| Date post: | 06-Apr-2018 |
| Category: |
Documents |
| Upload: | aisha00052693 |
| View: | 217 times |
| Download: | 0 times |
of 28
8/3/2019 Wages Micro Data
1/28
Microdata from the real world
Wage data - 534 observation
Education, work experience, sector, occupation, sex, marital status and other variables
Country: USA
Year: 1985
Elaboration: Economics Web Institute http://www.economicswebinstitute.or
Source: http://lib.stat.cmu.edu/datasets/CPS_
For other datasets, see: http://lib.stat.cmu.edu/datasets/
Reference: Berndt, ER. The Practice of Econometrics. 1991. NY: Addison-Wesley.
Authorization: Public Domain
Description: The datafile contains 534 observations on 11 variables sampled from the Current Population Survey of 1
Summary:
The Current Population Survey (CPS) is used to supplement census information between census years. These dat
Based on residual plots, wages were log-transformed to stabilize the variance. Age and work experience were alm
Adjusting for all other variables in the model, females earned 81% (75%, 88%) the wages of males (p < .0001).
In summary, many factors describe the variations in wages: occupational status, years of experience, years of edu
Therese Stukel
Dartmouth Hitchcock Medical Center
One Medical Center Dr.
Lebanon, NH 03756
e-mail: [email protected]
While every care has been taken in the compilation of this information and every attempt made to present accurate inf
http://www.economicswebinstitute.org/http://lib.stat.cmu.edu/datasets/CPS_85_Wageshttp://lib.stat.cmu.edu/datasets/http://lib.stat.cmu.edu/datasets/http://lib.stat.cmu.edu/datasets/CPS_85_Wageshttp://www.economicswebinstitute.org/8/3/2019 Wages Micro Data
2/28
5_Wages
85. This data set demonstrates multiple regression, confounding, transformations, multicollinearity, categorical
consist of a random sample of 534 persons from the CPS, with information on wages and other characteristics of
ost perfectly correlated (r=.98). Multiple regression of log wages against sex, age, years of education, work experi
ages increased 41% (28%, 56%) for every 5 additional years of education (p < .0001). They increased by 11% (7
ation, sex, union membership and region of residence. However, despite adjustment for all factors that were avail
rmation, we cannot guarantee that inaccuracies will not occur. For doubts, see the original data.
8/3/2019 Wages Micro Data
3/28
ariables, ANOVA, pooled tests of significance, interactions and model building strategies.
the workers, including sex, number of years of education, years of work experience, occupational status, region o
ence, union membership, southern residence, and occupational status showed that these covariates were related to
%, 14%) for every additional 10 years of experience (p < .0001). Union members were paid 23% (12%, 36%) mor
able, there still appeared to be a gender gap in wages. There is no readily available explanation for this gender ga
8/3/2019 Wages Micro Data
4/28
f residence and union membership. We wish to determine (i) whether wages are related to these characteristics an
wages (pooled F test, p < .0001). The effect of age was not significant after controlling for experience. Standardi
e than non-union members (p < .0001). Northerns were paid 11% (2%, 20%) more than southerns (p =.016). Man
.
8/3/2019 Wages Micro Data
5/28
(ii) whether there is a gender gap in wages.
zed residual plots showed no patterns, except for one large outlier with lower wages than expected. This was a ma
agement and professional positions were paid most, and service and clerical positions were paid least (pooled F-te
8/3/2019 Wages Micro Data
6/28
le, with 22 years of experience and 12 years of education, in a management position, who lived in the north and w
st, p < .0001). Overall variance explained was R2 = .35.
8/3/2019 Wages Micro Data
7/28
as not a union member. Removing this person from the analysis did not substantially change the results, so that th
8/3/2019 Wages Micro Data
8/28
e final model included the entire sample.
8/3/2019 Wages Micro Data
9/28
ID WAGE OCCUPATION SECTOR UNION EDUCATION EXPERIENCE AGE SEX
Wage
(dollars
per hour).
Occupational
category
(1=Managemen
t, 2=Sales,
3=Clerical ,
4=Service,
5=Professional,
6=Other)
Sector
(0=Other,
1=Manufa
cturing,
2=Constru
ction)
Indicator
variable for
union
membership
(1=Union
member, 0=Not
union member)
Number of
years of
education
Number of
years of work
experience
Age
(years)
Indicator
variable for
sex
(1=Female,
0=Male).
