andrew_gelman_stats andrew_gelman_stats-2012 andrew_gelman_stats-2012-1501 knowledge-graph by maker-knowledge-mining
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Introduction: After writing yesterday’s post , I was going through Solomon Hsiang’s blog and found a post pointing to three studies from researchers at business schools: Severe Weather and Automobile Assembly Productivity Gérard P. Cachon, Santiago Gallino and Marcelo Olivares Abstract: It is expected that climate change could lead to an increased frequency of severe weather. In turn, severe weather intuitively should hamper the productivity of work that occurs outside. But what is the effect of rain, snow, fog, heat and wind on work that occurs indoors, such as the production of automobiles? Using weekly production data from 64 automobile plants in the United States over a ten-year period, we find that adverse weather conditions lead to a significant reduction in production. For example, one additional day of high wind advisory by the National Weather Service (i.e., maximum winds generally in excess of 44 miles per hour) reduces production by 26%, which is comparable in order of magnitude t
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1 In turn, severe weather intuitively should hamper the productivity of work that occurs outside. [sent-3, score-0.599]
2 Using weekly production data from 64 automobile plants in the United States over a ten-year period, we find that adverse weather conditions lead to a significant reduction in production. [sent-5, score-0.608]
3 Furthermore, the location with the best weather (Arlington, Texas) only loses 2% of production per year due to the weather, whereas the location with the most adverse weather (Lordstown, OH) suffers an annual production loss of 11%. [sent-9, score-1.136]
4 Our findings are useful both for assessing the potential aggregate productivity shock associated with inclement weather as well as guiding managers on where to locate a new production facility – in addition to the traditional factors considered in plant location (e. [sent-10, score-0.859]
5 Welfare Costs of Long-Run Temperature Shifts Ravi Bansal, Marcelo Ochoa Abstract: This article makes a contribution towards understanding the impact of temperature fluctuations on the economy and financial markets. [sent-13, score-0.758]
6 We present a long-run risks model with temperature related natural disasters. [sent-14, score-0.714]
7 The model simultaneously matches observed temperature and consumption growth dynamics, and key features of financial markets data. [sent-15, score-0.98]
8 We use this model to evaluate the role of temperature in determining asset prices, and to compute utility-based welfare costs as well as dollar costs of insuring against temperature fluctuations. [sent-16, score-1.753]
9 78% of consumption, and the total dollar costs of completely insuring against temperature variation are 2. [sent-18, score-0.92]
10 If we allow for temperature-triggered natural disasters to impact growth, insuring against temperature variation raise to 5. [sent-20, score-0.803]
11 We show that the same features, long-run risks and recursive-preferences, that account for the risk-free rate and the equity premium puzzles also imply that temperature-related economic costs are important. [sent-22, score-0.59]
12 Our model implies that a rise in global temperature lowers equity valuations and raises risk premiums. [sent-23, score-0.997]
13 Temperature, Aggregate Risk, and Expected Returns Ravi Bansal, Marcelo Ochoa Abstract: In this paper we show that temperature is an aggregate risk factor that adversely affects economic growth. [sent-24, score-1.036]
14 Our argument is based on evidence from global capital markets which shows that the covariance between country equity returns and temperature (i. [sent-25, score-0.93]
15 , temperature betas) contains sharp information about the cross-country risk premium; countries closer to the Equator carry a positive temperature risk premium which decreases as one moves farther away from the Equator. [sent-27, score-1.774]
16 The differences in temperature betas mirror exposures to aggregate growth rate risk, which we show is negatively impacted by temperature shocks. [sent-28, score-1.768]
17 That is, portfolios with larger exposure to risk from aggregate growth also have larger temperature betas; hence, a larger risk premium. [sent-29, score-1.434]
18 We further show that increases in global temperature have a negative impact on economic growth in countries closer to the Equator, while its impact is negligible in countries at high latitudes. [sent-30, score-1.351]
19 Consistent with this evidence, we show that there is a parallel between a country’s distance to the Equator and the economy’s dependence on climate sensitive sectors; in countries closer to the Equator industries with a high exposure to temperature are more prevalent. [sent-31, score-0.955]
20 We provide a Long-Run Risks based model that quantitatively accounts for cross-sectional differences in temperature betas, its link to expected returns, and the connection between aggregate growth and temperature risks. [sent-32, score-1.592]
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same-blog 1 1.0000001 1501 andrew gelman stats-2012-09-18-More studies on the economic effects of climate change
Introduction: After writing yesterday’s post , I was going through Solomon Hsiang’s blog and found a post pointing to three studies from researchers at business schools: Severe Weather and Automobile Assembly Productivity Gérard P. Cachon, Santiago Gallino and Marcelo Olivares Abstract: It is expected that climate change could lead to an increased frequency of severe weather. In turn, severe weather intuitively should hamper the productivity of work that occurs outside. But what is the effect of rain, snow, fog, heat and wind on work that occurs indoors, such as the production of automobiles? Using weekly production data from 64 automobile plants in the United States over a ten-year period, we find that adverse weather conditions lead to a significant reduction in production. For example, one additional day of high wind advisory by the National Weather Service (i.e., maximum winds generally in excess of 44 miles per hour) reduces production by 26%, which is comparable in order of magnitude t
