Half of today’s international trade is composed of intermediate goods, so statistics of gross exports do not reflect the complexity of globalization. Trade flows should be expressed in terms of value added, taking advantage of modern international input-output tables, which show how different sectors in different countries interact with each other. The problem is that handling big input-output tables is complicated and many indicators are not available. This is where exvatools comes to the rescue. Its purpose is double:
Installing exvatools from the CRAN repository follows the usual procedure:
install.packages("exvatools")To make exvatools available:
library(exvatools)exvatools produces basic input-output tables from three types of data:
Raw data can be directly downloaded from the web pages of their respective institutions. Two sources are currently supported:
The advantage of these databases is threefold: they are widely used
in the economic literature, they are quite rich in terms of countries
and sectors, and they are directly downloadable from the web page of
their supporting institutions, normally as zipped files containing
comma-delimited files (.csv), Excel files
(.xlsx) or R data files (.rData).
For instance, if we want to use exvatools with the
latest edition of the OECD ICIO tables (2021, with data up to 2018), we
must first download to our computer the source file “ICIO_2015-2018.zip”
(96 MB) from the ICIO
web page. Then we will use the command make_wio(),
specifying the edition, the year and the folder where the source zip
file is saved (just the directory). For instance, if we had downloaded
it in C:\Users\Username\Documents\R and we wanted the year
2018, we would just need to type:
wio <- make_wio("icio2021", year = 2018,
src_dir = "C:/Users/Username/Documents/R")and exvatools will take care of the rest: it will
extract the .csv files from the zip file and produce the
basic input-output matrices.
If we just want to check the features of exvatools,
there is no need to download any data. The package includes two sets of
fictitious data, one replicating an ICIO-type database
(wiotype = "iciotest", with Mexico and China disaggregated)
and another one replicating a WIOD-type database
(wiotype = "wiodtest").
wio <- make_wio("iciotest")Alternatively, exvatools can use custom data to
create basic input-output matrices. In this case, we just need as input
a numeric matrix (or data frame) with the intermediate inputs
Z and the final demand Yfd, and a vector with
the names of the countries (names of sectors and final demand components
are optional). The command make_custom_wio() will be used
in this case.
wio <- make_custom_wio(df, g_names = c("C01", "C02", "C03"))For didactic purposes, we will use here ICIO-type fictitious data
made with make_wio("iciotest").
The contents of the created wio object can be checked
with summary():
summary(wio)
#>
#> ======================================================================
#> TEST INPUT OUTPUT TABLE, ICIO-TYPE, 2022 EDITION
#> Data for year: 2022
#> ======================================================================
#>
#> Element Description Dimensions
#> Z Intermediate inputs 30 x 30
#> Zd Domestic intermediate inputs 30 x 30
#> Zm Foreign intermediate inputs 30 x 30
#> A Coefficient matrix 30 x 30
#> Ad Domestic coefficient matrix 30 x 30
#> Am Foreign coefficient matrix 30 x 30
#> B Global Leontief inverse 30 x 30
#> Bd Domestic global Leontief inverse 30 x 30
#> Bm Foreign global Leontief inverse 30 x 30
#> Ld Local Leontief inverse matrices 30 x 30
#> Yfd Final demand, with components 30 x 12
#> Y Final demand 30 x 6
#> Yd Domestic final demand 30 x 6
#> Ym Foreign final demand 30 x 6
#> VA Value added 30 x 1
#> V Value added coefficients 30 x 1
#> W Diagonalized VA coefficients (V-hat) 30 x 30
#> X Production 30 x 1
#> EXGR Gross bilateral exports 30 x 6
#> E Diagonalized total gross exports (E-hat) 30 x 30
#> dims Dimensions
#> names Names of countries, sectors and demand components
#> type Type of input-output table
#> year Year
#>
#> Available countries, including rest of the world (G): 6
#> ESP, FRA, MEX, USA, CHN, ROW
#>
#> Extra disaggregated countries: 4
#> MEX : MX1 MX2
#> CHN : CN1 CN2
#>
#> Total countries, including disaggregations (GX): 10
#>
#> Available sectors (N): 3
#> D01T09, D10T39, D41T98
#>
#> Demand components (FD): 2
#> CONS INVST
#> exvatools provides multiple commands that make
operating with international input-output tables extremely easy: thus,
we can multiply a diagonal matrix by an ordinary one with
dmult(), an ordinary by a diagonal with
multd(), or make a block-by-block Hadamard product of
matrices with hmult().
