nep-big New Economics Papers
on Big Data
Issue of 2020‒06‒15
25 papers chosen by
Tom Coupé
University of Canterbury

  1. Making text count: economic forecasting using newspaper text By Kalamara, Eleni; Turrell, Arthur; Redl, Chris; Kapetanios, George; Kapadia, Sujit
  2. The Evolution of Inequality of Opportunity in Germany: A Machine Learning Approach By Paolo BRUNORI,; Guido NEIDHOEFER
  3. Comparison of tree-based models performance in prediction of marketing campaign results using Explainable Artificial Intelligence tools By Marcin Chlebus; Zuzanna Osika
  4. Machine Learning, the Treasury Yield Curve and Recession Forecasting By Michael Puglia; Adam Tucker
  5. Forecasting gasoline prices with mixed random forest error correction models By Wang, Dandan; Escribano Saez, Alvaro
  6. Applications of Machine Learning to Estimating the Sizes and Market Impact of Hidden Orders in the BRICS Financial Markets By Maake, Witness; Van Zyl, Terence
  7. Using Machine Learning to Forecast Future Earnings By Xinyue Cui; Zhaoyu Xu; Yue Zhou
  8. Mortality data correction in the absence of monthly fertility records By Alexandre Boumezoued; Amal Elfassihi
  9. Forecast combinations in machine learning By Qiu, Yue; Xie, Tian; Yu, Jun
  10. Priority to unemployed immigrants? A causal machine learning evaluation of training in Belgium By Bart Cockx; Michael Lechner; Joost Bollens
  11. Machine learning time series regressions with an application to nowcasting By Andrii Babii; Eric Ghysels; Jonas Striaukas
  12. Temporal mixture ensemble models for intraday volume forecasting in cryptocurrency exchange markets By Nino Antulov-Fantulin; Tian Guo; Fabrizio Lillo
  13. Daily Middle-Term Probabilistic Forecasting of Power Consumption in North-East England By Roberto Baviera; Giuseppe Messuti
  14. Nowcasting Tail Risks to Economic Activity with Many Indicators By Andrea Carriero; Todd E. Clark; Marcellino Massimiliano
  15. Breiman's "Two Cultures" Revisited and Reconciled By Subhadeep; Mukhopadhyay; Kaijun Wang
  16. Understanding the Sources of Earnings Losses After Job Displacement: A Machine-Learning Approach By Andreas Gulyas; Krzysztof Pytka
  17. Well-being through the Lens of the Internet By Yann Algan; Fabrice Murtin; Elizabeth Beasley; Kazuhito Higad; Claudia Senik
  18. The Impact of Artificial Intelligence on Individual Performance: Exploring the Fit between Task, Data, and Technology By Sturm, Timo; Peters, Felix
  19. Towards digital globalization and the covid-19 challenge By Schilirò, Daniele
  20. Random forest versus logit models: which offers better early warning of fiscal stress? By Jarmulska, Barbara
  21. Inference Using Simulated Neural Moments By Michael Creel
  22. Consumer Panic in the COVID-19 Pandemic By Michael Keane; Timothy Neal
  23. Exports vs. Investment: How Public Discourse Shapes Support for External Imbalances * By Federico Maria Ferrara; Jörg Haas; Andrew Peterson; Thomas Sattler
  24. The "merchantable gratuitousness" platforms and the Free Digital Labor controversy: a new form of exploitation? By Carlo Vercellone
  25. Presence and mobility of the population during Covid-19 outbreak and lockdown in Italy By Beria, Paolo; Lunkar, Vardhman

  1. By: Kalamara, Eleni (King’s College London); Turrell, Arthur (Bank of England); Redl, Chris (International Monetary Fund); Kapetanios, George (King’s College London); Kapadia, Sujit (European Central Bank)
    Abstract: We consider the best way to extract timely signals from newspaper text and use them to forecast macroeconomic variables using three popular UK newspapers that collectively represent UK newspaper readership in terms of political perspective and editorial style. We find that newspaper text can improve economic forecasts both in absolute and marginal terms. We introduce a powerful new method of incorporating text information in forecasts that combines counts of terms with supervised machine learning techniques. This method improves forecasts of macroeconomic variables including GDP, inflation, and unemployment, including relative to existing text-based methods. Forecast improvements occur when it matters most, during stressed periods.
