Winner's curse arises in cases where several bidders in an auction are unaware of the true value of the object.   In this auction, the most optimistic of the bidders will bid the most for the object.  Unfortunately, for the bidder --- the best estimate of the value of the object is the average of the bids.  Since the winning bidder bids more than the average bid, the winner overpays for the object and is "cursed".   If the bidders "know that they do not know" the value of the object, then they have an incentive to shade down their bid. 

The New York Times has a nice piece about the Nash Game that cities are now playing as they position to attract the new Amazon HQ.    A few thoughts.  First, the counter-factual; in the absence of local government incentives where would Amazon HQ locate?  Second, what ex-ante economic model do cities use to predict how aggressively they should bid for the Amazon HQ.  For example, suppose that Chicago offers $2 billion dollars worth of tax cuts.  It must be the case that some economic consulting firm has a model predicting that if Chicago lures the Amazon HQ that this will generate more than $2 billion in new income for the economy. But, what model did the consultants use? Is it a simple input/output model?  How does this economic consulting firm know how the Chicago's counter-factual path would be affected by attracting the HQ? 

I would point my readers to my 2017 JUE paper for some insights on economic agglomeration where we study the impact of new industrial parks in China on the local economy.

A final point is for the local mayors to consider Tim Bartik's finding from his 1991 Upjohn book.  When states attract a big factory, the jobs do not go to the incumbent unemployed or to those out of the labor force, instead new migrants move in and gain the bulk of the jobs.  So, if a mayor seeks to trigger a "brain importing" then the Amazon HQ luring effort makes more sense.

In a Nash game, everyone would be better off if they could commit to co-operate but this is not a nash equilibrium.     Richard Florida has an excellent piece published here exploring the point that the efficient allocation of resources is unlikely to be an equilibrium in this case. 

As we enter the recruiting season for both new MA and PHD students and recruiting new faculty, it is useful to collect some facts about where USC Economics ranks.   I find that vanilla rankings such as US News and World Report are backwards looking and based on outdated information.  So, permit me to present some facts based on REPEC;

We are ranked #20 among U.S economics departments (Columbia is double counted).

We are ranked #23 in the world among research universities.  (I"m not counting 4 non-universities ranked above us).   We hold this same rank if you restrict to our publications over the last 10 years.

In Econometrics,  we are ranked #7 in the world.

In natural resources economics, we are ranked #5 in the world (and I'm ranked #2!). 

In terms of individual economists, we hold the following ranks among the world's 55,000 ranked economists.   So, Hashem is the #24 ranked economist in the world.  Impressive!

24	24. M Hashem Pesaran Department of Economics, University of Southern California, Los Angeles, California (USA) 125 Joshua Aizenman Department of Economics, University of Southern California, Los Angeles, California (USA)

The NY Times has published a very interesting piece on the unintended consequences of Clean Air regulation in China.   The piece makes some excellent points and then at the end of the piece it takes a punch at the autocratic state.   Permit me to make some economics points that build on the piece.

The author is correct about the short run costs of compliance with the regulation but in the medium term and long term several market and firm level adjustments will take place that will sharply reduce the cost of compliance with this regulation. 

For example, the author says that in the short run that the supply of natural gas (the clean fuel) is inelastic so rising demand for this fuel raises the price of natural gas and this raises costs for small businesses.  But, Alaska has a major LNG port and can ship such gas to China.  This will soon occur and the medium term supply of natural gas to China will be much more elastic and thus higher demand will not translate into higher prices for this clean fuel.

Second, the author does not discuss industrial organization. As the price of energy rises, there will be some firms in the same industry who figure out how to produce more efficiently and thus will suffer a smaller marginal cost increase induced by the regulation. The author is implicitly assuming that energy is "Leontief" in the isoquants and that all firms in the same industry have the same production technology.  This is false. 

In our work on China, the median urbanite wants cleaner air --- while there is no free lunch the government is responding to this pressure by increasing the supply of such public goods.  The author of this piece may be correct that the "little guys" in business bear the incidence of this regulation but this doesn't mean that the regulation is bad.  I think that PHD economists should further explore this economic incidence point.

Here are my relevant papers on this topic.

1.  Siqi Zheng & Matthew E. Kahn, 2017. "A New Era of Pollution Progress in Urban China?," Journal of Economic Perspectives, American Economic Association, vol. 31(1), pages 71-92, Winter.
2.  Matthew E. Kahn & Pei Li & Daxuan Zhao, 2015. "Water Pollution Progress at Borders: The Role of Changes in China's Political Promotion Incentives," American Economic Journal: Economic Policy, American Economic Association, vol. 7(4), pages 223-242, November.
3.  Zheng, Siqi & Kahn, Matthew E. & Sun, Weizeng & Luo, Danglun, 2014. "Incentives for China's urban mayors to mitigate pollution externalities: The role of the central government and public environmentalism," Regional Science and Urban Economics, Elsevier, vol. 47(C), pages 61-71.
4.  Sun, Cong & Kahn, Matthew E. & Zheng, Siqi, 2017. "Self-protection investment exacerbates air pollution exposure inequality in urban China," Ecological Economics, Elsevier, vol. 131(C), pages 468-474.

Roughly 15,000+ economists are trying to get to Philadelphia today for the annual ASSA meetings.   In a typical year, I greatly enjoy the meetings.  It is a place to see old friends, mentors, past students, co-authors and to attend sessions, meet with book editors and talk about economics.  This is a special year for USC Economics this year because we will hire 3 new PHDs to join our faculty.  As we upgrade our department, we need "new blood".  We will be interviewing 50 people.

The challenge we face is that the ASSA meetings take place in the first week of January and they typically rotate between Boston, Chicago, Philly (and warmer cities including Atlanta, Washington Dc and San Diego).  The cold winter cities offer cheap hotels and decent airports but as the winters grow harsher there is rising discontent with bringing thousands of us into "harm's way".

UCLA has moved all of its interviews over to Skype.  (UPDATE, I'm wrong about this -- some of the UCLA interviewers will be listening in on Skype).  I think this is brilliant.  What is lost by not having face to face contact for a 25 minute interview? My hunch is nothing.  So, the Economists will adapt by either moving the timing of the conference, the city or an unraveling of the conference as more of the participates who are mainly there for interviewing Skype in.

A general equilibrium point.  A young reduced form researcher seeking to answer the question;  "how is bad weather affecting the economy"  Would run a regression by year of the form:

Total Conference revenue_t  =   constant + b1*bad weather_jt  +   b2*great city dummy_j   +  U_Jt

so we expect that b1<0 and="" b2="">0 as great cities such as San Diego and San Fran generate more business.

The b1 would be interpreted by some reduced form researchers as the "cost of climate change" but let's be more specific, the $ not spent in Philly this weekend (because economists have chosen to stay home) will be spent in the economist's origin cities.  The reduced form researchers ignore such cross-elasticities.  Why?   There are too many cross-elasticities and to properly model them would require a structural model.   So, while "b1" can be estimated and it is of interest to Philly's local boosters -- it really isn't that interesting of an economic parameter.  The $ that would have been spent in Philly had the weather been good does not vanish when the weather is bad, it is spent elsewhere.