Thomas Monk

Bio

Research

Abstract: This paper looks to understand the long-term dynamics of within-occupation task change in response to technological shocks. We develop a unique longitudinal dataset by harmonizing the full historical corpus of the Dictionary of Occupational Titles (DOT) records with modern O*NET data to construct an eighty-year panel of detailed, long form, task measures. This rich dataset enables us to capture within-occupation task evolution, revealing how jobs reinvent themselves in response to technological shocks that static occupational classifications overlook. We map these detailed occupational task descriptions to technological waves, creating a time-varying index of technology exposure at the task level. We exploit the natural variation in timing of these shocks to causally identify the extent to which a given technology drives changes in occupational task content and further explore how technology changes the wage structure both within- and between-occupations.

This project is kindly supported by grants funded by the LSE's Research Impact and Support Fund 2024 and the Economic and Social Research Council.

Abstract: This paper examines the long-run evolution of technology by uncovering “technological waves” from a century of patent data. We compile a comprehensive corpus of patent texts—focusing on abstracts and claims from 1930 until the present day to extract rich, domain-specific terminology. Employing unsupervised dynamic topic modeling techniques, we track the rise, peak, and decline of endogenously identified patent clusters, allowing the data to delineate the lifecycle of each technological wave rather than imposing a priori classifications. This approach captures established waves such as ICT, robotics, and artificial intelligence but also offers a framework for detecting nascent trends, such as Artificial Intelligence.

Working Papers

Abstract: Technological change events fundamentally change the type of tasks performed by human labour within occupations. We develop a predictive model, utilising machine learning techniques, and find that occupational skill intensity data can predict, to a high degree of accuracy, technological change event exposure, as measured by indices developed by Webb (2020). We link these predictions to skills data from a library of newspaper job vacancy adverts to understand how skill intensities have changed over time, and use this this to predict historical occupational technological exposure. Change in occupational technological exposure, as predicted by changing skill intensities, is highly associated with important labour market outcomes.

Abstract: Precautionary saving is a key driver of wealth inequality within models of the Bewley-Huggett-Aiyagari canon. However, models with savings rates calibrated solely to idiosyncratic income risk find it difficult to replicate the vast wealth inequality empirically observed in the United States. This paper looks at a potential source of increased precautionary savings - idiosyncratic medical expenses shocks. This paper: i. establishes an identification procedure for medical expenditure shocks across the entire life cycle, ii. finds that idiosyncratic shocks are very highly persistent, iii. establishes the extent to which these shocks contribute to wealth inequality through the effect on savings behaviour.

Abstract: Immigration in many high-income countries is often a fraught political issue. The share of migrants has been rising yet typically more people want lower than higher immigration, though views on the issue are often very polarised with strongly-held views on both sides. Given this, understanding public opinion on immigration is obviously important and there is a large and growing academic literature on the subject. These studies often investigate how attitudes vary with demographics among people in the same country at the same point in time. But it is also likely that attitudes respond to country-level macro variables like the level and mix of immigration and the general state of the economy. To investigate the influence of macro-level variables requires data on multiple countries and years so that there is enough variation in the variables of interest. This data is relatively rare. To address this, we introduce a novel high-dimensional dataset, harmonising data on the 28 EU countries from Eurobarometer surveys over the period 2002-2019, and investigate the influences of macro-level variables on attitudes towards immigration.