nep-big New Economics Papers
on Big Data
Issue of 2023‒04‒24
twenty-two papers chosen by
Tom Coupé
University of Canterbury

  1. AI language models: Technological, socio-economic and policy considerations By OECD
  2. A Macroscope of English Print Culture, 1530-1700, Applied to the Coevolution of Ideas on Religion, Science, and Institutions By Peter Grajzl; Peter Murrell
  3. Linking Alternative Fuel Vehicles Adoption with Socioeconomic Status and Air Quality Index By Anuradha Singh; Jyoti Yadav; Sarahana Shrestha; Aparna S. Varde
  4. Cryptocurrency Price Prediction using Twitter Sentiment Analysis By Haritha GB; Sahana N. B
  5. Stock Price Prediction Using Temporal Graph Model with Value Chain Data By Chang Liu; Sandra Paterlini
  6. AI Watch: Artificial Intelligence Standardisation Landscape Update By SOLER GARRIDO Josep; TOLAN Songul; HUPONT TORRES Isabelle; FERNANDEZ LLORCA David; CHARISI Vasiliki; GOMEZ GUTIERREZ Emilia; JUNKLEWITZ Henrik; HAMON Ronan; FANO YELA Delia; PANIGUTTI Cecilia
  7. European Low-Carbon Policy: Impact on fossil energy markets By Jacques Minlend
  8. Deep Calibration With Artificial Neural Network: A Performance Comparison on Option Pricing Models By Young Shin Kim; Hyangju Kim; Jaehyung Choi
  9. Taureau: A Stock Market Movement Inference Framework Based on Twitter Sentiment Analysis By Nicholas Milikich; Joshua Johnson
  10. Impact of COVID-19 shock on a segmented labour market: Analysis using a unique panel dataset By Das, Satadru; Ghosh, Saurabh; Mazumder, Debojyoti; Tushavera, Jitendra
  11. Factors of formation of dividend payment strategies By Shukina Polina
  12. Two Approaches to Saving the Economy: Micro-Level Effects of Covid-19 Lockdowns in Italy By Cseres-Gergely, Zsombor; Kecht, Valentin; Le Blanc, Julia; Onorante, Luca
  13. Cross-category spillovers in medical research By Aslan, Yasemin; Yaqub, Ohid; Rotolo, Daniele; Sampat, Bhaven N.
  14. Form 10-K Itemization By Yanci Zhang; Mengjia Xia; Mingyang Li; Haitao Mao; Yutong Lu; Yupeng Lan; Jinlin Ye; Rui Dai
  15. Trade Liberalization and Local Development in India: Evidence from Nighttime Lights By Priyaranjan Jha; Karan Talathi
  16. China 2.0 - Status and Foresight of EU-China Trade, Investment and Technological Race By ALVES DIAS Patricia; AMOROSO Sara; BAUER Peter; BESSAGNET Bertrand; CABRERA GIRALDEZ Marcelino; CARDONA Melisande; CARRARA Samuel; CHRISTOU Michail; CIANI Andrea; CONTE Andrea; DE PRATO Giuditta; DI GIROLAMO Francesca; DIAZ LANCHAS Jorge; DIODATO Dario; DIUKANOVA Olga; DOMNICK Clemens; FAKO Peter; FAZIO Alessandro; GAVIGAN James; GENTILE Stefano; GENTY Aurelien; GEORGAKAKI Aliki; GKOTSIS Petros; GOENAGA BELDARRAIN Xabier; GONZALEZ VAZQUEZ Ignacio; GREGORI Wildmer; HERVAS SORIANO Fernando; HIRSCHBUEHL Dominik; LACAL ARANTEGUI Roberto; LEWIS Adam; LOPEZ COBO Montserrat; MAGHIROS Ioannis; MANDRAS Giovanni; MARSCHINSKI Robert; MARTINEZ CILLERO Maria; MARTINEZ TUREGANO David; NARDO Michela; NDACYAYISENGA Nathalie; PAGANO Andrea; PREZIOSI Nadir; PUGLIESE Emanuele; PUNIE Yves; RIGHI Riccardo; RUEDA CANTUCHE Jose; SAMOILI Sofia; SHAMUILIA Sheron; SHEVTSOVA Yevgeniya; TACCHELLA Andrea; TELSNIG Thomas; TESTA Giuseppina; THIEL Christian; TRAVAGNIN Martino; TUEBKE Alexander; VAZQUEZ-PRADA BAILLET Miguel; VIGNATI Elisabetta
  17. Breakthrough Innovations and Productivity: An International Perspective By Kuusi, Tero; Nevavuo, Jenni
  18. Using publicly available remote sensing products to evaluate REDD + projects in Brazil By Gabriela Demarchi; Julie Subervie; Thibault Catry; Isabelle Tritsch
  19. The need for data products in personal finance. By Edouard Ribes
  20. The effects of personal data management on competition and welfare By Jiajia Cong; Noriaki Matsushima
  21. Nightless City: Impacts of Policymakers' Questions on Overtime Work of Government Officials By Natsuki Arai; Masashige Hamano; Munechika Katayama; Yuki Murakami; Katsunori Yamada
  22. Chorus in the cacophony: Dissent and policy communication of India's Monetary Policy Committee By Rounak Sil; Unninarayanan Kurup; Ashima Goyal; Apoorva Singh and Rajendra Paramanik

  1. By: OECD