    Keywords: Text; forecasting; machine learning
    JEL: C55 J42
    Date: 2020–05–22
  2. By: Paolo BRUNORI,; Guido NEIDHOEFER
    Abstract: We show that measures of inequality of opportunity fully consistent with Roemer (1998)'s inequality of opportunity theory can be straightforwardly estimated adopting a machine learning approach. Following Roemer, inequality of opportunity is generally defined as inequality between individuals exerting the same degree of effort but characterized by different exogenous circumstances. Due to difficulties of measuring effort, most empirical contributions so far identified groups of individuals sharing same circumstances, and then measured inequality of opportunity as between-group inequality, without considering the effort exerted. Our approach uses regression trees to identify groups of individuals characterized by identical circumstances, and a polynomial approximation to estimate the degree of effort exerted. To apply our method, we take advantage of information contained in 25 waves of the German Socio-Economic Panel. We show that in Germany inequality of opportunity declined immediately after the reunification, surged in the first decade of the century, and slightly declined again after 2010. The level of estimated unequal opportunity is today just above the level recorded in 1992.
    Keywords: Inequality, Opportunity, SOEP, Germany
    JEL: D63 D30 D31
    Date: 2020
  3. By: Marcin Chlebus (Faculty of Economic Sciences, University of Warsaw); Zuzanna Osika (Faculty of Economic Sciences, University of Warsaw)
    Abstract: The research uses tree-based models to predict the success of telemarketing campaign of Portuguese bank. The Portuguese bank dataset was used in the past in different researches with different models to predict the success of campaign. We propose to use boosting algorithms, which have not been used before to predict the response for the campaign and to use Explainable AI (XAI) methods to evaluate model’s performance. The paper tries to examine whether 1) complex boosting algorithms perform better and 2) XAI tools are better indicators of models’ performance than commonly used discriminatory power’s measures like AUC. Portuguese bank telemarketing dataset was used with five machine learning algorithms, namely Random Forest (RF), AdaBoost, GBM, XGBoost and CatBoost, which were then later compared based on their AUC and XAI tools analysis – Permutated Variable Importance and Partial Dependency Profile. Two best performing models based on their AUC were XGBoost and CatBoost, with XGBoost having slightly higher AUC. Then, these models were examined using PDP and VI, which resulted in discovery of XGBoost potenitial overfitting and choosing CatBoost over XGBoost. The results show that new boosting models perform better than older models and that XAI tools could be helpful with models’ comparisons.
    Keywords: direct marketing, telemarketing, relationship marketing, data mining, machine learning, random forest, adaboost, gbm, catboost, xgboost, bank marketing, XAI, variable importance, partial dependency profile
    JEL: C25 C44 M31
    Date: 2020
  4. By: Michael Puglia; Adam Tucker
    Abstract: We use machine learning methods to examine the power of Treasury term spreads and other financial market and macroeconomic variables to forecast US recessions, vis-à-vis probit regression. In particular we propose a novel strategy for conducting cross-validation on classifiers trained with macro/financial panel data of low frequency and compare the results to those obtained from standard k-folds cross-validation. Consistent with the existing literature we find that, in the time series setting, forecast accuracy estimates derived from k-folds are biased optimistically, and cross-validation strategies which eliminate data "peeking" produce lower, and perhaps more realistic, estimates of forecast accuracy. More strikingly, we also document rank reversal of probit, Random Forest, XGBoost, LightGBM, neural network and support-vector machine classifier forecast performance over the two cross-validation methodologies. That is, while a k-folds cross-validation indicates tha t the forecast accuracy of tree methods dominates that of neural networks, which in turn dominates that of probit regression, the more conservative cross-validation strategy we propose indicates the exact opposite, and that probit regression should be preferred over machine learning methods, at least in the context of the present problem. This latter result stands in contrast to a growing body of literature demonstrating that machine learning methods outperform many alternative classification algorithms and we discuss some possible reasons for our result. We also discuss techniques for conducting statistical inference on machine learning classifiers using Cochrane's Q and McNemar's tests; and use the SHapley Additive exPlanations (SHAP) framework to decompose US recession forecasts and analyze feature importance across business cycles.
