Skip to main content

Do Hybrid Organizational Forms of the Social Economy have a Greater Chance of Surviving? An Examination of the Case of Montreal

Abstract

The objective of this article is to contribute to an understanding of the evolution of a population of social economy enterprises faced with the economic crisis, namely by referring to the case of Montreal. We apply a two-step approach. For one, we use an innovative discrete-time survival model that takes spatial heterogeneity into account. In a second step, this model is used to predict the survival of different forms of the social economy, according to various proposed typologies for identifying hybrid organizational forms. It is understood that certain organizational forms (professional social economy) have fared better than others (emerging social economy). Organizations combining several sources of financing and several forms of paid or volunteer work likewise have greater chances of survival.

Résumé

L’objectif de cet article est de contribuer à une compréhension de l’évolution d’une population d’entreprises d’économie sociale confrontée à la crise économique, en nous appuyant sur l’exemple de Montréal. Nous mobilisons une approche en deux étapes. Nous utilisons un modèle novateur de survie en temps discret tenant compte de l’hétérogénéité spatiale. Ce modèle est ensuite utilisé pour prédire la survie de différentes formes d’économie sociale, suivant différentes typologies proposées identifiant des formes organisationnelles hybrides. On constate que certaines formes organisationnelles (économie sociale professionnelle) ont mieux survécu que d’autres (économie sociale émergente). De même, les organisations combinant plusieurs sources de financement et plusieurs formes de travail salarié ou bénévole ont eu plus de chances de survivre.

Zusammenfassung

Ziel dieser Abhandlung ist es, zu einem Verständnis über die Entwicklung einer Reihe von sozialwirtschaftlichen Unternehmen beizutragen, die von der Wirtschaftskrise betroffen sind, insbesondere unter Bezugnahme auf das Beispiel Montreal. Wir wenden ein zweistufiges Konzept an. Zunächst nutzen wir ein innovatives Überlebensdauermodell in diskreter Zeit, das die räumliche Heterogenität berücksichtigt. Anschließend prognostiziert man anhand dieses Modells die Überlebensdauer verschiedener Sozialwirtschaftsformen gemäß den unterschiedlichen vorgeschlagenen Typologien zur Bestimmung hybrider Organisationsformen. Man geht davon aus, dass es bestimmten Organisationsformen (professionelle Sozialwirtschaft) besser ergangen ist als anderen (neue Sozialwirtschaft). Organisationen, die mehrere Finanzquellen und verschiedene Formen bezahlter oder ehrenamtlicher Arbeit verbinden, haben entsprechend größere Überlebenschancen.

Resumen

El objetivo del presente artículo es contribuir a la comprensión de la evolución de una población de empresas de economía social enfrentadas a la crisis económica, haciendo referencia al caso de Montreal. Aplicamos un enfoque de dos pasos. Para uno de ellos, utilizamos un modelo de supervivencia discreto innovador que toma en cuenta la heterogeneidad espacial. En un segundo paso, este modelo se utiliza para predecir la supervivencia de diferentes formas de la economía social, de conformidad con diversas tipologías propuestas para identificar formas organizativas híbridas. Se entiende que determinadas formas organizativas (economía social profesional) se han comportado mejor que otras (economía social emergente). Las organizaciones que combinan varias fuentes de financiación y varias formas de trabajo pagado o voluntario tienen también mayores posibilidades de supervivencia.

摘要

本文通过蒙特利尔的例子旨在帮助人们理解面临经济危机的社会经济企业的发展过程,我们的方法分两步:第一步,运用一个新的、考虑到空间异质性的离散时间生存模型。第二步,根据各种提议的、用于识别混合组织机构形式的类型学,用这个模型预测不同类型社会经济企业的存活率。大家理解,某些形式的组织机构(专业的社会经济组织)会比其他一些形式的组织机构(新兴的社会经济组织)发展更好, 融资来源丰富而且采用多种类型的付薪工作或自愿性的工作的组织机构的存活率更高。

