*clear all
*macro drop _all
*set more off
set scheme gg_tableau
use "pwt_10_dload_02032021.dta", clear
* Merge with natural resources share
merge 1:1 year countrycode using "natural_resources.dta"
drop if _merge==2
Result Number of obs
-----------------------------------------
Not matched 7,050
from master 2,010 (_merge==1)
from using 5,040 (_merge==2)
Matched 10,800 (_merge==3)
-----------------------------------------
(5,040 observations deleted)
keep countrycode year rgdpo emp avh cn cgdpo hc ctfp natural_res
describe
Contains data from pwt_10_dload_02032021.dta
Observations: 12,810
Variables: 10 3 Feb 2021 17:45
--------------------------------------------------------------------------------
Variable Storage Display Value
name type format label Variable label
--------------------------------------------------------------------------------
countrycode str3 %9s 3-letter ISO country code
year int %10.0g Year
rgdpo float %14.3g Output-side real GDP at chained
PPPs (in mil. 2017US$)
emp float %9.0g Number of persons engaged (in
millions)
avh double %10.0gc Average annual hours worked by
persons engaged (source: The
Conference Board)
hc float %9.0g * Human capital index, see note hc
cgdpo float %14.3g Output-side real GDP at current
PPPs (in mil. 2017US$)
cn float %9.0g Capital stock at current PPPs (in
mil. 2017US$)
ctfp float %9.0g TFP level at current PPPs (USA=1)
natural_res float %8.0g total natural resources rents,
%GDP. WDI
* indicated variables have notes
--------------------------------------------------------------------------------
Sorted by:
Note: Dataset has changed since last saved.
keep if countrycode=="USA" | countrycode=="HKG" | countrycode=="SGP" | countrycode=="FRA" |countrycode=="DEU" | countrycode=="GBR" |countrycode=="JPN" |countrycode=="KOR" |countrycode=="ARG" |countrycode=="MEX" | countrycode=="ZAF" |countrycode=="BRA" |countrycode=="THA" | countrycode=="CHN" | countrycode=="IDN" | countrycode=="IND"
(11,690 observations deleted)
keep if year==2007 | year==2017
(1,088 observations deleted)
sum
Variable | Obs Mean Std. dev. Min Max
-------------+---------------------------------------------------------
countrycode | 0
year | 32 2012 5.080005 2007 2017
rgdpo | 32 4116422 5272712 326278.7 1.97e+07
emp | 32 120.3982 207.0305 2.79702 799.1861
avh | 32 1957.752 296.6315 1391.34 2484.901
-------------+---------------------------------------------------------
hc | 32 3.002113 .5662002 1.8998 3.974208
cgdpo | 32 4130251 5298790 326245.8 1.97e+07
cn | 32 1.63e+07 1.87e+07 1221346 7.28e+07
ctfp | 32 .7015366 .2102634 .3987721 1.029516
natural_res | 32 2.285404 2.740917 .0003952 10.55797
list countrycode year rgdpo emp avh hc, sep(16)
+--------------------------------------------------------------+
| countr~e year rgdpo emp avh hc |
|--------------------------------------------------------------|
1. | ARG 2007 665359 16.96141 1,787.545 2.805215 |
2. | BRA 2007 2191561 81.35378 1,774.266 2.341256 |
3. | CHN 2007 10521342 771.0384 2,152.65 2.413548 |
4. | DEU 2007 3664031 39.81446 1,454.087 3.627008 |
5. | FRA 2007 2545481 26.99143 1,536.816 3.00605 |
6. | GBR 2007 2411403 29.31574 1,665.821 3.643975 |
7. | HKG 2007 402072 3.487902 2,327.78 3.103403 |
8. | IDN 2007 1253070 100.3124 2,078.155 2.344984 |
9. | IND 2007 4286492 459.0289 2,098.121 1.8998 |
10. | JPN 2007 5269969 66.8419 1,826.9 3.451233 |
11. | KOR 2007 1716280 23.73085 2,303.538 3.382792 |
12. | MEX 2007 1773710 44.23125 2,045 2.556516 |
13. | SGP 2007 326279 2.79702 2,484.901 2.808781 |
14. | THA 2007 783612 36.1892 2,329.801 2.45509 |
15. | USA 2007 16702442 146.3958 1,785.884 3.657095 |
16. | ZAF 2007 638066 15.84869 2,322.123 2.370744 |
|--------------------------------------------------------------|
17. | ARG 2017 1022416 20.03366 1,648.813 3.035439 |
18. | BRA 2017 2968772 90.50139 1,709.594 2.94925 |
19. | CHN 2017 19685860 799.1861 2,168.919 2.648536 |
20. | DEU 2017 4234832 43.59322 1,391.34 3.670468 |
