How to Test The Parallel Trends Assumption Of Difference in Differences Estimation

[Mitchell Grant]

Hello All,

I apologize for this question, as I know that it has been asked before but I am unable to follow along with the response given.

Post that I am referencing: https://www.statalist.org/forums/for...did-estimation)

I am currently working with an unbalanced panel dataset which tracks per capita ethanol consumption at the state-level. My goal is to look at the impact of a UBI program in Alaska that I believe had an impact on their ethanol consumption, but, before preceding, I need to verify the parallel trends assumption.

Unfortunately, I am still a beginner at STATA and am struggling to find the right code to run. Could any of you please give me a hand? I have thus far created dummies y82, which is an indicator for whether or not the UBI program has been established (= 1 if year >=1982); UBI which indicates whether or not the observation belongs to the treatment or control group; and UBIy82 which is an interaction term between the two.

Any help would be GREATLY appreciated.

All the best,

Mitchell

[Clyde Schechter]

I recommend that you begin with a graphical exploration of the parallel trends assumption:

preserve
collapse (mean) ethanol_consumption, by(UBI year)
reshape wide ethanol_consumption, i(year) j(UBI)
graph twoway connect ethanol_consumption* year if year < 1982
restore

It should be fairly easy to judge visually whether the two groups' mean ethanol consumption exhibits parallel variation prior to 1982.

If the two curves also appear to be more or less straight lines, then you can quantify this with:

xtreg ethanol_consumption i.UBI##c.year if year < 1982
margins UBI, dydx(year)

The -margins- output will then give you estimated time trends for ethanol consumption in each group prior to 1982, and you can make a judgment whether they are close enough for practical purposes.

If the graph showed parallel curves, but they are not approximately linear, then quantifying it gets more complicated because you have to pick some way to specify the ethanol-consumption vs time relationship in the regression. Frankly, I usually don't bother with this unless forced to by a reviewer, since the specification process can be somewhat arbitrary and represents an unnecessary researcher degree of freedom. Visual assessment of parallel trends from graphs is really OK by itself.

[Mitchell Grant]

I was not expecting such a timely and detailed response. Thank you kindly for the help, Clyde. It was exactly was I needed.

[Neelakanda Krishna]

Withmissing.gphDear Stata members
I have decided to learn Differences-in-Differences and before proceeding with the methodology I though to graphically plot the outcome variable so that I can visually inspect the existence of the parallel trend. Here is my sample dataset

* Example generated by -dataex-. For more info, type help dataex
clear
input long co_code int year float(time_dummy lever_w del_lever_w fispro nppe_ta_w treat2 treat4)
  11 2011 0    .5504084             .   57.5825   .3451234 . 1
  11 2012 0    .5697871     .13673742   61.9625  .29354736 . 1
  11 2013 0    .5391156     .02014651     60.62   .4899267 . 1
  11 2014 0   .51597077     .03468611     53.69   .4550365 . 1
  11 2015 0    .4759782    -.02909484   51.4375  .42844895 . 1
  11 2016 0    .4625662   -.008831292    49.085   .4218914 . 1
  11 2017 1   .39045715   -.028536247    43.595   .4503756 . 1
  11 2018 1   .33070305    -.04365781   43.7825  .45255655 . 1
  11 2019 1    .2839154    -.02297148     44.23   .4434838 . 1
 289 2011 0   .54094803             .   57.8775   .3158847 . 1
 289 2012 0    .5555683     .04196965   60.6875    .425365 . 1
 289 2013 0   .51333773    -.12417872   63.6125   .4299606 . 1
 289 2014 0   .48577145    -.09322661     63.62   .3889136 . 1
 363 2011 0    .4761355             .      64.9   .6755958 1 1
 363 2012 0    .4731195    -.04081633      64.9   .6276743 1 1
 363 2013 0   .53195494       .084656      64.9   .5426359 1 1
 363 2014 0    .4994195    -.04058379 64.832504   .4036047 1 1
 363 2015 0   .51551414    -.04872707     64.44    .349384 1 1
 363 2016 0    .5284721     .01808228     64.44     .47962 1 1
 363 2017 1    .5849603     .02759484     64.44   .4654458 1 1
 363 2018 1    .4416465    -.13557772     64.44   .2198459 1 1
 363 2019 1     .689352      .2355951     64.44  .20597345 1 1
 414 2016 0    .4542443             .     74.19  .03726708 . .
 414 2017 1  .006420134     -.3973959     74.19   .0480226 . .
 414 2018 1  .005577936  -.0021691974   74.3125  .05113108 . .
 415 2018 1           .             .     64.93   .2330087 . .
 415 2019 1           .             .     64.93  .24387904 . .
 771 2013 0    .4651715             .         .  .29406333 0 .
 771 2014 0    .5203515     .16422585     44.68  .24340977 0 .
 771 2015 0     .461695     .04225349     45.03   .2141985 0 .
 771 2016 0    .5473848     .23217165    57.025  .15930188 0 .
 771 2017 1    .5459776     .08907863  57.98667  .12169435 0 .
 771 2018 1    .4951338  -.0006741511     57.03  .11059617 0 .
 771 2019 1    .4645513     .01636094     67.16  .10220815 0 .
 783 2011 0   .19831736             .     40.21  .25982013 0 .
 783 2012 0    .2092255     .02108219    41.865  .24346343 0 .
 783 2013 0   .19775394     .02563058     45.51   .1896934 0 .
 783 2014 0   .13007422   -.070008256     49.73  .18619993 0 .
 783 2015 0   .08677534     -.0432274    52.345  .24750434 0 .
 783 2016 0  .033318035    -.05364846    53.755  .22629647 0 .
 783 2017 1  .025697127    -.00810018   38.8675  .22433777 0 .
 783 2018 1  .014560171   -.011117832     31.88   .2284505 0 .
 783 2019 1 .0009790963    -.01434376   34.3175   .2313115 0 .
1120 2011 0  .016338103             .     61.65   .2067098 . .
1120 2012 0  .032134537    .017975256     61.65  .22745897 . .
1120 2013 0   .08688986     .06097838     61.65   .1946061 . .
1120 2014 0   .05394961    -.01804284     61.65   .1633293 . .
1120 2015 0   .03865692  -.0086235255     61.65   .2076624 . .
1120 2016 0   .06567325    .030431677     61.65  .23243997 . .
1120 2017 1     .043705    -.01358125     61.65  .20205106 . .
1120 2018 1   .03458266   -.004701423     58.47  .18189026 . .
1120 2019 1  .031668555   .0011949443     58.47   .2030323 . .
2248 2017 1    .1482503             .     63.53   .4664207 . .
2248 2018 1   .15626974     .01871116     63.53   .4828343 . .
2717 2011 0    .3987688             .    64.755   .5158676 1 1
2717 2012 0    .5396248     .12750345   63.8525   .5372157 1 1
2717 2013 0     .521483     .07072685   64.2625   .4523312 1 1
2717 2014 0    .5139446    -.07991093     64.49   .4870189 1 1
2717 2015 0    .4747386    .025524806    64.445   .4551124 1 1
2717 2016 0   .31001255   -.068927504   64.4375   .4537038 1 1
2717 2017 1   .26234335    -.02177348   56.1875   .4684093 1 1
2717 2018 1    .2209677     .14709762     54.02   .2361021 1 1
2717 2019 1   .19191967    -.04047067    55.575  .22883637 1 1
2842 2011 0   .24950735             .    60.135  .09140518 . 0
2842 2012 0    .3842184     .19543524     60.78  .08590434 . 0
2842 2013 0    .3622974     .05196851   61.4275   .0832793 . 0
2842 2014 0    .3780394     .04436482   61.8375   .0765293 . 0
2842 2015 0    .3651952    -.01095076     61.84  .07563667 . 0
2842 2016 0   .19154836     -.2856034     61.84  .08096717 . 0
2842 2017 1  .012137886    -.26735717     61.84  .09775044 . 0
2842 2018 1   .11468551     .10386612     61.84  .07905366 . 0
2842 2019 1    .1701168      .0781774     61.84   .0624494 . 0
3335 2011 0    .3992996             .   74.3925   .2621498 1 .
3335 2012 0    .1974824    -.08724902     74.33   .1961064 1 .
3335 2013 0   .15542907   -.014229422     74.33   .1971979 1 .
3335 2014 0   .02286336    -.12579037     74.33   .2117583 1 .
3335 2015 0  .028543843    .005624817 74.337494  .22432104 1 .
3335 2016 0   .03890818    .011772218     74.35  .23201247 1 .
3335 2017 1   .05525101    .020087996   74.3525   .2071913 1 .
3335 2018 1   .10858244     .06246426   74.3875  .16878895 1 .
3335 2019 1    .2077677      .1157242    74.845  .25074002 1 .
3990 2011 0    .5109358             .   54.8725   .4358847 . 1
3990 2015 0    .4456229             .      61.4   .4448767 . 1
3990 2016 0   .43831205    .032197233     62.34    .441005 . 1
3990 2017 1    .4056455 -.00022670903     62.51   .4827797 . 1
3990 2018 1    .3955588     .04801606    62.375   .4300584 . 1
3990 2019 1    .3502277    -.02746735   61.7375    .427183 . 1
3998 2011 0   .37776425             .      51.3  .28901437 . .
3998 2012 0    .3780212     .05985012     55.82   .2645878 . .
3998 2013 0   .39086115     .10227986   60.5675  .30952805 . .
3998 2014 0    .3920296     .07350206     59.93   .3094044 . .
3998 2015 0     .409129     .05291026   55.8675   .3290353 . .
3998 2016 0    .4354037     .03017044   54.4825   .4198247 . .
3998 2017 1    .4468606     .07788963   53.1675   .4841216 . .
3998 2018 1    .4743695     .11812042   52.0425   .4545583 . .
3998 2019 1    .4099103     .05429557    48.315  .36622944 . .
4024 2018 1    .4125189             .      65.8    .393684 . .
4024 2019 1    .4504824      .0569354      65.8   .4029671 . .
4030 2016 0   .17944816             .         .  .14157945 . .
4030 2017 1    .1738609     .06079136     73.39    .085012 . .
4030 2018 1   .03812203    -.10253177     73.39  .06227568 . .
4030 2019 1   .07597651     .03933999     73.39  .11335882 . .
4253 2011 0    .8300624             .   54.0225   .5575733 . 1
4253 2012 0     .794663     .02767152     54.11  .57819426 . 1
4253 2013 0     .762911     .00574854     54.08  .56922567 . 1
4253 2014 0    .7336145     .04414408     47.95  .55584306 . 1
4253 2015 0     .674849   -.023860365    47.005  .53919816 . 1
4253 2016 0    .7137975     .01831754    46.865   .7835729 . 1
4253 2017 1    .7916883    .005366382    46.155   .7873657 . 1
4253 2018 1    .9276171     .05226211        46   .7873657 . 1
4253 2019 1    1.276667      .1286042        46   .7873657 . 1
4671 2016 0    .3638019             .     40.46  .26672852 . .
4671 2017 1     .324754    -.06472622    40.415  .27678385 . .
4671 2018 1   .24909975    -.08717715   40.3725  .28023773 . .
4671 2019 1    .1459749    -.11077465   40.3325   .3019211 . .
4709 2012 0    .6662203             .   54.1675   .6243214 . 1
4709 2013 0    .6553847   -.012827914     56.34   .5741598 . 1
4709 2014 0    .5561621    -.11275136     67.73  .53494626 . 1
4709 2015 0   .54422885      .1514203    66.555   .6034432 . 1
4709 2016 0    .5054402     .13312131     67.45   .6715156 . 1
4709 2017 1    .4432106     -.1016096     67.77   .6700959 . 1
4709 2018 1    .4445682    -.00819917   69.4425   .6121173 . 1
4709 2019 1    .3930347     -.0584223     71.06   .5926768 . 1
5003 2011 0   .10918014             . 68.270004 .033950724 1 0
5003 2012 0   .29134154     .19747414     66.78  .06476504 1 0
5003 2013 0    .3004852    .020962145 71.597496  .06723971 1 0
5003 2014 0    .4097476     .12455592  85.58749  .06458065 1 0
5003 2015 0    .4219355     .09092648   75.0725  .05246822 1 0
5003 2016 0    .5785052     .03890598        75  .04491449 1 0
5003 2017 1    .6178731     .10533543     63.75  .03372275 1 0
5003 2018 1    .6006519     -.2439089    61.245 .022257343 1 0
5003 2019 1    .4547431   -.011841748     69.95  .01299266 1 0
5284 2011 0  .018213866             .   61.8175  .17273796 . 0
5284 2012 0  .009815243  -.0080831405   62.0475  .16281755 . 0
5284 2013 0   .01208981     .00230282   62.8725   .1646517 . 0
5284 2014 0  .002744237   -.008781558   63.2775   .1553238 . 0
5284 2015 0           .             .     64.07  .12738526 . 0
5284 2016 0  .002447381             .     66.43   .2207538 . 0
5284 2017 1           .             . 66.604996   .1927176 . 0
5284 2018 1           .             .    66.735   .1716542 . 0
5284 2019 1  .013277693             . 67.255005   .3334598 . 0
5574 2011 0    .3480836             .     46.55  .34627405 0 1
5574 2012 0    .3784089    .036953352     46.55   .4456995 0 1
5574 2013 0   .39252335      .0963627   46.7275   .4030058 0 1
5574 2014 0    .3991644     .08244169   47.6025   .3521859 0 1
5574 2015 0    .4926058      .2281045     43.63  .27024108 0 1
5574 2016 0   .33564585    -.02844879     42.42  .22484528 0 1
5574 2017 1    .3323925     .05325419     42.42   .3098954 0 1
5574 2018 1    .3418147     .04305329     42.42   .3253619 0 1
5574 2019 1    .3234833    .017563183    43.335  .31440115 0 1
5747 2011 0    .5204021             .     78.32  .26642716 1 1
5747 2012 0    .6293864      .3236734   78.5625   .3298945 1 1
5747 2013 0    .6302891   .0009027272        75   .4137909 1 1
5747 2014 0    .5998325      .0220675        75  .56066585 1 1
5747 2015 0    .6428508     .08915923        75   .6034054 1 1
5747 2016 0    .4589231     -.3973959 74.979996   .1681758 1 1
5747 2017 1     .436603     .03510929     74.92   .2149724 1 1
5747 2018 1    .3116025    -.05669611     74.92  .12631397 1 1
5747 2019 1   .26978314    -.13927868     74.92  .13326196 1 1
5757 2011 0    .6948834             .      73.5   .2480412 1 1
5757 2012 0    .7510648     .27430385    72.895   .3074789 1 1
5757 2013 0    .7640706     .05841199        75   .5316219 1 1
5757 2014 0    .7093325     .03783143        75   .7447475 1 1
5757 2015 0    .6998872    .010760627   59.3775   .7033976 1 1
5757 2016 0    .6356643     .09663028   65.0325   .6821414 1 1
5757 2017 1    .6636555    -.00497447    70.545    .685263 1 1
5757 2018 1    .7051849    .007421096     74.97   .6905315 1 1
5757 2019 1    .6302482    -.08133278     74.97   .6737728 1 1
5838 2017 1    .1730994             .    48.295          . . .
5838 2018 1   .12154696    -.04198895     53.43          . . .
5838 2019 1   .29709467      .1940019    57.965          . . .
6584 2014 0           .             .     35.59  .17262547 0 .
6584 2015 0           .             .   35.7275  .16551033 0 .
6584 2016 0  .006420786             . 36.045002   .1776915 0 .
6584 2017 1   .01701869     .01048473   36.4825   .1644127 0 .
6584 2018 1    .0165893  -.0004565866     37.05  .15523933 0 .
6584 2019 1   .02165312    .005970794   37.4725  .13423495 0 .
6585 2016 0       .3712             .   37.8675      .1744 . .
6585 2017 1    .2507317    .024390243     38.36   .5141463 . .
6585 2018 1    .0801282     -.3317308   37.9425  .20352563 . .
6585 2019 1    .0673516    .010273973   39.6375  .25114155 . .
6819 2011 0    .4224311             .         .  .33186385 . .
6819 2013 0    .4634391             .         .  .29120478 . .
6819 2014 0   .41481665    -.03896134         .  .28286982 . .
6819 2015 0    .2757323    -.12826598         .   .2663119 . .
6819 2016 0   .22421573    -.03026416  67.69333  .25299764 . .
6819 2017 1   .09270376     -.0811265   71.3125  .26018798 . .
6819 2018 1   .09099706     .01733537    64.805   .2271315 . .
6819 2019 1   .04310589    -.03905741    57.725   .1990771 . .
6923 2013 0  .036216702             .   56.6875   .3040631 . .
6923 2014 0   .05898787     .02825576     56.69  .24103117 . .
6923 2015 0  .001538993    -.05681711     56.69   .3023336 . .
6923 2016 0 .0007068081  -.0007757745     56.69  .26598364 . .
6923 2017 1   .08505154     .08457427     56.69   .2221029 . .
6923 2018 1   .17396885     .09772377      56.7   .2017585 . .
6923 2019 1   .13776949    -.03461726    56.715  .22542557 . .
7068 2011 0    .2255373             .     62.43   .4061304 . .
7068 2012 0    .7127093      .3236734     62.43  .07452057 . .
7068 2013 0    .3287078     -.3973959   62.4325   .2761831 . .
7068 2014 0     .270276    -.10195497    62.715   .4603901 . .
7068 2015 0   .23166804    -.02430449     62.93  .46644855 . .
7068 2016 0    .2082776   -.032873705   61.8575   .5058107 . .
7068 2017 1   .14068018      .0481768   60.7575   .3665709 . .
7068 2018 1    .1372827    .007937861   60.5625  .57788414 . .
7068 2019 1    .0975527    -.02666694   59.8225   .5419554 . .
7077 2011 0   .10389227             .   59.8425   .3433261 1 .
7077 2012 0  .017583195    -.10715128      67.2  .41385415 1 .
7077 2013 0     .084119    .068510376      67.2   .3831972 1 .
7077 2014 0    .0899088    .010309272      67.2   .3787669 1 .
7077 2015 0    .1970248      .1220661      67.2  .25454545 1 .
7077 2016 0    .3171595     .12967914      67.2  .20603964 1 .
7077 2017 1   .18523507    .020649694      67.2   .5800278 1 .
7077 2018 1   .24368845      .0698004      67.2   .5597058 1 .
7633 2013 0   .09921045             .     72.71  .27874687 1 .
7633 2014 0   .15525705     .06707676     72.71  .22930136 1 .
7633 2015 0    .1780412     .05717413     72.71  .17021276 1 .
7633 2016 0   .22957626     .05288886     72.71  .16778368 1 .
7633 2017 1    .5346775      .3211457     72.71  .14916554 1 .
7633 2018 1    .4951785    -.23943974    70.505   .1963312 1 .
7633 2019 1   .58521026      .1795093   46.5625  .15390864 1 .
8183 2011 0           .             .     49.94    .123459 . .
8183 2012 0           .             .     51.77  .15354125 . .
8183 2013 0           .             .     51.77  .14628233 . .
8183 2014 0   .03893272             .     51.77  .23652928 . .
8183 2015 0   .05951943      .0249954     51.77  .27865493 . .
8183 2016 0    .1848452     .13487032     51.77  .28920108 . .
8183 2017 1   .04137121    -.18321203     51.77   .3979338 . .
8183 2018 1           .             .     51.77    .375936 . .
8183 2019 1           .             .     51.77   .3279054 . .
8312 2011 0    .1763868             .     72.61  .17171596 1 0
8312 2012 0   .21140324     .03746872     72.61  .16444176 1 0
8312 2013 0   .25428674    .024261447     72.61  .14606898 1 0
8312 2014 0    .2266215   -.033824813 70.255005   .1267885 1 0
8312 2015 0   .16589355    -.06255081 64.652504  .11423653 1 0
8312 2016 0    .1230484   -.026483895    61.715  .09990086 1 0
8312 2017 1  .072696775    -.04238765     57.96   .0845092 1 0
8312 2018 1   .02358453    -.04777054     57.96  .07656267 1 0
8312 2019 1   .04078949     .02089869     57.99 .066213675 1 0
8523 2014 0  .024087144             .    52.555 .069193006 . 0
8523 2015 0  .025539907  -.0039436608   52.7775  .08976526 . 0
8523 2016 0 .0007068081   -.022773974     52.78 .066438355 . 0
8523 2017 1  .012088436     .01161126     52.37  .11340863 . 0
8523 2018 1  .017797846    .007703546     51.32  .18780713 . 0
8523 2019 1  .007172038    -.00499319   50.8475   .1493418 . 0
8628 2017 1   .25019747             .      73.6          . . .
8628 2018 1    .1524776     .02512815   73.5925  .56679064 . .
8628 2019 1   .11357084    -.04262767 73.582504   .5686779 . .
8893 2011 0    .3596167             .   68.1725   .4024749 1 .
8893 2012 0   .30688825    .013834873     72.56   .3564017 1 .
8893 2013 0    .1736542    -.10404785    73.565   .3648074 1 .
end

