Live·50 states tracked·190+ countries covered·Updated weekly
The Premise
Most financial hardship doesn't arrive as a single event. It compounds — quietly, across years — through housing pressure, medical bills, and debt that never quite closes. We study the signals before the crisis.
A moment worth saving — pinned to our research wall.
Low (<90)
Moderate (90-120)
Elevated (120-150)
High (>150)
Figure 1Financial Health Barometer composite index, by U.S. state. Hover a state for its current reading; switch indicators above. Source: FinMango Research, 2026.
We identify the systemic barriers endangering young adults' financial health — housing, healthcare, debt, predatory lending. We diagnose root causes. We provide the evidence decision-makers need to build solutions that actually work.
01
Identify What Others Miss
Market failures. Policy gaps. Structural inequalities. Finding them before they become crises.
02
Diagnose Root Causes
Economic theory. Big data. Google Health Trends. Understanding WHY systems fail young adults.
03
Radically Transparent
All research on GitHub. Every methodology. Every insight. Every limitation. Total transparency.
04
Empower Decisions
WHO. World Bank. IMF. Policymakers worldwide. We provide evidence. They build solutions.
A research agenda is really a list of honest questions. These are ours — the quiet ones, the inconvenient ones, the ones the data keeps hinting at.
?
Can search behavior predict an eviction before it's filed in court?
Housing · Mental Health
?
Does one medical event really erase a generation of savings — and for whom?
Medical Debt · Equity
?
How do community banks absorb the financial aftershock of a tornado?
Climate · Resilience
?
What does AI adoption in banking mean for the people behind the teller window?
AI · Labor Markets
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Where does financial literacy change behavior — and where is it just theater?
Literacy · Behavior
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When does student debt stop compounding disadvantage, if ever?
Student Debt · Mobility
§
§ 02partnerships
Google Collaborations
Google Health Trends API Active
We employ the Google Health Trends API to access actual conditional probability values, providing greater mathematical rigor and cross-term comparability not possible with traditional 0-100 RSV scales.
COVID-19 Open Data Project Archived
A comprehensive meta-dataset containing epidemiological information from 22,579 unique locations within 190+ countries and territories. Recognized by Google.org during the pandemic.
We leverage data-driven insights, predictive analytics, and economic theory to tackle the most pressing challenges facing financial health today.
01
Disaster Relief
Leveraging real-time data and predictive analytics to develop solutions helping communities prepare for, survive, and recover from disasters.
02
Mental Health
Investigating how psychological factors influence economic decision-making and how financial circumstances impact mental health outcomes.
03
Housing
Addressing the housing affordability crisis where home prices have far outpaced income growth, threatening financial stability.
04
Climate & Financial Resilience
Investigating how climate disasters and environmental shifts threaten the financial stability of vulnerable communities, and building data tools for resilience.
05
Artificial Intelligence
Leveraging AI and machine learning to develop more equitable financial systems and predict economic vulnerabilities.
06
Student Debt
Investigating how student loan debt impacts young adults' financial health, economic mobility, and long-term wealth building.
Most studies using Google Trends rely on normalized indices. We use conditional probabilities — an approach yielding greater mathematical rigor, and genuine cross-term comparability.
FinMango Technical Whitepaper, v2.4
Referenced & cited by
World Health OrganizationWorld BankInt'l Monetary FundGoogle HealthNature
§ 04publications
Published Research
2025Technical Whitepaper · v2.4Open Source
Financial Health Barometer: 2025 Methodology & Data Architecture
The whitepaper documents the data pipeline behind the Financial Health Barometer — a composite index combining Google Health Trends conditional probabilities, BLS and Census administrative data, and state-level housing and credit signals. We detail indicator construction, normalization choices, and the reasoning behind four composite tiers (Low → High).
2023Am. J. of Health Education7 authorsPeer-Reviewed
The Mango Model: Best Practices in the Creation of a COVID-19 Open Data Project Through a Partnership with Google Health and the Non-Profit FinMango
Oscar Wahltinez, Scott Glasgow, Aurora Cheung, James F. Glasgow, Martin Noguera, James W. Glasgow & Pamela Neidert Hoalt
A practitioner's account of how a nonprofit-tech partnership can produce globally useful public-health data. We describe the decisions, missteps, and governance structures behind a dataset that ultimately spanned 22,579 locations across 190+ countries — and argue that the partnership model, not the tooling, is what made it scale.
2022Nature · Scientific Data12 authorsOpen Access
COVID-19 Open-Data: A global-scale spatially granular meta-dataset for coronavirus disease
Oscar Wahltinez, Aurora Cheung, Ruth Alcantara, Donny Cheung, Mayank Daswani, Anthony Erlinger, Matt Lee, Pranali Yawalkar, Paula Lê, Ofir Picazo Navarro, Michael P. Brenner & Kevin Murphy
A harmonized, spatially granular meta-dataset covering epidemiology, hospitalization, mobility, policy, and demographic data across 190+ countries — updated daily throughout the pandemic. The paper documents the schema, sourcing strategy, and reconciliation logic used to merge thousands of heterogeneous government feeds into a single, queryable resource.
§ 04.1in progress
Working Papers
2026Housing · Mental HealthIn Progress
Predictive Signals: Do Mental Health-Related Google Searches Reflect Eviction Trends in Real Estate?
with Eren Çifi, PhD — Austin Peay State University
2026AI · Labor MarketsIn Progress
AI Adoption in Banking: Efficiency Gains and Employment Trade-offs
with Eren Çifi, PhD — Austin Peay State University
2026Climate · Community BankingIn Progress
Tornadoes and Financial Resilience: Assessing the Effects of Natural Disasters on Community Banks
with Eren Çifi, PhD — Austin Peay State University
2026ESG · Market BehaviorIn Progress
Sustainable Investment Search Behavior as a Predictor of Market Activity
with Madhavi Venkatesan, PhD — Northeastern University
2026Financial LiteracyIn Progress
Comparative Analysis of Financial Literacy Search Patterns
with John Longo, PhD — Rutgers University, and Danny Jang — Financial Futures
Our team brings experience from Google, Microsoft, Bloomberg News, and Wall Street. We combine technical expertise with academic rigor to tackle complex financial challenges.
§ 05.1academic partners
Collaborators
Eren Çifi, PhD
Austin Peay State University
Madhavi Venkatesan, PhD
Northeastern University
Chen Zhang, PhD
Iowa State University
John Longo, PhD
Rutgers University
Christos Makridis, PhD
Arizona State / Stanford
§ 06methodology
Our Data Approach
Most studies using Google Trends rely on normalized Relative Search Volume (RSV) indices. Our unique approach employs the Google Health Trends API to access actual conditional probability values.
This approach yields:
Greater mathematical rigor, avoiding distortions introduced by normalization
More robust statistical analysis due to interpretable, probabilistically grounded metrics
Cross-term and cross-time comparability
Deeper insights into search behavior and more precise linkages
