Traditional economics views growth as an aggregate phenomena and leaves the major driver of growth - the advancement of human knowledge - largely unexplained. INET Oxford, in collaboration with a number of scholars around the world, is leading several projects that are attempting to develop a bottom-up theory of growth that is empirically grounded and has a truly endogenous view of innovation. At the core of the work is the idea that the economy is a constantly evolving network of technologies that make possible networks of productive capabilities, that in turn enable the creation of products and services. It is the evolution of these networks, the search for new combinations of technologies, capabilities, and products in enormous combinatorial spaces of possibility, that drives economic growth. Our research is also examining the implications of this "bottom-up" view for policies for growth and innovation, as well as implications for inequality and sustainability.
INET Oxford's Research Themes
Economic Growth and Innovation
Employment, Equity and Growth
We show that while rich countries have faced common challenges in an era of globalization and rapid technological change, some sets of institutions and policy responses proved more capable than others in still underpinning widely-share improvements in incomes and living standards.
Estimating parameters and initial conditions for Agent Based Models
ABMs leverage the flexibility offered by computational simulations in order to avoid a number of problematic assumptions required by traditional modelling approaches. .
Forecasting technological progress
In this project we use networks and time series analysis to analyse performance curves and the large historical records of patenting activity to map the evolution of technological ecosystems.
How Ecological Properties Of Economic Production Networks Amplify Growth
In this project we developed a theory for the amplification of technological improvement by the production network structure of the economy.
Recent advances in the availability of cheap computational power and real-world data present researchers with an unprecedented opportunity to construct and test mathematical models of human behaviour.
Our World in Data
On this web publication we are presenting the long-term research on how and why living conditions around the world are changing.
Understanding macroeconomics from the bottom-up
Our models are based on fine-grained data sets at the level of firms, products and industries, with the goal of providing a detailed understanding of the rich and heterogeneous behaviour underlying business cycles, inflation and interest rates, innovation, and long-run growth.
No. 2023-14 - Intellectual Property Rights, Climate Technology Transfer and Innovation in Developing Countries
07 Aug 23
A longer study about intellectual property rights and their role for climate technologies in develop...
No. 2023-08 - Firm-level production networks: what do we (really) know?
16 May 23
Are standard production network properties similar across all available datasets, and if not, why?
Three growth-friendly reforms for the UK’s broken planning system
17 Nov 22
Land value capture can fund new development and ease local fears about lack of infrastructure for ne...
The Thatcher Legacy: Lessons for the future of the UK economy
17 Nov 22
In this essay, part of our Navigating Economic Change series, economists John Muellbauer and David S...
Empirically grounded technology forecasts and the energy transition
13 Sep 22
Future energy system costs are estimated for three different scenarios. A rapid green energy transit...
Real Estate Booms and Busts: Implications for Monetary and Macroprudential Policy in Europe
11 Jul 22
This paper examines the empirical evidence on the complex channels of transmission of monetary polic...
Can Stimulating Demand Drive Costs Down? World War II as a Natural Experiment
11 Jul 22
U.S. military production during World War II increased at an impressive rate and led to large declin...
No. 2022-11 - Human Wellbeing and Machine Learning
21 Jun 22
We here use data from Germany, the UK, and the US to assess the potential of Machine Learning (ML) t...
No. 2022-08 - Why is productivity slowing down?
29 May 22
We review recent research on the slowdown of labor productivity and examine the contribution of diff...
No. 2019-14 - Measuring productivity dispersion: a parametric approach using the Lévy alpha-stable distribution
28 Apr 22
We examine in detail the distribution of labor productivity levels and growth, and observe that they...
How production networks amplify economic growth
04 Jan 22
Technological improvement is the most important cause of long-term economic growth. We study the eff...
No. 2021-01 - Empirically grounded technology forecasts and the energy transition
14 Sep 21
Rapidly decarbonising the global energy system is critical for addressing climate change, but concer...
No. 2021-12 - Why is productivity slowing down?
09 May 21
We review recent research on the slowdown of labor productivity and examine the con- tribution of di...
The rise of science in low-carbon energy technologies
21 Jan 21
Designing efficient allocation of R&D budgets requires a better understanding of how Low-Carbon Ener...
Occupational mobility and automation: a data-driven network model
21 Jan 21
In this article, we develop a data-driven model to analyse how workers move through an empirically d...
No. 2020-17 - The Covid-19 Crisis Response Helps the Poor: The Distributional and Budgetary Consequences of the UK lock-down
02 Jun 20
We nowcast the economic effects of the Covid-19 pandemic and related lock-down measures in the UK an...
No. 2020-02 - Can stimulating demand drive costs down? World War II as a natural experiment
01 Jun 20
Our results indicate that decreases in cost can be attributed roughly equally to the growth of exper...
We need an ‘emergency survey’ based on random samples. Urgently.
01 Apr 20
During times of massive socio-economic change, it should be possible to quickly launch an emergency ...
No.2020-04 - Technological interdependencies predict innovation dynamics
02 Mar 20
We propose a simple model where the innovation rate of a technological domain depends on the innovat...
Learning From Automation Anxiety of the Past
12 Nov 19
Much like during the industrial revolution, today’s automation anxiety is entirely justified. But hi...
The Technology Trap: Capital, Labor, and Power in the Age of Automation
18 Jun 19
How the history of technological revolutions can help us better understand economic and political po...
No. 2019-09 - Has the Middle Secured Its Share of Growth or Been Squeezed?
17 Jun 19
In striking contrast to the notion that democracy is under threat because ‘the middle’ has been ‘squ...
No. 2019-07 - Inequality and Real Income Growth for Middle and Low-income Households Across Rich Countries in Recent Decades
06 Jun 19
This paper places what has happened to income inequality in rich countries over recent decades along...
No. 2019-08 - Firm Heterogeneity and the Aggregate Labour Share
04 Jun 19
Using a static model of firm behaviour with imperfect competition on the product and labour markets,...
Giving up on Growth? On the Validity of Post-Growth Arguments (in German)
28 May 19
No. 2019-04 - What Happened to the 'Great American Jobs Machine'?
18 Apr 19
In the 1980s and 1990s the US employment rate increased steadily. Since then it has declined both in...
Wright meets Markowitz: How standard portfolio theory changes when assets are technologies following experience curves
01 Apr 19
We apply portfolio theory to technologies following experience curves.
Dr Carl Benedikt Frey: Saving labour: automation and its enemies
28 Feb 19
In this talk Dr Carl Benedikt Frey will discuss the societal consequences of the accelerating pace o...
Long-run dynamics of the U.S. patent classification system
04 Jan 19
We argue that classification system changes and patent reclassification are common and reveal intere...
2. Background and data 3. Empirical strategy and results 4. Conclusions Appendix A. Supplementary materials Research Data References Figures (4) Fig. 1. Uber’s rollout in the United States Fig. 2. The spatial diffusion of Uber in the United States Fig. 3
01 Nov 18
A frequent belief is that the rise of so-called “gig work” has led to the displacement of workers in...
Prof Chris Magee: How useful and reliable is a simplified perspective on Technological Change?
24 Oct 18
Chris Magee will share a perspective that can help all of us better understand the complex pattern o...