Geography and Innovation: Evidence from Nobel Laureates*
October 2011
John C. Ham
University of Maryland
[email protected]
Bruce A. Weinberg
Ohio State University, IZA, and NBER
1945 N. High St.
Columbus, OH 43215
[email protected]
Knowledge spillovers are viewed as central for understanding economic growth, urban
agglomerations, and international trade, but there is little quantitative, micro-evidence for
these effects. Using rich data Nobel laureates in Chemistry, Medicine, and Physics, we
estimate the importance of spillovers by estimating hazard models for the probability that
one i) starts Nobel work and ii) does Nobel work depending on the number of researchers
who have or will win the Nobel Prize in their field in their locations. We first adjust the
standard approach to estimating hazard models to account for the fact that our sample
consists only of Nobel Prize winners. We then modify our approach to deal with the fact
that more able researchers are likely to have better colleagues. An increase in the number
of laureates in a person’s own field in the same location are associated with an increase in
the probability that he begins his Nobel research agenda. This suggests that spillovers
help identify important lines of research. Interestingly, laureates are no more likely to do
their Nobel work when they are around other laureates. Further we find that being in
multiple locations in a given year is beneficial in terms of starting Nobel work but not
doing the work.
*We are grateful for comments from seminar participants at Copenhagen Business School, Ohio
State University; Cleveland State University, the University of Southern California, the
University of Sussex, and conference participants at the Federal Reserve Bank of Cleveland. We
are also very grateful to Ben Jones, Sharon Levin, and Paula Stephan for sharing their data and to
Melanie Bynum, Damian Hruszkewycz, Tom LaPille, Andrew Morbitzer, and Adam Kaufmann
for excellent research assistance. Both authors thank the John Templeton Foundation. Ham also
thanks the NSF for financial support. Weinberg also thanks the National Institutes of Health. Any
opinions, findings, and conclusions or recommendations in this material are those of the authors
and do not necessarily reflect the views of the National Science Foundation, the National
Institutes of Health, the Federal Reserve Bank of San Francisco or the Federal Reserve System.
Of course, we take responsibility for all errors.
1
I. Introduction
Knowledge spillovers are viewed as central for understanding economic growth,
urban agglomerations, and international trade (Romer [1986]; Lucas [1988]; Glaeser,
Kallal, Scheinkman, and Schleifer [1992]; and Krugman [1991]. Spillovers are also
important for policy in that standard externality logic implies that decentralized, private
actors will both underinvest in innovation and be overly dispersed (because they do not
account for gains to others from clustering). If one wants to understand clusters of
important innovations, such as in Silicon Valley or at top research institutions, it is
particularly important to evaluate knowledge spillovers affecting the production of the
most important innovations.
This paper contributes to the small, emerging literature on knowledge spillovers
(reviewed below) by studying spillovers in a sample of highly important scientists –
Nobel laureates in Chemistry, Medicine, and Physics from 1901 to 2008. 1 Significantly,
our unique sample allows us to access the rich biographic data available on Nobel
laureates, separating when each laureate begins the research agenda for which he (or she)
received the Nobel Prize, which is likely to be the point where knowledge spillovers are
most important, from when he did his prize winning work. Our data also allow us to
construct histories of each Laureate’s institutional affiliations and determine the number
of other Nobel laureates each laureate is around in each year of his career. 2
1
We exclude Nobel laureates in Economics because the Economics Prize is more recent meaning that there
is less data, which affects both the number of observations and our right-hand side measures. It is also
possible that there are spillovers between Chemists, Medical scientists, and Physicists, while it is less likely
that there will be spillovers between Economists and researchers in these fields.
2
Biographical accounts suggest that a small number of Nobel laureates make multiple contributions of the
level to receive a Nobel Prize. While some people have been awarded multiple Noble Prizes, in most cases,
people only receive a single prize. Under this assumption, our estimates can be seen as the determinants of
2
While focusing on Nobel laureates has a number of important advantages, it also
poses an interesting challenge. We estimate the importance of spillovers using duration
models. Specifically we estimate the parameters of the conditional probability for when
people start or do the work for which they received the Nobel Prize among a sample of
people who ultimately do Nobel work. 3 We first modify the standard approach to
estimating hazard models to account for the fact that our sample consists only of people
who make Nobel contributions.
