miscellaneous factZ

It would be interesting to chart over time the progress of open-source, standards compliant, Mozilla-type web browsers (e.g. Firefox) versus Microsoft’s Internet Explorer. As is often the case in other areas, it is not easy to get good (open) data over a reasonable time period. The graph below shows browser market share as measured by the browser usage of visitors to the W3Schools website (data source on Open Economics plus the code to extract original data into this usable form).

Browser Market Share (NB: Firefox was released in Nov 2004 but not listed separately by W3Schools until 2005)

Given the source, and therefore the bias towards more technically savvy users, these figures probably overstate Firefox’s market share somewhat, though the overall trend is probably largely correct. What we see is a steady and continuing increase in Mozilla (Firefox) market share ever since Firefox’s launch in Autumn 2004 and a concomitant decline in market share of IE (the little dip for Firefox at the end appears to be directly attributable to the launch of Google’s chrome). What is particularly interesting is that, at least for W3Schools users, we are almost at the point where there are as many people using Firefox as IE. This is significant for several reasons.

First, because of its level of usage it will no longer be possible for websites to only ‘work in IE’ but instead will always have to work in Firefox as well. This is both good for Firefox and for the standards-compliant browsers more generally (while, of course, Firefox itself is not perfectly standards compliant it has traditionally been much better than IE).

Second, it is an (unusual) example of a case where dominance has not been maintained. Generally a firm with established dominance in a given area is able to maintain — witness the robustness of Microsoft’s established dominance in other areas. By contrast, in this market, as the graph shows, Firefox has almost drawn level with IE and may soon surpass it if the trend of the last few years continues.

The human problem of ’scarce resources and unlimited wants’ is oft-posited as a primary motivation for studying economics. As this phrase makes clear, ‘wants’ (’preferences’ to use the more usual terminology) are a central part of what we study, and the existence, and stability, of those ‘wants/preferences’ therefore merit serious consideration.[^1]

[^1]: It is interesting how the term ‘preference’ is studiously neutral, and almost anodyne in comparison with a term such as ‘want’, ‘desire’ or even ‘need’, each of which is a potential synonym. One might imagine, and this is simply conjecture, that the term was intentionally adopted in order to remove any overtone of judgement. After all, across most culture and over much of human history, the formation and satisfaction of ‘preferences’ has been a process laden with ethical, and religious, significance.

Few of us have difficulty accepting the fundamental nature of our desire for food and shelter. However, many of us might have greater difficulties assigning the same fundamentality to the desire for a particular brand of designer perfume or a digital music player. In fact, it is unclear to what extent one can want what one has never known (or conceived of), and thus, while it is not difficult to imagine any human desiring food and shelter — especially when they are absent, it is hard to imagine a stone-age nomad, say, even being able to conceive of designer perfume or iPods (let alone feel their lack).

It is also telling that so many of the consumer goods, especially those away from the necessity end of the spectrum, appear to require active promotion to the public. Of course it is true, as economists are particularly fond of pointing out, that advertising has an informational component — simply letting you know about the existence and attributes of products. However, it is also hard to deny that advertising also has a substantial ‘persuasive’ component, operating either to create preferences or alter existing ones.

If so this has important implications. In particular, it strongly suggests that our wants aren’t simply given but are, at least to some extent, formed by our experience and choices.[^2] This raises some deep and important questions for economists to answer — questions with a major bearing on the state and direction of many modern societies. It also has some direct connections with one of the oldest, and most philosophical, of the world’s religious traditions: Buddhism. Central to Buddhist teaching are the Four Noble Truths. Succintly put these are, in order:[^3]

[^2]: In economics jargon: preference are endogenous (i.e. determined within the system) rather than exogenous (fixed externally — e.g. by ‘nature’). The study of endogenous preferences is certainly not new. See for example the review of Bowles (1998) or the early incorporation of changeable preferences into the ‘traditional’ framework by Becker and Stigler (1977).

[^3]: These translations of the Dhammacakkappavattana Sutta are taken from http://www.accesstoinsight.org/tipitaka/sn/sn56/sn56.011.piya.html

1. (Dukka — The Nature of Suffering) ”Birth is suffering, aging is suffering, sickness is suffering, death is suffering, association with the unpleasant is suffering, dissociation from the pleasant is suffering, not to receive what one desires is suffering — in brief the five aggregates subject to grasping are suffering.”
2. (Samudaya — The Origin of Suffering) ”It is this craving (thirst) which produces re-becoming (rebirth) accompanied by passionate greed, and finding fresh delight now here, and now there, namely craving for sense pleasure, craving for existence and craving for non-existence (self-annihilation).”
3. (Nirodha — The Cessation of Suffering) ”It is the complete cessation of that very craving, giving it up, relinquishing it, liberating oneself from it, and detaching oneself from it.”
4. (Marga — The Path to Cessation of Suffering) ”It is the Noble Eightfold Path, and nothing else, namely: right understanding, right thought, right speech, right action, right livelihood, right effort, right mindfulness and right concentration.”

Why is this teaching relevant here? First, observe a commonality: both economics and Buddhism takes unsatisfied ‘wants’ (or ‘cravings’) as a source of unhappiness. But how do go about solving this problem? Here economics and Buddhism part ways, and rather dramatically, with the Four Noble Truths presenting a path to the achievement of well-being which is almost diametrically opposite to that advocated by economics.

Specifically, the ‘economics’ approach, is based on taking preferences as given and focusing on generating the goods to satisfy them. By contrast, Buddhism sees ‘wants’ as ultimately unsatisfiable, and instead proposes that the way to well-being is not to satisfy them but to relinquish them — while some ‘cravings’ can be temporarily satisfied more will always be generated, moreover there some fundamental desires, such as the wish not to die, cannot be addressed in the material world.

Put starkly: economic thought directs our energy efforts to satisfying our wants taking them as given while Buddhism directs those self-same energies to altering our wants, and views most attempts to satisfy them by obtaining ever more ‘things’ as inevitably doomed to failure — in fact, actively counter-productive as more ‘wants’ are generated by the very process of satisfaction.

As part of my research work on knowledge production, particularly how it relates to the intellectual property regime I’ve recently been looking at film/movie production over time. Thanks to the semi-openness of http://www.imdb.com/ we have a fairly comprehensive database of statistics available. Combining this with the excellent IMDbPY scripts and my own home-brewed code and I was able to start extracting some basic information on movie production over time.

