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cohorts.log
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___ ____ ____ ____ ____ ®
/__ / ____/ / ____/ 18.0
___/ / /___/ / /___/ MP—Parallel Edition
Statistics and Data Science Copyright 1985-2023 StataCorp LLC
StataCorp
4905 Lakeway Drive
College Station, Texas 77845 USA
800-STATA-PC https://www.stata.com
979-696-4600 [email protected]
Stata license: Single-user 2-core perpetual
Serial number: 501806323834
Licensed to: Miklos Koren
CEU MicroData
Notes:
1. Stata is running in batch mode.
2. Unicode is supported; see help unicode_advice.
3. More than 2 billion observations are allowed; see help obs_advice.
4. Maximum number of variables is set to 5,000 but can be increased;
see help set_maxvar.
. do src/cohorts.do
. use "temp/data.dta", clear
.
. do "src/create/variables.do"
. generate lnL = ln(employment_from_balance)
. generate exporter = export > 0
. generate lnQ = ln(sales)
. generate lnK = ln(tanass_clean)
(2 missing values generated)
. generate lnM = ln(ranyag)
(2 missing values generated)
. generate foreign = fo3
.
. regress lnQ lnK lnL lnM
Source | SS df MS Number of obs = 114
-------------+---------------------------------- F(3, 110) = 1391.14
Model | 244.381324 3 81.4604414 Prob > F = 0.0000
Residual | 6.44124363 110 .05855676 R-squared = 0.9743
-------------+---------------------------------- Adj R-squared = 0.9736
Total | 250.822568 113 2.21966874 Root MSE = .24199
------------------------------------------------------------------------------
lnQ | Coefficient Std. err. t P>|t| [95% conf. interval]
-------------+----------------------------------------------------------------
lnK | .0519114 .0245512 2.11 0.037 .0032568 .1005661
lnL | .1977643 .0369054 5.36 0.000 .1246266 .2709021
lnM | .7586059 .023559 32.20 0.000 .7119176 .8052943
_cons | 2.277995 .2074069 10.98 0.000 1.866963 2.689027
------------------------------------------------------------------------------
. predict TFP, resid
(4 missing values generated)
.
. replace birth_year_firm = foundyear
(91 real changes made)
.
end of do-file
.
. local T1 1945
. local T2 1995
. local k 5
. local offset 25
. rename first_year_in_market birth_year_entry
.
. replace birth_year_firm = birth_year_firm - `offset'
(118 real changes made)
. replace birth_year_entry = birth_year_entry - `offset'
(118 real changes made)
.
. foreach i in ceo respondent firm entry {
2. generate cohort_`i' = birth_year_`i'
3. replace cohort_`i' = `T1' if cohort_`i' <= `T1'
4. replace cohort_`i' = `T2' if cohort_`i' > `T2' & !missing(cohort_`i')
5. replace cohort_`i' = int(cohort_`i'/`k') * `k'
6. * people who were 20 in 1985 could already been exposed to business ed
> ucation
. generate byte modern_`i' = birth_year_`i' >= 1965 if !missing(birth_year_
> `i')
7. generate byte goldrush_`i' = inrange(birth_year_`i', 1965, 1974)
8. }
(3 missing values generated)
(2 real changes made)
(0 real changes made)
(93 real changes made)
(3 missing values generated)
(1 real change made)
(0 real changes made)
(90 real changes made)
(4 real changes made)
(0 real changes made)
(87 real changes made)
(1 real change made)
(0 real changes made)
(88 real changes made)
. replace birth_year_firm = birth_year_firm + `offset'
(118 real changes made)
. replace birth_year_entry = birth_year_entry + `offset'
(118 real changes made)
. replace cohort_firm = cohort_firm + `offset'
(118 real changes made)
. replace cohort_entry = cohort_entry + `offset'
(118 real changes made)
.
. foreach X in comp_tenure post_tenure {
2. replace `X' = int(`X'/`k') * `k'
3. }
(76 real changes made)
(75 real changes made)
.
. tabulate cohort_firm
cohort_firm | Freq. Percent Cum.
------------+-----------------------------------
1970 | 5 4.24 4.24
1980 | 1 0.85 5.08
1985 | 6 5.08 10.17
1990 | 46 38.98 49.15
1995 | 29 24.58 73.73
2000 | 17 14.41 88.14
2005 | 8 6.78 94.92
2010 | 6 5.08 100.00
------------+-----------------------------------
Total | 118 100.00
. tabulate cohort_ceo
cohort_ceo | Freq. Percent Cum.
