a regional impact analysis of european policies in rural...

26
87 th EAAE-Seminar. Assessing rural development of the CAP A Regional Impact Analysis of European Policies in Rural Areas Andrea Bonfiglio 1 Chiodo, E. 2 Abstract The objective of this paper is to evaluate the overall impact produced by the application of European development policies for rural areas of the Italian Marche region for the period 2000-2003. Towards this aim, the input-output approach is adopted. A Marche regional I-O table is firstly constructed using a hybrid method. To evaluate the overall impact throughout the territory, two models are developed and applied recursively: an interregional I-O model and a gravity model. As for the former, sub-interregional I-O tables are constructed, where the regions under study are, on one hand, the functional area receiving funds and, on the other hand, the rest of the Marche region. The gravity model is instead used to allocate among the other areas the impact calculated for the rest of the region. To show how the im- pact is distributed among regional areas, a Geographical Information System is used. The paper concludes illustrating the main results in terms of overall impact generated by policy and providing some considerations related to policy effectiveness. Keywords: Input-Output Approach, Policy Impact, Rural Development 1 Department of Economics, Polytechnic University of Marche, Piazzale Martelli, 8, 60121 Ancona, Italy, Tel: +39 071 220 70 [email protected] 2 Department of Food Science, University of Teramo (Italy).

Upload: others

Post on 18-Mar-2020

5 views

Category:

Documents


0 download

TRANSCRIPT

Page 1: A Regional Impact Analysis of European Policies in Rural Areasecsocman.hse.ru/data/135/673/1219/87EAAE_Bonfiglio_Chiodo.pdf · A Regional Impact Analysis of European Policies in Rural

87th EAAE-Seminar. Assessing rural development of the CAP

A Regional Impact Analysis of European Policies in Rural Areas

Andrea Bonfiglio1 Chiodo, E.2

Abstract

The objective of this paper is to evaluate the overall impact produced by the application of European development policies for rural areas of the Italian Marche region for the period 2000-2003. Towards this aim, the input-output approach is adopted. A Marche regional I-O table is firstly constructed using a hybrid method. To evaluate the overall impact throughout the territory, two models are developed and applied recursively: an interregional I-O model and a gravity model. As for the former, sub-interregional I-O tables are constructed, where the regions under study are, on one hand, the functional area receiving funds and, on the other hand, the rest of the Marche region. The gravity model is instead used to allocate among the other areas the impact calculated for the rest of the region. To show how the im-pact is distributed among regional areas, a Geographical Information System is used. The paper concludes illustrating the main results in terms of overall impact generated by policy and providing some considerations related to policy effectiveness. Keywords: Input-Output Approach, Policy Impact, Rural Development

1 Department of Economics, Polytechnic University of Marche, Piazzale Martelli, 8, 60121 Ancona, Italy, Tel: +39 071 220 70

[email protected] 2 Department of Food Science, University of Teramo (Italy).

Page 2: A Regional Impact Analysis of European Policies in Rural Areasecsocman.hse.ru/data/135/673/1219/87EAAE_Bonfiglio_Chiodo.pdf · A Regional Impact Analysis of European Policies in Rural

87th EAAE-Seminar. Assessing rural development of the CAP

2

1 Introduction In the past, rural development has been often associated to agriculture. The decrease in im-portance of agriculture in the developed economies has contributed to separate the concept of rural development from agriculture. Nowadays, it is largely acknowledged that rural devel-opment is something going beyond the concept of agricultural development. If we wanted to give a definition to rural development, we might define it as a long term strategy, oriented to maintain complexity and balance among components and to integrate rural areas in a com-plex process of sustainable development. From the economic standpoint, it means to organ-ize functions and non-agricultural employment forms within rural areas, promoting exchanges among sectors and territories and, so, breaking both isolation and mono-functionality charac-terizing the previous agricultural specialization (Sotte, 2002). This change in view has also had consequences in the definition of policies. Indeed, the process of CAP reform, from the introduction of the “MacSharry” reform to the mid-term review of Agenda 2000, has at-tempted to cope with the problem of rural development dedicating to the latter a more and more defined and wide chapter and transferring resources from the main pillar related to ag-riculture to the pillar of rural development. The declared aims of European rural development policies are: sustaining development in less advantaged areas through multi-sectoral pro-jects, preserving landscape and environment, reducing territorial disparities in order to favour more uniform and balanced development. In spite of these noble intentions, several criti-cisms have been arisen about credibility of these policies and the real political will to redraw the old agriculture-based policy towards the support to multi-sectoral development. A further element of reflection is that these policies tend to apply in an undifferentiated way to all areas within a given territory without specific identification of rural areas. Actually, in comparison with the previous EU programming period, in the current policy framework all structural poli-cies have been included within Objective 2 and a specific definition of policies for rural areas has not been accomplished. The criticisms arisen surely increase the usefulness of any analysis oriented to evaluate im-pact and effectiveness of these policies for rural areas. The objective of this research is just to attempt to assess impact in terms of income and em-ployment at a regional level produced by development policies in rural areas. The region under study is the Italian Marche region whilst the period considered is 2000-2003. Estima-tion of impact in this region represents an important objective for regional institutions, also considering that a relatively great volume of financial resources has been apportioned in this region, in order to stimulate economic development. But the difficulty of evaluating the real impact generated by these policies, taking account of specific peculiarities in terms of pro-ductive structure and territorial differences, impedes to express definitive judgment on the actual effectiveness of policies. For this reason, there is a strong need for methodologies able to assess impact considering regional peculiarities and isolating effects produced by policy from effects produced by other changes during the period in which policy has oper-ated. Among these methodologies, the input-output approach can represent a valid tool. In effect, input-output analysis is still considered an effective instrument to assess the impact in terms of output, and by a little extension, of income and employment generated by a varia-tion of final demand. This instrument permits not only to capture direct effects induced by a change in final demand in the directly involved sector but also those effects that are related to relationships among productive sectors and, through further extension, among different areas. This paper is articulated in the following way. In the first part, a description of policies ana-lysed is given. The second part is oriented to illustrate the methodology employed to assess impact, also facing the problem of allocating expenditure among sectors. The third part shows the results of the impact analysis at both territorial and sector levels. The last part is finally aimed at summarising the main results and provides some conclusions about policy effectiveness.

Page 3: A Regional Impact Analysis of European Policies in Rural Areasecsocman.hse.ru/data/135/673/1219/87EAAE_Bonfiglio_Chiodo.pdf · A Regional Impact Analysis of European Policies in Rural

87th EAAE-Seminar. Assessing rural development of the CAP

3

2 Development Policies in rural areas 2.1 An overview of policies in the Marche region Economic development policies co-financed by EU structural funds represent the main in-strument of programming and intervention at a regional level. Development policies, for the period 2000-2006, can be schematized as follows: structural policies for farms, policies for rural development, policies for revitalising areas facing struc-tural difficulties and policies for improving human resources. However, in the Marche region, none of these development policies has been specifically defined for rural areas (except for the initiative Leader +). In the Marche region, specific development policies for rural areas (except for the initiative Leader +) have not been defined for the period 2000-2006. The existing development poli-cies are: structural policies for farms, policies for rural development, policies for revitalising areas facing structural difficulties and policies for improving human resources. Here, we will focus on structural policies for farms and policies for rural development (Rural Development Plan) and policies for revitalising areas facing structural difficulties (Objective 2 Programme)3. The Marche region Rural Development Plan (RDP) is financed by the European Agricultural Guidance and Guarantee Fund (EAGGF). RDP is divided into three axes:

• Axis 1: Improvement of competitiveness and efficiency of agricultural and agro-industrial systems; Improvement of products quality. Measures included concern structural policies for farms and agro-food industries.

• Axis 2: Conservation and valorisation of rural landscape and environmental re-sources. In this axis, there are included measures pursuing objectives of environment and landscape conservation.

• Axis 3: Support to rural development. Measures included regard many different inter-vention: multifunctionality of agriculture, diversification, improvement of rural services, infrastructures, rural tourism.

Of 22 measures established by European Regulations, the Marche RDP includes 20 meas-ures. This choice is shared by most of the Northern and Central Italian regions. The area to which RDP is applied is the whole region. Only two measures are specifically bound to rural areas (Measure E – Less favoured areas; Measure O – Renovation and development of vil-lages and protection and conservation of the rural heritage). Public expenditure estimated is EUR 451.8 million whilst total expenditure (public and pri-vate) estimated is EUR 686 million. In terms of public expenditure, Axes 1, 2 and 3 absorb 42%, 46%, 11% of total, respectively. With reference to total expenditure, axis 1 absorbs most of resources, taking about 56% of total. Axes 2 and 3 instead absorb 31% and 13%, respectively (Chiodo, 2003). The other policy considered is the Objective 2 Programme. This Programme is aimed at revi-talising areas facing structural difficulties. It concerns areas with different types of socio-economic difficulties whether industrial, rural, urban or dependent on fisheries. Objective 2 policies are financed by the European Regional Development Funds (ERDF), whose main objective is to promote economic and social cohesion within the European Union through reduction in imbalances between regions or social groups.

3 Policies for improving human resources (Objective 3 policies) were excluded from the analysis for the difficulty of identifying

the territorial distribution of funds among sub-regional areas.

Page 4: A Regional Impact Analysis of European Policies in Rural Areasecsocman.hse.ru/data/135/673/1219/87EAAE_Bonfiglio_Chiodo.pdf · A Regional Impact Analysis of European Policies in Rural

87th EAAE-Seminar. Assessing rural development of the CAP

4

In the Marche Region, Objective 2 Programme involves 146 municipalities (of these, 28 are only involved partially), with a population of 351.000 inhabitants (28% of the regional popula-tion) and a surface equal to 66% of the total regional surface. Three types of areas with socio-economic difficulties are considered:

• Rural areas: this is a wide and relatively homogeneous rural area, comprising all the inland area of the region and characterized by mountains and hills; 125 munici-palities of all the four provinces of the region are concerned.

• Industrial areas: it comprises a group of 14 municipalities in the province of Ascoli Piceno, characterized by high unemployment and crisis in industrial sectors (textile, agro-food, chemicals and mechanics).

