Twenty years ago, a group of experts – the “Boskin Commission” – concluded that the U.S. consumer price index (CPI) systematically overstated inflation by 0.8 to 1.6 percentage points each year. Taking these findings to heart, the Bureau of Labor Statistics (BLS) got to work reducing this bias, so that by the mid-2000s, experts felt it had fallen by as much as half a percentage point.

We bring this up because there is a concern that as a consequence of the way in which we measure information technology (IT), health care, digital content and the like, the degree to which conventional indices overestimate inflation may have risen.

Measurement of prices is central to how we understand the economy. We use prices to distinguish nominal and real quantities in order to measure economic growth. We also use prices to deflate nominal incomes to measure the evolution of living standards. And, in the belief that price stability is the foundation for strong, sustainable and balanced growth, central bankers obsess over whether inflation is rising or falling, too high or too low. In economies accounting for about two thirds of global GDP, central banks have specified a level or range of inflation as a policy target.

When indices like the consumer price index (CPI) or the personal consumption expenditure price index (PCE) persistently overstate inflation, there are important consequences. So long as the upward bias is constant, central bankers can (and do) modify their inflation targets. Yet, these price indexes also are used to adjust entitlement benefits without correcting for any persistent bias. And, they can have an important impact on public discourse. In particular, upward bias means that the median real wage may have risen substantially over past decades, in contrast to reported stagnation.

If the overstatement of inflation has increased during the past decade, this also has profound consequences. For one thing, the reported slowdown in annual productivity growth – from something like 2½% in the decade prior to the crisis to about 1% today – could be more apparent than real. For another, true inflation may be even further below the Federal Reserve’s long-run objective of 2% on the PCE than current readings imply.

There is good reason to think that the price mismeasurement problem has gotten worse, but quantifying that deterioration is another thing. The impact on inflation may turn out to be small – perhaps an extra ¼% annually – leaving it well within the range of uncertainty that the Boskin Commission highlighted 20 years ago.

To help you follow our analysis, we'll summarize the big points first:

To measure price changes, we need to compare apples with apples. But, advances in technology (especially IT and medical care) lead to the introduction of new goods and services, as well as massive improvements in the quality of goods and services. Neither of these is likely to be accurately captured in current official price measures.

It is much easier to measure the impact of innovation on the quality of hardware than on the quality of software and new digital content (like Google, Facebook, Twitter, YouTube, etc.).

Medical care inflation has already collapsed, but perhaps not enough to reflect quality improvements.

Because of the low weight of IT goods and services and the high weight of medical care in overall consumption, the potential measurement of medical care costs matters far more for price bias.

Since computer hardware and software have a higher weight in investment than in consumption, the impact of their price mismeasurement on GDP and productivity growth is larger than their impact on consumer price inflation.

When it comes to technology, the primary issue is adjusting for the change of quality and the introduction of new products. (For a discussion of various sources of bias in price measurement, see here.) If the quality of a good or service improves but the reported price stays the same, its true cost has fallen. For example, the graphics card in an iPhone 5s (last year’s model!) has the capacity to do nearly 1,000 times the number of floating point operations per second (FLOPS) as a 1975 Cray-1 supercomputer, which (adjusting for inflation measured by the CPI basket) cost something like 100,000 times as much. That’s an almost incomprehensible price decline.

The official price changes for computer hardware (plotted as the black line in the chart below) are far less dramatic. Over the past 40 years, reported hardware deflation has averaged nearly 13% annually (compared to the 35%-plus annual decline in the estimated cost of FLOPS). And, even that reported hardware deflation has abated markedly in the past 5 years. In fact, while deflation averaged 15% annually from 1975 to 2010, since 2010, it has been less than 2%.

Personal consumption expenditure chained price index: computer hardware and software (percent change from a year ago)