The 2025 Mount Washington Valley Ice Climbing Study

Post-data collection, the anonymous survey responses were downloaded into Excel for cleaning. Cleaning included removing cases with no responses and/or who declined to participate, transitioning word responses into numbers (“seven” into “7”), removing odd responses (such as a year with five numerals), turning numeric ranges into single numbers by selecting the first number listed (“30-35” into “30”), and removing unclear responses as needed. Additionally, categorical responses (where a respondent selects their answer from a list of categories) were recoded as dichotomous measures where 0=the absence of selecting that response and 1=the presence of selecting this response. This allows interpreting mean responses in the tables that follow as percentages (.53 =53% of cases fit into that category). This is discussed again in detail in the table summaries.

In preparation for the economic impact portion of the study, several questions required additional recoding. These practices are aligned with United States Forest Service and National Park Service methodologies to reduce risks of overestimating economic impacts. [7] [8] [9] For example, groups larger than eight persons in the paid group were excluded from expenditures to prevent unusually large groups from inflating results. Persons with unusually long trips (over 30 days in this study) were excluded as their results, again, could inflate results due to longer stays. Additionally, expenditure patterns were adjusted to exclude atypically high expenditures that are more than three deviations from the mean. Retail expenditures are also capped at $500 to reduce risk of overestimation.

Due to the number of survey responses, it was not possible to conduct a more detailed visitor segmentation approach as utilized in National Park Service studies which typically study much larger visitor communities (see Thomas et al., 2019). Instead, the researchers delineated local resident expenditures (persons living within 34 miles of the Mount Washington Valley) and visitor expenditures (persons living 35 or more miles from the Mount Washington Valley. Visitor expenditures were further delineated by those staying overnight and day users. This approach has been effectively used in previous similar studies. [10]

Table 1 summarizes the demographics of ice climbers in the Mount Washington Valley. Recall that categorical responses in this table are recoded as dichotomous measures where 1=the presence of the trait measured and 0=absence of the trait measured. For example, in the sex measure, 0.22 or 22% of respondents self-described as being female. The largest number of responses came from persons identifying as males (76% or 0.76) while ~2% (0.02) identified as another sex category not listed. Overall, respondents’ average age was ~40 years, but this is influenced by excluding persons under age eighteen from the survey.

As has been found in past studies of climbing communities, participants were overwhelmingly well-educated. [11] [12] [13] For example, one in ten respondents had terminal college degrees such as a doctorate while one in four had a master’s degree as their highest degree. Additionally, 45% reported a Bachelor’s degree as their highest educational attainment. Overall, 83% of the sample had at least a Bachelor’s degree at the time of the survey. Respondents were also asked about their personal incomes. Note these are individual respondent incomes, not household incomes. Overall, respondents indicated having higher than typical personal incomes, ostensibly due to having advanced degrees. For example, the largest category included low-six figure personal incomes ($100K-150K). On a related note, nearly one in five respondents indicated owning their own business.

Table 1 also includes a summary of respondent’s self-identified race. Here, respondents could check as many categories as they felt appropriate. Overall, respondents identified as white (91%) with Asian (6%) being the next largest racial category. Notably, around 2% of respondents checked more than one racial category.

Table 1 closes with two additional measures related to Leave No Trace (LNT) principles. First, half of respondents had signed the Climber’s Pact. This program hosted via Access Fund asks climbers to publicly sign and adhere to practicing LNT ideas such as packing out all items brought into the backcountry, remaining on existing and official trails, and reducing impacts through environmentally-mindful use patterns. Respondents were also asked about their LNT formal experience, and 12% indicated they were official LNT Certified Trainers. One notable finding from this portion of the survey is that respondents more readily recognized LNT training in comparison to Access Fund’s Climber’s Pact. This is evidenced by a high count of missingness (in the form of “I’m not sure.” responses) for the Climber’s Pact question. It is conjecture, but this may indicate some differences in how ice climbers think about LNT and/or a lack of outreach for the Climber’s Pact to ice climbing communities.

