Economics – current debates from a policy perspective

1. Economics – current debates from a policy perspective Jonathan Cave

2. Outline The role of economics in policy Challenges to conventional economics Some policy challenges and debates

3. Economics and policy “Practical men who believe themselves to be quite exempt from any intellectual influence, are usually the slaves of some defunct economist. Madmen in authority, who hear voices in the air, are distilling their frenzy from some academic scribbler of a few years back”

4. How economics drives policy • Creating policy imperatives – “it’s the economy, stupid” • Framing issues and options • Incentives – why do people do what they do? • A common basis of comparison – adding Apples ™ and Oranges ™ • The animating force of the market – societal infrastructure, primordial network, measuring stick and channel of influence • Evidence of what works – empirical and experimental • Regulatory and other policy instruments • Impact assessment and evaluation EconomicTheory Administrations Research Business,MarketModels Citizens Business Policy

5. Building blocks • Levels: • Micro – Rationality (and psychology and sociology) • Meso – Firms, households, sectors (and networks) • Macro – Countries, blocs and aggregate measures • ‘Scale-free’ results – each level looks like the others • Interactions: • Competition, collusion and conflict • Game theory • Players, strategies, preferences (payoffs), information • Non-cooperative, bargaining, cooperative • Mechanism design – from contracts to auctions

6. Policy • Basis – market failure (to do what?): • Allocational efficiency – can everyone be made better off? • Technical efficiency – can we produce more of everything? • Dynamic efficiency – including growth, recovery and innovation • Equity • Delivery of external and public societal benefits • Mechanism: • selection (who plays) • incentives (what they do) • Tools • Property rights (to allow trade and investment) • Ex ante restrictions (licenses, standards) and Ex post rules (conduct or outcome-based) • Taxes and subsidies • Contests

7. Internet challenges to the standard model • Economic policy assumes that things are done for money • Rationality and meaningful consent may not be reliable • Non-human actors • Herding and contagion • Information goods involve access as much as ownership • Two-sided (platform) markets • Network externalities - tipping, excess volatility or inertia • Complex systems behaviour • New stuff: IoT, Cloud, Big Data

8. Public goods and the value of information • Standard theory assumes exclusive and transferrable property rights • A few exceptions are recognised (in red): • Externality determines how we aggregate costs and benefits to decide what is efficient • Permissiondetermines whether we can use markets to determine efficient outcome, organise production and access, and pay the costs

9. Value of information • Common sense assures information is valuable – we can always ignore it, right? • Consider the game of Prisoners’ Dilemma • Each person can choose between selfish and cooperative behaviour e.g. selfishness is traffic shaping (S) and cooperation is net neutrality (N) • Selfishness helps the person less than it hurts the other, regardless of what the other does • There is a unique individually rational equilibrium – selfishness all round • But all the other outcomes are collectively rational (Pareto optimal) – it is not possible to make anyone better off without hurting someone else • Now suppose that either strategy could be cooperative (shaping may help traffic types to cluster together; net neutrality may treat unequal parties equally) • Suppose that player 1 (row) knows which is which, and player 2 observes 1’s move • If the informed player (1) uses this information, the result is always the inefficient equilibrium • If the informed player randomises, so does the other; not perfect, but better than before • So the information has negative value to both players!

10. Policy 1: Digital Single Market • Policy context: Europe 2020 strategy • A policy chapeau – many initiatives, e.g. • Future Internet PPP • Horizon 2020 • Flagships • EFSI • Digital Agenda for Europe – now Digital Single Market • The challenge of the Internet • Jurisdictional problems – regional, national, EU, global • Market definition and regulatory traction • Regulators not set up for Internet, with different cultures • Conflicting economic objectives – competition, competitiveness, GDP, employment… • Tension between harmonisation and comparative advantage • The challenge of Grands Projects

11. Economic case for the Digital Single Market* • Massive potential for Europe • 315 million daily Internet users • €415 billion in additional GDP/year • Substantial (unknown) contributions to employment) • Europe’s digital market is not her own : • 42% of online economic activity lies within Member State borders; only 4% is cross-border. • 15% of consumers bought cross-border; 44% bought domestically; Cross-border competition could save them up to €11.7 Billion per year • 7% of SMEs managed significant cross-border sales – average extra cost of €9000 per year; uniform rules would increase proportion to at least 57%; VAT compliance adds €5000/country • Shipping costs are a barrier for 90% of e-shoppers and 62% of companies • 52% of attempted cross-border orders within the EU are geo-blocked • 54% of online economic activity involves US-based services • Framework conditions are also lagging • Data protection reforms stalled, Safe Harbour is sunk, TTIP risks • Patchy penetration of fast broadband (22.5%) and 4G 59%-15% (rural) • New opportunities • Cloud data storage (20%-40% in next 6 years) • Data analytics could save top 100 mfgs €415 billion, raise GDP growth by 1.9% * Data from Eurostat and Digital Agenda Scorecard

