This chapter investigates the mechanisms through which immigration may affect productivity growth and reviews the available evidence from OECD countries. It considers the effects of immigration on the skill composition of the workforce, firms’ investment and technological choices, and the contribution of immigrants to innovation and entrepreneurship. The chapter also assesses how the distribution of immigrants across firms and sectors with different productivity levels shapes aggregate productivity outcomes. It concludes with a discussion of the implications for migration and integration policy.
International Migration Outlook 2026
50th Edition
Abstract
Key findings
Productivity growth is a key driver of long-term economic growth and living standards. In a context of demographic ageing and productivity slowdown, reviving productivity growth is top of the policy agenda in most OECD countries. Given the growing contribution of immigrants to OECD labour markets and economies, understanding how immigration affects productivity growth has become increasingly important.
This chapter investigates the mechanisms through which immigration affects productivity and the available evidence from OECD countries. The analysis suggests that immigration is neither systematically productivity-enhancing nor productivity-reducing. Its impact depends on migrant characteristics, firms’ responses, labour market context and institutions, as well as the design and effectiveness of migration and integration policies.
Immigration changes the skill composition of the host country workforce. In OECD countries, immigrants are over-represented both at the top and the bottom of the educational distribution. However, the translation of education into productivity is not automatic. On average, immigrants have lower literacy and numeracy skills than the native‑born with the same educational attainment. They also face more barriers in the labour market, which make them more likely to be over-skilled for their jobs.
Immigration influences workforce productivity not only directly through immigrants’ productivity but it may also change the productivity of the native‑born and affect firms’ investment and production choices. Evidence from academic literature shows that native‑born workers often upgrade towards more complex and communication-intensive tasks in response to immigration. Greater labour availability may slow the adoption of labour‑saving technologies, particularly following low‑skilled migration inflows. At the same time, immigration can stimulate investment and productivity, as firms expand and invest in complementary capital.
In addition, migration contributes to productivity growth through its effect on innovation and entrepreneurship. Migrants contribute disproportionately to research and innovation in many OECD countries, reflecting their overrepresentation among STEM graduates, and their concentration in research and development activities. While migrants are over‑represented among founders of firms in key innovation hubs in the OECD, most migrant entrepreneurs are own-account workers and small business owners, which limits the contribution of these firms to aggregate productivity.
The allocation of immigrants across firms and sectors affects aggregate labour productivity. Within sectors, immigrants are disproportionately employed in lower-productivity firms, and sectors with higher migrant shares tend to have below-average labour productivity, across OECD countries.
A novel decomposition of productivity growth suggests that immigrant employment relocated more to more productive sectors than native-born employment in most countries in the decade to 2024.
Migration and integration policies can help maximise the productivity potential of immigration and minimising possible negative impacts. Migration and integration policies can boost countries’ attractiveness for top talent and increase their retention. Integration policies can promote the effective utilisation of the skills and potential of all immigrants, both recent and settled, and address barriers to immigrant job mobility.
Labour migration policies need to balance short- and long-term productivity objectives. Recruitment from abroad may help alleviate short-term labour shortages in strategic sectors, potentially supporting productivity growth. However, if migrant recruitment is used to circumvent structural adjustments, it may hinder longer-term productivity growth.
While effective migration and integration policies can contribute to supporting productivity growth, labour migration systems must balance a broader set of economic and social objectives, such as ensuring the provision of essential services, notably in the care sector, or safeguarding national food security.
Productivity growth is a central driver of long-term economic growth and living standards. Productivity growth has gradually slowed down in the OECD in the past five decades and slowed further since the mid‑2000s (Box 5.1). Understanding the sources of productivity growth in order to put in place the right policies to revive productivity growth is top of the policy agenda in most OECD countries.
In fact, in the context of ageing populations across the OECD, productivity growth is key to increasing living standards. If the growth rate of labour productivity remains constant,1 the projected contraction of the share of employed persons in the total population implies that GDP per capita growth in the OECD area will be reduced by about 40%, falling from 1.0% per year in the 2010s to 0.6% per year on average over the period 2024‑2060. This corresponds to a loss equivalent to around 14% of GDP per capita by 2060 (OECD, 2025[1]).
Immigrants account for an increasing share of the employed population and employment growth in the OECD. In 2024, immigrants represented 16% of the employed population on average across OECD countries. In ten countries, immigrants represent more than 20% of the employed population and in 15 countries more than 15% (Figure 5.3). Immigrants have accounted for 50% or more of total employment growth from 2014 to 2024 in half of OECD countries (Figure 5.4).
Given the growing contribution of immigration to OECD economies, it is increasingly critical to understand how immigration affects productivity growth. Immigration is not considered in the economics literature as a main determinant of productivity growth (Manning, 2025[2]) nor is it an explanation for the productivity slowdown (Goldin et al., 2024[3]). Nevertheless, migration has an impact on productivity through many different channels. Immigrants are workers, inventors and entrepreneurs. They also influence decision making of other stakeholders and affect the allocation of factors of production.
From a migration policy perspective, the question is: How can migration and integration policies contribute to the overarching goal of reviving productivity growth? How can countries maximise the economic benefits of migration and minimise any potential negative effects?
This chapter examines the mechanisms through which immigration may affect productivity growth and reviews the evidence from OECD countries. Throughout the chapter, productivity refers to labour productivity defined as output per worker. Labour productivity growth includes multi factor productivity (MFP) growth as well as changes in the capital-labour ratio. See Box 5.2 for a discussion of the definition and measurement of productivity. The evidence reviewed concerns both labour productivity and MFP, and the measure used is specified whenever necessary.
To structure the discussion, the effects of immigration on aggregate macro-level productivity are split into effects at the firm level, at the sector level and across sectors (Figure 5.1). First, immigration affects firm level productivity by changing the skill distribution of the workforce and the capital-labour ratio in the host economy (squares 1 and 2). Second, migrants may affect productivity at the firm level through their effect on R&D and innovation (square 3). Third, migrant entrepreneurs directly contribute to entry and exit of firms, consequently changing sectoral productivity (square 4). Fourth, immigrant allocation across firms of different productivity levels also changes sectoral productivity (square 5). Fifth, the allocation of immigrants across sectors with different productivity levels affects aggregate productivity (square 6).
Source: Based on André and Gal (2024[4]), “Reviving productivity growth: A review of policies”. https://doi.org/10.1787/61244acd-en.
The remainder of the chapter is organised as follows. The first section focusses on human capital. It considers how immigration influences productivity both by altering the skill composition of the workforce and by shaping the skills used and acquired by native‑born workers. The second section analyses the effect of immigration on capital intensity. It discusses whether investment keeps pace with increased employment due to migration in the short run and how migration impacts firms’ investment and technological choices. The third section examines the role immigrants play in innovation and firm creation through entrepreneurship. The fourth section considers the allocation of immigrants across firms with different productivity levels within sectors. The last section describes how immigrants sort into sectors of different productivity levels and how this affects aggregate productivity in the host country.
The evidence reviewed in this chapter suggests that immigration is neither systematically productivity-enhancing nor productivity-reducing. Its impact depends on migrant characteristics, labour market institutions, firms’ responses, and the effectiveness of migration and integration policies.
Productivity growth across OECD economies has been on a long-term downward trajectory since the 1970s, with a particularly marked slowdown since the mid‑2000s. Average annual OECD labour productivity growth fell from close to 2% in the 1990s to around 0.8% over the 2010s, and the decline has been broad-based across almost all large economies (Figure 5.2).
Labour productivity, average annual growth rate
Note: EU corresponds to the simple average of Belgium, Denmark, France, Germany, Ireland, Italy, Luxembourg, the Netherlands and Greece. OECD is the simple average of the average annual growth rate of Australia, Austria, Belgium, Canada, Denmark, Finland, France, Germany, Greece, Iceland, Ireland, Italy, Japan, Luxembourg, the Netherlands, New Zealand, Norway, Portugal, Spain, Sweden, Switzerland, Tûrkiye, the United Kingdom and the United States.
Labour productivity is computed as the gross value added over number of workers in real terms (chain value added rebased to 2020 prices).
Source: OECD Productivity Data Base (PDB).
Several structural factors explain this trend. First, earlier waves of transformative innovation have either matured or yielded diminishing returns. While the diffusion of ICT drove a temporary productivity revival in some countries, especially the United States, these gains proved short-lived and were less pronounced elsewhere. At the same time, the shift towards service‑based economies, where productivity growth is typically lower than in manufacturing, has exerted a drag on aggregate productivity.
Second, the global financial crisis has had lasting effects, notably through subdued investment. Business investment has remained sluggish limiting both capital deepening and the adoption of new, more productive technologies.
Third, TFP growth has slowed markedly. This may reflect a range of factors, including slower innovation, reduced firm dynamism, and barriers to the reallocation of resources towards more productive uses. In fact, the pace of productivity-enhancing innovation and its diffusion has weakened. Although firms at the global technological frontier continue to record solid productivity gains, the majority of firms lag behind.
Finally, measurement challenges, particularly related to intangibles and digital services, may have led to some underestimation of productivity growth, although this accounts only for a small share of the slowdown.
Source: Goldin et al. (2024[3]), “Why Is Productivity Slowing Down?”, https://doi.org/10.1257/jel.20221543; André and Gal (2024[4]), “Reviving productivity growth: A review of policies”. https://doi.org/10.1787/61244acd-en.
This box discusses two main measures of productivity: labour productivity and multi factor productivity (MFP also referred to as total factor productivity, TFP).
Labour productivity is measured as Output divided by Labour Input, where labour input is measured as hours worked or total employment when hours worked are not available.
Labour productivity is a measure of how effectively labour is used in production. Labour productivity does not measure worker’s individual productivity. Changes in capital available to workers, as well as technological, organisational and efficiency changes are also captured in labour productivity growth. The main advantage of labour productivity as a productivity measure is its wide availability and simplicity, as its measurement does not rely on assumptions.
Multi factor productivity (MFP) reflects the ability to produce more output by combining inputs more efficiently driven by new ideas, technological breakthroughs, and improvements in business models. MFP growth is the part of output growth not explained by growth in labour and capital.1 The advantage of MFP is that it does not depend on changes in production factors. A positive change in MFP raises the volume of output that can be produced with a given volume of capital and labour.
In practice, distinguishing between the contributions of MFP and production factors is not always clear-cut. MFP is not directly observed but estimated. Hence, its measure depends on model assumptions and is sensitive to model misspecification and mismeasurement of inputs.2 For instance, any change in the quality of the inputs that is not captured in the measured inputs (e.g. change in skill composition of the workforce or intangible capital) will translate into changes in MFP.
Labour productivity growth depends on MFP growth and capital deepening (i.e. the change in capital services per worker). OECD growth‑accounting decompositions show that, since the 1980s, total factor productivity has accounted for roughly half of labour‑productivity growth on average across OECD economies, with the remainder explained mainly by capital deepening (OECD, 2025[5]).
1. After weighting these inputs by their importance in production.
2. The usual key underlying assumptions are that firms use resources efficiently; that prices reflect costs without distortions; output, labour and capital inputs are accurately measured; full utilisation of capital and labour; constant returns to scale.
Source: OECD (2025[5]), OECD Compendium of Productivity Indicators 2025, https://doi.org/10.1787/b024d9e1-en.
Share of foreign-born in the employed population, 2024
Note: OECD is the average for the countries represented in the figure.
Source: European countries: Labour Force Surveys (Eurostat); Australia, Canada, New Zealand, the United Kingdom: Labour Force Surveys; Chile: Encuesta de Caracterización Socioeconómica Nacional (CASEN); Colombia: Gran Encuesta Integrada de Hogares (GEIH); Costa Rica: Encuesta Nacional de Hogares (ENAHO), Mexico: Encuesta Nacional de Ocupacion y Emploe (ENOE); United States: CPS.
Contribution of foreign-born and native‑born to employment growth, 2014‑2024
Note: OECD is the average for the countries represented in the figure. For Canada it shows 2014-2025 growth.
Source: European countries: Labour Force Surveys (Eurostat); Australia, Canada, New Zealand, the United Kingdom: Labour Force Surveys; Chile: Encuesta de Caracterización Socioeconómica Nacional (CASEN); Mexico: Encuesta Nacional de Ocupacion y Emploe (ENOE); United States: CPS.
Human capital is a key determinant of productivity. Workers with higher skills generally generate greater value added per hour worked.
Whether migration ultimately raises or lowers productivity depends on how the average productivity of immigrants compares with that of native‑born workers and also on the extent to which immigration affects the productivity of the native‑born.
