Kiryas Joel, New York has the lowest per capita income of any location with over 10,000 population in the US.
Posts tagged with economics
Summary: skip to the pictures after the
<big> text under heading 2.
Since 2009, pundits have concerned themselves with economic inequality. Robert Reich’s infographic about the US I’ll treat as a summary.
Let me dummyise the opinionscape into three camps:
The third view is the one I want to challenge just now.
When I see a manual farmer being destroyed by Nature, I feel:
And somehow, gut reactions are part of real morality and ethics.
So here’s my challenge to the Paretians. Which image galls you more:
To the extent that these gut reactions translate into legitimate morals, the Robespierreans win over the Galtists and over the Paretians.
Envy exists. From this one infers that when the rich get richer but the poor don’t, that their individual utilities can still drop. But let’s go beyond society-as-a-collection-of-independent-individuals.
The image of the Monopoly Man merrily dancing next to the poor (or even indifferently ignoring their plight) curdles the blood. Gucci little piggies go first against the wall for a reason.
Advertising rises and falls with the economy, though how much is a matter of debate. Randall Rothenberg … points to the remarkable stability of advertising at about 2% of GDP since 1919, when the data began to be collected.
I’m not the first person to say "ceteris paribus is a lie". What this aphorism means is that if you make a c.p. assumption in order to think something through, then the conclusion you reach may be irrelevant to the real world.
Worse, because people don’t understand models, someone might take your careful “A implies B” statement to mean “Both A and B are the case”. For example rather than Edgeworth boxes implying that trade be always mutually beneficial, people might take you to mean that
which is not at all what the theory’s saying. The theory is just connecting assumptions to conclusion: yes, if this were true, then that would surely follow. Which is great because some people don’t actually think such things through.
Anyway. Ceteris paribus assumptions make thinking easier, but they hamstring whatever you find out—so that it may be useless, or (hopefully not) worse than useless: misleading.
But maybe it’s possible to keep the crutch of c.p. and make it less foolish.
composite = .4 × X₁ + .2 × X₂ + 1.7 × X₃
This is what I’m calling a “super-dimension”.
You hold all other things constant so you can think logically about a situation that has the geometry of a single straight line. By creating a composite dimension maybe one could still use the handy ceteris-paribus assumption but roll more of real-life into the model too.
For example let’s say as wealth ↑, trips to the emergency room ↓. Then you could form a composite dimension with a positive coefficient on wealth, negative on emergency room visits, and talk about both at once with everything else held constant. One step forward relative to talking only about only wealth ↑↓.
But wait — maybe these are only linearly related around a small neighbourhood of some point. Well, we could still create a composite “super-dimension” by varying the coefficients. This could either come in the form of pre-transforming wealth to be log of wealth, or something else — like a threshold effect where we use two or three linear pieces (eg, rich enough with slope=0, way too poor with slope=0, and middle with a linear decrease). In general, whereas linear means
+k+k+k+k+k+k+…, nonlinear can be interpreted as
+1.2k+k+.9k+.8k+k+1.1k+1.3k+1.2k+1.4k+…. So instead of constructing a composite dimension with fixed coefficients before ignoring everything else, perhaps one could vary the coefficients along with the space.
That’s all. This may not be a new idea.
@condoroptions Interviews Tadas Viskanta of Abnormal Returns - Part 1
Since , the [US] labor force participation rate (LFPR) has dropped from 66 percent to 63 percent. [Out of 314M people.] Many people have left the labor force because they are discouraged … (U.S. Bureau of Labor Statistics data indicate that a little under 1 million people fall into this category)….
…Knowing the reasons why people have left (or delayed entering) the labor force can help us [guess] how much of the ↓ might … ↑ if the economy ↑ and how much is permanent. (For more on this topic, see here, here, and here.)
The chart … shows the distribution of reasons in the fourth quarter of 2013…. Young people [usually say they] are not in the labor force … because they are in school. Individuals 25 to 50 years old who are not in the labor force mostly [say they] are taking care of their family or house. After age 50, disability or illness becomes the primary reason [given]—until around age 60, when retirement begins to dominate.
Of the 12.6 million increase in individuals not in the labor force, about 2.3 million come from people ages 16 to 24, and of that subset, about 1.9 million can be attributed to an increase in school attendance (see the chart below).
off-topic sidenote: the natural cohort —vs— year adjustments, like “the baby boom has shifted 7 years since 7 years ago” are an economic example of the covariant/contravariant distinction