Tourism Industry Dataset (Tour2018-2023AU24)

The dataset series Tour2018–2023AU24 contains quantitative data about the U.S. tourism and travel-related industries from 2018 to 2023. Each annual dataset presents detailed production, expenditure, and value-added information across multiple travel service categories such as traveler accommodations, food services, air, rail, and water transportation, urban transit, sightseeing, and vehicle rentals. The datasets include key economic indicators(industry output, intermediate inputs, compensation of employees, taxes, and gross operating surplus) measured in millions of dollars.

The datasets show us not only the economic indicators but also focuses on humanistic factors as well, which our project is primarily focused on. By comparing data across years, researchers can explore how big events like the COVID-19 pandemic affected the tourism economy, particularly shifts in consumption, labor income, and industrial recovery. For example, the 2020 dataset shows sharp declines in accommodations and air transportation, aligning with global travel restrictions, due to the pandemic while later years (2022–2023) show a gradual recovery.

Timeline Dataset (CDC Museum COVID-19 Timeline)

The CDC Museum COVID-19 Timeline documents major events throughout the pandemic, including public health announcements, policy shifts, travel advisories, border restrictions, scientific updates, and reopening guidelines. Unlike numerical datasets, this timeline captures the chronological context of the crisis, helping researchers understand how government actions and public health conditions evolved over time.

For our project, the timeline provides essential background for interpreting changes in U.S. tourism activity. Many events, such as national emergency declarations, travel bans, mask mandates, and phased reopening plans, directly impacted mobility and travel demand. When combined with the tourism industry dataset (Tour2018–2023AU24) and international arrivals data, the CDC timeline helps explain why economic output, employment, and visitor numbers dropped sharply in 2020 and gradually recovered in later years, offering a more complete understanding of how COVID-19 reshaped the tourism sector.

International Arrivals Dataset (NTTO, 2000-2023)

This dataset, released by the National Travel and Tourism Office (NTTO), provides the number of international travelers arriving in the United States and worldwide annually from 2000 to 2023, disaggregated by World Region and Country of Origin. It allows for long-term trend analysis and shows how global mobility changes over time, and especially how travel to the U.S. collapsed in 2020 and gradually recovered. The dataset reveals sharp declines in arrivals from every region in 2020, uneven recovery across world regions, and shifts in economic growth and COVID-19 policies around the World.   

Together, these datasets give both a domestic economic view and a global mobility view, offering a more complete picture of how tourism changed before, during, and after COVID-19.

How the Data Was Generated

Corporations like the Bureau of Economic Analysis (BEA) and Bureau of Transportation Statistics (BTS) assisted in creating this data. Oftentimes these are taken from multiple sources such as surveys of businesses (e.g., Census Bureau’s Annual Business Survey), administrative tax data, and national accounts. The National Travel and Tourism Office uses data from country-of-residence reporting, air carrier flights, and arrival records. 

Data cleaning and integration steps likely involved standardizing economic classifications (NAICS codes), adjusting for inflation, and converting all values into consistent millions-of-dollars units. Because of this modeling process, the numbers represent estimates rather than direct observations.

Original Sources and Funding

The datasets draw from publicly funded federal sources, primarily through the U.S. Department of Commerce and its sub-agencies. Funding likely comes from government appropriations for economic statistics programs, with the goal of supporting public policy, economic forecasting, and academic research. There is no evidence that these datasets were funded or influenced by private corporations; however, economic modeling choices made by the agencies reflect particular institutional priorities, such as emphasizing national economic growth over local social outcomes.

What the Dataset Reveals

The tourism dataset allows us to examine macroeconomic relationships within the travel sector:

• The relative economic weight of different travel services. e.g., accommodations vs. transportation.
• Post-COVID recovery trends, such as whether spending returned faster in domestic air travel than in international travel.
• Sectoral interdependence, showing how one industry (e.g., accommodations) affects others (e.g., food services or vehicle rentals).
• Employment structures through “compensation of employees” data, offering clues about which sub-industries rely more heavily on labor versus capital.

The NTTO international arrivals dataset provides a long-term view of how global mobility around the world has shifted from 2000 to 2023:

• Showing the trends in international travel to the U.S. has changed from 2000 to 2023 across different world regions.
• The scale and direction of international travel flows, revealing which regions and countries contribute to the most travelers around the world and how patterns shift over time.
• The magnitude of the COVID-19 impact on global tourism and regional differences in travel recovery.
• Structural changes in global travel behavior around the world before, during, and after COVID-19.

