prompt_front: Now, answer the following question based on the text below, only give me the answer and do not output any other words.

Question: In which electorate was Simon English elected to the New Zealand Parliament?

Read the following text and answer briefly.

Context 1: Sir Simon William English  (born 30 December 1961) is a New Zealand former National Party politician who served as the 39th prime minister of New Zealand from 2016 to 2017  He had previously served as the 17th deputy prime minister of New Zealand and minister of finance from 2008 to 2016 under John Key and the Fifth National Government 

A farmer and public servant before entering politics, English was elected to the New Zealand Parliament in  as the National Party's candidate in the Wallace electorate  He was elevated to Cabinet in 1996 and in 1999 was made minister of finance, although he served for less than a year due to his party's loss at the 1999 general election  In October 2001, English replaced Jenny Shipley as the leader of the National Party (and consequently as Leader of the Opposition)  He led the party to its worst defeat at the 2002 general election, and as a consequence, in October 2003 he was replaced as leader by Don Brash 

In November 2006, after Brash's resignation, English became deputy leader under John Key  After National's victory at the 2008 general election, he became deputy prime minister and was also made minister of finance for the second time  Under English's direction New Zealand's economy maintained steady growth during National's three terms of government  He became a list-only MP after stepping down as an electorate MP at the 2014 general election 

John Key resigned as leader of the National Party and prime minister in December 2016  English won the resulting leadership election unopposed and was sworn in as prime minister on 12 December 2016  His tenure was only ten months, and included a three-month election campaign  In the 2017 general election, National won the largest number of seats but fell short of a majority  The parties holding the balance of power declined to support the existing government, and English was subsequently replaced as prime minister by Jacinda Ardern, leader of the Labour Party  English initially continued on as Leader of the Opposition, but resigned as leader of the National Party on 27 February 2018 and left parliament two weeks later 

Early life
English was born on 30 December 1961 at Lumsden Maternity Centre in Lumsden  He is the eleventh of twelve children of Mervyn English and Norah (née O'Brien) English  His parents purchased Rosedale, a mixed sheep and cropping farm in Dipton, Southland from Mervyn's uncle, Vincent English, a bachelor, in 1944  English was born in the maternity unit at Lumsden 

English attended St Thomas's School in Winton, then boarded at St  Patrick's College in Upper Hutt, where he became head boy  He played in the first XV of the school's rugby team  English went on to study commerce at the University of Otago, where he was a resident at Selwyn College, and then completed an honours degree in English literature at Victoria University of Wellington 

After finishing his studies, English returned to Dipton and farmed for a few years  From 1987 to 1989, he worked in Wellington as a policy analyst for the New Zealand Treasury, at a time when the free market policies favoured by Labour's finance minister Roger Douglas (known collectively as "Rogernomics") were being implemented 

English joined the National Party in 1980, while at Victoria University  He served for a period as chairman of the Southland branch of the Young Nationals, and became a member of the Wallace electorate committee  After moving to Wellington, he served for periods on the Island Bay and Miramar electorate committees, respectively 

Fourth National Government (1990–1999)

At the 1990 general election, English stood as the National candidate in Wallace, replacing the retiring Derek Angus, and was elected with a large majority  He would hold this seat, renamed Clutha-Southland in 1996, until 2014  He and three other newly elected National MPs (Tony Ryall, Nick Smith, and Roger Sowry) were soon identified as rising stars in New Zealand politics, and at various points were dubbed the "brat pack", the "gang of four", and the "young Turks"  In his first term in parliament, English chaired a select committee into social services  He was made a parliamentary under-secretary in 1993, serving under the Minister of Health 

First period in cabinet (1996–1999)
In early 1996, English was elevated to cabinet by Prime Minister Jim Bolger, becoming the Minister for Crown Health Enterprises and Associate Minister of Education (to Wyatt Creech)  He was 34 at the time, becoming the cabinet's youngest member  After the 1996 general election, the National Party was forced into a coalition with New Zealand First to retain government  In the resulting cabinet reshuffle, English emerged as Minister of Health  However, as a condition of the coalition agreement, NZ First's Neil Kirton (a first-term MP) was made Associate Minister of Health, effectively becoming English's deputy  This arrangement was described in the press as a "shotgun marriage", and there were frequent differences of opinion between the two ministers  After their relationship became unworkable, Kirton was sacked from the role in August 1997, with the agreement of NZ First leader Winston Peters 

As Minister of Health, English was responsible for continuing the reforms to the public health system that National had begun after the 1990 general election  The reforms were unpopular, and health was perceived as one of the government's weaknesses, with the health portfolio consequently being viewed as a challenge  English believed that the unpopularity of the reforms was in part due to a failure in messaging, and encouraged his National colleagues to avoid bureaucratic and money-focused language (such as references to "balance sheets" and "user charges") and instead talk about the improvements to services the government's reforms would bring  He also rejected the idea that public hospitals could be run as commercial enterprises, a view which some of his colleagues had previously promoted 

By early 1997, as dissatisfaction with Bolger's leadership began to grow, English was being touted as a potential successor, along with Jenny Shipley and Doug Graham  His age (35) was viewed as the main impediment to a successful leadership run  National's leadership troubles were resolved in December 1997, when Bolger resigned and Shipley was elected to the leadership unopposed  English had been a supporter of Bolger as leader, but Shipley reappointed him Minister of Health in her new cabinet 

English was promoted to Minister of Finance in a reshuffle in January 1999, a position which was at the time subordinate to the Treasurer, Bill Birch  After a few months, the pair switched positions as part of Birch's transition to retirement, with English assuming the senior portfolio  In early interviews, he emphasised his wish to be seen as a pragmatist rather than an ideologue, and said that the initiatives of some of his predecessors (Roger Douglas's "Rogernomics" and Ruth Richardson's "Ruthanasia") had focused on "fruitless, theoretical debates" when "people just want to see problems solved" 

Opposition (1999–2008)

After the National Party lost the 1999 election to Helen Clark's Labour Party, English continued on in the shadow cabinet as National's spokesperson for finance  He was elected deputy leader of the party in February 2001, following the resignation of Wyatt Creech, with Gerry Brownlee being his unsuccessful opponent 

Leader of the Opposition
In October 2001, after months of speculation, Jenny Shipley resigned as leader of the National Party after being told she no longer had the support of the party caucus  English was elected as her replacement unopposed (with Roger Sowry as his deputy), and consequently became Leader of the Opposition  However, he did not openly organise against Shipley, and according to The Southland Times "there was almost an element of 'aw, shucks, I'll do it then' about Mr English's ascension" 

Aged 39 when he was elected, English became the second-youngest leader in the National Party's history, after Jim McLay (who was 38 when elected in 1984)  He also became only the third Southlander to lead a major New Zealand political party, after Joseph Ward and Adam Hamilton  However, English failed to improve the party's performance  In the 2002 election, National suffered its worst electoral defeat ever, gaining barely more than twenty percent of the vote  English described it as "the worst day of my political life"  Both party insiders and the general public were split as to how much to blame him for the loss, but most of the party believed that English would be able to rebuild National's support 

By late 2003, however, National's performance in opinion polls remained poor  The party had briefly increased its popularity in the year following the election, but by October its support had fallen to levels only slightly better than what it achieved in the last ballot  English also appeared in a boxing match for a charity against entertainer Ted Clarke  This did not boost his polling or that of the National party either, with suggestions that it devalued his image as a serious politician  Don Brash, former governor of the Reserve Bank and a relative newcomer to politics, began to build up support to replace English  On 28 October, Brash gained sufficient backing in Caucus to defeat English in a leadership contest 

Shadow cabinet roles and deputy leader
On 2 November 2003, when Brash changed responsibilities for certain MPs, English became National's spokesman for education, ranked at fifth place in the party's parliamentary hierarchy  He remained in parliament after the 2005 election  In his new shadow education portfolio, English performed strongly, and remained a party favourite despite his election defeat as leader in 2002, eventually being returned to the finance portfolio in August 2004 as deputy spokesman (while still retaining responsibility for education) 

In November 2006, Brash resigned as leader  English was considered as a potential replacement leader (running against John Key) or deputy leader (against incumbent Gerry Brownlee) in the ensuing leadership election  However, a contest was avoided when the MPs agreed a Key/English ticket would run unopposed in a display of party unity  English took over the deputy leadership and the finance portfolio in the Key shadow cabinet 

Fifth National Government (2008–2017)

Deputy Prime Minister and Minister of Finance (2008–2016)

At the 2008 election, English was re-elected by his electorate, winning by a margin of about 15,500 votes  He became Deputy Prime Minister of New Zealand and Minister of Finance in the fifth National Government, being sworn into office on 19 November 2008 and continued to serve in those roles until becoming Prime Minister on 12 December 2014  He was also made Minister of Infrastructure in National's first term of government and Minister responsible for Housing New Zealand Corporation and minister responsible for the New Zealand flag consideration process in its third 

He was comfortably re-elected in Clutha-Southland in the 2011 election but opted to run as a party-list candidate in 2014  

The pairing of John Key as leader of the National Party and English as his deputy has been compared to that of Bob Hawke and Paul Keating (in Australia) and Tony Blair and Gordon Brown (in the UK) 

English acceded to the role of Finance Minister in the continuing wake of the financial crisis  In response to New Zealand's rising debt, English made budget deficit-reduction his main priority  His first budget outlined three focuses in New Zealand's financial recovery: "improving the business environment and removing roadblocks to growth; investment in productive infrastructure; and improving the way government works"  One of his first acts was creating the National Infrastructure Unit, charged with formulating a plan for infrastructure projects and investments  He commissioned a government-wide spending review, with an aim to reducing government expenditure—with the exceptions of a two-year stimulus package and long-term increases on infrastructure spending 

In April 2011, the Opposition criticised English for suggesting that New Zealand businesses could use New Zealand's low wages to help it compete with Australia  The National Government campaigned for re-election in 2011 on its economic record  The Government boasted growth for five consecutive quarters up to mid-2010, totalling 1 6% of real GDP 

Strong growth resulted in a surplus of $473 million for the 2015/16 financial year, projected to rise to $8 5 billion by 2020/21  In his 2016 Economic and Fiscal Update address, English stated that reducing debt and tackling the costs of the 2016 Kaikōura earthquake were higher priorities than reducing rates of tax 

Allowances issue
In 2009, the media, including TVNZ and TV3 revealed that English was receiving about NZ$900 a week as part of a living allowance for ministers, to live in his own NZ$1 2 million Wellington home  At the time, English also received $276,200 in his annual salary as Deputy Prime Minister  It was also revealed other ministers with homes in the capital city were also claiming accommodation allowances  On 3 August 2009, Prime Minister John Key started a review of the housing allowances claimed by cabinet ministers  English subsequently paid back $12,000 and only claimed about $24,000 a year in living allowances  The Auditor-General's office said in September 2009 that they were making "preliminary enquiries" into parliamentary housing expenses in response to a letter of complaint from Progressive party leader Jim Anderton  Two days later English stated that he would no longer take up any housing allowance and had paid back all the allowance he had received since the November 2008 election 

Prime Minister (2016–2017)

John Key resigned on 12 December, and endorsed English as his successor in the resulting leadership election  Following the drop-out of both Judith Collins and Jonathan Coleman from the leadership election, English was sworn in as the 39th Prime Minister of New Zealand on 12 December 2016 

English appointed his first cabinet on 18 December  In a reshuffle, he appointed Steven Joyce to succeed him as Finance Minister, while most ministerial portfolios remained the same 

In February 2017, English did not attend Waitangi Day commemorations at the historic treaty grounds, reportedly in response to the Ngāpuhi iwi's decision to stop the Prime Minister from speaking at the marae  Ngāpuhi have protested the Government's negotiation of the Trans Pacific Partnership Agreement (TPPA), which the iwi believe infringes upon Māori sovereignty, and thus does not adhere to the Treaty of Waitangi  English had been invited to attend in an official capacity; his non-attendance was criticised by a Ngāpuhi elder and Opposition leader Andrew Little 

In his first overseas trip as Prime Minister, English travelled to Europe to discuss trade ties, including a prospective New Zealand–European Union free trade agreement  He first travelled to London on 13 January 2017 to meet British Prime Minister Theresa May  Discussing trade relations, English said the two nations were "natural partners" and would "continue to forge ties" after the UK's withdrawal from the EU  He also arranged to meet with London Mayor Sadiq Khan, Belgian Prime Minister Charles Michel and German Chancellor Angela Merkel   In a meeting with Merkel, English received crucial backing from Germany for a trade deal with the EU  On 16 January, English stated that his government would continue to promote TPPA, despite the United States' decision to withdraw from the agreement  He explained that Southeast Asian countries would now be treated as a priority in negotiations—he also asserted that the United States was ceding influence to China by its rejection of the trade pact 

