Topics in the Economic Analysis of Law

To answer this question we will use Cost–Benefit Analysis, together with the Legal Incentives Model.

Object and counterfactual The object is the legal rule that theft is criminal (backed by police investigation, prosecution, and punishment). The counterfactual is a setting where taking others’ property is not criminally sanctioned (or is sanctioned so weakly that behavior is close to “no criminalization”).

To make the trade-offs concrete, consider a theft of a company laptop.

Actors and decision margin Key actors are (i) potential thieves choosing whether to steal, (ii) victims (firms/employees) choosing how much to invest and how much to spend on protection, and (iii) the state choosing enforcement intensity and sanctions. The decision margins are theft/avoid theft and investments in productive assets versus investments in protection.

Incentives and behavioral responses Under the Legal Incentives Model, a potential thief compares the private benefit \(B\) from stealing to the expected sanction \(pS\). Theft is deterred when \(pS\) is high enough that \(B \le pS\). Criminalizing theft raises expected sanctions relative to the counterfactual (through higher \(S\), higher \(p\), or both), so fewer thefts occur. Fewer thefts in turn reduce victims’ need to divert resources into security and make investments in valuable assets (like IT equipment and long projects) more attractive.

A simple illustration: suppose the thief can quickly resell the laptop for \(B = 500\) (cash), while the expected criminal sanction is \(pS = 0.25 \times 4{,}000 = 1{,}000\). Then \(B < pS\), so theft is deterred. If theft were not criminal (so \(pS \approx 0\)), the same person would steal.

Real costs and benefits

Misallocation of resources (benefit of criminalization)

Theft reallocates the laptop without consent, often from a higher-valuing user to a lower-valuing user. If the laptop is worth 2,000 to the firm because it contains customized software, data, and ongoing work, but only 500 to the thief (quick resale value), society loses 1,500 of value when theft occurs. Criminalization reduces this misallocation by preventing thefts.

Reduced investment and innovation incentives (benefit of criminalization)

If theft is common, firms rationally scale back investment in theft-prone but productive assets (equipment, data-intensive projects, long development tasks). Criminalization reduces expected theft, so it supports higher investment and more productive activity.

Private protection expenditures (benefit of criminalization)

When theft risk is high, firms spend real resources on alarms, guards, insurance administration, and security software, diverting resources from production. Criminalization lowers expected theft and can reduce these deadweight protection costs.

Public enforcement and adjudication costs (cost of criminalization)

Making theft criminal requires police prevention/investigation and the use of courts and prosecution. These use labor, time, and capital (vehicles, surveillance, court administration).

Punishment costs (cost of criminalization)

Imprisonment and other punishments require prison resources and supervision, and they remove convicted offenders from productive work. In addition, punishment imposes a substantial welfare cost on the convicted person through loss of liberty.

A central trade-off is that stronger deterrence can reduce the underlying incidence of theft, which can make some of the downstream costs (investigation, trials, imprisonment) smaller in equilibrium because there are fewer cases to process. But raising \(p\) and \(S\) can itself be costly: higher \(p\) requires more policing and detection effort, and higher \(S\) (especially prison) raises punishment costs.

Note that the laptop itself is largely a transfer (it moves from firm to thief). Transfers are not counted as real social costs. The main social losses come from misallocation and the induced protection and underinvestment, while sanctions (fines/damages) matter mainly for incentives rather than as real resource costs.

Verifiability and enforcement Deterrence depends on \(p\), which is limited by verifiability and enforcement capacity: many thefts are hard to detect, and even detected thefts may be hard to prove. Raising \(p\) typically requires costly monitoring, investigation, and evidence collection. This is why criminalization cannot be evaluated solely by saying “set \(S\) very high”; if \(p\) is low due to detection and proof constraints, expected sanctions may remain low, and achieving higher \(pS\) may require either more enforcement resources (to raise \(p\)) or more reliance on severe sanctions (raising punishment costs).

Conditional conclusion On economic grounds, theft should be criminal when the expected reduction in the real social costs of theft (misallocation, lower investment, and protection spending) exceeds the real resource and welfare costs of enforcement and punishment. This conclusion is strongest when stolen assets tend to have much higher value in the hands of the owner than to thieves (large misallocation), when theft risk would otherwise significantly depress investment, and when deterrence can be achieved without very large enforcement and imprisonment costs. If instead theft is hard to detect/prove (low \(p\)) so that effective deterrence requires very costly enforcement or very severe punishment, the net case for criminalization becomes weaker at the margin (though it may still be justified depending on how large theft-related losses are).

To answer this question we will use Legal Incentives Model and Cost–Benefit Analysis.

Object and counterfactual The object is the sentencing rule that punishment should be higher when (i) the evaded amount is large and/or (ii) the evasion was carried out in a way that makes discovery difficult (e.g., foreign-registered companies and transactions designed to obscure reality). The counterfactual is a sentencing practice where punishment does not increase with the private gain from evasion or with the difficulty of detection.

Incentives and behavioral responses Under the Legal Incentives Model, a potential evader compares the expected private gain from evasion with the expected punishment. Let saved tax be the gain if not detected, let \(p\) be the probability that evasion is detected and punished, and let \(T\) be the severity of punishment. Then expected gain is \(\text{saved tax}\times (1-p)\) and expected punishment is \(T\times p\). Evasion is deterred when \[ \text{saved tax}\times (1-p) < T\times p. \] These two statutory factors map directly into this comparison:

• A larger evaded amount means higher saved tax, i.e., a higher temptation to evade, so \(T\) must be higher to keep evasion unattractive.
• More sophisticated concealment lowers \(p\), so the expected punishment \(pT\) falls for any fixed \(T\); deterrence can be restored by increasing \(T\) when \(p\) is low.

A simple numerical illustration shows why both factors should increase punishment. Consider three cases:

• Evasion A: saved tax \(=100\), \(p=10\%\). Deterrence requires

\[ 100 \times 0.9 < T \times 0.1 \Rightarrow 900 < T. \]

• Evasion B: saved tax \(=100\), \(p=1\%\) (harder to detect). Deterrence requires

\[ 100 \times 0.99 < T \times 0.01 \Rightarrow 9{,}900 < T. \]

• Evasion C: saved tax \(=1{,}000\), \(p=10\%\) (larger amount). Deterrence requires

\[ 1{,}000 \times 0.9 < T \times 0.1 \Rightarrow 9{,}000 < T. \] Intuition: when the private upside is higher (more tax saved) or when the chance of punishment is lower (harder discovery), punishment must rise to keep expected punishment large enough to offset expected gain. This supports general deterrence: “it should not pay” to evade.

Real costs and benefits

• Public enforcement and adjudication costs: Hard-to-detect schemes typically require more investigative resources (international information gathering, forensic accounting). A rule that increases punishment when detection is hard can reduce the needed reliance on costly increases in enforcement intensity.
• Private resource waste on concealment: If concealment reduces \(p\), actors may spend real resources on structures and transactions designed to hide evasion. Higher punishment targeted at such methods reduces the incentive to waste resources on concealment.
• Distortions from shifting activity into harder-to-monitor forms: When evasion is “optimized” through offshore structures, economic activity may be reorganized toward opacity rather than productivity. Increasing punishment for such methods reduces this misallocation.
• Punishment costs: Harsher punishment (especially imprisonment) is itself a real social cost (lost productive time, incarceration resources). The efficiency case for higher punishment is therefore strongest where it yields large deterrence gains because \(p\) is low or the private gain is large.

Fines and collected taxes are transfers and are not counted as real resource benefits in this list, but they matter for incentives because they change the evader’s private payoff.

Verifiability and enforcement The “difficult to detect” factor is fundamentally about enforcement: when evasion is executed through foreign entities and layered transactions, the tax authority’s monitoring and proof options are limited, so \(p\) is lower. Raising \(p\) is possible but typically costly (more audits, international cooperation, complex evidence), so adjusting \(T\) in precisely those cases is an enforcement-responsive way to maintain deterrence despite verifiability constraints.

Conditional conclusion If the policy goal is general deterrence—making tax evasion unprofitable on average—then higher punishment is economically justified when (i) the evaded amount is large (higher private benefit) and/or (ii) detection is difficult (lower \(p\)). Under these conditions, a fixed punishment level would under-deter. If instead \(p\) could be made high at low cost (easy detection) or if the private gain were small, the same deterrence could be achieved with a lower punishment, reducing the real costs of severe sanctions.

To answer this question we will use Cost–Benefit Analysis and the Legal Incentives Model, treating AML as a policy that changes expected payoffs from profit-motivated crime by making laundering (and sometimes detection) more likely and more costly.

Object and counterfactual The object is large-scale AML regulation and compliance (reported global cost USD 274.1 billion annually). The counterfactual is weaker or no AML, meaning lower compliance spending and (likely) cheaper/easier laundering of criminal proceeds.

Incentives and behavioral responses Under the Legal Incentives Model, a potential offender compares the private benefit of the crime to the expected sanction. A simple illustration:

• Suppose a fraud yields private benefit \(B = 1{,}000{,}000\) NOK.
• If the probability the fraud is detected is \(p = 0.10\) and the sanction is \(S = 5{,}000{,}000\) NOK, the expected-sanction term is \(pS = 500{,}000\).
• The fraud is privately profitable if \(B > pS\), i.e. \(1{,}000{,}000 > 500{,}000\).

But in practice the proceeds must be laundered. Introduce a launderer who faces detection probability \(p = 0.20\) and sanction \(S = 1{,}000{,}000\) NOK. The launderer will supply laundering only if the fee (the launderer’s private benefit) is high enough: \[ (1-p)B > pS \;\;\Rightarrow\;\; 0.8B > 0.2\cdot 1{,}000{,}000 \;\;\Rightarrow\;\; B > 250{,}000. \] So the crime’s net proceeds fall by at least 250,000 NOK, leaving 750,000 NOK. The fraud can still be privately profitable: \[ 750{,}000 > 0.10\cdot 5{,}000{,}000 = 500{,}000. \] However, if AML increases the detection probability for laundering enough (raising \(pS\) for the launderer), the fee required to compensate the launderer rises further, and at some point the underlying fraud becomes privately unprofitable. AML can also increase the probability that the fraudster is caught (raising \(p\) for the predicate crime), reinforcing deterrence. This shows why “only 0.2% seized” does not by itself measure the benefit: the main benefit channel is deterrence—fewer crimes committed—rather than confiscation ex post.

Real costs and benefits

• Reduced crime (benefit): If AML raises the expected cost of monetizing criminal proceeds (higher laundering fee, higher chance proceeds are seized, higher chance the offender is caught), some crimes become unprofitable and are not committed. The benefit is the reduction in real social costs generated by those crimes (e.g., misallocation of resources into crime and protection, reduced investment incentives, and resources wasted on avoidance and concealment).
• Compliance and administrative burden (cost): The USD 274.1 billion annual AML cost is a real resource cost (labor, systems, audits, reporting), not merely a transfer.
• More complex laundering (cost): By making laundering harder, AML can force launderers to spend more real resources (time and effort) on evasion (e.g., routing through real companies rather than straw structures). These resources could otherwise have been used productively. This cost increase may be partly offset if deterrence reduces the demand for laundering services, but the complexity channel remains a real cost where laundering still occurs.

Transfers matter for incentives but are not counted as real resource costs: for example, the laundering fee is mainly a transfer from the criminal to the launderer, while the key social question is how AML changes real resource use and the amount of crime.

A key implication of the numbers in the prompt is that the “seizure probability” channel appears small on average: if only 0.2% of criminal proceeds are seized through AML, then (holding other things equal) AML’s direct effect through confiscation alone may reduce criminals’ expected returns only slightly. That pushes the evaluation toward whether AML substantially (i) raises the price of laundering and/or (ii) increases detection of the predicate crime, thereby deterring enough crime to justify large compliance costs.

Verifiability and enforcement AML works through detection and proof: increasing \(p\) (for laundering and/or for the underlying crime) requires monitoring, reporting, investigation, and successful adjudication, all of which are costly and imperfect. Effectiveness also depends on whether there are “holes” in the system. If there exists a laundering method or intermediary that is not effectively covered by AML and does not add cost for launderers, then criminals will shift to that channel and the deterrence effect may be close to zero. Concretely, if one bank (or one relevant gatekeeper) has weak AML, launderers can concentrate activity there, undermining systemwide deterrence.

The observed fact from lectures that launderers may take a large share (e.g., around 30%) of the proceeds is consistent with AML substantially increasing the cost/risk of laundering relative to a low-AML baseline. But whether this price effect is large enough to deter crime at scale is ultimately an empirical question.

Conditional conclusion The large AML expenditures are justified if (and only if) the induced reduction in the real social costs of crime exceeds USD 274.1 billion per year plus the real resource costs created by more complex laundering. That depends on (i) how much AML increases the price and risk of laundering and/or increases the probability that offenders are caught, (ii) how many crimes are close enough to the margin that a reduction in net proceeds (e.g., on the order of 30% plus a small seizure rate such as 0.2%) makes them unprofitable, and (iii) how large the real social costs of those deterred crimes are (which can be much larger or smaller than the stolen amount). Finally, a large volume of money still being laundered globally does not by itself imply AML is a failure: the objective is not necessarily to eliminate laundering, but to make laundering sufficiently costly and risky that some profit-motivated crimes are deterred.

To answer this question we will use Cost–Benefit Analysis, and the Legal Incentives Model.

Object and counterfactual The object is the criminal proof requirement “beyond reasonable doubt,” modeled as a bright-line conviction threshold (here, 99%). The counterfactual is a more continuous rule that would impose some positive prison term even when the decision-maker believes guilt is high but below the threshold (e.g., 98%), so that punishment varies smoothly with assessed guilt.

