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.
Estimating the Effects of a Large For-Profit Charter School Operator -- by Susan Dynarski, Daniel Hubbard, Brian Jacob, Silvia Robles
In this paper, we leverage randomized admissions lotteries to estimate the impact of attending a National Heritage Academy (NHA) charter school. NHA is the fourth largest forprofit charter operator in the country, enrolling more than 56,000 students in 86 schools across 9 states. Unlike several of the other large for-profit companies that operate virtual charters, NHA only has standard bricks-and-mortar schools. Our estimates indicate that attending a NHA charter school for one additional year is associated with a 0.04 standard deviation increase in math achievement. Effects on other outcomes are smaller and not statistically significant. In contrast to most prior charter school research which find the largest benefits for low-income, underrepresented minorities in urban areas, the benefits of attending an NHA charter network are concentrated among non-poor students attending charter schools outside urban areas. Using data from a survey of school administrators in traditional public and charter schools, we document several aspects of school organization, culture and instructional practice that might explain these positive effects.
Mortgage Market Credit Conditions and U.S. Presidential Elections -- by Alexis Antoniades, Charles W. Calomiris
Voters punish incumbent Presidential candidates for contractions in the local (county-level) supply of mortgage credit during market-wide contractions of credit, but they do not reward them for expansions in mortgage credit supply in boom times. Our primary focus is the Presidential election of 2008, which followed an unprecedented swing from very generous mortgage underwriting standards to a severe contraction of mortgage credit. Voters responded to the credit crunch by shifting their support away from the Republican Presidential candidate in 2008. That shift was particularly pronounced in states that typically vote Republican, and in swing states. The magnitude of the effect is large. If the supply of mortgage credit had not contracted from 2004 to 2008, McCain would have received half the votes needed in nine crucial swing states to reverse the outcome of the election. The effect on voting in these swing states from local contractions in mortgage credit supply was five times as important as the increase in the unemployment rate; if unemployment had not increased from 2004 to 2008, that improvement in local labor markets would only have given McCain only 9% of the votes needed to win the nine crucial swing states. We extend our analysis to the Presidential elections from 1996 to 2012 and find that voters' reactions are similar for Democratic and Republican incumbent parties, but different during booms and busts of mortgage credit. These asymmetric results indicate that voters react strongly and negatively to credit supply contraction; however, organized political bargaining (the "smoke-filled room channel") rather than voting was the primary vehicle for rewarding politicians for supporting government subsidies for mortgage risk during booms.
Transportation engineers are taught that as demand for travel goes up, this decreases not only speed but also the capacity of the road system, a phenomenon known as hypercongestion. We revisit this idea. There is no question that road systems experience periods in which capacity falls. However, we point out that capacity is determined by both demand and supply. Road construction, lane closures, stalled vehicles, weather, and other supply shocks provide an alternative explanation for the empirical evidence on hypercongestion. Using data from the Caldecott Tunnel in Oakland, California, we show that a naive regression recovers the standard hypercongestion result in the literature. However, once we use instrumental variables to isolate the effect of travel demand this effect disappears and across specifications we find no evidence that capacity decreases during periods of high demand. This lack of evidence of hypercongestion calls into question long-standing conventional wisdom held by transportation engineers and implies that efficient "Pigouvian" congestion taxes should be substantially lower than implied by hypercongestion models.
EPA to roll back car-emissions standards. The move could change the composition of the U.S. auto fleet.
The push to rewrite the Obama-era carbon limits on cars and SUVs, which aimed for vehicles to average 50 miles per gallon by 2025, is sure to spark a major political and legal battle.
ANTISOCIAL MEDIA: Zuckerberg slams Apple, unveils network’s chilling ambitions. The founder of the world’s most popular social platform outlined his ambitions for Facebook to act as a democratic system, with an independent “Supreme Court”, which people will be able to petition for their content to be restored. “I think in any kind of good-functioning democratic […]
To make a non-stop 10,000-km-long flight, and to hunt for an enemy submarine which lurked at great depths in an unfamiliar area: this task was completed by the naval aviation pilots in the Mediterranean Sea.
Rolling back Obama-era standards could result in a two-tier system, with California imposing stricter requirements.