------------+-----------------------------------
1945 | 7 6.09 6.09
1950 | 14 12.17 18.26
1955 | 19 16.52 34.78
1960 | 18 15.65 50.43
1965 | 16 13.91 64.35
1970 | 22 19.13 83.48
1975 | 10 8.70 92.17
1980 | 6 5.22 97.39
1985 | 3 2.61 100.00
------------+-----------------------------------
Total | 115 100.00
. tabulate cohort_respondent
cohort_resp |
ondent | Freq. Percent Cum.
------------+-----------------------------------
1945 | 3 2.54 2.54
1950 | 4 3.39 5.93
1955 | 11 9.32 15.25
1960 | 9 7.63 22.88
1965 | 13 11.02 33.90
1970 | 20 16.95 50.85
1975 | 26 22.03 72.88
1980 | 21 17.80 90.68
1985 | 9 7.63 98.31
1990 | 2 1.69 100.00
------------+-----------------------------------
Total | 118 100.00
. tabulate modern_ceo modern_respondent
| modern_respondent
modern_ceo | 0 1 | Total
-----------+----------------------+----------
0 | 23 35 | 58
1 | 4 53 | 57
-----------+----------------------+----------
Total | 27 88 | 115
. tabulate modern_respondent goldrush_respondent
modern_res | goldrush_respondent
pondent | 0 1 | Total
-----------+----------------------+----------
0 | 27 0 | 27
1 | 58 33 | 91
-----------+----------------------+----------
Total | 85 33 | 118
.
. summarize management [aw=weight], detail
ADJUSTED MANAGEMENT SCORE
-------------------------------------------------------------
Percentiles Smallest
1% 1.259873 1.167021
5% 1.597149 1.259873
10% 1.763816 1.263816 Obs 118
25% 2.20826 1.263816 Sum of wgt. 199.285845
50% 2.936157 Mean 2.839376
Largest Std. dev. .722534
75% 3.319371 4.055909
90% 3.763816 4.097149 Variance .5220553
95% 3.989866 4.222576 Skewness -.3005594
99% 4.222576 4.269489 Kurtosis 2.387017
. regress management i.cohort_respondent [pw=weight], cluster(tax_id)
(sum of wgt is 199.2858448)
Linear regression Number of obs = 118
F(9, 117) = 20.52
Prob > F = 0.0000
R-squared = 0.2292
Root MSE = .66023
(Std. err. adjusted for 118 clusters in tax_id)
------------------------------------------------------------------------------
| Robust
management | Coefficient std. err. t P>|t| [95% conf. interval]
-------------+----------------------------------------------------------------
cohort_res~t |
1950 | .1623757 .3313033 0.49 0.625 -.4937532 .8185046
1955 | .9955519 .1798927 5.53 0.000 .6392839 1.35182
1960 | 1.061157 .2810014 3.78 0.000 .5046488 1.617666
1965 | 1.523044 .2018675 7.54 0.000 1.123256 1.922832
1970 | 1.47129 .1583107 9.29 0.000 1.157764 1.784816
1975 | .9173748 .2000262 4.59 0.000 .5212334 1.313516
1980 | 1.211346 .1877505 6.45 0.000 .8395156 1.583176
1985 | 1.206054 .2702804 4.46 0.000 .6707778 1.74133
1990 | 1.643269 .1303114 12.61 0.000 1.385194 1.901344
|
_cons | 1.719479 .1101622 15.61 0.000 1.501308 1.937649
------------------------------------------------------------------------------
. outreg2 using "output/tables/management-cohort-resp.tex", replace tex(frag pr
> )
output/tables/management-cohort-resp.tex
dir : seeout
.
. foreach X in cohort_firm cohort_ceo cohort_respondent cohort_entry comp_tenur
> e post_tenure {
2. regress management i.`X' [pw=weight], cluster(tax_id)
3. outreg2 using "output/tables/management-`X'.tex", replace tex(frag pr)
4. }
(sum of wgt is 199.2858448)
Linear regression Number of obs = 118
F(6, 117) = .
Prob > F = .
