The Cost of AI "Tax Revenue losses & Cost of Universal Income"

Study examines projected AI job losses and estimates the tax revenue losses incurred by governments worldwide. The report further explores the implementation costs, both per person and for the system, of a Universal Income scheme aimed at supporting individuals unable to rejoin the workforce.

  • The countries with the highest number of jobs at risk from AI are the United States with just over 12 million jobs very likely to be lost, followed by Japan with 5.8 million and Mexico with 5 million lost jobs.
  • The countries that will experience the largest fiscal income decrease are the United States with a loss of AUD $985 billion, followed by Germany and Japan with a decrease of AUD $222 billion.
  • Switzerland would have the highest cost per capita for Universal Income among OECD countries, at an estimated AUD $43,883 per person per year, not including additional costs for children and dependents.
  • The United States would have the most expensive Universal Income program, estimated at over AUD $985 billion per year, followed by Germany and Japan with an estimated yearly cost of AUD $222 billion.*

*Please note that the total cost of Universal Income mentioned here is only for those who lost their jobs due to AI and does not include others who are currently not working or may stop working in the future.

At ThePayStubs.com, we’ve been studying the impact of new Ai technology on our operations. As we’ve seen remarkable efficiency gains, it’s become evident that work processes are changing, with new technologies taking over millions of jobs. To understand the implications for our industry and society, we partnered with Researchers of Urbanity Impact to investigate how this trend will affect people’s paystubs.

According to Dany Moussa, CEO of FinancialDocs, the company behind ThePayStubs.com, while AI will generate new startups and jobs, it’s also expected to cause significant job losses that won’t be easily replaced. This calls for innovative approaches from governments as this digital revolution will fundamentally transforms industries and society.

Our study focuses on three main areas. Firstly, we estimate the number of jobs at risk in each country, considering the potential for companies to reduce workforces through AI-based technologies. Secondly, we examine the cost of implementing a Universal Income Scheme to support those unable to rejoin the workforce. Lastly, we analyze the tax revenue losses governments will face due to job losses and the necessary increase in corporate tax to compensate for the decreased tax income and fund the universal salary.

In estimating jobs at risk, we evaluate productivity gains from Ai technologies and their impact on core tasks for each occupation. Our analysis takes into account productivity increases, redundancy percentages, and occupation/industry matrices for each country.

Since the 2016 failed vote attempt in Switzerland, Universal Income is one of the leading options to deal with the rise of AI and automation and its effect on the Job market. The average cost of universal income per person is calculated based on the ILO’s recommendation of 50% of the median disposable income in rich countries.

Lastly, considering the future of taxation, we focus on the use of Ai advancements to reduce workforces and its impact on corporate tax revenues. We also present the required increase in corporate tax revenues in each country to cover the potential cost to the system. The estimated lost tax revenues due to unemployment per person reflect the annual value of tax revenues lost when a worker becomes redundant, assuming they don’t find new employment. This includes income taxes, payroll taxes, and compulsory social contributions.

Legend

The index is default ranked by the final column, ordered from highest to lowest. Each individual column can be filtered, and the full methodology explaining how each factor was evaluated can be found underneath the table.

