As the Saver’s Match Program must soon transition from legislative concept to operational reality, one question looms larger than any other:
How will the Program operate at scale, in the real world, across tens of millions of people annually, thousands of institutions, and billions of federal dollars?
That question sits at the heart of the Saver’s Match Simulation (SMS), a ten-year, discrete-event simulation developed by Retirement Clearinghouse (RCH) to evaluate not just the policy, but the infrastructure required to deliver federal matching contributions accurately and efficiently. The simulation is notable not simply for its scope – more than 700 million modeled matching events – but for the rigor with which it defines a specific operational solution and tests it under a range of behavioral and adoption scenarios.
The results point to a clear conclusion: the Saver’s Match Program can succeed at national scale, but only if it is paired with a specific solutions model built around a centralized clearinghouse, sophisticated locate-and-match technology, and transitional retirement accounts known as Saver’s Match IRAs, or SMIRAs.
Download the full research report: Modeling the Future of the Saver’s Match Program here.
A Simulation Built Around a Realistic Solutions Model
What distinguishes the Saver’s Match Simulation from higher-level policy modeling is that it does not assume an abstract “black box” that magically routes federal dollars to retirement accounts. Instead, the authors defined a detailed, end-to-end operating framework and simulated how it would behave over time.
In that framework, federal matching contributions are first delivered to individual SMIRAs – special-purpose, transitional IRAs established in the name of the eligible saver. These accounts act as a staging area, allowing matching dollars to be received immediately upon IRS processing of a tax return, even when the saver’s ultimate retirement account has not yet been identified or validated.
From there, a clearinghouse applies multiple, increasingly expansive locate-and-match processes. The simulation models three primary matching pathways:
- Self-designated account matching, where taxpayers provide target account information at the time of filing.
- Data-driven matching, using Form W‑2 and Form 5498 data to identify active plans or IRAs.
- Broadcast matching, in which SMIRA account data is securely distributed to participating “market hubs” representing DC recordkeepers and IRA providers, enabling automated confirmation of matches.
Each of these processes is modeled in detail, with explicit probabilities, timing assumptions, and error-handling logic. This level of specificity matters because it reflects how matching should actually occur in a fragmented, dynamic retirement market – particularly one characterized by frequent job changes.
Sensitivity Analysis: Claiming and Adoption Are Critical
To test the resilience of this solutions model, the simulation executes four scenarios that vary two critical levers: taxpayer claiming behavior and institutional adoption of the matching infrastructure.
Claiming behavior is modeled along a “low” curve (50% rising to 60%) and a “high” curve (75% rising to 80%). Market adoption ranges from a low scenario starting at 25% and reaching 50%, to a high scenario starting at 70% and reaching 90%. These combinations produce four distinct operating environments, from best case to worst case.
What the simulation reveals is that end-to-end performance is highly sensitive to both variables – but in different ways.
High claiming drives volume. High adoption drives velocity and completion. When both are strong, the results are striking: over ten years, over 198 million matching contributions are successfully delivered to qualified retirement accounts, representing $133 billion in savings. But even in lower-adoption scenarios, the system continues to improve over time, with match rates climbing well beyond initial adoption levels.
Why Match Rates Exceed Adoption Rates
One of the most interesting findings in the simulation is that match rates consistently outpace underlying market adoption. At first glance, this might seem counterintuitive. But the model shows how compounding effects emerge when locate-and-match processes operate continuously across multiple years.
Unmatched SMIRAs do not simply sit idle. As individuals change jobs, open new accounts, or move into providers that have adopted the system, new matching opportunities are created. The simulation explicitly models “savers in motion,” capturing job turnover, enrollment behavior, retirement, disability, and mortality.
The result is a cross-year matching effect: contributions that could not be matched in year one can often be matched in year two, three, or later as adoption expands and employment circumstances change. In the high-adoption scenarios, this dynamic produces years in which matching activity exceeds 100% of annual SMIRA funding, as prior-year balances finally find a home.
The Central Role of SMIRAs
Across all scenarios, the Saver’s Match IRA emerges as a linchpin of program stability and success. In operational terms, SMIRAs solve a fundamental timing mismatch between tax administration and retirement account logistics.
Without a transitional account, matching contributions would have to be held, delayed, or risk misdirection while account ownership and eligibility are resolved.
Instead, SMIRAs serve four essential functions:
- They receive and safeguard federal dollars immediately, preserving program integrity
- They decouple Treasury operations from private-sector plan complexity, reducing friction
- They enable automated, multi-path matching, rather than a single process
- They ensure no forced distributions or leakage, even when matching takes time
In every modeled scenario, the presence of SMIRAs materially increases the number of successful matches and reduces stranded contributions.
Stress-Testing the System
The simulation’s “stress test” scenario – where high claiming is paired with low market adoption – is particularly instructive. Even under these constrained conditions, nearly three-quarters of all matching contributions are eventually delivered to qualified accounts over the ten-year window.
That outcome is not accidental. It reflects careful modeling of locate-and-match logic, realistic assumptions about data availability, and the persistence afforded by transitional IRAs. Importantly, it also shows that early-year inefficiencies do not doom long-term performance when the system is designed to learn and improve over time.
Policy Implications
The SMS highlights several policy levers that meaningfully influence outcomes:
- Simplifying the claiming process increases front-end participation
- Improving the timing and availability of W‑2 and 5498 data accelerates matching
- Encouraging provider adoption enhances throughput and reduces residual SMIRAs
- Educating taxpayers increases the effectiveness of self-designation
These are not abstract recommendations; they are parameters that visibly move the simulation’s results.
A Blueprint, Not a Thought Experiment
Ultimately, the Saver’s Match Simulation offers more than validation of a policy concept. It provides a blueprint for implementation – demonstrating that success depends not on generic assumptions, but on detailed operational design.
A national Saver’s Match Program can strengthen retirement security at scale, but only if it is built on proven clearinghouse technology, powered by sophisticated locate-and-match processes, and anchored by transitional SMIRAs that keep federal dollars safe, trackable, and mobile until they reach their rightful destination.