# Knowledge Worker Global Compensation - Research Compilation

**Source ID:** DS-00005
**Record Created:** 2025-10-25
**Last Updated:** 2025-12-10
**Cataloger:** Substrate Data Curation
**Review Status:** Reviewed

---

## 🎯 CURRENT BEST ESTIMATE

| Metric | Value | Confidence |
|--------|-------|------------|
| **Global Knowledge Worker Compensation** | **$35-50 trillion/year** | 65% |
| **U.S. (Professional Definition)** | **$6-8 trillion/year** | 95% |
| **U.S. (Broad Definition)** | **$10-12 trillion/year** | 85% |

**December 2025 Revision:** Global estimate reduced from $50-70T to $35-50T after mathematical validation against global labor share (~$58T total labor compensation). The $70T upper bound exceeded plausible labor share.

---

## Bibliographic Information

### Title Statement
- **Main Title:** Knowledge Worker Global Compensation: Summary Table (2024-2025)
- **Subtitle:** Multi-Source Research Compilation on Global Knowledge Worker Salaries
- **Abbreviated Title:** Knowledge Worker Compensation
- **Variant Titles:** Global Tech Salaries, Knowledge Economy Compensation Data

### Responsibility Statement
- **Publisher/Issuing Body:** Substrate Data Curation (Kai Personal AI Infrastructure)
- **Department/Division:** Multi-Agent Research System
- **Contributors:** 10 parallel AI research agents (Perplexity, Claude, Gemini researchers)
- **Contact Information:** Research compiled via automated research system

### Publication Information
- **Place of Publication:** Digital research compilation
- **Date of First Publication:** 2025-10-19
- **Publication Frequency:** On-demand research updates
- **Current Status:** Active

### Edition/Version Information
- **Current Version:** 2025-12-10 research snapshot (revised)
- **Version History:**
  - 2025-12-10: Revised global estimate from $50-70T to $35-50T based on labor share validation
  - 2025-11-25: 40-agent comprehensive synthesis with Bayesian reconciliation
  - 2025-10-19: Initial research compilation
- **Versioning Scheme:** Date-based research snapshots

---

## Authority Statement

### Organizational Authority

**Issuing Organization Analysis:**
- **Official Name:** Substrate Data Curation (Kai Personal AI Infrastructure)
- **Type:** Research compilation via multi-agent AI system
- **Established:** 2025
- **Mandate:** Aggregate authoritative compensation data from multiple sources
- **Parent Organization:** Independent research project
- **Governance Structure:** Automated research with human validation

**Domain Authority:**
- **Subject Expertise:** Aggregation of authoritative salary data from government agencies, consulting firms, industry reports
- **Recognition:** Synthesizes data from BLS (U.S. Bureau of Labor Statistics), OECD, ILO, major consulting firms
- **Publication History:** Initial research compilation (2025)
- **Peer Recognition:** Sources include recognized authorities (BLS, OECD, Dice, Glassdoor, Robert Half)

**Quality Oversight:**
- **Peer Review:** Multi-agent cross-validation (10 parallel agents)
- **Editorial Board:** Human review of aggregated findings
- **Scientific Committee:** Source validation against authoritative data providers
- **External Audit:** Not applicable (research compilation)
- **Certification:** None (data aggregation from certified sources)

**Independence Assessment:**
- **Funding Model:** Independent research project
- **Political Independence:** No political affiliations
- **Commercial Interests:** None (non-commercial research)
- **Transparency:** Full source attribution; research methodology documented

### Data Authority

**Provenance Classification:**
- **Source Type:** Tertiary (aggregates secondary and primary sources)
- **Data Origin:** Multi-source aggregation from government agencies, consulting firms, industry reports
- **Chain of Custody:** Primary sources (BLS, OECD, ILO, consulting firms) → AI research agents → Data synthesis → Public documentation

**Tertiary Source Characteristics:**
- Synthesizes data from 20+ primary and secondary sources
- Adds value through cross-validation and regional aggregation
- Provides confidence levels based on source quality
- Identifies data gaps and methodological limitations

---

## Scope Note

### Content Description

**Subject Coverage:**
- **Primary Subjects:** Labor Economics, Knowledge Worker Compensation, Technology Sector Salaries, Global Workforce Statistics
- **Secondary Subjects:** AI/ML Premium Roles, Regional Salary Comparisons, Freelance Knowledge Work
- **Subject Classification:**
  - LC: HD (Labor Economics), HB (Economic Theory)
  - Dewey: 331.2 (Labor Economics - Wages)
- **Keywords:** knowledge workers, technology salaries, global compensation, tech workers, AI/ML roles, software engineer salaries, consulting compensation, STEM workers, professional services salaries

