A Framework for Quantifying the Economic Spillovers from Government Activity
Applied to Science
May 2011
Subhra B. Saha (Cleveland State University)
s.b.saha@csuohio.edu
Bruce A. Weinberg (Ohio State University, IZA, and NBER)
weinberg.27@osu.edu
ABSTRACT
Governments invest heavily in science and those investments are increasingly being
justified in terms of the economic spillovers they generate, such as “jobs created.” Yet
there are no accepted methods for quantifying these benefits and their magnitude is
widely disputed. We analyze the ways in which science generates economic benefits; lay
out how to (and not to) quantify those benefits; and provide a range of estimates. While
our estimates vary considerably across specifications, our baseline estimates indicate that
a $1B increase in science spending might raise wages by $1.68B and that these wage
effects are likely to understate the effects on productivity. We also find that a $1B
increase in science might generate 92,500 jobs, with 90% of these jobs being missed even
using state-of-the-art “job creation” methods. Our methods can be applied to measure the
local productivity spillovers from other government activity as well.
JEL Codes: O33, O38, H54, J20
Keywords: Economic Impacts of Science, Knowledge Spillovers, Job Creation
We wish to thank Paul Bauer, Karen Berhnardt-Walther, David Blau, Steve Cosslett,
Timothy Dunne, Don Haurin, Francisca Richter, Steve Ross, Mark Schweitzer, and
Stephan Whitaker for helpful discussions; seminar participants at the Federal Reserve
Bank of Cleveland, Ohio State University, the London School of Economics, and the
Science of Science Measurement workshop, and the National Academies for comments;
and the Federal Reserve Bank of Cleveland and the National Science Foundation for
financial support. Paul Bauer and Mark Schweitzer also provided patent data. Mason
Pierce and Anna Winston provided excellent research assistance.
1
I. Introduction
Governments undertake a wide range of activities and are increasingly measuring
the benefits of those activities in terms of their economic impacts, such as the number of
“jobs created.” This paper provides a formal analysis of the ways in which one
government activity, namely investments in science, generates economic benefits; lays
out the best ways to (and not to) quantify those benefits; and provides a range of
estimates. Science investments provide a useful window into measuring the impact of
government activity. Governments are major supporters of scientific research (OECD
[2008]); and since 2003, the United States Federal Government spent roughly $60 billion
annually (in 2009 dollars) on basic and applied research (Clemins [2009]). Like many
government activities, these investments are increasingly being justified in terms of the
economic spillovers they generate, but there is no widely-accepted method for
quantifying these benefits and the size of these benefits is widely disputed (Macilwain
[2010]).
It is important to be clear about the ways in which science generates economic
benefits. Perhaps the greatest benefits from science come from the new knowledge,
products, and processes that derive from it (just as the primary benefit from building
roads, for instance, is to improve transportation). We refer to these benefits as the “direct
benefits” of science. At least since Vannevar Bush’s 1945 Science: The Endless Frontier
report, government support for science has been motivated by these direct benefits. In
recent years, however policy discussions have increasingly focused on what we will refer
to as the “spillover benefits” from science, the benefits that arise over and above the
direct benefits, such as the number of “jobs created” by science. There are many reasons
2
why science would generate spillover benefits, especially locally. Scientists and their
students often work for (or start) companies, increasing the translation and absorption of
science; graduates from research institutions may be better equipped to perform in the
knowledge economy; and science may generate infrastructure, including equipment and
facilities that support industrial innovation, or provide a hub for innovation (e.g. the
research institutions in San Francisco may have attracted venture capital that then
generated more innovation).1 Our focus is in this area, on the benefits that are generated
by science over and above the knowledge and innovations that arise from it directly, an
area of increasing policy importance.
At a theoretical level, our work points to the advantages of measuring the
economic spillovers from science and other government activities using the effects on
wages rather than the number of jobs created. Despite the policy interest in job creation
metrics, we show that creating jobs per se does not increase social welfare, although it
may drive up wages, benefiting households, albeit at the expense of firms. By contrast,
the effect of science on wages provides a useful measure of the economic spillovers from
science. Specifically, it provides a direct measure of the increase in surplus received per
worker and, if the job creation benefits of science are small relative to spillovers, the
effects of science on wages provides a lower bound for the effects of science on
productivity and total surplus.
To estimate the spillover benefits from science, we relate wages and changes in
employment in a metropolitan area to academic R&D as a measure of scientific activity.
One challenge to this approach is that academic R&D may well be endogenous – cities
1
Salter and Martin [2001] and Scott, Steyn, Geuna, Brusoni, and Steinmueller [2001] discuss mechanisms
and survey the literature.
3
with more academic R&D may have more (or less) desirable amenities. In this case,
workers would tend to move to those cities depressing wages. This effect will be offset if
people with higher (unmeasured) abilities disproportionately move to cities with more
academic R&D. On the other hand, universities may be located in cities with lower
productivity for economic or political reasons or because they were founded by
industrialists in cities that have lagged economically during deindustrialization (Crispin,
Saha, and Weinberg [2011]). To address these concerns, we include metropolitan area
fixed effects. These account for all time-invariant differences across cities (e.g. in climate
or geography). We also employ a share-shift index for academic R&D. This instrument
exploits initial differences across metropolitan areas in the academic fields in which their
R&D is concentrated interacted with changes in federal support for different fields (e.g.
Baltimore is heavily focused in the life sciences, so fluctuations in life sciences funding
affect Baltimore more than most cities).
While our estimates vary considerably across specifications, our baseline
estimates indicate that a $1 billion increase in science spending would likely raise wages
by $1.68 billion and that the wage effects are likely to understate the effects on
productivity. Our baseline estimates also indicate that a $1 billion increase in science
would generate 92,500 jobs, and that roughly 90% of them would be missed even using
state-of-the-art “job creation” methods.
It is important to note that while our analysis focuses on science, our points about
measuring impacts (and the associated empirical challenges) apply far beyond science
and research. Wage impacts are likely to measure the increase in social welfare and
household surplus better than the number of jobs created for many public works,
4
including investments in local infrastructure. Similarly, the challenge of unobserved
differences across cities applies to cross-city regression analysis quite generally.
Although the literature on the spillover benefits from science is small, there are a
few related studies. An early study by Beeson and Montgomery [1993] finds that
university activities are related to the share of scientists and workers with science
degrees, but not significantly related to income, employment rates, net migration rates, or
the share of employment in high tech industries in a cross-section of cities. Using panel
data and instrumental variables to account for the endogeneity of science spending, Saha
[2008] finds positive and significant effects of academic R&D and science and
engineering degrees in a city on income. Kantor and Whalley [2009] find small but
statistically significant agglomeration spillovers caused by university spending induced
by shocks to university endowments, although their university activities include a
substantial amount of non-scientific activity. There is some evidence that the benefits of
science are increasing over time (Goldstein and Renault [2004]; Saha [2008]; and
Crispin, Saha, and Weinberg [2010]). Hausman [2010] links these increasing spillovers to
the Bayh-Dole Act of 1980 giving universities the rights to license their research.
Given that research universities produce large numbers of students as well as
science, it is important to control for both individual education and the education
distribution of the population. Abel and Deitz [2009] find that controlling for the share of
college graduates in the population reduces the estimated effect of academic R&D on
wages.
Lastly, there is evidence linking University research to innovation. Zucker, Darby,
and Brewer [1998] find that the presence of star scientists is associated with more
5
biotechnology startups. Carlino and Hunt [2009] find academic R&D has positive and
significant effects on innovative activity as measured by patents Bauer, Schweitzer, and
Shane [2009] find that patenting and human capital levels are important determinants of
per capita income. These results complement each other – science generates patents,
which in turn raise income – providing a first step toward understanding the ways in
which science affects wages.
Thus the literature has explored how science is related to a wide range of
economic outcomes, but there are no accepted methods nor is there agreement on the
outcomes and the results are mixed. We contribute to this literature by using rigorous
economic principles to compare the relative merits of different approaches. We also
produce a set of estimates using a strong research design that allow us to compare the
various estimates empirically.
II. Theory
We model how a government paying to employ people in science (or another
activity) affects employment, wages, surplus, and the division of surplus. Our model
comprises 2 sectors demanding homogenous labor, a science sector, which is government
funded, and a competitive, private, non-science sector. Our model accounts for the fact
that science employment may generate productivity spillovers that raise the value of the
marginal product of labor in the non-science sector as well as direct benefits in the form
of better knowledge and technological advances. We do not account for any multipliers,
which might be applied to the job creation estimates.2
2
The Congressional Budget Office [2010] provides a convenient overview of the literature on multipliers.
It argues for multipliers for government purchases of goods and services between 1 and 2.5. Even an
estimate at the high end of this range would not qualitatively alter our results. Moreover, the benefits
captured by the multiplier may be geographically dispersed, while we focus only on local benefits.
6
In the non-science sector, the inverse demand curve for labor is given by the value
of the marginal product of labor, V q, s  . The value of the marginal product is decreasing
in the quantity of labor employed by the non-science sector, q, and non-decreasing in the
number of people employed producing science, s because of productivity spillovers. In a
competitive equilibrium, the wage paid by firms satisfies, W  V q, s  . Science generates
direct benefits (in the form of knowledge and technological improvements) of Bs  for
the population as a whole. The reservation wage for the qth worker is W  Rq  , defining
the inverse supply curve for labor.
The equilibrium is a wage, W, an (exogenously set) level of employment in
science, s, and a total employment level, Q, which includes employment in both the
science and non-science sectors (employment in the non-science sector is Q-s) such that,
W  RQ  and W  V Q  s, s  .
(II.1)
(Here and below, capital Q and W denote equilibrium values.)
In analyzing the effect of science on the market, we will refer to Figure 1. The
equilibrium in the absence of any science is indicated by (i). We decompose the effect of
science on the equilibrium into two components, the “productivity spillovers” from
science, given by the movement from (i) to the counterfactual point (*), and a “job
creation” effect due to the effect of hiring s people doing science, given by the movement
from (*) to (ii).
Effect of Science on Employment and Wages
We begin by determining the effect of science on employment and wages. The
effect of increasing science on the equilibrium can be obtained by totally differentiating
the equilibrium conditions in (II.1). Formally,
7
dW
dQ
dW
 dQ 
and
 Rq
 Vq 
 1  Vs .
ds
ds
ds
 ds

