As a guide to the issues facing the City of London during Brexit, who could be better than Tony Norfield, a university Marxist-turned-City-trader now sporting a Ph.D. in economics from the University of London?
With 20 years of experience in dealing rooms at money center banks in London, he explains how finance concentrates power and profits in just a few financial centers, and London leads as an international center. As England and Europe prepare to set the terms of Brexit, Norfield’s historical perspective on how England has promoted finance is fascinating.
For fintech firms wondering if they should stay in London, follow the siren calls from Berlin, apply to the French Tech Ticket program, or look at Lisbon. Norfield has some history to offer: The City, with backing from both Labor and Conservative governments and the Bank of England, has adapted remarkably well to change. It shifted from lending to acting as an intermediary. It used the Big Bang to bring in foreign competition and capital. It shifted from sterling to doing business in the U.S. dollar and dozens of other currencies. And it has welcomed Arab capital with 22 Islamic banks, more than all other western countries combined.
Writing before the Brexit referendum, Norfield predicted that Europe might act against the financial domination of the UK and U.S., and that does appear, sporadically, to be happening. French President François Hollande recently called for a hard Brexit to discourage other countries from following the UK’s example, but the erratic nature of French politics confirms Norfield’s emphasis on the value arising from the security and centuries-long stability of the British state. Unlike Paris, London did not erupt in 1968 and it hasn’t experienced a leadership assassination attempt like the one on DeGaulle. (Norfield kindly omits comparisons with the U.S. on this topic.)
London,which holds a substantial lead over New York as an international financial center, has a geographical advantage, sitting in a time zone between Asia and the U.S., so it can trade with both during regular working hours. It also enjoys a regulatory climate that has long favored finance by allowing its banks, for example, to work with communist countries during the Cold War.
The author occasionally mentions his personal experience at some key points in finance. He was working at a Japanese bank when that country’s surging economy imploded, and he was at ABN AMRO when it moved its securities business to London, but left FX in Amsterdam. A stint in Sydney showed him Australia’s time-zone disadvantage, and he was in regular chats with a Bundesbank officer in London when Britain pulled out of the Exchange Rate Mechanism (ERM).
Norfield uses the term imperialism to describe the role of financial power centers.
The concentration of financial power is a little startling. In 2013, Norfield writes, 75% of the top 100 international corporations were in just six countries — the U.S., UK, France, Germany, Japan, and Switzerland.
“All forms of international finance, and even commerce, can also be seen as parasitic on the value created elsewhere,” he wrote. Countries with advanced finance find customers in countries, rich or poor, with less sophisticated finance. In 2013, the U.S. had a net gain of $209 billion from its foreign investments, equivalent to receiving the entire economic output of the Czech Republic.
Finance is intertwined with politics, with corporations and with competition between the U.S. and UK for primacy. American banking laws have sometimes handicapped the U.S. financial services industry. Even as the U.S. was trying to gain access to the British Empire and trade with Commonwealth countries, American restrictions on foreign banking clashed with its large corporations which were seeking international expansion.
One result was the euromarket, which grew from $1 billion in 1960 to $1,050 billion by 1983, drawing European finance houses to expand their London operations. German banks, Norfield notes, do most of their securities business in London. U.S. banks also expanded London offices because they could serve international corporate clients with funding that was not so readily, or cheaply, available in the U.S. By 1971 the eurodollar market was equal to the money supply of France, and London had attracted 160 banks from 48 countries.
Staying outside the euro has also been a key to London’s position. British political leaders saw in 1976 and 1992 that inside Europe’s monetary system, it would be constrained by other states, particularly Germany.
Norfield believes global developments, often first visible in the financial sphere, can destabilize relationships between major powers.
It is likely that finance will continue to have the support of the British government to help it adjust.
“Above all, there was more consistent promotion of international financial business by the British authorities, which included an implicit promise not to distract financial interests with any changes in legislation,” he wrote.
Fintech firms and banks based in London might find Britain’s consistency encouraging. For updates, check Norfield’s blogs about Brexit and other finance topics. And for regular reports on the status of international finance centers, see Z/YEN’s Global Financial Centres Index.
Article published by Tom Groenfeldt. It originally appeared on Financial Technology and has been republished with permission.
