In paulofelipe/cp2015: Caliendo and Parro (2015) Quantitative Trade Model

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

Intro

This vignette uses the cp2015 package to reproduce the simulation results presented in section 5.1 of the article "Estimates of the Trade and Welfare Effects of NAFTA".

First, load the package:

library(cp2015)

## Additional packages used in this vignette
library(kableExtra)
library(tidyr)
library(dplyr)

# Change NA to - in kable
options(knitr.kable.NA = "")

Data

To use the package, it is necessary to obtain data on intermediate consumption, final consumption, value added, trade and tariff data, deficits and the shape parameter of the Fréchet distribution, $\theta_j$ (also called trade elasticity).

The package provides a set of data for testing and so that the user can check the required data. The cp2015_nafta object has the data for the NAFTA tariff reduction simulation experiment. Below, you can see the structure of the data.

# Data for example
# cp2015_nafta is the data available in the package
data <- cp2015_nafta
str(data)

Running the simulation

In section 5.1, Caliendo and Parro (2015) consider zero aggregate deficits in the baseline and counterfactual scenarios. To do this, just use the run_cp2015() function with the P zero_aggregate_deficit = TRUE to calculate the results. Use help(run_cp2015) to see more details about this function.

# Get the results
results <- run_cp2015(data = cp2015_nafta, zero_aggregate_deficit = TRUE)

str(results)

Reporting the results

We have extended the welfare decomposition formula to account for the possibility of changes in iceberg trade costs (variations in technical efficiency).

$$d \ln W_n = \frac{1}{I_n}\sum_{j = 1}^J\sum_{i = 1}^N \underbrace{\left(E_{ni}^j d \ln c_n^j - M_{ni}^j d \ln c_i^j \right)}{\text{Terms of trade}} + \frac{1}{I_n} \sum{j = 1}^J\sum_{i = 1}^N \underbrace{\tau_{ni}^ j M_{ni}^j \left(d \ln M_{ni}^j - d \ln c_i^j \right)}{\text{Volume of trade}} - \frac{1}{I_n} \sum{j = 1}^J\sum_{i = 1}^N \underbrace{M_{ni}^j (1 + \tau_{ni}^j) d \ln d_{ni}^j}_{\text{Technical efficiency}}$$

# Welfare for NAFTA countries
nafta <- c("Canada", "Mexico", "USA")
results$welfare %>%
  filter(region %in% nafta) %>%
  mutate(region = factor(region, levels = c("Mexico", "Canada", "USA"))) %>%
  kable(
    format = "html",
    digits = 2,
    col.names = c(
      "Country", "Terms of trade", "Volume of trade", "Technical efficiency",
       "Total", "Real wages"
      ),
    caption = "Welfare effects from NAFTA’s tariff reductions"
  ) %>%
  kable_styling(full_width = TRUE) %>%
  add_header_above(
    c(" " = 1, "Welfare" = 4, " " = 1)
  )

tot_bilateral <- results$tot %>%
  filter(region %in% nafta) %>%
  mutate(
    partner = case_when(
      partner %in% nafta ~ "NAFTA",
      TRUE ~ "ROW"
    ),
    category = "ToT"
  ) %>%
  group_by(region, partner, category) %>%
  summarise(
    value = sum(tot),
    .groups = "drop"
  )

vot_bilateral <- results$vot %>%
  filter(
    region %in% nafta
  ) %>%
  mutate(
    partner = case_when(
      partner %in% nafta ~ "NAFTA",
      TRUE ~ "ROW"
    ),
    category = "VoT"
  ) %>%
  group_by(region, partner, category) %>%
  summarise(
    value = sum(vot),
    .groups = "drop"
  )

bind_rows(tot_bilateral, vot_bilateral) %>%
  unite(partner_cat, partner, category) %>%
  pivot_wider(
    names_from = "partner_cat",
    values_from = "value"
  ) %>%
  mutate(region = factor(region, levels = c("Mexico", "Canada", "USA"))) %>%
  arrange(region) %>%
  kable(
    format = "html",
    digits = 2,
    col.names = c("Country", "NAFTA", "Rest of the world", "NAFTA", "Rest of the world"),
    caption = "Bilateral welfare effects from NAFTA’s tariff reductions"
  ) %>%
  kable_styling(full_width = TRUE) %>%
  add_header_above(
    c(" " = 1, "Terms of trade" = 2, "Volume of trade" = 2)
  )