• Areas dependent on fishery: it aggregates 7 municipalities (considered partially) with structural difficulties in reorganizing fishery.

As clearly emerges from this classification, most of areas involved by the Marche region Ob-jective Programme have been classified as rural areas. The objective of the programme is to strengthen production and to promote tourism by ex-ploiting the region's environmental and cultural assets guaranteeing sustainable develop-ment. Analogously to RDP, even Objective 2 Programme is articulated into three axes:

• Axis 1: Development and improvement of manufacturing industry The Measures of this Axis are finalised to support industrial and craft businesses, en-vironmental investments, investments for the purchase of services, interventions for SMEs, start-up and development of local businesses, and to offer assistance.

• Axis 2 : Ecological system and territorial improvement This second Axis is devoted to protection and improvement of the region's environ-mental and cultural heritage. There are included measures finalized to optimise the waste management system, sustain the protected areas system and the environ-mental education centres network, rationalise the transport system, improve rural vil-lages, associated infrastructures and port infrastructures.

• Axis 3 : Economic diversification and development of local potential The third Axis relates to diversification of economic activities towards tourism, culture and sectors able to promote improvements in quality of life and social well-being. In order to favour this diversification, assistance will be given to the Objective 2 areas in order to improve their network of care services. Development of information society at a local level and development of commercial and craft businesses are necessary measures that have to be adopted in order to guarantee diversification and develop-ment.

The public expenditure estimated for the period 2000-06 is EUR 251 million. Of public ex-penditure, Axis 1 absorbs 46%, Axis 2 absorbs 26% of resources, Axis 3 absorbs 26% of resources. The remaining resources are for Technical Assistance.

2.2 The measures analysed This research focuses on total expenditure (public commitments4 and private co-financing) in favour of rural areas, related to measures put into action until 2003 in the Rural Development Plan and in the Objective 2 Development Programme5.

4 Public commitment is the first definition level of expenditure which allows identifying beneficiaries and localizing projects at a

municipal level. The focus on public commitments instead of payments is justified by the need to consider the widest range of activated measures.

5 Focusing on “total expenditure” instead of public expenditure only, we are considering the whole investment on territory.

Page 5: A Regional Impact Analysis of European Policies in Rural Areasecsocman.hse.ru/data/135/673/1219/87EAAE_Bonfiglio_Chiodo.pdf · A Regional Impact Analysis of European Policies in Rural

87th EAAE-Seminar. Assessing rural development of the CAP

5

As rural areas, we considered the 125 rural municipalities that have been identified by Re-gion Marche within the Objective 2 Development Programme6. For the period 2000-03, the Marche region RDP has put into action 13 measures, with a total public expenditure of EUR 183.1 million and a total investment (public and private) of EUR 388.3 million. This is a conspicuous part of the total amount of resources programmed for the period 2000-06: 47% of public expenditure and 86% of the total investment (the difference depends from the different percentage of application of the measures and from the different percentage of private contribution in every measure). In particular, 11 Measures are considered. They correspond to public expenditure of EUR 176.7 million and total investment (public and private) of EUR 383.1 million. Two measures were not considered for lack of municipal data; within each measure, some projects of small amount were excluded since it was not possible to identify a specific beneficiary at a munici-pal level (i.e. projects concerning more than one municipality and managed by super-local authorities). The total amount of resources assigned to rural areas is equal to EUR 192 million (total in-vestment) which is equal to 50% of the analysed amount of resources (Tab. 1).

Tab. 1: Total expenditure in rural areas for RDP Measures, Marche (Italy), 2000-03 (Thousand Euro)

Marche Region Rural Municipalities Measure Intervention

Total investment

Public commitments

Total investment

Public commitments

A Investments in agricultural hold-ings 207,025 83,456 126,862 50,329

B Setting up of young farmers 7,282 7,282 5,144 5,144

C Training 951 802 84 67D Early retirement 1,123 1,123 876 876

G Improvement processing and marketing of agricultural products 87,956 35,182 22,970 9,188

M Marketing of quality agricultural products 2,200 1,460 n.a n.a,

V Financial engineering 10,000 5,000 n.a n.a, TOTAL AXIS 1 316,536 134,305 155,937 65,605E Less-favored areas 7,933 7,933 6,989 6,989

F Agri-environment Measures 13,335 13,335 6,637 6,637

H Forestation of agricultural land 3,690 3,690 1,843 1,843

I Other forestry Measures 8,097 7,550 6,673 6,134

Q Agricultural water resources man-agement 4,428 4,130 0 0

TOTAL AXIS 2 37,482 36,637 22,142 21,603

P Diversification of agricultural activities 34,289 12,205 13,925 5,313

TOTAL AXIS 3 34,289 12,205 13,925 5,313 TOTAL 388,308 183,148 192,003 92,521Source: artwork by the author on Marche Region data The Marche Region Objective 2 Programme, in the period 2000-03, has a different level of realization because of delay in programming and executing intervention: total public expendi- 6 Criteria the Marche Region used for defining municipalities as rural are: (a) population less than 100 inhabitants per kilome-

tre; (b) substantial decrease in population in the last ten years; (c) socio-economic difficulties deriving from a big decrease in employment in agricultural sector. Through the application of these criteria, 125 rural municipalities have been identified.

Page 6: A Regional Impact Analysis of European Policies in Rural Areasecsocman.hse.ru/data/135/673/1219/87EAAE_Bonfiglio_Chiodo.pdf · A Regional Impact Analysis of European Policies in Rural

87th EAAE-Seminar. Assessing rural development of the CAP

6

ture is equal to EUR 91,5 million (36% of the total). 2001 is the first year of application. The main part of expenditure is concentrated in the year 2003. We only considered the years 2001 and 2002, since these were the only years in which data were available at a municipal level. The total amount of resources considered is equal to EUR 35.5 million of public expenditure; the total investment (including private amount) is equal to EUR 80.1 million. Resources assigned to rural areas amount to EUR 45 million (total investment), i.e. 56% of the analysed amount of resources (Tab. 2).

Tab. 2: Total expenditure in rural areas for Objective 2 Programme Measures, Marche (Italy), 2000-02 (Thousand Euro)

Marche Region Rural Areas Measure Intervention Total

investment Public

commitments Total

Investment Public

commitments

1_1_2 Aids to investments for SMEs 54,301 22,156 32,825 6,562

1_3_1 Quality and technological innovation services for SMEs 6,302 2,190 2,891 997

1_3_2 Marketing and internationalisa-tion services for SMEs 1,757 527 1,281 384

1_4_1 Research and innovation ser-vices infrasctructures 1,428 428 1,136 341

1_4_2 Production areas infrasctructu-res 3,168 819 2,415 676

TOTAL AXIS 1 66,955 26,121 40,548 8,9592_5 Inter-modal infrastructures 6,685 5,345 0 0

2_6_1 Public transport services 2,555 2,024 2,522 1,996

2_6_2 Equiped areas and parking for public transportation 1,873 1,124 1,519 911

TOTAL AXIS 2 11,113 8,494 4,041 2,9083_1_1 Tourist promotion 1,607 439 0 03_2_3 Regional Museum system 519 397 311 190

TOTAL AXIS 3 2,126 835 311 190 TOTAL 80,194 35,450 44,900 12,057Source: artwork by the author on Marche Region data

3 Methodology to evaluate the overall impact In order to assess the overall impact in terms of employment and income generated by de-velopment policies for rural areas in the Marche region, the I-O methodology is adopted. Pol-icy funds are addressed to subjects located in specific areas having peculiar characteristics. Therefore, expenditure can produce effects that differ according to the localization of recipi-ents. An I-O approach oriented to the whole region would not allow capturing specificities characterising the different areas benefiting from public funds since it is based on the as-sumption that industries of different areas have the same pattern of consumption. To take account of these peculiarities, an interregional I-O approach is employed where the regions under study are, on one hand, the area receiving funds, and, on the other hand, the rest of the region Marche. This approach allows not only taking into account the specific characteris-tics of the area considered but also analysing the economic relationship existing between the recipient area and the rest of the regional economy. The territorial focus is on the concept of functional economic area, i.e. an area in which businesses and households tend to concen-

Page 7: A Regional Impact Analysis of European Policies in Rural Areasecsocman.hse.ru/data/135/673/1219/87EAAE_Bonfiglio_Chiodo.pdf · A Regional Impact Analysis of European Policies in Rural

87th EAAE-Seminar. Assessing rural development of the CAP

7

trate most of their services. In Italy, these areas have been identified for all regions defining the so-called Labour Local Systems (LLS)7 (ISTAT, 1991). In the Marche region, 44 different LLSs have been identified for the year 1991. Of these, we have analysed 29 LLSs (66% of the number of LLSs) 8 into which rural municipalities, benefit-ing from the development policies analysed, fall9. The basis of the interregional I-O model is a regional I-O table constructed by a hybrid method. Interregional and intersectoral matrices at sub-regional level are instead derived by a non-survey method. Finally, the impact calculated for the rest of the region is distributed throughout the territory by a gravity model10.

3.1 Constructing a regional I-O table The first step is construction of an I-O table for the Marche region. Towards this aim, a hybrid approach was used. This approach is currently considered the most feasible method to de-rive regional I-O tables (Lahr, 2001a; Fritz et al., 2002). It combines non-survey techniques for estimating regional direct requirements tables with superior data, which are obtained from experts, surveys and other reliable sources (primary or secondary). It is therefore a compro-mise between the survey and non-survey approaches in order to gain the advantages of both and to avoid the main disadvantages. Therefore, hybrid methods are less costly than survey methods and more accurate than non-survey methods in generating re-gional I-O tables. Phibbs and Holsman (1982) estimated that hybrid tables can be produced at about one-tenth of the cost of survey-based tables and in three months instead of two years which is the usual time necessary to construct survey-based tables. A further advantage lies in the modu-lar separability and thus in a major possibility of revision and updating (Greenstreet, 1989). Within the hybrid approach, several methods have been conceived. For an overview of these methods, see, for instance, Bonfiglio (2004). For this analysis, we employed the well-known GRIT methodology (Jensen et al., 1979). GRIT is a system producing variable-interference non-survey based tables. It relies on a series of mechanical steps to derive regional coeffi-cients from a national matrix, but provides the possibility at different stages for the insertion of superior data, which analysts consider to be more reliable than those obtained by me-chanical processes. Data collection efforts are concentrated on larger coefficients given their bigger impact on multipliers, while smaller coefficients can be neglected. This allows the cal-culation of tables to the degree of accuracy definable as “free from significant error”.

