Assignment 7.
Introduction to Econometrics. Name:________________________
Due date: in class, Monday, June 18, 2017 This data is from the Annual Social & Economic Supplement of the Current Population Survey,
March 2007 - 2015. The sample includes only persons 16 – 64 years of age during the work-year,
that is, 2006 - 2014. All individuals are civilian non-institutionalize adults. All individuals reside
in the Florida
Table 1. Descriptive statistics, N = 27,906
Variable
Lnwage
Educate
Exper
Exper2
Male
Married
Divorced
Widowed
Seprated
Never-married
Black
Othrrace
Amerind
White
Hispanic
Trend
Recovery Mean
6.49
13.65
21.54
616.58
0.5035
0.5742
0.1221
0.0152
0.0280
0.2606
0.1491
0.0436
0.0034
0.8039
0.2878
3.2845
0.4618 Std. Dev.
0.8642
2.53
12.35
561.53
0.5000
0.4945
0.3274
0.1222
0.1649
0.1927
0.3562
0.2042
0.0579
0.1577
0.4527
2.2557
0.4986 Min
-1.42
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0 Max
11.07
18
59
3481
1
1
1
1
1
1
1
1
1
1
1
7
1 Lnwage = natural logarithm of weekly wage
Educate = years of education
Exper = Age – Years of education – 6.
Exper2 = exper*exper
Male = 1 if individual is male; 0, female
Married = 1 if currently married; 0, otherwise
Divorced = 1 if currently divorced; 0, otherwise
Widowed = 1 if currently widowed; 0, otherwise
Seprated = 1 if currently seperated; 0, otherwise
Black = 1 if African American; 0, otherwise
Amerind = 1 if Native American, Native Alaskan, Native Hawaiian; 0, otherwise
Othrrace = 1 if Asian or Pacific Islander or other; 0 otherwise
Hispanic = 1 individual is Hisapnic; 0, otherwise (ethnic identifier, may belong to any race)
Trend = 0 (2006 work-year), 1 (2007 work-years), …, 7 (2014 work-year)
Recovery = 1 if work-year >= 2010; 0, otherwise. Table 2. Natural logarithm of weekly wage regressions: various specifications
N
F-statistic
p-value
R2
Adjusted R2
SER
Educate (1)
Exper (2)
Exper2 (3)
Married (4)
Divorced (5)
Widowed (6)
Seprated (7)
Black (8)
Othrrace (9)
Amerind (10)
Hispanic (11)
Trend (12)
Recovery (13)
Constant (0)
ESS
SSR
TSS All (1)
27,906
710.27
0.0000
0.2487
0.2484
0.7492
Coef.
0.1163
0.0530
-0.000903
0.1968
0.0760
-0.0897
0.0180
-0.1332
-0.0864
-0.0035
-0.1076
-0.0290
0.0604
4.3174
5,182.73
15,655.59
20,838.32 Std. Err.
0.0019
0.0015
0.00003
0.0128
0.0175
0.0387
0.0288
0.0132
0.0231
0.0805
0.0104
0.0041
0.0184
0.0291 All (2)
27,906
1000.78
0.0000
0.2441
0.2438
0.7515
Coef.
0.1200
0.0524
-0.000884
0.2088
0.0863
-0.0876
0.0048 -0.0296
0.0603
4.2085
5,086.08
15,752.24
20,838.32 A. Evaluate and interpret result.
H0: β8 = 0, β9 = 0, β10 = 0, β11 = 0.
H1: At least one βk ≠ 0 for k = {8, 9, 10, 11}. Std. Err.
0.0018
0.0015
0.00003
0.0128
0.0176
0.0388
0.0289 0.0041
0.0185
0.0279 Men (3)
14,050
485.31
0.0000
0.3101
0.3095
0.7245
Coef.
0.1152
0.0534
-0.000926
0.3332
0.1693
-0.1328
0.1380
-0.2130
-0.1310
0.2183
-0.1217
-0.0351
0.0751
4.4411
3,311.18
7,366.52
10,677.70 Std. Err.
0.0024
0.0020
0.00004
0.0174
0.0258
0.0738
0.0443
0.0188
0.0318
0.1174
0.0142
0.0055
0.0251
0.0384 Women (4)
13,856
302.11
0.0000
0.221
0.2203
0.7269
Coef.
0.1253
0.0471
-0.000787
0.0735
0.0872
0.0183
0.0101
-0.0258
-0.0269
-0.1536
-0.0938
-0.0212
0.0428
4.1007
2,075.14
7,313.66
9,388.79 Std. Err.
0.0027
0.0020
0.00004
0.0178
0.0230
0.0445
0.0362
0.0175
0.0315
0.1045
0.0144
0.0056
0.0254
0.0421 B. Evaluate and interpret result.
H0: 03 = 04 , 13 = 14 , 23 = 24 , 33 = 34 , 43 = 44 , 53 = 54 , 63 = 64 ,
3
4
3
3
4
3
4 73 = 74 , 83 = 84 , 93 = 94 , 10
= 10
, 11
= 11
, 12
= 12
, 13
=
4
13 H1: At least one k3 ≠ k4 , for k = {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13}. C. Use equation 1. Evaluate and interpret result.
H0: β1 = 0, β2 = 0, β3 = 0, β4 = 0, β5 = 0, β6 = 0, β7 = 0, β8 = 0, β9 = 0, β10 = 0, β11 = 0,
β12 = 0, β13 = 0.