R-squared = 0.1064
Root MSE = .70442
(Std. err. adjusted for 118 clusters in tax_id)
------------------------------------------------------------------------------
| Robust
management | Coefficient std. err. t P>|t| [95% conf. interval]
-------------+----------------------------------------------------------------
cohort_firm |
1980 | -1.204616 .189401 -6.36 0.000 -1.579715 -.8295176
1985 | .0579992 .2886512 0.20 0.841 -.5136594 .6296578
1990 | -.4958509 .2146589 -2.31 0.023 -.9209716 -.0707303
1995 | -.5490595 .2486811 -2.21 0.029 -1.041559 -.0565596
2000 | -.371701 .2443857 -1.52 0.131 -.855694 .112292
2005 | .1764411 .2788041 0.63 0.528 -.3757158 .728598
2010 | -.6461351 .3890489 -1.66 0.099 -1.416626 .1243559
|
_cons | 3.253306 .189401 17.18 0.000 2.878208 3.628405
------------------------------------------------------------------------------
output/tables/management-cohort_firm.tex
dir : seeout
(sum of wgt is 195.0050857)
Linear regression Number of obs = 115
F(8, 114) = 23.30
Prob > F = 0.0000
R-squared = 0.2318
Root MSE = .65806
(Std. err. adjusted for 115 clusters in tax_id)
------------------------------------------------------------------------------
| Robust
management | Coefficient std. err. t P>|t| [95% conf. interval]
-------------+----------------------------------------------------------------
cohort_ceo |
1950 | .1744273 .3131544 0.56 0.579 -.4459292 .7947837
1955 | .8212159 .2908211 2.82 0.006 .2451015 1.39733
1960 | 1.076304 .2440508 4.41 0.000 .5928415 1.559767
1965 | .9643005 .2734234 3.53 0.001 .4226509 1.50595
1970 | .8986009 .2637848 3.41 0.001 .3760452 1.421157
1975 | .7804872 .3011455 2.59 0.011 .1839203 1.377054
1980 | .8420469 .3059595 2.75 0.007 .2359435 1.44815
1985 | .0197241 .2206452 0.09 0.929 -.4173724 .4568206
|
_cons | 2.086665 .2192866 9.52 0.000 1.65226 2.52107
------------------------------------------------------------------------------
output/tables/management-cohort_ceo.tex
dir : seeout
(sum of wgt is 199.2858448)
Linear regression Number of obs = 118
F(9, 117) = 20.52
Prob > F = 0.0000
R-squared = 0.2292
Root MSE = .66023
(Std. err. adjusted for 118 clusters in tax_id)
------------------------------------------------------------------------------
| Robust
management | Coefficient std. err. t P>|t| [95% conf. interval]
-------------+----------------------------------------------------------------
cohort_res~t |
1950 | .1623757 .3313033 0.49 0.625 -.4937532 .8185046
1955 | .9955519 .1798927 5.53 0.000 .6392839 1.35182
1960 | 1.061157 .2810014 3.78 0.000 .5046488 1.617666
1965 | 1.523044 .2018675 7.54 0.000 1.123256 1.922832
1970 | 1.47129 .1583107 9.29 0.000 1.157764 1.784816
1975 | .9173748 .2000262 4.59 0.000 .5212334 1.313516
1980 | 1.211346 .1877505 6.45 0.000 .8395156 1.583176
1985 | 1.206054 .2702804 4.46 0.000 .6707778 1.74133
1990 | 1.643269 .1303114 12.61 0.000 1.385194 1.901344
|
_cons | 1.719479 .1101622 15.61 0.000 1.501308 1.937649
------------------------------------------------------------------------------
output/tables/management-cohort_respondent.tex
dir : seeout
(sum of wgt is 199.2858448)
Linear regression Number of obs = 118
F(8, 117) = 46.13
Prob > F = 0.0000
R-squared = 0.1950
Root MSE = .67165
(Std. err. adjusted for 118 clusters in tax_id)
------------------------------------------------------------------------------
| Robust
management | Coefficient std. err. t P>|t| [95% conf. interval]
-------------+----------------------------------------------------------------
cohort_entry |
1980 | 1.004777 .1518024 6.62 0.000 .7041402 1.305414
1985 | .3501748 .429663 0.81 0.417 -.5007502 1.2011
1990 | .9499741 .1537211 6.18 0.000 .6455376 1.254411
1995 | .7791883 .138256 5.64 0.000 .5053795 1.052997
2000 | 1.237835 .126911 9.75 0.000 .986494 1.489175
2005 | 1.68701 .1566962 10.77 0.000 1.376681 1.997338
2010 | 1.09337 .1945208 5.62 0.000 .7081322 1.478609
2015 | .745309 .2150484 3.47 0.001 .3194169 1.171201
|
_cons | 1.763816 8.98e-08 2.0e+07 0.000 1.763816 1.763816
------------------------------------------------------------------------------
output/tables/management-cohort_entry.tex
dir : seeout
(sum of wgt is 182.2477209)
Linear regression Number of obs = 108
F(10, 107) = .
Prob > F = .
