Economics

Why do biased predictions arise? What interventions can prevent them? We evaluate 8.2 million algorithmic predictions of math performance from ≈ 400 AI engineers, each of whom developed an algorithm under a randomly assigned experimental condition. Our treatment arms modified programmers' incentives, training data, awareness, and/or technical knowledge of AI ethics. We then assess out-of-sample predictions from their algorithms using randomized audit manipulations of algorithm inputs and ground-truth math performance for 20K subjects. We find that biased predictions are mostly caused by biased training data. However, one-third of the benefit of better training data comes through a novel economic mechanism: Engineers exert greater effort and are more responsive to incentives when given better training data. We also assess how performance varies with programmers' demographic characteristics, and their performance on a psychological test of implicit bias (IAT) concerning gender and careers. We find no evidence that female, minority and low-IAT engineers exhibit lower bias or discrimination in their code. However, we do find that prediction errors are correlated within demographic groups, which creates performance improvements through cross-demographic averaging. Finally, we quantify the benefits and tradeoffs of practical managerial or policy interventions such as technical advice, simple reminders, and improved incentives for decreasing algorithmic bias.

Many researches have discussed the phenomenon and definition of sharing economy, but an understanding of sharing economy's reconstructions of the world remains elusive. We illustrate the mechanism of sharing economy's reconstructions of the world in detail based on big data including the mechanism of sharing economy's reconstructions of society, time and space, users, industry, and self-reconstruction in the future, which is very important for society to make full use of the reconstruction opportunity to upgrade our world through sharing economy. On the one hand, we established the mechanisms for sharing economy rebuilding society, industry, space-time, and users through qualitative analyses, and on the other hand, we demonstrated the rationality of the mechanisms through quantitative analyses of big data.

We analyse the Bihar assembly elections of 2020, and find that poverty was the key driving factor, over and above female voters as determinants. The results show that the poor were more likely to support the NDA. The relevance of this result for an election held in the midst of a pandemic, is very crucial, given that the poor were the hardest hit. Secondly, in contrast to conventional commentary, the empirical results show that the AIMIM-factor and the LJP-factor hurt the NDA while benefitting the MGB, with their presence in these elections. The methodological novelty in this paper is combining elections data with wealth index data to study the effect of poverty on elections outcomes.

The carbon footprint of Bitcoin has drawn wide attention, but Bitcoin's long-term impact on the climate remains uncertain. Here we present a framework to overcome uncertainties in previous estimates and project Bitcoin's electricity consumption and carbon footprint in the long term. If we assume Bitcoin's market capitalization grows in line with the one of gold, we find that the annual electricity consumption of Bitcoin may increase from 60 to 400 TWh between 2020 and 2100. The future carbon footprint of Bitcoin strongly depends on the decarbonization pathway of the electricity sector. If the electricity sector achieves carbon neutrality by 2050, Bitcoin's carbon footprint has peaked already. However, in the business-as-usual scenario, emissions sum up to 2 gigatons until 2100, an amount comparable to 7% of global emissions in 2019. The Bitcoin price spike at the end of 2020 shows, however, that progressive development of market capitalization could yield an electricity consumption of more than 100 TWh already in 2021, and lead to cumulative emissions of over 5 gigatons by 2100. Therefore, we also discuss policy instruments to reduce Bitcoin's future carbon footprint.

The outbreak of COVID-19 in March 2020 led to a shutdown of economic activities in Europe. This included the sports sector, since public gatherings were prohibited. The German Bundesliga was among the first sport leagues realising a restart without spectators. Several recent studies suggest that the home advantage of teams was eroded for the remaining matches. Our paper analyses the reaction by bookmakers to the disappearance of such home advantage. We show that bookmakers had problems to adjust the betting odds in accordance to the disappeared home advantage, opening opportunities for profitable betting strategies.

We suggest use continuous numerical risk grades [0,1] of R for a single risk or the unit cube in Rn for n risks as the economic domain. We consider risk ratings of economic agents as their coordinates in the economic domain. Economic activity of agents, economic or other factors change agents risk ratings and that cause motion of agents in the economic domain. Aggregations of variables and transactions of individual agents in small volume of economic domain establish the continuous economic media approximation that describes collective variables, transactions and their flows in the economic domain as functions of risk coordinates. Any economic variable A(t,x) defines mean risk XA(t) as risk weighted by economic variable A(t,x). Collective flows of economic variables in bounded economic domain fluctuate from secure to risky area and back. These fluctuations of flows cause time oscillations of macroeconomic variables A(t) and their mean risks XA(t) in economic domain and are the origin of any business and credit cycles. We derive equations that describe evolution of collective variables, transactions and their flows in the economic domain. As illustration we present simple self-consistent equations of supply-demand cycles that describe fluctuations of supply, demand and their mean risks.

