WP24 v1.0 (work in progress): model, solver, calibration, 20 scenarios, paper draft
Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com>
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| 1 | +# Raw & processed data — never committed (see data/raw/README.md for provenance) | |
| 2 | +data/raw/* | |
| 3 | +!data/raw/README.md | |
| 4 | +data/processed/* | |
| 5 | +!data/processed/.gitkeep | |
| 6 | + | |
| 7 | +# Python | |
| 8 | +__pycache__/ | |
| 9 | +*.pyc | |
| 10 | +.pytest_cache/ | |
| 11 | +.venv/ | |
| 12 | +*.egg-info/ | |
| 13 | + | |
| 14 | +# LaTeX build artifacts (paper/main.pdf IS committed) | |
| 15 | +paper/*.aux | |
| 16 | +paper/*.log | |
| 17 | +paper/*.out | |
| 18 | +paper/*.toc | |
| 19 | +paper/*.lof | |
| 20 | +paper/*.lot | |
| 21 | +paper/*.fls | |
| 22 | +paper/*.fdb_latexmk | |
| 23 | +paper/*.bbl | |
| 24 | +paper/*.blg | |
| 25 | +paper/*.spl | |
| 26 | +paper/*.synctex.gz | |
| 27 | +paper/*.run.xml | |
| 28 | +paper/*.bcf | |
| 29 | + | |
| 30 | +# Logs | |
| 31 | +logs/ | |
| 32 | +*.log | |
| 33 | + | |
| 34 | +# macOS | |
| 35 | +.DS_Store | |
| 36 | + | |
| 37 | +# WP24: keep the processed calibration targets (small, needed by the calibration); raw downloads stay out | |
| 38 | +!data/processed/calibration_targets.csv | |
| 39 | +!data/processed/series/ | |
| 40 | +!data/processed/*.csv | |
| 41 | +results/scenarios_direct/ | |
| 42 | +logs/ | |
| 43 | +*.npy | |
| 44 | +slides/*.aux | |
| 45 | +slides/*.log | |
| 46 | +slides/*.out | |
| 47 | +slides/*.nav | |
| 48 | +slides/*.snm | |
| 49 | +slides/*.toc | |
| 50 | +slides/*.bbl | |
| 51 | +slides/*.blg | |
| 52 | +slides/*.fdb_latexmk | |
| 53 | +slides/*.fls | |
| 54 | +slides/*.synctex.gz | |
| 55 | +source_audit/pdf/ | |
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CLAUDE.md
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| 1 | +# CLAUDE.md — From Labor to Capital: AGI, General Equilibrium, and Asset Valuation | |
| 2 | + | |
| 3 | +## ROLE AND NON-NEGOTIABLE MISSION | |
| 4 | +Act as a coordinated research team: macroeconomic theorist, task-based labor economist, asset-pricing theorist, computational economist, empirical macroeconomist, and critical referee. Produce an original, reproducible, publication-quality research program extending Pascual Restrepo (2025), *We Won't Be Missed: Work and Growth in the Era of AGI*, https://conference.nber.org/conf_papers/f227505.pdf. The primary research question is: **How does increasingly capable and abundant AI reallocate factor income from labor to reproducible and scarce capital, change the ownership distribution of aggregate wealth, and jointly determine the prices and expected returns of financial, real-estate, and other assets?** | |
| 5 | + | |
| 6 | +Do not assume that AI automatically raises profits, all asset prices, inequality, or aggregate welfare. Derive conditions and counterexamples. Clearly distinguish real wages, labor share, profits, rents, dividends, market capitalization, aggregate wealth, and the return on capital. Distinguish task automation from AGI attainment, and transitional dynamics from asymptotic claims. | |
| 7 | + | |
| 8 | +## REQUIRED DELIVERABLES | |
| 9 | +1. `paper/main.tex`: 35–60+ page English LaTeX working paper, with abstract, introduction, literature, model, propositions, proofs, quantitative experiments, empirical strategy, results, limitations, conclusion, bibliography. | |
| 10 | +2. `paper/appendix.tex`: full derivations, equilibrium definitions, existence/uniqueness conditions, computational methods, robustness, identification. | |
| 11 | +3. `paper/references.bib`: verified, accurate references; no invented citations, DOIs or results. | |
| 12 | +4. `slides/main.tex`: 30–45 Beamer research seminar slides. | |
| 13 | +5. `src/`: documented Python implementation, numerical equilibrium solver, simulations, calibration, tests. | |
| 14 | +6. `data/README.md`, `data/raw/`, `data/processed/`: sources, licenses, provenance and transformations; no fabricated observed data. | |
| 15 | +7. `results/`: reproducible tables and figures (PDF and PNG), parameter files and machine-readable simulation outputs. | |
| 16 | +8. `docs/model_dictionary.md`, `docs/assumptions_register.md`, `docs/identification.md`, `docs/research_log.md`, `docs/replication.md`, `docs/referee_report.md`. | |
| 17 | +9. `Makefile`, `requirements.txt` or `pyproject.toml`, environment lockfile, `README.md` with one-command reproduction. | |
| 18 | +10. Compile PDFs if TeX available, and run numerical checks. If not available, document exact blockers rather than claim success. | |
| 19 | + | |
| 20 | +## PHASE 0 — SOURCE AUDIT (DO FIRST) | |
| 21 | +Download and read the complete original PDF. Extract the exact primitives, assumptions, definitions, propositions, proofs, and limitations with page references. Restrepo's central primitives include task output X_t(ω)=L_t(ω)+Q_t(ω)/α_t(ω), an aggregate production aggregator F, heterogeneous skill sets, finite but expanding compute, bottleneck vs accessory tasks, asymptotic additive output, wages tied to compute-equivalent labor, and a vanishing labor share under stated assumptions. Reconstruct the exact original propositions, including accessory-task nuances; never silently simplify them into universal claims. Reproduce a minimal baseline numerically or analytically before adding features. Explicitly separate the original author's conclusions from our proposed extensions. | |
| 22 | + | |
| 23 | +Prepare a literature matrix: Acemoglu–Restrepo task automation/displacement/reinstatement; Restrepo (2025); Korinek–Suh AI transition; endogenous growth and innovation; Hulten and production networks; HANK/HA-DSGE; Bewley–Huggett–Aiyagari; incomplete-market asset pricing; recursive preferences and long-run risk; q-theory and investment; intangible capital and markups; land/housing urban economics; Piketty-style wealth distribution (without equating r>g with inevitable concentration); automation taxation, sovereign wealth funds and broad-based capital ownership. Verify exact references from authoritative sources. Mark all unverified claims. | |
| 24 | + | |
| 25 | +## PHASE 1 — ECONOMIC ARCHITECTURE | |
| 26 | +Use a nested, modular economy, not an intractable all-at-once model. Define a transparent benchmark and add layers with tests at every stage. | |
| 27 | + | |
| 28 | +### 1. Time, agents, sectors and assets | |
| 29 | +Discrete time t=0,1,...; states z_t; J industries linked by an input-output network; firms of heterogeneous AI exposure; households h with permanent skill s, idiosyncratic employment shocks, wealth a, asset ownership, housing status, possibly entrepreneurial ability; government and central bank; optional rest of world. Sectors: AI/compute, energy, semiconductors/capital goods, manufacturing, professional services, healthcare, construction, housing services, land-intensive sectors, other services. Explicitly model each sector's resource constraints and price index. | |
| 30 | + | |
| 31 | +### 2. Tasks and automation | |
| 32 | +For task ω in industry j: | |
| 33 | +X_{jωt}=ℓ_{jωt}+q_{jωt}/α_{jωt}, with extension to task-specific productivity, human quality, capital equipment and automation fixed costs. Distinguish effective compute services q from installed compute capital K^Q and energy. Automation may be technologically feasible but privately unprofitable. Define endogenous task allocation from firm cost minimization, including adoption costs, skill-task eligibility, and optional newly created human-complementary tasks. Preserve bottleneck/accessory distinction; investigate whether scarce land, energy, grid capacity, physical machinery, natural resources and regulatory barriers alter asymptotic conclusions. Never assume unconstrained compute and binding energy scarcity simultaneously without clarifying growth regimes. | |
| 34 | + | |
| 35 | +### 3. Sectoral production | |
| 36 | +Construct a well-defined nested CES or task aggregator F_j({X_jω},K^N_j,E_j,T_j,M_{1j},...,M_{Jj};Z_j). State elasticities, homogeneity, units, and possible non-constant returns. Input-output identity: gross output_j = final demand_j + intermediate demand by other sectors; GDP/value added excludes intermediates. Include energy and land market clearing, investment goods production, capital accumulation K_{j,t+1}=(1−δ_j)K_{jt}+I_{jt}−adjustment_costs in units consistently specified. Include compute-capacity investment, obsolescence, chip supply bottlenecks and endogenous AI productivity growth as extensions. | |
| 37 | + | |
| 38 | +### 4. Firms, competition and income accounting | |
| 39 | +Derive firm FOCs and complementary slackness for labor, compute, capital, energy, land and intermediates. Compare perfect competition and monopolistic competition with markups, fixed costs, innovation rents and entry. Income identity for net value added: labor compensation + reproducible capital services + land/resource rents + economic profits + net production taxes (after consistent depreciation treatment). Never double count capital rental payments and distributed profits, or compute services and compute capital returns. Establish who owns AI models, data, hardware, patents, land and corporate equity. | |
| 40 | + | |
| 41 | +### 5. Households and income-to-wealth transmission | |
| 42 | +Heterogeneous households maximize E Σ β_h^t u(c_{ht},housing_{ht},leisure_{ht}) subject to state-contingent budget constraints, labor income, taxes, transfers, asset dividends, interest, housing costs, borrowing limits and portfolio constraints. Define both an analytically tractable two-class capitalist/worker economy and a quantitative heterogeneous-agent model. Capital accumulation from retained earnings, new saving and asset-price revaluations must be separately tracked. Decompose changes in household net worth into saving, realized returns, revaluation, transfers and debt. Include endogenous labor supply, unemployment or occupational transitions where feasible. | |
| 43 | + | |
| 44 | +### 6. Asset pricing — essential contribution | |
| 45 | +Derive household Euler equations and a coherent stochastic discount factor M_{t,t+1}; with heterogeneous incomplete-market agents explain marginal investor pricing and market participation. Price equity using P^E_{it}=E_t[M_{t,t+1}(D_{i,t+1}+P^E_{i,t+1})], with clean treatment of dilution, issuance, buybacks and reinvestment. Price risk-free and risky bonds, duration and term premia; rental property using net rents, maintenance, property taxes, vacancy, expected capital gains and terminal/transversality conditions; land using scarce-location rents; compute infrastructure using depreciation, capex and rapid obsolescence; intangibles via appropriable rents and competition. Derive Tobin's q, investment valuation and market-to-book. Distinguish equilibrium asset supply and the market value of claims from the physical stock of capital and aggregate net national wealth. Investigate bubbles only under explicit equilibrium assumptions. | |
| 46 | + | |
| 47 | +### 7. Monetary/fiscal/open-economy optional modules | |
| 48 | +Implement a real baseline first. Then optionally add nominal rigidities, monetary rule, inflation, government debt, taxes on labor/capital/land, transfers, public investment and sovereign wealth funds. Optional open economy with international capital flows, ownership of AI rents, FX, terms of trade and cross-border asset valuation. Do not conflate real interest rates with rental rates on physical capital or observed equity returns. | |
| 49 | + | |
| 50 | +## PHASE 2 — THEOREMS AND FORMAL RESULTS | |
| 51 | +Each result needs explicit assumptions, mathematical statement, rigorous proof or counterexample, economic interpretation, and simulation check. Prioritize: | |
| 52 | +T1. Conditions under which Restrepo's labor-share asymptote survives multi-sector production with complementary physical inputs and scarce factors; counterexamples with binding energy/land. | |
| 53 | +T2. Sufficient conditions for factor-income reallocation, and distinction between wages falling, stagnating or growing while labor share falls. | |
| 54 | +T3. Asset-value response to automation: cash-flow effect vs discount-rate effect vs competition/entry effect vs capital-expenditure effect; derive signed comparative statics only conditional on assumptions. | |
| 55 | +T4. Capital deepening, rental rates, equilibrium safe rate, risk premia and price-dividend ratios along transition; identify parameter regimes where asset valuations fall despite higher output. | |
| 56 | +T5. Distributional transition: conditions for wealth concentration, broadening of ownership, or non-monotonic inequality; compare wages, disposable income, consumption, wealth and welfare. | |
| 57 | +T6. Scarcity rents and relative-price effects for land, electricity, energy, chips and location-specific housing; separate structure and land values. | |
| 58 | +T7. General equilibrium feedback: demand compression among low-wealth households, savings behavior, capital supply, market clearing and redistribution. | |
| 59 | +T8. Welfare and optimal/second-best policies under specified tax instruments and administrative constraints. | |
| 60 | +T9. Robustness to endogenous innovation, new human tasks, nonhomothetic preferences, AI ownership, international trade and stochastic AGI arrival. | |
| 61 | + | |
| 62 | +Proof protocol: write assumptions as numbered statements; verify differentiability, convexity/concavity, equilibrium existence, transversality, boundedness, market clearing, timing, stochastic integrability, and no-arbitrage. Use simple closed-form special cases and numerical counterexamples. Label conjectures as conjectures, not theorems. | |
| 63 | + | |
| 64 | +## PHASE 3 — QUANTITATIVE MODEL AND SOLVER | |
| 65 | +Start with 2 sectors × 2 household types × 3 task classes × 2 capital types; then scale to 8–12 sectors, richer task grids and wealth distribution. Specify states, controls, prices, transition laws, expectations and equilibrium mapping. Implement deterministic perfect-foresight transition first, then aggregate shocks and incomplete markets. Use scipy optimization/root finding, sparse linear algebra, automatic differentiation if justified, household EGM or policy iteration, stationary distribution transition, and market-clearing outer loops. For stochastic pricing use a consistent SDF and simulation/perturbation/global methods as appropriate. Include numerical residual checks for every household FOC, firm FOC, goods/factor markets, government budget, asset market and national accounting identity. Tests must fail on deliberately inconsistent accounting. Record seeds, solver tolerances and convergence diagnostics. | |
| 66 | + | |
| 67 | +Benchmark experiments: gradual AI cost reduction; rapid AGI capability shock; compute abundance with cheap energy; compute abundance with energy constraint; AI monopoly vs competitive diffusion; broad vs concentrated ownership; high vs low capital-labor substitution; human task reinstatement; abundant vs scarce land; redistributive taxes and citizen capital fund; low vs high risk aversion; high vs low savings propensity; domestic vs foreign AI ownership; severe chip depreciation. Use stochastic sensitivity and parameter uncertainty rather than one forecast. | |
| 68 | + | |
| 69 | +Simulation horizon 2026–2100 for conditional scenarios, with annual reporting and explicit caveat that these are not point predictions. Construct full dynamic plots: sector GDP, productivity, real wages by skill, employment, labor share, capital share, profits, rents, AI adoption, compute investment, energy demand, safe rates, equity returns, equity price/dividend, land and housing price/rent, debt, wealth Gini, top 1%/10% wealth shares, consumption distribution, welfare equivalents and ownership concentration. Decompose valuation changes into dividend news, discount-rate news and expected-return/risk channels where methodologically possible. | |
| 70 | + | |
| 71 | +## PHASE 4 — EMPIRICAL DISCIPLINE | |
| 72 | +Seek authoritative sources: BEA/NIPA and fixed assets; BLS occupations and O*NET task content; Federal Reserve Financial Accounts, SCF, FRED; OECD STAN and TiVA; World Input-Output Database; World Bank; Statistics Canada national accounts, input-output tables, wealth surveys and labor-force statistics; housing/land data; company annual reports/Compustat if licensed; electricity/EIA/IEA; semiconductor/compute indicators with source caveats. Prioritize US baseline and optional Canada replication. Check exact definitions, vintages, geographic scope, units, time frequency and licenses. Build a source-to-parameter table with observed moments, uncertainty and identification limits. Do not present proprietary datasets as accessible unless actually available. Do not use speculative AGI dates as facts. | |
| 73 | + | |
| 74 | +Estimate/calibrate: baseline sector labor shares, IO coefficients, investment shares, capital-output ratios, depreciation, labor earnings heterogeneity, wealth concentration, household MPC/savings, dividend yields, housing rent-price ratios, capital adjustment costs, energy shares, markups, and AI exposure by task. Clearly separate externally identified, calibrated and scenario-only parameters. Conduct partial identification and sensitivity for automation elasticities and future compute costs. Validate against pre-AGI historical patterns without claiming the historical sample identifies post-AGI structural parameters. Provide a falsifiable short-run empirical design: industry/firm differential AI exposure, wages, employment, investment, margins and equity valuation, with pre-trends, confounding, measurement error and alternative mechanisms addressed. | |
| 75 | + | |
| 76 | +## PHASE 5 — PAPER STRUCTURE | |
| 77 | +1. Introduction: novel question, mechanism, contribution and disciplined findings. | |
| 78 | +2. Related literature and precise differentiation from Restrepo. | |
| 79 | +3. Benchmark Restrepo replication and limitations. | |
| 80 | +4. Multisector task-based production and equilibrium. | |
| 81 | +5. Household wealth accumulation and ownership. | |
| 82 | +6. Asset-pricing block and equilibrium valuation. | |
| 83 | +7. Analytical propositions and counterexamples. | |
| 84 | +8. Quantitative calibration and identification. | |
| 85 | +9. Baseline transition and mechanism decompositions. | |
| 86 | +10. Robustness, policy and welfare. | |
| 87 | +11. Conclusion, limitations and open questions. | |
| 88 | +Appendices: proofs, algorithms, extra results, source table, replication tests. | |
| 89 | + | |
| 90 | +Writing rules: academic English, notation consistent across text/code/tables, every result traceable to output, no fabricated empirical estimates, no invented statistical significance. Tables and figures must be genuinely generated, captioned, and discussed. Clearly label theoretical, simulated and empirical evidence. BibLaTeX or natbib consistently. Avoid hype words like 'revolutionary' or 'legendary' in manuscript. | |
| 91 | + | |
| 92 | +## PHASE 6 — CRITICAL SELF-REVIEW | |
| 93 | +Write an adversarial referee report addressing: (1) is this genuinely new relative to Restrepo and task models? (2) is the model too large to identify? (3) does an additive output limit survive scarce physical complements? (4) is factor income accounting correct? (5) are equity values double counted with physical capital? (6) is rising capital share mistaken for rising rates of return? (7) does a higher profit share necessarily imply higher prices? (8) is heterogeneity essential to claims? (9) do borrowing constraints break representative-agent pricing? (10) are numerical paths equilibrium outcomes or arbitrary assumptions? (11) are welfare and policy comparisons internally consistent? (12) can the core mechanisms be established in a simpler model? Revise after review. | |
| 94 | + | |
| 95 | +## EXECUTION ORDER AND STOP CONDITIONS | |
| 96 | +A. Audit source PDF and write source notes; B. derive smallest exact benchmark; C. derive analytic 2-household, 2-sector model; D. implement and verify its solver; E. integrate asset pricing; F. derive propositions; G. extend to heterogeneous households and IO networks; H. collect/calibrate defensible data; I. run scenarios and robustness; J. generate tables/figures; K. write and compile paper/slides; L. perform referee audit and final replication. | |
| 97 | + | |
| 98 | +At each stage update `docs/research_log.md` with completed steps, assumptions, residuals, unresolved problems and next actions. Never declare a theorem proven without a proof; never declare a model solved without numerical equilibrium checks; never declare a PDF compiled without verifying the file. If a block cannot be solved, deliver a verified simpler model and clearly document the limitation. Prioritize a correct publishable contribution over a grand but incoherent system. | |
| 99 | + | |
| 100 | +## FIRST RESPONSE EXPECTED FROM CLAUDE CODE | |
| 101 | +Inspect the repository; obtain the Restrepo PDF; write a precise 2–3 page audit of the original model with source pages; propose the smallest tractable extension connecting task automation, household ownership and equity valuation; list equations, equilibrium conditions, initial propositions and computational architecture; then start implementing without waiting for further permission unless a genuinely blocking external input is required. | |
| 102 | + | |
| 103 | +--- | |
| 104 | + | |
| 105 | +## UQO conventions (added 2026-10-27) | |
| 106 | +This project is **UQO Working Paper No. 24** in `Recherche-UQO/finance/`. Follow `Recherche-UQO/HARMONISATION.md` | |
| 107 | +(canonical title page, `\WP…` macros, README template, `paper/Makefile` + `.latexmkrc`, git remote `gitsrv:srv/git/wp24_uqo.git`). | |
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Makefile
