Table 4: Programs impacts on any employment and migration (Continued)

||**Impact o**|**n Any Em**|**ployment**|**Impa**|**ct on Migr**|**ation**|**Elasticity**|
|---|---|---|---|---|---|---|---|
|Intervention|Sample<br>size|Control<br>mean|ITT|Sample<br>size|Control<br>mean|ITT||
|_Panel F: Ghana M_|_icroenterpri_|_ses_||||||
|Up to 1 year|1,520|0.960|0.030*|1,884|0.004|-0.002|-0.061|
|after treatment|||(0.017)|||(0.006)|(0.236)|
|3 years|314|0.815|0.089**|314|0.025|-0.013|-0.143|
|after treatment|||(0.039)|||(0.016)|(0.191)|
|_Panel G: Nigeria Y_|_ouWin!_|||||||
|2 years|1,686|0.860|0.108***|1,477|0.041|0.002|0.018|
|post application|||(0.013)|||(0.011)|(0.094)|
|3 years|1,562|0.850|0.103***|1,279|0.056|-0.021*|-0.195*|
|post application|||(0.014)|||(0.012)|(0.114)|
|5 years|1,685|0.812|0.077***|1,106|0.083|-0.036**|-0.455**|
|post application|||(0.018)|||(0.015)|(0.211)|

_Notes:_ Columns (1) to (6) report impacts of several programs on self-employment and migration. The last column reports estimates from a two-stage least squares regression of the migration indicator on an indicator for self-employment instrumented by treatment status. Panel A uses the sample of participants to the Uganda YOP (Blattman et al., 2014, 2019). Each coefficient comes from a weighted least squares regression that controls for district fixed effects and a vector of baseline covariates. Regressions are weighted by inverse probabilities of attrition and selection into the endline tracking sample. Standard errors are clustered at the group level. Panel B uses data from the Uganda WINGS program (Blattman et al., 2016). Treatment effects are computed as the linear combination of the impact of an indicator for assignment to the general treatment group, and an indicator for assignment to the group dynamics training. Each regression controls for a vector of baseline covariates and strata fixed effects. Standard errors are clustered at the village level. Panel C uses data on the sample of participants to the Nairobi microfranchising interventions discussed in Brudevold-Newman et al. (2017). We further distinguish between franchise and grant treatment in sub-panel (a) and (b), respectively. Each regression controls for baseline covariates and fixed effects for randomization stratum, survey enumerator and survey month. Standard errors are robust. Panel D is based on the sample of male Sri Lanka microenterprise owners first studied in DeMel et al. (2008). The treatment variable is equal to the size of the one-time grant given to each enterprise. Standard errors are robust. Panel E uses data on the sample of potential owners that received the SIYB business training. All regressions include randomization strata fixed effects. The 8-months and 25-months regressions pool two survey rounds each. Round fixed effects are included in both regressions and standard errors are clustered at the individual level. Panel F takes data from the sample of male Ghana entrepreneurs receiving microenterprise grants as discussed in Fafchamps et al. (2014). Each regression includes strata (matched quadruplets) fixed effects and standard errors are clustered at the firm level. The 1-year regression pools data from the first 6 waves and additionally controls for wave fixed effects. Lastly, panel G presents results on the sample of winners to the YouWin! competition evaluated by McKenzie (2017b). Each regression controls for randomization strata fixed effects and standard errors are robust. See section C of the online appendix for more details on the randomized experiments analyzed and the variables used in each regression.

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