Downtown Economic Vitality and Business Mix in Small Wisconsin Communities

Jon Wolfrath

Master of Science Professional Project 12/5/2019

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Acknowledgements I would like to thank Bill Ryan from the University of Wisconsin-Extension for inspiring the idea for this project and providing insight into how it should be executed. I also want to acknowledge my advisor, James LaGro, and Dave Marcouiller for assisting in the review process.

Table of Contents

Contents Abstract...................................................................................................................................... 3

Introduction ................................................................................................................................ 3

Applied Methods Used ............................................................................................................... 4

Sample Communities ............................................................................................................. 4

Variable Data .......................................................................................................................... 6

Results ....................................................................................................................................... 7

Correlations with Other Variables ..........................................................................................10

Discussion ................................................................................................................................11

Limitations .............................................................................................................................11

Applications ...........................................................................................................................12

Further research ....................................................................................................................15

Appendix A: Sample Communities ............................................................................................16

Appendix B: Complete Data Set ................................................................................................17

Works Cited ..............................................................................................................................23

Tables & Figures Figure 1: Sample Municipalities ................................................................................................. 5 Figure 2: Example of .5-Mile Ring (Surgeon Bay) ...................................................................... 6 Figure 3: Menomonie .5-Mile Ring ............................................................................................12 Table 1: Metric Equations………………………………………………………………………………..3 Table 2: Top Performing Medium Communities……………………………………………………………8 Table 3: Top Performing Small Communities……………………………………………………………….8 Table 4: Downtown Industry Mix by Median Number of Businesses……………………………….9 Table 5: Downtown Retail Business Mix (Median)……………………………………………………9 Table 6: Economic Vitality Measurements (Medium Cities)………………………………………..…..10 Table 7: Economic Vitality Measurements (Small Cities)…………………………………………...…..10 Table 8: Downtown Businesses per Capita Correlations…………………………………………...11 Table 9: Downtown Employment per Capita Correlations……………………………………………....11 Table 10: Downtown Population Change Correlations…………………………………………...…11 Table 11: Lake Geneva Business Mix Comparison……………………………………………………...14

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Abstract This study examines downtown economic vitality and business mix in Wisconsin communities with 2,500 to 25,000 residents. These communities were ranked based on numerous economic vitality metrics and business mix medians were compiled for comparisons. This data is expected to be used for peer community evaluation and simple market analyses. Optimal peer communities will score well in these economic vitality metrics and have similar characteristics to your own community. Other variables were also compared to economic vitality metrics and correlations were found with county seat and urbanicity variables. This could provide some explanation for the success of communities that scored well in economic vitality.

Introduction This study examines 140 Wisconsin municipalities in the population range of 2,500 to 25,000 to identify communities with economically vital downtowns. There are many ways to define and quantify economic vitality.1 The measures calculated in this study are shown in Table 1. Table 1: Metric Equations

Metric Equation Downtown Businesses per Capita

number of businesses located within the downtown ring population of municipality

Downtown Employment per Capita number of employees that work within the downtown ring population of municipality

Percent of Businesses Downtown number of businesses located within the downtown ring total number of businesses in the municipality

Percent of Employment Downtown number of employees that work within the downtown ring total number of employees that work in the municipality

Percent of Population Downtown number of people living within the downtown ring total population of the municipality

2010-2018 Downtown Population Change

(2018 population – 2010 population) 2010 population

These metrics are intended to provide an assessment of how economically successful a community is by looking at three primary aspects of economic vitality: businesses, employees, and residents. These measures were also chosen because they are being used in an ongoing study conducted by the University of Wisconsin-Extension (UWEX) called “Business Mix and Economic Vitality of Downtowns in Small to Middle Size Midwest Cities”. Part of the intention with this work is to replicate the ongoing study for a different population range in Wisconsin. These Wisconsin communities were ranked based on their performance with each of these metrics to identify the most economically vital downtowns. Data was assembled to show the median performance in each of these measures. Additionally, a dynamic mix of business types is essential to create multifaceted communities where people can live, work, and play. This study provides median downtown business mix data using North American Industry Classification System (NAICS) codes to provide an understanding of which industries are

