Congratulations to Nick Kristof of the New York Times.  I am really impressed with this discussion between him and Carol in Berkeley.   My mother's name is Carol and she has been to Berkeley and I'm wondering if my mom wrote the following response;

Here is the NY Times Source and my remarks appear below.  

By all means, let’s talk about “personal responsibility”’ — Nicholas Kristof

Carol in Berkeley, Calif., on “Who Killed the Knapp Family?” (Jan. 9):

So long as poverty is seen as an individual or cultural failing (e.g. the culture of poverty which was linked to race, even though the evidence was nonexistent) we will not treat this with the seriousness it deserves. Yes, every individual has responsibility for their lives. But pulling oneself up by one’s bootstraps, after we take away not only your boots but your capacity to buy or make boots is unfair and is also emblematic of how poverty is understood. We need to understand that collectively this costs us all — both morally and financially. The solution is collective. It is jobs that pay a living wage, it is opportunities for upward mobility for oneself and one’s children, it is training for these jobs and it is a real safety net. Will some people still be poor? Will they self destruct? Of course. But the numbers will be far smaller. And we will be far richer as a society.

Nick: This observation by Carol struck me as exactly right. One of America’s mistakes over the last half century was to go too far down the track of extolling “personal responsibility” and haranguing people to lift themselves up by their bootstraps. When an infant in three counties in the United States has a shorter life expectancy than an infant in Bangladesh, that’s not because the American newborn is making “bad choices”; it’s because we as a country are. So by all means, let’s talk about “personal responsibility” — it’s real — but also about our collective responsibility to help America’s children and give them a fighting chance to succeed.

MEK:

This is a very important discussion.  Note that it is vague about what are the steps to implement this new "American Dream" social contract.   

How would Gary Becker respond to this important discussion?  I think he would trace out the information challenge.  Children do not select their parents.  When a child is born, will the parent invest efficiently in the child or will under-investment take place? If under-investment takes place, why does this occur?  Did the parent face financing constraints? What role can the state play to augment efficient investments in the child so that she has a higher probability of achieving her full potential?  Why Children Fail?

My starting point here would begin with investing in Jim Heckman's policy proposal on early childhood development and helping parents to shape the course of their children's life.

If more children receive early life interventions, and if these children live close to each other, then spatial neighborhood positive externalities occur. See this new John List and Yves Zenou co-authored paper.  

These early life intervention programs are expensive and would be very expensive if all disadvantaged children participate in them.  I have co-authored an under-appreciated paper on the local public finance issues that arise for such interventions.  Read my paper, I think that we make a key point (but few agree!).

Matthew E. Kahn & Kyle Barron, 2015. "The Political Economy of State and Local Investment in Pre-K Programs," NBER Working Papers 21208, National Bureau of Economic Research, Inc.

Abstract

My co-authored JHU Press book further explores these themes of how to help the urban poor who often live in poor cities to achieve their full potential.  I do not believe that the answer here is a higher minimum wage or strict rent control laws.   While Econ 101's perfect competition models are not in fashion right now, the lessons those models teach us lurk and will manifest themselves in the medium term in terms of unintended consequences if well meaning policy makers seek to help the poor through price controls.

An alternative path forward is to address the investment co-ordination failure that emerges for the urban poor who live in poor places.  Read Chapter 1 of our new book.

There are several  key issues in thinking about the economics of personal responsibility and personal growth; 

1. What are each new person's "rights"?

2. If the parent does not implement this plan, how does the state step in to augment the resources and the nurturing of this child?

3. How does society pay for this early intervention program?  

4.  Does the private sector or the public sector supply these interventions?

If this long winding post interests you, then read this one as well.

 In recent decades, U.S Clean Air Regulations have focused on reducing the emissions of new cars, new factories and new power plants.  Old cars, old factories and old power plants have often been "grandfathered" and haven't faced regulations.  Given the durability of capital, this differential regulation creates long lags between "cause" and "effect". In this case, the cause is the new regulations and the effect is environmental progress.

The Gruenspecht effect refers to an economist who in his Yale PHD thesis argued that the Clean Air Act can unintentionally make air pollution worse in the short run if car drivers keep their old cars longer because regulation raises the price of new cars (a regulation induced substitution effect).  Rob Stavins wrote a nice survey paper on this issue.

