American University Research Outputs and Local Housing Prices*
Qing Hong
Abigail Payne
April 2003
Abstract
The goal of this paper is to examine the effects of local housing prices on university
research outputs. The underlying hypothesis is that increases in housing prices lower
faculty members’ utilities, and then lead faculty members to re-allocate labor supply,
which is reflected as changes in university research outputs. We set up a simple model of
the behavior patterns of two kinds of agents: faculty members and universities, and the
interactions between the two patterns in the procedure of research production. A faculty
member maximizes utility, which is a function of job components (salary and effort) and
location components (local housing prices and amenities) by choosing where to work
and, simultaneously, where to live. Universities maximize profit (calculated using
shadow prices of research outputs) by picking the optimal salary and effort level. This
model predicts that housing prices have positive effects on both salaries and research
outputs. Using the data of 227 American universities over the period of 15 years, 19851999, our empirical findings suggest: (1) the overall effects of housing prices are
significantly positive on both published articles and salaries; (2) housing prices have a
larger positive impact on research outputs for more highly ranked universities; and (3) the
competition from outside offers has negative effects on research outputs and positive
effects on faculty salaries.
*
We are grateful to Aloysius Siow for very helpful and precise guidance. We thank William Strange,
Philip Oreopoulos, Michelle Alexopoulos and Robert McMillan for many constructive comments and
discussions.
I.
Introduction
“I know of no greater possible threat to the academic vitality of Stanford
than our current housing market,” Provost Condoleezza Rice wrote in a
May 7 letter to the president of the campus homeowners’ group. “Our
future depends on our ability to attract outstanding faculty, students and
staff to Stanford, but the lack of affordable housing has made that effort
far more difficult.”
……
“The housing market in the area has been bad forever, but by last year it
had become a problem,” Cox [the vice provost for institutional planning]
says. “Faculty, graduate students, medical residents and postdocs, every
category of person, find themselves in a completely untenable housing
market. I’ve heard around the country that [due to housing price] coming
to Stanford is just a joke.”
From Stanford Report [on line], issue of May 13, 1998
The study is motivated by the concern about high local housing prices and tight
housing markets in the real world. As shown in the above citation, at least one American
university is concerned that its ability to attract good quality faculty is being menaced by
high housing prices and tight housing markets. As shown in Panel A of Figure 1, the
trend of the index of median housing prices in San Francisco, CA (PMSA), where
Stanford University is located, is steeper than that of the annual salaries per faculty
member at Stanford since 1995, though salaries keep increasing during the whole sample
period.
The median housing prices in San Francisco raise fast even relative to that
averaged across all the MSA areas since 1995.
In the case of facing increasing housing prices, faculty members may react by making
new joint job- and residence-location decision, or labor supply decision, or both. As a
result, universities’ educational outputs may change. Of these outputs, the research
outputs are of most interest since research outputs are one of the most important bases on
which universities build up their academic and educational prestige. 1 In this paper, we
explore the net effects of local housing prices on university research outputs.
We start by setting up a simple theoretical model of two kinds of agents: faculty
members and universities. In the model, a faculty member maximizes utility, which is a
function of job components (salary and effort) and location components (local housing
prices and amenities) by choosing where to work and, simultaneously, where to live.
Universities maximize profit (calculated using shadow prices of research outputs) by
picking the optimal salary and effort level. The underlying logic for the interactions
between the behavior patterns of the two agents is that: (1) given that increased housing
prices lower faculty members’ utilities, and that the more able faculty members have
more alternative job opportunities than the less able members, universities faced with
high local housing prices are forced to offering higher salaries2 to attract faculty
members, or accepting lower research outputs; (2) if universities do not offer higher
salaries, then outstanding faculty member will choose to accept an offer from another
university, or may choose to exert less research.
With some standard restrictions on parameters restricted in the model, and using the
results, from previous studies, of positive effects of housing prices on personal earnings,
our predictions are that the effects of housing prices on both research outputs and salaries
are positive; and the effects of amenities are negative. The prediction that the effects of
housing prices on research outputs and salaries have the same signs implies that facing
increasing local housing prices, universities’ optimal reaction rules are, theoretically, to
offer higher salaries and to obtain higher effort levels at the same time.
After setting up the theoretical model, we estimate the empirical model to examine
the effects predicted in the theoretical analysis, using the data on 227 universities over the
period of 1985-19993.
We have two measures of research outputs: the number of
published articles and citations per article.
1
Someone may argue that some universities exert very few research activities. It is not an issue in this
study since our analysis focuses on Research and Doctoral Universities under Carnegie Classifications. See
section III of Data for the definitions of the two university categories of by Carnegie Classifications.
2
In reality, except for increasing salaries, universities may increase compensations in alternative forms,
such as providing low-interest-rate mortgage and building houses and then selling (or renting) to faculty at
low price. In this paper, we mainly focus on the channel of increasing faculty salaries.
3
Some universities have missing data in one year or several years in this period.
Our empirical results suggest that, first, the overall effects of housing prices are
significantly positive on both salaries and research outputs; second, the estimated effects
of housing prices on research outputs are ranked from high to low as we move from the
top ranked universities to the lowest ranked ones. Finally, we found that the value of
potential job opportunities has negative effects on research outputs and positive effects on
salaries, which verify the effect of competition from outside offers.
The analysis proceeds as follows. In section II, we set up the theoretical framework.
In section III we summarize the data sources and some statistics of main variables. In
sections IV and V, the explanation of the empirical strategies and the estimate results are
given out, respectively. And, the concluding remarks appear in section VI.
II.
Theoretical framework
To detect the relation between local housing prices and university research outputs,
we develop a theoretical model, in which simplified behavior patterns of two kinds of
agents – university and faculty members – and their interactions are considered. There
are three assumptions for the model: (1) labor is mobile; (2) there is no asymmetric
information between a given university and its faculty members – in other words, here is
no principle-agent problem; and (3) there is no commuting, that is, faculty members live
in the same MSA in which their universities are located.
(1) The faculty member’s problem
We assume that each faculty member has a utility function over two components: the
job component and location component, U lsi = U ( J lsi , Lil ) , with superscript representing
individual i and the subscripts location l and school s , respectively. Each component
plays a nonnegative role on faculty member utility, that is, U J > 0 and U L > 0 .
Valuation of the job component is formed over salary ( S ) and effort ( E ),
J lsi = J ( S lsi , Elsi ) , with J S > 0 and J E < 0 (job valuation rises with increased salary and
falls with increased effort). ( S lsi , Elsi ) is set by each university; each faculty member
makes a take-it-or-leave-it decision. Each individual is endowed with a unit of time,
which could be allocated to leisure and/or effort. Let E denote the fraction of the time
endowment spent in exerting effort, then (1 − E ) denotes the fraction of leisure time.
Next, the location component is a function of local housing prices ( HP ) and amenity
conditions ( A ), Lil = L( HPl i , Ali ) , with LHP < 0 and L A > 0 (faculty members value
housing prices negatively, and amenities positively). It is assumed that local housing
prices ( HP ) and amenity conditions ( A ) are exogenous to faculty member and university
decisions – in other words, the faculty group is too small relative to the local housing
market and residence environment to affect housing prices and amenities.4 In a reduced
form, the utility function can be written as
U lsi = U (S lsi , Elsi , HPli , Ali )
(1)
We also make the assumptions that all faculty members have some outside offer(s)5,
are mobile and able to move without costs. The optimization problem for a faculty is to
maximize utility, by making the decision about where to work and, simultaneously,
where to live. Specifically, university s brings up a job offer, ( S lsi , Elsi ) , to faculty i ;
faculty i makes a take-it-or-leave-it decision, taking the location valuation into
accounts.6
With the above assumptions of labor mobility and zero moving cost, in equilibrium,
all faculty members obtain the unique level of utility from any university-location
combinations,
U = U ( J lsi 1 , HPli , Ali )
∀i,l , and s .