Table 2 summarizes use patterns among ice climbers in this study. The average participant began climbing as a young adult (~22) and has been climbing since (on average) 2009. Not surprisingly, nearly all respondents indicated they participate in ice climbing (94%). Other popular climbing type responses included traditional climbing (75%) and alpine climbing (75%). Additionally, over half (54%) also participated in indoor gym climbing.

Thinking ahead to the economic impact study, respondents were asked several questions about their home location and their most recent climbing trip. Fifteen percent of participants indicated living very close (within 34 or fewer miles) from the Mount Washington Valley and are defined as local residents for this study. The remaining 85% of cases living 35 or more miles away are defined as visitors for this study. Looking at these visitors, 82% of those participants included an overnight stay on their visit to ice climb in the Mount Washington Valley. This overnight stay
covered, on average, three nights.

Respondents were also asked about their group sizes. The first question helps researchers better understand group sizes at local crags which can lead to LNT impacts. In this study, respondents reported climbing groups of, on average, around two to three people. The second group question is used in tandem with expenditures questions covered later in this report. There, respondents reported paying for, on average, groups of around one to two persons.

Table 2 concludes with two questions about the most current climbing visit. Nearly all cases noted their most recent ice climbing trip in the Mount Washington Valley happened during the current (2024-25) climbing season (80%) with the remaining cases generally falling in the prior 2023-24 climbing season. Similarly, nearly all respondents (88%) indicated climbing was the primary purpose for that trip.

Table 3 examines per person expenditures created by local residents on their most recent visit to ice climb in the Mount Washington Valley. For this study, recall that the research team defined local residents as persons having their primary residence 34 miles or less from the Mount Washington Valley. For this study, it is expected that local residents did not participate in overnight lodging due to living nearby, so no estimates are offered for lodging.

Based on survey expenditures across common economic impact questions, local residents spent an estimated $248 dollars on their most recent ice climbing trip with some caveats. The greatest expenses were in purchasing retail climbing gear ($80). However, the survey’s delivery at the start of the climbing season may have inflated this result. Other notable expenditures include gasoline purchases ($46) dine-in restaurants ($36), and groceries ($35). These results should be treated cautiously due to a relatively low number of cases being considered.

Table 4 examines per person visitor expenditures as a result of ice climbing in the Mount Washington Valley. For this study, recall that visitors are described as persons living 35 or more miles from the Mount Washington Valley. Unlike local resident expenditures, visitor expenditures are also separated into two categories: expenditures within 34 miles of the the Mount Washington Valley and expenditures more than 35 miles from the Mount Washington Valley but still within the state of New Hampshire. This allows researchers to capture visitor costs of traveling to and from the Mount Washington Valley in addition to their expenditures there.

First, Table 4 describes lodging expenditures using hotels and resorts as well as rental cabins and houses. Note that expenditures here only describe persons who stayed overnight per their survey responses and 82% of respondent visitors elected to stay overnight during their trip. On average, visitors spent $246 over their entire visit for lodging in hotels/resorts and $492 on rental cabins/houses. Note these expenditures have been adjusted to focus only on persons staying overnight in their respective lodging category, excludes atypically high expenditures in relation to three standard deviations above the mean, and excluding persons who reported no lodging expenditures. Recall that the average overnight stay for visitors was around three days.

Next, Table 4 summarizes non-lodging purchases such as retail and food purchases. This includes expenditures from day users with no overnight stay as well as overnight users as there were too few single day users to model in this study. The greatest expenditures occurred at dine- in restaurants ($73), purchasing climbing gear ($63), and gasoline purchases ($45). In sum, visitors spent $275 on food, retail, and services as a result of their visit to the Mount Washington Valley to ice climb.