7 LLSs are aggregations of Italian communes deriving from a research carried out by ISTAT and IRPET in collaboration with

the University of Newcastle Upon Tyne on the basis of data on commuting of households’ components for work reasons. The main objective is construction of a territorial grid determined by population movements for work reasons. The resulting territorial areas are geographical areas in which population movements tend to be concentrated. In this way, basic adminis-trative units (communes) are aggregated on the basis of social and economic relationships. Criteria used to identify LLSs are: (a) self-containing; (b) contiguity; (c) space-time relationship. Self-containing refers to an area where businesses are concentrated and offer work and residential opportunities to most people living in this territory; self-containing also refers to the capability of an area of containing most of human relationships between workplaces and social activities (places in which population lives). An area having the characteristic of self-containing is defined as a local system i.e. a social and economic entity aggregating employment, purchases, social opportunities and relationships. Contiguity means that communes within a LLS must be contiguous. Finally, space-time relationship refers to the distance and time necessary to reach workplace from living place. By these criteria and using proper clustering techniques, 784 LLSs have been identified on the national territory (ISTAT, 1997).

8 Some LLSs, located on borders of the Marche region, comprise both Marche communes and communes of other regions. In this analysis, since focus was on the Marche region, only the Marche communes have been considered. Therefore, bounda-ries of LLSs including foreign communes have been redrawn eliminating communes outside the Marche region. As a possi-ble result, effects measured for these LLSs can be underestimated.

9 We decided to focus on this territorial level instead of municipalities, for two main reasons. First, municipalities are integrated into a wider system of economic and social relationships, which the concept of LLS attempts to represent. Therefore, funds given to subjects residing in a certain municipality do not produce effects limited to the same municipality but spread in a larger and relatively self-sufficient territory characterized by intense interchange of goods, services and people (i.e. the La-bour Local System). Second, as better described in par. 3.3, the methodology employed here is suited to be applied at this territorial level. A lower territorial level could have introduced a factor of impact overestimation in this analysis.

10 This methodology has been already employed by Bonfiglio (2002). The main differences lie in the extension of the analysis to all rural development policies (not only policies to agri-tourism sector), the focus on functional areas instead of municipali-ties, ex-novo construction of a regional I-O table by a hybrid method, the use of a different non-survey method to derive in-terregional and intersectoral matrices.

Page 8: A Regional Impact Analysis of European Policies in Rural Areasecsocman.hse.ru/data/135/673/1219/87EAAE_Bonfiglio_Chiodo.pdf · A Regional Impact Analysis of European Policies in Rural

87th EAAE-Seminar. Assessing rural development of the CAP

8

Simplifying, the GRIT methodology like other hybrid methods is composed of 5 main phases: (a) mechanical derivation of coefficients by a non-survey method; (b) insertion of superior data in terms of coefficients; (c) derivation of a full I-O table; (d) insertion of superior data in terms of flows; (e) final balancing. For the application of the first phase, three different kinds of data requirements are identifi-able. First requirement of the GRIT methodology is the availability of a national I-O table. We took a national I-O table dated from 199711 (Rampa, 2001). It is symmetric, containing 42 sectors, valued at basic prices, expressed in total flows (domestic plus import flows), in current values and with imputed banking services allocated among intermediate costs. The table is consis-tent with the ESA1979 European accounting methodology12. Second requirement is the availability of employment data if other data (such as output or value added data) are not available. Employment data at both regional and national level were available for 24 sectors from 1996 middle Census of industry and services and ISTAT regional accounting (as for agriculture). At national level, employment data were also avail-able for 42 sectors corresponding to those of the national I-O table (Rampa, 2001). One pos-sibility was to aggregate national sectors up to 24 sectors for which regional data were avail-able and then to regionalize. However, this could have caused significant error due to aggre-gation in terms of multipliers (Lahr and Stevens, 2002). To minimize error, practice suggests aggregating after regionalizing. If detailed regional data are lacking, regional data could be disaggregated using national ratios. So, we decided to estimate regional employment of missing sectors by national weights represented by employment ratios. In so doing, it was possible to obtain employment data at regional level for all sectors represented in the na-tional I-O table. In order to ensure consistency, national employment was derived using cen-sus data and disaggregating these latter on the basis of national shares calculated from em-ployment data available for 42 sectors. Using data about national coefficients and employment, estimates of regional I-O and import coefficients were obtained applying the Simple Location Quotient (SLQ). Other non-survey methods could be used. However, recent empirical evidence has demonstrated that the kind of non-survey method does not affect significantly results within the hybrid procedure since the final balancing procedure tends to level considerably the initial differences characterizing different methods in order to make the structure of the regional table consistent with the sys-tem of the inserted superior data (Bonfiglio, 2004). Superior data at a coefficient level were not available; therefore we proceeded to the third phase of derivation of a complete I-O table. In this phase, estimates of regional output are necessary. These data were not available therefore were indirectly estimated by labour in-come ratios (Lahr, 2001b). A full 1997 42-sector regional I-O table was derived, where pri-mary inputs were value added, imports and other final payments (including all the other cate-gories such as taxes, subsidies and transfers)13 and, following the example of GRIT, final demand was disaggregated into household consumption, exports and other final demands (including public expenditure, investments and changes in inventory). After deriving a full regional I-O table, superior data were inserted about: value added, labour income, exports outside Italy, household consumption, other final demands. Moreover, fur-ther data were collected about agriculture sector, i.e.: total intermediate costs related to agri-

11 We decided not to update the national table for two main reasons. First, employment data at disaggregated sectoral level,

necessary to regionalize the national table, were available only for 1996. Second, empirical evidence has shown that an in-put-output table can remain valid for a certain number of years (Tilanus and Rey, 1964; Carter, 1970; Conway, 1980).

12 As known, the accounting system has been reformed introducing the so-called ESA1995 (ISTAT, 1996). For Italy, the only available table constructed by this new system (when this paper was written) was dated from 1992. For this, it was preferred to employ a more recent table even if constructed by the previous methodology.

13 In GRIT, only two categories of final payments are considered: value added and imports. However, there exist other compo-nents that do not fall into the mentioned categories. We classified them as “other final payments”.

Page 9: A Regional Impact Analysis of European Policies in Rural Areasecsocman.hse.ru/data/135/673/1219/87EAAE_Bonfiglio_Chiodo.pdf · A Regional Impact Analysis of European Policies in Rural

87th EAAE-Seminar. Assessing rural development of the CAP

9

cultural sector (including hunting, forestry and fishing) and seed, fertilizer and pesticide con-sumption in agricultural sector. The insertion of superior data raised consistency problems internal to the table. To solve these problems, the regional I-O table was balanced using a non-linear programming tech-nique based on the Friedlander’s minimand, adjusted for negative values (Friedlander, 1961; Bulmer-Thomas, 1982). Finally, the regional I-O tables was aggregated into 24 sectors to fit the I-O table to the sim-pler regional economic structure (see Appendix A).

3.2 An interregional I-O model The interregional I-O model considers two regions: the functional areas receiving public funds (the financed LLS) (region 1) and the rest of the region (region 2). In matrix form, the I-O system can be written as:

(1) |||

− − = − − − − × − − + − −

1 11 12 1 1

2 21 22 2 2

X A A X FD

X A A X FD

where X is output vector, A is the coefficient matrix and FD is the final demand vector. The solution of the system is the following:

(2)

1| || || |

− − − = − − − − − − − − − × − −

1 11 12 1

2 21 22 2

X I 0 A A FD

X 0 I A A FD

The system (2) represents the interregional I-O model which allows determining the output variation in the regions under study induced by a change in final demand. Output change takes also account of interrelationships between the regions through spill-over and feedback effects (Miller and Blair, 1985). To estimate employment and income impact the system (1) has to be modified transforming goods and services flows into employment and income flows respectively. The system (2) becomes:

(3)

1ˆ ˆ| |

| |ˆ ˆ| |

− − − = − − − − − − − − − × − −

1 11 -1 12 -1 11 1

2 21 -1 22 -1 22 2

E I 0 A e A e FD

E 0 I A e A e FD

(4)

1ˆ ˆ| || |

ˆ ˆ| |

− − − = − − − − − − − − − × − −

1 11 -1 12 -1 11 1

2 221 -1 22 -12 2

Y I 0 A h A h FD

Y 0 I FDA h A h

Where E indicates employment; Y is income; [ ]1 2, , , ne e e=e K is the employment coeffi-

cient vector where i i ie E X= ; [ ]1 2, , , nh h h=h K is the income coefficient vector where

i i ih Y X= . As can be easily deduced, a linear relationship between employment (income) and output is supposed. It is also hypothesised that local employment (income) coefficients are equal to those at a regional level, i.e. 1 2e = e = e and 1 2h = h = h .