In estimating spillovers it is important to control for sorting based on
unobservables, whereby more able scientists are likely to have better colleagues. We
further modify the approach to consider the distribution of unobserved heterogeneity
among those who won a prize, because unobservables in this distribution are much less
likely to be correlated with quality of ones colleagues. Of course, we need to
acknowledge that our estimates may still be subject to bias. However, this bias is likely
to be much smaller given the approach we take. This is important, since an approach that
would allow the quality of ones colleagues to be endogenous may be impractical given
the small number of highly important innovators.
Our analysis of when people both begin and do their prize-winning work provides
a valuable check on our estimates. Specifically, a positive relationship between the
number of Nobel laureates in a person’s location and the probability of doing, but not
starting, Prize-winning work might suggest that people who are working on particularly
making the first contribution that would qualify for a Nobel Prize.
3
Biographical accounts suggest that a small number of Nobel laureates make multiple contributions of the
level to receive a Nobel Prize. While some people have been awarded multiple Noble Prizes, in most cases,
people only receive a single prize. Under this assumption, our estimates can be seen as the determinants of
3
promising projects are likely to be successfully recruited by places with many important
scientists. On the other hand, an association between the probability of starting, but not
doing, Prize-winning work, as we find below, is less subject to reverse causality concerns.
This piece falls into an emerging literature estimating knowledge spillovers using
individual-level data on scientists. 4 Two creative, recent studies emphasizing
identification find little evidence of traditional knowledge spillovers. 5 Azoulay, GraffZivin, and Wang [2011] find that the death of superstar bio-scientists reduces the
productivity of both close and distant collaborators, suggesting spillovers but interestingly
to the vibrancy of entire subfields. Their approach requires a large sample of superstars,
generating some arbitrariness, and then identify a good comparison for each scientist
affect by the treatment. 6 Waldinger [2011] finds that the dismissals of scientists by the
Nazis had little effect on the people remaining at their home institutions. He faces the
difficult issue of how to treat scientists who move, the sole source of identification in
specifications with university fixed effects. He makes the strong assumption that movers
are a random subsample of the population, (i.e. that moving is exogenous). Kim, Morse,
and Zingales (2006) assemble rich data on economists in top 25 departments and show
that spillovers have declined over time. Motivated by clusters of extremely important
innovations, such as Silicon Valley, we focus on horizontal spillovers among important
making the first contribution that would qualify for a Nobel Prize.
4
Scientists have proven a valuable laboratory for studying knowledge spillovers (they are used in all of the
individual-level studies of which we are aware) because of the rich data on then and their contributions.
5
The finding that Nobel laureates are frequently associated with one another dates back at least to
Zuckerman’s [1977] well-known work, which shows that over half of American Nobel Laureates studied
with or worked under another Nobel Laureate. But this finding does not imply that spillovers operate
because of endogenous sorting among important scientists or nepotism.
6
The uncertainty in picking a comparison scientist must also be accounted for estimating standard errors
(e.g. in propensity score matching one allows for uncertainty in the coefficients used to estimate the
4
innovators. This approach yields a well-defined definition for a top researcher (Nobel
laureats). 7 Our work also highlights the importance of separating when people identify
important research agendas as opposed to when they do (or publish) their work.
A variety of other approaches have been used to estimate local knowledge
spillovers. Jaffe, Trajtenberg, and Henderson [1993] and Thompson and Fox-Kean [2005]
study the geographic concentration of patent citations while Glaeser, Kallal, Scheinkman,
and Schleifer [1992] and Glaeser and Ellison [1997] study industries. Like us, Zucker,
Darby, and Brewer [1998] study star researchers (in biotechnology), showing that they are
associated with more startups in an area. Mairesse and Turner [2006] find that immediate
proximity increases the probability of collaborating using data on scientists. The
existence of an urban wage premium (e.g. Gould [2005]) has also been viewed as
evidence of spillovers. Kaiser [2005] provides a particularly interesting analysis, tracking
the diffusion of Feynman diagrams through the physics community.
