We have said that decentralizing development policies is not necessarily a solution in itself; what is more important is the local institution‟s real capacity to govern. This conclusion is borne out by the history of local development in Italy from the 1950s to the 1990s. On the basis of the per capita value added growth rate in the Italian provinces between 1950 and 1996149, as well as of 18 other indicators that have influenced this main one150, the provinces can be classified into groups151 as in the map in Figure 29.
149
The data stops at 1996. However, since 1996 there has been a new cycle and therefore the post-war cycle could be said to come to an end at about that time. The new cycle is also due to new countries coming onto the international market, the gap created by new technologies, and reforms implemented in Italy (Barca, 2006).
150
See Arrighetti, Seravalli, 1999 and Arrighetti, Lasagni, Raimondi, 2000). The classification is more complex (by about 5 groups), but the essential message is clear in this simplified version.
151
These variables include, as well as those indicated: an index of completeness of the original production structure, productive sectors and services to firms (1951), obtained on the basis of Utton‟s diversification index, modified taking into account the effect of sectorial specialization, number of firms with more than 50 employees (1961); an index of infrastructure endowments (1980s); a factorial index for cultural and economic association building (1982); a factorial index of social capital endowments (1960s-1970s); indices of civic maturity, measured by literacy, political activism, participation in referendums (1970s); a factorial index of activities supporting the economy on behalf of the chambers of commerce, local banks, professional training, territorial administration (1950s); a factorial index of infrastructure endowment regarding cultural services and social services (1950s); distance as the crow flies from Milan; proportion of mountainous territory; state incentives for agriculture (1972-81); state incentives for industry (1972-81). Using regressions and semi-parametric methods (tree-like classifications), the most significant independent variables were in order: distance from Milan, number of firms with more than 50 employees in 1961, social infrastructure, local institutions, completeness of the industrial structure, professional training, and civic sense.
Fig. 29 - Geography of development per province in Italy (1950-1996) The darker grey tones indicate provinces in group 1; light grey group 2; white group 3.
Source: Our elaboration using data from ISL (Databank Department of Economics, University of Parma).
Let us examine some of the data for these three macro-areas.
Table 4 - Italian provinces by group, per capita value added growth rate, role of local institutions, and maximum growth of neighbouring provinces.
Growth Index of per capita income to start with Local institutions (professional training) Maximum growth in neighbouring provinces
Gruppo 1 (Industrial Triangle) 2.6% 0.41 0.23 3.46%
Gruppo 2 (Centre-North-East) 3.5% 0.26 5.27 3.82%
Gruppo 3 (South) 3.1% 0.20 -7.97 3.52%
Source: Our elaborations with ISL data.
Massa-Carrara Lucca
Pisa Pesaro E Urbino
Viterbo T erni
The provinces in the first group belong to the industrial triangle with the addition of Florence, Livorno and Rome. All together, these provinces had a higher per capita income in the 1950s, while the other two groups had a lower per capita income that was quite similar. In the fifty years that followed, there was greater growth in group 2 and lower growth in group 1. It is interesting to observe that this difference between growth and starting conditions corresponds to a marked difference – above all between groups 2 and 3, spread throughout the country but prevalently in the South – in the local institutions and professional training available to them. This variable was constructed by considering (in the 1950s) the proportion of the population of a similar age that attended schools, technical institutes or professional training centres, and by using the value of this variable that exceeds the share that in the regression is explained by the industrialisation rate at the time. It is therefore a proxy of the activism of local institutions in the field of professional training, which goes beyond what firms would have called for. Entrepreneurial spirit appears to be an important factor in the development of these provinces.
The last variable in the table (maximum growth of neighbouring provinces) was constructed by calculating the average per group of the maximum growth rate in neighbouring provinces for each of the provinces in the group. It therefore picks up the spill-over effect of development and also evidences the extent that agglomeration takes place. The highest values are to be found in the provinces of group 2 that are quite well distributed over the country but tend to be mostly in the central and north- eastern provinces.
The most relevant consideration for the analysis we have conducted in this chapter regards the partial but significant dissemination of group 2 towards the south. This shows that even in some backward southern regions there have been high rates of growth, such as in Avellino (3.9%), Potenza (3.6%), Matera (3.3%), and that this growth corresponds to positive values in the column that measures the activism of local institutions in professional training (Avellino +1.5; Potenza +1.8; Matera +6.2). By contrast, in other southern provinces with lower growth rates, this variable is negative, such as Naples (growth 2.2%, activism -5.9), Brindisi (growth 2.5%, activism -7.9) and Massa Carrara (growth 2.6%, activism – 8.7)
This data confirms that the hypothesis we have presented is plausible. Local institutions have a role in economic development but this role does not necessarily
depend on the existing economic conditions. It would seem, in fact, that we must consider their role as autonomous, and their policies as decidedly intentional.