Potential Consequences of Manipulating Economic Data in America, as Illustrated by Past Actions in Other Countries
In recent times, the importance of maintaining the neutrality and credibility of economic data agencies has come under scrutiny. The examples of the United States Bureau of Labor Statistics (BLS) and Argentina serve as cautionary tales, highlighting the potential consequences of political interference in these institutions.
The fakery of economic data in Greece during the global financial crisis of 2008 and 2009 significantly worsened the country's economic situation. In the United States, a partisan nomination to lead the BLS can lead to political interference that undermines the agency’s neutrality, accuracy, and public trust. This can have far-reaching consequences for policymaking and economic decision-making.
Recent events at the BLS illustrate these risks. The firing of BLS Commissioner Erika McEntarfer by President Trump after the agency released a weak jobs report was widely seen as political retaliation aimed at changing or influencing economic statistics to fit a political narrative. This is unprecedented and feared to weaken the agency’s independence.
Federal employees and experts warn that such actions amount to silencing the truth and undermining the foundation of democratic governance by introducing bias into data that should be objective. The integrity of publicly available data is essential for accurate economic analysis, policymaking, and private sector decisions. The consequences include erosion of trust in official economic data, making it harder for businesses, governments, and consumers to make informed decisions.
In Argentina, accusations of untrustworthy inflation and economic growth data have dogged the country for decades, scaring off investors despite a wealth of natural resources. Official inflation data was treated as suspect for years, contributing to the country's credit ratings staying in junk territory for years.
The US economy, with over $30 trillion in size, is significantly larger than both Greece and Argentina. The US economy experienced a relatively robust annualized growth rate of 3% in the second quarter. However, the President of the United States, Donald Trump, fired the head of the BLS this month following disappointing jobs data.
Despite the concerns, the US is considered a world leader in providing high-quality data, with the BLS being a world-class institution. The country has other sources of data, both public and private, to round out a fuller picture of the economy, such as the Census Bureau and the Bureau of Economic Analysis. These other data institutions, like the Census Bureau and the Bureau of Economic Analysis, are made up virtually 100% by statisticians and economists and are considered nonpolitical.
In Greece, similar political pressures led to the confession of faked numbers on the national deficit and debt in 2004 to qualify for entry into the eurozone in 2001. Countries like Greece and Argentina have been punished by investors for putting out manufactured numbers in the past.
In conclusion, a partisan-led nomination that compromises the independence of an economic data agency can cause loss of data credibility, reduce trust in government information, and distort economic policymaking and business planning. The example of the BLS commissioner’s firing highlights these dangers clearly. It is crucial to maintain the neutrality and integrity of economic data agencies to ensure accurate economic analysis, sound policymaking, and informed decision-making.
- The partisan interference in economic data agencies, like the one experienced by the BLS in the United States, can lead to disputed economic data, causing challenges for both policymaking and business planning by eroding trust in government information.
- The manipulation of economic data, such as the fakery of data in Greece during the global financial crisis and the inconsistent data in Argentina, can have far-reaching consequences, including deterring investments due to uncertain economic conditions and undermining the foundation of democratic governance by introducing bias into data that should be objective.
