As noted elsewhere in this issue, California Gov. Gavin Newsom signed a bill last week that allows college athletes to profit from the use of their “name, image or likeness.” But someone glancing at the following headline from Reuters – California to Let College Athletes be Paid in Blow to NCAA Rules - might reasonably conclude there needs to be a clarification of “profit.” And then perhaps someone taking a closer look at headlines before they go live. Just sayin’...
No state has a car culture quite like California, where the mythology of the hot rod still reigns supreme. Which may be why Gov. Gavin Newsom signed legislation last week that overturns a law which allowed police to issue a ticket carrying a hefty fine to motorists with loud exhaust systems. Now, rather than an automatic $197 levy, drivers will be issued a “fix-it” ticket that allows them to bring the car’s noise level down to legal levels and pay only a $25 fee. Which leaves car enthusiasts at least enough money to fill the tank once or twice. But just barely.
-- By RICH EHISEN
In some ways, academics’ methods of teaching and research have hardly changed since Plato and Aristotle were discussing philosophy and mathematics in the Academy in Ancient Greece. Students still learn from a tutor in small discussion groups and lectures, and many scholars still study physical manuscripts in libraries. But in this blog, we will explore a recent change that has revolutionized academia: big data analytics
AI-powered research
The application of big data analytics has generated new and innovative partnerships between universities and companies on research projects. Companies have access to a lot of data on their customers, and universities have data science experts who can interpret and analyze that data. The result is mutually beneficial—companies get better insights about their customers and products, and universities get new research ideas and much-needed funding from partner firms. For example, earlier this year the pharmaceutical firm Novartis announced a partnership with the University of Oxford’s Big Data Institute. The Oxford experts are using machine learning to analyze data from Novartis’ clinical trials to identify patterns in how patients respond to different medicines.
Academics in virtually every academic discipline are exploring how AI and big data analytics could change the way they do their research. A study in the journal Nature found that ‘big data’ and AI were the third and fourth most-searched terms on a scholarly database in 2018. LexisNexis attended Dubai’s Global Education and Skills Forum earlier this year and found many examples of academics who were using AI to do research that would otherwise be have been impossible. Milo Comerford, a senior analyst at the Tony Blair Institute for Global Change, said that AI and machine learning have “changed the game” for academic research. He gave the example of his own research which uses AI to trawl through thousands of extremist propaganda documents and come up with new insights. “I wouldn’t have been able to do [this] manually and through my own study,” he said.
Personalized teaching
Universities’ core purposes to research and to teach, and big data analytics are changing the latter as well as the former. Teachers and lecturers in universities are using big data to gather data on their students and then customize their lesson plans. Lecturers no longer have to wonder whether their lectures are going over students’ heads—now they can use data from e-learning software to work out which parts of the course students struggle with, and give extra support to those who find a particular area difficult. Back in 2015, in a paper in the journal Procedia Economics and Finance, Logica and Magdalena argued that big data offers teachers “a chance to refine the educational process”. “If [teachers] want relevant insights on their real efficiency and the progress of their students, they need to integrate this kind of solutions,” they wrote. In the four years since this paper was published, more and more educators have followed this advice.
The next step could be for some of these lecturers to be replaced by AI—indeed, some universities are already employing AI in their teaching process. In 2016, students on Georgia Tech University’s online master’s in computer science who asked questions on the course’s online forum were often answered by Jill Watson, who was apparently a teaching assistant at the university. When the course finished, it was revealed to them that Jill was in fact an AI bot based on IBM’s Watson platform. Most students had had no idea. AI and big data offers universities a way to save money on teaching, and reach a greater number of students.
Automated admissions
Universities are even using big data to help them decide which students to admit every autumn. Traditionally, universities have employed a large team of admissions officers who weigh up thousands of applications before deciding who to admit, but big data analytics allows them to automatically sort candidates by test scores and other admissions criteria. Universities rely on attracting large numbers of eligible students to enroll, and data analytics expert Eric Spear says big data analytics “holds the key to solving the problem of declining enrolment numbers”. Analytics allow an institution to track which courses are more popular with students from different demographics, and which courses have a problem with drop-outs. Removing the element of human judgement in the admissions process could also benefit universities’ reputations—earlier this year, top U.S. colleges were alleged to have accepted donations from wealthy Americans in exchange for university places. An AI-driven process would likely have prevented that from happening.
What should you do?
