slide1
Download
Skip this Video
Download Presentation
Measuring Labor Input

Loading in 2 Seconds...

play fullscreen
1 / 32

Measuring Labor Input - PowerPoint PPT Presentation


  • 126 Views
  • Uploaded on

Measuring Labor Input. Dale Jorgenson, Mun Ho, Jon Samuels Harvard University World KLEMS Conference, Harvard University August 19, 2010. Topics Measurement Issues and Methodology Data and Implementation Results Contribution of labor input to productivity revival

loader
I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
capcha
Download Presentation

PowerPoint Slideshow about 'Measuring Labor Input' - garth


An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript
slide1
Measuring Labor Input

Dale Jorgenson, Mun Ho, Jon Samuels

Harvard University

World KLEMS Conference, Harvard University

August 19, 2010

slide2
Topics
  • Measurement Issues and Methodology
  • Data and Implementation
  • Results
  • Contribution of labor input to productivity revival
  • Criticisms of this method
slide3
Information Technology and the American Growth Resurgence

Jorgenson, Ho and Stiroh (2005); Chapter 6

New Data on U.S. Productivity Growth by Industry

Jorgenson, Ho and Samuels (2010)

issues in measuring labor input
Issues in Measuring Labor Input
  • Number of workers, or Hours worked, are not suitable units of measure for heterogenous labor
  • Wide range of market wages indicate wide range of productivities
  • A wage-weighted index have been growing faster than simple sum of hours, productivity residual using hours will overstate the growth of TFP.
  • Need tractable method of handling this great heterogeneity
slide6
Methodology for a tractable measure of labor input
  • Cross classify workers in each industry by demographic characteristics
  • * In Jorgenson, Gollop & Fraumeni (1987):
  • sex, class, age, education, occupation
  • * Now: sex, class, age, education
  • -Define industry labor input as a Tornqvist index of the demographic components
index of labor input for industry j l jt as tornqvist index of components
Index of labor input for industry j, Ljt as Tornqvist index of components

scae: sex, class, age, education

j: industry j or aggregate economy

slide9
Index Ljt, cont. Constant Quality Index

Assume labor input is proportional to hours worked:

Qscaeis the quality of hours of group scae, fixed for all t.

Thus input index becomes:

Compared to simple hours:

index l jt cont
Index Ljt, cont.

Price of industry labor input is simply value/Lj

after choosing a normalization like:

Quality of industry labor input is labor input index divided by hours worked:

decomposing the labor input index
Contribution of age to labor qualityDecomposing the labor input index

How much of the quality change is due to changes

..in educational attainment?

..in the aging of the labor force?

Partial indices of labor input.

E.g. first-order index by age

slide12
Data

Need number of workers, hours and compensation to fill matrices of dimension (2sex, 2 class, 7age, 6educ, 70indus).

Total of 11760 cells.

Household survey data

(hours/week, weeks/year, wages/year, demographics, industry)

Census of Population.

- every 10 years

- 1% percent sample (1 million workers)

Current Population Survey, Annual Supplement (ASEC)

- every year, 1964+

- about 100,000 households

Establishment survey data

Bureau of Economic Analysis tabulations of total employment,

total compensation, wages for 72 industries;

annual hours for 18 industries

implementation
Implementation

-Begin with Census microdata (1% sample, ~1 mil. workers) to populate EMP, HOURS, COMP matrices for benchmark years

-From CPS annual microdata, construct marginal matrices: EMP, HOURS, COMP matrices of lower dimension (e.g. indus x edu, sex x age x edu, …)

-Interpolate between benchmark years using these annual marginal matrices

-Scale to industry totals in the National Accounts

data issues
Data Issues
  • -Change from SIC to NAICS classification
  • (CPS 2003+ and Census 2000 uses NAICS)
  • -Change in education classification in 1992
  • -Small sample size in CPS (use fewer industries)
  • -Household data is “top-coded” for wages
  • Workers in multiple jobs (multiple industries)
  • Estimating wages for self employed
  • No data on fringe (non-wage) benefits by person
criticisms of this methodology
Criticisms of this methodology
  • Equation of wages with marginal product is not valid with non-competitive markets and discrimination
  • Small sample sizes for many industries give poor estimates of cell averages
  • Education is not directly productive and merely a “signal”
  • Intensity of work effort is not recognized
summary
Summary
  • Simple sum of hours understate labor contribution, overstate TFP growth
  • Our labor input index – an aggregate over hours by demographic groups, weighted by wages – is a tractable measure with the use of U.S. Census microdata.
  • The growth of labor quality was about 0.4% per year, or, ¼ of the labor contribution to GDP growth is due to labor quality and ¾ due to hours growth.
ad