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on Big Data |
| By: | John A. List; Ian Muir; Gregory K. Sun |
| Abstract: | This study investigates how to use regression adjustment to reduce variance in experimental data. We show that the estimators recommended in the literature satisfy an orthogonality property with respect to the parameters of the adjustment. This observation greatly simplifies the derivation of the asymptotic variance of these estimators and allows us to solve for the efficient regression adjustment in a large class of adjustments. Our efficiency results generalize a number of previous results known in the literature. We then discuss how this efficient regression adjustment can be feasibly implemented. We show the practical relevance of our theory in two ways. First, we use our efficiency results to improve common practices currently employed in field experiments. Second, we show how our theory allows researchers to robustly incorporate machine learning techniques into their experimental estimators to minimize variance. |
| JEL: | C9 C90 C91 C93 |
| Date: | 2022–12 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberwo:30756&r=big |
| By: | Prateek Jain; Alberto Garcia Garcia |
| Abstract: | Within this decade, quantum computers are predicted to outperform conventional computers in terms of processing power and have a disruptive effect on a variety of business sectors.It is predicted that the financial sector would be one of the first to benefit from quantum computing both in the short and long terms. In this research work we use Hybrid Quantum Neural networks to present a quantum machine learning approach for Continuous variable prediction. |
| Date: | 2022–12 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2212.04209&r=big |
| By: | Hoang, Daniel; Wiegratz, Kevin |
| Abstract: | We study how researchers can apply machine learning (ML) methods in finance. We first establish that the two major categories of ML (supervised and unsupervised learning) address fundamentally different problems than traditional econometric approaches. Then, we review the current state of research on ML in finance and identify three archetypes of applications: i) the construction of superior and novel measures, ii) the reduction of prediction error, and iii) the extension of the standard econometric toolset. With this taxonomy, we give an outlook on potential future directions for both researchers and practitioners. Our results suggest large benefits of ML methods compared to traditional approaches and indicate that ML holds great potential for future research in finance. |
| Keywords: | Machine Learning, Artificial Intelligence, Big Data |
| JEL: | C45 G00 |
| Date: | 2022 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:kitwps:158&r=big |
| By: | N'yoma Diamond; Grant Perkins |
| Abstract: | In its semi-strong form, the Efficient Market Hypothesis (EMH) implies that technical analysis will not reveal any hidden statistical trends via intermarket data analysis. If technical analysis on intermarket data reveals trends which can be leveraged to significantly outperform the stock market, then the semi-strong EMH does not hold. In this work, we utilize a variety of machine learning techniques to empirically evaluate the EMH using stock market, foreign currency (Forex), international government bond, index future, and commodities future assets. We train five machine learning models on each dataset and analyze the average performance of these models for predicting the direction of future S&P 500 movement as approximated by the SPDR S&P 500 Trust ETF (SPY). From our analysis, the datasets containing bonds, index futures, and/or commodities futures data notably outperform baselines by substantial margins. Further, we find that the usage of intermarket data induce statistically significant positive impacts on the accuracy, macro F1 score, weighted F1 score, and area under receiver operating characteristic curve for a variety of models at the 95% confidence level. This provides strong empirical evidence contradicting the semi-strong EMH. |
| Date: | 2022–12 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2212.08734&r=big |
| By: | Asati, Akshita |
| Abstract: | CPI often referred to as the Consumer Price Index is a crucial and thorough method employed to estimate price changes over a fixed time interval within a country which is representative of consumption expenditure in a country‘s economy. CPI being an economic indicator engenders therefore the popular metric called inflation of the country. Thus, if we can accurately forecast the CPI, the country‘s economy can be controlled well in time and appropriate decision-making can be enabled. Hence, for a decade CPI index forecasting, especially in a developing country like India, has been always a matter of interest and research topic for economists and policy of the government. To forecast CPI, humans (decision makers) required vast domain knowledge and experience. Moreover, traditional CPI forecasting involved a multitude of human interventions and discussions for the same. However, with the recent advancements in the domain of time series forecasting techniques encompassing dependable modern machine learning, statistical as well as deep learning models there exists a potential scope in leveraging modern technology to forecast CPI of India which can technically aid towards this important decision-making step in a diverse country like India. In this paper, a comparative study is carried out exploring MAD, RMSE, and MAPE as comparison criteria amongst Machine Learning (XGBoost), Statistical Learning (Theta, ARIMA, Prophet) and Deep Learning (LSTM) algorithms. Furthermore, from this comparative univariate time series forecasting study, it can be demonstrated that technological solutions in the domain of forecasting show promising results with reasonable forecast accuracy. |
