- BLS Handbook of Methods
- Bureau of Economic Analysis
- Bureau of Labor Statistics
- Congressional Budget Office
- Economic Data - FRED® II, St. Louis Fed
- Office of Management and Budget
- Statistics: Releases and Historical Data, Board of Governors
- U.S. Census Bureau Economic Programs
- White House Economic Statistics Briefing Room
November 21, 2019
Private and Central Bank Digital Currencies
The Atlanta Fed recently hosted a workshop, "Financial System of the Future," which was cosponsored by the Center for the Economic Analysis of Risk at Georgia State University. This macroblog post discusses the workshops discussion of digital currency, including Bitcoin, Libra, and central bank digital currency (CBDC). A companion Notes from the Vault post provides some highlights from the rest of the workshop.
The introduction of Bitcoin has sparked considerable interest in cryptocurrencies since its introduction in the 2008 paper "Bitcoin: A Peer-to-Peer Electronic Cash System" by Satoshi Nakamoto. However, for all its success, Bitcoin is not close to becoming a widely accepted electronic cash system. Why it has yet to achieve its original goals is the topic of a paper by New York University professors Franz Hinzen and Kose John, along with McGill University professor Fahad Saleh titled "Bitcoin's Fatal Flaw: The Limited Adoption Problem."
Their paper suggests that the inability of Bitcoin to achieve wider adoption is the result of the interaction of three features: the need for agreement on ledger contents (in blockchain terminology, "consensus"), free entry for creating new blocks (permissionless or decentralized), and an artificial supply constraint. The supply constraint means that an increase in demand leads to higher Bitcoin prices. Such a valuation increase expands the network seeking to create new blocks (that is, increases the number of Bitcoin "miners"). But an increase in the network size slows the consensus process as it takes time for newly created blocks to reach all of the miners across the internet. The end result is an increase in the time needed to make a payment, reducing the value of Bitcoin as a means of payment—a significant consideration, obviously, for any type of currency.
As an alternative to the Bitcoin consensus protocol, they suggest a public, permissioned blockchain that results in faster transactions because it imposes limits on who can create new blocks. In their system, new blocks would be selected based on a weighted vote based on the blockchain's cyptocurrency held by validators (in other words, approved block creators). If validators were to approve new and malicious blocks, that would erode the value of the validator's existing cryptocurrency holdings and thus provide an incentive to behave honestly.
Federal Reserve Bank of Atlanta visiting economist Warren Weber presented some work with me on Libra, the new digital coin proposed by Facebook. Weber began by pointing to another problem with using Bitcoin in payments: the cryptocurrency's volatile value. Libra solves this problem by proposing to hold a portfolio of assets denominated in sovereign currencies, such as the U.S. dollar, that will provide one-for-one backing of the value of Libra. This approach is similar to that taken by some other "stablecoins," with the exception that Libra proposes to be stable relative to an index of several currencies whereas other stablecoins are designed to be stable with respect to only one sovereign currency.
Drawing on his background in economic history, Weber observes that introducing a new private currency is hard, but not impossible. For example, he pointed to the Stockholm Bank notes issued in Sweden in the 1660s. These notes worked because they were more convenient than the alternatives used in that country. The fact that other U.S. payments systems are heavily bank-based might afford an advantage to Libra.
Although no one is certain of the public's interest in using Libra, policymakers around the world have taken considerable interest in the potential implications of Libra for monetary policy and financial regulation. Could Libra significantly reduce the use of the domestic sovereign currencies in some countries, thus reducing the effectiveness of monetary policy? How might financial institutions providing Libra-based services be regulated?
One of the other possible policy responses to Libra is central banks' introduction of digital currency. Economists Itai Agur, Anil Ari, and Giovanni Dell'Ariccia from the International Monetary Fund consider some of the issues in developing a CBDC in their paper "Designing Central Bank Digital Currencies." They start by observing some important differences between cash and bank deposits. Cash is completely anonymous in that it reveals nothing about the identity of the payer. However, lost or stolen cash can't be recovered, so it lacks security. Deposits have the opposite properties—they are not anonymous, but there is a mechanism to recover lost or stolen funds.
