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Social Capital and Inequality in Immigrant Entrepreneurship: Pathways and Barriers

2025· article· en· W4416001624 on OpenAlexaffabout
Inara Tareque, Exequiel Hernández, Chris Rider, Nada Basir, Astrid Marinoni, Sandra Portocarrero

Bibliographic record

VenueAcademy of Management Proceedings · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicMigration, Ethnicity, and Economy
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsSocial capitalEntrepreneurshipImmigrationFace (sociological concept)Inclusion (mineral)Context (archaeology)InequalitySocial mobility

Abstract

fetched live from OpenAlex

Immigrant entrepreneurs who belong to marginalized populations face significant financial, social, cultural, and legal barriers (Hernandez, 2024). While founding and sustaining a new business is not an equal experience for all (Guzman & Kacperczyk, 2019), entrepreneurship offers marginalized people a pathway to greater economic inclusion and social mobility (Min & Bozorgmehr, 2003; Hwang & Phillips, 2023; Rider et al., 2023). Furthermore, despite the risks associated with new enterprises, immigrants are more likely than their native-born counterparts to become entrepreneurs (Kerr & Kerr, 2020). Considering this evidence, entrepreneurship has the potential to offer marginalized immigrants a pathway to economic inclusion and social mobility. As organizational scholars and members of an unequal society with growing anti-immigrant sentiment, it is crucial to investigate the mechanisms that could reduce barriers to entrepreneurial entry and growth for marginalized immigrants. The literature on social capital identifies it as a powerful resource facilitating entrepreneurial success (Burt, 1992; Lin et al., 2001; Adler and Kwon, 2002; Samila & Sorenson, 2017; Portes & Sensenbrenner, 1993). However, in the context of immigrant entrepreneurship, the role of social capital is far from straightforward.. Immigrants, being foreign to the host country, often lack access to the social networks that facilitate entrepreneurial entry, especially in the absence of resource-rich ethnic enclaves (Portes & Stepick, 1985). Even when such networks exist, cultural norms or an overreliance on insular perspectives within these enclaves can limit entrepreneurial ambition and growth (Portes, 2014). Finally, first-order barriers such as marginalized identities may further constrain immigrants’ ability to cultivate resourceful social ties. This symposium tackles such intricacies in the literature to advance our understanding of social capital and inequality in the context of immigrant entrepreneurship. It will feature research that explores how social capital shapes inequities in immigrant entrepreneurship and examines interventions to mitigate these disparities. Key questions addressed include: a) Can social capital offset financial inequities that hinder entrepreneurial entry? b) How do multiple overlapping identities of people influence their engagement with entrepreneurial ecosystems and networks? c) What interventions, such as macro policy changes or digital tools, can reduce the social network-driven inequities faced by immigrant entrepreneurs? The Impact of Financial Constraints on Entrepreneurship: The Moderating Role Of Social Capital Author: Inara Tareque; Columbia Business School Navigating Identity Networks in Entrepreneurial Ecosystems Author: Nada Basir; University of Waterloo Author: Bessma Momani; University of Waterloo Author: Melissa Finn; University of Waterloo Author: Leslie Nichols; Wilfrid Laurier University The Entrepreneurial Dynamics of Trade Liberalization: Immigrants as Agents of Change Author: Ashlee Li; Author: Astrid Marinoni; Georgia Institute of Technology A Digital Refuge: How WhatsApp Offers Stability Amidst Mobility to NYC Asylum Seekers Author: Sandra Portocarrero; The London School of Economics & Political Science Author: Rohini Jalan; McGill University

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.478
Threshold uncertainty score0.401

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.025
GPT teacher head0.293
Teacher spread0.269 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations1
Published2025
Admission routes2
Has abstractyes

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