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About Tom Groenfeldt
Tom Groenfeldt is a freelance reporter who focuses largely on finance and technology including trading, risk, back-office systems, big data, analytics, retail banking, international banking, and e-commerce. His work appears in several publications, including Forbes.com in the U.S. and Banking Technology in London. In 2015, he was named to the "FinServ 25," the top 25 top global influencers in banking, by The Financial Brand.
Many recent studies show that internal audit has not yet fully embraced digital transformation and is therefore not making use of the full potential of analytics. In this blog post, I’d like to offer my thoughts about the potential benefits for internal audit on leveraging enterprise-wide risk management and compliance platforms. These go under different names, but in essence, I am referring to these software tools that capture risk and controls information, including key indicators, and reflect them together side-by-side rather than in separate silos.
By reviewing the business, operational, and strategic risks associated with the company’s objectives, and by comparing the residual risk level against the documented risk appetite, internal audit can focus its attention on what matters the most for the business.
Even if the risk levels aren’t critical, if internal audit can focus on the risks that would seriously endanger important objectives, then it no longer has to be considered a super firefighter. Instead, it can serve as a true business partner with the very same purpose as all business owners: making the company run better and sustainably.
In this approach, internal audit becomes more proactive, but only with the support of complete risk and control profiles can it really achieve this objective.
Now, what can happen when internal audit unleashes the full power of governance, risk, and compliance (GRC) integration and of data analytics?
The icing on the cake: preventative auditing
“Preventative” is a term more familiar to those who have worked in asset-intensive companies where “preventative maintenance” – mending a machine before a failure is even detected – is the ultimate goal. That’s not just because the cost of doing so is less than a full-blown repair, but also because it prevents unplanned shutdowns.
I believe this concept can be applied to auditing to prevent business disruptions. In this case, it isn’t linked to a deficient asset, but to a deficient process or a negative context.
Internal audit could, in my opinion, focus as much on key risk indicators as it does on risk levels. Indeed, internal auditors are extremely knowledgeable about the business and the context in which the organization operates. They can detect that key risk indicators are demonstrating signs of a “pattern of failure.”
Risk indicators taken in isolation might not make it easy to detect what this means for the organization. But consolidated and aggregated at the department, business unit, or even company level, it could signal clear and existing danger.
Using all the information available in risk and control solutions, along with its own knowledge of the business, puts internal audit in a perfect position to help the organization navigate to less troubled waters. It would also put internal audit in the role that I personally believe it deserves: true strategic partner.
Do auditors in your company already leverage the wealth of risk and control information to plan their next audits? If not, are there any plans to do so in the near future?
Before we delve into the individual aspects of the accounting and financial close, the record-to-report (RTR) process that I discussed in my last blog, let’s first address change management. The third main point discussed in the Ventana Research paper says:
Adopt a continuous improvement approach to overcome inertia and the “we’ve always done it that way” mindset to which finance staffs are particularly prone.
This is a huge paradigm shift to overcome. The financial close process is not a “wish list” process. It is something that is mandatory, not only for regulatory reasons delegated by the government of every country, but also for the owners of private companies. Financial reporting and the delivery of financial information is essential to the operation of any organization. Your business produces financial reports, and if it works, why fix what ain’t broke?
That’s the thing—if you did it last year, just do it again and you’re good. NO! That is when organizations need to understand and embrace the move toward continuous accounting. How can you change what you are doing now to adopt the technology that exists to enable continuous accounting? Yes, you close the books every month, but is it efficient?
You need to evaluate how “continuous accounting integrates people, processes, information, and technology to achieve a transformation of the finance function and its corporate role.” (see this Ventana Research paper)
Think about that for a moment. This blog series is designed to parse the different steps in the accounting and financial close: the RTR process. The technology is there now. Your processes are based on historical processes based on traditional technologies. You need to think about how people in your accounting and financial close departments, and how your exceptional accountants (perhaps you yourself) can transform the processes that happen at the end of a period.
What processes are you doing now because you’ve always done them that way? What reports are being created because “Mary,” who worked there 10 years ago, said she needed them? Does your organization still need them? Are there more self-service tools that you can utilize to get Mary’s information out to the organization?
Let’s not cross the road just because Mary did it last year.
So, as you read the blogs in the following weeks that dissect the accounting and financial close process, think about how you can transform your accounting and close processes and how you can impact transformation.
