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.
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.
China's economic reforms over the past 40 years have led to a mixed economic structure with the government playing a key role in an increasingly market-driven economy. This paper expands a standard growth model of Barro (1990) to incorporate this structure, with a particular focus on including the agency problem between the central and local governments. To incentivize local governors, the central government has established an economic tournament, which generates not only intended incentives to develop local economies, a la Holmstrolm (1982), but also short-termist behaviors, a la Stein (1989). The latter channel helps to explain a series of challenges that confront the Chinese economy, such as overleverage through shadow banking and unreliable economic statistics.
The Contribution of Patients and Providers to the Overuse of Prescription Drugs -- by Carolina Lopez, Anja Sautmann, Simone Schaner
Overuse of medical care is often attributed to an informed expert problem, whereby doctors induce patients to purchase unnecessary treatments. Alternatively, patients may drive overuse of medications by exerting pressure on doctors to overprescribe, undermining the doctor's gatekeeping function for prescription medications. We develop a theoretical framework and designed a randomized trial to identify the importance of patients in driving overuse of antimalarials in community health clinics in Mali. Holding doctors' financial incentives constant, we vary patients' information about the availability of a discount for standard malaria treatment. We find evidence of patient-driven demand: directly informing patients about the price reduction, instead of allowing doctors to choose whether to share this information, increases use of the discount by 35 percent and overall rates of antimalarial use by 11 percent. This increase is driven by patients least likely to have malaria, leading to a worse match between treatment and cause of illness. We find no evidence that doctors use their information advantage to sell more powerful malaria treatment or increase revenue.
We study long run correlations between safe real interest rates in the U.S. and over 30 variables that have been hypothesized to influence real rates. The list of variables is motivated by an intertermporal IS equation, by models of aggregate savings and investment, and by reduced form studies. We use annual data, mostly from 1890 to 2016. We find that safe real interest rates are correlated as expected with demographic measures. For example, the long run correlation with labor force hours growth is positive, which is consistent with overlapping generations models. For another example, the long run correlation with the proportion of 40 to 64 year-olds in the population is negative. This is consistent with standard theory where middle-aged workers are high-savers who drive down real interest rates. In contrast to standard theory, we do not find productivity to be positively correlated with real rates. Most other variables have a mixed relationship with the real rate, with long run correlations that are statistically or economically large in some samples and by some measures but not in others.
... simply counting SEPs is not an accurate metric for leadership in the standard or the value of the patents to the end products. Only the quality of the inventions and their intrinsic value to the standards will determine market value and represent an accurate measure of leadership.
During a year when drug overdose deaths jumped to a record high 72,000 - roughly one every seven minutes - it's probably not surprising that overall life expectancy for Americans declined for the third straight year in 2017. But according to data from the CDC, drugs weren't the only factor at play: Deaths from suicide, the flu, diabetes and many other causes also increased. In 2017, US life expectancy at birth for the total population declined by 0.1 to 78.6 years for the total U.S. population. The drop in overall rates was driven by an increase in deaths for men (who are more likely to die of drug overdoses and suicide), with their life expectancy dropping by 0.1 to 76.1, while life expectancy for women was steady at 81.1. The spread between life expectancy for men and women also widened (in the women's favor) by 0.1 to 4.9 years. As more baby boomers die off, some might assume that the increase in rates has been driven by demographics, but this simply isn't accurate. Because even when adjusted for age (which should