Do classes of gas stations contribute differently to fuel prices? Evidence to foster effective competition in Spain

Despite the relatively large number of gas stations reached in Spain after decades of sectorial reforms, pre-tax fuel prices in the country remain systematically among the highest in the EU. The literature provides evidence suggesting that a low intensity of competition in the retail distribution could contribute to these casual observations. With the purpose of shedding light on ways to design e↵ective competition measures, we conduct an empirical analysis of more than ten million observations containing information about prices, brands, and locations at the station level. This allows us to know whether the exit (entry) of some classes of stations have the ability to reduce the prices of nearby competitors. Our results suggest that the presence in a local market of a station belonging to the network of the dominant market companies will tend to generate prices above the average. This is not only because these stations set higher prices but also because their presence will give rise to overpricing by local competitors. The opposite occurs with the self-advertised as “low-cost” stations. Policy measures promoting the gradual exit of stations associated with the dominant companies seem quite reasonable in view of the commitment to the transition toward transport decarbonization

A simheuristic for routing electric vehicles with limited driving ranges and stochastic travel times

Green transportation is becoming relevant in the context of smart cities, where the use of electric vehicles represents a promising strategy to support sustainability policies. However the use of electric vehicles shows some drawbacks as well, such as their limited driving-range capacity. This paper analyses a realistic vehicle routing problem in which both driving-range constraints and stochastic travel times are considered. Thus, the main goal is to minimize the expected time-based cost required to complete the freight distribution plan. In order to design reliable Routing plans, a simheuristic algorithm is proposed. It combines Monte Carlo simulation with a multi-start metaheuristic, which also employs biased-randomization techniques. By including simulation, simheuristics extend the capabilities of metaheuristics to deal with stochastic problems. A series of computational experiments are performed to test our solving approach as well as to analyse the effect of uncertainty on the routing plans.Peer Reviewe

Diversity and employment prospects: neighbors matter!

This paper explores how diversity affects individuals’ employment prospects, using data from the French Labor Force Survey. Employment correlates positively with local labor market diversity, but negatively with neighborhood diversity. Using an instrumental variable approach to deal with local labor market diversity drives the positive correlation to zero, confirming the suspicion of self-selection. Regarding neighborhood diversity, I adopt the strategy of Bayer et al. (2008), taking advantage of the very precise localization of the data: the negative effect of diversity is reinforced. I also show that nationalitybased diversity matters more than parents’ origin-based diversity, giving insights on the underlying mechanisms