    Keywords: Shapley; Probit; XGBoost; Treasury yield curve; Neural network; LightGBM; Recession; Tree ensemble; Support-vector machine; Random forest
    JEL: C45 C53 E37
    Date: 2020–05–20
  5. By: Wang, Dandan; Escribano Saez, Alvaro
    Abstract: The use of machine learning (ML) models has been shown to have advantages over alternative and more traditional time series models in the presence of big data. One of the most successful ML forecasting procedures is the Random Forest (RF) machine learning algorithm. In this paper we propose a mixed RF approach for modeling departures from linearity, instead of starting with a completely nonlinear or nonparametric model. The methodology is applied to the weekly forecasts of gasoline prices that are cointegrated with international oil prices and exchange rates. The question of interest is whether gasoline prices react asymmetrically to increases in oil prices rather than to decreases in oil prices, the "rockets and feathers" hypothesis. In this literature most authors estimate parametric nonlinear error correction models using nonlinear least squares. Recent specifications for nonlinear error correction models include threshold autoregressive models (TAR), double threshold error correction models (ECM) or double threshold smooth transition autoregressive (STAR) models. In this paper, we describe the econometric methodology that combines linear dynamic autoregressive distributed lag (ARDL) models with cointegrated variables with added nonlinear components, or price asymmetries, estimated by the powerful tool of RF. We apply our mixed RF specification strategy to weekly prices of the Spanish gasoline market from 2010 to 2019. We show that the new mixed RF error correction model has important advantages over competing parametric and nonparametric models, in terms of the generality of model specification, estimation and forecasting.
    Keywords: Mixed Random Forest; Random Forest; Machine Learning; Nonlinear Error Correction; Cointegration; Rockets And Feathers Hypothesis; Forecasting Gasoline Prices
    JEL: L71 L13 D43 C53 C52 C24 B23
    Date: 2020–06–04
  6. By: Maake, Witness; Van Zyl, Terence
    Abstract: The research aims to investigate the role of hidden orders on the structure of the average market impact curves in the five BRICS financial markets. The concept of market impact is central to the implementation of cost-effective trading strategies during financial order executions. The literature of Lillo et al. (2003) is replicated using the data of visible orders from the five BRICS financial markets. We repeat the implementation of Lillo et al. (2003) to investigate the effect of hidden orders. We subsequently study the dynamics of hidden orders. The research applies machine learning to estimate the sizes of hidden orders. We revisit the methodology of Lillo et al. (2003) to compare the average market impact curves in which true hidden orders are added to visible orders to the average market impact curves in which hidden orders sizes are estimated via machine learning. The study discovers that : (1) hidden orders sizes could be uncovered via machine learning techniques such as Generalized Linear Models (GLM), Artificial Neural Networks (ANN), Support Vector Machines (SVM), and Random Forests (RF); and (2) there exist no set of market features that are consistently predictive of the sizes of hidden orders across different stocks. Artificial Neural Networks produce large R^2 and small MSE on the prediction of hidden orders of individual stocks across the five studied markets. Random Forests produce the ˆ most appropriate average price impact curves of visible and estimated hidden orders that are closest to the average market impact curves of visible and true hidden orders. In some markets, hidden orders produce a convex power-law far-right tail in contrast to visible orders which produce a concave power-law far-right tail. Hidden orders may affect the average price impact curves for orders of size less than the average order size; meanwhile, hidden orders may not affect the structure of the average price impact curves in other markets. The research implies ANN and RF as the recommended tools to uncover hidden orders.
    Keywords: Hidden Orders; Market Features; GLM; ANN; SVM; RF; Hidden Order Sizes; Market Impact; BRICS(Brazil, Russia, India, China, and South Africa)
    JEL: C4 C8 D4
    Date: 2020–02–28
  7. By: Xinyue Cui; Zhaoyu Xu; Yue Zhou
    Abstract: In this essay, we have comprehensively evaluated the feasibility and suitability of adopting the Machine Learning Models on the forecast of corporation fundamentals (i.e. the earnings), where the prediction results of our method have been thoroughly compared with both analysts' consensus estimation and traditional statistical models. As a result, our model has already been proved to be capable of serving as a favorable auxiliary tool for analysts to conduct better predictions on company fundamentals. Compared with previous traditional statistical models being widely adopted in the industry like Logistic Regression, our method has already achieved satisfactory advancement on both the prediction accuracy and speed. Meanwhile, we are also confident enough that there are still vast potentialities for this model to evolve, where we do hope that in the near future, the machine learning model could generate even better performances compared with professional analysts.
