NIST Special Publication 1101 

Office of Applied Economics

Building and Fire Research Laboratory 

Gaithersburg, Maryland 20899 

U.S. Department of Commerce

National Institute of Standards and 

Technology 

Metrics and Tools for Measuring Construction 

Productivity: Technical and Empirical Considerations 

Allison L. Huang, Robert E. Chapman, and David T. Butry 

          

   
 

   
 

NIST Special Publication 1101

U.S. Department of Commerce

National Institute of Standards and 

Technology 

Office of Applied Economics

Building and Fire Research Laboratory 

Gaithersburg, Maryland 20899

Metrics and Tools for Measuring Construction 

Productivity: Technical and Empirical Considerations 

Allison L. Huang, Robert E. Chapman, and David T. Butry 

Sponsored by: 

National Institute of Standards and Technology 

Building and Fire Research Laboratory 

September 2009 

U.S. DEPARTMENT OF COMMERCE 

Dr. Gary Locke, Secretary 

NATIONAL INSTITUTE OF STANDARDS AND TECHNOLOGY 

Dr. Patrick D. Gallagher, Deputy Director 
 
 

Abstract 

Although the construction industry is a major component of the U.S. economy, it has 

experienced a “perceived” prolonged period of decline in productivity. Due to the critical 

lack of measurement methods, however, the magnitude of the productivity problem in the 

construction industry is largely unknown. The measurement problem is exacerbated by 

the fact that the construction industry is composed of four sectors that differ significantly 

in the outputs produced, firm size, and use of technology. The four sectors, which taken 

together define the construction industry, are residential, commercial/institutional, 

industrial, and infrastructure. 

This report describes efforts underway that focus on the measurement of construction 

productivity at three levels—task, project, and industry—and how such measurements 

can be developed. This report analyzes the measurement challenges associated with the 

development of meaningful measures of construction productivity at the task, project, and 

industry levels and establishes a framework for addressing those challenges. 

Specifically, this report identifies the metrics, tools, and data needed to move forward in 

collaboration with key construction industry stakeholders. Once produced, these metrics, 

tools and data will help construction industry stakeholders make more cost-effective 

investments in productivity enhancing technologies and improved life-cycle construction 

processes; they will also provide stakeholders with new measurement and evaluation 

capabilities. Finally, this report lays the foundation for future research and for 

establishing key industry collaborations that will enable more meaningful measures of 

construction productivity to be produced at the task, project, and industry levels. 

Keywords 

Building economics; construction; economic analysis; information technology; labor 

productivity; metrics; performance measurement; productivity 

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Preface 

This study was conducted by the Office of Applied Economics in the Building and Fire 

Research Laboratory at the National Institute of Standards and Technology. This report 

analyzes the measurement challenges associated with the development of meaningful 

measures of construction productivity at the task, project, and industry levels and 

establishes a framework for addressing those challenges. The intended audience is the 

National Institute of Standards and Technology, as well as, other government agencies 

that compile and publish construction-related statistics, private sector organizations 

concerned about the perceived decline in construction productivity, and standards 

development organizations that produce standards used by the construction industry. 

Disclaimer 

Certain trade names and company products are mentioned in the text in order to 

adequately specify the technical procedures and equipment used. In no case does such 

identification imply recommendation or endorsement by the National Institute of 

Standards and Technology, nor does it imply that the products are necessarily the best 

available for the purpose. 

Disclaimer Regarding Non-Metrics Units 

The policy of the National Institute of Standards and Technology is to use metric units in 

all of its published materials. Because this report is intended for the U.S. construction 

industry that uses U.S. customary units, it is more practical and less confusing to use U.S. 

customary units rather than metric units. Measurement values in this report are therefore 

stated in U.S. customary units first, followed by the corresponding values in metric units 

within parentheses. 

Cover Photograph Credits 

DigitalVision Construction in Action clip gallery image used in compliance with 

DigitalVision’s royalty free digital stock photography use policy. 

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Acknowledgements 

The authors wish to thank all those who contributed so many excellent ideas and 

suggestions for this report. They include Mr. Mark E. Palmer, Leader of the ComputerIntegrated Building Processes Group in the Building and Fire Research Laboratory 

(BFRL) at the National Institute of Standards and Technology (NIST), for his technical 

guidance, suggestions, and support. Special appreciation is extended to Mr. Douglas 

Thomas of BFRL’s Office of Applied Economics (OAE) for his thorough review and 

many insights and to Ms. Carmen Pardo for her assistance in preparing the manuscript for 

review and publication. Special appreciation is also extended to Professor Paul M. 

Teicholz, Professor Emeritus in Civil and Environmental Engineering at Stanford 

University, for the many stimulating exchanges that have truly improved this report. The 

report has also benefited from the review and technical comments provided by Dr. Nicos 

S. Martys and Ms. Geraldine Cheok of BFRL’s Materials and Construction Research 

Division. 

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Table of Contents

Abstract ............................................................................................................................. iii

Preface ................................................................................................................................ v

Acknowledgements ......................................................................................................... vii

Acronyms and Abbreviations ....................................................................................... xiii

Acronyms and Abbreviations ....................................................................................... xiii

1 Introduction ............................................................................................................... 1

1.1 Background .......................................................................................................... 1

1.2 Purpose ................................................................................................................. 3

1.3 Scope and Approach ............................................................................................. 3

2 Construction: An Engine for Economic Growth ................................................... 7

2.1 Value of Construction Put in Place ...................................................................... 7

2.2 Overview of the Construction Industry Supply Chain ....................................... 11

2.3 Research and Innovation in the Construction Industry ...................................... 18

3 Productivity and Competitiveness: A Survey of the Literature ......................... 21

3.1 Three Dimensions of Construction Productivity: Task, Project, and Industry .. 21

3.2 Factors Affecting Construction Productivity ..................................................... 22

3.2.1 Skilled Labor Availability......................................................................... 22

3.2.2 Technology Utilization ............................................................................. 27

3.2.3 Offsite Fabrication and Modularization .................................................... 29

3.2.4 Use of Industry Best Practices .................................................................. 30

3.3 Task Level Productivity Metrics ........................................................................ 31

3.3.1 Task Level Productivity Measures ............................................................ 31

3.3.2 Task Level Productivity Estimates ........................................................... 33

3.4 Project Level Productivity Metrics .................................................................... 35

3.4.1 Project Level productivity Measures ........................................................ 35

3.4.2 Project Level Productivity Estimates ........................................................ 36

3.5 Industry Level Productivity Metrics .................................................................. 38

3.5.1 Industry Level Productivity Measures ...................................................... 38

3.5.2 Industry Level Productivity Estimates ...................................................... 39

3.6 Reconciling Industry-Level Productivity Estimates with Task-Level 

Productivity Estimates ............................................................................................... 41

3.6.1 Quality of Industry-Level Productivity Estimates ............................ 41

3.6.1.1 Appropriateness of Output Measure .......................................... 41

3.6.1.2 Lack of Output Deflators ........................................................... 42

3.6.1.3 Quality of Input Measures ......................................................... 44

3.6.2 Changes in Output Mix ............................................................................. 44

3.6.3 Task-Level Productivity Does Not Completely Reflect Industry-Level 

Productivity ........................................................................................................ 47

3.6.4 Different Definitions of Productivity Measures and Different Definitions 

of the Construction Industry ............................................................................... 48

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3.7 Conclusions and Observations ........................................................................... 49

4 Challenges and Opportunities in Construction Productivity Measurement 

Using National Statistics ..................................................................................... 51

4.1 Discussion of the Teicholz Diagram .................................................................. 51

4.3 Approaches for Measuring Construction Productivity ....................................... 54

4.3.1 Focus on Building Types, Gross Output ................................................... 55

4.3.2 Focus on Building Types, Value Added ................................................... 57

4.3.3 Focus on Infrastructure Type, Value Added ............................................. 59

4.3.4 Focus on Specialty Trades ........................................................................ 59

4.3.5 Focus on Residential Building Construction ............................................ 60

5 Summary and Recommendations for Future Research ...................................... 63

5.1 Summary ............................................................................................................ 63

5.2 Recommendations for Future Research ............................................................. 63

5.2.1 Standard Practice for Measuring Task-Level and Project-Level 

Productivity ........................................................................................................ 63

5.2.2 Database of Project-Level Productivity Measures for Capital Facilities .. 64

5.2.4 Industry-Level Productivity Metrics ......................................................... 65

References ........................................................................................................................ 67

Appendix A Metrics of Productivity ......................................................................... 75

A.1 Industry-Level Productivity Measures ............................................................... 75

A.1.1 Labor Productivity .................................................................................... 75

A.1.2 Multifactor Productivity............................................................................ 76

A.1.3 Value-Added Function, Choice of Output Measure, and the Role of 

Intermediate Inputs ............................................................................................. 78

A.1.4 Output Measures ....................................................................................... 80

A.1.4.1 Gross Output versus Value Added ........................................... 80

A.1.4.2 Price Deflators .......................................................................... 81

A.1.5 Labor Input................................................................................................ 83

A.1.5.1 Hours (Production Workers) .................................................... 83

A.1.5.2 Conversion from Hours Paid to Hours Worked ....................... 83

A.1.5.3 Hours (Nonproduction Workers) .............................................. 84

A.1.5.4 Self-Employment ...................................................................... 85

A.1.5.5 Labor Costs ............................................................................... 86

A.1.5.6 Labor Quality ........................................................................... 86

A.1.6 Capital Input.............................................................................................. 88

A.1.7 Intermediate Inputs ................................................................................... 90

A.2 Aggregation Methods ......................................................................................... 92

A.3 Overview of BLS Productivity Program ............................................................ 93

A.4 Classification Issues ........................................................................................... 93

Appendix B Productivity and Competitiveness: An Annotated Bibliography ..... 97

B.1 Construction Productivity and Related Issues .................................................... 97

B.2 Construction Data ............................................................................................. 108

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B.3 Productivity Data and Measurement ................................................................ 109

Appendix C Sources of Construction Data Related to Productivity and Their 

Availability......................................................................................................... 117

C.1 Sources of Construction Data Related to Productivity ..................................... 117

C.2 Classification Systems, Variables, and Coverage ............................................ 125

Appendix D Glossary of Selected Terms ............................................................... 161

List of Figures

Figure 2.1 2008 Breakdown of $1072 Billion Construction Market ............................... 11

Figure 2.2 Impacts of Construction Industry Supply Chain in 2008 ............................... 12 

Figure 2.3 Value of Construction Work and Value of Business done by Size of 

Establishment........................................................................................17

Figure 3.1 Annual Average Number of Employed Persons in the Construction Industry 

by Age Groups, 2000 through 2008 .................................................................................. 23

Figure 3.2 Percentages of Employed Persons in the Construction Industry by Age 

Groups, 2008 through 2008 .............................................................................................. 24

Figure 3.3 Percentage of Private Construction Workers with Union Membership and 

Percentage of Private Construction Workers under Collective Bargaining Agreement, 

1973 through 2008 ............................................................................................................ 25

Figure 3.4 Construction Industry Production Worker Average Weekly Hourly Wage as a 

Percentage of Total Private Sector Production Worker Average Hourly Wage ............... 26

Figure 3.5 Ratio of Total Installed Cost to Work Hour (Normalized to the Value of Year 

2002................................................................................................................................... 37

Figure 3.6 Changes in Construction Output Mix (1964-2007) ........................................ 46

Figure 3.7 Dollar Amount Single-Family House Construction as a percentage of 

Residential, Commercial, Industrial, and Institutional Construction (1964-2002) ........... 47

Figure 4.1 Construction Labor Productivity and Non-Farm Business Labor Productivity 

Comparison ....................................................................................................................... 51

List of Tables 

Table 2.1 Value of Construction Put in Place in Millions of Constant 2008 Dollars ........ 9

Table 2.2 Value of Construction Put in Place: Sector Totals and Sum Total in Millions of 

Constant 2008 Dollars....................................................................................................... 10

Table 2.33Number of Establishments by Size of Establishment in the Construction 

Industry (2002).................................................................................................................. 16

Table 2.4 Percentage and Cumulative Percentage of Value of Business Done by Size of 

Establishement .................................................................................................................. 18

Table 3.15Compounded Annual Rate of Change in Labor and Multifactor Productivity 

for Activities by Division from 1976 to 1998 ................................................................... 34

Table 3.26Changes in Equipment and Material Technology versus Changes in Labor 

Productivity ....................................................................................................................... 34

Table C.17Standard Industry Classification (SIC) ......................................................... 126

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Table C.28North American Industry Classification System (NAICS) 1997 .................. 127

Table C.39North American Industry Classification System (NAICS) 2007 .................. 128

Table C.410Current Employment Statistics Survey with SIC Classification (Bureau of 

Labor Statistics) .............................................................................................................. 130

Table C.511Current Employment Statistics Survey with NAICS Classification (Bureau of 

Labor Statistics) .............................................................................................................. 132

Table C.612GDP by Industry (Bureau of Economic Analysis) ...................................... 136

Table C.713Producer Price Indices (Bureau of Labor Statistics) ................................... 137

Table C.814Producer Price Indices for Materials and Supply Inputs to Construction 

Industries (Bureau of Labor Statistics) ........................................................................... 138

Table C.915National Income and Product Accounts Private Fixed Investment by 

Structure Type (Bureau of Economic Analysis) ............................................................. 139

Table C.1016National Income and Product Accounts Investment in Government Fixed 

Assets (Bureau of Economic Analysis) .......................................................................... 141

Table C.1117Value of Construction Put in Place (C30 Data), Old Classification System 

(Census Bureau) .............................................................................................................. 144

Table C.1218Value of Construction Put in Place (C30 Data), New Classification System 

(Census Bureau) .............................................................................................................. 146

Table C.1319Industry Series of the 1992 Economic Census .......................................... 148

Table C.1420Industry Series of the 1997 Economic Census .......................................... 149

Table C.1521Industry Series of the 2002 Economic Census .......................................... 151

Table C.1622Variables Reported by Economic Census ................................................. 152

Table C.1723Source Data and Price Indices for BEA’s Annual Estimates of Private Fixed 

Investment in Structures by Type ................................................................................... 155

Table C.1824Web Links to Key Sources ........................................................................ 159

xii 
 

Acronyms and Abbreviations 

AAR Additions, Alterations, and Reconstruction 

BEA Bureau of Economic Analysis 

BFRL Building and Fire Research Laboratory 

BIM Building Information Modeling 

BLS Bureau of Labor Statistics 

CERF Civil Engineering Research Foundation 

CES Current Employment Statistics 

CII Construction Industry Institute 

CPS Current Population Survey 

CURT Construction Users Roundtable 

ECI Employment Cost Index 

GDP Gross Domestic Product 

GPS Global Positioning Satellite 

HWS Hours-at-Work Survey 

ICS Industry Classification System 

KLEMS Capital, Labor, Energy, Materials, and Services 

M&R Maintenance and Repair 

NAICS North American Industry Classification System 

NCS National Compensation Survey 

NIPA National Income and Product Accounts 

NIST National Institute of Standards and Technology 

NRC National Research Council 

NSF National Science Foundation 

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xiv 

OAE Office of Applied Economics 

OECD Organization of Economic Co-Operation and Development 

PPI Producer Price Index 

PPMOF Prefabrication, preassembly, modularization, and offsite fabrication 

R&D Research and Development 

RFID Radio-Frequency Identification 

SIC Standard Industry Classification 

SOC Standard Occupational Classification

TFP Total Factor Productivity 
 

1 Introduction 

1.1 Background 

Although the construction industry is a major component of the U.S. economy, it has 

experienced a “perceived” prolonged period of decline in productivity. Due to the critical 

lack of measurement methods, however, the magnitude of the productivity problem in the 

construction industry is largely unknown. Construction productivity is a highly important 

topic. An analysis of articles published in the American Society of Civil Engineers’ 

Journal of Construction Engineering and Management (JCEM) during 1985-2002 

indicates that productivity is a second highest ranked topic, in terms of number of 

articles.1

 The measurement problem is exacerbated by the fact that the construction 

industry is composed of four sectors that differ significantly in the outputs produced, firm 

size, and use of technology. The four sectors, which taken together define the 

construction industry, are residential, commercial/institutional, industrial, and 

infrastructure. 

To address these challenges, the National Institute of Standards and Technology (NIST) 

requested that the National Research Council (NRC) appoint an ad hoc committee of 

experts to provide advice for advancing the competitiveness and productivity of the U.S. 

construction industry. The committee’s specific task was to plan and conduct a workshop 

to identify and prioritize technologies, processes, and deployment activities that have the 

greatest potential to advance significantly the productivity and competitiveness of the 

capital facilities sector of the U.S. construction industry over the next 20 years.2

 

To assist the committee in its planning for the workshop, NIST prepared a white paper3

describing efforts underway that focus on the measurement of construction productivity 

at three levels: task, project, and industry.4

 The NIST white paper discussed how such 

                                                           

1 Osama Abudayyeh, Amber Dibert-De Young, and Edward Jaselskis, “Analysis of Trends in Construction 

Research: 1985-2002,” Journal of Construction Engineering and Management May/June (2004): 433-439. 

2

 The capital facilities sector includes commercial/institutional buildings (including high-rise and 

multifamily residential), industrial, and infrastructure projects. It does not include single-family and lowrise residential projects. 

3

 Robert E. Chapman and David T. Butry, Measuring and Improving the Productivity of the U.S. 

Construction Industry: Issues, Challenges, and Opportunities, NIST White Paper (Gaithersburg, MD: 

National Institute of Standards and Technology May 2008).

4 Tasks refer to specific construction activities such as concrete placement or structural steel erection. 

Projects are the collection of tasks required for the construction of a new facility (e.g., the construction of a 

new commercial office building, bridge, or power plant) or renovation (i.e., additions, alterations, and 

major replacements) of an existing constructed facility. Industry measures are based on the North 

1 
 

measurements can be developed, how they are related to the use of information and 

automation technologies and construction processes over the life of the project, and how 

to build on several ongoing collaborative efforts aimed at improving the efficiency, 

competitiveness, and innovation of the construction industry. 

NIST briefed the NRC committee in July 2008 on the Building and Fire Research 

Laboratory’s overall research program, its Measurement Science for Advanced 

Infrastructure Delivery goal that focuses on metrics and tools for construction 

productivity, and the contents of the white paper. Members of the NRC committee 

discussed on-going productivity-related research with NIST and asked for 

recommendations of researchers who might be willing to prepare white papers that would 

be presented as part of a major workshop planned for November 2008. As a result of 

NIST’s input and input from other subject-matter experts, three white papers were 

commissioned. The three white papers were presented at the November workshop, which 

was attended by the NRC committee members, several key NIST staff, and 

approximately 50 additional experts. At the end of the workshop, the participants 

identified a range of activities that could improve construction productivity. From among 

these, the committee identified five that could lead to breakthrough improvements in 

construction efficiency and productivity in 2 to 10 years. These activities are highlighted 

in the NRC report which states “If implemented throughout the capital facilities sector, 

these activities could significantly advance construction efficiency and improve the 

quality, timeliness, cost-effectiveness, and sustainability of construction projects.”5 The 

five activities, entitled “Opportunities for Breakthrough Improvements,” are: 

1. Widespread deployment and use of interoperable6

 technology applications, also 

called Building Information Modeling (BIM); 

2. Improved job-site efficiency through more effective interfacing of people, 

processes, materials, equipment, and information; 

3. Greater use of prefabrication, preassembly, modularization, and off-site 

fabrication techniques and processes; 

                                                                                                                                                                                

American Industrial Classification System (NAICS) codes for the construction sector and represent the 

total portfolio of projects. 

5 National Research Council. Advancing the Competitiveness and Efficiency of the U.S. Construction 

Industry. (Washington, DC: National Academies Press, October 2009). 

6

 Interoperability is the ability to manage and communicate electronic data among owners, clients, 

contractors, and suppliers, and across a project’s design, engineering, operations, project management, 

construction, financial, and legal units. 

2 
 

4. Innovative, widespread use of demonstration installations; and 

5. Effective performance measurement to drive efficiency and support innovation.7

Although the focus of this report is on effective performance measurement (activity 5), it 

also provides limited coverage of activities 1 through 4. This is due in part to the 

treatment of those activities in the NIST white paper and the expansion of that treatment 

in various sections of this report. 

1.2 Purpose 

The purpose of this report is to analyze the measurement challenges associated with the 

development of meaningful measures of construction productivity at the task, project, and 

industry levels and establish a framework for addressing those challenges. Measuring 

construction productivity is challenging because on the one hand construction industry 

stakeholders, such as building owners and managers, want easy answers to complicated 

questions that are made available through task-level metrics, while, on the other hand, 

industry leaders, policy makers at the federal and state levels, construction industry 

researchers/academics, and industry specialists demand complicated data-intensive 

metrics to assess national and industry-wide trends and challenges facing this critical 

sector of the U.S. economy. To address these challenges, this report identifies the 

metrics, tools, and data needed to move forward in collaboration with key construction 

industry stakeholders. Once produced, these metrics, tools, and data will help 

construction industry stakeholders make more cost-effective investments in productivity 

enhancing technologies and improved life-cycle construction processes; they will also 

provide stakeholders with new measurement and evaluation capabilities. 

1.3 Scope and Approach 

This report contains four chapters and three appendices in addition to the Introduction. 

Chapters 2 through 4 are the core components of the report. These chapters lay the 

foundation for future research and for establishing key industry collaborations that will 

enable more meaningful measures of construction productivity to be produced at the task, 

project, and industry levels. 

Chapter 2 provides a snapshot of the U.S. construction industry. As such, it provides the 

context within which the scope and size of the construction productivity measurement 

problem is defined. The chapter contains three sections. Section 2.1 presents 

information on the value of construction put in place to show the size of the construction 

industry and each of its four sectors. The four sectors, which taken together define the 

                                                           

7

 National Research Council, Advancing the Competitiveness and Efficiency of the U.S. Construction 

Industry. Op. cit. 

3 
 

construction industry, are residential, commercial/institutional, industrial, and 

infrastructure. Section 2.2 uses information on the construction supply chain to highlight 

the critical importance of manufactured products (materials, components, and systems). 

Section 2.3 places special emphasis on the role of research and innovation in the 

construction industry. 

Chapter 3 provides a survey of the literature on productivity and competitiveness. The 

chapter contains seven sections. Section 3.1 describes the three dimensions of 

construction productivity—task, project, and industry. Section 3.2 discusses the factors 

affecting construction productivity. Sections 3.3 through 3.5 describe existing 

productivity measures and present estimates of construction productivity measures at the 

task, project, and industry levels, respectively. Section 3.6 discusses the divergence 

between task-level and industry-level productivity estimates and presents possible 

explanations for the divergence. Section 3.7 synthesizes a number of conclusions and 

observations from the literature survey. 

Chapter 4 analyzes the challenges and opportunities for using national statistics in 

construction productivity measurement. The chapter is divided into two sections. 

Section 4.1 discusses the widely-referenced productivity comparison diagram produced 

by Paul Teicholz. The discussion focuses on the productivity calculations by Teicholz 

with particular emphasis on the data challenges associated with construction productivity 

measurement. Section 4.2 examines the types of data that are available in national 

statistics and suggests ways in which they would enable the development of meaningful 

productivity measures for the construction industry. 

Chapter 5 concludes with a summary and a discussion of topics for future research. 

Appendix A presents a mathematically-oriented discussion of productivity metrics. The 

metrics described in Appendix A are largely based on the Bureau of Labor Statistics 

productivity methodology. Both single factor labor productivity and multifactor 

productivity methodologies are presented and discussed. 

Appendix B presents an annotated bibliography on productivity and competitiveness. 

The annotated bibliography consists of three sections. Section B.1 focuses on documents 

with particular emphasis on productivity measurement and other related issues in the 

construction industry. Some of the measurement issues covered are deflators, quality 

adjustments of output, and the definition of what constitutes the construction industry. 

Section B.2 focuses on construction data-related documents. Section B.3 focuses on 

documents that treat the general topic of productivity methods and measurement. 

Appendix C identifies sources of construction productivity data and discusses their 

availability. Appendix C contains two sections. Section C.1 provides a description of 

data sources that may be relevant to construction productivity measurement. Section C.2 

4 
 

describes classification systems, variables, and availability. The section concludes with a 

series of tables cross-referencing key sources of data and their availability. 

Appendix D is a glossary of terms used in economics and construction. 

5 
 

6 
 

2 Construction: An Engine for Economic Growth 

Construction is an engine of growth for the U.S. economy. Investment in plant and 

facilities, in the form of construction activity, provides the basis for the production of 

products and the delivery of services. Investment in infrastructure promotes the smooth 

flow of goods and services and the movement of individuals. Investment in housing 

accommodates new households and allows existing households to expand or improve 

their housing. It is clear that construction activities affect nearly every aspect of the U.S. 

economy and that the industry is vital to the continued growth of the U.S. economy. 

This chapter provides a snapshot of the U.S. construction industry. As such, it provides 

the context within which the scope and size of the construction productivity measurement 

problem is defined. The chapter contains three sections. Section 2.1 presents 

information on the value of construction put in place to show the size of the construction 

industry and each of its four sectors. The four sectors, which taken together define the 

construction industry, are residential, commercial/institutional, industrial, and 

infrastructure. Section 2.2 uses information on the construction supply chain to highlight 

the critical importance of manufactured products (materials, components, and systems). 

Section 2.3 places special emphasis on the role of research and innovation in the 

construction industry. 

2.1 Value of Construction Put in Place 

This section provides information on a key indicator of construction activity; the value of 

construction put in place. Data published by the U.S. Bureau of the Census are used to 

establish the composition of construction expenditures by type of construction/function 

(e.g., non-residential/office building). These expenditures are then assigned to four key 

construction industry sectors. The reference document used throughout this section is the 

Current Construction Reports series C30 publication Value of Construction Put in Place. 

The data presented in the C30 report are summarized in Table 2.1. To facilitate 

comparisons between this report and the C30 report, Table 2.1 uses the same row and 

column headings as are used in the C30 report. Table 2.1 records annual values in 

millions of constant 2008 dollars for the years 2002 through 2008.8

                                                           

8 Inflation reduces the purchasing power of the dollar over time; deflation increases it. When amounts are 

stated in actual prices as of the year in which they occur, they are said to be in current dollars. Current 

dollars are dollars of any one year’s purchasing power, inclusive of inflation/deflation. That is, they reflect 

changes in purchasing power of the dollar from year to year. In contrast, constant dollars are dollars of 

uniform purchasing power, exclusive of inflation/deflation. Constant dollars indicate what the same good 

or service would cost at different times if there were no change in the general price level to change the 

purchasing power of the dollar. For additional information on conducting economic analyses using either 

constant dollars or current dollars, see Sieglinde K. Fuller and Stephen R. Petersen, Life-Cycle Costing 

7 
 

Reference to Table 2.1 reveals that total construction expenditures in real terms increased 

gradually from 2002 ($1015 billion) to 2006 ($1247 billion) and then declined in 2007 

($1195 billion) and 2008 ($1072 billion). Table 2.1 is organized to allow for in-depth 

analyses of the components/subcomponents of total construction expenditures. To 

facilitate such analyses, the data presented in Table 2.1 are initially divided into two 

parts: (1) private construction; and (2) public construction. 

Private construction contains two major components—residential buildings and nonresidential buildings—plus a number of subcomponents. Both the two major components 

and the subcomponents are shown as headings in the first column of Table 2.1. 

The residential buildings component includes new private housing and improvements. 

New private housing includes new houses and town houses (single family) and 

apartments and condominiums (multifamily). The value of improvements put in place is 

a direct measure of the value of residential additions and alterations activities. 

The non-residential buildings component includes manufacturing (industrial), office 

buildings, lodging, and commercial. Also falling under the non-residential buildings 

component are religious, educational, health care, and public safety. 

Rounding out the private construction component are farm non-residential, public 

utilities, and “all other private.” These are generally of a non-residential nature, but are 

not part of non-residential buildings. Farm non-residential construction includes 

structures such as barns, storage houses, and fences. Land improvements such as 

leveling, terracing, ponds, and roads are also a part of this subcomponent. Privately 

owned public utilities construction is categorized by industry rather than function of the 

building or structure. This subcomponent includes expenditures made by utilities for 

telecommunications, railroads, petroleum pipelines, electric light and power, and natural 

gas. “All other private” includes privately owned streets and bridges, sewer and water 

facilities, airfields, and similar construction. 

For public construction, there are two major components—residential and nonresidential. Both the two major components and the various subcomponents are shown as 

headings in the first column of Table 2.1. The non-residential building component 

contains subcomponents similar to those for private construction, with educational 

buildings being the largest subcomponent. Expenditures for the non-building 

                                                                                                                                                                                

Manual for the Federal Energy Management Program. NIST Handbook 135. (Gaithersburg, MD: 

National Institute of Standards and Technology, 1996). 

8 
 

subcomponents overwhelmingly consist of outlays for highways and streets, with sewer 

systems being a distant second subcomponent.9

Table 2.1 Value of Construction Put in Place in Millions of Constant 2008 Dollars9

2002 2003 2004 2005 2006 2007 2008

Total Construction 1,014,728 1,043,163 1,130,154 1,215,644 1,246,914 1,194,869 1,072,132

Total Private Construction 759,287 790,267 879,195 957,501 974,170 894,697 766,170

Residential 474,763 521,917 607,385 674,571 655,447 512,184 350,078

New Housing Units 357,651 404,502 475,856 530,052 500,665 367,740 229,934

New single family 318,214 363,412 430,329 477,911 444,273 316,902 185,776

New multi-family 39,437 41,090 45,527 52,141 56,392 50,839 44,158

Improvements 117,112 117,415 131,529 144,519 154,782 144,444 120,144

Nonresidential 284,524 268,351 271,811 282,930 318,723 382,513 416,092

Lodging 12,527 11,619 13,657 13,963 18,822 28,536 35,379

Office 42,242 35,781 37,475 41,094 48,785 55,881 57,084

Commercial 70,620 67,288 72,028 73,404 78,355 89,155 81,495

Health Care 26,854 28,337 29,944 31,414 34,192 36,954 39,101

Educational 15,689 15,708 14,476 14,098 14,780 17,332 18,585

Religious 9,975 10,015 9,293 8,505 8,266 7,811 7,097

Public Safety 260 216 329 450 447 618 650

Amusement and Recreation 8,950 9,105 9,611 8,276 9,960 10,584 10,316

Transportation 8,106 7,685 7,797 7,854 9,242 9,355 9,896

Communication 22,002 16,915 17,630 20,776 23,695 28,543 25,496

Power 39,025 39,338 31,184 28,998 33,282 49,184 68,702

Sewage and Waste Disposal 294 325 377 265 326 424 548

Water Supply 475 460 462 359 509 536 696

Manufacturing 27,220 25,080 26,975 32,947 37,471 47,042 60,784

Other 286 476 573 528 591 558 263

Total Public Construction 255,441 252,896 250,958 258,143 272,744 300,172 305,962

Residential 6,300 6,103 6,278 6,182 6,496 7,499 7,330

Nonresidential 249,141 246,792 244,681 251,961 266,248 292,674 298,632

Office 10,750 10,343 10,856 9,356 9,085 11,884 13,222

Commercial 4,203 4,709 4,402 4,033 3,572 3,974 3,447

Health care 5,626 5,982 6,738 6,543 6,895 8,493 8,598

Educational 72,709 71,251 70,152 73,751 75,921 83,142 85,496

Public safety 9,108 8,163 7,671 7,613 7,850 9,975 12,286

Amusement and recreation 11,790 10,608 9,418 8,520 10,367 11,442 11,172

Transportation 22,747 21,228 20,766 19,764 20,623 23,746 24,057

Power 5,022 9,163 9,158 10,099 9,174 12,398 11,457

Highway and street 68,636 66,667 66,442 70,323 76,431 79,176 81,592

Sewage and waste disposal 19,138 19,078 20,058 21,637 24,436 25,403 24,596

Water supply 14,415 14,159 13,922 15,106 15,467 15,869 16,255

Conservation and development 4,208 4,322 4,410 4,765 5,390 5,353 5,350

Other 790 1,120 688 450 1,037 1,818 1,104

Type of Construction Millions of Constant Dollars (2008)

Source: Census C30 Report. Individual entries may not sum to totals due to independent rounding.

To get the sector totals, each subcomponent was assigned to a sector and summed. The 

sector totals and the overall total are recorded in Table 2.2. Reference to the table reveals 

                                                           

9 Value of construction put in place is reported in current dollars by the Census Bureau. Constant 2008 

dollars are obtained using consumer price indices. 

9 
 

that sector totals vary considerably, with residential normally being the largest and 

industrial the smallest. 

Table 2.2 reveals that the commercial/institutional, industrial, and infrastructure sectors 

grew more or less consistently in real terms over the entire seven-year period. In real 

terms, expenditures in the commercial sector grew from $301.8 billion in 2002 to $384.4 

billion in 2008, an increase of almost 30 %. Real expenditures for two of the four 

sectors, industrial and infrastructure, were essentially constant between 2002 and 2005 

and then increased sharply between 2006 and 2008. Real expenditures for the industrial 

sector grew from $27.4 billion in 2002 to $61.3 billion in 2008, an increase of almost 

125 %. Over the 2002 to 2008 period, real expenditures for infrastructure increased by 

slightly more than 30 %. Real expenditures for the residential sector exhibited a cyclical 

pattern that highlights the magnitude of the current housing crisis. Real expenditures for 

the residential sector first increased sharply, from $481.1 billion in 2002 to $680.8 billion 

in 2005, declined gradually in 2006 (to $661.9 billion), and then fell precipitously in 

2007 (to $519.7 billion) and 2008 (to $357.4 billion). 

Table 2.2 Value of Construction Put in Place: Sector Totals and Sum Total in 

Millions of Constant 2008 Dollars 

2002 2003 2004 2005 2006 2007 2008

Residential 481,063 528,020 613,663 680,753 661,944 519,684 357,408

Commercial/Institutional 301,784 290,052 296,490 301,233 327,855 377,068 384,394

Manufacturing 27,438 25,167 27,136 33,117 37,913 47,475 61,269

Public Works 204,443 199,924 192,868 200,543 219,204 250,642 269,062

TOTAL 1,014,728 1,043,163 1,130,154 1,215,644 1,246,914 1,194,869 1,072,132

Type of Construction

Millions of Constant Dollars

Source: Census C30 Report. Note that due to rounding the values entered in the “Total” row in Table 2.2, 

differ slightly from the values entered in the “Total Construction” row in Table 2.1.

The data contained in Table 2.2 provide the basis for calculating each sector’s relative 

share of total construction expenditures. Each sector’s relative share of total construction 

expenditures is shown graphically in pie chart form in Figure 2.1. It was constructed 

using 2008 data from Table 2.2. Figure 2.1 reveals that in 2008 the commercial sector 

accounted for 36 % of total construction expenditures, followed by the residential sector 

with 33 % of total construction expenditures. Over the longer term, the 

commercial/institutional sector’s relative share of total construction expenditures is 

usually exceeded by the residential sector, which normally constitutes about 45 % of the 

total. However, due to the current housing crisis, their relative shares were reversed. 

Historically, the commercial sector’s relative share tends to exceed the combined total for 

the industrial and infrastructure sectors. 

10 
 

11 

Figure 2.1 2008 Breakdown of $1072 Billion Construction Market 

Residential, 

33%

Commercial 

and 

Institutional, 

36%

Industrial, 5%

Infrastructure, 

25%

2.2 Overview of the Construction Industry Supply Chain 

A total industry supply chain for construction gives a more complete representation of 

construction work in the United States. Complete data is not gathered on an annual basis; 

however, there is sufficient data in the 1997 and 2002 Census of the Construction 

Industry reports to extrapolate construction data that is gathered on an annual basis. 

Using the Census Bureau’s C30 annual figures for construction put in place along with 

Census data from 1997 and 2002, one can calculate values for five components of the 

construction industry: facility design; facility construction; renovation; maintenance and 

repair; and a value for materials, components, supplies, and fuels. Other components of 

the construction supply chain include contents and furnishings, operation and use, 

demolition, and losses. Each of these components is labeled in Figure 2.2, which records 

both the linkages between supply chain components and their estimated values. 
Figure 2.2 Impacts of Construction Industry Supply Chain in 2008 

12 
 

In 2008, the construction industry’s contribution to gross domestic product (GDP) was $582 

billion (see Figure 2.2), or 4.1 % of GDP.10 In 2008, the value of construction put in place was 

$1072 billion ($750 billion for new construction, $323 billion for additions, alterations, and 

reconstruction (AAR).11 Table 2.2 reveals that the value of construction put in place declined by 

6.8 % from 2007 to 2008. This decline was caused by a 34.3 % decline in new residential 

construction and a 13.6 % decline in residential renovations (see Table 2.1). The total of these 

two declines resulted in a -28.6 % change in the value of residential construction put in place. 

The remaining sectors of construction, commercial/institutional, industrial, and infrastructure, 

grew by 5.9 %, 34.0 %, and 11.5 % respectively. Overall, new construction declined by 9.4 % 

while renovations declined by 0.2 %. 

Maintenance and repair activities are an integral part of the construction industry. Expenditures 

for maintenance and repair (M&R) amounted to $134 billion in 2008.12 Thus, the total volume 

of construction work in 2008—equal to the value of construction put in place plus expenditures 

for maintenance and repair—was $1207 billion. It is important to note that expenditures for 

maintenance and repairs declined by 9.4 % from 2007 to 2008. 

Approximately 30 % of the volume of construction work—$329 billion—was due to the demand 

for manufactured products (materials, components, and systems).13 Note that expenditures for 

manufactured products are derived as percentages of expenditures for facility design services, 

new construction, AAR, and M&R. Thus, expenditures on manufactured products are tied to the 

volume of construction work done. Consequently, these expenditures decreased by 7.1 % from 

2007 to 2008. 

Figure 2.2 is organized so that expenditures are not double counted. Since expenditures for 

manufactured products (materials, components, and systems) are derived as percentages of 

expenditures for facility design services, new construction, AAR, and M&R, the values for the 

latter items are reduced by the appropriate percentage. Facility design services is also a derived 

                                                           

10 Bureau of Economic Analysis, “Gross-Domestic-Product-(GDP)-by-Industry Data.” Industry Economic Accounts 

(Washington, DC: Bureau of Economic Analysis), http://www.bea.gov/bea/dn2/gdpbyind_data.htm (accessed July 

2009). 

11 United States Census Bureau: Manufacturing and Construction Division, “Annual Value of Construction Put in 

Place.” Current Construction Report (CCR) C30 (Washington, DC: United States Census Bureau, July 2009), 

http://www.census.gov/const/C30/total.pdf (accessed July 2009). 

12 The value for maintenance and repair is calculated by using the ratio of maintenance and repair to new 

construction put in place from the 1997 census and multiplying it by the current value for new construction put in 

place. 

13 The value of manufactured products, materials, components, and systems is calculated using ratios from the 2002 

census. United States Census Bureau. “2002 Economic Census: Construction Subject Series.” Industry General 

Summary: 2002. EC02-23SG-1 (Washington, DC: U.S. Census Bureau, October 2005). 

13 
 

calculation; it is derived based on data from the 2002 Census of the Construction Industry for 

architectural services, surveying services, and engineering services. The total thus derived for 

facility design services is allocated according to the percentage shares between the value of new 

construction and AAR put in place, also from the 2002 Census of the Construction Industry. 

