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March 02, 2017
Gauging Firm Optimism in a Time of Transition
Recent consumer sentiment index measures have hit postrecession highs, but there is evidence of significant differences in respondents' views on the new administration's economic policies. As Richard Curtin, chief economist for the Michigan Survey of Consumers, states:
When asked to describe any recent news that they had heard about the economy, 30% spontaneously mentioned some favorable aspect of Trump's policies, and 29% unfavorably referred to Trump's economic policies. Thus a total of nearly six-in-ten consumers made a positive or negative mention of government policies...never before have these spontaneous references to economic policies had such a large impact on the Sentiment Index: a difference of 37 Index points between those that referred to favorable and unfavorable policies.
It seems clear that government policies are holding sway over consumers' economic outlook. But what about firms? Are they being affected similarly? Are there any firm characteristics that might predict their view? And how might this view change over time?
To begin exploring these questions, we've adopted a series of "optimism" questions to be asked periodically as part of the Atlanta Fed's Business Inflation Expectations Survey's special question series. The optimism questions are based on those that have appeared in the Duke CFO Global Business Outlook survey since 2002, available quarterly. (The next set of results from the CFO survey will appear in March.)
We first put these questions to our business inflation expectations (BIE) panel in November 2016 . The survey period coincided with the week of the U.S. presidential election, allowing us to observe any pre- and post-election changes. We found that firms were more optimistic about their own firm's financial prospects than about the economy as a whole. This finding held for all sectors and firm size categories (chart 1).
In addition, we found no statistical difference in the pre- and post-election measures, as chart 2 shows. (For the stat aficionados among you, we mean that we found no statistical difference at the 95 percent level of confidence.)
We were curious how our firms' optimism might have evolved since the election, so we repeated the questions last month (February 6–10).
Among firms responding in both November and February (approximately 82 percent of respondents), the overall level of optimism increased, on average (chart 3). This increase in optimism is statistically significant and was seen across firms of all sizes and sector types (goods producers and service providers).
The question remains: what is the upshot of this increased optimism? Are firms adjusting their capital investment and employment plans to accommodate this more optimistic outlook? The data should answer these questions in the coming months, but in the meantime, we will continue to monitor the evolution of business optimism.
March 2, 2017 in Books, Business Inflation Expectations, Economic conditions, Economic Growth and Development, Forecasts, Inflation Expectations, Saving, Capital, and Investment, Small Business | Permalink
February 07, 2017
Net Exports Continue to Bedevil GDPNow
Real gross domestic product (GDP) grew at an annualized rate of 1.9 percent in the fourth quarter, according to the advance estimate from the U.S. Bureau of Economic Analysis (BEA), 1.0 percentage point below the Atlanta Fed's final GDPNow model projection. This was a sizable miss relative to other forecasts. Both the consensus estimate from the January Wall Street Journal Economic Forecasting Survey and the January 20 staff nowcast from the New York Fed were expecting 2.1 percent growth last quarter.
The miss was also large relative to the historical accuracy of the GDPNow model. As the table below shows, almost all of GDPNow's error for fourth quarter growth was concentrated in real net exports. For the other broad subcomponents, GDPNow was more accurate than usual, as the last two columns of the table show. But net exports subtracted 1.70 percentage points from real GDP growth last quarter, whereas GDPNow forecasted they would only reduce growth by 0.64 percentage points. All but 0.02 percentage points of this error was in the "goods" category as opposed to services.
Three months ago, I wrote a macroblog post showing that nearly all of GDPNow's 0.8 percentage point error for third-quarter growth was concentrated in goods net exports. That analysis explained how GDPNow's goods net exports forecast is a weighted average of two forecasts. One of these forecasts is a "bean counting" model that uses monthly source data on nominal values and price deflators for goods imports and exports. The other is a quarterly econometric model that uses subcomponents of real GDP for prior quarters. In the GDPNow model, the "bean counting" model gets nearly 60 percent of the weight just before the advance GDP release.
To see how this approach matters for the GDP forecast, the following chart shows the "real-time" forecasts of the contribution of goods net exports to growth just before BEA's advance GDP estimate from the two models alongside the advance estimate of the contribution and the final GDPNow forecast.
