To further advance the understanding regarding the spillover effects of FDI on
domestic manufacturing enterprises in emerging countries, future research could
explore a number of potential directions as follows:
Firstly, researchers could investigate the role of absorptive capacity in
determining the spillover effects of FDI on domestic firms‘ TFP. This could involve
examining the impact of R&D activities, technology transfer, and other knowledge-
related factors on the ability of domestic firms to absorb the spillover effects of FDI
enterprises.
Secondly, researchers could explore the impact of FDI origin on the spillover
effects of FDI on domestic firms‘ TFP. By examining how the home country‘ s
technological prowess and industry composition affect the spillover effects of FDI in
the host country, researchers could gain a deeper understanding of the underlying
mechanisms driving FDI spillovers.
Thirdly, there is a need to investigate the influence of informal institutions, listed
as cultural elements, on the spillover effects of FDI on domestic firms‘ productivity
gains. A comprehensive understanding of the impact of institutions on FDI spillovers
could be gained by exploring the relationship between informal institutions and
productivity spillovers from FDI. Future research could explore solutions for the role
of local governments in attracting FDI and facilitating its positive spillovers.
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ises in Vietnam
Pursuant to Decree 56/2009/ND-CP dated 30/06/2009 regarding classifying enterprises into
total capital or average number of employees per year in the area of agricultural, forestry and fishery
enterprises, details are as follows:
Enterprise scale
Super-small
enterprises
L ≤ 10
Small enterprises 10< L≤200
Average enterprises 200<L≤300
Large enterprises L>300
172
Appendix A8: TFP contributions towards output increase
Source: APO Database 2021
Country Period % Growth value Contribution (100%)
Out- put
Growth
Labor
Growth
Capital
Growth
TFP
Growth
Labor Capital TFP
Vietnam 1995–2000 7.1 1.2 6.2 -0.3 17% 87% -4%
Vietnam 2000–2005 7 1.5 5.5 0 21% 79% 0%
Vietnam 2005–2010 6.1 2.3 5.3 -1.6 38% 87% -26%
Vietnam 2010–2015 5.3 0.4 3.5 1.3 8% 66% 25%
Vietnam 2015–2019 6.4 1.7 3.3 1.4 27% 52% 22%
Philippines 1995–2000 4.4 1.7 2.7 0.1 39% 61% 2%
Philippines 2000–2005 4.7 1.3 1.7 1.7 28% 36% 36%
Philippines 2005–2010 4.9 1.4 2.1 1.3 29% 43% 27%
Philippines 2010–2015 5.7 1.1 3.1 1.4 19% 54% 25%
Philippines 2015–2019 6.6 1.6 4.6 0.5 24% 70% 8%
Indonesia 1995–2000 0.7 2.2 3.7 -5.2 314% 529% -743%
Indonesia 2000–2005 4.5 1.9 2.6 0 42% 58% 0%
Indonesia 2005–2010 5.4 1.7 3.5 0.1 31% 65% 2%
Indonesia 2010–2015 5.3 2.5 4.2 -1.3 47% 79% -25%
Indonesia 2015–2019 4.8 2.3 3.5 -1 48% 73% -21%
Thailand 1995–2000 0.7 1.7 1.8 -2.8 243% 257% -400%
Thailand 2000–2005 5.3 1.9 1 2.4 36% 19% 45%
Thailand 2005–2010 3.7 1.3 2.1 0.3 35% 57% 8%
Thailand 2010–2015 3 0.9 1.8 0.3 30% 60% 10%
Thailand 2015–2019 3.6 0.4 1.7 1.5 11% 47% 42%
Malaysia 1995–2000 4.8 1.9 4.2 -1.4 40% 88% -29%
Malaysia 2000–2005 5.4 1.6 2.1 1.6 30% 39% 30%
Malaysia 2005–2010 4.8 1.5 2.6 0.7 31% 54% 15%
Malaysia 2010–2015 5.1 1.5 3.2 0.4 29% 63% 8%
Malaysia 2015–2019 4.2 1.1 2.8 0.4 26% 67% 10%
Brunei 1995–2000 2.8 0.8 2.4 -0.3 29% 86% -11%
173
Brunei 2000–2005 0.9 0.7 1.6 -1.3 78% 178% -144%
Brunei 2005–2010 0.1 0.6 2.9 -3.4 600% 2900% -3400%
Brunei 2010–2015 0.9 0.3 5 -4.5 33% 556% -500%
Brunei 2015–2019 1.9 -0.1 2.1 0 -5% 111% 0%
Singapore 1995–2000 6.2 2.1 3.5 0.5 34% 56% 8%
Singapore 2000–2005 4.9 1.5 2.1 1.3 31% 43% 27%
Singapore 2005–2010 7.2 2.8 2.4 2 39% 33% 28%
Singapore 2010–2015 4.7 1.6 2.7 0.3 34% 57% 6%
Singapore 2015–2019 3.7 0.5 2 1.1 14% 54% 30%
Korea 1995–2000 5.6 0.7 3 1.9 13% 54% 34%
Korea 2000–2005 5 1.4 2.8 0.7 28% 56% 14%
Korea 2005–2010 4.4 0.9 2.2 1.3 20% 50% 30%
Korea 2010–2015 3 1.2 1.7 0.2 40% 57% 7%
Korea 2015–2019 2.8 -0.2 1.6 1.4 -7% 57% 50%
Japan 1995–2000 1 -0.2 0.8 0.4 -20% 80% 40%
Japan 2000–2005 1.2 0.2 0.4 0.7 17% 33% 58%
Japan 2005–2010 0 0 0.2 -0.3 0% 0% 0%
Japan 2010–2015 1 0.2 0 0.9 20% 0% 90%
Japan 2015–2019 0.8 0.1 0.3 0.4 13% 38% 50%
US 1995–2000 4.2 1.4 1.7 1.1 33% 40% 26%
US 2000–2005 2.5 0.6 1.2 0.8 24% 48% 32%
US 2005–2010 0.9 -0.1 0.8 0.1 -11% 89% 11%
US 2010–2015 2.2 1 0.6 0.5 45% 27% 23%
US 2015–2019 2.3 1 0.9 0.4 43% 39% 17%
ASEAN 1995–2000 2.5 1.7 3.2 -2.3 68% 128% -92%
ASEAN 2000–2005 5 1.6 2.2 1.3 32% 44% 26%
ASEAN 2005–2010 5.1 1.7 3 0.4 33% 59% 8%
ASEAN 2010–2015 4.8 1.4 3.4 0.1 29% 71% 2%
ASEAN 2015–2019 4.8 1.4 3.2 0.2 29% 67% 4%
174
Appendix A9: Contribution of FDI sector towards Vietnam’s economy
Value Ratio
Year Total (Billion
VND)
State
economy
Private
economy
FDI
economy
Total (%) State
economy
Private
economy
FDI
economy
2005 914000.84
343,883
431,548
138,570
100 37.62 47.22 15.16
2006 1061564.52
389,533
501,432
170,600
100 36.69 47.24 16.07
2007 1246769.29
440,687
594,617
211,465
100 35.35 47.69 16.96
2008 1616047.13
566,812
767,632
281,604
100 35.07 47.5 17.43
2009 1809148.95
628,074
867,810
313,265
100 34.72 47.97 17.31
2010 2739843.17
662,604
1,362,614
413,937
100 24.18 49.73 15.11
2011 3539881.31
834,970
1,797,178
544,774
100 23.59 50.77 15.39
2012 4073762.29
959,672
2,121,219
633,507
100 23.56 52.07 15.5
2013 4473655.6
1,045,291
2,280,493
760,130
100 23.3 50.98 16.99
2014 4937031.68
1,139,544
2,500,528
857,689
100 23.08 50.65 17.37
2015 5191323.73
1,185,894
2,628,289
906,511
100 22.84 50.63 17.46
2016 5639401
1,284,522
2,832,253
1,003,123
100 22.78 50.22 17.79
2017 6293904.55
1,404,435
3,145,811
1,170,973
100 22.31 49.98 18.6
2018 7009042.13
1,495,494
3,514,624
1,369,513
100 21.34 50.2 19.54
2019 7707200.29
1,587,127
3,895,948
1,534,823
100 20.59 50.6 19.91
2020 8044385.73
1,662,352
4,067,451
1,609,112
100 20.66 50.5 20
2021 8479666.5
1,796,228
4,243,095
1,697,904
100 21.18 50.04 20.02
175
Appendix A10: Over-15-year-old labor structure in terms of economic sectors
Unit: thousand people
Year Total State economy Non-state economy FDI sector % Contribution FDI
2000 37075.3 4358.2 32358.6 358.5 1
2001 38180.1 4474.4 33356.6 349.1 0.9
2002 39275.9 4633.5 34216.5 425.9 1.1
2003 40403.9 4919.1 34731.5 753.3 1.9
2004 41578.8 5031 35633 914.8 2.2
2005 42774.9 4967.4 36694.7 1112.8 2.6
2006 43980.3 4916 37742.3 1322 3
2007 45208 4988.4 38657.4 1562.2 3.5
2008 46460.8 5059.3 39707.1 1694.4 3.6
2009 47743.6 5040.6 41178.4 1524.6 3.2
2010 49124.4 5025.2 42370 1729.2 3.5
2011 50547.2 5024.8 43423.8 2098.6 4.2
2012 51690.5 5017.4 44423.3 2249.8 4.4
2013 52507.8 4994.9 44994.6 2518.3 4.8
2014 53030.6 4893.2 45269.3 2868.1 5.4
2015 53110.5 4779.9 45132.8 3197.8 6
2016 53345.5 4702.3 45052.2 3591 6.7
2017 53708.6 4595.4 44905.4 4207.8 7.8
