Выбор редакции
Выбор редакции
31 декабря 2017, 23:55

The Causal Effect of Limits to Arbitrage on Asset Pricing Anomalies -- by Yongqiang Chu, David Hirshleifer, Liang Ma

  • 0

We examine the causal effect of limits to arbitrage on 11 well-known asset pricing anomalies using Regulation SHO, which relaxed short-sale constraints for a random set of pilot stocks, as a natural experiment. We find that the anomalies became weaker on portfolios constructed with pilot stocks during the pilot period. Regulation SHO reduced the combined anomaly long-short portfolio returns by 72 basis points per month, a difference that survives risk adjustment with standard factor models. The effect comes only from the short legs of the anomaly portfolios.

31 декабря 2017, 23:54

A Comparison between the College Scorecard and Mobility Report Cards

  • 0

​ 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.[1]   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.[2]) 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.[3]   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.[4] 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.[5] 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. [1] https://www.washingtonpost.com/news/rampage/wp/2015/02/17/why-do-americans-go-to-college-first-and-foremost-they-want-better-jobs [2] 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. [3] For instance, in the 2002 Scorecard entry cohort, 42 percent of students were over age 22 when they first received aid.     [4] The Scorecard data base does include the fraction of borrowers without earnings, which allows for the computation of unconditional mean earnings. [5] 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.

31 декабря 2017, 23:54

Harnessing the Power of Financial Data

​ For more than 200 years, Treasury has been managing the resources of the Federal government and embracing advancements and cutting-edge practices. Today we have an opportunity to create a more data-driven government that empowers our leaders to make more strategic decisions and provide the public with greater access and insight on how taxpayer money is spent. The ongoing Digital Accountability and Transparency Act (DATA Act) implementation, in which Treasury is playing a leading role, is providing that opportunity as agencies work to meet new standards that could enable the use of data and analytics. In 1990, the Chief Financial Officers Act of 1990 (CFO Act) established a vision for federal financial management to “provide for the production of complete, reliable, timely, and consistent financial information for use by the executive branch of the Government and the Congress in the financing, management, and evaluation of Federal programs.” Significant achievements have been made to maintain and report high-quality financial data — but the full vision of the CFO Act is still a work in progress. The 24 CFO Act agencies have been successful at promoting new accounting and reporting standards, generating auditable financial statements, strengthening internal controls, improving financial management systems and enhancing performance information. However, there is room for growth in the way financial reporting adapts to the evolving information technology landscape. Through the DATA Act implementation process Treasury has developed a DATA Act Information Model Schema (DAIMS) that links the financial data produced by agency CFOs with other spending data on Federal awards — including grants, loans and procurement data (as well as other related attributes). This new data set includes more than 400 data elements and significantly expands the data available to agency CFOs and other agency leadership. The DAIMS can also be extended to link to other administrative and program data to support data-driven decision-making.   A New Vision for Federal Financial Management   Treasury’s vision for a 21st century Federal Finance Organization includes five key levels based on leading private sector benchmarks for finance organizations. The first level covers the basics for any finance organization — budget formulation and transaction processing. The second level includes fundamental financial policies and regulatory controls to ensure appropriate accountability. Most agencies have achieved levels one and two. Levels three and above are where agencies can begin to see the added value in the investment of high-quality data and internal controls. This data can now be managed and used to support decision-making and to improve operations and outcomes.     In addition to leading the government-wide implementation of the DATA Act, Treasury is also required to implement the law as an individual agency. As an implementing agency, Treasury is taking a data management and service delivery perspective, satisfying both internal and external customers who are demanding dynamic visualizations of data, meaningful reports and management dashboards. The DATA Act provides a unique opportunity to provide authoritative and standardized data across the enterprise to meet various needs, which fits into the new vision for Federal Financial Management above.    At Treasury, we are expanding our data analytics and reporting efforts to gain more value from our data. The Department has been working internally to link existing enterprise data management activities to a financial data governance program working across the C suite and internal organizations. Treasury is also envisioning a new financial data service portal that will serve as the central repository for all Treasury financial data where agency leadership will have access to data, tools and resources to conduct program research and visualize the data in new ways, starting with DATA Act related insights. This data infrastructure will allow us to provide greater transparency and also create a more modern 21st century Federal Finance Organization that is a better steward of public resources. We believe that better data leads to better decisions and ultimately a better government.   Christina Ho is the Deputy Assistant Secretary for Accounting Policy and Financial Transparency and Dorrice Roth is the Deputy Chief Financial Officer at the Department of the Treasury.

