Economics - General

For many years price increases in the medical sector has outpaced overall inflation by a significant amount. According to the Bureau of Labor Statistics, here is the increase in consumer prices over the last few years.

Medical inflation is outpacing general inflation by an average of 1.5% per year. But is this measure of medical inflation accurately measured? Not according to paper by Joseph Newhouse (1992). Here are 4 reasons why not.

1. Medical CPI measures input, not final goods. The CPI for medical services focuses on inputs such as physician visits or hospital days. However, the service the patient consumers is treatment for a specific disease. An increase or decrease in the requisite number of doctors visits is a change in the input towards treatment. A true measure of medical CPI would measure how the price to treat a disease changes over time.
2. Actual Prices not observed. Generally, statisticians use the list price as the price of medical services. However, very few people pay this list price. Most individuals have insurance and these insurance companies negotiate bulk discounts. Thus, the list price is not the relevant price for most individuals.
3. Quality changes. Even if one uses the same amount of inputs in treating a disease, the quality of medical care has likely increased over time. Of course, observing quality changes in medical care is extremely difficult.
4. Medical CPI weight out-of-pocket expenses. Medical CPI weighs the cost to consumers of medical spending. However, since most people have health insurance, items which are paid more frequently out of pocket receive a higher weight. For instance, dental care is more frequently paid out of pocket and thus receives a higher weight in the CPI. [I am not sure if this weighting has changed in more recent versions of the medical CPI].

• Joseph P. Newhouse (1992) “Medical Care Costs: How Much Welfare Loss?” The Journal of Economic Perspectives, Vol. 6, No. 3 (Summer, 1992), pp. 3-21

John Tierney writes in The New York Times (”Appeasing the Gods…“) that “”We buy insurance not just for peace of mind or to protect ourselves financially, but because…we think buying health insurance will keep us from getting sick.”

A rational person would believe that buying insurance against an event will not alter the probability that it will occur–ignoring issues of moral hazard.  For instance, the act of buying health insurance should not make us less likely to be sick.  Using more preventive care which is cheaper due to insurance can prevent illness, but the act of buying health insurance should not effect the probability one gets sick holding constant the medical care levels.

A better example may be travel insurance.  “Last year, tens of millions of people bought life insurance for scheduled flights of airlines in the United States. Not one of those insured passengers died in a crash.”  Is this a waste of money?  Not if you are superstitious and believe that the act of buying life insurance affects the probability your plane will crash.

“So when we think about passing up flight insurance, we conjure up disaster just as easily as ancient Greeks imagined a thunderbolt from Olympus, and we too figure we can avert it through the equivalent of a bull sacrifice. Intuitively, we haven’t made great strides since Homer’s day. But at least our gods take credit cards.”

• Hat tip to Arnold Kling at EconLog.

The average person believes that they are above average in almost all respects. This phenomenon is often called the Lake Wobegon effect after Garrison Keillor’s fictitious town in which all people are above average.

A paper by Dunning, Heath and Suls (2004) gives some great examples of Lake Wobegon in action.

“Motorcyclists believe they are less likely to cause an accident than is the typical biker (Rutter, Quine, & Albery, 1998). Business leaders believe their company is more likely to succeed than is the average firm in their industry (Cooper, Woo, & Dunkelberg, 1988; Larwood & Whittaker, 1977). People think they are less susceptible to the flu than their contemporaries, and as a result avoid getting flu shots (Larwood, 1978). Of college professors, 94% say they do above-average work (Cross, 1977). People signing up to bungee jump believe they are more likely to avoid injury than the average bungee jumper, although their friends and family do not share this impression (Middleton, Harris, & Surman, 1996). Ironically, people even state that they are more likely than their peers to provide accurate self-assessments that are uncontaminated by bias (Friedrich, 1996; Pronin, Lin, & Ross, 2002).”

…Surgical trainees place too much confidence in their diagnoses after looking at X-ray evidence (Oksam, Kingma, & Klasen, 2000). After looking over a client’s case materials, clinical psychologists overestimate the chance that their predictions will prove accurate (Oskamp, 1965).

Despite the prevalence of overconfident self-assessment, I suffer from no such problem. That is of course because I am an above-average blogger.

For all my economist peers who are sick and tired of monetary policy, financial option valuations, and esoteric econometric specifications, it may be time for a change. The American Association of Wine Economists (AAWE) is holding their second annual conference August 14-16 in Portland, Oregon. The AAWE also publishes the Journal of Wine Economics.

I am sure the members of the AAWE do not mind conducting “research” as to which wines are the highest quality.

Who says economics is the dismal science?

Eric Crampton argues against the paternalistic view some economists have taken in a recent editorial in Health Economics. Here’s an excerpt:

“Of course, most economists would disagree vehemently [that taxing unhealthy behaviors is a good thing]. Raising taxes does tend to reduce consumption and, where consumption generates large negative externalities (costs borne by uninvolved parties) can even be efficient: Pigovean taxes (taxes proportionate to those external costs) can push us closer to socially-optimal outcomes. But, there is no inefficiency caused by people choosing to live lifestyles they view as preferable despite the health costs.

If I decide to enjoy a shorter life rather than eek out a miserable existence without wonderfully-marbled steaks, a beer or several, or even the occasional cigar, zero inefficiency is induced thereby.

…what evidence there is suggests that to the extent smoking induces a “fiscal externality,” the sign of the effect is wrong: smokers pay more in cigarette taxes than they ever cost the public purse. They die earlier of cheaper diseases and collect less in superannuation than do non-smokers. And, as a 10% increase in cigarette taxes correlates with a 2% increase in obesity, one wonders whether increased cigarette taxes consequently require further increases in taxes on fatty foods.

Crampton supports the idea of “De gustibus non est disputandum,” we should not criticize individuals’ preferences.

“‘Libertarian paternalism’, ‘optimal paternalism’ and ‘cautious paternalism’ have been promulgated by prominent economists.” A recent Health Economics editorial by Jody L. Sindelar contradicts the economist conventional wisdom that correcting externalities, providing information and protecting youths are the only role for the government in the health policy arena.

I agree with Sindelar that making general economic theory more flexible to the practicalities of the real world is important. For instance, she cites the effectiveness of the PROGRESA program in Mexico. The program has been so sucessful that it has been adopted in New York City. The authors also cite the fact that small conditional cash payments conditional on drug abstinence have also been effective in help those addicted to drugs quit their habit.

