The era of “expert failure” appears to be upon us, with pollsters, Wall Street analysts, and tech futurists all under scrutiny for their miscalculations. However, it is in the realm of economics where this skepticism has taken on a particularly fervent tone. Recently, we’ve witnessed a surge in Orwellian distrust of government data, leading to an unsettling proposition: that the numbers themselves are fabrications, rather than mere misinterpretations by economists.
I understand the impulse behind this skepticism. If the government stumbles in so many of its endeavors, why should we put faith in its statistics? However, this line of thinking conflates two distinct issues: the government’s failure to address economic challenges with its ability to manage technical problems. Recognizing this difference is crucial; we can justifiably critique economic planning initiatives while still trusting the Bureau of Labor Statistics to deliver employment data accurately.
To elucidate, economic problems often involve conflicting objectives and trade-offs. For instance, should we allocate titanium for railway tracks or for golf clubs? Should corn be transformed into ethanol or used as cattle feed? Markets resolve these dilemmas through mechanisms of pricing, profit, and loss. Conversely, as F.A. Hayek aptly illustrated, governments lack the capability to effectively weigh these trade-offs. Technical problems, however, have a singular focus. The goals are clear: construct the railway tracks, feed the cattle, and count the total number of jobs in the U.S. Here, execution is paramount, not conflict.
While market players can tackle technical challenges, governments can as well. Take the Soviet Union, which successfully launched the first satellite but could not keep grocery shelves stocked. This disparity was no coincidence; technical issues possess definitive endpoints, while economic issues require evaluating countless trade-offs that market dynamics illuminate.
It’s important to note that this analysis doesn’t address the cost-effectiveness of government solutions or their overall worth. Landing on the moon in 1961 was undeniably an impressive achievement, but a far greater accomplishment is ensuring the sustenance of one’s populace. The USSR excelled in the former yet faltered in the latter, ultimately leading to its collapse.
So, what’s the relevance of this discussion to government statistics? In a nutshell: everything. The process of data collection and analysis is a technical endeavor with a clear objective: precision in measurement. The Bureau of Labor Statistics, for example, does not grapple with trade-offs or resource allocation; it simply aims for accuracy.
Take a moment to assess the BLS’s performance. Unlike China’s National Bureau of Statistics, which operates as a virtual mouthpiece for the State Council, the BLS functions with legal independence. The much-criticized revision of 911,000 jobs in total non-farm job growth still leaves the Bureau with an accuracy rate exceeding 99%—considering there are over 150 million non-farm employees in the U.S. In contrast, the 2020 Census was estimated to have undercounted as many as 782,000 individuals, yet with a total population exceeding 330 million, the Census Bureau managed to maintain an accuracy rate of 0.25%.
Does this mean the data collected perfectly reflects reality? Certainly not. There are significant, legitimate discussions surrounding what should be counted in GDP, how to adjust the CPI for quality changes, and the criteria for defining “unemployment,” among other metrics. These debates focus on the nature of measurement rather than questioning the accuracy or technical competency of the measurements themselves.
This distinction is particularly crucial for classical liberals. While we rightfully question the government’s ability to choose winners, allocate resources, and orchestrate economic plans, dismissing government statistics as categorically flawed conflates technical skill with economic strategy. Could the private sector gather this data more efficiently? Perhaps. However, one must consider that Bloomberg Terminals, which come with a hefty price tag of over $24,000 per user annually, rely on government data.
Should we trust governments to orchestrate economies? Absolutely not. But when it comes to trusting government statistics—specifically those from the U.S.—the evidence leans toward yes. It’s not about the inherent virtue of governments; rather, it’s the recognition that measurement is fundamentally different from the interpretation of those measurements.