I remind you of these points because President Biden seeks to enact an aggressive set of low carbon policies.  If they are enacted, when will their effects be observed?

If the U.S slips into low economic growth over the next 5 years because of rising regulations and taxes, this will actually slow down the diffusion of the new green capital that will be mandated by the Biden Administration.  Why?

When people are poorer, they are less likely to buy new cars.  Academic economists will recognize that I'm talking about non-homothetic Engel curves.  See my 2011 paper with Lucas Davis. 

This point generalizes. When an economy slows down and expectations about medium term growth are bleak, new investment in capital slows down and older capital is relied upon to deliver the required services.  The updating of the capital stock (and thus the incorporation of the new green capital) is slowed down during bad times.  This suggests that President Biden needs an economic boom to accelerate his low carbon agenda.

Matt Kotchen and my 2011 paper takes this logic a step further.  We document that when a state is in a recession that its residents are less concerned about the climate change challenge.  Many environmentalists believe that recessions are good for the environment because the economy slows down.  We challenge this conventional wisdom and argue that to have a meaningful change in creating a bipartisan "low carbon economy", a society needs to be in an economic boom so that there is broad support for pursuing medium term goals.  Our core thesis is about to be tested.

I do agree with green tech optimists that if the price of low carbon technology goes to zero (so solar panels and electric vehicles) that their diffusion will grow sharply.  My family owns a famous electric vehicle and we may soon buy solar panels.   But, is the price of low carbon technology really shrinking to zero so quick?  

So, the question I want you to consider is the following;  Does President Biden need an economic boom in order to achieve his important low carbon goals?  I believe that the answer is yes.  

Finally, note that unlike the new capital regulation approach --- a carbon price would treat all capital equally and wouldn't introduce any perverse capital substitution incentives.  In fact, such carbon pricing would accelerate investment in green R&D that promotes the next generation of low carbon transportation and power generation.  The tricky political economy here is for the Biden Administration to figure out how to offset the negative income effects of this tax through strategic transfers to non-environmentalists. Yes, I support explicitly purchasing the "veto" of those who do not prioritize the issue of climate change. Greens need to vote with their pocket book the blocking coalition's veto.  

 Post-pandemic many educated people will be working from home 3 days a week.  Some of these people will choose to live further from city centers.  How will such "sprawl" and reduced work trips affect the profitability of ride sharing companies?

For urban economists to be useful here, we need to make some progress on a few core questions.  Does Uber make more money on short urban trips versus picking people up in the far flung suburbs?  On days when urban residents work from home, will they make more on less car trips than on days when they commute to work?  

To repeat my question, does the commute to work or accessing the "consumer city" generate more profit for Uber?   Given that public transit is a technology for taking you downtown, will Work from Home workers make fewer weekly trips to the center city?  Is this good news or bad news for Uber?  If public transit's share of trips declines in a city, does that mean that the demand for ride sharing has increased?

So, a key point to note here is that Work from Home bundles a daily time allocation with a low frequency residential locational choice.  Both of these decisions affects ride share demand.    

Another issue that arises here is demographics;  partition potential riders into 5 year age groups; so 20 to 24, 25 to 29 , etc.   Which of these age groups will most change their ride share demand based on their new work arrangements?  How does our place of residence, place of work, place of urban consumption and frequency of going to work jointly determine our urban trip demand and how we split them between using our own car versus Uber?

Given that Uber is a for profit firm, what is its best response if some of these elasticities are large and predict that ridership from certain segments will fall because of WFH?

 Several excellent urban economics research teams have access to U.S geocoded cell phone data.  Since I am not part of any of these teams, I will use this blog post to offer them some unsolicited advice.  For some examples of these teams; skim this  and this .  

Geotagged data provide researchers with a spatial and temporal high frequency database to know where each person spends her day.

I carry a cell phone with me 98% of the day when I am awake.  This suggests that I am geotagged.    An observer with access to my high frequency cell phone geotagged data would see the following patterns in my 2020 data;

1.  I am not in Baltimore even though I work at Johns Hopkins.  I have adapted to the crisis by going West.

2.  80% of the time I am within 1.5 miles of our Los Angeles house.

3.  20% of the time, I am elsewhere in California.

4.  I am rarely in a retail district in LA.

5.   I walk 2.5 hours  a day.

These five facts don't add up to a QJE paper.   The real action begins if one could access my Amazon Prime data in 2018, 2019 and 2020 as well as my geotagged data.    The observer would see a more than $1 to $1 crowd out of face to face shopping in 2020 as we do almost all of our non-fruit shopping online.

