The Analysis of Automation by Means of Automation: A Machine Learning Approach to Job Tasks.

Computers and automation have had a massive impact on working life since the 1980s and the latest technological changes strongly suggest that this trend will continue. Inventions such as the self-driving car, general improvements in 'drone'-based robotics, and the emergence of 'expert systems' for medical diagnosis create the prospect that automation will soon be having an impact on jobs that were previously resilient to being replaced by machines. In turn, the nature of earnings and job demand could change in unexpected ways that could cause upheaval for our welfare system and general social cohesion. In particular, even more low skill jobs could be replaced by machines and jobs in the middle and upper end of the wage distribution could face new threats. The process of offshoring could also be accelerated as new technology reduces the need for physical co-location and face-to-face interactions in services.

This project provides a new analysis of automation that is itself based on automatic, computer driven classification techniques. Since 1938, US job experts have been compiling and regularly updating rich, detailed task descriptions across approximately 12,000 jobs. These job descriptions and their associated numerical 'characteristics scores' have been the basis of a new 'job tasks' view of the labour market. This job tasks view has split jobs into bundles of tasks best summarised by firstly 'routine' tasks (ones that can be codified and programmed into a machine of some type) and secondly 'nonroutine' tasks (those tasks that require advanced manual or analytical skills that cannot yet be programmed into a computer). In turn, this task view has had much success in explaining the pattern of jobs in the labour market.

The analysis that this project will put forward will be based directly on the text job descriptions in these expert-written job databases. Machine learning methods associated with the 'Big Data' approach will be used to decompose the job description text to discover the underlying, latent structure of the tasks that make up our occupations. For example, these methods will allow us to pick up the diffusion or spread of phrases and concepts associated with computers across occupations. These statistical methods are based on the principle of automated 'pattern recognition' of clustered and repeated text, so in an ironic twist automated methods will themselves be used to study the structure of automation in the economy. The main contribution of this approach is to provide measures of what has been changing within occupations. Recent analysis of the labour market has shown that much change has been occurring at the 'within group' level, that is, dispersion inside specific educational, industry or occupational categories. The methods used in this project will measure in detail what has been changing within occupations by studying the pattern of tasks as suggested by the text job descriptions.

The project also features a novel dissemination plan. The usual tools of academic publication and presentation will be deployed along with a comprehensive media campaign to raise public awareness of the research. The Principal Investigator has a strong track record in both these areas of academic publication and public engagement. This will be enhanced by new publicly available data that will be supported with a data/policy blog and online code repository. The aim will be to bring the 'open source' code-sharing culture into the social sciences where these practices have not yet taken root.

The project will have a diverse set of beneficiaries both inside and outside academia. In the following, I list these beneficiaries and describe in detail how they will benefit.
1. ACADEMIC BENEFICIARIES
A special advantage of this project is that it has academic benefits that accrue both in terms of the content of the research (that is, the focus on the economics of job tasks) and in terms of methodology (namely, the high-profile implementation of 'Big Data' machine learning techniques as an example for applications on other social and economic topics).
(i) Content -Based Benefits.
The project will provide new knowledge about the changing structure of job tasks and the role of technology in the modern labour market. This has been a major front for labour economics research since the early 2000s when the key study by Autor, Levy and Murnane (2003) was first published. A literature on tasks represented by over 2,000 citations has emerged since this work and this proposal outlines a research design that will make major advances in the measurement of tasks. These text-derived measures of tasks will be released as publicly available data for researchers to use in diverse applications related to inequality, the future of low-skill work and the nature of off-shoring to name three prominent areas.
(ii) Methodology
The project will be a major, high profile and accessible implementation of 'Big Data' machine learning techniques in economics. Importantly, the PI has a comprehensive dissemination plan outlined that will maximize the accessibility of the work across economics and other social sciences. Code will be provided and promoted in a specialist policy and data-oriented blog, a tool which is popular in the heavily open source data science community but less common in the social sciences. Furthermore, this will be supported long with an extensive programme of academic presentations across fields and disciplines.
(iii) Interdisciplinary Engagement
The proposal shows a unique and well-considered plan for engagement with the core 'data science' disciplines of statistics and computer science. The PI has undertaken serious 'proof-of-concept' programming work to study the feasibility of the project and make himself aware of the technical demands that will be faced in this project. This has been achieved through personal research and through an emerging network that the PI has been building at Warwick with the relevant data science departments and centres. This FRL will allow the PI to capitalize on this work and build an important bridge between the disciplines.

2. PUBLIC BENEFICIARIES
The main public beneficiaries will be those policy agencies whose concerns cover the performance of the labour market. The following represents three agencies where the PI has had personal experience as an invited research presenter.
- Department of Work and Pensions (DWP): This department is responsible for welfare policies that are closely related to the economic prospects for workers in the low-skill labour market. As automation has developed, this has displaced the jobs of many low-skill workers. Changes in the scope of automation are set to increase this challenge and the research in this project will assist in identifying these trends.
- Department for Business, Innovation and Skills (BIS): This department deals in part with business-related trade and their interaction with skill demand in the economy. One major current policy challenge is offshoring, that is, the transfer of work to other non-UK locations. Again, progress in automation and communication are set to increase the future scope for off-shoring, with fewer jobs being constrained by the need for local geographical interactions.
- OECD: As the developed world's primary economic think tank the OECD is also concerned with off-shoring and low-skill work. The PI has an extensive network at the OECD and this research will be of detailed interest to them across policy areas.