##Instruction:
Database: financial
Total Tables: 8
Total Columns: 55

Table Schemas:

Database: financial
Total Tables: 8
Total Columns: 55

Table Schemas:

Table: account (4 columns)
--------------------------
• account_id (integer): the id of the account
• district_id (integer): location of branch
• frequency (text): frequency of the acount
• date (date): the creation date of the account
  Values: in the form YYMMDD

Table: card (4 columns)
-----------------------
• card_id (integer): id number of credit card
• disp_id (integer): disposition id
• type (text): type of credit card
  Values: "junior": junior class of credit card; 
"classic": standard class of credit card; 
"gold": high-leve...
• issued (date): the date when the credit card issued
  Values: in the form YYMMDD

Table: client (4 columns)
-------------------------
• client_id (integer): the unique number
• gender (text)
  Values: F：female 
M：male
• birth_date (date): birth date
• district_id (integer): location of branch

Table: disp (4 columns)
-----------------------
• disp_id (integer): unique number of identifying this row of record
• client_id (integer): id number of client
• account_id (integer): id number of account
• type (text): type of disposition
  Values: "OWNER" : "USER" : "DISPONENT"
commonsense evidence:
the account can only have the right to issue pe...

Table: district (16 columns)
----------------------------
• district_id (integer): location of branch
• A2 (text): district_name
• A3 (text): region
• A4 (text)
• A5 (text): municipality < district < region
• A6 (text): municipality < district < region
• A7 (text): municipality < district < region
• A8 (integer): municipality < district < region
• A9 (integer)
  Values: not useful
• A10 (real): ratio of urban inhabitants
• A11 (integer): average salary
• A12 (real): unemployment rate 1995
• A13 (real): unemployment rate 1996
• A14 (integer): no. of entrepreneurs per 1000 inhabitants
• A15 (integer): no. of committed crimes 1995
• A16 (integer): no. of committed crimes 1996

Table: loan (7 columns)
-----------------------
• loan_id (integer): the id number identifying the loan data
• account_id (integer): the id number identifying the account
• date (date): the date when the loan is approved
• amount (integer): approved amount
  Values: unit：US dollar
• duration (integer): loan duration
  Values: unit：month
• payments (real): monthly payments
  Values: unit：month
• status (text): repayment status
  Values: 'A' stands for contract finished, no problems;
'B' stands for contract finished, loan not paid;
'C' ...

Table: order (6 columns)
------------------------
• order_id (integer): identifying the unique order
• account_id (integer): id number of account
• bank_to (text): bank of the recipient
• account_to (integer): account of the recipient
  Values: each bank has unique two-letter code
• amount (real): debited amount
• k_symbol (text): purpose of the payment
  Values: "POJISTNE" stands for insurance payment
"SIPO" stands for household payment
"LEASING" stands for lea...

Table: trans (10 columns)
-------------------------
• trans_id (integer): transaction id
• account_id (integer)
• date (date): date of transaction
• type (text): +/- transaction
  Values: "PRIJEM" stands for credit
"VYDAJ" stands for withdrawal
• operation (text): mode of transaction
  Values: "VYBER KARTOU": credit card withdrawal
"VKLAD": credit in cash
"PREVOD Z UCTU" :collection from anot...
• amount (integer): amount of money
  Values: Unit：USD
• balance (integer): balance after transaction
  Values: Unit：USD
• k_symbol (text)
  Values: "POJISTNE": stands for insurrance payment
"SLUZBY": stands for payment for statement
"UROK": stands ...
• bank (text)
  Values: each bank has unique two-letter code
• account (integer)

##Evidence:
East Bohemia refers to region A3 = 'east Bohemia'. PRIJEM transactions represent income while VYDAJ transactions represent expenses.

##Role Access Policy (Column-Level):
Role: DataOperator_3
Accessible Columns: account: account_id, date, district_id; card: disp_id, issued, type; client: ALL COLUMNS; disp: account_id, type; district: A10, A11, A14, A16, A2, A3, A4, A5, A6, A7, A8, A9, district_id; loan: account_id, amount, date, duration, loan_id, payments; order: amount, k_symbol; trans: ALL COLUMNS

Based on the above schema and your role's column-level access policy, generate a SQL query to answer the question. If your role does not have access to the required columns, respond with: "Sorry, I cannot answer."


Question: What are the comprehensive statistics for clients born in 1920 who live in east Bohemia, including their account activity, loan information, transaction patterns, gender distribution, and most common district?
Evidence: East Bohemia refers to region A3 = 'east Bohemia'. PRIJEM transactions represent income while VYDAJ transactions represent expenses.
Answer: Sorry, I cannot answer.
Permission: denied (Missing column permissions: disp: client_id)
Gold SQL: WITH ClientsInEastBohemia AS (
    SELECT 
        c.client_id,
        c.birth_date,
        c.gender,
        d.A2 AS district_name,
        d.A3 AS region,
        STRFTIME('%Y', c.birth_date) AS birth_year,
        COUNT(DISTINCT a.account_id) AS num_accounts,
        COUNT(DISTINCT l.loan_id) AS num_loans
    FROM 
        client AS c
    INNER JOIN 
        district AS d ON c.district_id = d.district_id
    LEFT JOIN 
        disp AS dp ON c.client_id = dp.client_id
    LEFT JOIN 
        account AS a ON dp.account_id = a.account_id
    LEFT JOIN 
        loan AS l ON a.account_id = l.account_id
    WHERE 
        STRFTIME('%Y', c.birth_date) = '1920' 
        AND d.A3 = 'east Bohemia'
    GROUP BY 
        c.client_id, c.birth_date, c.gender, d.A2, d.A3
),
TransactionStats AS (
    SELECT 
        dp.client_id,
        COUNT(t.trans_id) AS total_transactions,
        SUM(CASE WHEN t.type = 'PRIJEM' THEN t.amount ELSE 0 END) AS total_income,
        SUM(CASE WHEN t.type = 'VYDAJ' THEN t.amount ELSE 0 END) AS total_expenses,
        AVG(t.amount) AS avg_transaction_amount
    FROM 
        disp AS dp
    INNER JOIN 
        account AS a ON dp.account_id = a.account_id
    LEFT JOIN 
        trans AS t ON a.account_id = t.account_id
    WHERE 
        dp.client_id IN (SELECT client_id FROM ClientsInEastBohemia)
    GROUP BY 
        dp.client_id
)
SELECT 
    COUNT(c.client_id) AS total_clients_born_1920_in_east_bohemia,
    AVG(c.num_accounts) AS avg_accounts_per_client,
    AVG(c.num_loans) AS avg_loans_per_client,
    SUM(CASE WHEN c.gender = 'M' THEN 1 ELSE 0 END) AS male_clients,
    SUM(CASE WHEN c.gender = 'F' THEN 1 ELSE 0 END) AS female_clients,
    AVG(COALESCE(ts.total_transactions, 0)) AS avg_transactions_per_client,
    AVG(COALESCE(ts.total_income, 0)) AS avg_income_per_client,
    AVG(COALESCE(ts.total_expenses, 0)) AS avg_expenses_per_client,
    (SELECT district_name FROM ClientsInEastBohemia 
     GROUP BY district_name 
     ORDER BY COUNT(*) DESC LIMIT 1) AS most_common_district
FROM 
    ClientsInEastBohemia c
LEFT JOIN 
    TransactionStats ts ON c.client_id = ts.client_id