(1)APPENDIX A: Figures and Tables
APPENDIX A.1: Figures
FIGURE 1.
Poverty trap at individual and country level
FIGURE 2.
Graphical representation of the Gini-coefficient and the Lorenz-curve
FIGURE 3.
Scatterplot of Log(GDP) against Gini-coefficient of income
FIGURE 4a.
Line graphs GDP series
2 0 0 0
3 0 0 0
4 0 0 0
5 0 0 0
6 0 0 0
7 0 0 0
5 0 5 5 6 0 6 5 7 0 7 5 8 0 8 5 9 0 9 5 0 0 0 5
1
5 0 0 0
1 0 0 0 0
1 5 0 0 0
2 0 0 0 0
2 5 0 0 0
5 0 5 5 6 0 6 5 7 0 7 5 8 0 8 5 9 0 9 5 0 0 0 5
2
1 5 0 0
2 0 0 0
2 5 0 0
3 0 0 0
3 5 0 0
4 0 0 0
4 5 0 0
5 0 0 0
5 5 0 0
5 0 5 5 6 0 6 5 7 0 7 5 8 0 8 5 9 0 9 5 0 0 0 5
3
4 0 0 0
8 0 0 0
1 2 0 0 0
1 6 0 0 0
2 0 0 0 0
2 4 0 0 0
5 0 5 5 6 0 6 5 7 0 7 5 8 0 8 5 9 0 9 5 0 0 0 5
4
4 0 0
5 0 0
6 0 0
7 0 0
8 0 0
9 0 0
1 0 0 0
5 0 5 5 6 0 6 5 7 0 7 5 8 0 8 5 9 0 9 5 0 0 0 5
5
1 0 0 0
2 0 0 0
3 0 0 0
4 0 0 0
5 0 0 0
6 0 0 0
7 0 0 0
5 0 5 5 6 0 6 5 7 0 7 5 8 0 8 5 9 0 9 5 0 0 0 5
6
4 0 0 0
5 0 0 0
6 0 0 0
7 0 0 0
8 0 0 0
9 0 0 0
5 0 5 5 6 0 6 5 7 0 7 5 8 0 8 5 9 0 9 5 0 0 0 5
7
1 0 0 0
2 0 0 0
3 0 0 0
4 0 0 0
5 0 0 0
6 0 0 0
5 0 5 5 6 0 6 5 7 0 7 5 8 0 8 5 9 0 9 5 0 0 0 5
8
5 0 0 0
1 0 0 0 0
1 5 0 0 0
2 0 0 0 0
2 5 0 0 0
5 0 5 5 6 0 6 5 7 0 7 5 8 0 8 5 9 0 9 5 0 0 0 5
9
3 0 0 0
4 0 0 0
5 0 0 0
6 0 0 0
7 0 0 0
8 0 0 0
9 0 0 0
1 0 0 0 0
1 1 0 0 0
1 2 0 0 0
5 0 5 5 6 0 6 5 7 0 7 5 8 0 8 5 9 0 9 5 0 0 0 5
10
0
1 0 0 0
2 0 0 0
3 0 0 0
4 0 0 0
5 0 0 0
5 0 5 5 6 0 6 5 7 0 7 5 8 0 8 5 9 0 9 5 0 0 0 5
11
1 0 0 0
1 2 0 0
1 4 0 0
1 6 0 0
1 8 0 0
2 0 0 0
2 2 0 0
5 0 5 5 6 0 6 5 7 0 7 5 8 0 8 5 9 0 9 5 0 0 0 5
12
2 0 0 0
2 4 0 0
2 8 0 0
3 2 0 0
3 6 0 0
4 0 0 0
4 4 0 0
4 8 0 0
5 2 0 0
5 6 0 0
5 0 5 5 6 0 6 5 7 0 7 5 8 0 8 5 9 0 9 5 0 0 0 5
13
3 0 0 0
4 0 0 0
5 0 0 0
6 0 0 0
7 0 0 0
8 0 0 0
9 0 0 0
1 0 0 0 0
1 1 0 0 0
5 0 5 5 6 0 6 5 7 0 7 5 8 0 8 5 9 0 9 5 0 0 0 5
14
4 0 0 0
8 0 0 0
1 2 0 0 0
1 6 0 0 0
2 0 0 0 0
5 0 5 5 6 0 6 5 7 0 7 5 8 0 8 5 9 0 9 5 0 0 0 5
15
5 0 0 0
1 0 0 0 0
1 5 0 0 0
2 0 0 0 0
2 5 0 0 0
5 0 5 5 6 0 6 5 7 0 7 5 8 0 8 5 9 0 9 5 0 0 0 5
16
1 5 0 0
2 0 0 0
2 5 0 0
3 0 0 0
3 5 0 0
4 0 0 0
4 5 0 0
5 0 5 5 6 0 6 5 7 0 7 5 8 0 8 5 9 0 9 5 0 0 0 5
17
8 0 0
1 2 0 0
1 6 0 0
2 0 0 0
2 4 0 0
2 8 0 0
3 2 0 0
5 0 5 5 6 0 6 5 7 0 7 5 8 0 8 5 9 0 9 5 0 0 0 5
18
0
4 0 0 0
8 0 0 0
1 2 0 0 0
1 6 0 0 0
2 0 0 0 0
5 0 5 5 6 0 6 5 7 0 7 5 8 0 8 5 9 0 9 5 0 0 0 5
19
6 0 0 0
8 0 0 0
1 0 0 0 0
1 2 0 0 0
1 4 0 0 0
1 6 0 0 0
1 8 0 0 0
5 0 5 5 6 0 6 5 7 0 7 5 8 0 8 5 9 0 9 5 0 0 0 5
20
4 0 0 0
8 0 0 0
1 2 0 0 0
1 6 0 0 0
2 0 0 0 0
2 4 0 0 0
5 0 5 5 6 0 6 5 7 0 7 5 8 0 8 5 9 0 9 5 0 0 0 5
21
4 0 0 0
8 0 0 0
1 2 0 0 0
1 6 0 0 0
2 0 0 0 0
2 4 0 0 0
5 0 5 5 6 0 6 5 7 0 7 5 8 0 8 5 9 0 9 5 0 0 0 5
22
4 0 0 0
8 0 0 0
1 2 0 0 0
1 6 0 0 0
2 0 0 0 0
2 4 0 0 0
5 0 5 5 6 0 6 5 7 0 7 5 8 0 8 5 9 0 9 5 0 0 0 5
23
2 0 0 0
3 0 0 0
4 0 0 0
5 0 0 0
6 0 0 0
7 0 0 0
8 0 0 0
9 0 0 0
1 0 0 0 0
5 0 5 5 6 0 6 5 7 0 7 5 8 0 8 5 9 0 9 5 0 0 0 5
24
9 0 0
1 0 0 0
1 1 0 0
1 2 0 0
1 3 0 0
1 4 0 0
1 5 0 0
