[DRAFT] Trade Flow Discrepancy Analysis Program

The current implementation is part of a broader context that involves tracing potentially illegal trade of forest products by analyzing anomalies in UN Comtrade data. It is a first milestone that represents the core retrieval and comparison mechanisms that will be needed before higher level functions for further processing can be added. Therefore, its results should be used analytically to identify suspicios reporting patterns, and not as a final detection of illegal trade.

This GitHub repository is yet to be populated; the raw original notebook is currently available at: https://colab.research.google.com/drive/1E5ST_SuhlCQgbmmdsaeZohgImgVIjwrO

The main purpose of this program is to analyze trade flow discrepancies across countries, by comparing what one reporting country says it traded with what its partners say in the corresponding mirror flows, and then quantifying the differences. At this stage, the code answers the question:

For a given reporter country, where do its exports / imports differ against their partners' reportings for the same bilateral flows?

The notebook is written for Google Colab and assumes:

• valid UN Comtrade API key stored in google.colab.userdata
• Google Drive is mounted to /content/drive
• hdx-python-country is installed (used to convert ISO3 to M49 codes)
• forest-product commodity scope preloaded as FULL_FOREST_PRODUCTS

The code defines FULL_FOREST_PRODUCTS as a list of HS-92 headings from chapters 44, 45, 46, 47, 48, 49, and 94. These represent a broad variety of forest products categories (e.g., wood, cork, paper, furniture, etc.) taken from Annex A.1 of Rougieux et al. (2017).

• a flow is identified by the composite key: refYear + reporterCode + partnerCode + flowCode + cmdCode
• duplicate rows for the same key are collapsed to one representative row
• if duplicate rows disagree in primaryValue, the row with the highest primaryValue is kept
• Quantities are standardized by preferring netWgt over qty (fallback)
• Values are standardized by preferring FOB for exports and CIF for imports, with fallback to primaryValue
• Percentage discrepancies are calculated relative to the partner-reported quantity / value whenever the partner value is not zero

This behaviour is also shown in detail as a sequence diagram in Appendix A.

This function is the notebook’s API wrapper. It sends a GET request to the UN Comtrade endpoint and returns the response as a DataFrame.

Signature:

comtrade_get(typeCode="C", freqCode="A", clCode="HS",
    reporterCode=None, partnerCode=None, period=None, cmdCode=None,
    flowCode="M,X", includeDesc=True, timeout=60,
)

Procedure:

• Builds the endpoint URL from BASE_URL, typeCode, freqCode, and clCode
• Sends the request and parses the JSON payload
• If the API returns no rows, it still constructs an empty DataFrame with the expected columns

Structure:

raw_df = comtrade_get(
  reporterCode=724,   #Spain
  partnerCode=620,    #Portugal
  period="2010,2011",
  cmdCode="4501,4502",
  flowCode="X"
)
print(raw_df[['cmdCode', ..., 'primaryValue']].head())

This function takes a raw DataFrame as returned from the Comtrade API and cleans it. It collapses multiple records with the same composite key into a single representative row for each flow.

Signature:

clean_flows(df, value_diff_pcts=None)

Procedure:

The behaviour of this function can be visualized in the function's flowchart available here: clean_flows() function flowchart. The function groups rows by the composite key: refYear, reporterCode, partnerCode, flowCode, cmdCode. For each composite-key group:

• If there is only one row, it is kept and assigned flag_conflict = 0
• If there are multiple rows but all non-null primaryValue values are identical, the first row is kept and the row is assigned flag_conflict = 0
• If there are multiple rows with different primaryValue values:
  • the row with the highest primaryValue is selected
  • each discarded primaryValue is expressed as a percentage of the selected representative and appended to the array value_diff_pcts (if provided)
  • flag_conflict is set to the number of discarded conflicting values

The result is a pandas.DataFrame with all original columns plus flag_conflict.

Structure:

clean_df = clean_flows(raw_df)
print(clean_df[["refYear", ..., "flag_conflict"]])

This function processes a cleaned DataFrame and adds the standardized columns of quantity and value, with their respective labels quantity_type and value_type. It selects the most appropriate quantity and values for each row and keeps track of their original units.

