robustness checks in addition to the battery of checks applied by Brueckner (2013), as discussed in Section 4.

# **2.3 Calculating Growth Contributions in Ethiopia**

Growth contributions over each time period can be calculated by first-differencing equation (1): as log-changes approximate growth rates of a variable. I.e. growth can be explained by a persistence effect _(θ[Δ lnyct-1])_ , changes in the explanatory variables _X_ , and a period-specific global shock _(Δbt)_ .<sup>9</sup> Note that the country fixed effect cancels out because it is time-invariant. To illustrate: growth in the Late 2000s can be explained by a dynamic persistence effect from the Early 2000s, the change in explanatory variables _X_ in the Late 2000s, and a global time-specific shock (relative to the previous period). In this setting, the persistence effect should be interpreted as an ‘echo’ (or fading out) of previous improvements. Finally, the residual part _Δect_ remains unexplained by the model.

For calculating Ethiopia's drivers of growth, we extend its explanatory variables by one 5-year period. As the original dataset by Brueckner (2013) contains average 5-year values across countries until 2010 only, we update the Ethiopia values using 2013 data. We treat the latter period (the ‘Early 2010s’) as if it represented a 5-year period average for 2011-2015. This can be motivated by the consideration that the 2013 value is the mid-point for this period and most macro variables are highly persistent. Since not all of the 2013 data were available from sources fully consistent with the original dataset we added to the logged original series the log changes between the 2006-2010 averages and the 2013 values of those data we had available.<sup>10</sup> Despite some caveats, this approach allows us to also analyze the most recent period of Ethiopia’s growth performance. Furthermore, it should be noted that this extension does not affect the model estimation results as it was performed after estimation for Ethiopia only.

To facilitate an analysis of the growth acceleration period since 2004 with the available data, it is useful to define time periods precisely. As illustrated in Figure 2, our analyzed period 1996-2013 consists of 4 data points ( _t0, t1, t2_ and _t3_ ), each capturing the average values of the following time periods: 1996-2000, 2001-2005, 2006-2010, and 2011-2015 (proxied by the 2013 value). In the remainder of the paper, we refer to ‘Early 2000s’ as the change between the 1996-2000 and 2001-

> 9 Our calculation differs slightly from the one in Brueckner (2013) and Araujo _et al_ . (2014) as we do not use the actual lag of the growth rate for calculating the persistence effect from equation (2) but instead take the growth rate as predicted from the model.  Furthermore, we take second differences of external factors as they enter the estimated levels equation already in first differences. To calculate effects for the 2000-2013 period, we proceed as follows to accommodate dynamic effects: we calculate the changes over the full 15-year period and multiply them by the respective coefficient Γ times (3+2θ+θ<sup>2</sup> )/3. This assumes that the change has been uniform over time and accommodates their dynamic effects. Similarly, the persistence effect is calculated as (θ+ θ<sup>2</sup> + θ<sup>3</sup> )/3 times the growth rate in the period prior to 2000.

> 10 Where series were not identical (e.g. education), adding log (i.e. percentage) changes still provides a good proxy. For the commodity price index growth, we decided to take the 90<sup>th</sup> percentile of previous index changes in Ethiopia. This was fairly consistent with our attempts to construct a similar index for this period. For the real exchange rate, we used IMF data for the Real Effective Exchange Rate (using the fiscal year 2013/14 as reference) as a proxy.

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