(2)
Equation (2) implies that, in equilibrium, there is no incentive for a faculty member to
move to another university, and location as well. Totally differentiating equation (2) we
derive how the changes in the job valuation is affected by changes in housing prices and
amenities,
dJ lsi = −
4
U
U HP
dHPli − A dAli
UJ
UJ
(3)
This assumption may be violated when a relative big university is located in a small town, with its
university size constituting a notable fraction of the local population. We will get back to this discussion in
the section of Robustness test.
5
This assumption is made to simplify analysis. One extension could be made by dividing the faculty
members into two groups: the tenured professors and the non-tenured ones, the two groups being supposed
to have great disparity between their abilities of on-job searching. However, being restricted by the
available data, we are not able to implement this method.
6
This assumes away the agent-principle problem.
From equation (3), we develop the empirical model of the reservation job valuation,7
i
ln J ls = ρ1 ln HPl + ρ 2 ln Al + ε lsi
(4)
The implication of equation (4) includes two points. First, ρ1 and ρ 2 are the simplified
expression of the coefficients on housing prices and amenity conditions, respectively, in
equation (3). Using the first order derivatives given previously, we get that ρ1 > 0 and
ρ 2 < 0 , which implies that the reservation job valuation increases when the housing
prices increase and the amenities decrease. So, changes in job valuation are required to
balance the changes in housing prices and amenities to reach the fixed utility level, U , in
equilibrium. Second, equation (4) shows the formation of the reservation job valuation,
that is, in equilibrium, given any level of housing price and amenity, a faculty forms a
reservation job valuation, J , which guarantee him/her the equilibrium utility level, U .
If the actual job valuation is lower than J , which means the utility is lower than U , then
the faculty member would be better off by quitting the current position and moving to
another job giving utility U .
(2) The university’s problem
In the general sense, universities are non-profit agents. For expository simplicity,
however, we model universities to maximize profits of research production, which are
calculated using the shadow price. In addition, in order to get the closed forms of the
solutions of optimal effort and salary, we assume that the job valuation is a CobbDouglas function, J ( S lsi , Elsi ) = ( S lsi ) µ (1 − Elsi )θ , and µ ,θ > 0 .
Recall that (1 − E )
denotes the fraction of leisure time of the time endowment. Then, the university’s
maximization problem is,
i
i
max P ⋅ Qls − S ls
{S , E }
i
ls
s.t.
i
ls
i
J ls ≤ ( S lsi ) µ (1 − Elsi )θ , µ , θ > 0 and Elsi ∈ [0,1]
Qlsi = ( RlL ) γ 1 ( RsS ) γ 2 (1 − Elsi ) −γ 3 , γ 1 , γ 2 , γ 3 > 0
7
(5)
We take logarithm on each variable because, with large disparities in the units of different variables, it
makes interpretation for the coefficients of variables easier.
where P is interpreted as the shadow price of research productivity, Qlsi . Considering
that universities sell research productivity on an international higher-education market,
P is not affected by university- or location-related factors. The first constraint is an
individual rationality constraint discussed in the proceeding subsection. The second
constraint gives the production technology. Qlsi , the research produced by individual i at
school s in location l , is a function increasing in a set of location-specific resources in l ,
RlL , and a set of university-specific resources of s , RsS , and decreasing in individual’s
leisure (or, inversely, increasing in effort exerted in research).8
To maximize profits, universities choose optimally the pair of contractual variables –
salaries and effort levels, subject to the binding constraint that faculty’s job valuation is
no less than the reservation level. We solve this maximization problem and get the
optimal solutions to S lsi and Elsi . And, using the optimal effort level, we write the
optimal research outputs,
γ 3µ
γ 1θ
γ 2θ
−γ 3
µ  θ −γ 3µ L θ −γ 3µ S θ −γ 3µ i θ −γ 3µ

Qlsi =  Pγ 3 
( Rl )
( Rs )
( J ls )
θ

(6)
The variable of salary in the theory does, precisely, refer to the research-related
T
remuneration, which differs from the total remuneration, S lsi
, and is not separately
T
= k ( S lsi )η ,
observable in practice.9 By setting up a simplified transformation function, S lsi
k > 0 , and η > 1 , we get the expression of the optimal total salaries,
θη
T
S lsi
γ 1θη
γ 2θη
−γ 3η
µ  θ −γ 3µ L θ −γ 3µ S θ −γ 3µ i θ −γ 3µ

( Rl )
( Rs )
( J ls )
= k  Pγ 3 
θ

(7)
Taking logarithm on both sides of equations (6) and (7), and combining with equation
(4), we obtain,
8
L
In this study, Rl includes three variables of location-specific resources, MSA population density, the
value of potential job opportunities in a given MSA, and a dummy variable which is set to equal to one if
S
the number of universities, which are in our sample, is greater than one in a given MSA; Rs includes two
variables of university-specific resources, university size and university total research funding per faculty
member. Different combinations of these variables are used in different specifications of estimation.
9
The idea comes from Graves, Marchand and Thompson (1982).
ln Qlsi =

γ 3µ
− γ 3ρ2
µ   − γ 3 ρ1

ln Pγ 3  + 
ln HPli +
ln Ali 
θ − γ 3µ 
θ   θ − γ 3µ
θ − γ 3µ

+
−γ3
γ 2θ
γ 1θ
ln RsS +
ln RlL +
ε lsi
θ − γ 3µ
θ − γ 3µ
θ − γ 3µ
Q
= α 0 + α 1 ln HPli + α 2 ln Ali + α 3 ln RsS + α 4 ln RlL + ε lsi
T
= ln k +
ln S lsi

− γ 3ηρ 2
θη
µ   − γ ηρ

ln Pγ 3  +  3 1 ln HPli +
ln Ali 
θ − γ 3µ 
θ   θ − γ 3µ
θ − γ 3µ

+
− γ 3η i
γ 2θη
γ θη
ln RsS + 1
ln RlL +
ε ls
θ − γ 3µ
θ − γ 3µ
θ − γ 3µ
S
= β 0 + β1 ln HPli + β 2 ln Ali + β 3 ln RsS + β 4 ln RlL + ε lsi
(8)
The two equations in (8) is the basis for our empirical model. We estimate the two
equations simultaneously in that their error terms are correlated.
To predict the signs of the coefficients on variables, we need to conjecture the sign of
(θ − γ 3 µ ) . As learned from previous studies, the sign of the effects of housing prices on
salary (or wage) is always positive, which implies, θ − γ 3 µ < 0 . Studying Great Britain
in 1972-1995, Cameron and Muellbauer (2001) found a long-run coefficient of around
0.075 for full-time men and around 0.10 for full-time women of relative regional house
prices on relative regional earnings. So, Orazem, and Otto (2001) used the U.S. census
data to examine the effects of housing prices, wages, and commuting time on joint
residential and job location choices. The data confirm that the higher metropolitan
housing costs require that wages be higher in the metropolitan market.
Taking as given that θ − γ 3 µ < 0 and using the given conditions of parameters,
ρ1 > 0 , ρ 2 < 0 , µ ,θ > 0 , and γ 1 , γ 2 , γ 3 > 0 , we are allowed to predict the signs of the
coefficients on housing prices and amenities in the two equations, respectively. In the
output equation, α 1 > 0 , α 2 < 0 ; and in salary equation, β 1 > 0 , β 2 < 0 . In words, the
predictions are,
•
Prediction1: The effects of local housing prices on research outputs are positive;
the effects of amenity conditions on research outputs are negative.