5 0 5 5 6 0 6 5 7 0 7 5 8 0 8 5 9 0 9 5 0 0 0 5
25
0
2 0 0 0
4 0 0 0
6 0 0 0
8 0 0 0
1 0 0 0 0
1 2 0 0 0
1 4 0 0 0
1 6 0 0 0
5 0 5 5 6 0 6 5 7 0 7 5 8 0 8 5 9 0 9 5 0 0 0 5
26
2 0 0 0
2 4 0 0
2 8 0 0
3 2 0 0
3 6 0 0
4 0 0 0
5 0 5 5 6 0 6 5 7 0 7 5 8 0 8 5 9 0 9 5 0 0 0 5
27
0
4 0 0 0
8 0 0 0
1 2 0 0 0
1 6 0 0 0
2 0 0 0 0
2 4 0 0 0
2 8 0 0 0
5 0 5 5 6 0 6 5 7 0 7 5 8 0 8 5 9 0 9 5 0 0 0 5
28
2 0 0 0
3 0 0 0
4 0 0 0
5 0 0 0
6 0 0 0
7 0 0 0
8 0 0 0
9 0 0 0
1 0 0 0 0
5 0 5 5 6 0 6 5 7 0 7 5 8 0 8 5 9 0 9 5 0 0 0 5
29
8 0 0
1 2 0 0
1 6 0 0
2 0 0 0
2 4 0 0
2 8 0 0
3 2 0 0
3 6 0 0
4 0 0 0
5 0 5 5 6 0 6 5 7 0 7 5 8 0 8 5 9 0 9 5 0 0 0 5
30
4 0 0
8 0 0
1 2 0 0
1 6 0 0
2 0 0 0
2 4 0 0
5 0 5 5 6 0 6 5 7 0 7 5 8 0 8 5 9 0 9 5 0 0 0 5
31
0
4 0 0 0
8 0 0 0
1 2 0 0 0
1 6 0 0 0
2 0 0 0 0
2 4 0 0 0
2 8 0 0 0
5 0 5 5 6 0 6 5 7 0 7 5 8 0 8 5 9 0 9 5 0 0 0 5
32
1 0 0 0
2 0 0 0
3 0 0 0
4 0 0 0
5 0 0 0
6 0 0 0
7 0 0 0
5 0 5 5 6 0 6 5 7 0 7 5 8 0 8 5 9 0 9 5 0 0 0 5
33
0
1 0 0 0
2 0 0 0
3 0 0 0
4 0 0 0
5 0 0 0
6 0 0 0
7 0 0 0
5 0 5 5 6 0 6 5 7 0 7 5 8 0 8 5 9 0 9 5 0 0 0 5
34
0
4 0 0 0
8 0 0 0
1 2 0 0 0
1 6 0 0 0
2 0 0 0 0
5 0 5 5 6 0 6 5 7 0 7 5 8 0 8 5 9 0 9 5 0 0 0 5
35
1 5 0 0
2 0 0 0
2 5 0 0
3 0 0 0
3 5 0 0
4 0 0 0
4 5 0 0
5 0 0 0
5 5 0 0
5 0 5 5 6 0 6 5 7 0 7 5 8 0 8 5 9 0 9 5 0 0 0 5
36
0
4 0 0 0
8 0 0 0
1 2 0 0 0
1 6 0 0 0
2 0 0 0 0
2 4 0 0 0
5 0 5 5 6 0 6 5 7 0 7 5 8 0 8 5 9 0 9 5 0 0 0 5
37
4 4 0 0
4 8 0 0
5 2 0 0
5 6 0 0
6 0 0 0
6 4 0 0
6 8 0 0
7 2 0 0
7 6 0 0
8 0 0 0
5 0 5 5 6 0 6 5 7 0 7 5 8 0 8 5 9 0 9 5 0 0 0 5
38
6 0 0
7 0 0
8 0 0
9 0 0
1 0 0 0
1 1 0 0
5 0 5 5 6 0 6 5 7 0 7 5 8 0 8 5 9 0 9 5 0 0 0 5
39
1 6 0 0
2 0 0 0
2 4 0 0
2 8 0 0
3 2 0 0
3 6 0 0
4 0 0 0
5 0 5 5 6 0 6 5 7 0 7 5 8 0 8 5 9 0 9 5 0 0 0 5
40
0
4 0 0 0
8 0 0 0
1 2 0 0 0
1 6 0 0 0
2 0 0 0 0
5 0 5 5 6 0 6 5 7 0 7 5 8 0 8 5 9 0 9 5 0 0 0 5
41
1 0 0 0
1 5 0 0
2 0 0 0
2 5 0 0
3 0 0 0
3 5 0 0
4 0 0 0
4 5 0 0
5 0 5 5 6 0 6 5 7 0 7 5 8 0 8 5 9 0 9 5 0 0 0 5
42
4 0 0 0
5 0 0 0
6 0 0 0
7 0 0 0
8 0 0 0
9 0 0 0
1 0 0 0 0
5 0 5 5 6 0 6 5 7 0 7 5 8 0 8 5 9 0 9 5 0 0 0 5
43
5 0 0 0
6 0 0 0
7 0 0 0
8 0 0 0
9 0 0 0
1 0 0 0 0
1 1 0 0 0
1 2 0 0 0
5 0 5 5 6 0 6 5 7 0 7 5 8 0 8 5 9 0 9 5 0 0 0 5
44
2 0 0 0
3 0 0 0
4 0 0 0
5 0 0 0
6 0 0 0
7 0 0 0
5 0 5 5 6 0 6 5 7 0 7 5 8 0 8 5 9 0 9 5 0 0 0 5
45
2 0 0 0
3 0 0 0
4 0 0 0
5 0 0 0
6 0 0 0
7 0 0 0
8 0 0 0
5 0 5 5 6 0 6 5 7 0 7 5 8 0 8 5 9 0 9 5 0 0 0 5
46
0
4 0 0
8 0 0
1 2 0 0
1 6 0 0
2 0 0 0
2 4 0 0
5 0 5 5 6 0 6 5 7 0 7 5 8 0 8 5 9 0 9 5 0 0 0 5
47
1 0 0 0
2 0 0 0
3 0 0 0
4 0 0 0
5 0 0 0
6 0 0 0
7 0 0 0
8 0 0 0
9 0 0 0
5 0 5 5 6 0 6 5 7 0 7 5 8 0 8 5 9 0 9 5 0 0 0 5
48
7 0 0
8 0 0
9 0 0
1 0 0 0
1 1 0 0
1 2 0 0
1 3 0 0
1 4 0 0
1 5 0 0
1 6 0 0
5 0 5 5 6 0 6 5 7 0 7 5 8 0 8 5 9 0 9 5 0 0 0 5
49
4 0 0 0
8 0 0 0
1 2 0 0 0
1 6 0 0 0
2 0 0 0 0
2 4 0 0 0
5 0 5 5 6 0 6 5 7 0 7 5 8 0 8 5 9 0 9 5 0 0 0 5
50
4 0 0 0
8 0 0 0
1 2 0 0 0
1 6 0 0 0
2 0 0 0 0
2 4 0 0 0
2 8 0 0 0
5 0 5 5 6 0 6 5 7 0 7 5 8 0 8 5 9 0 9 5 0 0 0 5
51
6 0 0 0
8 0 0 0
1 0 0 0 0
