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May 23, 2016
Can Two Wrongs Make a Right?
In a recent macroblog post, I showed that forecasts from the Atlanta Fed's real gross domestic product (GDP) nowcasting model—GDPNow—have been about as accurate a forecast of the U.S. Bureau of Economic Analysis's (BEA) first estimate of real GDP growth as the consensus from the Wall Street Journal Economic Forecasting Survey. Because GDPNow essentially uses a "bean-counting" approach that tallies the forecasts of the various main subcomponents of GDP, the total GDP forecast error can be broken up into the forecast errors coming from each piece of GDP. For most of the subcomponents of GDP, the contribution to total GDP growth is approximately its real growth rate multiplied by its expenditure share of nominal GDP (the exact formulas are in the working paper for GDPNow). The following chart shows the subcomponent contributions to the GDPNow forecast errors since the third quarter of 2011. (I want to note that the forecast errors are based on the final GDPNow forecasts formed before the BEA's first estimates of GDP are released.)
The forecast errors for the subcomponents can sometimes be quite large. For example, for the fourth quarter of 2013, GDPNow underestimated the combined contributions of net exports and inventory investment by nearly 2 percentage points. However, these misses were nearly offset by overestimates of the other contributions to growth (consumption, business and residential fixed investment, and government spending).
The pattern of large but largely offsetting GDP subcomponent errors has been attributed to the work of a fictional "Saint Offset," as former Fed Governor Laurence Meyer noted in a 1998 speech. Unfortunately, "Saint Offset" doesn't always come to the forecaster's aid. For example, in the fourth quarter of 2011, GDPNow predicted 5.2 percent growth—well above the BEA's first estimate of 2.8 percent—and the subcomponent errors were predominantly on the high side.
A closer look at the chart also reveals that GDPNow has had a tendency to overestimate the contribution of business fixed investment to growth and underestimate the growth contribution of inventory investment. Although these subcomponent biases have nearly offset one another on average, we really don't want to have to rely on "Saint Offset." We would like the subcomponent forecasts to be reasonably accurate because the subcomponents of GDP are of interest in their own right.Have the subcomponent biases been a unique feature of GDPNow forecasts? It appears not. Both the Survey of Professional of Forecasters (SPF), conducted about 11 weeks prior to the first GDP release, and Blue Chip Economic Indicators, conducted as close as three weeks prior to the first release, provide consensus forecasts for some GDP subcomponents. The following table provides an average forecast error (as a measure of bias) and average absolute forecast error (as a measure of accuracy) of the subcomponent growth contributions for the two surveys and comparably timed GDPNow forecasts.
We see that the biases in GDPNow's subcomponents have been fairly similar to those in the two surveys. For example, all three sources have underestimated the average inventory investment contribution to growth by fairly similar magnitudes.
The relative accuracy of GDPNow's subcomponent and overall GDP forecasts has also been similar to the accuracy of the two surveys. "Saint Offset" has helped all three forecasters; the standard errors of the real GDP forecasts are 20 percent to 40 percent lower than they would be if the forecast errors of the subcomponents did not cancel each other out.
Finally, notice that some GDP subcomponents appear to be much more difficult to forecast than others. For instance, the bias and accuracy metrics for consumer spending are smaller than they are for inventory investment. This differential is not really that surprising, because more monthly source data are available prior to the first GDP release for consumer spending than for inventory investment.
Can we take any comfort in knowing that private forecasters have mirrored the biases in GDPNow's subcomponent forecasts? An optimistic interpretation is that the string of one-sided misses are the result of bad luck—an atypical sequence of shocks that neither GDPNow nor private forecasters could account for. A more troubling interpretation is that there have been structural changes in the economy that neither GDPNow nor the consensus of private forecasters have identified. Irrespective of the reason, though, optimal forecasts should be unbiased. If biases in some of the subcomponents continue, then forecasters will need to look for a robust way to eliminate them.
May 16, 2016
GDPNow and Then
Real-time forecasts from the Atlanta Fed’s real gross domestic product (GDP) nowcasting model—GDPNow—have been regularly updated since August 2011 (the model was introduced online in July 2014). So we now have a nearly five-year history to allow us to evaluate the accuracy of the model’s forecasts. The chart below shows forecasts from GDPNow (red dots) alongside actual first estimates of real GDP growth (gray bars) from the U.S. Bureau of Economic Analysis (BEA). For comparison, the blue dots in the chart are the consensus (average) forecasts from the Wall Street Journal Economic Forecasting Survey (WSJ Survey).