Points to note about the dataset:

• IMDb includes information both on films scheduled to be released, in production etc. This explains why there are data points for years in the future. These values should probably be ignored.
• It is not clear exactly how comprehensive the IMDb dataset is — and more importantly how its comprehensiveness varies over time. One might be concerned that some periods (especially early ones) are under-represented in the database in which case the figures shown will be somewhat biased.

Production as Number of Titles

Movie production as measured by titles has shown marked rises and falls over time. Specifically there is a major expansion in the 1910s followed by a sharp fall in the 1920s with continuing decline until the end of WWII (1945). Following that production steadily increased up until the 1990s when is started to grow much faster. By the mid 2000s the number of titles had broken the 10k a year barrier. We do need to be cautious here, as the number of titles may be highly misleading measure of production over time (see next section). However it is reasonable can compare across countries.

World film (blue) and US film (red) production (number of titles)

UK film production (number of titles).

While production is substantially lower than the US the overall trend is very similar.

Indian film production (number of titles).

This shows a rather different trend. First production only really begins in the mid-1920s, and perhaps most interesting of all, production actually drops from the mid-1980s through to the mid 2000s. Here, however one needs to be especially concerned about IMDb’s coverage (the number of titles looks rather low particularly for recent years).

Production as Running Time

It is not clear that the number of titles is the correct measure of film production. After all a short of 5 minutes and 2.5 hour blockbuster both get counted equally. Thus, here we use total running time as a measure of production rather than the raw number of titles. As one can see this gives a rather different picture: there is a relatively smooth (and almost linear) trend upwards from 1900 to around 1990 followed by a massive explosion.

World production (running time)

Last Friday I attended an ESRC Research Workshop on Well-Being held at the LSE. According to the blurb:

The time is ripe for a major expansion of well-being research in Britain – in conjunction with leading overseas colleagues. Among public policy-makers, there is an increasing desire to promote well-being and a need for evidence on what works to promote it. And among social scientists there is a new capacity to throw light on well-being: its causes and its effects. Worldwide, research on these topics has already demonstrated the scope for rapid and important advances in knowledge. But the scale of such research in Britain is far too small. This one-day workshop has been organised to explore the possible intellectual content of such a cooperative endeavour.

Some of the most prominent researchers in this area were in attendance to give an overview of current work and I took some ‘impressionistic’ notes which can be found below.

Well-being Research: the Way Forward by Daniel Kahnemann

• Living and thinking about it
• Attention
• There are 2 selves
  • Experiencing self
  • Remembering/Score-keeping self
• Used to think that experiencing self was what was important (Edgeworth)
• Remembering self not very accurate — cites own research on pain for medical procedures
• But now thinks remembering self is more important
  • Implicit in this is an acceptance that there are at least 2 distinct dimensions
• Current well-being/happiness questions are problematic because they are mixed containing some experiencing self and some evaluative/remembering self
• New, huge, dataset from Gallup is making a big difference
  • 1000 people polled a day with 40 questions on well-being
• Ladder of Life question in Gallup measures ‘Life Evaluation’
• Despite having different questions replicates existing results from DRM etc
• Attention and ‘Focusing Illusion’
  • Norbert Schwarz study: how much pleasure do you get from your car
    • Reasonable correlation with car monetary value
  • Also asked: how much pleasure did you have in your commute this morning
    • Zero correlation with monetary value
  • How many dates did you have last month and how happy are you these days
    • Happy first, dating second no correlation in response
    • Reverse order: large correlation
  • Leads to errors in prediction since we know attention alters valuation
    • e.g. to predict pleasure/utility from car need to ask: how much enjoyment do I get from car when I do not think about it
    • How happy would you be if you moved to be the california
    • But this is mistaken [ed: is this not often taken into account as evidenced by phrase ‘always think the grass is greener’]
• Gallup data: huge correlation with money
  • Remembered happiness: Money worries, health coverage, general health are main predictors
  • Experienced happiness: pain + social activities
  • Children: negative impact on general impact but when asked about children people are very positive — both views are correct
• Easterlin hypothesis:
  • Some questioned this (Stevenson and Wolfers)
  • But focus on ladder of life question
  • Looking at positive/negative affect still find that within-group slope with income is steeper than across group/time effect (i.e. Easterlin hypothesis)

Income and happiness in developed countries by Steve Nickell

• No obvious relationship on the ladder of life question
  • But cross-country regressions are pretty dubious (too many variables)
• Time-series data
  • Happiness regressed on log and quadratic in log income plus controls — pretty good fit
  • Curvature for classic CES: y^{1-rho} get rho ~ 1.2 across a whole variety of countries (1.1 - 1.4)
  • Time series: happiness is pretty horizontal (in the US) though income risen lot (even taking account of dispersion)
  • Some reasonable support for relative income hypothesis
• But really want panel data (deals with endogeneity)
  • Only one such panel: GSOEP (West Germany)
  • Regress happiness on log income, log reference income, controls (state,year,individual dummies etc)
  • Income alone: large +ve coefficient
  • Include relative income: income coefficient disappears

Income and the Evaluation of Life by Angus Deaton

• Gallup’s World Poll
  • Why it’s great [ed: the value of having early access to proprietary data!]
• Gallup result is very similar to the World Values Survey (His paper from last year — [ed] see my comments last year)
• Could argue that steep and then flat but log sems to fit better
  • Difference here with Steve Nickell
• Within country analysis
  • Collecting income data within country is hard particularly in poorer countries
  • Get figure of about 0.6 (effect of increase of 1 in log income on ladder)
• What about Easterlin?
  • Does some analysis in the US and does not get relative income affects at all (with ladder question)
• Suppose people do care about relative income. There are serious (’ethical’) problems with a consumption tax or not worrying about GDP growth: you hurt the non-envious and help the envious

Questions on Preceding

• My question:
  1. If focusing illusion is common across goods does it actually end up leading to bias in/incorrect choices
  2. Once we accept that attention has such large effects it poses difficult questions since it suggests that people’s preferences/enjoyment has a significant endogenous component.
• Several on relative income
• Replication across countries

Workshop on happiness research by Michael Marmot, Andrew Steptoe, and Jane Wardle