•
Prediction2: The effects of local housing prices on faculty salaries are positive;
the effects of amenity conditions on faculty salaries are negative.
The fact, that the coefficients on housing prices have the same signs in output and
salary equations, indicates that, when local housing prices increase, universities’ optimal
reactions are to increase salary payments enough to induce higher effort levels. In
equilibrium, the increases in faculty member’s utility caused by increasing salary are
balanced by the decreases in utility caused by increasing effort level.
III.
Data.
(1) Data sources:
A. Research outputs
We have two measures for universities’ research outputs: the published articles per
faculty and the citations per article, which are constructed by Payne and Siow (2001) (PS
later on), using the Institute for Scientific Information (ISI) dataset. Data on articles
published and citations to articles are available annually for the period from 1981 through
1998, being collected from approximately 4,800 journals. PS use data at the institutional
level for papers published during that year for all disciplines. They construct the citations
per articles by dividing the total number of citations to articles published in a particular
year, accumulated to 1998, by the number of articles published in that year. “Thus, the
number of citations per article in earlier years will be higher on average than the number
of citations per article near the end of the sample period; the year fixed effects should
control for this difference.” (PS, p.15)
The trend of the index of published articles per faculty member averaged for the
whole sample universities (1990=100) is shown in Figure 2. We see that, basically, the
number of published articles per faculty member keeps increasing over time, and that the
line for the whole sample universities is smoother than that for Stanford.
B. Local housing prices
The measure for local housing prices is the median price of single-family homes at
MSA level, excluding the effect of the sale price of condominiums and the rental rate.
The median home price is an important indicator widely used in housing markets reports
and analysis, the data on which come from the National Association of Realtors10. The
sale price of single-family homes may vary dramatically due to the structure, area, and
other physical characteristics of houses. Changes in the median price reflect the changes
in purchasing costs, but not the building costs.
C. School characteristics
The measures of school characteristics used in this study include faculty salary,
university size11, research funding12, public university or private university, and the
Carnegie classifications: Research University I (R1), Research University II (R2),
Doctoral University I (D1), or Doctoral University II (D2).13 Data on these variables
come from CASPAR data, which is a compendium of data sources on higher educational
institutions and funded by the National Science Foundation (NSF).14
D. Value of potential job opportunities and outside-offer competition
The measure for the value of potential job opportunities ( VPO ) to faculty is
constructed by averaging the per capita private earnings in two sectors, the sector of
Finance, Investment and Real Estate and the sector of Services, at the MSA level. Data
come from the Bureau of Economic Analysis (BEA), an agency of the Department of
Commerce.15
10
Web page of the National Association of Realtors is: http://www.realtor.org.
University size is defined regarding to the number of faculty members, not of students.
12
There are two kinds of data regarding to research funding in this data source, total research funding and
federal research funding. For the purposes of this study, we use total research funding data and name it
simply as research funding.
13
The Carnegie Classification of Institutions of Higher Education is the leading typology of American
colleges and universities. It is the framework in which institutional diversity in U.S. higher education is
commonly described. We use its 1994 edition, in which all American universities and colleges are
classified into Doctoral-Granting Institutions and other 5 categories. Doctoral-Granting Institutions, which
we are interested in this study, comprises 4 sub-categories: Research University I, Research University II,
Doctoral University I, and Doctoral University II. “Research universities are defined as those that offer a
full range of baccalaureate programs, are committed to graduate education through the doctorate, and give
high priority to research, awarding at least 50 doctoral degrees each year. Doctoral schools differ from
Research schools in that they do not meet minimum requirements with respect to federal support and they
may award fewer doctorate degrees. The Research and Doctoral schools are further divided into classes II
and I. Research I differs from Research II in that Research I schools receive more than $40 million
annually in federal support. Doctoral I differs from Doctoral II in that Doctoral I schools must offer at least
40 doctoral degrees in at least five disciplines; Doctoral II schools must award 20 or more doctorate
degrees in at least one discipline or more than 1-0 degrees in at least three disciplines.” (PS, P.14)
11
14
Website for this data source is http://caspar.nsf.gov. Data from this source are at the institutional and
academic discipline level and are available on a yearly basis from as far back as 1972.
15
The website is: http://www.bea.gov/bea/regional/reis/.
The underlying reason for picking the two sectors is that we do not have the perfect
proxy of VPO to faculty members, and people who work in the two sectors have the
characteristics close to those of university faculty members in terms of education
background and income level. Therefore, we choose the private earnings in the two
sectors to construct VPO .
There are two inherent limitations of this measure for VPO . First of all, it is not a
comprehensive measurement, without covering all the possible potential opportunities to
university faculty members. On the other hand, it does not capture the value of the
potential job opportunities from other MSA areas.
However, how important this
disadvantage is depends on how mobile the faculty members are. Specifically, in an
extreme case, if a professor is perfectly immobile, then this disadvantage of the measure
for VPO does not decrease the accuracy of our estimations. Aggregately, we need to be
cautious when interpreting the estimate results when we use the variable of VPO .
The measure of VPO is one of the two measures we use to indicate the competition
from outside offers faced by universities.
The higher the degree of outside-offer
competition, the more difficult it is for universities keeping their faculty members,
especially those more able ones.16 The other one is a school number dummy, SCHN l .
SCHN l is set equal to one if the number of universities in MSA l is greater than one and
is zero otherwise. The coefficients on school number dummy provide us the relative
effects of competition for the group facing more universities, equivalently, higher degree
of competition, to the group facing no local competition.
E. University ranking
The data on university ranking come from the Top American Research Universities
(TARU) reported by TheCenter at the University of Florida.17 There are 3 annual reports
available on-line for the years 2000, 2001, and 2002. We chose the 2000 report, which is
16
Note that we assume perfect mobility in the model. In reality, mobility, other than ability, should be
taken into account.
17
An overview of TheCenter and the Top American Research Universities annual report can be found at
the website: http://thecenter.ufl.edu. TheCenter determines the Top American Research Universities by
their rank on nine different measures: Total Research, Federal Research, Endowment Assets, Annual
Giving, National Academy Members, Faculty Awards, Doctorates Granted, Postdoctoral Appointees, and
Median SAT Scores. The Top American Research Universities (1-25) identifies the institutions that rank in
the top 25 nationally on at least one of the nine measures. The Top American Research Universities (2650) identifies the institutions that rank 26 through 50 nationally on at least one of the nine measures.
based on the universities’ performance in 9 measures in 1998-1999, because its reported
period is also the closest to the studied period in our article, 1988-1998.
The TARU reports the universities ranked 1-25, and 26-50, which are defined as the
Rank1 and Rank2, respectively, in our study. We define all the remaining research
universities, which also are classified as Research Universities under Carnegie (1994)
classifications scheme, as Rank3.18 Finally, Rank4 includes all the doctoral universities
under Carnegie (1994) classifications. The major advantage of this rank grouping is that
it performs better in reflecting the gaps in schools’ research capabilities among subgroups
than Carnegie classification groupings – R1, R2, D1, and D2 universities, which is
obvious in Tables 3 and 4.
F. Local amenity index
Blomquist, Berger, and Hoehn (1988) provide a ranking of life quality for 253 urban
counties using 1980 Census data. We use their ranking of counties to construct the
measure for local amenity conditions of universities. To give the county with better
amenity conditions a higher amenity index, we calculate the amenity index by subtracting
254 by county’s ranking order number. Then we get a descending amenity index system
corresponding to the descending ranking of life quality for the 253 counties, with the
highest value of 254 and the lowest value of 1.