filter out most of the impact from the aging US population), mortality rates increased by 0.4% from 728.8 per 100,000 standard population in 2016 to 731.9 in 2017. White men and white women were responsible for most of the increase, with the age adjusted mortality rate for men climbing 0.6% while the rate for women climbed 0.9%. But the rise in mortality rates for white women was offset by a 0.8% decline in rates for black women. But by far the most significant increase for a given demographic group was the 2.9% rise for all Americans between the ages of 25 and 34, which more than offset a 1% drop for Americans aged 45-55. The 10 leading causes of death - heart disease, cancer, unintentional injuries, chronic lower respiratory diseases, stroke, Alzheimer disease, diabetes, influenza and pneumonia, kidney disease, and suicide - were unchanged from 2016 (these are ranked by number). These accounted for 74% of all deaths last year. But when adjusted for age, the data showed that more Americans are dying at younger ages from nearly all of the causes above - with the biggest jump seen in the age-adjusted rate for suicides (up a staggering 3.7%). Meanwhile, rates of infant mortality declined slightly, but were not statistically significant (the US continues to struggle with one of the highest infant mortality rate in the developed world). Overall, a total of 2,813,503 Americans died last year - 69,255 more than in 2016. In a series of tweets published after the CDC released its report, the organization's director said it is committed to "putting science into action" to ensure all Americans live longer, healthier lives. Latest CDC data show U.S. life expectancy has declined over past few years. Tragically, this trend is largely driven by deaths from drug overdose and suicide. This is a wakeup call. We are losing too many Americans, too early and too often, to conditions that are preventable. https://t.co/fiTD0zg01p — Dr. Robert R. Redfield (@CDCDirector) November 29, 2018 CDC is committed to putting science into action to protect U.S. health, but we must all work together to reverse the trend of declining life expectancy and help ensure that all Americans live longer and healthier lives. — Dr. Robert R. Redfield (@CDCDirector) November 29, 2018 Life expectancy rates are so broad that most Americans who see these headlines probably don't realize that falling life expectancy rates can affect their lives in myriad ways that might not be immediately obvious. Take the Dow, for example: The last time US life expectancy declined for 2+ years was in 1963 - right around the beginning of a secular bear market that lasted for more than a decade.
Charlie Gao Security, It has some problems. We explain. Russia has a good track record of adopting firearms that are reliable and durable. The Kalashnikov rifles and machine gun are known for their durability worldwide. But sometimes problems do occur. Quality standards for practically everything fell during the birth of the Russian Federation in the 1990s. Even AK-74Ms that were manufactured during this period are known to have quality control issues. At the same time, the explosion of organized crime resulted in a resurgence of submachine gun designs. Submachine guns were largely ignored during the Soviet Union following the 1940s, compact carbines like the AKMS and AKS-74U or automatic pistols took their place in most military roles. But these weapons were often unsuitable for police work. In response to the new demand for SMGs, design bureaus churned out a myriad of different SMG designs. Some of these were successful and see use to the current day, such as the PP-19-01 Vityaz. But many were not. One in particular, a small submachine gun called the PP-90M1, could be considered one of Russia’s worst military firearms. The PP-90M1 was conceived as a cheaper alternative to the PP-19 Bizon, a blowback 9x19mm SMG that was based on the AK receiver. The most radical feature of the Bizon was the helical magazine, which had a very large capacity of sixty-four rounds while retaining a fairly slim profile. But the helical magazine came with its own set of disadvantages. The Bizon’s magazine was not that reliable. It jammed often, anecdotally, almost every ten rounds. It also created balance issues by shifting the weapon’s center of gravity as the weapon expended ammunition. This resulted in the Bizon evolving into the PP-19-01 Vityaz, which stuck to a conventional stick magazine and slightly redesigned the ergonomics of the SMG. The PP-90M1 incorporated the helical magazine, and then tacked on plenty of other dubious design features. The majority of parts on the gun were made of polymer to save weight and money. Instead of the proven AKS side folding stock, the gun utilized an over folding stock of dubious quality. Worst of all, instead of a traditional charging handle on the side or top of the gun, a charging “stick” was added to the gun above and coaxial to the barrel. Read full article