1 2 0 0 0
1 4 0 0 0
1 6 0 0 0
1 8 0 0 0
2 0 0 0 0
5 0 5 5 6 0 6 5 7 0 7 5 8 0 8 5 9 0 9 5 0 0 0 5
52
4 0 0
8 0 0
1 2 0 0
1 6 0 0
2 0 0 0
2 4 0 0
5 0 5 5 6 0 6 5 7 0 7 5 8 0 8 5 9 0 9 5 0 0 0 5
53
2 0 0 0
2 4 0 0
2 8 0 0
3 2 0 0
3 6 0 0
4 0 0 0
4 4 0 0
5 0 5 5 6 0 6 5 7 0 7 5 8 0 8 5 9 0 9 5 0 0 0 5
54
8 0 0
1 2 0 0
1 6 0 0
2 0 0 0
2 4 0 0
2 8 0 0
5 0 5 5 6 0 6 5 7 0 7 5 8 0 8 5 9 0 9 5 0 0 0 5
55
2 0 0 0
3 0 0 0
4 0 0 0
5 0 0 0
6 0 0 0
7 0 0 0
8 0 0 0
9 0 0 0
5 0 5 5 6 0 6 5 7 0 7 5 8 0 8 5 9 0 9 5 0 0 0 5
56
0
2 0 0 0
4 0 0 0
6 0 0 0
8 0 0 0
1 0 0 0 0
1 2 0 0 0
1 4 0 0 0
1 6 0 0 0
5 0 5 5 6 0 6 5 7 0 7 5 8 0 8 5 9 0 9 5 0 0 0 5
57
1 0 0 0
1 5 0 0
2 0 0 0
2 5 0 0
3 0 0 0
3 5 0 0
4 0 0 0
4 5 0 0
5 0 5 5 6 0 6 5 7 0 7 5 8 0 8 5 9 0 9 5 0 0 0 5
58
4 0 0 0
5 0 0 0
6 0 0 0
7 0 0 0
8 0 0 0
9 0 0 0
5 0 5 5 6 0 6 5 7 0 7 5 8 0 8 5 9 0 9 5 0 0 0 5
59
7 0 0
8 0 0
9 0 0
1 0 0 0
1 1 0 0
1 2 0 0
5 0 5 5 6 0 6 5 7 0 7 5 8 0 8 5 9 0 9 5 0 0 0 5
60
0
4 0 0 0
8 0 0 0
1 2 0 0 0
1 6 0 0 0
2 0 0 0 0
2 4 0 0 0
5 0 5 5 6 0 6 5 7 0 7 5 8 0 8 5 9 0 9 5 0 0 0 5
61
2 0 0 0
3 0 0 0
4 0 0 0
5 0 0 0
6 0 0 0
7 0 0 0
8 0 0 0
5 0 5 5 6 0 6 5 7 0 7 5 8 0 8 5 9 0 9 5 0 0 0 5
62
6 0 0 0
7 0 0 0
8 0 0 0
9 0 0 0
1 0 0 0 0
1 1 0 0 0
5 0 5 5 6 0 6 5 7 0 7 5 8 0 8 5 9 0 9 5 0 0 0 5
63
9 0 0 0
1 0 0 0 0
1 1 0 0 0
1 2 0 0 0
1 3 0 0 0
1 4 0 0 0
1 5 0 0 0
1 6 0 0 0
1 7 0 0 0
5 0 5 5 6 0 6 5 7 0 7 5 8 0 8 5 9 0 9 5 0 0 0 5
64
4 0 0 0
8 0 0 0
1 2 0 0 0
1 6 0 0 0
2 0 0 0 0
2 4 0 0 0
5 0 5 5 6 0 6 5 7 0 7 5 8 0 8 5 9 0 9 5 0 0 0 5
65
0
1 0 0 0
2 0 0 0
3 0 0 0
4 0 0 0
5 0 0 0
6 0 0 0
7 0 0 0
8 0 0 0
5 0 5 5 6 0 6 5 7 0 7 5 8 0 8 5 9 0 9 5 0 0 0 5
66
5 0 0
1 0 0 0
1 5 0 0
2 0 0 0
2 5 0 0
3 0 0 0
3 5 0 0
4 0 0 0
4 5 0 0
5 0 5 5 6 0 6 5 7 0 7 5 8 0 8 5 9 0 9 5 0 0 0 5
67
1 5 0 0
2 0 0 0
2 5 0 0
3 0 0 0
3 5 0 0
4 0 0 0
4 5 0 0
5 0 0 0
5 0 5 5 6 0 6 5 7 0 7 5 8 0 8 5 9 0 9 5 0 0 0 5
68
1 0 0 0
2 0 0 0
3 0 0 0
4 0 0 0
5 0 0 0
6 0 0 0
7 0 0 0
8 0 0 0
5 0 5 5 6 0 6 5 7 0 7 5 8 0 8 5 9 0 9 5 0 0 0 5
69
0
4 0 0 0
8 0 0 0
1 2 0 0 0
1 6 0 0 0
2 0 0 0 0
5 0 5 5 6 0 6 5 7 0 7 5 8 0 8 5 9 0 9 5 0 0 0 5
70
4 0 0
4 4 0
4 8 0
5 2 0
5 6 0
6 0 0
6 4 0
6 8 0
5 0 5 5 6 0 6 5 7 0 7 5 8 0 8 5 9 0 9 5 0 0 0 5
71
2 0 0 0
3 0 0 0
4 0 0 0
5 0 0 0
6 0 0 0
7 0 0 0
5 0 5 5 6 0 6 5 7 0 7 5 8 0 8 5 9 0 9 5 0 0 0 5
72
8 0 0 0
1 2 0 0 0
1 6 0 0 0
2 0 0 0 0
2 4 0 0 0
2 8 0 0 0
3 2 0 0 0
5 0 5 5 6 0 6 5 7 0 7 5 8 0 8 5 9 0 9 5 0 0 0 5
73
2 8 0 0
3 2 0 0
3 6 0 0
4 0 0 0
4 4 0 0
4 8 0 0
5 2 0 0
5 0 5 5 6 0 6 5 7 0 7 5 8 0 8 5 9 0 9 5 0 0 0 5
74
6 0 0 0
7 0 0 0
8 0 0 0
9 0 0 0
1 0 0 0 0
1 1 0 0 0
1 2 0 0 0
5 0 5 5 6 0 6 5 7 0 7 5 8 0 8 5 9 0 9 5 0 0 0 5
75
1 0 0 0
2 0 0 0
3 0 0 0
4 0 0 0
5 0 0 0
6 0 0 0
7 0 0 0
5 0 5 5 6 0 6 5 7 0 7 5 8 0 8 5 9 0 9 5 0 0 0 5
76
2 4 0 0
2 8 0 0
3 2 0 0
3 6 0 0
4 0 0 0
4 4 0 0
4 8 0 0
5 0 5 5 6 0 6 5 7 0 7 5 8 0 8 5 9 0 9 5 0 0 0 5
77
bla bla
* The number above the graphs refers to the countries. See appendix C for a complete list. Time is
FIGURE 4b.
