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\begin{comment}
Please include the following acknowledgements in any reproductions of this material.
Source document: Kevin A. Bryan and Heidi L. Williams, 2021. 
Figure #: © .  All rights reserved. [Citation with link to source]
Table #: © .  All rights reserved. [Citation with link to source]
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\title[]{Innovation: Market Failures and Public Policies} 
\subtitle{Lecture 2: Science as a non-market incentive \vspace{3cm}}
\institute[]{\justifying The material covered in these slides is drawn from: Bryan, Kevin and Heidi Williams (2021) "Innovation: Market Failures and Public Policies," Chapter 13 in the \emph{Handbook of Industrial Organization Volume 4}, edited by Kate Ho, Ali Hortacsu, and Alessandro Lizzeri.}
\date{} 

\graphicspath{{graphics/}}

\begin{document}
	
\frame{\titlepage}

\begin{frame}
\frametitle{This lecture}
\begin{itemize}
\item Before focusing on market-based policies, we first discuss an additional motivator of research: scientific norms and institutions

\vspace{3mm}
\item Scientists are often incentivized by some combination of academic tenure, scientific credit, accolades, and pure curiosity

\vspace{3mm}
\item Rather than provide a comprehensive review of the economics of science literature, we hope to give a few illustrative examples of how non-market incentives encourage innovation in science
\end{itemize}
\end{frame}

\begin{frame}
\frametitle{Two caveats}
Exposit the core problems in the economics of innovation:
\begin{enumerate}
    \item First, science policy affects innovation beyond the production of ``basic’’ or ``fundamental’’ research
    \begin{itemize}
        \item Incentives to create, publish, and promulgate new ideas and inventions in the absence of profit from commercially-sold products
    \end{itemize}
    \item Second, fundamental science can be produced by private firms, in addition to academic institutions
    \begin{itemize}
        \item Scientific research within firms can often generate useful products and provide a foundation to build on academic science.
    \end{itemize}
\end{enumerate}
\end{frame}

\begin{frame}
\frametitle{Brief history of fundamental science in firms}
\begin{itemize}
\item In the early twentieth century, industrial R\&D labs like Bell Labs were prolific - in 1940, scientific publications from non-academic researchers were, on average, receiving more citations than those of academic researchers

\vspace{3mm}
\item Since the mid-twentieth century, industrial science in large firms has declined substantially

\vspace{3mm}
\item The frequency with which large firms cite scientific research has not declined over time, though their own research has become narrower and more specialized
\end{itemize}
\end{frame}

\begin{frame}
\frametitle{This lecture}
Understanding innovative activity requires understanding the norms/ institutions of science that motivate researchers. Specifically:
\begin{itemize}
    \item What drives the rate and direction of scientific research?
    \item How does the structure of knowledge production change over time?
    \item How should science be funded?
\end{itemize}
\end{frame}

\section{Rate and direction of scientific research}

\begin{frame}
    \frametitle{Outline}
    \tableofcontents[currentsection]
\end{frame}

\begin{frame}
\frametitle{Establishing priority of discovery}
Robert Merton: scientists are primarily motivated by establishing priority of discovery {\scriptsize \textcolor{red!75!green!50!blue!25!gray}{[Merton 1957, 1968, 1969]}}
\begin{itemize}
    \item Publication of scientific results establishes priority for a researcher
    \begin{itemize}
        \item Citations to publications provide one (imperfect) measurement of the importance of a contribution
    \end{itemize}
    \item Secondary incentives may include naming discoveries (e.g. Planck’s constant) or winning prizes (e.g. Nobel Prize), which confer prestige and monetary reward
\end{itemize}
\end{frame}

\begin{frame}
\frametitle{Winner-take-all contests}
Given the reward for establishing priority, science is often described as a winner-take-all contest {\scriptsize \textcolor{red!75!green!50!blue!25!gray}{[Stephan 2010]}}
\begin{itemize}
    \item Even when researchers solve problems near-simultaneously, the ``winner of the race'' reaps most of the reward
    \begin{itemize}
        \item As a result, winners receive even more resources (funding, research assistants, etc.), and are more likely to be first to future discoveries - the ``Matthew Effect'' {\scriptsize \textcolor{red!75!green!50!blue!25!gray}{[Merton 1968]}}
     \end{itemize}
     \item In surveys, scientists predict that teams that lose priority races face at 59\% penalty in citations (compared to the hypothetical winner) 
     \item Evidence from structural biology suggests that ``scooped’’ teams face only a 20\% citation penalty – at odds with a ``winner-take-all’’ model {\scriptsize \textcolor{red!75!green!50!blue!25!gray}{[Hill and Stein 2020]}}
\end{itemize}

