Addressing uncertainty in basic income
Written by: Michael A Lewis
As someone interested in basic income (BI), I read a fair amount about the topic. I read pieces by supporters and opponents, as well as those who might be considered more neutral. I’m often struck by the degree of uncertainty concerning implementation of BI.
A popular argument for BI these days is based on concerns about the possibility of mass technological unemployment. Some in the “tech industry” contend that BI will become necessary as automation replaces more and more human laborers in the years to come. This has led to a debate among economists and others regarding whether automation will result in a net loss of jobs (for humans) big enough to warrant the need for something like BI. Both sides of this debate bring evidence to make their cases. But in the end, we simply don’t know for certain if and when automation will lead to a net loss of jobs for us human beings.
Assuming BI might be implemented in a society which would still require a fair amount of human labor power, we’d like to know what impact BI would have on people’s inclination to sell their labor or, more commonly, “work.” A BI could affect labor supply in at least two ways.
One is that people who received an income they didn’t have to work for may be inclined to work less. The second possible effect has to do with how BI would be financed. If it were financed by an increase in income taxes, this could also reduce labor supply. The reason is that a large proportion of many people’s incomes are earnings, meaning that an income tax is largely a wage tax. A higher wage tax has two possible effects on labor supply.
On the one hand, such an increase could cause people to work less because with the higher tax (and all else equal) their take home pay is smaller than it was before, creating an incentive to work less. On the other hand, a smaller take home pay means one would have to work more than before to maintain their standard of living. This would create an incentive for people to work more not less. If BI were implemented, we have no way of knowing which of these effects would dominate the other.
Leaving the labor market (but still related to it), another area of uncertainty has to do with how people would spend their time, assuming they did reduce their labor supply. Opponents of BI worry that people would use their time “unproductively”, while proponents tend to argue that individuals would engage in more care work or pursue “self-actualization” through pursuing education, writing poetry, starting a business, and the like. But if we’re being honest, regardless of which side of the debate we’re on, we must admit that we don’t have much of an idea what the relative proportion of unproductive to productive activities would be, assuming we could even agree on how to categorize activities as unproductive or productive.
A third area of uncertainty is related to personal relations and household composition. BI could have an effect on who lives with whom, who marries whom, who has kids or not (as well as how many to have), etc. As a society, we obviously differ when it comes to our values about such matters, meaning we might differ on the desirability of BI. But we don’t really know for sure how implementation of BI would affect “family life.”
Now I’m not saying we’re completely in the dark when it comes to questions of BI’s effect on labor supply, use of non-wage time, etc. Economists, sociologists, and others can draw on theory to help us think through these matters. And, by this point, there’ve been several experiments/studies (as well as more recent “startup” studies) which offer a lens on what might happen if BI were implemented. But we should be careful not to overestimate how much help we can receive from such experts, as well as the studies that have been (and are being) conducted.
Considering the many BI experiments (as well as proposed ones) around the world, we need to be cautious about what lessons might be learned. The philosopher Nancy Cartwright, well known for her work in the philosophy of science, has a phrase that’s quite relevant to this discussion: “it works somewhere.” Cartwright frequently utters this phrase within the context of discussing randomized controlled trials (RCTs), the so called gold standard of empirical research in the social sciences. Her point is that even if a well-designed RCT shows that a policy works in one context, that doesn’t necessarily mean it’ll work in another one. This is relevant to BI studies because they’re being conducted, or proposed, in a variety of different contexts. So if we find out that something works in India or Finland, that doesn’t mean it’ll work in Japan or the U.S. In the article cited above, Cartwright goes into great detail about why generalizing experimental findings from one context to another can be so difficult. For those interested in what we might learn from BI experiments, I think her work is quite instructive.
When engineers design systems, such as buildings, bridges, etc., they also must face uncertainties. To be double sure of the approaches that they take, many engineers tend to avail the services of engineering consultancy firms, so that they can rest easy knowing they are backed up by the same opinion. However, they don’t know for sure what loads the systems will end up having to bear, they don’t know if there will be earthquakes, they don’t know how forceful the winds will be, etc. One of the things engineers do to deal with such uncertainties is to include safety factors in their designs.
For example, suppose an engineer is designing a structure and wind, seismic, and other data indicate that it’ll have to bear a load of 1000 kg. Suppose also that the engineer wants a safety factor of five. Then the load which the structure should be able to bear isn’t 1000 kg but 5×1000 = 5000 kg. So a safety factor is a multiple used to increase the strength or robustness of a system beyond that which is thought to be required to account for uncertainty in what’s thought to be required.
Those of us designing policies don’t have the luxury of being able to use simple equations, which include safety factors, the way engineers do. But perhaps we should adopt a similar safety factor mentality. Implementation of BI would be a complicated undertaking, involving a great deal of uncertainty. Perhaps BI supporters should consider how to increase its robustness in response to labor supply reductions, as well as other unanticipated effects. I admit I’m not exactly sure how to do this. But I believe it’s something worth thinking about.
Michael Lewis