So, my first point is that Amazon has access to its own data and knows each person's home address who is already a member of Amazon Prime or has purchased anything on the platform. IF Amazon's researchers can access anonymous cell phone data then they can geocode those data and match these "anonymous" people to people it can identify in its database.  In then can track which people are still engaging in more retail shopping and target them with special incentives to substitute to the Amazon platform.  The geocodes from our cell phone indicate which people originating at which homes make weekly trips to which retail outlets.  Food shopping patterns will quickly emerge even though no $ statements concerning actual expenditures are observed.

With a data merge of the Amazon platform data and the anonymous geotagged cell phone data, a researcher can study many interesting research questions.  For example, when wild fires occur or when other natural disasters occur;  how quickly do people start to adapt to the emerging crisis? How do they use the market products that Amazon sells to protect themselves?   

Do only the rich engage in this activity?  By scrapping Zillow data, one could know each person's neighborhood home value.  One could also use monthly Amazon expenditures to infer a person's income.  Armed with these wealth indicators, the researcher can study whether poor people are engaging in similar adaptation strategies as richer people during air pollution and natural disaster crises.

Returning to low frequency events, one could geocode the public schools in a city and see which adult cell phones commute to those schools during school days. This would indicate which people have school aged children.  This is an example of how to fleshout the set of X covariates about the cell phone owner even without surveying her. She reveals who she is by what she does all day long.

Once the observer knows who has school aged children (and the Amazon platform purchases can also be used to establish this), one can use the cell phone data to explore summer vacation patterns.  Are summer experiences a complement or substitute with the amenities where one lives?  Do LA people go somewhere sunny in summer?  

Returning to environmental economics, on highly polluted days --- how much more time do people spend indoors? How much less time do they exercise on those days?  

In 2021 as the pandemic subsides, who will go back to work and how often will they go?  The geocoded cell data will be ideal for studying this.   Suppose a researcher has the universe of LA cell phone geocoded data, by calculating the distance between points (assuming this can be done in a 3 dimensional space such as an office building's 17th floor), the researcher can infer which people work together! What new questions can then be answered?   

Turning to local macro-economics,  how does time outside of the home change as an economic boom begins?  Due to the rise of Working from Home, do people spend more time inside working during a boom?

As I think more about this topic I will add more points.  What I hope I have conveyed is that the cell phone data create a great natural experiment laboratory.   

A weakness here is that this yields a time allocation facts, it does not report "output".  In Gary Becker's time allocation model, you allocate time across tasks and you gain utility from these tasks.  To clarify, here is an example.   Suppose you are shopping for Christmas and your goal is to make your spouse happy.  The researcher observes how many minutes you spend at each store but we do not observe your spouse's marginal utility gain if you shopped for one more minute at one store versus another.  The optimizer will think this through and the "bang per buck" condition will hold but the observer won't know your personal gains from this optimizing behavior.  Instead, the observer will observe your optimal time allocation based on your geocoded data.

An active research field in climate economics uses natural disasters as "natural experiments" to learn about the economic effects of such shocks.  Amine Ouazad and I study how major hurricanes impact bank lending and securitization patterns in this study.   Gary Lin, Rhiannon Jerch and I study local public finance dynamics in the aftermath of disasters in this study.    In both papers, our estimation strategy relies on the fact that we have a "treatment group" and a "control group".   Intuitively, there are places that are roughly similar that have and have not been recently hit with a shock.  This allows us to use standard statistical methods to estimate the impact of the shock on those places that did get hit.  An assumption in such work is that "general equilibrium effects" can be ignored so that the set of places not hit by the shock are not affected by the responses in the places that were hit by shock. Intuitively, if all of the people in the places hit with a hurricane move to the areas not hit by the hurricane, then this is an example of an enormous general equilibrium effect!   

 In this blog post, I'd like to introduce a "mashup" where I combine ideas about green buildings, the rise of Work from Home, climate change adaptation, and non-linear pricing of electricity to make some predictions about the rise of the green economy. People who know my work, know that I've published several papers on green buildings.  

To make my main point, I need to establish several other points;

Point #1;  In California, there is a sharp geographic temperature gradient such that areas closer to the water (Santa Monica, Berkeley) are much cooler than more inland places (say 30 or 40 miles inland).

