Raphael ThysThe ultimate contribution of this article is to make the case for BI as a policy to solve the dual issues of poverty and environmental damage. We do this by estimating the positive impact that BI could have on GDP using fiscal multipliers on government spending. We then estimate revenue from a flat tax on global carbon emissions to demonstrate that it is possible to raise the funds needed to implement BI while reducing environmental degradation.
Results and discussion
Economic impacts
Our findings indicate that implementing BI programs could potentially increase global GDP by a significant amount. Specifically, our analysis suggests that BI initiatives could lead to an increase in GDP ranging from USD 1,043 billion to USD 48,606 billion, equivalent to 0.82% and 38.6% of global GDP, respectively, when considering only individuals living below poverty lines in low human development index (HDI) countries (949 million people) and the entire global population (7.7 billion people) (Table 1), respectively.
Moreover, the economic impact is amplified, with projections showing a potential boost to GDP ranging from USD 1,445 billion to USD 163,383 billion, equivalent to 1.16% and 129.8% of the current global GDP, respectively, when BI coverage extends to encompass all individuals residing in low HDI countries and the entire global population (Table 1), respectively.
Costs and affordability of BI
We have determined that the costs associated with implementing BI programs vary, ranging from USD 216 billion (0.17% of global GDP) to USD 7,070 billion (5.6%), depending on whether BI coverage is limited to individuals living below the poverty line in low HDI countries or extended to encompass the entire global population, respectively (Table 1). When considering BI coverage for all individuals in low HDI countries and the entire world, the estimated costs amount to USD 442 billion (0.35% of global GDP) and USD 41,567 billion (33%), respectively (Table 1). For context, according to the Stockholm Peace Research Institute’s 2013 report, health expenditures ranged from 2% (Africa) to 8% (North America) of GDP, while military expenditures ranged from 1% (Latin America and Caribbean) to 5% (Middle East) of GDP.
Therefore, providing BI to those living below the poverty line appears financially feasible.
Furthermore, our analysis reveals that for every dollar invested in implementing BI, approximately USD 7 and USD 4 of economic impacts are generated when considering coverage solely for individuals living below the poverty line and the entire population, respectively. This ratio ranges from approximately USD 5 for individuals living below the poverty line in low HDI countries, Africa, and Asia, to over USD 7 for all high HDI countries combined or independently for the continents of Europe, North America, Oceania, and South America (Table 1). Detailed country-level economic-impact-to-cost ratios are presented in Figure 1.
Figure 1Economic impact of UBI-to-cost ratio by countryShow full caption • View Large Image • Figure Viewer • Download Hi-res image • Download (PPT)
Financing BI
Table 2 provides an overview of a portfolio of financing options that meet the double dividend hypothesis in environmental economics (e.g., implementing taxes that both reduce environmental degradation, which is the first dividend, and reduce poverty using the revenue generated, the second dividend),
including, for example, taxes on CO2 emissions (Table 3, experimental procedures). We estimate that CO2 emissions taxes alone may be able to generate double dividend financing of about USD 2.3 trillion a year (Table 3), which is enough to cover the BI cost for people living below the poverty line in low and medium HDI countries combined; in Africa, Oceania, and South America combined; or in Asia, Europe, and North America separately (Table 1).
Although there are a variety of potential financing mechanisms for BI (Table 2), we focus on assessing carbon taxes in this study because of the global push to reduce carbon emissions to reach sustainability goals. Williams
demonstrates the potential of this source of income, it reports that a $30 per ton carbon tax, which is lower than estimates of the marginal damage from carbon pollution) in the USA could raise an average of USD 226 billion per year in 2012 dollars over the first 10 years of implementation. Our approach is similar to a study by the IMF,
which estimated that a $ 70 per ton carbon tax, which is roughly mid-point of our assessed carbon tax rate of USD 50–100 per ton, could raise revenue of around 1%–3% of GDP in most countries. Using the 2023 global GDP of USD 105 trillion puts the total revenue at between USD 1.05 and USD 3.15 trillion.
Aside from carbon taxes, there are other sources of BI financing that can also simultaneously benefit the environment. For instance, Peszko
estimated that a plastic tax of $280 per ton applied to the 20 most common plastic consumer products that contain less than 30% recycled content could bring in up to $1.3 billion annually in tax revenue and reduce plastic waste generation by nearly a third below the baseline in Europe. Another possible funding source for BI that can provide a double dividend is the redirecting of environmentally harmful subsidies, such as those for agriculture and fisheries.
