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Governance Structures and the Promotion of the Public Interest: Evidence from Brazilian Resource-Scarce Communities
Last registered on July 16, 2020


Trial Information
General Information
Governance Structures and the Promotion of the Public Interest: Evidence from Brazilian Resource-Scarce Communities
Initial registration date
March 07, 2018
Last updated
July 16, 2020 11:11 PM EDT

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Primary Investigator
University of Toronto
Other Primary Investigator(s)
PI Affiliation
University of Toronto
Additional Trial Information
In development
Start date
End date
Secondary IDs
The goal of this research is to examine the performance of public, private (NGO) and hybrid (public-private) governance (Mahoney, McGahan, & Pitelis, 2009; McGahan, Zelner, & Barney, 2013; Quélin, Kivleniece, & Lazzarini, 2017) in a resource-scarce environment (Baker & Nelson, 2005; George, McGahan, & Prabhu, 2012). Therefore, this research aims to develop an understanding of how different governance structures deploy resources to promote the public interest by evaluating a job training program targeting residents of Brazilian favelas. The research objective is to analyze alternative forms of commissioning resources under public versus private versus hybrid governance mode. Specifically, we analyze how governance structure influences both the fulfillment of the public interest and the distribution of value among stakeholders engaged in the deployment. Our setting allows a comparison of how much a non-profit organization can foster social prosperity – measured by the increased level of employment (formal and informal), income, the confidence level of individuals, the optimism level of individuals, among other outcomes. The approach is to compare these outcomes between individuals who undertook the training and those who did not. Also, the research design enables us to understand whether the training is more effective when performed through the public organization, the NGO, or through a hybrid form.
Baker, T., & Nelson, R. E. (2005). Creating Something from Nothing: Resource Construction through Entrepreneurial Bricolage. Administrative Science Quarterly, 50(3), 329–366.
George, G., McGahan, A. M., & Prabhu, J. (2012). Innovation for Inclusive Growth: Towards a Theoretical Framework and a Research Agenda. Journal of Management Studies, 49(4), 661–683. http://doi.org/10.1111/j.1467-6486.2012.01048.x
Mahoney, J. T., McGahan, A. M., & Pitelis, C. N. (2009). The interdependence of private and public interests. Organization Science, 20(6), 1034–1052. http://doi.org/10.1287/orsc.1090.0472
McGahan, A. M., Zelner, B. A., & Barney, J. B. (2013). Entrepreneurship in the Public Interest: Introduction to the Special Issue. Strategic Entrepreneurship Journal, 7(1), 1–5.
Quélin, B. V., Kivleniece, I., & Lazzarini, S. (2017). Public-Private Collaboration, Hybridity and Social Value: Towards New Theoretical Perspectives. Journal of Management Studies, 54(6), 763–792. http://doi.org/10.1111/joms.12274

