Month: December 2020
Vaccine approval in the UK, protein folding advances, isn’t there a SpaceX launch today?, and now this:
Cultured meat, produced in bioreactors without the slaughter of an animal, has been approved for sale by a regulatory authority for the first time. The development has been hailed as a landmark moment across the meat industry.
The “chicken bites”, produced by the US company Eat Just, have passed a safety review by the Singapore Food Agency and the approval could open the door to a future when all meat is produced without the killing of livestock, the company said.
…The product would be significantly more expensive than conventional chicken until production was scaled up, but Eat Just said it would ultimately be cheaper.
…The growth medium for the Singapore production line includes foetal bovine serum, which is extracted from foetal blood, but this is largely removed before consumption. A plant-based serum would be used in the next production line, the company said, but was not available when the Singapore approval process began two years ago.
Masao Fukui, job market candidate from MIT, has made some significant progress on this problem, paper here. You should cringe if you just hear ‘wage stickiness” — for the incumbents, maybe, due to morale effects, because a grumpy worker who just took a pay cut might wreck things. But why is there wage stickiness for the new, not yet hired workers? Isn’t the new wage bargain what they need to negotiate in the first place? Other than postulating stubborn unemployed workers who overestimate their worth, how might we generate microfoundations for wage stickiness for the not yet hired, also known as “the unemployed”? Here is Fukui’s abstract:
I develop a new theory of wage rigidity and unemployment fluctuations. The starting point of my analysis is a generalized version of Burdett and Mortensen’s (1998) job ladder model featuring risk-neutral firms, risk-averse workers, and aggregate risk. Because of on-the-job search, my model generates wage rigidity both for incumbent workers, through standard insurance motives, and for new hires, through novel strategic complementarities in wage setting between firms. In contrast to the conventional wisdom in the macro literature, the introduction of on-the-job search implies that: (i) the wage rigidity of incumbent workers, rather than new hires, is the critical determinant of unemployment fluctuations; (ii) fairness considerations in wage setting dampen, rather than amplify, unemployment fluctuations; and (iii) new hire wages are too flexible, rather than too rigid, in the decentralized equilibrium. Quantitatively, the wage rigidity of incumbent workers caused by the insurance motive alone accounts for about one fifth of the unemployment fluctuations observed in the data.
As for wage stickiness for the not yet hired workers, here is I think the key point:
I show using simple phase diagrams that new hire wages must always feature rigidity at the top of the job ladder. This comes from the fact that at the very top of the job ladder, potential new employers have no incentive to increase wages above what the incumbent firms offer because there would be no additional workers to poach. This extremely strong strategic complementarity spills over toward lower job ladder rungs, and the wages are asymptotically rigid regardless of functional forms or parameter values. This result provides an explanation for the recent evidence on new hire wage rigidity.
The paper has many other interesting features. For instance, once wage rigidity for incumbent workers is a larger cause of unemployment, as opposed to just wage rigidity for new hires, the Shimer empirical critiques of labor market matching models dissipate. So matching models are strengthened, as are models of real rather than nominal rigidity of wages.
I am not yet sure if Fukui is right, but in any case this paper is a major contribution to the theory of wage-setting and it seems he is getting closer to the truth than anyone else has.
Tricky stuff! Via Ivan Werning.
We document that since 1997, the rate of startup formation has precipitously declined for firms operated by U.S. PhD recipients in science and engineering. These are supposedly the source of some of our best new technological and business opportunities. We link this to an increasing burden of knowledge by documenting a long-term earnings decline by founders, especially less experienced founders, greater work complexity in R&D, and more administrative work. The results suggest that established firms are better positioned to cope with the increasing burden of knowledge, in particular through the design of knowledge hierarchies, explaining why new firm entry has declined for high-tech, high-opportunity startups.
2. New Canadian paper: “…for most parameter values, the optimal policy is to adopt an initial shutdown level which reduces the reproduction number of the epidemic to close to 1. This level is then reduced once a vaccination program is underway.”
4. Steve McQueen’s Lovers Rock is one of the very best movies of this year; search Amazon Prime for “Small Axe,” select episode two. Short too.
6. Covid-19 in America detected as early as Dec.13-16 2019? See here for good criticisms, still an open question.
Alex has had numerous posts on Modern Principles, but here is my two cents. A textbook, as the name indicates, is a book. It has to be conceived of as a book, and thought of as a book, and written as a book, and ideally it should be read as a book. There are many other textbooks out there, and I do not wish to name names, but consider the following question. Which are the authors who really love books? Who spend their lives reading books? And indeed writing books. And who spend their lives studying what makes books good or bad? Who view books as truly essential to their overall output?
An ancillary question to ask is who are the authors who are truly dedicated to video, and to on-line communication more generally, as an independent outlet for their efforts and creativity?
Here is information on our new fifth edition, better than ever. Because we love books.
Here is a new and very important paper by Victor Stango and Jonathan Zinman, here are some of the main results, noting that each and every paragraph is important:
Our first finding is that biases are more rule than exception. The median consumer exhibits 10 of 17 potential biases. No one exhibits all 17, but almost everyone exhibits multiple biases; e.g., the 5th percentile is 6.
Our second finding is that cross-consumer heterogeneity in biases is substantial. The standard deviation of the number of biases exhibited is about 20% of its mean, and several results below suggest that this variance is economically meaningful and not substantially inflated by measurement error.
Our third finding is that cross-consumer heterogeneity in biases is poorly explained by even a “kitchen sink” of other consumer characteristics, including classical decision inputs, demographics, and measures of survey effort. Most strikingly, we find more bias variance within classical sub-groups widely thought to proxy for behavioral biases than across them. E.g., we find more bias variation with the highest-education group than across the highest- and lowest-education groups.
Our fourth finding is that our 17 biases are positively correlated with each other within-consumer, especially after accounting for measurement error following Gillen et al. (2019).1Across all biases, the average pairwise correlation is 0.13, and 18% have p-values < 0.001. Within six theoretically-related groups of biases (present-biased discounting, inconsistent and/or dominated choices, risk biases, overconfidence, math biases, and limited attention/memory), the average pairwise correlation is 0.25 and 29% have p < 0.001.
Our fifth finding is that there are also some important correlations between biases and classical inputs. Classical inputs and demographics may not explain much of the variance in biases (per finding #3), but some of them are correlated with biases in patterns that align with prior work. Most notably, the average pairwise correlation between cognitive skills and biases is -0.25. Cognitive skills are strongly negatively correlated with most biases, but positively correlated with loss aversion and ambiguity aversion. Other classical inputs are relatively weakly correlated with biases, except for a few expected links between patience and present bias, risk aversion and aversion to uncertainty and losses, and risk aversion and math biases that can lead to undervaluation of returns to risk-taking.
Overall not encouraging! But perhaps some of that is also what makes life more meaningful, at a high cost admittedly.