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Speaking from experience, research projects often require many grueling hours of deciphering obtuse data dictionaries, recoding variable definitions to be consistent, and checking for data errors. Inevitably, you miss something, and you can only hope that it does not change your results when it's time to publish the results. It would be far less difficult if data sets came prebuilt with time-consistent variable definitions and a guidebook that makes the data relatively easy to use. Not only would research projects be more efficient, but also the research would be easier to replicate and extend.

To this end, we have worked closely with our friends at the Kansas City Fed's Center for the Advancement of Data and Research in Economics (CADRE) to produce what we call a harmonized variable and longitudinally matched (HVLM) data set. This particular data set uses the basic monthly Current Population Survey (CPS) data published by the U.S. Census Bureau and the Bureau of Labor Statistics. The HVLM data set underlies products such as the Atlanta Fed's Wage Growth Tracker and the various tools on the Atlanta Fed's Labor Force Participation Dynamics web page.

You may be wondering how this data set is different from the basic monthly CPS data available at IPUMS. Like the IPUMS-CPS data, the HVLM-CPS data set uses consistent variable names and includes identifiers for longitudinally linking individuals and households over time. Unlike the IPUMS-CPS data, the HVLM-CPS also has time-consistent variable definitions. For example, the top-coded values for the age variable in the IPUMS-CPS is not the same in all years, whereas the HVLM-CPS age variable is consistently coded by using the most restrictive age top-code. As another example, the number of race categories is not the same in every year in the IPUMS-CPS (having increased from 3 to 26), while the race variable in the HLVM-CPS data set is consistently coded by using the original three race categories. Applying these types of restrictions means that the resulting data set can be more readily used to make comparisons over time.

The screenshot below shows how accessible the HVLM-CPS data are. For a visual of each variable over time, click on Charts at the top to see a PDF file of time-series charts. Code Book is an Excel file containing the details of how each variable has been coded. You can see in the screenshot how each variable ends with two numbers. These two numbers correspond to the first year that variable is available. For example, mlr76 is coded with consistent values (1 = employed, 2 = unemployed and 3 = not in labor force) from 1976 until today. The Data File is a Stata (.dta) format file with variable labels already attached. For users wishing to use the panel structure of the CPS survey, lags of many variables are provided on the data set already—for example, mlr76_tm12 is an individual's labor force status from 12 months ago).

Clicking on the c icon under Code Book opens a screen with the values of the corresponding variable. The screenshot shows lfdetail94 and nlfdetail94 as examples. The first variable, lfdetail94, contains a large amount of detail on those engaged in the labor market, while nlfdetail94 contains detailed categories for those not engaged in the labor market.

The HVLM-CPS data set is freely available to download and is updated within hours of when the CPS microdata are published, thanks to sophistical coding techniques and the fast processors at the Kansas City Fed. To access the data, go to the CADRE page (using Chrome or Firefox). At the top right, select Sign in, then Google Login. Then, under schema, select Harmonized Variable and Longitudinally Matched [Atlanta Federal Reserve] (1976–Present).

By Ellyn Terry, an economic policy analysis specialist in the Atlanta Fed's research department

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The Atlanta Fed recently hosted its 24th annual Financial Markets Conference, whose theme was Mapping the Financial Frontier: What Does the Next Decade Hold? The conference addressed a variety of issues pertinent to the future of the financial system. Among the sessions touching on macroeconomics was a keynote speech on corporate debt by Federal Reserve Board chair Jerome Powell and another on revitalizing America by Massachusetts Institute of Technology (MIT) professor Simon Johnson. The conference also included a panel discussion of the Fed's plans for implementing monetary policy in the future. This macroblog post reviews these macroeconomic discussions. A companion Notes from the Vault post reviews conference sessions on blockchain technology, data privacy, and postcrisis developments in the markets for mortgage backed securities.

Chair Powell's thoughts on corporate debt levels
Chair Powell's keynote speech focused on the risks posed by increases in corporate debt levels. In his speech, titled "Business Debt and Our Dynamic Financial System" (which you can watch or read), Powell began by observing that business debt levels have increased by a variety of measures including the ratios of debt to gross domestic product as well as the debt to the book value of corporate assets. These higher debt ratios alone don't currently pose a problem because corporate profits are high and interest rates are low. Powell noted some reasons for concern, however, including the reduced average quality of investment-grade bonds, with more corporate debt concentrated in the "lowest rating—a phenomenon known as the 'triple-B cliff'".

