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:
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.
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:
- 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
- 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
- Address the inequality implications inherent in the displacement of jobs that require particular skill sets and the creation of jobs that require another
- Identify the type of jobs that are created and the quality of those jobs
- 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.
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
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.