In fact, almost 90% of the USD 540 billion in global farm subsidies are harmful—redirecting these huge subsidies could potentially benefit initiatives for ending poverty, eradicating hunger, and restoring nature while improving the livelihoods of 50 million smallholder farmers worldwide.
With regards to fisheries subsidies, Teh et al.
report that the estimated harmful subsidies in Sumaila et al.
for 30 low-income countries could cover up to 80% of the current gap in poverty line income for the fishers in these countries. Similarly, the IMF
reports that USD 7 trillion was provided to the fossil fuel sector globally in 2022, and they project that this number would increase to USD 8 trillion in 2030 or about 8% of global GDP.
Challenges to BI implementation
Despite the high economic-impact-to-cost ratios, a number of concerns and questions have limited the implementation of BI. These include
how do we govern its implementation, and how do we mitigate the additional negative effects of BI implementation? These issues are discussed below.
BI can be implemented at a range of scales, from local (e.g., city-scale implementation like Stockton, USA)
to global. Larger scales may require novel institutions for raising funds (Table 2) and delivering payments. Although a universal cash transfer undoubtedly poses challenges in terms of administration and governance, these challenges are not insurmountable as providing BI will involve similar processes and systems as those used to deliver social protection programs that most governments already deliver nationally or via international organizations where applicable. The universality of some BI programs is an advantage and can reduce the cost of implementation over eligibility-based social programs that involve relatively more complex recipient targeting. Additionally, this universality may remove the negative social stigma associated with many social programs, leading to higher uptake, and reduce negative unintended consequences for non-beneficiaries when cash transfers are conditional.
The simpler administrative demands of BI also reduce the scope for corruption and abuse of power by frontline officials, particularly in countries where state capacity is low.
Innovations in information and communications technology can facilitate BI implementation. For example, mobile phone money transfers have been widely adopted in many low-income countries, even where access to financial infrastructure is limited.
Still, mobile money transfers require reliable electricity and mobile networks, which could unintentionally disadvantage groups that lack access to these infrastructure.
Additional barriers to BI include the perception that it may weaken incentives to work, save, and invest and the risk that it may increase inflation. It is argued that BI may create negative social outcomes such as alcohol addiction or reduce people’s motive to work,
although evidence suggests these concerns to be unsubstantiated or overblown.
Evidence from two BI schemes, Alaska’s Permanent Fund Dividend and Iran’s national cash transfer, does not support this claim. The Alaska Permanent Fund has been providing an annual cash dividend to all residents since 1982. A recent study found that the dividend had no effect on employment and, in fact, increased part-time work by 17%.
Moreover, a high percentage of Alaskans save their children’s dividends or use them to set up college funds or pay down debt.
In Iran, a national cash transfer program was started in 2011 in which monthly cash deposits amounting to 28% of median per capita household income were made into individuals’ accounts. A study found that these cash transfers did not reduce labor force participation or number of hours worked.
Instead, the cash transfers had positive effects on the labor supply of women and self-employed men, although there was a negative effect on the number of hours worked for men aged 20–29 years old. However, this reduction in hours worked was likely due to youth not being attached to the job market and because many in this age group had the option to enroll in higher education.
Although not provided universally, negative income tax experiments carried out in the US and Canada, which provided an unconditional income floor to a targeted group of people, also found no, or only moderate, reductions in work participation.
BI can in fact have positive effects on the labor market by increasing human capital investments (e.g., using cash transfers to acquire a new skill). The security of having an income floor can also increase entrepreneurship by reducing the risk of trying out new business ventures.
Inflation can occur if income transfers increase aggregate purchasing power without an accompanying increase in the supply of goods and services, as was the case during COVID.
This situation is unlikely to manifest given the widespread unemployment and/or underemployment in many countries. The question of whether or not BI will increase inflation depends on factors that likely differ across countries, such as peoples’ responses to the extra income, individual countries’ current political and economic conditions, as well as how governments plan to finance BI. Nikiforos et al.
found that the effect BI has on inflation is moderate at an annual increase of less than half a percentage point in most scenarios studied. Based on results from smaller cash transfer programs, Ortiz et al.
argue that BI has no significant effect on inflation. However, a recent study reported that there was a 136.5% cumulative rise in prices 5 years after the introduction of an unconditional, universal cash transfer in Iran.