Michael Marmot

• Health as a measure of well-being
• 28 year gap in life-expectancy between poorest part of Glasgow (Galton - 54) and richest (Lenzie - 82)
• Major wealth effects on health outcomes even though (e.g. in the UK) people have all got enough to have pretty good healthcare
  • Relative effects of income (status?) has a major impact on health
  • Relative position not relative income (income != status — at least not always)
• Control for environment
  • Whitehall II study: look at poor physical health by deprived living area and grade level in civil service. Deprivation really matters when you are in the lower grades. [ed]: Suggests a) interaction effect b) that status matters more than area you live in
• Work stress: Coronary heart disease strongly linked to work stress
• Social relationships: mainly important on negative side (bad interactions are bad for you …)
• [ed: general murmurings from room throughout data presentation about what these correlations imply. Significant issues of causality and selection bias …]

Andrew Steptoe

• Meta analysis of positive affect and health
  • 18% reduction in prob. of mortality (even when controlling for other variables: smoking, BMI, social position etc)
• Issues:
  • Confounding: even though have controls direction of causation goes the other way (health to positive affect)
  • Genetics: simple correlation
  • Lifestyle: happier people lead healthier lives (or vice-versa)
  • Biology: positive affect associated with
    • Lower cortisol over working and non-working days
    • Lower heart rate over day
    • Lower systolic BP over the day
    • Reduced inflammatory responses
    • Independent of socio-demographic factors
• Happiness measure matters (a lot)
  • Using retrospective questionnaire measures find no relationship of positive affect with other stuff
  • But using EMA or DRM (i.e. more instantaneous stuff) find relationships
• Cross-cultural comparisons: Japan vs. the UK
  • Japan reports less +ve affect then UK (e.g. Gallup)
  • Find this in DRM studies of university women
  • And, importantly, find impact on cortisol levels (UK women lower than Japan)

Mapping Pain and Well-Being in Real Time and Yesterday by Alan Krueger

• Study in the Lancet (w/ Arthur Stone) on pain in general population (using diary study)
  • Data came from PATS, ~3900 people (by Gallup)
  • Pain rises with age but very flat 45 - 65 (for men and women)
  • Correlated with SES: poorer people in more pain (~20% of people with income under $30k in reasonable to severe pain compared to 7% for > $100k)
  • People in pain work less and watch more television
• Now doing EMA-PATS study + biological info (Krueger and Stone)
  • Check EMA and PATS are related (strongly correlated ~ 0.94 corrected for pain, and 0.92 corrected for happiness)
  • Not a representative sample (v. hard to get participants)
• A world of pain — use Gallup survey to look at pain across countries
  • Strong connection of GDP per capita and pain (~ -0.42 correlation)
• Questions:
  • Why SES-Pain gradient and Age-Pain gradient? Many possible explanations
  • Source/duration of pain
  • Biomarkers

Knott and Scott

• Examples of kids with cerebral palsy and some other bad thing: expectations matter (despite having serious disabilities kids evaluated their life as as good as others)
• Support only: no effect
  • Homestart: no effect or negative!
  • Surestart: also been shown not to work
  • …
• Skills and support: slightly better
• Child Antisocial behaviour: benefits
• Quality of mental health professionals: matters a lot
• Very little long-term follow-up data
  • Perry pre-school: good effects at age 27
  • 10 years follow-up of Scott et al (2001) finds some long-term effects
• More evidence based psychiatry
  • Quite a lot we can do if we do it in a skillful way

Well-being and Aging by Felicia Huppert

• Negative stereotyping has large impact
  • Older people exposed to -ve stereotypes do worse on stress, cognitive performance etc
• Causes of well-being
  • Separate +ve and -ve in GHQ (found a big difference in impact of e.g. unemployment on +ve vs -ve affect)
  • Magnitudes (as opposed to pure significant)
  • What are important drivers
• Environmental affects likely to be large (much larger than genetic affects)
• Study in US IT company: RCT of mindfulness meditation found substantial impact
• How much is society losing from people not flourishing [ed: losing seems to mean losing money/GDP here]

Work, Stress and Well-being by Richard Freeman

• Questions
  1. Does working environment affect worker well-being
  2. Can we specify workplace policies/practices make work lives better
  3. Do measures of job satisfaction and well-being provide different information
  4. Moving beyond survey measures
• Job satisfaction - one of most widely studied variables
  • Correlated with health and turnover (people leaving associated with dissatisfaction)
  • Two-factor model needed to explain some patterns
    • Puzzle: unionized workers quit less but also less satisfied (expectations?)
  • Job satisfaction and well-being
  • When people quit and go to a new job their satisfaction goes up
• Results from various datasets they used (WERS - several people per workplace + a lot of detail)
  1. Working environment matters a lot (could be workplace policy, culture, or selectivity)
     • Workplaces bad (good) in one dimension often bad (good) in others
     • [ed: so not some simple trade-off/optimization]
     • Large changes in well-being after quitting and moving elsewhere (bigger than money impact)
       1. Policy/practices matter but causality unclear
     • Major endogeneity problems (if i have a job satisfaction policy is that because people are miserable)
     • Well-being related with job attributes (hazardous, stress etc) in normal way
       1. Job satisfaction and worker well-being
     • Not that correlated
     • Job-satisfaction important for well-being but less important that health and various other variables
• 3 things to do
  1. Biomarkers at high/low satisfaction workplaces
  2. Impact of change of jobs
  3. Harvard network on work, family and health
     • Check company work policy carefully
     • Look at health outcomes, stress, sleep
     • Found big correlation of manager’s attitudes and practices correlated with cardiovascular outcomes

Co-operation and well-being by Armin Falk and David Skuse

David Skuse (development neuro-psychiatrist)

• Individual differences in happiness
• Role of genes and brain on behaviour
• Mechanisms of mental functioning underlying mental health
• Compensation for deficiencies …

Armin Falk

• [ed: computer battery ran out so this is very partial]
• Relative pay and fMRI results. Big impact of relative pay (Science 2007)
• Unfairness in principal agent setup (dull task and unfair division of revenue. impact on heart rate variability)
• Oxytocin study: look at genetic variations affecting oxytocin and see how they impact on trust in trust game (amount sent at stage 1)
• Mentioned current/future research on cultural formation on preferences

Last Friday and Saturday I was at the 2008 European Policy for Intellectual Property (EPIP) conference, held this year in Bern. I presented my paper on the optimal term of copyright and discussed a paper of Luca Spinesi’s on ‘Imperfect IPR enforcement, inequality, and growth’. Below can be found ‘impressionistic’ notes from some of the other sessions I had a chance to attend.

Jim Bessen: How can and how should economics inform patent policy?

