August 2017710-151 Slater Street
Ottawa, Ontario
K1P 5H3
613-233-8891
csls@csls.ca

CENTRE FOR
THE STUDY
OF LIVING
STANDARDS

What Explains the Post-2004 U.S.
Productivity Slowdown?

Alexander Murray
CSLS Research Report 2017-05
Prepared for Innovation, Science and Economic Development Canada
August 2017

What Explains the Post-2004 U.S. Productivity
Slowdown?
Abstract
Economic theory and history show that labour productivity growth is the main driver of risingliving standards, so changes in the trend rate of productivity growth have profound implications
for a society’s future prosperity. The average annual rate of business sector labour productivity
growth in the United States declined by 1.9 percentage points between the 1995-2004 period andthe 2004-2015 period, from 3.2 per cent to 1.3 per cent.
Based on original data analysis and a review of existing literature, this report summarizes thestate of knowledge on the causes of the post-2004 slowdown in U.S. productivity growth.
Official growth accounting estimates indicate that 60-65 per cent of the labour productivity
decline is accounted for by a decline in total factor productivity growth, while 30-35 per cent isaccounted for by a decline in the rate of capital deepening (i.e. growth of capital per hour
worked). Three industries, collectively representing 28 per cent of business-sector hours worked
in 2015, account for over 80 per cent of the aggregate labour productivity decline:
manufacturing, wholesale trade, and retail trade.
The aggregate productivity slowdown is traceable to a decline in the productivity contributions
arising from industries that produce or intensively use information and communication
technology (ICT) products. The decline in the productivity contribution of ICT was driven bysome combination of a) slower technological progress in ICT and b) a reduction in businessdynamism in the ICT sector resulting in a decline in the rate of resource reallocation from lessproductive to more-productive firms.

iWhat Explains the Post-2004 U.S. Productivity
Slowdown?
Table of Contents

Abstract ............................................................................................................................................ i
Executive Summary ....................................................................................................................... iii
List of Tables .................................................................................................................................. v
List of Charts................................................................................................................................... v
I. Introduction ................................................................................................................................. 1
II. Data Sources and Aggregate Trends in U.S. Productivity ......................................................... 2
A. Data ........................................................................................................................................ 2
B. Recent U.S. Productivity Performance .................................................................................. 3
III. Industry Sources of U.S. Productivity Growth ......................................................................... 8
A. Decomposition Method.......................................................................................................... 8
B. Decomposition Results......................................................................................................... 10
IV. Growth Accounting ................................................................................................................ 13
V. Explanations for the U.S. Productivity Slowdown .................................................................. 16
A. Measurement Issues ............................................................................................................. 16
i. Measuring Output in the Digital Economy........................................................................ 16
ii. Mismeasurement of Information and Communication Technology (ICT)....................... 18
iii. International Profit Shifting............................................................................................. 19
vi. Conclusions Regarding Mismeasurement ....................................................................... 20
B. Supply-side Explanations..................................................................................................... 20
i. Frontier Innovation: The Exhaustion of Major Innovation Opportunities ........................ 20
ii. Technology Diffusion: Growing Gap between Frontier Firms and Laggards.................. 22iii. Reallocation ..................................................................................................................... 23
iv. Labour Quality Growth.................................................................................................... 26
C. Aggregate Demand and Productivity Growth ...................................................................... 26
VI. Conclusion .............................................................................................................................. 28
Appendix A: Industry Decomposition Results Based on the GEAD ........................................... 31
Appendix B: Structural Breaks in Trend Productivity Growth .................................................... 33
References..................................................................................................................................... 38ii

What Explains the Post-2004 U.S. Productivity
Slowdown?
Executive Summary
Economic theory and history show that labour productivity growth is the main driver ofrising living standards, so changes in the trend rate of productivity growth have profound
implications for a society’s future prosperity. Business sector labour productivity growth in the
United States averaged 3.22 per cent per year over the 1995-2004 period, then declined to 1.33
per cent per year over the 2004-2015 period. Had labour productivity maintained its 1995-2004
pace, U.S. business sector real output would have been some $2.9 trillion higher in 2015 (in 2009
dollars); the implied cumulative loss of real output due to slower productivity growth over the
2005-2015 period was $13.8 trillion.
What explains the U.S. productivity slowdown in the mid-2000s? In this report, weaddress this question by undertaking original data analysis and by reviewing the research
literature. After an introduction in Section I, Section II discusses sources of data on U.S.
productivity and reviews aggregate trends in both labour productivity and total factor
productivity (or TFP) over the 1987-2015 period. We then review the literature on the timing of
the productivity slowdown in the mid-2000s. In Section III, we decompose U.S. labour
productivity growth into its industry sources. In Section IV, we decompose it into its proximate
growth accounting sources: total factor productivity growth, capital deepening, and labour
quality growth. In Section V, we review research on possible explanations for the slowdown.
Section VI discusses implications for Canada and concludes.
The key results of the report are as follows:Business sector labour productivity growth in the United States averaged 3.22 per centper year over the 1995-2004 period, then declined to 1.33 per cent per year over the
2004-2015 period. Private business sector TFP growth declined from 1.75 per cent per
year to 0.51 per cent per year between the same two periods.



There is a general consensus in the literature, based on a variety of methods of statisticalanalysis, that U.S. productivity growth experienced a structural break in 2004. Thismeans that the productivity growth decline was not a temporary phenomenon associatedwith the business cycle or other short-lived influences, but a structural decline in theeconomy's trend growth rate.



Three industries collectively accounted for 80.5 per cent of the labour productivity
slowdown: manufacturing (42.0 per cent), wholesale trade (21.7 per cent), and retail trade(16.9 per cent). These industries’ contributions to the slowdown were driven by withinsector productivity slowdowns. Labour reallocation across industries accounted for only
1.9 per cent of the labour productivity slowdown.
iiiOf the 2.0 percentage-point decline in U.S. private business sector labour productivity
growth after 2004, 61 per cent was accounted for by a decline in TFP growth and 36 percent was accounted for by a decline in the rate of capital deepening. Changing growth oflabour quality played a comparatively small role.

The slowdown is traceable to a decline in the productivity contributions arising fromindustries that produce or intensively use information and communication technology(ICT) goods and services. The decline in the productivity contribution of ICT was driven
by some combination of a) slower technological progress in ICT production and b) a
reduction in business dynamism in the 'high tech' sector (which is mostly ICT-producing
industries) resulting in a decline in the rate of resource reallocation from less-productive
to more-productive firms.

Error in the measurement of productivity growth explains at most a small fraction of the
post-2004 slowdown.



The U.S. productivity slowdown has slowed the labour productivity divergence between
the United States and Canada. Indeed, since 2010, the gap has turned negative; annual
labour productivity growth over the 2010-2015 period averaged 0.57 per cent in the
United States and 1.02 per cent in Canada. While this bodes well for the growth of
Canadian living standards relative to those of the United States in the near term, aprolonged slowdown in U.S. productivity growth portends slower growth in Canada in
the longer term. The United States is at the global technological frontier in many sectors,
including the important information technology sector. To the extent that the U.S.
slowdown reflects a decline in the pace of technological change at the frontier, then it isalmost certain that productivity growth in the countries chasing the frontier – like Canada– will eventually fall as well.

iv

What Explains the Post-2004 U.S. Productivity
Slowdown?
List of Tables
Table 1: Measures of Productivity Growth, United States, Compound Annual Growth Rates,
1987-2015 ....................................................................................................................................... 4
Table 2: Contributions of Investment Sector and Consumption Sector to Business Sector
Utilization-Adjusted Total Factor Productivity Growth, United States, 1987-2015 ...................... 7
Table 4: Industry Contributions to Labour Productivity Growth in the United States, Private
Industries, 1987-2015, CSLS Decomposition .............................................................................. 11
Table 5: Decomposition of U.S. Productivity Slowdown between 1995-2004 and 2004-2015,
Private Industries .......................................................................................................................... 12
Table 6: Sources of Labour Productivity Growth, Private Business Sector and Total Economy,
United States, 1987-2015.............................................................................................................. 15
Table 7: Labour Productivity Growth in the United States and Canada, Business Sector, 19872015............................................................................................................................................... 29

List of Charts
Chart 1: Labour Productivity Growth, Per Cent per Year (Five-Year Moving Average), UnitedStates, 1988-2015............................................................................................................................ 4
Chart 2: Total Factor Productivity Growth, Per Cent per Year (Five-Year Moving Average),
United States, 1987-2015................................................................................................................ 5
Chart 3: Total Factor Productivity Growth in the Investment and Consumption Sectors, Per Cent
per Year (Five-Year Moving Average), United States, 1987-2015................................................ 7
Chart 4: Total Contributions to the U.S. Labour Productivity Slowdown between 1995-2004 and2004-2015, Percentage Points, Private Industries......................................................................... 13v

What Explains the Post-2004 U.S. Productivity
Slowdown?
I. Introduction1
Business sector labour productivity growth in the United States averaged 3.22 per centper year over the 1995-2004 period, then declined to 1.33 per cent per year over the 2004-2015
period.2 (From 2010 to 2015, U.S. labour productivity grew by just 0.57 per cent per year.)Economic theory and history show that labour productivity growth is the main driver of risingliving standards, so the 1.89 percentage-point decline in the trend rate of productivity growth
after 2004 has profound implications for the future prosperity of the United States. 3 Had labour
productivity maintained its 1995-2004 pace, U.S. business sector real output would have beensome $2.9 trillion higher in 2015 (in 2009 dollars); the cumulative loss of real output due toslower productivity growth over the 2005-2015 period was $13.8 trillion.
What explains the U.S. productivity slowdown in the mid-2000s? In this report, weaddress this question by contributing original data analysis and by reviewing the research
literature. We find that the trend rate of U.S. productivity growth (whether measured using
labour productivity or total factor productivity) declined around 2004. About 60-65 per cent of
the labour productivity decline is accounted for by a decline in total factor productivity growth,
while 30-35 per cent is accounted for by a decline in the rate of capital deepening (i.e. growth ofcapital per hour worked). Three industries, collectively representing 28 per cent of businesssector hours worked in 2015, account for over 80 per cent of the aggregate labour productivity
decline: manufacturing, wholesale trade, and retail trade.
The aggregate productivity slowdown is traceable to a decline in the productivity
contributions arising from industries that produce or intensively use information andcommunication technology (ICT) products. The decline in the productivity contribution of ICT
was driven by some combination of a) slower technological progress in ICT and b) a reduction inbusiness dynamism in the 'high tech' sector (mainly industries producing ICT goods andservices) resulting in a decline in the rate of resource reallocation from less-productive to moreproductive firms.
The report is structured as follows. In Section II, we discuss sources of data on U.S.
productivity and review aggregate U.S. productivity trends over the 1987-2015 period. We thenreview the literature on the timing of the productivity slowdown in the mid-2000s. In Section III,
1

This report was written by Alexander Murray while he was an economist at the Centre for the Study of Living
Standards (CSLS). The project was supervised by CSLS Executive Director Andrew Sharpe and was prepared forInnovation, Science and Economic Development Canada. The author thanks Patrick Alexander, Robert Gordon,
Dale Jorgenson, Martin Baily, John Fernald and Jianmin Tang for comments.
2
Labour productivity is defined as real output per hour worked.
3
Productivity growth makes rising material living standards for the average person possible, but it does notguarantee it. Indeed, in recent decades median real wage growth in many OECD countries (including Canada andthe United States) has failed to keep pace with labour productivity growth. Uguccioni and Sharpe (2016) study thispuzzle for OECD countries.

1

we decompose U.S. labour productivity growth into its industry sources. In Section IV, wedecompose it into its proximate growth accounting sources: total factor productivity growth,
capital deepening, and labour quality growth. In Section V, we review research on possible
explanations for the slowdown. Section VI discusses implications for Canada and concludes.

II. Data Sources and Aggregate Trends in U.S. Productivity
In this section, we discuss sources of data on U.S. productivity and review the recent U.S.
productivity performance. It is well-established that U.S. productivity growth experienced astructural shift toward a slower growth path in 2004. 4 We provide a descriptive illustration of thisusing a variety of measures of labour productivity and total factor productivity (TFP) for the U.S.
business sector, private business sector, and total economy.

A. Data5
Our main source of U.S. productivity data is the Bureau of Labor Statistics (BLS). Two
BLS programs provide productivity estimates: the Labor Productivity and Costs program and theMultifactor Productivity program. 6 The Labor Productivity and Costs program provides
estimates of labour productivity for the U.S. business sector; the Multifactor Productivity
program provides estimates of labour productivity and total factor productivity (TFP) for theprivate business sector and for the total economy. 7
We augment the official BLS data with estimates from two additional sources. The first is
the growth accounting database maintained by John Fernald at the Federal Reserve Bank of San
Francisco. It is described in Fernald (2014b). Fernald’s estimates are based mainly on
disaggregated industry data from the BLS and the Bureau of Economic Analysis (BEA),
augmented with data from other sources in some cases. We include it in this report because it
allows us to examine the influence of variable factor utilization on productivity growth rates.
For our industry decomposition, we use labour productivity estimates for private
industries constructed by CSLS researchers using hours data from the BLS and output data fromthe BEA’s GDP by Industry database. This approach allowed for the construction of labour
productivity estimates in both levels and growth rates for a broad set of industries up to 2015.

4

A short review of the literature providing statistical evidence for this claim is presented in Appendix B.
We provide a brief overview of the data sources here. The database associated with this report contains URLs forthe specific source data files on the web sites of the BLS, the BEA , and the San Francisco Federal Reserve. The
database is available online at the following URL: http://www.csls.ca/reports/USslowdown_database.xlsx.
6
Multifactor productivity (or MFP) is another name for total factor productivity (or TFP). MFP is the term used by
the BLS. We use TFP throughout this report.
7
The total economy encompasses all paid activity in the economy, whether undertaken by the government or by theprivate sector. The business sector is the total economy minus general government, the output of household workers
and nonprofits, the gross housing product of owner-occupied dwellings and the rental value of nonprofit institutional
real estate. The private business sector is the business sector minus government enterprises.
52

B. Recent U.S. Productivity Performance
Table 1 summarizes measures of aggregate productivity growth in the United States overthe 1987-2015 period. Consider first the labour productivity growth measures in the left panel ofthe table. According to the official BLS measure of labour productivity in the U.S. businesssector, labour productivity grew at 2.0 per cent per year over the 1987-2015 period. The
alternative measures for the business sector and the private business sector yield the same 2.0 per
cent compound annual growth rate for the 1987-2015 period. The total economy measure grew
somewhat more slowly, at 1.7 per cent per year. This is not surprising; the total economy
measure includes government, non-profit and household sector activities that exhibit low
productivity growth relative to the business sector. The private industries measure excludes
government activities (both general government and government-owned enterprises) and theimputed rental value of owner-occupied housing. Its growth was similar to that of the total
economy measure over the full 1987-2015 period.8
The full-period average growth rates mask substantial variation across sub-periods. The
remaining rows of Table 1 present growth rates for sub-periods within the 1987-2015 period.9
Chart 1 plots the five-year moving averages of the annual growth rates of the five labour
productivity measures from the table and illustrates the pattern of accelerations and slow-downs.
Business sector labour productivity grew by 1.55 per cent per year between 1987 and 1995,
accelerated to 3.22 per cent per year over the 1995-2004 period, then fell back to 1.33 per centper year post-2004. Much of the labour productivity growth after 2004 occurred during a brief
burst of fast growth associated with the Great Recession; over the 2008-2010 period, labour
productivity grew by 3.25 per cent per year as hours worked fell much faster than real output.
Since 2010, labour productivity growth has averaged 0.57 per cent per year − well below therates observed in earlier periods.10
Total economy labour productivity exhibits the same pattern as the business sector
measures: fast growth between 1995 and 2004, a slowdown thereafter except for the 2008-2010
period, and very slow growth since 2010. Within each sub-period, the growth rates for the total
economy are smaller than those for the business sector, especially during the fast-growth periods.

