Build talent, import talent, or move the firm
Modified
Input
Firms facing a skill constraint can train local workers, recruit across borders, contract remotely, redesign the task, or relocate part of the organization. The right choice depends on capability gaps, transferability, coordination costs, time horizon, and the location of knowledge and customers. A global talent strategy should optimize the whole production system rather than minimize wages or maximize local headcount.
A Firm-Level Choice
When a firm cannot find a required capability locally, public discussion often presents two options: train domestic workers or recruit migrants.
The firm's actual choice set is wider. It can:
- build capability internally;
- hire a worker who already lives locally;
- bring a worker to the firm's location;
- employ or contract the worker remotely;
- move a team or function toward the talent;
- automate or redesign the task; or
- abandon the activity.
These options differ in cost, speed, control, learning, and strategic value.
The Make-Buy-Move Framework
Let strategy $s$ have expected value
where $Y$ is output, $w$ is labor cost, $C^{coord}$ is coordination cost, $C^{inst}$ is institutional and regulatory cost, $C^{tech}$ is technological cost, and $F_s$ is the fixed cost of establishing the strategy.
The firm chooses
The lowest wage strategy need not maximize value. A remote specialist may have a lower wage but impose high coordination cost. Internal training may be expensive initially but create cumulative organizational knowledge.
Comparing the Options
Table 1. Strategic options for accessing scarce capability
| Strategy | Main advantage | Main limitation | Best fit |
|---|---|---|---|
| Train internally | Builds firm-specific capability | Slow and uncertain | Recurring needs with teachable foundations |
| Hire locally | Low relocation and integration cost | Limited candidate pool | Common or adjacent capability |
| Recruit internationally | Places talent near the core team | Migration and retention friction | High-value, interdependent work |
| Contract remotely | Fast access and flexible scale | Control and knowledge-retention risk | Modular, observable tasks |
| Build a distributed team | Access to several labor markets | Requires strong documentation and management | Long-lived, separable functions |
| Move a function | Places work inside a stronger ecosystem | High fixed and governance cost | Capability tied to a location or cluster |
| Automate or redesign | Reduces labor requirement | May shift rather than remove the constraint | Standardizable tasks |
The table is a starting point. The same role may move across categories as the firm learns.
Build: The Economics of Training
Suppose the firm invests $c_T$ in training worker $i$. Capability evolves as
where $T_{i,t}$ is training effort and $\lambda_i$ is learning effectiveness.
The firm captures a return only if the worker remains and the new capability is used:
where $p_{i,t}$ is the probability the worker remains through period $t$.
Training is attractive when the skill is cumulative, the firm can provide good feedback, and the capability will be used repeatedly. It is less attractive when the need is immediate, the prerequisite gap is large, or the firm cannot offer a developmental environment.
Training should not be used as a slogan for postponing a difficult hiring decision.
Import: Bringing Talent to the Organization
International recruitment can improve coordination for tasks requiring close interaction with the core team. It also transfers local institutional knowledge to the worker over time.
The net benefit is
where $H_F$ is the worker's capability, $I_D$ and $P_D$ are the destination firm's knowledge and capital, $m$ is match quality, and the final terms are moving and integration costs.
This strategy works when the organization offers a productive platform that the worker values. If the destination weakens the worker's research access, professional options, or household welfare, compensation must offset the loss and retention remains uncertain.
The firm cannot assume that a vacancy is an attraction.
Remote: Moving the Task Instead of the Person
Remote work allows the task to cross the border.
Let a project consist of modules $j=1,\ldots,J$. Remote suitability can be represented as
where $M_j$ is modularity, $O_j$ is observability of output, $D_j$ is documentation quality, and $T_j$ is dependence on tacit knowledge.
Tasks with high $R_j$ can be distributed more easily.
Read as a diagnostic rather than a second scorecard, the framework becomes clear: Clear interface and acceptance test — Less remote-suitable: Ambiguous problem definition. Reproducible data and environment — Less remote-suitable: Access depends on informal local knowledge. Output can be reviewed asynchronously — Less remote-suitable: Constant real-time negotiation. Limited security or licensing friction — Less remote-suitable: Sensitive data and local regulation. Stable task ownership — Less remote-suitable: Rapidly changing authority.
Remote work fails when firms transfer ambiguity rather than a well-designed task.
Move: Locating the Firm Near Talent
Sometimes the knowledge base, professional network, customers, and suppliers are concentrated elsewhere. Moving a team can be more rational than trying to recreate the ecosystem.
Let location $r$ provide cluster productivity $G_r$:
The firm should compare the cluster gain with fixed and coordination costs:
A small representative office rarely captures the full $G_r$. The team needs enough authority, work, and local participation to join the knowledge system.
Moving a function is most credible when the location supplies a capability the firm cannot efficiently reproduce.
Organizational Knowledge and the Boundary of the Firm
Contracting can provide immediate expertise, but the firm may fail to retain what it learns.
Let intellectual capital evolve as
where $X_{s,t}$ is knowledge produced under strategy $s$ and $\lambda_s$ is the share absorbed by the firm.