Big data analytics can make your life easier, whether you’re an academic, a university president or a corporation considering a research partnership with data scientists. But the insights available from this analysis are only as good as the data being fed into them. LexisNexis’ Data as a Service provider helps universities by integrating data with flexible APIs that deliver normalized big data in a semi-structured XML format. This enables universities to add bespoke tags and other enrichments to meet the unique requirements of the research being undertaken. Additionally, when retrieving data, researchers can take advantage of our own robust topical classifications to identify and retrieve highly-relevant result datasets, ensuring a faster time to insight.
Next Steps:
How confident are you that a supplier you vetted two years ago hasn’t seen a change in financial stability that might lead to supply chain disruption? Or that a reseller isn’t putting you in danger of an FCPA violation? Or that a disgruntled customer’s online tweet hasn’t been picked up by mainstream media and turned into a reputational nightmare?
The days when a company could protect against financial, regulatory, strategic and reputational risk with a one-and-done process are long over. The global nature of business, the speed of communication and reliance on extensive third-party networks means that companies need a more proactive approach to risk management—and for many, ongoing risk monitoring is the answer.
What’s Third-Party Monitoring Got to Do with It?
Recently, we partnered with RIMS to talk about how a risk management workflow can be enhanced by continuous monitoring. It starts with understanding the influence that third parties have on your organization. We’re not talking about employees—the people inside the four walls of a business. Yes, they can expose an organization to risk, but most companies set clear ethical and compliance standards to manage internal workforces. Instead, we’re using the ISO definition of third parties which classifies them as persons or entities that are independent of the organization.
So, what are the three reasons you need ongoing monitoring of third parties?
Regulatory landscape is broad and complex.
Some industries are more alert to the potential risks posed by third parties. Banks and other financial services organizations, for example, must address Know Your Customer requirements to meet anti-money laundering and terrorist financing laws. Likewise, companies in the extractives industry are well-acquainted with the need for the third parties they rely on to adhere to a variety of regulations—from sanctions or anti-bribery and corruption compliance to environmental standards.
However, you need only look at recent FCPA enforcement actions to see that regulatory risk has become more prevalent across many industries and companies of varying sizes. And the same holds true for other types of risk. No business is immune in the digital age, where bad news can spread like wildfire.
The world has gotten smaller.
Another factor that should be considered is location—whether it's the location of the third party or where the location where a transaction will occur. High-speed travel and communications have empowered companies to expand into emerging markets and build out global supply chains. But operating across borders means companies need to stay alert to a variety of Political, Economic, Socio-Cultural, Technological, Legal and Environmental considerations.
The more you depend on third parties, the bigger your exposure.
Think about the money a company may spend with a third party. If an organization is highly-dependent on a third party—doing a large volume of purchasing from a vendor, for example—then the risk exposure is higher because the potential for disruption if the third party fails to deliver as promised due to financial instability or violates regulations while conducting business on your behalf.
Ready to learn even more about third-party risk? Check out the on-demand recording of the RIMS webinar for an informative look at the current risk landscape and how LexisNexis Entity Insight makes it easy to continuously monitor for threats that could impact your business.
Many states have learned lessons from the Great Recession of 2007-09 and are better prepared for the next economic downturn, according to findings by the National Association of State Budget Officers (NASBO) and other analysts.
“Rainy day funds are growing as a share of state budgets,” said Kathryn Vesey White, director of budget processes for NASBO. “We see that as a positive sign.”
With the longest economic expansion in U.S. history in its eleventh year, concerns about a prospective downturn have increased. An August survey by the National Association of Business Economics found that 38 percent of economists expect a recession in 2020.
The economy has been growing since the Great Recession ended in June 2009. But states and cities were so hard hit that some of them have not yet fully recovered, according to the Pew Charitable Trusts.
Total employment by state and local governments was 19.8 million before the Great Recession. Today it’s slightly less. Economist Sarah Crane of Moody’s Analytics sees this as a sign that some governments have moved cautiously on hiring despite a job surge in the private sector.
The biggest change in state behavior has been a hefty increase in rainy day funds. The median rainy day fund balance as a percentage of general fund spending has risen to 7.5 percent, White said. Before the Great Recession, it was 4.8 percent.
There are huge differences among the states, however. California has set aside reserves of $19 billion, most of it in a rainy day fund. Illinois, Kansas and New Jersey have no rainy day funds at all.
