#AI ChatGPT Grok: How much credibility should one give to official economic data?

One example on where I come from on this subject

Here another

I asked:

"With respect to economic data, e.g., inflation rate, GDP growth and unemployment rate, given by official entities e.g., a Bureau of Labor Statistics, political considerations set aside, how much credibility should one give to that those figures accurately reflect the reality?"

ChatGPT answered:

Economic data such as the inflation rate, GDP growth, and unemployment rate, when published by official entities like the Bureau of Labor Statistics (BLS) or the Bureau of Economic Analysis (BEA), generally deserve a high level of credibility—but not blind trust. Here's why:

Why they are broadly credible:

1. Transparent methodology: Agencies like the BLS and BEA publicly disclose how data is collected and processed. Methodologies are subject to academic scrutiny.

2. Professional staff: These agencies are staffed by career statisticians and economists with technical expertise, not political appointees.

3. Consistency over time: The data is collected using well-established procedures that allow for comparisons across time and countries.

4. Third-party validation: Independent researchers, journalists, and global institutions (e.g., IMF, World Bank) constantly review and critique the data.

But here are the caveats:

1. Statistical limitations: Reality is messier than averages. Unemployment figures, for example, exclude discouraged workers, and inflation may not reflect personal cost-of-living experiences.

2. Revisions: Initial GDP and job figures are often revised—sometimes substantially—based on better or fuller data.

3. Assumptions and models: Seasonal adjustments, imputations, and hedonic adjustments (in inflation) are necessary, but they can also obscure intuitive reality.

4. Political pressure: While rare in advanced economies, pressure to "frame" narratives exists. However, data tampering in the U.S. remains unlikely due to strong institutional norms and watchdogs.

Bottom line:

Treat official statistics as broadly reliable indicators of trends and magnitudes, but supplement them with independent analysis and understand the definitions and limitations behind each number. Statistics are a map, not the territory.

I said: Thanks!

It replied: You're very welcome! If you'd ever like to dig into the specific quirks of how, say, inflation or unemployment is calculated—or how alternative measures (like U-6 unemployment or shadow stats) compare—I’d be glad to help.

Grok 4 answered:

Official economic data from entities like the Bureau of Labor Statistics (BLS), Federal Reserve, or Bureau of Economic Analysis (BEA) are generally credible but not infallible. Here’s a breakdown of their reliability, focusing purely on methodological and statistical considerations:

Inflation Rate (e.g., Consumer Price Index - CPI):

Strengths: The CPI, produced by the BLS, uses a fixed basket of goods and services, updated periodically, to measure price changes. Data collection is rigorous, involving thousands of price quotes from diverse geographic areas and sectors. Methodologies are transparent, with detailed documentation available.

Limitations: The CPI may not fully capture individual experiences due to regional cost-of-living differences or personal consumption patterns. Substitution bias (consumers shifting to cheaper goods) and quality adjustments (e.g., tech improvements) can skew perceptions of "real" inflation. Hedonic adjustments for quality changes can be subjective but are based on statistical models.

GDP Growth:

Strengths: GDP, reported by the BEA, is calculated using standardized methods (e.g., expenditure or income approaches) aligned with international guidelines (SNA 2008). Data comes from surveys, tax records, and industry reports, with revisions to improve accuracy as more data arrives.

Limitations: Revisions can be significant, especially for initial estimates, as seen in past discrepancies (e.g., Q1 2023 GDP revised from 1.1% to 2.0%). Non-market activities (e.g., household work) and environmental costs are excluded, which may understate or overstate economic "reality." Seasonal adjustments can also introduce noise.

Unemployment Rate:

Strengths: The BLS’s unemployment rate (U-3) comes from the Current Population Survey, a large, randomized sample of households. Definitions follow international standards (ILO). The process is systematic, with clear criteria for who counts as unemployed.

Limitations: The U-3 measure excludes discouraged workers, underemployed individuals, and those marginally attached to the labor force, which the broader U-6 measure captures. For example, in 2023, U-3 was ~3.6%, while U-6 was ~6.7%, showing a gap in "felt" unemployment. Survey response rates have also declined, potentially affecting representativeness.