Incentives and behavioral responses A discontinuity is not automatically a mistake. It can be an efficient way to limit the expected cost of wrongful convictions when the sanction is very costly (21 years in prison) and when mistakes are unavoidable.

Using the Legal Incentives Model, the deterrence of homicide depends on the expected sanction \(pS\), where \(S\) is the prison term and \(p\) is the probability the offender is convicted (which is affected by the proof standard). A higher proof threshold lowers \(p\) for the guilty, weakening deterrence unless \(S\) rises; but raising \(S\) is costly and may be constrained. The system therefore chooses a proof standard by balancing (i) deterrence benefits from a higher \(p\) against (ii) the expected costs of convicting innocents when evidence is imperfect.

A key economic reason to set the standard very high is that “partial punishment for likely guilt” also partially punishes innocents. If B were punished “a bit” despite not meeting the threshold, then some fraction of innocent defendants—those who happen to look similar in the evidence—would also receive “a bit” of prison. With severe sanctions, even small increases in the chance of punishing an innocent generate large real costs. A numerical illustration makes the point: if the court’s posterior is 99% for A, then there is a 1% chance A is innocent; convicting implies an “innocent-prison” expectation of \(0.01 \times 21 = 0.21\) years. For B at 98%, convicting implies \(0.02 \times 21 = 0.42\) years—double the expected imprisonment imposed on innocents. The bright-line rule is a way of refusing to impose prison once the expected innocent-imprisonment component is judged too high relative to the gains from convicting more guilty defendants.

The threshold also affects upstream behavior. If courts awarded “probability-weighted” prison terms, prosecutors and police could obtain some punishment with weaker evidence. That reduces incentives to invest in costly evidence gathering and careful case screening, which can increase both (a) wrongful punishment (because weaker cases are still brought) and (b) overall enforcement spending on marginal cases that generate little additional deterrence. A high threshold forces the state to concentrate resources on cases where proof is strong, which can reduce error and can save resources relative to chasing many weak cases for small expected sentences.

Verifiability and enforcement “98% sure” and “99% sure” are not directly verifiable objects that a reviewing court can reliably audit. What is verifiable are pieces of evidence and whether the legal standard was met. A continuous punishment scheme would require the system to (i) elicit a precise probability assessment from fact-finders, (ii) prevent strategic manipulation of reported probabilities, and (iii) review these probability claims—each of which is administratively costly and error-prone. A bright-line proof standard is a coarse but enforceable rule: it channels decision-making into a binary, reviewable outcome and limits the scope for discretionary “split-the-difference” sentencing that would systematically impose prison on some innocents.

Real costs and benefits

• Expected harm from crime (deterrence): A lower threshold increases convictions and raises \(p\), increasing deterrence and reducing expected victimization; a higher threshold reduces \(p\), weakening deterrence.
• Imprisonment resource costs: Convicting more defendants increases real incarceration costs (public spending, lost lawful production, and other real burdens of confinement).
• Wrongful conviction costs (innocents imprisoned): A lower threshold increases the expected number of innocents convicted and thus increases real imprisonment costs imposed on people who did not commit the act.
• Wrongful acquittal costs (guilty released): A higher threshold increases acquittals of guilty defendants, which can increase expected future harms if released offenders reoffend and can reduce deterrence.
• Enforcement and adjudication costs: A high threshold can increase spending per prosecuted case (more investigation, better evidence), but it can also reduce the number of weak cases pursued; a probability-weighted scheme can add costly disputes over “how sure” the fact-finder was.

Prison itself is not a transfer; it is a real resource cost. (If fines were used, they would affect incentives but would largely be transfers aside from collection and distortion costs.)

Conditional conclusion Treating A and B very differently can be economically justified if (i) the real cost of wrongful imprisonment is large relative to the marginal deterrence benefit of convicting additional borderline cases, (ii) fact-finders’ precise probability assessments are not verifiable or cheaply reviewable, and (iii) allowing “partial punishment under uncertainty” would reduce incentives to generate strong evidence and would expand punishment of innocents. Under those conditions, a bright-line “beyond reasonable doubt” rule rationally produces discontinuities: it sacrifices some deterrence (more guilty acquittals) to sharply reduce expected wrongful punishment and to keep enforcement administrable. If, instead, probabilities could be credibly verified and reviewed at low cost, and wrongful imprisonment costs were relatively lower, a more continuous sanction scheme could become more attractive on cost–benefit grounds.

To answer this question we will use Cost–Benefit Analysis and the Legal Incentives Model.

Object and counterfactual The object is the bank’s investment in a real-time system that detects and stops suspicious BankID transactions. The counterfactual is the status quo where more fraud attempts are completed (and where detection happens later, if at all).

Actors and decision margin The key actors are the bank, customers, and scammers. The bank’s decision margin is whether (and how intensively) to invest in detection technology and related processes. Scammers’ decision margin is whether to attempt fraud, given the expected payoff under the bank’s security level.

Incentives and behavioral responses If the bank must cover 100% of fraud losses, the bank’s private benefit from the system is the amount of fraud losses it avoids. Then the investment is profitable if the system’s cost is lower than the value (in kroner) of fraud it prevents.

The system can reduce fraud in two ways:

1. Direct effect: It stops ongoing scams before money is transferred, so completed fraud falls mechanically.
2. Indirect effect (deterrence): It makes fraud less attractive, so fewer attempts are made.

Using the Legal Incentives Model, a scammer attempts fraud when the private benefit exceeds the expected sanction: \[ B > pS, \] where \(B\) is the gain from a successful fraud attempt, \(p\) is the probability the scammer is detected/held liable, and \(S\) is the sanction if caught. A real-time system can deter by:

• Reducing \(B\): blocking transfers reduces the expected gain from attempting fraud.
• Increasing \(p\): if the system generates evidence and alerts law enforcement quickly, the probability of being caught and sanctioned can rise.

A simple numerical illustration Suppose the system (i) stops 200 fraud attempts worth 100,000 kroner each, and (ii) deters 300 similar attempts that would otherwise have been made. Then the gross avoided fraud amount is: \[ 200 \times 100{,}000 + 300 \times 100{,}000 = 50 \text{ million kroner}. \] If the bank bears 100% of the loss, the investment is profitable if the system costs less than 50 million kroner over the relevant period.

Real costs and benefits

• System development and operation costs: building, maintenance, data, staffing, and ongoing monitoring increase real resource use relative to the status quo.
• Customer friction and false positives: legitimate transactions may be delayed or blocked, creating time costs and inefficient payment disruptions; handling complaints and manual review uses resources.
• Reduced resource waste from fraud handling: fewer completed scams reduce investigation, dispute resolution, customer support, and remediation costs inside the bank.
• Reduced fraud activity overall: deterrence can reduce scammers’ attempts and the bank’s need to spend resources responding to incidents.

Payments that the bank makes to reimburse victims are transfers (not real resource costs in a social accounting sense), but they are central to the bank’s private incentives: if the bank pays 100% of losses, avoided reimbursement payments are a primary private benefit from investment.

Verifiability and enforcement Real-time suspicion signals may be observable to the bank but not always verifiable to a court or usable for sanctioning scammers. The deterrence channel therefore depends on whether the system can (i) reliably identify suspicious behavior quickly enough to block transfers (raising the effective probability that fraud fails), and/or (ii) produce usable evidence and reporting that increases \(p\). Increasing \(p\) can be costly because it may require better data retention, documentation, cooperation routines, and human follow-up.

Conditional conclusion The investment is profitable for the bank if the system’s total cost is lower than the bank’s expected avoided fraud-related losses, including both the direct prevented transfers and the indirect deterrence effect from lowering \(B\) and/or raising \(p\). The main determinants are: (1) the cost of the system, (2) how much it increases the probability that fraud is stopped in time, (3) how much it increases the probability scammers are detected and sanctioned, and (4) how many attempts are deterred because fraud is no longer profitable. It is therefore incorrect to evaluate profitability only by comparing “detected fraud” to system cost: the system can be profitable even if it detects no completed fraud, if deterrence is strong enough that scammers stop trying. If the bank does not bear all losses (customers bear some), the bank’s reimbursement savings fall and its incentive to invest weakens—though it may still invest if weak protection leads to customer loss and higher resource costs from complaints and churn.

To answer this question we will use the Legal Incentives Model and Cost–Benefit Analysis.

Object and counterfactual The object is the bank’s internal compliance and monitoring system (training, controls, audit, escalation, sanctions, and workflow design) chosen under a liability regime where the bank faces a large monetary sanction if employees fail to follow AML procedures, even if senior management is not directly involved. The counterfactual is a weaker or cheaper compliance system (less monitoring, looser controls, softer internal sanctions), which reduces compliance cost but increases expected liability.

Incentives and behavioral responses The bank should design the organization so that employees actually comply with required AML procedures (customer due diligence, escalation of red flags, reporting obligations) instead of shortcutting them. Under the Legal Incentives Model, an employee’s temptation to skip compliance is reduced when the bank (i) lowers the private benefit from non-compliance \(B\), (ii) increases the internal probability that non-compliance is detected \(p\), and/or (iii) increases the internal consequence conditional on detection \(S\). The firm’s problem is to choose the mix of these levers that induces compliance at the lowest total expected cost, given the firm-level expected liability from failures.

Lever 1: reduce the employee private benefits from non-compliance \((B)\).

• Rebalance performance metrics so speed, volume, or revenue in high-risk segments is not rewarded unless compliance quality is met (reduces career/bonus gains from “looking the other way”).
• Limit discretion by standardizing onboarding and transaction approval, especially for high-risk customers/transactions, and require extra approvals.
• Separate tasks so that no single employee controls onboarding, approval, and execution without independent checks.
• Increase documentation and standardized processes, making shortcutting more time-consuming and risky.

Economic logic: lowering \(B\) is attractive when non-compliance is mainly driven by sales targets and time pressure. But lowering \(B\) often slows legitimate business and increases internal effort, so the bank trades off foregone legitimate surplus against a lower expected liability from AML failures.

Lever 2: increase the probability that non-compliance is detected \((p)\).

• Invest in KYC and transaction monitoring systems that flag missing documentation, suspicious patterns, and failures to escalate.
• Conduct regular internal audits and compliance reviews, with extra focus on high-risk units and subsidiaries.
• Make escalation/reporting lines independent from profit centers so compliance concerns are not suppressed.
• Build credible whistleblowing channels and follow-up procedures that raise both actual and perceived detection probability.

Economic logic: increasing \(p\) matters especially when employees are wealth-constrained and cannot be deterred using large monetary penalties alone. Monitoring is costly and can create false positives, delays, and rigidity, but the bank is often a relatively low-cost monitor compared to external regulators because it observes transactions and processes in real time.

Lever 3: increase internal consequences conditional on detection \((S)\).

• Use clear internal sanctions: demotion, dismissal, loss of responsibilities, exclusion from sensitive roles.
• Reduce bonuses or apply clawbacks tied explicitly to compliance outcomes, not only financial performance.
• Use mandatory retraining, adverse evaluations, and internal restrictions for documented failures.

Economic logic: raising \(S\) can be effective because internal sanctions can be timely and targeted. But \(S\) is constrained by credibility, employment law, fairness concerns, and the risk of overdeterrence (e.g., excessive fear may reduce initiative and harm recruitment and retention).

A simple numerical illustration: suppose an employee gains \(B=5\) (saved effort/time and sales credit) from skipping due diligence. If the bank’s monitoring implies \(p=0.1\) and the expected internal consequence is \(S=20\), then the expected internal sanction is \(pS=2\), so the employee still prefers non-compliance (\(B>pS\)). The bank can restore compliance by raising \(p\) (e.g., better audits so \(p=0.3\Rightarrow pS=6\)) and/or raising \(S\) (e.g., credible dismissal/reassignment so \(S=60\Rightarrow pS=6\)), or by redesigning workflow so \(B\) falls below the expected sanction.

Verifiability and enforcement AML compliance is partly observable inside the bank (documentation, workflow steps, escalation logs), but not all failures are easy to prove as deliberate misconduct rather than error. This creates two design implications. First, improving verifiability through standardized procedures and audit trails increases the bank’s ability to raise \(p\) cheaply and apply \(S\) credibly. Second, when errors are likely, relying heavily on severe \(S\) risks punishing mistakes and chilling legitimate business, so the bank may rationally emphasize clearer procedures (lower \(B\) by reducing discretion) and better monitoring (higher \(p\)) rather than extreme sanctions.

Real costs and benefits

• Monitoring and control costs: investment in KYC/transaction monitoring systems, audits, documentation infrastructure (increase relative to the baseline if the bank raises \(p\)).
• Employee compliance/precaution costs: time and effort spent on due diligence, escalation, reporting, and training (increase when the bank lowers \(B\) by making compliance more mandatory and structured).
• Delay and foregone legitimate business: slower onboarding/transactions and reduced ability to serve high-risk customers or jurisdictions (often increases when lowering \(B\) via stricter process and reduced discretion).
• Expected real consequences of enforcement actions: business disruption from supervisory measures, forced remediation, and internal reorganization (decrease when compliance improves).
• Reputational harm translated into real effects: loss of customers, higher funding/financing costs, and reduced ability to do business with counterparties (decrease when compliance improves).

Monetary fines and damages are primarily transfers and are not counted as real resource costs in this section, but they strongly matter for incentives: large firm-level fines increase the bank’s private payoff from investing in internal compliance and monitoring.

