Description

Cliometrics: historical assessments of long run economic changes (10h – DIEBOLT Claude, BETA - CNRS):

Economic historians have contributed to the development of economics in a variety of ways, combining theory with quantitative methods, constructing new databases, promoting interdisciplinary approaches to historical topics, and using history as a lens to examine the long-term development of the economy. The aim of the course is to explain past economic experiences and to understand how, why and when economic change occurs.  

Data Science and Complexity Measurement for Innovation and S&T Studies (10h – GUERZONI Marco, University of Milan-Bicocca):

In this course, we will discuss how to empirically measure the concepts of variety and innovation based on advanced models of unsupervised machine learning. A particular focus will be on the concept of complexity and its relation with firm perfomancce. Specifically, the course is divided into the following parts:

1. Introduction: Variety, innovation, and complexity.
2. Complexity: Definitions and empirical evidence.
3. Complexity at the firm level: Idea and empirical challenge.
4. Patent for measuring complexity: An unsupervised machine learning approach.
5. Complexity and performance: Evidence.

The course will include lecture parts and moments of interaction with the students. The second part of the course is hands-on, and reproducible codes and algorithms in R will be presented to analyze the data. 

Econometrics and Measurement of Networks (10h – COWAN Robin and MÜLLER Moritz, University of Strasbourg) :

This course, given jointly by Cowan and Müller, builds on the foundations laid in UE 2 Economics of Changes and Transitions Economics of Networks, and uses it to examine several research papers. The object is to discuss those papers, to understand what they are trying to say, the methods they use, and how they can be understood to advance our knowledge of networks and innovation. The goal here is not to be able to prove theorems in the econometrics of networks, but rather to understand the challenges network systems pose for statistical and econometric analysis. We will see how those challenges are addressed in the literature. This set-up also serves to enhance the students’ literacy of empirical research papers more generally. Our focus is on sampling network data and `peer effects in networks’. The course will include presentations by the lecturer and involve teamwork, presentations and discussions among students. 

Scientometrics and Measurement of R&D and S&T Trajectories (10h – BIANCHINI Stefano and MÜLLER Moritz, University of Strasbourg) :

Keeping track of progress in science and technology (S&T) has become imperative for various stakeholders, including policymakers, researchers, businesses and investors. The objective of this course is to provide students with a broad understanding of the main indicators and metrics used to study and assess scientific and technological progress. The course begins with a theoretical discussion on what constitutes “science” and “technology”, exploring some foundational concepts such as paradigm shift and technological trajectories, while also examining the boundaries and interrelationships between these two domains. It further discusses the role of the key actors in the economic system contributing to S&T, including academia, government research institutions, private enterprises, and non-profit organizations. Finally, the course covers some practical methodologies for measuring and assessing scientific and technological progress through various indicators derived from scientific publications, patent data, and web and social media visibility. 

Measurement of Structural Changes and Long-run Transformations (5h – BORSATO Andrea or LORENTZ André, University of Strasbourg) :

The course synthetizes and assesses recent advances in the research of growth and structural change, namely the reallocation of economic activity across the sectors of agriculture, manufacturing, and services. The course begins by presenting the stylized facts of structural transformation across time and space. A multi-sector extension of the one-sector growth model that encompasses the main existing theories of structural transformation is then developed. The multi-sector model can capture many important features of structural transformation and provides a benchmark for the study of structural change. This framework also provides fresh evidence into economic development, regional income convergence, aggregate productivity trends, hours worked, business cycles, wage inequality, and green-house gas emissions. 

Physical measures of environmental changes (5h – HAMBYE-VERBRUGGHEN Jérôme, University of Strasbourg) :

This course introduces to some measure of environmental changes outside usual economic considerations, starting with foundational concepts like the Planetary Boundaries and the stock-flow circuit from Ecological Economics. Students will briefly be exposed to a variety of environmental accounting frameworks and indicators, including carbon accounting, footprints, and life cycle analysis. A significant focus will then be on energy and materials, introducing input-output accounting and modelling as well as supply-use tables. Principles of exergy and thermodynamics will also be introduced for a deeper understanding. Finally, the course will emphasize a practical application of measuring energy dynamics using decomposition analysis, providing students with a precise understanding of a tool used both in academia and in policy institutions. This should give them a good basis as well as specific tools and methodologies to assess physical dimensions of sustainability.

Compétences visées

• Acquire a broad range of empirical approaches and data analysis methods to address the issues related to the long-run dynamics and transformations of economies and economic systems, technological as well as environmental transformations and their consequences

Disciplines

• Sciences économiques

Contacts

Responsable(s) de l'enseignement

• Andre Lorentz