sectors <- c(
  "Agriculture", "Mining", "Food", "Textile",
  "Wood", "Paper", "Petroleum", "Chemicals", "Plastic", "Minerals",
  "Basic metals", "Metal products", "Machinery n.e.c", "Office",
  "Electrical", "Communication", "Medical", "Auto", "Other Transport",
  "Other"
)

tot_sector <- results$tot %>%
  filter(region %in% nafta) %>%
  group_by(region, sector) %>%
  summarise(
    tot = sum(tot),
    .groups = "drop"
  ) %>%
  group_by(region) %>%
  mutate(
    category = "ToT",
    value = tot / sum(tot) * 100
  ) %>%
  select(-tot) %>%
  filter(sector %in% sectors)


vot_sector <- results$vot %>%
  filter(region %in% nafta, sector %in% sectors) %>%
  group_by(region, sector) %>%
  summarise(
    vot = sum(vot),
    .groups = "drop"
  ) %>%
  group_by(region) %>%
  mutate(
    category = "VoT",
    value = vot / sum(vot) * 100
  ) %>%
  select(-vot) %>%
  filter(sector %in% sectors)

bind_rows(tot_sector, vot_sector) %>%
  unite(region_cat, region, category) %>%
  pivot_wider(
    names_from = "region_cat",
    values_from = "value"
  ) %>%
  select(sector, contains("Mexico"), contains("Canada"), contains("USA")) %>%
  kable(
    format = "html",
    digits = 2,
    col.names = c(
      "Sector", "Terms of trade", "Volume of trade", "Terms of trade",
      "Volume of trade", "Terms of trade", "Volume of trade"
    ),
    caption = "Sectoral contribution to welfare effects from NAFTA’s tariff reductions (%)"
  ) %>%
  kable_styling(full_width = TRUE) %>%
  add_header_above(
    c(" " = 1, "Mexico" = 2, "Canada" = 2, "USA" = 2)
  )

results$trade %>%
  filter(importer %in% nafta, exporter %in% nafta) %>%
  group_by(importer, exporter) %>%
  summarise(
    trade_bln = sum(trade_bln),
    trade_cfl = sum(trade_cfl),
    .groups = "drop"
  ) %>%
  mutate(
    change = (trade_cfl / trade_bln - 1) * 100,
    change = ifelse(exporter == importer, NA, change)
  ) %>%
  select(importer, exporter, change) %>%
  mutate(
    importer = factor(importer, levels = c("Mexico", "Canada", "USA")),
    exporter = factor(exporter, levels = c("Mexico", "Canada", "USA"))
  ) %>%
  arrange(importer, exporter) %>%
  pivot_wider(
    names_from = "exporter",
    values_from = "change"
  ) %>%
  kable(
    format = "html",
    digits = 2,
    col.names = c("Import Contry", "Mexico", "Canada", "USA"),
    captions = "Trade effects from NAFTA’s tariff reductions"
  ) %>%
  kable_styling(full_width = TRUE)

results$trade %>%
  filter(exporter %in% nafta, exporter != importer) %>%
  group_by(exporter, sector) %>%
  summarise(
    trade_bln = sum(trade_bln),
    trade_cfl = sum(trade_cfl),
    .groups = "drop"
  ) %>%
  group_by(exporter) %>%
  mutate(
    share_bln = trade_bln / sum(trade_bln) * 100,
    share_cfl = trade_cfl / sum(trade_cfl) * 100
  ) %>%
  select(exporter, sector, contains("share")) %>%
  pivot_longer(
    cols = c("share_bln", "share_cfl"),
    names_to = "category",
    values_to = "value"
  ) %>%
  unite(exporter_cat, exporter, category) %>%
  pivot_wider(
    names_from = "exporter_cat",
    values_from = "value"
  ) %>%
  select(sector, contains("Mexico"), contains("Canada"), contains("USA")) %>%
  filter(sector %in% sectors) %>% 
  kable(
    format = "html",
    digits = 2,
    col.names = c("Sector", "Before", "After", "Before", "After", "Before", "After"),
    caption = "Export shares by sector before and after NAFTA’s tariff reductions (%)"
  ) %>%
  kable_styling(full_width = TRUE) %>%
  add_header_above(
    c(" " = 1, "Mexico" = 2, "Canada" = 2, "USA" = 2)
  )