1 Edwards, Mary, et al. “Downtown Success Indicators.” University of Illinois at Urbana-Champaign, Aug.2014.

https://cced.ces.uwex.edu/files/2014/12/Downtown_Success_Indicators_2014.pdf

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typically located downtown. Business mix is analyzed by general industries and a specific breakdown of retail types. Other economic vitality variables were compiled in an attempt to find a connection between performance in economic vitality and other variables. These variables include:

1. Business improvement districts (BIDs) 2. Main street program 3. Connected community program 4. County seat 5. Urbanicity

These variables were chosen because they are thought to have an influence on downtown economic vitality and the data for these variables is readily accessible.2

Applied Methods Used This study defines downtown differently based on population size. For communities with a population greater than 10,000, downtown is defined as the area within a .5-mile radius of the center point of downtown. For communities with a population of less than 10,000, downtown is considered everything within .25 miles of the downtown center. UWEX has completed similar studies using .25-mile and .5-mile rings to define downtown.3 The downtown ring is halved for smaller communities to minimize the inclusion of land that would not be considered part of the downtown core. Based on these definitions of downtown, information was gathered to assess downtown economic vitality.

Sample Communities

The 140 communities included in this study are in the population range of 2,500 to 25,000 based on the 2010 census. For simplicity, cities in the range of 10,000 to 25,000 are referred to as “Medium” and cities in the population range of 2,500 to 10,000 are referred to as “Small”. And although not all these municipalities are “cities”, they are occasionally referred to as cities for simplicity. The sample originally included every Wisconsin community in this population range, but not all these communities are represented in this study. Each community was manually analyzed to determine if it had a downtown and communities without a traditional downtown or city center—predominantly suburbs—were excluded from the data set. A complete list of municipalities included in the final sample set can be found in Appendix A.

2 Ryan, Bill, Michael Burayidi, and Steven Deller. "Business Mix and Economic Vitality of Downtowns in Small to

Middle Size Midwest Cities." Working paper, University of Wisconsin, Madison, WI, 2019.

3 Ryan, Bill, Beverly Stencel, and Jangik Jin. "Retail and Service Business Mix Analysis of Wisconsin's Downtowns."

Downtown Market Analysis. Last modified September 1, 2010. https://fyi.extension.wisc.edu/downtown-market-analysis/files/2011/02/ Retail_and_Service_Business_Mix_Analysis041711.pdf.

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Figure 1: Sample Municipalities

The sample set provides an in depth look at Wisconsin communities and the results might be sufficient to apply it to other Midwest states. According to the National League of Cities, there are an estimated 5,257 municipalities in the 2,500 to 25,000 population range in the United States.4 The 140 sample communities represent 2.7% of the national total of municipalities in this population range.

The business data presented in this report is from Infogroup and was gathered using ESRI Business Analyst. The center of downtown was selected for each municipality by manually placing a center point or “centroid” in ESRI Business Analyst at what was visually determined to be the center of downtown. 2018 data from the downtown ring area was then extracted and compiled into a spreadsheet for manipulation and comparison. This process was completed for each municipality in the sample set. The data for citywide business and demographic data was also gathered through ESRI Business analyst to maintain data consistency.

4 "Number of Municipal Governments & Population Distribution." National League of Cities.

https://www.nlc.org/number-of-municipal-governments-population-distribution.