Kindly see my additional information about the data

time_dummy is 0 if the year is in the range 2011-2016 and 1 otherwise (2017-2019).
treat2 is the first treatment group based on fispro, 1 if fispro>68 and 0 if fispro<45.
treat4 is the second treatment group based on nppe_ta_w, 1 if nppe_ta_w>68 and 0 if nppe_ta_w<9

*Now for the parallel trend I have used the following codes

preserve
collapse (mean) lever_w, by(treat2 year)
reshape wide lever_w, i(year) j(treat2) 
variable treat2 contains missing values
r(498);

 
graph twoway connect lever_w* year if year < 2017  

. restore
nothing to restore
r(622);

*I assume the problem is with 'treat2' which has missing values. Hence how to deal? Can I drop those? I dropped them

drop if treat2==.
*(138 observations deleted)
 preserve

. collapse (mean) lever_w, by(treat2 year)

. reshape wide lever_w, i(year) j(treat2)
(j = 0 1)

Data                               Long   ->   Wide
-----------------------------------------------------------------------------
Number of observations               18   ->   9           
Number of variables                   3   ->   3           
j variable (2 values)            treat2   ->   (dropped)
xij variables:
                                lever_w   ->   lever_w0 lever_w1
-----------------------------------------------------------------------------

. graph twoway connect lever_w* year if year < 2017

. restore

Which is one is correct?
Without missing.gph


I followed the instructions of Clyde Schechter based on this post in #2.

[Neelakanda Krishna]

While trying to find an answer for my post in #4, I found something similar in the post https://www.statalist.org/forums/for...19#post1596819 by Andrew Musau and I tweaked the code in the way I understood

preserve
collapse lever_w, by(year time_dummy)
tw (line lever_w year if time_dummy) (line lever_w year if  !time_dummy)
restore

But the graph look something strange for me. So I am squarely back to my basic question that if I have an unbalanced panel as I have demonstrated in post#4, how to plot parallel trend graphs?

[Maria Boutchkova]

Hello Neelakanda,
I tested your code and find the twoway command in #4 produces what you want. It shows you the average of lever_w by treated status pre shock. Before going further I would caution that you need to ensure covariate balance and common support. I would also point out that after dropping your missing values of treated2, you are left with only 4 controls and 10 treated countries, which at the very least means low statistical power, so you cannot detect significance even if there is an effect. For treated4, things are worse with only 1 control and 6 treated countries. You can see this in action if you run Clyde Schechter helpful code in #2 and both time trends for treated and controls are not statistically signifcantly different from 0 but one is positive, the other negative. This invites the obvious suspicion - if we had more observations then we could catch a significant difference in the trends and therefore a violation of the parallel trends assumption. Given the low power any claim that it holds is not reliable.

[Neelakanda Krishna]

Hi Professor Maria Boutchkova. Happy to see you here and I have read your papers related to CG.

I tested your code and find the twoway command in #4 produces what you want

Professor, which one should I pursue if I may ask it again? The one without removing missing (hence graph is not understandable) versus the one after removing missing .
While browsing through the forum I got to see your post in https://www.statalist.org/forums/for...94#post1622794. I have tweaked it and found some graphs but I am not quite sure are they correct or not?

xtset co_code year,yearly

local tr_var treat4
local vars lever_w nppe_ta_w fispro
local v_max: word count `vars'
local yr_st 2011
local yr_end 2019
local cond year >= `yr_st' & year <= `yr_end'
forvalues i= 1/`v_max' {
local v: word `i' of `vars'
local lab: var label `v'
egen mean_`v' = mean(`v'), by(`tr_var' year )
line mean_`v' year if `tr_var' == 1 & `cond', c(L)
    || line mean_`v' year if `tr_var' == 0 & `cond', c(L)
    legend(order(1 "Treated" 2 "Controls") size(small) ) scheme(sj)
    title("`lab'", size(small)) ylabel(, labsize(vsmall) ) xlabel(`yr_st'(1)`yr_end')
    xscale(r(`yr_st' `yr_end')) xtitle("") ytitle("")
    saving(par_tr_`v', replace)
graph export par_tr_`v'.png, replace
}
*

forvalues i= 1/`v_max' {
local v: word `i' of `vars'
local lab: var label `v'
egen mean_`v' = mean(`v'), by(`tr_var' year )
line mean_`v' year if `tr_var' == 1 & `cond', c(L) || line mean_`v' year if `tr_var' == 0 & `cond', c(L) legend(order(1 "Treated" 2 "Controls") size(small) ) scheme(sj) title("`lab'", size(small)) ylabel(, labsize(vsmall) ) xlabel(`yr_st'(1)`yr_end') xscale(r(`yr_st' `yr_end')) xtitle("") ytitle("") saving(par_tr_`v', replace)
graph export par_tr_`v'.png, replace

As you suggested where is the problem? Is it because of lack of data?
I made treated2 by classifying ownership (ownership, earlier labelled as fispro) into terciles and labelled 0 for those value in the top decile and 1 in the bottom decile (hence leaving the median).