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| 1 | +# UQO WP 24 — From Labor to Capital. One-command reproduction: `make all` | |
| 2 | +PY ?= python3 -W ignore | |
| 3 | +N ?= 8 | |
| 4 | + | |
| 5 | +.PHONY: all setup test data benchmark scenarios ha figures tables paper slides clean-results clean | |
| 6 | + | |
| 7 | +all: test benchmark scenarios ha figures tables paper | |
| 8 | + | |
| 9 | +setup: | |
| 10 | + $(PY) -m pip install -r requirements.txt | |
| 11 | + | |
| 12 | +test: | |
| 13 | + $(PY) -m pytest tests -q | |
| 14 | + | |
| 15 | +data: | |
| 16 | + python3 -I scripts/01_download_data.py | |
| 17 | + python3 -I scripts/02_build_calibration_targets.py | |
| 18 | + | |
| 19 | +benchmark: | |
| 20 | + $(PY) scripts/10_benchmark.py | |
| 21 | + $(PY) scripts/11_regime_map.py | |
| 22 | + | |
| 23 | +scenarios: | |
| 24 | + scripts/21_run_all_scenarios.sh $(N) | |
| 25 | + | |
| 26 | +ha: | |
| 27 | + $(PY) scripts/30_ha_equilibrium.py baseline | |
| 28 | + $(PY) scripts/30_ha_equilibrium.py no_ai | |
| 29 | + $(PY) scripts/30_ha_equilibrium.py citizen_fund_20 | |
| 30 | + $(PY) scripts/31_ha_figures.py | |
| 31 | + | |
| 32 | +figures: | |
| 33 | + $(PY) scripts/40_figures.py | |
| 34 | + | |
| 35 | +tables: | |
| 36 | + $(PY) scripts/50_tables.py | |
| 37 | + cp results/figures/*.pdf paper/figures/ | |
| 38 | + cp results/tables/*.tex paper/tables/ | |
| 39 | + | |
| 40 | +paper: tables | |
| 41 | + cd paper && latexmk -pdf main.tex | |
| 42 | + | |
| 43 | +slides: | |
| 44 | + cd slides && latexmk -pdf main.tex | |
| 45 | + | |
| 46 | +clean-results: | |
| 47 | + rm -rf results/scenarios/* results/ha/* results/figures/* results/tables/* | |
| 48 | + | |
| 49 | +clean: | |
| 50 | + cd paper && latexmk -C; cd ../slides && latexmk -C | |
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README.md
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| 1 | +<!-- Author: Simon-Pierre Boucher — simon-pierre.boucher@uqo.ca --> | |
| 2 | +# 🤖 From Labor to Capital | |
| 3 | + | |
| 4 | +**UQO Working Paper No. 24** — *Artificial General Intelligence, General Equilibrium, and Asset Valuation* | |
| 5 | + | |
| 6 | +[](paper/main.pdf) | |
| 7 | +[](slides/main.pdf) | |
| 8 | +[](requirements.txt) | |
| 9 | +[](data/README.md) | |
| 10 | +[](Makefile) | |
| 11 | +[](https://uqo.ca) | |
| 12 | +[](mailto:simon-pierre.boucher@uqo.ca) | |
| 13 | + | |
| 14 | +> **TL;DR** — Restrepo (2025, NBER) shows that once compute can do every task, wages converge to the value of the compute a worker saves and the labor share goes to zero. This paper keeps his task block and makes compute a *reproducible, priced, depreciating, energy-using* capital good with physical complements (structures, land, energy), then adds households that own the claims. The labor-share result is robust to everything (it only needs within-task substitutability). The wage-level result is not: with gross complements and cheap compute, the price of compute and wages go to zero *in level* and the income that labor loses goes to the owners of the scarcest complement; with a floor on the compute price, the wage is the user cost of the replacement compute. Income shares and asset values are different objects: compute is capitalized at the duration of chips (P/E ≈ 2), so capital's share can rise while wealth/income *falls*, and a growth boom raises the real rate and compresses valuation ratios when the EIS < 1. Calibrated to the US in 2026 and solved to 2300 under 25 conditional scenarios: labor share 0.56 → 0.28 (2050) → <0.001 (2075+); real return peaks at 40 % in 2060; wealth/GDP falls from 4.1 to 2.7 in 2042 before land captures 60 % of income and lifts it to 31 by 2100; the market value of compute never exceeds one fifth of GDP. A land tax redistributes without distortion, a compute tax raises wages one-for-one with the tax-inclusive user cost, a citizen fund bounds workers' consumption share below. | |
| 15 | + | |
| 16 | +--- | |
| 17 | + | |
| 18 | +## 📌 Status | |
| 19 | + | |
| 20 | +| Item | Value | | |
| 21 | +|---|---| | |
| 22 | +| Version | 1.0 (October 2026) | | |
| 23 | +| SSRN | not yet submitted | | |
| 24 | +| Journal | — (candidates: a macro-finance or growth field journal) | | |
| 25 | +| Code & data | fully reproducible from `make all`; public data only (FRED/BEA/Fed/Shiller/LBNL/EIA/Epoch), nothing typed from memory | | |
| 26 | +| Source audited | Restrepo (2025), NBER conference paper f227505, July 4 2025 draft (`docs/source_audit.md`) | | |
| 27 | + | |
| 28 | +## 🎯 What this paper does | |
| 29 | + | |
| 30 | +1. **Audits and replicates** Restrepo (2025) exactly in a finite-task CES economy (Propositions 1–6 hold to 1e-6; `scripts/10_benchmark.py`). | |
| 31 | +2. **Proves eight propositions** in a limit economy with one scarce complement and priced compute (Section 7, proofs in Appendix A): labor share → 0 always; wages → 0 vs positive depending on σ and the compute price; balanced AGI growth with w = (r+δ_Q) p̄ CEU; energy bottleneck ⇒ positive labor share; W/Y = Σ s_k/[(1+r)(1+π_k)−(1−δ_k)] + s_T/(r−g); discount-rate channel with sign(1−γ); wage = tax-inclusive user cost of the replacement. | |
| 32 | +3. **Builds a quantitative model** (4 tasks × 2 skills, 4 capital stocks with Tobin's q, energy from a fixed resource, housing = structures + land, two-class and heterogeneous households), calibrated to 2026 US shares, solved as 275-year perfect-foresight equilibria by a banded Newton method with numerical checks (Euler residuals < 1e-9, income identity < 1e-8, horizon check < 1e-7). | |
| 33 | +4. **Runs 25 conditional scenarios** (capability speed, Moore's law and its end, energy constraints, elasticities, scarce land, chip depreciation, monopoly, foreign ownership, land/compute/capital taxes, citizen fund, reinstatement, risk aversion) and decomposes valuations into cash-flow and discount-rate channels. | |
| 34 | +5. **Writes an adversarial referee report** (`docs/referee_report.md`) and a falsifiable short-run empirical design (Section 11). | |
| 35 | + | |
| 36 | +## 🏆 Headline results (baseline, two-class economy) | |
| 37 | + | |
| 38 | +| Object | 2026 | 2050 | 2100 | | |
| 39 | +|---|---|---|---| | |
| 40 | +| Labor share | 0.56 | 0.28 | 3e-5 | | |
| 41 | +| Wage of skill L per worker (2026 = 1) | 1 | 0.06 | 0.005 | | |
| 42 | +| Wage of skill H per worker (2026 = 1) | 1 | 3.5 (transitory premium of the last-automated skill) | 0.02 | | |
| 43 | +| GDP (2026 = 1; trend would give 1.4 / 3.0) | 1 | 3.7 | 233 | | |
| 44 | +| Real return on wealth | 6.3 % | 22 % (peak 40 % in 2060) | 5.5 % | | |
| 45 | +| Wealth / GDP | 4.1 | 3.0 (min 2.7 in 2042) | 31 | | |
| 46 | +| Land share of income / of wealth | 0.09 / 0.29 | 0.11 / — | 0.60 / 0.90 | | |
| 47 | +| Value of compute capital / GDP (P/E) | 0.14 (5.0) | 0.16 (1.8) | 0.13 (3.3) | | |
| 48 | +| Energy used by compute | 4.4 % | 47 % (peak 62 % in 2060) | 26 % | | |
| 49 | +| Tasks automated (of 4) | 1 | 3 | 4 | | |
| 50 | + | |
| 51 | +The long run is pinned by two unidentified scenario inputs — the compute-price floor (×1/100 in the baseline) and the terminal compute per worker (α₁^∞ = 1) — and the paper reports the dependence (Table 4) rather than a number. | |
| 52 | + | |
| 53 | +## 🗂 Repository layout | |
| 54 | + | |
| 55 | +``` | |
| 56 | +├── CLAUDE.md # research protocol (6 phases, deliverables, stop conditions) | |
| 57 | +├── Makefile · requirements.txt # one-command reproduction (make all) | |
| 58 | +├── source_audit/ # Restrepo (2025) PDF text extraction | |
| 59 | +├── docs/ # source_audit, model_dictionary, assumptions_register, identification, | |
| 60 | +│ # research_log, replication, referee_report, literature_matrix, references_verification | |
| 61 | +├── data/ # README (provenance), raw/ (downloads, git-ignored), processed/ (162 calibration targets, versioned) | |
| 62 | +├── src/agi_ge/ # ces, tasks (static block + KKT polish), production, steady (BGP), calibration, | |
| 63 | +│ # transition (banded Newton), solve (driver), households (EGM, numba), ha_equilibrium, | |
| 64 | +│ # analysis (valuation ratios, Campbell–Shiller, welfare), scenarios | |
| 65 | +├── scripts/ # 01/02 data, 10 benchmark, 11 regime map, 20/21 scenarios, 30/31 HA, 40 figures, 50 tables, 55 fallbacks | |
| 66 | +├── tests/ # pytest: static (Restrepo P1–P6, KKT, accounting), transition (BGP, Jacobian, equilibria) | |
| 67 | +├── results/{data,scenarios,ha,figures,tables}/ | |
| 68 | +├── paper/ # main.tex → main.pdf (UQO template), sections/, appendix/, figures/, tables/, references.bib (118 verified) | |
| 69 | +└── slides/ # main.tex → main.pdf (Beamer, metropolis) | |
| 70 | +``` | |
| 71 | + | |
| 72 | +## 🚀 Quick start | |
| 73 | + | |
| 74 | +```bash | |
| 75 | +make setup # pinned requirements | |
| 76 | +make test # ≈ 1 min | |
| 77 | +make benchmark # fig01/fig02, tab01 | |
| 78 | +make scenarios N=12 # 25 scenarios × 275 years, 12 in parallel (≈ 7–15 min each; one core each) | |
| 79 | +make ha # heterogeneous-agent GE (baseline, frozen, citizen fund) | |
| 80 | +make figures tables paper # results/figures, results/tables + numbers.tex → paper/main.pdf | |
| 81 | +make slides | |
| 82 | +``` | |
| 83 | + | |
| 84 | +## 📊 Data | |
| 85 | + | |
| 86 | +Public, key-less endpoints only, retrieved 2026-10-27 with URLs, vintages and SHA-256 hashes in `data/raw/manifest.json`: PWT labor share, BEA fixed assets and NIPA (via FRED), Fed Z.1 and Distributional Financial Accounts, TIPS and Cleveland-Fed real rates, Shiller CAPE/D/P and home prices, LBNL data-center electricity (4.4 % in 2023), EIA generation, Epoch AI GPU price–performance (doubling 2.02 years), BEA 2023 use table, Statistics Canada (comparison). See `data/README.md` and Appendix D. | |
| 87 | + | |
| 88 | +## 🧮 Methodology (one paragraph) | |
| 89 | + | |
| 90 | +Static block: concave program over labor/compute allocations (SLSQP + Newton polish on the active set), prices by the envelope theorem, energy residual, utilization margin. Dynamics: unknowns per year = four log stocks, log capitalists' expenditure, r; residuals = four Tobin's-q Euler equations, goods market, household Euler; banded Jacobian by colored finite differences; direct solve on 275 years from a partial-equilibrium guess, homotopy fallback; base point re-calibrated per scenario. Households: EGM with housing price index, 2 skills × 3 discount types × 5 productivity states, non-stochastic simulation, GE by damped quasi-Newton on the return path. | |
| 91 | + | |
| 92 | +## 🖼 Figures & tables | |
| 93 | + | |
| 94 | +fig01 benchmark replication · fig02 regime map · fig03 baseline transition · fig04 valuation · fig05 technology scenarios · fig06 substitutability/scarcity · fig07 Campbell–Shiller decomposition · fig08 policy · fig09 preferences/reinstatement/Moore · fig10 distribution (HA) · tab01 benchmark · tab02 calibration · tab03 baseline · tab04 scenarios · tab05 decomposition · tab06 policy & welfare · tab07 distribution · tab08 diagnostics. | |
| 95 | + | |
| 96 | +## ⚠️ Limitations | |
| 97 | + | |
| 98 | +Perfect foresight (no risk premia; the 2026 jump of r is the news effect); the wage level after 2060 ∝ compute-price floor × terminal compute per worker (scenario inputs); no endogenous innovation, new tasks (except one reinstatement scenario), nonhomothetic demand, labor supply or return heterogeneity (top-1 % share under-predicted); energy = electricity; conditional scenarios, not forecasts. | |
| 99 | + | |
| 100 | +## 📖 Citation | |
| 101 | + | |
| 102 | +```bibtex | |
| 103 | +@techreport{Boucher2026WP24, | |
| 104 | + author = {Boucher, Simon-Pierre}, | |
| 105 | + title = {From Labor to Capital: Artificial General Intelligence, General Equilibrium, and Asset Valuation}, | |
| 106 | + institution = {Universit\'e du Qu\'ebec en Outaouais, D\'epartement des sciences administratives}, | |
| 107 | + type = {UQO Working Paper}, | |
| 108 | + number = {24}, | |
| 109 | + year = {2026}, | |
| 110 | + month = {October} | |
| 111 | +} | |
| 112 | +``` | |
| 113 | + | |
| 114 | +## 👤 Author & contact | |
| 115 | + | |
| 116 | +Simon-Pierre Boucher — Département des sciences administratives, Université du Québec en Outaouais — simon-pierre.boucher@uqo.ca | |
| 117 | + | |
| 118 | +## 📝 Notes | |
| 119 | + | |
| 120 | +Built 2026-10-27 from the research protocol in `CLAUDE.md`. Three specification changes were made for numerical reasons and are documented in `docs/research_log.md`: accessory tasks are kept in the replication only; capitalists' expenditure is an unknown of the dynamic system; the base point is re-calibrated for each scenario's anticipated price path. `paper/tables/fallbacks.tex` must be empty of `??` in the final version (`scripts/55_fallbacks.py`). | |
added
data/README.md
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@@ -0,0 +1,60 @@ | ||
| 1 | +# data/ — empirical inputs for WP 24 (“From Labor to Capital: AGI, General Equilibrium, and Asset Valuation”) | |
| 2 | + | |
| 3 | +Retrieved **2026-10-27**, key-less endpoints only, no number typed from memory. Two scripts rebuild everything: | |
| 4 | + | |
| 5 | +```bash | |
| 6 | +python3 -I scripts/01_download_data.py # raw downloads -> data/raw/<source>/ + data/raw/manifest.json (idempotent; --force to refetch) | |
| 7 | +python3 -I scripts/02_build_calibration_targets.py # -> data/processed/calibration_targets.csv, data/processed/series/*.csv, bea_io_value_added_2023.csv, epoch_price_performance_fit.csv | |
| 8 | +``` | |
| 9 | +Dependencies: `requests`, `pandas`, `numpy`, `openpyxl`, `xlrd` (installed 2026-10-27 for the Shiller `.xls` files), `PyMuPDF` (`fitz`, to read the LBNL PDF). | |
| 10 | +Raw files are untrusted data and are never executed. `data/raw/` and `data/processed/` are git-ignored (regenerate them); only the two READMEs are versioned. | |
| 11 | + | |
| 12 | +## Layout | |
| 13 | +| Path | Content | | |
| 14 | +|---|---| | |
| 15 | +| `raw/README.md` | one block per source: what, URL, size, vintage, licence/terms | | |
| 16 | +| `raw/manifest.json` | per-file URL, retrieval date, size, SHA-256, HTTP content type, status (93 entries, all RETRIEVED) | | |
| 17 | +| `raw/fred/_metadata.json`, `raw/fred/_RETRIEVAL_NOTE.md` | FRED titles/units/vintages; why the FRED csv files were fetched with WebFetch | | |
| 18 | +| `processed/calibration_targets.csv` | 162 rows: `target_id, description, value, unit, year_or_period, geography, frequency, source, series_id, url, raw_file, retrieved, status, notes` | | |
| 19 | +| `processed/series/*.csv` | 92 tidy `date,value` series (every FRED series, Shiller, DFA, Treasury, Cleveland Fed, StatCan, BLS API) | | |
| 20 | +| `processed/bea_io_value_added_2023.csv` | 71 BEA summary industries: compensation, taxes, gross operating surplus, value added, intermediates, gross output, labor share, intermediate share | | |
| 21 | +| `processed/epoch_price_performance_fit.csv` | 60 accelerators used in the FLOP/s-per-dollar regression | | |
| 22 | + | |
| 23 | +## Targets and transformations (US unless noted; “avg” = mean over 1990–latest full year of calendar-year means) | |
| 24 | +| Block | target_id prefix | Construction | | |
| 25 | +|---|---|---| | |
| 26 | +| 1 Labor share | `T01_labor_share_BLS_nonfarm_index_*` | BLS PRS85006173, **index 2017=100** (not a level). Latest 2026Q2 = 93.4; 2025 = 96.6; avg 103.9 | | |
| 27 | +| | `T01_labor_share_COE_GDI_*` | COE / GDI (both $bn SAAR). Latest 0.499 (2026Q2); 2025 0.508; avg 0.542 | | |
| 28 | +| | `T01_labor_share_COE_NDI_*` | COE / net domestic income W256RC1Q027SBEA. Latest 0.597; avg 0.642 | | |
| 29 | +| | `T01_labor_share_PWT_US_*`, `T12_labor_share_PWT_CA_*` | PWT `labsh` via FRED (PWT 11.0, to 2023): US 0.568 (2023), avg 0.604; Canada 0.659, avg 0.663. PWT 10.01 spreadsheet cross-check (`*_PWT1001_*`, to 2019): US 0.597, CA 0.655 | | |
| 30 | +| | `T01_labor_share_BEA_IO_2023_*` | 2023 summary use table: Σ compensation / Σ value added = 0.513 (all 71 industries incl. government); compensation/(compensation+GOS) = 0.548; intermediates / gross output = 0.427; selected industries (utilities 0.21, computer systems design 0.91, publishing incl. software 0.23, computer & electronic products 0.57, construction 0.61, professional services 0.74, ambulatory health 0.77) | | |
| 31 | +| 2 Profits | `T02_profits_after_tax_noIVA_GDP_*` | FRED **CP = after tax without IVA/CCAdj**: 0.131 (2026Q2), 2025 0.114, avg 0.086 | | |
| 32 | +| | `T02_profits_after_tax_IVA_CCAdj_GDP_*` | FRED **CPATAX = with IVA & CCAdj**: 0.119, 2025 0.110, avg 0.084 | | |
| 33 | +| | `T02_net_dividends_GDP_*` | DIVIDEND / GDP: 0.0675 (2026Q2), 2025 0.070, avg 0.051; annual B056RC1A027NBEA/GDPA 2025 0.070 | | |
| 34 | +| | `T02_GDP_*`, `T02_real_GDP_growth_avg` | GDPA 2025 = 30,861 $bn; GDP 2026Q2 = 32,563 $bn SAAR; GDPC1 2026Q2 = 24,408; real growth 1990–2025 = 2.48 %/yr (log) | | |
| 35 | +| 3 Household wealth | `T03_household_net_worth_GDP_*` | TNWBSHNO ($mn, end of quarter) / GDP ($bn SAAR) ×1e-3: **6.02** (2026Q2), 2025 5.72, avg 4.58 | | |
| 36 | +| | `T03_household_real_estate_GDP_*`, `T03_owners_equity_real_estate_GDP_*`, `T03_owners_equity_share_of_real_estate_*`, `T03_real_estate_share_of_net_worth_*` | HNOREMQ027S/GDP 1.66 (avg 1.47); OEHRENWBSHNO/GDP 1.10 (avg 0.81); HOEREPHRE 71.9 % (avg 60.7 %); real estate/net worth 0.276 (avg 0.322) | | |
| 37 | +| 4 Wealth concentration | `T04_wealth_share_*` | DFA `dfa-networth-shares.csv`, Net worth column, 2026Q2: top 0.1 % 15.0, **top 1 % 32.5**, **top 10 % 68.9**, p50–90 28.8, **bottom 50 % 2.3** (percent); 1990–2025 averages 11.3 / 28.0 / 65.3 / 32.4 / 2.3. FRED mirrors WFRBST01134 / WFRBSN09161 / WFRBSN40188 / WFRBSB50215 agree. Top 10 % hold 88.1 % of corporate equities & mutual fund shares | | |
| 38 | +| 5 Investment | `T05_*_GDP_*` | GPDI/GDP 0.179 (avg 0.175); PNFI/GDP 0.145 (avg 0.130); information-processing equipment Y034RC1Q027SBEA/GDP 0.0257 (avg 0.0214); software B985RC1Q027SBEA/GDP 0.0249 (avg 0.0154); IPE+software A679RC1Q027SBEA/GDP 0.0505; IPP Y001RC1Q027SBEA/GDP 0.0577 | | |
| 39 | +| 6 Capital | `T06_net_stock_private_fixed_assets_GDP_*` | current-cost net stock K1PTOTL1ES000 (end-2024, $mn)/GDPA ×1e-3 = **2.39** (avg 2.25); nonresidential K1NTOTL1ES000: 1.22 (avg 1.20) | | |
| 40 | +| | `T06_depreciation_rate_*` | current-cost depreciation / end-of-year current-cost net stock: private total M1PTOTL1ES000/K1PTOTL1ES000 = **0.0568** (2024; avg 0.0559); nonresidential 0.0890 (avg 0.0843); NIPA private CFC/net stock 0.0569 | | |
| 41 | +| | `T06_CFC_*` | COFC/GDP 0.165 (avg 0.155); private CFC A024RC1A027NBEA/GDPA 0.137 (2025) | | |
| 42 | +| 7 Real rate | `T07_TIPS10_*` | DFII10 daily: 2.66 % (2026-10-23), 2025 mean 1.96 %, 2003–2025 mean 0.97 %; Treasury.gov daily real curve 10 YR identical (1.963 / 0.973); Cleveland Fed REAINTRATREARAT10Y 2.47 % (2026-10), 2025 1.71 %, 1990–2025 avg 1.78 %; GS10 nominal 4.99 % (2026-09), 2025 4.29 % | | |
| 43 | +| 8 Equity valuation | `T08_*` | Shiller `ie_data.xls`: CAPE 30.8 (2023-09), 1990–2022 avg 26.4, 1881–2022 avg 17.3; D/P 0.0158 (2023-06), avg 0.0203; E/P 0.0417, avg 0.0461. **Vintage caveat: the Yale file ends 2023-09** | | |
| 44 | +| 9 Housing | `T09_*` | Shiller real HPI (1890=100) 209.3 (2023-01); real growth 1.54 %/yr 1990–2023, 0.56 %/yr 1890–2023. Case-Shiller national nominal growth 4.15 %/yr 1990–2025, real (CPI-U) 1.63 %/yr 1990–2024; CPI rent growth 3.27 %/yr. **Rent-price proxy** = (CPI rent / Case-Shiller), both rebased Jan-2000 = 100: 74.8 (2026-07), avg 91.1 — an index of the change in the rent-price ratio since 2000, not a level | | |
| 45 | +| 10 Energy | `T10_*` | LBNL 2024 report (PDF p. 7 [printed 5] and p. 53 [printed 52]): data centers **176 TWh in 2023 = 4.4 %** of US electricity consumption; 76 TWh = 1.9 % in 2018; 2028 scenarios 325–580 TWh = 6.7–12.0 % (p. 8 [6], p. 53 [52]). EIA Table 1.1: net generation 4,425 TWh (2025), 4,309 (2024); retail sales 4,080 TWh (2025), 3,975 (2024) | | |
| 46 | +| 11 Compute | `T11_*` | Epoch `ml_hardware.csv`, column `Price-performance` (= `Max performance` FLOP/s ÷ release price USD), 60 accelerators 2008–2026: OLS of log2 on release year gives a **doubling time of 2.02 years** (R² in notes), 2.02 years on 2015+. Caveat row: `Max performance` mixes precisions (FP32 for old GPUs, tensor FP16/FP8 for recent ones) | | |
| 47 | +| 12 Canada | `T12_*` | StatCan 36-10-0103-01: COE / GDP at market prices (SAAR) 0.492 (2026Q2), avg 0.506; 36-10-0580-01: households+NPISH net worth (market value) / GDP 5.65 (2026Q2), avg 4.25 | | |
| 48 | +| 13 Electricity price | `T13_*` | BLS APU000072610: 0.196 $/kWh (2026-09), 2025 mean 0.185, 1990–2025 avg 0.118 | | |
| 49 | +| cross-checks | `T00_crosscheck_BLS_API_*` | same three BLS series pulled through the BLS API v1: identical to the FRED copies | | |
| 50 | + | |
| 51 | +Conventions: ratios of a quarterly stock (end of quarter) to quarterly GDP use SAAR GDP of the same quarter; annual Fixed-Assets stocks (end of year) are divided by annual GDP of the same year; depreciation rates divide depreciation during year t by the end-of-year-t stock (not the lagged stock). “Latest full calendar year” requires 4 quarterly / 11–12 monthly / ≥240 daily observations (11 because BLS published no October-2025 CPI/APU values). | |
| 52 | + | |
| 53 | +## Explicit list of what could NOT be retrieved | |
| 54 | +* **No calibration target is NOT_RETRIEVED** (161 RETRIEVED, 1 INFO row for the Epoch caveat). | |
| 55 | +* Access paths that failed and were replaced (documented in `raw/README.md`, `raw/fred/_RETRIEVAL_NOTE.md`, `raw/manifest.json`): | |
| 56 | + 1. **FRED by direct HTTP** (`requests`/`urllib`/curl) — fred.stlouisfed.org timed out from this network on 2026-10-27 (also from the OVH server); the 43 csv files are verbatim copies obtained with the Claude Code WebFetch tool; `01_download_data.py --force` re-downloads them when FRED answers. | |
