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on Big Data |
| By: | Hannes Mueller; André Groeger; Jonathan Hersh; Andrea Matranga; Joan Serrat |
| Abstract: | Satellite imagery is becoming ubiquitous and is released with ever higher frequency. Research has demonstrated that Artificial Intelligence (AI) applied to satellite imagery holds promise for automated detection of war-related building destruction. While these results are promising, monitoring in real-world applications requires consistently high precision, especially when destruction is sparse and detecting destroyed buildings is equivalent to looking for a needle in a haystack. We demonstrate that exploiting the persistent nature of building destruction can substantially improve the training of automated destruction monitoring. We also propose an additional machine learning stage that leverages images of surrounding areas and multiple successive images of the same area which further improves detection significantly. By combining these steps, we construct an automated classification of building destruction which allows real-world applications and we illustrate this in the context of the Syrian civil war. |
| Keywords: | conflict, destruction, deep learning, remote sensing, Syria |
| JEL: | C45 C23 D74 |
| Date: | 2021–05 |
| URL: | http://d.repec.org/n?u=RePEc:bge:wpaper:1257&r= |
| By: | Victor Klockmann (Goethe University Frankfurt, Theodor-W.-Adorno-Platz 4, 60323 Frankfurt, Germany. Center for Humans & Machines, Max Planck Institute for Human Development, Lentzeallee 94, 14195 Berlin, Germany); Alicia von Schenk (Goethe University Frankfurt, Theodor-W.-Adorno-Platz 4, 60323 Frankfurt, Germany. Center for Humans & Machines, Max Planck Institute for Human Development, Lentzeallee 94, 14195 Berlin, Germany.); Marie Claire Villeval (Univ Lyon, CNRS, GATE UMR 5824, 93 Chemin des Mouilles, F-69130, Ecully, France. IZA, Bonn, Germany) |
| Abstract: | With Big Data, decisions made by machine learning algorithms depend on training data generated by many individuals. In an experiment, we identify the effect of varying individual responsibility for moral choices of an artificially intelligent algorithm. Across treatments, we manipulated the sources of training data and thus the impact of each individual’s decisions on the algorithm. Reducing or diffusing pivotality for algorithmic choices increased the share of selfish decisions. Once the generated training data exclusively affected others’ payoffs, individuals opted for more egalitarian payoff allocations. These results suggest that Big Data offers a “moral wiggle room” for selfish behavior. |
| Keywords: | Artificial Intelligence, Pivotality, Ethics, Externalities, Experiment |
| JEL: | C49 C91 D10 D63 D64 O33 |
| Date: | 2021 |
| URL: | http://d.repec.org/n?u=RePEc:gat:wpaper:2111&r= |
| By: | Anesti, Nikoleta (Bank of England); Kalamara, Eleni (King’s College London); Kapetanios, George (Bank of England) |
| Abstract: | By employing large panels of survey data for the UK economy, we aim at reviewing linear approaches for regularisation and dimension reduction combined with techniques from the machine learning literature, like Random Forests, Support Vector Regressions and Neural Networks for forecasting GDP growth at monthly frequency for horizons from one month up to two years ahead. We compare the predictive content of surveys with text based indicators from newspaper articles and a standard macroeconomic data set and extend the empirical evidence on the contribution of survey data against text indicators and more traditional macroeconomic time series in predicting economic activity. Among the linear models, the Ridge and the Partial Least Squares models report the largest gains consistently for most of the forecasting horizons, and for the non‑linear machine learning models, the SVR performs better at shorter horizons compared to the Neural Networks and Random Forest that seem to be more appropriate for longer‑term forecasting. Text based indicators appear to favour more the use of non‑linear models and the expansion of the information set with macroeconomic time series does not appear to add much more predictive power. The largest forecasting gains are overwhelmingly concentrated at the shorter horizons for the majority of models and datasets which provides further empirical support that non‑linear machine learning models appear to be more useful during the Great Recession. |
| Keywords: | Forecasting; survey data; text indicators; machine learning |
| JEL: | C53 C55 |
| Date: | 2021–05–28 |
| URL: | http://d.repec.org/n?u=RePEc:boe:boeewp:0923&r= |
| By: | Oren Barkan (Ariel University); Jonathan Benchimol (Bank of Israel); Itamar Caspi (Bank of Israel); Allon Hammer (Tel-Aviv University); Noam Koenigstein (Tel-Aviv University) |
