“Economists pay too little attention to what is happening around them,” claims Diane Coyle (herself an economist) in the 8 November New York Times. This isn’t the common complaint that the profession is too concerned with elegant mathematical abstraction to notice what’s going on in the real world. Instead, it’s a discussion of how, if we measure the health of an economy only by looking for fluctuations in the GDP statistics, we rely on one of the “least useful” economic barometers, one which is also a poor forecasting tool. “It lags events, is frequently revised and provides no meaningful detail,” Diane writes, “paying close attention to worldly detail could make forecasting more reliable.”
Which worldly detail might help? Diane discusses the merits of noticing the length of hemlines, counting construction cranes, trends in the price of modern art, measuring sunspot activity and the number of shops selling scented candles, which have all been suggested as economic forecasting tools at some time with varying degrees of success (you will have to read the article to find out why). If some of these unconventional indicators seem anecdotal at best, they are certainly much quicker to measure than GDP. Steering an economy using GDP, Diane writes, is “like driving a car using only the rearview mirror”.
Full disclosure: Diane is helping to write our ebook. In 2016 we will publish our unit on innovation, for which she is a co-author (GDP also fails to capture the amount of digital innovation in an economy, she points out in the article). For CORE’s explanation of the usefulness and limitations of GDP as a measure of a nation’s wealth, as a policymaking tool, and as a way of measuring inequality, consult Unit 1, Unit 12 and Unit 19. For more of Diane’s analysis of GDP (and for some more about the complementary indicators that help economic forecasters do their job) she has written a book about the topic* (right), which she also discusses in this podcast.
* Coyle, Diane. 2014. GDP: A Brief but Affectionate History. Princeton, NJ: Princeton University Press.