要約

本論文の目的は、モントリオールの事例を参照して、経済危機に直面している社会的経済企業の人口進化の理解に貢献することである。2段階のアプローチを適用する。1つ目は、空間的不均一性を考慮した革新的な離散時間生存モデルを使用する。2つ目のステップでは、ハイブリッド組織形態を識別する様々な提案型の類型に基づいて、このモデルを社会経済における異なる形態の残存の予測に用いる。ある特定の組織の形態 (プロ社会経済) が他 (新興社会経済) よりも実践されていることがわかる。同様に資金調達源と有償もしくは無償ボランティアの業務形態が組み合わさった組織では生き残るための大きなチャンスが残されている。

ملخص

الهدف من هذه المقالة هو المساهمة في فهم تطور عدد الأشخاص في مؤسسات الإقتصاد الإجتماعي واجهوا الأزمة الإقتصادية، أي بالإشارة إلى حالة مونتريال.طبقنا نهج من خطوتين. الخطوة الأولى، نحن نستخدام سلسلة زمنية مبتكرة لنموذج البقاء على قيد الحياة التي تأخذ بعين الإعتبار التنوع المكاني. في الخطوة الثانية، يتم إستخدام هذا النموذج لتوقع البقاء على قيد الحياة لأشكال مختلفة من الإقتصاد الإجتماعي، وفقا” لمختلف الأنماط المقترحة لتحديد الأشكال التنظيمية الهجينة. من المعلوم أن أشكال تنظيمية معينة (الإقتصاد الإجتماعي المهني) أفضل حالا” من غيره (الإقتصاد الاجتماعي الناشئ). منظمات تجمع بين عدة مصادر للتمويل والعديد من أشكال العمل المأجور أو التطوع لديهم أيضا المزيد من فرص البقاء على قيد الحياة.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3

Notes

  1. 1.

    Hannan and Freeman (1989) define organizational forms as “ideal types” of organizations that are defined by a limited set of common characteristics, such as their structures, practices, members, and organizational routines. See also Hsu and Hannan (2005).

  2. 2.

    They are determined as being altruistic/egoistic based on an experimental game made with focus groups of each organization.

  3. 3.

    However, these authors, with the notable exception of Burger and Owens (2013), do not examine the endogenous characteristic of the sources of income, which vary over time, in relation to survival.

  4. 4.

    The credit union movement Desjardins and La Coop fédérée (through its head office), which are integral parts of the social economy, were processed and handled differently in this survey due to their organizational features and specificities at the economic level. As such, they are not included in this sample.

  5. 5.

    These sectors are: 1000 (natural resources, manufacturing, processing and construction), 2000 (trade, finance, insurance), 3000 (home and rental), 4000 (recreation, tourism, accommodation, catering), 5000 (health and social services), 6000 (arts, culture and communication) and 7000 (other services).

  6. 6.

    The classification of missions includes 13 categories (one of which is residual) established by crossing common classifications (Johns Hopkins University, Chantier de l’économie sociale, Centraide, Secrétariat du Québec à l’action communautaire et à l’innovation): Food; Arts and culture; Fair trade; Defense of social rights; Economic and/or community development; Popular education and literacy; Employment and job integration; Environment; Housing; Immigration and/or cultural communities; Recreation and Tourism, Health, Other.

  7. 7.

    The result of the logit model will nevertheless be presented in the “Appendices 2 and 3” sections, as a complementary robustness test.

  8. 8.

    For a logit, the exponential \(\exp \left( {\beta X_{i} } \right)\) is interpreted in terms of the odds ratio.

  9. 9.

    This point allows in particular to avoid the endogeneity between survival and certain explanatory variables, such as resources or size.

  10. 10.

    For the RESET on the non-heteroskedastic cloglog: F (3672) = 2.03, p value = 0.1078.

  11. 11.

    This can be seen as sufficient and as allowing for valid inferences, even if the hypothesis of a model applying even better to the data cannot be ruled out.

  12. 12.

    A hazard ratio (h) is interpreted as a percentage change of the mortality risk for an additional unit of the explanatory variable using the following rule: (h−1) * 100. Thus, an hr of 0.819 for the number of people on the board can be interpreted as an evolution of (0.819–1) * 100 = −18.1 % for an additional person to the board on the risk of mortality.

  13. 13.

    Which corresponds to a density of \({100}/{\pi } = 31.84\) organizations for each km2.

  14. 14.