    Date: 2020–05
  8. By: Alexandre Boumezoued (R&D, Milliman, Paris - Milliman); Amal Elfassihi (R&D, Milliman, Paris - Milliman)
    Abstract: Since the conjecture of Richards (2008), the work by Cairns et al. (2016) and subsequent developments by Boumezoued (2016), Boumezoued et al. (2018) and Boumezoued et al. (2019), it has been acknowledged that observations from censuses have led to major problems of reliability in estimates of general population mortality rates as implemented in practice. These issues led to misinterpretation of some key mortality characteristics in the past decades, including "false cohort effects". To overcome these issues, the exposure estimates for a given country can be corrected by using monthly fertility records. However, in the absence of birth-by-month data, the recent developments are not applicable. Therefore, this paper explores new solutions regarding the construction of mortality tables in this context, based on machine learning techniques. As a main result, it is demonstrated that the new exposure models proposed in this paper allow to provide correction with high quality and to improve the fitting of stochastic mortality models without cohort component, as it is the case for the existing correction method based on monthly fertility data.
    Keywords: Human Mortality Database,cohort effect,anomalous mortality data,stochastic mortality models,machine learning,neural network
    Date: 2020–05–27
  9. By: Qiu, Yue (Shanghai University of International Business and Economics); Xie, Tian (Shanghai University of Finance and Economics); Yu, Jun (School of Economics, Singapore Management University)
    Abstract: This paper introduces novel methods to combine forecasts made by machine learning techniques. Machine learning methods have found many successful applications in predicting the response variable. However, they ignore model uncertainty when the relationship between the response variable and the predictors is nonlinear. To further improve the forecasting performance, we propose a general framework to combine multiple forecasts from machine learning techniques. Simulation studies show that the proposed machine-learning-based forecast combinations work well. In empirical applications to forecast key macroeconomic and financial variables, we find that the proposed methods can produce more accurate forecasts than individual machine learning techniques and the simple average method, later of which is known as hard to beat in the literature.
    Keywords: Model uncertainty; Machine learning; Nonlinearity; Forecast combinations
    JEL: C52 C53
    Date: 2020–05–11
  10. By: Bart Cockx (Department of Economics, Ghent University); Michael Lechner (Swiss Institute for Empirical Economic Research (SEW), University of St. Gallen); Joost Bollens (Vlaamse Dienst voor Arbeidsbemiddeling en Beroepsopleiding (VDAB))
    Abstract: Based on administrative data of unemployed in Belgium, we estimate the labour market effects of three training programmes at various aggregation levels using Modified Causal Forests, a causal machine learning estimator. While all programmes have positive effects after the lock-in period, we find substantial heterogeneity across programmes and unemployed. Simulations show that “black-box” rules that reassign unemployed to programmes that maximise estimated individual gains can considerably improve effectiveness: up to 20% more (less) time spent in (un)employment within a 30 months window. A shallow policy tree delivers a simple rule that realizes about 70% of this gain.
    Keywords: Policy evaluation, active labour market policy, causal machine learning, modified causal forest, conditional average treatment effects
    JEL: J68
    Date: 2020–05–04
  11. By: Andrii Babii; Eric Ghysels; Jonas Striaukas
    Abstract: This paper introduces structured machine learning regressions for high-dimensional time series data potentially sampled at different frequencies. The sparse-group LASSO estimator can take advantage of such time series data structures and outperforms the unstructured LASSO. We establish oracle inequalities for the sparse-group LASSO estimator within a framework that allows for the mixing processes and recognizes that the financial and the macroeconomic data may have heavier than exponential tails. An empirical application to nowcasting US GDP growth indicates that the estimator performs favorably compared to other alternatives and that the text data can be a useful addition to more traditional numerical data.
