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  • Survey cross section regression


    Hello everyone!
    I am a new member on forum and particularly a new stata user.

    I have a dataset like this (example)[see image below].

    and I want to run the regression :
    employment growth = a + b0 (size dummy for medium firms) + b1 (size dummy for large firms) + b2 (firm age) + country dummies + sector dummies + year dummies + e .

    It is right if I just use the command : reg employment_growth i.firm_size firm_age i.country i.firm_sector_type i.survey_year ?

    Thanks for the help!

    Sincerely,
    John Economou.


    Click image for larger version

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  • #2
    John:
    assuming your dependent variable is actually continuous, your code looks ok to me.
    You may want to take the matter further and look for interactions between the predictors, though.
    Kind regards,
    Carlo
    (Stata 18.0 SE)

    Comment


    • #3
      Greetings Carlo!
      Yes, its is a continuous variable from -1 to 1.
      By thw way, how can i look for interactions betwwen the predictors ?
      It is , let's say i want to look for interaction of both firm size and age , so i can add in the regression command, for example, ... i.firm_size#c.firm_age ...?

      Comment


      • #4
        John:
        your code might be something along the following lines:
        Code:
        reg employment_growth i.firm_size##c.firm_age i.country i.firm_sector_type i.survey_year *note the double hash*
        As an aside, if your dataset embeds other elements that usually characterizes surveys (es principal sample units), you may want to consider prefixing -regress- with -svy:-.
        Kind regards,
        Carlo
        (Stata 18.0 SE)

        Comment


        • #5
          Carlo:
          could you please help me with svyset for my dataset for regression,
          any idea based on the image I attached ?
          Thank you,
          John.

          Comment


          • #6
            John:
            if the survey structure of your dataset is actually relevant, I meant something like:
            Code:
            svy: reg employment_growth i.firm_size##c.firm_age i.country i.firm_sector_type i.survey_year
            Kind regards,
            Carlo
            (Stata 18.0 SE)

            Comment


            • #7
              Greeting again Carlo,
              in the case I have 3 dummies for firm size [ firm_size = (1),(2),(3) ] and 3 dummies for firm age [ firm_age= (1),(2),(3) ],

              could I use
              Code:
                 
              i.firm_size##i.firm_age
              for the interaction effects, is it right ?
              I remind you that my dependent variable is employment_growth, a continuous variable from -1 to 1.
              Thank you,
              John.

              Comment


              • #8
                John:
                yes, that's correct if you meant that you have two dummies (one for -firm_size- and the other one for -firnm -age-) composed of three levels each.
                Last edited by Carlo Lazzaro; 15 Jan 2018, 04:10.
                Kind regards,
                Carlo
                (Stata 18.0 SE)

                Comment


                • #9

                  Carlo:
                  last question, if you could answer me


                  My question is for interpreting the results

                  First of all, let me present you the dummy variables more specific:
                  firm_size
                  (1): small firm (5-19 employees)
                  (2): medium firm (20-99 employees)
                  (3): large firm (100+ employees)
                  firm_age
                  (1): young firms (<=5 years)
                  (2): medium-age firms (6-10 years)
                  (3): mature firms (11+ years)

                  I get these results from Stata using the interaction effect command i.firm_size##i.firm_age:
                  size#age Coef
                  Medium(20-99)#
                  medium_age(6-10 years)
                  -.0046145
                  Medium(20-99)#mature(11+)
                  .0083574
                  Large(100 and over)#
                  medium_age(6-10 years)
                  -.0077434
                  Large(100 and over)#
                  mature(11+)
                  -.0007782
                  So for interpreting, for example for the first one, could I say that medium age and size firms have lower employment growth than medium size young firms (coef:
                  -.0046145)
                  and medium size mature firms have more employment growth than medium size young firms (coef:
                  .0083574)...?

