It can be hard to draw broad lessons about macroeconomics from the experience of one country alone, or from the experience of one or two recessions. Thus, a group of researchers including Òscar Jordà, Katharina Knoll, Dmitry Kuvshinov, Moritz Schularick, and Alan M. Taylor have been working to compile macroeconomic and financial data for 16 high-income countries going back to 1870. Alan Taylor provides a readable overview of several puzzles that emerge from the long-run financial data in \”The Rate of Return on Everything\” (NBER Digest, December 2018, pp. 20-23).
(The detailed research behind this short article is available as National Bureau of Economic Research Working Paper #24112: Ò. Jordà, K. Knoll, D. Kuvshinov, M. Schularick, and A. Taylor, \”The Rate of Return on Everything, 1870–2015,\” December 2017. It\’s also available as a Centre for Economic Policy Research Discussion Paper #12509: Jordà, O, K Knoll, D Kuvshinov, M Schularick, and A M Taylor (2017), “The Rate of Return on Everything, 1870–2015.” A short readable overview of the work that is very similar to the version I\’m referring to here, with the same title but listing Òscar Jordà, Katharina Knoll, Dmitry Kuvshinov, Moritz Schularick, Alan Taylor as the authors appeared at the VOX website on January 2, 2018).
#1 The Housing Puzzle
In general, economists would expect that assets with more risk–that is, more likely to rise or fall over time–will tend to have higher returns on average. From the standpoint of investors, the higher returns are needed to make up for the higher risk. This logic suggests that over the long run, a risky asset with volatile prices like corporate stock should have a higher average rate of return than a less risky asset with less volatile prices like housing. But that doesn\’t seem to be true. The blue line shows returns to housing, while the black line shows returns to corporate stock across the 16 countries in this sample.. Corporate stock is more volatile, but the average rates of return are quite similar.
Why might this pattern hold true? One possibility is that the risks for housing are higher than they at first appear, because it\’s harder to diversify the risks of owning housing, and perhaps also because it\’s harder to buy and sell housing quickly or in incremental chunks when prices change. But these factors don\’t seem nearly enough to explain the housing puzzle.
#2 The \”Safe Rate\” Puzzle
A lot of theories in finance and macroeconomics start with idea of a \”safe\” investment that pays a low rate of return but also has low risk. A common example would be investing in US Treasury debt, where the risk of default is near-zero. The theory then discusses how the safe assets might be combined with riskier assets. The puzzle is that \”safe\” assets like government debt actually can have quite volatile rates of return, once factors like inflation are taken into account. Here\’s a figure showing international returns on government debt.
For a concrete example, think about US experience since the 1970s. When inflation went way up in the 1970s, it mean that those who were holding government debt paying a low fixed rate were experiencing negative real returns for a time. The nominal rates paid on government debt rose by the early 1980s, but then when inflation declined substantially, those holding the \”safe\” asset for a time had substantially positive real returns for a time. Since then, a combination of declining nominal interest rates and low inflation have meant a steady decline in the real rate of return on \”safe\” assets. In real terms, the \”safe\” rate doesn\’t look all that safe.
Indeed, if you look at the \”risky\” assets like housing and corporate stock, but focus on moving averages over any given ten-year period rather than annual returns, the returns on the \”risky\” assets actually look rather stable.
#3 The r > g Question
If wealthy people can invest and receive a rate of return r, while the economic grows at a slower rate g, then wealth might grow faster than the economy over time (at least if wealthy people don\’t spend all of the returns on wealth), leading to greater inequality of wealth. This was a common interpretation of the work of Thomas Piketty in Capital in the Twenty-First Century on causes of income and wealth inequality a few years back. The findings here are that returns on risky assets like stocks and housing are often twice as large as rates of economic growth, or more.
But interestingly, Piketty himself doesn\’t view this r>g dynamic as central to the processes that generate wealth inequality. In an article her wrote for the Winter 2015 issue of the Journal of Economic Perspectives, \”Putting Distribution Back at the Center of Economics: Reflections on Capital in the Twenty-First Century,\” Piketty commented:
\”[T]he way in which I perceive the relationship between r > g and wealth inequality i soften not well-captured in the discussion that has surrounded my book—even in discussions by research economists. … I do not view r > g as the only or even the primary tool for considering changes in income and wealth in the 20th century, or for forecasting the path of income and wealth inequality in the 21st century. … I certainly do not believe that r > g is a useful tool for the discussion of rising inequality of labor income: other mechanisms and policies are much more relevant here, for example, the supply and demand of skills and education. … The gap between r and g is certainly not the only relevant mechanism for analyzing the dynamics of wealth inequality. As I explained in the previous sections, a wide array of institutional factors are central to understanding the evolution of wealth. Moreover, the insight that the rate of return to capital r is permanently higher than the economy’s growth rate g does not in itself imply anything about wealth inequality. Indeed the inequality r > g holds true in the steady-state equilibrium of most standard economic models …\”
What are some of the main factors that affect the rise or fall of wealth inequality over time? Examples would include taxes on wealth, the extent to which wealth is saved or consumed, and even the birth and death rates of the population, which affects how long concentrations of wealth will stay together and how many slices they will be divided into when passed to a new generation. There are questions about the extent to which whether the new fortunes being created in businesses around the globe will displace earlier fortunes, and whether the new fortunes will be long- or short-lived. There are also events of history like World Wars, and events of politics like surges of populist sentiment. For more on these topics, see \”Piketty and Wealth Inequality\” (February 23, 2015), or the four-paper symposium on these issues in the Winter 2015 issue of the Journal of Economic Perspectives.