    Date: 2020–05
  12. By: Nino Antulov-Fantulin; Tian Guo; Fabrizio Lillo
    Abstract: We study the problem of the intraday short-term volume forecasting in cryptocurrency exchange markets. The predictions are built by using transaction and order book data from different markets where the exchange takes place. Methodologically, we propose a temporal mixture ensemble model, capable of adaptively exploiting, for the forecasting, different sources of data and providing a volume point estimate, as well as its uncertainty. We provide evidence of the outperformance of our model by comparing its outcomes with those obtained with different time series and machine learning methods. Finally, we discuss the difficulty of volume forecasting when large quantities are abruptly traded.
    Date: 2020–05
  13. By: Roberto Baviera; Giuseppe Messuti
    Abstract: Probabilistic forecasting of power consumption in a middle-term horizon (months to a year) is a main challenge in the energy sector. It plays a key role in planning future generation plants and transmission grid. We propose a new model that incorporates trend and seasonality features as in traditional time-series analysis and weather conditions as explicative variables in a parsimonious machine learning approach, known as Gaussian Process. Applying to a daily power consumption dataset in North East England provided by one of the largest energy suppliers, we obtain promising results in Out-of-Sample density forecasts up to one year, even using a small dataset, with only a two-year In-Sample data. In order to verify the quality of the achieved power consumption probabilistic forecast we consider measures that are common in the energy sector as pinball loss and Winkler score and backtesting conditional and unconditional tests, standard in the banking sector after the introduction of Basel II Accords.
    Date: 2020–05
  14. By: Andrea Carriero; Todd E. Clark; Marcellino Massimiliano
    Abstract: This paper focuses on tail risk nowcasts of economic activity, measured by GDP growth, with a potentially wide array of monthly and weekly information. We consider different models (Bayesian mixed frequency regressions with stochastic volatility, classical and Bayesian quantile regressions, quantile MIDAS regressions) and also different methods for data reduction (either the combination of forecasts from smaller models or forecasts from models that incorporate data reduction). The results show that classical and MIDAS quantile regressions perform very well in-sample but not out-of-sample, where the Bayesian mixed frequency and quantile regressions are generally clearly superior. Such a ranking of methods appears to be driven by substantial variability over time in the recursively estimated parameters in classical quantile regressions, while the use of priors in the Bayesian approaches reduces sampling variability and its effects on forecast accuracy. From an economic point of view, we find that the weekly information flow is quite useful in improving tail nowcasts of economic activity, with initial claims for unemployment insurance, stock prices, a term spread, a credit spread, and the Chicago Fed’s index of financial conditions emerging as particularly relevant indicators. Additional weekly indicators of economic activity do not improve historical forecast accuracy but do not harm it much, either.
    Keywords: mixed frequency; big data; pandemics; downside risk; forecasting; quantile regression.
    JEL: C53 E17 E37 F47
    Date: 2020–05–11
  15. By: Subhadeep (DEEP); Mukhopadhyay; Kaijun Wang
    Abstract: In a landmark paper published in 2001, Leo Breiman described the tense standoff between two cultures of data modeling: parametric statistical and algorithmic machine learning. The cultural division between these two statistical learning frameworks has been growing at a steady pace in recent years. What is the way forward? It has become blatantly obvious that this widening gap between "the two cultures" cannot be averted unless we find a way to blend them into a coherent whole. This article presents a solution by establishing a link between the two cultures. Through examples, we describe the challenges and potential gains of this new integrated statistical thinking.
    Date: 2020–05
  16. By: Andreas Gulyas; Krzysztof Pytka
    Abstract: We implement a generalized random forest (Athey et. al. 2019) to a difference-in-difference setting to identify substantial heterogeneity in earnings losses across displaced workers. Using administrative data from Austria over three decades we document that a quarter of workers face cumulative 11-year losses higher than 2 times their pre-displacement annual income, while almost 10% of individuals experience gains. Our methodology allows us to consider many competing theories of earnings losses. We find that the displacement firm's wage premia and the availability of well paying jobs in the local labor market are the two most important factors. This implies that earnings losses can be understood by mean reversion in firm wage premia and losses in match quality, rather than by a destruction of firm-specific human capital. We further show that 94% of the cyclicality of earnings losses is explained by compositional changes of displaced workers over the business cycle.