2018 54282.5 4525.9 45215.4 4541.2 8.4
2019 54659.2 4226.2 45664.6 4768.4 8.7
2020 53609.6 4098.4 44777.4 4733.8 8.83
2021 49072 3951.7 40534 4586.3 9.35
176
Appendix A11: Vietnamese productivity
Year Productivity
(Milion VND/ People)
Growth (%)
2005 21.4 0
2007 25.3 18%
2008 32 26%
2009 37.9 18%
2010 43.9 16%
2011 55 25%
2012 62.8 14%
2013 68.3 9%
2014 74.3 9%
2015 78.9 6%
2016 84.4 7%
2017 93.2 10%
2018 102.1 10%
2019 110.5 8%
2020 117.4 6%
177
Appendix A12: Export share in terms of industries in Vietnam
2005 2010 2015 2020 2021
Exports 56.3 65.3 84.7 104.2 104.2
Heavy industrial goods and minerals
20.3 20.3 38.5 55.1 55.1
Light industrial goods and handicrafts
23.1 30.1 33.9 38.4 38.4
Agricultural produce
7.7 9.6 7.7 6.3 6.3
Forest products
0.4 0.7 1.2 1.3 1.3
Seafood products
4.7 4.5 3.4 3.1 3.1
Non-monetary gold
0 0 0 0 0
178
Appendix A13: Number of businesses operating until December 31 of each year
in terms of locality by Province/City and Year
No Provinces 2017 2018 2019 2020 2021
1 Hồ Chí Minh 204,918 228,267 239,623 254,699 268,465
2 Hà Nội 133,981 143,119 155,940 165,875 178,493
3 Bình Dương 22,976 27,566 31,599 34,836 37,668
4 Đồng Nai 18,865 21,183 22,398 24,270 25,055
5 Đà Nẵng 18,417 20,375 22,566 23,666 24,703
6 Hải Phòng 21,598 21,613 19,918 20,195 19,806
7 Thanh Hóa 9,557 11,127 11,763 13,152 14,088
8 Bắc Ninh 5,674 6,398 7,069 12,769 13,944
9 Nghệ An 11,456 11,706 10,855 11,636 12,414
10 Bà Rịa - Vũng Tàu 8,956 9,611 10,097 10,946 11,393
179
Appendix A14: Employees’ average monthly income in operating enterprises
with production and business results classified in terms of enterprise types
divided by Enterprise types and Year
Employees’ income 2010 2015 2016 2017 2018 2019 2020
Total number of 4,124 6,966 7,514 8,269 8,836 9,325 9,547
State enterprises 6,553 9,509 11,411 11,887 12,556 14,210 15,330
Private enterprises 2,950 4,588 4,515 5,599 5,369 5,821 5,221
Enterprises with foreign
investment
4,252 7,502 8,504 9,035 9,764 10,066 10,516
180
Appendix A15: The number of enterprises in operation until December 31
of each year in terms of economic sectors
The number of enterprises in terms of
industry classification
2017 2018 2019 2020 2021
Total number of 654,633 714,755 758,610 811,538 857,551
Agriculture, Forestry and Fisheries
9,951 10,766 10,085 11,398 12,011
Industry and Construction
212,170 228,147 239,755 258,431 270,549
Service 432,512 475,842 508,770 541,709 574,991
Structure %
Agriculture, Forestry and Fisheries
2% 2% 1% 1% 1%
Industry and Construction
32% 32% 32% 32% 32%
Service
66% 67% 67% 67% 67%
181
Appendix A16: Total factor productivity spillovers from FDI in manufacturing:
the role of FDI’s market orientation
1. Fixed Effects Estimation Method
2. Random Effects Estimation Method
F test that all u_i=0: F(87409, 89118) = 3.07 Prob > F = 0.0000
rho .68127524 (fraction of variance due to u_i)
sigma_e .66359289
sigma_u .97018626
_cons 2.719611 .0199911 136.04 0.000 2.680428 2.758793
hhi -2.530509 .0880722 -28.73 0.000 -2.70313 -2.357888
technologygap -.154755 .0089031 -17.38 0.000 -.1722051 -.1373049
labourquality .0448815 .0033749 13.30 0.000 .0382667 .0514963
forAB1 -.0040275 .0022729 -1.77 0.076 -.0084825 .0004274
forward .0041677 .0124175 0.34 0.737 -.0201705 .0285059
backexportAB1 -.1165186 .0136725 -8.52 0.000 -.1433166 -.0897206
backexport2 -.4569167 .0575519 -7.94 0.000 -.5697179 -.3441155
backdomesAB1 .2259611 .0106533 21.21 0.000 .2050807 .2468414
backdomes1 .6385526 .0878987 7.26 0.000 .4662719 .8108333
horexportAB1 .1637251 .0149207 10.97 0.000 .1344807 .1929694
horexport2 .5057921 .0618648 8.18 0.000 .3845377 .6270464
hordomesAB1 -.7975797 .0133911 -59.56 0.000 -.8238262 -.7713331
hor_domestic1 -.2873066 .0108911 -26.38 0.000 -.308653 -.2659602
Ln_Ab .5105409 .0027394 186.37 0.000 .5051716 .5159101
Ln_TFP Coef. Std. Err. t P>|t| [95% Conf. Interval]
corr(u_i, Xb) = 0.3109 Prob > F = 0.0000
F(14,89118) = 4345.50
overall = 0.5471 max = 4
between = 0.6195 avg = 2.0
within = 0.4057 min = 1
R-sq: Obs per group:
Group variable: madn Number of groups = 87,410
Fixed-effects (within) regression Number of obs = 176,542
> ort2 backexportAB1 forward forAB1 labourquality technologygap hhi, fe
. xtreg Ln_TFP Ln_Ab hor_domestic1 hordomesAB1 horexport2 horexportAB1 backdomes1 backdomesAB1 backexp
182
3. Hausman test
rho .48866746 (fraction of variance due to u_i)
sigma_e .66359289
sigma_u .64871915
_cons 3.643831 .0148878 244.75 0.000 3.614652 3.673011
hhi -2.540293 .0522023 -48.66 0.000 -2.642607 -2.437978
technologygap -.19632 .0075819 -25.89 0.000 -.2111804 -.1814597
labourquality -.0020074 .0022653 -0.89 0.376 -.0064474 .0024326
forAB1 -.0039723 .0021099 -1.88 0.060 -.0081078 .0001631
forward -.0642216 .0116055 -5.53 0.000 -.0869679 -.0414753
backexportAB1 -.2398176 .0119183 -20.12 0.000 -.263177 -.2164582
backexport2 -.7376189 .0503172 -14.66 0.000 -.8362389 -.6389989
backdomesAB1 .2972109 .0085209 34.88 0.000 .2805102 .3139115
backdomes1 1.051723 .0502994 20.91 0.000 .9531381 1.150308
horexportAB1 .3047544 .0129964 23.45 0.000 .2792819 .3302269
horexport2 .7518374 .0541405 13.89 0.000 .6457239 .8579508
hordomesAB1 -.9374829 .0104351 -89.84 0.000 -.9579354 -.9170304
hor_domestic1 -.386625 .0065098 -59.39 0.000 -.3993839 -.3738661
Ln_Ab .6393428 .0020045 318.96 0.000 .6354141 .6432715
Ln_TFP Coef. Std. Err. z P>|z| [95% Conf. Interval]
corr(u_i, X) = 0 (assumed) Prob > chi2 = 0.0000
Wald chi2(14) = 194488.62
overall = 0.5675 max = 4
between = 0.6523 avg = 2.0
within = 0.3997 min = 1
R-sq: Obs per group:
Group variable: madn Number of groups = 87,410
Random-effects GLS regression Number of obs = 176,542
> ort2 backexportAB1 forward forAB1 labourquality technologygap hhi, re
. xtreg Ln_TFP Ln_Ab hor_domestic1 hordomesAB1 horexport2 horexportAB1 backdomes1 backdomesAB1 backexp
Prob>chi2 = 0.0000
= 20325.78
chi2(14) = (b-B)'[(V_b-V_B)^(-1)](b-B)
Test: Ho: difference in coefficients not systematic
B = inconsistent under Ha, efficient under Ho; obtained from xtreg
b = consistent under Ho and Ha; obtained from xtreg
hhi -2.530509 -2.540293 .0097835 .0709341
technology~p -.154755 -.19632 .041565 .0046669
labourqual~y .0448815 -.0020074 .0468889 .0025017
forAB1 -.0040275 -.0039723 -.0000552 .0008452
forward .0041677 -.0642216 .0683893 .0044167
backexpor~B1 -.1165186 -.2398176 .123299 .0067002
backexport2 -.4569167 -.7376189 .2807022 .0279356
backdomesAB1 .2259611 .2972109 -.0712498 .0063943
backdomes1 .6385526 1.051723 -.4131705 .0720844
horexportAB1 .1637251 .3047544 -.1410293 .0073293
horexport2 .5057921 .7518374 -.2460453 .0299342
hordomesAB1 -.7975797 -.9374829 .1399032 .0083923
hor_domest~1 -.2873066 -.386625 .0993184 .0087315
Ln_Ab .5105409 .6393428 -.1288019 .0018672
FE RE Difference S.E.
