Running a PPML regression in python using GME package from USITC

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A couple of month ago, I just learned from a professor from my course about the new gravity dataset made by the United States International Trade Commission (USITC). Turns out, the USITC has not only a gravity dataset, but a dedicated page called "gravity portal" which contain some other data and software related to gravity estimation. And boy how excited I was when I just learned that they have made a python package to run a PPML regression.

For non-economics bunch, us trade people called international trade estimation models as "gravity model". According to wikipedia, this model first introduced by Walter Isard, an American economics, in 1954, it is a model constructed to mimic a gravity. It takes the form of:

$$ Trade_{ij}=\frac{GDP_{i}*GDP_{j}}{Distance_{ij}} ..............(1) $$

That is, the amount of bilateral trade made by two countries is related to their economic size (mass in the normal gravity equation) and their distance, as a proxy for trade cost. Obviously these days there are many extension to this model, but I won't dwell on it for now. For now, let's focus on the PPML extension.

Normally, we linearised (1) by log transformation. That way, it is easy to estimate (1) (or it's extensions) with OLS. Silva and Tenreyro (2006) critised the use of log-log OLS by showing that it is very hard to treat heteroskedasticity in trade data with OLS extension, let alone 0 trade data. In the paper, they show that non-linear regression especially PPLM performs much better. The paper became wildly popular among trade modeler. These days, it is hard to find a gravity modeler not using this method.

Running PPLM is pretty easy in STATA. Specialised packages such as this really makes it hard for me to switch to other language like R or python. As an international trade scholar, I have to rely on this package so I can focus on my research context. But now that I know PPLM package is out there, moving to python full-time become extra compelling!

So let's try how this works. First, import needed packages and grab the data that I used to run my thesis:

In [55]:
import os
import pandas as pd
import numpy as np
import gme as gme
os.chdir('C:\github')
trade=pd.read_stata('gravitytest.dta')
trade
Out[55]:
DISIC508 psid HS2012 hs10 country imk imu year hs6 imu6 ... entry_tp_d eu_o eu_d iso lgdp_o lgdp_d ldistw lmt lntma lntmb
0 15497 47623.0 9029109000 9.029109e+09 4.0 187.0 2008.0 902910 24568.0 ... NaN NaN NaN master only (1) NaN NaN NaN 1.791759 0.000000 0.0
1 29270 17530.0 9306900000 9.306900e+09 1112.0 8030.0 2008.0 930690 563383.0 ... NaN NaN NaN master only (1) NaN NaN NaN 0.000000 0.000000 0.0
2 28113 9703.0 8483109000 8.483109e+09 REUNION 172.0 2369.0 2008.0 848310 532539.0 ... NaN NaN NaN master only (1) NaN NaN NaN 1.791759 0.000000 0.0
3 26202 54660.0 8538901900 8.538902e+09 REUNION 1.0 24.0 2008.0 853890 12746.0 ... NaN NaN NaN master only (1) NaN NaN NaN 1.791759 0.000000 0.0
4 26202 54660.0 8413811000 8.413811e+09 REUNION 738.0 18487.0 2008.0 841381 20723.0 ... NaN NaN NaN master only (1) NaN NaN NaN 1.791759 0.000000 0.0
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
416912 13085.0 5201000000 5.201000e+09 ZIMBABWE 4208425.0 8388479.0 2012.0 520100 183987968.0 ... 58.0 0.0 0.0 matched (3) 23.246786 27.499453 9.056228 0.000000 1.098612 0.0
416913 13805.0 5201000000 5.201000e+09 ZIMBABWE 306143.0 732298.0 2012.0 520100 19769164.0 ... 58.0 0.0 0.0 matched (3) 23.246786 27.499453 9.056228 0.000000 1.098612 0.0
416914 64381.0 2401201000 2.401201e+09 ZIMBABWE 10830.0 87615.0 2012.0 240120 6828994.0 ... 58.0 0.0 0.0 matched (3) 23.246786 27.499453 9.056228 1.791759 1.098612 0.0
416915 27480.0 2401201000 2.401201e+09 ZIMBABWE 57600.0 77760.0 2012.0 240120 77760.0 ... 58.0 0.0 0.0 matched (3) 23.246786 27.499453 9.056228 1.791759 1.098612 0.0
416916 27475.0 2401101000 2.401101e+09 ZIMBABWE 495000.0 3019500.0 2012.0 240110 68412784.0 ... 58.0 0.0 0.0 matched (3) 23.246786 27.499453 9.056228 1.791759 1.098612 0.0