8

The main conceptual difference between the business sector and private industries is that the former includes
government enterprises and excludes the nonprofit sector and the output of household workers. Empirically, most of
the discrepancy between the BLS's business sector labour productivity measure and our private industries measure(based on BEA and BLS data) over the 1987-2015 period is traceable to a difference in the growth rates of theimplicit deflators used to transform nominal output into real output. The BLS's business sector price deflator grewby 1.84 per cent per year over the period, while our private industries price deflator grew by 2.04 per cent per year.
9
As will become apparent below, the cut-off dates for the main sub-periods are chosen to reflect structural breaks intrend productivity growth in the U.S. economy. An alternative approach would be to choose business cycle peaks, so
as to minimize the influence of business cycle factors on comparisons across sub-periods. Since we are primarily
interested in trend productivity growth, we opted to use the structural break dates for trend productivity growth fromthe literature discussed below. Sprague (2017) discusses productivity growth across U.S. business cycles and showsthat U.S. labour productivity growth in the current business cycle (i.e. the period since 2008) is the lowest among theten post-war U.S. business cycles.
10
We end our analysis in 2015, but U.S. business sector labour productivity estimates for 2016 are now available
from the BLS. According to those data, U.S. labour productivity grew by 0.27 per cent between 2015 and 2016,
below the 2010-2015 average of 0.57 per cent per year.

3

Table 1: Measures of Productivity Growth, United States, Compound Annual Growth
Rates, 1987-2015
Labour Productivity

Total Factor Productivity

Business
Sector(BLS)

Business
Sector(Fernald)

PrivateBusiness
Sector(BLS)

PrivateIndustries(BEA and
BLS)

Total
a
Economy(BLS)

PrivateBusiness
Sector(BLS)

Business
Sector,Unadjusted(Fernald)

2.00

2.05

2.01

1.75

1.74

0.93

0.93

Business
Sector,Utilizationadjusted(Fernald)
0.96

1987-1995

1.55

1.51

1.61

1.37

1.41

0.60

0.46

0.47

0.57

1995-2004

3.22

3.32

3.27

2.94

2.65

1.75

1.83

2.04

1.34

2004-2015

1987-2015

Total
aEconomy
(BLS)
0.76

1.33

1.41

1.27

1.07

1.20

0.51

0.54

0.44

0.39

2004-2008

1.33

1.21

1.33

1.11

1.28

0.27

0.04

-0.01

0.22

2008-2015

1.33

1.53

1.23

1.04

1.14

0.66

0.83

0.71

0.50

2008-2010

3.25

3.37

3.36

2.97

2.43

1.36

1.22

1.45

0.46

2010-2015

0.57

0.80

0.40

0.28

0.51

0.38

0.68

0.41

0.52

Sources: Series labelled 'BLS' are drawn directly from BLS sources. Series labelled 'Fernald' are taken from the growth accounting
database of John Fernald, described in Fernald (2014b). Fernald's series were constructed using data from the BLS and BEA. Series
labelled 'BEA and BLS' were constructed by CSLS researchers using BEA output data and BLS hours data. For links to the source data,
see Tables 1-5 in the database accompanying this report: http://www.csls.ca/reports/USslowdown_database.xlsx.
Note a. Series for the Total Economy end in 2014 rather than 2015.

Chart 1: Labour Productivity Growth, Per Cent per Year (Five-Year Moving Average),
United States, 1988-2015
4.0
3.5

Per cent

3.0
2.5
2.0
1.5
1.0
0.5
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015

0.0
Business Sector (BLS)
Total Economy (BLS)
Private Industries (BEA/BLS)Private Business Sector (BLS)Business Sector (Fernald)

Source: Tables 1-5 in the database, http://www.csls.ca/reports/USslowdown_database.xlsx.

4

Chart 2: Total Factor Productivity Growth, Per Cent per Year (Five-Year Moving
Average), United States, 1987-2015
3.00
2.50

Per Cent

2.00
1.50
1.00
0.50
0.00
-0.50
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015

-1.00

TFP, Private Business Sector (BLS)

TFP, Total Economy

TFP, Business Sector (Fernald)
Utilization-adjusted TFP, Business Sector (Fernald)
Source: Bureau of Labor Statistics and Fernald (2014b).
See Tables 1-4 in the database, http://www.csls.ca/reports/USslowdown_database.xlsx.

The measure for private industries tended to grow faster during high-growth periods and slower
during low-growth periods.
The right-hand panel of Table 1 presents measures of TFP growth. Chart 2 depicts the
five-year moving averages of TFP growth implied by these TFP measures. According to theofficial BLS measure for the private business sector, TFP grew by 0.93 per cent per year over the
1987-2015 period. The business sector measures from Fernald’s database yield an identical
average growth rate for the full period. As in the case of labour productivity, TFP growth was
slower in the total economy, at 0.76 per cent per year.
TFP growth varied across the sub-periods. Private business sector TFP grew by 0.60 per
cent per year between 1987 and 1995, accelerated to 1.75 per cent per year between 1995 and2004, then fell to 0.51 per cent per year after 2004. As in the case of labour productivity, TFP
growth experienced a short-lived spike associated with the Great Recession. Our main focus inthis report is on productivity growth trends, not on the relationship between productivity andbusiness cycles.11 In breaking down the 2004-2015 period into sub-periods, our purpose is to11

That labour productivity surged during the Great Recession is not especially surprising. Capital and labour quality
tend to change slowly relative to hours worked, resulting in countercyclical labour productivity growth. The spike inbusiness-sector TFP growth during the Great Recession is perhaps more surprising. The business cycle literaturedoes show that positive TFP shocks−especially after adjusting for variation in factor utilization−cause hours worked
to decline; everything else equal, this tends to make TFP countercyclical (Basu et al., 2006). See Fernald and Wang
(2016) for an extensive analysis of the cyclical properties of U.S. productivity measures.

5emphasize that since 2010 (i.e. after the end of the Great Recession) productivity growth has
been especially slow for a sustained period of time.
A TFP measure commonly used in the academic literature is the utilization-adjusted
measure of Fernald (2014b), which is also summarized in Table 1 and depicted in Chart 2. Thismeasure strips out the influence of variable factor utilization (i.e. variation over time in theintensity with which labour and capital are used, conditional on total hours worked and on the
total capital stock) from TFP, thereby yielding a purer indicator of technological progress. 12 Overa long span of time this should not matter because factor utilization rates tend to revert to theirlong-run averages. (This is true by construction under Fernald's approach.) Indeed, theutilization-adjusted and unadjusted measures for the business sector in Table 1 exhibited almost
identical growth over the full 1987-2015 period.
Nevertheless, the utilization-adjusted series is worth considering for two reasons. First,
factor utilization has a non-negligible influence on TFP growth within sub-periods. During the
fast-growth period of 1995-2004, in particular, the unadjusted measure appears to understate
annual TFP growth by 0.2 percentage points. 13 Second, the adjustment affects the timing of thetrend growth break in the mid-2000s. With the utilization-adjusted measure, TFP growth slows
around 2005, more or less consistent with labour productivity. With the unadjusted businesssector measures, the turnaround appears to occur somewhat later.
Fernald also presents utilization-adjusted TFP series for the consumption sector and theinvestment sector separately.14 Chart 3 displays the growth rates of these sector-specific TFP
measures (in five-year moving averages). Table 2 presents a breakdown of investment sector andconsumption sector TFP growth into the same sub-periods used in Table 1, as well as thepercentage-point contributions of each sector to overall utilization-adjusted TFP growth in thebusiness sector.
TFP growth has consistently been far faster in the investment sector than in the
consumption sector. Over the full 1987-2015 period, TFP grew by 3.34 per cent per year in theinvestment sector and by only 0.21 per cent per year in the consumption sector. The investment
sector accounted for 24 per cent of business sector nominal output on average over the period, so

12

TFP is often regarded as a proxy for technological progress, but it is influenced by other economic forces (as wellas measurement error). In addition to variation in factor utilization, these include non-constant returns to scale, static
resource misallocation, and departures from perfect competition. Moreover, even leaving aside these issues, therelationship between TFP growth and technological progress is complicated and requires subtle interpretation. See
Murray (2016) for a comprehensive discussion of these issues.
13
This implies that the factor utilization rate declined over the 1995-2004 period. This decline was largely
associated with the 2001 U.S. recession. Factor utilization fell 2.49 per cent (a large one-year decline) in 2001.
14
Here, the 'investment' sector refers to industries producing equipment, intellectual property and consumer
durables. It does not include structures, either residential or non-residential. The 'consumption' sector encompasses
all of business sector output other than those parts included in the investment sector (Fernald, 2014b). On the
differing macroeconomic effects of technology growth in the investment sector and the consumption sector, see
Basu et al. (2013).

6

8.0
7.0
6.0
5.0
4.0
3.0
2.0
1.0
0.0
-1.0
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015

Per Cent

Chart 3: Total Factor Productivity Growth in the Investment and Consumption Sectors,
Per Cent per Year (Five-Year Moving Average), United States, 1987-2015

Investment Sector TFP
Source: Fernald database described in Fernald (2014b).

Consumption Sector TFP

Table 2: Contributions of Investment Sector and Consumption Sector to Business Sector
Utilization-Adjusted Total Factor Productivity Growth, United States, 1987-2015
Compound Annual Growth(Per Cent per Year)Business
Sector0.96

Investment
Sector3.34

Consumption
Sector
0.21

1987-1995

0.47

1.76

0.08

1995-2004

2.04

5.46

2004-2015

0.44

2004-2008
2008-2015

Investment
Share

Percentage-Point
Contributions to Annual
Business Sector TFP Growth

0.24

Investment
Sector0.79

Consumption
Sector
0.16

0.23

0.40

0.06

0.91

0.25

1.36

0.68

2.78

-0.25

0.23

0.64

-0.20

-0.01

3.42

-1.06

0.24

0.82

-0.81

0.71

2.41

0.21

0.22

0.54

0.16

2008-2010

1.45

1.80

1.33

0.22

0.39

1.04

2010-2015

0.41

2.65

-0.24

0.22

0.60

-0.18

Percentage-Point Change,
1995-2004 to 2004-2015

-1.59

-2.68

-1.17

--

-0.72

-0.88

Per cent of Total Change,
1995-2004 to 2004-2015

100.0

--

--

--

45.3

55.2

1987-2015

Source: Data are taken from the growth accounting database of John Fernald, described in Fernald (2014b).
Fernald's series were constructed using data from the BLS and BEA.
Notes: TFP series are adjusted for variable factor utilization as described in Fernald (2014b). The 'investment sector'
refers to industries that produce equipment, intellectual property, and consumer durables. The 'consumption
sector' encompasses all of business sector output other than the investment sector. The investment share is theshare of the investment sector in nominal aggregate output. The per cent contributions to the total change (in thelast row of the table) are approximations because they do not account for the reallocation effect arising from thedecline in the investment share between 1995-2004 and 2004-2015. This is why they sum to 100.5 per cent rather
than 100 per cent.

7the investment and consumption sectors' percentage-point contributions to business sector TFP
growth were 0.79 and 0.16 percentage points, respectively.
Between the 1995-2004 and 2004-2015 periods, the rate of investment sector TFP growthdeclined by 2.68 percentage points (from 5.46 per cent per year to 2.78 per cent per year) and therate of consumption sector TFP growth declined by 1.17 percentage points (from 0.91 per centper year to -0.25 per cent per year). While the decline in the investment sector was larger, theconsumption sector made a slightly larger contribution to the overall slowdown in businesssector TFP growth because of its large output share. The consumption sector contributed -0.88
percentage points (or 55 per cent) of the total change in TFP growth between the two periods,
compared to a contribution of -0.72 percentage points (or 45 per cent) from the investment
sector. Thus, the aggregate TFP growth decline is attributable to both sectors in roughly equal
proportion.15
A final noteworthy detail from Table 2 is that the investment sector's share of businesssector nominal output fell from an average of 25 per cent in the 1995-2004 period to an averageof 22 per cent in the 2010-2015 period. Since TFP growth is faster in the investment sector, thedecline in that sector's output share has contributed negatively to aggregate TFP growth. Overthe 2010-2015 period, TFP grew at a robust 2.65 per cent per year in the investment sector but
declined by 0.24 per cent per year in the consumption sector.

III. Industry Sources of U.S. Productivity Growth
We decompose aggregate U.S. labour productivity growth into two-digit NAICS industry
contributions. This procedure reveals which industries have been drivers of U.S. growth andwhich have been hindering growth. We perform the decomposition for the 1987-1995, 19952004, and 2004-2015 periods and study the industry sources of the changes in trend productivity
growth between periods.

A. Decomposition Method
There is a large body of research on methods for decomposing aggregate productivity
growth into industry contributions. A comparison of some leading methods is provides by de
Avillez (2012).16 In this report, we use the CSLS decomposition (Sharpe, 2010; Sharpe andThomson, 2010). The CSLS decomposition is a variant of a standard approach, used frequently
in the literature, that decomposes aggregate labour productivity growth into industry
contributions from within-sector productivity growth and labour reallocation across industries. It
does not count relative price increases as part of an industry’s productivity growth contribution. 17
15

Similar calculations reveal that the 1.57 percentage-point acceleration of business sector TFP growth between the
1987-1995 and 1995-2004 periods was the result of contributions of 0.95 percentage points (or 60 per cent) from theinvestment sector and 0.62 percentage points (or 40 per cent) from the consumption sector.
16
Important recent contributions include Tang and Wang (2004), Diewert (2015; 2016), Reinsdorf and Yuskavage(2010), Reinsdorf (2015), Calver and Murray (2016), and Baldwin and Willox (2016).
17
We have also produced results based on an alternative decomposition, the Generalized Exactly Additive
Decomposition (GEAD) of Tang and Wang (2004) and Diewert (2015). This decomposition method does include
relative price increases as part of an industry’s productivity growth contribution. These results are in the appendix.

8

Define the following notation:
=
, =
, =
ℎ, =

ℎ
ℎ

ℎ

The CSLS decomposition expresses the relationship between aggregate labour productivity
growth and industry-level labour productivity growth as follows: 18
∆

=

,

∆
=

,

,

,

+ ∆ℎ ,
,

+

−
,

+

+ ∆ℎ ,

∆

,

−∆

,

(The notation ∆ denotes the change−
for any variable .) Thus, the contribution if
each industry to aggregate productivity growth is the sum of three components:Within-sector Effect (raises aggregate, ): productivity growth within industry
productivity growth;
Reallocation Level Effect (increases, ): reallocation of labour into industry
aggregate productivity growth if industry has an above-average productivity level;
Reallocation Growth Effect (increases, ): reallocation of labour into industry
aggregate productivity growth if industry has an above-average productivity growthrate.

The CSLS decomposition thus embodies the idea that the sign of an industry's reallocation effectshould reflect the productivity performance of that industry relative to the other industries fromwhich it is gaining (or to which it is losing) labour share. This treatment of the reallocation
effects is what distinguishes the CSLS decomposition from other commonly-used decomposition
methods.
We have applied this decomposition to the labour productivity measure for U.S. private
industries, the aggregate growth of which was displayed in Table 1. Alternatively, the industry
decomposition could have been performed using the KLEMS Multifactor Productivity Tables for
manufacturing and non-manufacturing industries from the BLS. Baily and Montalbano (2016)18

See de Avillez (2012) for details of the derivation of this decomposition. The key assumption underlying it is the
additivity of real output, i.e. aggregate real output equals the sum of industry-level real outputs. The BLS measuresreal output using chained Fisher indexes, which do not satisfy this additivity assumption. As a result, there will be asmall discrepancy between aggregate productivity growth and the sum of the industry contributions generated by the
CSLS decomposition. This discrepancy is evaluated quantitatively below; it turns out to be small.

9performed an industry decomposition of U.S. TFP growth using these data (and a different
decomposition method based on Domar weights). Their results on the industry sources of themid-2000s productivity slowdown are similar to ours. We regard our analysis as complementary
to theirs.

