Results for “"Cowen's first law"”
6 found

Sunday assorted links

1. Some red states take action to limit the power of their public health authorities.

2. Redux of my earlier post “Don’t judge Covid conditions by the current rate of Covid growth.”

3. Spencer Greenberg podcast with me:

Why might it be the case that “all propositions about real interest rates are wrong”? What, if anything, are most economists wrong about? Does political correctness affect what economists are willing to write about? What are the biggest open questions in economics right now? Is there too much math in economics? How has the loss of the assumption that humans are perfectly rational agents shaped economics? Is Tyler’s worldview unusual? Should people hold opinions (even loosely) on topics about which they’re relatively ignorant? Why is there “something wrong with everything” (according to Cowen’s First Law)? How can we learn how to learn from those who offend us? What does it mean to be a mentor? What do we know and not know about success? What is lookism? Why is raising someone else’s aspirations a high-return activity?

4. More on pan-coronavirus vaccines.

5. FDA had banned home testing for HIV/AIDS.

China Ethiopia donkey estimate of the day

There are estimated to be 44 million donkeys in the world, almost all of which are maintained for work. China has the highest population (eleven million) followed by Ethiopia (five million)…

Here is the source, via pointers from Yves-Marie Stranger and Michelle Dawson.  That estimate seems to be from the 1990s, the article has much more data of donkey interest, and it is a good example of Cowen’s Second Law and indeed Cowen’s First Law: “It has been made clear that the estimates of donkey populations presented here should be treated with great caution.”

And here is a 2011 report on donkeys, horses, and mules in Ethiopia.  Ethiopia has the third largest total equine population in the world.

Will a higher minimum wage *reduce* automation?

That’s why they have Cowen’s First Law!  Here is new research by Mitch Downey at UCSD (pdf):

Recent research emphasizes the pressure technological change exerts on middle-wage occupations by automating routine tasks. I argue that technology only partially automates these tasks, which often still require labor. Rather, technology reduces task complexity enabling a less skilled worker to do the same job. The costs of automation, then, are not only the costs of the technology itself but also of low-wage workers to use it. By raising the cost of low-wage labor, the minimum wage reduces the profitability of adopting automating technologies. I test this prediction with state variation in the minimum wage and industry variation in complementarity between low-wage workers and technology. I show that accounting for state price differences induces new and useful minimum wage variation, derive new measures of complementarity from the Dictionary of Occupational Titles and the CPS Computer Use Supplement, and build a measure of technology based on IT employment, the largest component of IT spending. My results imply a $1 decrease in the minimum wage raises the average industry’s technology use by 30% and decreases the routine share of the wage bill by 1 percentage point (3.3%), both relative to a counterfactual without complementarity. Routine-intensive industries often exhibit high complementarity, making the minimum wage an important policy lever to influence the pace of routine-biased technical change.

I owe this link to someone other than myself, but can no longer remember who that is…sorry!

Tyler Cowen’s three laws

Many of you have been asking for a canonical statement of what I sometimes refer to as Cowen’s Laws.  Here goes:

1. Cowen’s First Law: There is something wrong with everything (by which I mean there are few decisive or knockdown articles or arguments, and furthermore until you have found the major flaws in an argument, you do not understand it).

2. Cowen’s Second Law: There is a literature on everything.

3. Cowen’s Third Law: All propositions about real interest rates are wrong.

I coined those some time ago, when teaching macroeconomics, yet I remain amazed how often I see blog posts which violate all three laws within the span of a few paragraphs.

There is of course a common thread to all three laws, namely you should not have too much confidence in your own judgment.

Addendum: Kevin Drum comments.

Natural experiments and the return to schooling

Cowen’s First Law: There is a literature on everything.

Responding to queries from Kling and Caplan and Henderson, let us turn the microphone over to Andrew Leigh and Chris Ryan:

How much do returns to education differ across different natural experiment methods? To test this, we estimate the rate of return to schooling in Australia using two different instruments for schooling: month of birth and changes in compulsory schooling laws. With annual pre-tax income as our measure of income, we find that the naıve ordinary least squares (OLS) returns to an additional year of schooling is 13%. The month of birth IV approach gives an 8% rate of return to schooling, while using changes in compulsory schooling laws as an IV produces a 12% rate of return. We then compare our results with a third natural experiment: studies of Australian twins that have been conducted by other researchers. While these studies have tended to estimate a lower return to education than ours, we believe that this is primarily due to the better measurement of income and schooling in our data set. Australian twins studies are consistent with our findings insofar as they find little evidence of ability bias in the OLS rate of return to schooling. Together, the estimates suggest that between one-tenth and two-fifths of the OLS return to schooling is due to ability bias. The rate of return to education in Australia, corrected for ability bias, is around 10%, which is similar to the rate in Britain, Canada, the Netherlands, Norway and the United States.

There are many other papers in this genre, such as by Joshua Angrist, and they yield broadly similar results.  Here is an Esther Duflo paper on Indonesia.  There is an excellent David Card survey on the causal returns to education, from 1999, but more recent results have shown the same.  Card’s conclusion:

Consistent with earlier surveys of the literature, I conclude that the average (or average marginal) return to education is not much below the estimate that emerges from a standard human capital earnings function fit by OLS. Evidence from the latest studies of identical twins suggests a small upward “ability” bias – on the order of 10%. A consistent finding among studies using instrumental variables based on institutional changes in the education system is that the estimated returns to schooling are 20-40% above the corresponding OLS estimates.

That last sentence is because the marginal student is especially in need of education.  The view that education is mostly about signaling is inconsistent with the established consensus on the returns to schooling and yet the writers at EconLog do not respond to this literature or, as far as I can tell, even acknowledge it.

Here is one of my earlier posts on education.  Here is my theory of (some) education.