During my undergraduate dissertation I have been learning how to use Stata. This resource has been exceptionally useful for answering a multitude of questions! However, I am struggling to find an answer to this particular question so I thought I would contribute to the forum.
I want to plot the impact of religious distance (reldist_weighted_WCD_migweight) on technology adoption (lcumulative_o_flow). From the results posted below, you will see that religious distance has a significant hump shaped impact on technology adoption, once controlled for country and year fixed effects and migration (lIV_level_stock). I want to plot the ceteris paribus effect of religious distance in a graph but am struggling to do so. The desired output is something similar to Ashraf and Galor's graph on page 30 where they use an augmented component-plus-residual plot to show the impact of (Predicted) Genetic homogeneity on log population density (https://www.aeaweb.org/articles?id=10.1257/aer.103.1.1)
So, after running my regression:
Code:
gen reldist_weighted_WCD_migweight2=reldist_weighted_WCD_migweight^2 xi: bootstrap, seed(12345) reps(100): reg lcumulative_o_flow lIV_level_stock reldist_weighted_WCD_migweight reldist_weighted_WCD_migweight2 i.year i.o_country if o_patoffice=="EPO_A"
Code:
. acprplot reldist_weighted_WCD_migweight reldist_weighted_WCD_migweight^2 is collinear with reldist_weighted_WCD_migweight
Any comments that you may have on the issue are welcome.
Many thanks,
Mattie
Code:
. xi: bootstrap, seed(12345) reps(100): reg lcumulative_o_flow lIV_level_stock reldist_weighted_WCD_mi
> gweight reldist_weighted_WCD_migweight2 i.year i.o_country if o_patoffice=="EPO_A"
i.year _Iyear_1980-2011 (naturally coded; _Iyear_1980 omitted)
i.o_country _Io_country_1-96 (_Io_country_1 for o_c~y==Algeria omitted)
(running regress on estimation sample)
Bootstrap replications (100)
----+--- 1 ---+--- 2 ---+--- 3 ---+--- 4 ---+--- 5
x...xx.x...xxx..xx......x.xxx..xx.x.x...x..x.x.... 50
xxxxx...x.xx.x..xx.........x.......x.x.x.x..xxxxxx 100
Linear regression Number of obs = 2,373
Replications = 58
Wald chi2(57) = .
Prob > chi2 = .
R-squared = 0.9006
Adj R-squared = 0.8949
Root MSE = 1.0493
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| Observed Bootstrap Normal-based
lcumulative_o_flow | Coef. Std. Err. z P>|z| [95% Conf. Interval]
--------------------------------+----------------------------------------------------------------
lIV_level_stock | .0433186 .0073221 5.92 0.000 .0289675 .0576696
reldist_weighted_WCD_migweight | 7.359744 .3721271 19.78 0.000 6.630388 8.0891
reldist_weighted_WCD_migweight2 | -6.583357 .6132874 -10.73 0.000 -7.785378 -5.381336
_Iyear_1981 | -.0674904 .2146398 -0.31 0.753 -.4881768 .3531959
_Iyear_1982 | .0178493 .2837429 0.06 0.950 -.5382765 .5739751
_Iyear_1983 | .3935321 .2537367 1.55 0.121 -.1037827 .890847
_Iyear_1984 | .5352107 .2333929 2.29 0.022 .0777689 .9926524
_Iyear_1985 | .4729476 .2358709 2.01 0.045 .0106491 .9352462
_Iyear_1986 | .4256533 .2245722 1.90 0.058 -.0145001 .8658067
_Iyear_1987 | .3569092 .2666282 1.34 0.181 -.1656724 .8794908
_Iyear_1988 | .5028509 .2476099 2.03 0.042 .0175444 .9881573
_Iyear_1989 | .5106573 .2313004 2.21 0.027 .0573168 .9639979
_Iyear_1990 | .6233263 .2228631 2.80 0.005 .1865227 1.06013
_Iyear_1991 | .7188436 .2138539 3.36 0.001 .2996976 1.13799
_Iyear_1992 | .7808771 .2379217 3.28 0.001 .3145592 1.247195
_Iyear_1993 | .7265691 .2206304 3.29 0.001 .2941414 1.158997
_Iyear_1994 | .9045846 .239801 3.77 0.000 .4345833 1.374586
_Iyear_1995 | .9959872 .2097023 4.75 0.000 .5849783 1.406996
_Iyear_1996 | 1.227218 .212677 5.77 0.000 .8103789 1.644057
