README.md
In jjchern/bartik.weight: Estimating Rotemberg Weights for a Bartik "Instrument"
About bartik.weight
The package bartik.weight is an implementation in R for estimating
Rotemberg weights outlined in Goldsmith-Pinkham, Sorkin and Swift
(2018).
The original Stata package by Paul Goldsmith-Pinkham can be found here:
https://github.com/paulgp/bartik-weight.
Installation
You can install the current version of bartik.weight from Github with:
# install.packages("devtools")
devtools::install_github("jjchern/bartik.weight")
# To uninstall the package, use:
# remove.packages("bartik.weight")
Examples
The package includes example datasets:
library(tidyverse)
#> ── Attaching packages ─────────────────────────── tidyverse 1.2.1 ──
#> ✔ ggplot2 2.2.1.9000 ✔ purrr 0.2.4
#> ✔ tibble 1.4.2 ✔ dplyr 0.7.4
#> ✔ tidyr 0.8.0 ✔ stringr 1.3.0
#> ✔ readr 1.1.1.9000 ✔ forcats 0.3.0
#> ── Conflicts ────────────────────────────── tidyverse_conflicts() ──
#> ✖ dplyr::filter() masks stats::filter()
#> ✖ dplyr::lag() masks stats::lag()
#> ✖ dplyr::vars() masks ggplot2::vars()
# index: czone, year
bartik.weight::ADH_master
#> # A tibble: 1,444 x 20
#> czone year d_sh_empl_mfg d_tradeusch_pw timepwt48 reg_midatl reg_encen
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 100. 1990. -3.81 5.29 0.00211 0. 0.
#> 2 100. 2000. -4.62 6.62 0.00207 0. 0.
#> 3 200. 1990. -0.681 3.03 0.000732 0. 0.
#> 4 200. 2000. -6.97 10.3 0.000815 0. 0.
#> 5 301. 1990. -2.17 2.06 0.000261 0. 0.
#> 6 301. 2000. -3.78 6.20 0.000240 0. 0.
#> 7 302. 1990. -2.83 1.08 0.00257 0. 0.
#> 8 302. 2000. -3.95 3.11 0.00260 0. 0.
#> 9 401. 1990. -5.04 1.02 0.00171 0. 0.
#> 10 401. 2000. -4.96 2.99 0.00176 0. 0.
#> # ... with 1,434 more rows, and 13 more variables: reg_wncen <dbl>,
#> # reg_satl <dbl>, reg_escen <dbl>, reg_wscen <dbl>, reg_mount <dbl>,
#> # reg_pacif <dbl>, l_sh_popedu_c <dbl>, l_sh_popfborn <dbl>,
#> # l_sh_empl_f <dbl>, l_sh_routine33 <dbl>, l_task_outsource <dbl>,
#> # t2 <dbl>, l_shind_manuf_cbp <dbl>
# index: czone, year, ind
bartik.weight::ADH_local
#> # A tibble: 563,160 x 4
#> czone year ind sh_ind_
#> <dbl> <dbl> <chr> <dbl>
#> 1 100. 1990. 2011 0.0101
#> 2 100. 1990. 2015 0.
#> 3 100. 1990. 2022 0.000433
#> 4 100. 1990. 2023 0.00209
#> 5 100. 1990. 2024 0.000991
#> 6 100. 1990. 2026 0.00401
#> 7 100. 1990. 2032 0.
#> 8 100. 1990. 2033 0.000499
#> 9 100. 1990. 2034 0.
#> 10 100. 1990. 2035 0.000340
#> # ... with 563,150 more rows
# index: year, ind
bartik.weight::ADH_global
#> # A tibble: 780 x 3
#> year ind trade_
#> <dbl> <chr> <dbl>
#> 1 1990. 2011 1.21
#> 2 1990. 2015 7.46
#> 3 1990. 2022 0.00698
#> 4 1990. 2023 3.16
#> 5 1990. 2024 0.