| Date: | 2022–12–21 |
| URL: | http://d.repec.org/n?u=RePEc:osf:osfxxx:hyqsb&r=big |
| By: | Raj G. Patel; Chia-Wei Hsing; Serkan Sahin; Samuel Palmer; Saeed S. Jahromi; Shivam Sharma; Tomas Dominguez; Kris Tziritas; Christophe Michel; Vincent Porte; Mustafa Abid; Stephane Aubert; Pierre Castellani; Samuel Mugel; Roman Orus |
| Abstract: | Recent advances in deep learning have enabled us to address the curse of dimensionality (COD) by solving problems in higher dimensions. A subset of such approaches of addressing the COD has led us to solving high-dimensional PDEs. This has resulted in opening doors to solving a variety of real-world problems ranging from mathematical finance to stochastic control for industrial applications. Although feasible, these deep learning methods are still constrained by training time and memory. Tackling these shortcomings, Tensor Neural Networks (TNN) demonstrate that they can provide significant parameter savings while attaining the same accuracy as compared to the classical Dense Neural Network (DNN). In addition, we also show how TNN can be trained faster than DNN for the same accuracy. Besides TNN, we also introduce Tensor Network Initializer (TNN Init), a weight initialization scheme that leads to faster convergence with smaller variance for an equivalent parameter count as compared to a DNN. We benchmark TNN and TNN Init by applying them to solve the parabolic PDE associated with the Heston model, which is widely used in financial pricing theory. |
| Date: | 2022–12 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2212.14076&r=big |
| By: | Laureti, Lucio; Costantiello, Alberto; Leogrande, Angelo |
| Abstract: | In this article we estimate the level of Control of Corruption for 193 countries in the period 2011-2020 using data from the ESG World Bank Database. Various econometric techniques are applied i.e.: Panel Data with Random Effects, Panel Data with Fixed Effects, Pooled OLS, WLS. Results show that “Control of Corruption” is positively associated, among others, to “Government Effectiveness” and “Political Stability and Absence of Violence/Terrorism”, while it is negatively associated among others to “Agriculture, Forestry, and Fishing Value Added as Percentage of GDP” and “GHG Net Emissions/Removals by LUCF”. A cluster analysis implemented with the k-Means algorithm optimized with the Elbow Method shows four clusters. A confrontation among eight Machine Learning algorithms is proposed for the prediction of Control of Corruption. Polynomial Regression is the best predictor for the training data. The level of Control of Corruption is expected to growth by 10.36% on average. |
| Keywords: | D7, D70, D72, D73, D78. |
| JEL: | D70 D72 D73 D78 |
| Date: | 2022–12–30 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:115837&r=big |
| By: | Laureti, Lucio; Costantiello, Alberto; Leogrande, Angelo |
| Abstract: | In this article we investigate the impact of “Renewable Electricity Output” on green economy in the context of circular economy for 193 countries in the period 2011-2020. We use data from World Bank ESG framework. We perform Panel Data with Fixed Effects, Panel Data with Random Effects, WLS, and Pooled OLS. Our results show that Renewable Electricity Output is positively associated, among others, to “Adjusted Savings-Net Forest Depletion” and “Renewable Energy Consumption” and negatively associated, among others, to “CO2 Emission” and “Cooling Degree Days”. Furthermore, we perform a cluster analysis implementing the k-Means algorithm optimized with the Elbow Method and we find the presence of 4 clusters. Finally, we confront seven different machine learning algorithms to predict the future level of “Renewable Electricity Output”. Our results show that Linear Regression is the best algorithm and that the future value of renewable electricity output is predicted to growth on average at a rate of 0.83% for the selected countries. |
| Keywords: | Environmental Economics, General, Valuation of Environmental Effects, Pollution Control Adoption and Costs, Recycling. |
| JEL: | Q50 Q51 Q52 Q53 Q54 Q55 |
| Date: | 2022–12–11 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:115627&r=big |
| By: | Xiaodong Li; Pangjing Wu; Chenxin Zou; Qing Li |
