WIPIP, plenary session 1

WIPIP, University of Washington School of Law
Plenary Session 1: Innovation Policy
Stephanie Bair, Promoting the Useful Arts: Corporate Edition
87% of patents are assigned to organizations, not
individuals.  How to motivate
individuals/employees?  Assumption is
usually that companies will offer optimal incentives, such as financial
bonuses.  But companies vary quite widely
in incentive structures, both financial and more importantly nonfinancial.  Amazon: works employees past endurance.  Amazon thinks challenging employees is good
for innovation.  Google: free massages,
food; policy allowing employees to take extended leave w/benefits to work for
nonprofit, travel, etc.  Google also
thinks its approach is good for innovation.
Findings: workplaces that function like economic exchanges—bonus
financially for innovation—don’t work very well. Social exchanges tend to
motivate creativity more.  Social
exchanges are less formal than economic exchanges, like friend/neighbor
relationship.  Loose exchange of favors
over time. Can be more efficient because of informality; reduces transaction
costs. Built on trust, dignity, and respect. 
Other factors good for creativity: autonomy, competence, feeling of relatedness
among employees, variety of tasks, work/life balance.  Promoting sense of choice; avoiding undue
control; giving credit for work; encouraging appropriate breaks/downtime.  Conclusion: Amazon has it wrong.  Controlling behaviors make employees feel
less competent.  [Does Amazon get fewer
patents relative to its non-warehouse employee cohort size than Google?  What other measures might we use?]
How to get companies to do what’s best?  The case for private ordering.  Evidence shows that creativity incentives
support the bottom line, are related to employee
satisfaction/productivity/loyalty; good press. 
Should want to do it of their own accord.  (Unless employer class has taste for control/status
inequality that overwhelms this, or is in a market with less need for ongoing large-scale
innovation and just wants bodies.  Cf. Edward
Baptist’s excellent and terrifying The
Half Has Never Been Told
torture can also elicit innovation and efficiency from human bodies.)
What causes these incentives to fail?  Bounded rationality.  Info processing limitations; status quo bias;
conformity bias.  Solution: company can
use metrics.  Info asymmetrics: potential
employees may not have as much info as they do at Apple.  Amazon announced new benefits after the bad
press.  Social norms: managers should
face constraints for behaving poorly. 
Employment law: promote employee mobility.
Glynn Lunney, Copyright’s Excess
We know © exists to solve a problem of supply/demand and
underproduction in the absence of legal protection; © tries to eliminate free
riders to push paying demand = actual demand, making supply & demand
intersect at optimal level.  Copyright’s
excess is: when we push © up we do it uniformly, not just for marginal works we
want to incentivize—transfer wealth from consumers to producers of works we
would have gotten anyway, with less ©. 
Traditional analysis: just redistribution, no welfare effects; maybe it’s
in pockets of Congress, but that doesn’t matter. Plus, given uncertainty, might
be incentive effects even for nonmarginal works.
But, traditional answer: higher prices mean higher
deadweight losses and higher transaction costs to be balanced against marginal
benefits. Deadweight loss will be half of what’s transferred to producers (in
perfect model).
Real world application: demand curves are highly
skewed.  One song streamed 60 million
times during 2005-before.  4 million
songs on Spotify have never been streamed by anyone.  5 million times for Goo Goo Dolls song.  To help one marginal song by $1 we have to
give $12 to that song at the top.  Very
little goes to the marginal songs overall.
Music has seen a radical transfer of wealth from consumers
to copyright owners from the 1970s to 2000s—followed by sharp decline in
sales.  What relationship between income
and output in the music industry?  Music
revenue is down from peak in 2000.  File
sharing traffic is up—1000 petabytes a month, 1.25 billion albums a month if they
were all albums; $75 billion in reduced deadweight loss.  Albums released in US from 1996-2012: 30,000
to 100,000, then recession took us to 80,000, well above the peak revenue year
of 1999.  Billboard Hot 100: 5200
slots/year; number of unique songs: in 60s/70s it was about 750.  Falls steadily until 2002 when it’s 300
songs, then up to 500, then recession and 400. 
Turnover isn’t exact proxy for high quality output—maybe we’re getting
some super-high quality songs sticking around. 
Did quality peak in 1999?  Rolling
Stone thinks it peaks in 1970s (top albums ever), but Spotify might be less
white-guy.  2014 play count: backside of
Spotify distribution curve; median user is 28 and mostly younger; normalized,
though, the play counts peak in 1983, when revenue was lowest, and 1997 is
nadir in terms of what people still listen to today. So we didn’t see peaks in
quality in 1999. 