But given Iran’s political and economic situation in recent years, it is difficult to attribute this increase to BI. One could argue that inflationary pressures could arise if the government decided to print money rather than pay for BI costs through increased taxes or similar measures.
Closing the inequality gap?
It should be noted that the fact that we set payments at poverty lines means there is the potential that global economic hierarchies may be reinforced because people in rich countries will continue to receive greater amounts of BI. What is more, our analysis shows that marginal propensity to consume (MPC) is greater in rich countries, and therefore the economic impacts of UBI will be greater in rich countries. We note here that the focus of this paper is mainly to get people out of extreme poverty and the inability to meet their essential needs. Clearly, this is only a start, because to truly reduce overall global inequality, we need measures such as investment in education and skills development, as argued for in World Bank,
implementation of progressive taxation and redistribution,
and the promotion of fair trade practices and labor rights.
Conclusions
We present a global estimation of BI’s economic impact and the associated implementation costs across 186 nations. Our findings show a positive economic-impact-to-cost ratio for BI implementation across all scenarios examined. Although acknowledging barriers beyond financial constraints, including systemic challenges, our study highlights several potential benefits that substantiate the rationale for BI adoption.
Existing evidence indicates that BI implementation can yield substantial dividends beyond mitigating financial costs. Improved social and health outcomes emerge as key components, presenting governments with a viable avenue to curtail economic burdens while concurrently fostering enhanced environmental stewardship, for example, through innovative double dividend financing strategies (Tables 2 and 3).
Given this compelling evidence of BI’s multifaceted benefits, we recommend earnest exploration of its implementation by nations, recognizing that its successful implementation hinges on a convergence of factors, including fiscal considerations, societal attitudes toward poverty alleviation, and political resolve. Critical to the viability of BI policies will be the meticulous and participatory design of effective programs
—a safeguard against potential individual misuse and systemic corruption that could undermine their efficacy.
We urge governments to perceive BI not merely as a reactive measure but as a proactive and anticipatory economic strategy.
By furnishing a guaranteed income stream to both the unemployed and the economically marginalized, BI serves as a more universal societal safety net, fostering financial confidence among the less affluent segments and fortifying resilience against devastating shocks, such as pandemics and climate disasters, that jeopardize the livelihoods of millions worldwide.
BI stands poised not only to redress existing extreme poverty, but it can also serve as an approach to help mitigate the rising specter of vulnerability worldwide, averting socio-economic instabilities that fuel unrest, conflict, and forced mass migrations, often triggered by such upheavals.
In harnessing the potential of BI, societies pave the way for proactive resilience-building, steering developmental trajectories toward reduced poverty and sustainable futures.
In an era characterized by unprecedented risks and uncertainties, embracing extraordinary policies like BI becomes a necessary stride toward fortifying societies against future shocks, aligning with the ambitions set forth in Agenda 2030.
In essence, we contend that these extraordinary times necessitate commensurate measures. The adoption of visionary policies like BI emerges as an important step, not merely to cushion against contemporary crises but to proactively shape a more socially resilient, sustainable global landscape. In sum, the convergence of evidence highlights the transformative potential of BI, not merely as an economic stimulant but as a catalyst for societal well-being, resilience, and sustainable progress—aligning with global aspirations for a world free from extreme poverty and environmental degradation.
Experimental procedures
Resource availability
Lead contact
The lead contact is U. Rashid Sumaila.
Materials availability
All data used in this study are available from the lead contact upon reasonable request.
Data and code availability
No codes were used in this study. All data are available from the lead contact upon reasonable request.
Computing the cost of implementing BI
UBI cost = direct and indirect cost in country i is the payment amount, times the number of people covered by the scheme. We assume for the indirect cost of raising taxes to be the same as the marginal cost of public funds (MCF), which, for a country, represents the welfare cost to a society from raising additional revenue for public expenditures (in our case, basic income [BI]). This welfare cost arises from the market distortions (the deviation from socially optimal consumption and production) created by the tax instrument used to raise revenues. In public economics, distortions differ based on the tax instrument used; for example, revenue raised through an income tax will have a different MCF than revenue raised through consumption or profit taxes.
We first collected data on average MCFs reported in the literature by country and found numbers for a total of 61 countries.
