1.  The Wall Street Journal reports that Amazon is opening retail stores in cities.  On one level, this poses a puzzle because Amazon's rise was fueled by its cost savings due to the fact that it is a virtual store.  Over the decades, Amazon has assembled a huge database about each of its customers.  Such data (and knowing where each of us lives) allows it to make educated predictions about what goods we will want to buy at its retail stores.  Amazon will stock their stores with such products.

    So, the point of this email is that spatial coded micro data allows a retailer to pinpoint exactly what to have in inventory at each week of the year.  This creates gains to trade.  Consumers anticipating that the store will have what they are looking for will be more likely to go to the store.   Nearby firms to the physical Amazon stores will gain from the increase in walking traffic.  

    The role of Big Data in fueling bricks and mortar retail optimization seems like a promising research topic.  The true urban CPI price index should decline.  Read this paper.

    Handbury, Jessie, and David E. Weinstein. "Goods prices and availability in cities." The Review of Economic Studies 82, no. 1 (2015): 258-296.

  2.  Michael Mann has co-authored a witty letter to the Wall Street Journal.  Here is a photo for you to appreciate his insights.


    His letter makes an analogy between climate change and Clint Eastwood in the first Dirty Harry movie. For those of you who are too young to have watched this then watch this YouTube Video.

    In this scene, the Bad Guy is uncertain about whether Clint still has a bullet in his gun. He guesses wrong and dies due to his decision.

    Professor Mann is implicitly saying that we also face uncertainty in the face of climate change and we are currently taking a gamble by allowing global greenhouse gas emissions to rise.  He is hinting that we will suffer the same fate as the Bad Guy in Clint's Dirty Harry movie.

    Is he right?  

    The theme of my 2021 Yale University Press book adapting to climate change is that more and more of us know that we don't know how hard Mother Nature will punch different points in the map.  This uncertainty actually creates an incentive to adapt and to learn about these risks.  In my book, I argue that capitalism is now evolving and gearing up to cope with this risk.  Free markets will help us to adapt to this scary risk exactly because it is so scary.  Aggregate demand creates supply. We have baldness medications because there are millions of bald men willing to pay for such a cure. In a similar sense, free market price signals lead to directed search for the new problems that people need solved.


    PHD economists need to work on the question of how people react to ambiguous risk.  When risk averse people know that they do not know what risks they face, what "real options" do they implicitly buy to protect their families so they can reoptimize in the future when they know more about the tradeoffs of living in a place such as a fire zone or flood zone? Modern climate economics today seeks to ignore the Lucas Critique because it makes the math messy but that's not a good reason to take choice under uncertainty seriously.  Induced innovation will occur because of the uncertainty. This means that our adaptation menu choice set expands over time. This means that the "envelope theorem" doesn't hold.   What it does mean is that the empirical climate damage function (the Key to Bill Nordhaus's Nobel Prize work) flattens over time due to adaptation efforts.



  3. In a series of pieces, I have explored how the for profit insurance industry can accelerate climate change adaptation progress. Here is my recent RMS interview. Here is my 2017 co-authored Harvard Business Review piece. 

    Imagine a free market economy where there is no government regulation of insurance markets and those who seek insurance can contract with for profit insurers. The for profit insurers will do their homework. They will research the emerging climate risks that different properties face. They will research the property's location, and the actions that the incumbent property owner has taken (stilts, trimmed vegetation) to protect the property from flooding and wildfire risk. The insurance company will quote a price for an insurance premium and the home owner will either accept it or reject it. 

    Riskier properties will face a higher premium to purchase insurance. Potential home buyers will get an insurance quote before they buy the home so that they know what the marginal cost of insuring the house will be. Firms like Jupiter can also provide a report about anticipated risks so the potential home buyer will know whether insurance rates for a specific home will rise sharply in future years. If this is the case, then the home buyer will bid less for the home and the current owner of the home will bear the expected future climate costs because the resale value of the home will be lower. This free market system incentivizes insurers to do their homework . They will lose profit if they sell an insurance policy for too cheap of a price because the risky properties in expected value will require payouts. They will also lose profit if they price too high for a given property because a rival insurer will undercut them and gain the customer. 

    In this age of Big Data, insurers can make more profit by getting the conditional risk probabilities right. They will make even greater profits if they can incentivize existing property owners to take self protection steps that lower disaster risks. If an insurer signs a medium term contract with a property owner that says; "if you take precautions x, y and z, we will charge you $X dollars per year for insurance and your premium will only rise each year by the state average for all other policies in our portfolio". Such a 10 year contract would provide the home owner with an incentive to invest in pre-cautions (especially if other home buyers are aware of the rising insurance costs if the current home owner doesn't take these precautions). 

     When government steps in and subsidizes insurance in flood zones and fire zones, this creates a moral hazard effect of reducing the likelihood that at risk property owners take these precautions. As the federal government crowds out private insurance sector investment in climate science, this slows down our nation's adaptation efforts. 

     My new economic consulting firm; Climate Economics, explores these issues.
  4. A few months ago, I posted a Twitter tweet about how to use REPEC data to rank academic couples.  I followed a symmetric transparent method.  My criteria takes the REPEC Ranking for one spouse + REPEC ranking for the other spouse.  I treat them as equals.  If an economist is not married to an economist or if a highly ranked economist is married to another economist who is not ranked on REPEC, then they are not included.  

    I used this list to find the top ranked women in economics.  

    For the actual rankings, I used the overall REPEC Ranking data.  (as of July 2021)

    Here are my data at the very top;

    Rank 
    spouse 1spouse 2sum
    Reinhardt2422652289
    duflo9378171
    Currie114610725
    Goldin19450246
    Ramey31813621680
    case40149450
    Athey40356459
    Grohe391258649
    Malmendier525355880
    Baxter621130751
    Blau5935441137
    Romer670158728
    Finkelstein8185481364
    Stokey83812850
    Bandiera8076671473
    costa9643201284


    A low rank is better.  So,  Esther Duflo and Abhijit win the competition with Claudia and Larry close behind.

    Dora and I rank #12 in the world.  

    Critics will say;  "Where is Akerlof and Yellen?  Where is Poterba and Rose?" . Follow the links above and do your homework.  




  5. Across all of the world's economists;

    #9 in Environmental Economics

    #25 in Urban economics

    #4 in Resource Economics

    #27 in Energy Economics


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