This section examines these two channels by focussing on the skills that immigrants bring to the host economy. It considers how immigration influences productivity both by altering the skill composition of the workforce and by shaping the skills utilised and acquired by native‑born workers.
Immigration increases both the high and low educated population in most OECD countries
How much immigration changes the educational composition of the total workforce in the host country depends on the total share of immigrants in the employed population as well as on the difference in the educational composition across the two groups.
Across the OECD, immigrants tend to be over-represented at both ends of the educational distribution. Immigrants are more likely than the native‑born to be tertiary educated (36% compared with 33% on average across OECD countries) and more likely to have completed at most lower-secondary education (25% compared with 23%).2 Differences in educational attainment between the two groups are particularly large among the very highly educated, defined as having graduated from a master or doctoral programme, and the very low educated, defined as having completed at most primary education.
In 2024, 18% of immigrants in employment on average in the OECD were very highly educated and 9% were very low educated, compared with 14 and 6% of the native‑born. Annex Table 5.A.1 presents the educational attainment of the native and foreign-born by country.
Immigrants are over-represented among the very highly educated population in almost all OECD countries. In Switzerland and Ireland, for example, immigrants account for 46% and 39% of the very highly educated employed population, well above their share in employment (35% and 30%). Immigrants who graduated from a master or doctoral programme account for 9% of the total employed population in Switzerland and 6% in Ireland. The contributions are also quantitatively large in Luxembourg or Sweden (Figure 5.5). Exceptions are Southern-European countries (Greece, Italy and Spain) and Slovenia where immigrants are under-represented among graduates of master and doctoral programmes.
Conversely, in most OECD countries, immigrants are over-represented among the very low educated. This is the case in Latin American OECD countries (Costa Rica, Colombia) and in many European OECD countries (Austria, Belgium, Switzerland, Germany, Luxembourg, the Netherlands, Sweden or France). In six OECD countries, very low educated immigrants account for 3% or more of the total employed population. Their share is particularly significant in Belgium, Germany, Luxembourg, or the Netherlands, where immigrants account for more than half of the very low educated employed population (Figure 5.5).
Share of very high and very low educated, 15‑64 years old, foreign and native‑born, 2024
Note: Very high education corresponds to ISCED levels 6/8. Very low education corresponds to ISCED 0/1. Canadian LFS does not allow to separately identify the Very Low education level.
Source: Labour Force Surveys.
Attracting highly educated immigrants is one way to strengthen the skill distribution of the resident population and support long-term economic growth. At the same time, improving a country’s human capital also depends on retaining highly skilled foreign and native‑born workers. As a result, many OECD countries now pursue a dual strategy: attracting and retaining international talent while limiting the loss of highly skilled native‑born workers. Box 5.3 presents evidence on the emigration of highly skilled populations to other OECD countries.
The most educated residents of the OECD are highly mobile. While efforts are made to attract immigrants (from OECD and non-OECD Member countries), many OECD countries are also trying to retain, or attract back, their own highly skilled citizens.
In 14 OECD countries, 10% or more of the highly educated native‑born live in another OECD country, including the United Kingdom, Switzerland and Portugal. The countries with the highest emigration rates of highly educated native‑born are Luxembourg and New Zealand. One‑quarter of highly educated individuals born in Luxembourg live elsewhere in the OECD, as do over 20% of New Zealanders. Only in Norway, Spain and the United States is the emigration rate under 5% (Figure 5.6).
Emigration rate of highly educated native‑born (ISCED 5 to 8), 2020‑2021
Note: The emigration rate of country j is the number of tertiary-educated individuals born in country j living in another OECD country divided by the total number of tertiary-educated individuals born in country j.
Source: OECD DIOC 2020.
In some OECD countries, the contribution of highly educated immigrants to the resident highly educated native‑born far outweighs the lost contribution from highly educated emigrants (Figure 5.7). For example, in Australia, immigrants add 78% to the resident native‑born highly educated population, whereas if Australian emigrants in other OECD countries would return to Australia they would only add 5% to this population.
In other countries, immigration and emigration have a more similar weight on the composition of the resident highly educated population. For example, in Italy, immigrants add 11% to the native‑born highly educated resident population but emigrants could potentially contribute to an 8% increase.
Relative migration rate (% of total native‑born highly educated resident population)
Note: OECD simple average excluding Luxemburg.
Source: OECD DIOC 2020.
Education and skills do not automatically translate into productivity gains
Higher education levels among migrants can raise labour productivity by increasing the economy’s stock of human capital, but only to the extent that immigrants’ educational attainment translates into skills effectively utilised in the host country labour market.
Tertiary-educated immigrants are more likely than the native‑born to work in occupations that do not require a tertiary education degree. According to Labour Force Surveys of OECD countries with available data in 2024, over one‑third (33%) of tertiary-educated foreign-born workers are overqualified on average across OECD countries, compared with less than one‑quarter (24%) of native‑born workers. This means they are working in medium to low-skilled occupations (Figure 5.8).
The overqualification rates of immigrants are particularly large relative to those of the native‑born (20 percentage points (p.p.) or more) in Italy, Chile, Costa Rica, Denmark, Estonia, Poland, Norway and Sweden.
Share of tertiary-educated employed population working in a low to medium skill occupation (Over-education rate), 2024
Note: OECD simple average across all OECD countries with available data. * Data available for the year 2022 instead of 2024.
Source: Labour Force Surveys.
Part of this overqualification gap is due to immigrants having on average lower skills than the native‑born with the same education level. Degrees acquired abroad in particular may not translate into the same skills as domestic degrees, due to differences in curricula, quality of the educational systems, or language proficiencies.
Immigrants with host country degrees have lower overqualification rates than immigrants with foreign degrees. Host-country degrees reduce the immigrant overqualification gap by 75% EU‑wide, and by even more in North America, German-speaking countries, France, Sweden and the United Kingdom (OECD/European Commission, 2023[6]).
According to the 2023 wave of the Survey of Adult Skills (PIAAC), immigrants have lower literacy and numeracy skills than the native‑born of the same level of education in all countries with available data (Figure 5.9). The average literacy score of tertiary-educated immigrants in all countries is at level 2, while for the native‑born it is at Level 3 in almost all countries. Level 3 in literacy proficiency indicates that individuals are able to interpret, evaluate, and integrate information across complex or lengthy texts. Numeracy-skills performance patterns are similar to those observed for literacy, though gaps tend to be smaller. Note that part of the observed skills gap is due to differences in language fluency given that skills are evaluated in the host country language, and this may matter more for literacy than numeracy literacy.
Mean literacy scores by country of birth and level of education
Note: NB and FB stand for native-born and foreign-born respectively.
Source: Survey of Adult Skills (PIAAC) 2023.
Differences in skill levels between immigrants and the native‑born are only part of the explanation for differences in labour market outcomes between the two groups. First, the skills immigrants bring to the host country may not be those in demand in the host country labour market. Ensuring that workers have the right skills in rapidly changing labour markets is a priority across the OECD. While labour immigrants are selected based on their contribution to the host country labour market, immigrants migrating for family, humanitarian or other reasons do not primarily migrate for work and may face higher mismatches in the host country labour market.
Second, immigrants may not be able to put their skills to use in the host country labour market due to barriers faced in the labour market. Immigrants educated abroad and in particular those not selected under labour migration channels may face barriers in integrating in the host country labour market due to among other difficulties in recognition of foreign qualifications, occupational licensing procedures and requirements, lack of experience and knowledge about the host country labour market, lack of professional networks, or discrimination. Consistent with barriers faced in the labour market, immigrants more often report being over-skilled for their current position. According to the latest wave of the Survey of Adult Skills (PIAAC), almost one‑third (31%) of the foreign-born report having more skills than needed for their current role,3 compared with 27% of the native‑born, across all countries participating in the survey (Figure 5.10).
Share of the employed population aged 16‑65 reporting higher skills than needed for job
Note: Countries with immigrant subsample sizes under 30 observations excluded. Hollow markers indicate statistically significant differences compared to the native‑born sample.
Source: Survey of Adult Skills (PIAAC) 2023.
Immigration changes the incentives for the native‑born to participate in the labour market and invest in skills
The impact of immigration on productivity extends beyond its direct effect on the average productivity of workers. Immigration can also influence the behaviour of the native‑born, including their decisions regarding labour market participation and investment in education and skills. These responses can, in turn, affect overall productivity.
A first channel through which immigration changes the productivity of the native‑born is through the reallocation of time between home and market production. Low-skilled immigrants often provide services that substitute for household production, such as childcare and cleaning. This reduces the cost of these services and allows higher-skilled native‑born workers, particularly women, to increase their participation in the labour market. As a result, time is reallocated from lower-productivity home activities to higher-productivity market work. Evidence for this mechanism has been documented for several countries, including the United States (Cortés and Tessada, 2011[7]), Spain (Farré, González and Ortega, 2011[8]), Italy (Barone and Mocetti, 2011[9]), and across a group of OECD countries, Australia, Germany, Switzerland, the United Kingdom and the United States (Forlani, Lodigiani and Mendolicchio, 2015[10]).
Whether this translates into higher measured aggregate labour productivity is not automatic. Home production, or unpaid domestic work, is not accounted for in standard measures of aggregate labour productivity, whereas paid domestic work is. The potential productivity gains from the native‑born moving from unpaid home production to market production may not materialise in estimated aggregate productivity gains. It will only do so if the average productivity (new market job and domestic work) is higher than the average economy wide productivity.
A second channel relates to human capital investment. Immigration may affect the incentives to invest in education and training of the native‑born. Increased competition may encourage the native‑born to avoid entering the most exposed educational tracks and occupations to immigration.
On the high-skill side, the concern is whether high-skilled migration drives the native‑born to shy away from potentially more productive educational and career paths taken by immigrants to avoid increased competition. This immigration competition effect is most direct in the case of international students as these compete directly with native‑born students. The evidence available from OECD countries points to little to no crowding out effects. Borjas (2007[11]) finds a crowd out effect of international graduate students in the United States for white native‑born men, but a positive effect for native‑born women, Asians and Hispanics. Shih (2017[12]) finds no crowding out effect on graduate programmes in the United States and Machin and Murphy (2017[13]) find also no effects in the United Kingdom. The results are interpreted as net tuition payments from international students contributing to subsidizing the cost of enrolling domestic students.
Lower skilled migration may also impact the choices of the native‑born. In Norway, Røed and Schøne (2016[14]) and Schøne and Strøm (2026[15]) show that the increase in immigrants in the building and construction industry following the 2004 EU enlargement led to a decrease in apprentice numbers. This was at least partly driven by a decrease in the number of students enrolling in tracks with the most immigration induced competition. However, this research does not look into which career tracks the native‑born chose instead, and so does not inform on potential aggregate effects on productivity.
Increased competition in low-skill occupations, driven by immigration, may also encourage native‑born workers to acquire additional skills in order to move into higher-paying or less exposed occupations. Evidence for the United States points to positive effects on educational attainment and school performance of the native‑born as a response to immigration. Hunt (2017[16]) shows that immigration led to an increase in high school completion rates of native‑born in the United States over a long historical period (1940‑2010).
In addition, firms may also have different incentives to train domestic workers depending on access to immigrants (already in the host country or from abroad) with the necessary skills for the domestic labour market.
A few studies for the United Kingdom and Switzerland show that the effect of migration on firm training is small. The results depend on the type of training and the sectors considered. In addition, other effects of immigration may counterbalance the disincentives to firm training due to increased migration.
For the United Kingdom, Mountford and Wadsworth (2023[17]) found a small positive association between skilled immigration and training rates of the native‑born in the 1995 to 2018 period. However, the correlation depends on the sectors immigrants work in. In traded goods sectors, there is a positive association between training of native‑born and immigration (both unskilled and skilled). In non-traded high-wage sectors, the relationship between skilled immigration and training of the native‑born is negative.
Focusing on general industry training provided by employers (publicly-funded on-the‑job training from the Individualised Learner Record), Manning, Mcnally and Ventura (2024[18]) show that for 2003‑2011 the effect of immigration on training is small. The most negative effects are for shorter, considered less valuable, trainings and for low-skilled occupations.
For Switzerland, Oswald-Egg and Siegenthaler (2021[19]) analyse how the opening of the Swiss labour market to workers from the European Union affected the number of apprenticeships that firms provide. The authors show that while the greater availability of foreign workers reduced firms’ incentive to train, firms also expanded following increased migration and this had a positive effect on firm training.