Overall, the bank should increase compliance investment until the marginal real resource cost of additional monitoring/training/process constraints is balanced against the marginal reduction in expected real disruption and reputational damage from AML failures. Which lever is cheapest depends on the setting: when monitoring technology is effective and relatively cheap, increasing \(p\) tends to dominate; when monitoring is noisy and error-prone, reducing \(B\) through process design and using targeted, credible \(S\) becomes more important.

Conditional conclusion The bank should implement the mix of (i) workflow and incentive design that reduces employees’ private gains from shortcutting \(B\), (ii) internal monitoring that raises detection probability \(p\), and (iii) credible internal consequences \(S\), such that for relevant employee roles \(pS\) is at least as large as \(B\) and the total expected cost (compliance costs plus expected liability-related losses) is minimized. This conclusion is strongest when (a) employees are wealth-constrained (making \(p\) and non-monetary \(S\) crucial), (b) the bank can monitor at lower cost than external regulators, and (c) compliance failures are sufficiently verifiable to avoid heavy error costs. If monitoring is very costly or non-compliance is hard to distinguish from mistake, the bank should rely more on simplifying and standardizing procedures (lower \(B\)) and on moderate, well-targeted sanctions rather than extreme \(S\). If the bank operates in higher-risk markets or with decentralized subsidiaries, stronger centralized oversight and independent reporting lines are more valuable because baseline risk is higher and weak reporting reduces perceived \(p\).

To answer this question we will use Legal Incentives Model and Cost–Benefit Analysis.

Object and counterfactual The object is a policy choice: impose large monetary fines on the firm for AML failures committed by employees/subsidiaries, even if owners and top management were not directly involved. The counterfactual is an enforcement regime relying only on sanctions against the individual employees who engaged in (or facilitated) the misconduct.

Incentives and behavioral responses Under the Legal Incentives Model, an employee engages in misconduct when the private benefit \(B\) exceeds the expected sanction \(pS\), i.e. when \(B > pS\). In AML settings, \(p\) (detection and successful enforcement) is imperfect and costly to raise. A central policy design question is therefore how to structure liability so that the relevant decision-makers face sufficient expected costs to deter misconduct, while minimizing total social costs of monitoring and enforcement.

1. Wealth constraints make individual-only deterrence expensive. Individual employees are often wealth-constrained, which caps the maximum feasible monetary sanction \(S\). If \(S\) is capped, deterring a given \(B\) requires increasing \(p\), but raising \(p\) through public monitoring and enforcement is costly. A simple illustration: if an employee’s misconduct yields \(B=100\) and the maximum collectible individual penalty is \(S=50\), then deterrence requires \(p \ge 2\), which is impossible. Even if \(B=100\) and \(S=200\), deterrence still requires \(p \ge 0.5\), which can be very expensive to achieve via public enforcement. Firms can pay very large fines, so firm-level liability allows policymakers to substitute sanction severity for detection probability, maintaining deterrence without requiring extremely intensive public monitoring.
2. Firm liability changes who invests in detection (and can raise effective \(p\) for employees). Monitoring is costly regardless of who performs it, and firm-level liability works partly by inducing firms to invest in internal monitoring, audits, and reporting systems that raise the effective probability that employee misconduct is detected and sanctioned. This does not eliminate public monitoring: the state must still “monitor the monitor” by auditing or supervising firms’ compliance systems. The economic rationale is comparative advantage: (i) firms often observe day-to-day transactions continuously and have better information than regulators, making them lower-cost monitors of employee conduct, and (ii) it can be cheaper for the state to audit a firm’s compliance systems than to directly detect each instance of employee money laundering. With firm liability, employees anticipate both greater internal detection and greater likelihood of referral/escalation, increasing the relevant \(p\) in \(pS\).
3. Firms can reshape employees’ payoffs ex ante in ways the state cannot. Firms can reduce the private benefits \(B\) employees obtain from tolerating or facilitating AML violations (e.g., through internal rules, approval requirements, task separation, and compensation design that reduces gains from processing suspicious transactions). Firms can also impose internal sanctions (e.g., termination, loss of bonuses), raising the employee’s effective \(S\) even when criminal or civil sanctions are uncertain, delayed, or hard to apply. These private instruments directly change employees’ expected payoffs and complement public enforcement.

A key implication is that if liability were imposed only on individual employees, the firm would not bear the expected cost of AML failures and would tend to underinvest in monitoring and compliance relative to the social optimum, because the external harm from money laundering is large and not fully internalized by the individual wrongdoer. Firm-level liability forces internalization by making the firm’s private payoff depend on expected misconduct losses.

Real costs and benefits

• Public monitoring and enforcement costs: Under individual-only liability, achieving deterrence may require very high \(p\) via costly public monitoring, investigation, and litigation. With firm liability, some detection effort is shifted to the firm, and public resources can focus on auditing compliance systems, which may be cheaper.
• Private compliance and monitoring costs: Firm-level liability increases firms’ spending on internal controls, audits, training, and reporting. These are real resource costs (even if they also improve detection).
• Residual external harm from money laundering: Individual-only liability risks underdeterrence because \(S\) is capped and \(p\) is imperfect, leaving more residual harm. Firm liability can reduce residual harm by increasing both effective \(p\) and the employee’s effective expected cost of misconduct.
• Error and chilling (over-compliance) costs: Strong firm sanctions can lead to excessive caution, delayed transactions, or denial of legitimate customers. These are real costs of false positives and overly strict internal rules. They can be larger under firm liability if firms respond defensively.

Overall, the relevant comparison is not “public vs. private” cost, but total social cost across regimes. Monitoring must occur under any regime. Firm-level liability is attractive when firms are relatively low-cost monitors of day-to-day conduct and when public oversight of firms (auditing compliance) is cheaper than direct public detection of employee misconduct.

Sanctions and fines themselves are transfers and do not enter directly as real resource costs or benefits, but they matter because they change incentives and therefore the levels of residual harm and compliance activity.

Verifiability and enforcement Money laundering and AML compliance involve verifiability constraints: regulators and courts cannot cheaply observe or prove all underlying conduct, and enforcement is imperfect, so \(p<1\). Increasing \(p\) through public enforcement requires resources and may be limited by evidentiary constraints. Firm-level liability partly addresses this by inducing firms to generate verifiable records (audits, logs, internal reports) that make misconduct easier to detect and prove, while the state focuses on supervising whether compliance systems are credible rather than reconstructing each underlying transaction.

Conditional conclusion Imposing large firm-level fines can be efficient when (i) individual sanctions are capped by wealth constraints so individual-only liability would generate underdeterrence unless \(p\) is raised at very high public cost, (ii) firms are lower-cost monitors with better information than public authorities, so shifting monitoring to firms increases effective detection at lower total cost, and (iii) firms can materially reduce employee misconduct by changing internal controls and internal sanctions. If instead firms are not meaningfully better monitors, public auditing of compliance is as costly as direct public enforcement, or chilling/over-compliance costs are very large, then relying more heavily on individual sanctions (and less on firm-level fines) becomes relatively more attractive.

To answer this question we will use Legal Incentives Model and Cost–Benefit Analysis.

Object and counterfactual The object is the legal choice to treat non-performance of a supply contract (PharmaChem failing to deliver the ingredient) as a matter for contract law (civil liability, typically compensatory damages), rather than as a crime (criminal liability with harsher sanctions such as fines or imprisonment). The counterfactual is a regime in which breach of contract is criminalized.

Incentives and behavioral responses Under the Legal Incentives Model, a party’s choice to breach depends on the private benefit from breaching \(B\) (e.g., saving production costs, selling the ingredient elsewhere) versus the expected sanction \(pS\), where \(p\) is the probability the sanction is imposed and \(S\) is its magnitude.

Crimes like theft share a key feature: \(p\) is often well below 1 because conduct can be hidden and offenders may “get away with it.” If theft yields \(B=100\) and \(p=0.2\), then to deter it you would need \(S\) large enough that \(pS \ge B\), i.e. \(S \ge 500\). That logic helps explain why criminal law relies on harsh sanctions: civil damages alone may not deter when \(p\) is low.

By contrast, many contract breaches—especially failure to deliver a promised input under a long-term supply agreement—are hard to conceal from the buyer. If PharmaChem does not deliver, Biotech likely observes the shortfall quickly and can sue or invoke contract remedies, so \(p\) is often close to 1. Then compensatory (expectation) damages can create adequate deterrence: PharmaChem anticipates it will have to pay for the buyer’s loss, and so it will not breach when breach is inefficient.

This connects to “efficient breach.” If expectation damages require the breacher to internalize the promisee’s loss, PharmaChem will breach if and only if the private gains from reallocating the ingredient exceed the buyer’s loss—i.e., when the breach is Kaldor–Hicks efficient (efficient in the sense that the breacher could compensate the promisee and still be better off). If instead breach were treated like a crime with additional punishment (prison, punitive damages, extra fines), PharmaChem might perform even when performance destroys surplus, because the expected sanction \(pS\) becomes too large relative to the real social cost of non-performance.

Finally, criminalization can change behavior on an earlier margin: willingness to contract. If breach is criminal, parties may avoid long-term contracts, draft overly rigid agreements, or invest less, because the downside risk of inadvertent or opportunistic non-performance becomes much more severe. That reduction in contracting and investment is typically inefficient relative to the Coasean benchmark in which parties are free to allocate risks and remedies by agreement.

Real costs and benefits

• Expected production disruption and expected harm from non-delivery: Civil liability with expectation damages already pushes PharmaChem to internalize the buyer’s loss when \(p\) is high; additional criminal sanctions reduce breaches further, but mainly by deterring breaches that would have increased total surplus (deterring efficient breach).
• Precaution/compliance and over-performance costs: Criminalization induces excessive “over-compliance” (e.g., costly precaution to avoid any risk of shortfall, inefficiently rigid allocation of inventory) and may force performance even when switching uses of the ingredient would save real resources.
• Enforcement and adjudication costs: A criminal regime requires public investigation, prosecution, and adjudication resources in addition to (or instead of) private civil litigation. These are real resource costs, not just transfers.
• Chilling of contracting and investment: If breach exposes managers to criminal liability, firms may contract less, choose shorter terms, or avoid specialized reliance investments, reducing gains from trade in settings like long-term supply for drug production.
• Cost of imprisonment (if used): Incarceration is a large real resource cost (and removes productive capacity), which is hard to justify when civil remedies already provide strong incentives.

Damages and fines themselves are transfers (from PharmaChem to Biotech or the state) and are not counted as real social costs in the cost–benefit accounting, though they matter for incentives through \(S\).

Verifiability and enforcement The core difference is typically \(p\). For many crimes \(p\) is low because detection and proof are difficult. For many contract breaches (like not delivering an input under an ongoing agreement), non-performance is quickly observed and documented, so \(p\) is often high. When \(p\) is high, raising \(S\) through criminal sanctions is less necessary for deterrence and more likely to create over-deterrence and other real costs.

A complication is that not all breaches are easy to detect or prove (e.g., hidden quality shading, subtle under-delivery, or breaches masked by complex supply chains). In those cases \(p\) may be meaningfully below 1, so expectation damages may under-deter unless coupled with stronger enforcement tools or sanctions.

Transaction costs and private ordering Under the Coasean benchmark/Coasean logic, voluntary contracting is generally evidence of efficiency when parties are informed and third-party effects are limited. A contract-law regime lets parties choose terms, monitoring, and remedies (including liquidated damages, auditing, and termination clauses) to address foreseeable supply risk. Criminalization limits this private ordering by imposing non-waivable public sanctions, and the resulting fear of criminal exposure can discourage efficient contracting even when parties could have priced and allocated the risk contractually.

Conditional conclusion Breach of contract is typically not a crime because, in many contract settings, breach is readily detected and enforced (high \(p\)), so compensatory remedies can deter inefficient breach while still allowing efficient (Kaldor–Hicks efficient) breach. Criminal sanctions would add substantial real costs (public enforcement, over-compliance, imprisonment) and would likely deter valuable contracting and relationship-specific investment. If, however, the relevant breach is systematically hard to detect or prove (low \(p\))—for example, hidden quality reduction—then the case for stronger sanctions (or stronger enforcement mechanisms) becomes relatively stronger than in the standard “missed delivery is obvious” scenario.

To answer this question we will use the Coasean benchmark and Adverse selection.

Object and counterfactual The object is a legal requirement that all households must buy home contents insurance. The counterfactual is the current regime where households can opt in or opt out, and insurers are free to contract and price risk.

Incentives and behavioral responses Under the Coasean benchmark, voluntary insurance contracts are presumptively welfare-improving because both parties agree only if they expect to be better off. In that world, a growing share of uninsured households could simply reflect heterogeneous preferences and risk exposure: some households may rationally choose to self-insure (bear the risk) rather than pay the premium.

A mandate changes behavior by eliminating the opt-out margin. It increases coverage mechanically, but it also changes who is in the pool and how contracts are priced. If the current voluntary market is unraveling because the remaining buyers are disproportionately high risk, forcing low-risk households into the pool can lower average payouts per policy and stabilize the market. At the same time, compulsory coverage can weaken incentives to avoid losses (and can increase fraud attempts), because more people will have a claim opportunity once insured.

Transaction costs and private ordering The Coasean intuition behind the Coasean benchmark holds when rights are well-defined and tradable and transaction costs are close to zero. Put more operationally, voluntary contracts are good evidence of efficiency when (i) the agreement is genuinely voluntary, (ii) both parties understand the contract and act rationally, and (iii) the contract does not impose relevant costs on third parties.

