An Economic Impact Study of Bicycling in Arizona
Out-of-State Bicycle Tourists & Exports

FINAL REPORT
PREPARED FOR:

MPD 64-12 CONTRACT NO. ADOT11-013181

PREPARED BY:
McClure Consulting LLC
2944 N. 44th Street, Suite 101
Phoenix, AZ 85018
Economic & Policy Resources, Inc.
400 Cornerstone Drive, Suite 310
Williston, VT 05495
Kimley-Horn and Associates, Inc.
333 East Wetmore Road, Suite 280
Tucson, AZ 85705
June 2013

ACKNOWLEDGEMENTS
PREPARED FOR
ARIZONA DEPARTMENT OF TRANSPORTATION
CONTRACT NO: ADOT11-013181
TECHNICAL ADVISORY COMMITTEE (TAC)
TAC members included representatives from the diverse set of stakeholders below:

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ADOT, Communications
ADOT, Multimodal Planning Division (MPD), Bicycle and Pedestrian Program
ADOT, MPD, Transportation Analysis
ADOT, MPD, Research Center
ADOT, MPD, Tribal Transportation
Arizona Office of Tourism
Arizona State Parks
Federal Highway Administration – Arizona Division
MPOs and COGs

KEY INFORMANTS
The authors appreciate the input of Key Informants who were contacted for this study and agreed to be
interviewed in order to provide additional insight into the study approach and processes. In addition to
the persons listed below, other individuals offered informal opinions, and these contributions were also
appreciated.
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Tom Armstrong, Cochise Bicycle Advocates
Bob Beane, Coalition of Arizona Bicyclists
Wayne Cullop, Greater Arizona Bicycling Association
Richard DeBernardis, President, Perimeter Bicycling Association of America
Karen Lamberton, Cochise County Transportation Planner
Mike Melley, Mountain Bike Association
Daniel Paduchowski, Verde Valley Cyclists Coalition
Eric Prosnier, White Mountain Road Club
Anthony Quintile, Flagstaff Biking Organization
Bob Richards, Verde Valley Cyclists Coalition
Todd Sadow, Epic Rides
Dave Singer, City of Sedona
Randy Victory, Arizona State Parks
Matthew Zoll, Bicycle and Pedestrian Program Manager, Pima County Department of
Transportation

CONTENTS
INTRODUCTION................................................................................................................................... 1.1
1

REVIEW & SUMMARY OF LITERATURE, METHODS AND DATA SOURCES ............... 1.2

1.1

Introduction ........................................................................................................................................... 1.2

1.2
Review and Interpretation of Previous Literature, with Sources ............................................................ 1.2
1.2.1 Introduction to Literature Review .............................................................................................................1.2
1.3
Review and Interpretation of Previous Literature .................................................................................. 1.3
1.3.1 User Orientation Focus: Health, Environmental and Transport System Benefits of Bicycling ..................1.4
1.3.2 Infrastructure Orientation Focus: Returns to Investment, Impact Assessment and Valuation of Bicycling
Facilities ..................................................................................................................................................................1.7
1.3.3 Industry Orientation Focus: Economic Impacts Associated with Bicycle Tourism and Bicycling-Related
Businesses. .............................................................................................................................................................1.9
1.3.4

Summary and Conclusions ......................................................................................................................1.12

1.3.5

Sources Cited/Reviewed .........................................................................................................................1.13

1.4
Data Categories, and Identification and Preliminary Evaluation of Alternative Data Sources ............... 1.18
1.4.1 Introduction ............................................................................................................................................1.18
1.4.2
1.5

2

Other Data Notes ....................................................................................................................................1.19
Key Informant Contacts ........................................................................................................................ 1.19

REVIEW AND REFINEMENT OF METHODOLOGY ............................................................. 2.1

2.1

Introduction ........................................................................................................................................... 2.1

2.2
Data and Methodological Categories Addressed: Secondary and Primary Data ..................................... 2.1
2.2.1 Bicycle-related industrial classifications ...................................................................................................2.1
2.2.2

Needed metrics and associated data for assessment ...............................................................................2.3

2.3
Analysis Systems .................................................................................................................................... 2.4
2.3.1 Secondary data: Overall processing and output formats, by type ............................................................2.4
2.3.2

Primary data: Format, output from on-line surveys, and how adapted for this project ..........................2.4

2.3.3

Synthesis of data: Combining information/data from secondary and primary collection activities ........2.4

2.3.4

Analysis reporting for sub-state areas ......................................................................................................2.5

2.3.5

Impact analysis methodology—selecting an I-O model ............................................................................2.5

2.3.6

Impact analysis methodology—rationale for I-O model selection ...........................................................2.6

2.3.7

Recommended I-O model—Redyn ...........................................................................................................2.7

2.3.8

Redyn Underlying Data Sources ................................................................................................................2.9

2.3.9

Inputs to Redyn for this Project ..............................................................................................................2.11

2.4

3

Non-Quantifiable Elements .................................................................................................................. 2.11

DATA COLLECTION AND ANALYSIS ...................................................................................... 3.1

3.1

The Fundamental Challenges of Studying Topics Related to Bicycling .................................................... 3.1

3.2

Overview ................................................................................................................................................ 3.1

3.3

Methodology – Overview ....................................................................................................................... 3.2

3.4
Methodology – Data elements and processing ....................................................................................... 3.7
3.4.1 Secondary data..........................................................................................................................................3.7
3.4.2

Primary data ............................................................................................................................................3.12

3.4.3

Data processing and analysis ..................................................................................................................3.15

3.5
Findings and conclusions: organization and results .............................................................................. 3.19
3.5.1 Geographic scales of data reporting; state and regions .........................................................................3.19
3.5.2

Bicycle retailing .......................................................................................................................................3.22

3.5.3

Manufacturing and wholesaling..............................................................................................................3.38

3.5.4

Bicycle tourism in Arizona .......................................................................................................................3.39

3.6
Plan for periodic updating of the study ................................................................................................ 3.71
3.6.1 General structural considerations ...........................................................................................................3.71

4
4.1

CONCLUSIONS AND RECOMMENDATIONS ......................................................................... 4.1
Policy implications of findings ................................................................................................................ 4.4

List of Tables and Figures
Tables
TABLE 2-1. BICYCLE-RELATED INDUSTRIES, NORTH AMERICAN INDUSTRY CLASSIFICATION SYSTEM
2.2
TABLE 3-1. RESEARCH QUESTIONS AND REQUIRED INFORMATION
3.3
TABLE 3-2. DISTRIBUTION OF RESPONDENTS TO BICYCLE DEALER SURVEY BY TYPE
3.17
TABLE 3-3. ARIZONA OFFICE OF TOURISM (AOT) REGIONS AND INCLUDED COUNTIES
3.19
TABLE 3-4. DISTRIBUTION OF SURVEY RESPONSES BY REGION
3.21
TABLE 3-5. BICYCLE-RELATED SALES BY STORE TYPE, US AND ARIZONA
3.25
TABLE 3-6. REPORTED 2011 REVENUE AND ESTABLISHMENT SIZE
3.26
TABLE 3-7. ARIZONA SALES ESTIMATES BASED ON ALLOCATION OF SALES BY CHANNELS OF TRADE
3.27
TABLE 3-8. PRODUCTS AND SERVICES IN ARIZONA BICYCLE SHOPS
3.28
TABLE 3-9. BICYCLE SHOP REVENUES AND OUT-OF-STATE SALES ESTIMATES
3.29
TABLE 3-10. BICYCLE DEALER EMPLOYMENT, BY TYPE, SURVEY RESPONDENTS
3.30
TABLE 3-11. DEGREE OF IMPACT OF THE RECESSION ON BUSINESS ACTIVITIES
3.31
TABLE 3-12. SURVEY RESPONSE COUNT AND NUMBER OF ESTABLISHMENT REPORTED BY REGION
3.32
TABLE 3-13. ECONOMIC EFFECTS OF BICYCLE RETAILING AND MANUFACTURING/WHOLESALING, ARIZONA
3.37
TABLE 3-14. DISTRIBUTION OF MANUFACTURES/WHOLESALERS BY ARIZONA REGION
3.38
TABLE 3-15. GEOGRAPHIC DISTRIBUTION OF EVENTS
3.47
TABLE 3-16. AVERAGE PER DAY EXPENDITURES (PER PERSON) BY CATEGORY AND EXPENDITURE TOTAL
3.52
TABLE 3-17. AVERAGE PER TRIP EXPENDITURES (PER PERSON) BY CATEGORY AND EXPENDITURE TOTAL
3.53
TABLE 3-18 AGE DATA COMPARISON
3.55
TABLE 3-19. HIGHEST LEVEL OF EDUCATION COMPLETED
3.56
TABLE 3-20. ACCOMMODATION BY TYPE AND REGION FOR SURVEY AND AOT DATASETS
3.58
TABLE 3-21. DETAIL OF DIRECT EFFECTS FOR ROAD-BICYCLE, CYCLO-CROSS, AND TOURING (NON-COMMERCIAL)
EVENTS, ARIZONA
3.61
TABLE 3-22. ECONOMIC EFFECTS OF ROAD-BICYCLE, CYCLO-CROSS, AND TOURING (NON-COMMERCIAL) EVENTS,
ARIZONA
3.62
TABLE 3-23. DETAIL OF DIRECT EFFECTS, MOUNTAIN BIKE EVENTS, ARIZONA
3.63
TABLE 3-24. ECONOMIC EFFECTS OF MOUNTAIN BIKE EVENTS, ARIZONA
3.64
TABLE 3-25. DETAIL OF DIRECT EFFECTS, TRIATHLONS/DUATHLONS, ARIZONA
3.65
TABLE 3-26. ECONOMIC EFFECTS OF TRIATHLONS/DUATHLONS, ARIZONA
3.66
TABLE 3-27. DETAIL OF DIRECT EFFECTS, BMX, TRAINING CAMPS, AND COMMERCIAL TOURS, ARIZONA
3.67
TABLE 3-28. ECONOMIC EFFECTS OF BMX, TRAINING CAMPS, AND COMMERCIAL TOURS, ARIZONA
3.68
TABLE 3-29. ECONOMIC EFFECTS OF THE PRECEDING FOUR CATEGORIES OF BICYCLE TOURISM COMBINED,
ARIZONA
3.70
TABLE 30. TOTAL ANNUAL ECONOMIC EFFECTS GENERATED BY OUT-OF-STATE CUSTOMERS/PARTICIPANTS
4.2

Figures
FIGURE 3-1. OVERALL STRUCTURE FOR THE DATA COLLECTION AND ANALYSIS PHASE ............................................3.6
FIGURE 3-2. BUSINESS DATABASE COMPILATION METHODOLOGY ...........................................................................3.8
FIGURE 3-3. EVENT AND TOUR DATABASE COMPILATION METHODOLOGY ............................................................3.11
FIGURE 3-4. PRIMARY DATA COLLECTION AND IMPLEMENTATION ACTIVITIES ......................................................3.14
FIGURE 3-5. DATA SCHEMATIC FOR ANALYSIS MODEL DEVELOPMENT...................................................................3.16
FIGURE 3-6. ARIZONA OFFICE OF TOURISM REGIONS AND INCLUDED COUNTIES ..................................................3.20
FIGURE 3-7. REGIONAL DISTRIBUTION OF BICYCLE DEALERS ...................................................................................3.33
FIGURE 3-8. REGIONAL SHARE OF ARIZONA EMPLOYMENT IN INDUSTRIES WITH A POTENTIAL CONNECTION TO
BICYCLING .......................................................................................................................................................3.34
FIGURE 3-9. PERCENT OF EVENTS BY TYPE, ARIZONA ..............................................................................................3.46
FIGURE 3-10. PERCENT OF EVENTS BY REGION ........................................................................................................3.47
FIGURE 3-11. TYPE OF EVENT BY TIME OF YEAR, SEASON........................................................................................3.48
FIGURE 3-12 . REGION EVENTS BY TIME OF YEAR, SEASON .....................................................................................3.49
FIGURE 3-13. PARTICIPANTS BY AZ/NON-AZ RESIDENCY, BY REGION, 2012 ............................................................3.50
FIGURE 3-14. REGIONAL SHARE OF ARIZONA EMPLOYMENT IN ALL NON-RETAIL INDUSTRIES WITH A POTENTIAL
CONNECTION TO BICYCLING ...........................................................................................................................3.59

Introduction
This study is specifically focused on the contribution to the Arizona economy from out-of-state
visitors engaged in organized bicycling activities in the state, and out-of-state customers,
wholesale or retail, of bicycle products made or sold in Arizona. Deriving those estimates
involves documenting:
 The manufacture of bicycles and bicycle parts, clothing, and accessories (e.g.
helmets, shoes, water bottle cages, cyclometers, locks, gloves, etc.),
 Wholesaling and distribution of these goods,
 Retail sales of bicycles and also bicycle parts, accessories, and clothing, and
bicycle servicing and renting,
 Inventorying the various types of bicycling events and of organized tours by
bicycle.
The ultimate purpose of the study is to provide information to serve as one basis by which the
Arizona Department of Transportation (ADOT) and potentially other state agencies could
expand support for bicycling activity, and thereby the bicycle industry, in Arizona.
This document primarily compiles, with selective edits, the contents of the three Working
Papers that each summarized various aspects of the study. A final section highlights certain
conclusions and recommendations on the part of the consulting team. Working Paper #1
consisted of a review of bicycle-impact-related literature, an annotated tabulation of
potentially relevant data sources, a preliminary listing of Arizona bicycling events and tour
operators (see Appendix document), and a list of “special contacts,” “key informants” (see
Acknowledgements). Working Paper #2 began with a presentation of data needs for this study,
followed by a discussion of data availability from various secondary sources. The resultant gaps
identified within these secondary data sources introduced a discussion of primary data
collection sources and survey techniques, with various methods recommended. Specific
research products—such as surveys and questionnaires—tied to target data groups were
drafted, and guidelines were presented for data analysis. Analytical systems for quantifying the
economic impacts were discussed.
Working Paper #3 described: 1) the methodologies for primary data collection and analysis, for
compiling and analyzing secondary data (with a focus on the inventories of bicycle-related
businesses and events), and for using the primary and secondary data in combination; 2) the
findings and conclusions from the data received, compiled, and analyzed, quantified to the
extent practical; and 3) designs and recommendations for updating the study in the future.

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1 Review & Summary of Literature, Methods and Data
Sources
1.1 Introduction
This Section includes a review of bicycle-impact-related literature, an annotated tabulation of
potentially relevant data sources (see Appendix A1.1) and a preliminary list of “special
contacts” – industry or organizational people who might be especially helpful in this assignment
(see Acknowledgements).

1.2 Review and Interpretation of Previous Literature, with Sources
1.2.1 Introduction to Literature Review
Arizona has long been known as a great place to live, work, visit, and recreate. Arizonans enjoy
a wide range of climates, topography, and natural beauty. The Arizona Department of
Transportation (ADOT) recognizes its role, in partner with other state, regional and local
government agencies, as a significant driver of the state’s economy. In addition to building
highways that facilitate the transport of goods and services as well as the movement of people,
ADOT recognizes the critical role played by transportation infrastructure in tourism and
recreation and economic development.1
The ADOT Bicycle and Pedestrian Program has taken significant strides to support tourism and
economic development. A number of related activities called out in the 2003 ADOT Bicycle and
Pedestrian Plan have been implemented. The economic importance of bicycling and its
attendant benefits for Arizonans is not well known. As noted in the prior studies discussed
herein, the activity of bicycling provides environmental, transportation, and health benefits as
well as economic benefits. Though such benefits are obviously enjoyed at the individual level, in
aggregate these benefit streams from bicycling activity are thought to flow to society in the
following forms:
 Reduced health costs (e.g., reduced risks of chronic diseases and ill-health;
reduction of sick days in the workplace and increased worker productivity);

1

As documented, for example, in the ADOT study, Tourism’s Impact on Future Transportation Needs, which notes
“Baby Boomers now enjoy Adventure Vacations as an option to the typical vacation. . . . the tremendous upswing
in adventure travel has caught the public, and the travel industry, by surprise. They want to hike, go white-water
rafting, fish, view wildlife, scuba dive, bicycle . . . “ (p. 7).

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 Reduced costs related to air pollution and greenhouse gas emissions;
 Reduced traffic congestion and increased vehicle operating costs savings (e.g.,
reduced maintenance costs, reduced oil/gas expenditures); and
 Increased demand for recreational/leisure goods and services.
In addition, bicycling represents an opportunity to grow the regional economy. As the number
of active transportation participants and individual trips in the region increases, so does the
impact of bicycling on state and local economies. For example, prior research has linked
investments in bicycling infrastructure and facilities with economic returns in the form of
increased visitation of travelers and tourism and related expenditures.
The purpose of this study is to estimate the economic benefits of bicycling in the State of
Arizona. The study emphasis is on developing an objective and defensible model for
determining the total economic benefits associated with (1) the bicycle industry of Arizona; and
(2) bicycle tourism and visitor spending associated with organized tours, rides, and races in
Arizona.
This Part 1 provides a review of literature useful for conducting this economic impact analysis of
bicycling in Arizona. Specifically, Part 1 (a) reviews and interprets literature evaluating the
benefits and economic impacts of bicycling; (b) evaluates the methods and approaches utilized
in prior studies and offers preliminary suggestions for this current study; and (c) reviews recent
and on-going activities of state department of transportation bicycle and pedestrian programs,
with a particular focus on agency-directed/supported bicycling impact studies. Part 1 also
includes a list of sources cited/reviewed, an annotated bibliography of prior relevant economic
impact studies, and a glossary of bicycle facilities terminology.
Bicycling is growing in popularity both as a form of transportation and for recreation. Bicycling
trips in the United States more than doubled between 1990 and 2010 (Alliance for Biking and
Walking, 2012). As levels of bicycling continue to increase, communities are realizing that the
convergence of cycling facilities, participants, and bicycle-related industry can be a contributor
to local economies. In the past few years, a growing number of public agencies, advocacy
organizations, industry groups, and academics have mobilized to document the growing
economic benefits of bicycling, both to justify the public spending on facilities and program
efforts and to demonstrate the economic value of this form of transportation and recreation.

1.3 Review and Interpretation of Previous Literature
This section is intended to review what has been learned about the economic impact of
bicycling from prior studies. Overall, this literature review is presented as a three-pronged

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analysis of (1) user orientation: studies focused on recreational, health and environmental
benefits for active transportation users; (2) infrastructure orientation: studies focused on the
economic returns of cycling and pedestrian infrastructure investments; and (3) industry
orientation: studies focused on the economic impacts associated with bicycling-related
businesses and tourism. An annotated bibliography of the selected studies can be found in
Appendix A1.1.
To set the stage for any effort estimating the economic benefits of bicycling, it is necessary to
summarize the main issues involved, the matters that confound such endeavors, and a
rationale for more structured research for this study in Arizona.
1.3.1 User Orientation Focus: Health, Environmental and Transport System Benefits
of Bicycling
Background
Advocacy groups point out that bicycling provides an enjoyable, convenient and affordable
means of physical activity and recreation. The most effective fitness routines are moderate in
intensity, individualized, and incorporated into our daily lives—both for personal transportation
(e.g., commuting to work) and for recreation.2
According to National Institutes of Health, approximately $142 billion in medical costs are spent
annually on obesity and physical inactivity in the United States (American Public Health
Association, 2010). The estimate includes healthcare costs, lost wages due to illness and
disability, and future earnings lost by premature death. Evidence suggests that improved
bicycling facilities leads to increased cycling use. Increased physical activity—as found in
cycling—could help to reduce the risk of coronary heart disease, premature death, high blood
pressure, obesity, adult-onset diabetes, depression, and colon cancer. A more active population
can help reduce the cost of health care, decrease workplace absenteeism, and maintain the
independence of older adults.
Sedentary lifestyles have serious consequences for public health. The most visible is the sharp
rise in obesity across the United States in recent years. Over two-thirds of adults in the United
States are either overweight or obese. In the United States, the prevalence of obesity has
steadily increased over the years from 13 percent in 1960 to 35 percent in 2008. (According to

2

Advocacy groups such as the Alliance for Biking and Walking, League of American Bicyclists, Australian Bicycle
Council, British Cycling, Canadian Cycling Association, and Vélo Québec.