The paper develops a model in which CBDC can be designed to operate at multiple points on a continuum between deposits and cash. The key concern from a public policy perspective is that the more CBDC operates like bank deposits, the more it will depress bank credit and output. However, if the CBDC operates too much like paper currency, then it could supplant paper currency and eliminate a payments method that some individuals prefer. The paper proposes that CBDC be designed to look more like currency to minimize the extent to which CBDC replaces bank deposits. The problem then becomes how to avoid CBDC reducing the usage of cash to the point where cash is no longer viable. (For example, merchants could decide to stop accepting cash because they find that the few transactions using cash do not justify the costs of accepting it.) The way the paper proposes to keep CBDC from being too attractive relative to cash by applying a negative interest rate to the CBDC. The result would be that those who most highly value CBDC will use it, but the negative rate will likely deter enough people so that cash remains a viable payments mechanism.
November 01, 2019
New Evidence Points to Mounting Trade Policy Effects on U.S. Business Activity
Trade worries remain at the forefront of economic news. Average tariffs on Chinese imports now stand at 21 percent, up from 3 percent in March 2018. Earlier this month, President Trump suspended plans for further tariff hikes on Chinese goods. Also this month, the U.S. is rolling out new tariffs on $7.5 billion worth of imports from Europe. On another front, fears are growing that Congress may not approve the U.S.-Mexico-Canada Trade Agreement, the intended successor to the North American Free Trade Agreement. Data from the folks at policyuncertainty.com say that articles about trade policy uncertainty in U.S. newspapers were more than 10 times as numerous in the third quarter of 2019 as the average from 1985 to 2010.
Trade policy worries extend beyond the newswires. We hear concerns about trade policy in reports from Main Street firms in the Sixth District collected through our Regional Economic Information Network and, more broadly, in the Federal Reserve's Beige Book. Amid reports of softening manufacturing conditions in the U.S., slowing growth in payroll employment, and a drop-off in business investment, it's natural to wonder whether trade policy is at least partly to blame. Professional forecasters seem to think so. For instance, the International Monetary Fund (IMF) forecasts that the U.S.-China trade dispute will shave roughly three-fourths of a percent from global output by 2020, which, as the IMF's managing director noted, is "equivalent to the whole economy of Switzerland."
Over the past year and a half, we have been keenly interested in how trade policy worries affect business decision making. In August 2018, we reported that trade concerns prompted about 1 in 5 firms to re-evaluate their capital investment decisions. At the same time, only 6 percent of the firms in our sample had then decided to cut or defer previously planned capital expenditures in response to trade policy developments. Early this year, we noted that the hit to aggregate investment from trade tensions and tariff worries was modest in 2018, but firms believed the impact would increase in 2019.
As U.S.-China trade tensions escalated during the third quarter of this year, we went back into the field, posing another set of trade-related questions to panelists in our Survey of Business Uncertainty (SBU). This time around, we asked both backward- and forward-looking questions about the perceived effects of trade policy, and we expanded the scope of our questions to cover employment and sales in addition to capital expenditures.
Overall, our results say that the negative effects of trade policy developments on U.S. business activity have grown over time, particularly for firms with an international reach. Trade policy effects on the business sector as a whole remain modest but larger than we saw six or 12 months ago.
Twelve percent of surveyed firms reported cutting or postponing capital expenditures in the first six months of 2019 because of trade tensions and tariff worries (see exhibit 1). That's twice the share when we asked the same question a year earlier. Given the capital-intensive nature of manufacturing, it is perhaps more concerning that one in five manufacturing firms now report cutting or postponing capital expenditures because of trade policy tensions.
We also find that tariff hikes and trade policy tensions now exert a larger negative impact on gross U.S. business investment. Exhibit 2 uses SBU data on whether firms changed their capital expenditures due to trade policy tensions and, if so, by how much and in which direction. Column (1) reports the average percentage impact in the sample, where we weight each firm's response by its capital stock value. To estimate the dollar impact of trade policy developments in column (2), we multiply the weighted-average percent change by actual U.S. business investment in the first half of 2019, which yields an estimated effect on U.S. business investment of about minus $40 billion.
This estimated trade policy hit to aggregate investment is modest but roughly double what we previously found for the second half of 2018. Our results say that investment is hardest hit in manufacturing and construction, though perhaps for different reasons. The larger response for manufacturing is likely due to its higher international exposure, both in direct goods trade and across the supply chain. For construction, the impact is likely due to an increased cost of imported materials and equipment.
