Jobs disappear, jobs are created, work changes, in a geographically uneven way. Some places cope better with adverse events that threaten its labour force into unemployment. Some places end up having better jobs. Why? Labour dynamics are particular of each place, influenced by a myriad of local factors, such as the type of jobs in each city. But jobs do not stand alone. Workers interact with and influence each other within and across firms. This ends up reflecting in the structure of labour of each city, region, or country. At each point in time, the structure sets the opportunities and boundaries for labour dynamics to unravel. This doctoral thesis investigates the way jobs relate to each other – relatedness – and how it shapes the evolving geography of jobs. Several bodies of literature – Evolutionary Economic Geography (EEG), Labour Economics, Urban Scaling, Innovation Studies, Regional Policy – come together to better understand how relatedness shapes labour dynamics in different spatial contexts. First, using employment data of six industrialized countries, we find that bigger cities have more than proportionally higher levels of relatedness between jobs, than smaller cities – relatedness self-reinforces as cities grow. Second, relatedness promotes job diversification and prevents exit of job specializations in USA cities – “magnet effects” – in three distinct ways: agglomeration of jobs that are complementary, and/or similar, and/or synergic. Third, the impacts of automation spread through the structure of relatedness – “diffusion effects”. More concretely, being complementary, but not similar, to local high-risk-jobs increases employment grow of a job in a USA city. Finally, policy can explore the potential of relatedness effects to reach desired outcomes. For instance, by stimulating the structure of relatedness around highly specialized jobs, which tend to be denser in innovative sectors. Accordingly, we found EU policy business incentives to innovation to have increased job quality in Portuguese firms. These findings may help design policy instruments that neutralize the negative effects of automation, while promoting the positive impacts. For instance, identifying which jobs in which cities might be at higher risk, given jobs’ technical feasibility of automation, but also how high-risk-jobs spread automation impacts to other jobs in each city. Workers will be in greater need of social support and training programs especially where their similarities to high-risk-jobs in the city out rule the complementarities. Moreover, place-based policy instruments can target the above-mentioned relatedness effects to foster employment with job quality in lagging regions.
|Qualification||Doctor of Philosophy|
|Place of Publication||Utrecht|
|Publication status||Published - 2020|
- j00 - Labor and Demographic Economics: General
- o15 - "Economic Development: Human Resources; Human Development; Income Distribution; Migration"
- o31 - Innovation and Invention: Processes and Incentives