To calculate the Global Macro Scoreboard, we use Predata Relative Volatility signals for each of the 35 issues we track. The Relative Volatility algorithm is designed to capture sustained digital conversation activity related to an issue. After the House passed its tax reform bill on November 16, the Relative Volatility Signal for U.S. Tax Reform trended downward, only picking back up the day before the Senate passed its version. Throughout, the signal was driven by traffic on general interest sources related to the American tax system and previous attempts at tax reform. It appeared that general internet audiences lost interest in tax reform once it passed the House.
However, the U.S. Tax Reform Anomaly Detection signal, which is based on a different aggregation algorithm, told a different story. Anomaly Detection signals reveal extreme levels of activity on individual web pages within broader conversations on a single day. Our U.S. Tax Reform Anomaly Detection signal began spiking last Monday, before it was clear the Senate bill would pass. The signal was driven by web sources related to tax policy insiders, including think tanks, advocacy groups, tax and accounting consultants, and trade associations. These stakeholders, it seems, were paying close attention to the GOP Senate's week of political maneuvering.
The two different aggregation algorithms reveal the disconnect between policy insiders and the general web-browsing public. Just because something gets picked up in the major papers does not mean it will gain traction in the broader conversation online. Looking back to the Global Macro Scoreboard, it seems web audiences have moved on from tax reform, swinging their attention to other U.S. policy issues such as the debt-ceiling and trade.
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