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Figure 2: Example of .5-Mile Ring (Surgeon Bay)

Variable Data

Influential economic vitality variables used in this study are:

1. Urbanicity ratings 2. Business improvement district (BIDs) 3. Main Street program 4. Connected Community program 5. County seat

Urbanicity ratings were created from Beale codes—an urban to rural classification system to quantify how urban a county is. 5 In this system, counties are given a rating between 1 (counties in metro areas of 1 million population or more) and 9 (nonmetro areas that are completely rural or less than 2,500 urban population and not adjacent to a metro area). Urbanicity plays a role in the economic independence of a community and can affect a community’s trade area size. Ideally, urbanicity would be measured at the municipal level, but measuring urbanicity at the county level can still be helpful to explain economic vitality or the lack thereof due to proximity to other trade areas. BID information was compiled from a directory created by the Local Government Center Division of UWEX and is accurate as of May 2019. It can be found here: https://lgc.uwex.edu/business-improvement-districts-bids/. BID’s are an important aspect of economic vitality, especially in small communities. BID’s allow business property owners in a

5 US Department of Health & Human Services. "NCHS Urban-Rural Classification Scheme for Counties." Centers for

Disease Control and Prevention. Last modified June 1, 2017. Accessed November 23, 2019. https://www.cdc.gov/nchs/data_access/urban_rural.htm.

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designated district to assess fees to themselves in order to pool money for business promotion, management, and future development within the BID.6 Main Street programs have been implemented to help support downtown community development by providing technical assistance with downtown revitalization projects.7 Similarly, Connect Community programs focus on training and networking in communities that are making downtown revitalization efforts.8 Both Main Street and Connect Community programs may create positive outcomes regarding the quantity and success of downtown businesses. The data regarding these programs was gathered from the Wisconsin Economic Development Corporation (WEDC) website. Finally, hosting government institutions as a county seat can be an economic catalyst for a community. Government centers are usually located in or near the downtown area and can create a stable downtown economy as a core employer.

Results Businesses per capita was used as the primary metric to describe economic vitality. The following rankings were discovered for downtown businesses per capita for both medium and small cities. Burlington topped the list of medium cities and Wisconsin Dells scored highest for the smaller population range. A full spreadsheet of the data is included in Appendix B.

Table 2: Top Performing Medium Communities

City (10k-25k) Downtown Businesses

per Capita

Rank

Burlington city 0.0267 1

Baraboo city 0.0263 2

Monroe city 0.0249 3

Elkhorn city 0.0237 4

Chippewa Falls city 0.0236 5

Marinette city 0.0206 6

Port Washington city 0.0202 7

Portage city 0.0197 8

Hudson city 0.0196 9

Platteville city 0.0189 10

In general, smaller communities had more downtown businesses per capita. Burlington, the highest performing medium sized community, had the 19th highest score overall. Perhaps this shows that many business types only require a small population base to support business operations.

6 University of Wisconsin - Extension. "Business Improvement Districts - BIDs." Local Government Center.

https://lgc.uwex.edu/business-improvement-districts-bids/.

7 Wisconsin Economic Development Corporation. "Main Street." Wisconsin Economic Development Corporation.

https://wedc.org/programs-and-resources/main-street/. 8 "Downtown Development Programs." Wisconsin Economic Development Corporation. https://wedc.org/wp-

content/uploads/2013/02/2016-Main-Street-Connect-Communities-Brochure.pdf.

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Table 3: Top Performing Small Communities

City (2.5k-10k) Downtown Businesses

per Capita

Rank

Wisconsin Dells city 0.0549 1

Spooner city 0.0528 2

Lancaster city 0.0373 3

Richland Center city 0.0342 4

Black River Falls city 0.0338 5

Amery city 0.0338 6

Rhinelander city 0.0335 7

Barron city 0.0327 8

Viroqua city 0.0325 9

Dodgeville city 0.0311 10

It is no surprise that Wisconsin Dells has the most downtown businesses per capita. Wisconsin Dells has a tourism industry that is unmatched for its size. According to the Wisconsin Dells Economic Impact Study, it hosts over 4 million visitors annually to visit the Waterpark Capital of the World.9

