Silicon Valley’s Y Combinator has concluded its pilot in Oakland and released a draft proposal for a large-scale randomized control trial of basic income in the United States.
In January 2016, Silicon Valley entrepreneur Sam Altman announced his intention to spearhead a privately funded trial of unconditional basic income in the United States, hiring social work and political science PhD Elizabeth Rhodes as Research Director later in the year, and eventually assembling a team of expert advisors.
Since this time, Y Combinator has conducted a feasibility study in Oakland, California, and is now working to finalize the design of its full scale experiment. (Contrary to some misconceptions, the Oakland project was not itself an experiment. Its purpose was merely to test and fine-tune the mechanisms for conducting the experiment–such as the selection of participants, disbursement of funds, and collection of data–not to analyze the effects of unconditional cash transfers on recipients. The latter will be the goal of the project described in the new research proposal, which has yet to be launched.)
Although some details of the experiment remain to be decided, including the precise outcome variables and methods of data collection, Y Combinator has decided to design the experiment as a randomized controlled trial, conducted on a random sample of poor and low-income young adults from two US states (using a stratified sample to ensure adequate representation across race, gender, and income categories).
On the tentative design, the researchers will select a total of 3000 participants, randomly assigning 1000 to the treatment group–who will receive a regular cash payment of 1000 USD per month unconditionally for the duration of the experiment–and the remaining 2000 to the control group. (Individuals in the control group will provide the same type of feedback and data to researchers but receive only a much smaller cash payment, tentatively set at 50 USD per month.) The experiment is planned to continue for three to five years.
Y Combinator expresses an interest in a “holistic approach to understanding the individual-level effects of basic income”, in contrast to past and present experiments which have focused on the labor market impacts of unconditional cash payments, such as Finland’s current basic income experiment and the negative income tax experiments conducted in the United States in the 1970s. Among these individual-level effects, the research group is particularly interested in time use, mental and physical health, subjective well-being, financial health, decision making and attitudes toward risk, as well as political and social attitudes. Furthermore, although individual-level effects will be the focus of the experiment, researchers also hope to examine spillover effects on recipients’ families, friends, and communities.
While the research group has not finalized its choice of data sources and collection methods (see its project proposal for a discussion of possibilities currently under discussion), it plans to combine quantitative analysis with regular surveys and interviews (in contrast, for example, to the Finnish experiment, in which researchers have abjured the use of surveys and interviews during the duration of the experiment). Rhodes has explained, however, that participation in surveys and interviews will be voluntary for participants; that is, the payments will continue for the duration of the experiments even if recipients do not respond to requests for data and information.
The research team acknowledges that the experiment does not, strictly speaking, test a universal basic income. For one, as mentioned, the sample will be limited to young adults (aged 21 to 40) with incomes below the area median. The researchers justify this limitation, however, by noting that “the marginal effect of the additional income on many of the outcomes is expected to be relatively small at higher income levels” and that, under most plans, “the benefit received by higher-income individuals would be paid back in taxes in order to fund the program”.
Additionally, due to the use of a randomized controlled trial, the research will not capture multiplier effects that might result from the implementation of a universal basic income (in contrast, for example, to the saturation study in Dauphin, Manitoba, or GiveDirectly’s recently launched village-level RCT of basic income in Kenya). However, researchers note that “ the intervention is very expensive and our sample size is constrained by the budget. We will not have enough statistical power to detect effects with a geographically saturated study and the increase in sample size required to allow for clustering is financially infeasible.”
To conduct the experiment, Y Combinator has partnered with the Center on Poverty and Inequality (CPI) at Stanford University. The research has been approved by Stanford’s Institutional Review Board for research involving human subjects.
Y Combinator is currently working with state and local governments to coordinate mechanisms for distributing payments without affecting recipients’ future eligibility for existing government benefits, and to obtain the use of registries to collect individual data.
With many details still to be settled, no specific launch date has been set for the experiment (although Rhodes stated at the recent BIEN Congress that the research group hopes to begin the study in “early 2018”), and the states from which subjects will be sampled have not been publicly announced.
The full research proposal can be read on Y Combinator’s blog (see “Basic Income Research Proposal,” published September 20, 2017).
The organization invites comments and feedback on its project proposal.