416917 rows × 107 columns

I construct that dataset from Indonesian Bureau of Statistics. It has information on import made by firms in Indonesia in 2008-2012. As you can see, that dataset has 107 columns. I won't use them all for this test. let's kick the rest out and keep only what we need.

In [56]:
trade=trade[['DISIC508','psid','country','imu6','lmt','lntma','lntmb','contig','comlang_off','ldistw','lgdp_o','lgdp_d','fta_wto','year']]
trade
Out[56]:
DISIC508 psid country imu6 lmt lntma lntmb contig comlang_off ldistw lgdp_o lgdp_d fta_wto year
0 15497 47623.0 24568.0 1.791759 0.000000 0.0 NaN NaN NaN NaN NaN NaN 2008.0
1 29270 17530.0 563383.0 0.000000 0.000000 0.0 NaN NaN NaN NaN NaN NaN 2008.0
2 28113 9703.0 REUNION 532539.0 1.791759 0.000000 0.0 NaN NaN NaN NaN NaN NaN 2008.0
3 26202 54660.0 REUNION 12746.0 1.791759 0.000000 0.0 NaN NaN NaN NaN NaN NaN 2008.0
4 26202 54660.0 REUNION 20723.0 1.791759 0.000000 0.0 NaN NaN NaN NaN NaN NaN 2008.0
... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
416912 13085.0 ZIMBABWE 183987968.0 0.000000 1.098612 0.0 0.0 0.0 9.056228 23.246786 27.499453 0.0 2012.0
416913 13805.0 ZIMBABWE 19769164.0 0.000000 1.098612 0.0 0.0 0.0 9.056228 23.246786 27.499453 0.0 2012.0
416914 64381.0 ZIMBABWE 6828994.0 1.791759 1.098612 0.0 0.0 0.0 9.056228 23.246786 27.499453 0.0 2012.0
416915 27480.0 ZIMBABWE 77760.0 1.791759 1.098612 0.0 0.0 0.0 9.056228 23.246786 27.499453 0.0 2012.0
416916 27475.0 ZIMBABWE 68412784.0 1.791759 1.098612 0.0 0.0 0.0 9.056228 23.246786 27.499453 0.0 2012.0

416917 rows × 14 columns

Much better. But let's make a two-digit ISIC code for fixed effect, and then show summary statistics of my data.

In [57]:
pd.options.mode.chained_assignment = None  # default='warn', dapat dari stackoverflow
trade['isic2']=trade['DISIC508'].str[:2] # bikin isic 2 digit
trade.describe(include='all')
Out[57]:
DISIC508 psid country imu6 lmt lntma lntmb contig comlang_off ldistw lgdp_o lgdp_d fta_wto year isic2
count 416917 416917.000000 416917 4.169170e+05 416917.000000 416917.000000 416917.000000 416528.000000 416528.0 416528.000000 416334.000000 416528.000000 416156.000000 416917.000000 416917
unique 251 NaN 207 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 24
top NaN JAPAN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
freq 346920 NaN 78312 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 346920
mean NaN 39929.765625 NaN 1.578270e+06 1.640825 0.046497 0.069130 0.039210 0.0 8.407879 28.012295 27.228710 0.626592 2003.304932 NaN
std NaN 19671.726562 NaN 1.813418e+07 0.921622 0.250223 0.280148 0.194094 0.0 0.755016 1.452945 0.225774 0.483710 7.055770 NaN
min NaN 1892.000000 NaN 1.000000e+00 0.000000 0.000000 0.000000 0.000000 0.0 6.194654 17.226353 26.958157 0.000000 2008.000000 NaN
25% NaN 17958.000000 NaN 2.010000e+03 1.791759 0.000000 0.000000 0.000000 0.0 8.184400 26.498676 27.014057 0.000000 2009.000000 NaN
50% NaN 44835.000000 NaN 2.078600e+04 1.791759 0.000000 0.000000 0.000000 0.0 8.532608 28.262346 27.287390 1.000000 2010.000000 NaN
75% NaN 56871.000000 NaN 1.993940e+05 2.397895 0.000000 0.000000 0.000000 0.0 8.609140 29.334930 27.463705 1.000000 2011.000000 NaN
max NaN 72930.000000 NaN 1.450820e+09 11.736077 3.401197 2.639057 1.000000 0.0 9.871479 30.413757 27.499453 1.000000 2012.000000 NaN

