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Do Nonresident Students Affect Prices for In-State Students at Public Colleges?


by Robert Kelchen - 2019

Background/Context: Public colleges and universities have sought to recruit and enroll more students from outside their home state in an effort to both enhance institutional prestige and generate additional revenue from the higher tuition rates than nonresident students generally pay. A body of research has shown that nonresident students tend to be more economically advantaged and less racially diverse than in-state students and that increases in out-of-state students crowd out lower income and minority state residents from more selective public colleges.

Purpose/Research Questions: Although research has shown some adverse effects of additional nonresident enrollment, no prior research has examined whether the additional money generated from out-of-state students’ higher tuition rates is used to help make college more affordable for state residents. In this article, I examined whether increased percentages of nonresident students were associated with changes in the sticker or net prices of attendance at four-year public colleges and whether any relationships were different between broad-access and selective public colleges.

Research Design: I used data on college pricing and nonresident enrollment from the U.S. Department of Education’s Integrated Postsecondary Education Data System from the 2000–01 through 2013–14 academic years from 505 public four-year colleges in this analysis. I used panel regressions with controls for other state-level factors that could have affected nonresident enrollment. In my preferred specifications, I used Arellano–Bond estimators to account for autoregressive processes in the data and used a one-year lag between nonresident enrollment and when prices paid by in-state students were measured.

Results and Conclusions: There was no systematic relationship between changes in the percentage of nonresident students and the sticker or net prices faced by in-state students. This suggests that although state residents do not appear to be subsidizing an amenities arms race to attract students from other states, the additional tuition revenue coming from nonresident students is not being used to help subsidize in-state students. Future research should investigate how public colleges are using the additional revenue if it is not being used for student financial aid.



Public four-year colleges and universities were created with the primary goal of serving and educating residents of that state (Thelin, 2011). For example, the University of Minnesota’s 1851 charter (which predates statehood by seven years) stated that “the object of the University shall be to provide the inhabitants of this Territory with the means of acquiring a thorough knowledge of the various branches of Literature, Science and the arts” (Regents of the University of Minnesota, 2017). Most public institutions still primarily serve residents of that state; in the 2012–13 academic year, 88% of students attending public four-year colleges were in-state residents (author’s calculations using data from the Integrated Postsecondary Education Data System).


Yet many public colleges are seeking to recruit and enroll larger numbers of “out-of-state” or “nonresident” students (these terms are used interchangeably throughout this article) in an effort to enhance institutional revenue and prestige. Sixty percent of over 400 public colleges analyzed by Burd (2015) saw larger shares of nonresident students between 2000 and 2012, while flagship universities in Iowa, Alabama, Mississippi, and Vermont had more first-year students come from outside the state than within the state in 2012–13 (author’s calculations using IPEDS data).


An increase in nonresident enrollment is often seen as a boon by many public colleges, particularly those institutions in regions of the country with declining pools of traditional-age students and excess capacity. A number of colleges in states such as North Dakota, Ohio, and Wisconsin offer nonresident tuition prices that are equal to or only slightly higher than in-state rates in an effort to meet enrollment targets. However, efforts by highly selective public colleges to attract increasingly mobile high-achieving students who place a higher value on selectivity than in the past (e.g., Hoxby, 2009; Long, 2004) have been met with resistance in many states because of concerns about crowding out in-state students (e.g., Phillips & Belkin, 2014; Pratt, 2014).


A flashpoint in the debate about crowding out has been in California, in which nonresident enrollment in the University of California system increased by 82% from 2010–11 to 2014–15, while resident enrollment declined by 1% amid the lowest levels of per-student state appropriations in decades (State Higher Education Executive Officers, 2017). A report issued by the state auditor found that the UC system violated the state’s master plan by admitting nonresident students who were less academically qualified than the median resident student (California State Auditor, 2016). As a result of public pressure, the California legislature recently passed a plan that provided financial incentives for the UC system to reduce its share of nonresident students (Saul, 2016).


A growing body of empirical research has found that the increasing admission competitiveness at selective public colleges has had adverse effects on the ability of low-income, first-generation, and minority students to access selective public colleges within their home state. Traditionally underrepresented students have made steady gains in academic preparation over time, but higher income, White, and Asian students have made even larger gains, reducing the ability of underrepresented students to get accepted to selective colleges (Bastedo & Jaquette, 2011; Posselt, Jaquette, Bielby, & Bastedo, 2012). This is particularly important because students who attend college out of state are more likely to be White and of higher socioeconomic status than students attending in state, placing further pressure on lower income in-state students (Niu, 2015).


Research by Jaquette, Curs, and Posselt (2016) indicated that increased numbers of out-of-state students have disproportionately crowded out low-income and minority students at public colleges, with the largest effects at the most selective public institutions and at flagship colleges in high-poverty states. This raises concerns of undermatching, in which students attend a less selective college than they could have given their academic profile (e.g., Belasco & Trivette, 2015; Hoxby & Turner, 2013; Smith, Pender, & Howell, 2013), which has been shown to negatively affect the earnings of disadvantaged students who were qualified to attend a more selective public institution (e.g., Dale & Krueger, 2014; Hoekstra, 2009).


Although research has examined whether nonresident students crowd out in-state students at public colleges, there has been no research to this point examining whether in-state students who are able to gain admission are affected by a greater concentration of nonresident students. Given concerns about college affordability—particularly for students from lower income families—I examine whether increased nonresident enrollment levels affect the prices paid by in-state students. If colleges are using a portion of the additional tuition revenue to help provide additional grant aid for state residents with financial need, these students’ prices may go down. But if colleges recruit wealthier nonresident students by increasing spending on facilities and other amenities (e.g., Armstrong & Hamilton, 2013; Jacob, McCall, & Stange, 2018; Mulholland, Tomic, & Sholander, 2014; Pope & Pope, 2009) that are often funded by student fees (Kelchen, 2016), in-state students may pay a higher price.


In this article, I examine the following research questions using panel data for four-year public colleges: (1) Do the listed cost of attendance and components such as tuition and fees and housing expenses for in-state students change when nonresident enrollment increases? (2) Does the net price of attendance (both overall and by family income bracket) for in-state students change when nonresident enrollment increases? (3) Do these relationships differ by institutional selectivity?


REVIEW OF THE LITERATURE


The existing body of literature points to three main reasons that public colleges and universities may pursue increasing nonresident enrollment levels. The first reason is to increase the diversity of the student body, particularly in the case of public universities that primarily serve a region with a rather homogenous population. This reason is often stated in justifying international student enrollment (NAFSA: Association of International Educators, 2016). One structured pathway through which several highly selective public universities recruit a diverse population of nonresident students is the Posse Scholars program, which operates in a number of large cities (The Posse Foundation, 2014). However, students from out of state (excluding international students) are nearly 10 percentage points more likely to be White than in-state students at four-year public colleges, suggesting that increasing nonresident enrollment may actually decrease racial and ethnic diversity (author’s calculations using data from the National Postsecondary Student Aid Study).


