A compact interface to the four decompositions: it dispatches on method and, given a list
of 'icio' objects (e.g. one per year), runs the decomposition on each and stacks the results.
Usage
decomp(x, method = c("bm", "leontief", "kww", "wwz"), ..., idcol = "Label")Arguments
- x
an 'icio' class object from
load_icioorload_icio_csv, or a (preferably named) list of such objects, e.g. one ICIO table per year.- method
character. The decomposition method:
"bm"(the default and recommended method, seebm),"leontief","kww"or"wwz".- ...
- idcol
character. Only used if
xis a list: the name of the identifier column prepended to the stacked result. It holds the names ofx, or the list indices ifxis unnamed. Set toNULLto omit it.
Value
A data.table - see bm, leontief, kww or
wwz for the columns of each decomposition. If x is a list, the results are
stacked with rbindlist and prefixed with idcol.
Details
Building an 'icio' object is by far the most expensive step (it involves inverting a
GN x GN matrix), so it is done once by load_icio and reused across
decompositions. Pass the object to bm, leontief, kww or
wwz directly if you prefer.
References
Hummels, D., Ishii, J., & Yi, K. M. (2001). The nature and growth of vertical specialization in world trade. Journal of international Economics, 54(1), 75-96.
Koopman, R., Wang, Z., & Wei, S. J. (2014). Tracing value-added and double counting in gross exports. American Economic Review, 104(2), 459-94.
Wang, Zhi, Shang-Jin Wei, and Kunfu Zhu (2013). Quantifying international production sharing at the bilateral and sector levels (No. w19677). National Bureau of Economic Research.
Borin, A., & Mancini, M. (2019). Measuring What Matters in Global Value Chains and Value-Added Trade. World Bank Policy Research Working Paper 8804.
Examples
# Load leather example data
data(leather)
# Explore the data
str(leather)
#> List of 5
#> $ inter : num [1:9, 1:9] 16.1 2.4 0.9 1.1 0.3 0 1.2 1.3 2.1 5.1 ...
#> $ final : num [1:9, 1:3] 21.5 16.2 11 7.5 8.9 1.2 9.2 7.9 25.1 6.1 ...
#> $ countries : chr [1:3] "Argentina" "Turkey" "Germany"
#> $ industries: chr [1:3] "Agriculture" "Textile_and_Leather" "Transport_Equipment"
#> $ out : num [1:9] 77.7 58.3 19 112.7 124.6 ...
#> - attr(*, "class")= chr "iot"
# Create the 'icio' object
m <- load_icio(leather)
## Decomposing gross exports:
# Borin-Mancini (2019), the recommended method
decomp(m)
#> Exporting_Country GEXP DC DVA VAX DAVAX REF
#> <fctr> <num> <num> <num> <num> <num> <num>
#> 1: Argentina 64.3 53.68996 52.81756 46.73209 34.79877 6.085473
#> 2: Turkey 113.6 92.46175 89.82482 77.34144 65.50360 12.483373
#> 3: Germany 147.6 111.29058 106.84240 96.71914 89.31689 10.123261
#> DDC FC FVA FDC GVC GVCB GVCF
#> <num> <num> <num> <num> <num> <num> <num>
#> 1: 0.8723949 10.61004 10.43484 0.1752063 29.50123 11.48244 18.01879
#> 2: 2.6369363 21.13825 20.55564 0.5826050 48.09640 23.77518 24.32122
#> 3: 4.4481800 36.30942 35.06489 1.2445289 58.28311 40.75760 17.52551