Internal teams may have a high $\lambda_s$ because they work inside the firm's systems. External contractors can also generate high absorption when documentation, review, and paired work are built into the contract. Without those mechanisms, deliverables arrive while capability remains outside.
The sourcing decision should therefore include a knowledge-retention objective.
Labor Cost Is Not Unit Cost
Comparing salaries across locations is insufficient.
Unit labor cost is
where $q_r$ is verified productive output.
A lower wage can coincide with a higher unit cost when rework, delay, management attention, or quality failure is substantial. A higher-wage specialist can be cheaper when the person selects a simpler model, prevents an invalid deployment, or builds reusable infrastructure.
This is especially important in AI, where an error can be fluent, scalable, and difficult to detect.
A Portfolio Strategy
Firms rarely need one global policy for all work. They can allocate tasks across strategies.
Let $x_{js}\in[0,1]$ be the share of task $j$ assigned to strategy $s$, with
The firm minimizes total cost
subject to quality, security, latency, learning, and regulatory constraints.
This produces a portfolio:
- core model governance may remain close to decision makers;
- specialized research may connect to an external cluster;
- modular engineering may be globally distributed;
- local staff may be trained for recurring domain work; and
- routine tasks may be automated.
The boundaries should follow task economics, not symbolism.
The portfolio should also preserve option value. A pilot with a remote specialist can reveal the true task before the firm pays the fixed cost of a new office. A temporary internal rotation can test whether local staff possess the prerequisites for deeper training. A small co-located team can discover which interfaces must remain close to decision makers before the firm distributes the rest.
These are not indecisive half-measures when they are designed as experiments. They produce information about $C_{js}$, task modularity, and knowledge absorption. The firm should specify in advance which result triggers expansion, redesign, or exit.
Implications for Education and Policy
Education should prepare students for a labor market in which firms can move tasks as well as people. Graduates compete and collaborate across locations.
This raises the value of:
- internationally legible technical communication;
- reproducible work;
- clear interfaces;
- independent problem solving;
- domain knowledge;
- remote collaboration; and
- the ability to learn inside unfamiliar institutional systems.
For policy, restricting labor mobility does not necessarily preserve local work. Firms may contract remotely, automate, or move functions. A durable local strategy must raise the productivity of the ecosystem, not rely only on barriers.
The four routes should be treated as a portfolio of real options. Internal training is slow but preserves organizational knowledge. Migration brings scarce capability into the existing team but creates integration and policy risk. Remote contracting offers speed and geographic reach but may weaken control over tacit knowledge. Relocation places the firm inside a deeper labor market but exposes it to high fixed costs and possible loss of existing relationships. Uncertainty makes a staged strategy valuable: a firm can begin with a remote team, use the project to learn which capability is truly scarce, then decide whether to train internally, sponsor migration, or establish a permanent location. The best choice is often a sequence rather than a single irreversible answer. Governance should assign an owner to the learning generated by each stage. If a remote pilot succeeds, the firm must record which expertise mattered, which interfaces failed, and whether the capability should remain external. Otherwise, the pilot reduces an immediate workload without improving the later location or hiring decision. Option value exists only when experimentation produces information that changes the next commitment. The staged approach also reduces false precision. Cost estimates made before the firm has observed the work are often dominated by assumptions about coordination, review, and tacit knowledge. A bounded pilot replaces part of that uncertainty with evidence. Management can then compare the options on observed unit cost, learning, retention of knowledge, and control rather than salary or tax rates alone. The strategic comparison should therefore value learning as well as the immediate delivery of scarce work.
Technological change makes the location decision more dynamic. The AI Trade Shock emphasizes worker reallocation across sectors and borders. Jevons Paradox and AI Adoption shows that firms compare total task economics rather than technical exposure alone, while The AI Data Center Jobs Debate demonstrates that the employment footprint of infrastructure extends beyond the facility itself. Building, importing, contracting remotely, and relocating are therefore portfolio choices whose costs depend on scale, demand, integration, and the surrounding production network.
Conclusion
A skill shortage does not imply one remedy. Firms can build, buy, import, distribute, move, redesign, or exit.
The decision depends on more than wages. It depends on the time required to develop capability, the transferability of knowledge, task modularity, coordination cost, cluster advantage, retention, and the firm's ability to absorb what external specialists produce.
The strategic objective is a productive system with cumulative learning. Sometimes that means bringing talent to the firm. Sometimes it means taking the firm to talent. Increasingly, it means designing an organization that can do both.
References
Ronald H. Coase, “The Nature of the Firm”, Economica, 1937.
Frank Levy and Richard J. Murnane, The New Division of Labor, Princeton University Press, 2004.
Richard Baldwin, The Globotics Upheaval, Oxford University Press, 2019.
The Economy Editorial Board (2026) ‘The AI Trade Shock Will Reward Countries That Move Workers, Not Just Data’, The Economy Review, 2 June.
The Economy Editorial Board (2026) ‘Jevons Paradox and AI Adoption: Why Comparative Advantage Matters More Than Exposure’, The Economy Review, 1 September.
The Economy Editorial Board (2026) ‘The AI Data Center Jobs Debate Is Counting the Wrong Workers’, The Economy Review, 17 August.
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