I have the following data. This dataset is at city level.
Plate=unique identifier of each province There are 81 provinces in this dataset.
Child Birth Year= It spans from 2003 to 2018. Indeed, they are just years.
Refugee Ratio= It is the number of refugees in each province divided by its own population. It takes 0 from 2003 to 2012 due to absence of refugees. Then, after 2013, it starts taking its values.
Region 5= It is region dummies. 1 includes provinces in North, 2 includes provinces in South, 3 includes provinces in West, 4 provinces cities in East, and 5 provinces cities in Central.
Instrument= It is an IV.
Log GDP Interpolated= GDP per capita at province level (only year 2003 is interpolated).
For example, when I try to look at the effect of refugees on GDP, I run the following codes to replicate the study I attached here:
(1) To include province and year FEs, I think it's okay. I replicate the author's Column (4) here.
ivreg2 log_gdp_interpolated ( refugeeratio =instrument_) i.plate i.child_birth_year, cluster(plate)
(2) To include province FE, year FE, and 5 Region Linear Time Trends, the regression says "Dropped collinear: 2.region5 3.region5 4.region5 5.region5. Here is the code I use.
ivreg2 log_gdp_interpolated ( refugeeratio =instrument_) i.plate i.child_birth_year i.region5, cluster(plate)
As you can see, I have just include region5 dummies. I also tried it without "i." and it was dropped again. So my question, how can I include the "5 Region Linear Time Trends" as the authors add as in their Column (5).
(3) Similarly, is this correct to replicate their Column (6) using the following code?
ivreg2 log_gdp_interpolated ( refugeeratio =instrument_) i.plate i.child_birth_year i.region5#i.child_birth_year, cluster(plate)
Thank you very much in advanced.
Array
Code:
* Example generated by -dataex-. To install: ssc install dataex clear input byte plate int child_birth_year float(region5 refugeeratio) double instrument_ float log_gdp_interpolated 1 2003 2 0 0 8.098686 1 2004 2 0 0 8.35625 1 2005 2 0 0 8.560877 1 2006 2 0 0 8.6270895 1 2007 2 0 0 8.833439 1 2008 2 0 0 8.950308 1 2009 2 0 0 8.775798 1 2010 2 0 0 8.951138 1 2011 2 0 0 8.99517 1 2012 2 0 0 9.039643 1 2013 2 .00899705 2137.502174204115 9.113099 1 2014 2 .028272137 7725.103785707176 9.062313 1 2015 2 .06261454 9552.797764008226 8.977292 1 2016 2 .0680111 10815.378820879647 8.983587 1 2017 2 .07022186 11869.59208789388 8.932284 1 2018 2 .08672575 13611.10083198924 8.81266 2 2003 5 0 0 7.53345 2 2004 5 0 0 7.804962 2 2005 5 0 0 8.018276 2 2006 5 0 0 8.095382 2 2007 5 0 0 8.301027 2 2008 5 0 0 8.4515085 2 2009 5 0 0 8.24615 2 2010 5 0 0 8.475749 2 2011 5 0 0 8.558379 2 2012 5 0 0 8.554743 2 2013 5 .017068442 1873.8649676118544 8.62631 2 2014 5 .05579131 6772.297838991608 8.59959 2 2015 5 .036632303 8374.566018545016 8.50638 2 2016 5 .04061859 9481.421693263867 8.488602 2 2017 5 .04283048 10405.609435989865 8.441897 2 2018 5 .04727203 11932.322374920604 8.375801 3 2003 3 0 0 8.039925 3 2004 3 0 0 8.265195 3 2005 3 0 0 8.448931 3 2006 3 0 0 8.52808 3 2007 3 0 0 8.732327 3 2008 3 0 0 8.862084 3 2009 3 0 0 8.706999 3 2010 3 0 0 8.930561 3 2011 3 0 0 8.954763 3 2012 3 0 0 8.963824 3 2013 3 0 775.6147412650209 9.015151 3 2014 3 .0007078433 2803.133697970472 9.02242 3 2015 3 .0040069674 3466.331335474366 8.934076 3 2016 3 .005851456 3924.471912625391 8.944426 3 2017 3 .006891223 4307.004085084045 8.93551 3 2018 3 .008190825 4938.928520185606 8.804566 4 2003 5 0 0 7.34143 4 2004 5 0 0 7.580326 4 2005 5 0 0 7.773012 4 2006 5 0 0 7.768281 4 2007 5 0 0 7.943454 4 2008 5 0 0 8.026946 4 2009 5 0 0 7.925411 4 2010 5 0 0 8.236918 4 2011 5 0 0 8.225596 4 2012 5 0 0 8.267767 4 2013 5 0 801.6362469490603 8.292835 4 2014 5 .00018200515 2897.17750035604 8.2031765 4 2015 5 .0014546518 3582.625103178801 8.123725 4 2016 5 .0015804373 4056.1360787997937 8.203612 4 2017 5 .0018795975 4451.50202371064 8.111795 4 2018 5 .0021050407 5104.627223064127 8.006121 5 2003 3 0 0 8.037372 5 2004 3 0 0 8.316185 5 2005 3 0 0 8.533969 5 2006 3 0 0 8.565304 5 2007 3 0 0 8.739339 5 2008 3 0 0 8.95324 5 2009 3 0 0 8.813373 5 2010 3 0 0 8.976414 5 2011 3 0 0 9.006424 5 2012 3 0 0 9.049225 5 2013 3 0 892.4989384510496 9.091903 5 2014 3 .0003106429 3225.562533397196 9.035556 5 2015 3 .0004469732 3988.703247419831 8.994672 5 2016 3 .0006649282 4515.885051754473 8.9999695 5 2017 3 .0012185954 4956.064356863975 8.93685 5 2018 3 .001688849 5683.21903495784 8.793315 6 2003 3 0 0 8.820829 6 2004 3 0 0 9.080735 6 2005 3 0 0 9.286835 6 2006 3 0 0 9.369869 6 2007 3 0 0 9.585378 6 2008 3 0 0 9.712936 6 2009 3 0 0 9.521501 6 2010 3 0 0 9.652315 6 2011 3 0 0 9.694221 6 2012 3 0 0 9.711046 6 2013 3 0 845.9475972730434 9.818029 6 2014 3 .005825161 3057.3222638415114 9.750788 6 2015 3 .01024025 3780.65875825695 9.638588 6 2016 3 .012557893 4280.343588669125 9.655186 6 2017 3 .014737303 4697.5638334046025 9.586901 6 2018 3 .017472614 5386.7912669373945 9.465929 7 2003 2 0 0 8.747828 7 2004 2 0 0 8.968632 7 2005 2 0 0 9.149391 7 2006 2 0 0 9.225716 end
0 Response to Adding linear time trends (dropped)
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