We can also easily obtain a block diagonal matrix with
bkd(), a block off-diagonal matrix with
bkoffd(), or a diagonal matrix with the sums of all columns
with diagcs(),
Additionally, as we are always operating with named rows and columns
with names of countries and sectors, we have improved commands to
consolidate matrices and provide names, like rsums() to sum
rows, csums() to sum columns, sumnrow() to sum
every nth row of a matrix, sumncol() to sum every nth
column, sumgrows() to sum groups of rows of a particular
size, sumncols() to do the same with columns, etc.
For instance, let us check that the production X is
equivalent to the product of the global Leontief inverse matrix
B and the final demand Y:
BY <- wio$B %*% wio$YWe can sum the rows and check that it coincides with the production vector:
BY <- rsums(BY, "BY")
print(cbind(head(BY, 10), head(wio$X, 10)))
#> BY X
#> ESP_01T09 1378.568 1378.568
#> ESP_10T39 1914.607 1914.607
#> ESP_41T98 2113.699 2113.699
#> FRA_01T09 1848.173 1848.173
#> FRA_10T39 1799.486 1799.486
#> FRA_41T98 1608.004 1608.004
#> MEX_01T09 0.000 0.000
#> MEX_10T39 0.000 0.000
#> MEX_41T98 0.000 0.000
#> USA_01T09 1895.742 1895.742In OECD ICIO tables two big industrial countries, China and Mexico,
are disaggregated into two. Calculations must be done with disaggregated
data, but countries must be later consolidated (e.g., CN1
and CN2 must be converted into CHN). This can
be done with the command meld().
For instance, to calculate the value added absorbed abroad
(VAX) we need to multiply the value added coefficients
matrix V-hat (represented here with W) by the
global inverse matrix B by the final demand matrix
Y, and then exclude the value added absorbed domestically.
This can be easily done with a few commands.
# To calculate all value added induced by demand:
VBY <- dmult(wio$W, wio$B) %*% wio$Y
VBY
#> ESP FRA MEX USA CHN ROW
#> ESP_01T09 33.25239 33.04328 29.13415 39.26315 44.36171 40.26254
#> ESP_10T39 105.86567 99.40932 118.73322 139.90848 115.91495 126.97831
#> ESP_41T98 221.93642 191.16663 158.37563 173.76610 145.22181 168.64254
#> FRA_01T09 48.48628 66.62310 51.62458 49.45643 47.66424 70.97642
#> FRA_10T39 120.80031 104.64030 128.78920 102.79096 97.91708 99.78527
#> FRA_41T98 134.74749 129.14500 99.99938 88.70168 86.75694 101.79354
#> MEX_01T09 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000
#> MEX_10T39 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000
#> MEX_41T98 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000
#> USA_01T09 175.01545 130.46673 128.74728 107.71747 102.20179 117.94400
#> USA_10T39 102.29790 84.03672 79.60012 121.89497 66.60450 100.75615
#> USA_41T98 115.50508 118.18725 129.51719 122.16680 95.86500 111.98916
#> CHN_01T09 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000
#> CHN_10T39 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000
#> CHN_41T98 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000
#> ROW_01T09 82.22487 42.82585 45.01008 60.06683 46.78138 51.62906
#> ROW_10T39 90.44372 97.64510 80.63597 95.00232 91.33130 91.05473
#> ROW_41T98 62.56171 51.01318 42.64560 58.70599 41.40437 50.09897
#> MX1_01T09 173.85507 180.14952 154.43342 149.69601 164.13090 149.43125
#> MX1_10T39 119.84480 74.11099 117.78742 97.93871 142.64384 121.97803
#> MX1_41T98 49.45281 29.23090 35.52473 39.69653 36.67997 31.42076
#> MX2_01T09 96.85254 83.89485 70.55941 97.36493 80.35587 66.40230
#> MX2_10T39 138.50490 86.35272 92.84516 108.88726 99.24637 102.95958
#> MX2_41T98 82.91973 67.68778 62.14849 77.03372 65.06803 75.12146
#> CN1_01T09 109.94070 114.47837 91.18810 152.37369 127.84703 118.37641