looks good. So many empty cells lol. Anyway, this data set has only 1 importing country, that is, Indonesia. Of course I have varied firms, so instead of a country, let's make these firms as the importer. I have many exporting countries, including tariff asociated with their purchase (varies with country of origin, type of goods in HS-6-digit code, and year) which I dubbed 'lmt' in the dataset. I also have the number of SPS (lntma) and TBT (lntmb) associated with goods purchased by those firms.

so first of all, we set the environment to prepare the data for the regression. This is like using xtset in STATA for xtreg, I think.

In [58]:
gme_data=gme.EstimationData(data_frame=trade,
                           imp_var_name='psid',
                           exp_var_name='country',
                           trade_var_name='imu6',
                           sector_var_name='isic2',
                            year_var_name='year')
print(gme_data)
number of countries: 2463 
number of exporters: 207 
number of importers: 2256 
number of years: 5 
number of sectors: 24 
dimensions: (416917, 15)

Now that it all set, let's let the machine go brrrr. Don't forget to set your fixed effects.

In [59]:
gme_model=gme.EstimationModel(estimation_data=gme_data,
                             lhs_var='imu6',
                             rhs_var=['lmt','lntma','lntmb','lgdp_o','lgdp_d','ldistw','fta_wto','contig','comlang_off'],
                             fixed_effects=['country','isic2'])
estimates=gme_model.estimate()
select specification variables: ['lmt', 'lntma', 'lntmb', 'lgdp_o', 'lgdp_d', 'ldistw', 'fta_wto', 'contig', 'comlang_off', 'imu6', 'psid', 'country', 'year', 'isic2'], Observations excluded by user: {'rows': 0, 'columns': 1}
drop_intratrade: no, Observations excluded by user: {'rows': 0, 'columns': 0}
drop_imp: none, Observations excluded by user: {'rows': 0, 'columns': 0}
drop_exp: none, Observations excluded by user: {'rows': 0, 'columns': 0}
keep_imp: all available, Observations excluded by user: {'rows': 0, 'columns': 0}
keep_exp: all available, Observations excluded by user: {'rows': 0, 'columns': 0}
drop_years: none, Observations excluded by user: {'rows': 0, 'columns': 0}
keep_years: all available, Observations excluded by user: {'rows': 0, 'columns': 0}
drop_missing: yes, Observations excluded by user: {'rows': 955, 'columns': 0}
Estimation began at 12:39 PM  on Aug 12, 2020
Omitted Columns: ['comlang_off', 'country_fe_PAPUA NEW GUINEA', 'country_fe_ZIMBABWE', 'isic2_fe_37', 'country_fe_MALAYSIA', 'country_fe_ZAMBIA', 'isic2_fe_36']
Estimation completed at 12:40 PM  on Aug 12, 2020
In [60]:
result=gme.combine_sector_results(estimates)
result.head(8)
Out[60]:
all_coeff all_stderr all_pvalue
lmt -0.639771 0.021540 7.396863e-194
lntma 0.561644 0.033192 3.152121e-64
lntmb 0.364746 0.036307 9.556518e-24
lgdp_o 0.393253 0.191870 4.040557e-02
lgdp_d 0.946128 0.157832 2.040966e-09
ldistw -2.106424 0.409394 2.671980e-07
fta_wto 0.073430 0.100158 4.634703e-01
contig -6.504605 1.125489 7.498616e-09

And there you go. Table above shows coefficients from the PPLM. I think the python ran this thing a bit faster than STATA but I have to check again since my PPLM in STATA has more stuff in it.

