youn

g ad

ults

(18‒

29 y

ears

old

)13

993

0.73

263

960

20.

786

adul

ts (3

0‒44

yea

rs o

ld)

−1,

250

−1,

152

0.56

77

800.

378

Mid

dle-

aged

adu

lts (4

5‒64

yea

rs o

ld)

1,07

677

00.

275

607

375

0.18

6se

nior

s (65

yea

rs o

ld a

nd o

ver)

−32

5−

373

0.52

7−

262

−27

00.

889

Hou

sing

uni

ts−

303

280.

196

−3,

122

−2,

768

0.42

9Va

cant

hou

sing

uni

ts99

01,

197

0.26

7−

236

290.

169

Cens

us b

lock

gr

oups

low

er-in

com

e ho

useh

olds

(< $

50,0

00)

−2,

401

−2,

039

0.05

280

363

80.

184

Mid

dle-

inco

me

hous

ehol

ds

($50

,000‒$

100,

000)

293

188

0.35

361

951

70.

300

upp

er-in

com

e ho

useh

olds

(> $

100,

000)

7823

20.

138

−26

790.

300

Popu

latio

n w

ith a

t lea

st a

bac

helo

r’s

degr

ee14

188

0.44

195

−15

0.06

6

Popu

latio

n w

ithou

t a b

ache

lor’s

deg

ree

−4,

310

−3,

336

0.01

960

852

00.

315

empl

oyed

civ

ilian

s−

1,73

9−

1,76

50.

919

950

580

0.04

6u

nem

ploy

ed c

ivili

ans

−24

923

90.

007

−2,

406

−1,

473

0.06

9

Dow

nloa

ded

by [

Tar

biat

Mod

ares

Uni

vers

ity]

at 2

1:43

05

Nov

embe

r 20

17

JOURNAL OF URBAN DESIGN 361

Tabl

e 6.

 T-t

ests

resu

lts c

ompa

ring

the

aver

age

soci

o-ec

onom

ic c

hang

e be

twee

n 20

00 a

nd 2

010

in ra

w d

ata

and

the

loca

l com

pone

nt o

f shi

ft-s

hare

ana

lysi

s fo

r are

as

with

in o

ne m

ile o

f des

tinat

ion

rede

velo

pmen

ts b

uilt

in th

e 20

00s a

nd ra

ndom

ly sa

mpl

ed si

tes i

n la

s Veg

as.

Raw

dat

aLo

cal c

ompo

nent

of s

hift

-sha

re a

naly

sis

Des

tinat

ion

rede

velo

pmen

ts

(n =

10)

Rand

om s

am-

ples

(n =

100

)p-

valu

e (α

=

0.05

)

Des

tinat

ion

rede

velo

p-m

ents

(n =

10)

Rand

om s

am-

ples

(n =

100

)p-

Valu

e

(α =

0.0

5)Ce

nsus

blo

cks

Tota

l pop

ulat

ion

−1,

614

3,02

60.

000

−6,

608

518

0.00

0W

hite

pop

ulat

ion

−2,

088

367

0.00

0−

278

−1,

088

0.00

0bl

ack

popu

latio

n17

126

00.

434

98−

400.

234

His

pani

c po

pula

tion

352

1,85

50.

000

−2,

242

517

0.00

0Ch

ildre

n (u

nder

18

year

s old

)−

671

741

0.00

0−

296

−55

0.00

4Yo

ung

adul

ts (1

8‒29

yea

rs o

ld)

201,

107

0.00

049

174

20.

000

adul

ts (3

0‒44

yea

rs o

ld)

−1,

228

1,05

20.

000

−21

270.

405

Mid

dle-

aged

adu

lts (4

5‒64

yea

rs o

ld)

1,03

01,

052

0.87

855

827

0.00

0se

nior

s (65

yea

rs o

ld a

nd o

ver)

−33

340

30.

000

−27

511

40.

