Thierry Dousset
email: thierry.dousset@fr.thalesgroup.com

Christian Renard

Hubert Diez
email: hubert.diez@cnes.fr

Julien Sarrazin
email: sarrazin@telecom-paristech.fr-anne-claire.lepage@telecom-paristech.fr

Anne Claire Lepage

Compact Patch Antenna for Automatic Identification System (AIS)

Keywords: UHF Antenna, Automatic Identification System, Microstrip Patch Antenna

This paper presents a radiating element developed for the space AIS application. This element is a compact sized and reduced mass microstrip patch antenna integrating frequency resonant adjustment devices. Theoretical and experimental results with good agreement are presented.

I. INTRODUCTION

An important emerging application for satellite communications in VHF band is Automatic Identification System (AIS) aimed at detecting messages sent by vessels for maritime tracking, safety and security purposes. Other potential functionalities like fisheries control campaigns, maritime border control operations, maritime pollution survey, search and rescue and anti-piracy can be also investigated.

AIS space segment is constituted of Low Earth Orbit micro or mini satellites which cannot accommodate large VHF antenna (central frequency about 162 MHz). Consequently dimensions and mass constitute two main driving factors for the selection of the antenna solution.

The aim of this paper is to present the antenna solution studied by Thales Systemes Aeroportes (TSA) in collaboration with the French space agency (CNES) and Telecom ParisTech (TPT) Comelec laboratory, for space AIS application.

II. OBJECTNES OF PERFORMANCES

The antenna solution investigated by CNES for the space AIS application is an array constituted of few radiating elements (4 to 6 typically). Depending on the size of the satellite (mini or micro), the array arrangement could be 1 D (linear) or 2D (cross-like arrangement, for example). For micro-satellite, deployable configurations are also investigated.

This study was performed under the CNES contract No. 104881100

The main objectives of performances fixed by CNES for the radiating element to satisfy the system and the accommodation requirements are:

-Frequency bandwidth: 162±0.050 MHz (AIS1&2) -Polarization: double linear (H and V) -Broadside directivity: > 6 dBi -Reflection coefficient on both ports: Sii:::; -10 dB -Isolation between H/V ports: Si j :::; -20 dB -Dimensions: L x W x H :::; 50 x 50 x 20 cm -Mass: :::; 5 kg I unit.

Degraded RF-performances could be accepted to respect the dimensional and mass constraints.

III. SOLUTIONS TRADE-OFF ANALYSTS

In the first part of the study, a trade-off analysis between potential solutions has been performed. The RF specifications and the mechanical constraints contributed to eliminate solutions like quadrifilar helix antenna (single circular polarization, prohibitive height), dipole, monopole and PIFA antenna (single linear polarization, insufficient directivity). Finally, the selected radiating element is a dual linearly polarized micros trip patch antenna. This microstrip patch antenna is a pertinent candidate for the AIS application because it is thin profile and potentially low weight solution.

IV. COMPACT MTCROSTRW PATCH ANTENNA

The proposed radiating element is mainly constituted of a square microstrip patch etched on a RogerslTMM6 ® substrate (f.,. = 6.00 ± 0.08 and tg8 = 0.023) with a thickness h = 0.635 cm. It is fed by two 50 Q coaxial probes (one for each polarization) located on the symmetry axis of the patch [START_REF] Carver | Microstrip Antenna Technology[END_REF], [START_REF] Garg | Microstrip Antenna Handbook[END_REF].

The choice of this specific material, compliant with space application, results from a comparative analysis on the following criteria: dielectric characteristics, specific gravity but also thermal considerations (dimensional expansion and variation of the dielectric permittivity).

In order to reduce the mass of the radiating element, the substrate is truncated all around the patch. The mass reduction induced by this modification is about 1.6 kg. Subsequently, the patch has to be stacked on metallic ground plane (dim. 50 x 50 cm2). In the current configuration, this part which integrates the coaxial probes fixtures and the mechanical interfaces is made of a waffled aluminum plate. The mass of the complete radiating element is less than 3.9 kg. In a further phase of the development, the substitution of the aluminum plate by a composite honeycomb panel will reduce the mass of the antenna about 0.5 kg.

The simulated S-parameters of the isolated patch antenna are given on Figure I (simulations were performed by TPT using the software CST Microwave Studio ® ). The operating frequency bandwidth (defined for Sii � -10 dB) is about 0.6 MHz (�0.37%) and six times larger than the required range.

The isolation Si j between the two ports (H and V polarizations) is better than -35 dB in the required bandwidth.

'� .. �>"� � 1 / 1i � m c,'
. The theoretical directivity of the patch antenna (see radiation pattern on Figure 2) is about 4.5 dBi at the center frequency. This directivity which is lower than the specified value (6 dBi) is due to the small dimensions of ground plane.

"9> " � !Em "' / 1\ - � " � � - -------------- � � ,
The theoretical level of the cross-polarization, not represented on this figure, is lower than -34 dB in both principal planes.

V. EFFECT OF THE DIELECTRIC PERMITTIVITY DISPERSION AND ADJUSTMENT DEVICE

The narrow operating bandwidth of the patch element results from the relatively high dielectric permittivity and the low thickness (h/"-� 0.0034) of substrate whose choices were imposed by dimension and mass constraints.

At this point, it is important to analyze the effect of the dispersion of the dielectric permittivity on the RF performances of the radiating element and particularly on its RF-matching. The variations of the substrate permittivity have two origins:

-The dispersion from sheet to sheet announced by the manufacturer (E,-= 6.00 ± 0.08),

-The dispersion induced by the temperature variations during operation in orbit (for the selected material TMM6 ® : d£jdT = -II ppm/QC).

In practice, for a same manufactured lot of sheets, a lower dispersion can be obtained. However, for a production that could extend over several years, the whole dispersion range has to be considered.

The effect of the manufacturing dispersion on the S parameters of the patch antenna is shown on Figure 3. A variation of ±0.08 of the dielectric permittivity induces a frequency-shift of about ±1 MHz which is larger than the operating bandwidth (0.6 MHz).

-1 0 ----------------------c-- -20 ----------------------r -------------------, -- co � -3 0 ----------------------I ----------------------I -- (/) '" � -4 0 ;;:. --------------------1 ----------------------1 F"oquence (MHz) Considering an AIS-satellite placed on a SSO orbit, a preliminary thermal study performed by CNES leads to a temperature, at the level of the patch, protected by a thin thermal radome, ranging from -SO°C to +60°C. The resulting frequency-shift is about 0.22 MHz and is less than half of the antenna bandwidth. An initial setting made for an average temperature of +soC will meet the Sjj-parameter specification (Sjj :::; ; -10 dB) in the operational space environment.

VI. EXPERIMENTAL RESULTS

During the study, a set of three prototypes of the radiating element have been manufactured and tested by TSA (S-parameters measurements) and CNES (radiation performances measurements: patterns, directivity and gain).

The performances of the isolated patch antennas and, then, of the three elements arranged in a linear array were tested.

Figure 6 gives the measured S-parameters of one of the three patch elements. The analysis of this figure shows that the maximum resonance frequency accessible after adjustment is 160.2 MHz for a design at 162 MHz corresponding to a frequency shift of about 1.1 %.

• E -10.01 ••••••••••••••• •••••••••• •••••••••••• ••••••••••••••• •••••••••••• •1 '1 •• ••••••• •• ••••••••• •••••• ,••• •••••• ••••••••• • •••••••••• •••••••••• 1 J � -12.5
� -15.0 ';:.;,:""'��� ---. _ _ • � -17.5 . . . . . c . . •., = , . . The ongm of the gap between simulation and measurement has been attributed to a discrepancy between the dielectric permittivity measured by ROGERS (for the considered patch, lOr = 6.0S7 was measured using the Full Sheet Resonance method) and the effective dielectric permittivity of the patch antenna. To recover the measured resonant frequency, a permittivity equal to 6.23 should be considered in the simulation.

For future development, it will be necessary to take into account the shift between the value of the permittivity measured by ROGERS and the value to be considered for the design, by reducing the patch dimensions, but also to broaden the range of adjustment accessible with the device for adjusting the resonant frequency.

Figure 7 shows a picture of the linear array constituted of the three radiating elements, with a spacing s = 1.-12 -92 cm, mounted on the positioner of the CNES antenna measurement facility. The analysis of the values in Table I shows a good agreement between measurements and simulations for the ports of same polarization (H-polarization for odd ports and V -polarization for even ports) corresponding to high level of coupling. The origins of discrepancies observed for the ports of different polarizations (HIV isolation) are: small manufacturing defects (patches #1 and #2) and quality of the electromagnetic environment of the array (efficiency of the absorbent panels in VHF band, disturbance caused by the coaxial cables used for the measurements ... ).

Concerning the isolation between ports of the same polarization, the highest values correspond to E-plane coupling (about -15 dB between two adjacent patches) and the lowest values correspond to H-plane coupling (around -23 dB between two adjacent elements). Complementary simulations have highlighted that the mechanical structure around the radiating elements contributes to increase by about 9 dB the E-plane coupling and about 6 dB the H-plane coupling.

CNES has performed radiating measurements with a spherical near field method suitable for small VHF antennas. The antenna radiating patterns have been then deduced by a near-field to far-field transformation. Figure 8 shows the far field radiation patterns of the isolated patch antenna (patch #2 -port 3 or 4) measured for different <j>-angles round the axis. The comparison of the two previous figures allows to appreciate the modification of the radiating pattern induced by array arrangement for this polarization. Figure 10 shows that the radiation pattern of the polarization perpendicular to the axis of the array (V-polarization), is less disturbed by the arrangement in array. These results are explained by the coupling effects that are higher in the E-plane than in the H plane. Table II gives the theoretical directivity and the measured gain and directivity of the isolated patch and of the central patch in the array. For both cases, the differences between simulations and measurements are explained by the mechanical environment of the element under tests (fixing parts, mechanical interface with the antenna positioner ... ) but also by the measurement un-accuracy estimated to ± IdB.