Authored by Alan Dershowitz via The Gatestone Institute, The recent guilty plea of Michael Cohen of lying represents the dominant trend in Mueller's approach to prosecution. The vast majority of indictments and guilty pleas obtained against Americans by Mueller have not been for substantive crimes relating to his mandate: namely, to uncover crimes involving illegal contacts with Russia. They have involved indictments and guilty pleas either for lying, or for financial crimes by individuals unrelated to the Russia probe. If this remains true after the filing of the Mueller report, it would represent a significant failure on Mueller's part. Mueller was appointed Special Counsel not to provoke individuals into committing new crimes, but rather to uncover past crimes specifically involving alleged illegal coordination between the Trump campaign and Russian agents. No one doubted that Russia attempted to influence the 2020 election in favor of Donald Trump and against Hillary Clinton. But Mueller's mandate was not to prosecute Russians or to point the finger at Vladimir Putin. His mandate was to uncover crimes committed by the Trump campaign with regard to Russia's attempts to influence the election. It was always an uphill struggle for Mueller, since collusion itself is not a crime. In other words, even if he could show that individuals in the Trump campaign had colluded with Russian agents to help elect Trump, that would be a serious political sin, but not a federal crime. Even if Mueller could prove that members of the Trump team had colluded with Julian Assange to use material that Assange had unlawfully obtained, that, too, would not be a crime. What would be a crime is something that no one claims happened: namely, that members of the Trump campaign told Assange to hack the Democratic National Committee beforeAssange did so. Merely using the product of an already committed theft of information is not a crime. If you don't believe me, ask the New York Times, the Washington Post, the Guardian and other newspapers that used material illegally obtained by Assange with full knowledge that it was illegally obtained. Not only did they use information from Assange, but also from Chelsea Manning and from the stolen Pentagon Papers. The First Amendment protects publication by the media of stolen information. It also protects use of such information by a political campaign, since political campaigns are also covered by the First Amendment. It is important to note that Special Counsel Robert Mueller does not have a roving commission to ferret out political sin, to provoke new crimes, or to publish non-criminal conclusions that may be embarrassing to the President. His mandate, like that of every other prosecutor, is to uncover past crimes. In Mueller's case those crimes must relate to Russia. He also has the authority to prosecute crimes growing out of the Russia probe, but that is collateral to his central mission. In the end, Mueller should be judged by how successful he has been in satisfying his central mission. Judged by that standard and based on what we now know, he seems to be an abysmal failure. Perhaps more will come out when his report is published, but it is unlikely that he uncovered anything dramatically new with regard to allegations that the Trump campaign acted illegally in an attempt to help Russia undercut Hillary Clinton's campaign. Even if the report alleges uncharged criminal behavior, it must be remembered that much of what will be in the report are merely allegations based on uncross-examined evidence. Some of that evidence seems to come from admitted liars, who have pleaded guilty for lying. These liars would make poor witnesses in an actual trial, but if their evidence serves as a basis for conclusions reached in the Mueller report, then these conclusions may seem more credible than they actually are. We must, of course, wait for the publication of the Mueller report before reaching any final judgments, but if the Mueller report merely catalogues all the guilty pleas and indictments achieved thus far for lying and unrelated financial crimes, and tries to build a case of guilt by association around them, the American public will be justly critical of the process.