Immigration may also have a direct impact on the educational outcomes of the native‑born through peer effects or through changes in resources in schools. This will have an indirect impact on productivity once the affected cohorts enter the labour market. A strand of research has investigated this question in OECD countries with mixed results pointing to the importance of migration and educational context in shaping the effects of immigration. Brunello and Rocco (2013[20]) find small negative effects on native‑born student outcomes on a sample of OECD countries based on PISA data. Gould, Lavy and Paserman (2009[21]) find a negative effect of immigrant concentration in elementary schools on native‑born educational outcomes following the mass migration wave to Israel in the 1990s. Small negative effects are also found in vocational education in Italy (Frattini and Meschi, 2019[22]). Many country studies find no effects, such as Ohinata and Van Ours (2013[23]) for the Netherlands, (Geay, Mcnally and Telhaj (2013[24]) for England, Figlio et al. (2024[25]) and Figlio and Özek (2019[26]) for the United States.
Immigration leads to greater task specialisation
Immigration changes not only the distribution of workers across skill levels but also the composition of skills available in the labour force. Even among workers with similar levels of formal education, immigrants and native‑born workers may differ in language proficiency, cultural knowledge and work experience. Since the skill profile of immigrants differs from that of native‑born, there are potential gains from task specialisation. Workers from each group will specialise in tasks in which they have a comparative advantage, thereby improving efficiency in production. As a result, immigration affects productivity not only through the direct contribution of immigrant workers but also through changes in the allocation of tasks among all workers.
There is evidence of task specialisation as a response to both low and high-skilled immigration. A strand of empirical research provides evidence that immigrants do tend to specialise in manual, routine or physically intensive tasks, while the native‑born shift towards tasks that require communication, co‑ordination and problem-solving skills.
For the United States, Peri and Sparber (2009[27]) show that low educated native‑born workers increasingly move into communication-intensive occupations as a response to immigration. Peri, Shih and Sparber (2015[28]) show that following an increase of STEM foreign workers (through the H‑1B programme), the native‑born shift toward managerial, communication, and complementary cognitive roles, while the foreign-born concentrate more in technical, innovation, and research tasks.
Evidence for other OECD countries also points in the same direction. Foged and Peri (2016[29]) find occupational upgrading among native‑born workers in Denmark as a response to refugee immigration in 1991-2008 (dispersion policies). Ortega and Verdugo (2014[30]) show that in France in the period 1968‑1999, the native‑born moved to higher paid occupations and performed more abstract tasks as a response to immigration. For Korea, Lee, Peri and Yang (2026[31]) show that the sudden suspension of immigration during the COVID‑19 pandemic led to reallocating Korean employees toward lower-skilled tasks leading to occupational downgrading. D’Amuri and Peri (2014[32]) document shifts towards more complex tasks across 15 OECD European countries during the 1996-2010 period. Reallocation of the native‑born towards more complex tasks was larger in countries with more flexible labour laws.
Despite the evidence on task specialisation as a response to immigration, direct evidence for positive effects on productivity through this channel remains elusive. This reflects both measurement challenges and the difficulty of isolating the contribution of specific mechanisms. Nonetheless, some studies provide suggestive evidence of positive effects, suggesting that specialisation and complementarities between immigrant and native‑born workers can contribute to higher overall efficiency. Peri (2009[33]) finds that immigration has positive and significant effects on total factor productivity (TFP) across US states over the 1960‑2006 period. The results in Peri, Shih and Sparber (2015[28]) imply, based on a model simulation exercise, that inflows of foreign STEM explain between 30% and 50% of the aggregate productivity growth that took place in the United States between 1990 and 2010.
Even when immigration improves the productivity of the workforce, productivity outcomes depend critically on firms’ responses. The next section therefore examines how migration influences investment decisions, capital intensity and technology adoption.
Beyond the direct skills that migrants bring to the host country, their effect on labour productivity also depends on how migration affects firms’ investment choices. When migration leads to a reduction in capital use per worker, either because it takes time for firms to invest or because of a deliberate production choice, this likely reduces firm-level labour productivity. This section reviews the evidence on the effect of migration on firms’ investment incentives, and which factors shape the response of the aggregate capital stock per worker.
Capital dilution: How quickly does investment adjust to immigration?
A longstanding concern among economists is that in the short run immigration dilutes capital. If immigration significantly increases the number of workers, but investment does not keep up, the capital labour ratio (the capital used per hour worked) decreases leading to lower labour productivity. This may particularly occur in the short run, if capital is relatively slow to adjust.
Standard growth models predict that a sudden increase in labour supply may temporarily reduce capital per worker if investment does not adjust immediately. In the classic Solow growth model, a one‑time increase in labour supply through immigration temporarily reduces the capital stock per worker and thereby labour productivity, until in the long run capital is accumulated from domestic savings and the initial levels of capital per worker and labour productivity are restored. In the transitional period the return to capital investment is high. Attracting foreign investment may also speed up the convergence to the new long run equilibrium.
A question in practice is then how quickly capital adjusts to an increase in labour supply. The academic literature has mainly focussed on the employment and wage effects of immigration. There is less empirical evidence on how capital flows respond to immigrant inflows. The adjustment of capital is in many cases assumed to be fixed in the short run and to fully adjust in the longer run. Nevertheless, the observed immigration impacts on the labour market are consistent with capital fully adjusting in less than a decade (Ottaviano and Peri, 2008[34]).
Recent evidence for Canada suggests, however, that in the 2010s capital investment has been sluggish to adjust to increased immigration (Doyle, Skuterud and Worswick, 2025[35]). The authors show that real per capita investment has been flat in Canada between 2010 and 2021, while migration to Canada grew significantly, indicating capital dilution. In addition, the authors show that the stocks of machinery and equipment and intellectual property per worker, which are more closely linked to innovation and future productivity growth, declined after 2014.
While macro data provide suggestive evidence, assessing how capital adjusts to an immigration surge is difficult using aggregate figures alone, as it is challenging to disentangle to which extent changes in the overall capital stock are driven by immigration rather than by other simultaneous shifts in the economic environment. Due to this identification challenge, much of the recent literature has turned to micro-level or subnational data, where these confounding factors can be better isolated. Identifying the adjustment mechanisms at play is also the key to any potential policy response.
Immigration-driven changes in production
While the simplest neoclassical theory predicts that a one‑time migration shock should temporarily lower the capital labour ratio (and therefore labour productivity) until the original ratio is restored in the long run, more recent work argues that firms may have incentives to either over-invest or under-invest relative to this neoclassical benchmark.
Firms may, under certain conditions, respond by increasing investment in machinery and equipment, potentially raising capital intensity if investment more than offsets the initial dilution of capital. Such a response is most likely when migrants’ skills differ from those of the native‑born and are particularly complementary to capital. The literature on capital-skill complementarity (Autor, Katz and Krueger, 1998[36]; Krusell et al., 2000[37]) suggests that skilled migration may therefore induce additional capital investment. For example, inflows of engineers or technicians can increase the profitability of adopting advanced technologies and equipment. Evidence for this mechanism is scarce and at most indirect (Glennon, 2024[38]).
Firms may also change their production processes due to increased immigration. If (low-skilled) labour is more abundant, and hence cheaper, firms may shift toward more labour-intensive techniques. This leads to firms using less capital per hour worked, a decline in labour productivity. In aggregate data, this response may be difficult to distinguish from capital dilution in the short run: both show capital per worker falling as immigration rises. The difference is that dilution is an involuntary gap between labour and a slow-adjusting capital stock, whereas this is a change in production, with longer term effects.
The rest of this section reviews the empirical evidence for OECD countries on how firms adapt their production choices in response to immigration.
Several studies have highlighted that following immigration surges, firms install less labour-saving equipment than they otherwise would.
In the United States, Lewis (2011[39]) studies metropolitan areas during the immigration wave of the 1980s and 1990s, when inflows of less-educated workers, mainly from Mexico and Central America, raised the supply of high-school dropouts faster in some cities than others. Linking plant-level technology surveys to local skill mix, he finds that plants in cities with faster growth in less-skilled labour added automation machinery more slowly between 1988 and 1993, despite similar initial adoption plans, and that capital-labour and capital-output ratios grew more slowly. Automation machinery substitutes for the least-skilled workers, so firms economised on it where such workers were abundant.
In Denmark, Mann and Pozzoli (2024[40]) examine firm-level robot adoption over 1995-2009 using employer-employee register data. Their variation comes from the inflow of non-Western immigrants, many of them refugees arriving from the mid‑1990s who were disproportionately low-skilled and concentrated in routine, blue‑collar jobs, distributed unevenly across municipalities. A higher local share of non-Western migrant workers lowers the probability of first-time robot adoption by around 9%, with the effect concentrated in labour-intensive and routine‑task industries.
The end of the Bracero programme in the United States provides a negative labour-supply shock. From 1942 to 1964 the programme admitted around half a million Mexican seasonal farm workers each year; their exclusion at the end of 1964 varied across states and crops by prior reliance on bracero labour. Clemens, Lewis and Postel (2018[41]) find that exclusion did little to raise wages or employment for domestic workers, as employers adjusted by changing technique where possible, most visibly through rapid mechanisation of the California tomato harvest.
Beyond the adoption of existing technologies, immigration may also alter the direction of innovation away from labour-saving technology. Acemoglu (2010[42]) argues that if a technology is strongly labour-saving, scarce and expensive labour raises the payoff to inventing it, while abundant labour lowers that payoff. Immigration, by making labour more abundant, should therefore slow the development of labour-saving technology. San (2023[43]) extends the analysis of the Bracero programme to the innovation of new technologies, classifying agricultural patents by crop and task, and finds that the shock raised labour-saving patenting, with the largest effect in the most labour-intensive tasks. An inflow of labour would, by the same logic, have discouraged such invention.
The resettlement of around 2.5 million ethnic Germans, predominantly low-skilled, who resettled in West Germany after 1990 provides a positive labour supply shock. An allocation policy in 1996‑1997 distributed them across regions administratively, providing exogenous variation in regional low-skilled labour supply. (Danzer, Feuerbaum and Gaessler (2024[44]) find that a larger inflow reduced the regional share of automation patents, with a 10% increase in the low-skilled workforce lowering the automation-patent share by around 3.3 p.p., while non-automation patenting was unchanged. The effect peaked about four years after the inflow and then faded.
The discouraging effect of migration on automation appears mainly where labour is the binding constraint. Danzer, Feuerbaum and Gaessler (2024[44]) find that efforts to automate are confined to tight labour markets. It also depends on the price and capability of the technology. Mann and Pozzoli (2024[40]) find the immigration-robot substitution weakening as robots became cheaper and more capable; when capital is expensive relative to labour, labour supply strongly affects adoption, but once the machine is cheap and effective firms adopt it regardless. The substitution is concentrated in routine, automatable, labour-intensive tasks (Mann and Pozzoli, 2024[40]; Danzer, Feuerbaum and Gaessler, 2024[44]; San, 2023[43]).
Beyond its effects on labour and capital inputs, migration may influence productivity through technological change itself. The next section examines the role of immigrants in innovation and entrepreneurship.
Technological progress has long been recognised as the driver of long-term productivity growth (Solow, 1957[45]). Unlike the accumulation of human and physical capital, which tends to face diminishing returns as more investment is added, technological progress can continuously shift the production frontier outward. Advances in technology allow firms to produce more output with the same amount of labour and capital by improving efficiency (See also Box 5.2). This section investigates the impact of immigration on innovation in the host country. It first discusses the contribution of immigrants to research and invention in the host country as an engine of productivity growth. It then focusses on migrant entrepreneurship and on innovation in immigrant founded firms.
How much does migration contribute to inventions in the host country?
Immigrants have made important contributions to scientific discovery and technological innovation throughout modern history. Between 2000 and 2025, immigrants have been awarded approximately 30% of the Nobel Prizes in chemistry, medicine and physics (Ahart, 2025[46]). Beyond the contributions of the world’s most exceptional researchers, immigrants also contribute to research and innovation more broadly. This section studies how immigration contributes to research output and inventions in the host country.
Immigrants may have a positive impact on innovation if they tend to concentrate in research activities, whether this is in universities and public research centres or in corporations. In addition, immigrants may contribute disproportionately to innovation in the host country if, compared with otherwise similar native‑born researchers, they are more productive or if their presence raises the productivity of native‑born researchers through spillover effects. By contrast, if immigrant researchers mainly displace native‑born researchers, immigrant innovation may largely substitute for innovation that would have occurred anyway, generating limited additional benefits.
Immigrants are over-represented among STEM graduates in most OECD countries
Among the highly educated population, STEM workers play a particularly central role, as they are directly involved in research and development (R&D) activities, the generation of innovation, and the adoption and diffusion of existing technologies.
