Spending on new vehicles has been a bright spot for the U.S. retail sector. March sales estimates come out on Tuesday, and most expect a strong recovery after sales were depressed by the bitterly cold months of January and February.
But a closer look at the data on new auto purchases reveals some potential worries. Nothing is conclusive at this point, but there is a chance we are seeing another debt-fueled spending spree that will prove unsustainable. We are going to take a closer look at some micro data in the next couple of weeks, but thought we would outline the case based on the aggregate data.
New auto purchases have driven the consumer spending recovery to a large degree. The chart below shows the spending recovery for new auto sales and for all other retail spending. We index both series to be 100 in 2009, so the percentage change from any year to 2009 can be seen by taking the value for that year and subtracting 100.
From 2009 to 2013, spending on new autos increased by 40% in nominal terms. All other spending increased by only 20%. Further, excluding autos, 2013 saw . . .
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The sharp rise in income inequality in the United States is well-established. But what about wealth inequality? Income represents the flow of cash that a household earns every year, whereas wealth is the total stock of assets that a household owns, either through accumulation or inheritance.
Wealth is as important as income for thinking about overall well-being. For example, wealth may be more important than income in predicting who can send their kids to an expensive college. And wealth also represents control. Corporations are controlled by shareholders. So a higher concentration of wealth naturally implies that fewer individuals control the decisions made by firms in the economy. Similarly, non-profit organizations (including universities) and political parties pay special attention to their wealthy donors.
How has wealth inequality changed over the years? This has been a difficult question to answer in the past because wealth is highly concentrated to begin with, and we do not have good time-series data on the wealth holdings of the very rich. For example, data sets such as the Federal Reserve’s Survey of Consumer Finances (SCF) do not capture the super-rich.
Emmanuel Saez and Gabriel Zucman have preliminary work that approaches this question from a new angle. We want to emphasize . . .
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The Federal Reserve directly controls the short-term interest rate. But what it really tries to target is inflation and its expectations. The Fed’s goal is to achieve the target of 2% inflation in the long-term, and its preferred price index is the core personal consumption expenditure price index that excludes the volatile food and energy sectors (or core PCE for short). So how has the Fed performed in achieving its target of 2% inflation in the past 15 years?
The chart above plots the implied core PCE index if inflation had met its 2% target (red line), and the actual core PCE index (blue line) starting from 1999. The blue line is consistently below the red line, the gap has only diverged further since the Great Recession. The cumulative effect is that today the price level is 4.7% below what it should have been had the Fed achieved its long-run target.
The divergence between target and actual inflation is all the more striking given the elevated rate of unemployment during the sample period. We have discussed in a previous post how the post-2001 and post-2009 recoveries were “jobless” – a recovery in output but not much in employment. The Fed . . .
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A central argument we have made on this blog and in our book is that the distribution of income/wealth matters a great deal for thinking about the macro-economy. Convincing some of this fact is not easy — many continue to work within a modeling framework in which all distributional considerations are assumed away, the so-called “representative-agent” framework.
Perhaps the easiest way to see the importance of the income distribution is to examine how households respond to a windfall of cash or wealth. Do they spend the money, or do they save it? And does the spending response to a windfall of cash depend on the income of the household?
The answer is a resounding yes: low income households spend a much higher fraction of cash windfalls than high income households. In the parlance of economics, low income households have a much higher marginal propensity to consume, or MPC, than high income households.
This is one of the most well-established facts in empirical research in macroeconomics. Here is a summary:
Low income households spend a much higher fraction of fiscal stimulus rebate checks. See the evidence here and here. For the 2001 stimulus checks, low income households spent 63% more of their rebate . . .
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We are crunching some numbers trying to figure out what is going on with the house prices. In the meantime, here are some links which suggest that perhaps the sharp rebound in house prices is cooling.
Zillow forecasting Case-Shiller February report sees slowdown.
From the housing master himself at calculatedriskblog.
Erin Carlyle at Forbes explains the weirdness of seasonal adjustment, which is something we are looking into as well.
. . .
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Here is a chart from Ed Leamer showing jobless recoveries after the last two recessions. There are several ways to see jobless recoveries in the data, but this one is particularly striking. It may take a little effort to follow it, but it is worth the effort.