#> CN1_10T39 119.64017 127.22095 126.45021 165.70116 164.43470 128.00251
#> CN1_41T98 109.34845 93.91197 106.67385 115.50129 111.77386 119.86482
#> CN2_01T09 84.74417 83.13258 69.33664 86.84078 80.88526 72.00972
#> CN2_10T39 73.17244 54.76049 43.08188 56.11525 70.19744 78.67920
#> CN2_41T98 158.98841 103.45062 108.60598 136.04997 128.63627 109.93866We can see that rows for Mexico and China are disaggregated. We can
meld them with meld():
VBY <- meld(VBY)
VBY
#> ESP FRA MEX USA CHN ROW
#> ESP_01T09 33.25239 33.04328 29.13415 39.26315 44.36171 40.26254
#> ESP_10T39 105.86567 99.40932 118.73322 139.90848 115.91495 126.97831
#> ESP_41T98 221.93642 191.16663 158.37563 173.76610 145.22181 168.64254
#> FRA_01T09 48.48628 66.62310 51.62458 49.45643 47.66424 70.97642
#> FRA_10T39 120.80031 104.64030 128.78920 102.79096 97.91708 99.78527
#> FRA_41T98 134.74749 129.14500 99.99938 88.70168 86.75694 101.79354
#> MEX_01T09 270.70761 264.04436 224.99283 247.06094 244.48677 215.83355
#> MEX_10T39 258.34970 160.46370 210.63258 206.82598 241.89021 224.93762
#> MEX_41T98 132.37254 96.91869 97.67322 116.73025 101.74800 106.54222
#> USA_01T09 175.01545 130.46673 128.74728 107.71747 102.20179 117.94400
#> USA_10T39 102.29790 84.03672 79.60012 121.89497 66.60450 100.75615
#> USA_41T98 115.50508 118.18725 129.51719 122.16680 95.86500 111.98916
#> CHN_01T09 194.68487 197.61095 160.52474 239.21447 208.73229 190.38614
#> CHN_10T39 192.81261 181.98144 169.53209 221.81640 234.63214 206.68171
#> CHN_41T98 268.33686 197.36260 215.27984 251.55127 240.41013 229.80349
#> ROW_01T09 82.22487 42.82585 45.01008 60.06683 46.78138 51.62906
#> ROW_10T39 90.44372 97.64510 80.63597 95.00232 91.33130 91.05473
#> ROW_41T98 62.56171 51.01318 42.64560 58.70599 41.40437 50.09897We just want the value added absorbed abroad (sometimes referred to
as value added exported). For that we need the block off-diagonal matrix
of VBY, that we can produce with bkoffd():
bkoffd(VBY)
#> ESP FRA MEX USA CHN ROW
#> ESP_01T09 0.00000 33.04328 29.13415 39.26315 44.36171 40.26254
#> ESP_10T39 0.00000 99.40932 118.73322 139.90848 115.91495 126.97831
#> ESP_41T98 0.00000 191.16663 158.37563 173.76610 145.22181 168.64254
#> FRA_01T09 48.48628 0.00000 51.62458 49.45643 47.66424 70.97642
#> FRA_10T39 120.80031 0.00000 128.78920 102.79096 97.91708 99.78527
#> FRA_41T98 134.74749 0.00000 99.99938 88.70168 86.75694 101.79354
#> MEX_01T09 270.70761 264.04436 0.00000 247.06094 244.48677 215.83355
#> MEX_10T39 258.34970 160.46370 0.00000 206.82598 241.89021 224.93762
#> MEX_41T98 132.37254 96.91869 0.00000 116.73025 101.74800 106.54222
#> USA_01T09 175.01545 130.46673 128.74728 0.00000 102.20179 117.94400
#> USA_10T39 102.29790 84.03672 79.60012 0.00000 66.60450 100.75615
#> USA_41T98 115.50508 118.18725 129.51719 0.00000 95.86500 111.98916
#> CHN_01T09 194.68487 197.61095 160.52474 239.21447 0.00000 190.38614
#> CHN_10T39 192.81261 181.98144 169.53209 221.81640 0.00000 206.68171
#> CHN_41T98 268.33686 197.36260 215.27984 251.55127 0.00000 229.80349
#> ROW_01T09 82.22487 42.82585 45.01008 60.06683 46.78138 0.00000
#> ROW_10T39 90.44372 97.64510 80.63597 95.00232 91.33130 0.00000
#> ROW_41T98 62.56171 51.01318 42.64560 58.70599 41.40437 0.00000The model we have used allows us to express value added induced by final demand, but we can also study how gross exports induce value added, not only from final exports but also from exports of intermediates. In this case, we need to consider the effect of the multiple times that intermediate products cross international borders, to avoid double counting.
