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Performance among Chilean regions

In Chile, there are 53 TL3 regions (provincias), according to the OECD typology, 42 of which are defined as predominantly rural (see the first section of this chapter). Because GDP data are not available for Chilean TL3 regions, this section starts by examining the performance of the larger 15 TL2 regions where GDP data are available using density and the degree of rurality as a proxy to capture the performance of rural areas in Chile. It then examines the impact of regional performance in Chile on national growth.

In Chile, before the crisis, regions with a lower population density had faster GDP per capita growth on average than regions with a higher density. GDP per capita growth and population density over the period 1995-2007 before the global, financial crisis (1995-2007) appear to be negatively correlated, suggesting that regions with lower population density, all things equal, display on average a higher rate of growth in per capita GDP (Figure 1.30). When the years of the crisis are included in the analysis, the relationship breaks down due to a wider variability in the performance of Chilean TL2 regions.

Chilean TL2 regions with a higher degree of rurality record higher growth rates in GDP per capita over 1995-2011. The degree of rurality captures the percentage of the population living in rural communities (comunas in Chile). Rural communities are defined as municipalities (comunas) with less than 150 inhabitants per km2. This proxy can suffer from a measurement bias, in particular when urban centres are embedded in a larger municipality. In Chile, the degree of rurality ranges from 7% in Santiago to 100% in six regions (Antofagasta, Atacama, Aysen, Coquimbo, Magallanes and Chilean Antartica, and Arica and Parinacota). On average, the higher the degree of rurality, the higher the annual average growth rate over the period before the crisis (1995-2007), and the relationship holds when including the years of the crisis (1995-2011). The trends also reveal a positive trend between the degree of rurality and the variability in the performance of rural regions similarly to the general trends among OECD rural regions (Box 1.4).

Figure 1.30. GDP per capita growth and population density among Chilean TL2 regions, 1995-2011

Source: OECD (2013), OECD Regional Statistics (database), http://dx.doi.org/10.1787/region-data-en, (accessed on 15 December 2013).

Figure 1.31. GDP per capita growth and degree of rurality among Chilean TL2 regions, 1995-2011

Source: OECD (2013), OECD Regional Statistics (database), http://dx.doi.org/10.1787/region-data-en. (accessed on 15 December 2013).

Grouping Chilean TL2 regions into three groups according to their degree of rurality - the first with less than 33%. the second between 33% and 66% and the third with more than 66% - permits to analyse the growth trends in these three groups over several time periods. The pattern reveals a similar picture as present among different types of OECD TL3 regions revealing a higher rate of growth in rural regions. Indeed. the analysis shows higher growth among Chilean rural regions with a higher degree of rurality over the three periods considered.

Box 1.4. Local labour markets in Atlantic Canada

Recent research on functional regions in the four Atlantic provinces of Canada shows that average productivity, defined as GDP per worker, does not vary greatly across size of region, but that the variability of productivity among regions within each size class increases as the size of region declines (see figure below). Five size classes of region are defined, with the largest consisting of the bigger urban places in Atlantic Canada and the smallest being very small autonomous communities that have no commuting flows. The largest urban region is the Halifax functional economic region with a population of about 400 000 people, while the smallest regions have populations of under 600. The table below shows the distribution across the five categories.

The main conclusion from this work is that regions of any size, even very small regions, can have high levels of productivity. Because productivity is ultimately a characteristic of firms and not places, it is possible for firms in any size of region to be highly productive. Indeed, we might think that in small regions that lack any agglomeration benefits, the survival of a firm hinges on being highly productive so it can be competitive in distant export markets.

The five categories of functional region in Atlantic Canada

Number of regions

Average population

Range in size

Urban centres

11

132 541

412 000-101 620

Small cities and regional towns

29

22 237

39 805-9 225

First order rural

31

4 568

7 950-2 140

Second order rural

39

1 055

2 139-1 810

Third order rural

149

236

600-95

While firms in large regions take advantage of agglomeration effects that enhance productivity and the presence of a large home market, firms in small rural regions can benefit from the presence of site-specific resources. Both types of regions make useful contributions to national economies, but in the case of small regions, there are clearly locations that have serious economic problems. The crucial task for rural policy is to identify ways to improve productivity in these lagging regions.

Source: Freshwater, David and Alvin Simms (2013), “Factors affecting productivity in local labour markets in Atlantic Canada”, paper presented at the North American Regional Science Association Annual Meeting, Atlanta, Georgia, 13-16 November 2013.

Table 1.12. Growth performance of Chilean TL2 regions in GDP per capita according to degree of rurality

Chilean TL2 regions with a degree of rurality of:

Number of regions

Growth in GDP per capita

1995-2007

2007-11

1995-2011

0-33%

2

1.96%

2.46%

2.08%

33-66%

3

2.14%

2.13%

2.14%

66-100%

8

2.94%

2.90%

2.93%

Source: OECD (2013), OECD Regional Statistics (database), http://dx.doi.org/10.1787/region-data-en. (accessed on 15 December 2013).

 
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