#> 6 1990. 2026 0.0333
#> 7 1990. 2032 0.176
#> 8 1990. 2033 3.78
#> 9 1990. 2034 12.2
#> 10 1990. 2035 5.11
#> # ... with 770 more rows
The main dataset bartik.weight::ADH_master contains the key variables
at the level of czone-year.
The Bartik instrument is formed by interacting local industry shares
(variable sh_ind_ in the ADH_local tibble) and the industry growth
rates (variable trade_ in the ADH_global tibble).
To estimate the Rotemberg weights, it’s necessary to transform the
local tibble from long to wide format:
bartik.weight::ADH_local %>%
mutate(ind = str_glue("t{year}_sh_ind_{ind}")) %>%
spread(ind, sh_ind_, fill = 0) %>%
print() -> ADH_local2
#> # A tibble: 1,444 x 782
#> czone year t1990_sh_ind_2011 t1990_sh_ind_2015 t1990_sh_ind_2022
#> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 100. 1990. 0.0101 0. 0.000433
#> 2 100. 2000. 0. 0. 0.
#> 3 200. 1990. 0.00165 0.00714 0.
#> 4 200. 2000. 0. 0. 0.
#> 5 301. 1990. 0.000581 0. 0.
#> 6 301. 2000. 0. 0. 0.
#> 7 302. 1990. 0.0215 0.00341 0.
#> 8 302. 2000. 0. 0. 0.
#> 9 401. 1990. 0.000242 0.00274 0.
#> 10 401. 2000. 0. 0. 0.
#> # ... with 1,434 more rows, and 777 more variables:
#> # t1990_sh_ind_2023 <dbl>, t1990_sh_ind_2024 <dbl>,
#> # t1990_sh_ind_2026 <dbl>, t1990_sh_ind_2032 <dbl>,
#> # t1990_sh_ind_2033 <dbl>, t1990_sh_ind_2034 <dbl>,
#> # t1990_sh_ind_2035 <dbl>, t1990_sh_ind_2037 <dbl>,
#> # t1990_sh_ind_2041 <dbl>, t1990_sh_ind_2043 <dbl>,
#> # t1990_sh_ind_2044 <dbl>, t1990_sh_ind_2045 <dbl>,
#> # t1990_sh_ind_2046 <dbl>, t1990_sh_ind_2047 <dbl>,
#> # t1990_sh_ind_2048 <dbl>, t1990_sh_ind_2051 <dbl>,
#> # t1990_sh_ind_2062 <dbl>, t1990_sh_ind_2064 <dbl>,
#> # t1990_sh_ind_2066 <dbl>, t1990_sh_ind_2067 <dbl>,
#> # t1990_sh_ind_2068 <dbl>, t1990_sh_ind_2074 <dbl>,
#> # t1990_sh_ind_2075 <dbl>, t1990_sh_ind_2076 <dbl>,
#> # t1990_sh_ind_2077 <dbl>, t1990_sh_ind_2079 <dbl>,
#> # t1990_sh_ind_2082 <dbl>, t1990_sh_ind_2083 <dbl>,
#> # t1990_sh_ind_2084 <dbl>, t1990_sh_ind_2085 <dbl>,
#> # t1990_sh_ind_2086 <dbl>, t1990_sh_ind_2087 <dbl>,
#> # t1990_sh_ind_2091 <dbl>, t1990_sh_ind_2095 <dbl>,
#> # t1990_sh_ind_2096 <dbl>, t1990_sh_ind_2097 <dbl>,
#> # t1990_sh_ind_2098 <dbl>, t1990_sh_ind_2099 <dbl>,
#> # t1990_sh_ind_2111 <dbl>, t1990_sh_ind_2131 <dbl>,
#> # t1990_sh_ind_2211 <dbl>, t1990_sh_ind_2221 <dbl>,
#> # t1990_sh_ind_2231 <dbl>, t1990_sh_ind_2241 <dbl>,
#> # t1990_sh_ind_2252 <dbl>, t1990_sh_ind_2253 <dbl>,
#> # t1990_sh_ind_2257 <dbl>, t1990_sh_ind_2258 <dbl>,
#> # t1990_sh_ind_2273 <dbl>, t1990_sh_ind_2281 <dbl>,
#> # t1990_sh_ind_2284 <dbl>, t1990_sh_ind_2295 <dbl>,
#> # t1990_sh_ind_2296 <dbl>, t1990_sh_ind_2297 <dbl>,