In our original data, we have local variables at MSA level, while Blomquist, et al
(1988) rank counties. In the first step, we match a county to a MSA. There are two
possibilities: some MSA areas have one or more than one matched county, while some
MSA areas have none. In the second step, in the former group of MSA areas, if we have
the rank of the county in which the school is located, we simply match the country’s
amenity index to the school; otherwise, we match the nearest matched county in the MSA
to the school. For the schools in the MSA areas with no matched counties, however, we
report their amenity index as missing data.
After merging the new data on amenity index into our original dataset, 268
observations are missing in the estimated sample (900 observations are missing in the full
18
We are allowed to define it in this way because the research universities referred by TheCenter and the
research universities defined by Carnegie classifications are of great overlap. In our dataset, all the
universities ranked 1-50 in the TARU report fall into the body of research universities under Carnegie
classifications, which is the combination of Research 1 and Research 2 universities.
sample) in the Article Case19. The distributions of missing observations over ranking
groups in two different samples are shown in Table 1. It is apparent that the observations
are missing more frequently among the lower ranked schools.
This will bias our
estimates to towards higher ranked schools.
Table 1: The distributions of missing observations across subgroups in different samples
(in the Article Case):
University ranks
In the full sample
In the estimated sample
Rank1
120
45
Rank2
180
63
Rank3
240
68
Rank4
360
92
Total
900
268
Another disadvantage of the amenity index data is that the Blomquist, et al (1988)’s
county ranking is estimated using 1980 Census data, but 1980 does not fall into the
period studied in the paper, 1990-1998. So, the accuracy of our estimate results is
influenced by how much the ranking of quality of life for urban counties changes over
time.
(2) Data sample
Separate data sources are matched at two levels: schools and MSA areas. By school,
we merged university research outputs measures, university characteristic measures, and
university ranking. And then, by MSA, we match MSA median housing prices and VPO
with all these school data.
There are 3323 observations in the data sample. Since we have missing data in
different years for different variables, eventually, in estimated sample of the benchmark
specification, we have 1241 observations of 143 universities in the Article Case (1238
19
We have two sets of estimates. In the first set, we use published articles per faculty as the quantity
measure of university research outputs. In the second set, we use citations per article as the quality
measure of research outputs. We call them Article Case and Citation Case, respectively. In the part of
empirical analysis of this paper, we mainly discuss the Article Case. We discuss the Citation Case briefly
in comparison to the Article Case.
observations of 143 universities in the Citation Case) over an 8-year period, 1991-1998.20
In the whole sample, the 227 universities are scattered in 40 states of the total 51.21
(3) Variable summary statistics
In Panel A of Table 2, we summarize descriptive statistics of two dependent variables
and two main explanatory variables, not only for the entire estimated sample, but also for
subgroups of estimated samples, separately by Carnegie classifications and by university
ranking as well. Panel B of Table 2 summarizes additional four explanatory variables –
MSA population density, MSA private earnings in two selected sectors, university size,
and total research funding per faculty member. All variable summaries in Tables 2 are
for the Article Case.
As reported in Panel A of Table 2, the means of published articles per faculty
member, annual salaries per faculty member, median housing prices, and amenity index,
are 1.45, $57890, $125670, and 121.46, respectively. Together with Panel B, we have 4
school characteristic variables: published articles per faculty member, annual salaries per
faculty member, university size, and total research funding per faculty. There are three
issues worth noting. First, by both the Carnegie Order, ranked as R1, R2, D1, and D2,
and the Rank Order, ranked from Rank1 through Rank4, the means of the four school
characteristic variables decrease as you move down the rankings. Second, the decreases
under the Rank Order are smoother than under the Carnegie Order. For example, the
means of published articles per faculty are, under the Carnegie Order, 2.62, 0.95, 0.56,
and 0.69. Under the Rank Order, the same statistics are 3.05, 1.66, 0.95, and 0.62. Third,
we do not observe the same decreasing patterns in the non-school-characteristic variables
under any order.
IV.
Empirical strategy
We develop two empirical strategies to detect the effects of housing prices on
research outputs.
(1) Strategy I
Based on the expressions in (8), Strategy I models are
20
The list of the university names is available on requests.
In our estimated sample, the 11 states with no observations are: Alabama, Arkansas, Maine, Montana,
New Hampshire, Vermont, West Virginia, and Wyoming, and Alaska, District of Columbia and Hawaii.
21
ln Qls ,t = α 0 + Yt + α 1 ln HPl ,t −1 + α 2 ln Al ,t −1 + α 3 ln RsS,t −1 + α 4 ln RlL,t −1 + ε lsQ,t
(12)
ln S lsT ,t = β 0 + Yt + β 1 ln HPl ,t −1 + β 2 ln Al ,t −1 + β 3 ln RsS,t −1 + β 4 ln RlL,t −1 + ε lsS ,t
(13)
The two equations are estimated simultaneously, with the assumption that the two error
terms, ε lsQ,t and ε lsS ,t , are jointly normally distributed. The model using the number of
published articles (citations to article) as the proxy of research outputs is called the
Article Case (the Citation Case). α 1 and β 1 are of most interest. If α 1 ( β 1 ) is not equal
to zero, then it is suggested that there are impact of local median housing prices on both
research outputs (salaries). Considering the time lag effects of information about local
median housing prices and amenities on individual utility expectation and of university
decision-making, we take one period lag on explanatory variables. Year fixed effects are
included in all specifications.
(2) Strategy II
The underlying assumption for Strategy I, that the effects of housing prices on
research outputs are identical across all universities, is untenable since universities differ
dramatically in some characteristics, for example, research productivity. This assumption
can be relaxed in different ways.
Our Strategy II model demonstrates one of the ways. In Strategy II, first, we define
four university ranking groups: Rank1 through Rank4, with Rank1 referring to the top
ranked universities and Rank4 the lowest ranked universities. Then, in both research
output and salary equations, we incorporate a full set of interaction terms with ranking
groups for the variables of housing prices and the two university-specific resources,
university size and total research funding per faculty member. But we do not include the
interaction terms for the variables of amenities and the location-specific resources, MSA
population density, VPO , and SCHN l .22 By doing so, we are permitted to look into
subgroups to explore the effects of housing prices and compare the differences. This
kind of nonlinearity is the only difference between Strategy I and Strategy II. Strategy II
models are,
22
We estimated different specifications, both including and excluding the interaction terms for amenity
index and the location-specific resource variables. However, we decide to use the latter model in that its
results are more significant and more consistent with the predicted signs of the coefficients than the results
from the former model.
ln Qls ,t = α 0 + Yt + RANK k
+ α 11 ln HPl ,t −1 + α 12 ln HPl ,t −1 ∗ Rank 2 + α 13 ln HPl ,t −1 ∗ Rank 3 + α 14 ln HPl ,t −1 ∗ Rank 4
+ α 2 ln Als ,t −1
+ α 31 ln RsS,t −1 + α 32 ln RsS,t −1 ∗ Rank 2 + α 33 ln RsS,t −1 ∗ Rank 3 + α 34 ln RsS,t −1 ∗ Rank 4
α 4 ln RlL,t −1 + ε lsQ,t
(14)
ln S lsT ,t = β 0 + Yt + RANK k
+ β11 ln HPl ,t −1 + β12 ln HPl ,t −1 ∗ Rank 2 + β13 ln HPl ,t −1 ∗ Rank 3 + β14 ln HPl ,t −1 ∗ Rank 4
+ β 2 ln Als ,t −1
+ β 31 ln RsS,t −1 + β 32 ln RsS,t −1 ∗ Rank 2 + β 33 ln RsS,t −1 ∗ Rank 3 + β 34 ln RsS,t −1 ∗ Rank 4
α 4 ln RlL,t −1 + ε lsS ,t
(15)
Ranking group dummies, RANK k , are introduced to control for each group’s fixed effect.