Point #2;  Climate Change is likely to exacerbate this spatial temperature gradient;  UCLA's Alex Hall makes some very nice maps of these likely impacts.  Here is an example documenting the past map of hot days in the recent past versus in the medium term future in the LA area.

Point #3;  Many California electric utilities feature non-linear increasing block tariffs for electricity such that the more electricity your household consumes per month, you will face a higher marginal price for consumer more power.  Frank Wolak and I explore this issue in our 2013 paper.  The introduction of dynamic pricing (such that the marginal price of electricity spikes during peak summer hours would only accentuate the discussion below because it would act as an incentive to "go green".)

Point #4;   Homes are durable goods that last for 50 years or longer and the initial attributes of the home often determine how the current owner uses the home.  A buyer of a used home faces adjustment costs for upgrading the home. This includes the financial costs and the hassle of the upgrade.

Point #5;  The rise of Work from Home in the post-covid economy could lead to more people seeking to live further inland where land is even cheaper even though these places get quite hot.  Nick Bloom's surveys indicate that people will go to work 2 days a week post-covid and this effectively reduces the cost of commuting per mile.  

Point #6;  The fixed cost of building green homes charged by solar panels are getting cheaper and there are increased synergies between having such a solar home charge a family's electric vehicles. See our Tesla paper.

Point #7;  Center Cities (especially progressive center cities) limit the ability of developers to build new housing due to costly red tape.   See my 2011 paper; 

Combining these points yields the following "theorem";  

The combination of climate change,  non-linear pricing for electricity, the rise of Work from Home, and center city NIMBYism limiting new housing construction will lead to a rising share of homes at the Suburban fringe and a rising % of these homes will be zero energy homes even if there is no new low carbon regulation.

Suppose that each household features 1 person and he chooses where to live.   If this person lives far from the city center in a hot place; he exposes himself to average temperature T_j for the year where "j" indexes the geographic location.    To keep this simple, let each location j differ on 2 dimensions; distance from the city center (d_j) and temperature T_j.

The non-linear pricing function of electricity is defined as p(E_j) which is an increasing and convex function of E.  E_j is an increasing and convex function of T_J and a convex function of the home's square footage.   Home square footage is an increasing function of distance from the city center because land prices fall with distance from city center.    

People who live further from the city drive more miles (see my 2000 paper).     Assume that a person who lives j miles from the city center drives M_j miles.  Assume the conventional vehicle achieves 30 miles per gallon and the price of gasoline per gallon = 2.

The annual energy bill for a person at location j = 2*M_j/30   + p(E_j)*E(T_j, d_j).

Note that if the person installs a solar panel home that fuels the EV then the annual energy bill = $0!

Assume a fixed cost $F for this install.  We now have the theorem. The cost minimizing person will "go green" even if he doesn't have Greta Thurnberg's preferences if the cost savings are large enough. He will compare the expected present discounted value of the cost savings from going green to the upfront cost.

Note that $F declines over time due to global supply chains.  My 2011 piece in the New York Times wasn't popular with their commenters but the logic was right!  

In calculating this expected PDV note that expectations about the rising T_j over time raise the likelihood that he installs the green durable upfront. This is my usual Investment under uncertainty point.  Many climate researchers ignore how expectations influence today's choices.  

This is key here.  The expectation of climate causes upfront adaptation!   Why?   The expected temperature increase raises the expected PDV of one's status quo electricity bill (i.e the bill he will receive from the local electric utility if he doesn't install solar and green the home).

This blog post is meant to teach you the microeconomics of climate change adaptation.  We are not passive victims.  This rising demand for green low operating costs homes will induce innovation.  This is a key theme of my 2021 Yale Press book Adapting to Climate Change.  Note that there is no government in this economy as its rational agents use markets to mitigate carbon (unintentionally as they seek to reduce their energy bills) and to adapt to the rising heat.

Let's return to point #2 above.  Climate Scholars such as Alex Hall do a great job documenting the likely climate gradient of future high heat but such scholars often assume that we are passive victims here as Riverside and other inland areas "heat up".  Such scholars focus on Government to solve the problem.  As this blog post has noted, the private sector offers an alternative adaptation strategy here.  We are not passive victims in the face of a serious threat.