    Note that this approach results in taking the typology thus constructed as a given fact without taking account of the uncertainty of the classification. The latent class analysis developed in Rousselière and Bouchard (2011) is a fuzzy classification. Using a most likely class membership strategy may lead to underestimate the heterogeneity of each cluster (Goodman 2007). Classification quality indicators (calculation of entropy index), however, allow for an application of this approach (Asparouhov & Muthen 2014). This also leads to interpret the result as being a prediction for an average individual of each category.

  15. 15.

    The studied population contained no mutuals and excluded foundations that had no goods or services production activity.

  16. 16.

    See the Quebec definition of the social economy, http://www2.publicationsduquebec.gouv.qc.ca/dynamicSearch/telecharge.php?type=2&file=/E_1_1_1/E1_1_1.html, accessed October 26, 2015.

References

  1. Aalen, O. D., Borgan, O., & Gjessing, H. K. (2008). Survival and event history analysis. New York: Springer.

    Book  Google Scholar 

  2. Abadie, A., & Imbens, G. W. (2006). Large sample properties of matching estimators for average treatment effects. Econometrica, 74, 235–267.

    Article  Google Scholar 

  3. Abadie, A., & Imbens, G. W. (2011). Bias-corrected matching estimators for average treatment effects. Journal of Business and Economic Statistics, 29, 1–11.

    Article  Google Scholar 

  4. Ai, C., & Norton, E. (2003). Interaction terms in logit and probit models. Economics Letters, 80, 123–129.

    Article  Google Scholar 

  5. Amin, A., Cameron, A., & Hudson, R. (2002). Placing the social economy. London: Routledge.

    Google Scholar 

  6. Arandoa, S., Gagoa, M., Podivinsky, J. M., & Stewart, G. (2012). Do labour-managed firms benefit from agglomeration? Journal of Economic Behavior & Organization, 84, 193–200.

    Article  Google Scholar 

  7. Archibald, M. E. (2007). An organizational ecology of national self-help/mutual-aid organizations. Nonprofit and Voluntary Sector Quarterly, 36(4), 598–621.

    Article  Google Scholar 

  8. Asparouhov, T., & Muthen, B. (2014). Auxiliary variables in mixture modeling. Structural Equation Modeling, 21(3), 329–341.

    Article  Google Scholar 

  9. Barron, D. N., West, E., & Hannan, M. T. (1994). A time to grow and a time to die: Growth and mortality of credit unions in New York, 1914–1990. American Journal of Sociology, 100, 381–421.

    Article  Google Scholar 

  10. Battilana, J., & Lee, M. (2014). Advancing research on hybrid organizing insights from the study of social enterprises. The Academy of Management Annals, 8(1), 397–441.

    Article  Google Scholar 

  11. Bertotti, M., Han, Y., Netuveli, G., Sheridan, K., & Renton, A. (2014). Governance in South Korean social enterprises. Social Enterprise Journal, 10(1), 38–52.

    Article  Google Scholar 

  12. Besel, K., Lewellen, Williams C., & Klak, J. (2011). Nonprofit sustainability during times of uncertainty. Nonprofit Management & Leadership, 22(1), 53–65.

    Article  Google Scholar 

  13. Billis, D. (Ed.). (2010). Hybrid organizations and the third sector: Challenges for practice, theory and policy. New York: Palgrave Macmillan.

    Google Scholar 

  14. Birchall, J. (2013). The potential of cooperatives during the current recession; theorizing comparative advantage. Journal of Entrepreneurial and Organizational Diversity, 2(1), 1–22.

    Google Scholar 

  15. Bontemps, C., Bouamra-Mechemache, Z., & Simioni, M. (2013). Quality labels and firm survival: Some first empirical evidence. European Review of Agricultural Economics, 40(3), 413–439.

    Article  Google Scholar 

  16. Borgonovi, F. (2006). Do public grants to American theatres crowd-out private donations? Public Choice, 126, 429–451.

    Article  Google Scholar 

  17. Bouchard, M. J., Ferraton, C., Michaud, V., & Rousselière, D. (2008b). Base de données sur les organisations d’économie sociale. La classification des activités, Montréal, Chaire de recherche du Canada en économie sociale, Collection Recherche no R-2008-1.

  18. Bouchard, M. J., & Rousselière, D. (2010). Cité créative et économie sociale culturelle: Etude de cas de Montréal. Études Canadiennes/Canadian Studies, 68, 139–158.