Line graphs of EDUAVER series
9.2
9.4
9.6
9.8
1 0.0
1 0.2
1 0.4
1 0.6
50 55 60 65 70 7 5 8 0 8 5 9 0 9 5 0 0 0 5
2
7.4
7.6
7.8
8.0
8.2
8.4
8.6
8.8
50 5 5 6 0 6 5 7 0 7 5 8 0 85 90 95 00 05
4
0 .5
1 .0
1 .5
2 .0
2 .5
5 0 5 5 6 0 65 70 75 80 85 90 95 00 0 5
5
5
6
7
8
9
1 0
50 55 6 0 6 5 7 0 7 5 8 0 8 5 90 95 00 0 5
6
2 .4
2 .8
3 .2
3 .6
4 .0
4 .4
4 .8
5 0 5 5 6 0 6 5 70 75 80 85 90 95 00 0 5
8
8.0
8.5
9.0
9.5
1 0.0
1 0.5
1 1.0
1 1.5
50 55 60 6 5 7 0 7 5 8 0 8 5 9 0 95 00 0 5
9
4
5
6
7
8
5 0 5 5 6 0 6 5 7 0 75 80 85 90 95 00 05
10
3.0
3.5
4.0
4.5
5.0
5.5
6.0
50 55 60 65 70 7 5 8 0 8 5 9 0 9 5 0 0 0 5
11
0.0
0.4
0.8
1.2
1.6
2.0
2.4
50 5 5 6 0 6 5 7 0 7 5 8 0 85 90 95 00 05
12
2 .0
2 .5
3 .0
3 .5
4 .0
4 .5
5 .0
5 .5
5 0 5 5 6 0 65 70 75 80 85 90 95 00 0 5
13
7.0
7.5
8.0
8.5
9.0
9.5
1 0.0
1 0.5
50 55 6 0 6 5 7 0 7 5 8 0 8 5 90 95 00 0 5
14
7 .6
7 .8
8 .0
8 .2
8 .4
8 .6
8 .8
9 .0
9 .2
5 0 5 5 6 0 6 5 70 75 80 85 90 95 00 0 5
15
8.6
8.8
9.0
9.2
9.4
9.6
9.8
1 0.0
1 0.2
50 55 60 6 5 7 0 7 5 8 0 8 5 9 0 95 00 0 5
16
2
3
4
5
6
7
5 0 5 5 6 0 6 5 7 0 75 80 85 90 95 00 05
17
1
2
3
4
5
6
50 55 60 65 70 7 5 8 0 8 5 9 0 9 5 0 0 0 5
18
3
4
5
6
7
8
50 5 5 6 0 6 5 7 0 7 5 8 0 85 90 95 00 05
19
8 .6
8 .8
9 .0
9 .2
9 .4
9 .6
9 .8
5 0 5 5 6 0 65 70 75 80 85 90 95 00 0 5
20
5
6
7
8
9
1 0
1 1
50 55 6 0 6 5 7 0 7 5 8 0 8 5 90 95 00 0 5
21
5 .6
6 .0
6 .4
6 .8
7 .2
7 .6
8 .0
8 .4
5 0 5 5 6 0 6 5 70 75 80 85 90 95 00 0 5
22
7.0
7.5
8.0
8.5
9.0
9.5
50 55 60 6 5 7 0 7 5 8 0 8 5 9 0 95 00 0 5
23
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
4.5
5 0 5 5 6 0 6 5 7 0 75 80 85 90 95 00 05
25
3
4
5
6
7
8
9
50 55 60 65 70 7 5 8 0 8 5 9 0 9 5 0 0 0 5
26
0.8
1.2
1.6
2.0
2.4
2.8
3.2
50 5 5 6 0 6 5 7 0 7 5 8 0 85 90 95 00 05
27
4
5
6
7
8
9
10
5 0 5 5 6 0 65 70 75 80 85 90 95 00 0 5
28
6.4
6.8
7.2
7.6
8.0
8.4
8.8
9.2
50 55 6 0 6 5 7 0 7 5 8 0 8 5 90 95 00 0 5
29
1
2
3
4
5
5 0 5 5 6 0 6 5 70 75 80 85 90 95 00 0 5
30
1.2
1.6
2.0
2.4
2.8
3.2
3.6
4.0
4.4
4.8
50 55 60 6 5 7 0 7 5 8 0 8 5 9 0 95 00 0 5
31
6.4
6.8
7.2
7.6
8.0
8.4
8.8
9.2
5 0 5 5 6 0 6 5 7 0 75 80 85 90 95 00 05
32
0
1
2
3
4
5
50 55 60 65 70 7 5 8 0 8 5 9 0 9 5 0 0 0 5
33
0
1
2
3
4
5
50 5 5 6 0 6 5 7 0 7 5 8 0 85 90 95 00 05
34
4 .0
4 .4
4 .8
5 .2
5 .6
6 .0
6 .4
6 .8
7 .2
5 0 5 5 6 0 65 70 75 80 85 90 95 00 0 5
35
1
2
3
4
5
6
7
8
50 55 6 0 6 5 7 0 7 5 8 0 8 5 90 95 00 0 5
36
6 .5
7 .0
7 .5
8 .0
8 .5
9 .0
9 .5
10 .0
5 0 5 5 6 0 6 5 70 75 80 85 90 95 00 0 5
37
8.4
8.6
8.8
9.0
9.2
9.4
9.6
50 55 60 6 5 7 0 7 5 8 0 8 5 9 0 95 00 0 5
38
1.0
1.5
2.0
2.5
3.0
3.5
4.0
4.5
5 0 5 5 6 0 6 5 7 0 75 80 85 90 95 00 05
39
3
4
5
6
7
8
9
1 0
1 1
50 55 60 65 70 7 5 8 0 8 5 9 0 9 5 0 0 0 5
41
3.0
3.5
4.0
4.5
5.0
5.5
6.0
6.5
50 5 5 6 0 6 5 7 0 7 5 8 0 85 90 95 00 05
42
8 .8
9 .0
9 .2
9 .4
9 .6
9 .8
5 0 5 5 6 0 65 70 75 80 85 90 95 00 0 5
43
9.0
9.2
9.4
9.6
9.8
1 0.0
1 0.2
50 55 6 0 6 5 7 0 7 5 8 0 8 5 90 95 00 0 5
44
8 .6
8 .8
9 .0
9 .2
9 .4
9 .6
5 0 5 5 6 0 6 5 70 75 80 85 90 95 00 0 5
45
2
3
4
5
6
7
50 55 60 6 5 7 0 7 5 8 0 8 5 9 0 95 00 0 5
46
0.8
1.2
1.6
2.0
2.4
2.8
5 0 5 5 6 0 6 5 7 0 75 80 85 90 95 00 05
47
2
3
4
5
6
7
8
50 55 60 65 70 7 5 8 0 8 5 9 0 9 5 0 0 0 5
48
5
6
7
8
9
1 0
50 5 5 6 0 6 5 7 0 7 5 8 0 85 90 95 00 05
50
6
7
8
9
10
11
12
5 0 5 5 6 0 65 70 75 80 85 90 95 00 0 5
51
9.2
9.6
1 0.0
1 0.4
1 0.8
1 1.2
1 1.6
50 55 6 0 6 5 7 0 7 5 8 0 8 5 90 95 00 0 5
52
0 .5
1 .0
1 .5
2 .0
2 .5
5 0 5 5 6 0 6 5 70 75 80 85 90 95 00 0 5
53
2
3
4
5
6
7
8
50 55 60 6 5 7 0 7 5 8 0 8 5 9 0 95 00 0 5
54
2
3
4
5
6
7
8
5 0 5 5 6 0 6 5 7 0 75 80 85 90 95 00 05
55
6.5
7.0
7.5
8.0
8.5
9.0
9.5
1 0.0
50 55 60 65 70 7 5 8 0 8 5 9 0 9 5 0 0 0 5
56
1.5
2.0
2.5
3.0
3.5
4.0
4.5
5.0
50 5 5 6 0 6 5 7 0 7 5 8 0 85 90 95 00 05
57
4
5
6
7
8
9
10
5 0 5 5 6 0 65 70 75 80 85 90 95 00 0 5
58
0.0
0.4
0.8
1.2
1.6
2.0
50 55 6 0 6 5 7 0 7 5 8 0 8 5 90 95 00 0 5
60
3
4
5
6
7
8
9
5 0 5 5 6 0 6 5 70 75 80 85 90 95 00 0 5
61
7.5
8.0
8.5
9.0
9.5
1 0.0
1 0.5
1 1.0
50 55 60 6 5 7 0 7 5 8 0 8 5 9 0 95 00 0 5
62
8.96
9.00
9.04
9.08
9.12
9.16
9.20
5 0 5 5 6 0 6 5 7 0 75 80 85 90 95 00 05
63
6.9
7.0
7.1
7.2
7.3
7.4
50 55 60 65 70 7 5 8 0 8 5 9 0 9 5 0 0 0 5
64
7
8
9
1 0
1 1
1 2
50 5 5 6 0 6 5 7 0 7 5 8 0 85 90 95 00 05
65
3 .0
3 .5
4 .0
4 .5
5 .0
5 .5
6 .0
6 .5
5 0 5 5 6 0 65 70 75 80 85 90 95 00 0 5
66
9.0
9.2
9.4
9.6
9.8
1 0.0
1 0.2
50 55 6 0 6 5 7 0 7 5 8 0 8 5 90 95 00 0 5
67
0
1
2
3
4
5
5 0 5 5 6 0 6 5 70 75 80 85 90 95 00 0 5
69
3
4
5
6
7
8
9
50 55 60 6 5 7 0 7 5 8 0 8 5 9 0 95 00 0 5
70
7
8
9
10
11
12
13
5 0 5 5 6 0 6 5 7 0 75 80 85 90 95 00 05
73
1
2
3
4
5
6
50 55 60 65 70 7 5 8 0 8 5 9 0 9 5 0 0 0 5
75
4.8
5.2
5.6
6.0
6.4
6.8
7.2
7.6
50 5 5 6 0 6 5 7 0 7 5 8 0 85 90 95 00 05
76
3
4
5
6
7
8
9
5 0 5 5 6 0 65 70 75 80 85 90 95 00 0 5
77
FIGURE 4c.