3.3 Deriving interregional and intersectoral matrices To construct an interregional and intersectoral matrix, much information is needed. This problem is even more marked at a local level where available information is quite scarce and

Page 10: A Regional Impact Analysis of European Policies in Rural Areasecsocman.hse.ru/data/135/673/1219/87EAAE_Bonfiglio_Chiodo.pdf · A Regional Impact Analysis of European Policies in Rural

87th EAAE-Seminar. Assessing rural development of the CAP

10

often limited to data on employment. For this reason, indirect estimation techniques are usu-ally adopted. For an overview of these techniques see Bonfiglio (2004). In this analysis, we adopted the interregional Round’s approach (Round, 1972; 1978; 1983) based on the use of the location quotient method. This approach allows estimating both interregional imports and exports and offers a higher degree of internal consistency than single region applications. The use of the location quotient method is reasonable for several reasons: it proved to out-perform other non-survey methods like supply-demand pool (Shaffer and Chu, 1969a; 1969b; Czamanski and Malizia, 1969, Morrison and Smith, 1974; Eskelinen and Suorsa, 1980; Sawyer and Miller, 1983); it produced good results at a small scale in reproducing sur-vey-based tables (Morrison and Smith, 1974); it is particularly suited when employment data are the only available data and, finally, it is applied, in this analysis, to functional economic areas. In this regard, Robison and Miller (1988) have demonstrated that location-quotient-based techniques produce better results when applied to self-sufficient areas (that which functional economic areas are) because, otherwise, at local level, where interregional trade is more intense, the location-quotient approach would fail in capturing the volume of trade, overestimating the intraregional one. Within the location approach, several methods can be included, such as: simple location quotient, purchases-only location quotient, West’s location quotient, cross industry location quotient, symmetric cross industry location quotient, semilogarithmic quotient and Flegg’s location quotient. The Flegg’s location quotient (FLQ) (Flegg et al. 1995; Flegg and Webber, 1996a, 1996b, 1997; Brand, 1997; McCann and Dewhurst, 1998; Flegg and Webber, 2000) has been designed to overcome some drawbacks related to the simple location quotient (SLQ) and the cross-industry location quotient (CILQ). SLQ takes account of the size of the region and the weight of the selling sector but it neglects the importance of the purchasing sector. Instead, CILQ considers the importance of the selling and purchasing sectors, whilst it neglects the size of the region. The common result is that input coefficients (expressing the local production) are overestimated while import coefficients (expressing the dependency from other regions) are underestimated. On the contrary, the FLQ is designed in a such way that all the three variables are considered: importance of selling sectors, importance of pur-chasing sector and size of the region. Moreover, recent empirical evidence (Flegg and Web-ber, 2000) has demonstrated that the FLQ outperforms both CILQ and SLQ in reproducing survey-based models. Given the stronger theoretical validity and the latest empirical results, we decided to apply the FLQ in this analysis. The FLQ takes the following form:

(5) * *R Ri j

ij ijN Ni j

E EFLQ CILQ

E Eλ λ= ⋅ = ⋅

where ( )*2log 1 R NE E

δλ = + , 0 1δ≤ < ; *0 1λ≤ ≤ . δ is a parameter that has to be esti-

mated. The larger the value of δ , the greater the adjustment for regional imports. So, δ is inversely related to the size of the region. Empirical evidence has demonstrated that an ap-proximate value for δ of 0.3 is good for even very different regions (Flegg and Webber, 1997). The FLQ is used to estimate for a given region both input coefficients and import coefficients (which are export coefficients for the other region). Formally, for the region 1, it results that:

(6) 1 1

111

if 1if 1

Mij ij ij

ij Mij ij

a FLQ FLQa

a FLQ <= ≥

(7) ( )1 1

211

1 if 1

0 if 1

Mij ij ij

ijij

a FLQ FLQa

FLQ

⋅ − <= ≥

Page 11: A Regional Impact Analysis of European Policies in Rural Areasecsocman.hse.ru/data/135/673/1219/87EAAE_Bonfiglio_Chiodo.pdf · A Regional Impact Analysis of European Policies in Rural

87th EAAE-Seminar. Assessing rural development of the CAP

11

Where Mija is the input coefficient for the Marche region; 11

ija is the input coefficient for the

region 1; 21ija is the import coefficient for the region 1 (export coefficient for the region 2). To

estimate coefficients for the region 2 ( 22ija and 12

ija ), the systems (6) and (7) are analogously

applied using 2ijFLQ instead of 1

ijFLQ . The logic behind the above systems is that if sector i

in region 1 has a lower relative importance than that at the Marche level ( 1 1ijFLQ < ), the lo-cal production is not able to satisfy the entire local demand and part of production is imported from the rest of the region. Technically, the Marche input coefficient is reduced and the dif-ference is attributed to the import coefficient. Otherwise, if sector i in region 1 has a bigger relative importance than that at the Marche level ( 1 1ijFLQ > ) then the local production can fulfil all local requirements and no goods and services will be imported from the rest of re-gion. In this case, the input coefficient of the region 1 will be given equal to the Marche input coefficient whilst the import coefficient will be null. The approach described above was used to estimate 29 interregional and intersectoral ma-trices, one for every directly financed LLS. The starting I-O table from which these matrices were derived was the 1997 24-sector regional I-O table constructed by GRIT methodology.

3.4 Distributing expenditure among sectors An often undervalued question related to the possibility of linking policy to the I-O approach refers to sector distribution of funds. Development policies consist of a series of measures that are addressed to enterprises operating in given sectors. If activity sector of recipients was known, fund destination would be certain and, consequently, the problem of distributing funds established by each measure among the sectors represented within an I-O table would not exist. However, when activity sector of recipients is not known, some assumptions on fund distribution have to be made necessarily. This is the case of this research. In the litera-ture, the allocation question has been often neglected (Morillas et al., 2000). Morillas et al. (2000) illustrate a possible strategy in this regard. In their study, the measures of the CSF (Common Structural Funds) program for the period 1988-1993 are reclassified into eight ar-eas (BIPE classification). Funds aggregated into these areas are then distributed among 44 NACE-CLIO sectors on the basis of fixed percentages. This procedure could not be em-ployed directly in this research for two main reasons: (a) the sector disaggregation of the regional I-O tables differs from the 44-sector NACE-CLIO classification; (b) the procedure was conceived to distribute the 1983-1993 CSF program funds. Therefore, it was decided to apply the Morillas’ general approach, making some adjustments to conform it to the policies under discussion and to the sector disaggregation of the Marche I-O table. The first step was to reclassify the 21 measures into 10 categories (Tab. 3): (1) Investments in agriculture; (2) Aids to farms; (3) Incentives to cease farm activity; (4) Investments in agro-tourist farms; (5) Investments in agro-food industries; (6) Industrial equipment; (7) Aids to non-agricultural en-terprises; (8) Infrastructure; (9) Education and Research; (10) Studies, advice and communi-cation. All categories refer to investments in one or more sectors. The only exception is the category (3) which is based on the assumption that farmers will destine incentives received in the form of money transfers towards the purchase of goods and services. Therefore, pro-duction increases because of the increase in consumption of farmers.

Page 12: A Regional Impact Analysis of European Policies in Rural Areasecsocman.hse.ru/data/135/673/1219/87EAAE_Bonfiglio_Chiodo.pdf · A Regional Impact Analysis of European Policies in Rural

87th EAAE-Seminar. Assessing rural development of the CAP

12

Tab. 3: Connection between measures and categories Measures of development policies Categories RDP - Measure A 1. Investments in agriculture RDP – Measure F RDP – Measure H RDP – Measure I (forestry investments) RDP – Measure B 2. Aids to farms RDP – Measure E RDP – Measure D 3. Incentives to cease farm activity RDP – Measure P 4. Investments in agro-tourist farms RDP – Measure G 5. Investments in agro-food industries Obj.2 – Sub-measure 1.1.2 6. Industrial equipment Obj.2 – Sub-measure 2.6.1 7. Aids to non-agricultural enterprises Obj.2 – Sub-measure 3.1.1 Obj.2 – Sub-measure 3.2.3 RDP – Measure Q 8. Infrastructure Obj.2 – Sub-measure 1.4.1 Obj.2 – Sub-measure 1.4.2 Obj.2 – Sub-measure 2.6.2 RDP – Measure C 9. Education and Research RDP – Measure I (forestry research) Obj.2 – Sub-measure 1.3.1 10. Studies, advice and communication Obj.2 – Sub-measure 1.3.2 Source: artwork by the author

Once measures were reclassified, funds assigned to every LLS were first distributed among categories (see Appendix B); then, funds related to each category were redistributed among the 24 sectors of the regional I-O table according to a rearranged version of the matrix of redistribution employed by Morillas et al. (2000) (see Appendix C). This allowed estimating the vector of final demand related to every financed area.

3.5 Modelling territorial and sectoral policy impact Evaluation of total effects on the economy of the Marche region produced by injection of final demand in functional areas is effected by a sequential and reiterated use of the interregional I-O model and a gravity model. Denote with S the LLS receiving public funds, with R the rest of the region, with k the sector directly financed by policy and with F the amount of total expenditure related to sector k . The impact in terms of income in the LLS and in the rest of the region is derived applying the interregional I-O model as follows:

(8)

1ˆ ˆ| || |

ˆ ˆ| |

− − − = − − − − − − − − − × − −

S SS -1 RS -1 Sk

R SR -1 RR -1

Y I 0 A h A h FD

Y 0 I 0A h A h

where SkFD is the n-dimensional vector of final demand related to the directly financed LLS

and it results that [ ]0,0, , , ,0F=SkFD K K . Note that the k-th element of the vector is equal to

F while the other elements are null. The impact in terms of employment can be derived in a very similar way.