#> # t1990_sh_ind_2298 <dbl>, t1990_sh_ind_2299 <dbl>,
#> # t1990_sh_ind_2311 <dbl>, t1990_sh_ind_2321 <dbl>,
#> # t1990_sh_ind_2322 <dbl>, t1990_sh_ind_2323 <dbl>,
#> # t1990_sh_ind_2325 <dbl>, t1990_sh_ind_2329 <dbl>,
#> # t1990_sh_ind_2331 <dbl>, t1990_sh_ind_2335 <dbl>,
#> # t1990_sh_ind_2337 <dbl>, t1990_sh_ind_2339 <dbl>,
#> # t1990_sh_ind_2341 <dbl>, t1990_sh_ind_2342 <dbl>,
#> # t1990_sh_ind_2353 <dbl>, t1990_sh_ind_2369 <dbl>,
#> # t1990_sh_ind_2371 <dbl>, t1990_sh_ind_2381 <dbl>,
#> # t1990_sh_ind_2384 <dbl>, t1990_sh_ind_2385 <dbl>,
#> # t1990_sh_ind_2386 <dbl>, t1990_sh_ind_2389 <dbl>,
#> # t1990_sh_ind_2391 <dbl>, t1990_sh_ind_2392 <dbl>,
#> # t1990_sh_ind_2393 <dbl>, t1990_sh_ind_2394 <dbl>,
#> # t1990_sh_ind_2395 <dbl>, t1990_sh_ind_2396 <dbl>,
#> # t1990_sh_ind_2399 <dbl>, t1990_sh_ind_2411 <dbl>,
#> # t1990_sh_ind_2421 <dbl>, t1990_sh_ind_2426 <dbl>,
#> # t1990_sh_ind_2429 <dbl>, t1990_sh_ind_2431 <dbl>,
#> # t1990_sh_ind_2434 <dbl>, t1990_sh_ind_2435 <dbl>,
#> # t1990_sh_ind_2436 <dbl>, t1990_sh_ind_2439 <dbl>,
#> # t1990_sh_ind_2448 <dbl>, t1990_sh_ind_2449 <dbl>,
#> # t1990_sh_ind_2451 <dbl>, t1990_sh_ind_2452 <dbl>,
#> # t1990_sh_ind_2491 <dbl>, t1990_sh_ind_2493 <dbl>,
#> # t1990_sh_ind_2499 <dbl>, t1990_sh_ind_2514 <dbl>, …
Once all the data is in proper format, the bw() function will return
the weight, and the just-identified IV estimates:
library(bartik.weight)
# Prepare variables in the master tibble
y = "d_sh_empl_mfg"
x = "d_tradeusch_pw"
controls = c("reg_midatl", "reg_encen", "reg_wncen", "reg_satl",
"reg_escen", "reg_wscen", "reg_mount", "reg_pacif", "l_sh_popedu_c",
"l_sh_popfborn", "l_sh_empl_f", "l_sh_routine33", "l_task_outsource",
"t2", "l_shind_manuf_cbp")
weight = "timepwt48"
# Prepare variables in the local tibble
Z = setdiff(names(ADH_local_wide), c("czone", "year"))
# Prepare variables in the global tibble
G = "trade_"
# Estimate the weight (alpha) and the IV estimates (beta)
bw = bw(ADH_master, y, x, controls, weight, ADH_local2, Z, ADH_global, G)
bw
#> # A tibble: 780 x 5
#> year ind trade_ alpha beta
#> <dbl> <chr> <dbl> <dbl> <dbl>
#> 1 1990. 2011 1.21 -0.00102 -2.80
#> 2 1990. 2015 7.46 -0.0104 -1.06
#> 3 1990. 2022 0.00698 0.000000663 7.74
#> 4 1990. 2023 3.16 0.0000676 8.82
#> 5 1990. 2024 0. 0. 3.93
#> 6 1990. 2026 0.0333 0.0000218 -0.0432
#> 7 1990. 2032 0.176 0.00000474 13.6
#> 8 1990. 2033 3.78 0.0000286 29.0
#> 9 1990. 2034 12.2 -0.00188 0.923
#> 10 1990. 2035 5.11 -0.000357 1.59
#> # ... with 770 more rows
The following table replicates Panel A of Table 5 in Goldsmith-Pinkham,
Sorkin and Swift
(2018):
bw %>%
mutate(Weight = if_else(alpha > 0, "Positive", "Negative")) %>%
