observation	wage	female	education	experience
1	11.55	1	12	20
2	5	0	9	9
3	12	0	16	15
4	7	0	14	38
5	21.15	1	16	19
6	6.92	1	12	4
7	10	1	12	14
8	8	1	12	32
9	15.63	0	18	7
10	18.22	1	18	5
11	20	0	20	31
12	4.35	1	12	7
13	5	0	5	31
14	8.25	0	12	14
15	15	0	12	15
16	19	1	14	26
17	18	0	14	23
18	7.07	0	16	4
19	8	0	14	16
20	25	0	14	27
21	17.3	1	12	44
22	16	1	12	38
23	5	1	12	19
24	8.25	0	12	13
25	8	1	12	14
26	13.69	1	12	20
27	19.9	0	12	26
28	22	1	12	17
29	6.5	1	12	1
30	12	1	12	19
31	13.39	0	12	34
32	36.85	0	20	21
33	27.47	0	16	25
34	6	0	12	2
35	21.54	1	18	19
36	12.43	0	12	7
37	19.7	0	12	33
38	7.5	1	12	11
39	25.95	0	18	12
40	25.95	1	16	13
41	11.53	1	18	29
42	5.5	1	12	2
43	9.62	0	12	10
44	5.25	1	12	3
45	11.5	0	12	9
46	17	0	16	30
47	11	0	12	30
48	13	1	12	29
49	7.32	0	12	2
50	5.5	0	12	6
51	11.52	0	12	33
52	15	0	12	37
53	13.5	0	12	16
54	6.75	1	11	12
55	12.5	0	12	12
56	7.1	0	9	28
57	6	0	11	35
58	14.43	0	16	9
59	13.78	1	16	2
60	5	1	12	7
61	4.49	1	10	31
62	10	0	5	42
63	5	1	12	37
64	17.68	1	16	33
65	4.38	1	12	23
66	6.88	1	12	12
67	12.5	1	12	25
68	13.9	1	16	8
69	7	0	12	41
70	12	0	11	32
71	9	0	12	15
72	6.5	0	11	6
73	16	0	12	42
74	2.13	1	12	12
75	9.62	0	12	21
76	20.2	1	18	21
77	14.43	1	20	2
78	35.83	0	20	20
79	16.67	1	14	11
80	23.08	0	18	25
81	4.25	0	14	7
82	5.5	1	12	25
83	15.59	0	12	36
84	6.94	1	14	26
85	25	1	14	30
86	5	0	12	3
87	15.68	1	16	21
88	9.53	1	12	19
89	11	0	12	7
90	4.25	1	12	0
91	4	1	12	34
92	8.25	0	12	15
93	22	0	12	15
94	5.15	1	12	1
95	4.7	0	16	1
96	8	0	1	26
97	12	0	11	32
98	2	1	5	14
99	5	0	9	12
100	7	0	12	3
101	12	0	5	30
102	17.49	0	12	14
103	6.75	0	10	9
104	6.5	1	14	5
105	4.5	1	1	26
106	8.65	1	16	18
107	10	0	9	8
108	5.5	1	1	20
109	7.5	0	12	2
110	3.57	1	16	1
111	18.25	1	16	21
112	16	0	14	26
113	25	0	18	33
114	12	1	12	41
115	15.35	1	12	22
116	16.67	1	12	28
117	6.25	0	12	38
118	4.25	1	12	37
119	17.3	0	16	15
120	20.83	0	12	38
121	18.46	0	16	30
122	10	0	10	2
123	11.55	0	12	15
124	11	1	14	7
125	15.89	1	16	24
126	23.08	0	16	14
127	4.25	1	5	22
128	5	0	7	7
129	17.45	1	12	23
130	12.82	1	16	25
131	6.8	0	12	2
132	4.25	1	10	5
133	8.65	1	12	38
134	8	1	12	36
135	13.25	1	14	25
136	6.75	0	12	17
137	5.5	1	1	24
138	7.5	0	12	19
139	9.25	1	16	21
140	29.12	0	16	22
141	24.75	0	16	19
142	8.96	1	12	41
143	8	0	12	6
144	7	1	12	1
145	17.3	1	16	37
146	12.03	0	12	10
147	9	0	16	4
148	5.5	0	12	0
149	4.45	1	11	1
150	6.06	1	12	1
151	21.63	0	16	12
152	5.83	0	12	2
153	43.25	0	18	13
154	37.5	0	12	22
155	8.19	0	12	13
156	21.63	0	16	12
157	6	1	12	17
158	9.75	1	16	7
159	8.65	1	16	10
160	7	1	12	24
161	6	0	7	24
162	10	1	12	7
163	12.5	1	12	13
164	25.48	1	18	30
165	5	1	12	4
166	14.43	1	16	5
167	24.47	1	16	33
168	28.85	0	20	23
169	42.73	0	20	13
170	7	0	16	15
171	9	1	16	23
172	7	0	12	3
173	18	0	12	22
174	18	0	12	22
175	13.55	1	16	24
176	9.3	1	12	20
177	14.24	0	16	17
178	18	1	18	9
179	9.09	1	12	16
180	17.64	1	12	13
181	11.53	1	16	14
182	19.15	0	14	11
183	12.2	1	12	15
184	14.44	0	12	7
185	15	1	16	29
186	9.54	1	12	27
187	17.45	0	12	23
188	24	1	14	16
189	14.43	0	18	8
190	16.35	0	16	8
191	5	1	12	6
192	18.7	1	16	11
193	8	0	12	47
194	18.75	0	12	16
195	5.5	1	12	1
196	4.92	1	12	40
197	7	1	16	1
198	4.5	0	12	4
199	8.2	1	12	33
200	17.25	0	12	27
Source: Gyjarati - Econometrics by Exmaple

The file contains data on 200 workers. For each, it contains the following information:

• Wage per hour.
• A dummy variable that takes the value 1 if the person is a female, and 0 otherwise.
• Years of education.
• Years of experience.

Researchers are interested in estimating the effect of gender, years of education, and years of experience on the wage per hour.

First, construct two variables:

1. A simple dummy variable called "experienced_worker" that take the value 1 if the worker has15 or moreyears of experience, and 0 if the worker has14 or lessyears of education.

2. A variable that is the interaction offemaleandexperienced_worker(i.e.femaleXexperienced_worker).

Then, estimate the following regression:

Notice that you use the newexperienced_workervariable in the regression and not the originalexperiencevariable.

1. What is the estimated regression equation?
2. Interpret the estimated coefficient onfemale. Be precise.
3. Interpret the estimated coefficient onyears of education. Be precise.
4. What is the predicted wage of a male with 12 years of education and 20 years of experience?
5. Which of the 4 Xs affect wages? Hint: which of the 4 coefficients are statistically significant?
6. What is the predicted wage equation for anon-experienced female worker?
7. What is the predicted wage equation for an experienced female worker?
8. What is the effect on wage of being an experienced worker (compared to non-experienced) for females? Hint: use the parts f and g.
9. What is the effect on wage of being an experienced worker (compared to non-experienced) for males? Hint: repeat parts f and g for males.
10. Is the effect of being an experienced worker on wage different for females and males? Hint: examine the statistical significance of a certain coefficient/s.
11. Does the model allow the effect ofyears of educationon wage to be different for females and males? Does it make sense?