    Keywords: Job displacement, Earnings losses, Causal machine learning
    JEL: J3 J64 C55
    Date: 2020–05
  17. By: Yann Algan (Département d'économie); Fabrice Murtin (Economics department); Elizabeth Beasley; Kazuhito Higad (Organisation de Coopération et de Développement Économiques (OCDE)); Claudia Senik (Paris-Jourdan Sciences Economiques)
    Abstract: We build models to estimate well-being in the United States based on changes in the volume of internet searches for different words, obtained from the Google Trends website. The estimated well-being series are weighted combinations of word groups that are endogenously identified to fit the weekly subjective well-being measures collected by Gallup Analytics for the United States or the biannual measures for the 50 states. Our approach combines theoretical underpinnings and statistical analysis, and the model we construct successfully estimates the out-of-sample evolution of most subjective well-being measures at a one-year horizon. Our analysis suggests that internet search data can be a complement to traditional survey data to measure and analyze the well-being of a population at high frequency and local geographic levels. We highlight some factors that are important for well-being, as we find that internet searches associated with job search, civic participation, and healthy habits consistently predict well-being across several models, datasets and use cases during the period studied.
    Date: 2019–01
  18. By: Sturm, Timo; Peters, Felix
    Date: 2020–06–15
  19. By: Schilirò, Daniele
    Abstract: Digital globalization is a new form of globalization. It brings about relevant changes regarding how business is conducted across borders, the flow of economic benefits, and broadening participation. The growth of data and information related to digital globalization determines that global economic, financial, and social connections increase through digital platforms. Covid-19 is causing a shock to the global economy that is proving to be both faster and more severe than the 2008 global financial crisis. If the current crisis is pushing towards deglobalization, at the same time, Covid-19 represents a challenge for digital globalization and the digital transformation of economies. This research contribution examines the process towards digital globalization that is characterizing the world economy, its impact on businesses, consumers, and governments. It also discusses the challenge that the crisis caused by the coronavirus pandemic is posing to the globalization and digital transformation of economies.
    Keywords: digital globalization; fourth industrial revolution; artificial intelligence; Covid-19; deglobalization; digital innovation policy
    JEL: D20 D78 F60 L86 O31
    Date: 2020–04
  20. By: Jarmulska, Barbara
    Abstract: This study seeks to answer whether it is possible to design an early warning system framework that can signal the risk of fiscal stress in the near future, and what shape such a system should take. To do so, multiple models based on econometric logit and the random forest models are designed and compared. Using a dataset of 20 annual frequency variables pertaining to 43 advanced and emerging countries during 1992-2018, the results confirm the possibility of obtaining an effective model, which correctly predicts 70-80% of fiscal stress events and tranquil periods. The random forest-based early warning model outperforms logit models. While the random forest model is commonly understood to provide lower interpretability than logit models do, this study employs tools that can be used to provide useful information for understanding what is behind the black-box. These tools can provide information on the most important explanatory variables and on the shape of the relationship between these variables and the outcome classification. Thus, the study contributes to the discussion on the usefulness of machine learning methods in economics. JEL Classification: C40, C53, H63, G01
    Keywords: early warning system, interpretability of machine learning, predictive performance
    Date: 2020–05
  21. By: Michael Creel
    Abstract: This paper deals with Laplace type methods used with moment-based, simulation-based, econometric estimators. It shows that confidence intervals based upon quantiles of a tuned MCMC chain may have coverage which is far from the nominal level. It discusses how neural networks may be used to easily and automatically reduce the dimension of an initial set of moments to the minimum number of moments needed to maintain identification. When estimation and inference is based on the neural moments, which are the result of filtering moments through a trained neural net, confidence intervals have correct coverage in almost all cases, and departures from correct coverage are small.
