I have run the following regression and am trying to analyse whether the initial level of Income of a country affects changes the effect of inequality on growth (i.e the relationship could be non-linear among developed and developing countries). I have included an interaction term to do this which shows that as inequality is negative and the interaction term is positive, inequality negatively affects the growth of a country with lower initial income.
However, I am trying to find if this weakens as the income per capita increases or turns positive for higher income countries and hence believe the
command is appropriate. But, I am unsure of the code required for this. i have the following:
But, I am not sure if this is the appropriate code for the information I am trying to get. Any help understanding the appropriate code would be greatly appreciated.
However, I am trying to find if this weakens as the income per capita increases or turns positive for higher income countries and hence believe the
Code:
margins
Code:
. xtdpdgmm growth_rate c.l.gini_disp##c.l.ln_Income l.EFW l.ln_pl_i l.fyr_sch_sec l.myr_sch_sec,
> model(fod) gmm(l.(c.gini_disp##c.ln_Income EFW ln_pl_i fyr_sch_sec myr_sch_sec), lag(0 0)) gm
> m(l.(c.gini_disp##c.ln_Income EFW ln_pl_i fyr_sch_sec myr_sch_sec), lag(0 0) diff model(level)
> ) two w(ind) teffects
note: standard errors can be severely biased in finite samples
Generalized method of moments estimation
Fitting full model:
Step 1 f(b) = .00142514
Step 2 f(b) = .88647575
Group variable: ncountry Number of obs = 708
Time variable: period Number of groups = 112
Moment conditions: linear = 156 Obs per group: min = 1
nonlinear = 0 avg = 6.321429
total = 156 max = 11
-------------------------------------------------------------------------------------------
growth_rate | Coef. Std. Err. z P>|z| [95% Conf. Interval]
--------------------------+----------------------------------------------------------------
gini_disp |
L1. | -.0092847 .0008463 -10.97 0.000 -.0109434 -.007626
|
ln_Income |
L1. | -.0522122 .0039198 -13.32 0.000 -.0598949 -.0445296
|
cL.gini_disp#cL.ln_Income | .0009942 .000096 10.36 0.000 .0008062 .0011823
|
EFW |
L1. | .0078056 .0006158 12.68 0.000 .0065987 .0090124
|
ln_pl_i |
L1. | -.0054779 .0009282 -5.90 0.000 -.0072971 -.0036586
|
fyr_sch_sec |
L1. | .0001479 .0015346 0.10 0.923 -.0028598 .0031556
|
myr_sch_sec |
L1. | .0119233 .0016257 7.33 0.000 .008737 .0151097
|
period |
1970 | .0047695 .0008684 5.49 0.000 .0030676 .0064715
1975 | -.0028515 .0012079 -2.36 0.018 -.0052189 -.000484
1980 | -.0152241 .0011472 -13.27 0.000 -.0174725 -.0129757
1985 | -.0064175 .0021772 -2.95 0.003 -.0106847 -.0021503
1990 | -.0024537 .0019021 -1.29 0.197 -.0061817 .0012744
1995 | -.0099781 .0023262 -4.29 0.000 -.0145373 -.0054189
2000 | -.017659 .0021014 -8.40 0.000 -.0217777 -.0135404
2005 | -.0088494 .0021685 -4.08 0.000 -.0130995 -.0045992
2010 | -.013366 .0027469 -4.87 0.000 -.0187499 -.0079821
2015 | -.0326874 .0030746 -10.63 0.000 -.0387135 -.0266612
|
