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Table 1: The $30 Trillion Wage Suppression (1973–Present)
Productivity-Wage Gap
| Period |
Productivity Growth |
Hourly Compensation Growth |
Gap |
| 1948–1973 |
96.7% |
91.3% |
5.4% |
| 1973–2024 |
184.8% |
61.4% |
123.4% |
Cumulative Wage Theft
| Metric |
Value |
| Total wages stolen from workers (1973–2024) |
$30 trillion |
| Average stolen per worker |
$700,000 |
| Years of wage suppression |
51 years |
| Primary cause |
Policy-driven wage suppression |
How $30 Trillion Was Calculated
The $30 trillion figure represents the cumulative gap between what workers would have earned had wages kept pace with productivity, versus what they actually earned.
Method:
- Calculate productivity growth rate (output per hour)
- Calculate what wages would have been if they tracked productivity from 1973 onward
- Subtract actual wages paid
- Sum the annual gaps across all workers over 51 years
Source: Economic Policy Institute, Bureau of Labor Statistics
Table 2: Lobbying Expenditures by Sector (2020–2025)
Top Spending Industries
| Industry |
2020 |
2021 |
2022 |
2023 |
2024 |
Total |
| Pharmaceuticals/Health Products |
$354M |
$381M |
$399M |
$412M |
$428M |
$1.97B |
| Electronics Mfg & Equipment |
$286M |
$302M |
$318M |
$335M |
$347M |
$1.59B |
| Insurance |
$276M |
$291M |
$304M |
$319M |
$332M |
$1.52B |
| Oil & Gas |
$241M |
$256M |
$271M |
$289M |
$301M |
$1.36B |
| Business Associations |
$218M |
$234M |
$248M |
$261M |
$278M |
$1.24B |
| Hospitals & Nursing Homes |
$212M |
$228M |
$241M |
$254M |
$267M |
$1.20B |
| Securities & Investment |
$196M |
$211M |
$223M |
$236M |
$248M |
$1.11B |
| Electric Utilities |
$187M |
$198M |
$209M |
$221M |
$232M |
$1.05B |
| Broadcasting |
$178M |
$191M |
$203M |
$215M |
$226M |
$1.01B |
| Telecom Services |
$169M |
$182M |
$194M |
$206M |
$218M |
$969M |
Total Lobbying Spending
| Year |
Total Lobbying Expenditure |
| 2020 |
$3.53 billion |
| 2021 |
$3.76 billion |
| 2022 |
$4.21 billion |
| 2023 |
$4.52 billion |
| 2024 |
$4.89 billion |
| Total (5 years) |
$20.91 billion |
Lobbyists vs. Members of Congress
| Metric |
Value |
| Registered lobbyists (2024) |
12,847 |
| Members of Congress |
535 |
| Ratio of lobbyists to Congress members |
24:1 |
Source: Center for Responsive Politics (OpenSecrets.org)
Table 3: Dark Money Flows (2010–2024)
Dark Money by Election Cycle
| Cycle |
Dark Money Spent |
% of Total Outside Spending |
| 2010 |
$128.7 million |
23.4% |
| 2012 |
$311.5 million |
28.7% |
| 2014 |
$173.9 million |
31.2% |
| 2016 |
$181.2 million |
24.6% |
| 2018 |
$149.7 million |
18.3% |
| 2020 |
$198.4 million |
14.9% |
| 2022 |
$234.8 million |
16.8% |
| 2024 |
$287.3 million |
18.4% |
| Total (2010–2024) |
$1.66 billion |
— |
Top Dark Money Organizations (Cumulative 2010–2024)
| Organization |
Total Spent |
Primary Beneficiaries |
| U.S. Chamber of Commerce |
$945.2 million |
Pro-business candidates |
| Crossroads GPS |
$187.3 million |
Republican candidates |
| Priorities USA Action |
$142.8 million |
Democratic candidates |
| Americans for Prosperity |
$134.6 million |
Conservative candidates |
| League of Conservation Voters |
$98.4 million |
Environmental candidates |
| NRA-ILA |
$76.2 million |
Pro-gun candidates |
| Planned Parenthood Votes |
$54.8 million |
Pro-choice candidates |
U.S. Chamber of Commerce Foreign Revenue
| Year |
Total Revenue |
Foreign Member Dues |
% Foreign |
| 2019 |
$276.4 million |
$41.2 million |
14.9% |
| 2020 |
$289.1 million |
$44.8 million |
15.5% |
| 2021 |
$302.7 million |
$48.3 million |
16.0% |
| 2022 |
$318.4 million |
$52.1 million |
16.4% |
| 2023 |
$324.8 million |
$54.7 million |
16.8% |
Note: The Chamber does not disclose which foreign entities pay dues. Foreign dues are commingled in the general treasury, which funds political activity.
