AI could shrink average household incomes in Ireland and widen inequality: under a central scenario about 7% of jobs are displaced in the short–medium term, with productivity gains and higher returns to capital accruing mainly to top earners while middle- and higher-income households suffer the largest average income losses; the tax-benefit system cushions poorer households but public finances face strain if job losses are large.

Artificial Intelligence and income inequality in Ireland

Karina Doorley, S. O’Connor, Richard O'Shea, Dora Tuda · Fetched April 11, 2026

semantic_scholar descriptive medium evidence 8/10 relevance DOI Source

Using scenario-based microsimulation, the report finds that a central estimate of roughly 7% short–medium-term job displacement from AI would, on average, reduce household disposable incomes in Ireland and moderately raise income inequality because productivity and capital gains concentrate benefits at the top while displacement hits middle- and higher-income households most.

Artificial intelligence (AI) is advancing quickly and is beginning to reshape the kinds of work people do, how incomes are earned, and the balance between labour and capital in modern economies. For Ireland, a country with a highly educated workforce and a strong technology sector, AI adoption presents a unique challenge and opportunity. This report explores what AI adoption could mean for Irish households, for the distribution of income, and for public finances over the short- to medium-term. Using evidence from international research, we simulate a range of plausible scenarios for AI adoption and its effect on employment, wages and capital income. Linking these scenarios to the Irish tax-benefit system using SWITCH, the ESRI’s microsimulation model, we investigate the potential distributional effects of AI adoption in Ireland. Unlike previous waves of technological change, AI tends to place higher earning and highly educated workers at greater risk of disruption, because the occupations most exposed to AI are predominantly in these groups. In our central scenario – drawn from credible international estimates – around 7 per cent of current jobs could be displaced in the short–medium run. Those most likely to experience this disruption are found in higher income households, where the share of workers transitioning into unemployment is substantially larger than in lower income families. For those who remain in work, AI is expected to increase productivity. We estimate that workers who are not displaced may see modest but broadly shared wage gains. These gains are not large enough to counterbalance the average fall in income due to job displacement. Increases in returns to capital as a result of AI adoption, while modest in percentage terms, benefit households at the very top of the income distribution, where the vast majority of Ireland’s capital income is concentrated. When these effects are combined, we find an average decline in household disposable income as a result of AI adoption. The largest average losses are experienced by middle and higher income households, for whom job displacement outweighs any wage or capital income gains. Lower income households also lose, but by much less. Ireland’s tax and welfare system absorbs most of the income loss for lower income households, and roughly half of the loss for households at the top of the income distribution. Income inequality, measured by the Gini index, rises moderately in every scenario we examine due to the polarising effect of job losses and wage and capital income increases on the income distribution. The implications for the public finances are also substantial. If employment losses are relatively small and productivity gains are realised, AI adoption could boost Exchequer revenues. But if job displacement is sizeable, tax receipts fall while welfare spending rises, resulting in potentially large pressures on the public finances. These findings underline the importance of forward-looking policy. AI has the potential to increase productivity and living standards, but only if the workforce is equipped with the skills needed to use it effectively or to transition into roles that are less exposed. Ireland’s high levels of educational attainment offer a strong foundation, but targeted educational support will be essential, particularly for older workers or those with lower formal qualifications. Investment in lifelong learning, retraining and programmes that help workers shift into AI-complementary or currently under-supplied occupations will be crucial. Beyond the labour market, our analysis highlights the need to consider the future resilience of Ireland’s tax base. If AI accelerates the shift from labour income to capital income, the current heavy reliance on labour taxation may become increasingly difficult to sustain. Broadening the tax base and strengthening taxation of wealth and capital may become necessary to ensure the long-term stability of public services and welfare supports. Although the long-term effects of AI remain uncertain, Ireland is well placed to benefit from the opportunities it brings – but only if the risks are managed carefully. AI adoption will create winners and losers, at least in the short to medium term. Policymakers will need to steer this transition in a way that supports displaced workers, protects the living standards of vulnerable households and ensures that the gains from AI contribute to inclusive and sustainable economic growth.

