nep-big New Economics Papers
on Big Data
Issue of 2020‒09‒21
24 papers chosen by
Tom Coupé
University of Canterbury

  1. A Survey on Data Pricing: from Economics to Data Science By Jian Pei
  2. Testing investment forecast efficiency with textual data By Foltas, Alexander
  3. Deep Learning in Science By Stefano Bianchini; Moritz M\"uller; Pierre Pelletier
  4. Towards Earnings Call and Stock Price Movement By Zhiqiang Ma; Grace Bang; Chong Wang; Xiaomo Liu
  5. Capturing dynamics of post-earnings-announcement drift using genetic algorithm-optimised supervised learning By Zhengxin Joseph Ye; Bjorn W. Schuller
  6. The Seven-League Scheme: Deep learning for large time step Monte Carlo simulations of stochastic differential equations By Shuaiqiang Liu; Lech A. Grzelak; Cornelis W. Oosterlee
  7. Financial Frictions and the Wealth Distribution By Jesús Fernández-Villaverde; Samuel Hurtado; Galo Nuño
  8. Exploring Urban Form Through Openstreetmap Data: A Visual Introduction By Boeing, Geoff
  9. Doubly Robust Semiparametric Difference-in-Differences Estimators with High-Dimensional Data By Yang Ning; Sida Peng; Jing Tao
  10. Heterogeneous Treatment Effects of Nudge and Rebate:Causal Machine Learning in a Field Experiment on Electricity Conservation By Kayo MURAKAMI; Hideki SHIMADA; Yoshiaki USHIFUSA; Takanori IDA
  11. Artificial Intelligence, Income Distribution and Economic Growth By Gries, Thomas; Naudé, Wim
  12. Improving Investment Suggestions for Peer-to-Peer (P2P) Lending via Integrating Credit Scoring into Profit Scoring By Yan Wang; Xuelei Sherry Ni
  13. Detecting and adapting to crisis pattern with context based Deep Reinforcement Learning By Eric Benhamou; David Saltiel; Jean-Jacques Ohana; Jamal Atif
  14. A Stock Prediction Model Based on DCNN By Qiao Zhou; Ningning Liu
  15. Algorithmic Pricing and Competition: Empirical Evidence from the German Retail Gasoline Market By Stephanie Assad; Robert Clark; Daniel Ershov; Lei Xu
  16. Hiring as Exploration By Danielle Li; Lindsey R. Raymond; Peter Bergman
  17. Neural model of conveyor type transport system By Pihnastyi, Oleh; Khodusov, Valery
  18. Identifying policy challenges of COVID-19 in hardly reliable data and judging the success of lockdown measures By Bonacini, Luca; Gallo, Giovanni; Patriarca, Fabrizio
  19. Big data comes to Hollywood: Audiovisuelle Medienmärkte im digitalen Zeitalter By Gänßle, Sophia
  20. Restrictions on Privacy and Exploitation in the Digital Economy: A Market Failure Perspective By Nicholas Economides; Ioannis Lianos
  21. Forecasting financial markets with semantic network analysis in the COVID-19 crisis By A. Fronzetti Colladon; S. Grassi; F. Ravazzolo; F. Violante
  22. A Study of Financial Cycles and the Macroeconomy in Taiwan By Chen, Nan-Kuang; Cheng, Han-Liang
  23. The social costs of crime over trust: An approach with machine learning By Angelo Cozzubo
  24. Inequality in Household Adaptation to Schooling Shocks: Covid-Induced Online Learning Engagement in Real Time By Andrew Bacher-Hicks; Joshua S. Goodman; Christine Mulhern

  1. By: Jian Pei
    Abstract: It is well recognized that data are invaluable. How can we assess the value of data objectively, systematically and quantitatively? Pricing data, or information goods in general, has been studied and practiced in dispersed areas and principles, such as economics, marketing, electronic commerce, data management, data mining and machine learning. In this article, we present a unified, interdisciplinary and comprehensive overview of this important direction. We examine various motivations behind data pricing, understand the economics of data pricing and review the development and evolution of pricing models according to a series of fundamental principles. We cover both digital products and data products. Last, we discuss a series of challenges and directions for future work.
    Date: 2020–09
  2. By: Foltas, Alexander
    Abstract: I use textual data to model German professional macroeconomic forecasters' information sets and use machine-learning techniques to analyze the efficiency of forecasts. To this end, I extract information from forecast reports using a combination of topic models and word embeddings. I then use this information and traditional macroeconomic predictors to study the efficiency of investment forecasts.