Edited 1000 OBSERVATIONS ADDED

* Example generated by -dataex-. For more info, type help dataex
clear
input long co_code int year float(time_dummy ownershi nppe_ta_w treat2 treat4 lever_w size_w)
   11 2011 0   57.5825    .3451234 . 1    .5504084  7.692159
   11 2012 0   61.9625   .29354736 . 1    .5697871  7.931967
   11 2013 0     60.62    .4899267 . 1    .5391156  8.025386
   11 2014 0     53.69    .4550365 . 1   .51597077  8.138857
   11 2015 0   51.4375   .42844895 . 1    .4759782  8.160204
   11 2016 0    49.085    .4218914 . 1    .4625662  8.169874
   11 2017 1    43.595    .4503756 . 1   .39045715  8.268808
   11 2018 1   43.7825   .45255655 . 1   .33070305  8.310906
   11 2019 1     44.23    .4434838 . 1    .2839154  8.385649
  289 2011 0   57.8775    .3158847 . 1   .54094803  7.413488
  289 2012 0   60.6875     .425365 . 1    .5555683  7.465369
  289 2013 0   63.6125    .4299606 . 1   .51333773  7.327781
  289 2014 0     63.62    .3889136 . 1   .48577145  7.207416
  363 2011 0      64.9    .6755958 1 1    .4761355  9.358657
  363 2012 0      64.9    .6276743 1 1    .4731195  9.282261
  363 2013 0      64.9    .5426359 1 1   .53195494  9.338382
  363 2014 0 64.832504    .4036047 1 1    .4994195  9.323365
  363 2015 0     64.44     .349384 1 1   .51551414   9.20133
  363 2016 0     64.44      .47962 1 1    .5284721   9.21132
  363 2017 1     64.44    .4654458 1 1    .5849603  9.158089
  363 2018 1     64.44    .2198459 1 1    .4416465  9.171402
  363 2019 1     64.44   .20597345 1 1     .689352   9.14435
  414 2016 0     74.19   .03726708 . .    .4542443  6.180017
  414 2017 1     74.19    .0480226 . .  .006420134  5.964607
  414 2018 1   74.3125   .05113108 . .  .005577936  5.776723
  415 2018 1     64.93    .2330087 . .           .  7.591811
  415 2019 1     64.93   .24387904 . .           .  7.536204
  771 2013 0         .   .29406333 0 .    .4651715  6.630683
  771 2014 0     44.68   .24340977 0 .    .5203515  6.897806
  771 2015 0     45.03    .2141985 0 .     .461695  7.113387
  771 2016 0    57.025   .15930188 0 .    .5473848  7.495042
  771 2017 1  57.98667   .12169435 0 .    .5459776  7.675732
  771 2018 1     57.03   .11059617 0 .    .4951338  7.772121
  771 2019 1     67.16   .10220815 0 .    .4645513  7.871731
  783 2011 0     40.21   .25982013 0 .   .19831736  7.452112
  783 2012 0    41.865   .24346343 0 .    .2092255  7.504777
  783 2013 0     45.51    .1896934 0 .   .19775394  7.699978
  783 2014 0     49.73   .18619993 0 .   .13007422  7.688272
  783 2015 0    52.345   .24750434 0 .   .08677534  7.688822
  783 2016 0    53.755   .22629647 0 .  .033318035  7.686621
  783 2017 1   38.8675   .22433777 0 .  .025697127  7.672339
  783 2018 1     31.88    .2284505 0 .  .014560171  7.673084
  783 2019 1   34.3175    .2313115 0 . .0009790963  7.622028
 1120 2011 0     61.65    .2067098 . .  .016338103   9.46848
 1120 2012 0     61.65   .22745897 . .  .032134537   9.61161
 1120 2013 0     61.65    .1946061 . .   .08688986  9.826855
 1120 2014 0     61.65    .1633293 . .   .05394961 10.014935
 1120 2015 0     61.65    .2076624 . .   .03865692  10.14689
 1120 2016 0     61.65   .23243997 . .   .06567325 10.239388
 1120 2017 1     61.65   .20205106 . .     .043705  10.37602
 1120 2018 1     58.47   .18189026 . .   .03458266 10.482662
 1120 2019 1     58.47    .2030323 . .  .031668555 10.609154
 2248 2017 1     63.53    .4664207 . .    .1482503  7.934478
 2248 2018 1     63.53    .4828343 . .   .15626974   8.00933
 2717 2011 0    64.755    .5158676 1 1    .3987688 10.212482
 2717 2012 0   63.8525    .5372157 1 1    .5396248 10.179546
 2717 2013 0   64.2625    .4523312 1 1     .521483 10.359493
 2717 2014 0     64.49    .4870189 1 1    .5139446 10.229534
 2717 2015 0    64.445    .4551124 1 1    .4747386  10.36415
 2717 2016 0   64.4375    .4537038 1 1   .31001255 10.589538
 2717 2017 1   56.1875    .4684093 1 1   .26234335 10.676764
 2717 2018 1     54.02    .2361021 1 1    .2209677  11.94411
 2717 2019 1    55.575   .22883637 1 1   .19191967 11.893708
 2842 2011 0    60.135   .09140518 . 0   .24950735  6.491785
 2842 2012 0     60.78   .08590434 . 0    .3842184  6.770675
 2842 2013 0   61.4275    .0832793 . 0    .3622974  6.984253
 2842 2014 0   61.8375    .0765293 . 0    .3780394  7.066552
 2842 2015 0     61.84   .07563667 . 0    .3651952  7.071573
 2842 2016 0     61.84   .08096717 . 0   .19154836  6.804171
 2842 2017 1     61.84   .09775044 . 0  .012137886  6.426327
 2842 2018 1     61.84   .07905366 . 0   .11468551  6.541318
 2842 2019 1     61.84    .0624494 . 0    .1701168  6.762383
 3335 2011 0   74.3925    .2621498 1 .    .3992996  7.243656
 3335 2012 0     74.33    .1961064 1 .    .1974824  7.581821
 3335 2013 0     74.33    .1971979 1 .   .15542907  7.733684
 3335 2014 0     74.33    .2117583 1 .   .02286336  7.778254
 3335 2015 0 74.337494   .22432104 1 .  .028543843  7.775822
 3335 2016 0     74.35   .23201247 1 .   .03890818  7.826403
 3335 2017 1   74.3525    .2071913 1 .   .05525101  7.927613
 3335 2018 1   74.3875   .16878895 1 .   .10858244  8.108293
 3335 2019 1    74.845   .25074002 1 .    .2077677 8.2735405
 3990 2011 0   54.8725    .4358847 . 1    .5109358  8.563141
 3990 2015 0      61.4    .4448767 . 1    .4456229  9.215437
 3990 2016 0     62.34     .441005 . 1   .43831205  9.308274
 3990 2017 1     62.51    .4827797 . 1    .4056455  9.385167
 3990 2018 1    62.375    .4300584 . 1    .3955588   9.53976
 3990 2019 1   61.7375     .427183 . 1    .3502277  9.585972
 3998 2011 0      51.3   .28901437 . .   .37776425  9.546169
 3998 2012 0     55.82    .2645878 . .    .3780212  9.717851
 3998 2013 0   60.5675   .30952805 . .   .39086115  9.987824
 3998 2014 0     59.93    .3094044 . .    .3920296 10.192468
 3998 2015 0   55.8675    .3290353 . .     .409129  10.28826
 3998 2016 0   54.4825    .4198247 . .    .4354037 10.297828
 3998 2017 1   53.1675    .4841216 . .    .4468606 10.463384
 3998 2018 1   52.0425    .4545583 . .    .4743695     10.69
 3998 2019 1    48.315   .36622944 . .    .4099103 10.978138
 4024 2018 1      65.8     .393684 . .    .4125189  9.126492
 4024 2019 1      65.8    .4029671 . .    .4504824  9.173573
 4030 2016 0         .   .14157945 . .   .17944816   6.26435
 4030 2017 1     73.39     .085012 . .    .1738609  6.726233
 4030 2018 1     73.39   .06227568 . .   .03812203  6.938188
 4030 2019 1     73.39   .11335882 . .   .07597651  6.977934
 4253 2011 0   54.0225    .5575733 . 1    .8300624 11.989892
 4253 2012 0     54.11   .57819426 . 1     .794663 12.068917
 4253 2013 0     54.08   .56922567 . 1     .762911 12.117258
 4253 2014 0     47.95   .55584306 . 1    .7336145 12.218475
 4253 2015 0    47.005   .53919816 . 1     .674849 12.267224
 4253 2016 0    46.865    .7835729 . 1    .7137975  12.23711
 4253 2017 1    46.155    .7873657 . 1    .7916883 12.140344
 4253 2018 1        46    .7873657 . 1    .9276171 12.039883
 4253 2019 1        46    .7873657 . 1    1.276667  11.82667
 4671 2016 0     40.46   .26672852 . .    .3638019 10.181612
 4671 2017 1    40.415   .27678385 . .     .324754 10.113408
 4671 2018 1   40.3725   .28023773 . .   .24909975  10.07854
 4671 2019 1   40.3325    .3019211 . .    .1459749 10.048293
 4709 2012 0   54.1675    .6243214 . 1    .6662203  10.44225
 4709 2013 0     56.34    .5741598 . 1    .6553847 10.439264
 4709 2014 0     67.73   .53494626 . 1    .5561621 10.418832
 4709 2015 0    66.555    .6034432 . 1   .54422885  10.76657
 4709 2016 0     67.45    .6715156 . 1    .5054402  11.14619
 4709 2017 1     67.77    .6700959 . 1    .4432106 11.071162
 4709 2018 1   69.4425    .6121173 . 1    .4445682  11.04983
 4709 2019 1     71.06    .5926768 . 1    .3930347 11.034452
 5003 2011 0 68.270004  .033950724 1 0   .10918014  7.997966
 5003 2012 0     66.78   .06476504 1 0   .29134154  8.149081
 5003 2013 0 71.597496   .06723971 1 0    .3004852  8.190493
 5003 2014 0  85.58749   .06458065 1 0    .4097476   8.24273
 5003 2015 0   75.0725   .05246822 1 0    .4219355  8.456126
 5003 2016 0        75   .04491449 1 0    .5785052  8.210152
 5003 2017 1     63.75   .03372275 1 0    .6178731  8.331225
 5003 2018 1    61.245  .022257343 1 0    .6006519  8.018691
 5003 2019 1     69.95   .01299266 1 0    .4547431  8.271267
 5284 2011 0   61.8175   .17273796 . 0  .018213866  5.136974
 5284 2012 0   62.0475   .16281755 . 0  .009815243  5.154447
 5284 2013 0   62.8725    .1646517 . 0   .01208981   5.15733
 5284 2014 0   63.2775    .1553238 . 0  .002744237  5.205105
 5284 2015 0     64.07   .12738526 . 0           .  5.267343
 5284 2016 0     66.43    .2207538 . 0  .002447381   5.31959
 5284 2017 1 66.604996    .1927176 . 0           .  5.416989
 5284 2018 1    66.735    .1716542 . 0           .  5.483136
 5284 2019 1 67.255005    .3334598 . 0  .013277693  5.574433
 5574 2011 0     46.55   .34627405 0 1    .3480836   6.41001
 5574 2012 0     46.55    .4456995 0 1    .3784089  6.429235
 5574 2013 0   46.7275    .4030058 0 1   .39252335  6.674309
 5574 2014 0   47.6025    .3521859 0 1    .3991644  6.888878
 5574 2015 0     43.63   .27024108 0 1    .4926058  7.300406
 5574 2016 0     42.42   .22484528 0 1   .33564585  7.602701
 5574 2017 1     42.42    .3098954 0 1    .3323925   7.78705
 5574 2018 1     42.42    .3253619 0 1    .3418147  7.893721
 5574 2019 1    43.335   .31440115 0 1    .3234833  8.004666
 5747 2011 0     78.32   .26642716 1 1    .5204021   13.3713
 5747 2012 0   78.5625    .3298945 1 1    .6293864  13.58085
 5747 2013 0        75    .4137909 1 1    .6302891  13.58085
 5747 2014 0        75   .56066585 1 1    .5998325  13.58085
 5747 2015 0        75    .6034054 1 1    .6428508  13.58085
 5747 2016 0 74.979996    .1681758 1 1    .4589231  12.94253
 5747 2017 1     74.92    .2149724 1 1     .436603  13.07622
 5747 2018 1     74.92   .12631397 1 1    .3116025  13.24635
 5747 2019 1     74.92   .13326196 1 1   .26978314 12.974212
 5757 2011 0      73.5    .2480412 1 1    .6948834 12.773135
 5757 2012 0    72.895    .3074789 1 1    .7510648 13.149864
 5757 2013 0        75    .5316219 1 1    .7640706 13.212224
 5757 2014 0        75    .7447475 1 1    .7093325  13.34137
 5757 2015 0   59.3775    .7033976 1 1    .6998872 13.370268
 5757 2016 0   65.0325    .6821414 1 1    .6356643  13.58085
 5757 2017 1    70.545     .685263 1 1    .6636555  13.58085
 5757 2018 1     74.97    .6905315 1 1    .7051849  13.53073
 5757 2019 1     74.97    .6737728 1 1    .6302482 13.521702
 5838 2017 1    48.295           . . .    .1730994 4.4485164
 5838 2018 1     53.43           . . .   .12154696 4.5053496
 5838 2019 1    57.965           . . .   .29709467  4.670021
 6584 2014 0     35.59   .17262547 0 .           .   7.09465
 6584 2015 0   35.7275   .16551033 0 .           .  7.167964
 6584 2016 0 36.045002    .1776915 0 .  .006420786  7.199977
 6584 2017 1   36.4825    .1644127 0 .   .01701869  7.182504
 6584 2018 1     37.05   .15523933 0 .    .0165893  7.180907
 6584 2019 1   37.4725   .13423495 0 .   .02165312  7.237131
 6585 2016 0   37.8675       .1744 . .       .3712  4.241327
 6585 2017 1     38.36    .5141463 . .    .2507317  4.629863
 6585 2018 1   37.9425   .20352563 . .    .0801282  4.241327
 6585 2019 1   39.6375   .25114155 . .    .0673516  4.472781
 6819 2011 0         .   .33186385 . .    .4224311   7.34116
 6819 2013 0         .   .29120478 . .    .4634391 8.2886095
 6819 2014 0         .   .28286982 . .   .41481665  8.309677
 6819 2015 0         .    .2663119 . .    .2757323 8.3361025
 6819 2016 0  67.69333   .25299764 . .   .22421573  8.416311
 6819 2017 1   71.3125   .26018798 . .   .09270376  8.670841
 6819 2018 1    64.805    .2271315 . .   .09099706  8.900767
 6819 2019 1    57.725    .1990771 . .   .04310589  9.002886
 6923 2013 0   56.6875    .3040631 . .  .036216702  7.890882
 6923 2014 0     56.69   .24103117 . .   .05898787  8.055095
 6923 2015 0     56.69    .3023336 . .  .001538993  8.065862
 6923 2016 0     56.69   .26598364 . . .0007068081  8.226359
 6923 2017 1     56.69    .2221029 . .   .08505154  8.340551
 6923 2018 1      56.7    .2017585 . .   .17396885  8.449856
 6923 2019 1    56.715   .22542557 . .   .13776949  8.458992
 7068 2011 0     62.43    .4061304 . .    .2255373  8.614955