The result is very close to my STATA result I think (again I never really ran it with only these number of variables for my thesis). tariff (lmt) and Indonesian GDP (lgdp_d) have an expected sign and somewhat expected magnitude Distance has an expected sign but it looks very high indeed (remember this is firm's purchase). FTA has no significance which is expected (must be absorbed by FE and tariff).

NTM still shows a positive and significant result, which is a bit intriguing. One argument that make sense is reverse causality. That is, the government seems to be wary of Current Account Deficit (CAD). The higher the observed import, the more likely it is willing to raise Non-Tariff Measures.

Of course this is just a quick thought. I am treating this causality problem a bit more serious on my paper with some ways or the other. Bottom line is, PPLM is now working on python and I have one less reason to stick with STATA. Now when the whole ANU switch to R and python, I think I will be ready. Ha ha ha ha!

Thanks for reading this post, I hope it's useful. Happy to reply to any comments. Here's a longer result of the regression.

In [61]:
# long result
estimates.keys()
results=estimates['all']
results.summary()
Out[61]:
Generalized Linear Model Regression Results
Dep. Variable: imu6 No. Iterations: 12
Model: GLM Df Residuals: 415640
Model Family: Poisson Df Model: 191
Link Function: log Scale: 1.0000
Method: IRLS Log-Likelihood: -1.5906e+12
Covariance Type: HC1 Deviance: 3.1813e+12
No. Observations: 415832 Pearson chi2: 3.96e+13
coef std err t P>|t| [0.025 0.975]
lmt -0.6398 0.022 -29.701 0.000 -0.682 -0.598
lntma 0.5616 0.033 16.921 0.000 0.497 0.627
lntmb 0.3647 0.036 10.046 0.000 0.294 0.436
lgdp_o 0.3933 0.192 2.050 0.040 0.017 0.769
lgdp_d 0.9461 0.158 5.995 0.000 0.637 1.255
ldistw -2.1064 0.409 -5.145 0.000 -2.909 -1.304
fta_wto 0.0734 0.100 0.733 0.463 -0.123 0.270
contig -6.5046 1.125 -5.779 0.000 -8.711 -4.299
country_fe_AFGHANISTAN 0.4090 0.466 0.877 0.380 -0.505 1.323
country_fe_ALBANIA -0.6846 0.795 -0.862 0.389 -2.242 0.873
country_fe_ALGERIA 1.4254 1.025 1.391 0.164 -0.583 3.434
country_fe_ANGOLA 0.5248 0.607 0.865 0.387 -0.664 1.714
country_fe_ANTIGUA DNA BARBUDA 1.7079 0.911 1.875 0.061 -0.078 3.494
country_fe_ARGENTINA -0.2130 0.746 -0.286 0.775 -1.676 1.249
country_fe_ARMENIA -2.6577 0.859 -3.095 0.002 -4.340 -0.975
country_fe_AUSTRALIA -3.7090 0.991 -3.741 0.000 -5.652 -1.766
country_fe_AUSTRIA -3.4246 0.725 -4.725 0.000 -4.845 -2.004
country_fe_BAHAMAS 3.1000 0.629 4.930 0.000 1.868 4.332