12. Policy case for the Digital Single Market

13. Other Digital Agenda for Europe policies • Innovation Union • Targets human capital, finance, patenting costs, regulations and procedures, standards, strategic public procurement, fragmentation • Large variations, faltering progress (scorecard leaders, followers) • Dimensions of innovation • European Fund for Strategic Investments • Regulatory and structural reforms to improve investment climate • European Investment Advisory Hub to channel finance to real economy • Supporting higher-risk financing • All owned by different players, and subject to internal and external shocks • All rely on others’ participation, but create unique risks • Each relies on uncertain (and potentially inconsistent) economic modelling

14. Policy 2: Net neutrality • What could be wrong with neutrality? • The separability of the transport layer – a bit is a bit? • The race to zero (rating) • Two-sided markets and walled gardens • Competition in the market or competition for the market • Is it all about content delivery? • Dimensions of performance and discrimination • Congestion externalities and crowding types – who interferes with whom? • The indirect value of a subscriber base – cream-skimming and sludge-passing • Quality of experience – latency, jitter and relative speeds

15. Net Neutrality 2: the necessity and efficiency of discrimination • Often, platforms or infrastructures have large fixed costs • Allocationally efficient marginal-cost pricing will not cover them • Any feasible single price regime will generate welfare loss • Have to price according to inverse elasticity of demand (Ramsey) • How to ensure the right kind of differentiation? • Secondary question: who should have market power? • How to balance regional and global interests, value capture and creation? • Future-proofing the rules • In diagrams, cost = area under MC plus fixed cost (shaded rectangle); revenue = sum of unshaded rectangles AC AC AC Price Price Price Monopoly Monop D D D MC MC MC Quantity Quantity Quantity Efficient multi-price – includes subsidies Extreme case: market would not exist without discrimination Inefficient single-price

16. Policy 3: Competitiveness and growth • Competitiveness is not the same as competition • Incubators vs. boot camps vs. Darwinian sandpits • The importance of failure • Building market share, IP, experience, organisational capital • Creative destruction - incumbents vs. new entrants • New forms of enterprise • Classifying and understanding them; guiding their development • Size matters – but indirectly • Networked and transitory affiliations; rules assume corporates and growth • Finance • Not just the amount, but the modalities • Competition regulation and financial regulation • Structure (SCP) • Understanding market networks • Macroprudentialregulation • Ecosystem services • New industrial policy and Better Regulation

17. Policy 4: Competition policy • What is a market? • Cooperation or collusion • New forms of enterprise • Standards and self-regulation • From promoting competition to promoting efficiency • Nudging the self-organisation of markets and products • Avoiding capture and foreclosure • Balancing public needs and private incentives

18. Policy 5: Privacy and security • Economic or fundamental right • Adequacy of legal roles • Third-party monetisation and the two-way value of personal information • The Right to be Forgotten – or remembered? • Privacy of information or of action? • The draft General Privacy Regulation • Safe Harbour type arrangements • Security concerns – and the effective placement of liability

19. Policy 6: Financial trading • Non-human actors, quants and complex systems • The rule of algorithms (e.g. Gaussian Copula) • The interaction of technical and financial efficiency • Behavioural responses and feedback loops • Seeing what is happening • “The regulators had all the data; the investment houses had all the brains”

20. Regulating the cloud: more, less or different regulation and competing agendas

21. Introduction • The cloud is: • a fad; • a metaphor; • a critical phase in complex ICT system development; • A microcosm for issues of Internet regulation • dead [choose all that apply] • Cloud-like things challenge regulation: • Unique issues • Existing issues made harder (or easier) • It shares this characteristic with “Internet regulation” • Not regulatory convergence, but a regulatory network • Rewards new models and approaches

22. Operational definition of the cloud • 5-3-4 … • …You know the rest

23. The cloud is already regulated • Technical: standards; interoperability; QoS; security… • Economic: general competition  consumer protection; IPR • Social: privacy; content; liability(?) • Sector-specific: finance, health, transport, services • Up- and down-stream regulation: cloud, cloud-based and cloud-enhanced services

24. Why should it be regulated? • Issues unique to the cloud (few) • Issues made harder or easier by the cloud • A convenient point of intervention – or at least discussion • A natural platform for self- and co-regulation • A ‘model’ for atomistic and dynamic competition (the cloud version of the app ecosystem)

