Tariff reductions and labor demand elasticities : evidence from Chinese firm-level data

International production fragmentation has been a global trend for decades, becoming especially important in Asia where the manufacturing process is fragmented into stages and dispersed around the region. This paper examines the effects of input and output tariff reductions on labor demand elasticities at the firm level. For this purpose, we consider a simple heterogenous firm model in which firms are allowed to export their products and to use imported intermediate inputs. The model predicts that only productive firms can use imported intermediate inputs (outsourcing) and tend to have larger constant-output labor demand elasticities. Input tariff reductions would lower the factor shares of labor for these productive firms and raise conditional labor demand elasticities further. We test these empirical predictions, constructing Chinese firm-level panel data over the 2000--2006 period. Controlling for potential tariff endogeneity by instruments, our empirical studies generally support these predictions.

Does trade liberalization boost quality upgrading? : evidence from Indonesian plant-product-level data

In this study, we examine the effects of tariff reduction on firms' quality upgrading by employing an Indonesian plant-product-level panel dataset matched with a plant-level dataset. We explore the effects of lower output and input tariffs separately, by focusing on the apparel industry. By estimating the Berry-type demand function, we derive product-quality indicators based on the Khandelwal (Review of Economic Studies, 2010) methodology, which enables us to isolate quality upgrading from changes in prices. Our findings are as follows. First, a reduction in output tariffs does not affect product quality upgrading. Second, a reduction in input tariffs boosts quality upgrading in general. In particular, this impact is greater for import firms, which is consistent with the fact that the source of the boost is the import of high-quality foreign inputs.

Uncertainties in the modal estimation from output-only data in civil structures

Cualquier estructura vibra según unas frecuencias propias definidas por sus parámetros modales (frecuencias naturales, amortiguamientos y formas modales). A través de las mediciones de la vibración en puntos clave de la estructura, los parámetros modales pueden ser estimados. En estructuras civiles, es difícil excitar una estructura de manera controlada, por lo tanto, las técnicas que implican la estimación de los parámetros modales sólo registrando su respuesta son de vital importancia para este tipo de estructuras. Esta técnica se conoce como Análisis Modal Operacional (OMA). La técnica del OMA no necesita excitar artificialmente la estructura, atendiendo únicamente a su comportamiento en servicio. La motivación para llevar a cabo pruebas de OMA surge en el campo de la Ingeniería Civil, debido a que excitar artificialmente con éxito grandes estructuras no sólo resulta difícil y costoso, sino que puede incluso dañarse la estructura. Su importancia reside en que el comportamiento global de una estructura está directamente relacionado con sus parámetros modales, y cualquier variación de rigidez, masa o condiciones de apoyo, aunque sean locales, quedan reflejadas en los parámetros modales. Por lo tanto, esta identificación puede integrarse en un sistema de vigilancia de la integridad estructural. La principal dificultad para el uso de los parámetros modales estimados mediante OMA son las incertidumbres asociadas a este proceso de estimación. Existen incertidumbres en el valor de los parámetros modales asociadas al proceso de cálculo (internos) y también asociadas a la influencia de los factores ambientales (externas), como es la temperatura. Este Trabajo Fin de Máster analiza estas dos fuentes de incertidumbre. Es decir, en primer lugar, para una estructura de laboratorio, se estudian y cuantifican las incertidumbres asociadas al programa de OMA utilizado. En segundo lugar, para una estructura en servicio (una pasarela de banda tesa), se estudian tanto el efecto del programa OMA como la influencia del factor ambiental en la estimación de los parámetros modales. Más concretamente, se ha propuesto un método para hacer un seguimiento de las frecuencias naturales de un mismo modo. Este método incluye un modelo de regresión lineal múltiple que permite eliminar la influencia de estos agentes externos. A structure vibrates according to some of its vibration modes, defined by their modal parameters (natural frequencies, damping ratios and modal shapes). Through the measurements of the vibration at key points of the structure, the modal parameters can be estimated. In civil engineering structures, it is difficult to excite structures in a controlled manner, thus, techniques involving output-only modal estimation are of vital importance for these structure. This techniques are known as Operational Modal Analysis (OMA). The OMA technique does not need to excite artificially the structure, this considers its behavior in service only. The motivation for carrying out OMA tests arises in the area of Civil Engineering, because successfully artificially excite large structures is difficult and expensive. It also may even damage the structure. The main goal is that the global behavior of a structure is directly related to their modal parameters, and any variation of stiffness, mass or support conditions, although it is local, is also reflected in the modal parameters. Therefore, this identification may be within a Structural Health Monitoring system. The main difficulty for using the modal parameters estimated by an OMA is the uncertainties associated to this estimation process. Thus, there are uncertainties in the value of the modal parameters associated to the computing process (internal) and the influence of environmental factors (external), such as the temperature. This Master’s Thesis analyzes these two sources of uncertainties. That is, firstly, for a lab structure, the uncertainties associated to the OMA program used are studied and quantified. Secondly, for an in-service structure (a stress-ribbon footbridge), both the effect of the OMA program and the influence of environmental factor on the modal parameters estimation are studied. More concretely, a method to track natural frequencies of the same mode has been proposed. This method includes a multiple linear regression model that allows to remove the influence of these external agents.