Reviewed by Dawn Howard
Photo (Martin Luther King Jr. Regional Shoreline, Oakland) CC BY-NC-ND 2.0 MagicMediaProduction
Jon Altman and Eva Cox. Credit to: Alfred Deakin Institute (Deakin University, Melbourne)
The Alfred Deakin Institute at Deakin University in Melbourne, Australia, hosted a forum on the 17th and 18th August discussing the concept of a universal basic income.
Workshop co-convenor Jon Altman (Deakin University and ANU) suggested that part of the impetus for the workshop was the sense that discussion of UBI in Australia was not as advanced as it was in other countries. As evidence of this he cited the comment made by Chris Bowen (Shadow Treasurer for the Labor party), who said that UBI was “a terrible idea”. Tim Hollo – Executive Director of the Green Institute – also highlighted the fact that the Greens were the only major party in Australia currently in support of the concept.
Dr Tim Dunlop – author of Why the Future is Workless – gave context to the discussion by talking about the state of work, technology and automation. He said the “salient point” in labour market analysis is that many problems are current. Evidencing this, he summarized some figures from the International Labour Organization, including; global unemployment exceeding 200 million in 2017; stagnation of real wage growth; decline in proportion of wealth going to wages; 760 million men and women worldwide in “vulnerable work”, defined as work unable to bring them above the the world poverty threshold of AUD $3.10 per day; millions in refugee camps and jails; record levels of over and under-employment; and the creation of “increasingly precarious” work.
Looking at future technology, Dr Dunlop said that the consistent finding was that “around 40 to 50% of jobs are at high risk of automation in the next twenty years” (Oxford Martins School Report, 2015) under “currently existing technologies” (McKinzie Report) and that it would be “close to a form of denialism”, therefore, to state, as many do, that “concerns about technological unemployment are overstated”. Associate Professor Karl Widerquist agreed with this point, stating that “people are not interchangeable parts” and often find that their “learn[t] skills” are “not needed any more”. In this regard, he said a UBI could compensate for the continual disruption of technology, and the inherent inability of workers to adapt and provide themselves with income. Phillip Ablett (USC), summarising work by Mullally, added that neo-liberalism’s emphasis “on ‘individual responsibility’ for poverty” contributed to this persecution of workers, where we tend “to blame individuals for their ‘failure’ to succeed in the market economy rather than consider the structural impediments to achievement”.
Professor Widerquist said a shift away from labour prosperity to capital prosperity has led to an “incentive problem” where employers don’t have an incentive to treat their employees appropriately since employees don’t have any power to refuse their conditions. The universal nature of a UBI, as such, would allow for a “voluntary participation economy instead of a mandatory participation economy”. Dr Frances Flanagan agreed that “capital accumulation” was central to the problem of “acute inequality”, however she expressed concerns that discussions around UBI focused too heavily on wage leverage and monetary incentive. Citing “care work” as an example “utterly antithetical” to the taylorisms of tasking and efficiency, Dr Flanagan said we need a more positive definition of ‘work’ since there are always ‘jobs’ that “require empathy, judgement and relationships”. UBI, consequently, needs to be “supportive of the fight for better jobs” and “[be] supportive of the fight against marketisation”. Professor John Quiggin (UQ) echoed Dr Flanagan’s concerns that UBI risks the possibility of replacing social services with a single payment, though he did point out that an unconditional stipend could destigmatise the concept of welfare payments to individuals, undermining the concept of the “deserving and undeserving poor”. Professor Eva Cox (AO) was also critical of UBI as a means to empowering a “protestant, male, Anglo” market system, where humans are economically judged as being good or bad “consumers”. She reiterated the need to revisit the concept of ‘work’ through a lense where humans were considered “social”, “dependent” and “interdependent”, advocating a UBI that was used to redefine “the social contract between the nation state and the individual”, with “reciprocity built into it”.
On the subject of evidence to support a UBI’s practical plausibility, both Professor Widerquist and Professor Greg Marston (University of Queensland) said that trials investigating the effects could be strategically dangerous since the trial conditions are often neither unconditional nor universal. Marston pointed to climate change as an example of where the accumulation of data has brought about, in many cases, confirmation bias in support of inactivity rather than impetus to instigate change. It was generally agreed that the issues of design and implementation were not, therefore, easily separated. Professor Quiggin, Troy Henderson and Dr Ben Spies-Butcher advanced the idea of a staged introduction, a “stepping-stone” approach which would retain the “big idea” excitement for voters and simultaneously satisfy technocrats. Quiggin’s preferred model was to favour the “basic” over the “universal” through various mechanisms and adjustments to tax regimes, introducing a full UBI payment to selected, vulnerable populations, and then gradually increase the number of people covered. The cost of everyone in Australia receiving a full UBI was estimated to be around 5-10% of GDP. Henderson and Spies-Butcher offered modelling that began by universalising the age pension, and by also introducing an “unconditional Youth Basic Income paid to those aged 20-24 based on a negative income tax model.”
