Financial Centrality and Liquidity Provision -- by Arun G. Chandrasekhar, Robert Townsend, Juan Pablo Xandri
We study an endowment economy in which agents face income risk, as if uncertain returns on a portfolio, and agents can only make transfers in states when they are actively participating in the market. Besides income risk, agents also have limited and stochastic market access, with a probability distribution governed by an underlying social network. While network connections may serve to dissipate shocks, they may also provide obstacles to the sharing of risk, as when participation frictions are generated through the network. We identify and quantify the value of key players in terms of whether they are likely to be able to smooth the resulting market participation risk and how valuable that smoothing would be when they are there. We define financial centrality in economic terms, given the model, as the ex ante marginal social value of injecting an infinitesimal amount of liquidity to the agent. We show that the most financially central agents are not only those who trade often - as in standard network models - but are more likely to trade when there are few traders, when income risk is high, when income shocks are positively correlated, when attitudes toward risk are more sensitive in the aggregate, when there are distressed institutions, and when there are tail risks. We extend our framework to allow for endogenous market participation. Observational evidence from village risk sharing network data is consistent with our model.
Estimating the Associations between SNAP and Food Insecurity, Obesity, and Food Purchases with Imperfect Administrative Measures of Participation -- by Charles J. Courtemanche, Augustine Denteh, Rusty Tchernis
Administrative data are considered the "gold standard" when measuring program participation, but little evidence exists on the potential problems with administrative records or their implications for econometric estimates. We explore issues with administrative data using the FoodAPS, a unique dataset that contains two different administrative measures of Supplemental Nutrition Assistance Program (SNAP) participation as well as a survey-based measure. We first document substantial ambiguity in the two administrative participation variables and show that they disagree with each other almost as often as they disagree with self-reported participation. Estimated participation and misreporting rates can be meaningfully sensitive to choices made to resolve this ambiguity and disagreement. We then document similar sensitivity in regression estimates of the associations between SNAP and food insecurity, obesity, and the Healthy Eating Index. These results serve as a cautionary tale about uncritically relying on linked administrative records when conducting program evaluation research.
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.
Authored by John Whitehead via The Rutherford Institute, Tolerance cuts both ways. This isn’t an easy pill to swallow, I know, but that’s the way free speech works, especially when it comes to tolerating speech that we hate. The most controversial issues of our day - gay rights, abortion, race, religion, sexuality, political correctness, police brutality, et al. - have become battlegrounds for those who claim to believe in freedom of speech but only when it favors the views and positions they support. “Free speech for me but not for thee” is how my good friend and free speech purist Nat Hentoff used to sum up this double standard. This haphazard approach to the First Amendment has so muddied the waters that even First Amendment scholars are finding it hard to navigate at times. It’s really not that hard. The First Amendment affirms the right of the people to speak freely, worship freely, peaceably assemble, petition the government for a redress of grievances, and have a free press. Nowhere in the First Amendment does it permit the government to limit speech in order to avoid causing offense, hurting someone’s feelings, safeguarding government secrets, protecting government officials, insulating judges from undue influence, discouraging bullying, penalizing hateful ideas and actions, eliminating terrorism, combatting prejudice and intolerance, and the like. Unfortunately, in the war being waged between free speech purists who believe that free speech is an inalienable right and those who believe that free speech is a mere privilege to be granted only under certain conditions, the censors are winning. We have entered into an egotistical, insulated, narcissistic era in which free speech has become regulated speech: to be celebrated when it reflects the values of the majority and tolerated otherwise, unless it moves so far beyond our political, religious and socio-economic comfort zones as to be rendered dangerous and unacceptable. Indeed, President Trump - who has been accused of using his very public platform to belittle and mock his critics and enemies while attempting to muzzle those who might speak out against him - may be the perfect poster child for this age of intolerance. Even so, Trump is not to blame for America’s growing intolerance for free speech. The country started down that sorry road long ago. Protest laws, free speech zones, bubble zones, trespass zones, anti-bullying legislation, zero tolerance