Chapter 4 Data
4.1 bfw_mp_data
This is the example vignette for function: bfw_mp_data from the PrjLabEquiBFW Package.
4.1.1 Get All Data
bl_verbose = false;
mp_data = bfw_mp_data(bl_verbose);4.1.2 Dataset 1
disp(mp_data('tb_data_pq'));
year category numberWorkers meanWage
____ ________ _____________ ________
1989 {'C001'} 1.4486e+06 1.942
1989 {'C002'} 1.1256e+06 3.2247
1989 {'C003'} 1.5156e+06 3.3738
1989 {'C004'} 8.4266e+06 NaN
1989 {'C011'} 9199 2.1604
1989 {'C012'} 1.1011e+05 5.6589
1989 {'C013'} 4.816e+05 5.8023
1989 {'C014'} 2.533e+05 NaN
1989 {'C101'} 4.4275e+06 2.3157
1989 {'C102'} 3.1277e+06 3.2178
1989 {'C103'} 1.9279e+06 4.329
1989 {'C104'} 4.8562e+05 NaN
1989 {'C111'} 96487 4.5245
1989 {'C112'} 2.7718e+05 5.4146
1989 {'C113'} 1.3868e+06 8.0437
1989 {'C114'} 1.187e+05 NaN
1992 {'C001'} 1.7431e+06 1.8431
1992 {'C002'} 1.3773e+06 3.4764
1992 {'C003'} 1.428e+06 4.079
1992 {'C004'} 8.7758e+06 NaN
1992 {'C011'} 18205 4.5495
1992 {'C012'} 1.6703e+05 5.752
1992 {'C013'} 6.2931e+05 7.0257
1992 {'C014'} 3.657e+05 NaN
1992 {'C101'} 4.7927e+06 2.052
1992 {'C102'} 4.0642e+06 2.9976
1992 {'C103'} 1.6709e+06 4.7971
1992 {'C104'} 5.5e+05 NaN
1992 {'C111'} 74782 4.0857
1992 {'C112'} 3.3189e+05 7.9404
1992 {'C113'} 1.4371e+06 10.001
1992 {'C114'} 1.3064e+05 NaN
1994 {'C001'} 2.5091e+06 1.9678
1994 {'C002'} 1.5404e+06 3.5099
1994 {'C003'} 1.5569e+06 4.3758
1994 {'C004'} 8.8237e+06 NaN
1994 {'C011'} 10653 2.8112
1994 {'C012'} 2.4128e+05 6.9136
1994 {'C013'} 7.5302e+05 8.6943
1994 {'C014'} 4.1888e+05 NaN
1994 {'C101'} 5.3134e+06 2.1107
1994 {'C102'} 4.0308e+06 3.1178
1994 {'C103'} 1.6829e+06 4.8591
1994 {'C104'} 7.1707e+05 NaN
1994 {'C111'} 1.5239e+05 7.0725
1994 {'C112'} 4.3682e+05 11.505
1994 {'C113'} 1.557e+06 12.719
1994 {'C114'} 1.2058e+05 NaN
1996 {'C001'} 2.8324e+06 1.459
1996 {'C002'} 2.1046e+06 2.4083
1996 {'C003'} 1.753e+06 2.7709
1996 {'C004'} 8.7805e+06 NaN
1996 {'C011'} 57074 2.3762
1996 {'C012'} 2.5339e+05 4.8631
1996 {'C013'} 9.465e+05 5.8817
1996 {'C014'} 5.1589e+05 NaN
1996 {'C101'} 5.4919e+06 1.7407
1996 {'C102'} 4.4873e+06 2.385
1996 {'C103'} 1.9182e+06 3.3137
1996 {'C104'} 6.9559e+05 NaN
1996 {'C111'} 2.0215e+05 5.7586
1996 {'C112'} 4.858e+05 6.221
1996 {'C113'} 1.6429e+06 7.9771
1996 {'C114'} 1.7307e+05 NaN
1998 {'C001'} 3.1189e+06 1.3076
1998 {'C002'} 2.0101e+06 2.5758
1998 {'C003'} 2.0265e+06 2.9886
1998 {'C004'} 8.8847e+06 NaN
1998 {'C011'} 36132 3.694
