Timothy Besley, “Reciprocity and the State.” LSE Public Policy Review
2(1): 1-10, 2021. - "Reciprocal obligation lies at the heart of state-citizen relations [p. 1]." We can see this as the state tries to grapple with handling COVID.
- How can a society build reciprocity, which is (often) a sort of informal norm or institution?
- Government sovereignty makes it hard for for the state to commit to its future conduct – it could always reverse course, even if the citizenry is relying on earlier promises (such as pension payments).
- Incidentally, the social contract theorists (Hobbes, Locke, Rousseau) all invoke reciprocity between the governed and the governors.
- Two elements that can build confidence in governments are (1) constraints on executive power and (2) open competition for leaders (elections).
- "Reciprocity kicks in when states deliver collective goods for citizens and, in exchange, when citizens offer their support, whether by paying taxes, volunteering for military duty, or obeying the law. However, their willingness to do so in the long-term is contingent on state behaviour [p. 5]."
- Taxes and social insurance are two areas where we can apply more concretely general considerations about reciprocity.
- The extent to which citizens believe that it is OK to cheat on their taxes varies quite a bit from country to country. People who trust their government, and believe that taxes are used for the social good, have higher tax morale.
- To bolster reciprocity, we might want to discourage tax avoidance (which is legal) along with tax evasion (which is illegal) – both can undermine tax morale.
- Social insurance, by sharing risks, brings broad public benefits. Universality helps to promote "quasi-voluntary compliance" that more targeted programs might put at risk.
Ted
O’Donoghue and Jason Somerville, “Modeling Risk Aversion in Economics.”
Journal of Economic Perspectives
32(2): 91-114, Spring, 2018.
• As Rabin and Thaler (2001) indicate, expected utility (EU) maximization seems incapable of explaining people’s risk preferences – even though it does suggest some nice measures of the degree of risk aversion.
• Other models of risk aversion, however, might prove more empirically sound, while maintaining tractability. That is, we might not need expected utility to analyze problems involving risk aversion, as alternative models could replicate current standard, EU-based results, while offering still more or avoiding the shortcomings associated with the assumption of EU maximization.
• Consider standard findings associated with insurance: (1) A more risk averse person is willing to pay more for insurance (than is a less risk averse person); and (2) at a fixed price per dollar of insurance (fixed in excess of the actuarially fair price), a more risk averse person will purchase more insurance (than will a less risk averse person).
• Consider standard findings associated with financial investments: (1) In a world with one safe (riskless) and one risky asset, more risk averse people invest less in the risky asset; and (2) if the population as a whole becomes more risk averse, the price of the risky asset must fall (equivalently, the expected return from holding the risky asset must rise).
• Consider standard findings of principal/agent analysis, say, when a risk neutral principal hires a risk averse agent: (1) if the agent’s effort is not observable, then to encourage effort, the agent will have to bear some risk (so that lower output leads to less pay); and (2) the unobservability of effort is costly to the principal, who would prefer to contract on effort directly.
• The various claims made concerning risk aversion in the three previous bullet points do require risk aversion – but they do not require expected utility maximization.
That is, many of the ideas that have been developed around the concept of risk aversion – developed in the context of expected utility maximization – remain valid even when expected utility maximization is not descriptively accurate.
• Consider loss aversion as an alternative approach, one where outcomes are judged against a reference point and “losses loom larger than gains.” For prospects with some loss and some gain outcomes, loss aversion can generate risk averse behavior. (This style of loss aversion does not require "diminished sensitivity," the feature of prospect theory that leads to risk averse behavior in the gains domain and risk seeking behavior in the losses domain.)
• A second alternative, also featured in prospect theory, is probability weighting. The general notion is that, in practice, decision weights might not equal objective probabilities. Specifically, probability weighting typically involves the overweighting of low probability events and the underweighting of high probability events. This type of probability weighting can generate, depending on the options, either risk seeking or risk averse behavior. Lotteries, for instance, might be attractive (induce risk seeking behavior) due to the overweighting of the low-probability outcome of a large win.
• Finally, consider contextual features and salience. Extreme or vivid outcomes (like deaths in terrorist attacks) might garner intense attention, leading to higher decision weights on those outcomes. The contextual feature of the available (although unchosen) options can exert influence by shifting the salience of other outcomes. Again, choices displaying risk aversion can arise from these factors. Expected utility maximization is neither necessary nor sufficient for explaining risk-averse behavior.
Saurabh Bhargava and George Loewenstein, “Behavioral Economics and
Public Policy 102: Beyond Nudging.” American Economic Review 105(5): 396-401, 2015.
• The early behavioral economics-influenced policy proposals were aimed at internalities, and at nudging decisions quite proximate to the perceived problem.
• The next stage, Bhargava and Loewenstein argue, should be to influence the design of policies that are more fundamental, but perhaps less proximate, to perceived problems. This approach need not be particularly controversial, given that the targeted problems often implicate externalities or other market failures.
• Successful nudges have included easing the way to save more for retirement, along with improving the disclosure of information so that people receive useful information in a manner that is easy to understand and respond to.
• Proposed principle #1: not only should choice environments be simplified, the objects of choice should be simplified. Financial products, for instance, could be required to be simple and to be presented in a standardized form.
• Proposed principle #2: policy should look to counter nudges by the private sector that are detrimental to consumers.
• Proposed principle # 3: traditional policy instruments, such as taxes, should be modified in a manner informed by behavioral considerations (framing, salience, inattention, etc.) to maximize the policies’ impacts.
• Examples that the authors discuss include health insurance (lots of room for simplification and standardization); privacy and disclosure (again, simplification and standardization, along with controls on misleading disclosures); and climate change (contending against the many psychological dispositions that make it hard to recognize or respond to this global public bad).
Dean Karlan and Jacob Appel, “To Buy: Doubling the Number of Families with a Safety Net.” Chapter 3, pages 39-54, in More than Good Intentions: Improving the Ways the World's Poor Borrow, Save, Farm, Learn, and Stay Healthy, New York: Dutton, 2011. [Also see this Q&A with the authors at the Freakonomics blog.]
• For a successful anti-poverty policy, you need an intervention that reduces poverty, of course; but further, you need your intervention to be taken up by the targeted poor people. Many policies that “work” do not achieve high levels of take-up, including oral rehydration therapy (for protection against the effects of diarrhea), and ant-malarial mosquito nets.
• Behavioral economist Sendhil Mullainathan talks about “The Last Mile Problem,” the relative neglect of take-up after the intervention has been designed.
• Decisions to borrow money are influenced by far more factors than the interest rate. Randomized controlled experiments indicate that a photo of a pretty woman on loan informational material makes it more likely that men will borrow, and providing but one instead of four sample loans helps, too. In-person meetings, especially when introduced by a trusted acquaintance, doubles take-up.
• In another randomized controlled experiment, a cell phone giveaway did not increase borrowing; indeed, that promotion had a perverse impact.
• Choice overload leads to “I’ll think about it tomorrow”- type responses.
• Rainfall insurance for poor farmers in India is woefully undersubscribed, although (so Karlan and Appel assert) very beneficial.
