Using stochastic frontier analysis and dynamic fixed-effects panel modeling, this study examines how changes in the x-inefficiency of bachelor's degree production are influenced by changes in state higher education policy. The findings from this research show that increases in need-based state financial aid help to mitigate the convergence among states in x-inefficiency with regard to bachelor's degree production.
Get full access to this article
View all access options for this article.
References
1.
AignerD.LovellK. & SchmidtP. (1977). Formulation and estimation of stochastic frontier function models. Journal of Econometrics, 6(1), 21–37.
2.
AlchianA. & DemsetzH. (1972). Production, information costs and economic organization. American Economic Review, 62(5), 777–795.
3.
ArchibaldR. B. & FeldmanD. H. (2006). State higher education spending and the tax revolt. Journal of Higher Education, 77(4), 618–644.
4.
ArellanoM. (1989). A note on the Anderson-Hsiao estimator for panel data. Economic Letters, 31, 337–341.
5.
ArellanoM. & BondS. R. (1991). Some tests of specification for panel data: Monte Carlo evidence and an application to employment equations. Review of Economic Studies, 58(2), 277–297.
6.
ArellanoM. & BondS. (1998). Dynamic panel data using DPD. Institute for Fiscal Studies Working Paper no. 88/15.
7.
ArellanoM. & BoverO. (1995). Another look at the instrumental variable estimation of error-components models. Journal of Econometrics, 68(1), 29–51.
8.
AstinA. W. (1984). Student involvement: A developmental theory for higher education. Journal of College Student Personnel, 25, 297–308.
9.
BallR. & WilkinsonR. (1994). The use and abuse of performance indicators in UK higher education. Higher Education, 27(4), 417–427.
10.
BatteseG. & CoelliT. (1995). A model for technical inefficiency effects in a stochastic frontier production function for panel data. Empirical Economics, 20(2), 325–332.
11.
BeanJ. P. (1990). Why students leave: Insights from research. In HosslerD. & BeanJ. P. (Eds.), The strategic management of college enrollments (pp. 147–169). San Francisco, CA: Jossey-Bass.
12.
BeckerG. S. (1964). Human capital: A theoretical and empirical analysis, with special reference to education. New York: National Bureau of Economic Research.
13.
Ben-PorathY. (1967). The production of human capital and the life cycle of earnings. Journal of Political Economy, 75(4), 352–365.
14.
BishopP. & BrandS. (2003). The efficiency of museums: A stochastic frontier production function approach. Applied Economics, 35(17), 1853–1858.
15.
BlundellR. & BondS. (1998). Initial condition and moment restrictions in dynamic panel data models. Journal of Econometrics, 87(1), 115–143.
16.
BottassoA. & SembenelliA. (2004). Does ownership affect firms' efficiency? Panel data evidence on Italy. Empirical Economics, 29(4), 769–786.
17.
BoundJ. & TurnerS. (2007). Cohort crowding: How resources affect collegiate attainment. Journal of Public Economics, 91(5/6), 877–899.
18.
BowlesS. & GintisH. (2000). Walrasian economics in retrospect. Quarterly Journal of Economics, 115(4), 1411–1439.
19.
CareyK. (2004). A matter of degrees: Improving graduation rates in four-year colleges and universities. Washington, DC: The Education Trust.
20.
CareyK. (2005). One step from the finish line: Higher college graduation rates are within our reach. Washington, DC: The Education Trust.
21.
CaselliF.EsquivelG. & LefortF. (1996). Reopening the convergence debate: A new look at cross-country growth empirics. Journal of Economic Growth, 1(3), 363–389.
22.
ClarkJ. A. & SiemsT. F. (2002). X-Efficiency in banking: Looking beyond the balance sheet. Journal of Money, Credit, and Banking, 34(4), 987–1013.
23.
De GrootH.McMahonW. W. & VolkweinJ. F. (1991). The cost structure of American research universities. Review of Economics and Statistics, 73(3), 424–451.