 7068 2012 0     62.43   .07452057 . .    .7127093 10.295435
 7068 2013 0   62.4325    .2761831 . .    .3287078  9.219914
 7068 2014 0    62.715    .4603901 . .     .270276  9.095569
 7068 2015 0     62.93   .46644855 . .   .23166804  9.149942
 7068 2016 0   61.8575    .5058107 . .    .2082776  9.109823
 7068 2017 1   60.7575    .3665709 . .   .14068018  9.921451
 7068 2018 1   60.5625   .57788414 . .    .1372827 10.005457
 7068 2019 1   59.8225    .5419554 . .    .0975527  10.10545
 7077 2011 0   59.8425    .3433261 1 .   .10389227  6.925005
 7077 2012 0      67.2   .41385415 1 .  .017583195  6.742173
 7077 2013 0      67.2    .3831972 1 .     .084119  6.861292
 7077 2014 0      67.2    .3787669 1 .    .0899088  6.916517
 7077 2015 0      67.2   .25454545 1 .    .1970248  7.098376
 7077 2016 0      67.2   .20603964 1 .    .3171595  7.148031
 7077 2017 1      67.2    .5800278 1 .   .18523507  7.804007
 7077 2018 1      67.2    .5597058 1 .   .24368845   7.86722
 7633 2013 0     72.71   .27874687 1 .   .09921045  8.865015
 7633 2014 0     72.71   .22930136 1 .   .15525705  8.982875
 7633 2015 0     72.71   .17021276 1 .    .1780412  9.233266
 7633 2016 0     72.71   .16778368 1 .   .22957626  9.240899
 7633 2017 1     72.71   .14916554 1 .    .5346775  9.313348
 7633 2018 1    70.505    .1963312 1 .    .4951785  8.995661
 7633 2019 1   46.5625   .15390864 1 .   .58521026 9.1949625
 8183 2011 0     49.94     .123459 . .           .  7.942611
 8183 2012 0     51.77   .15354125 . .           .   7.96419
 8183 2013 0     51.77   .14628233 . .           .  8.113966
 8183 2014 0     51.77   .23652928 . .   .03893272  8.256556
 8183 2015 0     51.77   .27865493 . .   .05951943  8.376735
 8183 2016 0     51.77   .28920108 . .    .1848452 8.5515175
 8183 2017 1     51.77    .3979338 . .   .04137121   8.35679
 8183 2018 1     51.77     .375936 . .           .  8.411943
 8183 2019 1     51.77    .3279054 . .           . 8.4857645
 8312 2011 0     72.61   .17171596 1 0    .1763868   9.35725
 8312 2012 0     72.61   .16444176 1 0   .21140324  9.371251
 8312 2013 0     72.61   .14606898 1 0   .25428674  9.286829
 8312 2014 0 70.255005    .1267885 1 0    .2266215  9.262895
 8312 2015 0 64.652504   .11423653 1 0   .16589355  9.254884
 8312 2016 0    61.715   .09990086 1 0    .1230484  9.358717
 8312 2017 1     57.96    .0845092 1 0  .072696775   9.42563
 8312 2018 1     57.96   .07656267 1 0   .02358453  9.444258
 8312 2019 1     57.99  .066213675 1 0   .04078949  9.614592
 8523 2014 0    52.555  .069193006 . 0  .024087144  6.479738
 8523 2015 0   52.7775   .08976526 . 0  .025539907  6.277583
 8523 2016 0     52.78  .066438355 . 0 .0007068081  6.369901
 8523 2017 1     52.37   .11340863 . 0  .012088436  6.443654
 8523 2018 1     51.32   .18780713 . 0  .017797846  6.623932
 8523 2019 1   50.8475    .1493418 . 0  .007172038  7.004428
 8628 2017 1      73.6           . . .   .25019747  6.227327
 8628 2018 1   73.5925   .56679064 . .    .1524776  6.902642
 8628 2019 1 73.582504    .5686779 . .   .11357084  6.878532
 8893 2011 0   68.1725    .4024749 1 .    .3596167  8.575688
 8893 2012 0     72.56    .3564017 1 .   .30688825  8.780373
 8893 2013 0    73.565    .3648074 1 .    .1736542  8.880307
 8893 2014 0     73.76   .29155213 1 .   .13743457  9.158489
 8893 2015 0    73.785   .24718437 1 .   .06304603  9.348405
 8893 2016 0     73.78    .2980761 1 .   .06197859  9.615505
 8893 2017 1 71.472496    .3127501 1 .  .003652219  9.833398
 8893 2018 1    70.485    .4222405 1 .  .001664384 10.116738
 8893 2019 1   70.5125    .3906587 1 .   .01824345 10.300138
 9395 2011 0     74.93   .05111475 1 .   .11527894  8.238959
 9395 2012 0     74.93    .2571288 1 .    .3116961 8.6090975
 9395 2013 0     74.93   .19746792 1 .    .3572313  8.834031
 9395 2014 0     74.93   .26023173 1 .     .457702  9.077061
 9395 2015 0     74.93   .21505913 1 .   .36436895  9.127795
 9395 2016 0     74.93   .25837252 1 .    .3786785  8.816601
 9395 2017 1     74.93   .17021357 1 .    .3189563  9.264687
 9395 2018 1     74.93   .05690428 1 .    .3941053  9.218665
 9395 2019 1     74.93   .04001473 1 .    .3029013  9.069848
 9505 2016 0     72.16           . . .     .152146  6.552936
 9505 2017 1     72.16   .12531224 . .   .06109492  6.867766
 9505 2018 1  74.58666   .10190892 . .    .1964527  7.193385
 9505 2019 1     74.59   .07547297 . .     .163536  7.303372
 9793 2011 0    17.705   .25910693 0 .   .21299487   8.50427
 9793 2012 0   29.3675    .2187088 0 .    .2090286   8.57731
 9793 2013 0   30.3325   .17818294 0 .   .16243246  8.608714
 9793 2014 0     30.14    .1556313 0 .   .22626795  8.667095
 9793 2015 0     31.02   .17787054 0 .    .2123294 8.7782955
 9793 2016 0   28.9775    .1507736 0 .   .15408486  8.815014
 9793 2017 1     27.64    .1498146 0 .    .1957748   8.94361
 9793 2018 1    27.795   .25802383 0 .    .2839797  9.160341
 9793 2019 1     27.95   .23870453 0 .    .3059011  9.225455
10714 2011 0   42.4325    .3999125 . 1    .3242179  6.124902
10714 2012 0   43.5225    .4100446 . 1    .3694196  6.104793
10714 2013 0   49.4075    .4615182 . 1    .3186611  5.938591
10714 2014 0   51.8375    .4776339 . 1   .22189525  5.828357
10714 2015 0     52.42   .42298645 . 1    .1945914  5.829534
10714 2016 0   51.5825    .3957268 . 1   .27926573  5.806038
10714 2017 1     50.53    .5967621 . 1    .1778991  6.275139
10714 2018 1     47.23    .4623206 . 1   .11722489  6.505485
10714 2019 1     45.67    .4818109 . 1   .11691818  6.444926
10735 2018 1    23.645   .01147052 . .   .56434965  6.077413
10735 2019 1      24.8  .007799805 . .    .4761131  6.422273
10867 2018 1 14.936667  .005804029 . .    .0938887  5.679831
10867 2019 1     16.27  .002844372 . .    .1117432  5.505738
10884 2015 0     66.71   .18231797 1 .   .24478763 10.884612
10884 2016 0    66.925    .1856919 1 .    .1184179 10.924565
10884 2017 1 66.432495    .2083274 1 .    .0985217 11.111485
10884 2018 1     65.96   .23392244 1 .   .12456716 11.290808
10884 2019 1   65.9975   .24438003 1 .   .10957008 11.363976
10903 2011 0     74.19    .3549336 1 1    .4359254  7.921028
10903 2012 0     74.19    .4041751 1 1    .4086082  8.013906
10903 2013 0     74.19    .3397412 1 1    .3936641  8.218517
10903 2014 0     74.19    .3510908 1 1    .3441462  8.384233
10903 2015 0    74.195   .34103554 1 1    .3081732 8.3817625
10903 2016 0    74.195    .4093392 1 1   .24548458  8.420682
10903 2017 1 74.192505    .3772037 1 1   .20690367  8.666079
10903 2018 1     74.19   .48871905 1 1   .24919914  8.891965
10903 2019 1     74.19    .4290367 1 1    .1883165   9.08022
11063 2012 0    71.375    .4086149 1 1   .25624168 10.305205
11063 2013 0   72.1425    .4173737 1 1    .2288777  10.36906
11063 2014 0    70.515    .3436972 1 1   .25744537 10.559558
11063 2015 0   69.9225    .3414371 1 1    .1753668   10.4644
11063 2016 0    69.455   .38253945 1 1   .16024715 10.374425
11063 2017 1     69.11    .3384885 1 1   .17343117 10.434072
11063 2018 1    69.755    .3018872 1 1   .13159414 10.492163
11063 2019 1     70.01   .27960724 1 1   .14713919 10.612953
11303 2011 0   43.6725    .2115609 0 .   .14599228  9.095243
11303 2012 0     43.71    .2617737 0 .   .13354772  9.075402
11303 2013 0     43.71   .25372303 0 .   .12613228  9.079833
11303 2014 0     43.97   .24178247 0 .   .12190944  9.029778
11303 2015 0   44.9175    .1917019 0 .   .16098036  9.041933
11303 2016 0    47.555    .2281567 0 .   .17040256  8.774746
11303 2017 1   48.9175   .07330542 0 .    .1236687  8.969402
11303 2018 1     49.01    .0849358 0 .   .10148738  8.732111
11303 2019 1     49.09   .07467589 0 .   .10207336  8.753482
11443 2011 0     30.61   .22240414 0 .  .022922635  7.290156
11443 2012 0      35.2    .2101533 0 .  .014463407  7.231866
11443 2013 0   35.3675   .16717307 0 .  .029391706  7.385665
11443 2014 0     35.43   .16508666 0 .   .12435195  7.208008
11443 2015 0     36.66    .1189514 0 .   .14920734  6.872231
11443 2016 0     40.35   .05612394 0 .   .05729319  7.221397
11443 2017 1     40.35   .03764386 0 . .0032314756  7.475283
11443 2018 1     40.35   .03989075 0 . .0040552844  7.790117
11443 2019 1     75.38   .04168704 0 .  .003015485  7.805475
11599 2011 0   30.2625    .4531034 0 1    .7372581 12.012906
11599 2012 0   34.0075    .4159955 0 1    .7589632 12.263445
11599 2013 0     37.95     .352842 0 1    .7361039  12.50905
11599 2015 0     37.31   .28738052 0 1    .6070604 12.611395
11599 2016 0   31.9075   .45181414 0 1    .6214933 12.816352
11599 2017 1     28.82    .4317759 0 1    .7913945 12.813807
11599 2018 1   10.1225    .4471545 0 1    .9886845 12.737926
11599 2019 1     8.985    .4414001 0 1    .9250221 12.717113
11664 2014 0     54.61   .57687455 . .   .18137333  7.144407
11664 2015 0     54.61    .0374094 . .    .2226639  7.157034
11664 2016 0     54.61   .03094051 . .   .16549416  7.288996
11664 2017 1     54.61    .0413414 . .    .0853911   7.13521
11664 2018 1   55.1225   .05035793 . .   .04920596  7.102746
11664 2019 1     56.66   .04757578 . .  .013711077  7.098954
11853 2011 0     34.61    .6427076 0 1  .018422889  6.574378
11853 2012 0   36.9125    .5668238 0 1   .04272465  6.488749
11853 2013 0   38.3625    .4422151 0 1   .06153023  6.616868
11853 2014 0   41.6675   .58521163 0 1   .04059007  6.995216
11853 2015 0   41.9125   .57417226 0 1  .013794567   6.84843
11853 2016 0   41.3225    .4956171 0 1  .015356068  7.354299
11853 2017 1     44.25    .3642129 0 1   .13055615  8.213273
11853 2018 1     44.67   .27576116 0 1   .11443117  8.310464
11853 2019 1    45.185   .21184663 0 1   .06489332  8.364788
11955 2011 0     27.06    .4782368 0 1    .9879131  9.485795
11955 2012 0     27.06   .53987557 0 1    1.198599  9.303639
11955 2013 0     27.06    .4990572 0 1   1.2171613  9.303567
11955 2014 0     27.06    .4631255 0 1    1.222319  9.306341
11955 2015 0     27.06    .4501233 0 1    1.272524  9.251165
11955 2016 0     27.06    .6535733 0 1     1.34504  8.800792
11955 2017 1     27.06    .7408362 0 1   1.3218213  8.563695
11955 2018 1     27.06    .7873657 0 1     1.34504  8.346523
11955 2019 1     27.06    .7873657 0 1     1.34504  8.246565
12011 2011 0    67.965   .10899951 1 0  .007140333 10.373095
12011 2012 0     68.56   .11574738 1 0 .0007068081 10.369103
12011 2013 0     68.56   .13533567 1 0 .0007068081 10.392533
12011 2014 0     68.56   .11730194 1 0 .0007068081 10.536582
12011 2015 0    68.565   .10378223 1 0 .0007068081 10.547185
12011 2016 0     68.58    .0857952 1 0 .0007068081 10.682072
12011 2017 1     68.58  .073976606 1 0           .  10.76361
12011 2018 1     68.58   .05640911 1 0 .0007068081 10.780345
12011 2019 1     68.58  .033312976 1 0           . 10.722203
12281 2011 0   22.5225           . 0 0           .  4.241327
12281 2012 0     22.52           . 0 0           .  4.241327
12281 2013 0     22.52  .008403362 0 0           .  4.241327
12517 2019 1     28.06    .4007563 . .  .012943916 10.717544
12577 2015 0    64.325    .5763921 1 1    .4458165  7.663784
12577 2016 0    59.535    .5683539 1 1    .3959631  7.571525
12577 2017 1     55.05    .4519971 1 1    .3570905  7.688547
12577 2018 1     55.05   .54441684 1 1    .5438875   8.01384
12577 2019 1     55.05    .5676948 1 1    .3533458  8.092943
12774 2011 0     30.43   .27334005 0 .   .08674587  7.772163
12774 2012 0     30.43    .2795186 0 .   .12888403  7.734121
12774 2013 0     30.43   .28621924 0 .    .1812474   7.67327
12774 2014 0     30.43    .2574035 0 .   .18667145  7.707647
12774 2015 0     30.59    .2443476 0 .    .2360392  7.697394
12774 2016 0     30.75    .2189945 0 .   .24185915  7.665894
12774 2017 1     30.75    .2752235 0 .   .27917102   7.45159
12774 2018 1    30.755    .1655004 0 .   .23691526  7.643195
12774 2019 1     30.76   .14636955 0 .   .20652993  7.721925
12790 2017 1         .   .28302872 . .    .3284595  5.948035
12790 2018 1     72.67    .2376362 . .     .336337  6.167936
12790 2019 1     72.67   .19319107 . .    .2405929  6.419832
12881 2015 0         .    .3497094 . .    .3629386  9.218685
12881 2016 0         .   .37893185 . .    .3203077  9.321703
12881 2017 1        44    .3649006 . .    .3452397  9.448183
12881 2018 1    44.015    .3145372 . .   .06739125  9.792305
12881 2019 1     44.02   .26908866 . .   .10363298 10.093902
