Tuition and enrollment yield at selective liberal arts colleges

Parker, Jeffrey; Summers, Jeffrey

doi:10.1016/0272-7757(93)90065-o

Cited by 37 publications

(17 citation statements)

References 6 publications

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“…Surprisingly, despite substantial variation in both the degree of aggregation and the time period of the data, enrollment elasticity estimates are consistently found to be inelastic for both public and private universities (Doyle & Cicarelli, 1980;Parker & Summers, 1993) and for different racial and income groups (Blakemore & Low, 1983;Wetzel, O'Toole, & Peterson, 1998). However, a survey of enrollment demand studies by Heller (1997) notes that the majority of work uses data that predate the significant real rise in tuition that began in the early 1980s and suggests that enrollment demand may have become more elastic over the last several decades.…”

Section: Introductionmentioning

confidence: 97%

Aim High or Go Low? Pricing Strategies and Enrollment Effects When the Net Price Elasticity Varies with Need and Ability

Curs

Singell

2010

The Journal of Higher Education

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Detailed data on individual applicants to a large public university are used to demonstrate that net price responsiveness decreases with need and ability. Enrollment effects are simulated and show a movement towards a high tuition/high aid (low tuition/low aid) policy significantly lowers (raises) tuition revenue with a modest increase (decrease) in the number of aid-eligible students. Aim High or Go Low? Pricing Strategies and Enrollment Effects when the Net Price Elasticity Varies with Need and AbilityThere is a broad national consensus among academics, policy makers, and college administrators that expanding college access to higher education for low-income students is crucial to developing an effective U.S. poverty policy (St. John, 2003). Education policy pundits disagree, however, on whether the most auspicious tuition policy to expand access should follow a high tuition/high aid (HH) model that targets aid or a low tuition/low aid (LL) model that provides universal aid (Mumper, 2003). Specifically, HH proponents contend that on efficiency and equity grounds list tuition should reflect the true cost of college, but that the actual price net of aid should reflect one's ability to pay (Mortenson, 1993;Schorr, 1988). Conversely, LL advocates argue that college subsidies should be spread more evenly over the student population, because targeted aid programs are unable to attract the necessary long-term popular support and funding (Wilson, 1987;Johnstone, 1997).Implicit in both the HH and LL models are some form of subsidy motivated by the notion that higher education has a public as well as a private return. Moreover, de facto public policy has led to the real cost of college net of aid increasing for all students (i.e., a higher real tuition, lower real aid) with the rationale that the return to college has increased in recent decades and is primarily private in nature (Card, 1999). Thus, the largely theoretical debate regarding the efficacy of the HH versus LL pricing strategies on access takes place in the context of an increasing public expectation that students are, and should be, responsible for covering the lion's share of the cost of college (Heller, 2001). 4This paper enters the HH-LL debate in the context of a single large public university, the University of Oregon (UO), by examining the access, quality, and revenue consequences of moving away or towards their explicit LL-mission for (needy) in-state students and implicit HH-policy for (able) out of state students. The UO is an ideal setting to study differences in price responsiveness by need and ability because prior work suggests that net price variation (i.e., tuition minus aid) is largely determined at the institution level and because flagship public institutions tend to attract a deep and heterogeneous pool of freshman applicants (Curs & Singell, 2002). On the other hand, McMillen, Singell, and Waddell (2007) find that there is a hierarchy of institutions that compete across different applicant pools comprised of students who likely d...

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Section: Introductionmentioning

confidence: 97%

Aim High or Go Low? Pricing Strategies and Enrollment Effects When the Net Price Elasticity Varies with Need and Ability

Curs

Singell

2010

The Journal of Higher Education

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“…More recently, Moore, Studenmund, and Slobko (1991) examine the effects of financial aid on enrollment at an individual, selective college. Parker and Summers (1993) examine the impact of tuition increases and financial aid on enrollments at liberal arts colleges. Alexander and Frey (1984) and McLain, Vance, and Wood (1984) discuss enrollment forecasts and admissions yield, respectively, in the context of master of business administration programs.…”

Section: Alternative Methodologies For Enrollment Forecastingmentioning

confidence: 99%

Methods and Techniques of Enrollment Forecasting

Brinkman¹,

McIntyre²

1997

New Drctns for Instit Rsrch

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Enrollment forecasters have much to consider. A combination of quantitative and qualitative methods can sometimes be the best approach. Methods and Techniques of Enrollment Forecasting Paul T. Brinkman, Chuck McIntyreInstitutional researchers are often called on to conduct or assist with enrollment forecasts. Occasionally, personnel in institutional-planning offices, in state agencies, or in the marketing offices of continuing education divisions will be called on to produce such forecasts. Enrollment forecasts are fundamental elements of planning and budgeting at any higher education institution that depends on student enrollments or at any agency or organization that has responsibilities for supporting those institutions. Numerous institutional policy issues are related to enrollment forecasts-tuition policy, budget forecasting, faculty staffing, institutional closure or consolidation, and optimizing objectives related to the size and composition of enrollment (Weiler, 1987a).There is no one right way to forecast enrollment. In this chapter, we discuss a variety of approaches and associated issues. We relate choices of method or technique to specific circumstances and situations in an effort to provide some guidance to readers in choosing their own approaches. We focus on forecasting institutional enrollments. Factors Affecting EnrollmentNo one factor determines enrollments at a college or university. For the economist, enrollment will be determined by the intersection of measured supplyand-demand curves. For the demographer, enrollments are related to numbers of people and where they are located. For the higher education administrator, enrollment is determined by the combined effects of many manageable and unmanageable factors, categories that are roughly, though not completely, equivalent to supply and demand. In addition, there is always the possibility that a

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“…In particular, such is the case using time-series variation in aggregate prices and enrollments for broad sets of universities (e.g., Campbell & Siegel, 1967) or single institutions (e.g., Seneca & Taussig, 1987), individual variation in net prices and decisions to enroll for a random cross-section of college-age persons (e.g., Tierney, 1982) and for applicants to a specific university (e.g., Ehrenberg & Sherman, 1984). In addition, more recent work also finds an inelastic price response for public and private universities (i.e., Dolyle & Cicarelli, 1980;Parker & Summers, 1993), for in-state and out-of-state students at public universities (i.e., Curs & Singell, 2002), and across different racial and income groups (i.e., Blakemore & Low, 1983;Wetzel et al, 1998). …”

Section: Identifying the Enrollment Effect Of Pell Programmentioning

confidence: 99%