macroblog

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The Atlanta Fed's macroblog provides commentary and analysis on economic topics including monetary policy, macroeconomic developments, inflation, labor economics, and financial issues.

Authors for macroblog are Dave Altig, John Robertson, and other Atlanta Fed economists and researchers.


January 03, 2017


Following the Overseas Money

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Though the holiday season has come to a close, the forthcoming policy season may bring with it serious debate about a holiday of a different sort: a tax "holiday" that would allow corporations to repatriate accumulated profits currently held overseas.

As with many of the policy proposals that the new Congress and administration will consider, our primary interest here at the Atlanta Fed is to assess how the policy, if enacted, will likely affect our own economic forecasts and the environment in which future monetary policy will be made.

The best starting point is usually to just determine the facts as we know them. In this case, the question is what we know about the nature of the foreign earnings of U.S. corporations.

U.S. corporations' undistributed foreign earnings have been accumulating rapidly for more than a decade, as companies have expanded their foreign operations. The income earned by U.S. domestic corporations' foreign subsidiaries is generally not subject to U.S. tax until the income is distributed to the parent corporation in the United States. According to a November 25, 2016, Wall Street Journal article, over the past decade total undistributed foreign earnings of U.S. companies have risen from about $500 billion to more than $2.5 trillion, a sum equal to nearly 14 percent of U.S. gross domestic product.

Though it is not uncommon to refer to these sums as "a pile of cash," this sort of terminology is perhaps a bit misleading. For one, some of that "pile of cash" is not cash at all. According to a report from the Joint Committee on Taxation , "the undistributed earnings may include more than just cash holdings as corporations may have reinvested their earnings in their business operations, such as by building or improving a factory, by purchasing equipment, or by making expenditures on research and experimentation."

More important, the portion of foreign earnings that hasn't been invested in business operations is not necessarily "trapped" or "stashed" overseas. In fact, much of it is in the United States, already working (albeit while untaxed) for the U.S. economy.

U.S. companies do not routinely disclose what their foreign subsidiaries do with undistributed earnings. To better understand the situation, in 2011 the Senate Permanent Subcommittee on Investigations conducted a survey of 27 large U.S. multinationals. Survey results showed that those companies' foreign subsidiaries held nearly half of their earnings in U.S. dollars, including U.S. bank deposits and Treasury and corporate securities (see the table).

A couple of years later, a June 13, 2013, Wall Street Journal report also found that Google, EMC, and Microsoft kept more than three-quarters of their foreign subsidiaries' cash in U.S. dollars or dollar-denominated securities.

So it turns out, then, that a large fraction of undistributed foreign profits is held at U.S. banks or invested in U.S. securities. Even dollar deposits held by U.S. companies in tax havens such as Ireland, the Cayman Islands, and Singapore ultimately live here in the United States because foreign banks typically hold their dollar deposits in so-called correspondent banks in the United States.

In fact, U.S. dollar balances always stay in the United States, even if they are controlled from outside the country. Those dollars in turn are available to be lent out to U.S. businesses. And when U.S. companies' foreign subsidiaries invest their cash holdings in U.S. Treasury bonds, they are in effect lending to the U.S. government.

Foreign subsidiaries of U.S. companies choose to invest their profits in dollar-denominated assets for much the same reasons that make the U.S. dollar an international reserve currency:

  • the dollar maintains its value in terms of goods and services (the dollar is a global unit of account);
  • U.S. financial markets are deep and liquid, providing ample investment choices; and
  • U.S. government obligations are considered virtually risk-free, making them a safe haven during times of global stress and risk aversion.

Companies also have operational reasons for keeping surplus cash in U.S. dollars. Most of the international trade invoicing is done in dollars, so U.S. companies' foreign subsidiaries hold dollars to pay suppliers and deal with customers. Also, nonfinancial companies prefer to avoid foreign exchange risk and volatility. Finally, holding most of the funds, which are not invested in foreign operations, in dollars mitigates potential accounting losses, since U.S. companies are required to report in dollars on their consolidated financial statements.

