Выбор редакции
24 ноября, 06:35

Friedman's Presidential Address in the Evolution of Macroeconomic Thought -- by N. Gregory Mankiw, Ricardo Reis

This essay discusses the role of Milton Friedman's presidential address to the American Economic Association, which was given a half century ago and helped set the stage for modern macroeconomics. We discuss where macroeconomics was before the address, what insights Friedman offered, where researchers and central bankers stand today on these issues, and (most speculatively) where we may be heading in the future.

24 ноября, 06:34

A Comparison between the College Scorecard and Mobility Report Cards

​ 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.

24 ноября, 06:34

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
24 ноября, 06:18

Семья Хворостовского попросила вместо цветов пожертвовать в Фонд исследований рака

Обращение опубликовано на странице скончавшегося 22 ноября певца в Facebook и на сайте Cancer Research UK — британской благотворительной организации, финансирующей исследования раковых заболеваний.

24 ноября, 03:05

Mortuary errors 'avoidable if bodies treated like living patients'

Many serious mistakes – including identity mix-ups – could be avoided, according to study of 132 incidents reported in EnglandSerious mistakes made in mortuaries – including identity mix-ups - might be avoided if the deceased received the sort of management standards given to the living, according to a study.Postmortems on the wrong bodies, and even people being buried or cremated by the wrong family, are some of the errors spotted by researchers who looked into 132 incidents reported in England to a national NHS database between 1 April 2002 and 31 March 2013. Continue reading...

Выбор редакции
24 ноября, 03:03

Семья Хворостовского призвала жертвовать на исследования рака

Семья всемирно известного оперного певца Дмитрия Хворостовского, скончавшегося на 56-м году жизни, обратилась к его поклонникам с просьбой делать пожертвования в пользу британской благотворительной организации Cancer Research UK, которая занимается исследованиями рака.

Выбор редакции
24 ноября, 02:08

THE SCIENCE WAS RIGGED: Researchers Publish Bombshell Report That Suggests Sugar Industry Conspirac…

  • 0

THE SCIENCE WAS RIGGED: Researchers Publish Bombshell Report That Suggests Sugar Industry Conspiracy.

24 ноября, 01:41

Bad Foods! 6 Processed Foods to Avoid at All Costs

If you find yourself buying frozen or packaged foods, then you'll want to make sure none of these six processed foods find their way into your grocery cart.

Выбор редакции
24 ноября, 01:09

Human form of 'mad cow' disease detectable in skin: study

Abnormal proteins involved in the brain-destroying Creutzfeldt-Jakob disease (CJD), a human form of "mad cow" disease, are detectable in the skin, researchers say, raising new concerns about transmission.…

23 ноября, 23:24

The Terrifying Reasons Your Cat Wants to Kill You

Inviting a natural born predator into your home has its share of risks.

Выбор редакции
23 ноября, 22:30

President Trump's Thanksgiving Day Message

Trump has far better communication skills than his predecessors. Truth is good! The Financial Armageddon Economic Collapse Blog tracks trends and forecasts , futurists , visionaries , free investigative journalists , researchers , Whistelblowers , truthers and many more [[ This is a content summary only. Visit http://FinanceArmageddon.blogspot.com or http://lindseywilliams101.blogspot.com for full links, other content, and more! ]]

23 ноября, 22:19

5 Grooming Products You Should Never Buy Name Brand

Step away from the designer brands, dear reader.

23 ноября, 22:15

The Guardian view on cheering health news: wake up and drink the coffee | Editorial

Consuming three cups a day is associated with more benefits to health than problems, for most peopleLet me drink three cups a day, the rebellious daughter demands in Bach’s Coffee Cantata. Her unreasonable father has attempted to ban the bean. Now it emerges that science is on her side. People who drink three to four cups are more likely to see health benefits than problems, according to an umbrella review of 200 studies by a team at the University of Southampton. That level of consumption is associated with lower risks of premature death and heart disease and other conditions compared with those who abstain. (Whether drinkers will “shrivel up like a piece of roast goat” without their fix, as Bach’s heroine warns, is not stated.)This is a rare piece of cheering research on consumption – for as the daughter also sings, coffee is lovelier than a thousand kisses; and all the more so as the temperature drops. No one is proposing mainlining it in the manner of Honoré de Balzac; the novelist boasted that he drank up to 50 cups a day, which helps to explain the volume and energy of his work but also, some have suggested, sped his death at 51. Nor, as spoilsport researchers remind us, will an accompanying cake improve our health. But there is no need to sacrifice that afternoon cortado for a camomile tea quite yet. The research indicates that, in moderation, our espressos, flat whites and americanos are fine for our bodies as well as – experience suffices here – a tonic for our souls. Coffee drinkers everywhere will raise a cup to that. Continue reading...

