Understanding Unemployment in the Era of Big Data: Policy Informed by Data-Driven Theory

Authored by Omar A Guerrero, Eduardo Lopez

Date Published: 2017

DOI: 10.1002/poi3.136

Sponsors: No sponsors listed

Platforms: JavaScript

Model Documentation: Other Narrative Pseudocode Mathematical description

Model Code URLs: Model code not found

Abstract

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 organizations, so ignoring realistic micro-mechanisms may lead to undesirable outcomes in the real world. Ironically, the data to inform theoretical frameworks at the micro-level have existed in labor studies since the 1980s. 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 theories of unemployment and improve policies. Based on previous empirical work with administrative big data, we introduce a data-driven model of unemployment dynamics that is validated at both the micro-and macro-levels. At a first glance, validation at the micro-level seems unnecessary since we focus on aggregate unemployment. However, by establishing a connection between our model and the ones commonly used to advice policy, we show that overlooking micro-level validity leads to erroneous predictions with significant real-world consequences.

Tags

networks Economics Network Policy Big data Search Unemployment Agent-based modeling Labor flows Matching function