21. | FRA 2017 2868908 27.88164 1,505.368 3.190702 |
22. | GBR 2017 2941387 32.24306 1,668.949 3.757822 |
23. | HKG 2017 364329 3.827966 2,147.574 3.239043 |
24. | IDN 2017 2816327 122.7806 2,019.923 2.316139 |
25. | IND 2017 8285657 487.191 2,122.941 2.12382 |
26. | JPN 2017 5047894 68.25327 1,743.7 3.572278 |
27. | KOR 2017 2088215 26.55355 2,012.471 3.694501 |
28. | MEX 2017 2362915 52.34075 2,148 2.7365 |
29. | SGP 2017 464688 3.669285 2,333.345 3.974208 |
30. | THA 2017 1153223 37.37199 2,092.99 2.743465 |
31. | USA 2017 19542980 154.6723 1,763.727 3.738714 |
32. | ZAF 2017 725931 18.30284 2,197.033 2.809246 |
+--------------------------------------------------------------+
* Physical Capital share
local alpha=0.33
* Relevant year of study
local year_base=2017
* Adjust real GDP by natural resources (assuming one uses the same deflator for GDP and nat_res)
replace rgdpo = (1-natural_res/100)*rgdpo
(32 real changes made)
* Adjust GDP at current prices by natural resources
replace cgdpo = (1-natural_res/100)*cgdpo
(32 real changes made)
gen output_per_worker = ln(rgdpo/(emp*avh))
gen capital_output_ratio = ln(cn/cgdpo)*`alpha'/(1-`alpha')
gen human_capital = ln(hc)
gen tfp_resid = output_per_worker - capital_output_ratio - human_capital
list countrycode year output_per_worker capital_output_ratio human_capital tfp_resid, sep(16)
+-------------------------------------------------------------+
| countr~e year output~r capita~o human_~l tfp_re~d |
|-------------------------------------------------------------|
1. | ARG 2007 3.037522 .6098746 1.03148 1.396167 |
2. | BRA 2007 2.660811 .6595789 .8506877 1.150545 |
3. | CHN 2007 1.77974 .5978479 .8810977 .3007945 |
4. | DEU 2007 4.145921 .6806464 1.288408 2.176867 |
5. | FRA 2007 4.116326 .757651 1.100627 2.258048 |
6. | GBR 2007 3.890548 .6135893 1.293075 1.983883 |
7. | HKG 2007 3.902407 .6815512 1.132499 2.088357 |
8. | IDN 2007 1.682002 .6724415 .8522787 .1572817 |
9. | IND 2007 1.444775 .6018438 .6417484 .2011832 |
10. | JPN 2007 3.764597 .8319523 1.238732 1.693913 |
11. | KOR 2007 3.446438 .6959226 1.218701 1.531814 |
12. | MEX 2007 2.916034 .6078814 .9386455 1.369507 |
13. | SGP 2007 3.848961 .6501795 1.032751 2.16603 |
14. | THA 2007 2.200679 .7523809 .8981633 .5501347 |
15. | USA 2007 4.146625 .6221023 1.296669 2.227853 |
16. | ZAF 2007 2.778497 .4459212 .8632039 1.469372 |
|-------------------------------------------------------------|
17. | ARG 2017 3.41938 .5860501 1.110356 1.722974 |
18. | BRA 2017 2.919388 .7619811 1.081551 1.075856 |
19. | CHN 2017 2.414968 .6512252 .974007 .7897353 |
20. | DEU 2017 4.245199 .7760065 1.300319 2.168874 |
21. | FRA 2017 4.224308 .8925919 1.160241 2.171475 |
22. | GBR 2017 3.996837 .8017488 1.32384 1.871249 |
23. | HKG 2017 3.791373 .955901 1.175278 1.660193 |
24. | IDN 2017 2.394288 .867594 .8399017 .6867918 |
25. | IND 2017 2.060172 .6685063 .7532164 .6384492 |
26. | JPN 2017 3.747187 .8068436 1.273203 1.66714 |
27. | KOR 2017 3.665354 .7976747 1.306846 1.560833 |
28. | MEX 2017 3.015939 .7538939 1.00668 1.255366 |
29. | SGP 2017 3.994062 .7256042 1.379825 1.888632 |
30. | THA 2017 2.674167 .7769223 1.009222 .8880229 |
31. | USA 2017 4.26685 .6087787 1.318742 2.339329 |
32. | ZAF 2017 2.839159 .6900046 1.032916 1.116239 |
+-------------------------------------------------------------+
foreach i in output_per_worker capital_output_ratio human_capital tfp_resid {
* Remove logs
replace `i' = exp(`i')
* Identify the value of each variable for the US
gen output_ = `i' if countrycode == "USA"
* Compute the cross-country average in each year
bys year: egen output_norm = mean(output_)
* Compute the relative values for each variable
gen norm_`i' = `i'/output_norm
drop output_ output_norm
}
(32 real changes made)
(30 missing values generated)
(32 real changes made)
(30 missing values generated)
(32 real changes made)
(30 missing values generated)
(32 real changes made)
(30 missing values generated)
