How would the policy rule of forecast targeting work for the Federal Reserve? To what extent is the Federal Reserve already practicing forecast targeting? Forecast targeting means selecting a policy rate and policy-rate path so that the forecasts of inflation and employment "look good," in the sense of best fulfilling the dual mandate of price stability and maximum employment, that is, best stabilize inflation around the inflation target and employment around its maximum level. It also means publishing the policy-rate path and the forecasts of inflation and employment forecasts and, importantly, explaining and justifying them. This justification may involve demonstrations that other policy-rate paths would lead to worse mandate fulfillment. Publication and justification will contribute to making the policy-rate path and the forecasts credible with the financial market and other economic agents and thereby more effectively implement the Federal Reserve's policy. With such information made public, external observers can review Federal Reserve policy, both in real time and after the outcomes for inflation and employment have been observed, and the Federal Reserve can be held accountable for fulfilling its mandate. In contrast to simple policy rules that rely on very partial information in a rigid way, such as Taylor-type rules, forecast targeting allows all relevant information to be taken into account and has the flexibility and robustness to adapt to new circumstances. Forecast targeting can also handle issues of time consistency and determinacy. The Federal Reserve is arguably to a considerable extent already practicing forecast targeting.
The Life-Cycle Dynamics of Exporters and Multinational Firms -- by Anna Gumpert, Andreas Moxnes, Natalia Ramondo, Felix Tintelnot
This paper studies the life-cycle dynamics of exporters and multinational enterprises (MNEs). We present a dynamic model of trade and MNE activity in which the mode of serving a market depends on the well-known proximity-concentration tradeoff. We show that the option of performing MNE activities in the model produces life-cycle patterns for exporters that differ from those in an export-only model. Calibrating our model to rich firm-level data from France and Norway, our main quantitative finding is that a reduction in trade costs triggers much larger responses in growth rates and exit rates, for young exporters, in the model with MNEs than in the model without MNEs. We also show that the model is largely consistent with a set of new facts on the joint life-cycle dynamic behavior of exporters and MNEs.
Introduction In 2015, the Department of Education launched the College Scorecard, a vast database of student outcomes at specific colleges and universities developed from a variety of administrative data sources. The Scorecard provides the most comprehensive and accurate information available on the post-enrollment outcomes of students, like whether they get a job, the rate at which they repay their loans, and how much they earn. While labor-market success is certainly not the end-all-be-all of higher education, the notion that a college education is a ticket to a good job and a pathway to economic opportunity is intrinsic to the tax benefits and financial support provided by federal and state governments, to the willingness of parents and families to shoulder the burden of college’s high costs, and to the dreams of millions of students. More than 86% percent of freshmen say that “to be able to get a better job” is a “very important” reason for going to college. That is why the College Scorecard is a breakthrough—for the first time, students have access to detailed and reliable information on the economic outcomes of students after leaving college, including the vast majority of colleges that are non-selective or otherwise fall between the cracks of other information providers. The data show that at every type of post-secondary institution, the differences in post-college earnings across institutions are profound. Some students attend institutions where many students don’t finish, or that don’t lead to good jobs. Moreover, the analysis behind the Scorecard suggested not only that there are large differences across institutions in their economic outcomes, but that these differences are relevant to would-be students. For instance, the evidence in the Scorecard showed that when a low-income student goes to a school with a high completion rates and good post-college earnings, she is likely to do as well as anyone else there. While there are large differences between where rich and poor kids are likely to apply and attend, there is little difference in their outcomes after leaving school: the poorest aid recipients earn almost as much as the richest borrowers. This pattern suggests, at least, that low-income students are not mismatched or underqualified for the schools they currently attend. But it is also consistent with powerful evidence from academic studies that show that when marginal students get a shot at a higher-quality institution their graduation rates and post-college earnings converge toward those of their new peers (Zimmerman 2014, Goodman et al. 2015). Hence, the Scorecard is likely to provide useful information for students, policymakers, and administrators on important measures of post-college success, access to college by disadvantaged students, and economic mobility. Indeed, the College Scorecard shows that great economic outcomes are not exclusive to Ivy-League students. Many institutions have both good outcomes and diverse origins—institutions whose admissions policies, or lack thereof, take in disproportionate shares of poor kids and lift them up the economic ladder. Nevertheless, the design of the Scorecard required making methodological choices to produce the data on a