    Keywords: Forecast Efficiency,Investment,Random Forest,Topic Modeling
    JEL: C53 E27 E22
    Date: 2020
  3. By: Stefano Bianchini; Moritz M\"uller; Pierre Pelletier
    Abstract: Much of the recent success of Artificial Intelligence (AI) has been spurred on by impressive achievements within a broader family of machine learning methods, commonly referred to as Deep Learning (DL). This paper provides insights on the diffusion and impact of DL in science. Through a Natural Language Processing (NLP) approach on the publication corpus, we delineate the emerging DL technology and identify a list of relevant search terms. These search terms allow us to retrieve DL-related publications from Web of Science across all sciences. Based on that sample, we document the DL diffusion process in the scientific system. We find i) an exponential growth in the adoption of DL as a research tool across all sciences and all over the world, ii) regional differentiation in DL application domains, and iii) a transition from interdisciplinary DL applications to disciplinary research within application domains. In a second step, we investigate how the adoption of DL methods affects scientific development. Therefore, we empirically assess how DL adoption relates to re-combinatorial novelty and scientific impact in the health sciences. We find that DL adoption is negatively correlated with re-combinatorial novelty, but positively correlated with expectation as well as variance of citation performance. Our findings suggest that DL does not (yet?) work as an autopilot to navigate complex knowledge landscapes and overthrow their structure. However, the 'DL principle' qualifies for its versatility as the nucleus of a general scientific method that advances science in a measurable way.
    Date: 2020–09
  4. By: Zhiqiang Ma; Grace Bang; Chong Wang; Xiaomo Liu
    Abstract: Earnings calls are hosted by management of public companies to discuss the company's financial performance with analysts and investors. Information disclosed during an earnings call is an essential source of data for analysts and investors to make investment decisions. Thus, we leverage earnings call transcripts to predict future stock price dynamics. We propose to model the language in transcripts using a deep learning framework, where an attention mechanism is applied to encode the text data into vectors for the discriminative network classifier to predict stock price movements. Our empirical experiments show that the proposed model is superior to the traditional machine learning baselines and earnings call information can boost the stock price prediction performance.
    Date: 2020–08
  5. By: Zhengxin Joseph Ye; Bjorn W. Schuller
    Abstract: While Post-Earnings-Announcement Drift (PEAD) is one of the most studied stock market anomalies, the current literature is often limited in explaining this phenomenon by a small number of factors using simpler regression methods. In this paper, we use a machine learning based approach instead, and aim to capture the PEAD dynamics using data from a large group of stocks and a wide range of both fundamental and technical factors. Our model is built around the Extreme Gradient Boosting (XGBoost) and uses a long list of engineered input features based on quarterly financial announcement data from 1,106 companies in the Russell 1000 index between 1997 and 2018. We perform numerous experiments on PEAD predictions and analysis and have the following contributions to the literature. First, we show how Post-Earnings-Announcement Drift can be analysed using machine learning methods and demonstrate such methods' prowess in producing credible forecasting on the drift direction. It is the first time PEAD dynamics are studied using XGBoost. We show that the drift direction is in fact driven by different factors for stocks from different industrial sectors and in different quarters and XGBoost is effective in understanding the changing drivers. Second, we show that an XGBoost well optimised by a Genetic Algorithm can help allocate out-of-sample stocks to form portfolios with higher positive returns to long and portfolios with lower negative returns to short, a finding that could be adopted in the process of developing market neutral strategies. Third, we show how theoretical event-driven stock strategies have to grapple with ever changing market prices in reality, reducing their effectiveness. We present a tactic to remedy the difficulty of buying into a moving market when dealing with PEAD signals.
    Date: 2020–09
  6. By: Shuaiqiang Liu; Lech A. Grzelak; Cornelis W. Oosterlee
    Abstract: We propose an accurate data-driven numerical scheme to solve Stochastic Differential Equations (SDEs), by taking large time steps. The SDE discretization is built up by means of a polynomial chaos expansion method, on the basis of accurately determined stochastic collocation (SC) points. By employing an artificial neural network to learn these SC points, we can perform Monte Carlo simulations with large time steps. Error analysis confirms that this data-driven scheme results in accurate SDE solutions in the sense of strong convergence, provided the learning methodology is robust and accurate. With a variant method called the compression-decompression collocation and interpolation technique, we can drastically reduce the number of neural network functions that have to be learned, so that computational speed is enhanced. Numerical results shows the high quality strong convergence error results, when using large time steps, and the novel scheme outperforms some classical numerical SDE discretizations. Some applications, here in financial option valuation, are also presented.
