and poorer individuals to start businesses, and may prop up the survivorship of less profitable businesses. De Mel et al. (2012) find that training leads to an increase in start-up among women with low Raven test scores (a measure of analytical ability), and actually reduces the rate of ownership among women with the highest test scores. Giné and Mansuri (2011) find that businesses that fail tend to be ones with lower land holdings and lower digitspan recalls (a measure of short-term recall), but do not examine whether training differentially affects who survives. Since many studies only examine impacts over a short time frame, rates of business failure are often low. Selectivity is more important in contexts where impacts are measured over longer time frames, or where business failure rates are high. For example, Bruhn and Zia (2012) find 36 percent of businesses shut down during their study period in Bosnia, with this high rate likely due in part to the downturn caused by the global economic crisis, while Calderon et al. (2012) find that 50 percent of the non-attriting businesses had closed by the time of their second follow-up survey 28 months after training.

# _Impacts on Business Practices_

A first link in the causal chain from business training through to business profitability and growth is that business training has to improve the knowledge of business owners and result in these owners implementing at least some of the practices and behaviors that training has taught them. One can think of other potential mechanisms through which training may affect business outcomes (e.g. through changing attitudes or work hours), but failure to find any change in practices should cast doubt on the ability of the training to improve firm outcomes.

Table 8 summarizes the impacts found by different studies on business practices. Almost all studies find a positive effect of business training on business practices, although the effect is often not significant once the sample is split by gender. Studies differ in the specific practices they measure, how comprehensively they measure them, and how (if at all) they aggregate them. Several studies measure only one to three basic practices, such as Calderon et al. (2012) who examine whether the firm uses formal accounting, and Mano et al. (2011) who record whether or not the firm keeps records, whether it analyzes them, and whether it visits customers. Others record a broader range of practices, including different types of record keeping, different marketing activities, and other specific practices taught in the training.

One common approach to aggregating different practices is to follow Kling et al. (2007) and normalize each practice as a z-score (subtracting the mean and dividing by the standard deviation), and then averaging these z-scores. A coefficient of 0.03 as in Karlan and Valdivia (2011) is then interpretable as an impact of 3 percent of a standard deviation. This is useful for considering the magnitude of the increase in relative terms, but does not provide much guidance as to the absolute size of the effect. Alternatively one can look at the percentage point increase in the likelihood a particular practice is implemented, or the change in the number of practices implemented out of some total, both of which provide more guidance on the absolute magnitude of the increase.

Many studies find baseline levels of business practices which are relatively low. For example Giné and Mansuri (2011) report only 18 percent of firms at baseline record money taken from the business and 14