Keynote Bartelsman

Understanding Production Technology
Eric J. Bartelsman
Vrije Universiteit Amsterdam, Tinbergen Institute
Global Forum on Productivity
Ottawa, June 27, 2018
Bartelsman (VU, TI) Production Technology 06/27/18 1 / 29

Is ICT a GPT?

Is ICT a GPT?
What is a GPT? (Bresnahan and Trajtenberg, 1995; Jovanovic and
Rousseau, 2005)
A GPT is pervasive, improving over time, innovation spawning
A GPT transforms the way households live and firms conduct business
A GPT requires complementary efforts at reorganization (in
society/economy, between and within firms)
What are known GPTs?
Steam, railroad
Electrification
Internal Combustion engine
Computers
Information, Computers and Telecommunication?

Electriﬁcation

Empirical Regularities of GPT
Productivity growth is slow during period of development and
adoption
Adoption causes uncertainty, disruption, creative-destruction (churn)
of jobs and ﬁrms
Entry, exit, mergers go up. Investment by young ﬁrms increase
Wage premium for skilled workers increases

Improvements over time

ICT is a GPT
While price declines for ICT seem slower than during Computer era,
Byrne-Corrado show continued steep decline. Measurement issues
with cloud, platform, product scope, etc make this hard. E.g.,
increases in real service flows from smartphone are hard to split
between GDP and consumer surplus
Evidence on firm dynamics, dispersion, entrants/exits, investment
could point to GPT (Foster et al. 2018)
Overalll, label of GPT is not defined precisely enough to offer insights
into what is changing in production/consumption and what the
impacts are on statistical measures
A more detailed over view of new technology as well as of statistical
developments can provide a more analytical framework

12 Disruptive Technologies, (MGI 2013)
Renewable Energy; Advanced Oil and Gas Exploration/Recovery;
Energy Storage
Next Generation Genomics
Advanced Materials; Additive Manufacturing (3D printing)
Autonomous Vehicles; Advanced Robotics; Automation of Knowledge
Work
Cloud Technology; Mobile Computing; Internet of Things

12 Disruptive Technologies, (MGI 2013)
Five years later: how are the developments?
Energy related: Rapid price declines in energy storage; Oil production
remains high
Genomics: continued decline in sequencing costs (now $35) ; a
surprise from CRISPR/Cas9 Editing.
Materials and 3D continue on pace
Continued hype in ICT. Valuations booming.
Autonomous vehicles proceding more rapidly than expected (level 5
broadly launched in 2020/21?)
Machine learning (GAN) has emerged to speed up development of
robotics and other technologies

Improvements over time

10 ’Breakthrough’ Technologies
MIT Technology Review, 119(2), March/April 2016
SolarCity Gigafactory (in 2018 ﬁnally ramping up production); Power
from the Air (now battery free streaming video)
Immune Engineering; Gene Editing in Plants; DNA App Store
Reusable Rockets; Robot Teachers (ROS, GAN);Tesla Autopilot
Conversational Interfaces; Slack;

The Intangible Production Technologies
Public-Private Technology Embodied Technology
Learning Technology Harvesting Technology

Digitisation through 2030
Technology for the coming decade is mostly available now
Timing of uptake, diffusion and impact is harder to assess
Uptake and diffusion depend greatly on economic environment
Policy can influence uptake and diffusion and can mitigate associated
problems
New technologies are changing the ’economic production technology’
used to convert primary inputs into utility
Data and analytical frameworks for economic policy will need to adapt

Productivity Growth (TFP) on a Declining Trend
Source: IMF SDN/17/04

Continued Weak Investment in EU
Source: Eurostat

Partial rebound in Investment in USA
Source: Eurostat

Labor Share of Income Declining

Capital Share of Income Declining
Source: Simcha Barkai (2018)

Increasing Markups and Proﬁt Margins
Source: de Loecker and Eeckhout (2017)

Frontier Growth is Robust
Source: Andrews, Criscuolo, Gal (2106)

What Can Explain these Features?
Rapidly Changing Technology!
Not just upward shift of production possibilities or interactions
between factors.
The familiar CRTS ’production technology’ AF(K, L) is being
replaced.
Lucas/Hopenhayn/Melitz production with fixed intangible investment,
stochastic productivity and firm dynamics (entry, optimal size, exit).
Alternatively, Kortum-Kramarz/Oberfield Network Production:
transformation of primary inputs (labor, land) into final goods and
services as a ’network’ with digitisation affecting not just production
in a node, but also search and matching costs for new vertices.
The new production technologies have implications for labor markets,
capital markets, output markets, savings-investment, labor-leisure
tradeoff, income distribution.