(b) (B) (b-B) sqrt(diag(V_b-V_B))
Coefficients
. hausman FE RE
183
4. Test for Serial Correlation
5. Test for Heteroskedasticity
6. Regression results with domestic manufacturing enterprises
Prob > F = 0.0000
F( 1, 20843) = 952.575
H0: no first-order autocorrelation
Wooldridge test for autocorrelation in panel data
> backexportAB1 forward forAB1 labourquality technologygap hhi
. xtserial Ln_Ab hor_domestic1 hordomesAB1 horexport2 horexportAB1 backdomes1 backdomesAB1 backexport2
Prob>chi2 = 0.0000
chi2 (87410) = 5.1e+36
H0: sigma(i)^2 = sigma^2 for all i
in fixed effect regression model
Modified Wald test for groupwise heteroskedasticity
. xttest3
rho .68930345 (fraction of variance due to u_i)
sigma_e .63243095
sigma_u .94199831
_cons 3.225714 .0280688 114.92 0.000 3.1707 3.280729
2014 -.5871885 .0071476 -82.15 0.000 -.6011978 -.5731792
2013 -.0027022 .0061857 -0.44 0.662 -.0148262 .0094217
2012 .2423602 .0062135 39.01 0.000 .2301817 .2545387
nam
hhi -2.099327 .1239816 -16.93 0.000 -2.34233 -1.856324
technologygap -.1148521 .0290654 -3.95 0.000 -.1718201 -.057884
labourquality .0387906 .0039516 9.82 0.000 .0310455 .0465357
forAB1 .012027 .0027477 4.38 0.000 .0066414 .0174126
forward .1552857 .0142965 10.86 0.000 .1272648 .1833067
backexportAB1 .0624045 .016214 3.85 0.000 .0306251 .0941839
backexport2 .2693714 .0624809 4.31 0.000 .1469093 .3918335
backdomesAB1 .1951248 .0178905 10.91 0.000 .1600594 .2301901
backdomes1 -.2731826 .1323126 -2.06 0.039 -.5325144 -.0138508
horexportAB1 -.0677401 .0175209 -3.87 0.000 -.102081 -.0333993
horexport2 -.2450879 .0666132 -3.68 0.000 -.3756494 -.1145265
hordomesAB1 -.8697116 .0214379 -40.57 0.000 -.9117297 -.8276935
hor_domestic1 -.206604 .0158822 -13.01 0.000 -.237733 -.1754751
Ln_Ab .6403022 .0046833 136.72 0.000 .6311229 .6494814
Ln_TFP Coef. Std. Err. t P>|t| [95% Conf. Interval]
Robust
(Std. Err. adjusted for 82,465 clusters in madn)
corr(u_i, Xb) = 0.0965 Prob > F = 0.0000
F(17,82464) = 2047.22
overall = 0.5461 max = 4
between = 0.5900 avg = 2.0
within = 0.4989 min = 1
R-sq: Obs per group:
Group variable: madn Number of groups = 82,465
Fixed-effects (within) regression Number of obs = 162,924
> ort2 backexportAB1 forward forAB1 labourquality technologygap hhi i.nam if sohuu==0 ,fe r
. xtreg Ln_TFP Ln_Ab hor_domestic1 hordomesAB1 horexport2 horexportAB1 backdomes1 backdomesAB1 backexp
184
7. Regression results for domestic manufacturing enterprises engaged in exports.
rho .96027006 (fraction of variance due to u_i)
sigma_e .44507848
sigma_u 2.1881361
_cons 1.025189 .1695909 6.05 0.000 .6927797 1.357598
2014 -.1100312 .0196652 -5.60 0.000 -.1485762 -.0714862
2013 .0374622 .0203121 1.84 0.065 -.002351 .0772753
2012 -.1191692 .0961744 -1.24 0.215 -.3076774 .069339
nam
hhi -.916977 .3765014 -2.44 0.015 -1.654944 -.1790096
technologygap -3.892457 1.270251 -3.06 0.002 -6.382232 -1.402683
labourquality .038325 .0170475 2.25 0.025 .0049107 .0717392
forAB1 .0440452 .0101407 4.34 0.000 .0241688 .0639217
forward .2131491 .0459987 4.63 0.000 .1229885 .3033096
backexportAB1 .2230895 .0689102 3.24 0.001 .088021 .358158
backexport2 .7464733 .2140343 3.49 0.000 .3269521 1.165995
backdomesAB1 -.2373426 .0419272 -5.66 0.000 -.3195225 -.1551626
backdomes1 -1.662685 .3144979 -5.29 0.000 -2.279122 -1.046249
horexportAB1 -.2310989 .0752543 -3.07 0.002 -.3786022 -.0835955
horexport2 -.7550556 .2180836 -3.46 0.001 -1.182514 -.3275975
hordomesAB1 .3140541 .0888648 3.53 0.000 .1398732 .488235
hor_domestic1 .1762927 .0456199 3.86 0.000 .0868747 .2657107
Ln_Ab .1564429 .0306687 5.10 0.000 .0963302 .2165556
Ln_TFP Coef. Std. Err. t P>|t| [95% Conf. Interval]
Robust
(Std. Err. adjusted for 23,366 clusters in madn)
corr(u_i, Xb) = -0.6165 Prob > F = 0.0000
F(17,23365) = 23.91
overall = 0.1336 max = 4
between = 0.1216 avg = 1.2
within = 0.2441 min = 1
R-sq: Obs per group:
Group variable: madn Number of groups = 23,366
Fixed-effects (within) regression Number of obs = 27,323
> ort2 backexportAB1 forward forAB1 labourquality technologygap hhi i.nam if sohuu==0 & export1==1 ,fe r
. xtreg Ln_TFP Ln_Ab hor_domestic1 hordomesAB1 horexport2 horexportAB1 backdomes1 backdomesAB1 backexp
185
8. Regression results for domestic manufacturing enterprises engaged in exports in
industrial zone
rho .95366849 (fraction of variance due to u_i)
sigma_e .47260809
sigma_u 2.1441819
_cons 1.013982 .2098006 4.83 0.000 .6027574 1.425206
2014 -.1895005 .0249161 -7.61 0.000 -.2383377 -.1406632
2013 -.0258445 .0265009 -0.98 0.329 -.0777882 .0260992
2012 -.0674349 .0995489 -0.68 0.498 -.2625579 .1276882
nam
hhi -1.326316 .4317657 -3.07 0.002 -2.172608 -.4800239
technologygap -3.659181 1.122269 -3.26 0.001 -5.858909 -1.459452
labourquality .039351 .0202703 1.94 0.052 -.0003803 .0790824
forAB1 .0604694 .0134542 4.49 0.000 .0340982 .0868406
forward .2907938 .0623837 4.66 0.000 .1685173 .4130704
backexportAB1 .3810321 .0851403 4.48 0.000 .214151 .5479131
backexport2 1.245868 .2933112 4.25 0.000 .670957 1.820779
backdomesAB1 -.3691735 .0502498 -7.35 0.000 -.4676667 -.2706803
backdomes1 -2.142975 .3759778 -5.70 0.000 -2.879918 -1.406031
horexportAB1 -.4095144 .0912995 -4.49 0.000 -.588468 -.2305607
horexport2 -1.301886 .3003484 -4.33 0.000 -1.89059 -.713181
hordomesAB1 .5093971 .1079234 4.72 0.000 .2978594 .7209348
hor_domestic1 .2638699 .0569255 4.64 0.000 .1522919 .3754479
Ln_Ab .1728073 .0380382 4.54 0.000 .0982497 .2473649
Ln_TFP Coef. Std. Err. t P>|t| [95% Conf. Interval]
Robust
(Std. Err. adjusted for 21,966 clusters in madn)
corr(u_i, Xb) = -0.6040 Prob > F = 0.0000
F(17,21965) = 22.27
overall = 0.1444 max = 4
between = 0.1332 avg = 1.1
within = 0.2947 min = 1
R-sq: Obs per group:
Group variable: madn Number of groups = 21,966
Fixed-effects (within) regression Number of obs = 24,727
> gnghiep==1 ,fe r
> ort2 backexportAB1 forward forAB1 labourquality technologygap hhi i.nam if sohuu==0 & export1==1 & con
. xtreg Ln_TFP Ln_Ab hor_domestic1 hordomesAB1 horexport2 horexportAB1 backdomes1 backdomesAB1 backexp
186
9. Regression results for domestic manufacturing enterprises engaged in exports,
located outside of industrial zones.