Line graphs EDUHIGHER series
6
8
10
12
14
16
18
50 55 60 6 5 70 75 80 85 90 95 00 05
2
2
4
6
8
10
12
14
16
18
50 55 6 0 65 70 75 8 0 85 90 95 00 05
4
0.0
0.4
0.8
1.2
1.6
2.0
2.4
5 0 55 60 65 70 7 5 80 85 90 9 5 00 05
5
2
4
6
8
10
12
14
16
18
50 55 6 0 65 70 75 8 0 85 90 95 00 05
6
1
2
3
4
5
6
5 0 55 60 65 70 7 5 80 85 90 9 5 00 05
8
4
6
8
10
12
14
16
50 55 6 0 65 70 75 8 0 85 90 95 00 05
9
0
2
4
6
8
10
12
50 55 60 65 70 7 5 80 85 90 9 5 00 05
10
0. 4
0. 8
1. 2
1. 6
2. 0
2. 4
50 55 60 6 5 70 75 80 85 90 95 00 05
11
0.0
0.4
0.8
1.2
1.6
2.0
50 55 6 0 65 70 75 8 0 85 90 95 00 05
12
0
1
2
3
4
5
6
7
5 0 55 60 65 70 7 5 80 85 90 9 5 00 05
13
2
3
4
5
6
7
8
50 55 6 0 65 70 75 8 0 85 90 95 00 05
14
0
1
2
3
4
5
6
5 0 55 60 65 70 7 5 80 85 90 9 5 00 05
15
8 .5
9 .0
9 .5
10 .0
10 .5
11 .0
11 .5
12 .0
12 .5
50 55 6 0 65 70 75 8 0 85 90 95 00 05
16
0
2
4
6
8
10
12
14
50 55 60 65 70 7 5 80 85 90 9 5 00 05
17
2
3
4
5
6
7
8
9
10
50 55 60 6 5 70 75 80 85 90 95 00 05
18
1
2
3
4
5
6
7
8
9
10
50 55 6 0 65 70 75 8 0 85 90 95 00 05
19
11.2
11.4
11.6
11.8
12.0
12.2
12.4
5 0 55 60 65 70 7 5 80 85 90 9 5 00 05
20
2
4
6
8
10
12
14
50 55 6 0 65 70 75 8 0 85 90 95 00 05
21
1
2
3
4
5
6
7
8
9
10
5 0 55 60 65 70 7 5 80 85 90 9 5 00 05
22
0
2
4
6
8
10
12
50 55 6 0 65 70 75 8 0 85 90 95 00 05
23
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
50 55 60 65 70 7 5 80 85 90 9 5 00 05
25
0
2
4
6
8
10
12
14
50 55 60 6 5 70 75 80 85 90 95 00 05
26
0
1
2
3
4
5
50 55 6 0 65 70 75 8 0 85 90 95 00 05
27
1
2
3
4
5
6
7
8
9
5 0 55 60 65 70 7 5 80 85 90 9 5 00 05
28
3
4
5
6
7
8
9
10
11
12
50 55 6 0 65 70 75 8 0 85 90 95 00 05
29
0.0
0.4
0.8
1.2
1.6
2.0
2.4
5 0 55 60 65 70 7 5 80 85 90 9 5 00 05
30
0 .0
0 .5
1 .0
1 .5
2 .0
2 .5
3 .0
3 .5
50 55 6 0 65 70 75 8 0 85 90 95 00 05
31
2
4
6
8
10
12
50 55 60 65 70 7 5 80 85 90 9 5 00 05
32
0
1
2
3
4
5
50 55 60 6 5 70 75 80 85 90 95 00 05
33
0
1
2
3
4
5
6
50 55 6 0 65 70 75 8 0 85 90 95 00 05
34
0
1
2
3
4
5
6
7
8
9
5 0 55 60 65 70 7 5 80 85 90 9 5 00 05
35
0
2
4
6
8
10
12
14
50 55 6 0 65 70 75 8 0 85 90 95 00 05
36
2
4
6
8
10
12
14
16
5 0 55 60 65 70 7 5 80 85 90 9 5 00 05
37
10 .0
10 .2
10 .4
10 .6
10 .8
11 .0
11 .2
11 .4
50 55 6 0 65 70 75 8 0 85 90 95 00 05
38
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
50 55 60 65 70 7 5 80 85 90 9 5 00 05
39
0
4
8
12
16
20
50 55 60 6 5 70 75 80 85 90 95 00 05
41
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
1.6
50 55 6 0 65 70 75 8 0 85 90 95 00 05
42
10.2
10.4
10.6
10.8
11.0
11.2
11.4
11.6
5 0 55 60 65 70 7 5 80 85 90 9 5 00 05
43
1 1.0
1 1.2
1 1.4
1 1.6
1 1.8
1 2.0
1 2.2
50 55 6 0 65 70 75 8 0 85 90 95 00 05
44
9.2
9.4
9.6
9.8
10.0
10.2
10.4
5 0 55 60 65 70 7 5 80 85 90 9 5 00 05
45
0
1
2
3
4
5
6
7
50 55 6 0 65 70 75 8 0 85 90 95 00 05
46
0.0
0.4
0.8
1.2
1.6
2.0
2.4
50 55 60 65 70 7 5 80 85 90 9 5 00 05
47
1
2
3
4
5
6
7
50 55 60 6 5 70 75 80 85 90 95 00 05
48
0
2
4
6
8
10
12
14
50 55 6 0 65 70 75 8 0 85 90 95 00 05
50
0
2
4
6
8
10
12
5 0 55 60 65 70 7 5 80 85 90 9 5 00 05
51
0
4
8
12
16
20
50 55 6 0 65 70 75 8 0 85 90 95 00 05
52
0.0
0.5
1.0
1.5
2.0
2.5
3.0
5 0 55 60 65 70 7 5 80 85 90 9 5 00 05
53
0
2
4
6
8
10
12
14
16
50 55 6 0 65 70 75 8 0 85 90 95 00 05
54
0
2
4
6
8
10
12
14
16
50 55 60 65 70 7 5 80 85 90 9 5 00 05
55
2
3
4
5
6
7
8
9
10
50 55 60 6 5 70 75 80 85 90 95 00 05
56
0
1
2
3
4
5
6
7
50 55 6 0 65 70 75 8 0 85 90 95 00 05
57
1
2
3
4
5
6
7
8
5 0 55 60 65 70 7 5 80 85 90 9 5 00 05
58
0.0
0.4
0.8
1.2
1.6
50 55 6 0 65 70 75 8 0 85 90 95 00 05
60
0
1
2
3
4
5
6
7
8
5 0 55 60 65 70 7 5 80 85 90 9 5 00 05
61
2
4
6
8
10
12
14
16
18
50 55 6 0 65 70 75 8 0 85 90 95 00 05
62
8.0
8.4
8.8
9.2
9.6
10.0
10.4
50 55 60 65 70 7 5 80 85 90 9 5 00 05
63
8. 5
9. 0
9. 5
10. 0
10. 5
11. 0
11. 5
12. 0
12. 5
50 55 60 6 5 70 75 80 85 90 95 00 05
64
4
6
8
10
12
14
50 55 6 0 65 70 75 8 0 85 90 95 00 05
65
0
2
4
6
8
10
12
5 0 55 60 65 70 7 5 80 85 90 9 5 00 05
66
9.4
9.6
9.8
1 0.0
1 0.2
1 0.4
1 0.6
1 0.8
50 55 6 0 65 70 75 8 0 85 90 95 00 05
67
0
1
2
3
4
5
5 0 55 60 65 70 7 5 80 85 90 9 5 00 05
69
2
3
4
5
6
7
8
9
10
50 55 6 0 65 70 75 8 0 85 90 95 00 05
70
8
12
16
20
24
28
32
50 55 60 65 70 7 5 80 85 90 9 5 00 05
73
0
2
4
6
8
10
12
14
50 55 60 6 5 70 75 80 85 90 95 00 05
75
0
1
2
3
4
5
6
7
8
9
50 55 6 0 65 70 75 8 0 85 90 95 00 05
76
0
1
2
3
4
5
6
7
8
9
5 0 55 60 65 70 7 5 80 85 90 9 5 00 05
77
FIGURE 5.