Выбор редакции
31 декабря 2017, 23:34

Mattis defends U.S. efforts to prevent civilian casualties in Yemen

'We are being held to a standard... that has never been achieved before in warfare,' Mattis said.

Выбор редакции
31 декабря 2017, 22:55

Arsène Wenger attacks refereeing standards after controversial penalty

• Arsenal manager says professionalism has not improved refereeing• Mike Dean penalises Calum Chambers for handball against West BromArsène Wenger delivered a withering attack on the standard of refereeing in the Premier League following the controversial penalty Mike Dean awarded West Bromwich Albion in the closing minutes of the 1-1 draw at The Hawthorns.Calum Chambers was harshly penalised for handball as Kieran Gibbs tried to lift the ball over the Arsenal defender, giving Jay Rodriguez the chance to equalise from the spot and prompting a furious reaction from Wenger. Continue reading...

31 декабря 2017, 21:43

Jay Rodriguez hits controversial penalty as West Brom grab draw against Arsenal

A landmark game for Arsène Wenger will stay in the Frenchman’s memory for all the wrong reasons. The Arsenal manager ended up face-to-face with Mike Dean on the touchline, angry with the referee’s decision to award West Bromwich Albion a late penalty, and signed off 2017 with a furious attack on the standard of officiating in the Premier League and the manic festive fixture schedule. Happy New Year, Arsène.It was that sort of evening for Wenger, who was in no mood to celebrate the fact that he had just clocked up his 811th Premier League game as manager to surpass Sir Alex Ferguson’s record. The Arsenal manager looked and sounded totally exasperated, his patience stretched to breaking point by the sight of Dean pointing to the spot in the 88th minute. Continue reading...

31 декабря 2017, 20:07

Further thoughts on corporate income taxes, by Scott Sumner

Stephen Williamson and John Cochrane have an interesting discussion of the effects of a corporate income tax. This comment by Cochrane is interesting: So how do you deduct investment and leave something left over to tax? It rests on two ideas. First, that the tax code can distinguish "real" investments like buying forklifts from "financial" investments like buying stocks and bonds, and only deduct the former. Second, that there is some pure "profit," some pure "rent," some "unreproducible input" (i.e. something that did not come from a past unmeasured investment), something like the classic "unimproved land" that can be taxed, without distorting any decision. It goes hand in hand with the complaints of greater monopoly. But I find it hard to find and name a concrete source of profits that, once named, does not distort the decision to undertake some useful activity to make those profits. Starting, organizing, and improving a business, figuring out the intangible organizational capital that makes it a successful competitor, creating a product and a brand name, are all crucial activities for which no investment tax credit will successfully offset a large profits tax. "Intangible capital" is about all most companies have these days. Then Gideon Magnus left this comment: Yes with full investment expensing the tax burden as well as the expected present value of tax revenues is essentially zero, and the only reason to levy the tax is to close the loophole of people incorporating and then paying zero taxes on their labor income. So putting this together, it seems like corporate profits involve a return on: 1. Land 2. Physical capital 3. Human capital 4. Innate talent (cleverness, etc.) In that case, why not do the following: 1. Tax corporate income as personal income (which would effectively be the top marginal tax rate for big companies--40.8%.) 2. Allow full expensing of physical investment. 3. Do not allow interest expenses to be deducted. 4. Do not tax interest, dividends and capital gains (as part of the personal income tax). Rationale: 1. You'd like to avoid taxing investment income. This system does that. 2. You'd like to tax labor income being disguised as capital income. This system does that. It also taxes land rents and the innate skills of entrepreneurs. 3. It treats debt and equity equally. One downside is that it taxes income from investments in human capital. But in practice that's not really much of a problem. Recall that wage taxes also tax income from investments in human capital. So the treatment is equalized between the corporate and non-corporate sectors. More importantly, the acquisition of human capital is already heavily subsidized by the government. Indeed I'm persuaded by Bryan Caplan that it's too heavily subsidized. So if this sort of corporate tax regime slightly discourages the acquisition of human capital, it may actually improve welfare for standard "second best" reasons. Consider how this would also simplify the taxation of personal income. For the vast majority of taxpayers, i.e. those paid wages and salaries, there would be no need to do taxes each April. The payroll and personal income tax systems could be combined into one---merely taxing wage income. For those claiming that savers are getting a tax break, just point out that they are actually paying taxes at the top rate, but at the corporate income level. Proprietorship income would also be taxed as personal income, again with full expensing of physical investments. What's wrong with this idea? (13 COMMENTS)