For young scholars looking for a research project, read this Acemoglu and Linn 2004 QJE paper and think about how to link its logic to this blog post.   Daxuan and I make a start on this problem in our 2019 paper.  

 The New York Times has published an excellent piece about an emerging coal shortage in China.  Let's use the supply and demand framework to study this issue.  Aggregate demand for coal in China is rising because it is winter and much of the nation's winter heating is supplied by burning coal.  The manufacturing sector is making a comeback and this sector uses plenty of electricity.  At the same time that aggregate demand is rising, aggregate supply has declined because the national Chinese government is in the midst of a feud with Australia. Australia is a leading exporter of coal to China.  A second reason is that China's domestic mines have faced major safety challenges as miners have died and the new regulations these mines face has reduced domestic supply.  I will return to this point below.

On an Econ 101 exam, students are asked the following question.  If aggregate demand for a good is rising and if aggregate supply is declining, what happens to the equilibrium price?  

The diligent student is supposed to draw the curves and show that the price of the good in question will rise.  Dear Reader, note that this prediction is for a free market economy. In "Communist" China, I doubt that this has taken place.  When prices do not change to reflect market fundamentals, a shortage emerges because the price sticks at the original "communist price".  At that "low price",  demand exceeds supply and the New York Times reporters see a shortage.  For some details, read this.

The solution to this "shortage" issue is to allow prices to float to reflect scarcity.  Price ceilings create a type of moral hazard effect as major consumers of electricity do not need to invest in energy efficiency because they know that their Communist Leaders will protect them against price spikes. Why incur a fixed cost to achieve energy efficiency if energy prices can never soar?       

Let me propose several new academic research projects here;

1.   I would like to see a study of the diffusion of electricity smart meters across China. How many firms and residential homes have  a smart meter measuring consumption so that these entities could be exposed to dynamic pricing?   I predict that the state run SOEs are less likely to have this equipment in place.  The elasticity of demand with respect to price would be more elastic if more of these entities had smart meters.   

The Chinese Communist Party has introduce free market features to many parts of their economy.  Has the electricity sector been exposed to dynamic pricing?  "Shortages" will vanish when price signals are informative.    I use the phrase "communist leaders" above to refer to this mixed system that incorporates both free market components and the heavy hand of the state.  This "shortage" case highlights the inefficiencies that can emerge from such a mixed system when prices are not used to allocate scarce resources to the entity who values them the most.  

2. Returning to the coal mining point above related to the safety at mines.  First, read this very interesting QJE paper.  

In 2004, Dora and I published a paper using U. S data documenting that the Value of a Statistical Life rises faster over time than a nation's per-capita growth rate.  As China grows richer, the society is willing to pay more to reduce the death risk in risky jobs such as coal mining.  We need new research studying the dynamics of the VSL in the developing world using revealed preference methods. Intuitively, what would I need to pay you for you to work for a week in a Chinese coal mine?  Now repeat this exercise but ask Jeff Bezos the same question. What would I need to pay him for a week for him to work in that same mine? The two answers would differ!   That's the income elasticity of avoiding risk!!

3.   A good economist could also write a big think paper on how China's energy efficiency efforts have been slowed down by its government's continuing rules that maintain energy price ceilings.  If every coal and electricity consumer anticipated that they face potential price spikes due to the law of supply and demand, how much more energy efficiency would they invest in?  So, I'm returning to the "moral hazard" point I made above.  The expectation of price spikes encourages investments in efficiency. In the case of gasoline, the fear of high gas prices nudges people to buy a Prius or a EV.  The same logic holds for an overall economy.  

 Mark Tercek's recently posted an interesting column where he makes the case for why major cash investments in the "blue chip" environmental interest groups is a wise use of Jeff Bezos' funds.

A quote from Tercek;

"When Jeff Bezos originally committed $10 billion to a new climate-focused philanthropic fund, environmental journalists and podcasts immediately began to discuss and debate where the money should go. (See here, here, and here).

While lots of interesting and cool ideas were proposed—investments in education, new far-out technologies, ambitious R&D programs, efforts to build stronger, bigger, and more diverse political coalitions—none of these smart and well-intended commentators (so far as I could find) suggested that the money go to funding the big NGOs so they could build on their success and do more of their important work. 

But that's just what Bezos did.  Bravo, I say.  

Unsung Heroes 

NGOs are the “essential workers” of the environmental movement."