Exhibit 3 reports the estimated effects of tariff hikes and trade policy tensions on private sector employment and sales in the first half of 2019. According to our results (reached by using the same procedure as in Exhibit 2), these developments subtracted about 40,000 jobs per month from nonfarm payrolls and about $259 billion in sales over the first half of the year. Though this employment impact is sizable, it is not estimated very precisely (one standard error corresponds to about 24,000 jobs per month). The estimates for the impact of tariff hikes and trade policy tensions imply about $110,000 in lost sales per lost job.
Notes on Exhibit 3: In Panel A, column (1) reports the employment-weighted mean response to questions about whether tariff hikes and trade policy tensions caused the firm to alter its employment level in the first half of 2019 and, if so, by what percentage amount. We deleted three questionable responses to the employment question that we could not verify. To obtain the aggregate employment impact in column (2), we multiplied the column (1) value by the average nonfarm private sector payroll employment in the first half of 2019. The "Reweighted" row reflects a re-weighting of the SBU data to match the one-digit industry distribution of private sector payroll employment. In Panel B, column (1) reports the sales-weighted mean response to questions about whether tariff hikes and trade policy tensions affected the firm's sales in the first half of 2019 and, if so, by what percentage amount. To obtain the aggregate sales impact in column (2), we multiplied the column (1) value by Nominal Gross Output: Private Industries. According to the U.S. Bureau of Economic Analysis, gross output is, "principally, a measure of an industry's sales or receipts. These statistics capture an industry's sales to consumers and other final users (found in GDP), as well as sales to other industries (intermediate inputs not counted in GDP). They reflect the full value of the supply chain by including the business-to-business spending necessary to produce goods and services and deliver them to final consumers." The "Reweighted" row reflects a re-weighting of the SBU data to match the one-digit industry distribution of private sector gross output. Standard errors are reported in brackets.
We also asked forward-looking questions to assess whether firms think trade policy worries will continue to dampen their business activities in the second half of 2019. Exhibit 4 summarizes our findings in this regard. SBU respondents anticipate that the impact of trade policy on their second-half sales revenue will be similar to what they reported for the first half of 2019, but they anticipate somewhat larger negative effects on their capital expenditures and employment. Across the private sector as a whole, SBU respondents see their capital expenditures as down by 3.8 percent in the second half of 2019 due to tariff hikes and trade policy tensions.
In sum, as trade policy tensions escalated in the first half of 2019, our results say that businesses took a hit to their sales and backed off on hiring and investment. Moreover, firms anticipate that the negative effects will continue during the second half of 2019. Our estimated impact magnitudes are rising over time but remain modest.
We should also note that our estimates do not capture certain effects. For instance, they don't capture the pass-through of tariff hikes to American consumers in the form of higher prices or to American companies in the form of compressed margins and lower profits. Tariff hikes and trade policy tensions also slow growth in the global economy, with negative effects on the U.S. economy. These blowback effects are also outside the scope of our investigation.
September 26, 2019
Digging into Older Americans’ Flat Participation Rate
The rate of labor force participation (LFP) by people age 55 and over had been rising during the decade leading up to the Great Recession. But more recently, as the following chart shows, the share of older individuals engaged in the labor market has barely budged. (We should note that for all the charts in this post, the data are from the Current Population Survey from the U.S. Bureau of Labor Statistics and the authors' calculations.) What behavioral and demographic factors could be underlying these trends?
We can use the Atlanta Fed's Labor Force Dynamics web page to explore why the 55-and-over population has a relatively flat LFP rate of late. One factor working to depress older Americans' LFP rate is the increase in the share of those 55 and older who are retired: from 46.9 percent in the second quarter of 2014 to 47.8 percent in the second quarter of 2019 (see the chart).
This change in the overall retirement rate of the 55-plus cohort is actually the result of the union of two opposite forces—one demographic and one behavioral. From the perspective of demographics, a greater share of the population is reaching the typical retirement age threshold of 65. For instance, the share of people aged 65 and older has increased from 18 percent in the second quarter of 2014 to 20.3 percent in the second quarter of 2019 (see the chart).