Authored by Ilana Mercer via The Unz Review, That’s the law. Nothing can be done about it. And that’s the liberal reaction to any rational action to stop the stampede of uneducated, unruly, fractious, antagonistic masses toward and over the U.S. southern border. Liberals call law-enforcement unlawful. Or, they shoehorn the act of holding the line into the unlawful category. Prevent uninvited masses from entering the country: Unlawful. Tear gas marauding migrants for stoning Border Patrol personnel: Illegal, immoral, possibly even criminal. Illegal. Unconstitutional. Immoral. Un-American. These are some of the refrains deployed by wily pitchmen, Democrats and some Republicans, to stigmatize and end any action to stop, disincentivize and summarily deport caravans of grifters, bound for the U.S. in their thousands. Our avatars of morality and legality seldom cite legal chapter-and-verse in support of their case for an immigration free-for-all. To go by the law, as professed by the liberal cognoscenti, claims-makers must be allowed to make their claims. Could the cuddly treatment mandated be predicated on the Christine Blasey Fordstandard? Brett Kavanaugh’s accuser claimed she had A Story to tell. So, the country had to hear her tell it. A compelling standard. That’s what happens when feelings and fancy replace reason and facts. No wonder the noise-makers are drowning-out the authentic claims-makers in society. Against the sainted noise-makers on the border all laws appear to be null and void or tantamount to torture The Left is creating reality on the ground, all right. But the prime real estate liberals hope to colonize is in every American’s head. Ruffians are breaching the U.S. border near Tijuana, demanding access to the American Welfare State. That’s the reality! Helped by the American left’s monopoly over the intellectual means of production—the average American is being encouraged to look at this aberrant apparition and “think”: “Awesome. This is who we are. American laws are amazing for inviting this.” Illegal, immoral, un-America: These are all pejoratives reserved not for the grifters making claims against Americans; but for the Americans resisting their claims. To listen to the liberal propagandist class is to come away believing that breaking into America is legal so long as you call yourself a refugee or an asylee and are “seeking a better life.” Moreover, provided an asylee, refugee or saint in disguise appears at a port of entry (San Ysidro, in our case), then he must be admitted into America. So, is The Law an ass or are those lying about the law the real asses? A bit of both. The Center for Immigration Studies provides something of a corrective. The gist of it is simple: “The Border Patrol has the authority to not only arrest those who enter illegally, but also to dissuade their entry. There is nothing in the law that requires the Border Patrol to allow aliens to enter the United States illegally, and then arrest them. Simply put, aliens do not have a right to illegally enter the United States.” Essentially, the opportunity to assert “a credible fear” of persecution, as explained by Andrew R. Arthur of the CIS, doesn’t give a scofflaw the right to enter the country and claim asylum. To the contrary: The “credible fear” provision, evidently being misused and misconstrued, doesn’t “exist to facilitate asylum claims.” Rather, “it exists to facilitate the removal from the United States of aliens who have attempted entry through fraud or without proper documents.” This charitable interpretation struggles to convince. Notwithstanding a defense of lousy and lax law—it nevertheless seems true to state that U.S. laws governing the admission of asylum-seekers and refugees will still process people based on a tale told at a port of entry, and despite disqualifying conduct: the brazen, even criminal, behavior evinced by the Central American caravanners rushing our border. As practiced, the law is worse than an ass. It’s perverse in the extreme. In the context of law misconstrued or reinvented, the chant about the 1878 Posse Comitatus Act is as telling. It’s the excuse parroted by almost everybody, Republicans included, for a lack of vigorous military action against an en masse breach of the southern border. With their Posse Comitatus chant, the no-borders crowd is claiming that sending the U.S. Military to the border is tantamount to deploying the military for civilian purposes. If an ongoing, sustained, intentional and international invasion of U.S. territory by foreign nationals is considered a domestic dispute to be handled by civil authorities—then America, plain and simple, is both defenseless and borderless; there is, seemingly, no law that’ll defend American borders. What those liberals colonizing our heads are attempting to convey is that a good America, a just America, a moral America is de facto and de jure a borderless America. In truth, and according to the Congressional Research Service, as relayed by the Military Times, Posse Comitatus means that “the U.S. military is not to be used to control or defeat American citizens on U.S. soil.” The hordes amassed on the border with Mexico, and rushing the port of entry in San Ysidro, California, are not American citizens. They are not even very nice.
