should result in an increase in total income of SSCBTS’ households.

Table 3: LATE estimates of the Effects of CTA adoption on SSCBT’s trade outcomes

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
|Outcome||D|aily Turnover|||
|CTA adoption|0.956***<br>(0.122)|0.275***<br>(0.092)|0.269***<br>(0.091)|0.211***<br>(0.101)|0.212***<br>(0.101)|
|Observations|1159|1145|1145|1145|1145|
|F Statistics (excluded instruments)|1337.96***|885.98***|879.89***|629.14***|631.05***|
|Under identifcation test p value|0.0000|0.0000|0.0000|0.0000|0.0000|
|Wald test (joint signifcance)|61.24***|8.86***|8.59***|4.19**|4.20**|
|Cragg-Donald Wald F|475.718|392.85|400.19|347.12|336.16|
|Stock-Yogo : critical values|(16.38)|(16.38)|(16.38)|(16.38)|(16.38)|
|Outcome|||Daily Proft|||
|CTA adoption|0.782***<br>(0.101)|0.360***<br>(0.088)|0.358***<br>(0.087)|0.310***<br>(0.093)|0.313***<br>(0.093)|
|Observations|1159|1145|1145|1145|1145|
|F Statistics (excluded instruments)|1337.96***|885.09***|879.89***|629.14***|631.05|
|Under identifcation test p value|0.0000|0.0000|0.0000|0.0000|0.0000|
|Wald test (joint signifcance)|59.43***|16.87***|17.02***|10.83***|11.05***|
|Cragg-Donald Wald F|475.7|392.85|400.19|347.12|347.15|
|Stock-Yogo : critical values|(16.38)|(16.38)|(16.38)|(16.38)|(16.38)|
|Socio-demographic Controls||_~~√~~_|_~~√~~_|_~~√~~_|_~~√~~_|
|Borders fxed efects|||_√_|_√_|_√_|
|Type of Product fxed efects<br>Province fxed efects||||_√_|_√_<br>_√_|

Notes: outputs are log-transformed. ***,**,* Denote significance at 1%, 5% and 10% levels, respectively. Standard errors are in parenthesis. Socio-demographic Controls includes variable such as gender, Age, Education, Living in urban, Marital status, Experience as SSCBT, whether the SSCBT has a fix place in the market and the logarithm of purchased volume amount.

To corroborate this significant impact, we applied two additional treatment effect models, namely the propensity score matching (PSM)<sup>9</sup> and the nearest-neighbor matching techniques (NNM).<sup>10</sup> In comparison to LATE method, PSM and NNM are free of instruments use and impose fewer constraints on the functional form of the treatment model, as well as fewer assumptions about the distribution of the error term. The estimation of Equation 2 shows that, though the coefficients provided are different in magnitudes, the result reported in Table 6 give an ATE and an ATT for the two methods (PSM and NNM) applied on the two trades outcomes (Turnover and Profit), are positive and significant, which confirm the results of LATEs estimators. The results are in line with Kariuki

> 9For the two approaches, ATT and ATE are estimated first, by propensity-score matching (when using PSM). In this case, It does not need bias correction, because PSM matches on a single continuous covariate. Second, by nearest-neighbor matching (when using NNM), and it is based on a weighted function of the covariates for each observation. Furthermore, it uses a bias-correction term when matching on more than one continuous covariate (STATA, 2017).

> 10Both NNM and PSM commands have a nneighbor(#) option in STATA. But this only specifies the number of matches per observation. The criteria used in matching differs for both commands. _teffects nnmatch_ , for the NNM model, uses a ”distance” metric criteria, while _teffects psmatch_ , for the PSM model, uses the criteria of the estimated predicted probabilities of treatment.

15