Methodology used to create Community Impact Snapshot reports
The Community Impact Snapshot is designed for community leaders interested in estimating the tourism-related economic impact of festivals and events hosted in their communities. It was developed collaboratively by the University of Minnesota Duluth’s Bureau of Business and Economic Research (BBER) and University of Minnesota Extension’s Department of Community Development (Extension).
An economic impact analysis looks at an initial spending event or "economic shock" — in this case, spending by visitors attending a community festival or event — and tracks multiple rounds of industry and consumer spending to show the ripple effects on the local economy. This technical report describes the standardized data, inputs, assumptions, and analytical processes used to estimate the economic impacts for the entire Community Impact Snapshot series.
This overall economic impact includes direct, indirect, and induced effects. The initial visitor spending or shock is the direct effect, whereas increased industry spending is the indirect effect, and increased consumer spending is the induced effect.
Study area
The study area for economic impact analysis is critical. It forms the distinction between the local and non-local economy. Only economic activity occurring within the local economy is captured in the analysis. Spending that is outside the local economy is a leakage. Once the dollars are spent outside the local economy, the dollars stop creating indirect and induced effects in the region. In most cases, the study area for the snapshot series is the county in which the festival or event occurs, unless otherwise noted.
Direct effect
The direct effect is the initial change in the economy due to an activity or event. When people travel to a community to attend a festival or event, they may also go out to eat, shop at a local store, and stay overnight in a hotel. This spending by festival and event attendees is the direct effect. In addition, the festival or event organizers spend money to host the activity. That spending is also part of the direct effect.
The formula for calculating the amount of spending by festival and event attendees is:
Festival or event attendee spending = Number of attendees x spending per attendee
Number of attendees
To determine event attendance and visitors’ home location, the research team uses Placer.ai — a location intelligence platform that relies on mobile analytics to track visitor trends. The first step in using the platform is to create a point of interest, which is a polygon defining the geographic boundary of the event location. For festivals and outdoor events, this might be the boundary of a park or entertainment center where the event takes place.
Once the point of interest is defined, the platform can then be used to obtain an estimate of the total number of visitors to the event, as well as the proportion of visitors that are local residents, day visitors, and overnight visitors. Visitors are classified into one of these three categories based on their travel distance. Day Visitors and Overnight Visitors represent the "new money" brought into the study area by the festival or event. Day visitors are those who are not considered local residents but live within 100 miles of the festival location. Overnight visitors are those who live more than 100 miles away (See Table 2). These definitions are also consistent with standard input-output methodology.
To ensure an accurate economic impact snapshot, the definition of a "Local Resident" is customizable based on the event’s scale and the community’s geography. Impact reports typically utilize one of three geographic filters: those living within a 20-mile radius of the event, those living within the zip code where the event takes place, or those living within the county where the event takes place. For events in small communities, event organizers or community leaders may only consider those within the zip code, or within a 20-mile radius to be local residents. For larger communities or regional events, Local Residents may be defined as anyone living within the same county where the event is held.
While the total spending by Local Residents is calculated to understand the overall size of the event, this spending is not included in the direct economic effect. This aligns with standard input-output methodology, which assumes spending by residents is money that would have been spent in the community regardless of the event. The primary goal of the economic analysis is to determine "new money," or money generated from outside the study area.
Table 1. Definition and spending profile used for each attendee type
| Visitor type | Travel distance |
|---|---|
| Local | Determined by community leaders/event organizers |
| Day | Determined by community leaders/event organizers |
| Overnight | More than 100 miles from event location |
It should be noted that data on event attendance obtained from Placer.ai are estimates. The platform relies on algorithms to estimate visitor volumes based on available mobile analytics data, and therefore, visitor counts can be inaccurate. However, it is assumed that estimates of event attendance using Placer.ai are adequate for the purposes of these studies.