    Abstract: AI language models are a key component of natural language processing (NLP), a field of artificial intelligence (AI) focused on enabling computers to understand and generate human language. Language models and other NLP approaches involve developing algorithms and models that can process, analyse and generate natural language text or speech trained on vast amounts of data using techniques ranging from rule-based approaches to statistical models and deep learning. The application of language models is diverse and includes text completion, language translation, chatbots, virtual assistants and speech recognition. This report offers an overview of the AI language model and NLP landscape with current and emerging policy responses from around the world. It explores the basic building blocks of language models from a technical perspective using the OECD Framework for the Classification of AI Systems. The report also presents policy considerations through the lens of the OECD AI Principles.
    Date: 2023–04–17
  2. By: Peter Grajzl; Peter Murrell
    Abstract: We combine unsupervised machine-learning and econometric methods to examine cultural change in 16th- and 17th-century England. A machine-learning digest synthesizes the content of 57, 863 texts comprising 83 million words into 110 topics. The topics include the expected, such as Natural Philosophy, and the unexpected, such as Baconian Theology. Using the data generated via machine-learning we then study facets of England's cultural history. Timelines suggest that religious and political discourse gradually became more scholarly over time and economic topics more prominent. The epistemology associated with Bacon was present in theological debates already in the 16th century. Estimating a VAR, we explore the coevolution of ideas on religion, science, and institutions. Innovations in religious ideas induced strong responses in the other two domains. Revolutions did not spur debates on institutions nor did the founding of the Royal Society markedly elevate attention to science.
    Keywords: cultural history, England, machine-learning, text-as-data, coevolution, VAR
    JEL: C80 Z10 N00 P10 C30
    Date: 2023
  3. By: Anuradha Singh; Jyoti Yadav; Sarahana Shrestha; Aparna S. Varde
    Abstract: This is a study on the potential widespread usage of alternative fuel vehicles, linking them with the socio-economic status of the respective consumers as well as the impact on the resulting air quality index. Research in this area aims to leverage machine learning techniques in order to promote appropriate policies for the proliferation of alternative fuel vehicles such as electric vehicles with due justice to different population groups. Pearson correlation coefficient is deployed in the modeling the relationships between socio-economic data, air quality index and data on alternative fuel vehicles. Linear regression is used to conduct predictive modeling on air quality index as per the adoption of alternative fuel vehicles, based on socio-economic factors. This work exemplifies artificial intelligence for social good.
    Date: 2023–03
  4. By: Haritha GB; Sahana N. B
    Abstract: The cryptocurrency ecosystem has been the centre of discussion on many social media platforms, following its noted volatility and varied opinions. Twitter is rapidly being utilised as a news source and a medium for bitcoin discussion. Our algorithm seeks to use historical prices and sentiment of tweets to forecast the price of Bitcoin. In this study, we develop an end-to-end model that can forecast the sentiment of a set of tweets (using a Bidirectional Encoder Representations from Transformers - based Neural Network Model) and forecast the price of Bitcoin (using Gated Recurrent Unit) using the predicted sentiment and other metrics like historical cryptocurrency price data, tweet volume, a user's following, and whether or not a user is verified. The sentiment prediction gave a Mean Absolute Percentage Error of 9.45%, an average of real-time data, and test data. The mean absolute percent error for the price prediction was 3.6%.
