Description
After reading ModelingResidentSpendingBehavior, discuss how the authors presented their study, from the questions and hypotheses to the findings.
Identify and discuss
Research method
Data collection
Journal of Sport Management, 2018, 32, 473-485
© 2018 Human Kinetics, Inc.
ARTICLE
Modeling Resident Spending Behavior During Sport Events:
Do Residents Contribute to Economic Impact?
Nola Agha
University of San Francisco
Marijke Taks
University of Ottawa
The role of residents in the calculation of economic impact remains a point of contention. It is unclear if changes in resident
spending caused by an event contribute positively, negatively, or not at all. Building on previous theory, we develop a
comprehensive model that explains all 72 possible behaviors of residents based on changes in (a) spending, (b) multiplier,
(c) timing of expenditures, and (d) geographic location of spending. Applying the model to Super Bowl 50 indicates that few
residents were affected and positive and negative effects were relatively equivalent; thus, their overall impact is negligible. This
leaves practitioners the option to engage in the challenging process of gathering data on all four variables on all residents or to
revert back to the old model of entirely excluding residents from economic impact. From a theoretical perspective, there is a
pressing need to properly conceptualize the time variable in economic impact studies.
Keywords: cost benefit analysis, crowding out, mega event, mega sport event
Positive economic impacts of large scale sport events, as well as
the methods for measuring economic impact have come under scrutiny
(e.g., Késenne, 2012). Nevertheless, sport event managers, local
organizations, and public authorities still rely on economic impact
studies to justify the public spending which is often required to cover
the high cost of organizing events. Therefore, it is imperative that they
can rely on trustworthy economic impact studies. One question that
continues to arise is whether residents should be included in economic
impact. To answer this question, we do not conduct an economic
impact study. Instead, we examine the theory behind economic impact,
build a model, and apply it to a large event to answer a critical question
about the methods currently used to conduct these studies.
Traditionally, resident spending was not considered in the
calculation of economic impact (Crompton, 1995; Getz, 1991).
Over time, researchers identified, categorized, and labeled exceptions to this rule and excluded or included residents if they were
home stayers, runaways, changers, or exhibited other forms of
nontraditional behavior (e.g., Coates & Depken, 2009; Cobb &
Olberding, 2007; Preuss, 2005). These previous studies mainly
focused on event-affected residents who were surveyed at events.
Yet, as Matheson and Baade (2006) stated, “A basic shortcoming of
typical economic impact studies, in general, pertains not to information on spending by those included in a direct expenditure survey,
but rather to the lack of information on the spending behavior for
those who are not” (p. 356). In other words, we have some
understanding of the role event-affected residents play in the calculation of economic impact (e.g., Kwiatkowski, 2016), but we do not
understand the role of residents that are not involved in the event but
may still be affected by it. Previous research has indeed indicated
that the residents who do not engage with the event may change their
spending by staying home (e.g., Coates & Depken, 2009), going out,
or otherwise altering their behaviors (e.g., Crompton & Howard,
2013, Preuss, 2005; Taks, Girginov, & Boucher, 2006).
Ultimately, the current conceptualizations of “other” types of
residents are incomplete. There is no model that encompasses the
universe of possible behavioral and spending changes incurred by
residents who are affected by events (e.g., disrupted, stimulated,
diverted), which then lead to either a positive, negative, or neutral
charge to the total calculation of economic impact. Therefore, the
primary purpose of this article is to develop a model that explains all
of the possible ways residents’ changes in behavior can affect impact.
Furthermore, primary data collected during a Type B event (Gratton
& Taylor, 2000) are used to illustrate an application of the model and
to determine if the overall effect of residents is positive, negative, or
neutral. Type B events are defined as “Major spectator events
generating significant economic activity, media interest and part
of an annual cycle of sport events” (Gratton & Taylor, 2000, p. 190).
This contribution clarifies a major point of contention, namely
whether or not to include resident spending in economic impact
studies based on a direct expenditure approach (DEA; Davies,
Coleman, & Ramchandani, 2013) or a cost benefit analysis
approach (CBA; Késenne, 2012; Taks, Késenne, Chalip, Green,
& Martyn, 2011). The question of whether and how residents affect
economic impact is also important for event managers, local
organizations, and public authorities, who continually read, conduct, and evaluate survey-based economic impact studies and use
them as a currency to justify their public spending.
Residents in Economic Impact Studies
Agha is with the University of San Francisco, San Francisco, CA. Taks is with the
School of Human Kinetics, University of Ottawa, Ottawa, Ontario, Canada. Address
author correspondence to Nola Agha at nagha@usfca.edu.
Economic impact studies based on a DEA engage in a series of
steps to measure new spending in a local economy due to an event.
473
474
Agha and Taks
Table 1
Previous Categories of Residents Affected by Events
Previous Cases
Description
Economic Outcome
Variables Affected
by the Event
Home stayers (Preuss, 2005)
Staycation (Getz, 1991)
Vacationing at home (Cobb & Olberding, 2007)
Residents forgo a vacation to
stay in the region to partake in
the event
Expenditures are an economic
benefit as they would not have
occurred locally without the event
Geographic location
of the spending
Timing
Leavers (Crompton & Howard, 2013)
Runaways (Preuss, 2005)
Skedaddle effect (Coates & Depken, 2009)
Residents specifically leave
the area to avoid the event
Expenditures are out of the area
and generate an economic cost
Geographic location
of the spending
Changers (Preuss, 2005)
Residents replace a preexisting vacation with one that allows them to avoid the event
No cost or benefit to the region
as the vacation would have
occurred regardless
Timing
This includes surveying spectators and/or participants and asking a
series of questions regarding their status as a visitor or as a local
resident, how much they spent, how long they are visiting, etc.
Traditional methods excluded all residents’ spending due to the idea
that these expenditures would occur locally regardless of the presence of an event and that these expenditures simply substitute for
others (e.g., Crompton, 1995). Over time, ad hoc attempts identified
the ways residents might have a nonneutral effect on impact (see
Table 1) and authors, such as Gelan (2003), advocated for including
resident spending, although the mistake in this approach is that it
only analyzed residents who were event spectators, not all residents.
CBA, on the other hand, is based on welfare economics and views
each dollar as a cost or benefit and results in a calculation of net
economic benefits (Késenne, 2012). In a framework developed by
Agha and Taks (2015), residents have the ability to add to impact
(e.g., residents tapping into their savings because of the event) or to
take away from it (e.g., event is crowding out residents or crowding
out local businesses). A CBA has some similarities to a DEA in that
it includes a survey-based approach to measure specific gains (or
losses) to the local economy because a researcher must know how
locals are changing their behaviors and spending to determine if it
generates a benefit or a cost.