_Iyear_1997 | 1.62892 .2226651 7.32 0.000 1.192505 2.065336
_Iyear_1998 | 1.349956 .2357112 5.73 0.000 .8879707 1.811941
_Iyear_1999 | 1.73312 .2162772 8.01 0.000 1.309225 2.157016
_Iyear_2000 | 1.957714 .2225118 8.80 0.000 1.521599 2.393829
_Iyear_2001 | 1.991195 .2228489 8.94 0.000 1.554419 2.427971
_Iyear_2002 | 1.680574 .2409684 6.97 0.000 1.208285 2.152864
_Iyear_2003 | 2.001597 .2245871 8.91 0.000 1.561415 2.44178
_Iyear_2004 | 2.108615 .2419064 8.72 0.000 1.634487 2.582743
_Iyear_2005 | 2.204265 .2125649 10.37 0.000 1.787646 2.620885
_Iyear_2006 | 2.014445 .2304086 8.74 0.000 1.562853 2.466038
_Iyear_2007 | 2.336512 .2175173 10.74 0.000 1.910186 2.762838
_Iyear_2008 | 2.315236 .2238853 10.34 0.000 1.876429 2.754044
_Iyear_2009 | 2.196182 .1960523 11.20 0.000 1.811927 2.580438
_Iyear_2010 | 2.225872 .2529522 8.80 0.000 1.730095 2.721649
_Iyear_2011 | 2.309962 .2218774 10.41 0.000 1.87509 2.744834
_Io_country_2 | 1.268477 .4919877 2.58 0.010 .3041991 2.232755
_Io_country_3 | 3.444388 .30816 11.18 0.000 2.840405 4.04837
_Io_country_4 | -.0893468 .3204862 -0.28 0.780 -.7174882 .5387947
_Io_country_5 | 6.646548 .3144022 21.14 0.000 6.030331 7.262765
_Io_country_6 | 7.260963 .2663222 27.26 0.000 6.738982 7.782945
_Io_country_7 | 2.336293 .3133768 7.46 0.000 1.722085 2.9505
_Io_country_8 | 7.586078 .2795772 27.13 0.000 7.038117 8.134039
_Io_country_9 | -.304022 .4799933 -0.63 0.526 -1.244792 .6367476
_Io_country_10 | .7309615 .4492355 1.63 0.104 -.1495239 1.611447
_Io_country_11 | 4.597047 .2866138 16.04 0.000 4.035295 5.1588
_Io_country_12 | 2.689356 .349596 7.69 0.000 2.004161 3.374552
_Io_country_13 | 7.286705 .2957281 24.64 0.000 6.707088 7.866321
_Io_country_14 | .5171735 .4199815 1.23 0.218 -.305975 1.340322
_Io_country_15 | 2.104829 .3050291 6.90 0.000 1.506983 2.702675
_Io_country_16 | 4.834861 .4218744 11.46 0.000 4.008003 5.66172
_Io_country_17 | 1.756597 .2645659 6.64 0.000 1.238057 2.275137
_Io_country_18 | .4905027 .301507 1.63 0.104 -.1004401 1.081446
_Io_country_19 | 2.801754 .3286333 8.53 0.000 2.157644 3.445863
_Io_country_20 | 1.801246 .2913432 6.18 0.000 1.230224 2.372269
_Io_country_21 | 1.28167 .29458 4.35 0.000 .7043042 1.859037
_Io_country_22 | 4.146615 .3501905 11.84 0.000 3.460254 4.832976
_Io_country_23 | 6.552384 .2842844 23.05 0.000 5.995197 7.109571
_Io_country_24 | -2.058116 .2730878 -7.54 0.000 -2.593358 -1.522874
_Io_country_25 | .0742726 .3942984 0.19 0.851 -.6985382 .8470834
_Io_country_26 | 1.655048 .3032595 5.46 0.000 1.060671 2.249426
_Io_country_27 | .799039 .3651613 2.19 0.029 .083336 1.514742
_Io_country_28 | 2.086525 .2883082 7.24 0.000 1.521451 2.651599
_Io_country_29 | 6.699462 .2908476 23.03 0.000 6.129412 7.269513
_Io_country_30 | 8.951348 .312096 28.68 0.000 8.339651 9.563045
_Io_country_31 | .2544531 .3141573 0.81 0.418 -.3612839 .8701901
_Io_country_32 | 10.56494 .3560433 29.67 0.000 9.867109 11.26277
_Io_country_33 | 4.071337 .2891144 14.08 0.000 3.504684 4.637991
_Io_country_34 | -.0779222 .5392577 -0.14 0.885 -1.134848 .9790034
_Io_country_35 | 4.989608 .3307975 15.08 0.000 4.341257 5.637959
_Io_country_36 | 5.097968 .3337297 15.28 0.000 4.443869 5.752066
_Io_country_37 | 3.180557 .3353782 9.48 0.000 2.523228 3.837887
_Io_country_38 | 4.741135 .3059514 15.50 0.000 4.141481 5.340789
_Io_country_39 | 1.495386 .3113511 4.80 0.000 .8851496 2.105623
_Io_country_40 | 1.509291 .32632 4.63 0.000 .8697156 2.148867
_Io_country_41 | 5.150211 .2949507 17.46 0.000 4.572118 5.728303
_Io_country_42 | 6.215447 .3061713 20.30 0.000 5.615362 6.815532
_Io_country_43 | 8.494306 .310636 27.34 0.000 7.885471 9.103142
_Io_country_44 | -.1430325 .4261938 -0.34 0.737 -.9783569 .692292
