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  3. 2022
  4. Preliminary Program
  5. Productivity
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Productivity

Paper Session

Sunday, Jan. 9, 2022 12:15 PM - 2:15 PM (EST)

Hosted By: Society of Government Economists
  • Chair: Lucy Eldridge, U.S. Bureau of Labor Statistics

Tier Structure Aggregation, KLEMS Growth Accounting and the Economic Impact of Sectoral Reallocations

Jon D. Samuels
,
U.S. Bureau of Economic Analysis
Mun S. Ho
,
Harvard University

Abstract

Resources moving from less productive to more productive sectors can increase aggregate output without any underlying change in production technology, yet the impact of these reallocations on GDP and aggregate TFP is challenging to measure because it involves measuring unobserved counterfactual production where resources have not moved. We construct measures of counterfactual production by implementing an industry-level production account with a tier structure – industries, sectors, GDP. Aggregate GDP and TFP growth constructed bottom-up from the micro (industry) level captures the true data generating process for the sources of growth. The counterfactual accounts impose the restriction that outputs and inputs had not shifted across industries, so that the difference between the two measures captures the economic impact of reallocations of outputs and inputs. Our tier structure allows us to discuss the impact of reallocation of industries within sectors, such as component industries within the Manufacturing sector, and across sectors, such as from Manufacturing to Services.

The Effect of Aging on Entrepreneurship and Aggregate Productivity

Huiyu Li
,
Federal Reserve Bank of San Francisco
Naomi Kodama
,
Nihon University

Abstract

This paper documents an inverted-U relationship between firm sales, sales per worker and entrepreneurship rate with entrepreneur age in Japan. To understand the implications of these patterns for aggregate productivity, we model these facts with a general equilibrium occupational choice model with endogenous entrepreneur rate, TFP and labor quality. We calibrate the model and simulate the change in output per capita and entrepreneurship rate when the age distribution in the population changes according to official projections. Preliminary results suggest that aging may lower aggregate productivity through a left shift in the productivity distribution of potential workers and entrepreneurs.

Intangible Capital and Productivity Growth in 61 Industries

Leo Sveikauskas
,
U.S. Bureau of Labor Statistics
Corby Garner
,
U.S. Bureau of Labor Statistics
Peter Meyer
,
U.S. Bureau of Labor Statistics
Matthew Russell
,
U.S. Bureau of Labor Statistics

Abstract

Evidence shows that intangibles are now growing more rapidly than other forms of capital. It is therefore important to understand how intangibles contribute to productivity growth. Information already exists on investments in R&D, artistic originals, and software in each of our 61 industries. Our work examines the impact of each of these intangibles, and adds data on advertising as a potential further intangible. We also consider whether the rate of return to intangibles found in industry data is equivalent to the returns typically found within firm data. Finally, we examine whether advertising tends to be central in consumer industries, whereas R&D is instead more important in capital goods industries.

Chaos Before Order: Productivity Patterns in U.S. Manufacturing

Sabrina Wulff Pabilonia
,
U.S. Bureau of Labor Statistics
Cindy Cunningham
,
U.S. Bureau of Labor Statistics
Lucia Foster
,
U.S. Census Bureau
Cheryl Grim
,
U.S. Census Bureau
John Haltiwanger
,
University of Maryland
Jay Stewart
,
U.S. Bureau of Labor Statistics
Zoltan Wolf
,
New Light Technologies

Abstract

Within-industry productivity dispersion is pervasive and exhibits substantial variation across countries, industries, and time. We build on prior research that explores the hypothesis that periods of innovation are initially associated with a surge in business start-ups, followed by increased experimentation that leads to rising dispersion potentially with declining aggregate productivity growth, and then a shakeout process that results in higher productivity growth and declining productivity dispersion. Using novel detailed industry-level data on total factor productivity and labor productivity dispersion from the Dispersion Statistics on Productivity along with novel measures of entry rates from the Business Dynamics Statistics and productivity growth data from the Bureau of Labor Statistics for U.S. manufacturing industries, we find support for this hypothesis, especially for the high-tech industries. An increase in entry rates in a two-year period t is associated with an increase in dispersion and decrease in aggregate productivity growth in two-year period t+1 and a decrease in dispersion and increase in aggregate productivity growth in two-year period t+2.

Discussant(s)
Daniel Sichel
,
Wellesley College
Benjamin Pugsley
,
University of Notre Dame
Rachel Soloveichik
,
U.S. Bureau of Economic Analysis
Chad Syverson
,
University of Chicago
JEL Classifications
  • O0 - General
  • J0 - General