General Considerations:

Data Integrity: These agencies operate with professional independence, adhering to statistical standards. Manipulation is unlikely due to oversight, peer review, and public scrutiny. However, methodological choices (e.g., weighting, seasonal adjustments) involve judgment calls that can influence outcomes.

Lag and Revisions: Initial releases are often based on incomplete data, leading to revisions. For instance, GDP estimates are revised multiple times over months or years. This can make real-time accuracy lower than final figures.

Scope and Interpretation: No single metric captures the full economy. CPI, GDP, and unemployment reflect specific aspects, not the lived experience of every individual. Structural changes (e.g., gig economy, automation) may not be fully reflected in traditional metrics.

Credibility Assessment: These figures are among the most reliable economic indicators available, grounded in robust, transparent methodologies. However, they are estimates, not perfect mirrors of reality. Assign them high credibility (80-90%) for broad trends, but cross-reference with alternative data (e.g., private indices like the Chapwood Index for inflation or ADP payroll data) and consider their limitations for specific contexts. Always check the latest methodologies on agency websites (e.g., bls.gov, bea.gov) for updates.

I said: Thanks!

It replied: You're welcome! Happy to help with any other questions.

Note: The answers are copied exactly from those given to me by ChatGPT and Grok

Here my many questions to ChatGPT and Grok on bank regulations

#AI ChatGPT- OpenAI: If provided real time data, would you be able to assist in providing transparent and reliable economic data e.g., GDP figures?

I asked OpenAI: 

"If provided real time data, would you be able to produce economic statistics of all sorts, e.g., GDP figures, in a transparent way less prone to errors so as to allow its various user to trust these more e.g., being less influenced by political manipulation? If yes, how would you go about it?'

OpenAI answered: 

"As of my last knowledge update in September 2021, I cannot access real-time data or perform real-time calculations or analysis. However, I can provide you with information on how modern technology and data science can be applied to improve the transparency and accuracy of economic statistics, including GDP figures, with the goal of reducing political manipulation and enhancing trust. Keep in mind that the specific methods and technologies available may have advanced since then.

Automated Data Collection: Real-time economic data collection is essential. Utilizing advanced web scraping techniques, APIs, and data feeds from authoritative sources (e.g., government agencies, central banks, and international organizations) can help ensure accurate and up-to-date data.

Data Validation and Cleaning: Implement robust data validation and cleaning processes to identify and rectify errors or inconsistencies in the data. This can involve outlier detection, time-series analysis, and data reconciliation techniques.

Machine Learning and Forecasting: Utilize machine learning models and time-series forecasting algorithms to predict economic indicators like GDP. These models can adapt to changing data patterns and improve the accuracy of predictions over time.

Blockchain and Data Integrity: Implement blockchain technology to securely record and verify economic data transactions. Blockchain can enhance transparency and ensure data integrity, making it difficult for any single entity to manipulate the data retroactively.

Decentralized Data Sources: Encourage the use of decentralized data sources where multiple entities contribute data. This reduces the likelihood of political manipulation because no single entity has complete control over the data.

Open Data Standards: Promote the use of open data standards and formats, making it easier for external experts, researchers, and organizations to verify and replicate economic statistics independently.

Transparency in Data Collection: Publish detailed information on data collection methodologies, sources, and any adjustments made to the data. Transparency in data collection helps build trust among users.

Independent Audits: Conduct regular independent audits of economic data collection and reporting processes. External auditors can help identify and rectify potential issues.

Cross-Verification with Alternative Data Sources: Use alternative data sources, such as satellite imagery, social media sentiment analysis, and other unconventional methods, to cross-verify economic statistics and identify potential discrepancies.

Data Visualization and Accessible Reporting: Present economic statistics in an easily understandable and accessible manner. Use data visualization techniques to help users interpret the data correctly.

User Education: Educate users, including policymakers, economists, and the general public, on how economic statistics are collected, processed, and reported. This can foster a better understanding of the limitations and nuances of economic data.