Finally, Table 4 includes visitor expenditures to and from The Mount Washington Valley in the state of New Hampshire. The first notable finding here is that nearly everyone visiting The Mount Washington Valley chose to stay very close to ice climbing opportunities rather than lodge further away elsewhere in the state. Next, visitors spent on average around $45 while traveling to and from their destination. This includes $12 on gasoline and $8 on food (across all categories). Additionally, a small but still notable number of respondents indicated flying to New Hampshire and getting a rental car to drive to the Mount Washington Valley, adding on average $19 to expenditures in this region.

At the time of this study, no formal count of ice climbing use patterns at the Mount Washington Valley existed so it was necessary to create this figure for the economic impact estimates. The researchers utilized a parking lot estimation approach to craft an initial visitation estimate. This approach provides a functional middle ground between a costly but detailed climbing crag-focused count with trail cameras and an imprecise gut-estimate based on user experiences of how many people might be using this area. This parking lot estimation approach has been utilized in prior similar studies that do not have more formal visitation estimates available and to also address how external factors (such as weather and access issues) can shape visitation estimates. [14]

The visitation estimate for this study began by identifying eight parking areas commonly used by ice climbers in this region. Study representatives were trained to visit each parking lot throughout the climbing season to count the number of vehicles found there. Using an online data entry form, counts would be recorded alongside date, time, and any notable issues (such as weather). These results were then compiled into an Excel file denoting every day in the climbing season and each parking lot. Weekly patterns would then be created using parking lot counts for each day across the season. Modifications were made for poor weather days (cases where temperatures were above 45 degrees Fahrenheit; cases with more than an inch of rain), snow accumulation which closed parking areas for the rest of the season, mixed use patterns (specifically hiking and other users at three parking areas shared by climbers), and festival events in the region (Ice Fest, which increases visitation rates to the area).

This approach is not without limitations. No parking lot estimate of this kind can ever fully account for the complexity of parking lot use patterns. For example, the models account for poor weather days but cannot account for weather changes by hour. The researchers could not fully account for mixed-use and utilized interviews with community members to estimate mixed use patterns at lots. While the approach allows for car turnover throughout the day during heavy use days, it cannot fully account for this idea. While a more detailed crag and trail account approach would be ideal, it was not feasible given its costs. With these limitations in mind, this approach provides an alternative which can be verified by future in-depth studies of the region.

Table 5 summarizes researcher visitation estimates for this study. In all, the parking lot estimate identified 6,605 cases attributed to climbers following the adjustments noted above. Using a prior study estimate of 1.8 climbers per car, the researchers estimate 11,889 ice climbing-related visits occurred in the The Mount Washington Valley in 2025. Note this figure is not a person count but rather a visit count. This means one person could (and likely will) visit more than once per season. When adjusted for local resident use patterns, the researchers attributed 15% of visits to local residents (1,783) and 85% to visitors (10,106 visits).

The researchers utilized IMPLAN (IMpacts for PLANning), a leading economic impact estimator, to model the economic impacts of ice climbing in the Mount Washington Valley. IMPLAN was created by the United States Forest Service in 1976 to examine the economic effects of resource outputs on local communities. In 1985, the University of Minnesota established IMPLAN as a standalone corporation to meet demands for regional modeling beyond the United States Forest Service. The company (then known as Minnesota IMPLAN Group, or MIG) would be sold in 2013 and officially change its name to IMPLAN. The online estimator utilized in the present study was released in 2018.

IMPLAN quantifies economic impacts across four measures: job estimates, labor wages, value added, and output. Job estimates are a count of full, part-time, and seasonal jobs supported by the activities being studied. The next three terms (labor income, value added, and output) are nested ideas. Labor income is a measure of the total employment-based income supported by the activity being studied. This includes both employee compensation and proprietor income. Value added measures changes in values generated by production of goods and services in the
analysis. Value added equals labor income plus taxes on production and imports plus other property income. Value added also can be treated as the same thing as Gross Domestic Product (GDP) in most studies. Finally, output represents the total value of production as it includes value added (which includes labor income) and intermediate inputs. Intermediate inputs are the purchase of non-durable goods and services used to create other goods and services but not intended for final consumption. Of the three, labor income is the most conservative measure of economic impact and often the most intuitive to understand. The analysis which follows includes all three, but focuses on jobs and labor income.