policies, hate crime laws and a host of other legalistic maladies dreamed up by politicians and prosecutors (and championed by those who want to suppress speech with which they might disagree) have conspired to corrode our core freedoms, purportedly for our own good. On paper - at least according to the U.S. Constitution - we are technically free to speak. In reality, however, we are only as free to speak as a government official - or corporate entities such as Facebook, Google or YouTube - may allow. Free speech is no longer free. What we have instead is regulated, controlled speech, and that’s a whole other ballgame. Just as surveillance has been shown to “stifle and smother dissent, keeping a populace cowed by fear,” government censorship gives rise to self-censorship, breeds compliance, makes independent thought all but impossible, and ultimately foments a seething discontent that has no outlet but violence. The First Amendment is a steam valve. It allows people to speak their minds, air their grievances and contribute to a larger dialogue that hopefully results in a more just world. When there is no steam valve - when there is no one to hear what the people have to say - frustration builds, anger grows and people become more volatile and desperate to force a conversation. By bottling up dissent, we have created a pressure cooker of stifled misery and discontent that is now bubbling over and fomenting even more hate, distrust and paranoia among portions of the populace. Silencing unpopular viewpoints with which the majority might disagree - whether it’s by shouting them down, censoring them, muzzling them, or criminalizing them - only empowers the controllers of the Deep State. Even when the motives behind this rigidly calibrated reorientation of societal language appear well-intentioned - discouraging racism, condemning violence, denouncing discrimination and hatred - inevitably, the end result is the same: intolerance, indoctrination and infantilism. The police state could not ask for a better citizenry than one that carries out its own censorship, spying and policing. This is how you turn a nation of free people into extensions of the omniscient, omnipotent, omnipresent police state, and in the process turn a citizenry against each other. So where do we go from here? If Americans don’t learn how to get along - at the very least, agreeing to disagree and respecting each other’s right to subscribe to beliefs and opinions that may be offensive, hateful, intolerant or merely different - then we’re going to soon find that we have no rights whatsoever (to speak, assemble, agree, disagree, protest, opt in, opt out, or forge our own paths as individuals). The government will lock down the nation at the slightest provocation. Indeed, the government has been anticipating and preparing for civil unrest for years now, as evidenced by the build-up of guns and tanks and militarized police and military training drills and threat assessments and extremism reports and surveillance systems and private prisons and Pentagon training videos predicting the need to impose martial law by 2030. Trust me: when the police state cracks down, it will not discriminate. We’ll all be muzzled together. We’ll all be jailed together. We’ll all be viewed as a collective enemy to be catalogued, conquered and caged. Indeed, a recent survey concluded that a large bipartisan majority of the American public already recognizes the dangersposed by a government that is not only tracking its citizens but is also being controlled by a “Deep State” of unelected government officials. Thus, the last thing we need to do is play into the government’s hands by turning on one another, turning in one another, and giving the government’s standing army an excuse to take over. So let’s start with a little more patience, a lot more tolerance and a civics lesson on the First Amendment. What this means is opening the door to more speech not less, even if that speech is offensive to some. It’s time to start thinking for ourselves again. It’s time to start talking to each other, listening more and shouting less. Most of all, as I make clear in my book Battlefield America: The War on the American People, it’s time to make the government hear us—see us—and heed us. This is the ultimate power of free speech.
Charlie Gao Security, Europe Warsaw's first line of defense could be in for a tough battle. Poland’s tank fleet is one of the most numerous in Europe, fueled by the country’s large (for Europe) military spending and based on the tank fleet inherited from the Polish People’s Army. It consists of a mix of plain T-72M1s, domestic T-72 variants and various versions of the Leopard 2. It would be the first line of defense in a theoretical ground conflict between Russia and the West. But exactly how capable is the Polish tank fleet? How effective are its upgrades? And what are its plans for the future? The most plentiful tank in Polish service is the T-72M1, with 350 tanks in the Polish inventory and two hundred in active service. These are pretty much standard T-72As from 1979, featuring an early-generation 125-millimeter 2A46 gun, active night vision and a laser rangefinder that feeds data to a 1A40 FCS. Armor is basic Soviet composite technology: a sandwich of textolite and steel. The turret had the “Dolly Parton” turret armor. These tanks provide a basic level of fighting capability but are terribly dated compared to any tank that Russia would field. The armor on these would likely be penetrated by any modern antitank munition, bar light RPGs. Recommended: 5 Worst Guns Ever Made. Recommended: The World’s Most Secretive Nuclear Weapons Program. Recommended: The Fatal Flaw That Could Take Down an F-22 or F-35. Read full article