None of this is to say that a tax holiday for U.S. corporations on undistributed foreign profits is a good or bad policy choice. But even without passing judgment, it may fall to macroeconomic forecasters to estimate the policy impact on business investment, job growth, and the like. Understanding the facts underlying the targeted funds is a reasonable starting point for answering the harder questions that may come.

January 3, 2017 | Permalink | Comments (2)

December 16, 2016


The Impact of Extraordinary Policy on Interest and Foreign Exchange Rates

Central banks in the developed countries have adopted a variety of extraordinary measures since the financial crisis, including large-scale asset purchases and very low (and in some cases negative) policy rates in an effort to boost economic activity. The Atlanta Fed recently hosted a workshop titled "The Impact of Extraordinary Monetary Policy on the Financial Sector," which discussed these measures. This macroblog post discusses the highlights of three papers related to the impact of such policy on interest rates and foreign exchange rates. A companion Notes from the Vault reviews papers that examined how those policies may have affected financial institutions, including their lending.

Prior to the crisis, central banks targeted short-term interest rates as a way of influencing the rest of the yield curve, which in turn affected aggregate demand. However, as short-term rates approached zero, central banks' ability to further cut their target rate diminished. As a substitute, the central banks of many developed countries (including the Federal Reserve, the European Central Bank, and the Bank of Japan) began to undertake large-scale purchases of bonds in an attempt to influence longer-term rates.

Central bank asset purchases appear to have had some beneficial effect, but exactly how these purchases influenced rates has remained an open question. One of the leading hypotheses is that the purchases did not have any direct effect, but rather served as a signal that the central bank was committed to maintaining very low short-term rates for an extended period. A second hypothesis is that central bank purchases of longer-dated obligations resulted in long-term investors bidding up the price of remaining longer-maturity government and private debt.

The second hypothesis was tested in a paper  by Federal Reserve Board economists Jeffrey Huther, Jane Ihrig, Elizabeth Klee, Alexander Boote and Richard Sambasivam. Their starting point was the view that a "neutral" policy would have the Fed's System Open Market Account (SOMA) closely match the distribution of the stock of outstanding Treasury securities. In their statistical tests, they find support for the hypothesis that deviations from this neutrality should influence market rates. In particular, they find that the term premium in longer-term rates declines significantly as the duration of the SOMA portfolio grows relative to that of the stock of outstanding Treasury debt.

The central banks' large-scale asset purchases not only took longer-dated assets out of the economy, but they also forced banks to increase their holdings of reserves. Large central banks now pay interest on reserves (or in some cases charge interest on reserve holdings) at an overnight rate that the central bank can change at any time. As a result, these purchases can significantly reduce the average duration (or maturity) of a bank's portfolio below what the banks found optimal given the term structure that existed prior to the purchases. Jens H. E. Christensen from the Federal Reserve Bank of San Francisco and Signe Krogstrup from the International Monetary Fund have a paper  in which they hypothesize that banks respond to this shortening of duration by bidding up the price of longer-dated securities (thereby reducing their yield) to restore optimality.

The difficulty with testing Christensen and Krogstrup's hypothesis is that in most cases central banks were expanding bank reserves by buying longer-dated securities, thus making it difficult to disentangle their respective effects. However, in 2011 the Swiss National Bank undertook a series of three policy moves designed to produce a large, rapid increase in bank reserves. Importantly, these moves were an attempt to counter perceived overvaluation of the Swiss franc and did not involve the purchase of longer-dated bonds. In a follow-up empirical paper , Christensen and Krogstrup exploit this unique policy setting to test whether Swiss bond rates declined in response to the increase in reserves. They find that the third and largest of these increases in reserves was associated with a statistically and economically significant fall in term premia, implying that the increase did lower longer-term rates.