In conclusion, the consistent theme of the two days was that UBI cannot be offered as a silver-bullet solution to issues around inequality, welfare, social security and the potential growing precarity of work. So while there is a tendency amongst advocates (worldwide) to present UBI as a single policy response for addressing many of the problems societies have with these issues, the very strong feeling of the workshop was that this could be a dangerous over-reach.
You can view some of the contributors speaking here.
I recently read Kate McFarland’s very informative overview of several basic income “experiments.” The quotes are around that last word in my previous sentence because, as McFarland notes, not all these projects are truly experiments, at least not if the word “experiment” is being used the way it is in the social and biomedical sciences. As we use this term in the social sciences, an experiment is a study with the following features:
Study participants or a cluster of them are randomly assigned to at least two groups
At least one of the groups is a treatment group, while at least one is a control group
The treatment group receives the intervention of interest, while the control group does not receive intervention.
Feature one above is key.
What random assignment does is make it very likely that the treatment and control groups will be balanced. “Balanced” roughly means that the distribution of variables related to both the intervention and outcome of interest are the same across treatment and control groups. So if after the data are analyzed we find a difference in the outcomes between treatment and control groups, we can attribute such a difference to the intervention of interest.
The random assignment feature is why Eight’s study in Uganda, as McFarland points out, has limited “usefulness as an experiment.” I think it is fair to say in fact, that social scientists would not consider what Eight is doing an experiment at all. I am not saying that Eight’s study has no usefulness whatsoever. It may be useful when it comes to keeping BI “in the spotlight” and, thereby, help to maintain attention on this movement. For those of us who, at least in principle, like the idea of a basic income, this is a good thing. But we should be careful when it comes to considering what we can learn from the Uganda “experiment.”
The study in Uganda is usually called a pre-test/post-test study. In such studies, measures are taken before an intervention of interest (the pre-test part), after the intervention is implemented (the post-test part), and then these “before and after” measures are compared to one another. If certain changes are observed, these may be attributed to the intervention in question. The problem with such studies is that we do not know what would have happened to the group which received the intervention had it not received it. Maybe the observed changes in the relevant measures would have occurred even if there had been no intervention. The reason we want control groups in experiments is to allow researchers to estimate what would have happened to the group that received the intervention had it not received it. Without a control group, the Uganda study simply may not tell us much about the effects of the cash grants they are testing.
The third feature above has to do with the intervention of interest. This is very pertinent to the experiments McFarland wrote about, as well as BI experiments in general. Following BIEN, McFarland defines BI as “a periodic cash payment unconditionally delivered to all on an individual basis, without means-test or work requirement.” As I read her piece, I thought she was interpreting this definition to mean that if a policy provides a cash payment, exactly as spelled out in the definition, but also decreases the payment if a recipient obtains an income from selling their labor, then such a policy wouldn’t be a basic income. Alaska has no income tax, but it does have the Permanent Fund Dividend. Since it gives folks the dividend but does not tax any of it back in the form of an income/earnings tax, its grant would be an example of a basic income. But if the U.S. or any other nation, granted people money unconditionally, periodically, on an individual basis, and without a means test but also taxed all sources of income, including earnings, then that country would not have a basic income. This may seem like a mere semantic point, having nothing to do with BI experiments. But I think it is incredibly relevant.
McFarland makes it clear that some places are assessing the effects of a BI as defined by BIEN. Others are testing the effects of programs similar to BI, as defined by BIEN, but with the added feature of a decrease in the BI grant if someone works. I think she refers to this as a guaranteed minimum income.
I suspect that if the U.S. ever did anything like a BI, it would be this guaranteed minimum income version. I think this is because of the vulnerability of a BI, as McFarland defines it, to what I call the “Bill Gates objection”—why give really rich people more money? If one can respond that rich people will not be net recipients because they would pay more in income taxes than they would receive in the BI, this might be a viable response to the objection.
