Archive for the ‘Economics’ Category

A few various trends taking place in the US:

  • Skill-biased technological change and a steady polarization of the workforce.
  • Repetitive tasks shifting away from labor to capital.
  • Women going to college in increasing numbers meaning more two-income households. The trend is so strong that in most undergraduate universities that women outnumber men.
  • This is leading to lower fertility, steady aging of the population and delayed child rearing.
  • “Great Stagnation”
  • Elevated unemployment (possibly structural).
  • Most job creation occurring in non-tradable sectors (so much for competitiveness).
  • Finance taking a larger slice of the aggregate GDP pie.

So I decided to look up income data by education and created a ratio of bachelors degree holders vs HS diploma holders. The higher the ratio, the higher the income premium in going to college is favored. This ratio is steadily going up over time for both genders. The ratio is a multiple of how many times your income will go up through completion of college.

For 2008 (the most recent year of numbers) men will experience a 2.939 income multiple by going to college.

Women will experience a 2.505 income multiple.

Men experience a larger premium from going to college than women do.

This could be self-selection bias as by eye-balling various majors in college such as engineering, one will notice a larger male presence. Engineering is one of the highest paid majors for those with a bachelors degree. Men are highly represented in finance and computer sciences too.

Also interesting to note, the ratio for women declining after the early 1990s recession and just before the dot-com bubble. Where college was guaranteeing less of a premium for women. Without looking at anymore numbers I would think that incomes of lower skilled women were improving very fast in the mid-90s rather than a decline in incomes for college-educated women.

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This describes a coordination failure with farmers being unable to collude because incentives to cheat and defect are high. The risk in coordination lies mostly with a farmer who can see that he is able to cheat, which would allow the farmer who did not cheat to bear the entire risk of his crops being destroyed.

This is similar to the prisoner’s dilemma where both players will miss a Nash Equilibrium and a socially optimal strategy by playing their dominant strategies.

Furthermore it touches upon the notion that reducing corruption and increasing trust are prerequisites for meaningful economic development.

Palanpur farmers sow their winter crops several weeks after the date at which yields would be maximised. The farmers do not doubt that earlier plantings would give them larger harvests, but no one, the farmer explained, is willing to be the first to plant, as the seeds on any lone plot would be quickly eaten by birds. I asked if a larger group of farmers, perhaps relatives, had ever agreed to sow earlier, all planting on the same day to minimise the loses. “If we knew how to do that,” he said looking up from his hoe at me, “we would not be poor.”

Via “Origin of Wealth” (chpt Design Spaces), Eric D. Beinhocker.

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1.) All data are from the American Time Use Survey.

2.) All data for Table 1, Chart 1, Chart 2 reflect the amount of hours per week that the average high school student puts in to various activities.  Chart 1 and Chart 2 are visualizations of Table 1.

3.) Data for Table 2 and Chart 3 reflect the average number of hours that mothers spend with their children on educational activities and all other activities.

4.) These averages control for differences across groups in the number and age of children, education of the mother and marital status.

5.) . This U.S. government survey measures the time use of thousands of individuals from 2003 to 2009 based on time diaries, which are considered the most accurate way to measure time use. It includes data on individuals ages 15 and older.

A few findings:

  • Not all Asian mom’s are hardcore tiger mom’s.
  • White students spend time doing a diverse array of activities.
  • Not many differences in terms of mother’s spending time with their kids with regards to educational activities. Though 0.6 hours in the large scheme of things may mean a lot.
  • More or less, it’s fairly concrete, you can infer your own conclusions.

Table 1

Chart 1Chart 2

Table 2

Chart 3

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Using ACS data that is easily available on the Census website for 2010, I was curious to see the composition of wealthy Hispanic households by national origin.

Mexican households are somewhere around 65% to 70% of all Hispanic households but the overall composition tilts to the other national groups as you filter through higher and higher income levels.  The general heuristic is that slightly less than half of wealthy Hispanics are Mexican, about a quarter are Cuban/Puero Rican and another quarter is Central American/South American.

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From the GSS, tracking turnout in the 2008 election by educational attainment. The overall partisan affect of this turnout pattern hurts the Democrats.

The partisan makeup of “partisan” Democrats and “partisan” Republicans as based on GSS data and defined as people who self-identified as “strong” in their leanings. I also restricted it to solely White voters just in case of race skewing or biasing the data.

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I was rearranging my closet and bookshelf and I dusted off a notebook that I ended up using for from Econ 142. I found a particularly cool problem that I encountered back than, that I will reproduce here.