This is why several methods have appeared to calculate full decomposition of value added in exports, distinguishing what part of value added is pure value added and which one is double counting, and also, within the pure double counting, what part is really exported and what part eventually returns back to the exporting country (‘false’ exports).
exvatools can then produce key value added
indicators (like bilateral value added exports or VAX) that
are not currently available in public databases.
There are several methodologies in the economic literature, and exvatools includes the most complete ones: Koopman et al. (2014), Wang et al. (2013), Borin and Mancini (2019) and Miroudot and Ye (2021).
For instance, to create create a full decomposition of value added in exports of Spain using the method of Borin and Mancini (2019), using a source-based approach, we would type::
exvabm <- make_exvadec(wio, exporter = "ESP", method = "bm_src")
#> ℹ Preparing DVA auxiliary matrices...
#> ℹ Calculating DVA terms...
#> ℹ Calculating FVA terms...
#> ℹ Preparing output ...
#> ✔ Done!
#> ======================================================================
#> DECOMPOSITION OF VALUE ADDED IN EXPORTS OF SPAIN IN 2022
#> Sector: All sectors
#> Destination: All countries
#> ======================================================================
#> VA_components USD_MM Percent
#> Gross exports of goods and services (EXGR) 4666.96 100.00
#> Domestic Content in VA (DC) 2165.17 46.39
#> Domestic Value Added (DVA) 1880.60 40.30
#> Value Added Exported (VAX) 1624.18 34.80
#> Reflection (REF) 256.41 5.49
#> Domestic Double Counting (DDC) 284.58 6.10
#> Foreign Content in VA (FC) 2501.78 53.61
#> Foreign Value Added (FVA) 2176.21 46.63
#> Foreign Double Counting (FDC) 325.58 6.98
#> Global Value Chain-related trade (GVC) 4034.25 86.44
#> GVC-related trade, backward (GVCB) 2786.36 59.70
#> GVC-related trade, forward (GVCF) 1247.89 26.74
#> ======================================================================
#> Method: Borin and Mancini (2019), source-based, standard output
#> Country perspective, source approachThe advantage is that, once we have obtained a decomposition, we can
play with the results in terms of sectors and countries of destination
just using the command get_exvadec_bkdown(). For instance,
to select the value added in Spanish exports of services (including
construction) to the United States, we just have to type:
get_exvadec_bkdown(exvabm, exporter = "ESP",
sector = "SRVWC", importer = "USA")
#> ======================================================================
#> DECOMPOSITION OF VALUE ADDED IN EXPORTS OF SPAIN IN 2022
#> Sector: Services, including construction (SRVWC)
#> Destination: United States (USA)
#> ======================================================================
#> VA_components USD_MM Percent
#> Gross exports of goods and services (EXGR) 276.71 100.00
#> Domestic Content in VA (DC) 159.63 57.69
#> Domestic Value Added (DVA) 145.95 52.74
#> Value Added Exported (VAX) 127.28 46.00
#> Reflection (REF) 18.67 6.75
#> Domestic Double Counting (DDC) 13.68 4.94
#> Foreign Content in VA (FC) 117.08 42.31
#> Foreign Value Added (FVA) 101.53 36.69
#> Foreign Double Counting (FDC) 15.55 5.62
#> Global Value Chain-related trade (GVC) 221.62 80.09
#> GVC-related trade, backward (GVCB) 130.76 47.26
#> GVC-related trade, forward (GVCF) 90.86 32.84
#> ======================================================================
#> Method: Borin and Mancini (2019), source-based, standard output
#> Country perspective, source approachAn alternative (although methodologically inferior) decomposition
would be that of Wang et al. (2013). In this case, instead of the normal
decomposition, we will use the "terms" output. (that shows
the 16 terms that compose the value added in exports):
exvawwz <- make_exvadec(wio, exporter = "all", method = "wwz",
output = "terms", quiet = TRUE)Note that here we have selected export = all, that
produces decompositions for all countries (not only a specific one), and
we have also used the option quiet = TRUE, that produces a
silent output.
