by George Taniwaki

SEIU_775_purple FFlogo Wa2016Yes1501

I’m a libertarian by nature. (That’s libertarian with a small L, meaning I believe in government transparency and clarity. Please don’t confuse it with Libertarian with a capital L, which I associate with mindless anarchy.) Every two year, I dutifully check for my ballot and voter pamphlet (Washington has voter by mail). The number of items seems to be getting longer, especially voter initiatives.

Here is my method of deciding how to cast my ballot on voter initiatives. First, I start skeptically. Most voter initiatives are funded by political extremists who do not consider the consequences of adopting their pet idea. But I do my online research, checking analysis produced by hopefully reputable and unbiased sources. Ultimately though, I usually vote against them.

This year in Washington, there a really bizarre ballot issue. It is Initiative Measure No. 1501. “Increased Penalties for Crimes Against Vulnerable Individuals”

This measure would increase the penalties for criminal identity theft and civil consumer fraud targeted at seniors or vulnerable individuals; and exempt certain information of vulnerable individuals and in-home caregivers from public disclosure.

Should this measure be enacted into law? Yes [ ] No [ ]

How could anyone be against this? We want to help seniors, right? Well, it’s not that simple.

A convoluted story

There is a very complex story about this initiative. It involves a union, an antiunion think tank, and the U.S. Supreme Court. Initiative 1501 is sponsored by the Service Employees International Union (SEIU) that represents healthcare workers that work in nursing homes or provide in-home care. Washington, like most states, requires certain workers, such as nurses, to have a license in order to provide services to the public. About one-third of all service workers in the U.S. require licenses. In many cases, these workers are also unionized.

Enter the Freedom Foundation. This antiunion policy group is headquartered in Olympia, Washington. It was founded by Bob Williams, who was formerly with the American Legislative Exchange Council (ALEC). You may have heard of ALEC; it is a corporate funded lobbying group that writes model legislation (which obviously is designed to further the goals of its corporate clients) which it then provides to state legislators to review. The legislators can then submit the bills for approval into law. The Freedom Foundation provides very similar services.

In 2014, the U.S. Supreme Court ruled 5-4 in Harris v. Quinn that an Illinois state law that allowed the SEIU to collect a representation fee (union dues) from in-home healthcare workers wages was unconstitutional. The reasoning was that the fee violated the First Amendment rights of the workers to not provide financial support for collective bargaining.

After the ruling, the Freedom Foundation complained that the SEIU was not doing enough to inform its members that they did not have to pay the representation fee in order to belong to the union. Though a public records act, it sued the union and the state, won, and started to send communications to members encouraging them to stop paying the fee.

Since a Supreme Court ruling covers the entire U.S., not just Illinois, the SEIU realized that it was very vulnerable to attack by the Freedom Foundation or other antiunion organizations.

Now the initiative makes sense

In Washington, the SEIU proactively sponsored Initiative 1501 as a direct attack against Freedom Foundation. The SEIU wants to avoid having to release the names, addresses, and phone numbers of its members (or having the state reveal these either). Initiative 1501 does this by saying that in-home caregivers are a protected class, like seniors or vulnerable individuals, that the state and the union cannot release personal information about.

After all that research, the story starts to make sense. This is a battle between two parties that a libertarian like me dislikes. But more transparency is better than less. So I will vote no. Sorry seniors and vulnerable individuals, you will have to rely on existing statutes to protect you.

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by George Taniwaki

Did you watch the debate on Monday night? I did. But I am also very interested in the post-debate media coverage and analysis. This morning, two articles that combine big data and the debate caught my eye. Both are novel and much more interesting than the tired stories that simply show changes in polls after a debate.

First, the New York Time reports that during the presidential debate (between 9:00 and 10:30 PM EDT) there is high correlation between the Betfair prediction market for who will win the presidential election and afterhours S&P 500 futures prices (see chart 1).