regular basis, and making it simple and accessible required choosing among specific measures intended to be representative. Some of these choices were determined by data availability or other considerations. Some choices have been criticized (e.g. Whitehurst and Chingos 2015). Other valuable indicators could not be reliably produced on a regular basis or in a way that evolved over time as college or student outcomes changed. In part to address these issues, we supported the research that lead to the creation of Mobility Report Cards, which provide a test of the validity and robustness of the College Scorecard and an expansion of its scope. Mobility Report Cards (MRCs) attempt to answer the question “which colleges in America contribute the most to helping children climb the income ladder?” and characterize rates of intergeneration income mobility at each college in the United States. The project draws on de-identified administrative data covering over 30 million college students from 1999 to 2013, and focuses on students enrolled between the ages of 18 and 22, for whom both their parents’ income information and their own subsequent labor-market outcomes can be observed. MRCs provide new information on access to colleges of children from different family backgrounds, the likelihood that low-income students at different colleges move up in the income distribution, and trends in access over time. Background on College Scorecard The College Scorecard provides detailed information on the labor-market outcomes of financial-aid recipients post enrollment, including average employment status and measures of earnings for employed graduates; outcomes for specific groups of students, like students from lower-income families, dependent students, and for women and men; and measures of those outcomes early and later in their post-college careers. These outcome measures are specific to the students receiving federal aid, and to the institutions those students attend. And the outcome measures are constructed using technical specifications similar to those used to measure other student outcomes, like the student loan Cohort Default Rate, which allows for a consistent framework for measurement while allowing institution outcomes to evolve from cohort to cohort. The technical paper accompanying the College Scorecard spelled out the important properties and limitations of the federal data used in the Scorecard, regarding the share of students covered, the institutions covered, the construction of cohorts, the level of aggregation of statistics, and how the earnings measures were used. These choices were made subject to certain constraints on disclosure, statistical reliability, reproducibility, and operational capacity, and with specific goals of making the data regularly available (updating it on an annual basis), using measurement concepts similar to those used in other education-related areas (like student loan outcomes), and providing measures that could evolve over time as characteristics of schools and student outcomes changed. These constraints imposed tradeoffs and required choices. Moreover, the research team producing the MRCs was not bound by certain of these methodological requirements or design goals, and thus could make alternative choices. Despite making different choices, however, the analysis below shows that on balance the outcome measures common to both projects are extremely similar. In brief, the Scorecard estimates are based on data from the National Student Loan Data System (NSLDS) covering undergraduate students receiving federal aid. NSLDS data provides information on certain characteristics of students, the calendar time and student’s reported grade level when they first received aid, and detailed information on the institution they attended (such as the 6- and 8-digit Office of Postsecondary Education Identification number OPEID). These data and identifiers are regularly used as the basis for reporting institution-specific student outcomes, like the Cohort Default Rate or disbursements of federal aid. For purposes of constructing economic outcomes using these data, all undergraduate aid recipients were assigned an entry cohort—either the year they first received aid if a first-year college student, or an imputation for their entry year based on the year they were first aided and their academic level. (For instance, if a student self-reported entering their second undergraduate year in the first year they received aid, they would be assigned a cohort year for the previous year.) If a student attended more than one institution as an undergraduate, that student was included in the cohorts of each institution (i.e. their outcomes were included in the average outcomes of each institution—just as is done with the Cohort Default Rate). These data were linked to information from administrative tax and education data at specific intervals post-entry (e.g. 6, 8, and 10 years after the cohort entry year). Adjacent cohorts were combined (e.g. entry cohorts in 2000 and 2001 were linked to outcomes in 2010 and 2011, respectively). Individuals who are not currently in the labor market (defined as having zero earnings) are excluded. And institution-by-cohort specific measures like mean or median earnings and the fraction of students that earn more than $25,000 (among those working), were constructed for the cohorts (e.g. mean earnings for non-enrolled, employed aid recipients ten years after entry for the combined 2000 and 2001 cohorts). Each year, the sample was rolled forward one year, with the earlier cohort being dropped and a new cohort being added, allowing the sample to evolve over time. This focus on aid recipients is natural for producing estimates related to aid outcomes, like student debt levels or the ratio of debt to earnings. Moreover, these data are