24.
DelaneyJ. A. & DoyleW. R. (2007). The role of higher education in budgets. In ShawK. M. & HellerD. E. (Eds.), State postsecondary education research: New methods to inform policy and practice. Sterling, VA: Stylus Publishing.
25.
DellerS. C. & RudnickiE. (1993). Production efficiency in elementary education: The case of Maine public schools. Economics of Education Review, 12(1), 45–57.
26.
DolanR. & SchmidtR. (1994). Modeling institutional production of higher education. Economics of Education Review, 13(3), 197–213.
27.
DriskillR. A. & HorowitzA. W. (2002). Investment in hierarchical human capital. Review of Development Economics, 6(1), 48–58.
28.
DuncombeW.MinerJ. & RuggieroJ. (1995). Potential cost savings from school district consolidation: A case study of New York. Economics of Education Review, 14(3), 265–284.
29.
FinnM. G.HoffmanD. L. & SchlagenhaufD. E. (1990). Intertemporal asset pricing relationships in barter and monetary economies: An empirical analysis. Journal of Monetary Economics, 25(3), 431–451.
30.
GrangerC. W. J. (1969). Investigating causal relations by econometric models and cross-spectral methods. Econometrica, 37(3), 424–438.
31.
HadriK. (2000). Testing for stationarity in heterogeneous panel data. The Econometrics Journal, 3(2), 148–161.
32.
HansenL. P. & SingletonK. J. (1983). Stochastic consumption, risk aversion, and the temporal behavior of asset returns. Journal of Political Economy, 91(2), 249–265.
33.
HansenL. P. & SingletonK. J. (1996). Efficient estimation of linear asset pricing models with moving average errors. Journal of Business and Economic Statistics, 14(1), 53–68.
34.
HemmeterJ. A. (2006). Estimating public library efficiency using stochastic frontiers. Public Finance Review, 34(3), 328–348.
35.
HearnJ. & LonganeckerD. (1985). Enrollment effects of alternative postsecondary pricing policies. The Journal of Higher Education, 56(5), 485–508.
36.
HoenackS. A. (1983). Economic behavior within organizations. Cambridge and New York: Cambridge University Press.
37.
HoflerR. A. & PayneJ. E. (1997). Measuring efficiency in the National Basketball Association. Economics Letters, 55(2), 293–299.
38.
HopkinsD. (1990). The higher education production function: Theoretical foundation and empirical findings. In HoenackS. A. & CollinsE. L. (Eds.), The economics of American universities. Albany, NY: State University of New York Press.
39.
HopkinsD. & MassyW. F. (1981). Planning models for colleges and universities. Palo Alto, CA: Stanford University Press.
40.
HosslerD.LundJ. P.RaminJ.WestfallS. & IrishS. (1997). State funding for higher education: A sisyphean task. The Journal of Higher Education, 68(2), 160–196.
41.
HoveyH. (1999). State spending for higher education in the next decade. Washington, DC: National Center for Public Policy and Higher Education.
42.
IzadiH.JohnesG.OskrochiR. & CrouchleyR. (2002). Stochastic frontier estimation of a CES cost function: The case of higher education in Britain. Economics of Education Review, 21(1), 63–71.
43.
JacobsR. (2001). Alternative methods to examine hospital efficiency: Data envelopment analysis and stochastic frontier analysis. Health Care Management Science, 4(2), 103–115.
44.
JalanJ. & RavallionM. (1998). Are there dynamic gains from a poor-area development program?Journal of Public Economies, 67(1), 65–85.
45.
JensenM. C. & MecklingW. H. (1976). Theory of the firm: Managerial behaviour, agency costs and ownership structure. Journal of Financial Economics, 3, 305–360.
46.
JohansenS. (1988). Statistical analysis of cointegration vectors. Journal of Economic Dynamics and Control, 12(2/3), 231–254.
47.