1998 {'C012'} 3.2575e+05 5.0667
1998 {'C013'} 9.3514e+05 5.6322
1998 {'C014'} 5.4905e+05 NaN
1998 {'C101'} 5.5182e+06 1.7357
1998 {'C102'} 4.8667e+06 2.4162
1998 {'C103'} 2.1473e+06 3.3496
1998 {'C104'} 6.7234e+05 NaN
1998 {'C111'} 1.6247e+05 4.0171
1998 {'C112'} 5.3722e+05 7.4345
1998 {'C113'} 1.6662e+06 8.7309
1998 {'C114'} 1.9321e+05 NaN
2000 {'C001'} 2.7625e+06 1.659
2000 {'C002'} 2.7297e+06 2.5901
2000 {'C003'} 2.2657e+06 3.2971
2000 {'C004'} 9.3772e+06 NaN
2000 {'C011'} 77107 2.8732
2000 {'C012'} 4.0734e+05 5.2881
2000 {'C013'} 1.1005e+06 6.5806
2000 {'C014'} 7.5089e+05 NaN
2000 {'C101'} 5.6807e+06 1.8978
2000 {'C102'} 5.3498e+06 2.4629
2000 {'C103'} 2.2554e+06 3.968
2000 {'C104'} 6.7471e+05 NaN
2000 {'C111'} 2.1108e+05 3.8076
2000 {'C112'} 6.6682e+05 7.0165
2000 {'C113'} 2.2414e+06 10.509
2000 {'C114'} 1.9925e+05 NaN
2002 {'C001'} 3.6671e+06 1.6863
2002 {'C002'} 2.5202e+06 2.826
2002 {'C003'} 2.4393e+06 3.292
2002 {'C004'} 9.291e+06 NaN
2002 {'C011'} 1.0685e+05 3.7516
2002 {'C012'} 4.5408e+05 5.83
2002 {'C013'} 1.3436e+06 7.9012
2002 {'C014'} 5.9194e+05 NaN
2002 {'C101'} 5.9945e+06 2.0088
2002 {'C102'} 5.2352e+06 2.7613
2002 {'C103'} 2.2663e+06 4.1455
2002 {'C104'} 6.7629e+05 NaN
2002 {'C111'} 2.4805e+05 4.0453
2002 {'C112'} 5.9178e+05 7.1763
2002 {'C113'} 2.0465e+06 8.9213
2002 {'C114'} 3.2278e+05 NaN
2004 {'C001'} 3.5389e+06 1.755
2004 {'C002'} 2.5059e+06 2.6069
2004 {'C003'} 2.5599e+06 3.1199
2004 {'C004'} 9.5136e+06 NaN
2004 {'C011'} 1.4496e+05 3.4155
2004 {'C012'} 4.5696e+05 5.4516
2004 {'C013'} 1.8123e+06 6.7
2004 {'C014'} 7.7668e+05 NaN
2004 {'C101'} 5.9652e+06 2.215
2004 {'C102'} 5.7124e+06 2.8839
2004 {'C103'} 2.3318e+06 3.8541
2004 {'C104'} 9.677e+05 NaN
2004 {'C111'} 2.8065e+05 5.1077
2004 {'C112'} 5.9455e+05 6.7843
2004 {'C113'} 2.2218e+06 8.6393
2004 {'C114'} 2.6115e+05 NaN
2005 {'C001'} 3.604e+06 1.8015
2005 {'C002'} 2.9152e+06 2.6792
2005 {'C003'} 2.4463e+06 3.3468
2005 {'C004'} 9.2417e+06 NaN
2005 {'C011'} 1.2085e+05 2.4982
2005 {'C012'} 5.9567e+05 4.9431
2005 {'C013'} 1.6771e+06 6.3435
2005 {'C014'} 8.2842e+05 NaN
2005 {'C101'} 5.9621e+06 2.2032
2005 {'C102'} 5.4187e+06 2.7741
2005 {'C103'} 2.5829e+06 3.7258
2005 {'C104'} 9.6341e+05 NaN
2005 {'C111'} 3.5414e+05 3.7752
2005 {'C112'} 6.5345e+05 6.9592
2005 {'C113'} 2.3148e+06 8.3387
2005 {'C114'} 2.8472e+05 NaN
2008 {'C001'} 3.9395e+06 1.8657
2008 {'C002'} 2.8968e+06 2.6475
