METHODS OF SPATIAL ECONOMIC ANALYSISLECTURE 06

Δρ. Μαρί-Νοέλ Ντυκέν, Αναπληρώτρια Καθηγήτρια, mdyken@prd.uth.grΤηλ. 24210-74438Γραφείο Γ.6

UNIVERSITY OF THESSALYFACULTY OF ENGINEERING

DEPARTMENT OF PLANNINGAND REGIONAL DEVELOPMENT

MASTER «EUROPEAN REGIONAL DEVELOPMENT STUDIES»

CALCULATING COMPOSITE INDICATORS FOR REGIONAL ANALYSIS

OBJECTIVE O

F THE LECTURE

Objective of the Lecture

1. Meaning of Composite Indicators.

2. A simple and empirical method for composite indicator’s calculation.

3. Example of empirical method (see Data_LECTURE06)

4. A systematic method in order to create composite indicators: Introduction to Factorial Analysis (Full presentation in Lecture 07)

MEANING OF COMPOSITE INDICATOR

DEFINITIO

NEXAMPLES of multi-

dimensional conceptsDEFINITION

“A composite indicator is formed when individual indicators are

compiles into a single index, on the basis of an underlying model of the multi-dimensional concept that is

being measured”.(OECD Glossary of statistical terms)

A composite indicator measures a multi-dimensional concept

(phenomena) that cannot be appropriately evaluated through

a single indicator.

Development level Welfare

Most popular composite indicators

Human Development Index Index of Economic Well-Being Regional Competitiveness Index Environmental Sustainability

Index Environmental Performance

Index

DEFINITIO

NEXAMPLES OF COMPOSITE INDICATORS

DEFINITIO

NUSEFULNESS OF COMPOSITE INDICATORS [01]

Statistical indicators are important for designing and assessing policies, especially as regards the progress of the national and / or regional economy and society.

The progress of the economy and society is not an one-dimensional concept. It is obviously a complex phenomena. Consequently, it is absolutely necessary to define an accurate measurement of welfare.

Even if the GDP per capita is often employed as a measure of development and progress, it is a very “simplest” approach: the increase of GDP pc does not mean systematically incomes’ increase for the majority of the citizens nor reduction of economic inequalities.

Nevertheless the major advantage of GDP pc is the fact that this indicator is frequently used, it is a wide and consistent measurementGDP per capita is systematically produced by important institutions (World Bank, OECD, Eurostat, etc), allowing comparisons (both between places and across time) to be made.

DEFINITIO

NUSEFULNESS OF COMPOSITE INDICATORS [02]

The need for the construction of a more relevant index of welfare and development is imperious.

Composite indicators are increasingly recognized as useful tools for the assessment of policies as well as for public communication. This is because they are able to capture and describe complex concepts with a single measure, allowing comparisons.

In many cases, it allows to take into account structural dimensions (see example)

Nevertheless, composite indicators still generate controversy, since their us present advantages and disadvantages. Yet, over the recent years, a proliferation in their use in various policy domains, is evident.

DEFINITIO

NADVANTAGES AND DISAVANTAGES OF COMPOSITE INDICATORS [02]

EMPIRICAL METHOD FOR COMPOSITE INDICATOR’S CALCULATION

CALCULATIO

N O

F COM

POSITE IN

DICATORS

THE SIX (6) STEPS FOR CALCULATION OF COMPOSITE INDICATORS

Step 1: Developing a theoretical or empirical framework for the composite indicator

Step 2: Identifying and developing relevant variablesStep 3: Data collection and treatment of missing values, if necessaryStep 4: Standardization of variables to allow a pertinent comparison

between single indicators, especially when they are measured in different scales

Step 5: Weighting variables and/or groups of variablesStep 6: Conducting sensitivity tests on the robustness of the

aggregated variable (composite index)

See for more details: Freudenberg (2003)

CALCULATIO

N O

F COM

POSITE IN

DICATORS

CONSTRUCTING A COMPOSITE INDEX OF WELFARE AND DEVELOPMENT [CIWD]

Main question with this method: the choice of the weighting system.

EMPIRICAL METHOD OF CALCULATION IN PRACTICE

EXAMPLE : CO

MPO

SITE INDICATO

RCALCULATION OF COMPOSITE INDICATOR FOR UNEMPLOYMENT

The data are available in: DATA_LECTURE06.xlsThey concern 4 single indexes of unemployment in the 13 regions of Greece, measured at three different dates: 2004, 2009, 2012 (See Sheet: Data for Composite Indicator)

The question:

How to better evaluate the unemployment problem at regional level?

The single measure through the total rate of unemployment is a pertinent indicator but it don’t reflect the complexity of the unemployment problem, that is:

(a) the groups of active population more affected by unemployment (Young, women et.c)(b) The duration of unemployment (long term unemployment)

Consequently, we suggest that the calculation of a composite indicator will better reflect the intensity and the structural problems of the 13 regions of Greece.