Table 1 provides an overview of research that has labeled
various categories of residents that should be accounted for in
economic impact studies. The types of changes in behaviors
described in these studies are rather limited, and various names
have been given to describe the same behavior. Note that the
conceptualizations of resident impact in Table 1 define eventrelated changes, or shifts, in two different variables, the timing
(changers and home stayers) and geographic location of the money
spent (home stayers and runaways). These studies do not take into
consideration possible shifts in the amount or businesses where the
money is spent. In the “Model Development” section, we propose a
model using four variables that captures many more possible ways
in which residents may shift their spending behaviors because of an
event. In doing so, we demonstrate that these previous categorizations are insufficient to capture the real economic outcome of
changes in resident spending behavior due to event hosting.
Model Development
a reallocation of funds and do not generate benefits or costs
(Table 2). From this simplistic definition, we see the amount spent
and the geographic location of the spending are the first of four
necessary variables that need to be considered. We also see that
economic impact implicitly considers two cases that capture a
shift—the actual spending and the alternate spending that would
have occurred without the event. In Table 2, these two variables
and the two cases are interacted: the geographic area where
the money is normally spent in the absence of the event
(i.e., “origination of expenditures”) and where the money is
actually spent because of the event (i.e., to the “location of
expenditures”). The third variable that needs attention is the
business industry in which spending occurred to derive a multiplier
that will estimate the indirect and induced effects of the initial
spending (Crompton, 1995). The fourth variable, the timing of the
expenditure, is not nearly as straight forward as the first three. This
is due to competing frameworks, limited research in this area, and
ad hoc operationalization of the variable. For instance, there is no
consensus which time frame should be considered. In the context of
the Olympics, tourism spending can change several years before an
event (Solberg & Preuss, 2007). With no sense of time scale, direct
survey questions ask, “Did you ‘reduce spending in the past’ or
‘reduce spending in the future,’ or will you ‘respend at a later date’”
although questions designed to find home stayers ask, “Did you
forgo another vacation (trip) in order to attend the (event)?”
(Preuss, Kurscheidt, & Schütte, 2009). In short, there is very little
alignment between theoretical models (conceptualizations) of time
shifted expenditures and the survey questions that are designed to
identify those shifts. Our solution is three pronged: (a) we acknowledge time as a theoretical necessity in calculating economic impact
and build it into our model, (b) we rely on current survey-based
questions to identify time-shifted behaviors (home stayers and
Table 2
Economic Impact
Location of Expenditures
Origination of
expenditures
Outside the area
Economic impact is new spending in a local economy less any
expenditures that have left the local economy due to the event in
question. At a basic level, expenditures made locally from residents
who would have otherwise made those expenditures are considered
Inside the area
Outside the Area
Inside the Area
Not related to
economic impact
Economic cost
Economic benefit
Zero economic impact
Note. Adapted from “The economic impact of visitors at major multi-sport
events,” by H. Preuss, 2005, European Sport Management Quarterly, 5,
pp. 281–301.
JSM Vol. 32, No. 5, 2018
Modeling Resident Spending During Sport Events
changers) and test those against an improved four variable conceptualization of shifted spending, and (c) we collect qualitative data
on time shifting to see if it aligns with multiple-choice questions.
In summary, there are four necessary variables related to
economic impact: the amount spent, the geographic location of
spending, the business industry in which it was spent (to derive the
multiplier), and the timing of the expenditure. To calculate impact,
one must capture the shift in these variables. For example, a
resident can spend more, less, or the same amount of money in
the presence of an event in the host region. A resident can spend
within the geographic area of impact or outside of it. Spending can
shift to a business with a higher multiplier, same multiplier, or
lower multiplier. Finally, spending can occur as normally planned
or a resident may shift the timing of their expenditures to before or
after the event.1
Given these four variables and their associated shifts, Table 3
illustrates that there are 18 potential behaviors for the case in which
a resident intended to, and did, spend their money locally (i.e.,
In-In). For example, an event can cause a resident to spend the same
amount but at a lower multiplier business, which will have a
negative effect on impact. An event can cause a resident to spend
more at a higher multiplier business, which will lead to economic
benefits regardless of whether the spending is time shifted or not.
From Tables 2 and 3, it is apparent that the geographic variable
has multiple dimensions, which generates four distinct cases for
residents:
(a) In-In: Spending that would have been spent in the area of
impact and stayed in the area (the cases illustrated in Table 3).
(b) In-Out: Spending that would have been spent in the area of
impact but shifted out. All of these 18 cases are negative and
are analogous to the economic cost in Table 2. Runaways
would be classified here but so would a resident who
intended to go to the local golf course, but it was booked
for a preevent tournament and instead drove to a nearby
course that was outside of the area of impact.
(c) Out-In: Spending that would have been spent outside the
region but shifted in. These 18 cases are all positive and
correspond to the economic benefit in Table 2. A home stayer
is a special case of this.
(d) Out-Out: Spending that would have been outside the region
and stayed outside the region. These 18 cases are not related
to economic impact. They include changers, but also residents who planned to take a day trip to visit family in a nearby
metropolitan area and did, in fact, take that planned trip.
The outcomes based on shifts in geography related to In-Out,
Out-In, and Out-Out are straight forward, but the shifts in behaviors
within the specific area, In-In, need to be accurately modeled and
estimated. Moreover, the cases in Table 3 illustrate three important
points.
(a) Economic impact can be affected regardless if residents are
positively or negatively affected by the event (e.g., a resident
spending more than usual on public transit to attend the
event, or a resident forced to spend more on public transit
because of event-related traffic). As illustrated here, it is
possible, although not necessary, for positive and negative
engagement to have the same effect (in this case, higher
spending on public transportation).
(b) The changes in behavior can begin with any of the four
variables. For example, a resident may stay away from
downtown because of traffic (geography), a resident may
475
Table 3 Theoretical Model of Resident Effects on
Economic Impact
Spending Multiplier Time Shift
Geography
In-In In-Out Out-In Out-Out
More
Higher
Same
Lower
Same
Higher
Same
Lower
Less
Higher
Same
Lower
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
+
+
0
+
?
?
+
+
0
0
−
−
?
?