Our use of Nobel laureates raises a couple of important issues. First, while the
Nobel prizes are awarded for scientific contributions, many of them have had important
commercial
and
technological
applications,
including X-rays,
nuclear power,
semiconductors, chemical synthesis, and biotechnology. Second, our focus on Nobel
laureates should not be viewed as an assumption that the Nobel laureates are the most
important innovators in their fields. Rather, we view them as a sample of people who
have made important contributions, one that is attractive given the rich historical data that
propensity score).
7
Azoulay, Graff-Zivin, and Wang [2011] focus on vertical spillovers (from the important innovators to
those who are less important). Like, Morse, Kim, and Zingales [2006], we focus on horizontal spillovers
(spillovers between people of roughly comparable stature), but our focus is on the most important
5
are available for them. 8
The outline of the paper is as follows. We discuss our methods in Section II,
including our approach to dealing with the possible endogeneity of the number of Nobel
laureates in a person’s location. We discuss our data in Section III and our results in
Section IV. Section V concludes.
II. Methods
We estimate the transition rate into both starting and doing Nobel Prize-winning
work. We use a discrete time hazard model with unobserved heterogeneity, time changing
explanatory variables (such as the number of individuals around who will eventually win
the Nobel Prize) and duration dependence (measured as time since three years before the
individual received their highest degree). The hazard function is defined as the
conditional probability an individual starts (does) their Prize-winning work in year t given
they have not started (done) their work in the previous t-1 years. We parameterize the
hazard functions as follows:
λik (t | θik )
1
, k b, d .
=
1 + exp {−hk (t ) − X i (τ i + t ) β k − θik }
(1)
In (1) b denotes beginning the work while d denotes doing the work that wins the Nobel
prize. (In what follows the k subscript is dropped for notational ease.) Further, i denotes
individual, t denotes time since three years before the individual received their highest
degree, h(t ) denotes duration dependence, τ i denotes the calendar time three years before
innovators. Waldinger’s [2001] estimates are a mix of horizontal and vertical spillovers.
8
Zuckerman [1977] contains an extended discussion of “errors” in award process, concluding that the
Nobel Prizes Committees have chosen to reduce false positives at the expense of false negatives (e.g. by
postponing awards until the importance of a contribution is firmly established).
6
the individual received their highest degree, X i (τ i + t ) denotes (possibly) time changing
explanatory variables. Finally, θi denotes an unobserved heterogeneity term with
distribution function Ι(θi ) . 9
There are several approaches that could be used to estimate these hazard rates.
One would be to take a random sample of scientists and use this for estimation. However
that presents four difficulties. First, Nobel Prize winners will be extremely rare in such a
sample. Second, it does not seem reasonable to assume that the hazard is constant over all
skill levels of scientists. Third, since one of our X (τ + t ) variables is the number of other
present and future Nobel Prize winners in the field at the institution, it is not reasonable to
treat it as independent of the unobserved heterogeneity θ among the population of all
scientists. Finally, dealing with endogenous variables in duration models is much harder
than in linear models even if one can find a good instrument. 10
Another possibility would be to use a version of choice-based sampling (Manski
and Lerman, 1977) to deal with the rare event
issue. However, while Imbens and
Lancaster (1990) have analyzed duration models within the context of choice based
sampling, they do not allow for unobserved heterogeneity, and do not deal with the other
problems raised immediately above. A third possibility is to use only data on Nobel Prize
winners. This allows us to address the four issues raised immediately above, albeit at the
cost of adjusting the estimation to address the fact that this is an extreme example of
9
In future work we will experiment with allowing duration for doing one’s prize winning work to start in
the year after one begins their prize winning work. Given time varying explanatory variables, this model is
identified (other than by functional form) even if the unobservables in the two hazard functions are
correlated.
