##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:
Withdrawal transactions refer to type = 'VYDAJ'. District name refers to A2. Unemployment category is based on 1995 unemployment rate: High (>2.0), Medium (1.0-2.0), Low (<1.0).

##Role Access Policy (Column-Level):
Role: RiskAnalyst
Accessible Columns: account: account_id, district_id, frequency; client: client_id, birth_date, district_id; district: ALL COLUMNS; loan: loan_id, account_id, date, amount, duration, payments, status; trans: account_id, date, type, operation, amount, balance, k_symbol

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 each district with withdrawal transactions, provide a comprehensive analysis including the total number and amount of withdrawals, demographic information, withdrawal per capita, and rank the districts by total withdrawal amount. Also categorize each district by unemployment level.
Evidence: Withdrawal transactions refer to type = 'VYDAJ'. District name refers to A2. Unemployment category is based on 1995 unemployment rate: High (>2.0), Medium (1.0-2.0), Low (<1.0).
Answer: Sorry, I cannot answer.
Permission: denied (Missing column permissions: trans: trans_id; client: gender)
Gold SQL: WITH district_withdrawal_counts AS (
    SELECT 
        T1.district_id,
        T1.A2 AS district_name,
        COUNT(DISTINCT T3.trans_id) AS withdrawal_count,
        SUM(T3.amount) AS total_withdrawal_amount,
        AVG(T3.amount) AS avg_withdrawal_amount
    FROM district AS T1 
    INNER JOIN account AS T2 ON T1.district_id = T2.district_id 
    INNER JOIN trans AS T3 ON T2.account_id = T3.account_id 
    WHERE T3.type = 'VYDAJ'
    GROUP BY T1.district_id, T1.A2
),
district_demographics AS (
    SELECT
        d.district_id,
        d.A2 AS district_name,
        d.A3 AS region,
        CAST(d.A4 AS INTEGER) AS population,
        d.A10 AS urban_ratio,
        d.A11 AS avg_salary,
        d.A12 AS unemployment_rate_95,
        COUNT(DISTINCT c.client_id) AS client_count,
        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
    FROM district d
    LEFT JOIN client c ON d.district_id = c.district_id
    GROUP BY d.district_id, d.A2, d.A3, d.A4, d.A10, d.A11, d.A12
)
SELECT 
    dw.district_name,
    dw.withdrawal_count,
    dw.total_withdrawal_amount,
    dw.avg_withdrawal_amount,
    dd.region,
    dd.population,
    dd.urban_ratio,
    dd.avg_salary,
    dd.unemployment_rate_95,
    dd.client_count,
    dd.male_clients,
    dd.female_clients,
    ROUND(dw.total_withdrawal_amount / CAST(dd.population AS REAL), 2) AS withdrawal_per_capita,
    RANK() OVER (ORDER BY dw.total_withdrawal_amount DESC) AS district_rank_by_withdrawal,
    CASE 
        WHEN dd.unemployment_rate_95 > 2.0 THEN 'High Unemployment'
        WHEN dd.unemployment_rate_95 BETWEEN 1.0 AND 2.0 THEN 'Medium Unemployment'
        ELSE 'Low Unemployment'
    END AS unemployment_category
FROM district_withdrawal_counts dw
JOIN district_demographics dd ON dw.district_id = dd.district_id
ORDER BY dw.total_withdrawal_amount DESC;