american university research outputs and local …homes.chass.utoronto.ca/~q_hong/files/2ndyr...

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, 1985-1999, 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 , with superscript representing

individual and the subscripts location l and school , respectively. Each component

plays a nonnegative role on faculty member utility, that is, U and U .

),( il

ils

ils LJU=

si

0>J 0>L

Valuation of the job component is formed over salary ( ) and effort (S E ),

, with and ),( ils

ils

ils ESJJ = 0>SJ 0<EJ

)ilsE

(job valuation rises with increased salary and

falls with increased effort). 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

,ilsS(

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 ( ), , withA ),( il

il

il AHPLL = 0<HPL and (faculty members value

housing prices negatively, and amenities positively). It is assumed that local housing

prices (

0>AL

HP ) and amenity conditions ( ) 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.

A

4 In a reduced

form, the utility function can be written as

),,, il

il

ils

ils AHPEi

lsU

i

U=

s )ilsE

),,1

il

il AHP(UU = ,, li s

J

Ail

J

HP dAUUdHP

U−

−=

(S (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 brings up a job offer, ( , to faculty ;

faculty makes a take-it-or-leave-it decision, taking the location valuation into

accounts.

,ilsS i

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, ilsJ ∀ and . (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,

il

ils

UdJ (3)

4 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

ilsll

ils AHPJ ερρ ++= lnlnln 21 (4)

The implication of equation (4) includes two points. First, 1ρ and 2ρ 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 01 >ρ and

02 <ρ , 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 Cobb-

Douglas function, , and θµ )1()(),( ils

ils

ils

ils ESESJ −= 0, >θµ . Recall that ( )1 E−

denotes the fraction of leisure time of the time endowment. Then, the university’s

maximization problem is,

{ }max

, ils

ils ES

ils

ils SQP −⋅

..ts θµ )1()( ils

ils

ils ESJ −≤ , 0, >θµ and ]1,0[∈i

lsE

, 321 )1()()( γγγ −−= ils

Ss

Ll

ils ERRQ 0,, 321 >γγγ (5)

7 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, . Considering

that universities sell research productivity on an international higher-education market,

ilsQ

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. Q , the research produced by individual i at

school in location l , is a function increasing in a set of location-specific resources in l ,

, and a set of university-specific resources of , , and decreasing in individual’s

leisure (or, inversely, increasing in effort exerted in research).

ils

sLlR s S

sR8

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 and . And, using the optimal effort level, we write the

optimal research outputs,

ilsS i

lsE

µγθγ

µγθθγ

µγθθγ

µγθµγ

θµγ 3

3

3

2

3

1

3

3

)()()(3−−

−−−

=

ils

Ss

Ll

ils JRRPQ (6)

The variable of salary in the theory does, precisely, refer to the research-related

remuneration, which differs from the total remuneration, , and is not separately

observable in practice.

TlsiS

9 By setting up a simplified transformation function, S ,

, and

η)( ils

Tlsi Sk=

0>k 1>η , we get the expression of the optimal total salaries,

µγθηγ

µγθθηγ

µγθθηγ

µγθθη

θµγ 3

3

3

2

3

1

3 )()()(3−−

−−−

=

ils

Ss

Ll

Tlsi JRRPkS (7)

Taking logarithm on both sides of equations (6) and (7), and combining with equation

(4), we obtain,

8 In this study, 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 the number of universities, which are in our sample, is greater than one in a given MSA; 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.

