My research interests lie in the intersection of economics, networks, and computation. I am currently studying labour dynamics as a process where people flow throughout the economy by moving from one firm to another. I study these flows by looking at detailed data about employment histories of each individual and every firm in entire economies. Using this information, I construct networks of firms in order to map the roads that people take throughout their careers. This allows to study labour markets at an unprecedented fine-grained level of detail. I employ agent-based computing methods to understand how economic shocks and policies alter labour flows, which eventually translate into unemployment and other related problems. My interests are extend to other fields such as development economics, political economy, and international trade. Here you can find some of my published and ongoing works and collaborators.

Published Works

Labor Flows and the Aggregate Matching Function: A Network-Based Test Using Employer-Employee Matched Records (with Eduardo López, forthcoming in Economic Letters)

The assumption of aggregate matching functions in labor markets is tested using a network configuration model for directed multigraphs. We use employer-employee matched records of the universe of employees and firms in Finland and find that aggregate matching functions, even at the level of submarkets, cannot explain the vast majority of the observed patterns of labor flows between firms. Our findings suggest the need for theoretical frameworks that take into account the structure of labor market frictions.

Employment Growth through Labor Flow Networks (with Rob Axtell)

We analyze the employment trajectories of millions of individuals across firms in order to study the underlying network that they use to find jobs. Using data from Mexico and Finland, we show that the traditional story of small firms driving most employment growth is not very accurate. Instead, we find that better connected firms are the ones driving a disproportionate amount of employment growth. Finally, we develop an agent-computing model that explains the emergence of these labor flow networks.

Using Agentization for Exploring Firm and Labor Dynamics (with Rob Axtell)

We discuss the challenges of mapping equation-based economic models into agent-computing models. We propose a systematic way to do it and provide an example related to labor markets and firm formation.

Works in Progress

Understanding Unemployment in the Era of Big Data: Policy Informed by Data-Driven Theory (with Eduardo López)

On one hand, unemployment is a central issue in all countries. On the other the economic policies designed to mitigate it are usually built on theoretical grounds that are validated at an aggregate level, but have little or no validity from a micro point of view. This situation is a cause for concern because policies are designed and implemented at the level of individuals and organisations, so ignoring realistic micro-mechanisms may lead to costly outcomes in the real world. Ironically, the data to inform theoretical frameworks at the micro-level has existed in labour studies since the 1980's. However, it is only now that we count with analytical methods and computational tools to take full advantage of it. In this paper we argue that big data from administrative records, in conjunction with network science and agent-computing models offer new opportunities to inform unemployment theories and improve policies. We introduce a data-driven model of unemployment dynamics and compare its predictions against a conventional theory built on assumptions that are common among policy models. We show that these assumptions, while reasonable at a first glance, lead to erroneous predictions that have real-world consequences.

The Network Composition of Aggregate Unemployment (with Eduardo López and Rob Axtell)

We study the effect of a specific kind of labor market friction on aggregate unemployment. In our model, unemployed workers search for jobs through a network of firms, the frictionless network. The lack of an edge between two companies indicates the impossibility of labor flows between them (at least in the short run) and is a kind of friction. Thus, any network topology other than the complete graph will have give rise to labor market frictions. Application of the method of random walks on graphs yields analytical solutions for the equilibrium unemployment rate. To accomplish this it is convenient to introduce a new statistic, firm-specific unemployment, in order to derive aggregate unemployment as a function of the structure of frictionless networks. Using comprehensive employer-employee matched records for the universe of workers and firms in Finland, we characterize frictionless networks and their influence on aggregate unemployment. We show that, to first order, the heterogeneity in firm connectivity is responsible for more than half of the equilibrium unemployment rate. Our theory provides new micro-foundations for the aggregate matching function and other stylized facts of labor markets, such as the Beveridge curve, wage dispersion, and the employer- size premium. We also develop a new way to estimate the hiring policy of each firm, without the need for data on firm vacancies. Overall, the theory and methods described represent new ways for employer-employee matched records to be used for the study labor dynamics.

The Network Picture of Labor Flow (with Eduardo López and Rob Axtell)

We construct a data-driven model of flows in graphs that captures the essential elements of the movement of workers between jobs in the companies (firms) of entire economic systems such as countries. The model is based on the observation that certain job transitions between firms are often repeated over time, showing persistent behavior, and suggesting the construction of static graphs to act as the scaffolding for job mobility. Individuals in the job market (the workforce) are modeled by a discrete-time random walk on graphs, where each individual at a node can possess two states: employed or unemployed, and the rates of becoming unemployed and of finding a new job are node dependent parameters. We calculate the steady state solution of the model and compare it to extensive micro-datasets for Mexico and Finland, comprised of hundreds of thousands of firms and individuals. We find that our model possesses the correct behavior for the numbers of employed and unemployed individuals in these countries down to the level of individual firms. Our framework opens the door to a new approach to the analysis of labor mobility at high resolution, with the tantalizing potential for the development of full forecasting methods in the future.

Urban NEETs in Mexico and their Dynamics: A Pseudo-Panel Approach (with César Bouillon, available upon request)

We analyze the relationship between youth unemployment and the recent increase in violence in Mexican municipalities. We use a pseudo-panel approach to analyze unemployment outcomes across age cohorts throughout 15 years of census data. We find a significant relationship among those municipalities that ranked among the most violent. However, other factors such as family disintegration seem to have a stronger effect in youth unemployment.

Talks and Media

My plenary talk at the International Conference on Computational Social Science (Helsinki, 2015)

A 10 minutes presentation of my research project on labor flow networks and aggregate unemployment.