0
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
Note. In-In is spending that would have occurred within the area of impact and did,
in fact, occur in the area of impact. In-Out is spending that would have occurred
within the area of impact but instead shifted out of the area because of the event.
Out-In is spending that would have occurred outside the area of impact but instead
shifted into the area because of the event. Out-Out is spending that would have
occurred outside of the area of impact and did, in fact, occur outside the area of
impact. + means positive effect, − means negative effect, 0 means no effect, and
blank cells in Out-Out indicate that these resident spending cases are irrelevant
because they do not relate to economic impact.
purchase tickets for the event (spending), a resident may go
grocery shopping on Thursday to avoid weekend crowds
(timing), or a resident may buy lunch from a grocery store
instead of going to her normal deli for lunch (business).
(c) Regardless of where it begins, the initial disruption can (but
does not necessarily have to) affect the other three variables.
We provide two examples of how multiple variables can be
affected and illustrate which variables shift. For instance, a
resident may purchase tickets for the event, which could be
more than she normally spends on entertainment and in a
different location. She makes up for it by not going out for
movies the following week. This is a shift in location, timing,
business multiplier, and spending. Or, a resident may go
grocery shopping on Thursday instead of his normal Sunday
shopping, but he spends more because he went to a different
store in a different city (within the area of impact) on his drive
home from work. This is a shift in timing and spending, but
the location and business multiplier are the same.
Of the 18 possible behaviors for the In-In group, five outcomes
are positive, five are negative, four are neutral (no effect), and four
are indeterminate. The cases of higher spending at lower multiplier
businesses and lower spending at higher multiplier businesses
make it clear that all four variables are necessary to calculate
the final impact as these are indeterminate, ex ante, and require the
actual values on a case by case basis to determine their effect.
JSM Vol. 32, No. 5, 2018
476
Agha and Taks
Based on the framework presented in Table 3, we can see there
are 72 possible behavioral combinations of which 22 have no effect
on economic impact, 23 are positive, 23 are negative, and four are
indeterminate. Thus, to determine the overall impact of residents
we must know (a) the shift in spending, business, time, and
geography; and (b) the proportion of residents in each of these
categories. Because mega events generally assume a large area of
impact, we hypothesize most resident geographic spending shifts
will be within the host region (i.e., In-In) and thus subject to the
variable impacts presented in Table 3.
Study Context
Super Bowl 50, the 2016 championship game for the National
Football League (NFL), was hosted in the San Francisco Bay area
and allowed for applying the model to different groups of residents
from three distinct geographical perspectives (see Figure 1). The
Super Bowl is traditionally the largest 1-day sporting event in the
United States in terms of viewership with 111.9 million viewers
in 2016 (Nielsen, 2016). The Super Bowl 50 Host Committee
defined the area of impact (i.e., geography variable) as the 6,900
square mile, nine-county San Francisco Bay Area (population
7.15 million). This nine-county Bay Area as whole was the first
geographic area we delineate for this study. The region is comprised of three major cities, San Francisco (population 805,235),
Oakland (population 390,724), and San Jose (population 945,942),
and 98 smaller municipalities (Bay Area Census, 2016). Host
Committee consultants reported 1.9 million residents and
300,000 out-of-area visitors attended a Super Bowl related event
(Repucom, 2016) although the game itself was played in front of
only 70,000 fans at Levi’s Stadium in Santa Clara. Seven miles
away from Levi’s Stadium, the city of San Jose hosted several
community events as well as the NFL Opening Night at SAP
Center where 7,000 fans paid to watch the media interview players
(Davidson, 2016). Santa Clara County, which includes the cities of
San Jose and Santa Clara, was the second geographic area taken
into consideration.
San Francisco, 45 miles away from Levi’s Stadium, hosted the
majority of the lodging and hundreds of hospitality events located
primarily in the downtown central business district. Two main fan
festivals were also in San Francisco. Super Bowl City was a free,
Figure 1 — Maps of the study area.
9-day fan festival featuring 64 free performances with attendance
estimates ranging from 5,000 per day (Lee, 2016) to a total of
900,000 (“Super Bowl,” 2016). The NFL Experience was a paid
fan experience that reported 150,000 attendees over 9 days
(Controller’s Office, 2016) and was located at the Moscone Center;
ticket prices ranged from $25 to $60.
Super Bowl City was located above the region’s busiest public
transit station and required the closure of over 14 streets, the
rerouting of 20 bus lines, and the closure of one streetcar line
for a total of 21 days (SFMTA, 2016). The NFL Experience was
located less than a mile away from Super Bowl City, and both
events were located within one mile of all six of San Francisco’s
Fortune 500 companies, two of which were asked to have their
employees work from home during Super Bowl City (Raymos,
2016). Similarly, the city of San Francisco encouraged residents to,
“Work remotely, stagger your work hours or take that vacation you
deserve” (SFMTA, 2016).2 In short, the bulk of the activity and
disruption (nongame related events) were held in a small portion of
downtown San Francisco, for multiple days. We expected the vast
majority of resident disruption to be related to San Francisco and
not Santa Clara where the game was played. San Francisco County
was the third geographic area under consideration.
Super Bowl 50 was somewhat different from other Super
Bowls in that it was geographically dispersed in a warm weather
city with a developed public transportation system. On the other
hand, Super Bowl 50 is highly comparable with other Super Bowls
and with many other large events on other event features such as
high levels of security, a multitude of hospitality events, crowding
out of nonevent visitors (it occurred during Chinese New Year), fan
events, the closing of the central business district, altering public
transportation, residents asked to stay home, geographic dispersion
(similar to the Olympics or the Commonwealth Games), thousands
of visitors, public subsidies, etc. Thus, Super Bowl 50 has event
features that make it generalizable to many other Type B events
(Gratton & Taylor, 2000) throughout the world.
Method
Survey Instrument
We used a survey method (using the Qualtrics platform, Provo,
UT) similar to traditional DEA impact studies to collect the data on
residents’ spending behavior. The survey was pretested multiple
times to develop the clearest questions for respondents. We
integrated the standard questions to identify the three categories
of home stayers, changers, and runaways and added questions to
capture all of the 72 categories.
A screening question first identified whether the respondent
was a Bay Area resident and then recorded their zip code. NonBay Area residents were exited from the survey. The zip code
determined to which of the three geographic areas the resident
belonged. Next, a series of questions determined whether the
respondent was aware of the Super Bowl, if they were attending,
and if so, how much they were spending on the event. Similarly,
respondents were asked if they were aware of the fan-related
events, if they were attending, and if so, how much they were
spending.
