Sources:

• John Bakija, Williams College ”Jobs and Income Growth of Top Earners and the Causes of Changing Income Inequality: Evidence from U.S. Tax Return Data.” November 2010, Working Paper.
• Hat tip: Mother Jones.

One way to answer this question is to use the Oaxaca decomposition.  This approach was originally formulated by Ronald Oaxaca. This document provides a nice overview of how to use the Oaxaca Decomposition and I apply that framework to the health spending case.

Differences in Health Spending

Assume that there are two regions: Region A and Region B. The spending for the two regions can be modeled using a linear regression framework:

• Y^A = β^AX + ε^A
• Y^B = β^BX + ε^B

The Y term represents spending and the variable X represents the patient’s health status. Health status could be measured as a vector of factors or as a single indicator (e.g., healthy or sick). The term β describes much an area spending on medical resources to treat a patient with a health status of X. Thus, average difference in spending per person the two regions is:

• Y^A – Y^B = β^AX^A – β^BX^B

where X^A is the average case mix in the area.

Determinants of Health Spending Differentials

Now the question is whether case mix or spending practices conditional on case mix is the key driver of the differences in spending between regions A and B. One can differentiate these two components using the following Oaxaca Decomposition:

• Y^A – Y^B = ΔXβ^B + ΔβX^A
• Y^A – Y^B = ΔXβ^A + ΔβX^B

In the first equation, the differences in health status (X‘s)are weighted by the coefficients for region B and the differences in the coefficients are weighted by the X’s from region A, whereas in the second, the differences in the X‘s are weighted by the coefficients of from region A and the differences in the coefficients are weighted by the X‘s of from region B.

There are basically three factors that effect health spending in the region: i) differences in health status across regions ii) differences in treatment patterns conditional on health status, and iii) the interaction of health status and conditional treatment effects. One can see this clearly below:

• Y^A – Y^B = ΔXβ^B + ΔβX^B + ΔXΔβ
• Y^A – Y^B = H + T + HT

The equations above show the health status effect (H), the treatment effect (T) and the interaction (HT).

The specification chosen for the Oaxaca decomposition determines whether the interaction effect is placed with the health status effect or the treatment effect.  More precisely:

• Y^A – Y^B = ΔXβ^B + ΔβX^A = H + (HT + T)
• Y^A – Y^B = ΔXβ^A + ΔβX^B = (H+ HT) + T

In effect, the first decomposition specification incorporates the interaction term with the treatment effect whereas the second specification places the interaction term together with the health status effect.

Sources:

• Oaxaca, R. 1973. “Male-Female Wage Differentials in Urban Labor Markets.” International Economic Review 14: 693–709.
• Owen O’Donnell, Eddy van Doorslaer, Adam Wagstaff, Magnus Lindelow “Explaining Differences between Groups: Oaxaca Decomposition” Chapter 12, Analyzing Health Equity Using Household Survey Data, World Bank, WBI Learning Resources Series, 2008.

Although there are numerous presentations on health care, such as the Health Insurance and Health Care Practice seminar lead by Amy Finkelstein. Other sessions of note include:

• The 2006 Massachusetts Health Care Reform
• Children’s Health and Education in China
• Selection and Moral Hazard in Health Insurance
• Behavioral Economics and Health
• The Dynamics of Health and Wealth among the Elderly
• Medicare and Prescription Drugs
• What Determines the Performance of US and International Healthcare?

The full schedule is available here.

“Previous publishing revolutions, such as the advent of printing, prompted similar concerns about trivialisation and extremism. But whatever you think about the impact of blogging on political, scientific or religious debate, it is hard to argue that the internet has cheapened the global conversation about economics. On the contrary, it has improved it.”

Sure, writing an economics blog improves the quality of intellectual debates, but why do bloggers do it?  Blogging takes time and resources away from other pursuits (e.g., publishing, other work, leisure).  For only a few blogs does the ad revenue earned make up for the time spent working on the site.

One reasons blogging is attractive is that it helps the reputation of the blogger.

“Research (by two blogging economists at the World Bank) suggests that academic papers cited by bloggers are far more likely to be downloaded. Blogging economists are regarded more highly than non-bloggers with the same publishing record. Blogs…have also given voice to once-obscure scholars advancing bold solutions to America’s economic funk and Europe’s self-inflicted crisis.”

If blogging can help promote the intellectual debate of a boring topic like macroeconomics, imagine what it is doing for the much more interesting (in my completely unbiased opinion) debates surrounding health economic issues.

The question is, how does one protect the animals?  In Namibia, the answer has been allowing hunters to kill them.  Hunting these animals has the obvious drawback that it decreases the number of these animals alive in the short run.  Further, most hunters prefer hunting adult male lions.

