How the Deflation Index is calculated, plus complete data downloads and alternative weighting methodologies.
The Deflation Index tracks technological cost reductions across four fundamental sectors: computing, communications, energy, and transportation. Each sector is measured using cost-per-performance metrics that capture the true force of technological progress.
The DI is a weighted geometric mean of these four sector indices, showing how technological deflation compounds over time.
Why geometric mean? Technological improvements multiply (compound), they don't add. A geometric mean properly captures this multiplicative relationship.
Where each sector represents fundamental technological progress:
Current Scope: These weights represent v3.0.2 composition with four sectors. Future versions (v4.0, 2027-2028) will add healthcare, education, and housing sectors with rebalanced weights. See limitations section for full methodology discussion.
Note: These weights represent the current v3.0.2 composition with four sectors. Future versions will add healthcare, education, and housing sectors with rebalanced weights.
Note on Future Expansion: v4.0 will add healthcare, education, and housing sectors. Current v3.0.2 composition uses four sectors with normalized weights totaling 100%.
For each component within each sector, we track the cost per unit of performance over time:
Each component is indexed to 100 in the base year (1990 for most, 2010 for energy/transport). As costs fall, the index falls proportionally.
Multiple components within each sector are combined using geometric means and their respective weights. For example, Computing = (GFLOPS^0.60 × Storage^0.30 × RAM^0.10).
The four sector indices are combined using the DI formula shown above, producing a single comprehensive measure of technological deflation.
Why these specific weights? The current weights (29.4%, 23.5%, 29.4%, 17.6%) reflect relative economic importance across three dimensions:
We tested four alternative weighting schemes to ensure our findings aren't artifacts of weight choices:
To test sensitivity, we calculated results under alternative weighting schemes:
| Weighting Approach | Weights (C/Co/E/T) | Annual DI | Cumulative DI |
|---|---|---|---|
| Current (Multi-Factor) | 29/24/29/18 | -9.21% | -96.25% |
| Equal Weights | 25/25/25/25 | -8.45% | -95.04% |
| Expenditure-Weighted | 25/20/30/25 | -8.43% | -94.99% |
| GDP-Only Weights | 20/15/40/25 | -8.37% | -94.88% |
Result: The core finding—approximately 94-96% cumulative deflation at 8.3-9.2% annually—is robust across reasonable weighting approaches. The current weighting produces the highest deflation rate because it gives more weight to the fastest-deflating sectors (Computing, Communications).
Detailed analysis: See Weight Justification document for complete methodology, derivations, and comparisons to alternative approaches.
v4.0 Plan: When healthcare, education, and housing sectors are added (2027-2028), we'll switch to expenditure-weighting as primary method while keeping current multi-factor approach as alternative for comparison.
Year-over-year percentage changes show the annual deflation rate. The average annual rate (1990-2024) is -9.21%.
All data comes from authoritative, publicly available sources. Every data point includes source citations in the Excel files.
| Source | What We Use | Reliability |
|---|---|---|
| Federal Reserve (FRED) | M2 money supply, CPI data | 100/100 (A+) |
| Bureau of Labor Statistics | CPI components, historical pricing | 100/100 (A+) |
| IRENA | Solar LCOE, renewable energy costs | 95/100 (A) |
| BloombergNEF | Battery costs, EV economics | 95/100 (A) |
| AI Impacts | Computing power costs (FLOPS) | 90/100 (A-) |
| DOE | LED efficiency, transportation data | 95/100 (A) |
| FCC | Broadband pricing, network costs | 90/100 (A-) |
| Backblaze | Hard drive cost tracking | 85/100 (B+) |
Average source reliability: 92/100 (A-grade). We prioritize government agencies, international organizations, and industry leaders with transparent methodologies.
All data is open and free. Download the Excel files, verify every formula, and build your own analysis.
Why provide variants? To demonstrate the robustness of our findings across different methodological approaches. All variants use the same source data and sector indices—only the weights differ.
Methodology: Balances three factors: GDP contribution, enabling effect (how much other sectors depend on it), and deflationary force (magnitude of cost reductions).
Weights: Computing 29.4% • Communications 23.5% • Energy 29.4% • Transportation 17.6%
Use case: Recommended default. Most comprehensive approach accounting for both direct and indirect economic impact.
Download PrimaryMethodology: Simple equal weighting across all sectors with no assumptions about relative importance.
Weights: Computing 25% • Communications 25% • Energy 25% • Transportation 25%
Use case: Simple baseline, conservative estimate, easy to explain and defend.
