John Halstead - Independent researcher. Formerly Research Fellow at the Forethought Foundation; Head of Applied Research at Founders Pledge; and researcher at Centre for Effective Altruism. DPhil in political philosophy from Oxford
Yeah that's fair enough re that part of the comment.
Yeah I suppose I would disagree with how a lot of researchers view the strength of evidence provided by cross-sectional studies. I think a lot of researchers seem to endorse the proposition 'if this could be confounded, it provides no evidence of causation', which I don't think is right. It depends on one's prior on how plausible the confounder is. I think this is why a lot of economics has stopped trying to focus on some of the more important macro questions, and I think this is a mistake.
eg consider the potential effects of climate change on economic performance. I do think cross-sectional evidence is highly relevant and should update one's view. If economic performance were very strongly climatically determined, I would expect this to show up strongly in the cross-section. I wouldn't expect to see California being way richer than Baja California. I wouldn't expect gross state product for US states to look like this as a function of state average temperature:
I would expect growth rates to be uniformly low in climatically exposed places like Vietnam, Bangladesh, Indonesia, India etc, which is not what we see. So, I do think this sort of evidence should update one's view, even though there are obviously loads of potential confounders.
In climate economics, people don't like this, so they have started using panel data approaches which aim to test the exogenous effects of weather changes on economic performance in particular periods of time. This supposedly provides better evidence of causation, but I think should be completely ignored because of huge researcher degrees of freedom, reporting bias and political bias. I think they leave the door open for econometric skullduggery to provide inflated estimates. In part because the cross-sectional evidence is more transparent, I think it is more reliable.
Re confounding, the headline estimate that James uses is adjusted for various potential confounders.
"Aside from controlling for all time-invariant factors using FE models, we control for a variety of moment-specific factors, including: what people were doing (40 activities), who they were doing it with (7 types), time of day (three-hour blocks split by weekday vs. weekend/bank holiday), location Page 16 (inside/outside/in vehicle and home/work/other), and how many responses the participant has previously given. OLS estimates also include time-invariant controls for gender, employment status, marital and relationship status, household income, general health, children, single parent status, region, age and age squared at baseline. Derivations/descriptive statistics are given in Web Appendix S5.
I don't have a strong view on what effect the potential selection effect would point.
the economic damage inflicted by natural disasters is much higher now than it was in the 1960s (as a share of GDP)
You cannot infer trends in the climate-related economic impact of natural disasters from trends in the total damage of natural disasters or trends in the per capita economic impact of natural disasters. The economic costs of natural disasters is influenced by the increasing economic value of areas that are vulnerable to climate change. Eg here is a Miami beach a century ago compared to today
You need to adjust for this by producing an estimate of normalised damages (discussed here by one of the most cited climate researchers). For example, here is the normalised cost to the US of hurricanes since 1900. i.e. suggestive of increased losses on the order of $5-10bn over the course of a century, which is about 0.2% of US GDP.
Despite what you read in the media, according to the IPCC, for the vast majority of extreme events, it is not yet possible to attribute with confidence any change to climate change. A white entry in the table means that a signal cannot yet be noticed with confidence. A blue entry means the signal is increasing. An orange entry means the signal is decreasing.
There is as yet no clear evidence of a climate signal for precipitation, flooding, drought, fire weather, wind speed, storms, cyclones, and coastal flooding.
I don't agree with your points on natural disasters. I am going to post below the charts from OWID on weather-related deaths, including absolute numbers and per capita numbers. Some comments:
I think it is a very hard area to provide an accurate outline of, and I think to do that you need to go beyond reading the abstracts of papers and to look at the assumptions in those paper which typically combine very pessimistic warming, very pessimistic economic growth, limited or no adaptation. I think a lot of your analysis errs in a pessimistic direction.
I'm not sure the phone study has the traditional weaknesses as cross-sectional studies. It's a bit more like a panel study where you can track in very fine-grained detail what events are happening and what happens to subjective wellbeing at the time. Because the event data is so fine-grained and there are so many contiguous datapoints, it provides very good evidence of causation. These sorts of studies also give intuitively plausible results for all other events. People don't like work, getting divorced, being unemployed, being widowed; people like sex, seeing their friends etc.
It's true that people opt in, but I don't see any particular reasons to think that this would have a bias towards 'happy drinkers'. The same is true for other life events. Like maybe there is some bias such that people who enjoy sex are more likely to opt in to phone-based subjective wellbeing studies, but I don't think that is what is driving the results.
(this was a joke)
Hi Nick, yeah I get that there are costs to alcohol, I just think it is important to consider the benefits when deciding what to do. A lot of public health policies are defended by only focusing on the costs of doing something, not the benefits. So, I think it is important to consider the benefits.
My own intuition is that if there were no alcohol, happiness would go down for the vast majority of people and would go up for a small minority. From my own experience, people often seem close to their happiest when drinking, and it is a very important social lubricant.
I think the best study in James' review was the subjective wellbeing one because it measures what, in my view, actually matters, which is people's moment to moment wellbeing. It's also a very large sample. I wouldn't class the benefits found in the study as 'tiny'. The increase in wellbeing while drinking is nearly as large as that produced during spending time with friends. And this is a benefit which would be spread across billions of people.
Yeah wrt to your botec, I wasn't sure whether you were implicitly writing off time spent asleep.
I suppose you would also have to do the same for measuring the effects of money on wellbeing. Do you do that?
Hangovers are a myth