    Date: 2023–03
  5. By: Chang Liu; Sandra Paterlini
    Abstract: Stock price prediction is a crucial element in financial trading as it allows traders to make informed decisions about buying, selling, and holding stocks. Accurate predictions of future stock prices can help traders optimize their trading strategies and maximize their profits. In this paper, we introduce a neural network-based stock return prediction method, the Long Short-Term Memory Graph Convolutional Neural Network (LSTM-GCN) model, which combines the Graph Convolutional Network (GCN) and Long Short-Term Memory (LSTM) Cells. Specifically, the GCN is used to capture complex topological structures and spatial dependence from value chain data, while the LSTM captures temporal dependence and dynamic changes in stock returns data. We evaluated the LSTM-GCN model on two datasets consisting of constituents of Eurostoxx 600 and S&P 500. Our experiments demonstrate that the LSTM-GCN model can capture additional information from value chain data that are not fully reflected in price data, and the predictions outperform baseline models on both datasets.
    Date: 2023–03
  6. By: SOLER GARRIDO Josep (European Commission - JRC); TOLAN Songul; HUPONT TORRES Isabelle (European Commission - JRC); FERNANDEZ LLORCA David (European Commission - JRC); CHARISI Vasiliki (European Commission - JRC); GOMEZ GUTIERREZ Emilia (European Commission - JRC); JUNKLEWITZ Henrik (European Commission - JRC); HAMON Ronan (European Commission - JRC); FANO YELA Delia (European Commission - JRC); PANIGUTTI Cecilia (European Commission - JRC)
    Abstract: The European Commission presented in April 2021 the AI Act, its proposed legislative framework for Artificial Intelligence, which sets the necessary regulatory conditions for the adoption of trustworthy AI practices in the European Union. Once the final legal text comes into force, standards will play a fundamental role in supporting providers of concerned AI systems, bringing the necessary level of technical detail into the essential requirements prescribed in the legal text. Indeed, harmonised standards provide operators with presumption of conformity with legal requirements. AI has been an active area of work by many standards development organizations in recent years. In this report, we analyse a set of specifications produced by the IEEE Standards Association covering aspects of trustworthy AI. Several of the documents analysed have been found to provide highly relevant technical content from the point of view of the AI Act. Furthermore, some of them cover important standardization gaps identified in previous analyses. This work is intended to provide independent input to European and international standardisers currently planning AI standardisation activities in support of the regulatory needs. This report identifies concrete elements in IEEE standards and certification criteria that could fulfil standardisation needs emerging from the European AI Regulation proposal, and provides recommendations for their potential adoption and development in this direction.
    Keywords: Artificial Intelligence, Standards, Technical Specifications
    Date: 2023–01
  7. By: Jacques Minlend (Université de Rennes, CNRS, CREM-UMR6211, F-35000 Rennes, France)
    Abstract: This paper proposes text-as-data methods relying on unsupervised machine learning algorithms applied to European Union (EU) law acts and newspapers. These are used to construct two monthly indices over a reference period 1997-2021: (i) First, a news-based index which underlies a conjunctural uncertainty about the international context in which the global energy and environment policy evolves (EnvPU). (ii) Second, a laws-based index which reflects structural changes of the European energy and environment regulations (EnvP). The main findings suggest both indices display, in some extent, a common evolutionary pattern around salient events in the history of the EU energy and environment policy. Moreover, EnvPU index appears to be more volatile and is driven in the short-run by EnvP index. Given the support of such a policy to carbon phase-out, we further examine, in what extent, each index relates to price uncertainty dynamics in fossil energy markets (oil, gas, and coal). As a result, we uncover that, increase in news-based EnvPU index has a positive impact on price uncertainty of all fossil energy markets, the effect being stronger and more significant for gas and coal markets. In contrast, while an exogenous shock in laws-based EnvP index has a negative effect on price uncertainty in oil and gas markets, it tends to increase the coal price uncertainty. Overall, EnvP index depicts a stabilizing effect on fossil energy prices.
