U.S. GDP slows down...

source: tradingeconomics.com

Are we counting the benefits of cloud computing to GDP?

A new NBER working paper by David Byrne, Carol Corrado and Daniel E. Sichel titled, "The Rise of Cloud Computing:  Minding Your P's, Q's and K's."

Abstract:

Cloud computing--computing done on an off-site network of resources accessed through the Internet--is revolutionizing how computing services are used.  However, because cloud is so new and it largely is an intermediate input to other industries, it is difficult to track in the U.S. statistical system.  Moreover, there is a paucity of systematic information on the prices of cloud services.  To begin filling this gap, this paper does three things.  First, we define the different segments of cloud computing and document its explosive expansion.  Second, we develop new hedonic prices indexes for cloud services based on quarterly data for compute, database, and storage services offered by Amazon Web Services (AWS) from 2009 to 2016.  These indexes fall rapidly over the sample period, with quickening (and double digit) rates of decline for all three products starting at the beginning of 2014. Finally, we highlight the puzzle of why investment in IT equipment in the NIPAs has been so weak while capital expenditures have exploded for IT equipment associated with cloud infrastructure.  We suggest that cloud service providers are undertaking large amounts of own-account investment in IT equipment and that some of this investment may not be captured in GDP.

Gated copy available here. 

Robert Gordon revisits the Phillips Curve

"Friedman and Phelps on the Phillips Curve Viewed from a Half Century's Perspective" a new NBER working paper by Robert J. Gordon.

Abstract:

In the late 1960s the stable negatively sloped Phillips Curve (PC) was overturned by the Friedman-Phelps natural rate model.  Their PC was vertical in the long run at the natural unemployment rate, and their short-run curve shifted up whenever unemployment was pushed below the natural rate.   

This paper criticizes the underlying assumption of the Friedman-Phelps approach that the labor market continuously clears and that changes in unemployment down or up occur only in response to "fooling" of workers, firms, or both.   A preferable and resolutely Keynesian approach explains quantity rationing by inertia in price and wage setting.  The positive correlation of inflation and unemployment in the 1970s and again in the 1990s is explained by joining the negatively sloped Phillips Curve with a positively sloped dynamic demand curve.  For any given growth of nominal GDP, higher inflation caused by adverse supply shocks implies slower real GDP growth and higher unemployment.  

This "triangle" model based on inflation inertia, demand, and supply worked well to explain why inflation and unemployment were both positively and negatively correlated between the 1960s and 1990s, but in the past decade the slope of the short-run Phillips Curve has flattened as inflation exhibited a muted response to high unemployment in 2009-13 and low unemployment in 2016-2018.   

It remains to be seen whether a continuation of low unemployment will cause a
modest and fixed extra amount of inflation, thus reviving the stable Phillips curve of the early 1960s, or whether inflation will continuously accelerate as Friedman and Phelps would have predicted.

Gated version available here. 

Robert Gordon's latest working paper: "Why has economic growth slowed when innovation appears to be accelerating?

From the eminent Robert Gordon of Northwestern University, a new working paper on the productivity slowdown in the West. 

Abstract:

Measured between quarters with identical unemployment rates, U. S.  economic growth slowed by more than half from 3.2 percent per year during 1970-2006 to only 1.4 percent during 2006-16, and only half of this GDP growth slowdown is accounted for diminished productivity growth.  The paper starts from the proposition that GDP growth matters, not just productivity growth, because slower GDP growth provides fewer resources to address the nation's problems, including faltering education, aging infrastructure, and the looming shortfall in funding for Social Security and Medicare, and it also implies lower net investment and a reduced rate at which new capital can embody the latest technology.   

The paper documents the contribution to slower GDP growth of the separate components of demography -- fertility, mortality, life expectancy, and immigration.  Particular emphasis is placed on the interaction between rising inequality and the slower secular rise of life expectancy in the U.S. compared to other developed countries, both in the form of a large gap in life expectancy between rich and poor, and the stagnation of life expectancy for the lowest income quintile.  Further contributions to slowing growth are made by a decline in the population share of both legal and illegal immigration and a turnaround from rising to declining labor force participation.  Rising inequality creates a gap between the growth of average real per-capita income relative to that of median real income, and alternative measures of the evolution of this gap are compared and assessed. 

Read more of the abstract at NBER.

Measuring economic freedom, what does government size have to do with it?

A recent paper by Jan Ott from the journal Social Indicators Research. 

Abstract: 

The Heritage Foundation and the Fraser Institute measure economic freedom in nations using indices with ten and five indicators respectively. Eight of the Heritage indicators and four of the Fraser-indicators are about specific types of institutional quality, like rule of law, the protection of property, and the provision of sound money. More of these is considered to denote more economic freedom. Both indices also involve indicators of ‘big government’, or levels of government activities. More of that is seen to denote less economic freedom. Yet, levels of government spending, consumption, and transfers and subsidies appear to correlate positively with the other indicators related to institutional quality, while this correlation is close to zero for the level of taxation as a percentage of GDP. Using government spending, consumption transfers and subsidies as positive indicators is no alternative, because these levels stand for very different government activities, liberal or less liberal. This means that levels of government activities can better be left out as negative or positive indicators. Thus shortened variants of the indices create a better convergent validity in the measurement of economic freedom, and create higher correlations between economic freedom and alternative types of freedom, and between economic freedom and happiness. The higher correlations indicate a better predictive validity, since they are predictable in view of the findings of previous research and theoretical considerations about the relations between types of freedom and between freedom and happiness.

When it comes to business data, is Yelp as good as the U.S. Census?

From the paper titled, "Nowcasting the Local Economy: Using Yelp Data to Measure Economic Activity," by Edward L. Glaeser, Hyunjin Kim and Michael Luca

Here's the abstract from NBER.

Abstract:

Can new data sources from online platforms help to measure local economic activity? Government datasets from agencies such as the U.S. Census Bureau provide the standard measures of local economic activity at the local level.  However, these statistics typically appear only after multi-year lags, and the public-facing versions are aggregated to the county or ZIP code level. In contrast, crowdsourced data from online platforms such as Yelp are often contemporaneous and geographically finer than official government statistics.  In this paper, we present evidence that Yelp data can complement government surveys by measuring economic activity in close to real time, at a granular level, and at almost any geographic scale.  Changes in the number of businesses and restaurants reviewed on Yelp can predict changes in the number of overall establishments and restaurants in County Business Patterns.  An algorithm using contemporaneous and lagged Yelp data can explain 29.2 percent of the residual variance after accounting for lagged CBP data, in a testing sample not used to generate the algorithm.  The algorithm is more accurate for denser, wealthier, and more educated ZIP codes.

Revised Gross Domestic Product 4th Quarter 2016: 2.1$

Real gross domestic product (GDP) increased at an annual rate of 2.1 percent in the fourth and final quarter of 2016. This latest report is the third estimate of U.S. GDP from the Bureau of Economic Analysis. Real GDP for 2016 grew at a 1.6 percent. 

Here is my commentary on yesterday's report.