Here there are reasons to doubt that the voluntary outcome reliably aggregates to an efficient market outcome. The facts point to a high-transaction-cost environment driven by information problems about risk type. If the households who most want insurance are those with higher expected losses, insurers must raise premiums, and then low-risk households are the first to opt out. A simple illustration: suppose some households have expected annual losses of 10,000 (high risk) and others 1,000 (low risk). If insurers cannot price perfectly by type and must charge a pooled premium around 5,500, low-risk households will rationally drop out, leaving a worse pool and pushing premiums up further. In that case, the observed increase in non-insurance is not strong evidence that “the market is working”; it may instead reflect adverse selection that prevents mutually beneficial trades with low-risk households.

Verifiability and enforcement The prompt also highlights rising fraud and disputed documentation (e.g., alleged theft and fire). That points to verifiability constraints: even if a loss is observable to the insured, it may be hard to prove to the insurer/court at acceptable cost. This raises real resource costs for claims handling (investigation, documentation, disputes), and it can lead to socially wasteful behavior (time and resources spent staging, hiding, or contesting claims).

A mandate does not remove these constraints. It may increase claim volume and therefore increase the resources spent on verification and anti-fraud. Enforcement also matters on the coverage side: a mandatory rule requires some monitoring of whether households actually hold a valid policy (administration and compliance checks), which is itself costly.

Real costs and benefits

• Risk-bearing and inefficient self-insurance: Mandatory coverage reduces the need for households to hold costly buffers (e.g., excessive savings or other costly “self-insurance”) and reduces the real distress and disruption from uninsured large losses, relative to a world where some remain uninsured.
• Misallocation from adverse selection (“market unraveling”): If adverse selection is driving low-risk households out, a mandate can reduce the inefficiency from missing mutually beneficial insurance trades by forcing broad pooling; this can lower the resources wasted on searching for, pricing, and re-pricing an increasingly risky pool.
• Administrative and enforcement costs: A mandate increases real resource costs of administering compliance (verification of coverage) and may increase insurers’ claim-processing and investigative costs because more households are insured and can file claims.
• Fraud and loss-avoidance behavior: Compulsory insurance can increase resources wasted on fraudulent activity and on defensive monitoring/investigation; it can also reduce private incentives to take loss-avoidance precautions if premiums or coverage terms do not reflect behavior well.
• Public relief and external costs (if present): To the extent uninsured households receive public emergency support after large losses, broader coverage can reduce tax-financed relief administration and other spillover costs. If such third-party costs are small, this channel is weak.

Premiums and claim payments are mainly transfers between insureds and insurers and are not themselves real resource costs or benefits, but they matter for incentives and for who opts in or out under voluntary insurance.

Conditional conclusion If voluntary contracting conditions are approximately satisfied (low transaction costs, good risk-based pricing, limited fraud, and little or no shifting of costs onto third parties), then the Coasean benchmark implies that mandatory home contents insurance is likely to reduce welfare by forcing coverage on households who rationally prefer to self-insure and by adding compliance administration. If instead adverse selection and verifiability problems are sufficiently severe that the market is unraveling (high-risk households remain insured, low-risk households exit, and pricing cannot correct this), then a mandate can increase welfare by restoring pooling and preventing inefficient non-insurance—provided the added enforcement and anti-fraud costs do not outweigh these gains. The stronger the fraud problem and the higher the costs of monitoring compliance and verifying claims, the weaker the efficiency case for a mandate (even if the distributional “social security” case remains politically salient).

To answer this question we will use the Coasean benchmark, and Cost–Benefit Analysis.

Object and counterfactual The object is a proposed public rule that increases banks’ responsibility for losses from BankID fraud (e.g., requiring banks to reimburse customers more often). The counterfactual is private ordering: banks and customers remain free to allocate fraud risk through terms and pricing of digital banking services, with minimal government intervention.

Incentives and behavioral responses Under the Coasean benchmark, voluntary contracts can be treated as evidence of efficiency when (i) agreements are truly voluntary, (ii) both parties are sufficiently informed and understand the terms, and (iii) relevant costs are not pushed onto outsiders. A Coasean intuition applies: if rights and liability are well-defined and tradeable and transaction costs are close to zero, the parties can bargain to a Pareto-efficient risk allocation (for example, customers who prefer low fees accept more fraud risk; customers who value protection pay for it).

If these conditions fail, private contracts may not deliver the efficient level of fraud prevention. A key mechanism is that liability affects who has the strongest incentive to invest in anti-fraud measures. If banks bear more of the loss when fraud succeeds, they have stronger incentives to invest in security design, monitoring, and friction-reducing but protective authentication; if customers bear most losses, banks may underinvest from a social perspective even if customers “agreed” in standard terms.

A simple illustration: suppose better security costs the bank 10 per customer per year and reduces expected fraud losses by 50. If customers are left bearing the loss, the bank may not find it privately profitable to spend 10 (it can keep fees low and let customers absorb the 50). If the bank must reimburse, the bank now saves an expected 50 by spending 10, so it invests. This is an internalization logic: shifting responsibility can move behavior toward the efficient precaution level when the bank is the low-cost avoider.

Transaction costs and private ordering Even if tailoring would be efficient in theory, Coase-style bargaining can be blocked by transaction costs and information problems. Individualized contracting is costly because banks serve many customers and would need to segment contracts: tech-savvy/low-risk customers could efficiently bear more responsibility, while elderly/less digital customers might efficiently be protected by the bank. In practice, asymmetric information makes such sorting difficult.

A related problem is adverse selection. A customer who insists on strong fraud protection may thereby signal that they are high-risk, leading the bank to set a high price for protection. Then some moderate-risk customers who would be efficient to cover may opt out because the price reflects the pool’s risk, not their own. When this happens, private ordering can produce too little coverage and too weak incentives to invest in prevention. In such settings, a mandatory “standard contract” (a default or non-waivable rule) where banks bear more responsibility can be efficiency-enhancing even though individualized contracts could dominate in a frictionless world.

Real costs and benefits

• Fraud losses (expected harm): Regulation that makes banks responsible can reduce expected losses if it induces more effective prevention, faster detection, and better account controls; without liability, banks may tolerate higher expected harm because customers absorb it.
• Precaution and compliance costs: Banks may incur higher real costs from stronger authentication, monitoring, customer support, and fraud-response systems; customers may also face more friction (time, failed transactions) if security tightens.
• Administrative and enforcement costs: Public rules can require supervision, complaint handling, and dispute resolution; banks may incur operational costs to document compliance and process reimbursements.
• Misallocation from distorted consumer choice: If customers cannot evaluate security quality or contract terms, they may choose banks based on price while underweighting fraud risk, leading to inefficiently low demand for security and therefore inefficiently low supply.
• System-wide trust and usage benefits: If reimbursement rules sustain trust in digital banking and payments, society can avoid real costs from reduced digital participation (e.g., reverting to slower/manual methods, extra verification steps across the economy).

Any reimbursements, fines, or damages are largely transfers between banks and customers and are not counted as real resource costs or benefits; their importance here is that they change incentives to prevent fraud and to take care in authentication and customer communication.

Whether the benefits exceed the costs depends on (a) how responsive banks’ security investment is to increased responsibility, (b) how large the incremental prevention effect is relative to added compliance/friction costs, and (c) whether banks can reduce fraud at lower real cost than individual customers can through vigilance and self-protection.

Conditional conclusion Public regulation that increases banks’ responsibility for BankID fraud is most plausibly justified when private ordering is unlikely to be reliably efficient: customers have limited information and understanding of security quality and contract terms, transaction costs and asymmetric information block efficient tailoring (including adverse selection in “buying” protection), and fraud has important third-party effects through reduced trust in digital payments that banks do not fully internalize. By contrast, if customers are well-informed, can compare banks’ security and liability terms, and can cheaply contract for the preferred mix of price and protection without spillovers to outsiders, the Coasean benchmark points toward leaving responsibility allocation to contract and competition, since intervention would mainly add compliance and service-cost increases without offsetting behavioral gains.

To answer this question we will use Cost–Benefit Analysis and the Legal Incentives Model.

Object and counterfactual The object is a targeted subsidy that covers NordCem’s costs of installing and operating carbon capture. The relevant counterfactual is a technology-neutral subsidy that pays for CO\(_2\) abatement regardless of method (fuel switching, different cement types, or capture), so that NordCem can choose the least-cost way to reduce emissions.

Incentives and behavioral responses Under the Legal Incentives Model logic, a subsidy changes the firm’s private payoff by reducing the private cost of the subsidized action. If carbon capture is subsidized but other abatement is not, NordCem is pushed toward carbon capture even when it is not the lowest real-cost way to cut emissions. A technology-neutral subsidy instead preserves the incentive to pick the cheapest available abatement method (or mix of methods), because NordCem can apply support to whichever option reduces emissions at the lowest real resource cost.

A simple illustration (from the prompt’s assumptions) shows the mechanism. If carbon capture costs 1,500 NOK per tonne and fuel switching costs 500 NOK per tonne, then with a fixed subsidy budget of 1.5 billion NOK:

• Targeted carbon-capture subsidy buys \(1.5 \text{ billion} / 1{,}500 = 1{,}000{,}000\) tonnes CO\(_2\) abatement.
• Technology-neutral subsidy can buy \(1.5 \text{ billion} / 500 = 3{,}000{,}000\) tonnes CO\(_2\) abatement (if the cheaper measures are available at that scale).

If the main benefit is the emissions reduction itself, the technology-neutral policy generates larger benefits for the same public spending because it delivers more tonnes abated.

Real costs and benefits

• Real abatement (resource) costs: A capture-only subsidy steers spending toward a higher-cost abatement technology (1,500 NOK/tonne in the example) rather than lower-cost measures (500 NOK/tonne). This raises the real resource cost per tonne abated relative to a technology-neutral policy that can fund the cheapest reductions first.
• Climate benefits from abatement: The benefit of reduced CO\(_2\) is increasing in total tonnes reduced. Holding the benefit per tonne fixed, a policy that achieves 3,000,000 tonnes rather than 1,000,000 tonnes yields larger total benefits.
• Administrative and implementation costs: Targeted programs can require specialized monitoring of eligible equipment and performance; technology-neutral programs may require measurement of abatement across heterogeneous methods. Whichever approach has lower measurement/administration costs (for the same integrity) is preferred on this margin.
• Additional dynamic benefits (if present): A capture subsidy could generate real benefits beyond current abatement, such as knowledge spillovers that reduce future capture costs (making later abatement cheaper) or accelerating deployment that would otherwise be delayed. These benefits can offset higher current resource costs.

Transfers note: subsidy payments themselves are not real resource costs; they matter because they change private incentives and therefore behavior.

Conditional conclusion The employee is right to worry about inefficiency when (i) there exist substantially cheaper abatement options (like fuel switching) that could have delivered the same emissions reductions at lower real cost, (ii) those options are scalable for NordCem (or in the relevant sector) within the policy’s budget, and (iii) carbon capture does not generate significant additional benefits beyond the tonnes abated. In that case, a technology-neutral abatement subsidy dominates because it achieves more emissions reduction per krone and therefore higher total surplus for a given public budget.

Conversely, subsidizing carbon capture rather than general abatement can be efficient if carbon capture delivers extra benefits not captured by “tonnes today,” such as large knowledge spillovers that lower future abatement costs, or if (despite higher unit costs) capture is the only practical way to achieve a needed level of reductions because other methods are limited, risky, or blocked by high upfront costs and liquidity constraints under a technology-neutral scheme.

To answer this question we will use Cost–Benefit Analysis, and (where relevant) Market power.

Object and counterfactual The object is a government decision to stop building additional foreign cables for electricity export. The relevant counterfactual is to allow further cable expansion, so Norway becomes more connected to neighboring markets and can both export more in surplus periods and import more in deficit periods. The policy question is not whether prices change, but whether the change increases total economic surplus once behavioral responses and real resource costs are included.

Incentives and behavioral responses More cable capacity tightens the link between Norwegian and foreign prices. When foreign prices are higher, the ability to export increases the opportunity cost of selling domestically, so domestic prices can rise; when Norway is short (e.g., low reservoir inflow), cables also allow imports that can limit extreme scarcity prices or even prevent rationing. Stopping cables weakens these arbitrage forces.

Behavioral responses matter:

• Consumption: Higher prices (from more integration in tight periods) induce conservation and shifting of use to off-peak/low-price periods; stopping cables blunts those incentives and can raise consumption in scarce periods.
• Production and investment: If producers expect better export opportunities, they have stronger incentives to maintain capacity and invest; stopping cables can reduce expected returns to such investments, potentially lowering future supply resilience.
• Risk allocation: Cables can increase exposure to foreign price shocks, but also provide an “insurance” margin via imports in dry years. A cable stop reduces that insurance.

A simple numerical illustration clarifies transfers versus real gains from trade. Suppose producing 1 MWh costs 40. Domestic customers value it at 50, while foreign buyers value it at 80. Without export capacity, the MWh stays at home: surplus is \(50-40=10\). With export capacity, exporting creates surplus \(80-40=40\). Domestic customers lose the 10 surplus they would have gotten, but society gains 40 instead; net surplus rises by 30 (and the higher domestic price is mainly a transfer from domestic buyers to sellers). This is the core efficiency case for allowing trade, even though some domestic customers face higher prices.