    Keywords: Energy and Environment Policy; News and media; Text-mining; Unsupervised machine learning; Commodity markets; Structural VAR.
    JEL: Q58 C55 C80 D80 Q02 C32
    Date: 2023–02
  8. By: Young Shin Kim; Hyangju Kim; Jaehyung Choi
    Abstract: This paper explores Artificial Neural Network (ANN) as a model-free solution for a calibration algorithm of option pricing models. We construct ANNs to calibrate parameters for two well-known GARCH-type option pricing models: Duan's GARCH and the classical tempered stable GARCH that significantly improve upon the limitation of the Black-Scholes model but have suffered from computation complexity. To mitigate this technical difficulty, we train ANNs with a dataset generated by Monte Carlo Simulation (MCS) method and apply them to calibrate optimal parameters. The performance results indicate that the ANN approach consistently outperforms MCS and takes advantage of faster computation times once trained. The Greeks of options are also discussed.
    Date: 2023–03
  9. By: Nicholas Milikich; Joshua Johnson
    Abstract: With the advent of fast-paced information dissemination and retrieval, it has become inherently important to resort to automated means of predicting stock market prices. In this paper, we propose Taureau, a framework that leverages Twitter sentiment analysis for predicting stock market movement. The aim of our research is to determine whether Twitter, which is assumed to be representative of the general public, can give insight into the public perception of a particular company and has any correlation to that company's stock price movement. We intend to utilize this correlation to predict stock price movement. We first utilize Tweepy and getOldTweets to obtain historical tweets indicating public opinions for a set of top companies during periods of major events. We filter and label the tweets using standard programming libraries. We then vectorize and generate word embedding from the obtained tweets. Afterward, we leverage TextBlob, a state-of-the-art sentiment analytics engine, to assess and quantify the users' moods based on the tweets. Next, we correlate the temporal dimensions of the obtained sentiment scores with monthly stock price movement data. Finally, we design and evaluate a predictive model to forecast stock price movement from lagged sentiment scores. We evaluate our framework using actual stock price movement data to assess its ability to predict movement direction.
    Date: 2023–03
  10. By: Das, Satadru; Ghosh, Saurabh; Mazumder, Debojyoti; Tushavera, Jitendra
    Abstract: This paper studies the impact of economic crisis caused by the COVID on the Indian labour market using the Periodic Labour Force Survey (PLFS). The unique dataset offers the opportunity to analyse sectoral transition and mobility of workers in response to a crisis due to its rotational panel framework. We employ transition matrices, non-parametric cumulative distribution functions, and machine learning techniques to identify the impact of COVID shock on formal and informal sector workers and whether this impact was heterogeneous. We find that labour market outcomes, both in terms of employment status and income, became even more divergent between the formal and informal sectors during the first wave of pandemic and remained divergent in the recovery phase. The classification analysis highlights that the sector in which the worker was employed (formal or informal sector), was an important predictor of income loss during the first wave.
    Keywords: Segmented Labour Market, Informality, COVID scarring.
    JEL: J31 J46 J62
    Date: 2023–03
  11. By: Shukina Polina (Department of Economics, Lomonosov Moscow State University)
    Abstract: In the context of increasing competition for capital in the market, it becomes important to increase the investment attractiveness of the company. One of the ways to increase the attractiveness of the company are dividend payments. As part of this work, the factors of dividend payments are identified and systematized, the impact of dividend payments on the yield of shares of the relevant company is shown, and dividend payment strategies in the form of Lintner's target dividend model are studied. To identify correlation relationships, the tools of econometric regression models and machine learning were used. As a result, it turned out that the factor of dividend payments is statistically significantly correlated with the profitability of the company's shares, and the stage of the company's life cycle is a statistically significant factor in influencing dividend payments.
    Keywords: dividend policy; life cycle stage; stock returns; target dividend.