rho .97147448 (fraction of variance due to u_i)
sigma_e .30565325
sigma_u 1.7837252
_cons 1.466247 .1607909 9.12 0.000 1.150874 1.78162
2014 .0451746 .0261587 1.73 0.084 -.0061327 .0964819
2013 .1195744 .0275564 4.34 0.000 .0655256 .1736231
2012 .3287882 .2390003 1.38 0.169 -.1399834 .7975597
nam
hhi 1.436742 .7417942 1.94 0.053 -.0182021 2.891686
technologygap -158.367 27.8594 -5.68 0.000 -213.01 -103.724
labourquality -.0200858 .0319157 -0.63 0.529 -.0826849 .0425133
forAB1 -.014162 .0143904 -0.98 0.325 -.0423872 .0140632
forward -.0484669 .059852 -0.81 0.418 -.1658597 .068926
backexportAB1 -.0387505 .1046172 -0.37 0.711 -.243945 .1664441
backexport2 -.394439 .3478854 -1.13 0.257 -1.076776 .2878981
backdomesAB1 -.0366411 .0682492 -0.54 0.591 -.170504 .0972218
backdomes1 -1.50636 .3551374 -4.24 0.000 -2.202921 -.8097991
horexportAB1 .072271 .1160284 0.62 0.533 -.1553054 .2998474
horexport2 .5667843 .3668198 1.55 0.123 -.1526904 1.286259
hordomesAB1 .0631289 .1167545 0.54 0.589 -.1658715 .2921293
hor_domestic1 .1222149 .0475979 2.57 0.010 .0288572 .2155727
Ln_Ab .0541038 .0302599 1.79 0.074 -.0052476 .1134552
Ln_TFP Coef. Std. Err. t P>|t| [95% Conf. Interval]
Robust
(Std. Err. adjusted for 1,672 clusters in madn)
corr(u_i, Xb) = -0.8437 Prob > F = 0.0000
F(17,1671) = 9.25
overall = 0.0761 max = 4
between = 0.0853 avg = 1.6
within = 0.2885 min = 1
R-sq: Obs per group:
Group variable: madn Number of groups = 1,672
Fixed-effects (within) regression Number of obs = 2,596
> gnghiep==0 ,fe r
> ort2 backexportAB1 forward forAB1 labourquality technologygap hhi i.nam if sohuu==0 & export1==1 & con
. xtreg Ln_TFP Ln_Ab hor_domestic1 hordomesAB1 horexport2 horexportAB1 backdomes1 backdomesAB1 backexp
187
10. Regression results for domestic manufacturing enterprises with fewer than 100
employees.
rho .67093563 (fraction of variance due to u_i)
sigma_e .65486146
sigma_u .93508142
_cons 3.333746 .0303158 109.97 0.000 3.274327 3.393165
2014 -.6322769 .0078894 -80.14 0.000 -.6477402 -.6168136
2013 .0144708 .0072306 2.00 0.045 .0002989 .0286426
2012 .265651 .0068685 38.68 0.000 .2521889 .2791131
nam
hhi -2.222556 .1422867 -15.62 0.000 -2.501437 -1.943675
technologygap -.1014633 .02798 -3.63 0.000 -.1563039 -.0466227
labourquality .0321059 .0042891 7.49 0.000 .0236994 .0405125
forAB1 .0054787 .0029158 1.88 0.060 -.0002363 .0111936
forward .1273767 .0156448 8.14 0.000 .0967129 .1580405
backexportAB1 .0625663 .017785 3.52 0.000 .0277078 .0974249
backexport2 .1526644 .0722698 2.11 0.035 .0110158 .2943129
backdomesAB1 .1751167 .020488 8.55 0.000 .1349604 .215273
backdomes1 -.3292828 .1495318 -2.20 0.028 -.6223644 -.0362013
horexportAB1 -.0688096 .0193783 -3.55 0.000 -.1067909 -.0308283
horexport2 -.1127223 .0784372 -1.44 0.151 -.2664588 .0410143
hordomesAB1 -.842437 .0245443 -34.32 0.000 -.8905437 -.7943303
hor_domestic -2.023594 .1807477 -11.20 0.000 -2.377859 -1.66933
Ln_Ab .6627657 .0048863 135.64 0.000 .6531886 .6723428
Ln_TFP Coef. Std. Err. t P>|t| [95% Conf. Interval]
Robust
(Std. Err. adjusted for 76,991 clusters in madn)
corr(u_i, Xb) = 0.0636 Prob > F = 0.0000
F(17,76990) = 2070.76
overall = 0.5529 max = 4
between = 0.5934 avg = 1.9
within = 0.5226 min = 1
R-sq: Obs per group:
Group variable: madn Number of groups = 76,991
Fixed-effects (within) regression Number of obs = 144,505
> <=100 ,fe r
> ort2 backexportAB1 forward forAB1 labourquality technologygap hhi i.nam if sohuu==0 & laodong_cuoi
. xtreg Ln_TFP Ln_Ab hor_domestic hordomesAB1 horexport2 horexportAB1 backdomes1 backdomesAB1 backexp
188
11. Regression results for domestic manufacturing enterprises with higher than 100
employees.
rho .81190589 (fraction of variance due to u_i)
sigma_e .37274327
sigma_u .77441732
_cons 1.661832 .0787897 21.09 0.000 1.507386 1.816279
2014 -.1299962 .0145536 -8.93 0.000 -.1585248 -.1014677
2013 -.0014436 .0115822 -0.12 0.901 -.0241473 .0212602
2012 .114586 .0139707 8.20 0.000 .0872002 .1419718
nam
hhi -.4693312 .1794759 -2.62 0.009 -.8211467 -.1175156
technologygap -.5362111 .075378 -7.11 0.000 -.6839699 -.3884523
labourquality .1031133 .0120054 8.59 0.000 .0795799 .1266467
forAB1 .0174767 .0056728 3.08 0.002 .0063567 .0285966
forward .1047649 .025999 4.03 0.000 .0538008 .1557291
backexportAB1 -.064938 .0332613 -1.95 0.051 -.130138 .0002621
backexport2 .0784408 .1060415 0.74 0.459 -.1294259 .2863075
backdomesAB1 .0416601 .024999 1.67 0.096 -.007344 .0906641
backdomes1 -.4648895 .2775293 -1.68 0.094 -1.008913 .0791341
horexportAB1 .0870541 .0353226 2.46 0.014 .0178135 .1562948
horexport2 -.0354865 .1028235 -0.35 0.730 -.2370451 .166072
hordomesAB1 -.360661 .0380982 -9.47 0.000 -.4353426 -.2859794
hor_domestic -.4500864 .3257489 -1.38 0.167 -1.088632 .1884591
Ln_Ab .2623193 .0151102 17.36 0.000 .2326997 .2919389
Ln_TFP Coef. Std. Err. t P>|t| [95% Conf. Interval]
Robust
(Std. Err. adjusted for 8,648 clusters in madn)
corr(u_i, Xb) = 0.1624 Prob > F = 0.0000
F(17,8647) = 51.01
overall = 0.2838 max = 4
between = 0.3002 avg = 2.1
within = 0.2177 min = 1
R-sq: Obs per group:
Group variable: madn Number of groups = 8,648
Fixed-effects (within) regression Number of obs = 18,419
> > 100 ,fe r
> ort2 backexportAB1 forward forAB1 labourquality technologygap hhi i.nam if sohuu==0 & laodong_cuoi
. xtreg Ln_TFP Ln_Ab hor_domestic hordomesAB1 horexport2 horexportAB1 backdomes1 backdomesAB1 backexp
189
12. Regression results for domestic manufacturing enterprises in the high-tech
industry.
