Proposal for a composite indicator for local development

Français non disponible
English

Single indicators are often used to simplify and interpret economic and social phenomena. However, often reality is more complicated and is composed of interconnected and multidimensional aspects. To address this problem, many researchers have tried to describe complex phenomena by combining sets of different variables into composite indicators as an alternative way to represent economic, social and environmental problems.

The IMAJINE project proposes a composite indicator for local development by synthesising several indicators available at the municipal level in three study countries (France, Spain, Italy) which are among the most populated in Europe. It is an experimental construction of a multi-dimensional indicator of different levels of local development, showing overall index and identifying the most significant factor explaining inequalities between local territories.

Composite indicator

Composite indicators are an alternative way to represent economic, social, and environmental problems. For instance, economic performance is usually measured with GDP, but composite indicators allow one to specify measures that target and support specific policies, entail a specific policy aim or social change desired by proponents of the measure and generally provide greater insight into complex phenomena, such as, economic performance, that could support local bottom-up strategies

Composite indicators also enable researchers and analysts to communicate to the general public and consequently, increase accountability. Defining composite indicators allows the specification of measures that target and support specific policies. Composite indicators are not just numbers pointing to an issue; they also entail a specific policy aim or social change desired by proponents of the measure. Hence, the ability of a composite indicator is to provide greater insight into a complex phenomenon, such as economic performance, which could serve to support local bottom up strategies.

The proposed indicator considers five dimensions based on data in the IMAJINE dataset:

Economic wealth

Economic wealth is considered proof of performance at a regional level. Hence, it includes disposable income of households at the municipal level.

Population at risk of poverty or social exclusion

The poverty dimension considers the at-risk-of-poverty rate (AROPE) before social transfers, calculated as the share of people having an equivalent disposable income before social transfers that are below the at-risk-of-poverty threshold (aro). This indicator is often taken as a relevant feature at regional (NUTS 2) level by the EU.

Level of education

In order to consider the lack of educational attainment at the local level, the indicator considers the rate of people (illiterate or not) who did not receive formal education (edu).

Local labour

Local labour (lab) is considered in the unemployment rate and is included in the composite indicator as a key input.

Share of agriculture

Other studies have also emphasised the share of agriculture as an important indicator of the sectorial mix at the local level (agr). Hence, the share of the agricultural sector, calculated as the ratio of agricultural employees to total employees within a municipality is considered in the development of the composite indicator.

A combination of Principal Component Analysis (PCA) and Geographically Weighted Principal Component Analysis (GWPCA) methodologies were used to develop the composite indicator.

PCA and GWPCA methodologies

Principal Component Analysis (PCA) is used to define weights and synthesise a composite indicator. Moreover, particular attention is paid to the spatial characteristics of the given data and a spatial extension of PCA, namely Geographically Weighted Principal Component Analysis (GWPCA), is used. GWPCA allows weights at the unit level to be defined, which can be used to tackle substantial heterogeneity. This feature may be appealing for understanding the importance of each dimension in space and help design regional policies that consider heterogeneity within regions.

As most of the variables have theoretically negative relationships with respect to local economic performance, household income is reversed in sign. In this fashion, a lower value of the composite indicator indicates a higher potential for local economic performance. Conversely, units characterised by a larger composite indicator must be interpreted as being located in more-disadvantaged areas.

The composite indicator has been calculated for France, Spain and Italy, at the level of municipalities/communes.

Report on economic growth and spatial inequalities

Presentation at the Good Practice Workshop of the Evaluation Helpdesk

Relevance for monitoring and evaluation of the CAP

A composite indicator like the one proposed by IMAGINE can be very useful for analysing the effects of local development strategies and therefore it can be inspiring for LEADER evaluations. This composite indicator represents a preliminary attempt to aggregate data from several variables in order to capture the structure of local economic performance at a very refined scale.

The analysis of the composite indicator must be considered as explorative within the sphere of local economic development as it is based on data from the IMAJINE dataset, but also opens the possibility for further development of local datasets. Therefore, in order for the indicator to be used in other contexts/Member States, the dataset may need to be adapted, while methodologies for collecting local level data should also be in place. Considerations include time and resources for applying it as well as knowledge of Principal Component Analysis, which is the IMAJINE recommended methodology for defining weights and synthesising the composite indicator.

This would be worthwhile as the indicator helps to reduce complexity in analysing multi-dimensional economic phenomena such as local economic performance and development, relevant in the context of LEADER for example. Evidence from the countries analysed by the IMAJINE project show that the methodological approach for developing the composite indicator helps produce accurate results.

In addition, the analysis stresses the need to pursue multidimensionality at a local level to prevent policy makers from setting ad hoc policies and interventions. Local composite indicators allow us to better consider differences at low geographical scale, target disadvantaged areas within certain regions and develop very accurate policies that may help ensure policy effectiveness, which again stresses the relevance for local development under LEADER. The performance of such indicators becomes then the subject of evaluation and feeds back into further policy improvement/development.

The transferability of the tool depends on the availability of data at the local level. Another IMAJINE output (the local level database of socio-economic indicators) offers an approach for acquiring local level data using official statistics, censuses and applying the Generalised Cross Entropy (GCE) method.

Last modification date:

09/12/2021

Project details

IMAJINE:
Integrative Mechanisms for Addressing Spatial Justice and Territorial Inequalities in Europe

Objectives

Formulate new integrative policy mechanisms to enable European, national and regional government agencies to more effectively address territorial inequalities within the European Union.
Imagine a future for European regions in which the distribution of resources is consistent with principles of social and spatial justice.
Respond to a pressing need to re-appraise the appropriateness and efficacy of existing policy instruments for tackling territorial inequalities, and to consider and develop alternative mechanisms.
Produce new primary data, apply new analytical tests to secondary data and integrate the results with insights from relational geographical theory and the concept of spatial justice.

Timeframe:

2017 to 2022

Contacts of project holder:

Professor Michael Woods, Aberystwyth University (UK)

Project website(s):

IMAJINE

CORDIS database

Territorial coverage:

Belgique, Finlande, France, Allemagne, Grèce, Irlande, Pologne, Roumanie, Espagne, Les Pays-Bas

Relevant CAP Objectives:

8. Vibrant rural areas

Data Collection Systems Used:

Ad-hoc data collection
Eurostat

Associated Evaluation Approaches:

Impact evaluation ex post
Impact evaluation ongoing

Type of Output:

New indicators

Spatial Scale:

Sub-regional / local
Regional

Keywords:

economic performance
local development
poverty