Table 4: Downtown Industry Mix by Median Number of Businesses

Industry Medium Cities Percent Small Cities Percent

Agric/Forestry/Fish/Hunt 0 0% 0 0%

Mining 0 0% 0 0%

Utilities 0 0% 0 0%

Construction 9 5% 4 6%

Manufacturing 6 3% 3 5%

Wholesale Trade 4 2% 1 2%

Retail Trade 27 14% 9 14%

Transportation/Warehouse 2 1% 1 2%

Information 4 2% 1 2%

Finance/Insurance 17 9% 5 8%

Real Estate/Rent/Leasing 9 5% 3 5%

Prof/Scientific/Tech Service 17 9% 4 6%

Mgmt of Comp/Enterprises 0 0% 0 0%

Admin/Support/Waste Mgmt 6 3% 1 2%

Educational Services 5 3% 2 3%

HealthCare/Social Assist 14 7% 5 8%

Arts/Entertainment/Rec 6 3% 1 2%

Accommodation/Food Service 18 9% 6 10%

Other Service excl Pub Admin 25 13% 10 16%

Public Administration 18 9% 4 6%

Unclassified Establishments 10 5% 3 5%

9 "Wisconsin Dells Economic Impact Summary 2017." Wisconsin Dells. Last modified 2017.

https://www.wisdells.com/media/facts/economic-impact.htm.

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Although larger communities had a larger number of businesses, industry mix in the medium and small communities were roughly the same analyzed as a percentage of all businesses. This suggests that business mix behaves similarly for both population ranges. Table 5: Downtown Retail Business Mix (Median)

Industry Medium

Cities

Percent Small Cities Percent

Motor Vehicles/Parts Dealers 2 8% 1 13%

Furniture/Home Furnish 1 4% 0 0%

Electronics/Appliance 1 4% 0 0%

Building Matl/Garden Equip 2 8% 1 13%

Food & Beverage Stores 3 12% 1 13%

Health/Personal Care 2 8% 1 13%

Gas Stations 1 4% 1 13%

Clothing/Accessory 4 15% 0 0%

Sports/Hobby/Book/Music 3 12% 1 13%

General Merchandise 1 4% 0 0%

Misc Store Retailers 6 23% 2 25%

Nonstore Retailers 0 0% 0 0%

Retail in small communities are difficult to analyze due to low medians in each retail category. Table 5 is a good illustration of the decline in GAFO (general merchandise, apparel, furniture, other) retailers. The GAFO retail categories are not typically present in small communities. Table 6: Economic Vitality Measurements (Medium Cities)

Downtown

Businesses per Capita

Downtown Employment per Capita

Percent of Businesses Downtown

Percent of Employment Downtown

Percent of Population Downtown

2010-2018 Downtown Population Change

Median 0.015 0.140 38% 25% 21% 1%

Mean 0.016 0.158 36% 26% 21% 1%

High 0.027 0.326 50% 47% 34% 13%

Low 0.006 0.056 14% 9% 9% -3%

Range 0.021 0.271 35% 38% 25% 16%

Medium and small sized communities had similar medians for economic vitality measurements, but small communities had much larger ranges in general. This small community variability was found on both ends containing higher highs and lower lows. It is impossible to give a single explanation for this variability; it could be simply because the smaller population range contains more communities and has proportionally larger population differences.

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Table 7: Economic Vitality Measurements (Small Cities)

Downtown Businesses per Capita

Downtown Employment

per Capita

Percent of Businesses Downtown

Percent of Employment Downtown

Percent of Population Downtown

2010-2018 Downtown Population

Change

Median 0.015 0.142 31% 24% 11% 1%

Mean 0.017 0.183 31% 25% 12% 1%

High 0.055 0.733 55% 54% 40% 56%

Low 0.003 0.018 8% 7% 2% -9%

Range 0.052 0.715 47% 47% 38% 65%

Correlations with Other Variables

These calculated metrics were then analyzed to find correlation with vitality variables — potential metric influencers. In general, there were not any strong correlations, but there were moderate and weak correlations with these variables. Downtown businesses per capita had a moderate correlation with the county seat variable in both medium and small communities. There also was a moderate correlation with small city urbanicity. Not that the urbanicity and county seat variables are the cause of the number of businesses per capita, but these variables could have an influence. A county seat could add economic security through an employment base and incentivize other organizations to locate in a community. Urbanicity could be correlated because rural communities in general will have a larger trade area due to greater distance from competing business districts.10 Table 8: Downtown Businesses per Capita Correlations