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the Arizona Department of Health Services, more than half [56%] of adult Arizonans are
overweight or obese.)
Obesity is more than a cosmetic issue; it is associated with serious health conditions, namely
increased risks of diabetes and cardiovascular diseases. Physical inactivity contributes
substantially to the burden of disease, death and disability. Increased physical activity, as
displayed in cycling, can lower the risks of obesity, lower the risks of hospitalization from
asthma, and address other health conditions such as heart disease, some cancers, and type 2
diabetes.
Policy changes at the local level have the potential to encourage increased physical activity over
the long term by making active transportation an easier choice for residents. Studies have
estimated that up to 40 percent of chronic illnesses could be prevented by regular physical
activity, suggesting that additional planning could offer opportunities for increased physical
activity by creating bicycling alternatives, such as dedicated on-road and off-road bikeways, to
motorized transportation. Other health benefits associated with increased physical activity
include improved mental health and well being (Bassett, Jr. et al, 2008; Chenoweth &
Associates, 2009).
Bicycling has been shown to confer other benefits (so-called “green dividends”) in the form of
externalities such as avoided costs, congestion reduction, and lessening of environmental
degradation.” For instance, bicycling is viewed as an energy-efficient, non-polluting mode of
travel. Short distance, motor vehicle trips are the least fuel-efficient and generate the most
pollution per mile. These trips have the greatest potential of being replaced by cycling (or
walking) trips. Shifting to this mode can help mitigate global climate change, local air pollution,
smog, acid rain, water pollution, and noise pollution and thus create “green dividends.”
Increasing the active transportation mode of bicycling (and thereby reducing motorized
transportation) can contribute to an environment that is pleasant and safe with less noise and
pollution (Victoria Transport Policy Institute, 2009).
Methodology review
Economic appraisal is a commonly applied method in transport planning. However, the healthrelated effects of bicycling are rarely taken into account. A review of studies on transportrelated physical activity reveals that most approaches utilize a cost-benefit analysis framework.
Such a framework can, in theory, be integrated into a comprehensive economic analysis of
transport-related physical activity, as well as assess the current situation of infrastructure
investments made in the past. However, among the more significant challenges in modeling are
the following:
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 The relationship between observed bicycling and total physical activity, including
assumptions about how bicycling might influence total physical activity, and that
all observed cyclists could be classified as sufficiently active (and therefore have
a reduced risk and/or reduced medical costs);
 Assessing physical activity as a positive effect on morbidity versus all-cause
mortality;
 Substitution of activity between bicycling and other active transport modes;
 Direct attribution of bicycling activity over a finite period (for instance, one year)
to quantifiable health benefits
In addition, an economic appraisal of cycling will need to be based on some consensus on
methods for valuing health and/or life. For instance, the standard “value of statistical life”
remains unresolved irrespective of the approach taken: a “cost of illness” approach (applying
costs to each specific disease) or “years of life lost” approach (Abelson, 2008). Furthermore,
there is a time dimension that is not explicitly taken into account. In other words, there is a
delay between increases in physical activity (such as walking and bicycling) and measurable
benefits. There is a need for including a level of uptake of bicycling into such an appraisal. Some
studies assume a five-year “build-up” period for reaching full effect of benefits.
Finally, any comprehensive economic appraisal of the health effects of bicycling will then be
discounted to take into account inflation and allow for the calculation of present value. Most of
these health-related benefit-cost analyses have been associated with increased bicycling use as
a direct result of expanding bicycle facilities/infrastructure (Buis and Wittink, 2000; Cavill et. al,
2008; Davis, 2010; Fishman et. al., 2011; Genter, et. al., 2008; Gotschi and Mills, 2008; Gotschi,
2011; Saelensminde, 2004; Wang, et. al., 2005). All of these studies indicate that benefits
exceed costs. Such consensus is a reflection of a variety of factors, including the inexpensive
nature of bicycle facilities and optimistic adoption rates of such facilities. However, these
studies are often troubled by the relatively unreliable manner in which demand is estimated
and benefit values (particularly health-related) are derived.
Many of these health-related effects of bicycling have been incorporated into an analytical
assessment tool by European health economic researchers (Cavil et. al, 2008; World Health
Organization, 2008). This analytical tool—health economic assessment tool (HEAT) for bicycling
can estimate the maximum and mean annual benefits and values in terms of reduced mortality
as a result of cycling. Such a tool can be utilized as an input to comprehensive cost-benefit
analyses of new transport infrastructures or assessments of existing infrastructures.

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1.3.2 Infrastructure Orientation Focus: Returns to Investment, Impact Assessment
and Valuation of Bicycling Facilities
Background
As noted above, several analyses of bicycling infrastructure projects have been completed in
the context of traffic congestion and increased travel times, environmental degradation, and
improved health/reduced mortality from enhanced physical activity transport. Generally, these
assessments are completed within the context of a cost-benefit analytical framework with the
following components:
 Costs associated with the plan and design, operation and maintenance of
cycling/pedestrian infrastructure, such as bikeways; and
 Benefits associated with the use of cycling infrastructure, identifying the full
range of direct and indirect benefits to cyclists.
Although costs are both easily identified and quantified, the methods (and related
transparency) of determining and quantifying benefits are more difficult to develop and are less
likely to achieve consensus. For instance, the data sources and methods utilized for the
aforementioned benefits of avoided costs, congestion reduction, improved health, and positive
environmental impacts are sometimes carelessly applied without deliberation to context or
procedures.
Bikeways
Another aspect of an infrastructure focus is documenting the economic impacts of a specific
bikeway.3 The underlying premise is that bikeways can provide a substantial boost in the
number of visitors attracted to an area, thus increasing the likelihood of money being spent
within the local area (Schoutens, 2006; Lawrie, et. al., 2004; Bowker, et. al., 2007). The majority
of users of these bikeways engage in bicycling as their primary activity. Thus, it is logical to
assume that most of the economic impacts of bikeway use can be attributed to bicycling.

3

A bikeway is a generic term for any road, street, path or way which in some manner is specifically designated for
bicycle travel, regardless of whether such facilities are designated for the exclusive use of bicycles or are to be
shared with other transportation modes. See Appendix A1.2: Glossary of Bicycle Facilities Terminology.

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Research on bikeway use and economic impacts originated in the early 1990s as some of the
first rail-trail conversions4 were complete, providing some of the first longer bikeway facilities
for cycling in the United States. Economic impact studies were often commissioned by the
agency responsible for the bikeway to demonstrate the economic benefit or return on
investment in the trail conversion. Many of these studies utilized some type of user survey,
such as self-administered or intercept (survey takers recruit respondents at specific locations)
to obtain information about trail user and expenditure patterns.
Research results indicate that bikeways generate revenue from users, mostly through
purchases on food, lodging and incidentals at nearby establishments. Such results vary
depending on the length and location of the bikeway as well as the area’s attractiveness to
potential visitors. One report on the economic impacts of trails claims that trail-related
expenditures range from $1 per day to more than $75 per day in the U.S. (Sjoquist, 2008). In
general, most of these studies focus on the number of visits or visitors (and their duration)
generated by the trail, the average amount that each visitor spent, and hence the total revenue
or sales that can be attributed to trail users. Developing bikeway user expenditure profiles
obtained from survey results is critical in economic impact estimation (Downward, et. al., 2009;
Bowker, et. al., 2007).
Valuation and trails
Finally, bicycle facilities, such as trails, are viewed as amenities that provide economic benefits
by increasing the value of nearby real estate. Increased property valuation in turn raises
property tax revenues for local governments. Models that calculate the impact of amenities,
such as parks, greenways, and trails on nearby real estate values are based on the concept of
enhancement valuation—the extent to which the amenity affects the surrounding land market.
The concept that people are willing to pay a premium for a home located close to amenities
such as parks and trails is known as the “proximate principle” (Crompton, 2001). This principle
was initially employed in studies on the economic benefits of parks and open space, but more
recently has been extended to examining the economic benefits of greenways and trails.
Most past studies attempting to document the influence of trails on property values have
focused primarily on measuring people’s perceptions of the trail’s impact on their property.
Measuring perceptions of increased valuation is generally done through surveys of homebuyers

4

Rail-Trail is a shared use path, either paved or unpaved, built within the right-of-way of a former railroad.
According to TrailLink of the Rails-to-Trails Conservancy, 11 of the identified 75 multi-use paths in Arizona are
classified as “rail-trails.” http://www.traillink.com/trailsearch.aspx?tn=&st=AZ&ct=&sp=N

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or homeowners. While these studies provide useful insights on opinions, they measure
potential actions rather than reality.
More recently, studies have utilized a “revealed preference” approach, which attempts to
identify the actual influence of the trail on property values (Krizek, 2006). Typically, these
studies utilize the hedonic method, a statistical analysis of property values. This method has the
benefit of estimating values on real choices—therefore measuring the results of actual or
observed behavior, rather than a hypothetical situation. As such, this method rests on the
notion that property markets are good indicators of economic value. Both of these methods—
stated and revealed preference approaches—are considered indirect methods because the
increased value is a secondary, not a primary benefit of the amenity.
In general, there is a paucity of studies on this topic and conclusions about the effects of trails
on property values indicate that there are a multitude of factors other than merely the trail
itself that affects property valuation. Trails vary significantly in their use, location, length, and
features that need to be considered separately to understand how they impact property values,
either positively or negatively (Karadeniz, 2008).
1.3.3 Industry Orientation Focus: Economic Impacts Associated with Bicycle
Tourism and Bicycling-Related Businesses.
Background
Economic impact studies of bicycling have been completed for a number of states and other
regions (Argys and Mocan, 2000; Barnes, 2004; Bicycle Federation of Wisconsin, 2006; Birk and
Roberts, 2006 and 2008; Lankford et. al., 2011; Grabow et. al., 2010; Wilbur Smith Associates,
2001; Resource Systems Group et. al., 2012). These states/regions (Colorado, Minnesota,
Wisconsin, Iowa, Vermont, Maine, and Portland, Oregon) have a locus of economic activity
related to bicycling (e.g., manufacturing of bicycles and parts and accessories) and/or tourism
oriented around bicycling. The primary finding of these studies is that their respective bicyclerelated industry represents an economic force and provides a source of both direct and indirect
revenues and jobs.
In general, the economic activity associated with bicycle-related businesses and bicycle-related
tourism is estimated and expressed in terms of various “metrics”—employment, personal
income, (value of) output, and tax revenues. The effects of bicycle-related economic activity
ripples outward throughout the region, providing businesses, earnings, and jobs both directly
and indirectly. Direct economic impacts represent the sum of the initial bicycle-related activity
of manufacturers, wholesalers, and retailers. Indirect economic impact is the economic activity

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generated by suppliers to the various businesses involved in the direct economic activity and by
suppliers to those suppliers. Induced economic activity is the activity generated within the
region when bicycle-related employees and employees from related supplier firms spend their
wages on consumer goods and services. All of this spending is income for the recipient
businesses and, in turn, is re-spent in the economy, creating a spin-off or ripple effect as
successive waves of spending occur. Each of these types of impacts affects regional
employment, earnings, and output.
Data sources related to bicycle-related business activity range from confidential individual
employer records obtained from state workforce/employment agencies to information
obtained from business surveys. As on the consumer/user side, sampling procedures and
survey results from businesses need to reflect the “universe” of bicycle-related businesses
within the region.
Bicycle-related tourism
The economic impact of bicycling tourism is based on the idea that facilities such as trails or
events either attract people to visit a region or induce them to stay longer. While bicyclerelated tourism has been shown to generate positive economic benefits to an area, there are
significant methodological issues related to data and valuation attributed to bicycling activity.
For instance, no one has been able to determine the magnitude and characteristics of visitors
engaged in bicycling and what portion of their stay is associated with this bicycling activity.
Moreover, there is the oft-neglected category of bicycle-related spending from in-state
residents. For any particular sub-state region, there will be a number of state residents that are
out-of-the-area and as such are classified as visitors. Furthermore, measurement and
attribution of expenditures for bicycle-related tourism are problematic. Any assessment of
bicycle-related tourism is dependent on survey data obtained from expenditure diaries or
intercepts of bicycle visitors (Downward et. al., 2009).
The economic impact of bicycle-related tourism is usually calculated by estimating or counting
the number of visitors (and local residents) who are participating in self-guided and organized
tours, rides, and events. These studies use an average expenditure approach of tourism
spending for food, lodging, and other goods and services while visiting to arrive at a total
economic value of visitation. For example, the Maine Department of Transportation estimated
that bicycle tourists in 2001 spent between $25 (day trips) to $115 per day (guided tours) on a
combination of retail, services, lodging, food, and transportation. Such a method yields an
estimate of economic activity attributable to bicycle-related tourism.

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Bike events, such as races and tours, represent a subset of bicycle-related tourism that attract
visitors to an area, either to participate or to watch. Economic impacts of these events are
easier to quantify because many of the factors are known, such as the number of days in the
region, number of participants and their origin, and their expenditure patterns. However, like
other bicycle-related tourism, data on these events are collected via participant survey.
Bicycle racing is a growing sport in a number of areas, here in the United States and beyond.
Organized racing can include road races, criterium races,5 mountain biking, multi-sport races,
and track racing in velodromes. Both the racers and their friends/family who travel to
participate in the event and the spectators who watch spend money on food, lodging, and
other shopping goods and services (Hong, 2007; Santos Tour Down Under, 2012).
Bicycling sector contribution to broader economy
Recent efforts in Wisconsin, Iowa, Australia, and the United Kingdom have attempted to
measure the holistic extent of bicycling’s contribution to the broader economy. This
comprehensive approach is analogous to an industry’s contribution to the nation’s gross
domestic product. Indeed, one study dubbed the “gross cycling product of the United Kingdom”
provided an economic overview of the bicycling sector and its associated benefits, determining
that the gross cycling contribution to the UK in 2010 was £2.9 billion (Grous, 2011). The report
also quantified the economic benefits generated by each individual cyclist, taking into account
factors including bicycle manufacturing, cycle and accessory retail, and employment. In 2010,
the annual gross cycling product was £230 per cyclist. In addition to the value of production
from the bicycling sector, investment in bicycling infrastructure and health savings of cycling
were added to the gross cycling product.
The Australia Bicycle Council (2012) utilized this same methodology to estimate its own gross
cycling product in 2011. Accounting conventions in this economic overview differ in that the
various “outputs” were not summed together, but reported in terms of various “accounts,”
such as savings from reduced congestion, worker productivity gains, etc. Such outputs include
congestion and environment cost savings; reduced costs related to inactivity—health savings
and improved worker productivity; bicycle-related industry production; bicycle tourism; cycling
infrastructure spending; industry employment; and participation and ownership.

5

Criterium Race: A bike race held on a short course that might consist of blocked-off city streets. The course is
short, usually less than 5 km, and is a closed circuit, where riders complete multiple laps. Riders typically race for a
given length of time, then complete a specified number of laps. Criteriums are especially convenient for watching
in-person as the riders pass by a given point many times over the course of a race.

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Recent efforts in Wisconsin (Grabow et. al., 2010) and Iowa (Lankford et. al., 2011) utilized an
aggregated approach in their comprehensive economic accounts of bicycling. Coupled with the
bicycle-related industry contribution and bicycling tourism/recreation were the monetized
values of additional physical activity, improvements in air quality, and reductions in greenhouse
gas emissions. The latter three effects account for about one-fifth of the estimated $1.928
billion contribution of bicycling to the Wisconsin economy for 2009. In similar fashion,
researchers at Northern Iowa University included the economic and health effects of bicyclists,
and bicycle tourism including the popular RAGBRAI (Register’s Annual Great Bicycle Ride Across
Iowa), as well as the value of bicycle business activity and bicycle organizations in their total
economic impact of $522.5 million to the Iowan economy in 2010.
1.3.4 Summary and Conclusions
This review is intended to address a relatively narrow frame of economic benefits associated
with bicycle-related activities. Based on the results of this review, it appears more is known
about the direct economic impacts of bicycling from industry and tourism than about the
increase of property valuation resulting from trails and other bicycling facilities. Still, the
number of studies is relatively small in each of the above categories and needs to be expanded,
both to provide further support for what is already known and to generalize findings beyond
those areas studied. Furthermore, most of these cited studies present challenges with respect
to data and analytical methods. Much of the requisite data obtained and utilized is via survey
questionnaire from the bikeway user and/or bicycle-related business owner/operator. For
many of these non-economic benefits, averages and estimates are often borrowed from
previous studies. Analytical approaches—particularly economic impact methods—are better
understood and more appropriately applied. Still, the key in using these economic models is
determining the level and extent of bicycling as an “export” sector.
This review of studies indicates that bicycling as an industry and/or tourism activity has the
potential to generate economic returns that exceed investments and make it an attractive
business sector for some regions and communities. The presence of attractive facilities, such as
trails, generates use by local residents and visitors. In addition, bicycle events and races attract
both participants and spectators, similar to other sporting events, who also stimulate the local
economy. The bicycle-related industry (including manufacturers, wholesalers, and retailers)
contributes to the local and regional economy by its sales revenue, employment and earnings.

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Cook, Ina Katherine. 2007. Bike/Ped: Transportation’s Contribution to Quality of Life and
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Grabow, Maggie, Micah Hahn, and Melissa Whited. 2010. Valuing Bicycling’s Economic and
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MMM Group in association with Stantec and TransActive Solutions. 2008. Town of Oakville
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1.4 Data Categories, and Identification and Preliminary Evaluation of
Alternative Data Sources
1.4.1 Introduction
The information in this Part provides an inventory of alternative data sources based on the
following data categories:
 Establishment-specific
 Business data with potential relevance to tourism
 Economic data

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 National data with potential applicability in benchmarking certain statistics
 Trade organization-generated data
 Tourism data
The evaluation provides general information related to the year, geography, variables and
relevant notes for each data source, and can be found in Appendix A1.3.
1.4.2 Other Data Notes
The MoveAZ Long Range Transportation Plan (report for ADOT by Cambridge Systematics, Inc.
and Lima & Associates, 2004) included the table, “Estimated Daily Bicycle Trips from 2002
through 2025” (data by county). In deriving these estimates, the authors followed a prescribed
methodology that combined certain national bicycle-usage factors (from Federal Highway
Administration, National Household Travel Survey) with Arizona-specific data in the U.S. Census
Journey to Work datasets (for 1990 and 2000), utilization data in the MAG Household Survey for
Maricopa County, and other data such as on population projections.

1.5 Key Informant Contacts
Approximately two dozen key informant contacts were identified by the consultant team and
the ADOT Project Manager who could represent the varied geographic and bicycle-activity
interests across Arizona. They included government representatives (including members of this
study’s Technical Advisory Committee), advocacy groups, bicycle clubs, and event organizers.
The intent of drafting the list was to contact some of these informants to solicit their guidance
on data and related issues. For example, these individuals could provide input on:
 How best to structure outreach procedures, survey instruments, etc. in order to
maximize research input from shop owners, event organizers, and others.
 Bike shop owners/managers who might be the most receptive to an economic
survey.
 The relationship of Arizona’s bicycle friendliness to bicycle-related tourism, etc.

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2 Review and Refinement of Methodology
2.1 Introduction
This section begins with a presentation of data needs for this study followed by a discussion of
data availability from various secondary sources. The resultant gaps within these secondary
data sources necessitate a discussion of primary data collection sources and survey techniques
with various methods recommended. Our recent interview series with key informants—
summarized here—was especially helpful in providing guidance with respect to business
contacts/event organizers and approaches and protocols. Specific research products—such as
surveys and questionnaires—are tied to target data groups, and guidelines are presented for
data analysis (e.g. missing data estimation, inclusion of non-quantifiable observations).
Analytical systems for quantifying the economic impacts are discussed with particular attention
to the input-output (I-O) model.

2.2 Data and Methodological Categories Addressed: Secondary and Primary
Data
As noted in the previous section, economic impact studies of bicycling have been conducted in
a number of states and regions. These states/regions have a locus of economic activity related
to bicycling and/or tourism oriented around bicycling. In these studies, bicycling was found to
be an economic force, providing a source of both direct and indirect revenues and jobs. The
documentation of such benefits involved defining the components of bicycle-related activity
that could be analyzed and devising a variety of analytical approaches.
2.2.1 Bicycle-related industrial classifications
What precisely is meant by bicycling-related activity? How should the bicycle-related industry
be defined? Prior to examining the specific data/information requirements for this economic
impact analysis of bicycling, the data need to be organized by detailed bicycle-related industry
segment (Table 2-1). According to the latest (2012) North American Industry Classification
System (NAICS), there is a group of twenty bicycle-related industries identifiable for this study.

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Table 2-1. Bicycle-related industries, North American Industry Classification System

These industries cover the broad industry spectrum from manufacturing and trade to
transportation and services. Manufacturing sectors include those that make bicycle clothing,
shoes, gloves, and other accessories, and those that produce bicycles and parts/components,
and bicycle pumps. Trade includes both wholesale and retail sales of bicycles and related
equipment;6 whereas Real Estate and Rental & Leasing include bicycle rentals. Bicycle repair
shops (those without new bicycle sales) are found in Other Services category. The broad sectors
of Transportation & Warehousing include bicycle couriers. Bicycle tour operators are covered
under Administrative and Support Services. Finally, the Arts, Entertainment and Recreation
sector includes mountain biking trail centers, bicycle tour guides, event organizers, and bicycle
riding clubs. The latter two segments have significant levels of volunteerism.
Without exception, bicycle-related activity represents a minor portion of the overall production
or services provided in each of these industry categories. In other words, none of the above
five-six digit NAICS industries is solely about bicycle-related production or services. More often,

6

Note that The Gluskin Townley Group estimates Internet sales of new bikes and parts hit $1.2 billion in 2011, about 20
percent of all bicycle-related sales.
http://www.bicycleretailer.com/sites/default/files/downloads/resource/Stats_7_1_12.pdf

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bicycle-related activity is relegated to the “other” or “miscellaneous” within the underlying
product detail (up to ten-digit product codes)7. Even within the motorcycle, bicycle & parts
manufacturing (NAICS 336991) segment, almost three-fourths of the 2007 total production
value is associated with motorcycles, based on the product code data.
2.2.2 Needed metrics and associated data for assessment
What are the various “metrics” used in assessing the economic effects of bicycling? Generally,
the metrics utilized in these studies include: employment, earnings8, output9, and taxable
revenues. Are there available data and information consistent with these metrics to accomplish
the goals of this economic impact of bicycling in Arizona study? Specific data requirements are
listed below under each of the two broad benefit categories—bicycle-related industry, and
bicycle tourism and visitor spending.
Bicycle-related industry, organized by detailed NAICS industry segment
 Establishments or business units, with or without employees (i.e., sole
proprietor);
 Number of employees by establishment;
 Amount of employee wages and salaries and (sole) proprietor income;
 Amount of annual sales or output value; and
 Portion of sales/output value sold to out-of-state residents/visitors.
Bicycle-tourism and visitor spending associated with organized tours, rides, and races










Event, tour, ride or race by location;
Number of participants (and their respective residences ) by event, tour or race;
Number of “companions” with event participants; and
Level of expenditures by event participants and their companions for such broad
categories as:
Event registration fees;
Lodging;
Food—restaurant and groceries;
Gas; and
Other/miscellaneous retail (including bicycle-related).