group_by(Weight) %>%
summarise(Sum = sum(alpha), Mean = mean(alpha)) %>%
knitr::kable(digits = 3, caption = "Negative and positive weights")
| Weight | Sum | Mean |
| :------- | ------: | ----: |
| Negative | -0.116 | 0.000 |
| Positive | 1.116 | 0.002 |
Negative and positive weights
Below is part of Panel D of Table 5:
bw %>%
top_n(5, alpha) %>%
arrange(desc(alpha)) %>%
mutate(ind = case_when(
ind == "3571" ~ "Electronic Computers",
ind == "3944" ~ "Games and Toys",
ind == "3651" ~ "Household Audio and Video",
ind == "3661" ~ "Telephone Apparatus",
ind == "3577" ~ "Computer Equipment"
)) %>%
rename(g = trade_) %>%
knitr::kable(digits = 3, caption = "Top five Rotemberg weight industries")
| year | ind | g | alpha | beta |
| ---: | :------------------------ | ------: | ----: | ------: |
| 2000 | Electronic Computers | 189.117 | 0.140 | -0.620 |
| 2000 | Games and Toys | 320.636 | 0.098 | -0.179 |
| 2000 | Household Audio and Video | 218.216 | 0.055 | -0.147 |
| 2000 | Telephone Apparatus | 94.577 | 0.051 | -0.308 |
| 2000 | Computer Equipment | 41.678 | 0.047 | -0.232 |
Top five Rotemberg weight industries
jjchern/bartik.weight documentation built on May 17, 2019, 7:28 p.m.
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About bartik.weight
The package bartik.weight is an implementation in R for estimating
Rotemberg weights outlined in Goldsmith-Pinkham, Sorkin and Swift
(2018).
The original Stata package by Paul Goldsmith-Pinkham can be found here: https://github.com/paulgp/bartik-weight.
Installation
You can install the current version of bartik.weight from Github with:
# install.packages("devtools")
devtools::install_github("jjchern/bartik.weight")
# To uninstall the package, use:
# remove.packages("bartik.weight")
Examples
The package includes example datasets:
library(tidyverse)
#> ── Attaching packages ─────────────────────────── tidyverse 1.2.1 ──
#> ✔ ggplot2 2.2.1.9000 ✔ purrr 0.2.4
#> ✔ tibble 1.4.2 ✔ dplyr 0.7.4
#> ✔ tidyr 0.8.0 ✔ stringr 1.3.0
#> ✔ readr 1.1.1.9000 ✔ forcats 0.3.0
#> ── Conflicts ────────────────────────────── tidyverse_conflicts() ──
#> ✖ dplyr::filter() masks stats::filter()
#> ✖ dplyr::lag() masks stats::lag()
#> ✖ dplyr::vars() masks ggplot2::vars()
# index: czone, year
bartik.weight::ADH_master
#> # A tibble: 1,444 x 20
#> czone year d_sh_empl_mfg d_tradeusch_pw timepwt48 reg_midatl reg_encen
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 100. 1990. -3.81 5.29 0.00211 0. 0.
#> 2 100. 2000. -4.62 6.62 0.00207 0. 0.
#> 3 200. 1990. -0.681 3.03 0.000732 0. 0.
#> 4 200. 2000. -6.97 10.3 0.000815 0. 0.
#> 5 301. 1990. -2.17 2.06 0.000261 0. 0.
#> 6 301. 2000. -3.78 6.20 0.000240 0. 0.