**Geographic Coverage:**
- **Spatial Scope:** Global (U.S., Europe, Asia-Pacific, Latin America)
- **Countries/Regions Included:** United States, Switzerland, Denmark, Germany, Singapore, Eastern Europe, China, India, Latin America
- **Geographic Granularity:** Country and regional aggregates
- **Coverage Completeness:** U.S. high (85% confidence); Global medium (65% confidence)
- **Notable Exclusions:** Africa, Middle East (limited data availability)

**Temporal Coverage:**
- **Start Date:** 2024 (primary data year)
- **End Date:** 2025 (latest projections)
- **Historical Depth:** 1-2 years (snapshot with growth trends)
- **Frequency of Observations:** Annual salary data
- **Temporal Granularity:** Year-level
- **Time Series Continuity:** Not applicable (single research snapshot)

**Population/Cases Covered:**
- **Target Population:** Global knowledge workers (~1+ billion estimated)
- **Inclusion Criteria:** Knowledge-intensive roles (software engineers, data scientists, consultants, healthcare professionals, financial analysts)
- **Exclusion Criteria:** Manual labor, routine clerical work, retail service workers
- **Coverage Rate:** U.S. ~100 million workers (38-42% of workforce); Global ~1+ billion
- **Sample vs. Census:** Aggregation of sample surveys and employment statistics

**Variables/Indicators:**
- **Number of Variables:** 15+ compensation and workforce metrics
- **Core Indicators:**
  - Total annual compensation by geography and sector
  - Average/median salaries by role
  - Year-over-year growth rates
  - Workforce size estimates
  - AI/ML premium percentages
  - Freelance knowledge worker statistics
- **Derived Variables:** Total market value calculations, regional averages, growth trend projections
- **Data Dictionary Available:** Yes - see knowledge-worker-compensation-data.md

### Content Boundaries

**What This Source IS:**
- Comprehensive multi-source aggregation of knowledge worker compensation data
- Best available synthesis of U.S. knowledge worker salaries (high confidence)
- Useful global overview with regional estimates (medium confidence)
- Identifies data gaps and methodological limitations transparently

**What This Source IS NOT:**
- NOT primary salary survey data (use BLS OEWS, Dice, Glassdoor directly)
- NOT real-time (2024-2025 snapshot; annual updates required)
- NOT granular below country/regional level (no city-specific data)
- NOT comprehensive for all countries (Africa, Middle East gaps)
- NOT peer-reviewed academic research (research compilation)

**Comparison with Similar Sources:**

| Source | Advantages Over This Source | Disadvantages vs. This Source |
|--------|----------------------------|-------------------------------|
| BLS OEWS (U.S. only) | Primary government data; high accuracy | U.S. only; no global coverage; annual lag |
| OECD Average Wages | Official international data; high credibility | Country-level only; no occupation-specific knowledge worker data |
| Dice Tech Salary Report | Tech sector depth; annual trends | U.S. tech only; narrow scope |
| Glassdoor Salaries | User-generated; granular roles | Self-reported bias; quality varies |
| Payscale/Robert Half | Consulting firm depth; market insights | Subscription models; narrower scope |

---

## Access Conditions

### Technical Access

**API Information:**
- **Endpoint URL:** N/A (static research compilation)
- **API Type:** N/A
- **API Version:** N/A
- **OpenAPI/Swagger Spec:** N/A
- **SDKs/Libraries:** N/A

**Authentication:**
- **Authentication Required:** No
- **Authentication Type:** None (public research documentation)
- **Registration Process:** Not applicable
- **Approval Required:** No
- **Approval Timeframe:** N/A

**Rate Limits:**
- Not applicable (static document)

**Query Capabilities:**
- Not applicable (static document)

**Data Formats:**
- **Available Formats:** Markdown (knowledge-worker-compensation-data.md)
- **Format Quality:** Structured markdown tables
- **Compression:** Not compressed
- **Encoding:** UTF-8

**Download Options:**
- **Bulk Download:** Yes - markdown file
- **Streaming API:** No
- **FTP/SFTP:** No
- **Torrent:** No
- **Data Dumps:** Single markdown document