The initial estimate of real GDP growth for a particular quarter is usually published at the end of the subsequent month. The WSJ Survey consensus forecasts plotted above were released about two weeks before these estimates. To maintain comparable timing with the WSJ Survey, the GDPNow forecasts shown in the chart are those constructed on or before the 12th day of the same month.
Occasionally, there has been relatively large disagreement between GDPNow and the WSJ consensus. For example, GDPNow predicted that GDP growth would be below 0.5 percent for five out of 19 quarters between 2011 and 2016, and the lowest WSJ Survey consensus forecast for any of those quarters was 1.3 percent. Nonetheless, the average accuracy of the GDPNow and WSJ Survey consensus forecasts has been similar: the average absolute forecast error (average error without regard to sign) for GDPNow was 0.56 versus 0.60 for the WSJ Survey consensus.
Studies have shown that the average or median of a set of professional forecasts tends to be more accurate than an individual forecaster (see, for example, here and here). Therefore, it’s surprising that GDPNow has been about as accurate on average as the WSJ Survey consensus. To see just how surprising this result is, I used the fact that the WSJ Survey provides both the names and forecasts of its respondents. From these, I constructed a panel dataset with each respondent’s absolute forecast errors and their absolute disagreement (difference) from the consensus forecast. Using a standard econometric technique (a two-way fixed-effects regression), we can then calculate each panelist’s average absolute GDP forecast error and their average absolute disagreement with the WSJ Survey consensus. These points are shown in the scatterplot below.
There is a clear inverse relationship between average forecast accuracy and average disagreement with the WSJ Survey consensus. However, GDPNow’s accuracy and disagreement statistics do not fit the general pattern. GDPNow (the orange diamond in the chart) was more accurate on average than all but six out of 49 WSJ panelists, though at the same time it differed from the consensus by more on average than all but four of the panelists.
What should one infer from all of this? Differences in forecasting method could be part of the explanation. GDPNow differs from many other approaches to nowcasting in that it is essentially a “bean counting” exercise. It doesn’t use historical correlations of GDP with other economic series in the way that commonly used dynamic factor models do, and it also doesn’t incorporate judgmental adjustments (see here for more discussion of these differences). During a period when the economy has been giving very mixed signals, perhaps it doesn’t come as a surprise that GDPNow’s forecasts occasionally deviate quite a bit from the WSJ Survey consensus. Time will tell if GDPNow continues to perform at least as well as the consensus.
July 01, 2015
Far Away Yet Close to Home: Discussing the Global Economy's Effects
In case you needed any motivation to take interest in the outcome of ongoing negotiations between the Greek government and its international creditors, this excerpt from the Wall Street Journal ought to do it:
Global growth is really important. We are all connected through the financial markets, through foreign-exchange markets," Fed governor Jerome Powell said last week in an interview with The Wall Street Journal. "If global growth weakens, or remains weak, and we get into a trend of that, then yes, that will be a big headwind for the United States economy."
Last week, I participated in the latest edition of our webcast, ECONversations, devoted to the theme "what to make of the first quarter?" (The webcast can be found here). The conversation revolved around the Atlanta Fed staff's view of why 2015 began with such a whimper and ideas on prospects for improvement through the balance of the year.
Not surprisingly, the international context loomed large. Between June 2014 and March 2015, the U.S. dollar appreciated by about 14 percent against a broad basket of currencies, and by about 20 percent against major currencies. The dollar has roughly remained in those neighborhoods since. As to the gross domestic product (GDP) side of the story, arithmetically net exports subtracted almost 2 percentage points off first quarter growth.
A key assumption of our current outlook is that the international environment (including the exchange rate) will stabilize, and smoother sailing without the "big headwind" referenced by Governor Powell is ahead.
That assumption generated some discussion (in the Q&A part of the webcast, and via online questions). With some paraphrasing, here are a few of the comments and questions we received, and my best attempt to respond:
Q: You associate the prior appreciation in the dollar with a several percentage point subtraction from growth in the first quarter. This seems quite large in context of available research on the elasticity of the trade balance to movements in the foreign exchange value of the dollar.