On the horizontal axis is the total amount of hours worked. On the vertical axis is the total output of the business sector. Leamer plots hours and output since WWII. Before 2001, we saw a steady expansion of both output and hours–we move in the northeast direction of the chart.
Recessions are times when we move to the southwest of the chart — hours and output drop. Recoveries should be times when we move back to the northeast–output and hours increase. That’s exactly what happened after all post WWII recessions, until 2001.
In 2001 and in the Great Recession, the recovery moves us straight north in the chart, not to the northeast. Output recovers, but jobs don’t.
. . .
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We all know that households cut back on spending dramatically during the Great Recession. Are they spending now? Has spending caught up to the trend the United States was on before?
The red line in the chart below plots retail spending in real terms in the United States from 1992 to 2013. We want to get a sense of the trend in spending so we plot spending on a logarithmic scale, and we subtract off the 1992 level to start the line at zero. A logarithmic scale is informative because a straight line in the chart would imply that spending was growing at a constant rate in real terms.
Prior to 2007, spending was growing at a constant rate of about 3% real growth per year. The black dots show the pre-2007 trend and where we would be if we had continued on that trend through 2013. The Great Recession is plainly evident in the chart: see the sharp decline in retail spending in 2008 and 2009 that took us well below trend.
So are we catching back up to our previous trend? Absolutely not. In fact, in . . .
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Alvin Hansen introduced the notion of “secular stagnation” in the 1930s. Hansen’s hypothesis has been brought back to life by Larry Summers in his November 2013 secular stagnation speech. The speech generated a huge amount of discussion, and for good reason. Summers’ provocative hypothesis is that the Great Recession was symptomatic of a longer-term problem: persistently inadequate demand that is evidenced by low real interest rates.
The basic idea and implications have been fleshed out by many: here is some backround reading by Ryan Avent, John Cassidy, Gavyn Davies, and Paul Krugman. A brief summary of the hypothesis goes something like this: A normally functioning economy would lower interest rates in the face of low current demand for goods and services in order to induce households and businesses to borrow and spend. When interest rates fall in a normally functioning economy, households borrow to buy cars or re-do their kitchens, and businesses find it profitable to invest. A lower interest rate helps boost demand.
But what if the interest rate needed to generate sufficient demand is negative? In other words, what if the economy needs to charge people for saving in risk-free assets such as U.S. Treasuries in order to get them to spend? Many find this . . .
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There are a number of examples in history where two countries that share the same fate and trajectory for a while suddenly start to diverge in their economic fortunes. A persistent difference in the growth rates of the two countries – i.e. a difference in long-run growth – translates into a huge difference in GDP per capita within a generation or two.
Berkeley’s Brad Delong provides a number of such historical comparisons: North vs South Korea, China vs Taiwan (until the 80s), Cambodia vs Thailand, Georgia vs Turkey, Cuba vs Mexico, and so on. Each pair of countries had similar “initial conditions” and yet something clicked in one country but not the other. The result was that within a few decades, the successful high-growth country was on average eight times richer than its twin counter-part!
This is how important long-run growth is. A country that fails to trigger what is necessary for long-run growth traps itself in the cycle of poverty.
India and Pakistan are a natural pair for long-run comparison. The two countries share a common colonial history and started off with a largely similar nature of economic activity. How does the long run trajectory of these two countries . . .
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An integral part of macroeconomics is understanding business cycles, i.e. figuring out what drives changes in aggregate output. We have devoted the last many years trying to understand why large declines in output and employment – as seen in the Great Recession – occur. In trying to answer such questions, a major obstacle in macroeconomics is the paucity of micro data. For example, while we know what happens to consumption in the aggregate, we do not have an accurate idea of movements in consumption at the neighborhood or even city level.
This is unfortunate because micro level data can greatly help us better understand the root causes of large falls in output and employment. For example, by comparing the neighborhoods that suffer the largest decline in consumption with neighborhoods that somehow escape recessionary consequences, we can hone in more precisely at the root cause of the problem. It is a bit like an epidemiologist comparing subjects that seem immune to a virus with those who quickly succumb to the epidemic. It is clear that such evidence is critical to treating and preventing future outbreaks.
As a result we are always on the lookout for measures of economic activity at finer geographical . . .
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