Bartik weights

[vdquadros]

Hi @econisaac,

Could you please take a look at this and make sure it makes sense/agrees with what we talked about in the meeting on Tuesday?

Thanks, Victoria

Dataset

To calculate the Bartik weights, I created a dataset of the form:

• 124 observations (one for each MSA).
• 130 variables:
  • rmsa
  • Dependent variables (2 variables):
    • For high school-equivalent workers:
      • resgap2: Difference between the mean wage residuals of HS-equivalent immigrant and native workers in each MSA in 2000.
    • For college-equivalent workers:
      • resgap4: Difference between the mean wage residuals of college-equivalent immigrant and native workers in each MSA in 2000.
  • Instruments (38 variables):
    • shric1-shric38: This corresponds to one variable for each of the 38 countries. It is the fraction of earlier immigrants from country k who lived in location l in 1980. For example, the variable shric1 takes on 124 distinct values, giving us the share of immigrants from Mexico that lived in each of the top 124 MSAs in 1980. In order to agree with Card's instrument, we divide the variables by the city population in 2000. So the values of shric1-shric38 for each row are divided by the corresponding MSA's population in 2000.
  • Regression controls (12 variables):
  • logsize80: Log city size in 1980
  • logsize90: Log city size in 1990
  • coll80: Share of the MSA population with college in 1980
  • coll90: Share of the MSA population with college in 1990
  • nres80: Mean wage residuals for natives living in the MSA in 1980
  • ires80: Mean wage residuals for immigrants living in the MSA in 1980
  • nres90: Mean wage residuals for natives living in the MSA in 1990
  • ires90: Mean wage residuals for immigrants living in the MSA in 1990
  • mfg80: Share of MSA workers in manufacturing in 1980
  • mfg90: Share of MSA workers in manufacturing in 1990
  • resgap902: Lagged dependent variable for high school-equivalent workers. Currently not included in the list of controls.
  • resgap904: Lagged dependent variable for college-equivalent workers. Currently not included in the list of controls.
  • "Growth rates" (76 variables):
  • For high school equivalent workers (38 variables):
    • hs_imm_ic1-hs_imm_ic38: Number of HS-equivalent immigrant workers who arrived in the US between 1990 and 2000. One variable for each country ic1-ic38. This variables are constant across MSAs, so their values are repeated for all of the 124 observations.
  • For college equivalent workers (38 variables):
    • coll_imm_ic1-coll_imm_ic38: Number of college-equivalent immigrant workers who arrived in the US between 1990 and 2000. One variable for each country ic1-ic38. This variables are constant across MSAs, so their values are repeated for all of the 124 observations.
  • Regression weight (1 variable):
    • count90: MSA population in 1990

Code for weights

I am going to have two scripts, one for the high school-equivalent workers and one for the college-equivalent workers. I am copying below the code for HS-equivalent workers.

set seed 12345
use data/prepared_bartik.dta, clear

local controls logsize80 logsize90 coll80 coll90 ires80 nres80 mfg80 mfg90
local weight count90

local y resgap2
local x relshs
*local z shric*

local ind_stub shric*
local growth_stub hs_imm_ic*

* local time_var year
local cluster_var rmsa

foreach ind_var of varlist `ind_stub'* {
    replace `ind_var' = `ind_var' * 100
    }

forvalues k = 1(1)38 {
    egen agg_sh_ind_`k' = rowtotal(shric`k')
    }

bartik_weight, z(`ind_stub'*) weightstub(`growth_stub'*) x(`x') y(`y') controls(`controls') weight_var(`weight')

[econisaac]

That seems right. It looks ok, but either the code will run or not....