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01-09-2015

Agglomeration externalities and technical efficiency in Italian regions

Authors: Massimiliano Agovino, Agnese Rapposelli

Published in: Quality & Quantity | Issue 5/2015

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Abstract

The aim of the present work is to estimate an aggregate production function for the 20 Italian regions by emphasizing the role that agglomeration externalities (localization externalities and urbanization externalities) and spatial spillovers have in influencing the technical efficiency of the production process. To this purpose, we use the stochastic frontier approach. The results highlight the relevance and the positive impact that localization and urbanization externalities have in improving the efficiency level of the production process of Northern and Central Italy regions. Furthermore, spatial spillovers represent a source of development and growth both for Northern-Central regions and Southern regions. Finally, we show that the beta-convergence analysis of regional performance depends on the structural characteristics of each economy (Northern-Central Italy and Southern Italy), and these structural differences imply that Northern-Central and Southern macro-areas will have different steady states with regard to production performance.

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The more the production structure of the corresponding region reflects the national economy diversity, the more the Hefindal index increases (Cirilli and Veneri 2009).

Porter externalities (Porter 1990), which represent a cross between MAR and Jacobs thesis, are expressed by the following indicator: \(P_{ijt} ={\left( {{firm_{ijt}}\big /s_{ijt}} \right) }\bigg /{\left( {{\sum \limits _i {\sum \limits _j {firm_{ijt}}}}\big /{\sum \limits _i {\sum \limits _j{s_{ijt}}}}} \right) }\), where \(firm_{ijt}\) indicates the number of firms in sector \(j\) in region \(i\) at time \(t\). Due to the unavailability of the number of firms by sector, we are not able to get this indicator and therefore it will be omitted from our analysis.

TFP measures the output growth attributable to technical progress and to efficiency in the combination of production factors.

Since there are a multitude of regional-specific and time-specific factors that could influence the efficiency of regions, we control, also in the inefficiency function, for the time effect \((\eta _{t})\) and the regional dummies \((\eta _{i})\).

Northern and Central Italy (NC): Piemonte (PIE), Valle d’Aosta (VDA), Lombardia (LOM), Liguria (LIG),Trentino Alto Adige (TAA), Veneto (VEN), Friuli Venezia Giulia (FVG), Emilia Romagna (EMR), Toscana (TOS), Umbria (UMB), Marche (MAR), Lazio (LAZ). Southern Italy (S): Abruzzo (ABR), Molise (MOL), Puglia (PUG), Campania (CAM), Basilicata (BAS), Calabria (CAL), Sicilia (SIC), Sardegna (SAR).

The analysis of beta convergence is usually focused on per capita income (see Arbia 2005).

It is important to highlight that in this study we only deal with absolute convergence.

The observation period is restricted to the years 1970-93 because at the moment more recent data are not available.

Regio-IT1970-2004 and Regio(cap)-IT_70-94 (www.crenos.it).

Minerals and non-metallic mineral products; Metal products and machinery and transport equipment; Food, beverages, tobacco; Textiles and clothing, leather and footwear; Paper and printing products; Wood, rubber and other industrial products.

Trade, hotels and public establishment; Transport and communication services; Credit and insurance institutions; Other market services.

That is, estimates that do not take into account spatial dependence.

We implement the same analysis controlling for a country-specific deterministic trend; in particular, we define a \(td_{i}\) variable, where \(t\) is a polynomial in time and \(d_{i}\) is a regional dummy. The results are similar to those reported in Table 1; for reasons of space we do not show them, interested readers can request them to the authors.

Table 2

Results

	Variables	Basic model		Regional model
	Variables	Without spatial spillovers (1)	With spatial spillovers (2)	Without spatial spillovers (3)	With spatial spillovers (4)
Frontier function	Constant	0.1675* (1.7499)	0.4540*** (4.3512)	0.0253 (0.3315)	0.4497*** (6.1125)
	Capital	0.5329*** (35.2112)	0.4585*** (25.4018)	0.5539*** (46.9051)	0.4595*** (35.6903)
	Labor	0.5534*** (39.0471)	0.6454*** (34.1017)	0.5341*** (49.1599)	0.6387*** (47.2472)
	Time effect	yes	yes	yes	yes
	Regional dummies	yes	yes	yes	yes
Inefficiency function	costant	\(-\)0.1061** (\(-\)2.4448)	3.1456*** (11.7304)	0.2720*** (6.4980)	2.3795*** (10.3614)
	MAR	0.4660*** (13.0338)	0.3675*** (8.5651)	0.0784** (2.2987)	\(-\)0.0086 (\(-\)0.2122)
	J	0.0470** (2.9873)	0.0597*** (3.6942)	\(-\)0.0379** (\(-\)2.1069)	\(-\)0.1016*** (\(-\)4.7308)
	WE		\(-\)0.4606*** (\(-\)11.5209)		\(-\)0.2867*** (\(-\)8.3364)
	D*MAR			\(-\)0.4426*** (\(-\)6.8938)	\(-\)0.2713*** (\(-\)4.1569)
	D*J			\(-\)0.0429*** (\(-\)4.9071)	0.0706*** (2.7527)
	D*WE				\(-\)0.0261*** (\(-\)2.7709)
	Time effect	yes	yes	yes	yes
	Regional dummies	yes	yes	yes	yes
	\(\sigma ^{2}\)	0.0082*** (10.0389)	0.0057*** (10.1064)	0.0045*** (10.5232)	0.0031*** (17.5473)
	\(\gamma =\frac{\sigma _u^2}{\sigma _v^2 +\sigma _u^2}\)	0.8662*** (20.3906)	0.7202*** (9.1487)	0.7421*** (10.4378)	0.1252*** (8.6684)
	LR test of \(\sigma _u^2 =0\)	371.2250***	503.1071***	642.0008***	711.4107***
	Loglikelihood	541.7245	607.6656	677.1125	711.8174
	Mean efficiency	0.8553	0.8562	0.8706	0.8874

***, **, *: 1 %, 5 %, 10 %; (): t statistics

Table 3

Efficiency change compared to percentage change in externalities

Externalities	Italy (basic model)		NC (regional model)		S (regional model)
	(1)	(2)	(3)	(4)	(3)	(4)
MAR	\(-\)4 %	\(-\)3 %	3 %	2.4 %	\(-\)0.7 %	Not significant
J	\(-\)0.4 %	\(-\)0.5 %	0.7 %	0.3 %	\(-\)0.3 %	0.9 %
WE		4 %		2.8 %		2.5 %

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Title: Agglomeration externalities and technical efficiency in Italian regions
Authors: Massimiliano Agovino
Agnese Rapposelli
Publication date: 01-09-2015
Publisher: Springer Netherlands
Published in: Quality & Quantity / Issue 5/2015
Print ISSN: 0033-5177
Electronic ISSN: 1573-7845
DOI: https://doi.org/10.1007/s11135-014-0056-1

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