At a press conference at the Beehive on 1 February 2017, English announced that the 2017 general election would be held on 23 September  The Prime Minister later confirmed that his party would approach ACT, United Future and the Māori Party if confidence and supply agreements were required to form a government following the election  In his second cabinet reshuffle on 24 April, English appointed Gerry Brownlee as his new Foreign Affairs Minister; he also promoted Nikki Kaye to the portfolio of Education Minister, and moved Mark Mitchell into the cabinet to become Defence Minister  The reshuffle was perceived as an election preparation 

On 13 February 2017, English welcomed Australian Prime Minister Malcolm Turnbull to Wellington  The two leaders reaffirmed their shared trade agenda, and discussed changes to the Australian citizenship pathway which will affect permanent residents originating from New Zealand 

On 19 June, it was reported that Todd Barclay, who succeeded English as MP for Clutha-Southland, had clandestinely recorded one of his employee's conversations the previous year, and that John Key's leaders' budget was used to pay a confidential settlement after the employee resigned  English admitted that he had been aware of the illegal recording and the settlement, and thus implicated in the scandal 

During the 2017 National campaign launch, English introduced a $379 million social investment package including digital learning academies for high school students, more resources for mathematics, and boosting support for teaching second languages in schools, and maintaining National Standards in the school curriculum  Prime Minister English also sought to defend National's financial management and economic track record and claimed that the opposition Labour Party would raise taxes  Early opinion polling had forecast a poor showing in the election for the Labour Party, but in early August 37-year-old Jacinda Ardern took over as Labour leader and seemingly energised younger voters 

At the 2017 general election, National won the largest share of the party vote (44 4%) and the largest number of seats (56) in the House Representatives  However, National lacked enough seats to govern alone due to two of the party's support partners, the Māori Party and United Future, losing their parliamentary seats  In response, English stated that the party would be entering into talks to form a coalition with New Zealand First  Following talks with the two largest parties, New Zealand First entered a coalition arrangement with the Labour Party  English was succeeded as prime minister by Jacinda Ardern on 26 October 

Opposition (2017–2018)

Leader of the Opposition
English was re-elected as National Party leader on 24 October 2017  At the time of his re-election, English announced his intention to stay on as leader until the next general election  On 13 February 2018, however, he stood down as National Party leader due to personal reasons, and instructed the party to put into motion the processes to elect a new leader  He also retired from Parliament  English's resignation followed weeks of speculation that he would step aside for a new leader  On 27 February, he was succeeded as party leader by Simon Bridges as the result of the leadership election held that day 

Post-premiership 
In 2018, English joined the board of Australian conglomerate, Wesfarmers  English serves in Chairmanships of Mount Cook Alpine Salmon, Impact Lab Ltd and Manawanui Support Ltd  He is also a director of The Instillery, Centre for Independent Studies and The Todd Corporation Limited, and is a member of the Impact Advisory Group of Macquarie Infrastructure and Real Assets 

Political and social views

English is regarded as more socially conservative than his predecessor, John Key  He has stated his opposition to voluntary euthanasia and physician-assisted suicide, same-sex civil unions, and the decriminalisation of prostitution  As Prime Minister he opposed any "liberalisation" of abortion law 

In 2004, English voted against a bill to establish civil unions for both same-sex and opposite-sex couples  In 2005, he voted for the Marriage (Gender Clarification) Amendment Bill, which would have amended the Marriage Act to define marriage as only between a man and a woman  English voted against the Marriage (Definition of Marriage) Amendment Bill, a bill that legalised same-sex marriage in New Zealand  However, in December 2016 he stated, "I'd probably vote differently now on the gay marriage issue  I don't think that gay marriage is a threat to anyone else's marriage" 

In 2009, English voted against the Misuse of Drugs (Medicinal Cannabis) Amendment Bill, a bill aimed at amending the Misuse of Drugs Act so that cannabis could be used for medical purposes 

Personal life 
English met his future wife, Mary Scanlon, at university  She was studying medicine at the time, and became a general practitioner  Both her parents were immigrants, her father being Samoan and her mother Italian, born on the island of Stromboli  They have six children: a daughter and five sons 

English is a practising Roman Catholic, but has stated that he considers his religious beliefs personal and thus separate from politics 

In June 2002, English took part in TV3's Fight For Life, a celebrity boxing fundraiser to raise money for the Yellow Ribbon anti-youth-suicide campaign, influenced by the death of a teenage nephew in 1997  He lost a split decision to former university colleague Ted Clarke 

Honours
In the 2018 Queen's Birthday Honours, English was appointed a Knight Companion of the New Zealand Order of Merit, for services of over 27 years to the State 

See also

List of New Zealand governments
Politics of New Zealand

References

External links

Profile at National Party 
Profile on Parliament nz
Releases and speeches at Beehive govt nz

