Elias Giannakis (p), Adriana Bruggeman

The impact of the economic crisis has been highly asymmetric across the European countries and regions. A three-level logistic regression model, in which 1,342 European NUTS3 regions were nested in 276 NUTS2 regions and in 28 countries, was applied to investigate the determinants of economic resilience to recessionary shocks across European NUTS3 regions. Regional economic resilience was assessed based on the performance of the regional labour markets during the period from 2008 to 2013. We analysed the effect of pre-crisis sectoral structure (share of agriculture, manufacturing, construction and services in gross value added - GVA), human capital (old-age dependency ratio), economic development (gross domestic product per capita), labour market performance, migration, accessibility and urban-rural typology on regional economic resilience. The findings of our analysis highlight the heterogeneous ability of European NUTS3 regions to withstand recessionary impacts; the performance of regional labour markets ranged from 35% job losses to 27% job gains as a percent of employment. The predominantly rural regions of the European Union had the highest losses in employment (-4.5%), compared to intermediate regions (-3%) and predominantly urban regions (-1.9%). The results of the multilevel logistic regression model highlight the magnitude of the country effects on the performance of the NUTS3 regional employment during the recessionary period; 67% of the variance in the probability of NUTS3 regions’ resilience is attributed to between-country effects, while 6% is attributed to between-NUTS2 regions effects. The share of the agricultural sector in total GVA positively affects regional economic resilience; for each 1% increase in the share of the agricultural sector in GVA the odds of regions to withstand the impact of economic downturn increase by 8%. On the other side, regions specialised in the manufacturing sector are less likely to resist the impact of economic crisis; a 1% increase in the share of the manufacturing sector in GVA decreases the odds of regions to resist the impact of recession by 2.2%. The ageing population negatively affects regional resilience. The positive effect of net migration flows and accessibility was also identified. A second analysis, which explored regional resilience at national level, also identified the significance of the above factors but, additionally, showed that regions with better pre-crisis labour market performance were more resilient to economic crisis than lagging regions. The results of the study reveal the importance of designing and formulating targeted regional development policies at country level that can reduce disparities among regions.

Gloria Cicerone (p), Philip Mccann, Viktor Venhorst

We adapt the product-space methodological approach of Hausmann and Klinger to the case of Italian provinces and regions in order to examine the extent to which the network connectedness and centrality of a province’s exports is related to its economic performance. We construct a new Product Space Position (PSP) index which retains many of the Hausmann-Klinger features but which is also much better suited to handling regional and provincial data. We also compare PSP performance with other export composition indices. A better positioning in the export-network product space is indeed associated with a better local economic outcomes.

William James (p), Nik Lomax

In common with the rest of the world, the UK is experiencing major shifts in dietary patterns, as evidenced by changing patterns of food and drink expenditure. These changes may be the result of socio-demographic factors, trade policies, agricultural practices and food industry marketing. Understanding the local scale patterns of food and drink expenditure over time is critical for assessing market stability and for forecasting future trends. Examining expenditure and consumption patterns is also important for research into environmental impact, health, obesity and other ‘lifestyle diseases’. Despite its importance, there has been relatively little research into the spatial patterns of food and drink expenditure in the UK.

In this research paper, the technique of spatial microsimulation is used to estimate household expenditure of 103 categories of food and drink for each local authority district of the United Kingdom for the years 2001 – 2016. GIS analysis is used to combine these new expenditure datasets with socio-demographic, economic and commercial datasets.

The living costs and food survey provides detailed expenditure and demographic data for approximately 12,000 individuals each year. We use spatial microsimulation to allocate these individuals to local authority districts by combining the living cost and food survey dataset with geographically aggregated datasets. Specifically, constraint variables of age, gender, ethnicity, unemployment, student status and weekly hours worked are used to generate spatial microdata. Data processing and microsimulation is automated using the R programming language and the ipfp package.

The methodology is specifically developed to feed into a resilience model for the UK pig industry as part of the BBSRC funded PigSustain project, but the framework can be applied to any household expenditure as required.

Calvin Jones (p)

There has been extensive debate about the automation of productive and distribution activities on the level of human employment. This has been driven by the rapid introduction of robots, algorithmic problem solving and artificial intelligence across different sectors of the economy. While previous waves of technological development and machine incursion have, in aggregate tended to create as many or more jobs as they destroy, concerns have been raised about ability of machines to undertake a much wider range of manual and white collar jobs. Discussion of these developments, while wide ranging, has tended to give far less attention to the impact of automation processes (and their speed of implementation) across space. With the above in mind, we develop an approach to measuring the economic vulnerability and resilience of different places to automation based on an index of automation vulnerability for NUTS2 regions, initially in the UK. This incorporates factors identified in the literature as important drivers of (or that benefit from) automation processes, including occupational and industrial structures, prior economic resilience, levels of capital ownership, and prevalence of machine-complementary activities. We then examine our ranking of vulnerability to comment on whether this analysis suggests future rounds of automation will accelerate or mitigate existing processes of uneven development.