The Birds and the Bees – What behavioural science and biology teach us about risk

A lecture advertised as Behavioural science and biology have much to teach us about risk obviously gets my attention.

Below my main take aways on this FCA Insights Lecture (FCA = Financial Conduct Authority in the UK, similar to the AFM in the Netherlands, where I work). Read the full Insight Lecture delivered by Chairman of Oxford Risk, Lord Krebs on 22 November, 2017.

Talk in three parts:

  1.  The biology of risk and decision making.
  2.  The role of regulation versus informed choice
  3. How we might use insights from behavioural science to help consumers of financial products to make better choices. Key point in this final section will be that behavioural science has not displaced classical economic models, but has the potential to enrich our understanding of human decision-making.

So what do biologists have to say about risk?

Biological models derive optimality functions from Darwinian fitness, or a proxy for fitness such as food intake, growth rate, or reproductive success. It can be argued that normative economic models, because they have no external reference point equivalent to Darwinian fitness, have an element of circularity: utility is that which is maximised.

‘Expected energy budget rule

Imagine a choice between two food sources, a ‘safe’ option and a ‘risky’ option. Which one is it better for the organism to choose? The theoretical answer depends on the organism’s internal state. (…) You may not think of peas as being very clever, but they have been equipped by natural selection with mechanisms for detecting nutrient concentration in the soil, a valuable survival device. When a seedling germinates, the young roots grow towards parts of the soil that are rich in nutrients.

Dener, E., Kacelnik, A., & Shemesh, H. (2016). Pea plants show risk sensitivity. Current Biology, 26(13), 1763-1767.

I want to make two general comments about this experiment.

First, the fact that pea plants follow the predictions of a normative evolutionary model of risk underscores the point that a brain, or conscious thought, isn’t needed to make the right decisions. Instead, the species we study rely on rules of thumb that have evolved because they yield the right answer, or at least an approximation to it.

Second, understanding these rules of thumb not only helps to gain insight into differences between the optimal solution and observed behaviour, but could also provide a general theory of decision making that complements and enriches the normative optimality models. There’s a parallel here with the difference between normative economic models and the rules that people actually use to make decisions (what Gerd Gigerenzer calls ‘fast and frugal heuristics’).

It’s easy to see how this finding, if applicable to humans, could be used to manipulate people’s choice of investment portfolios. But also “for good”, an example (from Knoef & Brügen, 2017):

Omdat hun default voor de meeste mensen de beste keuze is, heeft NEST de low risk-optie ‘NEST lower growth fund’ genoemd (in plaats van low risk), terwijl de high risk-optie ‘NEST higher risk fund’ heet (in plaats van high return).

The role of the regulator

If the construction of options for investment can be used to steer people’s decisions, should they be regulated, or is it a case of caveat emptor?

[W]hy regulators may not want to ban things. If you have objective criteria and apply them consistently, you may come up with some unintended consequences. Be careful of what you wish for.

My question, for discussion, is whether or not similar externalities of poor financial decisions by consumers could cause “indirect harms” and therefore justify regulation

labelling is not a panacea for the problem of dietary ill health. Again, I pose a question for discussion: are there parallels here for the labelling and marketing of financial products?

I very much agree with this last remark; warnings, disclosure and more inforamtion are (too) often hailed as the solution to everything.

Helping people to make better financial decisions

Encouraging people to make better choices through nudging, and as an alternative to regulation, has been advocated in recent years for many areas of policy, including financial services. The latest annual report from the Behavioural Insights Team or “Nudge Unit”, published last month, lists their key success stories from field scale trials. (…) On the face of it, these are impressive results, although it remains to be seen how long lasting the effects of nudges are. But even if these success stories are sustained, I think nudging has its limits. (…) nudges are likely to be of limited effect, compared with more interventionist measures such as investment in infrastructure, taxation or regulation. (…) No amount of nudging will compensate for lack of investment in the appropriate infrastructure.

we should not see behavioural science as an alternative to traditional optimisation models. In biology the actual mechanisms by which animals or plants make decisions are seen as complementary to, and not alternatives to, normative optimality models.

awareness of how people actually make decisions must be relevant to the ways in which advice is presented.

Beyond nudge

Our view at Oxford Risk is that the best financial decisions will be made by consumers

  • when they have the relevant knowledge,
  • when they are engaged with the decision and
  • when they feel comfortable about making the decision.

The challenge for the financial services industry is to harness the power of behavioural science to help people to make decisions about their money that

  • will give them what they want,
  • what they need and
  • what they understand.

Consumers and competition: Delivering more effective consumer power in retail financial markets

Triggered by these tweets, I read a position paper by the FS-CP, the Financial Services Consumer Panel (Don’t rely on consumers to boost competition, pretty much sums up the main conclusion) and a ‘think-piece’ Consumers and competition: Delivering more effective consumer power in retail financial markets.

About the FS-CP: We work to advise and challenge the FCA from the earliest stages of its policy development to ensure they take into account the consumer interest.

The think piece was written by Jonquil Lowe and aims to

  • consider possibilities to deliver more effective ‘consumer power’
  • generate a set of real-world metrics that can measure how well markets work for consumers
  • influence FCA thinking on competition and consumer responsibility

using a consumer survey and a literature review (p2).