Although developed countries' monetary policy has focused on their domestic economies, these policies can have significant spillovers into emerging countries. Large changes in the rates of return available in developed countries can lead investors to shift funds into and out of emerging countries, causing potentially undesirable large swings in the foreign exchange rate of these emerging countries. Developing countries' central banks may try to counteract these swings via intervention in the foreign exchange market, but the effectiveness of sterilized intervention is the subject of some debate. (Sterilized intervention occurs when the central bank buys or sells foreign currency, but then takes offsetting measures to prevent these from changing bank reserves.)

Once again, determining whether exchange rates are influenced and, if so, by what mechanism can be econometrically difficult. Marcos Chamon from the International Monetary Fund, Márcio Garcia from PUC-Rio, and Laura Souza from Itaú Unibanco examine the efforts of the Brazilian Central Bank to stabilize the Brazilian real in the aftermath of the so-called "taper tantrum." The taper tantrum is the name given to the sharp jump in U.S. bond yields and the foreign exchange rate value of the U.S. dollar after the May 23, 2013, statement by Board Chair Ben Bernanke that the Federal Reserve would slow (or taper) the rate at which it was purchasing Treasury bonds (see a brief essay by Christopher J. Neely). Chamon, Garcia, and Souza's paper  takes advantage of the fact that Brazil preannounced its intervention policy, which allows them to separate the impact of the announcement to intervene from the intervention itself. They find that the Brazilian Central Bank's intervention was effective in strengthening the value of the real relative to a basket of comparable currencies.

All three of the studies faced the difficult challenge in linking specific central bank actions to policy outcomes, and each tackled the challenge in innovative ways. The evidence provided by the studies suggests that central banks can use extraordinary policies to influence interest and foreign exchange rates.

December 16, 2016 in Exchange Rates and the Dollar, Interest Rates, Monetary Policy | Permalink | Comments (2)

December 05, 2016


Using Judgment in Forecasting: Does It Matter?

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Many professional forecasters use statistical models when making their near-term projections for real gross domestic product (GDP) growth. A 2013 special survey on the forecasting methods of the Survey of Professional Forecasters  found that 18 out of 21 respondents featured a statistical model prominently in their current-quarter economic projections. Nevertheless, there is fairly compelling evidence that many professional forecasters incorporate judgment in their forecasts of the first estimate of real GDP growth for a quarter—even when much of the source data used to construct the GDP estimate are available.

In the October 2016 Wall Street Journal Economic Forecasting Survey (WSJ), the most common panelist projection for annualized third-quarter real GDP growth was 2.5 percent, and the second most common one was 3.0 percent. The first digit after the decimal point, or tenths digit, of these two numbers are "5" and "0." Of the 58 individual forecasts of third-quarter growth in the survey, 21 had a tenths digit of "0" or "5," a total that is almost twice as large as we would expect if all tenths digits were equally likely to be submitted.

This pattern isn't unique to the most recent quarter's GDP forecast. The following chart shows the historical frequency of the tenths digit in past WSJ surveys for first estimates of real GDP growth over the period from the first quarter of 2003 to the third quarter of 2016, made about three weeks before the release.

Almost 40 percent of these 2,390 forecasts have a tenths digit of "0" or "5." In contrast, the historical distribution of published first estimates of real GDP growth from the fourth quarter of 1991 to the third quarter of 2016 and real gross national product (the most common measure of U.S. production in an earlier era) growth from the third quarter of 1965 to the third quarter of 1991 has a tenths digit of either "0" or "5" only 18 percent of the time. The historical Atlanta Fed's GDPNow forecasts have a "0" or a "5" tenths digit only 15 percent of the time.

More formally, one easily can reject the hypothesis at the 1 percent significance level that the tenths digit of the WSJ panelist forecasts are either uniformly distributed or follow the Benford distribution for tenths digits after rounding to the nearest tenth (see this paper by economists Stefan Gunnel and Karl-Heinz Todter, who found similar relative frequencies of "0s" and "5s" in professional forecasts of German GDP growth and consumer price index inflation).

