FRBSF Economic Letter
2006-02; February 24, 2006
Productivity Growth: Causes and Consequences—Conference
Summary
This Economic Letter summarizes the papers
presented at the conference "Productivity Growth:
Causes and Consequences" held
at the Federal Reserve Bank of San Francisco on November
18-19, 2005, under the sponsorship of the Bank's Center
for the Study of Innovation and Productivity. The papers
are listed at the end and are available on-line.
The study of productivity growth is among the most important
pursuits of economic science. Assessments of it influence
macroeconomic policy and in the long run productivity
growth drives improvements in the standard of living, the
mix
of goods and services available, as well as the mix of
jobs in an economy. The seven papers presented and discussed
at the conference covered the entire spectrum of the
process of productivity growth, from its fundamental cause—invention—to
the diffusion and adoption of invented technologies,
to
the consequences of technological change, such as longer
life spans.
Causes of productivity
growth
A paper by Jones explored the genesis of technology and
productivity growth—that is, the process of invention.
In particular, he examined how this process, which typically
builds on prior knowledge, is affected by the growing volume
of knowledge. In Jones's model, inventors decide on the
balance between acquiring knowledge that is narrow but
deep and knowledge that is broad but shallow. The model
predicts that, as the volume of knowledge grows deeper
and broader over time, invention requires levels of depth
and breadth that are increasingly difficult, in general,
for a single individual to attain. As a result, inventors
(researchers) would likely deepen their knowledge and become
more specialized by spending more time learning rather
than inventing, and they would likely gain more breadth
of knowledge by engaging in more teamwork. Jones tested
this hypothesis using U.S. data from 1975-2000 and found
favorable results: the average time students spent in doctoral
programs increased, the average age of inventors at the
time of their first invention increased, and the number
of inventors per patent increased. Another testable implication
of Jones's model is that, looking across technological
fields, inventions in deeper, more mature fields should
be generated by larger teams with more specialized team
members. As Jones demonstrated, this prediction is supported
by data on U.S. patents.
Two closely related papers explored how new technologies
diffuse or spread across parts of an economy. Conley
and Udry considered the role of social networks in this
diffusion
process. Identifying such networks has been an elusive
goal in the field of productivity research. Conley and
Udry found a unique setting ideal for achieving this
identification: pineapple farming in Ghana. In recent years,
Ghanaian farmers
have increasingly switched from traditional crops, such
as maize and cassava, to the more profitable crop of
pineapples, which has involved learning new technologies,
such as the
use of modern fertilizers. A basic part of the learning
is discovering the best trade-off between the cost of
the fertilizer and the value of the crop per acre. Conley
and
Udry use surveys to identify the social networks for
a sample of farmers, some of whom adopted pineapple farming
and some of whom did not. They find that communication
with farmers who have successfully adopted pineapple
farming
is a strong predictor of whether a given farmer subsequently
adopts the same technology (that is, the same use of
fertilizer) for growing pineapples. In contrast, and consistent
with
their model, such networking is found to play no role
in cultivation decisions for other crops whose technologies
are widely known. These findings point to the potential
importance of networking as a channel for the diffusion
of technology. They also suggest, though, that factors
that limit social networks, such as barriers to communication,
may slow technology adoption in developing countries.
Skinner and Staiger explored technology diffusion by
investigating the empirical patterns of technology
adoption among the
U.S. states. It is widely recognized that the pace
and extent of adopting new technologies—from telephones
to color television to computers—starts out slowly
and
picks
up speed over time. This pattern generally reflects
the fact that newly rolled out technologies tend to be
costly,
which limits the number of purchasers or users; then,
over time, as quality improves and costs decline, these
technologies
are diffused more quickly and more widely. Cross-country,
or regional, differences in the timing and pace of
technology adoption generally are attributed in part to
differences
in income levels, with lower-income regions typically
the slower to adopt.
However, such economic differences may not account
for all regional differences in the pace of adopting
technology.
For example, Skinner and Staiger found that some
states were much slower to adopt the use of beta blockers—that
is, they found that doctors as a group in those states
were much slower to prescribe beta blockers to patients
in the hospital recovering from heart attacks. Skinner
and Staiger argue that, because the drug's cost is
low and its benefits are clear, economic factors,
such
as
differences across states in income or prices, are
unlikely to explain
the wide cross-state differences in the rate at which
this medical practice is adopted. In developing an
alternative explanation, the authors point out that
states that were
slow to adopt hybrid corn during the first half of
the twentieth century generally are the same states
that
recently
have been slow to adopt beta blockers as a treatment
for heart attacks. In fact, they find this pattern
for the
adoption of other technologies, as well. The authors
posit that states with faster adoption rates may
have characteristics
that facilitate technology diffusion more generally.
One characteristic correlated with faster adoption
rates appears
to be education, and communications networks may
also play a role.
Baily et al. investigate whether competitive pressures
influence technology adoption decisions. Specifically,
their paper looks at the timing and extent of process
innovations adopted by U.S. automakers from 1987
to 2002, a period
in which foreign automakers increasingly penetrated
the U.S. market and were themselves adopting these
innovations.
