miscellaneous factZ

Over the last couple of months for the purpose of my research on happiness/subjective-well-being I’ve been putting together some notes on theories of contextual judgement. The first part of these is now in a form suitable for public consumption and I’ve posted them at:

http://www.rufuspollock.org/economics/notes/theories-of-contextual-judgement/

Following on from the third workshop a month ago, yesterday saw the third in the series of “Workshops on Well-being” take place at the LSE. This time the presentations were given by Mat White of Plymouth University and Andrew Steptoe of UCL. Below are some (very) impressionistic notes.

Presentation by Mat White (+ Paul Dolan): Accounting for the richness of our daily activities

1. Social psychologist: started out on risk perception, trust etc. (Fear of crime)

2. General problems with life satisfaction data

   • lots of it deals with attributes which are beyond realm of govt intervention (e.g. race, gender)
   • Response/cross person comparisons issue: same externals result in different reported happiness levels across individuals (e.g. old, poor people are happiest in Dolan’s Welsh data, perhaps because of a “Don’t grumble” attitude). [ed: essence is the qualia problem: can we compare different people’s report of their internal states, both across people and across time. Or more pithily: is my ‘Good’ or ‘OK’ the same as your ‘Good’ or ‘OK’?]
   • Subjective well-being isn’t one thing but a composite: SWB = Feelings + Thoughts + Time

3. Solutions

   • Experience Sampling Method: ask people during day
   • Problems: costly, only points in time, no duration etc
   • Day Reconstruction Method (DRM): solve duration issues
   • Can now base utility as integral of well-being function over time (ed: what utility always was but we just didn’t have the moment by data)
   • Find what one might expect re. what activities are nice
   • However no/v. weak correlation with e.g. income
     • But maybe because those payoffs are in the future
     • Or maybe because there are rewards in terms of thoughts, feelings about themselves etc (Eudamonia)

4. This project: add thoughts (about activities) to DRM

   • 625 Germans
   • 5815 Episodes (3057 single activities)
   • Online panel
   • Have 12 adjectives they can use which break down into ‘pleasurable’ and ‘rewarding’

5. Adding in Eudamonia makes a big difference!

   • Nice graph contrasting the DRM with ‘pleasure’ vs. ‘rewarding’ (at least partially inversely correlated).
   • Argue that we should sum both ‘eudaimonic’ and ‘hedonic’ evaluations over whole day.
   • Can now plot activities on x-y graph with x=hedonia, y=eudamonia (normalized about the mean values)
   • Get a slight -ve correlation
   • ed: this makes sense due to selection effects. Let w be total well-being and h hedonia score, e is eudamonia score. Suppose w is a linear combination of these underlying factors: w = h + k e. Now we would generally choose only to do activites with w > w0 (some outside option) => h+ke > w0 which gives the -ve correlation.
   • If reweight with duration [ed: equivalent to doing integral] then get a slight +ve correlation

   • ed: this reweighting by duration causes major changes to the form of the data. In particular all longer activities receive a positive shift while short ones receives a negative shift (explanation below). Whether this is what could/should do with the data was not entirely clear.

     Why does this shift occur. Results are plotted as ‘relative’ values (i.e. normalized about the mean). Thus if original value (x,y) it is plotted at (x-m1,y-m2) where m1 is the overall x-mean and m2 is overall y-mean. Adding duration means original values are now (dx,dy) and these are plotted relative to n1,n2 where n1,n2 are new duration weighted means.

     Letting dbar be the mean duration we could make the rough approximation that n1 = dbar m1, n2 = dbar y1. Then the new x position is: dx-n1 = dx - dbar m1 = d(x-m1) + (d-dbar)m1. Hence the new x-position will be a combination of a linear scaling out from the origin by d plus some offset of (d-dbar)m1. Since m1 is always positive this offset is positive (negative) as the duration of the activity is greater (less) than the mean duration of an activity.

