ATV Recreation in Vermont

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 <- "Motorcycling / ATVing"
state_chosen    <- "Vermont"
year_chosen     <- 2023

ATV 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 ATV/UTV 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 Motorcycling/ATVing sector experienced an impressive 43.5% growth in economic value from 2017 to 2023 in the United States. This remarkable expansion underscores both the increasing popularity of ATV/UTV activities and their substantial economic impact.

ATV Recreation in Vermont

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}"),
         subtitle = " Value Added in Millions of Dollars",
         y = NULL, x = NULL)

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, Vermont’s ATV/UTV recreation generated $16 million in value added, reflecting a 31.9% growth since 2019, compared to 25.2% 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}"),
         subtitle = str_glue("Value Added as a Share of State GDP, {year_chosen}"),
         x = "Percent of State GDP", y = NULL)

Code

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

In 2023, ATV/UTV recreation accounted for 0.04% of Vermont’s gross domestic product (GDP) — attaining number 28 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

ATV_rank <- df %>%
    arrange(-OR_VA) %>%
    filter(Year == year_chosen) %>%
    mutate(rank = row_number()) %>%
    filter(Act == "Motorcycling / ATVing") %>%
    pull(rank)

The chart above illustrates key trends in conventional outdoor recreation activities across Vermont since 2017. In 2023, “Motorcycling/ATVing” is ranked in number 6 outdoor recreational activity in the state.

ATV 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}: Value Added in {year_chosen}'))

The maps above highlight the significance of Vermont’s ATV/UTV recreation compared to other states. The map on the left presents the industry’s value added in billions of dollars, with large states such as California and Texas prominently standing out.

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 ATV/UTV recreation in Midwestern states.

ATV 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}: Value Added during 2019-2023')
    )

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

Percent Growth Since the Pandemic (Left Map): 45 states experienced industry growth compared to pre-pandemic levels, underscoring a national resurgence. Indiana led with a remarkable 123% growth, while New Mexico saw a -19% growth. This variation highlights how state-level policies, regional demand, and other factors influence industry dynamics.

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 ATV/UTV recreation growth. This adjustment demonstrates that in 27 states, the industry’s growth outpaces general economic expansion, underscoring its robust recovery and increasing economic significance 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 ATV/UTV recreation:

Exclusion of Multi-Purpose Expenditures: Purchases that serve multiple purposes, such as towing trucks used for transporting ATVs, 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 ATV/UTV activities, diluting the industry’s perceived economic contribution.
Aggregated Reporting Categories: ORSA groups multiple activities into broad categories (e.g., Motorcycling / ATVing), 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/