Here, the key coefficients are two sets of parameters, {α 1k } and {β 1k }, with k = 1, 2, 3,
and 4, corresponding to 4 ranking groups. The estimate of α 11 ( β 11 ) indicates the housing
price effects on research outputs (faculty salaries) for Rank1 universities; the estimated
coefficients on interaction terms, α 1k ( β 1k ), k = 2, 3, and 4, test whether housing prices
affect research outputs (faculty salaries) more for Rank k than for Rank1.
V.
Estimate results
In this section, we interpret the results for Strategy I and II sequentially, focusing on
the Article Case, while briefly discussing the Citation Case.
(1) Strategy I
A. The Article Case
Table 3 reports the estimates of Strategy I for the Article Case in two panels, with the
top panel being for the article equation, and the lower panel for the salary equation.
The results shown in the top panel indicate that housing prices have a significant
positive impact on research outputs, with the range of value from 0.28~0.72. This is
consistent with Prediction1 that the effects of local housing prices on research outputs are
positive. On the other hand, the coefficients on amenity index are significantly positive,
which is inconsistent with Prediction1. A 1% increase in amenity index leads the
number of published articles to increase 8~10%.
In the lower panel, being consistent with Prediction2, the effects of housing prices on
salary are positive. The effects of amenity index are positive, which is inconsistent with
Prediction2, while they are not statistically significant. The estimated results suggest that
salaries increase 14~23% if median housing prices increase 1%.
There are some issues regarding the other explanatory variables worth noting. First,
the variable of VPO plays a negative role on the numbers of published articles and a
positive role on salaries. It is consistent with the hypothesis that in the region with more
competition, the universities need to pay higher salaries to prevent faculty members from
dropping their current positions, while professors, taking advantage of the competition,
tend to make less effort in academic activities.23 When we introduce the variable of MSA
population density in column (2), the effect of VPO becomes insignificant in both the
article and salary equations. This demonstrates that the variable of MSA population
density acts as a measure of outside competition in research production. The fact that the
two variables of VPO and MSA population density have a correlation of 0.8 at the 5%
level of significance confirms the proceeding interpretation. However, the estimated
effects of SCHN are not consistent with the hypothesis of outside-offer competition, even
being significantly positive in columns (3) in the top panel. Additionally, the effects of
university sizes and average research funding are strongly significant and positive on
both the published articles and salaries. A 1% increase in research funding per faculty
member leads to a 63% increase in the number of published articles, and 6% increase in
salaries per faculty member.
B. The Citation Case
The estimate results of the Citation Case are reported in Table 4. Overall, the
estimates from the Citation Case are qualitatively similar to the Article Case. Except for
the estimates of school number dummy, the values of all the other estimates are smaller
compared to their counterparts in the Article Case reported. This result suggests that
23
In the situation with more than one alternative job opportunities, faculty members have various possible
choices. Basically, there are two categories, either picking up a new job or keeping the current job and also
having a second job at the same time. In either case, we will observe the drop in the research outputs at the
university the faculty member are currently serving for.
local housing market prices and amenity conditions outputs affect the quality measure of
research outputs less than the quantity measure. Besides, we also see that the effects of
amenity index in article equations become insignificant and, compared to their
counterparts in the Article Case, the estimated coefficients of salary equations change
very little.
(2) Strategy II
A. The Article Case
In Table 5, the estimates of Strategy II for the Article Case are reported in two panels,
the top one being for article equations and the lower one for salary equations.24
Most of all, there is a great disparity in the effects of housing prices across the four
ranking groups. The coefficients on housing prices within Rank1, being positive and, in
most cases, statistically significant, are close to the coefficients on housing prices within
Rank2. In column (2), the effects of housing prices are high in Rank1 and Rank2, and
then go down dramatically through Rank3, while still being positive, and become
negative for universities in Rank4. The net effects of housing price on the number of
published articles are 79%, 90%, 12%, and –31% for Rank1 through Rank4s,
respectively. There are two important points relevant to these results. First, they provide
the evidence to reject the validity of the underlying assumption for Strategy I that the
effects of housing prices on research outputs are identical across all universities. Second,
the effects of housing prices keep being significant even after the quality of universities’
research, represented by the university ranking, is controlled for. This reflects that the
significance of the effects of housing prices we observed in Table 3 is not the result of the
correlation between the university quality and the local housing prices, or put in another
way, the fact that the universities of higher quality always locate in the regions with
higher amenity conditions and higher housing prices.25
Our hypothesis is two-folded. First, universities faced with high local housing prices
are forced to offering higher salaries to attract faculty members, or accepting lower
research outputs. Higher ranked universities may have more room to increase salaries,
24
The estimates of university size and average total research funding and their complete set of interaction
terms with ranking groups are not reported in the table, which are available on requests.
25
The statement is also supported by the low degree of correlation between the two variables, school rank
and local amenity index in the dataset studies.
being more influential and powerful to get extra funding, relative to the lowest ranked
universities. If research funding per faculty member is a proxy for the ability to get extra
funding to increase salaries, then we do see that, with including the variable of research
funding, the coefficient on housing prices on research outputs becomes positive, though
non-significant. This guess is supported by the comparison of the relative salaries to
local housing prices among the four ranking groups shown in Figure 3. During the years
1995-1998, in which all the four groups experienced increases in local housing prices and
salaries26, the relative salaries of Rank4 universities drop fast relative to the other three
groups. And, the level of relative salaries goes down when we move from Rank1 group
through Rank3. The differences in the coefficients on housing prices on articles among
the four ranking groups can be explained by the differences in level and speed of the
changes in relative salaries. However, the remaining puzzle is that even the relative
salaries kept decreasing since 1995, the outputs of articles increased.
In the second fold, we hypothesize that faculty members may behave heterogeneously
given the same changes in relative salaries. When housing prices increase, professors
those have better paid outside offers would, with higher possibility, get promotions. The
professors in higher ranked universities are prone to process in the way in that they have
relative advantage in research in terms either of less teaching and more time in research,
or better colleague environment, or higher IQ. On the contrary, for the professors in
lower ranked universities, it is harder to get better paid outside offers to compete with the
current job. Corresponding to the relative advantages of professors in higher ranked
universities, the disadvantages of professors in low ranked universities may be too much
teaching, less inspiring colleague environment, or low IQ. Seeing that the possibility to
get promoted on current positions is very low, they may decide decrease effort level
exerted in research or other academic activities and switch part or all of time and effort to
another job, a second job or a new job. In either way, these professors could get better
off. When there is an increase in local housing prices, and given the same changes in
relative salaries, it seems possible that professors in higher ranked universities work
harder to be more productive, higher valued, and higher paid in the future, while
professor in lower ranked universities show less career concerns. To go in this way,
26
See Appendix Figure 4.
however, we need to extend the theory to a dynamic model, simply, a two-period model,
to allow individuals to value future salaries. So, the great disparities of the effects of
local housing prices across the four ranking groups may be the net results for both
reasons.
Second, row (5) reports the elasticities of the number of published articles with
respect to housing prices. They are statistically significant in columns (2)-(4), with the
value of 21~28%, which implies that local housing prices increasing 1% will result in a
21~28% increase in published articles. Just the result in column (1) is not significant.