| Abstract: | We present a hierarchical architecture based on Recurrent Neural Networks (RNNs) for predicting disaggregated inflation components of the Consumer Price Index (CPI). While the majority of existing research is focused on predicting headline inflation, many economic and financial institutions are interested in its partial disaggregated components. To this end, we developed the novel Hierarchical Recurrent Neural Network (HRNN) model, which utilizes information from higher levels in the CPI hierarchy to improve predictions at the more volatile lower levels. Based on a large dataset from the US CPI-U index, our evaluations indicate that the HRNN model significantly outperforms a vast array of well-known inflation prediction baselines. Our methodology and results provide additional forecasting measures and possibilities to policy and market makers on sectoral and component-specific prices. |
| Keywords: | Inflation forecasting, Disaggregated inflation, Consumer Price Index, Machine learning, Gated Recurrent Unit, Recurrent Neural Networks |
| JEL: | C45 C53 E31 E37 |
| Date: | 2021–03 |
| URL: | http://d.repec.org/n?u=RePEc:boi:wpaper:2021.06&r= |
| By: | Saide Aránzazu Salazar; Jaime Oliver Huidobro; Alvaro Ortiz; Tomasa Rodrigo; Ignacio Tamarit |
| Abstract: | El documento propone una nueva metodología que combina datos de operaciones con tarjeta e información de operaciones en efectivo en supermercados. Se estudian los cambios en patrones de consumo en relación con las variaciones de ingresos, que incluye la evolución del consumo de bienes y el uso de distintos canales de pago. This paper proposes a novel methodology combining high frequency card transaction data and point-of-sale (POS) data from cash operations registered at convenience stores to study changes in consumption patterns relative to variations in income, including changes in the items consumed and the payment channel. |
| Keywords: | e-Payments, Pagos electrónicos, cash, efectivo, Big Data, Big Data, machine learning, aprendizaje automático, consumption patterns, patrones de consumo, Mexico, México, Global, Global, Analysis with Big Data, Análisis con Big Data, Working Papers, Documento de Trabajo |
| JEL: | C32 D12 O17 O54 |
| Date: | 2021–05 |
| URL: | http://d.repec.org/n?u=RePEc:bbv:wpaper:2105&r= |
| By: | Victor Klockmann (Goethe University Frankfurt, Theodor-W.-Adorno-Platz 4, 60323 Frankfurt, Germany. Center for Humans & Machines, Max Planck Institute for Human Development, Lentzeallee 94, 14195 Berlin, Germany); Alicia von Schenk (Goethe University Frankfurt, Theodor-W.-Adorno-Platz 4, 60323 Frankfurt, Germany. Center for Humans & Machines, Max Planck Institute for Human Development, Lentzeallee 94, 14195 Berlin, Germany.); Marie Claire Villeval (Univ Lyon, CNRS, GATE UMR 5824, 93 Chemin des Mouilles, F-69130, Ecully, France. IZA, Bonn, Germany) |
| Abstract: | Humans shape the behavior of artificially intelligent algorithms. One mechanism is the training these systems receive through the passive observation of human behavior and the data we constantly generate. In a laboratory experiment with a sequence of dictator games, we let participants’ choices train an algorithm. Thereby, they create an externality on future decision making of an intelligent system that affects future participants. We test how information on training artificial intelligence affects the prosociality and selfishness of human behavior. We find that making individuals aware of the consequences of their training on the well-being of future generations changes behavior, but only when individuals bear the risk of being harmed themselves by future algorithmic choices. Only in that case, the externality of artificially intelligence training induces a significantly higher share of egalitarian decisions in the present. |
| Keywords: | Artificial Intelligence, Morality, Prosociality, Generations, Externalities |
| JEL: | C49 C91 D10 D62 D63 O33 |
| Date: | 2021 |
| URL: | http://d.repec.org/n?u=RePEc:gat:wpaper:2110&r= |
| By: | Pierre-Philippe Combes (Institut d'Études Politiques [IEP] - Paris, CNRS - Centre National de la Recherche Scientifique); Laurent Gobillon (PSE - Paris School of Economics - ENPC - École des Ponts ParisTech - ENS Paris - École normale supérieure - Paris - PSL - Université Paris sciences et lettres - UP1 - Université Paris 1 Panthéon-Sorbonne - CNRS - Centre National de la Recherche Scientifique - EHESS - École des hautes études en sciences sociales - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement, PJSE - Paris Jourdan Sciences Economiques - UP1 - Université Paris 1 Panthéon-Sorbonne - ENS Paris - École normale supérieure - Paris - PSL - Université Paris sciences et lettres - EHESS - École des hautes études en sciences sociales - ENPC - École des Ponts ParisTech - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement); Yanos Zylberberg (University of Bristol [Bristol]) |