Correlation Coefficient Medium Cities Small Cities

Urbanicity 0.14 0.51

BID 0.22 0.31

Main Street 0.17 0.11

Connect Community -0.26 -0.0019

County Seat 0.59 0.46

Employment per capita correlations are similar to those of businesses per capita. The quantity of businesses will likely have a large influence on the number of employees downtown. Employees can be a better metric because the number of businesses does not compensate for the footprint of a business. A large employer and a small employer would have the same value in the business count. County seat is still moderately correlated with downtown employment and urbanicity has a slightly weaker correlation than with businesses per capita.

10 Kures, Matt, et al. "Trade Area Analysis." Downtown Market Analysis. Last modified March 30, 2011.

https://fyi.extension.wisc.edu/downtown-market-analysis/understanding-the-market/trade-area-analysis/#Types.

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Table 9: Downtown Employment per Capita Correlations

Correlation Coefficient Medium Cities Small Cities

Urbanicity 0.15 0.36

BID 0.17 0.09

Main Street 0.15 0.02

Connect Community -0.15 0.09

County Seat 0.50 0.47

The final metric that showed weak to moderate correlation was downtown population change. All correlation coefficients were negative, showing that as a community’s population grows, there is a smaller chance that it has one of these variables. Urbanicity is the most highly correlating measure which may reflect urbanization. This is illustrating that an increase in population is slightly correlated with being in an urban area.

Table 10: Downtown Population Change Correlations

Correlation Coefficient Medium Cities Small Cities

Urbanicity -0.27 -0.42

BID -0.26 -0.22

Main Street -0.09 -0.14

Connect Community -0.36 -0.018

County Seat -0.24 -0.29

Correlations results with other variables were not included in tables because there were no noticeable correlations discovered. Unfortunately, the factors that seem to be correlated are essentially out of the control of decision makers and cannot be actively changed.

Discussion Key lessons can be learned from communities that scored well in economic vitality. These leading communities can be found throughout the state and can be used as a benchmark for comparing business mix and identifying programs undertaken to improve downtown economic vitality. Business mix varies depending on the type of community and although there are many formulas for success, it can be beneficial to make comparisons based on this data. It is recommended to examine the full data set (in Appendix B) to find your community’s economic performance and discover strengths and weaknesses. A business mix spreadsheet can be requested to compare your business mix with medians and peer communities (see Further Research section).

Limitations

This study has many limitations and assumptions. Regarding methodology, manually placing downtown centroids can be subjective and subject to error. Centroids were placed to provide the best representation of downtown as possible. Commonly, the centroid was placed in the visual center of the densely developed downtown area. However, the data gathered through this method would not be able to be completely replicable due to manual placements. If this study was repeated, the data would be slightly different. Due to geography, some downtowns are not developed evenly in all directions from the centroid. For example, many communities are

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adjacent to waterbodies like the City of Menomonie shown in Figure 3. This has data implications because other communities may perform better with these metrics if there is only developable land within the .5-mile ring.

Figure 3: Menomonie .5-Mile Ring

A ring is not necessarily the best representation of downtown either, many downtowns are linear rather than a cluster. This impacts the results of downtown data due to the inclusion of more land area that may not be considered part of the downtown core. However, this method was chosen to maintain a normalized area so that data can be compared. The Infogroup data used in this study inherently has inconsistencies as well. Although the issue is claimed to be resolved, ATM’s, electric charging stations, and other revenue generators without employees have been considered a business in the past. And although referred to as businesses, all organizations that employ people are included as a business in this study. This includes nonprofits, government institutions, and schools. Finally, the specified downtown economic vitality metrics do not paint the whole picture. Economic vitality is multi-dimensional and is difficult to summarize in just a handful of measures. A community may score highly in these metrics but lack key elements of a vital downtown. For example, this study is under the assumption that all businesses are good for the community, while in reality, there are businesses that could have a net negative effect on local economies. Also, these metrics are strictly quantitative and cannot account for other factors like aesthetics, liveliness, perceived safety, and other aspects that create a vibrant and successful downtown.