rho .64911018 (fraction of variance due to u_i)
sigma_e .44550813
sigma_u .60593977
_cons 3.118525 .1181933 26.38 0.000 2.88683 3.35022
2014 .2598344 .0209378 12.41 0.000 .2187899 .3008788
2013 -.8831212 .0363613 -24.29 0.000 -.9544003 -.811842
2012 .2586283 .0240804 10.74 0.000 .2114233 .3058333
nam
hhi -1.093244 .1384118 -7.90 0.000 -1.364573 -.8219149
technologygap -.3473149 .1637945 -2.12 0.034 -.668402 -.0262278
labourquality .0302578 .0096804 3.13 0.002 .0112812 .0492344
forAB1 .0429065 .0114506 3.75 0.000 .0204599 .0653531
forward -.9110923 .0709238 -12.85 0.000 -1.050124 -.7720601
backexportAB1 .2191326 .0739097 2.96 0.003 .0742472 .364018
backexport2 -.3077886 .239298 -1.29 0.198 -.7768855 .1613083
backdomesAB1 .1154853 .0551541 2.09 0.036 .0073665 .2236041
backdomes1 2.119358 .2117423 10.01 0.000 1.704279 2.534437
horexportAB1 -.2972723 .0794144 -3.74 0.000 -.4529487 -.1415959
horexport2 .2254556 .2529871 0.89 0.373 -.2704761 .7213874
hordomesAB1 -.5960233 .1249462 -4.77 0.000 -.8409558 -.3510907
hor_domestic -3.011847 .3526747 -8.54 0.000 -3.703197 -2.320497
Ln_Ab .8746659 .0391728 22.33 0.000 .7978754 .9514565
Ln_TFP Coef. Std. Err. t P>|t| [95% Conf. Interval]
Robust
(Std. Err. adjusted for 6,976 clusters in madn)
corr(u_i, Xb) = 0.3156 Prob > F = 0.0000
F(17,6975) = 336.68
overall = 0.7743 max = 4
between = 0.8166 avg = 2.1
within = 0.6423 min = 1
R-sq: Obs per group:
Group variable: madn Number of groups = 6,976
Fixed-effects (within) regression Number of obs = 14,581
> anh ==21|nganh25,fe r
> ort2 backexportAB1 forward forAB1 labourquality technologygap hhi i.nam if sohuu==0 & nganh==20| ng
. xtreg Ln_TFP Ln_Ab hor_domestic hordomesAB1 horexport2 horexportAB1 backdomes1 backdomesAB1 backexp
190
13. Regression results for domestic manufacturing enterprises in the low-tech industry
rho .70380012 (fraction of variance due to u_i)
sigma_e .61180201
sigma_u .94306827
_cons 3.250212 .0278898 116.54 0.000 3.195549 3.304876
2014 -.5507479 .0077768 -70.82 0.000 -.5659904 -.5355054
2013 -.0098216 .0068305 -1.44 0.150 -.0232094 .0035663
2012 .2470868 .0068638 36.00 0.000 .2336339 .2605398
nam
hhi -2.551018 .1551461 -16.44 0.000 -2.855104 -2.246932
technologygap -.0670705 .0185122 -3.62 0.000 -.1033544 -.0307866
labourquality .0278419 .0037794 7.37 0.000 .0204343 .0352495
forAB1 .0010494 .0026817 0.39 0.696 -.0042067 .0063055
forward .0996975 .0140463 7.10 0.000 .0721669 .1272282
backexportAB1 .0021142 .016847 0.13 0.900 -.0309059 .0351344
backexport2 -.0285038 .0660992 -0.43 0.666 -.1580581 .1010506
backdomesAB1 .1467029 .027513 5.33 0.000 .0927775 .2006282
backdomes1 -1.25279 .2040134 -6.14 0.000 -1.652655 -.8529239
horexportAB1 -.0143878 .0183492 -0.78 0.433 -.0503521 .0215766
horexport2 .0107172 .0716031 0.15 0.881 -.1296248 .1510591
hordomesAB1 -.8078949 .0266168 -30.35 0.000 -.8600639 -.755726
hor_domestic -.4658805 .2218174 -2.10 0.036 -.9006421 -.0311189
Ln_Ab .6605507 .0046233 142.87 0.000 .651489 .6696124
Ln_TFP Coef. Std. Err. t P>|t| [95% Conf. Interval]
Robust
(Std. Err. adjusted for 70,602 clusters in madn)
corr(u_i, Xb) = 0.0151 Prob > F = 0.0000
F(17,70601) = 1897.26
overall = 0.5606 max = 4
between = 0.5968 avg = 2.0
within = 0.5353 min = 1
R-sq: Obs per group:
Group variable: madn Number of groups = 70,602
Fixed-effects (within) regression Number of obs = 138,690
> ganh > 21 & nganh 30 ,fe r
> ort2 backexportAB1 forward forAB1 labourquality technologygap hhi i.nam if sohuu==0 & nganh < 20 |n
. xtreg Ln_TFP Ln_Ab hor_domestic hordomesAB1 horexport2 horexportAB1 backdomes1 backdomesAB1 backexp
191
Appendix A17: Total factor productivity spillovers from FDI in manufacturing:
the role of domestic firms’ absorption
1. Fixed Effects Estimation Method
rho .69801476 (fraction of variance due to u_i)
sigma_e 1.2724599
sigma_u 1.9345659
_cons 1.718591 .0620489 27.70 0.000 1.596977 1.840205
hhi .1667539 .025267 6.60 0.000 .1172313 .2162765
technologygap -.5402557 .0650122 -8.31 0.000 -.6676776 -.4128339
scale 5.520026 1.631526 3.38 0.001 2.322285 8.717766
fs1export1 -.1534613 .0694835 -2.21 0.027 -.2896469 -.0172758
bs1export1 .2740409 .1287256 2.13 0.033 .0217427 .5263391
hs1export1 .4450559 .1447406 3.07 0.002 .1613689 .7287429
fs1 -.0359186 .0267291 -1.34 0.179 -.0883069 .0164697
bs1 -.2840863 .0605951 -4.69 0.000 -.4028507 -.1653218
hs1 -.2703354 .0687417 -3.93 0.000 -.4050669 -.1356038
export1 .3792597 .0099569 38.09 0.000 .3597446 .3987749
labourquality .0420799 .0022163 18.99 0.000 .037736 .0464237
lnTFP2 Coef. Std. Err. t P>|t| [95% Conf. Interval]
Robust
(Std. Err. adjusted for 489,347 clusters in ID)
corr(u_i, Xb) = 0.1179 Prob > F = 0.0000
F(11,489346) = 371.33
overall = 0.0489 max = 6
between = 0.0509 avg = 2.3
within = 0.0219 min = 1
R-sq: Obs per group:
Group variable: ID Number of groups = 489,347
Fixed-effects (within) regression Number of obs = 1,104,994
> nologygap hhi if sohuu_16==0 & nam_1 <2016, fe r
. xtreg lnTFP2 labourquality export1 hs1 bs1 fs1 hs1export1 bs1export1 fs1export1 scale tech
192
2. Random Effects Estimation Method
3. Hausman test
rho .61688215 (fraction of variance due to u_i)
sigma_e 1.2724599
sigma_u 1.6146522
_cons 1.77953 .0059609 298.54 0.000 1.767847 1.791213
hhi -.3839234 .0227381 -16.88 0.000 -.4284892 -.3393576
technologygap -.6231247 .0046973 -132.65 0.000 -.6323313 -.613918
scale 10.21084 .357681 28.55 0.000 9.509797 10.91188
fs1export1 -.1464495 .0723117 -2.03 0.043 -.2881779 -.0047212
bs1export1 .3244973 .1272696 2.55 0.011 .0750536 .573941
hs1export1 .9882278 .1450164 6.81 0.000 .7040009 1.272455
fs1 .0005281 .0179316 0.03 0.977 -.0346172 .0356734
bs1 -.584503 .040107 -14.57 0.000 -.6631113 -.5058946
hs1 .1290143 .0453879 2.84 0.004 .0400557 .2179729
export1 .8377975 .009447 88.68 0.000 .8192818 .8563132
labourquality .1413505 .0018092 78.13 0.000 .1378044 .1448965
lnTFP2 Coef. Std. Err. z P>|z| [95% Conf. Interval]
corr(u_i, X) = 0 (assumed) Prob > chi2 = 0.0000
Wald chi2(11) = 42814.37
overall = 0.0689 max = 6
between = 0.0848 avg = 2.3
within = 0.0185 min = 1
R-sq: Obs per group:
Group variable: ID Number of groups = 489,347
Random-effects GLS regression Number of obs = 1,104,994
> nologygap hhi if sohuu_16==0 & nam_1 <2016, re
. xtreg lnTFP2 labourquality export1 hs1 bs1 fs1 hs1export1 bs1export1 fs1export1 scale tech
Prob>chi2 = 0.0000
= 29157.30
chi2(11) = (b-B)'[(V_b-V_B)^(-1)](b-B)
Test: Ho: difference in coefficients not systematic
B = inconsistent under Ha, efficient under Ho; obtained from xtreg
b = consistent under Ho and Ha; obtained from xtreg
hhi .1667539 -.3839234 .5506773 .0106864
technology~p -.5402557 -.6231247 .082869 .003146
scale 5.520026 10.21084 -4.690813 .2733536
fs1export1 -.1534613 -.1464495 -.0070118 .0380725
bs1export1 .2740409 .3244973 -.0504564 .0717424
hs1export1 .4450559 .9882278 -.5431719 .0827647
fs1 -.0359186 .0005281 -.0364466 .0144054
bs1 -.2840863 -.584503 .3004167 .0320617
hs1 -.2703354 .1290143 -.3993497 .03665
export1 .3792597 .8377975 -.4585378 .0046812
labourqual~y .0420799 .1413505 -.0992706 .0009147
FE1 RE1 Difference S.E.
