Robot Tax Policy Part 1 – Before

I recently had the pleasure of seeing Brianna Wu on a couple of panels about AI. One of the policies she was proposing was “taxing the robot which takes your job.” I asked if she would be open for an interview, which she was, and hopefully I’ll have that for you in the near future. Before I went back to Ms. Wu, I wanted to dive into the issue myself with the currently available materials so that I could present a before and after of my thoughts on the idea. Here’s the ‘before.’ I’ll go through the literature and write down how I feel after reading the articles and papers I find on the topic.

Let’s start with the problem and a (moderately) dense paper on the topic. Acemoglu and Restrepo present a very well researched and defined background on automation from the industrial revolution to the present and how – so far – automation has produced only temporary disruption to the overall job market. The main issue facing any disruptive automation is task-based: what tasks are automated, what tasks remain, and what tasks are created as a result of the automation. If the sum of tasks gained is positive, then the disruption is likely temporary. If the sum of tasks is negative, then the automation is probably eating away at jobs. The complexity of these tasks is also taken into consideration, with more complex tasks being weighted more heavily in the equation as being better for overall quality of life (under the assumption that they pay better).

Take AI as an example. An example of a low-complexity task created by AI is the necessity of feeding examples into a classifier. AIs can learn to classify data (images being the most prevalent), but require enormous amounts of curated data to function. Thus, there is a need for humans to find pictures and label them as either containing what the classifier is looking for, or not. However, this job is so low-complexity that the CAPTCHA people are using it now. People who want access to websites find pictures containing cars, which proves they’re human, and those pictures are then passed to an AI training set. However, a high-complexity task created by AI is setting them up to run, supervising them, and improving the algorithms.

Another key takeaway is the concept of excessive automation. This is a perverse incentive in the tax code which makes it cheaper to automate than to have a human employee even in cases where the automatic process is less productive. Another example: a machine that costs $1,000,000 with a 10-year useful life, and a human who is paid that in the course of 10 years in salary and benefits. Because of the option of accelerated depreciation in many tax codes, the company can write off 50% in the first year, and then a steadily decreasing percentage over the remaining 9. In the first 3 years, the company may write off 900,000 of that million dollars. Meanwhile, the employee is not only not a depreciable resource, but actually costs the company in payroll and medicare taxes over and above their salary and benefits. Thus, the machine may produce 30% less than the employee, but is still a better investment. True, there are maintenance costs to consider, but there’s also the possibility it will last much longer than the expected 10 years.

This is bad for the government as well. In addition to losing out on the revenue from the written off depreciation, there is also the loss of the worker’s income tax, and the payroll/medicare taxes. In the current structure, automation is heavily subsidized. South Korea recently debated robot taxation, settling so far on lowering the amount of write-off they allow for automation equipment.

In the next paper by Abbot and Bogenschneider, the authors start with a brief introduction to the issues around automation, concluding with the assertion that the current industrial revolution is fundamentally different from previous ones. They follow with an overview of the taxation problems with automation. An interesting fact that is not included in the previous paper is that while it may seem as if a sales tax or VAT would help with this, they do in fact make the problem of automation worse. This is because a machine doesn’t buy goods and so don’t incur the end-result costs of VAT. Thus, though a company may buy more and thus pay more VAT with an automated process, they then send that cost down the line to the end consumer, which is usually a human.

After that, they get to the main part of the paper where they compare different tax strategies to try to curb excessive automation. I will present them in brief along with my take on what difficulties they would face:

  • Reduce or eliminate the deductions around automated processes. These include the capital depreciation previously mentioned, as well as business expenses around provisioning and repairing the machine. The authors further suggest that the more automated the company, the less they are able to deduct. This is the simplest solution and likely would remove several of the tax incentives for automation.

Problems: Mainly those of categorization. What is an expense and what is an automated process? Is it just the direct costs around a piece of equipment, and what types of equipment are considered automation for the purposes of the tax code? If a company, for example, buys a robot that uses the same raw materials to produce goods as the worker it replaced, are the raw materials now included or are they allowed because they would have been purchased whether or not there was a robot? If the robot is faster and more material is purchased, would we tax the difference?