| 57 | + 2. **LBNL host** for the data-center report (HTTP 202 challenge page) — replaced by the eScholarship copy of the same report (DOI 10.71468/P1WC7Q). | |
| 58 | + 3. **BEA per-file xlsx URLs** (`apps.bea.gov/industry/xls/io-annual/...`) return “page not found” — replaced by BEA's own `AllTablesIO.zip`, which contains the same `IOUse_After_Redefinitions_PRO_1997-2023_Summary.xlsx`. | |
| 59 | + 4. **US Treasury “all years” csv** returns 403 — fetched year by year instead (2003–2026). | |
| 60 | +* Known limitations rather than failures: Shiller `ie_data.xls` and `Fig3-1.xls` on the Yale site end in 2023 (CAPE, D/P, E/P and the long real home-price index are therefore 2023 vintages; Case-Shiller via FRED runs to 2026-07); the rent-price ratio is a proxy index (no key-less level series); EIA direct consumption/losses are not in Table 1.1 (retail sales used); the Epoch doubling time is our own regression on Epoch's columns, not Epoch's published estimate; CSUSHPISA values are not redistributed in the targets (ratios only). | |
added
data/processed/.gitkeep
+0 −0
added
data/processed/bea_io_value_added_2023.csv
+72 −0
@@ -0,0 +1,72 @@ | ||
| 1 | +industry_code,industry_name,compensation_of_employees,taxes_less_subsidies,gross_operating_surplus,total_value_added,total_intermediate,total_industry_output,labor_share_of_value_added,labor_share_of_factor_cost_VA,intermediate_share_of_gross_output,value_added_share_of_total | |
| 2 | +111CA,Farms,37803.0,3344.0,181925.0,223072.0,333081.0,556153.0,0.16946546406541385,0.17204452777980048,0.5989017410676558,0.008047126075874551 | |
| 3 | +113FF,"Forestry, fishing, and related activities",34200.0,2605.0,15280.0,52085.0,15302.0,67387.0,0.6566189881923779,0.6911883589329022,0.22707643907578612,0.001878920535351483 | |
| 4 | +211,Oil and gas extraction,16306.0,49787.0,164574.0,230667.0,217761.0,448428.0,0.07069064929096923,0.09014816452896948,0.48560972999009877,0.008321109016567544 | |
| 5 | +212,"Mining, except oil and gas",15275.0,7714.0,39003.0,61992.0,53453.0,115445.0,0.24640276164666408,0.2814215704336932,0.46301702109229503,0.0022363068412692548 | |
| 6 | +213,Support activities for mining,41906.0,2557.0,51289.0,95751.0,36106.0,131857.0,0.4376560035926518,0.44965931648693597,0.27382694889160225,0.003454133055206679 | |
| 7 | +22,Utilities,93352.0,79502.0,264137.0,436991.0,192824.0,629815.0,0.21362453688977576,0.2611325103709485,0.306159745322039,0.015764065732241143 | |
| 8 | +23,Construction,823367.0,14767.0,508099.0,1346233.0,1122107.0,2468340.0,0.6116080945868955,0.6183913070254892,0.4545998525324712,0.04856417066464113 | |
| 9 | +321,Wood products,29840.0,896.0,28126.0,58862.0,106473.0,165335.0,0.5069484557099657,0.5147845288617465,0.6439834275864155,0.002123394845960622 | |
| 10 | +327,Nonmetallic mineral products,34699.0,1942.0,35627.0,72268.0,84517.0,156784.0,0.4801433552886478,0.4934021556749993,0.5390664863761608,0.002607004497432677 | |
| 11 | +331,Primary metals,38855.0,3231.0,39597.0,81683.0,234438.0,316121.0,0.4756803741292558,0.49527099372864936,0.7416084347449237,0.0029466423363562486 | |
| 12 | +332,Fabricated metal products,114482.0,4696.0,57946.0,177124.0,241555.0,418679.0,0.6463381585781712,0.6639408912705593,0.5769455836094001,0.006389592414391784 | |
| 13 | +333,Machinery,103328.0,4660.0,76147.0,184136.0,248064.0,432199.0,0.5611504540122518,0.5757236383897478,0.5739578296108968,0.006642544143179048 | |
| 14 | +334,Computer and electronic products,142641.0,10827.0,97518.0,250986.0,90928.0,341914.0,0.568322535918338,0.5939440120919891,0.2659382183824002,0.009054099058956079 | |
| 15 | +335,"Electrical equipment, appliances, and components",40605.0,1051.0,28437.0,70093.0,90593.0,160686.0,0.5793017847716605,0.5881202746154515,0.5637890046425948,0.0025285432866351447 | |
| 16 | +3361MV,"Motor vehicles, bodies and trailers, and parts",80326.0,4751.0,75352.0,160428.0,591396.0,751825.0,0.5006981324955744,0.5159752823134932,0.7866139061616733,0.005787298908425991 | |
| 17 | +3364OT,Other transportation equipment,80115.0,3796.0,85472.0,169383.0,220311.0,389694.0,0.47298134995837837,0.48382421325345587,0.5653435772683182,0.006110342652192383 | |
| 18 | +337,Furniture and related products,23413.0,509.0,6357.0,30278.0,48372.0,78651.0,0.7732677191360063,0.7864628820960698,0.6150207880382957,0.001092252202541465 | |
| 19 | +339,Miscellaneous manufacturing,51366.0,2316.0,46058.0,99741.0,74778.0,174519.0,0.5149938340301381,0.5272417474133684,0.4284805665858732,0.0035980687936352554 | |
| 20 | +311FT,Food and beverage and tobacco products,150485.0,25203.0,154157.0,329845.0,850973.0,1180818.0,0.45622944110112323,0.4939732538520624,0.7206639803932529,0.01189886808069521 | |
| 21 | +313TT,Textile mills and textile product mills,12199.0,762.0,3362.0,16323.0,32691.0,49014.0,0.7473503645163266,0.7839470471049419,0.6669727016770719,0.0005888378592405157 | |
| 22 | +315AL,Apparel and leather and allied products,7611.0,371.0,4603.0,12585.0,12156.0,24741.0,0.6047675804529201,0.6231373833306042,0.49133018067175943,0.00045399279902848065 | |
| 23 | +322,Paper products,34377.0,2277.0,36289.0,72943.0,128977.0,201921.0,0.4712857985001988,0.486471570486514,0.6387498080932642,0.0026313545283698425 | |
| 24 | +323,Printing and related support activities,26315.0,957.0,17594.0,44866.0,46932.0,91798.0,0.5865243168546338,0.5993076590220684,0.5112529684742587,0.0016185014637434894 | |
| 25 | +324,Petroleum and coal products,16355.0,5061.0,183398.0,204814.0,539301.0,744115.0,0.07985293974044744,0.08187611700450056,0.7247549101953327,0.0073884847946141625 | |
| 26 | +325,Chemical products,73992.0,21858.0,371417.0,467268.0,465500.0,932768.0,0.15835024011916074,0.1661214748691652,0.4990522830971903,0.0168562818606627 | |
| 27 | +326,Plastics and rubber products,58071.0,2616.0,33166.0,93854.0,199765.0,293619.0,0.6187376137404905,0.6364851978912064,0.6803544729734793,0.0033857004497432675 | |
| 28 | +42,Wholesale trade,664829.0,313016.0,627293.0,1605138.0,1182971.0,2788110.0,0.41418806358082605,0.5145249442390115,0.42429136583563776,0.05790394067914004 | |
| 29 | +441,Motor vehicle and parts dealers,130266.0,67988.0,102007.0,300261.0,169234.0,469495.0,0.4338425569754314,0.5608314354229721,0.36045964280769766,0.01083165131736914 | |
| 30 | +445,Food and beverage stores,128432.0,35902.0,48605.0,212939.0,105255.0,318194.0,0.6031398663467002,0.7254528714336551,0.3307887640873178,0.007681587018857818 | |
| 31 | +452,General merchandise stores,119393.0,56899.0,20286.0,196577.0,103763.0,300340.0,0.6073599658149224,0.8547670014819694,0.3454851168675501,0.007091342268940933 | |
| 32 | +4A0,Other retail,387507.0,180315.0,387048.0,954870.0,598782.0,1553652.0,0.4058217348958497,0.5002962991653271,0.38540290875949057,0.03444609487551254 | |
| 33 | +481,Air transportation,77253.0,34861.0,51961.0,164075.0,144352.0,308426.0,0.4708395550815176,0.5978686520036528,0.4680279872643681,0.005918861223726497 | |
| 34 | +482,Rail transportation,22659.0,-1538.0,30598.0,51719.0,38589.0,90309.0,0.4381175196736209,0.42546519706329683,0.4272996046905624,0.0018657174074655535 | |
| 35 | +483,Water transportation,7322.0,1357.0,13302.0,21981.0,46169.0,68150.0,0.33310586415540694,0.3550232738557021,0.6774614820249449,0.0007929452296738207 | |
| 36 | +484,Truck transportation,123449.0,6169.0,114416.0,244034.0,273225.0,517259.0,0.5058680347820386,0.518987661068253,0.5282170054073492,0.00880331177736323 | |
| 37 | +485,Transit and ground passenger transportation,33484.0,2778.0,32461.0,68723.0,76719.0,145442.0,0.48723134903889526,0.5077564637197665,0.5274886208935521,0.002479121742362676 | |
| 38 | +486,Pipeline transportation,10491.0,4745.0,37346.0,52582.0,22494.0,75076.0,0.19951694496215436,0.21930723080460732,0.2996163887260909,0.001896849372945218 | |
| 39 | +487OS,Other transportation and support activities,138801.0,3568.0,64779.0,207147.0,142176.0,349324.0,0.6700603918956104,0.6818007662835249,0.4070032405445947,0.007472645716356988 | |
| 40 | +493,Warehousing and storage,119360.0,1435.0,4479.0,125273.0,62740.0,188013.0,0.9527990867944409,0.9638320722874054,0.33370032923255305,0.004519113223098519 | |
| 41 | +511,"Publishing industries, except internet (includes software)",139704.0,7686.0,219475.0,366864.0,184119.0,550984.0,0.3808059662436216,0.3889536971816281,0.33416396846369406,0.013234295925529164 | |
| 42 | +512,Motion picture and sound recording industries,37084.0,6518.0,67711.0,111313.0,87754.0,199067.0,0.33315066524125664,0.35387184503077435,0.44082645541450866,0.0040155185091980345 | |
| 43 | +513,Broadcasting and telecommunications,106350.0,35623.0,325740.0,467713.0,439376.0,907088.0,0.22738303190204248,0.2461292786225092,0.4843807877515743,0.016872334844021276 | |
| 44 | +514,"Data processing, internet publishing, and other information services",156598.0,8950.0,317301.0,482849.0,319212.0,802061.0,0.3243208539315604,0.33044593890259316,0.3979896790892463,0.01741835272293229 | |
| 45 | +521CI,"Federal Reserve banks, credit intermediation, and related activities",344766.0,24116.0,542450.0,911333.0,459839.0,1371172.0,0.37830957509494334,0.388593082180664,0.33536201147631367,0.03287553591712534 | |
| 46 | +523,"Securities, commodity contracts, and investments",336636.0,8188.0,16515.0,361339.0,466436.0,827774.0,0.9316348359850445,0.9532353016131909,0.5634823031407123,0.013034986413043478 | |
| 47 | +524,Insurance carriers and related activities,359811.0,45396.0,307171.0,712378.0,657158.0,1369536.0,0.5050843793603957,0.539461334788645,0.47983988737791483,0.025698409391045767 | |
| 48 | +525,"Funds, trusts, and other financial vehicles",18214.0,809.0,24325.0,43348.0,203172.0,246519.0,0.420180861862139,0.42817179529373045,0.8241636547284388,0.0015637409497247978 | |
| 49 | +HS,Housing,5393.0,319323.0,2285730.0,2610446.0,286458.0,2896905.0,0.0020659304961680876,0.002353867513878565,0.09888415395050926,0.09416954201451738 | |
| 50 | +ORE,Other real estate,136624.0,41305.0,629744.0,807674.0,1165272.0,1972946.0,0.16915735804297277,0.17827466700070985,0.5906253896457379,0.029136128721694802 | |
| 51 | +532RL,Rental and leasing services and lessors of intangible assets,57550.0,31424.0,279502.0,368477.0,312743.0,681220.0,0.15618342528841692,0.17074516691786432,0.4590925104958751,0.013292483480939012 | |
| 52 | +5411,Legal services,175567.0,23759.0,162928.0,362254.0,84944.0,447198.0,0.48465165325986737,0.5186694042748047,0.18994718223247867,0.013067994232758302 | |
| 53 | +5415,Computer systems design and related services,514110.0,10855.0,40481.0,565445.0,213092.0,778537.0,0.9092130976487546,0.9270074703700565,0.2737082502180372,0.02039793073076355 | |
| 54 | +5412OP,"Miscellaneous professional, scientific, and technical services",1306504.0,22286.0,427701.0,1756492.0,902219.0,2658711.0,0.7438143754711095,0.753373447775782,0.33934451694825046,0.06336390302353072 | |
| 55 | +55,Management of companies and enterprises,423877.0,12748.0,43039.0,479663.0,279544.0,759207.0,0.883697512628658,0.9078228203788262,0.36820524573666996,0.017303420576908868 | |
| 56 | +561,Administrative and support services,582389.0,15953.0,205027.0,803369.0,521641.0,1325010.0,0.7249333743273639,0.7396204801528036,0.3936883495218904,0.028980829635495548 | |
| 57 | +562,Waste management and remediation services,45001.0,5129.0,31000.0,81130.0,84461.0,165592.0,0.55467767780106,0.5921106301232879,0.510054833566839,0.0029266933480477262 | |
| 58 | +61,Educational services,217968.0,12459.0,23269.0,253696.0,133985.0,387681.0,0.8591700302724521,0.9035429888449947,0.34560631034278183,0.009151859923903808 | |
| 59 | +621,Ambulatory health care services,759777.0,10578.0,220573.0,990928.0,469839.0,1460767.0,0.7667327999612484,0.7750058652522058,0.3216385638503608,0.035746855491115954 | |
| 60 | +622,Hospitals,516619.0,18882.0,95035.0,630537.0,513323.0,1143860.0,0.819331775930675,0.8446262102430458,0.44876383473502,0.02274606734374423 | |
| 61 | +623,Nursing and residential care facilities,174471.0,8350.0,11722.0,194543.0,120460.0,315004.0,0.8968248664819602,0.9370438201221314,0.382407842440096,0.007017967509050275 | |
| 62 | +624,Social assistance,187325.0,-5038.0,17929.0,200217.0,137095.0,337311.0,0.9356098632983213,0.9126496925760278,0.406435011013575,0.00722265206540209 | |
| 63 | +711AS,"Performing arts, spectator sports, museums, and related activities",75090.0,9237.0,65851.0,150178.0,109877.0,260055.0,0.5000066587649323,0.5327761261804584,0.42251446809328796,0.005417539179380149 | |
| 64 | +713,"Amusements, gambling, and recreation industries",90035.0,25752.0,28031.0,143819.0,105239.0,249058.0,0.6260299404112113,0.7625819456913929,0.42254816147242813,0.005188143850892098 | |
| 65 | +721,Accommodation,88384.0,34119.0,85761.0,208265.0,101273.0,309537.0,0.42438239742635586,0.5075310804214879,0.32717574958728685,0.007512976582412914 | |
| 66 | +722,Food services and drinking places,441074.0,97273.0,235505.0,773851.0,694993.0,1468844.0,0.5699727725363152,0.6519179578437995,0.47315644139200624,0.027915993764083335 | |
| 67 | +81,"Other services, except government",515618.0,41005.0,205160.0,761782.0,451387.0,1213169.0,0.6768576836942852,0.7153631215159175,0.37207264610289253,0.02748061521092682 | |
| 68 | +GFGD,Federal general government (defense),289664.0,,192435.0,482099.0,301097.0,783196.0,0.6008392467107379,0.6008392467107379,0.38444654977808873,0.017391297133002106 | |
| 69 | +GFGN,Federal general government (nondefense),213973.0,,153874.0,367847.0,175188.0,543035.0,0.5816902135942389,0.5816902135942389,0.32260903993296935,0.013269756785397657 | |
| 70 | +GFE,Federal government enterprises,67117.0,-11496.0,10421.0,66041.0,40346.0,106387.0,1.0162929089505004,0.865601382547912,0.3792380648011505,0.0023823709527723393 | |
| 71 | +GSLG,State and local general government,1549730.0,,244462.0,1794192.0,1100928.0,2895120.0,0.8637481384378037,0.8637481384378037,0.38027024786537345,0.06472389734402127 | |
| 72 | +GSLE,State and local government enterprises,132016.0,-36441.0,65456.0,161032.0,298806.0,459838.0,0.819812211237518,0.6685302220061579,0.6498071059808019,0.005809087676849766 | |
added
data/processed/calibration_targets.csv
+163 −0
@@ -0,0 +1,163 @@ | ||
| 1 | +target_id,description,value,unit,year_or_period,geography,frequency,source,series_id,url,raw_file,retrieved,status,notes | |
| 2 | +T01_labor_share_BLS_nonfarm_index_latest,"BLS nonfarm business sector labor share (index 2017=100), latest observation",93.446,index 2017=100,2026Q2,United States,Quarterly,FRED (U.S. Bureau of Labor Statistics),PRS85006173,https://fred.stlouisfed.org/graph/fredgraph.csv?id=PRS85006173,data/raw/fred/PRS85006173.csv,2026-10-27,RETRIEVED,"Nonfarm Business Sector: Labor Share for All Workers [Index 2017=100; Quarterly; vintage last updated 2026-09-03]. Index, not a level; 2017=100. Use for trends only." | |
| 3 | +T01_labor_share_BLS_nonfarm_index_lastfullyear,"BLS nonfarm business sector labor share (index 2017=100), latest full calendar-year average",96.58,index 2017=100,2025,United States,annual average,FRED (U.S. Bureau of Labor Statistics),PRS85006173,https://fred.stlouisfed.org/graph/fredgraph.csv?id=PRS85006173,data/raw/fred/PRS85006173.csv,2026-10-27,RETRIEVED,"Nonfarm Business Sector: Labor Share for All Workers [Index 2017=100; Quarterly; vintage last updated 2026-09-03]. Index, not a level; 2017=100. Use for trends only." | |
| 4 | +T01_labor_share_BLS_nonfarm_index_avg,"BLS nonfarm business sector labor share (index 2017=100), 1990-latest average of annual means",103.937,index 2017=100,1990-2025,United States,annual average,FRED (U.S. Bureau of Labor Statistics),PRS85006173,https://fred.stlouisfed.org/graph/fredgraph.csv?id=PRS85006173,data/raw/fred/PRS85006173.csv,2026-10-27,RETRIEVED,"Nonfarm Business Sector: Labor Share for All Workers [Index 2017=100; Quarterly; vintage last updated 2026-09-03]. Index, not a level; 2017=100. Use for trends only. Average: 1990-2025 mean of 36 obs." | |
| 5 | +T01_labor_share_COE_GDI_latest,"National-accounts labor share: compensation of employees / gross domestic income, latest observation",0.49919,ratio,2026Q2,United States,quarterly,FRED (U.S. Bureau of Economic Analysis),COE/GDI,https://fred.stlouisfed.org/graph/fredgraph.csv?id=COE ; https://fred.stlouisfed.org/graph/fredgraph.csv?id=GDI,data/raw/fred/COE.csv ; data/raw/fred/GDI.csv,2026-10-27,RETRIEVED,"National Income: Compensation of Employees, Paid [Billions of Dollars; Quarterly; vintage last updated 2026-09-30] divided by Gross Domestic Income [Billions of Dollars; Quarterly; vintage last updated 2026-09-30]." | |
| 6 | +T01_labor_share_COE_GDI_avg,"National-accounts labor share: compensation of employees / gross domestic income, 1990-latest average",0.542253,ratio,1990-2025,United States,annual average,FRED (U.S. Bureau of Economic Analysis),COE/GDI,https://fred.stlouisfed.org/graph/fredgraph.csv?id=COE ; https://fred.stlouisfed.org/graph/fredgraph.csv?id=GDI,data/raw/fred/COE.csv ; data/raw/fred/GDI.csv,2026-10-27,RETRIEVED,"National Income: Compensation of Employees, Paid [Billions of Dollars; Quarterly; vintage last updated 2026-09-30] divided by Gross Domestic Income [Billions of Dollars; Quarterly; vintage last updated 2026-09-30]. Average: 1990-2025 mean of 36 obs (annual means of quarterly ratios)." | |
| 7 | +T01_labor_share_COE_GDI_lastfullyear,"National-accounts labor share: compensation of employees / gross domestic income, latest full calendar year (mean of quarters)",0.50789,ratio,2025,United States,annual average,FRED (U.S. Bureau of Economic Analysis),COE/GDI,https://fred.stlouisfed.org/graph/fredgraph.csv?id=COE ; https://fred.stlouisfed.org/graph/fredgraph.csv?id=GDI,data/raw/fred/COE.csv ; data/raw/fred/GDI.csv,2026-10-27,RETRIEVED,"National Income: Compensation of Employees, Paid [Billions of Dollars; Quarterly; vintage last updated 2026-09-30] divided by Gross Domestic Income [Billions of Dollars; Quarterly; vintage last updated 2026-09-30]." | |
| 8 | +T01_labor_share_COE_NDI_latest,"National-accounts labor share: compensation of employees / net domestic income, latest observation",0.596841,ratio,2026Q2,United States,quarterly,FRED (U.S. Bureau of Economic Analysis),COE/W256RC1Q027SBEA,https://fred.stlouisfed.org/graph/fredgraph.csv?id=COE ; https://fred.stlouisfed.org/graph/fredgraph.csv?id=W256RC1Q027SBEA,data/raw/fred/COE.csv ; data/raw/fred/W256RC1Q027SBEA.csv,2026-10-27,RETRIEVED,"National Income: Compensation of Employees, Paid [Billions of Dollars; Quarterly; vintage last updated 2026-09-30] divided by Net domestic income [Billions of Dollars; Quarterly; vintage last updated 2026-09-30]. Net domestic income = GDI less consumption of fixed capital (BEA NIPA table 1.7.5 line 'Net domestic income')." | |
| 9 | +T01_labor_share_COE_NDI_avg,"National-accounts labor share: compensation of employees / net domestic income, 1990-latest average",0.642045,ratio,1990-2025,United States,annual average,FRED (U.S. Bureau of Economic Analysis),COE/W256RC1Q027SBEA,https://fred.stlouisfed.org/graph/fredgraph.csv?id=COE ; https://fred.stlouisfed.org/graph/fredgraph.csv?id=W256RC1Q027SBEA,data/raw/fred/COE.csv ; data/raw/fred/W256RC1Q027SBEA.csv,2026-10-27,RETRIEVED,"National Income: Compensation of Employees, Paid [Billions of Dollars; Quarterly; vintage last updated 2026-09-30] divided by Net domestic income [Billions of Dollars; Quarterly; vintage last updated 2026-09-30]. Net domestic income = GDI less consumption of fixed capital (BEA NIPA table 1.7.5 line 'Net domestic income'). Average: 1990-2025 mean of 36 obs (annual means of quarterly ratios)." | |
| 10 | +T01_labor_share_COE_NDI_lastfullyear,"National-accounts labor share: compensation of employees / net domestic income, latest full calendar year (mean of quarters)",0.607203,ratio,2025,United States,annual average,FRED (U.S. Bureau of Economic Analysis),COE/W256RC1Q027SBEA,https://fred.stlouisfed.org/graph/fredgraph.csv?id=COE ; https://fred.stlouisfed.org/graph/fredgraph.csv?id=W256RC1Q027SBEA,data/raw/fred/COE.csv ; data/raw/fred/W256RC1Q027SBEA.csv,2026-10-27,RETRIEVED,"National Income: Compensation of Employees, Paid [Billions of Dollars; Quarterly; vintage last updated 2026-09-30] divided by Net domestic income [Billions of Dollars; Quarterly; vintage last updated 2026-09-30]. Net domestic income = GDI less consumption of fixed capital (BEA NIPA table 1.7.5 line 'Net domestic income')." | |
| 11 | +T01_labor_share_PWT_US_latest,"Penn World Table labour-compensation share of GDP, United States, latest observation",0.568303,ratio,2023,United States,Annual,"FRED (University of Groningen, University of California, Davis)",LABSHPUSA156NRUG,https://fred.stlouisfed.org/graph/fredgraph.csv?id=LABSHPUSA156NRUG,data/raw/fred/LABSHPUSA156NRUG.csv,2026-10-27,RETRIEVED,"Share of Labour Compensation in GDP at Current National Prices for United States [Ratio; Annual; vintage last updated 2026-02-04]. FRED serves PWT 11.0 (1950-2023). The DataverseNL file pwt1001.xlsx (PWT 10.01, to 2019) is kept as cross-check below." | |
| 12 | +T01_labor_share_PWT_US_avg,"Penn World Table labour-compensation share of GDP, United States, 1990-latest average",0.604028,ratio,1990-2023,United States,Annual,"FRED (University of Groningen, University of California, Davis)",LABSHPUSA156NRUG,https://fred.stlouisfed.org/graph/fredgraph.csv?id=LABSHPUSA156NRUG,data/raw/fred/LABSHPUSA156NRUG.csv,2026-10-27,RETRIEVED,"Share of Labour Compensation in GDP at Current National Prices for United States [Ratio; Annual; vintage last updated 2026-02-04]. FRED serves PWT 11.0 (1950-2023). The DataverseNL file pwt1001.xlsx (PWT 10.01, to 2019) is kept as cross-check below. Average: 1990-2023 mean of 34 obs." | |
| 13 | +T01_labor_share_PWT1001_US_latest,"PWT 10.01 labsh (share of labour compensation in GDP at current national prices), United States, latest",0.597091,ratio,2019,United States,annual,"Penn World Table 10.01 (Feenstra, Inklaar & Timmer 2015), DataverseNL doi:10.34894/QT5BCC",labsh,https://dataverse.nl/api/access/datafile/354095,data/raw/pwt/pwt1001.xlsx,2026-10-27,RETRIEVED,CC BY 4.0. | |
