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on Big Data |
| By: | Philippe Aghion (Harvard University); Céline Antonin (Observatoire français des conjonctures économiques); Simon Bunel |
| Abstract: | In this survey paper, we argue that the effects of artificial intelligence (AI) and automation on growth and employment depend to a large extent on institutions and policies. We develop a two‑fold analysis. In a first section, we survey the most recent literature to show that AI can spur growth by replacing labor by capital, both in the production of goods and services and in the production of ideas. Yet, we argue that AI may inhibit growth if combined with inappropriate competition policy. In a second section, we discuss the effect of robotization on employment in France over the 1994‑2014 period. Based on our empirical analysis on French data, we first show that robotization reduces aggregate employment at the employment zone level, and second that non‑educated workers are more negatively affected by robotization than educated workers. This finding suggests that inappropriate labor market and education policies reduce the positive impact that AI and automation could have on employment. |
| Keywords: | Artificial intelligence; Growth; Automation; Robots; Employment |
| JEL: | J24 O3 O4 |
| Date: | 2019–12–18 |
| URL: | http://d.repec.org/n?u=RePEc:spo:wpmain:info:hdl:2441/7n49nkmngd8448a5ts5gt5ade0&r=all |
| By: | Nicolas Woloszko |
| Abstract: | This paper introduces the OECD Weekly Tracker of economic activity for 46 OECD and G20 countries using Google Trends search data. The Tracker performs well in pseudo-real time simulations including around the COVID-19 crisis. The underlying model adds to the previous Google Trends literature in two respects: (1) the data are adjusted for common long-term bias and (2) the data include variables based on both Google Search categories and topics (the latter being a collection of related keywords), thus further exploiting the potential of Google Trends. The paper highlights the predictive power of specific topics, including "bankruptcies", "economic crisis", "investment", "luggage" and "mortgage". Calibration is performed using a neural network that captures non-linear patterns, which are shown to be consistent with economic intuition using machine learning interpretability tools ("Shapley values"). The tracker sheds light on the recent downturn and the dynamics of the rebound, and provides evidence about lasting shifts in consumption patterns. |
| Keywords: | COVID-19, Google Trends, high-frequency, interpretability, machine learning, nowcasting |
| JEL: | C45 C53 C55 E37 |
| Date: | 2020–12–01 |
| URL: | http://d.repec.org/n?u=RePEc:oec:ecoaaa:1634-en&r=all |
| By: | Tamer Boyaci, (ESMT European School of Management and Technology); Caner Canyakmaz, (ESMT European School of Management and Technology); Francis de Véricourt, (ESMT European School of Management and Technology) |
| Abstract: | The rapid adoption of AI technologies by many organizations has recently raised concerns that AI may eventually replace humans in certain tasks. In fact, when used in collaboration, machines can significantly enhance the complementary strengths of humans. Indeed, because of their immense computing power, machines can perform specific tasks with incredible accuracy. In contrast, human decision-makers (DM) are flexible and adaptive but constrained by their limited cognitive capacity. This paper investigates how machine-based predictions may affect the decision process and outcomes of a human DM. We study the impact of these predictions on decision accuracy, the propensity and nature of decision errors as well as the DM's cognitive efforts. To account for both flexibility and limited cognitive capacity, we model the human decision-making process in a rational inattention framework. In this setup, the machine provides the DM with accurate but sometimes incomplete information at no cognitive cost. We fully characterize the impact of machine input on the human decision process in this framework. We show that machine input always improves the overall accuracy of human decisions, but may nonetheless increase the propensity of certain types of errors (such as false positives). The machine can also induce the human to exert more cognitive efforts, even though its input is highly accurate. Interestingly, this happens when the DM is most cognitively constrained, for instance, because of time pressure or multitasking. Synthesizing these results, we pinpoint the decision environments in which human-machine collaboration is likely to be most beneficial. |
| Keywords: | Machine-learning, rational inattention, human-machine collaboration, cognitive effort |
| Date: | 2020–11–30 |
| URL: | http://d.repec.org/n?u=RePEc:esm:wpaper:esmt-20-02&r=all |
| By: | Michael Allan Ribers; Hannes Ullrich |