                  Comment


                  • #10
                    John:
                    you should report the whole Stata output within CODE delimiters, Thanks
                    Kind regards,
                    Carlo
                    (Stata 18.0 SE)

                    Comment


                    • #11
                      Code:
                      . reg eg i.size_k i.age_dum i.year i.country_num i.sector_num i.size_k##i.age_dum if age_dum!=., cluster(country_num)
                      note: 179.country_num omitted because of collinearity
                      note: 188.country_num omitted because of collinearity
                      note: 200.country_num omitted because of collinearity
                      note: 209.country_num omitted because of collinearity
                      note: 211.country_num omitted because of collinearity
                      note: 230.country_num omitted because of collinearity
                      note: 233.country_num omitted because of collinearity
                      note: 237.country_num omitted because of collinearity
                      
                      Linear regression                                      Number of obs =   61700
                                                                             F( 36,   111) =       .
                                                                             Prob > F      =       .
                                                                             R-squared     =  0.0688
                                                                             Root MSE      =  .16811
                      
                                                                                      (Std. Err. adjusted for 112 clusters in country_num)
                      --------------------------------------------------------------------------------------------------------------------
                                                                         |               Robust
                                                                      eg |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
                      ---------------------------------------------------+----------------------------------------------------------------
                                                                  size_k |
                                                          Medium(20-99)  |   .0034212   .0140741     0.24   0.808    -.0244676    .0313099
                                                    Large(100 and over)  |  -.0307564   .0188659    -1.63   0.106    -.0681404    .0066275
                                                                         |
                                                                 age_dum |
                                              Mid-age Firms (6-10years)  |   -.053392   .0130224    -4.10   0.000    -.0791969   -.0275872
                                                Mature Firms (11+years)  |   -.076172   .0206118    -3.70   0.000    -.1170157   -.0353284
                                                                         |
                                                                    year |
                                                                   2010  |   .0073426   .0053396     1.38   0.172    -.0032381    .0179232
                                                                   2011  |  -.1360537   .0065298   -20.84   0.000    -.1489928   -.1231145
                                                                   2012  |  -.0170113   .0040012    -4.25   0.000    -.0249399   -.0090827
                                                                   2013  |  -.0271925   .0048602    -5.59   0.000    -.0368234   -.0175616
                                                                   2014  |  -.0400436    .004304    -9.30   0.000    -.0485724   -.0315149
                                                                   2015  |   .0286996   .0070836     4.05   0.000      .014663    .0427363
                                                                   2016  |  -.0361149   .0060998    -5.92   0.000     -.048202   -.0240278
                                                                   2017  |   .1088204   .0068626    15.86   0.000     .0952218    .1224191
                                                                         |
                                                             country_num |
                                                          6. Angola2010  |   .0575781   .0037882    15.20   0.000     .0500716    .0650846
                                               7. Antiguaandbarbuda2010  |  -.0183307   .0064038    -2.86   0.005    -.0310203   -.0056412
                                                        10. Armenia2009  |   .0303054   .0038704     7.83   0.000      .022636    .0379748
                                                     13. Azerbaijan2013  |  -.0271405   .0008113   -33.45   0.000    -.0287482   -.0255328
                                                        14. Bahamas2010  |  -.0082289   .0061167    -1.35   0.181    -.0203496    .0038917
                                                     16. Bangladesh2013  |   .0215917   .0033379     6.47   0.000     .0149774    .0282061
                                                       17. Barbados2010  |  -.0320311     .00542    -5.91   0.000    -.0427712   -.0212909
                                                        19. Belarus2013  |   .0035934   .0010698     3.36   0.001     .0014735    .0057133
                                                         20. Belize2010  |  -.0126911   .0050723    -2.50   0.014    -.0227422   -.0026401
                                                          21. Benin2009  |   .0258311   .0069035     3.74   0.000     .0121513    .0395109
                                                         24. Bhutan2015  |   .0108221   .0058092     1.86   0.065    -.0006893    .0223335
                                                            26. Bih2013  |   .0039855   .0019036     2.09   0.039     .0002134    .0077577
                                                        29. Bolivia2017  |  -.0453606   .0041589   -10.91   0.000    -.0536018   -.0371194
                                                       31. Botswana2010  |   -.001303   .0027385    -0.48   0.635    -.0067295    .0041235
                                                         32. Brazil2009  |   .0319665   .0052881     6.05   0.000     .0214878    .0424453
