Not-for-profit or for-profit? Research on the high-quality development path of private universities in China based on system dynamics

The study addresses whether not-for-profit or for-profit management better supports high-quality development in private universities, a critical issue in countries where private higher education enrolls a large share of students. While private universities in developed countries often achieve high quality, many in developing countries—including China and India—lag behind public institutions. China’s 2016 legal reforms require private universities to reregister as not-for-profit or for-profit, but implementation has been slow. The research aims to analyze which orientation more effectively drives high-quality development by modeling and simulating different development paths for a representative Chinese private university (K University) from 2022 to 2031, focusing on financial policy levers and their impact on quality-related outcomes.

Global expansion of private higher education has been driven by demand growth, constrained public capacity, and shifting funding perceptions. Countries exhibit varied management models, including for-profit and not-for-profit forms (e.g., the US, UK, South Africa, Vietnam). Debate persists about whether not-for-profit status leads to higher quality; some argue profit motives can undermine quality, while others find status alone does not determine outcomes. Private universities face risks and quality challenges worldwide: for-profit groups in the US have faced crises and allegations, Japanese family-owned institutions carry higher risk, and quality gaps exist in India and China. In China, despite reforms pushing classification into not-for-profit and for-profit, reregistration progress is limited, with investor incentives and administrative constraints complicating implementation. System dynamics has been applied to aspects of private university management (competitive advantage, internal control, finance). Prior work provides a basis for building a comprehensive model linking student, teacher, finance, and quality subsystems to evaluate development paths under different policy and management orientations.

The study uses system dynamics to construct and simulate a development model for private universities, following four steps: (1) identify subsystems and framework based on literature and system analysis; (2) specify variables and build the model; (3) test the model; (4) simulate four development paths using K University as a case. - Subsystems and framework: The development system is artificial, open, and dynamic, comprising four interacting subsystems—students, teachers, finance, and quality. Students generate income via tuition and fees and consume funds through instructional expenditure; finance allocates resources to teacher, student, and quality activities; teachers provide educational services; quality improvements attract students and faculty and require funding. - Variables: The model includes 33 variables: 4 state variables (number of students; financial surplus; number of current full-time teachers; quality evaluation index), 6 rate variables (e.g., number of freshmen enrolled; number of graduates; accounting income; accounting expenditure; number of teachers introduced; number of teachers leaving), 7 auxiliary variables (e.g., tuition income; accommodation income; government financial allocation; daily administrative expenditure; land cost; teaching expenditure; growth rate of teachers), and 16 constants (e.g., growth rate of students; proportion of graduates; dropouts; transfers; tuition growth rate; service income; admin expenditure growth; land area; transfer fees; capital construction; teaching expenditure growth; enrollment cost; tax expenditure; teacher turnover rate; retirees; quality index growth rate). - Case selection and data: K University in western China is used due to data accessibility from long-term investigation. It is recognized provincially for advantageous characteristics and applied talent training. - Model testing: Historical test from 2019–2021 with a one-year time step compares simulated to actual data from K University’s annual quality reports. Errors across key variables (students, freshman enrollment, full-time teachers, graduates) range from −2.02% to 6.93%, below 10%, supporting model validity. - Path design and control variables: Four development paths are simulated by varying financial control variables: government financial allocation; tuition growth rate; land cost; tax expenditure. Assumptions reflect China’s legal framework: • Path 1: Not-for-profit, steady development—no government allocation; tuition growth 0%; land cost 0; tax set at 6% of service income; all surplus reinvested in the school. • Path 2: Not-for-profit, rapid development—same as Path 1 plus government allocation (assumed RMB 1,000 per student per year), adjusted with student numbers; all surplus reinvested. • Path 3: For-profit, steady development—tuition growth 7.8% annually; land cost 0.0376 ten thousand yuan/m² paid over 10 years; tax 6% of accounting income; 10% of financial surplus reserved for school development, remainder distributable. • Path 4: For-profit, rapid development—same costs and tax as Path 3, but all financial surplus reinvested to accelerate quality improvement. Simulation horizon: 2022–2031. Vensim PLE is used for model implementation and runs.