    Google Scholar 

  19. Bouchard, M. J., Rousselière, D., Ferraton, C., & Michaud, V. (2008a). Portrait statistique de la région administrative de Montréal, Montréal, Chaire de recherche du Canada en économie sociale et Conférence régionale des élus de Montréal, Collection Hors-série, HS-2008-01, mai, p. 81.

  20. Burger, R., & Owens, T. (2013). Receive grants or perish? The survival prospects of Ugandan non-governmental organisations. Journal of Development Studies, 49(9), 1284–1298.

    Article  Google Scholar 

  21. Carter, D. B., & Signorino, C. S. (2010). Back to the future: Modeling time dependence in binary data. Political Analysis, 18(3), 271–292.

    Article  Google Scholar 

  22. Cazzuffi, C., & Moradi, A. (2012). Membership size and cooperative performance: evidence from ghanaian cocoa producers societies, 1930–36. Economic History of Developing Regions, 27(1), 67–92.

    Article  Google Scholar 

  23. Chaganti, R. S., Mahajan, V., & Sharma, S. (1985). Corporate board size, composition, and corporate failures in retailing industry. Journal of Management Studies, 22(4), 400–417.

    Article  Google Scholar 

  24. Chambré, S. M., & Fatt, N. (2002). Beyond the liability of newness: Nonprofit organizations in an emerging policy domain. Nonprofit and voluntary sector quarterly, 31(4), 502–524.

    Article  Google Scholar 

  25. Chevallier, M. (2013). Les atouts des coopératives: stabilité et expérience. Revue internationale de l’économie sociale, 327, 63–74.

    Article  Google Scholar 

  26. Chih-Hui, L. (2014). Can our group survive? An investigation of the evolution of mixed-mode groups. Journal of Computer-Mediated Communication, 19, 839–854.

    Article  Google Scholar 

  27. Ciampi, F. (2015). Corporate governance characteristics and default prediction modeling for small enterprises. An empirical analysis of Italian Firms. Journal of Business Research, 68, 1012–1025.

    Article  Google Scholar 

  28. Clément, M., Bouchard, C., & Jacob, L. (2008). Taux de survie des coopératives au Québec, Québec, Ministère du développement économique, de l’innovation et de l’exportation.

  29. Coombes, S. M. T., Morris, M. H., Allen, J. A., & Webb, J. W. (2011). Behavioural orientations of non-profits boards as a factor in entrepreneurial performance: Does governance matter? Journal of Management Studies, 48(4), 829–856.

    Article  Google Scholar 

  30. Cornforth, C. (2004). The governance of co-operatives and mutual associations: A paradox perspective. Annals of Public and Cooperative Economics, 75(1), 11–32.

    Article  Google Scholar 

  31. Fernandez, J. J. (2008). Causes of dissolution among Spanish nonprofit associations. Nonprofit and Voluntary Sector Quarterly, 37, 113–137.

    Article  Google Scholar 

  32. Geroski, P. A. (1995). What do we know about entry? International Journal of Industrial Organization, 13(4), 421–440.

    Article  Google Scholar 

  33. Goodman, L. A. (2007). On the assignment of individuals to latent classes. Sociological Methodology, 37(1), 1–22.

    Article  Google Scholar 

  34. Goodstein, J., Gautam, K., & Boeker, W. (1994). The effects of board size and diversity on strategic change. Strategic Management Journal, 15, 241–250.

    Article  Google Scholar 

  35. Gras, D., & Mendoza-Abarca, K. (2014). Risky business? The survival implications of exploiting commercial opportunities by nonprofits. Journal of Business Venturing, 29, 392–404.

    Article  Google Scholar 

  36. Greene, W. (2003). Econometric analysis (5th ed.). Prentice Hall: Upper Saddle River.

    Google Scholar 

  37. Greene, W., Harris, M. N., Hollingsworth, B., & Weterings, T. A. (2013). Heterogeneity in ordered choice models. Journal of Economic Surveys, 28(1), 109–133.

    Article  Google Scholar 

  38. Guo, S. & Fraser, M. W. (2010). Propensity score analysis. Statistical Methods and Applications. Thousand Oaks, Sage Publications, Advanced Quantitative Techniques in the Social Sciences Series.