Four components recorded in Figure 2.2 are of particular importance in understanding how the 

double counting of expenditures is avoided; they are: (1) facility design; (2) facility construction; 

(3) renovation; and (4) maintenance and repair. The value of facility design recorded in 

Figure 2.2, $109 billion, equals the sum of architectural services ($32.0 billion), surveying 

services ($5.4 billion), and engineering services ($73.7 billion) for a total of $111.2 billion14 less 

manufactured products associated with these services ($2.2 billion). The value for facility 

construction in Figure 2.2, $467 billion, equals the value of new construction put in place 

($749.7 billion) less new construction-related facility design services ($79.7 billion) and new 

construction-related manufactured products ($202.7 billion). The value for renovation recorded 

in Figure 2.2, $204 billion, equals the value of AAR ($323.4 billion) less AAR-related facility 

design services ($31.6 billion) and AAR-related manufactured products ($87.5 billion). The 

value for maintenance and repair recorded in Figure 2.2, $97 billion, equals M&R expenditures 

($133.6 billion) less M&R-related manufactured products ($36.4 billion). Thus, the value of 

manufactured products (materials, components, and systems) recorded in Figure 2.2, 

$329 billion, equals the sum of manufactured products associated with: (1) facility design 

services ($2.2 billion); (2) new construction ($202.7 billion); (3) AAR ($87.5 billion); and (4) 

M&R ($36.4 billion). 

The large value of manufactured products that appear in the construction industry supply chain is 

noteworthy because any productivity improvements associated with those products is not 

captured in productivity calculations for the construction industry. Construction activities often 

involve on-site assembly of manufactured products, which would be captured in part by 

construction productivity calculations. However, recent trends have emphasized the increased 

use of pre-assembly and off-site fabrication, particularly for many industrial applications.15 This 

trend poses a serious measurement challenge for the industry. Consequently, it is discussed in 

detail in Chapter 3. 

                                                           

14 The value of facility design services is allocated according to the percentage shares between the value of new 

construction and AAR put in place. Thus, $79.7 billion is for new construction-related facility design services and 

$31.6 billion is for AAR-related facility design services. 

15 Construction Users Roundtable, “Pre-Assembly Perks: Discover Why Modularization Works,” The Voice. (Fall 

2007), pp 28-31. 

14 
 

Construction also has a major impact on U.S. employment. In 2008, 11.0 million persons were 

employed in the construction industry.16 This translates into 7.6 % of the total U.S. workforce. 

During the 2007 to 2008 period, the construction industry shed 882 000 jobs representing 7.4 % 

of all construction jobs, according to the Current Population Survey. This loss was the most 

severe among all industries in terms of percent lost and number of jobs lost. No other industry 

exceeded a loss of more than 3 % of employment or more than 400 000 jobs. 

The composition of the construction workforce differs from much of the U.S. workforce due to 

the large number of self-employed workers (sole proprietorships and partnerships). Within the 

construction industry, there are 1.8 million self-employed workers. In contrast, manufacturing, 

which employs 15.9 million workers, has only 308 thousand self-employed workers.17 The large 

number of self-employed workers both reduces the size of the average firm and increases 

fragmentation within the construction industry. Table 2.3 shows number of establishments in 

construction industry by size of establishment.18 Nonemployers, which are businesses without 

paid employees that are subject to federal income tax, constitute about 2 million establishments 

and represents 74.46 % of all establishments in the construction industry. Establishments with 1 

to 4 employees constitute another 15.17 % of all establishments. Nonemployers, together with 

establishments with 1 to 4 employees, represent nearly 90 % of all establishments. Figure 2.3 

shows value of construction work and value of business done by size of establishment. Value of 

construction work is defined as receipts, billings, or sales for construction work. Value of 

business done is the sum of value of construction work and other business receipts.19 For 

nonemployers, only receipts data are available, and this variable is labeled “value of business 

done” in Figure 2.3. Figure 2.3 shows that value of construction work or value of business done 

is much more evenly distributed among size categories. Table 2.4 shows percentage and 

cumulative percentage of value of business done in each size category. Nonemployers and 

establishments with 1 to 4 employees each perform about 9 % of total value of business done. In 

other words, establishments with 5 or more employees, which constitute 10 % of all 

establishments, perform 82 % of total value of business done. The prevalence of self-employed 

workers and small-sized establishments complicates the adoption of new technologies and 

practices. Construction employment is affected by both the weather and the business cycle. 

Thus, year-to-year changes in employment can be substantial, resulting in layoffs and hiring 

surges. The cyclical nature of construction employment produces shortages in many highly-

                                                           

16 United States Bureau of Labor Statistics, “Household Data: Employed Persons in Nonagricultural Industries by 

Sex and Class of Worker.” Current Population Survey (Washington, DC: Bureau of Labor Statistics), 

http://www.bls.gov/cps/cpsaat16.pdf (accessed July 2009). 

17 Ibid. 

18 Nonemployer Statistics. 

19 2002 Economic Census.

15 
 

skilled trades. These shortages adversely impact productivity in the construction industry. 

Finally, declining construction productivity is exacerbated by the influx of unskilled labor from 

abroad, many of whom find their first employment opportunity in the construction industry. 

Table 2.33Number of Establishments by Size of Establishment in the Construction 

Industry (2002) 

Number of establishments Percentage of total number of establishments

nonemployers 2 071 317 74.5 %

1 to 4 employees 421 959 15.2 %

5 to 9 employees 140 498 5.1 %

10 to 19 employees 78 917 2.8 %

20 to 49 employees 46 625 1.7 %

50 to 99 employees 13 649 0.5 %

100 to 249 employees 6640 0.2 %

250 to 499 employees 1434 0.05 %

500 employees or more 585 0.02 %

Source: 2002 Nonemployer Statistics and 2002 Economic Census. 

 

16 
 

Figure 2.3 Value of Construction Work and Value of Business done by Size of 

Establishment 

0

50

100

150

200

250

Billions of Dollars

Value of construction work

Value of business done

Source: 2002 Nonemployer Statistics and 2002 Economic Census. 

17 
 

Table 2.4 Percentage and Cumulative Percentage of Value of Business Done by Size of 

Establishement 

Percentage of value 

of business done

Cumulative percentage of 

value of business done

nonemployers 8.7 % 8.7 %

1 to 4 employees 9.4 % 18.1 %

5 to 9 employees 8.9 % 27.0 %

10 to 19 employees 11.2 % 38.2 %

20 to 49 employees 16.7 % 54.9 %

50 to 99 employees 13.2 % 68.0 %

100 to 249 employees 15.4 % 83.4 %

250 to 499 employees 7.9 % 91.3 %

500 employees or more 8.7 % 100 %

Source: 2002 Nonemployer Statistics and 2002 Economic Census. 

2.3 Research and Innovation in the Construction Industry 

Given the demonstrated large impact of construction on the nation’s macroeconomic objectives, 

effective construction research becomes critical to the economy. Key drivers for change in 

construction research are sustainability; competition due to globalization and offshoring; 

homeland security and disaster resilience; infrastructure renewal; demand for better, faster, and 

less costly construction; and information technology. 

The problem is that the U.S. construction industry invests little in research relative to its 

significant GDP contribution to the economy. A landmark study co-sponsored by the Civil 

Engineering Research Foundation (CERF) and the National Science Foundation (NSF) involved 

a nationwide survey of civil engineering-related research and development (R&D). The study, 

later published by CERF,20 is especially noteworthy because it includes R&D associated with 

each of the key construction industry stakeholders. The CERF study reported that all key 

construction industry stakeholders combined invested in R&D at a rate that corresponds to only 

0.5 % of the value of construction put in place. This translates into approximately $5.4 billion in 

2008. A recently published NSF study covering companies performing industrial R&D provides 

a useful contrast.21 Private sector R&D investments in manufacturing totaled nearly $167 billion 

in 2007. Total R&D investments in construction were even surpassed by segments of the 

manufacturing industry (e.g., $9.8 billion for machinery, a mature segment of the industry). 

                                                           

20 Civil Engineering Research Foundation, A Nationwide Survey of Civil Engineering-Related R&D. CERF Report 

#93-5006 (Reston, VA: American Society of Civil Engineers, 1994). 

21 http://www.nsf.gov/statistics/infbrief/nsf079316/ (accessed July 2009). 

18 
 

Underinvesting reduces the potential for research-inspired innovations that contribute to 

substantial national benefits—namely constructed facilities that are more user and 

environmentally friendly, affordable, productive, and that are easier, faster, and more life-cycle 

cost effective to build, operate, and maintain. Given the impact of construction spending on the 

economy’s health, and that construction research helps make construction workers more 

productive and the construction industry more globally competitive and profitable, construction 

research becomes a critical variable in generating economic growth. 

Although the generally accepted perception of the construction industry views innovation as a 

rare occurrence, in actuality it occurs consistently throughout the industry. Construction 

innovation offers the potential for significant company, industry, and societal benefits. As the 

demand rises for increasingly complex facilities, and the traditional sources of skilled labor 

shrinks, many construction firms are looking for design and technology innovations to improve 

their products and services and reduce their costs. Owners and clients seek construction 

innovations to increase the technical feasibility of their proposed projects and improve the 

performance of the completed facility. 

Slaughter’s paper on “Models of Construction Innovation” is especially instructive.22 In that 

paper, five models of construction innovation are presented as a basis for construction firms to 

plan and carry out activities to effectively use specific construction innovations. These models 

are based upon established theories in management and economics but are modified to reflect the 

special conditions associated with constructed facilities, such as their scale, complexity, 

durability, and organizational contexts. For the purposes of project incorporation, the five 

categories of innovation are differentiated with respect to their degree of change from current 

practices and their links to other components and systems. Based on these models of innovation, 

firms can evaluate what they must do to implement the innovations. This framework can provide 

firms with a means through which to reduce the perceived risks of using construction 

innovations, and thereby somewhat lower the barriers to those innovations throughout the 

industry.23

                                                           

22 E. Sarah Slaughter, “Models of Construction Innovation.” Journal of Construction Engineering and Management. 

Vol. 124 (May/June 1998), pp. 226-231. 

23 Ibid. 

19 
 

20 
 

3 Productivity and Competitiveness: A Survey of the Literature 

This chapter provides a survey of the literature on productivity and competitiveness. The chapter 

contains seven sections. Section 3.1 describes the three dimension of construction 

productivity—task, project, and industry. Section 3.2 discusses the factors affecting construction 

productivity. Sections 3.3 through 3.5 describe existing productivity measures and present 

estimates of construction productivity measures at the task, project, and industry levels, 

respectively. Section 3.6 discusses the divergence between task-level and industry-level 

productivity estimates and presents possible explanations for the divergence. Section 3.7 

synthesizes a number of conclusions and observations from the literature survey. 

3.1 Three Dimensions of Construction Productivity: Task, Project, and Industry 

The nature of the construction process points to a need for measures of construction productivity 

at three levels: (1) task; (2) project; and (3) industry. Tasks refer to specific construction 

activities such as concrete placement or structural steel erection. Projects are the collection of 

tasks required for the construction of a new facility (e.g., the construction of a new commercial 

office building) or renovation (i.e., additions, alterations, and major replacements) of an existing 

constructed facility. Industry measures are based on the North American Industrial 

Classification System (NAICS) codes for the construction sector and represent the total portfolio 

of projects. 

Producing measures of construction productivity at each level involves the development of both 

metrics (i.e., the definition of the appropriate measure [parameter] that forms the basis for the 

calculation) and tools (i.e., the means through which construction industry stakeholders can 

perform the calculation for the selected metrics). Once produced, these metrics and tools will 

help construction industry stakeholders make more cost-effective investments in productivity 

enhancing technologies and life-cycle construction processes; they will also provide stakeholders 

with new measurement and evaluation capabilities (e.g., enabling them to simulate key elements 

of the project delivery process). 

The basic concept underlying construction industry productivity measures is a comparison of the 

output of a task, project, or industry with the corresponding factors of production (inputs) 

required to generate that output.24 The output and inputs of production thus constitute the basic 

components of every productivity measure. Typically, productivity measures are formulated as a 

ratio of output to one or more inputs. If only one of the inputs is used, then the ratio is a single 

factor productivity measure. A common example of this type of measure is output per labor 

hour. If all of the inputs are used, then the ratio is a multifactor productivity measure. 

                                                           

24 Stephen F. Weber and Barbara C. Lippiatt, Productivity Measurement for the Construction Industry. NBS 

Technical Note 1172 (Gaithersburg, MD: National Bureau of Standards, February 1983). 

21 
 

3.2 Factors Affecting Construction Productivity 

Much has been published about the factors that affect construction productivity. Although a 

comprehensive treatment is beyond the scope of this study, several key factors are usually cited 

in the literature. These factors are: (1) skilled labor availability; (2) technology utilization; (3) 

offsite fabrication and modularization; and (4) use of industry best practices. 

3.2.1 Skilled Labor Availability 

One of the greatest challenges facing the construction industry is its ability to attract and retain 

qualified workers. This is underscored by the fact that shortages of skilled workers continue to 

plague the construction industry.25 A 1996 survey by the Business Roundtable, for example, 

found that over 60 % of its members who responded to the survey reported shortages of skilled 

labor on construction projects. Furthermore, 75 % indicated that the trend had worsened during 

the past five years.26 Nearly 90 % of chemical and petrochemical companies have experienced 

difficulty in recruiting skilled craft workers.27 Craft worker shortages appear to be the most 

severe for electricians, pipefitters, and welders. But the survey results also suggest labor 

shortages among all other types of craft workers.28 Most respondents believe this skilled labor 

shortage is driven more by a shrinking skilled workforce, and less by increasing demand.29 

Many industry practitioners have suggested the shortage of skilled labor is a result of aging 

construction workforce, with fewer young people entering the industry. Figure 3.1 shows the 

annual average number of employed persons in the construction industry by age groups from 

1994 through 2008. The median age is plotted against the secondary axis for 2000 through 2008 

with a clear upward trend.30 The median age has risen from 38.7 years old in 2000 to 40.3 years 

old in 2008. Figure 3.2 plots the same data using percentages. The decline of young workers 

(34 years old or younger) in proportion is evident. Part of the decline in 2008 may be due to the 

economic downturn, as inexperienced workers, who tend to be younger, tend to be laid off first. 

Since experienced workers tend to be more productive, as the proportion of experienced workers 

increases, productivity is likely to increase. 

                                                           

25 Construction Industry Institute, The Shortage of Skilled Craft Workers in the U.S. RS 182-1 (Austin, TX: 

Construction Industry Institute, 2003). 

26 The Business Roundtable, Confronting the Skilled Construction Work Force Shortage—A Blueprint for the Future

(The Business Roundtable, October, 1997). 

27 Ibid.

28 Ibid.

29 Ibid.

30 Current Population Survey is the data source, and median age by industry is not available prior to 2000. 

22 
 

With an aging workforce, one concern for the viability of the construction industry is that skills 

and knowledge processed by experienced workers are not being passed onto younger generations 

because there is not enough new blood entering the industry. This challenge is compounded by 

the decline in training programs. Typically, training programs are funded by both owners and 

contractors through union and collective bargaining agreements. While open shop training 

programs exist, they tend to be rare.31 Figure 3.3 shows that the percentage of private 

construction workers that are union members and the percentage of private construction workers 

that are covered by collective bargaining agreements have declined since the 1970s. With the 

decline of union membership and collective bargaining agreements, training programs and the 

number of apprentices also have declined. 

Figure 3.1 Annual Average Number of Employed Persons in the Construction Industry by 

Age Groups, 2000 through 200832

38.7

38.6

39

39.1

39

38.9 38.9

39.3

40.3

37.5

38

38.5

39

39.5

40

40.5

0

2000

4000

6000

8000

10000

12000

14000

Median age

Construction annual average number of employed persons

(thousands)

65 years and older

55‐64  years

45‐54  years

35‐44  years

25‐34  years

20‐24  years

16‐19  years

Median age

                                                           

31 Construction Industry Institute, Construction Industry Craft Training in the United States and Canada. RS 231-1 

(Austin, TX: Construction Industry Institute, 2007). 

32 Current Population Survey. Median age data were not collected prior to 2000. 

23 
 

Figure 3.2 Percentages of Employed Persons in the Construction Industry by Age Groups, 

2008 through 200833

0

10

20

30

40

50

60

70

80

90

100

1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008

Percentage

65 years and older

55‐64  years

45‐54  years

35‐44  years

25‐34  years

20‐24  years

16‐19  years

Difficulty in staffing projects has resulted in increasing costs and schedule delays.34 Skilled 

labor shortage might pose a greater challenge in years to come, as the Bureau of Labor Statistics 

has projected, prior to the current financial crisis, an annual 1 % increase of jobs in the 

construction sector by 2016, reaching a level of 8.5 million.35 This increase in employment 

amounts to 10.2 % from 2006 to 2016. This projected growth in construction jobs is based on a 

projected output growth at a rate of 1.4 % per year to reach $1.9 trillion by 2016.36

                                                           

33 Current population Survey. 

34 Construction Users Roundtable, Confronting the Skilled Construction Workforce Shortage. WP-401. (Cincinnati, 

OH: Construction Users Roundtable, June, 2004). 

35 Eric B. Figueroa and Rose A. Wood, “Industry Output and Employment Projections to 2016,” Monthly Labor 

Review November (2007): 53-85. 

36 Ibid.

24 
 

37

Figure 3.3 Percentage of Private Construction Workers with Union Membership and 

Percentage of Private Construction Workers under Collective Bargaining Agreement, 

1973 through 200837

0.0

5.0

10.0

15.0

20.0

25.0

30.0

35.0

40.0

45.0

1973 1978 1983 1988 1993 1998 2003 2008

Percentage

% Union member % Covered by collective bargaining

Employers have attempted to identify the root causes and to develop strategies to overcome these 

shortages. Construction Industry Institute (CII) and others have funded research on the problem 

and generated potential solutions.38 For instance, using the CII model plant, actual data from 

companies that had implemented training programs, and estimations of benefits from experts, CII 

estimated the return for each dollar invested in training to be between $1.30 to $3.00.39 These 

benefits are in the form of increased productivity and reductions in turnover, absenteeism, and 

                                                           

37 Barry T. Hirsch and David A. Macpherson, "Union Membership and Coverage Database from the Current 

Population Survey: Note," Industrial and Labor Relations Review, 56(2003): 349-54. 

38 Construction Industry Institute, The Shortage of Skilled Craft Workers in the U.S. Op. cit.

39 Construction Industry Institute, Construction Industry Craft Training in the United States and Canada. Op. cit.

25 
 

rework.40 Craft training benefits project financial performance by increasing the craft workers’ 

average duration on a project and reducing turnover.41 Craft training also benefits individual 

workers by increasing their skills and knowledge, income, and job satisfaction. It is also 

essential for providing the skilled labor the industry needs. Despite this research and efforts to 

stem the problem, the construction industry’s skilled worker pool continues to shrink. The 

decreasing number of young people entering the work force and the failure to recruit from nontraditional labor pools exacerbate this trend. Over the past 30 years, real wages of construction 

workers have declined relative to those of other workers (Figure 3.4). Poor industry image, 

tough working conditions, the industry’s perceived poor safety record, and limited career 

development opportunities also have contributed to the decline in the number of people willing 

to enter and remain in the industry. 

42

Figure 3.4 Construction Industry Production Worker Average Weekly Hourly Wage as a

Percentage of Total Private Sector Production Worker Average Hourly Wage42

115

120

125

130

135

140

145

1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010

Percentage

                                                           

40 Ibid. 41 Ibid. 42 Source: Bureau of Labor Statistics. 

26 
 

3.2.2 Technology Utilization 

Technology utilization impacts construction productivity in a number of ways. Historical 

changes in construction equipment have resulted in sustained improvements in task level labor 

productivity. Goodrum and Haas have shown, using commercially available cost estimation 

data, that these improvements stem from better control, amplification of human energy, 

increased functionality, better ergonomics, and better information processing and feedback.43 

Improved level of control refers to advances in machinery and hand tools with built-in capability 

to automatically adjust the level of power or other characteristics of the equipment. One 

example is a concrete vibrator that automatically adjusts the vibration frequency to match the 

concrete’s slump. Better information processing and feedback refers to advances in heavy 

machinery that have the capability of performance monitoring and self-diagnosis systems. 

Overall, these technological advancements have enabled labor productivity to improve by 30 % 

to 45 %.44 Goodrum et al. came to a similar conclusion regarding material characteristics that 

lead to reductions in unit weight and installation flexibility.45 Reductions in unit weight enable 

ease of handling. Installation flexibility refers to the environmental conditions under which a 

material can be installed, such as temperature or moisture ranges. Comparing activities that 

experience such changes in materials with activities that did not, Goodrum et al. found labor 

productivity improved at least twice as much in activities with material improvements over the 

period of study (1977-2004). 

Preliminary analyses of CII Benchmarking data covering information integration and automation 

technologies revealed significant task level productivity improvements.46 Automation 

technologies focus on the degree to which individual work functions (e.g., supply management 

and project management) are automated. Integration technologies focus on the ability to 

exchange information between work functions and their associated databases (e.g., exchanges of 

information among supply management and project management functions). For the four trades 

examined—concrete, structural steel, electrical, and piping—labor productivity was about 30 % 

higher for projects with a high level of automation compared to projects with a low level of 

automation. The difference in labor productivity was about 45 % between projects with different 

levels of integration. 

                                                           

43 Paul M. Goodrum and Carl T. Haas, “Long-Term Impact of Equipment Technology on Labor Productivity in the 

U.S. Construction Industry at the Activity Level,” Journal of Construction Engineering and Management

January/February (2004): 124-133. 

44 Construction Industry Institute, Leveraging Technology to Improve Construction Productivity. RS 240-1 (Austin, 

TX: Construction Industry Institute, October, 2008). 

45 Goodrum, Paul M., M. Yasin, and Z. Dong. “The Relationship Between Changes in Material Technology and 

Construction Productivity.” Mimeo. (Lexington, KY: University of Kentucky). 

46 Construction Industry Institute, Leveraging Technology to Improve Construction Productivity. Op. cit.

27 
 

A field test was conducted to examine how materials tracking and locating technologies can 

contribute to productivity. The use of Radio-frequency identification (RFID) tags and a Global 

Positioning Satellite (GPS) system were coupled to track materials in lay down areas in two CII 

member projects.47 Improved materials tracking was shown to increase productivity at the 

workface because material retrieval became efficient. 

Previous paragraphs describe how technology can enhance productivity of individual tasks. 

Note that while technology can generally improve labor productivity, there is a cost associated 

with employing technology. Improvement in labor productivity is not an ultimate goal. For 

example, capital investment in technology can be increased to improve labor productivity, but 

this approach may not be the optimal solution when overall costs and benefits are considered. 

Building Information Modeling is one technology that can enhance productivity of an entire 

project, from the planning phase to the decommissioning phase. Eastman et al. describe Building 

Information Modeling (BIM) as “a new approach to design, construction, and facility 

management in which a digital representation of the building process is used to facilitate the 

exchange and interoperability of information in digital format.”48 A previous NIST study on 

interoperability estimated the cost of inadequate interoperability in the U.S. capital facilities 

industry to be $15.6 billion per year,49 and therefore enhanced interoperability has a great 

potential in efficiency gains. The National Research Council has identified the use of Building 

Information Modeling as a key activity that could lead to breakthrough improvements in 

construction productivity.50 

Concerns over the perceived decline in construction productivity have stimulated interest in ways 

to use technology and management practices to address this challenge. Current industry efforts 

aimed at the seamless flow of information in an interoperable design and construction 

environment seek to promote labor productivity both by enabling the project team to respond 

quickly and effectively to new requirements, changes in scope, site conditions, and delivery 

delays and by promoting the use of value adding processes and technologies. The CII Strategic 

Plan,51 the FIATECH Capital Projects Technology Roadmap,52 CURT’s efforts to address owner 

                                                           

47 Ibid.

48 Chuck Eastman, Paul Teicholz, Rafael Sacks, and Kathleen Liston, BIM Handbook: A Guide to Building 

Information Modeling for Owners, Managers, Designers, Engineers, and Contractors, (Hoboken, New Jersey: John 

Wiley & Sons, Inc., 2008). 

49 National Institute of Standards and Technology, Cost Analysis of Inadequate Interoperability in the U.S. Capital 

Facilities Industry. NIST GCR-04-867. (Gathersburg, MD: National Institute of Standards and Technology, 2004). 

50 National Research Council, Advancing the Competitiveness and Efficiency of the U.S. Construction Industry, Op. 

cit. 

51 Construction Industry Institute. Strategic Plan (Austin, TX: Construction Industry Institute, 2005). 

28 
 

issues associated with productivity improvement and cost reduction, the American Institute of 

Steel Construction CIS/2 protocol, the Hydraulics Institute’s initiative on electronic data 

exchange, and ASTM’s E 57 Committee are several noteworthy examples. 

3.2.3 Offsite Fabrication and Modularization 

Prefabrication,53 preassembly,54 modularization,55 and offsite fabrication56 (PPMOF) offer 

potential benefits in the increasingly competitive global marketplace. Owners want better 

facilities faster, at the lowest possible cost, and with increased safety. Both owners and 

contractors view PPMOF as a means to meet challenges of demanding schedules, adverse site 

conditions, and limited availability of skilled labor. Offsite fabrication and modularization can 

enable speedier delivery because offsite manufacturing of building components and onsite field 

preparation can proceed in parallel.57 Costs can be reduced because moving part of the onsite 

construction work to a controlled environment offsite can reduce the impact of adverse site 

condition on the project and can enhance safety and productivity.58 Additionally, offsite 

fabrication and modularization is a way to mitigate skilled labor shortage. Modularization has 

been used in the industrial sector for decades. As recent developments in modular construction 

have made this concept more versatile and applicable to the commercial sector,59 increasing 

demand for modularization may emerge. 

However, CII research shows that effective use of these methods requires careful consideration 

of their implications for engineering, transportation, coordination, and project organization.60 To 

                                                                                                                                                                                               

52 FIATECH. Capital Projects Technology Roadmapping Initiative (Austin, TX: FIATECH, October 2004). 

53 Prefabrication: a manufacturing process, generally taking place at a specialized facility, in which various materials 

are joined to form a component part of a final installation. 

54 Preassembly: a process by which various materials, prefabricated components, and/or equipment are joined 

together at a remote location for subsequent installation as a sub-unit; generally focused on a system. 

55 Module: a major section of a plant/building resulting from a series of remote assembly operations and may 

include portions of many systems; usually the largest transportable unit or component of a facility. 

56 Offsite fabrication: the practice of preassembly or fabrication of components both off the site and onsite at a 

location other than at the final installation location. 

57 Construction Users Roundtable, “Pre-Assembly Perks: Discover Why Modularization Works.” Op. cit.

58 Charles M. Eastman and Rafael Sacks, “Relative Productivity in the AEC Industries in the United States for Onsite and Off-site Activities,” Journal of Construction Engineering and Management 134, no. 7 (2008): 517-526. 

59 Construction Users Roundtable, “Pre-Assembly Perks: Discover Why Modularization Works.” Op. cit. 

60 Construction Industry Institute, Prefabrication, Preassembly, Modularization, and Offsite Fabrication in 

Industrial Construction: A Framework for Decision-Making. RS 171-1 (Austin, TX: Construction Industry Institute, 

2002). 

29 
 

successfully incorporate offsite fabrication and modularization in projects, careful upfront 

planning and early decision making are essential.61 The use of PPMOF may also increase the 

level of details required in the design, it may increase the requirement for procurement logistics, 

and it may also limit the ability to inspect work in progress if the fabrication is done remotely.62

Recent advances in design and information technologies, combined with increasing emphasis 

within the industry to address cost, schedule, and labor issues, have proven the use of PPMOF to 

be more viable than ever. In a recent Construction Users Roundtable (CURT) publication, CII 

Director Wayne Crew noted that the use of PPMOF has increased in the last 10 years, especially 

with new technologies such as building information modeling and internet design capabilities.63 

Future workforce shortages will likely encourage the use of PPMOF. PPMOF benefits such as 

reduced construction time, decreased costs, and increased safety have all contributed to its 

popularity, and while many companies in the oil and gas industries have used it for decades, 

others are realizing its full set of benefits. Widespread use of PPMOF has also been identified by 

the National Research Council as a key activity that could lead to breakthrough improvements in 

construction productivity.64

3.2.4 Use of Industry Best Practices 

Management practices affect productivity over the life cycle of a construction project in a 

number of ways, including planning, resource supply and control, and supply of information and 

feedback. Management practices that are inflexible or applied inappropriately can introduce 

inefficiencies that reduce productivity. A key opportunity for breakthrough improvement in 

productivity identified by the National Research Council is improved job-site efficiency through 

effective interfacing of people, processes, materials, equipment, and information.65 To address 

issues associated with management of resources, organizations such as CII have developed a 

suite of best practices aimed at improving the project execution process.66 These practices are 

directed at all phases of the project life cycle, from design, through procurement, fabrication, 

                                                           

61 Construction Users Roundtable, “Pre-Assembly Perks: Discover Why Modularization Works.” Op. cit. 

62 Construction Industry Institute, Prefabrication, Preassembly, Modularization, and Offsite Fabrication in

Industrial Construction: A Framework for Decision-Making. Op. cit. 

63 Construction Users Roundtable, “Pre-Assembly Perks: Discover Why Modularization Works.” Op. cit. 

64 National Research Council, Advancing the Competitiveness and Efficiency of the U.S. Construction Industry. Op. 

cit. 

65 Ibid.

66 For a list of CII knowledge areas, practices, and information resources, see: http://www.constructioninstitute.org/source/Orders/CII_Matrix.cfm?section=orders&OrdersSection=Matrix 

30 
 

construction, commissioning, and operations and maintenance.67 One example of a best practice 

is to incorporate maintainability as a project goal in the design process to enhance reliability and 

reduce total life-cycle costs.68 Other examples of best practices include front-end planning, 

alignment during front-end planning, partnering, team building, project delivery and contract 

strategy, constructability, project risk assessment, change management, zero accident techniques, 

and planning for startup.69 In-depth analyses of the value of best practices on cost and schedule 

control, as well as field rework have been performed.70 Increasing use of best practices is 

associated with improved cost, schedule, and safety performance, for both owners and 

contractors. For owners, the potential cost benefits are estimated to be $1.7 million to $3.4 

million, depending on industry group and project size. For contractors, the potential cost 

benefits can be $7.2 million for the typical $88 million heavy industrial project. Owners benefit 

most from schedule reductions, which can be as much as 16 % or 27 weeks for large projects. 

Finally, in terms of CII’s zero accident best practice, the difference between a 4th quartile (lowest 

practice use) project to a 1st quartile (highest use) project amounts to potential savings of more 

than $200 000 from lost workday cases avoided.71 Note also that the use of BIM can facilitate 

effective planning and management, which are the foundation for efficient processes that 

contribute to overall project success. 

3.3 Task Level Productivity Metrics 

3.3.1 Task Level Productivity Measures 

Tasks refer to specific construction activities such as concrete placement or structural steel 

erection. Task-level metrics are widely used within the construction industry. Most task-level 

metrics are single factor measures and focus on labor productivity. For example, R.S. Means has 

published task level metrics for many years. Typical task-level metrics published by R.S. Means 

estimate how much a given output is produced by a designated crew in a normal 8-hour day.72 

                                                           

67 Construction Industry Institute, Design for Maintainability: Improving Project Return on Investment. RS142-1 

(Austin, TX: Construction Industry Institute, 1999). 

68 Ibid.

69 Youngcheol Kang, William O’Brien, Jiukun Dai, Stephen P. Mulva, Stephen R. Thomas, and Pin-Chao Liao, 

Measuring Interoperability and Best Practices Impacts on Capital Project Productivity. NIST GCR 09-925 

(Gaithersburg, MD: National Institute of Standards and Technology, 2009). 

70 Construction Industry Institute, Benchmarking and Metrics Value of Best Practices Report. BMM 2003-4 (Austin, 

TX: Construction Industry Institute, 2003). 

71 Ibid.

72 R.S. Means. Building Construction Cost Data: 2009. 67th Edition. (Kingston, MA: R.S. Means, 2008). 

31 
 

In this case, the denominator is the number of hours associated with a designated “crew day.” 

Thus, for a designated crew day, higher output is better. In this case higher output equates to 

higher task labor productivity. For some tasks, equipment may be involved, in such cases, R.S. 

Means provides estimates of output that is produced by a designated crew in an 8-hour day along 

with the equipment they use, and these measures can be considered multifactor. 

The CII Benchmarking and Metrics Program uses a different metric to measure task labor 

productivity. CII fixes the output (e.g., cubic yards of concrete put in place) and measures the 

labor hours required to produce that output. In this case, the denominator is the fixed output and 

the numerator is the number of labor hours. Thus, for a given amount of output, lower labor 

hours is better. In this case, lower labor hours equates to higher task labor productivity. 

Both the R.S. Means and the CII task labor productivity metrics include explicit measures of 

output and labor hours in the values reported. Such metrics are easy to understand and are 

widely used within the industry as a basic estimating tool. In addition to resorting to cost 

estimating guides, such as R.S. Means, some contractors collect output and labor hour 

information from their projects and these data become the basis for cost estimation for their 

future projects. To differentiate these metrics from alternative formulations, we use the term 

“raw metrics” to refer to these ratios of input and output. These metrics are raw in the sense that 

they include the units of measure and are based on unadjusted outputs and labor hours. For 

example, the relative prices for selected labor inputs and the given output may vary over time. 

The CII Benchmarking and Metrics Program collects data on a project basis, where productivity 

is but one data element. The raw task level metrics produced by CII include not only the average 

productivity for that task—referred to as a baseline measure—but the full set of observed values. 

The observed raw task productivity values are then rank ordered into a distribution. Once this is 

done, the raw task productivity values can be assembled into quartiles. CII researchers can then 

examine the characteristics for a given task associated with projects in the best performing 

quartile and in the worst performing quartile. 

A task productivity index is an alternative to the raw metrics discussed previously. An index is a 

dimensionless number, pegged to a reference data set, where the reference data set establishes 

the baseline value for one or more components of the index. An index can be a ratio of raw 

metrics. For example, the denominator could correspond to the baseline value for that task’s 

labor productivity (e.g., labor hours per cubic yard of concrete) and the numerator could be the 

value for a specific project. In that case, the computed value of the index shows how that 

project’s task productivity compares to the overall average of the reference data set. 

Alternatively, the numerator could correspond to an average value for a new data set of task 

productivity values collected at some future point in time. Thus, the index can be used to track 

how task productivity is changing over time. 

An index can also incorporate additional information, such as the value of a deflator to help 

32 
 

control for changes in relative prices over time. Because the index is a dimensionless number, 

users can focus on the changes in the index value rather than the functional form of the metric 

underlying the index. If for example, the index value was pegged at 100.0 at time zero and 

higher values are better, then a future value of 102.5 indicates improvement in the amount of 

2.5 %. 

3.3.2 Task Level Productivity Estimates 

Goodrum and Haas examined productivity measures for 200 construction activities over a 22-

year period. The data sources were cost estimating guides.73 They found that average activity 

productivity has increased. Table 3.1 lists compounded annual rate of change in labor and 

multifactor productivity for activities by division from 1976 to 1998. This table is reproduced 

from Goodrum et al. (2002). Labor productivity and multifactor productivity increased for all 

divisions. One exception is that labor productivity for electrical work has stayed the same. 

Furthermore, studies by Goodrum and Haas show that activities that experienced a significant 

change in equipment technology (i.e., hand tools and machinery) generally also witnessed 

substantially greater long-term productivity improvements. Activities that experienced a 

significant change in material technology in terms of modularization, reduction in unit weight, or 

installation flexibility, also experienced greater productivity improvements. These results are 

summarized in Table 3.2. The partial factor productivity used by Goodrum and Haas was 

defined as units of physical output divided by the sum of labor costs and fixed capital costs. 

These authors also conducted other related studies and reached similar conclusions.74 

 

                                                           

73 Paul M. Goodrum and Carl T. Haas, “Partial Factor Productivity and Equipment Technology Change at Activity 

Level in U.S. Construction Industry,” Journal of Construction Engineering and Management 128 (2002): 463-472. 

74 Paul M. Goodrum, Carl T. Haas, and Robert W. Glover, “The Divergence in Aggregate and Activity Estimates of 

US Construction Productivity,” Construction Management and Economics 20, no. 5 (2002): 415-423; Paul M. 

Goodrum and Carl T. Haas, “Long Term Impact of Equipment Technology on Labor Productivity in the U.S. 

Construction Industry at the Activity Level” Op. cit.; E. Allmon, C. T. Hass, J. D. Borcherding, and P. M. 

Goodrum, “U.S. Construction Labor Productivity Trends, 1970-1998,” Journal of Construction Engineering and 

Management 126, no. 2 (2000): 97-104. 

33 
 Table 3.15Compounded Annual Rate of Change in Labor and Multifactor 

Productivity for Activities by Division from 1976 to 199875

Construction Division

Change in labor 

productivity 1976-1998 

(compound annual rates)

Change in multifactor 

productivity 1976-1998 

(compound annual rates)

Sitework +2.8 % +2.4 %

Doors and Windows +1.6 % +1.8 %

Metals +1.5 % +1.0 %

Finishes +1.2 % +1.6 %

Masonry +1.2 % +0.8 %

Concrete +1.1 % +1.4 %

Mechanical +1.0 % +1.4 %

Wood and Plastic +0.3 % +0.4 %

Moisture and Thermal Protection +0.2 % +0.6 %

Electrical +0.0 % +0.8 %

75 76

Technology Characteristic

Equipment Technology 

Characteristic

No Change in Equipment 

Technology Characteristic

Change in Equipment 

Technology 

Characteristic

Δ

Energy 3.6 % 39.8 % 36.2 %

Control 14.9 % 16.6 % 31.7 %

Functional Range 13.5 % 51.8 % 38.3 %

Information Processing 21.0 % 56.4 % 35.4 %

Material Technology 

Characteristic

No Change in Material 

Technology Characteristic

Change in Material 

Technology 

Characteristic

Δ

Modularization 8.1 % 24.2 % 16.1 %

Reduction in Unit Weight 10.4 % 48.6 % 38.2 %

Installation Flexibility 8.7 % 23.1 % 14.4 %

Change in Labor Productivity 

Table 3.26Changesin Equipment and Material Technology versus Changes in Labor 

Productivity76

                                                           

75 Table is reproduced from Paul M. Goodrum, Carl T. Haas, and Robert W. Glover, “The Divergence in Aggregate 

and Activity Estimates of US Construction Productivity” Op. cit.

76 Table is reproduced from Construction Industry Institute, Leveraging Technology to Improve Construction 

Productivity. Research Summary 240-1. October 2008. 

34 
 

3.4 Project Level Productivity Metrics 

3.4.1 Project Level productivity Measures 

Projects are the collection of tasks required for the construction of a new facility (e.g., the 

construction of a new commercial office building) or renovation (i.e., additions, alterations, and 

major replacements) of an existing constructed facility. Since a project is a collection of tasks, 

project level metrics are more complicated. The inputs and outputs for a given task, say concrete 

placement, differ from those of another task, say structural steel erection. Thus, it is not possible 

to aggregate the individual raw task productivity metrics into a project productivity metric unless 

adjustments are made. 

One way to make these adjustments is to use a reference data set to calculate baseline values for 

each task. Information is still needed, however, to calculate a meaningful project level 

productivity metric. For instance, information yielding the task weight (share that it represents to 

the overall project) is required, as is an understanding of the task flows. Because some tasks are 

completed in parallel, while other in series, the composition of the task flows affects overall 

project productivity. Therefore, each component of the project productivity metric contains: (1) 

the task weight; (2) the raw task productivity baseline value in the denominator; (3) the raw task 

productivity value for that project in the numerator; and (4) a measure of the task mix (in parallel 

versus in series task flows). The project productivity index value is a function of the individual 

components. 

The project level productivity metric just described is useful in measuring how an individual 

project compares to the overall average in the reference data set. In addition, data from all 

projects can be compiled into a distribution. Further analyses can then be conducted to identify 

characteristics associated with the best performing or worst performing projects. 

A project level productivity index can also be used to track changes in project productivity over 

time. In this case, the reference data set corresponds to time zero. For each index component, 

the values for the task weights and the task baseline values appearing in the denominator are 

equal to values computed in the reference data set. The numerator in each index component then 

becomes the average value of the corresponding task productivity in the future data set. As 

noted earlier, an index can also include a deflator to adjust for changes in relative prices over 

time. 