(II.2.a and II.2.b)
Equating these and solving for the change in quantity implies,
 Vq
Vs
dQ


 0.
ds Rq  Vq Rq  Vq


 


Job Creation
(II.3)
Productivity
Spillover
Using this expression, the log change in employment from an increase in science
employment (relative to total employment) is
d ln Q dQ
S
1
Q

 D

Vs
 0 .3
S
ds
1
1
ds 
W



D 
S
Q
Job Creation



(II.4)
Productivity Spillover
As reflected by the first term, creating science jobs raises total employment less than 1for-1 with the increase in science employment because science employment crowds out
non-science employment by raising wages. These effects can be seen in Figure 1, where
total employment increases from (*) to (ii) by less than the increase in science jobs,
indicated by the distance s, because wages are higher at (ii) than at (*). Put somewhat
differently, the number of jobs created doing science, s, overstates the net increase in
employment. The greater the labor demand elasticity (the smaller the labor supply
elasticity), the more job creation overstates the number of jobs created net of those
crowded out.
Increasing science also raises the value of the marginal product in the non-science
sector if Vs  0 . In Figure 1, the movement from (i) to the counterfactual point (*) gives
3
To derive these expressions, we use the conditions
Vq  
denotes the demand elasticity (written as positive) and
8
S
1 W
1 W
D
and Rq  S
, where 
D
 Q
 Q
denotes the supply elasticity.
the effect of productivity spillovers on employment in the private sector. As is intuitive
and can be seen from (II.4), the productivity spillover effect on employment is increasing
in both the labor demand and supply elasticities.
Given that the labor supply curve is stable, wages increase by the amount that it
necessary to induce the increase in total employment. Multiplying the slope of the inverse
supply curve (II.2.a) by the employment response (II.3) and rearranging yields,
dW
1
W
D
 D

Vs  0 .
S
S
ds 
Q 



 D


Job Creation
(II.5)
Productivity Spillover
Wages increase both because demand increases in the science sector (given by the
distance s in the figure labeled “job creation”) and because of the productivity spillover to
the non-science sector. These effects can be seen separately in Figure 1 as the movement
from (*) to (ii) and the movement from (i) to (*). The effect of science on wages is
decreasing in the elasticity of labor supply – as the labor supply elasticity increases, the
same increase in employment can be achieved with a smaller increase in wages.
Below we argue that on the order of 90% of the impacts of science operate
through productivity spillovers and that job creation accounts for on the order of 10% of
the effects. If so, the wage response will be increasing in the demand elasticity.
Moreover, any changes in wages will be dominated by the productivity spillover effect,
and the effect of science on will provide a convenient lower bound for the productivity
spillovers from science. Under this assumption, the change in wages approaches the
change in productivity as  D   and/or  S  0 .
Effect of Science on Surplus
Having characterized the effect of increasing science on employment and wages,
9
we turn to the effects of increasing science on total surplus and its division. Again, we
distinguish the job creation effect from the productivity spillover effect. We also assume
that science generates social benefits (in the form of new knowledge and technologies).
These benefits are captured in a reduced form way by the curve Bs  and generate social
welfare, but not employment or wages.
Total surplus in the model is
TS  
Qs
0
s
Q
V q, s dq   B~
s d~
s   Rq dq .
0
0
(II.6)
Intuitively, total surplus is the value of the marginal product received by firms in the nonscience sector plus the direct benefits of science less the opportunity cost of the workers
who are employed in science. (Here and elsewhere we ignore the deadweight loss of
taxation, although it may be considerable.)
The effect of increasing science on total surplus is,
Qs
dTS
  Vs q, s dq  Bs   RQ  .
0
ds
(II.7)
The first term gives the increase in surplus from productivity spillovers as the value of
marginal product increases for each of the Q-s workers employed in the private sector.
This is reflected in Figure 1 by the parallelogram (a). The second two terms combine the
direct benefits of science and the job creation effects. The science itself generates direct
benefits but imposes a cost in the form of the opportunity cost of the people employed to
produce it. This difference is given by the region (b) in Figure 1. If the direct benefits of
science are less than the amount it costs then (b) becomes a loss of surplus. It is
noteworthy that job creation per se only generates deadweight loss. If, for instance, the
science being produced generates no direct benefits (v(s)=0) then job creation generates a
10
deadweight loss equal to workers’ opportunity costs, given by the area (c). Thus, from a
welfare perspective, the gains from science are the direct benefits and the productivity
spillovers, with job creation itself generating a deadweight loss.
Policy discussions frequently focus on the benefits received by households, so we
look particularly at the producer surplus, which accrues to workers. The producer surplus
is PS   W  Rq dq . The change in producer surplus from increasing science is
Q
0
equal to the increase in the wage
dPS
dW
. Thus, if one wants to estimate the effects
Q
ds
ds
of increasing government spending on science, one wants to focus on wage benefits.
Our model has a number of implications. First, it highlights the limitations of job
creation as a measure of the benefits from science (or other government activity). Job
creation per se does not increase social welfare and, insofar as job creation drives up
wages, the number of jobs created will overstate the net increase in employment. At the
same time, the model points clearly to the advantages of measuring the benefits of
science (and other government activities) using changes in wages. If productivity
spillovers dominate the effect of science on wages, then the effect of science on wages
provides a lower bound on productivity spillovers. Under any circumstances, the increase
in wages measures the increase in consumer surplus received by each worker. Lastly, it
generates specific predictions for how the demand and supply elasticities affect how
wages (and employment) respond to science.
III. Estimation
To estimate the relationship between science and employment and wages, we run
reduced-form cross-city regressions. There are large variations in the sizes of cities and
11
researchers have emphasized that flows of workers respond to demand shocks slowly
(Blanchard and Katz [1992]), so we estimate the effects of science on employment using
10-year changes in (log) employment. As a price, wages respond to demand more
quickly, so we a use contemporaneous wage measure. (Wage regressions with lags of
science are generally similar, but less precise than those reported here. Consequently, our
ability to comment on the timing of impacts is limited.) Our models are of the form,
W
ln wcti  S ct  W  Z ct  W  xcti W   tW   cW   ctW   cti
(III.1)
E