• What is aim of ‘Property Rights’
• Look at example of tradable permits for pollution
  1. Do institutions do their jobs
  2. Resources (is air cleaner)
  3. Social welfare
• For patent system, thanks to recent work, first two are within our reach (though not within our grasp)
• Institutions. Want:
  1. Specificity
  2. Searchability
  3. Predictability
  4. Transactability
  5. Enforceability
• Patent system is not doing so well
  1. Specify: reasonable but lots of debate about what claims mean (40% overturn rate on appeal of district court decision re. claim construction)
  2. Search: pretty poor (esp. in ICT). Many firms do not bother to search.
  3. Predictability: low (e.g. no defense insurance)
  4. Transact: can be anti-commons
  5. Enforce: pretty unpredictable
• Resources (Innovation)
  • Patent system is not doing so well due to overlapping claim (pooling problem)
  • Fuzzy boundaries: dispute costs
    • Value patents (upper bound from renewal, re-assignment, int’l filings, firm market value, surveys, case-studies)
    • Dispute costs (lower bound)
    • For pharma: value ~ $12 billion/year, costs ~ $1 billion
    • Other industries: value ~ $2 billion/year (from 80s to present), costs ~ $1 billion / year up until mid 90s since when they have spiked and now much higher than value — e.g. in late 90s costs 3x value
    • Could use fees to address this (raise from ~$5000 to ~$30000)

Reto Hilty: Enforcement of intellectual property rights on Enforcement of IPRs

• Huge figures circulate about losses from piracy
  • Most figures are (very) dubious and produced by the industry
• History of IPRED (and IPRED2)
• More intl stuff:
  • TRIPS+
  • FTAs (US)
  • EPAs (EU)
  • ACTA
• Why has this focus on enforcement happened
  • General mantra that strengthening IP rights is good for innovation
  • Patents: probably have over-protection
    • Full patent protection (EPC 1973) — i.e. patent covers subsequent uses even if not anticipated. (probably a mistake)
    • Biological substances — full patent protection particularly problematic
    • Software patents …
    • Drugs and developing countries
  • Copyright law
    • Internet users see constriction not justice
    • Entertainment + TPMs — “unjustified profits”
    • Scientific research: unnecessary constrictions (Open Access)
  • Industrial design
  • Trade-mark law — large extensions in the last 80s (protection of colours, shapes unjustified)
  • Eventually this constant extension generated such opposition that it is now at a standstill
  • Thus, rightsholders move focus to enforcement (focus on ‘efficiency’)
• But stronger enforcement also causes problems [ed: the strength of a right in fact is is product of enforcement and strength in theory]
  • will there be a backlash?
• Also extension of IP geographically — esp. to developing countries
• What justifications are there for IP enforcement
  • IPR not valuable without some enforcement, certainty …
• One size cannot fit all: whether for IP itself or for enforcement
  • If IPR is misused enforcement can make things worse
• Suggestions:
  • Decriminalize where too much IP protection
  • Strengthen enforcement where IP truly detrimental
  • Distinguish IP protection from consumer protection (counterfeiting not the same as IP protection)
  • [ed: one concern here is that it seems here we are using enforcement/non-enforcement to correct IP rights which are themselves wrong — enforce where good, don’t enforce where not good. But if that were agreed why couldn’t we correct the underlying problem]

Davis, Davis and Hoisl: Leisure time invention

• PatVal data (10.5k German patents sampled with survey of inventors)
• Leisure time has +ve impact on inventive output
• Leisure time invention +vely linked to interactions with co-workers and outsiders
• More leisure time invention in conceptual-based technologies rather than science-based technologies
• Incidence of leisure time invention will be -vely related to project size
• Most hypotheses confirmed

Ashish Arora: Patents and Innovation

• Evidence for benefits of patents on innovation is mixed
  • Example of early Swiss and German dye and chemical industries
  • Surveys main evidence which show there are rents from patents but with equivalent subsidy ratio that is not that high
• Kyle and McGahan: no inducement of research in diseases of poor countries after TRIPs
  • Even if patent protection is important no reason for developing countries to have them (already have protection in developed countries)
• Thickets, patent litigation and trolls
  • Cockburn MacGarvie and Mueller (2008): fragmentation increasing across all industries
  • Substantial litigation costs
  • Geraldin, … find no thicket problem in 3G telephony
• Anti-commons
  • Completely unpersuaded by the evidence
  • All examples came from universities: US research universities have made a mess of tech-transfer and patenting, alienating faculty and angering corporate partners (Bayh-Dole has had significant unintended bad consequences)
• Markets for technology (specialization)
  • The first order effect of patents may be on trade in technology
  • Having people whose business it is to sell technology is really important (particularly if you are a developing country)
  • Licensing flows in US: $66 billion in 2006 (Carol Robbins). Good proportion of domestic R&D
  • Hall and Ziedonis evidence on specialist semiconductor firms
  • Gambardella and Giarratana (2007): software security patents
• Making patents more useful
  • Much of the problem is bad patents due to:
    1. Invention is poorly understood (underlying knowledge base is poor)
    2. The claims are written with the intent of claiming as much while revealing as little as poorly understood
  • ‘Metes and bounds’ of the patent are unclear to all except handful of patent lawyers
  • Not new: cf. German chemical industry back in 19th century
  • Solution:
    1. Force patents to be written using (i) standard terms (ii) without legal jargon (whose only justification is a futile reach for precision)
    2. Patents should be (i) published expeditiously (ii) transactions (licenses, assignments, beneficial interests) in patents should be recorded and disclosed

Survey on Patent Licensing: Dominique Guellec (OECD)

• Why licensing out:
  • Value from unused inventions
  • Inventions with applications elsewhere
  • Fabless firms
  • Establishing technology as a standard (may raise Competition issues)
  • Cross-licensing deals (ditto)
• Expected Economics Effects (+ve)
  • Increases diffusion
  • Reduces duplication
  • Boost downstream competition
  • Facilitates specialization
• Can also be -ve (mirror image of +ve ones e.g. reduced duplication = less competition)
• Graph showing huge increase in royalty/license payments since mid 80s: ~$10B/year to ~$110B/year) (source: world bank)
  • But how much of this real (i.e. not tax manipulation etc) — and also includes copyright etc
• OECD survey implemented by EPO by JPO/University of Japan on licensing behaviour
  • focuses on licensing out
  • response rate: 42% in europe, 34% in japan [ed: japan responses are less reliable for reasons not entirely clear to me]
  • no questions on revenues (people don’t respond when you ask this — either don’t know or don’t what to tell)
• Results:
  • 35% of european companies license out, 59% of japanese firms
  • Licensing to non-affiliated companies: 20% of Eur, 27% of Japanese
  • U-shaped prob of licensing as a function of size
  • By tech field: highest in chemistry and electronics
  • Younger companies do it more (controlling for size) [ed: issues here though. Old firms which are small are not the same as young firms that are small]
  • Why do it?
    • Earning revenue: 60% EUR, 52% JPN; cross-licensing: 18%, 18%
  • Patents you would have licensed but could not/did not: ~20%
    • Why? Difficulty of finding a partner (25% of EUR and 18% of JPN)
    • Not important: problems of drafting contracts or technology not mature
• Difficulty of finding partners could be for several reasons but suggests could be role for more/better intermediaries to facilitate transactions (INPIT in Japan)