                                                       35. Bulgaria2013  |   -.051065    .001697   -30.09   0.000    -.0544278   -.0477022
                                                    36. Burkinafaso2009  |   .0142456   .0042075     3.39   0.001     .0059081     .022583
                                                        38. Burundi2014  |   .0429111   .0062537     6.86   0.000     .0305189    .0553032
                                                       40. Cambodia2016  |   .0395046   .0030945    12.77   0.000     .0333727    .0456365
                                                       42. Cameroon2016  |    .064602   .0026282    24.58   0.000     .0593941      .06981
                                                      43. Capeverde2009  |  -.0022608   .0063184    -0.36   0.721    -.0147812    .0102596
                                         44. Centralafricanrepublic2011  |   .1914999   .0074841    25.59   0.000     .1766696    .2063301
                                                           45. Chad2009  |   .0653134   .0068049     9.60   0.000     .0518291    .0787977
                                                          47. Chile2010  |  -.0572089   .0037695   -15.18   0.000    -.0646785   -.0497393
                                                          48. China2012  |   .0433443   .0040747    10.64   0.000     .0352701    .0514185
                                                       50. Colombia2010  |  -.0285064   .0038056    -7.49   0.000    -.0360474   -.0209653
                                                          51. Congo2009  |   .0365993    .007122     5.14   0.000     .0224865    .0507121
                                                      52. Costarica2010  |  -.0272428   .0026884   -10.13   0.000      -.03257   -.0219156
                                                        54. Croatia2013  |   .0044778   .0011885     3.77   0.000     .0021226     .006833
                                                 57. Côte d'Ivoire2009  |   .0386955   .0036638    10.56   0.000     .0314354    .0459555
                                                       59. Djibouti2013  |   .0358921   .0005655    63.47   0.000     .0347715    .0370127
                                             61. Dominican Republic2010  |  -.0025694   .0031078    -0.83   0.410    -.0087278    .0035889
                                                            65. Drc2013  |   .0275857   .0026083    10.58   0.000     .0224172    .0327542
                                                        67. Ecuador2010  |   -.017476   .0029246    -5.98   0.000    -.0232714   -.0116807
                                                          70. Egypt2016  |  -.0191268   .0034696    -5.51   0.000     -.026002   -.0122516
                                                    72. El Salvador2010  |  -.0581503   .0029091   -19.99   0.000    -.0639148   -.0523858
                                                        74. Eritrea2009  |  -.0986808   .0070897   -13.92   0.000    -.1127295   -.0846322
                                                       78. Ethiopia2015  |  -.0093623   .0054471    -1.72   0.088    -.0201562    .0014316
                                                           79. Fiji2009  |   .0057425   .0078205     0.73   0.464    -.0097544    .0212394
                                                          80. Fyrom2009  |   .0340808   .0052105     6.54   0.000     .0237558    .0444057
                                                          82. Gabon2009  |    .074344   .0074464     9.98   0.000     .0595884    .0890995
                                                        85. Georgia2013  |   .0486941   .0021319    22.84   0.000     .0444696    .0529187
                                                          87. Ghana2013  |   .0320564   .0026154    12.26   0.000     .0268737    .0372391
                                                        88. Grenada2010  |   .0037471   .0067543     0.55   0.580    -.0096369    .0171311
                                                      90. Guatemala2010  |  -.0579288   .0024863   -23.30   0.000    -.0628557    -.053002
                                                         92. Guinea2016  |   .1056168   .0078238    13.50   0.000     .0901134    .1211202
                                                         94. Guyana2010  |  -.0062648   .0055753    -1.12   0.264    -.0173127    .0047831
                                                       96. Honduras2010  |  -.0629901   .0027055   -23.28   0.000    -.0683513    -.057629
                                                        98. Hungary2009  |  -.0262187   .0054231    -4.83   0.000    -.0369648   -.0154725
                                                         100. India2014  |   .0257313   .0047323     5.44   0.000      .016354    .0351086
                                                     101. Indonesia2009  |  -.0022592   .0049925    -0.45   0.652    -.0121521    .0076337
                                                       105. Jamaica2010  |  -.0350269   .0027631   -12.68   0.000    -.0405021   -.0295517
                                                        106. Jordan2013  |   .0092901   .0023927     3.88   0.000     .0045487    .0140314
                                                    108. Kazakhstan2013  |   .0098234   .0013402     7.33   0.000     .0071678     .012479
                                                         110. Kenya2013  |  -.0021744   .0022785    -0.95   0.342    -.0066895    .0023406
                                                        111. Kosovo2009  |   .0015746   .0049901     0.32   0.753    -.0083135    .0114627
                                                    114. Kyrgyzstan2013  |  -.0303036    .001447   -20.94   0.000    -.0331709   -.0274363
                                                       115. Lao PDR2009  |  -.0535652   .0031928   -16.78   0.000     -.059892   -.0472384
                                                       122. Lesotho2016  |   .0287063   .0053811     5.33   0.000     .0180434    .0393692
                                                       123. Liberia2009  |   .0218449   .0062164     3.51   0.001     .0095267     .034163
                                                      131. Malaysia2015  |   -.039885   .0053414    -7.47   0.000    -.0504693   -.0293007
                                                          133. Mali2010  |  -.0263993    .002904    -9.09   0.000    -.0321539   -.0206448
                                                    136. Mauritania2014  |   .0549315   .0068122     8.06   0.000     .0414326    .0684304