| Abstract: | A recent literature has used a historical perspective to better understand fundamental questions of urban economics. However, a wide range of historical documents of exceptional quality remain underutilised: their use has been hampered by their original format or by the massive amount of information to be recovered. In this paper, we describe how and when the flexibility and predictive power of machine learning can help researchers exploit the potential of these historical documents. We first discuss how important questions of urban economics rely on the analysis of historical data sources and the challenges associated with transcription and harmonisation of such data. We then explain how machine learning approaches may address some of these challenges and we discuss possible applications. |
| Keywords: | urban economics,history,machine learning |
| Date: | 2021–05 |
| URL: | http://d.repec.org/n?u=RePEc:hal:psewpa:halshs-03231786&r= |
| By: | Combes, Pierre-Philippe (GATE, University of Lyon); Gobillon, Laurent (Paris School of Economics); Zylberberg, Yanos (University of Bristol) |
| Abstract: | A recent literature has used a historical perspective to better understand fundamental questions of urban economics. However, a wide range of historical documents of exceptional quality remain underutilised: their use has been hampered by their original format or by the massive amount of information to be recovered. In this paper, we describe how and when the flexibility and predictive power of machine learning can help researchers exploit the potential of these historical documents. We first discuss how important questions of urban economics rely on the analysis of historical data sources and the challenges associated with transcription and harmonisation of such data. We then explain how machine learning approaches may address some of these challenges and we discuss possible applications. |
| Keywords: | machine learning, history, urban economics |
| JEL: | R11 R12 R14 N90 C45 C81 |
| Date: | 2021–05 |
| URL: | http://d.repec.org/n?u=RePEc:iza:izadps:dp14392&r= |
| By: | Adams-Prassl, Abigail; Boneva, Teodora; Golin, Marta; Rauh, Christopher |
| Abstract: | Using large, geographically representative surveys from the US and UK, we document variation in the percentage of tasks workers can do from home. We highlight three dimensions of heterogeneity that have previously been neglected. First, the share of tasks that can be done from home varies considerably both across as well as within occupations and industries. The distribution of the share of tasks that can be done from home within occupations, industries, and occupation-industry pairs is systematic and remarkably consistent across countries and survey waves. Second, as the pandemic has progressed, the share of workers who can do all tasks from home has increased most in those occupations in which the pre-existing share was already high. Third, even within occupations and industries, we find that women can do fewer tasks from home. Using machine-learning methods, we extend our working-from-home measure to all disaggregated occupation-industry pairs. The measure we present in this paper is a critical input for models considering the possibility to work from home, including models used to assess the impact of the pandemic or design policies targeted at reopening the economy. |
| Keywords: | Coronavirus; COVID-19; Industry; Occupations; Telework; Working from Home |
| JEL: | J21 J24 |
| Date: | 2020–06 |
| URL: | http://d.repec.org/n?u=RePEc:cpr:ceprdp:14901&r= |
| By: | Jing Xiao; Ron Boschma; |
| Abstract: | The purpose of this study is to investigate how a regional knowledge base in Information and Communication Technologies (ICTs) influences the emergence of AI technologies in European regions. Replying on patent data and studying the knowledge production of AI technologies in 233 European regions in the period of 1994 to 2017, our study reveals three results. First, ICTs are a major knowledge source of AI technologies and their importance has been increasing over time. Second, a regional knowledge base in ICTs is highly relevant for regions to engage in AI inventing. Third, the effects of regional knowledge base of ICTs are stronger for regions that recently caught up in AI inventing. Our findings suggest that ICTs play a critically enabling role for regions to diversify into AI technologies, especially in catching-up regions. |
| Keywords: | Artificial intelligence (AI), regional diversification, Information and Communications Technologies (ICTs), technological relatedness, General Purpose Technologies (GPTs), Europe |
| JEL: | O33 R11 O31 |
| Date: | 2021–05 |
| URL: | http://d.repec.org/n?u=RePEc:egu:wpaper:2117&r= |
