Précis
Perceiving inflation
Explaining economic growth
Building organizational capital
The public does not predict inflation very well. In fact, according to an Economic Commentary released recently by the Federal Reserve Bank of Cleveland, the average perception of past rates of price increase also tends to be quite wide of the mark. Write Michael F. Bryan and Guhan Venkatu, "… the average rate at which respondents thought prices had risen over the previous 12 months was about 6.0 percent. This ‘perception’ of inflation is more than twice the rise recorded by the Consumer Price Index (CPI) over the same period (2.7 percent)."
Their data also suggest that perceptions of inflation vary with many demographic characteristics of the respondent: higher incomes are associated with lower estimates and predictions of inflation, married respondents see lower inflation than singles, whites less than nonwhites, and so forth.
But most salient to Bryan and Venkatu was the fact that men and women—even after holding many of these other variable constant—subscribe to different ideas as to the rate of inflation. Women, after the regression-based adjustment, perceived current and expected inflation to be about 2 percentage points higher than men did after a similar adjustment.
None of several factors that might explain the gap—different consumption patterns, differing familiarity with the CPI itself, different frequencies of shopping—appeared to Bryan and Venkatu to be large enough to explain it. So they leave their readers with a puzzle, one they think will make for interesting, even "provocative" conversation.
Economic growth occurs due to increases in the inputs of production—such as labor, capital, and materials—and increases in efficiency of input use (which is often referred to as total factor productivity or TFP). A question that arises is which of these, input or TFP, is more responsible for income differentials across countries.
In "Technological Diffusion, Conditional Convergence, and Economic Growth," (NBER Working Paper Number 8713), David E. Bloom and Jaypee Sevilla (both of Harvard University) and David Canning (of Queen’s University of Belfast) tackle this question. They note that microeconomic studies often suggest that income differentials across countries are explained mostly by differences in TFP. However, in some macroeconomic studies, inputs appear to have more of a role; such studies may "pick up externalities to physical and human capital that appear at the aggregate level but do not affect private returns."
In their study, Bloom, Sevilla, and Canning focus on modeling the dynamics of TFP. Their model allows for technology diffusion and for differentials in TFP in the long run across countries due to differences in geography and institutions.
The researchers estimate their model using data from the Penn World Table (which displays national accounts time series for many countries) and the International Labor Organization, among other sources. They do not find evidence for externalities at the aggregate level. As they observe, this "puts the emphasis in explaining cross-country differences in income levels on how and why TFP varies across countries." Their results indicate that there is systematic variation in steady-state TFP across countries related to their geography and institutions, but that convergence to steady-state levels via technological diffusion is slow.
Building organizational capital
In their NBER Working Paper (Number 8722), Andrew Atkeson and Patrick J. Kehoe report that nearly 9 percent of the output of the manufacturing sector is not accounted for as payments either to physical capital—structures and equipment—or to labor. They believe this shortfall indicates payments to unmeasured forms of capital or to monopoly rents.
They argue that a substantial share of the unaccounted-for payments goes to the specific knowledge accumulated within plants about the more effective use of their technologies of production. This "organizational capital" is determined by the vintage of the plant’s technology and the staff’s accumulated knowledge of how to use it.
The model used by Atkeson and Kehoe shows that "learning is both prolonged and substantial" and that "the aggregate of specific productivities across a cohort of plants grows substantially for 20 years." In their analysis of the data, they suggest that about 4 percent of manufacturing output—nearly half the missing piece—can plausibly be attributed to the generally unmeasured capital that "the turbulent and time-comsuming process of building up a stock of organization-specific knowledge" creates.
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