- Student and teacher baseline dynamics: The choice of for-profit vs not-for-profit is assumed to have negligible direct impact on student enrollment or teacher retention. By 2031 across all paths, K University reaches 21,778 students, 5,488 freshmen, 5,257 graduates, and 72 teacher departures. - Tuition income: For-profit paths (3 and 4) yield higher tuition income due to a 7.8% annual tuition increase. In 2031, tuition income (ten thousand yuan) is 49,268.7 for Paths 3/4 vs 45,712.2 for Paths 1/2—a difference of about 3,556.5 (≈ RMB 35.565 million). - Accounting income: 2031 accounting income (ten thousand yuan): Path 3/4 = 60,502.1; Path 2 = 59,023.6; Path 1 = 56,945.7. Thus, for-profit paths exceed not-for-profit paths, and not-for-profit with allocation (Path 2) exceeds not-for-profit without allocation (Path 1) by ~2,078–3,078 (≈ RMB 20.78–30.78 million) over the period. - Accounting expenditure: For-profit paths incur higher expenditures due to land costs and higher taxes. 2031 accounting expenditure (ten thousand yuan): Path 3/4 = 54,023.5; Path 2 = 46,547.5; Path 1 = 45,139.2. - Government allocation, land cost, taxes: Path 2 receives annual allocations roughly RMB 19.478–20.7783 million. For-profit paths incur average annual land costs of RMB 42.349 million and annual tax expenditures of RMB 34.2173–36.3013 million; not-for-profit paths have zero land cost and ~RMB 3 million tax (6% of service income). - Financial surplus and investment capacity: Despite lower tuition growth, not-for-profit paths generate higher financial surpluses because avoided land and tax burdens outweigh extra tuition revenue in for-profit paths. 2031 financial surplus (ten thousand yuan): Path 2 = 149,122; Path 1 = 140,236; Path 3/4 = 95,151.4. Investment in school operations follows: Path 2 > Path 1 > Path 4 > Path 3. Under policy constraints, Paths 1 and 2 reinvest all surplus; Path 3 reinvests 10% of surplus; Path 4 reinvests all surplus. By 2031, cumulative development funds are largest under Path 2, then Path 1, then Path 4, and smallest under Path 3. - Overall: For-profit orientation increases tuition and accounting income but cannot offset added land and tax costs, leading to higher expenditures and lower surpluses than not-for-profit orientations. The rapid not-for-profit path with government allocation provides the strongest support for high-quality development as measured by financial surplus and school investment capacity.

The research question—whether not-for-profit or for-profit management better supports high-quality development—was addressed by modeling the interdependence of student, teacher, financial, and quality subsystems under realistic policy constraints. Simulations show that while for-profit status raises tuition revenue, legally and fiscally induced costs (land and taxes) substantially increase expenditures and reduce net financial capacity. Conversely, not-for-profit status benefits from lower land costs and preferential tax treatment; when paired with government allocations (Path 2), it maximizes financial surplus and investment into academic quality and resources. Given that quality improvements require sustained investment in faculty, facilities, and teaching, the higher surplus and mandated reinvestment under the not-for-profit paths—especially with public funding—more directly enable high-quality development. These findings underline the significance of policy design: differentiated support and fiscal incentives shape institutional financial trajectories more than tuition autonomy alone, guiding strategic choices for private universities in developing contexts.

This study develops and validates a system dynamics model to compare four development paths (steady/rapid, not-for-profit/for-profit) for a Chinese private university. It finds that not-for-profit management—particularly the rapid path with government financial allocation—produces higher financial surpluses and larger school operation investments than for-profit alternatives, thereby better supporting high-quality development. Policy-relevant strategies include: (1) encouraging private universities to adopt not-for-profit status through clearer governance rights, stakeholder remuneration structures, and expanded academic autonomy; (2) increasing government investment and implementing supportive policies (land allocation, tax relief) targeted to not-for-profit institutions; and (3) strengthening financial oversight to ensure allocated and surplus funds are fully and lawfully reinvested in educational quality. Future research should extend the model to multiple institutions and regions, refine parameter estimates (e.g., tax regimes, land policies), incorporate dynamic feedback from quality to demand more explicitly, and test sensitivity to policy shocks and demographic changes.

The analysis models a single case (K University) due to data availability, which may limit generalizability across diverse private universities and provinces. Financial subsystem data for private universities are difficult to obtain; several parameters (e.g., government allocation levels, land cost schedules, tax applications) rely on assumptions aligned with current regulations and provincial practices. The model assumes minimal direct impact of not-for-profit vs for-profit status on student enrollment and teacher retention, which may not hold in all contexts. Potential inconsistencies in unit reporting in the text (e.g., ten thousand yuan vs million yuan) may affect interpretation of absolute magnitudes, though relative comparisons are robust. Results reflect current Chinese policy environments and may vary with regulatory changes.