For the isolated patch, the differences of directivity and gain between the two polarizations (H and V) are explainable by the presence of the mast antenna positioner.

In the array configuration, the presence of the mechanical structure and of the adjacent radiating elements contributes to increase the gain in both polarizations. The increase appears to be more important for the vertical polarization, perpendicular to the axis of the array.

These results confirm the performances obtained by numerical simulation for different forms of array and different satellite platforms, namely that the directivity and the gain of the radiating elements depend on their position in the array and polarization (H or V) considered.

VII. FUTURE WORKS

Additional works to be carried out as part of a future development of the solution are: the refocusing of the radiating element operating frequency band taking into account the effective dielectric permittivity of the substrate, -the enlargement of the tuning range of the device for adjustment the resonance frequency, -the qualification of the radiating element in a space environment: mechanical tests (vibration, shock) and thermal (cycles at ambient pressure), thermal vacuum space, -in a more distant future, the extension of the operating bandwidth of the radiating element to cover both AIS frequency bands (AIS 1&2 and AIS 3&4).

VIII. CONCLUSION

This study allowed us to identify, define and validate a pertinent solution of radiating element, for space Automatic Identification System. The simulations and measurements performed on the prototypes manufactured have proven that the proposed solution is compliant with the RF and integration requirements of the application.

It has been highlighted that this radiating element solution can be used in a conventional phased array (requiring a phase-shifter behind each radiating element) or in an array using the technique of Digital Beam Forming (DBF -requiring one numerical receptor behind each radiating element). This radiating element of compact dimensions and reduced mass, is compatible with a generic payload on all types of AIS-satellite platform (micro or mini) in a fixed accommodation or a deployable configuration.

Figure I :

 I Figure I: Variation of the S-parameters of the radiating element vs frequency

Figure 2 :

 2 Figure 2: Radiation pattern of the radiating element at 162 MHz

  -;:: -� -• t -: : -, ' ,:-. _ I�_ ._. ---11 r � -50 -:;.::-.-=----=---.:: -- " \ "\ /-/-" : .... . +----.. = ; ... .--: .. ::-=-=-:-.. -8 21 £ r = 6 � " / I -60 -_ .. -------------------� .. ---_ .. --i-I--'�� ---------i -________ .. -5 11 £ r = 6 . 0 a I \i I -8 21 £ = 6 . 0 8 -70 ----------------------�--------U-------------: --------__ -5

Figure 3 :

 3 Figure 3: Effect of the substrate dielectric permittivity dispersion on the S parameters of the radiating element These results led us to design an adjustment device for the central operating frequency of the radiating element. This device consists of four sub-devices incorporating etched elements and located in the middle of the four sides of the radiating patch.

Figure 4 Figure 4 :

 44 Figure4shows the evolution of the resonance frequency as a function of the value of one of the adjustment parameters and confirms that it is possible to compensate for manufacturing variations of the substrate.

1 LFrequencyFigure 5 :

 15 Figure 5: Variation of the resonance frequency vs. the temperature

FrequencyFigure 6 :

 6 Figure 6: Measured S-parameters the radiating element (patch #2)

Figure 7 :

 7 Figure 7: Linear array of three radiating elements Table I gives the S-parameters simulated (upper part) and measured (lower part) of the three patch elements.

  PATCH 2 seul-Acces V3-F=160.2 MHz

Figure 8 :

 8 Figure 8: Measured radiation pattern of the isolated patch antenna

Figure 9 Figure 9 :

 99 Figure9shows the radiation pattern measured on the port 3 (H-polarization parallel to the array axis) of the same patch placed in the center of the array.r�a PATCH 2 entoure des des patchs 1 et 3 -Acces V3 sur structure-F=160.2 MHz

PATCH 2

 2 entoure des patchs 1 et: 3 -Acce�s V4 sur structure-F=160.2 MHz THETA ID"gl

Figure 10 :

 10 Figure 10: Measured radiation pattern of the central element in the array (port 4: V-polarization perpendicular to the array axis)

TABLE T .

 T Su-PARAMETERS OF ARRAY AT 160.2 MHZ

	SijdB)	Patch	#1		#2		#3
	Patch	Ports	1	2	3	4	5	6
		1		-58.3	-14.8	-59.1	-15.6	-67.5
	#1	2	-30.4		-61.0	-24.4	-66.0	-35.8
		3	-14.0	-37.9		-52.8	-14.9	-61.9
	#2	4	-41.5	-23.2	-38.9		-57.5	-24.4
		5	-15.7	-42.5	-14.0	-37.8		-59.9
	#3	6	-46.7	-42.0	-46.1	-23.6	-55.4
	.. Upper part (SI]): SnTIUlatlOns -Lower part (S]I): Measurements ..

TABLE n .

 n DIRECTIVITY AND GAIN OF THE RADIATING ELEMENT (ISOLA TED AND IN THE MIDDLE OF THE ARRAY)

	Configuration		Simulation	Measurement
	Patch	Ports	Polar.	Direct. (dBi)	Direct. (dBi)	Gain (dBi)
	Isolated	3 4	H V	4.40 4.40	5.00 4.85	1.80 0.50
	Central in the array	3 4	H V	4.10 4.10	4.60 5.85	2.45 2.05

ACKNOWLEDGMENT

Thales Systemes Aeroportes would like to thank the French space agency (CNES) that funded the study and for its contribution to the radiation measurements of the prototype antenna.


Jean-François Girres

Guillaume Touya²

A framework to formalize multi-scales land-use generalization in the ScaleMaster 2.0

Keywords: Generalization, MRDB, Land-use, ScaleMaster 2.0

This article presents a general framework to formalize the generalization of land-use coverages within the ScaleMaster2.0 model, a multiscale automatic generalization system, developed to facilitate smooth transitions (i.e. cartographic continuum) between level of details in a multirepresentation database (MRDB). The article describes the land-use simplification method developed, the strategy used to manage generalization on large areas, and the way to formalize multi-scales land-use generalization rules in the ScaleMatsre2.0. Finally, an experiment is provided on two datasets to illustrate the proposition: a land-use coverage in the area of Dakar (Senegal) and a map of sedimentary floor off the coast of Brittany (France).

Introduction

Multi-representation databases (MRDB) allow representing the same real world entities in different cartographic databases with their own level of detail. Thus, it enables navigating from a scale to another using multi-scales geoportals. To facilitate smooth transitions (i.e. cartographic continuum) between representations, cartographic generalization allows deriving intermediate representations, based on the most detailed database (Figure 1). In this context, the ScaleMaster2.0 [START_REF] Touya | ScaleMaster 2.0: a ScaleMaster Extension to Monitor Automatic Multi-Scales Generalizations[END_REF] was developed as a multi-scales generalization system, allowing the derivation of intermediate levels of detail by formalizing generalization rules from a MRDB. The ScaleMaster2.0 is an automatic generalization system, developed as an extension of the ScaleMaster, initially proposed by [START_REF] Brewer | Framing guidelines for Multi-Scale map Design using databases at multiple resolutions[END_REF]. The ScaleMaster2.0 is implemented using the CartAGen library [START_REF] Renard | Capitalisation problem in researchexample of a new platform for generalization: CartAGen[END_REF], a Java platform developed since 2009 by the generalization team of the COGIT lab. The parameterization of the ScaleMaster2.0 model is realized using three XML interfaces associated to the Java core: the ScaleMaster.xml file (to formalize generalization rules), the Parameters.xml (to specify the input databases used and the output results scale), and the Symbology.xml file. Since the beginning of the development the ScaleMaster2.0, several advances in the field of cartographic generalization have been proposed and formalized in the framework, as for instance the generalization of unusual map themes from OpenStreetMap (Touya & Girres, 2014), or methods to derive intermediate scales by taking account of the consistency between level of details, called multi-representation aware (MR-aware) generalization (Girres & Touya, 2014). Nevertheless, the question of land-use (or more generally of categorical data) generalization in the ScaleMaster2.0 model has not been studied yet. Then, this article presents in section 2 the different strategies proposed to handle land-use generalization. Section 3 presents the framework proposed to manage land-use generalization in the ScaleMaster2.0. Finally, an experiment of the model will be proposed in section 4, before concluding and evocating further works.

Land-use generalization strategies

Land-use coverages, as far as all categorical data (e.g. geological or pedological maps) constitute a partition of the real world, where a land-use class is affected for each element of the partition. Land-use is generally represented in geographic databases as a single coverage composed of different land-use classes. But in some cases, geographical databases represent land-use as the joining of independent layers, each of them composing a land-use class.

In a land-use generalization process, each land-use class can be handled differently according to the desired output scale, the geographical context of the data, or the point of view of the map (i.e. road, touristic or military maps). For instance, in a given context, a small polygon can be enlarged if it represents an isolated urban area, but can be deleted if it represents a piece of land. Thus, land-use generalization processes require specific rules for each land-use class, according to the geographical context of the objects and the goal of the output map.

Several land-use generalization models have been proposed in the literature. We can specially differentiate:  Models guided by the geometry, where for example small polygons are aggregated with bigger ones, or thin polygons are enlarged [START_REF] Van Oosterom | The GAP-tree, an approach to 'on the fly' map generalization of an area partitioning[END_REF]  Models guided by the data schema, where two object classes are merged in the same class at a smaller scale, as proposed by Le [START_REF] Men | Généralisation cartographique pour l'occupation du sol : application au passage v1 à v2 pour la BD carto[END_REF], [START_REF] Yaolin | Categorical database generalization aided by data model[END_REF] or [START_REF] Revell | Automated generalization and representation of ordnance survey polygonal landcover data at 1:10 000 scale[END_REF]  Models guided by the functionnality, where new concepts are created by aggregation [START_REF] Ruas | Data and knowledge modelling for generalization[END_REF]. For instance, an airport is the aggregation of several buildings and other surfaces.