    JEL: G11 G31 G32
    Date: 2022–11
  12. By: Cseres-Gergely, Zsombor (European Commission); Kecht, Valentin (University of Bonn); Le Blanc, Julia (European Commission); Onorante, Luca (European Commission)
    Abstract: In response to the two waves of Covid-19 in 2020, the Italian government implemented a general lockdown in March, but geographically targeted policies during fall. We exploit this natural experiment to compare the effects of the two policies in a difference-in-differences design, leveraging a unique database combining traditional, municipality-level and big data at weekly frequency. We find that the general lockdown of the first wave strongly reduced mobility at a high price in terms of employment, while the targeted policies during the second wave induced a lower decrease in mobility and little additional economic cost. We also study the role of pre-existing municipality characteristics and labour market policies in shaping these responses. Our results suggest that working from home and short-term work schemes buffered the adverse consequences of the drop in economic activity on the labour market. Both mechanisms, however, acted more strongly in high-income areas and among white collar workers, exacerbating existing inequalities.
    Keywords: Covid-19, human mobility, lockdowns, big data, differences-in-differences
    JEL: I12 I18 H12 D04 C33 H51
    Date: 2023–02
  13. By: Aslan, Yasemin; Yaqub, Ohid; Rotolo, Daniele; Sampat, Bhaven N.
    Abstract: Whether research funding is targetable is one of the central unresolved questions of science policy. A particular question is how often research aimed at understanding one disease or problem spills over to others. This has been a perennial topic of debate at the world’s largest single funding body of biomedical research, the U.S. National Institutes of Health (NIH). Critics of the agency’s priority-setting process have repeatedly called for better alignment between funding and disease burden, and patient advocates for specific diseases for more funding for their causes. In response, opponents of planning have argued that research in one area frequently leads to advances in others. In this paper, we provide new evidence to inform these debates by examining the extent to which research funding (grants) in one scientific or disease area leads to research findings (publications) in another. We used the NIH’s Research, Condition, and Disease Categorization (RCDC) to identify categories for NIH grants awarded between 2008 and 2016. We applied machine-learning to map text to these categories and use this model to categorize publications resulting from these grants. We categorized over 1.2 million publications, resulting from over 90, 000 grants. We found that 70% of the publications have at least one RCDC category not in its grant, which we termed “unexpected” categories. On average, 40% of categories assigned to a publication were unexpected. After adjusting for similarity across some of the RCDC categories by empirically clustering the categories, we found 58% of the publications had at least one unexpected category and, on average, 33% of publication categories were unexpected. Our results suggest that disease-orientation and clinical research were less likely to be associated with spillovers. Grants resulting from targeted requests for applications were more likely to result in publications with unexpected categories, though the magnitude of the differences was relatively small.
    Date: 2023–03–23
  14. By: Yanci Zhang; Mengjia Xia; Mingyang Li; Haitao Mao; Yutong Lu; Yupeng Lan; Jinlin Ye; Rui Dai
    Abstract: Form 10-K report is a financial report disclosing the annual financial state of a public company. It is an important evidence to conduct financial analysis, i.e., asset pricing, corporate finance. Practitioners and researchers are constantly designing algorithms to better conduct analysis on information in the Form 10-K report. The vast majority of previous works focus on quantitative data. With recent advancement on natural language processing (NLP), textual data in financial filing attracts more attention. However, to incorporate textual data for analyzing, Form 10-K Itemization is a necessary pre-process step. It aims to segment the whole document into several Item sections, where each Item section focuses on a specific financial aspect of the company. With the segmented Item sections, NLP techniques can directly apply on those Item sections related to downstream tasks. In this paper, we develop a Form 10-K Itemization system which can automatically segment all the Item sections in 10-K documents. The system is both effective and efficient. It reaches a retrieval rate of 93%.
    Date: 2023–02
  15. By: Priyaranjan Jha; Karan Talathi
    Abstract: We study the impact of the Indian trade liberalization of 1991 on development at the district level using satellite nighttime lights per capita as a proxy for development. We find that on average trade liberalization increased nighttime lights per capita but there was considerable heterogeneity in the effect. In particular, districts in states with flexible labor laws, districts with better road networks, proximity to the coast, or higher female labor force participation rate seem to have benefited more than other districts.