The second reason to enroll more out-of-state students is to raise additional revenue. Public universities, the vast majority of which have relatively small per-student endowments, operate under resource dependency theory (e.g., Aldrich & Pfeffer, 1976) because they are heavily dependent on others to provide the funds they need to operate. State funding for higher education has kept up with inflation but not enrollment growth or maintenance costs in the last several decades (State Higher Education Executive Officers, 2017). At the same time, most public colleges do not have the ability to unilaterally increase tuition to fully account for declining state support and increasing costs, with colleges in most states having their tuition set by a coordinating or governing board with members appointed by legislators and/or the state’s governor (Carlson, 2013). Yet they have the ability to set tuition for nonresident students with few limitations, and the small number of states with rules on out-of-state tuition typically set minimum—not maximum—values (Carlson, 2013).


Because state appropriations and in-state tuition and fees are increasingly limited revenue sources, nonresident students have received significant attention in the enrollment management literature as they have become an increasingly attractive option to colleges (e.g., Holley & Harris, 2010; Hossler, 2000). For example, Jaquette and Curs (2015) showed that public colleges increase nonresident student enrollment in the aftermath of per-student declines in state appropriations. The push to maximize tuition revenue from nonresidents has also contributed to the convergence of nonresident tuition rates across public research universities (Titus, Vamosiu, & Gupta, 2015), particularly because out-of-state students attending selective public colleges are relatively insensitive to price increases (Adkisson & Peach, 2008; Zhang, 2007).


In addition to seeking additional revenue, many colleges also seek to maximize their prestige and reputation. This is particularly salient for flagship public institutions and a smaller number of other public universities that are relatively selective and are often in better financial shape than regional public institutions. As colleges strive to enhance their national and international brands (Brewer, Gates, & Goldman, 2002; Hazelkorn, 2015; O’Meara, 2007), having students from a broader geographic area can be beneficial in several ways. First, recruiting nonresident students can help to raise a college’s academic profile as institutions become more selective. An example of this is the National Merit Scholar program, which provides a $2,500 merit-based grant to the highest achieving high school students. Some public colleges recruit National Merit Scholars by giving them large scholarships and heavily advertise the number of high-achieving students they recruit. For example, the University of Oklahoma enrolls more sponsored National Merit Scholars than any other university (National Merit Scholarship Corporation, 2016), and a majority of these students come from out of state (Perez-Pena, 2012).


The additional prestige gained from becoming more selective can benefit colleges that seek to move up in the array of college rankings and guidebooks, some of which explicitly reward prestige. For example, the U.S. News & World Report and Wall Street Journal/Times Higher Education both include reputation as components in their rankings and reward colleges for having higher per-student revenues—which implicitly rewards public colleges with more students from out of state (Morse, Brooks, & Mason, 2016; Times Higher Education, 2016). Moving up in the rankings can have a substantial effect on selective colleges’ reputations and finances (Alter & Reback, 2014; Bowman & Bastedo, 2009; Griffith & Rask, 2007; Luca & Smith, 2013; Meredith, 2004). Notably, Bastedo and Bowman (2011) found that moving up in the U.S. News & World Report rankings was associated with higher nonresident tuition at public research universities. Yet moving up in the rankings is an expensive proposition (Gnolek, Falciano, & Kuncl, 2014), making the implications for in-state prices unclear for selective colleges attempting to enhance their reputation.


DATA, SAMPLE, AND METHODS


To explore whether increases in the percentage of nonresident students at public four-year colleges and universities were associated with changes in the sticker and net prices for in-state students, I used up to 14 years of data on student prices, combined with information about state-level and institutional-level conditions over the corresponding period.


DATA

Data on two sets of pricing measures came from the Integrated Postsecondary Education Data System (IPEDS) and are adjusted for inflation into 2013 dollars using the Consumer Price Index. The first set of pricing measures included the posted cost of attendance (COA) for first-time, full-time in-state undergraduate students, as well as the individual components: tuition and required fees, room and board, books and supplies, and a category for other living expenses that includes allowances for transportation, entertainment, laundry, and personal care. I included these components for both on-campus students and off-campus students living away from their family, omitting the category of students who live off-campus with their family because these students do not receive a room and board allowance. These measures are available for the 2000–01 through 2013–14 academic years. The room and board and miscellaneous expense measures best reflect whether nonresident students’ preferences for additional amenities raise the posted prices for in-state students.


The second set of pricing measures represents the net price of attendance, which is defined as the total cost of attendance less any grant or scholarship aid received by a student. This captures whether nonresident tuition funds subsidize in-state students. Two different measures of net price were available in IPEDS for in-state students at public colleges. The first was the average net price faced by all students receiving grant aid from the federal government, state government, or the college, which was available beginning in the 2006–07 academic year. The second measure was the average net price for students receiving any federal financial aid by income bracket, with five income brackets ranging from below $30,000 per year to above $110,000 per year in nominal dollars; this was first available in 2008–09 (National Center for Education Statistics, 2015). For the sake of simplicity, my models for net price only included data from 2008–09 forward; results for overall net price beginning in 2006–07 are qualitatively similar and available on request from the author.


The key measure of interest in my study was the percentage of first-time undergraduate students who are from out of state, which came from IPEDS and was available from 2000–01 through 2013–14. Colleges are only required to report the geographic distribution of students in even-numbered years, and about one fifth of colleges did not report in odd-numbered years. In those cases, I interpolated during even-numbered years by taking an average of the previous and following year’s percentages. The percentage of out-of-state students excluded a small percentage of students (less than 1% in all but two years of the data set) whose residency status was unknown.


Because colleges may respond to additional nonresident students in different ways based on their market power, I used institutional selectivity to divide colleges into two groups. My proxy for selectivity is the Barron’s (2009) admissions competitiveness index, which groups colleges into six main competitiveness categories based on standardized test scores, high school grades, class rank, and the percentage of students admitted. I grouped unrated colleges and colleges in the special, noncompetitive, and less competitive categories as being less selective, while colleges in the very competitive, highly competitive, and most competitive categories were considered more selective in my analyses.


I then included a set of institutional-level and state-level characteristics that could potentially affect the prices faced by in-state students, with additional details and summary statistics for these measures available in Table 1. Measures of the percentage of full-time equivalent (FTE) enrollment at the undergraduate level and per-FTE state and local appropriations came from IPEDS. Research has shown a strong relationship between appropriations and in-state tuition (e.g., Delaney & Doyle, 2011; Koshal & Koshal, 2000; Rizzo & Ehrenberg, 2004), and I controlled for the percentage of undergraduate enrollment to account for states typically focusing appropriations on undergraduate education. For a small number of colleges reporting appropriations at the OPEID level instead of the IPEDS UnitID level (see Jaquette & Parra, 2014, for more details), I allocated appropriations equally on a per-FTE basis for institutions sharing the same OPEID.



