Globalisation in the OECD has come to regions more strongly than to nations. Technological change and the gradual reduction of the working age population are two main challenges influencing the economic performance of many regions. While some regions are able to adapt to these challenges and reap the benefits of globalisation, others remain stagnant and struggle to compete in the global arena.
Acronyms and Abbreviations
Differences across regions within countries are often greater than differences between countries, yet economists, policy makers and international organisations have paid less attention to regional development than national growth. Marked variations in economic performance among OECD regions reflects the regions’ great diversity in income levels, employment rates, mixes of high and low productivity activities, assets, comparative advantages, stages of development and public policies.
Overall Growth Trends
OECD regions vary more in their economic performance than do individual OECD countries (see Box 1.1 for a definition of regions). At the national level the main determinants of growth are macroeconomic factors, institutions and policies. The latter two factors have a strong regional dimension. OECD regions are very heterogeneous. Each is endowed with very different production capacities, comparative advantages, geographic characteristics, institutions, policies and assets. It is no surprise, therefore, that some regions are in a better position to reap the benefits of globalisation than others.
Analysing the Components of GDP Growth
In today’s integrated world, regions are required to compete beyond national borders to remain competitive. There has been a recent paradigm shift in regional policies from subsidy dependency to integrated polices with growth-enhancing objectives. This has forced regions to compete in global markets to attract foreign direct investment, human capital and private firms from all over the world. Some regions have been successful in this task while others have not. This chapter examines common characteristics of successful and unsuccessful OECD regions. It does so by breaking regional growth rates down into: i) national factors; ii) labour productivity (GDP per worker); iii) population; iv) employment rates (employment to the labour force); v) participation rates (labour force compared to working age population); and vi) activity rates (working age population to total population). We then compare these components among successful and unsuccessful OECD regions.
Assessing the Impact of the Main Determinants of Regional Growth
Chapter 1 described how, between 1995 and 2005, there was a significantly greater disparity in growth (three times larger) across OECD regions than across countries. Furthermore, we showed that growth does not occur uniformly within similar types of regions (i.e. predominately urban and rural regions). Not only was there a significant number of urban regions growing faster than rural regions, but also a significant number of rural regions out-performed urban regions in terms of GDP per capita growth rates over that decade.
Due to availability of data, average productivity at the regional level is defined by GDP per worker, where employment is measured at place of work. A rise in the regional share of GDP may be due to rapid growth – relative to the country’s growth rate – in average productivity. Average productivity, in turn, depends on technology, labour skills, production capital and infrastructure. All of these factors can be mobilised through regional infrastructure investment policies, through education and training to promote higher skill levels; and through research and innovation to create more efficient production technology. Therefore, the proportion of regional growth that is due to growth in average productivity tends to be based on regional assets.
Krugman’s 1991 model includes two a priori identical regions in endowment factors; two factors of production – agriculture with its constant-returns tied to the land, and manufacturers with increasing-returns (though a monopolistic Dixit-Stiglitz model) – that can be located in each region; and transportation costs for manufacturing goods. Workers are mobile across regions. The model finds that as transportation costs decrease and economies of large-scale production are present, a region with a relatively large non-rural population (or larger initial production) will be an attractive place to produce because of the large local market and because of the availability of goods and services produced there. This will attract more people increasing local demand and profits and attracting more firms. The forces of agglomeration depend on the level of trade cost and the proportion of mobile population in response to wage differentials. The external economies are pecuniary (not technological), arising from the desirability of selling to and buying from a region in which other producers are concentrated.
Simple accessibility indicators consider only intra-regional transport infrastructure expressed by measures of motorway length, number of railway stations or travel time to the nearest nodes of interregional networks. More complex accessibility indicators take into account the connectivity of transport networks by distinguishing between the network itself, i.e. its nodes and links, and the "activities" (such as work, shopping or leisure) or "opportunities" (such as markets or jobs) that can be reached by it. In general terms accessibility can be constructed using two separate functions, one representing the activities or opportunities to be reached and the other representing the effort, time, distance or cost needed to reach them...
i) Point or georeferenced data, where each point in space has a unique spatial identifier (e.g. longitude and latitude coordinates) and where the vector of observations is random and varies continuously over a fixed space. This is often referred to as geostatistical data. We thus have a continuous fixed space where the location of each data point is random. ii) Point pattern data, similar to georeferenced data but where the space is also random. Such datasets are, for example, used to count events and their clustering. iii) Areal (or lattice) data, where the space (of a regular or irregular shape) is fixed but partitioned into a finite number of areal units with well-defined boundaries, for example census (or other administrative) tracts.
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