    Keywords: trade liberalization, nighttime lights, per capita income, tariffs, labor laws
    JEL: F13 F14 O11 O24
    Date: 2023
  16. By: ALVES DIAS Patricia (European Commission - JRC); AMOROSO Sara (European Commission - JRC); BAUER Peter (European Commission - JRC); BESSAGNET Bertrand (European Commission - JRC); CABRERA GIRALDEZ Marcelino (European Commission - JRC); CARDONA Melisande (European Commission - JRC); CARRARA Samuel (European Commission - JRC); CHRISTOU Michail (European Commission - JRC); CIANI Andrea (European Commission - JRC); CONTE Andrea (European Commission - JRC); DE PRATO Giuditta (European Commission - JRC); DI GIROLAMO Francesca (European Commission - JRC); DIAZ LANCHAS Jorge (European Commission - JRC); DIODATO Dario (European Commission - JRC); DIUKANOVA Olga (European Commission - JRC); DOMNICK Clemens (European Commission - JRC); FAKO Peter (European Commission - JRC); FAZIO Alessandro (European Commission - JRC); GAVIGAN James (European Commission - JRC); GENTILE Stefano (European Commission - JRC); GENTY Aurelien (European Commission - JRC); GEORGAKAKI Aliki (European Commission - JRC); GKOTSIS Petros (European Commission - JRC); GOENAGA BELDARRAIN Xabier (European Commission - JRC); GONZALEZ VAZQUEZ Ignacio (European Commission - JRC); GREGORI Wildmer (European Commission - JRC); HERVAS SORIANO Fernando (European Commission - JRC); HIRSCHBUEHL Dominik (European Commission - JRC); LACAL ARANTEGUI Roberto (European Commission - JRC); LEWIS Adam (European Commission - JRC); LOPEZ COBO Montserrat (European Commission - JRC); MAGHIROS Ioannis (European Commission - JRC); MANDRAS Giovanni (European Commission - JRC); MARSCHINSKI Robert (European Commission - JRC); MARTINEZ CILLERO Maria (European Commission - JRC); MARTINEZ TUREGANO David (European Commission - JRC); NARDO Michela (European Commission - JRC); NDACYAYISENGA Nathalie (European Commission - JRC); PAGANO Andrea (European Commission - JRC); PREZIOSI Nadir (European Commission - JRC); PUGLIESE Emanuele (European Commission - JRC); PUNIE Yves (European Commission - JRC); RIGHI Riccardo (European Commission - JRC); RUEDA CANTUCHE Jose (European Commission - JRC); SAMOILI Sofia (European Commission - JRC); SHAMUILIA Sheron (European Commission - JRC); SHEVTSOVA Yevgeniya (European Commission - JRC); TACCHELLA Andrea (European Commission - JRC); TELSNIG Thomas (European Commission - JRC); TESTA Giuseppina (European Commission - JRC); THIEL Christian (European Commission - JRC); TRAVAGNIN Martino (European Commission - JRC); TUEBKE Alexander (European Commission - JRC); VAZQUEZ-PRADA BAILLET Miguel (European Commission - JRC); VIGNATI Elisabetta (European Commission - JRC)
    Abstract: As shown by first JRC China Flagship report, China's improvements and performance in high-tech sectors appear to originate from a combination of productivity-enhancing investments and technological developments while benefiting from sheltering foreign investment conditions. The second Flagship report – ‘China 2.0’ – adopts a holistic approach to analyse China's performance on international and capital markets as well as technological developments. Such an approach combines information on three pillars of analysis often found disconnected in the literature: economic, financial and trade statistics (1); innovation and patent data (2); energy and environmental policy (3). Furthermore, the second Flagship report encompasses all levels of (dis)aggregation, ranging from macro-country statistics to sectors, firms, and product –level information. Most importantly, it does so by consistently comparing China vis-à-vis the EU and including the US whenever suitable. The extent and granularity of the data used as well as the multi-country (China-EU-US), -level (country-sector-product), and -field (economy – innovation system – energy and environment) analytical approach aim to provide scientific evidence to a policy audience on a wide range of issues and allow to draw policy relevant conclusions.