country_fe_BAHRAIN 0.3896 0.659 0.592 0.554 -0.901 1.680
country_fe_BANGLADESH -2.7922 0.787 -3.549 0.000 -4.334 -1.250
country_fe_BARBADOS 2.9386 0.751 3.912 0.000 1.466 4.411
country_fe_BELARUS 1.4382 0.532 2.705 0.007 0.396 2.480
country_fe_BELGIUM -2.0392 0.744 -2.741 0.006 -3.497 -0.581
country_fe_BELIZE 1.1173 1.105 1.011 0.312 -1.048 3.282
country_fe_BENIN 1.7086 0.593 2.883 0.004 0.547 2.870
country_fe_BOLIVIA -1.5380 0.663 -2.319 0.020 -2.838 -0.238
country_fe_BOSNIA AND HERZEGOVINA 0.4275 1.082 0.395 0.693 -1.693 2.548
country_fe_BOTSWANA -7.9549 0.479 -16.601 0.000 -8.894 -7.016
country_fe_BRAZIL -0.6946 0.935 -0.743 0.458 -2.528 1.139
country_fe_BRUNEI DARUSSALAM -5.0344 0.860 -5.851 0.000 -6.721 -3.348
country_fe_BULGARIA -0.8735 0.579 -1.508 0.132 -2.009 0.262
country_fe_BURKINA FASO 0.7212 0.604 1.194 0.232 -0.462 1.905
country_fe_CAMBODIA -2.6752 0.871 -3.073 0.002 -4.382 -0.969
country_fe_CAMEROON 0.0639 0.578 0.111 0.912 -1.069 1.197
country_fe_CANADA -0.9668 0.924 -1.046 0.295 -2.778 0.844
country_fe_CAPE VERDE 3.3896 0.832 4.075 0.000 1.759 5.020
country_fe_CENTRAL AFRICAN REPUBLIC 0.6676 0.796 0.839 0.402 -0.892 2.228
country_fe_CHAD 1.4187 0.739 1.919 0.055 -0.030 2.868
country_fe_CHILE -0.7792 0.664 -1.174 0.241 -2.080 0.522
country_fe_CHINA -5.9689 1.306 -4.571 0.000 -8.528 -3.410
country_fe_COLOMBIA 0.5011 0.764 0.656 0.512 -0.997 1.999
country_fe_COMOROS -4.4323 1.051 -4.217 0.000 -6.492 -2.372
country_fe_COSTA RICA 1.3587 0.671 2.024 0.043 0.043 2.674
country_fe_COTE D'IVOIRE 0.7333 0.552 1.329 0.184 -0.348 1.815
country_fe_CROATIA -0.8040 0.744 -1.080 0.280 -2.263 0.655
country_fe_CUBA 1.4541 0.761 1.911 0.056 -0.037 2.946
country_fe_CYPRUS 0.2204 0.596 0.370 0.712 -0.948 1.389
country_fe_CZECH REPUBLIC -2.0958 0.645 -3.251 0.001 -3.359 -0.832
country_fe_DENMARK -2.8564 0.701 -4.074 0.000 -4.231 -1.482
country_fe_DJIBOUTI 1.6503 0.755 2.187 0.029 0.171 3.129
country_fe_ECUADOR 0.2109 0.792 0.266 0.790 -1.341 1.763
country_fe_EGYPT -0.7402 0.680 -1.089 0.276 -2.073 0.592
country_fe_EL SALVADOR 2.7996 0.642 4.363 0.000 1.542 4.057
country_fe_ESTONIA 0.6244 0.550 1.135 0.256 -0.454 1.703
country_fe_ETHIOPIA -1.1273 0.722 -1.561 0.118 -2.542 0.288
country_fe_FIJI -0.4756 0.866 -0.549 0.583 -2.174 1.222
country_fe_FINLAND -2.3117 0.668 -3.460 0.001 -3.621 -1.002
country_fe_FRANCE -3.2455 1.010 -3.213 0.001 -5.225 -1.266
country_fe_GABON 0.9253 0.591 1.566 0.117 -0.233 2.083
country_fe_GAMBIA 3.0057 0.869 3.459 0.001 1.303 4.709
country_fe_GEORGIA 0.7857 0.535 1.469 0.142 -0.263 1.834