ln  ct 10   S ct  E  Z ct  E   tE   cE   ctE .
 Ect 
(III.2)
Here ln wcti denotes the log wage of person i in city c at time t; E ct denotes employment
in city c at time t; S ct denotes academic R&D per capita in city c at time t; Z ct denotes
other characteristics of city c at time t; x cti denotes characteristics of person i in city c at
W
time t; and the  t ,  c ,  ct , and  cti
denote time, city, city-time, and individual level
effects, which can be treated as fixed effects or error components. It is worth noting that
we estimate our wage equation at the individual level, adjusting our standard errors for
the presence of metropolitan area effects. Employment growth is estimated at the
metropolitan area using 10-year changes between census years.
As indicated, uncontrolled differences in productivity or amenities will bias ̂ W .
Changes in productivity or amenities will also bias ˆ E . If, for instance, universities tend
to be located in places where productivity would otherwise be low (e.g. because of a
lower opportunity cost of real estate) or, in the case of employment, where productivity is
decreasing, ̂ W and ˆ E will both be biased downward. (The opposite is true if the most
12
science is in places where productivity would otherwise be high or increasing.)
Interestingly, if science is highest in places with (increasingly) desirable consumption
amenities then ̂ W will be biased downward and ˆ E upward. (The opposite patterns will
generate the opposite bias.) It is also possible that cities with higher S ct will attract the
most skilled workers. If so, we would expect ̂ W to be biased upward (it is unclear what
effect that would have on ˆ E ).
To address these biases, we estimate (III.1) with fixed effects and with
instrumental variables. The fixed effects estimates control for time-invariant differences
in production and/or consumption amenities across cities, but not time-varying
differences, including changes in innovation and average education that are driven by
changes in wages. We estimate equation (III.2) in differences (which are equivalent to
fixed effects given that we have 2 10-year changes) and with instrumental variables. The
difference estimates will account for secular trend differences in growth rates across
cities (e.g. the migration from the Northeast and Midwest to the South and West).
Instrumental Variables Strategy
To address these time-varying differences across cities, we turn to an instrumental
variables strategy, relying on share-shift indexes for scientific activity. These instruments
exploit historic variations in the research focus of different cities interacted with trends in
federal support for different fields. To illustrate our approach, consider a simple, stylized
example with two sectors – information and computing technology and bio-medical
technology. West Lafayette, Indiana and Baltimore both have considerable academic
R&D, but R&D in West Lafayette is more focused in engineering while Baltimore is
more heavily focused on the life sciences technology. An increase in life sciences R&D
13
will likely raise R&D in the Baltimore more than in the West Lafayette.
Formally, Let e fnt and e fct denote spending on field f in year t nationally and in
city c. Total spending in year t in city c is ect   f e fct . Field f’s share of all spending in
city c in year t is s f |ct 
e fct
ect

e fct

f
e fct
. The share shift index starts with the shares in
some base year, t=0, which we take to be 1973. Then for each city c the imputed growth
(where 1 equates to no change) in spending between 0 and year t is,
e fnt
ect 
  f s f |c 0
ec 0 
e fn 0
 
 
  f
 
e fc 0

f
e fc 0
e fnt 
e fn 0 

(III.3)
For each city, the implied growth is a weighted average of the growth in academic R&D
spending in each field where the weights for each city correspond to the share of
spending in that city in field f. We then interact these growth rates with per capital
spending in the base year ( ecPC
0 ) to get

