How do economists estimate the fiscal impact of illegal immigration on local and state economies?

1. How analysts count the people who matter — the unauthorized population puzzle

Economists begin with population estimates because fiscal impacts scale with the number, age, and family composition of unauthorized immigrants. The most widely used approach is the residual method, which subtracts the count of legally resident foreign-born from total foreign-born totals to infer unauthorized numbers; this method uses DHS administrative data and Census/ACS microdata and explicitly adjusts for survey omissions and errors ^[2]. Alternative tabulations rely on ACS-based microdata or administrative records compiled by research teams like New American Economy and the American Immigration Council, which define immigrant cohorts and estimate household structures and earnings at metro, state, and county levels ^{[1] [4]}. The choice of counting method creates the largest single source of variation in fiscal estimates because small changes in the population base multiply across spending categories.

2. Money in and money out — the revenue-expenditure accounting framework

After estimating population size, economists model tax contributions (income, payroll, sales, and property taxes) and public expenditures consumed (public schooling, Medicaid, emergency health, local law enforcement, and other services). Reports that follow a federal budget-window approach project lifetime or multi-year impacts by combining demographic characteristics — age, education, labor force participation — with tax incidence models and benefit eligibility rules to derive net fiscal balances ^{[5] [6]}. State and local analyses reweight these national rules to reflect differing tax bases and service rules, which is why a program that looks budget-neutral nationally can be costly for particular localities that fund education and emergency healthcare from local revenues ^{[5] [6]}. Analysts explicitly note that time horizon and which government level is measured drive outcomes in opposite directions.

3. Data sources and model knobs — why results diverge

Estimates diverge because economists pick different data and elasticities. Researchers using ACS microdata and ITEP or CBO tax-rate assumptions estimate household incomes, effective tax rates, and spending power at granular geographies; other teams adjust the ACS to account for undercounting or use administrative records that reclassify admission categories ^{[1] [4]}. Some studies update methods to the congressional budget window, aligning with federal scoring conventions and discounting, while others produce point-in-time local impact snapshots without discounting future fiscal flows ^{[6] [4]}. Each methodological “knob” — undercount adjustment, tax-rate source, public service cost per user, and discounting — materially changes whether the net fiscal impact is positive or negative for a given jurisdiction.

4. Where the costs concentrate — education, health, and criminal justice case studies

Case studies repeatedly identify education and emergency healthcare as the largest near-term local fiscal burdens because K–12 costs are immediate and locally funded, and unauthorized immigrants without access to non-emergency Medicaid still generate uncompensated hospital and clinic costs. Workshop and NRC-style reviews document these sectoral concentrations and emphasize the importance of local context: school district demographics, state K–12 funding formulas, and county hospital systems determine magnitude ^{[3] [7]}. Incarceration and criminal justice expenditures are smaller in aggregate but can be politically salient and concentrated in specific jurisdictions. Researchers stress that longer-term labor market integration and tax contributions can offset early-care costs, but those offsets typically accrue to different government levels and over decades ^[3].

5. Recent methodological updates and what they change

Newer 2024–2025 updates refine inputs: analysts incorporate recent ACS waves, revise age-education matrices, and align assumptions with OMB and CBO conventions to estimate impacts on the congressional budget window and state/local fiscal years ^{[6] [5]}. These updates often reduce uncertainty around earnings trajectories and tax elasticities but confirm that results remain heterogeneous by state and metro. The methodological trend is toward greater transparency: teams publish code, microdata tables, and sensitivity ranges to show how assumptions affect net balances ^{[6] [4]}. That transparency highlights that disagreements are not about arithmetic but about policy-relevant choices — which costs to count, which benefits to credit, and what time horizon to use.

6. What to watch — interpretive limits and policy relevance

Fiscal estimates should be read as contextual tools, not final verdicts. Population estimation error, choice of fiscal window, allocation of shared federal funds, and local funding formulas all mean that a single headline number can mislead; credible analyses therefore present sensitivity ranges and sectoral breakdowns to show trade-offs ^{[2] [5]}. Policymakers deciding on local service funding or immigration policy must ask which government level bears costs, whether offsetting revenues accrue later or elsewhere, and how labor-market integration will change the distribution of burdens and benefits over time ^{[6] [7]}. The literature is robust on methods and data; remaining disputes are largely about normative choices — what time frame and which fiscal actors matter most.