Current approach: “competition policy focuses heavily on how to make consumers more engaged and closer to the rational decision-makers that traditional economic theory suggests are a prerequisite for well-functioning markets.” (p6). This focus on timing and framing of information “underplays other potential policy options and overlooks the possibility that some seemingly irrational lack of engagement might in fact be rational

Price discrimination

3.12 With ‘third-degree’ price discrimination, consumers do not self-select. Instead the firm has to be able to distinguish groups of consumers who are willing to pay more. (…)

This type of price discrimination is common in the general insurance market, where customers who stay with the same insurer year after year typically pay higher premiums than new customers. Consumers may view this practice as an unfair penalty on loyalty.

However, a report for the Financial Services Practitioner Panel from Clayton et al (2013) documents industry views that, given existing customers can choose to switch if they want to, this form of price discrimination amounts to consumer choice rather than consumer detriment and: ‘there is an inherent value to customers to not having to shop around and transfer and this should be reflected in the price they pay (i.e they should pay more)’ (p.22)

p.13-15: More on price discrimination:

  • A common price strategy is price obfuscation (…) For example:
    • discontinuous pricing occurs where a small change in the consumer’s circumstances or behaviour triggers additional charges, such as a sudden shift from free-if-in-credit current account banking to steep charges for even a small, short-lived unauthorised overdraft.
    • Exit charges that are triggered if a consumer wants to switch their mortgage or investment-type life insurance product may be downplayed at the time of purchase.
    • Firms may ‘game’ any price disclosure rules or price comparison services by keeping down headline charges, but charging extra for product features that might normally be considered integral to the basic product, for example, some insurers charge a fee for administrative adjustments, such as change of address.
  • Bait and switch
  • Opportunism (taking advantage of an external event or requirement to charge higher prices)

Product-feature strategies

  • Product bundling and add-ons
  • Product complexity (eg many characteristics or technically complex; many similar but slightly different products)
  • Product differentiation (genuine or spurious); product differentiation may also be used spuriously to create a degree of market power provided consumers are convinced the differences have some meaning and value. (p.15)
  • Brand and advertising
  • ‘Hollowing out’ (reducing core features or services); stripped-back policies’ (…) akin to the ‘shrinking Mars Bar’ (p.16)

Exploit biases

Firms can turn System 1 thinking to their advantage by providing consumers with cues designed to influence heuristic-based decisions. (p.17). A few are mentioned (inertia, framing, present bias, anchors, salience), but I feel this chapter does not provide convincing evidence for the conclusion on p.52: “Consumers are prone to behavioural traits that get in the way of their ability to drive competition and may be deliberately exploited by firms.”

Barriers to competition


“[C]ompetition regulators may be underestimating the extent to which consumers’ decisions to occupy the Repeat-Passive space could be a rational choice rather than the result of behavioural biases that need to be changed or harnessed (…) : the wide nature of search and switching costs; and satisficing. (p.29)

Enhancing consumer power

Building on Grubb’s (2015) policy types, the following policy options are considered:

Simplify choice: This could entail policies to simplify products, information and process.
‘Advice’: Policies that provide or facilitate expert advice to consumers, such as, comparative information from regulators and price comparison websites and requirements for firms to share customer data with these services.
Choose for the consumer: Policies that involve consumers delegating choice decisions to a third party, and
Other policies: These include the type of behaviourally informed measures being deployed by the FCA, such as smarter information that is better framed and more timely, as well as triggers, incentives and financial education. (p.33)


Automated shopping-and-switching

6.24 To summarise, automated shopping-and-switching has three novel aspects:

• It essentially digitises the role of a human broker or adviser, but going further than price comparison websites by taking full account of product and service features other than just price.

• It changes the default from status quo to switch if a consumer gain is identified, and

• It passively gets on with repeating the process as necessary, releasing consumers from the merry-go-round of repeated active engagement across multiple financial and household markets. (p.40)

Meaningful metrics

Switching data is a particularly contentious measure because the rational outcome of shopping around might be not to switch if expected benefits do not outweigh costs. Moreover, as a market approaches the ideal of perfect competition, the necessity for, and pay-off from, switching would decline. (Paradoxically, this would also reduce the incentive for consumers to shop around even though continued shopping around would be required to maintain perfect competition.) (p.49)


8.2 However, the expectations placed on consumers [reliance on rational, active consumers to drive competition by shopping around] look unrealistic because:

  • Perfect competition seldom, if ever, exists in reality. In most retail financial services markets, it is in firms’ interests to create and maintain market power. They do this through strategies, such as price discrimination, price obfuscation, product bundling and complexity and promotion of brands. The result is markets that are overly complicated and products that are difficult or impossible to compare. Even the most financially capable consumers face a battle to find value-for-money in markets like these.
  • Price comparison websites are designed to help consumers make product comparisons, but often focus too heavily on headline price, ignoring other essential factors, such as product features and quality of service. Firms can ‘game’ price comparison sites by ‘hollowing out’ products and using a variety of ancillary charges.
  • Consumers are prone to behavioural traits that get in the way of their ability to drive competition and may be deliberately exploited by firms. Competition regulators have a growing understanding of these traits, treating them as a new type of barrier to competition, and seeking either to alter consumer behaviour or adopt policies that work with the grain of consumers’ actual behaviour. However, the aim of these policies is still to foster more active shopping around and switching.
  • Competition regulators may be misinterpreting widespread consumer decisions not to engage with shopping around as behavioural barriers, when in reality they may be rational choices based on consumers’ preferences about how they wish to spend their time and mental effort.