PresidentSandP500

Chart 1. Betfair prediction market for Mrs. Clinton compared to S&P 500 futures. Courtesy of New York Times

Correlation between markets is not a new phenomena. For several decades financial analysts have measured the covariance between commodity prices, especially crude oil, and equity indices. But this is the first time I have seen an article illustrating the covariance between a “fun” market for guessing who will become president against a “real” market. Check out the two graphs above, the similarity in shape is striking, including the fact that both continue to rise for about an hour after the debate ended.

In real-time, while the debate was being broadcast, players on Betfair believed the chance Mrs. Clinton will win the election rose by 5 percent. Meanwhile, the price of S&P 500 futures rose by 0.6%, meaning investors (who may be the same speculators who play on Betfair) believed the stock market prices in November were likely to be higher than before the debates started. There was no other surprise economic news that evening, so the debate is the most likely explanation for the surge. Pretty cool.

If the two markets are perfectly correlated (they aren’t) and markets are perfectly efficient (they aren’t), then one can estimate the difference in equity futures market value between the two candidates. If a 5% decrease in likelihood of a Trump win translates to a 0.6% increase in equity futures values, then the difference between Mr. Trump or Mrs. Clinton being elected (a 100% change in probability) results in about a 12% or $1.2 trillion (the total market cap of the S&P 500 is about $10 trillion) change in market value. (Note that I assume perfect correlation between the S&P 500 futures market and the actual market for the stocks used to calculate the index.)

Further, nearly all capital assets (stocks, bonds, commodities, real estate) in the US are now highly correlated. So the total difference is about $24 trillion (assuming total assets in the US are $200 trillion). Ironically, this probably means Donald Trump would be financially better off if he were to lose the election.

****

The other article that caught my eye involves Google Trend data. According to the Washington Post, the phrase “registrarse para votar” was the third highest trending search term the day after the debate was broadcast. The number of searches is about four times higher than in the days prior to the debates (see chart 2). Notice the spike in searches matches a spike in Sep 2012 after the first Obama-Romney debate.

The article says that it is not clear if it was the debate itself that caused the increase or the fact that Google recently introduced Spanish-language voting guides to its automated Knowledge Box, which presumably led to more searches for “registrarse para votar”. (This is the problem with confounding events.)

After a bit of research, I discovered an even more interesting fact. The spike in searches did not stop on Sep 27. Today, on Sep 30, four days after the debates, the volume of searches is 10 times higher than on Sep 27, or a total of 40x higher than before the debate (see chart 3). The two charts are scaled to make the data comparable.

VotarWashPost

Chart 2. Searches for “registrarse para votar” past 5 years to Sep 27. Courtesy of Washington Post and Google Trends

VotarToday

Chart 3. Searches for “registrarse para votar” past 5 years to Sep 30. Courtesy of Google Trends

I wanted to see if the spike was due to the debate or due to the addition of Spanish voter information to the Knowledge Box. To do this, I compared “registrarse para votar” to “register to vote”. The red line in chart 4 shows Google Trend data for “register to vote” scaled so that the bump in Sept 2012 is the same height as in the charts above. I’d say the debate really had an unprecedented effect on interest in voting and the effect was probably bigger for Spanish speaking web users.

VoteToday

Chart 4. Searches for “register to vote” past 5 years to Sep 30. Courtesy of Google Trends

Finally, I wanted to see how the search requests were distributed geographically. The key here is that most Hispanic communities vote Democratic and many states with a large Hispanic population are already blue (such as California, Washington, New Mexico, New Jersey, and New York). The exception is Florida with a large population of Cuban immigrants who tend to vote Republican.

VotarRegionToday

Chart 5. Searches for “registrarse para votar” past 5 years to Sep 30 by county. Courtesy of Google Trends

If you are a supporter of Democrats like Mrs. Clinton, the good news is that a large number of queries are coming from Arizona, and Texas, two states where changes in demographics are slowly turning voting preferences from red to blue.

In Florida, it is not clear which candidate gains from increased number of Spanish-speaking voters. However, since the increase is a result of the debate (during which it was revealed that Mr. Trump had insulted and berated a beauty pageant winner from Venezuela, calling her “miss housekeeping”), I will speculate many newly registered voters are going to be Clinton supporters.