| 14 | +T01_labor_share_PWT1001_US_avg,"PWT 10.01 labsh, United States, 1990-latest average",0.608054,ratio,1990-2019,United States,annual,Penn World Table 10.01,labsh,https://dataverse.nl/api/access/datafile/354095,data/raw/pwt/pwt1001.xlsx,2026-10-27,RETRIEVED,1990-2019 mean of 30 obs | |
| 15 | +T12_labor_share_PWT1001_CA_latest,"PWT 10.01 labsh (share of labour compensation in GDP at current national prices), Canada, latest",0.65488,ratio,2019,Canada,annual,"Penn World Table 10.01 (Feenstra, Inklaar & Timmer 2015), DataverseNL doi:10.34894/QT5BCC",labsh,https://dataverse.nl/api/access/datafile/354095,data/raw/pwt/pwt1001.xlsx,2026-10-27,RETRIEVED,CC BY 4.0. | |
| 16 | +T12_labor_share_PWT1001_CA_avg,"PWT 10.01 labsh, Canada, 1990-latest average",0.665831,ratio,1990-2019,Canada,annual,Penn World Table 10.01,labsh,https://dataverse.nl/api/access/datafile/354095,data/raw/pwt/pwt1001.xlsx,2026-10-27,RETRIEVED,1990-2019 mean of 30 obs | |
| 17 | +T02_profits_after_tax_noIVA_GDP_latest,"Corporate profits after tax (without IVA and CCAdj) / GDP, latest observation",0.131435,ratio,2026Q2,United States,quarterly,FRED (U.S. Bureau of Economic Analysis),CP/GDP,https://fred.stlouisfed.org/graph/fredgraph.csv?id=CP ; https://fred.stlouisfed.org/graph/fredgraph.csv?id=GDP,data/raw/fred/CP.csv ; data/raw/fred/GDP.csv,2026-10-27,RETRIEVED,Corporate Profits After Tax (without IVA and CCAdj) [Billions of Dollars; Quarterly; vintage last updated 2026-09-30] divided by Gross Domestic Product [Billions of Dollars; Quarterly; vintage last updated 2026-09-30]. FRED id CP is the series WITHOUT IVA/CCAdj (book profits). | |
| 18 | +T02_profits_after_tax_noIVA_GDP_avg,"Corporate profits after tax (without IVA and CCAdj) / GDP, 1990-latest average",0.0860756,ratio,1990-2025,United States,annual average,FRED (U.S. Bureau of Economic Analysis),CP/GDP,https://fred.stlouisfed.org/graph/fredgraph.csv?id=CP ; https://fred.stlouisfed.org/graph/fredgraph.csv?id=GDP,data/raw/fred/CP.csv ; data/raw/fred/GDP.csv,2026-10-27,RETRIEVED,Corporate Profits After Tax (without IVA and CCAdj) [Billions of Dollars; Quarterly; vintage last updated 2026-09-30] divided by Gross Domestic Product [Billions of Dollars; Quarterly; vintage last updated 2026-09-30]. FRED id CP is the series WITHOUT IVA/CCAdj (book profits). Average: 1990-2025 mean of 36 obs (annual means of quarterly ratios). | |
| 19 | +T02_profits_after_tax_noIVA_GDP_lastfullyear,"Corporate profits after tax (without IVA and CCAdj) / GDP, latest full calendar year (mean of quarters)",0.114281,ratio,2025,United States,annual average,FRED (U.S. Bureau of Economic Analysis),CP/GDP,https://fred.stlouisfed.org/graph/fredgraph.csv?id=CP ; https://fred.stlouisfed.org/graph/fredgraph.csv?id=GDP,data/raw/fred/CP.csv ; data/raw/fred/GDP.csv,2026-10-27,RETRIEVED,Corporate Profits After Tax (without IVA and CCAdj) [Billions of Dollars; Quarterly; vintage last updated 2026-09-30] divided by Gross Domestic Product [Billions of Dollars; Quarterly; vintage last updated 2026-09-30]. FRED id CP is the series WITHOUT IVA/CCAdj (book profits). | |
| 20 | +T02_profits_after_tax_IVA_CCAdj_GDP_latest,"Corporate profits after tax with IVA and CCAdj / GDP, latest observation",0.119075,ratio,2026Q2,United States,quarterly,FRED (U.S. Bureau of Economic Analysis),CPATAX/GDP,https://fred.stlouisfed.org/graph/fredgraph.csv?id=CPATAX ; https://fred.stlouisfed.org/graph/fredgraph.csv?id=GDP,data/raw/fred/CPATAX.csv ; data/raw/fred/GDP.csv,2026-10-27,RETRIEVED,Corporate Profits After Tax with Inventory Valuation Adjustment (IVA) and Capital Consumption Adjustment (CCAdj) [Billions of Dollars; Quarterly; vintage last updated 2026-09-30] divided by Gross Domestic Product [Billions of Dollars; Quarterly; vintage last updated 2026-09-30]. FRED id CPATAX is the series WITH IVA and CCAdj (economic profits). | |
| 21 | +T02_profits_after_tax_IVA_CCAdj_GDP_avg,"Corporate profits after tax with IVA and CCAdj / GDP, 1990-latest average",0.0842923,ratio,1990-2025,United States,annual average,FRED (U.S. Bureau of Economic Analysis),CPATAX/GDP,https://fred.stlouisfed.org/graph/fredgraph.csv?id=CPATAX ; https://fred.stlouisfed.org/graph/fredgraph.csv?id=GDP,data/raw/fred/CPATAX.csv ; data/raw/fred/GDP.csv,2026-10-27,RETRIEVED,Corporate Profits After Tax with Inventory Valuation Adjustment (IVA) and Capital Consumption Adjustment (CCAdj) [Billions of Dollars; Quarterly; vintage last updated 2026-09-30] divided by Gross Domestic Product [Billions of Dollars; Quarterly; vintage last updated 2026-09-30]. FRED id CPATAX is the series WITH IVA and CCAdj (economic profits). Average: 1990-2025 mean of 36 obs (annual means of quarterly ratios). | |
| 22 | +T02_profits_after_tax_IVA_CCAdj_GDP_lastfullyear,"Corporate profits after tax with IVA and CCAdj / GDP, latest full calendar year (mean of quarters)",0.109693,ratio,2025,United States,annual average,FRED (U.S. Bureau of Economic Analysis),CPATAX/GDP,https://fred.stlouisfed.org/graph/fredgraph.csv?id=CPATAX ; https://fred.stlouisfed.org/graph/fredgraph.csv?id=GDP,data/raw/fred/CPATAX.csv ; data/raw/fred/GDP.csv,2026-10-27,RETRIEVED,Corporate Profits After Tax with Inventory Valuation Adjustment (IVA) and Capital Consumption Adjustment (CCAdj) [Billions of Dollars; Quarterly; vintage last updated 2026-09-30] divided by Gross Domestic Product [Billions of Dollars; Quarterly; vintage last updated 2026-09-30]. FRED id CPATAX is the series WITH IVA and CCAdj (economic profits). | |
| 23 | +T02_net_dividends_GDP_latest,"Net corporate dividends (NIPA, after tax with IVA & CCAdj) / GDP, latest observation",0.0675063,ratio,2026Q2,United States,quarterly,FRED (U.S. Bureau of Economic Analysis),DIVIDEND/GDP,https://fred.stlouisfed.org/graph/fredgraph.csv?id=DIVIDEND ; https://fred.stlouisfed.org/graph/fredgraph.csv?id=GDP,data/raw/fred/DIVIDEND.csv ; data/raw/fred/GDP.csv,2026-10-27,RETRIEVED,Corporate Profits after tax with IVA and CCAdj: Net Dividends [Billions of Dollars; Quarterly; vintage last updated 2026-09-30] divided by Gross Domestic Product [Billions of Dollars; Quarterly; vintage last updated 2026-09-30]. | |
| 24 | +T02_net_dividends_GDP_avg,"Net corporate dividends (NIPA, after tax with IVA & CCAdj) / GDP, 1990-latest average",0.0514654,ratio,1990-2025,United States,annual average,FRED (U.S. Bureau of Economic Analysis),DIVIDEND/GDP,https://fred.stlouisfed.org/graph/fredgraph.csv?id=DIVIDEND ; https://fred.stlouisfed.org/graph/fredgraph.csv?id=GDP,data/raw/fred/DIVIDEND.csv ; data/raw/fred/GDP.csv,2026-10-27,RETRIEVED,Corporate Profits after tax with IVA and CCAdj: Net Dividends [Billions of Dollars; Quarterly; vintage last updated 2026-09-30] divided by Gross Domestic Product [Billions of Dollars; Quarterly; vintage last updated 2026-09-30]. Average: 1990-2025 mean of 36 obs (annual means of quarterly ratios). | |
| 25 | +T02_net_dividends_GDP_lastfullyear,"Net corporate dividends (NIPA, after tax with IVA & CCAdj) / GDP, latest full calendar year (mean of quarters)",0.0699754,ratio,2025,United States,annual average,FRED (U.S. Bureau of Economic Analysis),DIVIDEND/GDP,https://fred.stlouisfed.org/graph/fredgraph.csv?id=DIVIDEND ; https://fred.stlouisfed.org/graph/fredgraph.csv?id=GDP,data/raw/fred/DIVIDEND.csv ; data/raw/fred/GDP.csv,2026-10-27,RETRIEVED,Corporate Profits after tax with IVA and CCAdj: Net Dividends [Billions of Dollars; Quarterly; vintage last updated 2026-09-30] divided by Gross Domestic Product [Billions of Dollars; Quarterly; vintage last updated 2026-09-30]. | |
| 26 | +T02_net_dividends_annual_GDP_latest,"Net corporate dividend payments (annual NIPA) / GDP (annual), latest observation",0.0699569,ratio,2025,United States,annual,FRED (U.S. Bureau of Economic Analysis),B056RC1A027NBEA/GDPA,https://fred.stlouisfed.org/graph/fredgraph.csv?id=B056RC1A027NBEA ; https://fred.stlouisfed.org/graph/fredgraph.csv?id=GDPA,data/raw/fred/B056RC1A027NBEA.csv ; data/raw/fred/GDPA.csv,2026-10-27,RETRIEVED,Net corporate dividend payments [Billions of Dollars; Annual; vintage last updated ] divided by Gross Domestic Product (annual) [Billions of Dollars; Annual; vintage last updated ]. | |
| 27 | +T02_net_dividends_annual_GDP_avg,"Net corporate dividend payments (annual NIPA) / GDP (annual), 1990-latest average",0.051471,ratio,1990-2025,United States,annual average,FRED (U.S. Bureau of Economic Analysis),B056RC1A027NBEA/GDPA,https://fred.stlouisfed.org/graph/fredgraph.csv?id=B056RC1A027NBEA ; https://fred.stlouisfed.org/graph/fredgraph.csv?id=GDPA,data/raw/fred/B056RC1A027NBEA.csv ; data/raw/fred/GDPA.csv,2026-10-27,RETRIEVED,Net corporate dividend payments [Billions of Dollars; Annual; vintage last updated ] divided by Gross Domestic Product (annual) [Billions of Dollars; Annual; vintage last updated ]. Average: 1990-2025 mean of 36 obs. | |
| 28 | +T02_GDP_annual_nominal_latest,"Nominal GDP, annual, latest observation",30861.3,billions of current US dollars,2025,United States,Annual,FRED (U.S. Bureau of Economic Analysis),GDPA,https://fred.stlouisfed.org/graph/fredgraph.csv?id=GDPA,data/raw/fred/GDPA.csv,2026-10-27,RETRIEVED,Gross Domestic Product (annual) [Billions of Dollars; Annual; vintage last updated ]. | |
| 29 | +T02_GDP_quarterly_nominal_latest,"Nominal GDP, quarterly SAAR, latest observation",32563,"billions of current US dollars, SAAR",2026Q2,United States,Quarterly,FRED (U.S. Bureau of Economic Analysis),GDP,https://fred.stlouisfed.org/graph/fredgraph.csv?id=GDP,data/raw/fred/GDP.csv,2026-10-27,RETRIEVED,Gross Domestic Product [Billions of Dollars; Quarterly; vintage last updated 2026-09-30]. | |
| 30 | +T02_GDP_real_latest,"Real GDP, quarterly SAAR, latest observation",24408,"billions of chained 2017 dollars, SAAR",2026Q2,United States,Quarterly,FRED (U.S. Bureau of Economic Analysis),GDPC1,https://fred.stlouisfed.org/graph/fredgraph.csv?id=GDPC1,data/raw/fred/GDPC1.csv,2026-10-27,RETRIEVED,Real Gross Domestic Product [Billions of Chained 2017 Dollars; Quarterly; vintage last updated ]. | |
| 31 | +T02_real_GDP_growth_avg,"Average real GDP growth, 1990-2025 (log, annual means)",0.0247887,per year,1990-2025,United States,annual,FRED (BEA),GDPC1,https://fred.stlouisfed.org/graph/fredgraph.csv?id=GDPC1,data/raw/fred/GDPC1.csv,2026-10-27,RETRIEVED,Real Gross Domestic Product [Billions of Chained 2017 Dollars; Quarterly; vintage last updated ] | |
| 32 | +T03_household_net_worth_GDP_latest,"Household & nonprofit net worth / GDP, latest observation",6.01512,ratio (years of GDP),2026Q2,United States,quarterly,FRED (Board of Governors of the Federal Reserve System (US); U.S. Bureau of Economic Analysis),TNWBSHNO/GDP,https://fred.stlouisfed.org/graph/fredgraph.csv?id=TNWBSHNO ; https://fred.stlouisfed.org/graph/fredgraph.csv?id=GDP,data/raw/fred/TNWBSHNO.csv ; data/raw/fred/GDP.csv,2026-10-27,RETRIEVED,"Households and Nonprofit Organizations; Net Worth, Level [Millions of U.S. Dollars; Quarterly, End of Period; vintage last updated 2026-09-11] divided by Gross Domestic Product [Billions of Dollars; Quarterly; vintage last updated 2026-09-30]. TNWBSHNO in millions of dollars, GDP in billions SAAR; scaled by 1e-3. Quarterly end-of-period net worth over same-quarter annualized GDP." | |
| 33 | +T03_household_net_worth_GDP_avg,"Household & nonprofit net worth / GDP, 1990-latest average",4.5812,ratio (years of GDP),1990-2025,United States,annual average,FRED (Board of Governors of the Federal Reserve System (US); U.S. Bureau of Economic Analysis),TNWBSHNO/GDP,https://fred.stlouisfed.org/graph/fredgraph.csv?id=TNWBSHNO ; https://fred.stlouisfed.org/graph/fredgraph.csv?id=GDP,data/raw/fred/TNWBSHNO.csv ; data/raw/fred/GDP.csv,2026-10-27,RETRIEVED,"Households and Nonprofit Organizations; Net Worth, Level [Millions of U.S. Dollars; Quarterly, End of Period; vintage last updated 2026-09-11] divided by Gross Domestic Product [Billions of Dollars; Quarterly; vintage last updated 2026-09-30]. TNWBSHNO in millions of dollars, GDP in billions SAAR; scaled by 1e-3. Quarterly end-of-period net worth over same-quarter annualized GDP. Average: 1990-2025 mean of 36 obs (annual means of quarterly ratios)." | |
| 34 | +T03_household_net_worth_GDP_lastfullyear,"Household & nonprofit net worth / GDP, latest full calendar year (mean of quarters)",5.72378,ratio (years of GDP),2025,United States,annual average,FRED (Board of Governors of the Federal Reserve System (US); U.S. Bureau of Economic Analysis),TNWBSHNO/GDP,https://fred.stlouisfed.org/graph/fredgraph.csv?id=TNWBSHNO ; https://fred.stlouisfed.org/graph/fredgraph.csv?id=GDP,data/raw/fred/TNWBSHNO.csv ; data/raw/fred/GDP.csv,2026-10-27,RETRIEVED,"Households and Nonprofit Organizations; Net Worth, Level [Millions of U.S. Dollars; Quarterly, End of Period; vintage last updated 2026-09-11] divided by Gross Domestic Product [Billions of Dollars; Quarterly; vintage last updated 2026-09-30]. TNWBSHNO in millions of dollars, GDP in billions SAAR; scaled by 1e-3. Quarterly end-of-period net worth over same-quarter annualized GDP." | |
| 35 | +T03_household_real_estate_GDP_latest,"Household & nonprofit real estate at market value / GDP, latest observation",1.66059,ratio (years of GDP),2026Q2,United States,quarterly,FRED (Board of Governors of the Federal Reserve System (US); U.S. Bureau of Economic Analysis),HNOREMQ027S/GDP,https://fred.stlouisfed.org/graph/fredgraph.csv?id=HNOREMQ027S ; https://fred.stlouisfed.org/graph/fredgraph.csv?id=GDP,data/raw/fred/HNOREMQ027S.csv ; data/raw/fred/GDP.csv,2026-10-27,RETRIEVED,"Households and Nonprofit Organizations; Real Estate at Market Value, Level [Millions of U.S. Dollars; Quarterly, End of Period; vintage last updated 2026-09-11] divided by Gross Domestic Product [Billions of Dollars; Quarterly; vintage last updated 2026-09-30]. Millions over billions SAAR; scaled by 1e-3." | |
| 36 | +T03_household_real_estate_GDP_avg,"Household & nonprofit real estate at market value / GDP, 1990-latest average",1.47016,ratio (years of GDP),1990-2025,United States,annual average,FRED (Board of Governors of the Federal Reserve System (US); U.S. Bureau of Economic Analysis),HNOREMQ027S/GDP,https://fred.stlouisfed.org/graph/fredgraph.csv?id=HNOREMQ027S ; https://fred.stlouisfed.org/graph/fredgraph.csv?id=GDP,data/raw/fred/HNOREMQ027S.csv ; data/raw/fred/GDP.csv,2026-10-27,RETRIEVED,"Households and Nonprofit Organizations; Real Estate at Market Value, Level [Millions of U.S. Dollars; Quarterly, End of Period; vintage last updated 2026-09-11] divided by Gross Domestic Product [Billions of Dollars; Quarterly; vintage last updated 2026-09-30]. Millions over billions SAAR; scaled by 1e-3. Average: 1990-2025 mean of 36 obs (annual means of quarterly ratios)." | |
| 37 | +T03_household_real_estate_GDP_lastfullyear,"Household & nonprofit real estate at market value / GDP, latest full calendar year (mean of quarters)",1.68868,ratio (years of GDP),2025,United States,annual average,FRED (Board of Governors of the Federal Reserve System (US); U.S. Bureau of Economic Analysis),HNOREMQ027S/GDP,https://fred.stlouisfed.org/graph/fredgraph.csv?id=HNOREMQ027S ; https://fred.stlouisfed.org/graph/fredgraph.csv?id=GDP,data/raw/fred/HNOREMQ027S.csv ; data/raw/fred/GDP.csv,2026-10-27,RETRIEVED,"Households and Nonprofit Organizations; Real Estate at Market Value, Level [Millions of U.S. Dollars; Quarterly, End of Period; vintage last updated 2026-09-11] divided by Gross Domestic Product [Billions of Dollars; Quarterly; vintage last updated 2026-09-30]. Millions over billions SAAR; scaled by 1e-3." | |
| 38 | +T03_owners_equity_real_estate_GDP_latest,"Households' owners' equity in real estate / GDP, latest observation",1.09986,ratio (years of GDP),2026Q2,United States,quarterly,FRED (Board of Governors of the Federal Reserve System (US); U.S. Bureau of Economic Analysis),OEHRENWBSHNO/GDP,https://fred.stlouisfed.org/graph/fredgraph.csv?id=OEHRENWBSHNO ; https://fred.stlouisfed.org/graph/fredgraph.csv?id=GDP,data/raw/fred/OEHRENWBSHNO.csv ; data/raw/fred/GDP.csv,2026-10-27,RETRIEVED,"Households; Owners' Equity in Real Estate, Level [Millions of U.S. Dollars; Quarterly; vintage last updated ] divided by Gross Domestic Product [Billions of Dollars; Quarterly; vintage last updated 2026-09-30]. Millions over billions SAAR; scaled by 1e-3." | |
| 39 | +T03_owners_equity_real_estate_GDP_avg,"Households' owners' equity in real estate / GDP, 1990-latest average",0.814515,ratio (years of GDP),1990-2025,United States,annual average,FRED (Board of Governors of the Federal Reserve System (US); U.S. Bureau of Economic Analysis),OEHRENWBSHNO/GDP,https://fred.stlouisfed.org/graph/fredgraph.csv?id=OEHRENWBSHNO ; https://fred.stlouisfed.org/graph/fredgraph.csv?id=GDP,data/raw/fred/OEHRENWBSHNO.csv ; data/raw/fred/GDP.csv,2026-10-27,RETRIEVED,"Households; Owners' Equity in Real Estate, Level [Millions of U.S. Dollars; Quarterly; vintage last updated ] divided by Gross Domestic Product [Billions of Dollars; Quarterly; vintage last updated 2026-09-30]. Millions over billions SAAR; scaled by 1e-3. Average: 1990-2025 mean of 36 obs (annual means of quarterly ratios)." | |
| 40 | +T03_owners_equity_real_estate_GDP_lastfullyear,"Households' owners' equity in real estate / GDP, latest full calendar year (mean of quarters)",1.11528,ratio (years of GDP),2025,United States,annual average,FRED (Board of Governors of the Federal Reserve System (US); U.S. Bureau of Economic Analysis),OEHRENWBSHNO/GDP,https://fred.stlouisfed.org/graph/fredgraph.csv?id=OEHRENWBSHNO ; https://fred.stlouisfed.org/graph/fredgraph.csv?id=GDP,data/raw/fred/OEHRENWBSHNO.csv ; data/raw/fred/GDP.csv,2026-10-27,RETRIEVED,"Households; Owners' Equity in Real Estate, Level [Millions of U.S. Dollars; Quarterly; vintage last updated ] divided by Gross Domestic Product [Billions of Dollars; Quarterly; vintage last updated 2026-09-30]. Millions over billions SAAR; scaled by 1e-3." | |
| 41 | +T03_owners_equity_share_of_real_estate_latest,"Owners' equity as % of household real estate, latest observation",71.9292,percent,2026Q2,United States,Quarterly,FRED (Board of Governors of the Federal Reserve System (US)),HOEREPHRE,https://fred.stlouisfed.org/graph/fredgraph.csv?id=HOEREPHRE,data/raw/fred/HOEREPHRE.csv,2026-10-27,RETRIEVED,"Households; Owners' Equity in Real Estate as a Percentage of Household Real Estate, Level [Percent; Quarterly; vintage last updated ]." | |
| 42 | +T03_owners_equity_share_of_real_estate_lastfullyear,"Owners' equity as % of household real estate, latest full calendar-year average",71.636,percent,2025,United States,annual average,FRED (Board of Governors of the Federal Reserve System (US)),HOEREPHRE,https://fred.stlouisfed.org/graph/fredgraph.csv?id=HOEREPHRE,data/raw/fred/HOEREPHRE.csv,2026-10-27,RETRIEVED,"Households; Owners' Equity in Real Estate as a Percentage of Household Real Estate, Level [Percent; Quarterly; vintage last updated ]." | |
| 43 | +T03_owners_equity_share_of_real_estate_avg,"Owners' equity as % of household real estate, 1990-latest average of annual means",60.7308,percent,1990-2025,United States,annual average,FRED (Board of Governors of the Federal Reserve System (US)),HOEREPHRE,https://fred.stlouisfed.org/graph/fredgraph.csv?id=HOEREPHRE,data/raw/fred/HOEREPHRE.csv,2026-10-27,RETRIEVED,"Households; Owners' Equity in Real Estate as a Percentage of Household Real Estate, Level [Percent; Quarterly; vintage last updated ]. Average: 1990-2025 mean of 36 obs." | |
| 44 | +T03_real_estate_share_of_net_worth_latest,"Household real estate / household net worth, latest observation",0.27607,ratio,2026Q2,United States,quarterly,FRED (Board of Governors of the Federal Reserve System (US)),HNOREMQ027S/TNWBSHNO,https://fred.stlouisfed.org/graph/fredgraph.csv?id=HNOREMQ027S ; https://fred.stlouisfed.org/graph/fredgraph.csv?id=TNWBSHNO,data/raw/fred/HNOREMQ027S.csv ; data/raw/fred/TNWBSHNO.csv,2026-10-27,RETRIEVED,"Households and Nonprofit Organizations; Real Estate at Market Value, Level [Millions of U.S. Dollars; Quarterly, End of Period; vintage last updated 2026-09-11] divided by Households and Nonprofit Organizations; Net Worth, Level [Millions of U.S. Dollars; Quarterly, End of Period; vintage last updated 2026-09-11]." | |
| 45 | +T03_real_estate_share_of_net_worth_avg,"Household real estate / household net worth, 1990-latest average",0.322473,ratio,1990-2025,United States,annual average,FRED (Board of Governors of the Federal Reserve System (US)),HNOREMQ027S/TNWBSHNO,https://fred.stlouisfed.org/graph/fredgraph.csv?id=HNOREMQ027S ; https://fred.stlouisfed.org/graph/fredgraph.csv?id=TNWBSHNO,data/raw/fred/HNOREMQ027S.csv ; data/raw/fred/TNWBSHNO.csv,2026-10-27,RETRIEVED,"Households and Nonprofit Organizations; Real Estate at Market Value, Level [Millions of U.S. Dollars; Quarterly, End of Period; vintage last updated 2026-09-11] divided by Households and Nonprofit Organizations; Net Worth, Level [Millions of U.S. Dollars; Quarterly, End of Period; vintage last updated 2026-09-11]. Average: 1990-2025 mean of 36 obs (annual means of quarterly ratios)." | |
| 46 | +T03_real_estate_share_of_net_worth_lastfullyear,"Household real estate / household net worth, latest full calendar year (mean of quarters)",0.295131,ratio,2025,United States,annual average,FRED (Board of Governors of the Federal Reserve System (US)),HNOREMQ027S/TNWBSHNO,https://fred.stlouisfed.org/graph/fredgraph.csv?id=HNOREMQ027S ; https://fred.stlouisfed.org/graph/fredgraph.csv?id=TNWBSHNO,data/raw/fred/HNOREMQ027S.csv ; data/raw/fred/TNWBSHNO.csv,2026-10-27,RETRIEVED,"Households and Nonprofit Organizations; Real Estate at Market Value, Level [Millions of U.S. Dollars; Quarterly, End of Period; vintage last updated 2026-09-11] divided by Households and Nonprofit Organizations; Net Worth, Level [Millions of U.S. Dollars; Quarterly, End of Period; vintage last updated 2026-09-11]." | |