2008 {'C003'} 2.361e+06 3.1947
2008 {'C004'} 9.2787e+06 NaN
2008 {'C011'} 1.5621e+05 3.0013
2008 {'C012'} 6.771e+05 5.3544
2008 {'C013'} 1.9227e+06 6.8198
2008 {'C014'} 9.0351e+05 NaN
2008 {'C101'} 6.0495e+06 2.3736
2008 {'C102'} 5.8662e+06 2.9056
2008 {'C103'} 2.4905e+06 3.7731
2008 {'C104'} 1.2219e+06 NaN
2008 {'C111'} 2.8368e+05 3.9143
2008 {'C112'} 7.9417e+05 6.3566
2008 {'C113'} 2.4155e+06 8.3053
2008 {'C114'} 2.6468e+05 NaN
2010 {'C001'} 3.9036e+06 1.7636
2010 {'C002'} 2.8717e+06 2.4062
2010 {'C003'} 2.7349e+06 2.8429
2010 {'C004'} 9.9169e+06 NaN
2010 {'C011'} 1.2713e+05 3.1825
2010 {'C012'} 6.661e+05 4.7299
2010 {'C013'} 2.2114e+06 6.1872
2010 {'C014'} 1.2068e+06 NaN
2010 {'C101'} 6.6858e+06 2.263
2010 {'C102'} 5.9638e+06 2.5991
2010 {'C103'} 2.4368e+06 3.6533
2010 {'C104'} 1.4088e+06 NaN
2010 {'C111'} 3.6653e+05 3.5758
2010 {'C112'} 7.4601e+05 6.2607
2010 {'C113'} 2.7576e+06 8.101
2010 {'C114'} 3.7913e+05 NaN
2012 {'C001'} 5.1813e+06 1.7308
2012 {'C002'} 3.049e+06 2.4089
2012 {'C003'} 3.0537e+06 2.7185
2012 {'C004'} 8.7224e+06 NaN
2012 {'C011'} 1.9743e+05 3.3489
2012 {'C012'} 7.3753e+05 4.1924
2012 {'C013'} 2.3311e+06 6.4194
2012 {'C014'} 1.0551e+06 NaN
2012 {'C101'} 7.139e+06 2.1453
2012 {'C102'} 6.2508e+06 2.5302
2012 {'C103'} 2.5895e+06 3.1115
2012 {'C104'} 1.512e+06 NaN
2012 {'C111'} 4.3101e+05 3.2287
2012 {'C112'} 9.0347e+05 5.0768
2012 {'C113'} 2.7373e+06 7.5722
2012 {'C114'} 3.9649e+05 NaN
2014 {'C001'} 4.5694e+06 1.7262
2014 {'C002'} 3.2584e+06 2.4145
2014 {'C003'} 2.8512e+06 2.6173
2014 {'C004'} 9.733e+06 NaN
2014 {'C011'} 2.5971e+05 2.9667
2014 {'C012'} 8.2213e+05 5.6007
2014 {'C013'} 2.5873e+06 6.1866
2014 {'C014'} 1.3462e+06 NaN
2014 {'C101'} 7.0339e+06 2.212
2014 {'C102'} 6.3219e+06 2.5069
2014 {'C103'} 2.7689e+06 3.1292
2014 {'C104'} 1.5334e+06 NaN
2014 {'C111'} 4.3522e+05 3.3786
2014 {'C112'} 8.8807e+05 5.4313
2014 {'C113'} 3.0431e+06 8.6421
2014 {'C114'} 4.8022e+05 NaN 4.1.3 Dataset 1 Aux
disp(mp_data('tb_category2sexskillocc_key'));
category sex skill occupation categoryhigher nesttier nestid nestidhigher group inputx1x2
________ __________ _____________ ___________________ ______________ ________ ______ ____________ __________ __________
{'C001'} {'Female'} {'unskilled'} {'Manual' } {'B001' } 3 11 10 {'G00' } {'x1' }
{'C002'} {'Female'} {'unskilled'} {'Routine' } {'B002' } 3 21 20 {'G00' } {'x1' }