Powell noted several differences between the recent increase in corporate debt and the increase in household debt prior to the 2007–09 crisis that offset these risks. These differences include a more moderate rate of increase in corporate debt, the lack of a feedback loop from debt levels to asset prices, reduced leverage in the banking system, and less liquidity risk.

Powell concluded his remarks by saying that although business debt does pose a risk of amplifying a future downturn, it does not appear to pose "notable risks to financial stability." Finally, he noted that the Fed is working toward a more thorough understand of the risks.

Simon Johnson on jumpstarting America
Simon Johnson started his keynote speech by discussing Amazon's search for a second headquarters city. The company received proposals from 238 cities across the country (and Canada). However, in the end, it selected two large metropolitan areas—New York and Washington, DC—that were already among the leaders in creating new tech jobs. Although many places around the country want growth in good jobs, he said the innovation economy is "drawn disproportionately to these few places."

Johnson's remedy for this disproportionate clustering is for the federal government to make a deliberate effort to encourage research and development in various technical areas at a number of research universities around the country. This proposal is based on his book with fellow MIT economist Jonathan Gruber. They argue that the proposal encourages "exactly what the U.S. did in the '40s, '50s, and '60s," which was to help the United States develop new technology to be used in World War II and the Cold War.

Johnson proposed that the funding for new technical projects be allocated through a nationwide competition that intentionally seeks to create new tech hubs. In making his case, Johnson observed that the view that "all the talent is just in six places is fundamentally wrong." Johnson said that he and his coauthor found 102 cities in 36 states that have a substantial proportion of college graduates and relatively low housing prices. Moreover, Johnson observed that existing tech centers' cost of living has become very high, and those cities have substantial political limits on their ability to sustain new population growth. If some of these 102 potential hubs received the funding to start research and provide capital to business, Johnson argued, overall growth in the United States could increase and be more evenly distributed.

Discussing the implementation of monetary policy
The backdrop for the session on monetary policy implementation was postcrisis developments in the Fed's approach to implementing monetary policy. As the Fed's emergency lending programs started to recede after the crisis, it started making large-scale investments in agency mortgage backed securities and U.S. Treasuries. This program, widely (though somewhat misleadingly) called "quantitative easing," or QE, pumped additional liquidity into securities markets and played a role in lowering longer-term interest rates. As economic conditions improved, the Fed first started raising short-term rates and then adopted a plan to shrink its balance sheet starting in 2018. However, earlier this year, the Fed announced plans to stop shrinking the balance sheet in September if the economy performs as it expected.

Julia Coronado, president of MacroPolicy Perspectives, led the discussion of the Fed's plans, and a large fraction of that discussion addressed its plans for the size of the balance sheet. Kevin Warsh, former Federal Reserve governor and currently a visiting fellow at Stanford University's Hoover Institution, provided some background information on the original rationale for QE, when many financial markets were still rather illiquid. However, he argued that those times were extraordinary and that "extraordinary tools are meant for extraordinary circumstances." He further expressed the concern that using QE at other times and for other reasons, such as in response to regulatory policy, would increase the risk of political involvement in monetary policy.

During the discussion, Chicago Fed president Charles Evans argued that QE is likely to remain a necessary part of the Fed's toolkit. He observed that slowing labor force growth, moderate productivity growth, and low inflation are likely to keep equilibrium short-term interest rates low. As a result, the Fed's ability to lower interest rates in a future recession is likely to remain constrained, meaning that balance sheet expansion will remain a necessary tool for economic stimulus.

Ethan Harris, head of global economics research at Bank of America Merrill Lynch, highlighted the potential stress the next downturn would place on the Fed. Harris observed that "other central banks have virtually no ammunition" to fight the next downturn, a reference to the negative policy rates and relatively larger balance sheets of some other major central banks. This dynamic prompted his question, "How is the Fed, on its own, going to fight the next crisis?"

The conference made clear the importance of the links between financial markets and the macroeconomy, and this blog post focused on just three of them. I encourage you to delve into the rest of the conference materials to see these and other important discussions.

By Larry Wall, director of the Atlanta Fed's Center for Financial Innovation and Stability

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Nearly two months have passed since tax day, but the full impact of the 2017 Tax Cut and Jobs Act (TCJA) has yet to be fully assessed. Both the data, and in fact the rules themselves, are still incomplete. Nonetheless, conventional wisdom seems to hold that the legislation created winners and losers, and that the losers primarily reside in so-called "blue" states—those where the majority of voters have consistently gone for the Democratic presidential candidate in recent elections.

