Modern Economy, 2013, 4, 592-595
http://dx.doi.org/10.4236/me.2013.49063 Published Online September 2013 (http://www.scirp.org/journal/me)
Finding Externalities: An Empirical Study on the US
Chong-Uk Kim1, Gieyoung Lim2*
1Department of Economics, Sonoma State University, Rohnert Park, USA
2Department of International Economics & Law, Hankuk University of Foreign Studies,
Email: email@example.com, *firstname.lastname@example.org
Received July 13, 2013; revised August 7, 2013; accepted August 13, 2013
Copyright © 2013 Chong-Uk Kim, Gieyoung Lim. This is an open access article distributed under the Creative Commons Attribution
License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
This paper searches for another empirical evidence suppo rting positive externalities from higher education. Using state-
level US data on agriculture and IT industries, we find that there are positive spillover effects from more-knowledge
intensive workers in the IT industry to less-knowledge intensive workers in the agricultural industry. According to our
empiri- cal findings, one well-educated IT worker generates and contributes $11,000 to th e agricultural industry, which
implies that the benefits of higher education are diffused from education beneficiaries to the other member of society.
Keywords: Externality; Spillover Effects; Higher Education
If we want America to lead in the 21st century, nothing is
more important than giving everyone the best education
possible-from the day start preschool to the day they
start their career. (Barack Obama, Weekly Presidential
Address, Aug 18, 2012).
Needless to say, many researchers and policy makers
have emphasized the importance of higher education on
economic outcomes. According to the White House,
more than half of the 30 fastest growing occupations in
the US require postsecondary education and the demand
for the higher degree beyond a high school diploma will
grow fast over this decade1. On average, according to a
report from the US Census Bureau, a high school gradu-
ate expects to make $1.2 million over her/his lifetime
while a college graduate expects to earn $2.1 million
over her/his lifetime2. It is now commonly accepted that
educational attainment is the most important factor of an
individual economic success .
In addition to an individual economic success, there
have been many academic research efforts focusing on
the role of higher education in enhancing economic
growth and development. As a result, it is commonly
accepted that higher education leads economic growth
through not only the production of knowledge but also
the diffusion of knowledge3. Economists, following Lu-
cas’ seminal 1988 paper , generally consider that the
diffusion of knowledge is the important contribution of
education on economic growth and have tried to measure
the economic size of this diffusion empirically. Empirical
findings, however, have not found the consensus yet and
the debate over empirical findings on educational exter-
nalities is by no means new. For example, Canton  and
Yamarik  find no empirical evidence that education
generates positive externalities while Moretti  and
Kim and Lim  support the existence of positive exter-
nalities from college education.
In this paper, we search for another empirical evidence
supporting positive externalities from higher education.
Using state-level US data on agriculture and IT industries,
we find that there are positive spillover effects from
more-knowledge intensive workers in the IT industry to
less-knowledge intensive workers in the agricultural in-
In the following section, we will discuss our model.
Section 3 will present our empirical results. Section 4 at
2. The Model
2Current Population Reports, the US Census Bureau, July 2002.
*Corresponding a uthor.
To estimate externalities from higher education, we
3See Becker and Lewis .
pen Access ME
C.-Uk KIM, G. LIM 593
choose two different industries in the United States, Ag-
riculture and Information Technology (IT). According to
the 2005-2009 American Community Survey d ata in Ta-
ble 1, workers engaging in IT industry have the highest
educational attainment, on average, while workers en-
gaging in agriculture show the lowest educational at-
tainment. In IT industry, 94.2% of workers have some
college experience and 73.6% of them have at least a
bachelor’s degree. In contrast, 69.9% of workers engag-
ing in agriculture do not have any college experiences
and only 10.9% of workers have at least a bachelor’s
degree or higher. Therefore, we can conclude that work-
ers engaging in IT industry are more-knowledge inten-
sive workers while workers in agricultural industry are
less-knowledge intensive workers.
Open Access ME
Based on these statistics on educational attain ment, we
use the following constant returns agricultural production
function to measure externalities4:
where Yit is the total agricultural product for each US
state in period t; A is a common intercept; Kit, Lit, and Nit,
are capital, labor, and land u sed in the agricultu ral Indus-
try for each US state in period t; Eit is the number of
workers engaging in IT industry for each US state in pe-
Since we assume that workers in IT industry most
likely have a bachelor’s degree and least likely affect
agricultural products directly, the statistical sign and sig-
nificance of will decide the existence of positive exter-
nalities from higher education. In other words, if more-
knowledge intensive workers engaging in IT industry
increases the market value of agricultural products which
are produced by less-knowledge intensive farmers, then
that can be the evidence of positive externalities from
To estimate Equation (1) empirically, we take natural
logs on both sides of Equation (1), which provides the
following estimation equation:
lnln ln ln
Since Eit is the number of workers engaging in IT in-
dustry, a statistically significant and positive
can be the
evidence of positive externalities from higher education.
