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  • Synthetic control command for multiple treatment units

    Hi,

    I am trying to use the synthetic control method to run a difference in difference model.

    I know that the synth command only works when there is only one treatment unit but I have multiple treatment units and I am struggling to combine them into one, without obtaining the error "repeated time values within panel - r(451)" when running the xtset command.

    Each observation in my dataset represents an individual, each labelled id (so each observation has a different value for id).
    Currently I have used xtset id month.

    The treatment affected two regions in which there are thousands of id observations so there are no unique combinations of region and month, since several observations of id have the same region and month values.
    This means that I cannot run the command "xtset region month" without obtaining the error "repeated time values within panel - r(451)".

    How can I collate my observations together such that I can set a valid panel identifier, and then run the synth command with only one treatment unit?

    I hope this makes sense. Any help would be hugely appreciated!

    Many thanks.

  • #2
    Hey Riley. I'm Jared. I'm an econometrician who studies SCM, and I'm the author of the scul command for Stata. So I can best help you, please, do give an example of your dataset so I can see what the issue is.

    You need to have one observation per time period per unit of analysis for this to work. That is, ever single treated unit NEEDS a unique ID. So, when you show me your dataset using dataex, I'll look and see what the issue is.

    Comment


    • #3
      Hi Jared,

      Thank you very much for getting in touch.

      This is my first time using dataex but I have tried to show a representative sample of my dataset below. I have shown some observations in a non-affected region before and after the treatment date (region 1), and some observations in an affected region (region 11) before and after the treatment date.
      The month variable is a number - I hope this doesn't confuse things too much. id identifies each observation.
      The treatment occurred in month 1293 in regions 11 and 15 (only region 11 is shown here).
      The whole dataset contains 24 regions and 70 months. Although not every region has observations in every month.
      I have used xtset id month to identify the panel dataset.


      Code:
      * Example generated by -dataex-. For more info, type help dataex
      clear
      input float id byte region int month float Treatment
      1098 1 1292 0
      1099 1 1292 0
      1100 1 1292 0
      1101 1 1292 0
      1102 1 1292 0
      1103 1 1292 0
      1104 1 1292 0
      1105 1 1292 0
      1106 1 1292 0
      1107 1 1292 0
      1108 1 1292 0
      1109 1 1292 0
      1110 1 1292 0
      1111 1 1292 0
      1112 1 1292 0
      1113 1 1292 0
      1114 1 1292 0
      1115 1 1292 0
      1116 1 1292 0
      1117 1 1292 0
      1118 1 1292 0
      1119 1 1292 0
      1120 1 1292 0
      1121 1 1292 0
      1122 1 1292 0
      259 1 1300 0
      260 1 1300 0
      261 1 1300 0
      262 1 1300 0
      263 1 1300 0
      264 1 1300 0
      265 1 1300 0
      266 1 1300 0
      267 1 1300 0
      268 1 1300 0
      269 1 1300 0
      270 1 1300 0
      271 1 1300 0
      272 1 1300 0
      273 1 1300 0
      274 1 1300 0
      275 1 1300 0
      276 1 1300 0
      277 1 1300 0
      278 1 1300 0
      279 1 1300 0
      280 1 1300 0
      281 1 1300 0
      282 1 1300 0
      283 1 1300 0
      48350 11 1290 0
      48355 11 1290 0
      48356 11 1290 0
      48357 11 1290 0
      48358 11 1290 0
      48359 11 1290 0
      48360 11 1290 0
      48361 11 1290 0
      48362 11 1290 0
      48363 11 1290 0
      48364 11 1290 0
      48365 11 1290 0
      48366 11 1290 0
      48367 11 1290 0
      48368 11 1290 0
      48369 11 1290 0
      48370 11 1290 0
      48371 11 1290 0
      48372 11 1290 0
      48373 11 1290 0
      48374 11 1290 0
      48375 11 1290 0
      48376 11 1290 0
      48377 11 1290 0
      48378 11 1290 0
      48034 11 1300 1
      48035 11 1300 1
      48036 11 1300 1
      48037 11 1300 1
      48038 11 1300 1
      48039 11 1300 1
      48040 11 1300 1
      48041 11 1300 1
      48042 11 1300 1
      48043 11 1300 1
      48044 11 1300 1
      48045 11 1300 1
      48046 11 1300 1
      48047 11 1300 1
      48048 11 1300 1
      48049 11 1300 1
      48050 11 1300 1
      48051 11 1300 1
      48052 11 1300 1
      48053 11 1300 1
      48054 11 1300 1
      48055 11 1300 1
      48056 11 1300 1
      48057 11 1300 1
      48058 11 1300 1
      end
      Any advice would be greatly appreciated! And let me know if I can provide any more data.

      To add, since my first post I have learnt a bit about the sdid command. Would synth or sdid be more appropriate here?

      Thank you

      Comment


      • #4
        Okay so this is odd for a few reasons.
        Code:
        clear *
        
        * Example generated by -dataex-. For more info, type help dataex
        clear
        input float id byte region int month float Treatment
        1098 1 1292 0
        1099 1 1292 0
        1100 1 1292 0
        1101 1 1292 0
        1102 1 1292 0
        1103 1 1292 0
        1104 1 1292 0
        1105 1 1292 0
        1106 1 1292 0
        1107 1 1292 0
        1108 1 1292 0
        1109 1 1292 0
        1110 1 1292 0
        1111 1 1292 0
        1112 1 1292 0
        1113 1 1292 0
        1114 1 1292 0
        1115 1 1292 0
        1116 1 1292 0
        1117 1 1292 0
        1118 1 1292 0
        1119 1 1292 0
        1120 1 1292 0
        1121 1 1292 0
        1122 1 1292 0
        259 1 1300 0
        260 1 1300 0
        261 1 1300 0
        262 1 1300 0
        263 1 1300 0
        264 1 1300 0
        265 1 1300 0
        266 1 1300 0
        267 1 1300 0
        268 1 1300 0
        269 1 1300 0
        270 1 1300 0
        271 1 1300 0
        272 1 1300 0
        273 1 1300 0
        274 1 1300 0
        275 1 1300 0
        276 1 1300 0
        277 1 1300 0
        278 1 1300 0
        279 1 1300 0
        280 1 1300 0
        281 1 1300 0
        282 1 1300 0
        283 1 1300 0
        48350 11 1290 0
        48355 11 1290 0
        48356 11 1290 0
        48357 11 1290 0
        48358 11 1290 0
        48359 11 1290 0
        48360 11 1290 0
        48361 11 1290 0
        48362 11 1290 0
        48363 11 1290 0
        48364 11 1290 0
        48365 11 1290 0
        48366 11 1290 0
        48367 11 1290 0
        48368 11 1290 0
        48369 11 1290 0
        48370 11 1290 0
        48371 11 1290 0
        48372 11 1290 0
        48373 11 1290 0
        48374 11 1290 0
        48375 11 1290 0
        48376 11 1290 0
        48377 11 1290 0
        48378 11 1290 0
        48034 11 1300 1
        48035 11 1300 1
        48036 11 1300 1
        48037 11 1300 1
        48038 11 1300 1
        48039 11 1300 1
        48040 11 1300 1
        48041 11 1300 1
        48042 11 1300 1
        48043 11 1300 1
        48044 11 1300 1
        48045 11 1300 1
        48046 11 1300 1
        48047 11 1300 1
        48048 11 1300 1
        48049 11 1300 1
        48050 11 1300 1
        48051 11 1300 1
        48052 11 1300 1
        48053 11 1300 1
        48054 11 1300 1
        48055 11 1300 1
        48056 11 1300 1
        48057 11 1300 1
        48058 11 1300 1
        end
        bys id: g obs = _N
        xtset id month
        br
        Some IDs (in fact, all of them) are observed only once for a given time period. So, this means you can't use id as the panel id. What you'll need to do is use the collapse command to take an average (or sum, depending) and get that for your actual treatment unit, region 11. To give a concrete example of how to do this with real data, consider the following:
        Code:
        version 17
        
        
        
        clear *
        
        frame rename default population
        
        mkf covid
        
        
        import delimited "https://www2.census.gov/programs-surveys/popest/datasets/2020-2021/counties/totals/co-est2021-alldata.csv", clear
        
        keep region division state county stname ctyname popestimate2021
        
        rename (state county popestimate2021) (sid cid pop)
        keep if cid==0
        collapse (sum) pop, by(div)
        br
        Here, I just summed up all the people living in each state in each division in the United States for the year 2021 by using collapse. You could do the average by doing (not that this makes sense)
        Code:
        version 17
        
        
        
        clear *
        
        frame rename default population
        
        mkf covid
        
        
        import delimited "https://www2.census.gov/programs-surveys/popest/datasets/2020-2021/counties/totals/co-est2021-alldata.csv", clear
        
        keep region division state county stname ctyname popestimate2021
        
        rename (state county popestimate2021) (sid cid pop)
        keep if cid==0
        collapse (mean) pop, by(div)
        br
        You can use sdid if you want to. Or my colleague's allsynth which is also suited for multiple treated units. So in the end you'll have 2 treated units and 22 donor units. Which, all things considered, isn't too bad for the classic SCM design. What's your question, out of curiosity? What causal question are you trying to study here?