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About Elizabeth Milne
Elizabeth Milne has over 20 years of experience improving the software solutions for multi-national, multi-billion dollar organizations. Her finance career began working at Walt Disney, then Warner Bros. in the areas of financial consolidation, budgeting, and financial reporting. She subsequently moved to the software industry and has held positions including implementation consultant and manager, account executive, pre-sales consultant, solution management team at SAP, Business Objects and Cartesis. She graduated with an Executive MBA from Northwestern University’s Kellogg Graduate School of Management. In 2014 she published her first book “Accelerated Financial Closing with SAP.” She currently manages the accounting and financial close portfolio for SAP Product Marketing. You can follow her on twitter @ElizabethEMilne
Last August, a woman arrived at a Reno, Nevada, hospital and told the attending doctors that she had recently returned from an extended trip to India, where she had broken her right thighbone two years ago. The woman, who was in her 70s, had subsequently developed an infection in her thigh and hip for which she was hospitalized in India several times. The Reno doctors recognized that the infection was serious—and the visit to India, where antibiotic-resistant bacteria runs rampant, raised red flags.
When none of the 14 antibiotics the physicians used to treat the woman worked, they sent a sample of the bacterium to the U.S. Centers for Disease Control (CDC) for testing. The CDC confirmed the doctors’ worst fears: the woman had a class of microbe called carbapenem-resistant Enterobacteriaceae (CRE). Carbapenems are a powerful class of antibiotics used as last-resort treatment for multidrug-resistant infections. The CDC further found that, in this patient’s case, the pathogen was impervious to all 26 antibiotics approved by the U.S. Food and Drug Administration (FDA).
In other words, there was no cure.
This is just the latest alarming development signaling the end of the road for antibiotics as we know them. In September, the woman died from septic shock, in which an infection takes over and shuts down the body’s systems, according to the CDC’s Morbidity and Mortality Weekly Report.
Other antibiotic options, had they been available, might have saved the Nevada woman. But the solution to the larger problem won’t be a new drug. It will have to be an entirely new approach to the diagnosis of infectious disease, to the use of antibiotics, and to the monitoring of antimicrobial resistance (AMR)—all enabled by new technology.
Keeping an Eye Out for Outbreaks
Like others who are leading the fight against AMR, Dr. Steven Solomon has no illusions about the difficulty of the challenge. “It is the single most complex problem in all of medicine and public health—far outpacing the complexity and the difficulty of any other problem that we face,” says Solomon, who is a global health consultant and former director of the CDC’s Office of Antimicrobial Resistance.
Solomon wants to take the battle against AMR beyond the laboratory. In his view, surveillance—tracking and analyzing various data on AMR—is critical, particularly given how quickly and widely it spreads. But surveillance efforts are currently fraught with shortcomings. The available data is fragmented and often not comparable. Hospitals fail to collect the representative samples necessary for surveillance analytics, collecting data only on those patients who experience resistance and not on those who get better. Laboratories use a wide variety of testing methods, and reporting is not always consistent or complete.
Surveillance can serve as an early warning system. But weaknesses in these systems have caused public health officials to consistently underestimate the impact of AMR in loss of lives and financial costs. That’s why improving surveillance must be a top priority, says Solomon, who previously served as chair of the U.S. Federal Interagency Task Force on AMR and has been tracking the advance of AMR since he joined the U.S. Public Health Service in 1981.
A Collaborative Diagnosis
Ineffective surveillance has also contributed to huge growth in the use of antibiotics when they aren’t warranted. Strong patient demand and financial incentives for prescribing physicians are blamed for antibiotics abuse in China. India has become the largest consumer of antibiotics on the planet, in part because they are prescribed or sold for diarrheal diseases and upper respiratory infections for which they have limited value. And many countries allow individuals to purchase antibiotics over the counter, exacerbating misuse and overuse.
In the United States, antibiotics are improperly prescribed 50% of the time, according to CDC estimates. One study of adult patients visiting U.S. doctors to treat respiratory problems found that more than two-thirds of antibiotics were prescribed for conditions that were not infections at all or for infections caused by viruses—for which an antibiotic would do nothing. That’s 27 million courses of antibiotics wasted a year—just for respiratory problems—in the United States alone.