#> 7 302. 1990. -2.83 1.08 0.00257 0. 0.
#> 8 302. 2000. -3.95 3.11 0.00260 0. 0.
#> 9 401. 1990. -5.04 1.02 0.00171 0. 0.
#> 10 401. 2000. -4.96 2.99 0.00176 0. 0.
#> # ... with 1,434 more rows, and 13 more variables: reg_wncen <dbl>,
#> # reg_satl <dbl>, reg_escen <dbl>, reg_wscen <dbl>, reg_mount <dbl>,
#> # reg_pacif <dbl>, l_sh_popedu_c <dbl>, l_sh_popfborn <dbl>,
#> # l_sh_empl_f <dbl>, l_sh_routine33 <dbl>, l_task_outsource <dbl>,
#> # t2 <dbl>, l_shind_manuf_cbp <dbl>
# index: czone, year, ind
bartik.weight::ADH_local
#> # A tibble: 563,160 x 4
#> czone year ind sh_ind_
#> <dbl> <dbl> <chr> <dbl>
#> 1 100. 1990. 2011 0.0101
#> 2 100. 1990. 2015 0.
#> 3 100. 1990. 2022 0.000433
#> 4 100. 1990. 2023 0.00209
#> 5 100. 1990. 2024 0.000991
#> 6 100. 1990. 2026 0.00401
#> 7 100. 1990. 2032 0.
#> 8 100. 1990. 2033 0.000499
#> 9 100. 1990. 2034 0.
#> 10 100. 1990. 2035 0.000340
#> # ... with 563,150 more rows
# index: year, ind
bartik.weight::ADH_global
#> # A tibble: 780 x 3
#> year ind trade_
#> <dbl> <chr> <dbl>
#> 1 1990. 2011 1.21
#> 2 1990. 2015 7.46
#> 3 1990. 2022 0.00698
#> 4 1990. 2023 3.16
#> 5 1990. 2024 0.
#> 6 1990. 2026 0.0333
#> 7 1990. 2032 0.176
#> 8 1990. 2033 3.78
#> 9 1990. 2034 12.2
#> 10 1990. 2035 5.11
#> # ... with 770 more rows
The main dataset bartik.weight::ADH_master contains the key variables
at the level of czone-year.
The Bartik instrument is formed by interacting local industry shares
(variable sh_ind_ in the ADH_local tibble) and the industry growth
rates (variable trade_ in the ADH_global tibble).
To estimate the Rotemberg weights, it’s necessary to transform the
local tibble from long to wide format:
bartik.weight::ADH_local %>%
mutate(ind = str_glue("t{year}_sh_ind_{ind}")) %>%
spread(ind, sh_ind_, fill = 0) %>%
print() -> ADH_local2
#> # A tibble: 1,444 x 782
#> czone year t1990_sh_ind_2011 t1990_sh_ind_2015 t1990_sh_ind_2022
#> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 100. 1990. 0.0101 0. 0.000433
#> 2 100. 2000. 0. 0. 0.
#> 3 200. 1990. 0.00165 0.00714 0.
#> 4 200. 2000. 0. 0. 0.
#> 5 301. 1990. 0.000581 0. 0.
#> 6 301. 2000. 0. 0. 0.
#> 7 302. 1990. 0.0215 0.00341 0.
#> 8 302. 2000. 0. 0. 0.
#> 9 401. 1990. 0.000242 0.00274 0.
#> 10 401. 2000. 0. 0. 0.
