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1 Michel Tenenhaus & Carlo Lauro A SEM approach for composite indicators building Michel Tenenhaus & Carlo Lauro.

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Presentation on theme: "1 Michel Tenenhaus & Carlo Lauro A SEM approach for composite indicators building Michel Tenenhaus & Carlo Lauro."— Presentation transcript:

1 1 Michel Tenenhaus & Carlo Lauro A SEM approach for composite indicators building Michel Tenenhaus & Carlo Lauro

2 2 Economic inequality and political instability Data from Russett (1964), in GIFI Economic inequality Agricultural inequality GINI : Inequality of land distributions FARM : % farmers that own half of the land (> 50) RENT : % farmers that rent all their land Industrial development GNPR : Gross national product per capita ($ 1955) LABO : % of labor force employed in agriculture Political instability INST : Instability of executive (45-61) ECKS : Nb of violent internal war incidents (46-61) DEATH : Nb of people killed as a result of civic group violence (50-62) D-STAB : Stable democracy D-UNST : Unstable democracy DICT : Dictatorship

3 3 Economic inequality and political instability (Data from Russett, 1964) 47 countries

4 4 GINI FARM RENT GNPR LABO Agricultural inequality (X 1 ) Industrial development (X 2 ) ECKS DEATH INST Political instability (X 3 ) Economic inequality and political instability

5 5 Building composite indicators 1.Separately for each block (without taking into account the other blocks). 2.For each block, taking into account all the other blocks (multi-block data analysis). 3.For each block, taking into account the causal model (Structural Equation Modelling).

6 6 1. Using SEM for factor analysis Measurement model

7 7 ULS algorithm S = Observed covariance matrix for MV Goodness-of-fit Index (Jöreskog & Sorbum): = PCA when

8 8 First result This solution is not admissible because

9 9 A solution The variance of residual e2 is fixed to a small value

10 10 Result 2 The variance of residual e2 is fixed to a small value:

11 11 Bootstrap Results Regression Weights: Composite indicator

12 12 Principal component analysis with SEM The variance of the residuals are fixed to 0 :

13 13 Result 3 The variance of the residuals are fixed to 0 :

14 14 Bootstrap Results Regression Weights: Conclusion

15 15 Result 4 The variance of the residuals are fixed to 0 :

16 16 Bootstrap Results Regression Weights: Composite indicator

17 17 2. Using SEM for multi-block data analysis

18 18 Result 5 This solution is not admissible because

19 19 Result 6 Var(e2) is fixed to a small value

20 20 Result 7 MacDonald (1996) proposal All Var(e) are fixed to 0:

21 21 Bootstrap Results Regression Weights: Conclusion

22 22 Result 8 MacDonald (1996) Proposals: (1) All Var(e) are fixed to 0: (2) Composite indicator:

23 23 3. Causal model estimation using SEM-ULS

24 24 Result 9 This solution is not admissible because

25 25 Result 10 Var(e2) is fixed to a small value

26 26 Bootstrap Results Regression Weights: Conclusion

27 27 Result 11 Var(e2) is fixed to a small value Composite indicator:

28 28 Bootstrap Results Regression Weights: Conclusion

29 29 Conclusion Agricultural inequality and Industrial development are drivers of political instability Russet hypotheses are validated: Other composite indicators:

30 30

31 31 Conclusion Agricultural inequality above the average Industrial development below the average DICTATORSHIP


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