Methodology
Established economic methods with better inputs for the economy you are analyzing.
Lumecon is the intelligent economic analysis platform. Cedar Impact, its economic impact analysis software, uses regionalized input-output analysis built from federal economic data. It applies current public indicators where appropriate, documents regionalization and validation and keeps the evidence behind each result available for review.
Proven foundations
A framework used across the field
Regionalized input-output modeling with household and government accounts, the framework used across economic impact practice for decades.
Modern evidence
Close to present conditions
Current public datasets and higher-frequency indicators keep the model close to present conditions where the data support it.
Continuous improvement
Advancing as the evidence does
We invest in the model itself, and in the software around it. Data, methods and validation keep advancing as the evidence does.
The core model
Lumecon analyses are built on input-output modeling, the same analytical foundation used across the economic impact field for decades. An input-output model maps how industries in a region buy from and sell to each other, so a change in one place (a new program, an expansion, a budget) can be followed through the purchases, wages and household spending it sets off.
The mathematics is compact. Each industry’s production recipe is summarized by a technical coefficient, the amount it purchases from every other industry per dollar of its own output. Collecting those coefficients into a matrix, a single inversion carries every round of purchasing at once: the direct requirement, the suppliers’ requirements, the suppliers’ suppliers and so on. That inverse is the Leontief system, and it is the arithmetic at the center of every impact estimate.
zij is what industry j buys from industry i; xj is industry j’s total output.
x is the total output required to satisfy a change in final demand f. The inverse of I − A is the Leontief inverse.
From national relationships to your region
The industry-to-industry relationships in the national accounts describe how the U.S. economy as a whole buys and sells. To analyze a county, a state or a reservation, those relationships are adjusted to the region: local industry mix, employment and wages come from the regional data listed below, and regional purchase behavior estimates how much of each purchase is met by suppliers inside the region rather than imported from outside it. Regional self-sufficiency is estimated sector by sector with location quotients in the Flegg family, and the adjusted table is then rebalanced with biproportional (RAS) scaling so the accounting identities still hold. Smaller and more specialized regional economies keep less of each dollar, and the model reflects that.
Suppressed and small-area data
Federal disclosure rules withhold some county-sector figures so individual employers cannot be identified. Where a suppressed sector clearly exists in the region, its employment is estimated from wider employment-per-establishment patterns, and wages fall back through a stated hierarchy of sources. Every figure carries a source tag (reported, apportioned or estimated) so a reviewer can see exactly which numbers came straight from the data and which were estimated.
e counts employment; i indexes the sector; r and n mark the region and the nation.
The exponent δ sets how strongly regional smallness discounts local purchasing. It is calibrated against observed regional data rather than fixed by assumption, and every coefficient is capped so no local share exceeds one.
R and S are diagonal scaling matrices, updated each round until the regional table converges on its control totals.
From one change to total impact
National accounts
The benchmark industry-to-industry relationships.
Your region
Regionalized with local data, location quotients and RAS.
Direct effects
The analyzed activity itself: spending, wages, jobs.
Indirect effects
The suppliers it buys from, and their suppliers.
Induced effects
Workers spending their earnings back into the region.
Total impact
Reported layer by layer, with a lineage behind every figure.
Every result separates the three layers of activity. Direct effects are the project’s own spending, wages and jobs. Indirect effects happen at the suppliers the project buys from. Induced effects come from workers spending their earnings locally. The layers are always shown separately, so a reviewer can see how the total is composed rather than taking one number on faith.
Induced effects come from a social accounting matrix formulation. The input-output table is extended with a household account that receives labor income and spends it back into the region, and a government account that collects taxes and returns them as procurement and transfers. Household income includes transfer income, which matters in regions where transfers are a large share of income, including many reservation economies. Local spending propensities govern how much of each household dollar recirculates in the region rather than leaking out, so induced effects stay tied to the region’s real spending patterns.
Lumecon reports the layers themselves rather than leading with a single multiplier, and validation flags any multiplier outside the ranges credible for an economy of the region’s size.
