Vulgar Economics: inequality
The D.E.N.N.I.S. System of Capitalism
I'm a pretty big fan of It's Always Sunny in Philadelphia being a 20-minutes-outside-of-Philly boy myself. The show itself centers around five sociopaths who run an Irish bar in the only city where it's worth getting a cheese steak.
One of the characters, Dennis, is an incredibly narcissistic womanizer. Throughout the course of the series, it is revealed that he has multiple bench warrants for sexual misconduct and keeps duct tape, a Maglight, and a bundle of zip ties in a hidden compartment in his car.
In one episode, he reveals that he has a systematized his pattern of abusive and manipulative behavior into a mnemonic eponymous acronym. Oddly enough, this system also works quite well to explain how the capitalist system conditions the working class into accepting their conditions of poverty.
Labels: exploitation, inequality, LTV, macrodynamics, unemployment
#tbt Matias Vernengo 2006 Technology, Finance, and Dependency: Latin American Radical Political Economy in Retrospect
Through my odd traverse through economics education, I admittedly haven't gotten into Dependency Theory. This past weekend, I decided to finally look into it. In general, I'm rather impressed. As an anarchist, I generally appreciate any theory which takes as its foundation relationships of power in thinking through social and political relations.
This paper by Matias Vernengo provides a great introduction to dependency theory. Vernengo, who maintains the blog Naked Keynesianism, outlines the development of Dependency Theory through the scholarship of primarily Latin American and US American neo-Marxist and structuralist thought.
Labels: capital, inequality, LTV, macrodynamics, tbt
Neoclassical Production Function Reader
Two-Factor Production Function
Charles Cobb & Paul Douglas - A Theory of Production
Robert Solow - Technical Change and the Aggregate Production Function
Anwar Shaikh - Laws of Production and Laws of Algebra: Humbug II
Kenneth Arrow, et al. - Capital Labor Substitution and Economic Efficiency
Jesus Felipe & John McCombie - The CES Production Function, the accounting identity, and Occam's razor
Paul Romer - Endogenous Technological Change
Francesco Caselli - Accounting for Cross-Country Income Differences
Michael Isaacson - Braaaaaaaains! The Undead Humbug Production Function: Now With Human Capital
Labels: capital, capital controversy, human capital, inequality, macrodynamics, new consensus, reader
Shallow Green Resistance
I never really got into the radical environmental movement. I'm sympathetic, but by the time I became any sort of radical leftist, the green scare was in full swing. As national law enforcement cracked down and with virtually all militant environmental activism labeled an act of terrorism, much of the core of the radical environmental movement either went to jail, went soft, or steered clear of the brand of heroics that had become a right of passage.
As corporations continued their push against any and all environmental conservation, advocacy for the environment was left to increasingly out-funded non-profits.
Additionally, a conspiratorial, post-9/11, post-crash political climate filled the environmental movement with entirely too many new age weirdos. This shift brought with it a distinct taming of the movement into a syncretic hodge-podge of lifestyle environmentalists.
Labels: capital, Discrimination, inequality, LTV
Sure #LoveWins. Now What?
Now that the institution of marriage -- though perhaps problematic in its own right -- has been successfully challenged on the basis of part of its exclusivity, perhaps now we can move on to issues that materially affect the queer community irrespective of their assimilation into heteromonogamous modes of romance and kinship. The next battles for queer rights will likely be against housing discrimination, healthcare access, and public services for homeless and low income folks. A while back I wrote about the perennial battle against job discrimination:
Even with their libertarian bent, mainstream economists have failed to pick up on studies showing that LGBT discrimination costs the US economy $64 billion each year in turnover costs. Additionally, skilled LGBT workers are being needlessly kept out of jobs that they would otherwise be highly qualified for. Economists refer to this as “opportunity cost.”
But they aren’t referring to it.
They aren’t referring to it despite the fact that the labor market is generally considered the economist’s domain. They aren’t referring to it despite the fact that LGBT workers make up about 8 million of the estimated 130 million US workers. They aren’t referring to it despite the fact that the President has made countless nods to the need to pass this piece of legislation. They aren’t referring to it, despite legal scholars asserting that the protections in place for sex discrimination are insufficient to protect LGBT workers.
Certainly, some commentators on the bill believe that it is once again destined to die in committee. There are certainly others who believe that the right to dignified employment free from harassment and bigotry is merely ideological. Further there are others who are concerned that ENDA will fail to protect those least likely to be given employment protection in the first place: those working for the military, religious institutions, and small businesses. Historically, landmark civil rights legislation has been anything but comprehensive or easy.
Regardless, there is absolutely no excuse for the failure of professional economists to say anything about what may be the first national labor rights victory for the trans* community in the United States.
Read the rest at the New School Economic Review
Labels: Discrimination, inequality, LGBTQ
The other day, Branko Milanovic wrote a blog post detailing his reservations of the Palma index. For those unfamiliar, Palma is a proposed substitute for the Gini index. Whereas Gini measures the longest perpendicular distance between the Lorenz Curve -- which shows the percentage of overall income going to given percentages of the population -- and a line representing complete equality, Palma is a simple ratio between the incomes of the top 10% and the bottom 40%.
Palma came about as the result of an observed regularity in inequality data showing that the 50% of the population between these two quantiles doesn't change that much in the short run. Unlike Gini, Palma is not on a particular scale (Gini can range from zero to one), and thus may be difficult to use in cross-country and intertemporal comparisons.
Milanovic's beef with Palma seems to be in its construction. His basic problem revolves around this immobile middle. In particular, what do you do if the middle does change? I think this is a valid concern, but Milanovic gives little indication as to why Gini is substantially better.
Gini itself has its own problems. Given that it presumes a relatively smooth exponential income distribution, any irregularities can throw off the index.
My major point of contention with Milanovic here is not so much on the superiority of one index over the other, but rather the implication that we should invest ourselves in finding a superior index for inequality. Indices, merely by virtue of distilling the data from an entire economy down to one number, are inherently going to be problematic in terms of universal application. The choice of index (or indices if you're into that whole "robustness" thing) should be guided by the data you have and the questions you intend to answer.
Gini is a pretty good measure of whether incomes are skewed towards the rich, but it doesn't give a very clear picture of the degree of stratification. Palma fills in this gap by focusing exclusively on the rich and poor rather than where the middle is. There are of course many other indices to choose from when investigating inequality, each with its own ideal application and limitations. It is our duty as data scientists to approach each problem with the appropriate tools rather than attempting to force a particular approach as the one-size-fits-all tool.
Labels: inequality, statistics