        Comment


        • #5
          Hi Jared,

          Thank you for your advice. I understand why id cannot be used as the panel identifier now.

          I am looking at the effect of the Peruvian earthquake of 2007 on several different micro dependent variables. One variable I am looking at is whether someone has a bank account. My dataset includes a binary variable for whether individuals have a bank account of not.

          Am I right in thinking that for the difference-in-difference approach I need to collapse the data by month as well as region? I did this using the code:
          collapse BankAccount Treatment, by(region month)

          When I run the synth command after doing this the error I receive is: control units: for 13 of out 14 units outcome variable BankAccount is missing in 1261 pre-intervention MSPE period - check mspeperiod()
          invalid syntax
          r(198);

          When I included an mspeperiod() term in my synth command I received the error: at least one time period specified in mspeperiod() not found in timevar
          r(198);

          So I don't really know how to progress from here. Could you advise please?

          I have a suspicion that the issue may be related to there being an unequal number of regions observed in each time period. See the tab results below (using the data collapsed by time and month). I may be wrong, of course: I'm not sure whether this is an important problem or if it doesn't matter.
          tab month

          date of |
          interview |
          (cmc) | Freq. Percent Cum.
          ------------+-----------------------------------
          1261 | 2 0.60 0.60
          1262 | 8 2.41 3.01
          1263 | 10 3.01 6.02
          1264 | 11 3.31 9.34
          1265 | 10 3.01 12.35
          1266 | 9 2.71 15.06
          1267 | 8 2.41 17.47
          1268 | 9 2.71 20.18
          1269 | 9 2.71 22.89
          1273 | 4 1.20 24.10
          1274 | 12 3.61 27.71
          1275 | 12 3.61 31.33
          1276 | 7 2.11 33.43
          1277 | 10 3.01 36.45
          1278 | 8 2.41 38.86
          1279 | 6 1.81 40.66
          1280 | 10 3.01 43.67
          1281 | 9 2.71 46.39
          1285 | 4 1.20 47.59
          1286 | 7 2.11 49.70
          1287 | 15 4.52 54.22
          1288 | 6 1.81 56.02
          1289 | 8 2.41 58.43
          1290 | 10 3.01 61.45
          1291 | 9 2.71 64.16
          1292 | 12 3.61 67.77
          1293 | 10 3.01 70.78
          1294 | 12 3.61 74.40
          1297 | 1 0.30 74.70
          1298 | 5 1.51 76.20
          1299 | 7 2.11 78.31
          1300 | 15 4.52 82.83
          1301 | 9 2.71 85.54
          1302 | 8 2.41 87.95
          1303 | 6 1.81 89.76
          1304 | 10 3.01 92.77
          1305 | 13 3.92 96.69
          1306 | 7 2.11 98.80
          1307 | 4 1.20 100.00
          ------------+-----------------------------------
          Total | 332 100.00

          Many thanks!

          Comment


          • #6
            Show me what Stata says when you do
            Code:
            xtset

            Comment


            • #7
              xtset

              Panel variable: region (unbalanced)
              Time variable: month, 1261 to 1307, but with gaps
              Delta: 1 unit

              Comment


              • #8
                Hi Jared,

                After some experimenting I have derived some code that gives me an output. My code is:

                synth BankAccount Wealth BankAccount(1264) BankAccount(1290) Insurance, trunit(0) trperiod(1293) counit (4 6 10 12 16 17 18 20 22 23) mspeperiod(1264 1290 1293 1300) resultsperiod(1264 1290 1293 1300) figure

                I chose these periods because they are the only ones for which there are observations for all the regions in the control unit. If I dropped more regions from my control unit I could use more time periods.

                Is this a valid regression for moving forward with my work? I'd just like to double check I'm not doing something completely wrong before using these results!

                My main hesitation is that for some figures that I generate with synth, the control line and the treatment line are not aligned in the pre-treatment period. In some cases they are completely separate and not even parallel, and in other cases they are initially together but then diverge before the treatment period.

                Is there any way to overcome this? Or is there any interpretation of these results?

                Finally, is there a way of getting numerical results from the synth command, rather than just a graph?

                Many thanks for all your help. It's greatly appreciated!

                Riley

                Comment


                • #9
                  Hey Riley. Can you show me your results dataset? That is run your SCM, and then show me the saved dataset using dataex. I have a few ideas about what's wrong, but, just to be sure, I wanna see what you got first. The way you do this is

                  Code:
                  synth y x, .... keep(results, replace).
                  this'll save a dataset called "results" of the graph. When you open it, you can use dataex to show all the results you got

                  Comment


                  • #10
                    In fact, could also you show me your data (your main dataset) when you do
                    Code:
                    dataex, count(970)

                    Comment


                    • #11
                      Hi,

                      Thanks for getting back to me.

