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How Do "Since the Pandemic" Headlines Distort Financial Context?

In the years since 2020, financial news has been flooded with a specific temporal anchor: "since the pandemic." Stock recovered "the most since the pandemic began." Inflation has "climbed the highest since the pandemic eased." Companies have "hired the fewest workers since the pandemic recession." This framing has become so common that it feels neutral—a natural reference point. But it's not. "Since the pandemic" is one of the most effective ways journalists distort financial reality by cherry-picking a starting point.

Quick definition: "Since the pandemic" is a temporal trap—it picks 2020 as a baseline, even when that baseline is historically abnormal and not relevant to the question being asked.

Key takeaways​

  • 2020 was an extraordinary economic moment, not a typical baseline—it had simultaneous supply shocks, demand shocks, and unprecedented policy responses.
  • Cherry-picking time windows is a standard way to make stories more dramatic: the same statistic can be "the worst since 2008" or "the best in 30 years" depending on where you start counting.
  • The pandemic baseline is attractive to journalists because 2020 was chaotic, making comparisons dramatic and recent enough to feel relevant.
  • Meaningful comparisons use longer time periods (5-10 years minimum) or cycle-relevant periods (start of recession to start of next recession).
  • Context depends on the specific metric: inflation compared to pandemic = potentially useful; unemployment compared to pandemic = less useful (since 2020 was a one-off labor shock).

Why 2020 is a misleading baseline​

To understand why "since the pandemic" is a trap, you need to understand what 2020 actually was: an economic anomaly stacked on other anomalies.

In March 2020, governments ordered businesses to shut down. Supply chains froze. Factories closed. Unemployment spiked to 14.7% (higher than at any point in the Great Depression except the trough of 1933). Consumer spending collapsed. Airlines saw revenue plummet 90% in some cases. This was not recession—it was economic arrest.

Then, within weeks, governments deployed the largest stimulus packages in peacetime history. The Federal Reserve slashed interest rates to zero and began buying assets. Trillions of dollars flooded into the economy. Supply chains began reopening, but gradually. Demand surged for goods and services. Prices rose. Unemployment fell faster than historical precedent. The stock market rebounded sharply.

None of this is typical. It's not a recession or a recovery. It's a policy-induced shock followed by an extraordinary rebound. Using 2020 as a baseline for "normal" behavior is like using a car crash as your baseline for how cars typically accelerate.

Here's the problem: if you compare any metric to pandemic lows, you can make almost any economic trend sound either alarming or impressive.

Example 1: Inflation. In April 2020, inflation was near zero. The Consumer Price Index fell. Fast-forward to 2021, and inflation began rising as supply chains recovered slowly while demand soared. Journalists wrote: "Inflation is highest since the pandemic began—a 40-year high!" Both facts are true, but the comparison is misleading. Comparing 2021 inflation to 2020 lows is like comparing a fever to the temperature right after you came out of a freezer. The pandemic suppressed inflation artificially. The rebound to "normal" inflation was presented as a crisis because it was compared to an abnormal baseline.

Example 2: Unemployment. In May 2020, unemployment hit 14.7%, the highest since the Great Depression. By 2023, it had fallen to 3.8%. Headlines declared: "Unemployment has fallen the most since the pandemic began—the fastest jobs recovery in history!" This is true, but it's a comparison that was made possible only by the pandemic creating an artificial starting point. The jobs recovery was fast relative to the worst month in modern history, which means almost any normal recovery would look "record-breaking" by this metric.

Example 3: Wage growth. During the pandemic shutdowns, lower-wage workers were disproportionately laid off, leaving a higher proportion of higher-wage workers in the labor force. This shifted the average wage upward. When news outlets reported "biggest wage growth since the pandemic," they were partially reporting workforce composition changes, not actual wage increases for individuals. Again, the pandemic baseline created an illusion.

The mechanics of baseline selection bias​

Journalists don't usually intend to deceive. The problem is structural. Time windows create dramatically different narratives of the same underlying reality.

Consider the stock market from 2008 to 2024. Take three different time windows:

  1. From 2008 bottom to 2024: "Stock market up 500%, the greatest bull market in history!"
  2. From 2020 pandemic lows to 2024: "Stock market up 90%, strong recovery!"
  3. From 2021 peak to 2023 trough: "Stock market down 25%, worst bear market since 2008!"

All three statements are factually true. But they tell completely different stories. By choosing "since the pandemic," journalists pick a window that is recent (so it feels relevant), dramatic (2020 was chaotic), and creates room for impressively large percentage gains (because 2020 was so far below trend). It's not fraud. It's baseline selection bias.

Here's why this matters. When you read "stocks up the most since the pandemic began," you don't know whether you're looking at:

  • A powerful recovery from an artificial low (neutral or even good news).
  • A genuine outperformance relative to long-term trends (more impressive).
  • A rebound that's barely keeping pace with long-term averages (less impressive than the headline suggests).