We can check any exporting country, any sector, and any destination country. For instance, we can produce the decomposition of the value added in US exports to China for the manufacturing sector:
get_exvadec_bkdown(exvawwz, exporter = "USA",
sector = "MANUF", importer = "CHN")
#> ======================================================================
#> DECOMPOSITION OF VALUE ADDED IN EXPORTS OF UNITED STATES IN 2022
#> Sector: Manufacturing (MANUF)
#> Destination: China (CHN)
#> ======================================================================
#> VA_components USD_MM Percent
#> EXGR (Gross exports of goods and services) 400.89 100.00
#> T01 DVA_FIN (DVA, finals) 12.79 3.19
#> T02 DVA_INT (DVA, interm. for absorption) 13.95 3.48
#> T03 DVA_INTrex1 (DVA, int. reexp. for finals 3rd) 8.81 2.20
#> T04 DVA_INTrex2 (DVA, interm. for finals reexp.) 36.55 9.12
#> T05 DVA_INTrex3 (DVA, interm. for reexp. interm.) 35.08 8.75
#> T06 RDV_FIN1 (Reflection, finals from partner) 13.25 3.31
#> T07 RDV_FIN2 (Reflection, finals from 3rd cou) 8.62 2.15
#> T08 RDV_INT (Reflection, intermediates) 2.19 0.55
#> T09 DDC_FIN (Domestic Double Counting, finals) 9.13 2.28
#> T10 DDC_INT (Domestic Double Counting, interm.) 23.82 5.94
#> T11 FVA_FIN1 (FVA from partner, finals) 6.11 1.52
#> T12 FVA_FIN2 (FVA from 3rd countries, finals) 12.33 3.08
#> T13 FVA_INT1 (FVA from partner, intermediates) 6.22 1.55
#> T14 FVA_INT2 (FVA from 3rd countries, interm.) 12.56 3.13
#> T15 MDC (Double Counting, partner) 66.07 16.48
#> T16 ODC (Double Counting, 3rd countries) 133.42 33.28
#> ======================================================================
#> Method: Wang et al. (2013), terms output
#> Mix of country and world perspective, mix of source and sink approachWe have seen that the foreign content in Spanish exports amounts to
USD 2501.78 million. Where does it come from? If we do not need a
detailed breakdown of the value added, but we are interested in knowing
the specific geographical and sector origin of the value added content
in exports, we can use the command make_exvadir():
exvadir <- make_exvadir(wio, exporter = "ESP", va_type = "FC",
flow_type = "EXGR")Please note that the exvadir object that we have
obtained is different from the exvadec object, in the sense
that ‘exporters’ in an exvadir object are the
different countries and sectors of origin of the value added included in
the exports of the country specified with make_exvadir()(in
this case, Spain). We can better understand this by typing
summary(exvadir):
summary(exvadir)
#>
#> ======================================================================
#> ORIGIN AND DESTINATION OF VALUE ADDED IN EXPORTS OF SPAIN IN 2022
#> ======================================================================
#> Value added type: Foreign VA content (FC)
#> In type of flow: Total gross exports (EXGR)
#> That goes via country: any
#> Using inputs from sector: all sectors
#> Of country: all countries
#> With sector perspective: exporter
#> ======================================================================
#>
#> Available countries of origin of VA (G): 6
#> ESP, FRA, MEX, USA, CHN, ROW
#>
#> Available sectors of origin of VA (N): 3
#> D01T09, D10T39, D41T98
#>
#> Available destinations of VA (G): 6
#> ESP, FRA, MEX, USA, CHN, ROW
#> We can use get_data() to summarize the foreign content
of Spanish exports, with a breakdown between EU and Non-EU origin
(specifying a few countries) and also distinguishing between goods (with
utilities) and services. We can also break down the destination of those
exports between EU and non-EU:
get_data(exvadir, exporter = c("WLD", "EU27", "FRA",
"NONEU27", "USA"),
sector = c("TOTAL", "GOODSWU", "SRVWC"),
importer = c("WLD", "EU27", "NONEU27"))
#> WLD EU27 NONEU27
#> WLD_TOTAL 2501.78421 367.55781 2134.22640
#> WLD_GOODSWU 1772.66226 236.16941 1536.49285
#> WLD_SRVWC 729.12195 131.38840 597.73355
#> EU27_TOTAL 340.74928 49.39794 291.35134
#> EU27_GOODSWU 252.80996 33.55120 219.25877
#> EU27_SRVWC 87.93932 15.84674 72.09258
#> FRA_TOTAL 340.74928 49.39794 291.35134
#> FRA_GOODSWU 252.80996 33.55120 219.25877
#> FRA_SRVWC 87.93932 15.84674 72.09258
#> NONEU27_TOTAL 2161.03493 318.15987 1842.87505
#> NONEU27_GOODSWU 1519.85229 202.61821 1317.23408
#> NONEU27_SRVWC 641.18263 115.54166 525.64097
#> USA_TOTAL 433.65389 64.94868 368.70521
#> USA_GOODSWU 286.90179 38.50383 248.39796
#> USA_SRVWC 146.75210 26.44485 120.30725On the other hand, the flexibility of get_data() allows
for the creation of custom-made groups of countries and/or sectors.