| Abstract: | Designing an intelligent volume-weighted average price (VWAP) strategy is a critical concern for brokers, since traditional rule-based strategies are relatively static that cannot achieve a lower transaction cost in a dynamic market. Many studies have tried to minimize the cost via reinforcement learning, but there are bottlenecks in improvement, especially for long-duration strategies such as the VWAP strategy. To address this issue, we propose a deep learning and hierarchical reinforcement learning jointed architecture termed Macro-Meta-Micro Trader (M3T) to capture market patterns and execute orders from different temporal scales. The Macro Trader first allocates a parent order into tranches based on volume profiles as the traditional VWAP strategy does, but a long short-term memory neural network is used to improve the forecasting accuracy. Then the Meta Trader selects a short-term subgoal appropriate to instant liquidity within each tranche to form a mini-tranche. The Micro Trader consequently extracts the instant market state and fulfils the subgoal with the lowest transaction cost. Our experiments over stocks listed on the Shanghai stock exchange demonstrate that our approach outperforms baselines in terms of VWAP slippage, with an average cost saving of 1.16 base points compared to the optimal baseline. |
| Date: | 2022–12 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2212.14670&r=big |
| By: | Julia Cagé (ECON - Département d'économie (Sciences Po) - Sciences Po - Sciences Po - CNRS - Centre National de la Recherche Scientifique, CEPR - Center for Economic Policy Research - CEPR); Nicolas Hervé (INA - Institut National de l'Audiovisuel); Béatrice Mazoyer (INA - Institut National de l'Audiovisuel, médialab - médialab (Sciences Po) - Sciences Po - Sciences Po) |
| Abstract: | Social media are increasingly influencing society and politics, despite the fact that legacy media remain the most consumed source of news. In this paper, we study the propagation of information from social media to mainstream media, and investigate whether news editors' editorial decisions are influenced by the popularity of news stories on social media. To do so, we build a novel dataset including around 70% of all the tweets produced in French between August 2018 and July 2019 and the content published online by 200 mainstream media outlets. We then develop novel algorithms to identify and link events on social and mainstream media. To isolate the causal impact of popularity, we rely on the structure of the Twitter network and propose a new instrument based on the interaction between measures of user centrality and "social media news pressure" at the time of the event. We show that the social media popularity of a story increases the coverage of the same story by mainstream media. This effect varies depending on the media outlets' characteristics, in particular on whether they use a paywall. Finally, we investigate consumers' reaction to a surge in social media popularity. Our findings shed new light on news production decisions in the digital age and the welfare effects of social media. |
| Keywords: | Internet, Information spreading, News editors, Network analysis, Social media, Twitter, Text analysis |
| Date: | 2022–07–30 |
| URL: | http://d.repec.org/n?u=RePEc:hal:spmain:hal-03877907&r=big |
| By: | Julia Cagé (ECON - Département d'économie (Sciences Po) - Sciences Po - Sciences Po - CNRS - Centre National de la Recherche Scientifique, CEPR - Center for Economic Policy Research - CEPR); Nicolas Hervé (INA - Institut National de l'Audiovisuel); Béatrice Mazoyer (médialab - médialab (Sciences Po) - Sciences Po - Sciences Po) |
| Abstract: | Social media affects not only the way we consume news, but also the way news is produced, including by traditional media outlets. In this paper, we study the propagation of information from social media to mainstream media, and investigate whether news editors' editorial decisions are influenced by the popularity of news stories on social media To do so, we build a novel dataset including a representative sample of all the tweets produced in French between August 1st 2018 and July 31st 2019 (1.8 billion tweets, around 70% of all tweets in French) and the content published online by 200 mainstream media outlets. We then develop novel algorithms to identify and link events on social and mainstream media. To isolate the causal impact of popularity, we rely on the structure of the Twitter network and propose a new instrument based on the interaction between measures of user centrality and "social media news pressure" at the time of the event. We show that story popularity has a positive effect on media coverage, and that this effect varies depending on the media outlets' characteristics, in particular on whether they use a paywall. Finally, we investigate consumers' reaction to a surge in social media popularity. Our findings shed new light on our understanding of how editors decide on the coverage for stories, and question the welfare effects of social media. |
| Keywords: | Internet, Information spreading, News editors, Network analysis, Social media, Twitter, Text analysis |
| Date: | 2022–05–31 |
| URL: | http://d.repec.org/n?u=RePEc:hal:spmain:hal-03811318&r=big |
| By: | Jan Ditzen; Francesco Ravazzolo |