From a humanistic perspective, the dataset can illuminate broader questions: How do economic patterns reflect social privilege in who can afford to travel after COVID-19? How did labor disruptions in hospitality affect low-wage workers? Which sectors benefited most from government recovery programs? Likewise, the NTTO international arrivals dataset raises additional social questions, like which regions regained mobility fastest, and how do these differences reflect unequal access to vaccines and varying public health policies? Together, these datasets provide a structural, quantitative view of both the U.S. tourism economy and global mobility, revealing systemic inequality indirectly through numbers while also pointing toward deeper cultural, political, and human experiences that lie beyond purely economic measurement.

What the Dataset Leaves Out

While economically rich, the datasets cannot capture lived experiences or cultural dimensions of travel. It omits:

• Demographic data such as traveler age, income, race, or nationality.
• Worker experiences, including job security, satisfaction, or working conditions.
• Environmental impacts of travel (e.g. emissions/ sustainability practices).
• Regional granularity – the data is aggregated nationally, masking local differences.
• Qualitative motivations behind travel, such as leisure vs. necessity, or emotional and psychological aspects of post-pandemic travel.Environmental impacts of travel (e.g. emissions/ sustainability practices).
• Reasons for travel, traveler demographics, and economic spending.
• Internal distributions, transit passengers, and carbon footprint or environmental impacts.

Because of this, these datasets risk flattening complex social realities into economic abstractions. They quantify movement and money, but not the human experience behind travel. For instance, “Traveler Accommodations = $233,762 million” conceals who those travelers are, what destinations they visited, and how their access to travel is structured by wealth, policy, and geography. 

Ontology and Ideological Effects

The ontology of both datasets reflects a distinctly economic and administrative view of travel. The U.S. tourism dataset defines tourism primarily through industries, outputs, and expenditures. The NTTO dataset similarly conceptualizes travel as the movement of travelers across borders, captured only when individuals interact with state systems' records. In both cases, what “counts” as travel is limited to activities that can be taxed, recorded, or monetized. This framing privileges formal economic transactions and official border crossings, while rendering invisible the cultural, emotional, or communal dimensions of travel. As a result, these datasets promote a worldview in which tourism is understood as an economic push rather than a social or cultural practice. The absence of variables related to equity, community impact, environmental sustainability, or traveler well-being demonstrates how institutional priorities shape data collection, guiding analysis toward economic optimization rather than human-centered outcomes.

Biases and Subjectivity

Although both datasets appear neutral, they contain subjective choices that introduce bias. The categorization of travel-related industries reflects institutional definitions that may not align with how travelers actually experience mobility; decisions about which sectors belong in the tourism economy privilege certain forms of commerce while excluding others. 

In the NTTO dataset, geographic and administrative biases can arise from where the data is collected and the completeness of arrival data across countries, reflecting disparities in global views. Economic bias is also in both datasets, as they prioritize financial measurements and market outcomes, suggesting that economic recovery equates to social recovery. 

These forms of bias reveal how data production is shaped by institutional perspective, assumptions about whose experiences matter, what travel represents, and which aspects of mobility deserve to be quantified.

Conclusion

The Tour2018–2023AU24 dataset provides a robust quantitative foundation for analyzing how the U.S. travel industry evolved through and after the pandemic. The data underlines macro structures globally and how different sectors have significant interdependence between themselves. Researchers can then find answers to questions about industry recovery, consumer willingness to spend and other interesting aspects. On the other hand, the data sidelines more human variables such as satisfaction or cultural change.

The International Arrivals dataset NTTO reveals a quantitative analysis of how many people traveled to the U.S., where they came from, how travel volumes changed over time, how each world region was affected by and recovered from COVID-19, and the structural differences in global mobility. It provides a global, long-term, and traveler-centered perspective which shows who is traveling and gives us a good understanding of travel throughout COVID-19.

Understanding these limitations reminds us that data are never neutral, they reflect institutional choices about what to measure and what to silence. To produce more ethical and inclusive analyses, future work should integrate this dataset with qualitative or demographic data that can capture the lived realities behind the numbers.

* We used AI to proof our work and give us ideas on the various subsections