Information about Sub-Saharan Africa África subsahariana
Building Integrated Economies in West Africa
Chapter

Chapter 21. Financial Inclusion

Author(s):
Alexei Kireyev
Published Date:
April 2016
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Information about Sub-Saharan Africa África subsahariana
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Author(s)
Monique Newiak and Rachid Awad 

West African Economic and Monetary Union (WAEMU) countries lag behind benchmark countries in several dimensions of financial inclusion: access to finance is low, especially for the most vulnerable parts of the population, and the financial sector appears to only modestly contribute to the population’s ability to deal with shocks as well as firms’ investment programs. Private sector credit-to-GDP ratios, however, appear broadly in line with WAEMU countries’ fundamentals. Public policies, such as investments in infrastructure and the social sectors, could help closing these gaps. From the perspective of firms, policies to reduce participation costs in the financial sector and to lower collateral requirements could increase firms’ access to financing, and thus significantly boost GDP.

Benchmarking Financial Access

Financial access in the WAEMU remains comparatively low. Figure 21.1 compares different indicators of financial access in the WAEMU against a group of fast-growing regional and Asian benchmark countries.2 It shows that:

Figure 21.1.Financial Access

  • WAEMU countries, on average, lag behind benchmark groups in the provision of basic financial infrastructure such as the density of ATMs and the number of bank branches.
  • The relative amount of deposit and loans at commercial banks is broadly in line with African benchmark groups, but significantly lower than those in Asian benchmark countries; the number of people with deposits at commercial banks is relatively low.
  • The shortcomings in financial access are also revealed by enterprise surveys in each WAEMU country, with more than half of respondents identifying access to finance as a major constraint for their businesses.

While modest in general, financial access appears to be lowest for the most vulnerable parts of the population (Figure 21.2). Young adults and the population at the bottom of the income distribution (bottom 40 percent) are the groups with the lowest relative number of bank accounts (less than 5 percent of the respective part of the population), but the population living in rural areas, those with less education, and women are also less often in the possession of a financial account than is the average WAEMU inhabitant. In general, accounts are most often used for business purposes or to receive payments such as wages or remittances.

Figure 21.2.Demographic Characteristics of Financial Access

Source: Findex, 2011.

Note: Three-letter International Organization for Standardization abbreviations used for country names.

SSA = Sub-Saharan Africa; WAEMU = West African Economic and Monetary Union.

The main modes to access finance and make deposits are similar to those in benchmark countries, but several payment methods are less pronounced in the WAEMU (Figure 21.3). As in benchmark countries, the use of a bank teller is the main way to make a deposit. Checks and electronic payments, however, are much less developed modes of payments in WAEMU countries than they are in the comparator groups, and a much smaller share of the WAEMU’s population is in possession of a credit or debit card.

Figure 21.3.Deposit and Payment Modes

Source: Findex 2011.

Note: Three-letter International Organization for Standardization abbreviations used for country names.

SSA = Sub-Saharan Africa.

The use of loans and the purposes of saving point to relatively weak social protection and only a modest contribution of the financial sector in shock mitigation (Figure 21.4). While the share of the population with outstanding loans for educational fees is comparable to that in benchmark countries, the share of indebted people due to health issues or other emergencies is relatively high in the WAEMU. The population appears relatively less covered by health insurance and, with the exception of Mali, by agricultural insurance. Fewer people (are able to) save for potential emergencies. While pointing to absolute and relative weaknesses in social protection, these indicators also suggest that the financial sector provides insufficient help to the population to insure against or deal with shocks.

Figure 21.4.Use of Loans

Source: Findex, 2011.

Note: Three-letter International Organization for Standardization abbreviations used for country names.

SSA = Sub-Saharan Africa.

The banking sector’s contribution to firms’ investment programs also appears limited (Figure 21.5). Enterprise surveys indicate that, while most firms possess a bank account, less than 30 percent of firms access a loan or a line of credit in most WAEMU countries. The majority of loans require collateral. The value of such collateral, on average, exceeds the value of the loan, indicating problems with liquidation of the collateral. Loans from banks constitute only a small fraction of firms’ investment financing, while internal funds appear to be the main source of financing investments.

Figure 21.5.Firms

Source: World Bank, Enterprise Surveys.

Note: Three-letter International Organization for Standardization abbreviations used for country names.