Gelezen in ESB: Het effect van framing op pensioenperceptie

In ESB van 12 oktober 2017 stond een artikel over consumentenonderzoek van Prast (ex DNB) en Teppa (DNB) onder 1034 mensen van het CenterPanel: Het effect van framing op pensioenperceptie [pdf].

Vier random groepen kregen op verschillende wijze een vervangingsratio van 50% bij pensionering te zien en hen werd gevraagd: wat is oordeel om bij pensioen van rond te komen? zeer onvoldoendeonvoldoendevoldoenderuim voldoende. Het goede antwoord is (zeer) onvoldoende

Het %frame luidde: “U krijgt 50% van uw huidige bruto-inkomen”. Andere frames: bedrag in euro per jaar, bedrag in euro per maand, en “0,5 maal uw huidige inkomen

Percentage werkt beter
Respondenten in het percentage frame de verwachte pensioenaanspraak significant vaker als onvoldoende of zeer onvoldoende beschouwen (…) Een aanwijzing dat communicatie in euro’s kennelijk onduidelijk is.” concluderen Prast en Teppa.

Want in het %-frame zegt 82% (zeer) onvoldoende (oranje omcirkeld in tabel hieronder); in de andere frames (blauw omkaderd) zegt 70% (zeer) onvoldoende.


Prast en Teppa extrapoleren: “Als een percentageframe leidt tot meer duidelijkheid (‘bewust­zijn’) bij de deelnemer over de mate waarin zijn pensioen­aanspraak voldoende is, kan de pensioenuitvoerder, met slechts weinig moeite, zijn bereik van de deelnemer verbe­teren en zo voldoen aan het doel van de Wet pensioencom­municatie.”

Op zich ondersteunen de uitkomsten wel deze conclusie, maar ik vind het nogal stellig. En intuïtief vreemd, en niet helemaal in lijn met andere onderzoeken waarin euro’s en %’s vergeleken worden (vaak vindt men euro’s duidelijk).

Prast en Teppa maken zelf ook deze kanttekening: “Een kanttekening daarbij is dat wij slechts heb­ben gekeken naar een vervangingsratio gelijk aan de helft van het huidige inkomen. Nader onderzoek is nodig om te zien of het framingeffect stand houdt bij hogere en lagere vervangingsratio’s.

Ik vind het opvallend dat de 50%-conditie (“50% van uw huidige bruto-inkomen”) zo anders scoort dan de 0,5 conditie (“0,5 maal uw huidige inkomen”). In het %-frame antwoordt 82% (zeer) onvoldoende, in het decimaal-frame is dat 67%.

Het biedt wel de kans om makkelijk te repliceren. Voor de euro-condities moet je het inkomen van de respondent weten, voor het decimaal-frame niet.

Mogelijk dat we in de komende AFM Consumentenmonitor onder steekproef van N=800 die representatief is voor Nederland 6 random groepen gaan ondervragen (2×3 opzet); met 2 soorten frames (% of decimaal) en 3 waardes: 50%/0,5 – 70%/0,7 – 90%/0,9.

De vraag zou dan luiden voor 50%/0,5 frames:

Stel, u krijgt de volgende informatie over uw toekomstige pensioen:
Als u tot uw pensionering blijft werken, kunt u vanaf het bereiken van de pensioenleeftijd het volgende pensioen verwachten:

Frame A: 50 procent van uw huidige bruto-inkomen
Frame B: 0,5 maal uw huidige bruto-inkomen.

Geef aan in welke mate u dit pensioen voldoende of onvoldoende vindt om van rond te komen. Laat hierbij het eventuele inkomen van uw partner buiten beschouwing.
1. Ruim voldoende
2. Voldoende
3. Onvoldoende
4. Zeer onvoldoende
5. Weet niet

Andere reacties
Guus Pijnenburg is minder enthousiast:


Werkt de wildwestwaarschuwing wel? Onderzoek naar de vrijstellingsvermelding Let op! U belegt buiten AFM-toezicht

In het september nummer van het Tijdschrift voor Toezicht beschrijven Nynke van Egmond- de Boer en ik (team Consumentengedrag) een onderzoek naar de vrijstellingsvermelding Let op! U belegt buiten AFM-toezicht. In experimenten in het AFM Consument&Panel leidt afwezigheid van deze waarschuwing tot een hogere investeringsintentie, vooral bij vermogende respondenten die de melding niet kennen.