LlR

SsR

9 The idea comes from Graves, Marchand and Thompson (1982).

−

−+

−−

+

−= i

li

lils AHPPQ lnlnlnln

3

23

3

133

3

3

µγθργ

µγθργ

θµγ

µγθµγ

ils

Ll

Ss RR ε

µγθγ

µγθθγ

µγθθγ

3

3

3

1

3

2 lnln−−

+−

+−

+

Qlsi

Ll

Ss

il

il RRAHP εααααα +++++= lnlnlnln 43210

−

−+

−−

+

−+= i

li

lTlsi AHPPkS lnlnlnlnln

3

23

3

133

3 µγθηργ

µγθηργ

θµγ

µγθθη

ils

Ll

Ss RR ε

µγθηγ

µγθθηγ

µγθθηγ

3

3

3

1

3

2 lnln−

−+

−+

−+

(8) Slsi

Ll

Ss

il

il RRAHP εβββββ +++++= lnlnlnln 43210

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

)( 3µγθ − . As learned from previous studies, the sign of the effects of housing prices on

salary (or wage) is always positive, which implies, 03 <− µγθ . 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 03 <− µγθ and using the given conditions of parameters,

01 >ρ , 02 <ρ , 0, >θµ , and 03 >,, 21 γγγ , we are allowed to predict the signs of the

coefficients on housing prices and amenities in the two equations, respectively. In the

output equation, 01 >α , 02 <α ; and in salary equation, 01 >β , 02 <β . 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. 11 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) 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/.

http://www.realtor.org/

http://caspar.nsf.gov/

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 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 .

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, .

is set equal to one if the number of universities in MSA 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.

lSCHN

lSCHN l

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 (26-50) identifies the institutions that rank 26 through 50 nationally on at least one of the nine measures.

http://thecenter.ufl.edu/

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. 21 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.

(12) Qtls

Ltl

Ststltlttls RRAHPYQ ,1,41,31,21,10, lnlnlnlnln εααααα ++++++= −−−−

(13) Stls

Ltl

Ststltlt

Ttls RRAHPYS ,1,41,31,21,10, lnlnlnlnln εβββββ ++++++= −−−−

The two equations are estimated simultaneously, with the assumption that the two error

terms, and , 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).

Qtls,ε S

tls,ε

1α and 1β are of most interest. If 1α ( 1β ) 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 .lSCHN 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.

kttls RANKYQ ++= 0,ln α

lnln 211,

11 HPHP tl ++ − αα

1,2 ln −+ tlsAα

lnln ,231,

13 RR S

tsS

ts ++ − αα

4ln3ln2 1,411,

311, RankHPRankHPRank tltltl ∗+∗+∗ −−− αα

4ln3ln2 1,431,

331 RankRRankRRank S

tsS

ts ∗+∗+∗ −−− αα

Qtls

LtlR ,1,4 ln εα +− (14)

ktT

tls RANKYS ++= 0,ln β

lnln 211,

11 HP tl ++ − ββ

1,2 ln −+ tlsAβ

lnln 231,

13 RR S

sS

ts ++ − ββ

4ln3ln2 1,4

11,3

11, RankHPRankHPRankHP tltltl ∗+∗+∗ −−− ββ

4ln3ln2 1,431,

331, RankRRankRRank S

tsS

tst ∗+∗+∗ −−− ββ

Stls

LtlR ,1,4 ln εα +− (15)

Ranking group dummies, , are introduced to control for each group’s fixed effect.

Here, the key coefficients are two sets of parameters,

kRANK

{ }k1α and { }k

1β

11

, with 1, 2, 3,

and 4, corresponding to 4 ranking groups. The estimate of ( ) indicates the housing

price effects on research outputs (faculty salaries) for Rank1 universities; the estimated

coefficients on interaction terms, ( ),

=k11α β

k1α k

1β =k 2, 3, and 4, test whether housing prices

affect research outputs (faculty salaries) more for Rank than for Rank1. k

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 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

SCHN

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 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.

SCHN

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?