Nevertheless, we must be careful how much believe the government should manipulate our lives. Sindelar claims that smoking, alcohol and drug abuse, and overeating all are examples of irrational behavior. While these activities are harmful, many people do enjoy having a cigarette, getting intoxicated, or eating copious amounts of desserts and the decision to smoke, drink, overeat or use drugs is likely not irrational. I do not believe that these activities should be taxed or prohibited just because they are harmful to the individual. Only if they lead to harm in other individuals (i.e.: externalties, such as second hand smoke, drunk driving, etc.) would specific tax be merited.

In my view, I do not believe that all government action is bad. Yet, I believe that the burden of proof should be that government action is truly beneficial. In the criminal court, people are “innocent until proven guilty.” In the public policy arena, there should be no government action, unless the government action has been proven beyond a reasonable doubt to be effective.

• JL Sindelar (2008) “Paying for performance: the power of incentives over habits.” Health Economics, 17(4): 449-451.

According to the U.S. News and World Report UC San Diego was ranked as the tenth best economics department in the U.S.

Most economics simplify markets and assume that there is one market price. In reality, however, we observe significant price dispersion. Because of this, we see that that searching for the lowest price–while costly–can buyers to superior outcomes. George Stigler (1961) is a seminal paper on the Economics of Information which I will review here.

When should a person search? This occurs when the marginal benefit to search is equal to the marginal cost. Let us assume that the marginal cost of searching is proportional to the individuals cost of time. The marginal benefit of searching is:

• q*|∂P_min/∂n|

The number of searches is denoted as n. We see that people who buy a higher quantity, q, of goods will search longer. One implication of this is that business professional will search more than casual shoppers since they will likely buy higher quantities. Since tourist have little information and purchase low quantities, Stigler accurately predicts that tourist will pay higher prices than experienced buyers.

Also, “the expected savings from search will be greater, the greater the price dispersion of prices.” However, when goods are one-of-a-kind or unique, then the efficiency of searching is extremely low since it is difficult to identify potential sellers.

Brokers can be brought into the market to make it more efficient. These brokers “will eliminate the profitability of quoting very high selling and very low buying prices and will render impossible some of the extreme price bids.” Price dispersion will not disappear entirely when there are brokers since these brokers must make at least some profit to compensate them for their time. As price dispersion nears zero, brokers will leave the market.

What other conclusions does Stigler derive?

1. The larger the fraction of the buyer’s expenditures on the commodity, the greater the savings from search and hence the greater the amount of search.
2. The larger the fraction of repetitive (experienced) buyers in the market, the greater the effective amount of search (with positive correlation of successive prices).
3. The larger the fraction of repetitive sellers, the higher the correlation between successive prices, and…the larger the amount of accumulated search.
4. The cost of search will be larger, the larger the geographical size of the market.

• George J. Stigler (1961) “The Economics of Information” Journal of Political Economy, 69(3): 213-225.

Most people do not understand what a health economist is. Where do they work? What do they do? How do they spend their time?  How are they trained?

A paper by Morrisey and Cawley (Health Econ 2008) attempts to answer this question. The authors conducted an online survey to achieve a better understanding of what health economists do.

Training

Ninety-three percent of health economists have a Ph.D. A few health economists have an MD (2.6%), an RN (1%) or a JD (<1%) in addition to their PhD. Of those with a Ph.D., 72% have a Ph.D. in Economics. Below are a list of the economics departments that have trained the most health economists in the sample:

Seventy-six percent of health economists wrote a health related dissertation, even though 2/3 of graduate programs lacked a formal health economics field. For instance, at UCSD I am writing my dissertation on health economics even though there is not established program.

Employment

Where do health economists work? Most work in academia (64%), but a large percentage also work for the government (12%), NGOs (15%) or the private sector (9%). Of those who are academically employed, below is a chart detailing where their principal appointment is located.

For those who work in public health or medical school about 50% of their salary is made up from funding from external grants and contracts.

Research Interest

Below is a chart detailing the subspecialty of the health economists in the survey.  Respondents could choose multiple options.

After reading this post, hopefully you now have some idea of who health economists are and what they do.

• Morrisey MA, Cawley J. 2008. “US Health Economists: Who we are and what we do” Health Economics, 17(4): 535-543.

Many economists espouse utilitarianism as a superior ethical framework to proposed alternatives. A paper in Nature magazine finds that “Damage to the prefrontal cortex increases utilitarian moral judgements.”

Maybe there is some merit to the paper…economists have always been a strange breed.

Freakonomics by Steven Levitt and Stephen J. Dubner is an extremely popular book that has made economics a (somewhat) sexy topic of discussion. Levitt’s research makes economics exciting and his quirky, controversial studies make interesting reading.

John DiNardo, however, thinks that even Freakonomics is “interesting” and “entertaining,” it may not be revealing truths. Dr. DiNardo has written three critical reviews of the book. DiNardo’s criticisms call into doubt the meaning of some of the conclusions derived from Levitt’s research. For instance, DiNardo discusses the logical meaning the causal effect of obesity on health.

Nonetheless, I would argue that it is unlikely that anyone will devise a severe test of the proposition that obesity causes an increase in all-cause mortality. Simply put, the effect of obesity (or of ideal weight) is inextricably implementation specific. That is, it is not helpful to think about the “effect” of obesity for the same reason it is not helpful to debate the “causal effect of race on income”(Granger 1986). Many of us suspect, for example, that encouraging obese individuals to “starve themselves” for short periods of time might help one lose weight, but wouldn’t necessarily promote longevity (although it might, who knows? ).

Similarly, we might expect weight loss that results from increased physical activity to be more protective than
weight loss that results from increased life stress. The experience in the U.S. with the drugs fenfluramine and dexfenfluramine (Redux) is a case in point. Despite good evidence that the causal effect of taking Redux was weight loss, the drugs were pulled from the market because a “side effect” of the medication was an increase in potentially serious heart problems (Food and [Drug] Administration 1997) . Indeed, it would appear that the presumption that obesity is a cause of ill health made it virtually impossible to debate whether non–obesity was the cause of the increased heart problems. Rather, the consensus seems to be that the heart problems were not caused by non–obesity, but rather by Redux’s “side effects.”

My point is simple: when each way of “assigning” obesity that we can imagine would be expected to produce a different effect on all–cause mortality or other outcome, it is not at all clear that it is helpful to debate the “effect of obesity.” It seems more intelligible (and more policy relevant) to discuss the effect of Redux or exercise than it is to talk about the “effect” of obesity.

One study that DiNardo does hold up as an example of fine research is Cullen, Jacob and Levitt (Econometrica 2006). This paper was written by Levitt as well as my dissertation advisor Julie Cullen.