000

Hou

sing

uni

ts−

243

1,47

60.

000

−3,

165

226

0.00

0Va

cant

hou

sing

uni

ts1,

139

452

0.00

0−

182

−66

0.01

9

Cens

us b

lock

gro

ups

low

er-in

com

e ho

useh

olds

(< $

50,0

00)

−2,

171

540.

000

803

−80

0.00

0M

iddl

e-in

com

e ho

useh

olds

($

50,0

00‒$

100,

000)

210

702

0.00

050

3−

540.

000

upp

er-in

com

e ho

useh

olds

(> $

100,

000)

140

1,02

40.

000

4032

80.

000

Popu

latio

n w

ith a

t lea

st a

bac

helo

r’s d

egre

e10

21,

177

0.00

047

−88

0.12

6Po

pula

tion

with

out a

bac

helo

r’s d

egre

e−

4,01

72,

141

0.00

057

982

0.00

0em

ploy

ed c

ivili

ans

−1,

680

2,65

70.

000

872

214

0.00

0u

nem

ploy

ed c

ivili

ans

−23

061

50.

000

−2,

330

−23

90.

000

Dow

nloa

ded

by [

Tar

biat

Mod

ares

Uni

vers

ity]

at 2

1:43

05

Nov

embe

r 20

17

362 J. J. DANKO AND D. M. HANINK

Tabl

e 7.

 T-t

ests

resu

lts c

ompa

ring

the

aver

age

soci

o-ec

onom

ic c

hang

e be

twee

n 20

00 a

nd 2

010

in ra

w d

ata

and

the

loca

l com

pone

nt o

f shi

ft-s

hare

ana

lysi

s fo

r are

as

with

in o

ne m

ile o

f des

tinat

ion

rede

velo

pmen

ts b

uilt

in th

e 20

00s a

nd o

ther

type

s of r

edev

elop

men

ts b

uilt

in th

e 20

00s i

n la

s Veg

as.

Raw

dat

aLo

cal c

ompo

nent

of s

hift

-sha

re a

naly

sis

Des

tinat

ion

rede

velo

pmen

ts

(n =

10)

Oth

er ty

pes

of

rede

velo

pmen

ts

(n =

68)

p-va

lue

(α =

0.0

5)

Des

tinat

ion

rede

velo

pmen

ts

(n =

10)

Oth

er ty

pes

of

rede

velo

pmen

ts

(n =

68)

p-va

lue

(α =

0.0

5)Ce

nsus

bl

ocks

Tota

l pop

ulat

ion

−1,

614

−1,

866

0.13

8−

6,60

8−

7,01

40.

398

Whi

te p

opul

atio

n−

2,08

8−

2,37

10.

012

−27

8−

331

0.63

2bl

ack

popu

latio

n17

114

10.

785

9898

1.00

0H

ispa

nic

popu

latio

n35

236

90.

929

−2,

242

−2,

247

0.99

1Ch

ildre

n (u

nder

18

year

s old

)−

671

−74

20.

410

−29

6−

312

0.80

8Yo

ung

adul

ts (1

8‒29

yea

rs o

ld)

2059

0.48

649

157

60.

041

adul

ts (3

0‒44

yea

rs o

ld)

−1,

228

−1,

243

0.90

8−

2132

0.20

2M

iddl

e-ag

ed a

dults

(45‒

64 y

ears

old

)1,

030

891

0.24

955

845

90.

240

seni

ors (

65 y

ears

old

and

ove

r)−

333

−34

90.

645

−27

5−

258

0.58

3H

ousi

ng u

nits

−2,

171

−2,

189

0.91

080

367

90.

073

Vaca

nt h

ousi

ng u

nits

210

212

0.94

1−

485

−53

10.

114

Cens

us

bloc

k gr

oups

low

er-in

com

e ho

useh

olds

(< $

50,0

00)

140

234

0.02

540

790.