{'C003'} {'Female'} {'unskilled'} {'Analytical' } {'B003' } 3 31 30 {'G00' } {'x1' }
{'C004'} {'Female'} {'unskilled'} {'Home Production'} {0x0 char} 3 41 NaN {'G00' } {'x1' }
{'C011'} {'Female'} {'skilled' } {'Manual' } {'B101' } 3 12 10 {'G01' } {'x1' }
{'C012'} {'Female'} {'skilled' } {'Routine' } {'B102' } 3 22 20 {'G01' } {'x1' }
{'C013'} {'Female'} {'skilled' } {'Analytical' } {'B103' } 3 32 30 {'G01' } {'x1' }
{'C014'} {'Female'} {'skilled' } {'Home Production'} {0x0 char} 3 42 NaN {'G01' } {'x1' }
{'C101'} {'Male' } {'unskilled'} {'Manual' } {'B001' } 3 11 10 {'G10' } {'x2' }
{'C102'} {'Male' } {'unskilled'} {'Routine' } {'B002' } 3 21 20 {'G10' } {'x2' }
{'C103'} {'Male' } {'unskilled'} {'Analytical' } {'B003' } 3 31 30 {'G10' } {'x2' }
{'C104'} {'Male' } {'unskilled'} {'Home Production'} {0x0 char} 3 41 NaN {'G10' } {'x2' }
{'C111'} {'Male' } {'skilled' } {'Manual' } {'B101' } 3 12 10 {'G11' } {'x2' }
{'C112'} {'Male' } {'skilled' } {'Routine' } {'B102' } 3 22 20 {'G11' } {'x2' }
{'C113'} {'Male' } {'skilled' } {'Analytical' } {'B103' } 3 32 30 {'G11' } {'x2' }
{'C114'} {'Male' } {'skilled' } {'Home Production'} {0x0 char} 3 42 NaN {'G11' } {'x2' }
{'B001'} {'All' } {'unskilled'} {'Manual' } {'A001' } 2 10 2 {0x0 char} {'x2' }
{'B002'} {'All' } {'unskilled'} {'Routine' } {'A002' } 2 20 2 {0x0 char} {'x2' }
{'B003'} {'All' } {'unskilled'} {'Analytical' } {'A003' } 2 30 1 {0x0 char} {'x2' }
{'B101'} {'All' } {'skilled' } {'Manual' } {'A001' } 2 10 2 {0x0 char} {'x1' }
{'B102'} {'All' } {'skilled' } {'Routine' } {'A002' } 2 20 2 {0x0 char} {'x1' }
{'B103'} {'All' } {'skilled' } {'Analytical' } {'A003' } 2 30 1 {0x0 char} {'x1' }
{'A001'} {'All' } {'All' } {'Manual' } {'AA01' } 1 2 1 {0x0 char} {'x2' }
{'A002'} {'All' } {'All' } {'Routine' } {'AA01' } 1 2 1 {0x0 char} {'x1' }
{'AA01'} {'All' } {'All' } {'ManualRoutine' } {'AA02' } 0 1 0 {0x0 char} {'x2' }
{'A003'} {'All' } {'All' } {'Analytical' } {'AA02' } 0 1 0 {0x0 char} {'x1' }
{'AA02'} {'All' } {'All' } {'All' } {0x0 char} NaN 0 NaN {0x0 char} {0x0 char}4.1.4 Dataset 2
disp(mp_data('tb_supply_potwrklei'));
year group gender skill numberPotentialWorkers shareMarried shareChildrenUnder5 WBL Appliances jobScarceAgree jobScarceDisagree numberPotentialWorkersAddBackAddMIgrants CtrPSklmGen89 CtrGenRelaSkl89MaleLvlAct
