One-to-one matching

[Lutifye Isikdemir]

Dear all,

I am using Stata 14 and I want to match treatment firms and control firms one-to-one through the following way: First, control firms have to be in the same two-digit SIC code as with the treatment firms and they should be in the same portfolio as well which is constructed based on the three firm characteristics (i.e. var2, var3 & var4). Then, control firms are chosen based on the smallest difference in var1 as compared to the treatment firms (i.e. treatment=0 if control firm and treatment=1 if treated firm). If there is any tie among control firms (i.e. Company 1 and Company 5 can be both matched to Company 6 in the below data-set based on the smallest difference in Var1), the one who has the lowest rank in terms of the smallest difference in the aforementioned firm characteristics as compared to the treatment firm is chosen.

Up to now, I was able to construct the first part (i.e. more than 100 portfolios are constructed). However, I could not figure out how to match control and treatment firms based on the smallest difference in Var1 for each portfolio and do the ranking in case of ties. To illustrate my data-set (i.e. only 9th portfolio is demonstrated below), part of my data-set is provided below:

Portfolio	Company Name	Treatment	var1	var 2	var 3	var 4
9	Company 1	0	6	39303.29	0.283071	0.300222
9	Company 2	0	5	53535	0.27397	0.234608
9	Company 3	0	32	96075.69	0.252854	0.234742
9	Company 4	1	16	95403.36	0.288711	0.253231
9	Company 5	0	6	4948.74	0.304504	0.25742
9	Company 6	1	7	5943.369	0.315158	0.232289
9	Company 7	1	15	7587.342	0.292909	0.274163
9	Company 8	0	29	54537.61	0.269264	0.304528
9	Company 9	0	27	23657.51	0.228177	0.33258
9	Company 10	0	39	30194.24	0.253387	0.279335

Since I have recently started to use Stata, I am a bit lost. I will appreciate any of your response.

Regards,

[Rich Goldstein]

I do not fully understand; also, you did not use -dataex- (see the FAQ) to show your data making it too much work (for me, at least) to put into Stata; however, there are a number of user-written routines for matching and you probably want to look at some of them; use -search- to find them and download them; some of the apparently relevant programs include: vmatch, iematch, calipmatch and kmatch

[Clyde Schechter]

Well, it has nothing to do with being new to Stata. The condition "the one who has the lowest rank in terms of the smallest difference in the aforementioned firm characteristics as compared to the treatment firm is chosen" is too vague to be implemented as an algorithm in any package you might choose. You have multiple characteristics. You could have a tie situation in which one of the potential matches is very close on var2 but not var3 and var4, another is very close on var3 but not var2 or var4, and another one which is moderately close on var2 and var4 but a wild miss on var3. Which would you consider to have the lowest rank in terms of the smallest difference in those characteristics? You need a specific set of rules. There are (infinitely) many different similarity metrics that can be used, and you have to settle on one. Once you resolve this conceptual problem, putting it into code will likely be simpler than the coding you have already done so far, though, of course you are welcome to post back for help on the coding should it prove more complicated.

[Lutifye Isikdemir]

Dear Rich & Clyde,

Thank you for your responses. I am stuck in the very beginning of matching. I have tried some of the commands that you proposed (i.e. vmatch, iematch and kmatch). I think kmatch suits better than the other two in my setting. However, the problem is that control firms match more than one treatment firm with this command. I want one-to-one matching; that is, each treatment firm should match with one and unique control firm. Although I searched for correcting this issue, I could not find the answer that I looked for. For illustrative purposes, I demonstrate the command used for matching below:

kmatch md treatment var1, att nn ematch(group_portfolio) generate(stub)

where group_portfolio is constructed based on the three firm characteristics that I mentioned in the first post (i.e. var2, var3, var4) as well as two digit SIC codes.

Do you have any suggestions how I can fix this problem? Below I am sharing my data with dataex.