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1961 births
21st-century New Zealand politicians
Candidates in the 2017 New Zealand general election
Deputy Prime Ministers of New Zealand
Leaders of the Opposition (New Zealand)
Living people
Members of the Cabinet of New Zealand
Members of the New Zealand House of Representatives
New Zealand farmers
New Zealand finance ministers
New Zealand list MPs
New Zealand MPs for South Island electorates
New Zealand National Party MPs
New Zealand National Party leaders
New Zealand Roman Catholics
New Zealand people of Irish descent
People educated at St  Patrick's College, Silverstream
People from Dipton, New Zealand
People from Lumsden, New Zealand
Prime Ministers of New Zealand
University of Otago alumni
Victoria University of Wellington alumni
Knights Companion of the New Zealand Order of Merit
New Zealand politicians awarded knighthoods
Context 2: Hospitals as critical infrastructures has been historically dependent on various types of devices and equipment that are being revolutionized with digitalized solutions  The digitalization of conventional healthcare equipment is added with the new inclusion of numerous new devices for data collection, analysis, communication, and so on  All in all, the futures digital hospitals in 5G will be exponentially more data-dependent and digital-intensive  For that, this paper looks to theorize how the security scenario in a futures digital hospital would look like, and what relevant business possibilities could emerge for cybersecurity providers in the healthcare context  In this paper, we open up discussions on business possibilities relevant to Internet of Things-mobile device management for critical infrastructures such as future digital hospital  We apply business models as a conceptual lens to analyze how cybersecurity business could evolve for 5G enabled IoT-Mobile device management providers as a cybersecurity vendor 
The healthcare sector has progressed significantly since the introduction of Internet and proliferation of network technologies   Among many issues, the use of data, availability of data, data mass, and access control of data in healthcare remain are critical for keeping healthcare services trustworthy and secure for the end users, both hospital staff and the patients  Disruptions in healthcare services would have severe effects on people’s lives  However, as hospital managers and professionals need to design their data-dependent and digital-intensive networks in a manner, which is highly secure, they also should provide the basis for uninterrupted service for business sustainability  Security is often observed as a tradeoff between risk and business gains   Investing in security is important in order to secure business-critical systems and data for meeting business goals and eventually for creating competitive advantage [3, 4] 
Innovative technologies have the power to disrupt industries and prompt business transformation   The coming of fifth generation (5G) of telecommunications networks is seen to result in this kind of disruption  As we are gradually moving towards 5G, it is worthwhile to theorize how the security scenario in a futures digital hospital would look like, and what relevant business possibilities could emerge from cybersecurity in the healthcare context  From this perspective, in this paper, we open up discussions on business possibilities relevant to Internet of Things mobile device management for critical infrastructures such as future digital hospital  We apply business models as a conceptual lens to analyze how cybersecurity business could evolve for 5G enabled IoT device management providers as a cybersecurity vendor 
A futures digital hospital facility is envisioned to consist tech-aided advanced critical medical devices, intelligent information systems, digital communication tools, hundreds of handheld mobile devices (smartphones, tablets), wireless clinical wearables, in addition to thousands of smart IoT nodes [6, 7]  These devices should be fully integrated to improve staff productivity, hospital functions, patient safety and privacy, and, overall improve patient experience through secure and reliable healthcare services  However, inclusion of these various kinds of digital devices to the hospital context make the overall device network quite complex and heterogeneous   Thus, from a critical infrastructures view point, to manage, configure, update and secure the immense fleet of digital devices besides all the high-tech medical equipment, the futures digital hospital will need to redefine device management policy and services  
Mobile device management (MDM) systems are usually referred to “support centralized control of an entire fleet of mobile devices (smartphones and tablets) and mobile applications by applying and ensuring pre-defined configuration settings” [8, 9]  In the scope of this paper, we broadly use the term IoT-MDM to refer to a device management system that is capable of managing, configuring and updating both handheld mobile devices and IoT devices in combination in a centralized manner  We will briefly open up the concept of MDM and IoT-MDM 
The purpose of this study is to identify business potential for IoT-MDM service providers as cybersecurity vendor in the context of the future digital hospital  In doing so, we apply the concept of business model in order to make sense of a ICT-oriented business environment   Among various available conceptualizations, business model is considered as a boundary-spanning unit of analysis that explain the underlying business logic and the value creation and value capturing logic of an organization [11–15] 
Traditionally, device management has been associated with management and configuration of handheld mobile devices , thus, mobile device management (MDM)  Gartner  perceives MDM software to be a policy tool to configure and manage mobile handheld devices  They also mark that MDM services need to ensure security in reference to connectivity and content that is being transmitted  Along with surge of smart mobile devices, the Internet of Things (IoT) is growing large during the last few years and promises to flood the market with billions of devices in the coming years too   Zhang et al   states scalability, transparency and reliability as important issues that differentiates IoT from the conventional Internet  To that end, there are several IoT platforms available currently in the market for managing, updating and configuring IoT nodes, e g  IBM Bluemix, Cumulocity, ARM mbed OS, etc    However, the transition raises the question about the differences and similarities between MDM and IoT device management as approaches 
Takalo  marks MDM and IoT device management to be quite close on a conceptual level: both need solution for automated management of large device fleets consisting different form factors, device models, and operating system  Additionally, such systems conceptually needs to support various communication channels like: WiFi, cellular network and Ethernet  However, on practical level, MDM is more strictly controlled by operating systems and device vendors  IoT device management, on the other hand, is characterized by multiple operating systems, multiple hardware platform variations, non-complete standards, multiple communication methods and protocols  This agile approach allows the coexistence of various types of devices and nodes in the same environment  Recently, services are appearing where MDM and IoT device management are brought together under the same platform, which in a way reduces the complexity in device management and also improves the overall security of the system 
While we are still in early phase of mass IoT deployment and 5G deployment is approaching, it is important to revisit some of the key security threats that has been identified in recent literature relevant mainly to IoT  Zhang et al  [19, 21] identified privacy preservation as a critical issue for information security in IoT ecosystems  They further state that conventional naming, identification and authentication policies need to be improved and rather needs approaching differently  Farooq et al   look at the four layer generic IoT architecture consisting perception layer, network layer, middle-ware layer & application layer  Additionally, they offer a list of security challenges for each layer  Some of the key challenges are: unauthorized access, tag cloning, eavesdropping, spoofing, RF jamming, sinkhole attack, sleep deprivation attack, denial of service attack, malicious code injection, man-in-the-middle attack, spear-phishing attack, sniffing attack  Backman et al   state, a comprehensive security solution needs to address endpoint security, management and monitoring security, and secure data distribution and storage 
Ortbach et al   enumerate the drivers for adoption of MDM in organization through a quantitative analysis, reflecting three broad drivers: organization, environment, and technology  From organizational perspective, the company size, mobile IT usage, employee innovativeness with IT and BYOD (bring your own device) culture were identified as important drivers  From environmental viewpoint, regulations and other business partner influence are significant drivers for MDM adoption  Finally, from technology point of view, perceived security benefits and perceived cost of the service seems to affect the managerial attitude towards MDM adoption 
MDM systems are today a very common tool to manage users’ devices  With MDM, all mobile device types, tablets and PCs with typical operating systems can be controlled centrally   It is often thought that MDM can manage only mobile phones, but actually the MDM framework includes also users’ identities and profiles  This makes MDM a viable tool for organizations to manage their employees identities, user profiles, all devices, all applications and security controls under same system 
From an emerging technology perspective, SDN (software-defined network)/NFV (network functions virtualization) based 5G Slicing will challenge some of the traditional MDM features   Especially end-to-end security from device to IT cloud is difficult to realize with MDM  Of course, it is possible to force the use of VPN in mobile device with MDM, but many aspects of communications security will be still unsolved  Thus, 5G slicing provides new tools to control and manage the end-to-end communications flow with network functions (VNFs)  In the advent of IoT, this is particularly important since the billions of IoT devices of the 2020s will have only a minimal processing power and memory compared to the smart phones of today  These IoT devices may connect to network only once a month and communicate only with network edge cloud servers  Therefore, managing these new IoT devices cannot be done with conventional MDM systems of today 
Fortunately, many features of MDM can be provided by dedicated 5G slices and their VNFs  If e g  a IoT device does not have the latest anti-virus updates, the network slice may still provide the isolation and security controls so that the IoT device can send the metering data  Moreover, if the IP flow from the IoT device includes other than actual metering data, eg  due to malware in IoT device, this IP flow can be analyzed and filtered by slice specific VNFs before passing it to IoT could 
As a summary, it can be argued that SDN/NFV based 5G Slicing will provide new tools for security management, and, when combined with IoT-MDM system functionalities, together these can deliver a better device management framework for different kinds of user devices of the 2020s 
Hospital organizations are considered as critical infrastructure (CI) to nation states [3, 25]  As a critical infrastructure, hospital organizations are prone to security threats that can affect health policies, public health, healthcare services, surgical procedures, electronic patient records, patient privacy, doctor-patient communication, etc  Lehto and Ahokangas  notes, new technology (e g  next generation mobile networks, smart data storage, IoT) adoption of CIs increases the cybersecurity touchpoints and hence making the CIs more vulnerable  Broadly, from cybersecurity perspective, the hospital organizations in future will be vulnerable from management perspective (e g  organizing healthcare services, managing huge amount of patient data, clinical data, medication data, communication between health professional and patients etc ), healthcare service delivery perspective (e g  in an unwelcoming case of denial of service attacks in hospital context: like wannacry), network perspective (e g  security of the overall hospital network), and last but not the least from an individual privacy perspective (e g  individual patient records, healthcare professional logs, etc ) 
A futures digital hospital will consist various advanced technologies, such as critical medical devices, intelligent information systems and digital communication tools, which are fully integrated to improve staff productivity, hospital operations, patient safety, and the overall patient experience  Among them, many will be wireless mobile devices, wireless wearables and thousands of smart IoT devices for various kinds of measurements  
Innovations help transform healthcare in forms of advanced telecommunications techonology, new drugs and treatments like biological sensor pills or implants, new medical devices, social media interation, etc  [1, 6, 7]  In future, hospitals and surgical tools will be revolutionized with intelligence and connectivity i e  providing more assistive functions by sensors, processors, data collection, software algorithms and interfaces which are, for example, embedded in the tools that will support surgeon’s decision-making in action   Robotic-assisted Surgery (RAS) can be considered as a good example of such transformation  The rapid adoption of Minimally Invasive Surgery (MIS) and today’s information revolution not only improves the surgical outcomes and patient’s life but also changes the patient-physician relationship 
‘Smart’ i e  IoT-based technologies are going to intellectualize medical devices and service systems into Smart Hospitals  Smart hospitals extrapolate from totally digitalized and automated data collection, tracking and delivery between systems, devices, patients, and health professionals and organization   In future, it is also envisioned that virtual hospitals and personalized medication will become part of patient care  Patients will be able to visit virtual hospitals or e-clinincs for clinical purposes without the presence of health professionals, but procedures and communication are managed via remote management and telemedicine solutions  
Other features that futures digital hospitals will include: use of different AR (augmented reality) and VR (virtual reality) applications for healthcare related purposes  While VR applications are quickly evolving in the gaming industry, some applications are deemed suitable in hospital environment too  Patients can be introduced to hospital environment through VR solutions, which can reduce patient stress prior to surgical procedures   Another application of VR in healthcare has been identified as rehabilitation support for temporary physical disabilities and mental trauma  Further, AR solutions can help patients navigate within a large hospital complex 
The term, Business model, has had its root in information systems (IS) and information communication technology (ICT) since the late 1970s originating from business informatics  However, it came into management and strategic management literature as a research interest from the mid-1990s [29, 30]  Although business model had its roots in IS and ICT, the amount of research work on cyber security as a context is still quite negligible harnessing the potential of business model concept 
The concept of business models lies at the intersection of entrepreneurship and strategy, it can be observed as a bridge between abstract strategies and the practical implementation of strategic decisions and actions amidst the uncertainties of the modern business context [11–14, 31]  For instance, Zott and Amit  conceptualize business model as a ‘boundary-spanning’ set of activities aimed at creating and appropriating value  Morris et al   viewed the concept of the business model as a set of decisions related to the venture strategy, architecture, and economics of firm (value creation and capture) that need addressing to create sustainable competitive advantage in the chosen markets and specific contexts 
As a boundary-spanning unit of analysis, business models , connects an organization with its business environment, other organizations, customers, individuals and society as well; with the overall ecosystem at large [34, 35]  Trying to bridge business models and cyber security under current context, there are two core issues  First, as almost all of the entities operating within the digital sphere face multifaceted cyber threats, how can business model approach help organizations to respond to such situations? Second, how can business model approach help to identify opportunities and monetize security in future 5G? In the next two sub-chapters, we present two business model approaches that are suitable for ICT contexts, and, can help find answers to the above mentioned questions 
As the mobile telecommunications industry advanced, so did business model related discussions in the literature about extending organizational boundaries through vertical and horizontal integration in industries   Around this, Wirtz et al   offered four mutually exclusive business models classifying Internet based business models to be precise  According to Wirtz et al  , these business models present in Web 2 0 are related to connection, content, context, and commerce  Building further, Yrjölä et al   interpreted these business models as chronological layers, where “lower” layer business models are pre-requisites for the “higher” layer business models to exist 
Figure 1 The layered 4C ICT business models archetypes [Adapted from 35, 37, 38] 
The first layer is concerned with connection-related business model where a stakeholder provides connection services   Connection- related business models are relevant to connectivity for all sorts of devices and nodes through various communication channels, e g  PCs, smartphones, tablets, IoT devices, etc  The second layer is the business model focusing on monetizing content  At the content layer, all sorts of online content services (e g  mobile video streaming) are classified (i e  relevant, up-to-date and interesting) and are accessible conveniently for the end user  The content might be peer-to-peer/user-oriented contents (i e  exchange of personal content), and web browsing content (audio, video, text etc ) 
