Snowmobile Recreation in Wisconsin

Analysis of 2023 Outdoor Recreation Satellite Account Data

Author

Daniel Lee Consulting LLC

Code

library(tidyverse)
library(usmap)
library(patchwork)
library(tidyquant)
library(CGPfunctions) # for slope graphs - activity rank

source("../../00_Scripts/get_static_map.R")
source("../../00_Scripts/select_activity_state_year.R")
source("../../00_Scripts/filter_by_act_by_state_by_year.R")

OR_cleaned       <- read_rds("../../00_Data/data_wrangled/OR_cleaned.rds")
OR_pct_of_GDP    <- read_rds("../../00_Data/data_wrangled/OR_pct_of_GDP.rds")
OR_growth_static <- read_rds("../../00_Data/data_wrangled/OR_growth_static.rds")


# Choose levels
activity_chosen <- "Other snow activities (includes snowmobiling) 2"
activity_chosen_lab <- "Snowmobiling"
snowmobile_data_caption <- "Snowmobiling data here represents the BEA ORSA category, 'Other Snow Activities,' 
which also includes dog mushing, sleighing, snowshoeing, and snow tubing."
state_chosen    <- "Wisconsin"
year_chosen     <- 2023

Snowmobile Recreation in the United States

Code

# number of years lag
numb_yr_lag <- year_chosen - 2017

OR_selected <- OR_pct_of_GDP %>%
    
    select_activity_state_year(act_txt = activity_chosen)

growth_2017_2023_scalar <- OR_selected %>%
    
    group_by(Year) %>%
    summarise(OR_VA_yearly_total = sum(OR_VA)) %>%
    ungroup() %>%
    
    mutate(growth = ((OR_VA_yearly_total / lag(OR_VA_yearly_total, numb_yr_lag)) - 1)*100) %>%
    filter(!is.na(growth)) %>%
    pull(growth)

growth_2017_2023_scalar

Advocating for the snowmobile recreation requires a solid foundation of data to highlight its significance. According to the U.S. Bureau of Economic Analysis (BEA) Outdoor Recreation Satellite Account (ORSA), the snowmobile recreation experienced an impressive 235.2% growth in economic value from 2017 to 2023 in the United States. This remarkable expansion underscores both the increasing popularity of snowmobile activities and their substantial economic impact.

Snowmobile Recreation in Wisconsin

Code

## Chart 1 Outdoor Recreation Value Added as a Share of GDP ----

OR_selected <- OR_pct_of_GDP %>%
    
    select_activity_state_year(act_txt = activity_chosen, 
                               state_txt = state_chosen)

OR_selected %>%
    
    ggplot(aes(Year, OR_VA)) +
    geom_line(linewidth = 1.5, color = "lightblue") +
    scale_y_continuous(label = scales::dollar_format(scale = 1e-3)) +
    scale_x_continuous(breaks = seq(2017,2023,1)) +
    geom_text(aes(label = scales::dollar(OR_VA/1000, accuracy = 1)), col = "midnightblue") +
    theme_tq() +
    labs(title = str_glue("{state_chosen}: {activity_chosen_lab}"),
         subtitle = " Value Added in Millions of Dollars",
         y = NULL, x = NULL,
         caption = snowmobile_data_caption)

Code

# number of years lag
numb_yr_lag <- year_chosen - 2019

OR_VA_scalar <- OR_selected %>%
    filter(Year == 2023) %>%
    pull(OR_VA)

OR_VA_scalar

growth_OR_2019_2023_state_scalar <- OR_selected %>%
    
    mutate(growth = ((OR_VA / lag(OR_VA, numb_yr_lag)) - 1)*100) %>%
    filter(Year == 2023) %>%
    pull(growth)

growth_OR_2019_2023_state_scalar

growth_GDP_2019_2023_state_scalar <- OR_selected %>%
    
    mutate(growth = ((GDP / lag(GDP, numb_yr_lag)) - 1)*100) %>%
    filter(Year == 2023) %>%
    pull(growth)

growth_GDP_2019_2023_state_scalar

pct_2023_scalar <- OR_selected %>%
    filter(Year == 2023) %>%
    pull(pct)

pct_2023_scalar

In 2023, Wisconsin’s snowmobile recreation generated $45 million in value added, reflecting a 164.3% growth since 2019, compared to 23.5% in the overall state economy during the same period.

Code

OR_selected <- OR_pct_of_GDP %>%
    
    select_activity_state_year(act_txt = activity_chosen, year_txt = year_chosen)

OR_selected %>% 
    arrange(-pct) %>% 
    head(10) %>%
    
    ggplot(aes(pct, fct_reorder(state, pct))) +
    geom_col(fill = "midnightblue") +
    geom_text(aes(label = scales::percent(pct, accuracy=0.01)), 
              hjust = 1, col = "white")  + 
    scale_x_continuous(labels = scales::percent_format(accuracy=0.01))  + 
    theme_tq() +
    labs(title = str_glue("Top 10 States in {activity_chosen_lab}"),
         subtitle = str_glue("Value Added as a Share of State GDP, {year_chosen}"),
         x = "Percent of State GDP", y = NULL,
         caption = snowmobile_data_caption)

Code

state_rank <- OR_selected %>% arrange(desc(pct)) %>% mutate(rank = row_number()) %>% filter(state == state_chosen) %>% pull(rank)

In 2023, snowmobile recreation accounted for 0.01% of Wisconsin’s gross domestic product (GDP) — attaining number 27 position in the United States.