A: In the webcast, I did loosely refer to the trade effect on first quarter GDP as a "dollar effect." But the questioner—Barclay's head of U.S. economics research, Michael Gapen— is completely correct in asserting that standard estimates wouldn't support exchange-rate appreciation as an all-encompassing explanation for the big first quarter trade deficit. Our own estimates imply that four quarters after an exchange rate shock that raises the real broad-dollar index by 10 percentage points, real GDP is about one-half a percentage point lower than it would have been without the shock. This impact is roughly the same as most standard estimates (including Barclay's).
Some analyses might imply a larger GDP impact for the pure dollar effect, but any reasonable estimate would leave a fair amount of the first quarter net export decline unexplained. In any event, exchange-rate movements are both cause and effect, which brings us to:
Q: I have a question regarding the impact of the U.S. dollar (USD) in the economy. We often learn that changes in the real exchange rate affect the economy with a lag. Take Japan, for instance. It had a substantial depreciation in Japanese yen (JPY) real exchange rate but with very minimal impact on Japan's trade performance so far. What makes you so confident that the strong USD has had a strong impact in the U.S. economy in such a short period of time? Wouldn't the negative contribution from net exports more likely be linked to delays in West Coast ports and the sharp slowdown in Asian economies (China, in particular)?
A: Yes, in our analysis (and most we know of), the effects of exchange rates occur with a lag. And, as noted above, only a fraction of the decline in net exports by the end of 2014 and into the beginning of this year can be plausibly attributed to dollar appreciation. But we do think those effects are there, and they are continuing (to a lesser extent) in the current quarter.
Of course, changes in the value of the currency are an effect of other developments as well as a cause of changes in exports, GDP, and the like. All else is not typically equal, which often makes simple correlations (or, in the Japanese case, the lack thereof) difficult to interpret.
One of those "not equal" things could well have been the port delays. We don't have a firm estimate of how the backlogs might have affected the first quarter GDP statistic. If the impact was indeed material, we should see some reversal in the second and third quarters now that things are apparently getting back to normal. We'll count that as an upside risk.
And looking forward?
Q: Shouldn't the economic crisis in Greece dampen the demand for American exports and decrease growth well into the fourth quarter?
A: The good news is that current forecasts suggest 2015 euro-area growth will exceed its 2014 pace (according to the World Bank). In fact, the 2015 forecast strengthened over the course of this year despite the ongoing uncertainty associated with the Greek crisis. By most accounts, Canadian economic activity this year is expected to follow a trajectory similar to the United States (in like a lamb, out like something less lambish).
Mexico, as well, is expected to show more growth this year than last, despite some softening of the outlook since the beginning of the year. Put those three together (expanding the euro area to the entire European Union), and you have the anticipation of some improvement in countries accounting for somewhere in the neighborhood of 55 percent of our export markets.
The bad news is the ongoing uncertainty associated with the Greek crisis. Further, the outlook in emerging economies is growing more downbeat. These realities—a continuing impact of prior dollar appreciation and the fact that better foreign growth still does not equate to great growth—has us reluctant to think that net exports will be a big positive number in this year's GDP calculations. That reluctance notwithstanding, for now we are writing in a smaller trade deficit over the course of the year than what we saw in the first quarter.
If you want to go into the July 4 holiday on a somewhat optimistic note, I'll note that our GDPNow estimates for the second quarter have strengthened substantially with the arrival of more recent data—notably including signals of a much lower trade deficit effect than in the first quarter and today's positive news on manufacturing and nonresidential construction. Those data may not be enough to generate full confidence in our forecast for a much better second half of 2015, but they are moving in the right direction.
April 20, 2015
What the Weather Wrought
At Seeking Alpha, Joseph Calhoun responds to Friday's macroblog post, which noted that, over the course of the recovery, first-quarter gross domestic product (GDP) growth has on average been slower than the quarterly performance over the balance of the year:
... the "between-the-lines" meaning of the Atlanta post is to ignore all of this since this weakness is being portrayed as "just like last year" a statistical problem in the one measure that economists think most represents the economy.
Rest assured, we try pretty hard to not place any messages "between the lines," and the penultimate sentence of Friday's piece was meant to strike the appropriately tentative tone: "As for the rest of the year, we'll have to wait and see."
