Fee-for-service payments encourage high-volume services rather than high-quality care. Alternative payment models (APMs) aim to realign financing to support high-value services.

The 2 main components of gastroenterologic care, procedures and chronic care management, call for a range of APMs. The first step for gastroenterologists is to identify the most important conditions and opportunities to improve care and reduce waste that do not require financial support.

We describe examples of delivery reforms and emerging APMs to accomplish these care improvements. A bundled payment for an episode of care, in which a provider is given a lump sum payment to cover the cost of services provided during the defined episode, can support better care for a discrete procedure such as a colonoscopy. Improved management of chronic conditions can be supported through a per-member, per-month (PMPM) payment to offer extended services and care coordination.

For complex chronic conditions such as inflammatory bowel disease, in which the gastroenterologist is the principal care coordinator, the PMPM payment could be given to a gastroenterology medical home. For conditions in which the gastroenterologist acts primarily as a consultant for primary care, such as noncomplex gastroesophageal reflux or hepatitis C, a PMPM payment can support effective care coordination in a medical neighborhood delivery model. Each APM can be supplemented with a shared savings component.

Gastroenterologists must engage with and be early leaders of these redesign discussions to be prepared for a time when APMs may be more prevalent and no longer voluntary.

Download "Shifting Away From Fee-For-Service: Alternative Approaches to Payment in Gastroenterology" »

Downloads

• Download "Shifting Away From Fee-For-Service: Alternative Approaches to Payment in Gastroenterology"

May’s jobs gains, released this morning, show that only 38,000 new jobs were added this May, down from an average of 178,000 over the first four months of the year, and the least new jobs added since September 2010.

This year’s monthly job gains and losses can indicate how the economy is doing once they are corrected to account for the pattern we already expect in a process called seasonal adjustment. The approach for this seasonal adjustment that is presently used by the Bureau of Labor Statistics (BLS) puts very heavy weight on the current and last two years of data in assessing what are the typical patterns for each month.

In my paper “Unseasonal Seasonals?” I argue that a longer window should be used to estimate seasonal effects. I found that using a different seasonal filter, known as the 3x9 filter, produces better results and more accurate forecasts by emphasizing more years of data. The 3x9 filter spreads weight over the most recent six years in estimating seasonal patterns, which makes them more stable over time than in the current BLS seasonal adjustment method.

I calculate the month-over-month change in total nonfarm payrolls, seasonally adjusted by the 3x9 filter, for the most recent month. The corresponding data as published by the BLS are shown for comparison purposes. According to the alternative seasonal adjustment, the economy actually lost about 4,000 jobs in May (column Wright SA), compared to the official BLS total of 38,000 gained (column BLS Official).

In addition to seasonal effects, abnormal weather can also affect month-to-month fluctuations in job growth. In my paper “Weather-Adjusting Economic Data” I and my coauthor Michael Boldin implement a statistical methodology for adjusting employment data for the effects of deviations in weather from seasonal norms. This is distinct from seasonal adjustment, which only controls for the normal variation in weather across the year. We use several indicators of weather, including temperature and snowfall.

We calculate that weather in May had a negligible effect on employment, bringing up the total by only 4,000 jobs (column Weather Effect). Our weather-adjusted total, therefore, is 34,000 jobs added for May (column Boldin-Wright SWA). This is not surprising, given that weather in May was in line with seasonal norms.

Unfortunately, neither the alternative seasonal adjustment, nor the weather adjustment, makes todays jobs report any more hopeful. They make little difference and, if anything, make the picture more gloomy.

a. Applies a longer window estimate of seasonal effects (see Wright 2013).
b. Includes seasonal and weather adjustments, where seasonal adjustments are estimated using the BLS window specifications (see Boldin & Wright 2015). The incremental weather effect in the last column is the BLS official number less the SWA number.

Editor’s note: The following remarks were prepared and delivered by Gary Burtless at a roundtable sponsored by the American Tax Policy Institute on January 7, 2016. Video of Burtless’ remarks are also available on the Institute’s website. Download the related slides at the right. 

