Authored by Jeffrey Tucker via The Epoch Times, Ever since I started writing about dubious government economic statistics, I've been inundated with feedback from current and retired accountants. They are thrilled that I've highlighted this issue and have shared various insights. One particularly compelling point stood out: many government employees lack the intuition to recognize nonsensical figures. My correspondent blames technology. In the past, using slide rules required a deep understanding of numbers and decimal manipulation.

However, the advent of calculators relieved the human brain of this responsibility, eroding our innate number sense. With the rise of computers, people became tool operators, often without questioning the results produced. The consequence is a data system that no one perceives as their responsibility to fix, contributing to persistent inaccuracies in GDP figures and inflation indices. These issues have long plagued government data collection. A notable example is the work of economist G. Warren Nutter, who questioned the Soviet Union's economic data in the 1950s and '60s. His research debunked predictions that the Soviet GDP would outpace that of the United States, revealing instead economic stagnation and fraudulent data.

Despite initially being dismissed, Nutter's findings were later confirmed. This historical example prompts reflection on whether similar data manipulation happens in the United States today. Bureaucracies often produce results that political leaders desire, unchecked by rigorous oversight. As Nutter observed, statistics are prone to error and manipulation without independent verification. The current U.S. administration, for example, has an incentive to present favorable data, despite observable discrepancies in consumer prices and job statistics. Data manipulation isn't unique to the U.S.; it's a global issue seen in countries like China, North Korea, and Russia. Realistically, the U.S. economic data might reveal a lack of genuine recovery over the past four years, but accurate revisions may never come. This underscores the need for skepticism towards official data sources, a lesson we can draw from Nutter's work.