    Keywords: neural networks, Laplace type estimators, simulation-based estimation
    JEL: C11 C12 C13 C45
    Date: 2020–06
  22. By: Michael Keane (School of Economics, UNSW Business School, UNSW Sydney); Timothy Neal (UNSW School of Economics)
    Abstract: We develop an econometric model of consumer panic (or panic buying) during the COVID-19 pandemic. Using Google search data on relevant keywords, we construct a daily index of consumer panic for 54 countries from January to late April 2020. We also assemble data on government policy announcements and daily COVID19 cases for all countries. Our panic index reveals widespread consumer panic in most countries, primarily during March, but with significant variation in the timing and severity of panic between countries. Our model implies that both domestic and world virus transmission contribute significantly to consumer panic. But government policy is also important: Internal movement restrictions - whether announced by domestic or foreign governments - generate substantial short run panic that largely vanishes in a week to ten days. Internal movement restrictions announced early in the pandemic generated more panic than those announced later. In contrast, travel restrictions and stimulus announcements had little impact on consumer panic.
    Keywords: Coronavirus, Hoarding, Consumption, Panel Data, Containment Policy
    Date: 2020–05
  23. By: Federico Maria Ferrara; Jörg Haas; Andrew Peterson (Georgia Institute of Technology [Atlanta]); Thomas Sattler (Research Unit Ecosystem Boundaries - WSL)
    Abstract: The economic imbalances that characterize the world economy have unequally distributed costs and benefits. This raises the question how countries could run long-term external surpluses and deficits without significant opposition against the policies that generate them. We show that economic ideas, and their emphasis in the public discourse , help to secure mass political support for these policies and the resulting economic outcomes. First, a content analysis of 32,000 newspaper articles finds that the dominant interpretations of economic outcomes in Australia and Germany concur with very distinct perspectives: external surpluses are seen as evidence of competitiveness in Germany, while external deficits are interpreted as evidence of attractiveness for investments in Australia. Second, survey experiments in both countries suggest that exposure to these diverging interpretations has a causal effect on citizens' support for their country's economic strategy. Economic ideas, thus, are crucial to provide the societal foundation of national growth strategies.
    Keywords: survey experiments,text analysis,trade,capital flows,ideas,public opinion
    Date: 2020–05–11
  24. By: Carlo Vercellone (CEMTI - Centre d'études sur les médias, les technologies et l'internationalisation - UP8 - Université Paris 8 Vincennes-Saint-Denis)
    Abstract: Cognitive capitalism and the informational revolution have gone hand in hand with a blurring of the boundaries between work and leisure time. At the heart of this evolution is the rise of platform capitalism, and in particular the "merchantable gratuitousness" platforms, like Google and Facebook, which have now taken first place in the ranking of world firms in terms of stock market capitalisation and profitability. Their profit model is based on the logic of multi-sided markets and combines the sale of online advertising and the extraction of user data. The users thus represent both the product and the producers of the main raw material underlying the organisation of the advertising market for merchantable gratuitousness platforms. This is called Free Digital Labor. This concept refers to the activity, apparently both gratuitous and self-governing, performed, often unknowingly, by a multitude of individuals on the internet for the benefit of big internet oligopolies and data industries. The Free Digital Labor thesis is highly controversial. It is often rejected by means of three main arguments: 1. it would be, not labor, but the intangible capital of the algorithm which, through an automated process, would extract and create most of the value; 2. the Free Digital Labor would escape not only the canonical criteria of wage labor, but also the anthropological definition of labor as a conscious and voluntary goal-oriented activity; 3. the free services proposed by the platforms would be remuneration in kind, excluding any relationship of exploitation. Our contribution aims to clarify the terms of this debate and to respond to these objections through a historical and theoretical analysis of the changes in the capital-labor relationship that occurred under the aegis of platform capitalism.
