Liberty Street Economics

Historical Echoes: Seeing through the Blackout of 1965 and Other Trials

2013-05-24T07:00:00-04:00

Amy Farber

In November 1965, the northeastern United States experienced a thirteen-hour blackout—the biggest in history to that date. Life magazine did a spread (p. 36) with some surreal and gloomy pictures of stranded, dazed, well-dressed passengers sleeping every which way all over New York City’s Grand Central Terminal. A book was written that same year by the staff of the New York Times, When the Lights Went Out, which describes in detail how people and various agencies in New York had to cope and make emergency adjustments.
On page 57, the book begins to describe the problems banks faced the day after the blackout and adjustments made by the New York Fed:

For bankers, trudging late to their offices in the city, the day was cause for weeping. They moved quickly to unsnarl the mountain of unsorted checks that piled up during the blackout. In a typical twenty-four-hour period, the city’s eight major money-market banks—plus the Federal Reserve Bank of New York—clear or sort some five million checks, representing a total dollar value of seven billion to ten billion. Practically all of this is done by computers that read magnetic characters imprinted on the checks. But these mazes of wires and circuits were cold and still Tuesday night. Auxiliary power systems, which kept lights burning at most of the banks and time locks operating on head-office vaults did not have the muscle—and, in some cases the proper current—to keep the check-clearing process going.

The banks faced a two-fold problem in their cleanup. First, was the purely mechanical business of catching up with the sorting. This phase they handled with little difficulty. Second was the more complicated problem of offsetting a vast—though undoubtedly temporary—increase of credit in the banking system that was certain to result from the check-clearing snarl. Normally, the Federal Reserve System gives credit to its member banks for checks that are in the process of clearing within two days after they have been deposited initially, whether or not they have been actually presented to the bank on which they have been drawn. Thus, a slowdown of the normal clearing process, such as occurred during the blackout, leads to a big increase in the so-called “float,” the credit granted on such uncleared or floating checks. The Federal Reserve left its two-day rule standing. But several other things were done to ease the clearance problem. The New York Clearing House Association, which handles the bulk of clearing within the city, scheduled three special times for its members to exchange checks. In addition, the Clearing House extended for a day, the deadline within which the Monday, Tuesday and Wednesday items could be presented. Usually these items have to be in by the next business day after they are deposited. For its part, the Federal Reserve Bank of New York said that in view of the difficulties the banks had encountered, it would not impose penalties on banks that failed to maintain their legally required reserves during the statement week that ended Wednesday.

In more recent times, the New York Fed has faced similar challenges not attributable to financial events. Recall the twenty-five-hour blackout of 1977 (depicted in Spike Lee’s film Summer of Sam), when “there was a stillness on Wall Street” (p. 6) as “Wall Street’s banks, brokerages, and stock and commodities exchanges shut down for a day” (p. 14). Then there was 1981’s four-hour blackout in lower Manhattan—when, thanks to the Bank’s backup generators—its computers failed for only twenty-seven minutes. After the 9/11 attacks, a Fed credit extension succeeded in ameliorating the financial effects of the shock. And during the Northeast Corridor blackout of 2003, the New York Fed’s payment systems and open market desk functioned normally.

The Bank was also impacted by October 2012’s Superstorm Sandy. It stayed open during and after the storm, but the storm did result in minor changes in open market operations, and many of the staff worked remotely.

Disclaimer
The views expressed in this post are those of the author and do not necessarily reflect the position of the Federal Reserve Bank of New York or the Federal Reserve System. Any errors or omissions are the responsibility of the author.

Amy Farber is a research librarian in the Federal Reserve Bank of New York’s Research and Statistics Group.

Just Released: The New York Fed Staff Forecast—May 2013

2013-05-23T10:30:00-04:00

Jonathan McCarthy and Richard Peach

As we did last year around this time, we’re presenting the New York Fed staff outlook for the U.S. economy to the Bank’s Economic Advisory Panel at today’s meeting. It provides an opportunity to get valuable feedback from leading economists in academia and the private sector on the staff forecast; such feedback helps us evaluate the assumptions and reasoning underlying our forecast and the risks to it. It’s important to open the staff forecast to periodic evaluation to inform the staff’s discussions with New York Fed President Bill Dudley about economic conditions. In the same spirit of inviting feedback, we’re sharing a short summary of our forecast; for more, see the material from the Panel’s meeting.

Summary
Here, we discuss the New York Fed staff forecast for real GDP growth, the unemployment rate, and inflation in 2013 and 2014.

The forecast anticipates moderate economic expansion over the next two years. Real GDP growth in 2013 is expected to be around 2½ percent, slightly below the rate in 2013:Q1 as well as below our year-ago projection of about 3 percent. A major factor behind the projected sluggishness over the rest of this year is fiscal policy. The combination of sequestration and the tax increases associated with the agreement to avert the “fiscal cliff” is anticipated to exert a significant drag on GDP growth of more than 1 percentage point. In 2014, the fiscal drag is expected to be smaller than in 2013. With a smaller fiscal drag, an expected further lessening of other headwinds—for example, the European sovereign debt crisis and the deleveraging of household balance sheets—and further improvement in labor market and financial conditions, we project real growth in 2014 of around 3¼ percent.

With the overall economy expected to grow rather sluggishly for the remainder of 2013, the staff projects the unemployment rate to be near the April level over the rest of the year, averaging about 7¼ percent in the fourth quarter. As stronger growth is anticipated for 2014, we project a more substantial decline in unemployment, to about 6½ percent in the fourth quarter, which would be roughly similar to its behavior at this stage of “long” economic expansions (as discussed in this post).