1 5.10 6 1 0 8 21 35 1
2 4.95 6 1 0 9 42 57 1
3 6.67 6 1 0 12 1 19 0
4 4.00 6 0 0 12 4 22 0
5 7.50 6 0 0 12 17 35 0
6 13.07 6 0 1 13 9 28 0
7 4.45 6 0 0 10 27 43 0
8 19.47 6 0 0 12 9 27 0
9 13.28 6 1 0 16 11 33 0
10 8.75 6 0 0 12 9 27 0
11 11.35 6 0 1 12 17 35 0
12 11.50 6 1 1 12 19 37 0
13 6.50 6 0 0 8 27 41 0
14 6.25 6 0 1 9 30 45 0
15 19.98 6 0 0 9 29 44 0
16 7.30 6 2 0 12 37 55 0
17 8.00 6 0 0 7 44 57 0
18 22.20 6 1 1 12 26 44 0
19 3.65 6 0 0 11 16 33 0
20 20.55 6 0 0 12 33 51 0
21 5.71 6 1 1 12 16 34 1
22 7.00 6 1 1 7 42 55 0
23 3.75 6 0 0 12 9 27 024 4.50 6 0 0 11 14 31 0
25 9.56 6 0 0 12 23 41 0
26 5.75 6 1 0 6 45 57 0
27 9.36 6 1 0 12 8 26 0
28 6.50 6 0 0 10 30 46 0
29 3.35 6 1 0 12 8 26 1
30 4.75 6 0 0 12 8 26 0
31 8.90 6 0 0 14 13 33 0
32 4.00 6 0 0 12 46 64 1
33 4.70 6 0 0 8 19 33 0
34 5.00 6 0 0 17 1 24 1
35 9.25 6 1 0 12 19 37 0
36 10.67 6 0 0 12 36 54 0
37 7.61 6 2 0 12 20 38 038 10.00 6 2 1 12 35 53 0
39 7.50 6 0 0 12 3 21 0
40 12.20 6 1 0 14 10 30 0
41 3.35 6 0 0 12 0 18 0
42 11.00 6 1 1 14 14 34 0
43 12.00 6 1 0 12 14 32 0
44 4.85 6 1 0 9 16 31 1
8/3/2019 Wages Micro Data
10/28
45 4.30 6 2 0 13 8 27 0
46 6.00 6 1 0 7 15 28 1
47 15.00 6 1 0 16 12 34 0
48 4.85 6 0 0 10 13 29 0
49 9.00 6 0 1 8 33 47 0
50 6.36 6 1 0 12 9 27 0
51 9.15 6 0 0 12 7 25 0
52 11.00 6 1 1 16 13 35 0
53 4.50 6 1 0 12 7 25 1
54 4.80 6 1 0 12 16 34 1
55 4.00 6 0 0 13 0 19 0
56 5.50 6 1 0 12 11 29 1
57 8.40 6 1 0 13 17 36 0
58 6.75 6 1 0 10 13 29 0
59 10.00 6 1 1 12 22 40 0
60 5.00 6 1 0 12 28 46 1
61 6.50 6 0 0 11 17 34 0
62 10.75 6 2 1 12 24 42 0
63 7.00 6 1 0 3 55 64 0
64 11.43 6 2 0 12 3 21 0
65 4.00 6 1 1 12 6 24 066 9.00 6 2 0 10 27 43 0
67 13.00 6 1 1 12 19 37 0
68 12.22 6 2 1 12 19 37 0
69 6.28 6 1 0 12 38 56 1
70 6.75 6 1 1 10 41 57 0
71 3.35 6 1 0 11 3 20 0
72 16.00 6 0 1 14 20 40 0
73 5.25 6 0 0 10 15 31 0
74 3.50 6 1 0 8 8 22 0
75 4.22 6 1 0 8 39 53 1
76 3.00 6 1 1 6 43 55 1
77 4.00 6 1 1 11 25 42 1
78 10.00 6 0 1 12 11 29 0
79 5.00 6 0 0 12 12 30 0
80 16.00 6 1 1 12 35 53 0
81 13.98 6 0 0 14 14 34 0
82 13.26 6 0 1 12 16 34 0
83 6.10 6 1 1 10 44 60 1
84 3.75 6 0 0 16 13 35 1
85 9.00 6 1 1 13 8 27 0
86 9.45 6 1 0 12 13 31 0
87 5.50 6 0 1 11 18 35 0
88 8.93 6 0 0 12 18 36 1
89 6.25 6 0 0 12 6 24 1
90 9.75 6 1 1 11 37 54 0
91 6.73 6 1 0 12 2 20 0
92 7.78 6 1 0 12 23 41 093 2.85 6 0 0 12 1 19 0
94 3.35 6 1 0 12 10 28 1
95 19.98 6 1 0 12 23 41 0
96 8.50 6 0 1 12 8 26 0
97 9.75 6 1 0 15 9 30 1
98 15.00 6 2 1 12 33 51 0
99 8.00 6 1 0 12 19 37 1
100 11.25 6 0 0 13 14 33 0
8/3/2019 Wages Micro Data