269

Mid

dle-

inco

me

hous

ehol

ds

($50

,000‒$

100,

000)

102

140

0.53

747

240.

728

upp

er-in

com

e ho

useh

olds

(> $

100,

000)

−4,

017

−3,

730

0.51

457

952

40.

253

Popu

latio

n w

ith a

t lea

st a

bac

helo

r’s

degr

ee−

1,68

0−

1,66

30.

955

−4,

669

−5,

159

0.08

9

Popu

latio

n w

ithou

t a b

ache

lor’s

deg

ree

−23

031

0.01

3−

3,39

0−

2,81

00.

062

empl

oyed

civ

ilian

s−

243

−90

0.05

4−

3,16

5−

2,98

10.

436

une

mpl

oyed

civ

ilian

s1,

139

1,20

40.

515

−18

2−

500.

008

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JOURNAL OF URBAN DESIGN 363

Tabl

e 8.

 T-t

ests

resu

lts c

ompa

ring

the

aver

age

soci

o-ec

onom

ic c

hang

e be

twee

n 20

00 a

nd 2

010

in ra

w d

ata

and

the

loca

l com

pone

nt o

f shi

ft-s

hare

ana

lysi

s fo

r are

as

with

in o

ne m

ile o

f all

dest

inat

ion

rede

velo

pmen

ts a

nd o

ther

type

s of r

edev

elop

men

ts b

uilt

betw

een

1990

and

201

0 in

las

Veg

as.

Raw

dat

aLo

cal c

ompo

nent

of s

hift

-sha

re a

naly

sis

Des

tinat

ion

rede

velo

pmen

ts

(n =

14)

Oth

er ty

pes

of

rede

velo

pmen

ts

(n =

68)

p-va

lue

(α =

0.

05)

Des

tinat

ion

rede

velo

pmen

ts

(n =

14)

Oth

er ty

pes

of

rede

velo

pmen

ts

(n =

68)

p-va

lue

(α

= 0

.05)

Cens

us b

lock

sTo

tal p

opul

atio

n−

1,64

3−

1,87

20.

095

−6,

710

−7,

002

0.43

0W

hite

pop

ulat

ion

−2,

100

−2,

394

0.00

5−

291

−35

50.

479

blac

k po

pula

tion

126

102

0.80

959

620.

980

His

pani

c po

pula

tion

381

422

0.79

7−

2,36

7−

2,19

20.

617

Child

ren

(< 1

8 ye

ars o

ld)

−68

0−

733

0.41

3−

293

−30

00.

895

Youn

g ad

ults

(18‒

29 y

ears

old

)54

650.

834

533

581

0.32

0ad

ults

(30‒

44 y

ears

old

)−

1,23

4−

1,22

60.

932

−13

410.

126

Mid

dle-

aged

adu

lts (4

5‒64

yea

rs o

ld)

1,04

386

80.

110

572

443

0.07

9se

nior

s (>

65

year

s old

)−

331

−35

30.

476

−27

1−

260

0.67

7H

ousi

ng u

nits

−26

0−

670.

021

−3,

153

−2,

940

0.28

3Va

cant

hou

sing

uni

ts1,

096

1,20

30.

216

−19

7−

350.

006

Cens

us b

lock

gr

oups

low

er-in

com

e ho

useh

olds

(< $

50,0

00)

−2,

236

−2,

160

0.55

080

367

10.

023

Mid

dle-

inco

me

hous

ehol

ds ($

50,0

00‒$

100,

000)

234

208

0.49

0−

496

−53

70.

088

upp

er-in

com

e ho

useh

olds

(> $

100,

000)

122

234

0.00

521

790.

106

Popu

latio

n w

ith a

t lea

st a

bac

helo

r’s d

egre

e11

313

00.

720

6116

0.36

9Po

pula

tion

with

out a

bac

helo

r’s d

egre

e−

4,10

0−

3,65

50.