| Abstract: | Human decision-making differs due to variation in both incentives and available information. This generates substantial challenges for the evaluation of whether and how machine learning predictions can improve decision outcomes. We propose a framework that incorporates machine learning on large-scale administrative data into a choice model featuring heterogeneity in decision maker payoff functions and predictive skill. We apply our framework to the major health policy problem of improving the efficiency in antibiotic prescribing in primary care, one of the leading causes of antibiotic resistance. Our analysis reveals large variation in physicians’ skill to diagnose bacterial infections and in how physicians trade off the externality inherent in antibiotic use against its curative benefit. Counterfactual policy simulations show the combination of machine learning predictions with physician diagnostic skill achieves a 25.4 percent reduction in prescribing and the largest welfare gains compared to alternative policies for estimated as well as plausible hypothetical weights on the antibiotic resistance externality. |
| Keywords: | prediction policy, expert decision-making, machine learning, antibiotic prescribing |
| JEL: | C10 C55 I11 I18 Q28 |
| Date: | 2020 |
| URL: | http://d.repec.org/n?u=RePEc:ces:ceswps:_8702&r=all |
| By: | Michael Allan Ribers; Hannes Ullrich |
| Abstract: | Human decision-making differs due to variation in both incentives and available information. This constitutes a substantial challenge for the evaluation of whether and how machine learning predictions can improve decision outcomes. We propose a framework that incorporates machine learning on large-scale data into a choice model featuring heterogeneity in decision maker payoff functions and predictive skill. We apply this framework to the major health policy problem of improving the efficiency in antibiotic prescribing in primary care, one of the leading causes of antibiotic resistance. Our analysis reveals large variation in physicians' skill to diagnose bacterial infections and in how physicians trade off the externality inherent in antibiotic use against its curative benefit. Counterfactual policy simulations show that the combination of machine learning predictions with physician diagnostic skill results in a 25.4 percent reduction in prescribing and achieves the largest welfare gains compared to alternative policies for both estimated physician as well as conservative social planner preference weights on the antibiotic resistance externality. |
| Date: | 2020–11 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2011.11017&r=all |
| By: | Kitova, Olga; Dyakonova, Ludmila; Savinova, Victoria |
| Abstract: | The article describes a system of hybrid models ‘SGM Horizon’ as intellectual forecasting information system. The system of forecasting models includes a set of regression models and an expandable set of intelligent models, including artificial neural networks, decision trees, etc. Regression models include systems of regression equations that describe the behavior of forecast indicators of the development of the Russian economy in the system of national accounts. The functioning of the system of equations is determined by scenario conditions set by expert. For those indicators whose forecasts do not meet the requirements of quality and accuracy, intelligent models based on machine learning are used. Using the ‘SHM Horizon’ tools, predictive calculations were performed for a system of 30 indicators of the social sphere of the City of Moscow using hybrid models, and for8 indicators a significant increase in the quality and accuracy of the forecast was achieved with artificial neural network models. The process of models building requires considerable time, in this regard, the authors see the further development of the system in the application of the multi-criteria ranking method. |
| Keywords: | Regional economics, Forecasting, Socio-economic indicators, Hybrid models, Machine learning, Neural networks, Decision trees |
| JEL: | C40 C45 |
| Date: | 2020–07–24 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:104234&r=all |
| By: | Dario Sansone; Anna Zhu |
| Abstract: | Using novel nation-wide social security data combined with machine learning tools, we develop predictive models of income support receipt intensities for any payment enrolee in the Australian social security system between 2014 and 2018. We show that off-the-shelf machine learning algorithms can significantly improve predictive accuracy compared to simpler heuristic models or early warning systems currently in use. Specifically, the former predicts the proportion of time individuals will be on income support in the next four years with greater accuracy, by a magnitude of at least 22% (14 percentage points increase in the R2), compared to the latter. This gain can be achieved at little extra cost to practitioners since it uses data currently available to them. Consequently, our machine learning algorithms can improve the detection of long-term income support recipients accruing a welfare cost nearly AUD 1 billion higher than individuals identified in the current system. |
| Date: | 2020–11 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2011.12057&r=all |
| By: | CERQUA, AUGUSTO; LETTA, MARCO |
