Omar A. Guerrero

The Network Picture of Labor Flow

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 modelled 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.

The Network Composition of Aggregate Unemployment

We develop an alternative framework to the aggregate matching function in which workers search for jobs through a network of firms: the labor flow network. The lack of an edge between two companies indicates the impossibility of labor flows between them due to high frictions. In equilibrium, firms’ hiring behavior correlates through the network, generating highly disaggregated local unemployment. Hence, aggregation depends on the topology of the network in non-trivial ways. This theory provides new micro-foundations for the the Beveridge curve, wage dispersion, and the employersize premium. Using employer-employee matched records, we find that the empirical topology of the network, in conjunction with the supply elasticity, may be a major contributor of aggregate unemployment.

The Resilience of Public Policies in Economic Development

This paper studies the resilience of public policies that governments design for catalyzing economic development. This property depends on the extent to which behavioral heuristics and spillover effects allow policymakers to attain their original goals when a particular policy cannot be funded as originally planned. This scenario takes place, for example, when unanticipated events such as natural disasters or political turmoil obstruct the use of resources to advance certain policy issues, e.g., infrastructure or labor reforms. Here, we analyze how the adaptive capacity of the policy-making process generates resilience in the face of disruptions. In order to estimate the allocation of resources across policies, we employ a computational model that accounts for diverse social mechanisms, for example, coevolutionary learning and network interdependencies. In our simulations, we use a data set of 117 countries on 79 development indicators over an 11-year period. Then, we calculate a resilience score corresponding to each development indicator via counter-factual analysis of policy disruptions. Next, we assess whether some development strategies produce resilient/fragile policy profiles. Finally, by studying the relationship between policy resilience and policy priority, we determine which issues are bottlenecks to economic development.

Uncovering Vote Trading Through Networks and Computation

We develop a new methodological framework for the empirical study of legislative vote trading. Building on the concept of reciprocity in directed weighted networks, our method facilitates the measurement of vote trading on a large scale, while estimating the micro-structure of trades between individual legislators. In principle, it can be applied to a broad variety of voting data and refined for various specific contexts. It allows, for example, to study how vote trading in a specific legislative assembly varies over time. We validate our method with a computational model in which we control the level of vote trading. Finally, we demonstrate our framework in an analysis of four decades of roll call voting in the U.S. Congress.

Diffusing Workers in a Multiplex World

The study of labor mobility across firms is crucial to understand economic performance, unemployment, skills reallocation and other aspects that shape the economic life of nations. Modeling labor flows between firms has been a challenge due to the complexity arising from the distributed and heterogeneous nature of labor flows. In this paper, we introduce a discrete-time model of labor flowing on a multi-layered network (i.e. a multiplex graph). By introducing multiple layers, the model accounts for different mobility patters (e.g. industries, geographies, occupations, etc.), which is important to understand the reallocation of human capital, skills and knowledge. We apply the model to UK empirical micro-data and find that our measure of regional preferences for low versus high skilled workers vary significantly from a single to a multi-layer representation of the world.

Labor Flows and the Aggregate Matching Function: A Network-Based Test Using Employer-Employee Matched Records

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.