Dual Coordination Frictions in Application-Based Matching

In Simple Words

Think of job hunting like sending college applications. The first friction happens when many students apply to the exact same university, leaving smaller colleges with zero applicants. The second friction happens when a university offers admission to top applicants, but those applicants choose other universities instead, leaving the original university with unfilled seats and no time to process waitlisted students.

Research question

Standard aggregate Cobb-Douglas matching functions assume constant returns and smooth matching efficiency, ignoring how increasing individual application volume creates discrete coordination bottlenecks on both the applicant and employer sides.

Participants

Theoretical matching framework calibrated using Indirect Inference on N = 40,403 Danish job seekers across 14 county labor markets.

Study Design

Micro-founded directed search matching function $m(a; \bar{a}, \theta) = 1 - (1 - \psi)^a$ with Poisson-distributed vacancy applications ($\psi = \frac{\theta}{\bar{a}}(1 - e^{-\bar{a}/\theta})$).

Results

While higher search effort per worker resolves the first friction (reducing empty vacancies), it exacerbates the second friction (increasing double-job offers and candidate loss), causing aggregate matching efficiency to flatten and diminish beyond optimal application thresholds.

Takeaway

Specify matching functions with explicit dual-coordination friction parameters to model non-linear matching returns under policy-induced search effort spikes.

Related findings

• Constant Returns to Marginal Job Search Effort →Labour & employment

  Think of fishing in a large lake using a net. Standard theory suggests that after catching the easiest fish, throwing the net additional times will yield smaller catches.

• Partially Directed Search under Behavioral Friction →Labour & employment

  Think of shopping at a large supermarket with quality items scattered across many aisles. A shopper prefers organic produce and selects it when spotted, but walking down every aisle takes physical effort, so they leave many top-tier items on the shelves.

• Urn-Ball Matching Function with Elastic Search Effort →Labour & employment

  Picture job seekers as throwing balls randomly into a set of urns representing job openings. If multiple workers throw their balls into the exact same urn, only one gets hired, while the extra balls represent wasted effort (congestion).

• Ability-Dependent Peer Screening →Labour & employment

  Imagine trying to pick the best chess player out of a crowd. Only a master chess player has the expertise to accurately assess who among their acquaintances truly plays at an elite level, whereas a beginner cannot distinguish between a decent amateur and a grandmaster.