Page 13: A Regional Impact Analysis of European Policies in Rural Areasecsocman.hse.ru/data/135/673/1219/87EAAE_Bonfiglio_Chiodo.pdf · A Regional Impact Analysis of European Policies in Rural

87th EAAE-Seminar. Assessing rural development of the CAP

13

The total variation related to the rest of the region, i.e. 1

nRi

iY

=∑ and

1

nRi

iE

=∑ , is then distributed

among the other LLSs through the application of a gravity model (Bonfiglio, 2002). The hy-pothesis is that the probability of employment (and income) variation attraction exerted by a LLS is an indirect function of its distance from the financed LSS and a direct function of its ability to attract such a variation. The attraction probability of the LSS L relative to the varia-tion induced by total expenditure in the financed LLS S is given by:

(9) 2

2

1

( ) ( )( )

L LSL N

j jSj

dP j Sd

α

α=

= ≠

where LSd is the geographical distance between LLS L and the financed LLS S (this is a straight line distance between the centres of the respective LLSs) and N is the number of LLS. jα is an attraction index which reflects employment structure in LLS j , weighted ac-cording to import flows of the financed sector k in the financed LLS. Formally,

1

nj jS

j i ikiE aα

=

= ⋅∑ . It is assumed that income or employment effects, which spread outside a

LLS, are mostly attracted by communes with high levels of employment in sectors supplying inputs to the financed sector of the financed LLS. The attraction index has a greater impor-tance than the distance factor, which is squared just to reduce its effects on the attraction probability. The amounts of employment or income effects spreading outside the financed LLS, which

are attracted by LLS L will be respectively: 1

nR

L ii

P E=

⋅∑ and 1

nR

L ii

P Y=

⋅∑ . Once attraction prob-

ability for non-directly financed LLSs are derived, it is possible to know how the variation re-lated to the rest of region is distributed territorially. Every financed LLS can receive funds to more than one sector. Therefore the application of the interregional I-O model followed by the application of the gravity model is repeated as many times as there are sectors receiving public aids. Finally, the whole procedure above described is recursively applied as many times as there are directly financed LLS. The procedure was written in Visual Basic programming language and implemented in a Excel workbook. In Fig. 1 the flowchart summarising the procedure is shown.

Page 14: A Regional Impact Analysis of European Policies in Rural Areasecsocman.hse.ru/data/135/673/1219/87EAAE_Bonfiglio_Chiodo.pdf · A Regional Impact Analysis of European Policies in Rural

87th EAAE-Seminar. Assessing rural development of the CAP

14

Fig. 1: Flowchart of the procedure for assessing territorial and sectoral impacts

Application of theInterregional I-O model

Application of thegravity model

i = n. of financedsectors of LLS j

i = i + 1

i = 0

j = n. offinanced

LLSs

j = 0

j = j + 1

Employment (income) variation inf inanced LLS j and in the rest of the

region

Territorial distribution of variationrelated to the rest of the region

END

F

T

T

F

Note: j indicates the financed LLS; i indicates the financed sector in the LLS j

source: artwork by the author

4 Assessing policy impact on employment and income

4.1 Distribution of territorial impact Through the application of the procedure previously described, it was possible to estimate impact generated by development policies for rural areas in the Marche region for the period 2000-2003. The estimated impact takes account of the diversity characterizing productive structure in the region and relationships among functional areas. It results that policies will increase employment by 13,423 labour units (9% of employment surveyed in 1996) and in-come by around 54 million of Euro. Income per capita will rise by 36.5 € (Tab. 4).

Tab. 4: Territorial impact on employment and income produced by development poli-cies for rural areas in Labour Local Systems, Marche (Italy), 2000-03

Employment variation Income variation LLSs N. Popolation 2001

(inhabitants) Jobs % Var % 96-03 VC* 96 VC 03 Euro

(000) % Per capita (Euro)

Financed LLs 29 704,254 12,894 96.1 16.9 90.0 80.4 48,222 89.8 68.5

Non financed LLs 15 728,243 529 3.9 0.7 77.0 77.0 5,489 10.2 7.2

TOTAL 44 1,470,581 13,423 100 9.0 90.4 83.0 53,711 100.0 36.5

*VC = variation coefficient obtained as a percent ratio between standard deviation and average of employment levels in the Marche LLSs. Source: artwork by the author

Page 15: A Regional Impact Analysis of European Policies in Rural Areasecsocman.hse.ru/data/135/673/1219/87EAAE_Bonfiglio_Chiodo.pdf · A Regional Impact Analysis of European Policies in Rural

87th EAAE-Seminar. Assessing rural development of the CAP

15

As is logical to expect, increases tend to be concentrated in the financed LLSs which attract 96% of employment variation and 90% of income variation. From 1996, employment is ex-pected to increase by 9%. By means of spatial interrelationships, some effects will spread outside the financed areas. The non-financed LLSs will capture 4% of employment variation and 10% of income variation. In these areas, employment will rise by 0.7% in the period 1996-2003. From the effectiveness standpoint, for every one million of Euro, policies generate an increase in employment by 57 units, whilst the rise in income is equivalent to 23% of total expenditure. To evaluate effectiveness of policy, it can be useful to verify if policy contributes or less to reduce territorial disparities. Through the analysis of employment distribution14, it clearly emerges that differences in terms of employment among areas tend to diminish. Analysing areas separately, differences among financed areas decrease (variability passes from 90% to 80%), while differences related to non-financed areas remain constant. This would induce us to think that the policy analysed succeeds in stimulating more balanced growth, attenuat-ing disparities among areas. Fig. 2 and Fig. 3 show the spatial distribution of employment and income per capita variation respectively. In both cases, the areas with the higher impacts are those of hinterland which coincide with rural areas receiving funds in the period considered. The coastal areas, in-stead, are those excluded by the considered policy, but, thanks to spatial relationships, even they benefit from some indirect effects produced by policy. In terms of employment, the area with the biggest increases takes the form of “T” and comprises the LLSs of Jesi, Tolentino, Fabriano, Comunanza, Sarnano, Offida. Besides these LLSs, also the LLS of San Benedetto registers a higher value of employment variation. Fig. 2: Employment variation produced by development policies for rural areas in

Labour Local Systems, Marche (Italy), 2000-03

14 To measure employment distribution, variation coefficients calculated for employment existing in LLSs in 1996 and 2003

were used. It would have been also interesting to analyse possible changes in income distribution. However, this was not possible for lack of data.

Page 16: A Regional Impact Analysis of European Policies in Rural Areasecsocman.hse.ru/data/135/673/1219/87EAAE_Bonfiglio_Chiodo.pdf · A Regional Impact Analysis of European Policies in Rural

87th EAAE-Seminar. Assessing rural development of the CAP

16

source: artwork by the author Fig. 3: Income per capita variation produced by development policies for rural areas

in Labour Local Systems, Marche (Italy), 2000-03

source: artwork by the author As for income per capita, LLSs, which benefit from the largest increases, tend to be located in the South of the region. The LLSs with the highest income per capita variation are those of Piandimeleto, Sassocorvaro (located in the North) and Sarnano (in the South of the region).

4.2 Distribution of sectoral impact One of the main advantages of the I-O methodology is the possibility of increasing the detail of research, analysing impact at a sector level. Tab. 5 shows how the overall impact is dis-tributed among the 24 sectors of the regional table. With reference to employment, most part of the increase has to be ascribed to agriculture, absorbing more than 96% of variation. This was forecasted since more than 60% of total final demand is destined to investments in agri-culture. The remaining 4% of employment variation is apportioned among trade, research and businesses services, construction, food and tobacco, hotels and businesses, transport and communication and machinery sector. In terms of percent variation from 1996 to 2003, agriculture is the sector registering the high-est increases. Variation associated to the other sectors is not significant. Of these, those which benefit from the largest increments are food and tobacco, chemicals and machinery sector. With regard to income, agriculture again represents the sector attracting the highest share of variation. However, differently from what occurs at a level of employment, the relevant share is by far smaller (43%). Moreover, it has to be noted that there is major redistribution of im-pact among the other sectors. Sectors having income impacts around 10% are: trade, food and tobacco and machinery sector. The application of an interregional I-O model also allows distinguishing impacts produced locally and directly by policy (local variation) from those depending on interrelationships among different areas (extra-local variation). In this connection, the amounts of employment

Page 17: A Regional Impact Analysis of European Policies in Rural Areasecsocman.hse.ru/data/135/673/1219/87EAAE_Bonfiglio_Chiodo.pdf · A Regional Impact Analysis of European Policies in Rural

87th EAAE-Seminar. Assessing rural development of the CAP

17

and income impact induced by territorial links are about 10% and 25%, respectively. Besides agriculture, sectors capturing the biggest extra-local employment increases are: trade, re-search and businesses services and food and tobacco sector. It is interesting to note that for some sectors (such as: trade, transport and communication, credit and insurance), the extra-local variation is bigger than the local one. This can be due to the fact that these sectors tend to be located in the non-financed areas. As for income, most of extra-local increases are absorbed by the following sectors: trade, food and tobacco sector, agriculture, credit and insurance and research and business ser-vices. It is worth mentioning that in terms of income, agriculture is not the sector having the highest impact but it is preceded by trade and food and tobacco sector, although most of final demand is concentrated on the primary sector. Also in this case, there are sectors whose extra-local impact is higher than the local one. These are: textile, leather and shoes, rubber and plastic products, trade, transport and communication, credit and insurance. The analysis of policy direction in terms of development can be extended also at a sector level. Examining sector employment distribution in 1996 and 2003, it emerges that variability goes from 182% to 197%. This means that policy would tend to increase disparities among sectors, favouring a non-uniform sector development.

