Description Usage Arguments Details Value
convert_io
takes an input output table in standardized long format and
converts it to the necessary model inputs for simulations.
1 2 3 4 5 6 7 8 | convert_io(
iot,
orig_col = "origin",
sec_col = "sector",
dest_col = "destination",
use_col = "use",
flow_col = "flow"
)
|
iot |
An input output table in standardized long format (see the package
'iotr') with the columns, |
orig_col |
alternative name for 'origin' column |
sec_col |
alternative name for 'sector' column |
dest_col |
alternative name for 'destination' column |
use_col |
alternative name for 'use' column |
flow_col |
alternative name for 'flow' column |
Our simulation requires data on country-sector revenues, trade deficits, sectoral import shares by use category (final or sectoral intermediate), country-sector consumption shares, labor cost shares and cost shares of material inputs, all of which can be derived from an input output table.
Returns a list of
vector of location ids
vector of sector ids
matrix of location-sector revenues with
N = length(location_id)
rows and
J = length(sector_id) columns
vector of locations' deficit transfers, i.e. positive for a trade deficit and negative for a trade surplus
vector of import shares across locations for each
sector-destination-use combination, where the import share of
origin o
in sector s
products used in destination
d
and use category u
is at position
o + (d-1)*N + (s-1)*N*N + (u-1)*N*N*J
matrix of sectoral consumption shares in each location with
N
rows and J
columns
vector of intermediate cost shares across sectrs for each
destination-use combination, where the cost share of
sector s
intermediates used in destination
d
and use category u
is at position
d + (s-1)*N + (u-1)*N*J
matrix of location-sector labor cost shares with
N
rows and J
columns
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