    Keywords: China, Global Value Chains, M&As, FDI, Venture Capital, R&I, Industrial Leadership, Artificial Intelligence, Quantum, New Vehicles, Wind Energy, Circular Economy, Air Quality, COVID19, import dependence, input-output
    Date: 2022–12
  17. By: Kuusi, Tero; Nevavuo, Jenni
    Abstract: Abstract In this paper, we shed new light on the productivity impact of breakthrough patents, as well as their role in the variability of productivity across countries. We use text analysis and machine learning–based estimates of the number of breakthrough patents and show that there was a significant drop in quantity in the early 2000s. According to our econometric analysis, the slowdown in innovation activity has a clear temporal connection with the later slowdown in productivity in the 2010s. Breakthrough patents increased productivity on a large scale until the beginning of the 2010s, in particular in industrial information and communications technology (ICT) industries. In sectors other than ICT, productivity growth was more differentiated so that productivity growth is observed in industries that invested significantly in R&D after the emergence of breakthrough patents. We also identify large differences across countries in the link between productivity and breakthrough patents.
    Keywords: Productivity, Innovation, Breakthroughs, Patents
    JEL: D24 O31 O33
    Date: 2023–03–31
  18. By: Gabriela Demarchi (CEE-M - Centre d'Economie de l'Environnement - Montpellier - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement - Institut Agro Montpellier - Institut Agro - Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement - UM - Université de Montpellier, CIFOR - Center for International Forestry Research - CGIAR - Consultative Group on International Agricultural Research [CGIAR]); Julie Subervie (CEE-M - Centre d'Economie de l'Environnement - Montpellier - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement - Institut Agro Montpellier - Institut Agro - Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement - UM - Université de Montpellier); Thibault Catry (UMR 228 Espace-Dev, Espace pour le développement - IRD - Institut de Recherche pour le Développement - UPVD - Université de Perpignan Via Domitia - AU - Avignon Université - UR - Université de La Réunion - UG - Université de Guyane - UA - Université des Antilles - UM - Université de Montpellier); Isabelle Tritsch (UPR Forêts et Sociétés - Forêts et Sociétés - Cirad - Centre de Coopération Internationale en Recherche Agronomique pour le Développement, Cirad-ES - Département Environnements et Sociétés - Cirad - Centre de Coopération Internationale en Recherche Agronomique pour le Développement)
    Abstract: Ensuring the perpetuity and improvement of REDD initiatives requires rigorous impact evaluation of their effectiveness in curbing deforestation. Today, a number of global and regional remote sensing (RS) products that detect changes in forest cover are publicly available. In this study, we assess the suitability of using these datasets to evaluate the impact of local REDD projects targeting smallholders in the Brazilian Amazonb] Firstly, we reconstruct the forest loss of 21, 492 farms located in the Transamazonian region for the period 2008 to 2018, using data from two RS products: Global Forest Change (GFC) and the Amazon Deforestation Monitoring Project (PRODES). Secondly, we evaluate the consistency between these two data sources and find that the deforestation estimates at the farm level vary considerably between datasets. Despite this difference, using microeconometric techniques that use pre-treatment outcomes to construct counter-factual patterns of REDD program participants, we estimate that about two hectares, or about four percent of the forest area, were saved on average on each of the 350 participating farms during the first years of the program, regardless of the data-source used. Moreover, we find that deforestation decreased on plots surrounding participating farms during the very first years of the program, suggesting that the program may have had a positive effect on neighboring farms as well. Finally, we show that participants returned to their business-as-usual pattern of clearing one to three hectares per year at the end of the program. The environmental gain generated by the program, however, was not offset by any catch-up behavior, as the two hectares saved on each farm before 2017 were not cleared in 2018. By calculating the monetary gain of the delayed carbon dioxide emissions, we find that the program's benefits were ultimately greater than its costs.