31 декабря 2017, 17:41

For the U.S. Army, the Future Is Robots

Kris Osborn Security, During the last decade and a half of ground wars in Iraq and Afghanistan, the Army acquired and fast-tracked as many as 7,000 unique robotic systems. Not only have robots been able to use GPS waypoint technology to travel from one location to another, but the systems have slowly learned how to maneuver independently around other objects or obstacles in real time. Systems like the well-known Packbot progressively leveraged technology to use different software packages for different sensing or detection missions with greater levels of autonomy. The Army is transforming its fleet of transportable robots to a common set of standards to expedite modernization, interoperability, autonomy and mission flexibility. During the last decade and a half of ground wars in Iraq and Afghanistan, the Army acquired and fast-tracked as many as 7,000 unique robotic systems in an effort to keep pace with the emerging threat of enemy IEDs. Building upon these developments, which included the deployment of multiple transportable cave- and road-clearing robots, the service now seeks to architect design a common fleet with a single robotic chassis configurable to a wide range of varying missions,  Bryan McVeigh, the Army's project manager for Force Projection, said in a service statement. Recommended: Uzi: The Israeli Machine Gun That Conquered the World Recommended: The M4: The Gun U.S. Army Loves to Go to War With Recommended: Why Glock Dominates the Handgun Market (And Better than Sig Sauer and Beretta) “Previous robots often had just one capability, used expensive, proprietary software, and required more resources for training and maintenance. That means soldiers can do more while learning and carrying less, and that makes a big difference," he added. Read full article

31 декабря 2017, 16:39

Стратегические силы России вышли на новый рубеж

Успехи России в борьбе с ИГИЛ (организация запрещена в РФ) войдут в учебники военной истории. Но они не привели к смене приоритетов в вопросе обеспечения безопасности страны. Фундаментом независимой политики России в международных делах остается ракетно-ядерный щит. Президент Владимир Путин, выступая перед высшим военным руководством 22 декабря, подчеркнул, что ядерные силы РФ находятся на уровне, обеспечивающем надежное стратегическое сдерживание. Необходимо развивать их дальше, поставил задачу президент.

31 декабря 2017, 15:43

A tarnished hospital tries to win back trust

Yale New Haven Hospital, once reviled for hounding low-income patients for money, wants to be a model for community outreach.

Выбор редакции
31 декабря 2017, 15:00

WELL, THE WOMEN’S STANDARDS ARE RIGHT AND THE MEN’S STANDARDS ARE WRONG BECAUSE THAT’S HOW IT WORKS:…

WELL, THE WOMEN’S STANDARDS ARE RIGHT AND THE MEN’S STANDARDS ARE WRONG BECAUSE THAT’S HOW IT WORKS: Number 1 in 2017: His Standards or Hers? How Men and Women Define Success.

31 декабря 2017, 14:00

How America Is Transforming Islam

Being young and Muslim in the U.S. means navigating multiple identities. Nothing shows that more than falling in love.

Выбор редакции
31 декабря 2017, 11:00

Matthew Bourne’s Cinderella review – still having a ball

Sadler’s Wells, LondonVisual magic outweighs occasional longueurs in this welcome revival of the choreographer/director’s wartime fairytaleMatthew Bourne’s latest revival of Cinderella is an ambitious production, even by the choreographer-director’s own elevated standards. Created in 1997, the piece is set in the London blitz, to a soundtrack which overlays Prokofiev’s famous ballet score with the overhead grumble of Heinkel and Dornier bombers, and the terrifying whistle of their falling cargo. Darkly atmospheric designs by Lez Brotherston further charge the piece with danger. Life is precarious, death strikes at random.Cinderella (Ashley Shaw), the daughter of a buttoned-up widower, is a slave to the whims of her stepfamily, and in particular of her spoilt drama queen of a stepmother (the scene-stealing Michela Meazza). Cinderella is also required to minister to her daft stepsisters and horrible clumping stepbrothers, one of whom has a fetishistic thing for her shoes. Continue reading...

Выбор редакции
31 декабря 2017, 03:04

Daliso Chaponda: from Malawi to a major UK tour with gags about slavery

Daliso Chaponda gets laughs from topics other comedians shun. After a whirlwind year of success on Britain’s Got Talent, he explains why he does itJokes about famine and slavery are not the standard fodder of a comedy routine, but Daliso Chaponda revels in crossing the line.The 38-year-old Malawian was a surprise star of Britain’s Got Talent this year, winning over millions with his cheeky but close-to-the knuckle gags about life as an African in Britain. Continue reading...

