The spatial econometrics of the coronavirus pandemic

In this paper we use spatial econometric specifications to model daily infection rates of COVID-19 across countries. Using recent advances in Bayesian spatial econometric techniques, we particularly focus on the time-dependent importance of alternative spatial linkage structures such as the number of flight connections, relationships in international trade, and common borders. The flexible model setup allows to study the intensity and type of spatial spillover structures over time. Our results show notable spatial spillover mechanisms in the early stages of the virus with international flight linkages as the main transmission channel. In later stages, our model shows a sharp drop in the intensity spatial spillovers due to national travel bans, indicating that travel restrictions led to a reduction of cross-country spillovers

Modelling European regional FDI flows using a Bayesian spatial Poisson interaction model

This paper presents an empirical study of spatial origin and destination effects of European regional FDI dyads. Recent regional studies primarily focus on locational determinants, but ignore bilateral origin- and intervening factors, as well as associated spatial dependence. This paper fills this gap by using observations on interregional FDI flows within a spatially augmented Poisson interaction model. We explicitly distinguish FDI activities between three different stages of the value chain. Our results provide important insights on drivers of regional FDI activities, both from origin and destination perspectives. We moreover show that spatial dependence plays a key role in both dimensions

A spatial multinomial logit model for analysing urban expansion

The paper proposes a Bayesian multinomial logit model to analyse spatial patterns of urban expansion. The specification assumes that the log-odds of each class follow a spatial autoregressive process. Using recent advances in Bayesian computing, our model allows for a computationally efficient treatment of the spatial multinomial logit model. This allows us to assess spillovers between regions and across land-use classes. In a series of Monte Carlo studies, we benchmark our model against other competing specifications. The paper also showcases the performance of the proposed specification using European regional data. Our results indicate that spatial dependence plays a key role in the land-sealing process of cropland and grassland. Moreover, we uncover land-sealing spillovers across multiple classes of arable land

Herding resilience: Surveys and Bayesian spatial models for Africa’s livestock

This paper proposes a novel method for mapping livestock distribution in Africa using the Living Standards Measurement Study-Integrated Surveys on Agriculture (LSMS-ISA). Using a Bayesian spatial statistical model, we produce maps of livestock distribution at a resolution of 1 km2. Our case study in Malawi, covering 2010 and 2019, demonstrates the effectiveness of the method in mapping five livestock species. We compare our results with the Gridded Livestock of the World (GLW) database and use the maps to assess livestock vulnerability to climate-related flood risks under different climate scenarios. This approach provides a rapid, data-rich tool for policy makers to assess climate risks to livestock, which is critical for sustainable agricultural development and environmental management in data-poor regions

A joint spatial econometric model for regional FDI and output growth

This paper studies the joint dynamics of foreign direct investments (FDI) and output growth in European regions by using spatially augmented systems of equations modeling framework that incorporates third-region and spillover effects. The joint framework is used to study the dynamic impacts of regional human capital endowments, which demonstrates the importance of explicitly accounting for an endogenous relationship. The relationship is highlighted in a stylized projection exercise, where the long-run impacts are pronounced in Eastern Europe and capital cities. Overall, ignoring the relationship of regional economic performance and FDI distorts the implied transmission mechanism, which is of utmost importance for policy makers

LAMASUS NUTS-level economic data 1980-2021

This dataset comprises spatial and temporal economic data compiled from the Annual Regional Database of the European Commission (ARDECO) and education attainment from Eurostat, covering the period from 1980 to 2021(2024). The dataset consists of three files, each corresponding to a different level of NUTS coding (NUTS 1-3) according to the 2016 NUTS specification. For each file, the following columns are included: Identifier: NUTS Code: The unique identifier for the NUTS (2016) region Year: The year of the data point Variables:       3. - 8. Hours Worked by NACE sector in 1000 hours (empHour_*)      9. - 15. Employment by NACE sector in 1000 jobs (emp_*)      16. Total employment in 1000 jobs (empl)      17. GDP at constant prices ref. 2015 in mio EUR (gdp)      18. - 23. GVA by NACE sector at constant prices ref. 2015 in mio EUR (gva_*)      24. Total Labour Force in 1000 jobs (labour)      25. Total Population (Regional Accounts) in persons (pop)      26. - 31. Compensation of Employees by NACE sector at constant prices ref. 2015 in mio EUR (wage_*)      32. Share of low education workers in per cent (loweduc) [not available for NUTS3]      33. Share of high education workers in per cent (terteduc) [not available for NUTS3] The temporal dimension is yearly, ranging from 1980 to 2021(2024). The spatial dimension is identified by NUTS codes (2016), with granularity ranging from level 1 to level 3. This dataset has been created as part of LAMASUS Project under the scope of Deliverable 3.2 titled "Database on EU policies and payments for agriculture, forest, and other LUM related drivers ". The data is directly linked to the work described on pages 45-47 belonging to section 3.3 Sectoral Income and Employment. The full text of the deliverable can be accessed via: https://www.lamasus.eu/wp-content/uploads/LAMASUS_D3.2_policy-and-payment-database.pdf. Please note that this dataset is intended for research and analysis in the fields of climatology, environmental science, and related disciplines. Users are encouraged to cite this dataset appropriately if utilized in academic or scientific publications

A Bayesian spatial autoregressive logit model with an empirical application to European regional FDI flows

In this paper, we propose a Bayesian estimation approach for a spatial autoregressive logit specification. Our approach relies on recent advances in Bayesian computing, making use of Pólya–Gamma sampling for Bayesian Markov-chain Monte Carlo algorithms. The proposed specification assumes that the involved log-odds of the model follow a spatial autoregressive process. Pólya–Gamma sampling involves a computationally efficient treatment of the spatial autoregressive logit model, allowing for extensions to the existing baseline specification in an elegant and straightforward way. In a Monte Carlo study we demonstrate that our proposed approach markedly outperforms alternative specifications in terms of parameter precision. The paper moreover illustrates the performance of the proposed spatial autoregressive logit specification using pan-European regional data on foreign direct investments. Our empirical results highlight the importance of accounting for spatial dependence when modelling European regional FDI flows