\end{frame}

\begin{frame}
\frametitle{Effect of priority on quality}
Theoretical models suggest there may be tension between investing time in a project (improving its quality) and patenting/publishing first to establish priority
\begin{itemize}
\item Bobtcheff, Bolte, and Mariotti (2016) creates a model of priority races, with the built-in feature that higher-quality projects take longer
\item Hill and Stein (2020) goes further, allowing projects to differ in ex-ante potential
\begin{itemize}
    \item High potential projects = higher payoffs $\rightarrow$ more competitive $\rightarrow$ premature publications
    \item The authors provide empirical evidence from research in structural biology that is consistent with this hypothesis
    \item In this framework, highest-potential projects will have lowest quality execution
\end{itemize}
\end{itemize}
\end{frame}

\begin{frame}
\frametitle{Autonomy vs. social value}
Scientists value the freedom to choose their own research projects, which don't always align with a social planner's preferences
\begin{itemize}
    \item Stern (2004) finds that postdoctoral students in biology are willing to accept lower pay for greater autonomy in research positions
    \item Aghion, Dewatripont, and Stein (2008) seeks to clarify the relative advantages and disadvantages of different stages of scientific research being conducted at universities versus in the private sector
    \begin{itemize}
        \item Academics have more creative control, and require a wage premium to take on more ``focused'' industry roles
        \item Though academics enjoy choice, it may not result in projects that maximize economic value
        \item The model points to tradeoffs in research funding faced by a social planner: academia may be a cheaper input to innovation production, but firms can direct scientists to work on more commercially valuable projects. Resolution of the tradeoff depends on how close to the point of commercialization the particular research stage is.
    \end{itemize}
\end{itemize}
    
\end{frame}

\begin{frame}
\frametitle{Directing scientific research}
Since scientists value autonomy, it can be challenging to use policy levers – including higher R\&D spending – to direct research efforts 
\begin{itemize}
    \item Goolsbee (1998) shows that up to half of increased defense spending on R\&D goes to wages of scientists working on defense-related projects
    \item Myers (2020) finds that shifting one NIH applicant toward a research project that is a small distance away from their prior work requires millions of dollars in funding
    \begin{itemize}
        \item Myers does find that the increased productivity of grants likely offsets additional funding costs
    \end{itemize}

\end{itemize}
\end{frame}

\begin{frame}
\frametitle{More on scientists' research choices}
Prominent senior researchers may deter younger scientists from working in their areas of research, but produce spillovers in other areas
\begin{itemize}
\item Azoulay, Fons-Rosen,and Graff Zivin (2019) argues – based on an analysis of surprise deaths of star scientists – that scientists are hesitant to do work similar to that of prominent senior researchers (while seniors are alive)
\item Many of these ``stars,'' if they work on high-value projects, may produce spillovers to other fields
    \item Sampat (2015) finds that half of drug patents citing NIH-funded research are in a different disease area than that intended by the original grant
\end{itemize}
    
\end{frame}

\section{Knowledge production}

\begin{frame}
    \frametitle{Outline}
    \tableofcontents[currentsection]
\end{frame}

\begin{frame}
\frametitle{The burden of knowledge}

Knowledge production in science – and optimal science policy – may be best thought of as dynamic processes\\
\vspace{3mm}
Jones (2009) finds that the age at which a scientist produces her ``first'' innovation has increased steadily, from just over 30.5 in 1985 to around 31.4 in 1999.
\begin{itemize}
    \item Further, the average number of inventors named on a patent has risen from 1.7 in 1975 to 2.3 in 1999.
    \item Thus, Jones wonders whether these trends are the result of an increasing ``burden of knowledge'' necessary to make scientific advances
        \item Burden of knowledge hypothesis: Since inventors must receive enough education to reach the frontier of knowledge, which expands over time, researchers must either a) increase the time spent in training, or b) specialize more and work more in teams
\end{itemize}


\end{frame}

\begin{frame}
\frametitle{Furthering the burden of knowledge hypothesis}

Various papers in recent years have advanced Jones' hypothesis
\begin{itemize}
\item Work by teams is increasingly dominating work by solo authors {\scriptsize \textcolor{red!75!green!50!blue!25!gray}{[Wuchty et al. 2007]}}
\item Rising educational burden evidenced by World Wars as an exogenous disruptor of scientists' education {\scriptsize \textcolor{red!75!green!50!blue!25!gray}{[Jones 2010]}}
\item  The frequency of early-life discoveries in physics, chemistry, and medicine is declining over time
\begin{itemize}
    \item 	This trajectory is less apparent for physics, where the introduction of quantum mechanics overturned existing knowledge, ``shortened'' distance to knowledge frontier {\scriptsize \textcolor{red!75!green!50!blue!25!gray}{[Jones and Weinberg 2011]}}
\end{itemize}
\end{itemize}
    