If the Google search trend continues, it may be driven by new reports that Mr. Trump may have violated the US sanctions forbidding business transactions in Cuba. Cuban-Americans searching for information on voter registration after hearing this story are more likely to favor Mrs. Clinton.

by George Taniwaki

LotteChocoPie

Moon pies for cheap. Photo by George Taniwaki

I love moon pies (apparently, I was a southerner in a past life). Surprisingly, they are big in South Korea too (who knew, for history see Wikipedia).

Incidentally, don’t confuse moon pies with moon cakes which are another Asian sweet (which I usually don’t like because of the salty egg flavor).

Anyway, today, I found a really cheap source of my favorite confection. Lotte brand is $3.50 for 335g or 29 cents a pie. Mysteriously, they are hidden next to weird spices in the international food aisle, not prominently displayed with the other cookies in the snack aisle. Perhaps it’s a form of American food protectionism by US cookie makers, Asian segregationist policy or redlining by the store, or the result of some other nativist conspiracy plot.

It’s crazy that a South Korean company can import all the ingredients, process them, ship them back to the U.S., and still be cheaper than US-made cookies. But I don’t care as long as I get my fix of graham cracker, marshmallow, and sugary goodness.

by George Taniwaki

Patients are often frustrated and confused when navigating the healthcare system. Part of the problem is that if you are sick or hurt, it reduces your cognitive abilities. But it also because hospitals are busy places with little funding for improving the user experience. Often the layout of the rooms, the signage, the forms and instructions, and the language used by the staff are not tailored to the needs of patients who are unfamiliar with the system.

Design to reduce patient violence

A significant problem in hospital emergency medical departments (called A&E in Britain, ER in America) is abusive and violent patients. According to the National Audit Office, violence and aggression towards hospital staff costs the NHS at least £69 million a year in staff absence, loss of productivity and additional security.

Some other statistics from the Design Council report: More than 150 incidents of violence and aggression are reported each day within the NHS system. In 2010, the incidence rate of violence and aggression was about 1 per 1000 patients. In 2009, 21% of staff report bullying, harassment, and abuse by patients, 11% report physical attacks by patients.

Working with the National Health Service, a design firm called PearsonLloyd developed some low-cost methods to reduce the incidence of violence and aggression, increase patient satisfaction, improve staff morale, and reduce security costs. They call their program, A Better A&E. The program was pilot tested at St. George’s Hospital in London and Southampton General. For an introduction, see the video below.

BetterAE

Figure 1. Still from video “A Better A&E. Image from Vimeo

Signage and brochure

The program consisted of three parts. First, improved signage was installed that included an estimated wait times along with a brochure that explained why a patient who arrived after you could be seen a doctor before you.

BetterAEbusyBetterAEWait

Figures 2a and 2b. Large screen monitor alternately shows how busy the A&E is and then how long the wait time is for different categories of patients. Images from Design Council report

BetterAEbrochure

BetterAESignage

Figure 3a and 3b. A page from brochure explaining why wait times differ among patients and what to expect at each station. Signage posted at each patient area keyed to the brochure. Images from Dezeen.com

Root cause analysis

The second part of the redesign was the introduction of program to capture information from doctors, nurses, and other staff about factors that led to violent and abusive behavior. The program included root cause analysis and a prominently posted Incident Tally Chart to record the “variables within the system that might hinder the ability of staff to deliver high quality care.”

BetterAEIncidentTally

Figure 4. Incident tally posted where staff can record any events during their shift. Images from Design Council Report

Toolkit and patterns

The final part of the program was to design a toolkit that would take the lessons from the A&E departments of the two pilot hospitals and generalize them so that they could be adopted by any hospital within the NHS system. The toolkit is presented as an easy to use website, http://www.abetteraande.com

Results

Surveys of patients and staff taken after the redesign indicated that both groups saw benefits.

