Selected articles on AI and job displacement

Given my hiatus from posting and recent time constraints (multidimensional vector spaces occupy most of my time now that summer session has started), I'm discussing here a few interesting papers rather than providing an in-depth review of a single topic or piece of literature as I usually do. But worry not, I will be back to discuss the econometric details in another post soon enough.

And, a thank you to Intelligent Economist for mentioning this blog in his "Top 100 Economics Blogs of 2018" and a thank you to everyone who is visiting as a result of that post. I've tried to add some value with this blog (both for myself and for my readers) and I hope that you've found it valuable. Thanks very much for reading.

Automation and labor
In an earlier post from January, I discussed Acemoglu and Restrepo (2017)'s paper that models the relationship between automation and labor force displacement.

In the earlier post I identified key policy items to focus on in a future defined by automation:

1. Identify market failures that contribute to "excessive" automation or the adoption of technologies that are only marginally more cost effective than labor and lead to little productivity gain or job creation
2. Determine whether and to what degree jobs will be created at all in the process of automation if the adoption of new technologies leads to marginal but limited productivity gains
3. Address the inequality implications inherent in the displacement of jobs that require particular skill sets and the creation of jobs that require another
4. Identify the type of jobs that are created and the quality of those jobs
5. Prepare the labor market for "new skills" and a culture of lifelong learning

A recent paper from Jason Furman and Robert Seamans discusses a lot of these key items and more tangible policy proposals, including universal basic income and guaranteed employment, that would address the labor market implications of a future that will come to depend heavily on artificial intelligence. One challenge associated with automation that they mention in the paper is the decline in the male labor force participation rate, which I discussed in my previous post on rising inequality in male labor market outcomes.

The decline in male labor force participation is a signal that, at least in part, existing policies have had limited positive impact on (3) addressing the inequality implications inherent in job displacement and creation - the disproportionate impact on low-skilled labor - and (5) preparing the labor market for "new skills" and a culture of lifelong learning - failure to re-integrate workers that have been displaced by the system into new jobs requiring new skill sets. The paper highlights that addressing labor market transitions for individuals who have been displaced from their jobs is more challenging than it appears. For a discussion on skills in the context of automation see this new report from McKinsey Global Institute.

The paper also discusses non-labor related policy issues with rising automation including a need for new approaches to antitrust regulation. In particular, they draw attention to the fact that large datasets can serve as a barrier to entry in the AI field. I mentioned the role of big data in competition in an earlier post in the context of Amazon's edge in entering the grocery market: Amazon's access to high quality data on consumer preferences through its dominance of e-commerce retail is non-negligible given that its competitors in the grocery store market that it entered will have much less of that type of data. Even more so in the case of AI, data could serve as a crucial factor for entrants meaning there need to be novel ways of thinking about competition (or lack thereof) in these markets due to this new barrier to entry. It is also interesting to think about how institutions and laws such as the recent European Union General Data Protection Regulation can play a role in this area by limiting data retention.

Another point of further reading is the European Commission's "Analysis of the impact of robotic systems on employment in the EU". It adds value to existing literature because it is one of the first studies to use firm-level data to assess the impact of robotics on productivity (finding a significant and positive effect on labor productivity but not identifying an effect on employment levels which is an interesting finding that will have to delve into further in another post). The common alternative - using macro level data on productivity - has a more limited scope in terms of understanding what happens at a granular level. This is perhaps not as relevant for isolating a causal impact as for using descriptive statistics to explore the topic in greater detail and to ask more refined questions about automation's effects on firm behavior. 

A third paper in the current issue of Labour Economics also merits a mention given that it adds another layer of complexity to the relationship between automation and job displacement. Lordan and Neumark (2018) find that increases in the minimum wage lead to significant decreases in automatable employment held by low-skilled workers and that, while there is significant heterogeneity across industries and demographics, well-intentioned minimum wage laws interact with rising automation to have adverse impacts on a vulnerable population. One important question for this and economic research on AI more broadly: to what extent can new technologies be grouped together to analyze the impact of their adoption on the labor force? This paper relies on the U.S. Consumer Population Survey from 1980-2015, during which time a range of new technologies were adopted with potentially different implications and benefits of adoption for firms.

One of the key takeaways from Acemoglu and Restrepo (2017) is that new technologies have varying effects on firm productivity and the creation of new jobs. They discuss the "so-so technologies" that are only marginally more cost-effective and lead to little job creation. This implies that nuances in the type of technology and to what extent they increase firm productivity are extremely important. The nuances are, however, more important in determining job creation rather than job displacement based on their model.

A new report debunking myths on agriculture in Africa

The World Bank came out with a notable publication discussing common myths and truths on agriculture in Africa for policymakers and practitioners. The publication discusses the common myths in the table below and identifies whether or not they are true based on detailed data from the World Bank's Living Standards Measurement Survey.

It is notable both as a primer for researchers to get a more accurate, big picture sense of small holder agriculture and because its format effectively marries the technical details with concise policy takeaways without eliminating the relevant nuances across countries and settings.

Automation and labor: insights from the American Economic Association meetings last week

As briefly mentioned in my previous post, the American Economic Association held its annual meetings last week in Philadelphia. While the panel on gender bias was not webcast, several other lectures and discussions are available online. Another session that caught my eye was on automation and the future of labor, seeking to answer: what are the projected effects of automation, artificial intelligence, and robotics on labor share, wages, and the nature of work?

