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

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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。

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.

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Notes

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

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

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

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

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

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

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

  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.

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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.

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Authors and Affiliations

  1. Department of Organization and Human Resources, Université du Québec à Montréal, CP 8888, Succursale Centre-Ville, Montréal, QC, H3C 3P8, Canada

    Marie J. Bouchard

  2. Department of Economics, Management and Society, AGROCAMPUS OUEST, 2 rue André Le Notre, 49045, Angers Cedex, France

    Damien Rousselière

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  1. Marie J. Bouchard
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  2. Damien Rousselière
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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
Full size table

Appendix 2: Results of Different Alternative Estimations

See Table 9.

Table 9 Results of different alternative estimations
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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
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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

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