For instance, let’s create a group of countries called
LATAM, with Spain and Mexico. We would just have to define
the variable in the current environment.
LATAM <- c("ESP", "MEX")And now we can use it as a normal variable, just introducing it as
"LATAM" (with double quotes). We will use the
wwz decomposition and extract the domestic value added in
intermediates (DVA_INT) from LATAM to
USA. Note that, if we use custom groups, we need to select
the option custom = TRUE in get_data().
get_data(exvawwz, "DVA_INT", exporter = "LATAM",
sector = c("TOTAL", "MANUF", "SRVWC"),
importer = "USA", custom = TRUE)
#> USA
#> LATAM_TOTAL 39.85574
#> LATAM_MANUF 11.80574
#> LATAM_SRVWC 14.75979Let us now see an example of the exception marker "x",
that allows to define exceptions for countries and for sectors. We can,
for instance, calculate the NAFTA exports, both intra-regional and
extra-regional, employing just two sectors: non-services and
services.
get_data(exvawwz, "EXGR", exporter = "NAFTA",
sector = c("TOTAL", "TOTALxSRVWC", "SRVWC"),
importer = c("WLD", "NAFTA", "WLDxNAFTA"), custom = TRUE)
#> WLD NAFTA WLDxNAFTA
#> NAFTA_TOTAL 13087.740 2602.338 10485.402
#> NAFTA_TOTALxSRVWC 8736.552 1502.340 7234.212
#> NAFTA_SRVWC 4351.188 1099.998 3251.189The flexibility of the commands make_exvadir() and
get_data() allows for the creation of several ready-made
commands in exvatools. One is
get_va_exgr(), to get a detailed sector and geographical
origin and destination of value added, i.e., how the inputs of specific
sectors in specific countries contribute to the value added in exports
of a particular sector of a particular country. For instance, if we want
to check the value added in US services incorporated in Spanish exports
of manufactures, we just have to type:
get_va_exgr(wio,geo_orig = "USA", sec_orig = "SRVWC",
geo_export = "ESP", sec_export = "MANUF")
#> [1] 51.31605Sometimes we are not only interested in the origin, but also in the
country of final absorption. For that we have
get_va_exgry(). For instance, if we want to know what part
of the US value added incorporated in China’s exports of manufactures
ends up absorbed back in the US, we can type:
get_va_exgry(wio, geo_orig = "USA", geo_export = "CHN",
sec_export = "MANUF", geo_fd = "USA")
#> [1] 53.81984At the beginning we manually calculated the value added induced by
final demand. There is also a specific command for that in
exvatools called get_va_fd(). This allows,
for instance, the calculation of the Chinese total value added (or GDP)
induced by US final demand for manufactures:
get_va_fd(wio, geo_orig = "CHN", sec_orig = "TOTAL",
geo_fd = "USA", sec_fd = "MANUF")
#> WLD
#> CHN_TOTAL 229.385Finally, if we want to get a list of common trade indicators
(exports, imports, value added, production) similar to those of the TiVA
database, we could just use make_exvadec() with the method
"oecd" and output = "tiva".