| Abstract: | Impact evaluations of the microeconomic effects of the COVID-19 upheavals are essential but nonetheless highly challenging. Data scarcity and identification issues due to the ubiquitous nature of the exogenous shock account for the current dearth of counterfactual studies. To fill this gap, we combine up-to-date quarterly local labor markets (LLMs) data, collected from the Business Register kept by the Italian Chamber of Commerce, with the machine learning control method for counterfactual building. This allows us to shed light on the pandemic impact on the local economic dynamics of one of the hardest-hit countries, Italy. We document that the shock has already caused a moderate drop in employment and firm exit and an abrupt decrease in firm entry at the country level. More importantly, these effects have been dramatically uneven across the Italian territory and spatially uncorrelated with the epidemiological pattern of the first wave. We then use the estimated individual treatment effects to investigate the main predictors of such unbalanced patterns, finding that the heterogeneity of impacts is primarily associated with interactions among the exposure of economic activities to high social aggregation risks and pre-existing labor market fragilities. These results call for immediate place- and sector-based policy responses. |
| Keywords: | impact evaluation; counterfactual approach; machine learning; local labor markets; firms; COVID-19; Italy |
| JEL: | C53 D22 E24 R12 |
| Date: | 2020–11–26 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:104404&r=all |
| By: | Naftali Cohen; Srijan Sood; Zhen Zeng; Tucker Balch; Manuela Veloso |
| Abstract: | Time series forecasting is essential for agents to make decisions in many domains. Existing models rely on classical statistical methods to predict future values based on previously observed numerical information. Yet, practitioners often rely on visualizations such as charts and plots to reason about their predictions. Inspired by the end-users, we re-imagine the topic by creating a framework to produce visual forecasts, similar to the way humans intuitively do. In this work, we take a novel approach by leveraging advances in deep learning to extend the field of time series forecasting to a visual setting. We do this by transforming the numerical analysis problem into the computer vision domain. Using visualizations of time series data as input, we train a convolutional autoencoder to produce corresponding visual forecasts. We examine various synthetic and real datasets with diverse degrees of complexity. Our experiments show that visual forecasting is effective for cyclic data but somewhat less for irregular data such as stock price. Importantly, we find the proposed visual forecasting method to outperform numerical baselines. We attribute the success of the visual forecasting approach to the fact that we convert the continuous numerical regression problem into a discrete domain with quantization of the continuous target signal into pixel space. |
| Date: | 2020–11 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2011.09052&r=all |
| By: | Sriubaite, I.; Harris, A.; Jones, A.M.; Gabbe, B. |
| Abstract: | We perform a prediction analysis using methods of supervised machine learning on a set of outcomes that measure economic consequences of road traffic injuries. We employ several parametric and non-parametric algorithms including regularised regressions, decision trees and random forests to model statistically challenging empirical distributions and identify the key risk groups. In addition to a traditional outcome of interest – health care costs – we predict net monetary benefits from treatment, and productivity losses measured by the probability to return to work after the injury. Using the predictions of each selected algorithm we construct an ensemble machine learning algorithm - the Super Learner algorithm. Our findings demonstrate that the Super Learner is effective and performs best in predicting all outcomes. Further analysis of predictions by different groups of patients play an important role in the understanding of key risk factors for higher costs and poorer outcomes and offers a deeper understanding of risk in the health care sector. |
| Keywords: | Prediction and classification; super learner; machine learning; healthcare costs; patient outcomes; road traffic injuries; |
| JEL: | I11 I19 C14 C38 C53 |
| Date: | 2020–11 |
| URL: | http://d.repec.org/n?u=RePEc:yor:hectdg:20/20&r=all |
| By: | Katleho Makatjane; Roscoe van Wyk |
| Abstract: | Exchange rate volatility is said to exemplify the economic health of a country. Exchange rate break points (known as structural breaks) have a momentous impact on the macroeconomy of a country. Nonetheless, this country study makes use of both unsupervised and supervised machine learning algorithms to classify structural changes as regime shifts in real exchange rates in South Africa. Weekly data for the period January 2003-June 2020 are used. |
| Keywords: | machine learning, Markov switching, Principal component analysis, South Africa |
| Date: | 2020 |
| URL: | http://d.repec.org/n?u=RePEc:unu:wpaper:wp-2020-162&r=all |
| By: | Balaji, S. J.; Babu, Suresh Chandra; Pal, Suresh |
