Marco Modica (p), Aura Reggiani , Peter Nijkamp

Zsuzsanna Zsibók

The economic recession which followed the 2008 financial crisis has raised important issues on differences in the impact, especially from a spatial perspective, of the socio-economic shocks ‒ at both the regional and the community level. These differences may be due to the different levels of resilience and vulnerability, and may arise because of dissimilarities in the intrinsic characteristics of regions or communities (e.g. the pre-crisis economic characteristics of regions, ageing, household income, and so on). In the scientific literature, a great deal of attention has recently been paid to the concept of resilience (e.g. the capacity to bounce back or to resist a given shock) and vulnerability (e.g. the inherent characteristics that create the potential for harm), while less attention has been paid to the
relationship between these two notions. The objective of this paper is the exploration of the above-mentioned links, since a closer analysis of these interrelations might produce different outcomes. After a short review of the existing literature on resilience and vulnerability, this paper aims, first, to understand, by means of the resilience framework, how different cohorts react to labour market shocks or, in general, to recessionary periods. This key question might be explored also in terms of spatial differences. The main idea is then to control for the resilience in the German labour market, both in time and space. For this purpose, we study the German districts during the period of the Great recession to define a resilience index able to capture spatial and age differences. Furthermore, we provide a link with the vulnerability of these spatial areas to the economic shock. The results show the necessity of an integrated conceptual framework for resilience and vulnerability, also for empirical and policy purposes.

Timothy Slaper (p), Ping Zheng

Marco Modica

see extended abstract

Zsuzsanna Zsibók (p)

Timothy Slaper

Economic development at the national level is often accompanied by territorial divergence at sub-national levels, which phenomenon became even more noticeable during and after the global financial and economic crisis. Current development policy have to face the well-known equity versus efficiency challenge to sustain the prosperity of the most advanced regions and, at the same time, to tackle the divergence between advanced and lagging regions. For this reason, a comprehensive knowledge is necessary about the long-run, interdependent dynamics of national and regional level growth. Our research contributes to this knowledge through studying alternative growth paths for Hungary in a comparative manner. We focus on the methods that apply a regionalisation procedure in order to downscale national level economic forecasts to the regional level. The practice of “regionalisation” or regional downscaling intends to translate information available at a coarse geographical resolution (e.g. the national level) to a finer geographical scale (e.g. the regional level).
There are two basic approaches to producing regional-level economic forecasts. Bottom-up models are full-fledged regional models, specified in a standard way with well-established interregional feedback mechanisms. They are also called generative models, since the national growth rate of the economy is the weighted sum of the regional growth rates, that is, the causal relationship runs from the regional level to the national level. The major drawback to this approach is the great data requirement (subject to availability problems), and the size of the model when working with a large number of regions and sectors. In this paper, I intend to focus on the other type of methods, which is called top-down or distributive approach. These “satellite” models forecast regional growth (and employment etc.) given the forecast of the national variables obtained from macro models. In other words, these methods allocate regional growth across the regions in a competitive manner according to a certain, e.g. statistical, rule.
The aim of my research is to compare the results of different regional-level forecasting methods regarding the period between 2020 and 2050 at the NUTS 3 level in Hungary with respect to the gross domestic product (GDP). The existing, state-of-the-art, bottom-up approaches are multi-sectoral, multi-regional structural models with quite large resource requirements. I intend to present the results of some more simplistic methods with a top-down approach, keeping in mind that there may be a trade-off between the resource requirement and the reliability of the model results.