Whistleblower Protections: Establish whistleblower protections and reporting mechanisms to encourage individuals within the data collection and reporting process to report any attempts at manipulation or fraud.

While these measures can improve the transparency and accuracy of economic statistics, it's essential to recognize that no system can be entirely immune to political influence or errors. Vigilance, independent oversight, and ongoing improvements in data collection and reporting processes are crucial for maintaining trust in economic statistics. Moreover, advancements in technology and data science may have led to even more innovative approaches to addressing these challenges beyond my last update in 2021."

I asked OpenAI: 

"But if you had access to all the needed data in real time, would that change your answer?"

OpenAI answered: 

"If I had access to all the needed data in real-time, it would certainly change the feasibility of generating real-time economic statistics. With real-time data access, I could potentially provide more accurate and up-to-date economic statistics, including GDP figures, using advanced data analysis techniques. However, the principles of transparency, data validation, and independent verification would still be essential to ensure the reliability and trustworthiness of these statistics.

The methods mentioned in my previous response, such as automated data collection, machine learning, blockchain, open data standards, and user education, would still apply to improve the accuracy and transparency of real-time economic statistics. The advantage of real-time data access would be the ability to provide more timely and responsive economic insights, which can be valuable for decision-makers and analysts.

Keep in mind that even with real-time data access and advanced technology, challenges related to data quality, biases, and potential manipulation would still need to be addressed to maintain the integrity of economic statistics. The goal would remain to create a robust and trustworthy system for producing economic statistics that can withstand scrutiny and ensure the highest level of confidence from users."

Thanks!

"You're welcome! If you have any more questions or need further information, feel free to ask. I'm here to help!"

Note: The answers are copied exactly from those given to me by OpenAI

A good way to fight the reigning Bureaucracy Autocracy is with a Citizens’ Democracy, empowered by Artificial Intelligence.

ChatGPT - OpenAI: Getting to know you!

No remittances without representation!

The June 8 news article "In Guatemala, Harris offers stern words on corruption" reported extensively on Vice President Harris's travel to Guatemala, where the United States, by offering financial cooperation, hopes to reduce the flow of illegal migration from Central America and stimulate better behaviors, e.g., less corruption.

Though those financial contributions are indeed important, they are peanuts when compared with the remittances sent home by the migrant workers. The reality is that what the migrant workers from many Central American nations earn abroad is often much more than the gross domestic product of their home countries. The sad reality is that their remittances help to keep in power those ineffective governments that made them immigrate and that keeps them from going home.

If the United States really wants to help, then look to politically empower as much as possible those migrants in their homelands. For instance, should they not have an important direct representation in their respective congresses? No remittances without representation!

http://theamericanunion.blogspot.com/

A letter published in the Washington Post 

We need to restate productivity, real salaries, unemployment and GDP, to account for the many “working hours” spent consuming distractions.

In Bank of England’s “bankunderground" blog we recently read: “With the rise of smartphones in particular, the amount of stimuli competing for our attention throughout the day has exploded... we are more distracted than ever as a result of the battle for our attention. One study, for example, finds that we are distracted nearly 50% of the time.”

And on a recent visit to a major shop in the Washington area, thinking about it, I noticed that 8 out of the 11 attendants I saw were busy with some type of activity on their cellphones, and I seriously suspect they were not just checking inventories.

The impact of that on productivity, with less effective working time is being put into production, could be huge.

Also, going from for instance a 10% to a 50% distraction signifies de facto that full time or paid by the hour employee’s real salaries have increased fabulously.

And what about the real employment rate if we deduct the hours engaged in distractions? A statistical nightmare? Will we ever be able to compare apples with apples again?

And how should all these working hours consumed with distractions be considered in the GDP figures?

It behooves us to make certain how we measure the economy gets updated to reflect underlying realities. 

Perhaps then we are able to understand better the growing need for worthy and decent unemployments.

Perhaps then we are able to better understand the need for a Universal Basic Income, not as to allow some to stay in bed, but to allow everyone a better opportunity to reach up to whatever gainful employments might be left, like those “low-wage jobs” that it behooves us all, not to consider as “low value jobs”

PS. When you buy a web driven distraction you usually pay one amount… but your consumption of the distractions could be one hour per month or 300 hours per month, a difference that seems not to be considered anywhere.