R-squared = 0.2063
Root MSE = .6845
(Std. err. adjusted for 108 clusters in tax_id)
------------------------------------------------------------------------------
| Robust
management | Coefficient std. err. t P>|t| [95% conf. interval]
-------------+----------------------------------------------------------------
comp_tenure |
5 | .2018033 .2648005 0.76 0.448 -.3231328 .7267394
10 | -.0029964 .2591081 -0.01 0.991 -.5166479 .5106551
15 | -.0618748 .2816866 -0.22 0.827 -.6202857 .496536
20 | -.2359843 .3702524 -0.64 0.525 -.9699664 .4979979
25 | -.6843466 .3217857 -2.13 0.036 -1.322249 -.046444
30 | -.7531794 .3539959 -2.13 0.036 -1.454935 -.0514238
35 | .3240495 .3401265 0.95 0.343 -.3502115 .9983105
40 | -1.671366 .212855 -7.85 0.000 -2.093326 -1.249406
45 | -.4200592 .3816065 -1.10 0.273 -1.17655 .336431
50 | .2765225 .2454688 1.13 0.262 -.2100909 .7631358
60 | -.0419013 .2216299 -0.19 0.850 -.4812568 .3974541
|
_cons | 2.935182 .212855 13.79 0.000 2.513222 3.357142
------------------------------------------------------------------------------
output/tables/management-comp_tenure.tex
dir : seeout
(sum of wgt is 181.6497976)
Linear regression Number of obs = 109
F(9, 108) = .
Prob > F = .
R-squared = 0.1821
Root MSE = .69026
(Std. err. adjusted for 109 clusters in tax_id)
------------------------------------------------------------------------------
| Robust
management | Coefficient std. err. t P>|t| [95% conf. interval]
-------------+----------------------------------------------------------------
post_tenure |
5 | .1614196 .2144631 0.75 0.453 -.2636835 .5865227
10 | -.1958137 .2537803 -0.77 0.442 -.6988503 .3072228
15 | .0736144 .249291 0.30 0.768 -.4205235 .5677523
20 | -.2102286 .3597409 -0.58 0.560 -.9232975 .5028402
25 | -.8827591 .2512964 -3.51 0.001 -1.380872 -.3846461
30 | -.3638394 .4010844 -0.91 0.366 -1.158858 .4311794
35 | -.8374197 .6204835 -1.35 0.180 -2.067326 .3924862
40 | .6051003 .1648179 3.67 0.000 .2784027 .931798
45 | -.121266 .3303818 -0.37 0.714 -.77614 .5336079
60 | -.2232719 .2291817 -0.97 0.332 -.6775498 .2310061
|
_cons | 2.959161 .1648179 17.95 0.000 2.632463 3.285858
------------------------------------------------------------------------------
output/tables/management-post_tenure.tex
dir : seeout
.
. local controls foreign entrepreneur
. local ceo_X ceo
. local ceo_controls `controls'
. local respondent_X respondent
. local respondent_controls `controls'
. local respcontrol_X respondent
. local respcontrol_controls i.cohort_ceo `controls'
. local ceodomestic_X ceo
. local ceodomestic_controls `controls' if !expat
. local ceoexpat_X ceo
. local ceoexpat_controls `controls' if expat
.
. foreach spec in ceo respondent respcontrol ceodomestic ceoexpat {
2. local X ``spec'_X'
3. local controls ``spec'_controls'
4. regress management ib`T1'.cohort_`X' `controls' [pw=weight], cluster(t
> ax_id)
5. outreg2 using "output/tables/management-cohort-`X'.tex", tex(frag pr)
6.
. * X axis: cohort of respondency, Y axis, estimated management score, rela
> tive to baseline, 1945
. margins, dydx(ib1945.cohort_`X')
7. marginsplot, recast(scatter) yline(0) nolabels xlabel(1 "1950" 2 "1955
> " 3 "1960" 4 "1965" 5 "1970" 6 "1975" 7 "1980" 8 "1985" 9 "1990") xtitle("Bir
> th year of `X'") ytitle("Management score, relative to 1945")
8. graph export "output/fig/cohort-`spec'-marginsplot.png", replace
9.
