Environmental and Urban Economics

Catherine Wolfram's excellent blog post about the proposed "Green New Deal" (GND) nudged me to write out a few thoughts.  I recognize that we do not have a clear definition of what policies would be bundled into such a GND.  At its heart, it must be a Keynesian large scale expenditure of federal $ to jump start the green economy.

When government spends $, balanced budget conditions require someone's taxes to rise.  I will not comment further on this issue here.  Let's focus on the winners from this increased government expenditure.

Can empirical economists working in the Chad Jones, Paul Romer endogenous technological change literature,  estimate the marginal increase in patents as a function of new government expenditure?  Patents can be measured both in terms of quantity and quality.

Productivity report cards have been posted for academic economists.  Without being too modest, here are my rankings;

overall   (#340)

last 10 years (#141)

h-index (#315)

environmental  (#11)

I think these imperfect indicators provide some benchmarks for judging whether tenured academics are still hard at work.

I support introducing a large carbon tax to mitigate climate change risk.   Economists recognize that such a policy will introduce price effects (substitution effects) that will help mitigate the climate change challenge.  We also recognize that this policy will introduce income effects that will lower the real incomes of several different sets of people in our economy.  This blog post will discuss 3 sets of people who would be adversely affected by carbon pricing.

The NY Times reports that iPhone sales are way down in China.  The recent 40% decline in Apple's stock price suggests that investors were unaware of the emerging trouble that Apple now faces.  How would an industrial organization economist explain these recent facts.

A traditional explanation is that iPhones are expensive and are a luxury good in China.

The NY Times has published a very interesting piece about Facebook's effort to prevent suicide.   Facebook must have coded up an algorithm for predicting a person's probability of committing suicide as a function of the content of one's posts to the platform.  A statistician might ask if the R2 in predicting actual suicide based on "public thoughts" posted to Facebook is .75 or .0002.   I will return to this point in a moment.