You need a longer baseline to answer that question.

Long-term vs. short-term comparisons​

A useful rule of thumb: meaningful comparisons use either a long time frame or a cycle-relevant time frame.

Long time frame: Compare to the same quarter five years ago, or the same month 10 years ago. This smooths out unusual annual patterns and captures genuine long-term trends. "Unemployment up 0.3 points year-over-year" is more informative than "unemployment up from pandemic lows" because it controls for seasonal hiring and other cycles.

Cycle-relevant time frame: Some metrics are best understood relative to business cycles. "Unemployment has risen from the previous cycle low" (comparing the last recession's trough to the current trough) is more meaningful than comparing to pandemic extremes. The pandemic wasn't a normal recession, so normal recession comparisons are more relevant.

"Since the pandemic" uses neither approach. It uses a recent, dramatic, abnormal baseline—which is precisely what makes it effective for creating impressions rather than conveying understanding.

Let's look at real examples:

Retail sales. A news outlet reports: "Retail sales fell 0.5% last month, the steepest decline since the pandemic recovery began!" But retail sales decline by 0.5-1% regularly in January (people spend less after the holidays). This is seasonal, not news. A headline comparing to pandemic recovery is deliberately avoiding seasonal adjustment, which would show this is normal. If the headline said "retail sales fell as much as expected for January," there's no story. By using the pandemic baseline, a routine monthly decline becomes "news."

Mortgage rates. "Mortgage rates hit 7%, the highest since the pandemic era!" This is true, but mortgage rates in 2012 were 3-4%, and in 2008 they were 6%. The headline implies unprecedented rates when comparing to the full span of modern mortgage history (since the 1990s) shows 7% is normal to low. The pandemic saw rates drop to 2%, an artificial low driven by near-zero Fed rates and emergency purchases. Returning to 5-7% is a return to normal, not a crisis. But "back to normal rates" doesn't headline.

The cherry-picking playbook​

There's a pattern to how journalists (accidentally or deliberately) cherry-pick time windows:

  1. Find a dramatic event or policy change. (The pandemic, a Fed rate hike, an election, a new regulation.)
  2. Use that event as a baseline. Even if it's abnormal.
  3. Compare recent months or quarters to that baseline.
  4. Report the largest difference in either direction.

This process reliably generates attention-grabbing headlines because unusual starting points create unusual comparisons.

Here's how it could work for four different sectors, all in the same week:

  • Tech stocks down 8% this month. Headline: "Tech down the most since the pandemic tech boom!" (Uses 2021, when tech soared, as the baseline.)
  • Utilities up 3% this month. Headline: "Defensive stocks surge—biggest rally since the pandemic sell-off!" (Uses 2020, when defensive stocks were stable, as the baseline.)
  • Bond yields up 0.5%. Headline: "Yields at highest since the pandemic rate cuts!" (Uses 2020 lows as the baseline.)
  • Inflation down 0.1% month-over-month. Headline: "Inflation relief—smallest monthly increase since the pandemic ended!" (Uses pandemic peak as the baseline.)

All four headlines could be true and published in the same week, yet they paint opposite pictures of the market. One journalist uses pandemic-peak as the baseline for dramatic effect. Another uses pandemic-low. The underlying statistics are identical; the framing is different.

How to evaluate "since the pandemic" claims​

When you see this phrase, ask these questions in order:

  1. Is this a meaningful baseline? Did the pandemic actually disrupt this metric? (Yes for inflation, unemployment, airline travel. No for, say, long-term Treasury yields, which had been falling for decades before 2020.)

  2. Is this metric currently above or below its pre-pandemic trend? If inflation returned to 2019 levels, "back to pre-pandemic inflation" is more informative than "down since 2021 highs."

  3. How does this compare to the broader historical trend? Ask: "Has this metric always been this high? Or is this high only relative to 2020?" If the latter, the pandemic baseline is being used to inflate the appearance of change.

  4. What would the headline be with a longer time frame? If you used a 10-year baseline instead of a 4-year baseline, would the story change? If yes, you've found baseline selection bias.

  5. Is the headline using the baseline to imply a forecast? Some headlines with "since the pandemic" backgrounds imply a trend is accelerating or concerning. Usually, they're just comparing to an abnormal baseline. The trend might be normal or even improving on longer time scales.

Real-world examples​

Example 1: Labor force participation. In 2020, millions of workers left the labor force due to childcare constraints, health concerns, and early retirements. Labor force participation dropped to 61.4%. Journalists spent 2021-2023 reporting "labor force participation at lowest since the pandemic began—where are the workers?" But this framing ignored that pre-2008, participation was also around 66%, and it had been declining for two decades due to aging demographics. The pandemic-era decline was real, but the headline implied it was unique to the pandemic. Using a 20-year baseline showed labor force participation was following a long-term demographic trend, with the pandemic as a minor acceleration.