(Don Boudreaux) Tweet… is from page 96 of the 2015 Matthew Dale translation of Weiying Zhang’s excellent 2010 book, The Logic of the Market: Any policy that leads entrepreneurs to rent-seek is not a good policy. Industrial policies lead to entrepreneurial rent-seeking. Many people, including some Americans, praise China’s industrial policies. Actually, China’s industrial policies have never […]
The 4th Global Automotive Lightweight Materials Asia Summit, which takes place on 31 July – 1 August 2018 in Shanghai, China, will focus on designing lightweight structures and selecting the optimal combination of high strength steel, aluminium and reinforced plastics to help OEMs create the next generation of light vehicles meeting new regulatory standards on safety, emissions and performance.The Summit will bring together the best of Chinese and international automotive engineers to lay out lightweight engineering approaches for mixed material bodies from across Asia, the US and Europe to deliver actionable, transferrable insight for Asian OEMs. To find out more about this event and to register, please go to: GALM Asia 2018
The no-drama Energy secretary has demonstrated a remarkable ability to avoid the negative headlines that have dogged other Cabinet members.
South Africa win by 492 runsVernon Philander bags six wickets in an incredible spell of bowlingFeel free to get in touch on email or tweet @JPHowcroft 10.36am BST This was a result that was feared at the start of the match following the week Australia had endured but even so, it remains one heck of a thumping. Biggest Test victory margins by runs:675 Eng beat Aus Brisbane 1928/29562 Aus beat Eng Oval 1934530 Aus beat SA Melbourne 1911492 SA beat Aus Johannesburg 2018491 Aus beat Pak Perth 2004 10.28am BST South Africa arrived on day five knowing they would be celebrating at some point today, they just couldn’t have dreamed it would be so soon. Vernon Philander struck with his opening ball and then again later in his first over to start the rot. Four more fell at his hand in a mesmerising spell of seam bowling before a mindless run-out sealed the deal. Under 90-minutes of play to secure seven wickets and complete a 3-1 series victory, South Africa’s first at home over Australia since 1970. Continue reading...
Authored by Mac Slavo via SHTFplan.com, The heads of schools from various parts of England and Wales have described differences in the appearance of some of their pupils. Poverty stricken children are “grey and pale” and fill their pockets with food so they can have something to eat. And the problem is only worsening as government regulations and tax burdens continue to make it hard for everyday people to get by. According to the BBC, on school head was quoted saying, “My children have grey skin, poor teeth, poor hair; they are thinner.” Lynn, a head teacher from a former industrial town in Cumbria, who did not want to give her full name, was one of a number of head teachers speaking to reporters at the National Education Union conference in Brighton. Even though the government said measures were in place to tackle poverty, much of the UK is sliding into abject poverty as the country inches closer to Communism. “Children are filling their pockets with food. In some establishments that would be called stealing. We call it survival,” Lynn said. Jane Jenkins, a head teacher from Cardiff, said children in her school often only brought a slice of bread and margarine for lunch and that teachers supplemented this. “It’s really difficult and when people are asking you about standards, why we don’t go up the league tables?" There’s also becoming a stark difference between the wealthy and the poor. “When you take children out to an event, maybe a sporting event, you see children of the same age from schools in an affluent area. It’s the grey skin, the pallor. It’s the pallor you really notice,” said another teacher. “Monday morning is the worst. There are a number of families that we target that we know are going to be coming into school hungry. By the time it’s 9:30 am they are tired.” The teachers are also washing the grubby clothes for the children who can’t afford to even clean the few pieces of destroyed clothing they own. “We have washing machines and we are washing the children’s clothes while they do PE,” said Lynn. “We wouldn’t have it that these children are stigmatized because their clothes are dirty.” Howard Payne, a head at an inner city school in Portsmouth, said there had been a four-fold increase in the number of children with child protection issues. “Every one of these issues has had something to do with the poverty that they live in,” he said. “It’s neglect. It’s because they and their families don’t have enough money to provide food, heating or even bedding.” As poverty increases too, governments usually attempt to solve the problem by raising taxes, which pushes those already borderline into poverty as well, exacerbating the problem exponentially. More government is never the solution to the problems the government created in the first place.