regularly used to produce institution-specific accountability measures, like the Cohort Default Rate, which are familiar to stakeholders and authorized and regularly used to report institution-specific outcomes. Constructing the sample based on entry year and rolling forward one year allowed for comparisons within schools over time, to assess improvement or the effects of other changes on student outcomes. The focus of and choices underlying the Scorecard also had several potential disadvantages, which were noted in the technical paper or by reviewers offering constructive criticism (e.g. Whitehurst and Chingos 2015). These limitations, criticisms, and omissions of the Scorecard include the following specific to the methodology and data limitations. First, the Scorecard’s sample of students includes only federal student aid recipients. While these students are an obvious focus of aid policies, and comprise a majority of students at many institutions, high-income students whose families cover full tuition are excluded from the analysis. Moreover, schools with more generous financial aid often have a smaller share of students on federal financial aid, implying that the share and type of students included in the Scorecard vary across colleges. Unfortunately, the information needed to assign students to a specific entry cohort at a specific educational institution and to report institution-specific data is not available at the same degree of reliability and uniformity for non-federal-aid recipients. For instance, Form 1098-T (used to administer tax credits for tuition paid) may not identify specific institutions or campuses (e.g. within a state university system) and does not report information on the academic level or entry year of the student. In addition, certain disclosure standards prevented the publication of institution-specific data. Estimates based on aggregated statistics (as are used in the Mobility Report Cards) include an element of (deliberate) uncertainty in the outcomes, and subjectivity in terms estimation methodology. Second, FAFSA family income may not be a reliable indicator of access or opportunity. FAFSA family income is measured differently depending on whether students are dependent or independent; it is missing for many that do not receive aid; and it can be misleading for those who are independent borrowers. Unfortunately, information on family background is generally only available for FAFSA applicants (aid recipients) who are dependents at the time of application. Mobility Report Cards provide a more comprehensive and uniform measure of family income, but only for the cohorts of students they are able to link back to their parents (e.g. those born after 1979.) Mobility Report Cards The above factors raised concerns about the Scorecard’s reliability and usefulness to stakeholders. In an effort to assess the validity and robustness of Scorecard measures using an alternative sample and with more consistent definitions of family income and more outcomes, we supported the analysis behind the study “Mobility Report Cards: The Role of Colleges in Intergenerational Mobility in the U.S.” (Chetty, Friedman, Saez, Turner, and Yagan 2017). Perhaps most importantly, the Mobility Report Card (MRC) uses records from the Treasury Department on tuition-paying students in conjunction with Pell-grant records from the Department of Education in order to construct nearly universal attendance measures at all U.S. colleges between the ages of 18 and 22. Thus the MRC sample of students is more comprehensive of this population relative to the Scorecard. However, older students are generally not included in the MRC sample and certain institutions cannot be separately identified in the MRC sample. Furthermore, the MRC methodology relies on producing estimates of institutional outcomes rather than producing actual data on institution outcomes. At certain institutions, particularly those that enroll a disproportionate share of older students (such as for-profit and community colleges) and where a large share students receive Title IV aid, the Scorecard provides a more comprehensive sample of student outcomes. Another area of difference is that the MRC organizes its analysis around entire birth cohorts who can be linked to parents in their adolescence. It then measures whether and where each member of the birth cohort attends college. By following full birth cohorts, cross-college comparisons of adult earnings in the MRC measure earnings at the same age (32-34), unlike the Scorecard which measures adult earnings across colleges at different points in the lifecycle, depending on when the students attended the college. The advantage of the MRC approach is that it allows a comprehensive analysis of the outcomes of the entire birth cohort at regular intervals. However, the disadvantage mentioned above is that there is no information on older cohorts born prior to 1980. In addition, the MRC includes zero-earners in its earnings measures, whereas the Scorecard excludes them from their measures of earnings outcomes. Because it is not possible to differentiate individuals who are involuntarily unemployed (e.g. who were laid off from a job) from those who are out of the labor force by choice (in school, raising children, or retired), the Scorecard focused on measuring earnings specifically for those who clearly were participating in the labor market. Finally, family income in the MRC is measured consistently across cohorts using a detailed and relatively comprehensive measure of household income: total pre-tax income at the household level averaged between the kid ages of 15 and 19, as reflected on the parents’ tax forms. The design choices made in developing the MRC come at the cost of published statistics not being exact and instead being granular estimates (see