Next, we used these reported MCFs to compute average numbers for low, medium, and high human development index (HDI) country groups, respectively. These averages were then used to fill the data gap, ensuring that we have MCF estimates for all 186 countries included in our study (Table S2).
Computing the benefit of UBI
A change in fiscal policy has a multiplier effect on the economy, which is a measure of the extent to which fiscal expenditures boost gross domestic product (GDP), i.e., it is a measure of the efficacy of expansionary fiscal policy.
For instance, a multiplier effect of 2 means that each dollar of stimulus will lead to USD 2 in income generated. This type of effect is due to increases in disposable income, which in turn affects spending, consumption, and investment levels in the economy. More technically, fiscal multipliers relate to the impact of a change in government spending (ΔG) or change in tax paid (−ΔT) on real national output (ΔY). Estimates of the value of fiscal multipliers vary widely—partly because of the use of different modeling methods
and because even a single modeling method will give different results at different times, under different economic circumstances, and using different assumptions.
Here, we estimate fiscal multipliers using the marginal propensity to consume (MPC), which captures the proportion of extra income that is used for consumption. For example, if 80% of all new income in a given period of time is spent on products in a country, the MPC would be 80/100, or 0.8. The multiplier effect generated from this expenditure is then calculated using the following equation: 1/(1 − MPC). Hence, if consumers spend 0.8 and save 0.2 of every dollar of extra income, the multiplier is equal to 5, which means that every dollar of new income generates USD 5 of extra income overall.
Many influential papers have studied the effects of direct fiscal transfers to individuals.
Parker et al.
and Sahm et al.,
for instance, examined the effect of the 2008 tax rebates provided by the US after the Great Recession. Both studies found an MPC of about 0.25 and as high as 0.67 for what the authors describe as “liquidity-constrained” households. That is, households that are unable to borrow when income is low and/or are unable to quickly sell assets to cover shortfalls. In general, there is evidence that MPC can be quite high during crises because those who lose their jobs are likely to use their transfer funds or UBI for basic needs such as food, housing, and utilities.
It is therefore reasonable to assume that people with incomes below the poverty line would also have high MPCs. Ganong and Noel
found evidence to support this notion, showing that spending by unemployed individuals decreased sharply when unemployment insurance benefits end, a clear indication that these benefits have a large impact on consumption, resulting in high MPC and fiscal multipliers. Vladová
reported mean MPC from gross indicators for 10 European countries (Belgium, Britain, the Czech Republic, Hungary, Poland, Portugal, Slovakia, Spain, Sweden, and Switzerland) to be 86.2% (range: 80.5%–89.2%) and 90.9% (range: 85.8%–94.5%) in 1993 and 2003, respectively. By including a further 6 non-European OECD countries (Australia, Canada, Japan, Korea, New Zealand, and the USA), the author found that the mean MPC from net indicators for 1993 and 2003 to be 90% (range: 79.2%–100.2%) and 95.5% (range: 88.9%–106.5%), respectively. Two conclusions may be drawn from Vladová’s study. First, MPC for OECD countries is high, and secondly, MPC in these countries is increasing with time. Carroll et al.
also find that the aggregate consumption response across 15 European countries (Austria, Belgium, Cyprus, Germany, Spain, Finland, France, Greece, Italy, Luxembourg, Malta, the Netherlands, Portugal, Slovenia, and Slovakia) ranges between 0.1 and 0.4 and that it is higher in economies with large wealth inequality, where a larger proportion of households have little wealth, as well as under larger transitory income shocks when only households using liquid assets (rather than net wealth) to smooth consumption are considered. The authors conclude that wealth inequality and differences in the dynamics of household income affect the response of economies to a “fiscal stimulus” in an economically relevant way. In a study of the impact of remittances on consumption in a selection of countries in the Middle East and Europe, Glytsos
found MPC in Egypt, Greece, Jordan, Morocco, and Portugal to be 0.735, 0.847, 0.531, 0.607, and 0.667, respectively.
