Question

U.S. productivity growth accelerated in the second half of the 1990s. How do you account for this speedup? Why is it still impossible to know if this speedup is the start of a long-term trend or simply a transitory change?

Answer 1

Q- U.S. productivity growth accelerated in the second half of the 1990s?

Answer- One of the big stories of the 1990s was the acceleration of productivity growth in the latter part of the decade. Productivity growth averaged 2.5% in the second, compared to 1.5% in the first part of the decade. This improvement primarily resulted from more investment in new technologies, mainly computers and software, and from a tightening labor market that forced firms to utilize their existing pool of workers better.

IT investment grew from 3% of GDP at the beginning of 1991 to 4.9% — more than one-third of total investment — at the end of 2000. Subsequently, innovation as measured by multifactor productivity growth more than doubled in second half of the 1990s.

While more private investment was instrumental in making widespread use of new, productivity enhancing technologies, public investment helped to ensure that the new technologies existed in the first place. Integral components to the implementation of the new technologies, such as hardware, software, and the internet were developed with public support through government funded research and development, defense contracts, or publicly funded university research (NRC, 1999).

Given how important public investment was in helping to create the new technologies that ultimately contributed to the productivity boom of the 1990s, it is disconcerting to see that public investment in R&D declined over time. In the 1990s, federal R&D spending dropped below 1% of GDP for the first time in the post-war era, thereby lowering the chances for a repeat performance of the late 1990s.

The acceleration of productivity growth also resulted from a tight labor market, as firms made better use of their workforces. In the second half of the 1990s, firms began to understand that workers constituted an important resource that was increasingly hard to come by. For instance, Morgan Stanley chief economist, Stephen Roach, argued that downsizing in the early 1990s may have led to a temporary surge in productivity growth, but that sustained productivity growth required better skill development through better training and retaining of workers (Roach, 1996).

How do you account for this speedup?

A consensus has arisen among economists that the acceleration was caused by technological innovations that decreased the quality-adjusted prices of semiconductors and related information-communications technology (ICT) products, including digital computers

Possible reasons for the economic boom: The mid to late 1990s was characterized by significantly low oil prices (the lowest prices since the Post World War 2 Economic Boom) , which would have reduced transportation and manufacturing costs, leading to increases in economic growth.

Why is it still impossible to know if this speedup is the start of a long-term trend or simply a transitory change?

Shifts in the long-run rate of productivity growth|such as those experienced by theU.S. economy in the 1970s and 1990s|are dicult, in real time, to distinguish fromtransitory fluctuations. In this paper, we analyze the evolution of forecasts of long-run productivity growth during the 1970s and 1990s and examine in the context ofa dynamic general equilibrium model the consequences of gradual real-time learningon the responses to shifts in the long-run productivity growth rate. We nd that asimple updating rule based on an estimated Kalman lter model using real-time datadescribes economists' long-run productivity growth forecasts during these periods ex-tremely well. We then show that incorporating this process of learning has profoundimplications for the e ects of shifts in trend productivity growth and can dramaticallyimprove the model's ability to generate responses that resemble historical experience.If immediately recognized, an increase in the long-run growth rate causes long-terminterest rates to rise and produces a sharp decline in employment and investment, con-trary to the experiences of the 1970s and 1990s. In contrast, with learning, a rise inthe long-run rate of productivity growth sets o a sustained boom in employment andinvestment, with long-term interest rates rising only gradually. We nd the character-ization of learning to be crucial regardless of whether shifts in long-run productivitygrowth owe to movements in TFP growth concentrated in the investment goods sectoror economy-wide TFP.