An alternative project level productivity index can be produced as follows. We can create an 

index which is the quotient of two ratios, in each ratio the numerator is the value of construction 

put in place and the denominator is the number of field work hours. As noted earlier, a reference 

data set can be used to fix a baseline value for the ratio of value put in place to field work hours. 

The baseline value for the ratio is then used as the denominator in the index calculation. How an 

individual project compares to the baseline is determined by inserting its ratio of value put in 

place to field work hours in the numerator of the index. Alternatively, this project level 

35 
 

productivity index can be used to track changes in productivity over time by following the 

process described in the previous paragraph. 

A related measure is cost per square footage data for a particular type of building. R.S. Means 

produces a square footage model that requires limited inputs, such as building type, exterior wall 

type, structural system, and square footage, and yields rough estimates for the overall cost of a 

project or its major components. 

3.4.2 Project Level Productivity Estimates 

Publicly available project-level productivity estimates tend to be rare. Construction firms collect 

data on project productivity for internal uses, such as cost estimation in bid preparation. As the 

information is pertinent to the competitiveness of the firms, it is not generally shared. The CII 

collects and compiles project-level data from its member organizations. The projects are 

predominantly industrial projects. The resultant dataset, Benchmarking and Metrics Productivity 

Database, is used to study project performance as influenced by factors such as technology and 

best practices. The CII studies are conducted such that information on individual projects 

remains confidential. Since the dataset contains projects of the member organizations, the 

dataset is considered to be representative of member organizations’ projects, which tend to be 

more progressive in terms of project performance improvements, but not of the industry as a 

whole. Project level productivity measures can be calculated using this dataset. Figure 3.5 

shows an index based on total installed cost per field work hour from 2000 through 2007.77 The 

sample size varies from 16 in 2007 to 49 in 2004. The trend suggests a general decline over the 

seven-year span. However, it needs to be noted that changes in productivity may reflect changes 

in the composition of projects, in addition to changes in productivity. Each construction project 

is unique, and the mix of projects in each year is different. This is an intrinsic challenge in 

construction industry productivity analysis. 

                                                           

77 Youngcheol Kang, William O’Brien, Jiukun Dai, Stephen P. Mulva, Stephen R. Thomas, and Pin-Chao Liao, 

Measuring Interoperability and Best Practices Impacts on Capital Project Productivity. Op cit.

36 
 

Figure 3.5 Ratio of Total Installed Cost to Work Hour (Normalized to the 

Value of Year 200278

78

0

20

40

60

80

100

120

2000 2001 2002 2003 2004 2005 2006 2007

Ratio

                                                           

78 Ibid.

37 
 

3.5 Industry Level Productivity Metrics 

3.5.1 Industry Level Productivity Measures 

At the industry level, productivity—the amount (or value) of output produced per unit of input—

provides a measure of industrial efficiency. The Bureau of Labor Statistics (BLS) publishes two 

common measures of productivity: (single factor) labor productivity and multifactor 

productivity. Labor productivity is an output per hour measure. In the case of an industry 

producing multiple outputs, a Tornqvist index (weighted sum of the natural log of the ratio of 

output in different time periods) is used to chain multiple output indices together to form a single 

output measure (see Appendix A). 

Increases in labor productivity may be due to increases in labor quality or labor efforts. 

However, it can also increase simply due to other factors such as technology or increased capital 

utilization, even when labor quality and worker efforts are held constant. 

BLS measures multifactor productivity using output, labor, capital, and intermediate purchases 

input. A Tornqvist index is used to combine the inputs into a single measure of production. 

Multifactor productivity captures growth in output that is not explained by growth in these 

quantifiable inputs. In the growth accounting framework, multifactor productivity is calculated 

as a residual. Multifactor productivity growth can be attributed to factors such as management 

practices, best practices in the production process, etc. Because multifactor productivity is the 

part of output growth not explained by input growth, labor hours in multifactor productivity need 

to be quality adjusted. For instance, labor hours worked by workers with different skill levels 

need to be distinguished in multifactor productivity calculations. When an input quality 

increases, the input can be considered to have grown in quantity at the original quality level. In 

contrast, labor hours used in labor productivity calculations are simply the raw numbers of hours 

worked. 

Multifactor productivity is often a preferred measure compared to labor productivity. This is 

because labor productivity measures are more prone to misinterpretation. Increases in labor 

productivity may reflect increases in the capital-labor ratio, rather than increases in labor quality 

and efforts. Additionally, a unit of production may achieve high levels of labor productivity, but 

the overall productivity may be compromised because the underlying capital-labor ratio may not 

be optimal. Similarly, low labor productivity might be efficient in the sense that low wages 

induce contractors to adopt more labor intensive practices and save on capital costs. Labor 

productivity measures are limited in the sense that they do not reveal a complete picture and are 

prone to misinterpretation. While labor productivity is often a less preferred measure of 

productivity compared to multifactor productivity, it is calculated with much more precision with 

38 
 

fewer assumptions.79 Obviously, the data requirement for labor productivity calculation is also 

significantly less compared to multifactor productivity calculation. 

3.5.2 Industry Level Productivity Estimates 

There are no official productivity measures published by the BLS for the construction industry 

due to lack of suitable data. Productivity estimates of the construction industry do, however, 

exist in the literature. These estimates are produced by scholars in governmental agencies and 

academia. One highly referenced work is the productivity comparison diagram plotted by 

Teicholz.80 In this diagram, constant contract dollars of new construction work per field work 

hour is shown to have trended downward over the past 40 years at an average compound rate of 

 -0.6 % per year. In contrast, labor productivity of all non-farm industries (which includes the 

construction industry) has trended upward at an average compound rate of 1.8 % per year. 

Teicholz believes the reasons for the declines in labor productivity are due to lack of R&D 

spending, fragmentation within the industry, and declining real wage rates. He also notes that 

despite the fact that there has been a significant adoption of new information technology by the 

construction industry over the past 35 years, these applications tend not to be integrated with 

other systems and therefore do not permit improved collaboration by the project team. 

Industry-level productivity estimates made by other scholars tend to show a similar trend. Allen, 

for instance, shows that construction productivity declined between 1968 and 1978 and argues 

that the biggest factor in the decline was the shift in the mix of output from large-scale 

commercial and industrial projects to residential construction and its associated lesser skill 

requirements.81 Stokes also argues that construction productivity declined between 1968 and 

1978 and asserts that the major contributing factor to that decline was slower growth in capital 

per worker.82 This belief that construction productivity is declining is shared by industry 

observers such as the Business Roundtable.83

Other scholars have analyzed productivity trends using more recent data. Harrison examined the 

period between 1961 through 2005 using data from the Bureau of Economic Analysis’ National 

                                                           

79 Jerome A. Mark, “Problems Encountered in Measuring Single- and Multifactor Productivity,” Monthly Labor 

Review (1986): 3-11. 

80 Paul Teicholz, “Discussion of ‘U.S. Construction Labor Productivity Trends, 1970-1998,’” Journal of 

Construction Engineering and Management 127 (2001): 427-428. 

81 Steve G. Allen, “Why Construction Industry Productivity is Declining,” Review of Economics and Statistics

67(1985): 661-669. 

82 H. Kemble Stokes, Jr, “An Examination of the Productivity Decline in the Construction Industry,” Review of 

Economics and Statistics 63 (1981): 495-502. 

83 The Business Roundtable, CICE—The Next Five Years and Beyond (New York, NY: The Business Roundtable, 

1988). 

39 
 

Economic Accounts and Industry Economic Accounts.84 He found the productivity growth was 

 -2.43 % for 1961-1981, 0.13 % for 1981-1989, -1.18 % for 1989-2000, and -0.53 % for 2000-

2005. Multifactor productivity calculated by Jorgenson et al. for the construction industry was 

-1.08 % for the period of 1977-2000.85 Faruqui et al. examined productivity growth for selected 

business sectors between 1987 and 2000.86 During the 1987 to 1996 period, construction 

experienced a slight increase in productivity, whereas between 1996 and 2000, construction 

experienced a sharp decline in productivity. Even during the 1987 to 1996 growth period, 

construction productivity improvements significantly lagged productivity improvements in 

manufacturing, services, and primary industries (i.e., agriculture, fishing, mining, and forestry). 

The general pattern of productivity decline is also found in other studies that used national 

statistics.87

It should be noted that not everyone in the construction industry agrees that construction 

productivity is declining. For example, Young and Bernstein, in their McGraw-Hill 

SmartMarket Report, contend that the U.S. construction industry is making productivity 

improvements through innovation with new technologies, processes, and services.88 Teicholz 

asserts, however, that a fragmented market with very small players makes application of these 

innovations less frequent than desired for a healthy increase in industry productivity. Another 

reason the Teicholz chart may show declining productivity is that it focuses on field work. For 

example, many of the improvements in construction productivity in the oil and gas industries 

over the past decade stem from the use of offsite fabrication facilities, where component 

production is well-controlled and highly-automated.89 The debate about whether construction 

industry is declining, holding its own, or increasing cannot be easily resolved, because there are 

                                                           

84 Centre for the Study of Living Standards, Can Measurement Error Explain the Weakness of Productivity in the 

Canadian Construction Industry? Research Report no. 2007-01 (Ontario: Centre for the Study of Living Standards, 

2007). 

85 Dale W. Jorgenson, Mun S. Ho, and Kevin J. Stiroh, Productivity Volume 3: Information Technology and the 

American Growth Resurgence (Cambridge, Massachusetts and London, England: MIT Press, 2005). 

86 Umar Faruqui, Wulong Gu, Mustapha Kaci, Mirielle Laroche, and Jean-Pierre Maynard, “Differences in 

Productivity Growth: Canadian-U.S. Business Sectors, 1987-2000,” Monthly Labor Review 126 April (2003): 16-29. 

87 Martin Neil Baily and Robert J. Gordon, “The Productivity Slowdown, Measurement Issues, and the Explosion of 

Computer Power,” Brookings Papers on Economic Activity 1988 No. 2 (1988): 347-420; Wulong Gu and Mun S. 

Ho, “A Comparison of Industrial Productivity Growth in Canada and the United States,” American Economic 

Review 90 (2000): 172-175; William Gullickson and Michael J. Harper, “Possible Measurement Bias in Aggregate 

Productivity Growth,” Monthly Labor Review February (1999): 47-67. 

88 Norbert W. Young Jr. and Harvey M. Bernstein, “Key Trends in the Construction Industry—2006.” SmartMarket 

Report (New York, NY: McGraw Hill Construction, July 2006). 

89 Construction Users Roundtable, “Pre-Assembly Perks: Discover Why Modularization Works.” Op. cit.

40 
 

no accurate industry-level measures of productivity for either the construction industry as a 

whole or its components (i.e., commercial, industrial, infrastructure, and residential). 

3.6 Reconciling Industry-Level Productivity Estimates with Task-Level Productivity 

Estimates 

The disparity between the conclusions of industry-level studies and task-level studies has been 

recognized.90 There are many explanations for the observed difference in productivity trends. 

3.6.1 Quality of Industry-Level Productivity Estimates 

The national statistics offices in the U.S. collect tremendous amounts of data. Many elements of 

data required for productivity measurement exist. However, there are challenges associated with 

different classification systems and incomplete coverage. These challenges arise primarily 

because the existing data collection approaches are not designed specifically for productivity 

measurement. 

Productivity measurement requires highly accurate measures of output, inputs, and deflators. 

This requirement applies to both labor productivity and multifactor productivity measurements. 

The requirement is particularly challenging for multifactor productivity measurement because 

multifactor productivity by definition requires more data and because multifactor productivity is 

the residual, the portion of growth in output not explained by growth of inputs. Since the data 

requirement is more limited for labor productivity, this discussion will focus mainly on labor 

productivity measurement in demonstrating the fundamental challenges in implementation. 

3.6.1.1 Appropriateness of Output Measure 

The appropriateness of the output measure is a major challenge in productivity measurement. In 

addition, Lawson et al. have noted the low quality of output data in the construction industry.91 

The Economic Census is a major survey of industries. Because it is an establishment-based 

survey, it only surveys and reports data on establishments with payrolls, and a large number of 

workers in the construction industry are self-employed. Furthermore, because the Economic 

Census covers both general contractors and subcontractors, there is a significant amount of 

double counting. Double counting is a concern if the output measure is gross output, and it is not 

a concern if the output measure of interest is value added. 

                                                           

90 Paul M. Goodrum, Carl T. Haas, and Robert W. Glover, “The Divergence in Aggregate and Activity Estimates of 

US Construction Productivity” Op. cit.

91 Ann M. Lawson, Brian C. Moyer, Sumiye Okubo, and Mark A. Planting, “Integrating Industry and National 

Economic Accounts, First Steps and Future Improvements,” In A New Architecture for the U.S. National Accounts, 

ed. Dale W. Jorgenson, J. Steven Landefeld, and William D. Nordhaus. (Chicago and London: University of 

Chicago Press, 2006). 

41 
 

Another source of output data is Value of Construction Put in Place from the C30 reports 

produced by the Census Bureau. Value Put in Place is collected at the project level. Compared 

to manufacturing, for which data are collected at the establishment level, there are more data 

collecting units for the construction industry. This is one reason for less accurate data for the 

construction industry.92 Another reason is the lack of annual data for benchmarking the value 

put in place data.93 An additional complication associated with the use of value put in place as a 

data source for output measure is that the C30 reports document the total project costs, including 

architectural services, engineering services, construction services, and materials. These different 

types of costs are not distinguished in the reported summary statistics. Contract construction 

cost is reported separately from owner supplied materials and labor and architectural, 

engineering, and miscellaneous costs in the survey form. An output measure based on the 

contract construction cost or the total construction cost would be a gross output measure, with a 

boundary that approximates the construction industry. 

3.6.1.2 Lack of Output Deflators 

An important element in productivity measurement is the price deflators. Price deflators are 

needed to derive a quantity index of output. This is done by dividing the monetary value of 

construction in current dollars with an appropriate price deflator. Deflators are needed to strip 

away price changes due to inflation. In the case of construction, there is an additional challenge 

associated with the product not being uniform. Construction projects are heterogeneous even 

within a well-defined category, such as single family houses. Moreover, what is considered a 

typical new house in 1960 is very different from a typical new house in 2009. In addition to 

stripping away changes in prices due to inflation, there is also a need to define a uniform and 

time invariant “standard” house so that the output quantity index time series is meaningful. The 

nominal price of a new house in 2009 is higher than the price of a new house in 1960 for two 

reasons. The first reason is inflation. The second reason is that the house in 2009 is probably 

larger with more amenities. If the typical house in 1960 is chosen as the “standard house,” the 

quantity index is defined in terms of units of this “standard house.” The larger house with more 

amenities in 2009 is counted as more units of the “standard house,” while taking into account 

price increase due to inflation. 

The construction industry has been known to be deficient in the area of price deflators. Two 

notable price deflators with long time series are associated with the residential sector. The 

Census Bureau publishes price indices for new one-family houses sold and for new one-family 

houses under construction using a hedonic regression model. The series are monthly from 1963 

and from 1964, respectively. Using a similar approach, the Bureau of Economic Analysis 

                                                           

92 Eddy M. Rojas and Peerapong Aramvareekul, “Is Construction Labor Productivity Really Declining?” Journal of 

Construction Engineering and Management 129 (2003): 41-46. 

93 Ibid.

42 
 

(BEA), in conjunction with the Census Bureau, has developed a price index for multifamily 

housing units.94 This price index series extends back to 1978. 

For many years, price deflators based on input data were used for nonresidential construction 

because appropriate deflators did not exist. While this practice of using input cost data still exists 

today, these input-based deflators are used to a lesser extent due to recent development of price 

deflators. Using a deflator based on inputs to deflate output biases multifactor productivity 

towards no change. In the growth accounting framework, multifactor productivity is the residual 

of output growth that is not explained by input growth. In other words, multifactor productivity 

is the ability to produce more output with the same inputs.95 It is also the residual between 

output and input prices. It "represents the means by which a competitive position may be 

enhanced in the absence of input price reductions; the means by which the effects of input price 

increases may be mitigated; or the means by which payments to labor and to the owners of the 

capital may rise without increasing price."96 If there is positive multifactor productivity growth, 

the prices of inputs should grow faster than the prices of outputs. In other words, when input 

cost data are used to deflate output, it is implicitly assumed that the relationship between inputs 

and outputs stays constant, which translates to constant productivity.97 The use of input costs to 

deflate output has been cited as a reason for downward bias in productivity.98 Note however, 

using a cost index to deflate output can affect labor productivity in different ways. That is, using 

a cost index to deflate output could bias output upward or downward. 

Goodrum and Haas point out that a possible source of underestimation of output comes from 

decreases in real wage in construction, which is one component of value of construction put in 

place.99 A properly constructed output price index takes into account decreases in real wage. To 

create an accurate output measure, a high quality price index is fundamental. 

                                                           

94 Frank de Leeuw, “A Price Index for New Multifamily Housing,” Survey of Current Business Feb (1993) 73(2): 

33-42. 

95 Jorgenson, Dale W., “Productivity and Economic Growth,” in Fifty Years of Economic Measurement—the 

Jubilee of the Conference on Research in Income and Wealth, ed. Ernst R. Berndt and Jack E. Triplett (Chicago and 

London: University of Chicago Press, 1990). 

96 William Gullickson, “Measurement of Productivity Growth in U.S. Manufacturing,” Monthly Labor Review. July 

(1995): 13-35. 

97 Paul Pieper, “The Measurement of Construction Prices: Retrospect and Prospect.” In Fifty Years of Economic 

Measurement: The Jubilee of the Conference on Research in Income and Wealth, Volume 54, ed. Ernst R. Berndt 

and Jack E. Triplett (Chicago: University of Chicago Press, 1990). 

98 Edwin R. Dean, “The Accuracy of the BLS Productivity Measures,” Monthly Labor Review February (1999): 24-

34. 

99 Paul M. Goodrum and Carl T. Haas, “Closure to ‘U.S. Construction Labor Productivity Trends, 1970-1998,’” 

Journal of Construction Engineering and Management 127 (2001): 427-429. 

43 
 

The BLS has recently developed producer price indices for the nonresidential sector of the 

construction industry. These new producer price indices cover four types of new building 

construction and four types of specialty trades. These newly available producer price indices 

have been incorporated by the BEA in its estimates of investments in private structures.100 More 

details on the BLS producer price indices are discussed in Appendix A. 

3.6.1.3 Quality of Input Measures 

The main source of labor hours data is the Current Employment Statistics (CES). CES reports 

total number of employees, number of production workers, and average weekly hours of 

production workers by NAICS code. A challenge for the construction industry is that the CES is 

an establishment survey, and the self-employed and unpaid family workers are not within the 

scope of the survey. Based on Current Population Survey data, about 15 % to 19 % of total work 

hours and total workforce are attributable to the self-employed and unpaid family workers. At 

the Bureau of Labor Statistics, the CPS is used to supplement the CES, for data on proprietors 

and unpaid family workers. One limitation of using the CPS to obtain information on the selfemployed is the sample size.101 An implication is that the number of self-employed, the total 

number of workers, and the data work hours may not be accurate, particularly at the industry 

level or a sub-industry level. In CPS, the construction industry is not further categorized at a 

finer level. Coding of industries and reporting are more accurate in establishment level surveys 

compared to household surveys. For this reason, data from the CES are used as a primary source 

of data, and data from the CPS are used as a supplemental source of data in the BLS productivity 

programs.102 For materials flows, which are needed for multifactor productivity measurement, it 

has been noted that although the input-output framework tracks materials flows, the data outside 

of manufacturing tends to be incomplete. 

3.6.2 Changes in Output Mix 

Rojas and Aramvareekul point out that productivity changes can simply be due to changes in 

output mix.103 Residential and commercial building construction is labor intensive, compared to 

industrial and heavy construction, which tends to be capital intensive.104 They contend labor 

                                                           

100 Paul R. Lally, “How BEA Accounts for Investment in Private Structures,” Survey on Current Business February 

(2009): 9-15. 

101 Edwin R. Dean, “The Accuracy of the BLS Productivity Measures,” Op. cit.

102 Lucy P. Eldridge, Marilyn E. Manser, and Phyllis Flohr Otto, “Alternative Measures of Supervisory Employee 

Hours and Productivity Growth,” Monthly Labor Review April (2004): 9-28. 

103 Eddy M. Rojas and Peerapong Aramvareekul, “Is Construction Labor Productivity Really Declining?” Op. cit.

104 Based on 2002 Economic Census, capital labor ratio for construction of buildings was 0.06, and it was 0.15 for 

heavy and civil engineering construction.

44 
 

productivity is lower for residential and commercial building construction than industrial and 

heavy construction. From 1964 through 2007, the output mix changed from 64 % residential and 

commercial and 36 % industrial and heavy construction to 76 % residential and commercial and 

24 % industrial and heavy construction.105 Figure 3.6 shows the general upward trend of 

residential and commercial construction as a fraction of total construction and the general 

downward trend of industrial and heavy construction as a percent of total construction. 

Following Rojas and Aramvareekul’s argument, changing the output mix intrinsically translates into 

decline in measured “labor productivity.”106 This decline in labor productivity due to change in 

output mix is a result of shifting labor and capital usage, and it does not necessarily indicate 

lower labor quality or effort. Allen also argues that labor intensity associated with single-family 

house construction is higher and that the decline in construction productivity between 1968 and 

1978 was partially due to the shift in output mix from large scale commercial, industrial, and 

institutional projects to single-family houses.107 Figure 3.7 shows dollar amount of single-family 

house construction as a percent of residential, commercial, industrial, and institutional 

construction for the period of 1964 and 2002.108 A relative increase in single-family house 

construction between 1968 and 1978 is evident. Between 1964 and 2002, we also observe a 

general upward trend. Estimates of industry-level labor productivity of the construction industry 

tend to show a declining trend. This decline could be partially explained by changes in the 

output mix. 

Rojas and Aramvareekul also point out that the increase in labor productivity in manufacturing 

may be partially due to changes in output mix. If the changes in output mix are taken into 

account, the increase in labor productivity is smaller. This example illustrates the importance of 

accounting for all inputs in productivity measures. It also indicates the importance of focusing 

on homogenous products. When productivity is calculated for homogenous building or 

infrastructure types, the influence of changes in output mix is taken away. 

 

                                                           

105 The raw data on which these values are based are originated from Census Bureau’s Value of Construction Put in 

Place. The same dataset is used to generate Figure 3.6. 

106 Eddy M. Rojas and Peerapong Aramvareekul, “Is Construction Labor Productivity Really Declining?” Op. cit.

107 Steve G. Allen, “Why Construction Industry Productivity is Declining,” Op. cit.

108 The data source is Census Bureau’s Value of Construction Put in Place. 

45 
 

Figure 3.6 Changes in Construction Output Mix (1964-2007) 

10

20

30

40

50

60

70

80

90

1964 1969 1974 1979 1984 1989 1994 1999 2004

Percentage

Dollar amount of residential and commercial construction as a percentage of total

Dollar amount of industrial and heavy construction as a percentage of total

46 
 

Figure 3.7 Dollar Amount Single-Family House Construction as a percentage of

Residential, Commercial, Industrial, and Institutional Construction (1964-2002) 

10

15

20

25

30

35

40

45

1964 1969 1974 1979 1984 1989 1994 1999

Pcrcentage

3.6.3 Task-Level Productivity Does Not Completely Reflect Industry-Level Productivity 

Construction projects tend to be unique and are increasingly more complex. Task-level 

productivity does not capture project-level uniqueness and complexity. The trend of increasing 

project complexity could partly explain productivity decline at the industry level. High 

productivity at the task level also does not necessarily translate into high productivity at the 

project level. A project level success depends on managerial coordination and planning, which 

task-level productivity does not capture. For instance, idle time is not included in task-level 

productivity measurement, but it certainly can impede progress and productivity at the project 

level. Regulation is sometimes cited as one reason for low productivity in the construction 

industry. Regulation does not generally apply to task-level productivity, but it does affect 

project-level and therefore industry-level productivity. 

47 
 

3.6.4 Different Definitions of Productivity Measures and Different Definitions of the 

Construction Industry 

When comparing productivity estimates, it is helpful to keep in mind the different definitions of 

productivity. In terms of labor productivity, two different output measures can be used. Labor 

productivity is often defined as output per hour. The output measure can be gross output or 

value added. The choice of the output measure is also related to the definition and scope of the 

construction industry. Industry practitioners tend to define labor productivity in concepts similar 

to the gross output based labor productivity.109 In contrast, statistical offices, such as the BLS, 

tend to use value-added labor productivity measures. One difference between the labor 

productivity measures based on these two output measures can be seen by looking at 

prefabrication. Eastman and Sacks, for instance, have studied a number of similar on-site and 

off-site activities.110 They have observed that off-site activities tend to have higher productivity 

than their on-site counterparts. These authors therefore argue that construction productivity is 

underestimated and that the production of prefabricated materials ought to be included in the 

construction productivity measurement. Eastman and Sacks’ concept of construction labor 

productivity involves the gross output measure. Offsite activities, such as prefabrication, are 

productivity enhancing. If they are incorporated in a construction project, then labor 

productivity based on gross output is expected to improve. On the other hand, labor productivity 

based on value added is not expected to change with the use of prefabrication.111 This difference 

in the definitions of output and the implied scope of the construction industry can lead to 

different estimates and may be one reason for the divergent perceptions of industry productivity 

trends. 

The most commonly calculated labor productivity measure is defined to be value added per hour. 

This definition, for instance, is used by the BLS. In task-level productivity studies, the 

definitions usually vary. For instance, Goodrum et al. defined task-level labor productivity in 

terms of physical units of output per hour.112 This measure was shown to have increased during 

1976-1988. Differences in estimates may be partly due to different definitions. 

The rate of change in multifactor productivity in the growth accounting framework is defined to 

be the rate of change in output minus the weighted rates of change in capital, labor, and 

                                                           

109 Centre for the Study of Living Standards, Can Measurement Error Explain the Weakness of Productivity in the 

Canadian Construction Industry? Op. cit.

110 Charles M. Eastman and Rafael Sacks, “Relative Productivity in the AEC Industries in the United States for Onsite and Off-site Activities” Op. cit.

111 Centre for the Study of Living Standards. Can Measurement Error Explain the Weakness of Productivity in the 

Canadian Construction Industry? Op. cit.

112 Paul M. Goodrum, Carl T. Haas, and Robert W. Glover, “The Divergence in Aggregate and Activity Estimates of 

US Construction Productivity” Op. cit.

48 
 

intermediate inputs. The weights are cost shares of the corresponding inputs. The multifactor 

productivity measure defined in Goodrum et al. for task-level productivity analysis is also 

different from the definition conventionally used at the industry level. It is defined as units of 

physical output divided by the deflated sum of labor cost and equipment cost. For multifactor 

productivity analysis, the productivity literature recommends the use of gross output (as opposed 

to value added) as the output measure along with symmetrical treatment of labor, capital, and 

intermediate inputs.113 While gross output measures are used in both formulations, task-level 

productivity defined by Goodrum et al. does not incorporate intermediate inputs. The presumed 

increase in prefabrication of materials could explain some of the increase in multifactor 

productivity and labor productivity defined in Goodrum et al., which are not reflected in 

productivity measures at the industry level.114 

3.7 Conclusions and Observations 

Task-level productivity estimates tend to show improvement in construction productivity over 

time, while industry-level productivity estimates tend to suggest otherwise. Some industry 

practitioners believe the construction industry has witnessed enhancements in productivity, while 

others believe productivity has been lagging. This divergence in estimates and in perceptions 

highlights the challenges associated with productivity measurement of the construction industry. 

If we set aside the issue of data not collected for the purpose of productivity measurement, we 

find that there is an intrinsic difficulty in construction productivity measurement. Much of this 

difficulty lies in the heterogeneous nature of the industry. Construction building or infrastructure 

types are heterogeneous. Within each building or infrastructure type, there is also heterogeneity 

as each project is unique. Building processes are heterogeneous, as demonstrated by the 

diversity of contract work on which the North American Industry Classification System (NAICS) 

is based. Finally, there is heterogeneity in the composition of construction firms, with large 

operations taking advantage of economies of scale and scope and making more profits than small 

companies. The heterogeneity that exists in these multiple dimensions means that productivity 

may be improving or deteriorating for a particular segment of the industry, at a particular level of 

analysis. Changes in productivity at an aggregated level may simply be caused by changes in the 

composition of projects or firms involved, rather than reflecting productivity change per se. The 

next chapter will discuss possible approaches of disaggregating the industry to create 

productivity measures that are more meaningful. 

                                                           

113 Frank M. Gollop, “Accounting for Intermediate Input: The Link Between Sectoral and Aggregate Measures of 

Productivity Growth,” in Measurement and Interpretation of Productivity, National Research Council (Washington, 

D.C.: National Academy of Sciences, 1979); Dale W. Jorgenson, Mun S. Ho, and Kevin J. Stiroh, Productivity 

Volume 3: Information Technology and the American Growth Resurgence (Cambridge, Massachusetts and London, 

England: The MIT Press, 2005). 

114 Ibid.

49 
 

 

50 
 

4 Challenges and Opportunities in Construction Productivity Measurement Using 

National Statistics 

4.1 Discussion of the Teicholz Diagram 

One highly referenced work in construction productivity is the productivity comparison diagram 

plotted by Teicholz.115 This following discussion focuses on the productivity calculations by 

Teicholz and highlights the data challenges associated with construction productivity 

measurement. In Teicholz’s diagram, constant contract dollars of new construction work per 

field work hour is shown to have trended downward over the past 40 years at an average 

compound rate of -0.6 % per year. In contrast, labor productivity of all non-farm industries, 

which includes the construction industry, has trended upward at an average compound rate of 

1.8 % per year. 116

Figure 4.1 Construction Labor Productivity and Non-Farm Business Labor Productivity 

Comparison116

0%

50%

100%

150%

200%

250%

Productivity Index

Non‐Farm Productivity Index (1964 = 100 %)

Construction Productivity Index (1964 = 100 %)

                                                           

115 Paul Teicholz, “Labor Productivity Declines in the Construction Industry: Causes and Remedies,” AECbytes 

Viewpoint. Issue 4, April 14, 2004. 

116 Ibid.

51 
 

In the Teicholz calculations, the output measure for the construction industry, constant contract 

dollars of new construction work, is from the C30 Value of Construction Put in Place reports 

produced by the Census Bureau. Value Put in Place, as defined by the C30 survey, includes 

architectural design, engineering costs, construction management (since 1997), force-account 

construction, and secondary construction, in addition to total construction cost as defined by the 

Economic Census.117 The Census reports that about two thirds of Value Put in Place 

corresponds to the work performed by the construction industry as defined by the Economic 

Census.118 Defined as such, the output measure contains contributions of industries outside of 

the construction industry. 

C30 reports published contract amounts in constant dollars, and these published figures were the 

output measure in the Teicholz calculations. The Census Bureau used an array of price and cost 

indices for the deflation. Possibly because of the lack of appropriate deflators, C30 reports 

currently only publish contract amounts in current dollars. 

Labor hours data that Teicholz used are field work hours, and they came from the Bureau of 

Labor Statistics. The BLS collects and reports labor hours data under the Current Employment 

Statistics Survey. Field work hours are work hours of the so-called production workers. 

Executive and managerial personnel, professional and technical employees, and workers with 

routine office jobs are considered non-production workers and therefore are excluded. In the 

BLS productivity program, hours of both production workers and non-production workers are 

combined to form total hours, which are used in labor productivity calculations.119 The CES is 

an establishment survey that covers establishments with payrolls. The self-employed are not 

under the sampling universe of the CES. About 15 % to 19 % of workers in the construction 

industry are self-employed or unpaid family workers, and therefore non-negligible.120 The labor 

hours data used in Teicholz productivity calculations come from the CES and do not include 

hours worked by the self employed. Excluding the self employed in the labor hours biases 

productivity measure upward. 

In addition, the Census notes that there is “a significant amount of construction work done in the 

underground economy.”121 The existence of an underground economy might be more likely to 

                                                           

117 United States Census Bureau. Construction Statistics Data Users’ Conference. October 28, 1997. Washington, 

DC. Document issued March, 1999. 

118 Ibid.

119 Appendix A discusses the estimation of non-production worker hours, as they are not collected under the Current 

Employment Statistics.

120 Current Population Survey.

121 Ibid. 

52 
 

affect the labor input than the output measure when the output measure is based on projects. The 

labor input is underestimated if the labor in the underground economy is ignored. 

Finally, changes in labor productivity may be a result of changes in the mix of outputs. 

Residential and commercial building construction is labor intensive, compared to industrial and 

heavy construction, which tends to be capital intensive. Rojas and Aramvareekul argue that 

Labor productivity is lower for residential and commercial building construction than industrial 

and heavy construction.122 From 1964 through 2007, the output mix changed from 64 % 

residential and commercial and 36 % industrial and heavy construction to 76 % residential and 

commercial and 24 % industrial and heavy construction.123 Increasing residential and 

commercial construction in the overall output mix could translate into decline in “labor 

productivity.” The decline in construction labor productivity illustrated in the Teicholz diagram 

might be partly explained by change in output mix. This illustration indicates the importance of 

focusing on homogenous products in productivity measurement. A productivity measure at the 

industry level alone is not sufficiently informative. 

4.2 Data Issues Associated with the Teicholz Diagram 

The C30 survey form specifically asks for contract construction cost, owner supplied materials 

and labor, and total construction cost, which is the sum of the former two. Architectural, 

engineering, and miscellaneous costs is asked of the survey respondent separately, as well as 

estimated amount of all other capital expenditures. These separate cost estimate data are not 

reported in the published C30 reports. Therefore, data on contract construction cost and total 

construction cost are not readily available. The contract construction cost and the total 

construction cost both contain labor costs and material costs. Value of Construction Put in Place 

includes architectural design, engineering costs, construction management, force-account 

construction, secondary construction, and total construction cost. In contrast, an output measure 

based on the contract construction cost or the total construction cost would be a gross output 

measure, with a boundary that approximates the construction industry. An additional 

investigation is needed to examine how labor hours data from the Current Employment Statistics 

treat owner supplied labor to determine whether contract construction cost or the total 

construction cost is a better output measure. 

One source of data challenge for productivity analysis in the construction industry is the lack of 

appropriate price deflators. The output data from the C30 reports used to be deflated using an 

array of price and cost indices, and the more recent data are no longer deflated and are reported 

only in current dollars. The Bureau of Economic Analysis (BEA), on the other hand, deflates 

output from the construction industry for GDP estimation. For the residential sector, price 

                                                           

122 Eddy M. Rojas and Peerapong Aramvareekul, “Is Construction Labor Productivity Really Declining?” Op. cit.

123 Data source is Census Bureau’s Value of Construction Put in Place. 

53 
 

deflators based on hedonic regressions are used and are considered reliable. For the 

nonresidential sector, the newly developed Producer Price Indices (PPI) by the Bureau of Labor 

Statistics are used. Two limitations are that these PPIs have only been available recently, and 

they have been developed for only four types of building construction projects. For the historical 

data, indices based on input costs are used for deflation. For the more recent data, this practice 

still continues as the price deflators do not exist for all construction types. 

The main source of data on labor hours is the CES, which does not cover the self-employed. The 

Current Population Survey is one data source where the self-employed are covered. The Current 

Population Survey is a household survey and covers the self-employed. Additionally, the CPS 

collects data on hours worked and weeks worked. Therefore, hours of the self-employed can be 

obtained from the CPS. CPS hours data are hours at work, while the CES hours data are hours 

paid. Ratios of hours at work to hours paid may be available from the National Compensation 

Survey to convert hours paid to hours worked. Additionally, the CPS data are reported for all 

workers in the construction industry, while the CES hours data are reported only for production 

workers. Official productivity measures published by the BLS are constructed using hours 

worked for both production and nonproduction workers. Therefore, the same definition needs to 

be used to calculate a labor productivity measure for the construction industry that is consistent 

with official labor productivity measures of other segments of the economy. 

4.3 Approaches for Measuring Construction Productivity 

Literature review reveals that there is no consensus on the trend of construction productivity. 

This document aims to examine what data are available in national statistics that would allow us 

to create meaningful productivity measures for the construction industry. This effort focuses on 

labor productivity, rather than multifactor productivity, because the data requirements for labor 

productivity measurement are more limited and therefore more feasible. 

The construction industry is highly heterogeneous. As a result, a single industry-level 

productivity measure alone is not sufficiently informative. Changes in the industry-level 

productivity may be due to changes in the composition of projects and therefore may not reveal 

true productivity changes. There are two possible types of approaches in classifying the industry 

into a finer level. The first approach is to focus on products. That is, productivity measures can 

be developed for different building types or infrastructure types. For each building or 

infrastructure type, productivity measures can be created, and these productivity measures can 

serve as benchmarks for practitioners who engage in such projects. The second approach is 

focused on production units, akin to how the NAICS codes are structured. The Economic 

Classification Policy Committee decided that “as a matter of principle, an industry classification 

54 
 

system should be based on producing units rather than products or services.”124 In 2002, the 

NAICS codes for the construction industry were structured such that there are three broad 

categories: (1) construction of buildings, (2) heavy and civil engineering construction, and (3) 

specialty trade contractors. This basic structure of categorization was the foundation for SIC 

codes of the construction industry that were in use until supplanted by the NAICS codes in 1997. 

Since much data from national statistics are based on the NAICS codes, creating productivity 

measures that follow NAICS structure is a natural possibility. Five possible specific approaches 

are described below. 

4.3.1 Focus on Building Types, Gross Output 

Output: 

Focusing on building types, say an office building, an output measure could be square footage of 

a project. Square footage values are collected in the C30 survey, which is part of the Census 

Bureau’s Value Put in Place Program. The sampling frame of the C30 survey for private 

nonresidential and for state and local government construction is based on F.W. Dodge 

reports,125 which is a compilation of construction projects, and the Dodge reports also contains 

the square footage values of projects. Dodge reports go back to 1967. Using square footage as 

an output measure avoids the problem of lack of good output deflators, and a long time-series is 

also available. 

Labor Input: 

Data for labor input can come from the Economic Census. The Economic Census is an 

establishment survey and it covers all large establishments and a sample of small establishments. 

These establishments include general contractors and specialty trade contractors. The Economic 

Census reports “number of construction workers.”126 The Economic Census also asks 

establishments regarding percentages of their work, based on sales, shipments, receipts, or 

revenue, in various building and infrastructure types. Starting with general contractors, we can 

first focus on establishments that specialize in office building construction. Obtaining the 

number of construction workers that work in office building construction is straight forward for 

establishments that specialize in office building construction. For establishments that work in 

                                                           

124 Yuskavage, Robert E. 2007. “Converting Historical Industry Time Series Data from SIC to NAICS” Paper 

prepared for the Federal Committee on Statistical Methodology 2007 Research Conference, November 5-7, 

Arlington, VA. 

125 United States Census Bureau. Construction Statistics Data Users’ Conference. October 28, 1997. Washington, 

DC. Document issued March, 1999. Op. cit.

126 Number of construction workers is available from the Economic Census for 2007 and 1992. For 1997, annual 

payroll costs for construction workers are reported instead of number of construction workers. For 2002, both 

number of construction workers and number of leased construction workers are reported. 

55 
 

office building construction but do not specialize, extrapolation is needed. For example, we can 

draw values of number of construction workers per dollar revenue from the distribution based on 

establishments that specialize. Using data on percentage revenue on office building construction 

and total revenue, we can obtain an estimate for number of construction workers. The same 

procedure can be applied to specialty trade contractors to obtain number of construction workers 

in office building construction. 

The number of construction workers can then be combined with average weekly hours of 

production workers from the Current Employment Statistics Survey to yield an estimate of 

annual hours, which is the labor input that can be used in labor productivity calculation. The 

Current Employment Statistics Survey is an establishment survey, and it contains monthly data 

for detailed classification, largely based on NAICS. In the case of office building construction, 

average weekly hours of production workers in commercial building construction can be used, 

and data for this variable is available monthly starting from January 1990. 