Reference: Bacolod, Marigee P. (2001). “The Role of Alternative Opportunities in the Female Labor Market In Teacher Supply and Quality: 1940-1990”, Balderston, Frederick E. (1995). Managing Today’s University: Strategies for Viability, Change, and Excellence, Jossey-Bass: Jossey-Bass Publishers. Cameron, Gavin, and John Muellbauer (2001). “Earnings, Unemployment, and Housing in Britain”, Journal of Applied Econometrics, vol.16, pp 203-220 (2001). Case, Karl E. and Christopher J. Mayer (1996). “Housing price dynamics within a metropolitan area”. Regional Science and Urban Economics, 26 (1996), 387-407. Chay, Kenneth Y. (1998). “Does Air Quality Matter? Evidence from the Housing Market.” NBER Working Paper 6826. De Groot, Hans, Walter W. McMahon, and J. Fredericks Volkwein (1991). “ The Cost Structure of American Research Universities.” Review of Economics and Statistics, vol.73, no. 3, August 1991, pp.424-31. Dundar, Halil and Darrell R. Lewis (1995). “Departmental Productivity in American Universities: Economics of Scale and Scope”. Economics of Education Review, vol.14, no.2, pp.119-144. Gayer, Ted and W. Kip Viscusi (2002). “Housing price responses to newspaper publicity of hazardous waste sites”. Resource an Energy Economics, 24 (2002), 33-51. Glass, J. C., D. G. McKillop, and G. O’Rourke (2002). “Evaluating the productive performance of UK universities as cost-constrained revenue maximizers: an empirical analysis”. Applied Economics, 2002, 34, 1097-1108. Glass, J. C., D. G. McKillop, and N. Hyndman (1995). “Efficiency in the Provision of University Teaching and Research: an Empirical Analysis of UK Universities”. Journal of Applied Econometrics, vol.10, 61-72. Graves, Philip E., James R. Marchand, Randall Thompson (1982). “Economics Departmental Rankings: Research Incentives, Constraints, and Efficiency”, American Economic Review, vol.72, issue 5, Dec. 1982, pp1131-1141. Johnes, Geraint, and Thomas Hyclak (1999). “House Prices and Regional Labor Markes”, the Annuals of Regional Science (1999) 33: 33-49. Olmo, Jorge Chica (1995). “Spatial Esimation of Housing Prices and Locational Rents”, Urban Studies, vol. 32, no. 8, 1331-1344.

Payne, A. Abigail and Aloysius Siow (2001). “Does Federal Research Funding Increase University Research outputs?” Department of Economics, University of Toronto, working paper. Roback, Jennifer (1982). “Wage, Rents, and the Quality of Life”, Journal of Political Economy, vol.90, issue 6, Dec. 1982, pp 1257-1278. Siow, Aloysius (1998). “Tenure and Other Unusual Personnel Practices in Academia”, Journal of Law, Economics and Organization, 14(1): 152-173. So, Kim S., Peter F. Orazem, and Daniel M. Otto (2001). “The Effects of Housing Prices, Wages, and Commuting Time on Joint Residential and Job Location Choices”, American Journal of Agricultural Economics, 83(4), November 2001: 1036-1048. Zucker, Lynne G., Michael R. Darby, and Maximo Toreto (1997). “Labor Mobility from Academe to Commerce.” NBER Working Paper 6050.

Figure 1 Changes in the Indexes of Local Median Housing Prices and Annual Salaries per Faculty Member* (1990=100)

Panel A: Stanford University

80

90

100

110

120

1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998

Index of Median Price of Single-Family Homes in San Francisco, CA (PMSA)Index of Annual Salaries per Faculty Member at Stanford University

Panel B: Universities in the Whole Sample

80

90

100

110

120

1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998

Index of Averaged Local Median Prices of Single-Family Homes for the WholeSampleIndex of Averaged Annual Salaries per Faculty Member for the Whole Sample

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

90

100

110

120

130

1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998

the Whole Sample Universities Stanford University

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)

0.8

0.85

0.9

0.95

1

1.05

1.1

1.15

1991 1992 1993 1994 1995 1996 1997 1998

Rank1 Universities Rank2 UniversitiesRank3 Universities Rank4 Universities

Appendix: Figure 4 Panel A: Rank1 Universities

90

100

110

1991 1992 1993 1994 1995 1996 1997 1998

Index of Annual Salaries per Faculty Member for Rank1 Universities

Index of Local Median Price of Single-Family Homes for Rank1Universities

Panel B: Rank2 Universities

9095

100105110

1991 1992 1993 1994 1995 1996 1997 1998

Index of Annual Salaries per Faculty Member for Rank2 UniversitiesIndex of Local Median Price of Single-Family Homes for Rank2 Universities