list countrycode year norm_output_per_worker norm_capital_output_ratio norm_human_capital norm_tfp_resid, sep(16)
+-------------------------------------------------------------+
| countr~e year norm_o~r norm_c~o norm_h~l norm_t~d |
|-------------------------------------------------------------|
1. | ARG 2007 .3298548 .9878468 .7670611 .4353147 |
2. | BRA 2007 .2263182 1.038188 .6401958 .3405108 |
3. | CHN 2007 .0937724 .9760373 .6599632 .1455757 |
4. | DEU 2007 .9992968 1.060292 .9917732 .9502915 |
5. | FRA 2007 .9701561 1.145165 .8219778 1.030656 |
6. | GBR 2007 .7740823 .9915231 .9964125 .783511 |
7. | HKG 2007 .7833174 1.061252 .8485979 .8697962 |
8. | IDN 2007 .0850409 1.051628 .6412151 .1261137 |
9. | IND 2007 .0670814 .9799453 .5194833 .1317736 |
10. | JPN 2007 .6824762 1.233493 .943709 .5862903 |
11. | KOR 2007 .4964926 1.076613 .9249943 .498556 |
12. | MEX 2007 .29212 .9858797 .6990566 .4238625 |
13. | SGP 2007 .7425508 1.028475 .7680362 .9400494 |
14. | THA 2007 .1428521 1.139146 .6713224 .1867996 |
15. | USA 2007 1 1 1 1 |
16. | ZAF 2007 .2545833 .8384661 .6482589 .4683772 |
|-------------------------------------------------------------|
17. | ARG 2017 .4284979 .9775277 .8118938 .5399086 |
18. | BRA 2017 .2598993 1.165561 .7888407 .2826707 |
19. | CHN 2017 .1569415 1.04336 .7084083 .2123342 |
20. | DEU 2017 .9785823 1.182024 .981746 .8432807 |
21. | FRA 2017 .9583508 1.328185 .8534225 .845477 |
22. | GBR 2017 .7633703 1.212847 1.005111 .6262033 |
23. | HKG 2017 .6215885 1.41499 .8663523 .507055 |
24. | IDN 2017 .1537293 1.295395 .6195017 .1915632 |
25. | IND 2017 .1100657 1.061547 .5680616 .1825228 |
26. | JPN 2017 .5947214 1.219042 .955483 .5105897 |
27. | KOR 2017 .5479912 1.207915 .9881743 .459096 |
28. | MEX 2017 .2862441 1.156173 .7319362 .3382522 |
29. | SGP 2017 .7612543 1.123923 1.062988 .6371838 |
30. | THA 2017 .2033793 1.183107 .7337991 .2342641 |
31. | USA 2017 1 1 1 1 |
32. | ZAF 2017 .2398624 1.084616 .7513936 .2943192 |
+-------------------------------------------------------------+
gen overall_diff = 1/norm_output_per_worker
gen contrib_capital = 1/norm_capital_output_ratio
gen contrib_labor = 1/norm_human_capital
gen contrib_TFP = 1/norm_tfp_resid
list countrycode year overall_diff contrib_capital contrib_labor contrib_TFP, sep(16)
+-------------------------------------------------------------+
| countr~e year overal~f contri~l contri~r contri~P |
|-------------------------------------------------------------|
1. | ARG 2007 3.031637 1.012303 1.303677 2.297189 |
2. | BRA 2007 4.418557 .963217 1.562022 2.936764 |
3. | CHN 2007 10.66412 1.024551 1.515236 6.869277 |
4. | DEU 2007 1.000704 .9431368 1.008295 1.052309 |
5. | FRA 2007 1.030762 .8732367 1.216578 .9702563 |
6. | GBR 2007 1.291852 1.008549 1.0036 1.276306 |
7. | HKG 2007 1.276622 .9422837 1.178414 1.149695 |
8. | IDN 2007 11.75904 .9509068 1.559539 7.929354 |
9. | IND 2007 14.90727 1.020465 1.92499 7.588774 |
10. | JPN 2007 1.465253 .8107058 1.059649 1.70564 |
11. | KOR 2007 2.014129 .9288386 1.081088 2.005793 |
12. | MEX 2007 3.423251 1.014323 1.430499 2.359256 |
13. | SGP 2007 1.346709 .9723133 1.302022 1.063774 |
14. | THA 2007 7.000249 .8778508 1.489597 5.353329 |
15. | USA 2007 1 1 1 1 |
16. | ZAF 2007 3.927988 1.192654 1.542593 2.135031 |
|-------------------------------------------------------------|
17. | ARG 2017 2.333734 1.022989 1.231688 1.852165 |
18. | BRA 2017 3.847645 .8579561 1.267683 3.537686 |
19. | CHN 2017 6.371799 .9584417 1.411615 4.709557 |
20. | DEU 2017 1.021886 .8460069 1.018593 1.185845 |
21. | FRA 2017 1.043459 .7529073 1.171753 1.182764 |
22. | GBR 2017 1.30998 .8245066 .994915 1.596925 |
23. | HKG 2017 1.608781 .7067189 1.154265 1.972172 |
24. | IDN 2017 6.50494 .7719656 1.614201 5.220209 |
25. | IND 2017 9.085481 .9420211 1.760372 5.478766 |
26. | JPN 2017 1.681459 .8203166 1.046591 1.95852 |
27. | KOR 2017 1.824847 .8278727 1.011967 2.178194 |
