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

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

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February 28, 2018

Weighting the Wage Growth Tracker

The Atlanta Fed's Wage Growth Tracker (WGT) has shown its usefulness as an indicator of labor market conditions, producing a better-fitting Phillips curve than other measures of wage growth. So we were understandably surprised to see the WGT decline from 3.5 percent in 2016 to 3.2 percent in 2017, even as the unemployment rate moved lower from 4.9 to 4.4 percent.

This unexpected disconnect between the WGT and the unemployment rate naturally led us to wonder if it was a consequence of the way the WGT is constructed. Essentially, the WGT is the median of an unweighted sample of individual wage growth observations. This sample is quite large, but it does not perfectly represent the population of wage and salary earners.

Importantly, the WGT sample has too few young workers, because young workers are much more likely to be in and out of employment and hence less likely to have a wage observation in both the current and prior years. To examine the effect of this underrepresentation, we recomputed median wage growth after weighting the WGT sample to be consistent with the distribution of demographic and job characteristics of the workforce in each year. It turns out that this adjustment is important when the labor market is tight.

During periods of low unemployment, young people who stay employed tend to experience larger proportionate wage bumps than older workers. In 2017, for example, the weighted median is 40 basis points higher than the unweighted version. However, both the unweighted version (the gray line in the chart below) and the weighted version of the WGT (the blue line) declined by a similar amount from 2016 to 2017. The decline in the weighted median is also statistically significant (the p-value for the test is 0.07, indicating that the observed difference is unlikely to be due to chance).

Another issue that could affect comparisons of wage growth over time is the changing demographic characteristics of the workforce. In particular, we know that workers' wage growth tends to slow as they approach retirement age, and the fraction of older workers has increased markedly in recent years. To examine this trend, we re-computed the weighted median, but fixed the demographic and job characteristics of the workforce so they would look as they did in 1997.

Our 1997-fixed version shows that median wage growth in recent years would be a bit higher if not for the aging of the workforce (the dashed orange line in the chart below). Moreover, this demographic shift appears to explain some of the slowing in median wage growth from 2016 to 2017. Whereas the 1997-fixed median also slows over the year, the difference is not statistically significant (a test of the null hypothesis of no change in the 1997-fixed weighted median between 2016 and 2017 yielded a p-value of 0.38).

Long story short, our analysis suggests that median wage growth of the population of wage and salary earners is currently higher than the WGT would indicate, reflecting the strong wage gains young workers experience in a tight labor market. Moreover, the increasing share of older workers is acting to restrain median wage growth. Although the decline in median wage growth from 2016 to 2017 appears to be partly the result of the aging workforce, there still may be more to it than just that, and so we will continue to monitor the WGT and related measures closely in 2018 for signs of a pickup. We also want to note that with the release of the February wage data in mid-March, we will make a monthly version of the weighted WGT available.


February 28, 2018 in Data Releases, Employment, Labor Markets, Wage Growth | Permalink


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February 13, 2018

GDPNow's Forecast: Why Did It Spike Recently?

If you felt whipsawed by GDPNow recently, it's understandable. On February 1, the Atlanta Fed's GDPNow model estimate of first-quarter real gross domestic product (GDP) growth surged from 4.2 percent to 5.4 percent (annualized rates) after a manufacturing report from the Institute for Supply Management. GDPNow's estimate then fell to 4.0 percent on February 2 after the employment report from the U.S. Bureau of Labor Statistics. GDPNow displayed a similar undulating pattern early in the forecast cycle for fourth-quarter GDP growth.

What accounted for these sawtooth patterns? The answer lies in the treatment of the ISM manufacturing release. To forecast the yet-to-be released monthly GDP source data apart from inventories, GDPNow uses an indicator of growth in economic activity from a statistical model called a dynamic factor model. The factor is estimated from 127 monthly macroeconomic indicators, many of which are used to estimate the Chicago Fed National Activity Index (CFNAI). Indices like these can be helpful for forecasting macroeconomic data, as demonstrated here  and here.