• John Dinardo (2007). “Interesting Questions in Freakonomics .” Journal of Economic Literature, Volume 45, Number 4, pp. 973-1000(28)
• Julie Berry Cullen, Brian A Jacob, Steven Levitt (2006) “The Effect of School Choice on Participants: Evidence from Randomized Lotteries.” Econometrica 74 (5) , 1191–1230 doi:10.1111/j.1468-0262.2006.00702.x

The American Economic Association (AEA) has some great resources for economists.  For instance,  it has links to:

• Rankings of the Top Economics Graduate programs
• Articles on Economists’ salaries and the Economist Job Market
• Articles on Economics Graduate Schools
• General information on Graduate study in Economics

Darkness at Noon by Arthur Koestler is a unique novel which describes the imprisonment of Communist revolutionary Nicholas Rubashov. Rubashov was a loyal supporter of the Communist cause in Russia, but his subsequent imprisonment on bogus charges causes him to reflect on whether his fight to bring Communism to Russia was truly beneficial to Russian society. The book demonstrates how Communist idealist notions became perversions as Stalin took power and instituted a cruel dictatorship.

One of the main themes of the book is that philosophical generalizations and abstract ideals often lead to disastrous results when they are applied without regard to individual circumstance. As Ferdinand Lassalle once said:

‘Show us not the aim without the way./ For ends and means on earth are so entangled/ That changing one, you change the other too;/ Each different path brings other ends in view.’

One passage from the book may be particular applicable to economists whose solutions to mathematical problems may not prove to be fruitful in the ‘real-world’.

‘A mathematician once said that algebra was the science for lazy people–one does not work out x, but operates with it as if one knew it. In our case, x stands for the anonymous mases, the people. Politics mean operating with the x without worrying about its actual natures. Making history is to recognize x for what it stands for in the equation.’

William Easterly is a famous development economist at NYU. Yet in a 2007 paper in the American Economic Review, Easterly asks “Was Development Assistance a Mistake?”

Easterly first recounts how development economics conventional wisdom on how to end poverty has changed over time.

• 1950-1970s: Raising investment is the key to reducing poverty. “…[d]evelopment (i.e. economic growth) was a simple matter of raising the rate of investment to GDP, including public investments like roads, dams, irrigation canals, schools, electricity and private investment. However, private investment was usually not trusted to do enough or do the right things, and so there was a strong role for the state to facilitate and direct investment, guided in turn by the development experts.”
• 1980s: Washington Consensus. This policy “…called for removing price distortions, opening to trade, and correcting macroeconomic imbalances (mainly budget deficits). The slogan of the new wave was ‘adjustment with growth.’”
• 1990s: New Growth Literature. Here economists would would use hundreds of right hand side variables and regress them on growth in order to find the determinants of economic growth. “Durlauf, Johnson, and Temple (2005) pointed out that 145 different right hand side variables were significant as determinants of growth in various studies with around 100 degrees of freedom.”
• 2000s: We don’t know. In 2005, the World Bank states that “different policies can yield the same result, and the same policy can yield different results, depending on country institutional contexts and underlying growth strategies.” The Barcelona Development Agenda proclaimed that “there is no single set of policies that can be guaranteed to ignite sustained growth.”

So how does Easterly sum up the contribution of development economists to the world?

“In sum, we don’t know what actions achieve development, our advice and aid doesn’t make those actions happen even if we knew what they were, and we are not even sure who “we” are that is supposed to achieve development. I take away from this that development assistance was a mistake.”

In fact, Easterly likens development economists advice to that of a communist central planner.

“The 20th century’s first development economist may have been Lenin, who wrote a famous pamphlet in 1902 called ‘What is to be done?,’ and said that the revolutionary intelligentsia had the answer. A long line of such diverse thinkers as Edmund Burke, Karl Popper, Friedrich Hayek, Isaiah Berlin, and James C. Scott have criticized the idea that experts can re-design society, all the way back to the French Revolution, and the catastrophic outcomes of the more extreme attempts to do so supported these criticisms. Yet the unquenchable demand for experts who can call tell “us” the right answers shows no sign of ending soon.”

Are smart people risk averse? Are dumb people impatient?

This is what Thomas Dohmen, Armin Falk, David Huffman, Uwe Sunde explore in their 2007 Discussion paper. Using data from a choice experiment of 1000 German adults, the authors tested for risk aversion using a Holt & Laury framework, and for impatience by varying the annual rate of return for a €100 investment. It is necessary to test the risk aversion and impatience parameter separately because in expected utility theory (EUT), a more concave utility function will cause more impatient choices, holding constant the discount rate. Cognitive ability was measured using questions similar to those on the Wechsler Adult Intelligence Scale (WAIS).

The authors found that individuals with higher cognitive abilities are less likely to be risk averse. Further, those who scored higher on the WAIS are significantly less impatient. This finding is true even after controlling for income, education, and credit constraint co-variates.

According to the authors:

“The paper also points to a different interpretation of reduced form models that have been estimated in the literature on cognitive ability and labor market outcomes. These models have typically included a measure of cognitive abilities, but not risk aversion or impatience, as explanatory variables (e.g., Cawley et al., 2001). Outcomes such as educational attainment or wages may by affected by risk aversion and impatience, and thus part of the impact of cognitive ability may reflect the correlation with these traits. In other words, our findings point to a potentially important source of omitted variable bias in this type of estimation.

Given that cognitive ability is known to be transmitted from parents to children, our findings could also be relevant for the literature on intergenerational transmission of preferences and socio-economic status.”

• Thomas Dohmen, Armin Falk, David Huffman, Uwe Sunde, 2007. “Are Risk Aversion and Impatience Related to Cognitive Ability?” IZA Discussion Paper No. 2735.

Sometimes, a coefficient isn’t what it seems to be. When using an ordinary least squares (OLS) regression, the regression coefficients indicate the proportion by which the dependent variable changes when the independent variables increases by one unit. Regression coefficients are more difficult to interpret, however, for more complicated regression specifications such as probit, mutlinomial logit, negative binomial, etc.

Let us assume that a regression coefficients are fitted to the following equation:

• y=f(Xβ)=f(β₀+β₁x₁+…+β_kx_k)

For instance, in the probit model, f(·)=Φ(·), where Φ(·) is the normal cdf. In the OLS setting,∂y/∂x_k=β_k. Thus, there are no interaction terms–unless explicitly states as one of the variables in X–in the OLS. However, in the probit case, ∂y/∂x_k=φ(xβ)β_k., where φ(·) is the pdf of the normal distribution. Thus, the marginal effects differ depending on the value of x.