____ _______ ________ ___________ ______________________ ____________ ___________________ _____ __________ ______________ _________________ ________________________________________ _____________ _________________________
1989 {'G00'} "Female" "unskilled" 1.2516e+07 0.88971 0.43358 0.613 0.63031 0.22 0.72 1.2516e+07 1.2516e+07 1.2516e+07
1992 {'G00'} "Female" "unskilled" 1.3324e+07 0.90306 0.45062 0.675 0.59086 0.22 0.72 1.3398e+07 1.3578e+07 1.3607e+07
1994 {'G00'} "Female" "unskilled" 1.443e+07 0.89015 0.41182 0.675 0.59767 0.25 0.05 1.4575e+07 1.4841e+07 1.4824e+07
1996 {'G00'} "Female" "unskilled" 1.547e+07 0.88061 0.40429 0.675 0.65548 0.25 0.05 1.5708e+07 1.6142e+07 1.6105e+07
1998 {'G00'} "Female" "unskilled" 1.604e+07 0.86928 0.38743 0.675 0.69916 0.25 0.05 1.6389e+07 1.6744e+07 1.6723e+07
2000 {'G00'} "Female" "unskilled" 1.7135e+07 0.85884 0.34545 0.675 0.73095 0.33 0.55 1.7603e+07 1.8227e+07 1.7962e+07
2002 {'G00'} "Female" "unskilled" 1.7918e+07 0.85017 0.34355 0.7 0.775 0.33 0.55 1.8647e+07 1.911e+07 1.8955e+07
2004 {'G00'} "Female" "unskilled" 1.8118e+07 0.82951 0.31998 0.7 0.84238 0.33 0.55 1.9094e+07 1.9948e+07 1.9783e+07
2005 {'G00'} "Female" "unskilled" 1.8207e+07 0.82472 0.31105 0.7 0.82212 0.25 0.67 1.9311e+07 2.006e+07 1.979e+07
2008 {'G00'} "Female" "unskilled" 1.8476e+07 0.82096 0.28599 0.725 0.85993 0.25 0.67 1.9613e+07 2.0721e+07 2.0427e+07
2010 {'G00'} "Female" "unskilled" 1.9427e+07 0.81832 0.27503 0.725 0.84777 0.17 0.71 2.0606e+07 2.2128e+07 2.1707e+07
2012 {'G00'} "Female" "unskilled" 2.0006e+07 0.81686 0.28519 0.725 0.8435 0.17 0.71 2.1178e+07 2.2773e+07 2.2296e+07
2014 {'G00'} "Female" "unskilled" 2.0412e+07 0.82762 0.26669 0.806 0.88849 0.17 0.71 2.1542e+07 2.3803e+07 2.3224e+07
1989 {'G01'} "Female" "skilled" 8.5421e+05 0.87768 0.54077 0.613 0.95588 0.22 0.72 8.5421e+05 8.5421e+05 8.5421e+05
1992 {'G01'} "Female" "skilled" 1.1802e+06 0.82152 0.49389 0.675 0.91667 0.22 0.72 1.1634e+06 9.2665e+05 8.9747e+05
1994 {'G01'} "Female" "skilled" 1.4238e+06 0.81836 0.44336 0.675 0.92079 0.25 0.05 1.3845e+06 1.0129e+06 1.0304e+06
1996 {'G01'} "Female" "skilled" 1.7729e+06 0.8449 0.44756 0.675 0.95833 0.25 0.05 1.7095e+06 1.1016e+06 1.1382e+06
1998 {'G01'} "Female" "skilled" 1.8461e+06 0.82028 0.3968 0.675 0.92982 0.25 0.05 1.7743e+06 1.1427e+06 1.1632e+06
2000 {'G01'} "Female" "skilled" 2.3358e+06 0.83775 0.39002 0.675 0.95161 0.33 0.55 2.2378e+06 1.2439e+06 1.5085e+06
2002 {'G01'} "Female" "skilled" 2.4965e+06 0.81416 0.34612 0.7 0.93981 0.33 0.55 2.4109e+06 1.3042e+06 1.4587e+06