11/28
101 14.00 6 0 1 11 13 30 0
102 10.00 6 2 0 10 12 28 0
103 6.50 6 0 0 12 8 26 0
104 9.83 6 1 0 12 23 41 0
105 18.50 6 1 0 14 13 33 1
106 12.50 6 0 0 12 9 27 0
107 26.00 6 0 1 14 21 41 0
108 14.00 6 2 0 5 44 55 0
109 10.50 6 0 1 12 4 22 0
110 11.00 6 1 0 8 42 56 0
111 12.47 6 0 1 13 10 29 0
112 12.50 6 2 0 12 11 29 0
113 15.00 6 2 1 12 40 58 0
114 6.00 6 2 0 12 8 26 0
115 9.50 6 2 0 11 29 46 0
116 5.00 6 0 1 16 3 25 0
117 3.75 6 2 0 11 11 28 0
118 12.57 6 0 1 12 12 30 0
119 6.88 6 0 0 8 22 36 1
120 5.50 6 0 0 12 12 30 0
121 7.00 6 0 1 12 7 25 0122 4.50 6 1 0 12 15 33 1
123 6.50 6 0 0 12 28 46 0
124 12.00 6 1 1 12 20 38 0
125 5.00 6 2 0 12 6 24 0
126 6.50 6 1 0 12 5 23 0
127 6.80 6 1 0 9 30 45 1
128 8.75 6 0 0 13 18 37 0
129 3.75 6 1 0 12 6 24 1
130 4.50 6 0 0 12 16 34 0
131 6.00 6 0 1 12 1 19 0
132 5.50 6 1 0 12 3 21 0
133 13.00 6 0 0 12 8 26 0
134 5.65 6 1 0 14 2 22 0
135 4.80 6 1 0 9 16 31 0
136 7.00 6 2 0 10 9 25 0
137 5.25 6 0 0 12 2 20 0
138 3.35 6 1 0 7 43 56 0
139 8.50 6 1 0 9 38 53 0
140 6.00 6 0 0 12 9 27 0
141 6.75 6 0 0 12 12 30 0
142 8.89 6 1 0 12 18 36 0
143 14.21 6 1 1 11 15 32 0
144 10.78 6 2 1 11 28 45 0
145 8.90 6 2 1 10 27 43 0
146 7.50 6 0 0 12 38 56 0
147 4.50 6 1 0 12 3 21 1
148 11.25 6 0 1 12 41 59 0149 13.45 6 0 1 12 16 34 0
150 6.00 6 1 0 13 7 26 0
151 4.62 6 1 0 6 33 45 1
152 10.58 6 1 0 14 25 45 0
153 5.00 6 0 0 12 5 23 0
154 8.20 6 0 0 14 17 37 0
155 6.25 6 0 0 12 1 19 0
156 8.50 6 1 0 12 13 31 0
8/3/2019 Wages Micro Data
12/28
157 24.98 1 0 0 16 18 40 0
158 16.65 1 0 0 14 21 41 0
159 6.25 1 0 0 14 2 22 0
160 4.55 1 0 0 12 4 22 1
161 11.25 1 0 0 12 30 48 1
162 21.25 1 0 0 13 32 51 0
163 12.65 1 0 0 17 13 36 1
164 7.50 1 0 0 12 17 35 0
165 10.25 1 0 0 14 26 46 1
166 3.35 1 0 0 16 9 31 0
167 13.45 1 0 0 16 8 30 0
168 4.84 1 0 1 15 1 22 0
169 26.29 1 0 0 17 32 55 0
170 6.58 1 0 0 12 24 42 1
171 44.50 1 0 0 14 1 21 1
172 15.00 1 1 0 12 42 60 0
173 11.25 1 1 0 16 3 25 1
174 7.00 1 0 0 12 32 50 1
175 10.00 1 0 0 14 22 42 0
176 14.53 1 0 0 16 18 40 0
177 20.00 1 0 0 18 19 43 1178 22.50 1 0 0 15 12 33 0
179 3.64 1 0 0 12 42 60 1
180 10.62 1 0 0 12 34 52 0
181 24.98 1 0 0 18 29 53 0
182 6.00 1 0 0 16 8 30 0
183 19.00 1 1 0 18 13 37 0
184 13.20 1 0 0 16 10 32 0
185 22.50 1 0 0 16 22 44 0
186 15.00 1 0 0 16 10 32 0
187 6.88 1 0 0 17 15 38 1
188 11.84 1 0 0 12 26 44 0
189 16.14 1 0 0 14 16 36 0
190 13.95 1 0 0 18 14 38 1
191 13.16 1 0 0 12 38 56 1
192 5.30 1 0 0 12 14 32 0
193 4.50 1 0 0 12 7 25 1
194 10.00 1 0 0 18 13 37 1
195 10.00 1 0 0 10 20 36 0
196 10.00 1 0 1 16 7 29 0