Example 2: Corporate profit margins. In 2020-2021, certain companies saw profit margins expand as supply constraints boosted pricing power. Journalists wrote: "Corporate profits at highest since the pandemic began!" But in the decades before 2008, corporate profit margins had been similar. The pandemic compressed them, and the rebound was just a return to normal. A longer baseline would have shown this was a reversion, not exceptional growth.

Example 3: Student loan payment pause. In 2020, the government paused student loan repayments. In 2023, it restarted. Journalists reported: "Student loan defaults could hit highest levels since the pandemic pause began!" This is circular—the baseline is literally the start of the policy being analyzed. The relevant baseline would be: "Defaults could return to pre-pandemic levels" or "Defaults will be lower than the 2008-2012 crisis period." Using the pause as the baseline only works if you want to imply the restart is shocking.

Common mistakes​

  1. Accepting the pandemic baseline without question. When you see "since the pandemic," treat it like any other time window choice. Ask why that time window. Often, there's a better one.

  2. Forgetting that 2020 was abnormal. The pandemic created simultaneous supply and demand shocks, policy interventions, and behavioral changes. It's not a natural baseline. Most economic metrics were pushed far from their trend in 2020, making almost any 2021-2024 value appear dramatic by comparison.

  3. Conflating "recent" with "true." Just because a comparison is recent doesn't make it meaningful. A recent event can still be a poor baseline.

  4. Not checking the pre-pandemic value. Before accepting a "since the pandemic" claim, look up: what was this metric in 2019? If it was similar to today, the pandemic baseline is irrelevant.

  5. Assuming "highest since the pandemic" means "high." This phrase can mean: "the highest in a 4-year window, but still below the 20-year average." That's not high. That's normal to low. Always check absolute levels, not just comparisons.

  6. Missing the implied narrative. Headlines with pandemic baselines often imply concern or exceptionalism. Sometimes that's warranted. Often, it's an artifact of baseline selection. Ask: "Would this headline exist if the comparison used a longer time frame?" If no, you've found bias.

Diagram: Evaluating "since the pandemic" baseline claims​

FAQ​

Q: Is "since the pandemic" ever a valid comparison?​

A: Sometimes, yes. If you're studying pandemic-specific effects (labor force participation, supply chain disruption, mortgage rates), comparing to pre-pandemic levels is exactly right. The trap is when journalists use "since the pandemic" for metrics unrelated to the pandemic shock. For example, "median home price at highest since the pandemic began" is less useful than "median home price at highest in 20 years"—unless you're specifically analyzing pandemic-era housing demand.

Q: What time frame should I use instead?​

A: Start with five years back, or 10 years if the metric is stable. For cyclical metrics (unemployment, interest rates), use the same point in the previous business cycle. For trending metrics (stock prices, GDP), use the previous 10 years or the previous comparable year. Avoid baselines created by policy shifts or disasters unless you're specifically analyzing that policy or disaster.

Q: Can the same headline be true but misleading if it uses a pandemic baseline?​

A: Yes, that's the entire point. "Stock market at highest since the pandemic began" is technically true, but if the stock market is also at its highest in 40 years, the headline is choosing a baseline that makes the true statement sound less impressive than it is. The headline is factual but strategically misleading.

Q: What if an article mentions both "since the pandemic" and a longer-term comparison?​

A: That's better. If a headline says "retail sales down 2%, but up 15% annually," you have both contexts. The longer-term context prevents the short-term baseline from misleading you. Read carefully for these dual baselines—they're the sign of more careful reporting.

Q: How do I know if a journalist chose the pandemic baseline deliberately to mislead or accidentally?​

A: You usually can't know intent. But it doesn't matter. Whether the misleading baseline was deliberate or lazy, your job is the same: recognize it and demand better context. Ask the journalist or editor why they chose that time window. Often, they'll realize they could have been clearer.

Q: Are there other "anchor events" besides the pandemic that get misused?​

A: Absolutely. The 2008 financial crisis, 9/11, the 2016 election, and major Fed policy shifts all serve as anchors for "since [event]" comparisons. Apply the same logic: is the event-based baseline meaningful for this specific metric? If not, demand a longer-term comparison.

Summary​

"Since the pandemic" is a trap because it uses a historically abnormal, recent event as a baseline—precisely the combination that makes almost any subsequent statistic appear dramatic. By asking whether the metric was actually disrupted by the pandemic, whether it's returned to pre-pandemic levels, and how it compares to longer time frames, you can distinguish genuine economic changes from illusions created by baseline selection. When you encounter this phrase, pause and demand a longer time horizon. You'll often find that "highest since the pandemic" is just "back to normal"—and normal isn't news.

Next​

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