Particle swarm grammatical evolution for energy demand estimation

[EN] Grammatical Swarm is a search and optimization algorithm that belongs to the more general Grammatical Evolution family, which works with a set of solutions called individuals or particles. It uses the Particle Swarm Optimization algorithm as the search engine in the evolution of solutions. In this paper, we present a Grammatical Swarm algorithm for total energy demand estimation in a country from macroeconomic variables. Each particle in the Grammatical Swarm encodes a different model for energy demand estimation, which will be decoded by a predefined grammar. The parameters of the model are also optimized by the proposed algorithm, in such a way that the model is adjusted to a training set of real energy demand data, selecting the more appropriate variables to appear in the model. We analyze the performance of the Grammatical Swarm evolution in two real problems of one-year ahead energy demand estimation in Spain and France. The proposal is compared with previous approaches with competitive results.Spanish Ministerial Commission of Science and Technology (MICYT), Grant/Award Number: TIN2017-85887-C2-2-P; Ministerio de Ciencia, Innovacion y Universidades, Grant/Award Number: PGC2018-095322-B-C22 and RTI2018-095180-B-I00; Comunidad de Madrid y Fondos Estructurales de la Union Europea, Grant/Award Number: S2018/TCS-4566 and Y2018/NMT-4668; GenObIA-CM, Grant/Award Number: S2017/BMD-3773; Ministerio de Economia, Industria y Competitividad, Grant/Award Number: MTM2017-89664-PMartínez-Rodríguez, D.; Colmenar, JM.; Hidalgo, JI.; Villanueva Micó, RJ.; Salcedo-Sanz, S. (2020). Particle swarm grammatical evolution for energy demand estimation. Energy Science & Engineering. 8(4):1068-1079. https://doi.org/10.1002/ese3.568S1068107984Safarzyńska, K., & van den Bergh, J. C. J. M. (2017). Integrated crisis-energy policy: Macro-evolutionary modelling of technology, finance and energy interactions. Technological Forecasting and Social Change, 114, 119-137. doi:10.1016/j.techfore.2016.07.033Li, F., Song, Z., & Liu, W. (2014). China’s energy consumption under the global economic crisis: Decomposition and sectoral analysis. Energy Policy, 64, 193-202. doi:10.1016/j.enpol.2013.09.014Salcedo-Sanz, S., Muñoz-Bulnes, J., Portilla-Figueras, J. A., & Del Ser, J. (2015). One-year-ahead energy demand estimation from macroeconomic variables using computational intelligence algorithms. Energy Conversion and Management, 99, 62-71. doi:10.1016/j.enconman.2015.03.109Sánchez-Oro, J., Duarte, A., & Salcedo-Sanz, S. (2016). Robust total energy demand estimation with a hybrid Variable Neighborhood Search – Extreme Learning Machine algorithm. Energy Conversion and Management, 123, 445-452. doi:10.1016/j.enconman.2016.06.050Ceylan, H., & Ozturk, H. K. (2004). Estimating energy demand of Turkey based on economic indicators using genetic algorithm approach. Energy Conversion and Management, 45(15-16), 2525-2537. doi:10.1016/j.enconman.2003.11.010Shaik, S., & Yeboah, O.-A. (2018). Does climate influence energy demand? A regional analysis. Applied Energy, 212, 691-703. doi:10.1016/j.apenergy.2017.11.109United Nations Climate Change Conference.The Paris Agreement. UNTC XXVII 7.d.Suganthi, L., & Samuel, A. A. (2012). Energy models for demand forecasting—A review. Renewable and Sustainable Energy Reviews, 16(2), 1223-1240. doi:10.1016/j.rser.2011.08.014Geem, Z. W., & Roper, W. E. (2009). Energy demand estimation of South Korea using artificial neural network. Energy Policy, 37(10), 4049-4054. doi:10.1016/j.enpol.2009.04.049Ünler, A. (2008). Improvement of energy demand forecasts using swarm intelligence: The case of Turkey with projections to 2025. Energy Policy, 36(6), 1937-1944. doi:10.1016/j.enpol.2008.02.018Kıran, M. S., Özceylan, E., Gündüz, M., & Paksoy, T. (2012). Swarm intelligence approaches to estimate electricity energy demand in Turkey. Knowledge-Based Systems, 36, 93-103. doi:10.1016/j.knosys.2012.06.009Kıran, M. S., Özceylan, E., Gündüz, M., & Paksoy, T. (2012). A novel hybrid approach based on Particle Swarm Optimization and Ant Colony Algorithm to forecast energy demand of Turkey. Energy Conversion and Management, 53(1), 75-83. doi:10.1016/j.enconman.2011.08.004Yu, S., Wei, Y.-M., & Wang, K. (2012). A PSO–GA optimal model to estimate primary energy demand of China. Energy Policy, 42, 329-340. doi:10.1016/j.enpol.2011.11.090Yu, S., Zhu, K., & Zhang, X. (2012). Energy demand projection of China using a path-coefficient analysis and PSO–GA approach. Energy Conversion and Management, 53(1), 142-153. doi:10.1016/j.enconman.2011.08.015Yu, S., & Zhu, K. (2012). A hybrid procedure for energy demand forecasting in China. Energy, 37(1), 396-404. doi:10.1016/j.energy.2011.11.015Geng, Z., Zeng, R., Han, Y., Zhong, Y., & Fu, H. (2019). Energy efficiency evaluation and energy saving based on DEA integrated affinity propagation clustering: Case study of complex petrochemical industries. Energy, 179, 863-875. doi:10.1016/j.energy.2019.05.042Han, Y., Long, C., Geng, Z., Zhu, Q., & Zhong, Y. (2019). A novel DEACM integrating affinity propagation for performance evaluation and energy optimization modeling: Application to complex petrochemical industries. Energy Conversion and Management, 183, 349-359. doi:10.1016/j.enconman.2018.12.120Han, Y., Wu, H., Jia, M., Geng, Z., & Zhong, Y. (2019). Production capacity analysis and energy optimization of complex petrochemical industries using novel extreme learning machine integrating affinity propagation. Energy Conversion and Management, 180, 240-249. doi:10.1016/j.enconman.2018.11.001Colmenar, J. M., Hidalgo, J. I., & Salcedo-Sanz, S. (2018). Automatic generation of models for energy demand estimation using Grammatical Evolution. Energy, 164, 183-193. doi:10.1016/j.energy.2018.08.199O’Neill, M., & Brabazon, A. (2006). Grammatical Swarm: The generation of programs by social programming. Natural Computing, 5(4), 443-462. doi:10.1007/s11047-006-9007-7O’Neill, M., & Ryan, C. (2001). Grammatical evolution. IEEE Transactions on Evolutionary Computation, 5(4), 349-358. doi:10.1109/4235.942529KennedyJ EberhartR.Particle swarm optimization. Vol.4. Proceedings of ICNN'95 - International Conference on Neural Networks.Perth WA;1995:1942‐1948.https://doi.org/10.1109/ICNN.1995.488968Tsoulos, I. G., Gavrilis, D., & Glavas, E. (s. f.). Neural network construction using grammatical evolution. Proceedings of the Fifth IEEE International Symposium on Signal Processing and Information Technology, 2005. doi:10.1109/isspit.2005.1577206Beni, G., & Wang, J. (1993). Swarm Intelligence in Cellular Robotic Systems. Robots and Biological Systems: Towards a New Bionics?, 703-712. doi:10.1007/978-3-642-58069-7_38Krause, J., Ruxton, G. D., & Krause, S. (2010). Swarm intelligence in animals and humans. Trends in Ecology & Evolution, 25(1), 28-34. doi:10.1016/j.tree.2009.06.016Marini, F., & Walczak, B. (2015). Particle swarm optimization (PSO). A tutorial. Chemometrics and Intelligent Laboratory Systems, 149, 153-165. doi:10.1016/j.chemolab.2015.08.020Ling, C. X. (1995). Overfitting and generalization in learning discrete patterns. Neurocomputing, 8(3), 341-347. doi:10.1016/0925-2312(95)00050-