197 9.37 1 0 0 16 26 48 1
198 5.80 1 0 0 16 14 36 0
199 17.86 1 0 0 13 36 55 0
200 1.00 1 0 0 12 24 42 0
201 8.80 1 0 0 14 41 61 0
202 9.00 1 0 0 16 7 29 0
203 18.16 1 0 0 17 14 37 0
204 7.81 1 0 0 12 1 19 1205 10.62 1 1 0 16 6 28 1
206 4.50 1 0 0 12 3 21 1
207 17.25 1 0 0 15 31 52 0
208 10.50 1 1 0 13 14 33 1
209 9.22 1 0 0 14 13 33 1
210 15.00 1 1 1 16 26 48 0
211 22.50 1 0 0 18 14 38 0
212 4.55 2 0 0 13 33 52 1
8/3/2019 Wages Micro Data
13/28
213 9.00 2 0 0 12 16 34 0
214 13.33 2 0 0 18 10 34 0
215 15.00 2 0 0 14 22 42 0
216 7.50 2 0 0 14 2 22 0
217 4.25 2 0 0 12 29 47 1
218 12.50 2 1 0 12 43 61 0
219 5.13 2 0 0 12 5 23 1
220 3.35 2 0 0 16 14 36 1
221 11.11 2 0 0 12 28 46 0
222 3.84 2 0 0 11 25 42 1
223 6.40 2 0 0 12 45 63 1
224 5.56 2 0 0 14 5 25 0
225 10.00 2 1 0 12 20 38 0
226 5.65 2 0 0 16 6 28 1
227 11.50 2 0 0 16 16 38 0
228 3.50 2 0 0 11 33 50 1
229 3.35 2 0 0 13 2 21 1
230 4.75 2 0 0 12 10 28 1
231 19.98 2 0 0 14 44 64 0
232 3.50 2 0 0 14 6 26 1
233 4.00 2 0 0 12 15 33 1234 7.00 2 0 0 12 5 23 0
235 6.25 2 1 0 13 4 23 1
236 4.50 2 0 0 14 14 34 0
237 14.29 2 0 0 14 32 52 1
238 5.00 2 0 0 12 14 32 1
239 13.75 2 0 0 14 21 41 0
240 13.71 2 0 1 12 43 61 0
241 7.50 2 0 0 12 27 45 1
242 3.80 2 0 0 12 4 22 1
243 5.00 2 0 0 14 0 20 0
244 9.42 2 0 0 12 32 50 0
245 5.50 2 0 0 12 20 38 0
246 3.75 2 0 0 15 4 25 0
247 3.50 2 0 0 12 34 52 0
248 5.80 2 0 0 13 5 24 0
249 12.00 2 1 0 17 13 36 0
250 5.00 3 0 0 14 17 37 1
251 8.75 3 0 0 13 10 29 1
252 10.00 3 0 0 16 7 29 1
253 8.50 3 0 0 12 25 43 1
254 8.63 3 0 0 12 18 36 1
255 9.00 3 1 0 16 27 49 1
256 5.50 3 0 0 16 2 24 1
257 11.11 3 0 0 13 13 32 0
258 10.00 3 0 0 14 24 44 1
259 5.20 3 0 0 18 13 37 1
260 8.00 3 0 1 14 15 35 1261 3.56 3 0 0 12 12 30 1
262 5.20 3 0 0 12 24 42 1
263 11.67 3 2 0 12 43 61 1
264 11.32 3 1 0 12 13 31 1
265 7.50 3 0 0 12 16 34 1
266 5.50 3 0 0 11 24 41 1
267 5.00 3 0 0 16 4 26 1
268 7.75 3 0 0 12 24 42 1
8/3/2019 Wages Micro Data
14/28
269 5.25 3 0 0 12 45 63 1
270 9.00 3 0 1 12 20 38 0
271 9.65 3 0 0 12 38 56 1
272 5.21 3 0 0 18 10 34 0
273 7.00 3 0 0 11 16 33 1
274 12.16 3 0 0 12 32 50 1
275 5.25 3 0 0 16 2 24 1
276 10.32 3 0 0 13 28 47 1
277 3.35 3 0 0 16 3 25 0
278 7.70 3 0 1 13 8 27 1
279 9.17 3 1 0 12 44 62 1
280 8.43 3 0 0 12 12 30 0
281 4.00 3 0 0 12 8 26 0
282 4.13 3 0 0 12 4 22 1
283 3.00 3 0 0 12 28 46 1
284 4.25 3 0 0 13 0 19 1
285 7.53 3 0 0 14 1 21 0
286 10.53 3 1 0 14 12 32 1
287 5.00 3 0 0 12 39 57 1
288 15.03 3 0 0 12 24 42 1
289 11.25 3 0 0 17 32 55 1290 6.25 3 0 0 16 4 26 0
291 3.50 3 0 0 12 25 43 1