Download Equal-WeightedMethodology: Weighted by household and business expenditure patterns—where money is actually spent.
Weights: Computing 25% • Communications 20% • Energy 30% • Transportation 25%
Use case: Consumer-centric perspective, "where did my money go?" analysis, policy discussions.
Download Expenditure-WeightedMethodology: Weighted by direct GDP contribution—traditional economic weighting.
Weights: Computing 20% • Communications 15% • Energy 40% • Transportation 25%
Use case: Macro-economic analysis, GDP impact studies, conservative estimate.
Download GDP-Weighted| Variant | 2024 DI | Cumulative | Annual Avg |
|---|---|---|---|
| Multi-Factor (Primary) | 3.74 | -96.26% | -9.21% |
| Equal-Weighted | ~4.2 | ~-95.8% | ~-9.0% |
| Expenditure-Weighted | ~4.5 | ~-95.5% | ~-8.8% |
| GDP-Weighted | ~5.8 | ~-94.2% | ~-8.3% |
Key Finding: All variants show substantial technological deflation (94-96% cumulative). The core finding is robust across all methodologies. Choice of weights affects magnitude but not direction.
Download sector-specific indices with detailed calculations and source data.
Every data point in the Deflation Index is graded, sourced, and verified. We maintain rigorous quality standards:
| Grade | Score | Criteria |
|---|---|---|
| A (Excellent) | 85-100 | Government agencies, peer-reviewed research, industry gold standards |
| B (Good) | 70-84 | Reputable industry reports, academic estimates, established analysts |
| C (Fair) | 50-69 | Secondary sources, interpolations, reasonable estimates |
| D (Poor) | <50 | Weak sources, speculative estimates (not used in index) |
Where uncertainty exists, we choose the more conservative estimate. This means the true technological deflation is likely higher than we measure. The index understates the gap, not overstates it.
Every Excel file includes:
Anyone can verify our work. That's the standard we hold ourselves to.
The Deflation Index methodology is open for formal scrutiny. We believe rigorous measurement improves through expert review, institutional feedback, and collaborative refinement.
We welcome formal review from:
Current Status:
We're looking for:
Bloomberg, Refinitiv, FactSet, and other platforms—let's discuss data feeds and API access
Co-author research, validate methodology, collaborate on sector expansion
Domain experts for v4.0: healthcare economists, education researchers, housing analysts
Challenge assumptions, propose alternatives, identify improvements
Institutional inquiries: For data licensing, API access, or partnership discussions, contact us directly.
Technical feedback: If you spot errors or have methodology suggestions, open an issue on GitHub with details.
Research use: Cite the Deflation Index in research, analysis, and publications. We encourage independent verification.
Contact: [email protected]
Our commitment: Precision, transparency, and intellectual honesty. If you see something wrong, we want to know. If you have ideas to improve the work, we're listening.
The core measurement reveals a systematic divergence between two opposing forces:
Technology delivered 9.21% annual deflation across the four sectors. Meanwhile, the money supply expanded 5.7% annually. These opposing forces create a 14.9 percentage point gap every year.
Over 35 years, this annual gap compounds into massive divergence. Technology made these sectors 96% cheaper while the money supply expanded 550%. The question: where does the productivity go?
The answer: Into asset prices, intermediaries, financial complexity, and system overhead—not into lower prices for consumers.
We're transparent about where the Deflation Index is currently limited. Better to acknowledge constraints openly than to overstate capabilities.
Sectors Covered (v3.0.2):
Coverage: The current four sectors represent approximately 40% of measurable technological deflation, not 100%. We prioritize defensible measurement over comprehensive coverage—better to measure four sectors perfectly than twenty sectors poorly.
Expansion Timeline: v4.0 (2027-2028) will add healthcare, education, and housing sectors with rebalanced weights across all seven sectors.
Weighting System:
Index Construction:
Conservative Assumptions:
Why These Limitations: We prioritize verifiable, reproducible data over comprehensive coverage. Each limitation represents an opportunity for future improvement and is documented for transparency.
Current:
Planned:
These limitations don't invalidate the core finding: massive technological deflation occurred (-96.26% cumulative) while consumer prices rose (+155%). This divergence exists regardless of methodological choices.
What changes with better methodology: The precise magnitude of the gap. Not whether it exists.
We welcome critique: Challenge our assumptions, propose alternatives, suggest improvements. The goal is the best measurement possible, not defending any particular method.
For comprehensive technical details, see our GitHub repository:
All data is public, all calculations are transparent, all assumptions are documented. Download the Excel files and verify every number.