                      Here is the results dataset you requested in your first message when running the synth command with BankAccount as the y variable:
                      Code:
                      * Example generated by -dataex-. For more info, type help dataex
                      clear
                      input float(_Co_Number _W_Weight) double(_Y_treated _Y_synthetic _time)
                       4  .21  .3899204134941101 .38982811587303867 1264
                       6 .058  .2609523832798004 .26076808803528545 1290
                      10 .033 .40799999237060547 .36239077445492146 1293
                      12 .431  .5483092069625854  .3808997351005674 1300
                      16 .055                  .                  .    .
                      17 .031                  .                  .    .
                      18 .057                  .                  .    .
                      20 .015                  .                  .    .
                      22 .048                  .                  .    .
                      23 .062                  .                  .    .
                      end
                      label values _Co_Number hv024
                      label def hv024 4 "arequipa", modify
                      label def hv024 6 "cajamarca", modify
                      label def hv024 10 "huanuco", modify
                      label def hv024 12 "junin", modify
                      label def hv024 16 "loreto", modify
                      label def hv024 17 "madre de dios", modify
                      label def hv024 18 "moquegua", modify
                      label def hv024 20 "piura", modify
                      label def hv024 22 "san martin", modify
                      label def hv024 23 "tacna", modify
                      Here is a portion of the dataset you requested in your second message:
                      Code:
                      * Example generated by -dataex-. For more info, type help dataex
                      clear
                      input int month byte region float(WealthIndex BankAccount sh09a sh2802 agriculture sh28bb hv109 hv206 sh25h hv104 hv110 earthquake time Treatment)
                      1237  0         5          .  .5555556        .         .         .  3.888889          1          . 1.2222222 .11111111 1 0 0
                      1248  0  4.178082          .  .3082192        .         .         .  2.691781          1          .  1.589041  .2328767 1 0 0
                      1249  0 4.1055098          . .43962485        .         .         .   3.12544    .975381          .  1.511137 .24736226 1 0 0
                      1250  0  4.237864          .   .592233        .         .         . 3.1262136          1          .  1.514563 .26213592 1 0 0
                      1251  0  4.234177          .  .4541139        .         .         .  3.164557          1          .  1.528481 .26898733 1 0 0
                      1252  0 3.8653295          .  .4040115        .         .         . 2.8825214   .9641834          . 1.4770774   .239255 1 0 0
                      1253  0 4.2093024          .  .3209302        .         .         .  3.116279          1          . 1.5023255  .2883721 1 0 0
                      1254  0  4.124629          .  .3946587        .         .         .  3.148368   .9777448          .  1.519288  .2240356 1 0 0
                      1255  0 2.5490196          . .39750445        .         .         . 2.3297682   .4723708          .  1.467023  .2798574 1 0 0
                      1261  0 3.6795454  .25454545  .4181818        .  35.14706  67.07353     2.575   .8840909         .4 1.4886364 .28863636 1 0 0
                      1262  0 4.0136986    .216895  .3493151        .  20.11111 33.037037  2.926941   .9543379  .19607843 1.4726027 .29223743 1 0 0
                      1263  0  3.296178   .3089172  .3598726        . 24.157894 25.210526  2.503185   .7738854  .29787233 1.4394904 .22611465 1 0 0
                      1264  0  4.132626   .3899204  .4270557        .  6.111111  37.77778  2.734748   .9230769  .24324325  1.469496 .24403183 1 0 0
                      1265  0 4.1691976   .3882863  .3904555        . 27.859156  44.11267 3.0498915   .8850325  .25742576 1.5097613  .2516269 1 0 0
                      1266  0  3.822335     .35533    .35533        . 27.419754  24.93827 2.8375635   .8426396   .7419355  1.461929 .19796954 1 0 0
                      1267  0  3.878412  .26799008  .3101737        .  3.107143  9.107142  2.756824          1   .1923077  1.508685 .27543426 1 0 0
                      1268  0  4.887574   .8106509  .7633136        .         2        20 4.0532546          1   .6068376 1.4970415 .18343195 1 0 0
                      1269  0   3.88412   .3690987  .4334764        .  11.07595  18.56962  2.693133   .9806867  .35820895  1.476395  .2532189 1 0 0
                      1273  0  4.320802   .4235589  .3659148 .9398496  22.77778 33.703705  3.243108          1  .29268292 1.5213033 .23809524 1 0 0
                      1274  0  3.972896    .318117 .35520685 .9172611 14.537635  22.87097 2.9800286   .9443652  .17419355 1.4935806  .2467903 1 0 0
                      1275  0 3.7258065 .032258064  .2580645        1         .         . 2.2258065    .983871          1 1.6451613  .3064516 1 0 0
                      1276  0  3.213115  .29508197   .442623        1        11        10  2.327869          1          0   1.47541 .27868852 1 0 0
                      1277  0  3.632653   .3241296  .3985594 .9339736 20.956043 17.972527  2.609844   .9231693   .2826087 1.4765906 .29411766 1 0 0
                      1278  0 4.3126683   .4986523  .4743935 .9838275         5        50 3.2156334   .9919137   .7563025  1.490566  .2398922 1 0 0
                      1279  0  4.124105  .39379475  .3675418 .9045346 27.954546 37.954544 3.0167065   .9952267   .2469136 1.4868735 .25775656 1 0 0
                      1280  0 4.1873903  .56742555  .4588441 .9807355  16.90909  8.181818  3.190893   .9544659   .4924623 1.5218915 .25218913 1 0 0
                      1281  0  2.074074          0  .2962963        1 22.266666 10.333333 1.8148148   .1234568          .  1.506173 .25925925 1 0 0
                      1286  0 4.3079586   .3217993  .2802768 .9273356 18.666666  87.22222  2.851211          1  .24561404 1.4290657 .29757786 1 0 0
                      1287  0  4.628118  .53741497 .50566894 .8866213  5.944445  17.36111  3.240363          1   .6564417 1.4852607  .2063492 1 0 0
                      1288  0  4.237942   .4115756 .34405145 .8424437  32.67857         5  2.797428          1    .479452 1.4855305 .23151125 1 0 0
                      1289  0  4.477041   .4566326  .3137755 .9209183         3        30  2.765306    .997449   .4901961  1.492347 .28061223 1 0 0
                      1290  0  3.575238  .26095238 .51428574 .9180952  9.481482  16.37963  2.544762   .9219047  .16949153  1.472381  .3085714 1 0 0
                      1291  0 3.9333334         .2        .3        1        65         5  2.833333          1          . 1.4333333 .26666668 1 0 0
                      1292  0 1.3703704          0  .2962963        1 26.045454 10.272727 1.4074074  .25925925          . 1.5185186  .2962963 1 0 0
                      1293  0     4.228       .408      .488     .926  8.563637 18.272728     3.154       .962   .5923077     1.458       .23 1 1 1
                      1294  0 4.3771186   .3983051 .37711865 .8983051 1.0909091 10.045455 2.8008475   .9872881   .6222222 1.5254238  .2584746 1 1 1
                      1299  0  3.833333         .5        .5        1         .         .  3.083333   .9166667         .5 1.4583334 .16666667 1 1 1
                      1300  0 4.0368357   .5483092  .4269324 .8967391  21.26887 32.867924 2.9885266   .9432367   .4987593 1.4746376 .24577294 1 1 1
                      1301  0  4.207858   .4359949  .4448669 .8897339    24.972    54.296         3    .887199     .69437 1.4771863 .24081115 1 1 1
                      1302  0  3.956564   .4517375  .4372587 .9305019 12.186666  39.33333  2.777027   .9324324   .5504587 1.4681467 .28088802 1 1 1
                      1303  0  4.620155  .53229976  .5503876 .9276486 1.4848485        10  3.124031   .9922481   .2857143 1.4754522 .20413436 1 1 1
                      1304  0 4.0761013   .5634179   .552737 .9025367  20.02273  49.61364   2.94526   .9599466   .5117371   1.52737   .246996 1 1 1
                      1305  0 4.5433526   .5346821  .4855491 .8453757 21.197184 134.50705  3.046243   .9985549   .8317757 1.4768786 .25722542 1 1 1
                      1306  0 3.6533334   .4773333      .544 .7946666 16.044777  19.71642 2.2373333   .9706666  .23255815 1.5253333 .29066667 1 1 1
                      1307  0       3.8          1        .9        1         .         .       3.2          1          1       1.5        .2 1 1 1
                      1241  1         4          .       .25        .         .         .         2          1          .       1.5       .25 0 0 0
                      1250  1  2.064838          .  .3017456        .         .         . 1.5885286   .7406484          . 1.4613466 .26184538 0 0 0
                      1253  1 2.2876713          . .29223743        .         .         .  1.869863   .5730594          . 1.4657534 .28538814 0 0 0
                      1256  1 1.4671533          .  .4209246        .         .         .  1.347932  .29683697          . 1.4768857  .2725061 0 0 0
                      1262  1  1.586207  .03448276  .3793103        .  33.13214  47.19643 1.3068966          0          .  1.410345        .3 0 0 0
                      1263  1 3.3728814  .40677965  .4745763        .        11      59.4  2.576271   .8813559          0 1.5254238  .3389831 0 0 0
                      1264  1         4          1         1        .        17        20 4.3333335          1          . 1.3333334  .3333333 0 0 0
                      1265  1         2  .08695652  .3478261        .   26.5914 26.903225 1.6086956         .4          . 1.4086957 .26956522 0 0 0
                      1266  1  3.011364   .4318182 .27272728        . 24.507246        60 2.0113637          1          0 1.5113636  .1818182 0 0 0
                      1267  1       2.5   .2012195 .35060975        . 31.596775  56.60215 2.0853658   .7439024          0  1.429878 .27743903 0 0 0
                      1268  1         1  .05660377  .5974843        . 19.303226  25.23226 1.2201258          0          . 1.4591194  .3773585 0 0 0
                      1274  1   2.07722  .12741312  .4749035 .9536679  41.32544   147.071 1.8301158  .45945945          0 1.5019305 .33976835 0 0 0
                      1275  1 1.4621212          0  .4772727 .7954546   47.2439 16.707317       1.5 .007575758          . 1.4848485       .25 0 0 0
                      1276  1 2.0585682  .03904555 .19088937  .856833  43.42274 130.37318 1.6312364   .4446855          . 1.4663775  .2537961 0 0 0
                      1277  1         3          0         1        1        50        30         5          1          .         1         0 0 0 0