“A male lion needs six years to establish himself in a pride and rear a new generation. Overhunting leads to continual turnover in the pride: when a new male takes the throne, he tends to kill the old crop of cubs so he can father his own. But when I asked if he would support a ban on trophy hunting, even Packer demurred.”

However, hunting may save the animals.  Hunters pay thousands of dollars to have the chance to hunt these animals.  Villagers can profit from these hunts.  Thus, they have the incentive to allow the hunting of these animals, but not to the degree that this asset is completely used up.  Furthers, villagers themselves may want to hunt the animals themselves if they trample their crops.  If they are valuable assets, however, the villagers will have an incentive to protect them.

Counterintuitively, hunting may be the only path for lions to survive.

• Ben S. Bernanke, “Financial Reform to Address Systemic Risk” at the Council on Foreign Relations, Washington, D.C., March 10, 2009

In a 2010 short paper, Brunnermeier, Hansen, Kashyap, Krishnamurthy and Lo (2010) argue that the field of economics has not adequately examined the topic of systemic risk. One of the reasons for this is that systemic risk is difficult to observe and quantify.  The Office of Financial Research (OFR) is President Obama’s attempt to fill this gap in knowledge.

Even though systemic risk is poorly measured, that does not mean that economists haven’t thought of ideas to combat systemic risk. The Fed Chairman’s quotation citing the need for holistic regulation is one approach. Is a holistic approach to regulation a good thing? Today, I give my 2 cents.

Merits

Financial and economic market are complex entities. Creating multiple government bodies where each one only regulates a piece of a given market can often be suboptimal. An individual agency may be in charge of making a given financial instrument safer or more transparent. Even if they government body succeeds in their mission, their regulations may create unintended consequence. For instance, another, more volatile, less transparent, unregulated financial instrument is created. Or the regulation could have an adverse effect on real markets. Having a single entity regulate all financial markets in an integrated fashion seems like a promising idea.

Problems

However, regulation generally requires more specialized knowledge than any one agency can maintain. For instance, credit default swaps are complicated entities. Regulators must have specific knowledge in order to properly regulate these instruments. Having a single body regulate all financial markets may create an entity with a wide breadth of knowledge but little depth. Further, if the regulatory scheme created by the central planner is poorly constructed, investors may have no other markets from which they can seek more rational regulation. If regulation by some of the government bodies is successful, investors could migrate to investments in more rationally regulated sectors (although this shift from well-regulated to poorly-regulated markets is a distortion in and of itself).

Motivation

In addition, one has to question the Chairman’s motivation for expanded regulation. He may have the best interests of the country at heart; integrated regulation may be the best mechanism through which one can decrease systemic risk. However, Dr. Bernanke is not an unbiased observer. Centralizing regulatory control increases the power of the Fed, the and the prestige of Dr. Bernanke. Thus, the desire to centralize regulation may not be a completely unbiased opinion.

Although I am not a macroeconomist myself, Marginal Revolution has a good description of the pair.

• Thomas Sargent.
• Christopher Sims.

My old professor, Jim Hamilton of UCSD explains macroeconomic impulse response functions which Sims helped develop.

• Richard Dowden, Africa: Altered States, Ordinary Miracles.

“Using retrospective claims data for a sample of commercially insured patients, we find that, on average, expenditures to treat diseases rose 11% from 2003Q1 to 2005Q4 and would have risen even faster, 18%, had the mix of services remained fixed at the 2003Q1 levels.  This suggests that fixed-basket price indexes, as are used in the official statistics, could overstate true price growth significantly.”

Much of the decrease in cost to treat specific conditions come from a shift of patients from inpatient care to outpatient surgical centers.  The question is, was this change a one time productivity gain, or does the health care system have other options for improving productivity (in the sense of reducing cost for the same quality).  It could be the case that Health IT and electronic medical records could produce synergies.  Alternatively, more intensive use of physician assistants and nurse practitioners could reduce the cost of treating many conditions.  We will see what the future holds.

• Source: Ana Aizcorbe and Nicole Nestoriak “Changing mix of medical care services: Stylized facts and implications for price indexes” Journal of Health Economics, 30 (2011) 568-574.

Notes: The authors analysis uses the Symmetry grouping algorithm to define episodes of care.  By using the groupers, the authors are not required to have extensive medical knowledge to perform this analysis.   On the other hand, because the groupers are proprietary, the algorithms can be seen as a ‘black box.’

The formula used to estimate the expenditure and price indices are:

• Price: {Σc_d2x_d1/N₁}/ {Σc_d1x_d1/N₁}
• Expenditure: {Σc_d2x_d2/N₂}/ {Σc_d1x_d1/N_d1}

Where c_dt is the cost per episode of type d in year t, x_dt is the number of episodes with disease d in time t, and
N_t is the total number of beneficiaries in time t.

• Kenneth Minogue