Patent Thickets and the Market for Ideas: Mark Schankerman (LSE)

• Market for ideas (patent licensing and sale of patents) [ed: this is obviously not the whole market for ideas …]
• Study market though new lens: settlement of patent infringement disputes
  • Do not know whether when settlements happen licensing actually occurs
• Focus on 2 key aspects:
  • Fragmentation of rights (’patent thickets’)
  • Certainty of enforcement (CAFC led to more certainty — not worrying here about pro-patent bias)
• Fragmentation:
  • Trad story: bad (higher transaction costs, bargaining failure …)
  • Dissenting voice (Lichtman 2006): greater fragmentation lowers the value at stake in each negotiation and this reduces the incentive to bargain hard. This speeds up settlement. Of course still leaves question of whether this reduces total negotiation time.
• Model gives us various hypotheses:
  • H1: more complementarity means longer negotiation
  • H2: more fragmentation means shorter negotiations
  • H3: Settlement negotiations will be shorter for patents litigated after CAFC (1982)
  • H4: Impact of fragmentation external rights will be lower after the introduction of CAFC
  • H5: CAFC has a bigger impact where the preceding circuit had more uncertainty
• Results
  • More fragmentation: leads to lower dispute duration (19.6 months for < 50th percentile frag vs. ~16 months for > 90th percentile)
  • CAFC has a big effect on dispute duration (~33 months to ~18months)
• Conclusion: looking at delay (not royalty stacking on other issues)
  • Certainty: good
  • Fragementation: not bad (and maybe good)

I’ve just put out an updated version of my paper on Search Engines entitled: “Is Google the next Microsoft? Competition, Welfare and Regulation in Internet Search”, the original version of which went up in June. The revised version is available either from SSRN:

http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1265521

or from here:

http://rufuspollock.org/economics/papers/search_engines.pdf

Abstract

Internet search (or perhaps more accurately `web-search’) has grown exponentially over the last decade at an even more rapid rate than the Internet itself. Search engine providers such as Google and Yahoo! have become household names, and the use of a search engine, like use of the Web, is now a part of everyday life. The rapid growth of online search and its growing centrality to the ecology of the Internet raise a variety of questions for economists to answer. Why is the search engine market so concentrated and will it evolve towards monopoly? What are the implications of this concentration for different ‘participants’ (consumers, search engines, advertisers)? Does the fact that search engines act as ‘information gatekeepers’, determining, in effect, what can be found on the web, mean that search deserves particularly close attention from policy-makers? This paper supplies empirical and theoretical material with which to examine many of these questions. In particular, we (a) show that the already large levels of concentration are likely to continue (b) identify the consequences, negative and positive, of this outcome (c) discuss the possible regulatory interventions that policy-makers could utilize to address these.

One of the active Open Knowledge Foundation projects is Open Economics. A substantial part of that effort ends up being data acquisition and ‘cleaning’: getting hold of economic data, parsing it into (computer) usable form and adding it to the Store. (Wouldn’t it be nice if that data was already nicely packaged up or at least in a decent raw form …).

Once this job is done, the data is there in a nice clean state for others to use — plus we can draw some nice graphs (as we will see below). As an illustration of this process, we’ll look at one particular dataset acquired earlier this year when, motivated by the large increases in commodity prices and the concerns expressed regarding their impact, I decided to see what data I could dig up on food prices (starting with Wheat).

As usual, it was US government material that was most easily available (in a decent format) and I decided to start off with historical information on wheat to be found in the Wheat Yearbook, in particular the contents of:

http://www.ers.usda.gov/data/wheat/yearbook/WheatYearbookTables-Recent.xls

While the data was available (and open — since US Govt provided) it was in a format that was not immediately computer usable (lots of blank lines etc). Thus, the first step was to parse this into standard csv file format (see script here) and then upload this to Open Economics. The result:

http://www.openeconomics.net/store/517d7c4e-3cb7-4e8f-aaa1-745dd665ad1f

Not only do we now have nice clean data but, thanks to plotkit, Open Economics has javascript graphing so without any more effort we can automatically have graphs of the resulting material. Not only does this allow us to answer our original question (see Fig 4) but these graphs also tell a fascinating historical story:

US Wheat: 1866 - 2007

NB: if the figures are too small click through for the full-size versions on Open Economics (the dates at the bottom run from 1866 to 2007)

Figure 1: Output (Millions of Bushels)

First up is output. As can be seen here output rose steadily (approximately linearly) up until the First World War. It then stayed flat or even fell during the inter-war period — the Great Depression and the Dust Bowl can be seen in the sharp dip in the early 1930s. Following the Second World War output rose, accelerating (exponentially?) up until the early 1980s when it has flattened out, even declining (with sharp variations) to the present.

Looking at these raw output figures the immediate question one asks (at least as an economic historian) is: what underlying causes drove these changes in output. In particular, output is the product of two factors: total acreage in use and yield (average output per acre) so it would be interesting to see time-series for them as well. Fortunately this data is also available:

Figure 2: Acreage (Millions of Acres)

The first thing to note is that these series start in 1866, the year after the American Civil War ended. This was a period of great westward expansion in cultivation in the United States — the “Opening of the Prairies”. The graph bears graphic witness to these changes: we can see that harvested acreage tripled between 1866 and the outbreak of WWI in 1914.

This massive expansion was to have a profound effect far outside of the US: food prices dropped around the world due to the increase in supply. In Western Europe this lead to a ‘Great Depression’ in agriculture right up until the First World War (which in turn had a significant effect on European politics creating protectionist alliances between peasants and landowners in many European countries). It also assisted industrialization by keeping the price of bread low for the fast growing industrial proletariat.