                                                     137. Mauritius2009  |   .0372199   .0038414     9.69   0.000     .0296079    .0448319
                                                        139. Mexico2010  |  -.0421678   .0020788   -20.28   0.000     -.046287   -.0380485
                                                       142. Moldova2013  |  -.0163294   .0012021   -13.58   0.000    -.0187115   -.0139473
                                                      144. Mongolia2013  |   .0321721   .0011668    27.57   0.000       .02986    .0344842
                                                    146. Montenegro2013  |   -.004597   .0014253    -3.23   0.002    -.0074212   -.0017727
                                                       147. Morocco2013  |    .019179   .0025941     7.39   0.000     .0140386    .0243194
                                                       149. Myanmar2014  |   .0285418     .00422     6.76   0.000     .0201796     .036904
                                                       152. Namibia2014  |   .0258852   .0029954     8.64   0.000     .0199497    .0318208
                                                         153. Nepal2009  |   .0160132   .0042284     3.79   0.000     .0076342    .0243921
                                                     157. Nicaragua2016  |   .0305665   .0024165    12.65   0.000     .0257781     .035355
                                                         158. Niger2009  |   .0498457   .0065178     7.65   0.000     .0369301    .0627612
                                                       161. Nigeria2014  |   .1154552   .0048078    24.01   0.000     .1059283    .1249822
                                                      163. Pakistan2013  |   .0351779   .0031406    11.20   0.000     .0289546    .0414011
                                                          171. Peru2010  |   .0289103    .003719     7.77   0.000     .0215408    .0362799
                                                        175. Poland2013  |    -.01472   .0020824    -7.07   0.000    -.0188464   -.0105936
                                                       177. Romania2013  |   -.003072    .001545    -1.99   0.049    -.0061336   -.0000104
                                                        179. Russia2012  |          0  (omitted)
                                                        181. Rwanda2011  |   .1452669    .008316    17.47   0.000     .1287881    .1617457
                                                         182. Samoa2009  |    .021916   .0082311     2.66   0.009     .0056056    .0382264
                                                       184. Senegal2014  |   .0427418   .0050847     8.41   0.000     .0326661    .0528175
                                                        186. Serbia2013  |  -.0290319   .0011821   -24.56   0.000    -.0313743   -.0266896
                                                  188. Sierra Leone2017  |          0  (omitted)
                                               193. Solomon Islands2015  |   .0379943   .0025661    14.81   0.000     .0329093    .0430793
                                                      196. SriLanka2011  |   .1113591   .0022255    50.04   0.000      .106949    .1157691
                                               197. Stkittsandnevis2010  |  -.0055203   .0065729    -0.84   0.403    -.0185449    .0075042
                                                       198. Stlucia2010  |  -.0238592   .0055929    -4.27   0.000    -.0349419   -.0127765
                                     199. Stvincentandthegrenadines2010  |   -.005507   .0057977    -0.95   0.344    -.0169956    .0059816
                                                         200. Sudan2014  |          0  (omitted)
                                                      201. Suriname2010  |  -.0073986   .0050508    -1.46   0.146     -.017407    .0026099
                                                     203. Swaziland2016  |   .0718659   .0052619    13.66   0.000     .0614392    .0822927
                                                    206. Tajikistan2013  |   .0214378   .0019015    11.27   0.000     .0176699    .0252057
                                                      208. Tanzania2013  |    .062372   .0029641    21.04   0.000     .0564983    .0682456
                                                      209. Thailand2016  |          0  (omitted)
                                                   211. Timor-Leste2015  |          0  (omitted)
                                                          212. Togo2009  |   .0155097   .0072371     2.14   0.034     .0011689    .0298506
                                             215. Trinidadandtobago2010  |  -.0106753   .0028661    -3.72   0.000    -.0163546   -.0049961
                                                       216. Tunisia2013  |  -.0073223   .0027728    -2.64   0.009    -.0128167   -.0018278
                                                        218. Turkey2013  |   .0421697   .0026061    16.18   0.000     .0370056    .0473338
                                                        220. Uganda2013  |  -.0153361   .0023245    -6.60   0.000    -.0199423   -.0107299
                                                       222. Ukraine2013  |  -.0551574   .0028281   -19.50   0.000    -.0607615   -.0495533
                                                       224. Uruguay2010  |  -.0070428   .0023387    -3.01   0.003    -.0116772   -.0024085
                                                    226. Uzbekistan2013  |   -.010429   .0021455    -4.86   0.000    -.0146805   -.0061776
                                                     229. Venezuela2010  |  -.0318256   .0027768   -11.46   0.000    -.0373281   -.0263231
                                                       230. Vietnam2009  |          0  (omitted)
                                            232. West Bank And Gaza2013  |   .0563602   .0010036    56.16   0.000     .0543716    .0583488
                                                         233. Yemen2010  |          0  (omitted)
                                                        236. Zambia2013  |   .0195371   .0027311     7.15   0.000     .0141254    .0249489
                                                      237. Zimbabwe2011  |          0  (omitted)
                                                                         |
                                                              sector_num |