We finally select 4 single indicators as a very basic approach of the question:

Un_young Unemployment Rate for young people (15-24 years old)Un_25p Unemployment Rate for people 25 years old and moreSR Sex Ratio for unemployedLT_Un Long term unemployment as % of total unemployment

Two main questions:1./ The scales present

important differences. So it is necessary to standardize the variables.

2./ The 2nd main question is the choice of the pertinent weights

EXAMPLE : CO

MPO

SITE INDICATO

R2. CALCULATION OF COMPOSITE INDICATOR FOR UNEMPLOYMENT

Un_young Unemployment Rate for young people (15-24 years old)Un_25p Unemployment Rate for people 25 years old and moreSR Sex Ratio for unemployedLT_Un Long term unemployment as % of total unemployment

2004 INITIAL VARIABLESRegions NUTS II Un_young Un_25p SR LT_Un

R00 GREECE 26,9 8,7 168,7 53,06

R01 Anatoliki Makedonia, Thraki 30,4 11,1 207,1 55,74

R02 Kentriki Makedonia 31,6 10,1 181,1 52,97

R03 Dytiki Makedonia 49,3 13,2 164,5 64,40

R04 Thessalia 25,4 8,2 213,6 66,06

R05 Ipeiros 33,1 8,7 152,4 62,08

R06 Ionia Nisia 23,8 9,8 134,0 18,97

R07 Dytiki Ellada 30,2 10,5 180,5 61,59

R08 Sterea Ellada 33,7 10,1 138,6 56,46

R09 Peloponnisos 28,4 7,1 185,7 59,88

R10 Attiki 22,0 7,8 156,2 51,63

R11 Voreio Aigaio 31,2 7,1 311,8 54,02

R12 Notio Aigaio 19,8 7,4 113,2 22,34

R13 Kriti 20,9 6,3 154,9 28,60

EXAMPLE : CO

MPO

SITE INDICATO

RCALCULATION OF COMPOSITE INDICATOR FOR UNEMPLOYMENT

2004 INITIAL VARIABLES STANDARDIZEDRegions NUTS II Un_youn

g Un_25p SR LT_Un ZUn_young ZUn_25p ZSR ZLT_UnR00 GREECE 26,9 8,7 168,7 53,06 R01 Anatoliki Makedonia, Thraki 30,4 11,1 207,1 55,74 35,9 69,6 47,3 78,1R02 Kentriki Makedonia 31,6 10,1 181,1 52,97 40,0 55,1 34,2 72,2R03 Dytiki Makedonia 49,3 13,2 164,5 64,40 100,0 100,0 25,8 96,5R04 Thessalia 25,4 8,2 213,6 66,06 19,0 27,5 50,6 100,0R05 Ipeiros 33,1 8,7 152,4 62,08 45,1 34,8 19,7 91,5R06 Ionia Nisia 23,8 9,8 134,0 18,97 13,6 50,7 10,5 0,0R07 Dytiki Ellada 30,2 10,5 180,5 61,59 35,3 60,9 33,9 90,5R08 Sterea Ellada 33,7 10,1 138,6 56,46 47,1 55,1 12,8 79,6R09 Peloponnisos 28,4 7,1 185,7 59,88 29,2 11,6 36,5 86,9R10 Attiki 22,0 7,8 156,2 51,63 7,5 21,7 21,6 69,4R11 Voreio Aigaio 31,2 7,1 311,8 54,02 38,6 11,6 100,0 74,4R12 Notio Aigaio 19,8 7,4 113,2 22,34 0,0 15,9 0,0 7,2R13 Kriti 20,9 6,3 154,9 28,60 3,7 0,0 21,0 20,5

Min value 19,80 6,30 113,21 18,97Max value 49,30 13,20 311,76 66,06

9,3580,1930,4980,194,30*100_*100_

MinMaxMinYoungUnYoungZUn

Standardization of the 4 single variables

EXAMPLE : CO

MPO

SITE INDICATO

RCALCULATION OF COMPOSITE INDICATOR FOR UNEMPLOYMENT

Calculation of the Composite Indicator with alternative weights

Conclusions:_______________________________________________ ______________________________________

_______________________________________________ ______________________________________

EXAMPLE : CO

MPO

SITE INDICATO

RSENSIBILITY ANALYSIS AS REGARDS WEIGHTS’ STRUCTURE

UNEMPLOYMENT

Conclusions:___________________________________________

___ ______________________________________

The most simple way in order to examine the robustness of our composite indicator is to compare this indicator with the total rate of unemployment that we have not included in our calculation.

METHODS OF SPATIAL ECONOMIC ANALYSISLECTURE 04

Δρ. Μαρί-Νοέλ Ντυκέν, Αναπληρώτρια Καθηγήτρια, mdyken@prd.uth.grΤηλ. 24210-74438Γραφείο Γ.6

UNIVERSITY OF THESSALYFACULTY OF ENGINEERING

DEPARTMENT OF PLANNINGAND REGIONAL DEVELOPMENT

MASTER «EUROPEAN REGIONAL DEVELOPMENT STUDIES»