exvativa <- make_exvadec(wio, exporter = "all", method = "oecd",
output = "tiva", quiet = TRUE)And then get the decomposition for Spain:
get_exvadec_bkdown(exvativa, exporter = "ESP")
#> ======================================================================
#> DECOMPOSITION OF VALUE ADDED IN EXPORTS OF SPAIN IN 2022
#> Sector: All sectors
#> Destination: All countries
#> ======================================================================
#> VA_components USD_MM Percent
#> Gross exports of goods and services (EXGR) 4666.96 100.00
#> Gross exports, finals (EXGR_FNL) 1343.03 28.78
#> Gross exports, intermediates (EXGR_INT) 3323.92 71.22
#> Gross imports (IMGR) 5292.12 113.40
#> Gross imports, finals (IMGR_FNL) 2386.14 51.13
#> Gross imports, intermediates (IMGR_INT) 2905.98 62.27
#> Domestic absorption (DOM) 739.92 15.85
#> Domestic absorption, finals (DOM_FNL) 224.26 4.81
#> Domestic absorption, interm. (DOM_INT) 515.66 11.05
#> Gross balance (BALGR) -625.17 -13.40
#> Domestic Content in VA (EXGR_DVA) 2165.17 46.39
#> Direct domestic VA content (EXGR_DDC) 1757.25 37.65
#> Indirect domestic VA content (EXGR_IDC) 123.34 2.64
#> Reimported domestic VA content (EXGR_RIM) 284.58 6.10
#> Value Added in final demand (FD_VA) 1985.24 42.54
#> DVA in dom. final dem. (VAD) (DXD_DVA) 361.05 7.74
#> DVA in foreign final dem. (VAX) (FFD_DVA) 1624.18 34.80
#> FVA in dom. final dem. (VAM) (DFD_FVA) 2249.35 48.20
#> Balance of VA (VAX - VAM) (BALVAFD) -625.17 -13.40
#> Foreign VA Content (EXGR_FVA) 2501.78 53.61
#> Backward participation in GVC (DEXFVAP) 2501.78 53.61
#> Forward participation in GVC (FEXDVAP) 3047.87 65.31
#> Value added (VA) 1985.24 42.54
#> Production (PROD) 5406.87 115.85
#> ======================================================================
#> Method: OECD (2021), TiVA output
#> Country perspective, source approachTo check the information about sectors, it suffices to print
info_sec():
info_sec("iciotest")
#>
#> ======================================================================
#> Test Input Output Table, ICIO-type, 2022 edition
#> ======================================================================
#>
#> Individual sectors:
#> PRIMARY: D01T09 (Primary sector), MANUF: D10T39 (Manufacturing),
#> SRVWC: D41T98 (Services, including construction)
#>
#> Sector groups:
#> TOTAL: D01T98 (Total goods and services), GOODSWU: D01T39 (Goods,
#> total, incl. utilities)To check the information about available countries, the command is
info_geo():
info_geo("iciotest")
#>
#> ======================================================================
#> Test Input Output Table, ICIO-type, 2022 edition
#> ======================================================================
#>
#> Individual countries:
#> FRA (France), MEX (Mexico), ESP (Spain), USA (United States), CHN
#> (China), ROW (Rest of the world)
#>
#> Groups of countries:
#> WLD (World), EU27 (EU-27), NONEU27 (Non-EU27), NAFTA (NAFTA), USMCA
#> (USMCA)This commands do not require to have a wio in the
environment, so we can just check what sectors are available in the
OECD’s ICIO tables, 2021 edition.