292 6.85 3 0 0 12 8 26 0
293 12.50 3 0 0 13 16 35 1
294 12.00 3 0 0 12 5 23 0
295 6.00 3 0 0 13 31 50 0
296 9.50 3 0 0 12 25 43 1
297 4.10 3 0 0 12 15 33 1
298 10.43 3 0 0 14 15 35 1
299 5.00 3 0 0 12 0 18 1
300 7.69 3 0 0 12 19 37 0
301 5.50 3 0 0 12 21 39 1
302 6.40 3 0 0 12 6 24 1
303 12.50 3 0 1 12 14 32 1
304 6.25 3 0 0 13 30 49 1
305 8.00 3 0 0 12 8 26 1
306 9.60 3 0 1 9 33 48 0
307 9.10 3 0 0 13 16 35 0
308 7.50 3 0 0 12 20 38 1
309 5.00 3 0 0 13 6 25 1
310 7.00 3 0 1 12 10 28 1
311 3.55 3 0 0 13 1 20 1
312 8.50 3 0 0 12 2 20 0
313 4.50 3 0 0 13 0 19 1
314 7.88 3 0 0 16 17 39 0
315 5.25 3 0 0 12 8 26 1
316 5.00 3 0 0 12 4 22 0317 9.33 3 0 0 12 15 33 1
318 10.50 3 0 0 12 29 47 1
319 7.50 3 0 0 12 23 41 1
320 9.50 3 0 0 12 39 57 1
321 9.60 3 0 0 12 14 32 1
322 5.87 3 0 0 17 6 29 1
323 11.02 3 0 1 14 12 32 0
324 5.00 3 0 0 12 26 44 1
8/3/2019 Wages Micro Data
15/28
325 5.62 3 0 0 14 32 52 1
326 12.50 3 0 0 15 6 27 1
327 10.81 3 0 0 12 40 58 1
328 5.40 3 1 0 12 18 36 1
329 7.00 3 0 0 11 12 29 1
330 4.59 3 2 0 12 36 54 1
331 6.00 3 0 0 12 19 37 1
332 11.71 3 1 0 16 42 64 1
333 5.62 3 0 0 13 2 21 1
334 5.50 3 0 0 12 33 51 1
335 4.85 3 0 0 12 14 32 1
336 6.75 3 0 0 12 22 40 0
337 4.25 3 0 0 12 20 38 1
338 5.75 3 0 0 12 15 33 1
339 3.50 3 0 0 12 35 53 0
340 3.35 3 0 0 12 7 25 1
341 10.62 3 1 0 12 45 63 1
342 8.00 3 0 0 12 9 27 1
343 4.75 3 0 0 12 2 20 1
344 8.50 3 0 0 17 3 26 0
345 8.85 3 0 1 14 19 39 1346 8.00 3 0 0 12 14 32 1
347 6.00 4 0 0 4 54 64 0
348 7.14 4 0 0 14 17 37 0
349 3.40 4 0 0 8 29 43 1
350 6.00 4 0 0 15 26 47 1
351 3.75 4 0 0 2 16 24 0
352 8.89 4 0 0 8 29 43 1
353 4.35 4 0 0 11 20 37 1
354 13.10 4 0 0 10 38 54 1
355 4.35 4 0 0 8 37 51 1
356 3.50 4 0 0 9 48 63 0
357 3.80 4 0 0 12 16 34 1
358 5.26 4 0 0 8 38 52 1
359 3.35 4 0 0 14 0 20 0
360 16.26 4 0 1 12 14 32 0
361 4.25 4 0 0 12 2 20 1
362 4.50 4 0 0 16 21 43 0
363 8.00 4 0 0 13 15 34 1
364 4.00 4 0 0 16 20 42 1
365 7.96 4 0 0 14 12 32 1
366 4.00 4 0 0 12 7 25 0
367 4.15 4 0 0 11 4 21 0
368 5.95 4 0 0 13 9 28 0
369 3.60 4 0 0 12 43 61 1
370 8.75 4 0 0 10 19 35 0
371 3.40 4 0 0 8 49 63 1
372 4.28 4 0 0 12 38 56 1373 5.35 4 0 0 12 13 31 1
374 5.00 4 0 0 12 14 32 1
375 7.65 4 0 0 12 20 38 0
376 6.94 4 0 0 12 7 25 1
377 7.50 4 1 1 12 9 27 1
378 3.60 4 0 0 12 6 24 1
379 1.75 4 0 0 12 5 23 1
380 3.45 4 0 0 13 1 20 1
8/3/2019 Wages Micro Data
16/28
381 9.63 4 0 1 14 22 42 0
382 8.49 4 0 0 12 24 42 1
383 8.99 4 0 1 12 15 33 1
384 3.65 4 0 0 11 8 25 1
385 3.50 4 0 0 11 17 34 1
386 3.43 4 0 0 12 2 20 0
387 5.50 4 0 0 12 20 38 0