B. Decomposition Results
The decomposition results for U.S. labour productivity growth over the full 1987-2015
period are presented in Table 3. The aggregate labour productivity growth of 1.76 per cent per
year is entirely attributable to labour productivity growth within industries; the total withinsector effect contributed 1.90 percentage points to the overall growth rate, while inter-industry
labour reallocation reduced overall growth by 0.14 percentage points (the sum of the reallocationlevel and growth effects).19
At the industry level, the largest total contributions were made by manufacturing (0.59
percentage points, or 33.7 per cent of total growth) and the finance, insurance and real estate(FIRE) industry (0.31 percentage points, or 17.8 per cent of total growth). These contributions
were driven almost entirely by within-sector productivity growth, with reallocation acrossindustries playing a minor role.
Our primary interest is the mid-2000s productivity slowdown. The decomposition results
are informative about the industry origins of that slowdown. Panel A of Table 4 presents the
CSLS decomposition of U.S. labour productivity growth for the 1995-2004 and 2004-2015
periods separately, while Panel B presents the implied changes in industry contributions to
productivity growth between the two periods.
Between the 1995-2004 and 2004-2015 periods, aggregate labour productivity growthdeclined by 1.87 percentage points (from 2.94 per cent to 1.07 per cent per year). Of this change,-1.84 percentage points (or 98.1 per cent of the total slowdown) was driven by the within-sector
effect. Labour productivity growth slowed in eleven of the fourteen industries between the two
periods. Labour reallocation across industries contributed negligibly to the productivity
slowdown. Together, the reallocation level and growth effects contributed -0.04 percentage
points (or 1.9 per cent) to the slowdown.
The industry total contributions to the aggregate productivity growth slowdown are
depicted in Chart 4. The three industries that made the largest contributions to the slowdown
were manufacturing (-0.79 percentage points), wholesale trade (-0.41 percentage points), and19

As noted earlier, the non-additivity of chained quantity indexes introduces a discrepancy between the growth rateof aggregate productivity growth and the sum of the total industry contributions. That discrepancy is small. For thefull 1987-2015 period, the aggregate labour productivity growth rate is 1.76 per cent per year while the sum of thetotal industry contributions is 1.66 per cent; the discrepancy is just 0.1 percentage points. The discrepancy issimilarly small within each sub-period we consider. In our tables of results, we have scaled the contributions by theratio 1.76/1.66 so that the industries' total contributions sum to the true aggregate growth rate.

10

Table 3: Industry Contributions to Labour Productivity Growth in the United States,
Private Industries, 1987-2015, CSLS Decomposition
Percentage Point
Contributions

Per Cent of Private Industries
Total

LabourProductivity
Growth

TC

WSE

RLE

RGE

TC

WSE

RLE

RGE

1.76

1.76

1.90

-0.09

-0.05

100.0

108.0

-5.3

-2.8

3.82

0.08

0.04

0.04

0.00

4.4

2.2

2.2

-0.1

2.32

0.05

0.07

-0.01

-0.01

2.6

4.0

-0.8

-0.6

Utilities

0.88

-0.02

0.03

-0.05

0.00

-1.0

1.8

-2.6

-0.1

Construction

-0.71

-0.05

-0.06

0.01

0.00

-2.8

-3.2

0.6

-0.2

Manufacturing

3.65

0.59

0.61

-0.01

-0.01

33.7

34.8

-0.5

-0.5

Wholesale trade

3.47

0.24

0.25

-0.01

0.00

13.8

14.3

-0.5

-0.1

Retail trade
Transportation andwarehousing
Information
Other services, except
government
FIRE
Professional andbusiness services
AERAF

2.64

0.23

0.21

0.02

0.00

13.1

12.0

1.1

0.0

1.22

0.05

0.05

0.00

0.00

2.7

2.7

0.0

0.0

4.26

0.21

0.24

-0.03

-0.01

11.9

13.8

-1.5

-0.4

0.25

0.02

0.02

0.00

0.00

1.0

0.9

0.2

-0.1

2.25

0.31

0.32

-0.01

0.00

17.8

18.4

-0.7

0.0

0.92

0.15

0.14

0.01

0.00

8.5

8.1

0.6

-0.2

0.71

0.01

0.04

-0.03

0.00

0.7

2.3

-1.5

-0.1

Private industries
Agriculture, forestry,
fishing, and hunting
Mining

EHSA-0.55-0.11 -0.07 -0.03 -0.01
-6.3
-4.0
-1.9
-0.5
Notes:
1. TC = Total contribution; WSE = within-sector effect; RLE = reallocation level effect;
RGE = reallocation growth effect.
2. FIRE = Finance, insurance, real estate, rental and leasing; AERAF = Arts, entertainment, recreation,accommodation, and food services; EHSA = Educational services, health care and social assistance.
3. Labour productivity is real output (in chained 2009 dollars) per hour worked. Year-over-year growth isdecomposed into industry contributions each year. The results in the table are the arithmetic averages of annual
contributions over the 1987-2015 period.
4. To account for the non-additivity of chained volume indexes, the CSLS decomposition results are scaled so thatthe industry total contributions sum to the average annual growth rate of aggregate labour productivity.
Source: CSLS calculations based on BEA and BLS data.

11

Table 4: Decomposition of U.S. Productivity Slowdown between 1995-2004 and 2004-2015, Private Industries
Panel A: Productivity Growth Decomposition by Period
1995-2004
LabourProductivity
Growth

2004-2015

Percentage Point Contributions
TC

WSE

RLE

RGE

LabourProductivity
GrowthPercentage Point Contributions
TC

WSE

RLE

RGE

2.94

2.94

3.11

-0.10

-0.06

1.07

1.07

1.27

-0.16

-0.04

Agriculture, forestry, fishing, hunting

6.20

0.10

0.07

0.03

0.00

2.58

0.03

0.03

0.00

0.00

Mining

0.02

-0.05

0.00

-0.04

0.00

1.91

0.10

0.06

0.06

-0.02

Utilities

2.15

-0.03

0.06

-0.08

0.00

-0.78

-0.02

-0.02

0.00

0.00

Construction

-0.75

-0.06

-0.07

0.01

-0.01

-1.21

-0.06

-0.09

0.03

0.00

Manufacturing

6.42
6.58
4.46

1.07
0.48
0.39

1.09
0.50
0.38

0.00
-0.02
0.01

-0.02
0.00
0.00

2.09
0.97
0.69

0.28
0.07
0.08

0.35
0.07
0.05

-0.06
0.00
0.02

-0.01
0.00
0.00

1.31

0.05

0.05

0.00

0.00

0.50

0.02

0.02

0.00

0.00

5.16

0.25

0.27

0.00

-0.01

5.24

0.25

0.33

-0.07

-0.01

Other services, except government

0.36

0.02

0.02

0.00

0.00

-0.80

-0.02

-0.03

0.01

0.00

FIRE
AERAF

2.78
1.73
1.64

0.45
0.27
0.06

0.39
0.27
0.09

0.06
0.01
-0.03

0.00
-0.01
0.00

2.36
0.96
-0.42

0.31
0.14
-0.06

0.34
0.15
-0.02

-0.04
-0.01
-0.04

0.00
0.00
0.00

EHSA

0.00

-0.06

0.00

-0.05

-0.01

0.27

-0.04

0.03

-0.07

0.00

Private industries

Wholesale trade
Retail trade
Transportation andwarehousing
Information

Professional and business services

Panel B: Industry Contributions to Productivity Slowdown between 1995-2004 and 2004-2015
Change inLabourProductivity
GrowthPercentage Point Contributions

Per Cent of Total Change

TC

WSE

RLE

RGE

TC

WSE

RLE

RGE

Construction

-1.87
-3.62
1.89
-2.93
-0.45

-1.87
-0.07
0.14
0.00
0.00

-1.84
-0.04
0.06
-0.08
-0.02

-0.06
-0.03
0.10
0.08
0.02

0.02
0.00
-0.01
0.00
0.00

100.0
3.7
-7.7
-0.2
-0.2

98.1
2.0
-3.0
4.2
1.1

3.0
1.7
-5.4
-4.4
-1.1

-1.1
0.0
0.7
0.0
-0.1

Manufacturing

-4.33

-0.79

-0.74

-0.06

0.01

42.0

39.3

3.2

-0.6

Wholesale trade

-5.61

-0.41

-0.43

0.02

0.00

21.7

23.0

-1.1

-0.2

Retail trade

-3.77

-0.32

-0.33

0.01

0.00

16.9

17.4

-0.5

0.0

Transportation and warehousing

-0.81

-0.04

-0.03

0.00

0.00

1.9

1.6

0.2

0.1

Information

AERAF

0.09
-1.16
-0.43
-0.77
-2.06

0.00
-0.04
-0.14
-0.13
-0.13

0.06
-0.04
-0.05
-0.12
-0.11

-0.07
0.01
-0.09
-0.02
-0.01

0.01
0.00
0.00
0.01
0.00

-0.1
1.9
7.5
7.0
6.7

-3.3
2.3
2.6
6.4
6.1

3.5
-0.4
4.9
0.9
0.6

-0.3
0.0
0.0
-0.3
0.0

EHSA

0.27

0.02

0.03

-0.01

0.00

-1.1

-1.6

0.7

-0.2

Private industries
Agriculture, forestry, fishing, hunting
MiningUtilities

Other services, except government
FIRE
Professional and business services

Notes:
1. TC = Total contribution; WSE = within-sector effect; RLE = reallocation level effect; RGE = reallocation growth effect.
2. FIRE = Finance, insurance, real estate, rental and leasing; AERAF = Arts, entertainment, recreation, accommodation, and food services; EHSA = Educational
services, health care and social assistance.

12

Chart 4: Total Contributions to the U.S. Labour Productivity Slowdown between 19952004 and 2004-2015, Percentage Points, Private Industries
Manufacturing
Wholesale trade
Retail trade
FIRE
Professional and business services
AERAF
Agriculture, forestry, fishing, and hunting
Other services, except government
Transportation and warehousing
Information
Construction
Utilities
EHSA
MiningPrivate industries
-2.50

-0.79
-0.41
-0.32
-0.14
-0.13
-0.13
-0.07
-0.04
-0.04
0.00
0.00
0.00
0.02
0.14
-1.87
-2.00

-1.50
-1.00
-0.50
Percentage Points

0.00

0.50

Source: Table 5, Panel B.
Note: FIRE = Finance, insurance, real estate, rental and leasing; AERAF = Arts, entertainment, recreation,accommodation and food services; EHSA = Educational services, health care and social assistance.

retail trade (-0.32 percentage points). Together, these three industries accounted for 80.5 per centof the total slowdown. The contributions of manufacturing, wholesale trade and retail trade to theaggregate slowdown were driven almost entirely by within-sector productivity growth
slowdowns in those industries.
While three industries account for 80 per cent (or four fifths) of the aggregate slowdown,nine of the fourteen industries contributed to it. In that sense, the slowdown has been broadbased. The only industry that contributed non-negligibly in the direction of faster productivity
growth in the 2004-2015 period relative to the 1995-2004 period was mining. Its 0.14
percentage-point positive contribution was driven by a substantial increase in within-industry
productivity growth and by a positive reallocation level effect as the industry (which has anabove average productivity level) increased its hours share.

IV. Growth Accounting
The previous section examined industry contributions to aggregate productivity growth.
An alternative perspective is provided by the growth accounting framework, which decomposes
aggregate labour productivity growth into contributions from the following proximate sources:
13





Labour composition: growth of the quality of the economy's labour input;
Capital deepening: growth of the ratio of physical capital services to labour hours;
Total factor productivity (TFP): the residual part of labour productivity growth
unexplained by the other two components.

For any variable , let ∆ denote the logarithmic first-difference of (which isapproximately equal to the per cent growth rate of ). Following standard procedures, labour
productivity growth ∆( − ℎ)(i.e. the log first-difference of the output-hours ratio) can be
expressed as∆( − ℎ) = ∆ + ∆( − ℎ) + (1 − )∆where ∆ is TFP growth, ∆( − ℎ) is capital deepening (i.e. growth of the capital-hours ratio),∆ is labour quality growth, and is the share of capital costs in nominal value added.

Table 5 presents growth accounting results for the U.S. private business sector and total
economy, based on the BLS measures of labour productivity for those sectors from Table 1. For
the private business sector (Panel A of the table), labour productivity growth over the full 19872015 period (2.01 per cent per year) was driven mainly by contributions from TFP growth (0.93
percentage points) and capital deepening (0.80 percentage points), with growth of labour quality
contributing 0.28 percentage points per year.
The bottom two rows of Panel A shows the changes in labour productivity growth and in
the contributions of the three components between the 1995-2004 period and the 2004-2015
period. The 2.0 percentage-point slowdown in labour productivity growth is attributable to a 1.22
percentage-point decline in TFP growth and a -0.72 percentage-point contribution from capital
deepening. Labour quality growth held steady between the two periods (although it exhibited
substantial variation within the post-2004 period, as shown in the table).
Panel B of Table 5 displays the growth accounting results for the total economy.
Productivity growth and its components tended to be smaller in the total economy than in theprivate business sector, but the relative magnitudes of the contributions from TFP, capital
deepening and labour quality are similar to those in Panel A. Over the full 1987-2015 period,
TFP growth and capital deepening contributed 0.75 and 0.73 percentage points, respectively, tolabour productivity growth in the total economy. The contribution of labour quality growth, at
0.26 percentage points, was comparatively small but certainly not negligible.
Again, we are primarily interested in the sources of the slowdown post-2004. The 1.45
percentage-point slowdown in labour productivity growth between 1995-2004 and 2004-2015
was the result of a -0.94 percentage-point contribution from slower TFP growth and a -0.44

14

Table 5: Sources of Labour Productivity Growth, Private Business Sector and Total
Economy, United States, 1987-2015
Panel A. Private Business Sector
LabourProductivity

Average
Capital
Share

2.01

1987-1995

Contributions(Percentage Points)
Total Factor
Productivity

CapitalDeepening

LabourQuality

0.35

0.93

0.80

0.28

1.61

0.33

0.60

0.63

0.37

1995-2004

3.27

0.33

1.73

1.24

0.25

2004-2015

1.27

0.37

0.51

0.52

0.25

2004-2008

1.33

0.36

0.27

0.81

0.23

2008-2010

3.36

0.37

1.33

1.58

0.44

2010-2015

0.40

0.38

0.37

-0.15

0.18

Percentage-point Change,
1995-2004 to 2004-2015

-2.00

0.04

-1.22

-0.72

0.00

Per cent of Total Change,
1995-2004 to 2004-2015

100.0

--

61.0

35.9

0.1

1987-2015

Panel B. Total Economy
Contributions(Percentage Points)
Total Factor
CapitalLabourProductivity Deepening
Quality
0.75
0.73
0.26

LabourProductivity

Average
Capital
Share

1.74

0.38

1987-1995

1.41

0.35

0.57

0.50

0.34

1995-2004

2.65

0.35

1.33

1.04

0.25

2004-2014

1.20

0.42

0.39

0.60

0.20

2004-2008

1.28

0.41

0.21

0.84

0.21

2008-2010

2.43

0.44

0.45

1.62

0.31

2010-2014

0.51

0.43

0.52

-0.16

0.13

-1.45

0.07

-0.94

-0.44

-0.06

1987-2014

Percentage-point Change,
1995-2004 to 2004-2015

Per cent of Total Change,
100.0
-64.9
30.1
3.9
1995-2004 to 2004-2015
Source: Bureau of Labor Statistics, Multifactor Productivity Program.
Notes: Labour productivity growth rates are compound annual growth rates. The contribution ofcapital deepening is the growth rate of the capital-hours ratio multiplied by the average capitalshare for the period. The contribution of labour quality is the growth rate of labour composition
multiplied by one minus the average capital share for the period. The time periods for the average
capital shares end one year earlier than the date indicated (e.g. the average is computed over
1995-2003 instead of 1995-2004) to avoid double counting cut-off years.
The sum of the contributions may differ slightly from labour productivity growth due to rounding.