10
See Eberwein, Ham and LaLonde (1997).
7
nonrandom sampling. We take this third approach.
To be in our sample, an individual must eventually win the Nobel Prize over their
lifetime or the end of the sample, which ever comes first. Denote this event by Prz*i ; we
use an i subscript since it will differ across individuals. Further, denote the duration at
which the individual starts his or her Prize-winning work as ti* , so that the individual
starts the Nobel Prize-winning work in calendar time (τ i + ti* ) . Let P () denote the
probability of a discrete event and P () denote the appropriate function for a mixture of
discrete and continuous variables. To form the likelihood function we need P(ti* | Prz*i ) .
The standard approach (e.g. Heckman and Singer 1984a) proceeds as follows
∞
P (ti* , Prz*i )
(ti* | Prz*i ) =
P=
P (Prz*i )
∫ P (t
*
i
, Prz*i | θi )Ι(θi )dθi
−∞
∞
∫ P (Prz
*
i
| θi )Ι(θi )dθi
−∞
∞
∫ P (t
*
i
=
(2)
| Prz*i , θi ) P (Prz*i | θi )Ι(θi )dθi
−∞
∞
∫
,
P (Prz*i | θi )Ι(θi )dθi
−∞
where again Ι(θi ) is the distribution function for the unobservables among all scientists.
The problem with estimation based on (2) is that in this approach θi plays the role of a
random effect in a regression, and estimation is only valid if X i (τ i + t ) and θi are
uncorrelated. As noted above, this is clearly an unrealistic assumption given that
X i (τ i + t ) contains the number of Nobel laureates present at time τ i + t . As indicated, in
future work we will explore simultaneous equation duration models to address this issue.
The approach we take here is to assume that X i (τ i + t ) and θi are uncorrelated when θi is
drawn from the distribution of unobservables among Prize winners g (θi | Prz*i ) . We
proceed as follows:
8
∞
P(ti* , Prz*i )
P=
(ti* | Prz*i ) =
P(Prz*i )
∫ P (t , Prz ,θ )dθ
*
i
*
i
i
i
−∞
∞
∫ P (Prz ,θ )dθ
*
i
i
i
−∞
∞
=
∫ P (t
*
i
| Prz*i , θi ) g (θi | Prz*i )dθi P(Prz*i )
−∞
∞
∫
g (θi | Prz*i )dθi P(Prz*i )
−∞
∞
=
∫ P (t
*
i
| Prz*i , θi ) g (θi | Prz*i )dθi
−∞
∞
∫
g (θi | Prz*i )dθi
−∞
∞
∫ P (t
| Prz*i , θi ) g (θi | Prz*i )dθi
−∞
=.
1
*
i
∞
∫ P (t
*
i
| Prz*i , θi ) g (θi | Prz*i )dθi . (3)
−∞
Note that
P (ti* | Prz*i , θi ) =
f (ti* | θi )
,
1 − S (Ti | θi )
(4)
where Ti is the last year that the individual can win the Prize and still be in the sample,
and
ti* −1
Ti
*
*
i
i
i i
i
i
i
i
i
r 1=
r 1
=
f (t | θ ) =
λ (t | θ )∏ (1 −λ (r | θ )) and S (T | θ ) =
∏ (1 −λi (r | θi )).
(5)
Following Heckman and Singer (1984b), we approximate the conditional
distribution of the heterogeneity given that an individual wins the Prize, g (θi | Prz*i ) , as a
discrete distribution with support points θ1 , θ 2 ,..., θ J −1 , θ J and associated probabilities
J −1
P1 , P2 ,..., PJ −1 , PJ , where PJ = 1 − ∑ Pj . As noted above, to obtain consistent estimates we
j =1
9
now only have to assume that the number of Nobel Prize winners present is orthogonal to
the unobserved heterogeneity drawn from a distribution of Nobel Prize winners, which is
much more reasonable than assuming that it is independent of unobserved heterogeneity
drawn from an unconditional distribution for all scientists, as would be done in the
standard adjustment for the choice based sample. Of course it may still be violated if
among Nobel Prize winners the best researchers have better colleagues and win the Prize
more quickly. On the other hand, as noted in the introduction, an association between the
probability of starting, but not doing, Prize-winning work, as we find below, would be
less subject to reverse causality concerns.