Overall inflation, after being fairly subdued in 2012 (1.6 percent for the PCE deflator and 1.9 percent for the CPI on a Q4/Q4 basis), is expected to slow a bit further in 2013, in part reflecting declines in oil and gasoline prices and the expectations of those prices embedded in futures market prices. Core inflation measures are also anticipated to moderate further in 2013. In 2014, the staff expects overall and core inflation measures to be higher, as they move toward long-term inflation expectations and the FOMC’s longer-run goal for inflation. The continued anchoring of longer-term inflation expectations and subdued compensation growth are consistent with inflation remaining near that objective.

Comparison with the Blue Chip and SPF Forecasts
We now compare the New York Fed staff forecast with the consensus forecasts from the May Blue Chip Economic Indicators and the second-quarter Survey of Professional Forecasters (SPF), both of which were published on May 10.

The staff forecast for real GDP growth in 2013 is roughly similar to the Blue Chip consensus forecast and the SPF median forecast, as most forecasts expect a significant amount of fiscal restraint. The staff forecast for 2014 is somewhat above the Blue Chip and SPF, which probably reflects our expectation of more receding of the headwinds and greater improvement in fundamentals than in the consensus forecast of the surveys. This pattern influences the patterns for the unemployment rate projections: the staff forecast is near the Blue Chip and SPF forecasts in 2013:Q4, but below both for 2014:Q4 as well as below the average of the bottom ten forecasts in the Blue Chip survey. The difference between the staff forecast and the consensus forecast from these surveys reflects our projected higher real GDP growth path and assessment that considerable slack remains in resource utilization. (The concluding post of our labor market series from 2012 provides estimates of how low the unemployment rate could go.)

The staff inflation forecast is somewhat below the Blue Chip consensus forecast (CPI inflation only) and the SPF median forecast (both CPI and PCE inflation) for 2013. The staff inflation projection for 2014 is modestly above the Blue Chip consensus and near the SPF median forecasts. The more rapid bounce-back in inflation in the staff forecast reflects the fact that we see a stronger influence on inflation from the pull of inflation expectations relative to the restraint from resource slack than do many private forecasters.

Risks to the Staff Forecast
Because modal forecasts most often don’t coincide with actual outcomes in retrospect, an important part of the staff analysis of the economic outlook is the assessment of risks. In a continued uncertain environment, the staff sees the risks to real economic activity as roughly balanced (after being skewed to the downside for much of the past year). A reduction in the risks associated with an intensification of the European sovereign debt crisis and the realization of greater fiscal restraint have contributed to this shift in the risk assessment. For inflation, the staff assesses that the risks are roughly balanced, similar to its assessment from a year ago. Compared with the means of the SPF respondents’ probability assessments for real GDP growth, unemployment, and inflation, the staff’s risk assessment displays considerably more uncertainty around its central forecast. Of course, New York Fed staff monitors developments to determine if such risks are beginning to materialize and adjusts the forecast accordingly.

Disclaimer
The views expressed in this post are those of the authors and do not necessarily reflect the position of the Federal Reserve Bank of New York or the Federal Reserve System. Any errors or omissions are the responsibility of the authors.

Jonathan McCarthy is a vice president in the Research and Statistics Group of the Federal Reserve Bank of New York.

Richard Peach is a senior vice president in the Group.

Foreign Borrowing in the Euro Area Periphery: The End Is Near

2013-05-22T07:00:00-04:00

Matthew Higgins and Thomas Klitgaard

Current account deficits in euro area periphery countries have now largely disappeared. This represents a substantial adjustment. Only two years ago, deficits stood at nearly 10 percent of GDP in Greece and Portugal and 5 percent in Spain and Italy (see chart below). This sharp narrowing means that spending has been brought in line with income, largely righting an imbalance that had left these countries dependent on heavy foreign borrowing. However, adjustment has come at a sizable cost to growth, with lower domestic spending only partly offset by higher export sales. Downward pressure on domestic spending should abate now that the periphery countries have been weaned from foreign borrowing. The risk, though, is that foreign creditors might demand that the countries pay down (rather than merely service) accumulated external debts, forcing them to reduce spending below incomes.

    A country’s current account balance measures how much it lends to or borrows from the rest of the world. A country where imports run ahead of exports is also spending more than it produces and must borrow abroad to make up the difference. Countries in the euro area periphery borrowed heavily from abroad during the several years leading up to the ongoing sovereign debt and banking crisis. As a result, they faced wrenching adjustment pressures when foreign investors became unwilling to extend new credit, even with the lure of sharply higher interest rates. These adjustment pressures forced changes in the balance between exports and imports, and equivalently in the balance between income and spending, without any help from an exchange rate channel because of the shared euro currency.

Exports and Imports
The loss in access to foreign credit meant that export revenues had to rise relative to import purchases. As seen in the table below, much of the recent adjustment in Italy, Spain, and Portugal has come from higher export revenues. From 2010 to 2012, nominal goods and services exports in these countries rose by 15 percent or more, roughly in line with Germany’s performance. Higher exports in these countries have helped support growth, softening ongoing economic downturns. Greece, in contrast, saw export sales rise only 8 percent over this two-year period; this increase provided only limited help easing the country’s severe downturn.

    A look at the geographic breakdown of goods exports shows that the lion’s share of the increase was from sales outside the euro area, particularly to markets in North America, Asia, and oil-exporting countries. Indeed, periphery countries’ sales outside the euro area rose by roughly 30 percent over the two-year period, compared with growth of less than 10 percent for sales inside the euro area.

    Discussions of external adjustment in the periphery often stress the need to improve external competitiveness. Unfortunately, competitiveness measures are at best imperfect. A common metric is based on unit labor costs, that is, on labor compensation growth relative to labor productivity. The intuition is simple: Higher wages erode competitiveness unless offset by productivity gains. However, this measure can be misleading in turbulent times. The shutdown of low-productivity firms boosts the economy’s average productivity, but does not mean that competitiveness has improved at surviving firms. Economy-wide unit labor cost measures can be particularly misleading when downturns are concentrated in low-productivity sectors such as construction. For example, much of the measured decline in Spain’s unit labor costs over the past several years owes to the country’s construction crash.