    Abstract: Le capitalisme cognitif et la révolution informationnelle sont allés de pair avec un effritement des frontières entre temps de travail et temps libre. Au centre de cette évolution se trouve l'essor du capitalisme des plateformes et notamment des plateformes de la « gratuité marchande » qui, à l'image de Google et Facebook, ont désormais conquis le premières places dans le classement des firmes mondiales en termes de capitalisation boursières et de rentabilité. Leur modèle de profit repose sur la logique des marchés multi-versants et associe la vente de la publicité en ligne et l'extraction des données des usagers. Ces derniers représentent ainsi à la fois le produit et les producteurs de la principale matière première à la base de l'organisation du marché publicitaire des plateformes de la gratuité marchande. C'est ce que l'on nomme le Free Digital Labor. Par ce concept on désigne le travail à la fois gratuit et apparemment libre qu'une multitude d'individus effectue sur internet, souvent inconsciemment, au profit des grands oligopoles du numérique et des data industries. La thèse du Free Digital Labor suscite une vive controverse. Elle est souvent rejetée au moyen de trois principaux arguments : ce serait, non le travail, mais le capital immatériel de l'algorithme qui, par un processus automatisé, extrairait et créerait l'essentiel de la valeur ; le Free Digital Labor échapperait non seulement aux critères canoniques du travail salarié, mais aussi à la définition anthropologique du travail vu comme une activité consciente et volontaire orientée vers un but ; les services gratuits offerts par les plateformes correspondraient à une rémunération en nature excluant tout rapport d'exploitation. Notre contribution se propose d'élucider les termes de ce débat et de répondre à ces objections par une analyse historique et théorique des mutations du rapport capital/travail intervenues sous l'égide du capitalisme des plateformes. ABSTRACT. Cognitive capitalism and the informational revolution have gone hand in hand with a blurring of the boundaries between work and leisure time. At the heart of this evolution is the rise of platform capitalism, and in particular the "merchantable gratuitousness" platforms, like Google and Facebook, which have now taken first place in the ranking of world firms in terms of stock market capitalisation and profitability. Their profit model is based on the logic of multi-sided markets and combines the sale of online advertising and the extraction of user data. The users thus represent both the product and the producers of the main raw material underlying the organisation of the advertising market for merchantable gratuitousness platforms. This is called Free Digital Labor. This concept refers to the activity, apparently both gratuitous and self-governing, performed, often unknowingly, by a multitude of individuals on the internet for the benefit of big internet oligopolies and data industries. The Free Digital Labor thesis is highly controversial. It is often rejected by means of three main arguments: 1. it would be, not labor, but the intangible capital of the algorithm which, through an automated process, would extract and create most of the value; 2. the Free Digital Labor would escape not only the canonical criteria of wage labor, but also the anthropological definition of labor as a conscious and voluntary goal-oriented activity; 3. the free services proposed by the platforms would be remuneration in kind, excluding any relationship of exploitation. Our contribution aims to clarify the terms of this debate and to respond to these objections through a historical and theoretical analysis of the changes in the capital-labor relationship that occurred under the aegis of platform capitalism.
    Keywords: Karl Marx KEYWORDS cognitive capitalism,Algorithmes,Free Digital Labor,platform capitalism,multi-sided markets,data,Algorithms,Free Digital Labour,Karl Marx,MOTS-CLES capitalisme cognitif,capitalisme des plateformes,marches multi-versants,données
    Date: 2020–04–13
  25. By: Beria, Paolo; Lunkar, Vardhman
    Abstract: The non-medical policies implemented to “flatten the curve” and to reduce the stress on the health system during the COVID-19 outbreak represents a critical event in the history of Italy. This kind of “lockdown” has left people stranded in their homes and, for some, out of their homes unable to return to their region of residence due to the disruptions in the mobility network. As a consequence, a vast scale of research is being performed to understand the patterns of mobility of people during the emergency. The availability of rich datasets has made it possible to quantify the dynamics of spatial distribution of people as a response to the strict measures. With the help of the data provided by the Facebook – Data for Good program, an effort is made to describe and to reason on the presence and of mobility patterns of the population at a regional and provincial scale during the lockdown. Our interpretation is that, initially, tourists left the country and later Italians abroad managed to return from abroad stabilising the population. Concerning internal mobility, it is evident that the earliest affected Regions see a higher number of stationary users in the initial days of the outbreak. On the other hand, the central and the southern regions does not display a positive relative change of staying home until the official lockdown is announced on the 9th of March, 2020. Before the stricter lockdown started there was not a significant exodus of people from the North to the rest of the country. To the contrary, a visible relocation of people occurred between the cities and their urban belts.
    Keywords: covid-19; outbreak; lockdown; mobility; Facebook data for good; location based mobility, big data; social network; Italy
    JEL: J61 R23 R41
    Date: 2020–06–05

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