Why might we be getting fewer high-quality songs?  New artists—peak revenue period in 1983-1999,
there is a slight upward slope in the number of new artists.  25% of songs were by new artists, up to 40%
at peak; filesharing kicks in and we go back to 15-20%.  Why not as many new songs?  Existing artists produce fewer hits when
record sales are going up. 
Hypothesis: backwards bending labor supply curve. Overpaying
superstars reduces their creative output. 
Backstreet Boys & ‘Nsync were making $200 million/album.  They produced fewer albums.  Top 250 acts of all time, according to RIAA:
early ones like Beatles produced average 14 albums in first 10 years of career
(Beatles were 12 albums + EP); by the 1990s, that was down to 5.  Lost: Sergeant Pepper’s, the White Album,
Abbey Road, Let it Be.  Adele’s delay between
albums will predictably be greater too. 
Top artist hit production from 1962-present: number of hits v. record
sales over first 10 years of career—the artists with lots of hits tend to be grouped
on low income portion of scale.
More revenue didn’t yield more and better works of
authorship.  Higher revenue = more new
artists, but fewer hits from existing artists; most of the new artists were
one-hit wonders. Second effect (fewer hits) outweighed the first.  Maybe we need to balance incentives for
marginal artists v. incentives for most popular, instead of incentives v.
Laura Pedraza-Fariña, Scaffolding Innovation: The Role of
Patents, Grants, and Informal Norms in Assembling Teams that Span Technological
Lumpy structure of architecture of knowledge
distribution.  We’ve made vast improvements
in the way we can cure cancer/prolong survival, particularly for childhood
cancers, but the side effects include infertility; fewer advances in that
area.  Why this disconnect?  Two communities involved in
infertility/cancer research: oncologists, who are interested in understanding
cell division; endocrinologists, who in part work on addressing infertility.
Though when you ask cancer patients their main concerns, infertility is second
only to fear of death, that research hasn’t happened.  These two communities are not talking to each
other though they have key complementary knowledge to address secondary
infertility from cancer treatment.
Accounts of innovation incentives often assume free flow of
information; don’t look at barriers to assembly of teams even once free-riding
and market-demand problems are eliminated. 
Architecture of knowledge distribution/social barriers to flow of
knowledge are often important.  NIH sought
proposals for interdisciplinary research—problems that can only be solved by
cooperation among disciplines; oncofertility is one example.
Interviewed key informants: barriers and benefits to such
research; what is the effect of current patent and grants policy/what should we
do to encourage it?  Requires someone in
a position to unite two groups—an endocrinologist who fortuitiously ended up in
charge of a cancer center.  Benefits of
collaboration: problem finding.  Huge
areas of unknowns—when the mouse ovary and monkey ovary people got together
(hadn’t previously been talking), they realized that these ovaries behaved very
differently and they needed to account for that instead of using mouse ovaries
as human models.  New field of research:
oncofertility. New products: gel matrix to grow follicles into eggs in
vitro.  New social connections: ongoing
collaborations between engineers, endocrinologists, oncologists, etc.  People w/40 years of experience described it
as the best program they’d been involved with in their lives.  Intrinsically motivating to work in
non-traditional teams.
Policy conclusion: create scaffolds.  Temporary bridges may be enough to bring
together communities previously separated by structural holes, b/c intrinsic
motivation may take care of the rest. 
Patents are not the right tool for the job; the type of research creating
nontraditional teams tends to be exploratory, low appropriability/high
spillovers, long time to market—patents tend to distort incentives against
these.  Grants aren’t currently
structured to do this b/c NIH institutes weren’t coordinating, but could
be.  Regulatory levers: FDA.  Collaborative R&D.
W. Nicholson Price II, Timo Minssen, & Arti Rai, Patent
Failures on Life Science Frontiers
Poster child for patents seems to be developing new
drugs.  Some literature says that’s not
true across pharma/bioscience—antibiotics, orphan drugs, biosimilars, manufacturing,
second uses for existing drugs, diagnostics—all these things are different.

Themes emerging from the literature: coordination—many of these policies cut
across areas—FDA, PTO, Fed. Cir., Congress think of different things and don’t
talk to each other.  Policy academics in
these areas also often don’t talk to each other. Maybe industry trade groups
are better at figuring out how to play off regulations/regulators.  Life sciences are different?  Maybe health is special: human flourishing,
market failure, FDA as giant regulator sitting on top of everything,
gatekeeping market entry.

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