The following section captured spending information. Specifically, respondents were asked to record their actual spending all
day “yesterday” using three variables: the amount of spending, the
business, and the city in which each expenditure took place. Next,
they were asked if the Super Bowl or its related events caused them
JSM Vol. 32, No. 5, 2018
Modeling Resident Spending During Sport Events
to change the amount and/or location of their spending yesterday. If
they believed their spending yesterday was affected by the Super
Bowl they recorded the amount, the business, and the city of each
of what their expenditures would have been in the absence of the
event, allowing for the capture of changes in dollar amounts spent,
multipliers, and geography.
In imagining what their behaviors would have been in the
absence of the event, respondents relied or either known or
hypothetical information. For example, in some cases, the alternate
activity was known (traffic was terrible so someone took public
transportation instead of Uber), sometimes it was partially known
(someone planned to go out to dinner but the restaurant was near a
busy event zone so they stayed home and ate dinner—the cost of
staying home exists but is generally not acknowledged), and was
sometimes completely unknown and responses were hypothetical
(someone definitely spent money to visit a fan festival but without
it they honestly did not know what they would have done or how
much they would have spent that Saturday afternoon).
To determine timing changes, all respondents were asked the
screening question, “Because of the Super Bowl or its related
events, was your total spending amount yesterday: the same as
usual, more than usual, or less than usual.” The three responses
were randomized so as not to lead respondents in any direction. If a
respondent spent more or less than usual, they were asked the
amount and then received a follow-up question on the timing of
their expenditures. Those who spent more than usual were asked
the source of their additional funds: their savings, borrowed money
(e.g., a credit card), reducing spending in the past, or reducing
spending in the future. Those who spent less than usual were asked
if they would respend at a later date in the Bay Area, respend at a
later date outside the Bay Area, or save.
To identify home stayers, runaways, and changers, all respondents (not just those who had indicated they were affected) were
asked if they were taking a vacation away from the Bay Area
between January 30 and February 7. Those who responded positively were asked follow-up questions to determine if they were
leaving because of the Super Bowl (runaways) or foregoing another
vacation at a different time to take a vacation during the Super Bowl
(changers). In addition, they were asked if they were renting out
their home on Airbnb or a similar service and how much they were
earning. Those who responded they were not taking a vacation
during the Super Bowl period were asked if they were foregoing a
vacation (at a different time) to stay and attend the Super Bowl or its
associated events (home stayers).
All respondents were asked if they lived or worked near four
primary event zones: Moscone Center in San Francisco, Justin
Herman Plaza/Ferry Building in San Francisco, Levi’s Stadium in
Santa Clara, and SAP Center in San Jose. Demographic questions
included gender identity, age (in years), and annual household
income (15 categories). To capture a respondent’s attitude toward
the Super Bowl, data were collected with the Sport Involvement
Inventory based on Shank and Beasley (1998), using a seven-point,
eight-item sematic differential (e.g., boring vs. exciting, uninteresting vs. interesting, etc.). Finally, respondents were asked to
share any additional comments regarding the amount, timing, and
location of their spending changes due to the Super Bowl and its
associated events.
Data Collection and Participants
When survey populations are geographically and demographically diverse Yun and Trumbo (2000) recommend using
477
multimode techniques to improve sample representativeness.
For that reason, we collected data throughout the 6,900 square
mile area in a variety of ways. First, 32 graduate students enrolled
in a research methods course were enlisted to probabilistically
sample (Jones, 2015) the nine-county Bay Area in person for the
9 days surrounding the Super Bowl by intercepting subjects at six
similar, predetermined locations in each county (public transit
station, low-price-point grocery store, high-price-point grocery
store, coffee shop, laundromat, and a strip mall or a busy
shopping street in a big city). To achieve a geographically
stratified sample, counties were sampled in approximation to
their overall portion of the Bay Area population (see the percentages in Figure 1). Second, given the complexity of surveying
such a geographically distributed area, during the 9-day collection period, 33 graduate students enrolled in a different research
methods class distributed the survey online through 33 different,
hyperlocal, digital news publications that covered news related to
Super Bowl 50. Third, to counter concern that we would oversample residents who were familiar with or who were interested
in the event, we targeted residents in the Bay Area who would
take our questionnaire when shared on various convenient digital
platforms. Finally, a sampling concern was related to obtaining
accurate spending information given that the survey only captured a single day of behavior and given that respondents are
often hesitant to respond to questions about money (Swan &
Epley, 1981). To address these concerns, we utilized a longitudinal convenience panel (also stratified by county population)
that was tracked over the 9-day data collection period using an
electronic diary method known to provide more accurate data
than onsite surveying (Breen, Bull, & Walo, 2001). Each panel
respondent received the full online survey on the first day and a
shortened online version that collected only spending information on subsequent days.
There were 1,227 surveys taken of which only 790 were
completed.3 A further 151 surveys were discarded because the
reported spending information (in dollar amounts) was not complete
or was not in alignment with the follow-up question of whether they
spent more, the same, or less. Table 4 indicates the descriptive
statistics of the samples for each geographic sample—the ninecounty Bay Area with respondents from all samples
(n = 572),4 San Francisco County (n = 127), and Santa Clara County
(n = 141) in terms of gender, household income, attitude, age, and
awareness of the event. The samples were highly representative of
the region regarding age and income distribution (https://
censusreporter.org/profiles/). At a 95% confidence level, the confidence intervals were 4.1% for the Bay Area sample, 8.7% for San
Francisco, and 8.3% for Santa Clara (Griffiths, Hill, & Judge, 1993).
Coding and Data Analysis
The survey collected shift data for the time variable through
multiple choice questions. For the spending, business, and geographic location variables, actual and alternate values were collected to generate the shifts. First, the location of the business was
translated into a county code, and business variables were assigned
an output multiplier5 from IMPLAN6 based on the appropriate
industry.7 Next, variables were coded into their respective shifts;
spending more, less, or the same was determined by evaluating
actual and alternate spending; a business multiplier that was higher,
lower, or the same was determined by the actual and alternate
businesses; spending that was In-In, In-Out, Out-In, or Out-Out
was determined by evaluating the county codes for the location of
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478
Agha and Taks
Table 4
Descriptive Statistics
Bay Area San Francisco Santa Clara
(n = 572)
(n = 127)
(n = 141)
Gender
Male
45%
54%
57%
Female
55%
46%
43%
Household income
under $50,000
15%
16%
12%
$50,000–$99,999
30%
27%
39%
$100,000–$149,999
22%
23%
23%
$150,000+
32%
34%
26%
Average attitude
4.0
3.1
4.3
(7 is highest)
Average age
43.4
38.7
42.3
Awareness
Aware of Super Bowl
96%
98%
99%
Attended Super Bowl
1%
1%
2%
Average spent to
$1,300 (n = 5) $3,500 (n = 1) $1,000 (n = 2)
attend Super Bowla
Aware of fan festivals
90%
98%
88%
Attended fan festivals
22%
35%
11%
Average spent to
$39.07
$28.52
$29.62
attend fan festivalsa
a
Average based on those who attended only.
the actual and alternate spending. All of this coding was performed
separately for the three different areas of impact, and observations
were assigned to these areas according to their zip code.
