Fire fighters in a city such as Baltimore have a better defined job than the police.  Fire fighters do not set fires.  They put out fires and save those at risk.  Fires occur at random times in random parts of the city.

The quality of the fire fighting unit can be measured using;  1. time until the unit arrives on the scene.  2. time until the fire fighters put out the fire.  3. total cost for putting out the fire.

Each member of the fire fighting unit knows his role and the individual team members know whether other complementary members of the team did their job. 

A KEY POINT.  Once the fire breaks out, this is not a strategic game.  The fire is a passive foe that makes no strategic moves.   A good applied scientist should be able to almost perfectly predict its path.

In 2019, Ian Coxhead wrote a tough Journal of Economic Literature review of my co-authored 2016 book Blue Skies Over Beijing: Economic Growth and the Environment (joint with Siqi Zheng of MIT).   In this blog post, I won't comment on specifics about his review but I would like to restate our main thesis.  I hope this helps young scholars think about some open research questions.

Almost 20 years ago, Brian Jacob, Steve Levitt and Mark Duggan taught economists how to detect cheating teachers and cheating sumo wrestlers.    Why haven't urban economists been as successful in identifying corrupt police officers?

The answer must be that the data on their daily performance output are not available in the public domain.  In past research, I have examined the annual compensation for the Police in Baltimore, Boston and New York City.  You can read our report here.

Information is highly valuable and costly to acquire.  Some of us know more about some things than others. A mother of a young child knows more about the child's health and personality than does the child's teacher.  A friend knows more about you than a stranger.

In the developing world, lenders have utilized these peer to peer networks to create incentives for sharing information.
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