I have a data set in which the basic units are Factories (which is denoted by Factory ID). The factories with less than 10 million investment stock in plant and machineries are considered as small and others large. My aim of this research is to understand the impact of small scale de-reservation on product innovation. The small scale de-reservation statrted in 2000. The policy was not being carried out all at once, rather some firms were de-reserved in 2000 and they continue to be de-reserved through out the years. Likewise some more firms got de-reserved in 2001 and they continue to be de-reserved once it is de-reserved. The process of de-reservation happens till 2015. So here the treatment happens in multiple periods for different groups.
In this research my airm is to evaluate the impact of small-scale de-reservation policy on firms' product innovation. My intution is that small-scale dereservation have made small firms in direct competion with large firms with greater capabilities and therefore they may not be able to innovate further due to cut throat competition. In some other cases, they might innovate if there intial capabilities are comparable with large firms.
Si in this case if I do a Difference in difference with multiple time periods, can large firms which were not subject to the policy be control group? if not, what would be my control group and how to identify them?
Since there is no fixed year for policy, we identify the year of first treatment and then mark ''treated'' after that year. In this case how to creat the variables of treatment and group expossed to the treatment.
My data-set is from the year 2000 so thay i dont have an year in which all the firms remain un treated. Further in the data if year_dereservation is missing, that mean the firm is a large one and is not subject to the policy change
I have attached a sample dataset. The dataset also contains the variables which I may use as covariates in the regression.
After a product (and therefore the corresponding factory), is removed from the reservation list or de-reserved, some factories may continue to produce them and some new factories will definitely enter into that product segment. These new factories thay have entrred into the product segment after de-resertvation are not small factories by definition.
The variable SSI_FACTORY_DUMMY refers to the dummy variable if a factory has manufactured any small scale product either before or after the de-reservation period.
In this research my airm is to evaluate the impact of small-scale de-reservation policy on firms' product innovation. My intution is that small-scale dereservation have made small firms in direct competion with large firms with greater capabilities and therefore they may not be able to innovate further due to cut throat competition. In some other cases, they might innovate if there intial capabilities are comparable with large firms.
Si in this case if I do a Difference in difference with multiple time periods, can large firms which were not subject to the policy be control group? if not, what would be my control group and how to identify them?
Since there is no fixed year for policy, we identify the year of first treatment and then mark ''treated'' after that year. In this case how to creat the variables of treatment and group expossed to the treatment.
My data-set is from the year 2000 so thay i dont have an year in which all the firms remain un treated. Further in the data if year_dereservation is missing, that mean the firm is a large one and is not subject to the policy change
I have attached a sample dataset. The dataset also contains the variables which I may use as covariates in the regression.
After a product (and therefore the corresponding factory), is removed from the reservation list or de-reserved, some factories may continue to produce them and some new factories will definitely enter into that product segment. These new factories thay have entrred into the product segment after de-resertvation are not small factories by definition.
The variable SSI_FACTORY_DUMMY refers to the dummy variable if a factory has manufactured any small scale product either before or after the de-reservation period.
