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November 15, 2017
Labor Supply Constraints and Health Problems in Rural America
A recent research study by Alison Weingarden at the Federal Reserve's Board of Governors found that wages for relatively low-skilled workers in nonmetropolitan areas of the country have been growing more rapidly than those in metropolitan areas. In a talk yesterday in Montgomery, Alabama, Atlanta Fed President Raphael Bostic provided some evidence that differences in labor supply resulting from disability and illness may be behind this shrinking urban wage premium.
For prime-age workers (those between 25 and 54 years old), the dynamics of labor force participation (LFP) differ widely between metropolitan and nonmetropolitan areas. (These data define a metropolitan statistical area, or MSA). The LFP rate in MSAs declined by about 1.1 percentage points between 2007 and 2017 versus a 3.3 percentage point decline in non-MSA areas.
The disparity is also evident within education groups. For those without a college degree, the MSA LFP rate is down 2.6 percentage points, versus 5.0 percentage points in non-MSAs. For those with a college degree, the MSA LFP rate is down 0.7 percentage points, versus a decline of 2.5 percentage points for college graduates in non-MSAs. Moreover, although LFP rates in MSAs have shown signs of recovery in the last couple of years, this is not happening in non-MSAs.
A recent macroblog post by my colleague Ellyn Terry and the Atlanta Fed's updated Labor Force Dynamics web page have shown that the decline in prime-age LFP is partly a story of nonparticipation resulting from a rise in health and disability problems that limit the ability to work. This rise is occurring even as the population is gradually becoming more educated. (Better health outcomes generally accompany increased educational attainment.)
The following chart explores the role of disability/illness in explaining the relatively larger decline in non-MSA LFP. It breaks the cumulative change in the LFP rates since 2007 into the part attributable to demographic trends and the part attributable to behavioral or cyclical changes within demographic groups.
The demographic changes—and especially the increased share of the population with a college degree—has put mild upward pressure on the prime-age LFP rate for both the MSA and non-MSA population. Controlling for the contribution from these demographic trends, increased nonparticipation because of poor health and disability pulled down the LFP rate in MSAs by 0.8 percentage points and lowered the rate in non-MSAs by 2.0 percentage points over the past decade. For those without a college degree, disability/illness accounted for about 1.2 percentage points of the 2.6 percentage point decline in the MSA participation rate, and it accounted for 2.6 percentage points of the 5.0 percentage point decline in the non-MSA participation rate.
Taken together with evidence from business surveys and anecdotal reports about hiring difficulties, it appears that the non-MSA labor market is relatively tight. The greater inward shift of the rural supply of labor is showing through to wage costs, and especially for rural jobs that require less education.
Although the move to higher wages is welcome news for those with a job, it also raises troubling questions about why labor force nonparticipation because of disability and illness has increased so much in the first place—especially among those with less education living in nonmetropolitan areas of the country.
It is clear that the health problems for rural communities have been intensifying. Several interrelated factors have likely contributed to this worsening trend, including poverty, deeply rooted cultural and social norms, and the characteristics of rural jobs, as well as geographic barriers and shortages of healthcare providers that have limited access to care. This complex set of circumstances suggests that finding effective solutions could prove difficult.
November 06, 2017
Building a Better Model: Introducing Changes to GDPNow
Among the frequently asked questions on GDPNow's web page is this one:
Is any judgment used to adjust the forecasts? Our answer:
No. Once the GDPNow model begins forecasting GDP growth for a particular quarter, the code will not be adjusted until after the "advance" estimate. If we improve the model over time, we will roll out changes right after the "advance" estimate so that forecasts for the subsequent quarter use a fixed methodology for their entire evolution.
This macroblog post enumerates a number of minor changes to GDPNow that were implemented on October 30, when it began forecasting fourth-quarter real gross domestic product (GDP) growth. Here is a summary of the changes, intended to improve the accuracy of the GDP subcomponent forecasts:
- Services personal consumption expenditures (PCE). Use industrial production of electric and gas utilities to nowcast real PCE on electricity and natural gas. Use international trade data on travel services to forecast revisions to related PCE travel data.
- Real business equipment investment. Use/forecast data from the advance U.S. Census Bureau reports on durable manufacturing and international trade in goods that, previously, hadn't been utilized until the full reports on manufacturing and/or international trade .
- Real nonresidential structures investment. Replace a discontinued seasonally adjusted producer price index for "Steel mill products: Steel pipe and tube" with a nonseasonally adjusted version. The index is used to construct a price deflator for private monthly nonresidential construction spending.
- Real residential investment. Use employment data for production and nonsupervisory employees of residential remodelers to help forecast real investment in residential improvements.
- Real change in private inventories. Use published monthly inventory levels in the U.S. Bureau of Economic Analysis's underlying detail tables 1BU and 1BUC after the third-release GDP estimate from the prior quarter to estimate inventory levels for a number of industries in the first month of the quarter forecasted by GDPNow.
- Federal, state, and local government spending. Forecast investment in intellectual property products for these subcomponents using autoregression models.
The first three columns of the following table decompose the official estimate of the third-quarter real GDP growth rate, and forecasts of the growth rate from the discontinued and modified versions of GDPNow, into percentage point contributions from the subcomponents of GDP.
As the table shows, the methodological changes did not have much of an impact on the final third-quarter subcomponent forecasts—apart from inventory investment, where the modifications lowered the contribution to growth from 0.80 percentage points to 0.60 percentage points—or on their accuracy. Nevertheless, the topline GDP forecast of the modified model (2.3 percent) was less accurate than the previous version (2.5 percent). In the discontinued version of GDPNow, an overestimate of the inventory investment contribution to growth partly canceled out underestimated contributions from each of net exports, government spending, and nonresidential fixed investment.
In the modified version, the inventory contribution was also underestimated and did not cancel out these other errors. The last two columns of the table show that all of the subcomponent errors of the modified model were at least as small as their historical average for the discontinued version. However, the topline GDP forecast was less accurate than average because of less cancellation of the subcomponent errors than usual. We hope that the cancellation of subcomponent errors in the modified model will be more similar to the historical average in the discontinued version in the future.
Although the methodological changes could have more of an impact than the table suggests, we do not expect them to have a substantial impact in general. For example, on October 30, the discontinued version of GDPNow projected 3.0 percent GDP growth in the fourth quarter, which was little different from the modified model forecast of 2.9 percent growth. We provide a more detailed explanation of the changes to GDPNow here . Going forward, this same document will document any further changes to the model and when we made them.
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