The standard solution to this problem is to calculate the marginal effects when x is set equal to its mean value. When x_k is a dummy variable (i.e.: x_k∈{0,1}), the marginal effects are calculated by setting x_-k equal to their mean and then finding the difference in y when x_k increases from 0 to 1. This difference is the marginal effect for the discrete variable.

Sometimes this method will lead to results that are difficult to interpret. For instance, one could calculate the marginal effect on health from taking a new drug for someone with a gender=.051, and minority status=0.24. Since someone can either be male or female, they can be a minority or not, finding the marginal effect for this hypothetical person may not be the very revealing.

Another method would set continuous variables equal to their mean (e.g.: age, income) and then we could calculate the marginal effects for a the typical white female, a typical black male, etc.

A final methodology is given in Boonen, Schut and Koolman (2008). The authors investigate whether or not health insurance company financial incentives are effective in directing enrollees towards preferred provider pharmacies. In the results section of their paper, they state:

“The marginal effects for discrete variables are computed by calculating the change resulting from a change in the discrete variable from 0 to 1 holding all other variables fixed at their mean (see, for example, McGuirk and Porell, 1984; Madden et al., 2005). An average individual does not exist, however, and in our research we are interested in the probability that a certain consumer does or does not visit the preferred supplier. The marginal effects are thus not computed over the average individual but represent the mean of the marginal effects over each individual. This is done by computing the effect of, for example, a one-year increase in age on the probability of visiting the preferred provider for each individual and then averaging these probabilities across all individuals in the sample (Strombom et al., [JHE] 2002; Greene, 2003). The standard errors for the marginal effects are computed through bootstrapping. ”

This post should give researchers some idea of how to calculate marginal effects for complex regression models. It should be noted that there is no one optimal method, but one should determine how best to analyze the data and then use the marginal effects method most appropriate for your data analysis.

“In 1988, the first year for which data are available, there were fewer than 14,000 patients waiting for a kidney transplant and about 7,000 deceased-donor kidneys. Today, the waiting list has grown more than fivefold — an increase fueled partly by higher rates of diabetes — but the number of deceased-donor kidneys has only inched up. ”

There is a serious kidney shortage in the U.S.  Any economist can tell you that shortages generally occur from either a natural disaster or when the government imposes a below market price on a commodity.  In this case, the government has said that one can not sell organs; thus the market price for a kidney is set to zero.   Could a market for kidneys solve this problem?

The Wall Street Journal has a pair of interesting articles (”Kidney Shortage…” and “A Market for Kidneys?“) on whether or not we should have a kidney market.  Julio Elias of the University of Buffalo comes out in favor while Alvin Roth of Harvard University is against.  Dr. Elias makes a case based on economic theory:

“The current system of live organ transplants resembles an autarkic economy in which patients in need of an organ transplant are constrained to the organs available in the pool of friends and relatives. The kidney exchange system developed by Al and others is a barter system, and clearly will provide an improvement over the current system.

But a general conclusion of economics is that barter is an inferior system when compared to a money system, since barter requires the coincidence of wants. With the use of computers, and a national registry, multilateral barter is a good possibility, but still less efficient than using general purchasing power; i.e., a market. The main disadvantages of the kidney exchange system are the limitations that only kidneys from relatives and friends can be used and that the exchange must happen at the same time. A market-based exchange does not have such serious limitations.”

Dr. Roth believes that creating a market is politically infeasible and socially repugnant.  Although he does not reject the merits of an organ market, he does say that more practical steps–such as an organ exchange or making all individuals organ donors upon death unless they explicitly opt out–will currently be more politically feasible.

“There would be more live-donor transplants if everyone who wanted to donate a kidney to someone could do so, but a healthy person’s kidney is often incompatible with his or her intended donor. So, one way economists have helped is in helping organize kidney exchanges, which allow incompatible patient-donor pairs to exchange with other such pairs. “

 What do you think?

Many health economists wonder how much individuals would be willing to pay for a treatment. Since most medical care is paid by third parties (i.e. private insurance companies or the government) we can not use revealed preference econometrics which has been used in other areas of economics. Instead, many economists ask individuals directly these valuation questions. Yet simply asking the question is not enough. Do you ask the person how much they should pay for a treatment for an illness they do not yet have? Or should we only ask patients who have the disease how much they would be willing to pay for their own treatment? In healthcare systems that are run by the government, one may also want to know how much a person would pay for treatment for other people’s diseases.

In order to understand, what perspective should be taken, a paper by Dolan et al. (Health Economics 2003) uses a simple chart to illustrate six different perspectives:

The term p_p gives of the probability of one’s own need for treatment, and p_o is the probability that others in society will need treatment.

Why is it important for researchers to keep track of all these different perspectives when measuring willingness to pay (WTP)? Dolan notes that empirically, “real patients often give higher valuations than hypothetical patients.” Yet this need not be the case. If patients, ex-post, have adapted to the disease to a significant degree, than hypothetical patients may have higher valuations that real patients.

Whenever a researcher is investigating WTP measures, they must very cautious as to the perspective under which the question is asked.

• Dolan P, Olsen JA, Menzel P, Richardson J. An inquiry into the different perspectives that can be used when eliciting preferences in health. Health Economics. 2003 Jul;12(7):545-51.

According to optimal tax theory, taxes should be highest on relatively inelastic activities.  For instance, most men work full-time and and the tax rate does not affect this.  On the other hand, it has been should that the labor supply of women is much more sensitive to wages and income tax rates.  If we follow the conclusions from the optimal taxation literature we should tax men more than women.

This is what Harvard economists Alberto Alesina and Andrea Ichino propose (see Vox EU).  The Free exchange blog as well as Gilles Saint-Paul of the Toulouse School of Economics, however, are more sensible and reject this proposition.  Free exchange concludes on a particularly interesting point:

“…The economist’s idealised utilitarian social planner does not take seriously the costs incurred by the conflicts that will flare up when some are made ‘more equal than others’.  Perhaps liberal constitutions prominently feature equality before the law for a good reason: official non-discrimination works as a truce, a way of keeping the social peace.“

Health economists, policy makers, physicians and public health officials all want to maximize the well-being of society. These groups evaluate different medical treatments or public health interventions and then determine if the benefit is worth the cost.

