concordance: Product Concordance

CRAN downloads

Authors: Steven Liao (steven.liao@ucr.edu), In Song Kim (insong@mit.edu), Sayumi Miyano (smiyano@princeton.edu), Feng Zhu (zucxjo@gmail.com)

This R package provides a set of utilities for matching products in different classification codes used in international trade research. It currently supports concordance between the classifications below:

Harmonized System
- “HS0” (1988/92)
- “HS1” (1996)
- “HS2” (2002)
- “HS3” (2007)
- “HS4” (2012)
- “HS5” (2017)
- “HS” (HS combined)
Standard International Trade Classification
- “SITC1” (1950)
- “SITC2” (1974)
- “SITC3” (1985)
- “SITC4” (2006)
North American Industry Classification System
- “NAICS” (combined)

Support between the above and the below classifications will be offered soon:

Broad Economic Categories (BEC)
International Standard of Industrial Classification (ISIC)
Standard Industrial Classification (SIC)

Additionally, the package provides functions for:

Code nomenclature / descriptions look-up (for HS, SITC, NAICS, BEC, ISIC classification codes)
Rauch classification (product differentiation) look-up (via concordance to SITC2)
Trade elasticity look-up (via concordance to HS0 or SITC3 codes)

Installation Instructions

concordance is available on CRAN and can be installed using:

install.packages("concordance")

You can install the most recent development version of concordance using the devtools package. First you have to install devtools using the following code. Note that you only have to do this once:

if(!require(devtools)) install.packages("devtools")

Then, load devtools and use the function install_github() to install concordance:

library(devtools)
install_github("insongkim/concordance", dependencies=TRUE)

Citation

To cite concordance in publications use:

  Steven Liao, In Song Kim, Sayumi Miyano, Feng Zhu (2020). concordance: Product Concordance. 
  R package version 2.0.0. https://CRAN.R-project.org/package=concordance

A BibTeX entry for LaTeX users is:

  @Manual{,
    title = {concordance: Product Concordance},
    author = {Steven Liao and In Song Kim and Sayumi Miyano and Feng Zhu},
    year = {2020},
    note = {R package version 2.0.0},
    url = {https://CRAN.R-project.org/package=concordance},
  }

Usage Examples

Getting Product Description

Users can look up the product description of different classification codes using the get_desc function. The example below focuses on HS codes.

# load package
library(concordance)

# get product description
get_desc(sourcevar = c("120600", "854690"), origin = "HS5")

[1] "Oil seeds; sunflower seeds, whether or not broken" "Electrical insulators; other than of glass and ceramics"

Users can also input codes with different digits. For HS codes, 2, 4, 6-digits are supported. Note that users should always include leading zeroes in the codes (e.g. use HS code 010110 instead of 10110) – results may be buggy otherwise.

# get product description
get_desc(sourcevar = c("1206", "8546"), origin = "HS5")

[1] "Sunflower seeds; whether or not broken" "Electrical insulators of any material"

# get product description
get_desc(sourcevar = c("12", "85"), origin = "HS5")

[1] "Oil seeds and oleaginous fruits; miscellaneous grains, seeds and fruit, industrial or medicinal plants; straw and fodder"     
[2] "Electrical machinery and equipment and parts thereof; sound recorders and reproducers; television image and sound recorders and reproducers, parts and accessories of such articles"

Concording Different Classification Codes

Users can concord between different classification codes using the concord function. The example below converts HS5 to NAICS codes.

Users can choose to retain all matches for each input by setting all = TRUE. This option will also return the share of occurrences for each matched output among all matched outputs at the user-specified digit level.

# HS to NAICS
concord(sourcevar = c("120600", "854690"),
        origin = "HS5", destination = "NAICS",
        dest.digit = 6, all = TRUE)

$`120600`
$`120600`$match
[1] "111120"

$`120600`$weight
[1] 1


$`854690`
$`854690`$match
[1] "326199" "335932"

$`854690`$weight
[1] 0.5 0.5

Alternatively, users can simply obtain the matched output with the largest share of occurrences (the mode match) with all = FALSE (default). If the mode consists of multiple matches, the function will return the first matched output.

concord(sourcevar = c("120600", "854690"),
        origin = "HS5", destination = "NAICS",
        dest.digit = 6, all = FALSE)

[1] "111120" "326199"

Users can double-check the validity of the matches with get_desc.

# get product description of NAICS ouput
get_desc(sourcevar = c("111120", "326199"), origin = "NAICS2017")

[1] "Oilseed (except Soybean) Farming" "All Other Plastics Product Manufacturing"

More technically, the function works by matching an input code to the most fine-grained level of destination codes in our package (e.g., the 6-digit NAICS codes above) and then calculates the occurrence share of each matched code at the user-specified digit-level. Mode(s) can occur when users choose destination codes at a more aggregated level and multiple finer-grained matched codes belong to certain groups at that level.