 Mixed approaches, where the model is guided by the geometry, the data schema and the functionnality [START_REF] Galanda | An agent-based framework for polygonal subdivision generalization[END_REF][START_REF] Haunert | Area aggregation in map generalization by mixedinteger programming[END_REF].

To formalize land-use generalization in the ScaleMaster2.0, we follow a mixed approach. Indeed, in the context of MRDB, the data schema of a land-use coverage can be modified between two databases representing the same real world entities with their own level of details. In the meantime, polygonal geometries are generally simplified (by aggregation and using simplification algorithms) or even collapsed to other geometries (for instance river areas become river lines under a given threshold).

The following section presents how land-use generalization is handled using this mixed approach in the ScaleMaster 2.0.

A framework to generalize land-use coverages in the ScaleMaster2.0

This section presents the general framework proposed to handle multi-scale land-use generalization processes in the ScaleMaster2.0 model. It describes the method developed to perform land-use coverages generalization, the strategy proposed to manage land-use generalization on large areas, and finally its formalization in the ScaleMaster 2.0.

Land-use coverages generalization method

This section presents the proposed method to simplify a land-use coverage at a given scale. As exposed before, we consider that each land-use class requires a minimum area to be conserved. Then, different methods are proposed to eliminate holes whether they are inside the polygon, at the border of the polygon, or at the border of the map area. Finally, a line simplification is performed using the [START_REF] Douglas | Algorithms for the reduction of the number of points required to represent a digitized line or its caricature[END_REF] filtering algorithm.

The method starts by processing each land-use class separately, in order to remove polygons smaller than the given threshold. Thus, after an optional union of merged land-use classes (in the case of a process guided by the data schema) and a preliminary union of neighboring polygons, small polygons completely located inside the coverage are removed according to the parameterized threshold (Figure 3). Small polygons located at the border of the coverage are preserved as they are potentially part of bigger polygon in the neighboring coverage (see section 3.2). In the second step, the method merges all land-uses classes in a global coverage (converted in a topological map), and processes the distribution of the holes according to their characteristics (Figure 4). If the set of land-use classes doesn't provide a complete coverage of the area, an empty class is temporary created. Three categories of holes are differentiated and processed differently:

 Holes entirely located in a polygon are sealed and affected to the landuse class of the surrounding polygon (a)

 Holes located at the border between two polygons of different classes are divided in two parts (using the polygon's skeleton) and each part is distributed to the neighboring land-use classes (b)

 Holes located at the border between more than two polygons are divided in the corresponding number of neighboring polygons (using a partition computed with a triangulation around the polygon's centroid), and each part is distributed to the neighboring class sharing the longest border (c) Once all holes are removed in the land-use coverage, the line simplification algorithm is applied to finalize generalization. At the moment, the [START_REF] Douglas | Algorithms for the reduction of the number of points required to represent a digitized line or its caricature[END_REF] filtering algorithm is used in the model.

When land-use generalization is applied on large areas, the method presented in this section can be time-consuming, or can faces memory limitations for the processing. To avoid this problem, a partitioning method of the coverage is proposed in the following section.

Partitioning to handle generalization on large areas

In order to manage land-use generalization on large areas with the Scale-Master2.0, a partitioning of the coverage can be handled preliminarily. Different methods can be proposed to manage area partitioning, but they all face the problem of the consistency of the joining tiles.

Area partitioning can be achieved using different methods, as proposed in previous studies [START_REF] Briat | Scalability of contextual generalization processing using partitioning and parallelization[END_REF] But other partitioning methods can be proposed using external data, or not.

For instance, the Collagen model (Touya, 2010) proposes partitioning using delimitations which have a geographic signification (urban or suburban areas, mountainous, rural areas...). Other partitioning methods, based on the road network [START_REF] Ruas | Strategies for automated map generalization, 7th International Symposium on Spatial Data Handling[END_REF] or on the road and river networks [START_REF] Duchêne | The CartACom model: transforming cartographic features into communicating agents for cartographic generalization[END_REF] have also been proposed. These examples show that no method can be considered as more efficient, as they all depend on the maximum number of objects tolerated in the memory, or computational time accepted to process an entire coverage.

The partitioning method using regular squares can be considered as the easiest to implement, but it supposes to define the appropriate size of the square in order to not extent the maximum number of tolerated object. The partitioning method using quad-trees can be relevant in this context, as it can guarantee a tolerated quantity of objects in each square, and a smaller number of them. But this method can be more difficult to handle, especially in terms of consistency in the joining of land-use classes processed independently in each square.

Indeed, once the area partitioning and the processing of each tile is performed, the question of the consistency of the merge of land-use classes processed separately need to be resolved. To anticipate this problem, an extension of each square of the partitioning can be achieved, as proposed by [START_REF] Briat | Scalability of contextual generalization processing using partitioning and parallelization[END_REF] and illustrated in figure 6, to generate a double covering of objects located at the border and facilitate their joining with neighboring objects. But the question of the parameterization of this extension needs to be resolved. Another method proposes to identify preliminarily the objects located at the border of the area, and performs specific methods a posteriori to manage the consistency between neighboring objects processed separately in the different tiles.

In the framework proposed to generalize land-use coverage in the Scale-Master2.0 model, the partitioning using regular squares was chosen, because of its facility to implement. The question of the consistency of the joining of neighboring land-use classes is still an ongoing problem. Then, to manage this problem, we decided, as exposed in the previous section, to preserve the small polygons (potentially eliminated) located at the border of the tile, as we consider they can be part of a bigger area, larger than the minimum area threshold. This solution supposes the development of a posteriori methods to manage these neighboring polygons, but these methods have not been implemented yet in the model.

Formalization in the ScaleMaster 2.0

The land-use generalization algorithm and the partitioning method have been integrated in the CartAGen library [START_REF] Renard | Capitalisation problem in researchexample of a new platform for generalization: CartAGen[END_REF], in order to be used by the ScaleMaster2.0 model. To formalize their parametrization in the model, the ScaleMaster.xml and Parameters.xml file interfaces have been adapted to manage the specific generalization rules of each land-use class and the partitioning of the coverage.

The developed method formalizes for each land-use class two parameters: a merging option to another class (to simplify the data schema) and a minimal area threshold (to eliminate small polygons). These parameters need to be defined for each scale range of the ScaleMaster.xml file if generalization has to be performed at different scales. An additional for line simplification needs to be defined for the entire land-use coverage (all landuse classes are simplified the same way).

In Figure 7, two land-use classes are presented with their own land-use simplification parameters at the scale interval 1:50k-1:150k. In this example, lake areas smaller than 100.000 sq. meters and built-up areas smaller than 200.000 sq meters are eliminated. The line simplification parameter is specified for lake areas, but is also applied to all the land-use classes. To manage land-use generalization on large areas, the area partitioning option using regular squares is integrated in the Parameters.xml file. In Figure 8, the generalization is performed at the scale 1:100k, using a parti-tioning of the area with regular squares of 0.2 decimal degrees (in the WGS84 coordinate system).

Experiments

The developed land-use generalization method was experimented on two different case studies: a land-use coverage in the area of Dakar (Senegal) and a map of sedimentary floor off the coast of Brittany (France).

The first experiment is performed on a land-use coverage composed of 12 classes in the area of Dakar, extracted from a spatial dataset with a reference scale of 1:50k. The output derived scales are the 1:100k and 1:200k with a line simplification parameterized respectively at 20 and 30 meters using the Douglas-Peucker algorithm. Minimal area thresholds used for each land-use class are defined in Table 1.

Results of land-use generalization in the area of Dakar at the scale 1:100k are shwon in Figure 9. These results show that the formalization of appropriate threshold for minimal area and line simplification can provide interesting results in the constitution of cartographic continuums, with acceptable time computation by the use of partitioning options. Nevertheless, the definition of the generalization rules parameters remains a difficult task, according to the dataset processed, its geographical context, and the point of view of the map. To illustrate a generalization process on another type of categorical data, the second experiment was performed using a sea floor dataset off the coast of Brittany (1:50k scale). The output derived scales are the 1:100k and 1:200k with a line simplification parameterized respectively at 20 and 30 meters using the Douglas-Peucker algorithm. Minimal area thresholds and merging options used for each sedimentology class are defined in Table 2. Results of the experiment on the dataset of sedimentology floor off the coast of Brittany are exposed in the figure below. We can see that using appropriates generalization rules on the geometries and in the data schema, combined with appropriate symbols, smooth changes can be created between intermediate representations in order to create a consistent cartographic continuum. It shows that a generalization system based on a ScaleMaster 2.0 is able to obtain results close to tGAP continuous representations of categorical data (van Oosterom et al, 2014).

Conclusion and further work

This article presents a general framework to manage multi-scales land-use generalization in the ScaleMaster2.0 model. The proposed framework can be seen as a preliminary contribution in order to formalize derivation rules of categorical data at intermediate scales, in order to create cartographic continuums. The two experiments performed show interesting results to derive intermediate land-use coverages from a MRDB, with acceptable time computation by the use of partitioning options. Nevertheless, several improvements should be provided concerning the consistency between neighboring partitions of the generalized land-use coverages, using postprocessing operations already proposed in this paper, but which have not been implemented yet in the model. Moreover, the integration of other generalization operations (e.g. amplification of polygons, or skeletonization) already implemented in the ScaleMaster2.0 should be added in the general process of land-use generalization. Finally, the preservation of the consistency with lower level of details, as evoked by Girres & Touya (2014), or with other generalized themes (road or river networks for instance) needs to be extended to categorical data in order to provide consistent cartographic continuums of land-use coverages.

Figure 1 .

 1 Figure 1. Intermediate scale (1:75k) derived by automatic generalization from a larger scale (1:50k) in a MRDB

Figure 2 .

 2 Figure 2. Implementation of the ScaleMaster2.0 model using the CartAGen library

Figure 3 .