And even in countries where there are national guidelines for prescribing antibiotics, those guidelines aren’t always followed. A study published in medical journal Family Practice showed that Swedish doctors, both those trained in Sweden and those trained abroad, inconsistently followed rules for prescribing antibiotics.
Solomon strongly believes that, worldwide, doctors need to expand their use of technology in their offices or at the bedside to guide them through a more rational approach to antibiotic use. Doctors have traditionally been reluctant to adopt digital technologies, but Solomon thinks that the AMR crisis could change that. New digital tools could help doctors and hospitals integrate guidelines for optimal antibiotic prescribing into their everyday treatment routines.
“Human-computer interactions are critical, as the amount of information available on antibiotic resistance far exceeds the ability of humans to process it,” says Solomon. “It offers the possibility of greatly enhancing the utility of computer-assisted physician order entry (CPOE), combined with clinical decision support.” Healthcare facilities could embed relevant information and protocols at the point of care, guiding the physician through diagnosis and prescription and, as a byproduct, facilitating the collection and reporting of antibiotic use.
Cincinnati Children’s Hospital’s antibiotic stewardship division has deployed a software program that gathers information from electronic medical records, order entries, computerized laboratory and pathology reports, and more. The system measures baseline antimicrobial use, dosing, duration, costs, and use patterns. It also analyzes bacteria and trends in their susceptibilities and helps with clinical decision making and prescription choices. The goal, says Dr. David Haslam, who heads the program, is to decrease the use of “big gun” super antibiotics in favor of more targeted treatment.
While this approach is not yet widespread, there is consensus that incorporating such clinical-decision support into electronic health records will help improve quality of care, contain costs, and reduce overtreatment in healthcare overall—not just in AMR. A 2013 randomized clinical trial finds that doctors who used decision-support tools were significantly less likely to order antibiotics than those in the control group and prescribed 50% fewer broad-spectrum antibiotics.
Putting mobile devices into doctors’ hands could also help them accept decision support, believes Solomon. Last summer, Scotland’s National Health Service developed an antimicrobial companion app to give practitioners nationwide mobile access to clinical guidance, as well as an audit tool to support boards in gathering data for local and national use.
“The immediacy and the consistency of the input to physicians at the time of ordering antibiotics may significantly help address the problem of overprescribing in ways that less-immediate interventions have failed to do,” Solomon says. In addition, handheld devices with so-called lab-on-a-chip technology could be used to test clinical specimens at the bedside and transmit the data across cellular or satellite networks in areas where infrastructure is more limited.
Artificial intelligence (AI) and machine learning can also become invaluable technology collaborators to help doctors more precisely diagnose and treat infection. In such a system, “the physician and the AI program are really ‘co-prescribing,’” says Solomon. “The AI can handle so much more information than the physician and make recommendations that can incorporate more input on the type of infection, the patient’s physiologic status and history, and resistance patterns of recent isolates in that ward, in that hospital, and in the community.”
Speed Is Everything
Growing bacteria in a dish has never appealed to Dr. James Davis, a computational biologist with joint appointments at Argonne National Laboratory and the University of Chicago Computation Institute. The first of a growing breed of computational biologists, Davis chose a PhD advisor in 2004 who was steeped in bioinformatics technology “because you could see that things were starting to change,” he says. He was one of the first in his microbiology department to submit a completely “dry” dissertation—that is, one that was all digital with nothing grown in a lab.
Upon graduation, Davis wanted to see if it was possible to predict whether an organism would be susceptible or resistant to a given antibiotic, leading him to explore the potential of machine learning to predict AMR.
As the availability of cheap computing power has gone up and the cost of genome sequencing has gone down, it has become possible to sequence a pathogen sample in order to detect its AMR resistance mechanisms. This could allow doctors to identify the nature of an infection in minutes instead of hours or days, says Davis.
Davis is part of a team creating a giant database of bacterial genomes with AMR metadata for the Pathosystems Resource Integration Center (PATRIC), funded by the U.S. National Institute of Allergy and Infectious Diseases to collect data on priority pathogens, such as tuberculosis and gonorrhea.