Challenges: 

In terms of the output measures, square footage and the value of construction put in place are 

both gross output measures. A labor productivity measure can be constructed using gross output 

measures, but we need to keep in mind that only part of the output is contributed by the 

construction industry. An additional caveat is that while square footage is a sensible proxy for 

gross output and using it as an output measure avoids the problem of deflators, it does not control 

for quality and complexity changes over time. 

There is no data on the self-employed (proprietorships and partnerships) in terms of number of 

construction workers or hours devoted to different building types. The Economic Census is an 

establishment survey that covers only establishments with payrolls. The Economic Census does 

report monetary amount of subcontract work, but this value includes both labor and materials. In 

addition, the subcontract work reported by a general contractor may be performed by a specialty 

trade contractor who is also included in the Economic Census. It is not possible to estimate the 

amount of work performed by the self-employed in particular types of building construction 

work. 

The Current Population Survey is one data source where the self-employed are covered. From 

the Current Population Survey, about 15 % to 19 % of workers in construction are self-employed 

or unpaid family workers. The self-employed therefore represent a non-negligible portion of the 

construction work force. While the Current Population Survey is a household survey and covers 

the self-employed, the construction industry is not further categorized at a finer level. Under the 

CPS occupation classification, there are 31 occupation types of construction trades. Some of the 

construction occupations may fall under categories such as installation, maintenance, and repair 

workers or management occupations. Using data from the CPS, the BLS compiles construction 

occupations with the most substantial percentage of self-employed workers. Additionally, the 

56 
 

CPS collects data on hours worked and weeks worked. Therefore, information on the 

occupations of the self-employed workers is available. What is missing is the project types the 

self-employed engage in. 

Another data source on the self-employed is the Nonemployer Statistics, annually published by 

the Census Bureau, as previously discussed. But this data source also does not allow us to link 

the self-employed to different project types. An additional issue is the lack of labor data from 

the underground economy. 

Under this approach, although square footage data is available monthly and average weekly 

hours are also available monthly, number of construction workers working in the office building 

construction is available only every five years. The C30 survey contains project cost 

information; however, the labor and materials costs are not distinguished from each other. Labor 

input cannot be extrapolated using C30 results. Another data source on establishments is the 

County Business Patterns, which is an annual data on number of employees, payroll, and number 

of establishments by NAICS codes. It also reports number of establishments by employmentsize class for NAICS categories. However, this data source also does not contain information 

pertinent for productivity measurement with a focus on project types. As a result, productivity 

estimates can only be made every five years. 

4.3.2 Focus on Building Types, Value Added 

Output: 

The previous approach suffers from the lack of labor data for the self-employed and the 

underground economy. An alternative approach is to confine the scope and focus on the output 

of establishments with payrolls and the labor input of establishments with payrolls. Economic 

Census surveys ask individual establishments about labor costs, materials costs, the amount of 

business done, and percentage dollar value of work done by different building types. “Value 

added for the construction industry is defined as the dollar value of business done less costs for 

construction work subcontracted to others and payments for materials, components, supplies, and 

fuels.”127 Therefore information needed to calculate value added at the individual establishment 

level is available. Since the individual components of value added are collected at the 

establishment level only (rather than at the establishment level by building types), value added of 

new construction by building types needs to be extrapolated. 

To estimate value added by building types, we can first obtain value added from establishments 

that specialize in, say, office building construction. We can then construct a distribution based on 

value added as a fraction of total revenue. Then for establishments that engage in office building 

construction but do not specialize, we can draw values from the distribution. This a sensible 

                                                           

127 Economic Census 2002. 

57 
 

approach to the extent that value added as a fraction of revenue is similar for the same building 

type. The Economic Census forms ask respondents to report percent of construction work by 

building type and by types of construction (new construction; additions, alterations, or 

reconstruction; and maintenance). Using the percent of construction work and the randomly 

drawn fraction of value added divided by total revenue, we can derive value added by building 

type for each establishment. If there are not enough establishments that specialize in certain 

building or infrastructure types, then it may be sensible to combine several building or 

infrastructure types together. 

Labor Input: 

Labor input data is the same as in the first approach (4.3.2). 

Challenges: 

To use the value added approach, two deflators are needed. The so-called double deflation 

method involves first deflating gross output by an appropriate price deflator and second deflating 

intermediate inputs by an appropriate deflator. Subtracting the deflated intermediate inputs from 

the deflated gross output yields a deflated value added measure. 

Currently, the Producer Price Index (PPI) published by the Bureau of Labor Statistics for the new 

office building construction is available monthly only from June 2006. PPI for new industrial 

building construction has been available monthly since June 2007. PPI for new warehouse 

building construction is available monthly from December of 2004. Finally, PPI for new school 

building construction is available from December 2005. The short data series of PPIs presents a 

limitation. 

In terms of deflators for intermediate inputs, the BLS constructs producer price indices for 

material and supply inputs to the construction industries. There is an index for the overall inputs 

to construction industries, and under which there are indices for new construction and 

maintenance and repair construction. The breakout of new construction is: single-unit 

residential, multi-unit residential, nonresidential buildings, highway and street construction, and 

other heavy construction. The breakout for maintenance and repair is: residential and nonresidential. Each of the indices is based on a list of NAICS industries that supply materials to the 

sector in question and their relative weights. The lists of NAICS industries and the relative 

weights come from the BEA’s input-output tables. Since the input-output tables are based on 

NAICS codes, data to reconstruct deflators for intermediate inputs to specific project or building 

types, such as office building construction, are not readily available. A compromise might be 

simply to use the index for non-residential building construction as a proxy for office building 

construction. Another limitation is the infrequent data collection of the Economic Census. 

 

58 
 

4.3.3 Focus on Infrastructure Type, Value Added 

Output: 

This approach is similar in nature to the previous approach. Instead of focusing on a particular 

building type, we focus on an infrastructure type. Under the category “Heavy and Civil 

Engineering Construction,” we could focus on “highway and street construction,” which includes 

both general contractors and specialty trade contractors who work in this area. The output 

measure is value added by establishments that specialize in this area plus imputed value added 

by establishments that work in this area but do not specialize. 

Labor Input: 

From the Economic Census we can obtain number of construction workers. From Current 

Employment Statistics Survey, available monthly from January 1990, we can obtain number of 

production workers for “highway, street, and bridge construction,” as well as total number of 

employees and average weekly hours of production workers in “highway, street, and bridge 

construction.” From the Current Employment Statistics survey under SIC, we can obtain 

monthly data for the period of January 1988 through March 2003, although the category is 

“highway and street construction.” 

Challenges: 

Because the output measure is value added by establishments with payrolls, this approach avoids 

the problem of lack of labor data associated with self-employment. Producer Price Index is 

available for material and supply inputs to “highway and street construction” from the Bureau of 

Labor Statistics monthly from June 1986. Whether a producer price index is available for the 

output needs to be investigated. 

Another challenge is that labor data from the Current Employment Statistics Survey are available 

for the combined highway, street, and bridge construction for the new series, while labor data is 

available for only highway and street construction in the old series. Assumptions are needed to 

use data from these different sources together. 

4.3.4 Focus on Specialty Trades 

Output: 

Since much of existing data are classified under the NAICS system, it is natural to follow the 

NAICS system when creating productivity measures. PPIs have been developed for four 

specialty trades by the BLS: concrete contractors (nonresidential building), roofing contractors 

(nonresidential building), electrical contractors (nonresidential building), and plumbing/HVAC 

contractors (nonresidential building). These four types of specialty trades are also covered under 

59 
 

the Economic Census. Gross output obtained from the Economic Census can be deflated using 

the corresponding PPIs. 

Labor Input: 

The Economic Census contains information on the number of construction workers and the 

number of total employees. Current Employment Statistics Survey contains average weekly 

hours, number of all employees, and number of production workers for “poured concrete 

structure contractors,” “steel and precast concrete contractors,” “roofing contractors,” “electrical 

contractors,” and “plumbing and HVAC contractors.” 

Challenges: 

These PPIs for the specialty trades have become available monthly since December 2007. As a 

result, there is not a long enough time series to construct a productivity trend using these data. 

Specialty trades may subcontract work to other contractors, and some of which may be selfemployed. This issue can be mitigated, as previously discussed, by focusing on value added by 

the establishments with payrolls and labor input by these establishments with payrolls. To obtain 

deflated value added, deflators of intermediate inputs are not currently available and therefore 

would also need to be constructed. 

4.3.5 Focus on Residential Building Construction 

Output: 

Focusing on establishments with payrolls avoids the lack of data associated with the selfemployed. Value added output measure can be derived using data from the Economic Census. 

The PPIs for “new one-family houses under construction” and “new one-family house sold” are 

available monthly for 1964 through 2007 and 1963 through 2007, respectively. These PPIs are 

published by the Census Bureau. The BEA and the Census also produce a PPI for multifamily 

housing. This latter PPI was introduced in 1991. The data series starts from 1958 in the BEA’s 

National Income and Product Accounts (NIPA) tables. The PPIs for the residential sector are 

derived using the hedonic regression approach and are considered high quality. In terms of 

deflators for intermediate inputs, deflators for material and supply inputs for single-unit 

residential construction and multi-unit residential construction from the BLS can be used. 

Labor Input: 

Number of all employees, number of production workers, and average weekly hours of 

production workers are available from the Current Employment Statistics Survey for 

construction of residential buildings and new single-family general contractors. These data are 

available monthly from January 1990, except the number of all employees for construction of 

residential buildings is available monthly from January 1985. Number of all employees is 

available for new multifamily general contractors from January 1990. Starting in January 2001, 

60 
 

the number of all employees is collected for residential specialty trade contractors, residential 

building foundation and exterior contractors, residential building equipment contractors, 

residential building finishing contractors, and other residential trade contractors. 

Challenges: 

Using data from Economic Census, we can produce a productivity trend using data from 1987, 

1992, 1997, 2002, and 2007. 

 

61 
 

62 
 

5 Summary and Recommendations for Future Research 

5.1 Summary 

Although the construction industry is a major component of the U.S. economy, it has 

experienced a “perceived” prolonged period of decline in productivity. Due to the critical lack of 

measurement methods, however, the magnitude of the productivity problem in the construction 

industry is largely unknown. The measurement problem is exacerbated by the fact that the 

construction industry is composed of four sectors that differ significantly in the outputs 

produced, firm size, and use of technology. The four sectors, which taken together define the 

construction industry, are residential, commercial/institutional, industrial, and infrastructure. 

This report describes efforts underway that focus on the measurement of construction 

productivity at three levels—task, project, and industry—and how such measurements can be 

developed. This report analyzes the measurement challenges associated with the development of 

meaningful measures of construction productivity at the task, project, and industry levels and 

establishes a framework for addressing those challenges. Specifically, this report identifies the 

metrics, tools, and data needed to move forward in collaboration with key construction industry 

stakeholders. Once produced, these metrics,tools, and data will help construction industry 

stakeholders make more cost-effective investments in productivity enhancing technologies and 

improved life-cycle construction processes; they will also provide stakeholders with new 

measurement and evaluation capabilities. Finally, this report lays the foundation for future 

research and for establishing key industry collaborations that will enable more meaningful 

measures of construction productivity to be produced at the task, project, and industry levels. 

5.2 Recommendations for Future Research 

The background work for this report uncovered additional areas of research that might be of 

value to private-sector organizations and government agencies concerned with the measurement 

and analysis of construction productivity. These areas of research are concerned with: (1) the 

development of a standard practice for measuring task-level and project-level productivity; (2) 

the establishment of a database of project-level productivity measures for selected types of 

capital facilities; (3) factors affecting the use of prefabrication, preassembly, modularization, and 

off-site fabrication techniques and processes; and (4) industry-level productivity metrics. 

5.2.1 Standard Practice for Measuring Task-Level and Project-Level Productivity 

Standards are an efficient way of translating research results into practice. Improved metrics, if 

embodied in a voluntary industry consensus standard, will increase the rate of investment in 

productivity enhancing technologies, including information, communication, and automation and 

integration technologies, conveying benefits on individuals, businesses, and government in the 

form of lower costs of building services and products. Future research aimed at developing, in 

collaboration with ASTM International, a standard practice for measuring task-level and project63 
 

level productivity will fill that need. Ideally, the standard practice will incorporate metrics that 

enable leading-indicators of performance to be calculated and used to identify areas for 

improvement during the construction phase. The Building Economics Subcommittee, ASTM 

E06.81, is the ideal venue for producing this standard practice. Furthermore, the 45 members of 

the ASTM E06.81 Subcommittee on Building Economics and over 600 ASTM E06 Committee 

members on Building Performance and Constructions provide an excellent user base as well as 

industry marketing spokespersons for such a standard practice. Over the longer term, the metrics 

defined in the standard practice can be embodied in supporting software products that will help 

implement the standard by various stakeholder groups. 

5.2.2 Database of Project-Level Productivity Measures for Capital Facilities 

Although there are a number of sources for task-level productivity data, no such sources exist for 

project-level productivity measures. A recent study, sponsored by NIST and conducted by the 

Construction Industry Institute (CII), discussed two promising approaches for reporting projectlevel productivity metrics.128 Additional research on these two approaches in conjunction with 

CII’s Benchmarking and Metrics Program and other key construction industry stakeholders, 

could result in a database of project-level productivity measures for selected types of capital 

facilities (e.g., industrial facilities, commercial and institutional buildings, and infrastructure 

projects). Such a database would offer a means for disseminating information on project-level 

productivity. The database would consist of both raw metrics (e.g., direct measures of inputs and 

outputs) and index-based metrics (e.g., a reference value of 100 pegged to some reference point 

in time). An advantage of index-based metrics is that they enable project-level productivity to be 

tracked over time and to spot trends. Ideally, the database would incorporate the capability to 

analyze how the use of industry best practices and automation and integration technologies affect 

project-level productivity. 

5.2.3 Prefabrication, Preassembly, Modularization, and Off-Site Fabrication 

Prefabrication, preassembly, modularization, and off-site fabrication (PPMOF) involve the 

fabrication or assembly of systems and components at off-site locations and manufacturing 

plants. Once completed, the systems or components are shipped to a construction job site for 

installation at the appropriate time. Both owners and contractors view PPMOF as a means to 

meet challenges of demanding schedules, adverse site conditions, and limited availability of 

skilled labor. PPMOF offers the promise—if used appropriately—of lower project costs, shorter 

schedules, improved quality, and more efficient use of labor and materials. However, various 

obstacles stand in the way of the widespread use of PPMOF, including building codes that hinder 

innovation as well as conventional design and construction practices. In addition to the obstacles 

referenced above, CII research shows that using PPMOF techniques and practices requires 

                                                           

128 Youngcheol Kang, William O’Brien, Jiukun Dai, Stephen P. Mulva, Stephen R. Thomas, and Pin-Chao Liao, 

Measuring Interoperability and Best Practices Impacts on Capital Project Productivity. Op. cit.

64 
 

careful consideration of their implications for engineering, transportation, coordination, and 

project organization.129 Additional research is needed to better understand why the successful 

use of PPMOF techniques and practices in the industrial sector has not been duplicated in the 

commercial/institutional and infrastructure sectors. Ideally, this research would be conducted as 

part of a broad-based initiative to understand the pros and cons of PPMOF techniques and 

practices from a market-based perspective. 

5.2.4 Industry-Level Productivity Metrics 

Statistical agencies, such as the Census Bureau, the Bureau of Labor Statistics (BLS), and the 

Bureau of Economic Analysis (BEA), fulfill many needs of the nation through collection and 

compilation of high-quality data. For instance, an accurate account of the size of the GDP and 

sizes of contributions by various industry sectors are fundamental for a basic understanding of 

the nation’s economy. Data on housing starts serve as an important indicator that captures the 

cyclic nature of the economy. To understand how industries make decisions on labor and capital 

utilizations, data on labor and capital investments are indispensible. To achieve goals like these, 

the national statistics offices have collected much of the data that are relevant to productivity 

measurement of the construction industry. 

One key element in productivity measurement is output deflators. BLS has recently produced 

several producer price indices in the nonresidential sector, and this effort has enhanced the 

estimates of investments in BEA’s National Income and Product Account tables. BEA and BLS 

may collaborate further to develop other nonresidential building construction indices, such as 

price indices for highways, hospitals, retail, communication, power, and lodging structures.130 

Efforts such as this improve the quality of existing statistics and have spillover benefits for 

productivity measurement. 

There are currently no official productivity statistics on the construction industry due to the lack 

of adequate data. One challenge that stands out is the mismatch of classification systems 

between different data sources. One potential remedy may be to classify micro-level data in two 

or more classification systems designed for different purposes. For instance, the updated 

classification system of Value of Construction Put in Place is based on end use. For productivity 

measurement of the construction industry, a classification system based on building type is 

preferable. Information on both the building type and the end use is available, and therefore it is 

possible to classify the same data under two systems for different uses. The use of microdata for 

reclassification can be applied to datasets that have undergone classification changes as well. 

For instance, the Economic Census and the Current Employment Statistics both transitioned 

                                                           

129 Construction Industry Institute, Prefabrication, Preassembly, Modularization, and Offsite Fabrication in 

Industrial Construction: A Framework for Decision-Making. Op. cit.

130 Paul Lally, “How BEA Accounts for Investment in Private Structures.” Op. cit.

65 
 

from SIC to NAICS, and as a result, there is a major break in the time series. Reconstructing 

historic SIC-based data by reclassifying establishments surveyed is one approach. 

Another remedy for reconciling the different classification systems under which output and labor 

input data are organized may be to ask additional questions on existing surveys. The Value of 

Construction Put in Place survey, for instance, is reported monthly. The main variable is 

construction costs, which includes costs of both materials and labor. The current Value of 

Construction Put in Place does not request owners to report material cost and labor cost 

separately. If information on labor costs and/or hours can also be collected in the same survey, 

then productivity measures can be developed for different project types and possibly for different 

geographic regions. Alternatively, the Current Employment Statistics Survey could ask 

respondents the types of projects they are currently working on. If monthly surveys such as the 

Value of Construction Put in Place or the Current Employment Statistics Survey can be slightly 

amended, a rich data set for productivity measurement could be within reach and can enable 

calculations of labor productivity by project types. Such a productivity measure could be used as 

a benchmark for owners and contractors engaged in specific project types. Another challenge is 

the under coverage of the self-employed in establishment-based surveys. A solution may be to 

develop a supplementary survey that aims to fill this gap. 

66 
 

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73 
 

74 
 

Appendix A Metrics of Productivity 

Methodologies described in this section are largely based on the approach use by BLS in its 

productivity programs. BLS has been recognized as a world leader in productivity measurement, 

and its approach, based on work by Jorgenson and others, has been incorporated in the 

Organization of Economic Cooperation and Development (OECD) manual titled “Measuring 

Productivity,” which sets the international standard.131

A.1 Industry-Level Productivity Measures 

A.1.1 Labor Productivity 

Labor productivity is an output per hour measure. It is defined as ொ೟

ொబ

ൊ

௅೟

௅బ

, in the BLS 

formulation, where ொ೟

ொబ

 is the index of output in the current year (t) and ௅೟

௅బ

 is the index of labor 

input in the current year. Output ܳ௧ can be a physical quantity measure or a deflated value of 

production, where output expressed in a monetary unit is divided by a price index, such as BLS 

producer price indices. Deflating value of production using appropriate producer price indices 

takes away changes in the value of output due to price changes. Lt islabor hors in year t. The 

construction industry produces different products—such as different building types and 

infrastructure types. To aggregate the different types of outputs, the output index can be 

calculated using a Tornqvist formula,132 in which quantities of different products are weighted 

using the value shares of the products: 

ܳ௧

ܳ௧ିଵ ൌ ݁݌ݔ ෍൥ ݓ௜,௧ ቆ݈݊ ݍ௜,௧ݍ௜,௧ିଵቇ

௡

௜ୀଵ

൩

where: 

ൌthe ratio of output in the current year (t) to previous year (t-1) ொ೟

ொ೟షభ

݊ ൌnumb

݈݊ ௤೔,೟

௤೔,೟షభ

er of products, 

ൌthe natural logarithm of the ratio of the quantity q of product i in current t year to the 

quantity in the previous year, and 

ݓ௜,௧ ൌthe average value share weight for product i

The average value share weight for product j is: 

                                                           

131 OECD (Organization for Economic Co-operation and Development), Measuring Productivity—Measurements of 

Aggregate and Industry-Level Productivity Growth (Paris: OECD, 2001).

132 Bureau of Labor Statistics, Handbook of Methods (Washington D.C.: Bureau of Labor Statistics, 1997). 

75 
 

ݓ௝,௧ ൌ ܵ௝,௧ ൅ ܵ௝,௧ିଵ

2

Where: ܵ ൌ ݌௝,௧ ൈ ݍ௝,௧ ൊ ൫∑ ݌௜,௧ ൈ ݍ௜,௧ ௡

௜ୀଵ ௝,௧ ൯

And ݌௜,௧ ൌprice of product i at time t

The Tornqvist formula yields the ratio of output in year t to output in year (t-1). The series of 

ratios can be chained to form the index of output used in the productivity formula. That is, 

ܳ௧

ܳ଴

ൌ ܳ௧

ܳ௧ିଵ

ൈ

ܳ௧ିଵ

ܳ௧ିଶ

ൈ...ൈ

ܳଶ

ܳଵ

ൈ

ܳଵ

ܳ଴

ݍ௜,௧ is generally calculated by dividing the value of output by the corresponding price index 

(BLS’s producer price index). This approach is conceptually equivalent to indices based on 

physical quantities of output. 

௅೟

௅బ

 is calculated by dividing total labor hours in year t with total labor hours in the base year. 

BLS does not distinguish between different types of labor hours in the output per hour 

measures.133 

Changes in labor productivity reflect changes in output that cannot be attributed to changes in the 

hours of labor in production.134 Labor productivity reflects influences such as changes in capital 

input per labor unit, changes in technology, rates of capacity utilization, level of output, 

managerial skill, and effort and quality of labor. Changes in labor productivity cannot be solely 

attributed to changes in labor effort or quality. 

A.1.2 Multifactor Productivity 

Multifactor productivity (or total factor productivity) is the ability to produce more output with 

the same inputs.135 It represents a shift in production function. Changes in multifactor 

productivity reflect changes in output that cannot be attributed to changes in capital inputs, labor 

inputs, and intermediate inputs. Changes in multifactor productivity reflect technological 

change, changes in capacity utilization, economies of scale, changes in managerial skills, 

changes in the organization of production, changes in the resource allocation, and measurement 

                                                           

133 Ibid.

134 Bureau of Labor Statistics, Multifactor Productivity Trends, 2007 (Washington DC: Bureau of Labor Statistics, 

2009). 

135 Dale W. Jorgenson, Mun S. Ho, and Kevin J. Stiroh, Productivity Volume 3: Information Technology and the 

American Growth Resurgence. Op. cit.

76 
 

error.136 Productivity represents the residual between output and inputs, and it also represents 

the residual between output prices and input prices.137 It is the ability to mitigate input price 

increases without increasing the price of output. Or it is the ability to gain a competitive edge 

without input price reductions.138

In the growth accounting framework, multifactor productivity growth is the growth in output 

minus weighted growth rates in capital, labor, and intermediate inputs. It is the residual, which is 

not accounted for by growth in labor and capital. The weights are the average value shares of the 

respective inputs in the value of the output between the two periods of consideration. In the 

equation form, the multifactor productivity growth is: 

ሶܣ

ܣ ൌ ܳሶ

כ ௞ܵ െ ܳ

ሶܭ

כ ௟ܵ െ ܭ

ሶܮ

כ ௫ܵ െ ܮ

ܺሶ

ܺ

where A is multifactor productivity, Q is output, K is capital, L is labor, X is intermediate input, 

ܵ௞, ܵ௟, and ܵ௫ are cost shares of capital, labor, and intermediate input, respectively, assuming 

competitive factor markets and constant returns to scale. That is, 

௜݅ כ ௜݌ ൌ௜ ܵ

ሻ௜݅ כ ௜݌ሺ∑

where ݌ ௜is the price of input i, and i is the quantity of input i. 

A specific functional form of the production function must be chosen for implementation. The 

translog function is used because the assumptions associated with it are the least restrictive.139 

The Tornqvist index, which is consistent with the translog function, is used for aggregation. By 

being consistent, it is meant that “changes in output consistent with the very general translog 

production function are exactly measured by changes in Tornqvist indices.”140 The 

                                                           

136 National Research Council, Measurement and Interpretation of Productivity (National Academy of Sciences, 

Washington D.C.: National Academy of Science, 1979); Bureau of Labor Statistics, Handbook of Methods

(Washington D.C.: Bureau of Labor Statistics, 1997); Jorgenson, Dale W., Mun S. Ho, and Kevin J. Stiroh, 

Productivity Volume 3: Information Technology and the American Growth Resurgence (Cambridge, MA and 

London, England: The MIT Press, 2005). 

137 William Gullickson, “Measurement of Productivity Growth in U.S. Manufacturing.” Op. cit.

138 Ibid. l

139 Bureau of Labor Statistics, Labor Composition and U.S. Productivity Growth, 1948-90 (Washington D.C.: 

Bureau of Labor Statistics, 1993). 

140 Ibid.

77 
 

instantaneous growth rates are replaced by annual growth rates. For instance, ௅ሶ

௅

 is replaced by 

∆ ln ܮ ൌ ln ܮ௧ െ ݈݊ ܮ௧ିଵ.

141

The Tornqvist index of multifactor productivity growth is: 

∆ ln ܣ ൌ ∆ ݈݊ܳ െ

1

2 כ ൫ܵ௞ሺݐሻ ൅ ܵ௞ሺݐെ1ሻ൯ כ ∆ ln ܭ െ 1

2 כ ൫ܵ௟ሺݐሻ ൅ ܵ௟ሺݐെ1ሻ൯ כ ∆ ln ܮ െ 12

כ ൫ܵ௫ሺݐሻ ൅ ܵ௫ሺݐെ1ሻ൯ כ ∆ ln ܺ

Note that the output measure should not include any commodity taxes, because the producers do 

not receive these taxes.142 Intermediate input costs, on the other hand, should include 

commodity taxes because these taxes are paid by the producers. 

For the multifactor productivity measures developed by the BLS, KLEMS inputs are used—

capital, labor, energy, materials, and purchased business services. The construction of the 

productivity statistic using more input types is analogous to the case presented above. 

A.1.3 Value-Added Function, Choice of Output Measure, and the Role of Intermediate 

Inputs 

Note in the multifactor productivity formula, all outputs—capital, labor, and intermediate 

inputs—are treated symmetrically. When all outputs are treated symmetrically, substitution 

between any inputs is allowed. In contrast, when a value-added sub-function is assumed, 

intermediate inputs cannot be substituted with capital or labor inputs.143 With the assumption of 

a value-added sub-function, the production function is written as: 

ܳ ൌ ݂ሺܸሺܭ, ܮ, ݐሻ, ܺሻ

This formulation assumes that the value added function is separable from intermediate inputs and 

that intermediate inputs do not influence the relative use of labor and capital. For instance, when 

the price of intermediate inputs decrease, a construction project may increase the use of 

intermediate inputs and reduce labor input. The assumption of a value-added function does not 

allow for such shifts in resource allocation. Additionally, this assumption implies that 

                                                           

141 Ibid.

142 W. Erwin Diewert, “Which (Old) Ideas on Productivity Measurement Are Ready to Use?” in New Developments 

in Productivity Analysis, ed. Charles Hulten, Edwin Dean, and Michael J. Harper. Conference on Research in 

Income and Wealth. (Chicago: University of Chicago Press, 2001). 

143 National Research Council, Measurement and Interpretation of Productivity. Op. cit.

78 
 

productivity growth can only be accomplished through the value-added function. That is, 

intermediate inputs cannot be the medium or source of productivity growth.144

Jorgenson et al. have developed an econometric method to test the existence of a value-added 

function.145 They rejected the existence of a value-added function for construction industry, 

which was among the 40 of the 45 industries that were rejected for this assumption in their 

analysis. They observed that intermediate inputs constitute a large proportion of gross output for 

about 70 percent of the industries studied, suggesting for the use of gross output concept rather 

than value-added concept for productivity studies at the industry level.146

Intermediate inputs are often substitutable with capital or labor in reality. For instance, at a 

construction site, putting together a door may involve cutting a door to fit certain dimensions, 

sanding the door, painting the door, and finally installing the door. In this process, all the 

activities are done by direct labor of the construction crew. The construction supervisor could 

also decide to purchase a pre-fabricated door. In this case, only the installation of the door 

involves direct labor, and the rest is accomplished through the purchase of an intermediate input. 

Traditionally, construction is defined to include only activities at the work site. Labor 

productivity of construction associates value-added with labor input. Off-site prefabrication is 

considered manufacturing in Census classification. Comparable on-site and off-site activities, 

such as precast concrete, cast-in-place reinforced concrete, sheetrock installation, and elevators 

and escalators, were studied by Eastman and Sacks, and it was observed that onsite activities are 

less productive than the counterpart off-site activities.147 The authors therefore argue that 

construction productivity is underestimated and that the production of prefabricated materials 

ought to be included in the construction productivity measurement. The observation by Eastman 

and Sacks and the theoretical and empirical work of Jorgenson et al. highlight the importance of 

treating intermediate inputs symmetrically in the productivity measurement. Including 

intermediate input in productivity measurement in this way recognizes the interdependence 

between sectors and makes it possible to evaluate the impact of contributions by other sectors, 

such as off-site prefabrication, on productivity growth. 

 

                                                           

144 Frank M. Gollop, “Accounting for Intermediate Input: The Link Between Sectoral and Aggregate Measures of 

Productivity Growth.” Op. cit.

145 Jorgenson, Dale W., Mun S. Ho, and Kevin J. Stiroh, Productivity Volume 3: Information Technology and the 

American Growth Resurgence. Op. cit. 

146 Ibid.

147 Charles M. Eastman and Rafael Sacks, “Relative Productivity in the AEC Industries in the United States for Onsite and Off-site Activities,” Journal of Construction Engineering and Management 134, no. 7 (2008): 517-526. 

79 
 

A.1.4 Output Measures 

A.1.4.1 Gross Output versus Value Added 

For multifactor productivity calculation, as discussed earlier, it is preferable to treat intermediate 

inputs symmetrically with capital and labor inputs. The proper measure of output is therefore 

gross output, rather than value added, where value added is defined to be gross output minus 

intermediate inputs. 

Some researchers use value-added as the output measure, and incorporate only capital and labor 

inputs in their multifactor productivity measurement. This is the approach BLS uses for 

multifactor productivity for the two major sectors, namely, private business and private nonfarm 

business sectors. Value-added and gross output may be close in value at this level of 

aggregation.148 But for disaggregated industries, gross output is preferred. And although it is 

more appropriate to use gross output, rather than value-added as the output measure, it might be 

preferable to use value-added as the output measure, for international productivity comparisons, 

as value-added data tend to be more available.149

Which output measure is the preferred output measure in the labor productivity calculation is not 

clear, however, and there has been little coverage of this issue in the literature.150 

One data source for gross output is the C30 Value of Construction Put in Place, which includes 

architectural and engineering design, construction management, force-account construction, and 

secondary construction, in addition to construction services performed by the construction 

industry. Value of construction Put in Place is collected from the owners at the project level. 

Therefore, construction by the self-employed, homeowner construction, and construction done as 

a secondary source of revenue by nonconstruction establishments are covered.151 In contrast, 

these types of construction are not covered by an establishment-level survey such as the 

Economic Census. Contract construction cost is reported separately from owner supplied 

materials and labor and architectural, engineering, and miscellaneous costs in the survey form. 

Therefore, it is possible to obtain separate data on total construction cost. An output measure 

based on the contract construction cost or the total construction cost would be a gross output 

measure, with a boundary that approximates the construction industry. The classification of the 

                                                           

148 Edwin R. Dean, Michael J. Harper, and Mark S. Sherwood, “Productivity Measurement with Changing-Weight 

Indices of Outputs and Inputs” In Industry Productivity: International Comparison and Measurement Issues, (Paris: 

OECD, 1996). 

149 Ibid.

150 Ibid.

151 Bureau of Economic Analysis, Concepts and Methods of the U.S. Input-Output Accounts (Washington D.C.: 

Bureau of Economic Analysis, 2009). 

80 
 

Value of Construction Put in Place data is based on end usage, and the pre-1993 classification 

system was based on building and infrastructure types. Data classified under these two systems 

are generally not comparable, particularly at a disaggregated level. Value of Construction Put in 

Place data are collected monthly and are not deflated. 

The only data source that allows the calculation of a value added output measure is the Economic 

Census. The Economic Census collects data on value of business done, costs for construction 

work subcontracted to others, and payments for materials, components, supplies, and fuels, 

which are components needed for calculating value added. One limitation of the Economic 

Census is that only establishments with payrolls are covered. The Economic Census is collected 

every five years with SIC/NAICS classification. 

A.1.4.2 Price Deflators 

An important element in productivity measurement is the price deflators. Price deflators are 

needed to derive a quantity index of output. This is done by dividing current dollars with an 

appropriate price deflator. Deflators are needed to strip away price changes due to inflation. 

Construction industry has been known to be deficient in this area, although many advances have 

been made in recent years. 

Two notable price deflators with long time series are associated with the residential sector. The 

Census Bureau publishes price indices for new one-family houses sold and for new one-family 

houses under construction using the hedonic regression model. The series are monthly from 

1963 and from 1964, respectively. Using a similar approach, the Bureau of Economic Analysis, 

in conjunction with the Census Bureau, has developed a price index for multifamily housing 

units.152 This price index series extends back to 1978. 

The hedonic approach is a multiple regression approach, where the price is regressed on a 

number of characteristics that determine the price. The regression coefficients tell us how much 

each of the characteristics contribute to the price. If we choose a "typical" house in 1970 as the 

"standard house," we can figure out what the price of this house, if constructed new, would be in 

a later year. This is done by substituting the characteristics of the "standard house" in the 

regression model of, say 2009, to get the price of the house in 2009. Using this approach, we can 

get a price series through time for the same "standard house." If we then divide the monetary 

value of construction with the corresponding price, we can get a quantity index series through 

time. This quantity index series is in terms of the number of "standard houses" in each year. In 

this way, we keep the "product" quality constant. This quantity index can be an output measure 

in productivity studies. A bigger house in 2009 would cost more to construct than the standard 

house in 1970, for two reasons. One reason is that there is inflation. The other reason is that the 

house is bigger. The price series obtained from the above approach addresses both of these 

                                                           

152 Frank de Leeuw, “A Price Index for New Multifamily Housing.” Op. cit.

81 
 

issues. Inflation is accounted for because the regression model from year 2009 was used to 

"predict" the price of the "standard house." Houses in 2009 are bigger, and since we are 

artificially keeping the "product" constant, we are giving more credit to the output of 2009. That 

is, the actual number of houses constructed in 2009 is inflated to reflect the fact that the unit is 

the smaller "standard house." 

In the previous section, two output data sources were discussed. The C30 Value of Construction 

Put in Place data are classified by project types or end usage while the Economic Census data are 

classified by SIC/NAICS. Two agencies currently create price deflators relevant to the 

construction industry. The BEA uses the C30 Value of Construction Put in Place data from the 

Census Bureau for its fixed investment and fixed assets data. In addition to construction 

spending defined by the Census Value of Construction Put in Place, BEA includes mining 

exploration, shafts, and wells, brokers’ commissions on the sale of new and used structures, 

mobile structures, manufactured homes, and net purchases of used structures. The BEA reports 

quantity and price indices for categories of structures based on end usage, which correspond to 

C30 classification. Although the added categories are not of relevance in terms of construction 

output, the BEA’s price deflators could potentially be used in conjunction with C30 Value of 

Construction Put in Place data to yield a constant-dollar output time series. 

The other agency that produces price deflators is the BLS. BLS has recently developed producer 

price indices for the construction industry that are based on prototypes of buildings. The 

appropriate price deflators contain the contractor’s profit, materials costs, and labor costs. 

Producer price indices are produced for the following new building construction categories: (1) 

New industrial building construction (NAICS 236211), (2) New warehouse building 

construction, (3) New school building construction (NAICS 236222), and (4) New office 

building construction (NAICS 236223). Producer price indices are also produced for four types 

of special trades in the nonresidential setting: (1) Concrete contractors (NAICS 23811X), (2) 

Roofing contractors (NAICS 23816X), (3) Electrical contractors (NAICS 23821X), and (4) 

Plumbing/HVAC contractors (NAICS 23822X). 

The PPIs are available for new industrial building construction starting from June 2007. The 

PPIs for new warehouse building construction are available from December 2004. For new 

school building construction, the PPIs are available from December 2005. And for new office 

building construction, June 2006 was the first time the PPIs are available. All of the newly 

available PPIs for nonresidential structures have been incorporated in BEA’s estimates of 

investments in private structures.153 For nonresidential structure types for which PPIs are not 

available, the BEA combines an input cost index with an output cost index to capture some of the 

productivity and quality changes in the industry and the costs for a particular building type.154 

                                                           

153 Paul R. Lally, “How BEA Accounts for Investment in Private Structures.” Op. cit.

154 Ibid.

82 
 

Using hospitals as an example, the BEA uses Census Bureau’s single-family houses under 

construction index along with the Turner Construction Company building cost index. BEA and 

BLS may collaborate further to develop other nonresidential building construction indices, such 

as price indices for highways, hospitals, retail, communication, power, and lodging structures.155 

For the four special trades groups, the PPIs are available starting from December of 2007. 

These PPIs correspond to NAICS categories, which is the basis of the Economic Census. 

However, the Economic Census is conducted every five years, and the PPIs have been available 

only since 2004. Coupling the output data from the Economic Census and the BLS PPIs would 

require longer time series than what is currently available. 

A.1.5 Labor Input 

A.1.5.1 Hours (Production Workers) 

The BLS Current Employment Statistics (CES) program is used as the primary source of 

industry employment and hours data. The data are collected monthly and the employment levels 

are benchmarked yearly using data from State unemployment insurance programs, which covers 

about 98 % of all nonfarm employees.156 The classification of industries in this survey is the 

NAICS system since 2003, and the historic data were classified by the SIC system. The CES 

reports the number of all employees, the number of production workers, the number of women 

workers, the average weekly hours of production workers, the average weekly earnings of 

production workers, and the average weekly hours of overtime of production workers. In the 

case of the construction industry, the production workers include “workers, up through the level 

of working supervisors, who are engaged directly in a construction project, either at the site or in 

shops or yards, at jobs ordinarily performed by members of construction trades.”157 For nonproduction workers, which are executive and managerial personnel, professional and technical 

employees, and workers in routine office jobs, only employment data is available from this 

survey. Note that the numbers of jobs are counted, not persons in the CES program. The hours 

are hours paid, not hours at work. Work hours of non-production workers are not collected, and 

therefore would need to be estimated. 

A.1.5.2 Conversion from Hours Paid to Hours Worked 

One disadvantage of the CES data set is that hours paid, instead of hours worked are reported. 

Hours paid include vacation, paid sick leave, and holidays, in addition to hours worked. Hours at 

work includes paid time for traveling between jobs sites, coffee breaks, and machine 

                                                           

155 Ibid.

156 Bureau of Labor Statistics, Handbook of Methods. Op. cit. 

157 Ibid.

83 
 

downtime.158 While some of the hours at work do not increase productivity, others do. One 

such example is activities that motivate workers and reduce shirking. Paid leave is best 

considered a benefit.159 BLS has been collecting annual establishment level data on actual hours 

worked for production and nonsupervisory workers (Hours-at-Work Survey) since 1981. Data 

from the Hours at Work Survey are used to derive ratios of hours at work to hours paid. This is 

done for 1-digit Standard Industrial Classification (SIC) industry groups on an annual basis. 

