Economic data flows and collects from sources both varied and unique. But which sources are significant and why? And how complex does this world of big data get when it comes to trying to explore the economic landscape? Cue Ms. Frizzle’s “Seatbelts, everyone!” line and let’s take a tour of economic data sources on the Magic School Bus!
Ever since the likes of IBM’s Watson, Google Trends, and Bloomberg Terminal emerged, data-driven decision making shifted from fad to fixture in the business world. But the fundamental shift that big data made in the world of research didn’t change which questions to ask, but rather how we ask those questions. Or, as Douglas Adams might say, you need to really know what you’re asking before you switch on Deep Thought.
No two sectors (or businesses) are alike. One fundamental differentiator is that some sectors make goods and some sectors distribute goods as a service. This simple but substantial distinction can significantly affect the quality or range of your results while modeling economic impacts.
Barely more than a year ago, e-scooters suddenly flooded city streets across the country. But their presence wasn’t always met with enthusiasm. Antagonistic cities and towns cited public health concerns, street congestion, and even harassment as cause for banning the popular short-trip transportation option altogether.
When scooters swooped into Richmond, Virginia overnight last August, battle lines emerged just as fast. Like other cities where the electric, dockless flock cropped up with little to no warning, some decried while others praised the newcomers. Several groups of residents urged government leaders to roll out the welcome mat instead of red tape, but politicians voiced concerns about safety as they issued directives to squash the permit-less scooters.
The concept of input-output analysis was theorized nearly a lifetime ago. But input-output modeling as we know it today has advanced in fits and spurts through an infancy that has lasted nearly 40 years. The latest advancement in technology and methodology is going to propel economic impact modeling into the future.
Technology has been scary lately if you’re employed in manual labor.
Every few weeks in the news cycle it seems as though apocalyptic headlines like “Technology could kill 5 million jobs by 2020 ” or “Robots will destroy our jobs – and we're not ready for it ” creep above the fold.