The DiTeCaM-CM project aims to shed light on the debate on the measurement of both innovative activity and the impact of public policies on the development and adoption of emerging technologies. In addition, we will examine the effects that technology adoption may have on the labour market and the survival of companies, with special emphasis on the Madrid region. Consequently, the project is structured around three general objectives:

Objective 1 focuses on innovation and has two specific objectives. The first specific objective seeks to identify the most appropriate policy measures to promote firms’ innovation and its efficiency, distinguishing between technology adopters and non-adopters located in Madrid region. The second specific objective centres on the evaluation of regional innovation systems and the search for improvement actions, comparing Madrid to similar regions and cities in Europe. To achieve these specific objectives, we use parametric and non-parametric techniques to measure firms’ innovation efficiency and to evaluate regional innovation systems, introducing methodological novelties aiming at extending and improve the existing approaches. As an example, we propose using machine learning techniques and other recent advances in the development of control functions to mitigate misspecification biases.

Objective 2 focuses on the effect on the labour market of new technology adoption and automation (robotization). It is structured into three specific objectives that analyse the
issue from different perspectives, with a special emphasis on Madrid. The first specific objective addresses the impact of the adoption of robots and artificial intelligence on employment. Although this type of analysis may seem standard, the approach and methodology proposed represent novel contributions to the existing literature. The second specific objective analyses the relationship between the adoption of robots and regional competitiveness from the point of view of economic complexity. Finally, the third objective analyses the strategies to adopt robots in the firms located in Madrid. The latter two research issues have not been addressed in the previous literature.

Objective 3 applies economic complexity theory to analyse employment-generating innovation activity in the Madrid region. This analysis is addressed from different perspectives, manifested in three specific objectives. The first specific objective analyses the innovation activity focusing on innovation poles of attraction, introducing a novel methodology to estimate economic complexity indicators. The second specific objective takes account of labour mobility, using geolocation data. Finally, the third specific objective incorporates the influence of institutional quality, through the use of fuzzy cognitive maps.