Code:
* Example generated by -dataex-. For more info, type help dataex clear input float(fact_id year) int year_dereservation float(new_product_dummy ownershipcode log_age factory_size factory_size_squared log_working_capital log_welfare_expenses sector_dummy_new) 58 1999 . 0 1 .6931472 2.6390574 6.964624 . . 20 37 1999 . 0 1 3.5263605 2.0794415 4.324077 13.22428 . 29 91 1999 . 0 1 3.970292 1.3862944 1.921812 8.863616 . 22 7 1999 2007 0 1 3.4011974 2.3025851 5.301898 13.370925 . 26 8 2000 . 0 1 1.3862944 2.833213 8.027098 13.969795 . 17 49 2000 . 0 1 3.0445225 2.944439 8.669721 12.398155 9.433484 26 1 2000 . 0 1 1.609438 2.564949 6.578965 13.417416 8.980172 18 13 2000 . 0 1 2.833213 2.564949 6.578965 13.261424 10.695756 22 83 2000 . 0 1 1.0986123 2.564949 6.578965 15.04651 9.807802 17 7 2000 2007 0 1 2.564949 2.397895 5.749902 13.876872 10.14655 26 40 2000 . 0 1 1.3862944 2.0794415 4.324077 14.27706 9.219894 20 39 2001 . 0 0 3.178054 2.564949 6.578965 13.266263 . 22 3 2001 2004 0 1 2.772589 3.218876 10.361162 15.09514 11.90896 28 49 2001 . 0 1 2.397895 2.995732 8.974412 11.006324 10.256923 26 75 2001 2008 0 1 4.624973 3.135494 9.831324 . . 21 7 2001 2007 0 1 2.833213 2.564949 6.578965 . 9.540148 26 37 2001 . 1 1 3.465736 1.609438 2.5902905 13.340822 8.56121 29 7 2002 2007 0 1 3.496508 2.1972246 4.827796 . 10.473478 26 93 2002 . 0 1 2.397895 1.609438 2.5902905 13.981338 . 17 56 2002 2007 0 1 1.7917595 2.397895 5.749902 10.208395 . 27 69 2002 . 0 1 2.3025851 2.397895 5.749902 13.631922 9.083756 28 1 2002 . 1 1 1.94591 1.3862944 1.921812 12.54669 . 17 75 2002 2008 0 1 2.772589 2.397895 5.749902 . . 21 56 2003 2007 1 1 1.94591 2.484907 6.174761 10.26399 . 27 14 2003 . 0 1 3.0910425 2.833213 8.027098 . 10.630094 26 37 2003 . 1 1 3.5263605 1.609438 2.5902905 11.346848 8.258681 29 18 2003 2015 0 1 2.890372 2.890372 8.354249 14.785128 . 28 8 2003 . 1 1 1.94591 2.564949 6.578965 13.96584 . 17 62 2003 . 0 1 2.6390574 3.637586 13.232033 15.523062 11.533395 24 47 2003 . 0 1 3.4011974 2.1972246 4.827796 12.983995 . 26 65 2003 . 0 1 3.0910425 3.465736 12.011326 15.311068 11.264618 26 74 2004 2007 0 1 4.2904596 4.905275 24.06172 17.726906 10.988018 26 40 2004 . 0 1 1.94591 2.944439 8.669721 14.657135 . 20 8 2004 . 1 1 2.1972246 2.944439 8.669721 13.977489 . 17 54 2004 . 0 1 3.2580965 2.397895 5.749902 14.7036 . 28 82 2004 2005 0 1 2.833213 2.3025851 5.301898 13.93892 . 24 56 2004 2007 1 1 2.0794415 .6931472 .480453 . . 27 19 2004 . 0 1 2.890372 4.969813 24.699045 13.889056 11.900804 24 65 2004 . 0 1 3.135494 3.8286414 14.658495 16.054665 11.13765 26 66 2004 2008 0 1 1.94591 1.94591 3.786566 . . 21 34 2004 . 0 1 1.94591 2.833213 8.027098 11.554893 7.809947 29 43 2004 . 0 1 1.7917595 2.1972246 4.827796 . 9.932318 24 27 2004 . 0 1 2.70805 2.70805 7.333536 16.354773 7.242083 24 18 2005 2015 0 1 2.995732 2.70805 7.333536 15.180366 . 28 98 2005 . 0 1 1.609438 3.178054 10.100026 13.679546 10.12755 17 27 2005 . 1 1 2.772589 2.772589 7.687248 16.365507 7.903965 24 68 2005 . 0 1 3.0445225 2.1972246 4.827796 14.514722 8.670601 28 7 2005 2007 1 1 3.583519 2.833213 8.027098 12.402966 10.217605 26 34 2005 . 1 1 2.397895 2.0794415 4.324077 . 