For decades, the world’s central bankers have almost lived and died by the Phillips curve, and it predicted inflation and wage growth reasonably well until the 1980s. Since then, however, the relationship between the factors it is meant to predict has been more complicated.
Some economists argue (paywall) that the ways in which we measure the variables at play, like wage growth, unemployment and inflation, need to change—not the underlying theory. But questioning the theory—and perhaps arguing against it—is no longer an arrestable offense.
Source: The US startup is disappearing
Fewer of the country’s most-educated workers are becoming entrepreneurs, and it’s happening across the American business landscape.
Source: The Psychology of Money – Collaborative Fund, by Morgan Housel
Go read the whole thing.
investing is not the study of finance. It’s the study of how people behave with money. And behavior is hard to teach, even to really smart people. You can’t sum up behavior with formulas to memorize or spreadsheet models to follow. Behavior is inborn, varies by person, is hard to measure, changes over time, and people are prone to deny its existence, especially when describing themselves. … The finance industry talks too much about what to do, and not enough about what happens in your head when you try to do it.
This report describes 20 flaws, biases, and causes of bad behavior I’ve seen pop up often when people deal with money.
Optimism is a belief that the odds of a good outcome are in your favor over time, even when there will be setbacks along the way. The simple idea that most people wake up in the morning trying to make things a little better and more productive than wake up looking to cause trouble is the foundation of optimism.
If you see someone driving a $200,000 car, the only data point you have about their wealth is that they have $200,000 less than they did before they bought the car. … Wealth, in fact, is what you don’t see. It’s the cars not purchased. The diamonds not bought. The renovations postponed, the clothes forgone and the first-class upgrade declined. It’s assets in the bank that haven’t yet been converted into the stuff you see.
Source: The epic mistake about manufacturing that’s cost Americans millions of jobs, by Gwynn Guilford
RE: Understanding the Decline of U.S. Manufacturing Employment, by Susan N. Houseman, Upjohn Institute for Employment Research
What’s odd is that, even as US factories laid off an historically unprecedented share of workers, the amount of stuff they made rose steadily—or at least, it appeared to. The sector’s growth in output, adjusted for inflation, had been chugging away at roughly the same pace as US GDP since the late 1940s. … That rests on the basic assumption that the manufacturing output data reflect the actual volume of stuff produced by US factories.
An economist at the Upjohn Institute, an independent organization that researches employment, Houseman specializes in measuring globalization. She had been working with a team of Federal Reserve economists with access to more granular data than was publicly available, which allowed them to strip away the computers industry output from the rest of the data.
In order to understand how the manufacturing sector is doing, economists look at how much stuff factories are making compared with previous years. … To make the output volume comparable from one year to the next, the statisticians aggregating the data adjust for price changes, as well as improvements in product quality. … But most economists and policymakers have failed to take into account how adjusting for quality improvements in a relatively small subsector skews the manufacturing output data.
There are also observable signs that automation wasn’t to blame. Consider the shuttering of some 78,000 manufacturing plants between 2000 and 2014, a 22% drop. This is odd given that robots, like humans, have to work somewhere. Then there’s the fact that there simply aren’t that many robots in US factories, compared with other advanced economies.
To be clear, automation did happen in manufacturing. However, throughout the 2000s, the industry was automating at about the same pace as in the rest of the private sector. And if booming robot-led productivity growth wasn’t displacing factory workers, then the sweeping scale of job losses in manufacturing necessarily stemmed from something else entirely.
Source: What to do when tech jobs go bad – Medium, by Alejandro Wainzinger
This is my humble attempt at describing what I’ve seen, what the problems are, and what some possible solutions could be.
A nice list of project management pitfalls.