  Labor market institutions are central for modern economies, and their polices
can directly affect unemployment rates and economic growth. At the individual
level, unemployment often has a detrimental impact on people's well-being and
health. At the national level, high employment is one of the central goals of
any economic policy, due to its close association with national prosperity. The
main goal of this thesis is to highlight the need for frameworks that take into
account the complex structure of labor market interactions. In particular, we
explore the benefits of leveraging tools from computational social science,
network science, and data-driven theories to measure the flow of opportunities
and information in the context of the labor market. First, we investigate our
key hypothesis, which is that opportunity/information flow through weak ties,
and this is a key determinant of the length of unemployment. We then extend the
idea of opportunity/information flow to clusters of other economic activities,
where we expect the flow within clusters of related activities to be higher
than within isolated activities. This captures the intuition that within
related activities there are more "capitals" involved and that such activities
require similar "capabilities." Therefore, more extensive clusters of economic
activities should generate greater growth through exploiting the greater flow
of opportunities and information. We quantify the opportunity/information flow
using a complexity measure of two economic activities (i.e. jobs and exports).