External Link(s)
Registration Citation
McGahan, Anita and Leandro Pongeluppe. 2020. "Governance Structures and the Promotion of the Public Interest: Evidence from Brazilian Resource-Scarce Communities." AEA RCT Registry. July 16. https://doi.org/10.1257/rct.2765-3.3000000000000003.
Former Citation
McGahan, Anita, Leandro Pongeluppe and Leandro Pongeluppe. 2020. "Governance Structures and the Promotion of the Public Interest: Evidence from Brazilian Resource-Scarce Communities." AEA RCT Registry. July 16. http://www.socialscienceregistry.org/trials/2765/history/72635.
Experimental Details
Intervention Start Date
Intervention End Date
Primary Outcomes
Primary Outcomes (end points)
Primary outcome variables include: formal employment, informal employment, wages (income) level, perceived confidence level, perceived optimism level, perceived self-stereotyping level.
Primary Outcomes (explanation)
Primary outcome explanation: Other than Labor Market Outcomes, an Instrument (questionnaire) will measure the perceived confidence, optimism, and stereotype of individuals. The instrument is based on the literature of behavioral economics and social-psychology (Chen, Gully, & Eden, 2001; John & Srivastava, 1999). Finally, the instrument is composed of 23 questions plus a “Ten Item Personality Inventory” questionnaire.
Chen, G., Gully, S. M., & Eden, D. (2001). Validation of a New General Self-Efficacy Scale. Organizational Research Methods, 4(1), 62–83. http://doi.org/10.1177/109442810141004
John, O. P., & Srivastava, S. (1999). Big Five Inventory (Bfi). Handbook of Personality: Theory and Research, 2, 102–138. http://doi.org/10.1525/fq.1998.51.4.04a00260
Secondary Outcomes
Secondary Outcomes (end points)
Secondary Outcomes (explanation)
Experimental Design
Experimental Design
The NGO will recruit participants from various favelas in Rio de Janeiro city (Brazil) to perform the training program in 2018. The research design is the following:
“Hybrid Mode” (Public and Private Organizations in partnership):
The NGO will recruit individuals interested in participating in the training program with the support of two public social service agencies. Among those interested in the training program, the NGO will select approximately 280 individuals, who will be “candidates for a spot” in the program. These candidates will provide some basic information on socioeconomic dimensions. A stratified randomized assignment (Bruhn & McKenzie, 2009) will follow, i.e., individuals will be stratified considering four elements: i) their age (median age cutoff), ii) their income level (only income source is the Brazilian social security, "Bolsa Familia", cutoff), iii) their social service agency (CRAS) filiation, and iv) the period they want to perform the training (morning or afternoon). Then, within each strata, the randomization will occur. Therefore, "half" of the members of the strata will be allocated to the NGO treatment (training program) and the other "half" to the control. The intervention will then follow. This procedure is the most adequate for the research objective given the sample size limitation (Bruhn & McKenzie, 2009).
“Pure Private Mode” (only NGO)
The NGO will also recruit, at least, additional 280 individuals from other favelas, in at least four different locations. These individuals will also receive the training treatment. However, the selection of these individuals will be performed through a self-selection process, i.e., the individuals will be enrolled in the program up to the point that there are no more spots available, based on a “first-in-first-serve” mechanism. Therefore, by the end of the intervention, the researchers will match each individual from the “Pure Private Mode” with one of the pairs in the “Hybrid Mode” and check whether there is any difference among the distinct modes of governance. Note that in this “Pure Private Mode”, the allocation is not random. Thus possible selection bias exists.
Bruhn, M., & McKenzie, D. (2009). In Pursuit of Balance: Randomisation in Practice in Development Field Experiments. American Economic Journal: Applied Economics, 1(4), 200–232. http://doi.org/10.1257/app.1.4.200
Experimental Design Details
Not available
Randomization Method
The stratified randomization will be performed on a computer, on March 16th. The NGO will be informed about the result right away. The intervention will then start on March 19th.
Randomization Unit
Individual level randomization.
Was the treatment clustered?
Experiment Characteristics
Sample size: planned number of clusters
N/A (clustering will not be used)
Sample size: planned number of observations
Up to 280 individuals in the “Hybrid Mode” (RCT), considering 140 treated and 140 control. At least additional 280 individuals in the “Pure Private Mode” (non-randomly selected), will be compared.
Sample size (or number of clusters) by treatment arms
"Hybrid Mode" (RCT)
Up to 140 treated individuals, and up to 140 control individuals.

"Pure Private Mode" (Non-randomized)
Up to 280 treated individuals
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
As our sample size of 280 individuals is fixed (given the NGO’s fixed number of spots), power calculations show that we will be able to detect from 0.15 to 0.25 standard deviations in the best and worst case scenarios, respectively, when controlling for other covariates. The power calculations were performed using the software Optimal Design Plus (Raudenbush, Spybrook, et al., 2011) References Raudenbush, S.W. Spybrook, J., Congdon, R., Liu, X., Martinez, A., Bloom, H., & Hill, C. (2011). Optimal Design Plus Empirical Evidence.
IRB Name
University of Toronto Research Ethics Board
IRB Approval Date
IRB Approval Number
Analysis Plan

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