25. Can it be regulated – and by whom? • Indirect relationships and regulatory traction • Implementation problems (e.g. jurisdiction) • Conflation of regulatory and stakeholder agendas – a hard problem for regulatory design • Limitations of existing statutory duties and powers • Need to assemble networked governance ecosystem to parallel – or to invade - cloud

26. How to frame the problem • Map regulators’ statutory remit and tools • Assess problem: • Whose problem is it • Is it due to or changed by the cloud • Context: • Can it be fixed without damaging other things • Will it go away by itself • Identify or develop instruments • Evidence and evaluation – finding the problem and fixing blame • Crafting and implementing a remedy • Changing behaviour • Monitoring and enforcement

27. Selection framework: legitimate interests • Citizen interests • Access to critical telecommunications services • Participation in society • Citizen protection • Consumer interests • Benefits of competition • Consumer protection • Consumer empowerment

28. Selection framework: specific duties • Ensuring the optimal use of the electro-magnetic spectrum • Ensuring that a wide range of electronic communications services – including high speed data services – is available throughout the UK • Ensuring a wide range of TV and radio services of high quality and wide appeal • Maintaining plurality in the provision of broadcasting • Applying adequate protection for audiences against offensive or harmful material • Applying adequate protection for audiences against unfairness or the infringement of privacy • Citizen and consumer interests

29. Screening test • Is there citizen harm? • Is there consumer harm? • Is ‘the market’ likely to mitigate or eliminate this harm? • Does it fit within regulator’s remit? • Is new intervention/power required? • Note: regulatory duties are not the same as policy objectives • Not the same for all nations (esp. within EU)

30. Citizen and consumer issues

31. Implementation issues

32. Modelling challenges • Complex adaptive system • Protean N-sided market • Salience vs. reality • Monetisation and participation rights • Technical issues: capacity management, privacy and security as a service, data access vs. processing • Challenging sectors: computerised/HF financial trading, health diagnostics, shared innovation, content sharing, supply chain data repositories

33. Three scenarios: dimensions • Sovereignty over cloud regulation: national, international, market/self-regulation • Locus of power in cloud services: Telcos (current EU situation), Google/Amazon (current US situation), Hypervisors (Vmware, MS/Citrix) • Balance of cloud deployment from regulatory perspective: public (consumer and citizen harms incl. privacy), private/enterprise (competition), hybrid • Architecture of (future) Internet: cloud as niche/overlay on current architecture or cloud-centric architecture (as in NEBULA).

34. Summary of scenarios

35. Cloud centric • Cloud develops under existing reg. framework • Increasingly central to Internet architecture and governance • Emergent issues tend to be managed through a combination of market solutions and self-regulation • Market and governance power lie with dominant B2C providers of global Internet • Dominant mode of computation, data storage and information-intensive communication and transaction • Could drive re-examination of regulators’ duties and mandate; in short run, most issues are out of scope

36. Nothing new • Continues trend as currently experienced in UK • Regulatory sovereignty according to existing mandates (some ad-hoc additions) • May create race to bottom amid contradictory requirements (especially at pan European level), as national regulatory agencies strive to attract profitable data centres. • Dominant model is public (B2C, SME) cloud; dominant players are telcos • Existing regimes continue in sub-optimal and fragmented fashion: • telecommunications regulators adopt consumer protection perspective • data protection authorities looking into how cloud service providers address privacy and data protection obligations • security and law enforcement agencies provide guidance on managing cloud risks

37. A new critical infrastructure • Currently, unlikely due to perceived security weaknesses (confidentiality, availability and data integrity) and regulatory interest • Private clouds are becoming more central, esp. in highly-regulated sectors subject to margin squeeze • Another reason is expansion of Big Data Analytics • Regulatory sovereignty will be increasingly joined up within and among nations • Critical core primarily provided as private/enterprise clouds; with the regulatory protection afforded by their centrality, they can compete successfully for citizen and consumer business as well. This reinforces the status of cloud service providers.

38. Conclusions • The cloud is primarily useful as a metaphor, and a means of raising challenges: • Definitions • Policy linkage (economic growth, finance,…) • Realigned roles and responsibilities • Sandbox for n-sided market, app ecosystem issues • The technical issues may need prior resolution • QoS • Self-organised complexity • Demand smoothing…

39. Algoarchy – the rise of formulÆ and machines

40. Debt derivatives and the Gaussian Copula The Internet connection Like the Internet itself, the simplicity of these formulae opened participation to all sorts of players Lines of information and accountability blurred Models that interpreted market data developed hidden bias and error Trading happened over the Internet, using big data analytics to which fast and stupid models were applied Systemic behaviour became harder to predict as individual elements became simpler.