Because the current inability to identify microbes quickly is one of the biggest roadblocks to making an accurate diagnosis, the team’s work is critically important. The standard method for identifying drug resistance is to take a sample from a wound, blood, or urine and expose the resident bacteria to various antibiotics. If the bacterial colony continues to divide and thrive despite the presence of a normally effective drug, it indicates resistance. The process typically takes between 16 and 20 hours, itself an inordinate amount of time in matters of life and death. For certain strains of antibiotic-resistant tuberculosis, though, such testing can take a week. While physicians are waiting for test results, they often prescribe broad-spectrum antibiotics or make a best guess about what drug will work based on their knowledge of what’s happening in their hospital, “and in the meantime, you either get better,” says Davis, “or you don’t.”
At PATRIC, researchers are using machine-learning classifiers to identify regions of the genome involved in antibiotic resistance that could form the foundation for a “laboratory free” process for predicting resistance. Being able to identify the genetic mechanisms of AMR and predict the behavior of bacterial pathogens without petri dishes could inform clinical decision making and improve reaction time. Thus far, the researchers have developed machine-learning classifiers for identifying antibiotic resistance in Acinetobacter baumannii (a big player in hospital-acquired infection), methicillin-resistant Staphylococcus aureus (a.k.a. MRSA, a worldwide problem), and Streptococcus pneumoniae (a leading cause of bacterial meningitis), with accuracies ranging from 88% to 99%.
Houston Methodist Hospital, which uses the PATRIC database, is researching multidrug-resistant bacteria, specifically MRSA. Not only does resistance increase the cost of care, but people with MRSA are 64% more likely to die than people with a nonresistant form of the infection, according to WHO. Houston Methodist is investigating the molecular genetic causes of drug resistance in MRSA in order to identify new treatment approaches and help develop novel antimicrobial agents.
The Hunt for a New Class of Antibiotics
There are antibiotic-resistant bacteria, and then there’s Clostridium difficile—a.k.a. C. difficile—a bacterium that attacks the intestines even in young and healthy patients in hospitals after the use of antibiotics.
It is because of C. difficile that Dr. L. Clifford McDonald jumped into the AMR fight. The epidemiologist was finishing his work analyzing the spread of SARS in Toronto hospitals in 2004 when he turned his attention to C. difficile, convinced that the bacteria would become more common and more deadly. He was right, and today he’s at the forefront of treating the infection and preventing the spread of AMR as senior advisor for science and integrity in the CDC’s Division of Healthcare Quality Promotion. “[AMR] is an area that we’re funding heavily…insofar as the CDC budget can fund anything heavily,” says McDonald, whose group has awarded $14 million in contracts for innovative anti-AMR approaches.
Developing new antibiotics is a major part of the AMR battle. The majority of new antibiotics developed in recent years have been variations of existing drug classes. It’s been three decades since the last new class of antibiotics was introduced. Less than 5% of venture capital in pharmaceutical R&D is focused on antimicrobial development. A 2008 study found that less than 10% of the 167 antibiotics in development at the time had a new “mechanism of action” to deal with multidrug resistance. “The low-hanging fruit [of antibiotic development] has been picked,” noted a WHO report.
Researchers will have to dig much deeper to develop novel medicines. Machine learning could help drug developers sort through much larger data sets and go about the capital-intensive drug development process in a more prescriptive fashion, synthesizing those molecules most likely to have an impact.
McDonald believes that it will become easier to find new antibiotics if we gain a better understanding of the communities of bacteria living in each of us—as many as 1,000 different types of microbes live in our intestines, for example. Disruption to those microbial communities—our “microbiome”—can herald AMR. McDonald says that Big Data and machine learning will be needed to unlock our microbiomes, and that’s where much of the medical community’s investment is going.
He predicts that within five years, hospitals will take fecal samples or skin swabs and sequence the microorganisms in them as a kind of pulse check on antibiotic resistance. “Just doing the bioinformatics to sort out what’s there and the types of antibiotic resistance that might be in that microbiome is a Big Data challenge,” McDonald says. “The only way to make sense of it, going forward, will be advanced analytic techniques, which will no doubt include machine learning.”
Reducing Resistance on the Farm
Bringing information closer to where it’s needed could also help reduce agriculture’s contribution to the antibiotic resistance problem. Antibiotics are widely given to livestock to promote growth or prevent disease. In the United States, more kilograms of antibiotics are administered to animals than to people, according to data from the FDA.
