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.
EVs are probably going to make this problem worse as they are typically heavier than the ICE equivalent. For example the Porsche Panamera weighs 2000kg and the Porsche Taycan weighs 2800kg. The VW Tiguan weighs 1500kg, where as the VW ID.4 weighs 1800kg.
I've heard this called "weaponized Keynesianism." The use of Keynesian here is somewhat off-base since this is nothing like what Keynes advocated, so a better term would be "politically weaponized stimulus."
In its strongest conspiratorial form it would consist of backing austerity policies until one's party or figurehead wins power, and then abruptly going spending happy. This creates the illusion of a sudden dramatic turnaround in the economy. The more opportunistic form would be to take power in a severely demand constrained economy and then go all-in on stimulus on a massive scale while being sure to claim credit for oneself or one's party rather than for the specific policy of stimulus (which is easy and any party could do).
AFIAK both the Nazis and the Soviets did this, among others, and perma-stimulus is also how the Chinese Communist Party has held onto power.
We are in a severely demand-constrained economy, so we certainly have a golden opportunity for someone to work a miracle with the Federal checkbook and then claim credit and use it to solidify their power. A friend of mine has called this Weimar America, and I really hope he's wrong.
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.
12 guys started Housing.com in 2012. (4 of them had more control of the company if I'm not wrong). Rahul Yadav was the CEO till yesterday. Good team, they executed things quickly, got attention from investors, got handsome funding early on. Bought a domain name like Housing.com. But Rahul Yadav is very brashy too and publicly ridiculed the VCs several times on not taking decisions on his terms.
Softbank (Japan) had nearly 30% stakes in housing.com . Rahul had ~4.5%. Anyway the investors eventually had enough power to expel Rahul "with immediate effect" after yesterday's board meeting saying "The board believed that his behaviour is not befitting of a CEO and is detrimental to the company".
I'm still not sure whether to side with Rahul or the investors. But he does like the spotlight for sure, even if it comes as negative publicity. I don't entirely disregard his opinion or frank talks, but there's a certain professionalism he lacked viz. bringing boardroom talks to media frequently, trying to sensationalise how he talked brazenly to the VCs.
They have raised some 120 mn in 5 rounds of funding (the last one at 250 mn valuation). I haven't seen the traction yet, which is probably ok for a 3 yr old company. The product is good, but the market in India is still nascent.
The problem is that the only reason I hear about them is Rahul's antics.
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
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.