To eliminate suspense from the start: Nothing I am about to say undermines the popular narrative about recent inequality trends.  For the past 35 years, U.S. inequality has increased.  Inequality has increased noticeably, no matter what income definition you care to use.  A couple of things you read in the newspaper are untrue under some income definitions. For example, under a comprehensive income definition it is false to claim that all the income gains of the past 2 or 3 decades have gone to the top 1 percent, or the top 5 percent, or the top 10 percent of income recipients.  Middle- and low-income Americans have managed to achieve income gains, too, as we shall see.

Tax policy certainly affects overall inequality, but I shall leave it for Scott, David, and Tracy to take that up. Let me turn to my main job, which is to distinguish between different reasonable income measures.

The crucial thing to know is that contradictory statements can be made about some income trends because of differences in the definition of income.  In general, the most pessimistic statements about trends rely on an income definition that is restrictive in some way.  The definition may exclude important income items, items, for example, that tend to equalize or boost family incomes.  The definition may leave out adjustments to income … adjustments that tend to boost the rate of income gain for low- or middle-income recipients, but not for top-income recipients.

The narrowest income definition commonly used to evaluate income trends is Definition #1 in my slide, “pretax private, cash income.”  Columnists and news reporters are unknowingly using this income definition when they make pronouncements about the income share of the “top 1 percent.”  The data about income under this definition are almost always based on IRS income tax returns, supplemented with a bit of information from the Commerce Department’s National Income and Product Account (NIPA) data file.

The single most common income definition used to assess income trends and inequality is the Census Bureau’s “money income” definition, Definition #2 on the slide.  It is just the same as the first definition I mentioned, except this income concept also includes government cash transfer payments – Social Security, unemployment insurance, cash public assistance, Veterans’ benefits, etc.

A slightly more expansive definition (#3) also adds food stamp (or SNAP) benefits plus other government benefits that are straightforward to evaluate. Items of this kind include the implicit rent subsidy low-income families receive in publicly-subsidized housing, school lunch subsides, and means-tested home heating subsidies.

Now we come to subtractions from income. These typically reflect families’ tax obligations.  The Census Bureau makes estimates of state and federal income tax liabilities as well as payroll taxes owed by workers (though not by their employers).  Since income and payroll taxes subtract from the income available to pay for other stuff families want to buy, it seems logical to also subtract them from countable income. This is done under income Definition #4.  Some tax obligations – notably the Earned Income Credit (EIC) – are in fact subtractions from taxes owed, which would not be a problem in the case of families that still owe positive taxes to the government.  However, the EIC is refundable to taxpayers, meaning that some families have negative tax liabilities:  The government owes them money.  In this case, if you do not take taxes into account you understate low-income families’ incomes, even as you’re overstating the net incomes available to middle- and high-income families.

Now let’s get a bit more complicated.  Forget what I said about taxes, because our next income definition (#5) also ignores them.  It is an even-more-comprehensive definition of gross or pretax income.  In addition to all those cash and near-cash items I mentioned in Definition #3, Definition #5 includes imputed income items, such as: 

• The value of your employer’s premium contribution to your employee health plan;
• The value of the government’s subsidy to your public health plan – Medicare, Medicaid, state CHIP plans, etc.
• Realized taxable gains from the sale of assets; and
• Corporate income that is earned by companies in which you own a share even though it is not income that is paid directly to you.

This is the most comprehensive income definition of which I am aware that refers to gross or pre-tax income.

Finally we have Definition #6, which subtracts your direct and indirect tax payments.  The only agency that uses this income definition is principally interested in the Federal budget, so the subtractions are limited to Federal income and payroll taxes, Federal corporate income taxes, and excise taxes.

Before we go into why you should care about any of these definitions, let me mention a somewhat less important issue, namely, how we define the income-sharing group over which we estimate inequality.  The most common assessment unit for income included under Definition #1 (“Pre-tax private cash income”) is the Federal income tax filing unit.  Sometimes this unit has one person; sometimes 2 (a married couple); and sometimes more than 2, including dependents.

The Census Bureau (and, consequently, most users of Census-published statistics) mainly uses “households” as reference units, without any adjustment for variations in the size of different households.  The Bureau’s median income estimate, for example, is estimated using the annual “money income” of households, some of which contain 1 person, some contain 2, some contain 3, and so on.