Voor beleggingsaanbiedingen aan particulieren die buiten toezicht vallen, geldt sinds 2012 een verplichte vrijstellingsvermelding in een vast formaat. In een gerandomiseerd experiment tonen we aan dat afwezigheid van de melding ‘Let op! U belegt buiten AFM-toezicht. Geen vergunning- en prospectusplicht voor deze activiteit’ leidt tot een hogere investeringsintentie (het effect op het daadwerkelijke gedrag is niet gemeten), vooral bij vermogende respondenten die de melding niet kennen (42 procent van de populatie).

Dit lijkt in lijn met een doel van de waarschuwing, namelijk consumenten beschermen door ze te wijzen op hun grotere eigen verantwoordelijkheid bij deze producten.

Met dit gerandomiseerde experiment laten we zien dat het nuttig en uitvoerbaar is voor toezichthouders om hun interventies te testen op effectiviteit.

We hebben onderzoek gedaan met een experimenten onder respondenten met meer dan €100.000 vermogen. Dat zijn potentiële beleggers want een van de mogelijke gronden voor een vrijstelling is namelijk als de waarde van de effecten ten minste €100.000 per belegger bedraagt (zie voor meer informatie). De €2,5 miljoen vrijstelling wordt verhoogd naar €5 miljoen. En vanaf 1 oktober 2017 geldt een meld- en informatieplicht voor aanbieders van vrijgestelde beleggingen.

Werkt als je ‘m niet al kent
In algemene zin was de koopintentie in het experiment laag. Toch vonden we een significant effect van aan/afwezigheid van de vrijstellingsvermelding; vooral respondenten die de waarschuwing nog niet kenden, gaven een minder lage koopintentie aan.


In dit experiment hebben we niet daadwerkelijk gedrag bestudeerd, wat we bijvoorbeeld wel gedaan hebben bij het onderzoek naar de kredietwaarschuwingszin; ‘Let op! Geld lenen kost geld’ geen onmiddellijk effect in verkoopomgeving (december 2016).

Wil je het hele artikel hebben? Stuur me een mailtje of download hier Werkt de wildwestwaarschuwing wel_TvT.

TIBER 2017 Symposium #TIBER2017

The TIBER 2017 Symposium on Psychology and Economics took place on August 25, 2017 in Tilburg [full program, my tweets from that day]. Interesting day, and good to see some people from the financial industry (ING, Rabobank).

Keynote Ralph Hertwig (from the Gigerenzer-school) kicked off with a talk on Preferential Heuristics, Uncertainty and the Structure of the Environment.

He started by quoting a 1967 paper Man as an intuitive statistician where Peterson and Beach argue that “Man gambles well”. But the Tversky and Kahneman-paradigm a couple of years later proved more influential, puzzling Hertwig.

Description vs. Experience
Tversky and Kahneman were influential with tasks where risks where described, instead of uncertainty experienced.

Hertwig shows figures from A meta-analytic review of two modes of learning and the description-experience gap (2016) DU Wulff, M Mergenthaler Canesco, R Hertwig.

Especially with low, true probabilities, there are large effects between answers to a choice formulated descriptively, versus the choice made after experiencing pay-offs. (DU =  discrete underweighting). From Wikipedia: “in experienced prospects, people tend to underweight the probability of the extreme outcomes and therefore judge them as being even less likely to occur.”


Adaptive decision maker has to make a trade-off between accuracy-effort. So heuristics can be effective, says Hertwig.

Hertwig on risk communication:

And research methods (why do adults score worse than babies or chimps?)

Parallel sessions
First, I went to Ozan Isler; Honesty, Cooperation & Social Influence, who:

we present a new mind-game that is powerful enough to measure honesty at the
individual level and fast enough to be implemented online. The game consists of forty rounds. In each round, the participant is first asked to think of a number between 0 and 9, then shown a single-digit random number, and finally asked to report whether the two numbers match.

Then I saw a talk about changing faces with FaceGen on a trustworthiness scale and playing a dictator game. The cool thing was the stopping rule: they started with N=30 participants, and would add N=5 until BayesFactor was either > 3 or < 1/3.

Next, I saw Tony Evans: The reputational consequences of generalized trust

Final morning talk: Stefan Trautmann – Implementing Fair Procedures? “We find that unfair outcomes are acceptable for the agents if procedures are perceived as fair. However, with opaque allocation decisions, it may be difficult to commit to fair allocation procedures. Indeed, we find a very high degree of favoritism by the decision
makers when they are forced to allocate unequal outcomes, and have no fair (random) procedure available

During lunch I read two interesting posters, one by my DNB-colleague Carin van der Cruijsen on DNB Working Paper No. 563: Payments data: do consumers want to keep them in a safe or turn them into gold?

And a poster by a collaborator of Stefan Zeisberger (Nijmegen), whom we’ll also collaborate with. Basically, people do trade on their beliefs if they are in the plus (have gained), but not/less so when they are in the red.

Afternoon sessions
I attended this talk because it used Bayesian statistics, but the unfortunately did not feature very much in the presentation: Peer effects on risky decision making from early adolescence to young adulthood: Specificity and boundary conditions.

Then Less Likely Outcomes Are Valued Less by Gabriele Paolacci (who is male, incidentally): “We found that people value the gift card less when its availability is uncertain.” Somewhat interesting, not very applicable. This effect might counter the scarcity-argument in marketing a bit, but doesn’t seem likely.