                      1280  1  2.019802   .1881188  .4009901 .8663366  24.12766  48.70821 1.8069307  .26980197  .23809524 1.4777228 .27227724 0 0 0
                      1286  1      4.25        .25        .5        1         .         .      2.25       .625          .       1.5         0 0 0 0
                      1287  1  4.295082   .6721311  .4590164 .8032787  43.85185 124.07407  2.934426          1   .4117647 1.4918033  .2295082 0 0 0
                      1288  1 1.9736842          0  .2105263        1  48.05263 35.736843 1.5921053  .36842105          . 1.4342105 .27631578 0 0 0
                      1289  1  2.172794  .04227941  .3621323 .9191176  24.50378  38.71033 1.3419118  .49264705          . 1.5018382       .25 0 0 0
                      1292  1  2.513859  .19189766  .3539446 .8294243  28.77027  62.67905 1.8059702   .6673774          0  1.466951 .26652452 0 0 0
                      1294  1         3          0        .5        1         .         .         2          1          .      1.75       .25 0 1 0
                      1300  1  2.875984  .27559054  .5472441 .9488189 19.890034  39.79382  2.062992   .6456693          0 1.5059055 .27165353 0 1 0
                      1301  1  2.204969  .12008282  .6335404 .9254658 22.482315  32.97106  1.716356  .35403726         .2  1.447205 .26915115 0 1 0
                      1302  1 1.8172044  .05645161  .4516129 .9354839  26.94643  100.4494  1.545699   .3575269          . 1.4327956 .27419356 0 1 0
                      1305  1  2.524927   .1319648  .5102639 .9472141 30.222857  54.31429  1.706745   .5366569          0  1.489736 .27859238 0 1 0
                      1306  1  2.626087  .19710144 .51014495 .8753623  23.89493  51.98188  1.794203   .9826087          0  1.484058  .2289855 0 1 0
                      1248  2  2.357377          . .29508197        .         .         .  2.029508   .6426229          . 1.4590164 .24918033 0 0 0
                      1251  2 2.8137536          . .28366762        .         .         . 1.9426934   .5329513          . 1.4899714 .28653294 0 0 0
                      1254  2 1.7244095          .  .2952756        .         .         .  1.503937  .28346458          .       1.5  .2559055 0 0 0
                      1262  2 2.8044555  .13118812  .2549505        . 25.075556  21.59111  1.930693   .6509901          .  1.509901 .23267327 0 0 0
                      1264  2 2.3414633   .0792683  .4146341        .  39.06612 10.975206 1.5914634   .2987805          0 1.4817073  .3719512 0 0 0
                      1265  2 3.9714286   .3857143  .4285714        . 10.941176  20.88235  2.657143          1          0 1.5714285 .34285715 0 0 0
                      1266  2 2.2205882 .029411765  .3529412        .  26.77273  8.886364  1.352941   .9705882          .  1.602941 .27941176 0 0 0
                      1268  2 3.8189654  .23275863  .3017241        . 21.864864  31.08108  2.801724   .9482759  .14705883 1.4655173  .3103448 0 0 0
                      1269  2 1.6419753          0  .1851852        .  40.89899   8.59596 1.4444444          0          . 1.4814814  .3333333 0 0 0
                      1273  2  2.823913   .2043478  .3630435 .9326087  19.44156 15.536797  2.347826   .7130435  .24444444  1.471739  .3478261 0 0 0
                      1274  2         1          0       .25        1        14        30      1.25          0          .      1.25       .25 0 0 0
                      1275  2         2          0         0        1         .         .         4          1          .         1         0 0 0 0
                      1276  2       2.4  .07058824  .1882353  .932353 18.254808 13.120193 1.7764705   .9147059          0  1.467647  .2647059 0 0 0
                      1277  2  1.764706          0  .2352941        1  25.42857 16.428572 1.1764706   .7647059          .  1.352941 .29411766 0 0 0
                      1281  2 2.1764705  .06417112 .19786096  .973262 24.993196 11.836735 1.8502674   .5294118          0 1.4973263  .2352941 0 0 0
                      1285  2  4.264151   .4528302 .33962265 .8679245       6.5 12.916667  3.207547          1   .1923077 1.5849056 .16981132 0 0 0
                      1286  2  2.798137   .1552795  .2453416 .9409938 37.568375 22.688034 1.6925466   .9223602          1  1.506211  .3198758 0 0 0
                      1288  2 3.1384616  .13076924  .3730769 .9192308  29.37037 18.555555 1.8384615   .9423077          0 1.4923077  .3269231 0 0 0
                      1289  2  2.515306  .09183674  .3214286        1 18.238094  5.422619 1.2908163   .8469388   .7777778 1.4744898 .29081634 0 0 0
                      1292  2  2.288889   .2777778  .3611111        1 18.647799  5.081761 1.4666667   .9833333          0       1.5 .23333333 0 0 0
                      1294  2         4          0         0        1         .         . 2.3333333          1          .         1         0 0 1 0
                      1298  2  3.537931  .24367817 .21149425 .9402299  22.38158 12.335526 2.2597702   .9586207   .3559322  1.464368 .27816093 0 1 0
                      1299  2 2.6911764   .3001337 .41243315 .9445187  25.35255 34.067627 1.7673796   .6564171   .4117647  1.493984  .2987968 0 1 0
                      1300  2  2.238532  .15137614  .3853211 .9541284 18.291891 15.383783  1.325688   .7614679   .4285714 1.4816514 .24770643 0 1 0
                      1303  2  2.736702        .25 .50797874 .9122341  22.24806  13.93411 1.7845745    .930851   .5263158  1.476064  .3085106 0 1 0
                      1304  2 4.3240743   .4259259  .4675926 .9675926       7.4        18 2.2592592   .9768519          0  1.486111 .22222222 0 1 0
                      1249 22  3.377193          . .48245615        .         .         .  2.464912   .9298246          . 1.5701754 .25438598 0 0 0
                      1250 22 2.4871795          .  .3846154        .         .         . 1.5641025   .3504274          . 1.4017094  .2991453 0 0 0
                      1253 22  2.671141          . .26510066        .         .         .  2.100671   .6778523          .   1.47651  .3154362 0 0 0
                      1256 22  2.446483          . .29357797        .         .         . 1.9816514  .50152904          . 1.5107033 .24770643 0 0 0
                      1257 22  1.048951          .  .3006993        .         .         . 1.3496504          0          . 1.4545455  .3706294 0 0 0
                      1262 22  3.728682   .4031008   .372093        . 19.833334  44.64286  2.829457   .9767442          0 1.4496124 .29457363 0 0 0
                      1264 22  3.166667  .23423423 .27027026        .  20.11111  88.08497 2.2207208   .9009009          1 1.4594594  .2792793 0 0 0
                      1266 22 1.6929578  .09014084   .371831        .  50.10239 138.51537 1.4366198   .4338028          0 1.4394367 .29295775 0 0 0
                      1267 22 2.3333333   .6666667  .6666667        .  63.66667  366.6667 4.3333335   .6666667          . 1.3333334         0 0 0 0
                      1269 22  2.836625  .15978456 .21543986        .  38.97193 130.39648 1.9712747   .7737882         .5  1.443447 .29443446 0 0 0
                      1273 22 1.7798742  .13522013 .30503145 .9245283 37.996227  152.5849  1.518868  .39308175          0  1.471698  .2672956 0 0 0
                      1274 22 3.8551724   .6206896  .5862069  .862069 13.458333  91.45834 2.9448276   .9586207          0 1.5448276 .28275862 0 0 0
                      1275 22         3          0        .5        1         .         .        .5          1          .       1.5        .5 0 0 0
                      1277 22  2.608333   .2777778  .3305556 .9138889  36.21134 115.76289 2.2694445   .5972222  .24444444  1.488889 .23333333 0 0 0
                      1278 22         3          1         1        0         1         8       4.5          1          .       1.5         0 0 0 0
                      1281 22       2.5   .3443396  .3537736 .8160377  49.76229  222.2131 2.2547169   .7830189          0 1.3915094  .2028302 0 0 0
                      1287 22  2.201646  .14403293  .3868313 .8559671  30.11253 105.64194  1.761317   .3847737          0 1.4609053  .3106996 0 0 0
                      1288 22 1.8214285          0  .5714286        1  32.57143 101.78571 1.1428572          0          . 1.5357143  .3214286 0 0 0
                      1290 22  2.455914         .2  .3698925 .9268817  29.28754 134.86263 1.9870968  .37419355        .44 1.4150537  .3032258 0 0 0
                      1292 22         4          1         1        0         .         .       2.5          1          .       1.5       .25 0 0 0
                      1293 22 2.3617885 .032520324  .2682927 .9065041  40.02857 119.90476  1.585366   .6626016          . 1.4512196  .3048781 0 1 0
                      1300 22  3.525281    .372191  .5266854 .8890449 25.346626  57.41104  2.380618   .8946629    .364486  1.469101 .24016854 0 1 0
                      1301 22  2.315847  .18360655  .6240437 .9355191  37.28397 160.25954 1.8874317  .52021855   .2631579  1.462295 .27431694 0 1 0
                      1302 22  2.364562  .11812627  .5743381 .9093686 32.050755 116.72565  1.725051   .5621181  .16666667  1.450102 .25050917 0 1 0
                      1304 22  2.952381  .30555555  .7579365 .8968254 32.369427   104.586 2.0912697    .765873          0 1.4801587 .25396827 0 1 0
                      1305 22  2.703846  .20384616  .7384616 .8730769  41.01948 141.68831 2.0038462   .7923077          0 1.4692308  .3653846 0 1 0
                      end
                      label values region hv024
                      label def hv024 1 "amazonas", modify
                      label def hv024 2 "ancash", modify
                      label def hv024 3 "apurimac", modify
                      label def hv024 4 "arequipa", modify
                      label def hv024 5 "ayacucho", modify
                      label def hv024 6 "cajamarca", modify
                      label def hv024 8 "cusco", modify
                      label def hv024 9 "huancavelica", modify
                      label def hv024 10 "huanuco", modify
                      label def hv024 12 "junin", modify
                      label def hv024 13 "la libertad", modify
                      label def hv024 14 "lambayeque", modify
                      label def hv024 16 "loreto", modify
                      label def hv024 17 "madre de dios", modify
                      label def hv024 18 "moquegua", modify
                      label def hv024 19 "pasco", modify
                      label def hv024 20 "piura", modify
                      label def hv024 21 "puno", modify
                      label def hv024 22 "san martin", modify
                      label def hv024 23 "tacna", modify
                      label def hv024 24 "tumbes", modify
                      label def hv024 25 "ucayali", modify
                      Region 0 is the treated one. Many of the variables have coded names so sorry if that makes it unclear/more difficult for you.