These ratios are then used to convert hours paid data from the establishment survey. Hours-atWork Survey by itself, however, is not detailed enough to be used in industry-level productivity 

measurement. The BLS terminated the Hours at Work Survey in 2000, and replaced the HWS 

with National Compensation Survey. The Employment Cost Index (ECI) from the National 

Compensation Survey is used to convert hours paid to hours worked. The Hours at Work Survey 

had a few limitations. Eventually, because of stringent data reporting requirements, the response 

rate decreased to the point where not enough data were usable. The ECI was designed to capture 

the hourly cost of wages and benefits, including paid leave. Ratios of hours at work to hours 

paid can also be constructed using the ECI data. These ratios are calculated using the ECI data 

since 2001. For the years before 2001, the ratios are based on Hours at Work Survey. The HWS 

survey included production and nonsupervisory workers in nonagricultural establishments. The 

National Compensation Survey, on the other hand, covers all workers. Another advantage of the 

National Compensation Survey is that it contains a bigger sample. The sample size is 37000 

occupations within 8500 private establishments whereas the HWS sampled fewer than 6000 

establishments. The response rate associated with the NCS is also higher than that of the HWS. 

A.1.5.3 Hours (Nonproduction Workers) 

The BLS Current Employment Statistics (CES) Survey reports the number of all employees, the 

number of production workers, the number of women workers, the average weekly hours of 

production workers, the average weekly earnings of production workers, and the average weekly 

hours of overtime of production workers. What the CES survey does not collect is the average 

weekly hours of supervisory and professional workers. For the non-production workers, only 

employment data are available from this survey. 

The Current Population Survey (CPS) collects data on hours worked. And it is used by the BLS 

in its productivity program to derive annual ratios of supervisory (or nonproduction) worker 

average weekly hours to nonsupervisory (or production) worker average weekly hours, and 

subsequently nonproduction worker hours. The CPS asks respondents for their occupation and 

                                                           

158 Bureau of Labor Statistics, Handbook of Methods. Op. cit. 

159 Lucy P. Eldridge, Marilyn E. Manser, and Phyllis Flohr Otto, “Alternative Measures of Supervisory Employee 

Hours and Productivity Growth.” Op. cit.

84 
 

employment status. The information on occupation and employment status is used to sort the 

data into supervisory (nonproduction) and nonsupervisory (production) categories.160 

Ratios of supervisory (nonproduction) worker average weekly hours to nonsupervisory 

(production) worker average weekly hours are calculated. These ratios are multiplied by 

nonsupervisory (production) worker average weekly hours from the CES. Note the hours data 

from the CES are for hours paid (rather than hours worked), and therefore some discrepancy is 

introduced. The ratios between hours worked and hours paid are available at major sector level 

(from the National Compensation Survey), but not available at detailed industry level. The 

resultant number, supervisory (nonproduction) worker average weekly hours, is then multiplied 

by the number of supervisory workers to yield total supervisory worker weekly hours. Total 

supervisory worker hours are obtained by multiplying total supervisory worker weekly hours by 

52. Total supervisory worker hours are then combined with total nonsupervisory worker hours 

from CES and total self-employed hours and unpaid family worker hours to yield total hours for 

an industry. 

A.1.5.4 Self-Employment 

The Current Population Survey (CPS) is used to supplement the CES, for data on proprietors and 

unpaid family workers. Self-employed individuals are not included in the CES. This is 

particularly a concern for the construction industry where a large proportion of the workers are 

self-employed. Starting in 1994, the CPS collects monthly data on employment and hours for 

primary job and all other jobs separately.161 In contrast, prior to 1994, CPS reports hours worked 

for all jobs a person holds, but only the primary job is recorded. 

The CPS is based on Census Bureau’s Industry Classification System (ICS). The CPS currently 

uses 2002 Census occupational classification and the 2007 Census industry classification. These 

are derived from the 2000 Standard Occupational Classification (SOC) and the 2007 North 

American Industry Classification System (NAICS). Crosswalks are available on BLS’s website 

to link census classification systems with SOC and NAICS. One limitation of using the Current 

Population Survey to obtain information on the self employed is the sample size. Coding of 

industries and reporting are more accurate in establishment level surveys compared to household 

surveys. For this reason, data from the Current Employment Statistics program should be used 

                                                           

160 Bureau of Labor Statistics, Construction of Average Weekly Hours for Supervisory and Nonproduction Wage and 

Salary Workers in Detailed Industries, Technical Note. (Washington D.C.: Bureau of Labor Statistics, 2005). 

161 Bureau of Labor Statistics, Construction of Employment and Hours for Self-employed and Other Nonfarm 

Workers and for all Farm Workers, Using Current Population Survey Data for Primary and Secondary Jobs,

Technical Note. (Washington D.C.: Bureau of Labor Statistics, 2006). 

85 
 

as a primary source of data, and data from the Current Population Survey should be used as a 

supplemental source of data.162

In a BLS study, self-employed were excluded from the analysis of productivity. The reason was 

that proprietors’ incomes include both returns from labor and capital.163 It is difficult to separate 

the income into these two components. The more recent BLS approach164 is similar to the 

Jorgenson et al. approach.165 First, it is assumed that the self-employed and unpaid family 

workers are paid the same hourly wages as employees with similar characteristics in the same 

sector. It is also assumed that the noncorporate rate of return is the same as after-tax rates of 

return for corporate businesses. These two rates are then adjusted proportionately such that the 

reported proprietor’s income matches with the sum of labor income and noncorporate income. 

A.1.5.5 Labor Costs 

In labor productivity calculations, labor input is simply expressed in hours. For multifactor 

productivity calculations, various inputs are combined using corresponding costs as weights. 

Since the price of labor includes both wage and benefits, from the producer’s point of view, both 

wage and benefits should be included in labor costs. BLS includes in the labor compensation 

wages, salaries, supplemental payments, including employer’s contribution to social security, 

unemployment insurance taxes, and payments for health insurance and pension plans.166 

Supplemental payments also include paid leave, such as vacation and holiday leave. The labor 

compensation data come from the National Income and Product Account (NIPA) developed by 

the BEA and is based on establishment-level data. Labor compensation for proprietors and 

unpaid family workers needs to be estimated. Real compensation per hour are calculated using 

Consumer Price Index.167 Labor costs also include a portion of noncorporate income.168 

A.1.5.6 Labor Quality 

Levels of labor quality are not distinguished in labor productivity calculations. However, 

accounting for labor quality is important in multifactor productivity. Jorgenson presented 

                                                           

162 Lucy P. Eldridge, Marilyn E. Manser, and Phyllis Flohr Otto, “Alternative Measures of Supervisory Employee 

Hours and Productivity Growth,” Op. cit.

163 Bureau of Labor Statistics, Labor Composition and U.S. Productivity Growth, 1948-90, Op. cit.

164 Bureau of Labor Statistics, Technical Information About the BLS Multifactor Productivity. Op. cit. 

165 Dale W. Jorgenson, Productivity, Volume 1: Postwar U.S. Economic Growth (Cambridge, MA and London: MIT 

Press, 1995). 

166 Bureau of Labor Statistics, Handbook of Methods, Op. cit.

167 Ibid.

168 Ibid.

86 
 

evidence suggesting that the assumption that labor and capital inputs are homogenous is not 

valid.169 For instance, for the period between 1947 and 1985, more than a third of labor input 

growth is from the growth of labor quality. It is important to take into account varying levels of 

labor quality. Jorgenson et al., for instance, categorizes labor into two gender, eight age, five 

education, two employment status (employed and self-employed), and ten occupation 

categories.170 In their approach, labor input growth is a function of growth in labor hours, as 

well as growth in labor quality. For each industry, a price matrix is established using labor 

categorizations. Characteristics of labor input include gender (2 groups), age (8 groups), 

employment class (2 groups), occupation (10 groups), education (5 groups), and industry (51 

groups). This matrix contains 81 600 cells, which is the product of the numbers of groups for 

each labor input characteristic. Each cell of this price matrix would be populated with the 

corresponding labor compensation of the particular labor category. Similarly, a quantity matrix 

is established with each cell of the matrix being populated with hours worked by labor in a 

particular category. With these two matrices, labor input can be obtained by summing labor 

inputs of various categories with the corresponding weights, where the weights are the average 

value shares of the two periods in consideration. 

BLS cross classifies the hours of workers by different schooling levels, gender, and age for its 

multifactor productivity measures, only at the major sector level. In contrast, for multifactor 

productivity measures at a less aggregated level, such as the manufacturing industries, labor 

input is simply a sum of all hours. For a few detailed industries, the number of employees is 

used as the labor input measure.171 Similarly, labor productivity is an output per hour measure 

and is calculated assuming all hours are homogenous. 

BLS labor classification used to include experience. The recent removal of experience from 

labor classification is consistent with Jorgenson et al.172, who assume that experience is 

implicitly included through data on education and age.173 The hours at work by the different 

types of workers are weighted and aggregated using an annually chained (Tornqvist) index. An 

earlier approach BLS used regarding labor costs was to estimate earnings using data from 

                                                           

169 Dale W. Jorgenson, “Productivity and Economic Growth,” in Fifty Years of Economic Measurement—the Jubilee 

of the Conference on Research in Income and Wealth. Op. cit.

170 Dale W. Jorgenson, F. M. Gollop, and B. M. Fraumeni, Productivity and U.S. Economic Growth. Op. cit.

171 Edwin R. Dean, Michael J. Harper, and Mark S. Sherwood, “Productivity Measurement with Changing-Weight 

Indices of Outputs and Inputs,” Op. cit. 

172 Dale W. Jorgenson, Mun S. Ho, and Kevin J. Stiroh, Productivity Volume 3: Information Technology and the 

American Growth Resurgence. Op. cit.

173 Bureau of Labor Statistics, “Changes in the Composition of Labor for BLS Multifactor Productivity Measures, 

2007.” Technical Report. (Washington D.C.: Bureau of Labor Statistics, 2009). 

87 
 

education, estimated experience, and other characteristics. The recent approach is to simplify the 

procedure by using actual earnings. 

An alternative approach utilized the Longitudinal Employer-Household Dynamics database to 

estimate human capital.174 Dale Jorgenson commented that this methodology is “taking 

productivity analysis to the next level.” 

A.1.6 Capital Input 

This section is based largely on Harper.175 Capital could include anything that is costly to obtain 

at the present, but it earns return in the future.176 Capital therefore could include equipment, 

structures, land, inventories, financial assets, human capital, and intangibles such as software 

development, advertising costs, or organizational efforts.177 In productivity studies, only 

equipment, structures, land, and inventories are accounted for as capital inputs. Although data 

on financial assets exist, they are not included in capital input calculations because it is difficult 

to link decisions about financial assets with production decisions.178 While intangible assets 

play direct roles in production, they are excluded because it is hard to quantify their service 

flows.179 

Property income of capital is defined as nominal revenues minus expenses for variable inputs. It 

represents the return of the capital to the investor who made the capital investment. It also 

represents the nominal cost paid by the production manager to the investor for the use of the 

capital. Property income of capital is readily available in firm’s accounting records.180

To construct capital input, the first step is to use the perpetual inventory method to convert 

investment data into capital stocks. Capital stocks then are combined with property income data 

                                                           

174 LEHD, “A Layman’s Guide to the LEHD Human Capital Measures” Longitudinal Employer-Household 

Dynamics Informational Document No. ID-2003-04. 2003; John M. Abdowd, Paul A. Lengermann, and Kevin L. 

McKinney. “The Measurement of Human Capital in the U.S. Economy,” Longitudinal Employer Household 

Dynamics. Technical Paper No. TP-2002-09. 2002 

175 Michael J. Harper, “Estimating Capital Inputs for Productivity Measurement: An Overview of U.S. Concepts and 

Methods,” International Statistical Review 67(1999):327-337. 1999. 

176 Ibid.

177 Ibid.

178 Ibid.

179 Ibid.

180 Ibid.

88 
 

to derive rental prices. These rental prices are then used as weights to aggregate capital services 

from different assets into a capital input index.181

The BEA uses BLS producer price indices as a basis to deflate nominal investments to yield real 

investments. The price indices used incorporate quality change such that investments of higher 

quality are treated as being higher in quantity, while the quality is kept constant. BLS then uses 

an age/efficiency function to weight real investments by age/efficiency, and weighted real 

investments are aggregated by asset types. The productive capital stock ܭ௜,௧ at time t for the ith

type of capital asset is a sum of past investments, ܫሺ݅, ݐ െ ߬ሻ, of asset type i and age ߬, weighted 

by the age/efficiency function, ܵఛ: 

ܭ௜,௧ ൌ ෍ܵఛ ܫሺ݅, ݐ െ ߬ሻ

ஶ

ఛୀ଴

The age/efficiency function, ܵఛ, used in BLS productivity program is of the following functional 

form: 

ܵఛ ൌ ܮെ߬

߬ܤ െ ܮ

Where L is service life of the asset, ߬ is the age of the asset, and B is a parameter. B is assumed 

to be 0.5 for equipment and 0.75 for structures. The age/efficiency profile is based on empirical 

evidence when it is available. However, such information tends to be limited. Note that the 

vintage aggregation is based on efficiency of the asset rather than its value. 

Property income,Ψ௧, is the total rent from if d ferent a se s ts at time t. That is, 

Ψ௧ ൌ ෍ܿ௜,௧ܭ௜,௧

௜

where ܭ௜,௧ is the productive stock of the ith asset and ܿ௜,௧ is the rental price. The rental price can 

be written as the following equation if the price of the asset is assumed to be the discounted sum 

of all future rents. 

ܿ௜,௧ ൌ ௧ ݌௜, ݎ௧ ൅ ݌௜,௧ߜ௜,௧ െ ሺ݌௜,௧ െ ݌௜,௧ିଵሻ

where ߜ௜,௧ is the rate of depreciation, ݎ௧ is the discount rate, and pi,t is the price. The rate of 

depreciation is derived from the age/price profile that corresponds to the age/efficiency profile 

used to aggregate assets of different vintages earlier. With data on property income, productive 

capital stock, the rate of depreciation, the two equations above are used to estimate the rate of 

return, ݎ௧ and thus ܿ௜,௧, the rental price. The rental price is implicit and needs to be estimated 

                                                           

181 Dale W. Jorgenson, Mun S. Ho, and Kevin J. Stiroh, Productivity Volume 3: Information Technology and the 

American Growth Resurgence, Op. cit.

89 
 

because companies often own the capital they use and there is no actual transactions that take 

place. 

Property taxes for specific asset types are then added to the rental price, and a multiplier is 

created to take into account corporate income tax, depreciation deductions, and credits. This 

procedure takes into account the different effective tax rates on different types of assets. For 

instance, the effective cost for equipment use is lower than that of structures due to investment 

tax credit for equipment and possible depreciation deductions for equipment over very short 

periods of time. 

Productive stocks of different asset types are then aggregated for each industry, using the rental 

price shares as weights. These aggregated productive stocks then constitute capital input for the 

industry. For the productivity measurement at the major sector level, such as the private 

business sector, then the industry-level capital inputs are aggregated using relative capital 

income as weights. 

Similar to labor, capital quality is accounted for in capital input calculation in the framework of 

Jorgenson et al.182 Capital is broken down by class of asset and legal form of organization. 

Capital stock at any time point is the sum of weighted past investments. The weights represent 

relative efficiencies of capital due to age differences. The cross-classification, however, was not 

done by industry. 

BLS classifies capital assets into 42 types for equipment, 21 types for nonresidential structures, 9 

types for residential structures, 3 types for inventories (by stages of processing), and land.183 

Notably, information processing equipment and software is included under the equipment 

category. 

A.1.7 Intermediate Inputs 

Intermediate inputs include energy, materials, and purchased business services inputs. These 

data are available from BEA’s input output tables. The role of intermediate inputs becomes 

more important when the focus is on a more disaggregated industry level.184 Intermediate inputs 

are constructed only for manufacturing industries in the BLS productivity program as 

manufacturing as a whole and 18 3-digit NAICS manufacturing industries are the only industries 

for which KLEMS productivity measures are published.185 Data for energy input come from 

price and quantity of fuels used for heat or power. For the productivity calculation of 

                                                           

182 Dale W. Jorgenson, F. M. Gollop, and B. M. Fraumeni, Productivity and U.S. Economic Growth, Op. cit.

183 Bureau of Labor Statistics, Technical Information About the BLS Multifactor Productivity, Op. cit.

184 Ibid.

185 Ibid.

90 
 

manufacturing industries, materials are non-energy inputs but include fuel-type materials that are 

used as raw materials in manufacturing. Purchased business services are purchased services 

from service industries by manufacturing industries. Costs associated with intermediate input 

purchases should include commodity taxes because they are paid by the producer.186

The present input-output framework is designed to track material flows. Data is limited 

regarding contracted labor services and leased capital equipments.187 As regards to materials 

flows, data tend to be incomplete outside of the manufacturing industries.188

                                                           

186 W. Erwin Diewert, “Which (Old) Ideas on Productivity Measurement Are Ready to Use?” in New Developments 

in Productivity Analysis. Op. cit.

187 Ibid.

188 Ibid.

91 
 

A.2 Aggregation Methods 

When choosing an aggregation method, it is best to choose one that uses weights that change 

over time. When fixed weights are used, the quality of estimates that are closer to the base year 

is generally high, whereas estimates that are further from the base year are likely to be error 

prone.189 For instance, when the price of capital increases, the quantity of capital decreases 

relative to labor. The value share of capital may increase, decrease, or stay constant, but the 

fixed-weight approach dictates the value share to be constant.190 Fisher Ideal191 and the 

Tornqvist indices are aggregated using weights that can change over time. Diewert shows that 

“certain index number formulas, which he coined “superlative,” such as the Tornqvist and the 

Fisher Ideal, are consistent with flexible production functions.”192 “Aggregation methods that 

use fixed weights are consistent with a more restrictive production function.” Diewert shows 

that “chained time series of superlative index numbers are approximately consistent.”193 On 

theoretical grounds, Fisher Ideal and Tornqvist indices are both good choices. There is also little 

difference between these indices in practical applications.194 As Dumagan shows, the Tornqvist 

index and the Fisher ideal index numerically approximate each other.195 As the Tornqvist index 

requires less data to calculate, it may be may be more practical to use.196 Although more 

researchers prefer the Tornqvist index, there is no strong reason to prefer the Tornqvist index or 

the Fisher ideal index.197 BLS uses the Tornqvist index in labor productivity calculations, while 

the BEA uses the Fisher ideal index for chain-type indices. 

                                                           

189 William Gullickson, “Measurement of Productivity Growth in U.S. Manufacturing,” Op. cit.

190 Edwin R. Dean, Michael J. Harper, and Mark S. Sherwood, “Productivity Measurement with Changing-Weight 

Indices of Outputs and Inputs” In Industry Productivity: International Comparison and Measurement Issues, OECD 

(Paris: OECD, 1996). 

191 For more information on Fisher Ideal Indices, please see R.D. Rossiter, “Fisher Ideal Indices in the National 

Income and Product Accounts,” Journal of Economic Education Fall (2000): 363-373.

192 W. Erwin Diewert, “Exact and Superlative Index Numbers,” Journal of Econometrics vol 4, no. 4 (1976): 115-

45. 

193 W. Erwin Diewert, “Superlative Index Numbers and Consistency in Aggregation,” Econometrica July (1978): 

883-900. 

194 William Gullickson, “Measurement of Productivity Growth in U.S. Manufacturing,” Op. cit.

195 Jesus C. Dumagan, “Comparing the superlative Tornqvist and Fisher ideal indices,” Economic Letters 76(2002): 

251-258. 

196 Ibid.

197 Edwin R. Dean, Michael J. Harper, and Mark S. Sherwood, “Productivity Measurement with Changing-Weight 

Indices of Outputs and Inputs,” Op. cit.

92 
 

The Tornqvist index can be used to aggregate different types of outputs. It is also the approach 

used to individually aggregate different types of labor inputs, capital inputs, or intermediate 

inputs. When different types of labor are aggregated, the weights used in the Tornqvist index 

formula are relative shares of labor compensation. For aggregation of different capital assets, 

relative shares of capital income the assets generate are used as weights. Again when finally 

calculating the productivity, all inputs are aggregated using the Tornqvist index, with weights 

being each input’s share of total costs. Generally it is desirable to use the most disaggregated 

data and then aggregate different components to a more aggregate level. 

A.3 Overview of BLS Productivity Program 

BLS does not publish labor productivity or multifactor productivity measures for the 

construction industry. BLS produces labor productivity for business, private nonfarm business, 

manufacturing (total, durable, and nondurable sectors), and nonfinancial corporations. Labor 

productivity is available also for over 400 selected industries in manufacturing, mining, utilities, 

wholesale and retail trade, and services. 

BLS has two multifactor productivity programs—the Major Sector Multifactor Productivity 

program and the Industry Multifactor Productivity program. In the Major Sector Multifactor 

Productivity program, the BLS publishes multifactor productivity the private business sector, the 

private nonfarm business sector, the aggregate manufacturing sector, and 18 3-digit NAICS 

manufacturing industries and the utility and gas industry. The productivity measures for the 

private business sector and the private nonfarm business sector are based on value added output, 

and labor and capital inputs. For the aggregate manufacturing sector and the 18 3-digit NAICS 

manufacturing industries, gross output, KLEMS inputs (capital, labor, energy, materials, and 

purchased business services) are used. 

In its Industry Multifactor Productivity program, the BLS publishes multifactor productivity for 

86 4-digit NAICS manufacturing industries, air transportation, and railroad transportation. For 

these industries, inputs include employee hours, capital services, and intermediate purchases. 

The BLS also produces multifactor productivity for manufacturing industries of U.S., France, 

and Germany for comparison of productivity trends. These measures are based on value-added 

and labor and capital inputs. 

A.4 Classification Issues 

There are two types of classification issues. One issue concerns the different classification 

systems used in different datasets. For instance, the Economic Census and the Current 

Employment Statistics are both establishment-based surveys, and they are organized using SIC 

and NAICS systems. Census Bureau’s Value of Construction Put in Place survey uses projects 

as units of data collection. To create labor productivity measures using output data from the 

93 
 

Value of Construction Put in Place is difficult because labor hours data are collected using 

NAICS/SIC, but not organized by project types. 

The other classification issue concerns the change of classification systems within a dataset. The 

most prominent example of this issue is the change of industry classification system from SIC 

(Standard Industrial Classification) system to NAICS (North American Industry Classification 

System) in 1997. Under the SIC system, establishments were mainly classified by product or 

activity types, but in some instances, end use, raw materials, or market structure was as the basis 

for classification. The classification system of SIC was not consistent. NAICS was devised to 

incorporate new industries that were not covered under SIC and also to provide a consistent 

framework for classification. With the rapid changes in the composition of the economy, a new 

classification was needed to accommodate the new and evolving economy. The NAICS system 

classifies industries by their production processes, as opposed to final products. 

Some of the categories of construction under the two classification systems appear to be similar. 

One such example is SIC 152 General Building Contractors-Residential and NAICS 2361 

Residential Building Construction. However, these two categories are not completely 

comparable due to rule changes, such as the treatment of auxiliary units.198 Efforts have been 

made to concord or bridge the two classification systems and reclassify older data using the new 

classification system. 

For instance, the microdata of the Current Employment Statistics survey from March 2001 were 

coded in both SIC and NAICS.199 For the data from March 2001, 97.2 % of the employment of 

the construction industry under NAICS can be classified under the construction industry in the 

SIC system. A small percent (0.3 %) of the employment of the construction industry falls under 

mining in the SIC system.200 Some of the employment (1.3 % and 1.2 %, respectively) fall 

under Finance, insurance, and real estate, and services in the SIC system. These ratios were used 

to reconstruct historic data. 

In the case of the Economic Census, establishments surveyed in 1997 were coded both with SIC 

and NAICS, and bridge tables were developed based on the 1997 data. The bridge tables list the 

NAICS codes and the corresponding SIC codes. Since the matches are not always exact, the 

tables also list the proportions of total SIC sales, receipts, or value of shipment under particular 

SIC codes that are under specific NAICS codes. Using these proportions to “translate” NAICS 

codes into SIC codes, or vice versa, could cause problems. 

                                                           

198 Teresa L. Morisi, “Recent Changes in the National Current Employment Statistics Survey,” Monthly Labor 

Review June (2003): 3-13. 

199 Ibid.

200 Ibid.

94 
 

For cases where the SIC code does not translate into a NAICS code, one approach is to directly 

look at micro-level data. If an establishment is surveyed in 1997, then its 5-digit identification 

number and its SIC and NAICS assignments can be used to assign NAICS codes to the older 

data associated with this particular establishment. This is possible because establishments 

surveyed in 1997 were assigned both a SIC code and a NAICS code. 

If establishments cannot be classified with a NAICS code using the above approaches, then they 

could be assigned NAICS codes by following the procedure outlined below using data from the 

1997 Economic Census. Information on the characteristics, such as shipments per worker or 

hourly wages (in the case of the manufacturing industry), can be used to derive probabilities of 

specific NAICS code assignment. A NAICS code can then be drawn from the distribution.201 

Another example is Klimek and Merrell,202 who used 1997 Economic Census data on retail and 

wholesale industries and established proportions of establishments originally assigned a SIC 

code that are assigned to a NAICS code. Using these proportions, the authors constructed a 

distribution from which NAICS codes are randomly drawn and assigned to individual 

establishments. The newly NAICS coded data were then used to produce aggregate data. This 

latter approach was shown to be reasonable as two thirds of the establishments that required 

random assignment were cases where over 90 % of the establishments in 1997 were coded into a 

single NAICS industry. The authors also suggested that multiple random assignments can be 

done and used to generate standard errors. Another possibility is to use a firm’s NAICS code to 

assign to its associated establishments. 

                                                           

201 Kimberly N. Bayard and Shawn D. Klimek, “Creating a Historical Bridge for Manufacturing Between the 

Standard Industrial Classification System and the North American Industry Classification System,” Proceedings of 

the American Statistical Association, Business and Economic Statistics Section [CD-ROM] (2004), pp. 478-84. 

202 Shawn D. Klimek and David R. Merrell, “On Reclassifying Industries from the Standard Industrial Classification 

System to the North American Industry Classification System,” presented at the Second International Conference on 

Establishment Surveys, Buffalo, New York, 2000. 

95 
 

96 
 

Appendix B Productivity and Competitiveness: An Annotated Bibliography 

This annotated bibliography summarizes key documents in productivity measurement with an 

emphasis on the construction industry. Challenges in construction productivity measurement 

have been recognized for many decades. While some aspects of construction productivity 

measurement have received attention, and notable improvements have been made, many 

fundamental challenges exist. This annotated bibliography provides a focused view of the state 

of knowledge and, for researchers and practitioners, an updated and centralized source of 

documents in this area. 

This annotated bibliography contains three sections. Section B.1 focuses on documents with a 

specific focus on the productivity, its measurement, and other related issues in the construction 

industry. Some of the measurement issues are deflators, quality adjustments of output, the 

definition of what constitutes the construction industry, and the underground economy. Most 

studies on construction productivity focus either on task-level productivity or industry-level 

productivity. Metrics used include labor productivity, multifactor productivity, and direct work 

rate. There are, however, different definitions for each of these metrics in the literature. Some 

of the causes for differences of changes in construction productivity are economies of scale, 

labor quality, capital-labor ratio, changes in output mix, and institutional issues (prevailing wage 

laws, unions, and collective bargaining). This section also includes studies from other countries, 

such as the United Kingdom and Canada. 

Section B.2 is titled “construction data.” It includes a document from the Construction Statistics 

Data Users’ Conference in 1997, published by the Census Bureau. This document discusses 

governmental statistics on construction and on how the data are collected and reported. 

Section B.3 contains studies that are on the general topic of productivity methods and 

measurement. It includes many documents published by the Bureau of Labor Statistics. It also 

includes documents from the OECD and academic sources. Methods and challenges on 

measurement of output, deflators, capital, labor, and data quality are presented. 

B.1 Construction Productivity and Related Issues 

Allen, Steve G. “Unionized Construction Workers are More Productive.” Quarterly Journal 

of Economics 99, no. 2 (1984): 251-274. 

This is an empirical paper that shows that unionized workers are more productive, controlling for 

capital-labor ratio, capital recentness, measurable labor quality, scale of production, industry, 

region, and interstate price differences. Productivity is defined as value added per worker. The 

factors that contribute to higher productivity levels among unionized workers may include better 

training, reduced use of unskilled labor, lower foreman to journeyman ratios, reduced recruiting 

and screening costs, and greater managerial ability. 

97 
 

Allen, Steve G. “Why Construction Industry Productivity is Declining.” Review of 

Economics and Statistics 67, no. 4 (1985): 661-669. 

This paper studies the sources of construction productivity change. Value added per employee 

deflated by the Dodge cost index was regressed on capital per employee, labor hours per 

establishment (economy of scale), labor quality, union, region variables, and building types. 

Data used was at the state level for 1972 and 1977. The coefficients from the regression model 

were then combined with data from these two years to yield percent productivity change due to 

the various factors. The total predicted productivity change from the regression was -8.8 %. The 

shift of output mix from commercial, industrial, and institutional projects to residential projects 

resulted in a reduction in skilled labor, and this was the most important factor that contributed to 

the decline in productivity. 

Alternative deflators were also devised. For instance, a deflator for nonresidential building 

construction was calculated using the difference between the rate of change of value put in place 

and the rate of change of square footage put in place. Adjusting for bias in the deflators accounts 

for -10.5 percentage points in reported productivity, which was -21.4 %. The predicted change 

from the regression, together with the adjustment of the deflators, therefore can explain 92 % of 

the productivity change. 

Allen, Steven G. “Why Construction Industry Productivity is Declining: Reply.” Review of 

Economics and Statistics 71, no. 3 (1989): 547-549. 

Allen responds to Pieper’s (1989) comments. The capital-labor ratio was shown to be decreasing 

in the original paper, but Pieper showed that it was increasing. The sources of data and the 

assumptions contribute to this discrepancy. Allen made several adjustments and reported that 

56.5 % of the observed decline in productivity can be explained, instead of 92 % as in the 

original paper. 

Allmon, E., C. T. Hass, J. D. Borcherding, and P. M. Goodrum. “U.S. Construction Labor 

Productivity Trends, 1970-1998.” Journal of Construction Engineering and Management

126, no. 2 (2000): 97-104. 

This study focuses on task-level productivity. Unit labor costs in constant dollars and daily 

output factors were obtained from Means cost manuals, for tasks such as hand trenching, welded 

steel pipe installation, ceiling tile installation, and compaction with a sheepsfoot roller, over three 

decades. The tasks were chosen such that tasks that are impacted by varying degrees of 

technology improvement are included in the study. The daily output increased for most of the 

tasks, and the unit labor costs decreased in real terms for all tasks. The two main reasons for the 

increase in productivity are low wages and technology improvement. Time use data from 72 

projects in Austin, Texas over 25 years were also studied. It was found that direct work rate is 

positively correlated with construction productivity. 

98 
 

Azari-Rad, Hamid, Peter Philips, and Mark J. Prus, eds. The Economics of Prevailing Wage 

Laws. Hampshire, England: Ashgate Publishing Limited, 2005. 

This book is a collection of chapters that examine the prevailing wage laws and how they affect 

various aspects of the American construction industry. The book presents a history of prevailing 

wage laws and an overview of the construction industry. The underlying vision of the prevailing 

wage laws is a society where labor is highly skilled, highly paid, and the industries are capital 

intensive and utilize advanced technologies. The main thesis of the book is that prevailing wage 

laws solve a free-rider problem and they allow long-term costs to be paid, such as costs on 

training, safety, insurance, and pensions, despite the short-term nature of projects. These 

arguments are supported by empirical evidence based on heterogeneity in prevailing wage laws 

across states and time. The discussion on productivity is limited. 

Baily, Martin Neil and Robert J. Gordon. “The Productivity Slowdown, Measurement 

Issues, and the Explosion of Computer Power.” Brookings Papers on Economic Activit, no. 2 

(1988): 347-431. 

This paper examines the source of U.S. productivity slowdown after 1973. There is a section 

that is devoted to construction productivity. The average annual growth rate in terms of GDP per 

hour in construction was estimated to be between -1.67 % to -1.99 % in the period of 1973-1987. 

Trends of output and inputs are examined, and the paper documents the “implausibility” of the 

data. This paper indicates data problems and the need for better data collection, particularly on 

output. Included at the end of the paper is a discussion by William Nordhaus and David Romer. 

Bosch, Gerhard and Peter Philips, eds. Building Chaos—An International Comparison of 

Deregulation in the Construction Industry. London: Routledge, 2003. 

This book contains case studies of 9 countries—the Netherlands, Germany, Denmark, Canada 

(Province of Quebec), Australia, Spain, the United States, the United Kingdom, and Republic of 

Korea, in descending order of the level of regulation of their construction labor markets. The 

construction industry is intrinsically volatile. Because construction projects are neither storable 

nor transportable, the industry is particularly vulnerable to economic downturns. It was shown 

that construction industries in these countries follow two paths of development. On one path of 

development, long term costs are paid for. These long term costs include training of workers, 

health insurance, retirement, compensation for instability of the industry, and development and 

use of advanced technologies. This model of development is capital intensive, human capital 

intensive, and “technically dynamic.” Productivity tends to be high in construction industries that 

are on the technically dynamic path. The other model of development is characterized by a freerider problem. The long term costs are not paid for because there is no legal requirement or 

because there are no arrangements made between contractors and organized labor. Labor quality 

tends to be low and not well equipped, and labor intensity tends to be high. 

99 
 

In the case of the United States, in some regions organized unions and organized employer 

associations engage in collective bargaining and develop agreements for apprenticeship 

programs, health insurance, and retirement. In other regions, no such agreements exist. 

Canadian Construction Innovation Council. Measuring the Performance of the Canadian 

Construction Industry: Metrics. Ontario: Canadian Construction Innovation Council, 2006. 

This is an initial document of the Canadian Construction Innovation Council’s effort to assess 

the performance of the Canadian construction industry. This document includes a literature 

review of benchmarking efforts and a preliminary set of metrics. 

Canadian Construction Innovation Council. Measuring the Performance of the Canadian 

Construction Industry: Pilot Project Final Report. Ontario, 2007. 

This report is the follow-up report of the Metrics report dated 2006 (see above). This document 

reports the findings of the pilot study where metrics of performance were applied to 37 projects, 

including buildings and water and wastewater piping systems. It was pointed out that only using 

productivity measurements may not capture a complete picture of the performance. In addition, 

the industry prefers descriptive measures. The metrics system used is based on benchmarking 

programs of the Construction Industry Institute in the U.S. and the Movement for Innovation 

(M4I) in the United Kingdom. Project and organizational performance metrics are the focus, and 

aggregation from the project or organizational level to industry level is feasible. A project 

timeline with 6 phases is defined, along with metrics in costs, time, quality, safety, scope, 

innovation, and sustainability. The results are presented using radar charts, box-and-whisker 

plots, and cumulative distribution curves. 

Centre for the Study of Living Standards. Productivity Trends in the Construction Sector in 

Canada: A Case of Lagging Technical Progress. CSLS Research Report, no. 2001-3. 

Ontario, 2001. 

This report documents the trends in productivity growth in Canada and uses regression models to 

explain output per hour in the total construction and residential construction sectors. The 

independent variables include capital intensity, educational attainment, capital utilization, and 

the unemployment rate. None of these variables can explain the productivity decline in the 

sector. Comparing the late 1970s with the late 1990s, capital-labor ratio and educational 

attainment have increased. While increases in both of these factors are expected to increase 

output per hour, the observed output per hour declined. After examination of other variables, the 

report concludes that measurement error and lack of technical progress are the main factors for 

the observed productivity decline. The section on taxation examines how taxation policy affects 

companies’ decisions to make investments in equipment and workforce. Allocation of these 

resources could potentially have a great impact on productivity growth. 

100 
 

Crawford, Paul and Bernard Vogl. “Measuring Productivity in the Construction 

Industry.” Building Research and Information 34, no. 3 (2006): 208-219. 

This paper provides an overview of methods of productivity measurement and presents data on 

construction productivity in the UK. It points out that labor productivity in the UK is relatively 

low compared to the rest of Europe, and it is likely a result of low capital intensity adopted in the 

UK. This observation is consistent with Bosch and Philips (2003), in which construction 

industries of nine countries are ranked by levels of regulation and two paths of development are 

identified. The construction industry of the UK is characterized by low levels of regulation, low 

labor wages, labor intensive production processes, and limited use of technology. Crawford and 

Vogl point out that high levels of labor productivity can be achieved at the expense of overall 

productivity due to suboptimal capital-labor allocations. Therefore, measures of labor 

productivity do not tell the whole story. There is a need to improve existing data and creating 

new data for productivity measurement. The paper also points out the need to have measures for 

the quality of inputs. 

Eastman, Charles M. and Rafael Sacks. “Relative Productivity in the AEC Industries in 

the United States for On-site and Off-site Activities.” Journal of Construction Engineering 

and Management 134, no. 7 (2008): 517-526. 

This paper examines on-site and off-site sectors of the construction industry. The authors found 

that productivity is higher for off-site sectors compared to on-site sectors. Furthermore, off-site 

sector productivity growth is also higher. Some of the off-site sectors are classified as 

manufacturing under Census. When construction industry productivity measurement does not 

properly account for the role of intermediate inputs, such as pre-fabricated construction products, 

productivity estimates could be biased downward. The empirical evidence presented in this 

paper highlights the importance of treating intermediate inputs properly in the growth accounting 

framework. More specifically, the evidence is consistent with the productivity measurement 

approach where output measure is gross output and all inputs, including intermediate inputs, are 

treated symmetrically. 

Goodrum, Paul and Carl T. Haas. “Partial Factor Productivity and Equipment Technology 

Change at Activity Level in U.S. Construction Industry.” Journal of Construction 

Engineering and Management 128, no. 6 (2002): 463-472. 

This paper examines task-level productivity for 200 activities between 1976 and 1998. More 

specifically, this paper looks at whether equipment technology enhances labor productivity. The 

data sources were cost estimating guides, including Means, Richardson, and Dodge. Partial 

factor productivity is defined to be units of physical output divided by the sum of labor costs and 

fixed capital costs. The authors concluded that activities that experienced a significant change in 

equipment technology also experienced a greater improvement in partial factor productivity. 

101 
 

Most of the activities examined experienced improvement in partial factor productivity during 

the study period. 

Goodrum, Paul M., Carl T. Haas, and Robert W. Glover. “The Divergence in Aggregate 

and Activity Estimates of US Construction Productivity.” Construction Management and 

Economics 20, no. 5 (2002): 415-423. 

This paper compares productivity estimates at the industry level and at the task level. While 

industry-level productivity estimates tend to show declining trends, task-level activity 

productivity estimates tend to suggest productivity increases. This paper discusses productivity 

output measures, particularly the construction of output deflators. It lists the types of indices 

used to deflate different types of construction outputs. For task-level productivity, the measure 

of labor productivity is defined to be units of physical output divided by work hours, and the 

measure of multifactor productivity is defined to be units of physical output divided by the sum 

of deflated labor cost and equipment cost. The data used for task-level productivity calculations 

are from estimating manuals. Labor productivity and multifactor productivity at the task level 

were shown to be increasing from 1976 to 1998. 

Goodrum, Paul M. and Carl T. Haas. “Long Term Impact of Equipment Technology on 

Labor Productivity in the U.S. Construction Industry at the Activity Level.” Journal of 

Construction Engineering and Management 130, no. 1 (2004):124-133. 

Using cost estimating guides, the authors identified 200 construction activities during 1976 and 

1998. Five technology factors were identified: amplification of human energy, level of control, 

functional range, ergonomics, and information processing. 43 types of hand tools and 31 types 

of machinery associated with the 200 activities were studied and a technology index was 

developed. The technology index captures changes in the equipment in terms of technology 

factors. The technology index was constructed for each activity. Using this approach, the 

authors found that 107 of the 200 activities increased labor productivity, while 30 activities 

showed a decline and 63 activities showed no change in labor productivity. Equipment 

technological advances can therefore explain some of the labor productivity increase during the 

22-year period. 

Centre for the Study of Living Standards. Can Measurement Error Explain the Weakness of 

Productivity in the Canadian Construction Industry? By Peter Harrison. Centre for the 

Study of Living Standards Research Report, no. 2007-01. Ontario, 2007. 