Final talk was Jan Stoop with a replication of The Rich Drive Differently, a Study Suggests (2013). Stoop et al found nothing, across wide range of settings and with 2.5x N of original study.

After tea
First up: Financial Incentives Beat Social Norms: A Field Experiment on Retirement Information Search [Netspar presentation, SSRN). Presentation by Inka Eberhardt. I had seen this work before, then presented by co-author Paul Smeets. It is a really big RCT (N=250,000) RCT, sending letters to get them to sign in to their personal webpage at their pension fund. Q&A was a bit disappointing, I didn’t think the answers were particularly strong.

Pollmann’s talk Let’s talk about money: Attachment style, financial communication, and
financial conflict concluded with a suggestion to Nibud for a web tool Let’s talk about money.

Final talk was Diffusion of culpability in reparations behavior. Results for a novel task were presented, fMRI results are forthcoming.

TIBER 2017 was concluded by Bertil Tungodden’s keynote: Fairness and Redistribution: Experimental Evidence.

Tungodden presented on a paper that is nicely summarized in this HBR article: Is It OK to Get Paid More for Being Lucky?

The Rise of Behavioural Discrimination & Virtual Competition

This blog post Big data and first-degree price discrimination (thanks Patricia) led me to the work of Ariel Ezrachi and Maurice Stucke. As Silvia Merler writes:

[Ezrachi and Stucke] argue that online behavioural discrimination will differ from the price discrimination we have seen in the retail world in three important respects:

  1. Big data allow the shift from third-degree, imperfect price discrimination to near perfect price discrimination;
  2. Sellers can use big data to target consumers with the right “emotional pitch” to increase overall consumption (the demand curve shifts to the right)
  3. As more online retailers personalise pricing and product offerings, it will be harder for consumers to discover a general market price and to assess their outside options, thus implying that behavioural discrimination becomes more durable.

Ezrachi and Stucke published a book in 2016: Virtual Competition (on my to read pile, reserved it at the University Library; book’s webpage also contains a lot of extra info/links).

Behavioural discrimination
I did read their paper The rise of behavioural discrimination (37 European Competition Law Review 484 (2016)).

New dynamics that reduce our welfare? (…) Our article explores how e-commerce and the personalisation of our online environment can give rise to behavioural discrimination, a durable, more pernicious form of price discrimination.”

I. Near perfect price discrimination

Third-degree price discrimination, which involves the charging of different prices to different groups. The price can depend, among other things, on your location (i.e. where you live), your age, or your sex. Cinemas, bus services, and restaurants, for example, may charge adults higher prices than children, students or senior citizens.

By contrast, in this article, our focus is on the possible shift to perfect, or first-degree, price discrimination—where firms can identify and charge for each individual the most he or she is willing to pay, i.e. the reservation price.

“Big Data, learning by doing, and the scale of experiments come into play to better approximate your reservation price.”

“In this data-driven economy, the algorithm—to maximise profitability—will estimate the likelihood of our shopping elsewhere or being aware of better deals and accordingly provide us with a convincing sales pitch.” (e.g. coupons and promotion codes for customers more sensitive to outside options, i.e. more price-sensitive customers who are likeley to compare option, more sophisticated consumers. Naieve consumers can be exploited more efficiently).

II. Shifting the demand curve to the right

Sellers using our personal data to induce us to buy more products or services than
we otherwise would have purchased.

A few consumer biases, which firms may exploit to promote consumption:

  • Use of decoys
  • Price steering, e.g. On Orbitz, Mac Users Steered to Pricier Hotels
  • Increasing complexity; facilitate consumer error or bias and manipulate consumer demand to their advantage (…) companies can, by designing the number and types of options they offer, better exploit consumers’ cognitive overload. In increasing complexity, the firms can also increase consumers’ search and switching costs, thereby reducing the visibility (and attraction) of outside options, and giving them more latitude to exploit consumers.
  • Imperfect willpower “framing effects” (how the issue is worded or framed) do matter. Credit cards are one example. Here they cite a Dutch study The abolition of the No-discrimination Rule from 2000 (!) with N=150 consumers (!) surveyed. Dutch
    merchants could impose surcharges or offer discounts based on how the customer was going to pay. Of the consumers surveyed, 74% thought it (very) bad if a merchant asked for a surcharge for using a credit card. But when asked about a merchant offering a cash discount, only 49% thought it (very) bad. A weak spot in an excellent paper.

The road to near-perfect behavioural discrimination will be paved with personalised coupons and promotions: the less price-sensitive online customers may not care as much if others are getting promotional codes, coupons, and so on, as long as the list price does not increase. (p.488)


Another way to frame behavioural discrimination in a palatable manner is to ascribe the pricing deviations to shifting market forces. Few people pay the same price for corporate stock. They accept that the pricing differences are responsive to market changes in supply and demand (dynamic pricing) rather than price discrimination (differential pricing). So once consumers accept that prices change rapidly (such as airfare, hotels, etc.), they have lower expectations of price uniformity among competitors. One hotel may be charging a higher price because of its supply of rooms (rather than discriminating against that particular user). (…) Thus, we may not know when pricing is dynamic, discriminatory, or both.