Table 1. Description of Institutional-Level and State-Level Control Variables

Characteristic (2013)

M

SD

Source

Percent undergraduate enrollment

88.6

8.1

IPEDS

State/local appropriations per FTE ($)

6,583

3,739

IPEDS

State unemployment rate (pct)

6.9

1.2

Bureau of Labor Statistics

State median income ($)

44,343

6,260

Bureau of Labor Statistics

Percent residents in poverty (pct)

14.8

3.7

Census Bureau

Pct state grant aid as need based

74.6

33.6

NASSGAP

State grant aid per 18- to 24-year-old ($)

312

177

NASSGAP, Census Bureau

Primary tuition-setting authority (pct)

   

  Governor and/or legislature

15.3

36.0

SHEEO

  System or coordinating board

76.8

42.3

SHEEO

Has fee-setting authority (pct)

   

  Governor and/or legislature

24.0

42.8

SHEEO

  State governing/coordinating board

66.7

47.2

SHEEO

Tuition cap in last three years (pct)

45.8

49.9

SHEEO

Fee cap in last three years (pct)

25.4

43.6

SHEEO

GOP control of state House (pct)

61.7

48.7

NCSL; Carl Klarner

GOP control of state Senate (pct)

59.3

49.2

NCSL; Carl Klarner

GOP governor (pct)

60.3

49.0

NCSL; Carl Klarner

Number of institutions

504

Index to acronyms:

   

IPEDS: Integrated Postsecondary Education Data System

 

NASSAGAP: National Association of State Student Grant and Aid Programs

SHEEO: State Higher Education Executive Officers

 

NCSL: National Conference of State Legislatures

 



Three measures of state economic conditions were included to reflect families’ ability to pay for college, particularly given that Jaquette et al. (2016) found crowding-out effects of nonresident enrollment to be largest for disadvantaged students living in high-poverty states, and Rizzo and Ehrenberg (2004) found state unemployment rates to be positively correlated with in-state tuition at flagship public universities. I used state-level unemployment rates and median income (both from the Bureau of Labor Statistics) and the percentage of state residents living in poverty (from the Census Bureau) to proxy for ability to pay. Annual surveys from the National Association of State Student Grant and Aid Programs (NASSGAP) provided information on the percentage of state aid for undergraduate students that is allocated based on financial need and the inflation-adjusted amount of total financial aid to undergraduates. I combined the latter measure with state population data from the Census Bureau for young adults between the ages of 18 and 24 to get an estimate of state grant aid funding effort for traditional-age college students.


The amount of flexibility that colleges have to set tuition and fee prices for in-state students has been shown to affect tuition and fee levels for in-state students (e.g., Kelchen, 2016; Kim & Ko, 2015; Knott & Payne, 2004). To capture this flexibility, I used five waves of surveys on state-level tuition and fee policies given by the State Higher Education Executive Officers Association to state fiscal officers. The measures taken from the survey include whether a tuition and/or fee cap had been enacted in the previous year, whether primary tuition-setting authority resides with the governor and/or legislature, state governing or coordinating board, or an individual college or system board, and whether any of those stakeholders have the authority to set student fees. These surveys were given in the 1998–99, 2002–03, 2005–06, 2010–11, and 2012–13 academic years, with at least 44 states participating in four or the five surveys. Between years of the survey and when states did not respond, I used the most recent year of data available.


Finally, I used data from Carl Klarner and the National Conference of State Legislatures to construct annual measures of partisan control of the governor’s office and state House and Senate. In general, prior research has found that states with more liberal or Democratic legislators have lower tuition at public colleges (Doyle, 2012) and higher levels of state appropriations (Archibald & Feldman, 2006; McLendon, Hearn, & Mokher, 2009; Weerts & Ronca, 2012), although some research has found that unified Democratic control is associated with less higher education funding and more K–12 education funding (Tandberg, 2010).


SAMPLE


My starting point was the population of four-year public colleges that primarily granted baccalaureate degrees (excluding primarily associate-level colleges with a few bachelor’s degree programs). I also excluded special-focus institutions such as health science colleges because of their different pricing structures as compared with typical colleges and the federal military academies because they do not belong to an individual state and do not charge tuition. This resulted in an initial sample of 538 colleges. I dropped 33 colleges for having missing data on some of the covariates, including all public colleges in Michigan and Nebraska. This resulted in an analytic sample of 505 colleges in 48 states. A small number of institutions did not have data across all years, but in general, the panel is strongly balanced.


Table 2 contains summary statistics by institutional selectivity for the cost of attendance and net price components for the 2013–14 academic year. The average less selective college had 16.7% of undergraduate students coming from out of state in fall 2013, whereas more selective colleges averaged 22.6% nonresident enrollment. The sticker prices for more selective colleges were about $3,000 higher for both on-campus and off-campus students than for less-selective colleges (p < .01). About $2,100 of this difference was due to higher tuition and fees, with most of the remainder due to higher room and board prices. There were no statistically significant differences in miscellaneous expense allowances by institutional selectivity.



Table 2. Summary Statistics of the Sample Institutions by Selectivity Levels

 

 

Less selective

 

More selective

 

Characteristic (2013–14)

M

SD

 

M

SD

Diff?

Percent of students from out of state

16.7

14.3

 

22.6

15.8

***

Cost of attendance ($)

      

  On campus

21,017

3,456

 

24,449

4,030

***

  Off campus, no family

21,718

3,645

 

24,689

4,075

***

Tuition and fees ($)

7,790

2,306

 

9,890

2,969

***

Books/supplies ($)

1,263

447

 

1,191

270

 

Room and board ($)

      

  On campus

8,760

1,979

 

10,004

1,995

***

  Off campus, no family

8,959

2,169

 

9,898

2,079

***

Other expenses ($)

      

  On campus

3,276

1,075

 

3,318

1,159

 

  Off campus, no family

3,750

1,176

 

3,694

1,382

 

Net price of attendance ($)

      

  All students

12,193

3,356

 

14,954

3,074

***

  Income $0–$30,000

10,038

3,321

 

10,318

3,292

 

  Income $30,001–$48,000

11,170

3,318

 

12,085

3,271

***

  Income $48,001–$75,000

14,108

3,316

 

15,881

3,413

***

  Income $75,001–$110,000

16,619

3,502

 

19,494

3,520

***

  Income $110,001+

17,322

3,715

 

21,226

4,216

***

Number of institutions

388

116

 

Sources:  Barron's (2009) (selectivity), IPEDS (all others).

 

 

 

 

Notes:

      

(1) The Barron's classifications of "special," "noncompetitive," and "less competitive" as well as unrated colleges were collapsed into less selective, and "very competitive," "highly competitive," and "most competitive" into more selective.

(2) All pricing measures are for first-time, full-time in-state students, while net price metrics are further restricted to students receiving any grant aid, and net price by family income metrics are restricted to students receiving any federal financial aid.

(3) The "Diff?" category tests for a statistically significant difference between less selective and more selective colleges.

*p <. 10. **p < .05. ***p < .01.

  



The net price of attendance varied considerably based on a student’s family income and the college’s level of selectivity. Among students receiving any grant aid, the average net price of attendance was $12,193 at less selective colleges and $14,954 at more selective colleges (p < .01). However, the average net prices for students receiving federal financial aid with family incomes below $30,000 per year were not significantly different across selectivity categories ($10,038 for less selective colleges and $10,318 for more selective colleges). Net prices then increased faster at more selective colleges as family incomes rose, reaching $17,322 for students with family incomes over $110,000 at less selective colleges and $21,226 at more selective colleges (p<.01).


