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on Evolutionary Economics |
| By: | Pawel Dlotko; Simon Rudkin |
| Abstract: | Understanding disease spread through data visualisation has concentrated on trends and maps. Whilst these are helpful, they neglect important multi-dimensional interactions between characteristics of communities. Using the Topological Data Analysis Ball Mapper algorithm we construct an abstract representation of NUTS3 level economic data, overlaying onto it the confirmed cases of Covid-19 in England. In so doing we may understand how the disease spreads on different socio-economical dimensions. It is observed that some areas of the characteristic space have quickly raced to the highest levels of infection, while others close by in the characteristic space, do not show large infection growth. Likewise, we see patterns emerging in very different areas that command more monitoring. A strong contribution for Topological Data Analysis, and the Ball Mapper algorithm especially, in comprehending dynamic epidemic data is signposted. |
| Date: | 2020–04 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2004.03282&r=all |
| By: | Fritsch, Michael; Obschonka, Martin; Wahl, Fabian; Wyrwich, Michael |
| Abstract: | We investigate whether the Roman presence in the southern part of Germany nearly 2,000 years ago had a deep imprinting effect with long run consequences on a broad spectrum of measures ranging from present-day personality profiles to a number of socioeconomic outcomes and why. Today's populations living in the former Roman part of Germany score indeed higher on certain personality traits, have higher life and health satisfaction, longer life expectancy, generate more inventions and behave in a more entrepreneurial way. These findings help explain that regions under Roman rule have higher present-day levels of economic development in terms of GDP per capita. The effects hold when controlling for other potential historical influences. When addressing potential channels of a long term effect of Roman rule the data indicates that the Roman road network plays an important role as a mechanism in the imprinting that is still perceptible today. |
| Keywords: | Romans,personality traits,culture,well-being,regional performance,Limes |
| JEL: | N9 O1 I31 |
| Date: | 2020 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:hohdps:052020&r=all |
| By: | Guglielmo Briscese; Nicola Lacetera; Mario Macis; Mirco Tonin |
| Abstract: | We study how intentions to comply with the self-isolation restrictions introduced in Italy to mitigate the COVID-19 epidemic respond to the length of their possible extension. Based on a survey of a representative sample of Italian residents (N=894), we find that respondents who are positively surprised by a given hypothetical extension (i.e. the extension is shorter than what they expected) are more willing to increase their self-isolation. In contrast, negative surprises (extensions longer than expected) are associated with a lower willingness to comply. In a context where individual compliance has collective benefits, but full enforcement is costly and controversial, communication and persuasion have a fundamental role. Our findings provide insights to public authorities on how to announce lockdown measures and manage people’s expectations. |
| Keywords: | COVID-19, social distancing, expectations |
| JEL: | C42 D91 H12 H41 I12 |
| Date: | 2020 |
| URL: | http://d.repec.org/n?u=RePEc:ces:ceswps:_8182&r=all |
| By: | Ivan Moscati |
| Date: | 2019 |
| URL: | http://d.repec.org/n?u=RePEc:baf:cbafwp:cbafwp19129&r=all |
| By: | Andreas Ziegler (University of Kassel) |
| Abstract: | This paper empirically examines whether environmental values are correlated with economic preferences from behavioral economics and considers possible consequences when independence is assumed. The data for this analysis stem from a large-scale computer-based survey among more than 3700 German citizens. Our indicators for environmental values are based on the New Ecological Paradigm (NEP), which is a standard instrument in social and behavioral sciences and increasingly common in economic studies. The econometric analysis with Generalized Poisson regression models reveals strong correlations between two NEP scales and several economic preferences, which are based on established experimental measures: While social preferences (measured in an incentivized dictator game) and positive reciprocity are significantly positively correlated, trust and (less robust) negative reciprocity are significantly negatively correlated with the NEP scales, respectively. Only risk and time preferences (also measured in an incentivized experiment) are not robustly significantly correlated with the NEP scales. These estimation results strongly recommend the additional inclusion of economic preferences in econometric analyses that use a NEP scale as explanatory factor of main interest for environmentally relevant behavior. In particular, not considering social preferences, trust, and positive and negative reciprocity can lead to strong distortions due to omitted variable biases. This conclusion is illustrated in an empirical example that reveals biased estimation results for the effect of a NEP scale on donation activities if not all relevant economic preferences are included as control variables. |
| Keywords: | Environmental values, New Ecological Paradigm (NEP), economic prefer-ences, individual behavior, artefactual field experiments |
| JEL: | Q50 D01 D91 Q57 A13 |
| Date: | 2020 |
| URL: | http://d.repec.org/n?u=RePEc:mar:magkse:202020&r=all |
| By: | Benjamin Avanzi; Gregory Clive Taylor; Phuong Anh Vu; Bernard Wong |
| Abstract: | In this paper, we develop a multivariate evolutionary generalised linear model (GLM) framework for claims reserving, which allows for dynamic features of claims activity in conjunction with dependency across business lines to accurately assess claims reserves. We extend the traditional GLM reserving framework on two fronts: GLM fixed factors are allowed to evolve in a recursive manner, and dependence is incorporated in the specification of these factors using a common shock approach. We consider factors that evolve across accident years in conjunction with factors that evolve across calendar years. This two-dimensional evolution of factors is unconventional as a traditional evolutionary model typically considers the evolution in one single time dimension. This creates challenges for the estimation process, which we tackle in this paper. We develop the formulation of a particle filtering algorithm with parameter learning procedure. This is an adaptive estimation approach which updates evolving factors of the framework recursively over time. We implement and illustrate our model with a simulated data set, as well as a set of real data from a Canadian insurer. |
