As you might have noticed, in my initial minimum wage-related salvo against Don Boudreaux, I used a somewhat unusual graph:

 

fedminwagepopfull

Needless to say, it did not come from nowhere. In fact, it came from a lot of thinking and a decent amount of work, partially inspired by, curiously enough, Don Bourdreaux’s “Catalog” project, as well as by Scott Sumner’s work in general as well as his frustration with conceptual understandings of inflation. Rather than beat around the bush any more, though, I’m going to tell you what I’ve created, then explain it and defend it, then provide examples:

I have created a new econometric index. It consists of dividing the nominal price or value of something across time by nominal GDP per capita and representing it as a percentage. This divides out the currency unit, and so is an index measure of quantity, not price. I am calling the index, as well as the index unit, the “Percappy” (plural “percappies”).

Now – why?

It is theoretically and empirically sound. The Percappy tells us what share NGDP-per-capita could obtain a single unit of a given thing at a given time. It requires only three measurements – GDP, population, and the nominal price/value at a given moment in time – none of which need to be additionally weighed, balanced, or adjusted in any way. It does not need to be controlled for inflation over time, since inflation would affect equally both numerator and denominator. It does not need to be controlled for PPP or exchange rates across national borders as long as correct local prices are used.

It tells us something new and useful. The Percappy tells us, essentially, what share of per-capita national income it would take to acquire a single unit of some good, service, or financial product. This allows us to easily compare living standards (again, controlled perfectly for inflation) across time as well as across national borders, even diagonally (for example, you could compare other countries’ present consumption frontiers to the United States’ current consumption frontier).

It is analytically powerful. With a minimum of computation and weighting, using only easily-accessible publicly-available data, many different things can be compared – the prices of goods, services, commodities, financial products, and even more (as we shall soon see) can be compared while controlling for any number of factors that tend to complicate such comparative analyses.

It is simple to understand. Unlike inflation, which is conceptually challenging and whose definition is not universally agreed on, both the idea and the process behind the Percappy is very simple and easily understood – it answers the question
“how much more or less of some thing can be bought in different times and places?” by dividing prices by per-capita national income.

What it doesn’t tell us (and other limitations). It tells us very little about inflation or exchange rate fluctuations unless a large amount of data is computed and compared. The Percappy index is powerful because it easily divides away these factors to look at “objective” standards of living; but the trade-off is that it can tell us very little about the monetary, financial, fiscal, and other policy-related factors that may drive those changes. It also tells us nothing about quality. Also, historical prices can be hard to come by.

Let’s look at some examples. Firstly, a simple one – the price of a McDonald’s hamburger over time (note: log scale; all prices are per-unit and all measurements in %; the formula is 100P/[GDP/POP])

chart_1

Unsurprisingly, we are richer in terms of McDonald’s burgers than we used to be. What about some other delicious items?

chart_1 (1) chart_1 (3) chart_1 (2)

 

Interestingly, while Oreos and Hershey’s Bars seem to both be trending downwards (albeit not as sharply as McDonald’s burgers) Cornflakes seem to have bottomed out in the early-to-mid ’90s and have since climbed back to 1960s levels.

What about some other consumer prices people care about?

chart_1 (6)

FRED has only been keeping track since 1990, but clearly we’re not doing well on that score.

But the true power of this is to tabulate more than just common consumer prices. What about the value of the S&P 500?

chart_1 (7)

What about the average dividend of the S&P 500?

chart_1 (8)

And, to be fair to the goldbugs, what about gold?

chart_1 (5)

Hopefully even these examples, as simple and unprocessed (and, thanks to Google Spreadsheets, amateurishly presented) as they are, show some of the power of this index. Essentially, it can “crunch” almost anything, including (at least in theory), other indices (I’m thinking about feeding it the Case-Shiller index).

I am hoping other people think this project has some value – if you do, you can help! Here’s how (bleg time):

1) Data! Find historical prices is harder than you might think! If you can send me time-series price data of just about anything I would be quite grateful – or, you can do it yourself! It’s pretty easy. This could be easily crowdsourced.

2) Mathematica. I got this software and I love it, but haven’t quite figured it out yet. Especially the “get large volumes of data into it so you can crunch with with panace” part. Any Mathematica aficionados out there want to send me tips?

Here’s hoping people find this useful, helpful, interesting, worth picking up on or helping out with, or some combination thereof.

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