#> # ... with 1,434 more rows, and 777 more variables:
#> # t1990_sh_ind_2023 <dbl>, t1990_sh_ind_2024 <dbl>,
#> # t1990_sh_ind_2026 <dbl>, t1990_sh_ind_2032 <dbl>,
#> # t1990_sh_ind_2033 <dbl>, t1990_sh_ind_2034 <dbl>,
#> # t1990_sh_ind_2035 <dbl>, t1990_sh_ind_2037 <dbl>,
#> # t1990_sh_ind_2041 <dbl>, t1990_sh_ind_2043 <dbl>,
#> # t1990_sh_ind_2044 <dbl>, t1990_sh_ind_2045 <dbl>,
#> # t1990_sh_ind_2046 <dbl>, t1990_sh_ind_2047 <dbl>,
#> # t1990_sh_ind_2048 <dbl>, t1990_sh_ind_2051 <dbl>,
#> # t1990_sh_ind_2062 <dbl>, t1990_sh_ind_2064 <dbl>,
#> # t1990_sh_ind_2066 <dbl>, t1990_sh_ind_2067 <dbl>,
#> # t1990_sh_ind_2068 <dbl>, t1990_sh_ind_2074 <dbl>,
#> # t1990_sh_ind_2075 <dbl>, t1990_sh_ind_2076 <dbl>,
#> # t1990_sh_ind_2077 <dbl>, t1990_sh_ind_2079 <dbl>,
#> # t1990_sh_ind_2082 <dbl>, t1990_sh_ind_2083 <dbl>,
#> # t1990_sh_ind_2084 <dbl>, t1990_sh_ind_2085 <dbl>,
#> # t1990_sh_ind_2086 <dbl>, t1990_sh_ind_2087 <dbl>,
#> # t1990_sh_ind_2091 <dbl>, t1990_sh_ind_2095 <dbl>,
#> # t1990_sh_ind_2096 <dbl>, t1990_sh_ind_2097 <dbl>,
#> # t1990_sh_ind_2098 <dbl>, t1990_sh_ind_2099 <dbl>,
#> # t1990_sh_ind_2111 <dbl>, t1990_sh_ind_2131 <dbl>,
#> # t1990_sh_ind_2211 <dbl>, t1990_sh_ind_2221 <dbl>,
#> # t1990_sh_ind_2231 <dbl>, t1990_sh_ind_2241 <dbl>,
#> # t1990_sh_ind_2252 <dbl>, t1990_sh_ind_2253 <dbl>,
#> # t1990_sh_ind_2257 <dbl>, t1990_sh_ind_2258 <dbl>,
#> # t1990_sh_ind_2273 <dbl>, t1990_sh_ind_2281 <dbl>,
#> # t1990_sh_ind_2284 <dbl>, t1990_sh_ind_2295 <dbl>,
#> # t1990_sh_ind_2296 <dbl>, t1990_sh_ind_2297 <dbl>,
#> # t1990_sh_ind_2298 <dbl>, t1990_sh_ind_2299 <dbl>,
#> # t1990_sh_ind_2311 <dbl>, t1990_sh_ind_2321 <dbl>,
#> # t1990_sh_ind_2322 <dbl>, t1990_sh_ind_2323 <dbl>,
#> # t1990_sh_ind_2325 <dbl>, t1990_sh_ind_2329 <dbl>,
#> # t1990_sh_ind_2331 <dbl>, t1990_sh_ind_2335 <dbl>,
#> # t1990_sh_ind_2337 <dbl>, t1990_sh_ind_2339 <dbl>,
#> # t1990_sh_ind_2341 <dbl>, t1990_sh_ind_2342 <dbl>,
#> # t1990_sh_ind_2353 <dbl>, t1990_sh_ind_2369 <dbl>,
#> # t1990_sh_ind_2371 <dbl>, t1990_sh_ind_2381 <dbl>,
#> # t1990_sh_ind_2384 <dbl>, t1990_sh_ind_2385 <dbl>,
#> # t1990_sh_ind_2386 <dbl>, t1990_sh_ind_2389 <dbl>,
#> # t1990_sh_ind_2391 <dbl>, t1990_sh_ind_2392 <dbl>,
#> # t1990_sh_ind_2393 <dbl>, t1990_sh_ind_2394 <dbl>,
#> # t1990_sh_ind_2395 <dbl>, t1990_sh_ind_2396 <dbl>,
#> # t1990_sh_ind_2399 <dbl>, t1990_sh_ind_2411 <dbl>,
#> # t1990_sh_ind_2421 <dbl>, t1990_sh_ind_2426 <dbl>,
#> # t1990_sh_ind_2429 <dbl>, t1990_sh_ind_2431 <dbl>,
#> # t1990_sh_ind_2434 <dbl>, t1990_sh_ind_2435 <dbl>,
#> # t1990_sh_ind_2436 <dbl>, t1990_sh_ind_2439 <dbl>,
#> # t1990_sh_ind_2448 <dbl>, t1990_sh_ind_2449 <dbl>,
#> # t1990_sh_ind_2451 <dbl>, t1990_sh_ind_2452 <dbl>,
