In 2015, Marshall Burke, Sol Hsiang, and Ted Miguel published a paper in Nature that looked at the relationship between temperature (climate) and economic output. They examined the historical impact of temperature changes (1960-2010) on 166 countries and then used this data to try and predict the potential future impacts of climate change on GDP per capita.
What they discovered is that temperature has a non-linear impact on economic production. Put differently, there’s an optimal annual average temperature. And it turns out to be 13 degrees celsius. If a country sits below this average number, then warming increases productivity. But if a country sits above this number, then warming has a negative impact on productivity. And the impact gets worse (stronger negative correlation) at higher temperatures.
Some of you are probably wondering whether the correlations they found should be interpreted as causation. For what it’s worth, the study tries to correct for non-temperature related economic changes (such as a recession or policy changes) and it also looks at how individual countries perform against themselves during temperature fluctuations. So the control and treatment groups are arguably pretty tight.
All of this suggests that there are a number of countries that stand to benefit from climate change (at least from this perspective). They are the ones that are cold today.
We then examined the correlation between hedge-fund volatility and office location in terms of number of stories above ground. We found that as the elevation of hedge-fund managers’ offices increased, they were more willing to take risks that resulted in more volatility. This was true even when statistically controlling for factors such as total assets, fund strategy and several other variables that could have led more resourceful hedge funds to occupy expensive offices that are often found on higher levels of buildings.
Does this mean taller cities are also more volatile cities? Assuming this is all true, it once again proves that we are maybe not the rational decision makers that many us probably think we are.
In 2015, Marshall Burke, Sol Hsiang, and Ted Miguel published a paper in Nature that looked at the relationship between temperature (climate) and economic output. They examined the historical impact of temperature changes (1960-2010) on 166 countries and then used this data to try and predict the potential future impacts of climate change on GDP per capita.
What they discovered is that temperature has a non-linear impact on economic production. Put differently, there’s an optimal annual average temperature. And it turns out to be 13 degrees celsius. If a country sits below this average number, then warming increases productivity. But if a country sits above this number, then warming has a negative impact on productivity. And the impact gets worse (stronger negative correlation) at higher temperatures.
Some of you are probably wondering whether the correlations they found should be interpreted as causation. For what it’s worth, the study tries to correct for non-temperature related economic changes (such as a recession or policy changes) and it also looks at how individual countries perform against themselves during temperature fluctuations. So the control and treatment groups are arguably pretty tight.
All of this suggests that there are a number of countries that stand to benefit from climate change (at least from this perspective). They are the ones that are cold today.
We then examined the correlation between hedge-fund volatility and office location in terms of number of stories above ground. We found that as the elevation of hedge-fund managers’ offices increased, they were more willing to take risks that resulted in more volatility. This was true even when statistically controlling for factors such as total assets, fund strategy and several other variables that could have led more resourceful hedge funds to occupy expensive offices that are often found on higher levels of buildings.
Does this mean taller cities are also more volatile cities? Assuming this is all true, it once again proves that we are maybe not the rational decision makers that many us probably think we are.
Brandon Donnelly
Daily insights for city builders. Published since 2013 by Toronto-based real estate developer Brandon Donnelly.
Opposition to Development or Opposition to Developers? Survey Evidence from Los Angeles County on Attitudes towards New Housing.
It is a study out of UCLA that was published earlier this year by Paavo Monkkonen and Michael Manville.
For the paper, they conducted a survey-framing experiment with over 1,300 people in Los Angeles County to test how strongly they felt about a number of common anti-housing sentiments; arguments such as traffic congestion, neighborhood character, and strain on local services.
However, they also introduced another argument: large developer profits. And interestingly enough, they discovered that respondents were 20 percentage points more likely to oppose a new hypothetical housing development when the survey was framed around the developer making a lot of money.
Here is a table from the paper showing the various frames, as well as the percentage of people who supported, had no opinion, and who opposed. Note that under the “developer” frame, the opposition number is 48%.
So their “takeaway for practice” is as follows: “Housing opposition is often framed as a form of risk aversion. Our findings, however, suggest that at least some opposition to housing might be motivated not by residents’ fears of their own losses, but resentment of others’ gains.”
Opposition to Development or Opposition to Developers? Survey Evidence from Los Angeles County on Attitudes towards New Housing.
It is a study out of UCLA that was published earlier this year by Paavo Monkkonen and Michael Manville.
For the paper, they conducted a survey-framing experiment with over 1,300 people in Los Angeles County to test how strongly they felt about a number of common anti-housing sentiments; arguments such as traffic congestion, neighborhood character, and strain on local services.
However, they also introduced another argument: large developer profits. And interestingly enough, they discovered that respondents were 20 percentage points more likely to oppose a new hypothetical housing development when the survey was framed around the developer making a lot of money.
Here is a table from the paper showing the various frames, as well as the percentage of people who supported, had no opinion, and who opposed. Note that under the “developer” frame, the opposition number is 48%.
So their “takeaway for practice” is as follows: “Housing opposition is often framed as a form of risk aversion. Our findings, however, suggest that at least some opposition to housing might be motivated not by residents’ fears of their own losses, but resentment of others’ gains.”
