In this paper, I explore how traditional working arrangements are affected by the introduction of a gig platform. I do so by forming a novel dataset that combines comprehensive employer-employee data from the Veneto region of Italy with delivery-level information from Italy's largest on-demand delivery service platform. By exploiting the platform's staggered rollout across the region, I find the platform’s availability increases registered unemployment followed by a delayed rise in employment. Individuals that take up gig work are more likely to have fixed-term contracts with a shorter duration and spend more time registered as unemployed after the platform’s introduction.

Policies targeting disadvantaged areas aim to improve their conditions, but the labels they impose carry consequences of their own. In this paper, we examine Denmark's Ghetto Plan, one of the first recent place-based policies explicitly targeting migrant populations. Under this policy, certain public housing deemed "problematic" were officially designated as "ghettos", with minimal additional implications. Using rich administrative data and a Difference-in-Differences approach, we show that the policy backfired, worsening spatial inequality through compositional shifts driven by native avoidance. In addition, the policy was particularly detrimental to exposed natives, who accepted a 4% annual income loss to leave stigmatized areas.

This study investigates supply-side contributors to earnings disparities between foreign-born and native-born workers in Italy’s on-demand delivery sector. Using detailed administrative data, we document that foreign-born riders earn significantly less than natives, even after accounting for individual and job-related characteristics. We identify three supply-side factors: familiarity with the host country (proxied by experience), network formation (via location choice), and earnings targets.