We do believe, like others, that weather was at play in the subpar performance of 2015's debut. Severe weather, in February in particular, can explain some of the first-quarter weakness, but "some" is the operative qualifier.
As the following chart illustrates, relative to a baseline forecast without weather effects—proxied with National Oceanic and Atmospheric Administration measures of heating and cooling days through March—we estimate that the severity of the winter subtracted about 0.6 percentage point from GDP growth:
Two points: First, to the extent that weather is a culprit in subpar first-quarter growth, we should see some payback in the current quarter (as, dare we say, we saw last year).
Second, we (the Atlanta Fed staff) did not begin the year projecting first-quarter growth at a mere 1.8 percent annualized (as the benchmark forecast in the experiment illustrated above implies). That rate of growth is a considerable step-down from our forecast at the beginning of the year, forced by the realities of the incoming data (as captured, for example, by GDPNow estimates). That gap leaves plenty of explaining left to do.
Observable developments can plausibly explain much of the forecast miss—mainly the initial, somewhat ambiguous, impact of energy price declines and the rapid, steep appreciation of the dollar, which has clearly been associated with a suppression of export activity. Our current view is that, as energy prices and the exchange rate stabilize, we will see a return to growth patterns that are closer to 3 percent than 1 percent.
We are not, however, selling the position that it is wise to be completely sanguine about the rest of the year. Here is the official word from Dennis Lockhart, president of the Atlanta Fed (subscription required for full citation):
I lean to a later lift-off date [for the federal funds rate target]. To the extent you want to simplify that debate to June versus September, I lean to September. I don't think, given the progress we have made, the state of the economy, and my confidence that the first quarter was an aberration, that it would be horribly damaging to go a little earlier versus later. But my preference would be to wait for more confirming evidence that we are on the track we think we are on and we expect to carry us back to inflation toward target.
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April 17, 2015
Déjà Vu All Over Again
In a recent interview, Fed Vice Chairman Stanley Fischer said, “The first quarter was poor. That seems to be a new seasonal pattern. It's been that way for about four of the last five years.”
The picture below illustrates the vice chair's sentiment. Output in the first quarter has grown at a paltry 0.6 percent during the past five years, compared to a 2.9 percent average during the remaining three quarters of the year.
What's causing this pattern? Well, it could be we just get really unlucky at the same time every year. Or, it could be a more technical problem with seasonal adjustment after the Great Recession (this paper by Jonathan Wright covers the topic using payroll data). It also seems likely that we can just blame the weather (see this Wall Street Journal blog post).
Whatever the reason for the first-quarter weakness, it appears to be happening again. Our current quarterly tracking estimate—GDPNow—has first-quarter growth hovering just above zero. As for the rest of the year, we'll have to wait and see. We of course hope it follows the postrecession pattern.
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April 02, 2015
What Seems to Be Holding Back Labor Productivity Growth, and Why It Matters
The Atlanta Fed recently released its online Annual Report. In his video introduction to the report, President Dennis Lockhart explained that the economic growth we have experienced in recent years has been driven much more by growth in hours worked (primarily due to employment growth) than by growth in the output produced per hour worked (so-called average labor productivity). For example, over the past three years, business sector output growth averaged close to 3 percent a year. Labor productivity growth accounted for only about 0.75 percentage point of these output gains. The rest was due primarily to growth in employment.
The recent performance of labor productivity stands in stark contrast to historical experience. Business sector labor productivity growth averaged 1.4 percent over the past 10 years. This is well below the labor productivity gains of 3 percent a year experienced during the information technology productivity boom from the mid-1990s through the mid-2000s.
John Fernald and collaborators at the San Francisco Fed have decomposed labor productivity growth into some economically relevant components. The decomposition can be used to provide some insight into why labor productivity growth has been so low recently. The four factors in the decomposition are:
- Changes in the composition of the workforce (labor quality), weighted by labor's share of income
- Changes in the amount and type of capital per hour that workers have to use (capital deepening), weighted by capital's share of income
- Changes in the cyclical intensity of utilization of labor and capital resources (utilization)
- Everything else—all the drivers of labor productivity growth that are not embodied in the other factors. This component is often called total factor productivity.
The chart below displays the decomposition of labor productivity for various time periods. The bar at the far right is for the last three years (the next bar is for the past 10 years). The colored segments in each bar sum to average annual labor productivity growth for each time period.
