Economics

Hardly any other area of research has recently attracted as much attention as machine learning (ML) through the rapid advances in artificial intelligence (AI). This publication provides a short introduction to practical concepts and methods of machine learning, problems and emerging research questions, as well as an overview of the participants, an overview of the application areas and the socio-economic framework conditions of the research. In expert circles, ML is used as a key technology for modern artificial intelligence techniques, which is why AI and ML are often used interchangeably, especially in an economic context. Machine learning and, in particular, deep learning (DL) opens up entirely new possibilities in automatic language processing, image analysis, medical diagnostics, process management and customer management. One of the important aspects in this article is chipization. Due to the rapid development of digitalization, the number of applications will continue to grow as digital technologies advance. In the future, machines will more and more provide results that are important for decision making. To this end, it is important to ensure the safety, reliability and sufficient traceability of automated decision-making processes from the technological side. At the same time, it is necessary to ensure that ML applications are compatible with legal issues such as responsibility and liability for algorithmic decisions, as well as technically feasible. Its formulation and regulatory implementation is an important and complex issue that requires an interdisciplinary approach. Last but not least, public acceptance is critical to the continued diffusion of machine learning processes in applications. This requires widespread public discussion and the involvement of various social groups.

In normal times, it is assumed that financial institutions operating in non-overlapping sectors have complementary and distinct outcomes, typically reflected in mostly uncorrelated outcomes and asset returns. Such is the reasoning behind common "free lunches" to be had in investing, like diversifying assets across equity and bond sectors. Unfortunately, the recurrence of crises like the Great Financial Crisis of 2007-2008 demonstrate that such convenient assumptions often break down, with dramatic consequences for all financial actors. In hindsight, the emergence of systemic risk (as exemplified by failure in one part of a system spreading to ostensibly unrelated parts of the system) has been explained by narratives such as deregulation and leverage. But can we diagnose and quantify the ongoing emergence of systemic risk in financial systems? In this study, we focus on two previously-documented measures of systemic risk that require only easily available time series data (eg monthly asset returns): cross-correlation and principal component analysis. We apply these tests to daily and monthly returns on hedge fund indexes and broad-based market indexes, and discuss their results. We hope that a frank discussion of these simple, non-parametric measures can help inform legislators, lawmakers, and financial actors of potential crises looming on the horizon.

In a recent article in the American Economic Review, Tatyana Deryugina and David Molitor (DM) analyzed the effect of Hurricane Katrina on the mortality of elderly and disabled residents of New Orleans. The authors concluded that Hurricane Katrina improved the eight-year survival rate of elderly and disabled residents of New Orleans by 3% and that most of this decline in mortality was due to declines in mortality among those who moved to places with lower mortality. In this article, I provide a critical assessment of the evidence provided by DM to support their conclusions. There are three main problems. First, DM generally fail to account for the fact that people of different ages, races or sex will have different probabilities of dying as time goes by, and when they do allow for this, results change markedly. Second, DM do not account for the fact that residents in New Orleans are likely to be selected non-randomly on the basis of health because of the relatively high mortality rate in New Orleans compared to the rest of the country. Third, there is considerable evidence that among those who moved from New Orleans, the destination chosen was non-random. Finally, DM never directly assessed changes in mortality of those who moved, or stayed, in New Orleans before and after Hurricane Katrina. These problems lead me to conclude that the evidence presented by DM does not support their inferences.

A substantial increase in illegal extraction of the benthic resources in central Chile is likely driven by an interplay of numerous socio-economic local factors that threatens the success of the fisheries management areas (MA) system. To assess this problem, the exploitation state of a commercially important benthic resource (i.e., keyhole limpet) in the MAs was related with socio-economic drivers of the small-scale fisheries. The potential drivers of illegal extraction included rebound effect of fishing effort displacement by MAs, level of enforcement, distance to surveillance authorities, wave exposure and land-based access to the MA, and alternative economic activities in the fishing village. The exploitation state of limpets was assessed by the proportion of the catch that is below the minimum legal size, with high proportions indicating a poor state, and by the relative median size of limpets fished within the MAs in comparison with neighbouring OA areas, with larger relative sizes in the MA indicating a good state. A Bayesian-Belief Network approach was adopted to assess the effects of potential drivers of illegal fishing on the status of the benthic resource in the MAs. Results evidenced the absence of a direct link between the level of enforcement and the status of the resource, with other socio-economic (e.g., alternative economic activities in the village) and context variables (e.g., fishing effort or distance to surveillance authorities) playing important roles. Scenario analysis explored variables that are susceptible to be managed, evidencing that BBN is a powerful approach to explore the role of multiple external drivers, and their impact on marine resources, in complex small-scale fisheries.