  • A “layoff tax”. Some States already track layoffs and charge more in unemployment insurance to firms that have greater layoffs over the course of the year. This would be expanded to the federal level and possibly include a way to determine whether the layoffs were motivated by decrease in corporate size or by increases in automation, perhaps by looking at year-over-year sales.

Problems: Categorization again, as well as fears of offshoring caused by the increase in the tax rate. Also, how long do we levy this tax? Is it based on layoffs for one year, five, ten, or what? Can a company hire more people and thus lower the rate, and if so, what if they’re just hiring low wage bodies to decrease their tax burden after laying off a lot of higher wage people?

  • A “human worker” tax incentive. This can either be a depreciation analogue for future wage costs, or repealing the payroll/medicare taxes.

Problems: Decreased tax revenue for the government either way. In the first case, it would significantly decrease revenue across the board. In the second, this would also effectively end Medicare, Social Security, and Unemployment Insurance.

  • Tax automation indirectly by looking at productivity per capita, as a ratio of either profit:employment or sales:employment.

Problems: Is there an average ratio they have to exceed before the tax is levied or is this a down-the-line to everyone ratio? What is the tax rate and how does the ratio affect it? That is, is it 1% for every 100:1 profit/person over a certain limit, or something more nuanced? The authors themselves suggest that sales ratio is a bad idea since it would disproportionately affect low-margin, high-volume companies. I think that this proposal may be the most fair, but would also be extremely hard to pass legislatively. Companies would successfully lobby to have the ratio set so high that it would tax almost no one, and even if in later years automation got to the point where the ratio was passed by most companies, they’d lobby to have the ratio increased based on spurious reasons.

  • Related and from the previous paper. A tax on that ratio, maintaining payroll and medicare taxes at the same quantity even after layoffs, or charging a tax on robot deployment.

Problems: The same as previous, plus these: If the layoffs weren’t automation-based, it punishes companies that do layoffs to try to survive in a competitive environment. If there’s a tax directly on robotic deployment, it brings up the categorization issue again, and is a tax on an entire industry without a view of whether tasks, wages, and employment are actually increased by some robotic deployments.

  • Direct increase to the corporate tax rate

Problems: Not only assumes that automation is an across-the-board problem that all firms should pay for, it also makes companies more likely to automate so as to get deductions from depreciation. Also from a practical standpoint, this is a nonstarter. Our government is unlikely to raise taxes on anyone for any reason ever. Arcane rule changes are one thing, but a straight up tax hike would die before it even reached a committee.

Out of these, I think that decreasing the deductions is most likely to pass (not requiring much predictiation power since it already has in SK, though likely not happening during this administration), but that ultimately the ratio of employees to profit would have the most societal benefit. Some would say it’s taxing business success because the owners have found ways to make more money with less outlay. To that, I say, yes. Yes it is. But you can’t really call yourself a “job creator” when you then say that success is the elimination of jobs.

Let’s look at the media discussion and policy movement so far. Starting with Bill Gates’ remarks which really set the ball rolling on taxation of robots and automation. He didn’t say what method he favors, leaving it instead to policy people to figure it out. Gates’ main thrust is that a worker can produce X amount, and a robot can produce the same, but the human worker is taxed, while the robot isn’t. This type of inequality is nothing new, and one might even call it a core facet of capitalism. The business owner owns the means of production and so they reap the benefits.

Lawrence Summers, former treasury secretary and economy adviser to Presidents Clinton/Bush and Obama respectively, rebutted this in (for a civil servant) very strong language. His arguments, in brief, were first that there is no way to draw the line, second that it improves living standards, and third that taxing robots decreases output and thus decreases overall wealth. The first argument has been covered in this article previously: it is difficult if not impossible to legally define “bad automation”, and even harder for an organization like the IRS to administer it. There will be either inevitable collateral harm, or a law so weak that almost no one will pay the tax. The third argument presupposes that robots and humans are currently on equal footing in the tax code, which has been discussed previously as well.