JohansenS. (1995). Likelihood-based inference in cointegrated vector autoregressive models. Oxford: Oxford University Press.
48.
JohnesJ. (2006). Data envelopment analysis and its application to the measurement of efficiency in higher education. Economics of Education Review, 25(3), 273–288.
49.
JohnstoneD. B.TeixeiraP.RosaJ. M. & VossensteynH. (2006). Introduction. In JohnstoneD. B.TeixeiraP.RosaJ. M.VossensteynH., (Eds.), Cost-sharing and accessibility in higher education: A fairer deal? (pp. 1–18). The Netherlands: Springer.
50.
JonesD. P. (2003). Financing in sync: Aligning fiscal policy with state objectives. Boulder, CO: National Center for Higher Education Management Systems.
51.
KaneT. (1999). The price of admission: Rethinking how Americans pay for college. Washington, DC: The Brookings Institute.
52.
KangB. & GreeneK. V. (2002). The effects of monitoring and competition on public education outputs: A stochastic frontier approach. Public Finance Review, 30(1), 3–26.
53.
KahaneL. (2005). Production efficiency and discriminatory hiring practices in the National Hockey League: A stochastic frontier approach. Review of Industrial Organization, 27(1), 47–71.
54.
KellyP. J. & JonesD. P. (2005). A new look at the institutional component of higher education finance: A guide for evaluating performance relative to financial resources. Boulder, CO: National Center for Higher Education Management Systems.
55.
KivietJ. (1995). On bias, inconsistency and efficiency of various estimators in dynamic panel data models. Journal of Econometrics, 68(1), 53–78.
56.
KivistoJ. A. (2007). Agency theory as a framework for the government-university relationship. Tempere: Higher Education Group/Tempere University Press.
57.
KoshalR. & KoshalM. (1999). Economies of scale and scope in higher education: A case of comprehensive universities. Economics of Education Review, 18(2), 269–277.
LaneJ. E. (2007). Spider web of oversight: Latent and manifest regulatory control in higher education. Journal of Higher Education, 78(6), 1–30.
60.
Le GrandJ. & BartlettW. (Eds.). (1993). Quasi-markets and social policy. Basingstoke: Macmillan.
61.
LeibensteinH. (1966). Allocative efficiency v. x-efficiency. American Economic Review, 56(3), 392–415.
62.
LiT. & RosenmanR. (2001). Cost inefficiency in Washington hospitals: A stochastic frontier approach using panel data. Health Care Management Science, 4(2), 73–81.
63.
LonganeckerD. (2006). State governance and the public good. In KezarA. J.ChambersT. C.BurkhardtJ. C., & Associates (Eds.), Higher education for the public good (pp. 57–70). San Francisco, CA: Jossey-Bass.
64.
LucasR. E. (1988). On the mechanics of economic development. Journal of Monetary Economics, 22(1), 3–42.
65.
Lumina Foundation for Education. (2006). Seeking solutions: A gathering at the summit. Indianapolis, IN: Author.
66.
McLendonM. K.DeatonS. B. & HearnJ. C. (2007). The enactment of reforms in state governance of higher education: Testing the political instability hypothesis. The Journal of Higher Education, 78(6), 645–675.
67.
MeeusenW. & van den BroeckJ. (1977). Efficiency estimation from Cobb-Douglas production functions with composed error. International Economic Review, 18(2), 435–444.
68.
National Association of State Budget Officers. (1992, June). Fiscal Survey of the States 2006. Washington, DC: National Governor's Association and National Association of State Budget Officers.
69.
NickellS. (1981). Biases in models with fixed effects. Econometrica, 49(6), 1417–1426.
70.
OkunadeA. A. (2004). What factors influence state appropriations for public higher education in the United States?Journal of Education Finance, 30(2), 123–138.
71.
PaulsenM. B. (2001). The economics of the public sector: The nature and role of public policy in higher education finance. In PaulsenM. B. & SmartJ. C. (Eds.), The finance of higher education: Theory, research, policy, and practice (pp. 95–132). New York: Agathon Press.
