The aim of this project is to analyse the long-term effects of immigration on attitudes of natives towards immigration, as well as the mechanisms through which immigration may affect natives’ attitudes. Moreover, I plan to study whether attitudes of natives towards migration have long-term economic consequences, particularly on the ability of immigrants to integrate in the natives’ society, by analysing economic outcomes of first and second generation migrants. The identification strategy relies on instrumental variables, combined with machine learning methods for causal inference.

Constructing accurate predictions on different macroeconomic variables is a key issue for any central bank and other policy making institutions. For example, obtaining accurate inflation forecasts is important for setting interest rates. These institutions typically rely on a set of models to construct their forecasts and the question that often arises is which of these models performs the best in terms of predictive ability. The purpose of this project is to show that, when strong identification on these models is lost (an issue that is prevalent in many models used for prediction), our inference based on standard tests, that compare these models' predictive accuracy, can be misleading. A policy maker could thus falsely conclude that a particular model outperforms some other models in her set of competing models. This project will answer thus the question of how to perform correct inference about predictions in the setting in which the models are affected by identification deficiencies. To this end, I propose methods that make the standard predictive ability tests robust to this issue, while appropriately accounting for the parameter estimation error. The asymptotic distribution of the statistic will be derived under loss of strong identification. Bootstrap inference will be developed in order to obtain correct critical values. Monte Carlo simulations will analyze the finite sample properties of bootstrap critical values. Empirical studies will illustrate the consequences of using a standard vs. a bootstrap critical value. Results emerging from this project, will be of interest to a large academic community, central banks and other governmental organizations - that could take-up the new knowledge for policy making, as well as businesses that produce predictions - that could improve their forecast evaluation methodologies.

BRANDSUS focuses on the study of inclusive governance arrangements for sustainable place development (SPD) responding to calls for more holistic models of regional development in the European Union (EU) following the goals of the 2030 Agenda for Sustainable Development, including economic social and environmental sustainability. BRANDSUS will increase scientific knowledge by developing participatory place branding (PPB) as an alternative, inclusive governance framework for SPD. The project develops an innovative mixed-methods comparative case study approach (Spain-Netherlands) for the study of citizen-led governance models: a large-scale survey and a participatory intervention methodology combining participatory action research with the method of sociological intervention. BRANDSUS will also contribute to advancing research methods in public administration and the social sciences at large. The practical application of the proposed methodology will not only support place development and economic growth but also ensure social and environmental sustainability. The project contributes to societal development by providing the governance framework for stakeholders to collectively enact positive change. Inclusive and effective governance models for stakeholder engagement will enhance collaboration towards a common vision. BRANDSUS will be conducted at the Department of Public Administration and Sociology at Erasmus University of Rotterdam, ranked 1st in 2017 and 2018 and 2nd in 2019 for the subject of Public Administration by the Shanghai Ranking. Beyond sharing my expertise and further developing innovative participatory governance methodologies, this fellowship will allow me to restart my career, as well as to acquire the knowledge and skills necessary to achieve my medium-term goal to secure a grant as a principal investigator to continue studying citizen-led governance models for SPD and my long-term goal to secure a tenured position in the EU.