Applications

The primary purpose of this study is to keep communities well-informed and provide resources to further understand community economic performance. Through community data and sample set medians, it will be simple to assess an individual community’s performance with these metrics. This data will also provide a better understanding of what business mix is characteristic

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of downtowns in small Wisconsin communities. It is recommended that this data be used for the following purposes:

1. Peer Community Evaluation

Peer community identification is recommended to compare your downtown relative to communities that have similar market and functional qualities. Once peer communities are identified, they can provide lessons about the success of similar downtowns. Economic vitality metrics presented in this study should be used to identify peer communities with successful downtowns or business districts to maximize learning opportunities from top performers. Case studies can then be carried out to see what kinds of businesses are supported in like communities. This can be executed through independent research, discussion with city staff, and site visits. According to the UWEX’s Downtown Market Analysis Toolbox, you can begin to answer the following questions through peer community analysis:

• “If your downtown leads your peers, what strengths can you build on?”

• “If your downtown lags your peers, are you missing opportunities to grow in certain sectors?”

• “Have your peer cities developed successful niches that attract additional customers to their downtowns?”

• “How has the peer city downtown or other business district defined a unique market position for itself?”

• “Specifically, what competitive advantages has the peer city cultivated through the goods and services it offers and the consumer groups (e.g., students, day workers, visitors, etc.) it serves?”

• “What specific economic development strategies have worked in the peer city downtown or other business district?”

• “What initiatives have helped expand or recruit businesses, including retail?”11

2. Market Analysis

Market analyses are commonly executed to understand what types of businesses are needed downtown and business mix data is crucial to performing these market assessments. Although consultants are commonly used for formal market analyses, city staff can complete a simplified analysis to better understand the downtown economy12. The data presented in this study is expected to be used for market analyses by:

a. Understanding and evaluating your own community’s downtown business mix. Once existing business mix data is gathered and assessed in relation to medians and peer communities, planners can infer what the community’s weaknesses and strengths are.

11 Clark, Jill, Ryan Pesch, and Bill Ryan. "Peer City Comparison." Downtown Market Analysis. Last modified 2011.

https://fyi.extension.wisc.edu/downtown-market-analysis/understanding-the-market/peer-city-comparison/.

12 Burayidi, Michael. Downtown Revitalization in Small and Midsized Cities. N.p.: American Planning Association,

n.d.

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Table 11: Lake Geneva Business Mix Comparison

NAICS Industry Lake Geneva

Percent Median Percent

Agric/Forestry/Fish/Hunt 0 0% 0 0%

Mining 0 0% 0 0%

Utilities 1 0% 0 0%

Construction 6 3% 4 6%

Manufacturing 6 3% 3 5%

Wholesale Trade 0 0% 1 2%

Retail Trade 55 24% 9 14%

Transportation/Warehouse 3 1% 1 2%

Information 2 1% 1 2%

Finance/Insurance 10 4% 5 8%

Real Estate/Rent/Leasing 12 5% 3 5%

Prof/Scientific/Tech Service 22 10% 4 6%

Mgmt of Comp/Enterprises 0 0% 0 0%

Admin/Support/Waste Mgmt 4 2% 1 2%

Educational Services 9 4% 2 3%

HealthCare/Social Assist 14 6% 5 8%

Arts/Entertainment/Rec 5 2% 1 2%

Accommodation/Food Service 36 16% 6 10%

Other Service excl Pub Admin 23 10% 10 16%

Public Administration 9 4% 4 6%

Unclassified Establishments 14 6% 3 5%

Table 11 is an example of a high-level business mix comparison between Lake Geneva and the median number of businesses per industry. This table can give enough information to make inferences about the community. Lake Geneva has many more businesses than typically found in communities of its size. It specifically contains a much higher percentage of retail businesses than the median. It also specializes in professional/scientific/tech services.