Authored by Nick Giambruno via InternationalMan.com, I think there’s a very high chance of a stock market crash of historic proportions before the end of Trump’s first term. That’s because the Federal Reserve’s current rate-hiking cycle, which started in 2015, is set to pop “the everything bubble.” I’ll explain how this could all play out in a moment. But first, you need to know how the Fed creates the boom-bust cycle… To start, the Fed encourages malinvestment by suppressing interest rates lower than their natural levels. This leads companies to invest in plants, equipment, and other capital assets that only appear profitable because borrowing money is cheap. This, in turn, leads to misallocated capital – and eventually, economic loss when interest rates rise, making previously economic investments uneconomic. Think of this dynamic like a variable rate mortgage. Artificially low interest rates encourage individual home buyers to take out mortgages. If interest rates stay low, they can make the payments and maintain the illusion of solvency. But once interest rates rise, the mortgage interest payments adjust higher, making them less and less affordable until, eventually, the borrower defaults. In short, bubbles are inflated when easy money from low interest rates floods into a certain asset. Rate hikes do the opposite. They suck money out of the economy and pop the bubbles created from low rates. It Almost Always Ends in a Crisis Almost every Fed rate-hiking cycle ends in a crisis. Sometimes it starts abroad, but it always filters back to U.S. markets. Specifically, 16 of the last 19 times the Fed started a series of interest rate hikes, some sort of crisis that tanked the stock market followed. That’s around 84% of the time. You can see some of the more prominent examples in the chart below. Let’s walk through a few of the major crises… • 1929 Wall Street Crash Throughout the 1920s, the Federal Reserve’s easy money policies helped create an enormous stock market bubble. In August 1929, the Fed raised interest rates and effectively ended the easy credit. Only a few months later, the bubble burst on Black Tuesday. The Dow lost over 12% that day. It was the most devastating stock market crash in the U.S. up to that point. It also signaled the beginning of the Great Depression. Between 1929 and 1932, the stock market went on to lose 86% of its value. • 1987 Stock Market Crash In February 1987, the Fed decided to tighten by withdrawing liquidity from the market. This pushed interest rates up. They continued to tighten until the “Black Monday” crash in October of that year, when the S&P 500 lost 33% of its value. At that point, the Fed quickly reversed its course and started easing again. It was the Chairman of the Federal Reserve Alan Greenspan’s first – but not last – bungled attempt to raise interest rates. • Asia Crisis and LTCM Collapse A similar pattern played out in the mid-1990s. Emerging markets – which had borrowed from foreigners during a period of relatively low interest rates – found themselves in big trouble once Greenspan’s Fed started to raise rates. This time, the crisis started in Asia, spread to Russia, and then finally hit the U.S., where markets fell over 20%. Long-Term Capital Management (LTCM) was a large U.S. hedge fund. It had borrowed heavily to invest in Russia and the affected Asian countries. It soon found itself insolvent. For the Fed, however, its size meant the fund was “too big to fail.” Eventually, LTCM was bailed out. • Tech Bubble Greenspan’s next rate-hike cycle helped to puncture the tech bubble (which he’d helped inflate with easy money). After the tech bubble burst, the S&P 500 was cut in half. • Subprime Meltdown and the 2008 Financial Crisis The end of the tech bubble caused an economic downturn. Alan Greenspan’s Fed responded by dramatically lowering interest rates. This new, easy money ended up flowing into the housing market. Then in 2004, the Fed embarked on another rate-hiking cycle. The higher interest rates made it impossible for many Americans to service their mortgage debts. Mortgage debts were widely securitized and sold to large financial institutions. When the underlying mortgages started to go south, so did these mortgage-backed securities, and so did the