decomp(m, aggregation = "bilateral")
#> Exporting_Country Exporting_Industry Importing_Country GEXP DC
#> <fctr> <fctr> <fctr> <num> <num>
#> 1: Turkey Agriculture Argentina 10.7 8.9591095
#> 2: Turkey Textile_and_Leather Argentina 12.1 9.8259679
#> 3: Turkey Transport_Equipment Argentina 1.6 1.1210406
#> 4: Germany Agriculture Argentina 14.9 12.7315559
#> 5: Germany Textile_and_Leather Argentina 10.3 8.5456405
#> 6: Germany Transport_Equipment Argentina 31.6 21.2976809
#> 7: Argentina Agriculture Turkey 14.0 12.5642780
#> 8: Argentina Textile_and_Leather Turkey 6.8 5.2626672
#> 9: Argentina Transport_Equipment Turkey 0.9 0.6361968
#> 10: Germany Agriculture Turkey 23.8 20.3363108
#> 11: Germany Textile_and_Leather Turkey 20.7 17.1742484
#> 12: Germany Transport_Equipment Turkey 46.3 31.2051464
#> 13: Argentina Agriculture Germany 19.2 17.2310098
#> 14: Argentina Textile_and_Leather Germany 21.7 16.7940996
#> 15: Argentina Transport_Equipment Germany 1.7 1.2017051
#> 16: Turkey Agriculture Germany 35.2 29.4729584
#> 17: Turkey Textile_and_Leather Germany 47.1 38.2481890
#> 18: Turkey Transport_Equipment Germany 6.9 4.8344878
#> DVA VAX DAVAX REF DDC FC FVA
#> <num> <num> <num> <num> <num> <num> <num>
#> 1: 8.7346635 8.224093 7.2163401 0.51057032 0.22444602 1.7408905 1.6914093
#> 2: 9.5634845 9.079825 8.0548444 0.48365898 0.26248341 2.2740321 2.2161439
#> 3: 1.0476459 1.018431 0.9626616 0.02921527 0.07339473 0.4789594 0.4625597
#> 4: 12.4755135 10.951482 9.6750702 1.52403152 0.25604243 2.1684441 2.0962209
#> 5: 8.3338317 7.706572 7.1641956 0.62725969 0.21180881 1.7543595 1.6952298
#> 6: 20.0216436 19.072854 18.2515939 0.94879005 1.27603729 10.3023191 9.9457626
#> 7: 12.4371868 11.215869 8.0001479 1.22131820 0.12709119 1.4357220 1.4094133
#> 8: 5.1463769 4.569764 2.9980790 0.57661309 0.11629029 1.5373328 1.5147708
#> 9: 0.6072535 0.575969 0.4840523 0.03128448 0.02894336 0.2638032 0.2574840
#> 10: 19.9273303 18.188155 16.9826710 1.73917503 0.40898052 3.4636892 3.3483259
#> 11: 16.7485744 13.780948 11.7517352 2.96762614 0.42567402 3.5257516 3.4069182
#> 12: 29.3355095 27.019131 25.4916229 2.31637845 1.86963692 15.0948536 14.5724306
#> 13: 17.0567133 15.324513 12.3850073 1.73220028 0.17429648 1.9689902 1.9329096
#> 14: 16.4229968 14.012735 10.0865741 2.41026176 0.37110283 4.9059004 4.8339009
#> 15: 1.1470343 1.033239 0.8449095 0.11379565 0.05467078 0.4982949 0.4863586
#> 16: 28.7345939 24.243961 20.4567369 4.49063336 0.73836447 5.7270416 5.5642624
#> 17: 37.2264561 30.622454 24.9707904 6.60400204 1.02173293 8.8518110 8.6264777
#> 18: 4.5179730 4.152680 3.8422274 0.36529320 0.31651476 2.0655122 1.9947889
#> FDC GVC GVCB GVCF
#> <num> <num> <num> <num>
#> 1: 0.049481197 3.4836599 1.9653365 1.5183234
#> 2: 0.057888186 4.0451556 2.5365155 1.5086400
#> 3: 0.016399618 0.6373384 0.5523541 0.0849843
#> 4: 0.072223200 5.2249298 2.4244865 2.8004433