If I am right about this, then studies like the one in Finland, which focuses on a BI, might not tell those of us in the U.S., or in other nations following a similar course, as much as we would hope. That is because the effects of a BI might differ from the effects of a guaranteed minimum income. As an example, if one could get a BI and keep all their earnings without any loss in the amount of their BI grant, such a policy could have a different effect on labor supply than one which would curtail the grant when income from earnings increased. All this means that BI supporters who get enthusiastic about findings from BI experiments ought to take a moment to see if what was studied is what they actually have in mind.
About the author:
Michael A. Lewis is a social worker and sociologist by training whose areas of interest are public policy and quantitative methods. He’s also a co-founder of USBIG and has written a number of articles, book chapters, and other pieces on the basic income, including the co-edited work The Ethics and Economics of the Basic Income Guarantee. Lewis is on the faculties of the Silberman School of Social Work at Hunter College and the Graduate and University Center of the City University of New York.
The People’s Republic of China has created the largest unconditional cash transfer program in the world. It is called dibao, meaning Minimum Livelihood Guarantee. A recently published book is taking a fresh look at how effective dibao is at improving the livelihoods of impoverished Chinese people.
Dr. Qin Gao is on the faculty of the Columbia University School of Social Work, where she researches poverty, income inequality, and social welfare programs in China.
Gao has done extensive work researching dibao, and has released the book “Welfare, Work, and Poverty: Social Assistance in China,” which evaluates how well dibao has achieved its goals of lowering the amount and intensity of poverty in China.
The UBI Podcast recently interviewed Gao on her new book about the dibao program and asked her to give her thoughts on universalizing dibao.
Dibao is important to understand for basic income researchers because it demonstrates on a large-scale how basic income operates when it is not universal (since it includes a means test).
The dibao program allows each locality to set its own dibao standard (essentially the poverty line). Anyone below that standard is technically eligible for dibao assistance. The assistance in theory gives an individual enough money to reach the dibao standard. Eligibility for dibao is based on individual income, so one individual in a household could qualify, while another may not, Gao said.
For example, a dibao standard in Beijing, China is 900 RMB per person per month. If an individual made 700 RMB per month, dibao would provide 200 RMB in assistance to reach the dibao line of 900.
While some may worry that officials will cut off dibao assistance once an individual goes over the line, Gao said the reality is more complicated.
“In reality, many local officials are very considerate of the fluctuation in people’s incomes and other family situations. For example, education needs, health care needs. So many localities actually have initiatives to not discontinue people’s dibao benefits right away if they have income that’s higher than the local dibao line,” she said.
Some localities may allow a family to stay on dibao for three months after extra income is earned to make sure they have job security and they “do not fall back into poverty right away.”
Once a family receives the cash, it is unconditional, meaning there are no (direct) behavioral conditions to continue receiving the money.
Gao said the evidence that dibao creates a poverty trap, where families remain under the poverty line intentionally to receive assistance, is not strong.
Some localities have families update their income and wealth information every three to six months. Certain villages will even publish the names of recipients to allow for public feedback on whether a family should qualify for dibao. Based on the feedback, localities will randomly select people to verify their income information.
“So it’s a very systematic and stringent process,” Gao said.
For some villages, allowing others to comment on a family’s poverty situation may further stigmatize the dibao and other forms of welfare.
“Because the dibao is an unconditional cash transfer, so by design the policy requires applicants to tell the truth and other community members and neighbors to share the responsibility of monitoring. That is part of the design of this program,” Gao said.
While on paper, the dibao is technically an “unconditional cash transfer,” the way dibao measures wealth creates its own form of conditions.
Depending on the locality, dibao recipients may face a myriad of asset tests that prevent them from owning pets, a larger than average home, a car, or luxury items. Expensive private schools and schools abroad are off-limits. In the past, even a cellphone was a disqualifier.
“I think now, many localities are more lenient on that, especially on the cell phone. But there are certain luxury goods (so-called), that you’re not supposed to have. That also features into the feedback from the neighbors and community members. They would get critical and jealous if you have certain luxury goods that they don’t have but you are getting dibao,” Gao said.
In Gao’s book, she also analyzes the subjective well-being and social participation of dibao recipients. She found dibao recipients “tend to be more isolated, and less active in their social participation” than similar peers.
Dibao recipients may feel stigmatized from participating in these activities, such as going to the movies, since it is not a “culturally acceptable use of the dibao income,” she said.
After China’s transition from a planned economy to a market-based economy, society’s expectations about how families earn their own living changed. Now it is expected that people “earn a living through their own work.” Although, Gao said China is currently going through a debate about who “deserves” welfare.