Real costs and benefits

• Gains from trade: Allowing exports when foreign willingness to pay exceeds domestic willingness to pay increases total surplus; stopping cables foregoes these gains.
• Security of supply / shortage risk: Cables provide import capacity in deficit periods. Stopping expansion can increase the expected real harm from shortages (lost load, production stoppages), especially in “dry year” states.
• Investment and maintenance incentives: Greater market access can strengthen incentives to invest in generation, storage flexibility, and grid reliability. A cable stop may reduce these incentives and raise long-run real costs (e.g., more expensive domestic backup solutions).
• Volatility and adjustment costs: Greater integration can increase price volatility in Norway. Volatility creates real adjustment costs for some actors (e.g., inefficient shutdowns of otherwise productive industrial capacity, costly emergency adaptation). A cable stop may reduce some of these volatility-related costs, but at the expense of less import insurance.
• Environmental and system-operation costs: If less trade forces more domestic high-cost/low-flexibility adjustments in tight periods, system operation can become more resource-intensive. Conversely, if exports lead to more domestic production in periods where it is socially costly (e.g., water value in reservoirs), that can be a real cost; the sign depends on system conditions.
• Administrative and political economy costs: Intervention can create ongoing costs of regulation and lobbying over exceptions, allocation rules, or accompanying price measures.

Higher domestic prices due to cables are largely transfers from consumers to producers and do not themselves constitute a net social cost, but they can matter indirectly if they cause real production losses (e.g., inefficient industrial shutdowns) or if risk-sharing markets are incomplete.

Overall magnitude depends on how often Norway is constrained by domestic scarcity versus having surplus, and on how valuable the import option is in extreme states. If a cable stop materially increases the probability or severity of shortages, the expected real cost can be large.

Market power and competitive constraints If domestic producers have market power in tight periods, additional interconnection can act as a competitive constraint by bringing in imports and disciplining price-setting. In that case, stopping cables can increase the scope for market power and raise real misallocation (too little consumption by high-value users or inefficient rationing). If instead foreign markets are the source of market power (e.g., constrained import routes into a broader region), more integration may import those distortions; then the case for more cables is weaker. Which direction dominates is an empirical question about where competitive constraints are strongest.

Conditional conclusion A stop in building additional export cables is a good idea only under conditions where the real costs created by further integration (e.g., large volatility-induced shutdown costs, or importing market power/distortions) are likely to exceed the real gains from trade and the security-of-supply benefits of import capacity. If, as is often the case, higher prices mainly reflect scarcity and trade opportunities rather than pure market failure, then stopping cables mainly converts a surplus-increasing trade margin into a politically attractive but inefficient price reduction (a transfer), while increasing long-run scarcity risk and weakening investment incentives. The conclusion therefore turns on (i) the frequency and severity of scarcity states, (ii) the ability of households and industry to adjust/hedge, and (iii) whether market power or other frictions prevent prices from reflecting real scarcity.

To answer this question we will use Cost–Benefit Analysis, supplemented by the Legal Incentives Model.

Object and counterfactual The object is a household “electricity price support” scheme where the state pays a share of the bill when the market price exceeds a threshold. The counterfactual is no support: households pay the full market price and therefore internalize scarcity through their usage decisions.

Incentives and behavioral responses The key mechanism is that the scheme lowers the marginal price households face when the market price is high. This weakens the incentive to reduce consumption in high-price (scarce) periods. In Legal Incentives Model terms, the policy reduces the “sanction” from consuming in scarcity: the household’s private cost of an extra kWh is no longer the full market price but the subsidized price.

Two market outcomes follow.

1. Quantity: When the consumer-facing marginal price is lower, households demand more electricity than they would at the full market price (less conservation, less shifting of consumption to off-peak).
2. Price that clears the market: Because total demand is higher at any given market price, the market-clearing price tends to rise, especially when supply is tight in the short run (capacity constraints, limited import availability, hydropower constraints). A higher market price means the subsidy also becomes more expensive for the state and can raise prices paid by non-covered consumers (e.g., firms) if they are not included in the support.

A simple illustration: Suppose the market price in a scarcity hour is 2 NOK/kWh. The scheme covers 50% of the price above 1 NOK/kWh, so the household pays \(1 + 0.5(2-1)=1.5\) NOK/kWh. The household therefore treats consumption as if electricity were 1.5 NOK/kWh, not 2. Relative to the counterfactual, more kWh are consumed in the very hours when the system is most costly to supply. If the system is tight, the extra demand pushes the market-clearing price above 2 NOK/kWh, raising both the fiscal cost and the resource cost of meeting demand.

Real costs and benefits

• Reduced expected harm from high bills (risk of non-payment, severe cutbacks in essential consumption): The scheme can reduce real hardship costs for liquidity-constrained households by smoothing extreme price spikes and preventing inefficiently large reductions in other essential spending.
• Higher real production and procurement costs in scarcity periods: Because the subsidy reduces demand response, more electricity must be produced/imported precisely when marginal supply is expensive (costly imports, activation of higher-cost generation, or using scarce water with an opportunity cost for later periods).
• Misallocation from weakened conservation and load shifting: The policy encourages consumption in high-cost hours and reduces incentives to invest in efficient appliances, insulation, or demand response. This is a real efficiency loss when the market price is a useful scarcity signal.
• Administrative and implementation costs: Metering, billing integration, eligibility rules, and verification generate real resource costs.
• Risk of non-price rationing or reliability measures when supply is fixed in the short run: If physical supply cannot expand in peak scarcity, higher subsidized demand increases the need for costly rationing arrangements or emergency measures.

(Transfers are central for incentives but are not real resource costs: the subsidy payment itself largely reallocates money from taxpayers to electricity consumers and, indirectly via higher market-clearing prices, to producers.)

Whether the efficiency costs dominate depends strongly on the supply situation. When supply is very inelastic (typical in the short run), the main effect of the subsidy is to raise the market-clearing price and shift money, while also increasing costly consumption in the tightest periods. When supply is more elastic (more available imports/generation), quantity expands more, but then the added kWh come with real marginal cost that must be compared to the consumers’ additional value from those kWh.

Conditional conclusion Electricity price support can be a reasonable tool for protecting households against extreme price spikes if (i) the main policy objective is distributional relief or hardship reduction, (ii) the scheme is tightly targeted to households with high marginal utility of income and limited ability to adjust, and (iii) it preserves at least some marginal price signal so that consumption still falls when electricity is truly scarce. It is less attractive as a general solution to “high prices” when the underlying problem is scarcity, because lowering the consumer-facing marginal price reduces conservation and tends to increase the market-clearing price and real supply costs. If the market is competitive and prices mainly reflect scarcity, broad price support trades short-run bill relief for weaker efficiency. If instead prices are elevated by limited competition or other frictions, the same subsidy risks being captured by producers through higher market-clearing prices, making it an especially costly way to help households.

To answer this question we will use Cost–Benefit Analysis, and Adverse selection.

Object and counterfactual The object is a contract clause making CleanCarbon pay NordCem’s additional EU ETS quota costs if the capture system underperforms. The counterfactual is a contract without that clause, where NordCem bears quota costs from underperformance (possibly reflected in a lower contract price).

Incentives and behavioral responses The clause changes who internalizes the cost of underperformance. If CleanCarbon expects to pay for quota costs when capture is low, it has stronger incentives to invest in design quality, installation quality, and troubleshooting that improve realized capture. If, instead, the main driver of capture is NordCem’s operating conditions (kiln gas composition management, temperature stability, and maintenance routines), the clause can weaken NordCem’s incentives to do those things, because NordCem is partly insured against the consequences.

The clause can also address asymmetric information about expected performance. If CleanCarbon knows more than NordCem about the true reliability of the technology under realistic plant conditions, willingness to accept liability (or the price required to accept it) can act as a credibility device: a supplier expecting frequent shortfalls will demand a large price increase or refuse the clause, while a confident supplier can accept at lower added price.

A simple numerical illustration: if quota price is 100 per tonne and underperformance is 50,000 tonnes in a year, the private quota bill is 5,000,000. Assigning that liability to CleanCarbon creates a strong incentive to avoid that shortfall—if CleanCarbon can prevent it cheaply—but is wasteful if the only way to reduce shortfall is costly overengineering while NordCem could have reduced it through low-cost maintenance.

Real costs and benefits

• Precaution / performance-improvement costs (technology side): likely increase because CleanCarbon will spend more on robust design, installation, and monitoring to avoid being liable; this is a real resource cost, but it can be efficient if it reduces costly shortfalls.
• Precaution / performance-improvement costs (operation side): may decrease if NordCem becomes less careful in maintenance and operation due to reduced exposure; this is inefficient if NordCem is the low-cost avoider of underperformance.
• Abatement-cost efficiency in the ETS system: if underperformance leads NordCem to buy additional quotas, the real social resource cost is the extra abatement undertaken elsewhere to free up quotas (or the production adjustments needed to reduce emissions). A clause is beneficial if it reduces expected shortfalls and therefore reduces these real abatement/adjustment costs.
• Risk of misallocated effort and “wrong-party” incentives: if the clause makes CleanCarbon bear costs driven by NordCem’s behavior, CleanCarbon may respond by excessive engineering, costly monitoring of NordCem, or refusing to contract—real costs that do not improve capture efficiently.
• Contracting and dispute costs: the clause can increase bargaining, monitoring, and conflict costs, especially if parties litigate over whether shortfalls were “underperformance” or caused by NordCem’s operation choices.

Quota payments themselves are transfers (NordCem pays another quota holder) and are not counted as real resource costs in the CBA, but they matter for incentives and for how abatement effort is allocated across firms.

Verifiability and enforcement The clause works only if underperformance and the triggering condition are verifiable. Measuring actual capture rates can be technically feasible, but attributing a shortfall to (i) technology/installation versus (ii) NordCem’s maintenance and operating conditions may be hard to prove to a court. If causation is hard to verify, the clause can generate high dispute costs and weak incentives (because liability becomes unpredictable). This is why a more precise clause may dominate: for example, NordCem pays quota costs attributable to improper maintenance or operation, while CleanCarbon pays costs attributable to technological failure or improper installation—provided those categories can be verified using objective indicators and documentation.

Conditional conclusion Including the clause is a good idea for NordCem if (i) CleanCarbon is the party that can reduce expected quota-cost exposure at lowest real cost (through better technology, installation, and performance support), (ii) CleanCarbon has private information about true expected performance and the clause helps screen low-quality suppliers or makes performance claims credible, and (iii) capture performance and causation can be verified at reasonable cost. If instead underperformance is mainly driven by NordCem’s own operating and maintenance choices, or if attribution is hard to verify so disputes are likely, the clause can be counterproductive; in that case a tailored, split-responsibility clause that conditions liability on verifiable causes is more likely to increase welfare.

To answer this question we will use Adverse selection and Moral hazard, focusing on how contract terms can reduce hidden-quality and hidden-action problems that make “performance as promised” hard to guarantee.

Object and counterfactual NordCem is choosing contract clauses for a carbon-capture installation. The counterfactual is a simple fixed-price purchase/install contract with no special performance terms, where CleanCarbon is paid largely regardless of realized capture and disputes about causes of underperformance are handled only ex post.

Incentives and behavioral responses Because capture rates depend on local conditions and ongoing routines, NordCem faces two related risks.

• Hidden quality at contracting (adverse selection): CleanCarbon may know more than NordCem about how robust the technology is under NordCem’s kiln-gas composition, temperature variability, and realistic maintenance constraints. Without safeguards, CleanCarbon can overstate expected performance.
• Hidden actions after signing (moral hazard): After installation, performance depends on effort and care (calibration, maintenance, troubleshooting). If CleanCarbon is paid regardless of performance, its incentive to incur these costly actions is weaker. If NordCem’s own operation strongly affects performance, NordCem may also underinvest in the routines that protect capture efficiency unless it bears the consequences.

Clauses that strengthen performance incentives or improve information before final commitment address these problems:

1. Performance guarantee (e.g., guaranteed capture efficiency). If CleanCarbon must stand behind a stated capture level, offering a strong guarantee can act as a signal of product quality and reduce adverse selection. A seller with low expected performance expects to pay more under the guarantee and therefore is less willing to offer it on the same terms. The guarantee also reduces CleanCarbon’s incentive to “oversell” in the letter-of-intent stage.
2. Performance-based payment (pay tied to verified tonnes captured or verified capture rate). This makes CleanCarbon’s payoff increase with the output NordCem cares about, addressing moral hazard by aligning incentives to deliver a working system and to sustain performance (maintenance, adjustments, rapid repair). A simple illustration: if extra maintenance costs CleanCarbon \(50\) but increases expected captured CO\(_2\) enough to raise its performance payment by \(80\), it will do the maintenance; under a fixed price, it would not.
3. Testing before final acceptance (independent third-party test before full payment / final handover). This reduces asymmetric information about system quality at the acceptance stage. A credible test changes bargaining and incentives: CleanCarbon anticipates that low quality will be revealed before final payment, which pushes it toward delivering higher quality and away from strategic delay or “hand-over and hope” behavior.
4. Split liability for CO\(_2\) quota costs (allocate based on cause of underperformance). If underperformance is due to system failure, CleanCarbon pays; if due to poor operation by NordCem, NordCem pays. The economic logic is to place consequences on the party controlling the relevant margin. That improves incentives on both sides: CleanCarbon internalizes the costs of design/installation defects it can prevent, and NordCem internalizes the costs of poor routines it can prevent. This reduces wasteful blame-shifting and encourages each side to take the efficient precautions it controls.
5. Make CleanCarbon responsible for operation and maintenance after installation. If CleanCarbon controls operation/maintenance, then performance is more directly “its” choice variable, and strong performance clauses become less problematic because responsibility and control are aligned. This reduces the moral-hazard concern that NordCem’s (possibly unobservable) poor operation undermines capture rates while CleanCarbon is still being judged/paid on performance.