Private Sector Credit Gaps

Private sector credit-to-GDP ratios are broadly in line with the benchmark for the WAEMU on average, but there are variations across countries (Figure 21.6). Following the methodology in Al Hussainy and others (2011) and Barajas and others (2013), we estimated a benchmark ratio of private sector credit to GDP based on a number of structural factors in a panel of over 120 emerging and developing countries for the period 1986–2013. We regressed the ratio of private sector credit to GDP on: (1) the log of GDP per capita and its square, (2) the log of the population to proxy for market size, (3) the log of population density to proxy for the ease of service provision, (4) the log of the age dependency ratio to account for demographic trends and the related savings behavior, and (5) an oil-exporters dummy and time dummies to control for global factors. The fitted values from these regressions serve as the private sector-to-GDP benchmark. While generally following the dynamics of the benchmarks well, actual credit to GDP has been lower than the benchmark in 2013 in four countries (Benin, Burkina Faso, Côte d’Ivoire, Guinea-Bissau), higher in three (Mali, Niger, Togo), and broadly consistent with the benchmark in Senegal.

Figure 21.6.Credit to the Private Sector

(Share of GDP)

Source: World Economic Outlook database.

A number of policies could help countries to increase private sector credit relative to the benchmark (Figure 21.7, Table 21.1). In the next step, a regression of the financial gap (actual private sector credit to GDP minus its benchmark) on macroeconomic, institutional, and policy variables helps identifying the drivers of the deviations from the benchmark for 2004–13. Table 21.1 highlights the factors that help increase private sector credit relative to the benchmark, while Figure 21.7 depicts the change in the private sector credit to GDP relative to the benchmark if these underlying factors are changed by one standard deviation. Factors that relate positively to private sector credit to GDP include trade openness and foreign direct investment inflows on the external side, lower inflation and higher social and educational spending on the macroeconomic (policy) side, and better infrastructure and institutions (in this exercise, the International Country Risk Guide index, ICRG).

Figure 21.7.Drivers of the Financial Gap

Table 21.1Determinants of Financial Inclusiveness Gaps, 2004–2013
(1)(2)(3)(4)(5)
Economic Environment
Growth−0.004***−0.004***
(−3.08)(−3.04)
U.S. Federal Funds Rate10.0030.008**
(0.69)(2.17)
External Stance
FDI/GDP0.002***0.002***
(2.92)(3.17)
Trade Openess0.209***0.217***
(3.49)(3.40)
Capital Controls0.076***0.115***
(3.96)(5.73)
Policies
Fiscal Balance

(cycl. adjusted)/GDP
−0.185**−0.247**
(−2.16)(−2.52)
Inflation−0.004***−0.003***
(−5.50)(−4.48)
FX Regime0.007
(1.15)
Health Spending/GDP1.575***1.202***
(3.61)(2.57)
Institutions and Infrastructure
Institutions (ICRG)0.295***0.234***
(4.38)(3.14)
Telephone Lines0.000***0.000***
(11.44)−11.74
Internet Use0.001**0.001*
(2.01)(1.75)
Credit Information Depth−0.002
(−0.69)
Constant0.019*−0.119***−0.033−0.183***−0.308***
(1.76)(−5.03)(−1.17)(−5.74)(−7.38)
Number of observations10551055105510551055
R-squared0.010.040.040.090.18
Source: Authors’ calculations.Note: Robust t-statistics in parentheses; significance levels at 10 percent (*), 5 percent (**), and 1 percent (***) levels, respectively. FDI = foreign direct investment; FX = foreign exchange regime; ICRG = International Country Risk Guide rating; cycl = cyclically adjusted.

Proxy for external environment.

Source: Authors’ calculations.Note: Robust t-statistics in parentheses; significance levels at 10 percent (*), 5 percent (**), and 1 percent (***) levels, respectively. FDI = foreign direct investment; FX = foreign exchange regime; ICRG = International Country Risk Guide rating; cycl = cyclically adjusted.

Proxy for external environment.

