##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:
A2 refers to district name. PRIJEM refers to income transactions and VYDAJ refers to expense transactions.

##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: For accounts opened in 1993, provide a monthly breakdown showing the percentage of accounts from Decin district, average transaction statistics, and indicate whether each month's Decin percentage is above, below, or equal to the overall yearly average.
Evidence: A2 refers to district name. PRIJEM refers to income transactions and VYDAJ refers to expense transactions.
Answer: Sorry, I cannot answer.
Permission: denied (Missing column permissions: district: accounts_count, avg_expense, avg_income, avg_transactions, comparison_to_overall, decin_accounts_count, decin_percentage, opening_month, overall_decin_percentage)
Gold SQL: WITH AccountsOpenedIn1993 AS (
    SELECT 
        a.account_id,
        a.district_id,
        d.A2 AS district_name,
        STRFTIME('%m', a.date) AS opening_month
    FROM 
        account a
    JOIN 
        district d ON a.district_id = d.district_id
    WHERE 
        STRFTIME('%Y', a.date) = '1993'
),
AccountsWithTransactions AS (
    SELECT 
        a.account_id,
        a.district_name,
        a.opening_month,
        COUNT(t.trans_id) AS transaction_count,
        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_expense
    FROM 
        AccountsOpenedIn1993 a
    LEFT JOIN 
        trans t ON a.account_id = t.account_id
    GROUP BY 
        a.account_id, a.district_name, a.opening_month
),
MonthlyStats AS (
    SELECT 
        opening_month,
        COUNT(*) AS accounts_count,
        SUM(CASE WHEN district_name = 'Decin' THEN 1 ELSE 0 END) AS decin_accounts_count,
        AVG(transaction_count) AS avg_transactions,
        AVG(total_income) AS avg_income,
        AVG(total_expense) AS avg_expense
    FROM 
        AccountsWithTransactions
    GROUP BY 
        opening_month
)
SELECT 
    opening_month,
    accounts_count,
    decin_accounts_count,
    CAST(decin_accounts_count AS REAL) * 100 / accounts_count AS decin_percentage,
    avg_transactions,
    avg_income,
    avg_expense,
    (SELECT CAST(SUM(CASE WHEN district_name = 'Decin' THEN 1 ELSE 0 END) AS REAL) * 100 / COUNT(*) 
     FROM AccountsWithTransactions) AS overall_decin_percentage,
    CASE 
        WHEN CAST(decin_accounts_count AS REAL) * 100 / accounts_count > 
             (SELECT CAST(SUM(CASE WHEN district_name = 'Decin' THEN 1 ELSE 0 END) AS REAL) * 100 / COUNT(*) 
              FROM AccountsWithTransactions)
        THEN 'Above Average'
        WHEN CAST(decin_accounts_count AS REAL) * 100 / accounts_count = 
             (SELECT CAST(SUM(CASE WHEN district_name = 'Decin' THEN 1 ELSE 0 END) AS REAL) * 100 / COUNT(*) 
              FROM AccountsWithTransactions)
        THEN 'Average'
        ELSE 'Below Average'
    END AS comparison_to_overall
FROM 
    MonthlyStats
ORDER BY 
    opening_month;