Summary

Main Finding

AI adoption in Ireland is likely to reduce average household disposable income in the short–to–medium run, with around 7% of jobs displaced in the central scenario. Job losses are concentrated among higher-earning and more-educated workers, producing a polarising effect: modest wage gains and small increases in capital returns partially offset losses but primarily benefit the top of the distribution. The tax–benefit system cushions low-income households, but overall inequality rises moderately and public finances face substantial downside risk if displacement is large.

Key Points

Central scenario: ~7% of current jobs could be displaced in the short–medium run.
Displacement is concentrated among higher-earning, highly educated workers; middle and higher income households face larger average losses.
Workers who remain employed see modest, broadly shared wage gains that are insufficient to fully offset displacement-related income losses on average.
Capital returns rise modestly in percentage terms but mainly benefit the top of the income distribution where capital income is concentrated.
Lower-income households suffer much smaller average losses because Ireland’s tax and welfare system absorbs most of their income shock.
The tax–benefit system absorbs roughly half of income losses for the top of the distribution; top households still bear large absolute losses due to concentrated capital gains and displacement patterns.
Income inequality (Gini) increases moderately across scenarios.
Public finances: small employment losses combined with productivity gains could raise revenues; large displacement reduces tax receipts and raises welfare spending, causing material fiscal pressure.

Data & Methods

Scenario construction: plausible short–to–medium run AI adoption scenarios drawn from international research on occupational exposure to AI and estimates of task automation/displacement.
Microsimulation: linked the scenario outcomes (changes in employment, wages, capital income) to SWITCH, the ESRI’s tax–benefit microsimulation model, to estimate effects on household disposable incomes and public finances.
Channels modelled:
- Job displacement (transition into unemployment).
- Wage effects for those who remain employed (productivity-induced wage changes).
- Changes in returns to capital.
- Automatic adjustments through the tax and welfare system (via SWITCH).
Horizon: short– to medium-term impacts (allowing for incomplete labour market reallocation and limited long-run general equilibrium adjustments).
Key assumptions (explicit or implicit): displacement rates ~7% in central case; modest wage and capital return increases; limited labour reallocation in the short–medium run. Results are scenario-based and sensitive to these inputs.
Limitations: uncertainty about AI diffusion speed, firm-level adoption, long-run reallocation, complementary policy responses, and general equilibrium effects not fully captured.

Implications for AI Economics

Distributional channel importance: Unlike past automation waves, AI disproportionately threatens higher-paid, highly educated occupations in the short–medium run, changing which groups need policy support.
Role of tax–benefit systems: Existing welfare and tax policies can substantially cushion low-income households, but may be insufficient to absorb large, aggregate employment shocks; fiscal revenues are vulnerable if labour income shrinks materially.
Policy priorities:
- Invest in lifelong learning, retraining and targeted reskilling — especially for older workers and those with lower formal qualifications — to help transitions into AI-complementary roles.
- Strengthen job-matching programs and active labour market policies to speed re-employment and reduce permanent earnings losses.
- Consider tax-base resilience: monitor and prepare for structural shifts from labour to capital income — broadening the tax base and revisiting wealth/capital taxation could be needed to sustain public finances.
- Design targeted supports for groups at elevated displacement risk (higher-earning but exposed occupations), not only traditional low-income recipients.
Research implications: need for more Ireland-specific estimates of occupational exposure to AI, firm-level adoption patterns, dynamic labour reallocation, and general equilibrium feedbacks to refine projections and policy design.

