‘In a Pinch, If You Have Nothing’: An Exploration of Money-Making Apps in Homeless Shelters

Gig Economy PlatformsDark Patterns RecognitionSurgical Assistance & Medical TrainingHomeless Services OrganizationsFood Delivery Riders & Ride-Hailing Drivers

Paper Title

‘In a Pinch, If You Have Nothing’: An Exploration of Money-Making Apps in Homeless Shelters

Publication Info

  • Topic area: Digital technologies and income generation among unhoused individuals.
  • Keywords: homelessness, gig economy, microtasks, low-pay apps, poverty industries, dark patterns, digital literacy, financial precarity, scams, mobile apps.

Background and Problem

  • Problem / challenge: Unhoused individuals face significant barriers to stable income, and while digital platforms offer new income opportunities, they often involve risks such as scams, low pay, and exploitative practices.
  • Significance: Understanding how unhoused individuals use digital tools for income generation can inform interventions to mitigate exploitation and improve financial outcomes for vulnerable populations.
  • Motivation and related work: Previous research has explored poverty industries and platform capitalism but has not adequately addressed how unhoused individuals in the Global North use gig and microtask apps. This study aims to fill this gap by investigating the financial ecosystems of unhoused individuals leveraging digital platforms.

Solution

  • Proposed approach: A mixed-methods study combining app analysis and interviews to explore how unhoused individuals use money-making apps and the associated risks and benefits.
  • Novelty:
    1. Identification of "Poverty Industry" (PovI) apps targeting low-income users with exploitative practices.
    2. Documentation of dark patterns and risks in low-pay apps used by unhoused individuals.
    3. Analysis of gig and microtask apps' role in the financial strategies of unhoused populations.
    4. Proposal of design principles and policy recommendations for ethical app development.
  • Procedure and key techniques:
    • Participant observation of six money-making apps to evaluate usability, earning rates, and risks.
    • Semi-structured interviews with 13 unhoused individuals across four shelters in Seattle.
    • Thematic analysis of qualitative data to identify patterns in app usage, barriers, and risks.

Results

  • Concrete findings:
    • Gig apps provided higher earnings ($88–$295/day) but were unstable and often required transportation.
    • Low-pay apps (e.g., surveys, games) yielded minimal earnings ($2.60–$30/month) and frequently involved scams, payout failures, or dark patterns.
    • 85% of participants had tried low-pay apps, with 45% reporting payout failures and 54% experiencing device theft or breakage.
  • Advantage over baselines:
    • Gig apps offered higher wages and flexibility compared to traditional jobs but lacked stability and protections.
    • Low-pay apps were accessible but often exploitative, providing minimal financial benefit relative to time invested.
  • Experiments / evaluation:
    • App testing revealed earning rates ranging from $0.002/hr (Fetch Rewards) to $4.00/hr (5 Surveys).
    • Interviews highlighted participants’ reliance on digital tools despite risks, with 75% using free government-provided phones.
  • Limitations and future work:
    • Small sample size limits generalizability.
    • Future research should explore broader populations and develop interventions to reduce exploitation in digital labor platforms.

Summary

This study examines how unhoused individuals in Seattle use gig and low-pay apps to generate income. While gig apps provide higher earnings, they are unstable and often inaccessible due to transportation barriers. Low-pay apps, including surveys and games, yield minimal financial returns and frequently involve scams or exploitative practices. The findings highlight the prevalence of dark patterns in these apps and propose design principles to mitigate harm. Future work should focus on ethical app development and leveraging digital tools for social good among vulnerable populations.

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https://hci.top/en/papers/chi/222379/2026

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DOI: https://doi.org/10.1145/3772318.3791541
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Source
CHI
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Year
2026
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Authors
6 authors
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Subtopics
Gig Economy Platforms, Dark Patterns Recognition, Surgical Assistance & Medical Training
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Professions
Homeless Services Organizations, Food Delivery Riders & Ride-Hailing Drivers
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