In an opinion piece by Dorte Gyrd-Hansen in Pharmacoeconomics (2005), two schools of thought are examined. Those who are ‘welfarists’ believe that “the output of healthcare should be judge according to the extent to which it contributes to overall welfare (i.e. the [weighted] sum of individual utilities….’extra-welfarists’ do not define the output of healthcare in terms of preferences for health vis-a-vis other goods, but according to its contribution to health itself, i.e. they wish to maximize health as against overall welfare.”

What does this mean in reality?

“From a welfarist theoretic framework, treating a person who copes well with her disease and thus generates a high level of personal utility despite a poor health state will not be as efficient as treating a person who copes poorly. Extra-welfarists would aim at constructing an outcome measure that would produce equal values for the two strategies thus overriding individual preferences.”

Let’s use a chart to compare these two philosophies:

From the chart we see that welfarists try to maximize [the sum of] individual utilities subject to a budget constraint. Extra-welfarist, try to maximize health which is done by choosing all medical procedures which are more cost-effective than a certain threshold. This threshold, C, is must be chosen by policymakers. Welfarists wisely see that some individuals value health more than others in comparison to other goods. The extra-welfarist assumes all individuals with the same disease are homogeneous. This may seem naive, but in practice, it is very difficult to find each individuals willingness to pay for an increased level of health.

Extra-welfarists often try to elicit willingness-to-pay (WTP) measures for an additional QALY (i.e. quality-adjusted life year). If one applies a single WTP for each QALY, this “will entail overriding individual preferences such as diminishing marginal utility of health and potential differences in the value of increment health across population groups.” If we could rank health on a scale from 0 to 100 where 0 is equivalent to death and 100 is perfect health, economists would argue that under diminishing marginal utility of health that and individual would value an increase in health from 50 to 60 more than they would an increase from 90 to 100.

So is using the extra-welfarist QALY acceptable? While the welfarist camp offers no practical, easily estimable alternative, the do bring out some short comings of using QALYs (e.g., diminishing marginal utility of health, individual heterogeneity in terms of valuation of health against other good). Thus, I think the QALY method is helpful to analyze the benefit of a particular medical treatment, but the cost per QALY should not be the only factor taken into account when analyzing whether or not to adopt a new medical procedure.

• Gyrd-Hansen D., 2005. Willingness to pay for a QALY: theoretical and methodological issues. Pharmacoeconomics, 23(5):423-32.

Hybrid cars are supposed to save the environment, but they will also increase traffic. How can this be?

Example 1

Let us suppose that Hybrid Harry has a hybrid car which gets 50 mpg. Gas-guzzler Gary has a truck which gets 20 mpg. Both Harry and Gary live in San Diego and have relatives in Los Angeles that they visit over the holidays. Gary is much more likely to take the train to LA than Harry. Let’s see why:

It is about 240 miles to go from San Diego to LA and back. This means that Harry will use 4.8 gallons of gas. If a gallon of gas costs $3.50, than Harry will spend $16.80 on his trip.

Gary will need to use 12 gallons of gas to get from San Diego to Los Angeles. At $3.50 per gallon, Gary’s trip will cost $42.

If a train ticket costs $30 round trip, then Hybrid Harry will decide to drive while Gas-guzzler Gary will make the environmentally friendly choice of taking the train.

Example 2

Using a similar logic, Hybrid Harry will be more willing to live in the suburbs or exurbs and have a longer commute to work. Since commuting is cheaper for Hybrid Harry than Gas-guzzler Gary, Gary is more likely to live closer to his work.

Thus, we see that Hybrid Harry will be driving more miles than Gary and creating more traffic.

Typically, economists when economists look at the health insurance market, they focus on the insurance side of it. By this I mean to define insurance as the purchase of a product which will reimburse the buyer in the case of an adverse event. However, one must also look at the concept of protection. Protection is defined as expending a costly effort to reduce the probability of an adverse event. This costly effort, however, will not effect the amount of the loss, only the probability that it occurs.

A seminal paper by Ehrlich and Becker (JPE 1972) finds the optimal levels of self-protection and how optimal self-protection change when insurance markets are introduced. Let us assume that the probability of a loss is p(e) where e is the effort expended and p’<0. An expected utility maximizer optimizes the following function:

• max_e [1-p(e)]*U(I -e) + p(e)*U(I - L - e)

The first order condition is:

• -p’*[U(I -e)-U(I - L - e)]=(1-p)*U’(I -e) + p*U’(I - L - e)

Ehrlich and Becker note that “[t]he term on the left is the marginal gain from the reduction in p; that on the right, the decline in utility due to the decline in both incomes, is the marginal cost.”

When we introduce an insurance market, the expected utility maximizer faces a new objective function.

• max_e,s ,s [1-p(e)]*U(I-e-s*π(e)) + p(e)*U(I - L - e + s)

Here s is the insurance benefit and π(e) is price of the insurance; s*π(e) is the insurance premium. Let U(0)=U(I-L-e+s) and U(1)=U(I-e-s*π(e)). The first order conditions now become:

• -(1-p)U’(1) + p*U’(0)=0
• -p’*[U(1)-U(0)] - (1-p)*U’(1)*[1+s*π'] - p*U’(0)=1

How does self protection change when insurance markets are introduced? According to Ehrlich and Becker “On the one hand, self protection is discouraged because its marginal gain is reduced by the reduction of the difference between the incomes and thus the utilities in different states, on the other hand, it is encouraged if the price of market insurance is negatively related to the amount spent on protection through the effect of these expenditures on the probabilities.”

If insurance companies are actually able to measure self-protection and can price insurance accordingly, then individuals will have some incentive to increase prevention in order to lower their premiums. If insurance is priced in an actuarially fair manner (i.e., π=p(e)/[1-p(e)]) we can show that premiums will drop when self-protection increases:

• ∂π/∂e=p’/(1-p)²<0

However, if insurance companies are not able to observe self-protection efforts, than it is likely that moral hazard will occur–self protection will decrease. In the words of the authors, “Self-protection would then usually be discouraged by market insurance–moral hazard would exist–because the main effect of introducing market insurance would be to narrow the differences between incomes in different states.”

• Ehrlich I, Becker G. 1972. “Market Insurance, Self-Insurance, and Self-Protection.” The Journal of Political Economy, 80(4), pp. 623-648.

What happens when your the government is run by those tax-and-spend, dovish, universal health care loving, welfare promoting big government Democrats? Is there a difference when the low tax, hawkish, drug company pawns, anti-equality, small government Republicans take over? Few would question that there are significant ideological differences between the two parties and that federal and state elections have a large impact on the direction this country will take. But do parties matter for local elections?