Automatic fault detection on BIPV systems without solar irradiation data

BIPV systems are small PV generation units spread out over the territory, and whose characteristics are very diverse. This makes difficult a cost-effective procedure for monitoring, fault detection, performance analyses, operation and maintenance. As a result, many problems affecting BIPV systems go undetected. In order to carry out effective automatic fault detection procedures, we need a performance indicator that is reliable and that can be applied on many PV systems at a very low cost. The existing approaches for analyzing the performance of PV systems are often based on the Performance Ratio (PR), whose accuracy depends on good solar irradiation data, which in turn can be very difficult to obtain or cost-prohibitive for the BIPV owner. We present an alternative fault detection procedure based on a performance indicator that can be constructed on the sole basis of the energy production data measured at the BIPV systems. This procedure does not require the input of operating conditions data, such as solar irradiation, air temperature, or wind speed. The performance indicator, called Performance to Peers (P2P), is constructed from spatial and temporal correlations between the energy output of neighboring and similar PV systems. This method was developed from the analysis of the energy production data of approximately 10,000 BIPV systems located in Europe. The results of our procedure are illustrated on the hourly, daily and monthly data monitored during one year at one BIPV system located in the South of Belgium. Our results confirm that it is possible to carry out automatic fault detection procedures without solar irradiation data. P2P proves to be more stable than PR most of the time, and thus constitutes a more reliable performance indicator for fault detection procedures. We also discuss the main limitations of this novel methodology, and we suggest several future lines of research that seem promising to improve on these procedures.

Nuevos Esquemas de Visualizaciones para Mejorar la Comprensibilidad de Modelos de Data Mining

