Only one inflation rate for the rich and the poor?
The story of GDP since 1940 is also the story of macroeconomics. (p20)
This is by Diane Coyle in her book “GDP: A Brief but Affectionate History”. Ever since the first Gross National Product (GNP) accounts were published for the United States in 1942, a great range of assumptions on what to include were necessary: Should we count services, the public sector or the financial sector? Ultimately these accounts are a social construct, so we need to decide which activities are worthwhile.
In macroeconomics, researchers have tried to get away from the model of the representative household by introducing heterogeneity among households. And similarly to these theoretical developments, new ideas for national accounts have been put forth: Thomas Piketty, Emmanuel Saez and Gabriel Zucman propose (pdf) to start using “distributional national accounts”. Previously we could answer what the aggregate economy produces and consumes, but these new accounts promise to tell us: How much has income grown for somebody at a particular place in the income distribution?
There already exist some indicators for this question, such as the top 1% income share estimated from tax returns. But what’s new is to provide accounts that are consistent with the macro data.
It’s interesting to ponder over the question of how to convert nominal to real values for income groups. Should we use one inflation rate for everyone or a different inflation rate for every income bracket?
Richer people spend less on food and other items relatively to their total income than do poorer people. The German statistical office, for example, offers this tool (in German) to calculate personalized inflation rates. But there are good reasons for and against using a single inflation rate and our choice should depend on how we want to think about income:
- Income as consumption. More income means you can buy and consume more goods now or in the future. Normally, this is what economists think of when they hear “income”.
- Income as economic power. Being rich also comes with more influence, so income might be a good indicator for who’s powerful in society.
The first concept is probably better suited for international or intra-temporal comparisons. We might ask: “How much better off is somebody in Switzerland relative to somebody in Kenya?” or “How much better off is somebody in Germany now than compared to 1950?” And for both questions we probably want to take into account that prices differ in the two countries and have been different in the past.
But within a single country at one point in time, the second concept is likely more useful. If both rich and poor people generally live nearby, compete for the same resources and participate through the same political entity, then we should probably use the same price indicator for both groups.
So it seems to make sense to just use one inflation rate in the distributional national accounts. But how large is the dispersion in prices that people actually pay?
Greg Kaplan and Sam Schulhofer-Wohl (pdf) look at scanner data for the prices of sales transactions by households [source: MR]. They find great variation among the prices that people pay for similar goods and this effect even dominates the movements of the aggregate price level:
[…] almost all of the variability in a household’s inflation rate over time comes from variability in household-level prices relative to average prices for the same goods, not from variability in the aggregate inflation rate.
And even similar households pay different prices for the same goods:
Households with low incomes, more household members, or older household heads experience higher inflation on average, […], but these effects are small relative to the variance of the distribution, and observable household characteristics have little power overall to predict household inflation rates.
So something else, apart from income, dominates individual inflation rates.
This is based on 500 mio. transactions by 50,000 U.S. households between 2004 and 2013. Coyle also argues in her book for using “user-generated statistics” (p138) to improve our understanding of economic activity. But it’s a pity that the time dimension for this kind of data is relatively short.
It’s previously been found that relevant economic actors (managers of firms in New Zealand) know remarkably little about the aggregate inflation rate. Kaplan and Schulhofer-Wohl offer the intriguing explanation that the aggregate inflation rate might simply matter little to individuals as they face different prices anyway. This probably also holds implications about how central banks should think about the transmission of monetary policy.
However, Kaplan and Schulhofer-Wohl say it’s important to know whether people can forecast their own personal inflation rate. If they cannot, then people might keep looking at the aggregate inflation rate as the best predictor of where also their personal price level will be in the future.
Coyle argues in her book that though GDP has many imperfections, it’s still the best way to measure economic activity and that instead replacing it we should use a “dashboard of indicators” (p118):
The U.S. Commerce Department called GDP one of the greatest inventions of the twentieth century, and so it was. There is no replacement for it on the horizon. (p138)
Not a replacement, but the authors Piketty, Saez and Zucman have a good point that we should add the cross-sectional dimension to it. So let’s hope that statistical agencies will take over this task, through maintaining and publishing these distributional national accounts.