Task and Purpose, Jared Keller Security, North America The P320 is here. “We are unaware if other branches have conducted official testing of the MHS,” Taylor told Task & Purpose in an email. “Army test standards are more stringent than those followed by the commercial industry and civilian law enforcement agencies in the U.S., and the MHS passed all of the Army safety testing.” It’s been an emotional roller coaster, but the moment of truth is finally here: Every branch of the U.S. armed forces, including the Coast Guard, has placed an order for the Army’s new Modular Handgun System. Sig Sauer Chief Marketing Officer Tom Taylor told Military.com on March 15 that each service plans on acquiring and fielding its own arsenal of the company’s P320 pistols as the M17 and M18, as part of the $580 million contract for 195,000 sidearms the Army awarded the company back in January 2017. The services have been eyeing the new MHS since at last May 2017, but Taylor said the remaining branches “have orders that will be fielded starting later this year and early next year.” But apart from the Marine Corps’ own explicit order for 35,000 of the sidearms as part of its fiscal 2019 budget proposal, it’s unclear if the other branches intend to adopt the M17 as widely as the two infantry services. Read full article
Barney Ronay: why England need to ditch BaylissEngland women ease to T20 victory over AustraliaAnd email Tim here or tweet him here, if you fancy 3.40am GMT And now, an email entitled The decline and fall of the OBO. Help. “Nice weather stuff there,” says Robert Wilson. Phew. But I feel a but coming... “But I can’t believe that the North and South game is dropping off the edge of the map without the merest attempt at an Elizabeth Gaskell joke (or failing that a sentimental YouTube clip of the sainted Patrick Swayze).” Ha.“Can I tell you a secret?” It’s twenty to four in the morning – you can tell me anything you like. “Although, like all my generation, I’m a King Viv worshipping Windies nut and practically an Australian fan nowadays, I can’t help feeling that England in New Zealand is my favourite bitter but chocolatey treat. Nobody goes. Nobody watches. Nobody cares. It’s often weird and desultory and since I was a nipper, it’s always accompanied by the direst predictions of the imminent death of Test cricket. By any standards, the NZ tour is oxen-stunningly depressing. You need a heart of oak to endure it. So why does it always make me feel so incredibly youthful and happy?” 3.35am GMT An email entitled simply Weather. “Greetings from a frustrated NZ cricket fan,” says Alison Cunningham. Greetings! “It’s glorious warm dry sunshine here in Dunedin.” You’re joking. “Just like yesterday. Thought I’d mention it for future Test schedulers to bear in mind.” Classy. “Although after that amazing ODI victory it would seem churlish to ask for more. Thanks for the entertaining coverage in the absence of actual cricket.” Ah, it’s a pleasure. The essence of cricket commentary is when there’s no cricket to commentate on. Continue reading...
Participants from around the world met at United Nations Headquarters from 12 to 23 March for the UN’s largest gathering on gender equality. This year’s Commission on the Status of Women focused on empowering rural women and girls, with discussions on critical issues such as ensuring adequate living standards, food security, access to land, technology, education, health, and ending all forms of violence and harmful practices.
Japan’s financial watchdog on Friday issued a warning to Hong Kong-based cryptocurrency exchange Binance for operating without registration, the latest move by regulators to tighten standards in the…
President Trump is replacing National-Security Adviser H.R. McMaster with former diplomat John Bolton.
(Don Boudreaux) TweetHere’s a letter that I sent a few days ago to the Washington Post: Richard Trumka’s brief for punitive taxes on Americans who buy steel and aluminum is a standard-fare mash-up of familiar canards and fallacies (“The politicians screaming about a trade war are beholden to Wall Street,” March 20). Nothing in it is new, […]
The president’s irate criticism of the omnibus spending bill demonstrates his continued attachment to a flawed theory of the presidency.
Barbie has always been an icon in American culture. This year, 19 new Barbies were released to celebrate inspiring female sheroes.
Health secretary faces backlash after telling trusts the standards will not have to be met next yearJeremy Hunt is facing a backlash from patient groups over his decision to let hospitals flout key NHS waiting time targets next year.The health and social care secretary has told NHS trusts they do not need to meet standards that require them to treat 95% of A&E patients within four hours and 92% of people awaiting non-urgent care within 18 weeks. Continue reading...
No wonder Trump is threatening a veto—Democrats are getting most of what they wanted.