23 ноября, 20:30

Cold war between Saudi Arabia and Iran intensified, UN warns of 'serious consequences'

Saudi Arabia shouldn't start a war with Iran because Iran will win and they will lose . Saudi land forces are much weaker than Iran The Financial Armageddon Economic Collapse Blog tracks trends and forecasts , futurists , visionaries , free investigative journalists , researchers ,... [[ This is a content summary only. Visit http://FinanceArmageddon.blogspot.com or http://lindseywilliams101.blogspot.com for full links, other content, and more! ]]

23 ноября, 19:31

Does Claiming ‘Sex Addiction’ Only Allow Sexual Predators to Get Away With Harassment?

Is sex addiction a real condition -- or just an excuse sexual predators use to get away with their crimes?

23 ноября, 19:01

Cloned sheep Dolly ‘aged normally’

When Dolly the sheep was put down before her seventh birthday in 2003, she was said to suffer from age-related osteoarthritis, raising red flags that clones may grow old faster.

Выбор редакции
23 ноября, 19:01

World’s only art particle accelerator switches on

THE world’s only particle accelerator dedicated to art was switched on at the Louvre in Paris yesterday to help experts analyze ancient and precious works. The 37-meter AGLAE accelerator housed underneath

23 ноября, 18:52

Frankfurters’ foray: In a pre-Brexit skirmish with the City, Eurex takes on LCH

Print section Print Rubric:  A German bid to loosen London’s grip on clearing euro interest-rate swaps Print Headline:  Frankfurters’ foray Print Fly Title:  Euro-denominated clearing UK Only Article:  standard article Issue:  A hated tax but a fair one Fly Title:  Frankfurters’ foray Main image:  20171125_FNP503.jpg SEEN from the continent, it just isn’t right. LCH, a firm mostly owned by the London Stock Exchange (LSE), dominates the clearing of interest-rate derivatives. Each day it clears $3.4trn-worth, counting both sides of a trade. (The simplest variety is a swap of fixed and floating rates, allowing counterparties to reduce or increase their exposure to changes in rates.) In euro-denominated derivatives, the biggest category after dollars, LCH’s market share comfortably exceeds 90%, according to Clarus Financial Technology, a research firm. Eurex, the ...

Выбор редакции
23 ноября, 18:42

Science fiction triggers 'poorer reading', study finds

US academics find words such as ‘airlock’ and ‘antigravity’ are cues for test subjects to assume a story isn’t worth a careful readIt might feature such thought-stretching concepts as time travel and warp drives, but reading science fiction actually makes you read more “stupidly”, according to new research. In a paper published in the journal Scientific Study of Literature, Washington and Lee University professors Chris Gavaler and Dan Johnson set out to measure how identifying a text as science fiction makes readers automatically assume it is less worthwhile, in a literary sense, and thus devote less effort to reading it. They were prompted to do their experiment by a 2013 study which found that literary fiction made readers more empathetic than genre fiction. Continue reading...

23 ноября, 18:13

DO YOUR PART AND FRY MORE FOODS: The Telegraph (UK) is good at finding “scientists” who will say any…

DO YOUR PART AND FRY MORE FOODS: The Telegraph (UK) is good at finding “scientists” who will say anything. Here, finally, they find some scientists who have useful information: “In large cities like London, cooking fat is known to be responsible for 10 per cent of small particles in the air, so researchers believe frying […]

23 сентября, 23:16

Где искать научные статьи в открытом доступе. Большая подборка легальных ресурсов