15

percentage-point contribution from slower capital deepening. The contribution of labour quality
growth was -0.06 percentage points. At 1.45 percentage points, the slowdown in the total
economy was smaller (in absolute terms) than the slowdown in the business sector (2.0
percentage points).
These results indicate that a large decline in TFP growth was the most important
proximate driver of the post-2004 labour productivity slowdown. Slower capital deepening also
made a substantial contribution to the slowdown. It is particularly noteworthy that the rate ofcapital deepening has been negative since 2010; hours worked are recovering from the GreatRecession, but capital investment has remained sluggish. Hall (2014) points to the failure of the
U.S. capital stock to rebound as a major lasting effect of the recession. Note, however, that thecontribution of capital deepening to productivity growth was already well below its 1995-2004
level (though not its 1987-1995 level) over the 2004-2008 period, before the recession.

V. Explanations for the U.S. Productivity Slowdown
Why did U.S. productivity growth decline in the mid-2000s and why has it remained low
since then? In this section, we discuss a number of potential explanations for the slowdown. The
discussion is based on a review of the research literature on the question, together with theempirical results from the previous sections of this report. We organize the discussion in terms of
three broad categories of explanation: mismeasurement, supply-side explanations, and demandside explanations.

A. Measurement Issues
Why did measured productivity growth decline in the mid-2000s? One possible answer isthat the measured slowdown is the result of measurement error and not a reflection of any real
economic phenomena. This explanation − the mismeasurement hypothesis − has been advanced
by several authors in both the popular press and the academic literature. 20
i. Measuring Output in the Digital Economy
Technological changes have occurred throughout the post-2004 period in areas such as
robotics (e.g. autonomous vehicles, drones), communication equipment (e.g. smart phones),
digital platforms (e.g. Uber, Airbnb), and data analytics (e.g. artificial intelligence, 'big data').
Official output statistics may fail to capture some of the output arising from these innovativetechnologies and business models, due either to conceptual or compilation challenges. Mokyr(2014, pp. 88) makes this point forcefully:"[Economists] are trained to look at aggregate statistics like GDP per capita andits derivatives such as factor productivity. These measures were designed for asteel-and-wheat economy, not one in which information and data are the most20

For discussions of the mismeasurement hypothesis in the popular press, see Ito (2013), Feldstein (2015), Smith(2015) and Aeppel (2015).

16

dynamic sector. Many of the new goods and services are expensive to design, but
once they work, they can be copied at very low or zero cost. That means they tend
to contribute little to measured output even if their impact on consumer welfare isvery large. ... The aggregative statistics miss much of what is interesting."
If these measurement problems have become more severe since 2004 as the use of thesetechnologies has expanded, the result could be a decline in the pace of measured real output
growth, and hence measured productivity growth. The decline would be illusory; it would reflecta statistical failure rather than a true slowdown in growth.
Ahmad and Schreyer (2016) provide a comprehensive discussion of the conceptual andpractical challenges to GDP and productivity measurement posed by the digital economy. 21 They
identify the following key output measurement challenges:







Rising importance of peer-to-peer transactions: New technology has facilitated thematching of consumers with non-business service providers (e.g. homeowners letting
their owner-occupied homes via Airbnb). How can the value added of the providers
be measured?
'De-marketization' of production: Digital technology has shifted some activities
from the market sector to the household production sector (e.g. online banking, online
flight booking and check-in, etc.). Should these activities be included in GDP?
Free products and services: Many digital services are provided to users at zero ornear-zero prices, which complicates their inclusion in national accounts. Suchservices may be financed by advertising revenues, may be traded for users'
behavioural data, or may represent an investment in the provider's brand. Different
methods of measuring the value of these 'payments' have different implications for
output measurement.
Price vs. quality changes: Digital technology has exacerbated problems inseparating price changes from quality changes. Specific challenges include increasing
product or service customization, non-volume-based pricing models (e.g. fee-foraccess rather than price-per-unit pricing), and substitution of new services for old
ones (e.g. substituting Airbnb for hotel rooms).

The first three challenges pertain to the measurement of current-dollar output in the digital
economy, while the fourth pertains to the measurement of prices and hence of real output
volumes.22
Much of the output from peer-to-peer transactions may already be captured in GDP (e.g.
in the imputed rental value of owner-occupied housing, which assumes full-time occupancy), and21

The digital economy refers to the matching of service providers with end users via intermediating digitalplatforms. Two characteristics distinguish the digital economy from the 'traditional' economy. First, digital platforms
are characterized by powerful network effects, which tend to encourage market concentration. Second, digitaltechnologies tend to reduce barriers to entry, and this tends to increase market contestability by new entrants.
22
Aside from these output measurement issues, an important implication of the rising digital economy for
productivity measurement is the blurring of the line between consumer durables and productive capital. Is an Uber
driver's car a consumer durable or a part of the capital stock of the transportation sector?

17

in principle the records of the intermediaries could be used to capture the rest. The activities thathave moved from the market sector to the non-market sector arguably should not be counted in
GDP; this is a long-standing issue in national accounts measurement. Ahmad and Schreyer(2016) argue that the latter two issues − free products and, especially, price versus quality
measurement − are the challenges for which further research efforts are most warranted.
That said, they suggest that the size of the digital economy may (so far) be too small forthese issues to have affected output growth or productivity growth to a significant degree. It istherefore unlikely that these measurement issues can explain away the post-2004 productivity
slowdown.
Recent quantitative research supports that conclusion. Syverson (2016) shows that eventhe largest estimates of the unmeasured value added associated with digital technology accountfor, at most, a third of the $2.9 trillion reduction in U.S. output in 2015 due to the productivity
slowdown. He also finds that the tendency of measured U.S. nominal GDI to exceed measured
nominal GDP − a pattern consistent with some output being produced and given away for free orat steep discounts, as is often the case in the digital economy − began in 1998, well before theU.S. productivity slowdown, and did not get worse after 2004. Byrne et al. (2016) argue that thevalue of 'free' digital services to consumers is largely non-market and ought not to be included inmarket sector output or productivity measures. If they nevertheless include it (using estimates of
the unmeasured value of 'free' digital services based on the opportunity cost of the time users
spend using them), they find that business sector labour productivity growth would be about 0.3
percentage-points higher over the 2002-2011 period, with slightly larger effects after 2005 than
before. Since business sector labour productivity growth slowed by about 2 percentage points
after 2004, most of the slowdown remains unexplained even after this adjustment.
ii. Mismeasurement of Information and Communication Technology (ICT)The U.S. productivity boom of 1995-2004 coincided with an acceleration in the rate of
decline of prices for ICT investment. After 2004, the prices of ICT goods and services fell more
slowly than before and the contribution of ICT production to productivity growth declined. 23
Some research has suggested that the official price indexes for ICT investment understate therate of decline of ICT prices in recent years (Byrne and Corrado, 2015 and 2016; Byrne andPinto, 2015; Byrne et al., 2015). If this measurement problem became worse around 2004 forsome reason, it might explain some part of the decline in measured productivity growth.
Byrne et al. (2015) find that this is not the case; that is, while there is substantial error in
the measurement of ICT price indexes, the measurement problems did not become worse after
2004. They correct for the mismeasurement of ICT prices using new price indexes and find thatthe correction actually exacerbates the post-2004 productivity growth decline rather than
explaining it away. ICT prices were mismeasured well before the slowdown, and since U.S.
production of these products has declined, the impact of their mismeasurement on U.S.
productivity was larger during the 1990s than it has been post-2004.

23

This is discussed further in Section V-B below.

18

Syverson (2016) also provides evidence against the view that ICT mismeasurement
explains the measured productivity growth slowdown. First, many countries experienced aproductivity slowdown in the 2000s and the size of the slowdown is uncorrelated with theintensity with which countries consume or produce ICT products. The same is true across U.S.
states (Cardarelli and Lusinyan, 2015). Second, the true output growth of ICT-producing
industries would have to have been some 500 per cent greater than their measured output growthsince 2004. This is an implausible degree of mismeasurement.
iii. International Profit Shifting
Another potential source of error in productivity measurement arises from a failure toproperly account for the economic activity of U.S. multinational enterprises (MNEs) that engage
in significant offshore profit shifting (usually for tax purposes). Using a variety of tax planning
strategies, MNEs are able to book profits in low-tax jurisdictions even though the productive
activity that gave rise to those profits occurred in other jurisdictions. 24 This leads to anunderstatement of GDP in the high-tax jurisdiction and an overstatement of GDP in the low-tax
jurisdiction. For example, suppose a U.S.-based MNE develops new intellectual property that isused in production abroad. The flow of payments for the services of this intellectual property
should be accounted for in U.S. value added as part of net exports. But if the U.S. MNE legallytransfers ownership of the intellectual property to a subsidiary in a relatively low-tax jurisdiction(and manages to 'underprice' it in the transfer), that value added will be attributed to thatjurisdiction rather than to the United States. 25
If the extent of international profit shifting was greater in the post-2004 period than inearlier times, then it could explain part of the measured productivity growth slowdown. Guvenen
et al. (2017) investigate this possibility and develop adjusted measures of U.S. GDP and labour
productivity to account for international profit shifting. They allocate the worldwide earnings of
U.S. MNEs across countries based on measures of the amount of the company's economic
activity that actually occurs in each country. 26
Guvenen et al. find that the effect of international profit shifting on measured U.S. GDP
was small from the 1970s to the late 1990s, but became more important in the early 2000s. Theiradjustments to GDP raise measured labour productivity growth by 0.1 percentage points per year
over the 1994-2004 period and by 0.25 per cent per year over the 2004-2008 period. However,they found that international profit shifting did not affect productivity growth after 2008. Thesefindings imply that the adjustments raise average productivity growth over the 2004-2015 period24

One such strategy is to underprice the services of intangible capital developed in the United States and used byforeign subsidiaries. Another is to arrange for loans from subsidiaries in low-tax jurisdictions to subsidiaries in hightax jurisdictions, thereby shifting profits to the low-tax jurisdiction.
25
The payments would still be captured in U.S. gross national income, as part of earnings on direct foreign
investment. Note that this is true even if the foreign earnings are not repatriated; the BEA accounts for U.S. foreign
direct investment income at the time the earnings are earned, not when they are distributed as dividends (Bureau of
Economic Analysis, 2006).
26
This approach is called 'formulary apportionment.' To measure the amount of a company's economic activity that
occurs in a given country, they use information on the company's labour compensation payments and sales tounaffiliated parties in that country.

19

by about 0.09 per cent per year, which is essentially the same as the pre-2004 adjustment. Thus,
accounting for international profit shifting explains none of the measured slowdown in U.S.
labour productivity growth between the 1994-2004 and 2004-2015 periods. At best, it explains asmall share of the slowdown in the 2004-2008 period. But productivity growth has remained low
after 2008 for reasons unexplained by international profit shifting.
vi. Conclusions Regarding Mismeasurement
While the measurement of productivity growth is subject to substantial error anduncertainty, the balance of the evidence suggests that the post-2004 slowdown in measured
productivity growth is a true economic phenomenon and not an illusion driven by worsening
measurement error. In the remainder of this section, we review possible supply-side and demandside explanations for the slowdown.

B. Supply-side Explanations
This section discusses possible structural explanations for the decline in U.S. productivity
growth in the mid-2000s. We first discuss whether the pace of technological progress has
declined, due either to a decline in the rate of frontier innovation or to a decline in the rate oftechnology diffusion. We then discuss the role of resource reallocation across firms and acrossindustries. Finally, we touch upon labour quality growth .
i. Frontier Innovation: The Exhaustion of Major Innovation Opportunities
A leading explanation of the post-2004 productivity slowdown is that it reflects theexhaustion of opportunities to enhance productivity via the adoption of new ICTs (together withthe absence of any other new technology to drive fast productivity growth). This is related to abroader idea: that opportunities for fundamental, economy- and society-altering technologicaladvances were exhausted by the early 1970s, and that − aside from a transitory ICT-driven blipin the 1995-2004 period − slow productivity growth since 1973 represents a permanent decline in
the economy's innovative potential relative to the 1920-1970 period.
We first discuss the narrower story about the role of ICT in the 1995-2004 productivity
acceleration and the subsequent slowdown. We then discuss the broader hypothesis that the paceof technological progress has slowed more or less permanently.
a. Information and Communication Technology
There is a sizable literature on the sources of the 1995-2004 U.S. productivity boom anda growing one on the reasons that boom ended in 2004. The leading explanation is that the twotrend breaks are tied together by a single factor: the diffusion of information andcommunications technology (ICT) throughout the U.S. economy. According to this explanation,
an increase in the rate of technological improvement in the ICT-producing industries after 1995
led to rapid price declines in ICT and a boom in ICT investment in other sectors. That investment
boom caused fast productivity growth across sectors that use ICT intensively. By 2004, the20

productivity gains from the adoption of ICT had all been exploited and productivity growthdeclined.
Rapid ICT price declines predate the mid-1990s. Before economists noticed the mid1990s increase in trend productivity growth, there had emerged a large body of research seeking
to explain the "Solow paradox" of high ICT investment and low productivity growth. 27When
trend productivity growth increased after 1995, however, ICT became the leading explanation.
Jorgenson (2001) decomposes aggregate U.S. productivity growth into industry contributions
using the Domar approach. He finds that the ICT sector (comprising the computer, software, andcommunication equipment industries) accounted for 49 per cent of the TFP growth increase
between 1990-1995 and 1995-1999, and 77 per cent of the labour productivity increase between
those two periods. He attributes this to an increase in the rate of technological advance in thedevelopment of semiconductors after 1995.
The distinction between labour productivity and TFP is important in terms of the ways inwhich ICT diffusion shows up in productivity statistics in the late 1990s and early 2000s. The
high rate of aggregate TFP growth from 1995 to 2000 was driven mainly by fast TFP growthwithin ICT-producing industries (especially semiconductor production). TFP growth in ICT
production slowed somewhat after 2000, but until 2004 that slowdown was offset by a surge in
TFP growth in industries that intensively use ICT (Jorgenson and Stiroh, 2000; Cette et al.,2016). That surge reflected complementary innovations and business reorganization that ICT
made possible in the intensive ICT-using industries. In terms of labour productivity, the growthincrease was broad-based across both ICT-producing and intensive ICT-using industries (but not
in industries that do not use much ICT) throughout the 1995-2004 period because TFP growth in
ICT-producing industries led to lower relative prices for ICT goods and, hence, to capital
deepening in ICT-using industries.
The ICT-based story is broadly consistent with our industry decomposition results from
Section III. Wholesale trade, retail trade, and finance − three of the industries that drove theproductivity slowdown after 2004 − are all ICT-intensive service industries according to Cette etal. (2016). The other key contributor, manufacturing, includes both ICT-producing and ICTusing industries, though it also includes some industries that are neither. The decomposition
results of Cette et al. (2016), which focus explicitly on ICT producers and users, confirm theimportance of intensive ICT-using industries in the post-2004 slowdown.
b. Slowdown in the Pace of Technological Progress

27

The fact that ICT investment was high and productivity growth was low in the 1980s and early 1990s was dubbed
the Solow paradox after Solow (1987) said, "You can see the computer age everywhere but in the productivity
statistics." Jorgenson and Stiroh (2000) showed that the rapid decline of ICT prices led to dramatic substitution
toward ICT at the expense of other inputs between 1990 and 1996, but that this substitution was accompanied bylittle growth of TFP and labour productivity. To explain this, they note that input substitution driven by relative
price changes corresponds to a movement along a production function, while TFP growth represents a shift in the
production function. Substitution toward ICT might raise TFP growth if one firm's ICT investments generates
spillover benefits for other firms, and it might raise labour productivity growth if ICT substitutes for labour rather
than for other types of capital. The authors find no evidence of these effects in data up to 1996.