  • 88% of patients felt the guidance solution was clear
  • 75% of patients felt the signage reduced their frustration during waiting times
  • Staff reported a 50% drop in threatening body language and aggressive behavior
  • NHS calculated that each £1 spent on design solutions resulted in £3 in benefits

by George Taniwaki

About comment spam

Comment spam is a real problem. Most websites that allow comments (like mine) receive over 100 spam messages that link to unethical or fraudulent websites for each legitimate comment they receive.

Luckily, there are excellent spam filters that identify and remove these annoying click-bait messages. For instance, the service that hosts this blog, WordPress, uses a service called Akismet. These spam filters use pattern recognition to find suspicious messages based on characteristics like message content, sender email address, sender IP address, web page commented on, etc. Suspect messages are tagged as spam and moved to a junk comment folder.

Naturally, in the spam arms race, the creators of spam campaigns need tools to rapidly create comments, ideally a unique one for every blog post, so as to avoid being detected.

The message

I recently received a comment on this blog that reveals how comment spammers create messages. The comment was actually not the intended comment. Rather, the spammer sent me over 300 lines of code they used to create custom-looking comments. Phrases that could be customized were enclosed in curly braces {}. The options for the words in a phrase were separated by vertical pipes |. The curly braces could be nested to allow multiple levels of customization. In fact, the entire comment starts with a curly brace so that different versions of the message could be sent. The spam message generator is partially reproduced below.

Note in particular how many of the characters (highlighted in yellow) are accented or Unicode homoglyphs, meaning they form words that look like English, but will not appear in any dictionary that might be used by a spam filter to detect phrases often used in spam messages. Of special note is that words used multiple times will often have a different glyph replacement in each instance.

{

{ӏ have|I’ve} bеen {surfing|browsing} online mοrе thаn {three|3|2|4} hours todaу, ƴet I
never found any іnteresting article like
yours. {It’s|It іs} pretty worth enoսgh for me. {Іn mу opinion|Personally|In my view}, іf
ɑll {webmasters|site owners|website owners|web owners} аnd
bloggers mаde gooԁ content as ƴou dіd, tҺe {internet|net|web} will bе {much moгe|a lot more} useful than ever beforе.|
I {couldn’t|could not} {resist|refrain fгom} commenting.

{Very wеll|Perfectly|Well|Exceptionally well} written!|
{ӏ wіll|І’ll} {rіght awaʏ|immeԀiately} {tɑke
hold of|grab|clutch|grasp|seize|snatch} уoսr {rss|rss feed} ɑs I {can not|ϲаn’t} {іn finding|fіnd|to find} yοur {email|е-mail} subscription {link|hyperlink} օr
{newsletter|e-newsletter} service. Ɗo {yoս ɦave|yoս’ve} any?
{Please|Kindly} {аllow|permit|lеt} me {realize|recognize|understand|recognise|кnow}
{sߋ tɦat|in orԁer that} I {may juѕt|may|cοuld} subscribe.
Ҭhanks.|

The string of faux-fawning gibberish continues for another 290 lines or so and finally ends with this heart-felt closing.

Thɑnks fоr {greɑt|wonderful|fantastic|magnificent|excellent} {іnformation|info} ӏ wɑs looking for thіs {informatіon|info} for my mission.|
{Hi|Hello}, i tɦink that і saw you visited my {blog|weblog|website|web site|site} {ѕo|thus}
i сame to “return the favor”.{I аm|I’m} {trying to|attempting tߋ} find thіngs to {improve|enhance}
mʏ {website|site|web site}!І suppose its ok to use {some of|a fеw of} уօur ideas!\

I’m somewhat surprised the code above can confuse a spam filter. A pattern recognition algorithm could be designed to detect which forms of phrases, misspellings, and glyph substitutions are most commonly seen in spam rather than in messages typed by honest but error-prone humans.

Anyway, I want to thank this incompetent spammer for providing me with content for this blog post. And of course, thanks for the {kind|wonderful|supporting} message.

