On October 1 ADP reported continued softening in the US labor market. CNBC explained, “Private payrolls saw their biggest decline in 2½ years during September, a further sign of labor market weakening that compounds the data blackout accompanying the U.S. government shutdown.”
On October 3 the Institute for Supply Management reported, “In September, the Services PMI® registered an unchanged reading of 50 percent, 2 percentage points lower than the August figure of 52 percent. The Business Activity Index moved into contraction territory in September, registering 49.9 percent, 5.1 percentage points lower than the reading of 55 percent recorded in August. This is the first time the index has entered contraction territory since May 2020.” (Here is the complete report.)
Both reports use credible data sources to reach plausible judgments regarding the current direction of travel for demand and supply. The similar direction found by two independent reports reinforces claims of validity. These retrospective indicators are suggestive–but far short of conclusive–for future speed and direction of travel.
The current hiatus in US government data outputs is a meaningful moment to reflect on the role and limitations of data — and related issues of fundamental epistemology and decision-making.
I conceive a ladder of learning consisting of:
Step 1: Occurrence, duration, and variation are simultaneously distributed across space.
Step 2: Observations of Step 1 from different spatial and temporal angles of more or less scope.
Step 3: Qualitative and quantitative measurements of Step 2 that generate usable data.
Step 4: Organizing data by various characteristics to generate targeted, often purposeful information.
Step 5: Contextualizing information by reference to dependent, interdependent, and independent variables to generate knowledge.
Step 6: Carefully and creatively applying knowledge to solve a problem or engage an opportunity.
Whether this ladder ascends to wisdom or descends into foolishness depends on the accuracy and validity of each Step 2-6 and, in my experience, especially on the quality of Steps 5 and 6.
Step 1 is infinite in scope. Subsequent steps attempt to narrow scope to fit human capacity for understanding. Steps 2, 3, and 4 are usually radically reductionist. Observations can be equally valid yet contradictory. Measures can be accurate but exclude crucial aspects of occurrence. Data can be thoughtfully and carefully curated while unable to elucidate known-unknowns, much less unknown-unknowns, or even (especially?) unknown-knowns. Contextualization can be highly contentious as echoes of infinity are reintroduced for purposes of reliable knowledge-making.
When widely accepted that the ladder of learning stands on flooded soggy soil and leans against a rather wobbly wall, the step-wise process can be helpful. Each step offers probabilistic insights for decision-makers to consider, but the ladder does not lead anyone into the delusion of risk free certainty. Problems will escalate and accelerate if certainty is perceived or claimed.
So, more data is better than less. Continued access to well-validated longitudinal data, with known limitations, is particularly helpful (such as BLS, BEA, and Census data). Honest, self-critical, and courageous decision-makers ready to boldly test, but not over-commit, to data-informed hypotheses can accelerate toward wisdom and divert from foolishness. Resilience is most likely to emerge from a humble relationship with reality.
The current absence of government data is unhelpful. There are other sources of data. Quality and quantity of data matter. But the quality and care of decision-making matters more.