Hi Guys,
my name is Johannes and I'm currently writing my thesis about the effects of China's Belt and Road Initiative on Institutional frameworks in participating countries. Here, I use the average score of the World Governance Index (issued by the World Bank), ranging from -2.5 to 2.5 as the dependent variable (av_wgi) My main variable to describe its variance is a dummy-variable BRI_state.
My prof. advised me to run a Difference-in-Difference estimation, even though he is not very keen on answering my questions about it... This seems okay to me, even though I already finished a FE-model, which would work just fine, in my opinion. However, in order to make sense of DID it is strongly advised to identify parallel paths between the control and treatment group. The control group in my example consists out of the states that do not take part in the Belt and Road. The treatment group, in contrast, is the group of countries who do take part.
To do the regression, I created a time variable (time) that is 0 before the treatment and jumps to 1 in the treatment year and afterwards (in my case the year 2014). I also created a dummy variable for treatment which I called BRI_state. It is a dummy-variable as well. It is 0 for all non-participating states and 1 for all participating states. As a last step I created the interaction term (time*BRI_state), which I called did. The code for these three steps was straightforward.
Now, however, I am running into a problem. The regression alone wouldn't be a big issue, as I can follow many online guides such as the on of Princeton university. The bigger problem is how to identify the trends, since it would be necessary for a holistic DD-Analysis to have parallel paths of treatment and control group before the treatment. Does anyone here has an idea for a command or any other way to do so? And how to illustrate this with a graph?
I read something about the dpd and didq command that I already installed. I also read something about the ddid command, which could not be installed in my stata, I don't know why. The didq and ddid command should however also be a solution for non-parallel paths...
At this point, I am kind of desperate and would be happy for any help or suggestion. I also created a simplified dataset with all necessary variables to do the testing (in my opinion) and paced it into this post via the dataex command (Note that it only displays 100 out of 1752 observations in this panel)
------------------ copy up to and including the previous line ------------------
Listed 100 out of 1752 observations
Use the count() option to list more
Thank you very much in advance,
Johannes
my name is Johannes and I'm currently writing my thesis about the effects of China's Belt and Road Initiative on Institutional frameworks in participating countries. Here, I use the average score of the World Governance Index (issued by the World Bank), ranging from -2.5 to 2.5 as the dependent variable (av_wgi) My main variable to describe its variance is a dummy-variable BRI_state.
My prof. advised me to run a Difference-in-Difference estimation, even though he is not very keen on answering my questions about it... This seems okay to me, even though I already finished a FE-model, which would work just fine, in my opinion. However, in order to make sense of DID it is strongly advised to identify parallel paths between the control and treatment group. The control group in my example consists out of the states that do not take part in the Belt and Road. The treatment group, in contrast, is the group of countries who do take part.
To do the regression, I created a time variable (time) that is 0 before the treatment and jumps to 1 in the treatment year and afterwards (in my case the year 2014). I also created a dummy variable for treatment which I called BRI_state. It is a dummy-variable as well. It is 0 for all non-participating states and 1 for all participating states. As a last step I created the interaction term (time*BRI_state), which I called did. The code for these three steps was straightforward.
Now, however, I am running into a problem. The regression alone wouldn't be a big issue, as I can follow many online guides such as the on of Princeton university. The bigger problem is how to identify the trends, since it would be necessary for a holistic DD-Analysis to have parallel paths of treatment and control group before the treatment. Does anyone here has an idea for a command or any other way to do so? And how to illustrate this with a graph?
I read something about the dpd and didq command that I already installed. I also read something about the ddid command, which could not be installed in my stata, I don't know why. The didq and ddid command should however also be a solution for non-parallel paths...
At this point, I am kind of desperate and would be happy for any help or suggestion. I also created a simplified dataset with all necessary variables to do the testing (in my opinion) and paced it into this post via the dataex command (Note that it only displays 100 out of 1752 observations in this panel)
Code:
* Example generated by -dataex-. To install: ssc install dataex clear input int year countryyear countryname av_wgi D_avwgi time BRI_state did 2010 "Afghanistan.2010" "Afghanistan" -1.7420540650685628 .029326656833291054 0 1 0 2011 "Afghanistan.2011" "Afghanistan" -1.7171535690625508 .024900496006011963 0 1 0 2012 "Afghanistan.2012" "Afghanistan" -1.5503835876782734 .16676998138427734 0 1 0 2013 "Afghanistan.2013" "Afghanistan" -1.5619265635808308 -.011542975902557373 0 1 0 2014 "Afghanistan.2014" "Afghanistan" -1.4660872220993042 .09583934396505356 1 1 1 2015 "Afghanistan.2015" "Afghanistan" -1.4758503933747609 -.009763170965015888 1 1 1 2016 "Afghanistan.2016" "Afghanistan" -1.5469506978988647 -.07110030204057694 1 1 1 2017 "Afghanistan.2017" "Afghanistan" -1.58739373087883 -.04044303297996521 1 1 1 2010 "Albania.2010" "Albania" -.1755655581752459 -.015180491842329502 0 1 0 2011 "Albania.2011" "Albania" -.2222826952735583 -.04671713709831238 0 1 0 2012 "Albania.2012" "Albania" -.23950260474036136 -.0172199085354805 0 1 0 2013 "Albania.2013" "Albania" -.19716660616298518 .04233599826693535 0 1 0 2014 "Albania.2014" "Albania" -.01998024806380272 .17718635499477386 1 1 1 2015 "Albania.2015" "Albania" -.01781945489346981 .0021607931703329086 1 1 1 2016 "Albania.2016" "Albania" -.004202986989791195 .013616467826068401 1 1 1 2017 "Albania.2017" "Albania" .013104683409134546 .017307670786976814 1 1 1 2010 "Algeria.2010" "Algeria" -.8736798961957296 .0035981934051960707 0 0 0 2011 "Algeria.2011" "Algeria" -.9118536313374838 -.03817373514175415 0 0 0 2012 "Algeria.2012" "Algeria" -.8859593669573466 .02589426375925541 0 0 0 2013 "Algeria.2013" "Algeria" -.8264579027891159 .059501465409994125 0 0 0 2014 "Algeria.2014" "Algeria" -.8565742274125417 -.030116325244307518 1 0 0 2015 "Algeria.2015" "Algeria" -.8556728561719259 .0009013712406158447 1 0 0 2016 "Algeria.2016" "Algeria" -.869491438070933 -.013818581588566303 1 0 0 2017 "Algeria.2017" "Algeria" -.853789766629537 .015701672062277794 1 0 0 2010 "American Samoa.2010" "American Samoa" .7077756424744924 -.008821715600788593 0 0 0 2011 "American Samoa.2011" "American Samoa" .706112802028656 -.0016628404846414924 0 0 0 2012 "American Samoa.2012" "American Samoa" .7061648070812225 .00005200505256652832 0 0 0 2013 "American Samoa.2013" "American Samoa" .7014415760835012 -.004723230842500925 0 0 0 2014 "American Samoa.2014" "American Samoa" .8676691323518753 .1662275493144989 1 0 0 2015 "American Samoa.2015" "American Samoa" .8810259521007537 .013356819748878479 1 0 0 2016 "American Samoa.2016" "American Samoa" .8861504524946213 .00512450048699975 1 0 0 2017 "American Samoa.2017" "American Samoa" .9469823002815246 .06083184853196144 1 0 0 2010 "Andorra.2010" "Andorra" 1.3374883929888408 -.015464206226170063 0 0 0 2011 "Andorra.2011" "Andorra" 1.4120391011238098 .07455071061849594 0 0 0 2012 "Andorra.2012" "Andorra" 1.4167765577634175 .0047374567948281765 0 0 0 2013 "Andorra.2013" "Andorra" 1.4112175504366558 -.005559007171541452 0 0 0 2014 "Andorra.2014" "Andorra" 1.320518175760905 -.09069937467575073 1 0 0 2015 "Andorra.2015" "Andorra" 1.3467688858509064 .02625071071088314 1 0 0 2016 "Andorra.2016" "Andorra" 1.3548247317473094 .008055846206843853 1 0 0 2017 "Andorra.2017" "Andorra" 1.4330222407976787 .07819750905036926 1 0 0 2010 "Angola.2010" "Angola" -1.015260676542918 .0015232762088999152 0 0 0 2011 "Angola.2011" "Angola" -1.0592858642339706 -.04402518644928932 0 0 0 2012 "Angola.2012" "Angola" -.9938524017731348 .06543346494436264 0 0 0 2013 "Angola.2013" "Angola" -1.0600128024816513 -.0661604031920433 0 0 0 2014 "Angola.2014" "Angola" -1.0254801213741302 .03453268110752106 1 0 0 2015 "Angola.2015" "Angola" -1.0106753905614216 .014804731123149395 1 0 0 2016 "Angola.2016" "Angola" -1.0044250190258026 .006250371690839529 1 0 0 2017 "Angola.2017" "Angola" -.9970676600933075 .007357358932495117 1 0 0 2010 "Anguilla.2010" "Anguilla" 1.32699520389239 .07568130642175674 0 0 0 2011 "Anguilla.2011" "Anguilla" 1.3460992972056072 .019104093313217163 0 0 0 2012 "Anguilla.2012" "Anguilla" 1.332432508468628 -.013666789047420025 0 0 0 2013 "Anguilla.2013" "Anguilla" 1.3390166362126668 .006584127899259329 0 0 0 2014 "Anguilla.2014" "Anguilla" .8491269286721945 -.4898897111415863 1 0 0 2015 "Anguilla.2015" "Anguilla" .8697997152805328 .020672786980867386 1 0 0 2016 "Anguilla.2016" "Anguilla" .9320305675268173 .062230851501226425 1 0 0 2017 "Anguilla.2017" "Anguilla" .8357231542468071 -.0963074117898941 1 0 0 2010 "Antigua and Barbuda.2010" "Antigua and Barbuda" .8009439756472906 .005523895379155874 0 0 0 2011 "Antigua and Barbuda.2011" "Antigua and Barbuda" .8033551027377447 .0024111270904541016 0 0 0 2012 "Antigua and Barbuda.2012" "Antigua and Barbuda" .8210189044475555 .017663801088929176 0 0 0 2013 "Antigua and Barbuda.2013" "Antigua and Barbuda" .795589491724968 -.025429412722587585 0 0 0 2014 "Antigua and Barbuda.2014" "Antigua and Barbuda" .4116133699814479 -.38397613167762756 1 0 0 2015 "Antigua and Barbuda.2015" "Antigua and Barbuda" .5801443209250768 .16853095591068268 1 0 0 2016 "Antigua and Barbuda.2016" "Antigua and Barbuda" .5536672423283259 -.02647707797586918 1 0 0 2017 "Antigua and Barbuda.2017" "Antigua and Barbuda" .3856189971168836 -.16804824769496918 1 0 0 2010 "Argentina.2010" "Argentina" -.26670753583312035 .10598119348287582 0 0 0 2011 "Argentina.2011" "Argentina" -.21089698001742363 .055810555815696716 0 0 0 2012 "Argentina.2012" "Argentina" -.31539386510849 -.10449688136577606 0 0 0 2013 "Argentina.2013" "Argentina" -.33874431252479553 -.023350447416305542 0 0 0 2014 "Argentina.2014" "Argentina" -.3868080103614678 -.048063699156045914 1 0 0 2015 "Argentina.2015" "Argentina" -.31294399484371144 .07386401295661926 1 0 0 2016 "Argentina.2016" "Argentina" -.04624137282371521 .2667026221752167 1 0 0 2017 "Argentina.2017" "Argentina" .011730817457040152 .05797218903899193 1 0 0 2010 "Armenia.2010" "Armenia" -.30583417788147926 -.05953597277402878 0 1 0 2011 "Armenia.2011" "Armenia" -.28470878799756366 .021125389263033867 0 1 0 2012 "Armenia.2012" "Armenia" -.18572189037998518 .09898689389228821 0 1 0 2013 "Armenia.2013" "Armenia" -.17203107724587122 .013690813444554806 0 1 0 2014 "Armenia.2014" "Armenia" -.2889581968386968 -.1169271171092987 1 1 1 2015 "Armenia.2015" "Armenia" -.2715284898877144 .017429707571864128 1 1 1 2016 "Armenia.2016" "Armenia" -.30548814808328945 -.033959656953811646 1 1 1 2017 "Armenia.2017" "Armenia" -.2996722062428792 .0058159418404102325 1 1 1 2010 "Aruba.2010" "Aruba" 1.2552866339683533 -.03081723116338253 0 0 0 2011 "Aruba.2011" "Aruba" 1.260848085085551 .005561450961977243 0 0 0 2012 "Aruba.2012" "Aruba" 1.2718956271807353 .011047542095184326 0 0 0 2013 "Aruba.2013" "Aruba" 1.2788068056106567 .006911178585141897 0 0 0 2014 "Aruba.2014" "Aruba" 1.106783886750539 -.17202292382717133 1 0 0 2015 "Aruba.2015" "Aruba" 1.2196964919567108 .1129126027226448 1 0 0 2016 "Aruba.2016" "Aruba" 1.2253518005212147 .005655308719724417 1 0 0 2017 "Aruba.2017" "Aruba" 1.2242043515046437 -.001147449016571045 1 0 0 2010 "Australia.2010" "Australia" 1.595369577407837 .0024842917919158936 0 0 0 2011 "Australia.2011" "Australia" 1.621867795785268 .02649821899831295 0 0 0 2012 "Australia.2012" "Australia" 1.6096359193325043 -.012231876142323017 0 0 0 2013 "Australia.2013" "Australia" 1.5786974430084229 -.03093847632408142 0 0 0 2014 "Australia.2014" "Australia" 1.6068808436393738 .028183400630950928 1 0 0 2015 "Australia.2015" "Australia" 1.550214409828186 -.056666433811187744 1 0 0 2016 "Australia.2016" "Australia" 1.5727197925249736 .0225053820759058 1 0 0 2017 "Australia.2017" "Australia" 1.537142554918925 -.035577237606048584 1 0 0 2010 "Austria.2010" "Austria" 1.5438798467318218 .01212503481656313 0 0 0 2011 "Austria.2011" "Austria" 1.4716352423032124 -.07224460691213608 0 0 0 2012 "Austria.2012" "Austria" 1.5228851636250813 .051249921321868896 0 0 0 2013 "Austria.2013" "Austria" 1.550333559513092 .027448395267128944 0 0 0 end
Listed 100 out of 1752 observations
Use the count() option to list more
Thank you very much in advance,
Johannes
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