181

588

523

0.11

5em

ploy

ed c

ivili

ans

−1,

697

−1,

682

0.94

989

570

50.

009

une

mpl

oyed

civ

ilian

s−

235

700.

000

−2,

352

−1,

786

0.00

5

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364 J. J. DANKO AND D. M. HANINK

destination redevelopments in terms of spurring positive socio-economic change may not initially be evident when examining the first set of census data published following the construction of the sites.

Las Vegas destination redevelopments built in the 2000s affected many of the same types of trends between 2000 and 2010 as destination redevelopments that were built in the 1990s (Table 6). Positive effects are that the destination redevelopments built in the 2000s attracted young adults, middle-aged adults, middle-income households, population with at least a bachelor’s degree and employed civilians, as well as a decline in vacancies and unemployed civilians. Although the upper-income households increased near destination redevelop-ments, they did not increase as much as randomly sampled locations in Las Vegas. Negative effects are that these projects cause a decline in the following characteristics of their neigh-bourhood: total population decline, Hispanic population, children, seniors and housing units, as well as an increase in lower-income households and population without a bachelor’s degree. In addition, although White population in the neighbourhoods around destination redevelopments declined, they did not decrease more than the White population of the neighbourhoods near randomly sampled sites during the 2000s.

Compared to other types of redevelopments built in the 2000s, destination redevelop-ments built in the 2000s had one advantage and one disadvantage in terms of local socio-eco-nomic change between 2000 and 2010 (Table 7). The advantage of destination redevelopments was that these projects helped reduce the number of unemployed civilians in their neigh-bourhoods. The disadvantage of destination redevelopments was that other types of rede-velopments attracted more young adults during this decade.

Examining the effects of all destination redevelopments built between 1990 and 2010 on changes of the socio-economic characteristics during the 2000s illustrate similar common trends as indicated above. Positive effects include attracting more young adults, middle-aged adults, middle-income households, population with at least a bachelor’s degree and employed civilians, as well as causing a decline in vacancies and unemployed civilians. Although the upper-income households increased in the neighbourhoods near these des-tination redevelopments during the 2000s, they did not increase as much as those near randomly sampled sites. Negative effects include these redevelopments being associated with a decline in total population, Hispanic population, children, seniors and housing units as well as spurring an increase in lower-income households and population without a bach-elor’s degree. While White population declined near destination redevelopments, this group did not decrease as much as in the neighbourhoods near randomly sampled sites.

Comparing local socio-economic change between 2000 and 2010 for all destination rede-velopments and other types of redevelopments between 1990 and 2010, destination rede-velopments have had more advantages than disadvantages. Advantages include an increase in employed civilians, as well as decreases in vacancies and unemployed civilians. The only disadvantage is an increase in lower-income households.

Discussion

An examination of the common factors in the local socio-economic changes in the raw data compared to those in the local component of shift-share analyses illustrates the utility of the approach presented in this paper (Tables 2–9). If relying solely on raw census data, the following local socio-economic trends might have been associated with destination

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JOURNAL OF URBAN DESIGN 365

Tabl

e 9.

 T-t

ests

resu

lts c

ompa

ring

the

aver

age

soci

o-ec

onom

ic c

hang

e be

twee

n 20

00 a

nd 2

010

in ra

w d

ata

and

the

loca

l com

pone

nt o

f shi

ft-s

hare

ana

lysi

s fo

r are

as

with

in o

ne m

ile o

f all

dest

inat

ion

rede

velo

pmen

ts a

nd o

ther

type

s of r

edev

elop

men

ts b

uilt

betw

een

1990

and

201

0 in

las

Veg

as.