METHODS


I used three different panel regression techniques to answer my research questions. All three of these models used logged values of the individual cost of attendance or net price components as the dependent variable and the percentage of nonresident students as the key independent variable of interest. These models also controlled for both the institutional-level and state-level characteristics, as discussed in Table 1, and included year fixed effects. All dependent variables and financial characteristics used as independent variables are transformed using natural logarithms. This reduces the influence of outliers and allows the dependent variables to be interpreted in percentage point terms. I estimated regressions with cost of attendance components using data from 2001–02 to 2013–14 and from 2008–09 to 2013–14, while regressions for the net price components could only be estimated from 2008–09 to 2013–14. In addition to estimating models for all colleges, I estimated separate models by a college’s selectivity level (based on its Barron’s competitiveness rating) to explore whether more selective and less selective colleges responded differently to a change in the percentage of nonresident students using an interaction term between selectivity and nonresident enrollment.


I began by using ordinary least squares (OLS) panel regressions with institutional fixed effects. A drawback of panel regression models is that they can contain autoregressive processes, in which past values of a variable can influence current values. This is a particular concern for college pricing models, when changes to the cost of attendance are often based on the previous year’s values, and colleges are often limited to changing tuition and fees by a certain amount each year. I estimated two separate models that accounted for a potential AR(1) framework in the data and have been used in other studies using higher education finance data (Curs, Bhandari, & Steiger, 2011; Doyle, 2010; Hillman, 2012; McLendon, Tandberg, & Hillman, 2014).


I first employed the Arellano–Bond (AB) estimator (via the xtabond command in Stata) that uses two lags of the dependent variable as instruments for possibly endogenous variables and first differences of the variables in a linear generalized method of moments model (Arellano & Bond, 1991). I also used the Arellano–Bover/Blundel–Bond (ABBB) estimator (via the xtdpdsys command in Stata) that uses lags of both the dependent and independent variables as instruments (Arellano & Bover, 1995; Blundell & Bond, 1998). Because of how lags are used in the models that account for autoregressive processes, I have one fewer year of data in the AB estimates and two fewer years in the ABBB estimates than in the OLS models. My preferred specification uses the AB estimator, but I do present the main results across all three regression models (in part because the results are broadly similar across all models). All results across all regressions are available on request from the author.


Another analytic decision to consider was when nonresident enrollment could plausibly affect how colleges set their prices. Public colleges typically set their prices for the academic year in the spring preceding that fall semester. This is generally after state appropriations for higher education are known and is approximately when students are negotiating financial aid packages with colleges, meaning that both the tuition and aid components of the net price are being determined at about the same time. Therefore, I used control variables from the previous year (a one-year lag) in my preferred specification to help explain how colleges set prices in the given year. This means that 2013–14 cost of attendance components and net prices are used alongside nonresident enrollment and other institutional-level and state-level characteristics from the 2012–13 academic year.


As robustness checks, I also ran models with a two-year lag that allow for a longer time period between when tuition is set and when students are enrolled, and a contemporaneous model that uses pricing components and covariates from the same academic year. I additionally conducted a falsification test that used nonresident enrollment and covariates from the year after cost of attendance and net price components were observed in the regressions (a one-year lead). Because future nonresident enrollment should not affect current prices, these results should be statistically insignificant unless there are underlying trends that are not captured in the control variables.


LIMITATIONS


A key limitation of the pricing measures used in this study is that they are based on a fraction of a college’s total undergraduate enrollment. The cost of attendance metrics were for full-time, first-year students, whereas the net price metrics covered a subset of full-time, first-year students receiving various types of financial aid. Most important, the net price by family income metrics excluded students who did not complete the Free Application for Federal Student Aid (FAFSA). Although this excludes students from high-income families who do not need assistance paying for college, Kantrowitz (2015) has estimated, using data from the 2011–12 National Postsecondary Student Aid Study, that about 2 million students would have received a Pell Grant if they had completed the FAFSA. This means that the net price metrics do not represent the prices that all financially needy families actually pay for college, although the use of listed tuition and fee prices in the models does reflect what these families would pay.


My measure of nonresident enrollment does not account for the presence of state-level tuition reciprocity agreements or institutional initiatives to recruit additional students from neighboring states. Because the prices charged to nonresident students under tuition discounting agreements often vary across program and proximity to the state’s border (Rizzo & Ehrenberg, 2004) and there is no centralized data source for capturing all these agreements (let alone a data source that breaks out resident and nonresident tuition revenue), I did not include this measure as a control. However, I did examine reports from several of the regional exchanges (the Midwest Student Exchange Program, the Southern Regional Education Board, and the Western Undergraduate Exchange) over the last decade to confirm that membership in these exchanges was relatively stable over the period of study and thus ruled out changes in tuition exchanges as a substantial driver of any enrollment changes. Finally, some states have placed caps on the number or percentage of nonresident enrollment at public colleges, but no systemic data are available on the level and prevalence of these caps. This limitation prevents me from describing the landscape of state-level policies to limit nonresident enrollment but should not substantially affect the results.


RESULTS


I first examined the relationship between the percentage of nonresident students and college pricing for all four-year public colleges regardless of selectivity. The regression results can be found in Panel A of Table 3 for the full length of the panel (2000–01 to 2013–14) and Panel B for the years that net price data were available (2008–09 to 2013–14). Using my preferred specification of Arellano–Bond panel regressions, there were no statistically significant relationships between nonresident enrollment and state residents’ tuition and fees or other components of the overall cost of attendance across either time period. (Coefficients for the control variables are not presented in the tables but are available on request from the author.) This suggests that colleges did not respond to an increased percentage of nonresident students by increasing room and board prices or other portions of the living allowance. This provides some evidence against the prevailing hypothesis that colleges recruit wealthy students through enhanced amenities, although it should also be noted that colleges face pressures to keep off-campus living allowances low to look more affordable, and on-campus housing choices often vary considerably in price.



Table 3. Regressions Between the Percentage of Nonresident Students and College Pricing

Panel A: 2000–01 to 2013–14

      

Change in price (pct) for 1 ppt increase in nonresident enrollment

Regression w/ Fixed Effects

Arellano–Bond

Arellano–Boyer/ Blundell–Bond

Coeff.

(SE)

Coeff.

(SE)

Coeff.

(SE)

In-state cost of attendance

      

  On campus

0.03

(0.02)

-0.04

(0.03)

-0.05

(0.03)

  Off campus, no family

0.00

(0.03)

-0.01

(0.02)

-0.02

(0.03)

In-state tuition and fees

0.00

(0.03)

-0.04

(0.03)

-0.05

(0.03)

Books/supplies

-0.10**

(0.04)

0.00

(0.05)

-0.05

(0.05)

Room and board

      

  On campus

-0.03

(0.03)

-0.05

(0.05)

-0.06

(0.05)

  Off campus, no family

0.01

(0.07)

0.00

(0.04)

0.10

(0.07)

Other expenses

      

  On campus

0.31***

(0.08)

0.04

(0.08)

0.16

(0.11)

  Off campus, no family

0.11

(0.08)

-0.01

(0.05)

-0.02

(0.06)

Maximum number of colleges

505

505

505


Panel B: 2008–09 to 2013–14

Change in price (pct) for 1 ppt increase in nonresident enrollment

Regression w/ Fixed Effects

Arellano–Bond

Arellano–Boyer/ Blundell-Bond

Coeff.