IMPLAN also describes economic impacts at three levels: direct, indirect, and induced. Direct impacts represent changes resulting from the activity being studied. For example, when ice climbers purchase a meal in the Mount Washington Valley, their expenditures create a direct impact in the analysis. IMPLAN now considers how that direct expenditure supports changes in the local economy starting with indirect impacts. Indirect impacts represent business to business transactions in purchasing goods and services to prepare for the next direct impact. For example, in our hypothetical meal purchase, the restaurant would now need to purchase more products to create future meals, as well as pay electric bills, pay worker wages, and cover any other costs needed to be ready for the next customer. Wages paid to workers represent our third and final form of impact: induced impact. Induced impact represents expenditures by households as a result of the direct effects being studied. Returning to our example, the employees of the restaurant would take their wages and pay housing costs, buy groceries and gasoline, and numerous other expenditures. Moreover, workers impacted at the indirect level (such as the company wholesaling food products and cooking supplies) would also likely generate induced spending as they pay their workers (indirect) and those workers spend their wages (induced). This process continues in IMPLAN until the expenditures being studied have fully leaked from the economy due to purchases of services and goods not available in the study area.

An economic impact study area considers the location of the direct impacts being studied (in the Mount Washington Valley) while also considering the immediate areas impacted by those direct impacts, such as where local businesses are located and where workers live. For this study (and based on conversations with local business owners, climbing organizations, and survey data), the researchers used a three-county study area (Coos, Carroll, and Grafton). This three-county study area will be referred to as Study Area One. Additionally, the researchers included the state of New Hampshire as a second study area for expenditures occurring outside of the initial study area but still inside the state as visitors travel to and from the Mount Washington Valley. This state model will be referred to as Study Area Two.

Supplemental tables A1-A6 summarize the expenditure patterns modeled in this economic impact analysis and are constructed based on mean expenditures from Tables 3-4 and visitation patterns in Table 5. The tables include three models examining the ice climbing season and three models examining Ice Fest, a nationally-famous festival which occurs during the ice climbing season in the Mount Washington Valley. [15] These supplemental tables also include mean values for reference alongside their estimated total expenditures, as well as methodological notes for how the expenditures are modeled in IMPLAN.

Table 6 summarizes the results of supplemental tables A1-A6. Based on visitation estimates and survey expenditure patterns, ice climbing supported $6.2 million in expenditures in the Mount Washington Valley during the 2024-2025 season.

Table 7 summarizes the economic impacts of the ice climbing season and the economic impacts of Ice Fest. These findings are based on the study’s Ice climbing visitation patterns (Table 5) and ice climbing expenditure patterns (Table 3, Table 4, and summarized in Table 6) plus the expenditures generated by Ice Fest (Mirkin et al., 2025). Table 7 breaks climbing season impacts down into three models: local resident expenditures, visitor expenditures (non-local resident) in the three-county study area (Study Area One), and visitor expenditures (non-local resident)
beyond the three-county study area but still in New Hampshire (Study Area Two). An additional model is included summarizing the overall economic impacts of Ice Fest which are also described in more detail in Mirkin and associates (2025). Note the Ice Fest model does not include job estimates as it is an annual event.

Based on the findings of this study, the researchers estimate ice climbers (local residents and visitors) spend $6.2 million annually, largely focused in Coos, Carroll, and Grafton counties. When analyzed in IMPLAN, the presence of ice climbing in the Mount Washington Valley supports $2.1 million in labor income alongside the presence of 36 jobs in the region. Additionally, the presence of ice climbing in the Mount Washington Valley adds $4.4 million annually to the region and state’s Gross Domestic Product. When examining the total value of production, ice climbing supports a total output of $6.4 million in the study areas examined in this report.