The source of this belief springs from the newly imposed limitations on federal deductions of state and local taxes, or SALT, and the disproportional impact of these limitations on taxpayers in high-tax, high-income states—the majority of which are blue. A CNBC report from last week on pushback from blue-state politicians summarizes a typical reaction: "Lawmakers from high-tax districts say their constituents have suffered from the provision in the tax plan."

Is this view justified? In our own research, we focus on the long-term effects of the TCJA with the assumption that the legislation's provisions eventually become permanent. (The individual tax cuts are currently scheduled to expire in 2025.) Examining individual households from the 2016 Federal Reserve Board of Governors' Survey of Consumer Finances and incorporating state-specific tax provisions, we reached a few major conclusions regarding TCJA's impact.

First, the overwhelming majority of taxpayers across the country stand to enjoy lifetime gains in after-tax income as a result of the TCJA. The following chart documents our estimates of lifetime gains in every state and the District of Columba, by state-specific wealth quintile. (Wealth here is defined inclusive of human wealth—that is, it includes the present-value of expected wage and salary income.) The chart has a lot of information, but the key point here is the preponderance of blue-shaded areas, which represent the proportion of gainers in each wealth quintile, in each state. Outright losers—represented in the chart by the red shaded areas in each row—are confined to a very small proportion of the wealthy.

What is true is that the tax cuts were relatively more favorable, in percentage terms, to red-state residents. Our estimates show that the percentage reduction in the present value of lifetime taxes for red states is nearly twice that of blue states—but not in absolute terms. We calculate the average change in lifetime after-tax income for individuals in blue states to be $25,781, compared to a $23,094 average for red states. (In absolute terms, "purple" states—those averaging less than a 5 percent margin for either party over the past five election cycles—had the largest average gain of all, at $27,042.)

Another point worth emphasizing: the relatively smaller blue-state gains don't result from the fact that they are high-income states but instead result from the fact that they are high-tax states. When we control for the demographic make-up of states—and hence keep the income distribution across states constant—we get essentially the same implications for the distribution of TCJA tax gains.

It is likely true that blue-state taxpayers didn't gain as much as their red-state counterparts as a result of the TCJA. But for the most part, our estimates suggest they did indeed gain.

By Alan Auerbach of the University of California at Berkeley; Darryl Koehler and Michael Leiseca of Economic Security Planning Inc.; Laurence Kotlikoff and Victor Ye of Boston University; and David Altig, Patrick Higgins, and Ellyn Terry of the Atlanta Fed

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In a recent recent macroblog post, my colleague John Robertson found that the recent rise in female prime-age (ages 25 to 54 years) labor force participation (LFP) over the last few years has been driven in large part by increased participation among Hispanic women. (Hispanic refers to people of Cuban, Mexican, Puerto Rican, South or Central American, or other Spanish culture or origin regardless of race.) Much of the LFP improvement among Hispanic women has come as they've shifted away from household duties.

To understand this development and determine whether it's a trend likely to continue, we look at trends in the activities of younger Hispanic women. In particular, we look at the so-called NEETs rate among women ages 16 to 24. The NEETs rate is the share of the youth population that is "Not Employed or pursing Education or Training." This group is sometimes referred to as "disconnected youth" or "opportunity youth" because they are generally less likely to be attached to the labor force as they move into their prime working years and are at higher risk of experiencing long-term unemployment, persistent poverty, poor health, and criminal behavior.

A look at the next chart shows substantial improvement in the NEETs rate among young Hispanic women over the last two decades. The gap has narrowed considerably and in recent years has tracked much more closely with black non-Hispanic women.

The declining NEETs rate for young Hispanic women primarily reflects shifting preferences toward more education and away from household responsibilities. As you can see in the next chart, the share of young Hispanic women who are in education or training has risen over the last two decades, up nearly 19 percentage points since 2000. Their share now more closely matches that of young black and white non-Hispanic women.

Mirroring the rise in educational activities has been a shrinking share of young Hispanic women who are not in the labor force because they are taking care of home or family, as the following chart shows.

Young Hispanic women have invested increased time in their education over the last two decades and as a result have higher average levels of educational attainment than earlier cohorts moving into their prime working years. To see this, the next chart shows the distribution of educational attainment over time for Hispanic women aged 25.