    Date: 2020–09
  7. By: Jesús Fernández-Villaverde; Samuel Hurtado; Galo Nuño
    Abstract: We postulate a nonlinear DSGE model with a financial sector and heterogeneous households. In our model, the interaction between the supply of bonds by the financial sector and the precautionary demand for bonds by households produces significant endogenous aggregate risk. This risk induces an endogenous regime-switching process for output, the risk-free rate, excess returns, debt, and leverage. The regime-switching generates i) multimodal distributions of the variables above; ii) time-varying levels of volatility and skewness for the same variables; and iii) supercycles of borrowing and deleveraging. All of these are important properties of the data. In comparison, the representative household version of the model cannot generate any of these features. Methodologically, we discuss how nonlinear DSGE models with heterogeneous agents can be efficiently computed using machine learning and how they can be estimated with a likelihood function, using inference with diffusions.
    Keywords: heterogeneous agents, wealth distribution, financial frictions, continuous-time, machine learning, neural networks, structural estimation, likelihood function
    JEL: C45 C63 E32 E44 G01 G11
    Date: 2020
  8. By: Boeing, Geoff (Northeastern University)
    Abstract: This chapter introduces OpenStreetMap—a crowd-sourced, worldwide mapping project and geospatial data repository—to illustrate its usefulness in quickly and easily analyzing and visualizing planning and design outcomes in the built environment. It demonstrates the OSMnx toolkit for automatically downloading, modeling, analyzing, and visualizing spatial big data from OpenStreetMap. We explore patterns and configurations in street networks and buildings around the world computationally through visualization methods—including figure-ground diagrams and polar histograms—that help compress urban complexity into comprehensible artifacts that reflect the human experience of the built environment. Ubiquitous urban data and computation can open up new urban form analyses from both quantitative and qualitative perspectives.
    Date: 2020–08–25
  9. By: Yang Ning; Sida Peng; Jing Tao
    Abstract: This paper proposes a doubly robust two-stage semiparametric difference-in-difference estimator for estimating heterogeneous treatment effects with high-dimensional data. Our new estimator is robust to model miss-specifications and allows for, but does not require, many more regressors than observations. The first stage allows a general set of machine learning methods to be used to estimate the propensity score. In the second stage, we derive the rates of convergence for both the parametric parameter and the unknown function under a partially linear specification for the outcome equation. We also provide bias correction procedures to allow for valid inference for the heterogeneous treatment effects. We evaluate the finite sample performance with extensive simulation studies. Additionally, a real data analysis on the effect of Fair Minimum Wage Act on the unemployment rate is performed as an illustration of our method. An R package for implementing the proposed method is available on Github.
    Date: 2020–09
  10. By: Kayo MURAKAMI; Hideki SHIMADA; Yoshiaki USHIFUSA; Takanori IDA
    Abstract: This study investigates the different impacts of monetary and nonmonetary incentives on energy-saving behaviors using a field experiment conducted in Japan. We find that the average reduction in electricity consumption from rebate is 4%, while that from nudge is not significantly different from zero. Applying a novel machine learning method for causal inference (causal forest) to estimate heterogeneous treatment effects at the household level, we demonstrate that the nudge intervention’s treatment effects generate greater heterogeneity among households. These findings suggest that selective targeting for treatment increases the policy efficiency of monetary and nonmonetary interventions.
    Keywords: Causal Forest, Rebate,Nudge, Randomized Controlled Trial, Energy, Machine Learning
    JEL: D9 C93 Q4
    Date: 2020–09
  11. By: Gries, Thomas; Naudé, Wim
    Abstract: The economic impact of Artificial Intelligence (AI) is studied using a (semi) endogenous growth model with two novel features. First, the task approach from labor economics is reformulated and integrated into a growth model. Second, the standard represen- tative household assumption is rejected, so that aggregate demand restrictions can be introduced. With these novel features it is shown that (i) AI automation can decrease the share of labor income no matter the size of the elasticity of substitution between AI and labor, and (ii) when this elasticity is high, AI will unambiguously reduce aggre- gate demand and slow down GDP growth, even in the face of the positive technology shock that AI entails. If the elasticity of substitution is low, then GDP, productivity and wage growth may however still slow down, because the economy will then fail to benefit from the supply-side driven capacity expansion potential that AI can deliver. The model can thus explain why advanced countries tend to experience, despite much AI hype, the simultaneous existence of rather high employment with stagnating wages, productivity, and GDP.
