My goal is to find out the impact of migration on several firm level performance measures, such as wage. Here is my data:
Code:
* Example generated by -dataex-. To install: ssc install dataex clear input int(id year) byte(sector wage ccode Density) float(meanID Treatment Time TT) 1 2000 10 43 5 10 40.56667 0 0 0 1 2001 10 14 5 41 40.56667 0 0 0 1 2002 10 10 5 96 40.56667 0 0 0 1 2003 10 44 5 33 40.56667 0 1 0 1 2004 10 78 5 6 40.56667 0 1 0 1 2005 10 59 5 24 40.56667 0 1 0 2 2000 10 27 5 3 40.56667 0 0 0 2 2001 10 52 5 28 40.56667 0 0 0 2 2002 10 21 5 22 40.56667 0 0 0 2 2003 10 55 5 69 40.56667 0 1 0 2 2004 10 34 5 39 40.56667 0 1 0 2 2005 10 23 5 72 40.56667 0 1 0 3 2000 10 34 5 24 40.56667 0 0 0 3 2001 10 9 5 9 40.56667 0 0 0 3 2002 10 20 5 68 40.56667 0 0 0 3 2003 10 57 5 22 40.56667 0 1 0 3 2004 10 9 5 51 40.56667 0 1 0 3 2005 10 85 5 80 40.56667 0 1 0 4 2000 10 67 5 33 40.56667 0 0 0 4 2001 10 66 5 50 40.56667 0 0 0 4 2002 10 54 5 49 40.56667 0 0 0 4 2003 10 53 5 16 40.56667 0 1 0 4 2004 10 76 5 36 40.56667 0 1 0 4 2005 10 27 5 63 40.56667 0 1 0 5 2000 10 98 5 42 40.56667 0 0 0 5 2001 10 99 5 36 40.56667 0 0 0 5 2002 10 24 5 20 40.56667 0 0 0 5 2003 10 6 5 30 40.56667 0 1 0 5 2004 10 13 5 80 40.56667 0 1 0 5 2005 10 15 5 65 40.56667 0 1 0 6 2000 10 89 6 24 42.43333 0 0 0 6 2001 10 11 6 33 42.43333 0 0 0 6 2002 10 84 6 7 42.43333 0 0 0 6 2003 10 76 6 61 42.43333 0 1 0 6 2004 10 90 6 19 42.43333 0 1 0 6 2005 10 14 6 80 42.43333 0 1 0 7 2000 10 25 6 21 42.43333 0 0 0 7 2001 10 42 6 15 42.43333 0 0 0 7 2002 10 5 6 26 42.43333 0 0 0 7 2003 10 21 6 12 42.43333 0 1 0 7 2004 10 31 6 0 42.43333 0 1 0 7 2005 10 28 6 36 42.43333 0 1 0 8 2000 10 72 6 96 42.43333 0 0 0 8 2001 10 22 6 19 42.43333 0 0 0 8 2002 10 62 6 24 42.43333 0 0 0 8 2003 10 59 6 51 42.43333 0 1 0 8 2004 10 81 6 93 42.43333 0 1 0 8 2005 10 70 6 25 42.43333 0 1 0 9 2000 10 32 6 52 42.43333 0 0 0 9 2001 10 65 6 27 42.43333 0 0 0 9 2002 10 35 6 89 42.43333 0 0 0 9 2003 10 47 6 54 42.43333 0 1 0 9 2004 10 73 6 90 42.43333 0 1 0 9 2005 10 22 6 10 42.43333 0 1 0 10 2000 10 8 6 100 42.43333 0 0 0 10 2001 10 40 6 20 42.43333 0 0 0 10 2002 10 49 6 68 42.43333 0 0 0 10 2003 10 2 6 8 42.43333 0 1 0 10 2004 10 63 6 22 42.43333 0 1 0 10 2005 10 59 6 91 42.43333 0 1 0 11 2000 10 57 7 55 53.96667 1 0 0 11 2001 10 23 7 9 53.96667 1 0 0 11 2002 10 26 7 100 53.96667 1 0 0 11 2003 10 44 7 10 53.96667 1 1 1 11 2004 10 72 7 12 53.96667 1 1 1 11 2005 10 76 7 96 53.96667 1 1 1 12 2000 10 12 7 95 53.96667 1 0 0 12 2001 10 4 7 81 53.96667 1 0 0 12 2002 10 86 7 35 53.96667 1 0 0 12 2003 10 15 7 52 53.96667 1 1 1 12 2004 10 27 7 56 53.96667 1 1 1 12 2005 10 3 7 16 53.96667 1 1 1 13 2000 10 78 7 2 53.96667 1 0 0 13 2001 10 31 7 80 53.96667 1 0 0 13 2002 10 79 7 14 53.96667 1 0 0 13 2003 10 65 7 49 53.96667 1 1 1 13 2004 10 54 7 78 53.96667 1 1 1 13 2005 10 42 7 69 53.96667 1 1 1 14 2000 10 70 7 5 53.96667 1 0 0 14 2001 10 86 7 69 53.96667 1 0 0 14 2002 10 88 7 96 53.96667 1 0 0 14 2003 10 6 7 76 53.96667 1 1 1 14 2004 10 44 7 32 53.96667 1 1 1 14 2005 10 37 7 75 53.96667 1 1 1 15 2000 10 24 7 61 53.96667 1 0 0 15 2001 10 14 7 97 53.96667 1 0 0 15 2002 10 96 7 50 53.96667 1 0 0 15 2003 10 70 7 18 53.96667 1 1 1 15 2004 10 94 7 78 53.96667 1 1 1 15 2005 10 54 7 53 53.96667 1 1 1 16 2000 11 86 8 77 46.7 0 0 0 16 2001 11 58 8 93 46.7 0 0 0 16 2002 11 98 8 14 46.7 0 0 0 16 2003 11 52 8 26 46.7 0 1 0 16 2004 11 51 8 15 46.7 0 1 0 16 2005 11 91 8 72 46.7 0 1 0 17 2000 11 37 8 80 46.7 0 0 0 17 2001 11 45 8 4 46.7 0 0 0 17 2002 11 69 8 99 46.7 0 0 0 17 2003 11 86 8 64 46.7 0 1 0 end
I define post and pre-treatment periods. The immigration starts at year 2003. I have data for the period 2000-2005. I define post treatment period as 2003-2005, and pre-treatment period in 2000-2002.
- I have immigration density (Density) calculated at the city level and yearly defined as the total migrants divided by the total population. I use these densities to define control and treatment groups. More specifically I define the city as treatment group if average immigration density (2003-2005) is above 51. Under this assumption a given city belongs to treatment region for the 2003-2005 period. However, under this assumption I am not able to exploit yearly changes in immigration densities. How can I do this?
- My first hypothesis is the firms in the treatment region should face lower wages due to the abundance in the low skill labor provided by the migrants. In order to do that I run the following codes. All of them give the same results
xtreg wage i.Treatment##i.Time i.year,fe
xtreg wage TT i.year, fe
- I also want to control for the industry. Can I do this by simply adding industry fixed effects to the firm level regression like the following:
- I also want to test the following hypotheses:
-The labour-intensive firms in the labour-intensive industries benefit more than the labour-intensive firms in the capital-intensive industry.
Many thanks in advance.
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