Third, the effects of amenity index on the number of published articles are positive
and statistically significant in all the four specifications. It is inconsistent with the
theoretical predictions that amenity conditions play a negative role on research outputs.
The estimates of amenity index fall into the range of [0.09, 0.16], which means that 1%
improvement on amenity index will lead to 9 ~ 16% increase in the quantity measure of
universities’ research outputs.
In the specification in column (4), we introduce the variable of research funding per
faculty member. In the first panel, the coefficients on the variable, which are not reported
in Table 5, are significantly positive. As a result, there is a sharp drop in most of the
estimates in column (4) relative to their counterparts in the previous specifications. For
an instance, the effects of housing prices within Rank1 decrease from 63% and 83% in
columns (1) and (3), respectively, to 36%. Within Rank2, it drops from 63% and 101%
to 58%. The only exception happens to the effects of housing prices within Rank4 shown
in row (4). Within Rank4, the effects of housing prices raise from –48% and –23% to
17%, while being insignificant in columns (3) and (4). If the amount of total research
funding per faculty member at a university captures school ranking to some extent in that
top research universities always have more research funding than those lower ranked,
then, consistently, we would expect that, after taking into account the research funding
effects, the gaps between the coefficients of different ranking groups are weakened
relative to those in columns (1) and (3).
Finally, the effects of MSA population density and SCHN on the number of
published articles are of the expected signs and, in most cases, significant. The variable
of VPO plays a similar impact as the variable of population density on articles when the
variable of population density is excluded in estimation model. The results provide a
strong support for the existence of negative effects from outside-offer competition on
articles.
B. The Citation Case
In Table 6, the estimates of Strategy II for the Citation Case are reported. They are
broadly consistent with the Article Case, except for several differences worthy of notice.
First, the estimates of all explanatory variables are smaller in terms of absolute value than
their counterparts in the Article Case. For instance, the effects of housing prices within
Rank1 are in the range of 36~83% in the Article Case, while they are in the range of
27~34% in the Citation Case. It suggests that the quality of research outputs is less
influenced by the exogenous housing prices than the quantity of research outputs.
Second, in the first panel, the effects of housing prices within Rank1 and Rank2 are still
significant, while those within Rank4 become insignificant. Lastly, the coefficients of
housing prices, amenity index, VPO, and SCHN on salaries in Citation Case are of few
difference from their counterparts in Table 5 for the Article Case, which implies that,
maximizing profits under either form of research outputs, universities adjust salary offers
with respect to exogenous changes in local housing markets to the similar extent.
VI.
Concluding remarks
In this study, we set up a simple two-agent model to examine the effects of housing
prices on university research outputs, taking the rational reaction forms of both university
and faculty into account. With some standard restrictions on parameters restricted in the
model, and taking as given the conclusion from previous studies that the effects of
housing prices on personal earnings are positive, the theory predicts that the effects of
housing prices on both research outputs and salaries are positive; and the effects of
amenities are negative.
Our empirical results are a mixture of consistency and
inconsistency with the theoretical predictions. First, in the specifications without looking
into ranking subgroups, the effects of housing prices are significantly positive on both of
published articles and salaries. Next, when looking into ranking subgroups, the estimated
effects of housing prices on research outputs are ranked from high to low as we move
from the top ranked universities to the Rank4. This diminishing pattern is observed in
both Article Case and Citation Case. The effects of housing prices are high in Rank1 and
Rank2s, and then go down dramatically through Rank3, while still being positive, and
become negative for universities in Rank4. Finally, the set of variables capturing local
outside-offer competition – the MSA population density, the value of potential job
opportunities, and a school number dummy – play a negative role on university research
outputs, and a positive role on salaries per faculty member.
There are two interesting issues we leave for future research.
First, how to
incorporate the heterogeneity among faculty members? One way to implement this idea is
to break faculty members into two groups of tenured and non-tenured professors. Since
the two groups differ substantially in terms of research ability and labor mobility, it will
be interesting to detect the effects of housing prices and amenity on the professors’
research outputs in each group. Restricted by available data, we cannot implement this
idea in the current study. Second, as we mention briefly in Footnote 3, in practice, except
for adjusting salaries and effort levels, universities may have alternative options to reduce
the passive impact of housing prices increasing on research outputs. These alternative
options include providing low-interest-rate mortgage and building houses and then selling
(or renting) to faculty at low price. Stanford University is using the latter scheme. It is
worth serious thinking that why some universities choose salary-effort scheme, while
other choose building or mortgage scheme favorable to faculty members.
How to
integrate these different schemes into one model and apply the benefit-cost analysis?
And, how do housing prices affect research outputs differently under different schemes?
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Figure 1 Changes in the Indexes of Local Median Housing Prices and Annual
Salaries per Faculty Member* (1990=100)
Panel A: Stanford University
Index of Median Price of Single-Family Homes in San Francisco, CA (PMSA)
Index of Annual Salaries per Faculty Member at Stanford University
120
110
100
90
80
1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998
Panel B: Universities in the Whole Sample
Index of Averaged Local Median Prices of Single-Family Homes for the Whole
Sample
Index of Averaged Annual Salaries per Faculty Member for the Whole Sample
120
110
100
90
80
1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998
Note:
* Both local median housing prices and annual salaries per faculty member are in 1996 dollars.
Figure 2 Changes in the Index of the Annual Number of Published Articles per
Faculty Member
the Whole Sample Universities
Stanford University
130
120
110
100
90
1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998
Note:
There are missing data on the number of published articles per faculty member in the years 1987-89 for the
whole sample, and in the years 1985-89 for Stanford University.
Figure 3 Relative Changes in Salary:
The ratio of the index of annual salaries per faculty member over the index of local
median housing prices (1991=100)
Rank1 Universities
Rank3 Universities
Rank2 Universities
Rank4 Universities
1.15
1.1
1.05
1
0.95
0.9
0.85
0.8
1991
1992
1993
1994
1995
1996
1997
1998
Appendix: Figure 4
Panel A: Rank1 Universities
Index of Annual Salaries per Faculty Member for Rank1 Universities
Index of Local Median Price of Single-Family Homes for Rank1
Universities
110
100
90
1991
1992
1993
1994
1995
1996
1997
1998
Panel B: Rank2 Universities
Index of Annual Salaries per Faculty Member for Rank2 Universities
Index of Local Median Price of Single-Family Homes for Rank2 Universities
110
105
100
95
90
1991
1992
1993
1994
1995
1996
1997
1998
Panel C: Rank3 Universities
Index of Annual Salaries per Faculty Member for Rank3 Universities
Index of Local Median Price of Single-Family Homes for Rank3 Universities
130
120
110
100
90
1991
1992
1993
1994
1995
1996
1997
1998
Panel D: Rank4 Universities
Index of Annual Salaries per Faculty Member for Rank4 Universities
Index of Local Median Price of Single-Family Homes for Rank4 Universities
110
105
100
95
90
1991
1992
1993
1994
1995
1996
1997
1998
Table 2 Variable summary statistics for the Article Case.