Global Jobs at Risk

Jobs-at-risk, by sector (thousands of persons) Potential cost to system Future of taxation
Rank Country population gdp employed goods services services tax tax revenue income support gain-eur gain-per
1 United States 333.3 million 114,155 147,886 th. 90.7 th. 1,945.5 th. 10,901.4 th. 12,856.5 th. A$40,136 A$49,037 A$89,173 A$1,146.4 B 11.1% 127.5%
2 Japan 125.5 million 69,916 67,124 th. 88.2 th. 1,221.3 th. 4,675.2 th. 5,886.8 th. A$21,778 A$19,615 A$41,392 A$243.7 B 9.9% 76.6%
3 Mexico 129.0 million 31,291 55,166 th. 286.4 th. 1,112.6 th. 3,699.2 th. 5,010.0 th. A$2,181 A$2,220 A$4,401 A$22.0 B 6.8% 33.1%
4 Germany 83.2 million 95,896 41,500 th. 22.3 th. 883.6 th. 3,192.3 th. 4,066.3 th. A$39,102 A$20,854 A$59,956 A$243.8 B 9.7% 162.4%
5 United Kingdom 67.4 million 82,788 32,407 th. 10.6 th. 416.2 th. 2,804.5 th. 3,223.6 th. A$23,921 A$22,437 A$46,358 A$149.4 B 9.3% 118.0%
6 France 67.7 million 83,267 27,728 th. 30.5 th. 413.6 th. 2,275.9 th. 2,690.9 th. A$41,786 A$18,147 A$59,934 A$161.3 B 8.1% 143.5%
7 South Korea 51.7 million 76,089 27,273 th. 64.4 th. 524.8 th. 2,061.4 th. 2,632.5 th. A$12,177 A$13,072 A$25,249 A$66.5 B 8.2% 63.9%
8 Turkey 84.1 million 52,242 28,827 th. 209.0 th. 608.1 th. 1,688.5 th. 2,481.2 th. A$4,069 A$4,985 A$9,054 A$22.5 B 8.0% 74.5%
9 Italy 59.1 million 78,427 22,554 th. 37.0 th. 480.2 th. 1,662.3 th. 2,136.9 th. A$34,538 A$16,019 A$50,557 A$108.0 B 7.9% 178.7%
10 Colombia 51.6 million 30,100 20,392 th. 136.2 th. 337.4 th. 1,498.3 th. 1,930.9 th. A$824 A$5,050 A$5,874 A$11.3 B 12.3% 52.2%
11 Spain 47.3 million 69,886 19,774 th. 32.7 th. 314.0 th. 1,606.4 th. 1,923.9 th. A$24,222 A$13,751 A$37,973 A$73.1 B 8.9% 126.8%
12 Poland 38.2 million 65,189 16,656 th. 58.1 th. 394.9 th. 1,074.7 th. 1,506.5 th. A$11,582 A$8,367 A$19,948 A$30.1 B 8.0% 112.6%
13 Canada 38.2 million 85,224 18,865 th. 2.5 th. 229.9 th. 1,468.4 th. 1,471.2 th. A$27,901 A$22,970 A$50,871 A$74.8 B 7.6% 64.3%
14 Australia 25.7 million 102,705 13,065 th. 12.7 th. 184.9 th. 1,132.2 th. 1,329.8 th. A$22,367 A$26,779 A$49,146 A$65.4 B 10.3% 54.5%
15 Netherlands 17.5 million 106,091 9,282 th. 7.6 th. 100.9 th. 826.9 th. 931.0 th. A$35,585 A$20,043 A$55,628 A$51.8 B 8.6% 87.4%
16 Chile 19.7 million 45,246 8,303 th. 23.6 th. 146.8 th. 645.0 th. 801.3 th. A$2,041 A$7,424 A$9,465 A$7.6 B 7.2% 42.1%
17 Sweden 10.4 million 97,485 5,120 th. 4.1 th. 68.7 th. 442.1 th. 512.4 th. A$45,702 A$17,608 A$63,310 A$32.4 B 8.0% 113.2%