                      Thanks in advance for your help!

                      Comment


                      • #12
                        Okay now I see the issue. Yeah, the amount of missing values you have for the outcome here are extremely problematic for the synthetic control method. The classical one, anyways. Workarounds do exist, but you'd need Python. So here's my advice to you:

                        The month of analysis cannot be used here! Too many missing values that result in unacceptable outcomes. You're fitting the preintervention periods perfectly, which is horrible for SCM because it induces what we call interpolation biases. My advice is to aggregate your time level up to the quarter of analysis. Hopefully, then you'd have little to no missing data, and any data that are missing can be reasonably interpolated/extrapolated.

                        Also, now that I look at your dataset again......... This isn't my project, so I can't tell you what to study, but why not do the Wealth Index as your outcome? Why not use that as your outcome and make your life easier? Then we can skip the whole missing data part and it would be much easier, right? Again it isn't my call, but it is what I would do, if this were my paper. You can even keep the month as the time unit of analysis, if you so choose.

                        Comment


                        • #13
                          Hi Jared,

                          Thank you for your reply. I will aggregate up my observations to the quarter level.

                          I am in fact studying the Wealth Index too! I am looking at a variety of different outcome variables in my study.

                          Now that I can make the synthetic control method work (!), how should I choose the predictor variables? Is it a bit of trial and error to see what combination yields the best results?

                          Also, when running the SCM for some outcome variables, Stata gives me a 'conformability error'. I can't work out what this means in the SCM context - do you have any idea what I could do about it?

                          Many thanks.

                          Comment


                          • #14
                            Stata gives me a 'conformability error'
                            Can you show the code you used and the exact error Stata issued?

                            Predictor selection is a pretty open problem in SCM. My method SCUL doesn't use predictors, because the LASSO I use in some cases does better than SCM with predictors. Covariates are super super subjective in terms of which ones you use. But some guidelines exist.


                            Also, I wanna see pretty much the whole dataset so I can diagnose it better. Can you also show me the results of
                            Code:
                            dataex, count(800)
                            It'll go easier if I can see the same (or pretty much the same) data that you're seeing

                            Comment


                            • #15
                              Hi,

                              Sorry for the delayed response. I get a conformability error when using ElectricityAccess as the dependent variable. This is what Stata produces when I run this command:

                              synth ElectricityAccess ElectricityAccess(1280) ElectricityAccess(1292) BankAccount Insurance Migration agriculture Land Education Internet Gender School, trunit(0) trperiod(1293) counit(1 3 6 10 14 19) mspeperiod(1280 1292) resultsperiod(1280 1292 1300)
                              ------------------------------------------------------------------------------------------------------------------------------------------
                              Synthetic Control Method for Comparative Case Studies
                              ------------------------------------------------------------------------------------------------------------------------------------------

                              First Step: Data Setup
                              ------------------------------------------------------------------------------------------------------------------------------------------
                              conformability error
                              r(503);

                              Here are the results from dataex, count(800):