_cons | .4437157 .0332909 13.33 0.000 .3784667 .5089647
-------------------------------------------------------------------------------------------
Instruments corresponding to the linear moment conditions:
1, model(fodev):
1965:L.gini_disp 1970:L.gini_disp 1975:L.gini_disp 1980:L.gini_disp
1985:L.gini_disp 1990:L.gini_disp 1995:L.gini_disp 2000:L.gini_disp
2005:L.gini_disp 2010:L.gini_disp 2020:L.gini_disp 2025:L.gini_disp
1965:L.ln_Income 1970:L.ln_Income 1975:L.ln_Income 1980:L.ln_Income
1985:L.ln_Income 1990:L.ln_Income 1995:L.ln_Income 2000:L.ln_Income
2010:L.ln_Income 2015:L.ln_Income 2020:L.ln_Income 2025:L.ln_Income
1965:cL.gini_disp#cL.ln_Income 1970:cL.gini_disp#cL.ln_Income
1975:cL.gini_disp#cL.ln_Income 1980:cL.gini_disp#cL.ln_Income
1985:cL.gini_disp#cL.ln_Income 1990:cL.gini_disp#cL.ln_Income
2000:cL.gini_disp#cL.ln_Income 2005:cL.gini_disp#cL.ln_Income
2010:cL.gini_disp#cL.ln_Income 2015:cL.gini_disp#cL.ln_Income
2020:cL.gini_disp#cL.ln_Income 2025:cL.gini_disp#cL.ln_Income 1965:L.EFW
1970:L.EFW 1975:L.EFW 1980:L.EFW 1990:L.EFW 1995:L.EFW 2000:L.EFW
2005:L.EFW 2010:L.EFW 2015:L.EFW 2020:L.EFW 2025:L.EFW 1965:L.ln_pl_i
1970:L.ln_pl_i 1980:L.ln_pl_i 1985:L.ln_pl_i 1990:L.ln_pl_i 1995:L.ln_pl_i
2000:L.ln_pl_i 2005:L.ln_pl_i 2010:L.ln_pl_i 2015:L.ln_pl_i 2020:L.ln_pl_i
2025:L.ln_pl_i 1970:L.fyr_sch_sec 1975:L.fyr_sch_sec 1980:L.fyr_sch_sec
1985:L.fyr_sch_sec 1990:L.fyr_sch_sec 1995:L.fyr_sch_sec 2000:L.fyr_sch_sec
2005:L.fyr_sch_sec 2010:L.fyr_sch_sec 2015:L.fyr_sch_sec
2, model(level):
1970:D.L.gini_disp 1975:D.L.gini_disp 1980:D.L.gini_disp 1985:D.L.gini_disp
1990:D.L.gini_disp 1995:D.L.gini_disp 2000:D.L.gini_disp 2005:D.L.gini_disp
2010:D.L.gini_disp 2015:D.L.gini_disp 2020:D.L.gini_disp 2025:D.L.gini_disp
1965:D.L.ln_Income 1970:D.L.ln_Income 1975:D.L.ln_Income 1980:D.L.ln_Income
1985:D.L.ln_Income 1990:D.L.ln_Income 1995:D.L.ln_Income 2000:D.L.ln_Income
2005:D.L.ln_Income 2015:D.L.ln_Income 2020:D.L.ln_Income 2025:D.L.ln_Income
1965:D.cL.gini_disp#cL.ln_Income 1970:D.cL.gini_disp#cL.ln_Income
1975:D.cL.gini_disp#cL.ln_Income 1980:D.cL.gini_disp#cL.ln_Income
1985:D.cL.gini_disp#cL.ln_Income 1990:D.cL.gini_disp#cL.ln_Income
1995:D.cL.gini_disp#cL.ln_Income 2000:D.cL.gini_disp#cL.ln_Income
2005:D.cL.gini_disp#cL.ln_Income 2010:D.cL.gini_disp#cL.ln_Income
2015:D.cL.gini_disp#cL.ln_Income 2020:D.cL.gini_disp#cL.ln_Income
2025:D.cL.gini_disp#cL.ln_Income 1965:D.L.EFW 1970:D.L.EFW 1975:D.L.EFW
1980:D.L.EFW 1985:D.L.EFW 1990:D.L.EFW 1995:D.L.EFW 2000:D.L.EFW
2005:D.L.EFW 2010:D.L.EFW 2015:D.L.EFW 2020:D.L.EFW 2025:D.L.EFW
1965:D.L.ln_pl_i 1970:D.L.ln_pl_i 1975:D.L.ln_pl_i 1980:D.L.ln_pl_i
1985:D.L.ln_pl_i 1990:D.L.ln_pl_i 1995:D.L.ln_pl_i 2000:D.L.ln_pl_i
2005:D.L.ln_pl_i 2010:D.L.ln_pl_i 2015:D.L.ln_pl_i 2020:D.L.ln_pl_i
2025:D.L.ln_pl_i 1965:D.L.fyr_sch_sec 1970:D.L.fyr_sch_sec
1975:D.L.fyr_sch_sec 1980:D.L.fyr_sch_sec 1985:D.L.fyr_sch_sec
1990:D.L.fyr_sch_sec 1995:D.L.fyr_sch_sec 2000:D.L.fyr_sch_sec
2005:D.L.fyr_sch_sec 2010:D.L.fyr_sch_sec 2015:D.L.fyr_sch_sec
2020:D.L.fyr_sch_sec
3, model(level):