Signature:

extract_metrics(clean_df)

Procedure:

The behaviour of this function can be visualized in the function's flowchart available here: extract_metrics() function flowchart. For each row found in the clean dataframe received:

• quantity
  • use netWgt if present and positive
  • else use qty
• quantity_type
  • "netWgt_kg" or "qty"
• value
  • export (X) → prefer fobvalue, else primaryValue
  • import (M) → prefer cifvalue, else primaryValue
• value_type
  • "FOB", "CIF", or "primaryValue"

The result is the clean pandas.DataFrame with four added columns: quantity, quantity_type, value, value_type

Structure:

metrics_df = extract_metrics(clean_df)
print(metrics_df[["cmdCode", ... "quantity", "quantity_type", "value", "value_type"]])

This is the core functionality of the notebook. It retrieves reporter and mirror flows, standardizes them, merges them, computes the discrepancies, and aggregates the results.

Signature:

discrepancy(reporter, period, cmdCode, flow, partners=None, aggregateBy="refYear")

Inputs:

• reporter: M49 reporter country code
• period: years (YYYY) or months (YYYYMM) as a comma-separated string
• cmdCode: commodity codes as a comma-separated string
• flow: Trade flow code ("X" for exports, "M" for imports)
• partners: optional list of M49 partner codes (defaults to all partners)
• aggregateBy: "refYear", "cmdCode", or "partner"

Flowchart:

1. Retrieve reporter-side flows with comtrade_get()
2. Clean and standardise reporter data via clean_flows() + extract_metrics()
3. Infer the partner list from reporter-side records if partners is None
4. Retrieve the mirror flow for each partner
5. Clean and standardise partner-side records
6. Outer-merge reporter and partner tables on:
   • reporter-side partnerCode
   • partner-side reporterCode
   • refYear
   • cmdCode
7. Flag quantity_type_mismatch and value_type_mismatch
8. Compute absolute discrepancies:
   • quantity_discrepancy
   • value_discrepancy
9. Compute percentage discrepancies relative to partner-side values
10. Carry over reporter-side flag_conflict as conflict_count
11. Aggregate the merged table by year, commodity, or partner

Returns:

aggregated_flows, merged_flows, value_diff_percentages_list = discrepancy(...)

Structure:

result = discrepancy(...)

Simple helper function that runs discrepancy() and saves the returned merged_flows DataFrame to an Excel file on the mounted drive. The function prevents duplicate entries by checking for existing data in the Excel file and only appending unique new records. The resulting file will be named after the reporter_iso3 code (e.g., ITA.xlsx).

Signature:

_download_discrepancy_data(reporter_iso3, commodity_codes_str,
    flow_direction, start_year, end_year, partners=None
)

This function is designed to build a broad trade panel on a national level, covering the majority of forest product codes, both imports and exports, and across multiple years, by repeatedly calling _download_discrepancy_data().

Signature:

complete_country_download(
    reporter_iso3, flows=["X", "M"], start=1992, end=2024
)

Procedure:

The only required parameter of this function is the reporting country ISO3 code reporter_iso3. By default, all other parameters are set to be as extensive a possible. The function then:

• Loops over selected flow directions
• Splits the year range and FULL_FOREST_PRODUCTS list into chunks of 10 HS-92 codes
• Repeatedly throttles calls to _download_discrepancy_data() to retrieve and save data chunks to excel

Note: Due to the many requests made by this function to populate the Excel file, a complete run can take longer than 1 hour to complete for a single country. A formal test still needs to be done to assess the function, along with checkpointing and a retry logic which still need to be implemented.

This function reads a saved Excel file for one reporter country, filters the data based on a series of user-defined filters, aggregates the filtered discrepancy measures, and passes the result to the plotting helper function _plot_discrepancy_charts() for visualization.

Signature:

plot_excel_discrepancies(
    reporter_iso3, commodity_codes_filter_str, partner_iso3_filter_str,
    start_year_filter, end_year_filter, flow_direction_filter, 
    aggregation_type
) 

Procedure:

• Reads {REPORTER}.xlsx from the mounted drive
• Filters by flow direction
• Optionally filters by specific year range, commodity codes, and/or partner ISO3 codes
• Aggregates percentage discrepancies and conflict counts
• Forwards data to _plot_discrepancy_charts() for the final plotting

Aggregated plotting fields

Current-state notes:

1. This function depends on the Excel workflow being operational, and having the data from reported country already downloaded
2. all_value_diff_percentages is always initialized as an empty list here, since it is not yet being saved in teh excel file
3. Partner aggregation currently uses partnerDesc_df1 but will be switched to partnerCode_df1