Taken at face value, the chart suggests that a primary reason for the sluggish average labor productivity growth we have seen over the past three years is that capital spending growth has not kept up with growth in hours worked—a reduction in capital deepening. Declining capital deepening is highly unusual.
Do we think this sluggishness will persist? No. In our medium-term outlook, we at the Atlanta Fed expect that factors that have held down labor productivity growth (particularly relatively weak capital spending) will dissipate as confidence in the economy improves further and firms increase the pace of investment spending, including on various types of equipment and intellectual capital. We currently anticipate that the trend in business sector labor productivity growth will improve to a level of about 2 percent a year, midway between the current pace and the pace experienced during the 1995–2004 period of strong productivity gains. That is, we are not productivity pessimists. Time will tell, of course.
Clearly, this optimistic labor productivity outlook is not without risk. For one thing, we have been somewhat surprised that labor productivity has remained so low for so long during the economic recovery. Moreover, the first quarter data don't suggest that a turning point has occurred. Gross domestic product (GDP) in the first quarter is likely to come in on the weak side (the latest GDPNow tracking estimate here is currently signaling essentially no GDP growth in the first quarter), whereas employment growth is likely to be quite robust (for example, the ADP employment report suggested solid employment gains). As a result, we anticipate another weak reading for labor productivity in the first quarter. We are not taking this as refutation of our medium-term outlook.
Continued weakness in labor productivity would raise many important questions about the outlook for both economic growth and wage and price inflation. For example, our forecast of stronger productivity gains also implies a similarly sized pickup in hourly wage growth. To see this, note that unit labor cost (the wage bill per unit of output) is thought to be an important factor in business pricing decisions. The following chart shows a decomposition of average growth in business sector unit labor costs into the part due to nominal hourly wage growth and the part offset by labor productivity growth:
The 1975–84 period experienced high unit labor costs because labor productivity growth didn't keep up with wage growth. In contrast, the relatively low and stable average unit labor cost growth we have experienced since the 1980s has been due to wage growth largely offset by gains in labor productivity. Our forecast of stronger labor productivity growth implies faster wage growth as well. That said, a rise in wage growth absent a pickup in labor productivity growth poses an upside risk to our inflation outlook.
Of course, the data on productivity and its components are estimates. It is possible that the data are not accurately reflecting reality in real time. For example, colleagues at the Board of Governors suggest that measurement issues associated with the price of high-tech equipment may be causing business investment to be somewhat understated. That is, capital deepening may not be as weak as the current data indicate. In a follow-up blog to this one, my Atlanta Fed colleague Patrick Higgins will explore the possibility that the weak labor productivity we have recently experienced is likely to be revised away with subsequent revisions to GDP and hours data.
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March 05, 2015
Could Reduced Drilling Also Reduce GDP Growth?
Five or six times each month, the Atlanta Fed posts a "nowcast" of real gross domestic product (GDP) growth from the Atlanta Fed's GDPNow model. The most recent model nowcast for first-quarter real GDP growth is provided in table 1 below alongside alternative forecasts from the Philadelphia Fed's quarterly Survey of Professional Forecasters (SPF) and the CNBC/Moody's Analytics Rapid Update survey. The Atlanta Fed's nowcast of 1.2 percent growth is considerably lower than both the SPF forecast (2.7 percent) and the Rapid Update forecast (2.6 percent).
Why the discrepancy? The less frequently updated SPF forecast (now nearly a month old) has the advantage of including forecasts of major subcomponents of GDP. Comparing the subcomponent forecasts from the SPF with those from the GDPNow model reveals that no single factor explains the difference between the two GDP forecasts. The GDPNow model forecasts of the real growth rates of consumer spending, residential investment, and government spending are all somewhat weaker than the SPF forecasts. Together these subcomponents account for just under 1.0 percentage point of the 1.5 percentage point difference between the GDP growth forecasts.
Most of the remaining difference in the GDP forecasts is the result of the different forecasts for real business fixed investment (BFI) growth. The GDPNow model projects a sharp 13.5 percent falloff in nonresidential structures investment that largely offsets the reasonably strong increases in the other two subcomponents of BFI. Much of this decline is due to petroleum and natural gas well exploration; a component which accounts for almost 30 percent of nonresidential structures investment and looks like it will fall sharply this quarter. The remainder of this blog entry "drills" down into this portion of the nonresidential structures forecast (pun intended). (A related recent analysis using the GDPNow model has been done here).