72.
PeterK. & CataldiForrest E. (2005). The road less traveled? Students who enroll in multiple institutions (NCES 22 2005–157). U.S. Department of Education, National Center for Education Statistics. Washington, DC: U.S. Government Printing Office.
73.
PindyckR. S. & RubinfeldD. L. (2000). Econometric models and economic forecasts (4th ed). New York: McGraw-Hill.
74.
RobstJ. (2000). Do state appropriations influence cost efficiency in public higher education?Applied Economic Letters, 7(11), 715–719.
75.
RobstJ. (2001). Cost efficiency in public higher education institutions. Journal of Higher Education, 72(6), 730–750.
76.
RomerD. (2001). Advanced macroeconomics. New York: McGraw-Hill.
77.
RothschildM. & WhiteL. J. (1995). The analytics of pricing in higher education and other services in which customers are inputs. Journal of Political Economy, 103(3), 573–586.
78.
SchankT. (2005). Are overtime plants more efficient than standard-time plants? A stochastic production frontier analysis using the IAB Establishment Panel. Empirical Economics, 30(3), 693–710.
79.
SnyderT. D.TanA. G. & HoffmanC. M. (2006). Digest of education statistics 2005. National Educational Statistics Center. (NCES No. 2006030). Washington, DC: U.S. Government Printing Office. Retrieved June 15, 2008 from http://nces.ed.gov/pubsearch/pubsinfo.asp?pubid=2006030.
80.
StevensA. P. (2005). A stochastic frontier analysis of English and Welsh universities. Education Economics, 13(4), 355–374.
81.
TaylorJ. & TaylorR. (2003). Performance indicators in academia: An x-efficiency approach?Australian Journal of Public Administration, 62(2), 71–82.
82.
The College Board. (2006a). Trends in college pricing 2005. Washington, DC: Author.
83.
The College Board. (2006b). Trends in student aid 2005. Washington, DC: Author.
84.
The Futures Project. (2005). Correcting course: How we can restore the ideals of public higher education in a market-driven era. Providence, RI: A. Alfred Taubman Center for Public Policy and American Institutions.
85.
TintoV. (1987). Leaving college: Rethinking the causes and cures of student attrition. Chicago, IL: University of Chicago Press.
86.
TitusM. A. (2006). No college student left behind: The influence of financial aspects of a state's higher education policy on college completion. The Review of Higher Education, 29(3), 293–317.
87.
TitusM. A. (in press). The production of bachelor's degrees and financial aspects of state higher education policy: A dynamic analysis. The Journal of Higher Education.
88.
U.S. Department of Education. (2006). A test of leadership: Charting the future of U.S. higher education. Washington, DC.
89.
VolkweinJ. F. & TandbergD. A. (2008). Measuring up: Examining the connections among state structural characteristics, regulatory practices, and performance. Research in Higher Education, 49(2), 180–197.
90.
WilliamsG. L. (1995). The marketization of higher education: Reforms and potential reforms in higher education finance. In HillD. D. & SpornB. (Eds.), Emerging patterns of social demand and university reform: Through a glass darkly (pp. 170–193). Tarrytown, NY: Elsevier Science Inc.
91.
WilliamsonO. E. (1975). Markets and hierarchies: Analysis and antitrust implications. New York: Free Press.
92.
WindmeijerF. (2004). A finite sample correction for the variance of linear two-step GMM estimators. Journal of Econometrics, 126(1), 25–51.
93.
WinstonG. C. (1999). Subsidies, hierarchy, and peers: The awkward economics of higher education. Journal of Economic Perspectives, 13(1), 13–36.
94.
ZumetaW. M. (1997). State policy and private higher education: Past, present, and future. In SmartJ. C. (Ed.), Higher education: Handbook of theory and research (Vol. XII, pp. 43–106). Bronx, NY: Agathon Press.