(SE)

Coeff.

(SE)

Coeff.

(SE)

In-state cost of attendance

      

  On campus

0.07**

(0.03)

0.04

(0.03)

0.03

(0.03)

  Off campus, no family

0.04

(0.05)

0.04

(0.05)

0.02

(0.05)

In-state tuition and fees

0.08**

(0.04)

-0.01

(0.03)

0.05

(0.05)

Books/supplies

0.02

(0.07)

0.13

(0.08)

0.00

(0.11)

Room and board

      

  On campus

-0.01

(0.05)

0.05

(0.06)

0.04

(0.06)

  Off campus, no family

-0.12

(0.08)

0.06

(0.07)

0.04

(0.07)

Other expenses

      

  On campus

0.19

(0.12)

0.10

(0.12)

0.31*

(0.18)

  Off campus, no family

0.08

(0.14)

-0.07

(0.16)

0.05

(0.15)

In-state net price of attendance

      

  All students

0.14

(0.17)

-0.01

(0.12)

0.42

(0.28)

  Income $0–$30,000

-0.21

(0.25)

-0.08

(0.06)

-0.45

(0.56)

  Income $30,001–$48,000

-0.23

(0.19)

-0.15

(0.18)

0.12

(0.45)

  Income $48,001–$75,000

-0.23**

(0.09)

0.08

(0.15)

0.16

(0.16)

  Income $75,001–$110,000

-0.13

(0.16)

0.18

(0.15)

0.24

(0.18)

  Income $110,001+

-0.20

(0.21)

0.03

(0.11)

-0.16

(0.21)

Maximum number of colleges

505

505

505

Sources: IPEDS (all outcomes listed), as listed in Table 1 (all covariates).

Notes:

      

(1) Each row reflects a separate regression. Coefficients for control variables are not shown but are available on request from the author.

(2) All pricing measures are for first-time, full-time in-state students, whereas net price metrics are further restricted to students receiving any grant aid, and net price by family income metrics are restricted to students receiving any federal financial aid.

(3) Not all colleges report net prices for the highest income brackets because of small sample sizes.

(4) All dollar values are adjusted to 2013 dollars using the Consumer Price Index.

 

*p < .10. **p < .05. ***p < .01.

  



The general pattern of null findings between nonresident enrollment and in-state tuition and fees (with the exception of the OLS regression model between 2008–09 and 2013–14, which found a small positive relationship) could potentially be explained by colleges increasing their tuition as much as allowed and then using the additional funds generated by nonresident students to help fund lower income in-state students. However, as Panel B of Table 3 shows, there is little consistent evidence that the additional revenue generated by increased nonresident enrollment is passed on to state residents in the form of lower net prices. Coefficients in the OLS model were generally negative, but the models that account for the autoregressive nature of college pricing had statistically insignificant net price coefficients on both sides of zero.


As a robustness check, I then explored whether the main results held across different lags and leads between when nonresident enrollment was measured and prices were measured, with the results using Arellano–Bond estimates found in Table 4. (Results using the other panel regression models are substantively similar and are available on request from the author.) The reasonable alternatives of a 2-year lag (for example, using nonresident enrollment from 2011–12 to influence prices in 2013–14) and using the same year of data for both measures had generally similar patterns of results, with mainly null findings across all metrics. The falsification test using a one-year lead (in which nonresident enrollment was used from the year after prices were measured) also had mainly insignificant results, with a few coefficients on other expenses and overall net price being negative and marginally significant at p<.10.



Table 4. Robustness Checks With Different Lags and Leads Between Nonresident Enrollment and Pricing Decisions

Panel A: 2000–01 to 2013–14

Change in price (pct) for 1 ppt increase in nonresident enrollment

2-year lag

1-year lag (preferred)

Same year

1-year lead

Coeff.

(SE)

Coeff.

(SE)

Coeff.

(SE)

Coeff.

(SE)

In-state cost of attendance

  On campus

0.01

(0.04)

-0.04

(0.03)

0.03

(0.02)

0.01

(0.04)

  Off campus, no family

0.01

(0.02)

-0.01

(0.02)

0.02

(0.03)

0.01

(0.02)

In-state tuition and fees

-0.02

(0.03)

-0.04

(0.03)

0.05

(0.04)

-0.02

(0.03)

Books/supplies

0.02

(0.04)

0.00

(0.05)

0.06

(0.05)

0.02

(0.04)

Room and board

        

  On campus

0.06

(0.05)

-0.05

(0.05)

0.03

(0.03)

0.06

(0.05)

  Off campus, no family

0.02

(0.04)

0.00

(0.04)

-0.02

(0.04)

0.02

(0.04)

Other expenses

        

  On campus

-0.14

(0.09)

0.04

(0.08)

0.02

(0.07)

-0.14

(0.09)

  Off campus, no family

-0.08

(0.06)

-0.01

(0.05)

0.06

(0.06)

-0.08

(0.06)

Maximum number of colleges

505

505

505

505


Panel B: 2008–09 to 2013–14

Change in price (pct) for 1 ppt increase in nonresident enrollment

2-year lag

1-year lag (preferred)

Same year

1-year lead

Coeff.

(SE)

Coeff.

(SE)

Coeff.

(SE)

Coeff.

(SE)

In-state cost of attendance

  On campus

-0.05

(0.04)

0.04

(0.03)

0.03

(0.03)

-0.05

(0.04)

  Off campus, no family

-0.03

(0.05)

0.04

(0.05)

-0.01

(0.05)

-0.03

(0.05)

In-state tuition and fees

-0.01

(0.04)

-0.01

(0.03)

-0.08**

(0.04)

-0.01

(0.04)

Books/supplies

-0.02

(0.09)

0.13

(0.08)

0.03

(0.08)

-0.02

(0.09)

Room and board

        

  On campus

0.04

(0.07)

0.05

(0.06)

0.03

(0.06)

0.04

(0.07)

  Off campus, no family

0.02

(0.08)

0.06

(0.07)

-0.06

(0.07)

0.02

(0.08)

Other expenses

        

  On campus

-0.26*

(0.13)

0.10

(0.12)

0.01

(0.13)

-0.26*

(0.13)

  Off campus, no family

-0.28*

(0.15)

-0.07

(0.16)

0.15

(0.13)

-0.28*

(0.15)

In-state net price of attendance

  All students

-0.44*

(0.23)

-0.01

(0.12)

-0.22*

(0.13)

-0.44*

(0.23)

  Income $0–$30,000

0.25

(0.50)

-0.08

(0.06)

0.25

(0.52)

0.25

(0.50)

  Income $30,001–$48,000

-0.35

(0.28)

-0.15

(0.18)

-0.09

(0.16)

-0.35

(0.28)

  Income $48,001–$75,000

-0.05

(0.18)

0.08

(0.15)

-0.01

(0.13)

-0.05

(0.18)

  Income $75,001–$110,000

-0.01

(0.15)

0.18

(0.15)

-0.62

(0.60)

-0.01

(0.15)

  Income $110,001+

-0.09

(0.15)

0.03

(0.11)

-0.19

(0.27)

-0.09

(0.15)

Maximum number of colleges

505

505

505

505

Sources: IPEDS (all outcomes listed), as listed in Table 1 (all covariates).