28. | MEX 2017 3.493521 .8649226 1.366239 2.956374 |
29. | SGP 2017 1.313622 .8897404 .9407445 1.569406 |
30. | THA 2017 4.916921 .8452324 1.362771 4.268687 |
31. | USA 2017 1 1 1 1 |
32. | ZAF 2017 4.169058 .9219853 1.33086 3.397671 |
+-------------------------------------------------------------+
gen share_due_to_TFP = contrib_TFP/(contrib_TFP + (contrib_labor*contrib_capital))
list countrycode year share_due_to_TFP, sep(16)
+----------------------------+
| countr~e year share_~P |
|----------------------------|
1. | ARG 2007 .6351256 |
2. | BRA 2007 .6612352 |
3. | CHN 2007 .8156626 |
4. | DEU 2007 .5252959 |
5. | FRA 2007 .4773435 |
6. | GBR 2007 .5577075 |
7. | HKG 2007 .508693 |
8. | IDN 2007 .8424432 |
9. | IND 2007 .7943733 |
10. | JPN 2007 .6650437 |
11. | KOR 2007 .6663877 |
12. | MEX 2007 .6191877 |
13. | SGP 2007 .4566049 |
14. | THA 2007 .8036857 |
15. | USA 2007 .5 |
16. | ZAF 2007 .5371403 |
|----------------------------|
17. | ARG 2017 .5951366 |
18. | BRA 2017 .7648551 |
19. | CHN 2017 .7768331 |
20. | DEU 2017 .579144 |
21. | FRA 2017 .5727713 |
22. | GBR 2017 .6606402 |
23. | HKG 2017 .707401 |
24. | IDN 2017 .8072925 |
25. | IND 2017 .7676488 |
26. | JPN 2017 .6952364 |
27. | KOR 2017 .722219 |
28. | MEX 2017 .7144338 |
29. | SGP 2017 .6521734 |
30. | THA 2017 .7875014 |
31. | USA 2017 .5 |
32. | ZAF 2017 .7346785 |
+----------------------------+
replace share_due_to_TFP =. if countrycode=="USA"
(0 real changes made)
corr share_due_to_TFP norm_output_per_worker if year==`year_base'
local corr0: display %5.2f r(rho)
reg share_due_to_TFP norm_output_per_worker if year==`year_base'
local std0: display %5.2f _b[norm_output_per_worker]/_se[norm_output_per_worker]
(obs=15)
| share_~P norm_o~r
-------------+------------------
share_due_~P | 1.0000
norm_outpu~r | -0.8563 1.0000
Source | SS df MS Number of obs = 15
-------------+---------------------------------- F(1, 13) = 35.74
Model | .059662186 1 .059662186 Prob > F = 0.0000
Residual | .021704098 13 .001669546 R-squared = 0.7333
-------------+---------------------------------- Adj R-squared = 0.7127
Total | .081366284 14 .005811877 Root MSE = .04086
-------------------------------------------------------------------------------
share_due_t~P | Coefficient Std. err. t P>|t| [95% conf. interval]
--------------+----------------------------------------------------------------
norm_output~r | -.2190058 .0366358 -5.98 0.000 -.2981525 -.139859
_cons | .8056751 .020224 39.84 0.000 .7619839 .8493664
-------------------------------------------------------------------------------
#tw sc share_due_to_TFP norm_output_per_worker
tw (scatter share_due_to_TFP norm_output_per_worker if year==`year_base', msize(small) mlabel(countrycode) mlabsize(vsmall) mcolor(pink) mlabcolor(black) mlabposition(6)), xscale(log) xticks(0.015 0.031 0.0625 0.125 0.25 0.5 1 2) xlabel(0.015 "1/64" 0.031 "1/32" 0.0625 "1/16" 0.125 "1/8" 0.25 "1/4" 0.5 "1/2" 1 "1" 2 "2") xtitle("Output per worker `year_base', PPP current 2017 US$ (USA=1)") ytitle("Share due to TFP") legend(off) graphregion(margin(right) fcolor(white) lcolor(white)) plotregion(margin(medlarge)) note(Correlation = `corr0' ; t-stat = `std0', position(7) ring(0)) legend(off)
Unknown #command
#tw sc share_due_to_TFP norm_output_per_worker
tw (scatter share_due_to_TFP norm_output_per_worker if year==`year_base', msize(small) mlabel(countrycode) mlabsize(vsmall) mcolor(pink) mlabcolor(black) mlabposition(6)), xscale(log) xticks(0.015 0.031 0.0625 0.125 0.25 0.5 1 2) xlabel(0.015 "1/64" 0.031 "1/32" 0.0625 "1/16" 0.125 "1/8" 0.25 "1/4" 0.5 "1/2" 1 "1" 2 "2") xtitle("Output per worker `year_base', PPP current 2017 US$ (USA=1)") ytitle("Share due to TFP") legend(off) graphregion(margin(right) fcolor(white) lcolor(white)) plotregion(margin(medlarge)) note(Correlation = `corr0' ; t-stat = `std0', position(7) ring(0)) legend(off)
Unknown #command
