May 2009

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An excerpt from a National Geographic article by Simon Worrall:

The world economy in the ninth century had two powerful engines. One was Tang dynasty China, an empire stretching from the South China Sea to the borders of Persia, with ports open to foreign traders from far and wide. The Tang welcomed diverse people to its capital, Changan, the site of modern-day Xian, and multiethnic groups lived side by side in a city of a million—a population unmatched by a Western city until London in the early 19th century. Then, as today, China was an economic powerhouse—and much of that power was built on trade.

The other economic engine was Baghdad, capital of the Abbasid dynasty from 762 onward. That dynasty inherited the Muslim world in the Middle East; by 750 it had spread as far as the Indus River to the east and Spain to the west, bringing with it trade, commerce, and the religion of Islam (the Prophet Muhammad himself had been a merchant).

Linking the two economic powerhouses were the Silk Road and its watery counterpart, the Maritime Silk Route. The overland road gets all the attention, but ships had likely been plying the seas between China and the Persian Gulf since the time of Christ. In tune with the cycle of the monsoon winds, this network of sea-lanes and harbors bound East and West in a continuous exchange of goods and ideas.

Tang China was hungry for fine textiles, pearls, coral, and aromatic woods from Persia, East Africa, and India. In return, China traded paper, ink, and above all, silk. Silk, light and easily rolled up, could travel overland. But by the ninth century, ceramics from China had grown popular as well, and camels were not well suited for transporting crockery (think of those humps). So increasing quantities of the dishes and plates that held the meals of wealthy Persian Gulf merchants arrived by sea in Arab, Persian, and Indian ships.

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Some diagnostic situations contain a lot of variables. Any given symptom may have several possible causes, and further, these causes may interact with one another and therefore be difficult to isolate. In deciding how to proceed, there often comes a point where you have to step back and get a larger gestalt. Have a cigarette and walk around the lift. The gap between theory and practice stretches out in front of you, and this is where it gets interesting. What you need now is the kind of judgment that arises only from experience; hunches rather than rules. For me, at least, there is more real thinking going on in the bike shop than there was in the think tank.

Vice

Economic Inquiry has some interesting articles on the vices of drinking and smoking:

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Mechanics not only diagnose what your car needs, they also fix it.  Doctors also both diagnose and treat the patient.  Both of these cases are examples of credence goods.

A working paper by Frankel and Schwarz (2009) looks at the economic environment where uninformed customers rely on experts to both diagnose and treat their problems. If experts can earn more money doing high margin procedures, then customers may not receive appropriate treatment.  However, experts must also take into account how doing unnecessary treatments will affect their reputation.

“In the 1950s, Bower was summoned to Los Angeles by billionaire Howard Hughes, who wanted him to study Paramount Pictures…. But Bower sensed that nothing good could come of working for Hughes. He found the entrepreneur’s approach to business ‘so unorthodox and so unusual’ that he felt he would never be able to help Paramount. Instead of taking the assignment and reaping a big fee, he walked away. The move was classic Bower. He built McKinsey into a global consulting powerhouse by insisting that values mattered more than money” (Byrne (2003)). In other words, by publicly rejecting a profitable action, McKinsey increased its future business. 

Because of reputational concerns within a  in a repeated game framework, the authors show a truthful equilibrium will emerge.  ”The promise of future business removes the incentive to play major treatments over minor ones. Customers only need to look at the most recent action taken. If it was a minor treatment, they return to the last period’s expert with high probability. If it was a major treatment, they return with a low probability.”

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The Sentinel Effect hosts a Spamalot version of the Cavalcade of Risk. Yours truly bats leadoff in this CoR.

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In February, I discussed a cross border insurance plan that covers doctors visits, but not catastrophic medical issues.  Now the No Insurance Club (NIC) offers similar coverage in the U.S.  According to the firm’s press release, “patients receive up to 12 office visits per year that also include immunizations, $4 or less in-office prescriptions, and additional services including blood tests” with no deductibles and no co-pays.  The site claims that there is no premium but the one-time “membership fee” of $480/year is in essence a premium.

