Introduction

Our group is examining how valuable is Outdoor Recreation in the U.S. This topic is important because outdoor recreation serves as a critical driver for many local economies, generating revenue through tourism, supporting small businesses, and creating jobs.Understanding the economic value of these activities helps highlight their role in development.

By analyzing trends at a state by state level, we aim to provide insights that are useful for policymakers, economists, and community planners. Identifying patterns in tourism and recreational spending can help guide investment decisions, and inform economic policy.

Data and Methods

Data:

The data for this study on the U.S. outdoor recreation economy was provided by the Bureau of Economic Analysis (BEA), a division of the U.S. Department of Commerce. The BEA collected the data from their own Outdoor Recreation Satellite Account statistics, covering the period from 2012 to 2017 at both the state and national-levels. For this analysis, the sample size includes all 50 states as well as national-level estimates. Our independent variables include activity category, industry, and state. The dependent variable is the economic value of outdoor recreation, measured in millions of U.S. dollars.

Data Source: https://apps.bea.gov/scb/issues/2019/10-october/pdf/1019-outdoor-recreation-account.pdf

Methods:

We focused on two tables from the BEA: “Table 3. Outdoor Recreation Value Added by State, Selected Conventional Activities, 2017” and “Table 4. Outdoor Recreation Value Added by State, Selected Industries, 2017”. Table 3 provides insights into which outdoor activities are the most popular across different states, while Table 4 highlights which industries experience the greatest economic impact from outdoor recreation. Together, these tables allow us to examine both the activity-level and industry-level contributions to the overall economic value of outdoor recreation.

Import Libraries:

library(tidytext) 
library(tidyverse) 
library(readxl) 
library(ggplot2) 
library(forcats)

Making data frames for activity and industry:

activities_df <- read_excel("recreation_dataset.xlsx", sheet = "Table 3") 
industries_df <- read_excel("recreation_dataset.xlsx", sheet = "Table 4")

Top Activities:

Filter only Washington (WA):

washington_data <- activities_df %>%
  filter(States == "Washington") %>%
  pivot_longer(-States, names_to = "Activity", values_to = "Value")

Summarizing Top Activities combining all the states:

top_activities <- activities_df %>%
  filter(!is.na(States)) %>%
  pivot_longer(-States, names_to = "Activity", values_to = "Value") %>%
  group_by(Activity) %>%
  summarise(Total = sum(Value, na.rm = TRUE)) %>%
  arrange(desc(Total))

Summarizing the Top 5 States for each activity + Include WA:

top_states_activities <- activities_df %>%
  filter(!is.na(States)) %>%
  pivot_longer(-States, names_to = "Activity", values_to = "Value") %>%
  group_by(Activity) %>%
  slice_max(Value, n = 5) %>%
  bind_rows(washington_data) %>%
  arrange(Activity, desc(Value)) %>%
  mutate(States = fct_reorder(States, Value, .desc = FALSE))

Top Industries:

Summarizing Top Industries combining all the states:

top_industries <- industries_df %>%
  filter(!is.na(States) & States != "United States") %>%
  pivot_longer(-States, names_to = "Industries", values_to = "Value") %>%
  group_by(Industries) %>%
  summarise(Total = sum(Value, na.rm = TRUE)) %>%
  arrange(desc(Total))

Summarizing Top 5 States for each industry:

top_states_industries <- industries_df %>% 
  filter(!is.na(States) & States != "United States") %>%
  pivot_longer(-States, names_to = "Industries", values_to = "Value") %>%
  group_by(Industries) %>%
  slice_max(Value, n = 5)

Filtering Washington Top 5 Activities:

WA_Top_Activities <- activities_df %>% 
  filter(States == 'Washington') %>% 
  pivot_longer(-States, names_to = 'Activity', values_to = 'Value') %>% 
  arrange(desc(Value)) %>% 
  slice_head(n = 5)

Results

Figure 1: Top 5 Outdoor Recreation Activities in Washington
Washington’s outdoor recreation economy is led by conventional outdoor recreation (about $3.4M), followed by boating/fishing ($0.66M) and RVing ($0.37M). Snow activities and equestrian also appear in the top five, but with smaller contributions compared to water- and vehicle-based recreation.

Figure 2: Total Economic Value by Activity (Nationwide)
At the national level, conventional outdoor recreation dominates with over $130M in value, far surpassing other categories. Boating/fishing ($20.9M) and RVing ($16.9M) are also major contributors. Activities like motorcycling/ATVing, hunting/shooting/trapping, and equestrian fall in the middle range, while bicycling and recreational flying contribute the least to the total value.

Figure 3: Top 5 States by Outdoor Recreation Activity (Including Washington)
Breaking down activity contributions by state shows that certain activities cluster geographically. For example, Florida and California rank highest in boating/fishing, while Colorado and Utah lead in skiing and snowboarding. Washington isn’t present in the top 5 for a majority of the activities except for 2 which is Snowboarding/Skiing ($0.14M) and Recreational Flying ($0.15M).

Discussion

When we started this project, we thought Washington would rank much higher nationally because of the Pacific Northwest’s reputation for outdoor activities. The data showed otherwise. Larger states like California, Texas, and Florida led in most categories because of their bigger populations and tourism industries. States with unique geographies, like Colorado with snow sports, also outperformed Washington in certain activities. This shows that reputation doesn’t always match up with actual economic value.

Looking closer at Washington, some of the top activities were surprising. Boating and fishing made sense, but equestrian and RVing were also among the biggest contributors. This was unexpected and shows that less obvious activities play a bigger role in Washington’s economy than we assumed.

Overall, outdoor recreation adds major value across the U.S., often on the scale of other big industries. For Washington, the results suggest there’s room to grow in certain areas, but also a strong base in activities like conventional outdoor recreation. Policymakers can use this kind of information to guide investments in parks, infrastructure, and tourism.

One limitation is that our data comes from 2017, so it doesn’t capture more recent changes, like the spike in outdoor activity during the COVID-19 pandemic. We also didn’t look at demographics or cultural factors, which could explain differences in participation. Future studies could dig deeper into those areas.