We see that the "bean counting" forecast has been much more accurate than the quarterly econometric forecast, particularly for the last two quarters of 2016. Not surprisingly given its name, the "bean counting" model was able to largely capture the 0.75 percentage points that soybean exports contributed to third-quarter real GDP growth and the just over 0.5 percentage points they likely subtracted from fourth-quarter growth. The econometric model was not.
The final forecasts of goods net exports from the "bean counting" model have also been more accurate than GDPNow since forecasts were first posted online in mid-2014. Does this imply that an alternative "bean counting" version of GDPNow would be preferable? The answer is less obvious than you might think. Not putting any weight on the quarterly econometric model for any GDP subcomponents yields an average error for GDP growth (without regard to sign) of 0.635 percentage points, and the same statistic for GDPNow is 0.589 percentage points. This is despite the fact that the "bean counting" approach has been more accurate than GDPNow in its forecasts of net exports and about as accurate, on balance, for the other GDP subcomponents.
The final forecast of real GDP growth last quarter of this alternative "bean counting" model was 2.8 percent—only slightly more accurate than GDPNow. (For each GDP subcomponent, I include the "bean counting" and quarterly econometric model forecasts in this excel spreadsheet.)
However, if variants like the aforementioned "bean counting" approach continue to outperform the GDPNow model in one or more dimensions, we may consider regularly reporting their forecasts along with the GDPNow forecast.
December 05, 2016
Using Judgment in Forecasting: Does It Matter?
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.
November 07, 2016
The Price Isn't Right: On GDPNow's Third Quarter Miss
The U.S. Bureau of Economic Analysis's (BEA) first estimate of third quarter annualized real gross domestic product (GDP) growth released on October 28 was 2.9 percent. A number of nowcasts were quite close to this number, including the median forecast of 3.0 percent from the CNBC Rapid Update survey of roughly 10 economists. The Atlanta Fed's GDPNow model forecast of 2.1 percent? Not so close.
What accounted for GDPNow's miss? The table below shows the GDPNow forecasts and BEA estimates of the percentage point contributions to third quarter growth of six subcomponents that together make up real GDP. The largest forecast error, both in absolute terms and relative to the historical accuracy of the projections, was for the contribution of real net exports to growth. The published contribution of 0.83 percentage points was much higher than the model's estimate of 0.07 percentage points.
Why did GDPNow miss so badly on net exports? For both goods and services real net exports, the GDPNow forecast is a weighted average of two forecasts. The "bean counting" forecast uses the monthly source data on the nominal values and price deflators of exports and imports. The econometric model forecast uses published values of 13 subcomponents of real GDP for the last five quarters to predict real net exports for both goods and services. The statistically determined weights on the bean counting forecast increase as we get closer to the first GDP release and accumulate more monthly source data. (More details are provided here.)
For real net exports of services, 89 percent of the weight was given to the bean counting forecast. This weighting worked out well last quarter as the forecasts of the contribution of real services net exports to third quarter growth from both the bean counting and combined models were within 0.01 percentage points of the published value of −0.14 percentage points. But for real net exports of goods, the bean counting forecast received only 59 percent of the weight in the final GDPNow forecast. It projected that real net exports of goods would add 0.76 percentage points to growth—reasonably close to the BEA estimate. In contrast, the econometric model projected a subtraction of 0.58 percentage points from growth.
Since GDPNow had monthly price and nominal spending data through September on goods imports and exports, why didn't it place more weight on the source data? One of the important reasons is that it's difficult to match the quarterly inflation rate of the BEA's import price deflator for goods. The BEA constructs its price deflator with detailed price indices from the Bureau of Labor Statistics (BLS) producer price index and import/export price index programs as well as a few other sources. GDPNow uses the BLS's import price data at higher levels of aggregation than the BEA uses and also differs in the manner that it handles seasonality. The chart below plots the difference between the BEA's quarterly goods import price inflation rate and the GDPNow proxy. These inflation measures have differed by 5 percentage points or more on a number of occasions. Since goods imports are 12 percent of GDP, a miss of this magnitude on the price deflator would lead to a miss on the real net exports of goods contribution to growth of 0.5 percentage points or more, even if the other ingredients in the calculation were all correct.
