The Atlanta Fed's macroblog provides commentary and analysis on economic topics including monetary policy, macroeconomic developments, inflation, labor economics, and financial issues.
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May 22, 2017
GDPNow's Second Quarter Forecast: Is It Too High?
Real gross domestic product (GDP) growth slowed from a 2 percent pace in 2016 to an annual rate of 0.7 percent in the first quarter of 2017. The Federal Open Market Committee viewed this slowdown in growth "as likely to be transitory," according to its last statement.
Indeed, current quarter GDP forecasting models maintained by the Federal Reserve Banks of New York, St. Louis, and Atlanta have been pointing toward stronger second quarter growth (2.3 percent, 2.6 percent and 4.1 percent, as reported on their respective websites on May 19, 2017).
The Atlanta Fed's model—GDPNow—is at the high end of this range and is also high relative to other professional forecasts. The median forecast for second quarter real GDP growth in the May Survey of Professional Forecasters (SPF) was 3.1 percent, for instance, and recent forecasts from Blue Chip Publication surveys displayed on our GDPNow page show some divergence from our model as well.
We encourage—and frequently receive—feedback on our GDPNow tool, and some users have suggested that our forecast for second quarter growth is too high. In fact, some empirical evidence supports that view. The evidence considered here correlates differences between consensus Blue Chip Economic Indicators Survey and GDPNow forecasts for growth about 80 days before the first GDP release with the GDPNow forecast errors (see the chart below).
A note about the chart: The horizontal axis shows the difference between the Blue Chip consensus forecasts and GDPNow's forecast. The vertical axis measures the 80-day-ahead GDPNow forecast error, defined as the difference between the first published estimate of real GDP growth and the GDPNow forecast at the time of the mid-quarter Blue Chip survey.
As the chart shows, there is a positive relationship between the Blue Chip-GDPNow discrepancy and the GDPNow forecast error. A simple linear regression would predict that the GDPNow forecast of 3.7 percent growth on May 5 was too high by nearly 1.0 percentage point. Moreover, the chart suggests that there has been a bias in GDPNow forecasts since the fourth quarter of 2015 of between 0.9 and 2.0 percentage points at the time of these mid-quarter Blue Chip surveys. If you are inclined to think the GDPNow forecast for second quarter growth is a bit too high, then this evidence will not change your mind.
Given this evidence, you might think that putting relatively little stock in the GDPNow forecast at this point in the quarter would be prudent. Indeed, if we calculate the weighted average of the historical Blue Chip consensus and GDPNow forecasts that produced the most accurate forecast of the first estimate of real GDP growth, then the optimal weight of the GDPNow forecast lies somewhere between 0.34 and 0.55 (see the chart below). The weight depends on the number of days until the first GDP release.
For example, the optimal weight of 0.55 on GDPNow about 54 days before the first GDP release means that 0.55 times the GDPNow forecast plus 0.45 times Blue Chip consensus survey forecast has been more accurate, on average, than any other weighted average of the two forecasts. The lowest weight on GDPNow corresponds to forecasts made about 83 days before the first GDP release—the time when GDPNow's bean-counting algorithms have the least amount of source data to work with.
A weighted average of the Blue Chip consensus and GDPNow forecasts at that time would put the GDP forecast about 0.6 to 0.7 percentage points below the current GDPNow forecast. However, the confidence bands around these estimates are wide, so the positive weight placed on GDPNow early in the quarter could just be the result of chance.
Let's cut to the chase—why, exactly, is the GDPNow forecast for second quarter GDP growth so high? The details of the GDPNow forecast provide some clues. We can compare the GDPNow forecasts of GDP components with those from the SPF. (The Blue Chip forecast does not provide detail on all the GDP components.) The following table translates the median SPF forecasts into contributions to second quarter real GDP growth. These contributions are shown alongside GDPNow's forecasted contributions as well as the average contributions to real GDP growth over the prior four quarters.
Clearly, more than half of the difference between the GDP growth forecasts from GDPNow and the SPF is due to inventories. For both forecasts, inventory investment also accounts for over half of the pickup in second quarter growth from the trailing four-quarter average.
A macroblog post I wrote last year showed that the growth-forecast contribution of mid-quarter inventory investment produced roughly equivalent accuracy in the SPF and GDPNow models, but it was much less accurate than the contribution forecasts of the other GDP components. Based on experience, we can't be confident that either forecast of inventory investment is likely to be very accurate or that one is likely to be much more accurate than another.
With very little hard data in hand for the second quarter for most of the GDP components—and for inventories in particular—we will continue to closely monitor if the data are as strong as GDPNow is anticipating or if they hew more closely to other forecasts. Check back with us to see.
May 11, 2017
Are Small Loans Hard to Find? Evidence from the Federal Reserve Banks' Small Business Survey
The Federal Reserve Banks recently released results from the nationwide 2016 Small Business Survey, which asks firms with 500 or fewer employees about business and financing conditions. One key finding is just how small the financing needs of many businesses are. One-fifth of small businesses that applied for financing in the prior 12 months were seeking $25,000 or less. A further 35 percent were seeking between $25,001 and $100,000.
The data also show that firms seeking relatively small amounts of financing (up to $100,000) receive a significantly smaller fraction of their funding than firms who applied for more than $250,000. Chart 1 shows the weighted average of the share of financing received by the amount the firm was seeking.