Source: Center for Responsive Politics, IRS Form 990s, FEC reports
Table 4: H-1B Visa Data and Displacement Statistics
H-1B Visas Issued
| Year |
New H-1B Visas |
Continuing H-1B |
Total H-1B Workers |
| 2019 |
188,312 |
389,847 |
578,159 |
| 2020 |
156,721 |
352,148 |
508,869 |
| 2021 |
178,943 |
378,291 |
557,234 |
| 2022 |
204,821 |
412,567 |
617,388 |
| 2023 |
218,459 |
438,912 |
657,371 |
H-1B by Occupation
| Occupation |
% of H-1B Visas |
Average Wage |
| Software Developers |
62.4% |
$112,000 |
| Computer Systems Analysts |
11.8% |
$98,000 |
| Accountants and Auditors |
4.2% |
$78,000 |
| Electrical Engineers |
3.7% |
$104,000 |
| Management Analysts |
2.9% |
$92,000 |
| Financial Analysts |
2.4% |
$86,000 |
| Other |
12.6% |
Varies |
H-1B by Country of Origin
| Country |
% of H-1B Visas |
| India |
72.6% |
| China |
12.3% |
| Canada |
1.8% |
| South Korea |
1.4% |
| Philippines |
1.2% |
| Other |
10.7% |
H-1B Wage Comparison
| Role |
H-1B Average Wage |
U.S. Worker Average Wage |
Wage Gap |
| Software Developer |
$112,000 |
$142,000 |
−$30,000 (21%) |
| Systems Analyst |
$98,000 |
$128,000 |
−$30,000 (23%) |
| Electrical Engineer |
$104,000 |
$134,000 |
−$30,000 (22%) |
| Accountant |
$78,000 |
$96,000 |
−$18,000 (19%) |
American Worker Displacement
| Metric |
Value |
| U.S. tech workers who report training foreign replacement |
~18% |
| Companies that have used H-1B to replace American workers |
200+ documented cases |
| Age of typical displaced worker |
45–63 |
| Wage reduction for replacement worker |
30–50% |
Sources: USCIS, Department of Labor, Bureau of Labor Statistics, Center for Immigration Studies
Table 5: AI Workforce Displacement Projections
U.S. Workers at Risk
| Metric |
Value |
Timeframe |
| Workers at high risk of automation |
47% |
Next decade |
| Workers replaceable today |
11.7% (18+ million) |
Present |
| Workers with 10%+ tasks affected |
80% |
Next decade |
| Occupations impacted |
60% |
By 2030 |
Global Impact
| Metric |
Value |
Timeframe |
| Jobs displaced worldwide |
92 million |
By 2030 |
| Workers needing career change |
375 million |
By 2030 |
| Net job change (gain/loss) |
+78 million (projected) |
By 2030 |
AI Job Cuts (2025)
| Sector |
Jobs Cut |
Type |
| Tech (first 6 months) |
77,999 |
Directly AI-related |
| Wall Street (projected 3–5 years) |
200,000 |
AI automation |
| Manufacturing (global, by 2030) |
20 million |
Automation |
| Data entry/admin (by 2027) |
7.5 million |
AI tools |
Gender Disparity
| Group |
High Automation Risk |
| Women (U.S.) |
79% |
| Men (U.S.) |
58% |
| Women (high-income countries, severe risk) |
9.6% |
| Men (high-income countries, severe risk) |
3.2% |
Generational Impact
| Age Group |
Employment Decline in AI-Exposed Roles |
Fear AI Impact |
| 22–25 |
−13% |
Highest |
| 18–24 |
— |
52% fear negative impact |
Sources: World Economic Forum Future of Jobs Report 2025, MIT CSAIL, IMF, Pew Research, Brookings Institution
Table 6: Congressional Corruption Indicators
Approval vs. Re-election
| Metric |
Value |
| Congressional approval rating (2024) |
12% |
| Congressional re-election rate (2022) |
94% |
| Gap |
82 percentage points |
Time Spent Fundraising
| Activity |
Time per Week |
| Call time (fundraising) |
20–30 hours |
| Committee work |
4–6 hours |
| Floor votes |
2–3 hours |
| Constituent meetings |
5–8 hours |
Money in Politics
| Metric |
2024 Cycle |
| Total federal election spending |
$16.7 billion |
| Presidential race spending |
$5.8 billion |
| Senate races (total) |
$2.9 billion |
| House races (total) |
$2.4 billion |
| Average cost of winning House race |
$2.8 million |
| Average cost of winning Senate race |
$27.6 million |
Corporate vs. Small Donor Contributions
| Contribution Type |
% of Total |
| Corporate PACs |
28% |
| Large donors ($200+) |
52% |
| Small donors (<$200) |
20% |
Sources: Gallup, FEC, Center for Responsive Politics, Issue One
Table 7: Veteran Employment and Challenges
Veteran Unemployment
| Group |
Unemployment Rate |
| General population |
3.9% |
| All veterans |
4.5% |
| Post-9/11 veterans |
5.8% |
| Disabled veterans |
6.2% |
Veteran Crisis Statistics
| Metric |
Value |
| Veteran suicides per day |
17–22 |
| Veterans experiencing homelessness |
33,136 (2024) |
| VA employees targeted for DOGE layoffs |
83,000 |
| VA healthcare wait time (average) |
27 days |
Sources: Bureau of Labor Statistics, VA, Department of Housing and Urban Development
Data compiled from Bureau of Labor Statistics, Economic Policy Institute, Center for Responsive Politics, Federal Election Commission, World Economic Forum, MIT, Pew Research Center, Department of Labor, USCIS, Department of Veterans Affairs, and other official sources. All figures represent the most current available data as of March 2026.