**Reliability Metrics:**
- Not applicable (static research document)

### Legal/Policy Access

**License:**
- **License Type:** Research compilation (individual sources retain original licenses)
- **License Version:** N/A
- **License URL:** See individual source licenses (BLS: public domain, OECD: CC-BY, etc.)
- **SPDX Identifier:** Mixed (varies by source)

**Usage Rights:**
- **Redistribution Allowed:** Yes (with source attribution)
- **Commercial Use Allowed:** Check individual source licenses
- **Modification Allowed:** Yes (with source attribution)
- **Attribution Required:** Yes - cite original sources
- **Share-Alike Required:** No

**Cost Structure:**
- **Access Cost:** Free

**Terms of Service:**
- **TOS URL:** N/A (research compilation)
- **Key Restrictions:** Cite original sources; verify currency of data
- **Liability Disclaimers:** Research compilation "as is"; users responsible for validating currency
- **Privacy Policy:** No personal data collected

---

## Collection Development Policy Fit

### Relevance Assessment

**Substrate Mission Alignment:**
- **Human Progress Focus:** Knowledge worker compensation central to understanding economic progress and human capital value
- **Problem-Solution Connection:**
  - Links to Problems: Wage stagnation, skills gaps, labor market inefficiencies
  - Links to Solutions: Education investment, skills development, labor mobility
- **Evidence Quality:** Medium-High for U.S. (85% confidence); Medium for global (65% confidence)

**Collection Priorities Match:**
- **Priority Level:** IMPORTANT - valuable for labor economics and human capital domain
- **Uniqueness:** Best available multi-source synthesis of global knowledge worker compensation
- **Comprehensiveness:** Fills critical gap for global knowledge economy salary data

### Comparison with Holdings

**Overlapping Sources:**
- None currently in Substrate

**Unique Contribution:**
- Only global knowledge worker compensation dataset in Substrate
- Multi-source validation (20+ authoritative sources)
- Identifies data gaps and confidence levels transparently
- U.S. + global coverage in single compilation

**Preferred Use Cases:**
- When global knowledge worker compensation overview needed
- Cross-country salary comparisons
- Understanding knowledge economy labor market
- AI/ML skills premium analysis

---

## Technical Specifications

### Data Model

**Schema Documentation:**
- **Schema Type:** Markdown tables (structured text)
- **Schema URL:** knowledge-worker-compensation-data.md
- **Schema Version:** 2025-10-19

**Entity Types:**
- Geographic regions (U.S., Global, countries)
- Sectors (Technology, Finance, Healthcare, Professional Services)
- Roles (Software Engineer, Data Scientist, Consultant, etc.)
- Compensation metrics (average, median, growth rates)

**Key Relationships:**
- Geography → Sector → Role → Compensation
- Time → Geography → Growth Rate

**Primary Keys:**
- Composite: (Geography, Sector, Role, Year)

**Foreign Keys:**
- Not applicable (summary tables)

### Metadata Standards Compliance

**Standards Followed:**
- [x] Dublin Core
- [ ] DCAT - not applicable
- [ ] Schema.org - not applicable
- [ ] SDMX - not applicable
- [ ] DDI - not applicable
- [ ] ISO 19115 - not applicable
- [ ] MARC - not applicable

**Metadata Quality:**
- **Completeness:** 80% (source attribution comprehensive; some estimates)
- **Accuracy:** High for U.S. (85% confidence); Medium for global (65% confidence)
- **Consistency:** Good - standardized table format

### API Documentation Quality

**Documentation Assessment:**
- Not applicable (static research document)

---

## Source Evaluation Narrative

### Methodological Assessment

**Data Collection Methodology:**

**Sampling Design:**
- **Method:** Multi-source aggregation (not original sampling)
- **Sample Size:** Varies by source (BLS: millions of establishments; Dice: survey data)
- **Sampling Frame:** Aggregates from multiple sampling frames
- **Stratification:** By source methodology (varies)
- **Weighting:** Not applicable (summary compilation)

**Data Collection Instruments:**
- **Instrument Type:** AI research agents querying authoritative sources
- **Validation:** Multi-agent cross-validation (10 parallel agents)
- **Question Wording:** Not applicable (secondary data aggregation)
- **Mode:** Web-based research + API queries

**Quality Control Procedures:**
- **Field Supervision:** Automated multi-agent validation
- **Validation Rules:** Cross-source consistency checks
- **Consistency Checks:** Regional and sector-level validation
- **Verification:** Human review of aggregated findings
- **Outlier Treatment:** Flagged inconsistencies noted in research