The authors argue that nearly half of the productivity
increase over this period in the U.S. domestic auto
industry was driven by the adoption of improved process
technologies,
such as "lean manufacturing" techniques.
Another quarter of the measured increase in productivity
is argued
to have come from the product innovation of introducing
new vehicle lines, especially SUVs, for which there
was apparently unmet demand and on which U.S. manufacturers
could realize larger mark-ups.
Gordon and Dew-Becker sought to determine the cause
of the rather stark divergence in productivity
growth in
the European Union (EU) relative to the strong
performance in U.S. since 1995. The authors pointed out
that
about half of the comparative slowdown in EU productivity
growth was due to the acceleration in growth in
the U.S., while
the other half was due to a deceleration in Europe.
Previous research had shown that information technology
(IT) played
a big role in the U.S. acceleration in the second
half
of the 1990s, so one might think the slowing in
EU productivity might be due to developments affecting
the IT sector
in
Europe. On the contrary, Gordon and Dew-Becker
show that the slowdown in Europe was quite broad-based
and not
due just to weakness in IT-related industries.
A common explanation
for the EU slowdown is that institutional and legal
barriers limit flexibility, and it is frequently
illustrated by
a story about zoning laws in Europe that prevent
big-box stores, like Wal-Mart and Target, from
expanding and
establishing the ultra-efficient distribution systems
they have in the
U.S., which some argue have contributed to higher
U.S. labor productivity.
Gordon and Dew-Becker offered a different story:
Somewhat ironically, the labor market reforms
enacted in the
mid-1990s in many EU countries actually had a
negative effect on
productivity growth—at least temporarily. The
authors claimed that, by relaxing rigid work
rules and high
wage floors, EU employers could hire more low-wage,
low-productivity
workers and substitute away from high-skill workers
and capital. Indeed, before the mid-1990s, productivity
growth
in the EU was above that for the U.S. By opening
the door to these low-productivity workers, Gordon
and
Dew-Becker argue, average productivity is pulled
down, at least
until
the economy adjusts to the new composition of
the workforce.
Consequences
of productivity growth
Two papers presented at the conference address some consequences
of innovation and productivity growth. Bloom, Schankerman,
and Van Reenen looked at the social returns to innovative
activity, as measured by research and development (R&D)
spending. The authors conceive of social returns to R&D
as the technology spillovers flowing from R&D-performing
firms to other firms, net of the social costs of having
rival firms engage in parallel, duplicative research rather
than working together. Using panel data on U.S. firms between
1981 and 2001, they find that both technology spillovers
and market rivalry effects are quantitatively important,
though the former dominate such that the net social returns
to R&D are several times larger than the private returns.
They argue that since large firms tend to produce greater
technological spillovers and engender less rival R&D,
their model implies that the current emphasis in U.S. R&D
policy on small and medium-sized firms may not be the most
effective use of government-provided incentives.
Hall and Jones consider the consequences of continued
productivity improvements in the U.S. health-care industry.
Much of
the discussion over public policy in the U.S., according
to these authors, assumes that the rapid growth in health
spending in recent decades has been excessive. One argument,
for example, is that the rise in health spending as a
share of GDP is due to the lack of cost controls and misaligned
incentives. Hall and Jones provide a plausible alternative,
or at least an additional view, by developing an economic
model of individual consumption behavior where life span
is a function of health-care spending. Calibrating their
model using standard parameters, they find that as income
grows over time, the optimal share of spending on health
also grows. Specifically, because individuals receive
diminishing
marginal utility from consumption in a given time period,
they optimally respond to increases in income by putting
a greater share of resources toward increasing the number
of periods in which they can consume (longer life spans).
That is, with rising incomes, individuals will choose
to spend proportionately less on food, cars, housing, and
so on, and more on health today so as to be able to consume
in more tomorrows. Based on projections of aggregate
income
growth, their model suggests the optimal health share
is likely to double over the next half century, exceeding
30% of GDP by 2050. Daniel Wilson
Economist
Conference
papers
Baily, Martin Neil, Diana Farrell, Ezra Greenberg, Jan-Dirk
Henrich, Naoko Jinjo, Maya Jolles, and Jana Remes. "Increasing
Global Competition and Labor Productivity: Lessons from
the U.S. Automotive Industry."
Bloom, Nick, Mark Schankerman, and John Van Reenen. "Identifying
Technology Spillovers and Product Market Rivalry."
Conley, Tim, and Christopher Udry. "Learning about
a New Technology: Pineapple in Ghana."
Gordon, Robert, and Ian Dew-Becker. "Why Did Europe's
Productivity Catch-up Sputter Out? A Tale of Tigers and
Tortoises."
Hall, Robert, and Chad Jones. "The Value of Life
and the Rise in Health Spending."
Jones, Ben. "The Burden of Knowledge and the Death
of the Renaissance Man: Is Innovation Getting Harder?"
Skinner, Jonathan, and Douglas Staiger. "Technology
Adoption from Hybrid Corn to Beta Blockers."
Opinions expressed in this newsletter
do not necessarily reflect the views of the management
of the Federal Reserve Bank of San Francisco or of the
Board of Governors of the Federal Reserve System. Comments?
Questions? Contact
us via e-mail or write us at:
Research Department
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