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  • #1

    how to create change in health with respect to change in hours worked



    Hello I want to create a variable that depicts change in health wrt to change in hours worked. Something like the derivative Health effect of effort ∂h/ ∂hoursworked.

    I have two health variables BMI and self rating of health EW10. WSHOURS is the hours worked in a year. I have two time periods depicted in SURVEY.

    I want to create change in BMI wrt change in WSHOURS across the two years and change in EW10 wrt to change in WSHOURS. How do I do that?



    Code:
    
	
    Code:
    
	
    * Example generated by -dataex-. To install: ssc install dataex
clear
input float BMI int(EW10 WSHOURS SURVEY)
 23.49591 3 2555 2
30.774214 1 1760 1
 21.41378 1 2190 2
 26.62042 3 1800 2
 30.93279 2 1825 2
 22.14876 2 4000 2
 23.93525 2 1050 1
 23.93767 2 2100 2
 31.70855 2 1560 2
  25.9788 3 2190 2
 30.69388 3 2190 2
21.854063 2 2190 2
32.105175 2 3150 2
 28.16055 3 2240 1
       20 2 1920 2
  25.2002 2 2400 2
 29.04866 3 2190 2
15.741002 2 1980 2
 23.33768 2 1800 1
24.105185 2   60 2
 25.72977 1  660 2
18.189388 2  960 2
33.014046 2 1680 2
18.754324 2  400 2
18.586313 3 1200 2
17.647774 3 1200 2
 19.11111 3  400 2
 25.86672 3 1825 2
 19.27907 2  640 2
18.507961 4  368 2
19.403263 2 1480 2
 25.53711 4  400 2
 20.43453 2  200 2
27.333334 2  176 2
22.352186 1  800 1
 18.98075 2 1350 2
 19.77107 3  480 2
 15.13938 2  320 2
 26.00975 2  600 2
 19.54614 2 1825 2
 26.22564 2  180 2
20.703323 1  960 1
 25.66822 2  440 2
31.034264 2  240 2
18.042686 2  400 2
 20.92458 2  400 2
25.077394 2 2480 2
 24.20114 1 2700 1
14.724116 2  560 2
 21.75973 2  420 2
 27.30469 3  900 2
17.893951 2  800 2
 20.80251 2 1740 2
 26.43039 2 2320 2
 26.55152 2 2920 2
29.355314 2 1860 2
 21.29674 2  240 2
18.798946 2   16 2
24.681847 3  800 2
17.377777 3  480 2
18.952898 3  240 2
  21.0101 2  120 2
 28.96076 3  640 2
 21.38119 3  360 2
26.594553 3  432 2
25.558846 3  800 2
18.552498 2  224 2
 18.36727 3 2560 2
 22.07481 3  440 2
21.231205 4  560 2
 22.10179 4  560 2
 27.78715 2 2640 1
 27.54679 3  800 2
 25.64922 3  680 2
18.411722 2  720 1
 26.22222 2  400 2
 20.21424 2  480 2
28.681374 2  120 2
 26.34457 2  200 2
30.827494 2  280 2
26.567747 2 1500 2
25.555555 2 2920 2
18.263971 3  320 1
18.263971 2  240 2
18.817272 3  480 2
  25.7632 2  400 2
  21.7095 3  224 2
17.346754 3  240 2
21.277534 3  400 2
18.107124 3  480 2
20.388186 3  320 2
15.181862 4  800 2
22.534153 3  400 2
  20.9072 3  112 2
26.805414 3  240 2
 21.33821 3  200 2
 21.28549 3  400 2
 21.21137 2  240 2
25.748217 2  480 2
17.535034 2  480 2
end
label values EW10 EW10
label def EW10 1 "Very good 1", modify
label def EW10 2 "Good 2", modify
label def EW10 3 "OK 3", modify
label def EW10 4 "Poor 4", modify
label values SURVEY SURVEY
label def SURVEY 1 "IHDS1 1", modify
label def SURVEY 2 "IHDS2 2", modify
    

    ------------------ copy up to and including the previous line ------------------

Listed 100 out of 57690 observations
Use the count() option to li
    Tags: None
  • #2
    You don't have a person indicator, so you can't get the difference across time.

    The derivative you seek could be obtained using

    Code:
    reg BMI WSHOURS i.SURVEY
reg EW10 WSHOURS i.SURVEY
    but such a model is poorly specified (and the coefficients are of opposing signs).