Panel C: Rank3 Universities

90100110120130

1991 1992 1993 1994 1995 1996 1997 1998


Panel D: Rank4 Universities

9095

100105110

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)

Variables

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) Estimated sample

1.45

(1.94)

57.89

(10.46)

125.67 (47.86)

121.46 (73.90)

1103 (142)

By Carnegie classifications Research I 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)

By school ranking group Rank1 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)

(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)

Variables

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) Estimated sample

1397.68

(2216.20)

28.82

(11.64)

744.53

(482.64)

118.83

(166.28)

1026 (134)

By Carnegie classificationsResearch I 1412.57

(2062.40) 29.15

(11.19) 1064.89 (537.09)

229.57 (205.79)

433 (56)

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)

By school ranking groupRank1 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)

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) (3) Article Equation: Dependent variable =ln(published articles per faculty member) Main explanatory variables: ln(local median housing prices)

0.660*** (0.156)

0.716*** (0.157)

0.283** (0.097)

ln(amenity index) 0.095** (0.042)

0.084** (0.042)

0.080*** (0.024)

University-specific resource variables: ln(university size) 0.785***

(0.051) 0.800*** (0.051)

0.149*** (0.034)

ln(research funding per faculty member)

0.633*** (0.015)

Location-specific resource variables: ln(MSA population density)

-0.181*** (0.066)

-0.058 (0.039)

ln(value of potential job opportunities)

-0.427** (0.182)

0.141 (0.274)

0.069 (0.166)

SCHN (=1, if the # of schools in a given MSA >1)

0.101 (0.089)

0.130 (0.089)

0.159*** (0.052)

Salary Equation: Dependent variable =ln(annual salaries per faculty member) Main explanatory variables: ln(local median housing prices)

0.188*** (0.020)

0.180*** (0.020)

0.133*** (0.018)

ln(amenity index) 0.002 (0.005)

0.004 (0.005)

0.004 (0.004)


(0.006) 0.083*** (0.006)

0.028*** (0.006)


0.055*** (0.003)


0.025*** (0.008)

0.034*** (0.007)


0.071*** (0.023)

-0.008 (0.035)

-0.017 (0.030)


0.007 (0.011)

0.002 (0.011)

0.004 (0.009)

Observations 1103 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) (3) Citation Equation: Dependent variable =ln(citations per article) Main explanatory variables: ln(local median housing prices)

0.344*** (0.069)

0.360*** (0.070)

0.168*** (0.052)


0.018 (0.019)

0.009 (0.013)


(0.023) 0.365*** (0.023)

0.147*** (0.018)


0.231*** (0.008)


-0.050* (0.029)

-0.033 (0.021)


-0.117 (0.081)

0.039 (0.122)

0.123 (0.089)


0.132*** (0.039)

0.140*** (0.039)

0.165*** (0.028)

Salary Equation: Dependent variable =ln(annual salaries per faculty member) Main explanatory variables: ln(local median housing prices)

0.191*** (0.020)

0.183*** (0.020)

0.133*** (0.018)


0.004 (0.005)

0.004 (0.004)


(0.006) 0.084*** (0.006)

0.028*** (0.006)


0.055*** (0.003)


0.026*** (0.008)

0.034*** (0.007)


0.070*** (0.023)

-0.010 (0.035)

-0.017 (0.030)


0.006 (0.011)

0.002 (0.011)

0.004 (0.009)

Observations 1100 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) Specifications (1) (2) (3) (4)

Article Equation: Dependent variable =ln(published articles per faculty member) Main explanatory variables: ln(local median housing prices)*Rank1

0.627*** (0.129)

0.789*** (0.148)