    Keywords: Technology,artificial intelligence,productivity,labor demand,income distribution,growth theory
    JEL: O47 O33 J24 E21 E25
    Date: 2020
  12. By: Yan Wang; Xuelei Sherry Ni
    Abstract: In the peer-to-peer (P2P) lending market, lenders lend the money to the borrowers through a virtual platform and earn the possible profit generated by the interest rate. From the perspective of lenders, they want to maximize the profit while minimizing the risk. Therefore, many studies have used machine learning algorithms to help the lenders identify the "best" loans for making investments. The studies have mainly focused on two categories to guide the lenders' investments: one aims at minimizing the risk of investment (i.e., the credit scoring perspective) while the other aims at maximizing the profit (i.e., the profit scoring perspective). However, they have all focused on one category only and there is seldom research trying to integrate the two categories together. Motivated by this, we propose a two-stage framework that incorporates the credit information into a profit scoring modeling. We conducted the empirical experiment on a real-world P2P lending data from the US P2P market and used the Light Gradient Boosting Machine (lightGBM) algorithm in the two-stage framework. Results show that the proposed two-stage method could identify more profitable loans and thereby provide better investment guidance to the investors compared to the existing one-stage profit scoring alone approach. Therefore, the proposed framework serves as an innovative perspective for making investment decisions in P2P lending.
    Date: 2020–09
  13. By: Eric Benhamou; David Saltiel; Jean-Jacques Ohana; Jamal Atif
    Abstract: Deep reinforcement learning (DRL) has reached super human levels in complex tasks like game solving (Go and autonomous driving). However, it remains an open question whether DRL can reach human level in applications to financial problems and in particular in detecting pattern crisis and consequently dis-investing. In this paper, we present an innovative DRL framework consisting in two sub-networks fed respectively with portfolio strategies past performances and standard deviations as well as additional contextual features. The second sub network plays an important role as it captures dependencies with common financial indicators features like risk aversion, economic surprise index and correlations between assets that allows taking into account context based information. We compare different network architectures either using layers of convolutions to reduce network's complexity or LSTM block to capture time dependency and whether previous allocations is important in the modeling. We also use adversarial training to make the final model more robust. Results on test set show this approach substantially over-performs traditional portfolio optimization methods like Markowitz and is able to detect and anticipate crisis like the current Covid one.
    Date: 2020–09
  14. By: Qiao Zhou; Ningning Liu
    Abstract: The prediction of a stock price has always been a challenging issue, as its volatility can be affected by many factors such as national policies, company financial reports, industry performance, and investor sentiment etc.. In this paper, we present a prediction model based on deep CNN and the candle charts, the continuous time stock information is processed. According to different information richness, prediction time interval and classification method, the original data is divided into multiple categories as the training set of CNN. In addition, the convolutional neural network is used to predict the stock market and analyze the difference in accuracy under different classification methods. The results show that the method has the best performance when the forecast time interval is 20 days. Moreover, the Moving Average Convergence Divergence and three kinds of moving average are added as input. This method can accurately predict the stock trend of the US NDAQ exchange for 92.2%. Meanwhile, this article distinguishes three conventional classification methods to provide guidance for future research.
    Date: 2020–09
  15. By: Stephanie Assad; Robert Clark; Daniel Ershov; Lei Xu
    Abstract: Economic theory provides ambiguous and conflicting predictions about the association between algorithmic pricing and competition. In this paper we provide the first empirical analysis of this relationship. We study Germany’s retail gasoline market where algorithmic-pricing software became widely available by mid-2017, and for which we have access to comprehensive, high-frequency price data. Because adoption dates are unknown, we identify gas stations that adopt algorithmic-pricing software by testing for structural breaks in markers associated with algo-rithmic pricing. We find a large number of station-level structural breaks around the suspected time of large-scale adoption. Using this information we investigate the impact of adoption on outcomes linked to competition. Because station-level adoption is endogenous, we use brand headquarter-level adoption decisions as instruments. Our IV results show that adoption in-creases margins by 9%, but only in non-monopoly markets. Restricting attention to duopoly markets, we find that market-level margins do not change when only one of the two stations adopts, but increase by 28% in markets where both do. These results suggest that AI adoption has a significant effect on competition.