**Error Characteristics:**
- **Sampling Error:** Varies by source (BLS low; industry surveys higher)
- **Non-sampling Error:** Aggregation errors; definitional differences across sources
- **Known Biases:** U.S.-centric (better data coverage); self-reported bias in some sources
- **Accuracy Bounds:** U.S. ±5-10% (high confidence); Global ±15-30% (medium confidence)

**Methodology Documentation:**
- **Transparency Level:** 4/5 (Comprehensive)
- **Documentation URL:** knowledge-worker-compensation-data.md (full source attribution)
- **Peer Review Status:** Multi-agent validation; not academic peer review
- **Reproducibility:** SPARQL/API queries documented; research snapshot date-stamped

### Currency Assessment

**Update Characteristics:**
- **Update Frequency:** On-demand research updates (not automated)
- **Update Reliability:** Requires manual re-research
- **Update Notification:** None (static snapshot)
- **Last Updated:** 2025-10-19

**Timeliness:**
- **Collection to Publication Lag:** Varies by source (BLS ~6 months; Dice ~1 month; OECD ~1 year)
- **Factors Affecting Timeliness:** Annual salary survey cycles; government reporting schedules
- **Historical Timeliness:** Single snapshot (not time series)

**Currency for Different Uses:**
- **Real-time Analysis:** Unsuitable (static snapshot)
- **Recent Trends:** Suitable for 2024-2025 estimates
- **Historical Research:** Not applicable (single snapshot)

### Objectivity Assessment

**Potential Biases:**

**Political Bias:**
- **Government Influence:** Government sources (BLS, OECD) professional independence
- **Editorial Stance:** Research compilation neutral
- **Political Pressure:** None

**Commercial Bias:**
- **Funding Sources:** Consulting firms (Robert Half, Payscale) have commercial interests in salary data
- **Advertising Influence:** Not applicable
- **Proprietary Interests:** Some sources proprietary (Glassdoor, Payscale)

**Cultural/Social Bias:**
- **Geographic Bias:** U.S.-centric (better data coverage); Western Europe overrepresented
- **Social Perspective:** Knowledge worker focus excludes non-knowledge sectors
- **Language Bias:** English-language sources predominate
- **Selection Bias:** "Knowledge worker" definition varies by source

**Transparency:**
- **Bias Disclosure:** Data gaps acknowledged; confidence levels provided
- **Limitations Stated:** Comprehensive limitation documentation
- **Raw Data Available:** Source links provided; original data at individual sources

### Reliability Assessment

**Consistency:**
- **Internal Consistency:** Cross-source validation performed
- **Temporal Consistency:** Not applicable (single snapshot)
- **Cross-source Consistency:** Reasonable agreement for U.S. (±10%); wider variation globally

**Stability:**
- **Definition Changes:** "Knowledge worker" definition varies by source
- **Methodology Changes:** Not applicable (single snapshot)
- **Series Breaks:** Not applicable

**Verification:**
- **Independent Verification:** 10 parallel AI agents cross-validated findings
- **Replication Studies:** Not applicable (research compilation)
- **Audit Results:** Multi-agent validation; human review

### Accuracy Assessment

**Validation Evidence:**
- **Benchmark Comparisons:** U.S. BLS data cross-checked with Dice, Glassdoor (±10% agreement)
- **Coverage Assessments:** U.S. 85% confidence; Global 65% confidence
- **Error Studies:** Not applicable

**Accuracy for Different Uses:**
- **Point Estimates:** Reliable for U.S. averages; moderate for global
- **Trend Analysis:** Limited (single snapshot with YoY growth rates)
- **Cross-sectional Comparison:** Reliable for U.S. sectors; moderate for cross-country
- **Sub-population Analysis:** Limited (sector-level; no demographics)

---

## Known Limitations and Caveats

### Coverage Limitations

**Geographic Gaps:**
- Africa (minimal data available)
- Middle East (limited coverage)
- Rural areas (knowledge workers concentrated in urban centers)

**Temporal Gaps:**
- Single snapshot (2024-2025)
- No historical time series

**Population Exclusions:**
- Non-knowledge workers (by design)
- Informal economy knowledge workers
- Gig economy workers (partial coverage)

**Variable Gaps:**
- Equity compensation not captured in most sources (stock options, RSUs)
- Benefits variation across countries
- Demographic breakdowns limited

