Supply prices

[nk027]

When calculating prices of supply we have some issues.

• We have six occurrences of negative USD values and 34 of negative m3. All stem from forestry data.
• There's 2824 NA values of USD, where tonnes are available (over 113 items)
• Vice versa there's 42251 NAs of tonnes, where USD are available (products not measured in tonnes presumably)
• There's 59 occurrences of Inf prices - all are related to Alcohol, since it is provided in litres instead of tonnes - we skipped estimating tonnes from prices so far

[nk027]

Estimating tonnes of Alcohol from prices is problematic -- we get:

     Min.   1st Qu.    Median      Mean   3rd Qu.      Max. 
        0         0         0     38062         0 100498539 

Maybe it make more sense to convert from litres to tonnes -- the density of ethanol is about 789kg/m^3, water is at 997kg/m^3 (thanks Wikipedia). While most alcoholic beverages are anecdotally more dense than water it might make sense to estimate conservatively -- for now I'll assume a density of 1000kg/m^3, since I am lazy and we'll have to think this through anyways.

[nk027]

When estimating prices value are capped at 20% / 500% of the world's mean. Quantile values may be considerably more robust. See here e.g. (grouped by items):

     q1                 q5                 q9          
Min.   :0.000045   Min.   :0.000122   Min.   :  0.0003  
1st Qu.:0.000180   1st Qu.:0.000495   1st Qu.:  0.0015  
Median :0.000444   Median :0.000963   Median :  0.0026  
Mean   :0.000555   Mean   :0.001786   Mean   :  4.6960  
3rd Qu.:0.000896   3rd Qu.:0.002416   3rd Qu.:  0.0065  
Max.   :0.002500   Max.   :0.013352   Max.   :557.8133  
NA's   :4          NA's   :4          NA's   :4         
      mean       
 Min.   :     0  
 1st Qu.:     0  
 Median :     0  
 Mean   :  2698  
 3rd Qu.:     0  
 Max.   :321084  
 NA's   :4       

Apparently this capping is done irrespectively of item, country or year. This might be a stretch.

[nk027]

• Also there are quite some occurrences of weird prices, i.e. tiny or huge, beside alcohol.
• Also there's some Inf prices (just usd, no other info) for Fish, Seafood.
• Also we know of a single Malian camelid in 1999, that has no additional information attached to it.

[nk027]

For now we calculate price with (after converting litres of alcohol to tonnes): ifelse(tonnes != 0, usd / tonnes, head != 0, usd / head, usd / m3)) Head applies to livestock and m3 to forestry.

Note that for the worldprices we sum using: na_sum <- function(x) {ifelse(all(is.na(x)), NA_real_, sum(x, na.rm = TRUE))} If we just use a sum with na.rm = TRUE we run into an issue when all values are NA -- we get a 0.

[nk027]

To get really good price estimates we would need some other source - FAO, Worldbank, WTO or similar. E.g. the World Bank Commodity Prices.