ABLE Accounts and Research Design: Measuring the Policy Impact of Expanded Eligibility

Hook: Why this matters now — and why it's hard

Researchers studying disability policy face familiar pain points: limited access to paywalled administrative files, tangled data linkages, uncertainty about causal identification, and the ethical responsibility to protect SSI and Medicaid beneficiaries. The 2026 federal expansion of ABLE accounts eligibility to include those with disability onset through age 46 presents a rare, high-impact natural experiment — but exploiting it well requires careful design, strong privacy protections, and reproducible workflows. This primer gives you a practical, step-by-step blueprint for measuring policy impact, selecting outcomes, constructing identification strategies, and handling sensitive administrative data in 2026.

Executive summary (inverted pyramid)

• Most important: The 2026 ABLE expansion creates quasi-experimental leverage if you can define who became newly eligible and when. Use eligibility rules and rollout timing to build contrasts between affected and unaffected cohorts.
• Key outcomes: ABLE take-up, account balances and contributions, SSI/Medicaid retention, employment, healthcare utilization, medical debt, and institutionalization.
• Identification strategies: fuzzy regression discontinuity on disability-onset age, difference-in-differences exploiting state-level take-up heterogeneity and outreach timing, IVs using eligibility, and synthetic control for aggregate fiscal outcomes.
• Data sources: ABLE program administrative records, SSA SSI/SSDI files, CMS T-MSIS/VRDC Medicaid data, surveys (SIPP, NHIS, ACS), and fintech/aggregate bank datasets available through new data collaboratives in 2025–2026.
• Privacy: Follow DUAs, IRB approvals, use secure enclaves, privacy-preserving record linkage (PPRL), cell-size suppression, differential privacy where possible, and release synthetic replicates for reproducibility.

The policy and the research opportunity in 2026

In late 2025 and early 2026, federal legislation expanded ABLE account eligibility to those with disability onset through age 46, increasing the eligible population by an estimated 14 million Americans. That expansion widens access to tax-advantaged savings without risking SSI/Medicaid, making it plausible that affected individuals change their financial and service-use behavior. From a research perspective, this is valuable because it introduces exogenous variation in eligibility that can be translated into causal estimates if you carefully define treated and control groups and account for take-up.

Step 1 — Define the estimand and policy-relevant outcomes

Start with a crisp causal question and primary estimand. Examples:

• Intent-to-treat (ITT): What is the effect of being newly eligible for ABLE accounts on SSI receipt and Medicaid enrollment rates after 2 years?
• Local average treatment effect (LATE): Among compliers, what is the effect of opening an ABLE account on financial stability (medical debt, savings) and employment?
• Spillovers: Does increased ABLE availability affect state Medicaid expenditures or institutionalization rates?

Recommended outcome categories (with operational measures):

• Program outcomes: ABLE account opening (binary), number of accounts per 1,000 eligible, contributions and balance (continuous), withdrawals for qualified expenses.
• Benefits receipt: SSI and Medicaid enrollment/exit, application denials, reassessments, and overpayment recoveries.
• Economic outcomes: Earned income, employment spells, bank account ownership, medical debt, bankruptcy filings (where available).
• Health and service use: Emergency department visits, inpatient admissions, long-term services and supports (LTSS) utilization.
• Equity indicators: Treatment effects by race/ethnicity, rurality, disability type, and socioeconomic status.

Step 2 — Identification strategies: choose one (or a mix)

Policy evaluation is stronger when you combine complementary strategies and present evidence from multiple angles. Below are practical designs tailored to the 2026 expansion.

Fuzzy regression discontinuity (RDD) on disability-onset age

Rationale: ABLE eligibility depends on disability onset before a cutoff age (extended to 46). If onset age is measured and not manipulable at the threshold, you can use RDD to estimate local causal effects.