This paper examines the evolution of business and consumer uncertainty amid the coronavirus pandemic in 32 European countries and the European Union (EU).Since uncertainty is not directly observable, we approximate it using the geometric discrepancy indicator of Claveria et al. (2019).This approach allows us quantifying the proportion of disagreement in business and consumer expectations of 32 countries.We have used information from all monthly forward-looking questions contained in Joint Harmonised Programme of Business and Consumer Surveys conducted by the European Commission (the industry survey, the service survey, the retail trade survey, the building survey and the consumer survey).First, we have calculated a discrepancy indicator for each of the 17 survey questions analysed, which allows us to approximate the proportion of uncertainty about different aspects of economic activity, both form the demand and the supply sides of the economy.We then use these indicators to calculate disagreement indices at the sector level.We graphic the evolution of the degree of uncertainty in the main economic sectors of the analysed economies up to June 2020.We observe marked differences, both across variables, sectors and countries since the inception of the COVID-19 crisis.Finally, by adding the sectoral indicators, an indicator of business uncertainty is calculated and compared with that of consumers.Again, we find substantial differences in the evolution of uncertainty between managers and consumers.This analysis seeks to offer a global overview of the degree of economic uncertainty in the midst of the coronavirus crisis at the sectoral level.

Social distancing interventions can be effective against epidemics but are potentially detrimental for the economy. Businesses that rely heavily on face-to-face communication or close physical proximity when producing a product or providing a service are particularly vulnerable. There is, however, no systematic evidence about the role of human interactions across different lines of business and about which will be the most limited by social distancing. Here we provide theory-based measures of the reliance of U.S. businesses on human interaction, detailed by industry and geographic location. We find that 49 million workers work in occupations that rely heavily on face-to-face communication or require close physical proximity to other workers. Our model suggests that when businesses are forced to reduce worker contacts by half, they need a 12 percent wage subsidy to compensate for the disruption in communication. Retail, hotels and restaurants, arts and entertainment and schools are the most affected sectors. Our results can help target fiscal assistance to businesses that are most disrupted by social distancing.

To prevent the outbreak of the Coronavirus disease (COVID-19), many countries around the world went into lockdown and imposed unprecedented containment measures. These restrictions progressively produced changes to social behavior and global mobility patterns, evidently disrupting social and economic activities. Here, using maritime traffic data collected via a global network of AIS receivers, we analyze the effects that the COVID-19 pandemic and containment measures had on the shipping industry, which accounts alone for more than 80% of the world trade. We rely on multiple data-driven maritime mobility indexes to quantitatively assess ship mobility in a given unit of time. The mobility analysis here presented has a worldwide extent and is based on the computation of: CNM of all ships reporting their position and navigational status via AIS, number of active and idle ships, and fleet average speed. To highlight significant changes in shipping routes and operational patterns, we also compute and compare global and local density maps. We compare 2020 mobility levels to those of previous years assuming that an unchanged growth rate would have been achieved, if not for COVID-19. Following the outbreak, we find an unprecedented drop in maritime mobility, across all categories of commercial shipping. With few exceptions, a generally reduced activity is observable from March to June, when the most severe restrictions were in force. We quantify a variation of mobility between -5.62% and -13.77% for container ships, between +2.28% and -3.32% for dry bulk, between -0.22% and -9.27% for wet bulk, and between -19.57% and -42.77% for passenger traffic. This study is unprecedented for the uniqueness and completeness of the employed dataset, which comprises a trillion AIS messages broadcast worldwide by 50000 ships, a figure that closely parallels the documented size of the world merchant fleet.

The purpose of this study is to investigate the effects of the COVID-19 pandemic on economic policy uncertainty in the US and the UK. The impact of the increase in COVID-19 cases and deaths in the country, and the increase in the number of cases and deaths outside the country may vary. To examine this, the study employs bootstrap ARDL cointegration approach from March 8, 2020 to May 24, 2020. According to the bootstrap ARDL results, a long-run equilibrium relationship is confirmed for five out of the 10 models. The long-term coefficients obtained from the ARDL models suggest that an increase in COVID-19 cases and deaths outside of the UK and the US has a significant effect on economic policy uncertainty. The US is more affected by the increase in the number of COVID-19 cases. The UK, on the other hand, is more negatively affected by the increase in the number of COVID-19 deaths outside the country than the increase in the number of cases. Moreover, another important finding from the study demonstrates that COVID-19 is a factor of great uncertainty for both countries in the short-term.