12933 2016 0     48.02    .3404883 . .    .5167695  7.019386
13499 2011 0     73.16    .1680148 1 .    .3365169   6.93352
13499 2012 0     73.16   .22514665 1 .     .383225  7.139977
13499 2013 0     73.16   .22730005 1 .    .4182201  7.193235
13499 2014 0     73.16    .2413149 1 .     .374256  7.275865
13499 2015 0     73.16    .2566621 1 .    .4491948  7.233239
13499 2016 0     73.16   .24739246 1 .    .4227747  7.244085
13499 2017 1 73.167496    .2225108 1 .   .36709955  7.388328
13499 2018 1     73.17    .2088714 1 .   .28482938  7.552238
13499 2019 1     73.17   .17132217 1 .    .3118983   7.83356
13702 2015 0     63.34           . 1 .           .  4.720283
13702 2016 0     63.34 .0013399805 1 .           .  4.767289
13702 2017 1     63.34 .0013399805 1 .           . 4.7104306
13702 2018 1     63.34 .0013399805 1 .           .  4.776599
13702 2019 1     63.34 .0013399805 1 .           .  4.784153
14204 2011 0    50.105    .2037802 . .   .19276504  7.376383
14204 2012 0    50.725    .2538099 . .   .17117046  7.149132
14204 2013 0     50.86   .23295635 . .   .11867736  7.149289
14204 2014 0   50.4775    .2161965 . .  .064850844  7.117692
14204 2015 0     50.46   .18829176 . .    .0196711   7.14748
14204 2016 0    50.135   .19981883 . .  .017417962  7.269129
14268 2011 0     37.75    .5490045 0 1    .4367616 11.891718
14268 2012 0   40.9975    .4624115 0 1    .4795205 12.133508
14268 2013 0    48.675    .5776019 0 1    .5747258 12.487267
14268 2014 0    49.075    .6154515 0 1    .5838352  12.61995
14268 2015 0   49.7325    .4871848 0 1   .53174776   12.4659
14268 2016 0    48.665    .5411593 0 1    .5945643 12.450246
14268 2017 1      52.4    .4596609 0 1   .58797324  12.50203
14268 2018 1      52.4    .7873657 0 1     1.34504 11.270436
14268 2019 1      52.4    .7873657 0 1     1.34504  11.22419
14271 2011 0     61.64    .6306323 . 1     .453347 10.636903
14271 2012 0    62.815    .6306323 . 1     .453347 10.636903
14271 2014 0  71.14751    .6305249 . 1    .6208992 11.473866
14271 2015 0   52.7325    .5761167 . 1    .5266047 11.593565
14271 2016 0     46.86    .5439104 . 1     .557457 11.558415
14271 2017 1     46.86    .5242019 . 1    .6961761 11.504733
14271 2018 1     46.86    .6274458 . 1    .8556132  11.38542
14271 2019 1     46.86    .6238958 . 1    .9395847 11.296848
14344 2015 0     35.19     .349695 0 .    .4225247    6.9712
14344 2016 0     35.19    .4273311 0 .    .3223858  6.965647
14344 2017 1   40.5525   .40843445 0 .   .16670793   7.09978
14464 2011 0     59.12   .00879586 . 0   .18110693  9.453757
14464 2012 0     59.12  .008740978 . 0   .12170008  9.431081
14464 2013 0     59.12  .008919334 . 0    .1186046  9.391428
14464 2014 0   59.1075  .010848297 . 0    .1169453  9.401993
14464 2015 0     63.38  .009181034 . 0   .22352976  9.501172
14464 2016 0     63.38   .03786523 . 0   .25968745  9.587632
14464 2017 1     63.38   .04701657 . 0    .2981866   9.63831
14464 2018 1   63.3925   .04324649 . 0   .37869385   9.77007
14464 2019 1     63.43   .03151614 . 0    .3441647  9.853352
14658 2012 0     28.11    .0974558 0 0   .09774328  6.544919
14658 2013 0     26.25    .0801863 0 0    .0876133  6.677587
14658 2014 0   19.6775   .07446808 0 0   .08710889  6.683361
14801 2019 1   72.9525  .003267974 . .           .  4.241327
14897 2011 0   61.8325    .3767906 . 1    .1981242 10.808938
14897 2012 0   62.1175    .3669052 . 1   .23977555 10.911803
14897 2013 0     63.07    .3435211 . 1   .25535345 10.970694
14897 2014 0     63.44    .4120774 . 1   .22669196 11.027598
14897 2015 0     63.44    .3952421 . 1   .22342873 11.018522
14897 2016 0   63.4625   .03754818 . 1   .24253124 11.047148
14897 2017 1     63.47  .036168903 . 1   .26850945 11.113307
14897 2018 1    63.485  .031891964 . 1    .3581223 11.224324
14897 2019 1 64.130005  .029592866 . 1    .3773145  11.26284
15303 2011 0   49.7225   .43459615 0 1    .2754681  9.448057
15303 2012 0     46.65    .4134943 0 1    .2463519  9.527572
15303 2013 0   46.4725    .4093911 0 1    .1980343   9.55261
15303 2014 0     46.48    .3623882 0 1     .196147  9.610692
15303 2015 0     46.78    .3616752 0 1    .2296943  9.590166
15303 2016 0     47.03   .37423825 0 1    .2074357  9.545469
15303 2018 1     47.03    .3587892 0 1   .14122188  9.802761
15303 2019 1     47.03    .3442616 0 1   .14843541  9.918032
15510 2011 0      43.9    .3211642 . .   .38540375   8.00159
15510 2012 0     47.65    .3455344 . .    .3998558  7.976698
15510 2013 0      53.9    .3282582 . .    .3760998  7.975771
15510 2014 0      53.9    .3097744 . .    .3406342  8.025844
15510 2015 0      53.9   .28051278 . .    .3755394  8.063031
15510 2016 0      53.9    .3028887 . .    .4008164  7.960812
15510 2017 1     55.15    .2416062 . .   .53366923  7.885969
15510 2018 1      58.9    .2045083 . .    .4630891  7.985076
15510 2019 1      58.9   .20838524 . .    .3007701  8.048565
15646 2011 0     72.55    .7827653 1 1    .7930869  6.032126
16191 2011 0    61.665    .6550139 . 1    .6665981  8.266987
16191 2012 0     61.74   .55845314 . 1    .6432609  8.378437
16191 2013 0     61.74   .57793474 . 1    .6016955   8.35385
16635 2017 1   74.6825    .5670979 . .    .4500298  8.556818
16814 2011 0     54.82  .024345484 . 0   .18821874 8.0924225
16814 2012 0     54.82   .02484509 . 0   .17029856 8.1235285
16814 2013 0     54.82   .02148834 . 0   .12567759  8.187939
16814 2014 0     54.82  .021003025 . 0   .15990807  8.142529
16814 2015 0     54.82  .015688172 . 0   .20536986  8.163998
16814 2016 0     54.82   .01453658 . 0   .24110033  8.234777
16814 2017 1     54.82   .01290581 . 0   .26430726  8.085363
16814 2018 1     54.82  .011523093 . 0   .23250626  8.079711
16814 2019 1     54.82  .008909901 . 0   .22508107  8.101587
16820 2011 0   53.8775   .04078431 . 0    .3400358   9.21097
16820 2012 0     55.84   .03228497 . 0   .29014024  9.505045
16820 2013 0     56.52   .02833611 . 0    .2704552  9.795084
16820 2014 0    57.405  .032054067 . 0    .3004074  9.927863
16820 2015 0     57.53  .034823984 . 0    .3332419  9.905954
16820 2016 0     58.43  .032262858 . 0    .3231088  9.913423
16820 2017 1   59.0375   .02914009 . 0    .3060628  9.910835
16820 2018 1     59.03  .025826396 . 0    .3111513  9.915939
16820 2019 1     52.97  .015694642 . 0   .25110266 10.159063
16823 2011 0   46.8025  .021538267 0 0   .27318934 10.949354
16823 2012 0      46.2     .020623 0 0   .24090333 11.034635
16823 2013 0   46.9375  .028494064 0 0    .2105913 11.131436
16823 2014 0     49.05   .03217752 0 0    .1897586 11.142515
16823 2015 0     49.07   .04226847 0 0   .21921203  11.21601
16823 2016 0     49.07   .04190286 0 0    .2129457  11.15751
16823 2017 1   48.9325   .06035662 0 0   .23068957 11.306005
16823 2018 1     48.77   .06297905 0 0    .2524263 11.218503
16823 2019 1     48.77   .05235154 0 0   .21126117 11.342288
17567 2018 1         .   .15672913 . .    .5775128  4.241327
17567 2019 1     68.81   .10500905 . .   .25407362  5.110179
17638 2016 0   39.3475  .014154552 . .     .748661  6.259199
17638 2018 1     43.28  .011836736 . .    .8404081  6.194406
17638 2019 1   45.2575 .0082223965 . .    .8371183  6.235978
17728 2011 0     57.02   .08972973 . 0    .3081181  9.888897
17728 2012 0   58.4625   .06427242 . 0    .3480985 10.258988
17728 2013 0     60.86   .07303164 . 0    .2996806 10.399503
17728 2014 0     62.48   .11089037 . 0    .2621909  10.31569
17728 2015 0   60.3325   .12406527 . 0   .16593075  10.30163
17728 2016 0   58.1475   .13065208 . 0   .13043155 10.291254
17728 2017 1     57.96    .1618275 . 0    .0873545  10.45943
17728 2018 1   58.3125    .1483636 . 0    .0853111 10.675185
17728 2019 1    59.075   .14047086 . 0    .0517748 10.821692
17809 2015 0   57.6675    .3473011 . .   .17141755  7.503124
17809 2016 0    57.895    .2996754 . .   .08507647  8.002326
18030 2011 0    57.785   .22439787 . .    .4360596  7.051163
18030 2012 0   59.1025    .2170503 . .   .53393006   7.06732
18030 2014 0     58.96    .3811303 . .   .50950694  7.329881
18030 2015 0     58.96    .4189741 . .    .5878348   7.18675
18030 2016 0     58.96    .4007029 . .    .6266636  7.198483
18102 2011 0   32.7425    .3989958 0 1   .26370823 10.499708
18102 2012 0 33.954998    .4627533 0 1   .20358023 10.599638
18102 2013 0     34.35    .4491437 0 1   .25034666 10.788535
18102 2015 0     34.35    .4680606 0 1    .3074747 11.078978
18102 2016 0     34.35    .4701015 0 1    .3681412 11.258473
18102 2017 1    34.375   .49253395 0 1    .3644827  11.37151
18102 2018 1      34.4   .43396965 0 1     .339621 11.521846
18102 2019 1   31.7025    .4925536 0 1    .3938263 11.443222
18139 2015 0     68.52  .066131495 1 0   .08505352  6.259964
18139 2016 0     68.52   .08122147 1 0   .05351802  6.454097
18139 2017 1     66.71   .05925579 1 0   .10303316  6.460843
18139 2018 1    64.745    .0483871 1 0  .007483871  6.652863
18139 2019 1     64.68   .04847658 1 0 .0026282487   6.93469
18151 2011 0   45.6975    .4152313 0 1      .31142 11.285304
18151 2012 0   44.2625    .4312755 0 1    .3165573 11.415607
18151 2013 0   43.4675   .43772715 0 1   .28543124 11.438933
18151 2014 0     44.06    .4340163 0 1    .1607172 11.516818
18151 2015 0    44.105    .4277259 0 1   .11377418 11.484847
18151 2016 0     44.15    .3478985 0 1   .11810684 11.782617
18151 2017 1     42.01    .3553223 0 1    .2022279  12.04317
18151 2018 1     40.67    .4298002 0 1   .21024673  12.30908
18151 2019 1      40.9    .4833959 0 1   .22698896 12.324544
18323 2014 0      1.04           . 0 0           .  5.021245
18323 2015 0      1.04 .0013399805 0 0           .  5.031744
18323 2016 0      1.04           . 0 0           .  5.244389
18396 2011 0 35.795002  .071096346 0 0           .  8.232839
18396 2012 0     38.56   .05913056 0 0           .  8.314049
18396 2013 0   43.2675   .04912692 0 0           .  8.358549
18396 2014 0     47.14   .07161471 0 0           .  8.016813
18396 2015 0    46.675  .064956054 0 0           .  7.996216
18396 2016 0   48.2975   .05872578 0 0           .   8.00933
18396 2017 1    49.305   .07074304 0 0           .   7.98912
18396 2018 1   49.3975   .04719703 0 0           . 8.1204405
18396 2019 1   49.3175   .04127335 0 0           .   8.14407
19420 2011 0     52.74    .2594083 . .      .32489  8.147404
19420 2012 0     52.74    .2184925 . .    .3952294  8.242309
19420 2013 0     52.74   .23192385 . .   .41234785  8.365696
19420 2014 0     52.74   .21969986 . .    .3685079  8.408338
19420 2015 0     52.74   .19315083 . .     .358139  8.398071
19420 2016 0      52.7   .19037414 . .    .3718859 8.4072895
19420 2017 1     52.66   .15734553 . .    .3051442  8.412744
19420 2018 1     52.66    .1830865 . .    .3577309  8.470877
19420 2019 1     52.66   .19210967 . .    .3469684  8.507729
19528 2011 0     42.75   .06201616 0 0   .27207366  8.458483
19528 2012 0     42.51   .07460237 0 0    .1968358  8.466574
19528 2013 0     42.51    .0265256 0 0   .28902325  8.574141
19528 2014 0     42.51  .024957715 0 0    .3611915  8.579416
19528 2016 0      42.6  .018453617 0 0    .3876345  8.771618
19528 2017 1   42.1425   .02461887 0 0    .3228424  8.451779
19528 2018 1     42.87  .024910787 0 0    .4028502  8.382884
19528 2019 1    45.265  .024431957 0 0     .414556  8.211673
20327 2011 0     56.32   .06120528 1 0    .4218456  5.358471
20327 2012 0     73.57   .04088398 1 0    .3675875  5.603962
20327 2013 0     74.17   .06205251 1 0   .10381862  5.527045
20327 2014 0    73.325    .1714564 1 0 .0041402825   6.01762
20327 2015 0    70.955    .1623549 1 0 .0008291874  6.401917
20327 2016 0     70.32   .16379783 1 0 .0007068081   6.81575
20327 2017 1     65.42    .2146683 1 0 .0007068081  7.054104
20327 2018 1     65.42   .20542933 1 0 .0007068081  7.069108
20327 2019 1   66.6525   .24197616 1 0 .0007068081  6.771248
20451 2011 0    52.015    .4748972 . .    .3158835  6.327401
20451 2012 0      54.5   .37286445 . .    .3587699  6.554503
20451 2013 0      54.5    .3114515 . .    .3609288   6.71247
20451 2014 0      54.5   .28309262 . .    .3307634  6.825352
20451 2015 0      54.5   .27005175 . .   .26304442  6.832601
20451 2016 0      54.5   .25119916 . .   .34827945  6.865891
20858 2013 0     31.74  .032334387 0 0  .074132495  5.941223
20858 2014 0 32.614998   .08275862 0 0   .06356822  5.809643