Authored by Alex Deluce via GoldTelegraph.com, Paper currency has led to the collapse of almost every economy that has tried to institute a fiat currency to trade for goods and services. It’s not looking very well for the once mighty dollar, either. Throughout history, attempts at using fiat currency, even today, has failed. When the government prints fiat money that isn’t backed by any value, disaster inevitably ensues. Still, the long history of failed fiat currency is being ignored by today’s money printers. At the start of the first century, the Roman denarius was a coin containing approximately 94 percent real silver. By the end of the century, the amount of silver was reduced to 85 percent. Devaluing the denarius meant Nero and succeeding emperors could pay off their bills more easily while becoming richer. A hundred years later, the denarius contained less than 50 percent silver, and in 244 A.D., Emperor Philip the Arab devalued the currency down to 0.05 percent silver content. By the time the Roman empire collapsed, the denarius was made of 0.02 percent silver, and it became useless as a currency. Copper backed China’s initial paper currency. When copper became scarce, China began to make iron coins. The iron currency became overissued and soon became devalued. By the 11th century, a Szechuan bank issued another paper currency that could be exchanged for valuable goods such as rare metals or silk. Continuous war with neighboring Mongols caused economic inflation. China lost the war to Genghis Khan, who was too busy with other conquests to take much interest in China. Genghis’ grandson Kublai brought China and its finances under Mongolian control. Kublai used fiat currency for the vast China trade, including trade with Marco Polo. Kublai simply continued to print vast amounts of money as he continued his marauding and conquering. But infusing the economy with worthless monopoly money ruined many and chaos became the norm. France has an interesting history with paper money. It may be the only country to face economic collapse not once, not twice, but three times by flooding the country with fiat currency. High-living Sun King Louis XIV left a debt of 3 billion livres for his successor to deal with. Louis XV desperately needed incoming tax payments and demanded these be paid in paper currency. Predictably, the paper currency was quickly overprinted and became worthless. In the 18th century, France began another attempt at printing paper currency called assignats. By the end of the century, the assignat suffered 13,000 percent inflation. Napoleon rode to the rescue by instituting the gold franc, which stabilized France’s erratic currency. One would think the French might have seen a connection between the stable economy and gold-backed currency. No such luck. France reverted to paper currency in the 1930s, the paper franc. In just more than a decade, the fiat franc became devalued by 99 percent. France’s third attempt at printing worthless monopoly money proved to be a dismal failure. Following WWI, Germany’s Weimar Republic faced historic debts. So, Germany put the printing presses to work – a total of 133 printing companies were kept busy. The mark became more than worthless. It was used to heat furnaces. Burning the paper currency to keep warm was more efficient than using it for trade. A wheel barrel became a wallet. Throughout the 20th century, many other countries flooded their economy with fiat currency – and collapsed as a result. The direct correlation between government interference with money and the devaluation of currency seems to escape many. Has the US learned anything about fiat money? The Colonies happily jumped on the fiat currency bandwagon and began flooding the land with their own paper money. When these currencies quickly became overissued, they became – surprise! – devalued. Like the marc to come a few centuries later, colonial currency made for excellent bathroom tissue. The Revolutionary War was financed with a paper currency called the continental. It, too, crashed on a grand scale. This, finally, brought about some healthy American distrust for fiat currency. The US dollar was now backed by actual gold and saw the most splendid and richest economic growth in history. People came from all parts of the globe to be a part of such a success story. As if on schedule, the Federal Reserve appeared. Currency once again fell under the aegis of government control and manipulation. In 1933, President Roosevelt made ownership of gold illegal. The ties between the US dollar and the country’s gold reserves were severed gradually until President Nixon eliminated the gold standard entirely in 1971. The once mighty US dollar instantly turned into fiat currency. This was followed by a decade of disastrous inflation. The US has followed the historical pattern of paper money. The government is currently printing dollars with abandon to pay off its record debt (sound like a familiar refrain?). Following a long historical precedence, the US dollar is losing value at an alarming rate as we head into a period of high inflation. As history has demonstrated, inflation is a guaranteed devaluer of currency. Following WWII, Greece saw an increase in inflation of 18 percent per day. The country’s deficit tripled within a year. And then things got really bad. An invasion by the Axis powers saw income plummet by 70 percent. Like so many other countries in the past, Greece started printing worthless currency and experienced one of the worse hyperinflations in history. As previously stated, Germany’s Weimar Republic faced unprecedented debt in the wake of WWI. No country would accept its worthless mark as repayment, forcing Germany to sell off marks for other foreign currencies at any price. This lead to Germany’s famous hyperinflation of 29,500 percent. Zimbabwe is famous for its 100 trillion-dollar note during its period of catastrophic hyperinflation. Prices doubled daily as Zimbabwe faced hyperinflation of 79 billion percent in 2008. A loaf of bread was priced at 35 million Zimbabwe dollars. Government intervention can invariably be found as the source of any hyperinflation. There is no escaping history. Paper money and out-of-control national debts have always devalued the currency and caused massive inflation. The US dollar remains on the cusp. It is anyone’s guess which way it will go. But the roadmap to a financial disaster was printed thousands of years ago. Is anyone paying attention?