#> 5: 0.059129673 3.1358044 1.9661683 1.1696361
#> 6: 0.356556480 13.3484061 11.5783564 1.7700497
#> 7: 0.026308772 5.9998521 1.5628132 4.4370389
#> 8: 0.022562035 3.8019210 1.6536231 2.1482979
#> 9: 0.006319211 0.4159477 0.2927465 0.1232012
#> 10: 0.115363232 6.8173290 3.8726697 2.9446593
#> 11: 0.118833420 8.9482648 3.9514256 4.9968392
#> 12: 0.522422944 20.8083771 16.9644905 3.8438866
#> 13: 0.036080601 6.8149927 2.1432867 4.6717060
#> 14: 0.071999434 11.6134259 5.2770032 6.3364227
#> 15: 0.011936287 0.8550905 0.5529657 0.3021248
#> 16: 0.162779264 14.7432631 6.4654061 8.2778570
#> 17: 0.225333351 22.1292096 9.8735439 12.2556656
#> 18: 0.070723354 3.0577726 2.3820270 0.6757457
# Leontief, Koopman-Wang-Wei and Wang-Wei-Zhu
decomp(m, method = "leontief")
#> Source_Country Source_Industry Using_Country Using_Industry
#> <fctr> <fctr> <fctr> <fctr>
#> 1: Argentina Agriculture Argentina Agriculture
#> 2: Argentina Agriculture Argentina Textile_and_Leather
#> 3: Argentina Agriculture Argentina Transport_Equipment
#> 4: Argentina Agriculture Turkey Agriculture
#> 5: Argentina Agriculture Turkey Textile_and_Leather
#> 6: Argentina Agriculture Turkey Transport_Equipment
#> 7: Argentina Agriculture Germany Agriculture
#> 8: Argentina Agriculture Germany Textile_and_Leather
#> 9: Argentina Agriculture Germany Transport_Equipment
#> 10: Argentina Textile_and_Leather Argentina Agriculture
#> 11: Argentina Textile_and_Leather Argentina Textile_and_Leather
#> 12: Argentina Textile_and_Leather Argentina Transport_Equipment
#> 13: Argentina Textile_and_Leather Turkey Agriculture
#> 14: Argentina Textile_and_Leather Turkey Textile_and_Leather
#> 15: Argentina Textile_and_Leather Turkey Transport_Equipment
#> 16: Argentina Textile_and_Leather Germany Agriculture
#> 17: Argentina Textile_and_Leather Germany Textile_and_Leather
#> 18: Argentina Textile_and_Leather Germany Transport_Equipment
#> 19: Argentina Transport_Equipment Argentina Agriculture
#> 20: Argentina Transport_Equipment Argentina Textile_and_Leather
#> 21: Argentina Transport_Equipment Argentina Transport_Equipment
#> 22: Argentina Transport_Equipment Turkey Agriculture
#> 23: Argentina Transport_Equipment Turkey Textile_and_Leather
#> 24: Argentina Transport_Equipment Turkey Transport_Equipment
#> 25: Argentina Transport_Equipment Germany Agriculture
#> 26: Argentina Transport_Equipment Germany Textile_and_Leather
#> 27: Argentina Transport_Equipment Germany Transport_Equipment
#> 28: Turkey Agriculture Argentina Agriculture
#> 29: Turkey Agriculture Argentina Textile_and_Leather
#> 30: Turkey Agriculture Argentina Transport_Equipment
#> 31: Turkey Agriculture Turkey Agriculture
#> 32: Turkey Agriculture Turkey Textile_and_Leather
#> 33: Turkey Agriculture Turkey Transport_Equipment
#> 34: Turkey Agriculture Germany Agriculture
#> 35: Turkey Agriculture Germany Textile_and_Leather
#> 36: Turkey Agriculture Germany Transport_Equipment