2004 {'G01'} "Female" "skilled" 3.1909e+06 0.81625 0.36267 0.7 0.95485 0.33 0.55 3.1063e+06 1.3614e+06 1.5265e+06
2005 {'G01'} "Female" "skilled" 3.2221e+06 0.79637 0.32913 0.7 0.96514 0.25 0.67 3.1493e+06 1.3691e+06 1.6397e+06
2008 {'G01'} "Female" "skilled" 3.6595e+06 0.77916 0.29259 0.725 0.9747 0.25 0.67 3.5792e+06 1.4142e+06 1.7082e+06
2010 {'G01'} "Female" "skilled" 4.2115e+06 0.76377 0.2913 0.725 0.95575 0.17 0.71 4.1208e+06 1.5102e+06 1.9315e+06
2012 {'G01'} "Female" "skilled" 4.3212e+06 0.77164 0.29254 0.725 0.95676 0.17 0.71 4.23e+06 1.5542e+06 2.0311e+06
2014 {'G01'} "Female" "skilled" 5.0153e+06 0.78454 0.30464 0.806 0.98007 0.17 0.71 4.9115e+06 1.6245e+06 2.2031e+06
1989 {'G10'} "Male" "unskilled" 9.9687e+06 0.94927 0.50626 0.613 0.54777 0.22 0.72 9.9687e+06 9.9687e+06 9.9687e+06
1992 {'G10'} "Male" "unskilled" 1.1078e+07 0.95081 0.52871 0.675 0.54377 0.22 0.72 1.104e+07 1.0982e+07 1.1078e+07
1994 {'G10'} "Male" "unskilled" 1.1744e+07 0.93806 0.47527 0.675 0.59521 0.25 0.05 1.1707e+07 1.1789e+07 1.1744e+07
1996 {'G10'} "Male" "unskilled" 1.2593e+07 0.94471 0.47819 0.675 0.63516 0.25 0.05 1.2663e+07 1.2702e+07 1.2593e+07
1998 {'G10'} "Male" "unskilled" 1.3205e+07 0.94507 0.45177 0.675 0.68226 0.25 0.05 1.347e+07 1.3263e+07 1.3205e+07
2000 {'G10'} "Male" "unskilled" 1.3961e+07 0.93873 0.40672 0.675 0.72171 0.33 0.55 1.4437e+07 1.4538e+07 1.3961e+07
2002 {'G10'} "Male" "unskilled" 1.4172e+07 0.9392 0.41105 0.7 0.73558 0.33 0.55 1.4941e+07 1.4625e+07 1.4172e+07
2004 {'G10'} "Male" "unskilled" 1.4977e+07 0.93382 0.38584 0.7 0.80806 0.33 0.55 1.6083e+07 1.5427e+07 1.4977e+07
2005 {'G10'} "Male" "unskilled" 1.4927e+07 0.93314 0.37762 0.7 0.78404 0.25 0.67 1.618e+07 1.5595e+07 1.4927e+07
2008 {'G10'} "Male" "unskilled" 1.5628e+07 0.91783 0.35084 0.725 0.82775 0.25 0.67 1.6938e+07 1.6311e+07 1.5628e+07
2010 {'G10'} "Male" "unskilled" 1.6495e+07 0.92582 0.34076 0.725 0.82052 0.17 0.71 1.7858e+07 1.7454e+07 1.6495e+07
2012 {'G10'} "Male" "unskilled" 1.7491e+07 0.91384 0.34491 0.725 0.81119 0.17 0.71 1.8899e+07 1.8477e+07 1.7491e+07
2014 {'G10'} "Male" "unskilled" 1.7658e+07 0.91783 0.31885 0.806 0.87218 0.17 0.71 1.8998e+07 1.8935e+07 1.7658e+07
1989 {'G11'} "Male" "skilled" 1.8792e+06 0.9391 0.54027 0.613 0.93209 0.22 0.72 1.8792e+06 1.8792e+06 1.8792e+06
1992 {'G11'} "Male" "skilled" 1.9744e+06 0.94708 0.51393 0.675 0.93969 0.22 0.72 1.9631e+06 2.0702e+06 1.9744e+06