(b) (B) (b-B) sqrt(diag(V_b-V_B))
Coefficients
. hausman FE1 RE1
193
4. Test for Serial Correlation
5. Test for Heteroskedasticity
6. Regression results with Driscoll-Kraay standard errors
Prob > F = 0.0000
F( 1, 257483) = 2783.459
H0: no first-order autocorrelation
Wooldridge test for autocorrelation in panel data
> echnologygap hhi
. xtserial lnTFP2 labourquality export1 hs1 bs1 fs1 hs1export1 bs1export1 fs1export1 scale t
Prob>chi2 = 0.0000
chi2 (489347) = 4.2e+40
H0: sigma(i)^2 = sigma^2 for all i
in fixed effect regression model
Modified Wald test for groupwise heteroskedasticity
. xttest3
_cons 1.718583 .0296687 57.93 0.000 1.660433 1.776732
hhi .1668243 .1774629 0.94 0.347 -.1809974 .514646
technologygap -.5402563 .0518135 -10.43 0.000 -.641809 -.4387035
scale 5.520015 1.985714 2.78 0.005 1.628078 9.411952
fs1export1 -.1534678 .0518095 -2.96 0.003 -.2550128 -.0519228
bs1export1 .2739808 .0848128 3.23 0.001 .1077503 .4402112
hs1export1 .4451127 .1823119 2.44 0.015 .0877871 .8024382
fs1 -.0359118 .112358 -0.32 0.749 -.2561299 .1843064
bs1 -.2839676 .1657472 -1.71 0.087 -.608827 .0408918
hs1 -.2704481 .2786909 -0.97 0.332 -.8166736 .2757775
export1 .3792618 .0915902 4.14 0.000 .1997478 .5587757
labourquality .0420772 .0208055 2.02 0.043 .0012991 .0828554
lnTFP2 Coef. Std. Err. t P>|t| [95% Conf. Interval]
Drisc/Kraay
within R-squared = 0.0219
maximum lag: 2 Prob > F = 0.0000
Group variable (i): ID F( 11,489347) = 236.00
Method: Fixed-effects regression Number of groups = 489348
Regression with Driscoll-Kraay standard errors Number of obs = 1104998
> nologygap hhi if sohuu_16==0 & nam_1 <2016, fe
. xtscc lnTFP2 labourquality export1 hs1 bs1 fs1 hs1export1 bs1export1 fs1export1 scale tech
194
7. Regression results using GMM (Generalized Method of Moments) estimation.
195
Estimated results of export ability and spillover effects
196
8. Fixed Effects Estimation Method
9. Random Effects Estimation Method
F test that all u_i=0: F(534138, 624586) = 1.53 Prob > F = 0.0000
rho .48939545 (fraction of variance due to u_i)
sigma_e 2.564435
sigma_u 2.5106104
_cons 2.968003 .0074337 399.26 0.000 2.953433 2.982573
fs1xk3 -.0387482 .0217932 -1.78 0.075 -.0814622 .0039658
bs1xk3 -.0023791 .0414502 -0.06 0.954 -.0836202 .078862
hs1xk3 .0597598 .0529081 1.13 0.259 -.0439385 .163458
scale 30.51423 1.332698 22.90 0.000 27.90219 33.12628
technologygap -.0237127 .0001793 -132.26 0.000 -.0240642 -.0233613
hhi -4.675948 .0721364 -64.82 0.000 -4.817333 -4.534563
fs1 .6622176 .0409403 16.18 0.000 .581976 .7424592
bs1 .0648765 .0878701 0.74 0.460 -.1073461 .2370992
hs1 -.2778513 .1007285 -2.76 0.006 -.4752759 -.0804268
xk3 -.0053819 .0035506 -1.52 0.130 -.0123409 .0015771
labourquality .5696095 .0048928 116.42 0.000 .5600198 .5791993
lnTFP2 Coef. Std. Err. t P>|t| [95% Conf. Interval]
corr(u_i, Xb) = 0.0468 Prob > F = 0.0000
F(11,624586) = 3622.50
overall = 0.0785 max = 7
between = 0.0826 avg = 2.2
within = 0.0600 min = 1
R-sq: Obs per group:
Group variable: ID Number of groups = 534,139
Fixed-effects (within) regression Number of obs = 1,158,736
. xtreg lnTFP2 labourquality xk3 hs1 bs1 fs1 hhi technologygap scale hs1xk3 bs1xk3 fs1xk3,fe
rho .27022909 (fraction of variance due to u_i)
sigma_e 2.564435
sigma_u 1.5605025
_cons 3.061875 .0058921 519.66 0.000 3.050327 3.073423
fs1xk3 -.0780969 .0171101 -4.56 0.000 -.111632 -.0445617
bs1xk3 .0352282 .0313374 1.12 0.261 -.0261921 .0966484
hs1xk3 .084146 .0379113 2.22 0.026 .0098412 .1584508
scale 39.82811 .7323943 54.38 0.000 38.39265 41.26358
technologygap -.0215637 .0001291 -167.05 0.000 -.0218167 -.0213107
hhi -5.635538 .0559125 -100.79 0.000 -5.745125 -5.525952
fs1 .5583557 .0249075 22.42 0.000 .509538 .6071735
bs1 -.157252 .0532868 -2.95 0.003 -.2616921 -.0528118
hs1 .7233739 .0607741 11.90 0.000 .6042587 .842489
xk3 .0016942 .0027906 0.61 0.544 -.0037752 .0071636
labourquality .705633 .0035444 199.08 0.000 .698686 .7125799
lnTFP2 Coef. Std. Err. z P>|z| [95% Conf. Interval]
corr(u_i, X) = 0 (assumed) Prob > chi2 = 0.0000
Wald chi2(11) = 92896.93
overall = 0.0826 max = 7
between = 0.0908 avg = 2.2
within = 0.0582 min = 1
R-sq: Obs per group:
Group variable: ID Number of groups = 534,139
Random-effects GLS regression Number of obs = 1,158,736
. xtreg lnTFP2 labourquality xk3 hs1 bs1 fs1 hhi technologygap scale hs1xk3 bs1xk3 fs1xk3,re
197
10. Hausman test
11. Test for Serial Correlation
12. Test for Heteroskedasticity
Prob>chi2 = 0.0000
= 3594.60
chi2(11) = (b-B)'[(V_b-V_B)^(-1)](b-B)
Test: Ho: difference in coefficients not systematic
B = inconsistent under Ha, efficient under Ho; obtained from xtreg
b = consistent under Ho and Ha; obtained from xtreg
fs1xk3 -.0387482 -.0780969 .0393487 .0134978
bs1xk3 -.0023791 .0352282 -.0376073 .0271309
hs1xk3 .0597598 .084146 -.0243862 .0369054
scale 30.51423 39.82811 -9.31388 1.113411
technology~p -.0237127 -.0215637 -.0021491 .0001244
hhi -4.675948 -5.635538 .9595902 .0455791
fs1 .6622176 .5583557 .1038619 .0324919
bs1 .0648765 -.157252 .2221285 .069869
hs1 -.2778513 .7233739 -1.001225 .0803289
xk3 -.0053819 .0016942 -.007076 .0021953
labourqual~y .5696095 .705633 -.1360234 .0033729
FE2 RE2 Difference S.E.
