What are all the employee ids and the names of the countries in which they work?
SELECT T1.employee_id, T4.country_name FROM employees AS T1 JOIN departments AS T2 ON T1.department_id = T2.department_id JOIN locations AS T3 ON T2.location_id = T3.location_id JOIN countries AS T4 ON T3.country_id = T4.country_id
``` yaml
countries: # name of dataframe
- COUNTRY_ID:
    dtype: string
    sample_values: AR, AU, BE
- COUNTRY_NAME:
    dtype: string
    sample_values: Argentina, Australia, Belgium
- REGION_ID:
    dtype: int64
    sample_values: 2, 3, 1

departments: # name of dataframe
- DEPARTMENT_ID:
    dtype: int64
    sample_values: 10, 20, 30
- DEPARTMENT_NAME:
    dtype: string
    sample_values: Administration, Marketing, Purchasing
- MANAGER_ID:
    dtype: int64
    sample_values: 200, 201, 114
- LOCATION_ID:
    dtype: int64
    sample_values: 1700, 1800, 1700

employees: # name of dataframe
- EMPLOYEE_ID:
    dtype: int64
    sample_values: 100, 101, 102
- FIRST_NAME:
    dtype: string
    sample_values: Steven, Neena, Lex
- LAST_NAME:
    dtype: string
    sample_values: King, Kochhar, De Haan
- EMAIL:
    dtype: string
    sample_values: SKING, NKOCHHAR, LDEHAAN
- PHONE_NUMBER:
    dtype: string
    sample_values: 515.123.4567, 515.123.4568, 515.123.4569
- HIRE_DATE:
    dtype: datetime
    sample_values: 1987-06-17, 1987-06-18, 1987-06-19
- JOB_ID:
    dtype: string
    sample_values: AD_PRES, AD_VP, AD_VP
- SALARY:
    dtype: int64
    sample_values: 24000, 17000, 17000
- COMMISSION_PCT:
    dtype: float64
    sample_values: 0.0, 0.0, 0.0
- MANAGER_ID:
    dtype: int64
    sample_values: 0, 100, 100
- DEPARTMENT_ID:
    dtype: int64
    sample_values: 90, 90, 90

locations: # name of dataframe
- LOCATION_ID:
    dtype: int64
    sample_values: 1000, 1100, 1200
- STREET_ADDRESS:
    dtype: string
    sample_values: 1297 Via Cola di Rie, 93091 Calle della Testa, 2017 Shinjuku-ku
- POSTAL_CODE:
    dtype: numeric
    sample_values: 989, 10934, 1689
- CITY:
    dtype: string
    sample_values: Roma, Venice, Tokyo
- STATE_PROVINCE:
    dtype: string
    sample_values: ', , Tokyo Prefecture'
- COUNTRY_ID:
    dtype: string
    sample_values: IT, IT, JP

```
Foreign Key constraints:
Column 'department_id' in dataframe 'employees' has a foreign key relation to column 'department_id' in dataframe 'departments'.
Column 'country_id' in dataframe 'locations' has a foreign key relation to column 'country_id' in dataframe 'countries'.

EMPLOYEE_ID,COUNTRY_NAME
100,United States of America
101,United States of America
102,United States of America
103,United States of America
104,United States of America
105,United States of America
106,United States of America
107,United States of America
108,United States of America
109,United States of America
110,United States of America
111,United States of America
112,United States of America
113,United States of America
114,United States of America
115,United States of America
116,United States of America
117,United States of America
118,United States of America
119,United States of America
120,United States of America
121,United States of America
122,United States of America
123,United States of America
124,United States of America
125,United States of America
126,United States of America
127,United States of America
128,United States of America
129,United States of America
130,United States of America
131,United States of America
132,United States of America
133,United States of America
134,United States of America
135,United States of America
136,United States of America
137,United States of America
138,United States of America
139,United States of America
140,United States of America
141,United States of America
142,United States of America
143,United States of America
144,United States of America
180,United States of America
181,United States of America
182,United States of America
183,United States of America
184,United States of America
185,United States of America
186,United States of America
187,United States of America
188,United States of America
189,United States of America
190,United States of America
191,United States of America
192,United States of America
193,United States of America
194,United States of America
195,United States of America
196,United States of America
197,United States of America
198,United States of America
199,United States of America
200,United States of America
201,Canada
202,Canada
203,United Kingdom
204,Germany
205,United States of America
206,United States of America