The data that is saved to Excel allows for many different types of analysis, and the 6 plots produced by plot_excel_discrepancies() represent only a fraction of the possibilities. The current implementation shows:

1. Average percentage quantity discrepancy aggregated by chosen metric;
2. Average percentage value discrepancy aggregated by chosen metric;
3. CFD of average percentage quantity discrepancy aggregated by chosen metric;
4. CFD of average percentage value discrepancy aggregated by chosen metric;
5. CFD of conflict_count;
6. Total conflicts aggregated by chosen metric;

For each paired flow the average percentage discrepancy in quantity is calculated as follows:

quantity_discrepancy = reporter_quantity - partner_quantity
quantity_discrepancy_pct = (quantity_discrepancy / partner_quantity) * 100

Plot 1 shows this result averaged out across the chosen aggregation metric (commodity code in this case), with the standard deviation used as an error bar. Therefore, each bar is the average percentage difference between reporter and partner(s) quantities for the chosen grouping.

The second plot represents the same elements as Plot 1, but for value instead of quantity, and therefore, the same interpretation applies. As can be expected, the two plots often resemble each other, however, this is not always the case.

Interpretation:

• Positive values: reporter quantities / values tend to be higher than partner quantities
• Negative values: reporter quantities / values tend to be lower
• Large error bars: high variability across individual flows within that group

Plots 3 and 4 represent the cumulative frequency distribution of the results shown in Plots 1 and 2, respectively. Each bar in the plot represents the average percentage of quantity discreapancies (%) across the chosen grouping, that greater than or equal to the indicated percentage.

Interpretation: Ideally, most quantity and value entries in the trade panel to have discrepancies no greater than 20%. The plot above can be interpreted as follows:

• ~40% of quantity discrepancies averaged out across commodity codes are lower than -20%
• ~30% of quantity discrepancies averaged out across commodity codes are higher than 20%
• ~30% of quantity discrepancies averaged out across commodity codes are between -20% and 20%

The plot currently averages the discrepancy across the chosen metric to reduce noise in the dataset, however, this can be adapted as needed.

This plot shows the CFD of how often single trade flows had duplicate record conflicts during the cleaning procedure.

Interpretation:

• A steep drop after 0 implies most flows are clean
• A long tail implies repeated duplicate inconsistencies in the raw data

This plot can suggest possible the patterns of deduplication conflicts in the data, by aggregating the total number of deduplication conflicts on the chosen metric (commodity code in the example shown).

Interpretation: This is an indicator of where the raw source data needed the most conflict resolutions.

The data leaves a lot of room for more analysis. For instance, we can plot the discarded duplicate primaryValue values during clean_flows() and plot them as a percentage of the kept (maximum) value. With this graphs we can visualize how similar discarded duplicate values were to the chosen retained value prior to being saved. This can be useful to investigate the severity of anomalies in Plots 5 and 6.

Interpretation:

• A high poroportion of high thresholds implies duplicates are often close in value
• A low poroportion of high thresholds implies more serious conflicts
• In the example chart above, we see that the majority of discarded values (~60%) were less than 10% similar to their chosen representative (i.e.,)

The current code is simpler than the full EFI flow refinement methodology. Compared with the broader methodology described in the EFI technical report 100 (Rougieux et al., 2017), the present notebook does not yet:

• estimate missing quantities (from regional conversion factors, regional prices, or across units)
• adjust out-of-bounds prices
• replace quantities based on price stability comparisons across multiple years

Instead, it focuses on direct mirror-flow discrepancy calculations.

The discarded-value similarity plot depends on value_diff_percentages_list, which is not yet stored in the Excel workflow and is reset to an empty list during plotting from excel.

If no specific partners have been specified (i.e., partners=None), only partners already present in the reporter’s data are checked. This can miss reports from partners if they have never been disclosed by the reporter. However for a long enough period, it is likely that at least one trade with said partner was reported by the reporting country.

The current program provides a solid foundation for the broader goal of detecting suspicious patterns in forest-products trade. Its main contribution at the moment is in transforming raw Comtrade bilateral flow data into comparable reporter–partner pairs and quantifying discrepancies. This makes it a suitable screening tool to identify where reported trade might need a closer investigation. While the present implementation does not yet perform the full range of quantity estimation and validation steps, it forms the central architecture needed for a scalable system that can make use of its Excel database for further analysis. New versions of the program will strengthen the data persistence methodology and scale up to larger multi-country panels.