(2002), Uy (2006) and Freire et al. (2006) in other regions and is another reason why African countries should consider expanding their mortgage markets.

In addition, the working population (indexed by the 15–64 age cohort of the population), investment stock, human capital, and infrastructure are determinants of inclusive growth in Africa. These results are also in line with previous literature on the subject. However, trade openness is negatively and significantly related to GDP growth and GDP per capita growth. We can attribute these contradictory results to the proxy used to index these variables.

The combined variables are all non-significant and are associated to a negative sign; without combining them, we still have the same non-significant result. We are able to conclude then that our decomposition is relevant—but in the wrong way in terms of housing finance development impact on per capita economic growth. The variables housing finance policy, transfers, and conflict dummy_ present the positive sign predicted by the theory, while the variables inflation, interest rate, and domestic credit to private sector present a negative sign, contrary to the theoretical predictions.

# **4.4. Robustness check**

In order to make sure that our results are robust, we implemented additional estimations, changing some variables by their proxy, changing the specification, and changing the estimation method. By changing the specification, we were guided by the maximization of the number of observations relative to the one of the principal estimations. Practically, we either added control variables that might affect the dependent variable or removed other control variables to maximize the number of observations. As shown in the annexed tables, our results are conclusive since we have the same sign and signification variable as in the principal results discussed.

By exchanging some variables with their proxy, we were able to verify whether we obtain the same result even when we use different measures for the same variable. By changing the estimation method from FGLS to GMM, we verified the robustness of our main results. In addition, we used alternative measures of growth (GDP per capita growth and GDP growth) and housing finance (mortgage depth and mortgage penetration) to verify the quality of our results.

Tables A.6, A.7, and A.8 present the results of our robustness check regression. The results overall are conclusive. However, by comparing table A.6 in the appendix to table 4.1, the interpretation of the impact of conflict dummy_ is either positive or negative and significant depending on how we change the housing finance index. So, the previous results of a positive impact of conflict dummy_ are plausible only for housing finance depth and not housing finance penetration. Also, our combined variables become positively and significantly related to housing finance when the proxy is housing finance penetration.

We can therefore confirm that there is a little relative difference depending on the variable we use to index housing finance even if results remain mostly similar. The rest of the variables retain the same sign and significance independently of the change of the proxy of housing finance development and the estimation method.

Comparing table A.7 in the appendix to table 4.3, we see that by changing specification and dependent variables, our results remain globally unchanged. However, labor becomes not significant. Additionally, trade openness is negatively related to economic growth when we use the FGLS estimation method. However, when we use the GMM estimation method, trade openness positively and significantly affects 18