8.410943 29 13 2005 . 1 1 1.94591 2.944439 8.669721 15.013954 11.860026 21 83 2005 . 1 1 2.0794415 2.944439 8.669721 14.736695 10.036181 17 74 2005 2007 0 1 4.304065 4.934474 24.349033 17.163048 11.568067 26 23 2005 2007 0 1 3.367296 2.484907 6.174761 14.416636 7.677864 26 96 2005 2006 0 1 3.5263605 2.772589 7.687248 13.824446 9.265207 24 82 2006 2005 0 1 2.890372 2.397895 5.749902 14.39739 5.451038 24 47 2006 . 0 1 3.496508 2.3025851 5.301898 14.10745 7.20786 26 33 2006 . 0 1 3.465736 4.477337 20.046545 14.201146 . 16 86 2006 2006 0 1 3.78419 3.496508 12.225566 13.52973 10.81215 28 51 2006 . 0 1 2.70805 2.564949 6.578965 14.529168 . 20 77 2006 . 0 1 3.0445225 4.3820267 19.20216 14.186303 . 16 17 2006 . 0 1 3.433987 4.4886365 20.14786 14.20142 . 16 10 2006 . 0 1 2.1972246 2.70805 7.333536 12.24688 . 16 30 2006 2007 0 1 3.2580965 1.609438 2.5902905 . 7.635304 26 85 2006 . 0 1 .6931472 4.3307333 18.75525 15.28628 . 26 95 2006 . 0 1 .6931472 4.394449 19.311184 15.752844 . 26 66 2006 2008 1 1 2.1972246 2.397895 5.749902 14.420888 . 21 74 2006 2007 0 1 4.317488 5.908083 34.905445 . 11.330684 26 85 2007 . 1 1 3.0445225 3.9318256 15.459253 16.554813 10.71286 26 95 2007 . 0 1 1.0986123 3.89182 15.146264 15.685586 10.704143 26 74 2007 2007 0 1 4.3307333 4.7095304 22.179676 . 12.41545 26 62 2007 . 1 1 2.890372 3.988984 15.911994 16.61673 10.446596 24 75 2007 2008 1 1 2.397895 2.3025851 5.301898 14.657434 . 21 37 2007 . 0 1 3.637586 1.7917595 3.210402 14.103975 . 29 17 2007 . 0 1 3.465736 5.556828 30.87834 15.25402 . 16 54 2007 . 1 1 3.367296 2.70805 7.333536 14.884635 . 28 26 2007 . 0 1 3.367296 1.7917595 3.210402 8.977525 . 16 32 2007 . 0 1 1.94591 2.564949 6.578965 . 9.177507 25 77 2007 . 0 1 3.0910425 4.5217886 20.44657 14.474142 . 16 94 2007 2004 0 1 1.3862944 2.3025851 5.301898 12.896386 7.95015 26 25 2007 . 0 1 1.94591 2.564949 6.578965 14.786767 10.094769 24 67 2007 2005 0 1 1.0986123 5.26269 27.69591 18.168055 15.138324 29 99 2007 2008 0 1 1.3862944 3.0910425 9.554543 15.776823 10.985157 19 77 2008 . 0 1 3.135494 4.574711 20.92798 14.485622 . 16 99 2008 2008 0 1 2.6390574 3.0910425 9.554543 15.991436 10.904064 19 95 2008 . 0 1 1.3862944 3.8066626 14.49068 16.21671 . 26 16 2008 . 1 1 1.7917595 5.117994 26.19386 15.16214 8.003029 24 17 2008 . 0 1 3.496508 5.497168 30.218857 14.856136 10.91509 16 65 2008 . 1 1 2.833213 4.787492 22.92008 14.36624 11.808397 26 59 2008 . 1 1 2.397895 1.609438 2.5902905 14.266218 . 24 94 2008 2004 1 1 1.609438 2.484907 6.174761 12.631243 7.951207 26 25 2008 . 0 1 2.0794415 2.6390574 6.964624 15.05743 10.176754 24 49 2008 . 1 1 2.890372 2.70805 7.333536 . 11.14829 26 85 2008 . 0 1 3.3322046 3.7612 14.146626 16.48742 . 26 67 2008 2005 1 1 1.3862944 5.429346 29.477795 18.285807 14.72949 29 32 2008 . 1 1 2.0794415 2.944439 8.669721 . 7.790696 25 74 2008 2007 1 1 4.3438053 4.65396 21.659346 . 12.794356 26 88 2009 . 0 1 2.484907 3.295837 10.86254 13.61992 11.82113 25 79 2009 . 0 0 3.135494 2.3025851 5.301898 13.745592 12.91417 28 74 2009 2007 1 1 4.356709 4.317488 18.640705 . 11.6725 23 16 2009 . 1 1 1.94591 4.85203 23.5422 15.197473 10.829867 32 end
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