Economics majors have the highest lifetime earnings, says Slate. Why?
Part of it is that the finance and consulting industries like recruiting them, not necessarily for their specific skills, but because they consider the major a basic intelligence test. Granted, we’re probably not seeing the effect of Goldman Sachs or Private Equity salaries in these charts, since they only stop at the 95th percentile of earners—but banking is a big industry, and it pays well.
Economics departments generally didn’t follow others in pursuing grade inflation. Grade-seeking students of lesser abilities drop economics for other majors; those who are left earn their grades.
But give it time. Combine hard bums-on-seats financial pressures with a dearth of numerate high-school graduates who can do serious versions of economics, then add on a Minister who reckons* that darn near every numerate student should be pushed into Science, Technology, Engineering or Mathematics rather than other disciplines that require numeracy.
Equilibrium then has a fairly substantial dumbing-down of econ offerings.
I do think that Slate has some of this wrong though. Plenty of degrees select for intelligence: just look at relative GRE scores by intended major. Econ fares well, but philosophy, physics, and mathematics are hardly slouches.
What economics provides instead is a very general purpose rationality technology. It forces thinking about human behaviour within a consistent framework allowing us to work through comparative statics and dynamics: if one part of the system changes, we can make reasonable assessments about what is likely to happen as consequence. You don’t necessarily need the full-calculus version of intermediate microeconomics to be able to do this, but it does help. And it especially helps with most of these kinds of high-value economics applications:
Economics and logistics. All businesses seek to control costs; they don’t need an economist to tell them why it’s important or how to do it. But there are some very important exceptions. Companies in the transportation and communications business face complex optimization problems that mathematicians and economists have figured out how best to solve through “linear” (and later “non-linear”) programming methods. Firms in these industries and their customers who thereby benefit from lower prices (admittedly through processes they never see) benefit greatly.Economists and big data. For several decades after World War II, economists used statistical techniques to build increasingly complex models to forecast key macroeconomic variables, notably, GDP growth, inflation and unemployment. Economists who had statistical skills worked at leading forecasting firms such as Data Resources, Inc and Wharton Econometric Forecasting Associates (the two have since merged and been absorbed into Standard & Poors). Many large banks, other financial institutions and some large manufacturing companies also had their own economic staffs.This has all changed. Macro models are now largely out of vogue, though still used along with human judgment at institutions like the Federal Reserve Board and the International Monetary Fund. Forecasters never were very good at predicting turning points in the economy — recessions and recoveries — and it is not clear they will get better over time, though some will try.Instead, the “Big Data” revolution ushered in by the ease of capturing, storing and analyzing large bodies of data has generated new demands for economists and statisticians. High tech companies like Amazon, Yahoo and Google, among others, now employ economists to sift through all kinds of data — retail transaction data, browsing patterns, mobile phone usage — to fine tune their product offerings, pricing and other business strategies.Economists and market design. Most markets “clear” by having prices signal producers to make just enough that purchasers are willing to purchase. But a relatively new strand of economics, known as “market design” or “matching theory,” has focused on markets where “fit” is much more important than price in directing resources or decisions is gender neutral: matching of medical residents to hospitals, organ donor banks and on-line dating. For example, drawing on his Nobel prize-winning work shared with Lloyd Shapley, Harvard Business School emeritus professor Alvin Roth has used matching theory to design the national medical resident assignment program and kidney donor exchanges.
* I don’t know whether he reckons it. But the big funding push for STEM is having that effect.