Quantifying Online Gambling Financial Harm
Budget: €250 – €750 EUR
The assignment is to produce a standalone, publish-ready academic research paper that quantifies how online sports gambling affects personal finances — with a focus on societal harm that is both mathematically rigorous and accessible to a general audience. The target outlet is a journal in applied economics, public health economics, or quantitative finance.
**Scope of the work**
- Build a mathematical model centred on Monte Carlo simulation to capture the stochastic nature of betting outcomes and cash-flow volatility across a realistic population of bettor profiles.
- Support the simulation with two sub-models: (1) a disposable income model drawing on household income and expenditure data, and (2) an expected loss model incorporating betting frequency, stake sizing, and house edge. These feed the core impact analysis.
- Calibrate the model using publicly available financial records — including household income surveys (e.g. US Bureau of Labor Statistics Consumer Expenditure Survey, UK ONS), debt statistics, and regulatory reports from the UK Gambling Commission and American Gaming Association. No proprietary gambling-platform data required.
- Perform a full statistical treatment: descriptive summaries of gambling behaviour by demographic, inferential testing to assess significance of loss patterns across groups, and predictive analytics showing long-term wealth trajectories for different bettor profiles.
**Narrative & structure**
Open with a concise literature review that situates the paper within existing harm-minimisation research and identifies gaps. Then present the model, assumptions, and simulation design, followed by results and policy implications. Emphasise effect sizes and relatable metrics throughout — for example, "expected months of disposable income lost per year of regular betting" or "probability of net negative savings after five years" — so findings are meaningful to non-technical readers as well as academics.
**Originality & ethics**
All writing and any code must be entirely original and properly cited. Plagiarism screening will be run before acceptance.
**Deliverables**
- Full manuscript in Word or PDF, formatted to standard academic conventions (abstract, introduction, methods, results, discussion, conclusion, references)
- Supplementary appendix containing key equations, parameter tables, and simulation code or pseudocode
- One-page plain-English summary distilling the key findings for a lay audience (optional but appreciated)
**Acceptance criteria**
1. Core model uses Monte Carlo simulation calibrated to publicly sourced data only
2. Includes descriptive, inferential, and predictive statistical sections
3. Provides clear, quantitative harm metrics tied to personal financial outcomes (disposable income, savings, debt risk)
4. Literature review identifies a genuine gap the paper addresses
5. Written at graduate-level English and passes originality screening
Please share at least one relevant research sample when applying so I can assess style, depth, and familiarity with quantitative modelling.
**Scope of the work**
- Build a mathematical model centred on Monte Carlo simulation to capture the stochastic nature of betting outcomes and cash-flow volatility across a realistic population of bettor profiles.
- Support the simulation with two sub-models: (1) a disposable income model drawing on household income and expenditure data, and (2) an expected loss model incorporating betting frequency, stake sizing, and house edge. These feed the core impact analysis.
- Calibrate the model using publicly available financial records — including household income surveys (e.g. US Bureau of Labor Statistics Consumer Expenditure Survey, UK ONS), debt statistics, and regulatory reports from the UK Gambling Commission and American Gaming Association. No proprietary gambling-platform data required.
- Perform a full statistical treatment: descriptive summaries of gambling behaviour by demographic, inferential testing to assess significance of loss patterns across groups, and predictive analytics showing long-term wealth trajectories for different bettor profiles.
**Narrative & structure**
Open with a concise literature review that situates the paper within existing harm-minimisation research and identifies gaps. Then present the model, assumptions, and simulation design, followed by results and policy implications. Emphasise effect sizes and relatable metrics throughout — for example, "expected months of disposable income lost per year of regular betting" or "probability of net negative savings after five years" — so findings are meaningful to non-technical readers as well as academics.
**Originality & ethics**
All writing and any code must be entirely original and properly cited. Plagiarism screening will be run before acceptance.
**Deliverables**
- Full manuscript in Word or PDF, formatted to standard academic conventions (abstract, introduction, methods, results, discussion, conclusion, references)
- Supplementary appendix containing key equations, parameter tables, and simulation code or pseudocode
- One-page plain-English summary distilling the key findings for a lay audience (optional but appreciated)
**Acceptance criteria**
1. Core model uses Monte Carlo simulation calibrated to publicly sourced data only
2. Includes descriptive, inferential, and predictive statistical sections
3. Provides clear, quantitative harm metrics tied to personal financial outcomes (disposable income, savings, debt risk)
4. Literature review identifies a genuine gap the paper addresses
5. Written at graduate-level English and passes originality screening
Please share at least one relevant research sample when applying so I can assess style, depth, and familiarity with quantitative modelling.