Hostname: page-component-68c7f8b79f-gnk9b Total loading time: 0 Render date: 2026-01-01T07:48:30.125Z Has data issue: false hasContentIssue false
  • English
  • Français

Panel Data Analysis: A Guide for Nonprofit Studies

Published online by Cambridge University Press:  01 January 2026

Yuhao Ba Open the ORCID record for Yuhao Ba [Opens in a new window] ,
Jessica Berrett  and
Jason Coupet
Yuhao Ba*
Affiliation:
Department of Public Administration, North Carolina State University, Campus Box 8102, Raleigh, NC 27695, USA
Jessica Berrett
Affiliation:
School of Public Affairs, University of Colorado–Colorado Springs, Colorado Springs, CO 80918, USA
Jason Coupet
Affiliation:
Department of Public Administration, North Carolina State University, Campus Box 8102, Raleigh, NC 27695, USA
*
yba@ncsu.edu
Get access

Abstract

The growing push in nonprofit studies toward panel data necessitates a methodological guide tailored for nonprofit scholars and practitioners. Panel data analysis can be a robust tool in advancing the understanding of causal and/or more nuanced inferences that many nonprofit scholars seek. This study provides a walk-through of the assumptions and common modeling approaches in panel data analysis, as well as an empirical illustration of the models using data from the nonprofit housing sector. In addition, the paper compiles applications of panel data analysis by scholars in leading nonprofit journals for further reference.

Keywords

Panel data analysisFixed effectsRandom effectsDynamic modelArellano–Bond

Information

Type
Research Papers
Information
Voluntas: International Journal of Voluntary and Nonprofit Organizations , Volume 34 , Issue 2 , April 2023 , pp. 193 - 208
Copyright
Copyright © International Society for Third-Sector Research 2021