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Introduction: I. State of the Climate report The National Oceanic and Atmospheric Administration recently released their “State of the Climate Report” for 2009 . The report has chapters discussing global climate (temperatures, water vapor, cloudiness, alpine glaciers,…); oceans (ocean heat content, sea level, sea surface temperatures, etc.); the arctic (sea ice extent, permafrost, vegetation, and so on); Antarctica (weather observations, sea ice extent,…), and regional climates. NOAA also provides a nice page that lets you display any of 11 relevant time-series datasets (land-surface air temperature, sea level, ocean heat content, September arctic sea-ice extent, sea-surface temperature, northern hemisphere snow cover, specific humidity, glacier mass balance, marine air temperature, tropospheric temperature, and stratospheric temperature). Each of the plots overlays data from several databases (not necessarily indepenedent of each other), and you can select which ones to include or leave
Introduction: I hate to keep bumping our scheduled posts but this is just too important and too exciting to wait. So it’s time to jump the queue. The news is a paper from Michael Betancourt that presents a super-cool new way to compute normalizing constants: A common strategy for inference in complex models is the relaxation of a simple model into the more complex target model, for example the prior into the posterior in Bayesian inference. Existing approaches that attempt to generate such transformations, however, are sensitive to the pathologies of complex distributions and can be difficult to implement in practice. Leveraging the geometry of thermodynamic processes I introduce a principled and robust approach to deforming measures that presents a powerful new tool for inference. The idea is to generalize Hamiltonian Monte Carlo so that it moves through a family of distributions (that is, it transitions through an “inverse temperature” variable called beta that indexes the family) a
Introduction: Solomon Hsiang shares some bad news: Persistently reduced labor productivity may be one of the largest economic impacts of anthropogenic climate change. . . . Two percent per degree Celsius . . . That’s the magic number for how worker productivity responds to warm/hot temperatures. In my 2010 PNAS paper , I [Hsiang] found that labor-intensive sectors of national economies decreased output by roughly 2.4% per degree C and argued that this looked suspiously like it came from reductions in worker output. Using a totally different method and dataset, Matt Neidell and Josh Graff Zivin found that labor supply in micro data fell by 1.8% per degree C. Both responses kicked in at around 26C. Chris Sheehan just sent me this NYT article on air conditioning , where they mention this neat natural experiment: [I]n the past year, [Japan] became an unwitting laboratory to study even more extreme air-conditioning abstinence, and the results have not been encouraging. After th
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Introduction: Just in time for the hot weather . . . Aleks points me to this link to a graph of % check-ins at NYC ice cream shops plotted against temperature in 2011. Aleks writes, “interesting how the ice cream response lags temperature in spring/fall but during the summer, the response is immediate.” This graph is a good starting point but I think more could be done, both in the analysis and purely in the graphics. Putting the two lines together like this with a fixed ratio is just too crude a tool. A series of graphs done just right could show a lot, I think!
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Introduction: A recent article on global warming reminded me of the difficulty of letting the data speak. William Nordhaus shows the following graph: And then he writes: One of the reasons that drawing conclusions on temperature trends is tricky is that the historical temperature series is highly volatile, as can be seen in the figure. The presence of short-term volatility requires looking at long-term trends. A useful analogy is the stock market. Suppose an analyst says that because real stock prices have declined over the last decade (which is true), it follows that there is no upward trend. Here again, an examination of the long-term data would quickly show this to be incorrect. The last decade of temperature and stock market data is not representative of the longer-term trends. The finding that global temperatures are rising over the last century-plus is one of the most robust findings of climate science and statistics. I see what he’s saying, but first, I don’t find the st
Introduction: Solomon Hsiang writes : I [Hsiang] have posted about high temperature inducing individuals to exhibit more violent behavior when driving, playing baseball and prowling bars. These cases are neat anecdotes that let us see the “pure aggression” response in lab-like conditions. But they don’t affect most of us too much. But violent crime in the real world affects everyone. Earlier, I posted a paper by Jacob et al. that looked at assault in the USA for about a decade – they found that higher temperatures lead to more assault and that the rise in violent crimes rose more quickly than the analogous rise in non-violent property-crime, an indicator that there is a “pure aggression” component to the rise in violent crime. A new working paper “Crime, Weather, and Climate Change” by recent Harvard grad Matthew Ranson puts together an impressive data set of all types of crime in USA counties for 50 years. The results tell the aggression story using street-level data very clearly [click to
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