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A weekend thought: my father is the kind of guy who likes to come up with big monocausal theories to explain every little thing; he missed his calling as a columnist for a major newspaper. Anyway, last week we were chatting and he expounded on one of these theories, in this case a coherent and compelling narrative for the dramatic increase in dog ownership in recent years. The theory is unimportant (it had to do with a decline in aggregate nachas) but afterwards I decided for the heck of it to fact-check his theory. And what do you know? According to the AVMA’s pet census, dog ownership rates have declined, very slightly, from 2007 to 2012.

Now, I know why my dad thought otherwise – over the past few years, dogs have become fantastically more visible in the environments he inhabits, mainly, urban and near-suburban NYC. I am certain that, compared to 5-10 years, ago, many more dogs can be seen in public, more dog parks have emerged, and there are many more stores offering pet-related goods-and-services. But these are intertwined with substantial cultural and demographic changes, and authoritatively  not driven by a change in the absolute number of dogs or dog-ownership rate.

It’s hard to prove things with data, even if you have a lot of really good data. There will always be multiple valid interpretations of the data, and even advanced statistical methods can be problematic and disputable, and hard to use to truly, conclusive prove a single interpretation. As Russ Roberts is fond of pointing out, it’s hard to name a single empirical econometric work that has conclusively resolved a dispute in the field of economics.

But what data can do is it can disprove things, often quite easily. While Scott Winship will argue to death that Piketty’s market-income data is not the best kind of data to understand changes in income inequality, but what you can’t do is proclaim or expound a theory explaining a decrease in market income inequality. This goes for a whole host of things – now that data is plentiful, accessible, available, and manipulable to a degree exponentially vaster than any before in human history, it’s become that much more harder to promote ideas contrary to data. This is the big hidden benefit to bigger, freer, better data – it may not conclusively prove things, but it can most certainly disprove them, and thereby help better hone and focus our understanding of the world.

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Yesterday a friend of mine tweeted an invitation via a new service called Feastly. The invitation was to come to her home and eat a delicious, home-cooked gourmet meal in exchange for money. The service, Feastly, is set up to do exactly that – while it is still in private beta (and therefore cannot be fully-explored until one is invited in) it clearly aggregates offerings of that sort, sortable by dietary restrictions, price, attire, pet-friendliness, and other criteria. It’s a great idea, and one I wish I thought of.

On a social scale, I think as we see more services like this that directly connect buyers and sellers – think eBay, Etsy, ebook self-publishing – it will throw further into question whether statistics like GDP/GNI are useful metrics, not just of broader concepts like "standard of living," but of what they purport to measure. Every meal eaten on Feastly and not at a formal restaurant is one that involves an exchange of goods and services for money, and most of them will likely not be counted by current methods of measuring GDP. This issue predates the internet, of course, but the internet’s amazing power to match small-scale producers to buyers will accelerate this trend, as will the advent of 3-D printing.

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