The third, context, layer concerns the ability to create and monetize user, content, equipment/user device and system profiles and turn (big) data into meaningful information and knowledge through systemic virtual contextualization  The fourth layer concerns commerce, the ability to monetize any or all of the connection, content, or context specific resources, actors or activities related to the ongoing communications  At this layer, we identify business, consumer and public/government types of communications   Thus, B2B (business-to-business), B2C and B2G communications as well as C2B, C2C and C2G or G2B, G2C and G2G communications may be monetized at this layer 
Casdesus-Masanell and Llanes’s  offers different software business models based on the openness of core software and extension software offering  According to the mixed source software business model approach, services can be: open source (open core- open extension), open core (open core- closed extension), open extensions (closed core- open extension) and proprietary (closed core- closed extension)  Although this model was offered specifically suiting the software industry, we argue that similar approach can be useful in other ICT contexts to scale up value offering by adjusting cooperation and industrial partnering model 
Figure 2 Mixed source business model approach [Adapted form 40] 
Depending on individual organization’s choice of mixed source options, it should eventually translate the value creation and value capture logic of the firm  Casadesus-Masanell & Llanes  argues that purely proprietary models results in higher captured value for organizations but lessens the scope of value creation for users  In contrast, purely open source models can deliver the maximum value to customers but reduce captured value  The key for organizations is to locate the best mix of openness-closeness for an optimum level of value capture and creation  From the business model elements collection, the mixed source approach explain key partners, key activities, key resources and technologies used  From a strategic perspective, this approach to business modeling helps an organization to find a way to scale up or down its business activities 
This study concerns business possibilities of IoT and mobile device management in the context of futures digital hospital  Since future is elusive and the speed of technology advancements has been rapid during recent decades, we consider the most suitable method for conducting this research to be through qualitative approach  Qualitative methods helps with flexibility and sensitivity to the context that is less explored, and it can facilitate understanding of how things work in a particularly complex setting   We adopt a single qualitative case study approach for this research to explain underlying business potential of a scantly explored industrial context within the existing literature  
Interview 1 Pediatric Surgery Administration, teaching, hospital development, technology and innovations Pediatric surgery patient journey, concept development of futures digital hospital, digitalization in healthcare 
Workshop 1 Network specialists, operator service provider, cybersecurity expert Business opportunity creation, new market exploration, security aspects Digitalization in healthcare, security business opportunities in health domain, business modeling 
Workshop 2 Operator service provider, cybersecurity expert Business opportunity creation, new market exploration, security aspects Digitalization in healthcare, security business opportunities in health domain, business modeling 
Table 1 summarizes the data used for this research  The empirical data used for this study was collected through two streams  First, in order to build the conceptual case of future digital hospitals, five interviews were conducted with pediatric surgery specialists, a nurse, and a general physician from two hospital districts in Finland  These semi-structured interviews were organized between April and May 2016  These interviews also focused on understanding the journey path in a pediatrics surgery case to clarify suitability of digitalization in the future  However, in the scope of this paper, we do not discuss the patient journey pediatrics surgery in detail  Second, we explored the cybersecurity and business perspectives in two workshops that were organized with telecommunications and cybersecurity experts from the industry  One of the workshops were organized in November 2016  The other workshop took place in August 2017  While the futures digital hospital context from a security perspective relates more towards hospital management and network functionality, the IoT devices and mobile devices perspective is closer to end user security needs  This paper focuses on the cyber security related business opportunities through IoT-MDM systems/services that broadly covers the above mentioned domains  Findings and analysis from the interviews and workshops are presented next 
In this section, we present our analysis in four steps  First, we briefly discuss the relevance of Internet business models (4C)  to the context of the study, addressing how this approach can help cybersecurity providers identify business opportunities in IoT-MDM  Second, we present a 4-quadrant matrix reflecting four different 5G security-provisioning scenarios, which are also relevant to IoT-MDM  Then, we connect each scenario back to business models by applying a mixed-source business model approach  and identify different possible business models for each kind of cybersecurity provider  Finally, we connect the overall discussion back to the case of future digital hospital 
The 4C business model archetypes helps decoding the boundaries between multiple business models operating either in the same verticals of the ecosystem or in the same horizontals  These archetypes can provide with a basis to classify and analyze business models of suppliers, competitors, and, at the same time, business models of customers  For this case, the 4C business model archetypes are mostly relevant for cybersecurity providers and IoT-MDM system/service providers as a tool to analyze customer business models and identify customer needs, the hospital management style  These customer needs can eventually be turned around as business opportunities  While IoT-MDM systems and services are not only concerned about cybersecurity, there is room for service providers to customize and tailor services based on customer needs  These service providers either can target customers from one layer (connection/content/context/commerce) across multiple industries, or, they can also target a specific industry (e g  healthcare) create customized services for all the layers in that industry 
To make sense of the overall cybersecurity provisioning for future 5G, we created the 4-quadrant matrix (Figure 3) by identifying major security drivers for new business creation in future 5G  These scenarios were created in workshops with telecommunication & cybersecurity experts from the industry  While these service provisioning scenarios are relevant to overall 5G, they are also significantly related to the case of IoT-MDM system/services 
Figure 3 Scenarios for 5G cybersecurity provisioning [Adapted from 35] 
As a result, we find four major drivers of security, which potentially will come with new business opportunities in the 5G era  Device driven security comprises distributed and D2D security techniques  Platform driven security will focus on centralized and D2D security techniques  Whereas, network infrastructure driven security should focus on centralized and infrastructure security methods  Lastly, location driven security should harness distributed and infrastructure security techniques 
In a quest to identify potential business entities operating in each of the quadrants relevant to device management, we recognize a ‘secure device manufacturer/provider’ focuses on device-driven security  Currently, multiple device manufacturers are developing devices where security features are built-in, regardless of which network or websites the users are accessing  This built-in security can be offered to multiple kinds of devices including smartphones, tablets, pcs, wearables, and even to IoT devices with communication and computation capability  These secure devices are built in a way that it will control access to potentially harmful networks, websites, and content; even without any commercially available security applications installed  However, when a customer enterprise (e g  the future digital hospital) is buying a fleet of hundreds of such secure devices, the question raises how to manage and configure all these devices from time-to-time  In such a case, even the secure devices will need IoT-MDM services for proper management and seamless upgrading when needed 
For the platform-driven security, we observe a ‘traditional IoT-MDM system provider’ can be a good example  In many cases, IoT-MDM system providers are selling there device management systems to enterprises directly  And, in other cases, they are selling the service through MNO’s bundled with connectivity and/or infrastructure  While as network infrastructure-driven security, a ‘mobile network operator/carrier’ or a ‘network infrastructure vendor’ can build own IoT-MDM system to offer their clients as well  And, finally, a location-specific micro operator can offer location-driven security  Micro operators offer mobile connectivity combined with specific, local services  The operation of a micro operator is spatially confined to either its premises or to a defined area of operation  As a part of the location-specific services, these micro operators can also offer IoT-MDM services for the users through outsourcing 
Further, we attempt to connect the aforementioned classification and examples of different players offering IoT-MDM services with the mixed source business model approach  Table 2 summarizes our understanding on how each kind of cybersecurity provider can open and mix the core value creation logic for end users  As mentioned previously, the mixed-source business model options are: open source (open core, open extensions), open core (open core, closed extensions), open extensions (closed core, open extensions), and proprietary (closed core, closed extensions)  In relation to these mixed-source options, we analyze the plausible options for each of the four distinct cybersecurity providers in the context of this study 
Secure device manufacturer/provider Secure devices Proprietary (own device, own IoT-MDM platform), Open extensions (own device, outsourced IoT-MDM service) 
From a secure device manufacturer perspective, device business can be considered as the core operation whereas IoT-MDM services would be extended solution  A secure device manufacturer/provider can have either a proprietary model or an open extensions model  In the proprietary model, the secure device manufacturer will offer their own devices alongwith their own IoT-MDM system/service  This is a viable case in a sense that customers who are purchasing the fleet of secure devices might prefer the IoT-MDM service from the same vendor, which is ideally less risk prone  Generally, when a customer organization buys a fleet of secure devices, they would expect the device management functions to be offered by the same service provider since integration of multiple systems/interfaces might increase vulnerability and reduce the customer’s confidence on the system  However, in practice, not all secure device manufacturer will specialize in automated device management solutions, and new R&D expenses to develop the IoT-MDM systems could result to be financially unfeasible  So, the second option for a secure device manufacturer is to have an open extensions model, where they will still offer the secure devices but outsource the IoT-MDM services to other vendors 
A micro operator’s core business relates to location specific network, while they are offering additional location specific services as extensions  Ideally, a micro operator’s business model in this case can be considered to be open source  At one hand micro operators are dependent on appropriate available spectrum resources on carriers and NIVs, and on the other hand, outsourcing the IoT-MDM services to other vendors seems more economically feasible than building own system  In contrast, a traditional IoT-MDM system/ service provider can have either a direct-open extensions business model or an indirect-open extensions business model  Unlike the other three archetypes in this discussion, IoT-MDM system/services are the core business for this kind of actor, as device and network related issues can be considered as extensions  An IoT-MDM system/service provider is characteristically dependent on connectivity providers, i e  MNOs, NIVs  In a direct-open extensions business model, while they are selling the IoT-MDM service directly to the end users, they are using the operator connectivity, but delivering their core value directly  Alternatively, in an indirect-open extensions business model, these IoT-MDM vendors can sell the services through MNOs/NIVs/device vendors to the end users 
Finally, a mobile network operator can either have an open extensions business model, or less likely a proprietary business model for IoT-MDM services  In an open extensions business model, operators will offer own connectivity to end users while outsourcing the IoT-MDM service to vendors  In very few cases though, operators might have their own IoT-MDM system on offering and thus they can sell both connectivity and IoT-MDM service as a proprietary bundle 
Looking back at the case of futures digital hospital, it is deemed to comprise various advanced technology-aided devices, let it be for clinical purposes or communications purpose that support healthcare  With the presence of devices like smartphones, tablets, wearables, connected TVs, VR touchpoints, AR touchpoints, robotic assistance for surgeries, thousands of smart IoT devices, makes the overall device network of the digital hospital complex and need automated centralized management  This centralized management of numerous IoT and mobile devices can be delivered through IoT-MDM system/services  Using the IoT-MDM system/service, the hospital organization can configure, secure, and time-to-time update their device network 
While procuring IoT-MDM services, the above analysis shows that futures digital hospitals can source it either directly from IoT-MDM service providers or through MNOs  Alternatively, if a digital hospital also plays the role of a micro operator, besides offering other location-specific services, the micro operator can also offer device management services by sourcing it from other vendors  In other cases, the future digital hospital can procure fleet of secure devices for the hospital from whom they can also source the device management service as a bundle  From the 4C business models perspective, the hospital organization seems to be a single organizational entity where different activities can be categorized in the 4C layers  Cybersecurity providers can specify and address such issues to highlight and customize their service offering for the future digital hospital 
In this article, we have looked at the futures digital hospital context from a device management perspective, and attempted to portray business model options based on how to create and capture economic value from cybersecurity business  Though in this research, we adopt a high-level stance on cybersecurity from a technical perspective, the overall discussions on business potential are relevant to issues like information security, communication security, storage security, security at vulnerable touchpoints in hospital context (end user interface layer, IoT nodes, system layer, network layer) 
Futures digital hospitals will be vulnerable to cybersecurity threats because of its data-dependency and digital-intensive device network  Thus, the business opportunity for cybersecurity providers in this case can be considered as the need for automated and centralized IoT-MDM service  To that end, this paper presents four distinct players who can provide such service to a critical infrastructure like a digital hospital  The mixed-source business model options further open up multiple alternatives that each type of vendor can consider while tailoring services for the future digital hospital   This paper also connects the 4C ICT business model archetypes to cybersecurity business context which can be used as an analytical tool to identify customer needs and scope for value creation  
Academically, this work contributes by filling up the void in discussing business models for cybersecurity as an industry  In addition, in the existing literature the hospital context has also been less discussed from a cybersecurity business perspective  From an industrial point of view, the business model options discussed in this research are timely and relevant to the market context and need  As mentioned, the mixed source business model options show how cybersecurity providers can extend their offering for different kinds of need for the hospital context based on their core businesses  This study can prove to be helpful for cybersecurity business entities and at the same time hospital managers  The scope of this paper explains business potential of cybersecurity vendors to an emerging industry from a higher level  In this paper, authors do not attempt to analyze the technical aspect of cyber security provisioning in healthcare context, however that is a forthcoming research possibility of this study  Also, since this research is based on a conceptual phenomenon, thus its empirical validation, both qualitatively and quantitatively is still yet to come, which can be considered as a limitation of the study 
All in all, we consider that applying a business perspective to IoT-MDM systems can solve many challenges of a modern mobile IT environment, not only in healthcare but also in other kinds of critical infrastructures   These IoT-MDM systems can be provided by various kind of vendors through a balanced and timely business model 
This study has been supported by the DIMECC Cyber Trust – Digital cyber security program 
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ground_truth: The Wallace electorate.