Где искать научные статьи, если у вас нет доступа к платным базам данных? На сайте «Индикатор» опубликована подборка из 10 открытых ресурсов для ученых.1. UnpaywallОдин из самых удобных инструментов для бесплатного доступа — расширение для браузеров Chrome и Firefox Unpaywall. Оно автоматически ищет полные тексты научных статей. Если вы заходите на страницу какой-нибудь публикации, справа на экране появляется иконка с изображенным на ней замком. Если она зеленая и замок открыт, то достаточно просто нажать на него, и вы автоматические перейдете на страницу с полным текстом статьи в формате PDF. Установить расширение можно на его сайте.2. Академия GoogleДругой сайт, который может помочь, — это Академия Google. Вы просто пишете название статьи в поисковой строке и читаете полный текст. Если он, конечно, есть в открытом доступе.3. Open Access ButtonЕсли ни Unpaywall, ни Академия Google вам не помогли, может пригодиться сайт Open Access Button. Большая волшебная кнопка справится с поиском нужной статьи.4. ArXiv.orgЭтот сайт был создан специально для того, чтобы решить проблему открытого доступа к статьям. На ArXiv ученые выкладывают препринты своих статей, то есть черновики, которые в итоге публикуются с некоторыми изменениями. Большинство авторов — математики и физики, но сейчас по инициативе фонда Присциллы Чан и Марка Цукерберга разрабатывается аналог для биологии и других естественных наук — BioRxiv.5. КиберЛенинкаНаучная электронная библиотека «КиберЛенинка» — крупнейшее в России собрание научных статей, в основном на русском языке, хотя есть и иностранные публикации.6. Библиотека eLibraryНа этом сайте выкладываются статьи и научные публикации, входящие в РИНЦ (российский индекс научного цитирования). Необходима регистрация, причем вас могут попросить указать специальный пароль вашей организации. В профиле сохраняются настройки поиска и ваши подборки статей.7. Электронные библиотеки, сотрудничающие с вузамиУ многих вузов все-таки есть подписки на разные научные журналы. Они заключают договоры с электронными библиотеками, например с ЭБС «Университетская библиотека онлайн» или IQ Library.Узнайте, с какой библиотекой сотрудничает ваш вуз и как получить к ней доступ. Например, в МГУ доступ ко всем подпискам университета автоматически активируется, если вы ищете статью в компьютерном классе или через Wi-Fi-сеть МГУ.8. Российская государственная библиотека (РГБ)У РГБ есть электронный каталог, в котором можно найти не только статьи, но и диссертации и монографии на разные темы. К сожалению, не все работы есть в электронном варианте, но в каталоге есть функция «проголосовать за перевод в электронный вид необходимой книги или статьи». Сроки, к сожалению, неизвестны.9. Авторы статей или коллеги-ученыеЕсли нигде на вышеперечисленных ресурсах не удается найти статью, можно попробовать написать напрямую авторам или их коллегам и попросить полный текст. В научном мире это довольно распространенная практика. И есть два отработанных способа: написать в твиттере пост с хэштегом #icanhazpdf и указать, какую статью вы ищете и куда вам ее прислать, или зарегистрироваться на сайте Research Gate, найти нужную статью в профиле автора и нажать на кнопку «попросить полный текст». Чаще всего авторы отвечают в течение недели и присылают файл на указанную в профиле почту. Кстати, в этом случае статью можно даже обсудить с самим автором. Аналогичный ресурс, но более популярный среди ученых, работающих в области социальных и гуманитарных наук, — Academia.edu. Там часто даже просить ничего не надо — статьи, препринты, доклады и даже главы из книг можно скачать прямо из профиля исследователя.10. Специализированные базы данныхПомимо перечисленных выше ресурсов, существуют различные специализированные базы полных текстов статей, вот список самых крупных из них:1.PubMedБаза в основном по медицине и биологии, иногда содержит ссылки на полные бесплатные тексты статей.2.JstorОбширная база англоязычных статей, журналов и научных работ по самым разнообразным темам.3.MedLineКрупнейшая библиографическая база статей по медицинским наукам (NLM). Интегрирована в сервис SciFinder.4.PsyjournalsСайт с электронными версиями психологических журналов.5.SciFinderНаиболее полный и надежный источник химической информации, охватывающий более 99% текущей литературы по химии, включая патенты. Также там можно найти информацию по биологическим и биомедицинским наукам, химической физике, инженерии.6.ERICАнглоязычная база данных со статьями и научными публикациями по психологии из разных стран мира.7.Сборники статей от FrontiersFrontiers делает подборки статей по разным темам и выкладывает их в открытый доступ.8.HEP SearchБаза данных по физике высоких энергий.Вы также можете подписаться на мои страницы:- в фейсбуке: https://www.facebook.com/podosokorskiy- в твиттере: https://twitter.com/podosokorsky- в контакте: http://vk.com/podosokorskiy- в инстаграм: https://www.instagram.com/podosokorsky/- в телеграм: http://telegram.me/podosokorsky- в одноклассниках: https://ok.ru/podosokorsky