financial institutions that held them. It created a cascading crisis that nearly collapsed the global financial system. The S&P 500 fell by over 56%. • 2018: The “Everything Bubble” I think another crisis is imminent… As you probably know, the Fed responded to the 2008 financial crisis with unprecedented amounts of easy money. Think of the trillions of dollars in money printing programs – euphemistically called quantitative easing (QE) 1, 2, and 3. At the same time, the Fed effectively took interest rates to zero, the lowest they’ve been in the entire history of the U.S. Allegedly, the Fed did this all to save the economy. In reality, it has created enormous and unprecedented economic distortions and misallocations of capital. And it’s all going to be flushed out. In other words, the Fed’s response to the last crisis sowed the seeds for an even bigger crisis. The trillions of dollars the Fed “printed” created not just a housing bubble or a tech bubble, but an “everything bubble.” The Fed took interest rates to zero in 2008. It held them there until December 2015 – nearly seven years. For perspective, the Fed inflated the housing bubble with about two years of 1% interest rates. So it’s hard to fathom how much it distorted the economy with seven years of 0% interest rates. The Fed Will Pop This Bubble, Too Since December 2015, the Fed has been steadily raising rates, roughly 0.25% per quarter. I think this rate-hike cycle is going to pop the “everything bubble.” And I see multiple warning signs that this pop is imminent. • Warning Sign No. 1 – Emerging Markets Are Flashing Red Earlier this year, the Turkish lira lost over 40% of its value. The Argentine peso tanked a similar amount. These currency crises could foreshadow a coming crisis in the U.S., much in the same way the Asian financial crisis/Russian debt default did in the late 1990s. • Warning Sign No. 2 – Unsustainable Economic Expansion Trillions of dollars in easy money have fueled the second-longest economic expansion in U.S. history, as measured by GDP. If it’s sustained until July 2019, it will become the longest in U.S. history. In other words, by historical standards, the current economic expansion will likely end before the next presidential election. • Warning Sign No. 3 – The Longest Bull Market Yet Earlier this year, the U.S. stock market broke the all-time record for the longest bull market in history. The market has been rising for nearly a decade straight without a 20% correction. Meanwhile, stock market valuations are nearing their highest levels in all of history. The S&P 500’s CAPE ratio, for example, is now the second-highest it’s ever been. (A high CAPE ratio means stocks are expensive.) The only time it was higher was right before the tech bubble burst. Every time stock valuations have approached these nosebleed levels, a major crash has followed. Preparing for the Pop The U.S. economy and stock market are overdue for a recession and correction by any historical standard, regardless of what the Fed does. But when you add in the Fed’s current rate-hiking cycle – the same catalyst for previous bubble pops – the likelihood of a stock market crash of historic proportions, before the end of Trump’s first term, is very high. That’s why investors should prepare now. One way to do that is by shorting the market. That means betting the market will fall. Keep in mind, I’m not in the habit of making “doomsday” predictions. Simply put, the Fed has warped the economy far more drastically than it did in the 1920s, during the tech or housing bubbles, or during any other period in history. I expect the resulting stock market crash to be that much bigger. * * * Clearly, there are many strange things afoot in the world. Distortions of markets, distortions of culture. It’s wise to wonder what’s going to happen, and to take advantage of growth while also being prepared for crisis. How will you protect yourself in the next crisis? See our PDF guide that will show you exactly how. Click here to download it now.
After meeting with Trump, Argentina's president had to downplay a White House readout that described China's economic behavior as "predatory."
Ever wonder how much the royals pay their staff? It's not as much as you think. Here's the surprising answer.
