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The FRED® Blog

Demystifying the trade balance Why a trade balance deficit isn't necessarily a sign of a poor economy

The trade balance is the amount of exports minus imports. While the number generally reported in the media pertains to goods (that is, physical gizmos that are counted as they cross the border), there’s also trade in services such as software, consulting, and tourism. The graph above shows the trade deficit in red (a negative number) for the United States. The current account—that is, the change in asset holdings of the U.S. with the rest of the world—is shown in blue. Indeed, the current account comprises the trade deficit (if we import more, we owe more) plus transfers of income across boundaries. The latter can be important for some developing countries that have a lot of foreign workers sending remittances back to their families. For more-developed economies, dividend and interest incomes are more important.

In the system of National Income and Product Accounts (NIPA, a nation’s economic accounting), a trade deficit automatically implies that the country is saving less than it’s investing. Another way to understand this is that the rest of the world is investing in that country, thereby contributing to its production capacity. This accounting pertains to the capital account, which is always the counterpart to the current account: Current account plus capital account always equals zero, which is quite apparent in the graph below.

Is it bad to have a trade account deficit? If this means that your economy is booming and local production cannot keep up with demand, then no. If it implies that there is a current account deficit and, hence, foreigners are investing in your country, then also no. If this means that you can have more investment without having to save more, because the rest of the world is picking up the slack, no again. If you are worried that in the future dividends will flow abroad, then yes. But that will happen only if your economy is in good shape in the first place and will be able to afford paying such dividends.

How these graphs were created: For both graphs, look for and the first series to the graph; use the “Edit Graph” tab to add the second line; then apply a formula to adjust the units so that both lines match. Note that the trade balance needs to be multiplied by 12, as it’s expressed in monthly numbers.

Suggested by Christian Zimmermann.

View on FRED, series used in this post: BOPGSTB, NETFI, RWLBCAQ027S

Let’s do the Twist Did the FOMC succeed in lowering long-term rates?

In September 2011, the Federal Reserve’s Open Market Committee (FOMC) announced a “Maturity Extension Program” involving the purchase of $400 billion of longer-term U.S. Treasury securities and liquidation of an equivalent amount of shorter-term securities over a 9-month period. In June 2012, the FOMC extended the program through the end of 2012, a period when the Committee purchased an additional $267 million of longer-term securities and liquidated a similar amount of short-term securities. The purpose of the program was to put downward pressure on longer-term interest rates without increasing the total size of the Fed’s securities portfolio. The program was popularly referred to as “Operation Twist,” reflecting the Committee’s intention to lower long-term interest rates relative to short-term rates and thus twist the yield curve.

The graph illustrates the program’s impact on the maturity composition of the Fed’s portfolio of U.S. Treasury securities. At its inception in September 2011, the portfolio consisted of approximately equal shares of Treasury securities with maturities of 5 years or less and securities with maturities of more than 5 years. When the program ended in December 2012, the shares of short-term and long-term securities in the Fed’s portfolio were about 22 percent and 78 percent, respectively. The Fed began to reverse the Maturity Extension Program in 2013 mainly by buying shorter-term securities as longer-term securities matured. By mid-2015, the Fed held roughly equal amounts of Treasury securities maturing in 5 years or less and longer-term securities. Since then, the Fed has continued to adjust its portfolio toward shorter-term securities, while maintaining a constant total portfolio size.

Did the Maturity Extension Program succeed in twisting the yield curve? As the graph shows, the spread between the yields on 10-year and 3-month Treasury securities fell some 75 basis points during the months when the program was in effect and then rose after the program had concluded. Longer-term yields had also declined by about 75 basis points during the 2 months before the program began, however, perhaps because market participants anticipated the program before it was formally announced. Of course, without formal analysis, it is impossible to say whether the program caused the yield curve to twist, but the behavior of the curve was consistent with the program’s intent.

How this graph was created: Search for “U.S. Treasury securities held by the Federal Reserve” and “All Maturities” should be near the top. From “Edit Graph,” add the matching series for “Maturing within 15 days,” “Maturing in 16 to 90 days,” “Maturing in 91 days to 1 year,” and “Maturing in over 1 year to 5 years” to Line 1. Apply the formula (b+c+d+e)/a. From “Edit Graph,” use the add line feature to search for the “All Maturities” series again and create Line 2. Add to that the matching series for “Maturing in over 5 years to 10 years” and “Maturing in over 10 years.” Apply the formula (b+c)/a. From “Edit Graph,” use the add line feature to add “10-Year Constant Maturity Minus 3-Month Treasury Constant Maturity” as Line 3. Modify the frequency to “Weekly, Ending Wednesday.” Under the “Format” tab, select “Right” for the “Y-Axis position” for Line 3. Finally, click on “10Y” next to the date selection pane to show the last 10 years of data.