    The Organisation for Economic Co-operation and Development calculates a measure of competitiveness based on export performance adjusted for the strength of trading partners’ economies. If exports grow more rapidly than trading partners’ total imports do, a country gains export market share; if exports grow more slowly, a country loses it.

    By this metric, Spain and Portugal have done well in recent years, with exports growing 6 to 8 percent faster than trading partners’ total imports from 2010 to 2012. (These figures are based on preliminary estimates from late 2012.) This matches up well with Germany’s performance of exports, which grew 5 percent faster than trading partners’ total imports. Italian exports, in contrast, just kept pace with imports in destination markets. Meanwhile, Greece lost substantial market share, with exports growing 10 percent more slowly than destination market imports did over the period.

    Import weakness also contributed to the external adjustment in the periphery. This was particularly true in Greece and Portugal, where purchases of foreign goods and services dropped 11 percent and 4 percent, respectively, from 2010 to 2012. Italy’s imports rose modestly, while Spain’s rose 6 percent. In all cases, import growth fell far short of export growth, helping to reduce external borrowing.

    Ongoing economic downturns have been the key driver of import weakness in the periphery. Indeed, import spending remains well below levels prevailing before the Great Recession; import spending in Germany, in contrast, has moved well above it pre-recession level. One concern is that periphery import spending might pick up quite strongly once economies recover, bringing a new round of large current account deficits. A more positive take is that the difficult adjustments have brought imports in line with exports; going forward, the two can grow together without the need for new borrowing.

Spending and Income
A current account deficit means that domestic spending is greater than domestic income. An alternative but equivalent measure of this gap is the difference between domestic investment and domestic saving. (Note that a country’s public and private spending goes either to consumption or investment, while income not spent on current consumption is by definition saved. Thus, the gap between spending and income is equal to investment spending minus saving. This same accounting logic means that a country’s fiscal deficit is the difference between government saving and investment spending.) The table below shows that much of the narrowing in periphery current account deficits over the past two years has come from lower investment spending, with declines equal to almost 2 percentage points of GDP to more than 4 percentage points. Higher saving also contributed to reduced current account deficits, with small increases in Italy and Spain and more substantial increases in Greece and Portugal.

    The drop in investment spending, particularly outside the housing sector, could limit improvement in periphery competitiveness and growth prospects. As the next chart shows, this is especially the case since the recent drop in investment comes on the heels of an already large drop in 2008 and 2009—during the Great Recession but before the euro area crisis took hold. In contrast, investment spending in Germany has seen no pullback. Notably, economic recoveries are often led by investment spending. One risk for the periphery, then, is that stronger growth could see current account deficits widening as investment spending is again financed by external borrowing. On the positive side, stronger growth would also help boost public and private saving by limiting government budget deficits and raising corporate profits.

Conclusion
Periphery countries have suffered painful contractions weaning themselves from foreign borrowing. The hope now is that the restoration of external balance in these countries will set the stage for meaningful recoveries. The debt burden from past borrowing remains, however, and argues for caution about the periphery outlook. Much will depend on the behavior of external investors. A further pullback would force periphery countries to lower spending below incomes to generate current account surpluses to pay down (rather than merely service) accumulated external debts. This would suggest an even steeper drop in periphery consumption and living standards. Recent balance-of-payments data have been encouraging on that point, however, as private investors stopped pulling money out of periphery countries in the second half of 2012, apparently reassured by the European Central Bank’s Outright Monetary Transaction program that was announced last August.

Disclaimer
The views expressed in this post are those of the authors and do not necessarily reflect the position of the Federal Reserve Bank of New York or the Federal Reserve System. Any errors or omissions are the responsibility of the authors.

Matthew Higgins is a vice president in the Federal Reserve Bank of New York’s Emerging Markets and International Affairs Group.

Thomas Klitgaard is a vice president in the Bank’s Research and Statistics Group.

Do Big Cities Help College Graduates Find Better Jobs?

2013-05-20T07:00:00-04:00

Jaison R. Abel and Richard Deitz

Although the unemployment rate of workers with a college degree has remained well below average since the Great Recession, there is growing concern that college graduates are increasingly underemployed—that is, working in a job that does not require a college degree or the skills acquired through their chosen field of study. Our recent New York Fed staff report indicates that one important factor affecting the ability of workers to find jobs that match their skills is where they look for a job. In particular, we show that looking for a job in big cities, which have larger and thicker local labor markets (that is, bigger markets with many buyers and sellers), can give workers a better chance to find a job that fits their skills.

    Theoretical research in urban economics suggests that the large and thick local labor markets found in big cities can increase the likelihood of job matching and improve the quality of these matches. These benefits arise because big cities have more job openings and offer a wider variety of job opportunities that can potentially fit the skills of different workers. In addition, a larger and thicker local labor market makes it easier and less costly for workers to search for jobs.

    Our research focuses on whether college graduates located in big cities are better able to find jobs that match the skills they’ve acquired through their college education. We utilize newly available census data that identify both an individual’s level of education and, for college graduates, undergraduate college major. We construct two measures of what we call job matching for those with a bachelor’s degree. Our first measure, which we refer to as college degree matching, determines whether an undergraduate degree holder is working in an occupation that requires at least a bachelor’s degree. Our second measure, which we call college major matching, gauges the quality of a job match by identifying whether a person is working in a job that corresponds to that person’s undergraduate major. For example, consider a college graduate who majored in Communications. If this person worked as a public relations manager, an occupation that both requires a college degree and relates directly to a Communications major, we would classify this person as matching along both measures. By contrast, if this person worked as a retail salesperson, he or she would be classified as not matching along either measure.