A December macroblog post I coauthored with Atlanta Fed research director Dave Altig presented some statistical evidence that in the past, large declines in oil prices have had a pronounced negative effect on oil and mining investment. Chart 1 below shows that history appears to be repeating itself.
The Baker Hughes weekly series on active rotary rigs for oil and natural gas wells has plummeted from 1,929 for the week ending November 21 to 1,267 for the week ending February 27. The Baker Hughes data are the monthly source series for drilling oil and gas wells industrial production (IP) and one of the two quarterly source series for the U.S. Bureau of Economic Analysis's (BEA) estimate of drilling investment (for example, petroleum and natural gas exploration and wells). The other source series for drilling investment is footage drilled completions from the American Petroleum Institute, released about a week before the BEA publishes its initial estimate of GDP.
Chart 2 displays three of these indicators of drilling activity. The data are plotted in logarithms so that one-quarter changes approximate quarterly growth rates. The chart makes clear that the changes in each of the three series are highly correlated, suggesting that the Baker Hughes rig count can be used to forecast the other series. The Baker Hughes data end on February 27, and we can (perhaps conservatively) extrapolate it forward by assuming it remains at its last reading of 1,267 active rigs through the end of the quarter. We can then use a simple regression to forecast the February and March readings of drilling oil and gas wells IP. Another simple regression with the IP drilling series and its first-quarter forecast allows us to project first-quarter real drilling investment. The forecasts, shown as dashed lines in chart 2, imply real drilling investment will decline at an annual rate of 52 percent in the first quarter. This decline is steeper than the current GDPNow model forecast of a 36 percent decline as the latter does not account for the decline in active rotary rigs in February.
A 52 percent decline in real nonresidential investment in drilling would likely subtract about 0.5 percentage point off of first-quarter real GDP growth. However, it's important to keep in mind that a lot of first quarter source data for GDP are not yet available. In particular, almost none of the source data for the volatile net exports and inventory investment GDP subcomponents have been released. So considerable uncertainty still surrounds real GDP growth this quarter.
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February 20, 2015
Business as Usual?
Each month, we ask a large panel of firms to compare their current sales with "normal times." In our February survey, the firms in our panel reported their sales were approaching normal. Indeed, on average, larger firms (those with 100 or more employees) tell us sales levels this month were right at normal. But smaller firms, although improving, are still lagging their larger counterparts (see the chart).
These qualitative assessments suggest a continuation of the trend we've seen in our quarterly quantitative data (these data are compiled at the end of each quarter). In December, our panel of firms reported sales levels about 2.7 percent below normal—virtually identical to the Congressional Budget Office's estimate of the output gap. Here, too, our survey data show that on average, sales of the larger firms in our panel were essentially back to normal, but smaller firms were still reporting ample slack (see the chart).
Our next quantitative assessment of slack in U.S. business is due for release on March 20.
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December 04, 2014
The Long and Short of Falling Energy Prices
Earlier this week, The Wall Street Journal asked the $1.36 trillion question: Lower Gas Prices: How Big A Boost for the Economy?
We will take that as a stand-in for the more general question of how much the U.S. economy stands to gain from a drop in energy prices more generally. (The "$1.36 trillion" refers to an estimate of energy spending by the U.S. population in 2012.)
It's nice to be contemplating a question that amounts to pondering just how good a good situation can get. But, as the Journal blog item suggests, the rising profile of the United States as an energy producer is making the answer to this question more complicated than usual.
The data shown in chart 1 got our attention:
As a fraction of total investment on nonresidential structures, spending on mining exploration, shafts, and wells has been running near its 50-year high over the course of the current recovery. As a fraction of total business investment in equipment and structures, the current contribution of the mining and oil sector is higher than any time since the early 1980s (and generally much higher than most periods during the last half century).
In a recent paper, economists Soren Andersen, Ryan Kellogg, and Stephen Salant explain why this matters:
We show that crude oil production from existing wells in Texas does not respond to current or expected future oil prices... In contrast, the drilling of new wells exhibits a strong price response...
In short, the investment piece really matters.
We've done our own statistical investigations, asking the following question: What is the estimated impact of energy price shocks in the second half of this year on investment, consumer spending, and gross domestic product (GDP)?