This is the question analyzed by Ferreira and Gyourko in their 2007 NBER working paper. The authors use a regression discontinuity frame work (à la Lee 2001, Lee 2007) to analyze the outcome of 4,543 elections in 413 cities between 1950 and 2005. The authors find that on the local level, party labels do not affect 1) the size of government, 2) the allocation of spending, or 3) crime rates. This is true despite the fact that the incumbent has a large political advantage.

Why would this be the case. The authors argue that Tiebout sorting can lead to fairly homogeneous preferences among residents. Thus, when mayors run for office, there is less room for idealism. Further, ‘big ticket’ questions such as abortion, national defense spending, and foreign policy are not decided on the local level and these ideological issues–which often dominate federal elections–are not important in national elections. For instance, the Republicans in general may be against allowing illegal immigrants to obtain citizenship, but New York elected two Republican mayors (Guiliani and Bloomberg) who were necessarily ‘pro-immigrant’ due to a constituency made up of many first and second generation immigrants.

• Fernando Ferreira, Joseph Gyourko (2007) “Do political parties matter? Evidence from U.S. Cities.” NBER Working Paper #13535.

Who has the best Economics blog of 2008? The Bayesian Heresy makes their selections. Topping the list is the UCSD professor Jim Hamilton’s Econbrowser blog. In 2006, the Bayesian Heresy named the Healthcare Economist as the #2 Specialized Economics blog.

Barack Obama wins Iowa and is predicted to win New Hampshire according to Gallup polls.  Then Hillary Clinton wins New Hampshire.  Why were Gallup poll predictions wrong?

The Statistical Modeling blog tries to make sense of this in their post “What was going on with the New Hampshire polls?“  The post gives three reasons why the Gallup polls could have been wrong:

1. The likely voter screen and its potential deficiencies.   The Gallup polls only count the opinions of people they deem to be “likely voters” and thus the polls may have incorrectly included or excluded people.
2. Problems in survey weighting, especially when Iowa turnout was so strange.  Surveys weight their responses in order that the poll results are more representative of the voting public.  The survey designers may have incorrectly weighted the observations.
3. Obama being black.  “Some people have a theory that people will lie in a poll and say they support the black candidate because they don’t want to seem racist, but then they actually vote for the white person.”

Development economists have long sought the answers as to why new innovations do or do not get implemented in developing countries. Giliches (1957) found that hybrid corn adoption has an S-shaped function over time. Other studies have found that an individual’s social network is the primary determinant of technology adoption. If your friends try out a new technology and it works, you will be more likely to hear about this advance if you have a large social network. Other economists blame credit constraints for the slow adoption of many farming technologies. A large up-front cost of some fertilizer or new seeds may be prohibitive, even if there is a high payback rate in terms of crop yield. Finally, it is possible that the “new and improved” technology may not be better. A pesticide developed in the West may work on American or European pests but may prove impotent against different other farm threats in other countries.

A paper by Elaine Liu, however, argues that there is another driving force which may explain technology adoption: risk preferences. To test this, Liu surveys Bt cotton adoption of farmers in the Henan, Shandong, Hebei and Anhui provinces in China. Bt cotton is slightly more expensive that regular cotton seeds, but farmers who use these new seeds spray 82% less pesticides than with the original seeds.

To test for risk aversion, Liu employs a Holt and Laury (2002) methodology but uses prospect theory to fit parameters of the individual’s utility functions. This allows individuals to be loss averse and also to use nonlinear probability weighting.

After controlling for various covariates, Liu finds the following results.

• Individuals with higher levels of risk aversion adopt Bt cotton later.
• Individuals with higher levels of risk aversion continue using higher levels of pesticide even though less pesticide is needed when Bt cotton is used compared to traditional cotton seeds.
• Farmers with more education were not found to adopt Bt cotton earlier, but once they did begin using the Bt seeds, they wisely used less pesticide.

Although Liu does not mention this, prudence may play a factor in these technology adoption decisions. Taking a sure loss from the higher price of Bt cotton may not outweigh the gain from decreasing the probability of crop loss.

By 2006, the adoption of Bt cotton was nearly 100% and it seems that Chinese farmers are reaping the rewards of this new technology. Once the farmers understood better the benefits of the new seed, risk decreased and farmers were more likely to adopt the new Bt cotton technology.

One of the biggest news stories this year is the collapse of the subprime mortgage lending market. Why did this happen? How much do we really know about subprime lending?

A working paper by William Adams, Liran Einav and Jonathan Levin examines the subprime market for automobile loans. The authors find that liquidity constraints are a major force in shaping the subprime loan market. They find that car loans spike in January through March. Why is this? Poor individuals often take out a loan against their tax rebates. These rebates can be very high–up to $4500–and these individuals will use these rebates to help finance a car purchase.

Finance charges for these loans are very high. Interest rates usually surpass 20% and often at the state-mandated 30% interest cap. A $11,000 loan paid off over 42 months would incur $6000 of finance charges.

Yet it seems that loan demand within the subprime market is not very responsive to interest rates. It is, however, much more responsive to the amount of the down payment.

We estimate that a 100 dollar increase in the minimum down payment reduces the probability that an applicant will purchase by 0.0301, while a 100 dollar increase in the car price reduces the purchase probability by only 0.0034. That is, a 100 dollar increase in the minimum down payment has the same e¤ect as a 900 dollar increase in car price. This can still be explained in the absence of liquidity constraints, but it requires a much higher annual discount rate of 427 percent.

The authors found evidence of both moral hazard and adverse selection in the subprime market. Moral Hazard means that individuals are more likely to default on large loans. Adverse selection occurs when high risk borrowers desire large loans. The authors find that when a loan amount increases $1000, the default rate increases 24%. Sixteen percentage points is due to moral hazard and the rest is due to adverse selection.

Are there any ways to mitigate these market failure problems? The authors find that “risk-based minimum payments play a substantial role in mitigating adverse selection in financing choices.” These factors lead to the observation that “in practice, observably risky buyers end up with smaller rather than larger loans because they face higher down payment requirements.” Modern credit scores give the lender more information regarding the credit-worthiness of the borrower and help to match high-risk borrowers with smaller loans.

• William Adams, Liran Einav, Jonathan Levin. (2007). “Liquidity Constraints and Imperfect Information in Subprime Lending” NBER Working Paper #13067.

It’s decision time for Medicare Part D purchasers. Seniors have until December 31st to make their Part D choice and this decision is not a painless one.