    Keywords: artificial intelligence, pricing-algorithms, collusion, retail gasoline
    JEL: L41 L13 D43 D83 L71
    Date: 2020
  16. By: Danielle Li; Lindsey R. Raymond; Peter Bergman
    Abstract: This paper views hiring as a contextual bandit problem: to find the best workers over time, firms must balance “exploitation” (selecting from groups with proven track records) with “exploration” (selecting from under-represented groups to learn about quality). Yet modern hiring algorithms, based on “supervised learning” approaches, are designed solely for exploitation. Instead, we build a resume screening algorithm that values exploration by evaluating candidates according to their statistical upside potential. Using data from professional services recruiting within a Fortune 500 firm, we show that this approach improves the quality (as measured by eventual hiring rates) of candidates selected for an interview, while also increasing demographic diversity, relative to the firm's existing practices. The same is not true for traditional supervised learning based algorithms, which improve hiring rates but select far fewer Black and Hispanic applicants. In an extension, we show that exploration-based algorithms are also able to learn more effectively about simulated changes in applicant hiring potential over time. Together, our results highlight the importance of incorporating exploration in developing decision-making algorithms that are potentially both more efficient and equitable.
    JEL: D80 J20 M15 M51 O33
    Date: 2020–08
  17. By: Pihnastyi, Oleh; Khodusov, Valery
    Abstract: In this paper, a model of a transport conveyor system using a neural network is demonstrated. The analysis of the main parameters of modern conveyor systems is presented. The main models of the conveyor section, which are used for the design of control systems for flow parameters, are considered. The necessity of using neural networks in the design of conveyor transport control systems is substantiated. A review of conveyor models using a neural network is performed. The conditions of applicability of models using neural networks to describe conveyor systems are determined. A comparative analysis of the analytical model of the conveyor section and the model using the neural network is performed. The technique of forming a set of test data for the process of training a neural network is presented. The foundation for the formation of test data for learning neural network is an analytical model of the conveyor section. Using an analytical model allowed us to form a set of test data for transient dynamic modes of functioning of the transport system. The transport system is presented in the form of a directed graph without cycles. Analysis of the model using a neural network showed a high-quality relationship between the output flow for different conveyor sections of the transport system
    Keywords: conveyor; PDE– model; distributed system; transport delay
    JEL: C02 C15 C25 C44 D24
    Date: 2020–05–01
  18. By: Bonacini, Luca; Gallo, Giovanni; Patriarca, Fabrizio
    Abstract: Identifying structural breaks in the dynamics of COVID-19 contagion is crucial to promptly assess policies and evaluate the effectiveness of lockdown measures. However, official data record infections after a critical and unpredictable delay. Moreover, people react to the health risks of the virus and also anticipate lockdowns. All of this makes it complex to quickly and accurately detect changing patterns in the virus’s infection dynamic. We propose a machine learning procedure to identify structural breaks in the time series of COVID-19 cases. We consider the case of Italy, an early-affected country that was unprepared for the situation, and detect the dates of structural breaks induced by three national lockdowns so as to evaluate their effects and identify some related policy issues. The strong but significantly delayed effect of the first lockdown suggests a relevant announcement effect. In contrast, the last lockdown had significantly less impact. The proposed methodology is robust as a real-time procedure for early detection of the structural breaks: the impact of the first two lockdowns could have been correctly identified just the day after they actually occurred.
    Keywords: COVID-19,coronavirus,lockdown,machine learning
    JEL: C63 I12 I18
    Date: 2020
  19. By: Gänßle, Sophia
    Abstract: Die Traumfabrik Hollywood dominiert seit Dekaden die internationale Filmindustrie. Die gro-ßen amerikanischen Studios produzieren und vertreiben Filme, wobei sie alle Innovationen des 20ten Jahrhundert, von Fernsehen über VHS bis Blu-ray, überstanden haben. Die Digitalisie-rung bringt tiefgreifende technische Innovationen und damit auch neuen Wettbewerb in den Markt. Die großen Hollywood Studios stehen nun der Konkurrenz von Streamingdiensten wie Netflix und Amazon Prime Video gegenüber. Die digitale Überlegenheit dieser neuen Giganten und deren Umgang mit Daten stellen einen elementaren Unterschied zu den bisherigen Strate-gien der Studios dar. Dieser Beitrag zeigt die Änderungen auf und analysiert die neue Wettbe-werbssituation in der Filmindustrie.