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Article purchase

Temporarily unavailable

References

Allen, M. P. (1997). The problem of multicollinearity. In Understanding regression analysis, pp. 176–180. Springer. Doi: https://doi.org/10.1007/978-0-585-25657-3_37Google Scholar
Amirkhanyan, A. A., Kim, H. J., & Lambright, K. T. (2009). Faith-based assumptions about performance: Does church affiliation matter for service quality and access?. Nonprofit and Voluntary Sector Quarterly, 38(3), 490–521. doi: 10.1177/0899764008320031.CrossRefGoogle Scholar
Anderson, T. W., & Hsiao, C. (1981). Estimation of dynamic models with error components. Journal of the American Statistical Association, 76(375), 598–606. doi: 10.2307/2287517.CrossRefGoogle Scholar
Arellano, M. (2003). Panel data econometrics. Oxford university Press. https://books.google.com/books/about/Panel_Data_Econometrics.html?id=-QBWO67VjsC 10.1093/0199245282.001.0001CrossRefGoogle Scholar
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. doi: 10.2307/2297968.CrossRefGoogle Scholar
Bae, K. B., & Sohn, H. (2018). Factors contributing to the size of nonprofit sector: Tests of government failure, interdependence, and social capital theory. VOLUNTAS: International Journal of Voluntary and Nonprofit Organizations, 29(3), 470–480. doi: 10.1007/s11266-017-9888-3.CrossRefGoogle Scholar
Baltagi, B. H. (2008). Econometric analysis of panel data. John Wiley & Sons.Google Scholar
Baltagi, B. H., Bresson, G., & Pirotte, A. (2003). Fixed effects, random effects or Hausman–Taylor?: A pretest estimator. Economics Letters, 79(3), 361–369. doi: 10.1016/S0165-1765(03)00007-7.CrossRefGoogle Scholar
Bartels, B. (2015). Beyond ‘fixed versus random effects’: A framework for improving substantive and statistical analysis of panel, TSCS, and multilevel data. In Franzese, R. (Ed.), Quantitative research in political science. SAGE Publications Ltd. https://us.sagepub.com/en-us/nam/quantitative-research-inpolitical-science/book243299Google Scholar
Beckman, C. M., & Burton, M. D. (2008). Founding the future: path dependence in the evolution of top management teams from founding to IPO. Organization Science, 19(1), 3–24. doi: 10.1287/orsc.1070.0311.CrossRefGoogle Scholar
Bell, A., & Jones, K. (2015). Explaining fixed effects: Random effects modeling of time-series cross-sectional and panel data. Political Science Research and Methods, 3(1), 133–153. doi: 10.1017/psrm.2014.7.CrossRefGoogle Scholar
Bromley, P., Schofer, E., & Longhofer, W. (2018). Organizing for education: A cross-national, longitudinal study of civil society organizations and education outcomes. VOLUNTAS: International Journal of Voluntary and Nonprofit Organizations, 29(3), 526–540. doi: 10.1007/s11266-018-9979-9.CrossRefGoogle Scholar
Brooks, A. C. (2000). Public subsidies and charitable giving: Crowding out, crowding in, or both?. Journal of Policy Analysis and Management, 19(3), 451–464. doi: 10.1002/1520-6688(200022)19:3<451::AID-PAM5>3.0.CO;2-E.3.0.CO;2-E>CrossRefGoogle Scholar
Bun, M. J., & Sarafidis, V. (2013). Dynamic panel data models. UvA-Econometrics Discussion Paper, 1.Google Scholar
Calabrese, T. D. (2011). The Accumulation of Nonprofit Profits: A Dynamic Analysis. Nonprofit and Voluntary Sector Quarterly, 41(2), 300–324. doi: 10.1177/0899764011404080.CrossRefGoogle Scholar
Cameron, A. C., & Trivedi, P. K. (2010). Microeconometrics using stata, Vol. 2. Stata press College Station.Google Scholar
Cheng, Y. (2018). Nonprofit spending and government provision of public services: Testing theories of government-nonprofit relationships. Journal of Public Administration Research and Theory, 29(2), 238–254. doi: 10.1093/jopart/muy054.CrossRefGoogle Scholar
Clark, T. S., & Linzer, D. A. (2015). Should I use fixed or random effects?. Political Science Research and Methods, 3(2), 399–408. doi: 10.1017/psrm.2014.32.CrossRefGoogle Scholar
Cooper, H. M. (1989). Integrating research: A guide for literature reviews. Sage Publications.Google Scholar
Coupet, J. (2018). Exploring the link between government funding and efficiency in nonprofit colleges. Nonprofit Management and Leadership, 29(1), 65–81. doi: 10.1002/nml.21309.CrossRefGoogle Scholar