Spending per attendee
To estimate spending per attendee, the research team develops two spending profiles: one for overnight visitors and one for day visitors. These profiles are constructed using data from 38 economic impact and visitor profile studies conducted between 2014 to 2025 by either Extension or the BBER. To standardize the data, all spending amounts are adjusted to the current year’s dollars based on the publication year of the study. Each spending profile is then classified as either “overnight” or “day” based solely on whether lodging was included as an expense category.
For a complete list of the studies used in developing the spending profiles, see the Appendix 2 section on this page.
Once the data are standardized and classified, the team calculates the average spending within each group (Overnight and Day) for every expense category to create a representative spending profile for the two visitor types. Each profile includes average spending on lodging, food, transportation, entertainment, and other categories. Table 1 summarizes the spending profiles.
Table 2. Average spending pattern by visitor type (2025 dollars)
| Spending Category | Visitor Type: Overnight | Visitor Type: Day |
|---|---|---|
| Lodging | 61.11 | |
| Restaurants | 37.17 | 21.73 |
| Grocery or convenience store | 9.18 | 1.56 |
| Gasoline | 21.61 | 8.41 |
| Other transportation costs | 2.81 | 0.36 |
| Entertainment | 9.50 | 4.17 |
| Shopping | 18.61 | 15.12 |
| Other (or Miscellaneous) | 7.84 | 2.54 |
| Total | 167.84 | 53.89 |
Before the spending patterns can be entered into IMPLAN, the economic impact modeling system, each spending category needs to be attributed to one or more IMPLAN sector. IMPLAN utilizes a classification scheme of 528 sectors for economic activity, where each sector refers to a group of establishments engaged in the same or similar type of economic activity.
The nine spending categories in the spending pattern (eight for the day visitor spending pattern) are mapped to a total of 25 different IMPLAN industries (23 for the day visitors). As an example, the “Restaurants” spending category maps to the IMPLAN sectors of Full-service restaurants, Limited-service restaurants, and All other food and drinking places. For spending categories such as restaurants that map to more than one industry, the spending is distributed evenly among the IMPLAN industries.
For a complete list of IMPLAN sectors used in modeling, see Appendix 1: IMPLAN modeling details.
Many of the studies our team uses to create these spending profiles include a category called “Miscellaneous” or “Other spending.” Since the specific nature of this spending is unknown, the total amount is allocated equally among the six or seven remaining categories (e.g., lodging, restaurants, groceries). Second, if a relevant IMPLAN sector is found to not exist within a specific study area, the spending mapped to that missing sector is redistributed equally to all remaining viable sectors within the same spending category.
Operations
The festival and event organizers provide their total spending budget to the analysis team for inclusion in the study. This information is entered into IMPLAN using an industry impact analysis event under the promoters of performing art and sports and agents for public figures industry (IMPLAN code 482). The impacts specific to the festival or events operational spending are then viewed as direct, indirect, and induced effects.
Indirect and induced effects
Once the initial change in the economy (direct effect) is quantified, it can be entered into an input-output model. The input-output model IMPLAN is used by this research team. Input- output models trace the flow of goods and services throughout an economy. Once the flow is known, the model can show how a change in one sector of the economy (say increased sales at restaurants) affects other parts of the economy (say the suppliers of the restaurant). The indirect and induced effects are often at businesses in the community that may never directly serve a festival or event attendee but benefit nonetheless from activity.
After estimates have been calculated for the spending pattern of event visitors and the number of local, day, and overnight-visitors, this information is combined in IMPLAN to obtain the total economic impacts on the study area of visitors attending the event or festival. The separate visitor spending patterns with and without lodging are imported into IMPLAN as industry output events. These events are then assigned to the corresponding group of visitors, either local, day, or overnight visitors. The scale is then set to the total number of visitors from each group. The results for the three types of visitors are combined to obtain the total economic impacts of visitors to the event.