21

A view associated most closely with Robert Gordon (2016a) holds that the 1995-2004
period of fast ICT-driven productivity growth was a temporary deviation from the normal stateof affairs, which is modest technological innovation and moderate productivity growth. Gordon
argues that the period between 1920 and 1970 represented a unique period in U.S. economic
history. A set of 'great inventions' developed during the decades before 1920 (electricity, theinternal combustion engine, etc.) spread throughout the economy during the 1920-1970 period
and led to an explosion of productivity growth that was unprecedented in history and that is − in
Gordon's view − unlikely ever to be repeated.28
The slowdown in U.S. productivity growth in the early 1970s marked the end of thisperiod of revolutionary technological transformation. Subsequent innovations, Gordon argues,
have been 'evolutionary rather than revolutionary'; that is, they represent incremental
improvements in some aspect of economic life but not fundamental transformations of the kindthat occurred during the 1920-1970 period. The ICT-driven productivity boom of the 1995-2004
period was short-lived because it did not take long for the associated opportunities for
productivity enhancement to be exhausted. The productivity improvements in communicationand information processing associated with ICT are simply smaller and less far-reaching in theirimpact than were the productivity improvements associated with, say, electrification.
Some other productivity and innovation experts disagree with Gordon’s pessimistic
outlook for U.S. productivity growth. Syverson (2013) points out that the productivity effects ofearlier general purpose technologies (e.g. electricity) occurred in waves separated by periods ofslow growth, and that there is no basis for certainty that the productivity growth benefits of ICT
have all been exhausted. The findings of Byrne et al. (2015) suggest that technological progress
in ICT continues at a fast pace. 29 Brynjolfsson and McAffee (2011; 2014) and Mokyr (2014)
suggest that emerging technologies such as 3-D printing, artificial intelligence and genomics
could lead to heretofore unimagined societal and economic transformations.
People on both sides of this debate agree that the post-2004 productivity slowdown istraceable to a decline in the measured contribution of ICT-producing and intensive ICT-using
industries to aggregate productivity growth. The debate is over how to interpret this decline. Is ita return to normal that we should expect to be permanent, as Gordon claims? Or will further
waves of fast productivity growth driven by ICT occur in the future, so that the post-2004
slowdown will turn out to have been temporary? Only time can answer this question.
ii. Technology Diffusion: Growing Gap between Frontier Firms and Laggards
The process of productivity growth occurs in two stages: technological innovation andtechnology diffusion. The Gordon view is that the pace of innovation has slowed; that is, thatnew ideas at the technological frontier are arriving more slowly or are less significant relative tothe past. An alternative possibility is that the process of diffusion is somehow being impeded; thefrontier continues to be pushed forward as in the past, but the new technologies are not being
adopted as readily by businesses.

28
29

Cowen (2011) presents a similar argument.
See Sichel (2016) for some challenges to Gordon’s outlook, and Gordon (2016b) for a response.

22

It has been known for some time that there is substantial dispersion in productivity levels
across firms within the same industry (Syverson, 2004; 2011). Decker et al. (2017a) show that
this dispersion has been rising over time, both in terms of TFP (for firms in the manufacturingsector) and in terms of labour productivity (for the entire U.S. private sector). Using firm-level
data from 23 OECD countries including the United States, Andrews et al. (2015) find that the
productivity gap between firms at the global productivity frontier (within an industry) and allother firms increased over the 2000s. Moreover, their econometric evidence suggests that, withincountries, the productivity gap between firms at the national productivity frontier (as opposed to
the global frontier) and laggard firms also increased.
If we expect best-practice technologies to diffuse through the productivity distribution
from productivity leaders to the laggards, an increase in the dispersion of productivity levels may
signal a problem in that diffusion process − which may result in slower productivity growth.
Taking a long-run perspective, Comin and Mestieri (2016) find that the diffusion of newtechnologies across countries has grown faster over time but that the diffusion of thosetechnologies within countries (following initial adoption by early adopters) has slowed.
By industry, Andrews et al. (2015) identify three sets of firms: the global frontier, the 100
firms with the world’s highest productivity levels; the national frontier, the ten firms with thehighest productivity levels within their countries; and the remaining firms, which they call the‘laggards.’ They find that productivity growth at the global frontier did not decline over the
2000s, but that the gap between the global frontier and the laggards has widened. For the UnitedStates, their simulations suggest that closing the gap between the national productivity frontier
and the global frontier in all industries would raise productivity by ten per cent.
While the evidence suggests that the dispersion of firm-level productivities has been
growing over time, there is no sign that this trend changed in any way around 2004. Indeed,
Decker et al. (2017a) find that productivity dispersion in the United States has been trending
upward at least since the late 1980s. It is therefore unlikely that the post-2004 productivity
slowdown can be attributed to a decline in the rate of diffusion of best-practice technologies
from the frontier to the laggards. That being said, problems in technology diffusion may be
imposing a longer-term drag on U.S. productivity growth. This is an area that warrants further
research.
Aggregate productivity depends on both the distribution of firm-level productivities andthe relative sizes of the firms (measured in terms of input shares). Having discussed the changing
productivity distribution, we now turn to the matter of resource reallocation.
iii. Reallocation
Recent evidence suggests that the tendency of resources to be reallocated toward highproductivity firms in the United States has grown weaker in recent years and that this is tied tothe productivity slowdown in the mid-2000s. We review this evidence in the first part of thissection. In the second part, we discuss a different reallocation issue: the reallocation of resources
across industries with different productivity levels and the so-called Baumol Effect.
23

a. Reallocation Across Firms: Declining Business Dynamism
An emerging literature documents a decline in the rate of labour reallocation across firms
in recent decades.30 Decker et al. (2017a; 2017b) study the relationship between labour
reallocation and aggregate productivity growth and find that a rise and fall in the pace ofreallocation in information industries helps to explain the 1990s U.S. productivity accelerationand the post-2004 slowdown.
Decker et al. (2017a) note that trends in within-industry labour reallocation differ
substantially across industries. Measures of business dynamism (e.g. start-up rates, firm exit
rates, cross-firm labour reallocation rates, etc.) in the aggregate U.S. economy have been
declining since (at least) the early 1980s, but the implications of changes in dynamism for
aggregate productivity depend on which industries are driving the aggregate labour reallocation
trends. In the 1980s and 1990s the decline in overall U.S. labour reallocation rates was being
driven by the retail trade industry, and this was productivity-enhancing because it reflected thegrowing concentration of activity in that industry in highly productive 'big box' retailers. In the
1990s and 2000s, changing rates of labour reallocation in 'high-tech' industries (a set of ICTrelated manufacturing industries) became relatively important. High-tech business dynamism
declined throughout the 1980s, increased throughout the 1990s, then declined again after 2000.
On the relationship between high-tech labour reallocation and aggregate productivity
growth, Decker et al. provide two main results. First, trends in high-tech labour reallocation were
driven not by changes in the distribution of firms' productivities but by changes in firms'
responsiveness to productivity shocks. The dispersion of productivity across high-tech firms rosesteadily over time, but the responsiveness of firms to productivity changes (measured in terms of
employment growth or equipment investment growth) increased in the 1990s and fell in the
2000s. This is what explains the pattern of labour reallocation rates in that sector.
Second, the rise and fall of labour reallocation rates in high-tech industries contributed
significantly to the rise and fall of productivity growth in high-tech manufacturing. Firms' rising
responsiveness to productivity shocks contributed about 0.5 percentage points to annual TFP
growth in the 1990s, while declining responsiveness subtracted as much as 2.0 percentage points
from TFP growth in the 2000s. The authors' econometric simulations suggest that declining
labour reallocation became a drag on productivity growth in the high-tech sector around 2003,
which is close to the date of the aggregate U.S. productivity slowdown.
As discussed earlier, a declining productivity growth contribution from the ICT sector
has been a major driver of the aggregate productivity slowdown (Fernald, 2014a). The results of
Decker et al. (2017a) are consistent with this view, but they suggest that the decline in the
productivity contribution of the ICT sector may be the result of a decline in the efficiency of theallocation of labour across firms in the ICT sector rather than (or in addition to) a decline intechnological progress in the sector. Efficiency-enhancing labour reallocation is supposed tooccur as the result of a number of forces: the exit of the least productive firms from themarketplace, the entry of firms with new ideas to challenge incumbents, and the reallocation of30

See Davis et al. (2007), Decker et al. (2014; 2016), Davis and Haltiwanger (2014), Hyatt and Spletzer (2013),
Malloy et al. (2016) and Alon et al. (2017).

24

market share toward relatively productive firms through competition. Evidently, these forces are
growing less effective at channelling resources toward the most productive firms in the techsector.
Decker et al. (2017b) provide further evidence in support of the view that declining
allocative efficiency is part of the explanation of the post-2004 slowdown. Using measures oflabour productivity and employment for firms in the U.S. business sector, they show that thepositive correlation between productivity and employment share declined in the early 2000s. As
a result, the positive contribution of labour reallocation to aggregate labour productivity growth
fell significantly. Both a decline in reallocation among continuing firms and a decline in thecontribution of net firm entry to reallocation led to slower productivity growth after the early
2000s.
These results suggest that changes in U.S. business dynamism may play a substantial rolein explaining aggregate productivity trends in recent years. That said, this line of research is stillin its infancy and there remains much uncertainty about the magnitudes of the effects. Much
more work needs to be done to develop more reliable quantitative estimates of the relationshipbetween business dynamism and aggregate productivity. One important issue that the literaturemust address is the possibility that the changes in business dynamism, rather than (or in additionto) being causes of changing productivity growth, have themselves been caused by underlying
changes in the rate of technological progress.
b. Reallocation Across Industries: The Baumol and Denison Effects
The previous subsection discussed the implications of labour reallocation across firms.
Aggregate productivity growth is also affected by the reallocation of labour (or other inputs)
across industries. For completeness, we address this now.
The idea that resource reallocation across industries may tend to cause a slowdown inaggregate productivity growth is associated with the work of William Baumol on unbalanced
growth and the 'cost disease.'31 If some industries (say, manufacturing) exhibit fast productivity
growth while others (say, health services) exhibit slow productivity growth, the relative prices ofthe outputs of the slow-growth industries will tend to rise. Baumol thought that this would leadto an increase in the share of slow-growing industries in total output, and hence to lower
aggregate productivity growth even if productivity growth rates within each industry remainunchanged over time.
On the other hand, there has been a long-run tendency for labour (and other inputs) tomove from low-productivity to high-productivity uses over time. Edward Denison pointed thisout in the context of the shift from agriculture to manufacturing in advanced economies in the
middle of the 20th century.32 In general, aggregate productivity growth will rise if labour isreallocated into industries with high productivity levels.
31

See Baumol (2012) for a recent exposition of the cost disease argument. Classic references include Baumol (1967)
and Baumol et al. (1985).
32
See Denison (1967), especially chapter 16.

25

We already touched upon both these ideas in our industry decompositions of productivity
growth in Section III. What we called the reallocation level effect and the reallocation growtheffect could have been called the 'Denison effect' and the 'Baumol effect', respectively. 33
Our industry decomposition revealed that reallocation accounted for only 0.04 percentage
points (or 1.9 per cent) of the labour productivity growth slowdown between the 1995-2004
period and the 2004-2015 period (Table 4). Of this, 0.06 percentage points (or 3.0 per cent of thetotal slowdown) were attributable to the reallocation level effect (or the 'Denison effect'). The
reallocation growth effect (or the 'Baumol effect') actually worked against the slowdown, but its
impact was quantitatively negligible. The contribution of the reallocation level effect to theslowdown was driven by the FIRE, information and manufacturing industries, while mining andutilities made substantial contributions in the opposite direction (i.e. toward faster overall
productivity growth) through the reallocation level effect.
Overall, our industry decomposition results make it clear that cross-industry reallocation
was not a dominant driver of the slowdown.
iv. Labour Quality Growth
Our growth accounting exercise in Section IV revealed that change in the growth oflabour quality was not a significant driver of the productivity slowdown (Table 5). Labour
quality growth contributed nothing to the productivity decline in the private business sector,
while in the total economy it contributed -0.06 percentage points (or 3.9 per cent of the total
decline).
That being said, it is noteworthy that the contribution of labour quality growth to labour
productivity growth was high during the 2008-2010 period and has been quite low since 2010.
The brief surge in labour quality growth during the Great Recession may reflect theconcentration of job losses among relatively low-wage workers. It remains to be seen whether
the low contribution of labour quality since 2010 is a temporary effect of the recovery from that
recession (as those relatively low-productivity workers return to work) or a permanent slowdown
in labour quality growth (perhaps due to declining growth in educational attainment). If it is thelatter, then the labour productivity growth slowdown may become even worse in the coming
years. The projection exercises of Bosler et al. (2016) suggest that slow U.S. labour quality
growth is likely going forward.

C. Aggregate Demand and Productivity Growth
All the explanations discussed so far might be described as supply-side explanations. An
alternative view (or perhaps a complementary one) is that, since 2008, part of the decline inproductivity growth has been the endogenous result of a shortfall in aggregate demand.
Theoretical models of endogenous growth provide reasons to think that demand shocks might
affect TFP growth (at least temporarily) through endogenous changes in R&D spending.
33

Indeed, this is the terminology used by Nordhaus (2001) in his industry decomposition of productivity growth.

26

Empirical evidence in support of this mechanism as a driver of the productivity slowdown islimited.
The recession of 2008-2009 involved both a financial crisis and a large decline inaggregate demand in the U.S. economy. A disruption of financial markets or an aggregate
demand shortfall could lead to lower TFP growth via a number of channels. Financial market
disruption could lead to a reduction in resource reallocation, and hence to lower TFP due to ahigher degree of misallocation.34 Financial disruption or low demand could lead firms to reduce
their investment in research and development and technology commercialization, leading toslower TFP growth (Queralto, 2015; Anzoategui et al., 2015; Bianchi et al., 2016). Both lower
capital investment and lower TFP growth contribute to lower labour productivity growth,
everything else being equal.
Reifschneider et al. (2015) develop an econometric model of potential output and findthat the financial crisis in 2007 and 2008 was followed by a substantial decrease in U.S. potential
output growth. The largest contributors to this decline were reductions in capital formation andin their estimates of trend TFP growth.35 In an accounting analysis, Hall (2014) measures thesources of the shortfall of U.S. output in 2013 relative to its 1990-2007 trend and finds that thelargest contributors were reduced capital accumulation and reduced TFP growth. Reifschneider
et al. speculate that the decline in TFP growth might be the endogenous result of demand-driven
declines in R&D activity. Hall is skeptical of this view, given that "similar slowdowns [of TFP
growth] over 6-year periods have been common."Blanchard et al. (2015) examine recoveries from 122 recessions in 23 countries. Theynote that, in many cases, output growth (not just the output level) remains below its pre-recession
trend for a long time after the recession. While they consider the possibility that these growth
slowdowns are caused by the recessions, they think it more likely that the reverse is true: therecessions were caused by the arrival of news that future growth would be slower than expected.
Along similar lines, Blanchard et al. (2013; 2017) argue that the weakness of U.S. aggregate
demand since the Great Recession is a reflection of Americans' downward revisions of
expectations for future growth, not a cause of them.
The cause and effect relationship between current aggregate demand and future
productivity growth is difficult to untangle. Estimated structural models such as those of
Anzoategui et al. (2015) and Bianchi et al. (2016) can do so, but their results are contingent upon
all the assumptions embedded in the models. Given that U.S. productivity growth remained highfollowing the 2001 recession and then declined a few years before the 2008 recession, it seems
likely that the post-2004 slowdown in trend productivity growth was not primarily a demanddriven phenomenon (Fernald, 2014b). Still, further research on the question is warranted.

34

Quantitative findings on the importance of financial market frictions for TFP are mixed. Some studies find that
financial frictions have a large effect on TFP through resource misallocation (e.g. Moll, 2014; Greenwood et al.,2012), while others find that the effects are small (e.g. Midrigan and Xu, 2012; Gilchrist et al., 2012). Further
research in this area is warranted.
35
They did find an increase in TFP growth in 2009 and a decline thereafter, broadly consistent with the numbers in
our Table 1.