This report presents productivity trends, reviews productivity literature, presents views of 

industry practitioners, describes methodology used by Statistics Canada, and discusses possible 

sources of mismeasurement. This report is an accessible, thorough, and comprehensive resource 

for construction productivity, with a focus on Canada. Statistics Canada’s productivity 

measurement methodology is described, with an emphasis on the construction of price deflators. 

This document also contains discussions on prefabrication and demonstrates that incorporating 

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more productive prefabrication results in higher productivity if the output measure is gross 

output. If the output measure is value added, then more productive prefabrication does not result 

in higher construction productivity. The document also indicates lack of empirical evidence 

regarding the increasingly important role of prefabrication perceived by industry practitioners. 

Haas, Carl T., James T. O’Connor, Richard L. Tucker, Jason A. Eickmann, and Walter R. 

Fagerlund. Prefabrication and Preassembly Trends and Effects on the Construction 

Workforce. Center for Construction Industry Studies Report No. 14. Austin, Texas, 2000. 

Prefabrication and preassembly use in the U.S. is studied in this report using a survey instrument. 

The survey respondents were 29 managers. The survey results indicate increased usage of 

prefabrication and preassembly, from 14 % in 1984 to 27 % in 1999, as a fraction of overall 

project work. The main reasons for using prefabrication and preassembly were schedule, 

workforce issues, and economic factors. The areas where prefabrication and preassembly are 

most often used are piping, mechanical, equipment, and structural assembly. In addition to the 

survey design and the results, this report also includes historic accounts and a literature review. 

Hendrickson, Chris. “Discussion of ‘Is Construction Labor Productivity Really Declining?’ 

by Eddy M. Rojas and Peerapong Aramvareekul” Journal of Construction Engineering and 

Management 131, no. 2 (2005): 269-270. 

Hendrickson discusses the paper by Rojas and Peerapong (2003), who argue that the data quality 

is so low that it is not possible to conclude whether construction productivity is declining or 

increasing. Hendrickson uses Census of Construction Industries data from 1982 through 1997 

and calculated a productivity measure defined as construction contribution to GDP divided by 

hours. Construction contribution to the GDP was deflated using the GDP price deflator index. 

This productivity measure increased from 1982 to 1987 and stayed constant through 1997. It is 

noted that real wage has declined during this period. The author also notes the lack of difference 

in trends for input costs, output prices, and general price indices. If there is productivity decline, 

then we would expect output prices to be increasing at a higher rate than input costs and general 

price inflation. 

This discussion is followed by a closure by Rojas. He argues that the conclusion of moderate 

improvement in productivity reached by Hendrickson was based on the assumption that the 

output measure is reliable, but he argues that the output measure is not reliable. Both 

Hendrickson and Rojas agree there is a need for meaningful measures of productivity. 

National Bureau of Standards. Productivity Measurement for the Construction Industry.

NBS Technical Note no. 1172. Washington, D.C., 1983. 

This report describes the measurement of single and total factor productivity. It summarizes the 

approaches and indicates how they apply to the construction industry. This document concludes 

with a discussion on data availability and challenges. The lack of appropriate price and cost 

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indices to convert output values into quantity indices is one obstacle. Another challenge is 

establishing a quantity index for capital input. It also points out that the definitions of some fourdigit construction industries have been changed between Economic Censuses. It would not be 

possible to construct five-year TFP growth estimates based on Census data for these industries. 

This report contains an appendix with an annotated bibliography on productivity measurement 

methods, productivity in the construction industry, and productivity measurement case studies. 

National Research Council. Advancing the Competitiveness and Efficiency of the U.S. 

Construction Industry. Washington D.C.: National Academies Press, 2009. 

In 2008, the National Institute of Standards and Technology requested the National Research 

Council to appoint an ad hoc committee of experts to provide advice for advancing the 

competitiveness and productivity of the construction industry. Over 50 industry experts were 

gathered at a two-day workshop in November of 2008 to brainstorm and identify activities that 

could lead to breakthrough improvements in construction productivity. The committee 

incorporated its expert opinions and the discussions from the workshop and produced this report. 

Five opportunities for breakthrough improvements were identified. They are: (1) Widespread 

deployment and use of interoperable technology applications, also called Building Information 

Modeling (BIM); (2) Improved job-site efficiency through more effective inferfacing of people, 

processes, materials, equipment, and information; (3) Greater use of prefabrication, preassembly, 

modularization, and off-site fabrication techniques and processes; (4) Innovative, widespread use 

of demonstration installations; and (5) Effective performance measurement to drive efficiency 

and support innovation. The report also provides three recommendations: (1) greater 

collaboration among construction industry stakeholders to implement interoperable technology 

applications, job-site efficiencies, off-site fabrication processes, demonstration installations, and 

effective performance measures; (2) the development of a technology readiness index to evaluate 

and mitigate risks of new technologies; and (3) collaborative efforts among governmental 

agencies to develop industry-level productivity measures. 

O’Grady, John, and Prism Economic Analysis. Estimates of Revenue Losses to Government 

as a result of Underground Practices in Ontario’s Construction Industry. Document prepared 

for the Ontario Construction Secretariat. Ontario, 2001. 

This document provides an update to the 1998 report titled “The Underground Economy in 

Ontario’s Construction Industry.” Estimates in the 1998 report were amended using new 

input/output data, and new estimates are developed for the 1998-2000 period. The rate of self 

employment and share of cash in total transactions are two indicators of underground activity. 

These two indicators have moderately increased. The share of underground income in total 

construction income has declined from 22 % to 19 %, as a result of changes in composition of 

the construction activities, increased enforcement, and increase in the share of housing starts by 

large developers. However, the amount of the underground income has increased, and the 

underground economy in construction remains a serious problem. 

104 
 

O’Grady, John, Greg Lampert, and Bill Empey. The Underground Economy in Ontario’s 

Construction Industry: Estimates of Its Size and the Revenue Losses to Government and the 

WISB. Document prepared for the Ontario Construction Secretariat. Ontario, 1998. 

This document presents estimates of the size of the underground economy in the construction 

industry in Ontario, in addition to estimates of the fiscal impact for the governments and the 

Ontario Workplace Safety and Insurance Board. It is estimated that between 1995 and 1997, the 

underground employment in the construction industry in Ontario was about 25 % to 35 % of total 

employment. For residential construction, the estimates are between 35 % and 48 %. For 

nonresidential construction, it is between 11 % and 17 %. Annual total fiscal cost to the 

governments is between 1.1 and 1.7 billion dollars. Factors contributing to the growth of the 

construction underground economy include the introduction of the GST (Goods and Services 

Tax) in 1991, increase in unemployment due to economic conditions, and increased competition 

in obtaining contracts. 

Oppedahl, David B. “Understanding the (Relative) Fall and Rise of Construction Wages.” 

Chicago Fed Letter July, no. 155 (2000). 

Construction wages relative to all private production worker wages peaked in the 1970s and has 

generally declined, with a slight increase from 1996 to 2000. The decline is a result of a number 

of factors. Increases in the wage premium associated with higher levels of education reduce the 

relative wage in construction because construction workers tend to have lower educational 

attainment. Another factor is immigration of low-skilled workers. Technology improvement is 

also cited as a reason for deskilling. As advanced technologies become available, the author 

argues that the skills required by the labor are lessened. An example given in the article is on 

prefabrication. However, it should be pointed out that when more advanced technologies (such 

as Building Information Modeling) are used in construction, perhaps more (and different) skills 

are needed, which may increase labor wages. Other reasons for wage declines include the 

existence of an informal economy, increased safety, decline in union representation and 

bargaining power, and wage laws that allow hiring of less skilled workers. 

Park, Hee-Sung, Stephen R. Thomas, and Richard L. Tucker. “Benchmarking of 

Construction Productivity.” Journal of Construction Engineering and Management 131, no. 

7 (2005): 772-778. 

This paper describes the Construction Industry Institute’s (CII) effort on construction 

productivity benchmarking. The Construction Productivity Metrics System (CPMS) was 

developed through a consensus of industry experts. CPMS is a framework for data collection 

and productivity analysis. With a focus on heavy industrial projects, seven activity areas were 

identified: concrete, structural steel, electrical, piping, instrumentation, equipment, and 

insulation. Specific tasks to be included in productivity measurement are identified, and units of 

105 
 

measurement are specified. This framework was applied to a sample of 16 industrial projects, 

and the results indicate this approach can yield meaningful productivity measures. 

Pieper, Paul. “Why Construction Industry Productivity is Declining: Comment.” Review of 

Economics and Statistics 71, no. 3 (1989): 543-546. 

In Allen (1985), construction productivity decline was explained largely by shifts from high to 

low productivity sectors, declines in average establishment size, labor quality, capital-labor ratio 

and percentage union members. Pieper challenges Allen’s conclusion. Pieper argues that the 

capital-labor ratio has increased, rather than decreased, as claimed by Allen (1985). Pieper 

agrees with Allen that there is a shift in the sector output, but he believes the effect on 

productivity is overestimated by Allen. He believes the problem lies in Allen’s use of a cost 

index to deflate value added. Allen claimed that the BEA deflator was largely based on cost 

indices. Pieper points out that only about one quarter of construction expenditures are deflated 

using cost indices. The majority of total construction expenditures are deflated using the Census 

Single Family Homes and the Federal Highway Administration indices, which are price indices. 

Pieper, Paul. “The Measurement of Construction Prices: Retrospect and Prospect.” In 

Fifty Years of Economic Measurement: The Jubilee of the Conference on Research in Income 

and Wealth, Volume 54. Edited by Ernst R. Berndt and Jack E. Triplett. Chicago: 

University of Chicago Press, 1990. 

This chapter discusses thoroughly the history of the development of construction price indices 

during the previous 40 years. This work has been highly cited in construction productivity 

measurement literature. Pieper argues that when the cost index is used to deflate output, 

productivity is assumed to be constant, and it is considered the least desirable type of price index. 

The cost index, however, has been commonly used to deflate output due to lack of appropriate 

output price deflators. Pieper also criticizes Dacy’s (1964, 1965) method of deflation by 

pointing out that an embedded assumption is that factors of production are not substitutable. He 

discusses alternative price indices, including bid prices, hedonic price indices, estimation indices, 

and cost indices. This chapter is followed by comments by Robert Parker from the BEA. Parker 

summarizes Pieper’s chapter and points out that the construction industry appears to lack a 

lobbying group that advocates the Congress to appropriate resources for improved construction 

statistics. Parker also describes BEA’s efforts in this area. 

Rojas, Eddy M. and Peerapong Aramvareekul. “Is Construction Labor Productivity Really 

Declining?” Journal of Construction Engineering and Management 129, no. 1 (2003): 41-46. 

This paper discusses factors that affect the quality of labor productivity measures at the industry 

level. It argues that the uncertainty in the data prevents researchers to reach a conclusion as to 

whether the construction labor productivity is indeed declining during 1979 to 1998. Value Put 

in Place is collected at the project level. Compared to manufacturing, for which data is collected 

at the establishment level, there are more data collecting units for the construction industry. This 

106 
 

is one reason for less accurate data for the construction industry. Another reason is the lack of 

annual data for benchmarking the value put in place data. Problems with lack of price indices for 

nonresidential construction are also discussed. There is also a disconnect between Construction 

Put in Place and Census of Construction Industries. For instance, architectural and engineering 

work, force-account construction, and secondary construction are included in the Construction 

Put in Place, but not the Census of Construction Industries. This paper also discusses the impact 

of changes in output mix on labor productivity and suggests that labor productivity should be 

calculated for different sectors within the construction industry. 

Schriver, William R. and Roger L. Bowlby. “Changes in Productivity and Composition of 

Output in Building Construction, 1972-1982.” Review of Economics and Statistics 67, no. 2 

(1985): 318-322. 

Cost of building per square foot in 1972 dollars was regressed on characteristics of buildings, 

such as number of stories, location, end-use, and framing type. The data used was from Dodge 

contract construction, which is based on ex ante cost estimates. The deflators used were the 

Census Bureau price index for a new one-family house, Turner Construction Company cost 

index, and American Appraisal Company cost indices. A shift to more office buildings and less 

residential construction was observed. Cost increase per square foot of output is interpreted as a 

decline in total factor productivity. This study concludes that there is a significant decline in 

total factor productivity in construction from 1980 through 1982, after composition of output is 

accounted for. There is no decline in productivity from 1972 through 1979. 

Stokes, H. Kemble, Jr. An Examination of the Productivity Decline in the Construction 

Industry. Review of Economics and Statistics 63 no. 4 (1981): 495-502. 

Labor productivity in the construction industry rose at an annual rate of 2.4 % between 1950 and 

1968, and it declined at an annual rate of 2.8 % between 1968 and 1978. Labor productivity is 

measured using real value added as the output measure. The decline in productivity appears to 

be robust, regardless of which labor input measure is used (employees, hours paid, or hours 

worked). This paper examines possible causes of the decline. Only 25 % of the decline could be 

explained by the factors examined, which include measurement of output, shifts in the output 

mix, changes in capital-labor ratio, demographic changes of the labor force, economies of scale, 

regional shifts, and shifts in work practices. BLS has conducted studies on construction labor 

productivity defined as gross output per employee hour. Labor productivity defined as such 

increased during the 1970s. This observation is consistent with the increased use of 

prefabricated materials during the same period. It is also consistent with the decline of value 

added and an increase in manufactured construction supplies during the same period. 

Teicholz, Paul, Paul M. Goodrum and Carl T. Haas. “U.S. Construction Labor 

Productivity Trends, 1970-1998.” Journal of Construction Engineering and Management 

127, no. 5 (2001): 427-429. 

107 
 

Teicholz discusses the paper titled “U.S. Construction Labor Productivity Trends” by Allmon, 

Haas, Borcherding, and Goodrum (2000). Allmon et al. (2000) report that decreasing real labor 

costs and more productive equipment are major factors which made labor costs lower for 

accomplishing tasks in the 1990s compared to the 1970s. Task-level productivity has increased 

because the same tasks cost less in terms of labor. Teicholz presents labor productivity trends 

from 1964 to 1999, for the construction industry and all non-farm industries. Labor productivity 

is calculated by dividing deflated value of construction put in place with field labor hours. Labor 

productivity trends downward for the construction industry while it trends upward for all nonfarm industries. This graph of labor productivity trends is frequently cited in reports on 

construction productivity. 

Goodrum and Haas respond to the discussion by Teicholz. They provide a concise and 

informative review of the problem of productivity measurement in construction. They point out 

the problem with output deflators, which is embedded in the deflated value of construction put in 

place data. Output deflators are often based on input cost indices, and as a result, tend to 

overestimate inflation and underestimate output and productivity. The Census Single-Family 

House Under Construction Index was constructed using a hedonic approach. The authors argue 

that this index, while preferred, may not capture changes in quality of output. As a result, output 

could be biased downward. This index is used for over half of the value of construction put in 

place. Another possible source of underestimation of output comes from decreases in real wage 

in construction, which is one component of value of construction put in place. Teicholz, 

Goodrum, and Haas agree there is a significant need for further studies to resolve the 

productivity puzzle in construction industry. 

B.2 Construction Data 

Census Bureau. Construction Statistics Data Users’ Conference. October 28, 1997. 

Washington, D.C. Document issued March, 1999. 

This report summarizes the discussions and presentations at the Construction Statistics Data 

Users’ Conference. The programs that survey and compile construction statistics are described. 

These programs include the Building Permits Program, Survey of Construction, Value Put in 

Place Program (VIP), Manufactured (Mobile) Home Program, and Economic Census: 

Construction Sector. The Building Permits Program, the Survey of Construction, and the 

Manufactured (Mobile) Home Program all focus on the residential sector. The Survey of 

Construction is used to develop price indices using hedonic regression models. The 

nonresidential sector is covered by the Value Put in Place Program and the Economic Census. 

The Value Put in Place Program is based on construction projects. The Economic Census is an 

establishment survey. The report points out that the VIP data are not comparable to the 

Economic Census data. The VIP construction costs include architectural and engineering design, 

construction management, force-account construction, and secondary construction, in addition to 

construction performed by the construction industry, as defined by the Economic Census. It also 

108 
 

points out that the Economic Census does not survey establishments with no payrolls. Sole 

proprietorships and partnerships are therefore not included. Also not included in the Economic 

Census is construction work performed in the underground economy. 

Census Bureau. Statistical Abstracts of the United States 

The Census Bureau compiles Statistical Abstracts of the United States using data from a variety 

of governmental agencies and private sources. Examples of construction related data include 

number of establishments, number of paid employees, and annual payroll from County Business 

Patterns, characteristics of commercial buildings from U.S. Energy Information Administration, 

producer price indices of construction materials from Bureau of Labor Statistics, and Value of 

Construction Put in Place from the Census Bureau. 

R.S. Means Building Construction Cost Data 

The R.S. Means Building Construction Cost Data provide, for individual construction tasks, 

estimates of daily output, crew requirement, labor hours, material cost, labor cost, equipment 

cost, and overhead and profits. It is a reference guide for budgeting and estimating. The data are 

based on surveys of contractors and suppliers. This book is published yearly. The latest version 

is the 67th edition (2009). 

B.3 Productivity Data and Measurement 

Bureau of Labor Statistics. Trends in Multifactor Productivity, 1948-81. Bureau of Labor 

Statistics Bulletin no. 2178. Washington, D.C., 1983. 

This is the first publication on multifactor productivity measurement in BLS. This document 

describes the methodology and data sources used in BLS’s multifactor productivity measurement 

program, and presents results. It describes the incorporation of recommendations from the Rees 

Report regarding aggregation methods (the adoption of Tornqvist index) and construction of 

capital inputs in the BLS productivity program. This document provides a brief and accessible 

derivation of the multifactor productivity growth equation starting from a production function. It 

also compares BLS estimates with productivity measures made by Denison, Jorgenson, and 

Kendrick. 

Bureau of Labor Statistics. “Chapter 10. Productivity Measures: Business Sectors and 

Major Subsectors.” In BLS Handbook of Methods. Washington, D.C., 1997. 

This chapter describes the construction of labor productivity and multifactor productivity indices 

by the BLS. This chapter, like other chapters in the BLS Handbook of Methods, is succinct and 

informative. The labor productivity is an output per hour measure. Labor quality is not taken 

into consideration in this construct. Multifactor productivity is constructed in two ways. First, it 

is constructed using labor and capital inputs for major sectors. It is also constructed using 

capital, labor, energy, materials, and purchased business services inputs (KLEMS inputs) for 

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more detailed manufacturing industries. For multifactor productivity, labor is categorized into 

1008 types by education, experience, and gender. Note, however, that BLS recently dropped the 

experience categorization. It discusses the data sources and procedures used. Inputs are 

aggregated using a Tornqvist chain index. Properties of this index are nicely described. This 

document points out that the output data for the construction industry are not satisfactory, and the 

productivity measures for this industry need to be used with caution. An annotated bibliography 

is also included. 

Bureau of Labor Statistics. “Chapter 11. Industry Productivity Measures.” In BLS 

Handbook of Methods. Washington, D.C., 1997. 

This chapter describes labor productivity and multifactor productivity measures. It begins with 

a history of BLS’s involvement in productivity measurement. It then describes the methodology 

the BLS uses to calculate the productivity measures, the sources of data, and the assumptions 

associated with the calculations. Specific details are included for industries for which BLS 

calculates productivity. This document also discusses the use and limitation of these measures. 

A list of technical references with brief annotation is included. 

Bureau of Labor Statistics. Labor Composition and U.S. Productivity Growth, 1948-90.

Bureau of Labor Statistics Bulletin no. 2426. Washington, D.C., 1993. 

This document is a study that examines labor productivity, taking into account the heterogeneity 

of labor. Labor is categorized into groups by educational attainment, work experience, and 

gender. Labor productivity growth is decomposed into two components—changes in labor hours 

and changes in labor composition. The estimation of earnings is done in two steps. First, 

experience is econometrically estimated using historic data on actual work experience. Then the 

estimated experience is used, along with education data, to estimate labor earnings. The hourly 

earnings for each type of workers were estimated using econometric models instead of using 

averages from the survey. One reason why this approach was used was that precision is 

increased due to small sample sizes of some of the worker types. An appendix describes how the 

productivity growth equation is derived starting from a production function and the assumptions 

used in the process. Note that BLS recently dropped experience from its labor classification and 

it uses actual wages rather than estimated wages. 

Bureau of Labor Statistics. Productivity: A Selected Annotated Bibliography, 1983-87.

Bureau of Labor Statistics Bulletin no. 2360. Washington, D.C., 1990. 

This annotated bibliography contains over 1000 publications published between 1983-87 on the 

concepts, methods, measurement, sources of productivity change, the relation between 

productivity to economic variables such as wages, prices, and employment, and economic 

growth. An author index and a subject index are included. This bibliography is the 7th in the 

series. Previous BLS bibliographies include Bulletin 1226 (1958), Bulletin 1514 (1966), 

Bulletin 1776 (1971), Bulletin 1933 (1977), Bulletin 2051 (1980), and Bulletin 2212 (1984). 

110 
 

Bureau of Labor Statistics. Construction of Average Weekly Hours for Supervisory and 

Nonproduction Wage and Salary Workers in Detailed Industries. Technical Note. 

Washington D.C., 2005. 

This document describes the construction of annual ratios of supervisory worker average weekly 

hours to nonsupervisory worker average weekly hours using occupation data from the Current 

Population Survey. It also discusses the issues with classification systems associated with CES 

and CPS. 

Bureau of Labor Statistics. Construction of Employment and Hours for Self-employed and 

other Nonfarm workers and for all Farm workers, using Current Population Survey data for 

primary and secondary jobs. Washington, D.C., 2006. 

This document describes the calculation of number of employed and hours worked for selfemployed using the Current Population Survey. Prior to 1994, CPS collected hours worked at all 

jobs, but only collected industry and occupation information on the primary job. Beginning in 

1994, CPS collected hours worked and industry and occupation data on all primary and 

secondary jobs. This document describes methodologies for estimating hours worked by the 

self-employed and unpaid family workers using historic and more recent CPS data. 

Bureau of Labor Statistics. Technical Information About the BLS Multifactor Productivity 

Measures. Washington, D.C., 2007. 

This document describes the BLS multifactor productivity program, including data sources and 

calculation procedures. It also discusses using a simplified methodology and preliminary data to 

generate estimates of productivity. The list of references is annotated and is very extensive. 

Dean, Edwin R. Michael J. Harper, and Mark S. Sherwood. “Productivity Measurement 

with Changing-Weight Indices of Outputs and Inputs.” In Industry Productivity: 

International Comparison and Measurement Issues. Paris: OECD, 1996. 

This paper details the history of BLS’s productivity program and its improvements over time. 

Changing-weight indices are more preferable than fixed-weight indices, and the properties of 

Tornqvist index are discussed. The paper discusses value-added being a more appropriate output 

measure than gross output in multifactor productivity measurement. However, it might be better 

to use value-added output measures for international productivity comparisons, since valueadded output measures tend to be more readily available in the international arena. 

Dean, Edwin R. and Michael J. Harper. “The BLS Productivity Measurement Program.” 

In New Developments in Productivity Analysis. Edited by Charles R. Hulten, Edwin R. 

Dean, and Michael J. Harper. Chicago: University of Chicago Press, 2001. 

This document is a historic account of BLS’s productivity program, including its expansion and 

improvements due to changes in data availability, developments in the literature, and needs of 

111 
 

data users. It provides an accessible summary of the literature on labor inputs and capital inputs 

and how they are operationalized in the BLS program. Topics of discussion include production 

theory, aggregation methods, labor composition, hours at work, the perpetual inventory method, 

capital deterioration and depreciation, and choices of output measures. In addition to providing 

links to the economic literature and data sources, the document points out best practices, 

limitations, and potential improvements. 

Diewert, W. Erwin. “Which (Old) Ideas on Productivity Measurement Are Ready to Use?” 

In New Developments in Productivity Analysis. Edited by Charles R. Hulten, Edwin R. 

Dean, and Michael J. Harper. Chicago: University of Chicago Press, 2001. 

This paper describes areas of improvement for productivity measurement. This paragraph 

summarizes some of the comments. The input-output framework tracks materials flows, but 

there is limited information on contracted labor services or rented capital equipment. Even for 

material flows, the data outside of manufacturing tend to be incomplete. The current System of 

National Accounts does not collect enough information on the self-employed. How the 

operating surplus of the self-employed is allocated between labor and capital incomes needs to 

be imputed. This problem could be more significant as the self-employed population grows. 

Issues associated with capital inputs include the limited data on efficiency declines of assets and 

service life of assets. Currently the opportunity cost associated with capital purchases is not 

included in the user cost. Another issue is the interest rate that should be used. There are also 

comments on a unified national statistical system in which surveys and resultant data are 

designed and organized in a coordinated way. 

Gullickson, William. “Measurement of Productivity Growth in U.S. Manufacturing.” 

Monthly Labor Review July (1995): 13-37. 

This paper focuses on the multifactor productivity of the manufacturing industry and describes 

basic principles in productivity measurement. First, inputs should be as comprehensive as 

possible. Second, double-counting should be avoided in input and output measures. The third 

principle is on aggregation. Changing weights, rather than fixed-weights, are preferred in 

aggregation. It contains a discussion on value-added output concepts and gross output concepts, 

indicating a distinct drawback associated with the value-added approach, particularly for 

disaggregated industry analysis. This paper also contains an informative discussion on 

productivity and prices. Productivity is a residual between output and inputs. It is also a residual 

between output and input prices. For instance, productivity is the means by which output price 

can stay constant while input prices increase. 

Harper, Michael. “Estimating Capital Inputs for Productivity Measurement: An Overview 

of U.S. Concepts and Methods.” International Statistical Review 67, no. 3 (1999): 327-337. 

This paper describes the concepts and methods of capital input calculation adopted by BLS. It is 

very readable and informative. It begins with a conceptual framework, in which there are two 

112 
 

agents: investors and production managers. The rental price of a capital stock is later tied back 

to these two decision-makers. This paper lays out the construction of capital input measures in a 

step-by-step fashion. It includes a discussion on what constitutes capital and why some types of 

capital are not included in productivity measurement. It discusses the perpetual inventory 

method for vintage aggregation, the assumption associated with the age/efficiency profile, 

procedures used to determine the rental prices of the assets, taking account of tax treatment of 

different assets, and finally, aggregation methods. 

Jorgenson, Dale W., Frank M. Gollop, and Barbara M. Fraumeni. Productivity and U.S. 

Economic Growth. Cambridge, Massachusetts: Harvard University Press, 1987. 

This book is a study of U.S. productivity from 1948 to 1979. The growth accounting framework 

has been adopted by the BLS in its productivity programs and has become the international 

standard (Jorgenson et al. 2005). The authors describe in detail their methodology in deriving the 

components in the productivity growth equation—output, labor, capital, and intermediate inputs. 

All the inputs were treated symmetrically. That is, labor, capital, and intermediate inputs can all 

contribute to growth in output, in contrast to a more restricted approach where a value-added 

function is assumed with an implication that intermediate inputs are not involved in productivity 

growth. All the inputs are also treated as being heterogeneous in their quality in this framework. 

Therefore, growth in an input can be due to both growths in the quantity and the quality of this 

input. Sectoral productivity is calculated and then aggregated to the economy level. Sources of 

growth were identified. Assumptions, such as Hicks neutrality and the existence of a valueadded function, were tested empirically. 

Jorgenson, Dale W., Mun S. Ho, and Kevin J. Stiroh. Productivity Volume 3: Information 

Technology and the American Growth Resurgence. Cambridge, Massachusetts and London, 

England: MIT Press, 2005. 

This book presents the Jorgenson’s productivity framework, incorporating information 

technology as an intermediate input. Intermediate inputs are a significant input in more than 

70 % of the industries studied. Explicitly accounting for them by using gross output as the 

output measure is the proper approach. This work also demonstrates that it is fruitful to separate 

heterogeneous components of inputs. For instance, IT-related capital was separated from other 

types of capital, and sources of growth can be properly allocated. Similarly, labor was separated 

into college educated and non-college educated types. More detailed categorization of inputs 

enables explanation of industry productivity trends. 

Lally, Paul R. “How BEA Accounts for Investment in Private Structures.” Survey on 

Current Business February (2009): 9-15. 

This paper describes the use of recently available BLS PPIs in BEA’s estimates on private 

nonresidential structure investments. These include PPIs for office buildings, warehouses, 

industrial buildings, and schools. For building types for which PPIs are not currently available, 

113 
 

alternative deflation methods are discussed. This paper also mentions the possibility of 

developing PPIs for highways, hospitals, retail, communication, power, and lodging structures. 

Lawson, Ann M. Brian C. Moyer, Sumiye Okubo, and Mark A. Planting. “Integrating 

Industry and National Economic Accounts, First Steps and Future Improvements.” In A 

New Architecture for the U.S. National Accounts. Edited by Dale W. Jorgenson, J. Steven 

Landefeld, and William D. Nordhaus. Chicago and London: University of Chicago Press, 

2006. 

This chapter discusses quality of value-added estimates in the I-O accounts and in the GDP-byindustry accounts. Value-added estimates from both the I-O accounts and the GDP-by-industry 

accounts for the construction industry were pointed out to be poor in quality. The poor data 

quality is due to incomplete coverage in the Economic Census and the large number of lowquality enterprise-establishment adjustments. 

Mark, Jerome A. “Problems Encountered in Measuring Single- and Multifactor 

Productivity.” Monthly Labor Review (1986): 3-11. 

This paper contains data sources and methods BLS uses for productivity calculation. It also 

discusses lack of good price deflators, particularly for the construction industry, among other 

challenges. Construction industry output for nonresidential structures is deflated using cost 

indices. This results in a productivity index that is biased towards no change. It was pointed out 

that the lack of appropriate price deflators is the determinant for whether a productivity measure 

can be derived in many cases. This paper recognizes that productivity measurement is not an 

easy task. BLS has made many improvements in its productivity program throughout the years, 

and more improvements will need to be made in the future. While labor productivity is often a 

less preferred measure of productivity compared to multifactor productivity, it is calculated with 

much more precision and with fewer assumptions. 

National Research Council. Measurement and Interpretation of Productivity. Washington 

D.C.: National Academy of Sciences, 1979. 

This document is also known as the Rees report, produced by the Panel to Review Productivity 

Statistics set up by the National Academy of Sciences. This book consists of two parts—a report 

that gives an overview of the productivity measurement issues and recommendations, and a 

collection of papers on productivity measurement. Much of BLS’s improvements on its 

productivity program can be traced to recommendations in this document. 

Notably, a paper by Gollop shows that the assumption of the existence of a value-added subfunction in the production function is too restrictive. This assumption implies that the marginal 

rates of substitution between the arguments of the value-added sub-function—capital, labor, and 

time—are independent of intermediate inputs. It also implies that the intermediate inputs are not 

involved in technological change and that technological change can only occur through capital 

114 
 

and labor. This work shows the importance of explicitly treating capital, labor, and intermediate 

inputs symmetrically in productivity measurement. 

OECD (Organization for Economic Co-operation and Development). Measuring 

Productivity—Measurement of Aggregate and Industry-Level Productivity Growth. Paris: 

OECD, 2001. 

“The OECD Productivity Manual is the first comprehensive guide to the various productivity 

measures aimed at statisticians, researchers and analysts involved in constructing industry-level 

productivity indicators.” This users’ guide focuses on productivity growth, rather than 

productivity levels, at the industry level, using non-parametric methods. A variety of 

productivity measurements are described. How to choose among the different options depends 

on the purpose and data availability. This manual is very accessible and practical. It points out 

desirable qualities associated with different approaches, but it also indicates practical challenges. 

It focuses on the index number approach in a production theoretic framework, but a section of 

the manual is devoted to the growth accounting approach. The growth accounting approach 

integrates the theory of the firm, index number theory and national accounts. The growth 

accounting technique looks at the rates of changes in output and the rates of changes in inputs. 

The multifactor productivity growth is determined as the “unexplained” residual. 

This manual points out that availability of data poses a significant challenge in the construction 

of productivity measures. Examples include price indices for output measures by industry, hours 

worked by industry (in particular, statistics for self-employed individuals, and crossclassification by productivity related characteristics), service life of assets, age-efficiency and 

age-price profiles of assets, and updated input-output tables integrated with national accounts. 

OECD. Measuring Capital—Measurement of Capital Stocks, Consumption of Fixed 

Capital and Capital Services. Paris: OECD, 2001. 

This OECD manual describes concepts related to capital measurement and provides guidelines 

for estimation of capital stocks, consumption of fixed capital, and capital services. In addition to 

established methods, it discusses alternative methods, what is commonly implemented in 

practice, alternative data sources, and some unresolved issues. It is a very detailed reference on 

measuring capital. 

Schreyer, Paul, and Dirk Pilat. Measuring Productivity. Economic Studies no. 33, OECD, 

Second Quarter. Paris: OECD, 2001. 

This document provides an overview of the growth accounting approach of productivity 

measurement, with discussions on the comparison issues of productivity growth and levels 

between countries and across time. Gross output and value added output measures are 

compared. Using value added output, the relationship between multifactor productivity and 

labor productivity is derived. Changes in value-added-based labor productivity are shown to be 

115 
 

the sum of labor productivity changes due to changes in capital-labor ratios and effects of 

multifactor productivity growth. In addition to capital-labor ratio and multifactor productivity 

growth, changes in gross-output-based labor productivity are also a function of the ratio between 

intermediate input and labor input. Multifactor productivity measures based on gross output are 

not comparable across different levels of aggregation due to interindustry transactions. 

Multifactor productivity measures based on value added are comparable across different levels of 

aggregation because interindustry flows are subtracted from the output measure. The authors 

point out that gross output and value added are useful complements. This document also 

contains an informative discussion on the interpretation of productivity measures. 

Zoghi, Cindy. Measuring Labor Composition: A Comparison of Alternate Methodologies. 

Bureau of Labor Statistics. Washington, D.C., 2007. 

This paper discusses the issues associated with calculating a labor composition index. Labor 

quality is often taken into account by sorting labor into types by education, experience, age, 

gender, occupation, and geographic region. It is not clear exactly which of these variables are 

the best to use to capture the difference in effectiveness of labor. What determines wage? Wage 

may not always reflect marginal productivity of labor. A number of theories are discussed. A 

second issue is regarding whether to use the actual wages as weights in labor input aggregation 

or to use the estimated wages from Mincer-type human capital wage regressions as weights. It is 

not possible to determine which approach is best regarding the choice of labor type 

categorizations and the use of estimated or actual wages. 

116 
 

Appendix C Sources of Construction Data Related to Productivity and Their Availability 

This appendix provides a description of data sources that may be relevant to construction 

productivity measurement. It then describes classification systems, variables, and availability. 

These materials are tabulated for key sources of data. 

C.1 Sources of Construction Data Related to Productivity 

County Business Patterns (Census Bureau) 

County Business Patterns contains annual data. The variables include number of employees, 

payroll, and number of establishments by NAICS codes. Number of establishments by 

employment-size class for NAICS categories is also reported. The online data is available for 

1998 through 2006. Country Business Patterns does not cover the self employed. 

Dodge Reports (McGraw-Hill Construction) 

Dodge Reports are lists of construction projects and are available since 1967. Variables include 

value, month started, square footage, dwelling units (for residential only), state, county, project 

type, number of stories, and ownership (private and four public categorization). Note that the 

value associated with a project is an ex ante estimate. Whether a project is new construction, 

addition, or alteration is also indicated. Framing type is also reported. Examples of building 

types in the nonresidential sector include stores and restaurants, warehouses, office and bank 

buildings, parking garages and automotive services, manufacturing plants, warehouses, labs, 

schools, libraries, hospitals, government service buildings, religious buildings, amusement, 

social, and recreational buildings, hotels and motels, and dormitories. Nonbuilding categories 

include streets and highways, bridges, dams, reservoirs, river development, sewage and waste 

disposal systems, water supply systems, power plants, gas, and communication systems. While 

the Value Put in Place data have undergone a classification system change, the classification 

system of the Dodge Reports has remained the same since 1967. 

Current Employment Statistics Survey 

The BLS Current Employment Statistics (CES) program is used as the primary source of 

industry employment and hours data. The data are collected monthly and the employment levels 

are benchmarked yearly using data from State unemployment insurance programs, which covers 

about 98 % of all nonfarm employees.203 The classification of industries in this survey is the 

NAICS system since 2003, and some of the historic data since 1990 were updated using the same 

                                                           

203 Bureau of Labor Statistics, Handbook of Methods (Washington DC: Bureau of Labor Statistics, 1997). 

117 
 

classification system.204 For industries that have the same or similar titles in the SIC and NAICS 

systems, there could still be some discrepancy in the data classified under these two systems. 

For example, under the SIC, auxiliary establishments were classified under the same code as the 

primary activity of the parent enterprise. Under NAICS, auxiliary establishments are classified 

based on their own primary activity.205 In 2003, in addition to the new classification system, the 

CES also switched from a quota-based sampling method to a probability based sampling 

method.206 The historic CES data that remain in the SIC classification system extend back to 

1988, 1972, or 1958 for different segments of the construction industry. The longest time series 

is for the total number of all employees, the annual data for which extend back to 1919. The 

CES reports the number of all employees, the number of production workers, the number of 

women workers, the average weekly hours of production workers, the average weekly earnings 

of production workers, and the average weekly hours of overtime of production workers. In the 

case of the construction industry, the production workers include “workers, up through the level 

of working supervisors, who are engaged directly in a construction project, either at the site or in 

shops or yards, at jobs ordinarily performed by members of construction trades.”207 For nonproduction workers, which are executive and managerial personnel, professional and technical 

employees, and workers in routine office jobs, only employment data is available from this 

survey. One disadvantage of the CES data set is that hours paid, instead of hours worked are 

reported. Hours paid include vacation, paid sick leave, and holidays, in addition to hours 

worked. Hours at work includes paid time for traveling between jobs sites, coffee breaks, and 

machine downtime.208 While some of the hours at work do not increase productivity, others do. 

One such example is activities that motivate workers and reduce shirking. Paid leave is best 

considered a benefit.209 Work hours of non-production workers are not collected, and therefore 

would need to be estimated. The Current Employment Statistics Survey is also an establishmentlevel survey, and it does not contain information on the self-employed. 

                                                           

204 Teresa L. Morisi, “Recent Changes in the National Current Employment Statistics Survey,” Monthly Labor 

Review June (2003): 3-13. For national series that had classifications with 90% or greater degree of 

interchangeability under SIC and NAICS, the historic data were reconstructed. 

205 Ibid.

206 Ibid.

207 Bureau of Labor Statistics, Handbook of Methods. Op. cit. 

208 Ibid. 

209 Lucy P. Eldridge, Marilyn E. Manser, and Phyllis Flohr Otto, “Alternative Measures of Supervisory Employee 

Hours and Productivity Growth,” Monthly Labor Review April (2004): 9-28. 

118 
 

Current Population Survey (Census Bureau and Bureau of Labor Statistics) 

The Current Population Survey (CPS) is a household survey. The CPS is used by the BLS to 

supplement the CES, for data on proprietors and unpaid family workers since self-employed 

individuals are not included in the CES. This is particularly a concern for the construction 

industry where a large proportion of the workers are self-employed. The CPS collects hours 

worked and weeks worked, in addition to industry and occupation information. The construction 

industry is included in the CPS as one category and is not further divided into subcategories. 

Number of workers, and average work hours are reported for wage and salary workers, selfemployed workers and unpaid family workers. These data are available from 1994 to present. 