However, by the end of WWI most of the acreage that could be cultivated was already in use. After that point, while there has been variation in planted acreage (perhaps driven by substitution between wheat and other crops) there has been no long term trend (whether increasing or decreasing). Thus, while the increase in output up to WWI can be largely explained by increases in acreage under cultivation [^1] the large increases in output in the post-WWII period can’t be. This brings us then to the second major factor in explaining changes in output: yields.

[^1]: a crude eyeballing suggests that output increased somewhere between 3-4 times between 1866 and WWI. This is in line with the increase in acreage. That said, diminishing returns arguments (best land is cultivated first) would suggest that to maintain yield per acre on a vastly increased acreage would have necessitated some increase in yields.

Figure 3: Yield (Bushels / Acre)

One could not ask for a sharper confirmation of our previous hypothesis than Figure 3. As it shows average yields were almost perfectly flat from 1866 up until the end of the Second World War. From that point yields took off growing sharply, but at an almost constant rate, up until the mid 70s, following which the growth rate slowed substantially (though yields still continued to grow albeit with increased variability). In concrete terms this corresponded to a rise in yield from around 12 bushels per acre at the end of WWII to somewhere around 35 bushels per acre in the 70s — and around 40 today.

To put this most starkly: there was a roughly 3-fold increase in yields in this 30 year period. Again this is a particularly ‘graphic’ testament to the ‘green revolution’ of the post-war period which was driven largely by the development and adoption of new corn varieties (hybrid corn), fertilizers etc.

Figure 4: Price ($ per Bushel)

Lastly we come to price. Here, despite substantial fluctuations the basic trends fit with our historical intuition. There is little change between 1866 and WWI, a sharp rise during the war, a substantial decline in the inter-war period, then another sharp-rise during WWII (wars are good for farmers!) followed by stabilization (or even slight decline) until the mid 1970s when there is another sharp rise. Following that there is substantial variation but no great changes until the present when the line shoots up again (doubling from around $3 per bushel to somewhere near $6 in a year).

As basic economics tell us, price should reflect the interaction of supply and demand. The marked stability of price over long periods (particularly those where supply has increased) suggests then that demand has matched supply (or vice-versa) fairly well over this period (one might also need to take account of the fact that there may also have been substantial government intervention to stabilize prices).

Given that supply has risen substantially through the whole period, and especially since WWII (see Fig 1) this means that demand has also been climbing sharply. This is true: world population has increased at least 5x since 1850 and roughly tripled since WWII (in addition many people, especially in developed countries have increased their per-capita consumption, by eating more and better — as well as wasting more).

It would be interesting to imagine what would have happened if this kind of population increase, particularly that since WWII, had occurred without the massive increase in yields shown in Figure 3 (part of the answer may be that population would not have increased so much …). Certainly the price increases seen recently may reflect the kind of growing surplus of demand over supply that we would have seen without the ‘green revolution’. As such, they may be signals of the significant readjustments that will be needed in the near future, whether that be increases in supply, reductions in demand or more efficient use of existing supplies.

June 2008, JEL, p. 426, in review of Robert Frank’s Falling Behind: How Rising Inequality Harms the Middle Class by Frank Levy:

… By that time [mid 1980s] many of the trends noted by Frank were already underway. Since the late 1960s, the American Council of Education has been measuring the attitudes of college freshmen. Between 1968 and 1972, about 40 percent of freshman felt that “being very well off financially” was important or very important. In the fall of 1973, this proportion jumped to 62 percent and continued to rise steadily after that leveling off at 75 percent in the late 1980s (American Council of Education [The American Freshman report]). It was also in the early and mid-1970s that majoring in business administration took off while majoring in sociology shrank.

Of course one might wonder if the late 1960s were just an unrepresentative period in which the importance of money was less than it usually had been. In that case the later trend would be a simple reversion to the mean.

Sören Auer posted today to the okfn-discuss lists about plans for Open Participatory Research. Reading this I was particularly struck by his mention of ‘open peer review’ as this seemed directly related to some recent ideas of my own. Specifically I’ve been working on an economics paper with an academic colleague on the subject of dissemination of scholarly information. This is still at an early stage but the basic ideas in it are quite simple — as set out in the current introduction which can be found below.

Introduction

It is well known that in order to (completely) address a given number of (independent) goals one needs an equal number of instruments. For example, if one is seeking to address both congestion and pollution in relation to road-traffic, a single instrument, for example petrol taxes, will be insufficient to address both goals exactly (of course it will allow one to address both goals partially). The same issues arise in relation to the dissemination of scholarly information.

Here too there are multiple independent goals. Traditional academic publishing provides but a single instrument. Originally there was nothing that could be done (for reasons discussed further below), but changes in technology render this restriction to a single instrument unnecessary. Unfortunately, the two-sided nature of the journal market (based on expectations), combined with the current evaluation structure of academia, continue to lock society into this inefficient restriction. Open-access journals provides one, though as we shall argue, not the only, or even most efficient, way to improve the current situation.

Goals and Instruments

Crudely put, the two main goals (or tasks), in relation to the dissemination of scholarly information are:

• Distribution (transmission of the data/information) — `Making material available for Reading’
• Filtering/Recommendation — `Deciding what to Read’

It seems clear that these are distinct and hence require distinct instruments for their achievement. Journals can be seen as a single instrument which traditionally have tried to address both ends simultaneously. The deficiency of academic publishing can then be seen as one of insufficient instruments. Initially, because of the limitations of reproduction and distribution technologies, there was little that could be done about this. Today with the advent of the computer and the Internet this is no longer the case and it is possible to these two distinct goals with two distinct instruments.

Why then did restricted-access Journals originally come about? The answer lies in technology, in particular the nature of the technology available in earlier periods to manage distribution (printing and transmission). When many journals were originally started the cost of transmitting information was very high. Journals essentially acted as a club good by which the costs of reproduction and distribution could be (efficiently) shared (the efficiency arising here from economies of scale).

At the same time, given the limited ‘bandwidth’ it was natural for Journals to take on some filtering role in order to economize on the scarce transmission capacity. In this situation, dissemination is limited and with only one instrument available (Journals) and it is natural to tie dissemination and filtering together (with filtering in many ways secondary). Once filtering is being done it is natural for journals to `tie’ material to the journal explicitly via copyright — though at an early stage given the scale economies of journals this explicit tying was not actually necessary and was probably done for simple legal convenience.