The Marketplace Money radio program recently reported (’Deciphering Part D‘) that “the most popular policies have increased their prices substantially, especially Humana and United Healthcare, the ones that most of the people are in. Some of the policies’ prices have even doubled. So even though the average prices have only increased by about 14 percent, if you’re in one of the more popular plans, it’s really important to look at what your costs will be next year because you may want to change to a different policy.”

How can some plans double their prices yet still retain customers? Neo-classical economists would say that if the price of insurance at one company would rise, all seniors would switch to the cheaper plan and there would be a competitive equilibrium at the market price. Yet in the presence of switching costs, the insurance companies may be able to raise prices significantly without losing many customers.

Switching costs for Medicare Part D include the time consuming process of selecting from the hundreds of Medicare Part D plans. Children of seniors may also have to aid their parents in selecting a plan. Thus, if the price of my Part D insurance went up 16% while the rest of the plans went up 14%, I may decide to pay the higher price since I do not want to incur the search costs of finding a new Medicare Part D plan.

Companies such as Humana and UnitedHealth knew this would be the case. In the first year of Part D, these companies likely under-priced their insurance plans to attract customers. Once the customers had settled on their policies, they could more easily raise prices.

Despite the market inefficiencies caused by switching costs, this is not a reason to completely abandon a free market system. If the price increases of an individual company get too high, they will eventually outweigh the switching cost and the senior will move to a new plan. Further, information technology advances can help reduce switching costs. For instance, Medicare has a Prescription Drug Plan Finder that helps to estimate the cost of different plans depending on which prescriptions you are taking.

The N.Y. Times ran an interesting pair of articles Sunday regarding how economists “got it wrong.”

Conflict of Interest

Ben Stein (in “The Long and Short of It at Goldman Sachs“) comments on the economic analysis conducted by economist Jan Hatzius of Goldman Sachs. Dr. Hatzuis concludes that the sub-prime mortgage ‘crisis’ will not only hurt the stock market, but adversely affect overall growth. Is Dr. Hatzius’ doomsday scenario going to happen?

Mr. Stein believes Dr. Hatzius’ assertion “…is a conclusion that is an estimation based upon a guess.” Economists, especially macro-economists, have had a very poor track record predicting the future. This is likely because the entire U.S. economy is extremely complex and even if it were to be modelled, it would be non-linear in nature and subject to the consequences of chaos theory.

More troubling, is Mr. Stein’s hypothesis of why Dr. Hatzius’ analysis is gaining prominence:

Perhaps as a token of Dr. Hatzius’s genuine intelligence, which is fine. But to me, his paper seemed like a selling document in the real Wall Street sense of selling — namely, selling short. (Dr. Hatzius notes that he has long been bearish on housing, since faraway 2006, but I respectfully note that that is a lot different from predicting a credit catastrophe. The spokesman for Goldman also noted the company’s bearishness on housing since 2006. He also noted that in the recent past, Goldman Sachs has moved to a considerably larger short posture and that the firm is net short.)

…But what leaps out at me from this story is that Goldman Sachs was injecting dangerous financial products into the world’s commercial bloodstream for years.

My pal, colleague and alter ego, the financial manager Phil DeMuth, culled data from a financial Web site, ABAlert.com (for “asset-backed alert�?), that Goldman Sachs was one of the top 10 sellers of C.M.O.’s for the last two and a half years. From the evidence I see, Goldman was doing this for years. It might have sold very roughly $100 billion of the stuff in that period, according to ABAlert…

…The point to bear in mind, as Mr. Sloan brilliantly makes clear, is that as Goldman was peddling C.M.O.’s [Collateralized Mortgage Obligations], it was also shorting the junk on a titanic scale through index sales — showing, at least to me, how horrible a product it believed it was selling.

Dr. Hatzius likely honestly believes in his analysis, but one can not doubt that there is a serious conflict of interest being that Goldman Sachs pays his–likely very large–salary.

Do-gooders gone wrong

In an article on famine in Malawi (”Ending Famine, Simply by Ignoring the Experts“), economists once again got it wrong.

• World Bank Economists advised Malawi “…to adhere to free market policies and cut back or eliminate fertilizer subsidies, even as the United States and Europe extensively subsidized their own farmers.”  The result was Malawi has been dependent on food aid from rich nations and international organizations.
• Malwai’s solution was to begin subsidizing fertilizer to increase crop yield.  The result is that “… this year, a nation that has perennially extended a begging bowl to the world is instead feeding its hungry neighbors. It is selling more corn to the World Food Program of the United Nations than any other country in southern Africa and is exporting hundreds of thousands of tons of corn to Zimbabwe.”

Some claim that that the recent positive result was due to good rains over the last 2 years.  Nevertheless, the results are indisputable.  While I am generally in favor of more freer markets, here is one example of where economists got it wrong again.  Most World Bank economists are idealistic and want to improve the world for the poorest of the poor.  However, it seems that an international bureaucracy–despite having some of the smartest minds in the world on its payroll–is not doing a great job.

Conclusion

What should we take from these stories?  First, is that one must always be on the lookout for conflicts of interest that may bias a given piece of economic analysis.  Any one piece of economic analysis should not be sufficient to persuade you of the veracity of a theory; multiple analyses who reach the same conclusion are needed in order to have an adequate degree of reliability.  Secondly, top-down governing does not work, even if the people enacting policy are extremely bright economists.

This leads us to a new motto: Don’t always believe your local economist…except the Healthcare Economist, of course.

Where does an aspiring health economist look for journals relevant to the interdisciplinary field of health economics?  Which data sets are relevant for the empirical word a health economist would conduct?

If you’re looking for answers to these questions, check out the Resources page of my personal website.  The Resources page has a list of journals and data sets that health economists will find useful.

Can a state run petroleum company be as efficient as a private sector company? The answer is a resounding, “yes but…” It is possible that state-run petroleum companies can be efficient as long as they stick to the business of producing oil. Yet Tina Rosenberg’s “The Perils of Petrocracy” article in the N.Y. Times Sunday Magazine examines what has happened in the case of Hugo Chávez and Venezuela.

Chávez has siphoned off millions of dollars from Venezuela’s oil company, Petróleos de Venezuela S.A. (Pdvsa), for his own political uses. Much of the money has gone towards funding schools, medical clinics, and misiones for Venezuela’s poor. While this providing health and educational infrastructure for Venezuela’s poor is a laudable goal, it is unclear that conditions in Venezuela have improved.

“…the percentage of those living without running water and living in inadequate housing, as well as the number of young children not attending school, has scarcely budged in the last 10 years.”

