clusters in Ethiopia could be natural clusters or those supported by government policy. The latter have involved policy interventions to promote activity for firms in particular industries in specific locations (Ali 2012).

The effect of industrial diversity (represented by the Herfindahl index) is negative but statistically insignificant in model (8). Given that most Ethiopian cities (with the exception of Addis Ababa and possibly Dire Dawa) are relatively small, it is no surprise that firms are yet to benefit from economic spillovers related to industrial diversity.

**Business Environment** : The analysis above indicates that a 1 percent increase in taxes leads to a 1.24 percent fall in firms’ employment growth. Since the regression controls for firm-specific controls (such as size, ownership, industry and location), this suggests that increases in taxes on manufacturing firms might have a deleterious effect on employment growth in the future. Currently, Ethiopia ranks 109 out of 189 economies with regard to paying taxes according to the 2014 Doing Business Survey (World Bank 2014).<sup>12</sup> and there has not been a significant overhaul of tax policy since 2004. The average tax burden on manufacturing enterprises has also remained broadly unchanged in the last decade or so, and increased somewhat after the 2004 reform. While Ethiopia uses fiscal instruments such as tax holidays, etc. to attract investment in particular locations and industries (KPMG 2012-2013), it is unclear that this policy is being used effectively. Tax policy ought to ensure a level playing field and in many cases can be misused in that special tax breaks are meted out to large, influential corporations (Gauthier and Gersovitz 1997). Indeed, there is research (see Tybout 2000 for an overview) to indicate that the tax burden falls disproportionately on medium-sized firms in developing countries – small firms often stay informal to avoid taxes and large firms are influential enough to obtain special treatment. Given the country context, it is often medium-sized firms that employ labor, both skilled and unskilled, in large quantities and thus a high tax burden could strongly and negatively affect employment growth.

Similarly the model suggests that a 1 percent increase in licensing fees (measured at the firm level) negatively affects employment growth in equal measure. License fees and lengthy license procedures are often barriers to entry and expansion, slowing economy-wide growth (Rodrik and Subramanian 2004). Djankov et al (2002) find that countries with heavier regulations (including high licensing fee burdens) suffer from higher corruption and larger informal economies. Similarly, Klapper et al (2004) find that high costs of entry negatively affects entrepreneurial growth across a variety of industries. According to the 2014 Doing Business Survey, Ethiopia ranks 166 (out of 189 economies) with regard to the ‘starting a business’ indicator, and licensing fees are used in the model as a proxy for entry regulations in Ethiopia.

**Infrastructure** : The infrastructure variables (transport, electricity) and business environment variables (taxes, licensing) are constructed using firm-level data from the LMMIS data set. The variables is averaged across firms at the level of industries and cities, and over time.

Transport costs negatively affect firm employment growth, after controlling for firm

> 12 The total number of hours taken to pay taxes has increased from 212 hours in 2004 to 306 hours in 2014.

24