Chetty Friedman Saez Turner Yagan 2016) and of not being as easily replicable over time. However, the MRC’s design addresses many of the critiques made of the Scorecard. If the critiques of the Scorecard are quantitatively important, one should find that the MRC and Scorecard values differ substantially. In other words, the MRC data provide an estimate of how much the data constraints and methodological choices affect the data quality. Comparison of the College Scorecard and Mobility Report Cards The most basic test of the robustness of the Scorecard to the variations embodied in the MRC is to compare the main Scorecard adult earnings measure—median earnings of students ten years after they attend a college—with the analogous measure from the MRC: median earnings in 2014 (age 32-34) of the 1980-1982 birth cohort by college. For shorthand, we refer to these measures as Scorecard median earnings and MRC median earnings, respectively. Figure 1 plots MRC median earnings versus Scorecard median earnings. Both median earnings measures are plotted in thousands of 2015 dollars. Overlaid on the dots is the regression line on the underlying college-level data. Figure 1 The graph shows an extremely tight, nearly-one-for-one relationship: a slope of 1.12 with an R2 of 0.92. Visually one can see that not only does each extra thousand dollars of Scorecard median earnings typically translate into an extra thousand dollars of MRC median earnings, but the levels line up very closely as well. Hence across the vast majority of colleges, Scorecard median earnings are very close to MRC median earnings. The close correspondence between MRC median earnings and Scorecard median earnings can also be seen when examining college-level comparison lists. For example, among colleges with at least 500 students, almost exactly the same colleges appear in the top rankings using either measure. (This is natural given the very high R2 reported in Figure 1.) Hence, the Scorecard and MRC share a very tight relationship. In unreported analysis, we find that two offsetting effects tend to explain this very tight relationship between Scorecard median earnings and MRC median earnings. On the one hand, the MRC’s inclusion of students who earn nothing as adults somewhat reduces each college’s median adult earnings. On the other hand, the MRC’s inclusion of students from high-income families somewhat increases each college’s median adult earnings, as students from high-income families are somewhat more likely to earn high incomes as adults. The two competing effects tend to offset each other in practice, yielding MRC median earnings that are quite close to Scorecard median earnings. While some schools are outliers, in the sense that the measures differ, those examples are often readily explained by differences in methodological choices. For instances, because the Scorecard conditions on having positive earnings, schools where an unusually high share of students voluntarily leave the labor force have different outcomes in the MRC than the Scorecard. The other important contributor to outliers is the MRC’s restriction to students enrolled between ages 18 and 22, which tends to exclude many older, mid-career workers. These individuals tend both to be employed, often have relatively high earnings, and tend to enroll at for-profit schools (or other schools aimed at providing mid-career credentials). The Scorecard includes these students, whereas the MRC tends to exclude them. Conclusion The College Scorecard was created to provide students, families, educators, and policymakers with new information on the outcomes of students attending each college in the United States, and improving the return on federal tax and expenditure programs. Mobility Report Cards expand the scope of the information on the outcomes and the characteristics of students attending American colleges. Our analysis finds a very high degree of agreement at the college level between Scorecard median adult earnings and Mobility Report Card median adult earnings, suggesting that the Scorecard is a reliable tool measuring the outcomes of students and institutions that benefit from federal student aid and tax expenditures. References Chetty, Raj, John N. Friedman, Emmanuel Saez, Nicholas Turner, and Danny Yagan. “Mobility Report Cards: The Role of Colleges in Intergenerational Mobility in the U.S.”. (2016). Goodman, Joshua, Michael Hurwitz, and Jonathan Smith. “Access to Four-Year Public Colleges and Degree Completion.” Journal of Labor Economics (2017). Whitehurst, Grover J. and Matthew M. Chingos. “Deconstructing and Reconstructing the College Scorecard.” Brookings Working Paper (2015). Zimmerman, Seth D. "The returns to college admission for academically marginal students." Journal of Labor Economics 32.4 (2014): 711-754. Adam Looney, Deputy Assistant Secretary for Tax Analysis at the US Department of Treasury.  https://www.washingtonpost.com/news/rampage/wp/2015/02/17/why-do-americans-go-to-college-first-and-foremost-they-want-better-jobs  This assignment was capped at two years, so that students reported entering their third, fourth, or fifth year were assigned a cohort two years prior.  For instance, in the 2002 Scorecard entry cohort, 42 percent of students were over age 22 when they first received aid.  The Scorecard data base does include the fraction of borrowers without earnings, which allows for the computation of unconditional mean earnings.  We also restrict to colleges with at least 100 MRC students on average across the 1980-1982 birth cohorts and to colleges that have observations in both the Scorecard and the MRC. For MRC colleges that are groups of Scorecard colleges, we use the count-weighted mean of Scorecard mean earnings across colleges within a group. See Chetty Friedman Saez Turner Yagan (2016) for grouping details.