Are there any lessons here for improving GDPNow? Ideally, GDPNow would be able to closely map the monthly source data to real goods net exports so that most of the weight would go to the bean counting forecast once all of the data are in—much as it does with nonresidential structures and residential investment. The BEA's estimates of real petroleum imports are based on similar data in the monthly international trade data publication. Because petroleum imports account for so much of the volatility of inflation for goods imports, it may be better to use the monthly real petroleum imports data directly and only worry about replicating the price index for nonpetroleum goods.
That said, a previous macroblog post illustrated that the method GDPNow currently uses has a reasonable forecasting track record for net exports when compared with several consensus estimates from professional forecasters. Net exports may remain difficult to nowcast even with refinements to GDPNow's methodology.
GDPNow has established a commendable track record. But sometimes when it misses the mark, an analysis of the error can provide insight into how GDPNow works and the limitations of the model.
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.
January 15, 2016
Are Long-Term Inflation Expectations Declining? Not So Fast, Says Atlanta Fed
"Convincing evidence that longer-term inflation expectations have moved lower would be a concern because declines in consumer and business expectations about inflation could put downward pressure on actual inflation, making the attainment of our 2 percent inflation goal more difficult."
—Fed Chair Janet Yellen, in a December 2, 2015, speech to the Economic Club of Washington
To be sure, Chair Yellen's claim is not controversial. Modern macroeconomics gives inflation expectations a central role in the evolution of actual inflation, and the stability of those expectations is crucial to the Fed's ability to achieve its price stability mandate.
The real question on everyone's mind is, of course, what might constitute "convincing evidence" of changes in inflation expectations. Recently, several economists, including former Treasury Secretary Larry Summers and St. Louis Fed President James Bullard, have weighed in on this issue. Yesterday, President Bullard cited downward movements in the five-year/five-year forward breakeven rates from the five- and 10-year nominal and inflation-protected Treasury bond yields. In November, Summers appealed to measures based on inflation swap contracts. The view that inflation expectations are declining has also been echoed by the New York Fed President William Dudley and former Minneapolis Fed President Narayana Kocherlakota.
Broadly speaking, there seems to be a growing view that market-based long-run inflation expectations are declining and drifting significantly away from the Fed's 2 percent target and that this decline is troublingly correlated with oil prices.
A problem with this line of argument is that the breakeven and swap rates are not necessarily clean measures of inflation expectations. They are really better referred to as measures of inflation compensation because, in addition to inflation expectations, these measures also include factors related to liquidity conditions in the markets for these securities, technical features of the inflation protection in each security, and inflation risk premia. Here at the Atlanta Fed, we've built a model to separate these different components and isolate a better measure of true inflation expectations (IE).
In technical terms, we estimate an affine term structure model—similar to that of D'Amico, Kim and Wei (2014)—that incorporates information from the markets for U.S. Treasuries, Treasury Inflation-Protected Securities (TIPS), inflation swaps, and inflation options (caps and floors). Details are provided in "Forecasts of Inflation and Interest Rates in No-Arbitrage Affine Models," a forthcoming Atlanta Fed working paper by Nikolay Gospodinov and Bin Wei. (You can also see Gospodinov and Wei (2015) for further analysis.) Essentially, we ask: what level of inflation expectations is consistent with this entire set of financial market data? And we then follow this measure over time.
As chart 1 illustrates, we draw a very different conclusion about the behavior of long-term inflation expectations. The chart plots the five-year/five-year forward TIPS breakeven inflation (BEI) and the model-implied inflation expectations (IE) for the period January 1999–November 2015 at a weekly frequency. Unlike the raw BEI, our measure is quite smooth, suggesting that long-term inflation expectations have been, and still are, well anchored.
After making an adjustment for the inflation risk premium, we term the difference between BEI and IEs a "liquidity premium," but it really includes a variety of other factors. Our more careful look at the liquidity premium reveals that it is partly made up of factors specific to the structure of inflation-indexed TIPS bonds. For example, since TIPS are based on the non-seasonally adjusted consumer price index (CPI) of all items, TIPS yields incorporate a large positive seasonal carry yield in the first half of the year and a large negative seasonal carry yield in the second half. Chart 2 illustrates this point by plotting CPI seasonality (computed as the accumulated difference between non-seasonally adjusted and seasonally adjusted CPI) and the five-year breakeven inflation.