20858 2015 0     36.07   .07636186 0 0   .04377432   6.01908
20858 2016 0     36.07   .04308024 0 0  .035361696  6.322745
20858 2017 1     36.07   .04110846 0 0   .02330703  6.300602
20858 2018 1     36.07    .0408322 0 0   .00816644  6.242807
20858 2019 1     36.07    .1334241 0 0           .  4.989752
21042 2011 0     43.33     .472199 0 1    .4415986 10.821465
21042 2012 0    43.125    .4556864 0 1    .3939978 10.897915
21042 2013 0   43.8825    .4039434 0 1    .4077016 11.056933
21042 2014 0   43.5275    .3734816 0 1    .4194146 11.214363
21042 2015 0   43.7825    .3736103 0 1    .4252894  11.29378
21042 2016 0   43.5975    .3910639 0 1    .4553202 11.340103
21042 2017 1     42.93    .3911558 0 1    .3296275 11.396042
21042 2018 1    43.025    .3414931 0 1    .3135202 11.572768
21042 2019 1   44.1675    .4238643 0 1    .3543422 11.242402
21275 2012 0      2.99  .013153888 0 0   .01004811  7.403731
21283 2011 0     55.14    .5384916 . 1    .7148452  9.986301
21283 2012 0   55.2175   .55450577 . 1    .6689526 10.069984
21283 2013 0   55.0075   .50636935 . 1     .637965  10.09677
21283 2014 0   54.4025     .488674 . 1    .5832811 10.083882
21283 2015 0   54.4125   .45635465 . 1    .6068287 10.060295
21283 2016 0    54.335     .451795 . 1   .56349903  10.06306
21283 2017 1     54.28    .5219706 . 1   .42109305  10.27623
21283 2018 1   54.2725    .5607329 . 1   .40620825 10.429836
21283 2019 1    54.275   .50813544 . 1    .4296285 10.553565
21290 2019 1 65.962494     .484126 . .   .19981287  8.038963
21420 2012 0        75   .15380254 1 0 .0007068081 11.112587
21420 2013 0        75    .1548405 1 0   .04704903 11.151188
21575 2011 0   44.7725    .3411993 0 .   .51989824   9.05586
21575 2012 0   45.7225    .3393856 0 .   .55182445  9.059203
21575 2013 0    47.075   .29074636 0 .     .509348  9.094447
21575 2014 0    48.855   .28556052 0 .    .3262722  9.194953
21575 2015 0     48.66   .28772926 0 .    .1924101  9.356223
21575 2016 0     48.43    .2392406 0 .    .1526733   9.57653
21575 2017 1   42.7475   .23871414 0 .   .12102727  9.506109
21575 2018 1     39.78    .2029666 0 .   .14278404   9.57921
21575 2019 1     39.78   .20079176 0 .    .2128139   9.30229
21663 2011 0     66.12   .12151284 1 0  .001990574  8.136255
21663 2012 0    66.735   .13204001 1 0   .06605031  8.101375
21663 2013 0   67.0525    .1025635 1 0   .03258854  8.369714
21663 2014 0    65.615   .08218724 1 0   .01878048   8.79844
21663 2015 0     61.04   .05513853 1 0   .03075496  9.398445
21663 2016 0     61.04   .05201994 1 0   .05652632   9.38939
21663 2017 1     61.04   .04623008 1 0  .070348054  9.442483
21663 2018 1     61.04   .04319919 1 0   .11162824  9.421767
21663 2019 1     61.04   .05110646 1 0   .13585292   9.39172
21707 2011 0     33.38    .7739512 0 1     1.34504  7.945343
21707 2019 1     60.92     .423114 0 1   .08836227  8.077044
21964 2017 1     54.79   .13014941 . .   .10052682  7.054363
21964 2018 1     54.79   .13145863 . .   .08893297  7.055572
21964 2019 1     54.79     .130692 . .    .0752958  7.017148
22104 2014 0    39.495    .3523677 0 .    .4782734 12.087976
22104 2015 0      44.6   .27308694 0 .    .4598155 12.192227
22104 2016 0     50.38    .2054939 0 .    .4840364 12.338692
22104 2017 1     51.28    .1822799 0 .    .4732378 12.536333
22104 2018 1   51.1225   .14673488 0 .    .4570676 12.752705
22104 2019 1    51.225    .1246203 0 .    .4710669 12.916332
22247 2011 0   70.2625   .05849731 1 0    .4859045 10.189226
22247 2012 0    67.405  .032396466 1 0    .3179112 10.892495
22247 2013 0     67.57   .01415265 1 0   .19718233  11.73149
22247 2014 0   67.5475   .01318743 1 0   .23605175  11.81488
22247 2015 0   57.0825  .010991214 1 0   .27654654 11.896138
22247 2016 0     56.68   .01122355 1 0    .2894565  11.94446
22247 2017 1     55.52  .018452706 1 0   .54156464  11.60587
22247 2018 1     54.22  .022233114 1 0    .5507748 11.672626
22247 2019 1   54.2775  .032457933 1 0    .4537198 11.799862
22659 2011 0        75    .1680465 1 .    .3020962  6.341593
22659 2012 0        75    .1506024 1 .    .3633391  6.364751
22659 2013 0        75   .13508473 1 .    .3752542  6.380123
22659 2014 0        75   .42527625 1 .    .3993134   6.83744
22659 2015 0        75    .4102024 1 .    .4443508  6.855198
22659 2016 0        75    .4295101 1 .    .4804611  6.765615
22659 2017 1        75    .3224232 1 .    .3801989  7.008505
22659 2018 1        75    .3087303 1 .     .319779  7.006876
22659 2019 1        75   .27323732 1 .   .24895327  7.085232
22793 2011 0      9.37   .06328013 0 0    .4414451  8.934178
22793 2012 0  8.627501   .06867984 0 0   .55584246  8.809744
22793 2013 0      8.38    .0669281 0 0   .57159555  8.779942
22819 2011 0        75    .6017244 1 1     .425644 10.092875
22819 2012 0        75    .5725497 1 1    .4386418 10.132736
22819 2013 0     73.74    .5305689 1 1    .4039551  9.916522
22819 2014 0     72.49    .6263654 1 1    .4680088  9.949722
22819 2015 0     72.49    .6630892 1 1    .5073323  9.958558
22819 2016 0     72.49    .6459412 1 1   .54116964  9.942732
22819 2017 1     72.49    .6138285 1 1    .5396565  9.972795
22819 2018 1     72.49    .5832189 1 1   .53134197  9.994767
22819 2019 1    69.125    .5656482 1 1   .54265887 10.009877
22855 2011 0        39    .3370331 0 .   .08920036  7.118259
22855 2012 0   38.9925    .3243515 0 .   .17037657  7.085901
22855 2013 0   46.3425    .3014996 0 .    .2217048  7.144407
22855 2014 0     56.34    .6520956 0 .    .2940647  7.482963
22855 2015 0     56.34  .032343235 0 .   .52547854  7.323171
22855 2016 0   45.0875  .016025083 0 .    .7347732  7.451416
22855 2017 1     60.61 .0078536365 0 .     .383668  7.714677
22855 2018 1     59.53   .00467964 0 .   .10100222  7.849441
22855 2019 1     59.61    .3823583 0 .   .04537999  7.693846
22859 2011 0   52.7525    .2696086 . .   .04991532 10.752894
22859 2012 0     52.79    .2154993 . .   .05903529  10.96441
22859 2013 0     52.79    .3456927 . .  .036537975  11.13728
22859 2014 0     52.79   .28753203 . .   .03050061  11.31065
22859 2015 0    52.795   .25482076 . .   .04625452  11.41207
22859 2016 0    52.795    .2857203 . .   .03022447 11.580238
22859 2017 1   52.7975    .2354133 . .   .04461519  11.74081
22859 2018 1     52.79   .22386397 . .   .03829306   11.8444
22859 2019 1     52.79    .3598776 . .    .0804693 12.007548
22896 2011 0     74.97   .12815695 1 0     .543906  9.408158
22896 2012 0     74.97   .10757075 1 0    .4572592  9.572515
22896 2013 0   74.8925   .09341236 1 0    .4962396  9.715192
22896 2014 0     74.66   .09617116 1 0    .5531166  9.804645
22896 2015 0     74.66   .08222745 1 0     .450556  9.907948
22896 2016 0     74.66   .06913854 1 0   .49733415  10.02658
22896 2017 1     74.66   .11629871 1 0     .436995 10.015744
22896 2018 1     74.66     .104017 1 0   .36408755 10.051537
22896 2019 1     74.66    .1110502 1 0    .3349312  9.933556
22900 2011 0   56.1325   .13949662 . .    .4900477   6.41001
22900 2012 0     60.65    .1571976 . .   .51675713  6.440309
22900 2013 0   63.2525    .1618904 . .   .29009554  6.391415
22900 2014 0    64.425   .16843453 . .   .09729335  6.304083
22900 2015 0 65.082504   .21110298 . .   .13245678  6.017863
22900 2016 0     65.28   .21493027 . .   .04511895  5.901814
22900 2017 1    65.445   .17719422 . .   .16205347  6.046663
22900 2018 1     65.56   .15789473 . .   .16907895  6.122493
22900 2019 1     65.69   .07556757 . .    .4551351  6.829794
23150 2011 0         .    .2967395 1 .    .1601341  7.455761
23150 2012 0         .    .2124907 1 .    .1995539  7.609614
23150 2013 0         .    .2956497 1 .   .12621468  7.478735
23150 2014 0         .   .23803034 1 .    .1930649  7.663079
23150 2015 0     64.46    .2393342 1 .   .17997895  7.645302
23150 2016 0     64.46   .22686502 1 .    .1709473   7.73434
23150 2017 1     64.46    .2613845 1 .   .12195122  7.662891
23150 2018 1     64.46   .21391638 1 .   .25865558  7.889384
23150 2019 1     64.46    .2611772 1 .   .25773513  7.868905
23320 2013 0     54.21    .5307026 . 1   .38661575  7.408349
23320 2014 0   58.3725    .5077623 . 1     .478595   7.55648
23320 2015 0     58.48    .5443506 . 1   .26387423  7.441086
23354 2011 0      50.3    .4308207 . 1   .04300041 11.710546
23354 2012 0      50.3    .5251528 . 1    .0421812 11.704206
23354 2013 0      50.3   .48557395 . 1  .013426432 11.707233
23354 2014 0      50.3    .4493551 . 1  .002834326  11.72474
23354 2015 0      50.3    .4364618 . 1           . 11.769953
23354 2016 0    52.435    .4082714 . 1           . 11.776696
23354 2017 1     54.53   .54501915 . 1           . 11.835437
23354 2018 1     54.53    .4736947 . 1           . 11.935005
23354 2019 1     54.53    .4298892 . 1           .  12.00549
23397 2013 0    6.2525           . 0 .           .  4.241327
23397 2014 0    1.9425           . 0 .  .002186589   4.92144
23397 2015 0     1.865   .17384014 0 .   .12185579  5.186827
23397 2016 0      1.85     .212223 0 . .0040295506  5.003275
23463 2017 1     62.12   .12199066 . .    .4277066   7.67976
23463 2018 1     62.12    .1089695 . .    .4955784  7.926783
23463 2019 1     62.12   .13102807 . .     .437983  7.622957
23482 2014 0      70.4    .6773409 1 1   .17492594  7.809501
23482 2015 0      70.4    .4442533 1 1    .2618889  8.267295
23482 2016 0      70.4   .58288753 1 1   .41248745  8.361965
23482 2017 1      70.4    .5164572 1 1     .355433  8.450797
23482 2018 1     70.42    .5456042 1 1    .3555654  8.463033
23482 2019 1     70.43    .4679827 1 1    .3707513   8.59881
23561 2011 0   55.7025    .4176121 . 1   .27063295  7.459512
23561 2012 0   56.0175    .3284362 . 1    .3105042  7.746862
23561 2013 0     57.77    .4616445 . 1    .3799954  7.868024
23561 2014 0     57.36    .4582778 . 1    .4000982  7.955004
23561 2015 0    59.125   .35151935 . 1    .3538567  8.198777
23561 2016 0     64.03   .27378148 . 1    .3736755  8.130825
23561 2017 1      66.7   .28913316 . 1    .3680247  8.142907
23561 2018 1     67.41   .27129743 . 1    .3009664   8.32821
23561 2019 1     69.54    .3572171 . 1   .38061705  8.437154
23668 2013 0   21.8775   .21605605 0 .   .09305305  8.516192
23668 2014 0   18.3225    .2228922 0 .   .10846654   8.64033
23668 2015 0   17.1075    .2851619 0 .    .1872684  8.510289
23668 2016 0   15.8475    .2474145 0 .   .12605771  8.618088
23668 2017 1    14.345    .2204228 0 .    .2028738   8.82356
23668 2018 1    13.065    .2787964 0 .   .13674614  8.850188
23668 2019 1      11.4    .2844757 0 .   .02680781  8.742431
23696 2011 0     63.82    .3242235 . .   .14248069  8.124832
23696 2012 0     63.82    .3361721 . .   .19119026  8.498663
23696 2013 0     63.82    .3804264 . .   .16286117  8.644002
23696 2014 0  61.33667    .4106733 . .   .20145996  8.861492
23696 2015 0     59.69     .292358 . .    .1738128  9.368224
23696 2016 0   59.3425    .3276415 . .   .15103705  9.479971
23696 2017 1     58.71    .3407802 . .   .22454095  9.607693
23696 2018 1    58.485    .3554997 . .   .21724068  9.742838
23696 2019 1   56.3375     .380422 . .   .19253725  9.965476
23873 2011 0     85.79  .014703907 1 0    .0473345  7.306129
23873 2012 0     86.31  .013901212 1 0   .02942916  7.209636
23873 2013 0     78.75   .01093509 1 0  .034067012  7.263049
23873 2014 0     71.19   .01375064 1 0   .16357106  6.881925
23873 2015 0     71.19  .015250836 1 0    .0619398  6.616735
23873 2016 0     71.17  .012936263 1 0   .06400755  6.609484
23873 2017 1     71.19  .012922332 1 0     .049401  6.610561
23873 2018 1     71.19  .012087027 1 0   .05842063  6.612847
23873 2019 1     71.19  .011194653 1 0   .06315789  6.394426
23885 2015 0      14.1    .3984733 0 1     1.34504  4.241327
23885 2016 0      14.1    .3893376 0 1     1.34504  4.241327
23885 2017 1      14.1    .3680672 0 1     1.34504  4.241327
23885 2018 1      14.1    .3467601 0 1     1.34504  4.241327
23885 2019 1      14.1    .3265683 0 1     1.34504  4.241327
24028 2011 0     73.65     .055954 1 0    .4773277  9.151185
24028 2012 0        72   .04151384 1 0    .4842474  9.355306
24028 2013 0   71.6175  .032182284 1 0    .4442827  9.538003
24028 2014 0   74.8725  .027946206 1 0    .4265666  9.755626
24028 2015 0   72.4925  .017277468 1 0    .4520321  9.889941
