How Mirror-Image Effects Shape Online Labour Markets

Introduction

The 'gig economy', where workers are hired through internet platforms to complete a one-time service task (a 'gig'), is growing into a major labour market. Yet, we still lack a theory of how it develops. Its online part, including tasks to be completed at the computer (e.g. programming or translations), constitutes the first truly global labour market.

Challenges Faced by Gig Workers

Faced with unprecedented competition, most gig workers offer their services at low rates that do not allow for insurances or building up pensions. Several governments therefore consider making social security contributions compulsory.

Regulatory Considerations

But can regulation at the national level protect workers in online markets, or will the demand for online gigs simply relocate to low-wage, low-protection economies?

Proposed Theory

Based on an interdisciplinary framework of theories on varieties-of-capitalism, innovation systems, and entrepreneurial ecosystems, I propose a new institutional theory on ‘mirror-image specialization’.

Hypothesis

I hypothesize that education and labour-market institutions lead requesters, platforms, and providers of online gig work to specialize in hiring, transacting, and offering those skills that are least available in their home labour markets. This leads to specialization patterns in a country’s online gig economy opposite to those in its traditional labour market.

Implications of the Theory

If this is the case, my theory breaks ground for a new paradigm in institutional research of online markets and indicates that national policy-making can protect gig workers without risking the relocation of gig demand.

Research Methodology

To enable this theoretical high-risk/high-gain contribution, I will go beyond the empirical state-of-the-art of single-platform studies by collecting novel, large-N data on:

1. Gig requesters (WP1)
2. Platforms (WP2)
3. Gig providers (WP3)

Using quantitative and qualitative methods, I will go beyond the analytical state-of-the-art of one-time studies and analyse panel and time-stamped data to gain over-time insights into how the online gig economy unfolds.