Panel A: The dependent variables and two main explanatory variables
(Standard deviations are in parenthesis)
Number of published
articles per faculty
member
Annual salaries per
faculty member
($thousands † )
Median price of
single-family homes
($thousands † )
Amenity index
Observations*
(School numbers)
1.45
(1.94)
57.89
(10.46)
125.67
(47.86)
121.46
(73.90)
1103
(142)
2.62
(2.42)
64.85
(9.87)
133.13
(53.30)
124.45
(73.96)
433
(56)
Research II
0.95
(0.39)
54.93
(7.06)
94.20
(16.98)
118.47
(63.99)
152
(19)
Doctoral I
0.56
(1.24)
52.63
(7.93)
121.09
(37.78)
120.40
(81.46)
260
(34)
Doctoral II
0.69
(0.95)
53.24
(8.71)
136.31
(51.24)
119.27
(71.46)
258
(33)
3.05
(2.78)
68.39
(9.76)
139.37
(59.08)
110.90
(68.97)
285
(37)
Rank2
1.66
(1.03)
59.79
(5.86)
115.20
(37.45)
134.78
(75.08)
172
(22)
Rank3
0.95
(0.41)
51.99
(4.43)
97.09
(17.11)
133.63
(68.16)
128
(16)
Rank4
0.62
(1.11)
52.93
(8.33)
128.67
(45.59)
119.84
(76.57)
518
(67)
Variables
Estimated sample
By Carnegie classifications
Research I
By school ranking group
Rank1
(To be continued)
Note:
† All dollars are constant ($1996).
* The observations in the specifications without the variable of research funding per faculty member. The period studied is 1991-1998.
Table 2 (continued)
Panel B: The variables of location- and university-specific resources
(Standard deviations are in parenthesis)
MSA population
density (persons
per square mile)
MSA private earnings
in two selected
sectors ‡ ($thousands † )
University size
(number of faculty
members)
Total research funding
per faculty member
($thousands † )
Observations*
(School numbers)
1397.68
(2216.20)
28.82
(11.64)
744.53
(482.64)
118.83
(166.28)
1026
(134)
By Carnegie classifications
Research I
1412.57
(2062.40)
29.15
(11.19)
1064.89
(537.09)
229.57
(205.79)
433
(56)
Variables
Estimated sample
Research II
422.20
(247.24)
23.06
(4.77)
711.34
(230.13)
66.61
(35.95)
152
(19)
Doctoral I
1973.35
(2809.79)
31.44
(14.15)
462.07
(236.85)
21.67
(20.97)
249
(34)
Doctoral II
1391.09
(2291.21)
29.27
(11.40)
414.62
(189.61)
36.45
(43.60)
192
(25)
1391.30
(1965.59)
29.69
(10.89)
1123.66
(610.77)
262.44
(237.17)
285
(37)
Rank2
1131.61
(2108.62)
27.87
(10.87)
900.28
(355.55)
141.19
(101.35)
172
(22)
Rank3
419.54
(282.86)
22.41
(4.92)
735.41
(188.50)
81.63
(51.91)
128
(16)
Rank4
1719.29
(2609.97)
30.49
13.06)
441.41
(218.59)
28.11
(33.57)
441
(59)
By school ranking group
Rank1
Note:
† All dollars are constant ($1996).
‡ The two sectors are the Sector of Finance, Investment, and Real Estate and the Sector of Services. Please see Section III for the explanation in detail.
* The observations in the specifications with the variable of research funding per faculty member. The period studied is 1991-1998.
Table 3 Strategy I: the Article Case.
(Standard errors are in parenthesis)
Specifications
(1)
(2)
Article Equation: Dependent variable =ln(published articles per faculty member)
Main explanatory variables:
ln(local median housing
prices)
ln(amenity index)
0.660***
(0.156)
0.095**
(0.042)
University-specific resource variables:
ln(university size)
0.785***
(0.051)
ln(research funding per
faculty member)
Location-specific resource variables:
ln(MSA population
density)
ln(value of potential job
-0.427**
opportunities)
(0.182)
SCHN (=1, if the # of
0.101
schools in a given MSA
(0.089)
>1)
(3)
0.716***
(0.157)
0.084**
(0.042)
0.283**
(0.097)
0.080***
(0.024)
0.800***
(0.051)
0.149***
(0.034)
0.633***
(0.015)
-0.181***
(0.066)
0.141
(0.274)
0.130
(0.089)
-0.058
(0.039)
0.069
(0.166)
0.159***
(0.052)
Salary Equation: Dependent variable =ln(annual salaries per faculty member)
Main explanatory variables:
ln(local median housing
prices)
ln(amenity index)
0.188***
(0.020)
0.002
(0.005)
University-specific resource variables:
ln(university size)
0.086***
(0.006)
ln(research funding per
faculty member)
Location-specific resource variables:
ln(MSA population
density)
ln(value of potential job
0.071***
opportunities)
(0.023)
SCHN (=1, if the # of
0.007
schools in a given MSA
(0.011)
>1)
Observations
1103
0.180***
(0.020)
0.004
(0.005)
0.133***
(0.018)
0.004
(0.004)
0.083***
(0.006)
0.028***
(0.006)
0.055***
(0.003)
0.025***
(0.008)
-0.008
(0.035)
0.002
(0.011)
0.034***
(0.007)
-0.017
(0.030)
0.004
(0.009)
1103
1026
Note:
*** (**, and *) represents statistically significant at the 1% (5%, and 10%) level. Year fixed effects are
included in all specifications. One period lag is taken on all the explanatory variables.
Table 4 Strategy I: the Citation Case.
(Standard errors are in parenthesis)
Specifications
(1)
(2)
Citation Equation: Dependent variable =ln(citations per article)
Main explanatory variables:
ln(local median housing
prices)
ln(amenity index)
0.344***
(0.069)
0.021
(0.018)
University-specific resource variables:
ln(university size)
0.361***
(0.023)
ln(research funding per
faculty member)
Location-specific resource variables:
ln(MSA population
density)
ln(value of potential job
-0.117
opportunities)
(0.081)
SCHN (=1, if the # of
0.132***
schools in a given MSA
(0.039)
>1)
(3)
0.360***
(0.070)
0.018
(0.019)
0.168***
(0.052)
0.009
(0.013)
0.365***
(0.023)
0.147***
(0.018)
0.231***
(0.008)
-0.050*
(0.029)
0.039
(0.122)
0.140***
(0.039)
-0.033
(0.021)
0.123
(0.089)
0.165***
(0.028)
Salary Equation: Dependent variable =ln(annual salaries per faculty member)
Main explanatory variables:
ln(local median housing
prices)
ln(amenity index)
0.191***
(0.020)
0.003
(0.005)
University-specific resource variables:
ln(university size)
0.087***
(0.006)
ln(research funding per
faculty member)
Location-specific resource variables:
ln(MSA population
density)
ln(value of potential job
0.070***
opportunities)
(0.023)
SCHN (=1, if the # of
0.006
schools in a given MSA
(0.011)
>1)
Observations
1100
0.183***
(0.020)
0.004
(0.005)
0.133***
(0.018)
0.004
(0.004)
0.084***
(0.006)
0.028***
(0.006)
0.055***
(0.003)
0.026***
(0.008)
-0.010
(0.035)
0.002
(0.011)
0.034***
(0.007)
-0.017
(0.030)
0.004
(0.009)
1100
1025
Note:
*** (**, and *) represents statistically significant at the 1% (5%, and 10%) level. Year fixed effects are
included in all specifications. One period lag is taken on all the explanatory variables.
Table 5 Strategy II: the Article Case.