Many online news publishers finance their websites by displaying ads alongside content. Yet, remarkably little is known about how exposure to such ads impacts users' news consumption. We examine this question using 3.1 million anonymized browsing sessions from 79,856 users on a news website and the quasi-random variation created by ad blocker adoption. We find that seeing ads has a robust negative effect on the quantity and variety of news consumption: Users who adopt ad blockers subsequently consume 20% more news articles corresponding to 10% more categories. The effect persists over time and is largely driven by consumption of "hard" news. The effect is primarily attributable to a learning mechanism, wherein users gain positive experience with the ad-free site; a cognitive mechanism, wherein ads impede processing of content, also plays a role. Our findings open an important discussion on the suitability of advertising as a monetization model for valuable digital content.

A major focus of debate about rationing guidelines for COVID-19 vaccines is whether and how to prioritize access for minority populations that have been particularly affected by the pandemic, and been the subject of historical and structural disadvantage, particularly Black and Indigenous individuals. We simulate the 2018 CDC Vaccine Allocation guidelines using data from the American Community Survey under different assumptions on total vaccine supply. Black and Indigenous individuals combined receive a higher share of vaccines compared to their population share for all assumptions on total vaccine supply. However, their vaccine share under the 2018 CDC guidelines is considerably lower than their share of COVID-19 deaths and age-adjusted deaths. We then simulate one method to incorporate disadvantage in vaccine allocation via a reserve system. In a reserve system, units are placed into categories and units reserved for a category give preferential treatment to individuals from that category. Using the Area Deprivation Index (ADI) as a proxy for disadvantage, we show that a 40% high-ADI reserve increases the number of vaccines allocated to Black or Indigenous individuals, with a share that approaches their COVID-19 death share when there are about 75 million units. Our findings illustrate that whether an allocation is equitable depends crucially on the benchmark and highlight the importance of considering the expected distribution of outcomes from implementing vaccine allocation guidelines.

Education has traditionally been classroom-oriented with a gradual growth of online courses in recent times. However, the outbreak of the COVID-19 pandemic has dramatically accelerated the shift to online classes. Associated with this learning format is the question: what do people think about the educational value of an online course compared to a course taken in-person in a classroom? This paper addresses the question and presents a Bayesian quantile analysis of public opinion using a nationally representative survey data from the United States. Our findings show that previous participation in online courses and full-time employment status favor the educational value of online courses. We also find that the older demographic and females have a greater propensity for online education. In contrast, highly educated individuals have a lower willingness towards online education vis-à-vis traditional classes. Besides, covariate effects show heterogeneity across quantiles which cannot be captured using probit or logit models.

Motivated reasoning is a bias in inference in which people distort their updating process in the direction of more attractive beliefs. Prior work has shown how motivated reasoning leads people to form overly "positive" beliefs that also serve to bolster one's self-image in domains such as intelligence, prosociality, and politics. In this paper, I study whether positivity-motivated reasoning persists in domains where self-image does not play a role. In particular, I analyze whether individuals motivatedly reason to think that the world is a better place for others. Building off of the design from Thaler (2020), I conduct a large online experiment to test for positivity-motivated reasoning on issues such as cancer survival rates, others' happiness, and infant mortality. I find no systematic evidence for positivity-motivated or negativity-motivated reasoning, and can rule out modest effects. Positivity is not a sufficient condition for motivated reasoning.

Ageing populations in developing countries have spurred the introduction of public pension programs to preserve the standard of living for the elderly. The often-overlooked mechanism of intergenerational transfers, however, can dampen these intended policy effects as adult children who make income contributions to their parents could adjust their behavior to changes in their parents' income. Exploiting a unique policy intervention in China, we examine using a difference-in-difference-in-differences (DDD) approach how a new pension program impacts inter vivos transfers. We show that pension benefits lower the propensity of receiving transfers from adult children in the context of a large middle-income country and we also estimate a small crowd-out effect. Taken together, these estimates fit the pattern of previous research in high-income countries, although our estimates of the crowd-out effect are significantly smaller than previous studies in both high-income and middle-income countries.

Do competent women receive unfavorable treatment than equally competent men? I study this question in a laboratory experiment where unfavorable treatment has material consequences. I find that neither men nor women treat competent women less favorably; if anything, both men and women treat competent women slightly more favorably than equally competent men. The findings provide a piece of evidence that competent women may not necessarily receive unfavorable treatment in settings with material consequences, which may shed new light on hiring and promotion practices in labor markets.