### Methodological Limitations

**Sampling Limitations:**
- Varies by source (BLS high quality; self-reported surveys lower quality)
- Self-selection bias in Glassdoor, Payscale user-generated data

**Measurement Limitations:**
- "Knowledge worker" definition inconsistent across sources
- Purchasing power parity adjustments not uniformly applied
- Exchange rate fluctuations affect cross-country comparisons

**Processing Limitations:**
- Aggregation across sources with different methodologies
- Confidence levels estimated (not statistically rigorous)

### Comparability Limitations

**Cross-national Comparability:**
- Definitional differences (what constitutes "knowledge worker")
- PPP adjustments needed for true cost-of-living comparisons
- Tax and benefit systems vary (gross vs. net compensation)

**Temporal Comparability:**
- Single snapshot (cannot assess trends)

**Sub-group Comparability:**
- Limited demographic data (gender, race, education level)

### Usage Caveats

**Inappropriate Uses:**
1. **DO NOT use for precise individual salary negotiations** - broad averages only
2. **DO NOT assume global estimates are highly accurate** - medium confidence (65%)
3. **DO NOT use for historical trend analysis** - single snapshot
4. **DO NOT assume equity compensation included** - most sources cash compensation only
5. **DO NOT use without PPP adjustment** - cross-country comparisons need cost-of-living adjustment

**Ecological Fallacy Risks:**
- National/sector averages do not reflect individual company or role compensation
- Regional averages mask within-country variation

**Correlation vs. Causation:**
- Compensation levels do not imply causation
- Appropriate for descriptive analysis only

---

## Recommended Use Cases

### Ideal Applications

**Research Questions Well-Suited:**
1. "What is the global knowledge worker compensation market size?"
2. "How do U.S. tech salaries compare to European tech salaries?"
3. "What is the AI/ML skills premium in 2025?"
4. "What percentage of the U.S. workforce are knowledge workers?"

**Analysis Types Supported:**
- Descriptive statistics (compensation averages by geography/sector)
- Cross-country comparison (regional salary differences)
- Sector analysis (technology vs. finance vs. healthcare)
- Skills premium analysis (AI/ML vs. general software engineering)

### Appropriate Contexts

**Geographic Contexts:**
- U.S. national analysis (high confidence)
- Western Europe comparison (medium confidence)
- Global overview (medium confidence)

**Temporal Contexts:**
- Current snapshot (2024-2025)
- Short-term growth trends (YoY)

**Subject Contexts:**
- Knowledge economy labor markets
- Technology sector compensation
- STEM workforce analysis
- Consulting and professional services

### Use Warnings

**Avoid Using This Source For:**
1. **Individual salary negotiation** → Use role-specific Glassdoor, Payscale
2. **Historical trend analysis** → Single snapshot; need time series data
3. **Precise global estimates** → Medium confidence (65%); use OECD, ILO for official stats
4. **Equity compensation analysis** → Most sources exclude stock options/RSUs
5. **Demographic analysis** → Limited demographic breakdowns

**Recommended Alternatives For:**
- U.S. official statistics → BLS OEWS (Occupational Employment and Wage Statistics)
- Global official statistics → OECD Average Wages, ILO Global Wage Report
- Tech sector depth → Dice Tech Salary Report, Stack Overflow Developer Survey
- Equity compensation → Carta Equity Report, Glassdoor total compensation
- Real-time data → Glassdoor, Payscale (updated continuously)

---

## Citation

### Preferred Citation Format

**APA 7th:**
Substrate Data Curation. (2025, October 19). *Knowledge worker global compensation: Summary table (2024-2025)* [Research compilation]. https://github.com/danielmiessler/substrate

**Chicago 17th:**
Substrate Data Curation. "Knowledge Worker Global Compensation: Summary Table (2024-2025)." Research compilation. October 19, 2025. https://github.com/danielmiessler/substrate.

**MLA 9th:**
Substrate Data Curation. *Knowledge Worker Global Compensation: Summary Table (2024-2025)*. Research compilation, 19 Oct. 2025, github.com/danielmiessler/substrate.

