The Effect of Short-Term Rentals on Local Consumption Amenities: Evidence from Madrid

This paper investigates the impact of the arrival of Airbnb on the local consumption amenities in Madrid. We exploit the exogenous variation created by the timing and the unequal distribution of Airbnb listings across the urban geography to identify its effects on food and beverage establishments. Using an instrumental variable strategy, we find positive local effects on both the number of restaurants and their employees: an increase in ten Airbnb rooms in a given census tract translates into one more restaurant, and the same increase in a given neighborhood generates nine new tourist-related employees. The results are robust to sample composition, spatial spillovers and alternative measures of local consumption amenities. This paper contributes to the literature on the economic impacts of the platform economy on urban areas by providing evidence of positive economic externalities from short-term rentals

Value creation mechanisms of cloud computing: a conceptual framework

The management literature has analysed Cloud Computing, mainly focusing on the impact of its technical properties (e.g. accessibility, elasticity, scaling) on firms' dynamics, without explicitly addressing the dynamic generation of value streams. With this paper we fill this gap, linking the unexplored potential sources of Cloud Computing with the literature on business model value creation. We define a conceptual model able to integrate existent technical knowledge on Cloud Computing with the understudied part on the value creation mechanisms, dynamically representing their interaction. Our approach is based on a mixed methodology built on three pillars: 1) systematic literature review of the properties of Cloud Computing with an impact on firms’ management in order to identify possible gaps, using value generation within business models as the unit of analysis; 2) multiple case studies to inductively derive the emerging properties using Gioia methodology, analysing 20 startups in the AWS business case repository; 3) dynamic representation between technical properties extracted by literature review and emergent properties, focusing on the value streams generation. Results confirm how the leveraging potentiality of Cloud Computing goes well beyond technical advantages, deeply inserting in the business model system and enabling different sources of value creation

A Neural Network Ensemble Approach for GDP Forecasting

We propose an ensemble learning methodology to forecast the future US GDP growth release. Our approach combines a Recurrent Neural Network (RNN) with a Dynamic Factor model accounting for time-variation in mean with a General- ized Autoregressive Score (DFM-GAS). The analysis is based on a set of predictors encompassing a wide range of variables measured at different frequencies. The forecast exercise is aimed at evaluating the predictive ability of each model's com- ponent of the ensemble by considering variations in mean, potentially caused by recessions affecting the economy. Thus, we show how the combination of RNN and DFM-GAS improves forecasts of the US GDP growth rate in the aftermath of the 2008-09 global financial crisis. We find that a neural network ensemble markedly reduces the root mean squared error for the short-term forecast horizon

Predicting Exporters with Machine Learning

In this contribution, we exploit machine learning techniques to predict out-of-sample firms' ability to export based on the financial accounts of both exporters and non-exporters. Therefore, we show how forecasts can be used as exporting scores, i.e., to measure the distance of non-exporters from export status. For our purpose, we train and test various algorithms on the financial reports of 57,021 manufacturing firms in France in 2010-2018. We find that a Bayesian Additive Regression Tree with Missingness In Attributes (BART-MIA) performs better than other techniques with a prediction accuracy of up to 0:90. Predictions are robust to changes in definitions of exporters and in the presence of discontinuous exporters. Eventually, we argue that exporting scores can be helpful for trade promotion, trade credit, and to assess firms' competitiveness. For example, back-of-the-envelope estimates show that a representative firm with just below-average exporting scores needs up to 44% more cash resources and up to 2:5 times more capital expenses to reach full export status

Improving the Prediction of Clinical Success Using Machine Learning

In pharmaceutical research, assessing drug candidates’ odds of success as they move through clinical research often relies on crude methods based on historical data. However, the rapid progress of machine learning offers a new tool to identify the more promising projects. To evaluate its usefulness, we trained and validated several machine learning algorithms on a large database of projects. Using various project descriptors as input data we were able to predict the clinical success and failure rates of projects with an average balanced accuracy of 83% to 89%, which compares favorably with the 56% to 70% balanced accuracy of the method based on historical data. We also identified the variables that contributed most to trial success and used the algorithm to predict the success (or failure) of assets currently in the industry pipeline. We conclude by discussing how pharmaceutical companies can use such model to improve the quantity and quality of their new drugs, and how the broad adoption of this technology could reduce the industry’s risk profile with important consequences for industry structure, R&D investment, and the cost of innovation

Measuring the Input Rank in Global Supply Networks

We introduce the Input Rank as a measure of relevance of direct and indirect suppliers in Global Value Chains. We conceive an intermediate input to be more relevant for a downstream buyer if a decrease in that input’s productivity affects that buyer more. In particular, in our framework, the relevance of any input depends on: i) the network position of the supplier relative to the buyer, ii) the patterns of intermediate inputs vs. labor intensities connecting the buyer and the supplier, iii) and the competitive pressures along supply chains. After we compute the Input Rank from both U.S. and world Input-Output tables, we provide useful insights on the crucial role of services inputs as well as on the relatively higher relevance of domestic suppliers and suppliers coming from regionally integrated partners. Finally, we test that the Input Rank is a good predictor of vertical integration choices made by 20,489 U.S. parent companies controlling 154,836 subsidiaries worldwide

Machine Learning for Zombie Hunting. Firms’ Failures and Financial Constraints.