Coupet, J., & Berrett, J. L. (2019). Toward a valid approach to nonprofit efficiency measurement. Nonprofit Management and Leadership, 29(3), 299–320. doi: 10.1002/nml.21336.CrossRefGoogle Scholar
David, R. J., & Han, S. (2004). A systematic assessment of the empirical support for transaction cost economics. Strategic Management Journal, 25(1), 39–58. doi: 10.1002/smj.359.CrossRefGoogle Scholar
De Los Mozos, I. S. L., Rodríguez Duarte, A., & Rodríguez Ruiz, Ó (2016). Resource dependence in non-profit organizations: Is It harder to fundraise if you diversify your revenue structure?. VOLUNTAS: International Journal of Voluntary and Nonprofit Organizations, 27(6), 2641–2665. doi: 10.1007/s11266-016-9738-8.CrossRefGoogle Scholar
de Wit, A., & Bekkers, R. (2017). Government support and charitable donations: A meta-analysis of the crowding-out hypothesis. Journal of Public Administration Research and Theory, 27(2), 301–319. doi: 10.1093/jopart/muw044.Google Scholar
Diebold, J., & Coggburn, J. D. (2018). The determinants and opportunity costs of external management fees for state-administered pension plans. Public Budgeting and Finance, 38(4), 3–31. doi: 10.1111/pbaf.12207.CrossRefGoogle Scholar
Einolf, C. J. (2017). Parents’ charitable giving and volunteering: Are they influenced by their children’s ages and life transitions? Evidence from a longitudinal study in the United States. Nonprofit and Voluntary Sector Quarterly, 47(2), 395–416. doi: 10.1177/0899764017737870.CrossRefGoogle Scholar
Elhorst, J. P. (2003). Specification and estimation of spatial panel data models. International Regional Science Review, 26(3), 244–268. doi: 10.1177/0160017603253791.CrossRefGoogle Scholar
Faulk, L., & Derrick-Mills, T. (2015). Shaping the Future of Nonprofit Data. Shaping the Future of Nonprofit Data. https://www.american.edu/spa/news/shaping-future-of-nonprofit-data-07062015.cfmGoogle Scholar
Frees, E. W. (2004). Longitudinal and panel data: Analysis and applications in the social sciences. Cambridge University Press doi: 10.1017/CBO9780511790928.CrossRefGoogle Scholar
Galaskiewicz, J., & Bielefeld, W. (1998). Nonprofit Organizations in an Age of Uncertainty: A Study of Organizational Change. A. de Gruyter.Google Scholar
Garson, G. D. (2013). Longitudinal Analysis. Statistical Associates Publishers.Google Scholar
Glanville, J. L., Paxton, P., & Wang, Y. (2015). Social capital and generosity: A multilevel analysis. Nonprofit and Voluntary Sector Quarterly, 45(3), 526–547. doi: 10.1177/0899764015591366.CrossRefGoogle Scholar
Greene, W. H. (2003). Econometric Analysis (5th, illustrated ed.). Prentice Hall. https://books.google.com/books?id=JJkWAQAAMAAJGoogle Scholar
Grossman, A., & Rangan, V. K. (2001). Managing multisite nonprofits. Nonprofit Management and Leadership, 11(3), 321–337. doi: 10.1002/nml.11306.CrossRefGoogle Scholar
Habitat for Humanity. (2014, November 11). HUD awards $4.2 million capacity building grant to Habitat for Humanity. https://www.habitat.org/newsroom/11-11-2014-HUD-Capacity-BuildingGoogle Scholar
Habitat for Humanity. (2020). Our mission, vision, and principles. https://www.habitat.org/about/mission-and-visionGoogle Scholar
Halaby, C. N. (2004). Panel models in sociological research: Theory into practice. Annual Review of Sociology, 30(1), 507–544. doi: 10.1146/annurev.soc.30.012703.110629.CrossRefGoogle Scholar
Hannan, M. T., & Freeman, J. (1984). Structural inertia and organizational change. American Sociological Review, 49(2), 149–164. doi: 10.2307/2095567.CrossRefGoogle Scholar
Hausman, J. A. (1978). Specification tests in econometrics. Econometrica, 46(6), 1251–1271. doi: 10.2307/1913827.CrossRefGoogle Scholar
Heist, H. D., & Vance-McMullen, D. (2019). Understanding donor-advised funds: how grants flow during recessions. Nonprofit and Voluntary Sector Quarterly, 48(5), 1066–1093. doi: 10.1177/0899764019856118.CrossRefGoogle Scholar
Heutel, G. (2014). Crowding out and crowding in of private donations and government grants. Public Finance Review, 42(2), 143–175. doi: 10.1177/1091142112447525.CrossRefGoogle Scholar
Hsiao, C. (2007). Panel data analysis—Advantages and challenges. TEST, 16(1), 1–22. doi: 10.1007/s11749-007-0046-x.CrossRefGoogle Scholar
Hsiao, C. (2014). Analysis of panel data. Cambridge University Press doi: 10.1017/CBO9781139839327.CrossRefGoogle Scholar