January 17, 2017 The Honorable Mitch McConnell Majority Leader United States Senate Washington, DC 20510 Dear Mr. Leader: As the 115th Congress begins, we write to underscore the need for additional legislation early in this session to address the economic and fiscal crisis in Puerto Rico. The Puerto Rico Oversight, Management, and Economic Stability Act (PROMESA) provided Puerto Rico with important fiscal oversight and debt restructuring tools, and now the Oversight Board and Puerto Rico’s new Governor must take the critical next steps required by this federal legislation. Working with the new Governor, the Oversight Board now must certify a Fiscal Plan and set a path to comprehensively restructure the debt before the expiration of PROMESA’s automatic stay. Treasury has continued to provide both the Oversight Board and the new Governor with technical assistance as requested, and will remain able to do so after the transition to the next Administration. Despite the important progress achieved to date with bipartisan support, the work is not done. As Puerto Rico moves forward on these next steps, Congress must enact measures recommended by both Republicans and Democrats that fix Puerto Rico’s inequitable health care financing structure and promote sustained economic growth. Without congressional action to address these issues, Puerto Rico’s return to growth and opportunity will be a significant challenge. Most urgently, Congress should address Puerto Rico’s “Medicaid cliff” funding issue before April as recommended last month by the Congressional Task Force on Economic Growth in Puerto Rico. Failure to do so would jeopardize health care for up to 900,000 poor U.S. citizens living in Puerto Rico. CONGRESSIONAL TASK FORCE REPORT On December 20, the Congressional Task Force on Economic Growth in Puerto Rico, established by PROMESA, released its Final Report. The bipartisan report provides an overview of the economic challenges facing Puerto Rico and a series of potential solutions that, if crafted well and enacted quickly, are necessary for a sustainable economic recovery. It is important that Congress not only turn ideas into action, but in doing so, address Puerto Rico’s significant remaining economic and social challenges in meaningful ways to help put Puerto Rico on a path of sustained economic growth. As the report acknowledges, Puerto Rico faces an imminent shortfall in health care funding that could leave up to 900,000 Americans without coverage if Congress does not act in the near future. Puerto Rico’s already vulnerable health care system is stretched further by a Zika outbreak that, as of January 4, has resulted in over 34,000 cases, and will affect numerous women, children, and families for years to come. It is time to provide a long-term solution to Puerto Rico’s historically inadequate federal Medicaid financing, which threatens the viability of Puerto Rico’s Medicaid program and worsens Puerto Rico’s fiscal crisis. If Congress fails to craft a long-term solution, immediate action is still needed to ensure full fiscal year 2018 financing to avoid the “Medicaid cliff” identified in the report. Without action before April, Puerto Rico’s ability to execute contracts for Fiscal Year 2018 with its managed care organizations will be threatened, thereby putting at risk beginning July 1, 2017 the health care of up to 900,000 poor U.S. citizens living in Puerto Rico. Additionally, Puerto Rico continues to suffer from double digit unemployment and a labor force participation rate that is only two-thirds that of the U.S. average. A federally-financed, locally-administered Earned Income Tax Credit (EITC) in Puerto Rico would create incentives for work and increase participation in the formal economy – just as it has done for decades in the 50 states and the District of Columbia. Instead of recommending the immediate enactment of an EITC, the Task Force only suggested Congress further explore the proposal. We strongly encourage Congress to enact this powerful economic driver to bolster Puerto Rico’s future. Our analysis of the situation over the last several years demonstrates that an EITC would be the most effective and powerful tool to address these structural challenges to economic growth. Beyond those two major issues, the Task Force recommended a number of other policies that we agree should be enacted. First, we appreciate the bipartisan recommendation for Congress to continue authorizing Treasury to provide technical assistance to Puerto Rico. Furthermore, while we recommend a different approach to expand the Child Tax Credit to more Puerto Rican families, one that is locally administered, we welcome the Task Force recommendation for Congress to expand the Child Tax Credit in Puerto Rico, to the extent it is well-designed and supplements an EITC program for Puerto Rico. We support the Task Force’s acknowledgment of the importance of data in benchmarking economic growth and fiscal developments in Puerto Rico and the recommendations to improve data quality and timeliness. Finally, we are pleased with the recommendations on small business incentives, and the need to include Puerto Rico in funding and training programs that address Puerto Rico’s differential treatment in some Federal programs. It is time for Congress to move quickly to put these recommendations into law. Last summer, Republicans and Democrats in Congress took decisive action in PROMESA to help improve Puerto Rico’s fiscal position by establishing an independent oversight board and providing it