 3 Figure 3. First-step of the method : Union of neighboring polygons in the same land-use class, and elimination of small polygons according to their size and location.

Figure 4 .

 4 Figure 4. Second-step of the method: Three different strategies to eliminate holes in the coverage according to their neighboring land-use classes.

  and illustrated in the figure below: a delimitation layer (a), regular squares (b) or quad-trees (c).

Figure 5 .

 5 Figure 5. Three different methods for area partitioning, from Briat et al. (2011)

Figure 6 .

 6 Figure 6. Extension of a partitioning area, from Briat et al. (2011)

Figure 7 .

 7 Figure 7. Formalization of generalization rules for each land-use class in the ScaleMaster.xml file.

Figure 8 .

 8 Figure 8. Parameterization of the area partitioning option, using regular squares, in the Parameters.xml file. Finally, on a functional point of view, the developed land-use generalization process follows the steps: 1. Partition the land-use coverage in regular squares (if needed), 2. Merge the classes (if needed), 3. Convert the landuse coverage into a topological map, 4. Eliminate the small objects, 5. Apply line simplification algorithms. The next section presents an experiment of the model.

Figure 9 .

 9 Figure 9. Original dataset (left) and derived land-use coverage at the scale 1:100k (right) in the area of Dakar (with the partitions limits displayed).

Figure 10 .

 10 Figure 10. Results of the generalization of the sedimentology map of the coast of Brittany at the scales 1:100k (left) and 1:200 (right).

Table 1 .

 1 Minimum area for each land-use class generalized at different scales in the area of Dakar.

		Minimal area (in square meters)
		1:100k (dp=20m.)	1:200k (dp=30m.)
	River area	100.000	200.000
	Lake area	100.000	200.000
	Built up area	200.000	300.000
	Quarry area	300.000	400.000
	Wooded area	300.000	400.000
	Grassland area	300.000	400.000
	Crop area	300.000	400.000
	Reservoir area	100.000	200.000
	Thicket area	300.000	500.000
	Marsh area	300.000	500.000
	Soil surface area	300.000	500.000
	Tidal water area	300.000	500.000

Table 2 .

 2 Minimum area and filtering thresholds used for the generalization of sedimentology maps if the board of Brittany.

		Minimal area (in square meters)
		1:100k (dp=20m.)	1:200k (dp=30m.)
	Rock area	100.000	200.000
	Pebble area	200.000	400.000
	Pebble gravel area	200.000	Merged with Pebble area
	Gravel area	200.000	400.000
	Sand area	200.000	400.000
	Fine Sand area	200.000	Merged with Sand area
	Sand Gravel area	200.000	Merged with Sand area
	Land area	50.000	100.000


Fortuna Casoria
email: casoria@gate.cnrs.fr

Marianna Marino
email: marianna.marino@skema.edu

Pierpaolo Parrotta
email: p.parrotta@ieseg.fr

Davide Sala
email: davide.sala@uni-passau.de

Local Government and Innovation: the case of Italian provinces

Keywords: local government, decentralization, innovation, mafia transplantation, difference-in-difference JEL codes: D72, H72, K42, O31 JEL Classification: D72, H72, K42, O31 local government, decentralization, innovation, mafia transplantation, differencein-difference

This paper evaluates the effect of decentralization on innovation at the provincial level in Italy. We exploit quasi-natural experiments associated with three waves of reforms occurred in 1992, 2001 and 2004, to establish 8, 4, and 3 new provinces, respectively. Using a difference-in-difference estimation approach, we find evidence of a significant detrimental effect of (further) decentralization on innovation for Northern and Central Italian provinces. We suggest a potential mechanism that may explain the reduction in innovation associated with the aforementioned reforms. We argue that this finding can be rationalized with the costs imposed by the \mafia transplantation" phenomenon, as we find that the new provinces that were more exposed to \mafiosi in confino" reduced their innovation output more extensively. We perform a number of robustness checks that corroborate our main findings.

Introduction

It is widely accepted among scholars in social sciences that the establishment of subnational tiers of government improves the overall economic efficiency in the provision of public goods or services (Bardhan, 2002). [START_REF] Acemoglu | Institutions as a fundamental cause of long-run growth[END_REF] Among the most recognized advantages of decentralization is the higher ability of local government to (i) match public spending to the heterogeneous preferences of individuals or firms -allocative efficiency -and to (ii) mobilize underused resources to compete against other local jurisdictions -productive efficiency (Rodriguez-Pose and Ezcurra, 2010). Local governments may for instance exploit information about territorial conditions, better than the central administration does, to design innovation policies tailored to the specific needs and demands of local innovators or to improve the matching between entrepreneurs with innovative business projects and investors seeking R&D opportunities (Taylor, 2007).

However, there are a number of countervailing factors that may in practice hinder the achievement of the benefits associated to administrative decentralization. First, there exist incentives to under-report the true willingness to pay in case people or companies fear the actual payment of this public provision (Feld et al., 2004). This argument alone undermines allocative efficiency as the sum of individuals'/firms' true marginal willingness to pay could not be possibly equalized to the actual marginal cost of the public good provision. Second, even if decentralization enhances the matching of the provision of goods and services to the preferences of the population/firms, local governments may be less efficient than the central one in delivering such goods and services due to the loss of economies of scale (Prud'homme, 1995). It is also questionable that different tiers of government may have similar levels of technical and administrative capacity. Moreover, and most importantly for our paper, local administrations are typically weaker and less accountable institutions, with the corruption of public officials resulting easier compared to the case of the national authority, particularly when local governments are empowered to allocate resources among individuals and companies and to bid for greater shares of the national budget (Rodriguez-Pose and Ezcurra, 2010). Therefore, there is scope within local governments for cronyism and the creation of a system of patronage in the management of public funds to the detriment of the economic efficiency. This paper centers around the question of whether mismanagement of local funds can overturn the recognized advantage of decentralization. The case of Italy is particularly suitable to address this question, not only because Italy has been historically the host of some of the most influential and powerful criminal organizations worldwide, but also because -and perhaps this is less known -it has undergone important decentralization reforms. Specifically, the combination of the following two events is of particular interest for our research question. The first one is known as the mafia transplantation phenomenon (or also the "Northern Passage" of mafia activities), which indicates that the links between mafia and politics have radicated, from the South of Italy, where they were rooted in, towards the Northern (and richer) regions, where the infiltration of criminal organizations was originally absent. The second event is the administrative reform of the territorial governance that has introduced eight, three, and four new provinces, respectively, in three subsequent waves of reforms (1992, 2001, and 2004).

These changes in the territorial governance allow us to implement a difference-indifference (DID) estimation strategy and therefore to study the effect of decentralization on innovation, which we consider as an indirect measure of economic efficiency.

Key to the implementation of this strategy is that our sampling period spans some years before the first reform (i.e. 1992) through some years after the last reform (i.e.

2004

). We proxy innovation output by means of patent applications at the European Patent Office (EPO) over 1977-2012. [START_REF] Angrist | Mostly harmless econometrics: An empiricist's companion[END_REF] The use of patents is strongly grounded in the New Growth Theory that regards the R&D sector as the primary input for innovation and ultimately as the engine of a sustained economic growth in the long run (Romer, 1990; Lucas, 1988).

In our estimation strategy, we control for province fixed effects and region time-varying effects to account for average differences between provinces and idiosyncratic shocks over time at the regional level, respectively. Additionally, we factor out potential inter-province spillovers by removing provinces that share borders with the new established ones, i.e. we remove the bias associated with the reallocation of resources that may occur among neighbor provinces or due to the province split.

We find evidence of a significantly negative effect of decentralization on innovation. This detrimental effect is however heterogeneous because it varies by reform wave and across macro areas in the Italian territory. More specifically, innovation seems to be significantly hindered mainly in provinces in the Northern-Central regions, which comprise the more economically active areas of the country. Moreover, the overall decentralization effect appears to be exclusively driven by the 1992 reform, which is the wave that introduced the deepest changes in the provincial territory.

Inspired by Buonanno and Pazzona (2014), we relate this finding to the "mafia transplantation" phenomenon thoroughly documented in Dalla Chiesa (2017). The infiltration of criminal organization into the economic and political activities in Northern Region spurred (partly and unintentionally) from the mandatory resettlement-residence ("confino") policy in place until 1970's, to which highly ranked mafia bosses were subjected to. We therefore collected information on bosses sent in confino (i.e. forced exile) to identify with reasonable accuracy, on the basis of anti-mafia parliamentary committee reports, the arrival municipality and associated province of forced exile. The forced exile of bosses has led to the necessity of recreating criminal cells elsewhere employing those strategies and methods already successfully experimented in the Southern regions.

The final result being that higher proximity between organized criminal groups and public officials has favored mafia infiltrations and its illicit activities (e.g. corruption of public authorities, diversion of public funds, unfair competition) with obvious negative consequences on both direct and indirect costs of innovations for knowledge intensive firms located in Northern Italy and investing significant resources in high-risk projects.

Consistently with this backdrop, the reduction in local government size due to the reform waves should increase the costs of innovation activities in Northern-Central provinces that have been more exposed to "mafiosi in confino". Indeed, we find evidence of a stronger detrimental effect for new established provinces that have been more exposed to transplanted criminal cells, compared to those less exposed or unexposed to them. Although other potential mechanisms may concur to explain the reduction in innovation output identified for the new Northern-Central provinces, we deem to have nevertheless nailed an important channel, which is at the present still underrated, especially with regard to its international implications. In fact, dynamics similar to those identified on the Italian territory might be at play also in the geographical administrations of other OECD countries, where mafia-type organizations have expanded their illegal businesses (Europol, 2013; Sergi and Lavorgna,

2016).

