Productivity Dynamics over the Medium term...Facts about Productivity and Inflation Slowdown in...
Transcript of Productivity Dynamics over the Medium term...Facts about Productivity and Inflation Slowdown in...
Productivity Dynamics over the Medium Term
Diego Comin
Dartmouth College
Facts about Productivity and
Inflation
Slowdown in Productivity Growth, since 2005
until recently
Slowdown in TFP concentrated in
followers
Wide-spread across countries
Recently, TFP grows at pre-GR growth rate
Inflation was higher than expected during
and after GR
But lower than expected once short-run
output converged
-0.06
-0.05
-0.04
-0.03
-0.02
-0.01
0
0.01
0.02
0.03
1987 1991 1995 1999 2003 2007 2011 2015
Detrended TFP
‐0.100
‐0.080
‐0.060
‐0.040
‐0.020
0.000
0.020
0.040
0.060
1984.75 1987.75 1990.75 1993.75 1996.75 1999.75 2002.75 2005.75 2008.75 2011.75 2014.75 2017.75
Labor Productivity
‐7
‐6
‐5
‐4
‐3
‐2
‐1
0
1
2
3
0
0.5
1
1.5
2
2.5
3
2000 2002 2004 2006 2008 2010 2012 2014 2016 2018
Inflation and CBO Gap
Core Inflation Output gap (right axis)
Goals
Present model that accounts for these
patterns
Developed to study productivity growth
during the GR
Accounts for productivity growth before
GR, and recent recovery
Also accounts for evolution of inflation
Key mechanism: Cyclical response of
technology adoption
Provide evidence on the mechanism
A historical account of productivity
dynamics and inflation
Model
1. 𝑦 𝑦 𝑦2. 𝑦 𝛼 χ 𝛼 𝑅
3. 𝑅 𝑅 𝑟
4. 𝑅 𝛼 𝜋 +𝛼 𝑦
5. ∆𝜋 𝜅𝑚𝑐 𝜀6. 𝑚𝑐 𝜂 𝑎 𝜂 𝑦
7. ∆𝑦 𝜌∆𝑎
8. ∆𝑎 𝜈 𝜆 𝜈 𝑧 𝑎9. 𝜆 𝛾 𝑦 𝛾 𝑅
10.∆𝑧 χ 𝜃𝑠
0 20 40 60-0.6
-0.4
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0
0 20 40 60-0.6
-0.4
-0.2
0
0 20 40 60-1.5
-1
-0.5
0
0 20 40 60
-0.2
-0.1
0
0 20 40 603.4
3.6
3.8
0 20 40 600
0.5
1
0 20 40 60
-0.4
-0.2
0
0 20 40 60-2
-1
0
0 20 40 604.7
4.8
4.9
5
EvidenceCyclicality of Adoption
Figure 2: R&D Expenditures by US Corporations, 1983-2013
1984 1987 1990 1993 1995 1998 2001 2004 2006 2009 2012-0.15
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0
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0.1
R&D Expenditure by US Corporations
Log-linearly detrended data. Source: R&D Expenditure by US corporations (National Science Foundation).
Data are deflated by the GDP deflator and divided by the civilian population older than 16 (see Appendix
A.1 for data sources).
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Cyclical Adoption: A Shred of Evidence
� Survey data: sample of 26 production technologies that di¤used at various timesover the period 1947-2003 in the US (5) and the UK (21).
� mit � fraction of potential adopters that have adopted technology i at time t
�! ratio of adopters to non-adopters rit = mit=(1�mit)�! speed of di¤usion Speedit = 4 ln(rit)
� Econometric speci�cation
Speedit = �i + �1lagit + �2(lagit)2 + � � byt + �it;
lagit time from introduction of technology i
byt �detrended output3
Table 1: Cyclicality of the Speed of Technology Diffusion
I II III IV
yt 3.73 3.7 3.64 4.12(3.59) (2.81) (3.94) (3.17)
yt * US 0.07 -0.74(0.04) (0.53)
lagit -0.057 -0.057(5.22) (4.76)
lag2it 0.001 0.001(2.52) (2.12)
ln(lagit) -0.29 -0.29(6.68) (6.65)
R2 (within) 0.11 0.11 0.13 0.13N technologies 26 26 26 26N observations 327 327 327 327
Notes: (1) dependent variable is the speed of diffusion of 26 technologies, (2) all regressions include technology
specific fixed effects. (3) t-statistics in parenthesis, (4) yt denotes the cycle of GDP per capita in the country
and represents the high and medium term components of output fluctuations, (5)yt*US is the medium term
cycle of GDP per capita times a US dummy, (6) lag represents the years since the technology first started
to diffuse.
speed of diffusion and the cycle. Diffusion speed was lowest in the deep 1981-82 recession;
it recovered during the 80s and declined again after the 1990 recession. It increased notably
during the expansion in the second half of the 90s and declined again with the 2001 recession.
