Algorithmic obsolescence is on the horizon, threatening the advanced nations with poverty. We have to decide how we are going to deal with it, before it’s too late. Universal Basic Income (UBI) is one way. But, is it the only way?

Giving away free money to alleviate poverty is not a new concept. Activists in every generation have campaigned for the cause in one or the other way. In his 1967 book —" Where Do We Go From Here: Chaos or Community?"  — Martin Luther King Jr wrote:

The solution to poverty is to abolish it directly by a now widely discussed measure: the guaranteed income.”

The 21st-century context for ensuring “guaranteed income,” particularly in the developed world, is completely different than any era of the past. Call it by any name, technological unemployment, algorithmic obsolescence, or automation apocalypse, by 2030 as many as 800 million jobs will be lost worldwide to robots and artificial intelligence.

Since tech stalwarts like Elon Musk, Mark Zuckerberg, et al professed support for the concept of universal basic income, UBI has become a buzz word not only among the anti-poverty activists, but within the Silicon Valley community. If not a cornerstone of an eventual utopia, it is certainly perceived as a panacea for poverty alleviation.

We present our take on the subject matter in two parts. The first part presented herein emphasizes the backdrop to UBI, while the second part embodies a zero cost alternative to UBI : Earned Minimum Income (EMI).

Algorithmic Obsolescence / Automation Apocalypse

There’s a fair chance the automation apocalypse will happen well within our lifetime. It’ll probably happen so quickly we won’t even see it coming. In fact some of it is already beginning to happen. Low-end labor jobs are fast evaporating.

A 2017 study found that each robot reduces local employment by 6.2 workers. An earlier Oxford study lists 170 professions that are facing a 90%-plus probability of algorithmic obsolescence. Algorithms have already taken over day-trading almost completely. Autonomous vehicle prototypes don’t anymore need a human on the driver’s seat. The trend will accelerate dramatically when critical-mass automation occurs

Without a doubt, algorithmic obsolescence is on the horizon. We have to decide how we are going to deal with it, before it’s too late.
If algorithms are making some of the income-generating activities obsolete, shouldn’t a solution be explored in developing new algorithms that neutralize the obsolescence? Or, should the solution lie in imposing new taxes on the economy by default?

Perhaps, algorithmic thinking is too underrated. :(

Nevertheless, for a rational mind, trial & error, hit or miss, arbitrary decision making, can never take precedence over algorithmic thinking. So we have done some algorithmic thinking and come up with a radical new way to nip this woe in the bud before it starts hitting us hard. But, let’s first look at the dynamics of the problem, its potential consequences, and how the world is currently dealing with it.

The Economic Cost Of Poverty In The U.S.

According to a recent study, childhood poverty alone costs the U.S. $1.03 trillion annually.

However, the total cost of poverty to the U.S economy as estimated by a UBI lobby is estimated at more than $4 trillion a year much less than the cost of UBI, they claim.

This minimum stipend can be supplemented with wages from work or any vocation the beneficiaries engage in. UBI advocates claim it will help fight poverty by giving people the flexibility to find work and strengthen their safety net. It offers a way to support people who might be negatively affected by automation.

UBI / Cash-Transfer Programs in North America

Stanford Basic Lab

The Global Activism

The idea of UBI is that all citizens receive a fixed amount of money from the government to cover food, housing, and clothing, without regard to income or employment status.

GiveDirectly is one of the leading NGOs in the forefront of UBI research. They believe that UBI program targeting only those below certain income levels may be affordable in the US, and even more so affordable in poorer countries. According to their estimates giving $2 a day minimum subsistence wages to the 250 million poor in India would cost less than 5% of Indian GDP.

Previous studies conducted on unconditional cash transfers and their differences to an ideal universal basic income experiment

GiveDirectly’s 2013 UBI pilot in Kenya provided $404 - $1,530 in monthly installments over nine months followed by an analysis of its effects on recipient consumption, wealth, welfare, psychological well-being, and the overall impact on the village. While GiveDirectly’s large scale UBI trials are still underway the results so far have been quite encouraging.

GiveDirectly Results

In 2016, Switzerland held a referendum on a basic income proposal, and it received just 23.1% support. 77% of Swiss voters basically rejected the UBI. Nevertheless, late last year a Swiss film maker Rebecca Panian took up the challenge personally, and recruited 650 participants for a year long UBI experiment that would pay each participant 2,500 francs/month. However her attempt at crowdfunding the project failed.

Early this year Finland reported results of its two-year UBI experiment in which the government gave away $640 / month, no strings attached, to 2,000 citizens. The interim results show that UBI might make people feel less stressed but doesn’t necessarily fix unemployment.

Last year Canada stopped its UBI experiment midway, claiming the high costs aren’t sustainable.

The Price Tag

The cost to give every American citizen $12,000 per year, the current poverty threshold, would be roughly $3.8 trillion annually. The figure represents 21% of US GDP or 78% of tax revenue. That’s unthinkable, unfathomable and unprecedented in history.

There are many others who think the UBI costs far less than one thinks:

“The key to understanding the real cost of UBI is understanding the difference between the gross (or upfront) and net (or real) cost.”

Really? Will any sane economist not consider the upfront cost when that cost is as gargantuan as $3.8 trillion, constitutes 21% of country’s GDP, and actually eats up 78% of the total taxes the country collects?

When the gross investment is preposterously exorbitant, who would bother looking at the net?

The Criticism

The two main criticisms of a universal basic income are its cost and the expectation that it would reduce or eliminate incentives to work.

Even in countries with most extensive and generous welfare programs, their citizens would struggle to receive a poverty level wage, if all of those social spending programs were scrapped to fund UBI.

A recent report from National Bureau of Economic Research (NBER) concludes that UBI would direct much larger shares of transfers to childless, non-elderly, non-disabled households than existing programs, and much more to middle-income rather than poor households. Reviewing the labor supply literature the authors conclude:

“the ongoing UBI pilot studies will do little to resolve the major outstanding questions.”

Conclusion

While the world mulls over the impending automation apocalypse, the major UBI questions remain unanswered. As averred, the so called algorithmic obsolescence, warrants some algorithmic thinking rather than the random mythic trial and error venturing into the unknown. The unknown, of which, we only know one thing for sure — it’s a $3.8 trillion behemoth constituting 21% of the country’s GDP. All of it coming from tax the taxpayer’s pocket.

It’s simple economics stupid! Free lunch can only come from the state exchequer. What anyone’s algorithmic thinking got to do with it?

Well then, stay tuned for more on the Algorithmic Thinking that we believe can potentially counter this $3 trillion UBI conundrum.

To be continued ……..

References:

1. Michalis Nikiforos, Marshall Steinbaum, Gennaro Zezza, “Modeling the Macroeconomic Effects of a Universal Basic Income”, The Roosevelt Institute, August 29th, 2017.
2. Hilary W. Hoynes, Jesse Rothstein. Universal Basic Income in the US and Advanced Countries. NBER Working Paper №25538, Issued in February 2019.

This article was first published on Medium

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