Pounds That Kill: The External Costs of Vehicle Weight
Heavier vehicles are safer for their own occupants but more hazardous for other vehicles. Simple theory thus suggests that an unregulated vehicle fleet is inefficiently heavy. Using three separate identification strategies we show that, controlling for own-vehicle weight, being hit by a vehicle that is 1000 pounds heavier generates a 40–50% increase in fatality risk. These results imply a total accident-related externality that exceeds the estimated social cost of US carbon emissions and is equivalent to a gas tax of $0.97 per gallon ($136 billion annually). We consider two policies for internalizing this external cost, a weight-varying mileage tax and a gas tax, and find that they are similar for most vehicles. The findings suggest that European gas taxes may be much closer to optimal levels than the US gas tax.
absolutely spot on. Differences in distribution is only one way in which you could disprove pg. There are others. For example, different "treatment effects". If conditional on getting selected, VCs pay more attention or are more useful for women, then that would be another reason that we would get the pattern pg proposes, but is not due to bias at selection.
For people who might not be economists, let me give some background.
Paul Romer is a quite influential and famous economist who is one of the creators of modern "endogenous growth theory". Growth theory is about coming up with simple models of how we think economies grow over time. His theory suggests that economic growth occurs because of innovations that come from features inherent to the society that is growing, and each step of growth has a feedback that affects the next phase of growth. So for example, the amount of knowledge in an economy determines the level of growth, and growth dictates the level of knowledge in the next phase. This is against exogenous growth theory where growth is as a result of exogenous technological innovations that arrive randomly and shock the system. He's widely tipped to win the Nobel sometime soon for this and related ideas.
Now, Bob Lucas, another famous macroeconomist and growth theorist (and Nobel Winner) and his co-authors have been in Romer's crosshairs for some time because of what he calls the "mathiness" of their work. He defines mathiness as something that "uses a mixture of words and symbols, but instead of making tight links, it leaves ample room for slippage between statements in natural versus formal language and between statements with theoretical as opposed to empirical content." (http://paulromer.net/mathiness/)
In a relentless series of posts and papers, he's been asking questions at the root of whether macro-economics is a science, how it works and how it could be improved. Hackers might like his "Illustrating Mathiness – Code Analogy" (http://paulromer.net/illustrating-mathiness-code-analogy) where he compares poorly done economic models with bad code.
very basic question. where does the list of startups come from and how do you know what country a startup belongs to? making a list like this is harder than you think, so im curious to hear how these guys address it.
kinda weird that all the timeseries charts have time going backwards. going lower to higher is certainly the standard way to do it. for eg: http://data.jobsintech.io/companies/google-inc
> Those strict criteria kept out one notable program: Y Combinator, which ranked #1 in 2014. Hochberg and Cohen note that Y Combinator, a pioneer whose graduates have included Dropbox, Airbnb, and Reddit, has “evolved” its model into that of a hands-off seed fund.
> “Y Combinator is cashing in on the name it made for itself,” says Hochberg. “They’re talking about raising a multi-billion dollar late stage fund to take advantage of their model that selects great entrepreneurs rather than mold them.”
> Other accelerators that remain on the rankings are more hands-on than Y Combinator, which by metrics alone would have ranked first again this year. Some entrepreneurs enjoy the relative freedom the program offers, versus highly structured programs elsewhere.
any ideas on what this is useful for? I know this probably has thousands of different applications -- but was wondering what people are most excited to use this for?
I'm curious about the "copyright" field. Do you return the original sources from where Watson learnt the information he is presenting? What are the major sources? Have you faced copyright or legal restrictions to access information and has this affected Watson's ability to answer questions in a certain area?
Pounds That Kill: The External Costs of Vehicle Weight
Heavier vehicles are safer for their own occupants but more hazardous for other vehicles. Simple theory thus suggests that an unregulated vehicle fleet is inefficiently heavy. Using three separate identification strategies we show that, controlling for own-vehicle weight, being hit by a vehicle that is 1000 pounds heavier generates a 40–50% increase in fatality risk. These results imply a total accident-related externality that exceeds the estimated social cost of US carbon emissions and is equivalent to a gas tax of $0.97 per gallon ($136 billion annually). We consider two policies for internalizing this external cost, a weight-varying mileage tax and a gas tax, and find that they are similar for most vehicles. The findings suggest that European gas taxes may be much closer to optimal levels than the US gas tax.