**Vancouver:**
Substrate Data Curation. Knowledge worker global compensation: summary table (2024-2025) [Internet]. Research compilation; 2025 Oct 19 [cited 2025 Oct 25]. Available from: https://github.com/danielmiessler/substrate

**BibTeX:**
```bibtex
@misc{substrate_knowledge_worker_2025,
  author = {{Substrate Data Curation}},
  title = {Knowledge Worker Global Compensation: Summary Table (2024-2025)},
  year = {2025},
  month = {October},
  howpublished = {Research compilation},
  url = {https://github.com/danielmiessler/substrate},
  note = {Accessed: 2025-10-25; Multi-source aggregation via 10 parallel AI research agents}
}
```

### Data Citation Principles

Following FORCE11 Data Citation Principles:
- **Importance:** Research compilation is citable output; cite original sources when possible
- **Credit and Attribution:** Citations credit both compilation and original sources (BLS, OECD, etc.)
- **Evidence:** Citations enable readers to verify compensation claims
- **Unique Identification:** Date + version for exact reproducibility
- **Access:** Citation provides access to research compilation
- **Persistence:** Static snapshot preserved; future updates versioned
- **Specificity and Verifiability:** Research date ensures snapshot reproducibility
- **Interoperability:** Standard citation formats for reference managers
- **Flexibility:** Adaptable to various research contexts

**IMPORTANT:** Always cite original sources (BLS, OECD, Dice, etc.) for primary data claims. This compilation provides aggregated overview with source attribution.

---

## Version History

### Current Version
- **Version:** 2025-10-19 Research Snapshot
- **Date:** 2025-10-19
- **Changes:** Initial multi-source research compilation (10 parallel AI agents)

### Previous Versions
- None (initial research)

---

## Review Log

### Internal Reviews
- **Date:** 2025-10-25 | **Reviewer:** Substrate Data Curation | **Status:** Approved | **Notes:** Initial catalog entry; research compilation with transparent methodology

### Quality Checks
- **Last Metadata Validation:** 2025-10-25
- **Last Authority Verification:** 2025-10-25 (source attribution verified)
- **Last Link Check:** 2025-10-25
- **Last Access Test:** 2025-10-25 (markdown file accessible)

---

## Related Resources

### Cross-References

**Related Substrate Entities:**
- **Problems:**
  - Wage stagnation
  - Skills gaps
  - Labor market inefficiencies
- **Solutions:**
  - Education investment
  - Skills development programs
  - Labor mobility initiatives
- **Organizations:**
  - U.S. Bureau of Labor Statistics
  - OECD
  - International Labour Organization
- **Other Data Sources:**
  - DS-00002: U.S. GDP (economic output context)
  - DS-00003: U.S. Inflation (real wage purchasing power)

**External Resources:**
- **Alternative Sources:**
  - BLS OEWS: https://www.bls.gov/oes/
  - OECD Average Wages: https://data.oecd.org/earnwage/average-wages.htm
  - ILO Global Wage Report: https://www.ilo.org/global/research/global-reports/global-wage-report/
- **Complementary Sources:**
  - Dice Tech Salary Report: https://www.dice.com/recruiting/ebooks/tech-salary-report/
  - Glassdoor Salaries: https://www.glassdoor.com/Salaries/
  - Stack Overflow Developer Survey: https://survey.stackoverflow.co/
- **Source Comparison Studies:**
  - Academic research on knowledge worker compensation trends

### Additional Documentation

**User Guides:**
- knowledge-worker-compensation-data.md (detailed summary tables)
- Full research report: https://gist.github.com/danielmiessler/2dc039762a202b083753b1400452614d

**Research Using This Source:**
- Initial research compilation (2025)

**Methodology Papers:**
- Research methodology: Multi-agent AI research system (10 parallel agents)
- Sources: BLS, OECD, ILO, Dice, Glassdoor, Robert Half, Payscale, industry reports

---

## Cataloger Notes

**Internal Notes:**
- Unique tertiary source; multi-agent research compilation
- U.S. data high confidence (85%); Global medium confidence (65%)
- Future updates require re-research (not automated)
- Consider annual update schedule (October/November)
- Equity compensation gap noted (most sources exclude stock options/RSUs)

**To Do:**
- [ ] Annual update (October 2026) with new research cycle
- [ ] Consider adding demographic breakdowns if data becomes available
- [ ] Explore equity compensation data sources (Carta, Glassdoor total comp)
- [ ] Add Africa and Middle East data if sources improve

**Questions for Review:**
- Should this be updated quarterly or annually?
- How to handle exchange rate fluctuations in cross-country comparisons?
- Should we add PPP-adjusted values?

---

**END OF SOURCE RECORD**