The reminder of the paper is as follows: Sections 2 and 3 describe the related literature and the institutional background, respectively. Section 4 informs on the deployed estimation strategy; section 5 describes the data used and provides descriptive statistics and evidence concerning the relationship under analysis. Section 6 reports the discussion of our results, and section 7 concludes the paper highlighting potential policy implications of our findings.

Related literature

The theoretical literature on fiscal federalism typically identifies two major advantages of decentralization. First, decentralization is believed to improve allocative efficiency.

By being closer to the citizens, local governments have better information on their preferences, and are therefore better equipped to match such preferences with adequate services (Tiebout, 1956; Musgrave, 1959; and Oates, 1972). Moreover, decentralization supposedly gives voters more control over politicians and allows for yardstick competition, so that voters are able to infer the quality and competence of their politicians by comparing the performance of different local governments (see Besley and Case, 1995 and subsequent literature). Second, it is argued that decentralization may increase productive efficiency by deploying underused resources to perform better than neighbor jurisdictions (Rodriguez-Pose and Ezcurra, 2010), and therefore alleviate issues such as waste collection and treatment in local communities. Other scholars have taken a more cautious approach in assessing the benefits of decentralization. Prud'Homme (1995) and Tanzi (1995), for instance, claim that decentralization exposes local governments to greater corruption and lobbying by local interest groups, precisely because of the proximity between such groups and local government officials.

The empirical literature has extensively studied the merits of fiscal decentralization and its relationship with government efficiency. There is broad, yet mixed, evidence on possible links between decentralization and corruption. Huther and Shah (1998) find a negative correlation between the degree of expenditure decentralization and the level of corruption, measured using a governance index for 80 developed and developing countries. However, by focusing on correlations alone, their analysis suffers from omitted variables bias, as it disregards the many factors that could be correlated with both decentralization and corruption. Mello and Barenstein (2001) find that decentralization is negatively correlated with corruption (measured using the International Country Risk Guide -ICRG -index), [START_REF] Asfaw | Fiscal decentralization and infant mortality: empirical evidence from rural India[END_REF] but that this effect depends on how subnational expenditures are financed. In particular, they show that there is a strong negative association between decentralization and corruption when subnational spending is financed with non-tax revenues. Fisman and Gatti (2002) examine a cross-country relationship between decentralization and corruption (also measured using the ICRG index). They find that decentralization is significantly associated with less corruption, but also that, conditional on the degree of fiscal decentralization, being a federal country has no significant effect on corruption. In these studies, the level of fiscal decentralization is measured by the percentage of government expenditures made at the subnational level, as recorded by the IMF's Government Financial Statistics. Treisman (2000) takes a different approach by accounting for a country federal structure. He uses an average of various corruption indices, and a dummy variable to indicate whether a country has a federal or unitary structure. He finds that federalist countries have higher rates of corruption. However, Treisman (2000)'s simple measure of decentralization may not accurately reflect the extent to which subnational governments have authority or autonomy in decision-making. Treisman (2002), instead, uses several constitutional indicators of decentralization (such as a dummy variable for a federal country and the number of tiers of government) and the World Bank corruption index. His results suggest that corruption is perceived to be higher in countries with more tiers of government.

Other studies take a broader perspective, and show that fiscal decentralization may be positively correlated with different indicators of good government. Barankay and Lockwood (2007) use a dataset of Swiss cantons and show that there is a positive relationship between fiscal decentralization and education. Robalino and Picazo (2001), Asfaw et al. (2007), Cavalieri and Ferrante (2016) find a positive association between decentralization and health outcomes, measured by infant mortality rates. [START_REF] Barankay | Decentralization and the productive efficiency of government: Evidence from Swiss cantons[END_REF] While the link between fiscal decentralization and government efficiency has received wide attention, the relationship between decentralization and technological innovation is largely unexplored. Theorists support the idea of a positive correlation between decentralization and innovation (see, for instance, Acemoglu et al., 2005).

Empirical evidence is so far limited to case studies, most of which do not establish a direct link between decentralization and innovation. The case study by Drezner (2001) represents an attempt at doing so. He tests the hypothesis that decentralized states are better suited to fostering innovation by looking at the United Kingdom-Germany rivalry for technological leadership in the XIX century, and at the US-Japan rivalry of the last decades. Drezner (2001) highlights that in both cases the more centralized state had difficulties over time to emerge as technological leader. Taylor (2007) provides a more rigorous analysis of the relationship between fiscal decentralization and innovation. He studies whether a general correlation between innovation and decentralization exists by using data from the European Patent Office, data on scientific publications, and high-technology exports that cover a span of 17 years. In particular, the data refer to five of the most decentralized industrialized countries (Australia, Canada, Germany, Switzerland, United States) and to five of the most centralized ones (Finland, France, Great Britain, New Zealand, Sweden). Taylor (2007) shows that decentralized states are not more innovative than centralized states. A further relevant contribution is due to Prud'homme (2016), who uses economic decentralization theories to evaluate the effectiveness of industrial specialization policies implemented within each of mainland China's 31 provinces. He finds that the Chinese system of economic decentralization has either facilitated the technological catching-up of some provinces or led to the deployment of risky or even likely ineffective industrial development strategies.

In our paper, we empirically assess the effect of fiscal decentralization on innovation 2017) in at least two elements. First, we implement a different estimation strategy, which exploits the afore-mentioned quasi-experimental design and that allows for the identification of a causal effect. Second, our main analysis is based on all Italian provinces and covers a longer time span.

Institutional context

Italy has a three-tier system of territorial governance, which includes regions, provinces and municipalities. [START_REF] Barone | Sull'ampiezza ottimale delle giurisdizioni locali: il caso delle province italiane[END_REF] According to the principles established by the Constitution, regions, provinces, and municipalities are autonomous bodies with their own statutes, powers and functions.

Both the State and the regions exercise legislative power; but the legislative power of regions is limited to matters not expressly reserved to the legislation of the State.

Municipalities, provinces, and regions have financial autonomy for revenues and expenditure. Specifically, they establish and apply their own taxes and manage their own income, according to the principles of coordination of public finance and the tax system. [START_REF] Besley | Incumbent behavior: Vote seeking, tax setting and yardstick competition[END_REF] At the birth of the Italian Republic there were 19 regions, which largely corresponded with the territorial entities of the former Kingdom of Italy. In 1963, the "Abruzzi and Molise" region was divided into the two regions of Abruzzo and Molise, increasing the number of regions to 20, which has not been subject to changes. [START_REF] Buonanno | Migrating Mafias[END_REF] For about twenty years after the end of the World War II, the provincial geography of the Italian Republic remained substantially unchanged. [START_REF] Caglayan | Organized crime and Technology[END_REF] Subsequent changes in the provincial geography occurred in 1968, in 1970 and 1974 with the establishment of the provinces of Pordenone (Veneto), 10 Isernia (Molise) [START_REF] Cavalieri | Does fiscal decentralization improve health outcomes? Evidence from infant mortality in Italy[END_REF] and Oristano (Sardinia), [START_REF] Dalla Chiesa | Passaggio a Nord: la colonizzazione mafiosa[END_REF] respectively. A first major re-definition in the size and number of provinces was introduced in 1992, when 8 new provinces were added to the existing 95 -Biella and Verbano-Cusio-Ossola (Piedmont), [START_REF] Drezner | State structure, technological leadership and the maintenance of hegemony[END_REF] Crotone and Vibo Valentia (Calabria), Lecco and Lodi (Lombardy), Prato (Tuscany), and Rimini (Emilia Romagna). [START_REF] Eaton | International patenting and the European Patent Office: A quantitative assessment[END_REF] These new administrations became operational in 1995. A second reform wave occurred in 2001 and affected exclusively the territory of Sardinia, where 4 new provinces (Olbia-Tempio, Ogliastra, Medio Campidano and Carbonia-Iglesias) were established. They became operational in 2004 and represented the first experience of new provinces created by regional law, which led to a series of coordination issues with the national legislation. [START_REF]Threat Assessment: Italian Organised Crime[END_REF] The third and last major redefinition of the provincial geography occurred in 2004, when the total number of geographical provinces increased to 110, with the inclusion of 3 new provinces -Monza and Brianza (Lombardy), Fermo (Marche) and Barletta-Andria-Trani (Apulia) -that became operational a few years later. [START_REF] Feld | Federalism, decentralization, and economic growth[END_REF] In 2012, people in Sardinia voted for the abolition of the 4 provinces established in 2001. The output of this referendum had however effect only in 2016, when the province of Sud Sardinia was created with the contextual redefinition of the province of Cagliari. [START_REF] Fisman | Decentralization and corruption: evidence across countries[END_REF] The current number of Italian provinces (including the metropolitan areas) is 107.

For the purpose of our study, we focus on the three main waves of reforms that have affected the Italian provincial geography, namely the ones approved in 1992, 2001 and 2004. Since several other laws may have had an impact on the actual power and functions of the provinces over time, in our analysis we also account for such changes (which however applied simultaneously to all provinces at the national or regional level). [START_REF] Huther | Applying a simple measure of good governance to the debate on fiscal decentralization[END_REF] Over our sample period, the functions of the provinces were: protection and enhancement of the environment (soil, water, energy resources) and prevention of disasters; enhancement of cultural heritage; transports; organization of waste disposal (detection, discipline and control of discharges of water and atmospheric emissions); health, hygiene and public prophylaxis services; education (including school buildings); data collection and processing, technical and administrative assistance to local authorities; promotion and coordination of productive, commercial, tourism as well as social and cultural activities.

In 2014, the Italian government approved a local government reform that has substantially redesigned the functions of provinces. [START_REF] Lucas | On the mechanics of economic development[END_REF] According to the new law, several matters provinces had competence on have been transferred to regions and municipalities. However, provinces are still in charge of the territorial planning (coordination, defense and enhancement of the environment), the planning of transport services, the construction and maintenance of provincial roads.

