Intlrnz/DigitalNomadPolicy

A Global Benchmark Dataset of Digital-Nomad Policy Adoption, Tourism Flows, and Labor-Market Indicators

Overview

This repository contains a harmonized country-year panel dataset designed to support empirical research on the relationships between cross-border remote work policies, international tourism activity, labor market dynamics, and macroeconomic indicators.

The core panel integrates statistical database series from the United Nations World Tourism Organization (UN Tourism) and the World Bank Open Data platform with a hand-coded policy tracker documenting dedicated residency and long-stay visa categories. By linking cross-sectional policy attributes to longitudinal economic performance indicators under a standardized framework, this dataset provides the empirical infrastructure required to model global mobility choices, pandemic-era structural adjustments, and causal evaluations of remote-work migration streams.

---

Dataset Characteristics

• Temporal Coverage: Annual country-level panel observations spanning 2008–2024.
• Geographic Coverage: Near-global reach encompassing 190 independent countries and territories.
• Dimensionality: Main longitudinal panel containing 2,464 country-year rows across 14 distinct attributes.
• Policy Granularity: Detailed cross-sectional mapping of 30 national digital nomad visa programs alongside an 11-country curated non-adopter comparison matrix.

---

Repository Structure & File Contents

The data is delivered in a single, well-structured Excel workbook: DigitalNomadDataset.xlsx. It comprises three relational worksheets linkable via the standardized ISO 3166-1 alpha-3 country identifiers (iso3).

1. tourism_and_macroeconomic_data

Annual country-year data tracking financial profiles, structural volatility, and human traffic vectors.

• country_name / iso3 / year: Primary identification keys (zero missingness).
• gdp / inflation_annual_pct / exchange_rate_lcu_per_usd: Core World Bank macroeconomic controls.
• unemployment_rate: Standardized ILO-modeled labor statistics.
• internet_usage_pct / price_level_index_gdp: Digital infrastructure metrics and time-varying purchasing power parity ratios (benchmarked to US = 100).
• arrivals_total / arrivals_business / expenditures: UN Tourism metrics tracking international border-crossings and balance-of-payments receipts.
• tourism_gdp_share / tourism_employment: Sectoral direct GDP contributions and labor headcounts.

2. policy_data

Cross-sectional profiles tracking regulatory barriers and explicit entry criteria across 30 pioneer jurisdictions.

• visa_adoption_year: The calendar year the formal digital-nomad or remote-work visa path was officially launched (ranging across 2020–2024).
• coarse_tax_treatment: Simplified characterization of the local fiscal regime applied to foreign-earned incoming remote revenue.
• min_income_to_apply_per_month_usd: Standardized baseline monthly income required to satisfy legal eligibility thresholds.
• visa_duration_months / min_visa_fee_usd: Maximum initial authorized period of stay and base government administrative entry fees.

3. non_adopters

Curated baseline matrix mapping adjacent mobility channels, non-resident business parameters, and subnational remote incentives for 11 major non-adopting global economies (Australia, Canada, China, Germany, Hong Kong SAR, India, Nigeria, Russia, South Africa, United States, and Vietnam).

• primary_remote_work_visa: Primary de facto immigration mechanism or traveler pathways used by location-independent personnel.
• supplementary_alternative_visas: Talent, working-holiday, or investor visa options.
• opc_corporate_setup_policy: single-member corporate legal frameworks and constraints facing non-residents.
• digital_nomad_community_policies: Localized municipal incentives, cash grants, or regional co-living hubs (e.g., Tulsa Remote, Silicon Cape).

---

Data Quality & Missingness Warning

Users should note that missing data values are preserved in their original form to maintain complete fidelity to source channels. Gaps are heavily concentrated inside the UN Tourism series (e.g., tourism_employment at 55.52% and tourism_gdp_share at 50.28% missingness), reflecting non-random national reporting frameworks and validation pipelines. Missingness patterns are primarily concentrated among small island states, microstates, or sanctioned territories (such as Cuba or the British Virgin Islands).

Macroeconomic indicators, price level indices, internet percentages, and hand-coded policy matrices exhibit near-complete coverage (under 5% overall missingness). We highly recommend applying appropriate missing-data treatments or missingness indicators prior to executing covariate adjustments.

---

Suggested Analytical Use Cases

Staggered Difference-in-Differences (DiD)

The staggered rollout of digital nomad programs between 2020 and 2024 across 30 jurisdictions creates an ideal environment for natural experiments. Because standard Two-Way Fixed Effects (TWFE) models suffer from negative-weighting bias under staggered adoption with heterogeneous effects, researchers should pair naive baseline regressions with group-time cohort estimators, such as Callaway and Sant'Anna's $ATT(g,t)$ or Sun and Abraham's interaction-weighted event-studies. The un-sampled non-adopter pool provides an extensive set of never-treated control matches.

Compositional Shifts and Structural Substitution

Researchers can evaluate whether visa implementations lead to a genuine level shift in tourist demographics by computing the dynamic ratio of business-purpose entries against total international tourist arrivals. Evaluating these trends over event-time facilitates robust parallel-trends falsification checks in the years immediately preceding official adoption.

Subnational Data Linkages

This macro-level framework serves as a natural anchor for localized, subnational empirical data. Continuous variables can be collapsed using population-weighted averages to anchor regional microdata arrays, such as the Thailand–Vietnam Socio-Economic Panel (TVSEP) or localized constituency polygon features (e.g., Hong Kong DCCA_21C census tables), mapping how high-income entry floors alter local hospitality dependencies and neighborhood housing pressures.

---

Croissant Metadata Compatibility

This dataset contains a machine-readable croissant.json manifest conforming to the Croissant 1.1 Specification. This index includes extensive Responsible AI (RAI) metadata tracking intended usage, known coverage limitations, data collection biases, and structural field definitions, ensuring immediate compatibility with major dataset hosting search architectures (such as Google Dataset Search and MLCommons indexing platforms).

---

License & Terms of Use

This benchmark dataset is distributed under the Creative Commons Attribution 4.0 International (CC BY 4.0) license. You are free to share, copy, adapt, and redistribute the material in any medium or format, provided you give appropriate credit, provide a link to the license, and indicate if changes were made.

Citation

License: Creative Commons Attribution 4.0 International license (CC-BY-4.0). Citation: DigitalNomadPolicy (Hugging Face Repository: https://huggingface.co/datasets/Intlrnz/DigitalNomadPolicy)