Public data foundation
Lumecon draws from authoritative public datasets widely used in economic and regional analysis. The sources used depend on the geography and analysis. Where applicable, benchmark sources set model structure and higher-frequency series help update levels; sources used in a result are citable.
- BEA Input-Output AccountsThe national industry-to-industry benchmark tables the model is built from.Benchmark
- BLS QCEWEmployment and wages by county and industry, the backbone of regionalization.Benchmark
- Census ACSDemographics, income and housing that shape regional household profiles.Benchmark
- Census CBPEstablishment counts by county, industry and size class.Benchmark
- Census LODESWhere people live and where they work, for commuting and earnings flows.Benchmark
- USDA NASSAgricultural production detail for farm-heavy regional economies.Benchmark
- Census TIGER/LineBoundary files for counties, states and American Indian and Alaska Native areas.Geography
- Census QWIQuarterly workforce indicators that track employment between benchmarks.Higher-frequency
- FREDHigher-frequency series for employment, wages and prices.Higher-frequency
- USAspendingFederal award and spending flows into regions and programs.Supplementary
- Federal Audit ClearinghouseSingle Audit filings: audited federal award spending and findings for organizations that receive federal funding.Supplementary
- CICD Native Community Data ProfilesThe Center for Indian Country Development’s harmonized profiles and Tribal Economic Zones, with their own documented source list, for Native community context.Supplementary
- NaNDAThe National Neighborhood Data Archive (ISR, University of Michigan): contextual measures of place used where neighborhood conditions matter.Supplementary
- BIA Federal Register listThe Bureau of Indian Affairs list of federally recognized tribes, maintained current from the Federal Register, for identifying and validating Native entities.Geography
- NOAAEnvironmental context for analyses where weather and climate matter.Supplementary
Data vintages
Every analysis states the data year it runs against, and that year is shown with the results (for example, 2025 data). Historical analyses run from 2015 to present where the underlying data support it. Where source datasets carry different vintages, the analysis uses the most recent vintage available for each source at the stated data year.
Between benchmarks
The benchmark input-output tables update on the schedule of the agencies that publish them. Between benchmarks, higher-frequency public series for employment, wages and prices update the levels the model scales against, where the data support it. The structural relationships between industries remain tied to the benchmark tables, so the proven core stays intact while the levels stay current.
Built to evolve
The model is continually evaluated and improved as better data, regionalization approaches and validation evidence become available. How Cedar supports that workflow.
The commitments behind every analysis
These five commitments apply to every analysis; open each section for methodology detail.
GeographyGeographies that follow the real economyCounties, states and the nation, with reservations and trust lands treated as first-class regions rather than approximations.
Where an analysis covers both a state and homelands, the two scopes are reported side by side with the subset relationship stated explicitly, so nothing is double-counted.
Reservation and trust-land regions are constructed from the federal boundary files for American Indian and Alaska Native areas and from the employment, industry and demographic data located within those boundaries. They are modeled geographies with their own regional characteristics, and results for a reservation are estimated for that region itself. We use homelands as the plain-language umbrella term for reservation and trust-land geographies. Tribal government analysis is an active area of methodological work for us, and this page will grow as that work does.
IndustriesIndustries at the two-digit NAICS levelClassification lives where administrative data coverage is strongest, so fewer values are imputed between a source and a multiplier.
Every dollar in an analysis is classified against the North American Industry Classification System at the two-digit sector level, from Agriculture (11) to Public Administration (92). Two digits is where administrative data coverage is strongest: the federal employment, wage and output series that anchor the model are published nearly complete at this level for counties and states, while finer detail is often suppressed for confidentiality in smaller geographies. Working where the data is complete means fewer imputed values and fewer assumptions between a source and a multiplier.
As our client base and data coverage grow, we intend to extend classification beyond two digits where published sources support it, and each analysis will state the level it uses. Tribal governments are tracked as their own Lumecon category rather than as local government within Public Administration (92), because a nation that runs administration, enterprises, housing, health and education programs is understated by that single line.
ValidationValidation and human reviewA run that fails a critical check stops rather than shipping a doubtful number, and a person confirms the assumptions before results are finalized.