country_fe_GERMANY -4.1535 1.053 -3.944 0.000 -6.217 -2.090
country_fe_GHANA 1.0493 0.552 1.899 0.058 -0.033 2.132
country_fe_GREECE -1.0759 0.695 -1.547 0.122 -2.439 0.287
country_fe_GUATEMALA 2.7991 0.699 4.007 0.000 1.430 4.168
country_fe_GUINEA 2.4787 0.655 3.782 0.000 1.194 3.763
country_fe_GUINEA BISSAU 3.0531 0.832 3.669 0.000 1.422 4.684
country_fe_GUYANA 3.3098 0.896 3.693 0.000 1.553 5.066
country_fe_HAITI 4.4323 0.876 5.060 0.000 2.715 6.149
country_fe_HONDURAS 2.4895 0.642 3.879 0.000 1.232 3.747
country_fe_HONG KONG -4.9675 0.869 -5.713 0.000 -6.672 -3.263
country_fe_HUNGARY -1.8897 0.607 -3.116 0.002 -3.078 -0.701
country_fe_ICELAND -0.5570 0.855 -0.652 0.515 -2.232 1.118
country_fe_INDIA -4.5392 1.069 -4.247 0.000 -6.634 -2.444
country_fe_INDONESIA -7.4981 1.514 -4.951 0.000 -10.466 -4.530
country_fe_IRAN -0.3197 0.805 -0.397 0.691 -1.898 1.259
country_fe_IRELAND -0.4954 0.650 -0.762 0.446 -1.770 0.779
country_fe_ISRAEL -3.9824 0.702 -5.676 0.000 -5.358 -2.607
country_fe_ITALY -3.9412 0.977 -4.034 0.000 -5.856 -2.026
country_fe_JAMAICA 2.6006 0.640 4.062 0.000 1.346 3.856
country_fe_JAPAN -5.5958 1.237 -4.525 0.000 -8.019 -3.172
country_fe_JORDAN 1.1906 0.522 2.282 0.023 0.168 2.213
country_fe_KAZAKHSTAN -1.2164 0.867 -1.403 0.160 -2.915 0.482
country_fe_KENYA -1.0302 0.635 -1.622 0.105 -2.275 0.214
country_fe_KIRIBATI -0.2963 1.067 -0.278 0.781 -2.388 1.795
country_fe_KOREA SELATAN -4.9764 0.980 -5.076 0.000 -6.898 -3.055
country_fe_KUWAIT 0.1261 0.703 0.179 0.858 -1.252 1.504
country_fe_KYRGYZSTAN -0.4947 0.661 -0.748 0.454 -1.790 0.801
country_fe_LAOS -3.7586 0.655 -5.736 0.000 -5.043 -2.474
country_fe_LATVIA 0.8944 0.549 1.628 0.104 -0.182 1.971
country_fe_LEBANON 0.2168 0.615 0.352 0.725 -0.989 1.422
country_fe_LESOTHO -1.5089 0.658 -2.295 0.022 -2.798 -0.220
country_fe_LIBERIA 3.2042 0.788 4.067 0.000 1.660 4.748
country_fe_LIBYAN ARAB JAMAHIRIYA 1.4757 0.576 2.562 0.010 0.347 2.605
country_fe_LITHUANIA 1.1799 0.560 2.107 0.035 0.082 2.277
country_fe_LUXEMBURG -1.7115 0.626 -2.734 0.006 -2.939 -0.484
country_fe_MACAU -3.3526 0.697 -4.810 0.000 -4.719 -1.987
country_fe_MADAGASKAR 0.3656 0.669 0.547 0.585 -0.945 1.676
country_fe_MALAWI 1.1928 0.604 1.974 0.048 0.008 2.377
country_fe_MALI 1.5867 0.683 2.323 0.020 0.248 2.925
country_fe_MALTA 0.3692 0.667 0.554 0.580 -0.938 1.676
country_fe_MAROCCO 0.9948 0.625 1.591 0.112 -0.231 2.220
country_fe_MARSHALL ISLANDS 1.4335 0.995 1.440 0.150 -0.517 3.384
country_fe_MAURITANIA 2.7633 0.674 4.103 0.000 1.443 4.083
country_fe_MAURITIUS -0.2280 0.620 -0.368 0.713 -1.444 0.988