Redemptions, reallocations, and hedging in the TIPS market after oil price drops and global financial market turbulence can further exacerbate this seasonal pattern. Taken together, these factors are the source of correlation between the BEI measures and oil prices. To confirm this, chart 3 plots (the negative of) our liquidity premium estimate and the log oil price (proxied by the nearest futures price).
Our measure of long-term inflation expectations is also consistent with long-term measures from surveys. Chart 4 presents the median along with the 10th and 90th percentiles of the five-year/five-year forward CPI inflation expectations from the Philadelphia Fed's Survey of Professional Forecasters (SPF) at quarterly frequency. This measure can be compared directly with our IE measure. Both the level and the dynamics of the median SPF inflation expectation are remarkably close to that for our market-based IE. It is also interesting to observe that the level of inflation "disagreement" (measured as the difference between the 10th and 90th percentiles) is at a level similar to the level seen before the financial crisis.
Finally, we note that TIPS and SPF are based on CPI rather than the Fed's preferred personal consumption expenditure (PCE) measure. CPI inflation has historically run above PCE inflation by about 30 basis points. Accounting for this difference brings our measure of the level of long-term inflation expectations close to the Fed's 2 percent target.
To summarize, our analysis suggests that (1) long-run inflation expectations remain stable and anchored, (2) the seemingly large correlation of market-implied inflation compensation with oil prices arises mainly from the dynamics of the TIPS liquidity premium, and (3) long-run market- and survey-based inflation expectations are remarkably close in terms of level and dynamics over time. Of course, further softness in the global economy and commodity markets may eventually drag down long-term expectations. We will continue to monitor the pure measure of inflation expectations for such developments.
By Nikolay Gospodinov, financial economist and policy adviser; Paula Tkac, vice president and senior economist; and Bin Wei, financial economist and associate policy adviser, all of the Atlanta Fed's research department
May 07, 2015
All Eyes on the Consumer
It appears that the first quarter may have been even worse than we thought. The CNBC rapid update—consensus estimates from a panel of forecasters—registered a decline of 0.3 percent as of yesterday.
Clearly, the year didn't start out so well, but here at the Atlanta Fed we have not yet lost faith. We are sticking to the narrative that 2015 will be another solid year of recovery.
That said, our faith is not blind and, befitting data-dependent policymakers, we need to make some call about what it will take to shake our confidence. In a speech delivered yesterday (May 6) in Baton Rouge, Louisiana, Atlanta Fed President Dennis Lockhart pointed to our current lodestar:
As I assess the possible and necessary contributors to a rebound in the second quarter and thereafter, attention has to fall on consumer spending, in my view.
Is there a case for optimism? We think so, and it is based on the assumption that the fundamentals supporting consumer spending have been stronger than the actual recent pace of expenditures. President Lockhart continues:
What's up with the consumer? It's puzzling. The fundamentals supporting consumption growth seem strong. I consider consumer fundamentals to be real personal income growth, household wealth, access to credit, and consumer confidence. Consumer confidence is, in turn, highly influenced by the broad employment outlook.
To be more precise about that sentiment, the chart below illustrates an experiment based on a simple model that incorporates President Lockhart's description of "fundamentals." To be even more precise, we ask the following question: What would we have predicted for consumer spending growth during the past four months based on the history of actual consumer spending and its relationship to income, employment (and unemployment), confidence measures, and wealth (specifically, equity prices)? We also threw inflation and oil prices into the mix for good measure.
Here's what we got:
In other words, the "fundamentals" suggest the four-month annualized growth of consumer spending should have been in excess of 4 percent, as opposed to the approximately 1.5 percent we actually saw. That is a story we don't expect to persist, and our current view of the year is that first-quarter consumer spending results are not indicative of future performance.
Consumers are, of course, a forward-looking bunch, and it is possible the recent weak spending reflects a looming reality not captured by the simple model described above. But our forecast for now is that consumers will move to the fundamentals, and not vice versa.
As President Lockhart said in Louisiana: "Stay tuned."
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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 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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