#dataineconomics

[stag28]

"It's true that services have been growing in importance for many decades, but in the 1950-2000 period we also saw a big increase in the consumption of stuff. My claim is that "stuff" is no longer growing a bit slower than services; in per capita terms it's probably declining---especially in crude physical terms. If the government still says it's still rising, it's because of "hedonic adjustments" which treat an iPhone like it's also a camera, stereo system, computer, clock, answering machine, calculator, etc. [..] Some of this can be passed off as unmeasured growth due to our new internet-oriented economy. But that doesn't fully explain the move away from stuff. The iPhone has replaced the answering machine, but not the car, boat, swimming pool, golf equipment, hunting rifle, and lots of other stuff. (OK, uber sort of replaces the car, but it doesn't explain the drop in miles driven.) [..] In that case you could argue that growth (properly measured) has not slowed at all. If so, then our current ways of measuring growth may have reached their limits. We have to accept on faith that someone spending all day on the computer is happier than someone golfing or boating. Revealed preference suggests they are happier, but of course if you truly accept that criterion then you also must apply it to heroin use. I spend all my time on the internet, but deep down believe I'd be happier outside. And I would argue that this move away from stuff is interesting, even if the output data is biased downwards by unmeasurable aspects of economic growth. Productivity in the services tends to grow much more slowly than productivity in making stuff. [..] As a quantum theorist might say, "When you measure the economy, you change it"."

[stag28]

"To an outsider or newcomer, macroeconomics would seem like a field that is haunted by its lack of data, especially good clean experimental data. In the absence of that data, it would seem like we would be hard put to distinguish among a host of theories with distinct policy recommendations. So, to the novice, it would seem like macroeconomists should be plagued by underidentification or partial identification. But, in fact, expert macroeconomists know that the field is actually plagued by failures to fit the data – that is, by overidentification. Why is the novice so wrong? The answer is the role of a priori restrictions in macroeconomic theory. Macroeconomists use a body of theory that imposes a number of a priori parametric restrictions on households and businesses. Households maximize expected discounted utility flows, with utility functions that are required to lie in a narrowly specified class. Businesses maximize profits and engage in monopolistic competition (or perfect competition). Everyone updates their beliefs according to a priori specified rules of some kind (usually Bayesian updating). The mistake that the novice made is to think that the macroeconomist would rely on data alone to build up his/her theory or model. The expert knows how to build up theory from a priori restrictions that are accepted by a large number of scholars [..] this approach to macro made a lot of sense in the mid-1970s or early 1980s. Data and estimation were both much more expensive than they are today. It was (I think) reasonable to substitute relatively cheap theory-driven restrictions for relatively expensive data-driven information."