END of QUOTE

Mr. Tercek's piece raises the question about how non-profits (such as The Nature Conservancy) compete against each other for $, attention and influence.

In the for profit world, I understand how firms compete. Starbucks offered higher quality coffee, at a higher price and managed to make billions creating a new experience for customers.  There is a market test for whether their product is good.  What is the "market test" for non-profits?  

What is the analogue in the non-profit world?  How does Jeff Bezos and other very rich people judge the productivity and the product quality of non-profits?  As highlighted by the work of Chad Syverson, it is challenging to measure and the explain the productivity differences of firms in the same for profit sector. His work highlights that this challenge is even harder for measuring and explaining cross NGO productivity in the green non-profit sector.  If potential investors cannot judge this, how do they know what to invest in? If they form a portfolio by diversifying their investments, how do they judge if their donations "had impact"?

This raises the question of how non-profit environmental groups "have impact".  What pieces of legislation would not have been implemented with the NGO''s efforts?  What causal effect does the green legislation actually have on protecting the environment?  Are there cases when the NGOs had "good intentions" but significant unintended consequences subsequently emerged due to the regulation in question?  

In for profit markets, Starbucks does not value extra coffee profits earned by Peet's. In the NGO space, if one green NGO doesn't have much impact but if a rival NGO does and the environment is positively impacted --- does the losing NGO still feel good that the dirty status quo does not persist?  Is the output produced by green NGOs a type of public goods game?

Mr. Tercek argues that it is a good thing if the "big get bigger" in the NGO space.  During our new democratic age, this is an unpopular view. Many who are using the Internet to advise Mr. Bezos are nudging him to donate to unproven green NGO startups. Mr. Bezos has chosen to ignore this advice.  Mr. Tercek applauds this as he argues that the major green organizations have a proven track record and just need more money to finance their operations.

Society appears to be uncomfortable with an industrial organization featuring just a few very large, very well functioning firms (either in the private sector or in this case in the non-profit sector).   

An obvious economics question that arises here concerns the "efficiency versus equity" tradeoff for the environmental non-profit sector.  In this specific case, what does this classic Econ 101 diagram look like? 

Johns Hopkins ranks as #10 or so in the United States among universities and is world renown in medical care and public health.   In this post, I will argue that an unintended consequence of being so focused on health care and public health is that there are other key research areas (such as the private sector's role in stimulating economic growth) that receive too little attention.  My Center's sole focus is to balance this asymmetry.

While I am well aware that we are in the midst of a pandemic, this blog post will focus on the fundamental importance of major universities investing in understanding the root causes and consequences of urban economic growth and the flip; the root causes and consequences of urban poverty.   

The 21st Century Cities Initiative at Hopkins is an Interdisciplinary Research and Outreach center that was originally directed by Sociologists. I am a fan of interdisciplinary scholarship and collaboration but I seek to take this center in a new direction.

Given that the Bloomberg School of Public Health has hundreds of faculty studying various issues related to the supply of public health for urbanites, how does my 21CC find our niche?  The key word here is "public".  My key word is "private"!   Of course, the private sector responds to the rules of the game introduced by the public sector.  

Here are the core questions that my center works on and almost no other scholars at Hopkins are working on these essential questions.   While I focus on Baltimore here, these same issues arise for many cities in the U.S and around the world.  

They are all related to the urban poverty trap.  America's poor cities face a high poverty rate and many young people lack hope.  The only way out of this trap is economic growth.  There is no amount of $ that President Biden can send to these cities that is sufficient to allow them to escape the trap. These cities must figure out how to escape this trap on their own.  Economic growth is the only way out.

Given this reality, here are some questions that fascinate me. 

1. private sector job growth in cities such as Baltimore and cities around the United States.  Why does Baltimore feature so few superstar companies? Why hasn't Baltimore and other post-industrial cities had more success helping small businesses to thrive?

2.  What local and state rules of the game facilitate capital investment and local private sector job growth?

3. What role does local quality of life improvements with respect to crime reductions and pollution reductions and climate resilience play in attracting college educated people to move to the area and remain there even once they have children?

4.  What new rules of the game are needed to attract new commercial and residential real estate investment in cities such as Baltimore?

5. What role can better transit infrastructure that connects Baltimore to Philly and DC play in helping Baltimore thrive?

6. If Baltimore stages a comeback, will the urban poor (many of whom are African-American) share in this prosperity or will gentrification hurt their quality of life? What are their rights in a new Baltimore?

