rate, an indicator for exchange rate misalignment, and the occurrence of banking crises. Note that the analysis in Section 3.2 uses only the inflation rate and real effective exchange rate given that none of the WAEMU countries experienced a banking crisis over the period of analysis.<sup>7</sup> External conditions constitute another potential growth driver. Akin to most SSA economies, WAEMU countries are vulnerable to fluctuations in global commodity prices. The analysis controls for externally induced economic shocks using terms of trade and commodity export prices. The role played by external factors is particularly important in light of the recent downward spiral in commodity prices. Commodity price fluctuations might entail only transitory growth effects; however, resource windfalls can also affect long-term aggregate supply by increasing capital accumulation and influencing institution building. To ensure consistency with the baseline model in Brueckner (2014), this paper constructed a commodity export price index for the WAEMU countries based on the procedure in Arezki and Brueckner (2012).

# **3.1 Empirical Model**

The baseline cross-country regression model specifies real GDP as a function of several growth determinants and takes the following form<sup>8</sup> : where _yi,t_ is the log of real GDP per capita of country _i_ at time _t_ ; _yi,t-_ 1 stands for one-period lagged GDP per capita, with _β_ � measuring the speed of convergence in income per capita; _Xi,t_ for variables capturing structural, stabilization and external effects; _β_ � is the key parameter of interest and captures the effect of the variables on output per capita; _ηi_ is a set of country fixed effects accounting for unobserved country-specific and time-invariant factors (such as geographical location, historical legacies, and legal origins) that may have significant impact on both income and the explanatory variables; _vi_ is a vector of time dummies capturing universal time trends; and finally, _εit_ represents the error term. The original analysis is based on data for 126 countries spanning the period 19702010. Variable definitions and data sources are shown in Table 1 (Annex). To smooth out potential business cycle effects and reduce measurement error, the data are organized into five-year periods.<sup>9</sup> The baseline model in Equation (2) is estimated using the System GMM. Endogeneity problems are circumvented by deploying suitably lagged values of the independent and dependent variables as instruments (Blundell and Bond, 1998). Araujo _et al_ . (2014) and Moller and Wacker (2017) show that the baseline results are quite robust to a wide array of robustness checks, which include using: alternative instruments, alternative samples, alternative specifications, alternative variables, 10-year averaged data, balanced panel data, time-varying coefficients, unconditional models (to mitigate weak instruments), and more control variables. The key findings also stand up well to parameter heterogeneity (See Section 5).

The regression results show that movements in GDP per capita across the 126 countries in the sample were mainly due to changes in structural features and, to a limited extent, stabilization and external factors. See Table 1. Column 1 shows the baseline results while columns 2-4 report results from sensitivity analyses.<sup>10</sup> The results are generally consistent with theoretical expectations. Structural

> 7Some WAEMU countries, including Togo, experienced banking crises in the 1980s.

> 8See Brueckner (2014) and Araujo et al. (2015) for more details on model specification.

> 9The dynamics of our model allows for longer-run effects beyond the five-year interval. The model formulation accommodates

> both contemporaneous impacts of changes in _Xi,t_ �1 (captured by _β1_ ) and lagged impacts on future income per capita (captured by the lagged dependent variable). Thus, the overall long-run effect is given by _β_ 1⁄ _1-β0_ .

> 10The robustness checks are discussed in more detail in Brueckner (2014) and Moller and Wacker (2017).

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