Perhaps not surprisingly, the CFNAI and the GDPNow factor are highly correlated, as the red and blue lines in the chart below indicate. Both indices, which are normalized to have an average of 0 and a standard deviation of 1, are usually lower in recessions than expansions.

A major difference in the indices is how yet-to-be-released values are handled for months in the recent past that have reported values for some, but not all, of the source data. For example, on February 2, January 2018 values had been released for data from the ISM manufacturing and employment reports but not from the industrial production or retail sales reports. The CFNAI is released around the end of each month when about two-thirds of the 85 indicators used to construct it have reported values for the previous month. For the remaining indicators, the Chicago Fed fills in statistical model forecasts for unreported values. In contrast, the GDPNow factor is updated continuously and extended a month after each ISM manufacturing release. On the dates of the ISM releases, around 17 of the 127 indicators GDPNow uses have reported values for the previous month, with six coming from the ISM manufacturing report.


[ Enlarge ]

For months with partially missing data, GDPNow updates its factor with an approach similar to the one used in a 2008 paper by economists Domenico Giannone, Lucrezia Reichlin and David Small. That paper describes a dynamic factor model used to nowcast GDP growth similar to the one that generates the New York Fed's staff nowcast of GDP growth. In the Atlanta Fed's GDPNow factor model, the last month of ISM manufacturing data have large weights when calculating the terminal factor value right after the ISM report. These ISM weights decrease significantly after the employment report, when about 50 of the indicators have reported values for the last month of data.

In the above figure, we see that the January 2018 GDPNow factor reading was 1.37 after the February 1 ISM release, the strongest reading since 1994 and well above either its forecasted value of 0.42 prior to the ISM release or its estimated value of 0.43 after the February 2 employment release. The aforementioned rise and decline in the GDPNow forecast of first-quarter growth is largely a function of the rise and decline in the January 2018 estimates of the dynamic factor.

Although the January 2018 reading of 59.2 for the composite ISM purchasing managers index (PMI) was higher than any reading from 2005 to 2016, it was little different than either a consensus forecast from professional economists (58.8) or the forecast from a simple model (58.9) that uses the strong reading in December 2017 (59.3). Moreover, it was well above the reading the GDPNow dynamic factor model was expecting (54.5).

A possible shortcoming of the GDPNow factor model is that it does not account for the previous month's forecast errors when forecasting the 127 indicators. For example, the predicted composite ISM PMI reading of 54.4 in December 2017 was nearly 5 points lower than the actual value. For this discussion, let's adjust GDPNow's factor model to account for these forecast errors and consider a forecast evaluation period with revised current vintage data after 1999. Then, the average absolute error of the 85–90 day-ahead adjusted model forecasts of GDP growth after ISM manufacturing releases (1.40 percentage points) is lower than the average absolute forecast error on those same dates for the standard version of GDPNow (1.49 percentage points). Moreover, the forecasts using the adjusted factor model are significantly more accurate than the GDPNow forecasts, according to a standard statistical test . If we decide to incorporate adjustments to GDPNow's factor model, we will do so at an initial forecast of quarterly GDP growth and note the change here .

Would the adjustment have made a big difference in the initial first-quarter GDP forecast? The February 1 GDP growth forecast of GDPNow with the adjusted factor model was "only" 4.7 percent. Its current (February 9) forecast of first-quarter GDP growth was the same as the standard version of GDPNow: 4.0 percent. These estimates are still much higher than both the recent trend in GDP growth and the median forecast of 3.0 percent from the Philadephia Fed's Survey of Professional Forecasters (SPF).

Most of the difference between the GDPNow and SPF forecasts of GDP growth is the result of inventories. GDPNow anticipates inventories will contribute 1.2 percentage points to first-quarter growth, and the median SPF projection implies an inventory contribution of only 0.4 percentage points. It's not unusual to see some disagreement between these inventory forecasts and it wouldn't be surprising if one—or both—of them turn out to be off the mark.

February 13, 2018 in Business Cycles, Forecasts, GDP, Productivity, This, That, and the Other | Permalink


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