III. The durability of behavioural discrimination

it will be harder to know what others see. (…) As personalised offerings increase, search costs will also increase for consumers seeking to identify the “true” market price.

Behavioural discrimination—while not always possible—could occur more often than we expect. Furthermore, as we shift more of our activities to a controlled online ecosystem, it is likely to intensify.

The power to discriminate may be curtailed by possible pushback from consumers (I personally doubt it).

Price comparison websites may foster, rather than foil, behavioural discrimination and switching costs may be higher than one assumes, despite perceived competition being only a click away. (from the footnotes related to this quote: As more consumers rely (and trust) an intermediary to deliver the best results (whether relevant results to a search query or array of goods and services), the less interested they become in multi-homing—that is, from checking the availability of products and prices elsewhere. And: many users who indicated that when a search result is fails to meet their expectations they will “try to change the search query—not the search engine.”


IV. The welfare effects of behavioural discrimination

sellers can manipulate our environment to increase overall consumption, without necessarily increasing our welfare.

Once one accounts the consumer perspective, the social welfare perspective, and the limited likelihood of total welfare increasing, behavioural discrimination is likely a toxic combination. Moreover, behavioural discrimination may blur into actual discrimination due to the limits and costs of refined aggregation.

The worrying thing is that we (and the enforcers) may not even know that we are being discriminated against. Under the old competitive paradigm, one might suspect one was discriminated against if access was inexplicably denied (e.g. restaurants for “whites only”) or was charged a higher price based on this single variable. Under the new paradigm, users may not detect the small but statistically significant change in targeted advertisements (or advertised rates).



As pricing norms change, price and behavioural discrimination eventually may be accepted as the new normal. Just as we have accepted (or become resigned to) the quality degradation of air travel, and the rise of airline fees—from luggage to printing boarding passes—our future norms may well include online segmentation and price discrimination.

The costs can be significant. The new paradigm of behavioural discrimination affects not only our pocketbook but our social environment, trust in firms and the marketplace, personal autonomy, privacy and well-being.


Some other relevant links:

Why controllers compromise on their fiduciary duties: EEG evidence on the role of the human mirror neuron system

Why controllers compromise on their fiduciary duties: EEG evidence on the role of the human mirror neuron system – Philip I. Eskenazi, Frank G.H. Hartmann & Wim J.R. Rietdijk. Accounting, Organizations and Society 50 (2016): 41-50.


Business unit (BU) controllers play a fiduciary role to ensure the integrity of financial reporting. However, they often face social pressure from their BU managers to misreport. Drawing on the literature on the human mirror neuron system, this paper investigates whether controllers’ ability to withstand such pressure has a neurobiological basis. We expect that mirror neuron system functionality determines controllers’ inclination to succumb to social pressure exerted by self-interested managers to engage in misreporting.

We measure mirror neuron system functionality using electroencephalographic (EEG) data from 29 professional controllers during an emotional expressions observation task. The controllers’ inclination to misreport was measured using scenarios in which controllers were being pressed by their manager to misreport.

We find a positive association between controllers’ mirror neuron system functionality and their inclination to yield to managerial pressure. In line with our expectation, we find that this association existed specifically for scenarios in which managers pressed their controllers out of personal rather than organizational interests. We conclude that BU controllers’ neurobiological characteristics are involved in financial misreporting behavior and discuss the implications for accounting research and practice.

Hypothesis: For BU [Business Unit] controllers, we expected that hMNS [human mirror neuron system] functionality predicts controllers’ vulnerability to the social pressure to misreport exerted by BU managers.

An important characteristic of the role of BU controllers is the combination of local (to support their BU managers in operational and strategic decision making) and functional (fiducary duty) responsibilities.

Method: N=29 study with 3 scenario’s x 2 contexts (managers’ personal/ self-interest, or organizational interest).

EEG: Individual levels of hMNS functionality can be observed in electroencephalogram (EEG) recordings of brain activity (…) The associated weakening of the EEG signal is called mu suppression. Mu suppression has been shown to be a robust and valid indicator of hMNS functionality (…) lower values indicate more “mirroring”, associated with higher levels of sensitivity to others’ emotions.

For example scenario below, correlation between cooperation and MU: r = .406, p = .029


Result: Our findings indicate a strong association between hMNS functionality and controllers’ inclination to yield to BU managers’ pressure to misreport when this pressure stems from BU managers’ personal interests rather than from managers’ concerns with organizational interests.

our study suggests that emotional influence may cause excessive alignment between the interests pursued by the BU manager and those served by the reporting behaviors of the BU controller. In designing internal control structures, organizations need to be aware of the reporting risks associated with the expansion of “business partner” controllers.

Low Interest Rates and Risk Taking: Evidence from Individual Investment Decisions

Low Interest Rates and Risk Taking: Evidence from Individual Investment Decisions (July 2017) Chen Lian, Yueran Ma, and Carmen Wang. SSRN version. My summary is from earlier, 2016 version.