Estimation strategy

To identify the effects of the three main reforms of provincial geography on firm innovation, we deploy a difference-in-difference estimation approach. Specifically, we estimate the following linear model:

P atApps it = α + βtreated i + θ 1 post 92 t + θ 2 post 01 t + θ 3 post 04 t + δtreated × post it + η t × ξ k + u i + v it . (1)
P atApps indicates the number of patent applications at EPO, which varies over time at the provincial level; treated is a dichotomous variable that takes value 1 if the province is/will be a "new province"; post 92, post 01, and post 04 are dummy variables indicating each post-treatment period; the interaction treated × post is our treatment [START_REF] Lucas | On the mechanics of economic development[END_REF] http://www.gazzettaufficiale.it/eli/gu/2014/04/07/81/sg/pdf. variable, shown in bold in model (1). We are interested in the sign and size of the parameter δ, which informs on the treatment effect associated with the birth of new provinces, regardless of the wave. To account for potential time-varying region specific effects we include the interaction term η × ξ. The term u controls for time-invariant province fixed effects, while the term v is an idiosyncratic term.

We also perform an alternative and more detailed specification of the model (1), where both treated and the interaction treated × post are split into three components, depending on whether we refer to the first, the second, or the third reform wave. This more flexible specification, model (2), allows us to identify the treatment effects associated with each reform and to capture the potential heterogeneity in their impact.

P atApps it = α + β 1 treated 92 i + β 2 treated 01 i + β 3 treated 04 i + θ 1 post 92 t + θ 2 post 01 t + θ 3 post 04 t + δ 1 (treated × post 92 it )+ δ 2 (treated × post 01 it ) + δ 3 (treated × post 04 it ) + η t × ξ k + u i + v it .
(2) 5 Data

Data sources

We retrieve data on patent applications at the European Patent Office (EPO), measured at the provincial (NUTS 3) level over the 1977-2012 time period, from the EU-ROSTAT website "Science and Technology". [START_REF] De Mello | Fiscal decentralization and governance: A cross-country analysis[END_REF] We are aware that using patent applications implies that only certain types of innovations, from a limited range of industries, are taken into account. We acknowledge as well that, since a number of firms may prefer secrecy over patenting, some of the data might not be available. On the other hand, patent applications are subject to formal tests of novelty and non-obviousness, and they are a good proxy of the companies' research efforts, which often result into successful innovations.

Information on the institutional change of the Italian provincial geography is obtained from the website of the Ministry of the Interior. [START_REF] Musgrave | The Theory of Public Finance[END_REF] A careful analysis of the anti-mafia parliamentary committee reports allowed us to collect data on the number of convicted high-rank bosses who faced forced exile (confino) to Northern and Central provinces until mid-70's. As in Buonanno and Pazzona (2014) and Scognamiglio (2018), we use the number of mafiosi in confino to identify the provinces that were more/less exposed to the transplantation of organized crime. This measure of mafia exposure is not free from criticism. Indeed, since it has not been kept track of every boss who moved to the North independently, the aforementioned parliamentary reports do not provide a comprehensive accounting on all resettlements from the historical mafia regions (Dalla Chiesa, 2017). Although we share this concern, we deem the information retrieved in these reports to be reliable and valuable in order to identify the areas that have been highly exposed to the pressures and activities of criminal groups led by top mafia bosses. for the 2004 reform. A general increase in the average number of patent applications is observable when comparing before-and after-reform periods. The provinces Monza and Brianza and Barletta-Andria-Trani represent two extreme cases: the former experiences an increase in the number of applications of more than 300%, whereas the latter reports a value of 0 applications for both periods. This is consistent with the evidence that Southern provinces typically show a smaller raise in their innovation activities than Northern ones.

Descriptive statistics and evidence

[Table 1

about here]

A broader picture of the contribution of each province to the national innovation outcome emerges from Table A1, which informs on the ranking (ascending order) by average patent applications at EPO over the period 1986-2012. This table shows that the 5 least innovative provinces are new ones, and all of them are located in Southern Italy. The most innovative new provinces are either in Lombardy (Lecco and Monza and Brianza are 81st and 82nd, respectively) or in Emilia Romagna (Rimini is 73rd). A1.

Figure A1 translates into a geographical dimension what just discussed for Table

It is worth noting that systematic differences between treatment (new provinces) and control groups (old provinces) in the sample are not an issue for the implementation of the DID approach, which does require a random assignment to treatment (Angrist and Pischke, 2008; and Cameron and Trivedi, 2005). Instead, a key identifying assumption is that treatment and control groups show similar trends prior to the treatment (parallel trends assumption). We provide descriptive evidence concerning parallel trends in Figures 456, which show linear fits of patent applications for both treated (blue dots) and controls (red dots) by reform wave. Parallel trends appear to emerge only for the 1992 reform. For the 2001 reform, non-parallel trends are particularly stark, as we observe a clear divergence in the pre-treatment values of patent applications between the two groups of provinces. The 2004 reform also shows nonparallel trends, it is however interesting to notice that the slope of the trend associated with old provinces turns negative after 2004, whereas the slope associated with new provinces becomes steeper in the after-reform period. This may suggest that the slight divergence in the pre-treatment period is not due to an anticipation effect in terms of innovation potential for the areas set to become provinces.

Our main findings are reported in Tables 2 and3, which show the average treatment effects carried by the interaction terms over different specifications. Table 2 reports the average overall treatment effect (i.e., the parameter δ), while Table 3 reports the average treatment effect by reform wave (i.e., δ 1 , δ 2 and δ 3 ). Standard errors are all clustered at the provincial level, which is the unit of analysis for each observational year in our sample period 1977-2012. In Table 2, we gradually augment the DID specification with no control variables (base specification) either by adding further covariates or by removing the 5 provinces that innovate the most, namely Milan, Turin, Bologna, Rome and Vicenza. More specifically, we sequentially (a) add to the base specification, column (i), time and province fixed effect, column (ii); (b) replace time with region-time specific effects, column (iii); (c) exclude the top 5 performers, column (iv). The inclusion of province fixed effects substantially improves the explanatory power of our model, which jumps from 3% to 81% and remains relatively stable in the subsequent specifications. The treatment effect is always negative and increases in its numerical value when we account for time-varying region specific effects, even though the point estimate becomes somewhat less precise. Once we exclude the top 5 performers, the coefficient estimate decreases by about a third of its size. We deem the estimate in specification (iv) to be the most reliable, as the underlying model allows for a better comparability between treatment and control groups. This estimate suggests that becoming a new province may decrease the number of patent application at EPO by about 6.5 units, as compared to the old provinces. Taking into account the already rather low average number of patent applications observed in the Italian territory, a reduction of this magnitude in our innovation measure is undoubtedly relevant.

[Table 2 about here]

In Table 3, we report the treatment effect by reform wave. The more flexible and informative model is reported in column (i), where we use the same covariate set as we did in the last specification of Table 2. Interestingly, we find that the overall treatment effect is driven exclusively by the 1992 reform. Indeed, the treatment effects associated with the two more recent waves are much smaller and not statistically significant. We find that, on average, a province created in 1992 experiences a significant decrease in the number of patent applications of slightly more than 9.5, with respect to old provinces. This reduction appears to be less pronounced if we shorten the time window and look at the 1986-2012 period. The justification for this restriction is twofold. First, patent applications at EPO are filed in more systematically from 1985 onward. [START_REF] Oates | Fiscal Federalism[END_REF] Secondly, the shortening of the time span improves the parallelism in pre-treatment innovation trends between new and old provinces. [START_REF] Prud'homme | The dangers of decentralization[END_REF] A confounding effect might come from the potential influence of geographical spillovers: externality effects associated with firms' location decisions or labor mobility are likely to affect neighbor provinces. Conceptually, these effects may significantly bias our estimates either upward or downward, depending on whether innovative companies or workers decide to move to/from new born provinces from/to old ones. In order to control for such effects, we exclude neighbor provinces from the sample and re-estimate the model in column (iii). [START_REF] Prud'homme | Dynamics of China's provincial-level specialization in strategic emerging industries[END_REF] It is worth noticing that, by dropping neighbor provinces off the sample, the size of economic activity (i.e. agglomeration economy effect), related to the split from "donor" provinces, drops as well. After the exclusion of neighbor provinces, we find that the absolute magnitude of the 1992 reform effect almost doubles, i.e. a province born in 1992 reduces its patent applications by about 11 units on average, compared to an old province. This entails that the aforementioned externality effects bias downward the estimates reported in columns (i) and (ii).

Therefore, taking the specification related to column (iii) as our preferred one, we now test whether such negative treatment effects are geographically heterogeneous.

Besides being relevant on its own, the rationale for this check finds roots in the his- [START_REF] Oates | Fiscal Federalism[END_REF] See Eaton et al. ( 2004). [START_REF] Prud'homme | The dangers of decentralization[END_REF] Figures 4-6 report the same time span. [START_REF] Prud'homme | Dynamics of China's provincial-level specialization in strategic emerging industries[END_REF] We define as neighbor any old province sharing a border with a new one. The set of neighbor provinces includes Arezzo, Ascoli Piceno, Bari, Bergamo, Bologna, Cagliari, Catanzaro, Como, Cosenza, Cremona, Florence, Foggia, Forl-Cesena, Macerata, Milan, Novara, Nuoro, Oristano, Pavia, Pesaro and Urbino, Piacenza, Pistoia, Potenza, Reggio di Calabria, Sondrio, Turin, Valle d'Aosta/Valle d'Aoste, Varese, Vercelli.

torical Italian North-South economic divide. We regress our preferred specification on Southern-Central, column (iv), and Northern-Central regions, column (v), separately. [START_REF] Robalino | Does fiscal decentralization improve health outcomes? Evidence from a cross-country analysis[END_REF] Somewhat unexpectedly, it turns out that the 1992 reform effect is completely led by Northern-Central regions. More specifically, while the parameters in column (v) are similar to those reported in column (iii), [START_REF] Rodriguez-Pose | Does decentralization matter for regional disparities? A cross-country analysis[END_REF] no significant treatment effects are found for Southern-Central regions, column (iv). We consider this finding as particularly relevant because it suggests that innovation is hindered the most in more developed areas of the country, where apparently several productive companies in new provinces lower their innovation output, as compared to those in the control group. Conversely, innovation activities in more depressed regions are not significantly affected by the change in the provincial geography.

