2. Empirical analysis
2.1. Data
In this second part of the thesis we will examine the importance of certain MSA
characteristics for choosing a particular MSA to invest R&D. For this a dataset
from the Financial Times will be employed. This dataset comprises of 462 crossborder R&D projects in the United States spread over a timeframe from 2003 tot
2012. Disclosed in this dataset were the project data, investing company, parent
company, source country, host location, industry sector and subsector, capital
invested, jobs created and the project type (Research and/or development).
After fine-tuning our dataset we ended up with 427 projects of which 139 where
research oriented and 288 development. Further we classified each project into
low-tech or high-tech category with the provided industry sector and using the
categorization of the OECED. To facilitate the classification for our model we
bundled the high- and medium high-tech and the medium low-and low-tech
class. Table x in the appendix represents the used OECED classification. This
altogether makes us able to craft a model discerning both horizontally the
research and development specification, and vertically the high-tech and lowtech specification.
To determine which MSA characteristic are the most valuable for firms in their
location decision we employed the conditional (fixed-effects) logit model.
In the next sections we will cover the dependent and independent variables that
were analyzed in this empirical part.
2.1.1. Dependent Variable
The dependent variable in our model is a dummy variable expressing whether
yes (=1) or not (=0) a company with a specific project i actually invested in a
specific MSA j. In total there are 381 MSA’s in in the United States. In our dataset
only 91 of them have been chosen as location to invest in.
The top five most frequently chosen MSA’s are Detroit-Warren-Dearborn (MI),
Boston-Cambridge-Newton (MA-NH), San Jose-Sunnyvale-Santa Clara (CA), Los
Angeles-Long Beach-Anaheim (CA) and San Francisco-Oakland-Hayward (CA)
with respectively 45, 28, 25, 22 and 22 projects attached to them.
2.1.2. Dependent Variables
To analyze which MSA characteristics are impactful to the location decision we
have included 16 variables in our model, with some of them serving as proxies.
The variables were lagged to account for non-instantaneous relation between
the decision making (dependent variable) and the MSA characteristics
(independent variables). Also, we have taken the natural logarithm of all
variables, except dummy and ratio variables. This is to depict the coefficients as
an elasticity and comfort the comparison of the variables.
As a proxy for the attractiveness of the MSA market we elected GDP per capita.
GDP per capita is a suitable indicator of the economic performance relative to the
population. Thus expecting a positive relation between chosen MSA’s and GDP
per capita. MSA data on the GDP per capita for the period of 2011-2012 was
retrieved from the Bureau of Economic Analysis.
To capture the market potential of an MSA and adding to the market
attractiveness we included the population density. In some cases R&D facilities
are also dedicated on customizing technology to a certain market (SOURCE). The
bigger the market the more rationale it is to invest in that market. Data on the
population density was retrieved from the US Census Bureau.
An important cost for a firm is its taxes. By incorporating taxes into our model
we can study for ourselves the effects of it on the decision as its importance has
been strongly debated. SOURCES. Nonetheless taxes should have a negative
impact on the location decision. MSA’s are not politically dived therefore we
employed corporate tax data on the state level and appropriately allocated. Data
on the state corporate tax rate was retrieved from the Taxfoundation.
Another significant element in a firm its cost structure is the labor cost. We used
the average annual salary of an engineer of all industries as a proxy for the labor
cost in a R&D facility. In the model we assume that engineers are the most
conventional employees. Previous research has been split on the effect of labor
cost in R&D. Some believe that it has a negative impact on the decision
(SOURCE). On the other hand others believe that the employment of the best
employees are necessary and obviously comes at a price and thus has a minimal
effect on the location decision (SOURCE). Data on wages from engineers was
retrieved from the Bureau of Labor Statistics.
For firms having a greater supply in employment than there is demand means
they have a higher possibility of choosing better employees. This can imply that
the unemployment rate has a positive relation with the location decision. From
the employee perspective this represents a reduction in leverage. Data on the
unemployment rate was retrieved from the Bureau of Labor Statistics.
The familiarity aspect of a location holds a major role in the location decision
(SOURCE). Therefor we considered the fact that firms invest R&D in places
where they previously occupied some activities, whether they were R&D related
or not. Data on previous investments by a company or its subsidiaries was
retrieved from the ORBIS database.
To evaluate an MSA’s technological and innovational capability & external
connectivity we integrated various PCT patent data, which is the patent
cooperation treaty that consolidates international patent application and
protection procedures. Patents are allocated to an MSA by the inventor’s
address.