In recent years, interest rates reached historic lows in many countries. We document that individual investors “reach for yield,” that is, have a greater appetite for risk taking when interest rates are low. Using an investment experiment holding fixed risk premia and risks, we show that low interest rates lead to significantly higher allocations to risky assets, among MTurk subjects and HBS MBAs. This behavior cannot be easily explained by conventional portfolio choice theory or by institutional frictions. We then propose and test explanations related to investor psychology. We also present complementary evidence using historical data on household investment decisions.

We provide evidence that individual investors “reach for yield”, that is, have a greater appetite for risk taking in low interest rate environment (…) We find significantly higher allocations to risky assets in the low rate condition.

Experiments (N=400) with 2 groups, allocate $100.000 between ;
[groep 1] risk free = 5% vs risky asset = 10%
[groep 2] risk free = 1% vs risky asset =6% (investment horizon = 1 year).
We show that individuals demonstrate a stronger preference for risky assets in their investment decisions when the risk-free rate is low. (…) The difference is about 7 to 9 percentage points, on a basis of roughly 60% allocations to the risky asset.


Why? (what mechanisms?)

  • People may form reference points of investment returns. we find that there is significant reaching for yield behavior when interest rates are below 3%, whereas investment decisions are not significantly different when interest rates are above this level. This cut-off seems consistent with the level of interest rates that most participants are used to prior to recent years
  • Salience of the higher average returns on the risky asset in different interest rate environment. Most simply, 6% average returns relative to 1% risk-free returns is more salient than 10% average returns relative to 5% risk-free returns. Reaching for yield behavior is dampened if investment returns are completely framed in gross terms (e.g. instead of saying 5% returns, we say that one will get 1.05 units for every unit of investment).


In the 2017 version they made the graph that I had made myself in my 2016 summary:


Section 5: Suggestive Evidence from Observational Data: the data suggest that portfolio shares of stocks and flows into risky assets increase (while portfolio shares of safe assets and flows into deposits fall) when interest rates decrease. (p.28)

  • In terms of magnitude, a one percentage point decrease in interest rates is associated with about 1.5 percentage points increase in allocations to stocks and a similar size fall in allocations to “cash”.
  • Interestingly, the magnitude of allocations’ response to interest rates seems to be similar in the experiment and in the observational data. (p.29)
  • We see that across different data sources, decreases in interest rates are associated with flows into risky assets and out of safe interest-bearing assets. (p30)
  • we hold the findings in this section 5 to be merely suggestive and complementary to our experimental evidence, yet we are intrigued that data across several different sources show consistent patterns.


Other interesting bits:

  • P1: A number of papers also provide empirical evidence that banks, money market mutual funds, and corporate bond mutual funds invest in riskier assets when interest rates are low (Maddaloni and Peydr_o, 2011; Jim_enez, Ongena, Peydr_o, and Saurina, 2014; Chodorow-Reich, 2014; Hanson and Stein, 2015; Choi and Kronlund, 2015; Di Maggio and Kacperczyk, 2016).
  • footnote 1, p1: The \reaching for yield” behavior we study in this paper, most precisely, is that people invest more in risky assets when interest rates are low, holding constant the risks and excess returns of risky assets.
  • Footnote 3, p5: For example, Di Maggio and Kacperczyk (2016) and Choi and Kronlund (2015) show that money market mutual funds and corporate fund mutual funds who reach for yield get larger in inflows, especially when interest rates are near zero. These inflows most likely come from yield seeking end investors. It seems plausible that households’ yield seeking behavior could be an important cause of some financial institutions reaching for yield.
  • P5: our evidence on risk taking and interest rate environment may also have implications for security design and consumer protection, as households’ biases could be exploited by institutions and asset managers that highlight returns and shroud risks (C_el_erier and Vall_ee, 2016).
  • P10-11 In our data, Harvard Business School MBAs and MTurk workers reach for yield by a similar degree. Nor do we find that reaching for yield declines with wealth, investment experience, or education among MTurks, or with investment and work experience in finance among MBAs
  • P32 The impact of the interest rate environment on investor behavior could have important implications for connections between key macroeconomic issues and capital market dynamics and financial stability.

Overcoming Negative Media Coverage: Does Government Communication Matter?

Liu, Brooke Fisher, J. Suzanne Horsley, and Kaifeng Yang. “Overcoming negative media coverage: Does government communication matter?.” Journal of Public Administration Research and Theory (2012): 597-621


Public administration scholars often note that government should engage in more effectiveexternal communication to improve citizen trust and maintain political legitimacy. An important part of the belief is that more effective communication can lead to more favorable media coverage that ultimately shapes citizen trust in government. However, the link between government communication and media coverage remains empirically untested.

Through a survey of 881 government and business communicators, this study tests the relationship between external communication activities and media coverage.

The study shows that government organizations report being less likely to have favorable news coverage than their private counterparts, but most government organizations do report that their media coverage is favorable. Moreover, the results show that active media interaction, organizational support for communication, and adequate communication budget are associated with reporting more favorable coverage. In comparison, a different set of variables, except adequate communication budget, are found to affect whether business organizations report having more favorable media coverage.

Empirical research on effects government communication
p598: “Given the importance and challenges, it is crucial for public administration scholars to more rigorously study government communication and its impact on media coverage and, in turn, citizen trust

it is reasonable to state that the mechanisms linking external communication and government performance have not been mapped out with empirical evidence.