  

Notes:

        

(1) Results presented here are from Arellano–Bond panel regressions, with separate regressions for each row of results. Results for control variables and from OLS regressions with fixed effects and Arellano–Bover/Blundell–Bond models (which are generally similar) are available on request.

(2) All pricing measures are for first-time, full-time in-state students, whereas net price metrics are further restricted to students receiving any grant aid, and net price by family income metrics are restricted to students receiving any federal financial aid.

(3) Not all colleges report net prices for the highest income brackets because of small sample sizes.

 

(4) All dollar values are adjusted to 2013 dollars using the Consumer Price Index.

  

*p < .10. **p < .05. ***p < .01.

    



Finally, I examined whether the relationship between sticker and net prices and nonresident enrollment varied by institutional selectivity. The results for the 2008–09 to 2013–14 time period are presented in Table 5; the results for the sticker price components for the full 2000–01 to 2013–14 time period or using different regression models are generally similar and are available from the author on request. Again, most results are statistically insignificant, although less selective colleges slightly increased textbook allowances (p < .05), and more selective colleges slightly decreased in-state tuition and fees (p < .10) as nonresident enrollment increased. The only statistically significant difference between less selective and more selective colleges was in the books and supplies category (p < .05), with both types of colleges being generally comparable on other metrics.



Table 5. Regressions Between the Percentage of Nonresident Students and College Pricing by Selectivity, 2008–09 to 2013–14

Change in price ($) for 1 ppt increase in nonresident enrollment

Less selective

More selective

 

 

Coeff.

(SE)

Coeff.

(SE)

 

Diff?

In-state cost of attendance

      

  On campus

0.05

(0.04)

-0.02

(0.06)

  

  Off campus, no family

0.04

(0.05)

-0.02

(0.07)

  

In-state tuition and fees

0.04

(0.04)

-0.11*

(0.06)

  

Books/supplies

0.18**

(0.09)

-0.08

(0.12)

 

**

Room and board

      

  On campus

0.05

(0.07)

0.00

(0.05)

  

  Off campus, no family

0.04

(0.08)

0.01

(0.11)

  

Other expenses

      

  On campus

0.06

(0.12)

0.27

(0.30)

  

  Off campus, no family

-0.19

(0.16)

0.40

(0.40)

  

In-state net price of attendance

      

  All students

0.02

(0.14)

0.27

(0.38)

  

  Income $0–$30,000

-0.47

(0.60)

-0.18

(0.17)

  

  Income $30,001–$48,000

-0.16

(0.23)

0.15

(0.33)

  

  Income $48,001–$75,000

0.03

(0.17)

0.10

(0.22)

  

  Income $75,001–$110,000

0.20

(0.18)

-0.10

(0.16)

  

  Income $110,001+

0.04

(0.16)

-0.12

(0.13)

  

Maximum number of colleges

389

116

 

 

Sources: IPEDS (all outcomes listed), as listed in Table 1 (all covariates).

Notes:

      

(1) Results presented here are from Arellano–Bond panel regressions, with separate regressions for each row of results. Results for control variables and from OLS regressions with fixed effects and Arellano–Bover/Blundell–Bond models (which are generally similar) are available on request.

(2) All pricing measures are for first-time, full-time in-state students, whereas net price metrics are further restricted to students receiving any grant aid, and net price by family income metrics are restricted to students receiving any federal financial aid.

(3) Not all colleges report net prices for the highest income brackets because of small sample sizes.

(4) "More selective" colleges have a Barron's rating of very competitive, highly competitive, or most competitive.

(5) The "Diff?" column reports the results of an interaction model between selectivity and the percentage of nonresident students.

(6) All dollar values are adjusted to 2013 dollars using the Consumer Price Index.

  

*p < .10. **p < .05. ***p < .01.

   



DISCUSSION AND FUTURE WORK


Amid tight state budgets and pressures to gain status and prestige, many public colleges and universities have attempted to recruit and enroll more out-of-state students. Prior research has found that increased numbers of nonresident students disproportionately crowd out in-state students who are from lower income families or underrepresented minorities (Jaquette et al., 2016), but little empirical research has examined the implications of increased nonresident enrollment for the in-state students who can gain admission. In this article, I examined the implications of increased nonresident enrollment on the prices (both sticker and net) faced by in-state students who are able to gain admission and enroll at public four-year colleges and whether the relationship differs by institutional selectivity.


I found very few statistically significant relationships between the proportion of undergraduate enrollment from out-of-state and either sticker or net prices faced by in-state students. This null finding suggests that although on-campus housing prices are rising faster than inflation, the price tag of the least expensive on-campus option (which is what is reported in IPEDS) is not being driven up as nonresident enrollment increases. However, available data do not fully reflect the increasing prevalence of high-end housing, both on-campus and off-campus, that has the potential to further separate price-sensitive lower income students from higher income students (Geyer, 2016; Marcus, 2016). Further research is needed to examine the extent to which nonresident student enrollment may be contributing to the growing disparity in on-campus and off-campus housing price options.


On the other hand, I did not find much support for the hypothesis that public universities would take the additional revenue gained from out-of-state students and use it to reduce the net price faced by low-income state residents. Although it is possible that a portion of the additional revenue is being used to provide financial assistance to a very targeted group of lower income resident students, the lack of significant results suggests that the pool of students who directly benefit is limited. This suggests that most colleges are not setting aside a portion of any additional tuition revenue to help subsidize in-state students.


Further research should examine exactly how nonresident tuition dollars above the cost of providing an education are used, given that colleges can generally use these funds as desired, and other sources of funds (such as state appropriations and research grants) often come with restrictions regarding their usage. For example, are these additional funds used to subsidize increased instructional expenses, student services, or administrative expenses? It is entirely possible that colleges are using these revenues to help provide additional academic and social supports to lower income students in an effort to improve retention and graduation rates. If that is the case, colleges must carefully examine whether additional financial aid or additional student support spending is a better use of the additional resources that come from nonresident students.


The generally null pattern of results by selectivity group bears further investigation, particularly given that highly selective colleges have a greater ability to recruit and enroll nonresident students than broad-access institutions that primarily serve a region within a state. Highly selective colleges may be able to attract nonresident students without having to discount tuition (providing more funds to potentially support resident students), whereas less selective institutions may have to discount tuition more heavily to induce students to cross state lines. A good example of this is the University of Maine, which has successfully increased enrollment from surrounding states by matching in-state tuition rates but has had to discount its normal nonresident tuition rates by about one-half (Seltzer, 2017). The presence of tuition discounting also suggests that separating nonresident domestic enrollment and international enrollment may be valuable because international students rarely receive grant aid from colleges.