Hutcheson, G. D., & Sofroniou, N. (1999). The multivariate social scientist: Introductory statistics using generalized linear models. SAGE Publications, Ltd. http://methods.sagepub.com/book/the-multivariate-social-scientistGoogle Scholar
James, R. N. III (2018). Cash is not king for fund-raising: Gifts of noncash assets predict current and future contributions growth. Nonprofit Management and Leadership, 29(2), 159–179. doi: 10.1002/nml.21334.CrossRefGoogle Scholar
Jaskyte, K. (2012). Boards of directors and innovation in nonprofit organizations. Nonprofit Management and Leadership, 22(4), 439–459. doi: 10.1002/nml.21039.CrossRefGoogle Scholar
Jensen, U. T., & Vestergaard, C. F. (2016). Public service motivation and public service behaviors: Testing the moderating effect of tenure. Journal of Public Administration Research and Theory, 27(1), 52–67. doi: 10.1093/jopart/muw045.CrossRefGoogle Scholar
Keele, L., & Kelly, N. J. (2006). Dynamic models for dynamic theories: The ins and outs of lagged dependent variables. Political Analysis, 14(2), 186–205. doi: 10.1093/pan/mpj006.CrossRefGoogle Scholar
Kim, M. (2015). Socioeconomic diversity, political engagement, and the density of nonprofit organizations in US counties. The American Review of Public Administration, 45(4), 402–416. doi: 10.1177/0275074013504616.CrossRefGoogle Scholar
Kim, Y. H., & Kim, S. E. (2018). What accounts for the variations in nonprofit growth? a cross-national panel study. VOLUNTAS: International Journal of Voluntary and Nonprofit Organizations, 29(3), 481–495. doi: 10.1007/s11266-016-9752-x.CrossRefGoogle Scholar
Light, R. J., & Pillemer, D. B. (1984). Summing Up: The Science of Reviewing Research. Harvard University Press. https://books.google.com/books?id=5Y14EDR7UicC 10.4159/9780674040243CrossRefGoogle Scholar
Maddala, G. S., & Wu, S. (1999). A comparative study of unit root tests with panel data and a new simple test. Oxford Bulletin of Economics and Statistics, 61(S1), 631–652. doi: 10.1111/1468-0084.0610s1631.CrossRefGoogle Scholar
Maier, F., Meyer, M., & Steinbereithner, M. (2016). Nonprofit organizations becoming business-like: A systematic review. Nonprofit and Voluntary Sector Quarterly, 45(1), 64–86. doi: 10.1177/0899764014561796.CrossRefGoogle Scholar
Matsunaga, Y., Yamauchi, N., & Okuyama, N. (2010). What determines the size of the nonprofit sector?: a cross-country analysis of the government failure theory. VOLUNTAS: International Journal of Voluntary and Nonprofit Organizations, 21(2), 180–201. doi: 10.1007/s11266-010-9125-9.CrossRefGoogle Scholar
Mayer, W. J., Wang, H., Egginton, J. F., & Flint, H. S. (2014). The impact of revenue diversification on expected revenue and volatility for nonprofit organizations. Nonprofit and Voluntary Sector Quarterly, 43(2), 374–392. doi: 10.1177/0899764012464696.CrossRefGoogle Scholar
Mosley, J. E., & Galaskiewicz, J. (2015). The relationship between philanthropic foundation funding and state-level policy in the era of welfare reform. Nonprofit and Voluntary Sector Quarterly, 44(6), 1225–1254. doi: 10.1177/0899764014558932.CrossRefGoogle Scholar
Nickell, S. (1981). Biases in dynamic models with fixed effects. Econometrica: Journal of the Econometric Society, pp. 1417–1426.10.2307/1911408CrossRefGoogle Scholar
Nikolova, M. (2015). Government funding of private voluntary organizations: Is there a crowding-out effect?. Nonprofit and Voluntary Sector Quarterly, 44(3), 487–509. doi: 10.1177/0899764013520572.CrossRefGoogle Scholar
Paarlberg, L. E., & Hwang, H. (2017). The heterogeneity of competitive forces: the impact of competition for resources on united way fundraising. Nonprofit and Voluntary Sector Quarterly, 46(5), 897–921. doi: 10.1177/0899764017713874.CrossRefGoogle Scholar
Pandey, S. K., & Johnson, J. M. (2019). Nonprofit management, public administration, and public policy: Separate, subset, or intersectional domains of inquiry?. Public Performance and Management Review, 42(1), 1–10. doi: 10.1080/15309576.2018.1557382.CrossRefGoogle Scholar
Pedrini, M., & Ferri, L. M. (2016). Doing well by returning to the origin. Mission drift, outreach and financial performance of microfinance institutions. VOLUNTAS: International Journal of Voluntary and Nonprofit Organizations, 27(6), 2576–2594. doi: 10.1007/s11266-016-9707-2.CrossRefGoogle Scholar