27

VI. Conclusion
The post-2004 U.S. productivity slowdown resulted in a decline of some $3 trillion in
U.S. real output in 2015 relative to the counterfactual in which the slowdown had not occurred.
In this report, we aimed to provide an overview of the current state of knowledge on the causes
and descriptive characteristics of the slowdown. We summarized recent trends in U.S.
productivity measures and identified the proximate sources of the slowdown in a standard
growth accounting framework. We presented new findings on the industry sources of theslowdown based on the CSLS labour productivity growth decomposition. We reviewed theextensive literature aimed at explaining the slowdown.
Based on our analysis, the state of knowledge about the slowdown can be summarized in
the following four points:
1. The trend rate of U.S. productivity growth declined around 2004. The decline
occurred in both labour productivity growth and TFP growth, and in both the businesssector and the total economy.
2. Labour productivity growth in the private business sector slowed by 2.0 percentage
points per year after 2004. Of this decline, 60-65 per cent was accounted for by adecline in TFP growth and 30-35 per cent was accounted for by a decline in the rateof capital deepening. Changing growth of labour quality played a comparatively smallrole.
3. Three industries collectively accounted for 80.5 per cent of the labour productivity
slowdown: manufacturing (42.0 per cent), wholesale trade (21.7 per cent), and retailtrade (16.9 per cent). These industries’ contributions to the slowdown were driven bywithin-sector productivity slowdowns. Labour reallocation across industries
accounted for only 1.9 per cent of the labour productivity slowdown.
4. The slowdown is traceable to a decline in the productivity contributions arising fromindustries that produce or intensively use information and communication technology(ICT) products. The decline in the productivity contribution of ICT was driven bysome combination of a) slower technological progress in ICT and b) a reduction inbusiness dynamism in the ICT sector resulting in a decline in the rate of resource
reallocation from less-productive to more-productive firms.
What implications might the U.S. productivity growth slowdown have for Canada? Overthe 1987-2015 period, business sector labour productivity growth in Canada lagged that of theUnited States by 0.78 percentage points per year (Table 6). The gap has tended to be larger
during periods of fast growth in the United States. During the 1995-2004 period (the ICT-driven
productivity boom), U.S. labour productivity grew by 3.22 per cent per year. Canadian labour
productivity grew by 1.69 per cent per year over the same period; while this was above theCanadian average for the 1987-2015 period, it was 1.53 percentage points below the U.S. growth28

Table 6: Labour Productivity Growth in the United States and Canada, Business
Sector, 1987-2015
United States

Canada2.00

1.22

GrowthGap
0.78

1987-1995

1.55

1.12

0.43

1995-2004

3.22

1.69

1.53

2004-2015

1.33

0.91

0.42

2004-2008

1.33

0.79

0.54

2008-2010

3.25

0.88

2.38

2010-2015

0.57

1.02

-0.45

1987-2015

Sources: U.S. data are from the Bureau of Labor Statistics, Labor
Productivity and Costs. Canadian data are from Statistics Canada,
CANSIM Table 383-0008.

rate – an enormous gap.36 During the slow-growth periods of 1987-1995 and 2004-2015, the gap
was about 0.4 percentage points. The U.S. productivity slowdown has slowed the labour
productivity divergence between the United States and Canada relative to the 1995-2004 period.
Indeed, since 2010, the gap has turned negative. U.S. labour productivity growth over the
2010-2015 period has averaged 0.57 per cent per year, the lowest rate of any extended period
since 1987. In Canada, by contrast, labour productivity growth picked up after 2010 and has
averaged 1.02 per cent per year over the 2010-2015 period. Thus, Canadian labour productivity
growth has exceeded U.S. growth by 0.45 percentage points. In the near term, this bodes well forthe growth of Canadian living standards relative to those of the United States.
In the longer term, however, a slowdown in U.S. productivity growth portends slower
growth in Canada as well. The United States is at the global technological frontier in many
sectors, including the important information technology sector. To the extent that the U.S.
slowdown reflects a decline in the pace of technological change at the frontier, then it is almost
certain that productivity growth in the countries chasing the frontier – like Canada – will
eventually fall as well.
We conclude by suggesting some areas for future research. First, we provided industry
decomposition results for labour productivity. It would be informative to perform a similar
decomposition for TFP growth.37 Second, our decomposition based on two-digit industries could
be extended to a more detailed level of industry disaggregation. This would allow us to identify
the sub-industries that drove the large contributions of two-digit manufacturing, retail trade, andwholesale trade to the productivity slowdown.

36

The gap was even larger during the 2008-2010 period, at 2.38 percentage points. This probably reflects the larger
impact of the Great Recession on hours worked in the United States relative to Canada.
37
Calver and Murray (2016) show how to apply the CSLS decomposition method to TFP data. They present
decomposition results for Canadian TFP growth over the 1997-2014 period.

29

The industry-level results could be examined in greater detail in other ways. For example,
how much of the decline in manufacturing is attributable to a slowdown in the rate ofsemiconductor price decline versus increased offshoring of ICT production? This would be
useful to quantify.
Our growth accounting results indicated that U.S. labour quality growth spiked during theGreat Recession and has been slow since 2010. We speculate that this pattern reflected cyclicalchanges in the skill composition of the workforce, but it would be worthwhile to examine thisformally.
The emerging literature using firm-level data to link aggregate productivity growth toindustry-level trends in business dynamism suggests that declining labour reallocation acrossfirms might be a key driver of the productivity slowdown. This should be a major focus of future
research, since this driver of slower aggregate productivity growth might carry very different
policy implications than a decline in the pace of fundamental technical progress would.

30Appendix A: Industry Decomposition Results Based on the GEAD
The tables in this appendix report an industry decomposition based on the Generalized
Exactly Additive Decomposition (GEAD). The GEAD was developed by Tang and Wang
(2004). We implement the reformulation due to Diewert (2015).

Appendix Table 1: Industry Contributions to Labour Productivity Growth in the UnitedStates, Private Industries, 1987-2015, GEAD Approach
Percentage Point
Contributions

Per Cent of Private Industries
Total

LabourProductivity
Growth

TC

WSE

RPE

LRE

TC

WSE

RPE

LRE

1.76

1.76

1.96

0.00

-0.20

100.0

111.2

0.2

-11.4

3.82

0.00

0.05

-0.03

-0.02

0.1

2.9

-1.5

-1.3

2.32

0.04

0.04

-0.02

0.02

2.2

2.3

-0.9

0.9

Utilities

0.88

0.00

0.02

0.02

-0.04

-0.1

1.2

1.2

-2.5

Construction

-0.71

0.08

-0.04

0.13

-0.01

4.5

-2.5

7.5

-0.5

Manufacturing

3.65

0.05

0.67

-0.23

-0.39

3.1

38.0

-13.0

-21.9

Wholesale trade

3.47

0.14

0.25

-0.07

-0.04

7.8

14.5

-4.2

-2.5

Retail trade

2.64

0.09

0.22

-0.10

-0.03

5.1

12.6

-5.6

-2.0

1.22

0.06

0.04

0.00

0.01

3.2

2.5

0.3

0.4

4.26

0.12

0.25

-0.09

-0.04

6.7

14.3

-5.2

-2.4

0.25

0.04

0.01

0.04

0.00

2.5

0.5

2.2

-0.1

2.25

0.34

0.32

0.03

-0.02

19.0

18.4

1.8

-1.1

0.92

0.42

0.13

0.12

0.18

23.9

7.2

6.6

10.2

0.71

0.11

0.03

0.05

0.03

6.5

1.7

2.9

1.8

Private industries
Agriculture, forestry,
fishing, and hunting
MiningTransportation andwarehousing
Information
Other services,
except government
FIRE
Professional andbusiness services
AERAF

EHSA
-0.55
0.27
-0.04
0.14
0.18
15.6
-2.5
8.1
10.0
Notes:
1. TC = Total contribution; WSE = within-sector effect; RPE = relative output price growth effect;
LRE = labour input reallocation effect.
2. FIRE = Finance, insurance, real estate, rental and leasing; AERAF = Arts, entertainment, recreation,accommodation, and food services; EHSA = Educational services, health care and social assistance.
3. Labour productivity is real output (in chained 2009 dollars) per hour worked. Year-over-year growth isdecomposed into industry contributions each year. The results in the table are the arithmetic averages ofannual contributions over the 1987-2015 period.

31Appendix Table 2: Decomposition of U.S. Productivity Slowdown between 1995-2004 and 2004-2015 byGeneralized Exactly Additive Decomposition, Private Industries
Panel A: Productivity Growth Decomposition by Period
1995-2004
LabourProductivity
Growth

2004-2015

Percentage Point Contributions

Percentage Point Contributions

TC

WSE

RPE

LRE

LabourProductivity
Growth

2.94

2.94

3.19

0.00

-0.25

1.07

1.07

1.21

0.01

-0.14

Agriculture, forestry, fishing, hunting

6.20

0.04

0.08

-0.03

-0.02

2.58

0.00

0.03

-0.03

0.00

Mining

0.02

0.09

-0.01

0.12

-0.02

1.91

0.07

0.05

-0.08

0.09

Utilities

2.15

-0.02

0.04

0.01

-0.08

-0.78

0.02

-0.02

0.04

0.00

Construction

-0.75

0.29

-0.04

0.24

0.09

-1.21

-0.04

-0.08

0.11

-0.07

Manufacturing

6.42
6.58
4.46

0.07
0.18
0.20

1.17
0.49
0.38

-0.51
-0.22
-0.15

-0.58
-0.08
-0.03

2.09
0.97
0.69

0.03
0.11
0.00

0.33
0.07
0.05

-0.03
0.05
-0.01

-0.27
-0.01
-0.05

1.31

0.08

0.05

0.04

-0.01

0.50

0.05

0.02

0.03

0.00

-0.16

-0.12

Private industries

Wholesale trade
Retail trade
Transportation andwarehousing
Information

TC

WSE

RPE

LRE

5.16

0.23

0.30

-0.06

-0.01

5.24

0.04

0.31

Other services, except government

0.36

0.07

0.01

0.07

-0.01

-0.80

0.01

-0.02

0.04

-0.01

FIRE
AERAF

2.78
1.73
1.64

0.56
0.65
0.19

0.41
0.23
0.07

0.03
0.22
0.08

0.12
0.19
0.04

2.36
0.96
-0.42

0.21
0.30
0.07

0.34
0.14
-0.02

-0.06
0.00
0.05

-0.06
0.15
0.04

EHSA

0.00

0.32

0.00

0.16

0.16

0.27

0.21

0.03

0.04

0.14

Professional and business services

Panel B: Industry Contributions to Productivity Slowdown between 1995-2004 and 2004-2015
Change inLabourProductivity
GrowthPercentage Point Contributions

Per Cent of Total Change

TC

WSE

RPE

LRE

TC

WSE

RPE

LRE

Construction

-1.87
-3.62
1.89
-2.93
-0.45

-1.87
-0.03
-0.03
0.04
-0.33

-1.98
-0.05
0.06
-0.06
-0.04

0.00
0.00
-0.20
0.03
-0.13

0.10
0.02
0.11
0.07
-0.16

100.0
1.7
1.4
-2.2
17.5

105.8
2.5
-3.2
3.2
2.1

-0.3
0.0
10.5
-1.5
6.8

-5.6
-0.9
-5.9
-4.0
8.6

Manufacturing

-4.33

-0.04

-0.84

0.48

0.31

2.2

44.7

-25.9

-16.6

Wholesale trade

-5.61

-0.07

-0.42

0.27

0.07

3.7

22.2

-14.7

-3.8

Retail trade

-3.77

-0.20

-0.33

0.14

-0.02

10.9

17.5

-7.7

1.0

Transportation and warehousing

-0.81

-0.03

-0.03

-0.02

0.01

1.8

1.6

0.8

-0.6

Information

AERAF

0.09
-1.16
-0.43
-0.77
-2.06

-0.19
-0.06
-0.34
-0.35
-0.13

0.01
-0.03
-0.07
-0.09
-0.10

-0.10
-0.02
-0.09
-0.22
-0.03

-0.11
0.00
-0.18
-0.04
0.00

10.3
3.3
18.2
18.5
6.7

-0.7
1.8
3.6
4.9
5.1

5.2
1.3
5.0
11.6
1.8

5.8
0.2
9.6
2.0
-0.2

EHSA

0.27

-0.11

0.02

-0.12

-0.02

6.0

-1.3

6.5

0.8

Private industries
Agriculture, forestry, fishing, hunting
MiningUtilities

Other services, except government
FIRE
Professional and business services

Notes:
TC = Total contribution; WSE = within-sector effect; RPE = relative output price growth effect; LRE = labour input reallocation effect. FIRE = Finance,
insurance, real estate, rental and leasing; AERAF = Arts, entertainment, recreation, accommodation, and food services; EHSA = Educational services, healthcare and social assistance. Labour productivity is real output (in chained 2009 dollars) per hour worked. Year-over-year growth is decomposed into industry
contributions each year. The results in the table are the arithmetic averages of annual contributions over the period indicated.

32

Appendix B: Structural Breaks in Trend Productivity Growth
The U.S. economy has switched between periods of high and low trend growth inproductivity over the postwar period, and there is broad agreement in the literature about thetiming of these switches. After a period of high growth in the 1950s and 1960s, trend
productivity growth declined around 1973 and remained low until the mid-1990s (Fernald,2007). Trend growth was high from the mid-1990s to the mid-2000s, then declined again and has
remained low until the present. This pattern can be discerned in Chart 1 and Chart 2. In principle,
such a pattern could reflect either true structural breaks in trend growth or, alternatively, lucky or
unlucky sequences of random productivity shocks with no underlying structural change. Here,
we review research that uses formal statistical tests to provide evidence that the growth
accelerations and slowdowns depicted in the charts reflect true structural changes.
Our primary interest is in the timing and sources of the most recent trend break in the
mid-2000s, but we also focus on the mid-1990s trend break. (As we will discuss later, a leading
hypothesis is that both trend breaks were caused by changes arising from the production and useof information technology.) It is important to correctly identify the trend break points because aninappropriate choice can lead to misleading findings on changes in trend productivity growth
between time periods.38
The consensus in the literature is that the trend growth rate of U.S. productivity increased
at the end of 1995. Many studies take as given a trend break in 1995 based on visual inspectionof the data or references to other research. 39 Appendix Table 3 summarizes the methods andfindings of several studies that contain formal statistical tests for structural breaks in trend
productivity growth. The results vary depending on the method used, as well as the time period
examined and the coverage and source of the data. 40
Most of the studies in Appendix Table 3 find some evidence for an increase in trend
productivity growth in the mid to late 1990s. When the structural break tests of Hansen (1997) or
Bai and Perron (1998) are applied to labour productivity series with broad coverage, the break is
usually dated in 1995. Tests that use data on the U.S. business sector typically find a break in the
mid-1990s. Tests that use data on the nonfarm business sector often fail to detect a break unless38

For example, Almon and Tang (2011) found that U.S. productivity growth was higher in the post-2000 period thanin the pre-2000 period. They failed to detect a slowdown in U.S. productivity growth in part because of their choiceof 2000 as the turning point; both of their time periods included observations from high and low-growth regimes.
We do not mean to suggest that Almon and Tang made a mistake. The subject of their analysis was Canada; theUnited States was a secondary subject of analysis used mainly for comparison with Canada. The year 2000 may be
an appropriate break point for Canadian productivity trends. Even for the United States, 2000 may be an appropriate
cut-off if the goal is to compare productivity growth across business cycles as in Sprague (2017).
39
Examples include Basu and Fernald (2006), Cette et al. (2016), Gordon (2000a; 2000b), Jorgenson (2001),
Jorgenson et al.(2006), Oliner and Sichel (2000), Oliner et al. (2007), and Whelan (2002).
40
Coverage refers to the part of the economy captured by the data (e.g. total economy vs. business sector vs.
nonfarm business sector vs. manufacturing sector). Sources of data on U.S. productivity include the Bureau of Labor
Statistics (BLS), the Bureau of Economic Analysis (BEA), the Total Economy Database maintained by theGroningen Growth and Development Center (GGDC), and the Fernald (2014b) database at the San Francisco Fed.
Differences between studies along any of these dimensions may lead to differences in results.