Another useful study to consider in the future would be to examine the extent to which the prices faced by state residents changed in response to a significant shock in the percentage of nonresident enrollment. In the last several years, the University of California system has moved to reduce the percentage of out-of-state students in response to pressure from legislators (Gordon, 2016), while the University of Wisconsin-Madison increased its nonresident enrollment percentage after the university’s cap was lifted (Brookins, 2016). Although no comprehensive database of nonresident enrollment caps and adoption dates currently exists, constructing such a database would be a great service to the higher education community.  


Acknowledgment


I would like to thank Olga Komissarova for her helpful assistance in preparing this manuscript.


References

Adkisson, R. V., & Peach, J. T. (2008). Non-resident enrollment and non-resident tuition at land grant colleges and universities. Education Economics, 16(1), 75–88.


Aldrich, H. E., & Pfeffer, J. (1976). Environments of organizations. Annual Review of Sociology, 2, 79–105.


Alter, M., & Reback, R. (2014). True for your school? How changing reputations alter demand for selective U.S. colleges. Educational Evaluation and Policy Analysis, 36(3), 346–370.


Archibald, R. B., & Feldman, D. H. (2006). State higher education spending and the tax revolt. The Journal of Higher Education, 77(4), 618–644.


Arellano, M., & Bond, S. (1991). Some tests of specification for panel data: Monte Carlo evidence and an application to employment equations. The Review of Economic Studies, 58(2), 277–297.


Arellano, M., & Bover, O. (1995). Another look at the instrumental variable estimation of error-components models. Journal of Econometrics, 68, 29–51.


Armstrong, E. A., & Hamilton, L. T. (2013). Paying for the party: How college maintains inequality. Cambridge, MA: Harvard University Press.


Barron's Educational Series, Inc. (2009). Barron's profiles of American colleges: Descriptions of the colleges. Hauppauge, NY: Author.


Bastedo, M. N., & Bowman, N. A. (2011). College rankings as an interorganizational dependency: Establishing the foundation for strategic and institutional accounts. Research in Higher Education, 52(1), 3–23.


Bastedo, M. N, & Jaquette, O. (2011). Running in place: Low-income students and the dynamics of higher education stratification. Educational Evaluation and Policy Analysis, 33(3), 318–339.


Belasco, A. S., & Trivette, M. J. (2015). Aiming low: Estimating the scope and predictors of postsecondary undermatch. The Journal of Higher Education, 86(2), 233–263.


Blundell, R., & Bond, S. (1998). Initial conditions and moment restrictions in dynamic panel data models. Journal of Econometrics, 87, 115–143.


Bowman, N. A., & Bastedo, M. N. (2009). Getting on the front page: Organizational reputation, status signals, and the impact of U.S. News and World Report on student decisions. Research in Higher Education, 50(5), 415–436.


Brewer, D. J., Gates, S. M., & Goldman, C. A. (2002). In pursuit of prestige: Strategy and competition in U.S. higher education. Piscataway, NJ: Transaction.


Brookins, A. (2016, October 5). Out-of-state enrollment up at UW-Madison year after lifting enrollment cap. Wisconsin Public Radio. Retrieved from http://www.wpr.org/out-state-enrollment-uw-madison-year-after-lifting-enrollment-cap


Burd, S. (2015). Are public universities becoming bastions of privilege? The Hechinger Report. Retrieved from http://hechingerreport.org/public-universities-becoming-bastions-privilege/


California State Auditor. (2016). The University of California: Its admissions and financial decisions have disadvantaged California resident students. Sacramento, CA: Author.


Carlson, A. (2013). State tuition, fee, and financial assistance policies for public colleges and universities. Boulder, CO: State Higher Education Executive Officers.


Curs, B. R., Bhandari, B., & Steiger, C. (2011). The roles of public higher education expenditure and the privatization of the higher education on U.S. states economic growth. Journal of Education Finance, 36(4), 424–441.


Dale, S., & Krueger, A. B. (2014). Estimating the effects of college characteristics over the career using administrative earnings data. Journal of Human Resources, 49(2), 323–358.


Delaney, J. A., & Doyle, W. R. (2011). State spending on higher education: Testing the balance wheel over time. Journal of Education Finance, 36(4), 343–368.


Doyle, W. R. (2010). Does merit-based aid “crowd out” need-based aid? Research in Higher Education, 51(5), 397–415.


Doyle, W. R. (2012). The politics of public college tuition and state financial aid. The Journal of Higher Education, 83(5), 617–647.


Geyer, A. (2016, May 12). Lap of luxury. The Isthmus. Retrieved from http://isthmus.com/news/news/posh-student-apartments-upend-madison-housing/


Gnolek, S. L., Falciano, V. T., & Kuncl, R. W. (2014). Modeling change and variation in U.S. News & World Report college rankings: What would it really take to be in the top 20? Research in Higher Education, 55(8), 761–779.


Gordon, L. (2016, June 16). Budget pushes UC to limit non-resident enrollment, CSU to boost graduation rates. EdSource. Retrieved from https://edsource.org/2016/budget-pushes-uc-to-limit-non-resident-enrollment-csu-to-boost-graduation-rates/565829


Griffith, A., & Rask, K. (2007). The influence of the US News and World Report collegiate rankings on the matriculation decision of high-ability students, 1995-2004. Economics of Education Review, 26, 244–255.


Hazelkorn, E. (2015). Rankings and the reshaping of higher education: The battle for world-class excellence. New York, NY: Palgrave Macmillan.


Hillman, N. W. (2012). Tuition discounting for revenue management. Research in Higher Education, 53(3) 263–281.


Hoekstra, M. (2009). The effect of attending the flagship state university on earnings: A discontinuity-based approach. The Review of Economics and Statistics, 91(4), 717–724.


Holley, K., & Harris, M. S. (2010). Selecting students, selecting priorities: How universities manage enrollment during times of economic crisis. Journal of College Admission, 207, 16–21.


Hossler, D. (2000). The role of financial aid in enrollment management. New Directions for Student Services, 89, 77–90.


Hoxby, C. M. (2009). The changing selectivity of American colleges. Journal of Economic Perspectives, 23(4), 95–118.


Hoxby, C. M., & Turner, S. (2013). Expanding college opportunities for high-achieving, low-income students. Stanford, CA: Stanford Institute for Economic Policy.


Jacob, B., McCall, B., & Stange, K. M. (2018). College as country club: Do colleges cater to students’ preferences for consumption? Journal of Labor Economics, 36(2), 309–348.


Jaquette, O., & Curs, B. R. (2015). Creating the out-of-state university: Do public universities increase nonresident freshman enrollment in response to declining state appropriations? Research in Higher Education, 56(6), 535–565.


Jaquette, O., Curs, B. R., & Posselt, J. R. (2016). Tuition rich, mission poor: Nonresident enrollment and the changing proportions of low-income and underrepresented minority students at public research universities. The Journal of Higher Education, 87(5), 635–673.


Jaquette, O., & Parra, E. E. (2014). Using IPEDS data for panel analyses: Core concepts, data challenges, and empirical applications. In M. B. Paulsen (Ed.), Higher education: Handbook of theory and research (Vol. 29, pp. 467–533). Dordrecht, The Netherlands: Springer.