with comprehensive debt restructuring tools. As you know, these tools were provided to Puerto Rico as an alternative to a federal bailout and provide Puerto Rico’s government and the Oversight Board with comprehensive authorities to address the debt crisis. Members of Congress now must work together quickly to enact well-crafted legislation to encourage growth and opportunity for our fellow citizens in Puerto Rico. The Treasury Department and the Department of Health and Human Services stand committed to working with you to achieving those goals throughout the remainder of the transition to the next Administration. Sincerely, Jacob J. Lew Sylvia M. Burwell Secretary Secretary Department of the Treasury Department of Health and Human Services Identical letter sent to: The Honorable Charles E. Schumer The Honorable Paul D. Ryan The Honorable Nancy Pelosi
Independent Workers Are Almost Three Times More Likely To Rely on Marketplace Coverage than Other Workers Today, Treasury released a report with new data on sources of health insurance coverage for small business owners and self-employed workers. These data show that the Affordable Care Act (ACA’s) Health Insurance Marketplaces are playing an especially crucial role in providing health coverage to entrepreneurs and other independent workers. Prior to the Affordable Care Act, workers without employer-sponsored health insurance often lacked options for affordable coverage. Not only did high uninsured rates impede access to care and worsen financial security, but the risk of ending up without health insurance coverage prevented some individuals from striking out on their own. Experts considered “job lock,” or individuals’ need to stay in an employment situation to maintain health coverage, a significant impediment to entrepreneurship. To help address these challenges, the ACA’s Marketplaces were designed to offer portable health insurance coverage to small business owners and other independent workers, a growing segment of the economy. One in five 2014 Marketplace consumers was a small business owner or self-employed New data included in today’s Treasury Department report on alternative work arrangements show that small business owners and self-employed workers are taking advantage of the opportunity to purchase health coverage through the Marketplaces. In 2014, 1.4 million Marketplace consumers were self-employed, small business owners, or both, indicating that about one in five 2014 Marketplace consumers was a small business owner or self-employed. Indeed, among the 5.3 million workers who purchased Marketplace coverage for themselves (excluding their children or non-working spouses), about 28 percent were workers whose income was not primarily earned from wages paid by an employer. In fact, small business owners and self-employed individuals were nearly three times as likely to purchase Marketplace coverage as other workers. Nearly 10 percent of small business owners and more than 10 percent of gig economy workers got coverage through the Marketplace in 2014. Among small business owners and other independent workers, those with annual incomes below $65,000 were the most likely to rely on the Marketplace for health insurance. Middle- and lower-income Americans who buy coverage through the Marketplace are eligible for tax credits to help keep coverage affordable. About 65 percent of small business owners and 69 percent of all self-employed or independent workers have incomes below $65,000. Between 2014 and 2015, the number of people who signed up for Marketplace coverage increased by around 50 percent. And enrollment increased further in 2016, and is poised to rise again in 2017. Marketplace coverage among independent workers has almost certainly risen as well. HHS is also partnering with outside companies that support freelance workers, entrepreneurs, and start-ups to reach more independent workers with information about Marketplace coverage and financial assistance. Geographic patterns in small business owners’ and independent workers’ health coverage Today’s report includes detailed state-by-state data on Marketplace participation among entrepreneurs and independent workers. In all 50 states and D.C., thousands of small business owners and independent workers bought Marketplace coverage in 2014. Of note: · The ten states with the highest share of small business owners relying on the Marketplace for coverage were Vermont, Idaho, Florida, Montana, Maine, California, New Hampshire, Washington, D.C., Rhode Island, and North Carolina. · The 10 states with the largest number of small business owners with Marketplace coverage were California, Florida, Texas, New York, Georgia, North Carolina, Pennsylvania, Michigan, Washington, and Virginia. Adam Looney is the Deputy Assistant Secretary for Tax Analysis at the U.S. Department of Treasury. Kathryn Martin is the Acting Assistant Secretary for Planning and Evaluation at the U.S Department of Health and Human Services.  The Treasury report defines small business owners as Schedule C filers whose business activities (measured by expenses and gross receipts) exceed certain de minimis thresholds (a minimum of $5,000 of business expenses and either $15,000 of gross receipts or $10,000 of business expenses). Self-employed workers are defined as individuals who earn at least 85 percent of their earnings from operating a sole-proprietorship. “Gig economy workers” are those whose self-employment income derives in part or in whole from activities conducted through an online platform.