“Previously people had guaranteed jobs, but many people during the economic transformation were laid off so able-bodied adults couldn’t support themselves through jobs anymore. And that group is making up about half of the dibao population,” Gao said.
One area of concern for policymakers is ensuring the dibao is reaching the “proper recipients;” that is people in poverty. There are reports of targeting errors in administering dibao “because of misreporting or difficulty to capture the real income or assets situation in rural areas.”
“The targeting error is real and local officials are very aware of it, but that will stay with the program because of the variations of family conditions and income,” Gao said.
The dibao standard is often used as a criteria for other welfare as well. This means that qualifying for dibao also gives a family access to a host of other assistance (including education, housing, and medical assistance). However, this could create a “welfare cliff” issue, where if a family exceeds the standard they may lose a lot more assistance than they gain as income.
“I think this is one of the policy design features of dibao that needs to be revised right now,” Gao said of dibao acting as a “gatekeeper” for other social assistance.
Overall, dibao has only reduced the rate of poverty to a “modest degree.” It is more effective at reducing the depth and severity of poverty, Gao said.
When asked about the potential to universalize dibao and remove the means-test, effectively creating a Universal Basic Income for China, Gao said this idea has been “very much on my mind recently.”
“I think the best possibility probably would be for certain more developed localities to experiment with such a program and see how it works,” she said.
As for creating a UBI program in China in the near-term, Gao said this would be challenging for many reasons.
“To make the dibao or a similar cash transfer universal all around China, I don’t think it’s very likely in the short-term, both in terms of fiscal challenges and also political and cultural challenges,” Gao said.
There are three steps in the plan. By 2020, the Chinese government expects its companies and research facilities to be at the same level as those in leading countries such as the United States. After another five years it is aiming for a breakthrough in aspects of AI that will drive economic transformation. Then by 2030 China aims to become the world’s premier artificial intelligence innovation center, establishing the key fundamentals for a great economic power.
However, rapid development of AI solutions is not without its drawbacks. In June, Kai-Fu Lee, the chairman and chief executive of one of China’s leading venture capital firms Sinovation Ventures and the president of its Artificial Intelligence Institute, expressed concerns about the downsides of AI, particularly the potential for mass unemployment. He raised basic income as a feasible solution.
According to Kai-Fu, the AI products that now exist are improving faster than most people realize and promise to radically transform our world, not always for the better. They will reshape what work means and how wealth is created, leading to unprecedented economic inequalities and even altering the global balance of power.
He highlighted the challenges brought about by two specific developments: enormous wealth concentrated in relatively few hands and vast numbers of people out of work.
Part of the solution to the loss of jobs will involve educating or retraining people in tasks where AI performs poorly. These include jobs that involve cross-domain thinking such as the work of a trial lawyer, however, retraining displaced workers to perform these highly skilled tasks will not be feasible in most cases. There is more scope for people to occupy lower-paying jobs involving the nuanced human interaction that AI struggles to perform, such as social workers, bartenders and concierges. But here too there is a problem: how many bartenders does society really need?
The solution to the problem of mass unemployment, Kai-Fu suspects, will involve “service jobs of love.” These are jobs that AI cannot do, that society needs and that give people a sense of purpose. Examples include accompanying an older person to visit a doctor, mentoring at an orphanage and serving as a sponsor at Alcoholics Anonymous – or, potentially soon, Virtual Reality Anonymous for those addicted to their parallel lives in computer-generated simulations. In other words, the voluntary service jobs of today may turn into the real jobs of the future. Other voluntary jobs may be more professional and therefore higher-paying, such as compassionate medical service providers who serve as the human interface for AI programs that diagnose cancer. In all cases, people will be able to choose to work fewer hours than they do now.
In order to pay for these jobs, it will be necessary to take advantage of the enormous wealth concentrated in relatively few hands.
Kai-Fu Lee writes:
“It strikes me as unavoidable that large chunks of the money created by AI will have to be transferred to those whose jobs have been displaced. This seems feasible only through Keynesian policies of increased government spending, presumably raised through taxation on wealthy companies.
As for what form that social welfare would take, I would argue for a conditional universal basic income: welfare offered to those who have a financial need, on the condition they either show an effort to receive training or commit to a certain number of hours of “service of love” voluntarism.
To fund this, tax rates will have to be high. The government will not only have to subsidize most people’s lives and work; it will also have to revenue previously collected from employed individuals.”