Outdoor Recreation Activity Rankings

Code

df <- OR_cleaned %>% 
    filter_by_act_by_state_by_year(state_lab = state_chosen)

# create slope graph
newggslopegraph(df, Year, OR_VA, Act) +
    labs(title = str_glue("Conventional Outdoor Recreation in {state_chosen}"),
         subtitle = "by value added in millions of current dollars", 
         caption = "Included are nine broad conventional outdoor recreation activities categorized by the Bureau of Economic Analysis")

Code

Snowmobile_rank <- df %>%
    arrange(-OR_VA) %>%
    filter(Year == year_chosen) %>%
    mutate(rank = row_number()) %>%
    filter(Act == "Snow activities") %>%
    pull(rank)

The chart above illustrates key trends in conventional outdoor recreation activities across Wisconsin since 2017. In 2023, “Snow activities” is ranked in number 8 outdoor recreational activity in the state.

Snowmobile Recreation in 2023 across States

Code

map_OR_dollar <- OR_selected %>% 
    get_static_map(variable = OR_VA, var_lab = "Billions of \nDollars") +
    labs(title = "in Billions of Dollars")

Code

map_OR_pct <- OR_selected %>% 
    get_static_map(variable = pct, var_lab = "Percent") +
    labs(title = "as Share of State GDP")

Code

map_OR_dollar + map_OR_pct  + 
    plot_annotation(title = str_glue('{activity_chosen_lab}: Value Added in {year_chosen}'),
         caption = snowmobile_data_caption)

The maps above highlight the significance of Wisconsin’s snowmobile recreation compared to other states. The map on the left presents the industry’s value added in billions of dollars, with prominent contributions from the Mountain West region, as well as large states like California and Texas standing out due to their sizable economies.

The map on the right adjusts for state size by showing value added as a percentage of state GDP. This comparison reveals the notable importance of the snowmobile recreation particularly in the Mountain West, Midwest, and Northeast regions, where it constitutes a significant portion of the local economy.

Snowmobile Recreation Growth across States

Code

# Select activity and year
OR_growth_selected <- OR_growth_static %>%
    
    select_activity_state_year(act_txt = activity_chosen, year_txt = year_chosen)

growth_max <- OR_growth_selected %>% slice_max(growth_OR)
growth_min <- OR_growth_selected %>% slice_min(growth_OR)

Code

map_OR_percent_change <- OR_growth_selected %>%
    
    get_static_map(variable = growth_OR, var_lab = "Percent") +
    labs(title = "Percent Change")

Code

map_OR_diff <- OR_growth_selected %>%
    
    get_static_map(variable = growth_diff, var_lab = "Percent") +
    labs(title = "Industry - State Economy")

Code

map_OR_percent_change + map_OR_diff  +
    plot_annotation(
        title = str_glue('{activity_chosen_lab}: Value Added during 2019-2023',
         caption = snowmobile_data_caption)
    )

The maps above reveal the growth trajectory of the snowmobile recreation in the United States from 2019 to 2023.

Percent Growth Since the Pandemic (Left Map): All states experienced industry growth compared to pre-pandemic levels, reflecting a nationwide resurgence in snowmobile recreation. Arkansas led the way with an extraordinary 1,678% growth, increasing from $2 million in 2019 to $36 million in 2023. New York saw a 48% growth. These trends underscore the significant expansion of snowmobile recreation, particularly in the Southeast and Midwest regions.

Growth Adjusted for State Economy (Right Map): To account for differences in overall economic growth, the second map subtracts each state’s GDP growth from the snowmobile recreation growth. This adjustment confirms the broader trend: the Southeast and Midwest regions continue to exhibit exceptional growth. Notably, in all 50 states, snowmobile recreation growth has outpaced general economic expansion, highlighting its strong recovery and rising economic importance nationwide.

Limitations of BEA ORSA Data

While the BEA Outdoor Recreation Satellite Account (ORSA) provides valuable insights, its statistics have certain limitations in fully capturing the economic impact of the snowmobile recreation:

Exclusion of Multi-Purpose Expenditures: Purchases that serve multiple purposes, such as towing trucks used for transporting snowmobiles, are not included in the statistics. This omission overlooks a significant portion of rider-related spending.
Generalized Tourism Spending: Travel and tourism expenditures, such as hotel stays and restaurant dining, are reported as part of an aggregate total for all outdoor recreation activities. These expenses are not attributed specifically to snowmobile activities, diluting the industry’s perceived economic contribution.
Aggregated Reporting Categories: ORSA groups multiple activities into broad categories (e.g., Other snow activities (includes snowmobiling)), which may obscure the detailed economic contributions of individual segments within the outdoor recreation sector.

Due to these limitations, stakeholders often commission consultants to conduct detailed economic impact studies, offering a more precise understanding of the industry’s role in the economy. Such tailored studies can provide actionable insights for advocacy and policymaking.

“Daniel Lee Consulting: Custom Economic Insights and Data Solutions for Your Distinctive Goals”

Daniel Lee

President

Daniel Lee Consulting LLC

https://danielleeconsulting.com/

https://danielleeeconomicconsulting.com

https://www.linkedin.com/in/daniel-lee-data-scientist/