@PerKurowski

“I honestly don't understand why people vote against Universal Basic Income UBI”. Well here are some reasons.

The most general reason, thanks to arguments spread around by interested redistribution profiteers, is that by means of a UBI many will receive more than they should… in terms of what they need… and without reflecting enough on what is being redistributed, a societal dividend, really signifies.

The second reason is that many might consider that for some, given their special needs, a UBI does not suffice to keep them out of unfair unacceptable hardships… and without reflecting that not only nothing with UBI states a “that’s all folks!”… and again without reflecting enough on what is being redistributed, a societal dividend, really signifies.

Three, most those who carry out research on the consequences of UBI are, one way or another, often the recipients of re-distributional favors, and therefore have a bias against UBI.

For, bad proposals, look at the Swiss referendum. “2500 Swiss Francs or $3500 — would be paid to every citizen, for their whole life, no matter where they live. Those with a job could still work but would have the monthly income deducted from their salary.” 

Sincerely in such as referendum, I would also vote NO!  At about 50 percent of the GDP per capita, it is way too much. In Switzerland a number like $1.000, and expressed as a percentage of GDP or a percentage of average salaries, would be a much more reasonable level at which to start this social experiment.

And of course the idea of those working not getting the UBI plays directly into the hands of those arguing that UBI could cause people to work less.

So what is a Societal Dividend? Here is my take on it.

It is an amount transferred to anyone independent of having been able to capitalize on society’s strengths, like having been able to get a job.

It could be seen as an effort to grease the real economy by combating the natural concentrations of wealth.

It could be seen as a substitute for all those redistribution efforts that because of their complexity is bound to attract the profiteers.

It is a well-funded transfer, no funny money, from citizens to citizens, not depending on government favors. It could therefore be seen as an effort by citizens to become more independent of that populism and demagoguery that often lies behind all societal redistribution.

The way it is funded, could help to align the incentives for other societal causes, for instance if with carbon taxes, with the efforts for a better environment.

An Unsustainable Sustainability

An Unsustainable Sustainability 

The latest fashion in the academic world of international finance is to calculate what is known as the Sustainable Debt Level (SDL). As you may have guessed, it has to do with the level of public debt a country can sustain without entering into a crisis. Normally the SDL is calculated based on the size of the economy (GNP) or on a country’s exports.

Whatever scientific approach is given to the SDL issue, it sure seems somewhat obscene to the citizenry of countries where it is evident that public debt engenders low or even no productivity.

If a credit is granted properly, the credit is repaid and then debt levels never become a problem. It is only the bad or mediocre loans that accumulate—those that do not generate their own repayment. So it could be said that what is really being calculated with the SDL is the level of bad debt that a country can get saddled with. Quite frankly, a developing country with real needs cannot afford the luxury of canceling even one cent in interest on a debt level arising from a series of credits that are nonproductive on the average.

From this perspective and since what we really mean is sustaining something that is unsustainable, this question remains: wouldn’t it be better to skip calculating this debt level and try to free ourselves once and for all from these mortgages, instead of condemning future generations to live forever under the weight of an SDL that has been perfectly calculated? How much torture can the torture victim take before passing out?

And who encouraged these countries to go into debt? Ask those who are well-acquainted with the temptation that credits pose to politicians. In China, they say that you wish for your enemies to live in interesting times. In Argentina, because of the suffering provoked by excessive debt, it would seem that what their enemies could have wished upon them was the trust and confidence of international markets.

On the day that our country Venezuela firmly and irrevocably sets upon the path of totally canceling its debt, on this day an enormous opportunity will open for all those private and collective initiatives that need financial oxygen. Unfortunately it will not be easy, since our politicians, while condemning past debts, have mastered the magic of simultaneously preaching the benefits of new credits.

From El Universal, Caracas, June 5, 2003
As translated in my "Voice and Noise" of 2006

http://unsustainabledebtsustainability.blogspot.com/2003/06/unsustainable-sustainability.html

And this article lead me here: