I want to measure the influence of trade agreements on the composition of the traded goods according to their complexity.
There's a Product Complexity Index
Code:
. sum PCIMean
Variable | Obs Mean Std. Dev. Min Max
-------------+---------------------------------------------------------
PCIMean | 74,196,336 .0990187 .9274192 -3.61 2.86Code:
* Example generated by -dataex-. To install: ssc install dataex clear input int(exporter importer year) str4 sitc_product_code float PCIMean double(PCIValue export_value) float(expr_is_AdvEcon impr_is_AdvEcon expr_is_LA impr_is_LA agreements_count) 4 8 2002 "8994" .04566666 . 0 0 0 0 0 0 4 8 2004 "8510" -1.1266 . 0 0 0 0 0 0 4 8 2007 "8946" .9043334 . 0 0 0 0 0 0 4 8 2009 "2690" -.8125 . 0 0 0 0 0 0 4 8 2009 "6596" -.17275 . 1267 0 0 0 0 0 4 8 2009 "9310" . . 0 0 0 0 0 0 4 8 2010 "2690" -.886 . 301 0 0 0 0 0 4 8 2010 "7611" . . 179 0 0 0 0 0 4 8 2010 "7638" .6273333 . 143 0 0 0 0 0 4 8 2010 "8459" -1.4354 . 182 0 0 0 0 0 4 8 2010 "8471" -.74844 . 803 0 0 0 0 0 4 8 2010 "8942" .4011667 . 143 0 0 0 0 0 4 8 2011 "2690" -.839 . 0 0 0 0 0 0 4 8 2011 "5989" .7885333 . 0 0 0 0 0 0 4 8 2011 "9310" . . 0 0 0 0 0 0 4 8 2012 "2690" -.894 . 114 0 0 0 0 0 4 8 2012 "8459" -1.3744445 . 2596 0 0 0 0 0 4 8 2013 "2690" -.774 . 1709 0 0 0 0 0 4 8 2013 "8482" -.524 -.524 2804 0 0 0 0 0 4 8 2014 "9310" . . 10344 0 0 0 0 0 4 8 2015 "9310" . . 6594 0 0 0 0 0 4 8 2017 "9310" . . 0 0 0 0 0 0 4 12 1993 "7491" . . 1184 0 0 0 0 0 4 12 1994 "7492" . . 8598 0 0 0 0 0 4 12 1995 "7415" .241 .241 34853 0 0 0 0 0 4 12 1996 "0752" -1.9233333 . 215315 0 0 0 0 0 4 12 1996 "2922" -2.05 -2.05 16834 0 0 0 0 0 4 12 1997 "0752" -1.8066666 . 200432 0 0 0 0 0 4 12 1997 "2924" -2.03 -2.03 2483 0 0 0 0 0 4 12 1998 "0752" -1.8183334 . 428651 0 0 0 0 0 4 12 1998 "8745" .8836667 . 2406 0 0 0 0 0 4 12 1999 "0752" -1.7016667 . 387053 0 0 0 0 0 4 12 1999 "5530" .02104 . 733 0 0 0 0 0 4 12 1999 "8942" .04066667 . 785 0 0 0 0 0 4 12 2000 "0752" -1.7633333 . 20919 0 0 0 0 0 4 12 2000 "2924" -2.08 -2.08 1948 0 0 0 0 0 4 12 2000 "5530" -.054 . 524 0 0 0 0 0 4 12 2000 "7493" 1.56 1.56 180 0 0 0 0 0 4 12 2001 "0752" -1.61 . 46905 0 0 0 0 0 4 12 2001 "2929" -1.5942 . 2490 0 0 0 0 0 4 12 2002 "0111" -.39245 . 2157053 0 0 0 0 0 4 12 2002 "0577" -1.8 . 4495 0 0 0 0 0 4 12 2002 "2924" -1.88 -1.88 3246 0 0 0 0 0 4 12 2002 "2929" -1.474 . 1097 0 0 0 0 0 4 12 2002 "6531" -.24085 . 14422 0 0 0 0 0 4 12 2003 "0577" -1.75 . 3932 0 0 0 0 0 4 12 2003 "2924" -1.98 -1.98 1181 0 0 0 0 0 4 12 2003 "7432" . . 2100 0 0 0 0 0 4 12 2003 "7721" .9265 . 375 0 0 0 0 0 4 12 2004 "0752" -1.55 . 6719 0 0 0 0 0 4 12 2004 "2924" -1.99 -1.99 2194 0 0 0 0 0 4 12 2004 "5989" .876625 . 118 0 0 0 0 0 4 12 2004 "7283" .375 .375 240 0 0 0 0 0 4 12 2005 "0577" -1.965 . 2729 0 0 0 0 0 4 12 2005 "0752" -1.6366667 . 14501 0 0 0 0 0 4 12 2005 "2929" -1.4346 . 473 0 0 0 0 0 4 12 2005 "5989" .9223125 . 111 0 0 0 0 0 4 12 2005 "7139" 1.24 1.24 25761 0 0 0 0 0 4 12 2006 "0752" -1.6083333 . 1203 0 0 0 0 0 4 12 2006 "2223" . . 0 0 0 0 0 0 4 12 2006 "2922" -2.17 -2.17 198 0 0 0 0 0 4 12 2006 "2924" -1.85 -1.85 1308 0 0 0 0 0 4 12 2006 "8748" 1.54 1.54 7618 0 0 0 0 0 4 12 2007 "0548" -.805 . 1357 0 0 0 0 0 4 12 2007 "0575" -.776 -.776 38490 0 0 0 0 0 4 12 2007 "0752" -1.5766667 . 26229 0 0 0 0 0 4 12 2007 "2924" -1.91 -1.91 2765 0 0 0 0 0 4 12 2007 "5541" -1.06 -1.06 0 0 0 0 0 0 4 12 2007 "5989" .96475 . 498 0 0 0 0 0 4 12 2007 "7849" 1.46 1.46 846 0 0 0 0 0 4 12 2007 "8219" .341 .341 2252 0 0 0 0 0 4 12 2008 "0548" -.8725 . 1226 0 0 0 0 0 4 12 2008 "0577" -1.85 . 2277 0 0 0 0 0 4 12 2008 "0752" -1.5716667 . 34192 0 0 0 0 0 4 12 2008 "2924" -1.84 -1.84 9742 0 0 0 0 0 4 12 2008 "2927" -1.53 . 108 0 0 0 0 0 4 12 2008 "7611" . . 395 0 0 0 0 0 4 12 2009 "0575" -.944 -.944 1043 0 0 0 0 0 4 12 2009 "0579" -1.0316666 . 511 0 0 0 0 0 4 12 2009 "2924" -1.86 -1.86 5874 0 0 0 0 0 4 12 2009 "7239" .894 .894 1133360 0 0 0 0 0 4 12 2009 "8462" . . 519 0 0 0 0 0 4 12 2009 "8484" -.7776 . 1430 0 0 0 0 0 4 12 2009 "8510" -.7622 . 1358 0 0 0 0 0 4 12 2010 "2924" -1.85 -1.85 17774 0 0 0 0 0 4 12 2010 "6991" .766 . 24386 0 0 0 0 0 4 12 2010 "7641" . . 49340 0 0 0 0 0 4 12 2010 "8219" .28 .28 6505 0 0 0 0 0 4 12 2012 "0752" -1.57 . 1126 0 0 0 0 0 4 12 2014 "0575" -1.07 -1.07 28449 0 0 0 0 0 4 12 2014 "0752" -1.61 . 303 0 0 0 0 0 4 12 2014 "5514" .304 .304 1245 0 0 0 0 0 4 12 2014 "6652" .332 .332 8383 0 0 0 0 0 4 12 2014 "6974" .143 .143 7034 0 0 0 0 0 4 12 2014 "6991" .704 . 310 0 0 0 0 0 4 12 2015 "2924" -2.03 -2.03 1121 0 0 0 0 0 4 12 2016 "0577" -2.04 . 24997 0 0 0 0 0 4 12 2016 "2922" -2.56 -2.56 1432 0 0 0 0 0 4 12 2016 "7436" . . 155 0 0 0 0 0 4 12 2016 "7492" 1.54 1.54 454 0 0 0 0 0 end label var exporter "Country Code" label var importer "Country Code" label var PCIMean "Mean of several HS-Code-PCI-Values belonging to this SITC-Code" label var PCIValue "PCI Value" label var expr_is_AdvEcon "exporter is an Advanced Economy according to IMF WEO 2019" label var impr_is_AdvEcon "importer is an Advanced Economy according to IMF WEO 2019" label var expr_is_LA "exporter is country of Latinamerica or Carribean" label var impr_is_LA "importer is country of Latinamerica or Carribean"
A) to a growth in the complex products (high PCI-Value) share of total exports from the Advanced Economy to the Latin American Country
B) to a growth in the simple products (low PCI-Value) export of total exports from the Latin American Country to the Advanced Economy.
I have one solution, which I do not like though:
I could implement a PCI-Value threshold (≈ neutral product) and than calculate the share of complex product in the total exports. Or the share of the 25 % most complex products / least complex products in the total exports.
But I feel like loosing lots of information with this approach. Is there a better way?
Thank you so much for taking your time!
Johannes
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