. histogram cohort_`X', discrete freq width(5) xtitle("Birth year of `X'")
> ytitle("Number of firms")
10. graph export "output/fig/cohort-`spec'-histogram.png", replace
11. }
(sum of wgt is 195.0050857)
Linear regression Number of obs = 115
F(10, 114) = 15.60
Prob > F = 0.0000
R-squared = 0.3757
Root MSE = .5989
(Std. err. adjusted for 115 clusters in tax_id)
------------------------------------------------------------------------------
| Robust
management | Coefficient std. err. t P>|t| [95% conf. interval]
-------------+----------------------------------------------------------------
cohort_ceo |
1950 | .2280387 .2496841 0.91 0.363 -.2665836 .7226611
1955 | .6255583 .2237666 2.80 0.006 .1822784 1.068838
1960 | .8709544 .1938728 4.49 0.000 .4868939 1.255015
1965 | .7476039 .2181344 3.43 0.001 .3154813 1.179727
1970 | .7195994 .2266457 3.17 0.002 .270616 1.168583
1975 | .7696551 .2756349 2.79 0.006 .2236245 1.315686
1980 | .8248062 .2632978 3.13 0.002 .3032153 1.346397
1985 | .1256901 .1764412 0.71 0.478 -.2238386 .4752188
|
foreign | .5185497 .1448787 3.58 0.001 .2315462 .8055532
entrepreneur | -.1541056 .143677 -1.07 0.286 -.4387285 .1305174
_cons | 2.075016 .2118047 9.80 0.000 1.655433 2.4946
------------------------------------------------------------------------------
output/tables/management-cohort-ceo.tex
dir : seeout
Average marginal effects Number of obs = 115
Model VCE: Robust
Expression: Linear prediction, predict()
dy/dx wrt: 1950.cohort_ceo 1955.cohort_ceo 1960.cohort_ceo 1965.cohort_ceo
1970.cohort_ceo 1975.cohort_ceo 1980.cohort_ceo 1985.cohort_ceo
------------------------------------------------------------------------------
| Delta-method
| dy/dx std. err. t P>|t| [95% conf. interval]
-------------+----------------------------------------------------------------
cohort_ceo |
1950 | .2280387 .2496841 0.91 0.363 -.2665836 .7226611
1955 | .6255583 .2237666 2.80 0.006 .1822784 1.068838
1960 | .8709544 .1938728 4.49 0.000 .4868939 1.255015
1965 | .7476039 .2181344 3.43 0.001 .3154813 1.179727
1970 | .7195994 .2266457 3.17 0.002 .270616 1.168583
1975 | .7696551 .2756349 2.79 0.006 .2236245 1.315686
1980 | .8248062 .2632978 3.13 0.002 .3032153 1.346397
1985 | .1256901 .1764412 0.71 0.478 -.2238386 .4752188
------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.
Variables that uniquely identify margins: _deriv
file output/fig/cohort-ceo-marginsplot.png written in PNG format
(start=1945, width=5)
file output/fig/cohort-ceo-histogram.png written in PNG format
(sum of wgt is 199.2858448)
Linear regression Number of obs = 118
F(11, 117) = 20.46
Prob > F = 0.0000
R-squared = 0.3990
Root MSE = .58849
(Std. err. adjusted for 118 clusters in tax_id)
------------------------------------------------------------------------------
| Robust
management | Coefficient std. err. t P>|t| [95% conf. interval]
-------------+----------------------------------------------------------------
cohort_res~t |
1950 | .0044804 .2570008 0.02 0.986 -.5044962 .5134571
1955 | .6380783 .1720374 3.71 0.000 .2973673 .9787893
1960 | .6871185 .2826744 2.43 0.017 .1272966 1.24694
1965 | 1.118883 .1748325 6.40 0.000 .7726367 1.46513
1970 | 1.112372 .1946141 5.72 0.000 .7269491 1.497795
1975 | .6389115 .2117917 3.02 0.003 .2194692 1.058354
1980 | .7807089 .1839539 4.24 0.000 .4163979 1.14502
1985 | .8665722 .2063547 4.20 0.000 .4578975 1.275247
1990 | .8935571 .1897415 4.71 0.000 .5177841 1.26933
|
foreign | .4633906 .1345306 3.44 0.001 .1969599 .7298213
entrepreneur | -.2863211 .1396407 -2.05 0.043 -.5628721 -.00977
_cons | 2.0058 .1785055 11.24 0.000 1.652279 2.359321
------------------------------------------------------------------------------
output/tables/management-cohort-respondent.tex
dir : seeout
Average marginal effects Number of obs = 118
Model VCE: Robust
Expression: Linear prediction, predict()
dy/dx wrt: 1950.cohort_respondent 1955.cohort_respondent
1960.cohort_respondent 1965.cohort_respondent
1970.cohort_respondent 1975.cohort_respondent
1980.cohort_respondent 1985.cohort_respondent
1990.cohort_respondent
------------------------------------------------------------------------------
| Delta-method
| dy/dx std. err. t P>|t| [95% conf. interval]
-------------+----------------------------------------------------------------
cohort_res~t |
1950 | .0044804 .2570008 0.02 0.986 -.5044962 .5134571
1955 | .6380783 .1720374 3.71 0.000 .2973673 .9787893
1960 | .6871185 .2826744 2.43 0.017 .1272966 1.24694
1965 | 1.118883 .1748325 6.40 0.000 .7726367 1.46513
1970 | 1.112372 .1946141 5.72 0.000 .7269491 1.497795
1975 | .6389115 .2117917 3.02 0.003 .2194692 1.058354
1980 | .7807089 .1839539 4.24 0.000 .4163979 1.14502
1985 | .8665722 .2063547 4.20 0.000 .4578975 1.275247
1990 | .8935571 .1897415 4.71 0.000 .5177841 1.26933
------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.