prompt_front: Now, answer the following question based on the text below, only give me the answer and do not output any other words.

Question: What is the definition of mobile device management (MDM)?

Read the following text and answer briefly.

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Julius Francis Gomes is pursuing his Ph D  in international business from the University of Oulu  He currently works at the Oulu Business School as a Doctoral Student to research the futuristic business models for digital intensive industries  His research focuses on using business models as a mean to look in to future industries  He is interested to research business ecosystems in different contexts like cyber security, healthcare, future’s network etc  with a business model perspective  He received his M Sc  (2015) in international business from the University of Oulu  Prior to that he acquired MBA (2011) specializing in managing information systems in business applications  Previously, he has also enjoyed about three years in a top tier bank in Bangladesh as a channel innovator 
Marika Iivari is a postdoctoral researcher at the Martti Ahtisaari Institute within Oulu Business School  She defended her doctoral dissertation on business models in ecosystemic contexts  She holds M Sc  in International Business from the Ulster University, Northern Ireland  Her research interests are in the areas of open innovation, business models and strategy in the context of innovation ecosystems and smart cities, digital and ICT business ecosystems  She has been involved in several research projects around 5G and the Internet of Things, most recently in the health care sector  She is also an active member of the Business Model Community, the Open Innovation Community and the Society for Collaborative Networks 
Petri Ahokangas received his M Sc  (1992) and D Sc  (1998) degrees from the University Vaasa, Finland  He is currently Adjunct Professor (International software entrepreneurship) and Senior research fellow at Martti Ahtisaari Institute, Oulu Business School, University of Oulu, Finland  His research interests are in how innovation and technological change affect international business creation, transformation, and strategies in highly technology – intensive or software – intensive business domains  He has over 100 publications in scientific journals, books, conference proceedings, and other reports  He is actively working in several ICT-focused research consortia leading the business-related research streams 
Lauri Isotalo has received his M Sc  from Helsinki University of Technology (currently Aalto University) in 1992  He has also a postgraduate Diploma in Business Administration  At first Lauri worked in Nokia Corp  in Mobile Technology & System Marketing unit specializing in Intelligent Networks  In 1992 he joined Elisa Corp  where he has held several managerial positions in value added services business, system and process security and mobile network development  Since 2005 Lauri has also led Elisa SME teams in various international collaboration projects and acquired a deep knowledge of the cyber security of legacy telecommunication networks, ip core, access networks, user terminals and modern virtualized data center IT platforms/cloud systems  From 2014 Lauri has headed SDN&NFV development in Elisa 
Riikka Niemelä, M Sc , MHSc , is a cross-disciplinary health-tech professional who obtained her degrees in Electrical Engineering (Master of Science) and Medical technology (Master of Health Sciences) from the University of Oulu, Finland  After R&D engineering, she worked as a research assistant to research the national eHealth development of the Finnish healthcare  Thereafter, she has been researching and promoting the adaptation of connected health technologies in surgical processes of a Nordic future hospital, resulting in scientific publications  Currently, she works as a project manager in Tuttlingen, Germany – the world center of Medical Technology – to generate R&D and innovation projects in cooperation with the ICT and LifeScience companies from Oulu, Finland  Her aim is to promote the digitalization and internationalization of the both high-tech regions and to generate innovations serving the increasing demands for healthcare 
Journal of ICT, Vol  5_1, 107–128 
Context 3: A Brief History of Benjamin Franklin's Residences on Craven Street, London: 1757 - 1775 - Journal of the American Revolution
Benjamin Franklin House, 36 Craven St, London  (Photo by Elliott Brown | Wikimedia Commons)
If one looked into Benjamin Franklin’s time on Craven Street, they might initially believe he lived at 36 Craven Street the entirety of his two stays in London based on the plethora of articles on the internet that say so  If they dug a little deeper they might read that he lived at No  27 Craven Street, previously numbered 7, but now numbered 36; or that he lived exclusively at No  7 Craven Street; or that he lived in multiple residences on Craven Street; or that he moved out of No  36 to another house on Craven Street and then moved back into No  36 the last year of his residence  What is one to believe with all of the conflicting accounts? What does the historical record have to say about Franklin’s time on Craven Street?
Figure 1  Spur Alley 1685  “A map of the parish of St Martins in the Fields, taken from ye last survey, with additions (1685)”  (© The British Library Board, Shelfmark: Maps Crace Port  13 2, Item number: 2)
Before Craven Street existed there was Spur Alley, a narrow passageway sandwiched between the Hungerford Market to the north (now Charing Cross Station) and Scotland Yard and the Northumberland House and Garden to the south  It was flanked on both ends by major thoroughfares, the Strand on the west, connecting Westminster to London by road, and the River Thames on the east, not only connecting the two cities to each other and to Southwark on the south side of the Thames, but connecting the entire metropolis to the rest of the world  Being located in the City of Westminster, Spur Alley had escaped the devastation of the Great Fire of London in 1666 leaving its wooden structures, built in the early part of seventeenth century, intact, but also in dire need of restoration or demolition  “The ratebooks show that during the last thirty years or so of their existence the houses in Spur Alley were in a very bad condition  Few of them were rated at more than a few shillings and many of them were unoccupied ”[1] The landowner, William, 5th Baron Craven, desiring to increase the profitability of his assets, tore down the derelict structures on Spur Alley around 1730 and leased the newly established lots to builders  By 1735, twenty brick houses in the Georgian style had been built on the west side and sixteen on the east side of the way now called Craven Street [2]
Figure 2  Craven Street 1746  (John Rocque London, Westminster and Southwark, First Edition 1746, Motco Enterprises Limited, motco com)
Letters to Franklin during his residence with Mrs  Margaret Stevenson, his landlady on Craven Street, were addressed rather vaguely; “Craven Street/Strand”, “Mrs  Stevensons in Craven Street”, or “Benjamin Franklin Esqr ” are but a few examples  Letters from Franklin referenced “London,” or sometimes “Cravenstreet,” but never included a number  Despite the absence of numbered addresses in Franklin’s correspondence, there was a sense of one’s place in the neighborhood based on entries in the Westminster Rate Books (tax assessments)  The Rate Books did not list house numbers during Franklin’s time there, but they did list the residents of Craven Street in a particular order that became the default numbering system for the street  Number one was associated with the first resident listed under “Craven Street” in the Rate Books and was the northernmost house on the west side of the street  The numbers increased counter-clockwise down the west side and up the east side in accordance with the list of residents  In 1748, the first year of Margaret Stevenson’s (Stevens in the Rate Books for that year) residence on Craven Street, she is listed as the twenty-seventh resident, the second house north of Court Street (later Craven Court, now Craven Passage) on the east side of the street [3]
In 1766, Parliament passed the London Paving and Lighting Act (6 Geo  3 c  26), “An act for the better paving, cleansing, and enlightening, the city of London, and the liberties thereof; and for preventing obstructions and annoyances within the same; and for other purposes therein mentioned ”[4] One of the other purposes therein mentioned was the numbering of houses  With an aim to bring order to the chaotic numbering systems or lack thereof on London streets the Act provided that “… the said commissioners … may also cause every house, shop, or warehouse, in each of the said streets, lanes, squares, yards, courts, alleys, passages, and places, to be marked or numbered, in such manner as they shall judge most proper for distinguishing the same ”[5] This was quite an undertaking that took years to accomplish  It was a decade later before numbered addresses on Craven Street in the City of Westminster appeared in The London Directory (1776)  The London Directory and its competitors were published primarily by booksellers or printers to supplement their income and were highly profitable  To say they were competitive is an understatement  “Some of the most hotly disputed struggles over copyright in the century concerned guidebooks  Many were optimistically emblazoned with a royal license and a notice that the work had been entered at Stationers’ Hall  Various struggles between rival guides intensified as the potential for profits became clear ”[6] The London Directory boldly proclaimed to contain “An ALPHABETICAL LIST OF THE NAMES and PLACES of ABODE of the MERCHANTS and PRINCIPAL TRADERS of the Cities of LONDON and WESTMINSTER, the Borough of SOUTHWARK, and their Environs, with the Number affixed to each House ”[7] Kent’s Directory made a similar proclamation: “An Alphabetical LIST OF THE Names and Places of Abode OF THE DIRECTORS of COMPANIES, Persons in Public Business, MERCHANTS, and other eminent TRADERS in the Cities of London and Westminster, and Borough of Southwark WITH THE NUMBERS as they are affixed to their Houses agreeable to the late Acts of Parliament ”[8] Mrs  Stevenson wasn’t included in the directories because she didn’t meet the criteria of being a merchant or trader, not because she was a woman  Although it is rare to see women listed in the directories, some examples do exist [9] If Mrs  Stevenson had appeared in the directories in 1776 it would not have been on Craven Street as she had moved to Northumberland Court, a stone’s throw away, the previous year [10] A comparison of Craven Street residents whose names and addresses do appear in the directories with the same residents as they appear in the Westminster Rate Books determines if the numbering systems were congruent  For the most part they were  For example, Joseph Bond at No  30, William Rowles at No  31, Samuel Sneyd at No  32, and Jonathan Michie at No  35 in The London Directory coincide with their places of residence in the Westminster Rate Books; however, errors did occur  The 1776 edition of The London Directory lists Brown & Whiteford, wine merchants, at No  9 Craven Street while the Westminster Rate Books list them as the twenty-ninth residents  Obviously, it makes no sense to have Brown & Whiteford at No  9 in The London Directory and their next-door neighbor, Joseph Bond, at No  30  The same error appears in Baldwin’s The New Complete Guide for 1783  The New Complete Guide may have “borrowed” the error from The London Directory  It was not uncommon for the owner of one directory to copy entries from another to save both time and money  Beginning in 1778 and contrary to The London Directory, Kent’s Directory faithfully followed the numbering system of the Westminster Rate Books in all of its editions and listed Brown & Whiteford at No  29 as did Bailey’s Northern Directory in 1781  Perhaps realizing their error, The London Directory changed their listing of Brown & Whiteford from No  9 to No  29 in their 1783 edition and maintained that listing thereafter 
Sometime prior to 1792, the embankment on the Thames at the south end of Craven Street had been sufficiently extended allowing for the construction of ten new houses below the original houses: “ … four houses, Nos  21–24, were built on the west side, and six houses, Nos  25–30, on the east side of the way ”[11] In a note in the same report, the new numbering system is explained  “The houses in the street, which had previously been numbered consecutively down the west side and up the east side, were then renumbered on the same system to include the additional houses ”[12] Because the new houses (21-24) on the west side were built below the existing houses (1-20), houses 1-20 retained their original numbering 
Figure 4  Craven Street 1799  (Richard Horwood’s Map of London, Westminster and the Borough of Southwark 1799, Motco Enterprises Limited, motco com)
One would think that the numbers of the sixteen original houses on the east side, Nos  21 – 36, would simply increase by ten with the addition of the ten new houses, but such was not the case; they increased by nine  How could that be? The only possible explanation is that No  21 of the original houses was demolished to make way for the construction of the northernmost of the six new houses on the east side (No  30)  Evidence of No  21’s demolition appears in the lease granted to Charles Owen by William, 7th Baron Craven, in 1792, which describes No  22 as: “All that messuage in Craven Street late in the occupation of Francis Deschamps undertaker … being the Southernmost house in the Old Buildings on the East Side of the said Street numbered with the No  22 ”[13] The lease describes No  22 as being the southernmost house in the old buildings on the east side of Craven Street  Clearly the house previously at No  21 did not exist when the lease granted to Charles Owen was written in 1792 as it used to be the southernmost house  It is also worth noting that in 1790, The London Directory listed Jacob Life at No  21 (original numbering)  In 1791-2, it listed him at No  6  With No  21 vacated, it would allow for its demolition and the construction of the tenth new house  By utilizing lot No  21 for the new construction, only nine additional lots were needed to build the ten houses, hence, Margaret Stevenson’s former residence at 27 became 36 (27 + 9) in the renumbering and not 37 
