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The first step is quantifying participants’ Defined Contribution plan savings behavior.

Explore Data Elements

  • Demographics: Participants and Eligibles

    • Age

    • Gender

    • Race/Ethnicity

    • Wage

    • Tenure

    • Unique identifier

    No Personally Identifiable Information required.

  • Savings Behavior: Participants

    • Contributions: Employee and Employer (Roth/Standard)

    • Account balances by fund

    • Loans/Withdrawal activity

    • Subject to/using automatic features

    No Personally Identifiable Information required.

The anonymous data elements needed for eligible, participants, and plan design information are highlighted below. Additionally, more detailed information on format, location, and scope are here. The request is for annual, calendar-year information for 2021, and 2022, when it is available.

Please note: we realize that not all data will be available across each employer. We are happy to work with you to prioritize and assess feasibility. The data needed typically resides across multiple systems. We are happy to assist merging data fields, and simply require a unique identifier to implement.

Notables:

  1. This project does not seek, nor require, personally identifiable information (“PII”).

  2. A data dictionary (or definitions of fields/variables) may be necessary

  3. We are available to support and discuss any specific questions or concerns

    • Employer contribution formulae

    • Roth provisions

    • Automatic tools: effective date and defaults

    • Loan/withdrawal provisions

    • Fund level, asset class mapping

    • If applicable, DB plan SPD

  • Data will need to be gathered from multiple sources, including: recordkeeper, HRIS, and/or payroll.

    Start with the recordkeeper and append with additional data as available. We can assist with merging data or multiple data files with a common, unique identifier (not PII) that is common between files.

Collecting and Sharing Data

Connect with your recordkeeper to begin discussions.

Often this may be a series of conversations, to collaborate and understand the request. We are happy to support these conversations and have experience with some recordkeepers who are already working with us.

The Morningstar Center for Retirement and Policy Studies is the hub for data analysis, collection and storage.

All information is separate and cannot be accessed outside of this team. Further, Data cannot be used for any other purposes and cannot be used publicly or sold. Once data is ready to be uploaded, Morningstar will set up an account for you.

Often, for data collection, the recordkeeping data can be foundational and built upon with the missing elements.

Data from payroll can be appended. A unique identifier is needed between the files, to support integration. We are able to help with these efforts, and complete that pairing.

File transfer protocols (FTP) protocols can be followed to upload information and for maximum safety and security.

A test file is utilized to ensure secure transfer and data field formatting. Once confirmed, all data is uploaded through the FTP site.

To date, this type of exploration by gender, race, and income has been difficult, as employee demographic information is stored in systems that are separate from the recordkeeping data. Linking that information is critical to better understanding behavior.

Please click below to understand the types of data elements, steps to create engagement within your organization, protocols for collecting and sharing data, as well as the data analysis and reporting process.

Note: no personally identifiable information (PII) is required for this project. All data is exclusively for the benefit of this initiative.

Annual data fields to support analysis

Customized Reporting

Modeling and projection capabilities

Plan sponsors will receive several types of analysis:

  • A detailed analysis of the distribution of account balances for each combination of gender and race/ethnicity

    • The account balance analysis will have breakouts for age, wage and tenure to provide more useful comparison

  • A detailed analysis of how the following vary by each combination of gender and race/ethnicity

    • Participation activity

    • Contribution activity

    • Asset allocation

    • Loan activity

    • Withdrawal activity

  • An analysis of how differences in account balances may be explained by differential behavior shown in category 2 above

  • A comparison of the plan sponsor's results with the universe of plans in the Retirement Savings for All project

  • If there is a sufficiently large sample, a comparison with plans in the same industry will also be provided

Data FAQs

  • Cleaning

    • Each plan sponsor's data submission will be subject to at least two rounds of confidential cleaning by Morningstar. Preliminary results will be shared with the plan sponsor to ensure that all fields were processed correctly.

    Aggregation

    • No individual outside of the Morningstar analysts will see individual results. Any results that are made available publicly will have been aggregated to the extent that no individual sponsor's results can be reverse engineered.

  • Morningstar will provide plan-specific analysis to whatever degree is possible with the data submission. It is likely that not all plan sponsors will have all the data elements requested. In that case only certain portions of the universe comparisons will be available.

  • Once the data has been cleaned and approved by both the plan sponsor and Morningstar, the plan analysis should be available within a week.

  • Each plan sponsor will receive a detailed comparison of how their employee's results compare with the universe of plan sponsors for this project. If there is sufficient information, the results will also be broken out by industry. These comparisons include:

    • A detailed analysis of the distribution of account balances for each combination of gender and race/ethnicity

      • The account balance analysis will have breakouts for age, wage and tenure to provide more useful comparisons

    • A detailed analysis of how the following vary by each combination of gender and race/ethnicity

      • Participation activity

      • Contribution activity

      • Asset allocation

      • Loan activity

      • Withdrawal activity

  • In addition to the empirical analysis mentioned above, Morningstar will work with each plan sponsor to provide projections of the account balances at a retirement age (or range of retirement ages) provided by the plan sponsor. Social Security benefits will also be projected to retirement age and various types of replacement rates will be provided.

    If the plan sponsor has a defined benefit plan, Morningstar will attempt to include that information as well.

  • We are planning to do a first universe run once we receive a sufficient number of plans providing year-end 2022 data. This should be available in the second quarter of 2023.

Creating Momentum and Gathering Support

Employers are often at different stages of creating momentum, obtaining necessary support, and ultimately getting approvals to gather and share data. We suggest project advocates find an executive-level sponsor to help the project be prioritized and engage key internal stakeholders early in the process.

Key stakeholders in your organization may include:

  1. Human resources

  2. Finance

  3. Information technology

  4. Legal

  5. Public relations and communications

  6. C-suite (e.g., chief diversity officer, chief financial officer, chief technology officer)

  7. Employee resource groups (or similar)

Don’t forget to engage our team! We are here to help, and happy to support you throughout the process.