If you are interested, you can find the details of the statistical model here. But here is the bottom line: the estimated impact of energy price shocks is a very sizeable decline in investment in the mining and oil subsector relative to baseline and, more importantly, an extended period of flat to slightly negative growth in overall investment relative to baseline (see chart 2).
In our simulations, the "baseline" is the scenario without the ex-post energy price shocks occurring in the third and fourth quarters of 2014, while the "alternative" scenario incorporates the (estimated) actual energy price shocks that have occurred in the second half of this year. These shocks lead to a cumulative 8 percent drop in consumer energy prices and a 6 percent drop in producer energy prices by the fourth quarter of this year relative to baseline. By the fourth quarter of 2017, 2 percentage points of these respective energy price declines are reversed. In chart 2 above, each colored line represents the percentage point difference between the "alternative" scenario and the "baseline" scenario.
As for consumption and GDP? Like overall investment, there is a short-run drag before the longer-term boom, as chart 3 shows:
So is the recent decline in energy prices good news for the U.S. economy? Right now our answer is yes, probably—but we may have to be patient.
Note: We have updated this post since it was originally released, clarifying a sentence in the paragraph above chart 2 and providing the data for the charts. The original sentence stated: But here is the bottom line: the estimated impact of energy price shocks is a very sizeable decline in investment in the mining and oil subsector and, more importantly, an extended period of flat to slightly negative growth in overall investment (see chart 2).
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September 15, 2014
The Changing State of States' Economies
Timely data on the economic health of individual states recently came from the U.S. Bureau of Economic Analysis (BEA). The new quarterly state-level gross domestic product (GDP) series begins in 2005 and runs through the fourth quarter of 2013. The map below offers a look at how states have fared since 2005 relative to the economic performance of the nation as a whole.
It’s interesting to see the map depict an uneven expansion between the second quarter of 2005 and the peak of the cycle in the fourth quarter of 2007. By the fourth quarter of 2008, most parts of the country were experiencing declines in GDP.
The U.S. economy hit a trough during the second quarter of 2009, according to the National Bureau of Economic Research, but 20 states and the District of Columbia recovered more quickly than the rest. The continued progress is easy to see, as is the far-reaching impact of the tsunami that hit Japan on March 11, 2011, which disrupted economic activity in many U.S. states. By the fourth quarter of 2013, only two states—Mississippi and Minnesota—experienced negative GDP.
The map shows that not all states are growing even when overall GDP is growing, and not all states are shrinking even when overall GDP is shrinking. But if we want to know more about which states are driving the change in overall GDP growth, then the geographic size of the state might not be so important.
Depicting states scaled to the size of their respective economies provides another perspective, because it’s the relative size of a state’s economy that matters when considering the contribution of state-level GDP growth to the national economy. The following chart uses bubbles (sized by the size of the state’s economy) to depict changes in states’ real GDP from the second quarter of 2005 through the fourth quarter of 2013.
This chart shows how the economies of larger states such as California, New York, Texas, Florida, and Illinois have an outsize influence on the national economy, despite some having a smaller geographic footprint. (Conversely, changes in the relatively small economy of a geographically large state like Montana have a correspondingly small impact on changes in the national economy.)
Overall GDP is now well above its prerecession peak. But have all states also fully recovered their GDP losses? The chart below depicts the cumulative GDP growth in each state from the end of 2007 to the end of 2013. The size of the circle represents the magnitude of the change in the level of real GDP between the end of 2007 and 2013. Most states have fully recovered in terms of GDP. (North Dakota’s spectacular growth stands out, thanks to its boom in the oil and gas industry.) However, Florida, Nevada, Connecticut, Arizona, New Jersey, and Michigan had not returned to their prerecession spending levels as of the end of 2013. For Florida, Nevada, and Arizona, the depth of the collapse in those states’ booming housing sectors is almost certainly responsible for the relative shortfall in performance since 2007.
The next release of the state-level GDP data, scheduled for September 26, will provide insight into the relative performance of state economies during the first quarter of 2014 at a time when overall GDP shrank by more than 2 percent (annualized rate). Some analysts have suggested that weather disruptions were a leading cause for that decline. The state-level GDP data will help tell the story.
By Whitney Mancuso, a senior economic analyst in the the Atlanta Fed's research department
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- January 2017
- December 2016
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