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Source: Gallup
Source: The Big Picture
Tag Archives: Data Analysis
Predicting Jobs Data Is Hard — and Useless, Too.
This morning, I tweeted out Spencer Jakab’s WSJ column on NFP — It’s a Hard Job Predicting Payrolls Number, with the annotation “Its pointless, too.”
While I understand the obligation many economists have to their employers to make a jobs forecast, you have no such obligation. You don’t have to make a prediction, weigh in, make a guess, create a forecast model or even read other people’s forecasts.
Why not?
Here are three reasons:
1) People are really, really bad at making accurate forecasts: Most forecasts are at best, an educated hypothesis and at worst, a blind guess. A glance at the history of these sorts of predictions reveals that everyone gets these things wrong. I have yet to see someone consistently forecast these things. Indeed, I have yet to see a good 3 month in a row streak forecast by any economist. We simply lack the ability to predict the future.
2) Modelling isn’t much better: The combination of a huge number of known variables, poor data assembly, and a number of unknown variables — plus a healthy dollop of unforeseen randomness — makes employment data forecasting at best slightly better than raw guessing.
3) Even if you could make an accurate forecast, it wont help you in the markets: That’s the funny part of all this — it is a meaningless exercise for investors, and a dubious one for traders. This is especially true in the present investment environment where the FOMC looms as large as they do. The next level analysis is whether the good news is bad (meaning less accommodation) or good (economic improvement) or conversely where bad news is good (meaning more accommodation) or bad (economic deterioration).
Our time would be better utilized trying to discern the current state of the labor market — what actually is (and recently was) rather than what might be. This is useful data for companies, policymakers and labor participants. It has actual utility. Predictions don’t.
Source: The Big Picture
On the Move
Following up on my recent post on Correlation & Causation 101, I delved into various migratory and population estimate datasets at Census.gov. Fascinating stuff. [Note to self: Get a life.] As promised, I also called and corresponded with a contact at Census to be 100 percent certain my criticism of Mr. Moore and Mr. Laffer was well-founded. It was.
That said, I also came upon some neat infographics from Atlas Van Lines - a company that probably moves as many Americans (and Canadians, apparently) as any other.
Here’s their infographic on 2012 Household Moving Migration Patterns. Also, here’s an older link to the reasons we move (based on the exact Census data I wound up digging into that I’m sure Moore and Laffer ignored).
Source: The Big Picture
Say It With Me: Correlation ≠ Causation
The WSJ recently ran an editorial piece that perfectly exemplified two things:
The (well-known) biases and intellectual dishonesty of its editorial board
The peril of confusing correlation with causation, which BR has written about countless times
In a piece titled The Red-State Path to Prosperity, Stephen Moore and Art Laffer argue that metropolitan migration nationwide is going from blue states to red states because “Workers and business owners are responding to clear economic incentives,” i.e. lower tax rates, less regulation, more employer-friendly. They state that:
Among the 10 fastest-growing metro areas last year were Raleigh, Austin, Las Vegas, Orlando, Charlotte, Phoenix, Houston, San Antonio and Dallas. All of these are in low-tax, business-friendly red states. Blue-state areas such as Cleveland, Detroit, Buffalo, Providence and Rochester were among the biggest population losers.
In an accompanying video, Mary Kissel opens the segment by stating: “So it turns out that tax rates do actually matter, according to the Census Bureau,” at which point she begins a conversation with the aforementioned Art Laffer about what he and Mr. Moore inferred from the data. (Census main page on migration is here.) The simple fact of the matter is that Census Bureau made no such claim; Ms. Kissel is distorting Census data and giving voice to it through the ideological lens of Mssrs. Moore and Laffer.
Now, before I proceed, let’s be clear on one point: It’s entirely possible that Mr. Moore and Mr. Laffer are on to something. Unfortunately, they offer up no evidence to support their thesis. (I’m assuming the migration to which they refer has actually occurred the way they describe it which, perhaps, could be a grievous error on my part.)
I’d note that Table 24 (Reason for Move of Movers 16 Years and Over, by Household Income in 2011, Labor Force Status, Major Occupation Group, Major Industry Group, and Type of Move (All Categories): 2011 to 2012) and Table 26 (Reason for Move of Movers 16 Years and Over, by Household Income in 2011, Labor Force Status, Major Occupation Group, Major Industry Group, and Type of Move (Collapsed Categories): 2011 to 2012) might have been of use to the authors, but they apparently weren’t aware of, or chose to ignore, actual data. Various tables are here. You see, it’s actually possible to study the data behind why people have moved instead of simply guessing about it. Of course, in doing so, one runs the risk that the data don’t cooperate (not saying that’s the case here, just sayin’).
Now, in the spirit of the Wall St. Journal editorial board, and performing the same rigorous analysis used by Moore and Laffer, I hereby advance my own thesis as to the reason for the migration patterns cited in their editorial: WEATHER. Yes, weather.
As we can see immediately below, average temperatures in the net-gaining areas surpass those of the net-losing areas 100 percent of the time. Every net-gainer has a higher average temp than every net loser. Let’s have a look at the areas they cite (see block quote above). Gaining areas are on the left, losers on the right:
Source: The Big Picture
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