7

Product codes are harmonized with their respective six-digit NAICS industries.
Earnings include both wages and salaries of business employees as well as proprietors’ income.
9
Output is an economic term related to the value of production. Business sales or revenues are often used as a
close proxy to output.
8

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2.3 Analysis Systems
2.3.1 Secondary data: Overall processing and output formats, by type
Secondary economic data, by industry and county, will be tabulated for use as gross indicators
of the extent of economic activity in the bicycle-related group of industries occurring in each
county, appropriately qualified to make clear that these industry sectors include much more
than bicycle-related activity. Similarly, general tourism data, on visitor activity in different
regions throughout the state for example, can potentially be used to help sensitize the findings
from the primary (survey) research involving bicycling events and related visitation. The final
selection, application, and formatting of this type of data will depend on the actual output of
the primary research, its level of completeness, compatibility with overall database, and similar
considerations.
Secondary data that indicate sales by product line, at the national level, will be used as
appropriate to supplement primary data and/or gaps in primary data related to department
stores, for example.
Secondary data consisting of a compilation of various business lists has been brought together
into a master database, which will require additional primary research, essentially by means of
direct contact of establishments, to be more completely verified and establish additional
contact information.
2.3.2 Primary data: Format, output from on-line surveys, and how adapted for this
project
The online surveys will automatically tabulate results, and output of questionnaires sent
directly to respondents will be set up for similar ease of data collation and tabulation of results,
through some combination of electronically fillable forms or other methods. Survey data will be
further summarized and also cross-tabulated according to the patterns of information received,
response levels and profiles of respondents, what findings appear to be most interesting and
informative, and similar considerations.
2.3.3 Synthesis of data: Combining information/data from secondary and primary
collection activities
The survey results will include information about the organizations and establishments that
responded to the survey. This information should facilitate comparison of the profile of the
sample (that is, questionnaires actually received) to the profile of organizations/establishments
from other databases compiled for this project. Depending on the extent to which the sample
profile appears to be similar to the universe based on this kind of comparison, extrapolations of

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the findings from the sample to the universe might require additional processing of the data, or
at the very least qualitative assessments of these differences.
Where additional processing is required, a spreadsheet model format will be established that
explicitly shows the input data, calibrating data/factors from other data sets, factors to adjust
the sample data to the universe, and the resulting calculations, all by selected subareas. To the
extent possible, this model format will be consistent across the various analysis categories.
2.3.4 Analysis reporting for sub-state areas
Ideally, bicycle-related activity (businesses and events) in Arizona should be analyzed at the
county level. However, given the limited number of businesses and/or events in a number of
nonmetropolitan/rural counties (as well as to preserve confidentiality of survey participants), it
may be necessary for the purposes of this study to subdivide the state into sub-state regions
(including the relevant Indian communities) such as the following:
(1)
(2)
(3)
(4)
(5)

Northern – Coconino, Navajo, and Apache
North Central – Yavapai and GIla
Phoenix & Central – Maricopa and Pinal
Tucson & Southern – Pima, Santa Cruz, Graham, Greenlee and Cochise
Western – Yuma, La Paz, and Mohave

2.3.5 Impact analysis methodology—selecting an I-O model
The principal economic analytical tool used to measure bicycle-related impacts on the overall
Arizona economy is an I-O model, which takes into account the direct impacts and the complex
inter-industry connections of the broader economy. One of the most important uses of an I-O
model is that it serves as the basis for estimating the multiplier or “ripple” effects of changes
for one sector of the economy upon other sectors. Because the model takes into consideration
the complex inter-industry connections within the regional economy, calculated impacts will
include not only those people directly involved in producing goods and services for export, but
also the indirect impacts, such as the increase in work and jobs in industries supplying directexport businesses. It also includes induced impacts by spending of wages and salaries of these
direct and indirect workers on consumer-related goods and services.
The multipliers described here are known in I-O parlance as Type II multipliers. Although Type II
multipliers are only one of several types of multipliers available, they are most commonly used
in regional economic impact analysis. Type II multipliers measure the direct effects on a metric
(in this case jobs, output, or labor earnings) plus the indirect impacts that arise as the result of
inter-industry linkages, and the impact induced by spending on household goods and services

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from disposable income paid to workers in affected industries. This concept assumes that there
is an association between industry output and household income, and a further association
between household spending and income. Other types of multipliers may exclude household
spending (Type I), or they may include state and local government spending (Type III). Type IV
multipliers also include investment spending, on the assumption that this is directly responsive
to demand for output. Types III and IV are generally larger than Type II and are generally
considered more speculative.
There are many common misconceptions about multipliers. For instance, there is no such thing
as the multiplier for an industry. For any industry, there are any number of multipliers
depending upon the form in which the multiplier is expressed, the metric under consideration
and other factors. The choice of unit can make the same multiplier look larger or smaller.
Multipliers in static models (such as those available in the IMPLAN modeling package) are
timeless. Production adjustments may take place in anticipation of changes in market demand,
or in contrast, reaction time may lag resulting in delayed multiplier responses.
Prior studies have shown that multipliers do change over time. There are several reasons why
multiplier values change. First, technological changes and inventions of new products and
processes alter input purchasing patterns. Second, relative price changes across commodities
induce substitution of the relatively cheaper inputs for the more costly ones. Finally, changes in
interregional and/or international trade patterns (due in part to changes in transportation costs
and exchange rates) could substantially alter patterns of trade, and hence multiplier values.
2.3.6 Impact analysis methodology—rationale for I-O model selection
In conducting an economic impact analysis, the direct effects—here, bicycle-related activity in
Arizona—are generally provided or known. In contrast, the calculation of indirect effects cannot
be measured and verified directly through records. Instead, indirect effects must be
determined through the use of various estimating methodologies. The most common
estimation techniques involve the utilization of I-O tools that involve a range from simple
mathematical calculations on a spreadsheet (e.g. under U.S. Bureau of Economic Analysis RIMS
II program) to integrated simulations using sophisticated dynamic I-O models. Even though
these approaches vary in sophistication and structure, the general approach to this estimating
process is essentially the same for all I-O tools in that they all use calculated coefficients based
on detailed secondary data.
Dynamic I-O models, such as the Regional Dynamics Model (hereafter “Redyn Model”),
simulate the economic relationships between sectors of an economy on several geographic
levels through time. Linkages exist between sectors and regions that provide commodities or
other inputs to each other. I-O models explicitly consider these linkages and allow analysts to
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see how changes in individual or multiple sectors in a state or regional economy ripple
throughout the system. Effects of an initial investment are felt in these other sectors and
regions as increased employment, output, disposable income, and household earnings. MIG,
Inc., the owner of the IMPLAN model, describes I-O analysis in another way as “…a means of
examining relationships within an economy, both between businesses and between businesses
and final consumers. It captures all monetary market transactions for consumption in a given
time period. The resulting mathematical formulae allow examination of the effects of a change
in one or several economic activities on an entire economy.”
2.3.7 Recommended I-O model—Redyn
The Regional Dynamics (Redyn) Model is the newest and most sophisticated of the advanced IO models available today. The Redyn Model has been used to assess the net job and other
economic benefits associated with economic development projects, new businesses, certain
types of policy changes, and utility power projects in various states throughout the country in a
variety of analytical settings.
The Redyn Model will be employed in this economic impact of bicycling in Arizona study. The IO model uses a dynamic (or a longitudinal) approach that simulates not only the relationship
between sectors in an economy, but also the interconnectivity of regions. The dynamic element
of the model has a well-developed transportation impedance database that is able to account
for temporal effects due to competitive differences between either geographic regions and/or
different sectors of the economy. Over time, these competitive pressures emerge and then
tend to bring the regional economy back to equilibrium. The process, in that way, depicts the
so-called “ripple effect” impact that economic changes have on a region.
The internet-based Redyn Model was developed by Regional Dynamics, Inc. currently of
Scottsdale, Arizona, through its principal creator, the late Thomas Tanner, Ph.D., a former
model manager at Regional Economic Models Inc. (known as REMI) of Amherst, Massachusetts.
Dr. Tanner’s work experience at REMI gave him the expertise to create an alternative to the
REMI model. The result is an integrated I-O model of the counties and cities of the U.S. which
has significant advantages over the existing menu of other commercially-available I-O models:
REMI (which is prohibitively expensive); IMPLAN (as maintained by the Minnesota IMPLAN
Group, Inc., originally developed for the U.S. Forest Service, which is a static I-O model); and
RIMS II final demand and/or employment multiplier tables (which is maintained by the U.S.
Department of Commerce, Bureau of Economic Analysis (BEA) and is also a static tool).
The Redyn Model employs a concept known as New Economic Geography (NEG), a theory first
developed by Paul Krugman while at the Massachusetts Institute of Technology. NEG employs
“fully general equilibrium” models that “derive aggregate behavior from individual
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maximization.” This competitive framework is translated into a model that makes the
assumption that “commodities produced by an industry are truly joint in the production
process, as prescribed by a uniform production function for all firms in each industry based on
competitive pressures to diffuse advantages quickly across all firms in an industry.”
NEG also focuses on agglomeration economies and the relationships between them in different
levels of geographic space. To illustrate this, consider a tight group of commercial
establishments configured as a mall, which is an agglomeration of retail establishments. This is
the smallest level of an agglomeration economy. It is related to the regional commercial
market, which is part of the national market and the world market. It is the linkages between
these levels of economic agglomeration that NEG seeks to explain and illustrate.
Like all I-O models, the Redyn Model calculates the effects of a new final demand stimulus (for
instance, a new bicycle manufacturer locating in Arizona) into three general categories. The first
category includes the direct effects, which measure the changes in the bicycle manufacturing
industry where the final demand change was effected. The second category refers to the
indirect effects, which are those changes in inter-industry purchases as they respond to the
new final demand change in the bicycle manufacturing sector. Lastly, there are those effects
due to changes in spending from affected workers and their households as labor income either
increases due to the change in final demand, known as induced effects. The Redyn I-O model
software calculates all three of these effects just like any other I-O tool, but as a dynamic I-O
model (like the dynamic REMI model), Redyn has the capability through its structure to analyze
the impacts over time and its components as part of a single integrated development scenario.
In its simplest form, the estimation process is comprised of two fundamental steps: i)
calculations to determine what are commonly referred to as national coefficients, which are
based on production functions; and ii) determinations as to what proportion of those goods
and services are purchased within the region. The first calculations estimate how much input is
used in any given year to produce a given amount of output in any given year. The second
group of calculations referred to above involves calculations of what are referred to as regional
purchase coefficients (RPCs). These regional purchase coefficients estimate what proportion of
those goods and services are purchased within the region in question.
The Redyn Model, like other I-O models such as IMPLAN and REMI, handles all these
calculations within the model software based on the geography and type of stimulus specified
by the analyst. The Redyn Model is geographically specified so that RPCs are calculated for all
involved sectors based on purchases of goods and services from firms within the region. The
matrix can be, and usually is, large and complex. RPCs tend to be higher for service-providing
industry categories and lower for goods-producing industry categories. The Redyn Model

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calculates indirect jobs through the I-O simulation process using quantified national
relationships from the I-O tables and RPCs as calculated within the software algorithms
embedded in the Redyn Model.
There is a crucial distinction to be made between I-O models, such as REMI and Redyn and tools
that use an extracted single number I-O coefficient (such as a RIMS II multiplier), which
measures an impact in a single year. The multiplier approach involves simple multiplication
where a single number estimate of a change in “final demand” can be done in a spreadsheet.
Redyn and REMI do not employ single multiplier calculations, but derive their predictive ability
on interconnected matrices of “make” and “use” tables for specific industries and specific
regions or geographies. Therefore, the I-O model tool uses a large matrix of multipliers that
literally includes hundreds, and sometimes thousands, of multipliers. Impact assessment
analyses undertaken using Redyn can treat a project as a single integrated development
scenario and compare the economy under that scenario to a “base case” for the regional
economy.
2.3.8 Redyn Underlying Data Sources
Redyn (similar to other recognized I-O models) is based on the concept of a production
function, which determines what are called “make and use” tables. These tables quantify the
amount of inputs that are required to produce a unit of output across a number of industries.
The basic data underlying these tables are collected by the U.S. Department of Commerce
(principally through its sub-agency, the BEA). The vast amount of data come from a variety of
sources and the data collected are converted into production accounts on a national income
basis and input-output accounts basis, called the National Income and Product Accounts (NIPA).
These accounts are assembled into various tables including transactions tables (which track the
economic flows between producers and users of commodities, as well as the commodity taxes,
transportation charges, and wholesale and retail trade margins between producers and users)
and final uses tables. These tables are the building blocks for the input-output accounts, from
which various multipliers are derived.
Regional I-O models can be constructed using a geographically tailored I-O matrix. Such a matrix
takes into account RPCs, which are calculated based on the proportion of goods and services
required to produce one unit of output that are produced within a defined geographical region.
These RPCs for most I-O tools are based on subsets of the same national data described above.
For Redyn, the underlying matrix has all these coefficients embedded in the model, as it is for
the REMI and IMPLAN tools.
In addition, Redyn uses a variety of supplemental data provided by U.S. Government entities.
The industry structure for the model is the NAICS codes to the five-digit level. This allows the
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model to provide detail for 703 industries and over 180 commodities for the geographic region
specified in the model. This classification system and level of detail makes Redyn’s output
compatible for comparison to government reported statistics, as this is the system used for
publishing economic data by the U.S. Department of Labor’s Bureau of Labor Statistics (BLS),
the U.S. Department of Commerce’s BEA, and the U.S. Census Bureau. These government
bureaus provide the original data that are accumulated and transformed into the Redyn
Model’s I-O (e.g. multipliers) and other specified relationships.
The Redyn Model also employs data from the U.S. Census Bureau’s County Business Patterns
(CBP). CPB data is the source for wage bill payroll data and employment data used in the model.
The model’s developers preferred this approach since it is an annual series that is more
complete in its coverage of the workforce than the Quarterly Census of Employment and Wages
(QCEW) series, as it includes self-employed persons (with no employees), employees of private
households, agricultural production workers, and railroad workers. These workers are not
“covered” by most state unemployment insurance systems and are therefore not reported in
the QCEW series.
The CBP data set also is preferred because it provides detail about employment and wages
down to the zip code level where concentration of industry is high enough to provide
reportable data. The suppression of data due to confidentiality issues is a common problem of
all types of wage and employment data used in specifying I-O models. Redyn has developed a
method to fill in the blanks called row and column sum (RAS).
After performing the RAS technique on the CBP data it is reconciled with Regional Economic
Information System (REIS) data from the BEA to confirm consistency. The REIS data are
especially helpful for providing data in the agriculture and governmental sectors. The REIS data
are also used to allocate national consumption numbers to the roughly 3,100 counties in the
United States that are provided on the aggregate level by the NIPA. The levels of consumption
of industries and households are a key building block in the creation of I-O models.
The input-output tables themselves are created using BEA I-O “make” and “use” tables
(described above). The data in these tables are augmented by the biennial 10-year I-O forecast
tables from BLS. A major difference between the Redyn Model and other I-O models is its use of
a new distance impedance database supplied by the Oak Ridge National Laboratory. This allows
the Redyn Model to add elements of trade flow and gravity theory based on distance
impedance specific to road, rail, water, air, and proxy transport. The combination of these data
sources gives the Redyn Model the power to predict with greater accuracy than ever before the
economic impacts of a wide range of economic changes.

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2.3.9 Inputs to Redyn for this Project
For this study, inputs to the model will be generated separately for each of the metrics that
apply to this research project, for example, value of output (manufacturing, retail and
wholesale sales), employment and labor earnings, and tourism spending. The input values will
be based on the results of the primary research and the integration of that research with
secondary data to produce overall estimates at the statewide and sub-state regional levels.
Inputs related to bicycle-related business activity and bicycle events and visitor spending
segments will be separated for related model-generated analyses.

2.4 Non-Quantifiable Elements
By undertaking an economic impact framework, it is inevitable that a number of considerations
are introduced in the course of data gathering, but are otherwise for the most part left out of
the overall calculus. Here are some examples:


American Association of State Highway and Transportation Officials (AASHTO), US
Bike Route System. ADOT is working with the neighboring states’ Departments of
Transportation, Adventure Cycling Association, and other organizations to coordinate
four AASHTO-designated bicycle routes: 66, 70, 79, and 90
(see http://www.adventurecycling.org/routes/nbrn/USBRSCorridorMap.pdf). Such a
designation does not necessarily imply that “pent-up” demand will be realized (as in
“if we build [or here, designate] it, they will come”) with increased bicycle use. A
related matter is the indeterminate level of bicyclists (origin and destination;
duration) involved in these cross-state routes.



Arizona as destination for cycle training. Arizona offers year-round cycling for elite
professional/amateur road biking teams/clubs. Unlike such sports as spring baseball,
elite cyclist teams/clubs do not establish a “home road,” rather training often
migrates to various portions of the state.10 These groups are often viewed as “under
the radar” and hence their presence is difficult to quantify.11



Arizona as destination for bicycle marketing. As in other transportation
manufacturers, bicycle companies (such as TREK, Giant, Cannondale, Specialized,
Raleigh, and Schwinn) will test and market their products in various natural and/or
urban settings, be that the mountain biking trails around Flagstaff and Sedona, the

10

See, for example. Jason Sumner, “Making a Winning Team Better,” Bicycling, February 2012,
http://www.bicycling.com/news/pro-cycling/making-winning-team-better
“Team director Gord Fraser and his 13-rider squad spent the second of week of February holed up in a spacious
rental house in the saguaro cactus–dotted outskirts of Tucson. On-the-bike training was job No. 1, with media
training, sponsor presentations, a round of anti-doping tests, and team-building exercises filling out the busy
days.”
11
A velodrome for track cyclists is much different; as such, a velodrome represents a significant focal point for
bicycle-related economic activity—both for participants and for spectators.

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on-roads within the White Mountains, or the urban bikeways of Tucson and Phoenix
metropolitan areas.12


Arizona as retirees’ winter destination. To the extent bicycling’s popularity increases
across the age-spectrum, retirees and seasonal vacationers will incorporate bicycling
as one of their recreational pastimes. Increased bicycling undoubtedly translates into
boosted sales for bicycles and ancillary products and increased participation in rides,
events and tours.



Integration of bicycling activity with overall tourism. The desirability of maximizing
tourism officials’ engagement in bicycle-related tourism, including defining,
quantifying, and promoting Arizona's position, nationally, as a center for bicycling.
There is also a broad economic development component of this, as in all forms of
tourism, which involves the exposure of the state to people who subsequently
relocate here.



Public land management coordination. Federal and state land-management agencies
can play pivotal roles in encouraging or discouraging off-road bicycle use, and there
are “mixed reviews” regarding, for example, the role of the Forest Service in Arizona.
The issue is particularly relevant for the smaller cities and rural area of
northern/eastern Arizona.



Rural area impacts compared to statewide impacts. Events in rural areas can be
important to that locality, as components of the redistribution of tourism dollars
within the state, even when participants are Arizonans and the statewide effects are
neutral.

In addition, there are a number of data sets that have been compiled in support of ADOT
Bicycle and Pedestrian Program planning efforts, but which are generally not directly applicable
to this study. They are mentioned here for general information purposes and to add context to
the discussion in this section of other bicycle-related issues.


ADOT Bicycle Safety Action Plan On-line Survey: An on-line survey was completed for
the ADOT Bicycle Safety Action Plan. Some of the available relevant data includes:
o List of bicycle clubs and organizations. Respondents were asked to selfidentify clubs and organizations with which they are affiliated. 811
individuals responded to this question, identifying 210 bicycle clubs and
organizations. These range from informal riding groups to clubs with
sponsorship.

12

See, for example. The Bicycling Magazine Test Staff, “Storming the Desert: 2012 Editors’ Choice Awards,”
Bicycling, June 2012, pp. 95 – 110. “Tucson’s idyllic weather, cycling-friendly roads, and monster descents made it
the ideal spot to test the best bikes of 2012.”

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o Areas to improve to encourage more bicycling and walking: Survey
respondents were asked to identify what could be improved to encourage
them to bicycle and walk more. A majority of respondents identified issues
related to infrastructure, including a general lack of shoulders or narrow
shoulders, unmaintained shoulders, debris in the shoulder, and disconnected
bicycle lanes.