With the advent of digital communications, in particular the Internet, bandwidth is no longer scarce. What is now scarce is attention. In this setup the importance of a journal is not its role in efficiently sharing reproduction and distribution costs but its role as a filtering mechanism. However, while when distribution is central it is natural to `add-in’ filtering, it is not natural, or necessary, to tie distribution in to filtering when filtering is central. In fact it seems clear that distribution and filtering can be done entirely separately (i.e. one can have two instruments focused on distribution and filtering respectively). The Open Access movement can be seen as largely about achieving this separation: with open access there is no longer a connection between access/distribution (which would be free) and the filtering mechanism (the choice of which articles go in a particular journal).

That said the `Open Access’ movement still has a large focus on journals — albeit open-access ones. This, in our view, is a mistake. Technology has also affected possibilities for filtering. In particular it is no longer clear why the centralized mechanism of official peer-review and journals is superior to alternative decentralized options. The last decade, has witnessed widespread, and often successful, experimentation with distributed voting and evaluation mechanisms (for example Slashdot’s story-ratings and Google’s link-based site rankings).

Thus, to be more radical, it may make sense not only to remove centralized control of distribution but also centralized control of filtering. A more distributed (market-like?) filtering mechanism would permit the same freedom (and same status?) to participate in reviewing and recommendation as it does in the production of scholarly information. At the same time it would deliver greater transparency, and by permitting `free-entry’ in filtering, would allow greater specialization, greater diversity, increased participation and greater competition.

As such, the gains from going ‘open’ are not simply wider access, but a reduction in the time and energy scholars spend finding and processing research information. Significantly, this second item, which is less frequently mentioned in discussions of ‘Open Access‘, may well be the most significant.

City University’s Centre for Competition and Regulatory Policy summer workshop took place today and yesterday and I was there to present The Control of Porting in Platform Markets. As well as presenting I had the chance to take some ‘impressionistic’ notes on some of talks which are included below.

Thursday

Session 1: Telecoms and Postal Services

PAUL SMITH - CEPA: Defining the universal postal service

CARLO REGGIANI – UNIVERSITY OF YORK: Network neutrality and non-discriminatory issues: An economic analysis

• 2 recent papers (2007): Economides and Yal + Yermelo and Katz
• 2 sided-model
• n-firms providing platform (telecoms)
• network externalities both sides
• Questions:
  • do telcos set prices on both side
  • What is form of the competition
  • net neutrality is always bad so why used

Session 2: Competition issues

RUFUS POLLOCK - CAMBRIDGE UNIVERSITY: The control of porting in two-sided markets

DAVID GILL – UNIVERSITY OF SOUTHAMPTON (with John Thanassoulis): The impact of bargaining on markets with price takers: Too many bargainers spoil the broth

• What happens if some consumers bargain a discount from list prices
• Some proportion of consumers z do not bargain
  • exogenous but endogenized later on
  • Cournot competition for these guys (with Bertrand this all goes wrong …)
• Of those that do bargain some get one quote some get multiple (Bertrand from multiple)
  • trade-off getting monopoly from single quote guys vs. purchase from multi-quoters
  • From Judd + Burnett 1983
  • [ed: is there a cost for getting quotes]
  • [ed: Very like Baye and Morgan and resulting in similar mixing results)
• Firms anticipate that higher list prices raises profits from bargainers
• So as number of bargainers go up firms raise list prices
• Results
  • As bargainers prop. increase price-takers do worse
    • Waterbed effect + fact that
  • Existing bargainers CS decreases as prop. bargainers rises
  • Swapping consumers (price-taking to bargaining) benefit
  • Overall effect: ambiguous
    • Overall negative and most bargainers get only one quote
    • Overall positive if most bargainers get multiple quotes

• Then endogenize number of bargainers by assuming some intermediate types who face cost c of bargaining

  • Results similar
  • Still do not endogenize choice of number of quotes — discussed in paper but not done

• Comments:

  • Waterbed effect: what if firm entry (i.e. zero profit condition) then better prices for bargainers => worse prices for price-takers
  • Baye, Morgan

Session 3: Electricity and Related Issues

GERT BRUNEKREEFT – JACOBS UNIVERSITY BREMEN: Ownership unbundling of the German electricity TSOs – A social cost benefit analysis

VINCENT RIOUS – SUPELEC (with Jean-Michel Glachant, Yannick Perez and Philippe Dessante): The diversity of design of TSOs

STEPHEN WOODHOUSE - POYRY: Wind generation – no limits?

• Everyone is signing up to incredibly optimistic renewable and CO2 targets.
• For UK wind is essential as we have a lot of it compared to any other renewable options
• However wind has major delivery issues and conventional wisdom is that its max penetration is 10%

• Problem is:

  • wind can be irregular
  • (more significant) demand shows pronounced fluctuations over the day while renewables don’t (on average). This means that your backup capacity to deal with peak load make renewables on avg. v. expensive.

• [ed]: Comments

  • why this debate about whether feasible or not — why can’t we simply price carbon efficiently
  • like a man who has a dislocated shoulder and spends all his time trying to fix the pain this causes in his hip rather than sorting out his shoulder

Session 4: Evaluation of Regulation and Competition Institutions

GORDON HUGHES – UNIVERSITY OF EDINBURGH: Efficiency frontiers, stranded assets and the X-factor for telecoms network operators

• Setting the X in RPI - X
• Stochastic frontier analysis
• Look at 68 US local exchange carriers (data from FCC)
  • Current costs from historic accounts
  • Stranded assets (from switch to digital)
• Structural break in 2000
  • To 1999 costs falling at -3.3%. From 2000 falling at -2.1%
• Stranded assets affect costs: cumulative impact of 5% annual decline in switched line equivalent to a cost increase of ~2.6% per year
• Slow convergence towards frontier: ~1.3% per year
• Accounting vs. economic cost important
  • Accounting cost: ~ -1.7% per year (post 2000)
  • Economic cost: ~ 1.7% per year (post 2000)
• RPI-X:
  • using accounting costs: X ~ 1.5-2.5%
  • using economic costs: X ~ -0.5 - 2.0% (i.e. -ve and prices rise faster than inflation)
  • In europe can justify + ~2.5% to X but this will all over time.

JOHN CUBBIN - CITY UNIVERSITY (with Jon Stern, Federica Maiorano and William Gboney): What can we learn from economic studies of infrastructure regulatory policies?