tw (scatter norm_tfp_resid norm_output_per_worker if year==`year_base') (lfit norm_tfp_resid norm_output_per_worker if year==`year_base'), xtitle("Output per worker `year_base', PPP current 2017 US$") ytitle("Total Factor Productivity") legend(off)
tw (scatter norm_capital_output_ratio norm_output_per_worker if year==`year_base') (lfit norm_capital_output_ratio norm_output_per_worker if year==`year_base'), xtitle("Output per worker `year_base', PPP current 2017 US$") ytitle("Capital-output ratio") legend(off)
tw (scatter norm_human_capital norm_output_per_worker if year==`year_base') (lfit norm_human_capital norm_output_per_worker if year==`year_base'), xtitle("Output per worker `year_base', PPP current 2017 US$") ytitle("Human capital") legend(off)
bys year: egen median_contrib_tfp = median(share_due_to_TFP)
list countrycode year share_due_to_TFP median_contrib_tfp
+---------------------------------------+
| countr~e year share_~P median~p |
|---------------------------------------|
1. | ARG 2007 .6351256 .6351256 |
2. | BRA 2007 .6612352 .6351256 |
3. | CHN 2007 .8156626 .6351256 |
4. | DEU 2007 .5252959 .6351256 |
5. | FRA 2007 .4773435 .6351256 |
|---------------------------------------|
6. | GBR 2007 .5577075 .6351256 |
7. | HKG 2007 .508693 .6351256 |
8. | IDN 2007 .8424432 .6351256 |
9. | IND 2007 .7943733 .6351256 |
10. | JPN 2007 .6650437 .6351256 |
|---------------------------------------|
11. | KOR 2007 .6663877 .6351256 |
12. | MEX 2007 .6191877 .6351256 |
13. | SGP 2007 .4566049 .6351256 |
14. | THA 2007 .8036857 .6351256 |
15. | USA 2007 . .6351256 |
|---------------------------------------|
16. | ZAF 2007 .5371403 .6351256 |
17. | ARG 2017 .5951366 .7144338 |
18. | BRA 2017 .7648551 .7144338 |
19. | CHN 2017 .7768331 .7144338 |
20. | DEU 2017 .579144 .7144338 |
|---------------------------------------|
21. | FRA 2017 .5727713 .7144338 |
22. | GBR 2017 .6606402 .7144338 |
23. | HKG 2017 .707401 .7144338 |
24. | IDN 2017 .8072925 .7144338 |
25. | IND 2017 .7676488 .7144338 |
|---------------------------------------|
26. | JPN 2017 .6952364 .7144338 |
27. | KOR 2017 .722219 .7144338 |
28. | MEX 2017 .7144338 .7144338 |
29. | SGP 2017 .6521734 .7144338 |
30. | THA 2017 .7875014 .7144338 |
|---------------------------------------|
31. | USA 2017 . .7144338 |
32. | ZAF 2017 .7346785 .7144338 |
+---------------------------------------+
tw (scatter median_contrib_tfp year if year>=1970), xtitle("year") ytitle("Median contribution of TFP") legend(off) graphregion(margin(right) fcolor(white) lcolor(white)) plotregion(margin(medlarge))
gen contrib_cTFP = 1/ctfp
gen share_due_to_cTFP = contrib_cTFP/overall_diff
bys year: egen median_contrib_ctfp = median(share_due_to_cTFP)
list countrycode year share_due_to_TFP median_contrib_tfp median_contrib_ctfp
>
+--------------------------------------------------+
| countr~e year sha~_TFP med~_tfp med~ctfp |
|--------------------------------------------------|
1. | ARG 2007 .6351256 .6351256 .5787544 |
2. | BRA 2007 .6612352 .6351256 .5787544 |
3. | CHN 2007 .8156626 .6351256 .5787544 |
4. | DEU 2007 .5252959 .6351256 .5787544 |
5. | FRA 2007 .4773435 .6351256 .5787544 |
|--------------------------------------------------|
6. | GBR 2007 .5577075 .6351256 .5787544 |
7. | HKG 2007 .508693 .6351256 .5787544 |
8. | IDN 2007 .8424432 .6351256 .5787544 |
9. | IND 2007 .7943733 .6351256 .5787544 |
10. | JPN 2007 .6650437 .6351256 .5787544 |
|--------------------------------------------------|
11. | KOR 2007 .6663877 .6351256 .5787544 |
12. | MEX 2007 .6191877 .6351256 .5787544 |
13. | SGP 2007 .4566049 .6351256 .5787544 |
14. | THA 2007 .8036857 .6351256 .5787544 |
15. | USA 2007 . .6351256 .5787544 |
|--------------------------------------------------|
16. | ZAF 2007 .5371403 .6351256 .5787544 |
17. | ARG 2017 .5951366 .7144338 .6894252 |
18. | BRA 2017 .7648551 .7144338 .6894252 |
19. | CHN 2017 .7768331 .7144338 .6894252 |
20. | DEU 2017 .579144 .7144338 .6894252 |
|--------------------------------------------------|