b. Guiding decisions on business expansion and recruitment efforts. Through business mix comparisons, communities can decide the most effective strategy to build on strengths or compensate for weaknesses. Downtown businesses mix information can be beneficial to prospective business owners by providing a basic understanding of local market potential and can be used in the business recruitment process.

c. Being well-informed about potential next steps. A simplified business mix analysis may be sufficient or show a need for a more sophisticated analysis. It can be beneficial to run a simple market analysis to gather baseline data and to be better prepared to work with a consultant on a formal market analysis.

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3. Correlation Data

Moderate correlations were discovered with county seat and urbanicity variables which may help explain some of the results. Main Street programs, Connected Community programs, and BIDs were included with the intention of finding correlation with variables that a community can act upon. Although the data in this study does not strictly endorse these variables, these programs can affect aspects of economic vitality that are overlooked in this study such as expansion and sales growth of existing businesses, infrastructure improvements and design, public safety, and building valuable partnerships.13

County seat and urbanicity variables were found to have a moderate correlation with certain metrics which may emphasize the importance to build on existing public institutions in your community. Although causation cannot be claimed for either of these variables, public institutions can be viewed as a strength and an organization that will essentially never go “out of business”. The influence of urbanicity should also be analyzed to understand how regional economies affect local economies. It would make sense for isolated communities with a large trade area to have a higher amount of businesses and employment per capita than a similar community in an urban county.

Further research

Further research should be done to include other aspects of economic vitality and variables that may be correlated with them. The data provides a basis for identifying economically successful communities, but an analysis of other aspects of economic vitality could support the validity of the data. Case studies of these communities could validate the results of this study and find commonalities in top performers. It would be interesting to see specific programs and initiatives taken by these communities to bolster downtown economies and redevelopment. If this study were to be repeated in a different geography or population range, it may be beneficial to gather NAICS code data that is at a further level of specificity. The results in this study leave some ambiguity with business categories like “miscellaneous store retailers” and “unclassified establishments”. A more in-depth analysis of business types could contain revealing information. Further research should also address other variables that may correlate with economic vitality such as the presence of downtown development authorities, historic preservation initiatives, vacancies, and new developments. These variables may be difficult to find data for, but they could shed light on other important factors that affect economic vitality. Also, there may be interesting relationships among economic vitality variables such as the relation between the number of downtown businesses and downtown residents per capita. Understanding this relationship could provide insight into the interconnectivity of variables. If interested in accessing the full data spreadsheet, send a request to jwolfrath@wisc.edu.

13 "Downtown Development Programs." Wisconsin Economic Development Corporation. https://wedc.org/wp-

content/uploads/2013/02/2016-Main-Street-Connect-Communities-Brochure.pdf.

Law, Charles. "Wisconsin Business Improvement Districts -BIDs." Local Government Center. Last modified September 2012. https://lgc.uwex.edu/files/2016/03/ fs9BusinessImprovementDistricts.pdf.

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Appendix A: Sample CommunitiesWatertown city De Pere city South Milwaukee city Marshfield city Wisconsin Rapids city Cudahy city Onalaska city Menasha city Beaver Dam city Menomonie city Oconomowoc city Kaukauna city River Falls city Whitewater city Hartford city Chippewa Falls city Hudson city Stoughton city Fort Atkinson city Waunakee village Baraboo city Two Rivers city Grafton village Cedarburg city Waupun city Port Washington city Platteville city Marinette city Monroe city Burlington city Little Chute village Portage city Elkhorn city Merrill city Sparta city Shawano city Reedsburg city Sturgeon Bay city Tomah city Hartland village Oregon village Holmen village DeForest village Delavan city Plymouth city Rice Lake city New Richmond city Antigo city