Friday

Session 1: Transport

ANNE YVRANDE-BILLON - UNIVERSITY PARIS SORBONNE (with Miguel Amaral and Stephane Saussier): Does competition for the field improve cost efficiency? Evidence form the London bus tendering model

• Competition for market
• Idea is that competition raises bids (whether charges for providing service or payment for right to run it)
• Little empirical testing
• Several confounding factors
  • Winner’s curse: can happen in common-value and in private value auctions if bidders systematically under-estimate their own costs (i.e. over-estimate their own values)
  • Renegotiation effect: a bid not be allowed if not good enough even if it wins (implies more aggressive bidding)
  • Entry effect: Larger number of expected bidders might discourage entry.
• Existing papers on impact of no. of bidders on outcome
  • Branman et al (1987), Thiel (1988), Dalen + Gomez-Lobo (2001), Hong + Shum (2002) — find strong winner’s curse, Nunez + Athias (2006)
  • Do not control for other extra factors
• France vs. London (Amaral, Saussier + Yvrande 2008)
  • French Urban Public Transport sector
  • declining productivity, huge deficit — basically a disaster
  • tendering model (for buses):
    • No clear selection criterion (intuitu personae) — right enshrined in law by vague definition of the ‘collective welfare’
    • No regulator
    • Few bidders (av 1.4)
    • 66% of auctions with only one bidder
    • Incumbent advantage (~88% renewed)
    • Collusion (fined by Comp. Commission 2005)
  • Bus auctions in France are for complete networks while for UK they are for routes
  • This excludes Paris as Paris directly administered
• UK model
  • Bus operation auctioned on route-by-route basis
  • Bids are annual price for service provision
    • Revenues occur to authority — so service provider has no demand risk (just ‘industrial’ risk)
  • Selection criterion ‘best economic value’ but:
    • Qualitative factors count (e.g. reputation, quality)
    • Discretionary power of the regulator (TfL) — may not select the lowest bidder if a) do not think firm can deliver b) would result in more than 20% market share c) …
    • A public benchmark exists (what was the old public operator)
  • Auction format: combinatorial first price auction. Aims to:
    • Encourage participation of small operators by unbundling the network
    • Benefit from coordination and scale and scope via package format
• Regarding initial concerns:
  • These are private value auctions so less risk of winner’s curse
    • Cantillon + Pesendorfer (2006): private information about opportunity costs
    • [ed: not sure here. would seem likely that there is a strong common component here]
  • No renegotiation of contracts: short term contracts and strong regulator
• Dataset: all auctions between March 2003 and May 2006 (294 individual routes)
  • all on the regulator’s website!
  • other information about the transport network
• Summary info:
  • Constant over time (unlike France)
  • Around avg 3 bidders per auction
  • Only 20% of auctions have one bidder
• Basic regression:
  • Av cost per mile (cpm) does decline with number of bidders
  • But clear endogeneity problem as av. bus miles correlated with number of bidders and costs
  • Deal with this by using predicted number of bidders based on number of operators in the vicinity of the route in the previous period.
  • However correlating actual and predicted number of bidders find -ve correlation (suggests people enter (and bid high) when the number of expected bidders is low and vice-versa)
    • confirms endogeneity of entry
• Results:
  • N effects bids in the way we would expect
  • Competition effect larger than (deterred-)entry effect
• Discussant comments:
  • Still carry some demand risk because demand may impact on cost of operation
  • Data on congestion would be useful

ALBERTO GAGGERO- UNIVERSITY OF ESSEX (with Claudio Piga): Pricing and competition on the UK- Irish aviation market

• Background
  • UK-Irish aviation market is largely dominated by Aer-Lingus (EI) and Ryanair (FR)
  • Ryanair launched takeover in 2006 but was blocked in 2007
• Test whether there is impact of competition
  • Plus a study with European data (most from US)
• Data
  • 84k flights
  • EI: 30%, FR: 55%, next biggest 10%
  • ~ 25 routes
  • Using web spider have full fares dataset
  • CAA: available seats, sold seats, flight frequency (aggregated)
  • Distance in km between 2 endpoints
• Put in most variables you could think of
• Endogeneity issues:
  • pricing and market structure may be simultaneously determined so do IV
  • IV approaches mostly based on the fact that the more likely one serves both ends of a route the more likely one serves that route
• Results:
  • (Surprisingly) market shares variables go wrong way (higher market share lower prices)
  • This holds with IVs or without
  • Different IVs do affect size of negativity but do not change the sign
  • (Route) market share up 1% reduces fares by 0.19% (Borenstein IVs) or 0.5% (their own IVs)
  • Check robustness (e.g. pooling all London airports)
  • All other regressors economically and statistically significant and of right sign

Session 2: Finance

ENRIQUE BENITO - FSA: Size, growth and bank dynamics

• Background
  • Banking in Europe has changed a lot (lots of deregulation)
  • Size in banking is important
  • Little examination of size of banks
  • General increase in concentration (more big banks)
  • Data on Spanish banks 1970-2006
• Traditional literature:
  • X-section regression to explain current sizes as function of underlying factors (and hence trends over time in size and concentration driven by these underlying factors)
• Here focus on classic Gibratian stochastic growth process (LPE)
  • S(i,t) = S(i,t)^beta exp(mu(i,t))
  • mu(i,t) = N(alpha(i) + delta(t), sigma)
• Predictions from LPE
  • P1: beta = 1
  • P2: No persistence in growth across periods (no correlation across periods)
  • P3: Variability of growth rates is independent of size
• If these hold (strong all 3, weak just P1) then growth rates of banks follow random walk with drift
• Data
  • Annual data for all banks
  • Reliable data maybe from 1980 so do everything both 1970-2006 and 1980-2006
  • Include firms that exit plus mergers [ed: not quite sure how they deal with mergers exactly]
• Results:
  • Beta less than 1 (significantly but not by much). Some (IMO) weak evidence that is has increased a little bit in more recent periods
  • Rho (measure of convergence) is significantly above 1 (which implies previous periods growth predicts growth today)
  • Heteroscedascity: yes (size matters)
  • Variability of growth: larger banks have more stable growth
  • So reject LPE over whole period but may be converging towards it over time
• Conclusion:
  • Size-growth relationships change over time
  • Converging towards LPE => more skewed size distribution in future (more concentration)

KAI KOHLBERGER - FSA (with Richard Johnson): Has MCOB regulation affected the suitability of subprime mortgage sales?

• Did introduction of new regulations (MCOB) affect mis-selling
  • Mortgages should be suitable (explicit defn)
• Approach
  • Look at arrears rate 12 months after sale
• Data
  • 15 firms, 590k mortgages
  • Regressions with 300k observations (due to missing values — check this is not systematic)
  • FSA Product sales database (PSD)
  • Macro vars
  • Subprime defined as in PSD
• Find no impact on arrears rates discernible from policy change