21. | FRA 2017 .5727713 .7144338 .6894252 |
22. | GBR 2017 .6606402 .7144338 .6894252 |
23. | HKG 2017 .707401 .7144338 .6894252 |
24. | IDN 2017 .8072925 .7144338 .6894252 |
25. | IND 2017 .7676488 .7144338 .6894252 |
|--------------------------------------------------|
26. | JPN 2017 .6952364 .7144338 .6894252 |
27. | KOR 2017 .722219 .7144338 .6894252 |
28. | MEX 2017 .7144338 .7144338 .6894252 |
29. | SGP 2017 .6521734 .7144338 .6894252 |
30. | THA 2017 .7875014 .7144338 .6894252 |
|--------------------------------------------------|
31. | USA 2017 . .7144338 .6894252 |
32. | ZAF 2017 .7346785 .7144338 .6894252 |
+--------------------------------------------------+
tw (scatter median_contrib_tfp year if year>=1970) (scatter median_contrib_ctfp year if year>=1970)
gen share_due_to_hcap = contrib_labor/(contrib_labor+contrib_capital)*(1-share_due_to_TFP)
bys year: egen median_contrib_hcap = median(share_due_to_hcap)
(2 missing values generated)
list countrycode year share_due_to_hcap median_contrib_hcap contrib_labor
+--------------------------------------------------+
| countr~e year share_~p media~ap contri~r |
|--------------------------------------------------|
1. | ARG 2007 .2053897 .2095477 1.303677 |
2. | BRA 2007 .2095477 .2095477 1.562022 |
3. | CHN 2007 .1099756 .2095477 1.515236 |
4. | DEU 2007 .2452772 .2095477 1.008295 |
5. | FRA 2007 .3042626 .2095477 1.216578 |
|--------------------------------------------------|
6. | GBR 2007 .2206023 .2095477 1.0036 |
7. | HKG 2007 .2730059 .2095477 1.178414 |
8. | IDN 2007 .0978774 .2095477 1.559539 |
9. | IND 2007 .1343865 .2095477 1.92499 |
10. | JPN 2007 .1897694 .2095477 1.059649 |
|--------------------------------------------------|
11. | KOR 2007 .1794415 .2095477 1.081088 |
12. | MEX 2007 .2228186 .2095477 1.430499 |
13. | SGP 2007 .3110853 .2095477 1.302022 |
14. | THA 2007 .1235209 .2095477 1.489597 |
15. | USA 2007 . .2095477 1 |
|--------------------------------------------------|
16. | ZAF 2007 .2610383 .2095477 1.542593 |
17. | ARG 2017 .2211694 .1708509 1.231688 |
18. | BRA 2017 .1402351 .1708509 1.267683 |
19. | CHN 2017 .1329191 .1708509 1.411615 |
20. | DEU 2017 .2299051 .1708509 1.018593 |
|--------------------------------------------------|
21. | FRA 2017 .2601012 .1708509 1.171753 |
22. | GBR 2017 .1855723 .1708509 .994915 |
23. | HKG 2017 .1814829 .1708509 1.154265 |
24. | IDN 2017 .1303633 .1708509 1.614201 |
25. | IND 2017 .1513565 .1708509 1.760372 |
|--------------------------------------------------|
26. | JPN 2017 .1708509 .1708509 1.046591 |
27. | KOR 2017 .1527879 .1708509 1.011967 |
28. | MEX 2017 .1748648 .1708509 1.366239 |
29. | SGP 2017 .1787592 .1708509 .9407445 |
30. | THA 2017 .1311533 .1708509 1.362771 |
|--------------------------------------------------|
31. | USA 2017 . .1708509 1 |
32. | ZAF 2017 .1567377 .1708509 1.33086 |
+--------------------------------------------------+
*corr share_due_to_hcap norm_output_per_worker if year==`year_base'
*local corr0: display %5.4f r(rho)
*reg share_due_to_hcap norm_output_per_worker if year==`year_base'
*mat V=e(V)
*local std0: display %5.4f _b[norm_output_per_worker]/_se[norm_output_per_worker]
*local year_base=2017
*tw (scatter share_due_to_hcap norm_output_per_worker if year==`year_base', msize(small) mlabel(countrycode) mlabsize(vsmall) mcolor(pink) mlabcolor(black) mlabposition(6)), xscale(log) xticks(0.015 0.031 0.0625 0.125 0.25 0.5 1 2) xlabel(0.015 "1/64" 0.031 "1/32" 0.0625 "1/16" 0.125 "1/8" 0.25 "1/4" 0.5 "1/2" 1 "1" 2 "2") xtitle("Output per worker `year_base', PPP current 2017 US$ (USA=1)") ytitle("Share due to Human Capital") legend(off) graphregion(margin(right) fcolor(white) lcolor(white)) plotregion(margin(medlarge)) note(Correlation=`corr0' t-stat=`std0', position(7) ring(0)) legend(off)
gen share_due_to_cap = contrib_capital/(contrib_capital+contrib_labor)*(1-share_due_to_TFP)
bys year: egen median_contrib_cap=median(share_due_to_cap)
