Tapping into talent: Coupling education and innovation policies for economic growth

Ufuk Akcigit, Jeremy Pearce, Marta Prato 10 October 2020



On 21 July 2020, EU leaders approved the new EU budget, together with a recovery plan from COVID-19. This includes an ambitious research and innovation programme of over €100 billion, Horizon Europe, to be launched in 2021. The approval of these funds has sparked a lively debate about how to best utilise them to recover from the damage caused by COVID-19 and to spur economic growth. The European Commission emphasises that investments in research and innovation are key for a sustainable and inclusive recovery (European Commission 2020).  But what are the most effective policies that turn taxpayers’ money into innovation and productivity growth?

The economic literature on endogenous growth theory has emphasised the important role subsidies play in private research and development (R&D) by encouraging the creation of new products and technologies. Yet, the empirical literature has not been supportive of a strong link between R&D subsidies and economic growth. Goolsbee (1998) and Romer (2000) suggest that R&D subsidies to the private sector stimulate the demand for scientists without addressing the limited capacity of the educational system to train more scientists and meet such an increase in demand. The result, they argue, is that R&D subsidies end up boosting wages of existing scientists rather than stimulating innovative activity. This observation calls for policy analysis regarding how individuals choose a career as a scientist or engineer. The development of human capital through education is the key component of the supply side of innovation. However, the economic literature so far has dedicated little attention to innovation policy aimed at the supply of human capital. 

Education, R&D policies, and economic growth: A framework

Our recent work (Akcigit et al. 2020) puts the development of human capital at the centre of a comprehensive framework to analyse the innovation toolkit of a policymaker. The framework incorporates the interaction between education and R&D policies for economic growth and connects it to Danish data to study real-world policies. This work can help inform policymakers about the role of education and R&D policies and their interaction.

We follow a ‘micro-to-macro’ approach, using micro level data to inform the key mechanisms of a macro-model of the economy. The theoretical framework is informed by micro-data from Denmark, with attention directed to higher education and PhDs in particular, due to their prevalence in innovation. We document the following ten facts about the relationship between talent, education, and innovation:

Fact 1: Individuals with a higher IQ are more likely to obtain a PhD.

Fact 2: Individuals with higher parental income are more likely to obtain a PhD.

Fact 3: Individuals' IQ is correlated with parental income, but not perfectly.

Fact 4: Only a fraction of people with a high IQ and high parental income obtain a PhD.

Fact 5: PhDs are 20 times more likely to become inventors compared with the average person in society.

Fact 6: Conditional on education level, higher IQ people are more likely to innovate.

Fact 7: Inventors work in teams and team size is heterogeneous.

Fact 8: The probability of innovating as a team leader over an inventor's lifecycle follows an inverted-U shape.

Fact 9: An increase in the number of PhD slots is associated with a decline in the average IQ of PhDs.

Fact 10: Denmark’s inventive talent is mostly homegrown; foreign inventors account for less than 10% of the population.

These facts provide important observations on how individuals sort into careers in science and motivate key features of the model. The facts are consistent with previous work: in 19th and 20th century America, individuals from wealthier families and highly educated individuals were more likely to be inventors (Akcigit et al. 2017). Aghion et al. (2017) find in Finland similar relationships between IQ, parental background and innovation. In our work, we highlight the role of higher education and build these results into a macro-model to understand the role of policy.

In the model, individuals are born with differences in talent, career preferences, and financial resources (Facts 1-4). They decide, depending on their talent and preferences, whether they want to pursue higher education to become a researcher and innovate. Figure 1 documents the relationship between IQ and PhD enrolment, as individuals with higher IQs are much more likely to obtain a PhD (Fact 1). More talented and educated individuals are more likely to innovate (Facts 5-6). Talent, education and innovation are thus tightly connected. Thus, allocating more talented individuals to higher education and work in research creates more innovation in the economy. Yet, some talented individuals might not like working in the research sector, or they might not have the financial resources to afford the educational costs and time away from work from college through PhD. 

Figure 1 The relationship between IQ and PhD enrollment

Note: We group individuals into IQ percentiles (x-axis) and plot the fraction of individuals in each percentile who enroll in a PhD (y-axis).

Once in the research sector, individuals join a research team led by a team leader to produce ideas, where the innovations depend on the team leader’s talent (Fact 6) and the size of the team (Fact 7). Individuals continue to learn on the job with the possibility of becoming a team leader (Fact 8). An important factor in determining the supply of human capital is that higher education institutions have a fixed amount of PhD slots and will give those slots to the most talented individuals who are not credit-constrained and want to obtain a PhD (Fact 9). A fundamental driving force in the model is that talent is local and scarce (Fact 10).