#> 37: Turkey Textile_and_Leather Argentina Agriculture
#> 38: Turkey Textile_and_Leather Argentina Textile_and_Leather
#> 39: Turkey Textile_and_Leather Argentina Transport_Equipment
#> 40: Turkey Textile_and_Leather Turkey Agriculture
#> 41: Turkey Textile_and_Leather Turkey Textile_and_Leather
#> 42: Turkey Textile_and_Leather Turkey Transport_Equipment
#> 43: Turkey Textile_and_Leather Germany Agriculture
#> 44: Turkey Textile_and_Leather Germany Textile_and_Leather
#> 45: Turkey Textile_and_Leather Germany Transport_Equipment
#> 46: Turkey Transport_Equipment Argentina Agriculture
#> 47: Turkey Transport_Equipment Argentina Textile_and_Leather
#> 48: Turkey Transport_Equipment Argentina Transport_Equipment
#> 49: Turkey Transport_Equipment Turkey Agriculture
#> 50: Turkey Transport_Equipment Turkey Textile_and_Leather
#> 51: Turkey Transport_Equipment Turkey Transport_Equipment
#> 52: Turkey Transport_Equipment Germany Agriculture
#> 53: Turkey Transport_Equipment Germany Textile_and_Leather
#> 54: Turkey Transport_Equipment Germany Transport_Equipment
#> 55: Germany Agriculture Argentina Agriculture
#> 56: Germany Agriculture Argentina Textile_and_Leather
#> 57: Germany Agriculture Argentina Transport_Equipment
#> 58: Germany Agriculture Turkey Agriculture
#> 59: Germany Agriculture Turkey Textile_and_Leather
#> 60: Germany Agriculture Turkey Transport_Equipment
#> 61: Germany Agriculture Germany Agriculture
#> 62: Germany Agriculture Germany Textile_and_Leather
#> 63: Germany Agriculture Germany Transport_Equipment
#> 64: Germany Textile_and_Leather Argentina Agriculture
#> 65: Germany Textile_and_Leather Argentina Textile_and_Leather
#> 66: Germany Textile_and_Leather Argentina Transport_Equipment
#> 67: Germany Textile_and_Leather Turkey Agriculture
#> 68: Germany Textile_and_Leather Turkey Textile_and_Leather
#> 69: Germany Textile_and_Leather Turkey Transport_Equipment
#> 70: Germany Textile_and_Leather Germany Agriculture
#> 71: Germany Textile_and_Leather Germany Textile_and_Leather
#> 72: Germany Textile_and_Leather Germany Transport_Equipment
#> 73: Germany Transport_Equipment Argentina Agriculture
#> 74: Germany Transport_Equipment Argentina Textile_and_Leather
#> 75: Germany Transport_Equipment Argentina Transport_Equipment
#> 76: Germany Transport_Equipment Turkey Agriculture
#> 77: Germany Transport_Equipment Turkey Textile_and_Leather
#> 78: Germany Transport_Equipment Turkey Transport_Equipment
#> 79: Germany Transport_Equipment Germany Agriculture
#> 80: Germany Transport_Equipment Germany Textile_and_Leather
#> 81: Germany Transport_Equipment Germany Transport_Equipment
#> Source_Country Source_Industry Using_Country Using_Industry
#> <fctr> <fctr> <fctr> <fctr>
#> FVAX
#> <num>
#> 1: 28.52278143
#> 2: 2.79395126
#> 3: 0.35606694
#> 4: 1.81066955
#> 5: 3.11738415
#> 6: 0.35901126
#> 7: 1.23641723
#> 8: 1.30283802
#> 9: 4.12087363
#> 10: 1.06206936
#> 11: 19.12053186
#> 12: 0.41813924