Many economists and sociologists find this unsatisfactory because they think a $20,000 annual income goes a lot farther if it is supporting just one person rather than 12.  Therefore, a number of organizations—notably, the Luxembourg Income Study (LIS), the Organisation of Economic Cooperation and Development (OECD), and the Congressional Budget Office (CBO)—assume household income is equally shared within each household, but that household “needs” increase with the square root of the number of people in the household.  That is, a household containing 9 members is assumed to require 1½ times as much income to enjoy the same standard of living as a family containing 4 members.  After an adjustment is made to account for the impact of household size, these organizations then calculate inequality among persons rather than among households.

How are these alternative income definitions estimated?  Who uses them?  What do the estimates show?  I’ll only consider a two or three basic cases.

First, pretax, private, cash income. By far the most famous users of this definition are Professors Thomas Piketty and Emmanuel Saez.  Their most celebrated product is an annual estimate of the share of total U.S. income (under this restricted definition) that is received by the top 1 percent of tax filing units.

Here is their most famous chart, showing the income share of the top 1 percent going back to 1913. (I use the Piketty-Saez estimates that exclude realized capital gains in the calculation of taxpayers’ incomes.) The notable feature of the chart is the huge rise in the top income share between 1970—when it was 8 percent of all pretax private cash income—and last year—when the comparable share was 18 percent.  

I have circled one part of the line—between 1986 and 1988—to show you how sensitive their income definition is to changes in the income tax code.  In 1986 Congress passed the Tax Reform Act of 1986 (TRA86). By 1988 the reform was fully implemented.  Wealthy taxpayers noticed that TRA86 sharply reduced the payoff to holding corporate earnings inside a separately taxed corporate entity. Rich business owners or shareholders could increase their after-tax income by arranging things so their business income was taxed only once, at the individual level.  The result was that a lot of income, once earned by and held within corporations, was now passed through to the tax returns of rich individual taxpayers. These taxpayers appeared to enjoy a sudden surge in their taxable incomes between 1986 and 1988.  No one seriously believes rich people failed to get the benefits of this income before 1987.  Before 1987 the same income simply showed up on corporate rather than on individual income tax returns.

A final point:  The chart displayed in SLIDE #6 is the source of the widely believed claim that U.S. inequality is nowadays about the same as it was at the end of the Roaring 1920s, before the Great Depression.  That is close to being true – under this income definition.

Census “money income”: This income definition is very similar to the one just discussed, except that it includes cash government transfer payments.  The producer of the series is the Census Bureau, and its most famous uses are to measure trends in real median household income and the official U.S. poverty rate. Furthermore, the Census Bureau uses the income definition to compile estimates of the Gini coefficient of household income inequality and the income shares received by each one-fifth of households, ranked from lowest to highest income, and received by the top 5 percent of households.

Here is a famous graph based on the Bureau’s “median household income” series.  I have normalized the historical series using the 1999 real median income level (1999 and 2000 were the peak income years according to Census data).  Since 1999 and 2000, median income has fallen about 10 percent.  If we accept this estimate without qualification, it certainly represents bad news for living standards of the nation’s middle class. The conclusion is contradicted by other government income statistics that use a broader, more inclusive income definition, however.

And here is the Bureau’s most widely cited distributional statistic (after its “official poverty rate” estimate).  Since 1979, the Gini coefficient has increased 17 percent under this income definition. (It is worth noting, however, that the portion of the increase that occurred between 1992 and 1993 is mainly the result of methodological changes in the way the Census Bureau ascertained incomes in its 1994 income survey.)

When you hear U.S. inequality compared with that in other rich countries, the numbers are most likely based on calculations of the LIS or OECD.  Their income definition is basically “Cash and Near-cash Public and Private income minus Income and Payroll taxes owed by households.”  Under this income definition, the U.S. looks relatively very unequal and America appears to have an exceptionally high poverty rate.  U.S. inequality has been rising under this income definition, as indeed has also been the case in most other rich countries. The increase in the United States has been above average, however, helping us to retain our leadership position, both in income inequality and in relative poverty.

We turn last to the most expansive income definition:  CBO’s measure of net after-tax income.  I will use CBO’s tabulations using this income definition to shed light on some of the inequality and living standard trends implied by the narrower income definitions discussed above.