For examples of actual blog spam that prey on people who might be persuaded to sell a kidney, see this previous blog post.

by George Taniwaki

There is an ongoing argument regarding whether we as a society should pay people to donate a kidney. These arguments, both pro and con, revolve around two issues, whether such payments are the right thing to do (ethics) and whether they would increase the number of available organs (economics). This blog post will describe the economic effects of payments.

Organized markets

Before analyzing the effect of payments on the supply of donors, I want to assure readers that payments can be regulated. For instance, nearly all the blood, plasma, and platelets in the U.S. is collected from unpaid donors. Yet at the same time, there is also an active government regulated market for plasma. Similarly, family members and friends are a common source of donor eggs, donor sperm, and surrogates to allow individuals to have a child. But there is an active market for these as well.

An organized market for donor organs would not likely include person-to-person transactions. Rather, it would involve highly regulated, non-profit entities that would act as intermediaries between donors and patients, similar to the existing network of organ procurement organization (OPO) that recover and distribute organs from deceased donors. In other words, ignore the image in Figure 1.

kidney_for_sale_tshirt

Figure 1. Kidney for Sale t-shirt. Image from zazzle.com

One of the arguments against paying donors for organs is that it will favor wealthy patients who can afford the price. That is not necessarily so. Laws can still be written to prohibit individuals or hospitals from making payments to donors. The payments can be regulated to only allow insurance companies and other government sanctioned groups to make payments. Similarly, the organs collected from donors need not be transplanted to patients based on ability to pay for the organ. They can be allocated by whatever method is deemed medically and ethically justified.

More patients could benefit from transplants

Many kidney disease researchers, ethicists, and economist agree that under the right circumstances, increasing transplant rates would be a good thing. First, transplants improve medical outcomes. Second, transplants save money.

Studies have shown that patients with end-stage renal disease (ESRD) who receive transplant therapy live longer than those who receive dialysis therapy (U.S. Renal Data System 2013 Report). This is true even after adjusting for the fact that transplant patients are healthier on average than the overall kidney patient population (R. Wolfe, et al., New Engl J Med Dec 1999).

The data also shows patients who receive transplant therapy report a better quality of life than those who receive dialysis therapy (W. Fiebiger, et al., Health and Qual Life Outcomes Feb 2004).

More transplants would save money

In addition to being better for the patient, transplants can save money. Dialysis therapy costs about $75,000 per year per patient. Transplant therapy costs about $150,000 for the first year (evaluation, surgery, recovery, and follow-up) and then $15,000 per year thereafter (antirejection medication, infection control, and monitoring). Over the lifetime of the graft, a living unrelated donor can save society $94,000 compared to dialysis (A.J. Matas and M. Schnitzler Amer J Transpl Feb 2004). Adding the value of the additional 3.5 quality-adjusted life years for the patient increases the social benefit to $269,000.

A recent paper by B. Manns et al. (Clin J Amer Soc Nephr Dec 2013) indicates that even a 5% increase in the number of donors would justify a payment of $10,000 each by providing an incremental cost-savings of $340 and a gain of 0.11 quality-adjusted life years.

There is a shortage of suitable organs

The reasons more kidney patients don’t pursue and receive transplant therapy are not fully understood. One thing is certain though. The number of viable organs that become available each year is significantly lower than the number of patients newly diagnosed with ESRD. Thus, the expected wait time for a transplant continues to get longer (up to 8 years in California).

About 15% of patients on the waiting list die each year, so the proportion of patients who die without ever getting a transplant increases as the wait gets longer (over 50% in California). This long wait may deter some patients (and their doctors) from even starting the transplant evaluation process. As of this writing, there are 98,935 people in the U.S. waiting for a kidney transplant.

According to the U.S. Renal Data System, there were 115,643 people newly diagnosed with ERSD in 2011, the latest year data is available. This includes 2,855 who received a preemptive transplant (meaning they received a transplant before having to go on dialysis). In contrast, the  Organ Procurement and Transplantation Network (OPTN), shows there were only 16,814 transplants performed in the U.S. in 2011. The breakdown by donor type is shown in the table below.