Page 18: A Regional Impact Analysis of European Policies in Rural Areasecsocman.hse.ru/data/135/673/1219/87EAAE_Bonfiglio_Chiodo.pdf · A Regional Impact Analysis of European Policies in Rural

87th EAAE-Seminar. Assessing rural development of the CAP

Tab. 5: Sectoral impact of development policies for rural areas, Marche (Italy), 2000-03

Employment variation Income variation Sectors % Final

Demand Local(1) (jobs) Extra-local(2) (jobs) Total (jobs) % Var. % 1996-2003 Local (Euro) Extra-local

(000 Euro) Total

(000 Euro) %

Agriculture 61.51 11886.1 1071.3 12957.4 96.5 28.7 21306.4 1920.3 23226.7 43.2 Mining 0.17 0.2 0.0 0.2 0.0 0.2 28.9 0.4 29.4 0.1 Food and tobacco 9.71 21.4 16.7 38.1 0.3 1.8 2769.9 2155.2 4925.2 9.2 Textile products and apparel 0.02 0.2 0.5 0.7 0.0 0.0 32.4 69.9 102.3 0.2 Leather and shoes 0.02 0.1 0.2 0.2 0.0 0.0 7.4 24.1 31.5 0.1 Timber and furniture 0.02 0.0 0.0 0.0 0.0 0.0 3.4 0.8 4.1 0.0 Paper, printing, publishing 0.28 0.7 0.4 1.1 0.0 0.1 127.8 83.5 211.3 0.4 Coke and nuclear fuel 0.02 0.1 0.0 0.2 0.0 0.7 6.5 1.5 8.0 0.0 Chemicals 0.83 1.4 0.8 2.2 0.0 1.8 1446.9 802.0 2248.9 4.2 Rubber and plastic products 0.02 0.1 0.1 0.2 0.0 0.0 12.1 29.0 41.1 0.1 Non-metal mineral products 0.55 1.6 0.5 2.1 0.0 0.3 618.6 194.2 812.8 1.5 Metal products 0.29 1.2 0.9 2.1 0.0 0.1 64.2 49.2 113.4 0.2 Machinery (except electricity) 7.95 11.5 0.0 11.5 0.1 1.0 4347.4 0.7 4348.1 8.1 Electrical and electronic equipment 1.40 2.6 0.5 3.1 0.0 0.2 758.0 136.8 894.8 1.7 Transportation equipment 4.55 1.1 0.0 1.1 0.0 0.6 2103.5 11.0 2114.5 3.9 Other manufacturing 0.10 1.2 0.0 1.2 0.0 0.0 17.1 0.0 17.1 0.0 Energy and water 0.19 0.0 0.0 0.0 0.0 0.1 133.2 15.8 149.0 0.3 Construction 2.16 35.0 6.9 41.9 0.3 0.3 1080.6 212.2 1292.9 2.4 Trade 0.33 54.4 186.8 241.2 1.8 0.7 1273.7 4369.9 5643.5 10.5 Hotels and businesses 5.90 24.8 1.1 25.9 0.2 0.4 1428.4 62.9 1491.2 2.8 Transport and communication 0.33 2.6 4.8 7.4 0.1 0.1 342.2 621.0 963.1 1.8 Credit and insurance 0.27 1.1 3.1 4.2 0.0 0.2 408.1 1121.2 1529.3 2.8 Real estate, renting, research, business services 2.64 43.8 35.8 79.6 0.6 0.5 1225.9 1000.5 2226.3 4.1 Other services 0.76 0.9 0.6 1.6 0.0 0.0 770.0 516.5 1286.4 2.4 TOTAL 100.00 12092.3 1330.9 13423.2 100.0 8.9 40312.5 13398.6 53711.0 100.0 (1) Local variation is that produced in the financed LLSs and due to policy (2) Extra-local variation is that due to spatial interdependencies source: artwork by the author

Page 19: A Regional Impact Analysis of European Policies in Rural Areasecsocman.hse.ru/data/135/673/1219/87EAAE_Bonfiglio_Chiodo.pdf · A Regional Impact Analysis of European Policies in Rural

87th EAAE-Seminar. Assessing rural development of the CAP

5 Concluding remarks This research has attempted to estimate impact generated by development policies for rural areas in the Marche region for the period 2000-2003. Results show that policies will produce an increase in employment of 13,423 units (9% of employment existing in 1996), in income of 54 million of Euro and in income per capita of 36.5 €. At a territorial level, increases will mainly involve financed areas (96% of total employment variation and 90% of total income variation) which are localized in the hinterland of the region. However, by means of economic relationships on the territory, even non-financed areas, located on the coast of the region, will benefit from positive effects, capturing 4% of employment variation and 10% of income varia-tion. These latter figures can be interpreted as indices of policy effects dispersion. At a sector level, the bigger increases will concentrate on agriculture (97% of employment impact and 43.2% of income variation) mainly because more than 60% of final demand is destined to this sector15. Further sectors will benefit from positive effects, such as: trade, transport and communication, research and business services, food and tobacco, machinery sector, hotels and businesses and construction. In terms of effectiveness, it emerges that for every one million of Euro, policy produces a rise in employment of 57 units, whilst the increase in income is 23% of total expenditure. More-over, policy appears to be effective in reducing disparities among areas. From this point of view, policy contributes towards more balanced development. However, this is not true at a sector level, where divergences are instead sharpened. Therefore, whilst territorial localiza-tion of expenditure can be judged as appropriate to attenuate imbalances, the same conclu-sion cannot be extended at a sector level. One possible factor explaining the increase in sec-tor employment disparities might be an excessively unbalanced distribution of expenditure. Actually, measures which absorb the highest share of expenditure are still those oriented to agricultural sector (more than 50% of total expenditure). So, even if it has been defined as multi-sectoral, policy continues to be sectoral and mainly oriented to agriculture. In this connection, once some restrictions are accepted (i.e.: assumptions on which I-O analysis is based), the procedure adopted here could reveal itself a valid tool to carry out policy experiments. It might be used to compare several policy alternatives, helping to iden-tify that allocation of funds which is both more effective, consistent with the principles of multi-sectorality and the objective of stimulating balanced growth.

Literature

BONFIGLIO A.: Input-Output Analysis and Rural Development Policies: An Application to Areas of Objective 5b

in Arzeni A., Esposti R. and Sotte F. (ed.), European Policy Experiences with Rural Develop-ment, Wissenschaftsverlag Vauk Kiel KG, Kiel (2002) 207-227.

BONFIGLIO A.: A Sensitivity Analysis of the Impact of CAP Reform. Alternative Methods of Constructing Regional I-O Tables

PhD Studies, Associazione “Alessandro Bartola”, Polytechnic University of Marche, Ancona 2004, forthcoming.

15 We recognize that an increase in terms of employment of more than 10,000 units in agriculture of the region Marche can be

judged by local experts as unrealistic. Besides high sector final demand, this result is affected by an high employment coef-ficient characterizing the agricultural sector associated to a supposed linearity between employment and output. However, although not all impact can be considered real, a part of this increase can be justified by the fact that most of investments are destined to more labour-intensive and eco-compatible agriculture. Moreover, an advantage-disadvantage of I-O analysis is that this methodology permits to isolate pure effects produced by policy from effects caused by exogenous factors. There-fore, further effects or tendencies, not considered in the analysis (i.e. the natural decline of agriculture in the economy), could intervene during the period of analysis, restraining forecasted increases. Finally, it has to be noted that the utility of the procedure developed here is above all to allow comparing different policy options and not to estimate precisely the impact generated by a given policy.

Page 20: A Regional Impact Analysis of European Policies in Rural Areasecsocman.hse.ru/data/135/673/1219/87EAAE_Bonfiglio_Chiodo.pdf · A Regional Impact Analysis of European Policies in Rural

87th EAAE-Seminar. Assessing rural development of the CAP

2

BRAND S.: On the Appropriate Use of Location Quotients in Generating Regional Input-Output Tables: A Com-ment Regional Studies 31 (1997) 791-794

BULMER-THOMAS V.: Input-Output Analysis in Developing Countries. Source, Methods and Applications

John Wiley & Sons Ltd 1982.

CARTER A. P.: Structural Change in the American Economy

Cambridge, Harvard University Press, 1970.

CHIODO E.: L'azione pubblica in agricoltura

in Arzeni A. (ed.), Il sistema agricolo e alimentare nelle Marche. Rapporto 2002, Edizioni Scien-tifiche Italiane (2003)

CONWAY R. S.: Changes in Regional Input-Output Coefficients and Regional Forecasting

Regional Science and Urban Economics 10 (1980) 158-71.

CZAMANSKI S. AND MALIZIA E. E : Applicability and limitations in the use of national input-output tables for regional studies

Regional Science Association Papers and Proceedings 23 (1969) 65-77.

ESKELINEN H. AND SUORSA M.: A note on estimating interindustry flows

Journal of Regional Science 20 (1980) 261-266.

ESPOSTI R.: Marche regione rurale

in Esposti R. and Sotte F. (ed.), Sviluppo rurale e occupazione, Franco Angeli (1999) 119-153

FLEGG T. A. AND WEBBER C.D.: Using location quotients to estimate regional input-output coefficients and multipliers

Local Economy Quarterly 4 (1996a) 58-86.

FLEGG T. A. AND WEBBER C.D.: The FLQ formula for generating regional input-output tables: an application and reformation

Working Papers in Economics 17 (1996b), University of the West of England, Bristol.

FLEGG T. A. AND WEBBER C.D.: On the Appropriate Use of Location Quotients in Generating Regional Input-Output Tables: Reply

Regional Studies 31 (1997) 795-805.

FLEGG T. A. AND WEBBER C.D.: Regional Size, Regional Specialization and the FLQ Formula

Regional Studies 34 (2000) 563-69.

FLEGG T. A., WEBBER C.D. AND ELLIOT M. V.: On the appropriate use of location quotients in generating regional input-output tables

Regional Studies 29 (1995) 547-561.

FRIEDLANDER D.: A Technique for Estimating a Contingency Table Given the Marginal Row and Column Totals and Some Supplementary Data

Journal of the Royal Statistical Society Series A 124 (1961) 412-420.

FRITZ O., KURZMANN R., STREICHER G. AND ZAKARIAS G.: Constructing Regional Input-Output Tables in Austria

Working Paper Series, 5, Joanneum Research, Vienna, Austria, 2002.

GREENSTREET D.: A conceptual framework for construction of hybrid regional input-output models

Socio-Economic Planning Sciences 23 (1989) 283-289.

ISTAT: I sistemi locali del lavoro

Argomenti 10, Roma 1997.

Page 21: A Regional Impact Analysis of European Policies in Rural Areasecsocman.hse.ru/data/135/673/1219/87EAAE_Bonfiglio_Chiodo.pdf · A Regional Impact Analysis of European Policies in Rural

87th EAAE-Seminar. Assessing rural development of the CAP

3

LAHR M. L.: A Strategy for Producing Hybrid Regional Input-Output Tables

in Lahr M. L. and Dietzenbacher (eds.), Input-Output Analysis: Frontiers and Extensions, Pal-grave, London, (2001a) 1-31.

LAHR M. L.: Reconciling Domestication Techniques, the Notion of Re-exports, and Some Comments on Regional Accounting

Economic Systems Research 13 (2001b) 166-179.

LAHR M. L. AND STEVENS B. H.: A Study of the Role of Regionalization in the Generation of Aggregation Error in Regional Input-Output Models

Journal of Regional Science 3 (2002) 477-507.