Before we can commence with estimation, we need to specify Ti , the latest year
someone can win the Prize. We specify the maximum age at which the individual can win
the Prize and be in our sample as the min(70, age in 2003) and adjust Ti accordingly. For
now we assume that X i (τ i + t ) is unaffected by winning the Prize when calculating
S (Ti | θi ) in (4); we will experiment with alternative assumptions in future work.
III. Data
Our data contains two components: (1) the institutional affiliations of each Nobel
Laureate in each year of his or her career and (2) the years in which each Nobel laureate
began or did his or her Nobel Prize-winning work. Paula Stephan and Sharon Levin
generously provided data on the year in which each laureate began (and ended) their
prize-winning research agenda (see Stephan and Levin [1993]). Benjamin Jones provided
data on the year in which each laureate did his or her Prize-winning work (see Jones
[2010]). We integrated and extended both series.
10
The data also contain a variety of other background information, including the
institutions that the laureates attended for any bachelors, masters, or doctoral work and
their institutional affiliations in every year of their career. We define the beginning of a
laureate’s career as 3 years before receipt of his or her first doctorate or highest other
degree (the earliest point at which a laureate began doing Prize-winning work) until age
70 (the latest). 11
In most specifications the independent variable of interest is the number of Nobel
Laureates in an individual’s own field present in a given year in the locations with which
they were affiliated. In the present version, we measure locations using the city of the
institution. A laureate with Li (τ i + t ) >2 affiliations in a given calendar year τ i + t was
treated as having spent 1/ Li (τ i + t ) of their time in each location both for computing the
number of laureates in that location in the year and for computing the number of laureates
they were e xposed to. 12
For each field, table 1 shows sum of the total number of years spent in the city
across all laureates who lived in a city at some point in their career (i.e. from 3 years
before receipt of the highest degree until age 70) for the 20 cities with the most Nobel
laureate-years. (Thus, a laureate who spent 10 years living in a city would contribute 10
years, while one who spent 20 years there would contribute 20 years.) The table shows
considerable clustering. Cambridge, Massachusetts and Cambridge, England are
consistently at or near the top of the rankings, with 1450 and 1409 laureate years
11
In the early years in the sample, not all Nobel laureates received doctorates, in which case the highest
degree was used. Some laureates, especially in Medicine or those trained in Germany, have 2 doctorates.
For these laureates, the first doctorate was used.
11
respectively across all 3 fields.
IV. Results
Table 2 reports our main results on the determinants of when the laureates begin
or do their prize winning work. The first column shows that being around one additional
laureate is associated with a .6 increase in the probability of beginning prize-winning
work. Focusing on the pooled estimates, adding one own-field laureate in a person’s area
is associated with a 5.4 percentage increase in the probability that they start their Nobel
Prize-winning work. 13 Thus, the presence of Nobel laureates appears to assist in
identifying important research agendas. Interestingly, being in multiple locations, which
occurs when people visit a place temporarily or move, is associated with a .8 increase in
the probability of beginning prize-winning work, suggesting that being exposed to a new
environment, different people, and new ideas may also play a role in identifying
important research agendas. The probability of starting prize winning work also increases
with experience for each laureate. These results are robust to the inclusion of
heterogeneity (in column 2).
The probability that people do their prize-winning work (in column 3) is unrelated
to the number of other laureates present or being in multiple locations. Both coefficients
are considerably smaller and statistically insignificant. The contrast between the estimates
for beginning and doing suggests that being around other Nobel laureates and in a new
environment helps generate the ideas and research agendas that lead to Prize-winning
12
Thus, the number of laureates in each location is given a weight of
13
1/ Li (τ i + t )
In future drafts we will use the Delta method to calculate standard errors for these conditional
probabilities.
12
creativity, but is not a factor in bringing that work to fruition.
