increases power, but if the training has large effects for a few firms and small effects for most, truncating or trimming may eliminate the very effect the study is trying to detect. Karlan et al. (2012) argue that the prospect of a “tail” event may induce firms to experiment with new methods and techniques, such as those taught in training or by consultants. Truncation should be preferred to trimming for this reason.

We should note also that power is generally much higher for detecting binary outcomes, such as whether or not a new business is started, whether or not a firm applies for a loan, or whether or not it implements a particular business practice. Studies with low power to inform us about the impact of training on ultimate business outcomes may therefore still be informative about other training impacts.

# _Timing of Effects_

The short and long-run impacts of many policies can differ substantially, so a key challenge for impact evaluation is figuring out when to measure outcomes (Woolcock, 2009). For business training, one might expect firms to make some changes relatively quickly after training. But the full impact of training may take some time. Impacts on business survival may also take time to materialize. However, firms could start some practices and then drop them, so that surveys which measure what is taking place in the business only several years after training may miss the period of experimentation. Ideally, then studies should trace the trajectories of impacts, measuring both short and longer-term effects.

Table 6 provides details on the number of follow-up surveys, their timing, and their attrition rate for the different studies. The majority of studies we review take a single follow-up survey, providing a snapshot of information on the training impact, but no details on the trajectory of impacts. Eight of the thirteen studies are very short-run, looking at impacts at one year or less after training. De Mel et al. (2012) find that the impacts differ in the short and medium term in their study. For example, examining impacts within the first year would show that business training for women out of the labor force led to large increases in business entry, whereas surveys 16 and 25 months after training show the control group had caught up in terms of business ownership rates.

# _Survey Attrition and Selective Survival or Start-up_

Survey attrition is another problem complicating inference, especially if the reasons for attrition are business failure, refusal because of disappointment with the training effects, or because successful business owners have moved out of the area. Survey attrition rates can be related to the timing of the follow-up surveys – a longer interval elapsing since baseline makes it more likely people have moved or that contact details have changed, but may also help reduce the chance that the control group attrits because they were disappointed not to be selected for training. Attrition rates range from as low as 5.3 percent in Field et al. (2010) and 6 to 8 percent in de Mel et al. (2012), up to 24 percent in Karlan and Valdivia (2011), 26% in Calderon et al. (2012), and 28 percent in Klinger and Schündeln (2011). It is perhaps no coincidence that the highest attrition rates come from three studies conducted in Latin America; our personal experience has been that in many Latin American countries firms are less willing to answer surveys than in Africa or South Asia, and transitions between wage work and self employment are frequent in the region (Fajnzylber et al, 2006).

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