According to NASBO, several states have over time made policy changes that automatically set aside some revenues. For example, North Carolina in 2017 adopted legislation that puts a share of forecasted general fund revenue into its rainy day fund. The same year North Dakota passed a law to deposit into its rainy day fund severance tax revenue above a certain threshold.
California, Maryland, Massachusetts and Minnesota have also made changes to divert above-average or unanticipated state revenues into their rainy day funds.
The changes have helped. Moody Analytics in 2018 found that 23 states had the funds needed to get them through a moderate recession and that another 10 states were within striking distance of this goal. That figure could improve when a follow-up report is issued later this year, as many states are pouring money into rainy day funds or other reserves during 2019.
“States have learned lessons of the past and are taking seriously the prospect of an eventual recession,” said Crane, a co-author of the Moody Analytics report.
Rainy day funds are not the sole measure of preparedness. Despite dramatic improvement in its financial position under former Gov. Jerry Brown (D), California could be vulnerable in a severe recession because the Golden State depends on high-end income taxes for much of its revenue. These revenues nosedived in the Great Recession.
The economic profiles of states also matter. As an example cited in the Moody Analytics report, Pennsylvania, which has a flat income tax, and Florida, which has no personal income tax, both have relatively stable tax structures.
“However, the level of potential fiscal shock in Florida is much larger than in Pennsylvania because of [Florida’s] high reliance on tourism and housing versus Pennsylvania’s reliance on the more noncyclical industries of health care and education,” the report said.
States and cities suffer in recessions because revenues from sales and income taxes decline while demand for services, especially in health care, increase. Unlike the federal government, which engages massively in deficit financing, all states except Vermont are required to balance their budgets.
During the Great Recession, joblessness and the accompanying loss of health benefits drove millions of people onto Medicaid, the joint federal-state program that provides health care for the poor and disabled. This proved an immense fiscal burden for the states.
The situation may be different in the next downturn because of the Affordable Care Act (ACA), which passed in 2010 on a party-line vote, becoming operative in 2014. Thirty-two states have expanded Medicaid under the ACA, often called Obamacare. As a result, many of those who would seek Medicaid during a recession are in the expansion states already on the rolls.
The view that an economic downturn is just around the corner spiked in August, spurred by the U.S.-China trade war, slowing global growth and an investment situation in which short-term bonds paid more interest than long-term bonds. This is known as an inverted yield curve, a predictor of the last four U.S. recessions.
Since then, the yield curve has returned to nearly normal and U.S.-China trade tensions have eased. The European Central Bank has taken steps intended to prod global growth. Many economists have become more optimistic that the United States can evade or at least postpone a downturn.
“The household sector remains nearly three-quarters of the U.S. economy and displays a healthy mix of low unemployment and rising wages,” said Douglas Holtz-Eakin, a former director of the Congressional Budget Office. “As long as that continues, we will avoid a recession.”
No one knows how long this “healthy mix” will last. The recent oil price spurt after a major Saudi Arabia oil facility was damaged in a drone attack dramatically demonstrated how an unpredictable event can affect the economy.
Most economists believe recessions are inescapable, but Australia hasn’t had one in 27 years. Writer-investor Zackary Karabell, author of The Leading Indicators, believes that most recession models do not adequately account for the impact on the economy of high tech, which he says is powering growth at lower cost.
Nor does anyone know if the next downturn will be mild or severe. CNBC senior market analyst Michael Santoli points out there’s a lot of room between the present 2 percent-plus growth rate and a recession, which is defined as two consecutive quarters of negative growth.
Adding to the uncertainty, recessions arrive at different times in the states. The Economist recently observed that four states in the U.S. heartland — Indiana, Ohio, Pennsylvania and Michigan — are among a “modest but growing number of states experiencing falling employment.” Donald Trump carried these states in 2016 and with them won the presidency.
States were surprised by the Great Recession. Unaware of the deepening crisis, many states increased budgets and hired new workers in the summer of 2008, more than half a year after the recession began.
Not until the investment bank Lehman Brothers collapsed on Sept. 15, followed ten days later by government seizure of Washington Mutual, a giant savings and loan association, did the full extent of the crisis become apparent. State budgets were hit hard in 2009 and in many states for several years afterward.
Today, it’s encouraging that so many states are putting money away to see them through the next fiscal crisis. Unprepared states should get the message.
While we can’t say with any certainty when the next downturn will arrive, we know that no economic expansion lasts forever. The lagging states should start saving for the inevitable rainy day.