    What percentage of college graduates match along these two dimensions? As the chart below shows, we find that close to two-thirds of college graduates in the labor force work in a job requiring a college degree, while a little more than a quarter work in a job that is directly related to their college major.

    In our staff report, we estimated the relationship between the size and density of a metropolitan area and the probability of job matching to examine whether big cities help college graduates match into better jobs. Estimating these relationships turns out to be quite challenging because biases may result if either the workers or job opportunities in big cities are systematically more or less conducive to job matching. To address this difficulty, our analysis controlled for a wide array of worker characteristics, such as age, gender, marital status, and college major. We also controlled for characteristics of the metropolitan area in which these individuals were located, such as industry structure and differences in economic performance.

    The chart below shows our estimates of the probability of job matching across the urban spectrum for each of our measures. The horizontal axis measures the size of a metropolitan area in terms of population and is marked in percentiles. (The correlation between metropolitan area size and density is quite high, so the results along either dimension are similar.) To put these percentiles into perspective, St. Cloud, Minnesota, which has a population of about 190,000, is at the 25th percentile; while Chicago, with a population of more than 9 million, lies above the 95th percentile.

    Our estimates suggest that both types of job matching are more likely in the larger and thicker local labor markets available in big cities, with job matching benefits concentrated at the top of the distribution. For example, the probability of a college graduate working in a job requiring a college degree increases from 61.1 percent to 64.5 percent when the population size of a metropolitan area increases from the 50th percentile to the 99.9th percentile. This implies that college degree matching is about 6 percent more likely in a place like New York City than in a place like Syracuse, New York. For the same movement across the urban spectrum, the probability of a college graduate working in a job related to his or her college major increases from 26.7 percent to 29.1 percent, implying that college major matching is about 9 percent more likely. Thus, the larger and thicker local labor markets of big cities appear to help college graduates find better jobs by increasing both the likelihood and the quality of a job match.

    Given the expense of college and the potential difficulty faced by graduates in finding a job that utilizes the skills obtained through higher education, improving the chances of finding a good job is clearly important. Our work suggests that living in a big city can help.

Disclaimer
The views expressed in this post are those of the authors and do not necessarily reflect the position of the Federal Reserve Bank of New York or the Federal Reserve System. Any errors or omissions are the responsibility of the authors.

Jaison R. Abel is a senior economist in the New York Fed’s Research and Statistics Group.

Richard Deitz is an assistant vice president in the Group.

Historical Echoes: The “Mississippi Bubble” – When One’s Back Could Be Rented Out as a Writing Desk

2013-05-17T07:00:00-04:00

Amy Farber

In 1720, the very same year that England was experiencing the “South Sea Bubble” (see our post), France was experiencing a bubble as well—the “Mississippi Bubble.” France’s bubble was brought on by government debt and the advice of the head of the country’s finance ministry, John Law (Scottish mathematician, convicted murderer [a duel], gambler, and financial genius), to create paper money and a bank and to invest in his Mississippi Company. (Indeed, at the height of the trading frenzy for shares of stock in Law’s company, a hunchbacked man rented his back out as a desk in the “Street of Speculators” and earned a considerable sum.) Over a three-year period (1718-20), things went very wrong and too much money was printed (the regent’s decision, not Law’s). The text accompanying this portrait of Law describes him as an:

18th century Scotsman, credited by some historians as being “the father of inflation.” Law turned gambling IOUs into “gold counters,” then state debts into paper money, and finally sold all France down the river on the “Mississippi Bubble.”

     In a 2008 Financial Times article, “How the French invented subprime in 1719” (available with subscription), James MacDonald compares the Mississippi Bubble with the 2008 financial crisis. He cites six major similarities, including significant public debt, a charismatic financial wizard (Law), and the power of securitization.

     The full story of the bubble is complex, but an easy way to understand it is to watch this clever and humorous 1978 animation (9.75 min.). Or, if you want to see what the players in this drama really looked like, another clever and wryly humorous video (4.75 min.) tells essentially the same story with a backdrop of portraits and landscapes and a narrator who falls just short of conveying an authentic upper-class British accent. A third video (12.5 min.) has wavering audio but is otherwise excellent. The first video has Law escaping France disguised as a man with an enormous mustache and a beret, but with his Scottish bagpipes visible; the third has him escaping dressed as a woman.

     Two American authors have addressed the topic: Washington Irving, of Rip Van Winkle and Legend of Sleepy Hollow fame, wrote The Great Mississippi Bubble: a Time of Unexampled Prosperity (or read the less authentic-looking online version) and included it in his 1825 book of essays, Crayon Papers. Could “unexampled prosperity” mean something similar to “irrational exuberance”? The essay is rich in detail regarding events and character traits of the players. Dallas Federal Reserve Bank President Richard Fisher has even referred to the Irving essay in 2008 and 2010 speeches. (Follow the links and note the wonderful Irving quotations included.) From the 2008 speech:

Irving had never heard of a subprime mortgage or an Alt-A loan, an SIV, a CDS, a CLO or a CMO. But he understood booms propelled by greed and tomfoolery and busts born of fear, and that these underlying forces are deeply rooted in human DNA.

The American writer Emerson Hough wrote a 1902 best-selling historical novel, The Mississippi Bubble (this chapter is part of the serialized newspaper version), with a curious subtitle: “how the star of good fortune rose and set and rose again, by a woman’s grace, for one John Law of Lauriston.” What woman? Is it Lady Catharine Knollys, whom Law married? And what does Hough mean by “grace”? Possibly, the woman forgave Law for being a failure.

Chauncey Haines composed a “March Two Step” titled “the Mississippi Bubble.” (Listen to it here.) The sheet music, also published in 1902, includes an advertisement for the Hough novel, which quotes a review from the Boston Journal describing it as “one of the truly great romances.”