| Date: | 2020–04 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2004.06880&r=all |
| By: | Watzek, Julia; Brosnan, Sarah (Georgia State University) |
| Abstract: | Humans make thousands of decisions every day, and in some situations, we make reliably bad ones. Much research has explored the circumstances in which such irrational decision-making occurs, but the underlying mechanisms are often unclear. One approach that has recently gained traction is to study other species’ responses to similar scenarios to better understand our own decision-making strategies. Here we provide a critical discussion of experimental studies of decision-making biases in animals. We begin by demonstrating how comparative research can yield unique insights into our own decision-making that cannot be gained from studying humans alone. In particular, while comparative research helps us better understand how and why decision-making biases have evolved and which mechanisms underlie them, such studies often overlook how these behaviors vary, both within and between individuals. Methodological concerns and a lack in the diversity of species studied and the number of animals tested complicate this issue and can limit the inferences we can draw. We emphasize the need to study why and when some animals would be expected to show these biases while others would not. Further, rather than just assess whether a given bias is present, comparative research should measure the extent to which it is. We argue that studying how susceptibility to biases varies both within and between individuals is crucial to better understanding the nature of irrational decision-making. We suggest practical steps that open up exciting avenues for future comparative research in this area. |
| Date: | 2020–04–08 |
| URL: | http://d.repec.org/n?u=RePEc:osf:osfxxx:4gu2f&r=all |
| By: | Jean Roch Donsimoni (Johannes Gutenberg University Mainz); René Glawion (Johannes Gutenberg University Mainz); Bodo Plachter (Johannes Gutenberg University Mainz); Klaus Wälde (Johannes Gutenberg University Mainz) |
| Abstract: | We model the evolution of the number of individuals that are reported to be sick with COVID-19 in Germany. Our theoretical framework builds on a continuous time Markov chain with four states: healthy without infection, sick, healthy after recovery or after infection but without symptoms and dead. Our quantitative so- lution matches the number of sick individuals up to the most recent observation and ends with a share of sick individuals following from infection rates and sickness probabilities. We employ this framework to study inter alia the expected peak of the number of sick individuals in a scenario without public regulation of social con- tacts. We also study the effects of public regulations. For all scenarios we report the expected end of the CoV-2 epidemic. We have four general findings: First, current epidemiological thinking implies that the long-run effects of the epidemic only depend on the aggregate long-run infection rate and on the individual risk to turn sick after an infection. Any measures by individuals and the public therefore only influence the dynamics of spread of CoV- 2. Second, predictions about the duration and level of the epidemic must strongly distinguish between the officially reported numbers (Robert Koch Institut, RKI) and actual numbers of sick individuals. Third, given the current (scarce) medical knowledge about long-run infection rate and individual risks to turn sick, any pre- diction on the length (duration in months) and strength (e.g. maximum numbers of sick individuals on a given day) is subject to a lot of uncertainty. Our predictions therefore offer robustness analyses that provide ranges on how long the epidemic will last and how strong it will be. Fourth, public interventions that are already in place and that are being discussed can lead to more and less severe outcomes of the epidemic. If an intervention takes place too early, the epidemic can actually be stronger than with an intervention that starts later. Interventions should therefore be contingent on current infection rates in regions or countries. Concerning predictions about COVID-19 in Germany, we find that the long-run number of sick individuals (that are reported to the RKI), once the epidemic is over, will lie between 500 thousand and 5 million individuals. While this seems to be an absurd large range for a precise projection, this reflects the uncertainty about the long-run infection rate in Germany. If we assume that Germany will follow the good scenario of Hubei (and we are even a bit more conservative given discussions about data quality), we will end up with 500 thousand sick individuals over the entire epidemic. If by contrast we believe (as many argue) that once the epidemic is over 70% of the population will have been infected (and thereby immune), we will end up at 5 million cases. Defining the end of the epidemic by less than 100 newly reported sick individuals per day, we find a large variation depending on the effectiveness of governmental pleas and regulations to reduce social contacts. An epidemic that is not influenced by public health measures would end mid June 2020. With public health measures lasting for few weeks, the end is delayed by around one month or two. The ad- vantage of the delay, however, is to reduce the peak number of individuals that are simultaneously sick. When we believe in long-run infection rates of 70%, this number is equally high for all scenarios we went through and well above 1 million. When we can hope for the Hubei-scenario, the maximum number of sick individuals will be around 200 thousand only Whatever value of the range of long-run infection rates we want to assume, the epidemic will last at least until June, with extensive and potentially future public health measures, it will last until July. In the worst case, it will last until end of August. We emphasize that all projections are subject to uncertainty and permanent mon- itoring of observed incidences are taken into account to update the projection. The most recent projections are available at https://www.macro.economics.uni- mainz.de/corona-blog/. |
| Keywords: | Corona, COVID19, SARS-CoV-2, spread of infection, Markov model, Germany, projection |
| Date: | 2020–03–26 |
| URL: | http://d.repec.org/n?u=RePEc:jgu:wpaper:2006&r=all |