Plümper, T., Troeger, V. E., & Manow, P. (2005). Panel data analysis in comparative politics: Linking method to theory. European Journal of Political Research, 44(2), 327–354. doi: 10.1111/j.1475-6765.2005.00230.x.CrossRefGoogle Scholar
Ranucci, R., & Lee, H. (2019). Donor influence on long-term innovation within nonprofit organizations. Nonprofit and Voluntary Sector Quarterly, 48(5), 1045–1065. doi: 10.1177/0899764019843346.CrossRefGoogle Scholar
Rao, C. R., & Toutenburg, H. (1995). Linear Models: Least Squares and Alternatives (1st ed.). Springer. https://doi.org/10.1007/978-1-4899-0024-1CrossRefGoogle Scholar
Rosenbaum, P. R. (2005). Heterogeneity and Causality. The American Statistician, 59(2), 147–152. doi: 10.1198/000313005X42831.CrossRefGoogle Scholar
Rotolo, T., Wilson, J., & Dietz, N. (2014). Volunteering in the United States in the aftermath of the foreclosure crisis. Nonprofit and Voluntary Sector Quarterly, 44(5), 924–944. doi: 10.1177/0899764014546669.CrossRefGoogle Scholar
Shearer, R., & Clark, T. (2016). Are we drawing the correct conclusions? Regression analysis in the nonprofit literature. Canadian Journal of Nonprofit and Social Economy Research, 7(1). https://doi.org/10.22230/cjnser.2016v7n1a213CrossRefGoogle Scholar
Sydow, J., Schreyögg, G., & Koch, J. (2009). Organizational path dependence: Opening the black box. The Academy of Management Review, 34(4), 689–709.Google Scholar
Szper, R. (2013). Playing to the test: Organizational responses to third party ratings. VOLUNTAS: International Journal of Voluntary and Nonprofit Organizations, 24(4), 935–952. doi: 10.1007/s11266-012-9290-0.CrossRefGoogle Scholar
Tsay, W.-J., & Chung, C.-F. (2000). The spurious regression of fractionally integrated processes. Journal of Econometrics, 96(1), 155–182. doi: 10.1016/S0304-4076(99)00056-.CrossRefGoogle Scholar
Wawro, G. (2002). Estimating dynamic panel data models in political science. Political Analysis, 10(1), 25–48. doi: 10.1093/pan/10.1.25.CrossRefGoogle Scholar
Weimar, D., Wicker, P., & Prinz, J. (2015). Membership in nonprofit sport clubs: A dynamic panel analysis of external organizational factors. Nonprofit and Voluntary Sector Quarterly, 44(3), 417–436. doi: 10.1177/0899764014548425.CrossRefGoogle Scholar
Wicker, P., & Frick, B. (2016). Recruitment and retention of referees in nonprofit sport organizations: the trickle-down effect of role models. VOLUNTAS: International Journal of Voluntary and Nonprofit Organizations, 27(3), 1304–1322. doi: 10.1007/s11266-016-9705-4.CrossRefGoogle Scholar
Winters, M. S., & Rundlett, A. (2015). The challenges of untangling the relationship between participation and happiness. VOLUNTAS: International Journal of Voluntary and Nonprofit Organizations, 26(1), 5–23. doi: 10.1007/s11266-014-9500-z.CrossRefGoogle Scholar
Witt, A., Kurths, J., & Pikovsky, A. (1998). Testing stationarity in time series. Physical Review E, 58(2), 1800–1810. doi: 10.1103/PhysRevE.58.1800.CrossRefGoogle Scholar
Wooldridge, J. M. (2005). Fixed-effects and related estimators for correlated random-coefficient and treatment-effect panel data models. Review of Economics and Statistics, 87(2), 385–390. doi: 10.1162/0034653053970320.CrossRefGoogle Scholar
Wooldridge, J.M. (2013). Introductory Econometrics: A Modern Approach (5th ed.). Cengage Learning. https://books.google.com/books?id=C0KHwUKxys0CGoogle Scholar
Woronkowicz, J., & Nicholson-Crotty, J. (2017). The effects of capital campaigns on other nonprofits’ fundraising. Nonprofit Management and Leadership, 27(3), 371–387. doi: 10.1002/nml.21255.CrossRefGoogle Scholar
Yi, H., & Chen, W. (2019). Portable innovation, policy wormholes, and innovation diffusion. Public Administration Review, 79(5), 737–748. doi: 10.1111/puar.13090.CrossRefGoogle Scholar
Yu, Z. (2016). The effects of resources, political opportunities and organisational ecology on the growth trajectories of AIDS NGOs in China. VOLUNTAS: International Journal of Voluntary and Nonprofit Organizations, 27(5), 2252–2273. doi: 10.1007/s11266-016-9686-3.CrossRefGoogle Scholar
Zhu, L. (2013). Panel data analysis in public administration: Substantive and statistical considerations. Journal of Public Administration Research and Theory, 23(2), 395–428. doi: 10.1093/jopart/mus064.CrossRefGoogle Scholar