Results
We present the results for each of the geographic areas of interest,
for individual variables, two variables, three variables, and all four
variables.
Spending
Depending on the geography, 76–84% reported their spending
amounts to be unaffected by the Super Bowl (see Table 5). Of the
remainder, more respondents reported spending more than spending less except in San Francisco. The average amount spent more
and spent less varied across geographic samples.
Business
Overall, the vast majority of respondents spent at businesses with
the same multiplier (see Table 5). Although the average multiplier
in the geographic samples was roughly the same, the shifts in
multipliers were negative in the Bay Area and Santa Clara samples.
The positive change in multiplier in San Francisco stems from the
behavioral shift toward public transportation which has one of the
highest multipliers.
Time
Residents classified as changers are those whose behaviors have
shifted on only the dimension of time. The results show that they
are less than 1% of the Bay Area population. Similarly, in the openended feedback, very few respondents reported shifting the timing
of their expenditures on the day in question or on other days during
the Super Bowl period (see the lines labeled “Qualitative” in
Table 5). The most common descriptions of time shifts were for
residents changing the timing of doctor appointments and other
meetings to avoid traffic and crowds. In the overall framework of
economic impact, changing the timing of an appointment is an
example of a behavioral disruption that begins with time (and does
not affect amount, business, or geography), whereas traditionally,
the timing variable is intended to capture deeper shifts in expenditure timing (e.g., spending more now on tickets to events and
less later on local leisure consumption). The qualitative feedback
listing only these time-disrupted activities begs the questions of
whether the right questions are being asked and if people are even
capable of truly answering time shifting questions. Certainly,
people have a general sense that they are being affected by these
different variables, but it is not clear that the data can capture these
shifts.
Geography
The majority of residents in all geographic areas spent their money
within the area of impact and had intended to do so regardless of
the event (Table 5). In addition, In-Out and Out-In behavior is
quite uncommon. Open-ended comments revealed that residents
perceived their spending to shift away from the area more often
than reported in their spending data. Generally, more people
reported shifting spending away from event-related areas than
toward them.
Spending × Business
Whereas the values in Table 5 reflect the difference of how much
more or less was spent, the values hereafter represent the more
precise case of the actual spending times the multiplier less the
alternate spending times a multiplier for all reported transactions,
and then averaged across respondents. The interaction of the
spending shift and the multiplier shift in Table 6 reinforce the
results from the spending section. The most important finding is
the overall negative effect in San Francisco where more people are
spending less than spending more and the amount less is greater
than the amount more. In the other areas, the reverse is true, and a
greater number of residents are spending more. Note that when
less is spent in higher multiplier businesses, the overall effect
remains negative. Given the much higher spending values in the
spent more category, we point to research showing that we
remember larger expenditures (Neter & Waksberg, 1964), whereas
the alternate case, spending less, is both hypothetical and harder to
remember. This suggests that the spent less values may be
underestimated.
Spending × Time
A single question was asked to identify if those who spent more,
time shifted their behaviors. Of those who spent more than
normal, the proportion of time shifting ranged from 50% in
the Bay Area sample to 22% in the San Francisco sample (see
Table 6). These time-shifted expenditures should be disregarded
for economic impact (unless the multiplier of the business is
taken into account, however, as can be seen from the multiplier
analysis (Table 5) there is no substantial shift in the actual and
JSM Vol. 32, No. 5, 2018
Modeling Resident Spending During Sport Events
Table 5
479
Single Variable Shifts
Bay Area
n
Spending
Spent more (%)
Reported how much more ($)a
Spent same (%)
Spent less (%)
Perception how much less ($)a
Business
Higher multiplier industry (%)
Same multiplier industry (%)
Lower multiplier industry (%)
Average actual multiplierb
Average alternate multiplierc
Average difference of actual − alternate multiplier
Time
Changers
Qualitative: shifted time this day
Qualitative: shifted time a different day
Geographyd
In-In
In-Out
Out-In
Out-Out
Runaways (a specific form of In-Out)
58
San Francisco
n
Santa Clara
n
10.39%
$93.21
84.23%
5.38%
−$30.08
11
11
93
18
18
9.02%
$62.39
76.23%
14.75%
−$63.07
22
22
105
11
11
15.94%
$48.18
76.09%
7.97%
−$32.73
19
489
18
485
72
526
3.32%
85.49%
3.15%
1.550
1.560
−0.001
8
98
8
112
25
114
6.30%
77.17%
6.30%
1.550
1.533
0.008
3
115
7
109
27
125
2.13%
81.56%
4.96%
1.518
1.554
−0.004
3
1
7
0.52%
0.17%
1.22%
2
0
1
1.57%
0.00%
0.79%
0
0
3
0.00%
0.00%
2.13%
554
1
0
30
8
96.85%
0.17%
0.00%
5.25%
1.40%
109
0
1
27
7
85.83%
0.00%
0.79%
21.26%
5.51%
114
8
7
17
3
80.85%
5.67%
4.97%
12.06%
2.13%
470
30
Note. The sum of the n’s may be lower than the total n’s due to missing values.
a
Average. bThis reflects the respondents who actually had transactions “yesterday.” Some reported zero spending “yesterday” and thus have no actual multiplier. cThis
reflects respondents whose business locations shifted and is comprised of some who had actual spending the prior day and some who had zero spending the prior day. dThe
sum of percentages can be more than 100% because a single respondent can have multiple types of geographically shifted expenditures on a single day.
alternate multiplier). In contemplating the reliability of these
results, we do wonder, do respondents really know what their
time shifts will be? Given that spending more in the past or future
requires pondering one’s budget and expenditures, we are not
confident that the majority of people know or track this kind of
behavior.