e fnt

ect ecPC
0    f s f |c 0
e fn 0

 PC 
ec 0  

 f


e fc 0

f
e fc 0
e fnt  PC
ec 0 .
e fn 0 

(III.4)
These are our estimates of predicted academic R&D spending, which vary across cities
and over time within cities.
Instrumental Variables Estimation
In our individual-level wage regressions, (III.1) we instrument for the
metropolitan area-level science variable, S ct . Our first stage equation contains individual
characteristics, x cti and, insofar as there is selection into cities, these characteristics may
themselves be endogenous. To address this concern, we estimate the mean of the
14
individual characteristics in city c in year t, xct , and use the deviation of the
characteristics from the city-time mean, xcti  xcti  xct as instruments for x cti .
Formally, the first stage equations for our wage regression (III.1) are of the form,
IEct  H ct  IE  Z ct  IE  xcti IE   tIE   cIE   ctIE   ctiIE
xcti  H ct  x  Z ct  x  xcti x   tx   cx   ctx   ctix
(III.5.a)
(III.5.b)
where H ct denotes the historic instruments. In both (III.5.a) and (III.5.b) the unit of
observation is an individual i in city c at time t, with all people in the city in that year
being assigned common values for the city-time variables, S ct , H ct , and Z ct .
Instrumenting for x cti with xcti eliminates any bias from selective migration and
eliminates noise in the predicted values of IE ct generated by the inclusion of individual
level variables in the first stage (because in (II.5.a), ˆ IE  0 by construction). The first
stage regressions for the employment growth equation (III.2) are straightforward because
this model is estimated at the metropolitan area-level without individual controls.
Comparison of Estimates from the Different Strategies
If cities where incomes are higher produce more science (e.g. because they invest
more in scientific institutions) including city fixed effects and using instrumental
variables should reduce the magnitude of the estimates. Interestingly, most work (Saha
[2008]; Bauer, Schweitzer, and Shane [2009]; and Kantor and Whalley [2009]) finds that
fixed effects and/or instrumental variable estimators are larger than the OLS estimates.
This finding suggests that scientific activity is highest in areas that are appealing places to
live or where productivity would otherwise be lower (e.g. because universities are sited in
out-of-the-way places).
15
The various strategies emphasize different sources of variation in innovation and
average education. In particular, the fixed effects estimates place more weight on the
high- to middle-frequency variation compared to models without fixed effects, including
our instrumental variables estimates. For a given magnitude of change, higher-frequency
shocks should have smaller effects on labor supply, consequently wages should respond
more.
There may be measurement error in scientific activity. Insofar as there is timevarying measurement error, fixed effects estimates are likely to suffer most from
attenuation bias. On the other hand, the instrumental variables estimates will correct for
attenuation bias.
IV. Data
We draw together data from a variety of sources. Our main independent variable
is academic R&D in a metropolitan area for 1980, 1990, and 2000. We also use data on
patenting and the share of the population with a college degree as controls. We use
historic data as instruments. Our outcomes are wages and employment, which are drawn
from and constructed from the Census Public Use Micro Samples. We also proxy for the
supply and demand elasticities using the demographic composition of the population and
the industrial composition of employment. Our data draw on and extend Saha [2008] and
Crispin, Saha, and Weinberg [2010].
Science, Patenting, and Average Education Variables
Data for academic R&D expenditures for individual colleges and universities are
obtained from the National Science Foundation’s Survey of Research and Development
Expenditures at Universities and Colleges. Spending is reported by field (physical
sciences, life sciences, math, engineering, geology, psychology, and social science, which
16
includes the humanities) and source (e.g. federal, state, local, and industrial) for 1980,
1990 and 2000.4 Matching these schools to the Carnegie Classification [2002], about 93%
of universities and colleges that have positive R&D are Ph.D. granting research schools,
or mining engineering schools. The national observatories and national laboratories,
which are large producers of scientific research, are excluded from this sample. R&D is
measured in thousands of dollars. Total R&D from all universities and colleges is 64.3
billion in 1980 rising to 110.5 billion in 1990 and to 158.2 billion in 2000. The data is
aggregated to a metropolitan area level by matching the schools to IPUMS metropolitan
area codes. The New York, Boston, San Francisco, Chicago, and Los Angeles metros
have the most R&D but, not surprisingly, university towns like College Station, TX; State
College, PA; Iowa City, IA; Lafayette, IN; and Champaign, IL have the most R&D in per
capita terms. Our instruments for academic R&D are constructed using these data and are
described above.
Data on patents for individual metropolitan areas were generously provided by
Mark Schweitzer and Paul Bauer of the Federal Reserve Bank of Cleveland. Patent data
was extracted from government patent files. See Bauer, Schweitzer, and Shane [2006] for
additional details. Data on the education distribution in cities was estimated from the
census.
Outcomes: Census Micro-Data
We measure wages and construct employment using Census data from the
Integrated Public Use Microdata Series (IPUMS; see Ruggles; Alexander; Genadek;
Goeken; Schroeder; and Sobek [2010]). We use the 1980 1% unweighted metro sample,
4
A limitation of the data is that it does not include information on subcontracts to other organizations or
from other organizations. This is only a problem for subcontracts that are to or from organizations that are
outside of the lead institution’s metropolitan area.
17
the 1990 1% weighted sample, and the 2000 1% unweighted sample from IPUMS. These
samples were chosen to maximize identification of metropolitan areas. These data contain
a range of individual characteristics including education, gender, race, ethnicity, marital
status as well as city of residence, employment status, earnings, and weeks worked.
The wage sample is limited to non-institutionalized civilians not currently
enrolled in school living in metropolitan areas age 18 and above. Earnings are measured
in real weekly wages (deflated to 1982-1984=100 dollars). Individuals whose real weekly
wages were below 40 dollars or above 4000 are excluded from the sample as were people
who did not report earnings. Lastly, to ensure that our estimates capture spillovers from
academic R&D on the local economy, we discard people who are post-secondary
teachers or who work in universities or colleges. Our wage sample includes 382,337
individuals in 125 metropolitan areas in 1980, 420,280 individuals in 127 metropolitan
areas in 1990, and 459,574 individuals in 129 metropolitan areas in 2000.
The employment sample is similar to the wage sample except that it includes all
employed, non-institutionalized civilians over 18 who are not currently enrolled in school
(i.e. people with high, low, and unreported earnings are included in this sample). We also
include people who work at colleges and Universities and post-secondary school
teachers. Thus, when we measure changes in employment for this sample, we will
capture effects on researchers, but not student employees.
Our theory implies an important role for the labor demand and supply elasticities,
for which we generate proxies based on the industrial and demographic composition of
cities. Industries that produce goods for local consumption or that rely on inelastically
supplied local inputs, such as natural resources or climate have fewer options in terms of
18
where they locate their production and hence a less elastic demands for local labor. We
divide industries into three groups, two of which have low demand elasticities. Industries
that produce goods consumed locally cannot service the market if they move (e.g.
