Introduction:
In recent years, there has been growing concern about the accuracy and reliability of government economic statistics. Many individuals, including current and retired bean counters, have voiced their opinions on this issue, shedding light on the innumeracy of the employees responsible for collecting and reporting data. One possible explanation for this lack of numerical intuition is the advent of technology, particularly calculators and computers, which have taken over the task of number manipulation previously performed by the human brain. This shift has resulted in a decline in basic mathematical skills and an increased reliance on tools without fully understanding the logic behind the numbers generated.
The Impact of Technology:
The introduction of calculators revolutionized the way mathematicians and students approached numerical calculations. While it undoubtedly made calculations faster and more efficient, it also eliminated the need for individuals to engage their minds in the process. The intuitive skills that were once essential for deducing larger numbers from smaller digits and ensuring consistency in decimal point manipulation gradually diminished. As a result, employees in government data collection roles lack the fundamental intuition necessary to identify when their figures don’t make sense.
The Role of the Computer:
The computer further exacerbated the problem by allowing individuals to operate tools without fully comprehending the underlying processes. In many cases, data collectors and those reporting the data perform routine jobs within a broken system. While everyone acknowledges the issues with the data, no one takes responsibility for fixing them. This lack of accountability perpetuates the existence of flawed figures in key economic indicators such as GDP, inflation, and jobs data.
Lessons from the Soviet Union:
The challenges surrounding government data collection are not new. In the 1950s and ’60s, economist G. Warren Nutter conducted a deep dive into the Soviet Union’s economic data and uncovered significant discrepancies. While mainstream economists predicted that the Soviet GDP would outpace the United States, Nutter’s revised figures revealed a different reality. He discovered that the Soviet economy experienced initial growth due to the deployment of resources during the Second World War, but it never fully recovered. Instead, the system began generating fake numbers to maintain the illusion of progress. Nutter’s work was initially dismissed by mainstream economics, but he was eventually proven correct after the end of the Cold War.
Applying Lessons to the United States:
The Soviet Union’s experience raises questions about the integrity of economic data in the United States. Bureaucracies have a tendency to generate the answers politicians desire, particularly within complex systems lacking effective checks and balances. The Biden administration, for example, has a strong incentive to produce favorable data, even if it contradicts alternative sources and the lived experiences of ordinary citizens. Grocery prices, housing costs, and healthcare insurance rates are just a few examples of areas where the private sector’s observations differ significantly from official reports.
The Incentive to Manipulate Data:
No one within the government has a real incentive to fix data reporting or revise collection methods to provide a more accurate representation of the economy. The party in power often manipulates statistics to serve its own interests. This pattern is evident in countries like China, Latin American nations, North Korea, and possibly even Russia. The United States is not exempt from this behavior. It is highly likely that a thorough examination of the last four years would reveal minimal real recovery since March 2020. However, the revisions necessary to reflect this reality may never occur.
The Importance of Skepticism:
In light of these concerns, it is crucial to approach government economic statistics with skepticism. As the renowned economist G. Warren Nutter warned, economic statistics are subject to error, manipulation, and distortion. Governments and statistical agencies are not immune to the temptation of stretching figures to suit their own agendas. This reality should make us question the credibility and reliability of official sources, regardless of how seemingly credible they may appear.
Conclusion:
The issue of inaccurate government economic statistics is a long-standing problem that deserves serious attention. The prevalence of technology, coupled with a lack of accountability and the inherent biases of those in power, contributes to the perpetuation of flawed data. By examining the lessons learned from the Soviet Union and adopting a healthy dose of skepticism, we can begin to challenge the status quo and demand greater transparency and accuracy in economic reporting. Only then can we trust that the numbers accurately reflect the true state of the economy.