Variables that uniquely identify margins: _deriv
file output/fig/cohort-respondent-marginsplot.png written in PNG format
(start=1945, width=5)
file output/fig/cohort-respondent-histogram.png written in PNG format
(sum of wgt is 195.0050857)
Linear regression Number of obs = 115
F(19, 114) = 66.32
Prob > F = 0.0000
R-squared = 0.4667
Root MSE = .5792
(Std. err. adjusted for 115 clusters in tax_id)
------------------------------------------------------------------------------
| Robust
management | Coefficient std. err. t P>|t| [95% conf. interval]
-------------+----------------------------------------------------------------
cohort_res~t |
1950 | -.0121196 .4635224 -0.03 0.979 -.9303539 .9061147
1955 | .3887929 .2561208 1.52 0.132 -.1185804 .8961663
1960 | .3540599 .4015288 0.88 0.380 -.4413655 1.149485
1965 | .7216085 .3689745 1.96 0.053 -.0093271 1.452544
1970 | .8641064 .3690627 2.34 0.021 .132996 1.595217
1975 | .2819256 .3737414 0.75 0.452 -.4584533 1.022304
1980 | .4385621 .3457334 1.27 0.207 -.246333 1.123457
1985 | .7816844 .36493 2.14 0.034 .0587609 1.504608
1990 | .5478422 .3587742 1.53 0.130 -.1628867 1.258571
|
cohort_ceo |
1950 | .0448644 .3656886 0.12 0.903 -.6795619 .7692908
1955 | .3780651 .2923413 1.29 0.199 -.2010607 .9571909
1960 | .5092909 .3301951 1.54 0.126 -.1448231 1.163405
1965 | .4222705 .3539211 1.19 0.235 -.2788445 1.123385
1970 | .2569953 .348205 0.74 0.462 -.4327962 .9467868
1975 | .6148924 .4271447 1.44 0.153 -.2312779 1.461063
1980 | .6300142 .3737893 1.69 0.095 -.1104594 1.370488
1985 | -.3344116 .3386679 -0.99 0.326 -1.00531 .3364869
|
foreign | .4754994 .1453464 3.27 0.001 .1875692 .7634296
entrepreneur | -.1875378 .1473818 -1.27 0.206 -.4795 .1044243
_cons | 1.907017 .1875287 10.17 0.000 1.535524 2.27851
------------------------------------------------------------------------------
output/tables/management-cohort-respondent.tex
dir : seeout
Average marginal effects Number of obs = 115
Model VCE: Robust
Expression: Linear prediction, predict()
dy/dx wrt: 1950.cohort_respondent 1955.cohort_respondent
1960.cohort_respondent 1965.cohort_respondent
1970.cohort_respondent 1975.cohort_respondent
1980.cohort_respondent 1985.cohort_respondent
1990.cohort_respondent
------------------------------------------------------------------------------
| Delta-method
| dy/dx std. err. t P>|t| [95% conf. interval]
-------------+----------------------------------------------------------------
cohort_res~t |
1950 | -.0121196 .4635224 -0.03 0.979 -.9303539 .9061147
1955 | .3887929 .2561208 1.52 0.132 -.1185804 .8961663
1960 | .3540599 .4015288 0.88 0.380 -.4413655 1.149485
1965 | .7216085 .3689745 1.96 0.053 -.0093271 1.452544
1970 | .8641064 .3690627 2.34 0.021 .132996 1.595217
1975 | .2819256 .3737414 0.75 0.452 -.4584533 1.022304
1980 | .4385621 .3457334 1.27 0.207 -.246333 1.123457
1985 | .7816844 .36493 2.14 0.034 .0587609 1.504608
1990 | .5478422 .3587742 1.53 0.130 -.1628867 1.258571
------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.