_Io_country_45 | 9.883642 .2631182 37.56 0.000 9.367939 10.39934
_Io_country_46 | -.0807251 .4360649 -0.19 0.853 -.9353967 .7739464
_Io_country_47 | .5850801 .4013168 1.46 0.145 -.2014863 1.371646
_Io_country_48 | -.2195897 .4246516 -0.52 0.605 -1.051892 .6127122
_Io_country_49 | 5.915556 .435658 13.58 0.000 5.061682 6.76943
_Io_country_50 | .4539481 .444065 1.02 0.307 -.4164033 1.324299
_Io_country_51 | 1.712448 .5273462 3.25 0.001 .6788683 2.746028
_Io_country_52 | 1.04545 .2863109 3.65 0.000 .4842913 1.606609
_Io_country_53 | 3.929057 .3001018 13.09 0.000 3.340869 4.517246
_Io_country_54 | 1.755124 .3897666 4.50 0.000 .9911955 2.519053
_Io_country_55 | 4.642693 .2963839 15.66 0.000 4.061791 5.223595
_Io_country_56 | 2.339257 .3838999 6.09 0.000 1.586827 3.091687
_Io_country_57 | 1.240186 .3085399 4.02 0.000 .6354584 1.844913
_Io_country_58 | 3.538404 .3296869 10.73 0.000 2.892229 4.184578
_Io_country_59 | .2464082 .3899152 0.63 0.527 -.5178115 1.010628
_Io_country_60 | 3.269398 .2879417 11.35 0.000 2.705043 3.833754
_Io_country_61 | -1.799663 .4815534 -3.74 0.000 -2.74349 -.8558354
_Io_country_62 | 1.048054 .2863119 3.66 0.000 .4868933 1.609215
_Io_country_63 | 8.062706 .2749048 29.33 0.000 7.523902 8.601509
_Io_country_64 | 5.024368 .2996043 16.77 0.000 4.437155 5.611582
_Io_country_65 | -.2536804 .395666 -0.64 0.521 -1.029171 .5218106
_Io_country_66 | 6.069716 .2978604 20.38 0.000 5.485921 6.653512
_Io_country_67 | .1440842 .4680351 0.31 0.758 -.7732478 1.061416
_Io_country_68 | .2109399 .3308315 0.64 0.524 -.4374779 .8593577
_Io_country_69 | .2318769 .3585559 0.65 0.518 -.4708797 .9346334
_Io_country_70 | 1.568518 .3384619 4.63 0.000 .9051452 2.231891
_Io_country_71 | 4.178507 .3077147 13.58 0.000 3.575397 4.781616
_Io_country_72 | 3.481913 .2690881 12.94 0.000 2.95451 4.009316
_Io_country_73 | .3789064 .3613142 1.05 0.294 -.3292563 1.087069
_Io_country_74 | 2.179181 .2840631 7.67 0.000 1.622428 2.735935
_Io_country_75 | 5.450685 .2944945 18.51 0.000 4.873486 6.027883
_Io_country_76 | 2.205877 .3379857 6.53 0.000 1.543437 2.868317
_Io_country_77 | 4.44928 .3099468 14.35 0.000 3.841796 5.056765
_Io_country_78 | 2.763328 .2502395 11.04 0.000 2.272868 3.253788
_Io_country_79 | 3.671347 .4292145 8.55 0.000 2.830102 4.512592
_Io_country_80 | 5.060437 .339139 14.92 0.000 4.395737 5.725137
_Io_country_81 | 6.822557 .2893951 23.58 0.000 6.255353 7.389761
_Io_country_82 | .5192109 .3162869 1.64 0.101 -.1007001 1.139122
_Io_country_83 | 8.263921 .308368 26.80 0.000 7.659531 8.868311
_Io_country_84 | 8.24959 .3480859 23.70 0.000 7.567354 8.931826
_Io_country_85 | 2.496365 .3206864 7.78 0.000 1.867831 3.124899
_Io_country_86 | -.0804472 .422946 -0.19 0.849 -.9094061 .7485117
_Io_country_87 | 1.105817 .3436936 3.22 0.001 .4321899 1.779444
_Io_country_88 | 3.20714 .3567206 8.99 0.000 2.50798 3.906299
_Io_country_89 | 2.711653 .3619893 7.49 0.000 2.002167 3.421139
_Io_country_90 | 1.396323 .3075033 4.54 0.000 .7936277 1.999019
_Io_country_91 | 9.259329 .3009099 30.77 0.000 8.669556 9.849102
_Io_country_92 | 10.90914 .305229 35.74 0.000 10.3109 11.50738
_Io_country_93 | 1.696042 .3752012 4.52 0.000 .9606615 2.431423
_Io_country_94 | .3005881 .4208345 0.71 0.475 -.5242324 1.125409
_Io_country_95 | 2.156715 .3426491 6.29 0.000 1.485135 2.828295
_Io_country_96 | 1.179125 .4956383 2.38 0.017 .2076921 2.150559
_cons | -1.777824 .3320964 -5.35 0.000 -2.428721 -1.126927
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Note: One or more parameters could not be estimated in 42 bootstrap replicates;
standard-error estimates include only complete replications.
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