18 Belgium 11.6 million 98,396 4,854 th. 1.9 th. 73.4 th. 408.9 th. 479.9 th. A$44,627 A$20,347 A$64,974 A$31.2 B 8.3% 92.0%
19 Czech Republic 10.5 million 74,683 5,213 th. 5.5 th. 146.0 th. 330.0 th. 474.2 th. A$15,825 A$11,278 A$27,103 A$12.9 B 9.0% 94.2%
20 Portugal 10.3 million 63,296 4,812 th. 5.7 th. 92.8 th. 366.8 th. 458.9 th. A$13,715 A$12,232 A$25,947 A$11.9 B 8.9% 130.1%
21 Switzerland 8.7 million 123,169 4,684 th. 4.6 th. 72.5 th. 379.5 th. 453.0 th. A$39,963 A$51,612 A$91,576 A$41.5 B 12.2% 113.4%
22 Hungary 9.7 million 63,783 4,642 th. 8.7 th. 114.3 th. 321.8 th. 439.0 th. A$9,630 A$6,943 A$16,572 A$7.3 B 7.8% 231.0%
23 Austria 9.0 million 102,650 4,306 th. 6.6 th. 89.5 th. 329.6 th. 423.3 th. A$46,437 A$21,338 A$67,776 A$28.7 B 9.2% 144.9%
24 Greece 10.7 million 55,631 3,928 th. 19.5 th. 50.4 th. 321.1 th. 386.4 th. A$13,973 A$9,186 A$23,159 A$8.9 B 7.1% 259.4%
25 Israel 9.4 million 73,535 3,957 th. 1.3 th. 47.0 th. 330.9 th. 375.6 th. A$24,034 A$24,504 A$48,538 A$18.2 B 7.7% 69.5%
26 Denmark 5.9 million 112,065 2,900 th. 2.7 th. 43.6 th. 240.6 th. 285.1 th. A$51,103 A$21,154 A$72,257 A$20.6 B 7.4% 92.2%
27 Norway 5.4 million 142,061 2,798 th. 2.6 th. 40.1 th. 224.5 th. 265.2 th. A$52,866 A$21,986 A$74,852 A$19.9 B 6.5% 28.2%
28 Ireland 5.0 million 191,868 2,389 th. 4.3 th. 37.3 th. 205.8 th. 245.7 th. A$31,266 A$21,150 A$52,417 A$12.9 B 8.1% 47.4%
29 Finland 5.5 million 89,411 2,573 th. 4.1 th. 42.7 th. 199.7 th. 244.7 th. A$42,960 A$19,778 A$62,738 A$15.3 B 8.0% 127.9%
30 Slovakia 5.4 million 55,850 2,561 th. 2.8 th. 71.8 th. 167.9 th. 240.2 th. A$13,181 A$9,562 A$22,743 A$5.5 B 8.9% 104.0%
31 New Zealand 5.1 million 78,099 2,790 th. 4.1 th. 33.3 th. 179.3 th. 216.7 th. A$18,817 A$18,441 A$37,258 A$8.1 B 6.3% 43.41%
32 Costa Rica 5.2 million 36,603 2,040 th. 12.6 th. 32.5 th. 151.9 th. 192.2 th. A$5,221 A$5,088 A$10,309 A$2.0 B 8.5% 82.6%
33 Lithuania 2.8 million 73,604 1,369 th. 3.3 th. 28.2 th. 103.6 th. 133.6 th. A$12,933 A$11,467 A$24,400 A$3.3 B 10.1% 155.2%
34 Slovenia 2.1 million 75,814 972 th. 1.8 th. 23.2 th. 68.1 th. 91.6 th. A$20,842 A$11,767 A$32,610 A$3.0 B 8.6% 165.9%
35 Latvia 1.9 million 60,823 870 th. 2.5 th. 16.3 th. 65.6 th. 83.6 th. A$10,869 A$9,002 A$19,870 A$1.7 B 9.2% 369.0%
36 Estonia 1.3 million 71,341 654 th. 0.7 th. 14.9 th. 49.4 th. 64.6 th. A$15,592 A$11,060 A$26,652 A$1.7 B 9.2% 191.4%