#> 13: 0.48370042
#> 14: 1.83290239
#> 15: 0.43058635
#> 16: 0.59370415
#> 17: 1.15375958
#> 18: 4.74903511
#> 19: 0.21043693
#> 20: 0.14228369
#> 21: 1.06369578
#> 22: 0.03329456
#> 23: 0.07905450
#> 24: 0.04024626
#> 25: 0.02318460
#> 26: 0.07482343
#> 27: 0.19326212
#> 28: 0.71952151
#> 29: 1.34237213
#> 30: 0.11504126
#> 31: 34.92704803
#> 32: 6.99949698
#> 33: 1.47711579
#> 34: 2.55430885
#> 35: 1.52213499
#> 36: 6.18062537
#> 37: 0.41201175
#> 38: 1.38523849
#> 39: 0.11764036
#> 40: 2.69291816
#> 41: 40.16714096
#> 42: 1.31799873
#> 43: 1.10939926
#> 44: 1.15207241
#> 45: 9.50690317
#> 46: 0.03482652
#> 47: 0.08553139
#> 48: 0.02667530
#> 49: 0.81210167
#> 50: 0.90751892
#> 51: 3.16041392
#> 52: 0.11511911
#> 53: 0.07448266
#> 54: 0.64647326
#> 55: 0.92530356
#> 56: 2.25142713
#> 57: 0.16222512
#> 58: 2.31122022
#> 59: 2.05958253
#> 60: 0.51211484
#> 61: 29.87633590
#> 62: 5.24719728
#> 63: 9.60069308
#> 64: 0.64666560
#> 65: 0.72785683
#> 66: 0.08244379
#> 67: 1.53837777
#> 68: 2.54889673
#> 69: 0.63316614
#> 70: 1.45935830
#> 71: 18.95868110
#> 72: 8.15831503
#> 73: 0.66638333
#> 74: 0.65080723
#> 75: 0.25807221
#> 76: 1.29066963
#> 77: 1.48802285
#> 78: 0.56934671
#> 79: 1.73217260
#> 80: 1.51401054
#> 81: 34.74381924
#> FVAX
#> <num>
decomp(m, method = "kww")
#> Country DVA_FIN DVA_INT DVA_INTrex RDV_FIN RDV_INT DDC
#> <fctr> <num> <num> <num> <num> <num> <num>
#> 1: Argentina 19.34940 18.97119 8.411491 5.259501 0.8259724 0.8723949
#> 2: Turkey 43.39461 26.20678 7.740053 10.475795 2.0075781 2.6369363
#> 3: Germany 78.73101 15.23967 2.748464 5.689669 4.4335916 4.4481800
#> FVA_FIN FVA_INT FDC
#> <num> <num> <num>
#> 1: 3.450595 3.388617 3.770832
#> 2: 10.205386 5.359698 5.573163
#> 3: 26.668992 4.455024 5.185401
decomp(m, method = "wwz")
#> Exporting_Country Exporting_Industry Importing_Country DVA_FIN
#> <fctr> <fctr> <fctr> <num>
#> 1: Argentina Agriculture Argentina 0.0000000
#> 2: Argentina Agriculture Turkey 5.4744354
#> 3: Argentina Agriculture Germany 7.5385668
#> 4: Argentina Textile_and_Leather Argentina 0.0000000
#> 5: Argentina Textile_and_Leather Turkey 1.4704511
#> 6: Argentina Textile_and_Leather Germany 3.9470004
#> 7: Argentina Transport_Equipment Argentina 0.0000000
#> 8: Argentina Transport_Equipment Turkey 0.3534427
#> 9: Argentina Transport_Equipment Germany 0.5655083
#> 10: Turkey Agriculture Argentina 6.2797497
#> 11: Turkey Agriculture Turkey 0.0000000
#> 12: Turkey Agriculture Germany 11.8896593
#> 13: Turkey Textile_and_Leather Argentina 7.2273648
#> 14: Turkey Textile_and_Leather Turkey 0.0000000
#> 15: Turkey Textile_and_Leather Germany 13.7238725
#> 16: Turkey Transport_Equipment Argentina 0.8407805
#> 17: Turkey Transport_Equipment Turkey 0.0000000
#> 18: Turkey Transport_Equipment Germany 3.4331870
#> 19: Germany Agriculture Argentina 7.8610949
#> 20: Germany Agriculture Turkey 15.2949565
#> 21: Germany Agriculture Germany 0.0000000