In this contribution, we exploit machine learning techniques to predict the risk of failure of firms. Then, we propose an empirical definition of zombies as firms that persist in a status of high risk, beyond the highest decile, after which we observe that the chances to transit to lower risk are minimal. We implement a Bayesian Additive Regression Tree with Missing Incorporated in Attributes (BART-MIA), which is specifically useful in our setting as we provide evidence that patterns of undisclosed accounts correlate with firms’ failures. After training our algorithm on 304,906 firms active in Italy in the period 2008-2017, we show how it outperforms proxy models like the Z-scores and the Distance-to-Default, traditional econometric methods, and other widely used machine learning techniques. We document that zombies are on average 21% less productive, 76% smaller, and they increased in times of financial crisis. In general, we argue that our application helps in the design of evidence-based policies in the presence of market failures, for example optimal bankruptcy laws. We believe our framework can help to inform the design of support programs for highly distressed firms after the recent pandemic crisis

Simulation of Covid-19 epidemic evolution: are compartmental models really predictive?

Computational models for the simulation ofthe severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) epidemic evolution would be extremely useful to support authorities in designing healthcare policies and lockdown measures to contain its impact on public health and economy. In Italy, the devised forecasts have been mostly based on a pure data-driven approach, by fitting and extrapolating open data on the epidemic evolution collected by the Italian Civil Protection Center. In this respect, SIR epidemiological models, which start from the description of the nonlinear interactions between population compartments, would be a much more desirable approach to understand and predict the collective emergent response. The present contribution addresses the fundamental question whether a SIR epidemiological model, suitably enriched with asymptomatic and dead individual compartments, could be able to provide reliable predictions on the epidemic evolution. To this aim, a machine learning approach based on particle swarm optimization (PSO) is proposed to automatically identify the model parameters based on a training set of data of progressive increasing size, considering Lombardy in Italy as a case study. The analysis of the scatter in the forecasts shows that model predictions are quite sensitive to the size of the dataset used for training, and that further data are still required to achieve convergent - and therefore reliable- predictions

Talents from Abroad. Foreign Managers and Productivity in the United Kingdom.

In this paper, we test the contribution of foreign management on firms’ competitiveness. We use a novel dataset on the careers of 165,084 managers employed by 13,106 companies in the United Kingdom in the period 2009-2017. We find that a domestic manufacturing firm becomes on average between 9% and 12% more productive after hiring at least one foreign manager. Interestingly, productivity gains by domestic firms after recruiting foreign managers are similar in magnitude to gains after foreign acquisitions as from previous literature. Eventually, we do not find significant gains by foreign-owned firms hiring foreign managers. Our identification strategy combines difference-in-difference and matching techniques to challenge reverse causality. We proxy firms’ competitiveness either by total factor productivity or by technical efficiency derived from stochastic frontier analyses. Eventually, we argue that limits to the circulation of talents, as for example in case of a Brexit event, may hamper the allocation of labor productive resources

Evidence for Mixed Rationalities in Preference Formation

Understanding the mechanisms underlying the formation of cultural traits is an open challenge. This is intimately connected to cultural dynamics, which has been the focus of a variety of quantitative models. Recent studies have emphasized the importance of connecting thosemodels to empirically accessible snapshots of cultural dynamics. In particular, it has been suggested that empirical cultural states, which differ systematically from randomized counterparts, exhibit properties that are universally present. Hence, a question about the mechanism responsible for the observed patterns naturally arises. This study proposes a stochastic structural model for generating cultural states that retain those robust empirical properties. One ingredient of the model assumes that every individual’s set of traits is partly dictated by one of several universal “rationalities,” informally postulated by several social science theories.The second, new ingredient assumes that, apart from a dominant rationality, each individual also has a certain exposure to the other rationalities. It is shown that both ingredients are required for reproducing the empirical regularities. This suggests that the effects of cultural dynamics in the real world can be described as an interplay of multiple, mixing rationalities, providing indirect evidence for the class of social science theories postulating such a mixing