Scatterplot of Log(GDP) against Gini-coefficient of land
TABLE 3.
Results for Panel Unit Root tests
Variable
Type of test
Level
First difference
Cross-probability
probability
sections
Obs.
GDP
1 Levin, Lin & Chu
1.000
0.000
77
3446
1 Breitung t-stat
0.000
0.000
77
3369
2 Im, Perasaran and Shin W-stat
1.000
0.000
77
3446
2 ADF - Fisher Chi-square
1.000
0.000
77
3446
2 PP - Fisher Chi-square
1.000
0.000
77
3517
3 Hadri Z-stat
0.000
0.000
77
3609
EDUAVER
1 Levin, Lin & Chu
0.8443
0.000
60
2469
1 Breitung t-stat
0.9802
0.000
60
2409
2 Im, Perasaran and Shin W-stat
1.000
0.000
60
2469
2 ADF - Fisher Chi-square
1.000
0.000
60
2469
2 PP - Fisher Chi-square
1.000
0.000
60
2606
3 Hadri Z-stat
0.000
0.365
66
2709
EDUHIGHER
1 Levin, Lin & Chu
1.000
1.000
60
2329
1 Breitung t-stat
0.738
0.000
60
2269
2 Im, Perasaran and Shin W-stat
1.000
0.000
60
2329
2 ADF - Fisher Chi-square
1.000
0.000
60
2329
2 PP - Fisher Chi-square
1.000
0.000
60
2602
3 Hadri Z-stat
1.000
0.000
66
2705
Levin, Lin and Chu (LLC), Breitung, and Hadri tests assume that there is a common unit root process.
This implies that for an AR(1) process for panel data,
y
it
=
ρ
i
y
it
−1
+
X
it
δ
t
+
ε
it
,
ρ
i
is identical across
cross-sections. LLC and Breitung tests employ a null hypothesis of unit root, whereas Hadri test has a
null hypothesis of no unit root. The Im, Pesaran and Shin (IPS), Fisher-ADF and PP tests all allow for
an individual unit root process so that
ρ
i
may vary across cross-sections. These tests are all
characterized by the combining of individual unit root tests to derive a panel-specific result. The null
hypothesis of all tests is the presence of an individual unit root.
TABLE 4.
Redundant Fixed Effects test
Effects Test
Statistic
d.f.
Prob.
Cross-section F
4.9
-43.294
0.0000
Cross-section Chi-square
184.9
43
0.0000
Test performed with basic equation (1):
( 1)
log
y
it
α β
i
log
y
i t
−
γ
GINI
it
ϕ
CMI
it
λ
GINI CMI
it
it
ε
it
∆
=
+ ⋅
+ ⋅
+ ⋅
+ ⋅
⋅
+
TABLE 5.
Goldfeld-Quandt test on Heteroskedasticity
Sample
Standard Error
(S.E.)²
Goldfeld-Quandt
Statistic
1950 - 1978
0.024
0.0006
1978 - 2005
0.036
0.0013
2.138
Test performed with basic equation (1):
( 1)
log
y
it
α β
i
log
y
i t
−
γ
GINI
it
ϕ
CMI
it
λ
GINI CMI
it
it
ε
it
∆
=
+ ⋅
+ ⋅
+ ⋅
+ ⋅
⋅
+
The critical value of the F-distribution with 27 degrees of freedom in the numerator and 26
degrees of freedom in denominator equal 1.70. Hence, heteroskedasticity is present when
GQ-statistic > 1.70.
TABLE 6.
Test for Multicollinearity
Dependent Variable
Independent Variable
Fixed effects
R
²
Gini
Bank_Deposits
0.736
Gini
Private_Credit
0.740
Gini
Real_Int
0.732
Gini
Log Y
(t-1)
0.744
Bank_Deposits
Gini
0.755
Bank_Deposits
Log Y
(t-1)
0.716
Private_Credit
Gini
0.627
Private_Credit
Log Y
(t-1)
0.721
Real_Int
Gini
0.480
Real_Int
Log Y
(t-1)
0.137
Log Y
(t-1)
Gini
0.701
Log Y
(t-1)
Bank_Deposits
0.739
Log Y
(t-1)
Private_Credit
0.749
Log Y
(t-1)
Real_Int
0.747
TABLE 7A.
TABLE 7B.
Akaike Information Criterion for EDUAVER and EDUHIGHER
EDUAVER
Treshold
AIC
7.6
-3.934
7.8
-3.932
8.0
-3.932
8.2
-3.933
8.4
-3.933
8.6
-3.933
8.8
-3.932
9.0
-3.936
9.2
-3.934
9.4
-3.948
9.6
-3.938
9.8
-3.933
10.0
-3.936
EDUHIGHER
Treshold
AIC
9,8
-3.931
10.0
-3.931
10,2
-3.932
10,4
-3.932
10,6
-3.932
10,8
-3.932
11.0
-3.933
11,2
-3.936
11,3
-3.938
11,4
-3.937
11,6
-3.934
11,8
-3.932
12.0
-3.934
The AIC values come from the basic equation (1):
(0, )
( 1)
( , )
( 1)
log
[
log
]
[
log
]
it
x
i
i t
it
it
it
it
x
i
i t
it
it
it
it
it
y
I
y
GINI
CMI
GINI CMI
I
y
GINI
CMI
GINI CMI
α β
γ
ϕ
λ
α β
γ
ϕ
λ
ε
−
∞
−
∆
=
+ ⋅
+ ⋅
+ ⋅
+ ⋅
⋅
+
+ ⋅
+ ⋅
+ ⋅
+ ⋅
⋅
+
Where
x
refers to the thresholds used. The first part of the equation thus estimates the coefficients for
the values lower than the threshold, while the second part estimates this for corresponding values that
are higher than the threshold.
Formally the Akaike Information Criterion is calculated as:
)
/
(
)
/
(
2
l
T
K
T
TABLE 8.
Basic regression output results
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
Regression
Sample
squared
R-
S.E.Regres
sion
Durbin-
Watson
sections
Cross-
Obs.
log(Y) = + log(Y)
(t-1)
+ GINI
+ CMI+ GINI CMI
Coefficient
Prob.
Coefficient
Prob.
Coefficient
Prob.
Coefficient Prob.
Coefficient Prob.
TABLE 9.
Extension regression output results
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
Regression
Sample
R-squared
S.E.Regres
sion
Durbin-Watson
Cross-sections
Obs.
log(Y) = + log(Y)
(t-1)
+ GINI
+ CMI+ GINI CMI
Coefficient
Prob.
Coefficient
Prob.
Coefficient
Prob.
Coefficient Prob.
Coefficient Prob.
TABLE 10.
Extension regression output results
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
Regression
Sample
squared
R-
S.E.Regres
sion
Durbin-
Watson
sections
Cross-
Obs.
log (Y) = + log(Y)
(t-1)
+ GINI +
GINI CMI + GINI log(Y)
(t-1)
Coefficient
Prob.
Coefficient
Prob.
Coefficient
Prob.
Coefficient Prob.
Coefficient Prob.