The higher levels of LFP in recent years among prime-age Hispanic women partly reflects the greater investment in education by younger Hispanic women. If this trend continues—and there is no obvious reason why it wouldn't—then it will help drive even higher labor force attachment for prime-age Hispanic women in the years to come.

By Whitney M. Strifler, an economic policy analysis specialist in the Atlanta Fed's research department

 

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Two interesting, and important, documents crossed our desk last week. The first was the 2019 edition of the Economic Report of the President. What particularly grabbed our attention was the following statement from Chapter 3:

Fundamentally, when people opt to neither work nor look for work it is an indication that the after-tax income they expect to receive in the workforce is below their "reservation wage"—that is, the minimum value they give to time spent on activities outside the formal labor market.

That does not strike us as a controversial proposition, which makes the second of last week's documents—actually a set of documents from the U.S. Department of Health and Human Services (HHS)—especially interesting.

In that series of documents, HHS's Nina Chien and Suzanne Macartney point out a couple of things that are particularly important when thinking about the effect of tax rates on after-tax income and the incentive to work. The first, which is generally appreciated, is that the tax rates that matter with respect to incentives to work are marginal tax rates—the amount that is ceded to the government on the next $1 of income received. The second, and less often explicitly recognized, is that the amount ceded to the government includes not only payments to the government (in the form of, for example, income taxes) but also losses in benefits received from the government (in the form of, for example, Medicaid or child care assistance payments).

The fact that effective marginal tax rates are all about the sum of explicit tax payments to the government and lost transfer payments from the government applies to us all. But it is especially true for those at the lower end of the income distribution. These are the folks (of working age, anyway) who disproportionately receive means-tested benefit payments. For low-wage workers, or individuals contemplating entering the workforce into low-wage jobs, the reduction of public support payments is by far the most significant factor in effective marginal tax rates and the consequent incentive to work and acquire skills.

The implication of losing benefits for an individual's effective marginal tax rate can be eye-popping. From Chien and Macartney (Brief #2 in the series):

Among households with children just above poverty, the median marginal tax rate is high (51 percent); rates remain high (never dipping below 45 percent) as incomes approach 200 percent of poverty.

Our own work confirms the essence of this message. Consider a representative set of households, with household heads aged 30–39, living in Florida. (Because both state and local taxes and certain transfer programs vary by state, geography matters.) Now think of calculating the wealth for each household—wealth being the sum of their lifetime earnings from working and the value of their assets net of liabilities—and grouping the households into wealth quintiles. (In other words, the first quintile would the 20 percent of households with the lowest wealth, the fifth quintile would be the 20 percent of households with the highest wealth.)

What follows are the median effective marginal tax rates that we calculate from this experiment:

Wealth percentile

Median Effective Marginal Tax Rate

Lowest quintile

44%

Second quintile

43%

Third quintile

32%

Fourth quintile

33%

Highest quintile

35%

Note: The methodology used in these calculations is described here and here.
Source: 2016 Survey of Consumer Finances, the Fiscal Analyzer

Consistent with Chien and Macartney, the median effective marginal tax rates for the least wealthy are quite high. Perhaps more troubling, underlying this pattern of effective tax rates is one especially daunting challenge. The source of the relatively high effective rates for low-wealth individuals is the phase-out of transfer payments, some of which are so abrupt that they are referred to as benefits, or fiscal, cliffs. Because these payments differ widely across family structure, income levels within a quintile, and state law, the marginal tax rates faced by individuals in the lower quintiles are very disparate.

Note: The methodology used in these calculations is described here and here.
Source: 2016 Survey of Consumer Finances, the Fiscal Analyzer

The upshot of all of this is that "tax reform" aimed at reducing the disincentives to work at the lower end of the income scale is not straightforward. Without such reform, however, it is difficult to imagine a fully successful approach to (in the words of the Economic Report) "[increasing] the after-tax return to formal work, thereby increasing work incentives for potential entrants into the labor market."

By David Altig, executive vice president and research director in the Atlanta Fed's Research Department, and

 

Laurence J. Kotlikoff, William Warren Fairfield Professor at Boston University

 

Patrick Higgins, Darryl Koehler, Michael Leiseca, and Ellyn Terry contributed to the calculations reported in this post.