MCCANN P. AND DEWHURST J.H.L.: Regional Size, Industrial Location and Input-Output Expenditure Coefficients

Regional Studies 32 (1998) 435-444.

MILLER R.E. AND BLAIR P.D.: Input-Output Analysis: foundations and extensions

Prentice-Hall, Inc., Englewood Cliffs, New Jersey 1985.

MORILLAS A., MONICHE L. AND CASTRO M.: Structural Funds. Light and Shadow from Andalusia

Paper presented at the XIII International Conference on Input-Output Techniques, University of Macerata, Italy, August 21-25th 2000.

MORRISON W. I. AND SMITH P.: Nonsurvey Input-Output Techniques at the Small Area Level: An Evaluation

Journal of Regional Science 14 (1974) 1-14.

PHIBBS P. J. AND HOLSMAN A.: Estimating Input-Output Multipliers – A New Hybrid Approach

Environment and Planning A 14 (1982) 335-342.

RAMPA G.: Yearly Series of Input-Output Tables (ESA1979) for the Italian Economy, 1959-1997

Unpublished paper, Department of Juridical Culture - Economic Section, Genova, 2001

ROBISON M. H. AND MILLER J. R.: Cross-hauling and Nonsurvey Input-Output Models: Some Lessons from Small-Area Timber econo-mies

Environment and Planning A 20 (1988) 1523-1530.

ROUND J. I.: Regional Input-Output Models in the U.K.: A Reappraisal of Some Techniques

Regional Studies 6 (1972) 1-9.

ROUND J. I.: An Interregional Input-Output Approach to the Evaluation of non-survey methods

Journal of Regional Science 18 (1978) 179-94.

ROUND J. I.: Nonsurvey techniques: a critical review of the theory and the evidence

International Regional Science Review 8 (1983) 189-212.

Sawyer C. and Miller R.: Experiments in regionalization of a national input-output table

Environment and Planning A 15 (1983) 1501-1520.

SCHAFFER W. AND CHU K.: Nonsurvey Techniques for Constructing Regional Interindustry Models

Papers and Proceedings of the Regional Science Association 23 (1969a) 83-101.

SCHAFFER W. AND CHU K.: Simulating Regional Interindustry Models for Western States

A Program on Regional Industrial Development, Discussion Paper 14 (1969b), Georgia Insti-tute of Technology, Georgia.

Page 22: A Regional Impact Analysis of European Policies in Rural Areasecsocman.hse.ru/data/135/673/1219/87EAAE_Bonfiglio_Chiodo.pdf · A Regional Impact Analysis of European Policies in Rural

87th EAAE-Seminar. Assessing rural development of the CAP

4

SOTTE F.: Introduction

in Arzeni A., Esposti R. and Sotte F. (ed.), European Policy Experiences with Rural Develop-ment, Wissenschaftsverlag Vauk Kiel KG, Kiel (2002) 9-16.

TILANUS C. B. AND REY G.: Input-Output Volume and Value Predictions for the Netherlands, 1948-1958

International Economic Review 5 (1964) 34-45.

Page 23: A Regional Impact Analysis of European Policies in Rural Areasecsocman.hse.ru/data/135/673/1219/87EAAE_Bonfiglio_Chiodo.pdf · A Regional Impact Analysis of European Policies in Rural

87th EAAE-Seminar. Assessing rural development of the CAP

Appendix A: 1997 Marche 24-sector I-O table constructed by GRIT (million of Lire, current prices) Sectors 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

1 Agriculture 90,393 35 573,761 38,090 38,210 29,669 4,883 0 1,833 13,419 875 23 30 234 6092 Mining 0 21 0 0 0 1 0 1,431 1 0 78 0 0 0 03 Food and tobacco 182,371 0 572,098 1,738 124,653 2,089 1,917 40 14,915 558 0 0 0 0 04 Textile products and apparel 2,344 291 2,496 856,046 99,463 32,507 3,575 44 958 26,123 1,444 5,792 1,742 2,367 6,9885 Leather and shoes 396 74 0 29,819 2,131,392 32,914 1,870 39 344 4,877 24 2,293 768 1,639 1,0826 Timber and furniture 0 0 0 0 0 228,046 0 0 0 0 0 0 0 0 07 Paper, printing, publishing 41 445 20,513 3,487 7,156 8,048 111,333 108 6,191 8,471 4,610 7,183 6,147 8,002 2,8178 Coke and nuclear fuel 35,720 67 468 200 112 1,055 22 2,013 268 0 206 269 107 66 1349 Chemicals 102,811 274 1,255 7,620 4,525 4,754 1,760 158 11,202 17,345 1,134 2,148 864 1,402 883

10 Rubber and plastic products 333 246 10,560 7,029 76,268 29,696 2,646 174 4,440 63,750 2,359 9,599 18,681 20,053 30,33811 Non-metal mineral products 215 1,797 13,718 374 1,645 15,136 1,335 77 9,452 3,097 48,275 12,489 1,707 9,437 5,58912 Metal products 406 1,679 11,117 3,899 13,754 29,257 1,631 892 2,190 13,070 3,572 98,251 133,103 29,104 72,70413 Machinery (except electricity) 0 15 7 0 0 290 0 0 0 0 0 0 0 0 014 Electrical and electronic equipment 93 585 1,540 849 746 1,164 1,216 233 1,257 4,364 1,043 11,767 39,911 234,231 36,80215 Transportation equipment 94 4 0 0 0 0 7 0 3 71 42 165 240 58 13,55116 Other manufacturing 0 0 0 0 0 0 0 0 0 0 0 0 0 0 017 Energy and water 543 470 2,568 2,326 1,553 2,177 828 207 814 2,027 1,074 2,496 914 648 55718 Construction 584 2,905 8,091 6,613 12,963 5,423 4,817 1,355 3,870 8,067 7,434 13,406 9,324 10,615 6,35419 Trade 134,876 52,559 447,869 401,381 823,755 293,294 165,427 2,834 104,012 291,569 142,753 481,335 315,307 277,515 261,18520 Hotels and businesses 84 1,552 8,156 10,444 11,953 11,767 2,843 2,860 5,256 5,848 5,828 16,459 16,031 17,620 9,76621 Transport and communication 1,865 11,085 68,480 39,978 72,561 52,772 20,666 7,595 26,360 32,632 25,840 91,234 61,876 48,608 37,81222 Credit and insurance 23,480 2,757 46,761 47,735 76,165 84,305 14,635 6,219 8,108 21,127 12,163 67,822 38,180 29,686 15,81523 Real estate, renting, research, business services 4,523 10,621 84,372 110,579 114,274 82,976 44,903 4,476 47,974 59,633 32,134 141,459 96,398 114,716 82,20324 Other services 2,601 1,396 36,465 12,440 7,515 4,399 12,368 384 9,446 10,266 2,858 14,212 10,850 15,434 25,006

Total intermediate costs 583,773 88,878 1,910,295 1,580,647 3,618,663 951,739 398,682 31,139 258,894 586,314 293,746 978,402 752,180 821,435 610,195Value Added 1,699,600 39,733 526,084 562,583 2,332,200 1,020,555 723,907 273,851 555,452 1,181,054 398,000 1,285,100 1,058,012 901,106 761,982Imports 1,354,500 469,113 934,995 1,296,358 1,227,930 769,065 530,283 339,529 591,885 1,171,843 368,188 1,501,627 1,209,118 999,961 968,305Other primary inputs -2,524,363 -115,177 2,696,628 1,322,235 1,994,695 726,886 322,785 921,298 431,887 360,150 140,129 983,458 678,734 1,596,490 1,371,573PRIMARY INPUTS 529,737 393,669 4,157,707 3,181,176 5,554,825 2,516,506 1,576,975 1,534,678 1,579,224 2,713,047 906,317 3,770,185 2,945,864 3,497,557 3,101,860INPUT 1,113,512 482,548 6,068,003 4,761,820 9,173,487 3,468,246 1,975,658 1,565,819 1,838,120 3,299,362 1,200,064 4,748,586 3,698,046 4,318,991 3,712,051

Source: artwork by the author

Page 24: A Regional Impact Analysis of European Policies in Rural Areasecsocman.hse.ru/data/135/673/1219/87EAAE_Bonfiglio_Chiodo.pdf · A Regional Impact Analysis of European Policies in Rural

87th EAAE-Seminar. Assessing rural development of the CAP

6

Appendix A: 1997 Marche 24-sector I-O table constructed by GRIT (million of Lire, current prices) (continued)

Sectors 16 17 18 19 20 21 22 23 24 TIS HC EXP OFD TFD OUTPUT1 Agriculture 2,877 7 471 43 105,213 1,476 86 91 9,170 911,498 129,117 108,935 -36,038 202,014 1,113,5122 Mining 0 4,671 0 0 2 2 1 3 12 6,223 497,582 27 -21,284 476,325 482,5483 Food and tobacco 174 0 0 487 969,125 4,440 0 0 45,033 1,919,638 2,586,071 1,119,648 442,646 4,148,365 6,068,0034 Textile products and apparel 1,807 0 3,929 4,779 10,912 10,797 636 2,009 20,635 1,097,684 707,953 2,844,802 111,381 3,664,136 4,761,8205 Leather and shoes 1,713 0 0 15,650 3 735 821 22 5,909 2,232,384 1,725,000 4,805,771 410,332 6,941,103 9,173,4876 Timber and furniture 0 0 0 0 0 1,619 0 3,160 0 232,825 0 3,254,227 -18,806 3,235,421 3,468,2467 Paper, printing, publishing 1,434 705 3,561 29,519 12,502 19,121 6,497 26,322 44,725 338,938 1,208,394 352,517 75,809 1,636,720 1,975,6588 Coke and nuclear fuel 43 1,468 363 619 0 15,170 47 1,606 1,577 61,600 1,547,654 244,003 -287,438 1,504,219 1,565,8199 Chemicals 384 352 1,651 604 1,008 495 89 907 14,832 178,457 1,226,454 392,813 40,396 1,659,663 1,838,120