| Abstract: | For western economies a long-forgotten phenomenon is on the horizon: rising inflation rates. We propose a novel approach christened D2ML to identify drivers of national inflation. D2ML combines machine learning for model selection with time dependent data and graphical models to estimate the inverse of the covariance matrix, which is then used to identify dominant drivers. Using a dataset of 33 countries, we find that the US inflation rate and oil prices are dominant drivers of national inflation rates. For a more general framework, we carry out Monte Carlo simulations to show that our estimator correctly identifies dominant drivers. |
| Date: | 2022–12 |
| URL: | http://d.repec.org/n?u=RePEc:bny:wpaper:0111&r=big |
| By: | MohammadAmin Fazli; Mahdi Lashkari; Hamed Taherkhani; Jafar Habibi |
| Abstract: | Solving portfolio management problems using deep reinforcement learning has been getting much attention in finance for a few years. We have proposed a new method using experts signals and historical price data to feed into our reinforcement learning framework. Although experts signals have been used in previous works in the field of finance, as far as we know, it is the first time this method, in tandem with deep RL, is used to solve the financial portfolio management problem. Our proposed framework consists of a convolutional network for aggregating signals, another convolutional network for historical price data, and a vanilla network. We used the Proximal Policy Optimization algorithm as the agent to process the reward and take action in the environment. The results suggested that, on average, our framework could gain 90 percent of the profit earned by the best expert. |
| Date: | 2022–12 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2212.14477&r=big |
| By: | Arthur Charpentier |
| Abstract: | The analysis of discrimination has long interested economists and lawyers. In recent years, the literature in computer science and machine learning has become interested in the subject, offering an interesting re-reading of the topic. These questions are the consequences of numerous criticisms of algorithms used to translate texts or to identify people in images. With the arrival of massive data, and the use of increasingly opaque algorithms, it is not surprising to have discriminatory algorithms, because it has become easy to have a proxy of a sensitive variable, by enriching the data indefinitely. According to Kranzberg (1986), "technology is neither good nor bad, nor is it neutral", and therefore, "machine learning won't give you anything like gender neutrality `for free' that you didn't explicitely ask for", as claimed by Kearns et a. (2019). In this article, we will come back to the general context, for predictive models in classification. We will present the main concepts of fairness, called group fairness, based on independence between the sensitive variable and the prediction, possibly conditioned on this or that information. We will finish by going further, by presenting the concepts of individual fairness. Finally, we will see how to correct a potential discrimination, in order to guarantee that a model is more ethical |
| Date: | 2022–12 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2212.09868&r=big |
| By: | Majid Ahmadi; Nathan Durst; Jeff Lachman; John A. List; Mason List; Noah List; Atom T. Vayalinkal |
| Abstract: | Recent models and empirical work on network formation emphasize the importance of propinquity in producing strong interpersonal connections. Yet, one might wonder how deep such insights run, as thus far empirical results rely on survey and lab-based evidence. In this study, we examine propinquity in a high-stakes setting of talent allocation: the Major League Baseball (MLB) Draft from 2000-2019 (30, 000 players were drafted from a player pool of more than a million potential draftees). Our findings can be summarized in four parts. First, propinquity is alive and well in our setting, and spans even the latter years of our sample, when higher-level statistical exercises have become the norm rather than the exception. Second, the measured effect size is consequential, as MLB clubs pay a significant opportunity cost in terms of inferior talent acquired due to propinquity bias: for example, their draft picks are 38% less likely to ever play a MLB game relative to players drafted without propinquity bias. Third, those players who benefit from propinquity bias fare better both in terms of the timing of their draft picks and their initial financial contract, conditional on draft order. Finally, the effect is found to be the most pronounced in later rounds of the draft, where the Scouting Director has the greatest latitude. |
| JEL: | C93 D4 J30 J7 |
| Date: | 2022–12 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberwo:30786&r=big |
| By: | Gabriele Pellegrino (Università Cattolica del Sacro Cuore); Orion Penner (Ecole polytechnique federale de Lausanne); Etienne Piguet (University of Neuchatel); Gaetan de Rassenfosse (Ecole polytechnique federale de Lausanne) |