CMI
1
Whole sample
1.487
0.000
-0.167
0.000
-0.032
0.000
0.000
0.902
0.003
0.000
0.539
0.029
1.659
44
342
2
if EDUAVER<9.4
1.468
0.000
-0.168
0.000
-0.034
0.000
0.000
0.861
0.004
0.000
0.524
0.030
1.901
41
252
3
Bank_Deposits
if EDUHIGHER<11.3
1.422
0.000
-0.161
0.000
-0.033
0.000
0.000
0.901
0.004
0.000
0.517
0.029
1.889
39
270
4
if EDUAVER=>9.4
6.859
0.000
-0.705
0.000
-0.173
0.000
0.001
0.006
0.017
0.000
0.851
0.012
0.819
10
83
5
if EDUHIGHER=>11.3
4.276
0.000
-0.461
0.000
-0.086
0.000
0.000
0.229
0.009
0.000
0.628
0.021
1.241
10
65
6
Whole sample
1.354
0.000
-0.152
0.000
-0.032
0.000
-0.001
0.0348
0.003
0.000
0.534
0.028
1.735
44
340
7
if EDUAVER<9.4
1.319
0.000
-0.150
0.000
-0.032
0.000
-0.000
0.167
0.003
0.000
0.503
0.029
2.024
41
250
8
Private_Credit
if EDUHIGHER<11.3
1.227
0.000
-0.139
0.000
-0.031
0.000
-0.001
0.0175
0.003
0.000
0.507
0.028
2.015
39
268
9
if EDUAVER=>9.4
6.572
0.000
-0.679
0.000
-0.167
0.000
-0.000
0.652
0.017
0.000
0.837
0.013
0.736
10
83
10
if EDUHIGHER=>11.3
4.375
0.000
-0.471
0.000
-0.082
0.000
0.001
0.262
0.008
0.000
0.627
0.021
1.232
10
65
11
Whole sample
1.241
0.000
-0.139
0.000
-0.027
0.000
0.000
0.000
0.003
0.000
0.578
0.026
1.901
43
315
12
if EDUAVER<9.4
1.152
0.000
-0.131
0.000
-0.026
0.000
0.000
0.039
0.003
0.000
0.521
0.027
1.885
37
217
13
Real_Interest
if EDUHIGHER<11.3
1.165
0.000
-0.131
0.000
-0.027
0.000
0.000
0.065
0.003
0.000
0.514
0.026
2.002
35
236
14
if EDUAVER=>9.4
7.122
0.000
-0.735
0.000
-0.182
0.000
0.000
0.009
0.018
0.000
0.849
0.013
0.719
10
80
15
if EDUHIGHER=>11.3
3.886
0.000
-0.424
0.000
-0.074
0.001
0.000
0.222
0.008
0.000
0.621
0.021
1.320
9
61
log (Y) = + log(Y)
(t-1)
+ GINI +
GINI CMI + HC
Human Capital
TABLE 10.
Continued
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
Regression
Sample
squared
R-
S.E.Regres
sion
Durbin-
Watson
sections
Cross-
Obs.
Coefficient
Prob.
Coefficient
Prob.
Coefficient
Prob.
Coefficient Prob.
Coefficient Prob.
log (EDUAVER) = + log(Y)
(t-1)
+
GINI + GINI CMI
CMI
20
Bank_Depositst
Whole sample
-2.064
0.000
0.451
0.000
-0.003
0.272
0.002
0.423
-
-
0.948
0.135
0.226
41
335
21
"+ AR(1)
Whole sample
0.402
0.598
0.192
0.027
-0.002
0.322
0.000
0.973
0.873
0.000
0.988
0.057
1.856
25
151
22
Private_Credit
Whole sample
-2.633
0.000
0.512
0.000
-0.013
0.7138
-0.001
0.336
-
-
0.947
0.136
0.226
41
333
23
"+ AR(1)
Whole sample
0.172
0.844
0.217
0.288
-0.001
0.419
-0.001
0.593
0.871
0.000
0.988
0.057
1.858
25
151
24
Real_Int
Whole sample
-2.087
0.000
0.441
0.000
-0.001
0.667
0.000
0.000
-
-
0.955
0.134
0.288
38
297
25
"+ AR(1)
Whole sample
0.369
0.636
0.187
0.034
-0.000
0.901
-0.000
0.188
0.898
0.000
0.990
0.057
1.772
25
134
log (EDUHIGHER) = + log(Y)
(t-1)
+ GINI + GINI CMI
CMI
TABLE 11.
Wald-coefficients tests
log(Y) = + log(Y)
(t-1)
+
GINI + CMI+ GINI CMI
Sample
Coefficient
Wald Test
F-stat
Chi-square
CMI
1
2
Null-hypothesis
Prob.
Prob.
APPENDIX B: List of Countries
TABLE 12.
Full list of countries with number of observations
Observations
Country
number
Country
code
Country name
GDP
Gini Eduaver Eduhigher Real_int
Private_
credit
Bank_de
posits
1
ARM
Armenia
17
1
0
0
0
11
11
2
AUS
Australia
56
15
45
45
26
44
44
3
AZE
Azerbaijan
17
2
0
0
0
0
0
4
BEL
Belgium
56
3
45
45
49
42
42
5
BGD
Bangladesh
55
10
45
45
35
8
8
6
BGR
Bulgaria
55
29
50
50
15
12
12
7
BLR
Belarus
17
3
0
0
14
9
9
8
BRA
Brazil
55
1
45
45
10
24
24
9
CAN
Canada
56
18
45
45
56
44
44
10
CHL
Chile
55
18
50
50
13
43
42
11
CHN
China
55
16
30
30
16
17
17
12
CIV
Côte d'Ivoire
55
1
45
45
46
42
42
13
COL
Columbia
55
11
50
50
56
41
41
14
CSK
Czech Republic*
56
2
35
35
13
10
10
15
DEW
Central African Republic
48
8
35
35
38
0
0
16
DNK
Denmark
56
15
45
45
56
44
44
17
ECU
Equador
55
3
50
50
36
44
44
18
EGY
Egypt
55
1
30
30
55
44
44
19
ESP
Spain
56
5
45
45
47
31
31
20
EST
Estonia
18
6
5
5
0
11
11
21
FIN
Finland
56
3
45
45
50
44
44
22
FRA
France
56
7
45
45
39
42
42
23
GBR
United Kingdom
56
15
50
50
31
44
44
24
GEO
Georgia
17
2
0
0
0
0
0
25
GHA
Ghana
55
1
45
45
46
34
34
26
GRC
Greece
56
1
50
50
50
44
44
27
GTM
Guatamala
55
4
50
50
35
44
44
28
HKG
Hong Kong
55
9
45
45
14
13
12
29
HUN
Hungary
56
2
45
45
21
21
21
30
IDN
Indonesia
55
1
45
45
16
23
23
31
IND
India
55
5
45
45
43
44
44
32
IRE
Ireland
56
1
45
45
48
44
44
33
IRN
Iran
55
1
50
50
31
39
36
34
IRQ
Iraq
55
1
45
45
0
17
17
35
ITA
Italy
56
0
50
50
49
40
44
36
JOR
Jordan
55
0
45
45
0
28
27
37
JPN
Japan
56
23
50
50
56
44
44
38
KAZ
Kazakhstan
17
2
5
5
13
10
10
39
KEN
Kenia
55
1
45
45
33
41
41
40
KGZ
Kyrgyz Republic
17
2
0
0
0
8
8
41
KOR
Korea
56
9
45
45
56
34
33
42
LKA
Sri Lanka
55
8
45
45
55
44
44
TABLE 12.