So what explains this variation in financing attainment across the amount requested? We've heard reports from small business owners that smaller loans are relatively more difficult to obtain, especially from traditional banks. One often-cited rationale is that the administrative burden associated with originating and managing a small loan is often just not worth the bank's time. However, this notion is not entirely consistent with data on the current holdings of small business loans on the balance sheets of banks. As of June 2015, loans of less than $100,000 made up about 92 percent of the number of business loans under $1 million.
So it seems originating a loan for less than $100,000 is not uncommon for a bank after all. So why, then, do business owners say that smaller loans are more difficult to get? Using data from the 2016 Small Business Survey, we can investigate the reason for this apparent disconnect.
Much can be explained by looking at the characteristics of those who borrow small amounts versus large amounts. Firms seeking $25,000 or less are more likely to be high credit risk and younger, have fewer employees, and have smaller revenues than firms applying for more than $250,000. The table below summarizes the differences:
Of particular importance is the credit risk associated with the firm. Controlling for differences in this factor, it turns out that smaller amounts of financing are not more difficult to obtain. Charts 2 and 3 show the weighted average share of financing received by amount sought for low credit risk firms and for middle to high credit risk firms separately.
As charts 2 and 3 demonstrate, low credit risk firms are able to obtain a similar share of the amount requested, regardless of how much they applied for. The same is true for higher risk firms. We also see that medium and high risk firms get less of their financing needs met than low credit risk firms that apply for similar amounts.
From this evidence, it seems that credit approval has more to do with the attributes of the firm than the amount of financing for which the firm applied. These results also highlight the potential importance of alternatives to traditional bank financing so that riskier entrepreneurs—including important contributors to the dynamism of the economy such as startups—have somewhere to turn. A later macroblog post will explore how low and high credit risk firms use financing differently, including where they apply and where they receive funding.
May 05, 2017
Slide into the Economic Driver's Seat with the Labor Market Sliders
The Atlanta Fed has just launched the Labor Market Sliders, a tool to help explore simple "what if" questions using actual data on employment, the unemployment rate, labor force participation, gross domestic product (GDP) growth, and labor productivity (GDP per worker).
We modeled the Labor Market Sliders after the popular Atlanta Fed Jobs Calculator. In particular, the sliders take the rate of labor productivity growth and the rate of labor force participation as given (not a function of GDP or employment growth) and then asks questions about GDP growth and labor market outcomes. Like the Jobs Calculator, the sliders require that things add up, a very useful feature for all those backyard economic prognosticators (we know you're out there).
Let's look at an example of using the sliders. The Congressional Budget Office (CBO) projects that the labor force participation rate (LFPR) will maintain roughly its current level of 62.9 percent during the next couple of years, as the downward pressure of retiring baby boomers and the upward pressure from robust hiring hold the rate stable. The CBO also projects that labor productivity growth will gradually increase to almost 1 percent over roughly the same period.
Suppose we want to know what GDP growth would be over the next couple of years (other things equal) if labor productivity, which has been sluggish lately, returned to 1 percent, as projected by the CBO. By moving the Labor Productivity slider in the tool to 1 percent and the Months slider to 24, you will see how productivity alone affects GDP growth: it increases to about 2 percent (see the image below). In this experiment, the unemployment rate, average job growth, and LFPR are constrained to current levels.
However, there's more than one way to achieve GDP growth of 2 percent over the next two years. Let's take a look.
Hit the reset button, and productivity, GDP growth, and months revert to their starting values. Then move the Months slider to 24 and the GDP Growth slider to 2 percent. You then see that—at current levels of labor force participation and labor productivity growth—achieving 2 percent GDP growth over the next two years would require the economy to create about 200,000 jobs per months (see the image below), which would push the unemployment rate down to 3.1 percent (a rate not seen since the early 1950s).
Hit the reset button again. Achieving 2 percent GDP growth over the next two years is also realistic with a higher LFPR, some other things equal. First, move the Months slider to 24, then move the Labor Force Participation Rate slider to 63.7 percent. The higher LFPR is consistent with about 2 percent growth in GDP and roughly 200,000 additional jobs added each month (see the image below). (This scenario constrains the unemployment rate and labor productivity growth rate to their current levels.) Of course, we haven't seen the LFPR at 63.7 percent since 2012, but that's another discussion.
What if we wanted something a bit more ambitious, such as averaging 3 percent GDP growth over the next couple of years? Hit the reset button again, and try this scenario. Keep Labor Force Participation Rate at its current level (consistent with the CBO's projection), set Labor Productivity growth to 1 percent (also using the CBO projection as a guide), move the Months slider to 24, and the GDP Growth slider to 3 percent. The Labor Market Sliders allow us to see that the economy would need to add an average of about 240,000 jobs each month for those two years. This scenario, the tight-labor-market method of achieving 3 percent GDP growth, would bring the unemployment rate down to 2.6 percent.
However, suppose the United States were somehow able to recapture productivity growth of around 2 percent, which we experienced in the late 1990s and early 2000s. In that case, 3 percent GDP could be achieved at the current employment growth and unemployment rate.
I encourage you to play around and devise your own "what if" scenarios—and use the Labor Market Sliders to make sure they add up.
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- Immigration and Hispanics' Educational Attainment
- Are Tariff Worries Cutting into Business Investment?
- Improving Labor Market Fortunes for Workers with the Least Schooling
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- Learning about an ML-Driven Economy
- Hitting a Cyclical High: The Wage Growth Premium from Changing Jobs
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