Actualizado: 18 de feb de 2020
We live in “constant-change” times, and this is not different for companies and how to manage their people, the key factor to be successful today and in the future. This context makes that we need a better understanding of how people behave and how we can improve the professional lives of workers. Behavioral Economics brings us a new perspective to increase team and individual performance providing us with really useful tools for HR departments.
Nowadays it is possible to systematically assess, for instance, the intertemporal, risk and social preferences of a large number of individuals quite easily, while also keeping a relatively high level of control over external confounding factors. Applying these procedures within companies allows managers to classify people into categories such as short-run/long-run oriented, risk averse/seeker, loss averse, cooperative or egalitarian. Being able to classify people is a value in itself for companies and these measures can be used in a similar fashion as more traditional ones such as personality and cognitive tests – i.e., to predict performance, to hire persons who fit the team or the boss, etc. Moreover, such classifications are typically based on measures given by numerical values which are fairly comparable across individuals.
Contrary to many psychological questionnaires, instead of asking someone how forward-looking or risk seeking she is, behavioral measures rely on real decisions that reveal the decision maker’s true preferences. Even if we abstract from the obvious influence that factors such as socially-desirable responding and self-misrepresentation may exert on self-reported measures, the interpersonal comparability of such measures is also compromised by the subjective perception of respondents about what levels of a particular personal attribute can be considered low, medium or high. Behavioral measures, on the other hand, are able to provide economically meaningful values (or ranges of values) based on formal models. The discount factor, for instance, is a measure of the relative importance the individual gives to later versus sooner rewards and is therefore economically meaningful and interpersonally comparable. For example, the annual subjective discount factor of worker A has been estimated to be between 0.94 and 0.95, whereas for worker B it is estimated to be in the 0.97-0.98 range. We can infer that worker B is more patient or long-run oriented than worker A (more precisely, she values rewards that will be received one year later 2-4% more than worker A). This means that worker A and worker B could differ in their response to a particular incentive scheme if the associated rewards/penalties are sufficiently delayed: therefore, worker A should be offered incentives with shorter realization times in order to reach the same motivation level as worker B (the exact preference values obtained offer valuable quantitative information that can be used to calibrate the appropriate incentives for each worker). Similarly applies to other Behavioral Economics measures such as those assessing risk or social preferences.
In the typical tasks/games of Behavioral Economics experiments, the individual is asked to make decisions over different outcomes involving real monetary stakes. This is the main difference with self-reported tests. Psychometrics can give us information about how much risk people think they might be able to take in a certain situation. On the contrary, Behavioral Economics allows us to measure the exact risk they take in a real decision-making game, with real economic implications. To measure a person’s risk preferences, s/he is asked to make choices over lotteries with different prizes and probabilities. For instance, “do you prefer receiving a lottery ticket with 50% probability of earning a prize of $100 or a lottery ticket with 100% probability of earning a prize of $50? The lottery of the ticket you choose will be played for real and you will receive the money associated with the outcome of the draw.” In a similar vein, to elicit someone’s discount factor, the individual typically address a series of decision problems of the form, “do you prefer receiving $100 today or $104 in a month? The option you choose will be implemented for real and you will receive the money (at the particular date) specified in it”.
The advantages of monetary stakes compared to other types of experimental rewards are nowadays broadly accepted (see Camerer & Hogarth 1999 and Read 2005, for meaningful discussions). Real monetary incentives are of course costly to implement but there are ways in which one can get a good cost-benefit balance, for instance, through the use of probabilistic rewards (e.g. Exadaktylos et al. 2013). In addition, technology makes it now possible to pay participants using mobile phone apps that protect personal data, which further simplifies the procedure. Another important feature of the economic games used in Behavioral Economics is their simplicity, which allows workers to answer in any place at any moment without creating much noise, contrary to many psychometric tests that need a tightly controlled environment.
Furthermore, not only the environment is important. People can lie when answering a questionnaire and even if they don’t want to lie, they can’t be conscious about their feelings. However, in an economic game with real implications from the financial point of view, making a choice, which is not the preferred one, entails monetary and/or utility losses. Although this method cannot completely avoid lies (or desirable responding), it makes lying costlier, thus revealing values closer to the true preferences. In fact, since Behavioral Economics measures are based on the direct observation of people’s behavior in real decisions, these measures are often used to validate survey questions: if responses to a survey question predict behavior in an economic game, this “certifies” the validity of the survey question (see for instance Falk et al. 2016). In sum, the mapping between the measure and the behavior is direct rather than indirect.
Whether introverts or extroverts are better in negotiations represents a long-lasting debate within Personality and Social Psychology (Cain 2013). Psychological personality tests can explore how introvert or extrovert a worker is and this information can be used to indirectly infer who are the workers with greater bargaining power, depending on the particular context.
From the point of view of Behavioral Economics, however, we do not test whether a worker is more or less introverted. What we do is to directly measure his/her negotiation capacity in a decision-making task where choices have direct economic consequences for the decision maker as well as for other bargaining parties. We believe that this is the most efficient way to predict the future behavior of a worker in his/her day-to-day life in the company.
Cain, S. (2013). Quiet: The power of introverts in a world that can’t stop talking. Broadway Books. Camerer, C. F., & Hogarth, R. M. (1999). The effects of financial incentives in experiments: a review and capital-labor-production framework. Journal of Risk & Uncertainty, 19(1), 7-42. Exadaktylos, F., Espín, A. M., & Brañas-Garza, P. (2013). Experimental subjects are not different. Scientific Reports, 3, 1213. Falk, A., Becker, A., Dohmen, T., Huffman, D. B., & Sunde, U. (2016). The Preference Survey Module: A Validated Instrument for Measuring Risk, Time, and Social Preferences. IZA Discussion Papers No. 9674. Read, D. (2005). Monetary incentives, what are they good for? Journal of Economic Methodology 12(2), 265–276.