Выбор редакции
30 декабря 2017, 22:00

The Bayern blueprint: how Pep Guardiola honed Manchester City’s unbeatables | Michael Butler

A win at Crystal Palace will give City 19 league wins on the trot, a feat the coach also achieved in Munich. How do Pep’s record breakers compare?Manchester City managers of the future have a problem. The football played at the end of 2017 will always be the standard to which they are held. This month, City fan Noel Gallagher, a man not known for his compliments and somebody that has seen 250,000 people at Knebworth sing his name, described what is going on at the Etihad as “the greatest thing I’ve ever seen in my entire life”.Hyperbole perhaps, but if you do not agree that Pep Guardiola’s side are playing the best football of the Premier League era, then nobody can argue with the record that City are furthering each week — it is 18 consecutive league wins and counting. Continue reading...

30 декабря 2017, 18:29

Без заголовка

**Should-Read**: This seems to be a counterproductive line to pursue: the people who would be triggered to go to the polls to vote to repeal the state gasoline tax hike are also people who would like more of their SALT deduction back. They would seem eager to punish McCarthy and company for taking that away. Does the California Republican House delegation have any polling indicating otherwise? **Dan Morain**: [California congressional Republicans seek gas tax repeal](http://www.sacbee.com/opinion/opn-columns-blogs/dan-morain/article192080624.html): "Now that most of California’s House Republicans have voted for a tax overhaul that will raise taxes for many of their constituents, you have to wonder what more good cheer they’ll bring.... I’m thinking roads and other infrastructure... >...November 2018 ballot... repeal the 12-cent per gallon gasoline tax increase... to pay for road repairs, bridge maintenance and some public transit.... Potholes don’t fill themselves. That’s not stopping House Majority Leader Kevin McCarthy, R-Bakersfield, and most of California’s Republican congressional delegation from backing that repeal–with a notable exception, Rep. Jeff Denham, R-Turlock. McCarthy, a guy who knows politics, dumped $100,000 into the initiative to repeal the gas tax. Rep. Mimi Walters, an Orange County Republican, and Rep. Ken Calvert, R-Corona, chipped in $50,000 each, recent campaign...

30 декабря 2017, 11:15

The 1 Nail Polish Color Kate Middleton and Meghan Markle Are Allowed to Wear, According to the Queen

When it comes to beauty, Queen Elizabeth II has strict rules about nail polish. In fact, she's been wearing the same shade for over 25 years. See the one nail polish color the queen swears by, here.

Выбор редакции
30 декабря 2017, 11:00

David Hare: my ideal theatre

During David Hare’s 50 years in theatre its fortunes have changed beyond recognition. In the face of cultural cuts and crises, he sets out his vision for a Playhouse for todayIn 1946, George Orwell wrote his last essay for the Evening Standard. He described an imaginary pub, the Moon Under Water, where the music was quiet enough for conversation, the bar staff knew all the customers’ names and where you could always get a cut off the joint and a jam roll for three shillings.For my whole life, I have dreamed of having all my plays done at a theatre which, sadly, exists only in my mind, although the important elements of it, happily, exist in many. Continue reading...