1970bn.period 1975.period 1980.period 1985.period 1990.period 1995.period
2000.period 2005.period 2010.period 2015.period
4, model(level):
_cons
Code:
. margins, dydx(l.gini_disp) at(l.ln_Income = (0(0.1)10))
Average marginal effects Number of obs = 708
Model VCE : Conventional
Expression : Linear prediction, predict()
dy/dx w.r.t. : L.gini_disp
1._at : L.ln_Income = 0
2._at : L.ln_Income = .1
3._at : L.ln_Income = .2
4._at : L.ln_Income = .3
5._at : L.ln_Income = .4
6._at : L.ln_Income = .5
7._at : L.ln_Income = .6
8._at : L.ln_Income = .7
9._at : L.ln_Income = .8
10._at : L.ln_Income = .9
11._at : L.ln_Income = 1
12._at : L.ln_Income = 1.1
13._at : L.ln_Income = 1.2
14._at : L.ln_Income = 1.3
15._at : L.ln_Income = 1.4
16._at : L.ln_Income = 1.5
17._at : L.ln_Income = 1.6
18._at : L.ln_Income = 1.7
19._at : L.ln_Income = 1.8
20._at : L.ln_Income = 1.9
21._at : L.ln_Income = 2
22._at : L.ln_Income = 2.1
23._at : L.ln_Income = 2.2
24._at : L.ln_Income = 2.3
25._at : L.ln_Income = 2.4
26._at : L.ln_Income = 2.5
27._at : L.ln_Income = 2.6
28._at : L.ln_Income = 2.7
29._at : L.ln_Income = 2.8
30._at : L.ln_Income = 2.9
31._at : L.ln_Income = 3
32._at : L.ln_Income = 3.1
33._at : L.ln_Income = 3.2
34._at : L.ln_Income = 3.3
35._at : L.ln_Income = 3.4
36._at : L.ln_Income = 3.5
37._at : L.ln_Income = 3.6
38._at : L.ln_Income = 3.7
39._at : L.ln_Income = 3.8
40._at : L.ln_Income = 3.9
41._at : L.ln_Income = 4
42._at : L.ln_Income = 4.1
43._at : L.ln_Income = 4.2
44._at : L.ln_Income = 4.3
45._at : L.ln_Income = 4.4
46._at : L.ln_Income = 4.5
47._at : L.ln_Income = 4.6
48._at : L.ln_Income = 4.7
49._at : L.ln_Income = 4.8
50._at : L.ln_Income = 4.9
51._at : L.ln_Income = 5
52._at : L.ln_Income = 5.1
53._at : L.ln_Income = 5.2
54._at : L.ln_Income = 5.3
55._at : L.ln_Income = 5.4
56._at : L.ln_Income = 5.5
57._at : L.ln_Income = 5.6
58._at : L.ln_Income = 5.7
59._at : L.ln_Income = 5.8
60._at : L.ln_Income = 5.9
61._at : L.ln_Income = 6
62._at : L.ln_Income = 6.1
63._at : L.ln_Income = 6.2
64._at : L.ln_Income = 6.3
65._at : L.ln_Income = 6.4
66._at : L.ln_Income = 6.5
67._at : L.ln_Income = 6.6
68._at : L.ln_Income = 6.7
69._at : L.ln_Income = 6.8
70._at : L.ln_Income = 6.9
71._at : L.ln_Income = 7
72._at : L.ln_Income = 7.1
73._at : L.ln_Income = 7.2
74._at : L.ln_Income = 7.3
75._at : L.ln_Income = 7.4
76._at : L.ln_Income = 7.5
77._at : L.ln_Income = 7.6
78._at : L.ln_Income = 7.7
79._at : L.ln_Income = 7.8
80._at : L.ln_Income = 7.9
81._at : L.ln_Income = 8
82._at : L.ln_Income = 8.1
83._at : L.ln_Income = 8.2
84._at : L.ln_Income = 8.3
85._at : L.ln_Income = 8.4
86._at : L.ln_Income = 8.5
87._at : L.ln_Income = 8.6
88._at : L.ln_Income = 8.7
89._at : L.ln_Income = 8.8
90._at : L.ln_Income = 8.9
91._at : L.ln_Income = 9
92._at : L.ln_Income = 9.1
93._at : L.ln_Income = 9.2
94._at : L.ln_Income = 9.3
95._at : L.ln_Income = 9.4
96._at : L.ln_Income = 9.5
97._at : L.ln_Income = 9.6
98._at : L.ln_Income = 9.7
99._at : L.ln_Income = 9.8
100._at : L.ln_Income = 9.9
101._at : L.ln_Income = 10
