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on Evolutionary Economics |
| By: | Tang, Can; Zhao, Zhong |
| Abstract: | Using a national representative sample, the China Family Panel Studies, this paper explores the influences of clan culture, a hallmark of Chinese cultural history, on the prevalence of child labor in China. We find that clan culture significantly reduces the incidence of child labor and working hours of child laborer. The results exhibit strong boy bias, and are driven by boys rather than girls, which reflects the patrilineal nature of Chinese clan culture. Moreover, the impact is greater on boys from households with lower socioeconomic status, and in rural areas. Clan culture acts as a supplement to formal institutions: reduces the incidence of child labor through risk sharing and easing credit constraints, and helps form social norms to promote human capital investment. We also employ an instrument variable approach and carry out a series of robustness checks to further confirm the findings. |
| JEL: | J21 J22 J81 O15 |
| Date: | 2022–10–10 |
| URL: | http://d.repec.org/n?u=RePEc:unm:unumer:2022032&r=evo |
| By: | Jawwad Noor; Fernando Payró Chew |
| Abstract: | With beliefs over the outcomes of coin-tosses as our primitive, we formalize the Law of Small Numbers (Tversky and Kahneman (1974)) by an axiom that expresses a belief that the sample mean of any sequence will tend towards the coin’s perceived bias along the entire path. The agent is represented by a belief that the bias of the coin is path-dependent and self-correcting. The model is consistent with the evidence used to support the Law of Small Numbers, such as the Gambler’s Fallacy. In the setting of Bayesian inference, we show how learning is affected by the interplay between two potentially opposing forces: a belief in the absence of streaks and a belief that the sample mean will tend to the true bias. We show that, unlike other learning results in the literature (Rabin (2002), Epstein, Noor and Sandroni (2010)), the latter force ensures that the agent at least admits the true parameter as possible in the limit, if not learn with certainty that it is true. In an evolutionary setting, we show that agents who believe in the Law of Small Numbers are never pushed out of the evolutionary race by “standard” agents who correctly understand randomness. |
| Keywords: | law of small numbers, belief biases, heuristics, gambler’s fallacy, learning, misspecified beliefs, evolution |
| JEL: | D01 D9 |
| Date: | 2022–09 |
| URL: | http://d.repec.org/n?u=RePEc:bge:wpaper:1364&r=evo |
| By: | Dufwenberg, Martin; Feldman, Paul; Servátka, Maroš; Tarrasó, Jorge; Vadovič, Radovan |
| Abstract: | Lab evidence on trust games involves more cooperation than conventional economic theory predicts. We explore whether this pattern extends to a field setting where we are able to control for (lack of) repeat-play and reputation: the taxi market in Mexico City. We find a remarkably high degree of trustworthiness, even with price-haggling which was predicted to reduce trustworthiness. |
| Keywords: | trustworthiness, honesty, reciprocity, field experiment, haggling, taxis, Mexico City |
| JEL: | C72 C90 C93 |
| Date: | 2022–09–18 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:115044&r=evo |
| By: | Guilhem Lecouteux (UCA - Université Côte d'Azur, GREDEG - Groupe de Recherche en Droit, Economie et Gestion - UNS - Université Nice Sophia Antipolis (1965 - 2019) - COMUE UCA - COMUE Université Côte d'Azur (2015-2019) - CNRS - Centre National de la Recherche Scientifique - UCA - Université Côte d'Azur); Ivan Mitrouchev (IESEG School of Management Lille) |
| Abstract: | We propose a precise definition of the notion of ‘context' in behavioural economics, and identify four axioms characterising the strategies implemented in standard and behavioural welfare economics to define welfare: (1) normative individualism, (2) behavioural context-independence, (3) normative contextindependence, and (4) consumer sovereignty. We then review the different approaches in behavioural normative economics in the light of those axioms. We highlight that the key distinction between those approaches is the axiom which is chosen as a way to infer normative preferences from behavioural preferences, with either normative context-independence or consumer sovereignty. We argue that preference purification requires the axiom of normative context-independence, whose justification is however limited when individual behaviour is contextdependent. This suggests that it might be impossible to offer a general strategy to infer true/normative preferences from possibly incoherent behavioural preferences. |
| Date: | 2022–09–29 |
| URL: | http://d.repec.org/n?u=RePEc:hal:wpaper:hal-03791972&r=evo |
| By: | Alessandro Bondi; Sergio Pulido; Simone Scotti |
| Abstract: | We study an extension of the Heston stochastic volatility model that incorporates rough volatility and jump clustering phenomena. In our model, named the rough Hawkes Heston stochastic volatility model, the spot variance is a rough Hawkes-type process proportional to the intensity process of the jump component appearing in the dynamics of the spot variance itself and the log returns. The model belongs to the class of affine Volterra models. In particular, the Fourier-Laplace transform of the log returns and the square of the volatility index can be computed explicitly in terms of solutions of deterministic Riccati-Volterra equations, which can be efficiently approximated using a multi-factor approximation technique. We calibrate a parsimonious specification of our model characterized by a power kernel and an exponential law for the jumps. We show that our parsimonious setup is able to simultaneously capture, with a high precision, the behavior of the implied volatility smile for both S&P 500 and VIX options. In particular, we observe that in our setting the usual shift in the implied volatility of VIX options is explained by a very low value of the power in the kernel. Our findings demonstrate the relevance, under an affine framework, of rough volatility and self-exciting jumps in order to capture the joint evolution of the S&P 500 and VIX. |
| Date: | 2022–10 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2210.12393&r=evo |