(b) (B) (b-B) sqrt(diag(V_b-V_B))
Coefficients
. hausman FE2 RE2
Prob > F = 0.0000
F( 1, 112105) = 717.475
H0: no first-order autocorrelation
Wooldridge test for autocorrelation in panel data
. xtserial lnTFP2 labourquality xk3 hs1 bs1 fs1 hhi technologygap scale hs1xk3 bs1xk3 fs1xk3
chi2 (534139) = 1.6e+42
H0: sigma(i)^2 = sigma^2 for all i
in fixed effect regression model
Modified Wald test for groupwise heteroskedasticity
. xttest3
198
13. Regression results with Driscoll-Kraay standard errors
_cons 3.4454 .0553666 62.23 0.000 3.33688 3.553921
scale 4.691518 1.671864 2.81 0.005 1.414593 7.968442
technologygap -.0746736 .0028428 -26.27 0.000 -.0802457 -.0691015
hhi -.7589196 .280013 -2.71 0.007 -1.307757 -.2100824
fs1xk3 -.0070641 .0147524 -0.48 0.632 -.0359795 .0218512
bs1xk3 -.1657138 .0952589 -1.74 0.082 -.3524253 .0209978
hs1xk3 .2920286 .1401943 2.08 0.037 .017242 .5668153
fs1 -.0044794 .0947464 -0.05 0.962 -.1901864 .1812275
bs1 .5592702 .2138084 2.62 0.009 .1401969 .9783436
hs1 -.545393 .2417155 -2.26 0.024 -1.019165 -.0716205
xk3 .0010251 .0050437 0.20 0.839 -.0088607 .0109108
labourquality .1982615 .0506641 3.91 0.000 .0989576 .2975653
lnTFP2 Coef. Std. Err. t P>|t| [95% Conf. Interval]
Drisc/Kraay
within R-squared = 0.1529
maximum lag: 2 Prob > F = 0.0000
Group variable (i): ID F( 11, 30412) = 118.40
Method: Fixed-effects regression Number of groups = 30413
Regression with Driscoll-Kraay standard errors Number of obs = 36930
> f export1==1 & w>1 & sohuu_16==0,fe
. xtscc lnTFP2 labourquality xk3 hs1 bs1 fs1 hs1xk3 bs1xk3 fs1xk3 hhi technologygap scale i
199
14. Regression results using GMM (Generalized Method of Moments) estimation.
200
201
Appendix A18: Total factor productivity spillovers from FDI in manufacturing:
the role of provincial institution.
1. Fixed Effects Estimation Method
2. Random Effects Estimation Method
F test that all u_i=0: F(61570, 74) = 11.48 Prob > F = 0.0000
rho .93279899 (fraction of variance due to u_i)
sigma_e .02143869
sigma_u .0798738
_cons .0371701 .072669 0.51 0.611 -.1076262 .1819663
HHI .0084495 .0033013 2.56 0.013 .0018716 .0150274
scale2 .0448647 .0088625 5.06 0.000 .0272057 .0625237
technologygap1 -.5771624 .1854025 -3.11 0.003 -.946585 -.2077398
papi .0021119 .0017816 1.19 0.240 -.0014379 .0056618
f1papi .0011083 .0147566 0.08 0.940 -.0282947 .0305114
hpapi -.0004215 .0003775 -1.12 0.268 -.0011737 .0003308
vertical -.0019485 .5784988 -0.00 0.997 -1.154632 1.150735
horizontal1 -.0026256 .0190073 -0.14 0.891 -.0404984 .0352472
labourquality .0236303 .0036422 6.49 0.000 .0163729 .0308876
lnTFP2 Coef. Std. Err. t P>|t| [95% Conf. Interval]
corr(u_i, Xb) = -0.3903 Prob > F = 0.0000
F(9,74) = 14.21
overall = 0.0194 max = 2
between = 0.0193 avg = 1.0
within = 0.6334 min = 1
R-sq: Obs per group:
Group variable: ID Number of groups = 61,571
Fixed-effects (within) regression Number of obs = 61,654
> u_16==0 , fe
. xtreg lnTFP2 labourquality horizontal1 vertical hpapi f1papi papi technologygap1 scale2 HHI if sohu
rho .91284784 (fraction of variance due to u_i)
sigma_e .02143869
sigma_u .06938384
_cons .1107025 .0098089 11.29 0.000 .0914773 .1299276
HHI .0043263 .000296 14.61 0.000 .003746 .0049065
scale2 .0053481 .0004034 13.26 0.000 .0045575 .0061386
technologygap1 .9967443 .0803081 12.41 0.000 .8393434 1.154145
papi .0002289 .0002657 0.86 0.389 -.0002919 .0007497
f1papi .0000812 .0010652 0.08 0.939 -.0020066 .002169
hpapi -.0002831 .0000663 -4.27 0.000 -.0004129 -.0001532
vertical -.0099259 .0393546 -0.25 0.801 -.0870596 .0672078
horizontal1 .00613 .0022461 2.73 0.006 .0017278 .0105322
labourquality .0168617 .0003946 42.73 0.000 .0160882 .0176352
lnTFP2 Coef. Std. Err. z P>|z| [95% Conf. Interval]
corr(u_i, X) = 0 (assumed) Prob > chi2 = 0.0000
Wald chi2(9) = 2798.62
overall = 0.0434 max = 2
between = 0.0435 avg = 1.0
within = 0.1033 min = 1
R-sq: Obs per group:
Group variable: ID Number of groups = 61,571
Random-effects GLS regression Number of obs = 61,654
> u_16==0 , re
. xtreg lnTFP2 labourquality horizontal1 vertical hpapi f1papi papi technologygap1 scale2 HHI if sohu
202
3. Hausman test
4. Test for Heteroskedasticity
5. Regression results with Driscoll-Kraay standard errors
Prob>chi2 = 0.0000
= 137.98
chi2(9) = (b-B)'[(V_b-V_B)^(-1)](b-B)
Test: Ho: difference in coefficients not systematic
B = inconsistent under Ha, efficient under Ho; obtained from xtreg
b = consistent under Ho and Ha; obtained from xtreg
HHI .0084495 .0043263 .0041232 .003288
scale2 .0448647 .0053481 .0395167 .0088534
technology~1 -.5771624 .9967443 -1.573907 .1671069
papi .0021119 .0002289 .001883 .0017616
f1papi .0011083 .0000812 .0010271 .0147181
hpapi -.0004215 -.0002831 -.0001384 .0003717
vertical -.0019485 -.0099259 .0079774 .5771586
horizontal1 -.0026256 .00613 -.0087557 .0188741
labourqual~y .0236303 .0168617 .0067686 .0036208
FE RE Difference S.E.
(b) (B) (b-B) sqrt(diag(V_b-V_B))
Coefficients
. hausman FE RE
Prob>chi2 = 0.0000
chi2 (61571) = 1.2e+31
H0: sigma(i)^2 = sigma^2 for all i
in fixed effect regression model
Modified Wald test for groupwise heteroskedasticity
. xttest3
.