State Highway Bicycle Infrastructure: ADOT accommodates bicyclists on state
highways with shared roadways (including paved shoulders and wide curb lanes). An
inventory of shoulder widths on the state highway system was compiled. The
inventory identifies sections of state highway with shoulder width four feet or
greater. Four feet is the minimum recommended shoulder width that is needed to
accommodate bicyclists (AASHTO). The inventory shows that approximately 49% of
the State Highway System has a shoulder width of four feet or greater.



Traffic Volume Data: The shoulder width information can be superimposed onto
shoulder width data to help bicyclists to identify roadway segments that are most
comfortable for them. State highways with lower traffic volumes and a shoulder
width of four feet or greater represent the minimum facility that is desired by most
bicyclists when choosing to bicycle on the state highway system (see ADOT’s Cycle
Arizona Bicycle User Map at http://www.azbikeped.org/maps.htm).



State Highway Shared Use Paths: An inventory of shared use paths that was
compiled.



Bicycle/Pedestrian Grade Separations: An inventory of pedestrian/bicycle grade
separated crossings over state highways is available. There are 48 grade-separated
crossings of state highways in Arizona.



Bicycle Usage/Counts: Pima Association of Governments conducts an annual bicycle
count at multiple locations and intersections throughout the City of Tucson and Pima
County. The Flagstaff Metropolitan Planning Organization has also conducted a travel
diary study that identifies the modes of travel used within by residents the Flagstaff
region. The study found that 7.1% of trips are made by bicycling in the Flagstaff
region. ADOT installed a permanent bicycle count station on SR 179 near Sedona in
April 2011. The data show a higher ridership in the spring and fall seasons.13

13

http://www.azbikeped.org/mpd/azbikeped/studyupdate/pdf/Working%20Paper%20No%20%202%20Assessmen
t%20of%20Current%20Conditions%20and%20Needs.pdf, pp. 10 & 11.

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3 Data Collection and Analysis
3.1 The Fundamental Challenges of Studying Topics Related to Bicycling
The focus of this study was clearly defined from the outset to be on estimating the economic
benefits associated with (1) the bicycle industry of Arizona; and (2) bicycle tourism and visitor
spending connected with bicycle tourism in the form of organized tours, rides, and races in
Arizona. In part, this specificity reflects the data limitations that lie at the heart of any study
dealing with bicycling in the United States. Not only is there little concerted effort to document
bicycle usage, generally speaking, the information that is available cannot be considered
reliable at even the statewide level.14
Virtually all aspects of the bicycle industry, from production to retailing to the staging of
bicycle-related events (including those solely devoted to bicycling and duathlons/triathlons in
which bicycling is only one component of activity), are highly competitive and dynamic – in
terms of both growth and sometimes contraction/consolidation. The methodological
consequence of this is that bicycling entities seem to be relatively protective of their business
information. Despite the efforts of ADOT, advisors to the study, and the consulting team,
efforts to obtain primary data from all categories of entities proved to be quite difficult.
This study was further defined by ADOT to generally emphasize the tourism and export
components (both referring to out-of-state customers) of bicycling in Arizona. As such, it is a
snapshot of certain kinds of economic activity, and economic implications of certain kinds of
consumer behavior, but is understood to be a partial, perhaps most defensible, component of
economic benefits to the state.

3.2 Overview
This section describes: 1) the methodologies for primary data collection and analysis, for
compiling and analyzing secondary data (with a focus on the inventories of bicycle-related
businesses and events), and for using the primary and secondary data in combination; 2) the
findings and conclusions from the data received, compiled, and analyzed, quantified to the
extent practical; and 3) designs and recommendations for updating the study in the future.

14

For example, the National Household Travel Survey, US DOT, Federal Highway Administration.

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3.3 Methodology – Overview
Table 3-1 (below) identifies the research questions and required information to be addressed
for this phase of the study. This set of research questions acted as a guide to the approach and
the processes involved with the collection, interpretation and analysis of data required to
evaluate the various facets of the Arizona bicycling activity targeted in this study.
An overview of the tasks addressed in this section, including primary and secondary data
collection, processing and subsequent analysis, is shown below on Figure 3-1. Roman numerals
refer to applicable report sections.
To obtain the primary data, surveys were distributed and collected between October 2012 and
February 2013. Surveys were distributed via e-mail and physical mail and collected from online
platforms, e-mailed editable pdf files and returned surveys through the post. The distribution of
questionnaires was preceded by a testing process, which involved certain key informants
identified and consulted in prior phases of this study, along with other identified
establishments15 and organizations selected to take part in this process. The intent was to get
representation from each of the primary categories (dealers, manufacturers, events and tours).
Invitations for the testing process were drafted and sent to the group of testers in September of
2012. Feedback from the testing process, although very limited, was used to refine the survey
questions.
In the process of compiling secondary data and collecting primary data through surveys, the
intent was to obtain information for calendar year 2011. However, given the fact that data
sources were limited, primary data were largely collected late in 2012, and survey responses
were limited in number, the 2011 convention was not always adhered to. Data for 2012 were
used where it was expedient to do so in order to expand the information base.

15

Note that in this report the term “establishment” refers to a single store, and the term “firm” refers to an entity
that might have several stores (establishments) under a single ownership.

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Table 3-1. Research questions and required information
Questions we are attempting to answer:
To answer question need to know:

Information will come from/ be derived from:

How to estimate economic activity of interest in this project, in general
Limitations of secondary data for estimating
purposes
Background research on data sources
Primary data concepts and challenges
Background research on surveying options
Ancillary data available to supplement
primary sources
Background research on surveying options
Total annual bicycle sales in Arizona
Distribution of sales by channel of trade:
bicycle shops, sporting goods stores,
department stores, etc., by volume and $
National data from trade organizations
In big-box stores, bicycle-related sales as % National data from trade organizations;
of total
economic census
Annual sales by bicycle shops in AZ
Number of shops and characteristics
Inventory of establishments
Other databases (e.g. Sweat magazine)
Project survey data for shops
National data from trade organizations
Retail sales to tourists
Proportion of sales to tourists by channel of Assumptions based on sales by dollar amount,
trade
in national data from trade organizations
Proportion of sales to tourists in bicycle
shops
Project survey data for shops
Assessment of similarity of survey sample to
universe
Project survey of event participants

Key considerations:

Arizona's similarity to nation
Has some use as calibrating factor

Arizona's similarity to nation

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Questions we are attempting to answer:
To answer question need to know:

AZ bicycle-related manufacturing export sales
Products produced, sales volume

Information will come from/ be derived from:

Key considerations:

Inventory of establishments
Number of firms and survey data
limited; protect from disclosure

Project survey data for manuf.
Proportion of sales exported
Project survey data for manuf.
Bicycle events/tours in AZ with a potential tourist draw
Number and categorizations of events
Inventory compiled from secondary sources
Listings in constant state of change
Project survey data for event/tour operators
Online posted event results
Annual spending by out-of-state participants in events/tours - total and attributable primarily to bicycling event/tour
Number of events/tours with measurable
tourism participation
Project survey data for event/tour operators
Number of tourist participants
Project survey data for event/tour operators
Spending for bicycle-related goods
Spending in AZ by tourist participants
Project survey of event participants
is accounted for above
General data on tourism spending in AZ
Proportion of spending attributable
primarily to bicycle activity
Project survey of event participants
Regional economic context in which bicycle-related economic activity occurs
How regions compare in economic activity Federal or Economic Modeling Specialists
in bicycle-related industries
International (EMSI) employment data
Tourism indicators from Arizona Office of
Tourism (AOT)
Regional distribution of event/tour activity
Event/tour locations
Inventory compiled from secondary sources
Project survey data for event/tour operators

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Questions we are attempting to answer:
To answer question need to know:
Information will come from/ be derived from:
Regional distribution of bicycle-related tourism spending
Spending by tourist participants, by AZ
Results of analysis of regional distribution of
region
events/tours
Results of analysis of spending by participants
Regional distribution of retail sales activity
Establishment locations
Inventory of establishments
Mix of establishment types by region, and
proportionality of mix among regions
Inventory of establishments
Identifiable trends that are of interest to this
study
Event/tour activity trends
Bicycle sales trends

Key considerations:

Project survey data for event/tour operators
Inventory compiled from secondary sources
National data from trade organizations

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Figure 3-1. Overall structure for the data collection and analysis phase

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3.4 Methodology – Data elements and processing
3.4.1 Secondary data
Types of data sources, general
Secondary data used for the topics discussed in this section were compiled from a variety of
sources. Business data sources included: InfoUSA, other online bicycle business directories,
individual business websites and other online resources. National-level data on bicycle-related
businesses were obtained primarily from business-organization websites, such as the National
Bicycle Dealers Association (NBDA) site. Event and tour data were obtained primarily from
event websites and listings of events and event results.
Overview of data available, and completeness and other limitations of data, in
secondary databases
Businesses
The following diagram (Figure 3-2, below) outlines the processes involved with the
identification of data sources, database creation and the verification of the identified bicyclerelated retail and manufacturing establishments. The database of bicycle dealership
establishments was derived from eight directory websites, local data compilations such as that
prepared by Sweat Magazine, and the individual firm websites. Mass merchants and other
major retailers and sporting goods stores were primarily identified through corporate websites.
Secondary data were also available from sources such as national trade organizations.
The result of this effort is a listing of approximately 450 identified bicycle-related retail
businesses in operation in Arizona, including department stores and other retailers for which
bicycles are only one of many product lines, and including 11 manufacturing/wholesaling
establishments. The inventory is included as Appendix A3-1.
Data variables from secondary data of interest in the analysis include business name and
location, along with estimated data as available (including “formula-based” estimates from
some sources that were largely disregarded) on revenues and number of employees, and
contact information (websites, phone numbers, e-mail addresses).

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Figure 3-2. Business database compilation methodology

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Tour/Event organizations
Overview of Events/Tours
An inventory of bicycle events held throughout the state was developed for this study, through
the process summarized in Figure 3-3. Most of these are annual events that have an established
history and were most recently held in 2012 or are planned to be held in 2013. Some events are
either being held for the first time in 2013 or were held for the first time in 2012. All events
were divided into the following categories:








Road and Criterium Bicycling Events
Mountain Bike Events
Bicycle Tours
Triathlon and Duathlon Events
Cyclo-cross Events
Training Camps
BMX (limited data for this unique category)

Bicycling events can attract participants from the local or regional area, from across the state,
and from outside the state, who gather in the event location and provide local economic
benefit. To make a clear distinction between local benefits and state-level benefits, this study
focuses on the out-of-state component of participants and their travel parties. Economic
benefits are associated with event participants, friends and family, and support staff who travel
from out-of-state and require lodging, food and other goods and services. Although “bicycle
tourism” also involves visitors who independently travel to Arizona primarily to ride, or are
traveling through Arizona on a regional or cross-country tour, this study does not attempt to
directly capture that component.16
Figure 3-3 on the following page illustrates the process used to complete the event inventory.
With the broad range of bicycling events around the state, it is possible that not all bicycling
events that occur throughout the state were identified. For example, while bicycling clubs that
host rides were included in the inventory, there are likely many informal clubs that host riding
events throughout the state. However, the inventory is considered to be representative of and
include the majority of formal bicycling events in Arizona. The complete inventory of events is
provided in Appendix A3-2.

16

However, a few respondents to the Participant Survey were independent travelers, as the survey was posted (by
others) on a website accessible to the bicycling public.

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The event inventory includes the time of year the events are held, event location, event
duration (some events are single-day events, whereas others are multiple-day events), average
event distance, a brief event description, and event contact information. Where data were
available or could be obtained from direct contact, the complete event database includes
information on number of participants. The events range from annual events to those held
weekly. Some events are nationally organized and part of race circuits while others are leisurely
rides for social purposes or to raise money for a cause. The intent of the database is to include
events that are current and have also occurred in the past.

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Figure 3-3. Event and tour database compilation methodology

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3.4.2 Primary data
Data collection implementation activities, overall process
The primary data collection efforts were focused on the following sources of economic benefits,
which fall into two primary categories, with the subcategories listed within:
1) Revenues, especially from out-of-state purchasers, generated by bicycle shops, other
retailers selling bicycles and bicycle-related goods, including sporting goods and mass
merchants (i.e. big-box stores), and manufacturers and wholesalers;
2) Bicycle events and tours, and the out-of-state participants in those events/tours.
For each category and sub-category, primary data collection required unique:
 Survey instruments and contact strategies
 Survey testing outreach
 Preparation and refinement of contact databases – the “universe” of the
targeted survey group. (In the case of participants, the database of events was
the key to outreach to this group)
 Follow-up on low-response groups
 Follow-up on event/tour responses, for contacting their participants
 Follow-up with selected bicycle dealer survey respondents to attempt to retrieve
additional data related to out-of-state sales (based on “permissions” given in the
questionnaire response)
 Follow-up with selected event participant respondents to verify expense data
reported by category

An overview of the primary data collection activities is detailed on Figure 3-4 (below). The
following survey materials can be found in the Appendix document:
 Invitation for key informants to participate in discussions (Appendix A2-1)
 Advance notices to participate in surveys (Appendix A2-2)
 Questionnaire Drafts (Appendix A2-3)

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Economic-benefit categories that present particular challenges in documenting due to
limited availability of primary and secondary data
The project consultants had anecdotal evidence that racing teams, product promotions, and
other private bicycling activities occur in Arizona on a regular annual basis. The Team was able
to obtain some information on elite (professional/amateur) racing teams, but other specific
information on product promotions for instance could not be documented.
Based on previous research experience, the consulting team had minimal expectations that
data relevant to this study would be forthcoming from any attempts to contact the retail store
types engaged in the sale of bicycles and bicycle-related products other than the bicycle shops;
that is, the mass merchants, sporting goods stores, and the like. Although attempts were made
to obtain information from stores of these types, these attempts were unsuccessful.

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Figure 3-4. Primary data collection and implementation activities

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3.14

3.4.3 Data processing and analysis
Overall processing and analysis (model development)
The overall process for the collection, processing and analysis of the primary and secondary
sources is outlined on Figure 3-5 (below). The Team developed a set of Computational Matrices
that brought together primary and secondary sources to serve as reference material for refining
estimates that would be included in the report of findings. This information would also be used
as inputs for other analysis models and to calibrate input to the I-O model. The following
sections will detail:
 Data processing methods for secondary data
 Processing methods for data categories for which no or minimal primary data
were collected
 Processing methods for primary data collected via survey for:
 Bicycle retailers
 Manufacturers/wholesalers
 Event/tour organizations
 Event/tour participants
Data processing methods for secondary business and event/tour databases
The wide variety of secondary sources, including the previously identified establishment and
event/tour databases, were segmented by these two types and added to the two
correspondingly themed Computational Models. The computational models act as an
intermediary platform for the primary and secondary data sources. Combining the two data
types in one modeling system made it easier to compare the findings from the two data types,
and to make comparisons such as the sample size from primary data to the full database
compiled from secondary data. Where appropriate, the primary data could be calibrated to
reflect conditions demonstrated by the secondary data.
Data processing and analysis methods for data categories for which no or minimal
primary data were obtained
Even though no primary data were obtained from retail store types engaged in the sale of
bicycles and bicycle-related products other than the bicycle shops – that is, the mass
merchants, sporting goods stores, and the like – it was possible to estimate the contribution to
total sales of bicycles and bicycle related parts in Arizona made by these kinds of
establishments, based on national data that indicate the share of sales attributable to the
various categories of stores.

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Figure 3-5. Data schematic for analysis model development

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Processing of primary data from survey results
Bicycle dealers and manufacturers/wholesalers
Results from an on-line survey, and from direct-mailed questionnaires in some case, were
collected from 41 bicycle dealer/retailer establishments representing 55 locations throughout
Arizona. The distribution of establishment types is shown in Table 3-2 (below).
Results to an on-line survey, and from direct-mailed questionnaires in some case, were
collected from 41 bicycle dealer/retailer establishments representing 55 locations throughout
Arizona. The distribution of establishment types is shown in Table 3-2 (below).
Table 3-2. Distribution of respondents to bicycle dealer survey by type
Response
% of
Type of establishments
count
respondents
Single location
36
87.8%
Multiple locations
5
12.2%
Total Responses
41
Total Number of Establishments Reported
55
Source: Arizona Bicycle Dealer Questionnaire, 2012

Survey results, from an e-mailed questionnaire,17 were obtained from three manufacturers/
wholesalers: one firm engaged in manufacturing only and the other two in both manufacturing
and wholesaling. No results were obtained from attempts to contact mass merchants (big-box
stores) or sporting goods stores.
Event and Tour Operators
Five event sponsors and one touring company responded to the survey that they received by email. Information on 2012 bicycling events and bicycle tour operators in Arizona was also
obtained by using Internet search engines. For nearly every bicycling event, participant results
were posted by the respective event coordinator on their website or by a third-party
“umbrella” group, such as Cycling Event Promotions, Inc. (www.ceptiming.com), Athlinks
(www.athlinks.com), and AZ Race Results (www.azraceresults.com).

17

In a few instances, questionnaires were sent by regular mail to businesses and event sponsors where no other
options were available.

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Out-of-State Event Participants
A total of 132 responses were received through the on-line survey from out-of-state bicycling
visitors to Arizona, all but a few of these from event participants (the other were independent
tourists). Within the process of distributing surveys to event organizers, the organizers were
encouraged to invite their participants to the on-line participant survey. Out-of-state/country
participants were also encouraged via Facebook and other social media to provide expenditure
information during their event participation stay in Arizona. As noted above, most events
posted results of participants. Standard posted results information includes name, place and
time of finish, team affiliation, and, often, city/state/country of residence.

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Relationship of benefit categories to potential assessment of indirect/induced effects
using I-O model, and rationale for establishing relationships
Direct economic benefits of the following types can be extended to indirect and induced
benefits that are estimated by the application of models that quantify the multiplier effect
within the local economy from such activities:
 For establishments dealing in bicycle and related products, the annual retail sales
to out-of-state visitors, as a special subset of all sales.
 Bicycling events and tours that include participation by out-of-state visitors:
Spending of all types within the state by out-of-state participants, focusing on
the extent of their visitation that can reasonably be related to the bicycling event
(and acknowledging the potential for double-counting of tourist spending
recorded in the data for bicycle shops).
Note that the key aspect of these two categories is the out-of-state connection.

3.5 Findings and conclusions: organization and results
3.5.1 Geographic scales of data reporting; state and regions
The basis for and definition of study regions
The regions utilized in this study were based on the five (5) regions used by the Arizona Office
of Tourism (AOT). The counties that comprise these regions are defined in Table 3-3 and shown
on Figure 3-6 (below).
Table 3-3. Arizona Office of Tourism (AOT) regions and included counties
AOT Region
Arizona Counties
Northern
Apache, Coconino, Navajo
North Central
Gila, Yavapai
Phoenix & Central Maricopa, Pinal
Tucson & Southern Cochise, Graham, Greenlee, Pima, Santa Cruz
West Coast
La Paz, Mohave, Yuma
Source: Arizona Office of Tourism

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Figure 3-6. Arizona Office of Tourism regions and included counties

Source: AOT, Kimley-Horn and Associates

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Counties aggregated to regions for purposes of confidentiality
The regional framework for reporting study results was particularly useful and appropriate,
given the limitations of the primary data collected. Table 3-4 below shows the number of
survey responses by type and region. A total of 41 dealer responses were received, half of
which were located in the Phoenix & Central region. Three manufacturing/wholesaler
responses were received along with five responses from event organizations and one from a
touring company.
Table 3-4. Distribution of survey responses by region
Manufacturing/
Region
Dealer
Event
Wholesalers
Northern
5
North Central
3
Phoenix & Central
21
2
Tucson & Southern
11
1
West Coast
1
Total
41
3
5

Tour

1

Note: The distribution of event organizers and tour operators are not shown by geography since survey responders
had events/tours that spanned multiple regions.
Source: Impacts of Bicycling in Arizona questionnaires (Manufacturing/Wholesale, Bicycle, Event and Tour), 2012

Limited primary (survey-based) data means that many data elements are reportable only at the
statewide, or at best regional level, in order to preserve confidentiality and to maximize the
confidence of any results. Consequently no attempt has been made in this study to report
findings at the county level.
Data elements and regional breakdowns
Based on the survey response counts and therefore data usability, comparisons among regions
in this report are generally treated as follows:
 Quantitative data are sometimes discussed in relative terms rather than
tabulated
 Non-quantitative data are generalized
Data elements (variables) with this treatment include:
Employment
Total annual revenue

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Revenue by product category
Out-of-state revenue estimates
Seasonality of out-of-state purchases
Recession impact
3.5.2 Bicycle retailing
The overall goals of the bicycle retailer analysis were to:
1) Use total sales figures as an indicator of economic activity;
2) Use sales data for bicycle dealers as a way to estimate sales from all store types; and
3) Estimate retail sales to out-of-state visitors.
Overall structure of the bicycle retailing environment
Retail sales of bicycles and related products take place in several distinct establishment types:
bicycle shops (dealers), mass merchants such as Wal-Mart and other discount or big-box stores,
chain sporting goods stores, and mail order or Internet sites. Bicycle shops account for,
depending on the particular data tabulation reviewed (the primary source for this information,
cited later in this section, uses differing figures to describe the total bicycle market), either
slightly more than or less than half of revenues from sales of all bicycle-related goods. Mass
merchants, the big-box stores, account for roughly one-quarter of sales, with the rest going to
chain sporting goods stores and the Internet. Among these “channels of trade” (categories of
stores including mass merchants, chain sporting goods, etc.), volume (units sold) percentages
vary significantly from sales dollar percentages. Mass merchants for example have a much
higher percentage of total unit sales (e.g. 70% in 2011 compared to 20 percent for bicycle
shops/OSR,18 according to the same source cited for the table); since, in general, the bicycles
sold in bicycle shops tend to have higher unit prices.
This type of data, showing the allocation of sales dollars among establishment types, was
particularly useful to this study. The data facilitated the process of estimating total sales from
all store types, based on sales estimates for bicycle shops derived from the primary data.