Appendix 1: IMPLAN modeling details
Table 3. IMPLAN Sectors Used in Modeling
| IMPLAN Sector | Description |
|---|---|
| 402 | Retail – Motor vehicle and parts dealers |
| 404 | Retail – Electronics and appliance stores |
| 406 | Retail – Food and beverage stores |
| 407 | Retail – Health and personal care stores |
| 408 | Retail – Gasoline stores |
| 409 | Retail – Clothing and clothing accessories stores |
| 410 | Retail – Sporting goods, hobby, musical instrument and bookstores |
| 411 | Retail – General merchandise stores |
| 412 | Retail – Miscellaneous store retailers |
| 420 | Scenic and sightseeing transportation and support activities for transportation |
| 429 | Motion picture and video industries |
| 447 | Other real estate |
| 450 | Automotive equipment rental and leasing |
| 451 | General and consumer goods rental except video tapes and discs |
| 453 | Commercial and industrial machinery and equipment rental and leasing |
| 475 | Investigation and security services |
| 479 | Waste management and remediation services |
| 500 | Promoters of performing arts and sports and agents for public figures |
| 501 | Museums, historical sites, zoos and parks |
| 503 | Gambling industries (except casino hotels) |
| 504 | Other amusement and recreation industries |
| 505 | Fitness and recreational sports centers |
| 507 | Hotels and motels, including casino hotels |
| 508 | Other accommodations |
| 509 | Full-service restaurants |
| 510 | Limited-service restaurants |
| 511 | All other food and drinking places |
| 512 | Automotive repair and maintenance, except car washes |
The following are suggested assumptions from IMPLAN for interpreting the model and input-output analysis:
Constant Returns to Scale: The same quantity of inputs is needed per unit of Output, regardless of the level of production. In other words, if Output increases by 10%, input requirements will also increase by 10%.
Fixed Input Structure: This structure assumes that changes in the economy will affect the Industry's Output level but not the mix of Commodities and services it requires to produce that Output. This means that the same recipe of inputs will always be used to create the Output unless changes to the production function are made by the user.
Industry Homogeneity: I-O models assume that all firms within an industry are characterized by a common production process. In IMPLAN, edits can be made to the production function of an industry in order to model a distinct firm.
No Supply Constraints: I-O assumes there are no restrictions to inputs, raw materials, and employment and assumes there is enough to produce an unlimited amount of product. It is up to the user to decide whether this is a reasonable assumption for their study area and analysis, especially when dealing with large-scale impacts.
Fixed Technology: An Industry, and the production of Commodities, uses the same technology to produce each of its products. In other words, an Industry's Leontief Production Function is a weighted average of the inputs required to produce the primary product and each of the byproducts, weighted by the Output of each of the products.
Constant Byproduct Coefficients: As a requirement of the technology assumption, Industry byproduct coefficients are constant. An Industry will always produce the same mix of Commodities regardless of the level of production.
The Model is Static: No price changes are built in IMPLAN and the underlying data and relationships are not affected by impact runs. Input-Output models do not account for general equilibrium effects such as offsetting gains or losses in other Industries or geographies or the diversion of funds from other projects.
Backward and Forward Linkages: Type I multipliers measure only the backward linkages, also known as upstream effects (Bess & Ambargis, 2011), examining an industry’s supply chain. To highlight this concept, consider the example of a new resort opening in a city. The resorts operations will result in an increased demand in supporting industries such as utilities, food and beverages, labor, and shopping. IMPLAN’s results will include those impacts. However, IMPLAN cannot predict if a new shopping center or restaurant might open due to the increased demand. Historically, I-O models only measured backward linkages. With the addition of the Forward Linkages Suite, IMPLAN can now quantify both upstream and downstream effects. Forward Linkage multipliers measure downstream effects, examining how an Industry’s production is used as an input for other production or for final use. These reports rely only on backward linkages, and forward linkages are excluded.
Time Dimension: The length of time that it takes for the economy to settle at its new equilibrium after an initial change in economic activity is unclear because time is not explicitly included in regional I-O models.