Today, the Office of Economic Policy at the Treasury Department released the fourth in a series of briefs exploring the economic security of American households. This brief focuses on the economic security of older women. In this brief, we ask: Are older women at greater risk of poverty or being unable to manage their expenses than other populations? Are there specific groups of women at risk? What are the implications for policy? Compared with men, we find that elderly women are much more likely to be economically insecure. We attribute this finding to a variety of factors. Women live longer than men, meaning they have to finance a longer retirement and that they are more likely to reach an age in which they must finance disability costs. In addition, women tend to have lower lifetime earnings than men. Finally, women are more likely than men to live alone and thus are less likely to live with someone with whom to share economic risks. In this brief, we assess economic insecurity in a number of ways but focus on two measures: the poverty rate and the “overextended” rate—the share of the population whose spending exceeds what it can afford based on its income and annuitized wealth. We view this latter measure as reflecting economic insecurity, because elderly women who are overextended and on fixed incomes must reduce spending to live within their means. For women with low levels of consumption, this could entail cutting back on necessities like food and medicine. Comparing different measures of economic security, we find that the overextended share of the female population is 29 percent, far higher than the poverty rate of 12 percent. The implication is that economic insecurity is broader than the poverty rate implies. We find that single women are far more economically insecure on all measures than married women and that widowhood dramatically increases the likelihood of becoming insecure relative to remaining married. Widowhood is associated with a large loss in income and wealth; and while widows experience a large drop in household spending at widowhood, they continue to cut spending at rates faster than single women and married households. We also find that disability is associated with economic insecurity. The median disabled woman’s household assets (including non-liquid assets like housing) are sufficient only to finance six months in a nursing home, and the median disabled woman’s household has financial wealth sufficient to cover less than half a month of nursing home expenses. Women who remain married throughout their elderly years, on the other hand, do not experience high rates of economic insecurity. And holding constant marital status and disability status, we do not observe sharp increases in economic insecurity as women age. Notably, even though the poverty rate rises for women as they age, the overextended rate falls as women rely more on wealth to support themselves. All told, our findings suggest that public policy should focus on specific risks associated with aging, particularly living alone and living with a disability. We note that married couples might benefit from shifting more of their wealth from periods in which both spouses are alive to periods in which only one spouse is alive. Such an outcome could be accomplished in the private sector with greater use of financial products with survivor benefits. Experts have also suggested ways that public policy could help address the challenge, such as by restructuring Social Security to increase survivor benefits. Looking at disability, we note that while Medicaid and private long-term care insurance provide protection for some households, there is still a large unmet need that is apparent when looking at the economic security risks posed by disability. Karen Dynan is the Assistant Secretary of Economic Policy at the Department of the Treasury.
Международное рейтинговое агентство Fitch подтвердило долгосрочный рейтинг Москвы в иностранной и национальной валюте на уровне «BBB-« с «позитивным» прогнозом. Соответствующая информация размещена на сайте рейтингового агентства.При этом, рейтинг приоритетного необеспеченного долга российской столицы также подтвержден на уровне...