info_sec("icio2021")
#>
#> ======================================================================
#> OECD's Inter-Country Input-Output Table (ICIO), 2021 edition
#> ======================================================================
#>
#> Individual sectors:
#> AGR: D01T02 (Agriculture), FSH: D03 (Fishing), MN1: D05T06 (Mining,
#> energy products), MN2: D07T08 (Mining, non-energy products), MN3: D09
#> (Mining, support activities), FOD: D10T12 (Food and beverages), TEX:
#> D13T15 (Textiles and clothing), WOD: D16 (Wood products), PPR: D17T18
#> (Paper products and printing), PET: D19 (Petroleum products), CHM:
#> D20 (Chemical products), PHR: D21 (Pharmaceutical products), RBP: D22
#> (Rubber and plastic products), NMM: D23 (Other non-metalic mineral
#> products), MET: D24 (Basic metals), FMP: D25 (Fabricated metal
#> products), CEO: D26 (Computer, electronic and optical products), ELQ:
#> D27 (Electrical equipment), OMQ: D28 (Other machinery and equipment),
#> VEH: D29 (Motor vehicles), OTQ: D30 (Other transport equipment), OMF:
#> D31T33 (Other manufacturing equipment), EGS: D35 (Electricity and
#> gas), WTR: D36T39 (Water and water treatment), CON: D41T43
#> (Construction), WRT: D45T47 (Wholesale and retail trade), LTP: D49
#> (Land transport), WTP: D50 (Water transport), ATP: D51 (Air
#> transport), STO: D52 (Warehousing and support activities), POS: D53
#> (Post and courier), HTR: D55T56 (Accommodation and food services),
#> PAV: D58T60 (Publishing, audiovisual activities), TEL: D61
#> (Telecommunications), CIS: D62T63 (Computer prog. & information
#> services), FIN: D64T66 (Financial and insurance services), REA: D68
#> (Real estate activities), PRS: D69T75 (Professional activities), ADM:
#> D77T82 (Administrative and support), GOV: D84 (Public admin., defence
#> and social security), EDU: D85 (Education services), HHS: D86T88
#> (Human health and social services), AER: D90T93 (Arts, entertainment
#> services), OSA: D94T96 (Other services), PVH: D97T98 (Private
#> household services)
#>
#> Sector groups:
#> TOTAL: D01T98 (Total goods and services), GOODSWU: D01T39 (Goods,
#> total, incl. utilities), GOODS: D01T33 (Goods, total), PRIMARY:
#> D01T09 (Primary sector), AGF: D01T03 (Agriculture and fishing),
#> MINWU: D05T09pD35T39 (Mining and utilities), MIN: D05T09 (Mining and
#> quarrying), MANUF: D10T33 (Manufacturing), MNFXPET: D10T33x19
#> (Manufacturing, except coke and petroleum), WPPR: D16T18 (Wood, paper
#> and printing), CHNMP: D20T23 (Chemical, pharma, plastic and
#> non-metallic products), CHPH: D20T21 (Chemical and pharma products),
#> BMMP: D24T25 (Basic metals and metal products), CEOE: D26T27
#> (Computer, electronic, optical and electrical equipment), TREQ:
#> D29T30 (Transport equipment), EGWT: D35T39 (Electricity, gas, water
#> supply), SRVWC: D41T98 (Services, including construction), SERVS:
#> D45T98 (Services), BIZSV: D45T82 (Business services), TPST: D49T53
#> (Transportation and storage), PTL: D53T61 (Post and
#> telecommunications), INFO: D58T63 (Information industries), ITCS:
#> D61T63 (ITC Services), OBZS: D69T82 (Other business services),
#> NONBIZSV: D84T98 (Non-business services), OSPS: D90T98 (Other social
#> and personal services), AEO: D90T96 (Arts, entertainment and other),
#> RDIHI: D21, D28 (R&D Intensity High), RDIMH: D20, D27T30, D62T65 (R&D
#> Intensity Medium-High), RDIME: D22T24, D31T33, D69T77 (R&D Intensity
#> Medium), RDIML: D05T09, D10T19, D25, D58T63 (R&D Intensity
#> Medium-Low), RDILO: D01T03, D35T56, D64T68, D77T82, D90T100 (R&D
#> Intensity Low), RDINM: D84T90 (Non-Market Activities)Additionally, the commands get_geo_codes() and
get_sec_codes provide details about the components of the
different groups. These commands are also directly applicable for any
available input-output table. For instance, for "wiod2016"
we would have the following components of EU27:
get_geo_codes("EU27", wiotype = "wiod2016")
#> [1] "AUT|BEL|CZE|DNK|EST|FIN|FRA|DEU|GRC|HUN|IRL|ITA|LVA|LTU|LUX|NLD|POL|PRT|SVK|SVN|ESP|SWE|BGR|HRV|CYP|MLT|ROU"And for "icio2021" we have the following components of
the business services sector:
get_sec_codes("BIZSV", wiotype = "icio2021")
#> [1] "D45T47|D49|D50|D51|D52|D53|D55T56|D58T60|D61|D62T63|D64T66|D68|D69T75|D77T82"