Real costs and benefits

• Expected performance shortfall (real compliance/abatement burden): Better clauses reduce the expected gap between promised and realized capture, lowering the real resources NordCem must spend to meet emissions constraints (e.g., operational adjustments, additional abatement measures, downtime to fix problems).
• Precaution, operation, and maintenance costs: Performance pay, guarantees, and shifting O&M responsibility can increase real effort and maintenance intensity. These are real costs, but they can be efficient if they prevent larger performance losses.
• Monitoring, measurement, and testing costs: Third-party testing and ongoing verification require engineers, measurement equipment, and administrative effort. These costs rise with more demanding clauses, but they also reduce information problems and disputes.
• Dispute and renegotiation costs: Performance-based clauses and split-liability rules can trigger disagreements about causation (system defect vs. operator behavior), which are real costs (time, experts, delay). Clear metrics and protocols can reduce these costs.
• Misallocation risk from distorted incentives: If the contract puts too much weight on measured capture, CleanCarbon may focus narrowly on what is measured (or on short-run test performance) rather than durable performance, unless the clause is designed to reflect long-run outcomes.

Payments for CO\(_2\) quotas (or damages triggered by quota costs) are largely transfers between parties or market participants, so they are not counted as real resource costs here. They matter because they shape incentives: making CleanCarbon bear some of the quota-cost consequence increases its incentive to prevent underperformance.

Verifiability and enforcement All of these clauses rely on measuring “performance.” Capture rates and tonnes captured must be verifiable to avoid turning every underperformance event into a costly dispute. Third-party testing works because it creates a verifiable signal at a defined time. For ongoing performance-based payment or split liability, the contract should specify verifiable metrics (measurement method, sampling frequency, baseline operating conditions, documentation of maintenance routines). If measurement is noisy or manipulable, the expected “sanction” from a guarantee is effectively reduced (lower \(p\) that underperformance is established), weakening deterrence and increasing dispute costs.

Conditional conclusion If capture performance can be measured and attributed with reasonably verifiable metrics, NordCem should combine (i) a performance guarantee and/or performance-based payment to address hidden quality and hidden effort, with (ii) third-party acceptance testing to reduce information gaps before full commitment. If underperformance is substantially driven by the operator’s choices, split-liability rules or assigning operation/maintenance to CleanCarbon becomes important so that responsibility tracks control; otherwise strong performance clauses risk inefficient disputes and risk-shifting. The more expensive or unreliable monitoring is, the more valuable clear testing protocols and simpler, more verifiable triggers become relative to complex causation-based liability.

To answer this question we will use Cost–Benefit Analysis and the Legal Incentives Model, focusing on how the damages clause shapes performance, breach, and Biotech’s post-signing investment incentives.

Object and counterfactual The object is a liquidated-damages clause: the amount PharmaChem must pay if it fails to supply the ingredient. The relevant counterfactual is (i) a lower-damages clause (including zero damages) that makes breach relatively cheap, or (ii) a higher-damages clause that makes breach very expensive, even when non-performance would save large real costs.

Actors and decision margin

• PharmaChem chooses whether to perform (supply as promised) or breach (fail to supply) when supply is costly or difficult.
• Biotech chooses how much to invest after signing (capacity expansion, regulatory approval efforts) given the risk of non-supply and the promised damage payment.

Incentives and behavioral responses Expectation damages put Biotech in (approximately) the position it would have been in if PharmaChem had performed. The economic rationale is that setting damages to Biotech’s expectancy loss makes PharmaChem internalize the social loss from non-delivery and therefore choose performance versus breach efficiently.

A simple illustration shows why.

• Let Biotech’s value from timely delivery be \(V=100\) (e.g., profits from selling BioCureX, net of downstream non-ingredient costs), and let the contract price for the ingredient be \(P=40\).
• Biotech’s expectancy from performance is then \(V-P=60\). So expectation damages are \(D=60\).

PharmaChem compares:

• Perform: payoff \(P-C\), where \(C\) is PharmaChem’s real cost of supplying.
• Breach: payoff \(-D\).

So PharmaChem performs when \(P-C \ge -D\), i.e. when \(C \le P + D\). With expectation damages \(D=V-P\), that condition becomes \(C \le V\), which is exactly the efficiency test (supply when value \(V\) exceeds cost \(C\)).

If damages are too high, PharmaChem supplies even when supply costs exceed the value created (inefficient performance). Example: if \(D=120\) and \(C=130\), then perform yields \(40-130=-90\) while breach yields \(-120\); PharmaChem performs even though \(C(=130) > V(=100)\). If damages are too low, PharmaChem breaches even when supply is efficient (inefficient breach). Example: if \(D=10\) and \(C=90\), then perform yields \(40-90=-50\) while breach yields \(-10\); PharmaChem breaches even though \(C(=90) < V(=100)\).

Why both parties would want this: an efficient damages rule maximizes total surplus from the relationship. Because the contract also sets a price \(P\), the parties can split the resulting larger surplus between them (a Pareto improvement relative to an inefficient contract). So Biotech should not treat “higher damages” as automatically better; it can shrink the total pie by forcing costly performance in states where breach is efficient.

Investment complication (reliance): after signing, Biotech will make relationship-specific investments (capacity expansion and approval) that are valuable only if the ingredient arrives, especially because PharmaChem is the exclusive supplier. If damages promise reimbursement of all sunk investments whenever supply fails, Biotech can be pushed toward overinvestment: it captures the upside if supply occurs but is insured on the downside if supply fails. Efficient “hypothetical expectation damages” avoid this by compensating only the portion of lost profits and reliance that would have been efficient (surplus-increasing, i.e., Kaldor–Hicks efficient) given the known risk of non-supply and the available alternatives (here, very limited substitution).

Real costs and benefits

• Biotech’s expected lost output (expected harm): a higher damages clause reduces inefficient breach and thus lowers expected lost production and delayed patient access relative to a low-damages baseline.
• PharmaChem’s real supply/contingency costs: higher damages induce PharmaChem to spend more on backup capacity, inventories, quality control, and expedited shipping to avoid breach; these are real resource costs that rise with damages.
• Biotech’s precaution and planning costs: if damages are low, Biotech must spend more on contingency planning (e.g., idle capacity, alternative R&D paths, slower scaling) to protect itself from supply risk; expectation damages reduce the need for inefficient “self-insurance.”
• Inefficient performance or inefficient breach: damages that are too high create real waste when PharmaChem supplies at cost exceeding the value created; damages that are too low create real waste when breach occurs despite value exceeding cost.
• Inefficient investment (over- or under-reliance): overcompensation of relationship-specific investments can lead to excessive capacity/approval spending that has low expected surplus given breach risk; undercompensation can lead to too little investment and underproduction even when expansion would be surplus-increasing.
• Dispute-resolution and administration costs: more complex, state-contingent damage definitions (e.g., including lost profits, delay, and reliance) can raise measurement, auditing, and litigation costs compared to a simple number.

Transfers (the damage payment itself) are not a real cost in the cost–benefit sense, but the size and design of the transfer matter because they change performance and investment incentives.

Overall, the main trade-off is: raising damages improves performance incentives and protects efficient reliance, but also increases costly precaution by PharmaChem, increases the risk of inefficient forced performance in high-cost states, and can distort Biotech’s investment incentives if it becomes “too insured” against supply risk.

Verifiability and enforcement Expectation damages require defining and proving what Biotech would have earned with timely supply (lost profits, delay losses, and the extent to which investments become unusable without the ingredient). In practice, some components (e.g., realized sales after approval) may be easier to verify than others (e.g., reputational losses, which should be tied to verifiable real effects like future lost sales or higher financing costs). If key losses are hard to verify, a liquidated amount (or a schedule tied to verifiable milestones such as regulatory stage, capacity installed, or committed sales) can reduce enforcement costs while approximating expectation damages.

Conditional conclusion I would recommend damages set to (a practicable approximation of) expectation damages: the amount that makes Biotech as well off as if PharmaChem had supplied, so that PharmaChem performs when \(V \ge C\) and breaches when \(V < C\). In this case, expectancy should include verifiable lost profits from delayed or foregone BioCureX sales and verifiable downstream losses caused by non-supply, and it may include some relationship-specific investments that become worthless because PharmaChem is the exclusive supplier. However, the damages clause should not automatically reimburse all investments Biotech chooses to make; otherwise Biotech may invest too early or too much given supply risk. A good contractual implementation is therefore “hypothetical expectation damages”: compensate reliance only to the extent the investment was efficient given foreseeable non-supply risk (and possibly condition higher damages on verifiable milestones), with the exact level depending on (i) the magnitude and verifiability of lost profits and delay costs, (ii) the degree of supplier exclusivity and Biotech’s ability to mitigate, (iii) the likelihood of non-supply and PharmaChem’s ability to reduce it through costly precautions, and (iv) enforcement and measurement costs.

To answer this question we will use Cost–Benefit Analysis and the Legal Incentives Model.

Object and counterfactual The object is a contractual term that fixes (liquidates) damages payable by the start-up if it breaches instead of delivering the app. The counterfactual is a contract without such a term (or with a different stipulated amount), so that the start-up’s decision to perform or breach is governed by whatever consequences breach creates.

Actors and decision margin The key actor is the start-up, which will learn the realized development cost and then choose between (i) performing (developing and delivering the app) or (ii) breaching and paying the stipulated damages. The customer’s relevant margin is whether to rely on the promise (e.g., invest in complementary technology) once the contract is signed.

Incentives and behavioral responses Use the Legal Incentives Model: after learning the realized cost, the start-up compares the private payoff from performance to the private payoff from breach. Performance creates value for the customer but costs the start-up the realized development cost; breach avoids that cost but triggers the sanction-like payment (damages) to the customer.

Using the numbers in the prompt sketch: the app is worth 120 to the customer. With probability 50% the cost is 80, and with probability 50% the cost is 150. If the cost is 150, performance is not efficient because it is not a Kaldor–Hicks improvement: the customer’s gain (120) is below the start-up’s real resource cost (150). Efficient behavior in that state is breach.

Therefore, the damages amount should be set so that the start-up performs if and only if realized cost is below the customer’s valuation (120). If damages were set above 150, the start-up would be induced to perform even when cost is 150, because breaching would be even more expensive than completing the project. That would force inefficient performance. Even damages of 150 are “too high” in this sense, because they still make breach unattractive in the high-cost state where breach is socially preferred.

Setting damages equal to 120 makes breach privately costly by exactly the customer’s lost value from non-delivery. Then the start-up will perform when cost is 80 (since 80 is below 120), and will breach when cost is 150 (since 150 is above 120). This aligns the start-up’s decision with the Kaldor–Hicks criterion: produce if and only if value (120) exceeds cost.

This is the logic of expectation damages: damages that put the customer in the position it would have been in had performance occurred, here equal to the customer’s valuation of receiving the app (120). With that clause, the contract induces efficient breach and efficient performance, and (because it maximizes total surplus) it leaves more value to be divided between the parties; exactly how they share that gain depends on their bargaining power.

One important complication is reliance. Suppose the customer can invest in complementary technology in reliance on delivery, raising the value of the app from 120 to 140. Standard expectation damages would then suggest damages of 140 upon breach (to make the customer indifferent between performance and damages). But that can be inefficient because it gives the customer too strong incentives to rely: the customer does not internalize that there is a 50% probability the start-up will breach (when cost turns out to be 150). It may be socially efficient for the customer to wait with the complementary investment until delivery is certain.

To correct that reliance incentive, the optimal damages level is “hypothetical expectation damages”: the customer is compensated only for the hypothetical harm it would have suffered absent excessive reliance. For example, if it was socially optimal to wait to invest, the customer should receive only 120 in damages (not 140), even if the customer in fact chose to invest and thereby increased its own valuation. In the United States, the doctrine of foreseeability can be understood as implementing this idea by limiting damages to reliance the developer could reasonably expect the customer to undertake.

Real costs and benefits

• Development (precaution/production) costs: Paying to develop the app is a real resource cost. The stipulated damages affect whether the start-up incurs the 80 cost (efficient) and whether it inefficiently incurs the 150 cost (inefficient if forced by overly high damages).
• Expected value created by performance (benefit to customer): When the app is delivered, the customer receives value (120 in the baseline; potentially higher with complementary investment). The damages clause affects whether this value is realized in low-cost states and forgone in high-cost states.
• Reliance and complementary-investment costs: If the customer invests in complementary technology, that investment is a real resource cost. Overly high damages (full reliance-based expectation damages when breach is likely) can induce inefficiently early or excessive investment.
• Waste from inefficient performance or inefficient reliance: If damages are too high, the start-up may perform when cost exceeds value (wasteful). If damages are designed without regard to breach probability, the customer may rely too much (wasteful).

Damages payments themselves are transfers between the parties, not real resource costs. They matter because they change incentives: setting damages near 120 tends to avoid the large inefficiency of producing at cost 150 while still protecting the customer against opportunistic breach in the low-cost state.

Conditional conclusion If the only margin is the start-up’s perform/breach choice after learning cost, the recommended stipulated damages amount is 120 (the customer’s valuation), i.e., expectation damages, because it induces performance if and only if realized cost is below 120 and breach otherwise. If, however, the customer can make reliance investments that raise the value of performance (e.g., to 140) and those investments are socially excessive given the chance of breach, then “hypothetical expectation damages” are preferable—e.g., keeping damages at 120 to avoid encouraging over-reliance. The conclusion depends on whether reliance is important in the setting and whether it is efficient for the customer to invest before the start-up’s cost uncertainty is resolved.

To answer this question we will use Moral hazard, and the Coasean benchmark.

Object and counterfactual The object is a proposed clause in a long-term bareboat charter making the shipowner responsible for maintenance throughout the contract period. The counterfactual is the typical bareboat allocation where the charterer (who operates the vessel) is responsible for maintenance and bears the operational consequences of poor upkeep.