Production Technology with Intangibles
Digitalisation has some properties of earlier GPTs, but important new
ones as well
Features of Hopenhayn (1992) production technology:
Entry fee generates draw of ’Intangible asset’; Continuation and scale
of firm are dependent on draw.
Ex-ante expected profit is zero, and profit among incumbents is skewed
Equilibrium with heterogeneous firms either through curvature in profit
function or demand curve
With shift of economy to new production technology, we are
observing:
Volatility of firm outcomes increase with use of new technology
Share of intangibles in total investment increases
Income share of flexible factors decrease
Total rents increase and distribution becomes more skewed

Why is Measured TFP Growth Low
Researchers are ruling out causes
Business sector has always had new goods and other causes of input
and output mismeasurement
Willingness-to-accept estimates are for consumer surplus, not
necessarily for productivity of suppliers at market prices
Brynjolfsson, Rock and Syverson: full eﬀects needs implementation of
waves of complementary innovations
Spending on intangibles in past decade may not have been measured
as output of capital goods, and returns to these investments will be
highly skewed and have variable and possibly long lags.
New technology could be shifting the GDP production and asset
boundaries

How to Identify Shift to New Production Technology
Aggregate data for industries will provide a lagged signal of change
Marginal responses for ’margins that matter’ are not the marginal
responses estimated from historical aggregates (averages)
Linked longitudinal data on (global) transactions on output and input
markets, with prices and quantities, would be ideal to estimate
impacts of technology
Realistically, models estimated with micro moments can provide a
path forward
Use distributed micro data approach to create data of ’representative
firms’ below industry level, e.g. by location in productivity distribution
and/or by direction and magnitude of demand shock.
Estimate elasticities for different sub-populations to find (time-varying)
aggregate elasticities
Do firms that are expanding output share, or that are hiring workers
look different from shrinking firms, in terms of productivity, profits,
wages?

Example: the Taylor Rule (1)
i = r∗ + π + απ(π − π∗) + αy (y − ¯y)
Traditional measures of potential GDP may be off.
Likely new technology is ’capital saving’, so low investment to GDP
may not mean low potential.
Historical trend extrapolations of TFP are useless.
Output gap requires a ’natural rate’ measure: For GDP-gap, assess
slack by looking at measure such as hours-to-output and marginal
cost changes split by growing and shrinking firms.
Note that high rents of productive (and growing) firms can decline as
technology diffuses to competitors or that slack can increase as
resources reallocate to most productive technologies
Historical measures of natural rate of unemployment are less useful in
a world of robots-taking-jobs.
Labor-leisure tradeoff and labor-force decisions may become more
granular with new technology.

i = r∗ + π + απ(π − π∗) + αy (y − ¯y)
Business perceptions of marginal returns to (technology) investment
may be improving, but indicators of tangible investment can remain
weak.
Lagging investment in an economy with low real interest rates may
lead to misinterpretation that aggregate demand is low, rather than a
sign that new profitable technology is a substitute for tangible capital
New technology may provide new paths of intertemporal substitution
for households (services vs durable goods; intangible asset
investment), changing supply and demand for loanable funds.
Intangible investments are difficult to finance owing to agency costs
and risky returns. Timing between intangible investment and return
(as well as depreciation) may be more variable/less predictable.

i = r∗ + π + απ(π − π∗) + αy (y − ¯y)
Effects of new technology on actual and measured inflation is requires
more research (Bank of Canada)
How do hedonic price declines from quality increases affect inflation
expectations?
Has new technology reduced ’menu costs’ and other pricing frictions
enough to matter?
Is the real price decline in two-sided markets mostly on the ’eyeballs’
side?
How will new technology change financial transactions and liquidity
preference?

Example: Structural Policy
How to stimulate the production of new ideas and new technology: IP
and market power vs open source+
How to encourage firms to invest in (adopt) welfare enhancing
technology: carrot and stick; flexible markets
How to keep circular flow of consumption and production going
smoothly: income distribution, mutualization of winner-take-all
How to allay societal fears about jobs, income, future: clear and
factual narrative
How to highlight an encourage socially beneficial aspects of new
technologies: social dialog and directed innovation

Structural Policy Directions
Rethinking intellectual property rights: crucial, especially wrt data.
Providers of data should remain owners (EU GDPR)
Platforms are markets: and markets are social/communal constructs.
Rethink regulatory approach to platform monopolies
Build public platform: open source infrastructure (e.g. EU platooning
platform)
Regional interventions (eg taxi, scooters, home rental) can be welfare
improving
New forms of income and job solidarity not tied to tangible capital (ie
traditional employer)
By occupation; geographical; by skill type
Trade-oﬀs in solidarity, moral hazard, adverse selection
Incentives for human capital investment
International coordination on taxing intangibles: G20 and OECD
Using AI for policy evaluation and decision making: Public-private
data sharing, continuous experimentation and learning.

Digital Caveats
Beware of hypes: AI is not yet ’general’, but solves very speciﬁc
problems
Watch out for projections of technological magic
Don’t worry unduly about ’singularity’, or machines taking over.
Beware of anthropomorphic actions attributed to machines (learn,
think, imagine, describe)
Positive spillovers often are balanced by negatives
Consider long adoption lags and possibly very low depreciation
Don’t overestimate the near future and don’t underestimate the
longer horizon
see: Rodney Brooks (2017)

Keynote Bartelsman

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