personal services and construction). We refer to these as “local consumption industries.”
We refer to industries that are capital intensive (e.g. manufacturing) or rely on natural
resources (e.g. agriculture and mining) and cannot relocate quickly or inexpensively as
“locationally-tied.” By contrast industries that produce broadly-consumed products and
that are not capital intensive to measure those with a high demand elasticity (e.g.
wholesale trade).5
Research shows that labor supply is less elastic for educated, prime age-men, than
women, less-educated men, or workers who are early or late in their careers
(Killingsworth and Heckman [1986]; Pencavel [1986]; Juhn [1992]; Blau [1994]; and
Blundell and MaCurdy [1999]). We proxy for the labor supply elasticity using one minus
the share of the workforce that is male with a high school degree or higher between ages
35 and 55. For convenience, we refer to this group as people with a “high” elasticity of
supply (although it might be more accurate to describe them as the share that does not
have a very low elasticity of supply).
The education level in a city may generate spillovers. Our use of micro-data
enables us to distinguish spillovers from an educated population from the direct effect of
the education of the individuals in a city.
5
Our local consumption industries are construction, transportation, retail trade, personal services, and
entertainment and our locationally-tied industries are agriculture, mining, manufacturing, and public
administration. The other industries are wholesale trade, finance, business and related services, and
professional services.
19
Other Metropolitan Area Characteristics
We also obtained data on a range of control variables for metropolitan areas like
population, crime rates and public school attendance from the State and Metropolitan
Data Set 1980, 1990 and 2000 from ICPSR. To measure the cost of living in each
metropolitan area, we obtained data for utilities, mortgages, and taxes, from the Places
Rated Almanac of 1972, 1980, 1990 and 2000. We include population and its square and
their interactions with year in the wage regressions we report here to control for
differences in the cost of living, but exclude the other variables, which are likely to be
endogenous. Our wage results are robust to including those variables as controls as well.6
Aggregation
Metropolitan areas are aggregated to Consolidated Metropolitan Statistical Areas
(CMSA), New England Consolidated Metropolitan Areas, (NECMA) and Metropolitan
Statistical Areas (MSA). The constituent metropolitan areas in CMSAs and NECMAs
change from year to year. For consistency, we use the CMSA, NECMA and MSA
classification in the State and Metropolitan Area Data Book 1997-1998 (U.S. Bureau of
Census [1998]).
Descriptive Statistics
Table 1 reports descriptive statistics. On average academic R&D spending is $60
per person in 1982-1984 dollars, with a standard deviation of $84. R&D close to doubles
over the period, increasing from $40 in 1980 to $61 in 1990 to $76 in 2000. It is also
worth noting that academic R&D is relatively weakly correlated with patenting (on the
order of .1 depending on the year). It is more highly correlated with the college graduate
6
We do not control for population in the employment regressions because we are interested in total
employment as an indication of the demand for labor, not variations in employment relative to population.
20
population share, frequently having correlations in the range of .3-.4. This correlations is
reflected in the estimates below where the coefficient on scientific activity frequently
declines once the college graduate population share is controlled. The table also shows
the distribution of our proxies for the labor supply and demand elasticities.
IV. Results
Employment
Columns (1a)-(1d) in the top panel of Table 2 report estimates of the (log)
employment growth for 1980-90 and 1990-2000 on scientific activity in the initial year
(i.e. the 1980-90 change is related to 1980 independent variables and the 1990-2000
change is related to 1990 independent variables), pooling data for both periods. The
estimates show a positive relationship between academic R&D (and other variables) in
the initial year and employment growth over the following 10 years. The estimate is
robust to the inclusion of patenting per capita, but is reduced substantially and becomes
insignificant once the college-graduate population share is controlled. Columns (1a)-(1d)
in the bottom panel report instrumental variables estimates. (The corresponding first stage
regressions are reported in Appendix Table 1 and are very strong, showing F-statistics on
the excluded instrument between 100 and 150.) These estimates are very close to those
reported in the top panel.
Columns (2a)-(2d) in the top panel of Table 2 report estimates of the change in
employment growth for 1990 to 2000 relative to 1980 and 1990 regressed on the change
in academic R&D between 1980 and 1990. These estimates are somewhat larger than the
pooled estimates in Columns (1a)-(1d), but imprecise. They also decline much less with
the inclusion of the college-graduate population share. Columns (2a)-(2d) in the bottom
21
panel of Table 2 report instrumental variables estimates. (Here too the first stage
equations are quite strong, with F-statistics on the excluded instrument between 22 and
27.) Although these estimates are noisy, they are, if anything, larger than the
corresponding cross-sectional estimates and the pooled estimates in Columns (1a)-(1d),
providing some reassurance that our estimates are not biased by endogeneity. Moreover,
the estimated effects of scientific activity remain quite large even controlling for the
college-graduate population share.
To get a sense of the magnitudes, here and below, we consider the effect of a $1
billion increase in academic R&D, which would raise R&D per capita by roughly $3.2
per person. Our baseline estimate in the top panel of Column (1a), of .208 (SE=.101) is
close to the midpoint of our estimates. Based on it, such an expansion would raise
employment by .0675%. With civilian employment of 139 million in 2010, this increase
in academic R&D would increase employment by 92.5 thousand people, implying a cost
of $10,809 in academic R&D per job, combining local job creation and local productivity
effects. This estimate should be interpreted with caution both because the coefficients are
estimated with uncertainty and because they vary across the models.
To estimate the relative sizes of the job creation and productivity effects, we turn
to estimates from the STAR-METRICS Program, the most sophisticated effort to date to
evaluate job creation by federal research funding. These estimates indicate that every $1
million spent generates just over 10 jobs and between 4.5 and 7.75 full time equivalent
jobs per year (including jobs held by students), for a cost of roughly $100 thousand per
job and $222-$129 thousand per full time job per year.7 Thus $1 billion in spending
7
Specifically the Aggregate Employment Estimates are that $1million of quarterly support generated 28
jobs and 18 full time equivalent jobs directly and an additional 13 jobs indirectly through vendors,
22
would generate roughly 10,000 jobs and between 4,500 and 7,750 full-time jobs per year.
Our baseline estimates are an order of magnitude larger than those from STARMETRICS, indicating that the productivity effects of science on employment are likely
considerably larger than those from job creation.
Main Wage Effects
Table 3 reports estimates for wages. The top panel of Columns (1a)-(1d) report
OLS estimates from regressions that pool data for the 3 census years. The first column
shows a positive relationship between science and wages. Including patenting (in the
second column) does little to change the estimates, but including the share of the
population with college degrees reduces it substantially. The bottom panel of Columns
(1a)-(1d) presents comparable estimates using instrumental variables. (The first stage
equation is in Appendix Table 2 and shows an F-statistic on the excluded instrument
between 110 and 160.) The estimates are quite similar to the OLS estimates. The top
