etr.py output

Median ETRs by income quintile alone:
quint
1    0.55
2    0.60
3    0.62
4    0.63
5    0.62
Name: etr, dtype: float64

Median ETRs by type alone:
type
1    0.69
2    0.65
3    0.60
4    0.53
Name: etr, dtype: float64

Median ETRs by type and income quintile:
type  quint
1     1        0.62
      2        0.69
      3        0.75
      4        0.77
      5        0.81
2     1        0.53
      2        0.63
      3        0.67
      4        0.70
      5        0.74
3     1        0.45
      2        0.54
      3        0.58
      4        0.62
      5        0.66
4     1        0.33
      2        0.44
      3        0.50
      4        0.52
      5        0.58
Name: etr, dtype: float64

Index of med_b:
MultiIndex([(1, 1),
            (1, 2),
            (1, 3),
            (1, 4),
            (1, 5),
            (2, 1),
            (2, 2),
            (2, 3),
            (2, 4),
            (2, 5),
            (3, 1),
            (3, 2),
            (3, 3),
            (3, 4),
            (3, 5),
            (4, 1),
            (4, 2),
            (4, 3),
            (4, 4),
            (4, 5)],
           names=['type', 'quint'])

Medians for type 3:
quint
1    0.45
2    0.54
3    0.58
4    0.62
5    0.66
Name: etr, dtype: float64

Medians for quintile 5:
type
1    0.81
2    0.74
3    0.66
4    0.58
Name: etr, dtype: float64

ETR of lowest quintile for each type:
type
1    0.62
2    0.53
3    0.45
4    0.33
Name: etr, dtype: float64

Difference of quintile's ETR from lowest quintle:
type  quint
1     1        0.00
      2        0.07
      3        0.13
      4        0.15
      5        0.19
2     1        0.00
      2        0.10
      3        0.14
      4        0.17
      5        0.21
3     1        0.00
      2        0.09
      3        0.13
      4        0.17
      5        0.21
4     1        0.00
      2        0.11
      3        0.17
      4        0.19
      5        0.25
Name: etr, dtype: float64