His second argument, that the goods and services provided by automation are naturally better than those of a human, is questionable. His examples are autonomous vehicles, online booking, and robotic surgical tools. The first does not yet exist, the second is arguably only efficient from the perspective of the services being booked, and the third isn’t (yet) replacing any labor. To take the second and third in more detail: An online booking system as a replacement for a travel agent is attractive to consumers because it means they spend their own time rather than spending money for a solution. This may take more time (depending on what the person needs) or less time (because they just wanted something quick that a search algorithm can easily replicate), and replaces expertise and judgment with algorithmic probability. I wouldn’t call this better, merely different. The third example increases the safety of surgery and decreases patient recovery time. This is good for just about everyone, and because the demand for surgery is so great, there is no significant loss anywhere. Should the robot get to the point where they can autonomously do a heart operation in an hour and send the patient home in two days, the impact would be far greater.

Let us now cherry-pick a counter-example: automated ordering kiosks at fast food outlets. This removes a job and lowers costs for the business, which will likely pocket that difference, write off the cost of the kiosk in depreciation, and thus screw the tax collector coming and going. There is a minimal change in the efficiency of ordering food, and because of the lack of expertise in using the system, the customer takes longer to order, requiring more kiosks than there were workers, which are all written off. There is an increase in kiosk-making employment, which would be an offset except the kiosks are likely made and assembled by robots.

Later that year, governments considered – and usually rejected – any kind of automation tax. The EU refused to do so, citing fears it would slow innovation. It was put forward in San Francisco, but it appears that so far only a tax on automated vehicle rides (not yet implemented) has been approved. The right-wing jabbernowls briefly took notice of it via Ms. Wu (who’s notoriety in other spheres makes her a focal point for the attention of extreme 1st amendment writers). They made no policy arguments, but the reaction was illustrative of how the debate would likely be presented in the Senate if the congress-critters believed the average voter was watching or cared.

In South Korea, however, the ball began to roll. As the most mechanized economy in the world began to notice that automation was beginning to become economically inefficient, they debated, proposed, and finally passed a measure which decreased the subsidies for automation. This was met with a mixed response, but it was a first step and given that SK remains a robotics hub globally 2 years later, it seems not to have stifled innovation.

Talk has continued in the US in 2018 and 2019, with little change in arguments for and against. The only concrete action that has been taken is the most recent GOP-sponsored tax bill, which made it possible to not only accelerate depreciation, but write off the entire cost of automated equipment the first year it is bought. Effectively an even greater incentive to automate, there are already signs that it has resulted in more automation, though the results remain preliminary. This is, however, to be expected. Some States and smaller countries may take action sooner, but the larger governments will not act until we have passed the crisis point, and the actions they take then will have to be extreme to correct the imbalance. Until then, pro-business lobbies will make sure that automation is the preferred solution, speeding up the job loss to a rate where new tasks cannot organically be discovered fast enough. We may not be in the big one, the moment when automation starts taking tasks away faster than it adds them, but the tax code and pro-automation policies currently in place will make the short-term situation feel like it.

Ultimately, the difficulty is that as the means of production focus ever more on machines, it is harder for we as humans to link ourselves to what is produced in our economy. If fewer people produce more goods, how do we as a society represent that output? Money is a rough representation of the value of time spent doing a productive task, and is exchanged for other peoples’ output. What is the value of money when everything we need as human beings is made by an autonomous system without any input from a human except as the owner of the system? What can we give them that is more valuable than what they already have? This is why universal basic income is gradually being taken more seriously; because we can’t be sure that this isn’t the big one. A post-scarcity society is in an even more Malthusian bind than a scarcity-based economy because its output is strictly measurable, mechanistic, and changes only with the increase in technological advancement. There is no “hidden hand” to absolve society of blame, no faceless economic force to excuse poverty and starvation.

And that may be the most dangerous thing of all. When everything is automated, who chooses the size of the population, its distribution, its upkeep? As long as economics is confusing, policy can be vague. When we know precisely how much our machines can produce, we can say with certainty that X people can live at Y standard or X+N people can live at Y-R standard. We can optimize the curve. Then it’s just a matter of determining how to control X in order to get our desired Y.

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