Immigrant graduates have more often completed a STEM degree than their native‑born counterparts (Figure 5.11). Exceptions are Italy and Norway. On average in the OECD, 28% of university graduates have a STEM degree compared with 23% of the native‑born. The over-representation of STEM graduates is large in the United States (32% vs. 17%), Australia (29% vs. 16%), Lithuania (39% vs. 27%) or the Netherlands (29% vs. 19%).
When considering all STEM graduates in the host country, one in five is an immigrant, on average across OECD countries with available data. In Luxembourg, Australia, Switzerland and Ireland, more than one in three STEM graduates is an immigrant. The share of immigrants among STEM graduates is much larger than their share in the employed population in Australia (+24 p.p.), Luxembourg (+13 p.p.) but also in Estonia, Switzerland and the United States (+9 p.p.). (Figure 5.12 and Figure 5.4).
Share of STEM graduates among all tertiary educated, 2024 or latest available
Note: STEM fields include ISCED-F 2013 fields: 05 (natural sciences, mathematics and statistics), 061 (ICT), and 07 (engineering, manufacturing and construction).
Source: Labour Force Surveys.
Share of foreign-born among university graduates from a STEM field
Note: STEM fields include ISCED-F 2013 fields: 05 (natural sciences, mathematics and statistics), 061 (ICT), and 07 (engineering, manufacturing and construction).
Source: Labour Force Surveys.
From a migration policy perspective, international students play an important role in “feeding” the talent pipeline. International students are over-represented at the highest levels of instruction. Across OECD countries, the proportion of international students increases with the level of higher education. On average, they account for 5% of bachelor’s students, rising to 15% of master’s students and 25% of those in doctoral programmes (OECD, 2025[47]).
Encouraging native‑born students to pursue a STEM education as well as attracting international students to STEM degrees are common objectives of most OECD countries. International students are more likely to enrol in STEM fields than their native‑born counterparts, with 30% of international students choosing STEM programmes compared to just 19% of domestic students (Figure 5.13). This trend is particularly evident in countries like Denmark, Sweden and Türkiye, where over 40% of international students pursue studies in these disciplines (OECD, 2025[47]).
A key issue is whether international students remain in the host country after finishing their studies and contribute to the host country economy. The retention rate of international students varies across OECD countries. OECD (2022[48]) estimates for the 2010 and 2015 entry cohorts that after five years, around 30% of international students are still in the host country.4 This share drops sharply in most countries when looking at a ten‑year horizon for the 2010 cohort but remains at almost 50% in Canada and Germany and around 30% in Australia and New Zealand. The retention rate for Australia is in line with recent research on International Students Pathways in the country that finds that 35 to 40% of international students commencing in the early 2010s received permanent residency within 10 years, with an estimated decline (to around 25 to 30%) for students who commenced in the late 2010s (Jobs and Skills Australia, 2025[49]).
Share of students enrolled in a STEM degree, 2022
Note: Mobile students are international students, defined as those who completed their prior education in a different country and do not reside permanently in the host country. In cases where data on international students are unavailable, foreign students – identified by their citizenship differing from that of the host country – may be used as a proxy.
Non-mobile students are students who already resided in the host country prior to enrolment, native‑born or not.
Source: OECD (2024[50]), Education at a Glance 2024: OECD Indicators, https://doi.org/10.1787/c00cad36-en.
Immigrants are over-represented among inventors in OECD countries
Data on immigrant inventors are not directly available in the standard datasets used in the migration literature. As a result, research on immigrant inventors typically relies on information on patent applicants or patent recipients, using patents as a proxy for inventive activity. While patents initially serve to protect private returns to innovation, once they expire or are licensed, they can be adopted by other firms, contributing to broader productivity gains. In addition to patent data, information on scientific publications is also used to assess the contribution of immigrant researchers to scientific output. Box 5.5 presents patent and bibliometric data sources used to study immigrant researchers and inventors.
Patent data
Patent-based analyses draw on records from national and international patent offices, such as the European Patent Office and the United States Patent and Trademark Office, and harmonised global patent databases. These data identify inventors named on patent applications, usually together with their country of residence or affiliation at the time of filing, as well as information on assignees, technological fields and patent citations. By linking inventors across multiple patents, it is possible to trace geographic mobility and to identify immigrant inventors through changes in country of residence or by combining current location with nationality or name‑based indicators of origin. Patent data are particularly well suited to studying applied research and industrial innovation.
Bibliometric data
Bibliometric sources are based on scientific publication databases that report authorship, institutional affiliations, publication dates and research fields. Researchers can be tracked across publications, and international mobility is inferred from changes in country of affiliation over time. Bibliometric data are especially informative for analysing academic and basic research and for studying international collaboration networks and scientific impact through citation measures.
Source: Lissoni and Miguelez (2024[51]), “Migration and Innovation: Learning from Patent and Inventor Data”, https://doi.org/10.1257/jep.38.1.27.
According to patents filed under the Patent Co‑operation Treaty, foreigners were over-represented among inventors in more than half of OECD countries in 2010 (Figure 5.14).5 This is the case for example in the United States, Belgium, the Netherlands, or Finland. Unfortunately, data for more recent years is not available as data on the country of nationality of inventors is no longer systematically collected. For more details on the data, see (Miguélez and Fink, 2013[52]).
Share of foreigners among researchers filing patents in 2010 compared with share of foreigners among population aged 15 to 64
Note: OECD corresponds to the simple average across OECD countries that appear in the Figure (with data available).
Source: Miguélez and Fink (2013[52]), “Measuring the International Mobility of Inventors: A New Database”, https://doi.org/10.34667/tind.28871 and OECD DIOC 2010/11.
The rest of this section reviews the evidence on the contribution of immigrants to innovation in OECD countries. A large body of literature seeks to establish a causal effect of immigration on innovation in host countries, using a range of empirical strategies, including sudden immigration waves, instrumental‑variable approaches, and policy‑induced variation in migration flows.
The contribution of immigrants to innovation: Evidence from OECD countries
A substantial share of the literature finds that high‑skilled immigration has a positive effect on innovation. For example, for the United States, Kerr and Lincoln (2010[53]) exploit large fluctuations on the H‑1B admissions cap over the 1995‑2008 period to examine city‑level innovation effects. Identifying immigrant inventors through name ethnicity, they show that increases in the H‑1B cap are associated with higher invention rates by inventors with Chinese and Indian names in cities more reliant on the programme. Positive effects of high skill immigration on innovation in the United States are also estimated in Hunt and Gauthier-Loiselle (2010[54]) or Bernstein et al. (2022[55]) for example.
For Australia, Crown, Faggian and Corcoran (2020[56]) also find more patent applications in regions with more immigrants in scientific occupations. Using an instrumental strategy, they find that a 1 p.p. increase in the regional employment of higher educated migrants relative to total employment leads, on average, to a 4.8% rise in regional patent applications in the medium run (five years). Positive effects of immigration on innovation in Australia are also estimated in OECD (2024[57]).
Evidence from Switzerland also points to a positive impact of migration on innovation. Cristelli and Lissoni (2026[58]) use the 1999 Agreement on the Free Movement of Persons (AFMP) between Switzerland and the European Union, which gradually removed mobility restrictions, starting with cross‑border workers. Using European Patent Office data for the period 1990‑2012, they find that the Agreement led to a substantial rise in cross‑border inventors and patenting in border regions, with annual patenting increases of between 15% and 54% during the first eight years after ratification.
For France, Mayda, Santoni and Orefice (2022[59]) show that skilled migrants increase the number of patents at both the district and firm level using an instrumental strategy. In addition, the authors provide evidence on task specialisation, with immigrants specializing in research-oriented tasks (see also section Immigration, skills and productivity).
In a cross-country setting, Bosetti, Cattaneo and Verdolini (2015[60]) use data on patent applications and on citations of published research on 20 EU countries between 1995 and 2008. The authors find a positive effect of high-skilled immigration (measured by educational attainment and also by the actual occupation) on innovation. Importantly, the authors also point out the large positive effect of the country’s knowledge stock (R&D past expenditure, previous patents) on innovation, indicating that investment in R&D is paramount.
Labour market integration is a key factor shaping the innovation impact of high-skilled immigrants. Evidence from some OECD countries suggests that the innovation benefits of high‑skilled immigration may be more muted. Blit, Skuterud and Zhang (2020[61]) analyse changes in the high-skilled immigrant share across 98 Canadian cities and find that increases in the university-educated immigrant population are associated with only modest gains in per‑capita patenting. Innovation effects are stronger for STEM educated immigrants working in STEM occupations, but this channel is constrained because only about one‑third of STEM educated immigrants in Canada are employed in STEM jobs, compared with around two‑fifths of the native‑born (see also section Immigration, skills and productivity).
Immigrant STEM graduates are also slightly less likely to work in STEM occupations in other OECD countries. Across OECD countries, 48% of immigrant STEM graduates are employed in a STEM occupation compared with 51% of the native‑born (Figure 5.15). The gap is largest in OECD Southern European countries, in Germany, and some Eastern or Baltic countries (Slovak Republic, Latvia, Estonia and Czechia).
Few academic papers directly evaluate the impact of specific migration channels on innovation. From a migration‑policy perspective, it is important to evaluate differences across migration channels, although data limitations often prevent such distinctions. Hunt (2011[62]) shows that immigrants who entered the United States on student, trainee, or temporary work visas have higher rates of patenting and patent commercialisation than the native‑born, with particularly strong performance among those arriving as postdoctoral fellows or medical residents. In contrast, immigrants admitted directly as legal permanent residents (largely through family reunification) perform similarly to native‑born individuals. For Australia, Crown, Faggian and Corcoran (2020[56]) find a positive effect of the Temporary Graduate Visa program from 2007 to 2014 on the number of patent applications comparing regions across Australia.
Share of STEM graduates working in STEM occupations, 2024
Note: STEM occupations are: Science and engineering professionals; Information and communications technology professionals; Science and engineering associate professionals; Information and communications technicians. STEM fields include ISCED-F 2013 fields: 05 (natural sciences, mathematics and statistics), 061 (ICT), and 07 (engineering, manufacturing and construction).
Source: Labour Force Surveys.
Beyond aggregate effects, an important question is whether immigrants are more innovative than comparable native‑born individuals. Hunt and Gauthier-Loiselle (2010[54]), using data from the 2003 National Survey of College Graduates, show that immigrants account for 24% of patents, about twice their population share. However, this advantage is entirely explained by their greater concentration in science and engineering fields. Gaulé and Piacentini (2013[63]), analysing data on around 16 000 PhD graduates in US chemistry programmes, find that Chinese students exhibit higher scientific output during their doctoral studies and perform on par with recipients of highly selective National Science Foundation fellowships, conditional on programme admission.
Immigration may also have an impact on innovation by the native‑born. A key issue is whether immigrant innovation complements or crowds out innovation by native‑born workers. There is limited evidence on immigration crowding out native‑born innovation. Borjas and Doran (2012[64]) provide evidence that the post-1992 influx of Soviet mathematicians crowded out American scholars, with total mathematical output remaining stable. Doran, Gelber and Isen (2022[65]) use H1‑ B lottery data to argue that additional H1‑B workers crowd out other workers at the firm and lead to only modest, insignificant impacts on total firm innovation. In contrast, Moser, Voena and Waldinger (2014[66]) show that the influx of Jewish immigrant chemists from Nazi Germany increased patenting in chemistry by 71%, driven by the entrance of new domestic scientists. Cristelli and Lissoni (2026[58]) also find no crowding out effect in Switzerland.
Finally, there is evidence of positive spillover effects from immigrant innovators to native‑born researchers. Moser, Voena and Waldinger (2014[66]) show that US inventors who collaborated with émigré professors increased their patenting substantially and remained highly productive in subsequent decades, suggesting long-term knowledge transmission. Bernstein et al. (2022[55]), analysing patenting in the United States between 1976 and 2012, estimate that immigrants account for 32% of total innovative output since 1990, despite representing only 16% of the inventor workforce and directly authoring 23% of patents. The remaining contribution reflects sizeable human‑capital externalities generated by immigrants for native‑born inventors.
The large body of literature briefly reviewed in this section focusses on patents as this is a measurable outcome of innovation. Although there is a positive correlation between patenting activity and productivity, patents do not automatically translate into a higher productivity in the host country. Only when firms can effectively diffuse and commercialise new knowledge within the domestic economy does productivity increase. When inventions are primarily exploited abroad or protected in ways that limit diffusion, domestic productivity effects are weaker.
Innovation in the OECD benefits the country where innovation takes place but also all other countries who adopt innovation. The expected gains from immigrant inventors are greater for the host country in cases where innovation takes place in areas of strategic importance and with little expected diffusion abroad. The gains are also greater worldwide if immigrants would have been less innovative or less able to commercialise their innovation elsewhere.