#> 22: Germany Textile_and_Leather Argentina 6.5544233
#> 23: Germany Textile_and_Leather Turkey 8.3797057
#> 24: Germany Textile_and_Leather Germany 0.0000000
#> 25: Germany Transport_Equipment Argentina 16.9168288
#> 26: Germany Transport_Equipment Turkey 23.7239990
#> 27: Germany Transport_Equipment Germany 0.0000000
#> Exporting_Country Exporting_Industry Importing_Country DVA_FIN
#> <fctr> <fctr> <fctr> <num>
#> DVA_INT DVA_INTrexI1 DVA_INTrexF DVA_INTrexI2 RDV_INT RDV_FIN
#> <num> <num> <num> <num> <num> <num>
#> 1: 0.0000000 0.00000000 0.00000000 0.000000000 0.000000000 0.00000000
#> 2: 2.6813686 1.13634407 1.40930027 0.504931544 0.168614730 0.70568372
#> 3: 5.1055954 0.41301548 2.07223830 0.184142503 0.241349852 1.40626760
#> 4: 0.0000000 0.00000000 0.00000000 0.000000000 0.000000000 0.00000000
#> 5: 1.6059520 0.51989101 0.73959485 0.238689217 0.078969730 0.33143582
#> 6: 6.4499428 0.53948810 2.81987967 0.236761536 0.317887034 1.98497120
#> 7: 0.0000000 0.00000000 0.00000000 0.000000000 0.000000000 0.00000000
#> 8: 0.1488951 0.02512465 0.05166467 0.011502878 0.004631660 0.01867612
#> 9: 0.3167888 0.02878668 0.13002229 0.012763061 0.014519380 0.09346627
#> 10: 1.1190525 0.42428189 0.32364669 0.127683939 0.154036261 0.17353911
#> 11: 0.0000000 0.00000000 0.00000000 0.000000000 0.000000000 0.00000000
#> 12: 9.1875645 0.44406842 2.46325705 0.104348906 0.693923875 3.73948515
#> 13: 1.0471326 0.46078259 0.29530382 0.140987907 0.151185856 0.13152490
#> 14: 0.0000000 0.00000000 0.00000000 0.000000000 0.000000000 0.00000000
#> 15: 12.0058480 0.62802955 3.90680904 0.127367338 0.950059562 5.57979048
#> 16: 0.1781916 0.02204374 0.02060577 0.006672856 0.008562439 0.01119518
#> 17: 0.0000000 0.00000000 0.00000000 0.000000000 0.000000000 0.00000000
#> 18: 0.6546040 0.03517716 0.21633307 0.007037041 0.049810123 0.31139842
#> 19: 2.0199906 0.28226589 0.28018271 0.060605877 0.820750168 0.57157404
#> 20: 2.0603035 0.11637081 0.47705677 0.015705424 0.736949683 0.97226390
#> 21: 0.0000000 0.00000000 0.00000000 0.000000000 0.000000000 0.00000000
#> 22: 0.7909465 0.11961386 0.15208027 0.027530616 0.309465907 0.25977474
#> 23: 3.6874998 0.18967992 0.78711215 0.023624112 1.222414373 1.69900744
#> 24: 0.0000000 0.00000000 0.00000000 0.000000000 0.000000000 0.00000000
#> 25: 2.3746384 0.18110921 0.25997068 0.039006620 0.436904740 0.42764724
#> 26: 3.2689405 0.14831124 0.60720101 0.018387426 0.907106729 1.37381855
#> 27: 0.0000000 0.00000000 0.00000000 0.000000000 0.000000000 0.00000000
#> DVA_INT DVA_INTrexI1 DVA_INTrexF DVA_INTrexI2 RDV_INT RDV_FIN
#> <num> <num> <num> <num> <num> <num>
#> RDV_FIN2 OVA_FIN MVA_FIN OVA_INT MVA_INT DDC_FIN
#> <num> <num> <num> <num> <num> <num>
#> 1: 0.000000000 0.00000000 0.00000000 0.00000000 0.00000000 0.000000000
#> 2: 0.347019751 0.41126356 0.21430105 0.19588405 0.10207118 0.064864146
#> 3: 0.084582830 0.29510308 0.56633015 0.19467191 0.37359342 0.087209223
