13. Asymmetric Information: Remarks Flashcards
Adverse selectionMkt for FINANCE of INNOVATION
- There are good and bad innovation projects (bad projects = high risk of failure);
- Those who provide external finance (i.e. banks) can not perfectly discern good vs. bad projects, and generally proponents are much more informed about odds of success.
- To shield from the risk, banks pose average (unfavorable) conditions for lending to everybody (i.e. high interest rates or credit-rationing).
- Only (often low-skilled and high-risk loving) “kamikaze” innovators will ask for money while capable innovators (those with sufficiently good quality but at the same time realistic projects) may prefer to give up searching for external debt finance (discouraged borrowers) or search for other alternative financing sources.
- Good innovation projects may risk not to be financed.
Moral HazardMkt for FINANCE of INNOVATION
- For an outsider (external investors) may be difficult to monitor the strategies, choices, decisions taken by the prospective innovative managers/entrepreneurs.
- Managers/entrepreneurs may put in place actions not in the interest of the investors, or that investors would not have agreed (e.g. divert funds to different aims, exert less effort than required).
Signals & Incentives in Mkt for FINANCE of INNOVATION
(Sort of) “signals”
- Established firms may exhibit their track record,
- Innovative start-ups may show their goodness to external investors:
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Of course, these dimensions are not pure signals, but they may still have a signaling function
->
-patenting (Hsu and Ziedonis, 2013, SMJ),
-endorsement by a reputable alliance partner (Stuart et al., 1999, ASQ; Stuart, 2000, SMJ).
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(Sort of) “incentives”
- Banks: Use of collateral to secure debt.
- VC: “active investors” (i.e. work side by side with entrepreneurs), co-investment with entrepreneurs as a guarantee of high effort, use of milestones (staged rounds of financing).
Signals & Incentives in Mkt for FINANCE of INNOVATION
(Sort of) “signals”
- Established firms may exhibit their track record,
- Innovative start-ups may show their goodness to external investors:
.
Of course, these dimensions are not pure signals, but they may still have a signaling function
->
-patenting (Hsu and Ziedonis, 2013, SMJ),
-endorsement by a reputable alliance partner (Stuart et al., 1999, ASQ; Stuart, 2000, SMJ).
.
(Sort of) “incentives”
- Banks: Use of collateral to secure debt.
- VC: “active investors” (i.e. work side by side with entrepreneurs), co-investment with entrepreneurs as a guarantee of high effort, use of milestones (staged rounds of financing).
Signaling & Incentives:help but are not capable to replicate full info
(We know from last lecture that)
Signaling can improve information in the market. But it involves a cost, and for this reason, we expect the equilibrium to be sub-optimal with respect to a full information scenario. In the Spence’s model, education is costly for high-ability workers (they would have preferred to be recognizable as such without the need to “signal”) so signaling may hurt market’s efficiency: to some extent resources are wasted in order to enforce a separating equilibrium.
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Incentives may make objective functions of the 2 sides more similar but not the same, so behavior/effort of the more informed party (the “agent”) is unlikely to be the one that would be optimal in a full info scenario. Sharecropping: the farmer only benefits for a fraction of the harvest, s/he will exert some effort in the interest of the “principal”, but this effort is unlikely to be as high as the effort s/he would exert in case s/he own the land her/himself.
Incentives as “signals”
If signals are exogenously absent, firms may always endogenously look to put in place some incentive schemes in order to make workers’ type reveals (“signals”) themselves
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EG: Zappos (from Levitt & Dubner “Think like a freak”, 2014)
Zappos founded in 1999 in Las Vegas for selling shoes on-line
Management team: “customer obsessed culture”
Customer service: key asset.
365 day-return window, free-shipping, but in team’s view the Call centre 24/7 with no limits talking (“protracted talk therapy”, as one observer noted) would have been their core advantage.
A call-centre job isn’t typically very desirable, nor does it pay well (e.g. 11$ per hour)….How could they obtain that? Paying workers more was not an option
—> More fun & power for customer-service representatives
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Zappos’s incentive:
But Zappos management team needs to know early who really was engaged with the project and share its customer-centric obsession culture and who is not.
To escape from a pooling eq. to reach a separating eq. where good customer-service representatives are separated from bad ones, Zappos use the following incentive scheme:
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From Levitt & Dubner (2014): “When new employees are in the on boarding period- they have already been screened, offered a job and completed a few weeks of training- Zappos offers them a chance to quit. Even better, quitters will be paid roughly 2000$ just for quitting”
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This is the cost the firm and “good guys” are willing to suffer to implement a separating equilibrium.
Incidentally note:
Very few “ new hires” accepted “The Offer”.
“In 2009, Zappos was bought by Amazon.com for 1.2$ billion.”
Signals and Incentives
Principal: output y = f(x) [p = 1 → output = value; x: agent’s effort]
Agent: payment s(y)
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Principal’s π = y – s(y) → π = f(x) – s(f(x))
Agent’s u: s(y) – c(x) → s(f(x)) – c(x)
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The agent will be willing to «work» for the principal as long as its u ≥ 0
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Principal’s problem ⏟𝑚𝑎𝑥┬𝑥 π = f(x) – s(f(x))
s.t. s(f(x)) – c(x) = 0
⏟𝑚𝑎𝑥┬𝑥 π = f(x) – c(x) MP(x*) = MC(x*) Optimal incentive scheme: Marginal product of Agent’s effort = its marginal cost If principal can observe the amount of effort exerted by the agent, wage works perfectly: . Agent’s problem: ⏟𝑚𝑎𝑥┬𝑥 u = wx + K – c(x) w = MC(x) principal sets w = MP (x*). But if principal can not observe x and y = f (x, ɛ), w is highly inefficient since received w, the agent has an incentive to shirk (e.g. ↓x since ↓c(x)). . In these cases, «sharecropping» or similar incentivizing methods (Agent gets s = αf(x) and Principal (1- α)f(x)) might be preferrable. But bear in mind that they are not optimal compared to the full information scenario: . Agent’s problem ⏟𝑚𝑎𝑥┬𝑥 u = αf(x) – c(x) αMP(x) = MC(x) with x ≠ x* . Facultative: Formal analysis, see Varian 2010, Ch. 37.7. Incentives mitigate but do not solve the problem (x ≠ optimal x*)
Asymmetric Information
Technologies (e.g. Internet, IoT and big data) may help reducing Asymmetric Info.
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But at which “price” from a societal point of view?
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See (among others) Zuboff, S. (2019). The age of surveillance capitalism: The fight for a human future at the new frontier of power. New York: Profile Books.
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“For example, the insurer John Hancock has offered its customers free Fitbits, which are watches that track the wearer’s exercise, heart and sleep patterns. Thus, the firm can offer healthier customers lower insurance rates”.
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“New York State’s Department of Financial Service has now allowed life insurance companies to utilize data from social media such as Facebook and Instagram in order to evaluate the risks of people’s lifestyles according to their posts” (and offer differentiated rates).”
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From Wilkinson, Managerial Economics, II Edition, Cambridge UP, 2022.