(Standard errors are in parenthesis)
(1)
Specifications
(3)
(2)
(4)
Article Equation: Dependent variable =ln(published articles per faculty member)
Main explanatory variables:
ln(local median housing
prices)*Rank1
ln(local median housing
prices)*Rank2
ln(local median housing
prices)*Rank3
ln(local median housing
prices)*Rank4
Elasticity of published
articles w.r.t. Housing
prices
ln(local amenity index)
0.627***
(0.129)
0.630***
(0.228)
-0.010
(0.471)
-0.484***
(0.115)
0.789***
(0.148)
0.900***
(0.250)
0.117
(0.472)
-0.306**
(0.151)
0.830***
(0.148)
1.005***
(0.251)
0.029
(0.471)
-0.229
(0.152)
0.358***
(0.127)
0.584***
(0.198)
0.055
(0.355)
0.170
(0.127)
0.036
(0.091)
0.214*
(0.124)
0.267**
(0.124)
0.277***
(0.102)
0.122***
(0.030)
0.122***
(0.030)
0.113***
(0.030)
0.089***
(0.023)
/
-0.172***
(0.137)
-0.195**
(0.066)
-0.152***
(0.049)
0.283
(0.199)
-0.169**
(0.066)
-0.091**
(0.038)
0.029
(0.159)
0.053
(0.052)
YES
YES
YES
YES
NO
NO
NO
YES
1111
1103
1103
Location-specific resource variables:
ln(MSA population
/
density)
ln(value of potential job
/
opportunities)
SCHN (=1, if the # of
/
schools in a given MSA
>1)
University-specific resource variables:
ln(university size) &
interactions with ranking
groups
ln(research funding per faculty
member) & interactions with
ranking groups
Observations
1026
(To be continued)
Note:
*** (**, and *) represents statistically significant at the 1% (5%, and 10%) level. Year fixed effects are included
in all specifications. One period lag is taken on all the explanatory variables. The estimated coefficients on both
ln(university size) and its interaction terms, and ln(research funding per faculty member) and its interaction terms,
with ranking groups respectively, are not reported in the table.
Table 5 (continued)
(Standard errors are in parenthesis)
(1)
Specifications
(3)
(2)
(4)
Salary Equation: Dependent Variable = ln(annual salaries per faculty member)
Main explanatory variables:
ln(local median housing
prices)*Rank1
ln(local median housing
prices)*Rank2
ln(local median housing
prices)*Rank3
ln(local median housing
prices)*Rank4
0.122***
(0.018)
0.104**
(0.031)
0.162**
(0.065)
0.228***
(0.016)
0.086***
(0.020)
0.051
(0.034)
0.152**
(0.065)
0.176***
(0.021)
0.078***
(0.020)
0.030
(0.034)
0.169***
(0.065)
0.160***
(0.021)
0.116***
(0.021)
0.015
(0.032)
0.163***
(0.058)
0.203***
(0.021)
Elasticity of salaries w.r.t.
Housing prices
0.173***
(0.013)
0.130***
(0.017)
0.120***
(0.017)
0.143***
(0.017)
ln(local amenity index)
0.0001
(0.004)
0.007
(0.004)
0.008**
(0.004)
0.009**
(0.004)
/
0.077***
(0.019)
-0.004
(0.009)
0.030***
(0.007)
-0.013
(0.027)
-0.009
(0.009)
0.030***
(0.006)
-0.071***
(0.026)
0.011
(0.008)
YES
YES
YES
YES
NO
NO
NO
YES
1111
1103
1103
1026
Location-specific resource variables:
ln(MSA population
/
density)
ln(value of potential job
/
opportunities)
SCHN (=1, if the # of
/
schools in a given MSA
>1)
University-specific resource variables:
ln(university size) &
interactions with ranking
groups
ln(research funding per faculty
member) & interactions with
ranking groups
Observations
Note:
*** (**, and *) represents statistically significant at the 1% (5%, and 10%) level. Year fixed effects are included
in all specifications. One period lag is taken on all the explanatory variables. The estimated coefficients on both
ln(university size) and its interaction terms, and ln(research funding per faculty member) and its interaction terms,
with ranking groups respectively, are not reported in the table.
Table 6 Strategy II: the Citation Case.
(Standard errors are in parenthesis)
(1)
Specifications
(3)
(2)
(4)
Citation Equation: Dependent variable =ln(citations per article)
Main explanatory variables:
ln(local median housing
prices)*Rank1
ln(local median housing
prices)*Rank2
ln(local median housing
prices)*Rank3
ln(local median housing
prices)*Rank4
0.329***
(0.061)
0.315**
(0.107)
0.270
(0.221)
-0.031
(0.054)
0.330***
(0.070)
0.321***
(0.118)
0.263
(0.223)
-0.025
(0.072)
0.340***
(0.070)
0.347***
(0.119)
0.241
(0.223)
-0.006
(0.073)
0.269***
(0.066)
0.278***
(0.102)
0.252
(0.183)
0.081
(0.065)
Elasticity of citations w.r.t.
Housing prices
0.153***
(0.043)
0.155***
(0.059)
0.168***
(0.059)
0.187***
(0.052)
ln(local amenity index)
0.036***
(0.013)
0.035**
(0.014)
0.033**
(0.014)
0.020*
(0.012)
/
-0.017
(0.065)
0.020
(0.031)
-0.037
(0.023)
0.094
(0.095)
0.026
(0.032)
-0.051***
(0.020)
0.105
(0.082)
0.103***
(0.027)
YES
YES
YES
YES
NO
NO
NO
YES
1108
1100
1100
1025
Location-specific resource variables:
ln(MSA population
/
density)
ln(value of potential job
/
opportunities)
SCHN (=1, if the # of
/
schools in a given MSA
>1)
University-specific resource variables:
ln(university size) &
interactions with ranking
groups
ln(research funding per faculty
member) & interactions with
ranking groups
Observations
(To be continued)
Note:
*** (**, and *) represents statistically significant at the 1% (5%, and 10%) level. Year fixed effects are included
in all specifications. One period lag is taken on all the explanatory variables. The estimated coefficients on both
ln(university size) and its interaction terms, and ln(research funding per faculty member) and its interaction terms,
with ranking groups respectively, are not reported in the table.
Table 6 (continued)
(Standard errors are in parenthesis)
(1)
Specifications
(3)
(2)
(4)
Salary Equation: Dependent Variable = ln(annual salaries per faculty member)
Main explanatory variables:
ln(local median housing
prices)*Rank1
ln(local median housing
prices)*Rank2
ln(local median housing
prices)*Rank3
ln(local median housing
prices)*Rank4
0.122***
(0.018)
0.104***
(0.031)
0.161**
(0.065)
0.235***
(0.016)
0.088***
(0.020)
0.052
(0.034)
0.151**
(0.065)
0.184***
(0.021)
0.079***
(0.020)
0.032
(0.034)
0.169***
(0.064)
0.168***
(0.021)
0.116***
(0.021)
0.015
(0.032)
0.163***
(0.058)
0.203***
(0.021)
Elasticity of salaries w.r.t.
Housing prices
0.176***
(0.013)
0.135***
(0.017)
0.124***
(0.017)
0.142***
(0.017)
ln(local amenity index)
0.0003
(0.004)
0.007*
(0.004)
0.008**
(0.004)
0.009**
(0.004)
/
0.074***
(0.019)
-0.004
(0.009)
0.030***
(0.007)
-0.017
(0.027)
-0.010
(0.009)
0.031***
(0.006)
-0.072***
(0.026)
0.011
(0.008)
YES
YES
YES
YES
NO
NO
NO
YES
1108
1100
1100
1025
Location-specific resource variables:
ln(MSA population
/
density)
ln(value of potential job
/
opportunities)
SCHN (=1, if the # of
/
schools in a given MSA
>1)
University-specific resource variables:
ln(university size) &
interactions with ranking
groups
ln(research funding per faculty
member) & interactions with
ranking groups
Observations
Note:
*** (**, and *) represents statistically significant at the 1% (5%, and 10%) level. Year fixed effects are included
in all specifications. One period lag is taken on all the explanatory variables. The estimated coefficients on both
ln(university size) and its interaction terms, and ln(research funding per faculty member) and its interaction terms,
with ranking groups respectively, are not reported in the table.