• Design: Running variable = age at disability onset. Cutoff = 46. If documentation shows imperfect compliance (not everyone with onset ≤46 opens an ABLE), use a fuzzy RDD with eligibility as instrument for ABLE take-up.
• Assumptions & diagnostics: Test continuity in pre-treatment covariates around the cutoff, density tests (McCrary), bandwidth sensitivity, and placebo cutoffs (e.g., 40, 42).
• Data needs: Accurate onset age (from SSA disability filing, medical records, or survey recall). Misreporting in surveys biases RDD; administrative onset date is preferable.

Difference-in-differences (DiD) exploiting cross-state uptake and outreach

Rationale: Federal policy changed eligibility, but states differ in ABLE program marketing, enrollment procedures, and outreach timing. Use those differences as treatment intensity.

• Design: Compare outcomes in states with high-exposure (rapid outreach, fee waivers) vs low-exposure states before and after 2026. Consider event-study plots to check parallel trends and use Sun & Abraham (staggered DiD correction) if rollout is staggered.
• Assumptions & diagnostics: Parallel trends check, pre-trends, control for time-varying state-level policies (e.g., Medicaid expansions).
• Data needs: State-level ABLE enrollment data by month, state-level outreach dates, and county-level Medicaid/SSI administrative records or T-MSIS aggregates.

Instrumental variables (IV) using eligibility or outreach intensity

Rationale: Use exogenous variation in eligibility (birth cohorts or onset-age eligibility) as an instrument for account ownership to estimate LATE.

• Design: First stage — eligible indicator predicts ABLE ownership. Second stage — predicted ownership effects on outcomes.
• Assumptions & diagnostics: Exclusion restriction concerns (eligibility must affect outcomes only via ABLE ownership). Test for weak instruments.

Synthetic control and aggregate fiscal outcomes

Rationale: For state-level fiscal outcomes (e.g., Medicaid expenditures), build a synthetic control to compare treated states (with large uptake) to weighted combinations of controls.

• Design: Create synthetic counterfactual for each treated state using pre-2026 predictors.
• Assumptions & diagnostics: Good pre-period fit, placebo inference, sensitivity to donor pool.

Step 3 — Data sources and linkage strategies

Combining administrative ABLE records with SSI/Medicaid files produces the most credible estimates—but it also triggers the strictest privacy controls. Below is a prioritized toolkit of data sources and practical notes for 2026.

High-priority administrative sources

• State ABLE program registers: Enrollment date, account balance, contributions, distributions, account holder demographics. States typically operate ABLE programs and many provide microdata under DUAs.
• Social Security Administration (SSA): SSI/SSDI receipt, disability onset dates (date of entitlement), demographic characteristics. Access via SSA restricted data centers; DUAs required.
• CMS T-MSIS / VRDC: Medicaid enrollment, claims, service utilization. T-MSIS data quality improved in 2025, and VRDC provides secure remote analysis in several centers as of 2026.
• State Medicaid agencies: For granular LTSS and waiver program data, often available under state DUAs.

Survey and commercial sources to supplement

• SIPP (Survey of Income and Program Participation): Rich income and program participation, though onset age may be self-reported. Useful for correlational analyses and heterogeneous effects.
• NHIS / ACS: Disability indicators and socioeconomic covariates; useful for external validity checks.
• Financial transaction/fintech aggregates: Aggregators and bank data partnerships became more common in 2025–2026. Useful for near-real-time measures of savings and debts when linked appropriately.

Linkage: practical, privacy-preserving approaches

Linking across datasets is the hardest operational step. Use the following techniques in 2026:

• Secure data enclaves / remote analysis environments: Perform linkages and analyses in agency or university secure enclaves (e.g., SSA, CMS, or state secure research environments). Be mindful of cross-border rules like EU data residency rules when partnering with institutions subject to different data residency constraints.
• Privacy-preserving record linkage (PPRL): Use Bloom filters or cryptographic hashing (HMAC) and match on stable identifiers (SSN when permitted, birthdate, name tokens). PPRL allows matching without revealing PII across parties.
• Honest-broker models: A trusted intermediary performs deterministic matching and returns de-identified linked data to analysts under DUA constraints. Operational checklists for consent and measurement impact can help here—see an operational playbook for consent impact.
• Synthetic public-use files: Generate synthetic datasets that mirror the joint distribution using differential privacy or generative models; release these for reproducibility while keeping real data in the enclave. For building internal pipelines that produce synthetic outputs and developer-friendly artifacts, developer workflows described in internal developer tooling can be helpful.