This demographic shift is important because the retirement rate is much higher for those 65 and older compared to those from 60 to 64 years old. For example, the retirement rate in 2019 for those 60–64 is around 25 percent, but it's 56 percent for those aged 65–69, and it's 81 percent for those 70 and older (see the chart).
However, the retirement rate among those 60–64 and those 65–69 has also declined in recent years. This change in behavior within older age groups is partly offsetting the downward pressure on participation coming from having a larger share of population over 65.
A second factor that has worked against the overall retirement effect and helped push up the LFP rate of the 55-plus population has been a decline in the share of older individuals saying they are not participating in the labor force because of disability or illness. This rate has decreased from 8.6 percent in the second quarter of 2014 to 8.1 percent in the second quarter of 2019 (see the chart).
As the chart below shows, this shift is largely a demographic effect. Nonparticipation because of disability/illness drops off significantly as people turn 65, so the fact that a greater share of people 55 and older are now 65 and older decreases the overall share of nonparticipation for disability/illness reasons as well. However, it's important to note that a lower disability/illness rate for those 65 and older doesn't mean that this older population is actually less disabled or sick. It is more that individuals in the data fall into only one category, and those 65 and older are more likely to say they are retired than to say they are disabled or ill.
A third force helping push up the LFP rate is the decline in the share of older individuals on the sidelines of the labor market—those who are not participating but nonetheless want a job—from 1.8 percent in the second quarter of 2014 to 1.6 percent in the second quarter of 2019 (see the chart).
This “shadow labor force” effect on participation is mostly the result of changes in behavior (that is, a reduced propensity to remain on the sidelines within age groups) rather than from demographic changes because the rate has declined within age groups, whereas the levels are roughly similar across age groups (see the chart).
To put these various pieces together, the following chart summarizes the overall contributions of demographic and behavioral forces on the LFP rate among the 55-plus population between the second quarter of 2014 and the second quarter of 2019. The chart shows that the contributions stemming from changes in demographics and behavior have largely offset each other.
As I've already described, the biggest demographic effects come from having more people at an age with two specific characteristics: a relatively high rate of nonparticipation because of retirement and a relatively low rate of nonparticipation because of disability or illness. But as the following chart shows, from the second quarter of 2014 to the second quarter of 2109, the retirement demographic dominates, so the overall demographic LFP effect is a negative one.
Conversely, the largest behavioral shifts are, first, a lower propensity within older age groups to stay out of the labor force because of retirement and, second, a lower share of older people wanting a job but not looking for one. As the following chart shows, from the second quarter of 2014 to the second quarter of 2019, these behavioral changes combine to push up LFP by enough to nearly offset the demographic shifts.
It seems reasonable to presume that the aging population will continue to be an important source of downward pressure on the LFP rate of older Americans over the next few years. What will be telling is whether or not the behavioral shifts we have seen will persist as strongly as they have up to this point and continue to provide a countervailing positive influence on participation.
The tools on the Atlanta Fed's Labor Force Dynamics web page are very useful for understanding what's behind changes in LFP for different demographic groups. In addition to cuts for different age groups, you can look at differences between men and women and among different racial and ethnic groups and levels of educational attainment. You can download the chart data—the charts in this blog are downloaded images from the web page—and even download the underlying Current Population Survey microdata from the Kansas City Fed's CADRE web page if you want to create your own cuts. Check it out!
August 05, 2019
What the Wage Growth of Hourly Workers Is Telling Us
The Atlanta Fed's Wage Growth Tracker has shown an uptick during the past several months. The 12-month average reached 3.7 percent in June, up from 3.2 percent last year. But in 2016, it depicted acceleration that eventually reversed course. So is this recent increase real or illusory?
Although using a 12-month average quiets much of the noise in the monthly data, it is possible that the smoothed series still may exhibit some unwanted variation due to the way the Wage Growth Tracker is constructed. For example, how the monthly Current Population Survey reports individual earnings might be a factor introducing unwanted noise into the Tracker. Specifically, some people directly report their hourly rate of pay, and some report their earnings in terms of an amount per week, per month, or per year.