For nearly a century and a half after Franklin departed London for America in March of 1775 the scales were tipped heavily in favor of his residence having been No  7 Craven Street  As early as 1807 in London; Being An Accurate History And Description Of The British Metropolis And Its Neighborhood, Volume 4, one would have read: “In Craven Street is a house, No  7, remarkable for having been the residence of Dr  Benjamin Franklin [14] In 1815, the identical phrase appeared in The Beauties of England and Wales [15] After 23 editions of not mentioning Franklin, his name finally appeared in the 24th edition of The Picture of London in 1826: “The house, No  7, Craven Street, in the Strand, was once the residence of Dr  Benjamin Franklin ”[16] In 1840, Jared Sparks referred to Franklin’s Craven Street residence appearing in London guide books in his voluminous The Works of Benjamin Franklin: “In the London Guide Books, ‘No  7, Craven Street,’ is still indicated as the house in which Dr  Franklin resided ”[17] In 1846, George Gulliver F R S , in his book, The Works of William Hewson, wrote: “She [Polly] had been upon terms of the warmest friendship with Dr  Franklin
Figure 5  No  7 Craven Street with Memorial Tablet  (Photo courtesy of British History Online, and the Survey of London)
since she was eighteen years of age  That eminent philosopher resided with her mother, Mrs  Margaret Stevenson, at No  7, Craven Street, Strand, during the fifteen years of his abode in London ”[18] Guide books mentioning Franklin at No  7 continued to proliferate throughout the century: Handbook for London; Past and Present, Volume I (1849);”[19] Handbook for Modern London (1851);”[20] The Town; Its Memorable Characters and Events (1859);”[21] London and Its Environs (1879) [22] There was an anomaly when London In 1880 Illustrated With Bird’s-Eye Views of the Principal Streets, Sixth Edition (1880) placed Franklin at 27 Craven street [23] The anomaly lasted for six years until his place of residence was changed to No  7 in the revised edition, London  Illustrated by Eighteen Bird’s-Eye Views of the Principal Streets (1886) [24] London Past and Present; Its History, Associations, and Traditions, Volume 1 (1891), copied the 1849 Handbook for London almost word-for-word and included, “The house is on the right from the Strand ”[25] In October of 1867, The Society of Arts in London declared that: “In order to show how rich the metropolis is in the memory of important personages and events, which it would be desirable to mark by means of tablets on houses, the Council have caused an alphabetical list to be prepared, … ”[26] Franklin had been elected a corresponding member to the Society in 1756 and was a popular choice among Council members deciding who they were to memorialize [27] By January of 1870, a tablet honoring him was affixed to the house they believed to have been his residence while in London, No  7 Craven Street in the Strand on the west side of the street [28] A majority of historians writing about Franklin in the nineteenth and early twentieth century placed him at No  7: O  L  Holley, The Life of Benjamin Franklin (1848); E  M  Tomkinson, Benjamin Franklin (1885); John Torrey Morse, Benjamin Franklin (1891); Paul Elmer More, Benjamin Franklin (1900); John S  C  Abbot, Benjamin Franklin (1903); Sydney George Fisher, The True Benjamin Franklin (1903)  A notable exception is D  H  Montgomery’s His Life Written by Himself published in 1896  He has Franklin at No  27 Craven Street  It seems then that depending upon the source, Franklin was thought to have lived at either No  7 or No  27, but not both, the overwhelming majority favoring No  7  As late as 2011, Franklin is still mentioned as living at No  7 [29]
In 1913, No  7 was scheduled to be torn down  An article in the March 1914 edition of The Book News Monthly, describes the situation:
As is well known to informed American pilgrims, it has been possible for all admirers of the famous philosopher and statesman to pay their respects to his memory before that house, No  7 Craven Street, just off the Strand, which was his chief home during his two sojourns in the British capital, but even as these lines are being written the London newspapers are recording that that interesting shrine is soon to be pulled down to make room for a restaurant  It is some mitigation of this misfortune to remember that at the most the Craven Street house was nothing more than a reproduction of the one in which Franklin had his suite of four rooms, for the structure has been rebuilt since Franklin’s time  When, then, some one makes a piteous plea that at least the philosopher’s bedroom shall be preserved, the soothing answer is that the apartment in question is only a replica of that in which the illustrious American enjoyed his well-earned slumbers in 1757-62 and 1764-75  The restaurant-builder, however, with an eye doubtless to possible American patronage, has assured the world that every effort will be made to preserve as much as possible of the entire structure [30]
Concerned with the possible demolition of Franklin’s residence, the Royal Society of Arts (formerly the Society of Arts[31]) initiated an inquiry into the matter [32] The London County Council, having taken over the responsibility of placing memorial tablets on notable houses from the Royal Society, was charged with the investigation  It ultimately fell to Sir George Laurence Gomme, a clerk to the Council, to come up with a response  A few years earlier Sir George had discovered Margaret Stevenson residing at No  27 Craven Street in the Westminster Rate Books  He must have wondered why No  7 on the west side of Craven Street was being celebrated as Franklin’s residence when the evidence clearly showed otherwise 
Sir George and his staff examined the various London directories discussed earlier and came up with a novel explanation for the discrepancy  They concluded that there had been two numbering systems on Craven Street  An anonymous author echoes Sir George’s conclusion about the two numbering systems in an article in The Journal of the Royal Society of Arts:
…an inspection of the directories of that time proves that there were at least two systems of numbering in Craven Street before the erection of the additional houses  According to one of these the numbers started from the top (Strand end) on the west side of the street, and ran down to the bottom to No  20, then crossed over and went back to the Strand along the east side – 21 to 36  According to the other system, the east side of the street was numbered from the bottom upwards, starting at No 1  This was not apparently in general use, but there is evidence that this numbering was at all events occasionally used 
The evidence of these two systems of numbering, and for believing that Mrs  Stevenson’s house was first No  7 under the oldest system, next No  27 under the second system, and finally No  36 under the latest and existing system, is to be found in the various directories and the Westminster rate-books [33]
The “evidence” mentioned above consisted of The London Directory’s listing of Brown & Whiteford at No  9: “The rate-books for 1781 and 1786 show the house next but one to the north of Mrs  Stevenson’s house as in the occupation of Brown and ‘Whiteford,’ while the old directories mention the business of the firm as wine merchants, and give their address as 9, Craven Street – then a little later, down to 1791, as 29, Craven Street  Curiously enough, in the years 1778 to 1780, or 1781, Lowndes gives it as No  9, and Kent as 29 ”[34] Ignoring Kent’s Directory having Brown and Whiteford as 29 and The London Directory (Lowndes) having Brown and Whiteford “a little later” as 29, and knowing that Mrs  Stevenson lived two doors south of them, Sir George concluded that her house must have been numbered 7, even though there is no listing in any of the directories of her residence ever being No  7  He surmised that the No  7 on the west side of Craven Street with the memorial tablet thought to have been Franklin’s residence had simply been confused with number 7 (27) on the east side  Again from The Journal of the Royal Society of Arts:
Taking all the evidence together, there cannot be any doubt whatever that Mrs  Stevenson’s house, in which Franklin lodged, was the house two doors north from Craven Court, first numbered 7, afterwards 27, and finally 36, and consequently that the house in which Franklin lived was that now numbered 36, not the one now numbered 7, on which the tablet is placed [35]
A response to The Royal Society of Arts was issued: “… the London County Council … informed the Society that it had made a mistake and that No  36 Craven street was the building that deserved commemoration ”[36] The Society accepted the Council’s conclusion, and despite assurances of preservation by the restaurant builder, No  7 was torn down the following year 
Sir George’s assertion “that Mrs  Stevenson’s house, in which Franklin lodged, was the house two doors north from Craven Court” was correct, however, his assertion that it was “first numbered 7, afterwards 27”, was not  It was only by association with the errant entry of Brown & Whiteford at No  9 from 1776-1782 in The London Directory that Mrs  Stevenson’s address was conjured to be No  7  The problem with associating her address exclusively with that of Brown & Whiteford at No  9 during those years is that, as previously demonstrated, The London Directory also listed four other Craven Street residents, Bond, Rowles, Sneyd, and Michie, who’s addresses did conform to the numbering system in The Westminster Rate Books  If Brown & Whiteford at No  9 was indicative of a numbering system different from The Westminster Rate Books, Bond, Rowles, Sneyd, and Michie would have been listed as Nos  10, 11, 12, and 15, respectively  So on one hand Sir George was relying on the Westminster Rate Books to establish Mrs  Stevenson at No  27 and on the other hand he was dismissing the Westminster Rate Books to establish her at No  7  Instead of using the anomalous listing of Brown & Whiteford at No  9, he could have just as easily, and more logically, used the Bond et al  listings, or the post-1782 Brown & Whiteford listing in the London Directory at No  29 to establish Mrs  Stevenson at No  27  Even if there had been two numbering systems, his assertion that No  27 was first numbered 7 would still be false  The earliest numbering system was the Westminster Rate Books dating from the early 1730s when the houses were constructed  Brown & Whiteford at No  9 didn’t appear until 46 years later and then only for a brief period 
There is ample evidence in Franklin’s correspondence and in a memoir by Polly Hewson (Mrs  Stevenson’s daughter) that Benjamin and Mrs  Stevenson lived in not one, but two houses on Craven Street  On July 6, 1772, Polly wrote to Benjamin from her house at Broad Street North in London: “My Mother I must tell you went off last friday week, took our little Boy with her and left Mr  Hewson [Polly’s husband, William] the care of her House [27 Craven Street]  The first thing he did was pulling down a part of it in order to turn it to his own purpose, and advantage we hope  This Demolition cannot affect you, who at present are not even a Lodger [Benjamin was traveling at the time], your litterary apartment remains untouch’d, the Door is lock’d …”[37] In a memoir about her husband written after his death Polly writes: “He [William Hewson] began his Lectures Sept  30, 1772, in Craven-street, where he had built a Theatre adjoining a house which he intended for the future residence of his family ”[38] On October 7, 1772, Benjamin wrote to his son William: “I am very well  But we [Mrs  Stevenson and I] are moving to another House in the same street; and I go down tomorrow to Lord LeDespencer’s to [stay a] Week till things are settled ”[39] To his son-in-law, Richard Bache, on the same day he wrote: “We are moving to another House in the [street] leaving this to Mr  Hewson ”[40] Writing to a friend on October 30, 1772 he explained: “I should sooner have answered your Questions but that in the Confusion of my Papers, occasioned by removing to another House, I could not readily find the Memorandums …”[41] On November 4, 1772 Benjamin informed his wife Deborah of the move  “We are removed to a more convenient House in the same street, Mrs  Stevenson having accommodated her Son-in-Law with that we lived in  The Removing has been a troublesome Affair, but is now over ”[42]
An agreement had been struck between the parties  Margaret and Benjamin would move to another house on Craven Street and allow Polly and William to move into No  27, the large yard behind the house being spacious enough to accommodate the anatomy school William wished to build [43] Perhaps the idea was inspired by Margaret’s next-door neighbor at No  26, Dr  John Leake, a man-midwife and founder of the Westminster Lying-in Hospital, who had built a theater adjoining his residence in which he practiced anatomy and taught midwifery [44]
After Margaret and Benjamin vacated No  27, Polly, William, their son William Jr , and William’s younger sister, Dorothy Hewson, took up residence there [45] In the 1773 Westminster Rate Books for Craven Street, Mrs  Stevenson’s (Stephenson in the Rate Books) name has been crossed out and replaced with “William Hewson ”[46] Further proof that the Hewsons had indeed moved into 27 Craven Street has been confirmed by the discovery of human and animal remains buried in the basement of No  36 (formerly No  27 and now the Benjamin Franklin House), a by-product of the dissections that took place at William’s anatomy school [47]
So what house on Craven Street did Mrs  Stevenson and Benjamin move into after vacating No  27? An examination of the Westminster Rate Books for the years 1774 and 1775 reveal them living not at No  7 on the west side of Craven Street as one might expect from the overwhelming consensus of nineteenth century guidebooks and biographies, but surprisingly at No  1 [48] The controversy of No  7 being torn down was all for naught as it had never been Franklin’s residence  Sir George was correct on that point  Unfortunately, No  1 was torn down as well in the early part of the twentieth century  The first time No  1 is mentioned as Franklin’s second residence is in the Survey of London: Volume 18, St Martin-in-The-Fields II: the Strand published by the London County Council in 1937, ironically the same County Council that had declared No  36 as Franklin’s only residence twenty-four years earlier 
