Primary survey data sources
Current Population Survey (CPS)
Description: The CPS is the primary source of labor force statistics for the U.S. population. The Annual Social and Economic Supplement (ASEC) provides detailed income and demographic data.
Key Features: - Large sample: ~95,000 households, ~200,000+ individuals - Annual cross-section (March ASEC) - Detailed income components including Social Security benefits - Demographics: age, sex, race, ethnicity, education, family structure - Employment and earnings information - Geographic detail: state, metro area
Strengths for Our Purpose: - Large sample enables state-level and demographic subgroup analysis - Official poverty statistics source ensures public validation - Available annually since 1962 with consistent time series - Public use files freely available - Well-documented and widely used
Limitations: - Cross-sectional only (no panel structure) - Top-coded high incomes - Some income underreporting - No asset or wealth data - Limited earning history (only current year)
Our Use: - Core cross-sectional input to the current public populace population layer - Validation of age-earnings profiles - Calibration targets for population characteristics - Starting point for longitudinal extension
Panel Study of Income Dynamics (PSID)
Description: The longest-running longitudinal household survey in the world, following families since 1968.
Key Features: - True panel structure: same families over decades - Biennial from 1997 (annual before) - ~9,000 families currently - Intergenerational linkages - Detailed earnings histories - Wealth supplements - Comprehensive demographic transitions
Strengths for Our Purpose: - Critical for longitudinal modeling: True earnings trajectories over careers - Demographic transitions: marriage, divorce, childbearing, mortality - Intergenerational links enable family modeling - Long time series captures cohort differences - Wealth data for comprehensive retirement security analysis
Limitations: - Smaller sample than CPS (not suitable for state-level detail) - Sample attrition over time - Top-coding of high incomes - Public use files have restricted geographic detail
Our Use: - Primary source for longitudinal extension of populace - Training data for quantile regression forests - Validation of lifetime earnings distributions - Demographic transition modeling - Calibration of earnings mobility
Survey of Income and Program Participation (SIPP)
Description: Census Bureau survey designed to measure income and program participation, with longitudinal panel structure.
Key Features: - Panel structure: follows individuals for 3-4 years - Monthly income data - Detailed program participation (SNAP, SSI, Social Security, etc.) - Wealth modules - Sample size: ~20,000-50,000 households (varies by panel) - New panels fielded every few years
Strengths for Our Purpose: - Detailed benefit receipt data - Short-term earnings dynamics - Program participation for validation - Wealth and asset holdings - Larger than PSID but retains panel structure
Limitations: - Shorter panels than PSID (3-4 years vs. decades) - Sample attrition - Complex survey design - Public use files have some restrictions
Our Use: - Validation of benefit calculations - Short-term earnings dynamics - Program participation rates for calibration - Supplementary source for transition modeling - Wealth distribution validation
Administrative Data Sources (for Validation)
While we cannot directly access individual-level administrative data, we use published aggregate statistics for validation:
Social Security Administration (SSA)
Annual Statistical Supplement: - Benefit distributions by age, type (retirement, disability, survivors) - Beneficiary counts and characteristics - Average benefit amounts - Earnings distributions of covered workers - Trust fund financial status
OASDI Trustees Reports: - Long-run actuarial projections - Demographic assumptions (fertility, mortality, immigration) - Economic assumptions (wage growth, inflation) - Financial projections by component
MINT Documentation: - Published analyses using MINT model - Methodological documentation - Validation statistics
Our Use: - Primary calibration targets for benefit receipt - Validation of benefit calculations - Long-run projection benchmarks - Demographic assumption alignment
Internal Revenue Service (IRS)
Statistics of Income (SOI): - Tax return data aggregates - Earnings distributions - Social Security benefit taxation - Income by source and filing status
Our Use: - Validation of earnings distributions - Tax calculations on benefits - High-income earner distributions
Census Bureau
Population Estimates and Projections: - Annual population by age, sex, race, ethnicity - Birth and death rates - International migration
American Community Survey (ACS): - Large sample (3+ million households) - Annual cross-sections - Detailed demographics and geography - Income and program participation
Our Use: - Population controls for weighting - Demographic validation - Geographic distributions
Supplementary Data Sources
Survey of Consumer Finances (SCF)
Description: Federal Reserve triennial survey providing the gold standard for U.S. household wealth data, with oversampling of high-wealth households.