                              Code:
                              clear
                              input int month byte region float(WealthIndex BankAccount Insurance Migration agriculture Land Education ElectricityAccess Internet Gender School earthquake time Treatment)
                              1252  0 1          .         1         .         .         .         3          0 .       1.5          0 1 0 0
                              1255  0 1          . .25547445         .         .         . 1.8248175          0 . 1.4671533   .3211679 1 0 0
                              1261  0 1          0         0         .         .         .         0          0 .         1          0 1 0 0
                              1263  0 1          0         0         .         2        20         1          0 .       1.5          0 1 0 0
                              1265  0 1          0         0         .      42.5        20 2.4545455          0 . 1.5454545   .4545455 1 0 0
                              1269  0 1          0 .11111111         . 16.333334 28.333334 2.1111112          1 . 1.5555556  .11111111 1 0 0
                              1274  0 1          0  .6666667         1         .         . 1.1666666          0 .       1.5  .16666667 1 0 0
                              1277  0 1          0 .48387095  .7741935  28.48148  18.74074  1.451613  .22580644 .       1.5   .3870968 1 0 0
                              1292  0 1          0 .29411766         1 22.583334 12.583333 1.2941177          0 . 1.4705882  .29411766 1 0 0
                              1300  0 1          0 .11111111         1         .         .  3.111111          0 . 1.4444444  .44444445 1 1 1
                              1301  0 1       .125       .25         1    38.375   105.625      1.75          0 .    1.3125      .1875 1 1 1
                              1302  0 1          0        .5         1         8         5      1.75          0 .       1.5       .375 1 1 1
                              1304  0 1          0         0         1         .         . 1.6666666          0 .         2   .3333333 1 1 1
                              1306  0 1          0         0         1         .         .        .5          0 .       1.5          0 1 1 1
                              1250  1 1          . .19402985         .         .         .  .9104478  .25373134 . 1.4179105   .2238806 0 0 0
                              1253  1 1          . .15789473         .         .         . 1.3070176  .05263158 .  1.377193  .27192983 0 0 0
                              1256  1 1          .  .4054054         .         .         . 1.1737452  .02702703 . 1.4710425  .25096524 0 0 0
                              1262  1 1          0      .325         .        30  45.13044 1.1416667          0 . 1.4416667  .24166666 0 0 0
                              1265  1 1          0  .2352941         . 37.148148  38.14815 1.3235294          0 . 1.3235294   .2647059 0 0 0
                              1267  1 1          0 .17142858         .  23.53125   37.8125  .8571429  .05714286 .       1.4  .14285715 0 0 0
                              1268  1 1  .05660377  .5974843         . 19.303226  25.23226 1.2201258          0 . 1.4591194   .3773585 0 0 0
                              1274  1 1         .1  .3666667  .8833333  41.48936  107.4468      1.35          0 .       1.5         .3 0 0 0
                              1275  1 1          0  .5694444  .7638889  46.51724  13.62069 1.2916666          0 .       1.5   .2638889 0 0 0
                              1292  1 1          0  .2631579  .8421053  34.17857 34.285713 1.0789474          0 . 1.4210526   .2368421 0 0 0
                              1300  1 1          0  .4230769         1  16.73077 24.423077         1          0 . 1.3846154  .15384616 0 1 0
                              1301  1 1          0  .7291667  .8645833   21.0597  29.47761 1.2083334          0 .     1.375        .25 0 1 0
                              1302  1 1          0      .488      .984 26.669643   98.4375     1.208       .064 .      1.44       .288 0 1 0
                              1305  1 1   .1076923  .6307693         1  27.02564 36.435898 1.3538462          0 .  1.476923   .3538462 0 1 0
                              1248  2 1          .  .2739726         .         .         . 1.5342466          0 .  1.369863   .2191781 0 0 0
                              1251  2 1          .       .16         .         .         .      .936          0 .      1.52       .312 0 0 0
                              1254  2 1          .  .3333333         .         .         .  .9308176  .05031446 . 1.5157232  .24528302 0 0 0
                              1262  2 1          0     .1875         .  18.57377  6.655738  1.140625          0 .  1.609375    .234375 0 0 0
                              1264  2 1  .13636364  .3863636         .  36.05263         9 1.0909091          0 . 1.5227273   .3863636 0 0 0
                              1274  2 1          0       .25         1        14        30      1.25          0 .      1.25        .25 0 0 0
                              1276  2 1          0 .14814815  .9444444 14.369565   6.23913   .962963   .6481481 .       1.5  .24074075 0 0 0
                              1277  2 1          0         0         1        53        30      1.25          0 .      1.75         .5 0 0 0
                              1281  2 1  .05660377 .20754717  .9622642  32.28846 13.461538 1.3396226          0 . 1.5849056  .16981132 0 0 0
                              1289  2 1          0        .2         1 12.266666       8.8       .75          0 .       1.7        .25 0 0 0
                              1292  2 1          0        .4         1        41         2        .4         .6 .       1.4         .2 0 0 0
                              1298  2 1          0  .3333333         1         4         1  .6666667          0 . 1.6666666   .3333333 0 1 0
                              1299  2 1   .5410628  .4830918  .9565217  19.46842 13.426316  .9613526 .014492754 . 1.5700483   .3285024 0 1 0
                              1300  2 1          0  .2142857         1      14.5  43.92857       .75          0 . 1.4285715   .3571429 0 1 0
                              1303  2 1          0 .06666667         1  9.266666  6.733333        .8  .13333334 . 1.4666667  .26666668 0 1 0
                              1250  3 1          .  .4740741         .         .         . 1.1851852  .25185186 . 1.5185186   .3703704 0 0 0
                              1253  3 1          . .27906978         .         .         .  .8255814   .3837209 .       1.5  .26744187 0 0 0
                              1256  3 1          .   .342723         .         .         .  1.314554  .03755869 .  1.488263   .3286385 0 0 0
                              1263  3 1          0 .39534885         .  38.85119  14.14881  .9244186   .1511628 .  1.517442    .372093 0 0 0
                              1266  3 1 .016129032  .3064516         . 24.422413  9.784483 1.1532258  .29032257 .  1.532258  .22580644 0 0 0
                              1277  3 1  .07741936  .4193548  .9225807 19.141304  5.717391 1.1935484  .05806452 . 1.4967742   .3612903 0 0 0
                              1280  3 1 .014778325  .5320197  .8275862    19.585      9.05  .8669951 .009852217 . 1.4482758  .27586207 0 0 0
                              1286  3 1   .3837209 .39534885  .8139535  41.51852 25.617285  1.488372          0 .  1.511628  .39534885 0 0 0
                              1291  3 1   .5277778  .5555556  .9444444        32  6.363636  .9444444  .08333334 .       1.5  .19444445 0 0 0
                              1292  3 1   .8823529  .4705882         1  24.05882  13.82353 1.2941177          0 .  1.352941   .4705882 0 0 0
                              1299  3 1       .085       .69      .815 23.608435  43.39759      .945       .085 .     1.485       .275 0 1 0
                              1300  3 1        .25       .75  .9821429  27.30189  55.96227  .4464286  .14285715 . 1.5892857       .125 0 1 0
                              1303  3 1  .03773585  .6415094  .9811321  35.48077 203.15384 1.0943396  .13207547 . 1.6226416  .24528302 0 1 0
                              1304  3 1  .46551725  .8965517  .7241379 29.465517  7.913793  .7413793          0 . 1.4827586  .29310346 0 1 0
                              1273  4 1          0  .3333333         1 14.142858  7.857143 1.4761904   .2857143 . 1.3809524   .4761905 0 0 0
                              1276  4 1          0  .4705882  .2352941      21.2       4.2  1.647059          0 .  1.352941   .3529412 0 0 0
                              1290  4 1  .17391305 .13043478  .8695652   39.6875    32.125  1.695652          0 .  1.521739  .17391305 0 0 0
                              1301  4 1  .05555556  .5555556  .9444444        13  9.714286  .9444444          0 .  1.388889  .05555556 0 1 0
                              1302  4 1 .023809524 .26190478  .8809524 26.285715         8 1.3333334          0 . 1.4285715  .16666667 0 1 0
                              1303  4 1          0  .3913043  .9565217  9.478261 15.304348  1.478261          0 . 1.3913044  .17391305 0 1 0
                              1304  4 1          0         0         1        31        15         1          0 .       1.5          0 0 1 0
                              1250  5 1          .     .3125         .         .         .    .90625      .0625 .   1.53125     .15625 0 0 0
                              1253  5 1          .  .2980392         .         .         . 1.3568628  .14117648 . 1.4588236   .3294118 0 0 0
                              1255  5 1          . .07762557         .         .         .  .9589041  .10502283 .  1.456621  .25570777 0 0 0
                              1256  5 1          .        .4         .         .         .      1.32          0 .      1.48   .3333333 0 0 0
                              1257  5 1          .  .3960396         .         .         . 1.1089109          0 . 1.5445545  .26732674 0 0 0
                              1264  5 1 .009433962 .20754717         .  37.94805 32.857143 1.1603774          0 .  1.490566  .33962265 0 0 0
                              1265  5 1 .020689655 .28965518         .  39.54167        14       1.2          0 . 1.4413793  .22068965 0 0 0
                              1267  5 1 .032786883  .4918033         .  24.14706 16.588236 1.7868853          0 . 1.4918033   .3934426 0 0 0
                              1269  5 1       .024      .336         .  28.66942 14.421488     1.232       .032 .     1.528        .28 0 0 0
                              1274  5 1          0 .23913044  .9347826 20.935484 11.032258 1.1521739  .04347826 .       1.5   .3478261 0 0 0
                              1275  5 1 .015151516  .3484848  .9090909 21.887096  7.112903         1          0 . 1.4848485   .1969697 0 0 0
                              1291  5 1  .03921569  .6078432         1        18  3.977273  .7647059  .07843138 . 1.4117647   .3529412 0 0 0
                              1298  5 1       .375      .875         1 13.714286  4.571429       .25       .125 .     1.625       .125 0 1 0
                              1299  5 1   .4899329  .8624161  .8926175 28.334545  9.967273  .9328859          0 . 1.4932886  .32550335 0 1 0
                              1300  5 1   .4634146  .6829268   .902439  21.38889 23.333334  .6585366          0 .  1.414634   .2195122 0 1 0
                              1305  5 1          0  .6086956  .9565217  6.478261 18.043478 1.1304348          0 .  1.478261  .13043478 0 1 0
                              1306  5 1   .4285714  .8311688  .9480519 26.044117 16.691177         1          0 . 1.4805195   .2077922 0 1 0
                              1250  6 1          .  .3196721         .         .         .  .9918033          0 . 1.5491803   .3852459 0 0 0
                              1252  6 1          .  .2535211         .         .         . 1.3661972          0 . 1.4647887   .3098592 0 0 0
                              1253  6 1          . .13274336         .         .         . 1.0353982          0 .  1.539823  .26548672 0 0 0