| Abstract: | This paper studies the relationship between migration and the productivity of high-skilled workers, as captured by inventors listed in patent applications. Using machine learning techniques to identify inventors across patents uniquely, we are able to track the worldwide migration patterns of nearly one million individual inventors. Migrant inventors account for more than ten percent of inventors worldwide. The econometric analysis seeks to explain the recurring finding in the literature that migrant inventors are more productive than non-migrant inventors. We find that migrant inventors become about thirty-percent more productive after having migrated. The disambiguated inventor data are openly available. |
| Keywords: | inventor; productivity; skilled migration |
| JEL: | F22 J61 O30 |
| Date: | 2023–01 |
| URL: | http://d.repec.org/n?u=RePEc:iip:wpaper:20&r=big |
| By: | Hamid Nasiri; Mohammad Mehdi Ebadzadeh |
| Abstract: | Financial time series prediction, a growing research topic, has attracted considerable interest from scholars, and several approaches have been developed. Among them, decomposition-based methods have achieved promising results. Most decomposition-based methods approximate a single function, which is insufficient for obtaining accurate results. Moreover, most existing researches have concentrated on one-step-ahead forecasting that prevents stock market investors from arriving at the best decisions for the future. This study proposes two novel methods for multi-step-ahead stock price prediction based on the issues outlined. DCT-MFRFNN, a method based on discrete cosine transform (DCT) and multi-functional recurrent fuzzy neural network (MFRFNN), uses DCT to reduce fluctuations in the time series and simplify its structure and MFRFNN to predict the stock price. VMD-MFRFNN, an approach based on variational mode decomposition (VMD) and MFRFNN, brings together their advantages. VMD-MFRFNN consists of two phases. The input signal is decomposed to several IMFs using VMD in the decomposition phase. In the prediction and reconstruction phase, each of the IMFs is given to a separate MFRFNN for prediction, and predicted signals are summed to reconstruct the output. Three financial time series, including Hang Seng Index (HSI), Shanghai Stock Exchange (SSE), and Standard & Poor's 500 Index (SPX), are used for the evaluation of the proposed methods. Experimental results indicate that VMD-MFRFNN surpasses other state-of-the-art methods. VMD-MFRFNN, on average, shows 35.93%, 24.88%, and 34.59% decreases in RMSE from the second-best model for HSI, SSE, and SPX, respectively. Also, DCT-MFRFNN outperforms MFRFNN in all experiments. |
| Date: | 2022–12 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2212.14687&r=big |
| By: | Gutiérrez, Antonio |
| Abstract: | This paper empirically analyses the entrepreneurship gender gap and geographic variations of the entrepreneurship culture in the United States. To do so, we use Google search engine queries. First, we construct a composite index using factor analysis on searches related to entrepreneurship. Second, we explored the degree of local sexism that exists across the United States. Therefore, we use a simple index that represents sexist queries made on the search engine. The results indicate a positive and statistically significant correlation between the composite index and the number of companies based in each market area. The local sexism index fails to explain the gender gap in entrepreneurship. Conversely, it is intra-household decisions and the proportion of individuals in each age group that show statistically significant correlations with the entrepreneurship gender gap. |
| Keywords: | entrepreneurship; gender gap; entrepreneurship culture; Google Trends; Big Data |
| JEL: | J16 J71 L26 O51 |
| Date: | 2023–01 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:115876&r=big |
| By: | Jean-Fran\c{c}ois Chassagneux; Junchao Chen; Noufel Frikha |
| Abstract: | We propose a new probabilistic scheme which combines deep learning techniques with high order schemes for backward stochastic differential equations belonging to the class of Runge-Kutta methods to solve high-dimensional semi-linear parabolic partial differential equations. Our approach notably extends the one introduced in [Hure Pham Warin 2020] for the implicit Euler scheme to schemes which are more efficient in terms of discrete-time error. We establish some convergence results for our implemented schemes under classical regularity assumptions. We also illustrate the efficiency of our method for different schemes of order one, two and three. Our numerical results indicate that the Crank-Nicolson schemes is a good compromise in terms of precision, computational cost and numerical implementation. |
| Date: | 2022–12 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2212.14372&r=big |
| By: | Ben Lockwood; Francesco Porcelli; Michela Redoano; Antonio Schiavone; Benjamin Lockwood |