| By: | Shen, Fei; Xia, Chuanli; Yu, Wenting; Min, Chen; Wang, Tianjiao; Wu, Yi; Ye, Qianying |
| Abstract: | This report presents a part of the findings from the Hong Kong Online Public Opinion Data Mining Project (http://www.webopinion.hk/) that aims to collect and analyze online public opinion towards different issues and topics in Hong Kong. The report provides an overview of public opinion on a variety of topics such as public figures, organizations, and social issues. A total of 12 online platforms including discussion forums, news portal sites, and online news media are included in the analysis (for methodological details, see Text-Mining Online Public Opinion in Hong Kong: Methods and Procedures https://osf.io/preprints/socarxiv/b2mex/). The time span of the analysis in this report is from July 2017 to December 2019. |
| Date: | 2021–05–23 |
| URL: | http://d.repec.org/n?u=RePEc:osf:socarx:tnxw4&r= |
| By: | Kazutoshi Kan (Deputy Director, Institute for Monetary and Economic Studies, Bank of Japan (E-mail: kazutoshi.kan@boj.or.jp)) |
| Abstract: | This paper clarifies the source of difficulties in machine learning security and determines the usefulness of the failure mode approach for capturing security risks of machine learning systems comprehensively. Machine learning is an inductive methodology that automatically extracts relationships among data from a huge number of input-output samples. Recently, machine learning systems have been implemented deeply in various IT systems and their social impact has been increasing. However, machine learning models have specific vulnerabilities and relevant security risks that conventional IT systems do not have. An overall picture regarding these vulnerabilities and risks has not been clarified sufficiently, and there has been no consensus about their taxonomy. Thus, this paper reveals the specificity of the security risks and describes their failure modes hierarchically by classifying them on three axes, i.e., (1) presence or absence of attacker's intention, (2) location of the vulnerabilities, and (3) functional characteristics to be lost. This paper also considers points for future utilization of machine learning in society. |
| Keywords: | Machine learning, Failure mode, Secuirty risk, Vulnerability |
| JEL: | L86 L96 M15 Z00 |
| Date: | 2021–05 |
| URL: | http://d.repec.org/n?u=RePEc:ime:imedps:21-e-03&r= |
| By: | Shen, Fei; Xia, Chuanli; Yu, Wenting; Min, Chen; Wang, Tianjiao; Wu, Yi; Ye, Qianying |
| Abstract: | This report presents a part of the findings from the Hong Kong Online Public Opinion Data Mining Project (http://www.webopinion.hk/) that aims to collect and analyze online public opinion towards different issues and topics in Hong Kong. The report provides an overview of public opinion on a variety of topics such as public figures, organizations, and social issues. A total of 12 online platforms including discussion forums, news portal sites, and online news media are included in the analysis (for methodological details, see Text-Mining Online Public Opinion in Hong Kong: Methods and Procedures https://osf.io/preprints/socarxiv/b2mex/). The time span of the analysis in this report is from January 2000 (i.e., the earliest data point obtained) to June 2017 (i.e., the end of the third Chief Executive Leung Chun-ying’s administration). |
| Date: | 2021–05–23 |
| URL: | http://d.repec.org/n?u=RePEc:osf:socarx:52zbm&r= |
| By: | Caterina Rho; Raúl Fernández; Brenda Palma |
| Abstract: | We apply text analysis to Twitter messages in Spanish to build a sentiment- based risk index for the financial sector in Mexico. We classify a sample of tweets for the period 2006-2019 to identify messages in response to positive or negative shocks to the Mexican financial sector. We use a voting classifier to aggregate three different classifiers: one based on word polarities from a pre-defined dictionary; one based on a support vector machine; and one based on neural networks. Next, we compare our Twitter sentiment index with existing indicators of financial stress. We find that this novel index captures the impact of sources of financial stress not explicitly encompassed in quantitative risk measures. Finally, we show that a shock in our Twitter sentiment index correlates positively with an increase in financial market risk, stock market volatility, sovereign risk, and foreign exchange rate volatility. |
| JEL: | G1 G21 G41 |
| Date: | 2021–05 |
| URL: | http://d.repec.org/n?u=RePEc:bdm:wpaper:2021-04&r= |
| By: | Simone Vannuccini (cience Policy Research Unit, University of Sussex Business School, University of Sussex); Ekaterina Prytkova (Friedrich Schiller University Jena, Department of Economics and Business Administration) |