We expect that all coefficients of Equation (2) are statis-
tically positive and significant.
3. Empirical Results
First of all, we estimate the market value of agricultural
products as a function of three factors of production us-
ing the state-level US data over two time periods of 2002
and 2007. Descriptiv e statistics are presented in Table 2 .
Since Lagrangian multiplier (LM) supports the random
effects estimator, empirical results from the ordinary
lea st s qu ares (OLS) and the random effects model (REM)
are both presented in Table 3. All coefficients from re-
gression 3.1 and 3.2 are statistically positive and signify-
cant. The elasticities of capital, labor, and land in agri-
cultural product are 0. 54, 0.33, and 0.17 respectively and
these results are not different from previous empirical
Table 1. Educational attainment.
Less than high
school diploma High school diploma
or equivalent Some college,
no degree Associate’s
degree Doctoral or
All occupations 9.9 26.8 21.4 8.8 20.8 8.4 3.9
IT industry 0.4 4.4 13.3 8.2 41.8 22.7 9.1
Agriculture 31.9 38.0 14.4 4.8 8.5 1.7 0.7
*Source: 2005-2009 American Community Survey, U.S. Censu s Bureau .
Table 2. Descriptive statistics.
Variable Obs Mean Min Max
Yit Market value of agricultural products*
(thousands of US$, 2002) 100 4,445,898 46,143 2.78e + 07
Kit Machinery and equipment on operation*
(thousands of US$, 2002) 100 3,314,083 41,853 1.59e + 07
Lit Hired farm labor* 100 56,729 1330 535,256
Nit Land in farms* (acres) 100 1.86e + 07 61,223 1.30e + 08
Eit The Total number of workers in IT industry** 100 59,035 1860 394,840
Source: *2002-2007 Census of A griculture, USDA. **2002-2007 Occupational Employment Statistics Survey, BLS.
4See E. Moretti  and C. Kim & G. Lim .
5See Martin and Mitra  and Ec hevarria .
C.-Uk KIM, G. LIM
Table 3. Empirical results.
Independent Variables 3.1 OLS 3.2 Random 3.3 OLS 3.4 Random 3.5 Canada6
0.535 0.618 0.46 0.427
(9.79)** (11.31)** (9.51)** (8.20)**
0.327 0.311 0.223 0.419
(6.19)** (5.22)** (4.31)** (2.93)**
0.169 0.117 0.256 0.159
(2.36)* (3.15)** (2.57)* (3.96)**
ln(Eit) 0.048 0.146
Constant 0.428 0.171 0.106
R-squared 0.94 0.94
Obs 100 100 100
F-Statistics 486.70 571.99W 365.87 587.38W
Prob. (F-Stati s t i c s ) 0.0000 0.0000 0.0000 0.0000
The numbers in the b rackets ar e absolu te value of t-statistics. **indicat es sign ificance at 1 % level o f signifi cance. *indicates signi ficance at 5% level of s ignifi-
cance. Windicates Wald chi-squares statistics.
Second of all, we now include ln(Eit), the total number
of workers in IT industry, in our empirical model and
estimates the Equation (2). According to the results from
LM and Hausman tests, the REM is the most appropriate
estimator in this case. Therefore, the results from regres-
sion 3.4 are our most prefer red one. The share of capital,
labor, and land in agricultural products are 46%, 22%,
and 25% respectively and statistically significant. The
coefficient on ln(Eit) is 0.146 and statistically significant,
too. Since this coefficient decides the existence of exter-
nalities, the positive coefficient supports that there are
positive spillover effects from more-knowledge intensive
IT workers to less-knowledge intensive workers in agri-
At last, since the coefficient of 0.146 represents the
elasticity, 1% increase in the total number of IT workers
will increase the market value of agricultural products by
0.146%. For example, on average, if the number of IT
workers increases by 590 which is 1%, then the market
value of agricultural products will increase by $ 6, 4 91 ,00 0 .
In other words, one well-educated IT worker generates
and contributes $11,000 to agricultural industry, which is
an evidence of economically meaningful externalities.
Using data from US Agricultural and IT industries, we
find that there are positive externalities from higher edu-
cation. According to our empirical findings, 1% increase
in the number of IT workers will increase the market
value of agricultural products by 0.146%, which is not
only statistically but also economically meaningful evi-
dence of the existence of externalities from higher educa-
This work was supported by Hankuk University of For-
eign Studies Research Fund. This is gratefully acknow-
ledged by the authors.
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C.-Uk KIM, G. LIM 595
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