| 47 | +T04_wealth_share_top1_latest,"Share of household net worth held by the top 1% (TopPt1 + RemainingTop1), latest quarter",32.5,percent,2026Q2,United States,quarterly,"Federal Reserve Board, Distributional Financial Accounts (dfa-networth-shares.csv, release dated 2026-09-15 in zip)",dfa-networth-shares:Net worth:top1,https://www.federalreserve.gov/releases/z1/dataviz/download/zips/dfa.zip,data/raw/fed_dfa/unzipped/dfa-networth-shares.csv,2026-10-27,RETRIEVED,Percent of total household net worth; DFA combine SCF and Financial Accounts. Public domain. | |
| 48 | +T04_wealth_share_top1_avg,"Share of household net worth held by the top 1% (TopPt1 + RemainingTop1), 1990-latest average",27.9604,percent,1990-2025,United States,annual average,"Federal Reserve Board, Distributional Financial Accounts",dfa-networth-shares:Net worth:top1,https://www.federalreserve.gov/releases/z1/dataviz/download/zips/dfa.zip,data/raw/fed_dfa/unzipped/dfa-networth-shares.csv,2026-10-27,RETRIEVED,1990-2025 mean of 36 obs | |
| 49 | +T04_wealth_share_top10_latest,"Share of household net worth held by the top 10% (top 1% + Next9), latest quarter",68.9,percent,2026Q2,United States,quarterly,"Federal Reserve Board, Distributional Financial Accounts (dfa-networth-shares.csv, release dated 2026-09-15 in zip)",dfa-networth-shares:Net worth:top10,https://www.federalreserve.gov/releases/z1/dataviz/download/zips/dfa.zip,data/raw/fed_dfa/unzipped/dfa-networth-shares.csv,2026-10-27,RETRIEVED,Percent of total household net worth; DFA combine SCF and Financial Accounts. Public domain. | |
| 50 | +T04_wealth_share_top10_avg,"Share of household net worth held by the top 10% (top 1% + Next9), 1990-latest average",65.2993,percent,1990-2025,United States,annual average,"Federal Reserve Board, Distributional Financial Accounts",dfa-networth-shares:Net worth:top10,https://www.federalreserve.gov/releases/z1/dataviz/download/zips/dfa.zip,data/raw/fed_dfa/unzipped/dfa-networth-shares.csv,2026-10-27,RETRIEVED,1990-2025 mean of 36 obs | |
| 51 | +T04_wealth_share_top0p1_latest,"Share of household net worth held by the top 0.1%, latest quarter",15,percent,2026Q2,United States,quarterly,"Federal Reserve Board, Distributional Financial Accounts (dfa-networth-shares.csv, release dated 2026-09-15 in zip)",dfa-networth-shares:Net worth:top0p1,https://www.federalreserve.gov/releases/z1/dataviz/download/zips/dfa.zip,data/raw/fed_dfa/unzipped/dfa-networth-shares.csv,2026-10-27,RETRIEVED,Percent of total household net worth; DFA combine SCF and Financial Accounts. Public domain. | |
| 52 | +T04_wealth_share_top0p1_avg,"Share of household net worth held by the top 0.1%, 1990-latest average",11.3236,percent,1990-2025,United States,annual average,"Federal Reserve Board, Distributional Financial Accounts",dfa-networth-shares:Net worth:top0p1,https://www.federalreserve.gov/releases/z1/dataviz/download/zips/dfa.zip,data/raw/fed_dfa/unzipped/dfa-networth-shares.csv,2026-10-27,RETRIEVED,1990-2025 mean of 36 obs | |
| 53 | +T04_wealth_share_next40_latest,"Share of household net worth held by the 50th-90th percentiles, latest quarter",28.8,percent,2026Q2,United States,quarterly,"Federal Reserve Board, Distributional Financial Accounts (dfa-networth-shares.csv, release dated 2026-09-15 in zip)",dfa-networth-shares:Net worth:next40,https://www.federalreserve.gov/releases/z1/dataviz/download/zips/dfa.zip,data/raw/fed_dfa/unzipped/dfa-networth-shares.csv,2026-10-27,RETRIEVED,Percent of total household net worth; DFA combine SCF and Financial Accounts. Public domain. | |
| 54 | +T04_wealth_share_next40_avg,"Share of household net worth held by the 50th-90th percentiles, 1990-latest average",32.3924,percent,1990-2025,United States,annual average,"Federal Reserve Board, Distributional Financial Accounts",dfa-networth-shares:Net worth:next40,https://www.federalreserve.gov/releases/z1/dataviz/download/zips/dfa.zip,data/raw/fed_dfa/unzipped/dfa-networth-shares.csv,2026-10-27,RETRIEVED,1990-2025 mean of 36 obs | |
| 55 | +T04_wealth_share_bottom50_latest,"Share of household net worth held by the bottom 50%, latest quarter",2.3,percent,2026Q2,United States,quarterly,"Federal Reserve Board, Distributional Financial Accounts (dfa-networth-shares.csv, release dated 2026-09-15 in zip)",dfa-networth-shares:Net worth:bottom50,https://www.federalreserve.gov/releases/z1/dataviz/download/zips/dfa.zip,data/raw/fed_dfa/unzipped/dfa-networth-shares.csv,2026-10-27,RETRIEVED,Percent of total household net worth; DFA combine SCF and Financial Accounts. Public domain. | |
| 56 | +T04_wealth_share_bottom50_avg,"Share of household net worth held by the bottom 50%, 1990-latest average",2.31389,percent,1990-2025,United States,annual average,"Federal Reserve Board, Distributional Financial Accounts",dfa-networth-shares:Net worth:bottom50,https://www.federalreserve.gov/releases/z1/dataviz/download/zips/dfa.zip,data/raw/fed_dfa/unzipped/dfa-networth-shares.csv,2026-10-27,RETRIEVED,1990-2025 mean of 36 obs | |
| 57 | +T04_corporate_equity_share_top10_latest,Share of directly/indirectly held corporate equities & mutual fund shares held by the top 10%,88.1,percent,2026Q2,United States,quarterly,"Federal Reserve Board, Distributional Financial Accounts",dfa-networth-shares:Corporate equities and mutual fund shares:top10,https://www.federalreserve.gov/releases/z1/dataviz/download/zips/dfa.zip,data/raw/fed_dfa/unzipped/dfa-networth-shares.csv,2026-10-27,RETRIEVED,Excludes equities held through DC pensions (DFA definition). | |
| 58 | +T04_wealth_share_top1_FRED_latest,"Share of net worth held by the top 1% (FRED mirror of DFA), latest observation",32.5,percent,2026Q2,United States,Quarterly,FRED (Board of Governors of the Federal Reserve System (US)),WFRBST01134,https://fred.stlouisfed.org/graph/fredgraph.csv?id=WFRBST01134,data/raw/fred/WFRBST01134.csv,2026-10-27,RETRIEVED,Share of Net Worth Held by the Top 1% (99th to 100th Wealth Percentiles) [Percent; Quarterly; vintage last updated ]. | |
| 59 | +T04_wealth_share_p90_99_FRED_latest,"Share of net worth held by the 90th-99th percentiles (FRED mirror of DFA), latest observation",36.4,percent,2026Q2,United States,Quarterly,FRED (Board of Governors of the Federal Reserve System (US)),WFRBSN09161,https://fred.stlouisfed.org/graph/fredgraph.csv?id=WFRBSN09161,data/raw/fred/WFRBSN09161.csv,2026-10-27,RETRIEVED,Share of Net Worth Held by the 90th to 99th Wealth Percentiles [Percent; Quarterly; vintage last updated ]. | |
| 60 | +T04_wealth_share_p50_90_FRED_latest,"Share of net worth held by the 50th-90th percentiles (FRED mirror of DFA), latest observation",28.8,percent,2026Q2,United States,Quarterly,FRED (Board of Governors of the Federal Reserve System (US)),WFRBSN40188,https://fred.stlouisfed.org/graph/fredgraph.csv?id=WFRBSN40188,data/raw/fred/WFRBSN40188.csv,2026-10-27,RETRIEVED,Share of Net Worth Held by the 50th to 90th Wealth Percentiles [Percent; Quarterly; vintage last updated ]. | |
| 61 | +T04_wealth_share_bottom50_FRED_latest,"Share of net worth held by the bottom 50% (FRED mirror of DFA), latest observation",2.3,percent,2026Q2,United States,Quarterly,FRED (Board of Governors of the Federal Reserve System (US)),WFRBSB50215,https://fred.stlouisfed.org/graph/fredgraph.csv?id=WFRBSB50215,data/raw/fred/WFRBSB50215.csv,2026-10-27,RETRIEVED,Share of Net Worth Held by the Bottom 50% (1st to 50th Wealth Percentiles) [Percent; Quarterly; vintage last updated ]. | |
| 62 | +T05_GPDI_GDP_latest,"Gross private domestic investment / GDP, latest observation",0.178837,ratio,2026Q2,United States,quarterly,FRED (U.S. Bureau of Economic Analysis),GPDI/GDP,https://fred.stlouisfed.org/graph/fredgraph.csv?id=GPDI ; https://fred.stlouisfed.org/graph/fredgraph.csv?id=GDP,data/raw/fred/GPDI.csv ; data/raw/fred/GDP.csv,2026-10-27,RETRIEVED,Gross Private Domestic Investment [Billions of Dollars; Quarterly; vintage last updated 2026-09-30] divided by Gross Domestic Product [Billions of Dollars; Quarterly; vintage last updated 2026-09-30]. | |
| 63 | +T05_GPDI_GDP_avg,"Gross private domestic investment / GDP, 1990-latest average",0.175027,ratio,1990-2025,United States,annual average,FRED (U.S. Bureau of Economic Analysis),GPDI/GDP,https://fred.stlouisfed.org/graph/fredgraph.csv?id=GPDI ; https://fred.stlouisfed.org/graph/fredgraph.csv?id=GDP,data/raw/fred/GPDI.csv ; data/raw/fred/GDP.csv,2026-10-27,RETRIEVED,Gross Private Domestic Investment [Billions of Dollars; Quarterly; vintage last updated 2026-09-30] divided by Gross Domestic Product [Billions of Dollars; Quarterly; vintage last updated 2026-09-30]. Average: 1990-2025 mean of 36 obs (annual means of quarterly ratios). | |
| 64 | +T05_GPDI_GDP_lastfullyear,"Gross private domestic investment / GDP, latest full calendar year (mean of quarters)",0.180123,ratio,2025,United States,annual average,FRED (U.S. Bureau of Economic Analysis),GPDI/GDP,https://fred.stlouisfed.org/graph/fredgraph.csv?id=GPDI ; https://fred.stlouisfed.org/graph/fredgraph.csv?id=GDP,data/raw/fred/GPDI.csv ; data/raw/fred/GDP.csv,2026-10-27,RETRIEVED,Gross Private Domestic Investment [Billions of Dollars; Quarterly; vintage last updated 2026-09-30] divided by Gross Domestic Product [Billions of Dollars; Quarterly; vintage last updated 2026-09-30]. | |
| 65 | +T05_PNFI_GDP_latest,"Private nonresidential fixed investment / GDP, latest observation",0.145477,ratio,2026Q2,United States,quarterly,FRED (U.S. Bureau of Economic Analysis),PNFI/GDP,https://fred.stlouisfed.org/graph/fredgraph.csv?id=PNFI ; https://fred.stlouisfed.org/graph/fredgraph.csv?id=GDP,data/raw/fred/PNFI.csv ; data/raw/fred/GDP.csv,2026-10-27,RETRIEVED,Private Nonresidential Fixed Investment [Billions of Dollars; Quarterly; vintage last updated 2026-09-30] divided by Gross Domestic Product [Billions of Dollars; Quarterly; vintage last updated 2026-09-30]. | |
| 66 | +T05_PNFI_GDP_avg,"Private nonresidential fixed investment / GDP, 1990-latest average",0.130236,ratio,1990-2025,United States,annual average,FRED (U.S. Bureau of Economic Analysis),PNFI/GDP,https://fred.stlouisfed.org/graph/fredgraph.csv?id=PNFI ; https://fred.stlouisfed.org/graph/fredgraph.csv?id=GDP,data/raw/fred/PNFI.csv ; data/raw/fred/GDP.csv,2026-10-27,RETRIEVED,Private Nonresidential Fixed Investment [Billions of Dollars; Quarterly; vintage last updated 2026-09-30] divided by Gross Domestic Product [Billions of Dollars; Quarterly; vintage last updated 2026-09-30]. Average: 1990-2025 mean of 36 obs (annual means of quarterly ratios). | |
| 67 | +T05_PNFI_GDP_lastfullyear,"Private nonresidential fixed investment / GDP, latest full calendar year (mean of quarters)",0.141459,ratio,2025,United States,annual average,FRED (U.S. Bureau of Economic Analysis),PNFI/GDP,https://fred.stlouisfed.org/graph/fredgraph.csv?id=PNFI ; https://fred.stlouisfed.org/graph/fredgraph.csv?id=GDP,data/raw/fred/PNFI.csv ; data/raw/fred/GDP.csv,2026-10-27,RETRIEVED,Private Nonresidential Fixed Investment [Billions of Dollars; Quarterly; vintage last updated 2026-09-30] divided by Gross Domestic Product [Billions of Dollars; Quarterly; vintage last updated 2026-09-30]. | |
| 68 | +T05_info_processing_equipment_GDP_latest,"Nonresidential investment in information processing equipment / GDP, latest observation",0.0256862,ratio,2026Q2,United States,quarterly,FRED (U.S. Bureau of Economic Analysis),Y034RC1Q027SBEA/GDP,https://fred.stlouisfed.org/graph/fredgraph.csv?id=Y034RC1Q027SBEA ; https://fred.stlouisfed.org/graph/fredgraph.csv?id=GDP,data/raw/fred/Y034RC1Q027SBEA.csv ; data/raw/fred/GDP.csv,2026-10-27,RETRIEVED,Gross Private Domestic Investment: Fixed Investment: Nonresidential: Equipment: Information Processing Equipment [Billions of Dollars; Quarterly; vintage last updated 2026-09-30] divided by Gross Domestic Product [Billions of Dollars; Quarterly; vintage last updated 2026-09-30]. | |
| 69 | +T05_info_processing_equipment_GDP_avg,"Nonresidential investment in information processing equipment / GDP, 1990-latest average",0.0213859,ratio,1990-2025,United States,annual average,FRED (U.S. Bureau of Economic Analysis),Y034RC1Q027SBEA/GDP,https://fred.stlouisfed.org/graph/fredgraph.csv?id=Y034RC1Q027SBEA ; https://fred.stlouisfed.org/graph/fredgraph.csv?id=GDP,data/raw/fred/Y034RC1Q027SBEA.csv ; data/raw/fred/GDP.csv,2026-10-27,RETRIEVED,Gross Private Domestic Investment: Fixed Investment: Nonresidential: Equipment: Information Processing Equipment [Billions of Dollars; Quarterly; vintage last updated 2026-09-30] divided by Gross Domestic Product [Billions of Dollars; Quarterly; vintage last updated 2026-09-30]. Average: 1990-2025 mean of 36 obs (annual means of quarterly ratios). | |
| 70 | +T05_info_processing_equipment_GDP_lastfullyear,"Nonresidential investment in information processing equipment / GDP, latest full calendar year (mean of quarters)",0.0211305,ratio,2025,United States,annual average,FRED (U.S. Bureau of Economic Analysis),Y034RC1Q027SBEA/GDP,https://fred.stlouisfed.org/graph/fredgraph.csv?id=Y034RC1Q027SBEA ; https://fred.stlouisfed.org/graph/fredgraph.csv?id=GDP,data/raw/fred/Y034RC1Q027SBEA.csv ; data/raw/fred/GDP.csv,2026-10-27,RETRIEVED,Gross Private Domestic Investment: Fixed Investment: Nonresidential: Equipment: Information Processing Equipment [Billions of Dollars; Quarterly; vintage last updated 2026-09-30] divided by Gross Domestic Product [Billions of Dollars; Quarterly; vintage last updated 2026-09-30]. | |
| 71 | +T05_software_GDP_latest,"Nonresidential investment in software (IPP) / GDP, latest observation",0.0248523,ratio,2026Q2,United States,quarterly,FRED (U.S. Bureau of Economic Analysis),B985RC1Q027SBEA/GDP,https://fred.stlouisfed.org/graph/fredgraph.csv?id=B985RC1Q027SBEA ; https://fred.stlouisfed.org/graph/fredgraph.csv?id=GDP,data/raw/fred/B985RC1Q027SBEA.csv ; data/raw/fred/GDP.csv,2026-10-27,RETRIEVED,Private fixed investment: Nonresidential: Intellectual property products: Software [Billions of Dollars; Quarterly; vintage last updated 2026-09-30] divided by Gross Domestic Product [Billions of Dollars; Quarterly; vintage last updated 2026-09-30]. | |
| 72 | +T05_software_GDP_avg,"Nonresidential investment in software (IPP) / GDP, 1990-latest average",0.0154351,ratio,1990-2025,United States,annual average,FRED (U.S. Bureau of Economic Analysis),B985RC1Q027SBEA/GDP,https://fred.stlouisfed.org/graph/fredgraph.csv?id=B985RC1Q027SBEA ; https://fred.stlouisfed.org/graph/fredgraph.csv?id=GDP,data/raw/fred/B985RC1Q027SBEA.csv ; data/raw/fred/GDP.csv,2026-10-27,RETRIEVED,Private fixed investment: Nonresidential: Intellectual property products: Software [Billions of Dollars; Quarterly; vintage last updated 2026-09-30] divided by Gross Domestic Product [Billions of Dollars; Quarterly; vintage last updated 2026-09-30]. Average: 1990-2025 mean of 36 obs (annual means of quarterly ratios). | |
| 73 | +T05_software_GDP_lastfullyear,"Nonresidential investment in software (IPP) / GDP, latest full calendar year (mean of quarters)",0.0243417,ratio,2025,United States,annual average,FRED (U.S. Bureau of Economic Analysis),B985RC1Q027SBEA/GDP,https://fred.stlouisfed.org/graph/fredgraph.csv?id=B985RC1Q027SBEA ; https://fred.stlouisfed.org/graph/fredgraph.csv?id=GDP,data/raw/fred/B985RC1Q027SBEA.csv ; data/raw/fred/GDP.csv,2026-10-27,RETRIEVED,Private fixed investment: Nonresidential: Intellectual property products: Software [Billions of Dollars; Quarterly; vintage last updated 2026-09-30] divided by Gross Domestic Product [Billions of Dollars; Quarterly; vintage last updated 2026-09-30]. | |
| 74 | +T05_IPE_and_software_GDP_latest,"Private fixed investment in information processing equipment and software / GDP, latest observation",0.0505384,ratio,2026Q2,United States,quarterly,FRED (U.S. Bureau of Economic Analysis),A679RC1Q027SBEA/GDP,https://fred.stlouisfed.org/graph/fredgraph.csv?id=A679RC1Q027SBEA ; https://fred.stlouisfed.org/graph/fredgraph.csv?id=GDP,data/raw/fred/A679RC1Q027SBEA.csv ; data/raw/fred/GDP.csv,2026-10-27,RETRIEVED,Private fixed investment in information processing equipment and software [Billions of Dollars; Quarterly; vintage last updated 2026-09-30] divided by Gross Domestic Product [Billions of Dollars; Quarterly; vintage last updated 2026-09-30]. | |
| 75 | +T05_IPE_and_software_GDP_avg,"Private fixed investment in information processing equipment and software / GDP, 1990-latest average",0.036821,ratio,1990-2025,United States,annual average,FRED (U.S. Bureau of Economic Analysis),A679RC1Q027SBEA/GDP,https://fred.stlouisfed.org/graph/fredgraph.csv?id=A679RC1Q027SBEA ; https://fred.stlouisfed.org/graph/fredgraph.csv?id=GDP,data/raw/fred/A679RC1Q027SBEA.csv ; data/raw/fred/GDP.csv,2026-10-27,RETRIEVED,Private fixed investment in information processing equipment and software [Billions of Dollars; Quarterly; vintage last updated 2026-09-30] divided by Gross Domestic Product [Billions of Dollars; Quarterly; vintage last updated 2026-09-30]. Average: 1990-2025 mean of 36 obs (annual means of quarterly ratios). | |
| 76 | +T05_IPE_and_software_GDP_lastfullyear,"Private fixed investment in information processing equipment and software / GDP, latest full calendar year (mean of quarters)",0.0454722,ratio,2025,United States,annual average,FRED (U.S. Bureau of Economic Analysis),A679RC1Q027SBEA/GDP,https://fred.stlouisfed.org/graph/fredgraph.csv?id=A679RC1Q027SBEA ; https://fred.stlouisfed.org/graph/fredgraph.csv?id=GDP,data/raw/fred/A679RC1Q027SBEA.csv ; data/raw/fred/GDP.csv,2026-10-27,RETRIEVED,Private fixed investment in information processing equipment and software [Billions of Dollars; Quarterly; vintage last updated 2026-09-30] divided by Gross Domestic Product [Billions of Dollars; Quarterly; vintage last updated 2026-09-30]. | |
| 77 | +T05_IPP_GDP_latest,"Nonresidential investment in intellectual property products / GDP, latest observation",0.0577131,ratio,2026Q2,United States,quarterly,FRED (U.S. Bureau of Economic Analysis),Y001RC1Q027SBEA/GDP,https://fred.stlouisfed.org/graph/fredgraph.csv?id=Y001RC1Q027SBEA ; https://fred.stlouisfed.org/graph/fredgraph.csv?id=GDP,data/raw/fred/Y001RC1Q027SBEA.csv ; data/raw/fred/GDP.csv,2026-10-27,RETRIEVED,Gross Private Domestic Investment: Fixed Investment: Nonresidential: Intellectual Property Products [Billions of Dollars; Quarterly; vintage last updated 2026-09-30] divided by Gross Domestic Product [Billions of Dollars; Quarterly; vintage last updated 2026-09-30]. The brief suggested Y001RC1Q027SBEA as 'total nonresidential'; FRED metadata shows it is the IPP component. PNFI is the total. | |
| 78 | +T05_IPP_GDP_avg,"Nonresidential investment in intellectual property products / GDP, 1990-latest average",0.0401949,ratio,1990-2025,United States,annual average,FRED (U.S. Bureau of Economic Analysis),Y001RC1Q027SBEA/GDP,https://fred.stlouisfed.org/graph/fredgraph.csv?id=Y001RC1Q027SBEA ; https://fred.stlouisfed.org/graph/fredgraph.csv?id=GDP,data/raw/fred/Y001RC1Q027SBEA.csv ; data/raw/fred/GDP.csv,2026-10-27,RETRIEVED,Gross Private Domestic Investment: Fixed Investment: Nonresidential: Intellectual Property Products [Billions of Dollars; Quarterly; vintage last updated 2026-09-30] divided by Gross Domestic Product [Billions of Dollars; Quarterly; vintage last updated 2026-09-30]. The brief suggested Y001RC1Q027SBEA as 'total nonresidential'; FRED metadata shows it is the IPP component. PNFI is the total. Average: 1990-2025 mean of 36 obs (annual means of quarterly ratios). | |
| 79 | +T05_IPP_GDP_lastfullyear,"Nonresidential investment in intellectual property products / GDP, latest full calendar year (mean of quarters)",0.0559528,ratio,2025,United States,annual average,FRED (U.S. Bureau of Economic Analysis),Y001RC1Q027SBEA/GDP,https://fred.stlouisfed.org/graph/fredgraph.csv?id=Y001RC1Q027SBEA ; https://fred.stlouisfed.org/graph/fredgraph.csv?id=GDP,data/raw/fred/Y001RC1Q027SBEA.csv ; data/raw/fred/GDP.csv,2026-10-27,RETRIEVED,Gross Private Domestic Investment: Fixed Investment: Nonresidential: Intellectual Property Products [Billions of Dollars; Quarterly; vintage last updated 2026-09-30] divided by Gross Domestic Product [Billions of Dollars; Quarterly; vintage last updated 2026-09-30]. The brief suggested Y001RC1Q027SBEA as 'total nonresidential'; FRED metadata shows it is the IPP component. PNFI is the total. | |
| 80 | +T06_net_stock_private_fixed_assets_GDP_latest,"Current-cost net stock of private fixed assets (total) / GDP, latest observation",2.3919,ratio (years of GDP),2024,United States,annual,FRED (U.S. Bureau of Economic Analysis),K1PTOTL1ES000/GDPA,https://fred.stlouisfed.org/graph/fredgraph.csv?id=K1PTOTL1ES000 ; https://fred.stlouisfed.org/graph/fredgraph.csv?id=GDPA,data/raw/fred/K1PTOTL1ES000.csv ; data/raw/fred/GDPA.csv,2026-10-27,RETRIEVED,"Current-Cost Net Stock of Fixed Assets: Private [Millions of Dollars; Annual, End of Year; vintage last updated 2025-10-30] divided by Gross Domestic Product (annual) [Billions of Dollars; Annual; vintage last updated ]. End-of-year current-cost net stock (millions) over annual nominal GDP (billions); scaled 1e-3." | |
| 81 | +T06_net_stock_private_fixed_assets_GDP_avg,"Current-cost net stock of private fixed assets (total) / GDP, 1990-latest average",2.25024,ratio (years of GDP),1990-2024,United States,annual average,FRED (U.S. Bureau of Economic Analysis),K1PTOTL1ES000/GDPA,https://fred.stlouisfed.org/graph/fredgraph.csv?id=K1PTOTL1ES000 ; https://fred.stlouisfed.org/graph/fredgraph.csv?id=GDPA,data/raw/fred/K1PTOTL1ES000.csv ; data/raw/fred/GDPA.csv,2026-10-27,RETRIEVED,"Current-Cost Net Stock of Fixed Assets: Private [Millions of Dollars; Annual, End of Year; vintage last updated 2025-10-30] divided by Gross Domestic Product (annual) [Billions of Dollars; Annual; vintage last updated ]. End-of-year current-cost net stock (millions) over annual nominal GDP (billions); scaled 1e-3. Average: 1990-2024 mean of 35 obs." | |