[Table 3 about here]

In this regard, it is interesting to understand the potential mechanism behind our results, i.e. to identify what factors may have caused a reduction in innovation activities in Northern-Central regions. Inspired by the papers studying the phenomenon of mafia transplantation in Italy (see Buonanno and Pazzona, 2014; and Dalla Chiesa, 2017), we test whether the exposure of provinces in Northern-Central regions to the influent mafia bosses sent in confino may provide a partial explanation to our findings. It is worth mentioning that highly ranked mafia bosses were sent in confino until mid-1970s (i.e. much earlier than the start of our sample period), and that, to our knowledge, the level of economic activity characterizing the arrival provinces was not a selection criterion (i.e. local economic development was orthogonal to the inflow of influent mafiosi).

We look at mafia transplantation as potential mechanism because mafia activities may contribute to increasing the cost of innovation in at least three ways: by making use of extortions or by imposing their input suppliers to firms; by corrupting public authorities and diverting public funds; and by promoting illegal and unfair competition. Thus, the establishment of a new province may increase the proximity of the transplanted mafia organization to the local authority, facilitating de facto the potential corruption of public officials or other illegal activities expected to hinder knowledge accumulation and innovation output. In Table 4, we split provinces in Northern-Central regions in 'abovemedian', 'between median and 1st quartile', and 'below 1st quartile' exposure to mafiosi in confino and run our preferred specification. This sample split is somehow driven by the number of new provinces included in each category: Lodi, Monza and Brianza in the 'above-median'; Biella and Lecco in the 'between median and 1st quartile'; and Fermo, Prato, Rimini, and Vebano-Cusio-Ossola in the 'below 1st quartile'. Consistently with the idea that mafia transplantation increases the innovation costs, we find that the new provinces that have been more exposed to mafiosi in confino significantly reduce their innovation output. This reduction amounts to about 17.7 patent applications for the 'above-median' category, and to about 10.7 and 1.1 for the remaining ones, respectively. Although the estimates are not significant for the last two categories, we find evidence of a monotonic decrease in the absolute value of the treatment effect with respect to mafia exposure at the provincial level.

[Table 4 about here] Finally, we report a number of robustness checks in Table 5. Specifically, columns (i) and (ii) show placebo tests to check the robustness of our main findings to a random assignment of the "new province" status to old provinces, maintaining the regional composition. This means that we treat new provinces as old ones, and some pre-existing provinces as new ones, while making sure that the number of new provinces remains the same within a region. We find no significant effects of placebo testing of models (1) and (2), i.e. for pooled and by wave estimates associated with randomly assigned treatment units. Column (iii) informs on the correlation between patent applications and mafiosi in confino on the pre-treatment period 1986-1992. This correlation is sizeable and significantly negative, supporting the hypothesis that provinces experiencing mafia transplantation to a higher extent show lower levels of innovation output. Lastly, in column (iv) we display the treatment effects from our baseline specification while disregarding the years related to the recent economic crisis (2009-2012). These estimates are in line with our main findings. Overall, the performed robustness checks corroborate our main results.

[Table 5 about here]

Discussion and conclusions

In this paper, we shed light on the impact of decentralization on innovation by focusing on the case of Italian provinces. More specifically, we use data on patent applications at the EPO (measured at the NUTS 3 level), and exploit changes in the provincial geography of the Italian territory, which occurred in different waves (1992, 2001 and 2004), to perform an empirical analysis that compares the innovation outcome between newly established and old provinces. By means of a DID estimation strategy, which accounts for province time-invariant and region time-varying effects as well as for interprovince spillover confounders, we find evidence of a significant detrimental effect of decentralization on innovation. This effect is quite heterogeneous as it varies both geographically and by reform wave. Indeed, it turns out that whereas decentralization significantly hinders innovation in provinces in Northern-Central regions (the more economically active areas of the country), it does not affect innovation in Southern-Central provinces. Moreover, we find that the decentralization effect seems to be entirely driven by the 1992 reform, which has been though the most extensive wave as it involved more regions than other waves did.

Although our findings may be the result of different mechanisms at play, we decided to dig deeper in our investigation by collecting information from anti-mafia parliamentary committee reports on the number of highly ranked mafia bosses who were subject to forced exile to municipalities in Northern-Central provinces. This mandatory resettlement-residence ('confino') policy that was in place until 1970's, well before the start of our sample period, unintentionally favored a process of mafia transplantation, as documented in Buonanno and Pazzona (2014) and Dalla Chiesa (2017). Furthermore, assuming that the level of economic development was not a selection criterion of the municipalities where such powerful 'mafiosi' were sent to, we deem reverse causation not to be an issue in our analysis. Thus, we consider mafia transplantation as potential mechanism involved because it may had increased innovation costs through the corruption of public authorities, the diversion of public funds, the promotion of unfair competition, and other illicit activities. We find evidence that newly established Northern-Central provinces that have been more exposed to mafiosi in confino suffered higher detrimental effect of decentralization, compared to the less exposed ones. This is consistent with the idea that corruption and collusion may be easier to organize and enforce in smaller subnational government domains, due to the higher proximity of public officials and various interest groups, even in areas with a relatively high level of economic development. Therefore, the reduction in local government size due to the reform waves may have favored the aforementioned illegal activities, increasing de facto the mafia's influence on the new provinces. It is worth emphasizing that the identification of the role of mafiosi in confino does not exclude other potential complementary mechanisms that we are not able to test in this study.

Our findings are policy relevant and provide recommendations not only to the Italian public administration but also to the territorial governance of several European and OECD countries that in the last decades have been widely infiltrated by mafia groups, as documented in the Europol's threat assessment on the Italian organized crime (Europol, 2013). Indeed, there exist increasing evidence of an ongoing process of transplantation on a global scale through the exploitation of foreign territories and communities which are silently under the control of Italian mafias (e.g. see Sergi and Lavorgna, 2016). Future studies in our agenda will shed more light on some of the consequences of this transplantation process. Note: New provinces are listed in bold.

  in the context of Italian provinces by exploiting the establishment of new provinces occurred in the over 1977-2012 time period. Two papers, Barone (2011) and Caglayan et al. (2017), are closely related to ours. Barone (2011) uses this same quasi-experiment and finds that the creation of new provinces in 1992 has not brought any benefits in terms of population growth, share of population that completed mandatory education, and quality of the roads. Differently from Barone (2011), we focus on the performance of Italian provinces in terms of innovation (i.e. number of patent applications) and use all reform waves implemented to establish new provinces over the years. Caglayan et al. (2017) provide empirical evidence that a greater presence of organized crime is related to lower innovation and firms technological level during 2005-2012. Similarly to Caglayan et al. (2017), we approximate the increase in the presence of organized crime with the number of mafiosi in confino, i.e. individuals who were forcedly resettled in areas other than the municipalities of residence (from Southern to Central and Northern Italian provinces) because convicted or strongly suspected to belong to criminal organizations. 5 Our paper differs from Caglayan et al. (

Figures 1 -

 1 Figures 1-3 show the geographical position of the new provinces by reform wave.

Figure 1 :Figure 2 :Figure 3 :

 123 Figure 1: New provinces -1992 reform

Figure 4 :

 4 Figure 4: Parallel trends -1992 reform

Figure 5 :

 5 Figure 5: Parallel trends -2001 reform

Figure 6 :

 6 Figure 6: Parallel trends -2004 reform

Table 1 :

 1 Descriptive statistics on treated by reform wave

	Province	Region	Patent applications EPO Patent applications EPO
			pre-treatment period	post-treatment period
	1992 reform		1986 -1992	1993 -1999
	Biella	Piedmont	2.21	4.89
	Crotone	Calabria	0	0.14
	Lecco	Lombardy	3.29	14
	Lodi	Lombardy	0.36	6.90
	Prato	Tuscany	1.71	6.26
	Rimini	Emilia Romagna	6.71	10.86
	Verbano-Cusio-Ossola	Piedmont	1.64	4.79
	Vibo Valentia	Calabria	0.07	0.14
	2001 reform		1995 -2001	2002 -2008
	Carbonia-Iglesias	Sardinia	0	0.67
	Medio Campidano	Sardinia	0.36	0.14
	Ogliastra	Sardinia	0.14	0.07
	Olbia-Tempio	Sardinia	1.21	2.14
	2004 reform		1998 -2004	2005 -2011
	Barletta-Andria-Trani	Apulia	0	0
	Fermo	Marche	0.14	3.36
	Monza and Brianza	Lombardy	17.07	53.55

Table 2 :

 2 Main results -average treatment effect

		(i)	(ii)	(iii)	(iv)
	Treatment effect	-11.16** -13.24** -19.25* -6.51*
		(4.86)	(5.52)	(11.08) (3.80)
	Province FE	No	Yes	Yes	Yes
	Year FE	No	Yes	No	No
	Region × year FE	No	No	Yes	Yes
	Exclusion of top5	No	No	No	Yes
	Observations	3,960	3,960	3,960	3,780
	Clusters	110	110	110	105
	R-squared	0.03	0.81	0.84	0.82
	Note: Standard errors are robust and clustered at the province
	level. *, **, and *** denote significance at the 10, 5, and 1 per-
	centage level, respectively. Top5 performers are Milan, Turin,
	Bologna, Rome, Vicenza.