One limitation of using the Current Population Survey to obtain information on the self 

employed is the sample size. Coding of industries and reporting are more accurate in 

establishment level surveys compared to household surveys. For this reason, data from the 

Current Employment Statistics program is used as a primary source of data, and data from the 

Current Population Survey is used as a supplemental source of data in BLS’s productivity 

program.210

The Current Population Survey is also used by the BLS to derive annual ratios of supervisory (or 

nonproduction) worker average weekly hours to nonsupervisory (or production) worker average 

weekly hours, and subsequently nonproduction worker hours. The CPS asks respondents for 

their occupation and employment status. The information on occupation and employment status 

is used to sort the data into supervisory (nonproduction) and nonsupervisory (production) 

categories. CPS collects data on hours worked. Ratios of supervisory (nonproduction) worker 

average weekly hours to nonsupervisory (production) worker average weekly hours are 

calculated. These ratios are multiplied by nonsupervisory (production) worker average weekly 

hours from the CES. Note the hours data from the CES are for hours paid, and therefore some 

discrepancy is introduced. The ratios between hours worked and hours paid are available at 

major sector level, but not available at detailed industry level. The resultant number, supervisory 

(nonproduction) worker average weekly hours, is then multiplied by the number of supervisory 

workers to yield total supervisory worker weekly hours. Total supervisory worker hours are 

obtained by multiplying total supervisory worker weekly hours by 52. Total supervisory worker 

hours are then combined with total nonsupervisory worker hours from CES and total selfemployed hours and unpaid family worker hours to yield total hours for an industry. 

Economic Census (Census Bureau) 

The Economic Census is an establishment survey and it covers establishments with payrolls. 

The focus of the Economic Census of the Construction Industries is establishments whose 

primary activity is construction. It is conducted every five years, in years ending with 2 or 7. It 

                                                           

210 Ibid.

119 
 

is classified under NAICS in recent years and under SIC in historic years. Many categories of 

the construction industry under SIC and NAICS are not comparable.211 This change in 

classification system presents itself as a break in the time series of Economic Census data. The 

Economic Census defines value added for construction industries to be “the dollar value of 

business done less costs for construction work subcontracted to others and payments for 

materials, components, supplies, and fuels.”212 All the components needed to calculate value 

added are collected in Economic Census surveys. In terms of labor input, the Economic Census 

contains data on number of construction workers employed and number of other employees.213 

There is, however, no information on the work hours or full-time vs. part-time status of the 

workers. The Economic Census does, however, collect data on labor costs. Percentage of 

construction work done in various project types, such as office building construction or tunnel 

construction, is also collected. Percentage of construction work done in different specialty trade 

activities, such as concrete work or structural steel erection, is also collected for contractors. 

While the C30 reports or the Dodge reports contain data on gross output of the construction 

industry, the Economic Census is the only data source that enables the calculation of a value 

added measure of output. However, because the Economic Census covers both general 

contractors and subcontractors, there is a significant amount of double counting in terms of 

output. This is the reason why output data for the construction industry are not used in the inputoutput tables produced by the Bureau of Economic Analysis.214 As the construction industry is 

one that wages often are paid in cash, labor wage data may be biased downward.215 The 

Economic Census also does not cover the self-employed. Note also that there has been a change 

in the definition of value of construction work in the Economic Census surveys. The Economic 

Censuses for 1987-1997 collected value of construction work. In 2002 Economic Census, 

receipts, billings, or sales for construction work were collected to enhance the accuracy of 

estimates by respondents.216

 

                                                           

211 http://www.census.gov/epcd/naics02/SICN02C.HTM#symbols 

212 2002 Economic Census. 

213 In 1997 Economic Census, number of construction workers is not available, however, annual payroll costs for 

construction workers is available. 

214 Bureau of Economic Analysis, Concepts and Methods of the U.S. Input-Output Accounts (Washington DC: 

Bureau of Economic Analysis, 2009). 

215 Ibid.

216 Census Bureau, 2002 Economic Census, Construction, Industry Series (Washington DC: Census Bureau, 2005). 

120 
 

Hours-at-Work Survey and National Compensation Survey (BLS) 

BLS has been collecting annual establishment level data on actual hours worked for production 

and nonsupervisory workers (Hours-at-Work Survey) since 1981. Data from the Hours at Work 

Survey are used to derive ratios of hours at work to hours paid. This is done for 1-digit Standard 

Industrial Classification (SIC) industry groups on an annual basis. These ratios are then used to 

convert hours paid data from the establishment survey. Hours-at-Work Survey by itself, 

however, is not detailed enough to be used in industry-level productivity measurement. The BLS 

terminated the Hours at Work Survey in 2000, and replaced the HWS with the National 

Compensation Survey. The Employment Cost Index (ECI) from the National Compensation 

Survey is used to convert hours paid to hours worked. The Hours at Work Survey had a few 

limitations. Eventually, because of stringent data reporting requirements, the response rate 

decreased to the point where not enough data were usable. The ECI was designed to capture the 

hourly cost of wages and benefits, including paid leave. Ratios of hours at work to hours paid 

can also be constructed using the ECI data. These ratios are calculated using the ECI data since 

2001. For the years before 2001, the ratios are based on Hours at Work Survey. The HWS 

survey included production and nonsupervisory workers in nonagricultural establishments. The 

National Compensation Survey, on the other hand, covers all workers. Another advantage of the 

National Compensation Survey is that it contains a bigger sample. The sample size is 37000 

occupations within 8500 private establishments where as the HWS sampled fewer than 6000 

establishments. The response rate associated with the NCS is also higher than that of the HWS. 

Input-Output tables (Bureau of Economic Analysis) 

The main data source of the input-output tables associated with the construction industry is the 

Value of Construction Put in Place data. Data from the Economic Census are generally not used 

to estimate output of the construction industry because of substantial double counting due to the 

inclusion of both general contractors and subcontractors.217 Economic Census data, however, are 

used for inputs to construction industries. Generally, the I-O tables are organized using NAICS, 

but for the construction industry, activities are used due to data limitation.218 

National Income and Product Accounts Fixed Investment and Fixed Assets Data (Bureau 

of Economic Analysis) 

The BEA compiles data on private fixed investment by structure types and investment in 

government fixed assets by structure types. One data source the BEA uses is the C30 data from 

the Census Bureau. In addition to construction spending as defined by the Census Value of 

Construction Put in Place, BEA includes mining exploration, shafts, and wells, brokers’ 

                                                           

217 Bureau of Economic Analysis, Concepts and Methods of the U.S. Input-Output Accounts (Washington DC: 

Bureau of Economic Analysis, 2009). 

218 Ibid.

121 
 

commissions on the sale of new and used structures, mobile structures, manufactured homes, and 

net purchases of used structures. The data series start from 1929 and are available annually 

through the present. In 1997, the classification system changed. The pre-1997 classification 

system is based on building types. The new classification system is based on function (or end 

use). This change in classification system was preceded by the change in the classification in the 

C30 reports. In the 2009 Comprehensive Revision of the NIPAs, historic data were updated to 

conform to the new classification system.219 For the private sector, real fixed investment is 

reported along with quantity index and price index. For the public sector, investment in fixed 

assets is reported in current dollars along with a quantity index. Data of investment by structure 

types could be used as a gross output measure in productivity analyses. 

In addition to fixed investment by structure types, the BEA also compiles data on assets by 

industry. For the construction industry as a whole, net stocks, depreciation, and investment data 

are reported both in terms of current dollars and chain-type quantity indices. These data are 

reported annually by 32 equipment types and 15 structure types. Net stocks, depreciation, and 

investment data by industry are elements of capital in productivity analysis. 

Nonemployer Statistics (Census Bureau) 

The Nonemployer Statistics contains annual data and is based on administrative records. The 

universe of this survey is businesses with no paid employees. The variables include number of 

establishments and receipts by NAICS codes and by type of establishment (corporations, 

individual proprietorships, and partnerships). More aggregated NAICS classification is used to 

report the number of establishments and receipts by states. 

Price Deflator (Fisher) Index of New One-Family Houses Under Construction (Census 

Bureau) 

Price deflators for new one-family houses under construction are developed using a hedonic 

regression approach and are available monthly since 1964. 

Price Deflator for New Multifamily Housing (Tabulated by the Census Bureau for the 

Bureau of Economic Analysis) 

Price deflators for new multifamily housing are developed using a hedonic regression approach. 

This index was first developed in 1993 and extends back to 1978.220

Producer Price Indices (Bureau of Labor Statistics) 

                                                           

219 Eugene P. Seskin and Shelly Smith, “Improved Estimates of the National Income and Product Accounts: Results 

of the 2009 Comprehensive Revision” Survey of Current Business September (2009): 15-35. 

220 Frank de Leeuw, “A Price Index for New Multifamily Housing,” Survey of Current Business February (1993):33‐

42.

122 
 

The Bureau of Labor Statistics has recently developed producer price indices for the 

nonresidential sector of the construction industry. These producer price indices are a Laspeyres 

index, which holds quality constant. These price indices are based on prototypical buildings. 

Producer price indices are produced for the following new building construction categories: (1) 

new industrial building construction (NAICS 236211); (2) new warehouse building construction 

(NAICS 236221); (3) new school building construction (NAICS 236222); and (4) new office 

building construction (NAICS 236223). For each of the four building types, model buildings are 

developed. The buildings are comprised of a collection of assemblies, or production elements. 

The estimation of pricing for each assembly includes materials and labor. Sometimes machinery 

is also required. These costs are estimated by a cost-estimating firm. BLS surveys contractors 

regarding their margin (overhead and profits). BLS tracks both the costs and the margin. 

Producer price indices have also been developed for four types of special trades in the 

nonresidential setting (commercial and industrial): 1) concrete contractors (NAICS 23811), 2) 

roofing contractors (NAICS 23816), 3) electrical contractors (NAICS 23821), and 4) 

plumbing/HVAC contractors (NAICS 23822). The producer price indices for the specialty 

trades are for both new nonresidential building construction and nonresidential building 

maintenance and repair. Excluded from these producer price indices are residential work, 

additions, renovations, and non-building construction. 

Producer Price Indices for Materials and Supply Inputs to Construction Industries 

(Bureau of Labor Statistics) 

The BLS produces PPIs for materials and supply inputs to construction industries for different 

types of new construction (single-unit residential, multi-unit residential, non-residential 

buildings, highway and street construction, and other heavy construction) and for residential and 

non-residential maintenance and repair construction. These indices only include costs of 

materials and supplies and do not take into account labor costs, contractor overhead, and profits. 

R.S. Means Square Foot Costs 

Square Foot Costs data from R.S. Means are available since early 1980s. The BEA has used data 

from the more recent years along with the hedonic regression approach to develop price deflators 

for several building types. In the BEA’s hedonic model, the natural logarithm of the cost per 

square foot is regressed on total square feet, dummy variables for combinations of exterior wall 

and interior supporting-frame type, and dummy variable for the year.221

Survey of Construction (Census Bureau) 

The Survey of Construction focuses on new residential buildings. Data are monthly and include 

start date, completion date, and physical characteristics of each housing unit, such as square 

                                                           

221 Carol E. Moylan and Brooks B. Robinson, “Preview of the 2003 Comprehensive Revision of the National 

Income and Product Accounts—Statistical Changes,” Survey of Current Business September (2003): 17-32. 

123 
 

footage and number of bedrooms. These variables are collected for both new single-family and 

multifamily housing units. In addition, sales date and sales price are collected for one-family 

houses. Housing starts data have been collected since 1959, housing completions data have been 

collected since 1963, and housing completions data have been collected since 1968. 

Value of Construction Put in Place (Census Bureau) 

Value of Construction Put in Place is collected by Census Bureau’s Manufacturing, Mining, and 

Construction Statistics (i.e., Current Construction Report, Series C30: Value of New 

Construction). Samples for the Value of Construction Put in Place Survey are drawn from the 

list of construction projects produced by McGraw-Hill Construction (Dodge Reports). Dodge 

Reports do not usually contain projects in nonpermit areas, and therefore projects in nonpermit 

areas are identified separately.222 Value of Construction Put in Place includes architectural and 

engineering design, construction management, force-account construction, and secondary 

construction, in addition to construction services performed by the construction industry, as 

defined by the Economic Census. Since the Value of Construction Put in Place data are 

collected from owners, this data capture some construction activities not captured by the 

establishment-based Economic Census. Examples are construction by the self-employed, 

homeowner construction, and construction done as a secondary source of revenue by 

nonconstruction establishments.223 The definitions of construction in the Value of Construction 

Put in Place and the Economic Census are also different. For instance, maintenance and repair is 

part of value of construction work in the Economic Census, but it is not included in the Value of 

Construction Put in Place. The value of land is excluded. For all sampled projects, a 

questionnaire is mailed to the owner of the project prior to the start of the project. Estimates of 

total construction cost, architectural, engineering, and miscellaneous costs are requested in the 

survey. Value of Construction Put in Place is collected monthly until the project is completed. 

The Census Bureau reports that about two thirds of Value Put in Place corresponds with work 

performed by the construction industry as defined by the Economic Census.224 Data of Value of 

Construction Put in Place are reported monthly by building types. These data are not deflated, 

but they are reported with and without seasonal adjustment. Total construction cost, which is the 

sum of contract construction cost and owner supplied materials and labor, can be obtained from 

the microdata, but it is not published. Square footage information is reported on survey forms, 

but it is not published. 

The classification system of the Value of Construction Put in Place data changed in 1993. The 

new system is based on project types by end usage while the older system is based on building 

                                                           

222 Census Bureau, Construction Statistics Data Users’ Conference. October 28, 1997. Washington, DC. Document 

issued March, 1999. 

223 Bureau of Economic Analysis, Concepts and Methods of the U.S. Input-Output Accounts. Op. cit.

224 C30 Construction Spending Latest News, July 1, 2003. 

124 
 

and nonbuilding types. Data collected under the two classification systems are generally not 

comparable, particularly at a finer level. Some categories appear to be similar in both 

classification systems, but there are within-category changes that made the data incomparable. 

For instance, private medical offices were classified as office buildings in the old classification 

system, but they are classified under health care in the new classification system. Direct 

comparisons can only be made at the more aggregate levels, specifically for total, total private, 

total state and local, total federal, and total public levels for annual and not seasonally adjusted 

monthly data.225

C.2 Classification Systems, Variables, and Coverage 

The Standard Industry Classification (SIC) of the construction industry is tabulated, in addition 

to the North American Industry Classification System (NAICS) 1997 and 2007. Data sources 

that are tabulated are: Current Employment Statistics Survey (Bureau of Labor Statistics) with 

SIC classification and with NAICS classification, GDP by Industry (Bureau of Economic 

Analysis), Producer Price Indices (Bureau of Labor Statistics), producer price indices for 

materials and supply inputs to construction industries (Bureau of Labor Statistics), Investment in 

government fixed assets and private fixed investment by structure type from National Income 

and Product Accounts (Bureau of Economic Analysis), Value of Construction Put in Place with 

old and new classification systems (Census Bureau), Economic Census of the Construction 

Industries (Census Bureau). For Economic Census of the Construction Industries, sub-industries 

under SIC or NAICS classifications are tabulated for 1992, 1997, and 2002. Variables and their 

availability are tabulated separately. This compilation of variables and their availability is based 

mainly on Industry Series of 1992, 1997, and 2002 and is therefore incomplete. Source data and 

price indices for BEA’s annual estimates of private fixed investment in structures by type are 

also tabulated. 

                                                           

225 Ibid. 

125 
 

Table C.17Standard Industry Classification (SIC)

SIC code Definition

15 Building Construction General Contractors And Operative Builders 

152 General Building Contractors-Residential

1521 General Contractors-Single-Family Houses

1522 General Contractors-Residential Buildings, Other Than Single-Family

153 Operative Builders

1531 Operative Builders

154 General Building Contractors-nonresidential

1541 General Contractors-Industrial Building and Warehouses

1542 General Contractors-Nonresidential Buildings, Other Than Industrial Buildings and Warehouses

16 Heavy Construction Other Than Building Construction Contractors

161 Highway and Street Construction, Except Elevated Highways

1611 Highway and Street Construction, Except Elevated Highways

162 Heavy Construction, Except Highway and Street 

1622 Bridge, Tunnel, and Elevated Highway Construction

1623 Water, Sewer, Pipeline, and Communications and Power Line Construction

1629 Heavy Construction, Not Elsewhere Classified

17 Construction Special Trade Contractors 

171 Plumbing, Heating And Air-conditioning 

1711 Plumbing, Heating and Air-Conditioning 

172 Painting And Paper Hanging 

1721 Painting and Paper Hanging 

173 Electrical Work 

1731 Electrical Work 

174 Masonry, Stonework, Tile Setting, And Plastering 

1741 Masonry, Stone Setting, and Other Stone Work 

1742 Plastering, Drywall, Acoustical, and Insulation Work 

1743 Terrazzo, Tile, Marble, and Mosaic Work 

175 Carpentry And Floor Work 

1751 Carpentry Work 

1752 Floor Laying and Other Floor Work, Not Elsewhere Classified 

176 Roofing, Siding, And Sheet Metal Work 

1761 Roofing, Siding, and Sheet Metal Work 

177 Concrete Work 

1771 Concrete Work 

178 Water Well Drilling 

1781 Water Well Drilling 

179 Miscellaneous Special Trade Contractors 

1791 Structural Steel Erection 

1793 Glass and Glazing Work 

1794 Excavation Work 

1795 Wrecking and Demolition Work 

1796 Installation or Erection of Building Equipment, Not Elsewhere 

1799 Special Trade Contractors, Not Elsewhere Classified 

126 
 

Table C.28North American Industry Classification System (NAICS) 1997

NAICS code

23 Construction

233 Building, Developing, and General Contracting

2331 Land Subdivision and Land Development

23311 Land Subdivision and Land Development

2332 Residential Building Construction

23321 Single Family Housing Construction

23322 Multifamily Housing Construction

2333 Nonresidential Building Construction

23331 Manufacturing and Industrial Building Construction

23332 Commercial and Institutional Building Construction

234 Heavy Construction

2341 Highway, Street, Bridge, and Tunnel Construction

23411 Highway and Street Construction

23412 Bridge and Tunnel Construction

2349 Other Heavy Construction

23491 Water, Sewer, and Pipeline Construction

23492 Power and Communication Transmission Line Construction

23493 Industrial Nonbuilding Structure Construction

23499 All Other Heavy Construction

235 Special Trade Contractors

2351 Plumbing, Heating, and Air-Conditioning Contractors

23511 Plumbing, Heating, and Air-Conditioning Contractors

2352 Painting and Wall Covering Contractors

23521 Painting and Wall Covering Contractors

2353 Electrical Contractors

23531 Electrical Contractors

2354 Masonry, Drywall, Insulation, and Tile Contractors

23541 Masonry and Stone Contractors

23542 Drywall, Plastering, Acoustical, and Insulation Contractors

23543 Tile, Marble, Terrazzo, and Mosaic Contractors

2355 Carpentry and Floor Contractors

23551 Carpentry Contractors

23552 Floor Laying and Other Floor Contractors

2356 Roofing, Siding, and Sheet Metal Contractors

23561 Roofing, Siding, and Sheet Metal Contractors

2357 Concrete Contractors

23571 Concrete Contractors

2358 Water Well Drilling Contractors

23581 Water Well Drilling Contractors

2359 Other Special Trade Contractors

23591 Structural Steel Erection Contractors

23592 Glass and Glazing Contractors

23593 Excavation Contractors

23594 Wrecking and Demolition Contractors

23595 Building Equipment and Other Machinery Installation Contractors

23599 All Other Special Trade Contractors

Source: U.S. Census Bureau

Definition

127 
 

Table C.39North American Industry Classification System (NAICS) 2007 

NAICS code

23 Construction 

236 Construction of Buildings 

2361 Residential Building Construction 

23611 Residential Building Construction 

236115 New Single-Family Housing Construction (except Operative Builders) 

236116 New Multifamily Housing Construction (except Operative Builders) 

236117 New Housing Operative Builders 

236118 Residential Remodelers 

2362 Nonresidential Building Construction 

23621 Industrial Building Construction 

236210 Industrial Building Construction 

23622 Commercial and Institutional Building Construction 

236220 Commercial and Institutional Building Construction 

237 Heavy and Civil Engineering Construction 

2371 Utility System Construction 

23711 Water and Sewer Line and Related Structures Construction 

237110 Water and Sewer Line and Related Structures Construction 

23712 Oil and Gas Pipeline and Related Structures Construction 

237120 Oil and Gas Pipeline and Related Structures Construction 

23713 Power and Communication Line and Related Structures Construction 

237130 Power and Communication Line and Related Structures Construction 

2372 Land Subdivision 

23721 Land Subdivision 

237210 Land Subdivision 

2373 Highway, Street, and Bridge Construction 

23731 Highway, Street, and Bridge Construction 

237310 Highway, Street, and Bridge Construction 

2379 Other Heavy and Civil Engineering Construction 

23799 Other Heavy and Civil Engineering Construction 

237990 Other Heavy and Civil Engineering Construction 

238 Specialty Trade Contractors 

2381 Foundation, Structure, and Building Exterior Contractors 

23811 Poured Concrete Foundation and Structure Contractors 

238110 Poured Concrete Foundation and Structure Contractors 

23812 Structural Steel and Precast Concrete Contractors 

238120 Structural Steel and Precast Concrete Contractors 

23813 Framing Contractors 

238130 Framing Contractors 

23814 Masonry Contractors 

238140 Masonry Contractors 

23815 Glass and Glazing Contractors 

238150 Glass and Glazing Contractors 

23816 Roofing Contractors 

238160 Roofing Contractors 

23817 Siding Contractors 

238170 Siding Contractors 

23819 Other Foundation, Structure, and Building Exterior Contractors 

238190 Other Foundation, Structure, and Building Exterior Contractors 

Definition

128 
 

129 

Table C.3 North American Industry Classification System (NAICS) 2007 

NAICS code

2382 Building Equipment Contractors 

23821 Electrical Contractors and Other Wiring Installation Contractors 

238210 Electrical Contractors and Other Wiring Installation Contractors 

23822 Plumbing, Heating, and Air-Conditioning Contractors 

238220 Plumbing, Heating, and Air-Conditioning Contractors 

23829 Other Building Equipment Contractors 

238290 Other Building Equipment Contractors 

2383 Building Finishing Contractors 

23831 Drywall and Insulation Contractors 

238310 Drywall and Insulation Contractors 

23832 Painting and Wall Covering Contractors 

238320 Painting and Wall Covering Contractors 

23833 Flooring Contractors 

238330 Flooring Contractors 

23834 Tile and Terrazzo Contractors 

238340 Tile and Terrazzo Contractors 

23835 Finish Carpentry Contractors 

238350 Finish Carpentry Contractors 

23839 Other Building Finishing Contractors 

238390 Other Building Finishing Contractors 

2389 Other Specialty Trade Contractors 

23891 Site Preparation Contractors 

238910 Site Preparation Contractors 

23899 All Other Specialty Trade Contractors 

238990 All Other Specialty Trade Contractors 

Source: Census Bureau

NAICS 2002 is very similar to NAICS 2007. The only difference is that under NAICS 2007, NAICS 23821 and NAICS 238210 are "Electrical 

Contractors and Other Wiring Installation Contractors," whereas, it is "Eletrical Contractors" under NAICS 2002.

Definition
Table C.410Current Employment Statistics Survey with SIC Classification (Bureau of Labor Statistics) 

SIC code Category Variable Seasonal adjustment Coverage

15-17 Construction number of all employees both monthly from January 115-17 Construction number of all employees no annual from 1919

15-17 Construction number of production workers both monthly from January 1

15-17 Construction average weekly hours of production workers both monthly from January 1

15 General building contractors number of all employees both monthly from January 115 General building contractors number of production workers no monthly from January 115 General building contractors average weekly hours of production workers no monthly from January 115 Residential building construction number of all employees no monthly from January 1152 Residential building construction number of production workers no monthly from January 1152 Residential building construction average weekly hours of production workers no monthly from January 1153 Operative builders number of all employees no monthly from January 1

153 Operative builders number of production workers no monthly from January 1153 Operative builders average weekly hours of production workers no monthly from January 1

154 Nonresidential building construction number of all employees no monthly from January 1154 Nonresidential building construction number of production workers no monthly from January 1154 Nonresidential building construction average weekly hours of production workers no monthly from January 116 Heavy construction, except building number of all employees both monthly from January 1

16 Heavy construction, except building number of production workers no monthly from January 1

16 Heavy construction, except building average weekly hours of production workers no monthly from January 1161 Highway and street construction number of all employees no monthly from January 1

161 Highway and street construction number of production workers no monthly from January 1161 Highway and street construction average weekly hours of production workers no monthly from January 1162 Heavy construction, except highway number of all employees no monthly from January 1

162 Heavy construction, except highway number of production workers no monthly from January 1162 Heavy construction, except highway average weekly hours of production workers no monthly from January 117 Special trade contractors number of all employees both monthly from January 1

17 Special trade contractors number of production workers no monthly from January 117 Special trade contractors average weekly hours of production workers no monthly from January 1171 Plumbing, heating, and air-conditioning number of all employees no monthly from January 1

171 Plumbing, heating, and air-conditioning number of production workers no monthly from January 1171 Plumbing, heating, and air-conditioning average weekly hours of production workers no monthly from January 1172 Painting and paper hanging number of all employees no monthly from January 1

172 Painting and paper hanging number of production workers no monthly from January 1172 Painting and paper hanging average weekly hours of production workers no monthly from January 1

130 
Table C.4 Current Employment Statistics Survey with SIC Classification (Bureau of Labor Statistics) 

SIC code Category Variable Seasonal adjustment Coverage173 Eletrical work number of all employees no monthly from Jan

173 Eletrical work number of production workers no monthly from Jan

173 Eletrical work average weekly hours of production workers no monthly from Jan

174 Masonry, stonework, and plastering number of all employees no monthly from Jan174 Masonry, stonework, and plastering number of production workers no monthly from Jan174 Masonry, stonework, and plastering average weekly hours of production workers no monthly from Jan175 Carpentry and floor work number of all employees no monthly from Jan

175 Carpentry and floor work number of production workers no monthly from Jan

175 Carpentry and floor work average weekly hours of production workers no monthly from Jan

176 Roofing, siding, and sheet metal work number of all employees no monthly from Jan176 Roofing, siding, and sheet metal work number of production workers no monthly from Jan176 Roofing, siding, and sheet metal work average weekly hours of production workers no monthly from Jan

131 
Table C.511Current Employment Statistics Survey with NAICS Classification (Bureau of Labor Statistics)

NAICS code Category Variable Seasonal adjustment Coverage

Construction number of all employees both monthly from 

Construction number of production workers both monthly from 

Construction average weekly hours of production workers both monthly from 

236 Construction of buildings number of all employees both monthly from 

236 Construction of buildings number of production workers no monthly from 237 Construction of buildings average weekly hours of production workers no monthly from 2361 Construction of residential buildings number of all employees both monthly from 

2361 Construction of residential buildings number of production workers no monthly from 2362 Construction of residential buildings average weekly hours of production workers no monthly from 236115 New single-family general contractors number of all employees no monthly from 236115 New single-family general contractors number of production workers no monthly from 236115 New single-family general contractors average weekly hours of production workers no monthly from 236116 New multifamily general contractors number of all employees no monthly from 236118 Residential remodelers number of all employees no monthly from 

236118 Residential remodelers number of production workers no monthly from 

236118 Residential remodelers average weekly hours of production workers no monthly from 2362 Construction of nonresidential buildings number of all employees both monthly from 

2362 Construction of nonresidential buildings number of production workers no monthly from 2362 Construction of nonresidential buildings average weekly hours of production workers no monthly from 23621 Construction of industrial buildings number of all employees no monthly from 23621 Construction of industrial buildings number of production workers no monthly from 23621 Construction of industrial buildings average weekly hours of production workers no monthly from 23622 Construction of commercial buildings number of all employees no monthly from 23622 Construction of commercial buildings number of production workers no monthly from 23622 Construction of commercial buildings average weekly hours of production workers no monthly from 237 Heavy and civil engineering construction number of all employees both monthly from 237 Heavy and civil engineering construction number of production workers no monthly from 

237 Heavy and civil engineering construction average weekly hours of production workers no monthly from 2371 Utility system construction number of all employees no monthly from 2371 Utility system construction number of production workers no monthly from 

2371 Utility system construction average weekly hours of production workers no monthly from 23711 Water and sewer system construction number of all employees no monthly from 23711 Water and sewer system construction number of production workers no monthly from 23711 Water and sewer system construction average weekly hours of production workers no monthly from 

132 
Table C.5 Current Employment Statistics Survey with NAICS Classification (Bureau of Labor Statistics) 

NAICS code Category Variable Seasonal adjustment C23712 Oil and gas pipeline construction number of all employees no m23712 Oil and gas pipeline construction number of production workers no m23712 Oil and gas pipeline construction average weekly hours of production workers no m

23713 Power and communication system construction number of all employees no m

23713 Power and communication system construction number of production workers no m23713 Power and communication system construction average weekly hours of production workers no m

2372 Land subdivision number of all employees no m

2372 Land subdivision number of production workers no m2372 Land subdivision average weekly hours of production workers no m2373 Highway, street, and bridge construction number of all employees no m2373 Highway, street, and bridge construction number of production workers no m

2373 Highway, street, and bridge construction average weekly hours of production workers no m

2379 Other heavy construction number of all employees no m2379 Other heavy construction number of production workers no m

2379 Other heavy construction average weekly hours of production workers no m

238 Specialty trade contractors number of all employees both m

238 Specialty trade contractors number of production workers no m238 Specialty trade contractors average weekly hours of production workers no m

part of 238 Residential specialty trade contractors number of all employees both m

part of 238 Nonresidential specialty trade contractors number of all employees both m

2381 Building foundation and exterior contractors number of all employees no m2381 Building foundation and exterior contractors number of production workers no m

2381 Building foundation and exterior contractors average weekly hours of production workers no m

part of 2381 Residential building foundation and exterior contractors number of all employees no m

part of 2381 Nonresidential specialty trade contractors number of all employees no m23811 Poured concrete structure contractors number of all employees no m23811 Poured concrete structure contractors number of production workers no m23811 Poured concrete structure contractors average weekly hours of production workers no m

23812 Steel and precast concrete contractors number of all employees no m23812 Steel and precast concrete contractors number of production workers no m

23812 Steel and precast concrete contractors average weekly hours of production workers no m

23813 Framing contractors number of all employees no m23813 Framing contractors number of production workers no m23813 Framing contractors average weekly hours of production workers no m

23814 Masonry construction number of all employees no m

23814 Masonry construction number of production workers no m

23814 Masonry construction average weekly hours of production workers no m

133 
Table C.5 Current Employment Statistics Survey with NAICS Classification (Bureau of Labor Statistics) 

NAICS code Category Variable Seasonal adjustment Coverag

23815 Glass and glazing contractors number of all employees no monthly fr

23815 Glass and glazing contractors number of production workers no monthly fr23815 Glass and glazing contractors average weekly hours of production workers no monthly fr23816 Roofing contractors number of all employees no monthly fr

23816 Roofing contractors number of production workers no monthly fr

23816 Roofing contractors average weekly hours of production workers no monthly fr23817 Siding contractors number of all employees no monthly fr

23819 Other building exterior contractors number of all employees no monthly fr2382 Building equipment contractors number of all employees no monthly fr

2382 Building equipment contractors number of production workers no monthly fr

2382 Building equipment contractors average weekly hours of production workers no monthly fr2382 Residential building equipment contractors number of all employees no monthly fr

part of 2382 Nonresidential building equipment contracotrsnumber of all employees no monthly fr

23821 Electrical contractors number of all employees no monthly fr

23821 Electrical contractors number of production workers no monthly fr23821 Electrical contractors average weekly hours of production workers no monthly fr

23822 Plumbing and HVAC contractors number of all employees no monthly fr

23822 Plumbing and HVAC contractors number of production workers no monthly fr

23822 Plumbing and HVAC contractors average weekly hours of production workers no monthly fr23829 Other building equipment contractors number of all employees no monthly fr

23829 Other building equipment contractors number of production workers no monthly fr

23829 Other building equipment contractors average weekly hours of production workers no monthly fr2383 Building finishing contractors number of all employees no monthly fr2383 Building finishing contractors number of production workers no monthly fr2383 Building finishing contractors average weekly hours of production workers no monthly fr

part of 2383 Residential building finishing contractors number of all employees no monthly fr

part of 2383 Non residential building finishing contractors number of all employees no monthly fr

23831 Drywall and insulation contractors number of all employees no monthly fr

23831 Drywall and insulation contractors number of production workers no monthly fr

23831 Drywall and insulation contractors average weekly hours of production workers no monthly fr23832 Painting and wall covering contractors number of all employees no monthly fr

23832 Painting and wall covering contractors number of production workers no monthly fr

23832 Painting and wall covering contractors average weekly hours of production workers no monthly fr

134 
Table C.5 Current Employment Statistics Survey with NAICS Classification (Bureau of Labor Statistics) 

NAICS code Category Variable Seasonal adjustment Co23833 Flooring contractors number of all employees no mon

23833 Flooring contractors number of production workers no mon

23833 Flooring contractors average weekly hours of production workers no mon

23834 Tile and terrazzo contractors number of all employees no mon23834 Tile and terrazzo contractors number of production workers no mon

23834 Tile and terrazzo contractors average weekly hours of production workers no mon23835 Finish carpentry contractors number of all employees no mon23835 Finish carpentry contractors number of production workers no mon

23835 Finish carpentry contractors average weekly hours of production workers no mon23839 Other building finishing contractors number of all employees no mon

23839 Other building finishing contractors number of production workers no mon23839 Other building finishing contractors average weekly hours of production workers no mon2389 Other specialty trade contractors number of all employees no mon2389 Other specialty trade contractors number of production workers no mon2389 Other specialty trade contractors average weekly hours of production workers no monpart of 2389 Other residential trade contractors number of all employees no mon

part of 2389 Other nonresidential trade contractors number of all employees no mon

23891 Site preparation contractors number of all employees no mon23891 Site preparation contractors number of production workers no mon

23891 Site preparation contractors average weekly hours of production workers no mon23899 All other specialty trade contractors number of all employees no mon23899 All other specialty trade contractors number of production workers no mon

23899 All other specialty trade contractors average weekly hours of production workers no mon

135 
136 

IO code Description Variables 230110 New residential 1-unit structures, nonfarm gross output, quantity indexes, price indexes 230120 New multifamily housing structures, nonfarm gross output, quantity indexes, price indexes 230130 New residential additions and alterations, nonfarm gross output, quantity indexes, price indexes 230140 New farm housing units and additions and alterations gross output, quantity indexes, price indexes 230210 Manufacturing and industrial buildings gross output, quantity indexes, price indexes 230220 Commercial and institutional buildings gross output, quantity indexes, price indexes 230230 Highway, street, bridge, and tunnel construction gross output, quantity indexes, price indexes 230240 Water, sewer, and pipeline construction gross output, quantity indexes, price indexes 230250 Other new construction gross output, quantity indexes, price indexes 230310 Maintenance and repair of farm and nonfarm residential structures gross output, quantity indexes, price indexes 230320 Maintenance and repair of nonresidential buildings gross output, quantity indexes, price indexes 230330 Maintenance and repair of highways, streets, bridges, and tunnels gross output, quantity indexes, price indexes 230340 Other maintenance and repair construction gross output, quantity indexes, price indexes 

Description Variables 

construction value added 

construction

compensation to employees, wages and salaries, supplements to wages and salaries, gross operating surplus 

construction

chain-type quantity indexes for energy inputs, chaintype quantity indexes for material inputs, chain-typ quantity indexes for purchased service inputs 

construction full-time and part-time employees 

Table C.612GDP by Industry (Bureau of Economic Analysis) 
 

Table C.713Producer Price Indices (Bureau of Labor Statistics) 

Code Product Coverage

236211 New industrial building construction monthly since June 2007

236221 New warehouse building construction monthly since December 2004

236222 New school building construction monthly since December 2005

236223 New office building construction monthly since June 2006

23816 Roofing contractors, nonresidential work monthly since December 2007

23811 Concrete contractors, nonresidential work monthly since December 2007

23821 Electrical contractors, nonresidential work monthly since December 2007

23822 Plumbing/HVAC contractors, nonresidential work monthly since December 2007

137 
 

138 

Table C.814Producer Price Indices for Materials and Supply Inputs to Construction Industries 

(Bureau of Labor Statistics) 

Code Grouping Coverage

BCON Inputs to construction industries monthly from June 1986

BNEW New construction monthly from June 1986

BRS1 Single-unit residential monthly from June 1986

BRSM Multi-unit residential monthly from June 1986

BBLD Non-residential buildings monthly from June 1986

BHWY Highway and street construction monthly from June 1986

BHVY Other heavy construction monthly from June 1986

BMRP Maintenance and repair construction monthly from June 1986

BMRS Residential monthly from June 1986

BMNR Non-residential monthly from June 1986
Table C.915National Income and Product Accounts Private Fixed Investment by Structure Type (Bureau of Econ

Variables Cove

Private fixed investment in structures real private fixed investment, quantity index and price index annu

Nonresidential real private fixed investment, quantity index and price index annu

Commercial and healthcare real private fixed investment, quantity index and price index annu

Office real private fixed investment, quantity index and price index annu

Health care real private fixed investment, quantity index and price index annu

Hospitals and special care real private fixed investment, quantity index and price index annu

Hospitals real private fixed investment, quantity index and price index annu

Special care real private fixed investment, quantity index and price index annu

Medical buildings real private fixed investment, quantity index and price index annu

Multimerchandise shopping real private fixed investment, quantity index and price index annu

Food and beverage establishments real private fixed investment, quantity index and price index annuWarehouses real private fixed investment, quantity index and price index annu

Other commercial real private fixed investment, quantity index and price index annu

Manufacturing real private fixed investment, quantity index and price index annu

Power and communication real private fixed investment, quantity index and price index annu

Power real private fixed investment, quantity index and price index annu

Electric real private fixed investment, quantity index and price index annu

Other Power real private fixed investment, quantity index and price index annu

Communication real private fixed investment, quantity index and price index annu

Mining exploration, shafts, and wells real private fixed investment, quantity index and price index annu

Petroleum and natural gas real private fixed investment, quantity index and price index annu

Mining real private fixed investment, quantity index and price index annu

Other structures real private fixed investment, quantity index and price index annu

Religious real private fixed investment, quantity index and price index annu

Educational and vocational real private fixed investment, quantity index and price index annu

lodging real private fixed investment, quantity index and price index annu

Amusement and recreation real private fixed investment, quantity index and price index annu

Transportation real private fixed investment, quantity index and price index annu

Air real private fixed investment, quantity index and price index annu

Land real private fixed investment, quantity index and price index annu

Farm real private fixed investment, quantity index and price index annu

Other real private fixed investment, quantity index and price index annu

Brokers' commission on sale of structures real private fixed investment, quantity index and price index annuNet Purchases of used structures

Category

139 
Table C.9 National Income and Product Accounts Private Fixed Investment by Structure Type (Bureau of Econo

Variables C

Residential real private fixed investment, quantity index and price index a

Permanent site real private fixed investment, quantity index and price index a

Single-family structures real private fixed investment, quantity index and price index aMultifamily structures real private fixed investment, quantity index and price index a

Other structures real private fixed investment, quantity index and price index a

Manufactured homes real private fixed investment, quantity index and price index a

Dormitories real private fixed investment, quantity index and price index a

Improvements real private fixed investment, quantity index and price index a

Brokers' commission on sale of structures real private fixed investment, quantity index and price index aNet Purchases of used structures

Addenda real private fixed investment, quantity index and price index a

Private fixed investment in new structures real private fixed investment, quantity index and price index a

Nonresidential structures real private fixed investment, quantity index and price index a

Residential structures real private fixed investment, quantity index and price index a

Category

140 
Table C.1016National Income and Product Accounts Investment in Government Fixed Assets (Bureau of Econom

Variables 

Government fixed assets investment in government fixed assets (current dollars and chain-type quantity indexes) 

Equipment and software investment in government fixed assets (current dollars and chain-type quantity indexes) Structures investment in government fixed assets (current dollars and chain-type quantity indexes) 