Authored by Lance Roberts via RealInvestmentAdvice.com, All it took was two 10% stock market corrections in a single year and some heavy “browbeating” from President Trump to reverse Jerome Powell’s hawkish stance on hiking interest rates. On Wednesday, Powell took to the microphone to give the markets what they have been longing for – the “Powell Put.” During his speech, Powell took to a different tone than seen previously and specifically when he stated that current rates are “just below” the range of estimates for a “neutral rate.” This is a sharply different tone than seen previously when he suggested that a “neutral rate” was still a long way off. Importantly, while the market surged higher after the comments on the suggestion the Fed was close to “being done” hiking rates, it also suggests the outlook for inflation and economic growth has fallen. With the Fed Funds rate running at near 2%, if the Fed now believes such is close to a “neutral rate,” it would suggest that expectations of economic growth will slow in the quarters ahead from nearly 6.0% in Q2 of 2018 to roughly 2.5% in 2019. Such will also correspond with a drop in inflationary pressures, as we noted previously, which is already occurring with the drop in energy prices. “More importantly, falling oil prices are going to put the Fed in a very tough position in the next couple of months as the expected surge in inflationary pressures, in order to justify higher rates, once again fails to appear. The chart below shows breakeven 5-year and 10-year inflation rates versus oil prices.” But here was the key comment that suggests the recent blasting by President Trump hit home: “Powell says moving too fast would risk shortening U.S. expansion, moving too slow could risk higher inflation and destabilizing financial imbalances.” President Trump has been adamant that Powell’s aggressiveness was jeopardizing the economic recovery. More interesting was when Powell reiterated they see “no major asset class, however, where valuations appear far in excess of standard benchmarks” I am not sure which benchmarks the Fed looks at exactly. The real risk to the market is not valuations at historically high levels by virtually every measure, but rather the risk of a credit related event due to the impact of higher rates on an abundance of lower-rated corporate debt. Nonetheless, in the short-term, the “bulls” got their Christmas wish as noted by Bloomberg economists “Tim Mahedy and Yelena Shulyatyeva: ‘Powell’s comment that rates are just below neutral is a step back from his comments earlier in the fall implying the FOMC still has a ways to go. This could be the first sign that the pace of rate hikes is set to slow next year.’ However, not all economists got the same dovish message as noted by Greg Robb via Marketwatch. “I really don’t think he was dovish, not really. He didn’t say inflation was weaker or the economy was weaker than we thought. It is a bit of a market overreaction.” -Paul Ashworth, chief U.S. economist at Capital Economics. “The Fed has said they wanted to go above neutral. If they wanted to be neutral, they could have walked that back. He gave no hint of a pause in December.” – Avery Shenfeld, chief economist at CIBC All the “bulls” need now is for President Trump to “cave in” on his demands on China, a problem he created in the first place, at this weekends G-20 summit. I would expect a deal that is well short of any original objective as China agrees to issues which are economically unimportant to them. However, such will “look like a win” for the Trump administration and should clear the way for “Santa to visit Broad and Wall.” After that, it’s anyone’s guess, but the real issues plaguing the economy and the markets have not been resolved. Just something to think about as you catch up on your weekend reading list. Economy & Fed US Economy Is Strong – 3 Signs It Won’t Last by Lydia Depillis via CNN Business Why US-China Ceasefire Is Coming Soon by Anatole Kaletsky via Project Syndicate Levered Companies Layer Loans Over Loans by Sally Bakewell & Kelsey Butler via Bloomberg Is The US Economy TOO Strong? by Joe Calhoun via MyPersonalCFO The Fed’s Cheat Sheet by Eric Cinnamond Why Economists Insist Powell Wasn’t That Dovish by Greg Robb via MarketWatch Fed Warns Of “Large Plunge” In Markets If Risks Materialize by Jeff Cox via CNBC The Scariest Economic Chart Is Coming From China by Pedro de Costa via Forbes Fed’s Speech Sends Stocks Soaring, But Should It? by Mike Shedlock via Mishtalk Jerome Powell Sends Markets Soaring by Binyamin Appelbaum via NYT GM & Trump Go To Blows by Bruce Yandle via Washington Examiner GE’s $15 Billion Money Pit by Katherine Chiglinsky via Bloomberg The Smashing Effects Of A Trade War by Seth Levine via The Integrating Investor 10-Years Later – Did Bailouts Work by