\end{frame}

\begin{frame}
\frametitle{More examples of the burden of knowledge}

\begin{itemize}

\item Specialization and team size increased in fields whose frontier shifted outward following the fall of Iron Curtain, arrival of Soviet mathematics {\scriptsize \textcolor{red!75!green!50!blue!25!gray}{[Agrawal et al. 2016]}}
\item Brendel and Schweitzer (2019) finds evidence consistent with the burden of knowledge hypothesis in mathematics, and argues that researchers strategically respond with a division of labor
\item Bloom, Jones, Van Reenenm and Webb (2020) shows evidence of declining productivity per researcher in semiconductors, agriculture, and biotechnology
\end{itemize}
    
\end{frame}

\begin{frame}
\frametitle{Policy challenges}
Burden of knowledge motivates a new set of policy challenges {\scriptsize \textcolor{red!75!green!50!blue!25!gray}{[Jones 2011]}}
\begin{enumerate}
    \item Entry into science may be reduced by the burden on young academics
    \item As research becomes more intricate and specialized, the burden of knowledge on gatekeepers of quality - patent examiners, grant reviewers, etc. - also increases
    \item If policy and evaluation methods favor more specialized, narrow research that could be performed by single researchers, the economy will systematically disincentivize broader and more team-based research
\end{enumerate}
    
\end{frame}

\begin{frame}
\frametitle{Team research}
While team research could mitigate the burden of knowledge, it presents its own challenges
\begin{itemize}
    \item Teams involve fundamental inefficiency due to moral hazard with credit sharing {\scriptsize \textcolor{red!75!green!50!blue!25!gray}{[Che and Yoo 2001]}}
    \item Reducing moral hazard by rewarding team effort could cause inefficient team production in the presence of a collaboration credit premium {\scriptsize \textcolor{red!75!green!50!blue!25!gray}{[Valiki et al. 2020]}}
    \item Large teams appear less likely to pursue highly-disruptive research {\scriptsize \textcolor{red!75!green!50!blue!25!gray}{[Wu et al. 2019]}}
\end{itemize}

\vspace{3mm}
What drives increase in specialization and team production among innovators: Increase in the difficulty of reaching the frontier, or inefficient changes in scientific funding, credit allocation, and researcher training?

\end{frame}

\section{Funding scientific research}

\begin{frame}
    \frametitle{Outline}
    \tableofcontents[currentsection]
\end{frame}

\begin{frame}
\frametitle{Background}
One of the most important ways in which the public sector supports innovation is through the direct funding of research

\vspace{6mm}

Federally funded R\&D used to be the primary component of total US R\&D spending, but that since about 1980 the private sector has grown to account for a higher share of US R\&D spending
\begin{itemize}
    \item  This federal research support involved fifteen federal government departments and a dozen other agencies
    \item By far the largest two supporters were the Department of Defense (DOD) and the Department of Health and Human Services (HHS, which includes the National Institutes of Health or NIH)
\end{itemize}
\end{frame}

\begin{frame}
\frametitle{US research and development as a share of GDP, by source of funds: 1953-2015}
    \begin{center}
    \includegraphics[scale=0.26]{figure2_source_of_funding_270.pdf}
    \end{center}
\scriptsize \textcolor{red!75!green!50!blue!25!gray}{Notes: This figure shows US research and development (R\&D) spending – total and by source – as a share of GDP from 1953 to 2015. Source: Appendix Table 4-1 of National Science Foundation (2018).}
\end{frame}

\begin{frame}
\frametitle{Use of federal funding}

Federal funding consists of two distinct activities:
\begin{itemize}
    \item Conducting research directly (e.g. federally funded research and development centers (FFRDCs) such as Los Alamos National Laboratory)
    \item Supporting external research (e.g. NIH grants to university researchers)
\end{itemize}

\vspace{6mm}

The following figure illustrates an important pair of asymmetries: the federal government funds more external research than it spends internally, whereas higher educational institutions spend more than they directly fund
    
