Comparing traded volume to CFI-based estimates

Here, we calculate proportions of direct and indirect trade per trader. To do this, we link trade data to the direct trade estimates that were calculated based on the number of mapped farms per trader (reported for CFI) and cocoa farm production estimates from the KIT study.

The trade data represents volumes of beans, but also traded cacao waste, paste, butter and powder. To link CFI direct trade estimates (in kg beans) to the trade data, we therefore need to convert the traded non-bean volumes to bean equivalent volumes.

Below we use the mean and confidence intervals of direct trade estimates and the following conversion factors, per HS code:

HS code	Product	Conversion factor
180100	cacao beans	1
180200	cacao waste material	0
180310	cacao paste	1.2
180400	cacao butter	1.2
180500	cacao powder	1.2

Cacao waste material is not taken into account as the conversion factors of 1.2 for paste, butter and powder account for the volume of waste lost in processing. For the 2019 trade data, cacao waste material represented 4.94 % of the traded volume and 3.57 % of the FOB.

#converse trade volumes to bean equivalent volumes, based on HS codes, add bean_equivalent_volume as a column
trade_2019 <- trade_2019 %>%
  mutate(bean_equivalent_volume = case_when(
    HS6 == "180100" ~ VOLUME_RAW,
    HS6 == "180200" ~ VOLUME_RAW * 0,
    HS6 == "180310" ~ VOLUME_RAW * 1.2,
    HS6 == "180400" ~ VOLUME_RAW * 1.2,
    HS6 == "180500" ~ VOLUME_RAW * 1.2)
         )

#Sum bean_equivalent_volume per trader
total_bean_equivalent_volume <- 
  aggregate(trade_2019$bean_equivalent_volume, by=list(trade_2019$EXPORTER), FUN=sum) %>% 
  rename(
    EXPORTER = Group.1,
    CD_VOLUME_EXPORTED = x
    )

#join total_volume_full_trade with cfi_estimates_summary
#calculate indirect trade and the proportion of indirect trade
direct_vs_indirect_trade_bean_equivalent <- 
  inner_join(cfi_estimates_summary, total_bean_equivalent_volume, by = c("company" = "EXPORTER")) %>%
  select(- direct_volume_est_sd, - direct_volume_est_se) %>%
  rename(direct_trade_est_mean = direct_volume_est_mean,
         direct_trade_est_LCI = lower_ci,
         direct_trade_est_UCI = upper_ci) %>%
  mutate(indirect_trade_est_LCI = CD_VOLUME_EXPORTED - direct_trade_est_UCI,
         indirect_trade_est_mean = CD_VOLUME_EXPORTED - direct_trade_est_mean,
         indirect_trade_est_UCI = CD_VOLUME_EXPORTED - direct_trade_est_LCI,
         proportion_indirect_LCI = (indirect_trade_est_LCI / CD_VOLUME_EXPORTED)*100,
         proportion_indirect_mean = (indirect_trade_est_mean / CD_VOLUME_EXPORTED)*100,
         proportion_indirect_UCI = (indirect_trade_est_UCI / CD_VOLUME_EXPORTED)*100)

Results

Cocoa volume exported from the Ivory Coast in 2019 and estimated proportions of direct and indirect cocoa trade, per trader.

Trader	Exported volume (kg)	Direct trade (kg)	Indirect trade (kg)	Indirect trade (%)
BARRY CALLEBAUT	241623378	106525407	135097972	55.91262
CARGILL	274373924	74422209	199951714	72.87563
CEMOI	33996270	17095569	16900701	49.71340
OLAM	229944177	85547344	144396833	62.79647
SUCDEN	112460846	21134179	91326667	81.20752
TOUTON	132417559	39115392	93302167	70.46057