| Abstract: | Artificial Intelligence (AI) has been quickly labelled a General Purpose Technology (GPT) for its many uses and the high expectations built around a technology that can perform tasks associated with natural intelligence. However, for now, the claim “AI equals GPT" is premature, and eventually, taking into account potential future scenarios, it can turn out to be incorrect. In fact, though every GPT is an influential technology, not every influential technology is a GPT. Checking AI against the definitional criteria of GPT, we come to the conclusion that GPT is a misspecified model of AI: what was meant to be a concept for an individual technology in this case is stretched to cover a growing infrastructural, system technology. For example, the pervasiveness featured in the GPT concept seems to be qualitatively different from the largeness that modern AI demonstrates. In this paper, we suggest an alternative framework drawn from the literature on Large Technical Systems (LTS) as more fit to represent the nature of AI. We map the building blocks of LTS on AI and describe its state-of-the-art through this novel viewpoint. This is a timely exercise, as we witness the formation of an AI industry. A correct understanding of its core technology is needed to identify mechanisms at work, problems in place and eventually the dynamics of this new industry. The LTS framework offers a broader grasp of the infrastructural nature of AI as a technology, with more convenient categories to describe AI and measures to test empirically. We investigate how the implications of AI being an LTS entail the design of adequate public policies and firm strategies. |
| Keywords: | artificial intelligence; large technical system; general purpose technology; infrastructural technology |
| Date: | 2021–05 |
| URL: | http://d.repec.org/n?u=RePEc:sru:ssewps:2021-02&r= |
| By: | Tiago Cravo Oliveira Hashiguchi (OECD); Luke Slawomirski; Jillian Oderkirk (OECD) |
| Abstract: | Artificial intelligence (AI) has the potential to make health care more effective, efficient and equitable. AI applications are on the rise, from clinical decision-making and public health, to biomedical research and drug development, to health system administration and service redesign. The COVID-19 pandemic is serving as a catalyst, yet it is also a reality check, highlighting the limits of existing AI systems. Most AI in health is actually artificial narrow intelligence, designed to accomplish very specific tasks on previously curated data from single settings. In the real world, health data are not always available, standardised, or easily shared. Limited data hinders the ability of AI tools to generate accurate information for diverse populations with potentially very complex conditions. Having appropriate patient data is critical for AI tools because decisions based on models with skewed or incomplete data can put patients at risk. Policy makers should beware of the hype surrounding AI and identify and focus on real problems and opportunities that AI can help address. In setting the foundations for AI to help achieve health policy objectives, one key priority is to improve data quality, interoperability and access in a secure way through better data governance. More broadly, policy makers should work towards implementing and operationalising the OECD AI Principles, as well as investing in technology and human capital. Strong policy frameworks based on inclusive and extensive dialogue among all stakeholders are also key to ensure AI adds value to patients and to societies. AI that influences clinical and public health decisions should be introduced with care. Ultimately, high expectations must be managed, but real opportunities should be pursued. |
| Keywords: | Artificial intelligence |
| JEL: | I10 F50 H51 H87 O38 |
| Date: | 2021–06–11 |
| URL: | http://d.repec.org/n?u=RePEc:oec:elsaad:128-en&r= |
| By: | Mateusz Buczyński (Interdisciplinary Doctoral School, University of Warsaw); Marcin Chlebus (Faculty of Economic Sciences, University of Warsaw) |
| Abstract: | This study proposes a new GARCH specification, adapting a long short-term memory (LSTM) neural network's architecture. Classical GARCH models have been proven to give substantially good results in the case of financial modeling, where high volatility can be observed. In particular, their high value is often praised in the case of Value-at-Risk. However, the lack of nonlinear structure in most of the approaches entails that the conditional variance is not represented in the model well enough. On the contrary, recent rapid advancement of deep learning methods is said to be capable of describing any nonlinear relationships prominently. We suggest GARCHNet - a nonlinear approach to conditional variance that combines LSTM neural networks with maximum likelihood estimators of probability in GARCH. The distributions of the innovations considered in the paper are: normal, t and skewed t, however the approach does enable extensions to other distributions as well. To evaluate our model, we have executed an empirical study on the log returns of WIG 20 (Warsaw Stock Exchange Index) in four different time periods throughout 2005 and 2021 with varying levels of observed volatility. Our findings confirm the validity of the solution, however we present several directions to develop it further. |