One company has developed a rapid, on-farm diagnostics tool to provide livestock producers with more accurate disease detection to make more informed management and treatment decisions, which it says has demonstrated a 47% to 59% reduction in antibiotic usage. Such systems, combined with pressure or regulations to reduce antibiotic use in meat production, could also help turn the AMR tide.
Breaking Down Data Silos Is the First Step
Adding to the complexity of the fight against AMR is the structure and culture of the global healthcare system itself. Historically, healthcare has been a siloed industry, notorious for its scattered approach focused on transactions rather than healthy outcomes or the true value of treatment. There’s no definitive data on the impact of AMR worldwide; the best we can do is infer estimates from the information that does exist.
The biggest issue is the availability of good data to share through mobile solutions, to drive HCI clinical-decision support tools, and to feed supercomputers and machine-learning platforms. “We have a fragmented healthcare delivery system and therefore we have fragmented information. Getting these sources of data all into one place and then enabling them all to talk to each other has been problematic,” McDonald says.
Collecting, integrating, and sharing AMR-related data on a national and ultimately global scale will be necessary to better understand the issue. HCI and mobile tools can help doctors, hospitals, and public health authorities collect more information while advanced analytics, machine learning, and in-memory computing can enable them to analyze that data in close to real time. As a result, we’ll better understand patterns of resistance from the bedside to the community and up to national and international levels, says Solomon. The good news is that new technology capabilities like AI and new potential streams of data are coming online as an era of data sharing in healthcare is beginning to dawn, adds McDonald.
The ideal goal is a digitally enabled virtuous cycle of information and treatment that could save millions of dollars, lives, and perhaps even civilization if we can get there. D!
Despite the progress made in some countries, I am also aware of others that are still resistant to digitizing their economy and automating operations. What’s the difference between firms that are digital leaders and those that are slow to mature? From my perspective in working with a variety of businesses throughout Europe, it’s a combination of diversity and technology availability.
European companies are hardly homogenous. Comprising 47 countries across the continent, they serve communities that speak any of 225 spoken languages. Each one is experiencing various stages of digital development, economic stability, and workforce needs.
Nevertheless, as a whole, European firms do prioritize customer acquisition as well as improving efficiency and reducing costs. Over one-third of small and midsize companies are investing in collaboration software, customer relationship management solutions, e-commerce platforms, analytics, and talent management applications. Steadily, business leaders are finding better ways to go beyond data collection by applying predictive analytics to gain real-time insight from predictive analytics and machine learning to automate processes where possible.
Small and midsize businesses have a distinct advantage in this area over their larger rivals because they can, by nature, adopt new technology and practices quickly and act on decisions with greater agility. Nearly two-thirds (64%) of European firms are embracing the early stages of digitalization and planning to mature over time. Yet, the level of adoption depends solely on the leadership team’s commitment.
For many small and midsize companies across this region, the path to digital maturity resides in the cloud, more so than on-premise software deployment. For example, the flexibility associated with cloud deployment is viewed as a top attribute, especially among U.K. firms. This brings us back to the diversity of our region. Some countries prioritize personal data security while others may be more concerned with the ability to access the information they need in even the most remote of areas.
Technology alone does not deliver digital transformation
Digital transformation is certainly worth the effort for European firms. Between 60%–90% of small and midsize European businesses say their technology investments have met or exceeded their expectations – indicative of the steady, powerhouse transitions enabled by cloud computing. Companies are now getting the same access to the latest technology, data storage, and IT resources.
However, it is also important to note that a cloud platform is only as effective as the long-term digital strategy that it enables. To invigorate transformative changes, leadership needs to go beyond technology and adopt a mindset that embraces new ideas, tests the fitness of business models and processes continuously, and allows the flexibility to evolve the company as quickly as market dynamics change. By taking a step back and integrating digital objectives throughout the business strategy, leadership can pull together the elements needed to turn technology investments into differentiating, sustainable change. For example, the best talent with the right skills is hired. Plus, partners and suppliers with a complementary or shared digital vision and capability are onboarded.
The IDC Infobrief confirms what I have known all along: Small and midsize businesses are beginning to digitally mature and maintain a strategy that is relevant to their end-to-end processes. And furthering their digital transformation go hand in hand with the firms’ ability to ignite a transformational force that will likely progress Europe’s culture, social structure, and economy.