Variables that uniquely identify margins: _deriv
file output/fig/cohort-respcontrol-marginsplot.png written in PNG format
(start=1945, width=5)
file output/fig/cohort-respcontrol-histogram.png written in PNG format
(sum of wgt is 152.5995714)
Linear regression Number of obs = 87
F(10, 86) = 12.48
Prob > F = 0.0000
R-squared = 0.4272
Root MSE = .58012
(Std. err. adjusted for 87 clusters in tax_id)
------------------------------------------------------------------------------
| Robust
management | Coefficient std. err. t P>|t| [95% conf. interval]
-------------+----------------------------------------------------------------
cohort_ceo |
1950 | .341011 .2655554 1.28 0.203 -.1868956 .8689175
1955 | .8801967 .2674956 3.29 0.001 .3484331 1.41196
1960 | 1.089583 .2190375 4.97 0.000 .6541513 1.525016
1965 | .965645 .2543484 3.80 0.000 .4600171 1.471273
1970 | .9397925 .2458336 3.82 0.000 .4510915 1.428493
1975 | .9525663 .307581 3.10 0.003 .3411155 1.564017
1980 | .9585323 .2789354 3.44 0.001 .404027 1.513038
1985 | .2667172 .1984547 1.34 0.182 -.1277977 .661232
|
foreign | .4319686 .1613706 2.68 0.009 .1111745 .7527627
entrepreneur | -.2110012 .1595069 -1.32 0.189 -.5280904 .106088
_cons | 1.968811 .2298865 8.56 0.000 1.511812 2.42581
------------------------------------------------------------------------------
output/tables/management-cohort-ceo.tex
dir : seeout
Average marginal effects Number of obs = 87
Model VCE: Robust
Expression: Linear prediction, predict()
dy/dx wrt: 1950.cohort_ceo 1955.cohort_ceo 1960.cohort_ceo 1965.cohort_ceo
1970.cohort_ceo 1975.cohort_ceo 1980.cohort_ceo 1985.cohort_ceo
------------------------------------------------------------------------------
| Delta-method
| dy/dx std. err. t P>|t| [95% conf. interval]
-------------+----------------------------------------------------------------
cohort_ceo |
1950 | .341011 .2655554 1.28 0.203 -.1868956 .8689175
1955 | .8801967 .2674956 3.29 0.001 .3484331 1.41196
1960 | 1.089583 .2190375 4.97 0.000 .6541513 1.525016
1965 | .965645 .2543484 3.80 0.000 .4600171 1.471273
1970 | .9397925 .2458336 3.82 0.000 .4510915 1.428493
1975 | .9525663 .307581 3.10 0.003 .3411155 1.564017
1980 | .9585323 .2789354 3.44 0.001 .404027 1.513038
1985 | .2667172 .1984547 1.34 0.182 -.1277977 .661232
------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.
Variables that uniquely identify margins: _deriv
file output/fig/cohort-ceodomestic-marginsplot.png written in PNG format
(start=1945, width=5)
file output/fig/cohort-ceodomestic-histogram.png written in PNG format
(sum of wgt is 42.4055143)
Linear regression Number of obs = 28
F(7, 27) = .
Prob > F = .
R-squared = 0.4277
Root MSE = .64982
(Std. err. adjusted for 28 clusters in tax_id)
------------------------------------------------------------------------------
| Robust
management | Coefficient std. err. t P>|t| [95% conf. interval]
-------------+----------------------------------------------------------------
cohort_ceo |
1950 | .4228733 .4368077 0.97 0.342 -.4733821 1.319129
1955 | .0509777 .4491485 0.11 0.910 -.8705989 .9725543
1960 | .1187331 .3339882 0.36 0.725 -.5665541 .8040204
1965 | -.1087033 .2987776 -0.36 0.719 -.7217442 .5043376
1970 | -.2646009 .7227735 -0.37 0.717 -1.74761 1.218408
1975 | -.3798129 .3080426 -1.23 0.228 -1.011864 .2522383
|
foreign | 1.162423 .4526303 2.57 0.016 .2337025 2.091144
entrepreneur | -.4977914 .3455643 -1.44 0.161 -1.206831 .211248
_cons | 2.092317 .5821646 3.59 0.001 .8978137 3.28682
------------------------------------------------------------------------------
output/tables/management-cohort-ceo.tex
dir : seeout
Average marginal effects Number of obs = 28
Model VCE: Robust
Expression: Linear prediction, predict()
dy/dx wrt: 1950.cohort_ceo 1955.cohort_ceo 1960.cohort_ceo 1965.cohort_ceo
1970.cohort_ceo 1975.cohort_ceo
------------------------------------------------------------------------------
| Delta-method
| dy/dx std. err. t P>|t| [95% conf. interval]
-------------+----------------------------------------------------------------
cohort_ceo |
1950 | .4228733 .4368077 0.97 0.342 -.4733821 1.319129
1955 | .0509777 .4491485 0.11 0.910 -.8705989 .9725543
1960 | .1187331 .3339882 0.36 0.725 -.5665541 .8040204
1965 | -.1087033 .2987776 -0.36 0.719 -.7217442 .5043376
1970 | -.2646009 .7227735 -0.37 0.717 -1.74761 1.218408
1975 | -.3798129 .3080426 -1.23 0.228 -1.011864 .2522383
------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.
Variables that uniquely identify margins: _deriv
(file output/fig/cohort-ceoexpat-marginsplot.png not found)
file output/fig/cohort-ceoexpat-marginsplot.png written in PNG format
(start=1945, width=5)
(file output/fig/cohort-ceoexpat-histogram.png not found)
file output/fig/cohort-ceoexpat-histogram.png written in PNG format
. predict management_p
(option xb assumed; fitted values)
(3 missing values generated)
.
