Guido Pialli

Welcome! I am a PostDoc at the University of Turin.

I am also Honorary Research Fellow at CASA, UCL.

Previously, I was a Postdoctoral Research Fellow at UCL, based at CASA, contributing to an ESRC funded project.

I obtained a PhD in Economics from the University of Torino and Maastricht University in December 2022.

I am an applied economist. My research broadly falls under the area of the economics of knowledge and innovation. I also work on topics related to labor and migration.

I am currently working on: i) the drivers and direction of firm and regional innovation; ii) the economics of diversity; iii) technological change and income inequality.

Here you can find my CV

Contacts:

g.pialli@ucl.ac.uk;

guido.pialli@unito.it

Here are some of my ongoing projects:

Population diversity and entrepreneurial motivation: evidence from second-generation Americans (with Olga Tcaci).

Abstract. This article examines how within-population diversity, proxied by the migratory distance of ancestral populations from East Africa, shapes contemporary entrepreneurial spirit. Empirically, we adopt the so-called epidemiological approach and examine this channel among US second-generation immigrants, using the distance of their parents' country of origin from East Africa as a proxy for variation in ancestral population diversity. Our results indicate a positive relationship between population diversity and entrepreneurial disposition: individuals whose ancestral origins are geographically closer to East Africa, and thus characterized by greater interpersonal diversity, are more likely to engage in entrepreneurship. To interpret this finding, we develop a conceptual framework in which greater ancestral population diversity increases the dispersion of skills and competencies and thickens the upper tails of distributions of human capital and job non-routine task content. We provide empirical evidence consistent with these mechanisms and show that these channels contribute to higher entrepreneurship rates within ancestral groups.

GRAPH-EE: building employer-employee panels from a knowledge graph and company microdata (with Max Nathan, Steve Gray, Tom Kemeny, Ceren Ozgen, Jon Reades, Anna Rosso, Mateo Serè and Anna Valero).

Linked employer-employee datasets are a hugely powerful tool for researchers and policymakers across the social sciences. However, access to such data is typically highly restricted and the quality of data varies substantially across countries. This paper presents GRAPH-EE, a large-scale employer-employee platform for larger companies in the UK. Leveraging a vast knowledge graph of the global public internet linked to UK company microdata and global patents data, GRAPH-EE is a rich complement to administrative data sources. This version of the platform covers over 10,500 companies and 800,000 workers active in the UK during 2007-2023. We show the potential to extend our approach to multiple other countries.

Migration, diversity and productivity in UK firms (with Max Nathan, Tom Kemeny, Ceren Ozgen, Anna Rosso, Mateo Serè and Anna Valero).

Abstract. We look at the effects of migrant workers and migrant diversity on firms’ innovation and productivity. We build a novel worker-firm dataset leveraging over 500,000 online worker profiles alongside rich company microdata. We combine individual education and career histories from the Diffbot knowledge graph with larger firms from the UK company register, plus detailed financial and patents data. We define migrants through lowest observed countries of education. To identify effects and mechanisms we use placebo tests and IVs based on historic name patterns. We have three main findings. We find positive but non-significant effects of firm migrant share on firm productivity, which are larger for more qualified migrants and for tech and STEM-intensive settings. Since this does not rule out a link, we use our granular worker-level data to explore potential channels in detail. We find that migrant workers carry distinctive, and more complex skills than natives in the same jobs, especially in tech and STEM roles. We also find that migrant specialisation into technical occupations is robustly linked to higher firm patenting.