Relative to those paid an hourly rate, there are at least a couple of reasons why using the earnings of nonhourly workers might introduce additional variability into the Wage Growth Tracker's overall estimate of wage growth. First, reported nonhourly earnings include base pay as well as any overtime pay, tips, and commissions earned, and hence can vary over time even if the base rate of pay didn't change. For a worker paid at an hourly rate, reported earnings exclude overtime pay, tips, and commissions and so are not subject to this source of variation. Second, the method we use to convert nonhourly earnings to an hourly rate is likely subject to some margin of error since it involves using the person's recollection of how many hours they usually work. These two factors suggest that the earnings of workers paid at an hourly rate might be a somewhat cleaner measure of hourly earnings.
To investigate whether this distinction actually matters in practice, we created the following chart comparing the 12-month average Wage Growth Tracker since 2015 (depicted in the green line) with a version that uses only the earnings of those paid at an hourly rate (blue line).
As the chart shows, the 12-month average of median wage growth for hourly workers generally tracks the overall series—both series are about a percentage point higher than at the beginning of 2015. However, the hourly series is a bit less variable, making the recent uptick in wage growth more noticeable in the hourly series than in the overall series. This observation suggests that as we monitor shifts in wage pressure, the hourly series could complement the overall series nicely. Versions of the Wage Growth Tracker series for both hourly and nonhourly workers are now available on the Wage Growth Tracker page of the Atlanta Fed's website.
If you would like to use the Wage Growth Tracker's underlying microdata to create your own versions (or to conduct other analysis), follow this link to explore the data on the Atlanta Fed's website. See this macroblog post, "Making Analysis of the Current Population Survey Easier," from my colleague Ellyn Terry to learn more about using this dataset.
July 15, 2019
Making Analysis of the Current Population Survey Easier
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).
June 24, 2019
Mapping the Financial Frontier at the Financial Markets Conference
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.
June 07, 2019
The Tax Cut and Jobs Act, SALT, and the Blue State Blues: It's All Relative
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.
May 06, 2019
Improving Labor Force Participation
Without question, the U.S. labor market has tightened a lot over the last few years. But a shifting trend in labor force participation—and especially a rise in the propensity to seek employment by those in their prime working years—seems to be relieving some labor market pressure.
From the first quarter of 2015 to the first quarter of 2019, the labor force participation (LFP) rate among prime-age workers (those between 25 and 54 years old) increased by about 1.5 percentage points (see the chart below), adding about 2 million workers more than if the participation rate had not increased.
Changes in the distribution of the prime-age population in terms of age, education, and race/ethnicity toward groups with higher participation rates and away from groups with lower rates accounts for about a third of the rise in the overall prime-age LFP rate. The other two thirds can be pinned on an increase in LFP rates within demographic groups—what we call "behavioral" effects.
Of the increased participation behavior within demographic groups, there has been a decline in the share of the prime-age population that say they want a job but are not actively looking for work at the moment. We refer to these individuals as the "shadow labor force" because even though they are not in the labor force this month, they have a relatively high propensity to have a job next month. Second, there's been a decline in the share of the prime-age population that are not participating because they are too sick or disabled to work. The contribution of the change in behavior in these two categories (as well as several others from the first quarter of 2015 to the first quarter of 2019) are shown in the following chart, which is taken from the Atlanta Fed's Labor Force Participation Dynamics tool.
In contrast, consider the period from the first quarter of 2008 through the first quarter of 2015, a time when the rate of prime-age LFP declined by almost 2 percentage points. During that period, even though slow-moving demographic changes were putting modest upward pressure on the prime-age participation rate, that support was more than swamped by negative changes in participation rates within demographic groups. The following chart shows the relative contributions of these behavioral changes.
Within demographic groups, the increased incidence of being too sick or disabled to work stands out as the largest contributor to the decline in prime-age labor force participation between 2008 and 2014.
Since 2014, prime-age LFP has benefited from the movement of both demographics and participation behavior. But so far, less than half of the overall behavioral decline between 2008 and 2014 has been reversed.
Though demographic trends are likely to remain positive, how much more participation behavior—especially as it is related to disability and illness—can shift as the labor market tightens remains unclear. The share of the prime-age population too sick or disabled to work had been on a rising trend for the decade prior to the last recession, suggesting that there may be some deeper and structural health-related issues that could keep the disability/illness rate elevated despite an increasingly tight labor market.
March 26, 2019
Young Hispanic Women Investing More in Education: Good News for Labor Force Participation
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.
March 22, 2019
A Different Type of Tax Reform
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:
Median Effective Marginal Tax Rate
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.
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."
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