Spending × Geography
Table 6 indicates that adding geographical shifts to net changes
in Spending × Multiplier provides different estimates than in
Table 5, which reports only spending. In the geographic samples,
the amount spent less and the amount spent more have higher
average values once geographic shifts are considered. We use this
to highlight the importance of including all variables because an
estimate of economic impact based on spending without geographic location would have been incorrect as the In-Out and
Out-Out expenditures would have been erroneously included as
positive gain in the calculations.
Time × Geography
The effect of home stayers on impact is positive, but there are more
runaways (negative) in Table 5 than home stayers (Table 6). Note,
however, that the numbers are low confirming that these behaviors
do not greatly impact shifts in economic impact.
Spending × Business × Geography
The interaction of three variables in Table 7 paints a different picture
than does the analysis of one or two variables. Both In-Outs and OutIns are negligible in size perhaps suggesting that surveyors should not
spend time capturing runaways and home stayers or other forms of
geographic shifts. Santa Clara County does appear to have more InOuts and Out-Ins than the other areas. Note that the average In-Out shift
of $106.70 in Santa Clara County is a loss as this money was shifted
out of the area. On the other hand, Out-Ins represent money that was
shifted in and generate a positive impact. In the case of Santa Clara
County, the Out-Ins spent $17.19 less than usual, but this was still a gain
to the county (without time shifting) and is still positive, as expected.
Spending × Time × Geography
Of those who spent less than normal, a single question determined
their time and geographic shifts. Few plan to respend later outside
the Bay Area suggesting that reductions in spending are mostly
retained locally (Table 7).
Spending × Business × Time × Geography
When time shifting is included to analyze all four variables
simultaneously, the total responses drop because time shifting
does not matter for those whose spending behavior was the
JSM Vol. 32, No. 5, 2018
480
Agha and Taks
Table 6
Net Change in Average Spending × Multiplier for Two Variable Shifts
Bay Area
n
Spending × Businessa
Spent more
Higher multiplier business
Same multiplier business
Lower multiplier business
Spent same
Higher multiplier business
Same multiplier business
Lower multiplier business
Spent less
Higher multiplier business
Same multiplier business
Lower multiplier business
Average for full sample
Spending × Time
Because you spent more than normal did you
Reduce spending past
Reduce spending future
Spending × Geographyb
In-In
Spent more
Spent same
Spent less
In-Out
Spent more
Spent same
Spent less
Out-In
Spent more
Spent same
Spent less
Out-Out
Spent more
Spent same
Spent less
Time × Geography
Home stayers (a specific form of Out-In)
San Francisco
n
15
10
15
$134.12
$34.94
$239.05
4
0
3
0
470
0
$0.00
0
93
0
4
9
3
558
−$37.72
−$31.87
−$5.35
$11.58
63
7
24
11.11%
38.10%
540
56
454
30
1
1
0
0
0
0
0
0
28
1
27
0
$11.90
$139.68
$0.00
−$46.56
$30.63
$30.63
5
0.87%
$0.16
$4.60
$0.00
Santa Clara
n
$64.02
3
6
7
$92.38
$47.92
$98.17
$0.00
0
105
0
$0.00
4
5
5
122
−$63.46
−$45.57
−$28.15
−$6.59
0
4
0
138
9
1
1
11.11%
11.11%
21
3
5
$50.16
104
10
76
18
0
0
0
0
1
1
0
0
26
2
24
0
−$9.41
$77.13
$0.00
−$97.22
1
0.79%
$3.72
$3.72
$9.69
$126.00
$0.00
−$36.38
$7.68
14.29%
23.81%
112
14
94
4
6
6
0
0
7
1
0
6
17
1
14
2
$6.02
$63.02
$0.00
−$59.62
$106.70
$106.70
−$28.32
−$7.81
$43.39
$0.00
−$88.08
1
0.71%
−$17.19
$49.59
a
The sum of the n’s may be lower than the total n’s due to missing values. bn’s are different from the geography results in Table 4 because some cases of missing spending or
missing multipliers. We remind readers that in the calculation of overall impact In-Out values are negative and Out-Out are irrelevant, as in Table 3.
same (which ranged from 59% to 83% of the samples in Table 8).
Only respondents who reported spending more or less provided
information on their time shifting behaviors and of these respondents, few answered the time shifting question.
From this, we derive two important conclusions. First, in
Table 8, only a small portion of residents engage in behavior that
leads to a change in impact. In the case of the entire Bay Area, only
0.2% of respondents engaged in In-Out behavior and 7% engaged in
In-In behavior that affected impact. In San Francisco, the geographic
area most impacted by the event, the results are similar: 0.8% were
Out-In and 11.2% were In-In with behaviors that affected impact.
Second, when the fourth variable for time is brought in, the average
values for each type of behavior shift from the values in Spending ×
Business × Geography analysis in Table 7. This further reinforces
the point that any estimate of economic impact performed with
values from one, two, or three variables will be incorrect.
Discussion
Using the model based on shifts in four spending dimensions, we
found In-In residents exhibited all 18 forms of behavioral shifts in
JSM Vol. 32, No. 5, 2018
Modeling Resident Spending During Sport Events
Table 7
481
Net Change in Average Spending × Multiplier for Three Variable Shifts
Bay Area
San Francisco
n
Spending × Business × Geography
In-In
Spent more
Higher multiplier business
Same multiplier business
Lower multiplier business
Spent same
Higher multiplier business
Same multiplier business
Lower multiplier business
Spent less
Higher multiplier business
Same multiplier business
Lower multiplier business
In-Out
Out-In
Out-Out
Spending × Time × Geography
Because you spent less than normal will you
Respend later in the Bay Area
Respend later outside the Bay Area
n
Santa Clara
n
540
$11.90
104
−$9.41
112
$6.02
15
9
14
$134.12
$35.42
$255.80
4
0
3
$64.02
1
4
3
$71.99
$49.68
$83.11
$0.00
0
76
0
0
94
0
$0.00
0
454
0
4
9
3
1
0
28
26
8
1
−$37.72
−$31.87
−$5.35
$30.63
$0.16
4
5
5
0
1
26
30.77%
3.85%
18
3
1
$50.16
$0.00
−$63.46
−$45.57
−$28.15
$3.72
$9.69
0
2
0
6
7
17
$106.70
−$17.19
−$7.81
16.67%
5.56%
8
1
1
12.50%
12.50%
−$45.96
Note. Spending and multiplier shifts are truncated for In-Out, Out-In, and Out-Out. We remind readers that in the calculation of overall impact In-Out values are negative
and Out-Out values are irrelevant, as in Table 3.