10 Rubber and plastic products 3,172 689 14,147 12,195 1,153 27,972 61 6,216 9,135 350,912 1,167,175 1,624,884 156,391 2,948,450 3,299,36211 Non-metal mineral products 4,068 435 143,275 1,356 9,348 550 0 750 3,300 287,425 157,224 581,640 173,775 912,639 1,200,06412 Metal products 8,889 3,858 47,157 44,265 4,132 8,821 1,575 7,580 10,206 551,112 594,420 2,008,970 1,594,084 4,197,474 4,748,58613 Machinery (except electricity) 0 0 0 0 0 2,397 0 802 0 3,511 15,122 3,762,952 -83,539 3,694,535 3,698,04614 Electrical and electronic equipment 2,666 8,353 42,904 46,558 2,560 14,047 1,699 7,830 22,864 485,282 1,095,759 1,476,012 1,261,938 3,833,709 4,318,99115 Transportation equipment 2 0 0 6,532 3 5,003 0 192 3,359 29,325 1,942,067 876,696 863,963 3,682,726 3,712,05116 Other manufacturing 0 0 0 0 0 0 0 140 0 140 406,170 1,070,293 -324,779 1,151,684 1,151,82417 Energy and water 221 4,668 410 2,473 3,933 1,870 713 1,764 6,037 41,291 2,578,813 7,048 -1,109,706 1,476,155 1,517,44618 Construction 698 39,718 96,720 24,130 37,819 117,453 25,829 247,450 172,212 863,850 90,324 110,943 2,953,375 3,154,642 4,018,49219 Trade 53,605 16,300 176,640 636,049 709,350 441,612 36,634 155,357 330,531 6,751,749 1,975,347 591,550 521,550 3,088,447 9,840,19620 Hotels and businesses 1,830 2,635 12,662 54,502 0 79,056 19,348 59,951 134,799 491,250 2,845,000 1,491 3,358,258 6,204,749 6,695,99921 Transport and communication 8,847 8,481 52,637 170,878 61,304 602,724 52,410 66,801 124,491 1,747,937 3,815,000 1,039,830 -985,882 3,868,948 5,616,88522 Credit and insurance 8,989 7,480 102,513 243,507 30,553 124,146 125,162 64,499 418,222 1,620,029 500,805 2,074,363 1,073,787 3,648,955 5,268,98423 Real estate, renting, research, business services 20,295 23,932 130,244 399,852 190,439 276,770 734,052 380,548 760,112 3,947,485 406,013 9,165,794 -8,091,692 1,480,115 5,427,60024 Other services 1,449 1,311 5,257 139,242 96,678 82,064 39,863 74,289 556,658 1,162,451 3,692,833 308,947 15,152,051 19,153,831 20,316,282

Total intermediate costs 123,163 125,063 834,541 1,833,240 2,246,037 1,838,340 1,045,523 1,108,289 2,693,819 25,312,996 30,910,297 37,824,156 17,230,572 85,965,025 111,278,021Value Added 298,639 648,400 2,300,200 6,207,900 1,923,500 1,036,700 2,373,700 2,203,600 8,858,200 39,170,058Imports 283,654 455,153 1,173,092 1,439,242 1,115,639 1,852,960 384,367 2,875,255 2,394,586 25,706,648Other primary inputs 446,366 288,831 -289,341 359,817 1,410,821 888,895 1,465,394 -759,548 6,369,679 21,088,322PRIMARY INPUTS 1,028,659 1,392,384 3,183,951 8,006,959 4,449,960 3,778,555 4,223,461 4,319,307 17,622,465 85,965,028INPUT 1,151,824 1,517,446 4,018,492 9,840,196 6,695,999 5,616,885 5,268,984 5,427,600 20,316,282 111,278,021

TIS – Total Intermediate sales; HC – Household consumption; EXP – exports; OFD – Other final demands; TFD – Total final demand

Source: artwork by the author

Page 25: A Regional Impact Analysis of European Policies in Rural Areasecsocman.hse.ru/data/135/673/1219/87EAAE_Bonfiglio_Chiodo.pdf · A Regional Impact Analysis of European Policies in Rural

87th EAAE-Seminar. Assessing rural development of the CAP

Appendix B: Total expenditure in Labour Local Systems according to categories, Marche (Italy), 2000-03 (Thousand Euro)

Categories Financed LLSs 1 2 3 4 5 6 7 8 9 10 TOTALMORCIANO DI ROMAGNA 1,011 108 0 0 0 900 0 18 0 0 2,038CAGLI 5,593 498 31 1,866 910 984 744 16 0 20 10,661FOSSOMBRONE 6,386 450 230 657 0 1,132 919 27 5 1,118 10,924MONDOLFO 3,144 54 0 0 1,855 2,195 0 35 0 72 7,356PENNABILLI 2,169 634 52 0 0 199 0 0 16 0 3,070PERGOLA 3,292 730 0 388 157 0 0 230 69 59 4,926PIANDIMELETO 3,302 420 0 576 0 722 0 380 0 22 5,422SANT'ANGELO IN VADO 1,539 321 0 29 0 742 0 0 2 0 2,633SASSOCORVARO 5,223 1,060 81 0 0 2,437 0 35 6 16 8,858URBANIA 2,631 294 0 244 0 302 321 148 0 24 3,964URBINO 7,152 461 105 0 523 538 0 429 0 240 9,447FABRIANO 9,796 509 0 865 477 5,324 0 1,152 8 567 18,698JESI 10,556 311 3 897 2,664 1,502 0 0 2 729 16,664OSTRA 1,888 0 0 257 1,963 88 0 0 0 23 4,219SASSOFERRATO 4,263 249 0 350 0 0 109 148 0 113 5,232SERRA DE' CONTI 4,182 52 0 873 1,665 1,017 0 0 0 36 7,825CAMERINO 5,377 1,145 0 724 0 2,265 453 369 3 58 10,395CINGOLI 5,372 139 0 0 1,577 839 287 34 7 35 8,290SARNANO 9,212 883 261 429 527 611 0 17 3 25 11,967TOLENTINO 10,462 609 34 601 7,443 7,051 0 1,393 3 512 28,108URBISAGLIA 2,264 85 0 244 0 228 0 591 0 14 3,426VISSO 2,543 1,091 0 423 0 155 0 48 0 21 4,280ASCOLI PICENO 2,801 704 48 0 991 200 0 0 0 0 4,743COMUNANZA 9,395 920 0 2,091 0 244 0 0 0 20 12,670FERMO 502 15 0 0 0 0 0 0 0 0 517MONTEFIORE DELL'ASO 4,575 45 0 768 660 636 0 0 0 102 6,786MONTEGIORGIO 1,938 45 0 0 0 2,345 0 0 0 0 4,327OFFIDA 7,490 212 31 1,413 423 0 0 0 16 46 9,631SAN BENEDETTO DEL TRONTO 7,894 90 0 230 1,136 167 0 0 7 301 9,826TOTAL 141,950 12,133 876 13,925 22,970 32,825 2,833 5,070 148 4,172 236,903(1) Investments in agriculture; (2) Aids to farms; (3) Incentives to cease farm activity; (4) Investments in agro-tourist farms; (5) Investments in agro-food industries; (6) Industrial equipment; (7) Aids to non-agricultural enterprises; (8) Infrastructure; (9) Education and Research; (10) Studies, advice and communication.

Source: artwork by the author on Region Marche data

Page 26: A Regional Impact Analysis of European Policies in Rural Areasecsocman.hse.ru/data/135/673/1219/87EAAE_Bonfiglio_Chiodo.pdf · A Regional Impact Analysis of European Policies in Rural

87th EAAE-Seminar. Assessing rural development of the CAP

8

Appendix C: Matrix of redistribution of expenditure among sectors

Categories Sectors 1 2 3 4 5 6 7 8 9 10Agriculture 1 0.3 0.04 0 0 0 0 0.02 0 0Mining 0 0 0.04 0 0 0 0 0.07 0 0Food and tobacco 0 0 0.04 0 1 0 0 0 0 0Textile products and apparel 0 0 0.04 0 0 0 0 0 0 0Leather and shoes 0 0 0.04 0 0 0 0 0 0 0Timber and furniture 0 0 0.04 0 0 0 0 0 0 0Paper, printing, publishing 0 0 0.04 0 0 0 0 0 0 0.15Coke and nuclear fuel 0 0 0.04 0 0 0 0 0 0 0Chemicals 0 0.15 0.04 0 0 0 0 0.02 0 0Rubber and plastic products 0 0 0.04 0 0 0 0 0 0 0Non-metal mineral products 0 0 0.04 0 0 0 0 0.25 0 0Metal products 0 0 0.04 0 0 0 0 0.13 0 0Machinery (except electricity) 0 0.15 0.04 0 0 0.5 0.2 0 0 0Electrical and electronic equipment 0 0.1 0.04 0 0 0.05 0.15 0 0 0Transportation equipment 0 0 0.04 0 0 0.3 0.17 0.08 0 0Other manufacturing 0 0 0.04 0 0 0 0 0 0 0.05Energy and water 0 0 0.04 0 0 0 0 0.08 0 0Construction 0 0.05 0.04 0 0 0.1 0.1 0.18 0 0Trade 0 0 0.04 0 0 0.02 0.03 0 0 0Hotels and businesses 0 0 0.04 1 0 0 0 0 0.05 0Transport and communication 0 0 0.04 0 0 0 0.12 0.08 0.05 0Credit and insurance 0 0.05 0.04 0 0 0 0 0 0 0Real estate, renting, research, business services 0 0.14 0.04 0 0 0.03 0.17 0.09 0.7 0.6Other services 0 0.06 0.04 0 0 0 0.06 0 0.2 0.2TOTAL 1 1 1 1 1 1 1 1 1 1(1) Investments in agriculture; (2) Aids to farms; (3) Incentives to cease farm activity; (4) Investments in agro-tourist farms; (5) Investments in agro-food industries; (6) Industrial equipment; (7) Aids to non-agricultural enterprises; (8) Infrastructure; (9) Education and Research; (10) Studies, advice and communication.

Source: artwork by the author on Morillas’ et al. (2000) data