From 1748 until 1772 Margaret ‘Stephenson’ occupied this house [No  27 (36)], and it was there that Benjamin Franklin settled after his arrival in London in 1757 as Agent to the General Assembly of Pennsylvania … In October, 1772, Mrs  Stevenson and Franklin removed to No  1, Craven Street (now demolished), and No  36 was for the next two years occupied by William Hewson, surgeon, who had married Mary Stevenson [49]
In the spring of 1774, William Hewson died unexpectedly of septicemia two weeks after cutting himself while dissecting a cadaver  Polly was left to care for their two young sons and was pregnant with a daughter she would give birth to in August of the same year  Is it possible that Margaret and Benjamin moved back into No  27 to assist Polly after the death of her husband as suggested in The Americanization of Benjamin Franklin?[50]
If the Westminster Rate Books are to be believed, the answer is no  For the year 1774, the Rate Books list Margaret Stevenson at No  1 and William Hewson at No  27  For the year 1775, they list Margaret Stevenson at No  1 and Magnus Falkner (Falconer/Falconar) at No  27  Magnus was William’s assistant at the anatomy school and fiancé to William’s sister, Dorothy  On his death bed, William instructed Polly, “let Mr  Falconar be my successor ”[51] Magnus would immediately take over the running of the anatomy school and continue William’s unfinished research  Four months later, he and Dorothy would marry [52] Essentially only two things changed at 27 Craven Street after William’s death: Polly gave birth to her daughter, and Magnus replaced William as the lease holder, so even if Margaret and Benjamin had wished to move back into No  27, there would have been no room for them  It is also interesting to note that considering the multiple times Benjamin wrote of his move out of No  27 (and complained of it), he never once mentioned moving back into No  27 in any of his correspondence after Mr  Hewson’s death 
Figure 6  No  36 Craven Street  (Photo courtesy of David Ross, britainexpress com)
In sum, based on the Westminster Rate Books[53] and Franklin’s correspondence, Mrs  Stevenson is known to have resided at No  27 (36) Craven Street from 1748 to 1772  It follows that, aside from the two years Franklin spent in Philadelphia from 1762 to 1764, he resided there from 1757 to 1772  Franklin’s correspondence also reveals that in the autumn of 1772, he and Mrs  Stevenson moved to another house on Craven Street  The 1773 Westminster Rate Books show her name crossed off at No  27 and William Hewson’s inserted  The following year the Rate Books list her at No  1 Craven Street  Evidence for Mrs  Stevenson and Benjamin remaining at No  1 after William’s death appears in the Westminster Rate Books for 1775 which have Mrs  Stevenson still residing at No  1 and Magnus Falkner residing at No  27  Further evidence can be construed from the lack of any mention of a move back into No  27 in Franklin’s correspondence  Despite the many theories one could devise as to why Franklin was thought to have lived at No  7 Craven Street by so many guide books and Franklin biographers of the nineteenth century, one thing is certain; at some point after Franklin’s departure to America in March of 1775, and no later than 1807, someone mistakenly associated him with No  7 on the west side of Craven Street, and it soon became his de facto residence  Credit must go to D  H  Montgomery in 1896 and Sir George in 1913 for setting the record partially straight by placing Franklin at No  27(36)  In 1937, the London County Council gave us the first accurate account of Franklin’s residences on Craven Street in the Survey of London at No  27(36) and No  1  It has been shown conclusively that No  27 was never previously numbered 7  It was, however, renumbered 36 in 1792 after ten additional houses were built at the southern end of the street and remains No  36 to this day 
[1] “Craven Street and Hungerford Lane”, in Survey of London: Volume 18, St Martin-in-the-Fields II: the Strand, ed  G H Gater and E P Wheeler (London, 1937), 27-39, Early History of the Site 
http://www british-history ac uk/survey-london/vol18/pt2/pp27-39
[2] “England, Westminster Rate Books, 1634-1900,” from database with images, Craven Street – 1735, FamilySearch from database by FindMyPast and images digitized by FamilySearch; citing Westminster City Archives, London 
[3] Ibid , Craven Street – 1748 
[4] The Statutes at Large, From Magna Charta to the End of the Eleventh Parliament of Great Britain  Anno 1761 Continued, Vol  XXVII, ed  Danby Pickering, (Cambridge, John Archdeacon, 1767), 96 
[6] James Raven, Publishing Business in Eighteenth-Century England, (Woodbridge: The Boydell Press, 2014), 201 
[7] The London Directory For the Year 1776, Ninth Edition, (London: T  Lowndes, 1776), title page 
[8] Kent’s Directory For the Year 1778, Forty-Sixth Edition, (London: Richard and Henry Causton, 1778), title page 
[9] A listing in Kent’s Directory for the Year 1882 on p  28 reveals, “Brown Sarah, Leather-seller, 1, Westmoreland-buildings, Aldersgate-street”, and in Kent’s Directory for the Year 1883 on p  175, “Whiteland Mary, Wine & Brandy Mercht  Jermyn-str  St  James ”
[10] “The Papers of Benjamin Franklin,” Sponsored by The American Philosophical Society and Yale University, Digital Edition by The Packard Humanities Institute, 22:263a 
http://franklinpapers org/franklin
Mrs  Stevenson wrote to Benjamin Franklin a letter from her new home at 75 Northumberland Court on November 16, 1775: “In this Court I have a kind friend, Mr  Lechmoen he comes and seats with me and talks of you with a hiy regard and friendship ”
[11] Survey of London, Early History of the Site 
[12] Survey of London, Footnotes/n 10 
[13] Survey of London, Historical Notes/No  31 
[14] David Hughson, LL D , London; Being An Accurate History And Description Of The British Metropolis And Its Neighbourhood, To Thirty Miles Extent, From An Actual Perambulation, Vol  IV, (London: W  Stratford, 1807), 227 
[15] The Reverend Joseph Nightingale, The Beauties of England and Wales: Or, Original Delineations, Topographical, Historical, and Descriptive, of Each County, Vol  X, Part III, Vol  II (London: J  Harris; Longman and Co ; J  Walker; R  Baldwin; Sherwood and Co ; J  and J  Cundee; B  and R  Crosby and Co ; J Cuthell; J  and J  Richardson; Cadell and Davies; C  and J  Rivington; and G  Cowie and Co , 1815), 245 
[16] John Britton, F S A  & Co , ed , The Original Picture of London, Enlarged and Improved: Being A Correct Guide For The Stranger, As Well As For the Inhabitant, To The Metropolis Of The British Empire Together With A Description Of The Environs, The Twenty-Fourth Edition (London: Longman, Rees, Orme, Brown, and Green, 1826), 479 
[17] Jared Sparks, The Works of Benjamin Franklin, Vol  VII, (Philadelphia: Childs & Peterson, 1840), 151 
[18] George Gulliver, F R S , The Works of William Hewson, F  R  S , (London: Printed for the Sydenham Society, MDCCCXLVI), xx 
[19] Peter Cunningham, Handbook for London; Past and Present, Vol  I, (London: John Murray, 1849), 245 
[20] F  Saunders, Memories of the Great Metropolis: or, London, from the Tower to the Crystal Palace, (New York: G P  Putnam, MDCCCLII), 138 
[21] Leigh Hunt, The Town; Its Memorable Characters and Events, (London: Smith, Elder and Co , 1859), 185 
[22] K  Baedeker, London and Its Environs, Including Excursions To Brighton, The Isle of Wight, Etc : Handbook For Travelers, Second Edition, (London: Dulau and Co , 1879), 133 
[23] Herbert Fry, London In 1880 Illustrated With Bird’s-Eye Views of the Principal Streets, Sixth Edition, (New York: Scribner, Welford, & Co , 1880), 50 
[24] Herbert Fry, London  Illustrated By Eighteen Bird’s-Eye Views of the Principal Streets, (London: W  H  Allen and Co , 1886), 40 
[25] Henry B  Wheatley, F S A , London Past and Present; Its History, Associations, and Traditions, Vol  1, (London: John Murray, New York: Scribner & Welford, 1891), 473 
[26] The Journal of the Society of Arts, Vol  XV, No  778, (October 18, 1867): 717 
[27] D  G  C  Allen, “Dear and Serviceable to Each Other: Benjamin Franklin and the Royal Society of Arts,” American Philosophical Society, Vol  144, No  3, (September 2000): 248-249 
Franklin was a corresponding member in 1756 because he was still residing in Philadelphia  He became an active member the following year when he moved to London 
[28] The Journal of the Society of Arts, Vol  XVIII, No  894, (Jan  7, 1870): 137 
“Since the last announcement, the following tablets have been affixed on houses formerly occupied by – Benjamin Franklin, 7 Craven-street, Strand, W C ”
[29] Franklin in His Own Time, eds  Kevin J  Haytes and Isabelle Bour, (Iowa City, University of Iowa Press, 2011), xxxvii 
“Takes lodgings with Margaret Stevenson at No  7 Craven Street ” It is unknown if the editors are referring to No  7 on the west side of Craven Street or No  36 on the east side using Sir George’s explanation of No  36 being previously numbered 7 
[30] Henry C  Shelly, “American Shrines on English Soil, III  In the Footprints of Benjamin Franklin,” in The Book News Monthly, September, 1913 to August, 1914, (Philadelphia: John Wanamaker, 1914), 325 
[31] The Journal of the Royal Society of Arts, Vol  LVI, No  2,880, (Jan  31, 1908): 245 
http://babel hathitrust org/cgi/pt?id=mdp 39015058423073;view=1up;seq=251
“His Majesty the King, who is Patron of the Society, has granted permission to the Society to prefix to its title the term ‘Royal,’ and the Society will consequently be known in future as the ‘Royal Society of Arts ’”
[32] Nineteenth Annual Report, 1914, of the American Scenic and Historic Preservation Society, (Albany: J  B  Lyon Company, 1914), 293 
http://babel hathitrust org/cgi/pt?id=wu 89072985302;view=1up;seq=4;size=150
[33] The Journal of the Society of Arts, Vol  LXII, No  3,183, (Nov  21, 1913): 18 
http://babel hathitrust org/cgi/pt?id=mdp 39015058422968;view=1up;seq=26
[36] Allen, “Dear and Serviceable,” 263-264 
[37] Papers of Benjamin Franklin, 19:20 
[38] Thomas Joseph Pettigrew, F  L  S , Memoirs of the Life and Writings of the Late John Coakley Lettsom With a Selection From His Correspondence, Vol  I, (London: Nichols, Son, and Bentley, 1817), 144 of Correspondence 
[39] Papers of Benjamin Franklin, 19:321b 
[40] Ibid , 19:314 
[41] Ibid , 19:353a 
[43] Simon David John Chaplin, John Hunter and the ‘museum oeconomy’, 1750-1800, Department of History, King’s College London  Thesis submitted for the degree of Doctor of Philosophy of the University of London , 202 
“Following Falconar’s death [1778] the lease [27 Craven Street] was advertised, and the buildings were described as:
A genteel and commodious house, in good Repair, with Coach-house and Stabling for two Horses…consisting of two rooms and light closets on each floor, with outbuildings in the Yard, a Museum, a Compleat Theatre, and other conveniences  (Daily Advertiser, 27 August 1778)”
[44] Simon Chaplin, “Dissection and Display in Eighteenth-Century London,” in Anatomical Dissection in Enlightenment England and Beyond: Autopsy, Pathology and Display, ed  Dr  Piers Mitchell, (Burlington: Ashgate Publishing Company, 2012), 108 
“Given that a nearby building at 35 [ No  26 in Franklin’s time] was occupied by the man-midwife John Leake, who advertised lectures – including lessons in the art of making preparations – at his ‘theatre’ between 1764 and 1788, it is possible that some facilities were shared  In both cases, however, the buildings [Leake’s residence at No  26 and Hewson’s residence next door at 27] served a dual function as domestic accommodation and as sites for lecturing and dissection ”
[45] George Gulliver, F R S , The Works of William Hewson, F  R  S , (London: Printed for the Sydenham Society, MDCCCXLVI), xviii 
[46] Westminster Rate Books, Craven Street – 1773, courtesy of the City of Westminster Archives 
[47] S W  Hillson et al , “Benjamin Franklin, William Hewson, and the Craven Street Bones,” Archaeology International, Vol  2, (Nov  22, 1998): 14-16 
http://dx doi org/10 5334/ai 0206
[48] Westminster Rate Books, Craven Street – 1774, 1775, courtesy of the City of Westminster Archives 
[49] Survey of London, Historical Notes/No  36, Craven Street (not sourced) 
[50] Gordon S  Wood, The Americanization of Benjamin Franklin, (New York: The Penguin Press, 2004), 261 
[51] Pettigrew, Memoirs, 146 of Correspondence 
[52] http://founders archives gov/documents/Franklin/01-22-02-0178, note 7  “Falconar married Hewson’s sister five months after the Doctor’s death; most of the Craven Street circle attended the wedding, and BF gave away the bride: Polly to Barbara Hewson, Oct  4, 1774, APS” (American Philosophical Society); “England Marriages, 1538–1973 ,” database, FamilySearch (https://familysearch org/ark:/61903/1:1:V52W-TGS : accessed September 15, 2015), Magnus Falconar and Dorothy Hewson, September 12, 1774; citing Saint Martin In The Fields, Westminster, London, England, reference ; FHL microfilm 561156, 561157, 561158, 942 B4HA V  25, 942 B4HA V  66 
[53] I chose to rely on the Westminster Rate Books for the numbering system on Craven Street  The books were consistent throughout the eighteenth century in the ordering of residents on the street and were used as the basis for the 1792 re-numbering  For the most part, commercial directories aligned with them as well  If by chance a directory didn’t initially align, it would inevitably produce future editions that did 
Benjamin Franklin, Benjamin Franklin House, London
More from David Turnquist
If one looked into Benjamin Franklin’s time on Craven Street, they might   
I think it’s very ironic that on the street maps included in your excellent article, Craven Street is so close to Scotland Yard  Because following the back and forth juxtapositions of numbers 7, 27 and 36 Craven Street (throw in 75 Northumberland Court and 1 Craven Street, too) was a case that could confound Sherlock Holmes 
Excellent job of deciphering street renumbering material spanning sixty years, including that of a wrong house number (# 7) being erroneously identified and then perpetuated in subsequent street map printings  It’s gratifying at least to know that the present day #36 Craven Street is the correct house for Ben Franklin tourists to visit  Except for #1 Craven Street for the last three years Franklin was in London, but we won’t get into that 
Again, excellent article, David!