24028 2016 0    70.765  .015532066 1 0    .5302631  9.800352
24028 2017 1 70.854996  .016451227 1 0   .58978933  9.768579
24028 2018 1   70.8575   .01878206 1 0    .6639892  9.725837
24028 2019 1     62.27  .009830026 1 0    .7124915  9.708056
24106 2011 0   45.5125     .207841 0 .    .2281833  8.417925
24106 2012 0   45.7475   .20126773 0 .    .2400401  8.431831
24106 2013 0     44.71   .19812244 0 .   .23205768  8.431875
24106 2014 0   44.7225    .2094311 0 .    .2541073  8.369968
24106 2015 0    44.725   .25543946 0 .   .28540146  8.190881
24106 2016 0    43.855   .27225253 0 .    .2052105  8.139645
24106 2017 1    43.655    .2341382 0 .    .2500202  8.220107
24106 2018 1   43.3025   .22649935 0 .   .13537095  8.267885
24106 2019 1    42.465     .276987 0 .    .2061227  7.985008
24230 2014 0     21.77   .04727152 0 0   .04279316  6.401752
24230 2015 0    39.785    .0322973 0 0   .06283784   6.60665
24230 2016 0     41.59    .0203405 0 0   .09659498  7.017506
24230 2017 1     42.89   .11867696 0 0   .13460837  7.813228
24230 2018 1   42.9075   .04275272 0 0  .034721553  7.639834
24230 2019 1     43.02   .03839644 0 0  .018351894  7.716461
24306 2016 0      52.7     .381331 . .           .   7.70224
24306 2017 1      52.7   .35576925 . .           .  7.805637
24306 2019 1      52.7   .25993985 . .           .  8.186353
24381 2011 0     50.05    .3231425 . .    .2582371  9.450208
24381 2012 0   50.3125    .3159158 . .   .26819706  9.593369
24381 2013 0   50.5575    .3343516 . .   .23500645  9.664285
24381 2014 0   50.6675   .32317445 . .    .2012895  9.813896
24381 2015 0   50.8325   .29126275 . .    .1632307   9.81739
24381 2016 0   46.1125   .29184303 . .   .12164748 10.164386
24381 2017 1    44.635    .3529758 . .   .05869978  10.26002
24381 2018 1    44.675    .3227113 . .  .005077648 10.353068
24381 2019 1    44.755   .27715668 . .  .014263538  10.55395
24496 2015 0        70    .3701553 1 1   .16191763  7.300473
24496 2016 0   70.2475    .3624045 1 1    .1996018   7.35058
24685 2011 0   54.5525    .2708975 . .    .4081365 10.987883
24685 2012 0     54.78     .308879 . .    .4812128 11.071877
24685 2013 0    54.755   .32768175 . .    .4615476 11.217662
24685 2014 0   54.1175   .26319364 . .    .3949067 11.466292
24685 2015 0   53.8825   .21985634 . .   .34157285  11.77768
24685 2016 0   52.8475   .20688073 . .    .3085148 12.003944
24685 2017 1    51.875   .24918982 . .    .2053145 12.006725
24685 2018 1     51.87    .2194213 . .   .22096717 12.282413
24685 2019 1    51.905   .21093296 . .   .25809795 12.505907
24774 2011 0   33.9325   .14407197 0 0   .10820804  6.698761
24774 2012 0   34.4025    .1335153 0 0   .09825999  6.779126
24774 2013 0     34.57   .12748693 0 0    .0902523  6.802062
24774 2014 0    34.265   .11332758 0 0   .06793185  6.832385
24774 2015 0     33.79   .09920716 0 0   .10317138  6.891423
24774 2016 0     33.79   .10263713 0 0   .09757384  6.854354
24774 2017 1     33.78   .10080465 0 0   .08426464    6.7966
24774 2018 1    33.785   .09279513 0 0   .06404329  6.787732
24774 2019 1      33.8    .0895291 0 0    .0837673  6.805169
24919 2011 0   29.2025    .4080004 0 1    .3706696  8.722531
24919 2012 0     29.49   .39934185 0 1    .3583558  8.825736
24919 2013 0   28.7125    .3999769 0 1    .3529938   8.84425
24919 2014 0    26.695    .4262346 0 1    .3981208  8.808997
24919 2015 0     28.82   .47294345 0 1    .3541228  8.551111
24919 2016 0   31.8375    .4339916 0 1    .3951327   8.51979
24919 2017 1 32.370003    .3973876 0 1    .4397702   8.53676
24919 2018 1     32.02    .3491762 0 1    .4481462  8.559045
24919 2019 1      29.7    .3095547 0 1    .4139386 8.6082945
24921 2015 0    52.215   .24255827 . .   .27519426  6.729227
24921 2016 0   52.6875   .22199447 . .     .361689  6.883155
24921 2017 1   53.9775    .3235626 . .    .3335212  6.970167
24921 2018 1   54.0125    .3148789 . .    .3105536  7.052721
24921 2019 1   54.1175     .334842 . .    .2897554   7.04656
25176 2011 0   47.6275   .17571467 0 .    .3571335  7.329881
25176 2012 0     43.22   .23294437 0 .   .26903665  7.518878
25176 2013 0   41.9375   .19471642 0 .    .2344968  7.864919
25176 2014 0   44.2475   .17870425 0 .   .12959926  8.373207
25176 2015 0     44.01    .1870362 0 .   .12205013  8.473053
25176 2016 0   43.9425   .16682994 0 .   .01720178 8.7365055
25176 2017 1     43.83    .1500758 0 .   .01802257  9.276746
25176 2018 1    43.765    .2043839 0 . .0045005204  9.633311
25176 2019 1     43.71   .17972955 0 .  .005743769  9.696531
25188 2017 1     36.37    .3468257 . .   .08938682  6.721546
26005 2011 0     54.55    .6279598 . 1    .3209262  7.042723
26005 2012 0   55.0775    .5562853 . 1    .3641243  7.255591
26005 2013 0     59.15    .6669448 . 1    .3135864  7.271148
26005 2014 0     59.15    .6520734 . 1   .29265717  7.322774
26005 2015 0     59.15    .6221831 . 1    .2626212  7.370608
26005 2016 0     59.15    .5990402 . 1    .2258103  7.431122
26005 2017 1     59.15    .4097476 . 1    .2985937  7.350709
26005 2018 1     59.15    .4608284 . 1    .2529521   7.41956
26005 2019 1     59.15     .447908 . 1   .25098103  7.500695
26039 2011 0     74.37   .06105264 1 0 .0042105266  4.241327
26039 2012 0     75.15   .04225352 1 0           .  4.241327
26039 2013 0     74.72   .04259259 1 0           .  4.241327
26039 2014 0     74.72   .03789127 1 0           .  4.241327
26039 2015 0   74.3625   .03958945 1 0           .  4.241327
26039 2016 0      73.6  .029782357 1 0           . 4.4693503
26039 2017 1      73.6  .025641026 1 0           . 4.5798526
26039 2018 1      73.6    .0169348 1 0           .  4.771532
26039 2019 1      73.6  .018121911 1 0           .  4.799091
26061 2011 0   33.1175     .264526 0 .    .2694123  8.009164
26061 2012 0     35.07     .334624 0 .    .3524633  7.969876
26061 2013 0     35.07     .353665 0 .     .497197  7.950256
26061 2015 0   38.9325    .1925421 0 .    .4615941  8.106092
26061 2016 0     41.25   .17181975 0 .   .44110096  8.162231
26061 2017 1     41.25    .1827573 0 .    .4298681   8.20762
26061 2018 1   41.9775     .154799 0 .    .4294178  8.168429
26061 2019 1    45.375   .11950392 0 .    .3596856  8.273516
26124 2012 0         .           . 0 .           .  4.241327
26124 2013 0         .           . 0 .           .  4.337291
26124 2014 0      .715           . 0 .           .  4.241327
26485 2011 0    66.165   .07198908 1 0    .7424736  9.950509
26485 2012 0    66.065   .03399515 1 0     .783945  9.924441
26485 2013 0     65.94   .02878166 1 0    .8477418 10.025143
26485 2014 0     65.64   .02550092 1 0    .8794777  10.02254
26485 2015 0 65.634995  .021702355 1 0     .830345 10.016954
26485 2016 0     60.92   .02102429 1 0    .8363574 10.018209
26485 2017 1     55.97   .02019757 1 0    .8602906  9.913215
26485 2018 1     55.98  .017695913 1 0    .8344527  9.898053
26485 2019 1   55.9875  .014246307 1 0    .8305733  9.970585
26633 2011 0     33.36    .3931211 0 1    .3813655  10.34797
26633 2012 0     32.61    .3476354 0 1    .3633102  10.53972
26633 2013 0     32.61   .27189368 0 1     .472803 10.703148
26633 2014 0    31.385    .4005054 0 1    .4815814 10.704552
26633 2015 0     31.48    .3770917 0 1    .5170122 10.707866
26633 2016 0     32.97    .5154776 0 1    .5434922  10.57235
26633 2017 1     30.01    .5464036 0 1   .54422814  10.51877
26633 2018 1     30.01    .4687877 0 1    .5576847 10.531275
26633 2019 1     30.01    .4653768 0 1    .6590759  10.44687
26647 2019 1     65.07   .10862445 . .           .  5.210578
26964 2011 0     71.11    .0874147 1 0   .31234425  9.821143
26964 2012 0 71.152504    .0883475 1 0    .3153983  9.915218
26964 2013 0     71.25   .07789543 1 0    .3192521  9.914462
26964 2014 0     71.25    .0753704 1 0    .3740211  9.822256
26964 2015 0 69.130005   .05057279 1 0    .4447149  9.777022
26964 2017 1     65.07   .04951578 1 0    .4198469  9.783566
26964 2018 1   63.5775   .04808583 1 0    .3462089  9.768527
26964 2019 1     61.45   .04322208 1 0     .302456  9.812551
27180 2014 0         .    .4639803 . 1   .54963595  9.388403
27180 2015 0         .   .46171725 . 1    .4960941   9.39648
27180 2016 0         .    .4663858 . 1    .4708638 9.3994055
27180 2017 1         .    .4611382 . 1    .3989139  9.380817
27180 2018 1         .    .4203436 . 1    .3488853  9.415491
27180 2019 1     70.08    .3978906 . 1    .3063653  9.403165
27289 2012 0     69.65   .14664586 1 .   .00624025  4.241327
27289 2013 0   68.3225      .26125 1 .        .085 4.3820267
27289 2014 0   69.0575   .25568944 1 .  .005354753   4.31348
27347 2017 1        75    .6565483 . .    .3697446 10.573635
27347 2018 1        75    .6695515 . .    .3437884  10.52639
27347 2019 1    75.015    .5742112 . .   .31871775 10.535586
27428 2011 0   65.0775   .13689007 1 0    .5806341  8.569216
27428 2012 0     64.13   .05464112 1 0     .445666  8.493843
27428 2013 0    63.775   .05230618 1 0    .3153255  8.718336
27428 2014 0     63.24   .05614451 1 0     .309625  8.608952
27428 2016 0   63.2425   .04013274 1 0    .6087663  7.970015
27747 2011 0   48.3825    .1215762 . 0   .23841095 13.153537
27747 2012 0     48.53   .12044526 . 0    .3069815 13.342514
27747 2013 0     48.53   .12459217 . 0    .3353193 13.423075
27747 2016 0   49.7025      .22116 . 0      .31654  13.58085
27747 2017 1   49.6475     .240385 . 0     .300313  13.58085
27747 2018 1      47.3     .239042 . 0     .296106  13.58085
27747 2019 1   32.2275   .12926964 . 0   .24439067  13.49693
27766 2016 0     71.35    .4556935 . .   1.0834535  9.114942
27766 2017 1     71.35   .50059885 . .   1.3027393  8.957395
27766 2018 1     71.35    .6077901 . .     1.34504  8.691769
28503 2012 0     39.31   .25473896 0 .    .3902617  7.691337
28503 2013 0   34.5225   .21022333 0 .    .3844117  7.865495
28503 2014 0 36.800003   .22626467 0 .    .4588475  7.765697
28503 2015 0    34.105   .27173966 0 .     .342104  7.604247
28503 2016 0   33.4175   .27189037 0 .      .41624  7.567087
28503 2017 1    31.655   .26820514 0 .    .5202893    7.5018
28503 2018 1     26.36    .3242267 0 .    .3363689  7.232444
28503 2019 1     26.37    .3098686 0 .    .3869591   7.08565
28920 2016 0     63.48   .09257442 . .    .3010662 10.304104
28920 2017 1   63.1375    .0993992 . .   .20016736  10.34793
28920 2018 1   62.7675   .08443535 . .   .19553806 10.519087
28920 2019 1   62.8025   .06536856 . .   .29425097  10.89858
28932 2011 0    40.575   .39556575 0 1    .6036246 11.885316
28932 2012 0     46.13    .3681275 0 1    .5700076 11.914035
28932 2014 0   43.6975     .388816 0 1    .4883164 11.763653
28932 2015 0    31.715    .3967145 0 1    .5899524  11.72385
28932 2016 0     26.02    .5569504 0 1     .494161 11.868226
28932 2017 1   20.7275     .509976 0 1    .4752882 11.929614
28932 2018 1    15.435   .50158525 0 1    .4636657 11.920947
28932 2019 1    15.435    .5008077 0 1    .4195412 11.902925
28974 2013 0     36.62    .1640504 0 .   .27563772  7.568689
28974 2014 0     36.62    .2474484 0 .   .29736596  7.715881
28974 2015 0     36.62   .22981104 0 .    .3574273  7.766501
28974 2016 0     36.62    .2086511 0 .    .3306683  7.799344
28974 2017 1   36.6025   .20801553 0 .     .374844   7.67939
28974 2018 1   36.6575   .22433013 0 .    .3543297  7.735302
28974 2019 1     36.64   .20222607 0 .   .28434986  7.885856
28981 2013 0   52.9475   .31494835 . .    .0352185  9.881861
28981 2014 0    58.845    .3153183 . .  .025881946   9.91818
28981 2015 0     60.12    .3026706 . .  .010080293   9.93423
28981 2016 0     60.59    .3247709 . .  .005818862 10.052666
28981 2017 1     61.06    .3141746 . .  .073380664 10.213047
28981 2018 1     61.46    .3233872 . .           . 10.198568
28981 2019 1     61.63    .3558392 . .   .09414333  10.32106
29203 2011 0   51.5725    .3530917 . .    .4285192  7.221251
29203 2015 0     48.83    .2873626 . .    .3032125  7.406528
29203 2016 0     44.35    .2432917 . .    .4214572  7.659407
29203 2017 1   44.5375   .23585346 . .    .4026694  7.760083
29203 2018 1     42.98   .24172376 . .    .4449845  7.868216
29203 2019 1     42.45    .2325715 . .    .4871579  7.869555
29258 2011 0     53.89    .4381484 . 1   .45295775  8.203331
29258 2012 0    53.885    .4208955 . 1    .4656072  8.446019
29258 2013 0   54.2275    .4513228 . 1      .46484  8.555144
end