. foreach X in management operations monitoring targets people {
2. regress `X' modern_firm [pw=weight], cluster(tax_id)
3. outreg2 using "output/tables/`X'-modernity.tex", replace tex(frag pr)
4. regress `X' modern_ceo [pw=weight], cluster(tax_id)
5. outreg2 using "output/tables/`X'-modernity.tex", tex(frag pr)
6. regress `X' modern_respondent [pw=weight], cluster(tax_id)
7. outreg2 using "output/tables/`X'-modernity.tex", tex(frag pr)
8. regress `X' modern_ceo modern_respondent [pw=weight], cluster(tax_id)
9. outreg2 using "output/tables/`X'-modernity.tex", tex(frag pr)
10. regress `X' modern_ceo modern_respondent lnL foreign [pw=weight], clus
> ter(tax_id)
11. outreg2 using "output/tables/`X'-modernity.tex", tex(frag pr)
12. }
(sum of wgt is 199.2858448000001)
Linear regression Number of obs = 118
F(1, 117) = 6.15
Prob > F = 0.0146
R-squared = 0.0290
Root MSE = .71506
(Std. err. adjusted for 118 clusters in tax_id)
------------------------------------------------------------------------------
| Robust
management | Coefficient std. err. t P>|t| [95% conf. interval]
-------------+----------------------------------------------------------------
modern_firm | -.4165305 .1679564 -2.48 0.015 -.7491593 -.0839017
_cons | 3.216115 .1505831 21.36 0.000 2.917893 3.514337
------------------------------------------------------------------------------
output/tables/management-modernity.tex
dir : seeout
(sum of wgt is 195.0050857000001)
Linear regression Number of obs = 115
F(1, 114) = 1.69
Prob > F = 0.1957
R-squared = 0.0158
Root MSE = .72142
(Std. err. adjusted for 115 clusters in tax_id)
------------------------------------------------------------------------------
| Robust
management | Coefficient std. err. t P>|t| [95% conf. interval]
-------------+----------------------------------------------------------------
modern_ceo | .181637 .139569 1.30 0.196 -.0948482 .4581221
_cons | 2.740934 .1072017 25.57 0.000 2.528569 2.9533
------------------------------------------------------------------------------
output/tables/management-modernity.tex
dir : seeout
(sum of wgt is 199.2858448000001)
Linear regression Number of obs = 118
F(1, 117) = 6.89
Prob > F = 0.0098
R-squared = 0.0672
Root MSE = .70084
(Std. err. adjusted for 118 clusters in tax_id)
------------------------------------------------------------------------------
| Robust
management | Coefficient std. err. t P>|t| [95% conf. interval]
-------------+----------------------------------------------------------------
modern_res~t | .4238812 .1615391 2.62 0.010 .1039616 .7438009
_cons | 2.526707 .1413925 17.87 0.000 2.246687 2.806728
------------------------------------------------------------------------------
output/tables/management-modernity.tex
dir : seeout
(sum of wgt is 195.0050857000001)
Linear regression Number of obs = 115
F(2, 114) = 3.21
Prob > F = 0.0442
R-squared = 0.0642
Root MSE = .70658
(Std. err. adjusted for 115 clusters in tax_id)
------------------------------------------------------------------------------
| Robust
management | Coefficient std. err. t P>|t| [95% conf. interval]
-------------+----------------------------------------------------------------
modern_ceo | .0517478 .1481378 0.35 0.727 -.241712 .3452075
modern_res~t | .3867813 .1758845 2.20 0.030 .0383554 .7352073
_cons | 2.518632 .1469082 17.14 0.000 2.227608 2.809656
------------------------------------------------------------------------------
output/tables/management-modernity.tex
dir : seeout
(sum of wgt is 195.0050857000001)
Linear regression Number of obs = 115
F(4, 114) = 20.67
Prob > F = 0.0000
R-squared = 0.4114
Root MSE = .56546
(Std. err. adjusted for 115 clusters in tax_id)
------------------------------------------------------------------------------
| Robust
management | Coefficient std. err. t P>|t| [95% conf. interval]
-------------+----------------------------------------------------------------
modern_ceo | .0700629 .1138035 0.62 0.539 -.1553809 .2955068
modern_res~t | .2555421 .1402014 1.82 0.071 -.0221958 .5332801
lnL | .3447063 .0577847 5.97 0.000 .2302353 .4591773
foreign | .4801112 .1125053 4.27 0.000 .257239 .7029834
_cons | .7031038 .3052278 2.30 0.023 .0984499 1.307758
------------------------------------------------------------------------------
output/tables/management-modernity.tex
dir : seeout
variable operations not found
r(111);
end of do-file
r(111);