This study uses the IMPLAN Group’s input-output modeling data and software (IMPLAN version 25.7). The IMPLAN database contains county, state, zip code, and federal economic statistics, which are specialized by region, not estimated from national averages. Using classic input-output analysis in combination with region-specific Social Accounting Matrices and Multiplier Models, IMPLAN provides a highly accurate and adaptable model for its users. IMPLAN data files use the following federal government data sources:
- U.S. Bureau of Economic Analysis Benchmark Input-Output Accounts of the U.S.
- U.S. Bureau of Economic Analysis Output Estimates
- U.S. Bureau of Economic Analysis Regional Economic Information Systems (REIS) Program
- U.S. Bureau of Labor Statistics Covered Employment and Wages (CEW) Program
- U.S. Bureau of Labor Statistics Consumer Expenditure Survey
- U.S. Census Bureau County Business Patterns
- U.S. Census Bureau Decennial Census and Population Surveys
- U.S. Census Bureau Economic Censuses and Surveys
- U.S. Department of Agriculture Census
IMPLAN data files consist of the following components: employment, industry output, value added, institutional demands, national structural matrices, and inter-institutional transfers.
Economic impacts are made up of direct, indirect, and induced impacts. The data used was the most recent IMPLAN data available,. All data are reported in current years’ dollars.
Economic impacts are made up of direct, indirect, and induced impacts. The following are suggested assumptions for accepting the impact model: IMPLAN input/output is a production-based model, and employment numbers (from U.S. Department of Commerce secondary data) treat both full- and part-time individuals as being employed.
Regional data for the impact models for value added, employment, and output are supplied by IMPLAN for this impact. Employment assumptions were provided to the model to enable construction of the impact model. From these data, social accounts, production, absorption, and byproducts information were generated from the national level data and was incorporated into the model.
Direct effect: Initial new spending in the study area resulting from the project. In this case, direct effect includes spending by festival and event attendees and organizers.
Economic impact: The effect of an event on the economy in a specified area, ranging from a single neighborhood to the entire globe. It usually measures changes in business revenue, business profits, personal wages, and/or jobs.
Employment: Estimates (from U.S. Department of Commerce secondary data) are in terms of jobs, not in terms of full-time equivalent employees. Therefore, these jobs may be temporary, part-time, or short-term.
Expenditure: The amount of money spent.
IMPLAN: A software system that uses a backward-linkage model which allows a user to develop models that can estimate the economic impact of different varieties such as when a new firm enters a study area, recreation and tourism, development, and more.
IMPLAN sector: Sectors are a way of describing a specific industry. All versions of the sectors are based on NAICS codes.
Indirect effect: The additional inter-industry spending from the direct impact. For example, increased sales in linen supply firms resulting from more motel sales would be an indirect effect of visitor spending.
Induced effect: The impact of additional household expenditures resulting from the direct and indirect impact. For example, motel employees spend the income they earn from increased tourism on housing, utilities, groceries, and other consumer goods.
Industry: A group of businesses based on their related primary business activities.
Input: Information or data that can be operated on by any process or system.
Labor income: All forms of employment income, including employee compensation (wages and benefits) and proprietor income.
Leakages: Leakages are any payments made to imports or value-added sectors, which do not in turn re-spend the dollars within the region. What’s more, a study area that is part of a larger functional economic region will likely miss some important linkages. For example, workers who live and spend outside the study area may hold local jobs.
Output: The value of local production required to sustain activities.
Spending pattern: A set of data describing a particular set of goods and services an individual is likely to buy.
Value added: A measure of the impacting industry’s contribution to the local community; it includes wages, rents, interest, and profits.