6. After discussion

   • pop-ups (thoughts either +ve or -ve) have a big impact
   • in a regression on day-satisfaction number of +ve and-ve popups explained more than hedonic or eudamonic variables (total value for whole day)
   • could be useful to look at something more than a simple integral [ed: e.g. use contextual judgment stuff]
   • Eudamonia: enters day satisfaction regressions negatively. This is what we would expect given association of ’satisfaction’ with ‘pleasurable’ activites and slight negative correlation of ‘rewarding’ (eudamonic) activities with ‘pleasurable’ (hedonic) ones.
   • ed: could interpret eudamonic value as discounted future value coming from associated payoffs. I.e. if I work hard now this might not be pleasurable but it has high eudamonic content reflecting the future hedonic payoffs (nice garden, good holidays etc) of doing that work (NB: this is intentionally putting things very crudely).

Andrew Steptoe: DRM Analyses

1. Primarily interested in ‘positive affect’ and health outcomes

2. Questions:

   • how accurate is DRM
   • what does DRM tell us about activities and feelings of depressed people

3. Data: Daytracker study

   • 200 healthy women in full-time work
   • 2 x 24hr starting @ 5pm (one work day and one non work-day)
   • International dimension
   • EMA and DRM

4. Comparing EMA and DRM

   • Across aggregate data already see some differences (DRM shows noticeable rise towards end day while EMA does not really show this)
   • Per individual: similar differences but also fairly close correlation
   • Doing actual correlation looking at 4 different aspects (happy, tired, stress, anger) find medium correlations (0.2-0.7) which is reasonable but not great
   • also noticeable that timepoints are important: worst correlations are generally 12noon and 3pm

5. How accurate is the DRM for estimating feelings (esp. in relation to depression)

   • Do depressed people: have diminished pleasure in all activities or is reduced exposure to good stuff?
   • Depressed people are less happy across most interactions (except with Grandchildren) though effect (of depression) does vary and is strongest for being Alone or with your Partner
   • Looking at time: depressed people seem to spend more time (compared to non-depressed) doing things they don’t like
   • Similarly, looking across activities, depressed people are less happy doing most stuff
   • Again looking at time, seem to find depressed people spending more time on things that they particularly dislike (relative to others)
   • [ed: Not sure what this is telling us. After all the activities depressed people spend more time on may still be better than other options even if those options do not get as large a negative ‘hit’ from being depressed — e.g. house-work may not be much worse when depressed than non-depressed but it still might be worse than everything else]

Several years ago I read Michael Kremer’s article entitled “Randomized Evaluations of Educational Programs in Developing Countries: Some Lessons” in the 2003 AER Papers and Proceedings issue (jstor link). This brief article reviewed some of the recent results of evaluating the effects of various different programs on educational outcomes in the developing world. What particularly caught my eye was this paragraph summarizing a teacher incentive program in Kenya:

Some parent-run school committees in the area provide gifts to teachers whose students perform well. Glewwe et al (2002a) evaluate a program that provided prizes to teachers in schools that performed well on exams and had low dropout rates. In theory, this type of incentive could lead teachers either to increase effort or, alternatively, to teach to the test. Empirically, teachers responded to the program not by increasing attendance, but by increasing prep sessions designed to prepare students for exams. Consistent with a model in which teachers responded to the program primarily by increasing effort devoted to manipulating test scores, rather than by increasing effort at stimulating long-term learning, test scores for pupils who had been part of the program initially increased but then fell back to levels similar to the comparison group at the end of the program. [p. 104, emphasis added]

This provides a nice ‘real-world’ example of exactly what can go wrong when providing incentives in a multi-task situation — that is one where the ‘agent’ (here the teacher) performs multiple tasks not all of which can be monitored equally. As such it should make us wary of the current trend to ever more performance-based reward structures in everything from schooling to health-care.

Last year I collated and distilled the notes and summaries accumulated over the PhD into a proper paper which could act as the literature review in my dissertation. While I submitted the PhD last August I’ve only just got around to posting this up and it can now be found at:

http://www.rufuspollock.org/economics/papers/economics_of_knowledge_review.pdf

From the abstract:

A selective review of the existing theoretical literature related to the economics of knowledge with particular attention to intellectual property, especially in the form of patents.

Note for those seeking the references they can all be found in the economics bibliography found at:

http://www.rufuspollock.org/economics/biblio/

I’ve now posted my slides from the Musicans, Fans and Online Copyright event which took place last Wednesday at LSE. They can be found on this site:

http://rufuspollock.org/economics/papers/musicians_fans_and_online_copyright_20080319/

For anyone with an interest in copyright issues, particularly in the online environment, there is an excellent event on today at the LSE organized by Ian Brown of the OII and at which I’ll be speaking (briefly) on the subject of “How can we maximise copyright’s return to society?” More details below.