18

OSR: Outdoor Specialty Retailers, which includes such stores as Scheels, All Sports and Sun & Ski.

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Broad national trends for bicycle shops
Nationally, bicycle retailing has experienced a number of trends that have major influences on
how these businesses are faring. Although a full discussion of this is beyond the scope of this
study, it is instructive to note the following, summarized from Bicycle Retailer magazine:19
 The number of bicycle dealers nationwide has decreased every year since 2003, from
5,358 (in 2003) to 4,089 in 2012.
 Internet sales have continued to increase as a percent of total sales, with some
estimates (the NBDA source cited) claiming that Internet sales are 20% of total bicycle
sales as of 2012.
 Internet direct sales from foreign suppliers, in Europe and Asia, are also increasing.
 Data commonly reported in trade magazines do not provide good source material for
tracking actual sales trends (in dollars) of bicycles and related products, but available
information suggests that sales can fluctuate considerably from year to year.
Bicycle-related sales estimates for Arizona retailers
Estimating total annual sales of bicycle-related goods and services in Arizona involved the
following steps:
 Preparing a modeling format that recognized the nature of the information available to
generate such estimates; namely that information about revenues to any particular
store type was limited to bicycle shops (due to the lack of response to our primary
research efforts from big-box and sporting goods retailers) .
 Estimating revenues for bicycle shops based on a combination of Arizona-specific
primary (from the survey) and secondary (shop inventory) data. Survey results at the
regional level were also examined to help calibrate estimates where regional results
varied significantly from the state average.
 Segmenting retail sales within bicycle shops from revenues derived from bicycle
servicing and rentals.
 Given that the intent of this analysis was to use bicycle shop data to “back out” the sales
figures for other store types, based on national data, verifying that the mix of stores in
Arizona was similar to the nation. This analysis is summarized in Table 3-5 below, which
shows that total bicycle-related sales by store type, as a percent of total bicycle-related
sales for all store types, is similar for the US and Arizona.

19

Annual Stats Issue, July 1, 2012. A publication of the National Bicycle Dealers Association (NBDA).
Bicycleretailer.com

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 Combining data on bicycle-related retail-sales revenues from bicycle shops with national
data showing the percentage of sales of these products among different channels of
trade, then calculating total sales based on the percent of sales applicable to bicycle
shops, from that percentage data.

The results of this analysis are summarized below in Table 3-6 and Table 3-7.

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Table 3-5. Bicycle-related sales by store type, US and Arizona

Store category

Products
and
services
code

Products and services code
description

Sporting goods stores
20512
Bicycles, parts & accessories
Hobby, toy, and game
stores
20512
Bicycles, parts & accessories
Department stores
(except discount
department stores)
20512
Bicycles, parts & accessories
Discount department
stores
20512
Bicycles, parts & accessories
Sub-total without warehouse category (suppressed for AZ in source
data)

Product line sales
US

AZ

Total bicycle-related sales
by store type as % of total
bicycle-related sales for all
store types
US
AZ

$2,543,565,000

$55,165,000

77.0%

78.8%

$299,825,000

$4,688,000

9.1%

6.7%

(amounts are negligible)

$459,531,000

$10,111,000

13.9%

14.5%

$3,302,921,000

$69,964,000

100.0%

100.0%

Source: 2007 Economic Census, Product Lines Statistics. Data for the store category of Warehouse Clubs and Supercenters was suppressed for Arizona, so is
not included in this analysis. Nationally, this store type accounted for 14 percent of sales in the “bicycles, parts & accessories” product line.

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Sample-based revenue/establishment size
Table 3-6 (below) shows the average reported revenue and establishment size for the survey
respondents. Based on the data collected, the average establishment revenue (accounting for
responding firms with multiple locations) is $528,616. The total revenue reported for the 17
respondents (representing 21 establishments) providing revenue data, out of the 41 total
responses received, was $11.1 million. Applying the same per-establishment average from the
survey results to the total bicycle shop inventory yields an estimated annual revenue for all
bicycle shops of $76.7 million. Average revenue per establishment is low compared to the
national average of $866,817 reported by the National Sporting Goods Association (for 2011).
Table 3-6. Reported 2011 revenue and establishment size
Responding firms (R)
EstablishResponse
/ Establishments (E)
ment
average
(1)
average
Annual revenue, 2011
17R; 21E
$652,997
$528,616
Establishment size, in square
feet
28R; 31E
3,073 sq. ft.
All Arizona bicycle dealers
158E

Estimated
total 2011
revenue
$11,100,944
$172/SF
$76,651,170

(1) Accounts for multi-location operations
Source: Impact of Bicycling in Arizona Bicycle Dealer Questionnaire, 2012

Although the sample sizes within regions were small, especially for the regions outside the
Phoenix area, and therefore findings have limited reliability, average revenues were lowest in
the North Central region and lower in the Tucson & Southern region than in Phoenix & Central.
Total revenue estimates
Table 3-7 (below) combines data on bicycle-related retail-sales revenues estimated for Arizona
bicycle shops with national data showing the percentage of sales of these products among
different channels of trade. Applying these same percentages to Arizona bicycle shop sales
produces an estimate of total sales. (Note that cells in the table are blank where data do not
exist or are irrelevant.)

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Table 3-7. Arizona sales estimates based on allocation of sales by channels of trade

Channels of trade (types of
retail stores)

Bike shops + OSR (1)
Mass merchant
Chain sporting goods
Other [represents Internet
sales in this table]
Total AZ sales, all channels,
excluding service/rentals and
including Internet
Total retail sales in AZ from
all channels of trade,
excluding Internet sales

Adjusted
market share,
with
assumption of
20% to Internet
(2)
45.7%
22.4%
11.9%

Total est. AZ
bicycle shop
revenues
excluding
service/rentals

Estimates for
other channels of
trade, based on
AZ bicycle shop
sales

Total for
Arizona (3)

$65,139,495

20.0%

100.0%

$31,874,083
$16,948,917
$28,484,299

$142,446,794

$113,962,494

(1) OSR: Outdoor Specialty Retailers, which includes such stores as Scheels, All Sports and Sun & Ski.
(2) Source: Figures are adjusted from data in BicycleRetailer.com (NBDA), Annual Stats Issue. July 1, 2012 (data for
2011), to reflect other material (in the same source) that states Internet sales constitute 20% of the total.
(3) Authors.

“Bicycle” sales by categories of goods sold by retailers
According to the survey data, Arizona bicycle shops secure on average about 45 percent of their
revenues from the sale of bicycles, based on 25 survey responses representing 30
establishments. Approximately one-fourth of revenue comes from bicycle parts and accessories
sales. The final component of revenues is divided among clothing and shoe sales (10%, based
on 23 responses representing 27 establishments), and bicycle repair and rentals (18%, based on
25 responses representing 30 establishments). Among regions, clothing/shoes was a higher
proportion of revenues in the Phoenix & Central region.
These figures for Arizona overall are similar to national data (NBDA, see Table 3-7 for source),
which show that, excluding revenues from servicing and rentals, bicycle sales are 60% of
revenues and parts, accessories, etc. are 40%. The corresponding figures for Arizona (ignoring
the service/rental category) are 62% and 38%.

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The database from Sweat Magazine provides additional insight into the product lines and other
operations within Arizona bicycle shops. Table 3-8 (below) shows the percent of shops that
carry the product lines or engage in the services or other activities listed in the table.
Table 3-8. Products and services in Arizona bicycle shops
% of AZ
Product lines
shops
Cycling Clothes
96.2%
Nutrition Products
84.9%
Women's Only Products
89.6%
Sell Used Bikes
50.0%
Services/activities
Club/Team Affiliation
74.5%
Group Discounts
72.6%
Store Seminars
56.6%
Rentals
45.3%
Group Rides From Store
66.0%
Source: Sweat Magazine. The Bike Issue 2012. Vol. 21, No. 2. S.W.E.A.T. Marketing, Inc.
Used with permission.

Arizona bicycle shop sales to out-of-state visitors
Purchases of bicycle-related goods and services in Arizona by visitors from other states
constitute a specific tourism-related benefit associated with bicycling activity in the state.
Estimating such purchases within this study involved two survey-based approaches:
1. Asking bicycle shop owners or managers, in the survey directed to them, to estimate
sales to out-of-state visitors;
2. Asking out-of-state visitors attending Arizona bicycling events, within the survey
directed to that group, about their expenditures, including bicycle-related purchases,
while they were in Arizona.
The first approach included the assumption that most sales to visitors would be made within
bicycle shops, rather than mass merchants, sporting goods stores, etc. This assumption is
somewhat conservative, but is also based on the reality that we were unable to obtain sales
information of any kind from the mass merchants or sporting goods stores. The assumption
also reflects the intuitive notion that bicycle tourists are most likely to be interested in the kind
of specialized products more likely to be found in bicycle shops rather than mass merchandise
outlets.

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Results of the survey of bicycle shops indicated that a relatively high proportion of sales are
made to out-of-state visitors, as shown in Table 3-9 (below). However, the sample size for this
information was small, 13 responses representing 15 establishments. Respondents were asked
whether the estimates they gave were based on a “review of sales records,” or a “best
guesstimate.” Estimates based on review of sales records (5 responses) tended to be smaller
than the guesstimates, so our reported estimates, shown below, were adjusted to reflect this
and the fact that the small overall sample size calls for a conservative approach to these
estimates (although service/rental activity might not be fully captured in either the survey of
shop owners or the participant surveys, and this topic should receive additional attention in any
future updates).
Table 3-9. Bicycle shop revenues and out-of-state sales estimates
Estimated Total Revenues, Estimated % Out-ofEstimated Dollars, Out-of-State Sales
Bicycle Shops
State Sales
For parts,
For parts,
For parts,
Bicycles
Bicycles
Bicycles
clothing,
clothing,
clothing,
Total
only
only
only
etc.
etc.
etc.
$29,465,968 $35,673,527

35.0%

25.0%

$10,313,089 $8,918,382 $19,231,470

Source: Arizona Bicycle Dealer Questionnaire, 2012, McClure Consulting, LLC

Seasonality of out-of-state purchases
Respondents were asked, “Which season(s), if any, do you estimate that sales to out-of-state
visitors are higher than the annual average?” According to the 32 responses to this question
(representing 41 establishments), spring and winter are essentially tied for the top seasons for
sales to out-of-state visitors (indicated by over 70% of respondents), fall is second (46% of
respondents), and summer (14%) a distant last. In the North Central and Northern regions,
winter was the low season and the other seasons had roughly equal designations as the top
sales seasons.
Of 30 firms responding, 21 stated that they observed an increase in sales to out-of-state visitors
when major bicycling events were occurring in the city (county) where their establishment was
located.
Employment, shop area, and other characteristics of Arizona shops, and comparisons
of Arizona to national data
Employment
The surveyed firms represented 294 full- and part-time employees (including seasonal
workers), constituting an estimated 232 Full Time Equivalent (FTE) employees, as shown in
Table 3-10 (below).
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Table 3-10. Bicycle dealer employment, by type, survey respondents
EstablishResponse
Type of establishments
Total
ment
count
average
Full Time Equivalent work force
Total for full-time, part-time,
seasonal/temporary workers
Full-time
Part-time
If applicable, average hours per
week for a part-time worker
Seasonal/temporary
If applicable, average number of
weeks for seasonal/temporary
workers

41

231.9

5.2

41
36
31

294.0
163.0
101.0

6.5
3.6
2.5

30.0

22.8
1.36

30
14

14

8.57

Source: Arizona Bicycle Dealer Questionnaire, 2012

According to the survey data, the average full-time equivalent (FTE) employment for all
establishments is 5.15. Based on this number, an estimate for all Arizona bicycle shops is 814
FTE (figures based on numbers that are not rounded). The Tucson & Southern and Northern
regions produced the highest FTE averages based on the survey responses. North Central and
West Coast figures were lowest. The overall establishment average for the total employment
count (including full-time, part-time, seasonal/temporary workers) is 6.5. Average figures for
seasonal/temporary employment (per establishment) among the regions range from less than 1
to 3.5. These figures are based on:
 Full-time: 36 responses representing 45 establishments
 Part-time: 31 responses representing 40 establishments
 Seasonal/temporary: 14 responses representing 22 establishments
Employment, estimate of total for all bicycle retailing in Arizona
The following process was used to estimate total employment in all types of bicycle-related
establishments in Arizona:
 Translating estimates of total employment in bicycle shops to “retail only”
employment, based on data in Sweat Magazine, which indicate that an average

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of 2.7 employees per establishment are mechanics (based on 103 cases in the
magazine’s database).20
 Taking the retail-only employment in bicycle shops as a starting point, assume
that employment in other store types is proportional to sales dollars attributable
to store types other than bicycle shops (a more conservative assumption than
one based on proportionality of units).
 Adding back the estimated number of bicycle-shop mechanics.

This process yielded a total employment estimate, for all store types, of 1,110 FTE, including
mechanics.
Survey respondents were asked whether the Recession constituted a “minimal” or “significant”
economic effect to their business. The results are shown below in Table 3-11.
Table 3-11. Degree of impact of the Recession on business activities
Minimal economic effect
36.7%
Significant economic effect
63.3%
Total responses
30
Source: Arizona Bicycle Dealer Questionnaire, 2012

Survey data indicated that the average size of an Arizona bicycle shop was 3,073 square feet (28
firms responding, representing 31 separate establishments). The Sweat Magazine database
indicates that the average size of Arizona bicycle shops, based on input from 101 shops, is 3,893
square feet. This difference suggests that the estimates in this report, based on the survey data,
are conservative.
The median number of years that stores have been open is 16, according to Sweat.

20

The Sweat Magazine database and the inventory database assembled for this study, derived from a
variety of sources and largely confirmed through direct contact, do not exactly match, at least in part
due to the different time periods over which the databases were assembled and the volatility of this
business type (in Arizona and also elsewhere in the U.S.). However, the type of database summaries
from the magazine included in this report should remain relatively valid, even with some additions and
subtractions to the list. Data are used with permission.

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Retailing data by Arizona region
Survey responses
Table 3-12 (below) provides a breakdown of the bicycle dealer survey responses by region.
However, not all questions were addressed by every respondent, as noted in the preceding
discussions of individual report topics. While compilations of survey data by region were
prepared for the topics of employment, revenues, sales by type of goods/services, and sales to
out-of-state buyers and the seasonality of those sales, limited responses to some of the
questions necessitated that regional-level data be consolidated for some data elements.
Table 3-12. Survey response count and number of establishment reported by region
Total
number
Number of
% of
Total
of bicycle
Arizona region
establishment
establishments
responses
dealers
reported
represented
identified,
universe
North Central
5
5
8
62.5%
Northern
3
4
10
40.0%
Phoenix &
21
32
95
32.6%
Central
Tucson &
11
13
42
31.0%
Southern
West Coast
1
1
8
12.5%
AZ Total
41
55
163
33.1%
Source: Arizona Bicycle Dealer Questionnaire, 2012

Regional comparisons
The following series of bullets provides a summarized comparison of estimates to bicyclerelated activity in the Arizona bicycle dealer industry, by region, based on data received during
the surveying process:
 North Central region: Of the regions with reportable data, the North Central
region had the lowest reported average annual revenue per establishment. This
region accounts for the second lowest percentage of annual revenue attributable
to bicycle sales and the highest percentage for bicycle parts/accessories and
bicycling clothing, shoes, etc. For bicycle rental and service, the region is
comparable to figures reported within the Phoenix & Central region.
 Northern region: Only one survey respondent from this region; unreportable.

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 Phoenix & Central region: The Phoenix & Central region had the second-highest
average annual revenue per establishment, but this figure was, however, still
well below the US bicycle shop average of $866,817 for 2011. The region also
had the highest reported percentage of annual revenue attributable to bicycle
sales, and the second-lowest reported percentage of revenues for bicycle
parts/accessories and the lowest for bicycle servicing and rentals. For bicycle
clothing/shoes, the region has the highest reported revenue percentage.
 Tucson & Southern region: The Tucson & Southern region has the lowest
reported percentage of annual revenue attributable to bicycle sales and the
highest report value for bicycle servicing and rentals. The region yielded the
second lowest reported percentage of revenues for bicycle clothing/shoes and
the second highest for bicycle parts/accessories.
 West Coast region: Only one survey respondent from this region; unreportable.
Figure 3-7 and Figure 3-8 (below) together compare the percent of bicycle dealers in Arizona
regions with the regions’ share of Arizona employment in 23 industries with a potential
connection to bicycling. (Figures are approximate because actual data are not available for all
industries in all counties.) A comparison of the two charts indicates that Tucson has a much
higher percentage of bicycle dealers than employment in the underlying industries.
Figure 3-7. Regional distribution of bicycle dealers

5% 5%

6%
North Central

26%

Northern
Phoenix & Central
Tucson & Southern

58%

West Coast

Source: Table 3-12

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Figure 3-8. Regional share of Arizona employment in industries with a potential connection to
bicycling

6% 5%

4%
Northern

16%

North Central
Phoenix & Central
Tucson & Southern
West Coast

69%

Source: EMSI employment data for Arizona counties, 2011.

Use of I-O models in the analysis of economic benefits
The principal tool used in ascertaining economic impacts associated with bicycling activity is an
I-O model. At its roots, an I-O model is an accounting method to describe a specific regional
economy. One can actually think of an I-O model as a spreadsheet of the regional economy
where the columns represent the buyers (demand) and the rows are the sellers (supply). Any
particular cell where a column and row intersect is the dollar flow between the buyer and seller
of a particular good or service. The sum of a particular row is the total supply (in dollar value of
output or sales) of that particular industry and the sum of any particular column is the total
demand of the industry. Given the laws of supply and demand within competitive markets,
total demand must be equivalent to total supply.
The utility of the input-output approach lies not solely in its effective data accounting
framework, but in its ability to trace small changes in one part of the economy throughout the
entire regional economy. There are several measures used to gauge the economic effects,
including industry output (sales), income, and employment. Because the input-output approach
is based on dollar flows or sales, the impacts are generally displayed in total output or sales. In
practice, impacts are most readily understood when industry output (sales) are converted to
income and employment.
The derived economic multipliers from the input-output analysis are composed of three
segments. The first part is the direct effect that caused the initial change in the economy.

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This initial direct action will have a rippling effect throughout the economy. This rippling effect
is captured by the second component of the economic multiplier (indirect effect) and the third
component, referred to as the induced effect. Indirect effects are generally business-tobusiness transactions. Induced effects refer to wages and salaries paid to employees and the
spending of their incomes in the regional economy.
There are a number of input-output modeling systems available for use in this study of the
economic effects of bicycling activities in Arizona. In this report, we are utilizing the Redyn
modeling system to ascertain the scope and scale of economic effects of bicycling activities in
Arizona.
In comparison to results of studies of the economic impact of bicycling in other states,
reviewers of this report might ask why these economic effects of bicycling activities in Arizona
are rather modest. First, an economic impact assessment focuses on export activity—
product/service sales to customers located outside of the region. Such export activity
introduces new spending into the regional economy. This new injection of money into the
economy causes a ripple (or “multiplier”) effect throughout the rest of the economy. Through
the use of an I-O model, we can track and measure this economic impact. Second, the
dominant market niche of Arizona in bicycling activities is hosting various events—bicycle road
races, “ironman” triathlons, extreme (e.g., 24 hours) mountain bike events, elite training
camps, and unique operated bicycle tours. All of these events draw thousands of participants to
the roads, trails, and scenic vistas of Arizona, but only for a brief period of time—for instance, a
competitive event with an additional two to three vacation days in Arizona with the
participants’ family.
Estimates from I-O modeling: retail and manufacturing
Impact results based on out-of-state bicycle-related retail sales were estimated through the use
of a calibrated I-O model for Arizona. Retail sales figures were adjusted to reflect the fact that
only the margins are applicable to local impacts, which more accurately and conservatively
expresses the multiplier effects of these purchases than the full retail sales figures.
Bicycle-related retail sales to non-residents include purchase/rental of bicycles and related
equipment; clothing, shoes, and accessories; parts and tires; and miscellaneous. The total
economic activity associated with these “export” retail sales is presented below in Table 3-13.
The table shows the total and direct (as a sub-set of the total) effects, for the Redyn model
output categories of:
Jobs
Total output

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Disposable income, and
Labor income
Due to the modest size of bicycle manufacturing and distribution activity in Arizona (see
following section), particularly at the regional level, impact modeling estimates for these
sectors were included within findings from the retail trade sector.