In a move to speed up deportations, the Justice Department has decided that it will link clearing cases to the performance reviews of federal immigration judges. The minimum quotas, first reported by the Wall Street Journal, will require judges to clear 700 cases a year and to see that fewer of their rulings are sent back by a higher court. According to the Journal's report, the Justice Department says that on average over the last five years judges have cleared 678 cases, but some judges completed far more cases. The system operates on a sliding scale, so judges who fail to meet the top standards could see their performance review downgraded.Along with those requirements, judges will also need to meet thresholds in other areas including a demand that 85 percent of cases for people that have been detained be completed in three days after a judge has heard the merits of the particular case.Attorney General Jeff Sessions has previously stressed the work his department is doing to reduce the immigration case backlog, which numbers into the hundreds of thousands. Clearing through those cases would allow the administration to speed up deportation proceedings, which would coincide with other reported efforts to step up the enforcement of immigration laws. President Donald Trump complained over the weekend that undocumented immigrants are taking advantage of "dumb laws." Trump has also repeatedly expressed dissatisfaction with some legal requirements that can slow down the process for deportation.The new quotas are set to take effect in October.
Submitted by Nicholas Colas of DataTrek Research Today we offer up our quarterly Off the Grid economic indicators to look at the state of the US economy with fresh eyes. Everything looks pretty good, even as expectations for Q1 GDP growth falter slightly. Our best observation, courtesy of some Google Trend data hounding: still disappointing wage growth may be a lingering after-effect of the Great Recession. You need genuine confidence to demand higher compensation, and many workers aren’t there yet. With estimates for US Q1 GDP coming down, today we turn to our quarterly “Off the Grid” indicators to see if first quarter growth is really as sluggish as more traditional measures say. We’ve been looking at non-standard measures of US economic growth since the Financial Crisis. Over the years we have found a handful that reveal both underlying growth trends and consumer confidence. One of our favorite vectors for this analysis is the US auto industry. Specifically: Full sized pickup truck sales are weakening. February 2018 posted an 8.0% decline to last year, the worst comp since March 2011. January was only 1.0% higher. Some of the decline may be due to storm damage replacement last year pulling demand forward, but this is a critical indicator of the health of American small business. And it is moving in the wrong direction. Used car prices, as measured by Manheim Auctions, have been ticking down from their post-hurricanes Q4 highs and are now equal to where they were last August. Since almost every new vehicle sales comes with a trade-in, the value of used cars is an important driver of new car sales/product mix.Now, used car prices have remained elevated far longer than most experts thought possible. The recent drops may not mean much, but they do bear watching as we go into spring selling season. Vehicle inventory on new car dealer lots is, at least, in good shape. At 74 days supply, current stock is the same as last year at this time. This bodes well for production schedules (and economic growth) in Q2, especially if consumers use some of their tax reform savings to purchase a new car or truck. Overall score here as it relates to US economic strength: C+. While light vehicle demand has remained very good for this point in the economic cycle, lower pickup truck sales and used car prices are worrisome. Turning to the strength of the US consumer and their confidence in the domestic economy and financial system: Google search volumes for “Food stamps” in Q1 2018 were less than half peak levels from October 2013 and near 12 month lows. Program participation at the end of last year was down to 41.3 million people (of which roughly half are children) from 47.7 million in 2013. Current program levels remain slightly elevated from last year’s storms, but are among the lowest levels since the Great Recession. Gold and silver coin sales from the US Mint, which reliably ran over $250 million on a monthly basis from 2010 to 2013, are now just $51 million/month. These are levels we haven’t seen since mid 2008. Google autocompletes (where the search box algorithm suggests common endings to your partial query) for “I want to buy” include: bitcoin, a house, a timeshare, and ripple (in that order). In Q3 2011, the top autocomplete for this partial search entry was “used”. Top autocompletes for “I want to sell my” are: car, fur coat, and furniture. As recently as 2015, “kidney” was a top Google suggestion here. Overall score for consumer confidence in the economy and banking system: B+/A-. While way too many Americans still need (and qualify for) government assistance to purchase necessities, the general trend is thankfully positive. Worries over the banking system (manifested in interest in precious metals) are very low. And if the top Google autocompletes for “buy” and “sell” are highly volatile crypto currencies, we trust consumers have the basics largely covered. Other items of note/interest related to the Federal Reserve’s dual mandate of inflation and employment: When we go off the grid, we measure consumer inflation expectations through the lens of a commonly purchased treat: a bacon cheeseburger. The Consumer Price Index subcomponents cover all the bases here, with long-term price records for all three ingredients. Through this greasy lens, we see that these commonly purchased food items are collectively exceeding the Federal Reserve’s 2% inflation target. Year to date, bacon, ground beef and cheese are up 3-4% over last year. Moreover, the momentum of these changes is broadly accelerating, for last year at this time price levels were declining by 1-3%. No word on whether new Fed Chair Powell likes his bacon cheeseburger rare, medium, or well done. But no matter his preference, consumers are seeing rising inflation in this all-American food item. And since consumer inflation expectations can anchor on things they purchase frequently, this should/could boost public perceptions of inflationary pressures. This was, for example, a large piece of the 1970s inflation story. “Take this job and shove it”. Jessica covers the monthly JOLTS data in detail, but we lift one piece of this report for Off the Grid. We have found that consumer confidence is closely aligned with the percentage of workers who quit their job rather than being let go. In January 2018 (latest data available), the percentage of workers who submitted a resignation letter rather than receiving a pink slip was 60.5%. While not the highest percentage in the dataset (December 2017 was 62.9%, for example), it is significantly above average. Since 2010, the mean quits/separation ratio is 51.5%. Overall score for inflation and labor markets: B. Our two Off the Grid items point to the conundrum the Fed will face this year. Labor markets are tight, so workers have their choice of employment and are quitting at near-record levels for (presumably) better opportunities. Inflation should be ticking higher. The missing piece: wage growth. On that point, we have one last Off the Grid Indicator: the number of domestic Google searches for “Ask for a raise” (polite) and “get a raise” (brusque). The former has not increased in popularity for more than 5 years. The latter has doubled in terms of Google search volumes in the same period. Our take: wages may be stagnant in part because workers are still a bit shy about pushing for a pay increase. Total US search volumes for “Ask for” and “get” a wage bump aren’t that different. And, we suspect, the people that Google “Get a raise” have a better chance of that result than those who “Ask”. The Great Recession damaged consumer (and therefore worker) confidence in many ways. The ability to push for a wage increase may well be one less appreciated bit of that damage.
Agency says Barack Obama’s timeline set standards ‘too high’ in move that could lead to legal showdown with CaliforniaUS environmental regulators announced on Monday they would ease emissions standards for cars and trucks, saying that a timeline put in place by Barack Obama was not appropriate and set standards “too high”.The Environmental Protection Agency (EPA) said it had completed a review that would affect vehicles for model years 2022-25 but it did not provide details on new standards, which it said would be forthcoming. Current regulations from the EPA require the fleet of new vehicles to get 36 miles per gallon in real-world driving by 2025. That’s about 10 miles per gallon over the existing 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?