The next section explores the role of immigrants as entrepreneurs.
Migrant entrepreneurship
Immigrants create more firms than the native‑born but their contribution to aggregate productivity growth is uncertain
In two‑thirds of OECD countries, immigrants are more likely to be self-employed than the native‑born. In 2022, 13.8% of the foreign-born employed population was self-employed compared with 13.4% of the native‑born, on average across 37 OECD countries. There are large differences across countries, however. Immigrants are substantially more likely to be self-employed than the native‑born in Colombia, Central and Eastern European countries, Canada and the United States, as well as Portugal and Spain. Conversely, immigrants are less likely to be self-employed in Japan and Korea, Greece and Italy, as well as Iceland (OECD, 2024[67]).
However, most immigrant businesses are unlikely to drive productivity growth. First, most self-employed, both native‑ and foreign-born, are own-account workers, i.e. they have no employees. This is particularly the case for migrants, as they are less likely than the native‑born to have employees in over three‑quarters of OECD countries. Second, among employers, migrants have on average fewer employees than the native‑born in almost all countries. Third, in almost all OECD EU-EFTA countries with available data, immigrants are also more likely to exit self-employment than the native‑born (OECD, 2024[67]).
Nevertheless, immigrants are over-represented among the founders of the most innovative and productive firms in the OECD (see also section below on innovation). Recent research emphasises the contribution of standout firms in driving aggregate productivity growth (McKinsey, 2025[68]). However, no research has estimated the contribution of immigrant founded firms to aggregate productivity growth in OECD countries.
Are immigrant firms more innovative?
There is evidence that immigrant entrepreneurs are over-represented among founders of some of the most innovative and successful firms in the OECD. In the United States, in 2022, the four most valuable private venture backed companies had immigrant founders as well as three of the ten most valuable public companies globally, founded in the United States (Chodavadia et al., 2024[69]). Immigrants have also been shown to be over-represented among founders of firms in the 2017 Fortune 500 ranking, the largest firms in the United States (Azoulay et al., 2022[70]), and among founders of unicorn firms, that is privately owned firms valued at USD 1 billion or more, in the United States (NFAP, 2022[71]) and Germany (Startup Verband, 2025[72]). Immigrants are also over-represented among founders in Silicon Valley’s technology businesses (Saxenian, 2002[73]), among founders of engineering and technology companies, and more recently among top AI firms in the United States (NFAP, 2023[74]).
The over-representation of immigrant founders among the most successful and innovative companies attracts a lot of attention in the media and public opinion given the disproportionate role of these firms in terms of revenue and employment. However, these are exceptional firms, and it is not straightforward to generalise the role of immigrant entrepreneurs in these firms to a more representative population of firms in the economy.
A systematic evaluation of the contribution of immigrant entrepreneurs to innovation across OECD countries is limited by data availability. Ideally, one would need data on measures of innovation at the firm level linked to the characteristics of the founder of the firm. The analysis on migrant entrepreneurs in OECD (2024[67]) showed that only a small share of self-employed individuals (5%) carry out their activities in high and medium-high R&D intensity sectors (the two highest levels of the OECD taxonomy).6 Immigrants are as likely as the native‑born to be self-employed in high R&D intensity sectors on average across the 14 OECD countries considered in the estimation. For Canada, Picot and Ostrovsky (2021[75]) show that incorporated immigrant entrepreneurs are more likely than Canadian-born to work in high and medium-high intensity R&D sectors according to the same OECD Taxonomy in 2016. Ten per cent of the foreign-born entrepreneurs work in these sectors, compared with 6.4% of the adult children of immigrants and 5.3% of the population with no immigrant parent or grandparent.
Recent research in some OECD countries has mobilised detailed firm-level data to study innovation and investment in R&D in immigrant and native‑born owned firms. Overall, immigrant firms are similar to those of the native‑born in terms of innovation and R&D. In Canada, Germany and the United States, immigrant firms perform as well, and in some cases better than firms of the native‑born.
Recent research for the United States finds that firms with at least one immigrant owner are on average more innovative along most of the innovation measures considered, including reported firms’ innovation activities, research and development, trademarks, and patents (Brown et al., 2019[76]; Lee et al., 2025[77]; Chodavadia et al., 2024[69]). Previous research based on a survey of 1 300 high-tech companies, found little difference between firms with and without immigrant founders (Hart and Acs, 2011[78]).
The main effect of immigration on innovative entrepreneurship is driven by the concentration of immigrants in key innovation hubs. Pekkala Kerr and Kerr (2020[79]) find that immigrant founded companies account for over 40% of startups in leading clusters in the United States. However, immigrant founded firms are similar to those founded by the native‑born, indicating that their impact comes mainly from the quantity of immigrant entrepreneurs agglomerating in key hubs.
For Canada, Ostrovsky and Picot (2020[80]) compare innovation of immigrant and native‑born owned SMEs and find little difference between the two groups. In immigrant owned firms, there was marginally more product and process innovation, but there was no difference in the use of intellectual property.
Immigrants are not evenly distributed across all areas of the economy. In all OECD countries, immigrants tend to concentrate in some sectors of activity (OECD, 2020[81]) and to disproportionately work in firms with a high share of immigrants (OECD, 2025[82]).
Where workers are employed matters for productivity, not only how productive individual workers are. If labour shifts from low-productivity firms or declining sectors to higher-productivity ones, aggregate productivity increases even without changes in sectoral or firm productivity. Conversely, misallocation, for example due to barriers to job mobility or other frictions, may slow productivity growth by keeping workers in lower productivity activities.
Given that immigration has accounted for a large share of employment growth in the past decade in many OECD countries (Figure 5.4), the distribution of immigrants across sectors and firms is likely to matter for aggregate productivity. A key question is therefore whether, and through which mechanisms, the allocation of immigrants across sectors and firms affects aggregate labour productivity.
This section documents the sorting of immigrants across sectors, as well as firms within sectors for selected countries with available data and explores the link between the allocation of immigrants across sectors and firms and aggregate labour productivity.
The allocation of immigrants across firms within sectors
Immigrants are concentrated in specific firms within a same sector. These tend to be firms that disproportionately employ immigrants. Thirty-nine per cent of immigrants worked in firms where 50% or more of the employees were also immigrants, on average across the eight countries with available administrative linked employer-employee data (Figure 5.16). Twenty-two per cent work in firms where three‑quarters of employees are immigrants. These shares have increased in the 2010s in all countries in the analysis except in France where it remained constant. This evidence adds to a growing academic literature on immigrant firm segregation from across the OECD.7 Different mechanisms may be at play that explain immigrant workplace segregation. Many workers find employment through job referrals and their informal networks. If immigrants’ networks are disproportionately composed of other immigrants, then immigrants will tend to work together. Barriers in the labour market and employer discrimination may also be at play, leading immigrants to concentrate in workplaces that are easier to access for immigrants or do not discriminate against immigrants at hire.
Share of immigrant workers working in firms where more than half of the workforce is immigrant, 2010 and 2019
Note: OECD represents the average of the eight countries in the figure.
Source: OECD (2025[82]), International Migration Outlook 2025, https://doi.org/10.1787/ae26c893-en, based on administrative linked employer-employee data.
Within sectors, immigrants concentrate in firms with lower labour productivity. On average across the seven countries with available data, 23% of immigrants work in the bottom 20% of jobs in terms of firm productivity, 3 p.p. more than if immigrants were evenly distributed across the firm productivity distribution. At the other extreme, 17% of immigrants work in the top 20% of jobs in terms of firm labour productivity, 3 p.p. less than an even distribution would predict. (Figure 5.17). In Italy and Portugal, the gap in firm labour productivity between immigrants and the foreign-born is larger. 25% of immigrants work in the bottom quintile in both countries and 13% and 11% work in the top quintile in Italy and Portugal. In New Zealand, the allocation of immigrants and native‑born across firms with different productivity levels is more similar.
This gap in firm labour productivity is estimated within narrowly defined industries (3‑digit level). Differences in labour productivity without netting out sector effects are larger given that immigrants tend to sort into lower productivity sectors as documented in the next section (Annex Figure 5.A.1).
Distribution of immigrant workers along the firm labour productivity distribution, within industry
Note: OECD represents the average of the seven countries in the figure. For Norway, the values for 2019 correspond to the year 2018.
Source: OECD (2025[82]), International Migration Outlook 2025, https://doi.org/10.1787/ae26c893-en, based on administrative linked employer-employee data.
The pattern is similar in Australia. Immigrants tend to concentrate in lower productivity firms within sectors and this concentration increased between 2011 and 2020 (Andrews et al., 2023[83]). The authors also show that there are marked differences between immigrants by category of migration. Labour immigrants and their dependents, that is immigrants under the under the Permanent Skilled and Temporary Skilled visas, are more likely than other immigrants and the native‑born to work in high productivity firms within sectors. In contrast, international students and working holiday makers work in lower productivity firms.
These results are based on administrative data, which do not cover fully the informal labour market. This is a segment of the labour market which tends to have lower labour productivity and a higher share of immigrants. Hence, the productivity gap between the firms in which immigrants and the native‑born work may be larger than the estimates presented.
Misallocation of labour across firms, that is when workers are not employed by the most productive firms, reduces sectoral labour productivity. High‑productivity firms, those that could generate more output per worker, are unable to expand employment sufficiently, while low‑productivity firms retain too many workers. As a result, within‑sector productivity is lower than if labour were efficiently allocated. More efficient reallocation of labour would shift employment toward frontier firms, raising average productivity within sectors and, ultimately, aggregate productivity.
In many OECD countries, firms at the productivity frontier (10% most productive firms in the industry) are two to five times more productive than the median firm in the same industry (OECD, 2025[84]). With efficient allocation, high‑productivity firms should employ a larger share of workers. Instead, labour tends to be more evenly distributed across firms. This implies that a significant share of workers remains in below‑average productivity firms, dragging down sectoral productivity.
Evidence on the extent immigrants concentrate in low-productivity firms is not available for all OECD countries despite growing interest and evidence on firm productivity gaps. Importantly, more evidence is needed by category of migration. In a medium-run perspective, a key question is whether immigrants move to more productive firms over time. Previous evidence shows that immigrants do move to more productive firms as they accumulate experience in the host country but that significant gaps relative to the native‑born remain pointing to barriers to job mobility faced by immigrants in the host country labour market (OECD, 2025[82]).
The allocation of immigrants across sectors and aggregate labour productivity
In all OECD countries, immigrants are concentrated in some sectors of the economy (Annex Table 5.A.3). Immigrants are overrepresented in the accommodation and food sector in all countries. They represent 30% or more of workers in the sector in, for example, Austria, Germany, Iceland, Norway, Spain and Sweden. Immigrants are also over-represented in the construction sector in many countries, such as Costa Rica, Czechia, France, Greece, Italy, Spain or the United States. They tend to be underrepresented in manufacturing, but there are exceptions such as Czechia, Estonia or Iceland.
While immigrants are over-represented in some sectors where many jobs are low-skilled, they are also overrepresented in sectors where most jobs are high skilled, such as in ICT. In Canada, Denmark, Estonia, Finland, Hungary, Poland, Luxembourg, Lithuania, New Zealand and the United States, the share of workers in ICT that are immigrants is significantly larger than the share in the overall employed population.
The sectors migrants concentrate in are on average low labour productivity sectors. The share of migrants in employment is higher in sectors with lower labour productivity than in the host country labour market as a whole (Figure 5.18). The correlation is negative in all countries, except Ireland and Luxembourg.
Correlation between sectoral log labour productivity and migrant share, 2024
Note: The figure is a bin scatter plot. It shows the average log labour productivity for each of the 20 bins of average share of migrant sectoral employment, net of country fixed effects. The bins are based on 345 country-sector observations (23 countries and 15 sectors).
Source: Productivity Data Base & Labour Force Surveys.
Some sectors are excluded from this correlation. These are sectors for which there is no good measure of labour productivity: real estate, public administration and defence, education, and health activities. Real-estate activities are excluded because their value added is largely imputed (it includes the value of both actual and imputed housing rents in the economy).8 Public administration and defence, education and health services are excluded because they are largely non-market. Hence, their output value is measured as the sum of input costs, and in several countries their output volume is measured by deflating input costs, thus conventionally excluding any productivity gains (OECD, 2025[5]).
Approximately one‑quarter of immigrants (22%) on average work in sectors for which there is no measure of labour productivity. Although this share is lower than for the native‑born (30%), immigrants contribute significantly to the employed population in some of these sectors, as for example, the health sector. On average across the countries in the analysis, 11% of immigrants work in the health sector (Annex Table 5.A.2). In this case, labour productivity is not the right lens through which to study the contribution of immigrants to the host country economy and society.