                              1255  6 1          .       .42         .         .         .      1.12          0 .      1.38        .22 0 0 0
                              1256  6 1          .       .25         .         .         .      1.25          0 .      1.45         .3 0 0 0
                              1263  6 1 .037037037  .1851852         .  33.05263 15.789474 1.5925926          0 . 1.3333334 .074074075 0 0 0
                              1264  6 1          0  .4642857         . 19.285715 16.964285  .7857143          0 .       1.5   .2857143 0 0 0
                              1266  6 1          0 .09836066         . 11.830508 13.847458  .8196721          0 . 1.5409836  .18032786 0 0 0
                              1267  6 1  .05095541  .3121019         . 29.340136 17.687075 1.2165605  .02547771 . 1.4968153   .2611465 0 0 0
                              1277  6 1          0  .3181818         1  29.22222 12.277778 1.2272727   .7272727 .  1.590909   .3636364 0 0 0
                              1279  6 1          0  .2967033         1 16.824324      25.5 1.0549451          0 . 1.5164835   .2747253 0 0 0
                              1280  6 1          0  .3846154  .8846154  11.88889 12.777778  1.346154          0 . 1.4615384  .26923078 0 0 0
                              1289  6 1 .035714287  .4464286  .9285714     23.46     26.01 1.0982143          0 . 1.5178572   .3035714 0 0 0
                              1290  6 1          0        .5         1   20.4375    5.4375  .7333333          0 . 1.4666667  .23333333 0 0 0
                              1292  6 1   .4285714  .3956044         1      11.8  11.41111  .7912088          0 . 1.4945055  .23076923 0 0 0
                              1293  6 1  .29090908  .4363636         1  19.90196  21.27451 1.1636363          0 . 1.4727273         .2 0 1 0
                              1249  9 1          .         0         .         .         .       .75        .25 .       1.5          0 0 0 0
                              1250  9 1          . .33962265         .         .         .  .7924528          0 . 1.4622642   .3301887 0 0 0
                              1252  9 1          . .25570777         .         .         . 1.4200913  .00456621 . 1.5159818   .3287671 0 0 0
                              1256  9 1          . .29225352         .         .         .  .9788733  .11619718 .       1.5  .29225352 0 0 0
                              1267  9 1   .0952381  .4642857         . 25.655737 11.934426 1.0535715   .4404762 . 1.4166666   .3869048 0 0 0
                              1268  9 1          0  .6219512         .  31.35156  19.16406  .9573171  .27439025 .  1.390244   .3841463 0 0 0
                              1273  9 1   .3421053 .36842105         1  28.96875   15.3125 1.1315789  .18421052 .       1.5   .3157895 0 0 0
                              1274  9 1          0  .6034483         1  13.11111 13.203704  1.275862   .8965517 . 1.4827586   .3793103 0 0 0
                              1289  9 1   .6666667 .16666667         1      23.5      10.5 1.1666666          0 . 1.6666666  .16666667 0 0 0
                              1290  9 1  .12307692  .3846154  .9692308 31.288136  8.508474  .8461539  .04615385 .  1.446154   .3076923 0 0 0
                              1299  9 1  .23790322      .625  .8225806 34.377483  10.62914 1.0725807  .08870967 . 1.5080645   .3104839 0 1 0
                              1300  9 1          1       .75         1        34        50       2.5          1 .      1.25        .75 0 1 0
                              1303  9 1   .6285715        .6  .9142857  51.54546 11.848485 1.2285714          0 . 1.5714285   .3714286 0 1 0
                              1304  9 1   .3018868  .6226415  .9433962 18.433962  9.905661 1.1698114  .03773585 . 1.4150944  .26415095 0 1 0
                              1249 10 1          .  .3798077         .         .         . 1.0721154  .04326923 . 1.5192307  .28365386 0 0 0
                              1252 10 1          .   .369863         .         .         . 1.1712328          0 . 1.5273973    .369863 0 0 0
                              1254 10 1          . .20833333         .         .         .  .6145833          0 . 1.4583334     .15625 0 0 0
                              1255 10 1          .  .3130435         .         .         . 1.0347826 .069565214 . 1.4869565   .2521739 0 0 0
                              1263 10 1          0  .4939759         .  40.53846 19.628204  1.192771          0 . 1.6024096   .3253012 0 0 0
                              1264 10 1          0  .4166667         .  54.75862     332.5 1.0833334          0 . 1.5166667   .3666667 0 0 0
                              1265 10 1          0  .3305085         . 31.842106  28.10526 1.1271186  .05932203 . 1.4661016   .3813559 0 0 0
                              1268 10 1          0  .3757962         . 35.664063   79.6875  1.044586          0 . 1.4840764  .29936305 0 0 0
                              1275 10 1          0   .373494   .939759  26.97561  20.45122 1.4096385 .012048192 .  1.493976  .38554215 0 0 0
                              1276 10 1          0  .5096774  .9548387 23.413794 10.405172  .7548387  .02580645 .  1.516129  .28387097 0 0 0
                              1281 10 1          0  .4418605  .8372093 16.828283  8.919192  .9379845          0 . 1.5736434   .3255814 0 0 0
                              1285 10 1          0  .6666667         1        33        10         1          0 . 1.6666666   .3333333 0 0 0
                              1287 10 1  .05376344   .344086         1  21.73034  9.011236 1.1021506  .06451613 .  1.467742   .3978495 0 0 0
                              1299 10 1  .53090906  .6763636  .9018182  30.54762  83.80159  .9745455   .1090909 .  1.421818  .27272728 0 1 0
                              1300 10 1   .4418605  .6666667  .9418604 25.683544  44.72996 1.0232558          0 . 1.4379845    .248062 0 1 0
                              1305 10 1  .27906978  .8139535  .8604651  20.23684  55.42105         1  .27906978 .  1.488372   .3023256 0 1 0
                              1306 10 1   .7217391  .9130435  .9304348 32.911766  22.15686 1.1565218  .06086956 . 1.5043478   .4434783 0 1 0
                              1307 10 1         .5      .875         1      22.5        25      1.25          0 .      1.25       .375 0 1 0
                              1255 12 1          . .26666668         .         .         .       1.6   .4666667 .       1.6  .26666668 0 0 0
                              1261 12 1          0  .2857143         .      14.4       5.5 1.3571428   .9285714 .       1.5   .2857143 0 0 0
                              1262 12 1          0         0         .         7         2         1          1 .         2          0 0 0 0
                              1277 12 1          0         0         1         .         .         0          0 .         2          0 0 0 0
                              1280 12 1          0 .20408164         1  30.28409  49.71591 1.4387755          0 .  1.510204   .2755102 0 0 0
                              1286 12 1          0         0         0        17         5 2.1666667          0 .       1.5  .16666667 0 0 0
                              1290 12 1          0 .08333334         1  29.11111 33.666668 1.0833334          0 .       1.5        .25 0 0 0
                              1291 12 1          0         0         1        11         3         1          0 .         1          0 0 0 0
                              1293 12 1          0         0         1         1       7.5        .5          0 .       1.5          0 0 1 0
                              1300 12 1          0         0  .3333333         7         3 2.3333333          0 . 1.6666666   .3333333 0 1 0
                              1301 12 1  .16666667  .4833333         1  19.18868 13.132075 1.0333333        .05 . 1.4666667  .26666668 0 1 0
                              1304 12 1          0       .25         1         .         .       .75          0 .      1.75          0 0 1 0
                              1252 13 1          .       .15         .         .         .      1.25          0 .       1.4        .05 0 0 0
                              1278 13 1          0  .3697479  .9411765     24.64     48.25   .907563          0 . 1.4957983  .22689076 0 0 0
                              1279 13 1          0        .4         1         7        10        .8          1 .       1.6         .2 0 0 0
                              1281 13 1  .08064516 .14516129         1 16.169811 12.471698 1.3064516          0 .  1.467742   .3548387 0 0 0
                              1290 13 1          0  .4583333         1  6.461538  31.53846     1.125   .6666667 . 1.4166666   .3333333 0 0 0
                              1291 13 1          0  .4864865  .9189189   40.7027 15.351352  .7027027          0 . 1.5675676  .27027026 0 0 0
                              1301 13 1   .3577586  .7155172   .987069  20.40187  28.72897  .9784483  .05172414 .  1.487069  .28448275 0 1 0
                              1304 13 1          0         0         1         0         2 1.6666666          0 . 1.3333334  .16666667 0 1 0
                              1248 14 1          .  .5833333         .         .         .  .5833333          0 . 1.5833334  .08333334 0 0 0
                              1254 14 1          .       .25         .         .         .      1.25          0 .      1.75        .25 0 0 0
                              1255 14 1          .  .3103448         .         .         .  .8965517          0 .  1.586207   .1724138 0 0 0
                              1263 14 1          0      .375         .         1        10      .625          0 .       1.5          0 0 0 0
                              1266 14 1          0 .19607843         .  38.52941 17.058823  1.392157          0 . 1.5686275  .25490198 0 0 0
                              1301 14 1          0  .6666667         1         .         .         2          0 . 1.5555556   .3333333 0 1 0
                              1302 14 1  .02727273 .55454546  .9545454  25.48837 13.302325  1.209091          0 .       1.5   .3363636 0 1 0
                              1249 16 1          .  .1818182         .         .         .  1.409091         .5 . 1.4545455   .4545455 0 0 0
                              1250 16 1          .  .2352941         .         .         . 1.4117647          0 . 1.4705882   .2352941 0 0 0
                              1251 16 1          .  .5285714         .         .         .  .8285714          0 . 1.5285715  .22857143 0 0 0
                              1252 16 1          .  .3490566         .         .         .  .8962264          0 .  1.471698  .12264151 0 0 0
                              1253 16 1          . .38235295         .         .         .  1.254902          0 .  1.519608   .3333333 0 0 0
                              1254 16 1          . .16923077         .         .         . 1.1153846          0 . 1.4692308   .2923077 0 0 0
                              1255 16 1          .        .2         .         .         .       1.1          0 .       1.6         .2 0 0 0
                              1256 16 1          .  .2857143         .         .         . 1.1428572          0 . 1.4285715  .14285715 0 0 0
                              1264 16 1          0 .50187266         . 23.393805  65.40266 1.3670412  .02621723 . 1.4494382  .19475655 0 0 0
                              1265 16 1          0 .36842105         . 30.935484  57.74194 1.4473684          0 . 1.4210526   .2631579 0 0 0
                              1290 16 1          0  .4615385         1        34       100         1          0 . 1.4615384  .15384616 0 0 0
                              1291 16 1          0      .304       .88  21.02804   38.5514     1.264       .304 .     1.424       .272 0 0 0
                              1293 16 1 .005847953 .57309943  .9122807  29.96988 32.590363 1.0818714          0 .  1.491228   .2748538 0 1 0