panel of Columns (2a)-(2d) reports estimates that include city fixed effects. The estimates
are noticeably larger than the ordinary least squares regression, although less precise. The
bottom panel of Columns (2a)-(2d) reports two stage least squares estimates with fixed
effects. (The first stage equation is in Appendix Table 2 and shows a T-statistic on the
excluded instrument between 18 and 19.) These estimates are considerably larger than the
previous estimates.
As discussed, our results are consistent with the literature, which generally finds
subcontracts, and institutional support. The total number of jobs is 41 per $1 million per quarter. Because
the indirect jobs are not expressed in full time equivalents, the total number of full-time jobs ranges from an
absolute minimum of just over 18 (if all indirect jobs are small, part time jobs) to 31 jobs (if all indirect
jobs are full time jobs) per $1 million per quarter. To obtain an annual figure per $1 million, we divide the
quarterly figures by 4. It is also noteworthy that STAR-METRICS indicates that roughly 25% of the full
time equivalent jobs created by science spending are held by students. These jobs are not included in our
estimates.
23
that including fixed effects increases the estimates and there are a few reasons why this
might happen. First, science may be performed in places that would otherwise be less
productive if, for instance, universities are sited in places where productivity is lower
(e.g. because the opportunity cost of land was lower) or if the cities with more research
institutions are experiencing negative shocks (see Crispin, Saha, and Weinberg [2010]).
The fixed effects and fixed effects instrumental variables estimates also capture higherfrequency variation in scientific activity, which may be more correlated with wages if
(for instance), supply is less elastic in the short run because population moves slowly (or
labor demand is more elastic) than in the long run. In either case, neither the inclusion of
fixed effects nor the use of instrumental variables suggest that endogeneity is biasing the
estimated effects of science upward and it is reassuring that the fixed effects instrumental
variables estimates, which are the most convincing a priori, are quite strong.
As above, we are hesitant to identify a particular magnitude from these estimates
given their range, but it may be helpful to quantify the estimated effects of science on
wages. Again, consider an increase of $1 billion in science spending, which would raise
academic R&D by $3.2 per capita. Taking a coefficient of .082 (from the first OLS
estimates, which is at the low end of the coefficients), this would lead to a .026% increase
in wages. Given that labor income was roughly $6 trillion in 2010, total wages would
increase by $1.68 billion. If, as we have argued, job creation has a small effect on wages,
then the effect of science on wages provides a convenient lower bound for the effect of
science on productivity and surplus. It is also worth noting that this return ignores the
direct benefits of science, which may well exceed the productivity spillovers.
Wage Elasticity Effects
The effects of science on wages depend on the labor demand and labor supply
24
elasticities. And, as indicated in equation (II.5), when productivity spillovers dominate
the wage effects, the higher the labor demand elasticity or the lower the labor supply
elasticity, the closer the greater the wage responses. Moreover, as the demand elasticity
approaches infinity or the supply elasticity approaches zero, the wage effect affects
approach the effects on productivity. This section tests whether wage effects are
increasing in the demand elasticity and decreasing in the supply elasticity and uses these
estimates to impute the effects of science on productivity.
As discussed, we employ proxies for the demand and supply elasticities because
we do not measure them directly. To proxy for an inelastic labor demand, we use the
share of employment in local consumption industries and in industries that are
locationally-tied in that they are capital intensive (e.g. manufacturing) or rely on natural
resources (e.g. agriculture and mining). We proxy for the labor supply elasticity using the
demographic composition of the population. Specifically, we estimate the share of the
workforce that is male with a high school degree or higher between ages 35 and 55.
Research shows that labor supply is less elastic for educated, prime age-men, so as the
share of workers in this group increases, the elasticity of labor supply decreases.
We incorporate these variables into the models by interacting them with academic
R&D. Note that we include the main effect of the share of people employed in various
industries or the share of the population with a high elasticity of labor supply as well as
individual characteristics to control for the direct effect of those variables on wages.
Columns (1a)-(1d) of Table 4A report OLS estimates that include interactions
between academic R&D and employment in local consumption industries and in
locationally-tied industries pooling data for 1980, 1990, and 2000 without fixed effects.
25
We include both of these variables separately because it is impossible to say a priori how
the two sets of industries compare in terms of their demand elasticities although, in
practice, they produce broadly similar coefficients. As expected, the less elastic the
demand for labor, the less wages respond to academic R&D. The magnitudes are large.
Using the estimates in column (1a), the effect of $1000 per capita science spending on
wages at the mean employment shares is .116. A standard deviation across cities in the
employment share of local consumption industries is .043 and locationally-tied industries
is .065, so a 1 standard deviation increase in the employment share of both industries
would lower the wage response by .162, eliminating the entire wage effect.
As indicated, the effect of scientific activity on wages approaches the effect of
scientific activity on productivity as the elasticity of demand is increased. We estimate
the effect of academic R&D on productivity by looking at the effect of science on wages,
where our proxies for the demand elasticity are both 1 standard deviation below their
mean. In a city where the employment shares of local consumption and locationally-tied
industries is 1 standard deviation above the average, a $1000 per capita increase in
science spending would raise wages by .278 percent. A $1 billion increase in science
spending would raise wages by .089% or $5.3 billion. Here it is important to bear in mind
that in addition to the uncertainty over the coefficients, there is uncertainty as to whether
a 1 standard deviation increase in the employment share of both industries corresponds to
an infinitely elastic labor demand.
Columns (2a)-(2d) of Table 4A report estimates that capture variations in the
labor supply elasticity. In keeping with the model, they show that the more workers in
groups with a high elasticity of labor supply, the less wages respond to academic R&D.
26
The magnitudes are quite large, with the interaction term being somewhat larger in
magnitude than the main effect of academic R&D. It is hard to trace out large variations
in the labor supply elasticity using this variable because the share of this high elasticity
group does not vary much across cities, but it is clear that when the labor supply is more
elastic, wages respond less to science.
Table 4B reports fixed effects estimates for both sets of models. These estimates
are similar to the estimates without fixed effects, but show an increase in magnitudes like
the fixed effects estimates reported above and in the literature.
V. Conclusions
This paper studies how scientific activity is related to employment and wages. We
contribute to this literature by using rigorous economic principles to compare the relative
merits of different methods and measures. While much of the policy discussion has
focused on the benefits of science in terms of the number of people who are employed
doing it, what we refer to as “job creation,” we show that job creation measures do not
reflect the benefits to workers or firms but rather reduce surplus. Insofar as one wants to