Continued
Observations
Country
number
Country
code
Country name
GDP
Gini Eduaver Eduhigher Real_int Private_
credit
Bank_de
posits
55
PHL
Philippines
56
POL
Poland
55
11
55
55
56
44
44
57
POR
Portugal
56
5
45
45
23
23
23
58
ROM
Romenia
55
1
45
45
49
44
44
59
RUS
Russia
17
5
50
50
0
7
7
60
SDN
Sudan
55
6
0
0
11
0
0
61
SGP
Singapore
55
1
50
50
0
38
38
62
SUN
USSR
55
7
45
45
0
40
40
63
SVK
Slovak Republic
55
4
45
45
0
0
0
64
SVN
Slovenia
17
5
15
15
13
10
10
65
SWE
Sweden
17
2
15
15
14
12
12
66
THA
Thailand
56
6
45
45
56
44
44
67
TJK
Tajikistan
55
11
45
45
30
39
38
68
TKM
Turkmenistan
17
2
5
5
9
0
0
69
TUR
Turkey
17
2
0
0
0
0
0
70
TWN
Taiwan
56
3
50
50
56
17
17
71
TZA
Tanzania
55
31
45
45
0
0
0
72
UKR
Ukraine
55
7
0
0
0
0
0
73
USA
United States
56
48
50
50
56
44
44
74
UZB
Uzbekistan
17
2
0
0
0
0
0
75
VEN
Venuzuela
55
12
50
50
56
43
43
76
YUF
Yugoslavia
55
9
40
40
0
0
0
77
ZAF
South Africa
55
3
45
45
56
36
36
APPENDIX C: Formal treatment of the model
1
Consider a small-open economy in a one good world. This good can be used for
consumption or investment and can be produced by two sets of technologies. One
technology used only skilled, the other only unskilled labor. Production in the skilled
labor sector is described by:
)
,
(
s
t
t
s
t
f
K
L
Y
=
,
(1)
Where
s
t
Y is output in this sector at time t ,
K is the amount of capital and
t
s
t
L is the
labor input. f is a concave production with constant returns to scale. Investments in
both human and physical capital are made in period
(
t
−
1
)
. Furthermore, there are no
adjustment costs to investment and no depreciation of capital. Production in the
unskilled labor sector is described as:
n
t
n
n
t
w
L
Y
=
⋅
,
(2)
where
n
t
Y and
n
t
L are output and unskilled labor input respectively, and
w > 0 is
n
marginal productivity in the unskilled labor sector.
Individuals live for two periods in overlapping generations. An individual can work as
unskilled in both periods, or investment in human capital in the first period and work
as a skilled worker in the second period, where h>0, is the investment in human
capital. Each generation has a continuum of individuals of size L. Individuals derive
utility both from consumption in the second period of life and from any bequest to
their offspring:
b
c
u
=
α
log
+
(
1
−
α
)
log
,
(3)
Where c is consumption in the second period, b is bequest, and 0 <
α
< 1. Hence
individuals only differ in the amount they inherit from their parents.
borrower. However, borrowers can still evade the lenders but only at a cost
β
z,
where
β
> 1. Firms cannot evade repayment and can therefore lend at interest rate r.
In the absence of adjustment costs to investment and to the fact that the number of
skilled workers is know one period in advance, the amount of capital in the skilled
labor sector is adjusted each period so that:
r
L
K
F
s
t
t
k
(
,
)
=
,
(4)
Hence, there is a constant capital labor ratio in this sector, which determines the wage
of skilled labor
W , which is constant as well. This wage
s
W depends on r and on
s
technology only.
An individual who borrows an amount d pays an interest rate of
I , which covers
d
lenders’ interest rate costs z.
z
r
d
i
d
⋅
d
=
⋅
+
,
(5)
Lenders choose z to be high enough to make evasion disadvantageous:
z
i
d
(
1
+ )
d
=
β
,
(6)
This is an incentive compatibility constraint, equations (5) and (6) determine
i :
d
r
r
i
i
d
>
−
+
=
=
1
1
β
β
,
(7)
Consider an individuals who inherits an amount x in the first period of life. Suppose
the individuals invests nothing in human capital and works as an unskilled worker for
two periods, the forthcoming lifetime utility is:
ε
+
+
+
+
=
log[(
n
)(
1
)
n
]
n
x
w
r
w
U
,
(8)
Where:
)
1
log(
)
1
(
log
α
α
α
α
ε
=
+
−
−
,
(9)
]
)
)(
1
)[(
1
(
)
(
n
n
n
x
r
x
w
w
b
=
−
α
+
+
+
,
(10)
An individual with inheritance
x
≥ , who invests in human capital is lender with
h
utility:
ε
+
+
−
+
=
log[
(
)(
1
)]
)
(
x
w
x
h
r
U
s
s
,
(11)
And a bequest of:
)]
1
)(
(
)[
1
(
)
(
x
w
x
h
r
b
s
=
−
α
s
+
−
+
, (12)
An individual who invests in human capital but has an inheritance
x
< is a
h
borrower, with life time utility:
ε
+
+
−
+
=
log[
(
)(
1
)]
)
(
x
w
x
h
i
U
s
s
, (13)
And a bequest of:
ε
α
+
−
+
+
−
=
(
1
)[
(
)(
1
)
)
(
x
w
x
h
i
b
s
s
, (14)
Next it is assumed that:
)
2
(
)
1
(
r
w
r
h
w
s
−
+
≥
n
+
,
(15)
(this is necessary because it is clear that if
w
s
−
h
(1
+ <
r
)
w
n
(2
+ then all individual
r
)
prefer to work as unskilled. Hence there is no capital and an excess supply of loans
prevails. This drives the world interest rate down until equation (above) is satisfied)
Hence, as investment in human capital pays back more than unskilled labor lenders
prefer to invest in human capital as is seen from equations (11) and (13). Borrowers
invest in human capital as long as,
U
s
(
x
)
≥
U
n
(
x
)
, that is as long as:
Individuals who inherit an amount smaller than f would prefer not to invest in
human capital but work as unskilled. Education is therefore limited to individuals with
high wealth, due to higher interest rate for borrowers.
The amount that an individual inherits in the first period of life fully determines the
decision to invest in human capital or work as unskilled laborer, and how much to
consume and bequeath. Let
D be the distribution of inheritances by individuals born
t
in period t . This distribution satisfies:
L
x
dD
t
t
=
∞
)
(
0
,
(17)
The distribution therefore fully determines economic performance in period t . It
determines the amount of skilled labor:
∞
=
f
t
t
s
t
dD
x
L
(
)
,
(18)
And unskilled labor:
)
(
0
t
f
t
n
t
dD
x
L
=
,
(19)
The distribution of wealth not only determines equilibrium in period t but also
determines next period distribution of inheritances
D
t
+
1
Individuals who inherit less than f work as unskilled and so are their descendants in
all future generations. Their inheritances converge to a long run level
x
n
)
2
(
)
1
)(
1
(
1
1
w
r
r
x
n
n
+
+
−
−
−
=
α
α
,
(20)
Individuals who inherit more than f invest in human capital but not all their
descendants will remain in the skilled labor sector in future generations. The critical
point is g, where g is:
Individuals who inherit less than g in period t may invest in human capital, but after
some generations their descendants become unskilled workers and their inheritances
converge to
x
n
. Individuals who inherit more than g invest in human capital and so do
their descendants, generation after generation. Their bequests converge to
x
s
:
)]
1
(
[
)
1
)(
1
(
1
1
w
h
r
r
x
s
s
−
+
+
−
−
−
=
α
α
,
(22)
The economy is divided into two groups: skilled workers with wealth
x
s
and
unskilled workers with wealth
x
n
. The relative size of these groups depends on the
initial distribution of wealth, since the long-run number of unskilled workers
∞
n
L is
equal to
g
t
L , the number of individuals who inherit less than g in period t :
)
(
0
t
g
t
g
t
dD
x
L
=
,
(23)
The long-run level of average wealth is:
)
(
s
n
g
t
s
L
x
x
L
x
−
−
,
(24)
Which is decreasing with
L
g
t
L
.