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Table 3-13. Economic effects of bicycle retailing and manufacturing/wholesaling, Arizona
Total
Effects

Direct
Effects

Jobs
North Central
Northern
Phoenix & Central
Tucson & Southern
West Coast
Arizona Statewide Total

16
13
221
54
13
317

9
9
97
29
9
154

$2013 (000s)
Output
North Central
Northern
Phoenix & Central
Tucson & Southern
West Coast
Arizona Statewide Total

$1,400,964
$1,116,846
$47,152,362
$6,725,950
$1,221,457
$57,617,579

$863,332
$792,622
$22,847,444
$3,826,439
$842,505
$29,172,344

$2013 (000s)
Disposable income
North Central
Northern
Phoenix & Central
Tucson & Southern
West Coast
Arizona Statewide Total

$833,596
$505,121
$13,455,591
$2,829,893
$528,204
$18,152,405

$476,083
$342,851
$6,193,731
$1,426,317
$362,866
$8,801,848

Source: Authors; Redyn model.

Qualifications of the findings
The findings reported in this document are, as described throughout the text, often based on
small survey samples. The findings were reviewed in comparison to other data available, at
both the local and national level. Based on these reviews, figures used in the report
represented either conservative or consistent results, in comparison to other information.
However, many of the findings should still be considered tentative and at best estimates rather
than statistically valid conclusions that can be extended to the entire “population” of interest,
for example Arizona bicycle retailers, with confidence.

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3.5.3 Manufacturing and wholesaling
Overview of domestic production versus imports, nationally
The manufacture of bicycles, and bicycle parts and accessories, of course occurs worldwide.
Nevertheless, based on information in Bicycle Retailer,21 imported bicycles constituted only
16% of the US bicycle retail market in 2011. A casual review of the literature suggests that
locations, demand for specific types or brands of products, and other aspects of bicycle-related
manufacturing change fairly rapidly and extensively. A full discussion of this kind of activity is
beyond the scope of this report, but this simple observation is worth noting in terms of how
prospects for additional manufacturing activity in Arizona can be perceived.
Summary of information obtainable for manufacturing and wholesaling in Arizona
Bicycle-related manufacturing and wholesaling in Arizona is limited to a few small firms, as
shown below in Table 3-14. Only eleven firms were identified in the process of compiling a
database for these activities, distributed among the regions used for this study as shown below.
Of these, three responded to the survey designed for manufacturers and wholesalers. A limited
amount of data was obtained through Internet searches of the remaining firms. Given the small
number of firms and survey responses, only very generalized information about Arizona
manufacturing/wholesaling firms can be shared in this report.
Table 3-14. Distribution of manufactures/wholesalers by Arizona region
# of Bicycle
Region
Manufacturers/
Wholesalers
North Central
0
Northern
1
Phoenix & Central
8
Tucson & Southern
2
West Coast
0
Totals
11
Source: Arizona Manufacturer/Wholesaler Dealer Questionnaire, 2012, and Internet searches.

We estimate that total Arizona FTE employment for this group is approximately 75-90, based on
a combination of web-based sources and survey responses. Several firms have only 1 to 3
employees. This employment estimate can be converted to an “earnings” estimate using data

21

Annual Stats Issue, July 1, 2012. A publication of the National Bicycle Dealers Association. Bicycleretailer.com

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from EMSI on average earnings by industry for Arizona.22 Using this factor, the earnings
estimate is $3.6 million to $4.32 million.
The firms produce a range of goods, both individually and as a group, including different types
of bicycles and custom bicycles, nutrition products, bicycling accessories, and bicycle racks.
Several firms are involved in both manufacturing and wholesaling (including Internet sales).
One firm is both a custom bike builder and a dealer of bicycles manufactured by others. Only
three firms are, to the best of our knowledge, exclusively engaged in wholesaling (in Arizona
operations). Two of the three survey respondents stated that their exports (from Arizona)
constituted 90% of their business; the third stated 20%. Given the nature of the other firms, it is
probably safe to assume, as an estimate, that out-of-state sales constitute well over 50%
overall. Survey responses from two of the three firms indicated that their experience with the
Recession constituted a “significant (as opposed to minimal) economic effect.”
3.5.4 Bicycle tourism in Arizona
Nature of bicycle tourism as subset of sports tourism—a spectrum of bicycle tourism
Sports tourism is an emerging component of tourism/economic development. According to
sportanddevelopment.org, self-described as “the international platform on sport and
development,” “Today, more and more national and international development organisations
[sic] are using sport to add to their approaches in local, regional and global development . . .”
Bicycle tourism, especially if defined as travel associated with organized, sometimes
competitive events, is a subset of the growing sports tourism phenomenon.
Focus on discrete events—organized rides
Bicycle event categories addressed in this report include the following:
 Road Bicycling Events
 Mountain Bike Events
 Bicycle Tours

22

The figure applied $48,000 (2011 dollars), is a rounded average of the earnings per employee, for Arizona, for
two industries (the earnings figures of the two are similar): Motorcycle, Bicycle, and Parts Manufacturing, and
Sporting and Recreational Goods and Supplies Merchant Wholesalers.

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




Triathlon and Duathlon Events
Cyclo-cross Events
Training Camps
BMX events, to a limited degree

Aspects of bicycle tourism
Most involve travel (other than bicycle) between places
Bicycling events are (except for the very largest, such as the Tour de France for example)
primarily participatory rather than primarily spectator events, and therefore involve
“committed” attendees who might include per-event training or other associated activity along
with direct event participation. Participant attendance involves therefore not just travel but
travel with distinct purposes. It is less “discretionary” than typical leisure travel.
On-road bicycling and off-road mountain biking
While on-road cycling has a direct connection to roadways that are of particular interest to
ADOT, off-road mountain biking, especially in a place like Arizona where opportunities for such
activity are abundant, also attracts out-of-state tourists. These visitors might participate in a
mountain-bike event or simply use mountain biking trails independently.
Individual and group bicycle tours
Although there are individuals who travel to Arizona primarily to tour on their bicycles, or are
traveling through Arizona on a regional or cross-country tour, no attempt has been made to
document this type of activity for this study. However, there are also commercial tours that
feature bicycles and operate in the state, and these tour operators might be based in Arizona or
elsewhere in the US. This study identified 11 tour operators for which data (mostly secondary
data) were compiled and analyzed.
Day bicycle rides where bicycling is one of a number of activities undertaken during
vacation excursion
For visitors using their bicycles while on vacation in Arizona, riding could be an incidental
activity or a primary purpose of the trip.
Some cycling takes place within a rather restricted spatial context
While road events and tours could have a wide geographic extent, some bicycling events
operate within specific areas, in relation to special facilities. These include mountain biking
events, which might occur on National Forest land, and cyclo-cross events. For these cases,
event benefits are more likely to be concentrated in a specific community.

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Role of recreation in bicycling
An “independently recreating bicyclist” (not participating in an event) could represent any one
of a number of conditions:23
 “Self-contained travelers: These cyclists and travelers take their gear along on the
ride and mainly need camping, grocery, and Internet access.
 Ride-centered travelers: These cyclists and travelers tend to stay overnight in one
location and go riding during the day.
 Event-centered travelers: These travelers participate in organized rides or event
rides. These kinds of travelers also include spectators at racing events.
 Urban-cycling travelers: These travelers arrive in a community and spend all or some
of their time in the community traveling by bicycle. These travelers may also
sightsee locally by bicycle.”
Bicycle tourism definitions
For the purpose of this study, bicycling events and tours are considered “bicycle tourism” and
are the core component of information through which economic benefits of bicycle tourism in
Arizona are estimated. Although local benefits accrue to locations where events are held from
any participant party that travels to that location from another community, the focus in this
study is on state-level benefits and consequently on out-of-state participants and their travel
parties. When compiling data for such participants, whether by survey or reviewing postings of
event results, the possibility that seasonal residents of Arizona might be listed (or responding to
a survey) based on their permanent address. Seasonal residents will not necessarily have the
same spending patterns as tourists. Future studies can include methods for taking this
possibility into account.
Bicycle tourism spectrum in Arizona
All bicycle tourism activity was categorized as follows:
 Road Bicycling Events. Road bicycling events include races and events that are held
on paved surfaces. These events vary by level of difficulty and type. Some are
competitive races or time trials, but many are social rides or rides for charities. Most
road bicycling events are held during the daytime, but many are held over multiple
days.

23

Heidi Beierle, (Department of Planning, Public Policy & Management, University of Oregon) Bicycle Tourism as a
Rural Economic Development Vehicle, June, 2011, p. 6.

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 Mountain Bike Events. Mountain bike events take place on unpaved or natural
surfaces. Mountain bike events can be leisurely rides or races, including time trial
races. In a time trial race, participants ride multiple laps of a fixed course. Although
most mountain biking events are held during the daytime, there are more nighttime
events for mountain bikes than for any other type of event.
 Bicycle Tours. Events categorized as Bicycle Tours are those that are hosted by
touring companies that take tourists of all levels, ages, and abilities on rides as part
of a vacation package or training. Most of these rides are for multiple days and vary
by degree of difficulty; some are family friendly, while others are geared towards
more competitive cyclists. Some of these tours specialize in training cyclists for
major rides, such as the Tour de Tucson. Most of bike-operator tours provide inn
accommodations and meals in their package price. These particular tours were
selected as part of this inventory because they are notable and are focused solely on
bicycling. These tours may pass or stop at historical or cultural sites, but the main
experience for the tour is the bicycling.
 Triathlon and Duathlon Events. Triathlon and duathlon events are mostly held
during the daytime for a single day. In addition to the traditional triathlons, some
triathlons and duathlons are held indoors. Duathlon events were only inventoried if
one of the legs included bicycling. As with the road and mountain biking types of
events, these events include both qualifying races and less competitive rides.
 Cyclo-cross. Cyclo-cross is a form of bicycle racing, generally consisting of a number
of laps of a short course featuring pavement, wooded trails, grass, steep hills and
obstacles requiring riders to quickly dismount, carry their bikes while navigating the
obstruction and then remount. Cyclo-cross bicycles are similar to racing bicycles
(e.g., lightweight, narrow tires, and drop handlebars), but also share characteristics
with mountain bikes such as knobby tread tires and cantilever style brakes for
clearance needed due to muddy conditions.
 Training Camps. Arizona, primarily the greater Tucson area, is home to several elite
bicycle and triathlete training camps. These multi-day residence camps—operated
during the spring and late fall months--provide coach-directed road training and
nutrition-based meals. In addition, southern Arizona has become a training venue
for world-class professional cycling teams.
 BMX (bicycle motocross) racing also occurs in Arizona, and while this category of
bicycling is, in the context of this study, a “fringe” activity with little direct
connection to bicycle infrastructure for which ADOT would be responsible, BMX
events do involve bicycles, even if these are very specialized, and BMX events attract

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participants from outside Arizona. BMX event riders use specialized tracks
(sometimes indoors) and typically do not rely on on-street infrastructure. For these
reasons, BMX events are included only selectively in the event tallies discussed
below, as noted.
Data pertaining to the BMX Winter and Southwest National events were retrieved from the USA
BMX (The American Bicycle Association) website (http://www.usabmx.com). BMX tracks in
Arizona, according to available data, are found in the cities of Phoenix, Tucson, Surprise,
Chandler, Goodyear, Yuma, and Bullhead City, and a track is expected to be developed in Mesa.
A major BMX organization, USA BMX, has its offices in Gilbert, Arizona.
Bicycle tourism coverage—link with survey data
When the on-line survey instruments for out-of-state participants were designed for this study,
the working assumption was that these people would be contacted by the event sponsors via
the sponsors’ e-mailing lists, so that the potential respondents would be screened for the
desired cohort – that is, the out-of-state event attendees. In practice, the notice of the survey
was publicly posted on one or more organizational websites (by others acting independently),
with the emphasis on out-of-state participants somewhat obscured in the process.
Consequently, both in-state and out-of-state bicyclists responded to the survey. Nevertheless,
the latter group consisted of 132 questionnaire respondents through the online survey system,
approximately half of which submitted usable expenditure information, and most of the other
travel characteristics described below are based on usable responses from 50 to 65 percent of
respondents. Some interpretation was applied to the expenditure results, given the nature of
some of the response patterns.24
While this report refers to this group as “event participants,” the fact that the notice of the
survey became public means that out-of-state respondents who were individually touring could
also have participated. In the questionnaire, respondents were asked, “How did you become
aware of the event?” Of the 127 respondents who answered this question (a very high response
rate for an open-ended question, and an (informal) indicator of the level of interest in Arizona

24

For example, although the expenditure question asked about “per day” expenditures, submitted transportation
costs were modified in some cases, because they appeared to reflect total transportation costs for the entire trip,
not per day. Also, the nature of open-end responses to most of the “other expenditure” category entries suggested
that these items were “per trip” rather than per day expenditures. Since some respondents provided follow-up
contact information, the consulting team contacted some of the respondents with these ambiguous figures and
obtained some level of confirmation on these assumptions. Some assumptions had to be applied to the trip-length
answer (among available options) of “5 or more days.” In all of these cases, conservative factors were applied in
order to produce calculations from certain imprecise or somewhat ambiguous data entries.

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bicycling by this group), only seven stated that they did not come for a specific event. Another
survey question asked respondents, “Was the bicycling event in which you participated the
primary purpose of your visiting Arizona?” Seventy-two percent answered “yes” to this
question. The most frequently listed alternative reason for visiting was “vacation/leisure”
(although 13 of the 30 respondents in the “no” category did not provide additional information
about their trip purpose). These results would tend to confirm that event participants were in
fact well represented in the survey.25 In this section of the report, the term “participant” is
intended to represent the group of respondents to this survey, although some respondents
might be independent travelers.
“Economic footprint” of bicycle tourism—methods and approach
Understanding economic impacts of bicycle tourism
Preparing estimates of economic benefits from out-of-state event participants is primarily a
matter of applying expenditure data obtained through the participant survey to the Redyn
modeling system. Because the expenditure data were available by the following categories,
expenditures were input to the model according to their corresponding industry categories.
Retail sales figures were adjusted to reflect the fact that only the margins are applicable to local
impacts, which more accurately and conservatively expresses the multiplier effects of these
purchases than the full retail sales figures.
 Lodging/camping
 Restaurant and grocery
 Transportation (in-state, including fuel)
 Purchases of bicycle-related goods (expenditures in this category were excluded
from the I-O analysis for Bicycle Tourism to avoid double-counting, because retail
bicycle-related sales were addressed in the Retail portion of the analysis)
 Recreation/tour's/entrance fees, etc.
 Other (respondents were asked to list)
Travel related expenditures varied somewhat among regions, according to the participant
responses received.

25

Of course, some of the “no” respondents could still be event participants, and some who answered “yes” could
have been independent travelers who interpreted this question differently. In any case, future survey instruments
could be adjusted to more directly address this issue.

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Findings: secondary and primary data
Because the participant survey notice became unexpectedly public (see Page 3.43, above), both
in the state and out-of-state cyclists responded to the survey. Of the 224 surveys received, 41%
were from Arizona residents. The in-state responses were not analyzed for this report.
Overview of events/tours/training camps
Below is a summary of the inventory of bicycling events held in 2012 or that are expected to be
held in 2013 throughout Arizona. Note that data for tours reflect the number of programs, and
individual programs typically have more than one event per year. Data were also obtained on
two “national scale” BMX events that occur in the state: the Winter Nationals and Southwest
Nationals, which have been held in Phoenix and Tucson respectively (although both are
scheduled for 2013, only one was held in 2012, and this report includes data for the single
event, in the I-O table of impacts only).
 199 total events/tour programs (not counting multiple tour events)
o 76 Road Bike Events
o 28 Mountain Bike Events
o 16 Cyclo-cross Bike Events
o 19 Guided Touring Programs (most with multiple events)
o 60 Triathlon/Duathlon Events
 Event Times (for events for which data are compiled)
o 129 daytime events
o 4 night events
o 2 day and night events
o 42 multi-day events
Figure 3-9 (below) depicts the percentage of each event type.

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Figure 3-9. Percent of events by type, Arizona
45%

38.2%

40%
35%

30.2%

30%
25%
20%

14.1%

15%
10%

9.5%

8.0%

5%
0%
Cyclo-cross

Road Bike

Mountain Bike

Tours

Triathlon

Source: Report authors’ bicycle event inventory.

Distribution of events/tours
The location and time-of-year data were used to analyze how events are distributed throughout
the State. The distribution analysis contributes to an understanding of when and where money
is coming into the state from bicycling events, which can inform how these bicycling events
impact regional as well as overall state economics.
Geographic distribution of events/tours
The inventory of events includes the location of the event (i.e. the city, town, park, etc.).
However, since most events cross multiple jurisdictions, a regional level of analysis was
considered more appropriate for determining the distribution of the events. (The regional level
also fit best with the data limitations of this study.) For this study, the Arizona regions are
consistent with those used by the Arizona Office of Tourism (AOT), details of which can be
found in Section 3.5.1.
Table 3-15 (below) depicts how many events are held in each of the five regions. Additionally,
Figure 3-10 (below) visually summarizes the data. The table and figure show that in the Tucson
& Southern region there are almost as many road bike events than there are other types of
events combined. The Phoenix & Central and Western regions have more triathlon events than
any other single type. The North Central region is the only region that has as many mountain

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bike events as other types of bicycling events combined. Figure 3-10 (below) shows that most
events are held in the Phoenix & Central region, followed by the Tucson & Southern region.

Event Type
Cyclo-cross
Road Bike
Mountain Bike
Tour Programs
Triathlon
Total

Table 3-15. Geographic distribution of events
Phoenix
Tucson &
North
&
Northern
Western
Southern
Central
Central
11
2
2
1
0
29
28
10
3
0
7
6
6
8
1
2
11
5
0
0
33
11
8
4
4
82
58
31
16
5

Multiple
regions

AZ
Total

0
6
0
1
0
7

16
76
28
19
60
199

Source: Report authors’ bicycle event inventory.
NOTE: Not included in this table are three road bike cross-state events and one touring cross-state event (these are
cross-country events and the information stated that the events ride through Arizona; no specific region was
identified), two road bike events that span the Southern and Central regions, and one road bike event that spans
the Northern and North Central regions.

Figure 3-10. Percent of events by region
35
30
25
Cyclo-cross
20

Road Bike

15

Mountain Bike

10

Tours
Triathlon

5
0
Phoenix &
Central

Tucson &
Southern

Northern North Central West Coast
Source: Table 3-15

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Seasonal distribution of events
The seasonal distribution of events was also examined, based on the event database. There are
more events by time of year than listed in the inventory because some events are held multiple
times throughout the year in different seasons. When an event is held in multiple seasons, it
was counted multiple times to account for its presence in all relevant seasons.
Figure 3-11 (below) shows the percent of events by season by type of event. All event types
experience two peaks throughout the year: one in the spring and another in the fall. With the
exception of mountain biking events, all of the events experience a higher peak in the spring.
Mountain bike events have a higher peak in the fall. All events take a rest in the summer
season. In the spring, nearly 16 percent of all events are road bike events. In the summer, that
drops to about 5 percent. Another interesting observation is that, for the tours in this database,
tours drop to an almost non-existent level in the hot summer months, suggesting that tours
operating in more temperate areas of the state are few in number or otherwise
underrepresented in the database. Very few events are held year- round.

Figure 3-11. Type of event by time of year, season
35%
30%
25%

20%
15%
10%
5%
0%
Winter
Road Bike

Spring
Mountain Bike

Summer
Tours

Fall
Triathlon

Year Round
Cyclo-Cross

Source: Report authors’ bicycle event inventory.

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The number of events held in each region seasonally was also analyzed. Figure 3-12 (below)
illustrates the percentage of total events held in each region for each season. As expected, the
number of events in the Phoenix & Central, Southern, and North Central regions declines during
the summer season. In contrast, in the northern and western regions where summers are more
temperate, bicycling event activity peaks in the summer. Similarly, the Northern region has
cold, snowy winters, and therefore, as expected, there are very few events held in the winter.
Additionally, Figure 3-12 (below) shows the small component of year-round events, by the
three regions that host such events.
Figure 3-12 . Region events by time of year, season
40%
35%
30%
West Coast
25%

North Central
Northern

20%

Tucson & Southern
15%

Phoenix & Central

10%
5%
0%
Winter

Spring

Summer

Fall

Year Round

Source: Report authors’ bicycle event inventory.

Data could be obtained on two “national scale” BMX events that occur in the state: the Winter
Nationals and Southwest Nationals, which have been held in Phoenix and Tucson respectively
(although both are scheduled for 2013, only one was held in 2012, and this report includes data
in the I-O modeling process for the single event).
Figure 3-13 (below) shows the number of Arizona and non-Arizona participants, by region, for
2012.

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Figure 3-13. Participants by AZ/non-AZ residency, by region, 2012
25,000
20,000
15,000
10,000
5,000
North Central

Northern

AZ Residents

Phoenix-Central Tucson-Southern

West Coast

Non-AZ residents

Source: Report authors’ bicycle event analysis.