Disclaimer
The views expressed in this post are those of the author and do not necessarily reflect the position of the Federal Reserve Bank of New York or the Federal Reserve System. Any errors or omissions are the responsibility of the author.

Amy Farber is a research librarian in the Federal Reserve Bank of New York’s Research and Statistics Group.

My Two (Per)cents: How Are American Workers Dealing with the Payroll Tax Hike?

2013-05-15T07:00:00-04:00

Basit Zafar, Max Livingston, and Wilbert van der Klaauw

The payroll tax cut, which was in place during all of 2011 and 2012, reduced Social Security and Medicare taxes withheld from workers’ paychecks by 2 percent. This tax cut affected nearly 155 million workers in the United States, and put an additional $1,000 a year in the pocket of an average household earning $50,000. As part of the “fiscal cliff” negotiations, Congress allowed the 2011-12 payroll tax cut to expire at the end of 2012, and the higher income that workers had grown accustomed to was gone. In this post, we explore the implications of the payroll tax increase for U.S. workers.

The impact of such a tax hike depends on two factors. One, how did U.S. workers use the extra funds in their paychecks over the last two years? And two, how do workers plan to respond to shrinking paychecks? With regard to the first factor, in a recent working paper and an earlier blog post, we present survey evidence showing that the tax cut significantly boosted consumer spending, with workers reporting that they spent an average of 36 percent of the additional funds from the tax cut. This spending rate is at the higher end of the estimates of how much people have spent out of other tax cuts over the last decade, and is arguably a consequence of how the tax cut was designed—with disaggregated additions to workers’ paychecks instead of a one-time lump-sum transfer. We also found that workers used nearly 40 percent of the tax cut funds to pay down debt.

To understand how the tax increase is affecting U.S. consumers, we conducted an online survey in February 2013. We surveyed 370 individuals through the RAND Corporation’s American Life Panel, 305 of whom were working at the time and had also worked at least part of 2012.

Did Individuals Notice the Tax Increase?
We began by asking respondents whether they noticed any change in their take-home pay since December 2012. Fifty-five percent reported a decrease, thirteen percent saw an increase, and the remainder reported no change. Nearly two-thirds of higher-income individuals (those with annual household income exceeding $75,000) in our sample reported a decrease in their take-home pay, compared with only half of lower-income individuals. Since many factors may affect take-home pay, such as changes in income, insurance premiums, or state taxes, we asked about specific factors that led to such changes. Sixty-three percent of respondents reported that an increase in federal tax withholdings played a role. Moreover, when asked specifically about the change in the Social Security tax, two-thirds of our respondents were aware of the increase.

How Are Consumers Planning to Cover the Tax Increase?
We asked respondents to indicate the share (out of 100 percent) of the lost income that they would cover by cutting spending, increasing debt, and reducing saving. The vast majority (79 percent) planned on mostly cutting consumption, with around 20 percent mostly cutting savings and 2 percent mostly increasing debt (borrowing).

We also asked how survey respondents had used the extra income from the tax cut during 2011-12. This allows us to analyze how plans to cover the decrease in income starting in January 2013 relate to what individuals had actually done with the increase in income during the last two years. For this comparison, we divide respondents into three groups—those who reported they had mostly consumed (that is, spent) the increased income, those who had mostly saved it, and those who had mostly used it to pay off debt.

Based on that classification, the chart below shows what these respondents are planning to do now that their income has decreased. We see a disproportionate shift toward primarily reducing spending. Regardless of what consumers reported doing with the increase in take-home pay over the last two years, a majority report that they will cut back on spending. For example, 86.2 percent of respondents who mostly consumed the extra funds from the tax cut plan to mostly cut spending now, and 80.0 percent of those who used the extra funds mostly for paying off debt now plan to mostly reduce spending. The chart also shows a (weak) positive relationship between past usage of the extra funds and intended plans to cover the tax increase, with those who saved the extra income being more likely to mostly cut back on saving now (31.8 percent, versus 14.0 percent for their counterparts), and those who mostly paid off debt somewhat more likely to mostly increase their debt now (4.3 percent, versus 0.76 percent for their counterparts).

How Will the Tax Hike Affect Consumption Spending by Richer and Poorer Households?
Policymakers are particularly interested in the marginal effect of a tax hike on spending—for every dollar that consumers lose, how much less will they consume? This is known as the marginal propensity to consume (MPC). We can estimate the MPC for the tax cut of 2011-12 and for the tax increase in 2013 (see table below). The average MPC for the tax cut was 0.38; that is, on average consumers reported that they had spent 38 percent of the additional income. For the average household making $50,000 (which received an additional $1,000 per year during the two-year tax cut), this means an extra $380 of annual spending during 2011-12. This estimate is almost identical to the one we found in a survey that we conducted in early 2011 with a different set of individuals. However, the planned average MPC for the tax hike in our sample is -0.72; that is, consumers are planning to cut spending on average by 72 percent of the tax increase. For the average household making $50,000, this would mean cutting annual spending by around $720. In response to the tax hike, we find that on average individuals plan to cut saving by 25 percent of the tax hike and increase debt by 3 percent, whereas when the tax cut was in place they saved 29 percent of it and used 33 percent to pay down debt. The figures in the table indicate that consumers are likely to cut spending sharply and to slow the paydown of debt.

There is substantial heterogeneity in the response of different demographic groups to the tax hike. For example, lower-income individuals (annual household income of $75,000 or less) report a sharper planned reduction in spending (by 77 percent of the income loss, versus 64 percent for higher-income individuals). Similarly, higher-income individuals plan to reduce their saving by a higher proportion than lower-income individuals do. This reported behavior is interesting because it’s very different from how the two groups reported using the tax cut: the average MPC for low-income respondents for the tax cut was in fact lower than that for higher-income respondents (0.33, versus 0.46), and their propensity to pay down debt was substantially higher (0.41, versus 0.20). In a companion survey, we also found that low-income respondents are more heavily indebted than their counterparts and expect to face binding liquidity constraints in the future. This heterogeneous behavior is then quite sensible, since low-income individuals expect that they cannot really borrow much more, and so use a large portion of any additional income to pay down their existing debt, and plan to cover any reduction in income by reducing their spending.