ground_truth: Centralized control of mobile devices and applications.

prompt_front: Now, answer the following question based on the text below, only give me the answer and do not output any other words.

Question: When did the London Paving and Lighting Act pass, which mandated the numbering of houses?

Read the following text and answer briefly.

Context 4: 98 and 3 35 for the top 2000 and to 3 08 and 3 58 for the top 200 cryptocurrencies 
Conversely, the asymmetry between positive and negative large price returns does not differ much among the three groups, with the condition α+ < α− holding only for a slightly larger fraction of top 2000 (69 1%) and top 200 (70 6%) cryptoassets compared to all cryptocurrencies (66 4%)  Moreover, all these patterns are robust when filtering out time series with sampling issues or when considering only cryptoassets that stay compatible with the power-law hypothesis in more than 90% of the positions of the expanding time window (Supplementary Figures ) 
We also investigate whether the patterns related to the median of the power-law exponents differ among groups of cryptocurrencies with different designs and purposes  To do so, we group digital assets using the 50 most common tags in our dataset (e g  "bnb-chain", "defi", and "collectibles-nfts") and estimate the probability distributions of the median exponents α+ and α− (Supplementary Figures ) 
These results show that design and purpose affect the dynamics of large price variations in the cryptocurrency market as the medians of typical exponents range from 2 4 to 3 7 among the groups  The lowest values occur for cryptocurrencies tagged as "doggone-doggerel" (medians of α+ and α− are 2 38 and 2 83), "memes" (2 41 and 2 87), and "stablecoin" (2 65 and 2 79) 
Digital currencies belonging to the first two tags overlap a lot and have Dogecoin (DOGE, rank 9) and Shiba Inu (SHIB, rank 13) as the most important representatives  Cryptoassets with these tags usually have humorous characteristics (such as an Internet meme) and several have been considered as a form of pump-and-dump scheme , a type of financial fraud in which false statements artificially inflate asset prices so the scheme operators sell their overvalued cryptoassets 
Conversely, cryptoassets tagged as "stablecoin" represent a class of cryptocurrencies designed to have a fixed exchange rate to a reference asset (such as a national currency or precious metal)   While the price of stablecoins tends to stay around the target values, their price series are also marked by sharp variations, which in turn are responsible for their typically small power-law exponents 
This type of cryptoasset has been shown to be prone to failures , such as the recent examples of TerraUSD (UST) and Tron's USDD (USDD) that lost their pegs to the US Dollar producing large variations in their price series  The asymmetry between positive and negative large returns also emerges when grouping the cryptocurrencies using their tags 
All 50 tags have distributions of α+ shifted to smaller values when compared with the distributions of α− , with differences between their medians ranging from −0 74 ("okex-blockdream-ventures-portfolio") to −0 14 ("stablecoin")  Indeed, only four ('stablecoin", "scrypt", "fantom-ecosystem" and "alameda-research-portfolio") out of the fifty groupings have both distributions indistinguishable under a two-sample Kolmogorov-Smirnov test (p-value > 0 05) 
Focusing now on the evolution of the power-law exponents quantified by the time series α t for positive and negative returns, we ask whether these exponents present particular time trends  For Bitcoin [Fig  )], α t seems to increase with time for both positive and negative returns  At the same time, the results of Fig  also suggest that market capitalization affects these power-law exponents 
To verify these possibilities, we assume the power-law exponents (α t ) to be linearly associated with the cryptocurrency's age (y t , measured in years) and the logarithm of market capitalization (log c t )  As detailed in the Methods section, we frame this problem using a hierarchical Bayesian model 
This approach assumes that the linear coefficients associated with the effects of age (A) and market capitalization (C) of each digital currency are drawn from distributions with means µ A and µ C and standard deviations σ A and σ C , which are in turn distributed according to global distributions representing the overall impact of these quantities on the cryptocurrency market 
The Bayesian inference process consists of estimating the posterior probability distributions of the linear coefficients for each cryptocurrency as well as the posterior distributions of µ A , µ C , σ A , and σ C , allowing us to simultaneously probe asset-specific tendencies and overall market characteristics 
Moreover, we restrict this analysis to the 2140 digital currencies having more than 50 observations of market capitalization concomitantly to the time series of the power-law exponents in order to have enough data points for detecting possible trends  When considering the overall market characteristics, we find that the 94% highest density intervals for µ A ([-0 01, 0 06] for positive and [-0 02, 0 03] for negative returns) and µ C ([-0 02, 0 03] for positive and [-0 001, 0 04] for negative returns) include the zero (see Supplementary Figure for their distributions) 
Thus, there is no evidence of a unique overall pattern for the association between the power-law exponents and age or market capitalization followed by a significant part of the cryptocurrency market  Indeed, the 94% highest density intervals for σ A ([0 87, 0 93] for positive and [0 63, 0 70] for negative returns) and σ C ([0 57, 0 61] for positive and [0 49, 0 52] for negative returns) indicate that the cryptocurrency market is highly heterogeneous regarding the evolution of power-law exponents associated with large price variations (see Supplementary Figure for the distributions of σ A and σ C )  Figure illustrates these heterogeneous behaviors by plotting the posterior probability distributions for the linear coefficients associated with the effects of age (A) and market capitalization (C) for the top 20 digital assets, where cryptocurrencies which are significantly affected (that is, the 94% highest density intervals for A or C do not include the zero) by these quantities are highlighted in boldface 
Even this small selection of digital  currencies already presents a myriad of patterns  First, we observe that the power-law exponents of a few top 20 cryptocurrencies are neither correlated with age nor market capitalization  That is the case of Shiba Inu (SHIB, rank 13) and Dai (DAI, rank 11) for both positive and negative returns, UNUS SED LEO (LEO, rank 18) and Polkadot (DOT, rank 12) for the positive returns, and USDCoin (USDC, rank 4) and Solana (SOL, rank 9) for negative returns 
There are also cryptocurrencies with exponents positively or negatively correlated only with market capitalization  Examples include Tether (USDT, rank 3) and Dogecoin (DOGE, rank 10), for which the power-law exponents associated with positive returns increase with market capitalization, and Binance USD (BUSD, rank 6), for which power-law exponents associated with positive and negative returns decrease with market capitalization 
We also observe cryptocurrencies for which age and market capitalization simultaneously affect the power-law exponents  Polygon (MATIC, rank 14) is an example where the power-law exponents associated with positive returns tend to increase with age and decrease with market capitalization  Finally, there are also cryptocurrencies with power-law exponents only associated with age 
That is the case of Bitcoin (BTC, rank 1), Ethereum (ETH, rank 2), and Cardano (ADA, rank 8), for which the power-law exponents related to positive and negative returns increase with age, but also the case of Uniswap (UNI, rank 19), for which the exponents decrease with age  Figure systematically extends the observations made for the top 20 cryptoassets to all 2140 digital currencies for which we have modeled the changes in the power-law exponents as a function of age and market capitalization 
First, we note that only 10% of cryptocurrencies have power-law exponents not significantly affected by age and market capitalization  The vast majority (90%) displays some relationship with these quantities  However, these associations are as varied as the ones we have observed for the top 20 cryptoassets 
About 52% of cryptocurrencies have power-law exponents simultaneously affected by age and market capitalization  In this group, these quantities simultaneously impact the exponents related to positive and negative returns of 34% of cryptoassets, whereas the remainder is affected only in the positive tail (9%) or only in the negative tail (9%) 
Moving back in the hierarchy, we find that the power-law exponents of 32% of cryptocurrencies are affected only by age while a much minor fraction (6%) is affected only by market capitalization  Within the group only affected by age, we observe that the effects are slightly more frequent only on the exponents related to negative returns (12%), compared to cases where effects are restricted only to positive returns (10%) or simultaneously affect both tails (10%) 
Finally, within the minor group only affected by market capitalization, we note that associations more frequently involve only exponents related to negative returns (3%) compared to the other two cases (2% only positive returns and 1% for both positive and negative returns)  Beyond the previous discussion about whether positive or negative returns are simultaneously or individually affected by age and market capitalization, we have also categorized the direction of the trend imposed by these two quantities on the power-law exponents 
Blue rectangles in Fig  represent the fraction of relationships for which increasing age or market capitalization (or both) is associated with a raise in the power-law exponents  About 28% of all cryptocurrencies exhibit this pattern in which large price variations are expected to occur less frequently as they grow and age 
Conversely, the red rectangles in Fig  depict the fraction of relationships for which increasing age or market capitalization (or both) is associated with a reduction in the power-law exponents  This case comprises about 25% of all cryptocurrencies for which large price variations are likely to become more frequent as they grow in market capitalization and age 
Still, the majority of associations represented by green rectangles refer to the case where the effects of age and market capitalization point in different directions (e g  exponents increasing with age while decreasing with market capitalization)  About 36% of cryptocurrencies fit this condition which in turn contributes to consolidating the cumbersome hierarchical structure of patterns displayed by cryptocurrencies regarding the dynamics of large price variations 
This complex picture is not much different when considering only cryptocurrencies in the top 200 market capitalization rank (Supplementary Figure )  However, we do observe an increased prevalence of patterns characterized by exponents that rise with age and market capitalization (37%), suggesting that large price variations are becoming less frequent among the top 200 cryptocurrencies than in the overall market 
)  Each of the previous three levels is further classified regarding whether both positive and negative returns are simultaneously affected or whether the effect involves only positive or only negative returns  Finally, the former levels are classified regarding whether the power-law exponents increase, decrease or have a mixed trend with the predictive variables 
Overall, 36% of the associations are classified as mixed trends (green rectangles), 28% are increasing trends (blue rectangles), and 26% are decreasing trends (red rectangles)  We have studied the distributions of large price variations of a significant part of the digital assets that currently comprise the entirety of the cryptocurrency market 
Unlike previous work, we have estimated these distributions for entire historical price records of each digital currency, and we have identified the patterns under which the return distributions change as cryptoassets age and grow in market capitalization  Similarly to conventional financial assets , our findings show that the return distributions of the vast majority of cryptoassets have tails that are described well by power-law functions along their entire history 
The typical power-law exponents of cryptocurrencies (α ∼ 3) are, however, significantly smaller than those reported for conventional assets (α ∼ 4)   This feature corroborates the widespread belief that cryptoassets are indeed considerably more risky for investments than stocks or other more traditional financial assets 
Indeed, we have found that about half of the cryptocurrencies in our analysis do not have a characteristic scale for price variations, and are thus prone to much higher price variations than those typically observed in stock markets  On the upside, we have also identified an asymmetry in the power-law exponents for positive and negative returns in about 2/3 of all considered cryptocurrencies, such that these exponents are smaller for positive than they are for negative returns 
This means that sizable positive price variations have generally been more likely to occur than equally sizable negative price variations, which in turn may also reflect the recent overall expansion of the cryptocurrency market  Using a hierarchical Bayesian linear model, we have also simultaneously investigated the overall market characteristics and asset-specific tendencies regarding the effects of age and market capitalization on the power-law exponents 
We have found that the cryptocurrency market is highly heterogeneous regarding the trends exhibited by each cryptocurrency; however, only a small fraction of cryptocurrencies (10%) have power-law exponents neither correlated with age nor market capitalization  These associations have been mostly ignored by the current literature and are probably related to the still-early developmental stage of the cryptocurrency market as a whole 
Overall, 36% of cryptocurrencies present trends that do not systematically contribute to increasing or decreasing their power-law exponents as they age and grow in market capitalization  On the other hand, for 26% of cryptocurrencies, aging and growing market capitalization are both associated with a reduction in their power-law exponents, thus contributing to the rise in the frequency of large price variations in their dynamics 
Only about 28% of cryptocurrencies present trends in which the power-law exponents increase with age and market capitalization, favoring thus large price variations to become less likely  These results somehow juxtapose with findings about the increasing informational efficiency of the cryptocurrency market  
In fact, if on the one hand the cryptocurrency market is becoming more informationally efficient, then on the other our findings indicate that there is no clear trend toward decreasing the risks of sizable variations in the prices of most considered cryptoassets  In other words, risk and efficiency thus appear to be moving towards different directions in the cryptocurrency market 
To conclude, we hope that our findings will contribute significantly to the better understanding of the dynamics of large price variations in the cryptocurrency market as a whole, and not just for a small subset of selected digital assets, which is especially relevant due to the diminishing concentration of market capitalization among the top digital currencies, and also because of the considerable impact these new assets may have in our increasingly digital economy 
Our results are based on time series of the daily closing prices (in USD) for all cryptoassets listed on CoinMar-ketCap (coinmarketcap com) as of 25 July 2022 [see Supplementary Figure (a) for a visualization of the increasing number cryptoassets listed on CoinMarketCap since 2013]  These time series were automatically gathered using the cryptoCMD Python package and other information such as the tags associated with each cryptoasset were obtained via the CoinMarketCap API  
In addition, we have also obtained the daily market capitalization time series (in USD) from all cryptoassets which had this information available at the time  Earliest records available from CoinMarketCap date from 29 April 2013 and the latest records used in our analysis correspond to 25 July 2022  Out of 9943 cryptocurrencies, we have restricted our analysis to the 7111 with at least 200 price-return observations 
The median length of these time series is 446 observations [see the distribution of series length in Supplementary Figure   We have estimated the power-law behavior of the return distributions by applying the Clauset-Shalizi-Newman method to the return time series r t   In particular, we have sampled each of these time series using an expanding time window that starts at the hundredth observation and grows in weekly steps (seven data points each step) 
For each position of the expanding time window, we have separated the positive returns from the negative ones and applied the Clauset-Shalizi-Newman method to each set  This approach consists of obtaining the maximum likelihood estimate for the power-law exponent, α = 1 + n/ (∑ n t=1 ln r t /r min ) , where r min is the lower bound of the power-law regime and n is the number of (positive or negative) return observations in the power-law regime for a given position of the expanding time window 
The value r min is estimated from data by minimizing the Kolmogorov-Smirnov statistic between the empirical distribution and the power-law model  The Clauset-Shalizi-Newman method yields an unbiased and consistent estimator , in a sense that as the sample increases indefinitely, the estimated power-law exponent converges in distribution to the actual value 
Moreover, we have used the implementation available on the powerlaw Python package   In addition to obtaining the power-law exponents, we have also verified the adequacy of the power-law hypothesis using the procedure originally proposed by Clauset et al  as adapted by Preis et al    This procedure consists of generating synthetic samples under the power-law hypothesis with the same properties of the empirical data under analysis (that is, same length and parameters α and r min ), adjusting the simulated data with the power-law model via the Clauset-Shalizi-Newman method, and calculating the Kolmogorov-Smirnov statistic (κ syn ) between the distributions obtained from the simulated samples and the adjusted power-law model 
Next, the values of κ syn are compared to the Kolmogorov-Smirnov statistic calculated between empirical data and the power-law model (κ)  Finally, a p-value is defined by calculating the fraction of times for which κ syn > κ  We have used one thousand synthetic samples for each position of the expanding time window and the more conservative 90% confidence level (instead of the more lenient and commonly used 95% confidence level), such that the power-law hypothesis is rejected whenever p-value ≤ 0 1 
We have estimated the effects of age and market capitalization on the power-law exponents associated with positive or negative returns of a given cryptocurrency using the linear model where α t represents the power-law exponent, log c t is the logarithm of the market capitalization, and y t is the age (in years) of the cryptocurrency at t-th observation 
Moreover, K is the intercept of the association, while C and A are linear coefficients quantifying the effects of market capitalization and age, respectively  Finally, N (µ, σ ) stands for the normal distribution with mean µ and standard deviation σ , such that the parameter ε accounts for the unobserved determinants in the dynamics of the power-law exponents 
We have framed this problem using the hierarchical Bayesian approach such that each power-law exponent α t is nested within a cryptocurrency with model parameters considered as random variables normally distributed with parameters that are also random variables  Mathematically, for each cryptocurrency, we have
12/16 where µ K , σ K , µ C , σ C , µ A , and σ A are hyperparameters  These hyperparameters are assumed to be distributed according to distributions that quantify the overall impact of age and market capitalization on the cryptocurrency market as a whole  We have performed this Bayesian regression for exponents related to positive and negative returns separately, and used noninformative prior and hyperprior distributions in order not to bias the posterior estimation  
Specifically, we have considered and ε ∼ U (0, 10 2 ) , where U (a, b) stands for the uniform distribution in the interval [a, b] and Inv−Γ(θ , γ) represents the inverse gamma distribution with shape and scale parameters θ and γ, respectively  For the numerical implementation, we have relied on the PyMC Python package and sampled the posterior distributions via the gradient-based Hamiltonian Monte Carlo no-U-Turn-sampler method 
We have run four parallel chains with 2500 iterations each (1000 burn-in samples) to allow good mixing and estimated the Gelman-Rubin convergence statistic (R-hat) to ensure the convergence of the sampling approach (R-hat was always close to one)  In addition, we have also verified that models describing the power-law exponents as a function of only age (C → 0 in Eq  3) or only market capitalization (A → 0 in Eq  3) yield significantly worse descriptions of our data as quantified by the Widely Applicable Information Criterion (WAIC) and the Pareto Smoothed Importance Sampling Leave-One-Out cross-validation (PSIS-LOO) (see Supplementary Table )  ) is larger than r 90 estimated from negative returns (r − 90 ) 
This fraction is calculated only for weeks in which the power-law hypothesis is not rejected for both tails  The percentage of cryptoassets for which r + 90 > r − 90 is shown in the panels  The first column of panels depicts the results when considering data from all cryptocurrencies, while the second and third columns present the results for the top 2000 and top 200 cryptocurrencies by market capitalization, respectively 
Sampling issues refer to missing data and problems caused by prices of cryptoassets decreasing to zero  We note that these distributions barely change when considering only cryptocurrencies without any sampling issue  Indeed, the distributions in this figure are not significantly distinguishable from their counterparts in Fig  (two-sample Kolmogorov-Smirnov test, p > 0 05) 
)  Each of the previous three levels is further classified regarding whether both positive and negative returns are simultaneously affected or whether the effect involves only positive or only negative returns  Finally, the former levels are classified regarding whether the power-law exponents increase, decrease or have a mixed trend with the predictive variables 
Overall, 35% of the associations are classified as mixed trends (green rectangles), 37% are increasing trends (blue rectangles), and 18% are decreasing trends (red rectangles) 



ground_truth: 1766.