388 6.93 4 0 1 12 26 44 0
389 3.51 4 0 0 10 37 53 1
390 3.75 4 0 0 12 41 59 1
391 4.17 4 0 0 12 27 45 1
392 9.57 4 0 1 12 5 23 1
393 14.67 4 0 0 14 16 36 0
394 12.50 4 0 0 14 19 39 1
395 5.50 4 0 0 12 10 28 0
396 5.15 4 0 1 13 1 20 0
397 8.00 4 0 1 12 43 61 1
398 5.83 4 0 0 13 3 22 0
399 3.35 4 0 0 12 0 18 1
400 7.00 4 0 0 12 26 44 1
401 10.00 4 0 1 10 25 41 1402 8.00 4 0 0 12 15 33 1
403 6.88 4 0 0 14 10 30 1
404 5.55 4 0 1 11 45 62 1
405 7.50 4 0 0 11 3 20 0
406 8.93 4 0 1 8 47 61 0
407 9.00 4 0 0 16 6 28 1
408 3.50 4 0 0 10 33 49 1
409 5.77 4 1 0 16 3 25 0
410 25.00 4 0 1 14 4 24 0
411 6.85 4 0 1 14 34 54 0
412 6.50 4 0 0 11 39 56 0
413 3.75 4 0 0 12 17 35 1
414 3.50 4 0 1 9 47 62 0
415 4.50 4 0 0 11 2 19 0
416 2.01 4 0 0 13 0 19 0
417 4.17 4 0 0 14 24 44 1
418 13.00 4 0 1 12 25 43 0
419 3.98 4 0 0 14 6 26 1
420 7.50 4 0 0 12 10 28 1
421 13.12 4 0 0 12 33 51 1
422 4.00 4 0 0 12 12 30 0
423 3.95 4 0 0 12 9 27 1
424 13.00 4 0 1 11 18 35 0
425 9.00 4 0 0 12 10 28 0
426 4.55 4 0 0 8 45 59 1
427 9.50 4 0 1 9 46 61 1
428 4.50 4 0 0 7 14 27 0429 8.75 4 0 0 11 36 53 1
430 10.00 5 2 1 13 34 53 0
431 18.00 5 0 0 18 15 39 0
432 24.98 5 1 0 17 31 54 0
433 12.05 5 1 0 16 6 28 1
434 22.00 5 0 0 14 15 35 0
435 8.75 5 0 0 12 30 48 0
436 22.20 5 0 0 18 8 32 0
8/3/2019 Wages Micro Data
17/28
437 17.25 5 1 0 18 5 29 0
438 6.00 5 0 1 17 3 26 1
439 8.06 5 0 0 13 17 36 0
440 9.24 5 1 1 16 5 27 0
441 12.00 5 0 0 14 10 30 1
442 10.61 5 0 0 15 33 54 1
443 5.71 5 0 0 18 3 27 0
444 10.00 5 0 0 16 0 18 1
445 17.50 5 0 0 16 13 35 0
446 15.00 5 0 0 18 12 36 0
447 7.78 5 0 0 16 6 28 1
448 7.80 5 0 0 17 7 30 0
449 10.00 5 0 1 16 14 36 0
450 24.98 5 0 0 17 5 28 1
451 10.28 5 0 0 15 10 31 1
452 15.00 5 0 0 18 11 35 1
453 12.00 5 0 0 17 24 47 1
454 10.58 5 1 0 16 9 31 0
455 5.85 5 0 0 18 12 36 0
456 11.22 5 0 0 18 19 43 0
457 8.56 5 0 0 14 14 34 1458 13.89 5 1 0 16 17 39 1
459 5.71 5 0 0 18 7 31 0
460 15.79 5 0 0 18 7 31 0
461 7.50 5 0 0 16 22 44 1
462 11.25 5 0 0 12 28 46 1
463 6.15 5 0 0 16 16 38 1
464 13.45 5 0 0 16 16 38 0
465 6.25 5 0 0 16 7 29 1
466 6.50 5 0 0 12 11 29 1
467 12.00 5 0 0 12 11 29 1
468 8.50 5 0 0 12 16 34 1
469 8.00 5 0 1 18 33 57 0
470 5.75 5 0 0 12 21 39 1
471 15.73 5 1 0 16 4 26 0
472 9.86 5 0 0 15 13 34 0
473 13.51 5 0 1 18 14 38 0
474 5.40 5 0 0 16 10 32 1
475 6.25 5 0 0 18 14 38 0
476 5.50 5 0 0 16 29 51 0
477 5.00 5 0 0 12 4 22 0
478 6.25 5 0 0 18 27 51 0
479 5.75 5 0 0 12 3 21 0
480 20.50 5 0 1 16 14 36 0
481 5.00 5 2 0 14 0 20 0
482 7.00 5 0 0 18 33 57 0
483 18.00 5 0 0 16 38 60 0