  39. Hager, M. A., Galaskiewicz, J., & Larson, J. A. (2004). Structural embeddedness and the liability of newness among nonprofit organizations. Public Management Review, 6(2), 159–188.

    Article  Google Scholar 

  40. Hannan, M. T. (1998). Rethinking age dependence in organizational mortality. American Journal of Sociology, 104(1), 126–164.

    Article  Google Scholar 

  41. Hannan, M. T., & Freeman, J. (1989). Organizational ecology. Cambridge: Harvard University Press.

    Google Scholar 

  42. Hannan, M. T., Polos, L., & Carroll, G. R. (2007). Logics of organization theory: Audiences, codes and ecologies. Princeton: Princeton University Press.

    Google Scholar 

  43. Hansmann, H. (1996). The ownership of enterprise. Cambridge: Harvard University Press.

    Google Scholar 

  44. Helmig, B., Infergfurth, S., & Pinz, A. (2014). Success and failure of nonprofit organizations, theoretical foundations, empirical evidence, and future research. Voluntas, 25, 1509–1538.

    Article  Google Scholar 

  45. Hsu, G., & Hannan, M. T. (2005). Identities, genres, and organizational forms. Organization Science, 16(4), 474–490.

    Article  Google Scholar 

  46. Hudson, R. (2009). Life on the edge: Navigating the competitive tensions between the ‘social’and the ‘economic’in the social economy and its relations to the mainstream. Journal of Economic Geography, 9, 493–510.

    Article  Google Scholar 

  47. Hung, C. K. R., & Ong, P. (2012). Sustainability of Asian-American nonprofit organizations in U.S. metropolitan areas. Nonprofit and Voluntary Sector Quarterly, 41(6), 1136–1152.

    Article  Google Scholar 

  48. Hustinx, L., Verschuere, B., & De Corte, J. (2014). Organisational hybridity in a post-corporatist welfare mix: The case of the third sector in Belgium. Journal of Social Policy, 43(2), 391–411.

    Article  Google Scholar 

  49. Jenkins, S. P. (1995). Easy estimation methods for discrete-time duration models. Oxford Bulletin of Economics and Statistics, 57, 129–138.

    Article  Google Scholar 

  50. Klein, J. L., Tremblay, D. G., & Bussières, D. R. (2010). Social economy-based local initiatives and social innovation: A Montreal case Study. International Journal of Technology Management, 51(1), 121–138.

    Article  Google Scholar 

  51. Lévesque, B. (2013). How the social economy won recognition in Québec at the end of the 20th century. In M.-J. Bouchard (Ed.), Innovation and the social economy: The Québec experience (pp. 25–70). Toronto: University of Toronto Press.

    Google Scholar 

  52. Leviten-Reid, C. (2012). Organizational form, parental involvement, and quality of care in child day care centers. Nonprofit and Voluntary Sector Quarterly, 41(1), 36–57.

    Article  Google Scholar 

  53. Levy, P., & Lemeshow, S. (2008). Sampling of populations: Methods and applications. Hoboken: Wiley.

    Book  Google Scholar 

  54. Litchfield, J., Reilly, B., & Veneziani, B. (2012). An analysis of life satisfaction in Albania: An heteroscedastic ordered probit model approach. Journal of Economic Behavior & Organization, 81, 731–741.

    Article  Google Scholar 

  55. Long, J. S. (2009). Group comparisons in logit and probit using predicted probabilities. Working paper draft 2009-06-25.

  56. Lumely, T. (2010). Complex survey. Hoboken: Wiley.

    Book  Google Scholar 

  57. Mair, J., Battilana, J., & Cardenas, J. (2012). Organizing for society: A typology of social entrepreneuring models. Journal of Business Ethics, 111, 353–373.

    Article  Google Scholar 

  58. Meng, X. L. (1994). Multiple-imputation inferences with uncongenial sources of input. Statistical Science, 9(4), 538–573.

    Google Scholar 

  59. Mood, C. (2010). Logistic regression: Why we cannot do what we think we can do, and what we can do about it. European Sociological Review, 26(1), 67–82.