Let’s consider some potential limitations of a couple of those definitions.  The limitations do not necessarily make them flawed or uninteresting.  They do mean the narrower income measures cannot tell us some of the things that users claim they tell us.

An obvious shortcoming of the “cash pretax private income” definition is that it excludes virtually everything the government does to equalize Americans’ incomes.  Believe it or not, the Federal tax system is mildly progressive.  It claims a bigger percentage of the (declared) incomes of the rich than it does of middle-income families’ and especially the poor.  Any pretax income measure will miss that redistribution.

More seriously, it excludes all government transfer payments.  You may think the rich get a bigger percentage of their income from government handouts compared with middle class and poorer households.  That is simply wrong.  The rich get a lot less.  And the percentage of total personal income that Americans derive from government transfer payments has gone way up over the years.  In the Roaring 1920s, Americans received almost nothing in the form of government transfers. Less than 1 percent of Americans’ incomes were received as transfer payments.  By 1970—near the low point of inequality according to the Piketty-Saez measure—8.3 percent of Americans’ personal income was derived from government transfers.  Last year, the share was 17 percent. None of the increase in government transfers is reflected in Piketty and Saez’s estimates of the trend in inequality.  Inequality is nowadays lower than it was in the late 1920s, mainly because the government does more redistribution through taxes and transfers.

Both the Piketty-Saez and the Census “money income” statistics are affected by the exclusion of government- and employer-provided health benefits from the income definition. This slide contains numbers, starting in 1960, that show the share of total U.S. personal consumption consisting of personal health care consumption.  I have divided the total into two parts. The first is the share that is paid for out of our own cash incomes (the blue part at the bottom).  This includes our out-of-pocket spending for doctors’ charges, hospital fees, pharmaceutical purchases, and other provider charges as well as our out-of-pocket spending on health insurance premiums. The second is the share of our personal health consumption that is paid out of government subsidies to Medicare, Medicaid, CHIP, etc., or out of employer subsidies to employee health plans (the red part). 

As everyone knows, the share of total consumption that consists of health consumption has gone way up.  What few people recognize is that the share that is directly paid by consumers—through payments to doctors, hospitals, and household health insurance premium payments—has remained unchanged.  All of the increase in the health consumption share since 1960 has been financed through government and employer subsidies to health insurance plans. None of those government or employer contributions is counted as “income” under the Piketty-Saez and Census “money income” definitions.  You would have to be quite a cynic to claim the subsidies have brought households no living standard improvements since 1960, yet that is how they are counted under the Piketty-Saez and Census “money income” definitions.

Final slide: How much has inequality gone up under income definitions that count all income sources and subtract the Federal income, payroll, corporation, and excise taxes we pay?  CBO gives us the numbers, though unfortunately its numbers end in 2011.

Here are CBO’s estimates of real income gains between 1979 and 2011.  These numbers show that real net incomes increased in every income category, from the very bottom to the very top.  They also show that real incomes per person have increased much faster at the top—over on the right—than in the middle or at the bottom—over on the left.  Still, contrary to a common complaint that all the income gains in recent years have been received by folks at the top, the CBO numbers suggest net income gains have been nontrivial among the poor and middle class as well as among top income recipients.

Suppose we look at trends in the more recent past, say, between 2000 and 2011.  That lower panel in this slide presents a very different picture from the one implied by the Census Bureau’s “money income” statistics.  Unlike the “money income numbers” [SLIDE #9], these show that inequality has declined since 2000.  Unlike the “money income numbers” [SLIDE #8], these show that incomes of middle-income families have improved since 2000.  There are a variety of explanations for the marked contrast between the Census Bureau and CBO numbers.  But a big one is the differing income definitions the two conclusions are based on.  The more inclusive measure of income shows faster real income gains among middle-income and poorer households, and it suggests a somewhat different trend in inequality.

Editor's Note: TechTakes is a new series that collects the diverse perspectives of scholars around the Brookings Institution on technology policy issues. This first post in the series features contributions from Scott Andes, Susan Hennessey, Adie Tomer, Walter Valdivia, Darrell M. West, and Niam Yaraghi on the Internet of Things.