Actors and decision margin Key actors are the shipowner (lessor) and the bareboat charterer (lessee/operator). The relevant margins are (i) the charterer’s intensity of use and day-to-day care (how hard the vessel is run), and (ii) maintenance effort and timing (preventive upkeep vs deferral), including how quickly emerging problems are addressed.

Incentives and behavioral responses With charterer-responsible maintenance, the charterer internalizes more of the consequences of rough use and delayed upkeep, which tends to create strong incentives to maintain the vessel in a cost-effective way because the charterer directly sees problems and can fix them quickly.

If the owner is responsible for maintenance while the charterer controls operations, a Moral hazard problem can arise. The charterer can gain private operating benefits from “running the ship harder” (higher revenue, lower operational slack) while shifting part of the resulting wear-and-tear costs to the owner. For example, suppose the charterer can earn an extra \(B=0.5\) (in arbitrary units) from more intensive use, but this increases expected maintenance/repair needs by \(0.8\). If the owner pays for maintenance, the charterer will tend to choose the intensive use (since it captures \(0.5\) while not bearing the \(0.8\)), even though it is socially inefficient.

There can also be a misalignment in maintenance timing and quality. The owner, paying the bill, may prefer cheaper or deferred maintenance if the immediate cost savings are large, while the charterer bears operational disruption (downtime) and safety/reliability risk during the charter period. Even if the owner cares about the vessel’s long-run condition and residual value, the owner’s incentives need not match the charterer’s preference for high uptime and reliability during the contract period.

Finally, making the owner responsible typically requires more back-and-forth: the charterer observes issues and must coordinate approvals, shipyard scheduling, and scope of work with the owner. This can slow responses and increase the likelihood of inefficient delays.

Real costs and benefits

• Maintenance and repair resource costs: Owner-responsible maintenance can increase real maintenance costs if the charterer overuses the vessel or takes less care (more breakdowns and heavier wear), compared to charterer-responsible maintenance.
• Operating/precaution costs (care in use): Shifting maintenance away from the charterer weakens incentives for careful operation, increasing real expected wear and accident risk relative to the baseline.
• Downtime and disruption costs: More coordination and slower decisions can increase time out of service and operational disruption costs during the charter period.
• Monitoring and coordination costs: Owner responsibility increases the need to monitor charterer behavior and to coordinate maintenance decisions, creating additional real administrative and organizational costs.
• Expected harm from failures/accidents: If weaker care incentives and delayed maintenance raise the probability of serious incidents, expected harm increases (even if liability transfers do not count as resource costs, the underlying harm does).

Overall, the main efficiency intuition is that the charterer is usually the lower-cost monitor and implementer of day-to-day maintenance because it operates the ship and observes condition continuously. Moving maintenance to the owner tends to raise real costs through (i) weaker care incentives and (ii) higher coordination/monitoring burdens, unless there are specific offsetting advantages (e.g., owner has uniquely low-cost access to maintenance capacity or expertise).

(Any changes in charter hire, reimbursements, or indemnities are largely transfers between the parties; they matter for incentives but are not counted as real costs or benefits.)

Verifiability and enforcement A central practical problem is that “proper use” and “adequate maintenance” are often hard to verify to a court. The charterer’s operational choices (how intensely the vessel is run) may be observable to the parties but difficult to prove and attribute causally to later maintenance needs. This limits how well the owner can contractually prevent overuse or enforce “care” standards when the owner is on the hook for maintenance. Similarly, if the owner controls maintenance decisions, the charterer may struggle to enforce timely, high-quality maintenance if the contract terms are not tied to verifiable benchmarks. These verifiability limits make the moral-hazard/coordination concerns more severe.

Transaction costs and private ordering Under the Coasean benchmark, a voluntary clause allocating maintenance to the owner could be efficient if it reflects joint gains (e.g., the owner truly can maintain more cheaply or reliably). In that case, the parties can adjust the charter hire so that the owner is compensated for expected maintenance costs and the charterer pays for the improved service. But this benchmark is less reliable when transaction costs and information problems are large—here, especially the difficulty of specifying and enforcing verifiable standards for use and maintenance, and the ongoing coordination costs over a long contract period.

Conditional conclusion The default economic presumption favors the standard bareboat allocation where the charterer bears maintenance responsibility, because it aligns control (operation and day-to-day observation) with responsibility and reduces moral hazard and coordination costs. The proposed owner-maintenance clause is more attractive only if specific conditions hold—most importantly, that (i) the owner can perform maintenance substantially more cost-effectively than the charterer, and (ii) the contract can credibly limit overuse and ensure timely maintenance using verifiable triggers (or otherwise keep monitoring/coordination costs low). If those conditions do not hold, I would advise against the clause, even though the parties can partially reprice the charter to reflect the allocation, because repricing does not eliminate the underlying incentive and enforcement problems.

To answer this question we will use Moral hazard, and Cost–Benefit Analysis.

Object and counterfactual The object is the contract term(s) in a long-term bareboat charter that allocate the costs of collisions, groundings, and similar accidents between (i) the shipowner/lessor and (ii) the charterer/lessee, who operates the vessel. The counterfactual is an alternative allocation (e.g., owner bears more of the accident losses; charterer bears more; or the allocation is split by cause).

Incentives and behavioral responses Because a bareboat charter gives the charterer operational control, accident risk is strongly affected by the charterer’s choices: crew quality, training, safety routines, maintenance while operating, routing, speed, and investment in navigation systems. If the charterer does not bear (most of) the accident cost, a moral hazard problem arises: the charterer captures the savings from cutting corners but pushes expected accident losses onto the owner.

A simple way to see the incentive is expected-value reasoning. Suppose “low care” saves the charterer \(B=2\) (e.g., cheaper crew and fewer safety routines) but raises the accident probability from \(p=0.01\) to \(p=0.05\). If the accident cost that must be paid by the responsible party is \(S=100\), then the increase in expected accident cost is \((0.05-0.01)\cdot 100=4\). If the charterer bears the loss, low care is not privately attractive (\(B < 4\)), so the charterer chooses higher care. If instead the owner bears the loss, the charterer compares only the private saving \(B\) and will tend to choose low care even when it is inefficient.

By contrast, some accident-related losses are not mainly driven by the charterer’s operational choices, especially:

• latent defects or structural weaknesses that existed before delivery into the charter, and
• normal wear from ordinary use that is not tied to negligent operation.

For these categories, placing the risk on the owner better aligns incentives: the owner has stronger incentives to invest in seaworthiness before delivery and to disclose known defects, while the charterer cannot efficiently reduce these risks through day-to-day operations.

Real costs and benefits

• Precaution and compliance costs (crew, routines, equipment): Allocating operational accident losses to the charterer increases incentives to spend real resources on safety where the charterer is the low-cost risk reducer; allocating them to the owner weakens these incentives and can raise expected accident frequency/severity.
• Expected accident harm (repairs, downtime, cargo/third-party harm): Efficient allocation reduces expected accident harm by inducing care by the party with control over the relevant margin (often the charterer for navigation/operation; the owner for pre-existing seaworthiness).
• Information and inspection costs: If owners bear losses tied to latent defects, they may invest more in pre-delivery inspection/maintenance; if charterers bear too much of this risk, they may duplicate inspections or underinvest due to limited ability to detect defects.
• Enforcement and dispute costs: Splitting losses by cause (operational vs. latent defect vs. force majeure) can lower moral hazard but can raise litigation and verification costs because parties will dispute causation.
• Risk-bearing and insolvency costs: Concentrating large accident losses on a party that cannot absorb them can create real distortions (e.g., inefficiently low activity, or inability to pay leading to under-deterrence). Better risk spreading through insurance can reduce these real distortions.

Payments between owner and charterer (e.g., indemnities, higher/lower charter hire to compensate for liability) are transfers and are not real social costs, but they matter for incentives and for whether the parties will accept a given allocation.

Verifiability and enforcement Efficient “split-by-cause” clauses depend on what can be proven. Operational negligence (e.g., violation of safety procedures) may be easier to document than whether a defect was truly latent and pre-existing. If latent defects are hard to verify in court, putting too much on the owner may invite opportunistic claims by the charterer after an accident; if operational care is hard to verify, putting too much on the charterer may not generate the intended safety investments unless liability is strict or backed by observable proxies (e.g., certification, logbooks). These verifiability limits push toward allocations that rely on clearer, more documentable events.

Conditional conclusion An efficient allocation typically puts the costs of operational accidents on the charterer, because the charterer controls the main safety margins and bearing the loss mitigates moral hazard by inducing investment in competent crew and safety systems. Costs tied to latent defects and pre-charter seaworthiness should typically remain with the owner, because the owner is the low-cost avoider and has superior information about the vessel’s condition at delivery; normal wear from ordinary use is also naturally treated as the owner’s residual risk (or priced into hire) unless the charter specifies otherwise. Residual, non-controllable “force majeure”-type risks are efficiently assigned to the party that can bear and diversify them at lowest real cost, often through insurance, with the contract price adjusting to compensate whichever side takes on more risk. This conclusion is strongest when causation is reasonably verifiable and when insurance/solvency constraints do not undermine deterrence.

To answer this question we will use Cost–Benefit Analysis and Adverse selection.

Object and counterfactual The object is a “warranties and indemnities” clause that makes the sellers of Norsk Teknologi AS liable if specified risks materialize after closing. The counterfactual is a deal with limited or no seller liability for these future contingencies, so the buyer must bear (and price) the uncertainty.

Incentives and behavioral responses From an efficiency perspective, a risk-allocation clause is desirable if it increases total surplus: i.e., if shifting the risk to the sellers increases the buyer’s willingness to pay by more than the expected real resource costs the sellers will incur when the risk materializes. If so, the deal can be a potential Pareto improvement (Kaldor–Hicks): the buyer pays a higher price, and the sellers accept liability but are compensated.

Adverse selection is key because the sellers typically know more about the firm’s past actions and “latent” legal exposures than the buyer. Without warranties/indemnities, sellers have an incentive to downplay or withhold negative information about (i) the probability of patent disputes based on past development choices and documentation, and (ii) the probability of future tax claims based on past tax planning. If the buyer cannot trust the seller’s statements, the buyer rationally discounts the firm’s value to protect itself against worst-case hidden risks. A credible liability clause reduces this adverse-selection problem by making it costly for the seller to misrepresent or omit material information: if the risk was higher than disclosed, the seller must later pay.

By contrast, several listed risks are mainly determined by post-closing actions or external changes. Making the sellers liable for those risks does little to elicit better pre-sale information, and it can also distort incentives by shifting responsibility away from the party who will actually choose the relevant actions after closing.

A simple illustration: suppose the expected real resource cost of a pre-closing tax exposure is \(pS = 0.2 \times 10 = 2\) (e.g., “2 million” in expected payments plus associated real compliance/litigation burden). If the absence of credible warranties causes the buyer to discount the price by 5 because it fears hidden tax issues, then adding the clause can increase total surplus by about 3: the buyer can pay up to 5 more, while the sellers expect 2 in costs. The extra 3 can be shared through the price, so seller liability need not make sellers worse off if priced correctly.

Real costs and benefits

• Information acquisition and disclosure costs: Warranties/indemnities can increase due diligence and internal investigation costs (the parties spend resources to identify and document risks), but they also reduce wasteful “precautionary discounting” by the buyer driven by distrust.
• Expected harm from inefficient trade or mispricing: Without credible protection against hidden pre-closing liabilities, the buyer may underpay, delay, or walk away from an otherwise efficient transaction. Credible seller liability reduces this expected inefficiency by making disclosed information more reliable.
• Precaution/compliance incentives tied to pre-closing conduct: For risks rooted in the seller’s past behavior (tax planning choices; IP clearance/documentation choices), seller liability can improve incentives to have taken (and documented) lawful/low-risk actions and to truthfully reveal what was done.
• Post-closing adaptation and management costs: For risks driven by future demand changes, integration decisions, or future regulatory changes, shifting liability to sellers can reduce the buyer’s incentive to invest in adaptation (product changes, compliance, organizational integration), potentially increasing real resource waste.
• Enforcement and dispute-resolution costs: Broad, vague indemnities can generate costly disputes about causation and scope; narrower, well-defined categories reduce expected litigation/enforcement costs relative to a blanket “seller pays for any bad outcome.”

(Any indemnity payment itself is a transfer between buyer and sellers and is not counted as a real resource cost; it matters because it changes incentives and credibility.)

Verifiability and enforcement The clause works best where the triggering facts are verifiable. “Tax planning near the legal boundary” and “risk of patent claims tied to pre-closing technology choices” can be anchored in verifiable records (tax filings, internal memos, product specifications, IP searches, correspondence). This makes it feasible to enforce seller liability and therefore to use it as a credibility device against adverse selection.

In contrast, “culture clash” and “future demand decline” are hard to verify as contractual breaches and are strongly influenced by GlobalTech’s post-closing actions. “Future EU rules making the technology illegal” is verifiable as a change in law, but it is not a fact about the seller’s pre-closing conduct; making sellers liable would largely insure the buyer against an external shock rather than solve an information problem, and it risks creating extensive scope disputes (what counts as “illegal,” what adaptation was feasible, etc.).

Conditional conclusion Sellers should generally accept warranties/indemnities for risks that (i) are tied to the pre-closing history of Norsk Teknologi AS and (ii) are relatively verifiable—especially (1) patent disputes to the extent they stem from pre-closing development/IP clearance and (4) future tax claims arising from pre-closing tax planning. This reduces adverse selection and can raise the purchase price enough to compensate the sellers, increasing total surplus.

