Distributional effects of ultra-low rates, Part 2
Somewhat humorously, the zero-bound is succeeding where our political system is failing: general discontent has given us some pretty strange bedfellows. The more hawkish voices of course want a more aggressive policy, and are being joined by a growing number of more progressive types who’d like to see corporations making money the hard way—developing new ventures and investing in labor and productivity—instead of through the financial engineering enabled by extremely low rates.
In a 2013 paper, “QE and ultra-low interest rates: Distributional effects and risks,” McKinsey estimated that in the US, the UK, and the Eurozone governments had garnered about $1.6 trillion while households lost about $630 billion in net interest income by the end of 2012. The authors note that some of those governmental profits fueled the automatic stabilizers, and broke out demographic groups by age, not income. The young were gainers and the old, losers. For households under age 35 who are net borrowers, the gain from lower rates has averaged $1,500 a year; for 35–44, $1,700. But older households with significant interest-bearing assets were losers. Those aged 65–74, -$1,900, and over 75, -$2,700. For the oldest group, that was a hit of 6% of income; for the youngest, a gain of almost 3%.
They note that non-financial corporations also benefitted, by about $710B, but that had yet to translate into investment, probably because the recession lowered demand expectations. At the time they reckoned the benefit boosted profits by about 5%, one-fourth of the gain since 2007. McKinsey argues that effects of near-zero rates on asset prices were "ambiguous,” but at least in the recent U.S. instance, we’re not really convinced.
As logic would predict, they forecast a reversal of these benefits as interest rates rise. Even with large percentages of fixed-rate mortgages, the authors estimate that in the US household debt service could rise by about 7% for each 100-basis-point increase. The younger net debtors would lose the largest benefit, while the older asset holders gain share. The more capital-intensive industries, utilities, manufacturing, extraction, would take the biggest hit, and this was all before oil prices fell, but companies that “use capital productively,” will be rewarded. What a concept.
And they forecast an increase in market volatility. No genius points for that, but a nice change.
The Federal Reserve’s most recent Survey of Consumer Finances, for 2013, tells us that increased interest income will be narrowly distributed across households. Although most recent data show that 13.8% of households own stocks directly—which rises to 48% if you include mutual funds and the like—only 1.4% of households own bonds directly. The SCF doesn’t report on bonds owned through mutual funds, but applying the indirect/direct ratio prevailing in stocks to bonds gets us only to 5% of households. About 2% of the middle decile owns bonds (9.2% own stocks), and 6.9% in the top decile. The Fed doesn’t even include a number for bond ownership for the lower decile because it’s very tiny, and the authors note that families in the highest 40% of the income distribution own bonds disproportionately, though much of that is savings bonds. “Ownership of any type of bond other than savings bonds is concentrated among the highest tiers of the income and wealth distributions,” they say.
Debts are more equally distributed. Half—52%—of the bottom 20% is in debt; 84% of the top decile. But the top has a leverage ratio (debt/income) of 8%, compared with 19% at the bottom. Leverage ratios are highest in the middle of the distribution.
Really, what jumps out at you putting this stuff together are the details, like the fact that the fraction of young families with education debt increased from 22.4% to 38.8% between 2001 and 2013, and the fraction of middle-income families who saved fell significantly.
What gas dividend?
Oil (and gasoline) prices have crept off their lows; they’re up about 25% this year, though they’re still well below year-ago prices. But many analysts have been scratching their heads wondering why the decline in energy prices hasn’t done more to goose up retail spending. It may be that's not really much of a conundrum.
Here are several ways of making that point. First, there’s the graph above, showing the annual growth rate in nominal expenditures on energy and all other forms of consumption from the national income accounts. If energy prices could add to or subtract from other forms of consumption, you’d expect the two lines to look more like mirror images. In fact, they move more together than apart. The correlation coefficient between the two series is 0.43. Lag the non-energy spending a year, on the supposition that there might be a delayed reaction time, and the correlation coefficient actually falls to 0.28.
Moves in energy consumption are overwhelmingly dominated by price. The correlation coefficient between the annual change in the gasoline price and energy expenditures is 0.97. (That’s made even more remarkable by the fact that more than a third of energy spending is on services like electricity, which are not priced like gas.) While people may drive more or less, or raise or lower the thermostat, based on price movements, it appears they don’t change their behavior all that much. They just take the price—and it doesn’t seem to have much effect on other spending.
One gets similar results looking at the BLS’s Consumer Expenditure Survey (often abbreviated CEX, so as not to confuse it with Current Employment Statistics, the official name of the payroll survey). The CEX can be volatile, and dependent on the unreliable memories of the surveyed in describing their spending habits, but it’s still the best household-based measure of expenditures we have—and it allows for fairly detailed demographic analysis.
As the first graph above shows, we don’t find the mirror image between energy and non-energy spending growth that you might expect in the aggregate. The correlation between energy and non-energy spending growth is 0.28—positive, not negative. And the story is similar when you look at poor (bottom quintile), middle-income (third quintile), and affluent (top quintile) households. The correlation weakens the higher you go up the income ladder (0.52 for the first, 0.42 for the third, and 0.14 for the fifth), but they remain positive for all five.
Not surprisingly, there’s a tight relation between income growth and spending. A regression of the annual change in non-energy spending in the CEX on the annual change in household income yields an impressively high r2 of 0.53. So the “mystery” of why lower energy prices are not translating into higher retail spending may not be a mystery at all. The best explanation of tepid retail growth is tepid income growth.