country_fe_MEXICO -1.1895 0.871 -1.365 0.172 -2.898 0.519
country_fe_MOLDOVA, REPUBLIC OF -1.1614 0.833 -1.395 0.163 -2.793 0.470
country_fe_MONGOLIA -3.6615 1.155 -3.169 0.002 -5.926 -1.397
country_fe_MOZAMBIQUE 0.5349 0.503 1.063 0.288 -0.452 1.521
country_fe_MYANMAR -2.7854 0.774 -3.600 0.000 -4.302 -1.269
country_fe_NAMIBIA 0.8391 0.551 1.524 0.128 -0.240 1.918
country_fe_NEPAL -3.5180 1.352 -2.602 0.009 -6.168 -0.869
country_fe_NETHERLANDS -2.6678 0.822 -3.244 0.001 -4.280 -1.056
country_fe_NEW ZEALAND -2.1391 0.611 -3.502 0.000 -3.337 -0.942
country_fe_NICARAGUA 2.4747 0.814 3.039 0.002 0.879 4.071
country_fe_NIGERIA -0.8554 0.758 -1.129 0.259 -2.340 0.630
country_fe_NORWAY -1.8189 0.759 -2.396 0.017 -3.307 -0.331
country_fe_OMAN 0.0868 0.727 0.119 0.905 -1.338 1.511
country_fe_PAKISTAN -1.9681 0.753 -2.613 0.009 -3.444 -0.492
country_fe_PANAMA 1.8940 0.606 3.125 0.002 0.706 3.082
country_fe_PARAGUAY 1.9121 0.626 3.053 0.002 0.684 3.140
country_fe_PERU -1.0463 0.649 -1.612 0.107 -2.319 0.226
country_fe_PHILIPPINES -3.7168 0.900 -4.128 0.000 -5.482 -1.952
country_fe_POLAND -1.6215 0.761 -2.130 0.033 -3.114 -0.130
country_fe_PORTUGAL -1.5515 0.699 -2.219 0.026 -2.922 -0.181
country_fe_PUERTO RICO 1.0642 0.644 1.653 0.098 -0.197 2.326
country_fe_QATAR 0.0772 0.680 0.113 0.910 -1.256 1.411
country_fe_REP. OF MACEDONIA -0.6957 0.630 -1.105 0.269 -1.930 0.539
country_fe_RUSSIA -1.5519 0.973 -1.595 0.111 -3.459 0.356
country_fe_RWANDA -0.6205 1.122 -0.553 0.580 -2.819 1.578
country_fe_SAINT KITTS AND NEVIS 3.7861 0.970 3.903 0.000 1.885 5.687
country_fe_SAINT LUCIA 2.4708 0.888 2.782 0.005 0.730 4.212
country_fe_SAMOA 0.7636 1.075 0.710 0.477 -1.343 2.870
country_fe_SAO TOME AND PRINCIPE -3.4500 1.057 -3.265 0.001 -5.521 -1.379
country_fe_SAUDI ARABIA -0.8471 0.802 -1.056 0.291 -2.420 0.726
country_fe_SENEGAL 1.7053 0.581 2.933 0.003 0.566 2.845
country_fe_SEYCHELLES -7.1690 0.736 -9.737 0.000 -8.612 -5.726
country_fe_SIERRA LEONE 2.6239 0.715 3.668 0.000 1.222 4.026
country_fe_SINGAPORE -7.8383 1.210 -6.478 0.000 -10.210 -5.467
country_fe_SLOVAKIA -1.9957 0.576 -3.465 0.001 -3.124 -0.867
country_fe_SLOVENIA -2.4731 0.550 -4.500 0.000 -3.550 -1.396
country_fe_SOLOMON ISLANDS -1.6206 0.828 -1.956 0.050 -3.244 0.003
country_fe_SOUTH AFRICA -1.3349 0.718 -1.860 0.063 -2.742 0.072
country_fe_SPAIN -2.3184 0.904 -2.563 0.010 -4.091 -0.546
country_fe_SRI LANKA -4.6596 0.689 -6.764 0.000 -6.010 -3.309
country_fe_SUDAN 0.4152 0.551 0.754 0.451 -0.665 1.495
country_fe_SURINAME 3.2029 0.764 4.192 0.000 1.705 4.700