We illustrate the above mechanics using HS5 code “8546” as an example. When users ask for 6-digit NAICS codes (the most fine-grained level available), HS5 code “8546” is matched to five NAICS codes: “327212”, “327113”, “327110”, “326199”, and “335932”, with weights of 0.2 (1/5) each.

concord(sourcevar = "8546",
        origin = "HS5", destination = "NAICS",
        dest.digit = 6, all = TRUE)

$`8546`
$`8546`$match
[1] "327212" "327113" "327110" "326199" "335932"

$`8546`$weight
[1] 0.2 0.2 0.2 0.2 0.2

Instead, when users ask for 4-digit NAICS codes, HS5 code “8546” is matched to four NAICS codes: “3271”, “3272”, “3261”, “3359”. NAICS code “3271” gets a weight of 0.4 since it consists of two finer-grained matches “327113” and “327110” out of the 5 total matches (2/5).

concord(sourcevar = "8546",
        origin = "HS5", destination = "NAICS",
        dest.digit = 4, all = TRUE)

$`8546`
$`8546`$match
[1] "3271" "3272" "3261" "3359"

$`8546`$weight
[1] 0.4 0.2 0.2 0.2

Thus, when all = FALSE, the function will retain the matched code with the largest weight “3271”.

concord(sourcevar = "8546",
        origin = "HS5", destination = "NAICS",
        dest.digit = 4, all = FALSE)

[1] "3271"

Getting Product Differentiation

Rauch (1999) classifies each SITC Rev. 2 industry according to three possible types:

Differentiated products (“n”)
Reference priced (“r”)
Homogeneous goods traded on an organized exchange (“w”)

The get_proddiff function concords users’ input codes to SITC2 codes and then extracts the corresponding Rauch classifications.

There are two main options. First, users can set prop = "n", prop = "r", or prop = "w", in which case the function will return the proportion of “w”, “r”, or “n” in the resulting vector of Rauch indices.

# get the proportion of type "r"
get_proddiff(sourcevar = c("120600", "854690"), origin = "HS5", prop = "r")

120600 854690 
     1      0

If prop is not set to any of these, then the function returns, for each input code, a dataframe that summarizes all the frequencies and proportions of “w”, “r”, and “n”.

get_proddiff(sourcevar = c("120600", "854690"), origin = "HS5", prop = "")

$`120600`
  rauch freq proportion
1     w    0          0
2     r    1          1
3     n    0          0

$`854690`
  rauch freq proportion
1     w    0          0
2     r    0          0
3     n    1          1

Second, users can choose Rauch’s conservative classification with setting = CON (default). setting = LIB returns Rauch’s liberal classification.

get_proddiff(sourcevar = c("120600", "854690"), origin = "HS5", setting = "LIB", prop = "")

$`120600`
  rauch freq proportion
1     w    1          1
2     r    0          0
3     n    0          0

$`854690`
  rauch freq proportion
1     w    0          0
2     r    0          0
3     n    1          1

Getting Product Elasticity

Broda and Weinstein (2006) estimate product-level import demand elasticities for 73 countries using HS0 3-digit codes.

The get_sigma function concords users’ input codes to 3-digit HS0 codes and then extracts the corresponding product-level elasticities in the country selected by the user.

There are two main options. First, when give_avg = TRUE (default), each output element will be a simple average of all elasticities (of matched codes) in the corresponding vector.

get_sigma(sourcevar = c("120600", "854690"), origin = "HS5",
          country = "USA", give_avg = TRUE)

[1] 3.733456 1.233216

Users can also set give_avg = FALSE to obtain the full vector of elasticities for all matching codes of each element in the input vector. In this case, there were only one matches per input.

get_sigma(sourcevar = c("120600", "854690"), origin = "HS5",
          country = "USA", give_avg = FALSE)

$`120600`
$`120600`$elasticity
[1] 3.733456


$`854690`
$`854690`$elasticity
[1] 1.233216

Second, for the United States (only), Broda and Weinstein (2006) have also estimated elasticities based on more fine-grained 5-digit SITC3 codes. Users can obtain elasticities in the United States via this method with use_SITC = TRUE.

get_sigma(sourcevar = c("120600", "854690"), origin = "HS5",
          country = "USA", use_SITC = TRUE, give_avg = TRUE)

[1] 2.562991 1.345522

References

Broda, Christian, and David E. Weinstein. 2006. “Globalization and the Gains from Variety,” Quarterly Journal of Economics, 121(2): 541–585.
Rauch, James E. 1999. “Networks Versus Markets in International Trade,” Journal of International Economics 48(1): 7–35.