Table:
statistic
median
average

years

demographics

individual's age
60.0
60.3

spouse's age
60.0
60.3

percent

female
..
49.8

male
..
50.2

married
..
100.0

immigrant
..
6.1

non-pension income

individual has labour income
..
62.9

spouse has labour income
..
62.6

family has capital gains
..
26.8

family has investment income
..
62.5

family has employment insurance income
..
12.5

family has social assistance income
..
1.8

pension income

individual has private pension income
..
28.4

spouse has private pension income
..
28.2

family has private pension income
..
44.2

nominal dollars

earnings

individual's labour income
10,000
27,500

spouse's labour income
9,750
26,950

individual's total after-tax income
28,950
38,650

spouse's total after-tax income
28,700
38,100

percent

allowances

family has disability allowances
..
3.9

family has medical expense allowances
..
42.5

marginal tax rates

individual's marginal tax rate
26.7
24.1

spouse's marginal tax rate
19.2
20.5

average tax rates

individual's average tax rate
18.2
14.8

spouse's average tax rate
14.7
14.3

row 33
row 18
row 37
row 22
Extract the contents of cells in the above rows, identified by row indices. Show the cells in a row in the JSON format {"row_id":"<row index>", "cell_list":"<a list of cells in this row>"}. For example, {"row_id":"1", "cell_list": ["A", "B", "C"]}.

The extracted row contents are listed below.
For row 33: {"row_id": "33", "cell_list": ["individual's marginal tax rate", '26.7', '24.1']}
For row 18: {"row_id": "18", "cell_list": ['pension income', '', '']}
For row 37: {"row_id": "37", "cell_list": ["spouse's average tax rate", '14.7', '14.3']}
For row 22: {"row_id": "22", "cell_list": ['', 'nominal dollars', '']}