Compute the Lorenz curve for microdata.
Arguments
- welfare
numeric: A vector of income or consumption values.
- weight
numeric: A vector of weights. Default is a vector of ones,
rep(1, length(welfare)).- nbins
numeric: number of points on the Lorenz curve. if
NULLthe returning Lorenz curve would be the length of the original welfare vector minus the number ofNAsof different observations inwelfareandweight. Default is100forlength(welfare) > 1000and20otherwise.- force_nbins
logical; Force the creation of exact nbins even there is no actual data that falls in the corresponding interval. This implies that some observations will be repeated.
Examples
md_compute_lorenz(welfare = md_ABC_2010_income$welfare,
weight = md_ABC_2010_income$weight)
#> welfare lorenz_welfare lorenz_weight
#> 1 527466.5 0.0003376957 0.01012139
#> 2 770944.8 0.0012620532 0.02032103
#> 3 956874.9 0.0024979306 0.03066850
#> 4 1050884.1 0.0039694182 0.04114639
#> 5 1088278.0 0.0055054984 0.05138952
#> 6 1213282.9 0.0069577274 0.06045007
#> 7 1286504.4 0.0086596025 0.07031060
#> 8 1412181.0 0.0105074124 0.08029286
#> 9 1476508.8 0.0125630830 0.09055337
#> 10 1560126.0 0.0146654700 0.10050955
#> 11 1647038.0 0.0170353490 0.11100483
#> 12 1737304.5 0.0193014485 0.12063059
#> 13 1848083.6 0.0219092632 0.13109979
#> 14 1903261.5 0.0242845665 0.14015165
#> 15 1971652.5 0.0271715228 0.15078606
#> 16 2047943.5 0.0298738151 0.16044659
#> 17 2143238.5 0.0335044068 0.17281138
#> 18 2221881.2 0.0360345836 0.18112413
#> 19 2271607.0 0.0390183463 0.19068902
#> 20 2378096.0 0.0425773133 0.20167994
#> 21 2423298.2 0.0463684614 0.21295781
#> 22 2468756.8 0.0490135520 0.22072276
#> 23 2528214.5 0.0522747303 0.23003548
#> 24 2590368.5 0.0562543999 0.24118292
#> 25 2691228.8 0.0595428468 0.25009565
#> 26 2807437.5 0.0634174534 0.26028660
#> 27 2843264.0 0.0672974389 0.27014713
#> 28 2899196.2 0.0713083959 0.28020764
#> 29 2941314.5 0.0754888626 0.29049424
#> 30 2983189.0 0.0794214244 0.30002435
#> 31 3039593.5 0.0836239417 0.31005878
#> 32 3092553.8 0.0883756119 0.32118013
#> 33 3156643.5 0.0925633553 0.33076241
#> 34 3249655.0 0.0973062044 0.34133595
#> 35 3284760.0 0.1013468147 0.35022260
#> 36 3370602.8 0.1058835431 0.36003965
#> 37 3483609.0 0.1110439114 0.37090014
#> 38 3574020.5 0.1165914849 0.38221279
#> 39 3630684.8 0.1205340003 0.39007339
#> 40 3701176.5 0.1258161459 0.40041216
#> 41 3797451.8 0.1308362005 0.41003791
#> 42 3851052.5 0.1362041929 0.42010713
#> 43 3988344.0 0.1417637218 0.43025460
#> 44 4126599.0 0.1479052345 0.44113248
#> 45 4175654.2 0.1531242354 0.45016695
#> 46 4278784.0 0.1598269479 0.46154047
#> 47 4330308.0 0.1649856396 0.47014886
#> 48 4450651.0 0.1710469151 0.48003548
#> 49 4583402.5 0.1786654814 0.49207854
#> 50 4676065.0 0.1840867873 0.50046955
#> 51 4799488.5 0.1906791219 0.51041703
#> 52 4921696.0 0.1975220263 0.52052103
#> 53 5056518.0 0.2043279407 0.53032938
#> 54 5222615.0 0.2116179827 0.54047685
#> 55 5289737.0 0.2194971847 0.55123300
#> 56 5325424.0 0.2261619839 0.56024138
#> 57 5465240.5 0.2339040035 0.57058015
#> 58 5664699.0 0.2417327990 0.58066676
#> 59 5734393.5 0.2494474363 0.59037947
#> 60 5786693.5 0.2573803035 0.60027477
#> 61 5921869.5 0.2656260514 0.61037007
#> 62 6020719.0 0.2736847308 0.62005669
#> 63 6078579.0 0.2833664089 0.63154325
#> 64 6165239.0 0.2909918505 0.64049946
#> 65 6313356.0 0.3014843679 0.65260339
#> 66 6400624.0 0.3082930402 0.66029877
#> 67 6598847.0 0.3183028915 0.67135926
#> 68 6723124.0 0.3278916467 0.68168064
#> 69 6917179.5 0.3358297870 0.69000209
#> 70 7015360.0 0.3459946283 0.70049737
#> 71 7169986.0 0.3559772027 0.71060137
#> 72 7399422.0 0.3680075043 0.72242705
#> 73 7537658.0 0.3760000452 0.73013113
#> 74 7890955.0 0.3879857456 0.74129595
#> 75 8235175.5 0.3989193499 0.75094779
#> 76 8477008.0 0.4121598970 0.76234740
#> 77 8677887.0 0.4223627064 0.77089493
#> 78 8974374.0 0.4341605146 0.78047720
#> 79 9307018.0 0.4470115532 0.79058989
#> 80 9510035.0 0.4598769242 0.80035477
#> 81 9731732.0 0.4733182799 0.81036312
#> 82 10219490.0 0.4874193707 0.82044972
#> 83 10415763.0 0.5098227161 0.83596223
#> 84 10547598.0 0.5157629876 0.84003165
#> 85 10824170.0 0.5306387951 0.85004869
#> 86 11252204.0 0.5481878186 0.86148308
#> 87 11522499.0 0.5624545003 0.87049146
#> 88 12320558.0 0.5798950032 0.88096066
#> 89 13034536.0 0.5964953435 0.89037773
#> 90 14178849.0 0.6188846955 0.90215123
#> 91 15090150.0 0.6363639799 0.91068137
#> 92 16546611.0 0.6580029663 0.92045494
#> 93 18590828.0 0.6863040844 0.93184585
#> 94 19756928.0 0.7086763293 0.94013252
#> 95 22015660.0 0.7380363025 0.95017565
#> 96 24556662.0 0.7700535669 0.96026225
#> 97 26591538.0 0.8061904272 0.97024451
#> 98 34345624.0 0.8546482546 0.98131369
#> 99 50928228.0 0.9040598086 0.99000035
#> 100 236313152.0 1.0000000000 1.00000000