Regards,

* Example generated by -dataex-. To install: ssc install dataex
clear
input float(group_portfolio treatment) long gvkey double var1 float(var2 var3 var4)
31 1 61811 10 1172.0183 1.0920043 .2268924
31 0 210607 4 1324.781 .92838 .1523701
80 0 20548 11 391.2549 1.4347194 .16476972
80 1 14633 7 414.2353 1.3749626 .15961675
89 1 14359 15 1288.6755 .9310839 .20543145
89 0 29873 2 1258 .8371637 .20498085
89 1 22668 13 1032.304 .836772 .19765966
89 1 22974 13 830.3805 .9802133 .191556
94 1 25956 7 280.599 2.606157 .10822867
94 0 22255 5 542.2042 1.622007 .13752659
105 1 11038 27 10214.843 .8931121 .21275896
105 0 14878 18 2598.329 .7447093 .3385013
109 0 18756 5 3863.868 1.1203008 .17840184
109 1 8549 22 11651.113 1.1628073 .20150575
110 0 8468 9 2632.8435 1.566464 .12083858
110 1 8068 17 7695.705 2.291408 .12566632
203 0 7146 12 1925.798 .6888619 .1621985
203 1 28895 13 2365.7344 .7208692 .1659508
210 1 3144 18 130562.26 .12378003 .2933915
210 0 2663 17 23758.75 .27185774 .28518346
210 0 6375 16 13758.15 .36705515 .26663366
318 1 23252 9 819.831 .52179533 .23904587
318 0 5690 2 980.529 .5237112 .24974
356 1 25305 9 750.6015 4.049102 .067214675
356 0 18375 1 1853.64 2.396366 .05042773
358 0 100806 2 2936.688 2.0893562 .08902459
358 1 2917 15 4132.754 2.3761375 .09010832
360 1 10426 13 3001.177 4.313974 .05298525
360 0 31691 1 2275 4.346682 .05366953
384 0 1468 3 2324.442 .9185517 .1585091
384 1 14163 4 1057.3844 .8281435 .15798832
390 1 10580 12 4812.3174 .7335057 .14874293
390 0 5038 5 3555.95 .9354007 .15787114
452 0 24908 9 2062.036 .24474356 .05541047
452 1 2226 20 2810.3826 .2257956 .05966699
452 0 13365 1 938.432 .2171729 .02405276
452 0 13308 9 1190.0757 .2242832 -.11128678
459 0 9472 4 822.2805 .7581087 .14057282
459 1 29511 5 1848.1937 .6823419 .17112045
459 0 30191 3 511.7857 .7968159 .14707245
459 1 9772 14 1772.6074 .6062154 .15273008
459 0 12233 12 1684.1235 .7300646 .10540789
459 0 7936 6 302.8725 .6674162 .1041446
459 0 6618 2 572.5311 .5119774 .12034197
459 0 1239 4 1206.4756 .7536547 .1520358
459 0 25870 5 926.6 .770409 .15913603
459 0 13406 7 486.0187 .8173574 .13291596
459 0 3026 4 444.9645 .6921248 .13101234
460 0 24782 9 956.45 .5942569 .1981871
460 0 3917 2 351.8224 .58837074 .21057284
460 0 4213 8 2438.1 .4956355 .21557437
460 0 29751 4 997.7087 .8482044 .194349
460 1 12895 10 929.4719 .6326162 .28657192
460 0 13542 7 1771.8552 .6771309 .19998616
460 1 63500 5 1397.989 .6195513 .18003154
465 0 5180 16 56285.77 .25258607 .3886896
465 1 7257 32 96075.69 .25285378 .23474155
465 1 1920 13 7587.342 .29290888 .27416307
465 1 1078 21 39303.29 .28307047 .30022165
465 0 6078 6 4948.74 .3045044 .25741965
465 1 8762 16 95403.36 .288711 .2532312
465 0 61567 7 5943.369 .31515795 .2322887
465 1 8530 31 53535 .2739703 .2346083
465 1 9459 27 23657.514 .228177 .33258
465 0 30938 8 30194.236 .25338665 .27933517
465 1 2403 29 54537.61 .26926374 .30452845
465 0 6266 23 66291.08 .3018506 .26546726
466 0 3121 9 6818.469 .4074158 .19700377
466 0 28272 8 26513.59 .3229099 .2446
466 1 6730 30 40356.08 .35452405 .1810487
466 0 14261 18 37404.42 .3641281 .22070484
466 1 11288 25 20340.3 .3538443 .20072804
468 1 9667 12 4811.296 .6224082 .1743938
468 0 7536 17 22717.62 .4926133 .16754535
468 0 8020 4 6457.391 .57922775 .15990695
468 1 16582 16 3874.47 .6438009 .1535335
468 1 15426 13 4138.637 .6777351 .16546044
468 0 1945 3 13532.4 .5362486 .16095774
468 0 2086 17 11170.04 .6800334 .15863612
468 1 11040 29 20140.715 .5547469 .1695158
469 0 7679 8 2421.242 .57594407 .22344926
469 1 4087 17 53043.86 .7161432 .2140206
469 1 9217 13 5154.21 .7630655 .21332316
469 1 3170 14 13573.11 .5821436 .18609124
469 0 28169 12 1930.1837 .8187319 .287262
469 0 5589 9 4385.6797 .54404336 .22129086
469 0 8247 11 10282.94 .6264161 .25756824
469 0 15708 6 2333.9006 .5783451 .1873611
470 1 63637 7 1372.5188 4.0808187 .0998036
470 0 13467 4 11119.4 2.4568214 .10174844
470 0 31831 2 498.8734 3.594034 .06719529
471 0 2085 6 1939.14 1.342554 .11465775
471 0 4371 5 2751.227 .9068333 .13053207
471 0 15334 4 9607.207 1.229874 .15867105
471 0 11559 4 1731.2715 1.381473 .11337017
471 0 8902 5 1490.037 1.096099 .13314308
471 0 3558 4 2541.25 1.0882055 .1458392
471 1 10857 15 5168.235 1.2665833 .1616254
471 1 1794 12 2544 2.857311 .10799284
471 0 1209 16 6437.499 1.0131265 .149954
end
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