Table 3 :

 3 Main results -average treatment effect by reform wave

		(i)	(ii)	(iii)	(iv)	(v)
	Treatment effect 92	-9.68** -5.76* -11.41* -0.25 -11.48*
		(4.20)	(3.44)	(6.09) (0.21) (6.17)
	Treatment effect 01	-1.11	-0.86	-0.26	-0.26	-
		(0.88)	(0.76)	(0.20) (0.20)	-
	Treatment effect 04	-0.84	2.88	-4.12	-1.33	-5.74
		(10.92) (11.44) (13.72) (2.85) (21.88)
	1986 -2012 time horizon	No	Yes	Yes	Yes	Yes
	Exclusion of neighbor provinces	No	No	Yes	Yes	Yes
	Northern-centre regions	Yes	Yes	Yes	No	Yes
	Southern-centre regions	Yes	Yes	Yes	Yes	No
	Observations	3,780	2,835	2,133	972	1,161
	Clusters	105	105	79	36	43
	R-squared	0.82	0.89	0.89	0.67	0.87
	Note: Standard errors are robust and clustered at the province level. *, **,
	and *** denote significance at the 10, 5, and 1 percentage level, respectively.
	All specifications include province FE and region × year FE, and exclude top5
	performers (Milan, Turin, Bologna, Rome, Vicenza). Northern-centre regions
	include Emilia Romagna, Friuli Venezia Giulia, Liguria, Lombardy, Marche,
	Piedmont, Trentino Alto Adige, Tuscany, Umbria, Valle d'Aosta, Veneto.

Table 4 :

 4 Potential explanation: mafiosi in confino among Northern-Central provinces Standard errors are robust and clustered at the province level. *, **, and *** denote significance at the 10, 5, and 1 percentage level, respectively. All specifications include province FE and region × year FE, and exclude top5 performers (Milan, Turin, Bologna, Rome, Vicenza). Time horizon is 1986 -2012. Estimates refer only to Northern-centre regions (Emilia Romagna, Friuli Venezia Giulia, Liguria, Lombardy, Marche, Piedmont, Trentino Alto Adige, Tuscany, Umbria, Valle d'Aosta, Veneto). New provinces in the "abovemedian" sample are: Lodi, Monza and Brianza. New provinces in the "between median and 1st quartile" sample are: Biella, Lecco. New provinces in the "below 1st quartile" sample are: Fermo, Prato, Rimini, Vebano-Cusio-Ossola.

		Above-median Between median and 1st quartile Below 1st quartile
		(i)	(ii)	(iii)
	Treatment effect 92	-17.74**	-10.73	-1.11
		(7.91)	(9.88)	(9.60)
	Observations	810	351	459
	Clusters	30.00	13	17
	R-squared	0.90	0.88	0.92

Note:

Table 5 :

 5 Robustness checks Standard errors are robust and clustered at the province level. *, **, and *** denote significance at the 10, 5, and 1 percentage level, respectively. All specifications include province FE and region × year FE, and exclude top5 performers (Milan, Turin, Bologna, Rome, Vicenza). Time horizon in placebo checks is 1986-2012. Placebo reform 1992 involves the following provinces: Alessandria, Cosenza, Mantua, Novara, Pavia, Pistoia, Ravenna, Reggio di Calabria. Placebo reform 2001 involves the following provinces: Cagliari, Nuoro, Oristano, Sassari. Placebo reform 2004 involves the following provinces: Brescia, Foggia, Macerata.

		Placebo checks Time horizon Time horizon
				1977-1992	1986-2008
		(i)	(ii)	(iii)	(iv)
	Placebo effect	0.81
		(3.99)
	Placebo effect 92		-3.22
			(3.85)
	Placebo effect 01		0.86
			(0.76)
	Placebo effect 04		9.71
			(13.81)
	Mafiosi			-0.35***
				(0.13)
	Treatment effect 92				-9.20*
					(5.41)
	Treatment effect 01				-0.06
					(0.36)
	Treatment effect 04				-6.71
					(10.54)
	Observations	2,835	2,835	960	1,817
	Clusters	105	105	60	79
	R-squared	0.89	0.89	0.75	0.89

Note:

Table A1 :

 A1 Ranking of provinces (ascending order) by average patent applications atEPO over 1986 -2012

	Position	Province	Patent applications EPO Position	Province	Patent applications EPO
	1	Barletta-Andria-Trani	0.04	56	Latina	7.64
	2	Ogliastra	0.06	57	Palermo	7.75
	3	Crotone	0.20	58	Belluno	7.88
	4	Medio Campidano	0.22	59	Ascoli Piceno	7.95
	5	Vibo Valentia	0.27	60	Salerno	7.96
	6	Enna	0.35	61	Ferrara	8.23
	7	Carbonia-Iglesias	0.36	62	Prato	8.84
	8	Agrigento	0.43	63	Arezzo	8.99
	9	Caltanissetta	0.52	64	Vercelli	9.22
	10	Oristano	0.62	65	Bari	10.29
	11	Campobasso	0.73	66	Trieste	10.90
	12	Trapani	0.74	67	Catania	10.92
	13	Ragusa	0.85	68	Macerata	11.29
	14	Nuoro	0.87	69	Piacenza	12.34
	15	Syracuse	0.94	70	Chieti	12.94
	16	Matera	0.96	71	Cremona	14.84
	17	Olbia-Tempio	1.04	72	Perugia	14.90
	18	Reggio di Calabria	1.10	73	Rimini	15.49
	19	Fermo	1.17	74	Forl-Cesena	17.71
	20	Grosseto	1.34	75	Napoli	18.00
	21	Rieti	1.48	76	Venezia	18.39
	22	Isernia	1.54	77	Siena	18.45
	23	Foggia	1.61	78	Ravenna	18.74
	24	Catanzaro	1.63	79	Pesaro and Urbino	18.78
	25	Benevento	1.73	80	Pavia	19.78
	26	Sassari	1.81	81	Lecco	20.70
	27	Viterbo	1.82	82	Monza and Brianza	20.73
	28	Potenza	1.87	83	Lucca	22.02
	29	Avellino	1.90	84	Pisa	22.99
	30	Messina	2.14	85	Trento	23.63
	31	Cosenza	2.26	86	Mantua	23.83
	32	Imperia	2.33	87	Bolzano/Bozen	24.24
	33	Taranto	2.46	88	Cuneo	25.31
	34	Sondrio	2.84	89	Novara	28.32
	35	Valle d'Aosta/Valle d'Aoste	3.02	90	Alessandria	29.12
	36	Massa-Carrara	3.03	91	Udine	40.54
	37	Lecce	3.62	92	Como	43.10
	38	Verbano-Cusio-Ossola	3.72	93	Ancona	43.11
	39	Terni	4.13	94	Genoa	45.74
	40	Gorizia	4.15	95	Pordenone	47.29
	41	Caserta	4.32	96	Verona	48.19
	42	La Spezia	4.32	97	Parma	50.30
	43	Biella	4.54	98	Reggio nell'Emilia	56.40
	44	Cagliari	4.58	99	Varese	58.41
	45	Livorno	4.80	100	Florence	70.72
	46	Rovigo	4.94	101	Padua	71.60
	47	Teramo	5.18	102	Modena	79.77
	48	Brindisi	5.33	103	Brescia	82.47
	49	L'Aquila	5.41	104	Bergamo	85.30
	50	Savona	5.43	105	Treviso	92.81
	51	Pistoia	6.04	106	Vicenza	111.60
	52	Pescara	6.08	107	Roma	140.97
	53	Asti	6.48	108	Bologna	190.51
	54	Lodi	6.60	109	Turin	257.15
	55	Frosinone	7.26	110	Milan	376.11

See the landmark contributions due to[START_REF] Tiebout | A pure theory of local expenditures[END_REF],[START_REF] Musgrave | The Theory of Public Finance[END_REF] and[START_REF] Oates | Fiscal Federalism[END_REF].

The availability of other data informing on the economic activity at the local level is very scant and certainly does not cover a similarly wide time period. For instance, data on provincial GDP are available only from 2000 onwards (see Eurostat website).

The ICGR is published by Political Risk Services, a business risk and forecasting organization that surveys business people and experts asking questions about levels of corruption in different countries.

[START_REF] Robalino | Does fiscal decentralization improve health outcomes? Evidence from a cross-country analysis[END_REF] use a panel of low-and high-income countries; Asfaw et al. (2007) study infant mortality rates in India, while Cavalieri and Ferrante (2016) consider Italy as institutional context.

[START_REF] Buonanno | Migrating Mafias[END_REF] use the number of mafiosi in confino to explain mafia transplantation to Central and Northern Italian regions; Scognamiglio (2018) uses it to analyze how the arrival of members of crime organizations affects the local economy, and find a positive effect on employment in the construction industry.

With the term "province" we refer to the provincial administrative level labeled with the NUTS 3 code by Eurostat.

Titolo V -Le Regioni, le Province e i Comuni http://www.governo.it/costituzione-italiana/ parte-seconda-ordinamento-della-repubblica/titolo-v-le-regionile-province-e-i.

https://dait.interno.gov.it/territorio-e-autonomie-locali/sut/1963.pdf.

The sole exception is the (part of the old) province of Trieste, which was reintegrated into the Italian national territory as a result of the London Memorandum in 1954. http://freeterritorytrieste. com/TREATIES/UN541005MEMORANDUMLONDRA.pdf.

Patent statistics derive from the raw database PATSTAT, see https://ec.europa.eu/eurostat/web/ science-technology-innovation/data.

http://www.interno.gov.it/en.

Northern-Central regions include: Emilia Romagna, Friuli Venezia Giulia, Liguria, Lombardy, Marche, Piedmont, Trentino Alto Adige, Tuscany, Umbria, Valle d'Aosta, Veneto. The Southern-Central area comprises the complementary set of regions.

[START_REF] Rodriguez-Pose | Does decentralization matter for regional disparities? A cross-country analysis[END_REF] 2001 treatment effects are not estimated for Northern-Central regions because the variation to identify such an effect is due to Sardinia, which is a region in the complementarity subset.