Immigrant sectoral reallocation and productivity growth
As immigrants account for a growing share of employment across the OECD, an important question is whether this growth has been concentrated in relatively high- or low-productivity sectors and how this may have affected aggregate productivity growth.
Changes in aggregate productivity reflect both productivity improvements within sectors and shifts of employment across sectors. Aggregate labour productivity growth may be decomposed into three components: productivity growth within sectors, a static reallocation component, and a dynamic reallocation component (OECD, 2025[5]). The two reallocation components capture the contribution of changes in the distribution of employment across sectors to aggregate productivity growth. The static component measures the effect of workers moving between sectors with different initial levels of labour productivity, while the dynamic component captures shifts towards sectors with different rates of productivity growth.
To assess the contribution of immigration to productivity growth through reallocation, this section suggests a novel decomposition of the static reallocation component.9 The general idea of the decomposition is that immigration may affect the reallocation component of aggregate productivity growth in two separate ways. First, immigrants differ from the native‑born in their initial distribution across sectors, as highlighted in the previous subsection. An increase in the share of immigrants in the workforce may therefore change the aggregate sectoral allocation of workers, even if the sectoral allocation of immigrants remains unchanged. Second, immigrant employment may shift to more, or less, productive sectors over time. The decomposition separates these two components and thereby provides a clearer picture of how immigration contributes to aggregate productivity growth (Box 5.6).
Before discussing the results, two caveats are worth bearing in mind. First, labour reallocation accounts for only a small share of overall productivity growth. Most productivity growth occurs within sectors rather than through the movement of workers across sectors (Annex Figure 5.A.2).
Second, the decomposition is descriptive rather than causal. The estimates capture how changes in the distribution of employment across sectors are associated with productivity growth and should not be interpreted as causal effects of immigration on productivity.
This box suggests a novel decomposition of aggregate labour productivity growth that distinguishes between changes in the overall share of immigrants in employment and changes in where immigrants work.
Labour productivity growth within and across sectors
Aggregate labour productivity is the average of sectoral labour productivity weighted by sectoral employment.
Labour productivity growth may be decomposed into within-sector labour productivity growth, a static reallocation term and a dynamic reallocation term as follows:
Where the subscript s indicates the sector and the superscript 0 indicates the initial level.
The static reallocation term indicates the productivity growth due to employment changes in sectors of different initial labour productivity. The dynamic reallocation term indicates the productivity growth due to employment changes in sectors of different labour productivity growth.
Static and dynamic immigrant and native‑born reallocation
The change in employment in each sector is due to changes in both native‑born and foreign-born employment.
Hence, the Static Reallocation Term (SRT) equals:
Noting that
A captures the immigrant reallocation effect, the effect of changes in the immigrant distribution across sectors over time, weighted by the initial share of immigrants in total employment.
B captures the native‑born reallocation effect, the effect of changes in the native‑born distribution across sectors over time, weighted by the initial share of native‑born in total employment.
C captures the immigrant share effect, the effect of the change of the share of immigrants in total employment holding fixed their initial sorting across sectors of different labour productivity levels.
D captures the combined reallocation and share effect, the effect of the change of the share of immigrants in total employment together with their change in sectoral allocation over time relative to the native‑born.
A similar decomposition may be done for the Dynamic Reallocation Term.
In two‑thirds of countries, from 2014 to 2024, the sum of the three immigration components is positive. The largest contributions are in Ireland (3.7 p.p.), Luxembourg (3.0 p.p.), Sweden (0.4 p.p.), Austria and Greece (0.3 p.p.). Countries with negative contributions are Norway (‑0.9 p.p.), Germany, Italy, Portugal and Spain (‑0.4 p.p.).
This total contribution is the sum of two main effects that partially offset each other. On the one hand, migration increased in most countries and immigrants were initially concentrated in lower productivity sectors. The increase in the share of immigrants in the employed population holding fixed their initial distribution across sectors (the immigrant share effect) weighted negatively in 70% of countries. For example, in Spain, 42% of immigrants in 2014 worked in Accommodation or Retail, which are relatively low productivity sectors.10 The increase in immigration from 2014 to 2024 increased the share of employment in these sectors, leading to a negative contribution to aggregate productivity (‑1 p.p.).
On the other hand, from 2014 to 2024, immigrant employment shifted to more productive sectors (the immigrant reallocation effect), leading to a positive contribution to productivity growth in almost all countries. The most positive contributions were in Ireland (2.3 p.p.), Luxembourg (1.8 p.p.), Sweden (0.7 p.p.), Lithuania and Spain (0.4 p.p.).
2014‑2024
Note: See Box 5.6 for the decomposition and definition of the different terms.
Source: Productivity Data Base and Labour Force Surveys.
Both native‑ and foreign-born employment reallocated to more productive sectors over the decade to 2024. The reallocation effects identified above are weighed by (foreign- and native‑born) population size. How does the reallocation towards more productive sectors compare for the foreign- and native‑born? Figure 5.20 compares the native‑born and foreign-born reallocation terms without the population weights for a more direct comparison. In most countries, the dots are above the 45‑degree line indicating a larger contribution of foreign- than native‑born reallocation across sectors to productivity growth. For example, in Ireland, the share of immigrants working in ICT (one of the sectors with highest initial labour productivity) increased from 8.8% to 14.2% from 2014 to 2024. The increase was more modest among the native‑born: 7.7% in 2024, up from 6.6% in 2014.
The larger reallocation effect of foreign-born employment is consistent with immigrants being more mobile in the labour market than the native‑born. This is line with previous evidence that immigrant earnings growth relative to the native‑born in the first years in the host country is partly due to immigrants moving to higher-paying, and more productive, sectors (OECD, 2025[82]). This result may also be driven by successive cohorts of new entrants in the labour market working in more productive sectors. Understanding the drivers and consequences of immigrant job mobility warrants further research.
2014‑2024
Note: Following the decomposition in Box 5.6, for each country, is the co‑ordinate on the y-axis and the co‑ordinate on the x-axis.
Source: Productivity Data Base and Labour Force Surveys.
As this chapter demonstrates, the channels through which migration flows may impact productivity are numerous and diverse. Migration can influence labour productivity and multi factor productivity growth. Its effects may arise at firm and sector level, while also generating broader macroeconomic impacts.
Migration affects short term and longer-term allocation of factors of production by influencing employers and workers investment decisions. It can induce changes in production technologies through innovation, adoption of labour-saving technologies and/or externalities.
In most cases, these effects can be either positive or negative, depending on the context and the composition of migration flows. Hence, it is impossible to draw a definite conclusion as to whether migration is a blessing or a curse for productivity growth within individual OECD countries or across the OECD as a whole.
That said, the chapter has clearly identified the mechanisms through which migration may either enhance or hinder productivity growth. In each of these cases, public policy can play a decisive role in fostering positive outcomes. At a time when the working age population is shrinking in all OECD countries, and the scope for further gains in labour market participation may be increasingly limited, raising labour productivity becomes crucial. Well-designed migration and integration policies can contribute to this objective, both directly and indirectly.
The chapter outlines a roadmap for maximising the contribution of migration to productivity growth. It identifies four main channels through which effective migration and integration policies could strengthen the positive relationship between migration and productivity growth.
Attracting, retaining and efficiently utilising skills
Highly skilled immigrants account for a significant and increasing share of the workforce in most OECD countries. Competition for international talent, notably in STEM occupations and, increasingly, in research activities more broadly, has intensified as countries have come to recognise its important contribution to productivity growth. International students constitute a particularly valuable pipeline of talent, as they are more likely to enrol in advanced and STEM-related fields of study. OECD Indicators of Talent Attractiveness shows that migration policies can play a significant role in enhancing a country’s talent attractiveness. Half of OECD countries could virtually close their attractiveness gap relative to the OECD top performer country by implementing less stringent migration policies (OECD, 2023[85]).
Beyond attracting talents, OECD countries are increasingly seeking to improve the retention of top talent. Relatively low retention rates in many OECD countries suggest that many could benefit from policies that encourage highly skilled migrants to build their careers over the longer term. Family reunification opportunities, access to permanent residence, exposure to discrimination, and broader career and mobility prospects all potentially affect return or secondary migration decisions. In addition, many OECD countries face significant emigration of highly educated native‑born workers. A balanced strategy is therefore needed, combining efforts to attract international talent, and measures to retain and attract back highly skilled nationals.
Realising the productivity gains from migration also requires that migrants’ skills are effectively utilised in host country labour markets. A persistent challenge is the high incidence of skills mismatch observed among immigrants, who remain more likely than native‑born workers to be overqualified or employed outside their field of study. Addressing this issue requires a comprehensive policy approach, including faster recognition of foreign qualifications, occupation-specific language training, addressing barriers to occupational licensing, and fostering hiring practices that place greater emphasis on individuals’ skills and competences. Reducing skill mismatches among both native and foreign-born workers could yield substantial labour productivity gains.
Striking the right balance between short term and longer-term objectives
Labour migration programmes in OECD countries are for the most part not designed at the sector level (OECD 2020). Aside from seasonal agricultural programmes, only a few countries operate explicit sector-focussed labour migration programmes, and these tend to be limited or targeted at specific labour market needs or strategic sectors. Examples include Italy’s sectoral quota system under the Decreto Flussi, Japan’s Specified Skilled Worker programme, introduced in 2018 to address shortages in selected sectors, and Korea’s Employment Permit System, which targets SMEs in agriculture, livestock, manufacturing, construction, fisheries and selected service activities. In addition, Australia targets highly skilled migrants in selected “future sectors” (e.g. AI, fintech, cybersecurity), while Italy has implemented sector-specific regularisation programmes, particularly in domestic work and agriculture.
Yet, even in the absence of explicit sectoral programmes, migration policy is frequently used to address short-term labour and skills shortages, notably through shortage occupation lists. This may contribute to the concentration of migrants in sectors with occupations in demand. In addition, immigrants tend to work, at least initially, in so called entry sectors (Piore, 1979[86]). These are typically sectors with difficult working conditions, such as irregular hours and low pay. As a result, immigrants tend to be overrepresented in low productivity sectors across the OECD.
The concentration of immigrants is not limited to sectors; it also occurs across firms within sectors. OECD (2025[82]) shows that immigrants concentrate in lower-paying and lower labour productivity firms, suggesting that the allocation of migrant workers across firms may also have implications for aggregate productivity outcomes.
Whether the concentration of immigrants ultimately supports or hinders productivity growth critically depends on its underlying drivers. When international recruitment helps firms respond to genuine labour shortage, migration can support structural transformation and productivity growth. However, when migrant recruitment is used to circumvent structural issues and enable firms to delay or avoid necessary adjustments to improve productivity, working conditions and worker retention, it may create a vicious circle. Continued reliance on migrant labour may in this case negatively affect long-term productivity growth within sectors, the main driver of aggregate productivity growth.
Striking the right balance between short-term labour market needs and longer-term productivity objectives is therefore strategic. Migration has sectoral consequences, whether these are intended or not. Being aware of this dimension is a necessary condition both to maximise the intended sectoral benefits of migration and to avoid unintended adverse effects. The question is, however, whether OECD countries will want to use migration policy more proactively to support the development of particular sectors. This question is becoming increasingly relevant as governments expand the use of industrial policies to build resilience to economic shocks, enhance economic security and strengthen their competitiveness. Carefully designed industrial policy, combined with international co‑operation, can help countries respond to market concentration or supply chain concerns, adapt to technological change and create new economic opportunities. In this context, migration policy has an important role to play in ensuring that the skills required to support these strategic objectives are available when and where they are needed.
Addressing misallocation and fostering immigrants’ job mobility
The efficient allocation of workers across firms and sectors is a key driver of productivity growth. Facilitating labour mobility across sectors is particularly important to sustaining productivity growth notably in the presence of structural labour shortages that require sectoral labour reallocation (OECD, 2022[48]). OECD analysis emphasises that policies improving job mobility are key to maintaining productivity growth in ageing and tightening labour markets (OECD, 2025[1]; André and Gal, 2024[4]). In this context, it is important to recognise that immigrants face greater barriers not only to entering employment but also to changing jobs and sector.
Similar challenges arise within sectors, where immigrants are often concentrated in lower-productivity firms. This pattern may reflect differences in recruitment practices, information asymmetries, weaker professional networks and unequal access to high-quality job opportunities. As a result, immigrant workers may be less likely to match with high-productivity firms, reducing overall efficiency. More broadly, the misallocation of labour across firms with different productivity levels is recognised as a key factor behind weak aggregate productivity performance in many OECD economies (André and Gal, 2024[4]).
