Blockchain: Fueling the Next Generation of Economic Statistics

Disclosure: I own cryptocurrency assets. This article represents my personal opinions and should not be construed as professional investment advice. I have no affiliation with Truflation and my views do not constitute any endorsement. The inflation measurement techniques I describe are based on my own research and analysis. All information provided here is for general education purposes only. Readers should conduct their own research to reach their own conclusions and consult with qualified financial advisors before making any investment decisions. I have made my best effort to provide accurate information, but make no warranties regarding the completeness or applicability of content. This article should not be used as a substitute for official government data or legal/tax/investment counseling. I am not responsible for any actions taken based on the information provided.

Background:

Recently I came across the blockchain-based platform Truflation , which inspired me to take a deeper look into how key economic statistics like inflation are actually measured. I was shocked to learn just how archaic and limited the data collection practices are behind the government's Consumer Price Index.

The CPI survey only collects around 100,000 price quotes each month to represent the entire U.S. economy. For critical categories like housing, the samples are absurdly small - just 8,000 rental units surveyed per month, with each one only priced twice a year. They then split those 8,000 units into six groups to extrapolate average rental costs. That means the CPI is estimating nationwide rent changes using price data from just 1,333 sample units in each group!

For most categories, data collection still involves in-person visits or manual phone calls to individual retailers. Only 8% of pricing samples come from modern online sources. This sparse, intermittent sampling leaves plenty of room for error and fails to capture real-time market volatility.

Rethinking Inflation Data for the 21st Century

Inflation metrics shape critical economic policies and financial decisions, yet rely on antiquated data gathering that fails to capture real price changes. Modernizing how inflation is measured with big data analytics, blockchain transparency, and higher frequency updating would vastly improve accuracy and policy impacts.

The Consumer Price Index (CPI) and Personal Consumption Expenditures (PCE) price index stand prominently among key economic indicators guiding the Federal Reserve, government agencies, businesses and households. But the methodologies underlying these familiar metrics have not kept pace with advancements in data and technology.

Both indexes depend on small samples of self-reported pricing data collected manually on a monthly basis. The Labor Department’s CPI survey covers just 80,000 products and services nationwide. The Commerce Department’s PCE calculation uses a mere 5,000 data points. This sparse sampling fails to represent the diversity of a $20 trillion economy.

Such limited scope introduces significant room for error and biases. Product categories and weightings do not reflect real-time consumer behaviors. Geographic sampling is not robust. Online prices go undercaptured. Infrequent data collection smooths over pricing volatilities. Government statisticians must fill gaps through imputation and modeling, adding guesswork.

The lack of transparency around data sources, collection protocols and index calculation algorithms compounds concerns over accuracy. Even small errors propagate into enormous impacts on inflation-sensitive policies and payments tied to CPI including Social Security, federal pensions, tax brackets and more. Reliability is paramount.

Fortunately, an explosion of new data sources and technologies offers solutions. The answer lies in combining large-scale real-time data ingestion, machine learning for efficiency and insight, blockchain for transparency and decentralization, APIs for broad access, and high-frequency reporting.

Data Collection Revolution

The first priority is vastly expanding the sheer quantity of price data compiled, from millions of sources rather than thousands. Granular product-level data should be captured across sectors like:

• Grocery items from retailers and consumer packaged goods firms
• Home prices and rents from real estate listing services
• Hospital and doctor prices from medical claims databases
• College tuition from registrars’ offices
• Gasoline from station pump swipes and tank monitoring systems
• Online prices scraped from e-commerce sites
• Automotive from dealership management platforms
• And more.

Large datasets reveal detailed dynamics within categories, geographies, and cohorts. Census-scale data is now attainable thanks to the proliferation of APIs, electronic payments, scanner systems, and Internet of Things sensors. Agreements to access proprietary data troves from companies like credit card processors would supplement public data.

Sophisticated algorithms can ingest millions of data points in bulk, reducing manual collection needs. Natural language processing and computer vision techniques can extract insights from unstructured data like text descriptions and images. Expanding to worldwide trade and production data would shed light on upstream inflation drivers.

With reams of timely data, the focus becomes parsing signals from noise, rather than chasing scarce data points. Advanced analytics like machine learning algorithms can handle data at scale while continually improving analysis as relationships evolve.

Trust Through Transparency

Many question government inflation statistics’ integrity given the black box of inputs and processes. Publishing detailed documentation on data sources, cleaning protocols, weighting mechanics and calculation models would boost transparency.

But static documents still allow changes without visibility. Blockchain’s immutable ledger technology offers a powerful solution. Recording all data and methodology transactions permanently on a tamper-proof decentralized public blockchain would create radical, real-time transparency.

Once logged on blockchain, data becomes non-falsifiable and fully auditable. Algorithmic smart contracts can encode automatic inflation calculation rules with no manipulation. This verifiability builds public trust in the integrity of the metrics.

Democratized Access

Today’s inflation data comes through PDF reports on government websites, a highly limited format. API interfaces allowing any platform or application to easily query and integrate the most current inflation data would democratize access and ignite innovation.

Developers could build personalized inflation dashboards, interactive charts, mobile apps, and embedded widgets. Other economic and financial indicators could be combined for context. Startups could develop analytics services leveraging machine learning on the data.

Making granular historical data freely available facilitates scholarly research. Open data supercharges what any organization or individual can build on top.

Near Real-Time Reporting

Inflation does not stand still between monthly reporting epochs. More responsive indicators require higher frequency updates. The ability to process millions of data points daily means inflation could be measured in near real-time.

Just as stock tickers constantly refresh, continuously updated inflation indexes would reveal daily or hourly shifts. This high-resolution view enables an understanding of inflation’s underlying volatility and sensitivity to events. Central bankers could adjust policies faster using real evidence.

Platforms like Truflation already demonstrate the feasibility of daily inflation indicators. Mimicking hihigh-frequencyrading, economists could develop algorithmic inflation forecasting and hedging strategies leveraging real-time data.

Future Outlook

Achieving this multi-pronged evolution in inflation measurement opens a new frontier for economic analysis, financial engineering, policymaking, and journalism.

With far greater transparency, volumes of high-fidelity data, and split-second updating, inflation would cease being a monthly mystery figure. The future lies in inflation metrics reflecting empirical realities, not antiquated statistical conventions.

Of course, overhauling century-old legacy government processes raises challenges. Legal, bureaucratic, and methodological obstacles will slow adoption. But pioneers like Truflation are proving advanced accurate inflation measurement is viable.

The potential payoffs for productivity and prosperity from sharpening the world’s monetary measuring stick are enormous. Just as redefining the meter and second, drove science forward, better inflation data will push economic advancement. The inflation index must be rebuilt for the 21st century.