Musicians, fans and online copyright

Wednesday 19 March 2008 14:00 - 17:00

• John Kennedy, CEO of IFPI
• Paul Sanders, Director of Strategy at Playlouder
• Becky Hogge, Open Rights Group
• Adrian Brazier, DBERR
• Lilian Edwards, Southampton University
• Rufus Pollock, Cambridge University
• Michelle Childs, Knowledge Ecology International
• Wendy Grossman, musician / freelance journalist

Location: Old Theatre, London School of Economics, Houghton Street, London, WC2A 2AE, United Kingdom.

This Wednesday afternoon we have a great selection of speakers for our free OII/LSE event on music and copyright. Come along to find out what the government, music industry, publishers and independent experts are thinking about ideas like 3-strikes-and-you’re-disconnected; scanning ISP traffic for copyright works; and notice and takedown regimes.

Full programme at: http://www.oii.ox.ac.uk/events/details.cfm?id=186

Following on from the second workshop a month ago, today saw the third in the series of “Workshops on Well-being” take place at the LSE. This time the presentation was given by Andrew Clark of PSE. Below are some (very) impressionistic notes.

Presentation by Andrew Clark on Job Satisfaction: What do we Know and What Next?

1. Job satisfaction (JS) and individual well-being (LS)

   • well-being/LS function LS = f(Job satisfaction, health satisfaction, leisure etc)
   • data in BHPS (waves 6-15 though 11 missing)
   • health/ income / house / spouse / job / social life / amout leisure / use leisure (scale 1-7)
   • all highly significant
   • social life is top, followed by health, use of leisure, income and job satisfaction is last
   • robust to demographic controls
   • But do individual personality types make any difference (fixed effects)

2. Panel results

   • all effects go down (there are ‘happy types’) except JS (which doubles) though still the smallest
   • Is this ranking unique to Britain?
   • Is it the same for everyone? (subregressions: old/young, men/women; or do a latent class analysis)

3. JS is important to firms as well as it will predict worker behaviour

   • Labour turnover
   • Absenteeism
   • Counter-and non-productive work/productivity
   • Worker quitting (but almost impossible to do properly as quitting is self-reported so unreliable)
   • P(quit(t+1)) = f(JS, X(t))

4. Compare quitting GB and Germany

   • pretty similar, JS is pretty significant

5. Cognitive biases and context in relation to quitting (SPELL data from BHPS)

   • have panel data so can look at series of JS for an individual
   • refers to Kahnemann and Riedelmeier on evaluation of colonoscopy
   • suggest Peak-End evaluation: evaluations of peak and end point
   • Apply to job quitting (peak-end, min, max, avg, current …)
   • peak-end does best (followed by running max (close), and current)
   • => behaviour would not then seem to max their utility

6. Try do the same with income but need variations in income (since normally just rises)

   • use truckers as they have exog changes
   • other potential sources: tax changes
   • peak-end divorce

7. Relative income

   • Traditional: W/LS/JS = W(y,…)
   • Comparisons: LS/JS = W(y/yr, …)
   • yr is comparison/relative income
   • to whom do we compare? (peers, others in HH, spouse, myself in the past, friends, neighbours, work, expectations)
   • Results:
   • +ve effect of income
   • But falling as other’s income rises
   • Overall effect is zero: if everyone’s income rises then no effect

8. Preference for structure of income

   • same income but in different ways
   • flat slope (A), steeper (+ve) slope (B), and v. +ve slope (C)
   • even though flat slope (using saving to mimic C) would result in being overall better off
   • asked about this (they were told they could this) they still chose C (apparently because of self-control issues — they wouldn’t be able to save)

9. Does other’s income always affect one negatively

   • Hirschmann’s tunnel effect (happy for something good for you because it means something good is going to happen to me)
   • Danish ECHP (1994-2001): fantastic data (which gave not only individuals but all of their colleage’s info including pay)
   • here one does find a +ve effect of others income on me (check how it varies across firm so not just selection effect at firm level)