Ashland city Pewaukee village Jefferson city Rhinelander city Sheboygan Falls city Ripon city Lake Geneva city Mukwonago village New London city Delafield city Mount Horeb village Jackson village Waupaca city Twin Lakes village Prairie du Chien city Lake Mills city Milton city Berlin city Edgerton city Waterford village Richland Center city Mayville city Slinger village Evansville city Columbus city Union Grove village West Salem village Dodgeville city Clintonville city Oconto city Mauston city Viroqua city Saukville village Medford city East Troy village Prescott city Kewaskum village Mosinee city Baldwin village Chilton city Prairie du Sac village Marshall village Lancaster city Kiel city Rochester village Horicon city Stanley city Black River Falls city

Bloomer city Pulaski village Omro city Peshtigo city Seymour city Barron city Ladysmith city Tomahawk city Waterloo city Cross Plains village Sauk City village Ellsworth village Brodhead city New Holstein city Thiensville village Boscobel city Howards Grove village Algoma city Brillion city Lodi city Genoa City village Paddock Lake village Kewaunee city Arcadia city Lake Delton village Amery city Oconto Falls city Oostburg village Wrightstown village Walworth village Juneau city Mondovi city Johnson Creek village Sherwood village Hortonville village Spooner city Wisconsin Dells city Somerset village Nekoosa city Osceola village Williams Bay village New Lisbon city Poynette village Luxemburg village

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Appendix B: Complete Data Set

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Works Cited Burayidi, Michael. Downtown Revitalization in Small and Midsized Cities. N.p.: American Planning

Association, n.d. Clark, Jill, Ryan Pesch, and Bill Ryan. "Peer City Comparison." Downtown Market Analysis. Last modified

2011. https://fyi.extension.wisc.edu/downtown-market-analysis/understanding-the-market/peer-city-comparison/.

"Downtown Development Programs." Wisconsin Economic Development Corporation.

https://wedc.org/wp-content/uploads/2013/02/2016-Main-Street-Connect-Communities-Brochure.pdf.

Edwards, Mary, et al. “Downtown Success Indicators.” University of Illinois at Urbana-Champaign,

Aug.2014. https://cced.ces.uwex.edu/files/2014/12/Downtown_Success_Indicators_2014.pdf Kures, Matt, et al. "Trade Area Analysis." Downtown Market Analysis. Last modified March 30, 2011.

https://fyi.extension.wisc.edu/downtown-market-analysis/understanding-the-market/trade-area-analysis/#Types.

Law, Charles. "Wisconsin Business Improvement Districts -BIDs." Local Government Center. Last

modified September 2012. https://lgc.uwex.edu/files/2016/03/ fs9BusinessImprovementDistricts.pdf.

"Number of Municipal Governments & Population Distribution." National League of Cities.

https://www.nlc.org/number-of-municipal-governments-population-distribution. Ryan, Bill, Beverly Stencel, and Jangik Jin. "Retail and Service Business Mix Analysis of Wisconsin's

Downtowns." Downtown Market Analysis. Last modified September 1, 2010. https://fyi.extension.wisc.edu/downtown-market-analysis/files/2011/02/ Retail_and_Service_Business_Mix_Analysis041711.pdf.

Ryan, Bill, Michael Burayidi, and Steven Deller. "Business Mix and Economic Vitality of Downtowns in

Small to Middle Size Midwest Cities." Working paper, University of Wisconsin, Madison, WI, 2019.

University of Wisconsin - Extension. "Business Improvement Districts - BIDs." Local Government Center.

https://lgc.uwex.edu/business-improvement-districts-bids/. US Department of Health & Human Services. "NCHS Urban-Rural Classification Scheme for Counties."

Centers for Disease Control and Prevention. Last modified June 1, 2017. Accessed November 23, 2019. https://www.cdc.gov/nchs/data_access/urban_rural.htm.

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