METHODS OF SPATIAL ECONOMIC ANALYSIS LECTURE 06 Δρ. Μαρί-Νοέλ Ντυκέν, Αναπληρώτρια Καθηγήτρια, Τηλ Γραφείο Γ.6 UNIVERSITY OF THESSALY FACULTY OF ENGINEERING DEPARTMENT OF PLANNINGAND REGIONAL DEVELOPMENT MASTER «EUROPEAN REGIONAL DEVELOPMENT STUDIES»

CALCULATING COMPOSITE INDICATORS FOR REGIONAL ANALYSIS

OBJECTIVE OF 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

DEFINITION EXAMPLES of multi- dimensional concepts DEFINITION “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

DEFINITION EXAMPLES OF COMPOSITE INDICATORS

DEFINITION USEFULNESS 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 measurement GDP per capita is systematically produced by important institutions (World Bank, OECD, Eurostat, etc), allowing comparisons (both between places and across time) to be made.

DEFINITION USEFULNESS 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.

DEFINITION ADVANTAGES AND DISAVANTAGES OF COMPOSITE INDICATORS [02]

EMPIRICAL METHOD FOR COMPOSITE INDICATOR’S CALCULATION

CALCULATION OF COMPOSITE INDICATORS 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 variables Step 3:Data collection and treatment of missing values, if necessary Step 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 variables Step 6:Conducting sensitivity tests on the robustness of the aggregated variable (composite index) See for more details: Freudenberg (2003)

CALCULATION OF COMPOSITE INDICATORS CONSTRUCTING A C OMPOSITE I NDEX OF W ELFARE AND D EVELOPMENT [CIWD] Main question with this method: the choice of the weighting system.

EMPIRICAL METHOD OF CALCULATION IN PRACTICE

EXAMPLE : COMPOSITE INDICATOR CALCULATION OF COMPOSITE INDICATOR FOR UNEMPLOYMENT The data are available in: DATA_LECTURE06.xls They 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_youngUnemployment Rate for young people (15-24 years old) Un_25pUnemployment Rate for people 25 years old and more SRSex Ratio for unemployed LT_UnLong 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 2 nd main question is the choice of the pertinent weights EXAMPLE : COMPOSITE INDICATOR 2. CALCULATION OF COMPOSITE INDICATOR FOR UNEMPLOYMENT Un_youngUnemployment Rate for young people (15-24 years old) Un_25pUnemployment Rate for people 25 years old and more SRSex Ratio for unemployed LT_UnLong term unemployment as % of total unemployment 2004INITIAL VARIABLES Regions NUTS II Un_youngUn_25pSRLT_Un R00 GREECE26,98,7 168,7 53,06 R01Anatoliki Makedonia, Thraki 30,411,1 207,1 55,74 R02Kentriki Makedonia 31,610,1 181,1 52,97 R03Dytiki Makedonia 49,313,2 164,5 64,40 R04Thessalia 25,48,2 213,6 66,06 R05Ipeiros 33,18,7 152,4 62,08 R06Ionia Nisia 23,89,8 134,0 18,97 R07Dytiki Ellada 30,210,5 180,5 61,59 R08Sterea Ellada 33,710,1 138,6 56,46 R09Peloponnisos 28,47,1 185,7 59,88 R10Attiki 22,07,8 156,2 51,63 R11Voreio Aigaio 31,27,1 311,8 54,02 R12Notio Aigaio 19,87,4 113,2 22,34 R13Kriti 20,96,3 154,9 28,60

EXAMPLE : COMPOSITE INDICATOR CALCULATION OF COMPOSITE INDICATOR FOR UNEMPLOYMENT 2004INITIAL VARIABLESSTANDARDIZED Regions NUTS IIUn_youngUn_25pSRLT_UnZUn_youngZUn_25pZSRZLT_Un R00GREECE26,98,7168,753,06 R01Anatoliki Makedonia, Thraki30,411,1207,155,7435,969,647,378,1 R02Kentriki Makedonia31,610,1181,152,9740,055,134,272,2 R03Dytiki Makedonia49,313,2164,564,40100,0 25,896,5 R04Thessalia25,48,2213,666,0619,027,550,6100,0 R05Ipeiros33,18,7152,462,0845,134,819,791,5 R06Ionia Nisia23,89,8134,018,9713,650,710,50,0 R07Dytiki Ellada30,210,5180,561,5935,360,933,990,5 R08Sterea Ellada33,710,1138,656,4647,155,112,879,6 R09Peloponnisos28,47,1185,759,8829,211,636,586,9 R10Attiki22,07,8156,251,637,521,721,669,4 R11Voreio Aigaio31,27,1311,854,0238,611,6100,074,4 R12Notio Aigaio19,87,4113,222,340,015,90,07,2 R13Kriti20,96,3154,928,603,70,021,020,5 Min value19,806,30113,2118,97 Max value49,3013,20311,7666,06 Standardization of the 4 single variables

EXAMPLE : COMPOSITE INDICATOR CALCULATION OF COMPOSITE INDICATOR FOR UNEMPLOYMENT Calculation of the Composite Indicator with alternative weights Conclusions: _______________________________________________ ______________________________________

EXAMPLE : COMPOSITE INDICATOR SENSIBILITY 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 ANALYSIS LECTURE 04 Δρ. Μαρί-Νοέλ Ντυκέν, Αναπληρώτρια Καθηγήτρια, Τηλ Γραφείο Γ.6 UNIVERSITY OF THESSALY FACULTY OF ENGINEERING DEPARTMENT OF PLANNINGAND REGIONAL DEVELOPMENT MASTER «EUROPEAN REGIONAL DEVELOPMENT STUDIES»