baseline model, again with a somewhat smaller infrastructure parameter (that is now only at the borderline of statistical significance). Overall, this provides some weak evidence that our benchmark model somewhat overestimates the positive impact of infrastructure (as proxied by telephone lines).<sup>18</sup> Overall, these results support the baseline model but suggest that the infrastructure results may be on the higher side. As demonstrated, our model is robust to a wide range of alternative specifications. Where results differ in magnitude, this difference is rarely statistically significant and thus often reflects random sample effects. The only case where infrastructure is statistically insignificant arises in a considerably reduced sample and the quantitative result is still in line with parameter estimates from other robustness checks. In total, these robustness checks suggest, however, that our estimate for infrastructure in the baseline model may be on the higher side. However, we find no evidence that any of these alternative models would be more appropriate to capture growth in Ethiopia over the recent past. Moreover, even if we assume one of the lower parameter values (which themselves might be on the lower side), the associated contribution of infrastructure to Ethiopia’s growth acceleration would still be substantial and in the range of one percentage point per year.

The results are also robust to the choice of alternative infrastructure variables, apart from diverging trends in Early 2010. Given the critical explanatory importance of infrastructure and the difficulty of appropriate measurement and interpretation, we also tested whether results are sensitive to alternative variable specification. The model results reported in Table 1 use fixed telephone line coverage. We tested two alternative infrastructure variables, namely mobile phone subscribers and road coverage and found nearly identical results up to 2010.<sup>19</sup> We note that all three types of infrastructure are provided exclusively by the public sector in the case of Ethiopia giving us greater confidence in drawing inferences between public infrastructure investment and growth. Both alternative infrastructure variables have a strong positive impact on growth in our model, although the effect of roads is only at the borderline of statistical significance (z statistic 1.55). The results are depicted in Figure 7.1 as annualized contributions to the overall predicted growth rate (in percentage points). The bars for telephone lines simply reproduces the results depicted in Table 1 to facilitate comparison. We can further see that the effect of fixed telephone coverage and roads for explaining growth in Ethiopia in 2000-13 is nearly identical, despite minor differences in magnitudes and with respect to the timing across sub-periods.<sup>20</sup> Differences only arise in the last period (the Early 2010s), which is then also reflected in the 2000-2013 predictions. Over this period, fixed telephone lines were declining but this does not adequately capture infrastructure developments, as some substitution towards cell phones took place and other infrastructure—such as roads—was increasing considerably as well (see Figure 4.1).

When correcting for alternative measurements, we are able to predict growth in Early 2010s with greater accuracy. To assess the impact of these alternative infrastructure measures on the overall

> 18 We also estimated the model for landlocked countries only but due to the small sample size (25 countries with a total of 85 observations), results were mostly insignificant and thus not very informative. See column (6) of Annex Table A.2.

> 19 As our original data set only covered roads data until 2007 in Ethiopia, we replaced this data using national sources. 20 For a firm-level analysis showing the positive effect of road infrastructure on firm location choice and startup size in Ethiopia, see Shiferaw _et al_ . (2013).

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