Living directed donor   3,761
Living exchange donor       575
Living nondirected donor       157
Deceased directed donor       123*
Deceased nondirected donor 12,198
Total 16,814

*Assumes that 1% of deceased donor transplants are directed (OPTN 2009)

Of the total, 3,761 came from living directed donors, meaning the donor and the recipient knew each other. 575 came from exchange donors, meaning the donor knew the intended recipient but was incompatible so donated to a stranger who was in the same position and they swapped kidneys (for details see Mar 2010 blog post).  157 came from living anonymous or nondirected donors, meaning the donor did not have an intended recipient (similar to most blood donations). Finally, 12,321 came from deceased donors (of which all but about 123 are nondirected).

Costs to becoming a live kidney donor are high

For now, we will ignore the impact of paying for deceased donor organs and focus on a possible market for live donors. Further, we will ignore the ethics and legality of paying people to become live kidney donors. We will cover these issues in a future blog post. For now, we will explore the economics of paying for live donors.

Being a living donor can be expensive. The evaluation and surgery are paid for by the recipient’s insurance. However, there are lots of out-of-pocket costs such as travel to and from the transplant hospital for evaluation. In some cases, there can be multiple trips and may require a hotel stay for out-of-town donors. There are also opportunity costs, such as lost wages (or foregone billings for the self-employed) for the time spent in evaluation, surgery, and recovery. The time spent at home after surgery can vary from a few days to over a month, so this is a real burden for people who don’t receive sick pay or disability insurance from an employer. I estimate the total out-of-pocket and opportunity costs for a typical donor to be about $2000.

Usually, all of these costs are borne by the donor, meaning most donors are wealthy. Sometimes, the recipient will pick up some of these costs, especially if they are wealthy. Sometimes the donor and recipient conduct a fund-raiser to pay these costs. Finally, there are several charities that provide reimbursement if the donor or the recipient cannot afford the financial burden of paying for a living donor transplant. The best known of these is the National Living Donor Assistance Center.

Supply curves for nonaltruistic, nondirected donors

To analyze the effect of payments for kidney donors, we will use the basic technique used by economists called a supply curve. The supply curve shows the quantity (Q) of organs supplied for any price (P). We will look at the impact of paying for kidneys on three groups of living donors.

The first group is the nonaltruistic, nondirected (NAND) donors. This consists of the population of people who are aware of the existence of people who need a kidney transplant but don’t know anyone personally who needs a kidney. Further, they may be willing to donate a kidney, but have no desire to donate a kidney for altruistic reasons.

Figure 2 shows a hypothetical supply curve for kidneys from this population. At the current offering price today (Pcur), the quantity of kidneys offered by NAND donors is zero. Note that Pcur is negative and reflects the costs associated with  being a donor.

Raising the offer price will not result in any donors appearing until an offer of PNANDmin is made and the first donor will step forward. This initial price may be quite high due to what is called the repugnance factor by economist Alvin Roth (J Econ Perspectives, Summer 2007). (Repugnance will be discussed again when we explore the moral and legal issues surrounding payments to donors.)

As the price rises, more donors appear. However, at some point there may be some proportion of these potential donors who will be very reluctant to volunteer, regardless of the amount of money offered (perhaps because of very high repugnance, fear, or dislike of pain). At this point the supply curve will rise steeply, until you reach the last person in the population (QNANDmax) where a very large sum of money must be offered before they will be willing to undergo kidney donor surgery.

DonorSupplyNAND

Figure 2. Supply curve for nonaltruistic, nondirected donors

Note that I made a simplification in the supply curves shown above and below. I assume the out-of-pocket costs and opportunity costs for all donors is the same and equal to Pcur. Actually, this is not true and these costs can vary widely. However, allowing for varying costs makes the analysis much more complex without adding any new insights.

As an aside, behavioral research shows that people’s preferences are not stable, called the endowment effect. For instance, many people may say they would not donate a kidney for $20,000. But imagine what would happen if we gave those people the $20,000 first and ask them to consider what they could do with that money. Then we wait a few minutes and ask them if they would rather give the money back or donate a kidney. At that point, many may decide donating the kidney is their preferred choice.

