You are now a sqlite data analyst, and you are given a database schema as follows:

    【Schema】
    【DB_ID】 software_company
【Schema】
# Table: Customers
[
(ID:INTEGER, Primary Key, Examples: [0, 1, 2]),
(SEX:TEXT, Examples: [Male, Female]),
(MARITAL_STATUS:TEXT, Examples: [Never-married, Married-civ-spouse, Divorced]),
(GEOID:INTEGER, Examples: [61, 70, 53]),
(EDUCATIONNUM:INTEGER, Examples: [7, 3, 4]),
(OCCUPATION:TEXT, Examples: [Machine-op-inspct, Handlers-cleaners, Exec-managerial]),
(age:INTEGER, Examples: [62, 78, 69])
]
# Table: Mailings1_2
[
(REFID:INTEGER, Primary Key, Examples: [0, 1, 2]),
(REF_DATE:DATETIME, Examples: [2007-02-01 12:00:00]),
(RESPONSE:TEXT, Examples: [false, true])
]
# Table: mailings3
[
(REFID:INTEGER, Primary Key, Examples: [60000, 60001, 60002]),
(REF_DATE:DATETIME, Examples: [2007-07-01 12:00:00]),
(RESPONSE:TEXT, Examples: [false, true])
]
# Table: Demog
[
(GEOID:INTEGER, Primary Key, Examples: [0, 1, 2]),
(INHABITANTS_K:REAL, Examples: [30.046, 36.25, 47.645]),
(INCOME_K:REAL, Examples: [2631.47, 3012.75, 2192.41]),
(A_VAR1:REAL, Examples: [6.084, 4.604, 4.911]),
(A_VAR2:REAL, Examples: [5.79, 8.309, 8.557]),
(A_VAR3:REAL, Examples: [8.595, 6.007, 5.934]),
(A_VAR4:REAL, Examples: [3.935, 5.938, 6.494]),
(A_VAR5:REAL, Examples: [6.362, 8.773, 9.172]),
(A_VAR6:REAL, Examples: [8.626, 3.579, 3.202]),
(A_VAR7:REAL, Examples: [4.624, 6.349, 6.157]),
(A_VAR8:REAL, Examples: [8.324, 4.694, 4.822]),
(A_VAR9:REAL, Examples: [5.233, 6.884, 7.942]),
(A_VAR10:REAL, Examples: [6.232, 7.062, 7.901]),
(A_VAR11:REAL, Examples: [5.205, 7.319, 7.928]),
(A_VAR12:REAL, Examples: [8.231, 3.72, 2.33]),
(A_VAR13:REAL, Examples: [6.746, 6.405, 6.029]),
(A_VAR14:REAL, Examples: [8.679, 7.202, 6.455]),
(A_VAR15:REAL, Examples: [5.292, 4.932, 4.72]),
(A_VAR16:REAL, Examples: [3.5, 7.969, 8.564]),
(A_VAR17:REAL, Examples: [5.512, 8.15, 8.342]),
(A_VAR18:REAL, Examples: [5.783, 5.633, 4.938])
]
# Table: Sales
[
(EVENTID:INTEGER, Primary Key, Examples: [0, 1, 2]),
(REFID:INTEGER, Examples: [0, 1, 2]),
(EVENT_DATE:DATETIME, Examples: [2006-12-21 12:00:00]),
(AMOUNT:REAL, Examples: [17.907, 17.401, 13.277])
]
【Foreign keys】
Customers.GEOID=Demog.GEOID
Mailings1_2.REFID=Customers.ID
Sales.REFID=Customers.ID

    【Question】
    List the income and number of inhabitants of customers with a reference ID greater than the 50% of average of number of false response? reference ID greater than the 50% of average of number of false response refers to REFID > DIVIDE(MULTIPLY(0.5, COUNT(RESPONSE = 'false')), COUNT(RESPONSE)); income refers to INCOME_K; number of inhabitants refer to INHABITANTS_K;

    【Evidence】
    reference ID greater than the 50% of average of number of false response refers to REFID > DIVIDE(MULTIPLY(0.5, COUNT(RESPONSE = 'false')), COUNT(RESPONSE)); income refers to INCOME_K; number of inhabitants refer to INHABITANTS_K;

    Please read and understand the database schema carefully, and generate an executable SQL based on the user's question and evidence. 

SQL: SELECT T2.INCOME_K, T2.INHABITANTS_K FROM Customers AS T1 INNER JOIN Demog AS T2 ON T1.GEOID = T2.GEOID INNER JOIN Mailings1_2 AS T3 ON T1.ID = T3.REFID WHERE T3.REFID > ( SELECT 0.5 * COUNT(CASE WHEN RESPONSE = 'false' THEN 1 ELSE NULL END) / COUNT(RESPONSE) FROM Mailings1_2 )