La gran cantidad de datos que se registran diariamente en los sistemas de base de datos de las organizaciones ha generado la necesidad de analizarla. Sin embargo, se enfrentan a la complejidad de procesar enormes volúmenes de datos a través de métodos tradicionales de análisis. Además, dentro de un contexto globalizado y competitivo las organizaciones se mantienen en la búsqueda constante de mejorar sus procesos, para lo cual requieren herramientas que les permitan tomar mejores decisiones. Esto implica estar mejor informado y conocer su historia digital para describir sus procesos y poder anticipar (predecir) eventos no previstos. Estos nuevos requerimientos de análisis de datos ha motivado el desarrollo creciente de proyectos de minería de datos. El proceso de minería de datos busca obtener desde un conjunto masivo de datos, modelos que permitan describir los datos o predecir nuevas instancias en el conjunto. Implica etapas de: preparación de los datos, procesamiento parcial o totalmente automatizado para identificar modelos en los datos, para luego obtener como salida patrones, relaciones o reglas. Esta salida debe significar un nuevo conocimiento para la organización, útil y comprensible para los usuarios finales, y que pueda ser integrado a los procesos para apoyar la toma de decisiones. Sin embargo, la mayor dificultad es justamente lograr que el analista de datos, que interviene en todo este proceso, pueda identificar modelos lo cual es una tarea compleja y muchas veces requiere de la experiencia, no sólo del analista de datos, sino que también del experto en el dominio del problema. Una forma de apoyar el análisis de datos, modelos y patrones es a través de su representación visual, utilizando las capacidades de percepción visual del ser humano, la cual puede detectar patrones con mayor facilidad. Bajo este enfoque, la visualización ha sido utilizada en minería datos, mayormente en el análisis descriptivo de los datos (entrada) y en la presentación de los patrones (salida), dejando limitado este paradigma para el análisis de modelos. El presente documento describe el desarrollo de la Tesis Doctoral denominada “Nuevos Esquemas de Visualizaciones para Mejorar la Comprensibilidad de Modelos de Data Mining”. Esta investigación busca aportar con un enfoque de visualización para apoyar la comprensión de modelos minería de datos, para esto propone la metáfora de modelos visualmente aumentados. ABSTRACT The large amount of data to be recorded daily in the systems database of organizations has generated the need to analyze it. However, faced with the complexity of processing huge volumes of data over traditional methods of analysis. Moreover, in a globalized and competitive environment organizations are kept constantly looking to improve their processes, which require tools that allow them to make better decisions. This involves being bettered informed and knows your digital story to describe its processes and to anticipate (predict) unanticipated events. These new requirements of data analysis, has led to the increasing development of data-mining projects. The data-mining process seeks to obtain from a massive data set, models to describe the data or predict new instances in the set. It involves steps of data preparation, partially or fully automated processing to identify patterns in the data, and then get output patterns, relationships or rules. This output must mean new knowledge for the organization, useful and understandable for end users, and can be integrated into the process to support decision-making. However, the biggest challenge is just getting the data analyst involved in this process, which can identify models is complex and often requires experience not only of the data analyst, but also the expert in the problem domain. One way to support the analysis of the data, models and patterns, is through its visual representation, i.e., using the capabilities of human visual perception, which can detect patterns easily in any context. Under this approach, the visualization has been used in data mining, mostly in exploratory data analysis (input) and the presentation of the patterns (output), leaving limited this paradigm for analyzing models. This document describes the development of the doctoral thesis entitled "New Visualizations Schemes to Improve Understandability of Data-Mining Models". This research aims to provide a visualization approach to support understanding of data mining models for this proposed metaphor visually enhanced models.

Impact of Spatial Soil and Climate Input Data Aggregation on Regional Yield Simulations

This work was financially supported by the German Federal Ministry of Food and Agriculture (BMEL) through the Federal Office for Agriculture and Food (BLE), (2851ERA01J). FT and RPR were supported by FACCE MACSUR (3200009600) through the Finnish Ministry of Agriculture and Forestry (MMM). EC, HE and EL were supported by The Swedish Research Council for Environment, Agricultural Sciences and Spatial Planning (220-2007-1218) and by the strategic funding ‘Soil-Water-Landscape’ from the faculty of Natural Resources and Agricultural Sciences (Swedish University of Agricultural Sciences) and thank professor P-E Jansson (Royal Institute of Technology, Stockholm) for support. JC, HR and DW thank the INRA ACCAF metaprogramm for funding and Eric Casellas from UR MIAT INRA for support. CB was funded by the Helmholtz project “REKLIM—Regional Climate Change”. CK was funded by the HGF Alliance “Remote Sensing and Earth System Dynamics” (EDA). FH was funded by the German Research Foundation (Deutsche Forschungsgemeinschaft, DFG) under the Grant FOR1695. FE and SS acknowledge support by the German Science Foundation (project EW 119/5-1). HH, GZ, SS, TG and FE thank Andreas Enders and Gunther Krauss (INRES, University of Bonn) for support. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Improving 3D Keypoint Detection from Noisy Data Using Growing Neural Gas

3D sensors provides valuable information for mobile robotic tasks like scene classification or object recognition, but these sensors often produce noisy data that makes impossible applying classical keypoint detection and feature extraction techniques. Therefore, noise removal and downsampling have become essential steps in 3D data processing. In this work, we propose the use of a 3D filtering and down-sampling technique based on a Growing Neural Gas (GNG) network. GNG method is able to deal with outliers presents in the input data. These features allows to represent 3D spaces, obtaining an induced Delaunay Triangulation of the input space. Experiments show how the state-of-the-art keypoint detectors improve their performance using GNG output representation as input data. Descriptors extracted on improved keypoints perform better matching in robotics applications as 3D scene registration.