Step 4 — Protecting SSI/Medicaid research subjects (legal and technical checklist)

SSI and Medicaid recipients are protected classes under many DUAs. Your project must minimize disclosure risk and comply with federal rules.

1. Obtain IRB approval with a detailed data protection plan and justification for use of identifiable data where necessary.
2. Execute required DUAs with SSA and CMS; these specify allowed outputs, cell-size suppression rules (commonly no cells < 11), and publication vetting processes.
3. Adopt the HIPAA de-identification safe harbor or, preferably, an expert-determined de-identification standard if working outside HIPAA. Document the chosen standard.
4. Keep direct identifiers and keys in separate, encrypted repositories. Limit access to named, trained staff only.
5. Use differential privacy or noise injection for public releases when feasible; otherwise release only aggregated tables with suppressed small cells.
6. Maintain an audit log and data destruction plan post-project as required by the DUAs.

Best practice: do the sensitive linking and analysis inside the secure enclave, and release only synthetic data and analysis code publicly.

Step 5 — Pre-analysis, power, and robustness

Before you request restricted files, pre-register your analysis plan. Compute statistical power for your primary estimand under plausible take-up rates. Practical pointers:

• Estimate anticipated first-stage (eligibility → ABLE take-up) using state ABLE program pilot data or 2025 pilot rollouts. Indicative take-up matters: a weak first-stage undercuts IV and fuzzy RDD power.
• Run placebo checks: false policy dates, non-eligibility cutoffs, and outcomes that should be unaffected (e.g., Medicare outcomes for under-65 cohorts).
• Report multiple specifications and use robust standard errors clustered at relevant levels (individual, county, or state).
• For staggered rollouts, use event-study approaches corrected for heterogeneous treatment timing (Sun & Abraham, 2021-style estimators).

Step 6 — Reproducibility and transparent code sharing

Reproducible research in 2026 requires more than posting scripts: you must enable external validation while protecting data. Recommended workflow:

1. Pre-register the protocol on AEA RCT Registry or OSF.
2. Version-control analysis code with Git, and expose a public repository containing data generation scripts, model code, and a clear README describing DUAs and data access steps. For teams wrestling with tool sprawl while building reproducible pipelines, a tool sprawl audit can be useful.
3. Produce and share synthetic datasets and a detailed codebook that reproduces all tables/figures using synthetic data; hold real-data outputs in the secure enclave subject to DUA review.
4. Containerize the environment (Docker) so reviewers can run code on synthetic data with matching software versions and to simplify enclave-to-local reproducibility.
5. Publish an appendix with falsification tests, sensitivity analyses (e.g., alternative bandwidths in RDD), and explanation of any analytic choices mandated by DUAs.

Practical example: an analysis blueprint

Below is a condensed analysis plan you could adapt.

1. Research question: Does ABLE eligibility at age 46 reduce the probability of SSI termination over 24 months?
2. Design: Fuzzy RDD using disability-onset age (≤46 eligible). Instrument: eligibility indicator. Outcome: SSI status at 24 months. Covariates: age, sex, race, pre-period earnings, state fixed effects. Cluster SEs at state level.
3. Data: SSA disability onset dates and SSI records linked to state ABLE enrollment data via secure honest broker. When negotiating DUAs, consider modern e-signature workflows described in e-signature evolution writeups to speed approvals.
4. Pre-analysis: McCrary test, covariate balance within narrow bandwidths, power calculation assuming 10% take-up and baseline SSI termination rate of 5%.
5. Robustness: placebo cutoffs at 40 and 50, bandwidth sensitivity, and an IV DiD using state outreach intensity.
6. Privacy: run all steps inside SSA secure enclave, output aggregated tables meeting cell suppression rules, and publish synthetic replication files.

