Your research is mainly in Economics, with a focus on the interdisciplinary approach to related subjects such as Management, Entrepreneurship, and even the Economics of Education. How has this journey unfolded, and which moments would you highlight as the most significant?
My main research line – Applied Labour Economics – was clearly shaped by the empirical research carried out during my Master’s and PhD theses, as well as by the unique scientific experience shared by my supervisor. Research in areas bordering Labour Economics arose naturally, without very advanced or rigid planning, motivated on one hand by an interest in addressing questions relevant to interdisciplinary fields connected to Labour Economics, and on the other hand, by the interest in using microdata to explain macroeconomic issues. For example, in management, the application of economic fundamentals to strategic recruitment decisions (emphasising the role of human capital and labour market contact networks on firm performance) arose from the possibility of using microdata at the employer-worker pair level and econometric methodologies that adequately deal with endogeneity issues in analysing the causal relationship between strategy and performance. One of the most appropriate ways to deal with endogeneity is by conducting experiments, whether in the laboratory or in the field. However, in real-world contexts involving complex and dynamic decision-making, such as recruitment and investment strategies, such experiments are not feasible (due to high costs and ethical concerns), as well as being unlikely to capture long-term effects. The opportunity to analyse fundamental issues in management practices and entrepreneurship using administrative and longitudinal data with detailed information on firms and their workers has allowed me to offer an innovative contribution to these fields.

Labour Economics is one of the specific niches in which you have specialised most. What do you consider to be the main current challenges in this area, especially in the context of immense technological progress and rapidly changing markets?
There are several trends shaping labour markets in developed economies, such as population ageing, globalisation, new forms of work organisation, the transition to a green economy, and technological changes resulting from automation and artificial intelligence (AI), among others. Highlighting technological changes, it can be said that the reconfiguration of human-machine interaction imposes various challenges on the labour market, particularly in the restructuring of job content, with significant impacts on job creation and destruction dynamics. In a recent McKinsey study (2024), it is estimated that about 1.3 million jobs in Portugal will need to be reskilled due to the rapid adoption of automation and generative AI. According to the latest McKinsey Global Survey on AI (2025), in 2024, 72% of organisations used AI tools in at least one business function (compared to only 55% in 2023). Skills updating and professional reskilling will be crucial in this context, especially for individuals performing more routine tasks, thus more susceptible to being replaced by technology. This need should also challenge universities to play an active role in lifelong learning, fostering closer interaction with companies. The growth of platform work (gig economy) and the consequent precariousness of labour relations is another highly relevant topic. The absence of social protection in these new work models should lead to reflection on the effectiveness of labour policies in digital and remote work contexts. On the other hand, increasing digitalisation of work and the difficulty in establishing a clear boundary between work time and leisure time pose various challenges regarding work-life balance and mental health preservation.

You publish in some of the most prestigious scientific journals in Economics and Management. How important is methodological rigour in scientific production for you? How do you balance technical complexity with the practical relevance of your topics?
Economists, especially applied ones, seek to establish cause-and-effect relationships. However, this objective proves difficult to achieve, as much empirical economic research relies on real-world data, not laboratory experiments as in exact sciences like Physics or Chemistry. Social phenomena are highly complex and influenced by multiple factors. In this context, claiming a causal effect requires both representative samples of the studied population and appropriate methodology that allows us to assert, without ambiguity, that the observed effect results from the behaviour of the economic variable of interest, not any other factor. It is this methodological rigour that enables us to adopt a robust and distinctive approach to practical themes, relevant not only to academics but also to companies, workers, policymakers, and civil society in general. The subsequent challenge is to reconcile methodological sophistication with the ability to convey the research’s relevance to a less academic audience. Throughout my career, balancing technical complexity and practical relevance has been achieved through various opportunities to participate in less academic debates and workshops on current issues, as well as collaboration in consultancy work requiring more practical knowledge application. These activities involve a stimulating exercise in clearly identifying the contribution of my research to contemporary social challenges, while also providing additional satisfaction from its societal impact.

Specifically regarding the economics of Entrepreneurship, which factors do you consider most decisive for its success and long-term sustainability, especially in more adverse economic contexts?
According to research conducted (in co-authorship), covering over 150,000 new entrepreneurs in Portugal using administrative and longitudinal data, I highlight three success factors: (i) human capital; (ii) previous experience in business creation/management; (iii) prior experience in executive roles. Regarding human capital, empirical evidence shows that firms with a more qualified workforce (measured by a composite human capital index) have higher productivity and survival rates; furthermore, the quality of the founders’ human capital also plays a crucial role in new firm performance, as more qualified entrepreneurs attract more qualified workers, positively impacting firm performance. Regarding previous experience, results indicate “learning-by-doing” effects, whereby individuals who start a new business after a longer experience with their first venture (successful or not) have a higher probability of success in the subsequent attempt, suggesting learning mechanisms from failure. This effect is more pronounced if the re-entry occurs in the same sector, showing that prior entrepreneurial experience allows the entrepreneur to acquire unique specific resources, knowledge, skills, and contacts that constitute important assets when embarking on a new entrepreneurial activity. Lastly, empirical results show that individuals with executive and managerial experience as employees are more likely to transition into entrepreneurship and achieve greater business success.

