“Nobody knows anything…. Not one person in the entire motion picture field knows for a certainty what’s going to work. Every time out it’s a guess and, if you’re lucky, an educated one.”

– William Goldman, Oscar-winning screenwriter

On the surface, the film industry and central banking have little in common. Each does its own thing with little regard for the other. But in fact, they’re more alike than either cares to acknowledge.

Film executives must analyze the vast, constantly shifting data surrounding public preferences, make long-term financial commitments that aren’t easy to reverse, and then live with the consequences. Central bankers must do the same. Hollywood execs dress more fashionably, but otherwise they have a lot in common with Fed governors.

There’s one big difference, though: Hollywood’s financial mistakes hurt mainly Hollywood, but the Fed’s mistakes hurt almost everyone. Hollywood executives have their own skin in the game. They live with the financial consequences of their decisions. The members of the Federal Open Market Committee not only have no skin in the game; if something goes wrong, they will blame capitalism and free markets and thereby relieve themselves of the consequences of their own decisions and manipulations. And then they will go on manipulating the markets to far more applause than they deserve, in the attempt to clean up the consequences of their own mistakes.

Let’s be clear. The financial crisis of 2007–08 was the result of Federal Reserve errors and the regulatory failures of government agencies.

When William Goldman wrote, “Not one person in the entire motion picture field knows for a certainty what’s going to work,” he could just as easily have been talking about monetary policy. Nobody really knows what’s going to work, for the reasons we covered last week in “Data-Dependent on Imaginary Data.”

However, if we ask who makes more blockbusters while operating on flawed and limited information, Hollywood wins easily. It has the occasional Gigli or Heaven’s Gate, but the Fed remakes Ishtar every few years and thinks everything is fine.

Today we’ll extend last week’s discussion by considering how twisted inflation data leads to less-than-ideal policies.

No One Is Average

In the US we have two different inflation measures, produced by agencies of two different cabinet departments. The Federal Reserve prefers to look at the Commerce Department’s Personal Consumption Expenditures (PCE) Index, because they believe it is more comprehensive and nuanced than the Labor Department’s Consumer Price Index (CPI).

Are they right? In a moment I will talk about the differences, which are important; but I think the Fed is exactly backwards here. Neither measure is particularly foolproof, but the flexibility and adjustments that make the Fed prefer PCE also take the index further from reflecting the average citizen’s economic condition. This bias shows up in Fed policy, and not in a good way.

That doesn’t mean the CPI is wonderful, though. Unlike some, I don’t believe it is intentionally manipulated. I think the wonks (and I say that in a complimentary way, as a fellow wonk) who compile price data do a nearly impossible job as well as anyone can. Browse through the methodological explanations on the CPI home page and you’ll quickly see how much effort goes into that work. They have a whole “data available” shopping list:

• Price indexes are available for the US, the four Census regions, nine Census divisions, two size of city classes, eight cross-classifications of regions and size-classes, and for 23 local areas. Indexes are available for major groups of consumer expenditures (food and beverages, housing, apparel, transportation, medical care, recreation, education and communications, and other goods and services), for items within each group, and for special categories, such as services.

• Monthly indexes are available for the US, the four Census regions, and some local areas. [You can see those indexes here.]  

• More detailed item indexes are available for the US than for regions and local areas.

• Indexes are available for two population groups: a CPI for All Urban Consumers (CPI-U) which covers approximately 94 percent of the total population and a CPI for Urban Wage Earners and Clerical Workers (CPI-W) which covers 28 percent of the population.

• Some series, such as the US City Average All items index, begin as early as 1913.

All that data gets worked into “baskets” that try to match the spending habits of typical consumers. That’s where the effort starts going wrong. The problem is quite simple and beyond anyone’s control: None of us are average.