When to pay the performance incentive? Caveats of loss-aversion phenomenon

Rewards for performance are commonly used strategies to motivate employees to work harder. The emerging research in behavioural science is showing us innovative ways of designing these incentive systems.  One such system based on the principle of loss aversion is gaining popularity. World Bank in its recent ‘Mind, Society and Behaviour’ report discusses the loss aversion phenomenon. Prof Raj Chetty also quotes it in a podcast on ‘Improving public policy through behavioural economics’.

The principle of loss aversion is based on this simple finding that people are sensitive to losses in income than gains in income. It is an income gain if the employees are paid at the end of the year based on their performance. Instead, if the money is given upfront at the beginning of the year and it is taken back at the end, on not meeting performance metrics, it is an income loss. An experiment conducted in Chicago schools by Prof List and others of the University of Chicago show that teachers who were subject to income loss design of incentives exerted more efforts and also resulted in higher student scores as compared to the teachers who were subjected to ‘income gain’ design. However, there are certain limitations to the incentive mechanisms designed on the principle of loss aversion depending on the context. This article, in particular, tries to point out five such differences in a context which can possibly change the nature of the results.

First, nature of work. Daniel Pink in his book ‘Drive: The surprising truth about what motivates us’ describes two categories of tasks; algorithmic and heuristic. Algorithmic tasks are those where you follow a set of defined instructions to solve the problem, like working as a grocery clerk. Heuristic tasks are those which require innovation, experimentation with multiple possibilities and devising a novel solution, like designing an ad campaign. Pink demonstrates through various examples and corroborating research that incentives work very well in former scenarios (algorithmic tasks) and might back fire in case of heuristic tasks. The argument being, the pressure to perform, which is likely to be highly especially if the incentive is designed as per loss aversion principle, narrows down the focus of people, limiting their world view and making them less risk averse. So, this type of design may not be desirable in highly creative environments.

State capacity and second-best solutions

Have you ever heard of advocacy for low-cost private schools as means to provide education or regularizing quacks (untrained doctors) as means to provide health care or encouraging solar home systems as means to provide electricity, in the context of countries like the UK, US etc.? Probably not. But these are widely debated in the context of developing countries.
The emergence of advocacy for such second-best solutions is the result of weak state capacity and prolonged periods of inaction. A strong public delivery system can provide a better quality of services at lower costs to the poor. The question then is - can the state deliver in that context and what's the time required to make these systems work? What do you do in the meantime, especially if there is no hope of change? This then leads to advocacy for second-best solutions. The potential to deliver outcomes is lower for the second-best solutions as compared to strong public systems. However, they can be a lifeline in contexts where there is either no public service delivery or the public service delivery is underperforming to the extent that even a lower potential second-best solution is comparatively better. Let us consider the examples of electricity, education and healthcare.
Electricity: On an absolute scale, providing grid electricity to all households may be better because households benefit from economies of scales in the form of lower charges and they also get to use many appliances. The questions however are - how much time does it take to provide such service? What to do in case of certain areas that witnessed government's neglect and inaction for long and there is no hope of change. Such scenario warrants some immediate action, even if it is a short-term measure. This leads to proposals to provide second-best solutions like providing solar home systems or microgrids etc. These are second-best solutions because they can provide households with enough electricity only to run limited appliances at a higher cost but they can be saviours when there is nothing. We may thus observe that the emergence of proposals for relying on such second-best solutions as means to deliver electricity is due to weak state capacity and prolonged periods of inaction.

Nuanced understanding of state capacity required to enforce female foeticide laws

Union Minister Maneka Gandhi has proposed to rethink ways to curb female foeticide. The proposal is to make determining gender compulsory and following the pregnant women till their delivery, instead of illegalizing the process to determine the gender of the foetus. This has elicited several responses.

1. Design: One of the common arguments is that design of laws in current format hasn't worked and hence we need to try a new design. This may not be the best way to approach the problem. Thinking of a new design assumes that the poor design is the critical constraint in this case which might not be the case. Quoting Gulzar Natarajan
When something is persistently wrong or a failure, we tend to over-react and assume that the existing design and processes have failed and we need to adopt something new. We have deeply internalized that failures are due to lack of innovation with design, process, and technology. Very rarely do we step-back to see whether the original design and processes themselves were rigorously implemented or not. It is very comforting to rationalize away failures by blaming it on the design and other extraneous factors, rather than questioning our implementation capability.
We are not sure if we introspected enough about design failure vs. implementation failure in this context before jumping on to new design.

2. Quantum of punishment: Some have argued that the quantum of punishment is not enough to deter people from committing this mistake. Others argue that quantum of punishment matters only if there is certainty of punishment. [Read Ajay Shah's comments on similar arguments in a different context]

3. Implementation: Some argue that the problem is with the implementation of existing laws and hence we need police and judicial reforms.

This is generally the conventional wisdom regarding most types failures but all of this is only partly true. We need to rethink some of these and understand state capacity in more detail to effectively address the problems. Some such details are discussed below.