The software checks that tables cross-foot, that scopes nest correctly and that inputs fall in plausible ranges. Estimated inputs and model outputs are checked against plausibility bounds, including multiplier ranges credible for a small regional economy, and a run that fails a critical check stops rather than shipping a doubtful number.
Automation assists with data processing, quality checks and documentation. Economic assumptions, regionalization choices and model design remain economically grounded and subject to human review, and the person running an analysis confirms the assumptions before results are finalized. Learn how Cedar works.
TraceabilityEvery number has a lineageEach figure traces to its effect layer, operations, industries and source tags, and exports carry the full detail.
Each headline figure carries a trace: which effect layer it comes from, which operations generate it and which industries it lands in, each with its share. The same traceability runs through the inputs, where every figure carries its source tag (reported, apportioned or estimated).
Exports carry the source references, the assumptions, the model inputs and the full set of tables behind each figure, so the person defending the number in a council meeting, a boardroom or a grant review holds the same detail the model used.
ComparisonHow Lumecon differs from established platformsEstablished platforms and Lumecon draw on the same national statistical system; the differences are architecture, workflow and price.
Input-output analysis is an established, openly published field. Established platforms and Lumecon draw on the same national statistical system and decades of economic research. Lumecon differentiates through its data architecture, regionalization, traceability, reservation and trust-land modeling, modern workflow and pricing. Lumecon is not affiliated with or endorsed by IMPLAN, REMI or any other platform named here.
What the headline measures mean
- Jobs supported
- The count of jobs supported by the activity across the direct, indirect and induced layers over the analysis period. It is a job count; where a conversion basis matters, the analysis states it.
- Labor income
- Wages, salaries and benefits.
- GDP contribution
- The value added by the analyzed activity: the measure that corresponds to a region’s contribution to GDP. Lumecon uses GDP contribution as the plain-language label for value added.
- Economic output
- The total value of production the activity supports: value added plus intermediate purchases.
- Tax impacts
- Estimates of the revenue the activity supports, reported by level of government: federal, state and local, and tribal government where the analysis includes one.
Bring your records into a method you can examine.
Review the equations, sources, limitations and result lineage, then evaluate the workflow with your own analysis during the private beta.
Lumecon results are decision-support estimates. They describe likely economic relationships given the data and assumptions stated in each analysis, and they are labeled as estimates everywhere they appear. See the glossary for the terms used across analyses, and write to contact@lumecon.ai with methodology questions.
Common questions
What model does Lumecon use?
A regionalized input-output model built from the national economic accounts, extended with household and government accounts (a social accounting matrix formulation) so induced effects and the government tax-and-spend cycle can be estimated alongside direct and indirect effects.
Is Lumecon an alternative to IMPLAN or REMI?
Organizations evaluate Lumecon as an alternative to IMPLAN, RIMS II and REMI. The input-output foundation is shared across the field; Lumecon differs in its fully public, citable data foundation, the per-figure lineage with source tags, reservation and trust-land modeling and flat subscription pricing. Lumecon is not affiliated with or endorsed by IMPLAN or REMI.
How does Lumecon handle suppressed government data?
Federal disclosure rules withhold some county-sector figures. Where a suppressed sector clearly exists in a region, Lumecon estimates its employment from wider employment-per-establishment patterns and tags the figure as estimated, so a reviewer can always tell reported values from estimates.
How are reservations and trust lands modeled?
As modeled geographies constructed from the federal boundary files for American Indian and Alaska Native areas and the data located within them, with regional purchase behavior estimated for the region itself. Household income includes transfer income, which is a meaningful share of income in many reservation economies.
What do direct, indirect and induced effects mean?
Direct effects are the analyzed activity itself: its spending, wages and jobs. Indirect effects happen at the suppliers it buys from. Induced effects come from workers spending their earnings in the region. Lumecon reports the three layers separately and shows how each total is composed.
Are Lumecon results estimates?
Yes. Results are decision-support estimates and are labeled as such wherever they appear. Every analysis states its data year and assumptions, and exports carry the full tables behind each figure so the estimates can be reviewed.