    Article  Google Scholar 

  60. Núñez-Nickel, M., & Moyano-Fuentes, J. (2004). Ownership structure of cooperatives as an environmental buffer. Journal of Management Studies, 41(7), 1131–1152.

    Article  Google Scholar 

  61. Parente, C., Lopes, A., & Marcos, V. (2014). Social entrepreneurship profiles: Lessons from organizational and management dynamics. Journal of Social Entrepreneurship, 5(1), 22–41.

    Article  Google Scholar 

  62. Pérotin, V. (2006). Entry, exit and the business cycle: Are cooperatives different? Journal of Comparative Economics, 34, 295–316.

    Article  Google Scholar 

  63. Peters, S. (2000). On the use of the RESET test in micro-econometric models. Applied Economics Letters, 7, 361–365.

    Article  Google Scholar 

  64. Prentice, R., & Gloeckler, L. (1978). Regression analysis of grouped survival data with application to breast cancer data. Biometrics, 34(1), 57–67.

    Article  Google Scholar 

  65. Quarter, J., Sousa, J., Richmond, B. J., & Carmichael, I. (2001). Comparing member-based organizations within a social economy framework. Nonprofit and Voluntary Sector Quarterly, 30, 351–375.

    Article  Google Scholar 

  66. Raftery, A. (1995). Bayesian model selection in social research. Sociological Methodology, 25, 111–163.

    Article  Google Scholar 

  67. Rosenbaum, P. R., & Rubin, D. B. (1983). The central role of propensity score in observational studies for causal effects. Biometrika, 70, 41–55.

    Article  Google Scholar 

  68. Rousselière, D., & Bouchard, M. J. (2011). A propos de l’hétérogénéité des formes organisationnelles de l’économie sociale : isomorphisme vs écologie des organisations en économie sociale. Canadian Review of Sociology/Revue Canadienne de Sociologie, 48(4), 414–453.

    Article  Google Scholar 

  69. Rousselière, D., & Joly, I. (2011). A propos de la capacité à survivre des coopératives : une étude de la relation entre âge et mortalité des organisations coopératives agricoles françaises. Revue d’études en agriculture et environnement, 92(3), 259–289.

    Google Scholar 

  70. Rubin, D. B. (1996). Multiple imputation after 18 + years. Journal of the American Statistical Association, 91(434), 473–489.

    Article  Google Scholar 

  71. Schenker, N., & Taylor, J. (1996). Partially parametric techniques for multiple imputation. Computational Statistics & Data Analysis, 22, 425–446.

    Article  Google Scholar 

  72. Simons, T., & Ingram, P. (2004). An ecology of ideology: Theory and evidence from four populations. Industrial and Corporate Change, 13(1), 33–59.

    Article  Google Scholar 

  73. Smirnov, O. A. (2010). Modeling spatial discrete choice. Regional Science and Urban Economics, 40(5), 292–298.

    Article  Google Scholar 

  74. Spear, R. (2011). Formes coopératives hybrides. Revue internationale de l’économie sociale, 320, 26–42.

    Article  Google Scholar 

  75. Staber, U. (1993). Worker cooperatives and the business cycle: Are cooperatives the answer to unemployment? American journal of economics and sociology, 52(2), 129–143.

    Article  Google Scholar 

  76. Stekhoven, D. J., & Bühlmann, P. (2012). Missforest—Non-parametric missing value imputation for mixed-type data. Bioinformatics, 28(1), 112–118.

    Article  Google Scholar 

  77. Stiglitz, J. (2009). Moving beyond market fundamentalism to a more balanced economy. Annals of Public and Cooperative Economics, 80(3), 345–360.

    Article  Google Scholar 

  78. Valentinov, V. (2007). The property rights approach to nonprofit organization: The role of intrinsic motivation. Public Organization Review, 7, 41–55.

    Article  Google Scholar 

  79. van Buuren, S., Brand, J. P., Groothuis-Oudshoorn, C. G., & Rubin, D. B. (2006). Fully conditional specification in multivariate imputation. Journal of Statistical Computation and Simulation, 76(12), 1049–1064.

    Article  Google Scholar 

  80. Varum, C. A., & Rocha, V. C. (2010). The effect of crises on firm exit and the moderating effect of firm size. Economics Letters, 114(1), 94–97.