#> 4: 0.000000000 0.00000000 0.00000000 0.00000000 0.00000000 0.000000000
#> 5: 0.166207540 0.24200608 0.18754280 0.26256584 0.20347560 0.027678457
#> 6: 0.107403534 0.50340436 0.64959526 0.81211670 1.04795907 0.106880311
#> 7: 0.000000000 0.00000000 0.00000000 0.00000000 0.00000000 0.000000000
#> 8: 0.007976699 0.09668098 0.04987633 0.04203798 0.02168679 0.001418669
#> 9: 0.005810004 0.07980213 0.15468957 0.04511074 0.08744330 0.005097011
#> 10: 0.182994956 0.83991301 0.38033734 0.15006977 0.06795601 0.107000467
#> 11: 0.000000000 0.00000000 0.00000000 0.00000000 0.00000000 0.000000000
#> 12: 0.057224342 0.72010537 1.59023530 0.55070767 1.21614811 0.452925213
#> 13: 0.200948224 0.91653494 0.75610026 0.13297218 0.10969609 0.101320108
#> 14: 0.000000000 0.00000000 0.00000000 0.00000000 0.00000000 0.000000000
#> 15: 0.074151999 1.43574094 1.74038658 1.24831079 1.51318619 0.597136653
#> 16: 0.009457655 0.24206509 0.11715443 0.05450683 0.02638016 0.005182273
#> 17: 0.000000000 0.00000000 0.00000000 0.00000000 0.00000000 0.000000000
#> 18: 0.004084652 0.47838059 0.98843243 0.09450248 0.19526151 0.031112965
#> 19: 0.131707311 0.89832585 0.44057920 0.22998309 0.11279400 0.605435276
#> 20: 0.029961455 0.85721388 1.74782965 0.11412326 0.23269341 0.531356894
#> 21: 0.000000000 0.00000000 0.00000000 0.00000000 0.00000000 0.000000000
#> 22: 0.058019038 0.70047263 0.64510407 0.08462563 0.07793643 0.224432812
#> 23: 0.046204332 0.82475330 0.89554095 0.36128082 0.39228915 0.921022483
#> 24: 0.000000000 0.00000000 0.00000000 0.00000000 0.00000000 0.000000000
#> 25: 0.084238072 5.26294538 2.92022578 0.77714301 0.43120969 0.314861124
#> 26: 0.035453172 4.09529671 7.38070428 0.58558374 1.05536198 0.673700534
#> 27: 0.000000000 0.00000000 0.00000000 0.00000000 0.00000000 0.000000000
#> RDV_FIN2 OVA_FIN MVA_FIN OVA_INT MVA_INT DDC_FIN
#> <num> <num> <num> <num> <num> <num>
#> DDC_INT ODC MDC texp texpint texpfd texpdiff
#> <num> <num> <num> <num> <num> <num> <num>
#> 1: 0.00000000 0.00000000 0.00000000 0.0 0.0 0.0 0
#> 2: 0.07171574 0.33673597 0.17546624 14.0 7.9 6.1 0
#> 3: 0.09804177 0.18474634 0.35454534 19.2 10.8 8.4 0
#> 4: 0.00000000 0.00000000 0.00000000 0.0 0.0 0.0 0
#> 5: 0.08379741 0.36155510 0.28018741 6.8 4.9 1.9 0
#> 6: 0.28388511 0.82641514 1.06640983 21.7 16.6 5.1 0
#> 7: 0.00000000 0.00000000 0.00000000 0.0 0.0 0.0 0
#> 8: 0.01286371 0.03530681 0.01821428 0.9 0.4 0.5 0
#> 9: 0.02894336 0.04466665 0.08658247 1.7 0.9 0.8 0
#> 10: 0.06712404 0.20829312 0.09432126 10.7 3.2 7.5 0
#> 11: 0.00000000 0.00000000 0.00000000 0.0 0.0 0.0 0
#> 12: 0.44050153 0.51423688 1.13560832 35.2 21.0 14.2 0
#> 13: 0.06941710 0.19656847 0.16216018 12.1 3.2 8.9 0
#> 14: 0.00000000 0.00000000 0.00000000 0.0 0.0 0.0 0
#> 15: 0.65512387 1.31733277 1.59685374 47.1 30.2 16.9 0
#> 16: 0.01834868 0.02618153 0.01267131 1.6 0.4 1.2 0
#> 17: 0.00000000 0.00000000 0.00000000 0.0 0.0 0.0 0
#> 18: 0.09174341 0.10075490 0.20818030 6.9 2.0 4.9 0