| Abstract: | Policy-making processes in developing countries often continue to operate devoid of evidence. In this study, we explore the research-policy linkages between the agroeconomic research system (AERS) and the agricultural policy system (APS) in India. Specifically, we examine questions directed to the Ministry of Agriculture and Farmers’ Welfare in the two houses of the national parliament—the House of the People (Lok Sabha) and the Council of States (Rajya Sabha)—and filter them for key issues that confront the APS. In addition, using the list of research articles published in two major national agricultural economics journals, we examine the alignment of the AERS toward addressing pressing policy issues. We use 6,465 questions raised by elected representatives in the parliamentary houses and 377 research articles, both during the period 2014–2018. We use machine learning techniques for information retrieval because the required information is hidden as non-numerical text. Using tag clouds (lists of words by frequency), we identify key divergences between the concerns of the APS and the research focus of the AERS, and explore their linkages. To broaden our understanding, we employ latent Dirichlet allocation, a natural language processing technique that identifies crucial issues and automates their classification under appropriate clusters, to examine synergies between the research and policy systems. Results show remarkable alignment between the AERS and the APS, invalidating the two-communities hypothesis. We identify persistent issues in the policy domain that require the support of the research system, as well as potential areas for research system realignment. |
| Keywords: | INDIA; SOUTH ASIA; ASIA; agricultural economics; machine learning; agricultural research; agricultural policies; policies; farmers; research-policy linkages; latent Dirichlet allocation; policy systems; agroeconomic research |
| Date: | 2020 |
| URL: | http://d.repec.org/n?u=RePEc:fpr:ifprid:1970&r=all |
| By: | Marc Sabate-Vidales; David \v{S}i\v{s}ka; Lukasz Szpruch |
| Abstract: | Using a combination of recurrent neural networks and signature methods from the rough paths theory we design efficient algorithms for solving parametric families of path dependent partial differential equations (PPDEs) that arise in pricing and hedging of path-dependent derivatives or from use of non-Markovian model, such as rough volatility models in Jacquier and Oumgari, 2019. The solutions of PPDEs are functions of time, a continuous path (the asset price history) and model parameters. As the domain of the solution is infinite dimensional many recently developed deep learning techniques for solving PDEs do not apply. Similarly as in Vidales et al. 2018, we identify the objective function used to learn the PPDE by using martingale representation theorem. As a result we can de-bias and provide confidence intervals for then neural network-based algorithm. We validate our algorithm using classical models for pricing lookback and auto-callable options and report errors for approximating both prices and hedging strategies. |
| Date: | 2020–11 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2011.10630&r=all |
| By: | Hamza Saad |
| Abstract: | Traditional statistical and measurements are unable to solve all industrial data in the right way and appropriate time. Open markets mean the customers are increased, and production must increase to provide all customer requirements. Nowadays, large data generated daily from different production processes and traditional statistical or limited measurements are not enough to handle all daily data. Improve production and quality need to analyze data and extract the important information about the process how to improve. Data mining applied successfully in the industrial processes and some algorithms such as mining association rules, and decision tree recorded high professional results in different industrial and production fields. The study applied seven algorithms to analyze production data and extract the best result and algorithm in the industry field. KNN, Tree, SVM, Random Forests, ANN, Na\"ive Bayes, and AdaBoost applied to classify data based on three attributes without neglect any variables whether this variable is numerical or categorical. The best results of accuracy and area under the curve (ROC) obtained from Decision tree and its ensemble algorithms (Random Forest and AdaBoost). Thus, a decision tree is an appropriate algorithm to handle manufacturing and production data especially this algorithm can handle numerical and categorical data. |
| Date: | 2020–11 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2011.12348&r=all |
| By: | - |
| Abstract: | The Internet & Jurisdiction and ECLAC Regional Status Report 2020 is Latin America and the Caribbean’s first comprehensive exercise in mapping the different policy trends relating to the cross-border nature of the Internet and the way this affects different stakeholders such as governments, companies and civil society. How might differing regional and national regulations create barriers to cross-border e-commerce and investment in digital markets? What economic and social benefits could be realized by harmonizing frameworks throughout the region? A better understanding of this situation is vital to efforts to foster investor confidence, promote innovation and economic diversification, create greater trust in e-commerce and boost a market of more than 600 million people, while opening up opportunities for businesses, most particularly small and medium-sized enterprises. Conversely, uncoordinated action by a wide range of actors and initiatives risks hampering the digitalization of economies, governments and societies. It is to help policymakers navigate the challenges ahead and to mutualize knowledge that the Internet & Jurisdiction Policy Network, in coordination with the Economic Commission for Latin America and the Caribbean (ECLAC), is presenting the Internet & Jurisdiction and ECLAC Regional Status Report 2020. |