    Comment

    • #3
      I forgot to add the person indicator HHPBASE
      Code:
      
	
      Code:
      
	
      * Example generated by -dataex-. To install: ssc install dataex
clear
input double HHPBASE float BMI int(EW10 WSHOURS SURVEY)
101020216001 22.436853 .  720 2
101020301009  22.63579 .  160 2
101020314001 24.317696 .  160 2
101020401001  21.67756 . 3650 2
101020502001 20.810326 .   80 2
101020512002 22.127113 . 2190 2
101020705001 21.026867 . 2555 2
101020706003  18.57004 . 1540 2
101020707001 17.065573 . 3285 2
101020710001  20.51576 . 2555 2
101020808003 22.790215 . 3285 2
101020812002 30.774214 1 1760 1
101020813001  22.10659 . 1400 2
101030103001  24.98391 . 2555 2
101030104001 22.576605 . 2920 2
101030113001  25.98871 . 3000 2
101030201001  27.74631 . 3240 2
101030207001 27.149696 . 3285 2
101030213002  21.41378 1 2190 2
101030308001 24.577536 . 2190 2
101030401001 18.947283 . 2190 2
101030414002  26.62042 3 1800 2
101030505001   28.1338 . 2555 2
101030508001   21.6136 . 2100 2
101030509001  24.21695 . 2555 2
101030513002  30.93279 2 1825 2
101030902001  25.82156 . 3600 2
101050105001   24.0999 . 1600 2
101050109001  16.82412 . 1800 2
101050112001 20.149014 . 1750 2
101050118001 18.668373 . 1400 2
101050201002  22.14876 2 4000 2
101050202001 20.929516 . 1260 2
101050205001  26.83883 . 2000 2
101050207001 18.658733 . 1200 2
101050212001  20.76665 . 1050 2
101050213001  29.36319 . 1250 2
101050409002  23.93525 2 1050 1
101050420003  24.50649 . 2160 2
101050501001 17.877977 . 1620 2
101050508001 23.773676 . 4000 2
101050510001 17.688074 . 1350 2
101050515001  22.05578 . 4000 2
101050601002  23.93767 2 2100 2
101050603003 24.353064 . 2190 2
101050605001 16.613266 . 2190 2
101050614002 27.091906 . 2190 2
101050618001   25.1607 . 1720 2
101120108001 18.715063 .  600 2
101120109001 17.579903 .  240 2
101120112001 17.916296 . 1200 2
101120118001 16.670502 . 1740 2
101120311001  18.28856 . 1825 2
101120406001 18.344666 .  360 2
101120407001 23.830976 . 1800 2
101120407002   25.9788 3 2190 2
101120408002  30.69388 3 2190 2
101120417001  19.89859 . 4000 2
101120604003  17.11009 .  744 2
101120607001 17.680883 .  480 2
101120613001 19.240423 . 2760 2
101120614001  22.11462 .  360 2
101120616001 17.734375 . 2920 2
101120617001  25.11126 .  950 2
101130103001 15.891325 . 1440 2
101130107001  21.67969 . 1920 2
101130108001  25.63275 . 2190 2
101130108002 21.854063 2 2190 2
101130203002 21.476116 . 1770 2
101130211001 16.773531 .  210 2
101130305001  19.26717 .  420 2
101130315001 17.407513 .  240 2
101130405003 20.454664 . 1640 2
101130417001  19.98891 . 2920 2
101130419001  25.71124 . 2720 2
101130420001  26.63466 . 2190 2
101130517002 32.105175 2 3150 2
101130705001  22.54421 . 2920 2
101130707001 27.666136 . 2040 2
101130815001  25.71166 . 2190 2
101131004002 32.776558 . 2920 2
101131005001 15.841413 . 1920 2
101131005002        20 2 1920 2
101131008001 27.215187 . 1080 2
101131011001  20.43658 . 2800 2
101131011002   25.2002 2 2400 2
101131209001 15.023842 . 2480 2
101131304002  29.04866 3 2190 2
101131313001 22.499065 . 2920 2
101131504001 23.894686 . 2190 2
101131504002 24.105185 2   60 2
101131512002  25.72977 1  660 2
101131514002 18.189388 2  960 2
102010102101  19.21248 .  240 2
102010105102 33.014046 2 1680 2
102010110101 20.957273 . 2400 2
102010112201 17.684334 . 2240 2
102010112202 18.754324 2  400 2
102010116101 18.856842 . 1440 2
102010201102  22.69712 . 2400 2
end
label values EW10 EW10
label def EW10 1 "Very good 1", modify
label def EW10 2 "Good 2", modify
label def EW10 3 "OK 3", modify
label values SURVEY SURVEY
label def SURVEY 1 "IHDS1 1", modify
label def SURVEY 2 "IHDS2 2", modify

      Comment

      • #4
        Only 1 obs per ID.

        If you xtset your data, then you can get a lag which should permit you to do what you want.

        Comment

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