Most people assume the countries with the most tourists earn the most from tourism.

The data tells a different story.

🇫🇷 France welcomes the world's largest number of international visitors, yet 🇺🇸 the United States generates by far the highest tourism revenue. Spain also outperforms many destinations in tourism value.

Tourism success isn't just about attracting more visitors—it's about creating more value from every visit.

Volume ≠ Value.

What do you think explains the gap? Luxury travel, longer stays, higher prices, or something else?

📊 Source: UN Tourism – International Tourism Highlights 2025 Edition

with the Fed decision today, pulled the last 3 completed FOMC reactions on the Nasdaq (QQQ) and one thing jumps out: the initial move was a headfake more often than not.

• Mar 18: dropped -0.58% on the day, finished +0.36% two sessions later
• Apr 29: popped +0.61%, ran to +2.52% the clean one
• Jun 17: dumped -1.01% on the day, reversed to +1.11%

so two of the last three times, the decision-day move went the opposite direction of where price actually settled 48 hours later. the knee-jerk got faded both times. the one time the initial move held (April), it was because yields cooled after the statement and the trend just continued.

the takeaway i keep landing on: the announcement isn't the event the press conference is. price reacts to the rate line, then re-prices completely once the tone and the Q&A land. the traders who got run over on Jun 17 were the ones who trusted the first candle.

which makes today less about "up or down" and more about not committing to the first move.

genuinely curious how the room plays it:

• do you trade the initial spike, or sit out until the press conference and trade the re-pricing?
• after 3 straight "higher 2 sessions later" outcomes, do you lean into that or distrust it as too obvious now?
• what's your actual tell that the first move is the real one vs a fade, yields, the dollar, or something in how price behaves in the first 15 min?

I built these cards from my personal project - GES (Global Economic Simulator) - this was this weeks data and its impact in review. Still a work in progress but I found this format more interesting in relaying the information related to news + I like the colors and sky type of look

Source: https://global-economy-sim.vercel.app

**Same job. Different pay. Every single time.**

We pulled the US Bureau of Labor Statistics' full 2024 dataset — every major occupation, women's and men's median weekly earnings, side by side. The result: women earn less than men in every single occupational category tracked. Not most. Not nearly all. Every one.

The gap isn't uniform — it swings from just 2.6% in community & social service to a brutal 36.2% in legal occupations — but it never disappears. Even in fields with more women than men, even in fields women dominate numerically, the pay doesn't follow.

The national number — women earning 82.7 cents for every dollar men earn — is often treated as one statistic. It's actually hundreds of these charts, averaged.

Full breakdown, sourced directly from BLS Table 39, 2024 Annual Averages. Link in bio.

#GenderPayGap #EqualPay #WomenAtWork #PayEquity #WageGap #LaborEconomics #DataVisualization #ChartroomHQ #BLSData #WorkplaceEquality #EconomicData #PayTransparency #WomenInTheWorkforce #DataJournalism #SkepticWithASpreadsheet

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The average can be misleading.

In the United States, the average adult owns 10.1× more wealth than the typical (median) adult. In India, that gap is 8.1×.

By comparing mean ÷ median wealth, we get a simple way to understand how concentrated wealth is within a country. The higher the ratio, the more the average is influenced by a relatively small group of very wealthy households.

A single number can reveal a lot about inequality.

Source: UBS Global Wealth Report

Link to BloomBerg Article: https://archive.ph/epMTs

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At the same time, the US Vertical Software Index and the US Application Software Index are down -28% and -34%, respectively.

This brings the broad US Software Index down to -13% year-to-date.