Just because fewer women approve of Moore doesn't mean they won't vote for him.
В Министерстве экономического развития представили рейтинг инвестиционной привлекательности особых экономических зон (ОЭЗ). Эксперты определили лучшие зоны промышленно-производственного и технико-внедренческого типа, сообщает пресс-служба ведомства...
Ингушетия вошла в тройку регионов, в которых эффективнее всего исполняются "майские указы" президента, - сообщили в компании "Медиалогия". Лидером рейтинга стала Москва, на втором месте расположился Татарстан, а …
Except for Amazon retailers such as Costco, Kroger, Macy’s and Walmart should be helped by a 20% tax rate
Oil-rich Venezuela has always paid its debts - even at the expense of its citizens. But this week, everything changed: Venezuela is now officially in default, which means it's officially bankrupt. Rating agency Standard & Poor's declared the nation in 'selective default' on Monday after... [[ This is a content summary only. Visit http://FinanceArmageddon.blogspot.com or http://lindseywilliams101.blogspot.com for full links, other content, and more! ]]
OHIO PLAYER: Ohio supreme court justice: Leave Al Franken alone because, just fyi, I’ve probably boinked 50 chicks myself. And possibly Robert Taft, if you read his Facebook post too quickly. It’s certainly one way to push back against today’s PC culture. And speaking of anti-PC, Ace of Spades has a great, if (not surprisingly) […]
Программа «Двойной агент» представляет зрителям дайджест самых рейтинговых новостей, опубликованных в twitter-аккаунтах ведущих мировых СМИ. В этом выпуске ведущий Тим Керби обсуждает в студии с генеральным директором компании RGG Capital Жоэлем Лотье топ-7 российских новостей. А с заместителем директора Института стратегических исследований и прогнозов РУДН Никитой Данюком - топ-7 англоязычных новостей, набравших наибольшее количество ретвитов за неделю. Стоит отметить, рейтинг, формируемый автоматически, максимально объективно отражает реальный интерес к тому или иному материалу пользователей – обычных, рядовых людей. Он показывает, что интересно русским, а что привлекло внимание американцев, какие вопросы и темы волнуют людей, какова реакция здесь и за океаном на те или иные события, насколько мы разные и насколько друг на друга похожи. Сайт Царьград ТВ: http://tsargrad.tv/ Подписывайтесь: https://www.youtube.com/tsargradtv Facebook — https://www.facebook.com/tsargradtv ВКонтакте — https://vk.com/tsargradtv Twitter — https://twitter.com/tsargradtv Одноклассники — http://www.ok.ru/tsargradtv Новости телеканала Царьград: https://www.youtube.com/channel/UC84v7yS6sxkw5ylGG-0fNig/videos?view_as=subscriber
THE WORKERS’ PARADISE: North Korean defector found to have ‘enormous parasites.’ “I’ve never seen anything like this in my 20 years as a physician,” South Korean doctor Lee Cook-jong told journalists, explaining that the longest worm removed from the patient’s intestines was 27cm (11in) long. “North Korea is a very poor country and like any […]
На 20 участников рейтинга приходится почти 85% российского экспорта нефти, а он в 2016 году достиг 254,8 млн т. На первом месте — Litasco, швейцарский трейдер «Лукойла»
Mercer, одна из крупнейших консалтинговых компаний в сфере HR, публикует свой ежегодный индекс качества жизни Quality of Living Index, в рамках которого города мира оцениваются с точки зрения качества жизни по целому ряду параметров.
Камбербэтч в роли Ван Гога — как вам это?
Мировые продажи легковых электромобилей и подключаемых гибридов в 2016 году составили 774 тыс.единиц с ростом в 42% к 2015 и долей 0,86%К концу 2016 общее количество электромобилей в мире превысило 2 миллиона.Продажи по регионам(Китай - данные только по легковым электромобилям без коммерческих, которых еще около 160 тысяч, в основном электробусов)Рейтинг производителейРейтинг по моделямПрогноз на 2017 годhttp://www.ev-volumes.com/
Искусственный интеллект вновь демонстрирует свое могущество: хедж-фонды, которые используют алгоритмы, выходят в лидеры рейтингов эффективности, что, тем не менее, делает математиков и программистов главными компонентами успешного инвестирования