measure the effects of science on employment, our model shows that it is important to
account for how science raises local productivity through spillovers. Empirically, we
show that the local productivity spillovers from science on employment are likely to be
considerably larger than the “job creation” effects.
Perhaps more fundamentally, we show that if one wants to measure the benefits of
science either to workers or on productivity, it is important to focus on the effects of
science on wages rather than employment. Specifically, under assumptions that seem to
hold, the effects of science on local wages provide a convenient lower bound for the
27
effects of science on local productivity.
We also produce a set of estimates using a strong research design that allow us to
compare the various estimates empirically. While our estimates vary considerably across
specifications, our baseline estimates indicate that a $1B increase in science spending
might raise wages by $1.68 Billion and that these wage effects are likely to understate the
effects on productivity. We also find that a $1B increase in science might generate 92,500
jobs, with the vast majority of these jobs being missed even using state-of-the-art “job
creation” methods. Perhaps most significantly, our arguments and methods can be
applied to measure the economic benefits from government activity more generally.
28
References
Bauer, Paul W.; Mark E. Schweitzer, and Scott Shane. 2006. State Growth Empirics: The
Long-Run Determinants of State Income Growth. Working Paper.
Beeson, Patricia and Edward Montgomery. “The Effects of Colleges and Universities on
Local Labor Markets.” The Review of Economics and Statistics 75 (No. 4,
November): 753-61.
Blanchard, Olivier Jean and Lawrence F. Katz. 1992. “Regional Evolutions.” Brookings
Papers on Economic Activity 1: 1-75.
Blau, David M. 1994. “Labor Force Dynamics of Older Men.” Econometrica 62
(January, No. 1): 117-56.
Blundell, Richard and Thomas MaCurdy. 1999. “Labor Supply: A Review of
Approaches.” In Handbook of Labor Economics, Volume 3. Orley Aschenfelter
and David Card, Editors. Amsterdam. North-Holland Press. Pp. 1559-1695.
Carlino, Gerald and Robert Hunt. 2009. “What Explains the Quantity and Quality of
Local Inventive Activity?” Brookings-Wharton Papers on Urban Affairs 10: 65123.
Crispin, Laura; Subhra B. Saha; and Bruce A. Weinberg. 2010. “Innovation Spillovers in
Industrial Cities.” Working Paper.
Clemins, Patrick J. 2009. “Historic Trends in Federal R&D Budgets.” in AAAS Report
XXXIV: Research and Development, FY 2010. Washington, D.C.: American
Association for the Advancement of Science. pp. 21-26.
Congressional Budget Office. 2010. “Estimated Impact of the American Recover and
Reinvestment Act on Employment and Economic Output from April 2010-June
2010.” Downloaded from http://www.cbo.gov/ftpdocs/117xx/doc11706/08-24ARRA.pdf on May 23, 2011.
Hausman, Naomi. 2010. “Effects of University Innovation on Local Economic Growth
and Entrepreneurship.” Working Paper.
Juhn, Chinhui. 1992. “The Decline in Male Labor Market Participation: The Role of
Declining Market Opportunities,” Quarterly Journal of Economics 107 (February,
No. 1): 79-122.
Killingsworth, Mark R. and James J. Heckman. 1986. “Female Labor Supply: A Survey.”
In Handbook of Labor Economics, Volume 1. Orley Aschenfelter and Richard
Layard, Editors. Amsterdam. North-Holland Press.
National Science Foundation. 2011. Initial Results of Phase I of the STAR METRICS
Program: Aggregate Employment Estimates. Washington, D.C.: Government
Printing Office.
29
OECD. 2008. OECD Science Technology and Industry Outlook. Washington, DC:
OECD.
Pencavel, John. 1986. “Labor Supply of Men: A Survey.” In Handbook of Labor
Economics, Volume 1. Orley Aschenfelter and Richard Layard, Editors.
Amsterdam. North-Holland Press.
Saha, Subhra B. 2008. “Economic Effects of Universities and Colleges.” Working Paper.
Zucker, Lynne G.; Michael R. Darby; and Marilynn B. Brewer. 1998. “Intellectual
Human Capital and the Birth of U.S. Biotechnology Enterprises.” American
Economic Review. (Vol. 88, No. 1, March): 209-306.
30
Figure 1. The Effect of a Subsidy on Wages and Employment with a Productivity Spillover.
ln(Wage)
Supply
B(s)
(b)
Wii
(a)
(ii)
(*)
Wi
(i)
(c)
Productivity Spillover
Demand
Qi
s Qii
ln(Employment)
Note. The original equilibrium is indicated by (i). We assume that demand is increased by a subsidy and by a productivity spillover
generated by the subsidy leading to a new equilibrium at (ii). The parallelogram (a) gives the increase in (total) surplus from the
productivity spillover; the triangle (b) gives the loss in surplus from the subsidy. The parallelogram (c) plus the triangle (b) are
transferred to firms.
31
Table 1. Descriptive Statistics.
Log Wage
Academic R&D (Thousand $) Per Capita
Patents Per Capita
College Grad. Population Share
Supply / Demand Elasticity Proxies
Highly Supply Elasticity Share
Locationally-Tied Industry Share
Local-Consumption Industry Share
Other Industry Share
People
Cities
All Years
Mean SD
5.854 (0.736)
0.060 (0.084)
0.000 (0.000)
0.281 (0.073)
1980
Mean SD
5.810 (0.703)
0.040 (0.055)
0.000 (0.000)
0.217 (0.040)
1990
Mean SD
5.835 (0.736)
0.061 (0.089)
0.000 (0.000)
0.274 (0.052)
2000
Mean SD
5.910 (0.758)
0.076 (0.094)
0.000 (0.001)
0.342 (0.063)
0.791 (0.034)
0.322 (0.043)
0.268 (0.065)
0.410 (0.052)
1,262,193
381
0.831 (0.013)
0.310 (0.044)
0.328 (0.057)
0.361 (0.037)
382,337
125
0.793 (0.014)
0.324 (0.040)
0.264 (0.047)
0.412 (0.039)
420,282
127
0.755 (0.015)
0.329 (0.042)
0.329 (0.042)
0.449 (0.039)
459,574
129
32
Table 2. Employment Regressions.
Sample:
Initial Academic R&D per Capita
Initial Patenting per Capita
Initial Col. Grad. Pop. Share
Period=1990-2000
Constant
Observations
R-Squared
Initial Academic R&D per Capita
Initial Patenting per Capita
Initial Col. Grad. Pop. Share
Period=1990-2000
Constant
Observations
R-squared
(1a)
(1b)
(1c)
(1d)
(2a)
(2b)
(2c)
(2d)
1980-90 and 1990-2000 Changes Pooled
(2000-1990)-(1990-80)
Log Changes
Changes in Log Changes
0.208*
0.225*
0.062
0.075
0.399
0.446
0.369
0.413
(0.101)
(0.102)
(0.107)
(0.105)
(0.302)
(0.299)
(0.255)
(0.257)
-53.642+
-58.329*
-114.308
-116.877
(27.806)
(27.799)
(123.979)
(121.360)
0.640*
0.661*
0.252
0.281
(0.315)
(0.305)
(0.941)
(0.945)
-0.095*** -0.093*** -0.125*** -0.124***
(0.022)
(0.022)
(0.023)
(0.024)
0.140*** 0.148***
0.030
0.036 -0.125*** -0.120*** -0.137* -0.134*
(0.020)
(0.021)
(0.056)
(0.056)
(0.024)
(0.027)
(0.064)
(0.066)
246
246
246
246
119
119
119
119
0.1034
0.1135
0.1314
0.1432
0.0061
0.0107
0.0069
0.0117
Instrumental Variables
0.227+
0.247+
0.045
0.062
0.227
0.342
0.156
0.273
(0.127)
(0.128)
(0.146)
(0.144)
(0.476)
(0.449)
(0.462)
(0.450)
-54.169*
-58.144*
-109.767
-111.399
(27.488)
(27.378)
(120.362)
(117.937)
0.652*
0.671*
0.324
0.327
(0.322)
(0.311)
(0.962)
(0.967)
-0.095*** -0.093*** -0.126*** -0.124***
(0.022)
(0.022)
(0.023)
(0.023)
0.139*** 0.147***
0.029
0.034 -0.121*** -0.118*** -0.136* -0.133*
(0.021)
(0.021)
(0.056)
(0.056)
(0.025)
(0.026)
(0.063)
(0.065)
246
246
246
246
119
119
119
119
0.1033
0.1133
0.1313
0.1432
0.005
0.0103
0.0053
0.011
33
Note. Columns (1a)-(1d) report estimates of the (log) employment growth for 1980-90 and 1990-2000 on science in the initial year
(i.e. the 1980-90 change is related to 1980 independent variables and the 1990-2000 change is related to 1990 independent variables),
pooling data for both periods. Columns (2a)-(2d) report estimates of the change in employment growth for 1990 to 1990 relative to
1980 and 1990 regressed on the change in academic R&D between 1980 and 1990. For the instrumental variables estimates, the
instrument is a share shift index for academic R&D. First stage regressions are reported in Appendix Table 1. Estimates weighted by
population in 1990. Standard errors, which are robust to an arbitrary error structure within metropolitan areas in Columns (1a)-(1d),
are reported in parentheses. Significance given by: *** p<0.001, ** p<0.01, * p<0.05, + p<0.10.
34
Table 3. Wage Estimates.
(1a)
Academic R&D per Capita
Patenting per Capita
Col. Grad. Pop. Share
Observations
Adjusted R-squared
Academic R&D per Capita
Patenting per Capita
Col. Grad. Pop. Share
Observations