484 12.00 5 0 1 18 18 42 1485 20.40 5 1 0 17 3 26 0
486 22.20 5 0 0 18 40 64 1
487 16.42 5 1 0 14 19 39 0
488 8.63 5 0 0 14 4 24 1
489 19.38 5 0 0 16 11 33 1
490 14.00 5 0 0 16 16 38 1
491 10.00 5 0 0 14 22 42 0
492 15.95 5 0 1 17 13 36 1
8/3/2019 Wages Micro Data
18/28
493 20.00 5 0 1 16 28 50 1
494 10.00 5 0 0 16 10 32 1
495 24.98 5 0 0 16 5 27 1
496 11.25 5 0 0 15 5 26 0
497 22.83 5 1 0 18 37 61 1
498 10.20 5 0 1 17 26 49 1
499 10.00 5 0 0 16 4 26 1
500 14.00 5 0 1 18 31 55 1
501 12.50 5 0 1 17 13 36 1
502 5.79 5 0 0 12 42 60 1
503 24.98 5 0 0 17 18 41 0
504 4.35 5 0 0 12 3 21 1
505 11.25 5 0 0 17 10 33 1
506 6.67 5 0 1 16 10 32 1
507 8.00 5 0 0 16 17 39 1
508 18.16 5 0 0 18 7 31 0
509 12.00 5 0 0 16 14 36 1
510 8.89 5 0 1 16 22 44 1
511 9.50 5 0 0 17 14 37 1
512 13.65 5 0 0 16 11 33 0
513 12.00 5 0 1 18 23 47 0514 15.00 5 0 1 12 39 57 0
515 12.67 5 0 0 16 15 37 0
516 7.38 5 0 0 14 15 35 1
517 15.56 5 0 0 16 10 32 0
518 7.45 5 0 0 12 25 43 1
519 6.25 5 0 0 14 12 32 1
520 6.25 5 0 0 16 7 29 1
521 9.37 5 0 1 17 7 30 0
522 22.50 5 1 0 16 17 39 0
523 7.50 5 0 1 16 10 32 0
524 7.00 5 0 0 17 2 25 0
525 5.75 5 0 1 9 34 49 1
526 7.67 5 0 0 15 11 32 1
527 12.50 5 0 0 15 10 31 0
528 16.00 5 0 0 12 12 30 0
529 11.79 5 0 1 16 6 28 1
530 11.36 5 0 0 18 5 29 0
531 6.10 5 0 0 12 33 51 1
532 23.25 5 0 1 17 25 48 1
533 19.88 5 0 1 12 13 31 0
534 15.38 5 1 0 16 33 55 0
8/3/2019 Wages Micro Data
19/28
MARR RACE SOUTH
Marital Status
(0=Unmarried,
1=Married)
Race
(1=Other,
2=Hispanic,
3=White).
Indicator
variable for
Southern
Region
(1=Person
lives in
South,
0=Person
lives
elsewhere).
1 2 0
1 3 0
0 3 0
0 3 0
1 3 0
0 3 0
0 3 1
0 3 0
1 3 0
0 3 0
1 3 0
0 3 0
1 3 1
0 3 1
1 3 1
1 3 0
1 3 1
1 3 0
0 3 0
1 3 0
1 3 0
1 1 0
0 3 01 1 1
1 3 0
1 3 1
1 3 0
1 3 0
1 3 0
1 3 0
0 3 0
0 3 1
1 3 0
0 3 1
0 3 0
0 1 0
1 1 11 1 0
0 3 0
1 3 1
0 3 0
1 3 1
1 3 0
1 3 0
8/3/2019 Wages Micro Data
20/28
0 3 1
1 3 1
1 3 0
0 3 1
1 3 0
1 3 0
1 3 0
1 3 0
1 3 0
1 3 0
0 3 0
0 3 0
0 3 0
1 3 0
0 1 0
1 3 0
0 3 0
1 3 0
1 2 1
0 3 1
0 1 01 3 0
1 1 1
1 3 0
1 3 0
1 1 1
0 1 1
1 3 0
1 3 0
1 2 1
1 3 1
1 2 0
1 3 1
1 3 0
1 1 0
1 3 1
0 3 0
1 3 0
0 3 0
0 3 1
0 1 0
0 3 0
1 3 0
1 3 0
0 3 1
1 3 1
1 3 1
1 3 00 3 0
1 1 1
1 3 0
1 1 0
1 3 0
1 3 0
1 3 0
1 3 0
8/3/2019 Wages Micro Data
21/28
1 3 0
1 3 0
0 3 0
1 3 0
0 3 0
1 3 1
1 3 0
1 3 1
1 3 0
1 3 0
1 3 0
0 3 0
1 3 0
0 3 0
1 3 1
0 3 0
0 3 0
1 3 0
1 2 0
1 3 0
1 3 00 3 0
1 3 0
1 3 1
0 3 1
0 3 1
1 3 1
1 3 0
1 1 1
0 2 1
0 2 1
0 3 0
1 3 0
0 3 0
0 1 0
1 3 1
0 3 0
1 3 1
1 3 0
1 3 0
1 3 1
1 3 0
0 3 0
1 1 1
1 3 1
1 3 1
0 3 0