To determine MPC for the 186 countries in our study, we adopted the following step-by-step approach: (1) record all published country-level MPCs; (2) set MPCs greater than 0.82 at 0.82, which is the average of MPCs for high HDI countries. This is done to ensure that our estimates are conservative; (3) use the MPCs recorded in (2) to calculate average MPCs for low, medium, and high HDI country groups and use these to fill data gaps for countries with no data reported in the literature (of the 186 countries studied, we found data for 78). These steps provide average aggregate MPCs for all countries in our study, which we assume match the MPCs for the total population of a country. From the literature summarized above, we can assume that the MPCs for people living below the poverty line within a country are greater than the MPCs for the total population. This is because households living below the poverty line are more likely to face “liquidity constraints” compared with those who are not.
But by how much are MPCs for households living below the poverty line higher than those of the total population? In their study of the effects of “bankruptcy flag” removal using a sample of over 160,000 bankruptcy filers whose flags were removed between 2004 and 2011, Gross et al.
find that in the year following flag removal, credit card limits increase by USD780 and credit card balances increase by roughly USD290, implying an “MPC out of liquidity” of 0.37. The authors also found a significantly higher MPC during the Great Recession, with an average MPC roughly 20%–30% larger between 2007 and 2009 compared with surrounding years. Carroll et al.
report that more pronounced wealth inequality increases both the proportion of households with little wealth and the MPC among the lower half of the population. The authors report a difference in MPC between the top 10% and bottom 50% of income earners of 33% and the difference between those employed and unemployed of about 50%. Given these pieces of evidence, we conservatively assume that the MPCs for people living below the poverty line are 10% higher than the MPC for the total population.
Several caveats to our methodology merit consideration. Firstly, one might question the usefulness of a 2-year time horizon. Also, filling in the data gaps in MCP country data could introduce a bias toward wealthier countries, as low-income countries are less likely to have comprehensive data. Additionally, it has been argued that fiscal multipliers are nullified in the long run. However, we maintain that the short-term boost remains valuable, echoing Keynes’ famous sentiment: “in the long run, we are all dead.” Given that BI is not a one-shot policy but rather a continuous one, we anticipate that the economic stimulus would persist over time. Furthermore, we encounter challenges when dealing with averages. One might question the validity of averaging MCFs for country income groups, as this approach may overlook important differences in the distributions of MCFs within and between groups.
Finally, it is worth noting that the analyses on funds/sources and payments/impacts could be brought together more tightly because, at the moment, the estimates of economic costs and BI payments are at a country level, but the estimates for funds raised through carbon taxes are at country group level. Hence, we acknowledge that there is more work to be done, and our hope is that this work would kick off a number of follow-up papers that would fill up the kind of typical gaps that are often inherent in global large-scale studies such this one.
It is crucial for the reader to be mindful of these caveats and questions as they navigate our analysis.
Sustainably funding UBI through environmental taxes
We present a simple illustrative exercise estimating revenues generated using a carbon tax on fossil fuels. We assume, justifiably,
that most countries have existing infrastructure in place to tax (solid, liquid, and gaseous) fossil fuels. This is because the primary supply of fossil fuels is concentrated within a limited number of state or private entities in almost all countries. We do not extend the carbon tax to carbon emissions from cement manufacturing, or to other greenhouse gases such as methane, and nitrous oxide emissions. Some of these emissions are from non-point sources (such as agriculture), or from specific industries, and to implement such a new tax and monitoring infrastructure may be needed. As carbon emissions are a global externality, we assume that the carbon tax is uniform across the world. This differs from the current approach to reducing emissions via the Paris Agreement, which allows countries to determine their own commitments to reduction.
A caveat is in order: we provide a business-as-usual financing estimate. This ignores the fact that fossil fuel taxes will reduce consumption. Our purpose is to illustrate the power of this financing mechanism, but a complete analysis of the benefits and costs from this tax will account for the elasticity of consumption from the tax. It would recognize that a reduction in consumption of fossil fuel from the tax generates benefits and, correspondingly, reduces revenues that finance the BI. Such an analysis is outside the scope of the current analysis.
Data on carbon dioxide (CO2) emissions from solid, liquid, and gaseous fossil fuel consumption (kt) is from the World Bank Indicators for the year 2016. Our low tax scenario is USD 50 per ton of CO2 emissions, and the high tax scenario is USD 100 per ton of emissions. These prices were inspired by the mean of USD 54 per ton reported in Wang et al.
and the ∼USD 130 proposed by the government of Canada to achieve its Paris Agreement commitments.
It should be noted that currently, prices are ∼USD 23 per ton of CO2 equivalent emissions in Canada,
USD2 in Japan, USD 31 in Denmark, USD 68 in Switzerland, and USD 168 in Sweden.