[/CODE]

[Neelakanda Krishna]

Assuming that I am permitted to extend this thread, I have done the following

I used the above 1000 observations

drop if treat2==. //missing treatment indicator if missing
*(411 observations deleted)
*After dropping 411 observations, we have 37 unique treatment firms (treat2==1) and 44 unique control firms (treat2==0)
* is this numbers okay??
preserve
collapse (mean) lever_w, by(treat2 year)
reshape wide lever_w, i(year) j(treat2)
graph twoway connect lever_w* year if year < 2017
restore
*nothing to restore

xtset co_code year
xtreg lever_w i.time_dummy##c.year if year < 2017
margins time_dummy , dydx(year)
*WHAT DOES THIS REGRESSION MEAN? HOW TO INTERPRET THE COEFFICIENT?
/*
xtreg lever_w i.time_dummy##c.year if year < 2017

Random-effects GLS regression                   Number of obs     =        363
Group variable: co_code                         Number of groups  =         76

R-squared:                                      Obs per group:
     Within  = 0.0010                                         min =          1
     Between = 0.0221                                         avg =        4.8
     Overall = 0.0004                                         max =          6

                                                Wald chi2(1)      =          .
corr(u_i, X) = 0 (assumed)                      Prob > chi2       =          .

-----------------------------------------------------------------------------------
          lever_w | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
------------------+----------------------------------------------------------------
     1.time_dummy |          0  (empty)
             year |   .0011711   .0027276     0.43   0.668    -.0041748    .0065171
                  |
time_dummy#c.year |
               1  |          0  (empty)
                  |
            _cons |  -2.031911   5.492529    -0.37   0.711    -12.79707    8.733249
------------------+----------------------------------------------------------------
          sigma_u |  .27320787
          sigma_e |  .08320824
              rho |  .91511662   (fraction of variance due to u_i)
-----------------------------------------------------------------------------------

. margins time_dummy , dydx(year)

Average marginal effects                                   Number of obs = 363
Model VCE: Conventional

Expression: Linear prediction, predict()
dy/dx wrt:  year

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
year         |
0.time_dummy |   .0011711   .0027276     0.43   0.668    -.0041748    .0065171
------------------------------------------------------------------------------

.

Does may graph make sense

[Maria Boutchkova]

This is explained in #2 above. The regression tells you how the outcome variable depends on a time trend interacted with treated status:

xtreg lever_w i.treated2##c.year if year < 2017
margins treated2, dydx(year)

Look at the two margins and their significance - this is what I was talking about in #6.

[Neelakanda Krishna]

Thanks, Maria Boutchkova. Can I assure from the graph in #8 is 'somewhat following a parallel trend' given the number of observations is not low. Also, I have another question if you think it is worth answering, please post it here. Based on your post
https://www.statalist.org/forums/forum/general-stata-discussion/general/1622575-parallel-trend-assumption?p=1622794#post1622794
I have edited those commands to suit my requirements. As I am a beginner in Stata, I haven't learned much about loops, nevertheless, I used them. I used the above dataex in #7 and run the following commands

drop if treat2==.
*(411 observations deleted)
xtset co_code year

local tr_var treat2
local vars lever_w  nppe_ta_w size_w // here lever_w is dep var and others are controls
local v_max: word count `vars'
local yr_st 2011              // I assume that this is the start period in the sample
local yr_end 2019        // I assume that this is the start period in the sample
local cond year >= `yr_st' & year <= `yr_end'
forvalues i= 1/`v_max' {
local v: word `i' of `vars'
local lab: var label `v'
egen mean_`v' = mean(`v'), by(`tr_var' year )
line mean_`v' year if `tr_var' == 1 & `cond', c(L) 
    || line mean_`v' year if `tr_var' == 0 & `cond', c(L) 
    legend(order(1 "Treated" 2 "Controls") size(small) ) scheme(sj) 
    title("`lab'", size(small)) ylabel(, labsize(vsmall) ) xlabel(`yr_st'(1)`yr_end') 
    xscale(r(`yr_st' `yr_end')) xtitle("") ytitle("") 
    saving(par_tr_`v', replace)
graph export par_tr_`v'.png, replace
}
*

I am not sure whether this code suits my case. Also, it is difficult to know which is treated and control groups as the colors are same
For controls I ran the below codes

forvalues i= 1/`v_max' {
  2. 
. local v: word `i' of `vars'
  3. 
. local lab: var label `v'
  4. 
. egen mean_`v' = mean(`v'), by(`tr_var' year )
  5. 
. line mean_`v' year if `tr_var' == 1 & `cond', c(L) || line mean_`v' year if `tr_var' == 0 & `cond', c(L) legend(
> order(1 "Treated" 2 "Controls") size(small) ) scheme(sj) title("`lab'", size(small)) ylabel(, labsize(vsmall) ) 
> xlabel(`yr_st'(1)`yr_end') xscale(r(`yr_st' `yr_end')) xtitle("") ytitle("") saving(par_tr_`v', replace)
  6. 
. graph export par_tr_`v'.png, replace
  7. 
. }
invalid syntax
r(198);

[George Ford]

I find lgraph useful for this purpose.

ssc install lgraph

lgraph y t , by(treatgroup)

[Neelakanda Krishna]

Dear George Ford
Thanks for helping me.

 
 ssc install lgraph *Input the data collapse (mean) lever_w, by(year treat2) lgraph lever_w year , by( treat2 )

The benefit was here I didnt have to drop missing observations in treat2. I hope this graph is correct! Thanks once again

[George Ford]

I pray that 2015 is your treatment date!

[Neelakanda Krishna]

Dear George Ford
God has some other plans I guess. The treatment year is from 2017-2019. Parallel trend is violated, right? But what to do?

[George Ford]

That's a big shift in 2015. I'd study the data for a peculiarity. If none, then PT is not satisfied. If the treatment is a law or policy of some sort, maybe the response precedes the formal date. Stock prices don't change when the merger is announced, but when it is leaked. Maybe you have cross sections entering and exiting. That would cause problems. Restrict sample to complete data and try again.