    Date: 2023–05
  19. By: Edouard Ribes (CERNA i3 - Centre d'économie industrielle i3 - Mines Paris - PSL (École nationale supérieure des mines de Paris) - PSL - Université Paris sciences et lettres - CNRS - Centre National de la Recherche Scientifique)
    Abstract: Background context: Current societal challenges around healthcare, education and retirement require households to increasingly leverage personal finance instruments. To meet this trends the lending, insurance and investment industries need to become more efficient and affordable. Specific knowledge gap the work aims to fill: To date, the distribution chains of financial instruments remains costly and inefficient. To transform, the associated industries need to further leverage digital medias to accelerate products distribution and maintenance. Some of the benefits of digitalization have already been capture & depicted in the recent literature sitting at the frontier between personal finance and financial technologies. However the scope of those studies has so far been limited to the distribution of those instruments & there has been little discussion about the opportunities associated to the maintenance of financial contracts, notably through the structuration of data products/ warehouses. This is a gap this article aims to address. Methods used in the study: This paper leverage standard economic modeling techniques and option theory to describe the impact of digital medias and notably data products on the financial instruments brokerage system. It also leverages order of magnitude founds in the literature to perform a high level calibration of those models to one of the Big 5 European financial market, namely the French investment industry. Key findings: The proposed models show 3 stylized facts about data products when applied to the French investment industry. First, such a market can only support two data product suppliers. Second, it comes with a large asymmetry in prices (prices differ by a factor 2 or 4 between actors) and clients profiles between the two data suppliers. Third, the market is not completely efficient as its equilibrium results in about 35\% of the market not being equipped with a data products. Implications: Data products can yield a 10 to 20% productivity increase for independent financial advisors and brokers distributing financial instruments. Those gains will likely be passed in some form to households, thereby increasing the overall efficiency of the financial system and supporting households financial professionalization.
    Keywords: Personal finance, households economics, wealth, technological change, financial services, The proposed models show 3 stylized facts about data
    Date: 2023–03–01
  20. By: Jiajia Cong; Noriaki Matsushima
    Abstract: This study examines how consumers' personal data management affects firms' competition in the data collection and data application markets and welfare outcomes. Consumers purchase products from differentiated firms in two markets. Firms compete to collect consumer data first to predict their preferences in the data application market, where each firm offers personalized prices to its targeted consumers and a uniform price to untargeted consumers. Before firms offer prices, their targeted consumers can erase data to become untargeted for a fixed cost. We show that consumers' privacy management mitigates price competition, reduces firms' profits, and harms consumer surplus and social welfare in the data application market; privacy management intensifies competition and improves consumer surplus in the data collection market. Across these two markets, profits and social welfare decline. The change in consumers' two-market surplus depends on their foresight regarding the outcomes in the data application market, with only forward-looking consumers having a higher surplus. We extend the model in several directions, including data-enabled product personalization, privacy costs, data portability, and data ownership, and discuss the implications for privacy laws.
    Date: 2023–03
  21. By: Natsuki Arai; Masashige Hamano; Munechika Katayama; Yuki Murakami; Katsunori Yamada
    Abstract: We quantify the impact of unexpectedly assigned tasks on overtime work in the context of Japanese government officials. Data on overtime work are typically less reliable. We overcome this problem by using mobile phone location data, which enables us to precisely measure the nighttime population in the government-office district in Tokyo at an hourly frequency. Exploiting the exogenous nature of task arrivals, we estimate impacts on overtime work. We find that, in response to a newly assigned task, overtime work initially decreases and then increases persistently. Institutional changes to relax the time constraint and improve the working environment of government officials play a part in mitigating overtime work, but persistent increases in overtime work remain. We provide a simple model of optimal work allocation and show that distortion in intertemporal task allocation can account for the observed responses.
    Date: 2023–03
  22. By: Rounak Sil (KPMG Global Services, India); Unninarayanan Kurup; Ashima Goyal (Indira Gandhi Institute of Development Research); Apoorva Singh and Rajendra Paramanik (Indian Institute of Technology, Patna)
    Abstract: Using minutes of consecutive Monetary Policy Committee (MPC) meetings of the Indian central bank, we have constructed two novel measures of implicit dissent at the individual level as well as across groups. We have used VADER sentiment analysis to arrive at the proposed measures and investigated their influence on anchoring Indian growth and inflation forecasts. Our empirical findings show discordance amongst members increases forecast accuracy. This implies promoting an environment that supports nuanced opinions could improve policy outcomes.
    Keywords: Monetary policy, Dissent, NLP, Supply shock, Linear Regression
    JEL: E52 E58 C22
    Date: 2023–03

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