    Article  Google Scholar 

  81. Waljee, A. K., Mukherjee, A., Singal, A. G., Zhang, Y., Warren, J., Balis, U., et al. (2013). Comparison of imputation methods for missing laboratory data in medicine. BMJ Open, 3, 1–7.

    Article  Google Scholar 

  82. Walker, E. T., & McCarthy, J. D. (2010). Legitimacy, strategy, and resources in the survival of community-based organizations. Social Problems, 57(3), 315–340.

    Article  Google Scholar 

  83. White, I. R., Royston, P., & Wood, A. (2011). Multiple imputation using chained equations: Issues and guidance for practice. Statistics in Medicine, 30, 377–399.

    Article  Google Scholar 

  84. Williams, R. (2009). Using heterogeneous choice models to compare logit and probit coefficients across groups. Sociological Methods & Research, 37, 531–559.

    Article  Google Scholar 

  85. Wollebaek, D. (2009). Survival in local voluntary associations. Nonprofit Management & Leadership, 19(3), 267–284.

    Article  Google Scholar 

  86. Wooldridge, J. (2012). Introductory econometrics, a modern approach. Mason, South-Western Cengage Learning.

  87. Yang, Y., & Land, K. (2013). Age-period-cohort analysis: New models, methods, and empirical applications. Boca Raton: CRC Press.

    Book  Google Scholar 

Download references

Acknowledgments

We thank the editor, two anonymous referees, and Julien Salanié for their valuable comments and insightful suggestions. We are grateful to Martin St Denis, François Laliberté-Auger, and Dario Enriquez for all the preliminary work on the Montreal social economy survey done at the Canada research chair on the social economy. This research was undertaken, in part, thanks to funding from the Canada Research Chairs program. Finally, we wish to acknowledge the support of our translator and editor Cathleen Poehler. Any remaining errors are our own.

Author information

Affiliations

Authors

Corresponding author

Correspondence to Damien Rousselière.

Appendices

Appendix 1: Robustness Test Results of Different Imputation Methods

Alternatively to the simple non-parametric imputation model used in the article, we tested a multiple imputation method with “chained equations” (multivariate imputation by chained equations—MICE) (van Buuren et al. 2006; White et al. 2011). MICE uses a “fully conditional specification” method and is based on an iterative series of univariate imputation models. Continuous variables are imputed according to the PMM methodology (Predictive Mean Matching) (Schenker and Taylor 1996). The ordered variables are imputed using an ordered logit model. An initial burn-in period of 50 iterations is necessary for the convergence of Markov chains. A total of 100 imputations were finally retained for the estimation (See Table 8).

Table 8 Results of the multiple imputations

Appendix 2: Results of Different Alternative Estimations

See Table 9.

Table 9 Results of different alternative estimations

Appendix 3: Robustness Test. What are the Differences in Survival Between Cooperatives and Associations?

In analyses of the social economy, an important distinction is often made between cooperatives and non-profit associations. Here, the focus is on differences in the practices or performance in order to see if there might be “status effects” (e.g., Quarter et al. 2001 or Leviten-Reid 2012).

The above analysis is based on the assumption of the absence of systematic differences between cooperatives and associations, in other words, the assumption that the two samples are “balanced.” This assumption has to be tested in order to allow for an interpretation of the coefficient of the legal status in the previous regressions as an ATE (Average Treatment Effect). An ATE corresponds to the thought experiment that asks what would happen if organizations changed their status.

The solution is to make a preliminary match to create two balanced samples (see Rosenbaum and Rubin (1983) and Guo and Fraser (2010) for the underlying theory). We apply the matching method proposed by Abadie and Imbens (2006, 2011) and implemented using Stata with the command teffects psmatch. As reported in Table 10, we do not observe significant differences for the survival between cooperatives and associations.

Table 10 Result of the effect of status on survival

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Bouchard, M.J., Rousselière, D. Do Hybrid Organizational Forms of the Social Economy have a Greater Chance of Surviving? An Examination of the Case of Montreal. Voluntas 27, 1894–1922 (2016). https://doi.org/10.1007/s11266-015-9664-1

Download citation

Keywords

  • Survival analysis
  • Social economy
  • Hybrid organizational forms
  • Discrete-time model
  • Hybridization