In the coming years, the number of devices around the world connected to the Internet of Things (IoT) will grow rapidly. Sensors located in buildings, vehicles, appliances, and clothing will create enormous quantities of data for consumers, corporations, and governments to analyze. Maximizing the benefits of IoT will require thoughtful policies. Given that IoT policy cuts across many disciplines and levels of government, who should coordinate the development of new IoT platforms? How will we secure billions of connected devices from cyberattacks? Who will have access to the data created by these devices? Below, Brookings scholars contribute their individual perspectives on the policy challenges and opportunities associated with the Internet of Things.

The Internet of Things will be everywhere

Darrell M. West is vice president and director of Governance Studies and founding director of the Center for Technology Innovation.

Humans are lovable creatures, but prone to inefficiency, ineffectiveness, and distraction. They like to do other things when they are driving such as listening to music, talking on the phone, texting, or checking email. Judging from the frequency of accidents though, many individuals believe they are more effective at multi-tasking than is actually the case.

The reality of these all too human traits is encouraging a movement from communication between computers to communication between machines. Driverless cars soon will appear on the highways in large numbers, and not just as a demonstration project. Remote monitoring devices will transmit vital signs to health providers, who then can let people know if their blood pressure has spiked or heart rhythm has shifted in a dangerous direction. Sensors in appliances will let individuals know when they are running low on milk, bread, or cereal. Thermostats will adjust their energy settings to the times when people actually are in the house, thereby saving substantial amounts of money while also protecting natural resources.

With the coming rise of a 5G network, the Internet of Things will unleash high-speed devices and a fully connected society. Advanced digital devices will enable a wide range of new applications from energy and transportation to home security and healthcare. They will help humans manage the annoyances of daily lives such as traffic jams, not being able to find parking places, or keeping track of physical fitness. The widespread adoption of smart appliances, smart energy grids, resource management tools, and health sensors will improve how people connect with one another and their electronic devices. But they also will raise serious security, privacy, and policy issues.

Implications for surveillance

Susan Hennessey is Fellow in National Security in Governance Studies at the Brookings Institution. She is the Managing Editor of the Lawfare blog, which is devoted to sober and serious discussion of "Hard National Security Choices.”

As the debate over encryption and diminished law enforcement access to communications enters the public arena, some posit the growing Internet of Things as a solution to “Going Dark.” A recently released Harvard Berkman Center report, “Don’t Panic,” concludes in part that losses of communication content will be offset by the growth of IoT and networked sensors. It argues IoT provides “prime mechanisms for surveillance: alternative vectors for information-gathering that could more than fill many of the gaps left behind by sources that have gone dark – so much so that they raise troubling questions about how exposed to eavesdropping the general public is poised to become.”

Director of National Intelligence James Clapper agrees that IoT has some surveillance potential. He recently testified before Congress that “[i]n the future, intelligence services might use the IoT for identification, surveillance, monitoring, location tracking, and targeting for recruitment, or to gain access to networks or user credentials.”

But intelligence gathering in the Internet age is fundamentally about finding needles in haystacks – IoT is poised to add significantly more hay than needles. Law enforcement and the intelligence community will have to develop new methods to isolate and process the magnitude of information. And Congress and the courts will have to decide how laws should govern this type of access.

For now, the unanswered question remains: How many refrigerators does it take to catch a terrorist?

IoT governance

Scott Andes is a senior policy analyst and associate fellow at the Anne T. and Robert M. Bass Initiative on Innovation and Placemaking, a part of the Centennial Scholar Initiative at the Brookings Institution.

As with many new technology platforms, the Internet of Things is often approached as revolutionary, not evolutionary technology. The refrain is that some scientific Rubicon has been crossed and the impact of IoT will come soon regardless of public policy. Instead, the role of policymakers is to ensure this new technology is leveraged within public infrastructure and doesn’t adversely affect national security or aggravate inequality. While these goals are clearly important, they all assume technological advances of IoT are staunchly within the realm of the private sector and do not justify policy intervention. However, as with almost all new technologies that catch the public’s eye—robotics, clean energy, autonomous cars, etc.—hyperbolic news reporting overstates the market readiness of these technologies, further lowering the perceived need of policy support.