by Pierre Lemieux ...a state's protectionist measures are imposed on its own citizens or subjects. When he announced broad tariffs against Chinese imports, President Donald Trump said about the trade deficit, "It's out of control" ("U.S. To Apply Tariffs on About $60 Billion of Chinese Imports," Wall Street Journal, March 22, 2018). What this means is that American consumers and businesses who import goods from China are out of control, and the federal government will control them. With threats of retaliation and trade war, indeed with just the standard talk of "concessions" in ordinary trade negotiations, we tend to forget one simple and basic fact: a state's protectionist measures are imposed on its own citizens or subjects. Protectionist measures include taxes called tariffs or duties, import quotas, or straight import bans. Straight bans are rarer, but they exist: an example I mentioned in a recent Econlog post is the prohibition of hiring foreign ships to transport goods between American ports (mandated by the Jones Act of 1920). But a tariff set at a prohibitive level is equivalent to a ban: there were many examples in 19th-century America. Contemporary examples include the 50% tariff duty on alcohol and the 100% duty on tobacco in the United Arab Emirates. A tariff is always a sort of ban because it forbids importing without the state taking a cut (and thus increasing the price of the imported good). One key to understanding this is to grasp an elementary but important point: a tariff (or its equivalent) generally ends up being paid by domestic purchasers, since they translate into a higher domestic price. Foreign producers pay the tax, but get reimbursed by the higher price they can charge in the protected market. It is precisely to increase the domestic price they get that domestic producers lobby for the tariff. Most protectionist measures hurt foreign exporters too, for their market is thereby limited and they may have to compensate by producing less profitable goods. But this is an indirect effect of the direct action of a state forbidding something to its own citizens. The fundamental problem with protectionist measures is that they interfere with the benefits of exchange: they prevent the realization of the mutual benefits of a voluntary exchange between two parties, one of whom is a citizen or subject of the state that imposes the measures. If an American imports a solar panel or a bed from a Chinese producer, both believe they benefit from the exchange; otherwise, one would have walked away from the deal. The argument is not changed by the presence of intermediaries such as Walmart or the car company that imports steel in order to manufacture the car that a consumer wants. Reciprocity cannot be an argument against free trade. Any voluntary trade is reciprocal by its very nature: one party pays for something he (or it) thinks is worth more than the asking price; the other party gets a payment that he thinks is worth more than what he sells. Only collectivist reciprocity can be an argument, when the state decides what individuals will exchange at which conditions. It is true that a voluntary exchange harms third parties--if we adopt a very general and neutral concept of "harm." Suppose you buy a lawn-mowing service from a gardener. Some of the latter's competitors have lost the sale (although the more customers in market, the less noticeable this harm will be). Also, other potential buyers have been overbid by you, one of the successful buyers. The typical free-market price is the result of an invisible auction: it is the price at which the winning bidders have outbid others. These harms are ignored because they are merely transfers in the sense that one wins what the other loses. On the contrary, coercive restrictions on exchange create harms that are not compensated by larger gains. This is the standard economic argument for free markets. An indication that harms to disappointed competitors and losing bidders should not count is that otherwise the argument against free international trade would also apply to free domestic trade. In both cases, as we know, trade enriches most people. But can't foreigners be discriminated against? Even if you answer yes, you must remember that a tariff first hits the residents of the country whose state imposes it. Even as tariffs favor domestic producers, the harm they cause to consumers is larger. I give an example with the case of washing machines in an article in the Spring issue of Regulation. Generally, moreover, the favored producers are located in different regions than the harmed consumers (or purchasers). Protectionist measures harm some fellow citizens in order to respond to the rent-seeking of other fellow citizens. In 1872, Congressman Samuel Cox (D-NY) understood that. As he put it, protectionism steals from consumers somewhere in the country in order to give to producers elsewhere. He sarcastically declared (I borrow the quote from Douglas Irwin's extraordinary history of foreign-trade policy in America): Let us be to each other instruments of reciprocal rapine. Michigan steals on copper; Maine on lumber; Pennsylvania on iron; North Carolina on peanuts; Massachusetts on cotton goods; Connecticut on hair pins; New Jersey on spool thread; Louisiana on sugar, and so on. Why not let the gentleman from Maryland steal coal from them? True, but a comparative few get the benefit, and it comes out of the body of the people. Some argue that retaliation can be productive if it succeeds bringing a foreign government to repeal its own protectionist measures (that is, to stop harming its own subjects). One problem with this argument is that retaliation can also have no effect on foreign protectionism or even start a trade war, making everybody worse off. In this perspective, Adam Smith prudently wrote, but he gives a well-deserved jab to politicians at the same time: There may be good policy in retaliations of this kind, when there is a probability that they will procure the repeal of the high duties or prohibitions complained of. ... To judge whether such retaliations are likely to produce such an effect, does not, perhaps, belong so much to the science of a legislator, whose deliberations ought to be governed by general principles which are always the same, as to the skill of that insidious and crafty animal, vulgarly called a statesman or politician, whose councils are directed by the momentary fluctuations of affairs. When there is no probability that any such repeal can be procured, it seems a bad method of compensating the injury done to certain classes of our people, to do another injury ourselves, not only to those classes, but to almost all the other classes of them. Another protectionist argument is that it is legitimate to restrict trading with foreigners who live under a tyrannical government or who man its state corporations. But this also amounts to coercing a state's own citizens into not participating in exchanges that each individually believes is beneficial to him. And there is something fishy in the argument that trade with individuals dominated by foreign tyrants, or even trade with foreign tyrants themselves, should be forbidden. Except perhaps in extreme cases, the argument amounts to the following: The government of a free country should prevent its citizens from trading with the subjects of a non-free country. Find the error. (4 COMMENTS)
The government may have declared victory on living standards, but we have been here before
Novartis' (NVS) Tasigna and Seattle Genetics' (SGEN) Adcetris receive regulatory approvals for label expansion. Pfizer's label expansion application for Xtandi gets priority review.
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?