From your multiple prestigious collaborations with public entities in Portugal and abroad, what role do you believe academic research can (or should) play in public policy formulation?
Policy evaluation is a crucial element of good governance, as it promotes public accountability and contributes to citizens’ trust in the state. Policy evaluation is a fundamental area of applied Economics, especially in impact evaluation, aiming to establish a causal relationship between policy measures and observed changes in target indicators. Economists have sought to develop appropriate empirical methodologies for this purpose. Many of these models emerged within Labour Economics to assess the impact of active and passive employment policies, such as the effect of unemployment benefits on its duration, or the role of training and internships on employability and wages. Notably, the 2021 Nobel Prize in Economics was awarded to David Card, Joshua D. Angrist, and Guido W. Imbens for their work using natural experiments to analyse causal relationships (e.g., changes in minimum wage legislation). The key is to use situations where random or policy changes affect groups differently (treated and untreated) in a way akin to clinical trials in medicine. Academics possess theoretical and empirical knowledge that should inform policymakers in designing and implementing public policies. This is a clear case where stronger links between academia and public institutions responsible for policy implementation benefit both parties. Given the relevance of this exercise and the knowledge available in academia, the School of Economics and Management (FEP) has sought to offer training in policy evaluation methodologies.

What excites you most currently in Economic research? Are there any emerging areas that intrigue or seem particularly promising to you?
What currently excites me in economic research is essentially what motivated me at the start of my career: the possibility to explore real microdata to deepen understanding of contemporary labour markets, particularly in the Portuguese economy. In this regard, Portuguese researchers, especially labour economists, have been privileged since the 1990s by access to a unique administrative longitudinal database linking detailed information on employers and workers, the “Quadros de Pessoal.” This enthusiasm is further fuelled by the increasing ease of linking various administrative databases and other sample surveys, anonymised and accessed under accreditation protocols, made available by official statistical information producers. At the intersection of Labour Economics and Economics of Education, the possibility of, in the future, linking education data with labour market data will allow investigation into how individuals’ academic paths – academic performance, quality of institutions/training, and contact networks, correlate with their professional trajectories. Indeed, the impact of social connections (parents, children, classmates, friends, colleagues) on social mobility is a promising area, especially with data linkage enabling more precise mapping of social networks.

In an increasingly competitive scientific environment, what advice would you consider most pertinent to share with young researchers aiming for an academic career in this field?
Pursuing an academic career in economics in a highly competitive scientific environment requires not only technical excellence but also strategic vision and perseverance. The most relevant advice I usually share with doctoral students is to prioritise research quality and not succumb to the intense competitive pressure, exacerbated by the proliferation of predatory ‘scientific’ journals, that easily leads to valuing quantity over quality. This choice, in the medium and long term, results in competitive disadvantages in career progression. In this context, collaboration with researchers from other universities (especially abroad) with diverse skills, complementary knowledge, and ambitious goals plays a fundamental role in conducting high-quality research and individual growth. Collaborating with leading research centres and participating in international conferences, workshops, and seminars are equally important elements in boosting a scientific career. Lastly, factors such as resilience, discipline, and perseverance are no less important than technical-scientific knowledge and constitute key elements for a successful research path. In this context, surrounding ourselves with the right people (supervisors, senior researchers, colleagues, family, and friends) and learning to manage frustration, criticism, rejection, and delays are fundamental ingredients in an academic career. As Thomas Edison (1847–1942), a notable American inventor and entrepreneur, said: “I have not failed. I’ve just found 10,000 ways that won’t work.”.

What challenges would you highlight for the future of teaching and research in Economics and Management in Portugal?
One of the main challenges in teaching, common across most areas, is associated with unprecedented technological changes in artificial intelligence, which should lead to redefining the role of the teacher and curricula, with significant impacts on teaching and learning methods. Professor Joseph E. Aoun published a book in 2019 titled "Robot-Proof: Higher Education in the Age of Artificial Intelligence", which provides an interesting reflection on the role of higher education institutions in the AI era. According to the author, education should prepare students for tasks that machines cannot perform. He proposes a “robot-proof” curriculum framework based on three fundamental dimensions: (i) data literacy, essential for managing and interpreting the vast flow of information from the internet and media; (ii) technological literacy, crucial for understanding how technology works; and (iii) human literacy, what makes us unique as humans and distinguishes us from machines: creativity, innovation, entrepreneurship, cultural agility, teamwork, empathy, critical thinking, ethics, and social responsibility. Today, the great challenge for universities is to continue providing excellent training that prepares students to compete in the labour market alongside intelligent machines, in a scenario where change is the only constant and many skills become quickly obsolete.

You can find more information on the professor and researcher here.