Methodology

Our study sets out to evaluate the prospective influence of Large Language Model (LLM) based chatbot technologies on labour markets in OECD nations, and the subsequent effects on taxation structures. This exploration is articulated through three key dimensions: Jobs-at-risk, the potential cost to the system, and the future of taxation.

Jobs-at-risk

By "Job-at-risk", we do not consider complete replacement of an occupation by an AI worker, but rather the opportunity for companies to trim workforces as a result of productivity increases resulting from the use of LLM-based chatbot technologies.

We consider productivity gains from LLM-based chatbot technologies with the following characteristics:

  • ChatGPT4 level capacity for language understanding and reasoning; and the limited capacity for creative problem solving and complex task resolution.
  • Access to the internet and to internal company documents and databases

We do not consider the following developments that may be coming to the field:

  • Further advancements in robotics based on LLM technology advancements.
  • Shifts to telemetry; that is, if the occupation today requires in-person contact, the model assumes this stays the same.
  • Development of domain specific AI systems that combine LLM technology with traditional AI and Machine Learning specifically developed to allow higher-order creative problem solving.

The model is driven by an evaluation of productivity gains on the tasks core to each occupation, as defined by the occupation database ONET, given these assumptions. The productivity gains for the tasks were averaged for each occupation, weighted by the importance of the task per the ONET database, giving an aggregate productivity increase; and then converted to a redundancy percentage.

The final jobs at risk was estimated based on occupation/industry matrices for each country. These matrixes were estimated using the US occupation/industry mix as a model; ILO estimated breakdowns of employment by industry were multiplied with the equivalent US occupation by industry matrix achieving an estimated occupation/industry matrix for each OECD country. The final total number of employed persons per occupation per industry was multiplied by the redundancy percentage for each occupation.

Potential cost to system

To estimate the potential cost to the tax system as a result of redundancies, we combined both lost tax revenues due to loss of employment with the cost of supporting redundant workers.

As Universal Basic Income has been proposed as a solution to the existential threat of AI on labour markets, we took the approach of using the Universal Basic Income regime proposed by ILO to estimate the cost of supporting redundant workers.

Future of taxation

In the scenario considered in this study is limited to the use of LLM advancements to trim workforces–it ignores the potential for higher quality services and new services and industries altogether–the benefit of LLM is solely concentrated to the bottom line of corporations. Therefore, we present the required increase in corporate tax revenues in each country should tax systems *only* seek to cover the potential cost to the system through corporate tax increases. This increase is compared to the increase in overall tax revenues should the potential cost to the system be carried by tax revenue increases across the board.

Column notes

Population

The 2022 population of the country, in millions of persons.

Source: OECD

GDP/Capita (USD)

The 2022 Gross domestic product per capita, in 2022 exchange rate US Dollars.

Source: OECD

Employed Persons

The total number of employed persons (full-time or part-time) in the country in 2022, in thousands of persons.

Source: OECD

Jobs-at-risk

The total number of jobs at risk of being made redundant due to productivity increases due to LLM-based chatbot technologies, in thousands of persons. Estimates are provided on a sector basis, per the North American Industry Classification Standard.

  • Agriculture: NAICS code 1
  • Industry: NAICS codes 2-3
  • Services: NAICS codes 4-8

Sources: ILO, ONET, ChatGPT (See methodology notes above)

Potential cost to system

Estimated lost tax revenues due to unemployment, per person

The annual value of lost tax revenues for each worker due to redundancy, assuming new employment is not found. Includes income taxes, payroll taxes and compulsory social contributions.

Calculated as total tax 2022 tax revenues from income, payroll and social contributions divided by *Employed Persons*.

Sources: OECD

Estimated average cost of universal income, per person

Per the ILO proposal of a minimum viable universal income, the base universal income in rich countries should be set to 50% of the median disposable income in the country. Using available OECD data, the universal income is calculated as 50% of the average disposable household income per capita.

Sources: OECD

Estimated total cost to system, per person

The sum of *Estimated average cost of universal income, per person* and *Estimated lost tax revenues due to unemployment, per person*.

Total potential cost to system

The product of the *Estimated total cost to system, per person* and total *Jobs-at-risk*, giving the total annual potential cost to the system under the assumption that:

  • Companies use productivity increases only to reduce total headcount through redundancies
  • Redundant workers are not able to find new employment
  • AI displaced workers are provided with the ILO proposed Universal Basic Income to endure redundancy

Future of taxation

Required Tax Revenue Growth to cover cost to system

The % increase in required total tax revenues in order to absorb *Total potential cost to system*, assuming:

  • constant government expenses

Sources: OECD

Required Corporate Tax Increase to cover cost to system

The % increase in required total corporate revenues in order to absorb *Total potential cost to system*, assuming:

  • constant government expenses
  • *Total potential cost to system* is only absorbed through increases in corporate taxes.

Sources: OECD