Food for thought:
“Wall Street” had a positive stake in the 2008 financial chaos that led to the Great Recession. By convincing everyone that the health of the financial sector was vital for the health of the economy, financial companies now have free reign over public opinion and policy. Their hierarchical centrality becomes a self-fulfilling prophecy.
Probably not why things happened the way things happened, but conspiracy theories always provide perspective on events going forward. Unintended consequences abound.
What are the opportunity costs of not investing in public transit? Who exactly are the principals and agents in this case, what are their incentives, and how are they distorted?
Under the “what if x disappeared tomorrow” protocol, what would happen in a truly free market if all transit infrastructure disappeared tomorrow? What would arise?
I found this via The Unbroken Window, where it was used to discuss whether we should care about climate change.
I actually care a lot about whether we should care about climate change, but not right now. Right now, I want to see the places people moved from linked to the places they moved to. The fact that people are moving to Florida is meaningless to me if I don’t know where they’re moving from.
There are 3,144 “county equivalents” (from Wikipedia) . That means the complete undirected graph has 3144(3143)/2 = 4,940,796 possible edges. I’m not sure how this is changes for a directed graph in which each edge appears exactly twice (once in each direction).
Consider an espresso machine that can make only one drink at a time. Adding baristas without adding machines cannot increase the rate of espresso production.
This is not strictly true. Having multiple bar-trained counter staff allows baristas to rotate as needed, potentially minimizing espresso output rate loss due to declining barista productivity (and increasing error rate) over the course of the day, as well as interruptions in using the machine. Capital investment in “systems” would determine the extent to which this strategy can be employed.
I just made this idea up right now but I kind of like it: “Systems” capital is the subset of capital that determines how labor interacts with “physical” capital. Since systems are affected by both capitalists and labor, a small endogeneity problem arises between L and K. The effect of a change in K on MPL depends, in part, on a component of K that depends on QL (quality of labor), which would equal MPL with homogeneous workers. The systems capital contribution by a particular worker depends on that worker’s personal stock of human capital, and some systems are more dependent on workers’ own capital than others: car showrooms are useless without shrewd salesmen, while McDonald’s workers follow specific instructions that only the worst workers can screw up.
I don’t think this effect is big enough to be economically interesting on a macro level, but it might help clarify the language of “investing in human capital.” Horizontal differentiation in human capital might explain horizontal segregation among workers; horizontal differentiation in contribution of human capital to horizontally differentiated systems capital might explain a (little) bit more. Also, low-capital workers segregate into jobs in which systems capital does not depend much on workers’ human capital stocks.
What’s more interesting is the higher-order feedback between K, L, MPK, and MPL, the opportunity for cumulative advantage that results, and how that advantage can turn into disadvantage if any one of those components receives a large negative shock. Back to my example, if all the good baristas quit and are replaced by scrubs like me, systems fall apart and firm output eventually suffers.
But consider a labor market with incomplete information. Wouldn’t employers be very interested to know how job applicants will fit into existing systems? Is this what Thurow was driving at with his job competition model? What applicant signaling behavior would be consistent with this framework, and do we observe that behavior? If the model is “true,” would its effect be large enough to notice?
Filed under Labor, Producers
More than a few New Yorkers work two full-time jobs but live in homeless shelters. At the same time, New York has more vacant buildings and lots than it has homeless people. The study cited in the second article was produced by an organization called Picture The Homeless, that places much of the blame on market reactions to policy and not markets themselves. Other sources corroborate this idea.
My first instinct is to ask what would happen if those policies suddenly ended. Because I was raised by Bill Clinton Liberals, my following instinct is to assume things would get a lot worse. Consider the “rip the bandage” off analogy, except the bandage is actually stitches. There’s a research question in this: what would happen if all of New York housing policy suddenly disappeared, but nothing else was touched?
I think this could become a valid research methodology for understanding many different kinds of policy that have settled into “the long run.” First, it would require a researcher to compile, coherently, what exactly constitutes housing/healthcare/whatever policy at the relevant level, and then, with an explicit ceteris paribus assumption, to isolate the effect of ending exactly that body of legislation.
Rather than being an unrealistic view of policy, I think this is an excellent hypothetical framework for extricating the effects of one policy from another. A question like “why are there poor people in New York?” is too big to answer. A question like “what effect does housing policy have on homelessness in New York?” is slightly less too big, because the topic is small but the scale and scope of the answer are arbitrary. A question like “what would happen to housing markets if, tomorrow, x, y, and z policies ceased unconditionally?” is answerable.
The answer to this question probably won’t contain a solution to the problem. It would establish a set of first principles and a counterfactual baseline.
Richard Freedman writes (via a former professor’s excellent blog) that
Connections between workers and firms in the United States have more the flavor of a dating game, with workers and firms changing partners frequently, whereas connections between workers and firms in most advanced countries have more the flavor of a stable marriage.
I’d love to see an actual map of (a sample of) the labor market “dating network.” The nodes could be color-coded by length of stay and/or size-coded by salary, and the edges could be color-coded by time spent unemployed between jobs. I’m sure this is doable by scraping LinkedIn and maybe matching salaries from Glassdoor. Better yet, divide the labor force into cohorts by age and see how the networks differ across generations. There are at least two generations of users with significant representation on LinkedIn.
Then again, I’d rather know if the map differs for non-LinkedIn users, and how. And whether demographic differences would be confounding or causal. UPDATE: This and this might help (via Victoria Stodden), with this and other projects.
The paper is also a great, easy read. It lays out a lot of facts about employment and unemployment in the US, and then runs through a few plausible explanations of why this recovery was more jobless than the Great Depression (in terms of aggregate “employment-output elasticity”).
I’m writing this mostly for myself, to keep track of ideas I have and keep the clutter out of my brain. If I ever have any followers, I hope they’d be interested enough to comment, criticize, and brainstorm with me.
Victoria Stodden, of Columbia University, runs a similar blog that I didn’t know about until after I had this idea. It isn’t updated often, but she’s probably a lot smarter than I am, so here’s a link: http://www.stanford.edu/~vcs/Ideas.html. Hers is a bit more broad-scale than I intend mine to be.