In the game Former Soviet Union Roulette, a number of bullets are loaded into a revolver with six chambers; an individual then points the revolver at his head, pulls the trigger, and is killed if and only if the revolver goes off. Assume the individual must play this game; that he is an expected-utility maximizer; and that each chamber is equally likely to be in firing position, so if the number of bullets is b his probability of being killed is b/6. Suppose further that the maximum amount he is willing to pay to have one bullet removed from a gun initially containing only one bullet is $x, and the maximum amount he is willing to pay to have one bullet removed from a gun initially containing 4 bullets is $y, where x and y are both finite. Finally, suppose that he prefers more money to less and that he prefers life (even after paying $x or $y) to death. Let UD denote his von Neumann-Morgenstern utility when dead, which is assumed to be independent of how much he paid (as suggested by empirical studies of the demand for money); and let UA0, UAx, and UAy denote his von Neumann-Morgenstern utilities when alive after paying $0, $x, or $y respectively.

  1. What restrictions are placed on UD, UA0, UAx, and UAy by the assumption that he prefers more money to less when alive?
  2. What restrictions are placed on UD, UA0, UAx, and UAy by the assumption that he prefers life (even after paying $x or $y) to death?
  3. Is it possible to tell from the information given above whether x > y for an expected utility maximizer? Does it matter whether he is risk-averse? Explain.

First things first, lets just write down what we know.

  • Expected Utility maximizer.
  • Probably concave utility function where second derivative is negative.
  • Homo Economicus
  • b/6, b= bullets, each additional bullet adds like a ~16% chance of being killed
  • He is willing to pay an amount $x to have a bullet removed from a gun that only has one bullet. Essentially letting him live.
  • He is willing to pay an amount $y to have a bullet removed from a gun that has 4 bullets.
  • More $ > Less $ is his preference.
  • Life > Death is also his preference.
  • UD is independent of payment.

Utility functions defined:

  • UD = Utility of being dead
  • UA0 = Utility of being alive and paying $0
  • UAx = Utility of being alive and paying $x
  • UAy = Utility of being alive and paying $y

Scaling of utilities gives us

  • U(a1) = 0
  • U(a2) = U(a3) = U(a4) = 1/3
  • U(a5) = U(a6) = U(a7) = 2/3
  • U(a8) = 1

1.) Using the last two bits of information allows us to solve the first part.  UA0 > UAy and UAx > UD

2.) UA0, UAy, UAx > UD
3.) For the last question we have

  • EU($x) = UD*(1/6)+EUAx*(5/6)
  • EU(Nothing) = UD*(1/6)+UA0*(5/6)
  • EU(Removing Bullet at Cost $x) = UAx
  • EU(Nothing) = UD*(1/6)+ UA0*(5/6)
  • EU(Removing Bullet at Cost $y) = UD*3/6 + UAy*(3/6)
  • U’ > more money, happier, life over death, UA0 > UAx ~ UAy > UD

Setting up a system of equations for this situation yields us

1/6*UD + 5/6 UA0 = UAx
3/6UD + 3/6*Ay = 4/6*UD + 2/6UA0
+ 3/6*Ay = 1/6*UD + 2/6UA0

Bottom equation falls out since it can be rearranged to look like the second one.

1/6*UD + 5/6 UA0 = UAx
3/6UD + 3/6*Ay = 4/6*UD + 2/6UA0

Solving yields us:

UA0 = 2UAx – UAy
UA0 = UAx   + Uax – UAy (this is positive)
So UAx > UAy

This paper attempts to reconcile the Russian Roulette problem to Kahneman and Tversky’s Prospect Theory.


Utilizing the Russian roulette problem as an exemplar, Kahneman and Tversky (1979) developed a weighting function p to explain that the Allais Paradox arises because people behave so as to maximize overall value rather than expected utility (EU). Following the way that ‘‘overweighting of small probabilities” originated from the Russian roulette problem, this research measured individuals’ willingness to pay (WTP) as well as their happiness for a reduction of the probability of death, and examined whether the observed figures were compatible with the nonlinearity of the weighting function. Data analysis revealed that the nonlinear properties estimated by straight measures differed from those derived from preferential choices [D. Kahneman, A. Tversky, Prospect theory: an analysis of decision under risk, Econometrica 47 (1979) 263–291] and formulated by [A. Tversky, D. Kahneman, Advances in prospect theory: Cumulative representation of uncertainty, Journal of Risk and Uncertainty 5 (1992) 297–323]. The controversies and questions to the proposed properties of the decision weight were discussed. An attempt was made to draw the research attention from which function was being maximized to whether people behave as if they were trying to maximize some generalized expectation.

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