\end{frame}

\begin{frame}
\frametitle{Spending on and funding of US R\&D by sector: 2008-2015}
    \begin{center}
    \includegraphics[scale=0.28]{figure3_sector_funding_1.pdf}
    \end{center}
\scriptsize \textcolor{red!75!green!50!blue!25!gray}{Notes: This figure shows spending on and funding of US R\&D by sector from 2008 to 2015. Source: Table 4-1 of National Science Foundation (2018)}
\end{frame}

\begin{frame}
\frametitle{Value of public research support}
On the social value of federal funding: How different is the set of innovations society has access to because of public research support?
\begin{itemize}
    \item Available evidence from qualitative case studies suggests that many or most scientific discoveries can trace their roots to both public research support and investment by private firms
    \item For example, Chakravarthy, Cotter, DiMasi, Milne, and Wendel (2016) traces the history of 19 transformative drugs in the past 25 years, and finds only 4 have been researched and developed solely by one sector
\end{itemize}
\end{frame}

\begin{frame}
\frametitle{Returns to public research funding}  
Central policy question: what is the return to incremental dollars of public research funding?
\begin{itemize}
    \item For example, consider the ``war on cancer''
\begin{itemize}
    \item 1971 – Pres. Nixon introduces National Cancer Act, which gives National Cancer Institute special authority to spend (flexibly) on priorities related to cancer
    \item Additional \$100 million for cancer research  
\end{itemize}    
\item Many have declared the War on Cancer a failure 
\item Rigorously analyzing success/failure is challenging 
\end{itemize}

\end{frame}

\begin{frame}
\frametitle{Challenge \#1: finding a paper trail }

We often lack a paper trail for measuring the impacts of research investments
\begin{itemize}
\item For example - if the war on cancer funded investments into cell signaling that formed the basis for the development of a leukemia treatment, what data would allow us to trace that connection?
\end{itemize}

\vspace{4mm}

Researchers have made progress on this measurement challenge; here, we'll focus on Sampat and Lichtenberg (2011) to illustrate this point
\end{frame}

\begin{frame}
\frametitle{Sampat and Lichtenberg (2011)}

Sampat and Lichtenberg (2011) developed direct and indirect measures of whether drugs approved by the US Food and Drug Administration (FDA) built on publicly supported research
\begin{itemize}
    \item Direct support: whether patents linked to the individual FDA-approved drugs are assigned to government agencies and whether they disclose a so-called government interest statement
    \item Indirect support: whether drug patents cite earlier patents assigned to government agencies or patents disclosing government interest statements, or cite published biomedical research articles that acknowledge public research support
    \end{itemize}
    
Authors estimate while only 9\% of new drugs approved by the FDA between 1988-2005 directly benefited from public research support, nearly half (47.8\%) benefited indirectly

\end{frame}

\begin{frame}
\frametitle{Challenge \#2: Constructing a control group}
Building appropriate control groups is important, but difficult, for measuring the impact of public research
\begin{itemize}
    \item Public research funding often targets areas that are scientifically promising
    \item Shifts over time in the scientific potential of a given area would be expected to affect incentives for both private and public research investments
    \item If a dollar of public research crowds out a dollar of private research, then public research could in theory have no real effect on total research spending {\scriptsize \textcolor{red!75!green!50!blue!25!gray}{[David et al. 2000]}}
\end{itemize}
    
\end{frame}

\begin{frame}
\frametitle{Jacob and Lefgren (2011)}
Key advances have been made in the context of understanding NIH support for biomedical research
\begin{itemize}
    \item Jacob and Lefgren (2011) make advances in constructing ``treatment'' and  ``control'' groups in the context of NIH support for biomedical research
    \item Applications are assigned priority scores based on independent scientific reviews
    \item Grant applications just above and just below the priority score cutoff should be similar ex ante, but differ ex post in their probability of receiving funding
    \item Using an RD design, the authors estimate that receiving an R01 grant (roughly \$1.7 million) leads to one additional publication (7\% increase) over the next 5 years
\end{itemize}
\end{frame}

\begin{frame}
\frametitle{Azoulay, Graff Zivin, Li, and Sampat (2019)}

Builds on the data from Sampat and Lichtenberg (2011) and the empirical approach of Jacob and Lefgren (2011) to estimate the impact of NIH funding on private-sector patenting

\begin{itemize}
    \item The NIH comprises 21 institutes/centers, each receiving congressional appropriations
    \item Applications are assigned raw scores which are normalized to ranks
    \item Institutes/centers make funding decisions with a rank cutoff
    \item With an RD approach similar to Jacob and Lefgren, and a linkage between grants, papers, and citing patents, the authors estimate that \$10 million in NIH funding leads to 2.7 additional patents
\end{itemize}