Event Participants: Expenditures and characteristics
The expenditure results for event participants shown in Table 3-16 and Table 3-17 (below) are
based on calculations involving only out-of-state event participants who responded to the
survey. Note that the average of total expenditures (per person per day or per trip) is based on
the sum of expenditures, line item (individual travel party) by line item, divided by the number
of respondents who reported at least one expense (68 respondents). This method was also
used to calculate the averages for the individual expense categories, as well, in an attempt to
make the results more consistent and provide a more accurate average of overall eventparticipant spending patterns. The data indicate substantial levels of spending, especially
compared to data for tourist spending compiled by AOT (see following section). Regionally, the
Tucson & Southern region is on the higher end of the spectrum ($353) in terms of per person
per day spending, while the West Coast ($95) and North Central ($148) regions are on the lower
end.
The expenditure results underscore the need for reviewing this portion of future surveys of
participants, and finding ways to obtain responses that are as reliable as possible. For example,
it is possible that the visiting bicyclist parties are less “household oriented” than a typical tourist
party, and consequently having more unrelated people with individual lodging. The 70
respondents to the question about number of people in travel party, which represented 173
attendees, reported having only 13 children under the age of 18.

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Just under half of respondents stated their trip length was “five or more nights.” 26 Twenty-four
percent reported that they stayed with friends or family as their primary means of
accommodation. Respondents were a relatively affluent and well-educated group: 45% had
annual household incomes of $105,000 or more, and 45% held advanced degrees.
The regional breakdowns in the tables are based on the answer to the question, “In what city
did you stay most of the time during [the visit being reported about].” The data show that a
disproportionate share came from people staying in the Tucson area, which could in part reflect
the popularity of the Tour de Tucson with out-of-state visitors.

26

Future surveys will need to take this finding into account and adjust the options for answering this question
accordingly. In general, the questionnaire was structured to mirror the questions typically included in surveys of
visitors sponsored by the Arizona Office of Tourism.

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Table 3-16. Average per day expenditures (per person) by category and expenditure total

North Central
Northern
Phoenix &
Central
Tucson &
Southern
West Coast
AZ Total

Respondents
Transportation Bicyclewho
Event Lodging/ Restaurant/
Recreation/tour's/
Other
Total
(including
related
identified
fees
camping
Grocery
entrance fees, etc. expenses expenses
fuel)
goods
one or more
expenses
(insufficient data)
$6.33
$71.81
$49.88
$44.48 $15.83
$12.22
$15.83 $203.43
12
$10.33

$63.85

$53.49

$26.75

$26.67

$10.98

$27.38

$189.22

21

$43.88

$71.06

$49.20

$40.69 $103.50

$37.83

$36.83

$352.63

30

$49.60

(insufficient data)
$35.35 $57.98

$23.78

$27.50

$260.01

68

$23.73

$67.74

Source: Event Participant Questionnaire (2012)

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Table 3-17. Average per trip expenditures (per person) by category and expenditure total
Average
Event
length
fees
of stay

Respondents
Recreation/
Transport BicycleAvg.
who
Lodging/ Restaurant/
tour's/
Other
Total
(including related
party identified
camping
Grocery
entrance
expenses expenses
fuel)
goods
size one or more
fees, etc.
expenses

N. Central

(insufficient data)

Northern
Phoenix &
Central
Tucson &
Southern
West Coast

3.47

$6.33

$215.56

$157.90

$94.13

$15.83

$12.22

$15.83

$517.80

2.8

12

4.12 $10.33

$244.15

$226.07

$111.22

$26.67

$10.98

$27.38

$656.79

2.3

21

4.07 $43.38

$267.75

$182.30

$122.58 $103.50
$37.83
(insufficient data)

$36.83

$794.18

2.9

30

Arizona

4.00 $23.50

$251.19

$190.24

$109.08

$27.50

$683.28

2.6

68

$57.98

$23.78

Source: Event Participant Questionnaire (2012)

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Event participant comparisons with visitor data from the Office of Tourism
In this section, data from the survey of event participants are compared to similar traveler data
from the Arizona Office of Tourism.27 28
Spending
Overall, the event participants reported considerably higher travel expenditures than are
shown in AOT reports for traveler segments that are reasonably comparable to the event
participants (see footnote referenced above). For example, the participant figures for the
Phoenix and Tucson regions, where the sample sizes are largest, are essentially double those
reported in the AOT/LI data, and this difference generally applies across all the expenditure
categories. This result raises the question of whether other differences between the two groups
might explain this kind of expenditure difference. Available data on demographic and travel
characteristics (age, income, accommodations, and travel mode) for the two groups are
discussed in the following sections.
In summary, the comparisons indicate that the participants, compared to the AOT/LI figures: 1)
are slightly older (this applies to the actual respondents, not necessarily the composition of the
travel party; however there were few children under the age of 18 in the participants’ travel
parties), 2) had considerably higher incomes, 3) had similar accommodation patterns, and 4)
used private autos to a greater degree. Although comparable data on education are not
available for the AOT/LI dataset, the participant group is highly educated. While these findings
suggest that participant expenditures could be expected to be somewhat higher, limitations in
sample size, the possibility that survey respondents could be overstating their expenditures
(respondents were aware of the purpose of the study and might not have the same level of
objectivity as a typical tourist), and a bias to err on the side of conservatism have resulted in the
consultants lowering the expenditures levels by 30 percent for input to the I-O model.

27

Demographic and (non-budgetary) travel characteristics provided by AOT (sourced from Longwoods
International) are from detailed tables provided by AOT to the consultant team that are the basis of the AOT flyer
“2011 Arizona Domestic Overnight Visitor Profile, Regional,” and are not directly comparable to the bicycle-event
participant population surveyed in this report, as the data provided by AOT (sourced from Longwoods
International) include both in-state and out-of-state overnight travelers, and business as well as leisure travelers.
Expenditure data from AOT/Longwoods are from a segment more closely aligned with the bicycle event
participants – out-of-state leisure travelers. However, the factors to convert expenditure figures to perperson/per-day values are not specific to this segment but to the same group as the regional profile.
28
The aforementioned reports were provided by AOT and originally sourced from Longwoods International. Future
references to this data source in the text will be attributed to AOT/LI.

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Future studies could incorporate methods to refine the expenditure data from participants, for
example by creating a control testing or focus group to probe how respondents interpret and
react to these types of questions.
Age
Table 3-18 (below) displays data related to median age for event participants (based on 69 total
survey responses) and tourists (data from AOT/LI), by region. The data reveal that, on average,
surveyed event participants have a higher overall median age (49) compared to AOT/LI data
(47). The Northern regions had the oldest median age figures for event participants (64). The
North Central (45) and Phoenix & Central (45) regions had the lowest reported median age
figures.
Table 3-18 Age data comparison
Median age,
Response
survey
count
results
North Central
45.2
5
Northern
64.0
12
Phoenix & Central
45.1
21
Tucson &
Southern
46.3
29
West Coast
53.0
1
Arizona total
49.1
68

Median age,
AOT/LI
51.6
46.1
48
47.8
45
47.2

Source: Event Participant Questionnaire (2012), Longwoods International

Income
Due to differences in household income ranges, direct comparisons between survey and AOT/LI
data were not possible. However, an approximate comparison can be made by comparing the
percentage of event participant respondents that reported a household income above (and
below) $85,000, to AOT/LI percentages of visitors with incomes above (and below) $75,000.
Overall, over 63% of event participant respondents had a median household income greater
than $85,000, while only 44% of AOT/LI respondents had an income over $75,000. The Tucson
& Southern (75%) and Phoenix & Central (70%) regions represented the highest percentage of
respondents with a median household income of at least $85,000. According to the AOT/LI
data, the Phoenix & Central region revealed the highest percentage of respondents (48%) with
a median household income of at least $75,000.

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Education
Overall, the event participant data (below in Table 3-19) show that 38% of respondents have a
Bachelor’s degree and 45% have an Advanced degree, based on 71 responses. For regions that
had more than one response, the North Central (80%) and Phoenix & Central (52%) regions had
the highest percentage of respondents with an Advanced degree.
No comparable data were available in the AOT/LI tables. When compared to the American
Community Survey (ACS) figures for Arizona (for 2011) as shown in Table 3-19 below, the
percentage of event participants with a Bachelor’s or Advanced degree is far greater than the
figures for Arizona residents (for the population 25 years and older).
Table 3-19. Highest level of education completed
High
School
Survey Results
North Central
0.0%
Northern
0.0%
Phoenix &
Central
0.0%
Tucson &
Southern
3.3%
West Coast
0.0%
Arizona total
2.8%
ACS 2011 1-yr estimates (1)
Arizona total

38.90%

Associates
degree

Bachelor's
degree

Advanced
degree

Response
count

20.0%
0.0%

0.0%
76.9%

80.0%
23.1%

5
13

14.3%

33.3%

52.4%

21

20.0%
0.0%
14.3%

33.3%
0.0%
38.6%

43.3%
100.0%
45.7%

30
1
70

34.50%

17.10%

9.50%

Source: Event Participant Questionnaire (2012), American Community Survey 2011 1-Yr estimates
(1) For population 25 years and older.

Accommodation
Table 3-20 (below) shows the distribution of accommodation usage by region for the event
participant and AOT/LI datasets. Overall, the figures between the two datasets are nearly
identical, with the exception of the higher rate of “Campground/RV Park” usage by event
participants. According to the survey data, the North Central (83%) and Northern (73%) regions
yielded the highest percentage of stays in “Hotel/motel/resort” accommodations. Over 36% of
Tucson & Southern region event participants “Stayed with friends and/or family,” a higher
percentage than the Phoenix & Central region (20%).

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Transportation
Due to issues of data compatibility, the “mode of transport” results for the survey and AOT/LI
data are not directly comparable. With this in mind, the data showed a higher percentage for
“Private auto” use by event participants.

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Table 3-20. Accommodation by type and region for survey and AOT datasets
Stayed with
Hotel/
Campground/
Second
friends
Motel/
Other
Total
RV Park
home
and/or
resort
relatives
Event Participant Survey data
North Central
16.7%
83.3%
0.0%
0.0%
100.0%
Northern
18.2%
72.7%
0.0%
9.1%
100.0%
Phoenix & Central
12.0%
64.0%
4.0%
20.0%
100.0%
Tucson & Southern
0.0%
57.6%
6.1%
36.4%
100.0%
West Coast
100.0%
0.0%
0.0%
0.0%
100.0%
Arizona total
9.2%
63.2%
3.9%
23.7%
100.0%
AOT/LI Data
North Central
7.2%
62.8%
4.9%
20.0%
5.1% 100.0%
Northern
8.2%
65.1%
2.8%
18.6%
5.3% 100.0%
Phoenix & Central
1.9%
57.5%
4.1%
28.7%
7.8% 100.0%
Tucson & Southern
3.4%
57.0%
1.2%
30.4%
8.0% 100.0%
West Coast
4.9%
68.9%
3.7%
16.7%
5.9% 100.0%
Arizona total
4.8%
63.6%
3.5%
22.0%
6.2% 100.0%

No. of
responses

Source: Event Participant Questionnaire (2012), Longwoods International

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6
11
25
33
1
76

Regional comparison with bicycle-specific economic base
Figure 3-14 shows the proportion of Arizona employment in each region, in 18 non-retail
industries with a potential connection to bicycling. These industries represent about 38,000
employees statewide. (Retail categories were excluded because they tend to overshadow the
other industry figures combined, retail distribution by region is addressed elsewhere in this
report, and this compilation was thought to be worthy of assembling.29 (Figures are
approximate because actual data are not available for all industries in all counties.) When
compared to the distribution of events by region, Figure 3-10, it is clear that, with the exception
of the West Coast, events are much more evenly distributed throughout the state than the
underlying economic activity.
Figure 3-14. Regional share of Arizona employment in all non-retail industries with a potential
connection to bicycling

5% 6%

5%

15%

Northern
North Central
Phoenix & Central
Tucson & Southern

69%

West Coast

Source: EMSI employment data for Arizona counties, 2011.

Findings: Direct and indirect impacts (from I-O model)
Economic effects of bicycle tourism are organized under four categories:
1. Road-bicycle, cyclo-cross, and touring (non-commercial) events
2. Mountain bike events

29

In fact, the regional figures with retail included are similar on a percentage basis to those shown in Figure 3-14.

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3. Triathlon and duathlon events
4. BMX, training camps, and commercial tours
Category 1. Road-bicycle, cyclo-cross, and touring (non-commercial) events
Estimated economic effects for this category of events are shown in Table 3-21 and Table 3-22.
The first table shows the industry categories through which the Redyn model performs the
estimates, by the Redyn model output categories listed below. The inputs generated for the
model are described in preceding sections on the results of the analysis of event participant
data, and are also based on additional detail on attendees at this category of event and that
yield an estimated number of out-of-state participants totaling 5,200. The second table shows
the total and direct (as a sub-set of the total) effects, for the following Redyn model output
categories:
Jobs
Total output
Disposable income, and
Labor income

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Table 3-21. Detail of direct effects for road-bicycle, cyclo-cross, and touring (non-commercial) events, Arizona
Direct effects ($2013, 000s)
Industry
NAICS
Jobs
Wage bill
Output
Disposable Income
Grocery stores
445
3.21
$127.725
$276.993
$136.328
Gasoline stations
447
2.55
$98.163
$212.870
$105.963
Sporting goods stores
45111
0.00
$0.000
$0.000
$0.000
Department stores
452
5.28
$207.560
$450.098
$222.573
Tour Operators
56152
0.00
$0.000
$0.000
$0.000
Sports & Recreation Instruction
61162
0.00
$0.000
$0.000
$0.000
Promoters-Per. Arts, Sports & Events w/o facilities
71132
8.34
$193.892
$342.139
$206.690
Amusement, gambling & recreation industries
713 30.52
$577.093 $1,296.847
$619.736
Travelers accommodation
7211 30.67 $1,080.112 $2,737.398
$1,157.689
Restaurants & other eating places
7225 20.20
$443.233
$999.973
$473.088
Total
100.77 $2,727.778 $6,316.318
$2,922.066
Source: Authors; Redyn model

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Table 3-22. Economic effects of road-bicycle, cyclo-cross, and touring (non-commercial)
events, Arizona
Total
Direct
Effects
Effects
Jobs
North Central
8
6
Northern
3
3
Phoenix & Central
37
21
Tucson & Southern
113
70
West Coast
1
Arizona Statewide Total
162
101
$2013 (000s)
Output
North Central
Northern
Phoenix & Central
Tucson & Southern
West Coast
Arizona Statewide Total

$447.34
$212.15
$3,829.56
$6,939.65
$64.90
$11,493.59

$2013 (000s)
Disposable income
North Central
Northern
Phoenix & Central
Tucson & Southern
West Coast
Arizona Statewide Total

$261.43
$100.21
$1,594.92
$3,272.72
$28.25
$5,257.54

$2013 (000s)
Labor income
North Central
Northern
Phoenix & Central
Tucson & Southern
West Coast
Arizona Statewide Total

$200.24
$94.66
$1,618.82
$3,012.60
$27.64
$4,953.96

$335.69
$149.19
$1,625.59
$4,205.85
$6,316.32

$188.86
$68.08
$693.61
$1,971.52
$2,922.07

$144.66
$64.30
$704.00
$1,814.82
$2,727.78

Source: Authors; Redyn model

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Category 2. Mountain bike events
Estimated economic effects for this category of events are shown in Table 3-23 and Table 3-24.
The first table shows the industry categories through which the Redyn model performs the
estimates, by the Redyn model output categories of total output, wages, jobs, disposable
income, and labor income. The inputs generated for the model are described in preceding
sections on the results of the analysis of event participant data, and are also based on
additional detail on attendees at this category of event and that yield an estimated number of
out-of-state participants totaling 2,000. The second table shows the total and direct (as a subset of the total) effects, for the following Redyn model output categories:
Jobs
Total output
Disposable income, and
Labor income
Table 3-23. Detail of direct effects, mountain bike events, Arizona
Direct effects ($2013, 000s)
Disposable
Industry
NAICS Jobs Wage bill
Output
Income
Grocery stores
445 1.27
$37.020
$80.260
$41.001
Gasoline stations
447 1.09
$31.050
$67.314
$34.678
Sporting goods stores
45111 0.00
$0.000
$0.000
$0.000
Department stores
452 1.98
$57.620
$124.950
$63.741
Tour Operators
56152 0.00
$0.000
$0.000
$0.000
Sports & Recreation Instruction
61162 0.00
$0.000
$0.000
$0.000
Promoters-Per. Arts, Sports &
71132 3.18
$57.900
$102.156
$64.223
Events w/o facilities
Amusement, gambling &
713 11.54 $155.640
$349.769
$171.937
recreation industries
Travelers accommodation
7211 13.13 $320.840
$813.134
$356.341
Restaurants & other eating
7225 7.92 $128.430
$289.749
$142.245
places
Total
40.11 $788.500 $1,827.332
$874.166
Source: Authors; Redyn model

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Table 3-24. Economic effects of mountain bike events, Arizona
Total
Direct
Effects
Effects
Jobs
North Central
16
13
Northern
5
4
Phoenix & Central
7
4
Tucson & Southern
31
19
West Coast
0
Arizona Statewide Total
60
40
$2013 (000s)
Output
North Central
Northern
Phoenix & Central
Tucson & Southern
West Coast
Arizona Statewide Total

$856.67
$320.55
$764.34
$1,846.75
$26.39
$3,814.70

$2013 (000s)
Disposable income
North Central
Northern
Phoenix & Central
Tucson & Southern
West Coast
Arizona Statewide Total

$462.05
$142.27
$321.29
$874.53
$11.91
$1,812.04

$2013 (000s)
Labor income
North Central
Northern
Phoenix & Central
Tucson & Southern
West Coast
Arizona Statewide Total

$374.75
$140.30
$322.79
$801.83
$11.59
$1,651.26

$648.99
$250.43
$318.71
$1,147.05
$2,365.19

$345.04
$109.52
$137.34
$539.56
$1,131.47

$279.67
$107.92
$138.04
$494.95
$1,020.59

Source: Authors; Redyn model

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Category 3. Triathlons/Duathlons
Estimated economic effects of triathlon/duathlon events are shown below in Table 3-25 and
Table 3-26. The first table shows the industry categories through which the Redyn model
performs the estimates, by the Redyn model output categories of total output, wages, jobs,
disposable income, and labor income. The inputs generated for the model are described in
preceding sections on the results of the analysis of event participant data, and are also based
on additional detail on attendees at this category of event and that yield an estimated number
of out-of-state participants totaling 5,400. The second table shows the total and direct (as a
sub-set of the total) effects, for the following Redyn model output categories:
Jobs
Total output
Disposable income, and
Labor income
Table 3-25. Detail of direct effects, triathlons/duathlons, Arizona
Direct effects ($2013, 000s)
Industry
Disposable
NAICS Jobs
Wage bill
Output
Income
Grocery stores
445 2.91
$126.004
$273.221
$126.697
Gasoline stations
447 1.89
$78.877
$171.032
$80.153
Sporting goods stores
45111 0.00
$0.000
$0.000
$0.000
Department stores
452 4.02
$172.716
$374.569
$174.798
Tour Operators
56152 0.00
$0.000
$0.000
$0.000
Sports & Recreation Instruction
61162 0.00
$0.000
$0.000
$0.000
Promoters-Per. Arts, Sports & Events
71132 7.03
$202.344
$357.034
$203.111
w/o facilities
Amusement, gambling & recreation
713 21.36
$443.259
$996.061
$450.076
industries
Travelers accommodation
7211 27.05
$985.884 $2,498.576
$994.841
Restaurants & other eating places
7225 18.79
$437.176
$986.359
$439.589
Total
83.05 $2,446.260 $5,656.853
$2,469.265
Source: Authors; Redyn model

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Table 3-26. Economic effects of triathlons/duathlons, Arizona
Total
Direct
Effects
Effects
Jobs
North Central
2
1
Northern
5
5
Phoenix & Central
81
48
Tucson & Southern
30
19
West Coast
14
11
Arizona Statewide Total
133
83
$2013 (000s)
Output
North Central
Northern
Phoenix & Central
Tucson & Southern
West Coast
Arizona Statewide Total

$100.60
$341.14
$8,364.76
$1,865.31
$797.00
$11,468.81

$62.87
$269.61
$3,621.73
$1,137.39
$565.25
$5,656.85

$2013 (000s)
Disposable income
North Central
Northern
Phoenix & Central
Tucson & Southern
West Coast
Arizona Statewide Total

$68.46
$152.19
$3,467.47
$902.80
$328.50
$4,919.42

$42.26
$116.86
$1,538.12
$537.38
$234.64
$2,469.26

$2013 (000s)
Labor income
North Central
Northern
Phoenix & Central
Tucson & Southern
West Coast
Arizona Statewide Total

$43.89
$151.32
$3,536.00
$824.52
$341.14
$4,896.87

$27.09
$116.19
$1,568.52
$490.79
$243.67
$2,446.26

Source: Authors; Redyn model

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Category 4. BMX, training camps, and commercial tours
Estimated economic effects for this category of events are shown in Table 3-27 and Table 3-28.
The first table shows the industry categories through which the Redyn model performs the
estimates, by the Redyn model output categories of total output, wages, jobs, disposable
income, and labor income. The inputs generated for the model are described in preceding
sections on the results of the analysis of event participant data, and are also based on
additional detail on attendees at this category of event and that yield an estimated number of
out-of-state participants totaling 1,300. The second table shows the total and direct (as a subset of the total) effects, for the following Redyn model output categories:
Jobs
Total output
Disposable income, and
Labor income
Table 3-27. Detail of direct effects, BMX, training camps, and commercial tours, Arizona
Direct effects ($2013, 000s)
Industry
Disposable
NAICS Jobs Wage bill
Output
Income
Grocery stores
445 0.85
$33.601
$72.878
$35.616
Gasoline stations
447 0.20
$8.426
$18.269
$8.705
Sporting goods stores
45111 1.02
$39.050
$84.668
$42.356
Department stores
452 0.53
$22.327
$48.402
$23.174
Tour Operators
56152 3.53 $170.607
$498.855
$185.049
Sports & Recreation Instruction
61162 8.24 $241.174
$257.724
$261.591
Promoters-Per. Arts, Sports &
71132 0.45
$13.707
$24.189
$13.501
Events w/o facilities
Amusement, gambling &
713 1.68
$36.461
$81.926
$36.978
recreation industries
Travelers accommodation
7211 7.82 $279.499
$708.350
$297.040
Restaurants & other eating
7225 5.34 $116.620
$263.099
$123.612
places
Total
29.66 $961.473 $2,058.360 $1,027.622
Source: Authors; Redyn model

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Table 3-28. Economic effects of BMX, training camps, and commercial tours, Arizona
Total
Direct
Effects
Effects
Jobs
North Central
0
Northern
0
Phoenix & Central
8
5
Tucson & Southern
40
25
West Coast
0
Arizona Statewide Total
49
30
$2013 (000s)
Output
North Central
Northern
Phoenix & Central
Tucson & Southern
West Coast
Arizona Statewide Total

$23.88
$15.31
$815.80
$2,898.73
$21.10
$3,774.83

$2013 (000s)
Disposable income
North Central
Northern
Phoenix & Central
Tucson & Southern
West Coast
Arizona Statewide Total

$20.37
$9.38
$339.69
$1,482.03
$9.19
$1,860.66

$2013 (000s)
Labor income
North Central
Northern
Phoenix & Central
Tucson & Southern
West Coast
Arizona Statewide Total

$11.84
$7.24
$344.87
$1,366.36
$9.02
$1,739.33

$353.22
$1,705.14
$2,058.36

$150.68
$876.94
$1,027.62

$152.98
$808.50
$961.47

Source: Authors; Redyn model

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Summation of economic benefits from bicycle tourism
The sum of the Economic Effects tables for all four categories of bicycle tourism assessed for
this report (in preceding tables, and including: road bicycle, mountain bike, cyclo-cross,
triathlons/duathlons, and BMX, camps, and commercial tours), is shown in Table 3-29 (below).