As indicated, the relationship between the number of laureates present and the
probability of starting, but not doing Prize-winning work reassuring from the perspective
of causality. A positive relationship between the number of Nobel laureates in a person’s
location and the probability of doing, but not starting, Prize-winning work might suggest
that people who are working on particularly promising projects are (successfully)
recruited by places with many important scientists. On the other hand, the association
between the probability of starting, but not doing, Prize-winning work, that we find is
less subject to reverse causality concerns.
The remainder of the table probes the robustness of these estimates. Many
laureates receive the prize for joint work. To address this concern, we identified all
laureates who received the prize for joint work and exclude them from sample in columns
4 and 5, which has little effect on the estimates.
Our main results focus on own-field Nobel laureates, but laureates in one field
may benefit from laureates in another field (Jacobs’ [1961] well-known work, for
instance, emphasizes spillovers across domains.). Moreover, insofar as the most
prestigious institutions, with laureates across multiple fields, identify and recruit people
who are about to begin or do prize-winning work, one would expect a strong relationship
between other-field laureates and the probability of beginning or doing prize winning
work. Columns 6 and 7 include the number of other-field laureates present. These
estimates are smaller than those for own-field laureates and imprecise. Given the
imprecision in the estimates, it is impossible to rule out cross-field spillovers, but the
estimates are reassuring from a causality perspective.
13
Lastly, nepotism may play a role, with laureates lobbying for their current or
former colleagues to win. Lobbying, in and of itself, would not bias our results toward
finding effects unless people lobby for their colleagues to win specifically for the work
that they began (but did not do) while they were together. Moreover, analyses of the
Nobel Prize indicate that the critical point politically is in the committees and in the
Swedish Academy, not at the nomination phase (e.g. Friedman [2001]). To test for this
effect, we note that in fields where laureates receive the prize at earlier ages, there is more
opportunity for such nepotism. Columns 8 and 9 include interactions between the mean
age at which people in a field received the prize and the number of own-field laureates
present. While the estimates are imprecise, they do not indicate a smaller effect in fields
where people receive the prize at later ages, pointing against a strong nepotism effect.
Changes with Age and Over Time
The benefits of spillovers may vary over the career both for the person generating
them and for the person receiving them. Thus, researchers who have recently done their
important work may generate more benefits for their colleagues, as may older researchers,
who have accumulated more knowledge. Zuckerman [1977] also points to the benefits of
training with someone, which might suggest that people benefit from spillovers the most
early in their careers. Table 3 explores these hypotheses. Column 1 shows that the people
who are in the prime of their careers, in the sense that they are within 10 years of doing
the prize-winning work generate considerably larger benefits than laureates who are
outside of their prime. Consistent with an accumulated knowledge story, column 2 shows
that older laureates generate larger benefits than younger laureates. Column 3 turns to
look at the point in a laureate’s career when he or she benefits most from spillovers, by
14
interacting the number of laureates present with a laureate’s own age. Interestingly, these
estimates indicate that more experienced researchers benefit the most from spillovers,
suggesting that any benefits of training around other laureates do not manifest themselves
immediately (e.g. by increasing the probability that one ever does prize-winning work
rather than increasing the probability of doing prize-winning work right at the outset of
the career).
The effect of spillovers may have changed over time with improvements in
transportation and communication technology (Morse, Kim, and Zingales [2006]). At the
same time, Nobel laureates, who were once heavily concentrated in Europe, have spread
out, especially to the United States (Weinberg [2007]), which may offset these
improvements. To test whether the net effect of improvements in transportation and
information technologies have reduced the importance of spillovers, Column 4 includes
an interaction between the number of own-field laureates present and calendar year.
Although the estimate is imprecise, it shows that the benefits of proximity have gone
down.