(2 missing values generated)
list countrycode year share_due_to_cap median_contrib_cap contrib_capital
+--------------------------------------------------+
| countr~e year sha~_cap med~_cap contri~l |
|--------------------------------------------------|
1. | ARG 2007 .1594847 .1579937 1.012303 |
2. | BRA 2007 .1292171 .1579937 .963217 |
3. | CHN 2007 .0743618 .1579937 1.024551 |
4. | DEU 2007 .2294269 .1579937 .9431368 |
5. | FRA 2007 .2183939 .1579937 .8732367 |
|--------------------------------------------------|
6. | GBR 2007 .2216901 .1579937 1.008549 |
7. | HKG 2007 .218301 .1579937 .9422837 |
8. | IDN 2007 .0596794 .1579937 .9509068 |
9. | IND 2007 .0712402 .1579937 1.020465 |
10. | JPN 2007 .1451869 .1579937 .8107058 |
|--------------------------------------------------|
11. | KOR 2007 .1541708 .1579937 .9288386 |
12. | MEX 2007 .1579937 .1579937 1.014323 |
13. | SGP 2007 .2323098 .1579937 .9723133 |
14. | THA 2007 .0727934 .1579937 .8778508 |
15. | USA 2007 . .1579937 1 |
|--------------------------------------------------|
16. | ZAF 2007 .2018214 .1579937 1.192654 |
17. | ARG 2017 .1836941 .1111161 1.022989 |
18. | BRA 2017 .0949098 .1111161 .8579561 |
19. | CHN 2017 .0902478 .1111161 .9584417 |
20. | DEU 2017 .1909509 .1111161 .8460069 |
|--------------------------------------------------|
21. | FRA 2017 .1671275 .1111161 .7529073 |
22. | GBR 2017 .1537876 .1111161 .8245066 |
23. | HKG 2017 .1111161 .1111161 .7067189 |
24. | IDN 2017 .0623442 .1111161 .7719656 |
25. | IND 2017 .0809948 .1111161 .9420211 |
|--------------------------------------------------|
26. | JPN 2017 .1339127 .1111161 .8203166 |
27. | KOR 2017 .1249931 .1111161 .8278727 |
28. | MEX 2017 .1107013 .1111161 .8649226 |
29. | SGP 2017 .1690674 .1111161 .8897404 |
30. | THA 2017 .0813453 .1111161 .8452324 |
|--------------------------------------------------|
31. | USA 2017 . .1111161 1 |
32. | ZAF 2017 .1085838 .1111161 .9219853 |
+--------------------------------------------------+
*corr share_due_to_cap norm_output_per_worker if year==`year_base'
*local corr0: display %5.4f r(rho)
*reg share_due_to_cap norm_output_per_worker if year==`year_base'
*mat V=e(V)
*local std0: display %5.4f _b[norm_output_per_worker]/_se[norm_output_per_worker]
*tw (scatter share_due_to_cap norm_output_per_worker if year==`year_base', msize(small) mlabel(countrycode) mlabsize(vsmall) mcolor(pink) mlabcolor(black) mlabposition(6)), xscale(log) xticks(0.015 0.031 0.0625 0.125 0.25 0.5 1 2) xlabel(0.015 "1/64" 0.031 "1/32" 0.0625 "1/16" 0.125 "1/8" 0.25 "1/4" 0.5 "1/2" 1 "1" 2 "2") xtitle("Output per worker `year_base', PPP current 2017 US$ (USA=1)") ytitle("Share due to Physical Capital") legend(off) graphregion(margin(right) fcolor(white) lcolor(white)) plotregion(margin(medlarge)) note(Correlation=`corr0' t-stat=`std0', position(7) ring(0)) legend(off)
*tw (scatter median_contrib_tfp year if year>1970) (scatter median_contrib_cap year if year>1970) (scatter median_contrib_hcap year if year>1970), xtitle("year") ytitle("Median contribution of TFP") legend(label(1 "TFP") label(2 "Physical Capital") label( 3 "Human Capital")) graphregion(margin(right) fcolor(white) lcolor(white)) plotregion(margin(medlarge))
list countrycode year median_contrib_tfp median_contrib_cap median_contrib_hcap
+--------------------------------------------------+
| countr~e year med~_tfp med~_cap med~hcap |
|--------------------------------------------------|
1. | ARG 2007 .6351256 .1579937 .2095477 |
2. | BRA 2007 .6351256 .1579937 .2095477 |
3. | CHN 2007 .6351256 .1579937 .2095477 |
4. | DEU 2007 .6351256 .1579937 .2095477 |
5. | FRA 2007 .6351256 .1579937 .2095477 |
|--------------------------------------------------|
6. | GBR 2007 .6351256 .1579937 .2095477 |
7. | HKG 2007 .6351256 .1579937 .2095477 |
8. | IDN 2007 .6351256 .1579937 .2095477 |
9. | IND 2007 .6351256 .1579937 .2095477 |
10. | JPN 2007 .6351256 .1579937 .2095477 |