\end{frame}

\begin{frame}
\frametitle{Areas for future work: going beyond the NIH}

Much of the progress on measurement and empirical approaches for estimating return to public research spending focused on NIH data
\begin{itemize}
    \item More work can be done to analyze the impacts of research funding from other federal departments and agencies
    \item Moretti, Steinwender, and Van Reenen (2019) analyzes how defense-related research spending affects private research spending
    \item Gross and Sampat (2020) uses World War II scientific funding to examine long-run shifts in the rate, direction, and location of US technological development
\end{itemize}
\end{frame}

\begin{frame}
\frametitle{Future work: funding structure}

Federal funding is often structured in different ways, which could vary in effectiveness
\begin{itemize}
    \item Some research grants like NIH R01 grants provide 3-5 years of funding to support specific projects
    \item In contrast, programs like the Howard Hughes Medical Institute (HHMI) Investigator Program fund researchers, who have more project flexibility, over a longer time period
    \item Theoretical work (e.g. Manso (2011)) finds differing incentives, backed by empirical evidence from Azoulay, Graff Zivin, and Manso (2011)
    \begin{itemize}
        \item R01-style contracts may motivate incremental refinements on past discoveries
        \item HHMI-style contracts provide researchers with more freedom to pursue radical and untested ideas
    \end{itemize}
\end{itemize}

\end{frame}

\begin{frame}
\frametitle{Future work: rethinking peer review}

Funding programs often have different methods for deciding which applications receive grants
\begin{itemize}
    \item Institutions such as the NIH rely heavily on peer review by field-specific experts
    \item Li (2017) finds that evaluators are both better informed and more biased about the quality of projects closer to their own work
    \item Li and Agha (2015) finds that higher peer-review scores are associated with better research outcomes
\end{itemize}

\vspace{3mm}

Are there better ways of selecting which grant applications receive funding?

\end{frame}

\begin{frame}
\frametitle{Future work: alternatives to peer review}

Criticisms of peer review:
\begin{itemize}
    \item Disadvantages early career scholars and scholars from diverse backgrounds
    \item Poorly evaluates the work of multidisciplinary teams
    \item Imposes large costs on both applicants and reviewers
\end{itemize}

\vspace{3mm}

Alternative allocation mechanisms:

\begin{itemize}
    \item Randomization for borderline grant decisions could reduce evaluator bias (currently used by the New Zealand Health Research Council) 
    \item Methods that cut down on evaluation delays and grant preparation time could also improve project quality
\end{itemize}
    
\end{frame}

\begin{frame}
\frametitle{Future work: developing innovation from universities}
Considerable academic and policy interest in how best to encourage the development of innovations out of universities, particularly following the 1980 Bayh-Dole Act 
\begin{itemize}
    \item The Bayh-Dole Act allowed federal grantees like universities to obtain patents on inventions created by their funded research
    \item Lach and Schankerman (2008) finds that raising researchers' royalty shares increases university licensing income
    \item Ouellette and Tutt (2020) argues this result is driven by errors in the coding of university policies, and that there is no evidence that royalties impact licensing outcomes at US universities
    \item Other work in this area has focused on university innovation in European contexts (e.g. Hvide and Jones (2018))
\end{itemize}
\end{frame}

\begin{frame}
\frametitle{Future work: investments in scientific infrastructure}
\begin{itemize}
    \item In addition to funding research applications or scientific education, governments also fund large, shared infrastructure such as the early internet, particle colliders, and biological repositories
    \item However, measuring the relative efficiency of infrastructure investments versus direct funding in the aggregate is challenging
    \item In the long-run, physical capital appears more replaceable than human capital
    \begin{itemize}
        \item Waldinger (2016): no long-run relationship between physical destruction during World War II and scientific output
        \item Dismissal of elite scientists from their posts before the war led to permanent declines in science in a given city
    \end{itemize}
\end{itemize}
\end{frame}

\begin{frame}
\frametitle{Summing up}
\begin{itemize}
    \item Scientific norms and institutions play a complementary role to market incentives for the creation of new ideas
    \vspace{3mm}
    \item Efficiency of science depends on concerns like priority, as well as funding mechanisms and researcher autonomy 
    \vspace{3mm}
    \item Scientific research may be getting more difficult, necessitating larger teams and increased specialization
\end{itemize}

 \vspace{3mm}

Understanding and optimizing these non-market policies is critical for a well-functioning innovation system

\end{frame}

\end{document}