size. This is precisely the challenge described as the “Penrose effect” which refers to managerial capabilities and the absorption of technology as the key binding constraint limiting firm growth (Penrose, 1959). Such a short-term trade-off between high growth in employment and productivity has been observed in Sweden over the period 1997-2005 (Daunfeldt et al., 2010). A report by the UK Department for Business, Enterprise and Regulatory Reform (BERR) (2008) also confirms that high-growth may not necessarily imply high productivity.

This relationship between productivity and high growth could be further nuanced based on whether productivity is measured in growth or in levels. Using data from 2001-2010, Du et al. (2013) find that firms in the United Kingdom are more likely to achieve high-growth when they exhibit higher TFP growth. By comparison, when productivity is measured in levels, the relationship with HGF incidence is negative. The rationale for this difference is that it is usually the larger firms that have higher productivity levels (Van Ark and Monnikhof, 1996) and hence their growth is limited by the large base. It could also be the case that a threshold level of productivity is needed before firms begin to strategize their expansion capacity, which may imply lower growth in the short term.<sup>13</sup> Additionally, the metrics used for assessing high-growth could also be crucial. One could argue that growth in employment is less sensitive to firm productivity but depends more on industry characteristics. Thus, revenue may be a more natural metric for computing the HGF status (Du and Temouri, 2015). However, if it is the more productive firm that expands into international markets (Melitz, 2003) or engages in foreign investments (Helpman et al., 2003), then productivity is likely correlated with higher employment growth. From the perspective of this paper, both employment and revenue metrics are critical, although, given that the focus of developing country policy makers on jobs, HGFs computed using employment metrics is more relevant.

# **_Agglomeration and Growth Entrepreneurship_**

There is a paucity of research linking clustering to high growth even in the context of advanced economies. Clustering matters because firms located in cities may benefit from thicker markets for capital, labor and material inputs (Fujita et al., 1999), knowledge spillovers and reduced transaction costs (Audretsch and Feldman, 1996). Clustering of economic activity can also yield dynamic benefits, such as increased innovation (Matsuyama, 1991) and competition, which may also reduce the scope for opportunistic behavior (Collier and Venables, 2008). Studies suggest that firms located in geographic regions with industry clustering exhibit higher rates of growth due to enhanced demand for their products and a higher ability to harness reputational advantage (Gilbert et al., 2006, 2008). Clustering can generate high firm growth by providing greater access to both customers and prospective suppliers (Lechner and Dowling, 2003).<sup>14</sup>

# **_Manufacturing and Industrial Policy Reforms in Ethiopia_**

Ethiopian manufacturing has been the focal point of several studies that document the performance, productivity as well as the challenges for the sector. Ethiopian manufacturing grew at an average rate of 10.5 percent over 1996–2009, which coincided with employment growth of 4.1 percent. During this period, manufacturing was dominated by smaller firms, with a highly-skewed size distribution. Overall, less than 5 percent of manufacturing firms exported, with the total value of their exports being less than 8 percent of

> 13 This would be true for young firms relative to incumbents (Du et al., 2013).

> 14 On specific works pertaining to geography and HGFs, Anyadike-Danes, Bonner, and Hart (2013) focus on the type of location, but not specifically on clustering. Likewise, the finding of a positive correlation of high-growth with human capital, industrial specialization and services agglomeration in Bogas and Barbosa (2015) speaks to locational traits rather than the importance of firm clustering.

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