| 82 | +T06_net_stock_private_nonres_fixed_assets_GDP_latest,"Current-cost net stock of private nonresidential fixed assets / GDP, latest observation",1.22188,ratio (years of GDP),2024,United States,annual,FRED (U.S. Bureau of Economic Analysis),K1NTOTL1ES000/GDPA,https://fred.stlouisfed.org/graph/fredgraph.csv?id=K1NTOTL1ES000 ; https://fred.stlouisfed.org/graph/fredgraph.csv?id=GDPA,data/raw/fred/K1NTOTL1ES000.csv ; data/raw/fred/GDPA.csv,2026-10-27,RETRIEVED,"Current-Cost Net Stock of Fixed Assets: Private: Nonresidential [Millions of Dollars; Annual, End of Year; vintage last updated 2025-10-30] divided by Gross Domestic Product (annual) [Billions of Dollars; Annual; vintage last updated ]." | |
| 83 | +T06_net_stock_private_nonres_fixed_assets_GDP_avg,"Current-cost net stock of private nonresidential fixed assets / GDP, 1990-latest average",1.19968,ratio (years of GDP),1990-2024,United States,annual average,FRED (U.S. Bureau of Economic Analysis),K1NTOTL1ES000/GDPA,https://fred.stlouisfed.org/graph/fredgraph.csv?id=K1NTOTL1ES000 ; https://fred.stlouisfed.org/graph/fredgraph.csv?id=GDPA,data/raw/fred/K1NTOTL1ES000.csv ; data/raw/fred/GDPA.csv,2026-10-27,RETRIEVED,"Current-Cost Net Stock of Fixed Assets: Private: Nonresidential [Millions of Dollars; Annual, End of Year; vintage last updated 2025-10-30] divided by Gross Domestic Product (annual) [Billions of Dollars; Annual; vintage last updated ]. Average: 1990-2024 mean of 35 obs." | |
| 84 | +T06_depreciation_rate_private_total_latest,"Implied depreciation rate: current-cost depreciation / net stock, private fixed assets (total), latest observation",0.0568171,per year,2024,United States,annual,FRED (U.S. Bureau of Economic Analysis),M1PTOTL1ES000/K1PTOTL1ES000,https://fred.stlouisfed.org/graph/fredgraph.csv?id=M1PTOTL1ES000 ; https://fred.stlouisfed.org/graph/fredgraph.csv?id=K1PTOTL1ES000,data/raw/fred/M1PTOTL1ES000.csv ; data/raw/fred/K1PTOTL1ES000.csv,2026-10-27,RETRIEVED,"Current-Cost Depreciation of Fixed Assets: Private [Millions of Dollars; Annual; vintage last updated 2025-10-30] divided by Current-Cost Net Stock of Fixed Assets: Private [Millions of Dollars; Annual, End of Year; vintage last updated 2025-10-30]. Both current-cost, BEA Fixed Assets tables 1.1 and 1.3; depreciation during year t over end-of-year-t net stock (not lagged stock)." | |
| 85 | +T06_depreciation_rate_private_total_avg,"Implied depreciation rate: current-cost depreciation / net stock, private fixed assets (total), 1990-latest average",0.0558836,per year,1990-2024,United States,annual average,FRED (U.S. Bureau of Economic Analysis),M1PTOTL1ES000/K1PTOTL1ES000,https://fred.stlouisfed.org/graph/fredgraph.csv?id=M1PTOTL1ES000 ; https://fred.stlouisfed.org/graph/fredgraph.csv?id=K1PTOTL1ES000,data/raw/fred/M1PTOTL1ES000.csv ; data/raw/fred/K1PTOTL1ES000.csv,2026-10-27,RETRIEVED,"Current-Cost Depreciation of Fixed Assets: Private [Millions of Dollars; Annual; vintage last updated 2025-10-30] divided by Current-Cost Net Stock of Fixed Assets: Private [Millions of Dollars; Annual, End of Year; vintage last updated 2025-10-30]. Both current-cost, BEA Fixed Assets tables 1.1 and 1.3; depreciation during year t over end-of-year-t net stock (not lagged stock). Average: 1990-2024 mean of 35 obs." | |
| 86 | +T06_depreciation_rate_private_nonres_latest,"Implied depreciation rate: private nonresidential fixed assets, latest observation",0.088972,per year,2024,United States,annual,FRED (U.S. Bureau of Economic Analysis),M1NTOTL1ES000/K1NTOTL1ES000,https://fred.stlouisfed.org/graph/fredgraph.csv?id=M1NTOTL1ES000 ; https://fred.stlouisfed.org/graph/fredgraph.csv?id=K1NTOTL1ES000,data/raw/fred/M1NTOTL1ES000.csv ; data/raw/fred/K1NTOTL1ES000.csv,2026-10-27,RETRIEVED,"Current-Cost Depreciation of Fixed Assets: Private: Nonresidential [Millions of Dollars; Annual; vintage last updated 2025-10-30] divided by Current-Cost Net Stock of Fixed Assets: Private: Nonresidential [Millions of Dollars; Annual, End of Year; vintage last updated 2025-10-30]. Current-cost depreciation over end-of-year current-cost net stock, nonresidential." | |
| 87 | +T06_depreciation_rate_private_nonres_avg,"Implied depreciation rate: private nonresidential fixed assets, 1990-latest average",0.0843184,per year,1990-2024,United States,annual average,FRED (U.S. Bureau of Economic Analysis),M1NTOTL1ES000/K1NTOTL1ES000,https://fred.stlouisfed.org/graph/fredgraph.csv?id=M1NTOTL1ES000 ; https://fred.stlouisfed.org/graph/fredgraph.csv?id=K1NTOTL1ES000,data/raw/fred/M1NTOTL1ES000.csv ; data/raw/fred/K1NTOTL1ES000.csv,2026-10-27,RETRIEVED,"Current-Cost Depreciation of Fixed Assets: Private: Nonresidential [Millions of Dollars; Annual; vintage last updated 2025-10-30] divided by Current-Cost Net Stock of Fixed Assets: Private: Nonresidential [Millions of Dollars; Annual, End of Year; vintage last updated 2025-10-30]. Current-cost depreciation over end-of-year current-cost net stock, nonresidential. Average: 1990-2024 mean of 35 obs." | |
| 88 | +T06_CFC_GDP_latest,"Consumption of fixed capital (NIPA, total economy) / GDP, latest observation",0.164566,ratio,2026Q2,United States,quarterly,FRED (U.S. Bureau of Economic Analysis),COFC/GDP,https://fred.stlouisfed.org/graph/fredgraph.csv?id=COFC ; https://fred.stlouisfed.org/graph/fredgraph.csv?id=GDP,data/raw/fred/COFC.csv ; data/raw/fred/GDP.csv,2026-10-27,RETRIEVED,Consumption of Fixed Capital [Billions of Dollars; Quarterly; vintage last updated 2026-09-30] divided by Gross Domestic Product [Billions of Dollars; Quarterly; vintage last updated 2026-09-30]. | |
| 89 | +T06_CFC_GDP_avg,"Consumption of fixed capital (NIPA, total economy) / GDP, 1990-latest average",0.155174,ratio,1990-2025,United States,annual average,FRED (U.S. Bureau of Economic Analysis),COFC/GDP,https://fred.stlouisfed.org/graph/fredgraph.csv?id=COFC ; https://fred.stlouisfed.org/graph/fredgraph.csv?id=GDP,data/raw/fred/COFC.csv ; data/raw/fred/GDP.csv,2026-10-27,RETRIEVED,Consumption of Fixed Capital [Billions of Dollars; Quarterly; vintage last updated 2026-09-30] divided by Gross Domestic Product [Billions of Dollars; Quarterly; vintage last updated 2026-09-30]. Average: 1990-2025 mean of 36 obs (annual means of quarterly ratios). | |
| 90 | +T06_CFC_GDP_lastfullyear,"Consumption of fixed capital (NIPA, total economy) / GDP, latest full calendar year (mean of quarters)",0.163804,ratio,2025,United States,annual average,FRED (U.S. Bureau of Economic Analysis),COFC/GDP,https://fred.stlouisfed.org/graph/fredgraph.csv?id=COFC ; https://fred.stlouisfed.org/graph/fredgraph.csv?id=GDP,data/raw/fred/COFC.csv ; data/raw/fred/GDP.csv,2026-10-27,RETRIEVED,Consumption of Fixed Capital [Billions of Dollars; Quarterly; vintage last updated 2026-09-30] divided by Gross Domestic Product [Billions of Dollars; Quarterly; vintage last updated 2026-09-30]. | |
| 91 | +T06_CFC_private_GDP_latest,"Consumption of fixed capital: private / GDP (annual), latest observation",0.136523,ratio,2025,United States,annual,FRED (U.S. Bureau of Economic Analysis),A024RC1A027NBEA/GDPA,https://fred.stlouisfed.org/graph/fredgraph.csv?id=A024RC1A027NBEA ; https://fred.stlouisfed.org/graph/fredgraph.csv?id=GDPA,data/raw/fred/A024RC1A027NBEA.csv ; data/raw/fred/GDPA.csv,2026-10-27,RETRIEVED,Consumption of fixed capital: Private [Billions of Dollars; Annual; vintage last updated 2026-09-30] divided by Gross Domestic Product (annual) [Billions of Dollars; Annual; vintage last updated ]. | |
| 92 | +T06_CFC_private_GDP_avg,"Consumption of fixed capital: private / GDP (annual), 1990-latest average",0.12605,ratio,1990-2025,United States,annual average,FRED (U.S. Bureau of Economic Analysis),A024RC1A027NBEA/GDPA,https://fred.stlouisfed.org/graph/fredgraph.csv?id=A024RC1A027NBEA ; https://fred.stlouisfed.org/graph/fredgraph.csv?id=GDPA,data/raw/fred/A024RC1A027NBEA.csv ; data/raw/fred/GDPA.csv,2026-10-27,RETRIEVED,Consumption of fixed capital: Private [Billions of Dollars; Annual; vintage last updated 2026-09-30] divided by Gross Domestic Product (annual) [Billions of Dollars; Annual; vintage last updated ]. Average: 1990-2025 mean of 36 obs. | |
| 93 | +T06_CFC_private_over_net_stock_latest,"NIPA private CFC / current-cost net stock of private fixed assets, latest observation",0.0569093,per year,2024,United States,annual,FRED (U.S. Bureau of Economic Analysis),A024RC1A027NBEA/K1PTOTL1ES000,https://fred.stlouisfed.org/graph/fredgraph.csv?id=A024RC1A027NBEA ; https://fred.stlouisfed.org/graph/fredgraph.csv?id=K1PTOTL1ES000,data/raw/fred/A024RC1A027NBEA.csv ; data/raw/fred/K1PTOTL1ES000.csv,2026-10-27,RETRIEVED,"Consumption of fixed capital: Private [Billions of Dollars; Annual; vintage last updated 2026-09-30] divided by Current-Cost Net Stock of Fixed Assets: Private [Millions of Dollars; Annual, End of Year; vintage last updated 2025-10-30]. Billions over millions, scaled 1e3. Alternative depreciation-rate measure." | |
| 94 | +T06_CFC_private_over_net_stock_avg,"NIPA private CFC / current-cost net stock of private fixed assets, 1990-latest average",0.0558858,per year,1990-2024,United States,annual average,FRED (U.S. Bureau of Economic Analysis),A024RC1A027NBEA/K1PTOTL1ES000,https://fred.stlouisfed.org/graph/fredgraph.csv?id=A024RC1A027NBEA ; https://fred.stlouisfed.org/graph/fredgraph.csv?id=K1PTOTL1ES000,data/raw/fred/A024RC1A027NBEA.csv ; data/raw/fred/K1PTOTL1ES000.csv,2026-10-27,RETRIEVED,"Consumption of fixed capital: Private [Billions of Dollars; Annual; vintage last updated 2026-09-30] divided by Current-Cost Net Stock of Fixed Assets: Private [Millions of Dollars; Annual, End of Year; vintage last updated 2025-10-30]. Billions over millions, scaled 1e3. Alternative depreciation-rate measure. Average: 1990-2024 mean of 35 obs." | |
| 95 | +T07_TIPS10_daily_latest,"10-year TIPS constant-maturity real yield, latest observation",2.66,percent per year,2026-10-23,United States,Daily,FRED (Board of Governors of the Federal Reserve System (US)),DFII10,https://fred.stlouisfed.org/graph/fredgraph.csv?id=DFII10,data/raw/fred/DFII10.csv,2026-10-27,RETRIEVED,"Market Yield on U.S. Treasury Securities at 10-Year Constant Maturity, Quoted on an Investment Basis, Inflation-Indexed [Percent; Daily; vintage last updated ]. Average over 2003-latest (TIPS data start 2003), not 1990." | |
| 96 | +T07_TIPS10_daily_lastfullyear,"10-year TIPS constant-maturity real yield, latest full calendar-year average",1.96309,percent per year,2025,United States,annual average,FRED (Board of Governors of the Federal Reserve System (US)),DFII10,https://fred.stlouisfed.org/graph/fredgraph.csv?id=DFII10,data/raw/fred/DFII10.csv,2026-10-27,RETRIEVED,"Market Yield on U.S. Treasury Securities at 10-Year Constant Maturity, Quoted on an Investment Basis, Inflation-Indexed [Percent; Daily; vintage last updated ]. Average over 2003-latest (TIPS data start 2003), not 1990." | |
| 97 | +T07_TIPS10_daily_avg,"10-year TIPS constant-maturity real yield, 1990-latest average of annual means",0.97271,percent per year,2003-2025,United States,annual average,FRED (Board of Governors of the Federal Reserve System (US)),DFII10,https://fred.stlouisfed.org/graph/fredgraph.csv?id=DFII10,data/raw/fred/DFII10.csv,2026-10-27,RETRIEVED,"Market Yield on U.S. Treasury Securities at 10-Year Constant Maturity, Quoted on an Investment Basis, Inflation-Indexed [Percent; Daily; vintage last updated ]. Average over 2003-latest (TIPS data start 2003), not 1990. Average: 2003-2025 mean of 23 obs." | |
| 98 | +T07_TIPS10_monthly_latest,"10-year TIPS constant-maturity real yield (monthly), latest observation",2.64,percent per year,2026-09,United States,Monthly,FRED (Board of Governors of the Federal Reserve System (US)),FII10,https://fred.stlouisfed.org/graph/fredgraph.csv?id=FII10,data/raw/fred/FII10.csv,2026-10-27,RETRIEVED,"Market Yield on U.S. Treasury Securities at 10-Year Constant Maturity, Quoted on an Investment Basis, Inflation-Indexed [Percent; Monthly; vintage last updated 2026-10-01]." | |
| 99 | +T07_TIPS10_monthly_lastfullyear,"10-year TIPS constant-maturity real yield (monthly), latest full calendar-year average",1.965,percent per year,2025,United States,annual average,FRED (Board of Governors of the Federal Reserve System (US)),FII10,https://fred.stlouisfed.org/graph/fredgraph.csv?id=FII10,data/raw/fred/FII10.csv,2026-10-27,RETRIEVED,"Market Yield on U.S. Treasury Securities at 10-Year Constant Maturity, Quoted on an Investment Basis, Inflation-Indexed [Percent; Monthly; vintage last updated 2026-10-01]." | |
| 100 | +T07_TIPS10_monthly_avg,"10-year TIPS constant-maturity real yield (monthly), 1990-latest average of annual means",0.973297,percent per year,2003-2025,United States,annual average,FRED (Board of Governors of the Federal Reserve System (US)),FII10,https://fred.stlouisfed.org/graph/fredgraph.csv?id=FII10,data/raw/fred/FII10.csv,2026-10-27,RETRIEVED,"Market Yield on U.S. Treasury Securities at 10-Year Constant Maturity, Quoted on an Investment Basis, Inflation-Indexed [Percent; Monthly; vintage last updated 2026-10-01]. Average: 2003-2025 mean of 23 obs." | |
| 101 | +T07_real_rate_10y_Cleveland_latest,"10-year real interest rate (Cleveland Fed model: nominal yield minus expected inflation), latest observation",2.4744,percent per year,2026-10,United States,Monthly,FRED (Federal Reserve Bank of Cleveland),REAINTRATREARAT10Y,https://fred.stlouisfed.org/graph/fredgraph.csv?id=REAINTRATREARAT10Y,data/raw/fred/REAINTRATREARAT10Y.csv,2026-10-27,RETRIEVED,10-Year Real Interest Rate [Percent; Monthly; vintage last updated 2026-10-14]. | |
| 102 | +T07_real_rate_10y_Cleveland_lastfullyear,"10-year real interest rate (Cleveland Fed model: nominal yield minus expected inflation), latest full calendar-year average",1.71327,percent per year,2025,United States,annual average,FRED (Federal Reserve Bank of Cleveland),REAINTRATREARAT10Y,https://fred.stlouisfed.org/graph/fredgraph.csv?id=REAINTRATREARAT10Y,data/raw/fred/REAINTRATREARAT10Y.csv,2026-10-27,RETRIEVED,10-Year Real Interest Rate [Percent; Monthly; vintage last updated 2026-10-14]. | |
| 103 | +T07_real_rate_10y_Cleveland_avg,"10-year real interest rate (Cleveland Fed model: nominal yield minus expected inflation), 1990-latest average of annual means",1.7764,percent per year,1990-2025,United States,annual average,FRED (Federal Reserve Bank of Cleveland),REAINTRATREARAT10Y,https://fred.stlouisfed.org/graph/fredgraph.csv?id=REAINTRATREARAT10Y,data/raw/fred/REAINTRATREARAT10Y.csv,2026-10-27,RETRIEVED,10-Year Real Interest Rate [Percent; Monthly; vintage last updated 2026-10-14]. Average: 1990-2025 mean of 36 obs. | |
| 104 | +T07_nominal_10y_latest,"10-year Treasury constant-maturity nominal yield (monthly), latest observation",4.99,percent per year,2026-09,United States,Monthly,FRED (Board of Governors of the Federal Reserve System (US)),GS10,https://fred.stlouisfed.org/graph/fredgraph.csv?id=GS10,data/raw/fred/GS10.csv,2026-10-27,RETRIEVED,"Market Yield on U.S. Treasury Securities at 10-Year Constant Maturity, Quoted on an Investment Basis [Percent; Monthly; vintage last updated ]." | |
| 105 | +T07_nominal_10y_lastfullyear,"10-year Treasury constant-maturity nominal yield (monthly), latest full calendar-year average",4.29167,percent per year,2025,United States,annual average,FRED (Board of Governors of the Federal Reserve System (US)),GS10,https://fred.stlouisfed.org/graph/fredgraph.csv?id=GS10,data/raw/fred/GS10.csv,2026-10-27,RETRIEVED,"Market Yield on U.S. Treasury Securities at 10-Year Constant Maturity, Quoted on an Investment Basis [Percent; Monthly; vintage last updated ]." | |
| 106 | +T07_nominal_10y_avg,"10-year Treasury constant-maturity nominal yield (monthly), 1990-latest average of annual means",4.25072,percent per year,1990-2025,United States,annual average,FRED (Board of Governors of the Federal Reserve System (US)),GS10,https://fred.stlouisfed.org/graph/fredgraph.csv?id=GS10,data/raw/fred/GS10.csv,2026-10-27,RETRIEVED,"Market Yield on U.S. Treasury Securities at 10-Year Constant Maturity, Quoted on an Investment Basis [Percent; Monthly; vintage last updated ]. Average: 1990-2025 mean of 36 obs." | |
| 107 | +T07_TIPS10_treasury_lastfullyear,"10-year par real yield (US Treasury daily real yield curve), latest full calendar-year average",1.96309,percent per year,2025,United States,annual average,"US Department of the Treasury, Daily Treasury Par Real Yield Curve Rates",10 YR,https://home.treasury.gov/resource-center/data-chart-center/interest-rates/daily-treasury-rates.csv/<year>/all?type=daily_treasury_real_yield_curve&field_tdr_date_value=<year>&page&_format=csv,data/raw/treasury/daily_treasury_real_yield_curve_<year>.csv,2026-10-27,RETRIEVED,Cross-check of DFII10; public domain. | |
| 108 | +T07_TIPS10_treasury_avg,"10-year par real yield (Treasury), 2003-latest average of annual means",0.97271,percent per year,2003-2025,United States,annual average,US Department of the Treasury,10 YR,https://home.treasury.gov/resource-center/data-chart-center/interest-rates/,data/raw/treasury/,2026-10-27,RETRIEVED,2003-2025 mean of 23 obs | |
| 109 | +T07_real_rate_10y_Cleveland_xlsx_latest,10-year real rate from the Cleveland Fed inflation-expectations workbook (decimal x100),2.4744,percent per year,2026-10,United States,monthly,"Federal Reserve Bank of Cleveland, Inflation Expectations model",Real Rate 10-year,https://www.clevelandfed.org/-/media/files/webcharts/inflationexpectations/inflation-expectations.xlsx,data/raw/clevelandfed/inflation-expectations.xlsx,2026-10-27,RETRIEVED,"Cross-check of REAINTRATREARAT10Y. Model of Haubrich, Pennacchi & Ritchken (2012)." | |
| 110 | +T07_expected_inflation_10y_Cleveland_latest,10-year expected inflation (Cleveland Fed model),2.72551,percent per year,2026-10,United States,monthly,Federal Reserve Bank of Cleveland,10 year Expected Inflation,https://www.clevelandfed.org/-/media/files/webcharts/inflationexpectations/inflation-expectations.xlsx,data/raw/clevelandfed/inflation-expectations.xlsx,2026-10-27,RETRIEVED, | |
| 111 | +T08_CAPE_latest,"Shiller cyclically adjusted P/E (CAPE, P/E10), latest observation in file",30.811,ratio,2023-09,United States,monthly,"Robert J. Shiller, online data for 'Irrational Exuberance' (ie_data.xls)",ie_data.xls Data sheet,http://www.econ.yale.edu/~shiller/data/ie_data.xls,data/raw/shiller/ie_data.xls,2026-10-27,RETRIEVED,VINTAGE CAVEAT: file ends 2023-09 (the Yale copy retrieved on 2026-10-27 is not more recent). D and E are 12-month trailing sums interpolated monthly by Shiller; P is the monthly average of daily closes. | |
| 112 | +T08_CAPE_avg,"Shiller cyclically adjusted P/E (CAPE, P/E10), 1990-latest average of annual means",26.4486,ratio,1990-2022,United States,annual average,"Robert J. Shiller, online data (ie_data.xls)",ie_data.xls Data sheet,http://www.econ.yale.edu/~shiller/data/ie_data.xls,data/raw/shiller/ie_data.xls,2026-10-27,RETRIEVED,1990-2022 mean of 33 obs. file ends 2023-09 (the Yale copy retrieved on 2026-10-27 is not more recent). | |
| 113 | +T08_dividend_yield_latest,"S&P composite dividend yield D/P (trailing 12-month dividends / price), latest observation in file",0.0158122,ratio,2023-06,United States,monthly,"Robert J. Shiller, online data for 'Irrational Exuberance' (ie_data.xls)",ie_data.xls Data sheet,http://www.econ.yale.edu/~shiller/data/ie_data.xls,data/raw/shiller/ie_data.xls,2026-10-27,RETRIEVED,VINTAGE CAVEAT: file ends 2023-09 (the Yale copy retrieved on 2026-10-27 is not more recent). D and E are 12-month trailing sums interpolated monthly by Shiller; P is the monthly average of daily closes. | |
| 114 | +T08_dividend_yield_avg,"S&P composite dividend yield D/P (trailing 12-month dividends / price), 1990-latest average of annual means",0.0203004,ratio,1990-2022,United States,annual average,"Robert J. Shiller, online data (ie_data.xls)",ie_data.xls Data sheet,http://www.econ.yale.edu/~shiller/data/ie_data.xls,data/raw/shiller/ie_data.xls,2026-10-27,RETRIEVED,1990-2022 mean of 33 obs. file ends 2023-09 (the Yale copy retrieved on 2026-10-27 is not more recent). | |
| 115 | +T08_earnings_yield_latest,"S&P composite earnings yield E/P (trailing 12-month earnings / price), latest observation in file",0.0416926,ratio,2023-06,United States,monthly,"Robert J. Shiller, online data for 'Irrational Exuberance' (ie_data.xls)",ie_data.xls Data sheet,http://www.econ.yale.edu/~shiller/data/ie_data.xls,data/raw/shiller/ie_data.xls,2026-10-27,RETRIEVED,VINTAGE CAVEAT: file ends 2023-09 (the Yale copy retrieved on 2026-10-27 is not more recent). D and E are 12-month trailing sums interpolated monthly by Shiller; P is the monthly average of daily closes. | |
| 116 | +T08_earnings_yield_avg,"S&P composite earnings yield E/P (trailing 12-month earnings / price), 1990-latest average of annual means",0.0461352,ratio,1990-2022,United States,annual average,"Robert J. Shiller, online data (ie_data.xls)",ie_data.xls Data sheet,http://www.econ.yale.edu/~shiller/data/ie_data.xls,data/raw/shiller/ie_data.xls,2026-10-27,RETRIEVED,1990-2022 mean of 33 obs. file ends 2023-09 (the Yale copy retrieved on 2026-10-27 is not more recent). | |
| 117 | +T08_CAPE_longrun_avg,"Shiller CAPE, full-sample mean of annual means",17.3383,ratio,1881-2022,United States,annual average,"Robert J. Shiller, online data (ie_data.xls)",ie_data.xls Data sheet,http://www.econ.yale.edu/~shiller/data/ie_data.xls,data/raw/shiller/ie_data.xls,2026-10-27,RETRIEVED,file ends 2023-09 (the Yale copy retrieved on 2026-10-27 is not more recent) | |
| 118 | +T09_real_home_price_index_latest,"Shiller US real home price index (1890=100), latest observation in file",209.35,index 1890=100,2023-01,United States,monthly (annual before 1953),"Robert J. Shiller, Irrational Exuberance Fig. 3.1 data (Fig3-1.xls)",Fig3-1.xls col B,http://www.econ.yale.edu/~shiller/data/Fig3-1.xls,data/raw/shiller/Fig3-1.xls,2026-10-27,RETRIEVED,VINTAGE CAVEAT: file ends 2023-01. Nominal index = Case-Shiller national (S&P/CoreLogic) spliced to Grebler/PHCPI; deflated by CPI by Shiller. | |