Key Features: - Comprehensive wealth measurement (financial assets, housing, pensions, debt) - Oversamples high-net-worth households for accurate tail measurement - ~6,000 households per wave, triennial since 1989 - Detailed retirement account data (401(k), IRA, DB pensions)
Why Wealth Matters for Social Security Analysis (per reviewer feedback): - Benefit claiming decisions: wealthier households can afford to delay claiming to age 70 for higher benefits - Replacement rates: Social Security replaces a higher share of consumption for wealth-poor households - Means-testing proposals: require wealth data to evaluate - Comprehensive retirement adequacy: Social Security + pensions + savings + housing
Our Use: - Validation of wealth distributions by age and lifetime earnings quintile - Impute wealth statistically using random forest matching on age, earnings history, education, and marital status (parallel to earnings history imputation from PSID) - Validate that imputed wealth distributions match SCF aggregates (net worth, financial assets, pension coverage) - Enable analysis of reforms that interact with wealth (means-testing, claiming behavior)
Note: We treat wealth as a validation target rather than a calibration target initially, checking whether our synthetic panel with imputed wealth produces realistic distributions without forcing weights to match SCF. This is because survey-based wealth measures have their own measurement challenges. Full wealth accumulation modeling is an extension beyond the core Phase 1–5 deliverables.
Health and Retirement Study (HRS)
Description: Longitudinal study of Americans over age 50, with detailed wealth and health data.
Strengths: - Excellent wealth measurement - Health and disability transitions - Retirement decisions and timing - Linked to administrative earnings records (restricted access)
Our Use: - Validation of retirement age distributions - Wealth holdings near retirement - Disability onset patterns - Retirement claiming behavior by AIME quintile
National Health and Aging Trends Study (NHATS) and National Study of Caregiving (NSOC)
Description: Panel studies of older Americans and their caregivers, with detailed information on disability, care needs, care receipt, and unpaid caregiving.
Strengths: - Rich measures of ADLs, IADLs, cognitive impairment, and supervision needs - Detailed care setting information (informal care, paid home care, residential care) - Direct measurement of unpaid caregiving hours and caregiver relationships - Especially useful for validating intermediate LTC states rather than only final fiscal aggregates
Our Use: - Calibrate or validate disability and care-need prevalence among older adults - Validate transitions between no care, home care, and institutional settings - Measure caregiver burden, co-residence, and labor-supply spillovers - Inform static and dynamic modeling of caregiver-support policies
Medicare Current Beneficiary Survey (MCBS)
Description: Survey of Medicare beneficiaries with detailed information on utilization, spending, supplemental coverage, and health status.
Strengths: - Detailed Medicare utilization and out-of-pocket spending - Information on home health, post-acute care, and other services relevant to older adults with care needs - Better direct measurement of Medicare-financed services than general household surveys
Our Use: - Validate spending and utilization for Medicare beneficiaries with functional limitations - Assess interaction between Medicare and LTC proposals such as home-care benefits - Benchmark static first-pass models of Medicare-at-home style proposals
National Health Interview Survey (NHIS) and Medical Expenditure Panel Survey (MEPS)
Description: Broad household surveys covering health, disability, utilization, and medical expenditures.