33

Appendix Table 3: Studies Containing Statistical Tests for Trend Breaks in U.S. Productivity Growth in the 1990s and 2000s
Study

Description

Productivity Concept

1990s
Speed-up

2000s
Slowdown

Benati (2007)

Period: 1947:Q1 to 2005:Q4. Coverage: business sector andvarious major subsectors. Uses Bai and Perron (1998) test for
multiple structural breaks.

Output per hour
● Business sector● Nonfarm business sector

1995:Q4
No break---Output per Worker● Business sector● Nonfarm business sector
Output per hour

No breakNo break
1998:Q3--

Erber and Fritsche(2005)

Period: 1947Q1 to 2003Q4. Coverage: nonfarm businesssector. Remove cyclical component of productivity byregressing it on a distributed lag of an output gap measure,then apply the Bai and Perron (1998) structural break test to
the residuals.

Gomez-Salvador etal. (2006)
Hansen (2001)

Period: 1950-2005. Coverage: total economy. Use Bai andPerron (1998) test for multiple structural breaks.
Period: 1947 to 2001. Monthly industrial production andquarterly real output series are used. Coverage:
Manufacturing/durables sector. Apply a series of structural
break tests to labour productivity measures. Main results use
Bai and Perron (1998) test.

Output per hour

1995

--

Output per worker● Monthly● Quarterly

April 1994
1997

---

Fernald (2014a)

Period: 1973:Q1 to 2013:Q4. Coverage: business sector. Uses
Bai and Perron (1998) test for multiple structural breaks.

Output per hour
● Expenditure-side measure● Income-side measure

1997:Q2
1995:Q3

2003:Q4
2006:Q1

Jimeno et al. (2006)

Period: 1974:Q1 to 2004:Q4. Coverage: nonfarm businesssector. Perform several tests for trend growth changes using
univariate and multivariate methods, Markov switching
methods, and VECM-based methods to account for
cointegration between productivity and other variables (Bai etal., 1998).

-1996:Q4 to
1997:Q4,
dependingon test2005:Q1--

34

Utilization-adjusted TFP
Output per hourAppendix Table 3 (Continued)
Study

Description

Productivity Concept
Output per hour

1990s
Speed-up
1997

2000s
Slowdown--

Kahn and Rich
(2007)

Period: 1947:Q1 to 2002:Q4. Coverage: nonfarm businesssector. Estimate a regime-switching model in which
productivity switches between high- and low-growth regimes.
Also allow for business cycle effects.

Kahn and Rich
(2011)

Period: 1947:Q1 to 2011:Q2. Coverage: nonfarm businesssector. Estimate a regime-switching model in which
productivity switches between high- and low-growth regimes.
Also allow for business cycle effects.

Output per hour

1997

2004

Maury and Pluyard(2004)

Period: 1948:Q1 to 2002:Q4. Coverage: unclear. Fourversions of the Bai and Perron (1998) test are conducted; allpoint to the same 1990s trend break in the U.S.

Output per hour

1995:Q3--

Stiroh (2002)

Period: 1974:Q1 to 2001:Q3. Uses Hansen (1997) test to
identify structural break.

Output per worker● Business sector● Nonfarm business sector● Manufacturing sector

1995:Q3
1995:Q3
1993:Q3----

Turner and Boulhol(2010)

Period and coverage: 1970:Q1 to 2007:Q4 for nonfarm
business sector; 1987:Q2 to 2007:Q4 for manufacturingsector. Use Bai and Perron (1998) test for multiple structural
breaks.

Output per hour
● Nonfarm business sector● Manufacturing sector

No break
1993:Q3---35

more elaborate statistical methods are used either to strip business cycle effects from the data or
to allow for changes in trend productivity growth to occur gradually rather than suddenly. (Thatdata for the business sector and the nonfarm business sector would tend to yield different results
is somewhat surprising, since the two sectors are not very different in their coverage.) Studies
that focus on the manufacturing sector tend to find a trend break in 1993, somewhat earlier thanthe break in the broader economy (Hansen, 2001; Stiroh, 2002; Turner and Boulhol, 2010).
Different methods yield break-point estimates somewhat earlier or later than 1995, but
none differs dramatically from the consensus. Kahn and Rich (2007; 2011) estimate an
econometric model that allows both trend productivity growth and the state of the business cycle
to switch between high and low regimes. The co-movement of labour productivity with twoother variables − consumption and labour compensation − is used to distinguish between regime
shifts in productivity growth and transitory business cycle effects. The estimated probability that
the U.S. economy was in the high-growth regime increased from near zero in 1996:Q1 to near
one in 1997:Q4, which suggests that trend productivity growth increased sometime in 1997.41
Erber and Fritsche (2005) regress productivity growth on lags of an output gap measure to
remove cyclical variation, then apply trend break tests to the remaining long-run component.42
This yields an estimated break point in 1998. These findings suggest that accounting for theeffects of business cycle fluctuations on productivity growth results in slightly later estimatedbreak points in the trend growth rate of productivity.
Appendix Table 3 also includes studies that estimate the date of the mid-2000s decline intrend productivity growth. There are fewer such studies, perhaps because there would not havebeen enough observations in the relevant time series to detect the break until recently. 43 The
consensus in the literature, based on informal inspection of the data, is that trend productivity
growth declined at the end of 2004. This consensus is supported by the findings of Fernald(2014a) using conventional structural break tests and by those of Kahn and Rich (2011) in an
update of their regime-switching model.44
In addition to the results summarized in Appendix Table 3, there is research that
addresses time variation in trend productivity growth using formal statistical methods but
without testing for discrete structural breaks or regime changes. Time-varying parameter (TVP)
models comprise a class of econometric models in which model parameters (such as the mean
41

The model attributes the strong productivity growth of 1996 to a business cycle effect. It is not uncommon for
productivity growth to be high during or immediately following an economic slowdown; recall the discussion in
footnote 9 above.
42
This approach is due to Gordon (2003), who uses it to study U.S. potential output.
43
Revision of the U.S. national accounts data by the BEA is another important factor affecting the detection of trend
breaks in productivity growth. Data revisions in August 2011 resulted in a downward revision of measured
productivity growth over the 2008-2010 period (though it remains the case that productivity growth was strong
during that time, as shown in Chart 1 and Chart 2). Structural break tests may not have been able to detect a decline
in trend productivity growth using the old vintage data due to measurement error that was corrected in the August
2011 revision. The model of Kahn and Rich (2011) detects a very short-lived growth decline in the mid-2000s using
the old data; using the revised data, it says that the United States has been in a low-growth regime since 2004.
44
Note that Fernald (2014b) is the only one of the studies in Table 2 to examine total factor productivity (TFP). All
the others use labour productivity. Fernald finds that trend TFP growth declined in 2005:Q1. Unfortunately, he does
not say whether there was a break in TFP growth in the mid-1990s.

36

growth rate of productivity) are themselves allowed to drift over time. 45 Using a TVP method
due to Stock and Watson (1996; 1998), Benati (2007) finds that the trend growth rate of labour
productivity increased throughout the 1990s, slowly at first and then quickly after 1995. It
reached a plateau at the end of 2003 and began to decline thereafter. These results hold for both
output per hour and output per worker, and in both the business sector and the nonfarm businesssector. Roberts (2001) estimates a multivariate model using similar methods and finds that thetrend growth rate of output per hour began to increase around 1992. 46
In summary, the literature supports the consensus view that trend productivity growth in
the U.S. increased at the end of 1995, remained high until the end of 2004, then declined and has
remained low ever since. Some evidence suggests that trend growth did not increase until around1997, with the strong growth between 1995 and 1997 being attributable to business cycle effects.
But other evidence suggests that growth had already begun to pick up in the early 1990s, so 1995
remains a reasonable compromise date based on the weight of all the evidence. There are few
formal tests of the timing of the mid-2000s slowdown, but those that exist point to 2004 as thedate. It is reassuring that these dates are consistent with the dates many economists have chosen
based on informal inspection of the data.

45

An advantage of this approach over conventional structural break tests is that those tests have low power when thetrue data generating process features slow and continuous parameter drift. Drifting parameters are usually modeled
as random walks. See Cogley and Sargent (2005) and Benati (2007).
46
Another similar study is Cogley (2005), although he studies output growth rather than productivity growth.
Cogley estimates a TVP vector autoregression using data on U.S. GDP and consumption for the period 1948:Q1 to
2002:Q4. Consumption data help to disentangle permanent and transitory movements in output growth (Cochrane,1994). He finds that trend output growth increased beginning around 1992 and reached a plateau around 2000.

37

References
Aeppel, Timothy (2015) "Silicon Valley Doesn't Believe U.S. Productivity is Down," Wall Street
Journal, July 16.
https://www.wsj.com/articles/silicon-valley-doesnt-believe-u-s-productivity-is-down1437100700.
Ahmad, Nadim, and Paul Schreyer (2016) "Are GDP and Productivity Measures Up to the
Challenges of the Digital Economy?" International Productivity Monitor, Number 30, Spring,pp. 4-27.
Almon, Michael-John, and Jianmin Tang (2011) "Industrial Structural Change and the Post-2000
Output and Productivity Slowdown: A Canada-U.S. Comparison," International Productivity
Monitor, No. 22, pp. 44-81.
Alon, Titan M., David Berger, Rob Dent and Benjamin Pugsley (2017) “Older and Slower: The
Startup Deficit’s Lasting Effects on Aggregate Productivity Growth,” working paper.
Andrews, Dan, Chiara Criscuolo and Peter N. Gal (2015) "Frontier Firms, Technology Diffusion
and Public Policy: Micro Evidence from OECD Countries," The Future of Productivity: Main
Background Papers, OECD.
https://www.oecd.org/eco/growth/Frontier-Firms-Technology-Diffusion-and-Public-PolicyMicro-Evidence-from-OECD-Countries.pdf.
Anzoategui, Diego, Diego Comin, Mark Gertler and Joseba Martinez (2015) "Endogenous
Technology Adoption and R&D as Sources of Business Cycle Dynamics," working paper, NewYork University, August.
http://www.econ.nyu.edu/user/gertlerm/ACGM_August2015_last.pdf.
Bai, Jushan, and Pierre Perron (1998) "Estimating and Testing Linear Models with Multiple
Structural Changes," Econometrica, Vol. 66, No. 1, pp. 47-78.
Bai, Jushan, Robin L. Lumsdaine and James H. Stock (1998) "Testing For and Dating Common
Breaks in Multivariate Time Series," Review of Economic Studies, Vol. 65, pp. 395-432.
Baily, Martin N., and Nicholas Montalbano (2016) "Why is US Productivity Growth So Slow?
Possible Explanations and Policy Responses," Brookings Institution, Hutchins Center Working
Paper No. 22, September.
Baldwin, John R., and Michael Willox (2016) "The Industry Origins of Canada's Weaker Labour
Productivity Performance and the Role of Structural Adjustment in the Post-2000 Period,"International Productivity Monitor, Number 31, Fall, pp. 19-36.

38

Basu, Susanto, and John G. Fernald (2006) "Information and Communications Technology as a
General-Purpose Technology: Evidence from U.S. Industries," Federal Reserve Bank of San
Francisco Working Paper 2006-29, December.
Basu, Susanto, John G. Fernald and Miles Kimball (2006) "Are Technology Improvements
Contractionary?" American Economic Review, Vol. 96, No. 5, pp. 1418-1448.
Basu, Susanto, John G. Fernald, Jonas Fisher and Miles Kimball (2013) "Sector-SpecificTechnical Change," working paper, Federal Reserve Bank of San Francisco, November.
http://www.frbsf.org/economic-research/files/bffk-sector-specific.pdf.
Baumol, William J. (1967) "Macroeconomics of Unbalanced Growth: The Anatomy of an Urban
Crisis," American Economic Review, Vol. 57, No. 3, pp. 415-426.
Baumol, William J. (2012) The Cost Disease: Why Computers Get Cheaper and Health Care
Doesn't, New Haven: Yale University Press.
Baumol, William J., Sue Anne Batey Blackman and Edward N. Wolff (1985) "Unbalanced
Growth Revisited: Asymptotic Stagnancy and New Evidence," American Economic Review, Vol.
75, No. 4, pp. 806-817.
Benati, Luca (2007) "Drift and Breaks in Labor Productivity," European Central Bank Working
Paper No. 718, January. https://www.ecb.europa.eu/pub/pdf/scpwps/ecbwp718.pdf
Bianchi, Francesco, Howard Kung and Gonzalo Morales (2016) "Growth, Slowdowns, andRecoveries," working paper, Duke University, August.
Blanchard, Olivier, Eugenio Cerutti and Lawrence Summers (2015) "Inflation and Activity: Two
Explorations and their Monetary Policy Implications," IMF Working Paper No.WP/15/230,
November.
https://www.imf.org/external/pubs/ft/wp/2015/wp15230.pdf.
Blanchard, Olivier, Guido Lorenzoni and Jean Paul L'Huillier (2013) "News, Noise, andFluctuations: An Empirical Exploration," American Economic Review, Vol. 103, No. 7, pp.
3045-3070.
Blanchard, Olivier, Guido Lorenzoni and Jean Paul L'Huillier (2017) "Short-Run Effects ofLower Productivity Growth: A Twist on the Secular Stagnation Hypothesis," Peterson Institutefor International Economics Policy Brief PB 17-6, February.
Bosler, Canyon, Mary C. Daly, John G. Fernald and Bart Hobijn (2016) "The Outlook for U.S.
Labor-Quality Growth," Federal Reserve Bank of San Francisco Working Paper No. 2016-14,
July.
http://www.frbsf.org/economic-research/files/wp2016-14.pdf.

39

Brynjolfsson, Erik, and Andrew McAfee (2011) Race Against the Machine: How the Digital
Revolution is Accelerating Innovation, Driving Productivity and Irreversibly Transforming
Employment and the Economy, Lexington: Digital Frontier Press.
Brynjolfsson, Erik, and Andrew McAfee (2014) The Second Machine Age: Work, Progress andProsperity in a Time of Brilliant Technologies, New York: W.W. Norton and Company.
Bureau of Economic Analysis (2006) "Frequently Asked Questions: How are the International
Transactions Accounts affected by an increase in direct investment dividend receipts, such as
those that may arise from the American Jobs Creation Act of 2004?" bea.gov, January 19.
https://www.bea.gov/faq/index.cfm?faq_id=166.
Byrne, David, and Carol Corrado (2015) "Recent Trends in Communications Equipment Prices,"
FEDS Notes, Washington: Board of Governors of the Federal Reserve System.
Byrne, David, and Carol Corrado (2016) "ICT Prices and ICT Services: What Do they Tell Us
about Productivity and Technology?" The Conference Board, Economics Program Working
Paper No. 16-05, July.
https://www.conference-board.org/pdf_free/workingpapers/EPWP1605.pdf.
Byrne, David, and Eugenio Pinto (2015) "The Recent Slowdown in High-Tech Equipment Price
Declines and Some Implications for Business Investment and Labor Productivity," FEDS Notes,
Washington: Board of Governors of the Federal Reserve System.
Byrne, David, Stephen D. Oliner and Daniel E. Sichel (2015) "How Fast are Semiconductor
Prices Falling?" NBER Working Paper No. 21074, April.
http://www.nber.org/papers/w21074.
Byrne, David M., John G. Fernald and Marshall B. Reinsdorf (2016) "Does the United States
Have a Productivity Slowdown or a Measurement Problem?" Brookings Papers on Economic
Activity Spring 2016, pp. 109-182.
Calver, Matthew, and Alexander Murray (2016) "Decomposing Multifactor Productivity Growthin Canada by Industry and Province, 1997-2014," International Productivity Monitor, Number
31, Fall, pp. 88-112.
Cardarelli, Roberto, and LusineLusinyan (2015) "U.S. Total Factor Productivity Slowdown:
Evidence from the U.S. States," IMF Working Paper WP/15/116, May.
https://www.imf.org/external/pubs/ft/wp/2015/wp15116.pdf.
Cette, Gilbert, John G. Fernald, and Benoit Mojon (2016) “The Pre-Great Recession Slowdown
in Productivity,” European Economic Review, Vol. 88, pp. 3-20.
Cochrane, John H. (1994) "Permanent and Transitory Components of GNP and Stock Prices,"
Quarterly Journal of Economics, Vol. 109, pp. 241-265.