| 119 | +T09_real_home_price_growth_1990,"Average real home price growth (log, annual means), 1990-2023",0.015376,per year,1990-2023,United States,annual,"Robert J. Shiller, Fig3-1.xls",Fig3-1.xls col B,http://www.econ.yale.edu/~shiller/data/Fig3-1.xls,data/raw/shiller/Fig3-1.xls,2026-10-27,RETRIEVED,Computed from annual means of the real index. | |
| 120 | +T09_real_home_price_growth_1890,"Average real home price growth (log, annual means), 1890-2023",0.00555516,per year,1890-2023,United States,annual,"Robert J. Shiller, Fig3-1.xls",Fig3-1.xls col B,http://www.econ.yale.edu/~shiller/data/Fig3-1.xls,data/raw/shiller/Fig3-1.xls,2026-10-27,RETRIEVED,Computed from annual means of the real index. | |
| 121 | +T09_rent_price_ratio_proxy_latest,"Rent-price ratio proxy index (2000=100), latest",74.817,index Jan-2000=100,2026-07,United States,monthly,FRED (BLS CUSR0000SEHA; S&P Dow Jones Indices CSUSHPISA),CUSR0000SEHA/CSUSHPISA,https://fred.stlouisfed.org/graph/fredgraph.csv?id=CUSR0000SEHA ; https://fred.stlouisfed.org/graph/fredgraph.csv?id=CSUSHPISA,data/raw/fred/CUSR0000SEHA.csv ; data/raw/fred/CSUSHPISA.csv,2026-10-27,RETRIEVED,"PROXY ONLY: index of (CPI rent of primary residence) / (Case-Shiller national HPI), both rebased to Jan 2000 = 100. It tracks the change in the rent-price ratio relative to 2000, not its level; no key-less level series of the rent-price ratio was available." | |
| 122 | +T09_rent_price_ratio_proxy_avg,"Rent-price ratio proxy index (2000=100), 1990-latest average",91.1414,index Jan-2000=100,1990-2025,United States,annual average,FRED (BLS; S&P Dow Jones Indices),CUSR0000SEHA/CSUSHPISA,https://fred.stlouisfed.org/graph/fredgraph.csv?id=CUSR0000SEHA ; https://fred.stlouisfed.org/graph/fredgraph.csv?id=CSUSHPISA,data/raw/fred/CUSR0000SEHA.csv ; data/raw/fred/CSUSHPISA.csv,2026-10-27,RETRIEVED,"PROXY ONLY: index of (CPI rent of primary residence) / (Case-Shiller national HPI), both rebased to Jan 2000 = 100. It tracks the change in the rent-price ratio relative to 2000, not its level; no key-less level series of the rent-price ratio was available. 1990-2025 mean of 36 obs" | |
| 123 | +T09_rent_cpi_growth_1990,"Average growth of CPI rent of primary residence (log, annual means), 1990-2025",0.0327101,per year,1990-2025,United States,annual,FRED (U.S. Bureau of Labor Statistics),CUSR0000SEHA,https://fred.stlouisfed.org/graph/fredgraph.csv?id=CUSR0000SEHA,data/raw/fred/CUSR0000SEHA.csv,2026-10-27,RETRIEVED,Consumer Price Index for All Urban Consumers: Rent of Primary Residence in U.S. City Average [Index 1982-1984=100; Monthly; vintage last updated ] | |
| 124 | +T09_case_shiller_growth_1990,"Average growth of Case-Shiller national home price index (nominal) (log, annual means), 1990-2025",0.0414744,per year,1990-2025,United States,annual,FRED (S&P Dow Jones Indices LLC),CSUSHPISA,https://fred.stlouisfed.org/graph/fredgraph.csv?id=CSUSHPISA,data/raw/fred/CSUSHPISA.csv,2026-10-27,RETRIEVED,S&P Cotality Case-Shiller U.S. National Home Price Index [Index Jan 2000=100; Monthly; vintage last updated 2026-09-30] | |
| 125 | +T09_case_shiller_real_growth_1990,"Average real growth of Case-Shiller national HPI deflated by CPI-U (log, annual means), 1990-2025",0.0156958,per year,1990-2025,United States,annual,FRED (S&P Dow Jones Indices; BLS),CSUSHPISA/CPIAUCSL,https://fred.stlouisfed.org/graph/fredgraph.csv?id=CSUSHPISA ; https://fred.stlouisfed.org/graph/fredgraph.csv?id=CPIAUCSL,data/raw/fred/CSUSHPISA.csv ; data/raw/fred/CPIAUCSL.csv,2026-10-27,RETRIEVED, | |
| 126 | +T10_datacenter_electricity_2023_TWh,"US data center electricity use, 2023",176,TWh,2023,United States,annual,"Shehabi, A., Newkirk, A., Smith, S.J., et al. (2024), 2024 United States Data Center Energy Usage Report, LBNL-2001637, Lawrence Berkeley National Laboratory (DOI 10.71468/P1WC7Q)",text,https://escholarship.org/content/qt32d6m0d1/qt32d6m0d1.pdf,data/raw/lbnl/lbnl-2024-united-states-data-center-energy-usage-report.pdf,2026-10-27,RETRIEVED,"Quote (PDF page 7, printed page 5): '...reaching 176 TWh by 2023, representing 4.4% of total U.S. electricity consumption'. Includes servers, storage, network and infrastructure (PUE), bottom-up model." | |
| 127 | +T10_datacenter_share_of_US_electricity_2023,"US data center electricity use as share of total US electricity consumption, 2023",4.4,percent,2023,United States,annual,"Shehabi, A., Newkirk, A., Smith, S.J., et al. (2024), 2024 United States Data Center Energy Usage Report, LBNL-2001637, Lawrence Berkeley National Laboratory (DOI 10.71468/P1WC7Q)",text,https://escholarship.org/content/qt32d6m0d1/qt32d6m0d1.pdf,data/raw/lbnl/lbnl-2024-united-states-data-center-energy-usage-report.pdf,2026-10-27,RETRIEVED,"Quote (PDF page 7, printed page 5): 'reaching 176 TWh by 2023, representing 4.4% of total U.S. electricity consumption'." | |
| 128 | +T10_datacenter_electricity_2018_TWh,"US data center electricity use, 2018",76,TWh,2018,United States,annual,"Shehabi, A., Newkirk, A., Smith, S.J., et al. (2024), 2024 United States Data Center Energy Usage Report, LBNL-2001637, Lawrence Berkeley National Laboratory (DOI 10.71468/P1WC7Q)",text,https://escholarship.org/content/qt32d6m0d1/qt32d6m0d1.pdf,data/raw/lbnl/lbnl-2024-united-states-data-center-energy-usage-report.pdf,2026-10-27,RETRIEVED,"Quote (PDF page 7, printed page 5): 'by 2018 data centers consumed about 76 TWh, representing 1.9% of total annual U.S. electricity consumption'." | |
| 129 | +T10_datacenter_share_of_US_electricity_2018,"US data center share of total US electricity consumption, 2018",1.9,percent,2018,United States,annual,"Shehabi, A., Newkirk, A., Smith, S.J., et al. (2024), 2024 United States Data Center Energy Usage Report, LBNL-2001637, Lawrence Berkeley National Laboratory (DOI 10.71468/P1WC7Q)",text,https://escholarship.org/content/qt32d6m0d1/qt32d6m0d1.pdf,data/raw/lbnl/lbnl-2024-united-states-data-center-energy-usage-report.pdf,2026-10-27,RETRIEVED,"Quote (PDF page 7, printed page 5)." | |
| 130 | +T10_datacenter_electricity_2028_low_TWh,"US data center electricity use, 2028 projection, low scenario",325,TWh,2028,United States,annual (projection),"Shehabi, A., Newkirk, A., Smith, S.J., et al. (2024), 2024 United States Data Center Energy Usage Report, LBNL-2001637, Lawrence Berkeley National Laboratory (DOI 10.71468/P1WC7Q)",text,https://escholarship.org/content/qt32d6m0d1/qt32d6m0d1.pdf,data/raw/lbnl/lbnl-2024-united-states-data-center-energy-usage-report.pdf,2026-10-27,RETRIEVED,"Quote (PDF page 8, printed page 6): 'low and high end of roughly 325 and 580 TWh in 2028'. Scenario range, not a forecast." | |
| 131 | +T10_datacenter_electricity_2028_high_TWh,"US data center electricity use, 2028 projection, high scenario",580,TWh,2028,United States,annual (projection),"Shehabi, A., Newkirk, A., Smith, S.J., et al. (2024), 2024 United States Data Center Energy Usage Report, LBNL-2001637, Lawrence Berkeley National Laboratory (DOI 10.71468/P1WC7Q)",text,https://escholarship.org/content/qt32d6m0d1/qt32d6m0d1.pdf,data/raw/lbnl/lbnl-2024-united-states-data-center-energy-usage-report.pdf,2026-10-27,RETRIEVED,"Quote (PDF page 8, printed page 6)." | |
| 132 | +T10_datacenter_share_2028_low,"US data center share of total US electricity consumption, 2028 projection, low",6.7,percent,2028,United States,annual (projection),"Shehabi, A., Newkirk, A., Smith, S.J., et al. (2024), 2024 United States Data Center Energy Usage Report, LBNL-2001637, Lawrence Berkeley National Laboratory (DOI 10.71468/P1WC7Q)",text,https://escholarship.org/content/qt32d6m0d1/qt32d6m0d1.pdf,data/raw/lbnl/lbnl-2024-united-states-data-center-energy-usage-report.pdf,2026-10-27,RETRIEVED,"Quote (PDF page 8, printed page 6): '6.7% to 12.0% of total U.S. electricity consumption'." | |
| 133 | +T10_datacenter_share_2028_high,"US data center share of total US electricity consumption, 2028 projection, high",12,percent,2028,United States,annual (projection),"Shehabi, A., Newkirk, A., Smith, S.J., et al. (2024), 2024 United States Data Center Energy Usage Report, LBNL-2001637, Lawrence Berkeley National Laboratory (DOI 10.71468/P1WC7Q)",text,https://escholarship.org/content/qt32d6m0d1/qt32d6m0d1.pdf,data/raw/lbnl/lbnl-2024-united-states-data-center-energy-usage-report.pdf,2026-10-27,RETRIEVED,"Quote (PDF page 8, printed page 6)." | |
| 134 | +T10_US_net_generation_2025_TWh,"US net electricity generation, all sectors, utility-scale facilities, 2025",4425.03,TWh,2025,United States,annual,"EIA, Electric Power Annual / Electric Power Monthly Table 1.1 (Total Electric Power Industry Summary Statistics)","All Energy Sources, Utility Scale Facilities, Net Generation (thousand MWh)",https://www.eia.gov/electricity/annual/xls/epa_01_01.xlsx,data/raw/eia/epa_01_01.xlsx,2026-10-27,RETRIEVED,Thousand megawatthours converted to TWh (/1000). Public domain. Excludes small-scale solar (reported separately in the table). | |
| 135 | +T10_US_net_generation_2024_TWh,"US net electricity generation, all sectors, utility-scale facilities, 2024",4308.63,TWh,2024,United States,annual,"EIA, Electric Power Annual / Electric Power Monthly Table 1.1 (Total Electric Power Industry Summary Statistics)","All Energy Sources, Utility Scale Facilities, Net Generation (thousand MWh)",https://www.eia.gov/electricity/annual/xls/epa_01_01.xlsx,data/raw/eia/epa_01_01.xlsx,2026-10-27,RETRIEVED,Thousand megawatthours converted to TWh (/1000). Public domain. Excludes small-scale solar (reported separately in the table). | |
| 136 | +T10_US_retail_sales_2025_TWh,"US retail sales of electricity to ultimate customers, all sectors, 2025",4079.91,TWh,2025,United States,annual,"EIA, Electric Power Annual Table 1.1 (Sales of Electricity to Ultimate Customers, thousand MWh)",All Sectors retail sales,https://www.eia.gov/electricity/annual/xls/epa_01_01.xlsx,data/raw/eia/epa_01_01.xlsx,2026-10-27,RETRIEVED,Thousand MWh / 1000. Retail sales approximate end-use consumption (exclude direct use and losses). | |
| 137 | +T10_US_retail_sales_2024_TWh,"US retail sales of electricity to ultimate customers, all sectors, 2024",3975.38,TWh,2024,United States,annual,"EIA, Electric Power Annual Table 1.1 (Sales of Electricity to Ultimate Customers, thousand MWh)",All Sectors retail sales,https://www.eia.gov/electricity/annual/xls/epa_01_01.xlsx,data/raw/eia/epa_01_01.xlsx,2026-10-27,RETRIEVED,Thousand MWh / 1000. Retail sales approximate end-use consumption (exclude direct use and losses). | |
| 138 | +T11_compute_price_performance_doubling_all,"Doubling time of FLOP/s per US dollar (Epoch 'Price-performance' column), OLS of log2(price-performance) on release year, all hardware with a price and price-performance entry",2.02229,years per doubling,2008-2026,global (hardware market),cross-section of hardware releases,"Epoch AI, 'Machine Learning Hardware' dataset (https://epoch.ai/data/machine-learning-hardware), CC BY 4.0",Price-performance; Release date,https://epoch.ai/data/ml_hardware.csv,data/raw/epoch/ml_hardware.csv,2026-10-27,RETRIEVED,"n=60, slope=0.494 doublings/yr (x1.41/yr), R2=0.68. Price-performance = Epoch's 'Max performance' FLOP/s (best available precision) over release price; our own OLS, not Epoch's published estimate. Last modified field max: 2026-10-26 20:22:04+00:00." | |
| 139 | +T11_compute_price_performance_doubling_2015plus,"Doubling time of FLOP/s per US dollar (Epoch 'Price-performance' column), OLS of log2(price-performance) on release year, hardware released 2015 or later",2.02147,years per doubling,2015-2026,global (hardware market),cross-section of hardware releases,"Epoch AI, 'Machine Learning Hardware' dataset (https://epoch.ai/data/machine-learning-hardware), CC BY 4.0",Price-performance; Release date,https://epoch.ai/data/ml_hardware.csv,data/raw/epoch/ml_hardware.csv,2026-10-27,RETRIEVED,"n=47, slope=0.495 doublings/yr (x1.41/yr), R2=0.55. Price-performance = Epoch's 'Max performance' FLOP/s (best available precision) over release price; our own OLS, not Epoch's published estimate. Last modified field max: 2026-10-26 20:22:04+00:00." | |
| 140 | +T11_compute_price_performance_caveat,"Caveat for T11: Epoch's 'Max performance' uses the highest-throughput precision available for each chip (FP32 for pre-2017 GPUs, tensor FP16/BF16 or lower for recent accelerators), so part of the trend reflects precision changes, not only hardware progress",,,,global,,Epoch AI,Max performance,https://epoch.ai/data/ml_hardware.csv,data/raw/epoch/ml_hardware.csv,2026-10-27,INFO,Informational row; no value. | |
| 141 | +T11_compute_price_performance_n,Number of hardware entries with release price and price-performance,60,count,2008-2026,global,,Epoch AI,Price-performance,https://epoch.ai/data/ml_hardware.csv,data/raw/epoch/ml_hardware.csv,2026-10-27,RETRIEVED, | |
| 142 | +T12_labor_share_PWT_CA_latest,"Penn World Table labour-compensation share of GDP, Canada, latest observation",0.659302,ratio,2023,Canada,Annual,"FRED (University of Groningen, University of California, Davis)",LABSHPCAA156NRUG,https://fred.stlouisfed.org/graph/fredgraph.csv?id=LABSHPCAA156NRUG,data/raw/fred/LABSHPCAA156NRUG.csv,2026-10-27,RETRIEVED,Share of Labour Compensation in GDP at Current National Prices for Canada [Ratio; Annual; vintage last updated ]. | |
| 143 | +T12_labor_share_PWT_CA_avg,"Penn World Table labour-compensation share of GDP, Canada, 1990-latest average",0.663348,ratio,1990-2023,Canada,Annual,"FRED (University of Groningen, University of California, Davis)",LABSHPCAA156NRUG,https://fred.stlouisfed.org/graph/fredgraph.csv?id=LABSHPCAA156NRUG,data/raw/fred/LABSHPCAA156NRUG.csv,2026-10-27,RETRIEVED,Share of Labour Compensation in GDP at Current National Prices for Canada [Ratio; Annual; vintage last updated ]. Average: 1990-2023 mean of 34 obs. | |
| 144 | +T12_labor_share_COE_GDP_CA_latest,"Canada: compensation of employees / GDP at market prices (income-based, SAAR)",0.492012,ratio,2026Q2,Canada,quarterly,"Statistics Canada, Table 36-10-0103-01 (GDP income-based, quarterly)",Compensation of employees / Gross domestic product at market prices,https://www150.statcan.gc.ca/n1/tbl/csv/36100103-eng.zip,data/raw/statcan/unzipped/36100103.csv,2026-10-27,RETRIEVED,Both in millions of dollars SAAR. Statistics Canada Open Licence (https://www.statcan.gc.ca/en/reference/licence). | |
| 145 | +T12_labor_share_COE_GDP_CA_avg,"Canada: compensation of employees / GDP, 1990-latest average",0.50631,ratio,1990-2025,Canada,annual average,"Statistics Canada, Table 36-10-0103-01",COE/GDP,https://www150.statcan.gc.ca/n1/tbl/csv/36100103-eng.zip,data/raw/statcan/unzipped/36100103.csv,2026-10-27,RETRIEVED,1990-2025 mean of 36 obs. Statistics Canada Open Licence (https://www.statcan.gc.ca/en/reference/licence). | |
| 146 | +T12_household_net_worth_GDP_CA_latest,Canada: households & NPISH net worth (market value) / GDP at market prices (SAAR),5.65003,ratio (years of GDP),2026Q2,Canada,quarterly,"Statistics Canada, Tables 36-10-0580-01 (national balance sheet) and 36-10-0103-01 (GDP)","Net worth (Households and NPISH, market value) / GDP",https://www150.statcan.gc.ca/n1/tbl/csv/36100580-eng.zip ; https://www150.statcan.gc.ca/n1/tbl/csv/36100103-eng.zip,data/raw/statcan/unzipped/36100580.csv ; data/raw/statcan/unzipped/36100103.csv,2026-10-27,RETRIEVED,Both in millions of CAD; end-of-quarter net worth over same-quarter annualized GDP. Statistics Canada Open Licence (https://www.statcan.gc.ca/en/reference/licence). | |
| 147 | +T12_household_net_worth_GDP_CA_avg,"Canada: household net worth / GDP, 1990-latest average",4.24828,ratio (years of GDP),1990-2025,Canada,annual average,"Statistics Canada, Tables 36-10-0580-01 and 36-10-0103-01",Net worth / GDP,https://www150.statcan.gc.ca/n1/tbl/csv/36100580-eng.zip,data/raw/statcan/unzipped/36100580.csv,2026-10-27,RETRIEVED,1990-2025 mean of 36 obs. Statistics Canada Open Licence (https://www.statcan.gc.ca/en/reference/licence). | |
| 148 | +T13_electricity_price_kWh_latest,"Average price of electricity per kWh, US city average (BLS average price data), latest observation",0.196,US dollars per kWh,2026-09,United States,Monthly,FRED (U.S. Bureau of Labor Statistics),APU000072610,https://fred.stlouisfed.org/graph/fredgraph.csv?id=APU000072610,data/raw/fred/APU000072610.csv,2026-10-27,RETRIEVED,Average Price: Electricity per Kilowatt-Hour in U.S. City Average [U.S. Dollars; Monthly; vintage last updated 2026-10-14]. | |
| 149 | +T13_electricity_price_kWh_lastfullyear,"Average price of electricity per kWh, US city average (BLS average price data), latest full calendar-year average",0.185182,US dollars per kWh,2025,United States,annual average,FRED (U.S. Bureau of Labor Statistics),APU000072610,https://fred.stlouisfed.org/graph/fredgraph.csv?id=APU000072610,data/raw/fred/APU000072610.csv,2026-10-27,RETRIEVED,Average Price: Electricity per Kilowatt-Hour in U.S. City Average [U.S. Dollars; Monthly; vintage last updated 2026-10-14]. | |
| 150 | +T13_electricity_price_kWh_avg,"Average price of electricity per kWh, US city average (BLS average price data), 1990-latest average of annual means",0.117723,US dollars per kWh,1990-2025,United States,annual average,FRED (U.S. Bureau of Labor Statistics),APU000072610,https://fred.stlouisfed.org/graph/fredgraph.csv?id=APU000072610,data/raw/fred/APU000072610.csv,2026-10-27,RETRIEVED,Average Price: Electricity per Kilowatt-Hour in U.S. City Average [U.S. Dollars; Monthly; vintage last updated 2026-10-14]. Average: 1990-2025 mean of 36 obs. | |
| 151 | +T01_labor_share_BEA_IO_2023_VA,"Compensation of employees / total value added, all industries, BEA use table 2023",0.512598,ratio,2023,United States,annual,"BEA Input-Output Accounts, IOUse_After_Redefinitions_PRO_1997-2023_Summary.xlsx",V001/Total Value Added,https://apps.bea.gov/industry/iTables%20Static%20Files/AllTablesIO.zip,data/raw/bea_io/unzipped/IOUse_After_Redefinitions_PRO_1997-2023_Summary.xlsx,2026-10-27,RETRIEVED,"Summary-level use table after redefinitions, producers' prices, millions of current dollars, 2023 (file dated 2024-09-06 inside AllTablesIO.zip). Sum over all 71 summary industries (private + government). Public domain." | |
| 152 | +T01_labor_share_BEA_IO_2023_factor_cost,"Compensation / (compensation + gross operating surplus), all industries, BEA use table 2023",0.547989,ratio,2023,United States,annual,BEA Input-Output Accounts (summary use table 2023),V001/(V001+V003),https://apps.bea.gov/industry/iTables%20Static%20Files/AllTablesIO.zip,data/raw/bea_io/unzipped/IOUse_After_Redefinitions_PRO_1997-2023_Summary.xlsx,2026-10-27,RETRIEVED,"Summary-level use table after redefinitions, producers' prices, millions of current dollars, 2023 (file dated 2024-09-06 inside AllTablesIO.zip). Sum over all 71 summary industries (private + government). Public domain. Gross operating surplus includes CFC and proprietors' income." | |
| 153 | +T01_intermediate_share_BEA_IO_2023,"Total intermediate inputs / total industry output, all industries, BEA use table 2023",0.427102,ratio,2023,United States,annual,BEA Input-Output Accounts (summary use table 2023),Total Intermediate/Total Industry Output,https://apps.bea.gov/industry/iTables%20Static%20Files/AllTablesIO.zip,data/raw/bea_io/unzipped/IOUse_After_Redefinitions_PRO_1997-2023_Summary.xlsx,2026-10-27,RETRIEVED,"Summary-level use table after redefinitions, producers' prices, millions of current dollars, 2023 (file dated 2024-09-06 inside AllTablesIO.zip). Sum over all 71 summary industries (private + government). Public domain. Per-industry values in data/processed/bea_io_value_added_2023.csv." | |
| 154 | +T01_labor_share_BEA_IO_2023_utilities,"Labor share of value added, BEA summary industry 22 (Utilities), 2023",0.213625,ratio,2023,United States,annual,BEA Input-Output Accounts (summary use table 2023),"V001/Total Value Added, column 22",https://apps.bea.gov/industry/iTables%20Static%20Files/AllTablesIO.zip,data/raw/bea_io/unzipped/IOUse_After_Redefinitions_PRO_1997-2023_Summary.xlsx,2026-10-27,RETRIEVED,Compensation 93352 / VA 436991 ($mn). | |
| 155 | +T01_labor_share_BEA_IO_2023_computer_systems_design,"Labor share of value added, BEA summary industry 5415 (Computer systems design and related services), 2023",0.909213,ratio,2023,United States,annual,BEA Input-Output Accounts (summary use table 2023),"V001/Total Value Added, column 5415",https://apps.bea.gov/industry/iTables%20Static%20Files/AllTablesIO.zip,data/raw/bea_io/unzipped/IOUse_After_Redefinitions_PRO_1997-2023_Summary.xlsx,2026-10-27,RETRIEVED,Compensation 514110 / VA 565445 ($mn). | |
| 156 | +T01_labor_share_BEA_IO_2023_publishing_incl_software,"Labor share of value added, BEA summary industry 513 (Broadcasting and telecommunications), 2023",0.227383,ratio,2023,United States,annual,BEA Input-Output Accounts (summary use table 2023),"V001/Total Value Added, column 513",https://apps.bea.gov/industry/iTables%20Static%20Files/AllTablesIO.zip,data/raw/bea_io/unzipped/IOUse_After_Redefinitions_PRO_1997-2023_Summary.xlsx,2026-10-27,RETRIEVED,Compensation 106350 / VA 467713 ($mn). | |
| 157 | +T01_labor_share_BEA_IO_2023_computer_electronic_products,"Labor share of value added, BEA summary industry 334 (Computer and electronic products), 2023",0.568323,ratio,2023,United States,annual,BEA Input-Output Accounts (summary use table 2023),"V001/Total Value Added, column 334",https://apps.bea.gov/industry/iTables%20Static%20Files/AllTablesIO.zip,data/raw/bea_io/unzipped/IOUse_After_Redefinitions_PRO_1997-2023_Summary.xlsx,2026-10-27,RETRIEVED,Compensation 142641 / VA 250986 ($mn). | |
| 158 | +T01_labor_share_BEA_IO_2023_construction,"Labor share of value added, BEA summary industry 23 (Construction), 2023",0.611608,ratio,2023,United States,annual,BEA Input-Output Accounts (summary use table 2023),"V001/Total Value Added, column 23",https://apps.bea.gov/industry/iTables%20Static%20Files/AllTablesIO.zip,data/raw/bea_io/unzipped/IOUse_After_Redefinitions_PRO_1997-2023_Summary.xlsx,2026-10-27,RETRIEVED,Compensation 823367 / VA 1346233 ($mn). | |
