SHB 2714
SignedHouse
Food assistance caseload
Concerning caseload forecasting for food assistance programs.
How does a bill become law?
- Introduced: The bill is filed and assigned a number.
- Committee: A subject-matter committee holds hearings, takes public testimony, and decides whether to advance the bill.
- Floor Vote: The full chamber (House or Senate) debates and votes on the bill.
- Opposite Chamber: The bill repeats the committee and floor vote process in the other chamber.
- Governor: The Governor reviews the bill and decides whether to sign or veto it.
- Signed: The bill has been signed into law.
AI Analysis
This bill establishes and formalizes the caseload forecast council, a bipartisan group responsible for projecting how many people will need services across many state programs—including food assistance, childcare, foster care, and college aid—over time. It expands forecasting to include new programs and population groups, and sets rules for how forecasts are developed and approved.
- Creates the caseload forecast council, with six members: two appointed by the governor and four appointed by legislative leaders (one from each of the two largest caucuses in the House and Senate).
- Requires the council to hire a caseload forecast supervisor (a staff position) to manage forecast preparation; employment decisions require approval by at least five council members.
- Expands the definition of 'caseload' to include not just food assistance (SNAP and state food assistance), but also programs like medical assistance, foster care, early childhood education, college grants and scholarships, and the working families' tax credit.
- Adds new forecasting requirements for specific populations, including youth in extended foster care, behavioral rehabilitation services, children in transition to kindergarten, and individuals receiving developmental disabilities services (e.g., waivers, supported living).
- Requires the council to produce forecasts for working families' tax credit recipients, broken down by number of qualifying children (none, one, two, or three+).
- Allows dissenting council members to request and receive an alternative forecast with their own assumptions if they do not vote to approve the official forecast.
Who is affected
- State agency staff — Staff in the Department of Social and Health Services (DSHS) and other state agencies who rely on caseload forecasts to plan budgets and program capacity for food assistance and other public programs.
- Food assistance recipients — Low-income families and individuals who receive food assistance (like SNAP) or other public benefits; accurate forecasts help ensure programs are adequately funded and accessible.
- General public and researchers — Members of the public who rely on transparent, nonpartisan projections to understand how state benefit programs are expected to grow or change over time.
- State legislators and budget staff — Legislators and state budget planners who use caseload forecasts to make informed decisions about funding and policy changes.
Pro/Con Analysis
Stronger case for benefits
Potential Benefits (5)
Creating a bipartisan council with balanced legislative and executive appointments—and requiring supermajority approval for key decisions—enhances transparency and reduces partisan manipulation of caseload projections, which helps ensure stable, predictable funding for essential programs like SNAP, foster care, and college aid.
Local GovernmentPeopleRef: Sec. 1(1), (4), (5)Requiring the working families’ tax credit (WFTC) forecast to be broken down by number of qualifying children (none, one, two, three+) enables more precise targeting of benefits and improves accountability for how tax credits serve low- and moderate-income families—helping policymakers and advocates assess whether the program meets its anti-poverty goals.
Public SafetyPeopleRef: Sec. 1(7)(e), (7)(e)(i)-(iv)Expanding the definition of 'caseload' to include medical assistance, developmental disabilities services, behavioral rehabilitation, and early childhood education improves holistic understanding of state service demand—enabling better long-term planning and reducing risk of underfunding high-need populations (e.g., people with disabilities, foster youth).
HealthcarePeopleRef: Sec. 1(7)(a)-(e), (9)-(15)Mandating separate forecasting for youth in extended foster care, behavioral rehabilitation services, and transition-to-kindergarten programs ensures vulnerable subgroups are not aggregated into broader totals—allowing targeted resource allocation and early intervention for at-risk children.
EducationPeopleRef: Sec. 1(12), (13), (15)Requiring the supervisor to submit forecasts even if the council fails to approve them on time—and allowing dissenting members to request alternative forecasts—preserves continuity of information for budget planners and prevents political delays from disrupting fiscal planning, benefiting agencies and service recipients alike.
Public SafetyPeopleRef: Sec. 1(4), (5)
Potential Concerns (5)
The requirement that employment decisions for the caseload forecast supervisor require approval by at least five council members may create bureaucratic delays and reduce operational agility for the supervisor and staff, potentially slowing forecast production and responsiveness to urgent budget or program changes.
Local GovernmentRef: Sec. 1(2), (3)Expanding caseload forecasting to include the working families’ tax credit (WFTC) with breakdowns by number of qualifying children improves transparency and enables more precise budgeting, but the bill does not mandate funding to support implementation of these expanded forecasts—potentially straining existing DSHS and legislative budget staff resources without additional staffing or technical support.
Public SafetyLean peopleRef: Sec. 1(7)(e), (7)(e)(i)-(iv)Formal inclusion of medical assistance, developmental disabilities waivers, behavioral rehabilitation, and early childhood education in the caseload definition improves forecasting accuracy for high-need populations, but without explicit funding for data infrastructure or interagency coordination, smaller or under-resourced agencies may struggle to meet new reporting and forecasting demands.
HealthcareLean peopleRef: Sec. 1(7)(a)-(e), (9)-(15)Allowing dissenting members to request alternative forecasts may increase transparency, but in practice could fuel political fragmentation—especially if minority caucus members produce forecasts that diverge significantly in assumptions—leading to public confusion and undermining confidence in the official forecast, particularly in election years.
Local GovernmentPeopleRef: Sec. 1(5)Expanding forecasting to include college grants, early childhood education, and transition-to-kindergarten programs is beneficial, but the bill does not require standardized data collection protocols or interoperable systems across agencies—meaning inconsistent or delayed data inputs could reduce forecast reliability, especially for vulnerable student populations.
EducationPeopleRef: Sec. 1(7)(e), (8)-(16)
Who Is Most Affected
State agency staff (e.g., DSHS, ESD, Higher Education Learning Council) benefit from clearer forecasting expectations and standardized data requests, but may face added administrative burden without new staffing or IT resources.
Low-income families and individuals benefit from more accurate and inclusive forecasting of benefits like SNAP, Medicaid, and the working families’ tax credit—reducing gaps in coverage and enabling better program outreach and enrollment.
Researchers and policymakers gain more reliable, transparent, and granular data on program demand, especially for vulnerable subgroups (e.g., youth in extended foster care, people with disabilities), improving evidence-based decision-making.
State legislators benefit from more robust, nonpartisan forecasting tools for budget deliberations, but may face increased pressure to reconcile divergent forecasts from dissenting council members—potentially complicating consensus-building.
Local governments (e.g., counties, school districts) that administer or partner on programs like early childhood education, foster care, and behavioral health may benefit from more accurate demand forecasts but could face pressure to align local reporting with new state standards.