1 3 01 3 1
1 3 1
0 1 1
1 3 0
1 3 1
0 1 1
0 3 1
1 3 0
8/3/2019 Wages Micro Data
22/28
1 3 0
1 3 1
0 3 0
0 2 1
1 2 1
0 3 0
1 3 0
0 3 0
1 3 0
0 3 0
0 1 0
1 3 0
1 3 1
1 3 0
0 3 0
1 3 0
0 1 0
1 3 0
0 1 0
1 3 0
1 3 01 3 0
1 3 0
1 3 1
1 3 0
0 3 1
0 3 0
0 3 0
1 3 0
1 3 1
1 3 0
1 3 0
0 3 0
1 3 0
1 3 0
1 1 1
1 3 0
0 3 1
1 3 0
1 2 0
1 3 0
1 3 0
0 3 0
1 3 0
1 3 1
1 1 0
0 3 1
0 3 11 3 0
1 3 0
1 3 0
1 3 0
1 3 0
1 1 0
1 3 0
1 3 0
8/3/2019 Wages Micro Data
23/28
1 3 0
1 3 0
0 3 0
0 3 0
1 3 1
1 3 0
1 3 0
1 1 1
1 3 1
1 1 1
1 3 0
0 3 1
1 3 1
1 3 0
1 3 0
1 3 0
1 3 1
0 3 1
1 3 1
1 3 1
0 3 01 3 0
1 3 0
1 3 0
1 3 0
1 3 0
1 3 0
1 3 0
1 1 1
0 3 0
0 2 0
1 3 1
1 3 0
0 3 1
1 3 0
0 3 0
1 3 0
1 2 0
1 3 1
1 3 0
0 3 0
1 1 0
1 3 0
0 3 0
1 3 0
0 3 0
1 2 1
0 3 00 2 1
1 3 0
1 3 0
1 3 0
1 3 1
1 3 0
1 3 1
1 3 0
8/3/2019 Wages Micro Data
24/28
1 3 0
1 3 0
1 3 0
1 3 1
1 1 0
1 1 1
0 3 1
0 3 1
0 1 0
0 3 0
1 3 0
1 3 1
1 1 1
1 3 0
1 3 1
0 3 1
0 3 1
1 3 0
1 3 0
1 3 0
1 1 00 1 0
0 1 0
0 1 0
1 3 0
0 3 1
0 3 0
0 3 0
1 3 0
1 3 1
0 3 0
1 3 0
0 1 0
0 3 0
1 3 0
1 3 0
0 3 0
0 3 0
0 2 0
0 3 1
1 3 1
1 3 0
0 3 1
0 1 1
0 3 1
1 1 0
0 3 0
0 3 10 3 0
1 3 0
1 1 1
1 3 1
1 3 1
0 1 1
1 3 1
0 3 1
8/3/2019 Wages Micro Data
25/28
1 3 0
1 3 0
1 3 0
1 3 0
0 3 0
1 3 1
1 3 0
0 3 0
1 2 0
1 3 0
1 3 1
0 3 0
1 3 0
1 3 0
1 3 0
1 3 0
0 3 0
0 3 0
1 3 1
0 3 1
1 1 01 3 1
1 3 0
1 3 0
1 1 0
0 3 1
0 2 0
0 1 0
1 3 0
1 1 1
1 1 1
0 3 0
0 3 0
1 3 0
0 1 0
0 1 0
1 3 0
1 3 0
1 3 0
0 3 0
1 3 0
0 2 1
1 3 0
1 3 1
1 2 1
0 3 1
0 3 0
1 3 01 3 0
1 3 0
0 3 0
0 3 0
1 3 0
0 3 0
1 3 1
0 1 1
8/3/2019 Wages Micro Data
26/28
1 3 0
1 3 0
0 3 0
1 3 1
1 3 1
0 1 1
1 3 1
1 3 0
1 1 1
0 3 0
1 3 0
1 3 0
1 1 0
1 3 0
1 3 0
0 3 1
1 1 0
0 1 0
0 3 0
1 3 1
1 3 01 3 0
0 3 1
0 3 0
0 1 0
1 2 0
1 1 0
0 3 1
0 3 0
0 2 0
1 1 0
1 3 1
1 3 1
1 3 0
0 3 0
0 3 1
0 3 0
1 1 0
0 3 0
0 3 0
1 1 0
0 3 0
1 3 1
1 3 1
1 3 0
0 3 1
1 3 0
1 2 10 3 0
1 3 0
1 3 0
1 3 0
0 3 0
1 3 1
1 3 0
1 3 0
8/3/2019 Wages Micro Data
27/28
1 3 0
0 3 0
1 3 1
1 1 0
1 3 0
0 3 0
1 3 0
0 3 0
1 1 1
1 3 0
1 3 0
1 3 0
1 3 1
0 3 0
1 3 1
1 3 0
1 3 0
0 3 0
1 3 1
1 3 0
1 3 00 3 0
0 3 1
1 3 0
1 3 0
1 3 0
0 3 0
0 1 1
1 3 0
0 3 0
1 3 0
0 3 0
0 3 0
1 3 1
1 3 0
1 3 0
1 3 0
1 3 0
1 3 1
1 3 1
0 2 0
1 1 0
1 3 0
1 3 1
1 3 0
1 3 0
1 3 1
1 3 00 3 0
0 3 0
0 3 0
0 3 0
1 3 0
1 3 0
1 3 0
0 3 0
8/3/2019 Wages Micro Data
28/28
1 3 1
1 3 0
0 3 1
0 3 0
0 3 0
1 3 0
1 3 1
0 3 0
1 3 0
1 3 0
1 2 0
1 3 0
0 3 0
0 3 0
1 2 0
1 3 0
1 3 0
1 3 0
1 3 0
1 3 0
1 3 01 3 0
1 3 0
0 2 0
0 3 0
0 3 1
1 3 0
1 2 1
1 3 0
1 3 0
1 3 0
1 3 1
1 1 1
1 3 0
0 3 0
1 3 1
0 3 0
0 3 0
1 1 0
1 1 0
1 3 1
1 3 0