H1: At least one βk ≠ 0, for k = {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13}. D. Use equation 1. Can you provide an economic interpretation of ˆ0 ? E. Use equation 1. At what age do men reach their peak earnings? Women? F. Use equation 1. Provide an economic interpretation of ˆ12 and ˆ13. In other words, translate
into everyday language the information provided by these two coefficients. G. What is the predicted wage for a man with 12 years of education, 10 years of work
experience, currently married, white, and working during 2006? Working during 2014?
NB: Wage = elnwage In excel, calculate lnwage then use =exp(lnwage) to get wage. H. What is the predicted wage for a man with 12 years of education, 10 years of work
experience, currently married, white, and working during 2006? Working during 2014? Table 3. Natural logarithm of weekly wages: men, various specifications
N
F-statistic
p-value
R2
Adjusted R2
SER
Educate (1)
Exper (2)
Exper2 (3)
Married (4)
Divorced (5)
Widowed (6)
Seprated (7)
Hispanic (8)
Trend (9)
Recovery (10)
White (11)
Deducate (12)
Dexper (13)
Dexper2 (14)
Dtrend (15)
DRecovery (16)
Constant (0)
ESS
SSR
TSS (1)
(2)
15,822
15,822
629.68
496.3
0.0000
0.0000
0.3046
0.3053
0.3042
0.3047
0.7254
0.7251
Coef. Std. Err.
Coef.
0.1143 0.0023
0.1193
0.0529 0.0019
0.0388
-0.000916 0.00004 -0.000596
0.3318 0.0164
0.3299
0.1588 0.0243
0.1573
-0.0999 0.0687
-0.1048
0.1352 0.0409
0.1390
-0.1237 0.0133
-0.1245
-0.0215 0.0045
-0.0214
0.0351 0.0232
0.0342
0.1852 0.0158
0.1318
-0.0056
0.0173
-0.0004 4.2549
3,645.08
8,320.03
11,965.11 0.0368 4.2920
3,653.48
8,311.62
11,965.11 Std. Err.
0.0058
0.0041
0.00009
0.0164
0.0243
0.0688
0.0409
0.0134
0.0045
0.0232
0.0923
0.0063
0.0044
0.0001 0.0849 (3)
15,822
434.21
0.0000
0.3053
0.3046
0.7252
Coef.
0.1193
0.0388
-0.000596
0.3299
0.1573
-0.1047
0.1389
-0.1245
-0.0214
0.0385
0.1345
-0.0056
0.0173
-0.0004
-0.00001
-0.0052
4.2897
3,653.50
8,311.61
11,965.11 Std. Err.
0.0059
0.0041
0.00009
0.0164
0.0243
0.0688
0.0409
0.0134
0.0106
0.0552
0.0947
0.0063
0.0044
0.0001
0.0117
0.0608
0.0868 Interaction terms
Deducate = white*educate
Dexper = white*exper
Dexper2 = white*exper2
Dtrend = white*trend
DRecovery = white*Recovery
I. Test the null hypothesis that white and non-white men different starting wages but identical
wage growth with experience. J. Test the null hypothesis that white and non-white men different starting wages and different
wage growth with experience K. What is the rate of return to education for white men? Non-white men?
Questions L – M refer to Table 4.
L. What is the preferred regression? Why? M. Using equation 5, what is the rate of return to education? (Allow education to increase from
12 to 13). Table 4. Log wage regressions: years of education polynomials
1
0.114***
[0.0023]
-0.124***
[0.0133]
0.185***
[0.0158] 2
-0.0573***
[0.0110]
-0.144***
[0.0133]
0.183***
[0.0156]
0.00672***
[0.0004] 3
-0.112***
[0.0336]
-0.143***
[0.0133]
0.183***
[0.0156]
0.0123***
[0.0033]
-0.000167*
[0.0001] 4
0.0303
[0.0699]
-0.145***
[0.0133]
0.181***
[0.0156]
-0.0135
[0.0116]
0.00161**
[0.0008]
-0.0000418**
[0.0000] 5
0.335***
[0.1259]
-0.145***
[0.0133]
0.182***
[0.0156]
-0.108***
[0.0343]
0.0131***
[0.0040]
-0.000654***
[0.0002]
0.0000119***
[0.0000] 6
-0.213
[0.1874]
-0.146***
[0.0133]
0.182***
[0.0156]
0.166**
[0.0774]
-0.0406***
[0.0142]
0.00434***
[0.0013]
-0.000209***
[0.0001]
0.00000375***
[0.0000] 7
educate
-0.372
[0.3008]
Hispanic
-0.146***
[0.0133]
white
0.182***
[0.0156]
educate2
0.288
[0.1959]
educate3
-0.0754
[0.0535]
educate4
0.00922
[0.0073]
educate5
-0.000569
[0.0005]
educate6
0.0000172
[0.0000]
educate7
-0.000000199
[0.0000]
R-sq
0.3046
0.3157
0.3158
0.3161
0.3164
0.3171
0.3171
N
15822
15822
15822
15822
15822
15822
15822
2
Standard errors in brackets * p<0.10, **p<0.05, *** p<0.01. Additional explanatory variables include experience, experience , marital status
indicators, trend, and recovery, and constant.

 

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