    Date: 2020
  20. By: Nicholas Economides (Professor of Economics, NYU Stern School of Business, New York, New York 10012); Ioannis Lianos (Professor of Global Competition Law and Public Policy, Faculty of Laws, University College London, and Hellenic Competition Commission)
    Abstract: We discuss how the acquisition of private information by default without compensation by digital platforms such as Google and Facebook creates a market failure and can be grounds for antitrust enforcement. To avoid the market failure, the default in the collection of personal information has to be changed by law to “opt-out.” This would allow the creation of a vibrant market for the sale of users’ personal information to digital platforms. Assuming that all parties are perfectly informed, users are better off in this functioning market and digital platforms are worse off compared to the default opt-in. However, just switching to a default opt-in will not restore competition to the but for world because of the immense market power and bargaining power towards an individual user that digital platforms have acquired. Digital platforms can use this power to reduce the compensation that a user would receive for his/her personal information compared to a competitive world. Additionally, it is likely that the digital platforms are much better informed than the user in this market, and can use this information to disadvantage users in the market for personal information.
    Keywords: personal information; Internet search; Google; Facebook; digital; privacy; restrictions of competition; exploitation; market failure; hold up; merger; abuse of a dominant position; unfair commercial practices; excessive data extraction; self-determination; behavioral manipulation; remedies; portability; opt-in; opt-out.
    JEL: K21 L1 L12 L4 L41 L5 L86 L88
    Date: 2020–09
  21. By: A. Fronzetti Colladon; S. Grassi; F. Ravazzolo; F. Violante
    Abstract: This paper uses a new textual data index for predicting stock market data. The index is applied to a large set of news to evaluate the importance of one or more general economic related keywords appearing in the text. The index assesses the importance of the economic related keywords, based on their frequency of use and semantic network position. We apply it to the Italian press and construct indices to predict Italian stock and bond market returns and volatilities in a recent sample period, including the COVID-19 crisis. The evidence shows that the index captures well the different phases of financial time series. Moreover, results indicate strong evidence of predictability for bond market data, both returns and volatilities, short and long maturities, and stock market volatility.
    Date: 2020–09
  22. By: Chen, Nan-Kuang; Cheng, Han-Liang
    Abstract: his paper studies the characteristics of financial cycles (credit and house prices) and their interactions with business cycles in Taiwan. We employ multivariate structural time series model (STSM) to estimate trend and cyclical components in real bank credit, real house prices, and real GDP. We find that financial cycles are roughly twice the length of the business cycles, and house price cycles lead both credit and business cycles. Nevertheless, the estimated length of business and financial cycles in Taiwan is much shorter than those in industrialized economies. We then use machine learning to evaluate the importance of a macroeconomic variable that predicts downturns of financial cycles, by conducting both in-sample fitting and out-of-sample forecasting. Those macro variables selected by machine learning reflects Taiwan's close linkage in trades and financial interdependence with other countries such as China and spillover effects from the Fed's monetary policy.
    Keywords: financial cycle, credit, house prices, wavelet analysis, machine learning
    JEL: E32 E37 E44
    Date: 2020–06–23
  23. By: Angelo Cozzubo (University of Chicago)
    Abstract: In Peru, 55% of the population considers insecurity as the country's main problem. The present study seeks to contribute to the understanding of the social costs of crime in Peru by measuring the impact of patrimonial crime on trust in public institutions, using victimization surveys and censuses of police stations and municipalities and using the newly implemented machine-learning techniques in Stata combined with propensity score matching. Results: reduction of 3 percentage points (pp.) in the probability of trusting in the police and Serenazgo in the short term and 2 pp. in judicial power in the long term. Female victims would lose more confidence in Serenazgo and the Public Ministry. Robustness in the presence of unobservables, different pairings, and falsification tests, which would suggest potential causal character.
    Date: 2020–08–20
  24. By: Andrew Bacher-Hicks; Joshua S. Goodman; Christine Mulhern
    Abstract: We use high frequency internet search data to study in real time how US households sought out online learning resources as schools closed due to the Covid-19 pandemic. By April 2020, nationwide search intensity for both school- and parent-centered online learning resources had roughly doubled relative to baseline. Areas of the country with higher income, better internet access and fewer rural schools saw substantially larger increases in search intensity. The pandemic will likely widen achievement gaps along these dimensions given schools’ and parents’ differing engagement with online resources to compensate for lost school-based learning time. Accounting for such differences and promoting more equitable access to online learning could improve the effectiveness of education policy responses to the pandemic. The public availability of internet search data allows our analyses to be updated when schools reopen and to be replicated in other countries.
    Keywords: online learning, school closures, internet search, Google trends, Covid-19
    JEL: I20
    Date: 2020

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