The problem with this perspective is twofold. First, greater scientific breakthroughs are still needed. The current rate of improvement in processing power and data storage, miniaturization of devices, and more energy efficient sensors only begin to scratch the surface of IoT’s full potential. Advances within next-generation computational power, autonomous devices, and interoperable systems still require scientific breakthroughs and are nowhere near deployment. Second, even if the necessary technological advancements of IoT have been met, it’s not clear the U.S. economy will be the prime recipient of its economic value. Nations that lead in advanced manufacturing, like Germany, may already be better poised to export IoT-enabled products. Policymakers in the United States should view technological advancements in IoT as a global economic race that can be won through sound science policies. These should include: accelerating basic engineering research; helping that research reach the market; supporting entrepreneurs’ access to capital; and training a science and engineering-ready workforce that can scale up new technologies.

IoT will democratize innovation

Walter D. Valdivia is a fellow in the Center for Technology Innovation at Brookings.

The Internet of Things could be a wonderful thing, but not in the way we imagine it.

Today, the debate is dominated by cheerleaders or worrywarts. But their perspectives are merely two sides of the same coin: technical questions about reliability of communications and operations, and questions about system security. Our public imagination about the future is being narrowly circumscribed by these questions. However, as the Internet of Things starts to become a thing—or multiples things, or a networked plurality—it is likely to intrude so intensely into our daily lives that alternative imaginations will emerge and will demand a hearing.

A compelling vision of the future is necessary to organize and coordinate the various market and political agents who will integrate IoT into society. Technological success is usually measured in terms set by the purveyor of that vision. Traditionally, this is a small group with a financial stake in technological development: the innovating industry. However, the intrusiveness and pervasiveness of the Internet of Things will prompt ordinary citizens to augment that vision. Citizen participation will deny any group a monopoly on that vision of the future. Such a development would be a true step in the direction of democratizing innovation. It could make IoT a wonderful thing indeed.

Applications of IoT for infrastructure

Adie Tomer is a fellow at the Brookings Institution Metropolitan Policy Program and a member of the Metropolitan Infrastructure Initiative.

The Internet of Things and the built environment are a natural fit. The built environment is essentially just a collection of physical objects—from sidewalks and streets to buildings and water pipes—that all need to be managed in some capacity. Today, we measure our shared use of those objects through antiquated analog or digital systems. Think of the electricity meter on a building, or a person manually counting pedestrians on a busy city street. Digital, Internet-connected sensors promise to modernize measurement, relaying a whole suit of indicators to centralized databases tweaked to make sense of such big data.

But let’s not fool ourselves. Simply outfitting cities and metro areas with more sensors won’t solve any of our pressing urban issues. Without governance frameworks to apply the data towards goals around transportation congestion, more efficient energy use, or reduced water waste, these sensors could be just another public investment that doesn’t lead to public benefit.

The real goal for IoT in the urban space, then, is to ensure our built environment supports broader economic, social, and environmental objectives. And that’s not a technology issue—that’s a question around leadership and agenda-setting.

Applications of IoT for health care

Niam Yaraghi is a fellow in the Brookings Institution's Center for Technology Innovation.

Health care is one of the most exciting application areas for IoT. Imagine that your Fitbit could determine if you fall, are seriously hurt, and need to be rushed to hospital. It automatically pings the closest ambulance and sends a brief summary of your medical status to the EMT personnel so that they can prepare for your emergency services even before they reach the scene. On the way, the ambulance will not need to use sirens to make way since the other autonomous vehicles have already received a notification about approaching ambulance and clear the way while the red lights automatically turn green. 

IoT will definitely improve the efficiency of health care services by reducing medical redundancies and errors. This dream will come true sooner than you think. However, if we do not appropriately address the privacy and security issues of healthcare data, then IoT can be our next nightmare. What if terrorist organizations (who are becoming increasingly technology savvy) find a way to hack into Fitbit and send wrong information to an EMT? Who owns our medical data? Can we prevent Fitbit from selling our health data to third parties? Given these concerns, I believe we should design a policy framework that encourages accountability and responsibility with regards to health data. The framework should precisely define who owns data; who can collect, store, mine and use it; and what penalties will be enforced if entities acted outside of this framework.