Supply curve for directed donors

The second group we want to look at is potential directed donors. These are people who know someone who needs a kidney transplant and may be willing to donate to that person. The reasons may be altruistic, self-interest (not wanting to lose a relative or friend), or perhaps even coercion by the recipient or family members. Regardless of the reason, we can draw a supply curve like the one shown in Figure 3.

This curve looks very similar to the one in Figure 2 except it is shifted down. That is, once a potential donor develops a connection to the recipient, the minimum reservation price drops. That’s because the act of donation generates utility for the donor. At the current price of Pcur there are QDDcur donors.

DonorSupplyDD

Figure 3. Supply curve for directed donors

Raising the price offered to this group should increase supply, even if the offered price is below PNANDmin. Just reimbursing every donor’s out-of-pocket and opportunity costs could have a significant impact on supply. However, the supply is limited to QDDmax based on the total number of people who know someone who needs a transplant.

Supply curve for altruistic nondirected donors

The third group we want to look at is altruistic nondirected (AND) donors. Even though these donors do not know the recipient, and in fact often will never know the recipient, the supply curve for this group looks very similar to that of the directed donors. The utility an AND donor derives from her donation is not from helping a known person. Perhaps, it comes from imagining that the donation is helping a deserving person, or helping society as a whole, or the donation represents an act of altruistic sacrifice. At the current price of Pcur there are QANDcur donors.

Similar to the case for directed donors, just reimbursing every donor’s out-of-pocket and opportunity costs could have a significant impact on supply. Offering a payment (which a truly altruistic donor could decline and donate to charity) may increase the supply as well. However, it is not likely to have a large effect. I suspect the supply of altruistic donors is inelastic. I also believe the total number of people who would be willing to donate to a stranger QANDmax is limited as well, though probably significantly larger than the current 150 per year.

DonorSupplyAND

Figure 4. Supply curve for altruistic, nondirected donors

Shifting the supply curve

There is an alternative response to raising the offering price to AND donors. Since the utility the AND donor receives is dependent on psychological reward, any action that reduces the value of that reward may shift the supply curve upward. At the limit, the AND donors will become a NAND donors. In the worst case, the former AND donors may have a higher reservation price than the NAND donors causing the supply curve to be above the curve for the NAND donors (dashed brown curve S’ in Figure 4).

If this supply curve shift occurs, then paying donors could have the perverse effect of reducing the total number of donors until price PNANDmin is exceeded and NAND donors begin to appear.

Conversely, a well-crafted marketing effort to encourage more people to become AND donors can keep the AND curve from shifting upwards. It can also convince some NAND donors to reconsider their position and become AND donors, causing the total number of NAND donors to shrink and the number of AND donors to rise (shifting QNANDmax to the left and QANDmax to the right).

Add it all up

Combining the three supply curves would create an overall supply curve that would look similar to the solid line in Figure 5. At the current price Pcur, the number of donors is QDD+ANDcur. When the price reaches PNANDmin, the NAND donors will begin to enter the market.

If making payments causes all the AND donors to become NAND donors, then the supply curve shifts to upward as shown by the dashed line S’.  At the current price Pcur, the number of donors falls to QDDcur. When the price reaches PNANDmin, the NAND donors will begin to enter the market. When the price reaches P’ANDmin, the former AND donors will enter the market. Note that even if all the AND donors become NAND donors, there will still be a market price somewhere above PNANDmin that will result in more donors than are currently available at the current price of Pcur.

DonorSupplyTOT

Figure 5. Cumulative supply curve for all donors

More resources

For more on the economic analysis of organ markets, see the following papers.

A. Tabarrok. Library Econ Liberty, Aug 2009. Discusses payment for deceased donor organs.

Scott Halpern, et al. Annals of Int Med, Mar 2010. 342 participants were asked whether they would donate a kidney with varying payments of $0, $10,000 and $100,000. The possibility of payments nearly doubled the number of participants in the study who said they would donate a kidney to a stranger. Payment did not influence those with low income levels more than those with high incomes.