10. Do 2 wrongs make a right?

    • Peak-end utility could be thought of as ‘correct’ as:
    • with adaptation
    • current utility (after something good) understates actual total flow benefits (as one has adapted)
    • PE corrects for this

11. Instrumental uses of JS

    • ‘Good job’ lit has mainly focused on money
    • But self-employed earn less but are happier (though significat issues about reporting bias)
    • Also why are there different avg. wages in different industries (when they look the same)
    • Compensating differentials vs. rents
    • So let’s use JS to explain different
    • looking at the data: high wage goes with high JS (so suggests this about rents not compensation)

12. Job Quality: Are things going to the dogs?

    • ISSP (repeated XS in 3 waves 1989 - 2005)
    • Multivariate regressions: JS is improving (went down 1989-1997 but bounced back in 2005)
    • But stressful/dangerous/difficult work has been rising
    • Good job content has been going down.
    • However enough other stuff has been getting better faster (income, opportunities, flexible hours)

Discussion

• Paul Dolan:
  • Causality
  • Experienced Utility? Kahnemann would be unhappy
  • Peak-end seems difficult for JS since already a retro-spective evaluation (so peak-end of a peak-end)
• Gordan:
  • relative ranked position not just compared to the avg
  • care more about those above than those below
  • need to be more specific about form of relativities
• All: Context, Context, Context
• RP: Peak-end vs. range-frequency. Take colonoscopy: PE predicts that increasing pain at a single point (early on) would worsen evaluation while range-frequence would predict it would improve evaluation (since you spend more time at a level relatively better than the worst)
• BHPS: now have a question asking for whether your LS is better/worse than last year
• Gordan: gratitude is single biggest predictor of happiness
  • individual differences
• Propensities to adapt
• Gordon: Andrew Oswald and he also found +ve avg income effects in workplace
• Judgment vs. Adaptation
• Paul Dolan: generally we overestimate our +ve attributes but underestimate (their relative) income level

The research report entitled Models of Public Sector Information Provision via Trading Funds was published today to coincide with its mention in the budget (para 3.49). This report was commissioned by HMT and BERR and jointly written by myself, Professor Newbery and Professor Bently (all at Cambridge University).

The formal announcement and details of the context in which the report was commissioned can be found here on BERR’s website. To quote at some length from the section dealing with this report:

As part of the response [to the OFT’s report on the Commercial Use of Public Information], the Government commissioned Cambridge University to do some analysis specifically around the pricing of public sector information held by trading funds[i].

This analysis has been released today as a Study Report (11 March 2008 - see Related Documents), and it sets out estimates of the costs and benefits of marginal-cost pricing, based on the assumptions used by the Cambridge team and the data they were able to collect.

Going forward, the Government will look closely at the public sector information held by trading funds to distinguish more clearly what is required by Government for public tasks, ensuring this information is made available as widely as possible for use in actual and potential downstream markets.

In the lead up to the next spending review, it will also ensure that it is priced appropriately. The underlying principle will be that information collected for public purposes will be made available at a price that balances the need for access while ensuring customers pay a fair contribution to the cost of collecting this information in the long-term.

[i] The study was commissioned jointly by the Department for Business, Enterprise and Regulatory Reform and HM Treasury in July 2007. The terms of reference for the study are available upon request by contacting the BERR Enquiry Unit

Meanwhile the budget had this to say (chapter 3 and chapter 5)

3.49 [Commercial Use of Public Sector Information] The Office of Fair Trading’s (OFT) market study into the commercial use of public information15 highlighted important issues around access to public sector information for commercial or other re-use. The Government commissioned Cambridge University to analyse the pricing of this information. This analysis is published alongside Budget 2008. The Government will look closely at public sector information held by trading funds to distinguish more clearly what is required by Government for public tasks and ensure that this information is made available as widely as possible for use in downstream markets. In the lead up to the next Spending Review the Government will ensure that information collected for public purposes is priced so that the need for access is balanced with ensuring that customers pay a fair contribution to the cost of collecting this information in the long term. These issues will be considered in conjunction with the assessment of trading funds (see Chapter 5).

….

