Hi I am trying to match treatment and controls on ROA using calipmatch. However, I'm still new to this code and need some help with the matching width.

My understanding is that caliperwidth(0.1) makes the difference of ROA between treatment and control groups within the value of +/- 0.1. However, I'd like to match ROA with a range of +/-10%, which is ROA from the control group is not 10% higher or lower than the corresponding ROA.

Is there any way I can use the percentage difference instead of the actual value difference as the caliper width?

Thanks a lot for any help and comment.

Code:
* Example generated by -dataex-. For more info, type help dataex
clear
input long stkcd int year double roa byte treatment
 4 2011   .046830003727583745 0
 4 2012    .04050635388779153 0
 4 2013   .023938467922043048 0
 4 2014   .046188643554358366 0
 4 2015    .01719991819670127 0
 4 2016     .1775298333690193 0
 4 2017    .02191692926187318 0
 4 2018  -.061543475694888994 0
 4 2019 -.0024115198348312527 0
 5 2010  -.003421735983836361 0
 5 2011    .00905503454132357 0
 5 2012   .016686805744591075 0
 5 2013  -.035624123614119804 0
 5 2014    .03158343984215735 0
 5 2015  -.024626937110126847 0
 5 2016    .04628077822456593 0
 5 2017   .007876100430552625 0
 5 2018   .050217034684912974 0
 5 2019   .058874672135832556 0
 6 2010   .057942059022990655 1
 6 2011    .05216514790634165 1
 6 2012    .06738054038720612 1
 6 2013    .07002386641567787 1
 6 2014    .04360112953523529 1
 6 2015   .034540541559403316 1
 6 2016    .06103667380740991 1
 6 2017    .06244191741112164 1
 6 2018    .06819427734297234 1
 6 2019    .05354384054264582 1
 7 2010    .06527903160641156 0
 7 2011   .022930988812767884 0
 7 2012   .005614041578245276 0
 7 2013   .028639181838631657 0
 7 2014  -.049025421173671084 0
 7 2015   .027560054552458434 0
 7 2016     .1585790901726705 0
 7 2017  .0017960226238898176 0
 7 2018    -.3601920345168437 0
 7 2019   .041651386601188126 0
 9 2010    .04405083092833007 0
 9 2011    .03227960025216681 0
 9 2012    .02333106279716629 0
 9 2013    .03262986444719718 0
 9 2014    .03209050794474974 0
 9 2015   .053323504502728505 0
 9 2016   .019399877156343247 0
 9 2017   .011923784396394046 0
 9 2018   .022280568703266983 0
 9 2019   .023563756371373736 0
10 2010   .018060520974888063 0
10 2012   .014986191541823363 0
10 2013  .0037077236091027977 0
10 2014   -.09365409043696454 0
10 2015  .0038693136262392333 0
10 2016   .008022458238600682 0
10 2017    -.3381495394535607 0
10 2018   -.22195259878634666 0
10 2019   .029388978524123463 0
11 2010    .06006921870396561 1
11 2011    .07356854094059356 1
11 2012    .09487472826957374 1
11 2013    .07767129748537889 1
11 2014    .10751164072079042 1
11 2015    .03580557881896992 1
11 2016   .053327058855217664 1
11 2017    .11550610763036104 1
11 2018    .10182837949860239 1
11 2019    .06889121555302187 1
14 2010   .048823281366409975 1
14 2011   .035960996932445165 1
14 2012    .01641921155453686 1
14 2013   .018601747834259164 1
14 2014   .028069165184644126 1
14 2015   .024607412315286306 1
14 2016   .015536601513141431 1
14 2017   .007189933303928599 1
14 2018     .0805957263225833 1
14 2019    .01629095073318841 1
17 2010     .2054995592161571 0
17 2011     .2054995592161571 0
17 2012    -.3204851871021661 0
17 2013     .2054995592161571 0
17 2014    .11231382336389677 0
17 2015  -.002309371267719172 0
17 2016    .07194867424034053 0
17 2017    .02146764955447785 0
17 2018  -.025674901391686884 0
17 2019   -.12455655866464031 0
19 2010   .013645884125180918 1
19 2011   .009080568766300312 1
19 2012    .06055948680180421 1
19 2013    .03382681749991461 1
19 2014   .012234575854182918 1
19 2015   -.04051128967325299 1
19 2016    .07648891097108283 1
19 2017   -.05722507167415348 1
19 2018   .049599337667664174 1
19 2019   .056754632952510584 1
20 2010   .012642698084252649 0
20 2011   .015521759986024827 0
end