Will Survey Respondents Follow Up on Their Plans?
It’s still early in the year, and as more months of macroeconomic data become available we’ll be able to see whether the responses we observe in our survey are reflected in the macroeconomy. However, because of several confounding factors (such as the fiscal cliff and budget negotiations), it will not be straightforward to assess the impact of the tax hike on consumers from any time series analysis of such aggregate measures. Therefore, use of survey data is probably the best approach to evaluating the impact of tax changes. The micro evidence reported here is based on consumers answering these questions meaningfully; for the 45 percent of respondents who don’t see a decrease in their take-home pay, this may not be straightforward. However, while consumers may not be able to accurately predict their actual behavior, two previous studies have shown that planned behavior elicited in surveys tends to be a good predictor of actual behavior.

Overall, our analysis suggests that the payroll tax cut during 2011-12 led to a substantial increase in consumer spending and facilitated the consumer deleveraging process. Based on consumers’ responses to our recent survey, expiration of the tax cuts is likely to lead to a substantial reduction in spending as well as contribute to a slowdown or possibly a reversal in the paydown of consumer debt. These effects are also likely to be heterogeneous, with groups that are more credit and liquidity constrained more likely to be adversely affected. Such nuances may be lost in the aggregate macroeconomic statistics, but they’re important for policymakers to consider as they debate fiscal policy.

Disclaimer
The views expressed in this post are those of the authors and do not necessarily reflect the position of the Federal Reserve Bank of New York or the Federal Reserve System. Any errors or omissions are the responsibility of the authors.

Basit Zafar is an economist in the Federal Reserve Bank of New York’s Research and Statistics Group.

Max Livingston is a research analyst in the Research and Statistics Group.

Wilbert van der Klaauw is a senior vice president in the Group.

Just Released: The Geography of Student Debt

2013-05-14T11:15:00-04:00

Andrew Haughwout, Donghoon Lee, Wilbert van der Klaauw, and Joelle Scally

This morning, the New York Fed released its Quarterly Report on Household Debt and Credit for 2013 Q1. The report uses the FRBNY Consumer Credit Panel to show that outstanding household debt declined approximately $110 billion (about 1 percent) from the previous quarter. The drop was due in large part to a reduction in housing-related debt and credit card balances. Meanwhile, delinquency rates for each form of consumer debt declined, with the overall ninety-plus day delinquency rate dropping from 6.3 percent to 6.0 percent.

One of the unique aspects of the FRBNY Consumer Credit Panel, which is itself based on Equifax credit data, is the detail we obtain for each kind of household debt. This quarter, we have taken advantage of the geographic information available in the data set and are introducing a set of maps of our student loan data, which indicate regional variation in several dimensions of student debt. They depict:

Student loan borrowers as a share of the population. The population with active student loan debts, or “SL borrowers,” as a share of the population with a credit record varies substantially over space. For example, in Hawaii, less than 12 percent of people with a credit report have student debt, while in the District of Columbia over 25 percent do.

Student loan balances per SL borrower. Student indebtedness is significant for SL borrowers in virtually all states. Educational indebtedness per SL borrower ranges from a low of just under $21,000 in Wyoming to a high of over $28,000 in Maryland. Again, Washington, D.C., stands out: the average SL borrower there owes over $40,000. In general, we find SL-borrower debt levels are highest in California and along the Atlantic and Gulf coasts.

Percent of balance ninety-plus days delinquent. Delinquency rates show a distinct regional pattern, with states in the south and southwest having generally higher rates than those in the north. The lowest delinquency rate is South Dakota, at just over 6.5 percent, while the highest is in West Virginia, at nearly 18 percent.

Student loan indebtedness and delinquency continue to generate intense interest and we look forward to sharing data and perspectives that help define the scope of this important issue.

Disclaimer
The views expressed in this post are those of the authors and do not necessarily reflect the position of the Federal Reserve Bank of New York or the Federal Reserve System. Any errors or omissions are the responsibility of the authors.

Andrew Haughwout is a vice president in the Research and Statistics Group.

Donghoon Lee is a senior economist in the Group.

Joelle Scally is an economic analyst in the Group.

Wilbert van der Klaauw is a senior vice president in the Group.

Capital Controls, Currency Wars, and International Cooperation

2013-05-13T07:00:00-04:00

Bianca De Paoli and Anna Lipinska

The debate over whether there’s a case for limiting capital flows has intensified recently—both in media and academic forums. The traditional view has generally been that the voluntary exchange of funds across borders makes everyone better off: Borrowers have access to cheaper credit while lenders enjoy higher returns on their investments. But, as a recent article in The Economist highlights, this view has been revisited. In this post, we review arguments on this issue and discuss how our recent research contributes to the debate.

     In a recent staff discussion note and Vox article, International Monetary Fund economists outline several arguments that could justify the use of capital controls by policymakers. One is that capital controls can be a valuable tool to avoid currency overvaluation and protect the export sector at times when the sector is crucial for economic growth. A similar line of thought has been put forward by Gianluca Benigno and Luca Fornaro in a recent research paper. The authors show how reserve accumulation may be justified in these circumstances—shedding light on the much-discussed Chinese-export-led growth strategy.

     Capital controls may also be a useful form of macroprudential policy—or a policy designed to ameliorate the macroeconomic consequences of financial crises. The general argument is that limiting the flow of funds between countries can lower the probability of financial crises or, at least, reduce their costs (for formal arguments see, for example, Bianchi [2011], Bianchi and Mendoza [2011], Korinek [2010], and Benigno et al. [2011]). In a world of credit booms and busts, it might be beneficial to restrict borrowing (or the inflow of foreign capital) in good times to avoid a sudden loss in access to credit in bad times. The avoidance of bubbles that can result in uncompetitive economies has also been put forward as a justification for the use of capital flow restrictions (see Korinek [2011]).