#> 19: 0.09794912 0.32658836 0.16017355 14.9 5.7 9.2 0
#> 20: 0.10138593 0.16842209 0.34340686 23.8 5.9 17.9 0
#> 21: 0.00000000 0.00000000 0.00000000 0.0 0.0 0.0 0
#> 22: 0.04935351 0.12817618 0.11804455 10.3 2.4 7.9 0
#> 23: 0.21797800 0.50430185 0.54758551 20.7 10.6 10.1 0
#> 24: 0.00000000 0.00000000 0.00000000 0.0 0.0 0.0 0
#> 25: 0.26247603 0.58577113 0.32502408 31.6 6.5 25.1 0
#> 26: 0.44822829 0.70583084 1.27207602 46.3 11.1 35.2 0
#> 27: 0.00000000 0.00000000 0.00000000 0.0 0.0 0.0 0
#> DDC_INT ODC MDC texp texpint texpfd texpdiff
#> <num> <num> <num> <num> <num> <num> <num>
#> texpdiffpercent texpfddiff texpfddiffpercent texpintdiff texpintdiffpercent
#> <num> <num> <num> <num> <num>
#> 1: 0 0 0 0 0
#> 2: 0 0 0 0 0
#> 3: 0 0 0 0 0
#> 4: 0 0 0 0 0
#> 5: 0 0 0 0 0
#> 6: 0 0 0 0 0
#> 7: 0 0 0 0 0
#> 8: 0 0 0 0 0
#> 9: 0 0 0 0 0
#> 10: 0 0 0 0 0
#> 11: 0 0 0 0 0
#> 12: 0 0 0 0 0
#> 13: 0 0 0 0 0
#> 14: 0 0 0 0 0
#> 15: 0 0 0 0 0
#> 16: 0 0 0 0 0
#> 17: 0 0 0 0 0
#> 18: 0 0 0 0 0
#> 19: 0 0 0 0 0
#> 20: 0 0 0 0 0
#> 21: 0 0 0 0 0
#> 22: 0 0 0 0 0
#> 23: 0 0 0 0 0
#> 24: 0 0 0 0 0
#> 25: 0 0 0 0 0
#> 26: 0 0 0 0 0
#> 27: 0 0 0 0 0
#> texpdiffpercent texpfddiff texpfddiffpercent texpintdiff texpintdiffpercent
#> <num> <num> <num> <num> <num>
#> DViX_Fsr
#> <num>
#> 1: 0.0000000
#> 2: 12.6653262
#> 3: 18.5127115
#> 4: 0.0000000
#> 5: 5.0399905
#> 6: 15.2000533
#> 7: 0.0000000
#> 8: 0.4855005
#> 9: 0.9139796
#> 10: 9.5810525
#> 11: 0.0000000
#> 12: 32.5558824
#> 13: 8.8144222
#> 14: 0.0000000
#> 15: 34.0847033
#> 16: 0.9503192
#> 17: 0.0000000
#> 18: 3.8384373
#> 19: 16.3782379
#> 20: 26.3978378
#> 21: 0.0000000
#> 22: 9.6143465
#> 23: 17.5471871
#> 24: 0.0000000
#> 25: 14.8384045
#> 26: 22.0663893
#> 27: 0.0000000
#> DViX_Fsr
#> <num>
# Multiple tables at once, e.g. one per year, stacked with a 'Year' column
decomp(list(`2015` = m, `2016` = m), idcol = "Year")
#> Year Exporting_Country GEXP DC DVA VAX DAVAX
#> <char> <fctr> <num> <num> <num> <num> <num>
#> 1: 2015 Argentina 64.3 53.68996 52.81756 46.73209 34.79877
#> 2: 2015 Turkey 113.6 92.46175 89.82482 77.34144 65.50360
#> 3: 2015 Germany 147.6 111.29058 106.84240 96.71914 89.31689
#> 4: 2016 Argentina 64.3 53.68996 52.81756 46.73209 34.79877
#> 5: 2016 Turkey 113.6 92.46175 89.82482 77.34144 65.50360
#> 6: 2016 Germany 147.6 111.29058 106.84240 96.71914 89.31689
#> REF DDC FC FVA FDC GVC GVCB GVCF
#> <num> <num> <num> <num> <num> <num> <num> <num>
#> 1: 6.085473 0.8723949 10.61004 10.43484 0.1752063 29.50123 11.48244 18.01879
#> 2: 12.483373 2.6369363 21.13825 20.55564 0.5826050 48.09640 23.77518 24.32122
#> 3: 10.123261 4.4481800 36.30942 35.06489 1.2445289 58.28311 40.75760 17.52551
#> 4: 6.085473 0.8723949 10.61004 10.43484 0.1752063 29.50123 11.48244 18.01879
#> 5: 12.483373 2.6369363 21.13825 20.55564 0.5826050 48.09640 23.77518 24.32122
#> 6: 10.123261 4.4481800 36.30942 35.06489 1.2445289 58.28311 40.75760 17.52551