At the 2026 ESCoE Conference on Economic Measurement, the Office for Statistics Regulation (OSR) led a session on how unofficial data can shed light on local economic performance. In this blog, Ed Humpherson, Director General of OSR, and Jonathan Price, Expert Adviser for Economic Statistics, consider its associated opportunities and risks.
Why local insight matters
If you want to understand how an economy is really performing, look locally. It is in towns, cities and regions that economic change is felt most directly – through jobs, businesses and investment decisions that shape people’s day-to-day lives.
Yet this is also where our statistical picture is often weakest. Official statistics – statistics produced by government departments or designated organisations – are designed to provide a coherent and consistent view of the economy. They do this well when looking at the whole country. But as you move to smaller areas, the challenges grow. Sample sizes fall, estimates become more uncertain, and methods often rely on distributing national totals using models and proxies. These approaches help maintain consistency, but they can smooth over the very local variation that users are often most interested in.
This creates a familiar tension. Official statistics offer quality and comparability, but they do not always capture the texture of local economies – the emerging sectors, the informal activity or the specific industrial mix that makes one place distinct from another.
The rise of “bottom-up” data
Given that context, it is not surprising that there is growing interest in what is often described as “bottom-up” data: information that is collected, generated or assembled locally, sometimes at a much more detailed level. These are non-official data, which can draw on a wide range of sources, from local surveys and administrative data to commercial transaction records, job postings, property listings and even mobility data derived from digital platforms. Increasingly, these sources are being combined with official data to build richer, more-detailed pictures of local economic activity.
At its best, this kind of data can provide insight that is simply not available elsewhere. It can be timelier, offering earlier signals of economic change. It can be more closely aligned with local realities, reflecting the structure of specific places in a way that national frameworks cannot. It also allows for greater flexibility – analysts can define sectors and activities in ways that make sense locally, rather than relying on standard classifications that may not keep pace with the economy as it evolves. And because it is often collected locally, bottom-up data can encourage engagement, creating a sense of ownership that improves both participation and transparency.
Alongside these developments, there has been growing interest in local economic modelling. From relatively simple input–output frameworks to more complex models, these approaches aim to provide a structured way of understanding how local economies work and how they might respond to change. When used carefully, they can support more-tailored analysis and help decision makers think through the potential impacts of policy choices.
Understanding the risks and limitations
These opportunities come with important caveats. The strengths of bottom-up data are closely linked to its risks. The very flexibility that allows it to reflect local conditions can make it harder to compare across areas or to align with official statistics. The diversity of sources and methods can make it difficult to assess quality. In some cases, the underlying data or modelling processes are not fully transparent – particularly where proprietary systems or “black-box” techniques are used – making it harder to validate results or understand how conclusions have been reached.
There are also more subtle risks. Timeliness, for example, is often seen as an advantage, but more up-to-date data are not always more useful. Many important local policy challenges are structural and long term, and a strong focus on short-term indicators can distract from that bigger picture. Similarly, modelling outputs – especially those that produce clear or favourable results – can be selectively used to support certain narratives or interests (including those of market incumbents). This may be a particular risk where funding or policy decisions are at stake.
Even in the best circumstances, local economic modelling faces inherent limitations. Many approaches provide only a partial view of the economy or focus on short-term demand effects rather than longer-term structural change. More-advanced models promise greater realism, but they are often complex, difficult to validate and still relatively underdeveloped in practice. And as with all economic modelling, uncertainty is unavoidable – arguably even more so at the local level, where data are more limited and external influences are harder to capture.
Transparency and good practice
None of this means that bottom-up data and modelling should be dismissed. On the contrary, they have an important role to play in improving our understanding of local economies. But they work best when they are used alongside, rather than instead of, official statistics, and when their strengths and limitations are clearly understood.
That points to a simple but important principle: transparency matters. Being clear about where data come from, how they have been processed, what assumptions have been made and where the uncertainties lie is essential if users are to interpret them correctly. It also helps guard against misuse – intentional or otherwise – and supports trust in the analysis.
Looking ahead
There is an opportunity to bring these different strands together more effectively. Improving the availability and use of administrative data could strengthen the local insight that official statistics can offer. At the same time, greater engagement with those producing local data and analysis – supported by shared standards and proportionate assurance – could raise quality and build confidence in them.
If we get this right, we can move towards a richer and more balanced picture of local economies: one that combines the coherence and credibility of official statistics with the insight and responsiveness of bottom-up approaches. That, in turn, would provide a stronger foundation for the decisions that shape places and the lives of the people in them.
During the autumn, ONS plans to convene a seminar of experts and other interested parties to consider how we can take the next steps on this journey together.