Interpretation of Estimates
Assuming that our estimates can be interpreted causally, they give the effect of
being around more other Nobel laureate on the probability of starting doing Prize-winning
work for people who at some point do Nobel Prize-winning work. These estimates give a
sense of how being around more other important scientists affects the probability of
starting important work for people who do or are close to the margin to do important
work. To see this, recognize that being around important scientists will have little effect
on the probability of starting important work for people who are far from the margin to do
15
important work (although it may have other benefits). Thus, one needs to focus on others
whose potential for important work is comparable to the Nobel laureates.
IV. Conclusions
We find a small, but meaningful relationship between the number of Nobel
laureates in a person’s field that he or she is around and his or her probability of starting
doing Nobel Prize-winning work, but no relationship between the probability of actually
doing Nobel Prize-winning work and the number of Nobel Laureates present. Insofar as
our estimates do not completely address causality, even our small spillovers might be
overstated somewhat. Thus, our estimates provide support for modest spillovers from
innovative clusters.
While economists have sought to identify the causal effect of knowledge
spillovers, it is worth noting that even in the absence of any causal spillovers, local
authorities can gain from generating knowledge clusters. If innovators are attracted to an
area by the presence of other innovators, institutions or governments that want to generate
innovative hot spots may have an incentive to build clusters even if there are no benefits
from spillovers.
16
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18
Table 1. Most Common Locations.
Chemistry
Laureate
Location
Years
Cambridge, England
465.00
Cambridge, MA
356.00
Berkeley
353.67
Zurich, Switzerland
306.33
Munich, Germany
305.17
London, England
242.00
Gottingen, Germany
213.00
New York City
211.33
Berlin, Germany
189.25
Oxford, England
181.50
Paris, France
147.83
Ithaca, NY
145.50
Chicago
137.83
Los Angeles
133.00
Heidelberg, Germany
121.00
Stockholm, Sweden
114.67
New Haven
105.33
Pasadena
101.83
Uppsala, Sweden
93.17
Stanford
90.00
Medicine
Physics
Laureate
Years
847.00
700.50
549.33
393.67
369.33
289.00
243.50
239.33
183.50
178.00
171.00
170.33
166.00
164.00
147.50
132.50
132.00
121.50
120.00
108.50
Location
New York City
Cambridge, MA
London, England
Cambridge, England
Paris, France
St. Louis
Pasadena
Stockholm, Sweden
Bethesda
Seattle
Oxford, England
Berlin, Germany
Basel, Switzerland
Copenhagen, Denmark
Munich, Germany
Vienna, Austria
Baltimore
Boston
La Jolla
Zurich, Switzerland
19
Location
Cambridge, MA
Cambridge, England
Paris, France
Moscow, Russia
New York City
Stanford
Berlin, Germany
Chicago
Princeton
Pasadena
London, England
Ithaca
Berkeley
Zurich, Switzerland
Copenhagen, Denmark
Geneva, Switzerland
Murray Hill, NJ, USA
Munich, Germany
Heidelberg, Germany
Amsterdam, Netherlands
Laureate
Years
593.08
550.17
483.33
410.33
366.50
312.50
289.83
280.75
247.17
244.00
182.33
178.75
173.92
153.25
136.50
135.42
133.17
100.17
99.17
95.50
Table 2. Being with other Laureates and the Probability of Beginning or Doing Nobel Work.