Strengths: - Coverage of noninstitutionalized populations below age 65 - Functional limitation and health-status measures outside retirement-age populations - Expenditure detail useful for near-term medical spending and disability interactions
Our Use: - Support modeling of younger disabled populations who may have LTC needs - Validate health-status and expenditure gradients outside the Medicare population - Provide an all-age complement to HRS and NHATS
CMS Long-Term Care Administrative Sources
Minimum Data Set (MDS): - Resident assessments for nursing home populations - Functional status, cognitive impairment, and care needs in institutional settings
Transformed Medicaid Statistical Information System (T-MSIS): - Medicaid enrollment, service use, and spending - HCBS and institutional LTSS utilization patterns - State-by-state variation in Medicaid LTSS programs
Our Use: - Validate institutional care prevalence and resident characteristics - Benchmark Medicaid LTSS participation, payer mix, and spending - Anchor state-level LTC rule encoding and downstream model validation
State LTC Policy Sources
Long-term care policy analysis also requires policy-source data, not just survey microdata. We will need to assemble and version: - State Medicaid manuals and eligibility rules - HCBS waiver documentation and assessment criteria - Home equity and asset treatment rules - Functional eligibility definitions (ADLs, cognitive impairment, level-of-care tests) - Spousal impoverishment parameters such as resource allowances, monthly maintenance allowances, and personal-needs allowances - State transfer-penalty divisors, private-pay nursing facility rate tables, and Qualified Income Trust or Miller Trust guidance - Estate recovery guidance, PACE manuals, and Single Entry Point or assessment-instrument documentation
These sources play the same role for LTC that statutes, tax forms, and program manuals play in PolicyEngine’s existing tax-benefit infrastructure.
This matters because a credible static LTC pilot must do more than check one income threshold. It should be able to tell a family whether they are eligible now or soon, what spend-down or trust path would be required, how spousal protections change the result, and what patient liability would look like after approval. That level of output depends on operational state documents as much as on survey data.
For the proposal, two supporting appendices make this more concrete. public-validation-inventory.md shows how much of the model can be judged against public evidence before restricted administrative access is available. appendix-colorado-ltc-rules-packet.md shows what a first-pass authoritative source packet looks like for a state LTC pilot.
American Time Use Survey (ATUS)
Description: Time use diary survey providing detailed labor supply.
Our Use: - Validation of labor supply patterns - Hours worked distributions
Mortality Data
National Vital Statistics System: - Death rates by age, sex, race, cause - Life tables
SSA Actuarial Life Tables: - Cohort life tables - Differential mortality assumptions
Our Use: - Mortality modeling in projections - Survivor benefit calculations - Differential mortality by socioeconomic status
Data Limitations and Challenges
Known Issues
Income Underreporting: Survey data underreport income compared to national accounts, particularly: - Transfer income (SNAP, SSI, etc.) - Self-employment income - Interest and dividend income - High incomes (top-coding)
Mitigation: Use external calibration targets from administrative data
Sample Size: Panel surveys (PSID, SIPP) have smaller samples than CPS
Mitigation: Use CPS for cross-sectional detail; panels for dynamics only
Attrition: Panel surveys lose respondents over time, potentially biasing dynamics
Mitigation: Attrition weights and validation against full population
Measurement Error: All survey data contain measurement error
Mitigation: Multiple imputation and sensitivity analysis
Strategic Choices
Cross-Sectional vs. Longitudinal Trade-off: - CPS: Large but no panel structure - PSID: Panel structure but small sample
Our Approach: Synthetic panel combining CPS size with PSID dynamics
Administrative Data Access: - Ideal: Linked administrative earnings records - Reality: Not publicly accessible - Solution: Statistical matching to published aggregates
Time Period: - Most recent data for current policy - Historical data for validation of dynamics - Balance: Use recent CPS for base, historical PSID for dynamics
Social Security Administration (SSA)
Annual Statistical Supplement: - Benefit distributions by age, type (retirement, disability, survivors) - Beneficiary counts and characteristics - Average benefit amounts - Earnings distributions of covered workers - Trust fund financial status
OASDI Trustees Reports: - Long-run actuarial projections - Demographic assumptions (fertility, mortality, immigration) - Economic assumptions (wage growth, inflation) - Financial projections by component
MINT Documentation: - Published analyses using MINT model - Methodological documentation - Validation statistics
Our Use: - Primary calibration targets for benefit receipt - Validation of benefit calculations - Long-run projection benchmarks - Demographic assumption alignment