Labour dynamics
I have been developing this research area since my PhD studies. Back then, I proposed the analysis of labour mobility through what I called Labour Flow Networks (LFNs). LFNs have become a common tool to study movements between jobs, firms, industries, occupations, and regions. While most people working in this domain use LFNs as a fixed structure of the labour market (including my earlier studies), I am currently working on models that emerge these complex structures endogenously from economic principles. These models are extremely useful to study longitudinal dynamics that are impossible to observe in surveys, which is why the UK government has adopted them. Here you can see the organic progression and development of my LFN ideas, all the way to my most recent work with Kathyrn Fair on endogenous LFN models.
Scaling and complexity of labor markets
Published on: 2011My first work on the complex dynamics of labour movements captured in administrative large-scale micro-data. My idea was to test whether certain statistical regularities that are prevalent in firm dynamics studies held when using worker flow large-scale datasets.
Employment growth through labor flow networks
Published on: 2013After my first work using worker flow data, I knew that there was a lot more to be learnt from such granular information. Hence, I flipped the problem to look at employees moving across firms rather than at firms themselves. By using network analysis, we found a plethora of empirical regularities that had never been documented in labour studies. This work coined the term Labor Flow Networks (LFNs) and set the foundations for many works from various researchers working on LFNs.
Firm-to-firm labor flows and the aggregate matching function
Published on: 2015After finding nuanced networked structures in labour flows across different countries, I wanted to know if the workhorse model of labour economics was capable of explaining them. Similar as with well-mixed contagion models in epidemiology, the matching function in economics fails to reproduce such details, even when broken down into many local functions. This paper presents evidence on the limitations of this modelling tool and calls for a different approach to study labour flow networks.
Understanding unemployment in the era of big data
Published on: 2016So far I had established the presence of characteristic networked structures in labour flows and the inability of neoclassical economic models to account for them. Next, I wanted to find out if these structures had implications in important outcomes such as unemployment. This paper presents a simple model of workers flowing on labour flow networks and shows how the structure of the network and its correlation with job opportunities may exacerbate unemployment in the presence of shocks.
A network theory of inter-firm labor flows
Published on: 2020Once we had evidence of the importance of labour flow networks, we wanted to give them a more formal mathematical treatment with standard tools from stochastic processes on networks. This paper develops a model that is analytically tractable and that allows to estimate a new highly granular measure: firm-specific unemployment.
Diffusing workers in a multiplex world
Published on: 2017A common question on our work on labour flow networks was whether certain types of workers would navigate specific types of networks, for example, as a function of their occupation. To address this, we developed a generalisation of the random walkers labour flow model to multiplex networks.
How do governments determine policy priorities?
Published on: 2022After developing a mathematical model of labour flows on networks, we thought it would be possible to integrate this toolkit into the conventional equilibrium framework of neoclassical models. This paper achieve this to demonstrate how the hiring decisions of firms may induce exacerbated unemployment and bottlenecks in the economy if they are interconnected in a labour flow network with topological properties like the ones observed in the real world.
Endogenous labour flow networks
Published on: 2023Despite our success in demonstrating the importance of labour flow networks, I always thought that real policy relevance would come from accounting for worker-level behaviour and heterogeneity; something quite challenging with the plain formalism of random walks on graphs. In this work, we crack down this challenge by implementing an agent-computing model with household micro data and leisure-consumption behaviour. Furthermore, it is the first model that can generate empirical labour flow networks endogenously (instead of assuming them as exogenous). Real-world impact came shortly after completing this work, as departments in the UK government have adopted this toolkit.