7. What role does political competition play in stimulating a city to have a greater menu of leaders and policies to choose from?  I am fan of competition. If cities  such as Baltimore had a competitive Republican Party would the city be better governed?  The field of urban political science needs a major investment of new talent. I would like to work at a University with 5 of such empirical scholars!

Do you see how my focus differs from the classic Public Health focus?  How do we use free markets to help poor people to achieve the American Dream?    I am interested in the Move to Opportunity project but such partial equilibrium research doesn't tell me why the "good areas" are good and whether they will continue to be good if many new people move there.  

Ideally, I want the 21CC to take the James Heckman research program on human development and to explore its spatial implications. Do great places emerge when people achieve their full potential and plant roots there? This is a different vision of core research than what I see being supplied by my colleagues (and rivals) at Bloomberg's School of Public Health.  

Within 21CC, we are a diverse intellectual community. My team has different views about how to build great cities and we continue to debate these ideas.  I am open to all ideas as long as they are grounded in data and logic.  My colleagues know that I have several old school University of Chicago muscles that they do not share!

I am trying to create something new that doesn't exist at Hopkins or any other University.  Wharton's Real Estate Zell center gets close but they do not really delve into the details of urban political economy and the governance challenge.

In our 2021 book; Unlocking the Potential of Post Industrial Cities we present our vision in depth. Read Chapter One here.  The point of this blog is to sell this book and to market our vision for the positive role that free markets play in helping all of us to achieve our own conception "of the good life".  

The modern University spends a lot of time punching market capitalism in our undergraduate classes. Gary Becker and Milton Friedman deserve their chance to offer some ideas about how to improve urban quality of life. 

 

 This blog post builds on my last post on a related topic.      My critics say to me;  "Matt, our centers of productivity are located in coastal places and these places face ever increasing climate risks.  In a Paul Krugman  style "History versus Expectations" economy, we have built our productive hubs in places that are now under siege and our economy could be wiped out by future punches."  Note that these persistence pessimists downplay the role of Expectations.  Lucas lives on.  Capital is finite lived and there is always a new generation of young people.  They don't need to follow the herd and sink as the seas rise.  

My counter-points takes several forms;

First, I can point to my 2017 JUE paper on Chinese Industrial Parks.  These recent phenomena highlight how new centers of local economic productivity can quickly be built. Yes, China's government has certain unique powers that U.S governments don't have but are you so pessimistic about the ability of private firms to co-ordinate activity and move to higher ground as they anticipate threats to where they currently locate?

Second, as I discuss in my 2022 Work from Home (WFH) book, the rise of WFH  will only increase our resilience to climate shocks for those who are eligible to work in such jobs.   WFH eligible workers have strong incentive to move to "higher ground" and service worker will follow them (the Moretti local multiplier effect revisited!).  

Third, forward looking firms have strong incentives to anticipate the new risks they will face and to invest in the fixed costs to continue to be productive.  See my 2016 paper with Josh Graff-Zivin.

Fourth, I can point to the Davis and Weinstein key work on city resilience in the face of enormous shocks.  The death toll from natural disasters declines with economic development (see my 2005 RETAT paper).   Was Nietzsche right?  What doesn't kill us makes us stronger?

Fifth,  U.S cities compete against each other to attract productive firms. If a firm such as Amazon is not productive in a specific location, it will look around the nation for alternative locations as a HQ2. This competition fuels resilience.  Competition is a key idea that is missing in the recent climate economics literature.  IO economists study the benefits of competition but environmental economists do not.  The latter are implicitly assuming that all firms and people are stuck in place.  Is persistence really that persistent? 

My work on climate change adaptation focuses on both people and places.  As I discuss in my 2021 Yale Press book, we need to hold more real options and not lock in to any one place. My book fleshes out all of the points I make in this blog post.  The ability to reoptimize as we learn about the spatial distribution of risks is crucial for adapting to the very serious challenge that we face.

When one drops the macro approach to measuring climate change damage (see Nordhaus and Weitzman) and introduces geography, the adaptation margins actually increase to almost infinity given the number of locations, building types, construction materials and permutations with club goods that can be used in the face of every location specific climate threat.   Such adaptation is costly but how costly in an economy featuring learning by doing and global supply chains?    This is the exciting research agenda that merits new work.