This study includes several limitations which should be considered in future studies to potentially collect more nuanced data and provide additional study results. The authors note the following limitations to this study:

1. This study uses a visitation methodology which could be improved upon using trail cameras, crag user counts, and other formalized ground-level visitation estimation techniques.
2. Ice Fest visitation estimates account for volunteers, participants, and speakers, but may potentially undercount attendance due to spouses/partners, children, and so forth traveling with those involved in the event but not actually attending.
3. As the population of individuals ice climbing has not been established, it is not possible to determine if the survey sampling is representative of this population. Although it is conjecture, the results of the study (such as use patterns and demographics) do align with other climbing studies cited throughout this study.

Economic impact studies in particular are also subject to several common limitations. These include:

1. Economic impact studies are snapshot estimates of a particular activity at a single moment in time. As such, the economic impact of any outdoor recreation activity will certainly vary from year to year based on weather, spending patterns, local business availability, and other variables. As such, the results in this study can be best understood
   as a scientific estimate of what expenditures would generally look like in a typical year barring major changes to the study area economy and its related activities.
2. Economic impact studies are not measures of returns on investment or benefit/cost relationships.
3. Economic impact studies are limited in their ability to demonstrate directly observable activities in the study area. Instead, economic impact studies are estimates of how the activity being studied creates/supports economic activities in a study area.
4. Economic impact studies in IMPLAN do not place limitations on inputs or outputs and cannot make assumptions about changes in demand for ice climbing-related services, shortages/surpluses of commodities, and so forth.

1. Lewis, S. Peter and Rick Wilcox. 2002. An Ice Climber’s Guide to Northern New England. TMC Books L.L.C.
2. Waterman, Laura, Guy Waterman, and Michael Wejchert. 2018. Yankee Rock and Ice: A History of Climbing in the Northeastern United States. Stackpole Books.
3. Eusden, J. Dykstra, Woodrow Thomoson, Brian K. Fowler, P. Thomson Davis, Wallace A. Bothner, Richard A. Boisvert, and John W. Creasy. 2013. The Geology of New Hampshire’s White Mountains. Durand Press.
4. Voorhis, Jimmy, Graham McDowell, Elizabeth Burakowski and Taylor Luneau. 2023. “The implications of warmer winters for ice climbing: A case study of the The Mount Washington Valley, New Hampshire, USA.” Frontiers in Human Dynamics 5.
5. Note 5 Bradley, Michael J. and James N. Maples. 2025. “The Economic Impact of Rock Climbing in Newton County, Arkansas.” Journal of Business Administration Online.
6. Bradley, Michael J. and James N. Maples. 2024. “The Economic Impact of Climbing in the Lander Area of Wyoming.” Journal of Outdoor Recreation, Education, & Leadership 16(3): 64-77
7. Thomas, Catherine Cullinane, Egan Cornachione, Lynne Koontz, and Christopher Keys. 2019. “National Park Service Socioeconomic Monitoring Pilot Survey Visitor Spending Analysis.” USGS Report 37.
8. Flyr, Matthew and Lynne Koontz. 2023. “National Park Visitor Spending Effects: Economic Contributions to Local Communities, States, and the Nation.” Science Report NPS/SR 1-68.
9. White, Eric. M. 2017. “Spending patterns of outdoor recreation visitors to national forests.” General Tech Report PNW-GTR-961.
10. Maples, James N., Michael J. Bradley, Mary Boujaoude, Mora Rehm, and Tim Golden. 2021. “Economic Impact of Rock Climbing in Bishop, California.” California Association for Health, Physical Education, Recreation and Dance 7(2): 24-30.
11. Bradley, Michael J. and James N. Maples. 2024. “The Economic Impact of Climbing in the Lander Area of Wyoming.” Journal of Outdoor Recreation, Education, & Leadership 16(3): 64-77.
12. Maples, James N., Ryan L. Sharp, Brian Clark, Katherine Gerlaugh, and Braylon Gillespie. 2017. “Climbing out of Poverty: The Economic Impact of Rock Climbing in Eastern Kentucky’s Red River Gorge.” Journal of Appalachian Studies 23(1): 53-71.
13. Maples, James N., Michael J. Bradley, Sadie Giles, Rhiannon Leebrick, and Brian Clark. 2019. “Climbing Out of Poverty: The Economic Impact of Climbing in West Virginia’s New River Gorge.” Journal of Appalachian Studies 25(2): 184-201.
14. Maples, James N., Michael J. Bradley, Mary Boujaoude, Mora Rehm, and Tim Golden. 2021. “Economic Impact of Rock Climbing in Bishop, California.” California Association for Health, Physical Education, Recreation and Dance 7(2): 24-30
15. Mirkin, Benjamin J., Eleanor Weisman-Rowell, Carson H. Zundel, James N. Maples, and Ryan L. Sharp. 2025. “The Mount Washington Valley Ice Fest Economic Impact Study.
16. Crompton, John. 2020. “Uses and abuses of IMPLAN in economic impact studies of tourism events and facilities in the United States: a perspective article.” Tourism Review 75 (1): 187-190.
17. Clouse, Candi. 2019. “Tourism Spending”. IMPLAN Support Article. Available at: https://support.implan.com/hc/en-us/articles/360026545913-Tourism-Spending