Housing Price Prediction Using Machine Learning Algorithms in COVID-19 Times

Machine learning algorithms are being used for multiple real-life applications and in research. As a consequence of digital technology, large structured and georeferenced datasets are now more widely available, facilitating the use of these algorithms to analyze and identify patterns, as well as to make predictions that help users in decision making. This research aims to identify the best machine learning algorithms to predict house prices, and to quantify the impact of the COVID-19 pandemic on house prices in a Spanish city. The methodology addresses the phases of data preparation, feature engineering, hyperparameter training and optimization, model evaluation and selection, and finally model interpretation. Ensemble learning algorithms based on boosting (Gradient Boosting Regressor, Extreme Gradient Boosting, and Light Gradient Boosting Machine) and bagging (random forest and extra-trees regressor) are used and compared with a linear regression model. A case study is developed with georeferenced microdata of the real estate market in Alicante (Spain), before and after the pandemic declaration derived from COVID-19, together with information from other complementary sources such as the cadastre, socio-demographic and economic indicators, and satellite images. The results show that machine learning algorithms perform better than traditional linear models because they are better adapted to the nonlinearities of complex data such as real estate market data. Algorithms based on bagging show overfitting problems (random forest and extra-trees regressor) and those based on boosting have better performance and lower overfitting. This research contributes to the literature on the Spanish real estate market by being one of the first studies to use machine learning and microdata to explore the incidence of the COVID-19 pandemic on house prices