Assessment

Paper Typedescriptive Evidence Strengthmedium — Uses credible international estimates and a well-established microsimulation tool to generate detailed, policy-relevant projections, but results rely heavily on scenario assumptions (displacement rates, productivity and capital return changes) rather than on causal empirical identification or natural experiments, leaving substantial uncertainty about realized effects. Methods Rigormedium — Appropriate and transparent use of microsimulation (SWITCH) and scenario analysis provides internally consistent projections and likely includes sensitivity checks, but lacks causal identification, firm- or longitudinal-level validation, and depends on parameter choices that drive outcomes. SampleRepresentative Irish household microdata as implemented in the ESRI SWITCH microsimulation model, combined with international empirical estimates of occupations' exposure to AI and assumed short– to medium-term rates of job displacement (~7% in the central scenario), plus assumed productivity/wage impacts and changes in returns to capital. Themesinequality labor_markets productivity adoption governance skills_training IdentificationNo causal identification; uses scenario-based simulations drawing on international estimates of occupation-level AI exposure, assumed job-displacement rates and productivity/wage changes, and links these assumptions to Irish household microdata via the ESRI SWITCH microsimulation model to project distributional and fiscal outcomes. GeneralizabilityCountry-specific: tailored to Ireland's labour market structure, tax-benefit system and capital ownership patterns; results may not transfer to other countries., Scenario-driven: outcomes depend heavily on assumed displacement rates, wage/productivity responses and capital returns which are uncertain and may differ in reality., Short-to-medium term focus: does not capture long-run general equilibrium adjustments (new occupations, firm entry/exit, endogenous retraining)., Aggregated treatment of occupations and households: may mask firm-, sector- and regional heterogeneity in AI adoption and impacts., Multinational presence and capital concentration in Ireland may lead to different capital income distribution than in other economies.

Claims (12)

Claim	Direction	Confidence	Outcome	Details
AI tends to place higher earning and highly educated workers at greater risk of disruption, because the occupations most exposed to AI are predominantly in these groups. Job Displacement	negative	high	risk of job disruption / occupational exposure to AI	0.18
In our central scenario — drawn from credible international estimates — around 7 per cent of current jobs could be displaced in the short–medium run. Job Displacement	negative	high	share of jobs displaced	around 7% of current jobs could be displaced 0.18
Those most likely to experience this disruption are found in higher income households, where the share of workers transitioning into unemployment is substantially larger than in lower income families. Job Displacement	negative	high	share of workers transitioning into unemployment by household income	substantially larger (for higher income households vs lower income families) 0.18
For those who remain in work, AI is expected to increase productivity. We estimate that workers who are not displaced may see modest but broadly shared wage gains. Wages	positive	high	wage gains for workers who remain employed	modest but broadly shared wage gains 0.18
These wage gains are not large enough to counterbalance the average fall in income due to job displacement. Consumer Welfare	negative	high	net effect on household income (wages versus displacement losses)	0.18
Increases in returns to capital as a result of AI adoption, while modest in percentage terms, benefit households at the very top of the income distribution, where the vast majority of Ireland’s capital income is concentrated. Inequality	positive	high	returns to capital and distributional benefits (who gains)	modest in percentage terms (returns to capital); benefits concentrated at the top 0.18
When these effects are combined, we find an average decline in household disposable income as a result of AI adoption. Consumer Welfare	negative	high	household disposable income (average change)	0.18
The largest average losses are experienced by middle and higher income households, for whom job displacement outweighs any wage or capital income gains. Lower income households also lose, but by much less. Inequality	negative	high	change in household disposable income by income group	largest average losses: middle and higher income households; lower income households lose much less 0.18
Ireland’s tax and welfare system absorbs most of the income loss for lower income households, and roughly half of the loss for households at the top of the income distribution. Social Protection	mixed	high	net income after taxes and transfers (absorption of income loss)	most (lower income households) and roughly half (top households) of income losses absorbed 0.18
Income inequality, measured by the Gini index, rises moderately in every scenario we examine due to the polarising effect of job losses and wage and capital income increases on the income distribution. Inequality	negative	high	Gini index (income inequality)	rises moderately (in every scenario examined) 0.18
If employment losses are relatively small and productivity gains are realised, AI adoption could boost Exchequer revenues. But if job displacement is sizeable, tax receipts fall while welfare spending rises, resulting in potentially large pressures on the public finances. Fiscal And Macroeconomic	mixed	high	Exchequer revenues / tax receipts and welfare spending	0.18
Ireland’s high levels of educational attainment offer a strong foundation for benefiting from AI adoption, but targeted educational support (especially for older workers or those with lower formal qualifications) and investment in lifelong learning and retraining will be essential. Skill Acquisition	positive	high	capacity to transition into AI-complementary roles / skill resilience	0.03