Ice Climbing season 2024-2025

Ice Fest 2025

Model 1: Local Resident Expenditures in Study Area One, 24-2025

Model 2: Visitor Expenditures in Study Area One, 24-2025

Model 3: Visitor Expenditures Outside Study Area One but Inside Study Area Two

Model 4: Ice Fest 2025, Total Expenditures (no jobs estimated; annual event)

Total, All Models

Supplemental tables A1-A6 summarize the expenditure patterns modeled in this economic impact analysis and are constructed based on mean expenditures from Tables 3-4 and visitation patterns in Table 5. These supplemental tables also include mean values for reference alongside their estimated total expenditures. These supplemental tables also include methodological notes on how the results are modeled in IMPLAN to create economic impact estimates. Items marked with a single asterisk (*) are modeled at one fifth of their value as these represent retail expenditures which could ostensibly be used in another location. In addition to capping retail expenditures at $500 or less, this step greatly reduces overestimating economic impacts based on expensive gear purchases which will be used well beyond the current visit. Items marked with two asterisks (**) denote items which are included as expenditures, but are not modeled in the economic impact study. These specifically relate to air transportation costs, which can be very difficult to pinpoint when using county or even state study areas in lieu of a national study area.
Although reported and included in total expenditures, these items are not modeled in IMPLAN to reduce the risk of overestimation.

The researchers elected, in accordance with National Park Service economic impact methodologies (see Cullinane et al., 2019), to model local resident expenditures as part of this economic impact study. There is some debate over the best approach to modeling (or not modeling) local resident expenditures when analyzing studies in IMPLAN. [16] The core of this debate focuses on if residents of a study area can be treated as new expenditures occurring in a study area. New expenditures are aptly described as expenditures that are being brought by someone living outside of that study area. The argument follows that residents are better described as redirected expenditures as their expenditure patterns are already part of the study area. That said, the National Park Service does elect to include local residents as a visitor segment and model this as part of the economic impact study. Second, IMPLAN has noted there can be cause to include local resident expenditures in tourism studies under the premise of import substitution. [17] For example, small study areas with few alternatives for recreation opportunities are suitable to model resident expenditures as these limited opportunities to recreate nearby prevent these expenditures from leaking due to being spent elsewhere. Based on National Park Service’s established approaches, the small study area (three counties), and the rarity of ice climbing opportunities across the broader region and nation, the researchers opted to include local expenditures in the economic impact models.