|--------------------------------------------------|
11. | KOR 2007 .6351256 .1579937 .2095477 |
12. | MEX 2007 .6351256 .1579937 .2095477 |
13. | SGP 2007 .6351256 .1579937 .2095477 |
14. | THA 2007 .6351256 .1579937 .2095477 |
15. | USA 2007 .6351256 .1579937 .2095477 |
|--------------------------------------------------|
16. | ZAF 2007 .6351256 .1579937 .2095477 |
17. | ARG 2017 .7144338 .1111161 .1708509 |
18. | BRA 2017 .7144338 .1111161 .1708509 |
19. | CHN 2017 .7144338 .1111161 .1708509 |
20. | DEU 2017 .7144338 .1111161 .1708509 |
|--------------------------------------------------|
21. | FRA 2017 .7144338 .1111161 .1708509 |
22. | GBR 2017 .7144338 .1111161 .1708509 |
23. | HKG 2017 .7144338 .1111161 .1708509 |
24. | IDN 2017 .7144338 .1111161 .1708509 |
25. | IND 2017 .7144338 .1111161 .1708509 |
|--------------------------------------------------|
26. | JPN 2017 .7144338 .1111161 .1708509 |
27. | KOR 2017 .7144338 .1111161 .1708509 |
28. | MEX 2017 .7144338 .1111161 .1708509 |
29. | SGP 2017 .7144338 .1111161 .1708509 |
30. | THA 2017 .7144338 .1111161 .1708509 |
|--------------------------------------------------|
31. | USA 2017 .7144338 .1111161 .1708509 |
32. | ZAF 2017 .7144338 .1111161 .1708509 |
+--------------------------------------------------+
preserve
keep if year==2017
foreach i in output_per_worker capital_output_ratio human_capital{
replace `i'=ln(`i')
su `i',d
local var_`i'=r(Var)
}
(16 observations deleted)
(16 real changes made)
output_per_worker
-------------------------------------------------------------
Percentiles Smallest
1% 2.060172 2.060172
5% 2.060172 2.394288
10% 2.394288 2.414968 Obs 16
25% 2.756663 2.674167 Sum of wgt. 16
50% 3.542367 Mean 3.354289
Largest Std. dev. .7376638
75% 3.99545 3.996837
90% 4.245199 4.224308 Variance .5441479
95% 4.26685 4.245199 Skewness -.2788819
99% 4.26685 4.26685 Kurtosis 1.697944
(16 real changes made)
capital_output_ratio
-------------------------------------------------------------
Percentiles Smallest
1% .5860501 .5860501
5% .5860501 .6087787
10% .6087787 .6512252 Obs 16
25% .6792555 .6685063 Sum of wgt. 16
50% .7689938 Mean .7575829
Largest Std. dev. .1009268
75% .8042962 .8068436
90% .8925919 .867594 Variance .0101862
95% .955901 .8925919 Skewness .0858957
99% .955901 .955901 Kurtosis 2.472026
(16 real changes made)
human_capital
-------------------------------------------------------------
Percentiles Smallest
1% .7532164 .7532164
5% .7532164 .8399017
10% .8399017 .974007 Obs 16
25% 1.007951 1.00668 Sum of wgt. 16
50% 1.135299 Mean 1.127884
Largest Std. dev. .1846986
75% 1.303582 1.306846
90% 1.32384 1.318742 Variance .0341136
95% 1.379825 1.32384 Skewness -.4370833
99% 1.379825 1.379825 Kurtosis 2.250925
g output_factor=human_capital+capital_output_ratio
su output_factor,d
local var_output_factor=r(Var)
output_factor
-------------------------------------------------------------
Percentiles Smallest
1% 1.421723 1.421723
5% 1.421723 1.625232
10% 1.625232 1.696406 Obs 16
25% 1.715208 1.707496 Sum of wgt. 16
50% 1.885526 Mean 1.885467
Largest Std. dev. .2194403
75% 2.092284 2.10452
90% 2.125588 2.10543 Variance .0481541
95% 2.131179 2.125588 Skewness -.4439377
99% 2.131179 2.131179 Kurtosis 2.125851
cap drop success*
gen successcap=`var_capital_output_ratio'/`var_output_per_worker'
su successcap
Variable | Obs Mean Std. dev. Min Max
-------------+---------------------------------------------------------
successcap | 16 .0187196 0 .0187196 .0187196
gen successhcap=`var_human_capital'/`var_output_per_worker'
su successhcap
Variable | Obs Mean Std. dev. Min Max
-------------+---------------------------------------------------------
successhcap | 16 .0626917 0 .0626917 .0626917
gen successoutput=`var_output_factor'/`var_output_per_worker'
su successoutput
Variable | Obs Mean Std. dev. Min Max
-------------+---------------------------------------------------------
successout~t | 16 .0884944 0 .0884944 .0884944
restore