##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:
Percentage of gold cards issued before 1998 = count(gold cards issued before 1998) / count(all gold cards) * 100. Region refers to A3 in district table.

##Role Access Policy (Column-Level):
Role: LoanOfficer
Accessible Columns: account: account_id, district_id, frequency, date; client: client_id, district_id; disp: disp_id, client_id, account_id, type; loan: ALL COLUMNS; trans: account_id, date, type, amount, balance

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 percentage of gold credit cards were issued before 1998, both overall and broken down by region?
Evidence: Percentage of gold cards issued before 1998 = count(gold cards issued before 1998) / count(all gold cards) * 100. Region refers to A3 in district table.
Answer: Sorry, I cannot answer.
Permission: denied (Missing column permissions: card: card_id, disp_id, issued, type; district: A2, A3, account_id, disp_id, district_id)
Gold SQL: WITH GoldCardStats AS (
    SELECT 
        c.card_id,
        STRFTIME('%Y', c.issued) AS issue_year,
        d.account_id,
        CASE WHEN STRFTIME('%Y', c.issued) < '1998' THEN 1 ELSE 0 END AS is_pre_1998
    FROM card c
    JOIN disp d ON c.disp_id = d.disp_id
    WHERE c.type = 'gold'
),
AccountDetails AS (
    SELECT 
        a.account_id,
        a.district_id,
        d.A2 AS district_name,
        d.A3 AS region
    FROM account a
    JOIN district d ON a.district_id = d.district_id
),
RegionalStats AS (
    SELECT 
        ad.region,
        COUNT(gcs.card_id) AS total_gold_cards,
        SUM(gcs.is_pre_1998) AS pre_1998_gold_cards,
        CAST(SUM(gcs.is_pre_1998) AS REAL) * 100.0 / NULLIF(COUNT(gcs.card_id), 0) AS percent_pre_1998
    FROM GoldCardStats gcs
    JOIN AccountDetails ad ON gcs.account_id = ad.account_id
    GROUP BY ad.region
)
SELECT 
    'Overall' AS category,
    COUNT(card_id) AS total_gold_cards,
    SUM(is_pre_1998) AS pre_1998_gold_cards,
    CAST(SUM(is_pre_1998) AS REAL) * 100.0 / NULLIF(COUNT(card_id), 0) AS percent_pre_1998
FROM GoldCardStats
UNION ALL
SELECT 
    'By Region: ' || region AS category,
    total_gold_cards,
    pre_1998_gold_cards,
    percent_pre_1998
FROM RegionalStats
ORDER BY percent_pre_1998 DESC;