Appendix 2: References
- Assessing the Annual Economic Impact of the Grand Rapids IRA Civic Center (Erkkila & Qian, 2015)
- Itasca Area Visitor Profile: 2014 Final Report (Erkkila, Qian, et al., 2016)
- The Economic Impact of the Duluth Amateur Hockey Association on the City of Duluth (Haynes, Chiodi Grensing, et al., 2015)
- The Economic Impact of the Duluth Amateur Hockey Association on the City of Duluth (Haynes, Chiodi Grensing, et al., 2015)
- The Economic Impact of the Lake Superior and Mississippi Railroad (Haynes, Chiodi Grensing, et al., 2018)
- Economic Impact of the Hermantown Amateur Hockey Association on Duluth, Hermantown, and Proctor, Minnesota (Haynes, Chiodi Grensing, et al., 2020)
- The Economic Impact of Local Hockey and Curling Programs on Lake County, Minnesota (Haynes, Chiodi Grensing, et al., 2022)
- 2022 Minnesota State Park Visitor Study (MN DNR, 2022)
- Mille Lacs Area Profile: September 2014–August 2015 (Qian & Teng, 2015)
- 2018 Fairmont Area Summer and Fall Visitor Profiles: Final Report (Qian, 2019)
- The Range Recreation Civic Center: Understanding Visitors and Measuring its Economic and Social Value (Qian & Tuck, 2018)
- Bemidji Area Visitor Profile: Second-Quarter Spring Summary (Qian & Erkkila, 2019)
- Bemidji Area Visitor Profile: Fourth-Quarter Fall Summary (Qian & Erkkila, 2019)
- Bemidji Area Visitor Profile: Final Report (Qian & Erkkila, 2019)
- Cultural and Economic Contribution of the Beltrami County Fair (Qian, Tuck, et al., 2019)
- 2019 Otter Tail County Summer and Fall Visitor Profiles: Final Report (Qian, 2020)
- The 2016 Economic Impact of Glensheen Historic Mansion in Duluth, MN (Rockport Analytics, 2017)
- The Minnesota Visitor Economy 2023 (Tourism Economics, 2024)
- Profile of Mesabi Trail Visitors (Tuck & Linscheid, 2016)
- Economic Contribution of 2019 Grandma’s Marathon Weekend (Tuck & Bennett, 2019)
- Economic Contribution of the Twin Cities Pride Celebration (Tuck, 2019)
- Potential Economic Impact of the Batcher Block Opera House (Tuck, 2020)
- Economic Contribution of Attendees: Vikings Verizon Training Camp 2019 (Tuck & Qian, 2020)
- Economic Contribution of Vikings Verizon Training Camp Attendees, 2021 (Tuck & Qian, 2021)
- Economic Contribution of Attendees of East Grand Forks Ice Arena Youth Events (Tuck & Bhattacharyya, 2022)
- Economic Impact of Bertram Chain of Lakes Regional Athletic Park Event Attendees, 2023 (Tuck, 2023)
- Potential Economic Impact of Expanding Homecoming Offerings at Minnesota State University, Mankato (Tuck & Wehe, 2023)
- Economic Contribution of ATV Trails in Koochiching, Lake, and St. Louis Counties, Minnesota (Tuck & Bennett, 2024)
- Economic Contribution of 2024 Grandma’s Marathon Weekend (Tuck & Bennett, 2024)
- Economic Impact of the U.S. Gymnastics Olympic Team Trials and Associated Events in Minnesota: 2024 (Tuck, 2024)
- Cook County Economic Impact Report (University of Minnesota Department of Community Development, 2024)
How can Extension help?
Extension has conducted dozens of economic impact studies for festivals and events across Minnesota — from local community events like the Henderson Classic Car Roll-In to events with a national draw like Grandma’s Marathon. We offer economic modeling, survey design, sampling strategy, data analysis, and reporting. We can also train local coordinators and surveyors.
To learn more, contact Brigid Tuck or Xinyi Qian at Extension or Monica Haynes at University of Minnesota Duluth. You can also reach out to your local regional educator.
Reviewed in 2026