                      Basic Metals/Fabricated Metals/Machinery & Equip.  |   .0059711   .0138319     0.43   0.667    -.0214377    .0333798
                                          Chemicals & Chemical Products  |   .0033843   .0042852     0.79   0.431     -.005107    .0118756
                                           Chemicals, Plastics & Rubber  |   .0247956    .003858     6.43   0.000     .0171506    .0324406
                                                           Construction  |   .0183036   .0038103     4.80   0.000     .0107533    .0258539
                                    Electronics & Communications Equip.  |  -.0034653   .0025474    -1.36   0.176    -.0085131    .0015825
                                              Fabricated Metal Products  |   .0044037     .00543     0.81   0.419    -.0063563    .0151637
                                                                   Food  |   .0082984   .0041629     1.99   0.049     .0000494    .0165475
                                                              Furniture  |   .0026005   .0055857     0.47   0.642    -.0084679    .0136689
                                                               Garments  |   .0009185   .0077686     0.12   0.906    -.0144755    .0163124
                                                  Hospitality & Tourism  |  -.0020749   .0109696    -0.19   0.850    -.0238119    .0196621
                                                   Hotels & Restaurants  |  -.0078607   .0094131    -0.84   0.405    -.0265133     .010792
                                                       IT & IT Services  |   .0299706   .0031525     9.51   0.000     .0237237    .0362175
                                                       Leather Products  |  -.0218102   .0140604    -1.55   0.124    -.0496718    .0060514
                                                  Machinery & Equipment  |   .0090903   .0025941     3.50   0.001     .0039499    .0142308
                          Machinery & Equipment, Electronics & Vehicles  |   .0319791   .0035463     9.02   0.000     .0249519    .0390063
                                                          Manufacturing  |   .0008294   .0047178     0.18   0.861    -.0085192     .010178
                                                    Manufacturing Panel  |  -.0053162   .0019859    -2.68   0.009    -.0092513   -.0013811
                                Minerals, Metals, Machinery & Equipment  |  -.0424255   .0035514   -11.95   0.000    -.0494627   -.0353882
                                                         Motor Vehicles  |  -.0002762   .0028149    -0.10   0.922    -.0058542    .0053018
                                      Motor Vehicles & Transport Equip.  |  -.0038993   .0092528    -0.42   0.674    -.0222343    .0144356
                                          Non-Metallic Mineral Products  |  -.0008028   .0062513    -0.13   0.898    -.0131901    .0115846
                                                    Other Manufacturing  |   .0049926   .0029853     1.67   0.097     -.000923    .0109082
                                                         Other Services  |   .0086354   .0035836     2.41   0.018     .0015342    .0157366
                                                   Other Services Panel  |   -.058822   .0020567   -28.60   0.000    -.0628975   -.0547465
                                  Petroleum products, Plastics & Rubber  |   .0250946   .0028186     8.90   0.000     .0195094    .0306798
                                                  Printing & Publishing  |   .0568329   .0019413    29.28   0.000      .052986    .0606798
                                                                 Retail  |    .024539   .0047168     5.20   0.000     .0151924    .0338856
                                                           Retail Panel  |  -.0146747   .0022043    -6.66   0.000    -.0190426   -.0103068
                                             Rubber & Plastics Products  |   .0015179   .0049788     0.30   0.761    -.0083479    .0113838
                                                               Services  |    .002405    .008345     0.29   0.774    -.0141312    .0189411
                                             Services of Motor Vehicles  |   .0043811   .0036616     1.20   0.234    -.0028745    .0116368
                            Services of Motor Vehicles/Wholesale/Retail  |   .0268123   .0026516    10.11   0.000     .0215578    .0320667
                                                               Textiles  |   .0029206   .0038214     0.76   0.446    -.0046517     .010493
                                                    Textiles & Garments  |  -.0279024    .009596    -2.91   0.004    -.0469174   -.0088874
                                                                Tourism  |  -.0140933   .0025116    -5.61   0.000    -.0190701   -.0091164
                                                              Transport  |   .0020214   .0183886     0.11   0.913    -.0344169    .0384597
                                   Transport, Storage, & Communications  |   .0039119   .0089148     0.44   0.662    -.0137533    .0215771
                                                              Wholesale  |   .0396491   .0096247     4.12   0.000     .0205771    .0587211
                                              Wood Products & Furniture  |  -.0068934   .0035294    -1.95   0.053    -.0138871    .0001003
                           Wood products, Furniture, Paper & Publishing  |   .0050564   .0024255     2.08   0.039     .0002502    .0098627
                                                                         |
                                                          size_k#age_dum |
                                Medium(20-99)#Mid-age Firms (6-10years)  |  -.0026519   .0124756    -0.21   0.832    -.0273732    .0220694
                                  Medium(20-99)#Mature Firms (11+years)  |  -.0131793   .0142904    -0.92   0.358    -.0414968    .0151381
                          Large(100 and over)#Mid-age Firms (6-10years)  |   .0049214   .0151592     0.32   0.746    -.0251176    .0349604
                            Large(100 and over)#Mature Firms (11+years)  |  -.0059369   .0186498    -0.32   0.751    -.0428928     .031019
                                                                         |
                                                                   _cons |   .1280257   .0169784     7.54   0.000      .094382    .1616695
                      --------------------------------------------------------------------------------------------------------------------