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Nearly two months have passed since tax day, but the full impact of the 2017 Tax Cut and Jobs Act (TCJA) has yet to be fully assessed. Both the data, and in fact the rules themselves, are still incomplete. Nonetheless, conventional wisdom seems to hold that the legislation created winners and losers, and that the losers primarily reside in so-called "blue" states—those where the majority of voters have consistently gone for the Democratic presidential candidate in recent elections.

The source of this belief springs from the newly imposed limitations on federal deductions of state and local taxes, or SALT, and the disproportional impact of these limitations on taxpayers in high-tax, high-income states—the majority of which are blue. A CNBC report from last week on pushback from blue-state politicians summarizes a typical reaction: "Lawmakers from high-tax districts say their constituents have suffered from the provision in the tax plan."

Is this view justified? In our own research, we focus on the long-term effects of the TCJA with the assumption that the legislation's provisions eventually become permanent. (The individual tax cuts are currently scheduled to expire in 2025.) Examining individual households from the 2016 Federal Reserve Board of Governors' Survey of Consumer Finances and incorporating state-specific tax provisions, we reached a few major conclusions regarding TCJA's impact.

First, the overwhelming majority of taxpayers across the country stand to enjoy lifetime gains in after-tax income as a result of the TCJA. The following chart documents our estimates of lifetime gains in every state and the District of Columba, by state-specific wealth quintile. (Wealth here is defined inclusive of human wealth—that is, it includes the present-value of expected wage and salary income.) The chart has a lot of information, but the key point here is the preponderance of blue-shaded areas, which represent the proportion of gainers in each wealth quintile, in each state. Outright losers—represented in the chart by the red shaded areas in each row—are confined to a very small proportion of the wealthy.

What is true is that the tax cuts were relatively more favorable, in percentage terms, to red-state residents. Our estimates show that the percentage reduction in the present value of lifetime taxes for red states is nearly twice that of blue states—but not in absolute terms. We calculate the average change in lifetime after-tax income for individuals in blue states to be $25,781, compared to a $23,094 average for red states. (In absolute terms, "purple" states—those averaging less than a 5 percent margin for either party over the past five election cycles—had the largest average gain of all, at $27,042.)

Another point worth emphasizing: the relatively smaller blue-state gains don't result from the fact that they are high-income states but instead result from the fact that they are high-tax states. When we control for the demographic make-up of states—and hence keep the income distribution across states constant—we get essentially the same implications for the distribution of TCJA tax gains.

It is likely true that blue-state taxpayers didn't gain as much as their red-state counterparts as a result of the TCJA. But for the most part, our estimates suggest they did indeed gain.

By Alan Auerbach of the University of California at Berkeley; Darryl Koehler and Michael Leiseca of Economic Security Planning Inc.; Laurence Kotlikoff and Victor Ye of Boston University; and David Altig, Patrick Higgins, and Ellyn Terry of the Atlanta Fed

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In a recent recent macroblog post, my colleague John Robertson found that the recent rise in female prime-age (ages 25 to 54 years) labor force participation (LFP) over the last few years has been driven in large part by increased participation among Hispanic women. (Hispanic refers to people of Cuban, Mexican, Puerto Rican, South or Central American, or other Spanish culture or origin regardless of race.) Much of the LFP improvement among Hispanic women has come as they've shifted away from household duties.

To understand this development and determine whether it's a trend likely to continue, we look at trends in the activities of younger Hispanic women. In particular, we look at the so-called NEETs rate among women ages 16 to 24. The NEETs rate is the share of the youth population that is "Not Employed or pursing Education or Training." This group is sometimes referred to as "disconnected youth" or "opportunity youth" because they are generally less likely to be attached to the labor force as they move into their prime working years and are at higher risk of experiencing long-term unemployment, persistent poverty, poor health, and criminal behavior.

A look at the next chart shows substantial improvement in the NEETs rate among young Hispanic women over the last two decades. The gap has narrowed considerably and in recent years has tracked much more closely with black non-Hispanic women.

The declining NEETs rate for young Hispanic women primarily reflects shifting preferences toward more education and away from household responsibilities. As you can see in the next chart, the share of young Hispanic women who are in education or training has risen over the last two decades, up nearly 19 percentage points since 2000. Their share now more closely matches that of young black and white non-Hispanic women.

Mirroring the rise in educational activities has been a shrinking share of young Hispanic women who are not in the labor force because they are taking care of home or family, as the following chart shows.