spending, multiplier, and time. On the other hand, we found In-Out
and Out-In behavior to be exceedingly rare, except in the case of
Santa Clara County. We find this unsurprising since the county is a
smaller subset of a major metropolitan area whose county lines are
indistinguishable in the physical landscape leading to higher rates
of cross-border transactions.8
The application of the model to an event allowed us to
demonstrate that using one, two, or three variables resulted in
incorrect estimates of resident impact. Moreover, we illustrated that
respondents were unwilling or unable to answer questions on time
shifts and qualitative responses indicated that respondents viewed
time shifting differently (short term) than academic conceptualizations of the variable (to pre-event or post-event periods). Finally,
the model included four indeterminate categories of In-In residents
(in Table 3). We found the multiplier effect was not stronger than
the spending shift in the cases of higher spending + lower multiplier
or lower spending + higher multiplier. Thus, although these situations are hypothetically indeterminate, the model can be simplified
by assuming that the higher spending + lower multiplier has a
positive effect and the lower spending + higher multiplier has a
negative effect. This means that of the 72 possible behavioral
combinations, 22 have no effect on economic impact, 25 are
positive, and 25 are negative.
Are Resident Effects Positive, Negative, or Neutral?
To determine if the overall effect of residents is positive, negative,
or neutral, we note that the value of impact is a function of the
definition of the area of impact. In this exercise, we looked at three
geographic areas: the entire metropolitan area, the county where the
game was hosted, and the county where the vast majority of the
Super Bowl week activities were held. Those counties, Santa Clara
and San Francisco, saw the highest percentages of residents who
were affected and who reported shifts in behaviors. San Francisco
had more residents spend less than more. The decline in spending
from those spending less was larger in magnitude than the
increased spending from those spending more, resulting in an
overall net decrease. Santa Clara saw an increase in In-Out
behavior of residents shifting their spending outside the county;
thus, resident behavior decreased economic activity. As illustrated
in Table 9, we found San Francisco, the event area with the most
disruption and activity, to be most negatively affected. Note that the
three areas under investigation represent three different event size
and city size contexts (e.g., Agha & Taks, 2015): a multiday event
concentrated in a central business district (San Francisco County), a
single day event in a suburban city (Santa Clara County), and an
annual, week-long mega event in a large metropolitan area (Bay
Area). From this perspective, a large event in a small area of impact
had a more negative impact (10% of San Francisco residents
leading to negative impact) compared with a large event in a large
area of impact (2% of Bay Area residents leading to negative
impact).
Although the purpose of this research is not to conduct an
economic impact analysis, the natural inclination of a researcher
is to extrapolate the values in Table 8 to the entire area to generate
an overall effect of residents. This would be incorrect because if
we applied the percentages in Table 9 to the entire population, we
would be suggesting that every person (including babies, children, seniors, and the unemployed) engaged in these spending
behaviors, clearly leading to an overestimation. Using the number
JSM Vol. 32, No. 5, 2018
482
Agha and Taks
Table 8
Outcome of Resident Effects on Economic Impact
Samples
Theoretical
Outcome
Bay Area
(n = 528)
Geography
Spending
Multiplier
Time Shift
In-In
More
Higher
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
+
+
0
+
?
?
+
+
0
0
−
−
?
1.1%
1.2%
0.7%
0.5%
0.9%
1.1%
0.0%
0.0%
79.4%
0.0%
0.0%
0.0%
0.4%
No
?
0.4%
Yes
0
0.4%
No
−
0.9%
Yes
−
0.0%
No
−
−
+
0.5%
0.2%
0.0%
Same
Lower
Same
Higher
Same
Lower
Less
Higher
Same
Lower
In-Out
Out-In
All cases
All cases
Out-Out
All cases
4.9%
Table 9 Percentages of Sample Leading to Positive,
Negative, and Neutral Effects
Effect on Impact
Bay Area
San Francisco
Santa Clara
Positive
Neutral
Negative
Not related to impact
5%
87%
2%
5%
3%
65%
10%
22%
11%
80%
7%
2%
$174.53
$128.93
$35.21
$51.66
$93.82
$481.90
$0.00
−
$26.44
−
$49.00
−
$15.32
−
$41.91
San Francisco
(n = 118)
0.0%
0.8%
0.0%
0.0%
0.8%
0.0%
0.0%
0.0%
59.8%
0.0%
0.0%
0.0%
0.0%
$85.21
$0.00
0.0%
7.0%
0.7%
2.1%
0.7%
0.7%
0.0%
0.0%
67.4%
0.0%
0.0%
0.0%
0.0%
3.2%
−$63.46
0.0%
0.8%
−$7.66
0.7%
2.4%
−
$196.53
−
$118.11
−$5.65
0.7%
0.8%
−$5.35
$30.63
$114.51
Santa Clara
(n = 121)
3.2%
0%
0.8%
$3.72
20.5%
$71.99
$43.76
$51.66
$144.40
$79.09
$0.00
−
$22.98
−
$68.93
0.0%
0.0%
5.7%
5.0%
$106.47
−
$25.94
1.4%
the sports economics literature. Possible explanations for the
negative or neutral effects of the Super Bowl and other large
events (e.g., Baade, Baumann, & Matheson, 2008; Matheson &
Baade, 2006) are crowding out of both visitors and locals. The
neutralizing behaviors of residents confirm that these nonpositive
ex post results are unlikely to derive from local crowding out of
residents.
Limitations and Future Research
of households in each geographic region could lead to similar
inaccurate results because a single household could include one
member who spent more, one who spent less, and one who was
unaffected.
Even without an exact value for resident impact for this event,
the results from the application of the model clearly support the
proposition that some local residents are crowded out during an
event (Késenne, 2012). We also found evidence of retained expenditures. Most importantly, we found that they are roughly
equivalent with slight differences based on the area of impact,
essentially neutralizing the overall impact.
Although our research is framed around the DEA and CBA
survey-based approaches to impact, the quantitative results indicate
important implications for the ex post approach that is common in
Although it is theoretically possible and conceptually simple to
gather information on all residents to compute an impact, Wilton and
Nickerson (2006) agree “the actual collection of such information is
extremely difficult” (p. 17). For instance, capturing all shifts in all
four variables was very challenging, and we acknowledge that there
are known imperfections in collecting survey data (Ritchie, 1984).