Healthcare Economist’s Take

I am not sure why these plans would be popular.  Since NIC does not cover catastrophic medical care, the enrollees do not receive the main benefit of insurance–protecting individuals against high cost, low probability events.  Consumers may like these basic medical plans since they are in essence forced savings; individual must pay the annual fee and after that do not have to worry about paying for future’s doctor’s visits.

It is also possible that NIC negotiated lower rates with doctors.  Instead of significant administrative costs inherent in the insurance claims systems, doctors may take a lower fee to receive a “retainer” from NIC.  Thus, the plan may provide a significant cost advantages for the uninsured over just paying for doctors visits yourself.  A final question is whether or not these doctors are of high quality.

Regardless of the merits of this type of insurance, it is nice to see new products emerging to meet consumers needs.

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A study looking at the effect of the Great Famine:

Our results indicate that in-utero and early childhood exposure to famine had large negative effects on adult height, weight, weight-for-height, educational attainment and labor supply.

Not a shocker.

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If we start with 1000 people and 10% of the population dies each year, how many people will be left in 10 years?  One could figure this out using manually.  However, for more complicated models, involving covariate predictors of survival, using survival analysis is helpful.

Survival analysis starts with a hazard function, λ(t), which gives the probability of failure each year for all survivors. In our simple example, λ(t)=.10  ∀ t.

From this we can calculate a hazard function:

  • λ(t)=limh →0 P(t≤T<t+h|T≥t)/h

The variable T is the number of periods the person survives. We also have an associated cdf, F(t), which is equal to the cumulative probability of failure for T≤t. Thus we can calculate a survival function, S(t)=1-F(t), which gives the probability a person will survive to some period after period t.  The pdf is equal to the derivative of the cdf, or also f(t)=S(t)*λ(t).

By knowing the hazard function, we can also calculate many probabilities of interest.  For instance, if a2>a1:

  • P(T≥a2|T≥a1)=exp{-∫a1 to a2 λ(s) ds}
  • P(a1≤T≤a2|T≥a1)=1-exp{-∫a1 to a2 λ(s) ds}

Wooldridge (2001) uses the example of recidivism.  Let λ(t) equal the hazard rate that criminals freed freed from jail commit another crime.  The term  λ(13) is equal to the probability a person is arrested 13 months after their release conditional on not having been arrested for a year.

Weibull Example

One example of a common hazard function is the Weibull function.  In the Weibull, we have:

  • f(t)=αγtα-1exp{-γtα}
  • λ(t)=γαtα-1
  • S(t)=exp{-γtα}

The Weibull is an attractive model because the hazard rate need not be constant over time. Also, the Weibull distribution is simple to understand. The parameter γ determines its shape and the parameter λ determines its scale. Further, if the hazard rate depends on individual characteristics, we can condition the value of λ on a vector of covariates.  If α=1, then the Weibull simplifies to the exponential distribution. This is a “memoryless” distribution where the hazard rate is constant over time (i.e., λ(t)=λ).

Proportional Hazard Models

Often, you will want to see how different covariates affect the hazard rate.  A very simple model to use is the proportional hazard model.  Here, the baseline hazard is constant and covariates have a multiplicative effect on this baseline hazard.  For instance:

  • λ(t;x) = κ(x) λ0(t)
    • κ(x) =exp{βX}
  • ln λ(t;x) = βX + ln{λ0(t)}

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According the a 2008 Leapfrog Hospital Survey, below are the proportion of each type of medical procedure that met quality standards:

  • Coronary artery bypass graft: 43%
  • Percutaneous coronary interventions: 35%
  • High-risk deliveries: 32%
  • Pancreatic resection: 23%
  • Bariatric surgery: 16%
  • Esophagectomy: 15%
  • Aortic valve replacement: 7%
  • Aortic abdominal aneurysm repair: 5%

Source: Binder and Rudolph (HSR 2009)

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