30 декабря 2017, 01:45

What "Off The Grid" Indicators Reveal About The True State Of The US Economy

By Nicholas Colas, from DataTrek Research It’s that time of quarter again; today we review our “Off the Grid” economic indicators. And they all look pretty good in terms of launching the American economy into 2018. Pickup truck sales and used car prices remain robust, and there’s some actual inflation in our Bacon Cheeseburger Index. One warning: “Bitcoin” is among the top Google search autofills for the phrase “I want to buy… We started our “Off the Grid” economic indicators in the aftermath of the Financial Crisis as a way to dig deeper into the longer-lasting effects of that event on the American consumer. It seemed to us that standard economic measures like unemployment or CPI inflation missed a lot about the state of the country. So we started gathering up a list of intuitive metrics that could fill those gaps. A few examples from these datasets over the years: #1 Participation in the Supplemental Nutrition Assistance Program (commonly called Food Stamps) went from 26 million Americans in November 2006 to a high of 48 million in late 2012. At that high water mark, 16% of the entire US population needed government support to put food on the table. And since participation in the program is based on income, this meant a substantial portion of the US population was living at/near/below the poverty line. The latest data is more upbeat: as of August 2017 (latest data available), there are 41 million people enrolled in the program. Some of this reduction comes as states return to pre-Crisis rules for program participation, and some comes from rising incomes that allow households to exit the program. Another positive: Google searches for “Food Stamps” are back to pre-Crisis levels after a blip higher in the wake of the hurricanes in Florida and Texas. #2 During the Financial Crisis and its aftermath, Americans bought large amounts of gold and silver coins as a hedge against instability in the banking system. In any given month from 2009 to 2013, the US Mint shipped over $100 million in gold coins and $75 – 100 million in silver coins to dealers for retail sale. Demand for gold and silver coins in the US is now a fraction of those levels, averaging just $15 million and $7 million, respectively, per month in the second half of 2017. Google search volume data confirms the decline in interest, with “Gold coin” queries lower than at any point since 2004 (the start of the time series). #3 Sales of large pickup trucks, most commonly purchased by small businesses, reached a low of 70,000 units a month in early 2009. In November 2017, they were 191,000. Just as important, sales of large pickup trucks have been stable since 2014, growing at mid-single rates even as overall vehicle sales have plateaued. That’s a positive sign – small businesses don’t buy pickups for show. These are work vehicles, and an investment in a new one means they see business conditions remaining strong in 2018. With those three examples, you get the idea: the US economy has not only recovered from the Financial Crisis, but in many ways is firing on all cylinders. Our other OTG indicators generally point to the same conclusion. Here they are: Despite many pundits predicting their decline, used car prices are holding up well. The Manheim Used Vehicle Index (real price data from thousands of auctions) is our data source here. Their November 2017 reading is up 7.8% from last year. And since new car buyers almost always trade in their existing vehicle to buy a new one, higher used car prices effectively lower the cost of purchasing a new vehicle.   New vehicle inventories at dealer lots are currently at a seasonally normal 71 days supply. The caveat here: the hurricanes in Florida and Texas destroyed hundreds of thousands of cars and trucks. Selling rates in Q4 were therefore higher than usual, and “Days supply” is based on current rates. But given a strong economy, we are not overly worried that sales will fall off a cliff in the New Year. The amount of cash spent by the average American on a daily basis is up to $98/day, a post-Crisis high. (Source: Gallup Organization) We’ve seen a lot of change in Google’s autofill suggestions for “I want to buy” and “I want to sell”. Recall that the search engine tries to complete your partially entered query with words commonly used by other users. In Q4 2017, the most common thing users finished “I want to buy” was “a timeshare” (an obviously discretionary purchase). It dethroned “House”, which has been the most common entry since Q2 2015. A word of warning: “Bitcoin” has never made it into the top 4 autofills for “I want to buy”. Until now. We measure visible consumer inflation with our “Bacon Cheeseburger Index (BCI)”, equal weights of ground beef, cheese and bacon price data from the Consumer Price Index data. Good news here for the US Federal Reserve: consumers should start feeling a little more inflation in 2018. After a long bout of cheaper inputs for America’s favorite meal (well, mine anyway), the BCI is up 1.4% year over year. A year ago at this time, it showed a -5.7% decline. Our “Take This Job and Shove It” indicator is also in very healthy territory. This is a measure of quits as a percentage of total separations from the monthly JOLTS data. In October 2017, 61.4% of workers who left their jobs did so with a resignation letter rather than a pink slip. That’s not quite as high as the record 62.2% in September 2016, but still quite strong. And since Quits/Total Separations is a good proxy for Consumer Confidence, that important economic barometer has a full head of steam as we enter 2018. * * * The bottom line here: with few exceptions (Food Stamps, notably), the US economy is in exceptionally strong shape as we enter 2018. Small business confidence is strong, and savers do not see the need to hedge their bank accounts with gold coins. Timeshare salespeople are busy. Inflation that consumers use to anchor their expectations is rising at a modest pace. All that may be “Off the Grid”, but it gives us confidence that the standard – and currently quite bullish - economic data is actually on the mark.  

23 июня 2014, 14:12

Standard & Poor.s: каковы основные риски для российской экономики

Невыплата долгов "Нафтогазом" не будет считаться дефолтом всей Украины. Тем не менее, страна может официально стать банкротом в ближайшие год - два. Такой прогноз озвучил Моритц Краемер - он возглавляет группу суверенных рейтингов Standard & Poor.s. Поможет ли Украине МВФ, что ждет российские госкомпании и как ответить на обвинения в политической ангажированности?