5.14 [Asset management and sales] The Government is committed to managing the stock of public assets to deliver optimal results for society and the economy. Following the 2007 CSR, departments are publishing asset management strategies setting out how they will deliver best value from their assets. To support the introduction of asset management strategies the Treasury and Shareholder Executive will undertake an assessment of the governance, business plans and future development strategies of each of the trading funds and a selection of public corporations (excluding those already subject to central government review).

I was much much struck by generally pessimistic tone of Gregory Clark’s lengthy review in the JEL’s September issue of Avner Greif’s Institutions and the Path to the Modern Economy. These comments have wider implications for the application of economic tools (especially game theory) to the analysis of historical outcomes, particularly in relation to institutions, and I have therefore thought it worth excerpting from the review here (at some length).

When You Have More Variables than Data You Aren’t Explaining Anything

As noted, Greif defines an institution as a self-reinforcing set of behaviors. Greif pioneered in applying game theory to historical institutional analysis and his 1993 study of the Maghribi traders remains a classic of this still modest genre. This was certainly an exciting development for economists. For the first time, [sic] seemingly grounded the explanation of informal institutions in optimizing individual rational behavior. Behaviors that would seem to the layman to be based on blind irrational custom could be shown to be consistent with individual optimization. Given the incredible intellectual elaboration of game theory, and its meager harvest in terms of actual economic applications, the finding was welcome to both game theorists and to economic historians. [ed: a tough but fair assessment …] The Maghribi study also allowed for the possibilities of institutional change resulting just from changes in parameters. Since the equilibrium depended on certain parameter values, changes in transportation costs or observability could terminate the old equilibrium and lead to a new one. The 1993 article seemed to point to new micro foundations for institutions that would ground them in individual maximizing behavior.

But this book is almost certainly not what many economists who welcomed the 1993 article expected as the generalization of its ideas. Some indeed will be shocked by, and perhaps hostile to, the path Greif has taken. Were economists of a more literary bent, the word apostasy would be on their lips. In a search for generality, Greif concludes that such a set of limited rational actor assumptions is not constraining enough to describe real-world institutions. For a start, “multiple equilibria usually exist in the repeated situations central to institutional analysis” (p. 125). There have to be more constraints on the structure of the interaction to explain the equilibrium. These constraints include “cognitive norms” (p. 128) as well as “the social and normative foundation of behavior” (p. 143). Issues such as “losses of esteem,” “norms,” “fairness,” or “social exchange” have to be introduced. Also such social and normative behavior is “situationally contingent” (p. 144). [ed: and we now have so many parameters we could probably explain anything …]

Greif posits this as just an extension and elaboration of the original individualistic rational-actor game theoretic ideas. Once we are compelled to admit, however, into the explanatory apparatus almost the entire sociological zoo of ill defined and unmeasurable constructs, we lose all explanatory power. Explanatory power requires few objects and small degrees of freedom. Greif notes that “a useful feature of game theory is that it allows us to study all intertransactional linkages—economic, coercive, social and normative—simultaneously” (p. 147). But he does not seem to appreciate the price of this generality in terms of testability. All we are left with is the idea that people operating within institutions act as they do because, given the cognitive, intellectual, cultural, and normative constraints they face, their actions seem to them as being the best available. But, in an informal sense, we knew that already. Without any consideration of the ins and outs of game theory, we can appreciate that any lasting institution likely constitutes some set of self-reinforcing behaviors. Yanomamo males, for example, engaged in recurrent raids against other bands aimed at capturing women and revenging previous raids (Napoleon A. Chagnon 1983). This was clearly an institution in the sense of Greif and must be maintained by some kind of self-reinforcing set of behaviors. But we knew that, even if we had never studied game theory. So what insights have we gained from page after page of elaboration on the idea of equilibria and the elements that enter into them (pp. 124-53)? If we were able to reduce all such social equilibria to a game theory equilibrium of purely self interested rational individuals interacting with common knowledge that would be a radical, novel, and testable theory. This book denies that possibility, but without providing any alternative that has empirical content. [pp. 735-736, emphasis added]

The Problem of Too Many Equilibria (in Dynamic Games with Beliefs)

… Greif here starts from the basis that we will never be able to predict institutional structure from exogenous features of the situation—including institutional history. … Given the many potential stable equilibria in each institutional context, the outcomes are inherently unknowable. After the attention given to elaborating the theory of institutional stability and dynamics in the preceding 350 pages, this conclusion comes as something of a surprise. The structure and tone of the previous discussion is that of laying the groundwork for a theory of institutions. The reader now learns that the extended theory encompasses a perhaps uncountable number of possible institutional equilibria, so that there can be no advance prediction.