First patent variable is a count of patents from the specific sector in which each
R&D project is specialized. This variable measures the overall patent output
capability of an MSA and serves as a proxy to indicate the MSA’s innovation
capability. The values of the patent variables are described as fractional patent
counts. This was done to contend the double counting that would occur from
patents with multiple inventors from different regions/countries and thus inflate
actual patent quantity (SOURCE OECED).
Secondly we also added the variable measuring the more radical innovations.
Breakthrough innovations have the possibility to disrupt the market (SOURCE).
The ability of an MSA to foster such an advanced technological growth
environment could lead to firms investing in that location. We expect the share
of breakthroughs to have a positive link with the chosen MSA and especially
concerning research. The variable indicates the share of breakthrough
innovation per MSA related to the project’s sector. This was derived from
inventions that were notably cited. (SOURCE)
The last two patent variables enable us to determine the outer connectivity of
technological knowledge. Technology can improve more rapidly when there is
some outer interaction (SOURCE). So firms should avoid isolating all their
technological knowledge. To establish this data patents with at least a coinventor from an other MSA were taken into account. The variable represents
the interregional share of patents of the projects’ sector from all other patents of
the MSA.
To gauge the international knowledge connectivity we did accordingly as was
done for the interregional connectivity. This time we observed for a foreign coinventor. For both variables we expect connectivity to have a positive relation
with the chosen MSA, since firms could also benefit from connectivity to add to
their knowledge (SOURCE).
Narrowing down the connectivity aspect we are dealing with proximity elements
like hands on collaboration and spillovers. They are common and encouraged in
R&D communities (SOURCE). Although firms should judge their net spillovers,
we expect the agglomeration affect the positively impact the location decision
(SOURCE). To seize agglomeration affects we practiced a location quotient that
denotes the industrial agglomeration degree of firms. The location quotient
quantifies the comparison of the economical strength of various clusters. Data on
the location quotient was retrieved from the U.S. Cluster Mapping Project.
Finally we are going to address the university related variables. Universities can
be valuable sources for firms investing in R&D (SOURCES). Universities provide
knowledge through collaborations, consultants and the output of high-skilled
students (SOURCE).
First we included the variable that indicates how many top American research
universities are located in an MSA. Data on a list of top American research
universities was retrieved from the Center of Measuring University Performance
(MUP). They constructed this list by ranking American universities on nine
measures: Total Research, Federal Research, Endowment Assets, Annual Giving,
National Academy Members, Faculty Awards, Doctorates Awarded, Postdoctoral
Appointees, and SAT Scores. The Center of MUP only included universities with
at least 20 million dollars in federal research expenditure and after the fiscal
year of 2006 the cut-off was 40 million dollars. A top 25 list listed was designed if
universities at least scored for one measure in the top 25. The 26-50 list in the
same fashion accounted for all the universities who ranked within the 26-50 list
on at least one of the nine measures. We aggregated both list to generate the
variable of amount top American research universities per MSA. We expect the
MSA’s with the most number of top research universities to invest in R&D in
those MSA’s.
After a more general variable we examined some of the nine measures that did
not evidence any strong correlation.
The endowment assets that a university holds have a strong impact on the
credibility to sustain activities over the long term. When possessing enormous
funds, universities have the ability to invest in star professors and other
educational resources for students (SOURCE). Our endowment assets variable
represents the sum per MSA of all the top American research universities
depicted previously. Expected is that higher endowment funds per MSA will have
a positive effect on the location choice. Data on endowment assets from top
research universities was retrieved from the Center of MUP.
Total university research spending has an almost immediate effect on the
research capabilities of university (SOURCE). Data on total university research
spending was retrieved from the Center of MUP. We expect that firms will want
to invest near universities where substantial funding is allocated towards
research.
As addressed in the literature review have great interest in high-skilled labour
for their research facilities. Universities play a key role in providing them
(SOURCES). We used as variable the concentration of doctorates which was
constructed by dividing the number of science and engineering doctorates by the
MSA population. We expect a positive relation regarding the concentration of
doctorates with the location decision.
Lastly we observed for the output publications of universities. (ELABORATE
FROM LITERATURE) To take the quality of publications into account we chose to
include a variable that ultimately does so, by dividing the number of citations by
the number of publications. Faculty that produces qualitative work can we
valuable to firm on a consultant position or more collaborative way (SOURCE).
We expect our variable to positively affect the location choice.
2.2. Methodology