The purpose of this study is two-fold:

  1. To identify the types of government communication activities
  2. To test how the activities affect perceived media coverage

p604: “The survey consisted of 68 questions (…) the dependent variable is measured by three dimensions: the extent to which the media coverage is perceived as favorable, accurate, and fair.

Media more negative on government
p599 “results of a survey of government and business communicators found that government communicators reported being covered more frequently and more negatively than business communicators (Liu, Horsley, and Levenshus 2010).”

Government communication people feel they receive more unfavorable press/less favorbale press than business communication professionals, see Table 4.

To further understand the dependent variable, table 4 presents the responses’ detail distribution in government and business subsamples. Note that 9 is the scale midpoint depicting a neutral evaluation. Consistent with the t-test, higher percentages of business communicators (as opposed to their government counterparts) had responses higher than 9. Among government respondents, although very few of them reported extremely low values (3, 4, and 5), 15.3% of them reported that on average their organization had experienced unfavorable media coverage in the past six months. In contrast, 65.3% of government respondents reported favorable media coverage.


Positive effect media interaction for government
p609: The results show that media interaction for government (Model 2, Beta = 0.29, p < .001) does lead to positive media coverage. Media Interaction is the composite of: Write news releases and advisories, Hold news conferences, Conduct media interviews, Respond to media inquiries, Pitch stories to the media, and Track media clips.

Media interaction (news releases, news conferences, media interviews, responding to media inquiries, pitching stories to the media, and tracking media clips) is found to positively affect media coverage for the government subsample, but no such relationship is found for the business subsample (p612)

Het meten van effecten van de handhaving door de Belastingdienst

In het laatste nummer van het Tijdschrift voor Toezicht van 2016 schrijven drie medewerkers van de Belastingdienst over effectmeting (“een onmisbaar element van ‘goed toezicht’”): “Centraal in dit (beschrijvende en verkennende) artikel staat de vraag hoe de Belastingdienst de effecten van zijn handhavings- en toezichtactiviteiten meet en wat de uitdagingen hierbij zijn.” (p9)

Het meten van effecten van de handhaving door de Belastingdienst (2016) Sjoerd Goslinga, Maarten Siglé en Lisette van der Hel [pdf]

“Met effectmeting vindt de beoordeling plaats of het uitvoeren van de handhavingsactiviteiten daadwerkelijk de determinanten van compliance heeft beïnvloed en of dit vervolgens effect heeft gehad op de compliance” schrijven Goslinga et al.

Bijvoorbeeld: Aangiftecampagne om de compliance (tijdig aangifte doen, voor 1 april) van burgers te verhogen door middel van voorlichting.


‘Outcome’ (=effect) representeert in deze effectketen de uiteindelijke impact van de activiteiten van de Belastingdienst op zijn strategische doel: compliance. ‘Output’ (=resultaat) daarentegen is datgene wat door de inspanningen van de Belastingdienst is geproduceerd (zoals het aantal verstuurde brieven of uitgevoerde controles. Het uitvoeren van handhavingsactiviteiten wordt ook wel omschreven als een interventie. In termen van de effectketen gaat het hier om input, proces en output.

De auteurs zijn eerlijk (en reëel) over de stand van zaken met effectmeting:


Naar onze mening is de kern van het probleem dat belastingdiensten überhaupt niet gewend zijn om effecten te meten, maar zich vooral beperken tot output omdat dat veelal gemakkelijker vast te stellen is dan effecten.

De auteurs noemen vijf uitdagingen voor effectmeting:

  1. Expliciteren aan hoe activiteiten bijdragen (de inzet van mensen en middelen) aan het realiseren van de doelstellingen. Bijvoorbeeld met een doelenboom.
  2. Vinden van de juiste achterliggende oorzaken van (non-)compliance
  3. Meten van effecten van preventieve activiteiten – “Nadenken over nieuwe soorten indicatoren, die verder weg lijken te staan van de outputindicatoren waar belastingdiensten voorheen sterk op stuurden.”
  4. Opzetten van methodologisch verantwoord onderzoek

    Om vast te stellen in hoeverre inspanningen van de Belastingdienst bepalend zijn (geweest) voor dat nalevingsniveau is het noodzakelijk om een vergelijking te maken met het nalevingsniveau in een situatie waarin de inspanningen niet zouden zijn geleverd (counterfactual). (…) Het ideale onderzoeksdesign is de zogenoemde randomized controlled trial of gecontroleerd veldexperiment (p26)

    Er zijn veel situaties denkbaar waarin het niet mogelijk is een hoger niveau van onderzoeksvaliditeit te bereiken; de eerste twee niveaus kunnen dan zeker van toegevoegde waarde zijn. (p25)

  5. Organisatorische inbedding van effectmeting.

    Een uitdaging voor veel toezichthouders is om effectmeting op een structurele manier te borgen in de organisatie en onderdeel te maken van de manier van werken.


Effectmeting is een continu proces omdat het bij het realiseren van de (algemene) beleidsdoelstelling gaat om een ‘duurzame’ verandering in het gedrag van belastingplichtigen en de borging van de continuïteit van belastingopbrengsten.