Young Hispanic women have invested increased time in their education over the last two decades and as a result have higher average levels of educational attainment than earlier cohorts moving into their prime working years. To see this, the next chart shows the distribution of educational attainment over time for Hispanic women aged 25.

The higher levels of LFP in recent years among prime-age Hispanic women partly reflects the greater investment in education by younger Hispanic women. If this trend continues—and there is no obvious reason why it wouldn't—then it will help drive even higher labor force attachment for prime-age Hispanic women in the years to come.

By Whitney M. Strifler, an economic policy analysis specialist in the Atlanta Fed's research department

 

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Two interesting, and important, documents crossed our desk last week. The first was the 2019 edition of the Economic Report of the President. What particularly grabbed our attention was the following statement from Chapter 3:

Fundamentally, when people opt to neither work nor look for work it is an indication that the after-tax income they expect to receive in the workforce is below their "reservation wage"—that is, the minimum value they give to time spent on activities outside the formal labor market.

That does not strike us as a controversial proposition, which makes the second of last week's documents—actually a set of documents from the U.S. Department of Health and Human Services (HHS)—especially interesting.

In that series of documents, HHS's Nina Chien and Suzanne Macartney point out a couple of things that are particularly important when thinking about the effect of tax rates on after-tax income and the incentive to work. The first, which is generally appreciated, is that the tax rates that matter with respect to incentives to work are marginal tax rates—the amount that is ceded to the government on the next $1 of income received. The second, and less often explicitly recognized, is that the amount ceded to the government includes not only payments to the government (in the form of, for example, income taxes) but also losses in benefits received from the government (in the form of, for example, Medicaid or child care assistance payments).

The fact that effective marginal tax rates are all about the sum of explicit tax payments to the government and lost transfer payments from the government applies to us all. But it is especially true for those at the lower end of the income distribution. These are the folks (of working age, anyway) who disproportionately receive means-tested benefit payments. For low-wage workers, or individuals contemplating entering the workforce into low-wage jobs, the reduction of public support payments is by far the most significant factor in effective marginal tax rates and the consequent incentive to work and acquire skills.

The implication of losing benefits for an individual's effective marginal tax rate can be eye-popping. From Chien and Macartney (Brief #2 in the series):

Among households with children just above poverty, the median marginal tax rate is high (51 percent); rates remain high (never dipping below 45 percent) as incomes approach 200 percent of poverty.

Our own work confirms the essence of this message. Consider a representative set of households, with household heads aged 30–39, living in Florida. (Because both state and local taxes and certain transfer programs vary by state, geography matters.) Now think of calculating the wealth for each household—wealth being the sum of their lifetime earnings from working and the value of their assets net of liabilities—and grouping the households into wealth quintiles. (In other words, the first quintile would the 20 percent of households with the lowest wealth, the fifth quintile would be the 20 percent of households with the highest wealth.)

What follows are the median effective marginal tax rates that we calculate from this experiment:

Wealth percentile

Median Effective Marginal Tax Rate

Lowest quintile

44%

Second quintile

43%

Third quintile

32%

Fourth quintile

33%

Highest quintile

35%

Note: The methodology used in these calculations is described here and here.
Source: 2016 Survey of Consumer Finances, the Fiscal Analyzer

Consistent with Chien and Macartney, the median effective marginal tax rates for the least wealthy are quite high. Perhaps more troubling, underlying this pattern of effective tax rates is one especially daunting challenge. The source of the relatively high effective rates for low-wealth individuals is the phase-out of transfer payments, some of which are so abrupt that they are referred to as benefits, or fiscal, cliffs. Because these payments differ widely across family structure, income levels within a quintile, and state law, the marginal tax rates faced by individuals in the lower quintiles are very disparate.

Note: The methodology used in these calculations is described here and here.
Source: 2016 Survey of Consumer Finances, the Fiscal Analyzer

The upshot of all of this is that "tax reform" aimed at reducing the disincentives to work at the lower end of the income scale is not straightforward. Without such reform, however, it is difficult to imagine a fully successful approach to (in the words of the Economic Report) "[increasing] the after-tax return to formal work, thereby increasing work incentives for potential entrants into the labor market."

By David Altig, executive vice president and research director in the Atlanta Fed's Research Department, and

 

Laurence J. Kotlikoff, William Warren Fairfield Professor at Boston University

 

Patrick Higgins, Darryl Koehler, Michael Leiseca, and Ellyn Terry contributed to the calculations reported in this post.

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