Suggested by David Wheelock.

View on FRED, series used in this post: T10Y3M, TREAS10Y, TREAS15, TREAS1590, TREAS1T5, TREAS5T10, TREAS911Y, TREAST

Slow…labor…productivity…growth How does productivity affect our future?

Since the beginning of 2011, growth in real output in the nonfarm business sector has been slow, averaging just 2.7% percent. And most of the economic growth has been driven by increases in labor inputs and not by increases in labor productivity. The graph shows real output growth (green line) decomposed into growth in labor input (red line) and growth in labor productivity (blue line), where productivity is measured as real output per hour. Given that the output growth rates are only slightly different from—either a little above or a little below—growth in hours, the majority of growth in output has come from increases in hours instead of increases in labor productivity. Labor productivity growth averaged 0.7% over this period, accounting for just 27% percent of real GDP growth.

Labor productivity growth amounts to the average growth of how much goods and services each individual can consume and, thus, is the driving force behind increases in the standard of living. More importantly, a small difference in labor productivity growth leads to a dramatic difference in the standard of living over the long run. For example, if labor productivity growth held steady at 2%, which is the rate seen in the expansion from 2001 to 2007, the living standard would double in 35 years. If labor productivity continues to grow at 0.7%, it would take 99 years to double the standard of living.

How this graph was created: After searching for “nonfarm business sector,” select “Index 2009=100” for the three series and click on “Add to Graph.” Then go to the “Edit Graph” section and select “Percent Change from Year Ago” under “Units.” Finally, click on “Copy to all” and change the starting date to “2011-01-01.”

Suggested by Yili Chien and Paul Morris.

View on FRED, series used in this post: HOANBS, OPHNFB, OUTNFB

The economics of greeting cards Can Valentine's Day dashes to the store help the industry?

Tomorrow is Valentine’s Day, a great opportunity to spread some love…of economics…and study the greeting card industry. A search for greeting cards yields 90 results! One of the more interesting is shown in the graph. Unfortunately, we have only annual data; a higher frequency could have helped us see how much the industry depends specifically on Valentine’s Day. But we can see that the revenue of this industry appears to be trending down. So, is the reason for the decline the rise of the Internet and mobile apps? Maybe. FRED data can’t reveal all the mysteries of the universe, so we leave it to the reader to explore. Yours truly, FRED.

How this graph was created: As mentioned, a search for “greeting cards” yields quite a few results. Click on the series you want to graph.

Suggested by Christian Zimmermann.

View on FRED, series used in this post: REVEF511191ALLEST

Can businesses get loans these days? A look at the state of commercial lending by banks

Businesses often need money and one way they get it is through commercial loans from banks. We gauge this environment by graphing the total mass of loans banks have made to commercial entities. Of course, the fact that the current mass of loans is the highest it’s ever been is hardly surprising: The economy is growing and loan levels aren’t adjusted for inflation, so this measure is bound to keep increasing. For this reason, we’ll deflate this indicator with a proxy for the size of the economy: nominal GDP (i.e., not real GDP).

Now we have a better way to compare commercial lending conditions over time. Things are still looking rather good right now, but consider these two caveats: 1. Businesses have other ways to finance—say, through private loans or issuing bonds or stock in equity markets. These options may change over time, which probably explains why there was an upward trend in the early decades, when this sort of financing was building up. 2. This reported loan mass shows only the results of supply and demand, but not how difficult it is to get a loan (actual supply) or how much businesses want these loans (actual demand).

To evaluate loan supply conditions, the Federal Reserve conducts a survey of loan officers, asking them whether they tightened loans conditions and for whom. The graph below shows this, with higher values indicating tighter lending conditions. It’s very clear how recessions have led bank officers to be more careful with their lending. But right now, conditions seem to be pretty good.

How these graphs were created: Top graph: Search for “commercial loans.” Middle graph: First, use the top graph. Then go to the “Edit Graph” panel to add “GDP” to the first line, making sure to use the nominal measure. Then apply formula a/b. Bottom graph: Start afresh and search for “loan standards”; select the two series you want and click on “Add to Graph.”

Suggested by Christian Zimmermann.

View on FRED, series used in this post: BUSLOANS, DRTSCILM, DRTSCIS, GDP


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