Against this backdrop, restrictions limiting migrants’ ability to change employer or sector before a minimal time of residence in the host country might therefore have sizeable unintended consequences for productivity. Policies aimed at improving labour market matching, reducing skills mismatches and facilitating access to opportunities in more productive firms and sectors should therefore pay particular attention to the barriers faced by immigrants.
Supporting migrant entrepreneurship
Immigrants are more likely to be entrepreneurs than the native‑born in most OECD countries. This reflects a dual reality. On the one hand, immigrants are over-represented among own-account self-employed workers and immigrant founded firms tend to be smaller than those founded by the native‑born. On the other hand, immigrants are over-represented among the founders of the most successful firms in the OECD and are essential players in key innovation hubs. The total contribution of immigrant founded firms to aggregate productivity growth is uncertain and little research has been undertaken on this question in OECD countries.
Recognising the potential contribution of entrepreneurship to innovation and productivity growth, most OECD countries have introduced dedicated migration pathways for start-up founders, scientists, researchers, and other highly skilled professionals. However, there has been limited systematic evaluation of these programmes. This applies not only to specialised entrepreneurial and scientific visas but also to broader talent-attraction policies and post-graduate work schemes, whose contribution to innovation and business creation remains insufficiently assessed.
At the same time, migrant entrepreneurs often face specific barriers that can limit their potential and constrain their economic contribution. These include more restricted access to finance, weaker integration into professional and business networks, and administrative obstacles linked to their residence status. Targeted policies can help address these constraints. These include improving access to credit (for example through credit guarantee schemes), facilitating participation in business networks and incubators, and providing support with administrative procedures for firm creation and operation.
While effective migration and integration policies can contribute to the broader policy objective of supporting productivity growth, it should be acknowledged that productivity cannot become the alpha and the omega of migration policies. Labour migration systems must balance a broader set of economic and social objectives. These include, for example, ensuring the provision of essential services, notably in the domestic and care sectors; safeguarding national food security through adequate staffing in agriculture or agrifood industries; addressing labour needs linked to housing supply and affordability challenges; or supporting the tourism industry and the Horeca sector (hotels, restaurants and catering), as well as other labour-intensive strategic sectors.
These are all sectors characterised by relatively low measured productivity or by outputs that are difficult to capture adequately in conventional productivity statistics. They are also sectors in which immigrants tend to be overrepresented. From this perspective, migration policy should be designed not only to support productivity growth but also to support the development of strategic sectors.
References
[42] Acemoglu, D. (2010), “When Does Labor Scarcity Encourage Innovation?”, Journal of Political Economy, Vol. 118/6, pp. 1037-1078, https://doi.org/10.1086/658160.
[46] Ahart, J. (2025), “More than 30% of this century’s science Nobel prizewinners immigrated: see their journeys”, Nature, Vol. 646/8086, pp. 784-785, https://doi.org/10.1038/d41586-025-03247-6.
[91] Andersson, F. et al. (2014), “Workplace Concentration of Immigrants”, Demography, Vol. 51/6, pp. 2281-2306, https://doi.org/10.1007/s13524-014-0352-3.
[4] André, C. and P. Gal (2024), “Reviving productivity growth: A review of policies”, OECD Economics Department Working Papers, No. 1822, OECD Publishing, Paris, https://doi.org/10.1787/61244acd-en.
[83] Andrews, D. et al. (2023), Misallocated migrants: Immigration and firm productivity in Australia, e61 Institute, https://e61.in/misallocated-migrants-immigration-and-firm-productivity-in-australia/.
[92] Ansala, L., O. Åslund and M. Sarvimäki (2021), “Immigration history, entry jobs and the labor market integration of immigrants”, Journal of Economic Geography, Vol. 22/3, pp. 581-604, https://doi.org/10.1093/jeg/lbaa038.
[88] Åslund, O. and S. Oskar (2008), “How to measure segregation conditional on the distribution of covariates”, Journal of Population Economics 2008 22:4, Vol. 22/4, pp. 971-981, https://doi.org/10.1007/S00148-008-0189-4.
[36] Autor, D., L. Katz and A. Krueger (1998), “Computing Inequality: Have Computers Changed the Labor Market?”, The Quarterly Journal of Economics, Vol. 113/4, pp. 1169-1213, https://doi.org/10.1162/003355398555874.
[70] Azoulay, P. et al. (2022), “Immigration and Entrepreneurship in the United States”, American Economic Review: Insights, Vol. 4/1, pp. 71-88, https://doi.org/10.1257/aeri.20200588.
[9] Barone, G. and S. Mocetti (2011), “With a little help from abroad: The effect of low-skilled immigration on the female labour supply”, Labour Economics, Vol. 18/5, pp. 664-675, https://doi.org/10.1016/J.LABECO.2011.01.010.
[55] Bernstein, S. et al. (2022), The Contribution of High-Skilled Immigrants to Innovation in the United States, National Bureau of Economic Research, Cambridge, MA, https://doi.org/10.3386/w30797.
[61] Blit, J., M. Skuterud and J. Zhang (2020), “Can skilled immigration raise innovation? Evidence from Canadian Cities”, Journal of Economic Geography, Vol. 20/4, pp. 879-901, https://doi.org/10.1093/JEG/LBZ029.
[11] Borjas, G. (2007), “Do foreign students crowd out native students from graduate programs?”, in Science and the University, University of Wisconsin Press, https://doi.org/10.2307/jj.36106180.11.
[64] Borjas, G. and K. Doran (2012), “The collapse of the soviet union and the productivity of american mathematicians”, Quarterly Journal of Economics, Vol. 127/3, pp. 1143-1203, https://doi.org/10.1093/qje/qjs015.
[60] Bosetti, V., C. Cattaneo and E. Verdolini (2015), “Migration of skilled workers and innovation: A European Perspective”, Journal of International Economics, Vol. 96/2, pp. 311-322, https://doi.org/10.1016/J.JINTECO.2015.04.002.
[76] Brown, J. et al. (2019), Immigrant Entrepreneurs and Innovation in the U.S. High-Tech Sector, National Bureau of Economic Research, Cambridge, MA, https://doi.org/10.3386/w25565.
[20] Brunello, G. and L. Rocco (2013), “The effect of immigration on the school performance of natives: Cross country evidence using PISA test scores”, Economics of Education Review, Vol. 32/1, pp. 234-246, https://doi.org/10.1016/J.ECONEDUREV.2012.10.006.
[89] Carneiro, A., N. Fortuna and J. Varejão (2012), “Immigrants at new destinations: How they fare and why”, Journal of Population Economics, Vol. 25/3, pp. 1165-1185, https://doi.org/10.1007/S00148-011-0387-3.
[69] Chodavadia, S. et al. (2024), Immigrant Entrepreneurship: New Estimates and a Research Agenda, National Bureau of Economic Research, Cambridge, MA, https://doi.org/10.3386/w32400.
[41] Clemens, M., E. Lewis and H. Postel (2018), “Immigration Restrictions as Active Labor Market Policy: Evidence from the Mexican Bracero Exclusion”, American Economic Review, Vol. 108/6, pp. 1468-1487, https://doi.org/10.1257/aer.20170765.
[7] Cortés, P. and J. Tessada (2011), “Low-Skilled Immigration and the Labor Supply of Highly Skilled Women”, American Economic Journal: Applied Economics, Vol. 3, pp. 88-123, https://doi.org/10.2307/41288640.
[58] Cristelli, G. and F. Lissoni (2026), “Free Movement of Inventors: Open-Border Policy and Innovation in Switzerland”, Journal of the European Economic Association, https://doi.org/10.1093/jeea/jvag003.
[56] Crown, D., A. Faggian and J. Corcoran (2020), “Foreign-Born graduates and innovation: Evidence from an Australian skilled visa program”, Research Policy, Vol. 49/9, p. 103945, https://doi.org/10.1016/J.RESPOL.2020.103945.
[32] D’Amuri, F. and G. Peri (2014), “Immigration, Jobs, and Employment Protection: Evidence from Europe before and during the Great Recession”, Journal of the European Economic Association, Vol. 12/2, pp. 432-464, https://doi.org/10.1111/jeea.12040.
[44] Danzer, A., C. Feuerbaum and F. Gaessler (2024), “Labor supply and automation innovation: Evidence from an allocation policy”, Journal of Public Economics, Vol. 235/5, p. 105136, https://doi.org/10.1016/j.jpubeco.2024.105136.
[65] Doran, K., A. Gelber and A. Isen (2022), “The Effects of High-Skilled Immigration Policy on Firms: Evidence from Visa Lotteries”, https://doi.org/10.1086/720467, Vol. 130/10, pp. 2501-2533, https://doi.org/10.1086/720467.
[35] Doyle, M., M. Skuterud and C. Worswick (2025), “The economics of Canadian immigration levels”, Canadian Journal of Economics, Vol. 58/1, pp. 109-135, https://doi.org/10.1111/CAJE.12760.
[8] Farré, L., L. González and F. Ortega (2011), “Immigration, family responsibilities and the labor supply of skilled native women”, B.E. Journal of Economic Analysis and Policy, Vol. 11/1, https://doi.org/10.2202/1935-1682.2875.
[25] Figlio, D. et al. (2024), “Diversity in Schools: Immigrants and the Educational Performance of U.S.-Born Students”, The Review of Economic Studies, Vol. 91/2, pp. 972-1006, https://doi.org/10.1093/RESTUD/RDAD047.
[26] Figlio, D. and U. Özek (2019), “Unwelcome guests? The effects of refugees on the educational outcomes of incumbent students”, Journal of Labor Economics, Vol. 37/4, pp. 1061-1096, https://doi.org/10.1086/703116.
[29] Foged, M. and G. Peri (2016), “Immigrants’ Effect on Native Workers: New Analysis on Longitudinal Data”, American Economic Journal: Applied Economics, Vol. 8/2, pp. 1-34, https://doi.org/10.1257/app.20150114.
[10] Forlani, E., E. Lodigiani and C. Mendolicchio (2015), “Impact of low-skilled immigration on female labour supply”, Scandinavian Journal of Economics, Vol. 117/2, pp. 452-492, https://doi.org/10.1111/SJOE.12101.
[22] Frattini, T. and E. Meschi (2019), “The effect of immigrant peers in vocational schools”, European Economic Review, Vol. 113, pp. 1-22, https://doi.org/10.1016/J.EUROECOREV.2018.12.005.
[87] Galindo-Rueda, F. and F. Verger (2016), “OECD Taxonomy of Economic Activities Based on R&D Intensity”, OECD Science, Technology and Industry Working Papers, No. 2016/4, OECD Publishing, Paris, https://doi.org/10.1787/5jlv73sqqp8r-en.
[63] Gaulé, P. and M. Piacentini (2013), “Chinese graduate students and U.S. scientific productivity”, The Review of Economics and Statistics, Notes, Vol. (2)/95, http://direct.mit.edu/rest/article-pdf/95/2/698/1917181/rest_a_00283.pdf.
[24] Geay, C., S. Mcnally and S. Telhaj (2013), “Non‐Native Speakers of English in the Classroom: What are the Effects on Pupil Performance?”, The Economic Journal, Vol. 123/570, pp. F281-F307, https://doi.org/10.1111/ECOJ.12054.
[38] Glennon, B. (2024), “Skilled Immigrants, Firms, and the Global Geography of Innovation”, Journal of Economic Perspectives, Vol. 38/1, pp. 3-26, https://doi.org/10.1257/jep.38.1.3.
[90] Glitz, A. (2014), “Ethnic segregation in Germany”, Labour Economics, Vol. 29, pp. 28-40, https://doi.org/10.1016/J.LABECO.2014.04.012.
[3] Goldin, I. et al. (2024), “Why Is Productivity Slowing Down?”, Journal of Economic Literature, Vol. 62/1, pp. 196-268, https://doi.org/10.1257/jel.20221543.
[21] Gould, E., V. Lavy and M. Paserman (2009), “Does immigration affect the long-term educational outcomes of natives? Quasi-experimental evidence”, Economic Journal, Vol. 119/540, pp. 1243-1269, https://doi.org/10.1111/J.1468-0297.2009.02271.X.
[78] Hart, D. and Z. Acs (2011), “High-Tech Immigrant Entrepreneurship in the United States”, Economic Development Quarterly, Vol. 25/2, pp. 116-129, https://doi.org/10.1177/0891242410394336.
[16] Hunt, J. (2017), “The Impact of Immigration on the Educational Attainment of Natives”, Journal of Human Resources, Vol. 52/4, pp. 1060-1118, https://doi.org/10.3368/JHR.52.4.0115-6913R1.
