Economic Geography and Spatial Economics

The Economic Geography and Spatial Economics seminar series brings together scholars who work at the intersection between spatial and urban economics and the more institutionally-oriented fields of economic geography.

Irrespective of the specific methodological approach, research presented in the series is anchored in a shared culture and core research values, which place strong emphasis on theoretically-informed, policy-relevant and evidence-based research.

Some of the seminars are co-hosted by the Department of Geography and Environment and the Centre for Economic Performance.

The series is organised by Dr Gabriele Cristelli, Dr Duygu Buyukyazici, Dr Felipe Carozzi, Prof Riccardo Crescenzi and Prof Olmo Silva.

Unless otherwise noted, the seminars take place at 12.30-2pm in CKK 3.15.

If you wish to attend any of the seminars, please email geog.research@lse.ac.uk.

Upcoming seminars will be advertised here.

Autumn Term 2026

Thursday 22 October
Dr Felix S. K. Agyemang (University of Manchester)
'Modelling land values in African cities with open data and machine learning'

Understanding the geography of land values in cities is essential for urban science, revenue mobilization and public policy. Yet most African countries lack accurate and up-to-date information on urban land values and rents. An accessible, affordable and scalable method of land value estimation could enhance both research and urban development policy in African cities, supporting achievement of Sustainable Development Goals 9 and 11. Here, we develop and validate an approach to estimating unit rents per square meter at high spatial resolution using open data and machine learning in two large African cities: Accra, Ghana, and Nairobi, Kenya. Hedonic price models traditionally combine data on location (e.g. proximity to amenities and transportation infrastructure), neighbourhood characteristics (e.g. socioeconomic conditions) and intrinsic property characteristics (e.g. size of unit, age of building) to estimate rental prices. In the absence of reliable and accessible data on these features at the building or unit scale, we use OpenStreetMap (OSM) data and features extracted from freely available satellite imagery with a convolutional neural network to predict rents per square meter. For target data, we collected rental prices for over 8,600 properties across Accra and Nairobi. We use these data to train and validate random forest models for each city, successfully explaining approximately 60% of the variation in rental prices across samples from each city. This low-cost, scalable approach could be used to advance urban science and improve revenue mobilisation across the continent.

Thursday 29 October
Dr Frank Neffke (Complexity Science Hub)
'The Changing Global Division of Labor in Software: Emergence and Diffusion of New Programming Skills across IT Hubs'

With the rise of new industries, often new jobs emerge. Evolutionary Economic Geography and in particular Industry Life Cycle perspectives predict that these activities first emerge in a limited number of cities to then diffuse to other locations as job descriptions become more standardized. Here, we focus on a particularly important new industry: software development, an activity that is economically important, quickly changing, and has a pronounced spatial concentration in a small number of global IT hubs. We use an online database of over 60 million questions and answers about problems in software development that yields a longitudinal dataset of 237 software skills. By geo-locating 3 million posting users at regular intervals, we link these skills to cities worldwide. We find that, in spite of its digital nature, the software industry exhibits similar spatial regularities as previously observed in more traditional sectors. First, cities diversify into skills that are related to their existing ones. Second, new skills first emerge in cities with large and diversified software sectors, and later diffuse mostly unhindered by geographical distance to smaller cities specialized in closely related skills. We find suggestive but limited support for a windows of locational opportunity account: although even brand-new skills still emerge first in cities with strong prior specialization in related skills, concentrations of related activities impact less the emergence of new skills than the diffusion of existing ones.

Thursday 19 November
Dr Marta Santamaria (University of Warwick)

Thursday 26 November
Prof Diego Puga (CEMFI)

Thursday 3 December
Dr Allison Green (LSE Department of Economics)

Thursday 10 December
Dr Francesco Scotti (Politecnico di Milano)

Thursday 17 December
Prof Lee Fleming (University of California, Berkeley)
'Do Technology Clusters Distort Innovation?'

While the geographical agglomeration of inventors appears to increase their raw patent productivity, we show that it also reduces their exploration of new technologies. We interpret this result through a stylized Marshallian model in which larger clusters enhance individual incentives to exploit. If truly novel ideas have greater social value, however, then an increase in cluster size may give rise to a wedge between private and social incentives. Highly saturated fixed effects and Bartik regressions with U.S. patent data from 1980-2010 support the model’s predictions of increased exploitation and decreased exploration with increasing cluster size. Consistent with narrower search patterns, we also predict and find that, on average, patents originating from larger clusters exhibit lower creativity, fewer future citations, and reduced financial value.

Winter Term 2027

Thursday 28 January
Dr Dalila Ribaudo (Aston Business School)

Thursday 4 February
Dr Cem Ozguzel (OECD)

Thursday 11 February
Prof Francesco Drago (EUI)

Thursday 25 February
Dr Fabrizio Colella (USI Lugano, Switzerland)

Thursday 11 March
Prof Sebastian Siegloch (University of Cologne)

Thursday 18 March
Dr Simon Franklin (Queen Mary University of London)