Just as deductive methods cannot succeed, Greif asserts also that inductive generalization about institutional forms will also fail to reveal any patterns. This is because unobservable elements of the situation—beliefs and norms—are crucial to the determination of the outcome. The same observable elements will be associated with radically different institutional equilibria. … [p.737]

Case Studies (and Historical Anecdote) Aren’t Economics (or Economic History

[The empirical approach recommended by Greif] As conducted in the book, [this] is essentially the method of “analytical narratives” popularized by Greif and Robert Bates, Margaret Levi, Jean-Laurent Rosenthal, and Weingast. An analytical narrative consists of matching institutional detail to a formal, or more often informal, interpretation of the situation as some kind of rational choice equilibrium, interpreted in the broad sense above (Bates et al. 1998). It is not clear how this is distinguished from such things as Harvard Business School case studies. As applied by Greif and his colleagues, an “analytical narrative” seems to be just an interpretation of an institution in terms of a loosely defined equilibrium. This is fine as an approach to generating hypotheses, but as an endpoint of analysis, as it generally is in the book, it offers little conviction. [p. 737]

In Conclusion: There Isn’t Much of a Future

… Greif intends in his book to develop at least the outline of a new, micro grounded theory of institutions. Stating, explaining, and elaborating this theory takes 503 densely written pages, including a primer on game theory. By the end, however, this reviewer, to the contrary, read it mostly as a demonstration of the impossibility of a systematic account of institutions along the lines he proposes. The efflorescence of concepts, combined with the constriction of possible empirical tests, makes … prediction and testing impossible. And this shows in the case studies conducted in the book. Each institution in his formulation has to be analyzed in its full idiosyncracy, aided by the expert judgment of the investigator as to the social and epistemological context. But, as we saw in the case of the Podesteria, that kind of analysis, even in the hands of a careful enquirer like Greif, is fraught with the danger of conflating conjecture and fact. Kant’s Prolegomena to any Future Metaphysics as a Science never led to his proposed science of metaphysics. Unfortunately Greif’s Prolegomena to a future institutional theory similarly serves mainly to indicate the barriers to a science of institutions.

Originally status would have developed from some kind of of stimulus-response setup:

    Beating Competitor
            |
            V
      Higher Status
            |
            V            
Better Access to 'Resources'
  (e.g. Food and Partners)
            |
            V            
  Higher Survival Rate /
    More Progeny etc
            |
            V            
  Development of Reward System(s)
   for these outcomes (Food etc)   
            |
            |  (short-circuiting
            |   as with e.g. sex)
            |
            V         
Development of Reward Systems
 for Success in Competition 
     (Higher Status)

So status now has two components:

1. Increase in status improves access to ‘basic’ goods we derive direct ‘utility’ from (food etc)
2. Increase in status provides direct ‘utility’ independent of any impact upon access ‘basic’ goods.

What about respect? It could be argued that respect is a ‘basic’ good directly equivalent to type (ii) status. However I’m not really convinced of this for two reasons. First, ‘respect’ is fundamentally different from ‘normal’ goods in that one can select what you respect (and whose respect you care about). Second, and more importantly, as just outlined above, the desire for ‘respect’ or ’status’ seems to me a ’secondary’ desire, which has come about via a short-circuiting of our basic reward systems for ‘primary/basic’ goods.

Leaving this aside, the crucial point is that type (ii) status results in a pure zero-sum game. Thus, reducing competition for it (perhaps by increasing compassion) might move us to a (more) positive sum situation. Furthermore, the clear distinction between type (i) and type (ii) allow us to separate out ‘competing to survive’ (which might be essential) and ‘competing (just) to win’. This seems an important distinction to make. After all, we can all accept that, in a whole set of situations, successfully competing may be crucial to obtaining the basic resources needed to survive. However as we get wealthier it would seem that this first aspect diminishes in importance and the second (less healthy) aspect of status looms ever larger.