_cons -.397578 .0033182 -119.82 0.000 -.4040818 -.3910742
HHI .0093174 .0010275 9.07 0.000 .0073035 .0113313
scale2 .0296108 .0031735 9.33 0.000 .0233907 .0358309
technologygap1 -.5135189 .0345144 -14.88 0.000 -.5811673 -.4458705
papi .0123293 .0003846 32.06 0.000 .0115755 .013083
f1papi -.0214378 .0038719 -5.54 0.000 -.0290266 -.0138489
hpapi -.0017618 .0002783 -6.33 0.000 -.0023072 -.0012163
vertical .8482849 .1473103 5.76 0.000 .5595563 1.137013
horizontal1 -.0170831 .0058175 -2.94 0.003 -.0284855 -.0056808
labourquality .0465572 .0061803 7.53 0.000 .0344437 .0586706
lnTFP2 Coef. Std. Err. t P>|t| [95% Conf. Interval]
Drisc/Kraay
within R-squared = 0.1589
maximum lag: 1 Prob > F = 0.0000
Group variable (i): ID F( 9, 61571) = 7.01e+10
Method: Fixed-effects regression Number of groups = 61572
Regression with Driscoll-Kraay standard errors Number of obs = 61656
> u_16==0 , fe
. xtscc lnTFP2 labourquality horizontal1 vertical hpapi f1papi papi technologygap1 scale2 HHI if sohu
203
6. Regression results using Driscoll-Kraay standard errors for enterprises with fewer
than 200 employees
7. Regression results using Driscoll-Kraay standard errors for enterprises with TFP
greater than 75% of the sample
_cons -.531728 .0428778 -12.40 0.000 -.6157688 -.4476873
HHI .0104649 .003207 3.26 0.001 .0041792 .0167507
scale2 .0985776 .0174634 5.64 0.000 .0643492 .132806
technologygap1 -.4763747 .0573766 -8.30 0.000 -.5888332 -.3639163
papi .015287 .0014951 10.22 0.000 .0123565 .0182175
f1papi -.0194797 .0154515 -1.26 0.207 -.0497647 .0108053
hpapi -.0039054 .0006753 -5.78 0.000 -.005229 -.0025819
vertical .7972944 .5977238 1.33 0.182 -.3742473 1.968836
horizontal1 -.0279036 .0063012 -4.43 0.000 -.040254 -.0155533
labourquality .0598313 .011113 5.38 0.000 .0380498 .0816128
lnTFP2 Coef. Std. Err. t P>|t| [95% Conf. Interval]
Drisc/Kraay
within R-squared = 0.1544
maximum lag: 1 Prob > F = 0.0666
Group variable (i): ID F( 9, 57505) = 1.78
Method: Fixed-effects regression Number of groups = 57506
Regression with Driscoll-Kraay standard errors Number of obs = 57567
> u_16==0 & laodong_8<200 , fe
. xtscc lnTFP2 labourquality horizontal1 vertical hpapi f1papi papi technologygap1 scale2 HHI if sohu
_cons .1544161 .0071708 21.53 0.000 .1403607 .1684716
HHI .0053359 .0009589 5.56 0.000 .0034564 .0072154
scale2 .0589109 .0002614 225.36 0.000 .0583985 .0594233
technologygap1 -1.231672 .3812199 -3.23 0.001 -1.978897 -.4844468
papi .0013304 .0003194 4.17 0.000 .0007044 .0019565
f1papi -.0083615 .0033969 -2.46 0.014 -.0150197 -.0017033
hpapi -.0006309 .0002315 -2.73 0.006 -.0010847 -.0001771
vertical .3264073 .1352183 2.41 0.016 .0613672 .5914474
horizontal1 .0128118 .0004029 31.80 0.000 .012022 .0136016
labourquality .0043558 .0002122 20.53 0.000 .0039398 .0047718
lnTFP2 Coef. Std. Err. t P>|t| [95% Conf. Interval]
Drisc/Kraay
within R-squared = 0.7428
maximum lag: 1 Prob > F = 0.0000
Group variable (i): ID F( 9, 18838) = 10.44
Method: Fixed-effects regression Number of groups = 18839
Regression with Driscoll-Kraay standard errors Number of obs = 18858
> _16==0 & quart >3 , fe
. xtscc lnTFP2 labourquality horizontal1 vertical hpapi f1papi papi technologygap1 scale2 HHI if sohuu
204
8. Regression results using Driscoll-Kraay standard errors for enterprises with TFP
greater than 50% of the sample.
9. Regression results using Driscoll-Kraay standard errors for enterprises with TFP
less than or equal to 50% of the sample.
_cons .1523567 .000332 458.91 0.000 .151706 .1530074
HHI .0095743 .0003375 28.37 0.000 .0089128 .0102357
scale2 .0387018 .0007075 54.70 0.000 .0373152 .0400885
technologygap1 -.3696987 .0702697 -5.26 0.000 -.507429 -.2319683
papi .0007877 .0001572 5.01 0.000 .0004797 .0010957
f1papi -.0071996 .0005242 -13.73 0.000 -.0082271 -.0061722
hpapi -.0004135 .0000685 -6.03 0.000 -.0005478 -.0002792
vertical .2865938 .0224444 12.77 0.000 .2426023 .3305854
horizontal1 .0032613 .0005407 6.03 0.000 .0022016 .0043211
labourquality .0104575 .0021302 4.91 0.000 .0062822 .0146327
lnTFP2 Coef. Std. Err. t P>|t| [95% Conf. Interval]
Drisc/Kraay
within R-squared = 0.6085
maximum lag: 1 Prob > F = 0.0000
Group variable (i): ID F( 9, 38349) = 2.25e+10
Method: Fixed-effects regression Number of groups = 38350
Regression with Driscoll-Kraay standard errors Number of obs = 38399
> _16==0 & quart >2 , fe
. xtscc lnTFP2 labourquality horizontal1 vertical hpapi f1papi papi technologygap1 scale2 HHI if sohuu
_cons .32944 .0217693 15.13 0.000 .2867707 .3721093
HHI .0030487 .0022376 1.36 0.173 -.0013371 .0074346
scale2 -.0849005 .1681048 -0.51 0.614 -.414397 .2445961
technologygap1 2.007043 .0765909 26.20 0.000 1.85692 2.157166
papi -.0059104 .0004797 -12.32 0.000 -.0068507 -.0049701
f1papi .0823854 .0056426 14.60 0.000 .0713255 .0934453
hpapi .0022179 .0001604 13.82 0.000 .0019034 .0025324
vertical -3.318772 .2393857 -13.86 0.000 -3.787984 -2.84956
horizontal1 .0759647 .0139121 5.46 0.000 .048696 .1032335
labourquality .010837 .0007288 14.87 0.000 .0094084 .0122656
lnTFP2 Coef. Std. Err. t P>|t| [95% Conf. Interval]
Drisc/Kraay
within R-squared = 0.8931
maximum lag: 1 Prob > F = 0.0000
Group variable (i): ID F( 9, 23244) = 2.44e+09
Method: Fixed-effects regression Number of groups = 23245
Regression with Driscoll-Kraay standard errors Number of obs = 23257
> _16==0 & quart <3 , fe
. xtscc lnTFP2 labourquality horizontal1 vertical hpapi f1papi papi technologygap1 scale2 HHI if sohuu
205
LIST OF THE ARTICLES RELATED TO THE THESIS
1. Ngoc, P. T. B., Vu, H. Q., & Long, P. D. (2022). Domestic total factor
productivity with trade and heterogenous foreign direct investment in
developing countries: a case of Vietnamese manufacturing. International
Journal of Emerging Markets. (SSCI/ISI, Q2)
2. Ngoc, P. T. B., Long, P. D., & Vu, H. Q., (2022). The impact of absorbing
productivity spillover on export ability: evidence from an emerging
market. Cogent Economics & Finance, 10(1), 2152938. (ESCI, Q2)
3. Vu, H. Q., Ngoc, P. T. B., & Quyen, N. L. H. T. T. (2022). The Effect of
Institutions on Productivity Spillovers from FDI to Domestic Firms: Evidence
from Vietnam. Global Business & Finance Review, 27(3), 28. (Scopus, Q4).
4. Ngọc, P. T. B., P., Long, P. D., & Vũ, H. Q., (2021). Sự lan tỏa năng suất từ
doanh nghiệp FDI dang doanh nghiệp Việt Nam: bằng chứng qua hoạt động
xuất khẩu và khả năng hấp thụ.Tạp chí Nghiên cứu kinh tế, 513(2), 12-22. (
Danh sách tạp chí HD9CDGSNN, mức 1 điểm).
5. Ngoc, P. T. B., Long, P. D., & Vu, H. Q., (2020). Absorbing productivity
spillover and export ability: Evidence from Vietnamese manufacturing. In
International Conference on Business and Finance 2020 (ICBF 2020) (pp. 152-
168). University of Economics Ho Chi Minh City, Vietnam.