| Keywords: | INTERNET, TECNOLOGIA DE LA INFORMACION, TECNOLOGIA DE LAS COMUNICACIONES, ASPECTOS ECONOMICOS, ASPECTOS SOCIALES, ASPECTOS JURIDICOS, LIBERTAD DE EXPRESION, REDES SOCIALES, SITIOS WEB, DERECHO A LA VIDA PRIVADA, REDES DE INFORMACION, SEGURIDAD DE DATOS COMPUTARIZADOS, TECNOLOGIA DIGITAL, COMERCIO ELECTRONICO, TRANSMISION DE DATOS, NOMBRES DE DOMINIO DE INTERNET, PEQUEÑAS EMPRESAS, EMPRESAS MEDIANAS, INTERNET, INFORMATION TECHNOLOGY, COMMUNICATION TECHNOLOGY, ECONOMIC ASPECTS, SOCIAL ASPECTS, LEGAL ASPECTS, FREEDOM OF EXPRESSION, SOCIAL MEDIA, WEBSITES, RIGHT TO PRIVACY, INFORMATION NETWORKS, COMPUTER SECURITY, DIGITAL TECHNOLOGY, ELECTRONIC COMMERCE, DATA TRANSMISSION, INTERNET DOMAIN NAMES, SMALL ENTERPRISES, MEDIUM ENTERPRISES |
| Date: | 2020–11–24 |
| URL: | http://d.repec.org/n?u=RePEc:ecr:col022:46421&r=all |
| By: | Xiao-Yang Liu; Hongyang Yang; Qian Chen; Runjia Zhang; Liuqing Yang; Bowen Xiao; Christina Dan Wang |
| Abstract: | As deep reinforcement learning (DRL) has been recognized as an effective approach in quantitative finance, getting hands-on experiences is attractive to beginners. However, to train a practical DRL trading agent that decides where to trade, at what price, and what quantity involves error-prone and arduous development and debugging. In this paper, we introduce a DRL library FinRL that facilitates beginners to expose themselves to quantitative finance and to develop their own stock trading strategies. Along with easily-reproducible tutorials, FinRL library allows users to streamline their own developments and to compare with existing schemes easily. Within FinRL, virtual environments are configured with stock market datasets, trading agents are trained with neural networks, and extensive backtesting is analyzed via trading performance. Moreover, it incorporates important trading constraints such as transaction cost, market liquidity and the investor's degree of risk-aversion. FinRL is featured with completeness, hands-on tutorial and reproducibility that favors beginners: (i) at multiple levels of time granularity, FinRL simulates trading environments across various stock markets, including NASDAQ-100, DJIA, S&P 500, HSI, SSE 50, and CSI 300; (ii) organized in a layered architecture with modular structure, FinRL provides fine-tuned state-of-the-art DRL algorithms (DQN, DDPG, PPO, SAC, A2C, TD3, etc.), commonly-used reward functions and standard evaluation baselines to alleviate the debugging workloads and promote the reproducibility, and (iii) being highly extendable, FinRL reserves a complete set of user-import interfaces. Furthermore, we incorporated three application demonstrations, namely single stock trading, multiple stock trading, and portfolio allocation. The FinRL library will be available on Github at link https://github.com/AI4Finance-LLC/FinRL- Library. |
| Date: | 2020–11 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2011.09607&r=all |
| By: | Cevat Giray Aksoy (King’s College London); Panu Poutvaara (University of Munich and Ifo); Felicitas Schikora (Freie Universität Berlin and DIW Berlin) |
| Abstract: | We study the causal effect of local labor market conditions and attitudes towards immigrants at the time of arrival on refugees’ multi-dimensional integration outcomes (economic, linguistic, navigational, political, psychological, and social). Using a unique dataset on refugees, we leverage a centralized allocation policy in Germany where refugees were exogenously assigned to live in specific counties. We find that high initial local unemployment negatively affects refugees’ economic and social integration: they are less likely to be in education or employment and they earn less. We also show that favorable attitudes towards immigrants promote refugees’ economic and social integration. The results suggest that attitudes toward immigrants are as important as local unemployment rates in shaping refugees’ integration outcomes. Using a machine learning classifier algorithm, we find that our results are driven by older people and those with secondary or tertiary education. Our findings highlight the importance of both initial economic and social conditions for facilitating refugee integration, and have implications for the design of centralized allocation policies. |
| Keywords: | International migration, refugees, integration, allocation policy |
| JEL: | F22 J15 J24 |
| Date: | 2020–12 |
| URL: | http://d.repec.org/n?u=RePEc:crm:wpaper:2024&r=all |
| By: | Timothy DeStefano; Nick Johnstone; Richard Kneller; Jonathan Timmis |
| Abstract: | Cloud computing presents a significant change in the way firms access digital technology and enables data-driven business models. Now, firms can acquire their storage, processing and software needs as a cloud computing service rather than making upfront fixed cost investments in capital. Yet, policies that encourage digital diffusion are still targeted towards investment in physical IT capital. This paper exploits a UK tax incentive for capital investment to examine firm adoption of cloud computing and big data analytics. Using a quasi-natural experimental approach our empirical results show that the policy increased investment in IT capital and hardware as one would expect; but it reduced the adoption of cloud and big data analytics. The adverse effects of the policy on cloud and big data adoption are particularly pronounced for small firms. |