Apart from Sweden, these numbers show that there is room for raising double dividend financing via carbon taxes.
Data used in analyses and definitions
Population
The population of each of the countries in our study for 2019 was taken from the CIA World Factbook. Total populations for low, medium, and high HDI for the 186 countries in our study are 472, 5,693, 1,499, and 7,664 million, respectively.
Percentage of the population below the poverty line
National estimates of the percentage of the population falling below the poverty line for 2019 were taken from the CIA World Factbook, which reports that the estimates are based on surveys of sub-groups, with the results weighted by the number of people in each group.
Labor force
The labor force represents the number of people who are employed plus the unemployed who are looking for work. The numbers used for our analysis are for 2019 and were taken from the CIA World Factbook.
Fiscal multipliers
Fiscal multipliers measure the effect that increases in fiscal spending will have on a nation’s economic output, or GDP. It is the response of output in percentage with an exogenous government spending of one percent of GDP. In this study, it is derived from the MPC (experimental procedures).
Purchasing power parity
Purchasing power parity is a measurement of prices in different countries that uses the prices of specific goods to compare the absolute purchasing power of the countries’ currencies. In many cases, purchasing power parity produces an inflation rate that is equal to the price of the basket of goods at one location divided by the price of the basket of goods at a different location. The purchasing power parity, inflation, and exchange rate may differ from the market exchange rate because of poverty, tariffs, and other transaction costs.
GDP at purchasing power parity (GDP)
GDP at purchasing power parity (GDP) is GDP converted to international dollars using purchasing power parity rates. GDP can tell us about the cost of living in a country. Take, for example, India’s nominal GDP of USD2.182 trillion and its GDP of USD8.027 trillion.
CO2 emissions from liquid fuel consumption (kt)
CO2 emissions from liquid fuel consumption (kt) is CO2 emissions from liquid fuel consumption refer mainly to emissions from use of petroleum-derived fuels as an energy source.
CO2 emissions from solid fuel consumption (kt)
CO2 emissions from solid fuel consumption (kt) is CO2 emissions from solid fuel consumption refer mainly to emissions from use of coal as an energy source.
CO2 emissions from gaseous fuel consumption (kt)
CO2 emissions from gaseous fuel consumption (kt) and CO2 emissions from liquid fuel consumption refer mainly to emissions from use of natural gas as an energy source.
Acknowledgments
This research was supported by the Social Sciences and Humanities Research Council of Canada (U.R.S., W.W.L.C., L.S.L.T., and N.J.B.) and the Canada Research Chairs programme (U.R.S. and W.W.L.C.) as well as the University Killam Professors Programme (U. R. S.). We are grateful to Scott Barrett and Daniel Pauly for providing friendly reviews of earlier drafts.
Author contributions
U.R.S. conceived the study and together with C.C.C.W., V.W.Y.L., L.S.L.T., L.C.L.T., and H.S. ran all data collection and analyses. C.F., K.H., J.E.C., N.J.B., S.G., and S.P. contributed to study design. U.R.S. wrote the first manuscript draft with contributions from C.C.C.W., V.W.Y.L., L.S.L.T., and L.C.L.T. W.W.L.C., I.I., K.H., J.E.C., N.J.B., C.F., S.G., and S.P. contributed to result interpretations as well as writing and editing revisions.
Declaration of interests
The authors declare no competing interests.
Supplemental information
- Download .pdf (.17 MB) Help with pdf files Document S1. Table S1, Notes S1 and S2, and supplemental references
- Download .xlsx (.01 MB) Help with xlsx files Table S2. Excel file containing Table S2 (marginal cost of public funds for each country), which exceeds three pages
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Article info
Publication history
Published: June 7, 2024
Accepted: May 1, 2024
Received in revised form: April 9, 2024
Received: July 13, 2023
Publication stage
In press, corrected proof
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DOI: https://doi.org/10.1016/j.crsus.2024.100104
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Figures
- Graphical Abstract
- Figure 1Economic impact of UBI-to-cost ratio by country
Tables
- Table 1Economic impact and cost of basic income
- Table 2Potential sources of funding to finance basic income, as well as a brief description of purposes and examples
- Table 3Potential total carbon tax (USD billions) by country grouping to finance basic income
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