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Bilateral trade data

Source: R/bilateral_trade.R
get_bilateral_trade.Rd

Clean CSV data from the given file_path. Fill missing data and balance trade totals.

Usage

get_bilateral_trade(file_path)

Arguments

file_path

The local path where the input CSV is located. It is recommended to use an autogenerated path by get_file_path() function.

Value

A tibble with the reported trade between countries. For efficient memory usage, the tibble is not exactly in tidy format. It contains the following columns:

  • year: The year in which the recorded event occurred.

  • item_cbs_code: FAOSTAT internal code for the item that is being traded. For code details see e.g. add_item_cbs_name().

  • bilateral_trade: Square matrix of NxN dimensions where N is the total number of countries being considered. The matrix row and column names are exactly equal and they represent country codes.

    • Row name: The code of the country where the data is from. For code details see e.g. add_area_name().

    • Column name: FAOSTAT internal code for the country that is importing the item. See row name explanation above.

    If m is the matrix, the value at m["A", "B"] is the trade in tonnes from country "A" to country "B", for the corresponding year and item. The matrix can be considered balanced. This means:

    • The sum of all values from row "A", where "A" is any country, should match the total exports from country "A" reported in the commodity balance sheet (which is considered more accurate for totals).

    • The sum of all values from column "A", where "A" is any country, should match the total imports into country "A" reported in the commodity balance sheet (which is considered more accurate for totals).

    The sums may not be exactly the expected values because of precision issues and/or the iterative proportional fitting algorithm not converging fast enough, but should be relatively very close to the desired totals.

The step by step approach to obtain this data tries to follow the FABIO model and is explained below. All the steps are performed separately for each group of year and item.

  • From the FAOSTAT reported bilateral trade, there are sometimes two values for one trade flow: the exported amount claimed by the reporter country and the import amount claimed by the partner country. Here, the export data was preferred, i.e., if country "A" says it exported X tonnes to country "B" but country "B" claims they got Y tonnes from country "A", we trust the export data X. This choice is only needed if there exists a reported amount from both sides. Otherwise, the single existing report is chosen.

  • Complete the country data, that is, add any missing combinations of country trade with NAs, which will be estimated later. In the matrix form, this doesn't increase the memory usage since we had to build a matrix anyway (for the balancing algorithm), and the empty parts also take up memory. This is also done for total imports/exports from the commodity balance sheet, but these are directly filled with 0s instead.

  • The total imports and exports from the commodity balance sheet are balanced by downscaling the largest of the two to match the lowest. This is done in the following way:

    • If total_imports > total_exports: Set import as total_exports * import / total_import.

    • If total_exports > total_exports: Set export as total_exports * export / total_export.

  • The missing data in the matrix must be estimated. It's done like this:

    • For each pair of exporter i and importer j, we estimate a bilateral trade m[i, j] using the export shares of i and import shares of j from the commodity balance sheet:

      • est_1 <- exports[i] * imports[j] / sum(imports), i.e., total exports of country i spread among other countries' import shares.

      • est_2 <- imports[j] * exports[i] / sum(exports), i.e. total imports of country j spread among other countries' export shares.

      • est <- (est_1 + est_2) / 2, i.e., the mean of both estimates.

      In the above computations, exports and imports are the original values before they were balanced.

    • The estimates for data that already existed (i.e. non-NA) are discarded. For the ones left, for each row (i.e. exporter country), we get the difference between its balanced total export and the sum of original non-estimated data. The result is the gap we can actually fill with estimates, so as to not get past the reported total export. If the sum of non-discarded estimates is larger, it must be downscaled and spread by computing gap * non_discarded_estimate / sum(non_discarded_estimates).

    • The estimates are divided by a trust factor, in the sense that we don't rely on the whole value, thinking that a non-present value might actually be because that specific trade was 0, so we don't overestimate too much. The chosen factor is 10%, so only 10% of the estimate's value is actually used to fill the NA from the original bilateral trade matrix.

  • The matrix is balanced, as mentioned before, using the iterative proportional fitting algorithm. The target sums for rows and columns are respectively the balanced exports and imports computed from the commodity balance sheet. The algorithm is performed directly using the mipfp R package.

Examples

if (FALSE) { # \dontrun{
get_bilateral_trade(get_file_path("bilateral_trade"))
} # }