country_fe_SWAZILAND -1.0519 0.623 -1.689 0.091 -2.272 0.169
country_fe_SWEDEN -2.2875 0.762 -3.001 0.003 -3.781 -0.794
country_fe_SWITZERLAND -2.9224 0.796 -3.672 0.000 -4.482 -1.362
country_fe_TAIWAN -5.3211 0.931 -5.717 0.000 -7.145 -3.497
country_fe_TAJIKISTAN 2.8975 0.638 4.540 0.000 1.647 4.148
country_fe_TANZANIA, UNITED REP. OF -0.0753 0.528 -0.143 0.887 -1.110 0.960
country_fe_THAILAND -4.9906 0.983 -5.079 0.000 -6.916 -3.065
country_fe_TOGO 2.5262 0.702 3.598 0.000 1.150 3.902
country_fe_TRINIDAD AND TOBAGO 1.7423 0.885 1.969 0.049 0.008 3.477
country_fe_TUNISIA -0.1215 0.554 -0.219 0.826 -1.207 0.964
country_fe_TURKEY -2.4305 0.831 -2.926 0.003 -4.058 -0.803
country_fe_TURKMENISTAN -0.1843 0.718 -0.257 0.797 -1.592 1.224
country_fe_TUVALU 1.5304 1.344 1.139 0.255 -1.103 4.164
country_fe_UGANDA 0.2314 0.566 0.409 0.683 -0.878 1.341
country_fe_UKRAINE -0.7418 0.624 -1.189 0.235 -1.965 0.481
country_fe_UNITED ARAB EMIRAT -1.1745 0.775 -1.515 0.130 -2.694 0.345
country_fe_UNITED KINGDOM -3.1306 1.010 -3.100 0.002 -5.110 -1.151
country_fe_UNITED STATES -3.0327 1.279 -2.370 0.018 -5.540 -0.525
country_fe_URUGUAY 0.5274 0.788 0.669 0.504 -1.018 2.072
country_fe_UZBEKISTAN -1.8922 0.563 -3.363 0.001 -2.995 -0.789
country_fe_VENEZUELA 1.8393 0.785 2.344 0.019 0.301 3.377
country_fe_VIET NAM -4.4794 0.880 -5.091 0.000 -6.204 -2.755
country_fe_YEMEN -2.2397 0.604 -3.705 0.000 -3.424 -1.055
isic2_fe_ 0.6834 0.094 7.247 0.000 0.499 0.868
isic2_fe_15 1.0921 0.131 8.337 0.000 0.835 1.349
isic2_fe_16 0.9401 0.236 3.981 0.000 0.477 1.403
isic2_fe_17 1.0258 0.121 8.456 0.000 0.788 1.264
isic2_fe_18 0.2840 0.102 2.790 0.005 0.085 0.483
isic2_fe_19 -0.7478 0.117 -6.404 0.000 -0.977 -0.519
isic2_fe_20 -1.8403 0.131 -14.100 0.000 -2.096 -1.584
isic2_fe_21 0.4099 0.121 3.380 0.001 0.172 0.648
isic2_fe_22 -0.3420 0.165 -2.070 0.038 -0.666 -0.018
isic2_fe_23 -0.8543 0.269 -3.174 0.002 -1.382 -0.327
isic2_fe_24 0.9828 0.107 9.222 0.000 0.774 1.192
isic2_fe_25 0.2573 0.118 2.181 0.029 0.026 0.489
isic2_fe_26 -0.0824 0.126 -0.653 0.514 -0.330 0.165
isic2_fe_27 2.1734 0.108 20.078 0.000 1.961 2.386
isic2_fe_28 0.7762 0.133 5.842 0.000 0.516 1.037
isic2_fe_29 0.6114 0.122 5.000 0.000 0.372 0.851
isic2_fe_30 -0.9750 0.339 -2.874 0.004 -1.640 -0.310
isic2_fe_31 0.7240 0.106 6.822 0.000 0.516 0.932
isic2_fe_32 0.8617 0.152 5.659 0.000 0.563 1.160
isic2_fe_33 2.6663 0.165 16.129 0.000 2.342 2.990
isic2_fe_34 0.7941 0.107 7.407 0.000 0.584 1.004
isic2_fe_35 0.4387 0.107 4.089 0.000 0.228 0.649
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