Daron Acemoglu presented a theoretical paper, co-authored with Pascual Restrepo, that provided a framework for understanding the relationship between artificial intelligence and labor share. He breaks down the impact of artificial intelligence into two countervailing effects: a displacement effect and a productivity effect. The displacement effect is the inevitable displacement of the labor force that takes place when firms substitute machines to complete specific tasks previously done by labor. The reason there is a displacement effect at all is that the capital is cost-saving for firms. One result of this cost-saving displacement is that there may be an increase in productivity associated with the firm's output. This productivity effect will lead to a demand for new skills and new job creation. But the question posed by Acemoglu and Restrepo is how large is that increase in productivity associated with employing machines instead of labor and will it lead to large enough job creation that it will balance out the displacement effect?

An example provided in the paper of these two effects comes from Bessen's (2016) analysis of the introduction of ATM machines. The paper found that the introduction and wide dispersal of ATM machines, a technology that took over many of the existing tasks of bank tellers (notably many existing tasks that were performed more expensively by bank tellers), allowed banks to cut costs. This cost saving allowed them to open more branches, which in turn led to an increased demand for bank tellers who could then focus on more specialized skills that the ATMs did not have. I don't review that paper here, but I note that it is contentious in its isolation of the causal effect of ATM machines on the banks' decisions to open new branches. The example, however, illustrates the mechanism by which the displacement and productivity effects work according to the paper (some bank tellers in existing branches displaced and bank tellers in new branches added).

The model in Acemoglu and Restrepo indicates that the effect of automation on labor share is unambiguous (labor share will decrease with the displacement effect holding productivity constant) but if the productivity effect leads to new job creation (demand for new skills leads to new job creation) then it has the ability to lessen the inevitable job displacement associated with AI. The productivity effect from employing ATM machines arguably led to an increase in the number of bank branches employed and the number of bank tellers employed who then needed to have new skills in the tasks that the ATM could not complete. I don't think that the "new skills" required in the bank teller positions are necessarily a good example of the demand for new skills modeled by Acemoglu and Restrepo since the new jobs created are effectively the same as the old jobs being displaced at existing branches but it's possible they may require improving on some existing skills in order to better advise the client on the different transaction opportunities available to them or bringing in new clients.

There are a couple of takeaways I think are important here:

• Will jobs be created at all?: 
• The paper highlights the case in which firms adopt technologies that are only marginally more efficient than labor at performing the same task ("so-so technologies"). The adoption of these technologies leads to few productivity gains and as a result lesser job growth through new skills. But the displacement effect will still be resounding and Acemoglu and Restrepo argue that it is these marginally more efficient technologies that will be the most harmful to the labor force since they don't lead to productivity gains. 
• In the case of the bank tellers, what if instead of investing in new physical branches (requiring employment of bank tellers), banks invested in improving their mobile and online infrastructure to better serve clientele online? Firms' productivity may be growing but the productivity gains do not necessarily translate into job creation at the same rate (creates jobs for those tasked with updating and maintaining the online infrastructure but would this be comparable to creating jobs for a new set of tellers at new locations?).
• Address inequality implications: This leads to the next point. It is clear that the jobs that are created through the demand for new skills will not employ the same skills as the jobs that are displaced (see the example of investing in new physical branches versus investing in a better online infrastructure and the skills needed to maintain each of those). Which raises the question of whether income and wealth inequality will be exacerbated by rising automation if the jobs that are displaced disproportionately impact those at the lower quintiles and the jobs that are created disproportionately require skills that those at the lower quintiles do not possess or cannot reasonably acquire. Perhaps anticipating the impacts on those at the lowest quintiles of the income ladder several prominent tech executives, including Elon Musk, have advocated for a universal basic income that they claim will be the only way to address the widespread job loss associated with automation. 
• Identify type of jobs created: The response from Ben Jones in the discussion directly following Acemoglu's presentation raised an important point: the model appears to assume that all of the new tasks that are created based on the productivity effect are essential, i.e. there would be no output if the task were not completed. How likely is it that the new jobs created in the aftermath of technology adoption would be essential jobs (essential to production)?
• If, as Jones hints at, the new jobs are non-essential, it is also likely that they may be of lower quality. Quality of employment is particularly important given the rise of the gig economy and trend towards temporary and part-time employment that offers fewer benefits and protections to workers. 
• Prepare for "new skills": In order to preempt the potentially negative implications on labor share and inequality the key would be to identify the kinds of new skills that will be most valuable in a future with automation and how governments, policymakers, and educators can effectively plan for such a future by preparing students for those skills. Furthermore, they would want to be able to prepare those outside of formal education (those who are not in schools, universities, or training programs) for retraining and lifelong learning so that they can better adapt to changing conditions in the labor market. 
• Identify market failures contributing to "excessive" automation: In their paper, Acemoglu and Restrepo outlined the phenomenon of "excessive" automation that is only marginally more cost effective than labor and that leads to few productivity gains and little job creation. They provided a few reasons for the "excessive" automation, one being that capital is potentially over-subsidized through the tax system which in turn encourages firms to automate.