                              1298 16 1   .3448276  .4137931         1      54.5     507.5 1.1724138          0 . 1.6206896  .27586207 0 1 0
                              1299 16 1          0  .4855967  .9958848  25.58371  60.99548  1.234568   .2057613 . 1.4609053   .3209876 0 1 0
                              1300 16 1          0  .3636364         1 19.363636  30.90909  1.818182          0 .       1.5   .4545455 0 1 0
                              1303 16 1  .06944445  .6944444      .875 15.072464  45.43478 1.5277778  .09722222 .  1.361111        .25 0 1 0
                              1304 16 1          0  .6846847         1 21.702703  34.54955 1.2252252  .06306306 . 1.4324324  .24324325 0 1 0
                              1249 19 1          .  .3809524         .         .         .         1          0 . 1.4761904   .1904762 0 0 0
                              1251 19 1          .  .2857143         .         .         . 1.34   .09581 . 1.4285715  .23809524 0 0 0
                              1268 19 1          0         0         .         .         .         0          0 .       1.5          0 0 0 0
                              1273 19 1          0         0         1         .         . 1.7          1 . 1.34  .445 0 0 0
                              1274 19 1          0 .20183486  .8073394  62.26213  8.097087 1.382 .036697246 . 1.4862385   .2385321 0 0 0
                              1278 19 1  .20833333 .04166667         1  51.1 11.291667 2.0416667        .75 .     1.375       .375 0 0 0
                              1280 19 1          0 .54545456         1      36.4        38         1          0 . 1.3636364   .4545455 0 0 0
                              1281 19 1 .014285714 .547  .8714285   19.7971  95.65218       1.4          0 . 1.4714285         .3 0 0 0
                              1287 19 1          0  .4117647  .8235294  6.05  28.82353  .8823529   .1764706 . 1.4705882  .29411766 0 0 0
                              1291 19 1  .09923664  .3206107  .7022901  77.14865  25.87838 1.3435115  .03053435 .  1.480916   .3129771 0 0 0
                              1292 19 1        .25        .3     .9375 28.074627  40.64179    1.5125          0 .      1.45      .3875 0 0 0
                              1298 19 1          0  .3636364         1        15         5 1.1818181          0 . 1.4545455   .1818182 0 1 0
                              1299 19 1  .04016064  .4859438  .8594378  40.85549  155.1214 1.1405623  .04819277 . 1.4056225  .22088353 0 1 0
                              1300 19 1          0 .10526316  .7368421         3        10 1.5263158          0 . 1.5263158   .2631579 0 1 0
                              1304 19 1          0  .7065217         1  24.47561  63.34146 1.2826087  .07608695 . 1.4347826    .423913 0 1 0
                              1251 20 1          .  .4603175         .         .         . 1.3015873          0 . 1.5238096   .4126984 0 0 0
                              1255 20 1          .         0         .         .         .         1          0 .         1          0 0 0 0
                              1262 20 1          0        .2         .  19.52941        10 1.2666667          0 .       1.5  .23333333 0 0 0
                              1264 20 1          0      .625         .         .         .       1.5          0 .      1.25         .5 0 0 0
                              1265 20 1  .10309278 .20618556         . 19.171053  28.68421 1.2886598          0 .  1.474227   .3195876 0 0 0
                              1268 20 1          0 .36842105         .         1         5  1.631579          0 . 1.4736842   .2105263 0 0 0
                              1280 20 1          0      .375      .375    21.375      22.5         1          0 .       1.5        .25 0 0 0
                              1281 20 1          0  .4827586         1   41.3125   22.8125 1.2413793          0 . 1.44   .293 0 0 0
                              1301 20 1          0  .3  .92 10.384615 10.884615  .9189189          0 . 1.495   .2882883 0 1 0
                              1302 20 1          0   .516129         1  20.56774 35.548386  .9419355          0 . 1.5225806   .30 0 1 0
                              1304 20 1         .5       .65         1 13.064102  25.76923        .9        .09 .      1.46        .38 0 1 0
                              1305 20 1          0       .75         1         9         3       1.5          0 .       1.5        .25 0 1 0
                              1250 21 1          .         0         .         .         .        .6          0 .       1.6         .2 0 0 0
                              1255 21 1          .         0         .         .         .  .33          0 .         2   .3 0 0 0
                              1256 21 1          .   .220339         .         .         . 1.4322034          0 .  1.483051   .253 0 0 0
                              1262 21 1          0  .2345679         .      13.1 14.742857 1.4074074   .4567901 . 1.4814814   .2962963 0 0 0
                              1265 21 1          0 .21359223         .  44.43478 20.847826 1.1553398  .05825243 . 1.5339806   .2815534 0 0 0
                              1268 21 1          0 .17143         . 14.652174  8.65 1.2857143          0 . 1.4285715  .17857143 0 0 0
                              1269 21 1  .01459854 .227         . 19.114  9.491803  1.291971          0 . 1.5467  .26372 0 0 0
                              1274 21 1          0        .2  .8666667  6.933333  7.53        .8         .6 . 1.46  .26 0 0 0
                              1289 21 1          0       .25  .9722222 15.194445  12.11111 1.2222222          0 . 1.56  .3055 0 0 0
                              1292 21 1  .03726708 .27329192  .8198758 34.885246  52.81967 1.2981366          0 . 1.4906832   .2484472 0 0 0
                              1298 21 1  .05128205  .3846154         1  64.71 281.14285 1.4102564          0 . 1.4871795   .3589744 0 1 0
                              1299 21 1  .05673759  .4609929   .929078  29.66346 141.00961 1.8439716          0 . 1.4893616   .3333333 0 1 0
                              1300 21 1          0  .4047619   .952381  26.09524 17.666666 1.3571428          0 . 1.4285715  .268 0 1 0
                              1303 21 1          0  .4482759         1 21.068966 15.206897  .8965517          0 . 1.5172414  .13793103 0 1 0
                              1249 22 1          .       .25         .         .         .      1.25          0 .      1.25          0 0 0 0
                              1250 22 1          . .04761905         .         .         .   .952381          0 . 1.3809524  .23809524 0 0 0
                              1253 22 1          .  .2352941         .         .         . 1.5441177          0 .  1.485294   .2352941 0 0 0
                              1256 22 1          .  .2142857         .         .         . 1.3928572          0 . 1.4910715  .16964285 0 0 0
                              1257 22 1          .  .3088235         .         .         . 1.3014706          0 . 1.323   .3602941 0 0 0
                              1264 22 1          0  .6666667         . 4.66 26.6  .33          0 .       1.5   .33 0 0 0
                              1266 22 1          0 .33490565         .  44.43005  115.3627 1.1698114   .1792453 . 1.442  .287 0 0 0
                              1267 22 1          0         0         .        53       300         4          0 .         1          0 0 0 0
                              1269 22 1          0 .57575756         .  42.13044  53.04348 1.1515151          0 . 1.3636364   .3030303 0 0 0
                              1273 22 1          0 .19266056   .880734  44.66327  89.72449 1.1926606 .036697246 . 1.4495413   .2201835 0 0 0
                              1274 22 1          0        .5         1 25.666666        90 1.1666666          0 .       1.5   .3333333 0 0 0
                              1277 22 1          0  .3191489  .8404256   38.8254 100.87302  1.351064  .05319149 . 1.4574468  .19148937 0 0 0
                              1293 22 1          0     .1875         1  85.44444  327.7778    1.1875          0 .     1.375      .0625 0 1 0
                              1300 22 1          0  .8571429         1  45.78571  63.9  .9285714   .2 . 1.4285715   .3571429 0 1 0
                              1301 22 1          0   .760479   .994012 37.208588 144.17178  1.275449          0 . 1.4251497  .25748503 0 1 0
                              1302 22 1  .02877698  .6618705  .9064748     38.95     149.5   1.47482 .021582734 .  1.417266  .25179857 0 1 0
                              1304 22 1          0         1         1  64.23077 178.46153 1.1428572          0 .       1.5  .14285715 0 1 0
                              1305 22 1          0  .9090909         1 23.045454 65.454544  .9545454   .3181818 . 1.3636364   .3636364 0 1 0
                              1251 23 1          . .06666667         .         .         . 1.4666667          0 . 1.3333334   .4666667 0 0 0
                              1264 23 1          0         0         .        16        10         2          0 . 1.3333334          0 0 0 0
                              1280 23 1          0         0         1         .         .         1          0 .       1.5          0 0 0 0
                              1289 23 1          0 .29411766         1  89.6 1312  1.1          0 . 1.4705882   .2352941 0 0 0
                              1300 23 1          0     .1875         1 25.285715  62.85714         2          0 .     1.375      .1875 0 1 0
                              1301 23 1          0  .4545455         1        21         5 1.5454545          0 . 1.6363636  .27272728 0 1 0
                              1306 23 1          0       .25         1     73.75      12.5      2.25          0 .       1.5        .25 0 1 0
                              1254 24 1          .         0         .         .         .         1          0 .         1          0 0 0 0
                              1262 24 1          0         0         .         1        10 1.5555556          0 . 1.3333334  . 0 0 0
                              1265 24 1          0        .3         .         .         .       1.1         .4 .       1.4         .2 0 0 0
                              1275 24 1          0         0         1         .         .         0          0 .       1.5          0 0 0 0
                              1281 24 1          0 .22222222         1         3         5 1.4444444          0 . 1.4444444  .11111111 0 0 0
                              1300 24 1          0  .6666667         0         .         .  .8333333          0 . 1.6666666   .3333333 0 1 0
                              1248 25 1          .        .5         .         .         .   1.34375          0 .   1.46875     .28125 0 0 0
                              1251 25 1          .  .4159292         .         .         . 1.2566372          0 . 1.5044248   .3362832 0 0 0
                              1268 25 1          0        .5         .  41.88235 174.41176 1.4166666          0 . 1.4166666   .5416667 0 0 0
                              1269 25 1          0 .59090906         . 36.772728 120.90909 1.2272727          0 . 1.3636364   .4545455 0 0 0
                              1275 25 1          0  .1857143  .8714285  47.02174 233.04347 1.4857143  .04285714 .  1.457143   .2142857 0 0 0
                              1277 25 1          0  .2121212         1        33       200 1.6969697          0 . 1.5757576   .5151515 0 0 0
                              1278 25 1          0  .3181818         1  72.63636  561.8182 1.3636364          0 . 1.4545455   .3181818 0 0 0
                              1279 25 1          0 .25641027         1 19.923077 100.76923 1.4358975  .05128205 . 1.3076923   . 0 0 0
                              1306 25 1  .11627907  .7906977         1 15.721312 18.852459 1.1744186          0 .  1.383721   .3023256 0 1 0
                              end
                              Thank you very much for your help and your patience! It's greatly appreciated.

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