40

Cogley, Timothy (2005) "How Fast can the New Economy Grow? A Bayesian Analysis of the
Evolution of Trend Growth," Journal of Macroeconomics, Vol. 27, pp. 179-207.
Cogley, Timothy, and Thomas J. Sargent (2005) "Drifts and Volatilities: Monetary Policies andOutcomes in the Post WWII US," Review of Economic Dynamics, Vol. 8, pp. 262-302.
Comin, Diego, and Marti Mestieri (2016) “If Technology Has Arrived Everywhere, Why Has
Income Diverged?” working paper, Dartmouth College.
http://www.dartmouth.edu/~dcomin/files/CM_rev.pdf.
Cowen, Tyler (2011) The Great Stagnation: How America Ate All the Low-Hanging Fruit ofModern History, Got Sick, and Will (Eventually) Feel Better, New York: Dutton.
Davis, Steven J., John Haltiwanger, Ron Jarmin and Javier Miranda (2007) “Volatility and
Dispersion in Business Growth Rates: Publicly Traded versus Privately Held Firms,” NBER
Macroeconomics Annual Vol. 21, pp. 107-180.
Davis, Steven J., and John Haltiwanger (2014) “Labor Market Fluidity and Economic
Performance,” NBER Working Paper No. 20479, September.
de Avillez, Ricardo (2012) "Sectoral Contributions to Labour Productivity Growth in Canada:
Does the Choice of Decomposition Formula Matter?" International Productivity Monitor,
Number 24, Fall, pp. 97-117.
Decker, Ryan A., John Haltiwanger, Ron S. Jarmin and Javier Miranda (2014) “The Role ofEntrepreneurship in US Job Creation and Economic Dynamism,” Journal of Economic
Perspectives, Vol. 28. No. 3, pp. 3-24.
Decker, Ryan A., John Haltiwanger, Ron S. Jarmin and Javier Miranda (2016) “Where Has All
the Skewness Gone? The Decline in High-Growth (Young) Firms in the U.S.,” European
Economic Review, Vol. 86, pp. 4-23.
Decker, Ryan A., John Haltiwanger, Ron S. Jarmin and Javier Miranda (2017a) “Changing
Business Dynamism and Productivity: Shocks vs. Responsiveness,” working paper, Universityof Maryland, February.
Decker, Ryan A., John Haltiwanger, Ron S. Jarmin and Javier Miranda (2017b) “Declining
Dynamism, Allocative Efficiency, and the Productivity Slowdown,” working paper, Universityof Maryland, February.
Denison, Edward F. (1967) Why Growth Rates Differ: Postwar Experience in Nine WesternCountries, Washington, D.C.: The Brookings Institution.
Diewert, W. Erwin (2015) "Decompositions of Productivity Growth into Sectoral Effects,"
Journal of Productivity Analysis, Vol. 43, No. 3, pp. 367-387.

41

Diewert, W. Erwin (2016) "Decompositions of Productivity Growth into Sectoral Effects: Some
Puzzles Explained," in Greene, W.H., L. Khalaf, R.C. Sickles, M. Veall and M.-C. Voia (eds.)
Productivity and Efficiency Analysis, Springer International Publishing.
Erber, Georg, and Ulrich Fritsche (2005) "Estimating and Forecasting Aggregate Productivity
Growth Trends in the U.S. and Germany," German Institute for Economic Research Discussion
Paper No. 471, January.
http://www.diw.de/documents/publikationen/73/diw_01.c.42903.de/dp471.pdf
Feldstein, Martin (2015) "The U.S. Underestimates Growth," Wall Street Journal, May 18.
https://www.wsj.com/articles/the-u-s-underestimates-growth-1431989720.
Fernald, John G. (2007) "Trend Breaks, Long-run Restrictions, and Contractionary Technology
Shocks," Journal of Monetary Economics, Vol. 54, No. 8, pp. 2467-2485.
Fernald, John G. (2014a) “Productivity and Potential Output Before, During, and After the GreatRecession,” NBER Macroeconomics Annual, Vol. 29, No. 1, pp. 1-51.
Fernald, John G. (2014b) "A Quarterly, Utilization-Adjusted Series on Total Factor
Productivity," Federal Reserve Bank of San Francisco Working Paper 2012-19.
Fernald, John G., and J. Christina Wang (2016) "Why has the Cyclicality of Productivity
Changed? What Does it Mean?" Federal Reserve Bank of San Francisco Working Paper 201607, April.
http://www.frbsf.org/economic-research/files/wp2016-07.pdf.
Fernald, John G., Robert E. Hall, James H. Stock, and Mark W. Watson (2017) "TheDisappointing Recovery of Output After 2009," Brookings Papers on Economic Activity, BPEA
Conference Drafts, March 23-24.
https://www.brookings.edu/wp-content/uploads/2017/03/1_fernaldetal.pdf.
Gilchrist, Simon, Jae W. Sim and Egon Zakrajsek (2012) "Misallocation and Financial Market
Frictions: Some Direct Evidence from the Dispersion in Borrowing Costs," working paper,
Boston University, October.
http://people.bu.edu/sgilchri/research/RED_GSZ_Final_Oct_2012.pdf.
Gomez-Salvador, Ramon, Alberto Musso, Marc Stocker, and JarkkoTurunen (2006) "Labour
Productivity Developments in the Euro Area," European Central Bank Occasional Paper Series
No. 53, October. https://www.ecb.europa.eu/pub/pdf/scpops/ecbocp53.pdf
Gordon, Robert J. (2000a) "Does the 'New Economy' Measure Up to the Great Inventions of the
Past?" Journal of Economic Perspectives, Vol. 14, No. 4, pp. 49-74.
Gordon, Robert J. (2000b) "Has the New Economy Rendered the Productivity Slowdown
Obsolete?" Working paper, Northwestern University, September.

42

Gordon, Robert J. (2003) "Exploding Productivity Growth: Context, Causes, and Implications,"
Brookings Papers on Economic Activity 2003, No. 2, pp. 207-298.
Gordon, Robert J. (2016a) The Rise and Fall of American Growth: the U.S. Standard of Living
Since the Civil War, Princeton: Princeton University Press.
Gordon, Robert J. (2016b) “Comments on Daniel Sichel’s Review Article on The Rise and Fallof American Growth,” International Productivity Monitor, Number 31, Fall, pp. 63-67.
Greenwood, Jeremy, Juan M. Sanchez and Cheng Wang (2012) "Quantifying the Impact ofFinancial Development on Economic Development," Review of Economic Dynamics, Vol. 16,
No. 1, pp. 194-215.
Guvenen, Fatih, Raymond J. Mataloni, Jr., Dylan G. Rassier, and Kim J. Ruhl (2017) "Offshore
Profit Shifting and Domestic Productivity Measurement," NBER Working Paper No. 23,324,
April.
https://www.nber.org/papers/w23324.
Hall, Robert E. (2014) "Quantifying the Lasting Harms to the US Economy from the Financial
Crisis," NBER Macroeconomics Annual, Vol. 29, No. 1, pp. 71-128.
Hansen, Bruce E. (1997) "Approximate Asymptotic P Values for Structural Change Tests,"
Journal of Business and Economic Statistics, Vol. 15, No. 1, pp. 60-67.
Hyatt, Henry R., and James Spletzer (2013) “The Recent Decline in Employment Dynamics,”
IZA Journal of Labor Economics, Vol. 2, No. 3, pp. 1-21.
Ito, Aki (2013) "The Free Web has Economists Puzzled," Bloomberg.com, November 21.
https://www.bloomberg.com/news/articles/2013-11-21/economists-gdp-calculations-may-missimpact-of-free-internet-services.
Jimeno, Juan F., Esther Moral and Lorena Saiz (2006) "Structural Breaks in Labor Productivity
Growth: The United States vs. the European Union," Banco de EspañaDocumentos de Trabajo
No. 0625.
http://www.bde.es/f/webbde/SES/Secciones/Publicaciones/PublicacionesSeriadas/DocumentosTr
abajo/06/Fic/dt0625e.pdf
Jorgenson, Dale W. (2001) "Information Technology and the U.S. Economy," American
Economic Review, Vol. 91, No. 1, pp. 1-32.
Jorgenson, Dale W., and Kevin J. Stiroh (2000) "Raising the Speed Limit: U.S. Economic
Growth in the Information Age," Brookings Papers on Economic Activity 1:2000, pp. 125-235.
Jorgenson, Dale W., Mun S. Ho and Kevin J. Stiroh (2006) "The Sources of the Second Surge of
U.S. Productivity and Implications for the Future," Working Paper, Harvard University, March.
http://www.bankofcanada.ca/wp-content/uploads/2010/09/jorgenson.pdf

43Kahn, James, and Robert Rich (2007) “Tracking the New Economy: Using Growth Theory to
Detect Changes in Trend Productivity,” Journal of Monetary Economics, Vol. 54, no. 6, oo.
1670-1701.
Kahn, James, and Robert Rich (2011) “The Productivity Slowdown Reaffirmed,” Liberty Street
Economics, Federal Reserve Bank of New York, September.
http://libertystreeteconomics.newyorkfed.org/2011/09/the-productivity-slowdownreaffirmed.html.
Maury, Tristan-Pierre, and Bertrand Pluyaud (2004) "The Breaks in Per Capita Productivity
Trends in a Number of Industrial Countries," Banque de France Notes d'Études et de Recherche
No. 111, April. https://www.banque-france.fr/uploads/tx_bdfdocumentstravail/ner111_02.pdf
Midrigan, Virgiliu, and Daniel Yi Xu (2014) "Finance and Misallocation: Evidene from plantLevel Data," American Economic Review, Vol. 104, No. 2, pp. 422-458.
Mokyr, Joel (2014) "Secular Stagnation? Not in Your Life," in Coen Teulings and Richard
Baldwin, eds., Secular Stagnation: Facts, Causes and Cures, London: CEPR Press.
Moll, Bejamin (2014) "Productivity Losses from Financial Frictions: Can Self-Financing Undo
Capital Misallocation?" American Economic Review, Vol. 104, No. 10, pp. 3186-3221.
Molloy, Raven, Christopher L. Smith, Ricardo Trezzi and Abigail Wozniak (2016)“Understanding Declining Fluidity in the U.S. Labor Market,” Brookings Papers on Economic
Activity Spring 2016, pp. 183-259.
Murray, Alexander (2016) "Partial versus Total Factor Productivity Measures: An Assessment of
their Strengths and Weaknesses," International Productivity Monitor, Number 31, Fall, pp. 113126.
Nordhaus, William D. (2001) "Alternative Methods for Measuring Productivity Growth," NBER
Working Paper No. 8095, January.
Oliner, Stephen D., and Daniel E. Sichel (2000) "The Resurgence of Growth in the Late 1990s:
Is Information Technology the Story?" Journal of Economic Perspectives, Vol. 14, No. 4, pp. 322.
Oliner, Stephen D., Daniel E. Sichel and Kevin J. Stiroh (2007) "Explaining a Productive
Decade," Finance and Economics Discussion Series Paper 2007-63, Federal Reserve Board.
https://www.federalreserve.gov/pubs/feds/2007/200763/200763pap.pdf
Queralto, Albert (2015) "A Model of Slow Recoveries from Financial Crises," working paper,
Division of International Finance, Federal Reserve Board, May.

44

Reifschneider, Dave, William Wascher and David Wilcox (2015) "Aggregate Supply in the
United States: Recent Developments and Implications for the Conduct of Monetary Policy," IMF
Economic Review, Vol. 63, No. 1, pp. 71-109.
Reinsdorf, Marshall (2015) "Measuring Industry Contributions to Labour Productivity Change:
A New Formula in a Chained Fisher Index Framework," International Productivity Monitor,
Number 28, Spring, pp. 3-26.
Reinsdorf, Marshall, and Robert Yuskavage (2010) "Exact Industry Contributions to Labour
Productivity Change,‖ in W.E. Diewert, B.M. Balk, D. Fixler, K.J. Fox and A.O. Nakamura(eds.) Price and Productivity Measurement: Volume 6 - Index Number Theory, Victoria, B.C.:
Trafford Press.
Roberts, John M. (2001) "Estimates of the Productivity Trend Using Time-Varying Parameter
Techniques," Contributions to Macroeconomics, Vol. 1, Article 3.
Sharpe, Andrew (2010) "Can Sectoral Reallocations of Labour Explain Canada's Abysmal
Productivity Performance?" International Productivity Monitor, Number 19, Spring, pp. 40-49.
Sharpe, Andrew, and Eric Thomson (2010) "Insights into Canada's Abysmal Post-2000
Productivity Performance from Decompositions of Labour Productivity Growth by Industry andProvince," International Productivity Monitor, Number 20, Fall, pp. 48-67.
Sichel, Daniel (2016) “Two Books for the Price of One: Review Article of The Rise and Fall of
American Growth by Robert J. Gordon,” International Productivity Monitor, Number 31, Fall,
pp. 57-62.
Smith, Noah (2015) "The Internet's Hidden Wealth," Bloomberg.com, June 10.
Solow, Robert (1987) "We'd Better Watch Out," New York Review of Books, July 12.
Sprague, Shawn (2017) "Below Trend: The U.S. Productivity Slowdown Since the GreatRecession," Beyond the Numbers: Productivity, Vol. 6, No. 2, U.S. Bureau of Labor Statistics,
January.
Stiroh, Kevin J. (2002) "Information Technology and the U.S. Productivity Revival: What do theIndustry Data Say?" American Economic Review, Vol. 92, No. 5, pp. 1559-1576.
Stock, James, and Mark Watson (1996) "Evidence of Structural Instability in Macroeconomic
Time Series Relations," Journal of Business and Economic Statistics, Vol. 14, No. 1, pp. 11-30.
Stock, James, and Mark Watson (1998) "Median-Unbiased Estimation of Coefficient Variance ina Time-Varying Parameter Model," Journal of the American Statistical Association, Vol. 93, No.
441, pp. 349-358.
Syverson, Chad (2004) “Product Substitutability and Productivity Dispersion,” Review of
Economics and Statistics, Vol. 86, No. 2, pp. 534-550.
45

Syverson, Chad (2013) “Will History Repeat Itself? Comments on ‘Is the Information
Technology Revolution Over?’” International Productivity Monitor, Number 25, Spring, pp. 3740.
Syverson, Chad (2011) “What Determines Productivity?” Journal of Economic Literature, Vol.
49, No. 2, pp. 326-365.
Syverson, Chad (2016) "Challenges to Mismeasurement Explanations for the U.S. Productivity
Slowdown," working paper, University of Chicago, June.
http://faculty.chicagobooth.edu/chad.syverson/research/productivityslowdown.pdf.
Tang, Jianmin, and Weimin Wang (2004) "Sources of Aggregate Labour Productivity Growth in
Canada and the United States," Canadian Journal of Economics, Vol. 37, No. 2, pp. 421-444.
Turner, Laure, and HervéBoulhol (2010) "Recent Trends and Structural Breaks in U.S. andEU15 Labour Productivity Growth," Paris School of Economics, G-MonD Working Paper No.
17, November.
Uguccioni, James, and Andrew Sharpe (2016) “Decomposing the Productivity-Wage Nexus in
Selected OECD Countries, 1986-2013,” CSLS Research Report 2016-16, November.
http://www.csls.ca/reports/csls2016-16.pdf.
Whelan, Karl (2002) "Computers, Obsolescence, and Productivity," Review of Economics andStatistics, Vol. 84, No. 3, pp. 445-461.

46