Kantrowitz, M. (2015). Leaving money on the table. Las Vegas, NV: Edvisors Network Inc.


Kelchen, R. (2016). An analysis of student fees: The roles of states and institutions. The Review of Higher Education, 39(4), 597–619.


Kim, M. M., & Ko, J. (2015). The impacts of state control policies on college tuition increase. Educational Policy, 29(5), 815–838.


Knott, J. H., & Payne, A. A. (2004). The impact of state governance structures on management and performance of public organizations: A study of higher education institutions. Journal of Policy Analysis and Management, 23(1), 13–30.


Koshal, R. K., & Koshal, M. (2000). State appropriation and higher education tuition: What is the relationship? Education Economics, 8(1), 81–89.


Long, B. T. (2004). How have college decisions changed over time? An application of the conditional logistic choice model. Journal of Econometrics, 121, 271–296.


Luca, M., & Smith, J. (2013). Salience in quality disclosure: Evidence from the U.S. News college rankings. Journal of Economics & Management Strategy, 22(1), 58–77.


Marcus, J. (2016, September 26). The business decision segregating college students by income and race. The Hechinger Report. Retrieved from http://hechingerreport.org/business-decision-segregating-college-students-income-race/


McLendon, M. K., Hearn, J. C., & Mokher, C. G. (2009). Partisans, professionals, and power: The role of political factors in state higher education funding. The Journal of Higher Education, 80(6), 686–713.


McLendon, M. K., Tandberg, D. A., & Hillman, N. W. (2014). Financing college opportunity: Factors influencing state spending on student financial aid and campus appropriations, 1990 through 2010. The ANNALS of the American Academy of Political and Social Science, 655, 143–162.


Meredith, M. (2004). Why do universities compete in the rankings game? An empirical analysis of the effects of the US News and World Report college rankings. Research in Higher Education, 45(5), 443–461.


Morse, R., Brooks, E., & Mason, M. (2016, September 12). How U.S. News calculated the 2017 best colleges rankings. U.S. News & World Report. Retrieved from http://www.usnews.com/education/best-colleges/articles/how-us-news-calculated-the-rankings


Mulholland, S. E., Tomic, A., & Sholander, S. N. (2014). The faculty Flutie factor: Does football performance affect a university’s US News and World Report peer assessment score? Economics of Education Review, 43, 79–90.


NAFSA: Association of International Educators. (2016, January). Bringing global diversity to U.S. campuses—an economic and national security imperative. Retrieved from http://www.nafsa.org/_/File/_/2016_campaign_recruitment.pdf


National Center for Education Statistics. (2015). Average institutional net price FAQs. Retrieved from https://nces.ed.gov/ipeds/Section/Institutional_net_price


National Merit Scholarship Corporation. (2016). 2014–15 annual report. Evanston, IL: Author.


Niu, S. X. (2015). Leaving home state for college: Differences by race/ethnicity and parental education. Research in Higher Education, 56(4), 325–359.


O’Meara, K. (2007). Striving for what? Exploring the pursuit of prestige. In J. C. Smart (Ed.), Higher education: Handbook of theory and research (Vol. 22, pp. 121–179). Dordrecht, The Netherlands: Springer.


Perez-Pena, R. (2012, December 11). Courting merit scholars opens door to questions. The New York Times. Retrieved from http://www.nytimes.com/2012/12/12/education/some-question-merit-aid-at-university-of-oklahoma.html


Phillips, E. E., & Belkin, D. (2014, October 8). Colleges’ wider search for applicants crowds out local students. The Wall Street Journal. Retrieved from http://www.wsj.com/articles/colleges-wider-search-for-applicants-crowds-out-local-students-1412790096


Pope, D. G., & Pope, J. P. (2009). The impact of college sports success on the quantity and quality of student applications. Southern Economic Journal, 75(3), 750–780.


Posselt, J. R., Jaquette, O., Bielby, R., & Bastedo, M. N. (2012). Access without equity: Longitudinal analyses of institutional stratification by race and ethnicity, 1972–2004. American Educational Research Journal, 49(6), 1074–1111.


Pratt, T. (2014, June 13). Residents are crowded out of college by out-of-state and foreign students. The Hechinger Report. Retrieved from http://hechingerreport.org/residents-crowded-college-state-foreign-students/


Regents of the University of Minnesota. (2017). University of Minnesota charter. Retrieved from https://regents.umn.edu/policies/charter


Rizzo, M., & Ehrenberg, R. G. (2004). Resident and nonresident tuition and enrollment at flagship state universities. In C. M. Hoxby (Ed.), College choices: The economics of where to go, when to go, and how to pay for it (pp. 303–349). Chicago, IL: University of Chicago Press.


Saul, S. (2016, July 12). After outcry, University of California increases in-state admission offers. The New York Times. Retrieved from https://www.nytimes.com/2016/07/13/us/after-outcry-university-of-california-increases-in-state-admission-offers.html


Seltzer, R. (2017, May 11). Tuition matching, take 2. Inside Higher Ed. Retrieved from https://www.insidehighered.com/news/2017/05/11/university-maine-sees-slower-growth-second-year-flagship-match-program


Smith, J., Pender, M., & Howell, J. (2013). The full extent of student-college academic undermatch. Economics of Education Review, 32, 247–261.


State Higher Education Executive Officers. (2017). SHEF: FY 2016 state higher education finance. Boulder, CO: Author.


Tandberg, D. A. (2010). Politics, interest groups and state funding of public higher education. Research in Higher Education, 51(5), 416–450.


Thelin, J. R. (2011). A history of American higher education (2nd ed.). Baltimore, MD: Johns Hopkins University Press.


The Posse Foundation. (2014). Partner colleges + universities. Retrieved from https://www.possefoundation.org/our-university-partners/participating-schools/


Times Higher Education. (2016, September 16). Wall Street Journal/Times Higher Education college rankings 2017 methodology. Retrieved from https://www.timeshighereducation.com/world-university-rankings/wall-street-journal-times-higher-education-college-rankings-methodology


Titus, M. A., Vamosiu, A., & Gupta, A. (2015). Conditional converge of non-resident tuition rates at public research universities: A panel data analysis. Higher Education, 70(6), 923–940.


Weerts, D. J., & Ronca, J. M. (2012). Understanding differences in state support for higher education across states, sectors, and institutions: A longitudinal study. The Journal of Higher Education, 83(2), 155–185.


Zhang, L. (2007). Nonresident enrollment demand in public higher education: An analysis at national, state, and institutional levels. The Review of Higher Education, 31(1), 1–25.




Cite This Article as: Teachers College Record Volume 121 Number 2, 2019, p. 1-27
https://www.tcrecord.org ID Number: 22575, Date Accessed: 5/12/2021 9:02:04 PM

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About the Author
  • Robert Kelchen
    Seton Hall University
    E-mail Author
    ROBERT KELCHEN is an assistant professor of higher education at Seton Hall University. His research interests include higher education finance, accountability policies and practices, and student financial aid. He is the author of Higher Education Accountability (Johns Hopkins University Press, 2018).
 
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