Residential investment in government fixed assets (current dollars and chain-type quantity indexes) Industrial investment in government fixed assets (current dollars and chain-type quantity indexes) Office investment in government fixed assets (current dollars and chain-type quantity indexes) Commercial investment in government fixed assets (current dollars and chain-type quantity indexes) Health care investment in government fixed assets (current dollars and chain-type quantity indexes) Educational investment in government fixed assets (current dollars and chain-type quantity indexes) Public safety investment in government fixed assets (current dollars and chain-type quantity indexes) Amusement and recreation investment in government fixed assets (current dollars and chain-type quantity indexes) Transportation investment in government fixed assets (current dollars and chain-type quantity indexes) Power investment in government fixed assets (current dollars and chain-type quantity indexes) Highways and streets investment in government fixed assets (current dollars and chain-type quantity indexes) Military facilities investment in government fixed assets (current dollars and chain-type quantity indexes) Conservation and development investment in government fixed assets (current dollars and chain-type quantity indexes) Other structures investment in government fixed assets (current dollars and chain-type quantity indexes) 

Federal investment in government fixed assets (current dollars and chain-type quantity indexes) 

National defense investment in government fixed assets (current dollars and chain-type quantity indexes) 

Equipment and software investment in government fixed assets (current dollars and chain-type quantity indexes) 

Aircraft investment in government fixed assets (current dollars and chain-type quantity indexes) Missiles investment in government fixed assets (current dollars and chain-type quantity indexes) Ships investment in government fixed assets (current dollars and chain-type quantity indexes) Vehicles investment in government fixed assets (current dollars and chain-type quantity indexes) Eletronics and software investment in government fixed assets (current dollars and chain-type quantity indexes) Other equipment investment in government fixed assets (current dollars and chain-type quantity indexes) 

Structures investment in government fixed assets (current dollars and chain-type quantity indexes) 

Buildings investment in government fixed assets (current dollars and chain-type quantity indexes) 

Residential investment in government fixed assets (current dollars and chain-type quantity indexes) Industrial investment in government fixed assets (current dollars and chain-type quantity indexes) 

Military facilities investment in government fixed assets (current dollars and chain-type quantity indexes) 

Category

141 
Table C.10 National Income and Product Accounts Investment in Government Fixed Assets (Bureau of Econom

Variables 

Nondefense investment in government fixed assets (current dollars and chain-type quantity indexes) 

Equipment and software investment in government fixed assets (current dollars and chain-type quantity indexes) Structures investment in government fixed assets (current dollars and chain-type quantity indexes) 

Office investment in government fixed assets (current dollars and chain-type quantity indexes) Commercial investment in government fixed assets (current dollars and chain-type quantity indexes) Health care investment in government fixed assets (current dollars and chain-type quantity indexes) Educational investment in government fixed assets (current dollars and chain-type quantity indexes) Public safety investment in government fixed assets (current dollars and chain-type quantity indexes) Amusement and recreation investment in government fixed assets (current dollars and chain-type quantity indexes) Transportation investment in government fixed assets (current dollars and chain-type quantity indexes) Power investment in government fixed assets (current dollars and chain-type quantity indexes) Highways and streets investment in government fixed assets (current dollars and chain-type quantity indexes) Conservation and development investment in government fixed assets (current dollars and chain-type quantity indexes) Other structures investment in government fixed assets (current dollars and chain-type quantity indexes) 

State and local investment in government fixed assets (current dollars and chain-type quantity indexes) 

Equipment and software investment in government fixed assets (current dollars and chain-type quantity indexes) Structures investment in government fixed assets (current dollars and chain-type quantity indexes) 

Residential investment in government fixed assets (current dollars and chain-type quantity indexes) Office investment in government fixed assets (current dollars and chain-type quantity indexes) Commercial investment in government fixed assets (current dollars and chain-type quantity indexes) Health care investment in government fixed assets (current dollars and chain-type quantity indexes) Educational investment in government fixed assets (current dollars and chain-type quantity indexes) Public safety investment in government fixed assets (current dollars and chain-type quantity indexes) Amusement and recreation investment in government fixed assets (current dollars and chain-type quantity indexes) Transportation investment in government fixed assets (current dollars and chain-type quantity indexes) Power investment in government fixed assets (current dollars and chain-type quantity indexes) Highways and streets investment in government fixed assets (current dollars and chain-type quantity indexes) Sewer systems investment in government fixed assets (current dollars and chain-type quantity indexes) Water systems investment in government fixed assets (current dollars and chain-type quantity indexes) Conservation and development investment in government fixed assets (current dollars and chain-type quantity indexes) Other structures investment in government fixed assets (current dollars and chain-type quantity indexes) 

Category

142 
Table C.10 National Income and Product Accounts Investment in Government Fixed Assets (Bureau of Econom

Variables 

Addenda: investment in government fixed assets (current dollars and chain-type quantity indexe

General government fixed assets investment in government fixed assets (current dollars and chain-type quantity indexe

Equipment and software investment in government fixed assets (current dollars and chain-type quantity indexeStructures investment in government fixed assets (current dollars and chain-type quantity indexe

Government enterprise fixed assets investment in government fixed assets (current dollars and chain-type quantity indexe

Equipment and software investment in government fixed assets (current dollars and chain-type quantity indexeStructures investment in government fixed assets (current dollars and chain-type quantity indexe

Government nonresidential fixed assets investment in government fixed assets (current dollars and chain-type quantity indexe

Equipment and software investment in government fixed assets (current dollars and chain-type quantity indexeStructures investment in government fixed assets (current dollars and chain-type quantity indexe

Federal investment in government fixed assets (current dollars and chain-type quantity indexe

Defense investment in government fixed assets (current dollars and chain-type quantity indexeNondefense investment in government fixed assets (current dollars and chain-type quantity indexe

State and local investment in government fixed assets (current dollars and chain-type quantity indexe

Category

143 
Table C.1117Value of Construction Put in Place (C30 Data), Old Classification System (Census Bureau) 

Category Variable

Seasonal adjustment

Constant or current dollars Coverage

Comparable wclassification s

Total construction value of construction put in place both both monthly from 1964 to 2002

yes for annual aseaonally adjustmonthly data

Private construction value of construction put in place both both monthly from 1964 to 2002

yes for annual aseaonally adjustmonthly data

Residential buildings value of construction put in place both both monthly from 1964 to 2002 no

New housing units value of construction put in place both both monthly from 1964 to 2002 no

1 unit value of construction put in place both both monthly from 1964 to 2002 no

2 or more units value of construction put in place both both monthly from 1964 to 2002 no

Improvements value of construction put in place both both annual from 1964 to 2002 no

Nonresidential buildings value of construction put in place both both monthly from 1964 to 2002 no

Industrial value of construction put in place both both monthly from 1964 to 2002 no

Office value of construction put in place both both monthly from 1964 to 2002 noHotels, motels value of construction put in place both both monthly from 1964 to 2002 noOther commercial value of construction put in place both both monthly from 1964 to 2002 noReligious value of construction put in place both both monthly from 1964 to 2002 no

Educational value of construction put in place both both monthly from 1964 to 2002 noHospital and institutional value of construction put in place both both monthly from 1964 to 2002 noMiscellaneous value of construction put in place both both monthly from 1964 to 2002 no

Farm nonresidential value of construction put in place both both annual from 1964 to 2002 noPublic utilities value of construction put in place both both annual from 1964 to 2002 no

Telecommunications value of construction put in place both both monthly from 1964 to 2002 noRailroad value of construction put in place both both annual from 1964 to 2002 noElectric light and power value of construction put in place both both annual from 1964 to 2002 noGas value of construction put in place both both annual from 1964 to 2002 noPetroleum pipelines value of construction put in place both both annual from 1964 to 2002 no

All other private value of construction put in place both both monthly from 1964 to 2002 no

Public construction value of construction put in place both both monthly from 1964 to 2002

yes for annual aseaonally adjustmonthly data

Buildings value of construction put in place both both monthly from 1964 to 2002 no

Housing and redevelopment value of construction put in place both both monthly from 1964 to 2002 noIndustrial value of construction put in place both both monthly from 1964 to 2002 no

Educational value of construction put in place both both monthly from 1964 to 2002 noHospital value of construction put in place both both monthly from 1964 to 2002 no

Other value of construction put in place both both monthly from 1964 to 2002 no

Highways and streets value of construction put in place both both monthly from 1964 to 2002 no

Military facilities value of construction put in place both both monthly from 1964 to 2002 noConservation and development value of construction put in place both both monthly from 1964 to 2002 no

144 
Table C.11 Value of Construction Put in Place (C30 Data), Old Classification System (Census Bureau) 

Category Variable

Seasonal adjustment

Constant or current dollars Coverage

Comparable with neclassification system

Other public construction value of construction put in place both both monthly from 1964 to 2002 no

Sewer systems value of construction put in place both both monthly from 1964 to 2002 nowater supply facilities value of construction put in place both both monthly from 1964 to 2002 noMiscellaneous nonbuilding value of construction put in place both both monthly from 1964 to 2002 no

Total public construction value of construction put in place both both monthly from 1964 to 2002

yes for annual and not seaonally adjusted monthly data

State and local construction value of construction put in place both both monthly from 1964 to 2002

yes for annual and not seaonally adjusted monthly data

Total building value of construction put in place both both monthly from 1964 to 2002 no

Housing and redevelopment value of construction put in place both both monthly from 1964 to 2002 noEducational value of construction put in place both both monthly from 1964 to 2002 noHospital value of construction put in place both both monthly from 1964 to 2002 noOther value of construction put in place both both monthly from 1964 to 2002 no

Highways and streets value of construction put in place both both monthly from 1964 to 2002 noConservation and development value of construction put in place both both monthly from 1964 to 2002 noOther state and local construction value of construction put in place both both monthly from 1964 to 2002 no

Sewer systems value of construction put in place both both monthly from 1964 to 2002 noWater supply facilities value of construction put in place both both monthly from 1964 to 2002 noMiscellaneous nonbuilding value of construction put in place both both monthly from 1964 to 2002 no

Federal construction value of construction put in place both both monthly from 1964 to 2002

yes for annual and not seaonally adjusted monthly data

Total building value of construction put in place both both monthly from 1964 to 2002 no

Housing value of construction put in place both both monthly from 1964 to 2002 no

Industrial value of construction put in place both both monthly from 1964 to 2002 noEducational value of construction put in place both both monthly from 1964 to 2002 noHospital value of construction put in place both both monthly from 1964 to 2002 noOther federal buildings value of construction put in place both both monthly from 1964 to 2002 no

Highways and streets value of construction put in place both both annual from 1964 to 2002 noMilitary Facilities value of construction put in place both both monthly from 1964 to 2002 noConservation and development value of construction put in place both both monthly from 1964 to 2002 noMiscellaneous nonbuilding value of construction put in place both both monthly from 1964 to 2002 no

Total private construction includes public safety, highway and street, sewage and waste disposal, water supply, and conservation and development, which are not repor

145 
Table C.1218Value of Construction Put in Place (C30 Data), New Classification System (Census Bureau)

Category Variable

Seasonal adjustment

Constant or current dollars Coverage

Comparable with old classification system

Total construction value of construction put in place both current dollars monthly since 1993

yes for annual and not seaonally adjusted monthly data

Residential value of construction put in place both current dollars monthly since 2002 noNonresidential value of construction put in place both current dollars monthly since 2002 no

Lodging value of construction put in place both current dollars monthly since 2002 noOffice value of construction put in place both current dollars monthly since 2002 noCommercial value of construction put in place both current dollars monthly since 2002 noHealth care value of construction put in place both current dollars monthly since 2002 noEducational value of construction put in place both current dollars monthly since 2002 noReligious value of construction put in place both current dollars monthly since 2002 noPublic safety value of construction put in place both current dollars monthly since 2002 noAmusement and recreation value of construction put in place both current dollars monthly since 2002 noTransportation value of construction put in place both current dollars monthly since 2002 noCommunication value of construction put in place both current dollars monthly since 2002 noPower value of construction put in place both current dollars monthly since 2002 noHighway and street value of construction put in place both current dollars monthly since 2002 noSewage and waste disposal value of construction put in place both current dollars monthly since 2002 noWater supply value of construction put in place both current dollars monthly since 2002 noConservation and development value of construction put in place both current dollars monthly since 2002 noManufacturing value of construction put in place both current dollars monthly since 2002 no

Total private construction value of construction put in place both current dollars monthly since 1993

yes for annual and not seaonally adjusted monthly data

Residential value of construction put in place both current dollars monthly since 1993 noNonresidential value of construction put in place both current dollars monthly since 1993 no

Lodging value of construction put in place both current dollars monthly since 1993 noOffice value of construction put in place both current dollars monthly since 1993 noCommercial value of construction put in place both current dollars monthly since 1993 noHealth care value of construction put in place both current dollars monthly since 1993 noEducational value of construction put in place both current dollars monthly since 1993 noReligious value of construction put in place both current dollars monthly since 1993 noAmusement and recreation value of construction put in place both current dollars monthly since 1993 noTransportation value of construction put in place both current dollars monthly since 1993 noCommunication value of construction put in place both current dollars monthly since 1993 noPower value of construction put in place both current dollars monthly since 1993 noManufacturing value of construction put in place both current dollars monthly since 1993 no

146 
147 

Table C.12 Value of Construction Put in Place (C30 Data), New Classification System (Census Bureau) 

Category Variable

Seasonal adjustment

Constant or current dollars Coverage

Total public construction value of construction put in place both current dollars monthly since 1993

Residential value of construction put in place both current dollars monthly since 2002 

Nonresidential value of construction put in place both current dollars monthly since 2002 

Office value of construction put in place both current dollars monthly since 2002 Commercial value of construction put in place both current dollars monthly since 2002 Health care value of construction put in place both current dollars monthly since 2002 Educational value of construction put in place both current dollars monthly since 2002 

Public safety value of construction put in place both current dollars monthly since 2002 

Amusement and recreation value of construction put in place both current dollars monthly since 2002 Transportation value of construction put in place both current dollars monthly since 2002 Power value of construction put in place both current dollars monthly since 2002 Highway and street value of construction put in place both current dollars monthly since 2002 Sewage and waste disposal value of construction put in place both current dollars monthly since 2002 Water supply value of construction put in place both current dollars monthly since 2002 Conservation and development value of construction put in place both current dollars monthly since 2002 

Total private construction includes public safety, highway and street, sewage and waste disposal, water supply, and conservation and dewhich are not reported separately.Total public construction includes lodging, religious, communication and manufacturing, which are not reported separately.
 

Table C.1319Industry Series of the 1992 Economic Census

SIC code Description

1521 General Contractors— Single-Family Houses

1522 General Contractors— Residential Buildings, Other Than Single-Family Houses

1531 Operative Builders

1541 General Contractors— Industrial Buildings and Warehouses

1542 General Contractors— Nonresidential Buildings, Other Than Industrial Buildings and Warehouses

1611 Highway and Street Construction Contractors, Except Elevated Highways

1622 Bridge, Tunnel, and Elevated Highway Construction Contractors

1623 Water, Sewer, Pipeline, and Communications and Power Line Construction

1629 Heavy Construction Contractors, Not Elsewhere Classified

1711 Plumbing, Heating, and Air-Conditioning Special Trade Contractors

1721 Painting and Paper Hanging Special Trade Contractors

1731 Electrical Work Special Trade Contractors

1741 Masonry, Stone Setting, and Other Stone Work Special Trade Contractors

1742 Plastering, Drywall, Acoustical and Insulation Work Special Trade Contractors

1743 Terrazzo,Tile,Marble, and Mosaic Work Special Trade Contractors

1751 Carpentry Work Special Trade Contractors

1752 Floor Laying and Other Floor Work Special Trade Contractors, Not Elsewhere Classified

1761 Roofing, Siding, and Sheet Metal Work Special Trade Contractors

1771 Concrete Work Special Trade Contractors

1781 Water Well Drilling Special Trade Contractors

1791 Structural Steel Erection Special Trade Contractors

1793 Glass and Glazing Work Special Trade Contractors

1794 Excavation Work Special Trade Contractors

1795 Wrecking and Demolition Work Special Trade Contractors

1796 Installation or Erection of Building Equipment Special Trade Contractors, Not Elsewhere Classified

1799 Special Trade Contractors, Not Elsewhere Classified

148 
 

Table C.1420Industry Series of the 1997 Economic Census 

NAICS code SIC code Description

233110 Land Subdivision and Land Development

655200 Land subdividers and developers, except cemeteries

233210 Single-Family Housing Construction 

152100 General contractors--single-family houses

153110 Operative builders (pt)

874121 Management services (pt)

233220 Multifamily Housing Construction 

152220 General contractors--residential buildings, other than single-family

153120 Operative builders (pt)

874122 Management services (pt)

233310 Manufacturing and Industrial Building Construction 

153130 Operative builders (pt)

154120 General contractors--industrial buildings and warehouses

874123 Management services (pt)

233320 Commercial and Institutional Building Construction 

152210 General contractors--residential buildings, other than single-family (pt)

153140 Operative builders (pt)

154110 General contractors--industrial buildings and warehouses (pt)

154200 General contractors--nonresidential buildings, except industrial buildings and warehouses

874124 Management services (pt)

234110 Highway and Street Construction

161100 Highway and street construction contractors, except elevated highways

874131 Management services (pt)

234120 Bridge and Tunnel Construction

162200 Bridge, tunnel, and elevated highway construction contractors

874132 Management services (pt)

234910 Water, Sewer, Pipeline Construction

162310 Water, sewer, pipeline, and communication and power line construction (pt)

874133 Management services (pt)

234920 Power and Communication Transmission Line Construction 

162320 Water, sewer, pipeline, and communication and power line construction (pt)

874134 Management services (pt)

234930 Industrial Nonbuilding Structure Construction 

162910 Heavy construction, not elsewhere classified (pt)

874135 Management services (pt)

234990 All Other Heavy Construction 

162920 Heavy construction, not elsewhere classified (pt)

735320 Heavy construction, equipment rental (pt)

874136 Management services (pt)

235110 Plumbing, Heating, and Air-Conditioning Contractors 

171100 Plumbing, heating, and air-conditioning special trade contractors

235210 Painting and Wall Covering Contractors 

172100 Painting and paper hanging special trade contractors

179910 Special trade contractors, not elsewhere classified (pt)

235310 Electrical Contractors 

173100 Electrical work special trade contractors

149 
 

Table C.14 Industry Series of the 1997 Economic Census 

NAICS code SIC code Description

235410 Masonry and Stone Contractors 

174100 Masonry, stone setting, and other stone work special trade contractors

235420 Drywall, Plastering, Acoustical, and Insulation Contractors 

174200 Plastering, drywall, acoustical, and insulation work special trade contractors

174310 Terrazzo, tile, marble, and mosaic work special trade contractors

177110 Concrete work special trade contractors (pt)

235430 Tile, Marble, Terrazzo, and Mosaic Contractors 

174320 Terrazzo, tile, marble, and mosaic work special trade contractors (pt)

235510 Carpentry Contractors

175100 Carpentry work special trade contractors

235520 Floor Laying and Other Floor Contractors

175200 Floor laying and other floor work special trade contractors, not elsewhere classified

235610 Roofing, Siding, and Sheet Metal Contractors 

176100 Roofing, siding, and sheet metal work special trade contractors

235710 Concrete Contractors 

177120 Concrete work special trade contractors (pt)

235810 Water Well Drilling Contractors 

178100 Water well drilling special trade contractors

235910 Structural Steel Erection Contractors 

179100 Structual steel erection special trade contractors

235920 Glass and Glazing Contractors 

179300 Glass and glazing work special trade contractors

179920 Special trade contractors, not elsewhere classified (pt)

235930 Excavation Contractors 

179400 Excavation work special trade contractors

235940 Wrecking and Demolition Contractors 

179500 Wrecking and demolition work special trade contractors

235950 Building Equipment and Other Machinery Installation Contractors 

179600 Install or erection of building equipment, special trade contractors, not elseswhere classified

235990 All Other Special Trade Contractors

179940 Special trade contractors, not elsewhere classified (pt)

150 
 

151 

Table C.1521Industry Series of the 2002 Economic Census

NAICS code Description

236 Construction of Buildings

236115 New Single-Family Housing Construction (except Operative Builders)

236116 New Multifamily Housing Construction (except Operative Builders)

236117 New Housing Operative Builders

236118 Residential Remodelers

236210 Industrial Building Construction

236220 Commercial and Institutional Building Construction

237 Heavy and Civil Engineering Construction

237110 Water and Sewer Line and Related Structures Construction

237120 Oil and Gas Pipeline and Related Structures Construction

237130 Power and Communication Line and Related Structures Construction

237210 Land Subdivision

237310 Highway, Street, and Bridge Construction

237990 Other Heavy and Civil Engineering Construction

238 Specialty Trade Contractors

238110 Poured Concrete Foundation and Structure Contractors

238120 Structural Steel and Precast Concrete Contractors

238130 Framing Contractors

238140 Masonry Contractors

238150 Glass and Glazing Contractors

238160 Roofing Contractors

238170 Siding Contractors

238190 Other Foundation, Structure, and Building Exterior Contractors

238210 Electrical Contractors

238220 Plumbing, Heating, and Air-Conditioning Contractors

238290 Other Building Equipment Contractors

238310 Drywall and Insulation Contractors

238320 Painting and Wall Covering Contractors

238330 Flooring Contractors

238340 Tile and Terrazzo Contractors

238350 Finish Carpentry Contractors

238390 Other Building Finishing Contractors

238910 Site Preparation Contractors

238990 All Other Specialty Trade Contractors
Table C.1622Variables Reported by Economic Census

Variables How values are reported Availability Notes

Number of establishments U.S. total 1977, 1982, 1987, 1992, 1997, 2002

by state (location of establishment) 1992, 1997, 2002by size of establishment (number of employees) 1992, 1997, 2002by size of establishment (value of business done) 1992, 1997, 2002by project-type specialization 1992, 1997, 2002by kind-of-business specialization 1997, 2002

Proprietors and working partners U.S. total 1977, 1982, 1987, 1992Total number of employees U.S. total 1977, 1982, 1987, 1992, 1997, 2002

by state (location of establishment) 1987, 1992, 1997, 2002by size of establishment (number of employees) 1987, 1992, 1997, 2002by size of establishment (value of business done) 1987, 1992, 1997, 2002by project-type specialization 1992, 1997, 2002by kind-of-business specialization 1997, 2002

Number of construction workers U.S. total 1977, 1982, 1987, 1992, 1997, 2002

by state (location of establishment) 1992, 1997, 2002

March U.S. total 1977, 1982, 1987, 1992, 1997, 2002

by state (location of establishment) 1992, 1997, 2002

May U.S. total 1977, 1982, 1987, 1992, 1997, 2002

by state (location of establishment) 1992, 1997, 2002

August U.S. total 1977, 1982, 1987, 1992, 1997, 2002

by state (location of establishment) 1992, 1997, 2002

November U.S. total 1977, 1982, 1987, 1992, 1997, 2002

by state (location of establishment) 1997, 2002

Number of other employees U.S. total 1987, 1992, 1997, 2002

by state (location of establishment) 1997, 2002

March U.S. total 1977, 1982, 1987, 1997, 2002

by state (location of establishment) 1997, 2002

May U.S. total 1987, 1992, 1997, 2002

by state (location of establishment) 1997, 2002

August U.S. total 1987, 1992, 1997, 2002

by state (location of establishment) 1997, 2002

November U.S. total 1987, 1992, 1997, 2002

by state (location of establishment) 1997, 2002

Total payroll U.S. total 1977, 1982, 1987, 1992, 1997, 2002

by state (location of establishment) 1992, 1997, 2002by size of establishment (number of employees) 1992, 1997, 2002by size of establishment (value of business done) 1992, 1997, 2002by project-type specialization 1992, 1997, 2002by kind-of-business specialization 1997, 2002

Construction workers U.S. total 1977, 1982, 1987, 1992, 1997, 2002

by state (location of establishment) 1992, 1997, 2002

Other employees U.S. total 1977, 1982, 1987, 1992, 1997, 2002

by state (location of establishment) 1997, 2002

First-quarter payroll, all employees U.S. total 1977, 1982, 1987, 1992, 1997, 2002Fringe benefits, all employees U.S. total 1977, 1982, 1987, 1992, 1997, 2002

Legally required expenditures U.S. total 1977, 1982, 1987, 1992, 1997, 2002Voluntary expenditures U.S. total 1977, 1982, 1987, 1992, 1997, 2002

152 
Table C.16 Variables Reported by Economic Census 

Variables How values are reported Availability NotesValue of business done U.S. total 1977, 1982, 1987, 1992, 1997, 2002

by size of establishment (number of employees) 1992, 1997, 2002by size of establishment (value of business done) 1992, 1997, 2002by type of business 1987, 1992, 1997, 2002

Value of construction work U.S. total 1977, 1982, 1987, 1992, 1997, 2002

by state (location of establishment) 1987, 1992, 1997, 2002by state (location of construction work) 1987, 1992, 1997, 2002by size of establishment (number of employees) 1987, 1992, 1997, 2002by size of establishment (value of business done) 1987, 1992, 1997, 2002by project-type specialization 1992, 1997, 2002by kind-of-business specialization 1997

Value of construction work on government owned projects U.S. total 1977, 1982, 1987, 1992, 1997, 2002

Value of construction work on federally owned projects U.S. total 1987, 1992, 1997, 2002

Value of construction work on state and locally owned projects U.S. total 1987, 1992, 1997, 2002

Value of construction work on privately owned projects U.S. total 1977, 1982, 1987, 1992, 1997, 2002

Other business receipts U.S. total 1997, 2002

Cost of construction work subcontracted out to others U.S. total 1977, 1982, 1987, 1992, 1997, 2002

by state (location of establishment) 1992, 1997, 2002by size of establishment (number of employees) 1992, 1997, 2002by size of establishment (value of business done) 1992, 1997, 2002by project-type specialization 1992, 1997, 2002by kind-of-business specialization 1997, 2002

Value of construction work subcontracted in from others U.S. total 1977, 1982, 1987, 1992

by state (location of establishment) 1992

Net value of construction work U.S. total 1977, 1982, 1987, 1992, 1997, 2002

by state (location of establishment) 1992, 1997, 2002by size of establishment (number of employees) 1992, 1997, 2002by size of establishment (value of business done) 1992, 1997, 2002by project-type specialization 1992, 1997, 2002by kind-of-business specialization 1997, 2002

Value added U.S. total 1977, 1982, 1987, 1992, 1997, 2002

by state (location of establishment) 1987, 1992, 1997, 2002by size of establishment (number of employees) 1987, 1992, 1997, 2002by size of establishment (value of business done) 1987, 1992, 1997, 2002by project-type specialization 1992, 1997, 2002by kind-of-business specialization 1997, 2002

Value of construction work

New construction U.S. total and by project type 1987, 1992, 1997, 2002

Additions, alterations, or reconstruction U.S. total and by project type 1987, 1992, 1997, 2002

Maintenance and repair U.S. total and by project type 1987, 1992, 1997, 2002

Selected costs U.S. total 1977, 1982, 1987, 1992, 1997, 2002

Materials, components, supplies, and fuel U.S. total 1992, 1997, 2002

by state (location of establishment) 1992, 1997, 2002by size of establishment (number of employees) 1992, 1997, 2002by size of establishment (value of business done) 1992, 1997, 2002

Materials, parts, and supplies U.S. total 1977, 1982, 1987, 1992, 1997, 2002

Power, fuels, and lubricants U.S. total 1977, 1982, 1987, 1992, 1997, 2002

Purchased eletricity U.S. total 1977, 1982, 1987, 1992, 1997, 2002

Natural gas and manufactured gas U.S. total 1977, 1982, 1987, 1992, 1997, 2002

Gasoline and diesel fuel U.S. total 1977, 1982, 1987, 1992, 1997, 2002

On-highway use of gasoline and diesel fuel U.S. total 1987, 1992, 1997, 2002Off-highway use of gasoline and diesel fuel U.S. total 1987, 1992, 1997, 2002

All other fuels and lurbicants U.S. total 1977, 1982, 1987, 1992, 1997, 2002

153 
154 

Variables How values are reported Availability 

End-of-year gross book value of depreciable assets U.S. total 1987, 1992, 1997, 2002

by state (location of establishment) 1992, 1997, 2002by size of establishment (number of employees) 1992by size of establishment (value of business done) 1992

Depreciation charges during year U.S. total 1987, 1992, 1997, 2002

Number of establishments with inventories U.S. total 1992, 1997, 2002

Value of construction work for establishments with inventories U.S. total 1992, 1997, 2002

End-of-year (Economic Census year), inventories of materials and supplies U.S. total 1992, 1997, 2002End-of-year (one year before Economic Census year), inventories of materials and supplies U.S. total 1992, 1997, 2002

Number of establishments with no inventories U.S. total 1992, 1997, 2002

Value of construction work for establishments with no inventories U.S. total 1992, 1997, 2002

Establishments not reporting inventories U.S. total 1992, 1997, 2002Value of construction work for establishments not reporting inventories U.S. total 1992, 1997, 2002The above variables are reported by the Economic Census for SIC/NAICS categories. This compilation is an incomplete list.

Table C.16 Variables Reported by Economic Census 
Table C.1723Source Data and Price Indices for BEA’s Annual Estimates of Private Fixed Investment in Structur

Component Major source data Price index used to deflate the estimates 

Private fixed investment in structures

Nonresidential

Commerical and health care

Office Census Bureau monthly construction survey (C30) BEA price index for office buildings

b

Healthcare

Hospitals and special care

Hospitals Census Bureau monthly construction survey (C30)

An unweighted average of Census Bureau's singlefamily houses under construction index and a Turner Construction Company building cost index

Special care Census Bureau monthly construction survey (C30) Same as those for hospitals

Medical buildings Census Bureau monthly construction survey (C30) Same as those for hospitals

Multimerchandise shopping Census Bureau monthly construction survey (C30) BLS PPI for warehouses

Food and beverage establishments Census Bureau monthly construction survey (C30) Same as those used for multimerchandise shoppingWarehouses Census Bureau monthly construction survey (C30) Same as those used for multimerchandise shopping

Other Commercial Census Bureau monthly construction survey (C30) and judgemental trend Same as that used for warehouses and BLS price index for mobile structures 

p

155 
Table C.17 Source Data and Price Indices for BEA’s Annual Estimates of Private Fixed Investment in Structure

Component Major source data Price index used to deflate the estimates 

Manufacturing Census Bureau monthly construction survey (C30) BEA price index for factories

b

Power and communication

Power

Electric Census Bureau monthly construction survey (C30) Weighted average of Handy-Whitman price indexes for eletric light and power plants and utility buildings

prb

Other power Census Bureau monthly construction survey (C30) Handy-Whitman price index for has plants

r

Communication Census Bureau monthly construction survey (C30) AUS Telephone Plant index

p

Mining exploration, shafts, and wells

Petroleum and natural gas

Footage drilled and cost per foot from trade sources extrapolated by BLS producer price index for oil and gas wells.

Weighted average of BLS PPIs for drilling oil and gas wells and for oil and gas field services

p

Mining Census Bureau annal capital expenditures survey Same as those used for hospitals

156 
Table C.17 Source Data and Price Indices for BEA’s Annual Estimates of Private Fixed Investment in Structure

Component Major source data Price index used to deflate the estimates 

Other structures

Religious Census Bureau monthly construction survey (C30) Same as those used for hospitals

Educational and vocational Census Bureau monthly construction survey (C30) BLS PPI for new school construction

Lodging Census Bureau monthly construction survey (C30) Same as those used for hospitalsAmusement and recreation Census Bureau monthly construction survey (C30) Same as those used for hospitalsTransportation

Air Census Bureau monthly construction survey (C30) Same as those used for hospitalsLand Census Bureau monthly construction survey (C30)

Weighted average of BLS employment cost index (ECI) for construction industry, Bureau of Reclamation construction cost trends for bridges and for power plants, the BLS PPIs for material and supply inputs into construction industries, BLS PPI for other communication equipment, and the price indexes used for hospitals.

Farm Census Bureau monthly construction survey (C30) Same as those used for hospitalsOther Census Bureau monthly construction survey (C30)

An unweighted average of the Handy-Whitman water utility plant index, Federal Highway Administration Composite index for highways, and those used for hospitals

pr

Brokers' commissions on sale of structures Trend-based estimates

BLS PPI for real estate brokage, nonresidential property sales and rental including land sales and rental

rrp

Net purchases of used structures BEA government fixed asset accounts

An unweighted average of the implicit price deflators for nonresidential buildings, for utilities, for farm buildings, and for other private structures

rb

157 
158 

Component Major source data Price index used to deflate the estimates 

Residential

Permanent site

Single-family structures Census Bureau monthly construction survey Census Bureau price index for single-family houses under construction index 

Multifamily structures Census Bureau monthly construction survey Census Bureau price index for multifamily houses under construction 

Other structures

Manufactured homes Shipments from trade source and average retail price from Census Bureau monthly survey BLS PPI for mobile structures 

p

Dormitories Census Bureau monthly construction survey Same as that used for single family structuresImprovements Census Bureau survey of residential alterations and repair and survey of consumer expenditures

Average of the Census Bureau index for single-family houses under construction, BLS PPI for home maintenance and repair, and BLS employment cost index for construction industry

Brokers' commissions on sale of structures

Number of single-family houses sold and mean sales price from Census Bureau monthly construction survey and trade source

BLS PPI for real estate brokerage, residential property sales and rental

rr

Net purchases of used structures BEA government fixed asset accounts Same as that used for single family structures

Source: Paul R. Lally, "How BEA Accounts for Investment in Private Structures," Survey of Current Business February 2009: 9-15.

Table C.17 Source Data and Price Indices for BEA’s Annual Estimates of Private Fixed Investment in Structure
 

Table C.1824Web Links to Key Sources

Table Number Web Link (URL)

C.1 http://www.osha.gov/pls/imis/sic_manual.html

C.2 http://www.census.gov/eos/www/naics/

C.3 http://www.census.gov/eos/www/naics/

C.4 http://www.bls.gov/ces/cesbtabs.htm

C.5 http://www.bls.gov/ces/

C.6 http://www.bea.gov/industry/gdpbyind_data.htm

C.7 http://www.bls.gov/ppi/

C.8 http://www.bls.gov/ppi/

C.9 http://www.bea.gov/national/nipaweb/SelectTable.asp?Selected=N

C.10 http://www.bea.gov/national/nipaweb/SelectTable.asp?Selected=N

C.11 http://www.census.gov/const/www/c30index.html

C.12 http://www.census.gov/const/www/c30index.html

C.13 http://www.census.gov/epcd/www/92result.html

C.14 http://www.census.gov/epcd/www/ec97stat.htm

C.15 http://www.census.gov/econ/census02/guide/INDRPT23.HTM

C.16 See Economic Censuses for specific years.

C.17 http://www.bea.gov/scb/pdf/2009/02%20February/0209_briefing_structures.pdf

 

159 
 

 

160 
 

Appendix D Glossary of Selected Terms 

Automation and integration technologies: 

Automation technologies: Automation technologies focus on the degree to which 

individual work functions are automated (e.g., supply management and project 

management). 

Integration technologies: Integration technologies focus on the ability to exchange 

information between work functions and their associated databases (e.g., exchanges of 

information among supply management and project management functions). 

Deflation: The meaning of deflation is the division of the value of some aggregate by a price 

index - described as a “deflator” - in order to revalue its quantities at the prices of the price 

reference period or to revalue the aggregate at the general price level of the price reference 

period.226

Establishment: An establishment is a business or industrial unit at a single physical location that 

produces or distributes goods or performs services.227

Free-rider: A person or organization who benefits from a public good, but neither provides it 

nor contributes to the cost of collective provision. They thus free ride on the efforts of others. 

The free-rider problem means that many public goods are under-provided, or have to be provided 

by governments which can collect taxes or pay for them.228

Intermediate inputs: Goods and services, other than fixed assets, used as inputs into the 

production process of an establishment that are produced elsewhere in the economy or are 

imported. They may be either transformed or used up by the production process. Land, labor, and 

capital are primary inputs and are not included among intermediate inputs.229

Nominal prices: Prices charged by providers of general government services such as health and 

education and prices that are heavily subsidized through government funding or regulated by 

                                                           

226 OECD Glossary of Statistical Terms. http://stats.oecd.org/glossary/detail.asp?ID=3019. Accessed September 3, 

2009.

227 Census Bureau web page on Economic Census. 

http://factfinder.census.gov/jsp/saff/SAFFInfo.jsp?_pageId=sp2_economic

228 John Black, Oxford Dictionary of Economics. (Oxford and New York: Oxford University Press, 2002). 

229 OECD Glossary of Statistical Terms. http://stats.oecd.org/glossary/detail.asp?ID=1395. Accessed September 3, 

2009. 

161 
 

government policy. Such prices are not economically significant and therefore do not provide 

signals of market driven inflation.230

Price index: A price index reflects an average of the proportionate changes in the prices of a 

specified set of goods and services between two periods of time. Usually a price index is 

assigned a value of 100 in some selected base period and the values of the index for other periods 

are intended to indicate the average percentage change in prices compared with the base 

period.231

Productivity: The basic concept underlying construction productivity measures is a comparison 

of the output of a task, project, or industry with the corresponding factors of production (inputs) 

required to generate that output. 

Three dimensions of productivity: 

Task: Tasks refer to specific construction activities such as concrete placement or 

structural steel erection. 

Project: Projects are the collection of tasks required for the construction of a new facility 

or renovation of an existing constructed facility. 

Industry: Industry measures are based on the North American Industrial Classification 

(NAICS) codes for the construction sector and represent the total portfolio of projects. 

Quantity index: A measure reflecting the average of the proportionate changes in the quantities 

of a specified set of goods and services between two periods of time. Usually a quantity index is 

assigned a value of 100 in some selected base period and the values of the index for other periods 

are intended to indicate the average percentage change in quantities compared with the base 

period. A quantity index is built up from information on quantities such as the number or total 

weight of goods or the number of services; the quantity index has no meaning from an economic 

point of view if it involves adding quantities that are not commensurate, although it is often used 

as a proxy for a volume index.232

                                                           

230 OECD Glossary of Statistical Terms. http://stats.oecd.org/glossary/detail.asp?ID=5660. Accessed September 3, 

2009.

231 OECD Glossary of Statistical Terms. http://stats.oecd.org/glossary/detail.asp?ID=2110. Accessed September 3, 

2009. 

232 OECD Glossary of Statistical Terms. http://stats.oecd.org/glossary/detail.asp?ID=2221. Accessed September 3, 

2009. 

162 
 

163 

                                                           

Real terms: Attempts to reduce changes in economic variables to changes in quantities. Real 

GDP, for example, is the value of gross national product, measured at current prices, deflated by 

a GDP deflator, or price index.233

Sustainability: Sustainability is defined as “meeting the needs of the present generation without 

compromising the ability of future generations to meet their own needs.”234

Value added for the construction industry: Value added for the construction industry is 

defined as the dollar value of business done less costs for construction work subcontracted to 

others and payments for materials, components, supplies, and fuels.235

Value of construction put in place: The value of construction put in place is a measure of the 

value of construction installed or erected at the site during a given period. For an individual 

project, this includes (1) cost of materials installed or erected, (2) cost of labor (both by 

contractors and force account) and a proportionate share of the cost of construction equipment 

rental, (3) contractor’s profit, (4) cost of architectural and engineering work, (5) miscellaneous 

overhead and office costs chargeable to the project on the owner’s books, and (6) interest and 

taxes paid during construction (except for state and locally owned projects).236

Workface Planning: Workface Planning is the process of organizing and delivering all the elements 

necessary, before work is started, to enable craft persons to perform quality work in a safe, effective and 

efficient manner.237

233 John Black, Oxford Dictionary of Economics. Op. cit. 

234 Brundtland GH, editor. 1987. Our common future: Report of the UN Commission on Environment and 

Development. http://www.un-documents.net/wced-ocf.htm. Accessed September 3, 2009. 

235 Economic Census 2002. 

236 Census Bureau Construction Spending Methodology. http://www.census.gov/const/C30/definitions.pdf

237 Construction Owners Association of Alberta (COAA) Best Practices XVI Conference – Workface Planning 

(WFP) Plenary Presentation. 2003. http://www.workfaceplan.com/archive.htm. Accessed September 4, 2009.