Common pitfalls and how to avoid them

• Mis-measured onset age: Use administrative onset dates rather than survey recall when possible. If relying on survey recall, quantify measurement error and use validation sub-studies.
• Assuming instantaneous effects: ABLE impacts may accumulate. Use multiple horizons and event-study designs to capture dynamics.
• Ignoring heterogeneity: Effects may be concentrated among specific disability types or income strata. Pre-specify subgroup analyses but beware multiplicity.
• Disclosure risk from rich tables: Many micro-aggregates (cross-tabulations by county, age, race) increase re-identification risk. Default to coarser aggregations or synthetic data for public release.

2026 trends and future directions

As of 2026, three developments make ABLE evaluation more feasible and policy-relevant:

• Improved data linkage capacity via state–federal data collaboratives, with standardized DUAs that shorten lead times compared with 2020–2024.
• Wider availability of privacy-preserving tools (PPRL libraries, open-source differential privacy toolkits) that let researchers publish richer replication materials without disclosures.
• Growing adoption of synthetic data as a standard deliverable from secure enclaves, enabling better external validation of methods. Teams building these pipelines should consider carbon and compute impacts; guidance on carbon-aware caching can help make reproducibility less resource-intensive.

Actionable checklist for research teams (quick start)

1. Articulate a precise causal question and estimand.
2. Map data needs: list variables and identify agencies holding them (SSA, CMS, state ABLE programs).
3. Pre-register analysis and secure IRB sign-off with a data protection plan.
4. Contact data stewards early to negotiate DUAs and enclave access (allow 3–6 months).
5. Create a reproducibility plan: containerize, publish synthetic data and code, and record provenance.
6. Run diagnostic and placebo checks and prepare an output vetting checklist aligned with DUA suppression rules.

Conclusion and call-to-action

The 2026 expansion of ABLE accounts to age 46 provides a unique opportunity to evaluate a major, equity-focused financial policy for people with disabilities. Doing so credibly requires a careful mix of identification strategies, high-quality administrative linkages, and robust privacy protections. As a next step, preregister your design, reach out to state ABLE program administrators and SSA/CMS data stewards, and build a reproducible pipeline that places privacy and transparency at the center. Share synthetic replicates and analysis scripts publicly so policymakers, practitioners, and fellow researchers can reproduce and build on your work.

Ready to start? If you’re designing an ABLE evaluation, draft your estimand and data inventory now. Contact your institution’s IRB and data governance office, and join the 2026 ABLE research collaborative to access DUA templates, PPRL toolkits, and exemplar code. Together we can produce rigorous, reproducible evidence that informs policy for millions of Americans.

Related Reading

• Beyond Banners: An Operational Playbook for Measuring Consent Impact in 2026
• The Evolution of E‑Signatures in 2026: From Clickwrap to Contextual Consent
• Edge Auditability & Decision Planes: An Operational Playbook for Cloud Teams in 2026
• News Brief: EU Data Residency Rules and What Cloud Teams Must Change in 2026
• Edge‑First Developer Experience in 2026: Shipping Interactive Apps with Composer Patterns and Cost‑Aware Observability
• Multi-Week Battery Smartwatches for Long Trips: Which Models Keep Up?
• The Daily Grind: What Baseball Creators Can Learn from Beeple's Streak to Build a Loyal Audience
• De-escalation on the Road: Two Calm Responses to Avoid Defensive Drivers and Road Rage
• Build-A-Banner Family Kits: Create Your Own 'Final Battle' Flag Moment
• How AI-Enabled Smoke Detectors Should Change Your Home Ventilation Strategy