Column 2 subsequently introduces control for plant location in one of the top five agglomeration centers and the corresponding distance bands (and henceforth town FE are dropped). It also controls for initial spatial concentration, using EGI. Finally, the specification in column 3 introduces the interaction of initial EGI with the spatial location dummy. The coefficient for this interaction can be used to ascertain if industry concentration in agglomeration centers is particularly important in enhancing the probability of highgrowth. Columns 4-5 repeat the specifications in columns 2 and 3 using a dynamic Probit random effects model. Columns 6 and 7 repeat the specifications in columns 2 and 3 using the Birch definition for high growth and estimate it using the static Probit model, while columns 8 and 9 repeat the latter two estimations using a dynamic Probit random effects model.

# **_Is Initial Plant Productivity Pivotal for High-Growth Episodes?_**

The most striking result of Table 3 is the consistently positive and significant coefficient of initial plantlevel TFP across all specifications, that is, irrespective of the estimation technique and definition of highgrowth, there is a strong and positive correlation between high-growth and initial plant-level productivity.<sup>38</sup> This provides strong evidence that, ceteris paribus, initial plant-productivity is tightly linked with the probability of high-growth. The magnitudes of the coefficient on initial TFP levels are also noteworthy: in both the static and the dynamic Probit models, the coefficient for initial TFP is larger for specifications using the Birch index to define HGFs relative to the OECD definition. Since the OECD definition captures smaller firms, while Birch has a bias towards picking up relatively mid to large sized firms, these results suggest that higher initial TFP is relatively more important for larger sized plants in terms of enhancing their probability of high-growth.

# **_Industry and Spatial Agglomeration for High-Growth_**

The positive and highly significant coefficient for the agglomeration center dummy suggests a significantly higher probability in achieving high-growth among plants located in agglomerations, relative to those located over 150 km from the nearest center. In fact, plants located in towns that are within 50 km of the nearest agglomeration center also have a higher chance of experiencing a high-growth episode relative to those located farther away (although the coefficient for the dummy variable recording plant location within 50 km from agglomeration center is not always significant). Qualitatively, these results are consistent with standard arguments that go in favor of agglomeration and clustering (e.g. due to noted benefits from thicker markets for resources, knowledge spillovers and reduced transaction costs).

By comparison, the coefficient on initial industry-level clustering, measuring using EGI, is negative and significant, implying that agglomeration within the same broadly defined industries generates congestion externalities and competition for similar resources which discourages a high-growth spell. These results are consistent with the findings of Bigsten et al. (2012) who use the same Ethiopian data-set to suggest that although agglomeration generates positive externalities, it also increases competitive pressures in underdeveloped factor and product markets.

By introducing the interaction of initial industry clustering with plant location, the full specification in column 3 seeks to understand the association of clustering in agglomeration centers with the probability of attaining high-growth. Although the coefficient for the interaction term is positive (but insignificant), its sum with EGI and with agglomeration center dummy is negative (e.g. -.054 in column 3), implying that

> 38 The coefficient size with respect to TFP growth rather than levels is smaller in magnitude. The effect is significant only with the Birch definition. First, firms that have higher levels of TFP are not the same as those with higher growth in TFP. Second, Birch HGF firms are more than double the size of OECD defined HGFs and their TFP levels are also, on average nearly three times higher.

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