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Table 3-29. Economic effects of the preceding four categories of bicycle tourism combined,
Arizona
Combined,
Combined,
Total
Direct
Effects
Effects
Jobs
North Central
26
20
Northern
14
11
Phoenix & Central
134
78
Tucson & Southern
214
134
West Coast
16
11
Arizona Statewide Total
404
254
$2013 (000s)
Output
North Central
Northern
Phoenix & Central
Tucson & Southern
West Coast
Arizona Statewide Total

$1,428.48
$889.15
$13,774.46
$13,550.44
$909.38
$30,551.92

$1,047.55
$669.24
$5,919.26
$8,195.42
$565.25
$16,396.72

$2013 (000s)
Disposable income
North Central
Northern
Phoenix & Central
Tucson & Southern
West Coast
Arizona Statewide Total

$812.30
$404.05
$5,723.38
$6,532.08
$377.84
$13,849.66

$576.16
$294.46
$2,519.76
$3,925.40
$234.64
$7,550.42

$2013 (000s)
Labor income
North Central
Northern
Phoenix & Central
Tucson & Southern
West Coast
Arizona Statewide Total

$630.73
$393.51
$5,822.47
$6,005.30
$389.40
$13,241.42

$451.41
$288.42
$2,563.54
$3,609.05
$243.67
$7,156.10

Source: Authors; Redyn model

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Description and general assessment of non-quantifiable benefit categories
Although the Team was able to obtain some information on elite (professional/amateur) racing
teams, other specific information on product promotions for instance could not be
documented, although the Team is aware of such activities through anecdotal evidence.
Discussions of how to add to the information about this type of activity appear in the Plan
Update section.

3.6 Plan for periodic updating of the study
3.6.1 General structural considerations
The rationale for periodically updating this baseline study is to better understand the
dimensions of growth and other change in Arizona’s bicycle-related industry as well as the
comparative opportunities in bicycle-related tourism. Periodic updating also helps assess the
effectiveness of public policies and private sector efforts pertaining to bicycling in Arizona.
Updating the economic impact analysis of bicycling in Arizona presents similar challenges as
conducting this inaugural study. Significant issues for updating the report could be grouped
under what, who, how and when? What data and methods are required? Who should assume
the on-going responsibility in monitoring bicycle-related business and event activity in Arizona?
How should the work be accomplished and under what time frame? Finally, what would be the
cost associated with the update?
To the extent possible, research methods and approaches used in the inaugural report will need
to be replicated in updating the report, particularly if the intent is to “re-benchmark” the
analysis. The most useful secondary data sources identified in this report, those that
contributed to building the inventory, represent the starting point for updating the information
base for this study. These will play a key role in updating the “universe” of bicycle-related
businesses and bicycle events occurring during the year. As with this original report,
crosschecks from other secondary sources along with informed experts are desirable for both
bicycle-related business and bicycle event databases.
Future efforts will require adherence to the following:
 A recognition of the full range of industry coverage, consistent with the group of
bicycle-related industries identified from the NAICS;
 A data collection plan for bicycle-related industry establishments and bicyclerelated events in Arizona that is at the least consistent with the original effort,
and at best takes advantage of the kinds of institutionalized database

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management concepts described below, for example leading to a calendar of
bicycle-related events in Arizona; and
 A comparable methodology and analytical approach consistent with this
inaugural study for integration into the survey databases and computational
models.

Furthermore, it is assumed that overall responsibility for these periodic updates will occur
within the same organizational context of the ADOT Pedestrian and Bicycle Program.
Concomitantly, it is advisable for ADOT to keep in place a working technical advisory group
consisting of state and local agencies, tribal governments, and leading state bicycle coalition
groups, which might also become involved in monitoring bicycle business activity and bicycle
events in Arizona.
Comparable methods and analytical approaches (including application of the same economic
impact model) should be maintained in future updates of this study. A third-party independent
research and analysis organization should be contracted to conduct such an updated economic
impact analysis study. Depending on the rate of the state's growth, and indications of the
growth of bicycling at a national level (or in Arizona if the data-monitoring processes exist to
track this), the frequency of such an update could vary between five and 10 years.
The survey databases and computational models generated for this report, and which will be
turned over to ADOT at the conclusion of the study (stripped of firm-level data to ensure
confidentiality), have been created with future updating in mind. This system, however, does
require a comparable methodology and survey structure to ensure compatibility within the
existing system. Future survey results can be easily integrated into the existing system, which
automates variable summarization and integration into the computational model and other
analysis components. Secondary data used in the computational models, which act as
calibrating factors for our primary data, will also need to be refreshed for future updates. If this
system is to be used, a data file package and a more detailed manual of usage and updating
instructions can be provided.
There are a few refinements that could be made to the methodology and incorporated into an
update process, even if this means deviating from the original format:
 The survey of event sponsors focused on obtaining data about participants, and
for the sake of minimizing the burden on responding organizations (and thereby
encourage responses), plus the fact that events can involve a number of
volunteers, the survey of event sponsors did not ask about data for the

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organization itself, as a business. However, there are a few event sponsors that
have substantial levels of employment, and by proportioning their employees to
their out-of-state participants, some employees could be assigned to “export,”
that is, money-importing, status. This concept of course is highly dependent on
convincing event sponsors to actually respond to the survey. In this study, these
organizations were very underrepresented in survey responses.
 The survey of participants needs to be more controlled or else adaptable to the
condition, as in this study, where the survey became public to Arizona residents
as well as the targeted out-of-state participants.
Addressing data challenges
There are a number of features of this study that underscore the challenges in periodically
updating this report. One of the most significant challenges is monitoring bicycle-related
business activity within Arizona. All industries undergo a dynamic process called churning, with
its various member businesses opening, closing, expanding or contracting in any given calendar
year. As a result, the scope and scale of each industry—including the bicycle-related industry—
will be different each year. Experience with this report has shown that bicycle shops, in
particular, could be prone to this type of churning.
Similarly, Arizona’s calendar of bicycle events will undoubtedly change and hence will require
monitoring. There are alternative means by which to accomplish this. One way is to establish a
coordinating body involving the various Arizona bicycle organizations, and designating some
group such as the Coalition for Arizona Bicyclists—or perhaps establishing a separate group—to
serve as an umbrella organization expressly for purposes of keeping this kind of information
current. For example, the group could serve as a clearinghouse for maintaining an overall
annual calendar of Arizona bicycle events. To make this list as effective as possible, a uniform
reporting format would need to be devised and distributed to those organizations that would
be submitting information for the calendar. Specifically for the purposes of updating the
economic study, the reporting format would need to include one or more data variables that
would provide an indication of the extent to which out-of-state participants would be expected
to attend these events.
The same group could perhaps also take on other aspects of monitoring bicycling activities that
are relevant to this study. ADOT could play a central or peripheral role in such an organization.
This current research effort can serve as a means of leveraging relationships with bicycle event
sponsors (and perhaps touring company operators as well) for on-going data collection efforts,
most notably for the following information: (a) events and their schedule; (b) historic and
projected number of participants; (c) place of residence of participants; and (d) entry fees.

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To establish some consensus on concepts for institutionalizing an ongoing database
management/update process (beginning with consensus on whether this concept has any hope
of being realized), a “Bicycle Community Building” meeting could be set up. This might be a
dedicated conference or sessions at some existing regularly held function, such as a statewide
Arizona Bicycle Conference held in prior years.
The Arizona Office of Tourism, which is currently underrepresented in documenting bicycling
activity throughout the state, could be encouraged to take a more active role in data-gathering
as part of an enhanced appreciation of the role of bicycling in tourism, in general. Within this
concept there would appear to be an opportunity for shared, enhanced marketing of bicycling
events in Arizona, which could benefit all the parties involved in this data maintenance project.
What are some cost-effective options for monitoring the dynamic change in bicycle-related
business activity in Arizona? One recommendation is to establish a state agency confidential
data exchange with the Arizona Department of Revenue, Tax Policy and Research Division, to
obtain business counts within each of the twenty bicycle-related industries, at a level of detail
more precise than the generally available six-digit NAICS so that bicycle-specific firms are
counted. If this level of inter-agency cooperation is not obtainable, then a data collection plan
for bicycle-related business establishments is recommended. The umbrella clearinghouse-type
organization described above could potentially play a role in this. Alternatively or in concert
with such a group, public and private personnel throughout the state could be tasked with
monitoring and recording changes to the baseline set of bicycle-related industry establishments
in Arizona. An informal data-gathering organization such as Sweat Magazine could be
encouraged to take on this responsibility and share certain aspects of the information obtained
for the purpose of facilitating the update. ADOT’s Pedestrian and Bicycle Program could assume
various levels of leadership in these business-monitoring efforts.
Future updates could also be improved, and the existing study supplemented, by instituting
methods for obtaining data on categories of bicycling activity that are not readily obtainable.
For example, during the course of this study the research team became aware of the fact that
Arizona is a popular location for organized “corporate level” competitive training, product
testing and promotion, and similar activities which would not necessarily be made public.
Another example is the out-of-state bicycle-using tourist, who might be drawn to the state
primarily for the mountain biking trails, for example, but whose presence is not tracked
because that person is not involved in a specific event or guided tour. This latter case might
only be illuminated by incorporating focused questions, special sampling methods, etc. into
regularly administered surveys of visitors such as those sponsored by the Arizona Office of
Tourism.

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Methods of reaching out to corporate sponsors of bicycling activities might require some trialand-error experimentation. One approach might be to target a sample of corporate prospects
(who might or might not have been identified as having previously come to the state for such
purposes) with special promotional literature about the advantages of hosting bicycling
activities in the state. The literature could encourage them to “check in” when they come to
Arizona and receive some token reward.
While all of the data management concepts mentioned above are described here in reference
to the advantages they offer for updating the economic impacts report, putting any or all of
these methods into effect would provide additional benefits:
 They would expedite efforts to market Arizona to bicyclists nationally and
internationally.
 The databases alone could be used to help inform ADOT and other public policy.
Updating options
The approach to updating the report, and costs associated with any updating concept, will be
heavily influenced by the existence, and effectiveness, of any bicycle-related business and
bicycle event database-compilation systems that are implemented.
Benchmarking with secondary data:
If the database-coordinating bodies recommended above are put in place, updating of
inventories would be a fairly straightforward task. To use these updated inventories for
benchmarking purposes, the following considerations would apply:
 For bicycle shops: Growth rate of bicycle shops (and/or sales, corrected for
inflationary effects, if such data were available) could be compared to the rate of
change in other growth measures such as population, and this could be done by
region.
 For events/tours: Additional activities would have to be categorized in keeping with
the system established for the database update as described above, that is, it would
be necessary to categorize events in terms of their likelihood of having out-of-state
participants. (This type of categorization was undertaken for this report, but only
informally and to help target follow-up inquiries to event organizations.) The growth
rate of events that had meaningful levels of out-of-state participants could then be
compared to other growth measures, by region, similarly to the methods applied to
bicycle shops. These inventory-based indicators could be used to proportionately
increase/decrease (inflation-adjusted) economic effects according to the values of

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the indicators. The most interesting component of this research however would be
the extent to which the indicators show that bicycling activity, in sales or in events, is
changing at a different rate than overall growth in the state or any particular region.
Replicating the original report
The implications of replicating this report are discussed below under two alternative conditions:
1. The database-coordinating bodies recommended above are put in place, and
2. Data-gathering challenges experienced in the preparation of this initial report remain
unchanged.
Under either condition, the preparation of an updated report would be considerably simplified,
based on the following:
 Survey forms and related materials could be largely reused, even if some
modification in the data sought, structuring of questions, etc. was desired. For
example, the inventory of bicycling events revealed that some bicycle shops are
either prime sponsors of or otherwise heavily involved in certain events. For
other shops, the survey of bicycle shops indicated that proprietors were
generally aware of significant levels of purchases by tourists. It would be
interesting to learn in future studies the extent to which shops advertise,
sponsor riders, offer specials, increase inventories, etc. targeting specific events.
 The computational models established to process survey-based information
could be reused in a subsequent report, assuming that the survey materials are
reasonably comparable.
Under the second scenario, with no database coordinating entities in existence, data gathering
would still be simplified to some degree because the various sources used in the original report
will have been documented, and the inventories will provide some measure of the usefulness
of various data sources. If updated databases have been maintained, administering a new
round of surveys will be greatly facilitated.
Although there is no particular reason to believe that new or improved secondary data sources
will be available in the near future, due diligence requires that a review of potential sources be
conducted as part of the update process. This activity would consist primarily of revisiting
original sources from trade organizations, government, and bicycling organizations.

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4 Conclusions and Recommendations
Table 30 shows the combined total for all benefit categories for which the I-O model was used
to generate multiplier effects from expenditures by out-of-state visitors/consumers:
 Event participant spending
 Retail sales to out-of-state customers combined with manufacturing/wholesaling
exports
The table shows the four measures used to gauge the economic effects, including industry
output (sales), income, employment, and disposable income.

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Table 30. Total annual economic effects generated by out-of-state customers/participants
Bicycle
Retail sales and
Total
Tourism
manuf./wholesaling
Jobs
16
26
North Central
42
13
14
Northern
27
221
134
Phoenix & Central
355
54
214
Tucson & Southern
268
13
16
West Coast
29
317
404
Arizona Statewide Total
721
Output in $2013
North Central
Northern
Phoenix & Central
Tucson & Southern
West Coast
Arizona Statewide Total

$1,428,000
$889,000
$13,774,000
$13,550,000
$909,000
$30,552,000

$1,401,000
$1,117,000
$47,152,000
$6,726,000
$1,221,000
$57,618,000

$2,829,000
$2,006,000
$60,926,000
$20,276,000
$2,130,000
$88,170,000

$812,000
$404,000
$5,723,000
$6,532,000
$378,000
$13,850,000

$834,000
$505,000
$13,456,000
$2,830,000
$528,000
$18,152,000

$1,646,000
$909,000
$19,179,000
$9,362,000
$906,000
$32,002,000

$631,000
$394,000
$5,822,000
$6,005,000
$389,000
$13,241,000

$610,000
$490,000
$13,730,000
$2,577,000
$543,000
$17,949,000

$1,241,000
$884,000
$19,552,000
$8,582,000
$932,000
$31,190,000

Disposable income in $2013
North Central
Northern
Phoenix & Central
Tucson & Southern
West Coast
Arizona Statewide Total
Labor income in $2013
North Central
Northern
Phoenix & Central
Tucson & Southern
West Coast
Arizona Statewide Total

Source: Authors; Redyn model

In comparison to results of studies of the economic impact of bicycling in other states,
reviewers of this report might ask why these economic effects of bicycling activities in Arizona

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4.2

are rather modest. First, an economic impact assessment typically focuses on export activity—
product/service sales to customers located outside of the region. Such export activity
introduces new spending into the regional economy. This new injection of money into the
economy causes a ripple (or “multiplier”) effect throughout the rest of the economy. Through
the use of an input-output model, we can track and measure this economic impact. Second, the
dominant market niche of Arizona in bicycling activities is hosting various events—bicycle road
races, “ironman” triathlons, extreme (e.g., 24 hours) mountain bike events, elite training
camps, and unique operated bicycle tours. All of these events draw thousands of participants to
the roads, trails, and scenic vistas of Arizona, but only for a brief period of time—for instance, a
competitive event with an additional two to three vacation days in Arizona with the
participants’ families.
Conclusions within this study address two different types of issues: 1) interpretation of the
findings, and 2) observations regarding the experience of working with the bicycle industry in
Arizona to accomplish the objectives of this study. The findings of the study that are perhaps
the most illuminating to readers with minimal or no involvement in bicycling have to do with
the extensive inventory of bicycling events, both in general and those that have some level of
out-of-state participation. As noted above, the study estimated that approximately 14,000 outof-state participants are involved annually in about 250 events held throughout the state. With
its mild winter in the desert areas and numerous scenic attractions, Arizona is well positioned
to increase this kind of activity, regardless of national trends in bicycle usage, and could market
itself as a destination for bicycle tourism. A velodrome could be an important complementary
asset for bicycle tourism in Arizona.
From an economic development standpoint, it is a logical to conclude that the popularity of the
state with bicyclists should provide one platform by which to leverage the capture of other
aspects of the bicycling industry, primarily manufacturing. It is also likely that other states that
have a much larger established base of bicycle manufacturing, such as California, have an
advantage over Arizona. This would particularly apply for those types of bicycles that are massproduced in large volumes (however this is the type of production most likely to be occurring
offshore). In any case, manufacturing of bicycles is likely to continue to be a highly dynamic,
global activity, and may perhaps become even more fragmented in terms of specialized
vehicles, the materials used, and the specific purposes for which bicycles can be designed and
built.
Arizona bicycle shops that responded to the survey for this study indicated high levels of sales
to out-of-state customers, although any conclusions in this regard are hampered by the small
sample size of respondents. To the extent these findings can be verified, for example in

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subsequent updates in this study, additional efforts could be justified to assess how this kind of
activity could be supported/encouraged, as it constitutes “export” activity relative to the state.
As noted elsewhere in this report, research efforts were constrained by widespread
unwillingness on the part of representatives within all categories of the state’s bicycling
industry, including both shop owners and event sponsors, to share information. A proactive
approach to involving businesses and the entire bicycling community in an ongoing datagathering process could greatly expedite preparing subsequent updates to this report. Any such
actions however must take into account what appear to be high levels of “competition anxiety”
among all categories of bicycle business owners.

4.1 Policy implications of findings
Integration of bicycling activity with overall tourism at both the state and region level. Two
aspects of policy related to this concept are: 1) To maximize the Arizona Office of Tourism’s
(AOT’s) engagement in bicycle-related tourism, including defining, quantifying, and promoting
Arizona's position, nationally, as a center for bicycling. There is also a broad economic
development component of this, as in all forms of tourism, which involves the exposure of the
state to people who subsequently relocate here; and 2) For policymakers generally to recognize
that, while this report focuses on statewide impacts as discussed above, bicycling events in
areas outside the major cities can be important to that locality, as components of the
redistribution of tourism dollars within the state, irrespective of the fact that some participants
are Arizonans and the statewide effects are neutral. These local effects, where potentially
significant, could be documented separately and taken into consideration in the course of
ADOT’s integration of agency plans with local planning.
Public land management coordination. Federal and state land-management agencies can play
pivotal roles in encouraging or discouraging off-road bicycle use, and there are “mixed reviews”
by informants to this report regarding the federal role in such matters in Arizona. These kinds of
issues are complex and part of a whole set of “multiple use” issues faced by federal land
management agencies, in which evolving planning processes are an increasingly important
component of resolving these issues.

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4.4