Constraints to Financial Inclusion3

A microfounded general equilibrium model helps identify the most binding constraints to financial inclusion from the perspective of firms. In this section, the microfounded general equilibrium model by Dabla-Norris and others (2014) is calibrated to quantify the most binding constraints to financial inclusion and, as a consequence, growth, productivity, and a more equal income distribution. Agents in the model differ from each other in wealth and talent and can choose to become entrepreneurs or supply labor for wages. They face three financial frictions:

  • Participation costs ψ, which limit access to credit, in particular for smaller and poorer entrepreneurs
  • Intermediation costs χ, due to asymmetric information between banks and borrowers, which result in deposit-lending spreads
  • Imperfect enforceability of contracts, which results in collateral requirements and thus smaller collateral leverage ratios λ

To determine the values of the parameters ψ, χ, and λ, as well as other parameters for the calibration, a range of macroeconomic and financial indicators were fed into the model (Table 21.2).

Table 21.2Target Moments
Savings (percent of GDP)14.5
Collateral (percent of loan value)170
Firms with Credit (percent of firms)20
Nonperforming Loans (percent of loans)17
Interest Rate Spread7.4
Source: Authors’ estimates.
Source: Authors’ estimates.

The results point to participation costs and high collateral requirements as the main borrowing constraints on average in the WAEMU. Based on calibration, Figures 21.8, 21.9, and 21.10 depict the effects of relaxing individually each of the three financial constraints on the number of firms accessing credit, GDP, productivity, income inequality, interest rate spreads, and the nonperforming loan ratio. They suggest that, while both lower participation costs and lower collateral requirements could yield significant GDP gains, they have differentiated effects on other variables, in particular:

Figure 21.8.Lowering Participation Costs

(From left to right, dot indicates initial position)

Source: Author’s estimate.

Figure 21.9.Lowering the Cost of Intermediation

(From left to right, dot indicates initial position)

Source: Authors’ estimates.

Figure 21.10.Lowering Collateral Constraints

(From left to right, dot indicates initial position)

Source: Authors’ estimates.

  • Increasing financial access (Figure 21.8)—Lowering participation costs (such as transaction costs), institutional impediments, and bureaucratic hurdles could increase the fraction of firms with credit substantially. With more access to credit, which leads to higher investments, GDP increases significantly. From the WAEMU’s current position, lower participation costs could also decrease income inequality as measured by the Gini coefficient, because previously constrained (less wealthy) entrepreneurs overproportionately benefit from the change when they enter the market. Overall productivity may decline for the same reason.
  • Lowering collateral constraints (Figure 21.10)—Policies that could help decrease collateral requirements, such as the introduction of collateral registries, could also yield large GDP gains and increase productivity through gains in efficiency. The latter effect differs from the impact of policies, which increase financial access described previously, as it overproportionately benefits more talented entrepreneurs. While relaxing the collateral constraints allows all firms to borrow more, less talented businesses do not scale up their businesses by the same magnitudes, as their maximum business scale is sooner achieved. As a consequence, the policy may lead to an increase in income inequality.
References

    Al HussainyEdAndreaCoppolaErikFeyenAlainIzeKatieKibbuka and HaocongRen.2011. “A Ready-to-Use Tool to Benchmark Financial Sectors Across Countries and Over Time.FinStats 2011World BankWashington.

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    BarajasAdolfoThorstenBeckEraDabla-Norris and Seyed RezaYousefi.2013. “Too Cold, Too Hot, or Just Right? Assessing Financial Sector Development Across the Globe.Working Paper WP/13/81International Monetary FundWashington.

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    Dabla-NorrisEraYanJiRobertTownsend and FilizUnsal.2014. “Identifying Constraints to Financial Inclusion and Their Impact on GDP and Inequality: A Structural Framework for Policy.Working PaperInternational Monetary FundWashington.

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1With valuable contributions from Filiz Unsal (IMF), Era Dabla-Norris (IMF), and Eva Van Leemput (University of Notre Dame). The findings should not be reported as representing the views of the IMF. The section “Identifying the Most Binding Constraints to Firms’ Financial Inclusion” is part of a research project on macroeconomic policy in low-income countries, supported by United Kingdom’s Department of International Development. The findings should not be reported as representing the views of this department.
2African benchmark countries include: Ghana, Kenya, Lesotho, Rwanda, Tanzania, Uganda, and Zambia. Asian benchmark countries include: Bangladesh, Cambodia, India, Laos, Nepal, and Vietnam. Sub-Saharan Africa is provided as a comparator in many cases as well.
3We thank Eva Van Leemput for calibration of the model by Dabla-Norris and others (2014) for this section.

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