Understanding Bias in Economic Data: Do Political Affiliation and Wealth Affect Recession Risks?

Understanding Bias in Economic Data: Do Political Affiliation and Wealth Affect Recession Risks?

Political Affiliation and Wealth Don't Influence Recession Risks

Understanding the Influence of Bias on Economic Data

It's often speculated that political affiliation and wealth could be making a recession seem more likely than it actually is, by introducing systematic bias into economic data. However, this is merely a distraction. The data that is most crucial for gauging the likelihood of a downturn is not significantly affected by potential bias. A solid recession framework reveals that the short-term risk of a slump remains low, but can quickly shift to a higher risk.

The Role of Bias in Economic Data

There is no such thing as unbiased data. The process of data collection and presentation always introduces user bias, and economic data is no exception. Some argue that the risk of recession is being overstated due to data biases. Others suggest that soft data is more relevant to those who are less well off, while hard data is more pertinent to the wealthier segment of society. Consequently, the recent decline in soft data performance, which has contributed to increased recession risks, could be a reflection of worsening wealth inequality rather than a genuine deterioration in economic conditions.

Does Bias Overestimate Recession Risk?

The straightforward answer is no. Despite the presence of some bias in economic data, it does not significantly distort recession prediction when done correctly. The near-term recession risk currently remains low, but this risk could escalate rapidly, regardless of data bias.

Political Bias in Economic Data

Political bias is a common factor in some economic data. For instance, the Michigan Consumer Sentiment Survey, which provides a breakdown of political affiliation, reveals that sentiment is being predominantly driven by those who identify as Democrats, who are currently significantly more optimistic than Republicans. However, the core question for investors is whether this bias matters when stock markets experience their worst falls, i.e., during recessions.

The Importance of Manufacturing ISM in Predicting Downturns

When it comes to predicting downturns, the manufacturing ISM is a more reliable indicator than the Conference Board and Michigan’s gauges of consumer sentiment or the NFIB. The ISM is particularly important as it sits at the intersection of soft and hard data and how they interact to trigger recessions. Recessions typically develop when a negative feedback loop forms between hard and soft data. The ISM plays a crucial role in this process, acting as a key link from survey data to the market, and then ultimately to hard data.

Recessions and Their Pervasiveness

During a recession, things generally start going bad everywhere simultaneously. Several Fed member banks produce their own regional manufacturing surveys. The states they cover are fairly evenly balanced overall, with two Democrat strongholds, two Republican ones, and two states that are generally closely contested. This balance helps to neutralize any potential bias.

The Influence of Wealth Inequality on Hard and Soft Data

The idea that hard and soft data are influenced by wealth inequality, with hard data more relevant to the wealthy and soft data to the less well off, is not supported by evidence. Both better and worse-off households by net worth show a negligible relationship with both hard and survey-based data.

Conclusion

While it's essential to understand your data, investors should not be distracted by speculating about who votes for whom, and how well-off they might be. There is little evidence of systematic wealth bias in the data, and it's equally challenging to find political bias in data that is crucial for predicting recession. Therefore, when it comes to avoiding steep market drawdowns ahead of downturns, these factors are of little relevance.

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Some articles will contain credit or partial credit to other authors even if we do not repost the article and are only inspired by the original content.