| Keywords: | ICT, cloud computing, big data analytics |
| Date: | 2020 |
| URL: | http://d.repec.org/n?u=RePEc:not:notgep:2020-06&r=all |
| By: | Yifan Yu; Shan Huang; Yuchen Liu; Yong Tan |
| Abstract: | Social media-transmitted online information, particularly content that is emotionally charged, shapes our thoughts and actions. In this study, we incorporate social network theories and analyses to investigate how emotions shape online content diffusion, using a computational approach. We rigorously quantify and characterize the structural properties of diffusion cascades, in which more than six million unique individuals transmitted 387,486 articles in a massive-scale online social network, WeChat. We detected the degree of eight discrete emotions (i.e., surprise, joy, anticipation, love, anxiety, sadness, anger, and disgust) embedded in these articles, using a newly generated domain-specific and up-to-date emotion lexicon. We found that articles with a higher degree of anxiety and love reached a larger number of individuals and diffused more deeply, broadly, and virally, whereas sadness had the opposite effect. Age and network degree of the individuals who transmitted an article and, in particular, the social ties between senders and receivers, significantly mediated how emotions affect article diffusion. These findings offer valuable insight into how emotions facilitate or hinder information spread through social networks and how people receive and transmit online content that induces various emotions. |
| Date: | 2020–11 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2011.09003&r=all |
| By: | Matthieu Stigler; David Lobell |
| Abstract: | Index insurance has been promoted as a promising solution for reducing agricultural risk compared to traditional farm-based insurance. By linking payouts to a regional factor instead of individual loss, index insurance reduces monitoring costs, and alleviates the problems of moral hazard and adverse selection. Despite its theoretical appeal, demand for index insurance has remained low in many developing countries, triggering a debate on the causes of the low uptake. Surprisingly, there has been little discussion in this debate about the experience in the United States. The US is an unique case as both farm-based and index-based products have been available for more than two decades. Furthermore, the number of insurance zones is very large, allowing interesting comparisons over space. As in developing countries, the adoption of index insurance is rather low -- less than than 5\% of insured acreage. Does this mean that we should give up on index insurance? In this paper, we investigate the low take-up of index insurance in the US leveraging a field-level dataset for corn and soybean obtained from satellite predictions. While previous studies were based either on county aggregates or on relatively small farm-level dataset, our satellite-derived data gives us a very large number of fields (close to 1.8 million) comprised within a large number of index zones (600) observed over 20 years. To evaluate the suitability of index insurance, we run a large-scale simulation comparing the benefits of both insurance schemes using a new measure of farm-equivalent risk coverage of index insurance. We make two main contributions. First, we show that in our simulations, demand for index insurance is unexpectedly high, at about 30\% to 40\% of total demand. This result is robust to relaxing several assumptions of the model and to using prospect theory instead of expected utility. |
| Date: | 2020–11 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2011.12544&r=all |
| By: | Davillas, A.; Jones, A.M. |
| Abstract: | Using monthly data from the Understanding Society (UKHLS) COVID-19 Survey we analyse the evolution of unmet need and assess how the UK health care system performed against the norm of horizontal equity in health care access during the first wave of COVID-19 wave. Unmet need was most evident for hospital care, and less pronounced for primary health services (medical helplines, GP consultations, local pharmacist advice, over the counter medications and prescriptions). Despite this, there is no evidence that horizontal equity, with respect to income, was violated for NHS hospital outpatient and inpatient care during the first wave of the pandemic. There is evidence of pro-rich inequities in access to GP consultations, prescriptions and medical helplines at the peak of the first wave, but these were eliminated as the pandemic progressed. There are persistent pro-rich inequities for services that relate to individuals’ ability to pay (over the counter medications and advice from the local pharmacist). |
| Keywords: | Prediction and classification; super learner; machine learning; healthcare costs; patient outcomes; road traffic injuries; |
| JEL: | C1 D63 I14 |
| Date: | 2020–12 |
| URL: | http://d.repec.org/n?u=RePEc:yor:hectdg:20/21&r=all |