(Agbor, 2012, Pattillo _et al_ ., 2008; Bagnai, 2010). However, a strand of the literature contends that the euro peg may render adjustment to external shocks difficult because a fixed exchange precludes a key policy instrument for redressing a protracted recession (IMF, 2016; World Bank, 1994, 2008).

The results in this paper are comparable with the findings of a recent IMF paper, though our analysis underscores the role of financial deepening more strongly. Using a benchmarking and cross-country regression approach, IMF (2018b) finds that high growth in the WAEMU region coincided with increased investment, improved macroeconomic stability, enhanced institutional quality, improved terms of trade, and productivity gains. It is important to note, however, that only the benchmarking and structural break analyses in Section 2 of this paper are directly comparable with the approach adopted in IMF (2018b). A notable difference in results is our finding of the strong growthfacilitating role played by financial deepening.

# **5. Model Accuracy and Robustness**

# **5.1 Model accuracy**

In general, the model predicts actual growth in WAEMU very well, although it under-predicts growth for some countries. Annex Figure 10 compares actual per capita growth with the predictions from the model. Predictions for Côte d’Ivoire are the least precise in absolute terms, although the model ‘error’ is modest proportionally. This perhaps suggests that our model may not be a good fit in explaining what happens in the aftermath of a deep recession such as Côte d’Ivoire’s during the 2000s. In general, prediction errors could arise from shortcomings of some of the model variables. For instance, institutional quality is proxied by the Polity IV index, which exhibits limited variability over the short-term. It may also be due to factors that remained unaccounted for by the model. Having said that, discrepancies for country-specific results derived from cross-country studies are not exceptional in the literature (see results for LAC countries in Araujo _et al_ . (2016), for Ethiopia in Moller and Wacker (2017), and for Tanzania in Haile (2016)).

# **5.2 Robustness**

The original model by Brueckner (2014) has already passed several robustness checks. For example, it was shown (by testing the relevant first-stage relationship) that the instruments in the GMM regression are sufficiently strong. The main results are also robust to taking 10-year non-overlapping panel data, balanced panel data, time-varying coefficients, or alternative specifications. Furthermore, unconditional models were estimated variable by variable as this limits the weak-instrument problem in the case where various instruments appear strong in isolation but are highly correlated so that they are weak when used together (Dollar and Kraay, 2003).

In addition, Moller and Wacker (2017) estimate three other specifications (see columns 1 to 4 in Table 1). Column (2) shows the results when the Polity IV variable, which had a counter-intuitive sign in the baseline specification, is excluded. The results are almost identical to the baseline model in column (1). Column (3) expands the instrument set by allowing for first _-,_ second-, and third _-_ order differences (as opposed to including only the first lag). The findings indicate that the baseline estimates do not likely suffer from too few instruments. The coefficient estimates are again similar to the baseline, though the infrastructure parameter is smaller but still significant and large. Column (4) reports fixed-effect (FE) estimates, again with similar results but a somewhat smaller (but positive and significant) infrastructure parameter. The coefficient on the lagged dependent variable is somewhat smaller than in the baseline. This confirms that our baseline model is well-specified because of the downward bias of this parameter in fixed effect estimation (Nickell, 1981).

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