association of productivity and employment growth, using an employment-based definition ensures that this link is not overstated.

# **_TFP Estimation_**

Several techniques tackle the simultaneity issue inherent in TFP estimations. These methods typically use readily observable data as proxies for productivity shocks, such as investment (Olley and Pakes, 1996) or intermediate inputs (Levinsohn and Petrin, 2003). The LMMIS database has rather poor information on investment but high-quality information on the use of intermediate inputs. Thus, the paper uses the Levinsohn and Petrin (LP) estimator to estimate plant-level TFP.<sup>27</sup> More recently, frontier literature on productivity measures emphasize the importance of estimating physical output based measures of TFP (referred as TFPQ), vis-à-vis the widely-used revenue based measure (referred as TFPR) (e.g. Foster et al., 2008). The criticism for using TFPR is that differences in revenue-based efficiency measures could reflect differences in price, elasticity and scale economies rather than efficiency or quality. One limitation of estimating TFPQ is that it requires detailed information on product-level prices. Although such information exists for Ethiopia and certain studies have successfully computed TFPQ (e.g. Bigsten et al. 2012), it is nonetheless less meaningful in the context of a study on HGFs. This is so because TFPQ can be meaningfully estimated only for products that are homogenous, that is, where quality differentiation cannot be argued to play a role in determining price differences among products. Considering such product categories dramatically reduces the observation count from 13,975 to 5,565.<sup>28</sup> Furthermore, this is even more complicated for multiproduct plants where the methodology approximates TFPQ by considering only the major product of the firm.<sup>29</sup> In an analysis where firm growth is measured by change in overall employment, it would be misleading to consider plant TFPQ of the major product of the plant.<sup>30</sup>

# **_Distances to Cities/Urban Agglomerations_**

This work uses GIS software to compute distances between every town in the LMMIS data and five major Ethiopian cities that are considered to be centers of broader agglomerations. The five cities/urban agglomerations are Addis Ababa, Awassa, Bahir Dar, Dire Dawa and Mekele. Besides the agglomeration center itself, the following distance bands are defined from the nearest agglomeration center à la Ghani et al. (2016): 0-50 kilometer (km), 50-100 km, 100-150 km, and the residual category of towns that are located over 50 km away.

# **_Measuring Spatial Clustering_**

The Ellison-Glaeser index (EGI) measures the extent of spatial concentration in a given industry. It takes negative values when the industry is more dispersed than what random assignment would lead to, while positive values where industries show greater levels of concentration.<sup>31</sup> The EGI for industry _s_ at time _t_ is defined as:

> 27 The project also experimented with alternative definitions of TFP and found the results to be robust to such changes. Another problem related to TFP estimation is measurement errors; these are arguably all the more pervasive in the context of developing countries.

> 28 Examples of such products include bricks of clay, cement blocks, nails, sugar, bread and wheat flour.

> 29 Since most data-sets do not have disaggregate information on inputs by products of the firms, the share of inputs feeding into the major product is extrapolated.

> 30 About 50 percent of observations pertain to single product plants but restricting to homogenous products, only 26 percent of observations are suitable for TFPQ estimation.

> 31 The main advantage of the index is its robustness to the level of spatial aggregation, provided that the agglomeration externalities apply within the defined spatial unit and not between units. It is also independent of the industry distribution and the size distribution of establishments.

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