Despite testing multiple variations of our instrument, there were
several indications that it did not precisely capture all behaviors. For
example, the open-ended qualitative responses indicated that respondents are better able to remember or identify higher expenditures despite a perception of a shift to lower spending. We also found
evidence that humans are hesitant to share information pertaining to
money (Furnham & Argyle, 1998).
Capturing residents’ actual activity on a previous day, as well
as any activity that was different from what would have occurred,
JSM Vol. 32, No. 5, 2018
Modeling Resident Spending During Sport Events
allowed us to identify intertemporal effects. There was a high
nonresponse rate when asking respondents if changes in spending
(reduced or increased) is at the benefit/expense of the past or future
or if they have saved/spent or plan to respend or save the money in
the future. Respondents struggled to know, understand, or properly
evaluate time shifting behavior. There is an important need for
future research on time shifting—clearly defining it, deciding what
time period matters, and finding ways to ask appropriate questions,
so respondents can both understand and correctly answer. Journaling expenses over a certain period of time could be an alternative
way to capture this.
To gather the required information with a large enough
number of responses, we used a variety of data collection
techniques. Based on the number of Super Bowl game attendees,
it appears we oversampled people who purchased Super Bowl
tickets. Based solely on the definition of runaways and hunkerdowns, these residents were not physically present in the region
or were at home. To overcome this inherent difficulty in sampling
a resident who is not present, it was necessary to utilize online
sampling (to reach those at home) and a lengthy data collection
period (9 days) to capture some runaways before they left. In
both cases, it is still possible we undersampled, which relates
back to the concern of Matheson and Baade (2006) that to
calculate the most precise estimate of resident impact with
survey techniques, it is necessary to sample residents who are
not physically present.
483
Third, in the case of this Type B event, changes in residents’
spending behavior had a negligible effect on impact although it
varied between positive and negative depending on the area of
impact. Thus, practitioners have the option to engage in the
challenging process of gathering data on all four variables on all
residents (including those who do not attend the event) or to revert
back to the old model of entirely excluding residents from economic impact (e.g., Crompton, 1995, 2006; Wilton & Nickerson,
2006). The findings from the case of the Super Bowl that the gains
and losses are roughly equivalent in all three geographic areas
suggest that studies would result in a relatively small error in the
overall impact estimation when entirely excluding residents from
the calculation of economic impact. However, it is advised that
researchers apply the model to other events to determine if these
relative equivalencies hold true for multiple event types, especially
given the recent focus on smaller events and impact (e.g., Agha &
Taks, 2015; Rascher & Goldman, 2015). Either way, sport event
managers, local organizations, and public authorities need to
accurately understand the implications of including or excluding
residents in the calculations.
Notes
1
Whereas a shift in amount, business, or geography will have an impact, a
shift in just timing of residents does not have to have an impact if the other
three variables are constant.
2
Conclusions
To date, the largest problem in including residents in impact has
been that researchers have named, and thus attempted to capture
through surveys, only a few of the possible behaviors of residents.
To solve this problem, we utilized the core principles of economic
impact to build a model with four variables that captured all 72
possible ways residents can affect impact. Next, the model was
applied, and primary data were collected in the context of Super
Bowl 50 to determine the extent to which residents’ spending was
affected by the event. We analyzed shifts in their spending behavior
because of the event (in the four variables spending, business, time,
and geography) but also asked what their behavior would have
been in the absence of the event.
We found support for the model in determining the effect of
changes in resident spending on economic impact for any event and
highlight three findings. First, economic impact studies capturing
only a few categories of residents (such as home stayers or runaways) using only one, two, or three variables are incomplete,
resulting in incorrect estimates of resident impact.
Second, we have illustrated that what must be done (gathering
data on four variables from residents who are mostly not at an
event) is extremely challenging because of the nature of the data
being collected (sometimes hypothetical and the reluctance to share
monetary information), nearly always cost prohibitive (because of
the necessity to find respondents who are geographically dispersed
and not in attendance at the event), and researchers have yet to
develop a sufficient method to gather the required information for
one of the variables (time). Although time is a core variable in
economic impact (e.g., historically measured through visitors as
time switchers or casuals and through residents as runaways or
home stayers), it has been poorly operationalized by academics,
and it is very difficult for survey respondents to report accurately.
There is a pressing need for considerable academic attention on this
aspect of economic impact.
Reduced productivity is an important issue in large scale events. Mills and
Rosentraub (2013) examine this issue in detail. Our method focuses on the
DEA and as such we do not investigate indirect costs.
3
Of the 437 incomplete surveys, 50% exited the survey once they reached
the questions about individual spending data which reinforces our statement about the difficulty in collecting spending information. An additional
28% were not located in the nine-county Bay Area and were thus not our
targeted sample. The remaining 22% opened the survey but answered zero
questions. These reasons for elimination do not raise concerns for a
nonresponse bias.
4
Although there were 639 useable responses in the nine-county Bay Area
sample, we oversampled in San Francisco and thus used a random number
generator to drop 55 responses from San Francisco so that the Bay Area
sample achieved the objective stratified sample resulting in 572.
5
We agree with Crompton (1995) that income multipliers are more useful
for a resident to understand the true value of an event to their personal
gain or to their elected leaders to make policy decisions to fund events
(Crompton, 2006). This study seeks to accomplish neither of these.
We analyze how residents shift their spending between industries. The
sales, or output multipliers, allow us to calculate the economic impact
of this shift (e.g., negative economic impact if a resident shifts behavior
from a business with a higher multiplier to a business with a lower
multiplier).
6
IMPLAN ( is one of three companies that
provide multipliers based on input-output tables from the U.S. Department
of Commerce. It is a common tool used in U.S.-based economic impact.
See Davies et al. (2013) for more information on input-output and other
methods of impact estimation.
7
Of the over 400 industries tracked by IMPLAN, respondents spent in 44
different industries ranging from auto repair to wineries. For the indirect
and induced effects, the mean = 0.62 (SD = 0.23), minimum = 0.27
(gasoline stations), and maximum = 1.55 (state and local government
passenger transit). The only other industry with a multiplier over one is
performing arts companies.
JSM Vol. 32, No. 5, 2018
484
Agha and Taks
8
Although San Francisco is also part of the major metropolitan area,
it is surrounded on three sides by water and bridges, making individual
expenditures in adjacent areas less common. Manhattan is likely an analogous region. Although there are common flows of business goods and
services in the region, residents are less likely to leave the area to make
purchases.
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