Research and analysis

The Economic Impact of Equal Opportunities in the Digital Economy: executive summary

Published 6 July 2026

Executive summary

This report estimates the potential economic gains that could be unlocked if barriers facing under-represented groups, unrelated to skills or productivity, were fully removed within the UK digital economy. To do this, we build and then utilise an economic model that allows us to estimate the impacts of improving the “allocation of talent” – that is, enabling people to work in the jobs in which their potential productivity is highest, rather than being pushed into other jobs by barriers unrelated to productivity.

The UK digital economy is less diverse than the rest of the UK economy across multiple dimensions. The most underrepresented groups among those we examine are women and older workers. The proportion of the female workforce who work in the digital economy is 39% lower than the proportion of the male workforce who do so (7% versus 11%). The proportion of workers aged 50-69 who work in the digital economy is 35% lower than for workers aged under 35 (9% versus 14%).

We estimate that the potential gains to the digital economy from fully addressing the under-representation of women would amount to £16 billion per annum (6% of digital economy Gross Value Added (GVA) based on 2024 GVA estimates). Fully removing the barriers preventing proportionate representation of those aged 50-69 could generate gains of £15 billion per annum (7%). Addressing under-representation of other groups, such as people from lower socioeconomic backgrounds (£5 billion per annum), black ethnic backgrounds (£1 billion per annum), or disabled people (£3 billion per annum), could also yield substantial gains for the digital economy. These gains cannot be summed - this would result in double-counting, since the groups in question are overlapping.

The economy as a whole also benefits through a better allocation of talent across sectors. Fully addressing the under-representation of women in the digital economy could deliver an increase in total GDP of around £4 billion per annum (based on 2024 GDP estimates), while addressing under-representation of those aged 50-69 could deliver a £7 billion per annum. These benefits are smaller than the benefits specifically to the digital economy, since additional workers and output in the digital economy would mean fewer workers and output in other sectors. However, there is still a substantial net benefit for the economy as a whole, arising from the improved match between workers’ skills and the sector they are in.

At the individual level, gains per person are also substantial. These gains measure how much better off members of an underrepresented group would be, on average, if the barriers that currently limit access to digital jobs were removed. All gains are expressed as a cash-equivalent £ per person per year. The gains can reflect higher pay, but they would also incorporate non-pay factors that people value (working conditions, flexibility, culture, or non-pay benefits). Women in the digital economy could see gains worth around £7,000/year on average from reduced barriers, while workers aged 50-69 could gain £15,000/year on average. There are other groups who could gain significantly on an individual basis, even though, because there are fewer of them, they would not affect the aggregate picture as much. These include black workers (gain of £4,000/year) and disabled workers (gain of £6,000/year). These figures all include those already working in digital jobs, who are currently doing so despite the barriers, but would benefit once barriers are removed. If we focus just on those workers who would switch into the digital economy as a result of reduced barriers, the gains are also substantial: around £3,000 per year for women, and £7,000 per year for those aged 50-69.

Supplementary Information

The modelling results are not forecasts of what could happen under any specific policy approach aimed at increasing representation. Instead, they are estimates of the potential scale of the prize: the economic gains that could be unlocked in the hypothetical scenario in which barriers to representation – whatever they are – were fully removed. The model is agnostic about whether this scenario is achievable in practice or what it would cost to achieve it. In addition, the model merely infers the magnitude of the barriers, whatever they are, based on the degree of under-representation. It is agnostic about what the barriers to representation are, or what policies might address them. These are crucial questions in their own right and the subject of other research. Finally, the results should be taken as “upper bounds,” in that partial but incomplete barrier removal would result in smaller gains (although still gains).

We estimate the potential gains from addressing under-representation using a model of individuals’ sectoral choices in the presence of group-specific barriers. The model links the set of people who choose to work in the UK digital economy and the rest of the economy to output in both the digital economy and the economy as a whole. It therefore captures the fact that, when workers are better able to choose roles that suit their skills best – rather than being influenced by other barriers that distort these choices – they are able to be more productive. This raises output and makes them better off as individuals.

Other under-represented groups included in our analysis were disabled workers, those from low socio-economic status, women with dependent children, and black workers. The modelling framework allows for groups to be defined by intersections of characteristics very flexibly, although only certain such analyses were feasible given data availability and sample sizes. In particular, we produced an analysis by seniority level for women and by region for women and disabled workers. Analysis for LGBTQ+ individuals was considered but excluded due to data limitations. Modelling results (i.e., the potential gains from addressing under-representation) cannot be added up across the different groups considered, since they are not mutually exclusive. For example, one cannot add up the gains from addressing under-representation of women and of disabled workers, since these groups overlap.

The modelling requires assumptions about productivity. We generally make the simple and transparent assumption that, in the absence of barriers, the distribution of productivity would be the same across all groups (but that, within any group, different individuals will have different aptitudes for digital sector work and other work). Hence, we do not in general assume that under-represented groups are more productive, or more productive in the digital economy, than other groups. The exception to this is for analysis by age, given its relationship to experience, which we know can be a fundamental driver of higher productivity. We assume that, in the absence of barriers specific to the digital sector, older workers would have the same productivity advantage over younger workers as the advantage we see they have (based on relative earnings levels) in other parts of the economy.

The modelling accounts for potential displacement effects. As barriers fall and more workers from under-represented groups enter the digital economy, the sector may not expand one-for-one, meaning that other workers may be displaced. The extent to which this may happen is uncertain. It depends on an economic parameter known as the labour demand elasticity – essentially, this determines how easily additional labour is absorbed into the digital sector. We therefore anchor the magnitude of displacement to empirical evidence on the labour demand elasticity, on which there has been much previous work. We also show the sensitivity of results to a plausible range of assumptions about displacement.

We estimate the gains from addressing under-representation at both an aggregate level (i.e., output gains in the digital sector and in the economy as a whole) and per person within the under-represented groups. It is important to consider the gains from both points of view. Aggregated gains are, all else equal, higher when addressing under-representation among larger groups (e.g., women). Smaller groups tend to have smaller impacts on the whole digital sector or economy, but the individual benefits of reduced barriers and hence increased access to the digital sector can still be substantial.

At an aggregate level, we model impacts on output within the digital sector and the economy as a whole. Attracting additional talent to the digital economy would draw workers out of non-digital sectors, reducing output in those sectors. This means that gains to digital economy output will exceed the net gain to the economy as a whole, because the expansion of the digital sector is partly offset by contraction in other sectors. However, the output gained in the digital sector would exceed the output lost in other sectors – in other words, there are still benefits to the economy as a whole, even though these are smaller than the benefits to the digital sector specifically. This is because overall output is highest when people work in the sector where they are most productive, i.e., where jobs best match their skills. Barriers to sectoral choice unrelated to productivity prevent this. Removing barriers means better ‘matches’ between workers and the jobs in which they work.