Spatial Interdependence of Local Public Expenditures: Selected Evidence from the Czech Republic

Local expenditures in neighbouring municipalities can be spatially interdependent due to spillovers, cooperation effects, competition effects or mimicking. In this paper, we aim to test the spatial interdependence of local public expenditures using data on 205 Czech municipalities. We found positive spatial interdependence in expenditures on housing and culture and negative spatial interdependence for expenditures on industry and infrastructure and environmental protection. Additionally, we observed that political characteristics affect the size of spending; left-wing parties tend to increase expenditures on culture and decrease expenditures on industry and infrastructure; and higher party fragmentation decreases overall capital expenditures and expenditures on housing.Spillovers, fiscal competition, local public finance

Spatial Interdependence of Local Public Expenditures: Selected Evidence from the Czech Republic

Local expenditures in neighbouring municipalities can be spatially interdependent due to spillovers, cooperation effects, competition effects or mimicking. In this paper, we aim to test the spatial interdependence of local public expenditures using data on 205 Czech municipalities. We found positive spatial interdependence in expenditures on housing and culture and negative spatial interdependence for expenditures on industry and infrastructure and environmental protection. Additionally, we observed that political characteristics affect the size of spending; left-wing parties tend to increase expenditures on culture and decrease expenditures on industry and infrastructure; and higher party fragmentation decreases overall capital expenditures and expenditures on housing.spillovers, fiscal competition, local public finance

The evolution of educational inequalities in Spain: Dynamic evidence from repeated cross-sections

A lack of longitudinal data prevents many countries from estimating dynamic models and, thus, from obtaining valuable evidence for policymaking in the field of education. This is the case of Spain, where recent education reforms have targeted secondary schools, but their design has been based on incomplete information regarding the evolution of student performance and far from robust evidence concerning just when educational inequalities are generated. This paper addresses the absence of longitudinal data required for performing such analyses by using a dynamic model with repeated cross-sectional data. We are able to link the reading competencies of students from the same cohort that participated in two international assessments at different ages (9/10 and 15/16) and so identify when educational gaps – in terms of gender, socio-economic status and place of birth – emerge. In addition, we provide new evidence on the effect on achievement of the main policy used in Spain for levelling the performance of secondary school students, namely, grade retention. Our results suggest that educational inequalities in Spain originate in lower educational levels. After controlling for reverse causality, the negative relationship between grade retention and performance at the lower secondary school level persists

R&D drivers and obstacles to innovation in the energy industry [WP-IEB]

The energy industry is facing substantial challenges that require innovation to be fostered. Nevertheless, levels of R&D investment and innovation remain quite low in comparison with other sectors. In this paper we analyse the main drivers of R&D investment and obstacles to innovation in the energy industry. We examine, firstly, whether the stated R&D objectives pursued by firms play a role in their R&D effort. Secondly, we analyse the effects of financial, knowledge and market barriers on the innovation outcomes of the firms. We rely on data from the Technological Innovation Panel (PITEC) for Spanish firms for the period 2003-2010. We use a structural model with three equations corresponding to the decision to carry out R&D or not, the R&D effort and the production of innovations. The results of the econometric estimations show, first, that R&D intensity is positively related to process innovation. Second, the main barriers that hamper innovation in the energy industry are related to market factors while financial and knowledge obstacles are not significant

Are energy market integrations a green light for FDI?

This paper studies the effect of energy market integration (EMI) on foreign direct investment (FDI). EMIs diminish energy uncertainty and price volatility in the host country and affect FDI through two channels: first, by harmonizing energy prices and, second, by reducing price dispersion. FDI may, as a result, increase both within and outside the EMI area, through energy stability mechanisms and price mechanisms, respectively. An empirical application on a global dataset including bilateral FDI data, during 2003-2012, using the gravity equation, shows that the integration of Portugal and Spain's electricity market in 2007 increased the amount of FDI's participants. Additionally, a positive increase in FDI from neighboring countries (in this instance, France), albeit lower in magnitude, is observed