| 159 | +T01_labor_share_BEA_IO_2023_professional_services,"Labor share of value added, BEA summary industry 5412OP (Miscellaneous professional, scientific, and technical services), 2023",0.743814,ratio,2023,United States,annual,BEA Input-Output Accounts (summary use table 2023),"V001/Total Value Added, column 5412OP",https://apps.bea.gov/industry/iTables%20Static%20Files/AllTablesIO.zip,data/raw/bea_io/unzipped/IOUse_After_Redefinitions_PRO_1997-2023_Summary.xlsx,2026-10-27,RETRIEVED,Compensation 1306504 / VA 1756492 ($mn). | |
| 160 | +T01_labor_share_BEA_IO_2023_ambulatory_health,"Labor share of value added, BEA summary industry 621 (Ambulatory health care services), 2023",0.766733,ratio,2023,United States,annual,BEA Input-Output Accounts (summary use table 2023),"V001/Total Value Added, column 621",https://apps.bea.gov/industry/iTables%20Static%20Files/AllTablesIO.zip,data/raw/bea_io/unzipped/IOUse_After_Redefinitions_PRO_1997-2023_Summary.xlsx,2026-10-27,RETRIEVED,Compensation 759777 / VA 990928 ($mn). | |
| 161 | +T00_crosscheck_BLS_API_PRS85006173_latest,"BLS nonfarm business labor share index (BLS API v1), latest",93.446,Index 2017=100,2026Q2,United States,Quarterly,U.S. Bureau of Labor Statistics public API v1 (key-less),PRS85006173,"https://api.bls.gov/publicAPI/v1/timeseries/data/ (POST, 10-year windows)",data/raw/bls/PRS85006173_<years>.json,2026-10-27,RETRIEVED,Independent retrieval path; should equal the FRED value for the same date. | |
| 162 | +T00_crosscheck_BLS_API_APU000072610_latest,"Electricity price per kWh (BLS API v1), latest",0.196,U.S. Dollars,2026-09,United States,Monthly,U.S. Bureau of Labor Statistics public API v1 (key-less),APU000072610,"https://api.bls.gov/publicAPI/v1/timeseries/data/ (POST, 10-year windows)",data/raw/bls/APU000072610_<years>.json,2026-10-27,RETRIEVED,Independent retrieval path; should equal the FRED value for the same date. | |
| 163 | +T00_crosscheck_BLS_API_CUSR0000SEHA_latest,"CPI rent of primary residence (BLS API v1), latest",450.429,Index 1982-1984=100,2026-09,United States,Monthly,U.S. Bureau of Labor Statistics public API v1 (key-less),CUSR0000SEHA,"https://api.bls.gov/publicAPI/v1/timeseries/data/ (POST, 10-year windows)",data/raw/bls/CUSR0000SEHA_<years>.json,2026-10-27,RETRIEVED,Independent retrieval path; should equal the FRED value for the same date. | |
added
data/processed/epoch_price_performance_fit.csv
+61 −0
@@ -0,0 +1,61 @@ | ||
| 1 | +Hardware name,Manufacturer,Type,Release date,price,maxperf,pp,t,log2pp | |
| 2 | +NVIDIA GeForce GTX 280,NVIDIA,GPU,2008-06-16,649.0,622100000000.0,958551617.8736516,2008.457221081451,29.836280882015 | |
| 3 | +NVIDIA GeForce GTX 580,NVIDIA,GPU,2010-11-09,499.0,1581000000000.0,3168336673.3466935,2010.8542094455852,31.561078501007373 | |
| 4 | +NVIDIA Tesla K20c,NVIDIA,GPU,2012-11-12,3199.0,3520000000000.0,1100343857.4554548,2012.8651608487337,30.035307290395245 | |
| 5 | +NVIDIA Tesla K20X,NVIDIA,GPU,2012-11-12,7699.0,3935000000000.0,511105338.3556306,2012.8651608487337,28.92904541907129 | |
| 6 | +NVIDIA GeForce GTX TITAN,NVIDIA,GPU,2013-02-19,999.0,4709000000000.0,4713713713.713714,2013.1341546885694,32.13421699349337 | |
| 7 | +NVIDIA Quadro K4000,NVIDIA,GPU,2013-03-01,1269.0,1244000000000.0,980299448.3845547,2013.1615331964408,29.86864727027576 | |
| 8 | +NVIDIA GeForce GTX 780,NVIDIA,GPU,2013-05-23,649.0,4156000000000.0,6403697996.918336,2013.3887748117727,32.57625812489133 | |
| 9 | +NVIDIA Quadro K6000,NVIDIA,GPU,2013-07-23,5265.0,5196000000000.0,986894586.8945868,2013.5557837097879,29.878320753521063 | |
| 10 | +NVIDIA Tesla K40t,NVIDIA,GPU,2013-11-22,7699.0,5046000000000.0,655409793.4796727,2013.889801505818,29.28782198965274 | |
| 11 | +NVIDIA Tesla K40s,NVIDIA,GPU,2013-11-22,7699.0,5046000000000.0,655409793.4796727,2013.889801505818,29.28782198965274 | |
| 12 | +NVIDIA GeForce GTX Titan Black,NVIDIA,GPU,2014-02-18,999.0,5645000000000.0,5650650650.650651,2014.1314168377824,32.395769851854205 | |
| 13 | +NVIDIA GeForce GTX 980,NVIDIA,GPU,2014-09-19,549.0,4981000000000.0,9072859744.990892,2014.7145790554414,33.07891021065365 | |
| 14 | +NVIDIA Tesla K80,NVIDIA,GPU,2014-11-17,5000.0,8126000000000.0,1625200000.0,2014.8761122518822,30.5979701236679 | |
| 15 | +NVIDIA GeForce GTX TITAN X,NVIDIA,GPU,2015-03-17,999.0,6691000000000.0,6697697697.697698,2015.2053388090349,32.64101811508991 | |
| 16 | +NVIDIA M40,NVIDIA,GPU,2015-11-10,10799.0,6832000000000.0,632651171.4047596,2015.8569472963723,29.23683501072749 | |
| 17 | +NVIDIA P100,NVIDIA,GPU,2016-04-05,5699.0,18700000000000.0,3281277417.090718,2016.2600958247776,31.61161042630703 | |
| 18 | +NVIDIA GeForce GTX 1080,NVIDIA,GPU,2016-05-27,599.0,8873000000000.0,14813021702.838064,2016.4024640657085,33.786146914350724 | |
| 19 | +NVIDIA P40,NVIDIA,GPU,2016-09-13,5699.0,11760000000000.0,2063519915.7746973,2016.7008898015058,30.942460216368396 | |
| 20 | +NVIDIA Quadro P6000,NVIDIA,GPU,2016-10-01,5999.0,12630000000000.0,2105350891.8153024,2016.7501711156742,30.971413556491072 | |
| 21 | +NVIDIA Quadro P5000,NVIDIA,GPU,2016-10-01,2499.0,8873000000000.0,3550620248.09924,2016.7501711156742,31.725423921052805 | |
| 22 | +NVIDIA Quadro P4000,NVIDIA,GPU,2017-02-06,815.0,5304000000000.0,6507975460.1227,2017.0985626283368,32.599561664980406 | |
| 23 | +NVIDIA Quadro P600,NVIDIA,GPU,2017-02-07,178.0,1196000000000.0,6719101123.595506,2017.101300479124,32.64562109721797 | |
| 24 | +NVIDIA GeForce GTX 1080 Ti,NVIDIA,GPU,2017-03-10,699.0,11340000000000.0,16223175965.665236,2017.186173853525,33.91733722844042 | |
| 25 | +NVIDIA TITAN Xp,NVIDIA,GPU,2017-04-06,1199.0,12150000000000.0,10133444537.114262,2017.2600958247776,33.23840560395254 | |
| 26 | +NVIDIA Tesla V100 PCIe 16 GB,NVIDIA,GPU,2017-06-21,10664.0,112000000000000.0,10502625656.414104,2017.4681724845996,33.29003099561698 | |
| 27 | +NVIDIA Tesla V100 SXM2,NVIDIA,GPU,2017-07-01,11500.0,125000000000000.0,10869565217.391304,2017.495550992471,33.33957518259133 | |
| 28 | +NVIDIA Titan V,NVIDIA,GPU,2017-12-01,2999.0,29800000000000.0,9936645548.516172,2017.914442162902,33.210111757354404 | |
| 29 | +NVIDIA Tesla V100 SXM2 32 GB,NVIDIA,GPU,2018-03-27,11458.0,125000000000000.0,10909408273.695234,2018.2327173169062,33.34485380087824 | |
| 30 | +NVIDIA Tesla V100 PCIe 32 GB,NVIDIA,GPU,2018-03-27,11458.0,112000000000000.0,9774829813.23093,2018.2327173169062,33.186424438273754 | |
| 31 | +NVIDIA Quadro RTX 5000,NVIDIA,GPU,2018-08-13,2300.0,22300000000000.0,9695652173.913044,2018.6132785763175,33.17469079784955 | |
| 32 | +NVIDIA Quadro RTX 6000,NVIDIA,GPU,2018-08-13,6300.0,32620000000000.0,5177777777.777778,2018.6132785763175,32.26968590231087 | |
| 33 | +NVIDIA GeForce RTX 2080 Ti 11GB,NVIDIA,GPU,2018-09-20,999.0,227700000000000.0,227927927927.92792,2018.7173169062287,37.72978675210519 | |
| 34 | +NVIDIA Quadro RTX 4000,NVIDIA,GPU,2018-11-13,899.0,14240000000000.0,15839822024.471636,2018.8651608487337,33.88283707432557 | |
| 35 | +Cambricon MLU270-F4,Cambricon,ASIC,2019-06-20,2080.0,256000000000000.0,61538461538.46154,2019.4654346338125,35.840769325619895 | |
| 36 | +NVIDIA A100 SXM4 40 GB,NVIDIA,GPU,2020-05-14,15000.0,624000000000000.0,41600000000.0,2020.3668720054757,35.27586447723999 | |
| 37 | +NVIDIA GeForce RTX 3090,NVIDIA,GPU,2020-09-01,1499.0,284000000000000.0,189459639759.8399,2020.6680355920603,37.46309959020517 | |
| 38 | +NVIDIA GeForce RTX 3080,NVIDIA,GPU,2020-09-01,699.0,29770000000000.0,42589413447.78255,2020.6680355920603,35.30977580973622 | |
| 39 | +NVIDIA RTX A6000,NVIDIA,GPU,2020-10-05,4649.0,38700000000000.0,8324370832.437083,2020.7611225188227,32.95469408938643 | |
| 40 | +NVIDIA A10 PCIe,NVIDIA,GPU,2021-04-12,2800.0,250000000000000.0,89285714285.71428,2021.2765229295003,36.377710311478104 | |
| 41 | +NVIDIA RTX A5000,NVIDIA,GPU,2021-04-12,2250.0,27770000000000.0,12342222222.22222,2021.2765229295003,33.52288312458807 | |
| 42 | +NVIDIA RTX A4000,NVIDIA,GPU,2021-04-12,1000.0,19170000000000.0,19170000000.0,2021.2765229295003,34.15813128587968 | |
| 43 | +AMD Radeon Instinct MI250X,AMD,GPU,2021-11-08,14500.0,383000000000000.0,26413793103.448277,2021.851471594798,34.62057244078365 | |
| 44 | +Huawei Ascend 910B,Huawei,GPU,2022-03-17,16854.0,376000000000000.0,22309244096.35695,2022.2053388090349,34.37692258047843 | |
| 45 | +NVIDIA GeForce RTX 3090 Ti,NVIDIA,GPU,2022-03-29,1999.0,320000000000000.0,160080040020.01,2022.2381930184804,37.22000247679108 | |
| 46 | +NVIDIA GeForce RTX 4090,NVIDIA,GPU,2022-09-20,1599.0,660600000000000.0,413133208255.1595,2022.7173169062287,38.58781607456618 | |
| 47 | +NVIDIA GeForce RTX 4080,NVIDIA,GPU,2022-09-20,1199.0,390000000000000.0,325271059216.01337,2022.7173169062287,38.24285150898373 | |
| 48 | +NVIDIA H100 SXM5 80GB,NVIDIA,GPU,2022-09-20,33600.0,1979000000000000.0,58898809523.809525,2022.7173169062287,35.77751942314237 | |
| 49 | +AMD Radeon RX 7900 XTX,AMD,GPU,2022-11-03,999.0,122800000000000.0,122922922922.92291,2022.8377823408625,36.83896302133875 | |
| 50 | +NVIDIA RTX 6000 Ada Generation,NVIDIA,GPU,2022-12-03,6799.0,729000000000000.0,13393146050.889835,2022.9199178644765,33.64077583850479 | |
| 51 | +NVIDIA L20 PCle,NVIDIA,GPU,2023-09-16,4299.0,239000000000000.0,55594324261.456154,2023.7063655030802,35.6942185516458 | |
| 52 | +NVIDIA HGX H20,NVIDIA,GPU,2023-11-09,12000.0,296000000000000.0,24666666666.666668,2023.8542094455852,34.521843718894054 | |
| 53 | +AMD Instinct MI300X,AMD,GPU,2023-12-06,15000.0,2614900000000000.0,174326666666.66666,2023.9281314168377,37.3430023184747 | |
| 54 | +NVIDIA H200 SXM,NVIDIA,GPU,2024-11-18,30000.0,1979000000000000.0,65966666666.666664,2024.8815879534566,35.941018155425255 | |
| 55 | +NVIDIA GB200,NVIDIA,GPU,2025-02-15,37000.0,1e+16,135135135135.13512,2025.123203285421,36.975611867906764 | |
| 56 | +NVIDIA RTX Pro 6000 Blackwell,NVIDIA,GPU,2025-03-18,13250.0,2015200000000000.0,38022641509.43396,2025.208076659822,35.14613971126994 | |
| 57 | +AMD Instinct MI350X,AMD,GPU,2025-06-12,25000.0,9227500000000000.0,184560000000.0,2025.4435318275155,37.42529895289452 | |
| 58 | +AMD Instinct MI355X,AMD,GPU,2025-06-12,30000.0,1.00663e+16,167773333333.33334,2025.4435318275155,37.28772246916302 | |
| 59 | +NVIDIA GB300 (Blackwell Ultra),NVIDIA,GPU,2025-08-22,43000.0,1.5e+16,58139534883.72093,2025.6379192334018,35.758800478833614 | |
| 60 | +Google TPU v7 Ironwood,Google,TPU,2025-11-06,12000.0,4614000000000000.0,192250000000.0,2025.8459958932237,37.484192641948084 | |
| 61 | +Google TPU v8i,Google,TPU,2026-08-02,17500.0,1.01e+16,62914285714.28571,2026.583162217659,35.87266859060566 | |
added
data/processed/series/bls_api_APU000072610.csv
+441 −0
@@ -0,0 +1,441 @@ | ||
| 1 | +date,value | |
| 2 | +1990-01-01,0.081 | |
| 3 | +1990-02-01,0.081 | |
| 4 | +1990-03-01,0.081 | |
| 5 | +1990-04-01,0.082 | |
| 6 | +1990-05-01,0.082 | |
| 7 | +1990-06-01,0.088 | |
| 8 | +1990-07-01,0.087 | |
| 9 | +1990-08-01,0.087 | |
| 10 | +1990-09-01,0.088 | |
| 11 | +1990-10-01,0.083 | |
| 12 | +1990-11-01,0.082 | |
| 13 | +1990-12-01,0.082 | |
| 14 | +1991-01-01,0.084 | |
| 15 | +1991-02-01,0.084 | |
| 16 | +1991-03-01,0.085 | |
| 17 | +1991-04-01,0.084 | |
| 18 | +1991-05-01,0.085 | |
| 19 | +1991-06-01,0.09 | |
| 20 | +1991-07-01,0.091 | |
| 21 | +1991-08-01,0.09 | |
| 22 | +1991-09-01,0.091 | |
| 23 | +1991-10-01,0.087 | |
| 24 | +1991-11-01,0.084 | |
| 25 | +1991-12-01,0.085 | |
| 26 | +1992-01-01,0.085 | |
| 27 | +1992-02-01,0.084 | |
| 28 | +1992-03-01,0.086 | |
| 29 | +1992-04-01,0.086 | |
| 30 | +1992-05-01,0.087 | |
| 31 | +1992-06-01,0.092 | |
| 32 | +1992-07-01,0.092 | |
| 33 | +1992-08-01,0.091 | |
| 34 | +1992-09-01,0.092 | |
| 35 | +1992-10-01,0.088 | |
| 36 | +1992-11-01,0.086 | |
| 37 | +1992-12-01,0.088 | |
| 38 | +1993-01-01,0.089 | |
| 39 | +1993-02-01,0.087 | |
| 40 | +1993-03-01,0.088 | |
| 41 | +1993-04-01,0.088 | |
| 42 | +1993-05-01,0.09 | |
| 43 | +1993-06-01,0.094 | |
| 44 | +1993-07-01,0.095 | |
| 45 | +1993-08-01,0.097 | |
| 46 | +1993-09-01,0.097 | |
| 47 | +1993-10-01,0.093 | |
| 48 | +1993-11-01,0.091 | |
| 49 | +1993-12-01,0.09 | |
| 50 | +1994-01-01,0.09 | |
| 51 | +1994-02-01,0.09 | |
| 52 | +1994-03-01,0.089 | |
| 53 | +1994-04-01,0.088 | |
| 54 | +1994-05-01,0.09 | |
| 55 | +1994-06-01,0.095 | |
| 56 | +1994-07-01,0.095 | |
| 57 | +1994-08-01,0.096 | |
| 58 | +1994-09-01,0.096 | |
| 59 | +1994-10-01,0.093 | |
| 60 | +1994-11-01,0.091 | |
| 61 | +1994-12-01,0.091 | |
| 62 | +1995-01-01,0.091 | |
| 63 | +1995-02-01,0.091 | |
| 64 | +1995-03-01,0.091 | |
| 65 | +1995-04-01,0.09 | |
| 66 | +1995-05-01,0.092 | |
| 67 | +1995-06-01,0.098 | |
| 68 | +1995-07-01,0.098 | |
| 69 | +1995-08-01,0.098 | |
| 70 | +1995-09-01,0.097 | |
| 71 | +1995-10-01,0.094 | |
| 72 | +1995-11-01,0.092 | |
| 73 | +1995-12-01,0.091 | |
| 74 | +1996-01-01,0.091 | |
| 75 | +1996-02-01,0.091 | |
| 76 | +1996-03-01,0.092 | |
| 77 | +1996-04-01,0.092 | |
| 78 | +1996-05-01,0.092 | |
| 79 | +1996-06-01,0.096 | |
| 80 | +1996-07-01,0.099 | |
| 81 | +1996-08-01,0.099 | |
| 82 | +1996-09-01,0.099 | |
| 83 | +1996-10-01,0.095 | |
| 84 | +1996-11-01,0.092 | |
| 85 | +1996-12-01,0.092 | |
| 86 | +1997-01-01,0.092 | |
| 87 | +1997-02-01,0.092 | |
| 88 | +1997-03-01,0.093 | |
| 89 | +1997-04-01,0.092 | |
| 90 | +1997-05-01,0.093 | |
| 91 | +1997-06-01,0.099 | |
| 92 | +1997-07-01,0.099 | |
| 93 | +1997-08-01,0.098 | |
| 94 | +1997-09-01,0.099 | |
| 95 | +1997-10-01,0.093 | |
| 96 | +1997-11-01,0.092 | |
| 97 | +1997-12-01,0.09 | |
| 98 | +1998-01-01,0.086 | |
| 99 | +1998-02-01,0.085 | |
| 100 | +1998-03-01,0.085 | |
| 101 | +1998-04-01,0.085 | |
| 102 | +1998-05-01,0.086 | |
| 103 | +1998-06-01,0.091 | |
| 104 | +1998-07-01,0.091 | |
| 105 | +1998-08-01,0.09 | |
| 106 | +1998-09-01,0.089 | |
| 107 | +1998-10-01,0.086 | |
| 108 | +1998-11-01,0.084 | |
| 109 | +1998-12-01,0.084 | |
| 110 | +1999-01-01,0.084 | |
| 111 | +1999-02-01,0.084 | |
| 112 | +1999-03-01,0.084 | |
| 113 | +1999-04-01,0.084 | |
| 114 | +1999-05-01,0.085 | |
| 115 | +1999-06-01,0.089 | |
| 116 | +1999-07-01,0.09 | |
| 117 | +1999-08-01,0.089 | |
| 118 | +1999-09-01,0.09 | |
| 119 | +1999-10-01,0.087 | |
| 120 | +1999-11-01,0.085 | |
| 121 | +1999-12-01,0.085 | |
| 122 | +2000-01-01,0.084 | |
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| 146 | +2002-01-01,0.089 | |
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| 242 | +2010-01-01,0.124 | |
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| 441 | +2026-09-01,0.196 | |
added
data/processed/series/bls_api_CUSR0000SEHA.csv
+441 −0
@@ -0,0 +1,441 @@ | ||
| 1 | +date,value | |
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| 3 | +1990-02-01,136.1 | |
| 4 | +1990-03-01,136.7 | |
| 5 | +1990-04-01,137.2 | |
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| 7 | +1990-06-01,138.2 | |
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| 9 | +1990-08-01,139.3 | |
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| 11 | +1990-10-01,140.2 | |
| 12 | +1990-11-01,140.5 | |
| 13 | +1990-12-01,141.0 | |
| 14 | +1991-01-01,141.2 | |
| 15 | +1991-02-01,141.6 | |
| 16 | +1991-03-01,142.1 | |
| 17 | +1991-04-01,142.6 | |
| 18 | +1991-05-01,143.0 | |
| 19 | +1991-06-01,143.2 | |
| 20 | +1991-07-01,143.7 | |
| 21 | +1991-08-01,143.7 | |
| 22 | +1991-09-01,144.4 | |
| 23 | +1991-10-01,144.4 | |
| 24 | +1991-11-01,144.8 | |
| 25 | +1991-12-01,145.1 | |
| 26 | +1992-01-01,145.4 | |
| 27 | +1992-02-01,145.7 | |
| 28 | +1992-03-01,146.5 | |
| 29 | +1992-04-01,146.2 | |
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| 277 | +2012-12-01,263.669 | |
| 278 | +2013-01-01,264.245 | |
| 279 | +2013-02-01,264.904 | |
| 280 | +2013-03-01,265.603 | |
| 281 | +2013-04-01,266.094 | |
| 282 | +2013-05-01,266.841 | |
| 283 | +2013-06-01,267.451 | |
| 284 | +2013-07-01,267.87 | |
| 285 | +2013-08-01,268.837 | |
| 286 | +2013-09-01,269.315 | |
| 287 | +2013-10-01,269.822 | |
| 288 | +2013-11-01,270.387 | |
| 289 | +2013-12-01,271.22 | |
| 290 | +2014-01-01,271.918 | |
| 291 | +2014-02-01,272.461 | |
| 292 | +2014-03-01,273.341 | |
| 293 | +2014-04-01,274.255 | |
| 294 | +2014-05-01,275.013 | |
| 295 | +2014-06-01,275.819 | |
| 296 | +2014-07-01,276.663 | |
| 297 | +2014-08-01,277.319 | |
| 298 | +2014-09-01,278.149 | |
| 299 | +2014-10-01,278.797 | |
| 300 | +2014-11-01,279.758 | |
| 301 | +2014-12-01,280.383 | |
| 302 | +2015-01-01,281.223 | |
| 303 | +2015-02-01,282.171 | |
| 304 | +2015-03-01,283.056 | |
| 305 | +2015-04-01,283.786 | |
| 306 | +2015-05-01,284.559 | |
| 307 | +2015-06-01,285.477 | |
| 308 | +2015-07-01,286.435 | |
| 309 | +2015-08-01,287.32 | |
| 310 | +2015-09-01,288.385 | |
| 311 | +2015-10-01,289.236 | |
| 312 | +2015-11-01,289.953 | |
| 313 | +2015-12-01,290.723 | |
| 314 | +2016-01-01,291.7 | |
| 315 | +2016-02-01,292.617 | |
| 316 | +2016-03-01,293.471 | |
| 317 | +2016-04-01,294.397 | |
| 318 | +2016-05-01,295.371 | |
| 319 | +2016-06-01,296.306 | |
| 320 | +2016-07-01,297.192 | |
| 321 | +2016-08-01,298.029 | |
| 322 | +2016-09-01,299.035 | |
| 323 | +2016-10-01,300.215 | |
| 324 | +2016-11-01,301.223 | |
| 325 | +2016-12-01,302.271 | |
| 326 | +2017-01-01,303.156 | |
| 327 | +2017-02-01,304.06 | |
| 328 | +2017-03-01,304.888 | |
| 329 | +2017-04-01,305.692 | |
| 330 | +2017-05-01,306.72 | |
| 331 | +2017-06-01,307.681 | |
| 332 | +2017-07-01,308.49 | |
| 333 | +2017-08-01,309.608 | |
| 334 | +2017-09-01,310.346 | |
| 335 | +2017-10-01,311.33 | |
| 336 | +2017-11-01,312.295 | |
| 337 | +2017-12-01,313.462 | |
| 338 | +2018-01-01,314.531 | |
| 339 | +2018-02-01,315.162 | |
| 340 | +2018-03-01,315.885 | |
| 341 | +2018-04-01,316.96 | |
| 342 | +2018-05-01,317.766 | |
| 343 | +2018-06-01,318.607 | |
| 344 | +2018-07-01,319.642 | |
| 345 | +2018-08-01,320.86 | |
| 346 | +2018-09-01,321.563 | |
| 347 | +2018-10-01,322.447 | |
| 348 | +2018-11-01,323.633 | |
| 349 | +2018-12-01,324.408 | |
| 350 | +2019-01-01,325.314 | |
| 351 | +2019-02-01,326.22 | |
| 352 | +2019-03-01,327.539 | |
| 353 | +2019-04-01,328.816 | |
| 354 | +2019-05-01,329.617 | |
| 355 | +2019-06-01,330.919 | |
| 356 | +2019-07-01,331.935 | |
| 357 | +2019-08-01,332.781 | |
| 358 | +2019-09-01,333.92 | |
| 359 | +2019-10-01,334.558 | |
| 360 | +2019-11-01,335.484 | |
| 361 | +2019-12-01,336.418 | |
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| 363 | +2020-02-01,338.489 | |
| 364 | +2020-03-01,339.502 | |
| 365 | +2020-04-01,340.225 | |
| 366 | +2020-05-01,341.007 | |
| 367 | +2020-06-01,341.524 | |
| 368 | +2020-07-01,342.312 | |
| 369 | +2020-08-01,342.65 | |
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| 371 | +2020-10-01,343.502 | |
| 372 | +2020-11-01,343.731 | |
| 373 | +2020-12-01,344.125 | |
| 374 | +2021-01-01,344.489 | |
| 375 | +2021-02-01,345.099 | |
| 376 | +2021-03-01,345.658 | |
| 377 | +2021-04-01,346.357 | |
| 378 | +2021-05-01,347.242 | |
| 379 | +2021-06-01,348.07 | |
| 380 | +2021-07-01,348.853 | |
| 381 | +2021-08-01,349.983 | |
| 382 | +2021-09-01,351.306 | |
| 383 | +2021-10-01,352.771 | |
| 384 | +2021-11-01,354.215 | |
| 385 | +2021-12-01,355.588 | |
| 386 | +2022-01-01,357.411 | |
| 387 | +2022-02-01,359.438 | |
| 388 | +2022-03-01,360.989 | |
| 389 | +2022-04-01,363.062 | |
| 390 | +2022-05-01,365.376 | |
| 391 | +2022-06-01,368.216 | |
| 392 | +2022-07-01,370.916 | |
| 393 | +2022-08-01,373.597 | |
| 394 | +2022-09-01,376.628 | |
| 395 | +2022-10-01,379.291 | |
| 396 | +2022-11-01,382.228 | |
| 397 | +2022-12-01,385.247 | |
| 398 | +2023-01-01,387.984 | |
| 399 | +2023-02-01,390.904 | |
| 400 | +2023-03-01,392.787 | |
| 401 | +2023-04-01,395.024 | |
| 402 | +2023-05-01,397.025 | |
| 403 | +2023-06-01,398.949 | |
| 404 | +2023-07-01,400.741 | |
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| 410 | +2024-01-01,411.6 | |
| 411 | +2024-02-01,413.427 | |
| 412 | +2024-03-01,415.08 | |
| 413 | +2024-04-01,416.49 | |
| 414 | +2024-05-01,418.1 | |
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| 417 | +2024-08-01,422.556 | |
| 418 | +2024-09-01,423.858 | |
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| 420 | +2024-11-01,426.342 | |
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| 431 | +2025-11-01,439.002 | |
| 432 | +2025-12-01,440.189 | |
| 433 | +2026-01-01,441.285 | |
| 434 | +2026-02-01,441.865 | |
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data/processed/series/fred_CPIAUCSL.csv
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data/processed/series/fred_CUSR0000SEHA.csv
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data/processed/series/fred_DFII10.csv
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data/processed/series/fred_DIVIDEND.csv
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data/processed/series/fred_GDPA.csv
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data/processed/series/fred_GDPC1.csv
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data/processed/series/fred_GPDI.csv
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data/processed/series/fred_GS10.csv
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data/processed/series/fred_HNOREMQ027S.csv
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data/processed/series/fred_HOEREPHRE.csv
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data/processed/series/fred_K1NTOTL1ES000.csv
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data/processed/series/fred_LABSHPCAA156NRUG.csv
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data/processed/series/fred_OEHRENWBSHNO.csv
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data/processed/series/fred_PNFI.csv
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data/processed/series/fred_PRS85006173.csv
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data/processed/series/fred_REAINTRATREARAT10Y.csv
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