Imagine we dropped cash on every household in the country. Who would spend it? Who would save it? The answer to this question matters a great deal given the rise in inequality before the Great Recession. It also matters because the economy is likely to be demand-constrained during severe downturns, especially if the economy hits the zero-lower bound on nominal interest rates. If all households reacted to a cash windfall the same, then the distribution of income or wealth wouldn’t matter much for cyclical policy.
We argued in a previous post that lower income/wealth households have a much higher propensity to spend out of cash windfalls. Another study supporting this claim is the Jappelli and Pistaferri (2013) study forthcoming in the American Economic Journal: Macroeconomics. They used answers to a 2010 survey in Italy that asked consumers how much of an unexpected cash windfall they would spend. The first notable result is that the average marginal propensity to consume out of a cash windfall shock was 48%. So 48% of the cash windfall would be spent on average.
Even more interesting, the authors found that the MPC was much larger for households that had lower “cash on hand.” . . .
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What are the determinants of inequality? The first step in answering this question is defining exactly what we mean by inequality. A working paper by Chetty, Hendren, Kline, and Saez takes an interesting approach: it measures inequality based on the likelihood that a child born into a poor family will rise in the overall income distribution.
They call this measure “absolute upward mobility.” If absolute upward mobility is high, it means a child born into a poor family has a good chance of rising in the overall income distribution. If it is low, that means the poor child will likely be poor when she grows up.
The authors construct upward mobility for different cities. A city with a high score is considered more equal; a child born to a relatively poor family in the city has a good chance of rising in the income distribution. A city with a low score is more unequal, as a poor child is likely to remain poor as an adult.
The part of the study that interests us most is the correlation between their measure of inequality and other variables at the city level. In other words, what characterizes the most “unequal” cities?
It . . .
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Actions by the Federal Reserve are having strong effects on the price of stocks, bonds, and other financial assets in the economy. You can see this effect clearly on Fed meeting days, which are associated with large market movements.
A question we tackled in an earlier post was: what assets bear the most risk of surprise Fed announcements on the direction of monetary policy? We are following up on that post because we now have another observation: the March 19th, 2014 Fed meeting which was Janet Yellen’s first meeting as Fed Chair.
The meeting released information that the Fed planned on tightening sooner than markets expected — many attributed the market’s reaction to some combination of FOMC forecasts and Yellen’s statement that there would likely be 6 months between tapering ending and increases in the target Federal Funds Rate.
So we now have three days where we observe the Fed “surprising” markets with new information on the pace of tightening:
06/19/2013, or the “taper tantrum,” when the Fed announced that they wanted to begin the conversation of tapering QE purchases of long-term government bonds and mortgage-backed securities, a signal of tightening. 09/18/2013, or the “taper headfake,” when the . . .
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Excessive household debt was crucial in explaining the severity of the Great Recession. So where are we now? Have households strengthened their financial position since 2009? Are household balance sheets strong enough to prevent another massive pull back in spending if there are significant job losses?
To answer to these questions, we look at evidence from the 2012 National Financial Capability Study by FINRA. (We are grateful to Annamaria Lusardi, an expert on financial literacy, for pointing us to the data used in this post.) This survey is a representative sample of 25,000 individuals who were asked mostly qualitative questions about their finances. The survey was put into the field three years after the worst of the Great Recession.
The survey responses are shocking, and should put fear into all of us about the financial vulnerability of U.S. households.
The survey asks the following question: “How confident are you that you could come up with $2000 if an unexpected need arose within the next month?” Here are the answers:
Almost 40% of individuals in the United States either could not or probably could not come up with even $2000 if an unexpected need arose.
Another question asks: “Have you set . . .
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From the very nice blog post by Annie Lowrey:
“I asked Mr. Summers what was behind secular stagnation, and he said he was still thinking through all of its causes. But globalization, automation, income inequality and changes in corporate finance might be important factors, he said.
Income is now more concentrated in the hands of the rich. Those well-off households tend to save and invest higher proportions of their earnings than middle-class or low-income families do. That might mean, on aggregate, less spending and less demand across the economy for a given level of income.”
And here is a snippet from our post last week:
“But perhaps even more interesting are the implications for the secular stagnation hypothesis, which holds that we are in a long-run stagnating economy because of inadequate demand. Is it a coincidence that the secular stagnation hypothesis is being revived exactly when income inequality is accelerating? If a higher share of income goes to the wealthiest households who spend very little of it, then perhaps these two trends are closely related.”
Rising income and wealth inequality is not just about distributing the economic pie. It may very well have an effect on the size of the . . .
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Lenders are showing an increasing willingness to take risk, especially over the last two years. One measure of risk tolerance that economists typically track in corporate bond markets is the credit spread–the difference between interest rates for risky debt versus safe debt. But for the household sector, it is much more informative to track the quantity of credit extended.
The Federal Reserve Consumer Credit Statistical Release has shown strong growth in consumer debt (excluding mortgages). But this doesn’t necessarily mean that lenders are taking on more risk. They could be extending more credit to very high credit score individuals who are unlikely to default.
But the microeconomic evidence suggests that the opposite is true: much of the growth in auto and credit card debt is among individuals most prone to default. We can see this using zip code level data.
We split up zip codes in the United States into four groups based on their 2009 default rate. The groups each contain 25% of the population. The highest default rate zip codes tend to be those with the lowest credit scores. Lending in these areas is almost by definition more risky. The lowest default rate zip codes are the safest. . . .
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Our post three weeks ago on the weather and auto sales argued that bad winter weather was responsible for the slowdown in consumer spending. We used variation across states in January temperature and showed that states where January was very cold had poor auto sales, whereas auto sales were pretty strong in areas that had normal weather.
We concluded: “When it comes to durable goods such as cars, it is likely that purchases will increase sharply when the weather improves in the states that had extremely cold winters.”
We now have an out-of-sample test of our conclusion: March estimates of new auto sales are out, and they are higher than at any other point since 2007. They also beat consensus forecasts, which suggests that analysts didn’t fully account for the weather-related boost.
From calculatedriskblog.com: (SAAR stands for seasonally adjusted annualized rate)
Here is Bill McBride’s comment:
“Severe weather clearly impacted sales in January and February, and some of the increase in March was probably a bounce back due to better weather.” We hope this example illustrates the power of using microeconomic data to answer macroeconomic questions. . . .
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We are in our fourth week of blogging, and so far it has been a lot of fun! And a lot of work … we are learning new appreciation for people who do this for a living.
We thought it would be a good time to share some links and thoughts in response to comments on our posts.
Ryan Avent of the Economist criticized our post on inflation. On some accounts he is exactly correct–for example, the Fed targets headline inflation, not core. But we disagree with his views that the Fed could generate significant inflation in a liquidity trap if they wanted to. We will have more on this in a later post.
Paul Krugman correctly pointed out that he did not invent the term “liquidity trap.” Of course he is correct. But he also being too modest. Almost everyone now associates the term with him given his seminal 1998 paper. It is an absolute must-read. It also has to be the easiest paper to find on google. Just google “baaack”. Seriously. Try it.
The Grumpy Economist, aka John Cochrane, took a shot at interpreting our event study of asset returns on Fed “surprise” days. We were pretty agnostic, but John did . . .
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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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