Sellers should generally resist (2) demand decline, (3) integration/culture problems, and (5) future EU regulatory changes as seller-liability items, because these are primarily post-closing/external risks that the buyer controls or must manage and where seller liability does less to improve pre-sale information and more to create enforcement disputes and weaken the buyer’s adaptation incentives. This recommendation is strongest when the parties can clearly separate pre- vs post-closing causes in the contract and when enforcement costs are kept low through narrow, well-defined clauses.

To answer this question we will use Legal Incentives Model, and Cost–Benefit Analysis.

Object and counterfactual TryggBolig AS must choose (i) reactions to detected exaggeration and (ii) control routines that make exaggeration harder and riskier. The counterfactual is a more lenient regime with weaker documentation and fewer audits, where claimants expect a low chance of being challenged and little downside if they inflate.

Actors and decision margin The relevant actor is the insured customer filing a claim. The decision margin is how much to inflate the reported loss above the true loss (including whether to fabricate supporting information). The insurer chooses monitoring intensity and the menu of consequences when exaggeration is discovered.

Incentives and behavioral responses Under the Legal Incentives Model, exaggeration is attractive when the private benefit \(B\) from inflating the claim exceeds the expected sanction \(pS\), where \(p\) is the probability the exaggeration is detected/proved and \(S\) is the consequence conditional on detection.

• Increasing \(p\): stronger documentation requirements, random audits, specialized fraud investigation for flagged claims, and cross-checks (e.g., purchase receipts, serial numbers, fire reports) raise the chance that inflated amounts are detected and can be proven.
• Increasing \(S\): credible reactions such as denying the inflated part (or the whole claim when exaggeration is material), requiring repayment, cancelling coverage, and placing the customer in a higher-risk premium category increase the downside of trying.
• Reducing \(B\): contract design can reduce the marginal gain from exaggeration even when the claim is paid. For instance, higher deductibles and co-insurance mean an extra “invented” krone produces less extra payout, and payout caps or replacement-by-voucher policies narrow the scope for overstatement.

A simple illustration: suppose inflating a claim yields \(B = 10{,}000\) (extra payout if not challenged). If TryggBolig only detects/proves exaggeration with \(p=0.10\) and the expected consequence is \(S=50{,}000\) (repayment and other penalties), then \(pS = 5{,}000 < B\), so exaggeration remains attractive. Deterrence can come from raising \(p\) (more audits) or \(S\) (clearer, tougher reactions), or by shrinking \(B\) (e.g., co-insurance so that the same inflation produces less extra payout).

The insurer should also anticipate offsetting behavioral responses. Very strict routines can increase customer effort costs and delays, and can push honest, low-risk customers to go uninsured or to choose other insurers, weakening the risk pool and further harming profitability.

Real costs and benefits

• Investigation and monitoring costs: More audits, documentation checks, and specialist personnel increase real resource use relative to a lenient regime.
• Claim-processing delay and administrative burden: Tighter routines can slow payouts and raise time/effort costs for both the insurer and honest customers (including repeated documentation requests).
• Wasteful fraud effort: Stronger deterrence reduces resources spent by claimants on inflating claims (fabricating narratives, staging losses, searching for fake documentation).
• Risk-bearing and insurance-market participation: Reducing systematic exaggeration can improve pricing and trust, which can increase take-up of insurance and reduce uninsured risk-bearing; conversely, overly aggressive controls can reduce take-up and increase inefficient self-insurance.
• Dispute-resolution costs: More denials and challenges can increase complaints and formal disputes, which are real costs even when the underlying payment is a transfer.

Insurance payouts, premium changes, and repayments are transfers and are not counted as real social costs or benefits. They matter here because they shape incentives (\(B\) and \(S\)) and participation decisions.

Overall, the efficient policy balances marginal deterrence gains from reduced fraud and reduced wasteful behavior against marginal monitoring, delay, and dispute costs. Targeted controls (focused on suspicious claims) often dominate blanket frictions because they raise \(p\) for high-risk cases while reducing burdens on honest claimants.

Verifiability and enforcement Deterrence depends on whether exaggeration is verifiable: the insurer must be able to document the discrepancy using evidence that would withstand challenge. If proof is hard (e.g., cash purchases, no serial numbers, ambiguous fire causation), then raising \(p\) may be expensive and error-prone. In that setting, TryggBolig should rely relatively more on (i) contract terms that reduce \(B\) mechanically (deductibles/co-insurance/caps, replacement-value rules), and (ii) reactions that are enforceable without proving intent beyond the discrepancy (e.g., denying the unverifiable portion, requiring standardized proof for high-value items). Conversely, if documentation is feasible at low cost (digital receipts, standardized inventories, third-party reports), then investments that raise \(p\) through audits and verification can be cost-effective and allow a lower \(S\) while still achieving deterrence.

Conditional conclusion TryggBolig should design a package that (1) raises the expected consequence of exaggeration \(pS\) and/or (2) reduces the gain \(B\), while minimizing burdens on honest claimants. If reliable verification is feasible, the company should emphasize higher \(p\) through targeted audits and documentation coupled with predictable, escalating sanctions (deny inflated amounts, repayment, cancellation/premium increases). If verification is inherently difficult or costly, the company should put more weight on reducing \(B\) via deductibles/co-insurance/caps and on simple, enforceable reactions tied to missing proof, because attempts to raise \(p\) in low-verifiability settings risk high monitoring costs and costly false positives that can drive good customers out of the pool.

To answer this question we will use Cost–Benefit Analysis, and Moral hazard.

Object and counterfactual TryggBolig is considering adding a contract clause requiring every policyholder to install an FG-approved security lock costing at least NOK 7,000, often requiring door replacement. The relevant counterfactual is the current contract without this mandatory minimum-security requirement (customers choose their own level of home security).

Incentives and behavioral responses The clause forces higher precaution. If customers otherwise underinvest in security because they do not bear the full expected cost of theft (the insurer pays), the clause can reduce a moral-hazard problem by imposing a minimum standard. The main behavioral channel is a reduced probability of theft, and potentially fewer “easy” fraudulent theft and fire claims if stronger security makes staged losses harder to carry out or easier to dispute. However, a uniform NOK 7,000+ requirement also changes who buys insurance: some low-risk customers (or customers with low insured value) may drop coverage because the up-front cost exceeds their expected benefit from reduced theft risk, worsening the insurer’s pool and reducing risk-sharing for those customers.

Real costs and benefits

• Precaution/compliance costs (real resource costs): Customers spend at least NOK 7,000 on the lock and, in many cases, additional resources on replacing the door and installation time/hassle. This is the main cost and varies across homes (some need a full door change; others do not).
• Reduced expected harm from theft (real resource benefit): If the lock materially lowers the probability of theft, real losses from theft (stolen goods, damage to the home, disruption) fall. This is larger when households have higher at-risk value and face higher baseline theft risk.
• Reduced resources spent on fraud and disputes (real resource benefit): If fraud is less frequent or easier to detect, fewer resources are wasted on investigations, claims processing, and contested cases (and fewer real losses from deliberately caused fires/thefts).
• Administrative and monitoring costs (real resource cost): The insurer may need procedures to check compliance (documentation, inspections, handling exemptions), which consumes resources.

A simple way to discipline the comparison is expected-value reasoning. The clause is socially attractive for a given household when the expected reduction in theft-related losses exceeds the real compliance and administration cost. For example, if the baseline theft probability is \(p=0.04\) and the lock reduces it to \(p=0.01\), the probability drop is 0.03. If the relevant at-risk value (expected loss conditional on theft) is NOK 300,000, the expected reduction in loss is \(0.03 \times 300{,}000 = 9{,}000\), which can justify a NOK 7,000 lock (and even some monitoring cost). But if the at-risk value is NOK 100,000, the expected reduction is only \(0.03 \times 100{,}000 = 3{,}000\), so a universal requirement would be inefficient for that customer type. (Any premium reduction or shifted payouts are transfers between insurer and customer and are not counted as real costs/benefits, though they matter for incentives to buy insurance and comply.)

Verifiability and enforcement The clause only changes behavior if compliance is verifiable and can be enforced in practice. If TryggBolig cannot cheaply verify installation (or whether the lock is actually used), customers may not comply while still being covered, so the intended reduction in theft risk and fraud will not occur. Conversely, if FG-approval and documented installation provide clear proof, the insurer can condition coverage/claims on compliance, raising the effective probability \(p\) that non-compliance is detected and sanctioned (e.g., by reduced payout), which strengthens deterrence but can increase monitoring and dispute costs.

Conditional conclusion TryggBolig should include the clause if, for the relevant customers, the reduction in expected theft and fraud-related losses is larger than the real resource costs of installation/door replacement plus the insurer’s added verification costs. The conclusion is therefore likely to depend on (i) how much the lock reduces the probability of theft and fraudulent claims, and (ii) the level of at-risk value in the home. If risk and value are very heterogeneous, a uniform clause for all customers is less likely to be efficient than a targeted requirement focused on high-risk locations or high insured values; otherwise the clause may drive low-risk/low-value customers out of the pool while forcing them to buy precautions whose expected benefit is below NOK 7,000.

To answer this question we will use Cost–Benefit Analysis, and Moral hazard.

Object and counterfactual NordEnergi must propose a pricing structure for a bilateral electricity contract with NordMetall. The relevant counterfactuals are (i) a pure spot-price contract (NordMetall pays the market price each hour/day), and (ii) a pure fixed-price contract (NordMetall pays a constant price per kWh, regardless of spot movements). A natural candidate structure is a hybrid: a fixed price for a contracted “base” quantity (to create predictability) combined with spot pricing (or spot with a cap) for incremental consumption (to preserve incentives and allow participation in low spot prices).

Incentives and behavioral responses The key behavioral response is NordMetall’s production/consumption choice on “cheap” versus “expensive” days. Use the instructor’s numbers: spot is \(0.40\) kr/kWh on cheap days and \(1.20\) kr/kWh on expensive days. NordMetall can choose to use \(50{,}000\) kWh or \(100{,}000\) kWh per day. The first \(50{,}000\) kWh generates \(1.50\) kr/kWh in revenue for the firm; the next \(50{,}000\) kWh generates only \(0.60\) kr/kWh in revenue.

• Under spot pricing, NordMetall internalizes the real scarcity of electricity. On cheap days, the incremental margin for the second block is \(0.60 - 0.40 = 0.20\) kr/kWh, so using \(100{,}000\) kWh is efficient for the firm. On expensive days, the incremental margin for the second block is \(0.60 - 1.20 = -0.60\) kr/kWh, so it will reduce to \(50{,}000\) kWh. Thus spot pricing gives an incentive to shift consumption away from high-price periods and toward low-price periods, which is exactly the desired demand response when the system is tight.
• Under a fixed price, the firm faces the same marginal price on cheap and expensive days. If the fixed price is set around an average level, NordMetall will tend to choose the same consumption every day. In the example, a fixed price above \(0.60\) kr/kWh would lead NordMetall to use only \(50{,}000\) kWh even on cheap days (foregoing valuable production when power is abundant). A fixed price below \(0.60\) could lead to \(100{,}000\) kWh even on expensive days (running electricity-intensive production when power is socially scarce). Either way, compared to spot pricing, fixed pricing blunts the incentive to adjust output across states of the world.

This is a moral-hazard-type problem in the sense that NordMetall’s post-contract usage choice is the key action the contract should motivate. A contract that fully insures NordMetall against high prices also insures it against the need to economize on electricity when it is expensive.

A hybrid structure targets both concerns. A fixed-price block for a baseline quantity provides predictability for operations that cannot easily be adjusted, while spot pricing (or spot with a cap) on incremental usage preserves incentives at the margin and lets NordMetall benefit when spot prices are low.

Real costs and benefits

• Misallocation of electricity across time (inefficient production/consumption patterns): Pure fixed price tends to increase misallocation (too much use in expensive periods and/or too little use in cheap periods). Pure spot reduces misallocation by inducing load shifting. A hybrid reduces misallocation relative to pure fixed price because at least marginal units face scarcity pricing.
• Costly production distortions from price risk (risk-bearing costs): Pure spot exposes NordMetall to large and unpredictable input cost swings, which can lead to conservative production planning, inefficient shutdowns, or expensive internal hedging. A fixed-price component reduces these real distortions by stabilizing the cost of baseline operations.
• Hedging and contract-administration costs: Any fixed-price promise requires NordEnergi to manage its own exposure (through procurement/hedging, reserves, and risk management). These are real resource costs that rise as more volume is put on fixed terms; a hybrid limits these costs by keeping only a base quantity fixed.
• System reliability and scarcity management (as a real benefit): When prices reflect scarcity, demand reductions on expensive days can reduce strain on the system and the need for costly adjustments elsewhere. Hybrid and spot pricing support this more than pure fixed pricing.

Note: the price paid (fixed vs spot) is mainly a transfer between NordMetall and NordEnergi; it is not itself a real resource cost. But the transfer matters for incentives and for how much risk each party bears.

Conditional conclusion A sensible proposal for NordEnergi is a hybrid contract: a fixed price for a contracted baseline quantity (to provide the predictability NordMetall demands) and spot pricing for additional consumption (optionally with a pre-agreed cap if NordMetall’s risk concerns are strong and NordEnergi charges for that insurance). This is preferred when (i) NordMetall can adjust at least some production margin in response to prices (so marginal spot exposure creates real efficiency gains), and (ii) full spot exposure would impose meaningful risk-bearing costs on the firm. If instead NordMetall cannot adjust production at all in the short run, the incentive benefit from spot exposure is smaller, and a larger fixed-price share (with an explicit risk premium) becomes more attractive.