     But capital flow restrictions can be harmful if they’re driven by unfounded vested interests of individual countries (or lobby groups within these countries). Fears that capital controls, as well as monetary policy and direct intervention in the currency market, could lead to damaging “currency wars” are now at the heart of the global policy debate (as exemplified by media coverage of recent developments in Japan, Latin America, and Europe).

     In a recent New York Fed staff report, we study the pros and cons of interventions in the international flow of capital. Using a theoretical framework, we highlight that such interventions can indeed help improve risk sharing across borders when international financial markets aren’t developed enough to ensure an efficient exchange of funds across countries. We find that, if the management of international capital flows is coordinated among countries, it can help these countries better share the burden of adverse shocks. For example, after a fall in productivity in Country A, a subsidy to international borrowing from Country B could help households in Country A share the burden of the shock with households in Country B.

     But our analysis finds that countries, when acting independently, tend to use capital controls with different aims. In particular, countries are inclined to impose restrictions on the flow of capital to strategically influence exchange rates. The theoretical analysis shows how countries in some circumstances benefit from having an appreciated exchange rate, as it improves domestic purchasing power vis-á-vis the rest of the world, while in others a depreciation may be a valuable way to promote domestic goods and enable domestic households to enjoy higher income and consumption.

     Our findings suggest that if interventions in capital are set by individual countries in an uncoordinated fashion, then they would go in the opposite direction than those designed to enhance global risk sharing. Recall that risk sharing can be improved with a subsidy to international borrowing from countries that face adverse productivity shocks—that is, international borrowing can help support domestic consumption after a fall in domestic output. However, policymakers in these countries actually prefer to tax, rather than subsidize, international borrowing. Taxing capital inflows decreases the appreciation in the exchange rate and therefore protects demand for domestic goods. This policy of capital inflow taxation reduces the decline in domestic output at the expense of a larger divergence in consumption across countries.

     Moreover, when countries simultaneously engage in such interventions in the international flow of capital, not only global but individual welfare is adversely affected. Our results thus suggest that uncoordinated policy actions could indeed lead to harmful capital control wars. The conclusion and policy recommendations that emerge from our work suggest that there’s a clear case for international coordination in the use of capital controls.

Disclaimer
The views expressed in this post are those of the authors and do not necessarily reflect the position of the Federal Reserve Bank of New York or the Federal Reserve System. Any errors or omissions are the responsibility of the authors.

Bianca De Paoli is a senior economist in the Federal Reserve Bank of New York’s Research and Statistics Group.

Anna Lipinska is an economist at the Board of Governors of the Federal Reserve System.

Historical Echoes: What Do the New York Fed and Grand Central Terminal Have in Common?

2013-05-10T07:00:00-04:00

Amy Farber

These two fine old entities—the New York Fed and Grand Central Terminal—have at least three things in common: they are both about 100 years old, they both feature beautiful vaulting in some part of their structure by the same “designer” masons, and they both go very deep into the ground.

     100 years old: The Federal Reserve System will celebrate its 100th anniversary in December 2013, and the New York Fed will celebrate along with it (although the New York Fed and its building are technically a little younger). Likewise, Grand Central Terminal celebrated its 100th birthday this year, in February. The media covered this event and the history of this edifice nicely with pieces from CNN, the Wall Street Journal (click tabs for video and slideshow) and The Daily Beast (an almost poetic 100 facts), as well as a great video from CBSNewsOnline. In 1968, the terminal was condemned to be demolished but found a champion in Jacqueline Kennedy Onassis, who spearheaded a movement to save it from demolition. She said, “If you destroy your past, something in people dies.”

     Designer vaulting artists: Both the New York Fed and Grand Central Terminal have beautiful vaulting in the Catalan style by the Guastavinos (father and son)—“vaulting” as in ceilings, not “vault.” We are not talking about the well-known gold vault here, folks. (Not yet, anyway.) At the New York Fed, the vaulted ceiling (see also older picture) can be seen on the first floor by visitors to the Fed’s museum. In Grand Central Terminal, this vaulting may be seen in the famous Oyster Bar (try to watch the ceiling, not the food!) and the attached Whispering Gallery (tour guide Dan Brucker does an amusing demonstration of how to communicate using the Whispering Gallery, but it sounds like he’s whispering none too softly). The Guastavinos, father Rafael from Spain (1842-1908) and son Rafael Jr. (1872–1950), have become the object of intense study by an enthusiastic professor of engineering at MIT: John Ochsendorf. In a two-part lecture (Professor Ochsendorf starts speaking about ten minutes in; here is Part 2), he describes the history of the Guastavinos, the many buildings in the United States that have examples of their vaulting, and why the structures are so strong (still a mystery—he is having his engineering students study and build them). Other examples of this special vaulting that are “closer to home” include the Ellis Island Registry Hall, the Queensboro Bridge Market, the City Hall Subway Station (never actually used for subways), the Cathedral of Saint John the Divine (bottom picture), the Prospect Park Tennis Pavilion (seen at the lower part of this blog entry), and the Bronx Zoo elephant house. A traveling exhibit called “Palaces for the People,” began at the Boston Public Library’s McKim Building, the first major American public commission for Guastavino Sr. The exhibit (along with a scale model of a Boston Public Library ceiling vault constructed by contemporary masons) will find its way to the Museum of the City of New York in fall 2013. NPR has a recent story about this exhibit and John Ochsendorf's role in the revival of interest in the Guastavinos.