0.830*** (0.148)

0.358*** (0.127)

ln(local median housing prices)*Rank2

0.630*** (0.228)

0.900*** (0.250)

1.005*** (0.251)

0.584*** (0.198)


-0.010 (0.471)

0.117 (0.472)

0.029 (0.471)

0.055 (0.355)


-0.484*** (0.115)

-0.306** (0.151)

-0.229 (0.152)

0.170 (0.127)

Elasticity of published articles w.r.t. Housing prices

0.036 (0.091)

0.214* (0.124)

0.267** (0.124)

0.277*** (0.102)

ln(local amenity index) 0.122***

(0.030) 0.122*** (0.030)

0.113*** (0.030)

0.089*** (0.023)


/ / -0.152*** (0.049)

-0.091** (0.038)


/ -0.172*** (0.137)

0.283 (0.199)

0.029 (0.159)


/ -0.195** (0.066)

-0.169** (0.066)

0.053 (0.052)

University-specific resource variables: ln(university size) & interactions with ranking groups

YES

YES

YES

YES

ln(research funding per faculty member) & interactions with ranking groups

NO

NO

NO

YES

Observations 1111 1103 1103 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)

Specifications (1) (2) (3) (4)

Salary Equation: Dependent Variable = ln(annual salaries per faculty member) Main explanatory variables: ln(local median housing prices)*Rank1

0.122*** (0.018)

0.086*** (0.020)

0.078*** (0.020)

0.116*** (0.021)


0.104** (0.031)

0.051 (0.034)

0.030 (0.034)

0.015 (0.032)


0.162** (0.065)

0.152** (0.065)

0.169*** (0.065)

0.163*** (0.058)


0.228*** (0.016)

0.176*** (0.021)

0.160*** (0.021)

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)


/ / 0.030*** (0.007)

0.030*** (0.006)


/ 0.077*** (0.019)

-0.013 (0.027)

-0.071*** (0.026)


/ -0.004 (0.009)

-0.009 (0.009)

0.011 (0.008)


YES

YES

YES

YES


NO

NO

NO

YES

Observations 1111 1103 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. 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) Specifications (1) (2) (3) (4)

Citation Equation: Dependent variable =ln(citations per article) Main explanatory variables: ln(local median housing prices)*Rank1

0.329*** (0.061)

0.330*** (0.070)

0.340*** (0.070)

0.269*** (0.066)


0.315** (0.107)

0.321*** (0.118)

0.347*** (0.119)

0.278*** (0.102)


0.270 (0.221)

0.263 (0.223)

0.241 (0.223)

0.252 (0.183)


-0.031 (0.054)

-0.025 (0.072)

-0.006 (0.073)

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)


/ / -0.037 (0.023)

-0.051*** (0.020)


/ -0.017 (0.065)

0.094 (0.095)

0.105 (0.082)


/ 0.020 (0.031)

0.026 (0.032)

0.103*** (0.027)


YES

YES

YES

YES


NO

NO

NO

YES

Observations 1108 1100 1100 1025

(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)

Specifications (1) (2) (3) (4)

Salary Equation: Dependent Variable = ln(annual salaries per faculty member) Main explanatory variables: ln(local median housing prices)*Rank1

0.122*** (0.018)

0.088*** (0.020)

0.079*** (0.020)

0.116*** (0.021)


0.104*** (0.031)

0.052 (0.034)

0.032 (0.034)

0.015 (0.032)


0.161** (0.065)

0.151** (0.065)

0.169*** (0.064)

0.163*** (0.058)


0.235*** (0.016)

0.184*** (0.021)

0.168*** (0.021)

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)


/ / 0.030*** (0.007)

0.031*** (0.006)


/ 0.074*** (0.019)

-0.017 (0.027)

-0.072*** (0.026)


/ -0.004 (0.009)

-0.010 (0.009)

0.011 (0.008)


YES

YES

YES

YES


NO

NO

NO

YES

Observations 1108 1100 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. 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.

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