entrepreneurs’ age, education, experience, and gender. Recent evidence shows that even a large scale highway upgrade program, that cannot possibly select firms into treatment groups, had effects that vary by plant age (Grover, Maloney and O’Connell, 2020). More research is needed to explore the varied impact of policies and firm support programs by firm characteristics. This includes not only outward features such as age, size, sector and location but also innate characteristics such as productivity, export orientation, innovation, management capabilities. This information would be most useful in improving the design of firm support programs, including having better targeting and screening criterion.

_Market failure underlying interventions_ . Most support programs do not justify (or attempt to identify) the market failure behind the proposed intervention. When proposing business training for firm upgrading programs it is critical to understand why firms do not buy business development services (BDS) from the market. Is it because firms do not know about the potential benefits of BDS or do not have information on the existence of local BDS providers (information failure); or that they are financially constrained firms (credit constraints)? Do firms not invest in BDS because of the absence of BDS suppliers or because they offer low quality services (supply constraints)? In each of these cases, the intervention should be adjusted based on the market failure. For example, if the problem is information failure, an information session with local BDS providers could be enough. If firms are credit constrained, then a free training program potentially complemented with financial support would be needed. In the latter case, it would be important to support the development of local BDS capabilities, in addition to firm training programs.

# **5.2.Selection and targeting**

_Selecting firms with high growth potential_ . In firm support programs, identification of high growth potential firms is not always been effective (such as judges’ scores, researchers’ prediction models, and machine learning approaches). However, the business plan competition in Ghana seems to have been effective in selecting firms to generate a positive impact where small and established firms with a potential for rapid growth were identified by a panel of experts and surveys. One reason why expert evaluation is informative is that small firms in developing countries are highly heterogeneous and hence careful firm selection could be critical in making an intervention successful (Fafchamps and Woodruff, 2017).<sup>19</sup> By comparison, the business plan competition in Nigeria, YouWin!, found that scores from judges are not able to predict survival and other firm outcomes (employment, sales and profits) three years after entry (McKenzie, 2017),

> 19 Prior to the competition, entrepreneurs were surveyed on their characteristics, motivation and experiences, which was followed by an invitation to attend a three-day training to prepare business plans. Next, submitted business plans and oral presentations were assessed by a panel of successful entrepreneurs, consultants and business experts. Based on the quartile of the ranking, 70 of the 140 applicants randomly received a business training course. Both survey responses and expert panels’ decisions could predict firm growth, but the former explained a larger share of the variance in firm outcomes (revenues, profits, and employment). The impact of post-competition training did not have any significant effect on firm growth.

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