If we assume that near-term GDP growth forecasts with a tenths digit of "0" or "5" typically involve more judgment than forecasts with another tenths digit, a natural question is whether these more judgmental forecasts are less accurate than others. Of the 2,390 WSJ growth forecasts mentioned above, the ones with a tenths digit of "0" or "5" (after rounding to the nearest tenth) had an average error of 0.786 percentage points without regard to sign, and the others had an average error of 0.743 percentage points. These accuracy metrics are not statistically different at even the 10 percent significance level. Moreover, because of the panel nature of WSJ forecasts, we can measure how often a forecaster has a tenths digit of "0" or "5" (after rounding). Of the 44 panelists who submitted at least 30 three-week-ahead GDP forecasts during the period of the first quarter of 2003 through the third quarter of 2016, the correlation of the panelists "0" or "5" tenth digit frequency and their average error without regard to sign is only 0.13 and not significantly different from 0.

Although at least some professional forecasters appear to make judgmental adjustments to their near-term GDP projections, the evidence presented here does not suggest it comes, on average, at the cost of accuracy.

December 5, 2016 | Permalink | Comments (1)

November 28, 2016


Does Lower Pay Mean Smaller Raises?

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I've been asked a few questions about the relative wage growth of low-wage versus high-wage individuals that are measured by the Atlanta Fed's Wage Growth Tracker. Do individuals who were relatively lower (or higher) paid also tend to experience lower (or higher) wage growth? If they do, then wage inequality would increase pretty rapidly as low-wage earners get left further and further behind.

The short answer is no. As chart 1 shows, median wage growth is highest for the workers whose pay was relatively low (in the bottom 25 percent of the wage distribution), and lowest for those who were the highest-paid (in the top 25 percent of the wage distribution). Median wage growth is reasonably similar for those whose pay was in the middle 50 percent of the wage distribution.

To understand what's going on, let's look at the construction of a Wage Growth Tracker sample. In simple terms, a person's wage is observed in one month, and then again 12 months later. But relatively low-wage workers are less likely to remain employed (and hence more likely not to have a wage when observed a second time) than other workers. Almost half of workers who are not employed 12 months later come from the lowest 25 percent of the wage distribution. For workers in a relatively low-wage job, a greater share who might otherwise have experienced a declining wage left their employment, resulting in a larger share of wage increases among those who remained employed.

In contrast, relatively high wage earners in the Wage Growth Tracker sample have a remarkably low median wage growth—zero in recent years. They also have a much greater chance of experiencing a wage decline than other workers (see chart 2).

However, getting a complete picture for high-wage individuals in the Current Population Survey is limited by the fact that observations are top-coded (or censored to preserve identifiable individuals' anonymity). For example, weekly earnings higher than $2,885 are currently simply recorded as $2,885. If a person in this circumstance gets a wage increase, it will still be reported as just $2,885, which would make it seem as if wages didn't increase, even if they did.

Top-coding itself has only a relatively small effect on the median wage growth for the whole sample because top-coded earnings aren't that common. But they are a reasonably large share of the upper part of the wage distribution, which makes the median wage growth pretty unrepresentative for people who were relatively high wage earners. In principle, one could try to surmount this problem by estimating the earnings for top-coded workers, but my experience has been that doing so is likely to add more noise than insight.

What about examining a worker's current wage instead of their prior wage? Is the median wage growth also higher for workers who are currently in the lowest part of the wage distribution? No. In fact, they are more likely than others not to have received a pay raise or even to have had the rate of pay reduced. Conversely, someone who is currently in the upper part of the wage distribution is more likely to have received a larger pay raise than other workers. Some workers move up the wage distribution—but not all.

The bottom line is that the point of reference matters a lot when looking at the tails of the wage distribution, and top-coding limits the ability to learn much about the wage growth of high wage earners. But for the middle part of the wage distribution, it doesn't matter so much. The median wage growth of the overall sample is pretty representative of the typical wage growth experience of workers in the heart the wage distribution.

November 28, 2016 | Permalink | Comments (0)

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