We use the model to study three types of policies: (i) R&D subsidies, (ii) subsidies to the cost of innovation, and (iii) an increase in PhD slots. In the model, an R&D subsidy boosts the wages of researchers, consistent with the observations of Goolsbee (1998) and Romer (2000). The subsidy works through two channels. First, existing scientists will buy some lab equipment to produce more innovation to take advantage of the higher returns. Second, some talented individuals who would otherwise dislike a career in research might be attracted to the research sector due to higher returns. As opposed to R&D subsidies, educational subsidies allow talented individuals who lack financial resources to enrol in higher education. Finally, an increase in PhD slots can expand the pool of scientists and engineers in the economy, but it will fail to attract those talented individuals that dislike the research sector or lack financial means. 

The model speaks to specific policy analysis by connecting to detailed data. In addition to comprehensive individual-level data, Denmark also served as an arena for R&D and education policies. Starting in 2002, the Danish government required universities to double PhD enrolment in the span of 10 years, with the goal of expanding the pool of scientists and inventors. As highlighted in Figure 2, the increase in PhD enrolment was associated with a decline in average IQ of enrolled PhDs, a fact that our model replicates. This empirical finding illustrates constraints the government faces when attempting to expand the pool of researchers and inventors due to the scarcity of talent, which is a key determinant of innovation outcomes. It also stresses the importance of tapping into other sources of talent in the economy – i.e. attracting into research those talented individuals who are financially constrained or work in other sectors.

Figure 2 PhD enrollment and average IQ pf PhD enrollees in Denmark 

Source: Denmark's Statistics Office.

Policy implications

The quantitative framework delivers four main policy findings. 

  • First, traditional R&D subsidies are less effective than policies for education that focus on the supply of human capital. 
  • Second, we show that when governments have large budgets directed towards innovation and economic growth, they should use education and R&D policies together to expand the supply of scientists and maximize growth. 
  • Third, a society with higher wealth inequality should tilt policy more towards education subsidies rather than R&D subsidies to help those who are financially constrained. 
  • Fourth, these policies all take time and may show their full effects along different time horizons. Figure 3 illustrates the impact of three distinct policies: R&D subsidies, education subsidies, and increases in the supply of educational slots. R&D policies have the most immediate effect, but after 5 to 10 years educational subsidies become the most effective.

Figure 3 Model simulation of the dynamics of the economy upon the introduction of (i) R&D subsidy (solid blue line), (ii) education subsidy (dotted light-blue line), and (iii) expansion in PhD slots (dashed grey line)

Both education and R&D policies play essential roles in innovation and economic growth. Policymakers interested in spurring innovation will likely want to use a combination of policies. Much is still to be explored to understand in detail the interaction of policies and economic growth. Innovation and economic growth are essential to human wellbeing and health, economic recovery, and a dynamic society. The value of having the right policies for innovation and human inventive development is crucial. Putting individuals at the centre of policy analysis provides new insights how inequality, innovation, and individual development shape society. We expect a textured analysis of potential policies for economic growth to continue.


Aghion, P, U Akcigit, A Hyytinen and O Toivanen (2017), “The Social Origins and IQ of Inventors” VoxEU.org, 23 December.  

Akcigit, U, J Grigsby and T Nicholas (2017), “Innovation and Inventors during the Rise of American Ingenuity”, VoxEU.org, 2 February.

Akcigit, U, J G Pearce and M Prato (2020), “Tapping into Talent: Coupling Education and Innovation Policies for Economic Growth”, NBER Working Paper No. 27862.

European Commission (2020), “The Role of Research and Innovation in Europe’s Recovery”.

Goolsbee, A (1998), “Does Government R&D Policy Mainly Benefit Scientists and Engineers?”, American Economic Review, Papers and Proceedings 88(2): 298–302.

Romer, P M (2000), “Should the Government Subsidize Supply or Demand in the Market for Scientists and Engineers?”, Innovation Policy and the Economy 1: 221–252.



Topics:  Education Productivity and Innovation

Tags:  innovation, inventors, R&D, R&D subsidies

Arnold C. Harberger Professor of Economics, University of Chicago

Postdoctoral Fellow, University of Chicago

PhD candidate, Department of Economics, University of Chicago


CEPR Policy Research