1994 {'G11'} "Male" "skilled" 2.2668e+06 0.92429 0.46898 0.675 0.96316 0.25 0.05 2.2374e+06 2.2223e+06 2.2668e+06
1996 {'G11'} "Male" "skilled" 2.5039e+06 0.92112 0.45925 0.675 0.96134 0.25 0.05 2.4644e+06 2.3945e+06 2.5039e+06
1998 {'G11'} "Male" "skilled" 2.5591e+06 0.90398 0.37354 0.675 0.9554 0.25 0.05 2.5227e+06 2.5003e+06 2.5591e+06
2000 {'G11'} "Male" "skilled" 3.3185e+06 0.90086 0.39763 0.675 0.95879 0.33 0.55 3.2767e+06 2.7407e+06 3.3185e+06
2002 {'G11'} "Male" "skilled" 3.2091e+06 0.90837 0.36003 0.7 0.97224 0.33 0.55 3.1947e+06 2.7569e+06 3.2091e+06
2004 {'G11'} "Male" "skilled" 3.3582e+06 0.89767 0.36152 0.7 0.97963 0.33 0.55 3.3706e+06 2.9082e+06 3.3582e+06
2005 {'G11'} "Male" "skilled" 3.6072e+06 0.89235 0.33468 0.7 0.97339 0.25 0.67 3.6356e+06 2.9398e+06 3.6072e+06
2008 {'G11'} "Male" "skilled" 3.758e+06 0.86831 0.29492 0.725 0.97782 0.25 0.67 3.7943e+06 3.0748e+06 3.758e+06
2010 {'G11'} "Male" "skilled" 4.2492e+06 0.87325 0.30987 0.725 0.97603 0.17 0.71 4.2952e+06 3.2903e+06 4.2492e+06
2012 {'G11'} "Male" "skilled" 4.4683e+06 0.83196 0.30303 0.725 0.97234 0.17 0.71 4.5219e+06 3.483e+06 4.4683e+06
2014 {'G11'} "Male" "skilled" 4.8466e+06 0.86615 0.31875 0.806 0.97465 0.17 0.71 4.9105e+06 3.5695e+06 4.8466e+06 4.1.5 Dataset 2 Aux
disp(mp_data('tb_group2category_key'));
group groupName category sex skill
_______ ____________________ ________ __________ _____________
{'G00'} {'female-unskilled'} {'C001'} {'female'} {'unskilled'}
{'G00'} {'female-unskilled'} {'C002'} {'female'} {'unskilled'}
{'G00'} {'female-unskilled'} {'C003'} {'female'} {'unskilled'}
{'G00'} {'female-unskilled'} {'C004'} {'female'} {'unskilled'}
{'G01'} {'female-skilled' } {'C011'} {'female'} {'skilled' }
{'G01'} {'female-skilled' } {'C012'} {'female'} {'skilled' }
{'G01'} {'female-skilled' } {'C013'} {'female'} {'skilled' }
{'G01'} {'female-skilled' } {'C014'} {'female'} {'skilled' }
{'G10'} {'male-unskilled' } {'C101'} {'male' } {'unskilled'}
{'G10'} {'male-unskilled' } {'C102'} {'male' } {'unskilled'}
{'G10'} {'male-unskilled' } {'C103'} {'male' } {'unskilled'}
{'G10'} {'male-unskilled' } {'C104'} {'male' } {'unskilled'}
{'G11'} {'male-skilled' } {'C111'} {'male' } {'skilled' }
{'G11'} {'male-skilled' } {'C112'} {'male' } {'skilled' }
{'G11'} {'male-skilled' } {'C113'} {'male' } {'skilled' }
{'G11'} {'male-skilled' } {'C114'} {'male' } {'skilled' }