[62] Hunt, J. (2011), “Which Immigrants Are Most Innovative and Entrepreneurial? Distinctions by Entry Visa”, Journal of Labor Economics, Vol. 29/3, pp. 417-457, https://doi.org/10.1086/659409.
[54] Hunt, J. and M. Gauthier-Loiselle (2010), “How much does immigration boost innovation”, American Economic Journal: Macroeconomics, Vol. 2/2, pp. 31-56, https://doi.org/10.1257/mac.2.2.31.
[49] Jobs and Skills Australia (2025), International Students Pathways and Outcomes Study, https://www.jobsandskills.gov.au/research/studies/international-students-pathways-and-outcomes-study (accessed on 29 July 2026).
[53] Kerr, W. and W. Lincoln (2010), “The supply side of innovation: H-1B visa reforms and U.S. ethnic invention”, Journal of Labor Economics, Vol. 28/3, pp. 473-508, https://doi.org/10.1086/651934.
[37] Krusell, P. et al. (2000), “Capital-skill complementarity and inequality: A macroeconomic analysis”, Econometrica, Vol. 68/5, pp. 1029-1053, https://doi.org/10.1111/1468-0262.00150.
[31] Lee, J., G. Peri and H. Yang (2026), “The Effects of a Sudden Stop in Low-Skilled Immigration: Evidence from Korea’s Guest Worker Program”, https://doi.org/10.3386/W34927.
[77] Lee, K. et al. (2025), “Are Immigrants More Innovative? Evidence From Entrepreneurs”, Journal of Economics and Management Strategy, https://doi.org/10.1111/jems.12633.
[39] Lewis, E. (2011), “Immigration, Skill Mix, and Capital Skill Complementarity”, The Quarterly Journal of Economics, Vol. 126/2, pp. 1029-1069, https://doi.org/10.1093/qje/qjr011.
[51] Lissoni, F. and E. Miguelez (2024), “Migration and Innovation: Learning from Patent and Inventor Data”, Journal of Economic Perspectives, Vol. 38/1, pp. 27-54, https://doi.org/10.1257/jep.38.1.27.
[13] Machin, S. and R. Murphy (2017), “Paying out and crowding out? The globalization of higher education”, Journal of Economic Geography, Vol. 17/5, pp. 1075-1110, https://doi.org/10.1093/JEG/LBX006.
[2] Manning, A. (2025), Why immigration policy is hard; And how to make it better, Polity.
[18] Manning, A., S. Mcnally and G. Ventura (2024), “Immigration and Vocational Training: Evidence from England”.
[40] Mann, K. and D. Pozzoli (2024), “Robots and immigration”, Journal of Economic Behavior and Organization, Vol. 227, https://doi.org/10.1016/j.jebo.2024.106708.
[59] Mayda, A., G. Santoni and G. Orefice (2022), “DP17662 Skilled Immigration, Task Allocation and the Innovation of Firms”, https://cepr.org/publications/dp17662 (accessed on 24 April 2026).
[68] McKinsey (2025), How standout companies grow national productivity, https://www.mckinsey.com/mgi/our-research/the-power-of-one-how-standout-firms-grow-national-productivity (accessed on 7 August 2026).
[52] Miguélez, E. and C. Fink (2013), Measuring the International Mobility of Inventors : A New Database, World Intellectual Property Organization, https://doi.org/10.34667/TIND.28871.
[66] Moser, P., A. Voena and F. Waldinger (2014), “German Jewish émigrés and US invention”, American Economic Review, Vol. 104/10, pp. 3222-3255, https://doi.org/10.1257/aer.104.10.3222.
[17] Mountford, A. and J. Wadsworth (2023), “‘Good jobs’, training and skilled immigration”, Economica, Vol. 90/359, pp. 851-881, https://doi.org/10.1111/ECCA.12472.
[74] NFAP (2023), AI and Immigrants, https://nfap.com/studies/ai-and-immigrants/ (accessed on 25 April 2026).
[71] NFAP (2022), Immigrant Entrepreneurs and U.S. Billion-Dollar Companies, https://nfap.com/research/new-nfap-policy-brief-immigrant-entrepreneurs-and-u-s-billion-dollar-companies/ (accessed on 25 April 2026).
[82] OECD (2025), International Migration Outlook 2025, OECD Publishing, Paris, https://doi.org/10.1787/ae26c893-en.
[5] OECD (2025), OECD Compendium of Productivity Indicators 2025, OECD Publishing, Paris, https://doi.org/10.1787/b024d9e1-en.
[1] OECD (2025), OECD Employment Outlook 2025: Can We Get Through the Demographic Crunch?, OECD Publishing, Paris, https://doi.org/10.1787/194a947b-en.
[84] OECD (2025), The Global Forum on Productivity at 10: Past and Future Perspectives on Reviving Productivity Growth, OECD Publishing, Paris, https://doi.org/10.1787/ca7295d9-en.
[47] OECD (2025), What are the key trends in international student mobility?, OECD Publishing, Paris, https://doi.org/10.1787/2a423a76-en.
[50] OECD (2024), Education at a Glance 2024: OECD Indicators, OECD Publishing, Paris, https://doi.org/10.1787/c00cad36-en.
[67] OECD (2024), International Migration Outlook 2024, OECD Publishing, Paris, https://doi.org/10.1787/50b0353e-en.
[57] OECD (2024), “Migration and regional innovation in Australia”, OECD Regional Development Papers, No. 66, OECD Publishing, Paris, https://doi.org/10.1787/6d6ff472-en.
[85] OECD (2023), What is the best country for global talents in the OECD?, OECD Publishing, Paris, https://doi.org/10.1787/5186ab2d-en.
[48] OECD (2022), International Migration Outlook 2022, OECD Publishing, Paris, https://doi.org/10.1787/30fe16d2-en.
[81] OECD (2020), International Migration Outlook 2020, OECD Publishing, Paris, https://doi.org/10.1787/ec98f531-en.
[6] OECD/European Commission (2023), Indicators of Immigrant Integration 2023: Settling In, OECD Publishing, Paris, https://doi.org/10.1787/1d5020a6-en.
[23] Ohinata, A. and J. Van Ours (2013), “How Immigrant Children Affect the Academic Achievement of Native Dutch Children”, The Economic Journal, Vol. 123/570, pp. F308-F331, https://doi.org/10.1111/ECOJ.12052.
[30] Ortega, J. and G. Verdugo (2014), “The impact of immigration on the French labor market: Why so different?”, Labour Economics, Vol. 29, pp. 14-27, https://doi.org/10.1016/J.LABECO.2014.05.002.
[80] Ostrovsky, Y. and G. Picot (2020), “Innovation in immigrant-owned firms”, Small Business Economics 2020 57:4, Vol. 57/4, pp. 1857-1874, https://doi.org/10.1007/S11187-020-00376-2.
[19] Oswald-Egg, M. and M. Siegenthaler (2021), “Train drain? Access to skilled foreign workers and firms’ provision of training”, https://doi.org/10.3929/ETHZ-B-000501254.
[34] Ottaviano, G. and G. Peri (2008), “Immigration and National Wages: Clarifying the Theory and the Empirics”, https://doi.org/10.3386/W14188.
[79] Pekkala Kerr, S. and W. Kerr (2020), “Immigrant entrepreneurship in America: Evidence from the survey of business owners 2007 & 2012”, Research Policy, Vol. 49/3, p. 103918, https://doi.org/10.1016/j.respol.2019.103918.
[33] Peri, G. (2009), The Effect of Immigration on Productivity: Evidence from US States, National Bureau of Economic Research, Cambridge, MA, https://doi.org/10.3386/w15507.
[28] Peri, G., K. Shih and C. Sparber (2015), “STEM workers, H-1B visas, and productivity in US cities”, Journal of Labor Economics, Vol. 33/S1, pp. S225-S255, https://doi.org/10.1086/679061.
[27] Peri, G. and C. Sparber (2009), Task Specialization, Immigration, and Wages, American Economic Association, https://doi.org/10.2307/25760175.
[75] Picot, G. and Y. Ostrovsky (2021), Immigrant and Second-Generation Entrepreneurs in Canada: An Intergenerational Comparison of Business Ownership, https://papers.ssrn.com/abstract=4051039 (accessed on 25 April 2026).
[86] Piore, M. (1979), Birds of Passage, Cambridge University Press, https://doi.org/10.1017/cbo9780511572210.
[14] Røed, M. and P. Schøne (2016), “Impact of Immigration on Inhabitants’ Educational Investments”, Scandinavian Journal of Economics, Vol. 118/3, pp. 433-462, https://doi.org/10.1111/SJOE.12159.
[43] San, S. (2023), “Labor Supply and Directed Technical Change: Evidence from the Termination of the Bracero Program in 1964”, American Economic Journal: Applied Economics, Vol. 15/1, pp. 136-63, https://doi.org/10.1257/APP.20200664.
[73] Saxenian, A. (2002), “Silicon Valley’s New Immigrant High-Growth Entrepreneurs”, Economic Development Quarterly, Vol. 16/1, pp. 20-31, https://doi.org/10.1177/0891242402016001003.
[15] Schøne, P. and M. Strøm (2026), “Train or hire? Labour immigration and firms’ investments in the skilling of workers”, Labour Economics, Vol. 100, p. 102878, https://doi.org/10.1016/J.LABECO.2026.102878.
[12] Shih, K. (2017), “Do international students crowd-out or cross-subsidize Americans in higher education?”, Journal of Public Economics, Vol. 156, pp. 170-184, https://doi.org/10.1016/J.JPUBECO.2017.10.003.
[45] Solow, R. (1957), “Technical Change and the Aggregate Production Function”, The Review of Economics and Statistics, Vol. 39/3, p. 312, https://doi.org/10.2307/1926047.
[72] Startup Verband (2025), Migrant Founders Monitor.
Educational attainment of native and foreign-born per country, 2024
Note: OECD average is the average of the countries in the Table. Very-low education corresponds to ISCED 0/1, low to ISCED 2, medium to ISCED 3/4, high to ISCED 5, very-high to ISCED 6/8.
Source: Labour Force Surveys.
2024
Note: The sectors with no measure of labour productivity are real estate, public administration and defence, education, and health activities.
Source: Labour Force Surveys.
Share of immigrant workers in the sector, per country, 2024
Source: Labour Force Surveys. Data for Canada and New Zealand is for 2019 from administrative data.
Share of immigrants across the firm labour productivity distribution (quintiles), 2010, 2019
Note: For Norway, data is for 2010 and 2018.
Source: OECD (2025[82]), International Migration Outlook 2025, https://doi.org/10.1787/ae26c893-en, based on administrative linked employer employee data.
2014-2024
Note: See Box 5.6 for the decomposition.
Source: Productivity Data Base.
← 1. Assuming also constant entry and exit rates by age groups, and constant net migration rates.
← 2. Average across 28 OECD countries based on labour force survey data for 2024.
← 3. In PIAAC 2023, respondents were asked “Overall, which of the following statements best describes your skills in relation to what is required to do your job?” Those who answered “My skills are higher than required by my job” were classified as over-skilled.
← 4. In EU-EFTA OECD countries, only international students with a permit were taken into account. Students from other EU-EFTA countries were not part of the estimation.
← 5. The share of immigrants among inventors should be compared with the share of immigrants among the working age population, and not among the total population, given that immigrants are over-presented among the working age population (Manning, 2025[2]).
← 6. A first approximation of R&D activity of entrepreneurs is the sector of activity they operate in. The OECD Taxonomy of Economic Activities Based on R&D Intensity classifies sectors in five groups (from lowest to highest) according to their level of R&D intensity, which is estimated as the ratio of R&D to value added within the sector (Galindo-Rueda and Verger, 2016[87]).
← 7. Several country studies provide evidence of immigrant workplace segregation across the OECD, such as Åslund and Oskar (2008[88]) for Sweden, Carneiro, Fortuna and Varejão (2012[89]) for Portugal, Andersson et al. (2014[91]) for the United States, Glitz (2014[90]) for Germany, and Ansala, Åslund and Sarvimäki (2021[92]) for Finland.
← 8. This value is disproportionate as compared to the corresponding work force in national accounts (mostly real-estate agents and employees of notary offices are attached to the real-estate industry in national accounts).
← 9. The exercise in this section focusses on the static reallocation term, which tends to be the dominant part of reallocation for most countries. A similar exercise can be applied to the dynamic reallocation term.
← 10. Note that the shares in this paragraph and the one above are shares of employment relative to employment in sectors with a measure of labour productivity, not in total employment including sectors with no available measure of labour productivity.