Kevin Williamson via National Review The Fed Finally Blinks by Kevin Muir via The Macro Tourist Markets Was Yesterday The “All Clear” Or “More Noise by Bryce Coward via Zerohedge Don’t Blame The Strong Dollar by Mark Hulbert via MarketWatch Bull Market Is More Fragile Than It Looks by Stephen Gandel via Fortune History Says FANG Feast Is Finished by Dana Lyons via The Lyons Share Blue Chip Companies Are Piling On Debt by William Cohan via NYT October Sucked, What Now? by Cliff Asness via AQR Capital Management Did Powell Just Push Investors Into A “Bear Trap” by Barbara Kollmeyer via MarketWatch The Uphill Struggle For Equities by Louis-Vincent Gave via Evergreen Gavekal Hope, Fear & Reality by Jamie Powell via FT Alphaville What You Need To Know To Sell Before It’s Too Late by Jared Dillian via MarketWatch Most Read On RIA Rising Rates Are Killing The Housing Market by Lance Roberts Lessons From Thanksgiving Dinner by John Coumarianos Why We Sold AAPL Stock by Vitaliy Katsenelson GM Cuts Jobs As Auto Bubble Begins To Burst by Jesse Colombo 15-Surprises For 2019 by Doug Kass The Difference Between A Bull & Bear Market by Lance Roberts UTX Faces Reality – Will Other Companies Follow Suit by Michael Lebowitz The Fallacy Of The Positive Impact From Falling Oil Prices by Lance Roberts Watch Research / Interesting Reads Goldman Sachs 2019 Economic Outlook via Goldman Sachs What Will The Next Financial Crisis Look Like by Daniel LaCalle via The Epoch Times GM: A Case Study To End Share Buybacks by Patrick Hill via The Progressive Ensign 21-Quotes From Henry Hazlitt by Gary Galles via FEE Ray Dalio’s Principles For Dummies by Matthew Walther via American Affairs Why MSFT Is A Better Bet Than AAPL by Paul La Monica via CNN Money Overparenting In America Created Generation Of Snowflakes by Shawn Langlois via Marketwatch Paul Volcker’s Wisdom For America by John Cassidy via The New Yorker Why GM Killed Cars & Jobs by Nathan Bomey via USA Today US Corporations Are Winning Their War On Capitalism by Jonathan Tepper via Bloomberg When Next Recession Hits, Will Benefits Be Enough? by Gary Burtless via Real Clear Markets “There is nothing like price to change sentiment.“ – Helene Meisler
A DHS memo obtained by POLITICO asks several other Cabinet departments to send civilian police to the U.S.-Mexico border to stop migrant “caravans.”
If you have received an OSHA citation and felt like the agency piled on multiple citation items for a single alleged violation, you are not alone. Multiple OSHA standards often apply to a single workplace condition or event, which can result in multiple violations of OSHA’s standards.
Interior Secretary Ryan Zinke on Friday accused the House Natural Resources Committee's top Democrat of "drunken" behavior and paying “hush money” after the Democrat called for the secretary to resign for his series of ethics scandals.“It’s hard for him to think straight from the bottom of the bottle,” Zinke wrote on Twitter after Rep. Raúl Grijalva (D-Ariz.) published an op-ed asking for Zinke’s resignation — a remark that in most eras would be a stunning breach of decorum for a Cabinet member."This is coming from a man used nearly $50,000 in tax dollars as hush money to cover up his drunken and hostile behavior. He should resign and pay back the taxpayer for the hush money and the tens of thousands of dollars he forced my department to spend investigating unfounded allegations,” Zinke added. Grijalva had pointed in his op-ed to the various ethical investigations into Zinke, including at least one that has been referred to the Department of Justice. The Arizona Democrat promised his committee would continue investigating Zinke when the Democrats take control of the House in January. Interior’s internal watchdog earlier this month cleared Zinke of improperly shrinking a national monument in New Mexico to benefit a Republican lawmaker, though the full report has not yet been released.But last month it found last month Zinke sought to skirt or alter department policies to justify his taxpayer-funded trips with his wife.Article originally published on POLITICO Magazine]]>
Labour calls for inquiry after health secretary praised firm in paid-for newspaper articlesLabour is demanding an inquiry after Matt Hancock, the health secretary, was accused of breaking the ministerial code by endorsing a private healthcare company in a sponsored newspaper supplement.An interview with Hancock appeared in the Evening Standard’s Future London Health supplement, which was paid for by Babylon. In the feature, Hancock praised the company’s GP at Hand app, which allows users to have video consultations with doctors via their smartphone. Hancock is an ally of George Osborne, the editor of the Evening Standard. Continue reading...
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?