Data, data, everywhere – Oh to be a central banker
There was an interesting Opinion piece in a recent US edition of the Wall Street Journal. It was titled, “Is the Philips Curve Dead? And other Questions for the Fed.” It was penned by Alan Blinder, a former vice chair of the Federal Reserve and now professor of Economy and public affairs at Princeton University. It so happens the piece links two of my favorite topics- data and analytics, and economics. First the data.
The article explains that the Fed is surrounded by all manner of data. In fact the mission of the Fed, to seek or maintain full employment and keep prices stable, is all data-driven. For example, unemployment is reportedly at very low levels: 4.1 percent. This suggests that the economy is doing well. History suggests that as unemployment reaches the floor of its ‘natural rate’, costs will increase as wages need to rise to capture ever decreasing labor availability. However inflation today remains fairly muted, at 2 percent (“inching up”).
From a data and analytics perspective this situation is generally quite common. I often hear the phrase, “we are data rich but insight poor”. The discrepancy between data (low unemployment but also low inflation) leads the author to suggest the relationship between employment and prices, as expressed by the Phillips Curve, may not hold at present. Worse, it seems we don’t have a model that explains the current observations. What is a Central Banker or any other business leader to do in such situations?
The Fed has to take decisions- when, if, and by how much, to change interest rates to help nudge the economy along, to manage as best they can unemployment and prices. A business leader has to likewise sign-off on an investment, not really knowing (at least confidently) what the outcome will be.
What about the economics here? Using my economic spider-sensors I have other data that I can bring to the table. The workforce participation rate helps examine (to a degree) that the 4.1 percent unemployment rate is not the whole story. Historic numbers of working-age people stopped looking for work in the last few years; partly due to sufficient transfer payments from the government to pay a pseudo “living wage” and partly due to lack of interest. The rate has started to increase recently demonstrating that some of those disillusioned are coming back to look for work – and find it.
Inflation itself is also a problem child. The analytic itself has different definition. I myself noticed several years ago that some household service prices had been going up, even though some household items in the usual ‘basket’ had not. The reduction in my remaining household disposable income was not being recognized in the official inflation numbers. So my own feeling was that real prices were higher and that official figures were not reporting the ‘real’ prices in the wider economy.
So if we assume the real unemployment rate is actually higher than 4.1 percen, and that inflation is a little higher than 2 percent, we might feel happier to assume the Philips Curve still operates! But since our raw analytics being used to guide actions is currently erroneous, we are driving a car without clear understanding of the situation around us. In economics we are only talking of a few percentage points of some very large numbers, and public policy (interest rates) that are extremely hard to relate to day to day behavior. In business, an investment in some project Iike IOT or customer engagement or advanced analytics will impact jobs, holidays and which school our kids go to. So it becomes very real for people like you and me.
And if you think that linking interest rates to day to day behavior is easy – check out this article I just spotted: Monetary Policy According to HANK. This research paper looks at the link between changes in interest rates and direct and indirect impact on consumer spending. It is quite interesting that the body of economists are still learning about how we behave!
But I do sit, marvel and wonder at the sheer scale and impact of decisions taken by titans of industry and folks like central bankers. It is hugely impressive and thought provoking. The questions, the decisions they need to make, are both fascinating and daunting. Oh to be a Central Banker!
(This post originally appeared on Andrew White's Forrester Research blog, which can be viewed here).