Adjusted R-squared
(1b)
(1c)
(1d)
(2a)
(2b)
(2c)
(2d)
Pooled
Fixed Effects
0.082+
0.076+
-0.036
-0.033
0.198
0.166
0.104
0.089
(0.044)
(0.039)
(0.037)
(0.037)
(0.131)
(0.117)
(0.110)
(0.104)
19.112
11.165
29.847+
21.720
(15.712)
(12.032)
(15.263)
(13.810)
0.446** 0.421***
0.833*** 0.758***
(0.138)
(0.120)
(0.231)
(0.219)
1,262,181 1,262,181 1,262,181 1,262,181 1,262,181 1,262,181 1,262,181 1,262,181
0.308
0.308
0.309
0.309
0.299
0.299
0.299
0.299
Instrumental Variables
Pooled
Fixed Effects
0.118*
0.106**
-0.018
-0.018
0.786*
0.666+
0.483+
0.416+
(0.052)
(0.039)
(0.039)
(0.040)
(0.397)
(0.353)
(0.264)
(0.238)
18.981
11.377
26.946+
20.524
(15.664)
(12.295)
(14.216)
(13.269)
0.431** 0.408***
0.757*** 0.697***
(0.133)
(0.118)
(0.210)
(0.204)
1,262,181 1,262,181 1,262,181 1,262,181 1,262,181 1,262,181 1,262,181 1,262,181
0.308
0.308
0.309
0.309
0.298
0.299
0.299
0.299
Note. Sample includes data for 1980, 1990, and 2000. Individual-level controls include education, a quartic in potential experience,
race (dummies for black and other race), Hispanic background, citizenship, and marital status interacted with gender. Regressions also
include the log of population and its square, year dummy variables, and a full set of interactions between them. Estimates weighted by
population weights. Fixed effects estimates include metropolitan area fixed effects. In the two-stage least squares estimates, the
instruments is a share shift index for academic R&D. The individual characteristics are also treated as endogenous. We include the
deviation of each individual variable from its mean in each metropolitan area in each year as instruments. First stage regressions for
academic R&D are reported in Appendix Table 2. Standard errors, which are robust to an arbitrary error structure within metropolitan
35
areas, are reported in parentheses. Significance given by: *** p<0.001, ** p<0.01, * p<0.05, + p<0.10.
36
Table 4A. Wage Estimates with Elasticities, Pooled Regressions.
Academic R&D per Capita
Academic R&D per Capita
* Locationally-Tied Industry Share
Academic R&D per Capita
* Local-Consumption Industry Share
Academic R&D per Capita
* 1-Share(35-55, Some College+ Men)
Patenting per Capita
(1a)
0.952*
(0.416)
-1.157*
(0.541)
-1.730+
(0.944)
(1b)
0.993*
(0.405)
-1.221*
(0.532)
-1.813+
(0.932)
20.787
(14.862)
Col. Grad. Pop. Share
Locationally-Tied Industry Share
Local-Consumption Industry Share
1-Share(35-55, Some College+ Men)
Observations
Adjusted R-squared
(1c)
0.441+
(0.261)
-0.934*
(0.465)
-0.826
(0.620)
0.370*
(0.142)
0.188
(0.172)
0.408**
(0.141)
0.251
(0.169)
0.765***
(0.126)
0.840***
(0.145)
0.743***
(0.169)
(1d)
0.482+
(0.260)
-0.980*
(0.473)
-0.905
(0.622)
12.480
(8.601)
0.741***
(0.107)
0.848***
(0.137)
0.763***
(0.163)
(2a)
1.036+
(0.527)
(2b)
1.099*
(0.511)
(2c)
1.117*
(0.468)
(2d)
1.151*
(0.456)
-1.221+
(0.639)
-1.308*
(0.624)
19.131
(14.897)
-1.478*
(0.610)
-1.518*
(0.591)
11.737
(11.709)
0.455***
(0.135)
0.480**
(0.149)
-0.066
0.002
0.399
0.416
(0.349)
(0.309)
(0.302)
(0.303)
1,262,181 1,262,181 1,262,181 1,262,181 1,262,181 1,262,181 1,262,181 1,262,181
0.308
0.309
0.310
0.310
0.308
0.308
0.309
0.309
[See notes beneath Table 4B.]
37
Table 4B. Wage Estimates with Elasticities, Fixed Effects Estimates.
(1a)
(1b)
(1c)
Academic R&D per Capita
1.542**
1.456*
1.099*
(0.589)
(0.568)
(0.439)
Academic R&D per Capita
-2.265*
-2.062*
-1.615*
* Locationally-Tied Industry Share
(0.998)
(1.023)
(0.748)
Academic R&D per Capita
-2.557** -2.514** -1.975*
* Local-Consumption Industry Share
(0.963)
(0.945)
(0.784)
Academic R&D per Capita
* 1-Share(35-55, Some College+ Men)
Patenting per Capita
28.980*
(13.915)
Col. Grad. Pop. Share
0.882***
(0.211)
Locationally-Tied Industry Share
0.252
0.267
0.502*
(0.247)
(0.231)
(0.250)
Local-Consumption Industry Share
0.280
0.378
0.630*
(0.263)
(0.256)
(0.249)
1-Share(35-55, Some College+ Men)
Observations
Adjusted R-squared
(1d)
1.067*
(0.441)
-1.509+
(0.789)
-1.986*
(0.803)
22.199+
(11.815)
0.815***
(0.198)
0.494*
(0.229)
0.679**
(0.247)
(2a)
1.239*
(0.552)
(2b)
1.096*
(0.526)
(2c)
0.795+
(0.412)
(2d)
0.736+
(0.406)
-1.487*
(0.738)
-1.323+
(0.731)
28.050*
(13.300)
-0.987+
(0.593)
-0.919
(0.598)
17.545
(10.757)
0.931***
(0.218)
1.000***
(0.225)
1.016**
0.951*
1.457*** 1.386***
(0.389)
(0.368)
(0.402)
(0.395)
1,262,181 1,262,181 1,262,181 1,262,181 1,262,181 1,262,181 1,262,181 1,262,181
0.299
0.299
0.299
0.299
0.299
0.299
0.299
0.299
Note. Sample includes data for 1980, 1990, and 2000. Individual-level controls include education, a quartic in potential experience,
race (dummies for black and other race), Hispanic background, citizenship, and marital status interacted with gender. Regressions also
include the log of population and its square, year dummy variables, and a full set of interactions between them. Estimates weighted by
population weights. Fixed effects estimates include metropolitan area fixed effects. Standard errors, which are robust to an arbitrary
error structure within metropolitan areas, are reported in parentheses. Significance given by: *** p<0.001, ** p<0.01, * p<0.05, +
p<0.10.
38
Appendix Table 1. Employment Regression, First Stage Equations.
Share Shift Index
Patenting per Capita
Col. Grad. Pop. Share
Period=1990-2000
Constant
F-Statistic on Share Shift
Observations
Adjusted R-squared
1980-90 and 1990-2000 Changes Pooled
Log Changes
0.619*** 0.619*** 0.616*** 0.616***
(0.051)
(0.051)
(0.060)
(0.060)
0.187
0.093
(10.631)
(10.839)
0.016
0.016
(0.120)
(0.121)
0.001
0.001
0.000
0.000
(0.002)
(0.002)
(0.005)
(0.005)
-0.000
-0.000
-0.003
-0.003
(0.003)
(0.004)
(0.018)
(0.018)
148.22
149.55
104.17
105.07
252
252
252
252
0.809
0.808
0.808
0.807
(2000-1990)-(1990-80)
Changes in Log Changes
0.617*** 0.619*** 0.524***
(0.118)
(0.119)
(0.110)
-3.402
(25.874)
0.064
(0.116)
0.001
(0.004)
26.51
127
0.498
0.001
(0.004)
25.16
127
0.494
0.000
(0.005)
22.67
121
0.450
0.528***
(0.113)
-8.262
(26.629)
0.065
(0.116)
0.000
(0.005)
21.69
121
0.446
Note. Columns (1a)-(1d) report estimates of the (log) employment growth for 1980-90 and 1990-2000 on science in the initial year
(i.e. the 1980-90 change is related to 1980 independent variables and the 1990-2000 change is related to 1990 independent variables),
pooling data for both periods. Columns (2a)-(2d) report estimates of the change in employment growth for 1990 to 2000 relative to
1980 and 1990 regressed on the change in academic R&D between 1980 and 1990. The instrument is the share shift index for
academic R&D. Estimates weighted by population in 1990. Standard errors, which are robust to an arbitrary error structure within
metropolitan areas, are reported in parentheses. Significance given by: *** p<0.001, ** p<0.01, * p<0.05, + p<0.10.
39
Appendix Table 2. Wage Regression, First Stage Equations.
Share Shift Index
Patenting per Capita
Col. Grad. Pop. Share
F-Statistic on Share Shift
Observations
Adjusted R-squared
Pooled
Fixed Effects
0.487*** 0.469*** 0.469*** 0.226*** 0.226*** 0.222*** 0.222***
(0.039)
(0.045)
(0.045)
(0.051)
(0.052)
(0.051)
(0.052)
-4.585
-6.662
0.167
-0.198
(6.948)
(7.695)
(3.371)
(3.529)
0.106
0.120
0.039
0.039
(0.106)
(0.108)
(0.074)
(0.077)
155.52
156.25
108.32
108.94
19.25
18.51
18.65
18.19
1,262,181 1,262,181 1,262,181 1,262,181 1,262,181 1,262,181 1,262,181 1,262,181
0.783
0.784
0.786
0.787
0.537
0.537
0.538
0.538
0.485***
(0.039)
Note. Sample includes data for 1980, 1990, and 2000. Individual-level controls include education, a quartic in potential experience,
race (dummies for black and other race), Hispanic background, citizenship, and marital status interacted with gender. Regressions also
include the log of population and its square, year dummy variables, and a full set of interactions between them. Estimates weighted by
population weights. Fixed effects estimates include metropolitan area fixed effects. The instrument is a share shift index for academic
R&D. Individual characteristics are also treated as endogenous. We include the deviation of each individual variable from its mean in
each metropolitan area in each year as instruments. Standard errors, which are robust to an arbitrary error structure within
metropolitan areas, are reported in parentheses. Significance given by: *** p<0.001, ** p<0.01, * p<0.05, + p<0.10.
40