Gary Becker and J.J. Elias. J. Econ Perspectives, Summer 2007. A thorough analysis of the cost and number of transplant performed if payments were allowed for the donation of both live and deceased donor kidneys. It also counters the arguments against payments.

by George Taniwaki

While in school, we often learn about a particular subject through textbooks that make it seem that the body of knowledge for that area is neat and tidy and always has been. As students we may take for granted that the ideas presented by our instructors and textbooks are static, or at least follow a linear progression of always increasing. Rarely is it ever discussed how ideas change over time, the controversies, errors, and long lead time for new information to be incorporated into the body of knowledge.

Even in graduate school, where critical analysis is considered an important skill to teach, very little time is spent on the historical context in which important ideas were formed. Very little time is spent describing how radical new ideas are vetted and if found useful, replace entrenched older ones, a process called a paradigm shift. The term comes from the excellent book by the philosopher Thomas Kuhn entitled The Structure of Scientific Revolutions (1962).

Even rarer than a historical description of how a new idea replaces an old one is a description written by the very people who were responsible for the paradigm shift. That’s why I was interested to read a paper entitled Ball and Brown (1968): A Retrospective. written by Ray Ball and Philip Brown.

Mr. Ball and Mr. Brown were PhD students at the University of Chicago in the 1960s. They were the first researchers to show that on the day that accounting information (like earnings) is released, it will affect stock prices. This seems obvious today and nearly all researchers believe it is true and base their own research on the assumption that it is true. Today this belief even has a well-recognized name, called the semi-strong form of the efficient market hypothesis. But in 1968 the idea was considered radical and many experts dismissed the paper.

The paper was groundbreaking in another respect. The basis of their paper was not theoretical, it was experimental. The authors looked at actual companies and conducted a statistical analysis of the historical stock price data on the days before and after each “event”, in this case the company’s release of accounting information. (This technique was championed by Eugene Fama, also of the U. of Chicago.) Again, this seems obvious today, but was radical in 1968. The story of how they came  to write the paper is quite interesting. (Or it is to me or anyone else interested in the history of economic theory.)

Their original paper is entitled “An empirical evaluation of accounting income numbers” and appeared in J. Acct. Res. 1968. To give you an idea of how important this paper is, below is a citation graph for this paper. It has been cited a total of 941 times since it was published 45 years ago, with the number of citations growing almost every year (the drop at the end is likely due to recent papers not yet indexed).

BallBrownCitations

Citations for Ball Brown (1968). Image from Microsoft Research

Much thanks to my wife, Susan Wolcott, for sharing this paper with me. Her PhD dissertation in accounting is based on a test of the semi-strong form of the efficient market hypothesis.

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As a follow-up to a recent blog post marking the passing of the Nobel prize-winning economist Ronald Coase, I want to feature two fine obituaries.

The first is by the Economist,  in an article entitled “The man who showed why firms exist”.

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Coase is dead, long live the firm. Photo from U. of Chicago

Another tribute was published by UChicago News, an official publication of Mr. Coase’s employer. The article has a link to a YouTube video that includes short excerpts (3:40) of a longer interview of Ronald Coase from 2012.

CoaseInterview

Accidental Economist. Video still from YouTube

Ronald Coase was active until his death. In 2012, he published a book with a U. of Chicago PhD graduate named Ning Wang. The book entitled How China Became Capitalist describes the economic transformation in China over the past 35 years. It argues that the credit for this change belongs to individual entrepreneurs, not to the central government. China’s new economic freedom has not been matched by the free flow of ideas. Until that changes, China will never reach its full potential.

A short (3:30) discussion of the ideas behind the book by its two authors is posted on YouTube. It is from the same interview from the video described above.

CoaseAndWang

Coase and Wang discuss their book. Video still from YouTube.

Disclosure: George Taniwaki is a graduate of University of Chicago’s Booth School of Business. The opinions expressed in this blog post are his own and do not reflect those of any organization.