Raw

dat

aLo

cal c

ompo

nent

of s

hift

-sha

re a

naly

sis

Des

tinat

ion

rede

velo

p-m

ents

(n

= 1

4)

Oth

er ty

pes

of

rede

velo

pmen

ts

(n =

68)

p-va

lue

(α =

0.0

5)

Des

tinat

ion

rede

velo

pmen

ts

(n =

14)

Oth

er ty

pes

of

rede

velo

pmen

ts

(n =

68)

p-va

lue

(α =

0.0

5)Ce

nsus

blo

cks

Tota

l pop

ulat

ion

−1,

643

−1,

872

0.09

5−

6,71

0−

7,00

20.

430

Whi

te p

opul

atio

n−

2,10

0−

2,39

40.

005

−29

1−

355

0.47

9bl

ack

popu

latio

n12

610

20.

809

5962

0.98

0H

ispa

nic

popu

latio

n38

142

20.

797

−2,

367

−2,

192

0.61

7Ch

ildre

n (u

nder

18

year

s old

)−

680

−73

30.

413

−29

3−

300

0.89

5Yo

ung

adul

ts (1

8‒29

yea

rs o

ld)

5465

0.83

453

358

10.

320

adul

ts (3

0‒44

yea

rs o

ld)

−1,

234

−1,

226

0.93

2−

1341

0.12

6M

iddl

e-ag

ed a

dults

(45‒

64 y

ears

old

)1,

043

868

0.11

057

244

30.

079

seni

ors (

65 y

ears

old

and

ove

r)−

331

−35

30.

476

−27

1−

260

0.67

7H

ousi

ng u

nits

−26

0−

670.

021

−3,

153

−2,

940

0.28

3Va

cant

hou

sing

uni

ts1,

096

1,20

30.

216

−19

7−

350.

006

Cens

us b

lock

gr

oups

low

er-in

com

e ho

useh

olds

(< $

50,0

00)

−2,

236

−2,

160

0.55

080

367

10.

023

Mid

dle-

inco

me

hous

ehol

ds

($50

,000‒$

100,

000)

234

208

0.49

0−

496

−53

70.

088

upp

er-in

com

e ho

useh

olds

(>

$100

,000

)12

223

40.

005

2179

0.10

6

Popu

latio

n w

ith a

t lea

st a

bac

helo

r’s

degr

ee11

313

00.

720

6116

0.36

9

Popu

latio

n w

ithou

t a b

ache

lor’s

de

gree

−4,

100

−3,

655

0.18

158

852

30.

115

empl

oyed

civ

ilian

s−

1,69

7−

1,68

20.

949

895

705

0.00

9u

nem

ploy

ed c

ivili

ans

−23

570

0.00

0−

2,35

2−

1,78

60.

005

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366 J. J. DANKO AND D. M. HANINK

redevelopments: a decline in White population, seniors, housing units, lower-income house-holds and employed civilians, as well as an increase in vacancies (Tables 2, 4, 6 and 8). These trends might be interpreted as being caused by destination redevelopments because the differences between the averages of each variable are statistically significant. However, these interpretations would not account for citywide or other macro-processes affecting these trends and thus not justify the association with the presence of destination redevelopments. For example, the increase in vacancies could be the consequence of the housing crash in Las Vegas during the 2000s and decline of lower-income households could be due to infla-tion, not due to the presence of these destination redevelopments.

In fact, the trends in the local component of shift-share analysis show that most of these trends are actually driven by macro-processes. The decline in the White population, low-er-income households and employed civilians, as well as the growth in vacancies evident in the raw data are caused by macro-processes and not the presence of destination redevel-opments (i.e. not evident in the trends of changes in the local component of shift-share analyses). In addition, although the decline in both seniors and housing units is present in the raw data, suggesting that these changes are affected by macro-scale processes, these decreases are also worsened by the presence of destination redevelopments due to the fact that this trend is also evident in the local component of shift-share analysis.

Macro-processes also actually mask some of the local socio-economic impacts of desti-nation redevelopments. The decline of lower-income households shown in the raw data trends actually hides the local impact of destination redevelopments, which shift-share analysis reveals is an increase in lower-income households. Destination redevelopments also influenced the following local socio-economic trends that were not visible in the raw data: a decline in the total population and Hispanic population, as well as an increase in lower-income households, middle-income households and population without a bachelor’s degree.

