Examinando por Autor "Sanz, Ismael"

Mostrando 1 - 4 de 4

Do 2 weeks of instruction time matter? Using a natural experiment to estimate the effect of a calendar change on students' performance
(Wiley, 2023) Sanz, Ismael; Tena, J. D.
This paper investigates the effect on academic performance of an exogenous educational reform that reduced the school calendar of non-fee-paying schools in the Madrid region (Spain) by approximately two weeks, leaving the basic curriculum unchanged. To identify the consequences of such a measure, we exploit the fact that it did not affect private schools (control group) and the existence of an external cognitive test that measures academic performance before and after its application in the region. We find that the reform worsened students' educational outcomes by around 0.13 of a standard deviation. This effect was especially strong in the subjects of Spanish and Mathematics. We further explored quantile effects across the distribution of exam scores, finding that the disruption had a more negative effect on students in the upper quartile than those in the lower quartile. Overall, the analysis shows a reduction in the gap across non-fee-paying schools and an increase in the gap between non-fee- and fee-paying schools.
Las habilidades no cognitivas en la educación española
(Octaedro, 2022) Alcañiz, Vicente; Sanz, Ismael; Pires, Luis
Robots in action
(Wiley, 2024-03-03) Shahin, Taraneh; Ballestar de las Heras, María Teresa; Sanz, Ismael
This empirical study delves into the intricate factors that shape firms' choices regarding the adoption of robots within the Spanish context. Using a dataset encompassing a diverse set of industries, we employ an empirical analysis to uncover the determinants of robot adoption and investigate the associated outcomes on market variables. Our findings reveal several key factors that significantly influence a firm's likelihood of adopting robots. We find that firm profitability, exporter status, the control variables including share of R&D, and capital intensity exhibit strong positive relationships with robot adoption. Conversely, the impact of the level of human capital on adoption decisions is less pronounced. Furthermore, our study explores the impact of robot adoption on firm performance. We observe that firms embracing robotisation experience notable improvements in the output, exporting activities, and reduction in labour cost share. This study incorporates a gradient boosting-based machine-learning model, specifically XGBoost, along with instrumental variable regression models, to conduct rigorous robustness analyses and validate the obtained results. These findings contribute to the understanding of the dynamics and implications of robot adoption in the manufacturing sector, explaining the factors that drive firms' decisions and the subsequent market effects
Why is your company not robotic? The technology and human capital needed by firms to become robotic
(Elsevier, 2022) Ballestar, María Teresa; García-Lazaro, Aida; Sainz, Jorge; Sanz, Ismael
The impact of companies’ adoption of robotics is increasingly interesting. This study aims to elucidate how the adoption of these technologies will affect companies and society. Companies that use these technologies expect to gain a competitive advantage, but robotization implies risks that must be managed by companies and governments. This research focuses on one of the most sensitive elements of this transformation process—the workforce. First, we analyze the characteristics of the workforce and the degree of adoption of robotics using a sample of 4,640 firms with 26 years of observation. We develop a predictive model using a supervised artificial neural network multilayer perceptron (ANN-MLP) to evaluate a company’s readiness to make this transformation according to its workforce’s characteristics. Second, we focus on the characterization and segmentation of the companies for which the ANN-MLP is unable to correctly predict the degree of adoption of robotics. This classification failure means that there are unidentified factors that determine why a company has a workforce composition and structure that do not correspond to its expected degree of robotization. For this analysis, we investigate the main business indicators of these companies and cluster them using an unsupervised artificial neural network, specifically the Kohonen self-organizing map. Our findings will enable companies to understand the importance of transforming to robotics at the right moment, considering factors such as the optimum structure and composition of the workforce. The combination of technology and human capital is the key to boosting the efficiency of the transformation process toward robotics. At this point, a recommendation model to determine whether the company has sufficient maturity to make the transition is crucial for decision makers.