------------------------------------------------------------------------------
| Delta-method
| dy/dx Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
L.gini_disp |
_at |
1 | -.0092847 .0008463 -10.97 0.000 -.0109434 -.007626
2 | -.0091853 .0008367 -10.98 0.000 -.0108253 -.0075453
3 | -.0090859 .0008272 -10.98 0.000 -.0107072 -.0074645
4 | -.0089864 .0008177 -10.99 0.000 -.0105891 -.0073837
5 | -.008887 .0008082 -11.00 0.000 -.010471 -.007303
6 | -.0087876 .0007987 -11.00 0.000 -.010353 -.0072222
7 | -.0086882 .0007892 -11.01 0.000 -.0102349 -.0071414
8 | -.0085887 .0007797 -11.02 0.000 -.0101169 -.0070606
9 | -.0084893 .0007702 -11.02 0.000 -.0099988 -.0069798
10 | -.0083899 .0007607 -11.03 0.000 -.0098807 -.006899
11 | -.0082905 .0007511 -11.04 0.000 -.0097627 -.0068182
12 | -.008191 .0007416 -11.04 0.000 -.0096447 -.0067374
13 | -.0080916 .0007322 -11.05 0.000 -.0095266 -.0066566
14 | -.0079922 .0007227 -11.06 0.000 -.0094086 -.0065758
15 | -.0078928 .0007132 -11.07 0.000 -.0092906 -.006495
16 | -.0077934 .0007037 -11.08 0.000 -.0091725 -.0064142
17 | -.0076939 .0006942 -11.08 0.000 -.0090545 -.0063333
18 | -.0075945 .0006847 -11.09 0.000 -.0089365 -.0062525
19 | -.0074951 .0006752 -11.10 0.000 -.0088185 -.0061717
20 | -.0073957 .0006657 -11.11 0.000 -.0087005 -.0060908
21 | -.0072962 .0006563 -11.12 0.000 -.0085825 -.00601
22 | -.0071968 .0006468 -11.13 0.000 -.0084645 -.0059291
23 | -.0070974 .0006373 -11.14 0.000 -.0083465 -.0058482
24 | -.006998 .0006279 -11.15 0.000 -.0082286 -.0057674
25 | -.0068985 .0006184 -11.16 0.000 -.0081106 -.0056865
26 | -.0067991 .000609 -11.17 0.000 -.0079927 -.0056056
27 | -.0066997 .0005995 -11.18 0.000 -.0078747 -.0055247
28 | -.0066003 .0005901 -11.19 0.000 -.0077568 -.0054438
29 | -.0065009 .0005806 -11.20 0.000 -.0076389 -.0053628
30 | -.0064014 .0005712 -11.21 0.000 -.0075209 -.0052819
31 | -.006302 .0005618 -11.22 0.000 -.007403 -.005201
32 | -.0062026 .0005523 -11.23 0.000 -.0072851 -.00512
33 | -.0061032 .0005429 -11.24 0.000 -.0071673 -.0050391
34 | -.0060037 .0005335 -11.25 0.000 -.0070494 -.0049581
35 | -.0059043 .0005241 -11.27 0.000 -.0069315 -.0048771
36 | -.0058049 .0005147 -11.28 0.000 -.0068137 -.0047961
37 | -.0057055 .0005053 -11.29 0.000 -.0066959 -.0047151
38 | -.005606 .0004959 -11.30 0.000 -.0065781 -.004634
39 | -.0055066 .0004866 -11.32 0.000 -.0064603 -.004553
40 | -.0054072 .0004772 -11.33 0.000 -.0063425 -.0044719
41 | -.0053078 .0004678 -11.35 0.000 -.0062247 -.0043908
42 | -.0052084 .0004585 -11.36 0.000 -.006107 -.0043097
43 | -.0051089 .0004492 -11.37 0.000 -.0059893 -.0042286
44 | -.0050095 .0004398 -11.39 0.000 -.0058716 -.0041474
45 | -.0049101 .0004305 -11.40 0.000 -.0057539 -.0040663
46 | -.0048107 .0004212 -11.42 0.000 -.0056363 -.0039851
47 | -.0047112 .0004119 -11.44 0.000 -.0055186 -.0039038
48 | -.0046118 .0004027 -11.45 0.000 -.005401 -.0038226
49 | -.0045124 .0003934 -11.47 0.000 -.0052835 -.0037413
50 | -.004413 .0003842 -11.49 0.000 -.0051659 -.00366
51 | -.0043135 .000375 -11.50 0.000 -.0050485 -.0035786
52 | -.0042141 .0003658 -11.52 0.000 -.004931 -.0034972