In addition, comparing the advantages regarding local socio-economic impacts of des-tination redevelopments and other types of redevelopments (Tables 3, 5, 7 and 9), changes in the raw census data suggest a completely different set of advantages and disadvantages of destination redevelopments than the trends present in the local component of shift-share analysis. This discrepancy suggests that all of the supposed advantages and disadvantages of destination redevelopments in the raw data are actually driven by macro-processes, not the presence of destination redevelopments. It shows that changes in the raw data actually mask the true advantages and disadvantages of destination redevelopments. In fact, the trends in the raw data would suggest that destination redevelopments almost always are less effective at spurring local socio-economic changes compared to other types of rede-velopments. However, changes in the local component of shift-share analysis illustrate that this is not completely true. Destination redevelopments actually possessed some advantages in terms of spurring local socio-economic change (e.g. an increase in employed civilians between 2000 and 2010, as illustrated by Tables 5 and 9) despite the fact that there are no common trends among all the examined time periods.

Conclusions

This paper illustrates how a combination of a novel neighbourhood-level and GIS-based spatial approach to shift-share analysis and t-tests create a relatively simple approach in

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JOURNAL OF URBAN DESIGN 367

terms of computation, data requirements and statistical interpretation of assessing the local socio-economic impact of particular types of sites in an urban setting. In this paper, this technique was applied to evaluate how Las Vegas destination redevelopments altered the socio-economic characteristics of their particular neighbourhoods between 1990 and 2010. It also examined whether destination redevelopments possessed an advantage over other types of redevelopments in terms of spurring particular types of local socio-economic change.

Results show that destination redevelopments had an overall mixed local socio-economic impact between 1990 and 2010. The t-tests of the changes during this time period in the local component of shift-share analysis illustrate that Las Vegas destination redevelopments built during these two decades consistently affected the following changes in the characteristics of their neighbourhoods: a decline in the total population, Hispanic population and seniors (i.e. individuals 65 years old and over), as well as an increase in lower-income households (i.e. households earning less than $50,000), middle-income households (i.e. households earning between $50,000 and $100,000) and population without a bachelor’s degree. When compared to other types of redevelopments, the results of the approach also suggest that the advantages and disadvantages of destination redevelopments in terms of spurring local socio-economic changes were related to the decade examined. Many of these changes can be associated with typical effects of gentrification and other neoliberal redevelopments.

The combination of shift-share analysis and t-tests also illustrates one major issue of typical neighbourhood change studies. Specifically, relying on raw data to evaluate the local socio-economic impact of particular sites can be problematic due to the influence of mac-ro-level processes. Macro-level processes, such as citywide trends (e.g. the Las Vegas housing crash in the 2000s) and/or national factors (e.g. the US recession of 2007–2009), can mask true local socio-economic impacts and can lead to analysts drawing misleading conclusions about such relationships in neighbourhood change research without such an approach as the one proposed in this paper.

Although this paper focused on evaluating the local socio-economic impact of destination redevelopments, this approach is applicable to other types of neighbourhood change research endeavours. The type of local changes analyzed with this technique is not limited to the socio-economic characteristics of a neighbourhood. The type of sites could extend to other potential catalysts of urban change (e.g. the focus of this special edition, flagship architecture). Beyond changing the type of characteristics and sites to be evaluated, future neighbourhood change research could also explore the utility of other versions and exten-sions of shift-share analysis, as well as the benefits of incorporating qualitative data to better understand the causes of any identified trends.

Acknowledgements

The authors would like to thank all anonymous reviewers for their useful feedback.

Disclosure statement

No potential conflict of interest was reported by the authors.

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368 J. J. DANKO AND D. M. HANINK

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