| Keywords: | Value-at-Risk, GARCH, neural networks, LSTM |
| JEL: | G32 C52 C53 C58 |
| Date: | 2021 |
| URL: | http://d.repec.org/n?u=RePEc:war:wpaper:2021-08&r= |
| By: | Johann Pfitzinger |
| Abstract: | Adoption of deep neural networks in fields such as economics or finance has been constrained by the lack of interpretability of model outcomes. This paper proposes a generative neural network architecture - the parameter encoder neural network (PENN) - capable of estimating local posterior distributions for the parameters of a regression model. The parameters fully explain predictions in terms of the inputs and permit visualization, interpretation and inference in the presence of complex heterogeneous effects and feature dependencies. The use of Bayesian inference techniques offers an intuitive mechanism to regularize local parameter estimates towards a stable solution, and to reduce noise-fitting in settings of limited data availability. The proposed neural network is particularly well-suited to applications in economics and finance, where parameter inference plays an important role. An application to an asset pricing problem demonstrates how the PENN can be used to explore nonlinear risk dynamics in financial markets, and to compare empirical nonlinear effects to behavior posited by financial theory. |
| Date: | 2021–06 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2106.05536&r= |
| By: | Koffi, Marlene |
| Abstract: | This paper analyzes the recognition of women's innovative ideas. Bibliometric data from research in economics are used to investigate gender biases in citation patterns. Based on deep learning and machine learning techniques, one can (1) establish the similarities between papers (2) build a link between articles by identifying the papers citing, cited and that should be cited. This study finds that, on average, omitted papers are 15%-20% more likely to be female-authored than male-authored. This omission bias is more prevalent when there are only males in the citing paper. Overall, to have the same level of citation as papers written by males, papers written by females need to be 20 percentiles upper in the distribution of the degree of innovativeness of the paper. |
| Date: | 2021 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:clefwp:35&r= |
| By: | Klaus Ackermann (SoDa Labs and Department of Econometrics and Business Statistics, Monash Business School, Monash University); Sefa Awaworyi Churchill (School of Economics, Finance & Marketing, RMIT University); Russell Smyth (Department of Economics, Monash Business School, Monash University) |
| Abstract: | We examine the effects of mobile phone coverage on violent conflicts in Africa using a new monthly panel dataset on mobile phone coverage at 55 55 km grid cell levels for 32 African countries covering the period from 2008 to 2018. The base rate of a conflict event in a month across our data set is 0.0039 with a standard deviation of 0.0620. We find that access to mobile phone coverage increases the probability of a conflict occurring in the next month by 0.0028. This finding is robust to a suite of sensitivity checks including the use of various specifications and alternative datasets. We examine heterogeneity on the impact of mobile phone coverage across state-based conflict, non-state-based conflict and one-sided conflict, and find that our results are being driven by non-state conflicts. We examine economic growth as a channel through which mobile phone coverage influences conflict. In doing so, we construct new satellite data for night-time light activity as a proxy for economic growth. We find that economic activity is a channel through which mobile phone coverage influences conflicts, and that higher economic growth weakens the positive effect of mobile phone coverage on conflict. |
| Keywords: | Mobile phones , Cell phone coverage, Violence, Conflict, Africa |
| JEL: | D74 C23 O13 Q34 |
| Date: | 2021–05 |
| URL: | http://d.repec.org/n?u=RePEc:ajr:sodwps:2021-06&r= |
| By: | Bart Cockx (UGent & UCLouvain) |
| Abstract: | Ce numéro de Regards économiques analyse la question de la formation des demandeurs d’emploi en Belgique, du côté néerlandophone en particulier. Ces formations sont-elles efficaces pour améliorer l’insertion professionnelle des chômeurs et si oui, pour quels demandeurs d’emploi en particulier ? |
| Date: | 2021–03–04 |
| URL: | http://d.repec.org/n?u=RePEc:ctl:louvrg:160&r= |