Infrastructure bias

Infrastructure bias refers to the systematic influence exerted by the presence, distribution, and accessibility of existing infrastructure on economic, social, and scientific activity. It arises when the location or availability of facilities such as roads, telecommunications networks, laboratories, telescopes, or particle accelerators shapes the patterns of development or the nature of data collected. As a result, outcomes become skewed toward what is easier to access or measure rather than what is most representative.

Infrastructure bias in economics and social policy

Within economics and social policy, infrastructure bias highlights how pre-existing physical networks can disproportionately direct social and economic development. Regions with abundant road systems, reliable telecommunications, and established transport corridors tend to attract greater investment and receive more public services. Conversely, rural or underdeveloped areas with limited infrastructure may see slower growth, reinforcing existing inequalities.
Examples frequently include:

• Concentration of businesses and employment opportunities along major highways or railway lines.
• Improved access to education, healthcare, and markets in areas served by telecommunications and transport networks.
• Reduced levels of investment in remote regions, where the absence of infrastructure discourages both public and private initiatives.

This form of bias can perpetuate socio-economic disparities by creating feedback loops: infrastructure attracts development, and development attracts further infrastructure.

Infrastructure bias in scientific research

In scientific research, infrastructure bias reflects the dependence of data, experiments, and discoveries on the available scientific facilities. The scope of research is often limited by the capacity, location, and nature of existing instruments. This may influence both the types of questions asked and the outcomes obtained.
Astronomy and particle physics
These disciplines offer clear illustrations of infrastructure bias. Large-scale scientific instruments, such as radio telescopes, optical observatories, and particle accelerators, are limited in number and geographically fixed. Researchers are therefore constrained to investigations that can be conducted with available technologies:

• Astronomical surveys emphasise the regions of the sky visible from existing observatories, leading to uneven sky coverage.
• Particle physics experiments are limited by the energies achievable in operational accelerators.
• Data collection may favour detectable phenomena over those requiring unavailable or experimental instrumentation.

Such constraints mean that scientific knowledge often advances in directions aligned with technological capability, potentially overlooking phenomena outside the observational reach of current infrastructure.

Procedural bias in scientific sampling

A related form of bias, procedural bias, arises when sampling or data-collection methods systematically favour certain locations over others due to accessibility. This is closely tied to infrastructure bias when existing roads, towns, or communication networks influence where samples are taken.
A documented example concerns irregular genetic sampling of Solanum species in Bolivia. A review conducted in 2000 found that approximately 60% of samples were collected near towns or roads. Had the sampling been random, or derived from equidistant points across the landscape, only about 22% would have been taken from such accessible areas. This discrepancy demonstrates how proximity to infrastructure can distort scientific sampling, generating results that fail to represent the full ecological or genetic diversity.
Sampling bias of this nature affects a range of scientific fields, including ecology, conservation biology, and environmental studies, where representative data are crucial for effective analysis.

Broader implications

Infrastructure bias, whether in socio-economic systems or scientific practice, can lead to skewed interpretations and decisions. In development policy, it may reinforce regional inequalities by over-serving already connected areas. In research, it may restrict the scope of inquiry and limit the generalisability of scientific findings.
Awareness of this bias has prompted efforts to design more representative sampling methods, distribute scientific infrastructure more equitably, and tailor economic policies to account for disparities in access. While infrastructure cannot be evenly distributed across all contexts, recognising its influence remains essential for producing fairer social outcomes and more reliable scientific knowledge.