     Deep into the ground: The New York Fed and Grand Central Terminal both have subbasements of considerable depth, but for very different reasons. The Fed’s basement is 50 feet below sea level and 80 feet (five stories) below street level and is home to the famous gold vault. The gold, much of which belongs to foreign governments, arrived around the time of World War II when countries wanted a safe place to store their gold reserves. Other account holders who store gold at the New York Fed are the U.S. government, central banks (but not the Federal Reserve itself), and official international organizations. All this gold needs to be stored atop something that is strong enough to support its great weight: bedrock. As for the basement at Grand Central Terminal, according to the same amusing tour guide already mentioned, one of the reasons for Grand Central’s extra deep subbasement (160 feet down!) was so that FDR could have his own private train (he called it FDR’s “emergency egress out of New York City”). There is also quite a lot of machinery down there.

Disclaimer
The views expressed in this post are those of the author and do not necessarily reflect the position of the Federal Reserve Bank of New York or the Federal Reserve System. Any errors or omissions are the responsibility of the author.

Amy Farber is a research librarian in the Federal Reserve Bank of New York’s Research and Statistics Group.

Are Stocks Cheap? A Review of the Evidence

2013-05-08T07:00:00-04:00

Fernando Duarte and Carlo Rosa

We surveyed banks, we combed the academic literature, we asked economists at central banks. It turns out that most of their models predict that we will enjoy historically high excess returns for the S&P 500 for the next five years. But how do they reach this conclusion? Why is it that the equity premium is so high? And more importantly: Can we trust their models?

The equity risk premium is the expected future return of stocks minus the risk-free rate over some investment horizon. Because we don’t directly observe market expectations of future returns, we need a way to figure them out indirectly. That’s where the models come in. In this post, we analyze twenty-nine of the most popular and widely used models to compute the equity risk premium over the last fifty years. They include surveys, dividend-discount models, cross-sectional regressions, and time-series regressions, which together use more than thirty different variables as predictors, ranging from price-dividend ratios to inflation. Our calculations rely on real-time information to avoid any look-ahead bias. So, to compute the equity risk premium in, say, January 1970, we only use data that was available in December 1969.

Let’s now take a look at the facts. The chart below shows the weighted average of the twenty-nine models for the one-month-ahead equity risk premium, with the weights selected so that this single measure explains as much of the variability across models as possible (for the geeks: it is the first principal component). The value of 5.4 percent for December 2012 is about as high as it’s ever been. The previous two peaks correspond to November 1974 and January 2009. Those were dicey times. By the end of 1974, we had just experienced the collapse of the Bretton Woods system and had a terrible case of stagflation. January 2009 is fresher in our memory. Following the collapse of Lehman Brothers and the upheaval in financial markets, the economy had just shed almost 600,000 jobs in one month and was in its deepest recession since the 1930s. It is difficult to argue that we’re living in rosy times, but we are surely in better shape now than then.

The next chart shows a comparison between those two episodes and today. For 1974 and 2009, the green and red lines show that the equity risk premium was high at the one-month horizon, but was decreasing at longer and longer horizons. Market expectations were that at a four-year horizon the equity risk premium would return to its usual level (the black line displays the average levels over the last fifty years). In contrast, the blue line shows that the equity risk premium today is high irrespective of investment horizon.

Why is the equity premium so high right now? And why is it high at all horizons? There are two possible reasons: low discount rates (that is, low Treasury yields) and/or high current or future expected dividends. We can figure out which factor is more important by comparing the twenty-nine models with one another. This strategy works because some models emphasize changes in dividends, while others emphasize changes in risk-free rates. We find that the equity risk premium is high mainly due to exceptionally low Treasury yields at all foreseeable horizons. In contrast, the current level of dividends is roughly at its historical average and future dividends are expected to grow only modestly above average in the coming years.

In the next chart we show, in an admittedly crude way, the impact that low Treasury yields have on the equity risk premium. The blue and black lines reproduce the lines from the previous chart: the blue is today’s equity risk premium at different horizons and the black is the average over the last fifty years. The new purple line is a counterfactual: it shows what the equity premium would be today if nominal Treasury yields were at their average historical levels instead of their current low levels. The figure makes clear that exceptionally low yields are more than enough to justify a risk premium that is highly elevated by historical standards.

But none of this analysis matters if excess returns are unpredictable because the equity risk premium is all about expected returns. So…are returns predictable? The jury is still out on this one, and the debate among academics and practitioners is alive and well. The simplest predictive method is to assume that future returns will be equal to the average of all past returns. It turns out that it is remarkably tricky to improve upon this simple method. However, with so many models at hand, we couldn’t help but ask if any of them can, in fact, do better.

The table below gives the extra returns that investors could have earned by using the models instead of the historical mean to predict future returns. For investment horizons of one month, one year, and five years, we pick the best model in each of the four classes we consider together with the weighted average of all twenty-nine models. We compute these numbers by assuming that investors can allocate their wealth in stocks or bonds, and that they are not too risk-averse (for the geeks again, we solved a Merton portfolio problem in real time assuming that the coefficient of relative risk aversion is equal to one). The table shows positive extra returns for most of the models, especially at long horizons.

At face value, this result means that the models are actually helpful in forecasting returns. However, we should keep in mind some of the limitations of our analysis. First, we have not shown confidence intervals or error bars. In practice, those are quite large, so even if we could have earned extra returns by using the models, it may have been solely due to luck. Second, we have selected models that have performed well in the past, so there is some selection bias. And of course, past performance is no guarantee of future performance.

Disclaimer
The views expressed in this post are those of the authors and do not necessarily reflect the position of the Federal Reserve Bank of New York or the Federal Reserve System. Any errors or omissions are the responsibility of the authors.

Fernando Duarte is an economist in the Research and Statistics Group of the Federal Reserve Bank of New York.

Carlo Rosa is an economist in the Markets Group of the Federal Reserve Bank of New York.