                      Comment


                      • #12
                        John:
                        some comments about your code (thank you for using CODE delimiters):
                        - -size_k#age_dum- results show no evidence of interaction between firm size and age: I would rule interaction out from the right-hand side of the regression equation;
                        - you have too many predictors (and this explains the missing F): I would be really worried about the best way to disseminate your results. I would go for a more parsimonious model;
                        - eventually, I would jointly -testparm- -sector_num- predictor and see whether it makes sense to keep it among the independent variables of the regression model.
                        Kind regards,
                        Carlo
                        (Stata 18.0 SE)

                        Comment


                        • #13
                          Thank you for your answer Carlo.
                          I am just using some trials on this model.
                          In general, how I interpret interaction effects based on the result I posted; because I am bit confused with this ...

                          Comment


                          • #14
                            John:
                            provided that I'm still pretty skeptical about the relevance of your interactions, let's assume that you want to explain the result concerning
                            Code:
                            Medium(20-99)#Mid-age Firms (6-10years)
                            :
                            - when adjusted for the remaining predictors, being a medium firm which operates from 6 to 10 years, reduces the dependent variable by -.0026519.
                            Kind regards,
                            Carlo
                            (Stata 18.0 SE)

                            Comment


                            • #15
                              You asked Carlo to write the svyset command from information in your attachment. Except for the firm identifier, none of the names of the variables in that document appear refer to survey design variables (such as weights) needed for svyset

                              The needed variables are probably in the original data set and should be described in the study codebook or data dictionary. Before advising how to svyset we need description of the sampling design, as described in the study . If possible, provide a link to the information; otherwise copy and paste into a post.

                              Also, study the Introduction to survey commands, in the Survey Manual. This starts on page 2 in the Stata 14 manual.
                              Last edited by Steve Samuels; 16 Jan 2018, 14:56.
                              Steve Samuels
                              Statistical Consulting
                              [email protected]

                              Stata 14.2

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