(1)
(2)
(3)
(4)
(5)
(6)
Begun
Begun
Did
Begun
Did
Begun
Own Field Laureates
0.589*** 0.598***
0.263
0.607**
0.333
0.328
(0.214)
(0.221)
(0.216
(0.247)
(0.247)
(0.3)
In Multiple Locations 0.758*** 0.81***
0.042
0.676***
-0.073
0.759***
(0.113)
(0.117)
(0.151)
(0.133)
(0.18)
(0.113)
Other Field Laureates
0.229
(0.189)
Own Field Laureates *
Mean Age at Winning
Year
-0.09
-0.104
0.058
-0.205
-0.011
-0.115
(0.292)
(0.301)
(0.421)
(0.336)
(0.478)
(0.291)
Year2
0.231
0.262
0.311
0.359
0.414
0.258
(0.358)
(0.367)
(0.451)
(0.407)
(0.507)
(0.358)
Chemist
-0.112
-0.119
-0.182
-0.056
-0.173
-0.137
(0.136)
(0.142)
(0.195)
(0.16)
(0.229)
(0.136)
Physicist
-0.193
-0.197
0.258*
-0.135
0.289
-0.203
(0.135)
(0.14)
(0.154)
(0.159)
(0.178)
(0.135)
Duration 0-5 Years
-0.986*** -1.12*** -1.818*** -0.963*** -1.657*** -0.993***
(0.133)
(0.152)
(0.194)
(0.155)
(0.212)
(0.133)
Duration 6-10 Years
-0.37*** -0.377*** -0.704*** -0.374** -0.746*** -0.372***
(0.129)
(0.129)
(0.134)
(0.152)
(0.157)
(0.129)
Constant
-1.942*** -1.945*** -2.343*** -1.95*** -2.35*** -1.943***
(0.147)
(0.15)
(0.154)
(0.171)
(0.181)
(0.148)
Type 2 Constant
21.554
(4.13e8)
Gamma
-3.431***
(0.815)
Share Type 1
96.87%
Likelihood
-1545.717 -1542.276 -1702.409 -1149.486 -1263.77 -1544.897
Observations
489
489
489
363
363
489
20
(7)
Did
0.252
(0.298)
0.043
(0.151)
0.01
(0.191)
(8)
Begun
-2.625
(6.521)
0.756***
(0.113)
(9)
Did
0.287
(7.017)
0.042
(0.151)
5.853
-0.039
(11.796) (12.645)
0.056
-0.083
0.053
(0.423)
(0.294)
(0.425)
0.314
0.223
0.317
(0.452)
(0.361)
(0.457)
-0.186
-0.094
-0.184
(0.197)
(0.141)
(0.204)
0.257*
-0.136
0.256
(0.154)
(0.17)
(0.212)
-1.818*** -0.987*** -1.818***
(0.194)
(0.133)
(0.194)
-0.704*** -0.371*** -0.704***
(0.134)
(0.129)
(0.134)
-2.343*** -1.967*** -2.342***
(0.154)
(0.157)
(0.172)
-1702.408 -1545.508 -1702.41
489
489
489
Note. Standard errors are reported in parentheses.
21
Table 3. Differential Spillovers on Beginning Nobel Work by Career Stage and Time.
(1)
(2)
(3)
Own Field Laureates
-0.315
(0.389)
Own Field Laureates w/in
0.953***
10 Years of Doing
(0.387)
Own Field Laureates outside
0.147
10 Years of Doing
(0.439)
Own Field Laureates <=10
-0.193
Years of Experience
(0.665)
Own Field Laureates >10
0.783***
Years of Experience
(0.27)
Own Field Laureates * Own
10.651***
Experience
(4.121)
Own Field Laureates *
Calendar Year
In Multiple Locations
0.76*** 0.756*** 0.757***
(0.113)
(0.113)
(0.112)
Year
-0.111
-0.042
-0.03
(0.137)
(0.297)
(0.294)
Year2
-0.197
0.165
0.156
(0.135)
(0.366)
(0.358)
Chemist
-0.978*** -0.107
-0.099
(0.133)
(0.136)
(0.136)
Physicist
-0.37***
-0.182
-0.196
(0.129)
(0.136)
(0.131)
Duration 0-5 Years
-1.943*** -0.94*** -0.723***
(0.147)
(0.138)
(0.159)
Duration 6-10 Years
-0.319*** -0.214
(0.133)
(0.133)
Constant
-1.983*** -2.094***
(0.153)
(0.149)
22
(4)
0.591***
(0.218)
-0.481
(0.34)
0.749***
(0.112)
-0.206
(0.294)
0.44
(0.373)
-0.127
(0.136)
-0.203
(0.137)
-0.976***
(0.132)
-0.36***
(0.128)
-1.907***
(0.147)
Likelihood
-1545.032 -1544.91 -1541.356 -1544.652
Note. Standard errors are reported in parentheses. All estimates include 489 specifications.
23