#> # t1990_sh_ind_2491 <dbl>, t1990_sh_ind_2493 <dbl>,
#> # t1990_sh_ind_2499 <dbl>, t1990_sh_ind_2514 <dbl>, …
Once all the data is in proper format, the bw() function will return
the weight, and the just-identified IV estimates:
library(bartik.weight)
# Prepare variables in the master tibble
y = "d_sh_empl_mfg"
x = "d_tradeusch_pw"
controls = c("reg_midatl", "reg_encen", "reg_wncen", "reg_satl",
"reg_escen", "reg_wscen", "reg_mount", "reg_pacif", "l_sh_popedu_c",
"l_sh_popfborn", "l_sh_empl_f", "l_sh_routine33", "l_task_outsource",
"t2", "l_shind_manuf_cbp")
weight = "timepwt48"
# Prepare variables in the local tibble
Z = setdiff(names(ADH_local_wide), c("czone", "year"))
# Prepare variables in the global tibble
G = "trade_"
# Estimate the weight (alpha) and the IV estimates (beta)
bw = bw(ADH_master, y, x, controls, weight, ADH_local2, Z, ADH_global, G)
bw
#> # A tibble: 780 x 5
#> year ind trade_ alpha beta
#> <dbl> <chr> <dbl> <dbl> <dbl>
#> 1 1990. 2011 1.21 -0.00102 -2.80
#> 2 1990. 2015 7.46 -0.0104 -1.06
#> 3 1990. 2022 0.00698 0.000000663 7.74
#> 4 1990. 2023 3.16 0.0000676 8.82
#> 5 1990. 2024 0. 0. 3.93
#> 6 1990. 2026 0.0333 0.0000218 -0.0432
#> 7 1990. 2032 0.176 0.00000474 13.6
#> 8 1990. 2033 3.78 0.0000286 29.0
#> 9 1990. 2034 12.2 -0.00188 0.923
#> 10 1990. 2035 5.11 -0.000357 1.59
#> # ... with 770 more rows
The following table replicates Panel A of Table 5 in Goldsmith-Pinkham, Sorkin and Swift (2018):
bw %>%
mutate(Weight = if_else(alpha > 0, "Positive", "Negative")) %>%
group_by(Weight) %>%
summarise(Sum = sum(alpha), Mean = mean(alpha)) %>%
knitr::kable(digits = 3, caption = "Negative and positive weights")
| Weight | Sum | Mean | | :------- | ------: | ----: | | Negative | -0.116 | 0.000 | | Positive | 1.116 | 0.002 |
Negative and positive weights
Below is part of Panel D of Table 5:
bw %>%
top_n(5, alpha) %>%
arrange(desc(alpha)) %>%
mutate(ind = case_when(
ind == "3571" ~ "Electronic Computers",
ind == "3944" ~ "Games and Toys",
ind == "3651" ~ "Household Audio and Video",
ind == "3661" ~ "Telephone Apparatus",
ind == "3577" ~ "Computer Equipment"
)) %>%
rename(g = trade_) %>%
knitr::kable(digits = 3, caption = "Top five Rotemberg weight industries")
| year | ind | g | alpha | beta | | ---: | :------------------------ | ------: | ----: | ------: | | 2000 | Electronic Computers | 189.117 | 0.140 | -0.620 | | 2000 | Games and Toys | 320.636 | 0.098 | -0.179 | | 2000 | Household Audio and Video | 218.216 | 0.055 | -0.147 | | 2000 | Telephone Apparatus | 94.577 | 0.051 | -0.308 | | 2000 | Computer Equipment | 41.678 | 0.047 | -0.232 |
Top five Rotemberg weight industries
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