Next, we turn our attention to study the evolution of the speed of technology diffu-
sion during the Great Recession. Due to its recent nature, the evidence we have is more
anecdotal. Eurostat provides information on the diffusion of three relevant internet-related
technologies in the UK.6 Figure 4 plots their average diffusion from 2004 until 2013 with
the business cycle downturns in the UK. The figure confirms the pro-cyclicality of the speed
of diffusion of these technologies. In particular, during the downturn corresponding to the
Great Recession (2008-2009), the average speed of diffusion of our three technologies sharply
declined by 75%. After the Great Recession, the speed of diffusion recovered and converged
6 The measures we consider are the fraction of firms that (i) have access to broadband internet, that(ii) actively purchase online products and services and that (iii) actively sell online products and services(actively is defined as constituting at least 1% of sales/purchases). For each of these three measures weconstruct the speed of technology diffusion using expression (2), and then filter the effect of the lag sincethe introduction of the technology using expression (3) and the estimates from column 3 of Table 1. Theresulting series are demeaned so that they can be interpreted as percent deviations from the average speedof technology diffusion.
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Figure 3: Speed of Diffusion
1980 1985 1990 1995 2000 2005
−0.3
−0.25
−0.2
−0.15
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0
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Avg. diffusion
Avg. diffusion (3 year MA)
to approximately 8% below average. Beyond its cyclicality, the second observation we want
to stress from the Figure is that fluctuations in the speed of diffusion are very wide, ranging
from 86% above average in 2004 to 74% below the average diffusion speed in 2009.
Finally, Andrews et al. (2015) have recently provided complementary evidence that tech-
nology diffusion in OECD countries may have slowed during the Great Recession. In their
study, they show that the gap in productivity between the most productive firms in a sector
(leaders) and the rest (followers) has increased significantly during the Great Recession.7
Andrews et al. (2015) show that the most productive firms have much greater stocks of
patents which suggests that they engage in more R&D activity. They interpret the increase
in the productivity gap as evidence that followers have slowed down the rate at which they
incorporate frontier technologies developed by the leaders.
These co-movement patterns between the business cycle and measures of investments in
technology development as well as measures of the rate of technology adoption is, in our
view, sufficiently suggestive evidence to motivate the quantitative exploration we conduct
through the lens of our model.
7In manufacturing the productivity gap increased by 12% from 2007 and 2009, and in services by ap-proximately 20%.
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Figure 4: Diffusion Speed for 3 Internet Technologies in the UK, 2004-2013
2004 2005 2006 2008 2009 2010 2012-0.8
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-0.4
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0
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0.6
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1
Diffusion Speed for 3 Internet Technologies in UK
Source: Eurostat; see footnote 6 for details of calculations. Shaded areas are UK recession dates as dated by
UK ONS.
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0
0.01
0.02
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0.06
2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017
Adoption Costs in Germany
Adoption costs pre& post 08
0
0.02
0.04
0.06
0.08
0.1
0.12
0.14
2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017
Sales from products introduced or improved
% sales from company improvements in products pre& post 08
0
0.002
0.004
0.006
0.008
0.01
0.012
0.014
0.016
2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017
% Reduction production costs
Unit cost reductions pre& post 08
Historical Account
Q3-87 Q1-93 Q3-98 Q1-04 Q3-09 Q1-15-6
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TFPEndogenous Component of TFPLabor Productivity
Q1-85 Q1-90 Q1-95 Q1-00 Q1-05 Q1-10 Q1-15-15
0
15
2
5
8
AZ
(right axis)
Take Away TFP
Slowdown in TFP largely due to endogenous component
During GR and post-GR: liquidity demand slows down adoption rate
Prior to GR: R&D productivity declines in 2001, and this leads to lower TFP growth from 2005-2008
In recent times: adoption rate stabilized, and TFP grows at pre-GR rate due to catch up
Heterogeneity in adoption between leaders and followers could explain TFP divergence
Q1-10 Q1-12 Q1-14 Q1-16 Q1-18-3
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Baseline Model (Data)Exogenous TFP Model
Q1-10 Q1-12 Q1-14 Q1-16 Q1-18-0.1
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Baseline Model (Data)
Take Away inflation
Higher than expected inflation during and after GR, due to lower level of endogenous TFP
(Due to TFP convergence) this effect has disappeared and now endogenous TFP leads to lower than expected inflation