Chávez heavy handed tactics have led to Pdvsa hirings based on political affiliation, not merit. Investment in oil rigs, maintenance, and other operational necessities has diminished during the Chávez era and it is likely that long-run oil production will steadily decrease. Pdvsa not longer published clear, S.E.C.-style financial reports regarding where funds are spent. Also, other governmental policies have lead to high inflation rates, leading to decreased investment in Venezuela. The cult of personality surrounding Chávez is growing and even spawning Chávez-emulating governors in Carabobo (see “An Imitator of Chávez“).

How is health care in Venezuela? While the Cuban physician-staffed misiones are providing more primary care to poor citizens, disinvestment in other forms of medical care has meant that Venezuela’s hospitals are “falling apart.”

What is the solution? While not as politically attractive as nationalization and spouting slogans such as “El Petróleo Es Nuestro,” privatizing the oil sector and extracting heavy taxes or royalties seems to be a much better solution. The tax revenue can be used to pay for social programs and the long run viability of oil production will remain high. One complication with privatization is that firms may bribe politicians in order to pay low taxes or reduce payments for mining rights below their fair-market value. Nevertheless, privatization is better than Chávez’s corrupt cronyism.

The winners of the 2007 Nobel Prize in Economics are: Leonid Hurwicz, Eric S. Maskin, and Roger B. Myerson.  The trio won the prize for their work on Mechanism Design.  See the Nobel Prize press release as well as the Scientific Background paper detailing exactly what is mechanism design.

The Economist’s View website provides a number of links to newspaper articles and blog posts regarding the 2007 Economics Nobel prize.  “Mechanism Design for Grandma” on the Marginal Revolution site gives a simple explanation of Mechanism Design Theory.

As an Applied Microeconomist who is generally skeptically of the practical importance of much of economic theory, I find Tyler Cowen’s post on the Marginal Revolution website particularly interesting:

No doubt mechanism design, and the general problem of inducing truth-telling, will be with us forever.  But how practical are these general results?  Or have the theorists simply provided us with cautionary notes and left the real applications to the context-specific world of practice?  Did these guys get at the real reasons why we don’t organize the entire economy as a second-price auction?

Part of me thinks: “Hey, let’s say Natasha wants Yana to tell her the truth about when she will clean her room.  This stuff isn’t useful!”

Another part of me thinks: “It is most important to get theory right.  These guys are brilliant.  Only the philistines demand that all scientific contributions have immediate applications.”

Some of you might argue: “These guys have already had a big impact on real world auctions and incentive schemes.”  In terms of the induced improvement in human welfare, I find that a difficult case to make.  The important progress has come from recognizing much simpler truths about incentives.

An explanation for the recent General Motors-United Auto Workers deal is pretty simple: it is a transfer of risk.  GM will set up a Voluntary Employee Beneficiary Association (VEBA) which will be controlled by the union.  According to the Detroit Free Press (” UAW ratifies”) GM will place about $30 billion dollars in the account which will pay for the health care benefits of GM retirees.  The benefit to GM is that it can now focus on cutting costs and improving quality in car production, rather than worrying about the risk of increasing health care premiums.  The health care inflation risk is now being transfered to GM retiree beneficiaries.  If health care costs in the future exceed the $30 billion in the VEBA, then GM retirees will have to pay for these costs out of their own pocket.

Why wold the UAW accept this agreement?  Although the UAW accepted increasing risk due to inflationary medical costs, it eliminated another type of risk: bankruptcy risk.  Because the VEBA is controlled by the UAW, GM retirees will still have funded health care even if GM goes bankrupt.  Thus, UAW retirees have eliminated the catastrophic risk (GM bankruptcy) in exchange for accepting increased risk of increasing health care costs.

I believe the VEBA will work out well for both sides.  The deal also may be the death knell for defined benefit programs.

• See also Joe Pauda’s analysis of the GM-UAW deal on Managed Care Matters.

The N.Y. Times has an interested article titled “Exploring Ways to Shorten the Ascent to a Ph.D.” The piece speaks of the difficulty of completing a Ph.D. and wonders whether a PhD should be considered more of a training exercise or a mandate for revolutionary research. An excerpt:

For those who attempt it, the doctoral dissertation can loom on the horizon like Everest, gleaming invitingly as a challenge but often turning into a masochistic exercise once the ascent is begun. The average student takes 8.2 years to get a Ph.D.; in education, that figure surpasses 13 years. Fifty percent of students drop out along the way, with dissertations the major stumbling block. At commencement, the typical doctoral holder is 33, an age when peers are well along in their professions, and 12 percent of graduates are saddled with more than $50,000 in debt.

Do students who attend better schools preform better academically? This is tautologically correct, but not very informative. What would happen if we randomly moved students from low quality schools to high quality schools? Would they do better?

Using the results from Chicago Public Schools randomized lotteries of elementary studies, Julie Cullen–my dissertation adviser–attempts to answer this question in her latest NBER working paper. It is a follow-up to a similar Econometrica paper written with Brian Jacob and Steven Levitt (of Freakanomics fame) which studied a similar subject but used data on high school students. Below is the abstract.

In this paper, we examine whether expanded access to sought-after schools can improve academic achievement. The setting we study is the “open enrollment” system in the Chicago Public Schools (CPS). We use lottery data to avoid the critical issue of non-random selection of students into schools. Our analysis sample includes nearly 450 lotteries for kindergarten and first grade slots at 32 popular schools in 2000 and 2001. We track students for up to five years and examine outcomes such as standardized test scores, grade retention and special education placement. Comparing lottery winners and losers, we find that lottery winners attend higher quality schools as measured by both the average achievement level of peers in the school as well as by value-added indicators of the school’s contribution to student learning. Yet, we do not find that winning a lottery systematically confers any evident academic benefits. We explore several possible explanations for our findings, including the possibility that the typical student may be choosing schools for non-academic reasons (e.g., safety, proximity) and/or may experience benefits along dimensions we are unable to measure, but find little evidence in favor of such explanations. Moreover, we separately examine effects for a variety of demographic subgroups, and for students whose application behavior suggests a strong preference for academics, but again find no significant effects.

• Cullen JB, Jacob, BA (2007) “The Effect of School Choice on Participants: Evidence from Randomized Lotteries” NBER Working Paper #13443
• Cullen JB, Jacob BA, Levitt S (2006) “The Effect of School Choice on Participants: Evidence from Randomized Lotteries” Econometrica, 74(5) p. 1191-1230.