41. Gaussian copulas – the formula that killed Wall Street? • In the US, 2007/2008 marked the bursting of a Housing Bubble • This triggered a major recession; people looked for scapegoats. • Initially, blame was fixed on major financial institutions (Bear Sterns, Goldman Sachs, AIG, etc.) • Later, the finger was pointed at the formulas they used to assess investment risk • Chief among these was David X. Li’s Gaussian Copula formula

42. Collateralized Debt Obligations • A CDO is a structured asset-backed security (ABS) whose value and payments come from an underlying portfolio of fixed-income assets: bonds; loans; credit default swaps (CDSs) and mortgage-backed securities • The first CDO was issued in 1987; they became steadily more popular starting in the late 1990s until the mid 2000s • The same period saw the growth of the CDS • The CDO offers different tranches of security • “Senior” tranche - paid first, most secure, most expensive • lowest (subordinate/equity) tranches are riskiest but cheapest • Investors have ultimate credit risk exposure to underlying entities, so banks used CDOs to transfer risk from themselves to investors

43. CDOs, 2 • On each tranche, investor has “attachment percentage” and a “detachment percentage” - when the total percentage loss of the entities in the CDO reaches: • The attachment percentage, investors start to lose money (not get paid fully) • The detachment percentage, investors won’t get paid at all

44. CDO Example • Tranche 1 (equity tranche) = 0% - 5% • Tranche 2 = 5% - 15% • Tranche 3 = 15% - 30% • Tranche 4 (senior tranche) = 30% - 70% • If CDO has 3% loss, Tranche 1 (the equity tranche) will absorb that loss; other investors unaffected. • If CDO has 35% loss, Tranche 1 and 2 gets no payment, Tranche 3 loses most of its payment; Tranche 4 unaffected

45. Credit default swaps (CDSs) • Like an insurance policy that pays off in the event of default • Unlike an insurance party in that it does not (necessarily) involve the original debtor • No limit to the amount of CDS that can be written on a single “underlying” credit. • Every underlying gets a certain amount of “basis points” (representing .01%) • These depend on stability/riskiness of underlying • The riskier the underlying, the higher the basis points. • Reflects market perception of default risk over riskless rate; like percentage odds that underlying will default before maturity

46. Gaussian copula • Purported to model correlation between default of two obligations (or the entities that control them) without using historical default data. • Instead, formula used CDS pricing data (initially had less than 10 years’ of observations) • Implicit assumption: CDS market was able correctly to price the default risk correctly on the underlying assets • A copula is used in statistics to couple behaviour of two or more variables and determine if they are correlated • With so many underlying entities in CDOs and portfolio/index CDSs, copula seemed ideal • Li’s Gaussian formula was the only copula used in practice

47. The formula itself • T = time period • and are the probabilities of A and B not defaulting during T using inverse of standard normal cumulative distribution function (cdf) • (the copula) couples individual probabilities associated with A and B to come up with a single number, using standard bivariate normal cdf of correlation coefficient γ • = probability of both groups A and B defaulting within T

48. Response • Financial industry embraced it, and used it to create and sell unprecedented amounts of “AAA-rated” securities • This was easy: no need to examine (or even identify) underlying entities, just use one number • If underlying entities were believed to be uncorrelated, the perceived risk of a CDO built of these CDSs was near 0, especially in senior tranche • Banks began combining all kinds of risky underlyings; if they did not appear correlated, CDO was highly rated • Markets grew rapidly: • CDS – from $921B end ‘01 to $62T by end ’07 • CDO – from $275B in ‘00 to $4.7T by end ‘06

49. Impacts • Used to be good practice to diversify underlyings • With copula, a group of (apparently) uncorrelated home loans (say) could be advertised as a safe asset, because you’d ‘never’ lose everything (in the senior tranche) • Banks started to sell riskier CDOs; they also made riskier loans (because they could lay off the risk) • Exacerbated by government pressure to make more loans • Sound familiar? • When the initial growth occurred, underlying (house prices) was increasing rapidly, meaning prices reinforced impression of low and uncorrelated default risk • By the time the bubble burst, this misleading price record was ‘set in stone’ – By the time defaults showed up, it was too late: AAA CDOs became worthless

50. What was wrong? • Underlying correlation assumptions defeated by derivative cross-linking • Model intended for analysis, not decision-making • Fundamentals not understood by model users • Certainly not scalable • Conspiracy of optimism lasted 6-7 years – more ‘good history’ to reinforce belief in model • Maybe we can do better now with • Network models • Big data to identify high-dimensional correlations • Avoids even possibility of ‘one formula to rule them all’