53 | -.0041147 .0003566 -11.54 0.000 -.0048136 -.0034158
54 | -.0040153 .0003474 -11.56 0.000 -.0046962 -.0033343
55 | -.0039158 .0003383 -11.58 0.000 -.0045789 -.0032528
56 | -.0038164 .0003292 -11.59 0.000 -.0044616 -.0031712
57 | -.003717 .0003201 -11.61 0.000 -.0043444 -.0030896
58 | -.0036176 .0003111 -11.63 0.000 -.0042272 -.0030079
59 | -.0035182 .000302 -11.65 0.000 -.0041101 -.0029262
60 | -.0034187 .0002931 -11.67 0.000 -.0039931 -.0028443
61 | -.0033193 .0002841 -11.68 0.000 -.0038762 -.0027624
62 | -.0032199 .0002752 -11.70 0.000 -.0037593 -.0026804
63 | -.0031205 .0002664 -11.71 0.000 -.0036426 -.0025983
64 | -.003021 .0002576 -11.73 0.000 -.0035259 -.0025161
65 | -.0029216 .0002489 -11.74 0.000 -.0034094 -.0024338
66 | -.0028222 .0002402 -11.75 0.000 -.003293 -.0023514
67 | -.0027228 .0002316 -11.76 0.000 -.0031767 -.0022688
68 | -.0026233 .0002231 -11.76 0.000 -.0030606 -.002186
69 | -.0025239 .0002147 -11.76 0.000 -.0029447 -.0021031
70 | -.0024245 .0002064 -11.75 0.000 -.002829 -.00202
71 | -.0023251 .0001982 -11.73 0.000 -.0027135 -.0019366
72 | -.0022257 .0001901 -11.71 0.000 -.0025983 -.001853
73 | -.0021262 .0001822 -11.67 0.000 -.0024834 -.0017691
74 | -.0020268 .0001745 -11.62 0.000 -.0023687 -.0016849
75 | -.0019274 .0001669 -11.55 0.000 -.0022545 -.0016003
76 | -.001828 .0001596 -11.46 0.000 -.0021407 -.0015152
77 | -.0017285 .0001525 -11.34 0.000 -.0020274 -.0014297
78 | -.0016291 .0001457 -11.18 0.000 -.0019146 -.0013436
79 | -.0015297 .0001392 -10.99 0.000 -.0018025 -.0012568
80 | -.0014303 .0001331 -10.74 0.000 -.0016912 -.0011693
81 | -.0013308 .0001275 -10.44 0.000 -.0015807 -.001081
82 | -.0012314 .0001223 -10.07 0.000 -.0014711 -.0009917
83 | -.001132 .0001177 -9.62 0.000 -.0013627 -.0009013
84 | -.0010326 .0001137 -9.08 0.000 -.0012554 -.0008097
85 | -.0009332 .0001104 -8.45 0.000 -.0011496 -.0007167
86 | -.0008337 .0001079 -7.73 0.000 -.0010452 -.0006223
87 | -.0007343 .0001061 -6.92 0.000 -.0009424 -.0005263
88 | -.0006349 .0001053 -6.03 0.000 -.0008412 -.0004286
89 | -.0005355 .0001053 -5.09 0.000 -.0007418 -.0003291
90 | -.000436 .0001061 -4.11 0.000 -.000644 -.000228
91 | -.0003366 .0001078 -3.12 0.002 -.000548 -.0001252
92 | -.0002372 .0001104 -2.15 0.032 -.0004535 -.0000209
93 | -.0001378 .0001137 -1.21 0.225 -.0003605 .000085
94 | -.0000383 .0001176 -0.33 0.744 -.0002689 .0001922
95 | .0000611 .0001222 0.50 0.617 -.0001785 .0003007
96 | .0001605 .0001274 1.26 0.208 -.0000892 .0004102
97 | .0002599 .000133 1.95 0.051 -8.34e-07 .0005207
98 | .0003593 .0001391 2.58 0.010 .0000867 .000632
99 | .0004588 .0001456 3.15 0.002 .0001734 .0007441
100 | .0005582 .0001524 3.66 0.000 .0002595 .0008569
101 | .0006576 .0001595 4.12 0.000 .0003451 .0009702
------------------------------------------------------------------------------
. marginsplot
Variables that uniquely identify margins: L.ln_Income
But, I am not sure if this is the appropriate code for the information I am trying to get. Any help understanding the appropriate code would be greatly appreciated.