| Abstract: | We exploit the introduction of an open data online platform - part of a transparency program initiated by the Italian Government in late 2014 - as a natural experiment to analyse the effect of data disclosure on mayors’ expenditure and public good provision. First, we analyse the effect of the program by comparing municipalities on the border between ordinary and special regions, exploiting the fact that the latter regions did not participate in the program. We find that mayors in ordinary regions immediately change their behaviour after data disclosure by improving the disclosed indicators, and that the reaction depends also on their initial relative performance, a yardstick competition effect. Second, we investigate the effect of mayors’ attention to data disclosure within treated regions by tracking their daily accesses to the platform, which we instrument with the daily publication of newspaper articles mentioning the program. We find that mayors react to data disclosure by decreasing spending via a reduction of service provision, resulting in an aggregate decrease in efficiency. Overall, mayors seem to target variables that are disclosed on the website at the expense of variables that are less salient. |
| Keywords: | open data, local government, media coverage, OpenCivitas |
| JEL: | H72 H79 |
| Date: | 2022 |
| URL: | http://d.repec.org/n?u=RePEc:ces:ceswps:_10155&r=big |
| By: | Rachna Saxena; Abhijeet Kumar; Mridul Mishra |
| Abstract: | Lead recommendations for financial products such as funds or ETF is potentially challenging in investment space due to changing market scenarios, and difficulty in capturing financial holder's mindset and their philosophy. Current methods surface leads based on certain product categorization and attributes like returns, fees, category etc. to suggest similar product to investors which may not capture the holder's investment behavior holistically. Other reported works does subjective analysis of institutional holder's ideology. This paper proposes a comprehensive data driven framework for developing a lead recommendations system in holder's space for financial products like funds by using transactional history, asset flows and product specific attributes. The system assumes holder's interest implicitly by considering all investment transactions made and collects possible meta information to detect holder's investment profile/persona like investment anticipation and investment behavior. This paper focusses on holder recommendation component of framework which employs a bi-partite graph representation of financial holders and funds using variety of attributes and further employs GraphSage model for learning representations followed by link prediction model for ranking recommendation for future period. The performance of the proposed approach is compared with baseline model i.e., content-based filtering approach on metric hits at Top-k (50, 100, 200) recommendations. We found that the proposed graph ML solution outperform baseline by absolute 42%, 22% and 14% with a look ahead bias and by absolute 18%, 19% and 18% on completely unseen holders in terms of hit rate for top-k recommendations: 50, 100 and 200 respectively. |
| Date: | 2022–11 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2212.09624&r=big |
| By: | Zhang, Shoucheng |
| Abstract: | Economic growth and development of the nation as a whole is greatly influenced by the development and growth of the data centers. At both a national and international level, data centers contribute to the growth of the economy for the benefit of citizens. The result of this is that governments are able to increase their competitiveness, ease of doing business, contribute to the growth of their economies, and attract investors to their countries, as a result of this. Data centers are widely used and re-used throughout the economy, which highlights the importance of data as a new form of capital for 21st century knowledge driven economies, and more specifically, the re-use of data centers within the economy, which highlights the importance of data as a new form of capital for 21st century knowledge driven economies. The fact that data centers are capable of being re-used for a theoretically unlimited range of purposes means that they cannot be depleted at all because they can never run out of use. In the event that data centers are repurposed for the purpose of generating opportunities for growth, or generating benefits for society on a large scale that could not have been imagined when the data centers were first created, then the result may be positive spill-over effects. Governments can enhance their reputation by investing in Data Centers and initiatives but they can also be able to drive innovation across the economy by taking data-driven decisions that enhance their reputation as well. By making data available as well as sharing it, spillover benefits may also be created, since the availability and sharing of data may enable "super-additive" insights that may be greater than the sum of insights derived from isolated parts (data silos), allowing data to be used more efficiently. |
| Keywords: | Technology and Economic Impact, Data Centre and Development, Impact of Technologies on Nations, Data Centre Technologies and Organizational Development |
| JEL: | O1 O14 O3 O32 O33 Q55 |
| Date: | 2022–12–24 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:115811&r=big |