Annex 1. Financial Possibility Frontier: Benchmarking Waemu Against Hgnoes
The results presented in this section are based on the concept of financial benchmarking methodology presented in Beck et al. 2009. The benchmarks were estimated using the FinStats, a tool developed by the World Bank that implements the methodology in Beck 2009 and estimates the financial benchmarks for the quasi-totality of countries in the world. The concept of financial benchmarking is predicated on the idea that there are structural factor that determine the level of financial development a country can attain. Some factors are conducive to financial sector development (e.g., income levels) and others inhibit it (e.g., low density of population, which makes infrastructure deployment costly relative to the population served and minimizes the benefit of economies of scale in banking). The benchmarking allows for cross-country comparisons to see how a specific country is doing relative to other countries with similar structural characteristics and at similar stage of development. The benchmarking approach in Beck, assumes that once appropriate controls are introduced, the process of financial development is broadly comparable across countries and stages of development. Financial sector development is affected three types of factors: economic development, other structural characteristics and the policy environment.
Where X is an indicator of financial sector development, Y is an indicator of economic development; P represents the policy environment, Z is a vector of structural characteristics, and ε is a residual.
Economic development is captured by income per capita. Demand for financial services increases as income grows. On the supply side, richer countries have better infrastructure and higher competition, which lower the price of financial services. Income per capita is endogenous, but financial sector development affects income per capita with a delay. The reason for this is that changes in the policy environment affect the financial sector first, and the financial sector in turn then affects economic growth. Therefore, we can write:
The policy environment does not change radically overnight. Good policies today are generally linked to the good policies of yesterday but also to today’s innovations.
The structural variables included in the benchmarking analysis are a set of factors that are considered as external to policy, at least in the short run. These factors include: population, age dependency, a time factor, and special circumstances. Countries with larger population and higher population density can have deeper and low cost of providing financial services thanks to economies of scale. The share of non-working young and old populations (age dependency) affects saving and lending patterns. Over time, all financial systems tend to improve, albeit at different speed, because of global factors that “lift all boats”. To account for this, a time trend is included in the regression. Many special factors affect financial sector development: in oil exporting countries, income per capita can be out of proportion with the financial and overall economic development of the country. In contrast, offshore financial centers have a financial sector that is disproportionately larger than the overall economy.
When one runs a regression of financial development on economic development and structural factors only, policy innovations are captured by the residual. To see this, one can substitute equations (2) and (3) into (1), and get the following reduced form expression:
The policy innovation factor is now in the residual. When the benchmark is constructed using the economic and structural variables (Y and Z), the distance between the benchmark and the actual level of financial development is assumed to reflect the country’s policy environment. Countries with better policies (higher υ) would tend to have more developed financial sectors compared to countries with worse policies.
FinStats estimates equation (4) via quantile regressions,1 using data from 177 countries. It then compares a given country to its own potential (benchmark) or to its comparator countries. In the first approach, the country’s benchmark is calculated using its economic and structural variables in equation (4). In the second approach, comparator countries are chosen based on their similarity with the reference country on two dimensions: GDP per capita and populations. The comparator countries are those with the smallest distance to the reference country, where distance is calculated as follows:
Where, PR is the percentile rank of the country. FinStats uses the expected 25th and 75th percentiles.
Annex 2. Benchmarking WAEMU Countries Against Their Potential
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We benefited from comments received at a seminar in the African Department of the IMF where an earlier version of the paper was presented. We are also grateful for useful comments from Michel Lazare, Doris Ross, Patrick Gitton, Patrick Imam, Paul Mathieu and Fabien Nsengiyumva.
The WAEMU countries are Benin, Burkina Faso, Côte d’Ivoire, Guinea-Bissau, Mali, Niger, Senegal, and Togo. The eight countries are all members of the CFAF currency zone.
The High Growth Non-Oil Exporters (HGNOEs) are the countries with an average per capita growth rate of at least 3 percent during 1995–2009. Eight frontier SSA countries fall into this category: Botswana, Cape Verde, Ethiopia, Mauritius, Mozambique, Rwanda, Tanzania, and Uganda. (cf. IMF, African Regional Economic Outlook, October, 2010).
Both indicators are measures of size of the banking system, and do not give any indications on its efficiency or access to financial services.
Demirguc-Kunt (2006) provides a comprehensive survey of the explanatory variables used in empirical analysis.
The excess liquidity in the WAEMU, in addition to the lower development of government securities market, suggests that the factor behind the lower ratio of private credit compared to the control group is not due to government crowding out of the private sector.
The first step estimates an OLS and collects the residuals; the second step estimates generalized least squares with a variance matrix build from the residuals collected in the first step.
The introduction of property rights slightly weakens the WAEMU dummy, which remains significant.
This result of this regression should be interpreted with care, as the sample size drops substantially due to limited data on credit coverage. Also, for this reason, we do not further explore the impact of credit coverage on credit to the private sector in the presence of other explanatory variables.
Financial infrastructure and activities were not much affected by the internal crisis of the last decade. In particular, the south of Côte d’Ivoire, the financial and economic center of the country accounting for the bulk of GDP was under the government’s control during the socio-political crisis of 2002–2007. However, in the absence of crisis the financial sector might have developed faster.
Based on GDP per capita and population, Feyen and Kibuuka (FinStats, 2012) find that Mozambique is the most structurally similar to Côte d’Ivoire among the control group of countries.
This is the value under which 75 percent of countries with the structural characteristics of Côte d’Ivoire are expected to be.
We also looked at where the other WAEMU countries stand relative their potential. We found that, except Senegal, all the other countries perform below their potential. The graphs are displayed in annex 2. These countries belong to the same monetary union, and they share the same monetary policy and a common regulator of the financial sector. As such, the financial sector development issues in these countries are similar to those we detail below for Côte d’Ivoire.
Some steps have been taken by the Ivoirian authorities in 2011–12. This includes the creation of commercial courts in January 2012 and the adoption of a decree on the enforcement of arbitration court decisions (exequatur) in February 2012. Efforts to simplify business activities (starting a business, registering properties) are also underway. Financial sector policies are mostly defined at the regional level, limiting the ability of Côte d’Ivoire to promptly take needed measures in this area; however, there is a scope to national financial sector policies: the restructuring of public banks, the definition of the role of the state in the financial sector.
The quantile regressions are used to reduce the impact of outliers and produce different expected values to gauge the range of financial sector performance.