World Bank - International Tourism Arrivals (ST.INT.ARVL)

One number per economy per year: the people who arrived. ST.INT.ARVL - 'International tourism, number of arrivals' - is the World Development Indicators series carrying UN Tourism's counts of inbound tourists: overnight visitors who travel to a country other than their usual residence, outside their usual environment, for less than twelve months and for a purpose other than being paid there. That single harmonized definition turns into the demand curve underneath every destination market on earth.

The footprint is roughly 265 World Bank economies and aggregates, national rows sitting beside regional and income-group rollups such as Europe & Central Asia, with about 8-9,000 populated country-year observations once empty cells drop out. And the series does not flinch from its biggest event: Spain logs 126.17 million arrivals in 2019 and 36.41 million in 2020, the clearest single-number account of what border closures did to lodging and cruise demand that official statistics produce.

The table arrives wide: identifying columns up front, then one column per year, blank wherever no estimate exists. Verified rows look like this:

# ST.INT.ARVL -- one row per economy, one column per year
country_name     iso3   y1995       y2016        y2019        y2020
Spain            ESP    52460000    -            126170000    36410000
France           FRA    -           115561000    217877000    117109000
United States    USA    79732000    -            165478000    45037000

Three rows, and the whole story of modern inbound travel is already visible. Spain's arrivals nearly two-and-a-half-folded between 1995 and 2019, then lost 71 percent of them in a single year. The United States traced the same arc (79.73 million rising to 165.48 million, then down 73 percent to 45.04 million). France halved to 117.11 million while still leading the world - partly resilience, partly the methodology change behind its 2019 peak. Missing years print as blanks, never zeros dressed up as data.

Five fields carry every analysis. Each definition below describes exactly what the delivered column holds; the example column shows the field at work on a real economy-year record.

Additional fields on request: region, income group and per-economy collection notes travel in the companion country-metadata table rather than the core data file. They come along with any delivery, and their exact conventions are confirmed against live records when we prepare your sample - the same pass locks down column naming for your pipeline.

Three chips summarize the footprint:

• Geography: ~265 economies and aggregates - roughly 200-plus countries report actual arrival counts in a typical covered year, alongside regional and income-group rollups, so emerging-market destinations and mature ones land in the same pull.
• Time frame: year columns reach into the mid-2020s, but the populated observations run 1995 through 2020 - 206 economies reporting in 1995, 223 in 2019, 132 in 2020. Later years sit empty until the underlying estimates are published, which is itself information about how far behind a given market reports.
• Granularity: one annual national total per economy per year. No subnational, port or operator cut exists here by design; splits like those live in companion datasets on the same shelf.

Against the wider Datadory catalog - average quality score 7.81 across all 1,744 datasets - this slice scores 9/10, carried by verified field documentation and a quarter-century of comparable annual history under one unchanged definition.

API, files, or your warehouse. Daily, weekly, or hourly.

You choose the channel and the cadence; the field dictionary above travels unchanged across all three. Bulk files suit overnight warehouse loads, structured payloads suit event-driven pipelines, and the economy-year table lands in CSV-, spreadsheet- and SQL-friendly shapes for analysts who live in a BI tool. Because the grain never changes, switching cadence later is a settings conversation, not a re-integration project.

Ranked by how directly the series answers their day job:

1. Market researchers & consultants. A single harmonized definition applied to 200-plus reporting economies is the only fair way to benchmark destination markets - no stitching together national statistical offices that each count differently, though per-economy collection notes still deserve a read before publication.
2. Data scientists & ML engineers. A quarter-century annual panel of pre-COVID, collapse and recovery regimes is exactly what a demand-forecasting model needs for training on shocks, and the economy-year grain drops straight into any dataframe feeding occupancy or RevPAR forecasts.
3. Investors & quants. Country arrivals anchor the demand side of lodging and cruise exposure models; joining this series to revenue lines turns 'popular destination' into a growth rate you can defend.
4. Competitive intelligence & product teams. Destination-attractiveness framing for hospitality portfolios, with one honest limit: no competitor-level granularity exists here, and cruise passengers count as arrivals in several markets - relevant if your comparison set is cruise-dependent.
5. Sales & growth teams. Ranking destination countries by inbound volume is the fastest territory-sizing exercise in travel, and the aggregate rows hand you regional rollups without building them.

Notes that pair well with this page:

• Hotels, Resorts & Cruise Lines data hub - the pooled view of the industry, from booking records to economic indicators (hub).
• World Bank - International Tourism Receipts (ST.INT.RCPT.CD) - the money behind these headcounts; divide receipts by arrivals on ISO3 plus year and you have yield per visitor (receipts).
• Eurostat Nights Spent at Tourist Accommodation (tour_occ_arn2) - the deeper European vein, regional NUTS 2 detail versus this series' national totals (Eurostat nights).
• US NTTO I-94 International Visitor Arrivals Program - monthly, visa-type and port-level detail for one market where this series holds only an annual total (US NTTO).
• Head-to-head: ST.INT.ARVL vs Eurostat Tourism Statistics Explained Hub - a dedicated page weighing global breadth against European depth (comparison).
• Industry guide - how hospitality and travel datasets fit together across the catalog (hotels-resorts-cruise-lines data guide).