# SFC Modeling. Commercial Banks's influence on macroeconomics dynamics and their impacts on the economic growth potentials in developing countries: the case of Côte d'Ivoire and Niger, Member of the West African Economic and Monetary Union 'countries (WAEMU).

Hadi Cissé

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Hadi Cissé. SFC Modeling. Commercial Banks's influence on macroeconomics dynamics and their impacts on the economic growth potentials in developing countries: the case of Côte d'Ivoire and Niger, Member of the West African Economic and Monetary Union 'countries (WAEMU). 2025. hal-05115536

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Université
Sorbonne
Paris Nord

Université Sorbonne Paris Nord

Avenue Jean-Baptiste Clément, 93430 Villetaneuse.

Master thesis

Master 2 Economic Analysis and Policy, International Macroeconomic and Financialization

Hadi CISSÉ

Commercial Banks's influence on macroeconomics dynamics and their impacts on the economic growth potentials in developing countries: the case of Côte d'Ivoire and Niger, Member of the West African Economic and Monetary Union 'countries (WAEMU).

Under the supervision of:

Prof. Dany Lang

Prof. Gary Dymski

Paris 2025
> "On the occasion of the thirty-seventh anniversary of Basil Moore's book Horizontalists and Verticalists, his fundamental insight remains central: loans create deposits. Credit availability, not prior savings, determines investment and economic expansion."

Adapted from Moore, B. J. (1988), Horizontalists and Verticalists: The Macroeconomics of Credit Money, *Cambridge University Press*.

## Dedication

To my family and friends,

whose love grounded me, whose patience sustained me, and whose unwavering support gave me the strength to see this journey through.

## Acknowledgment

I am deeply grateful to **Prof. Dany Lang** (Université Sorbonne Paris Nord) and **Prof. Gary Dymski** (University of Leeds) for their unwavering support and supervision throughout this thesis. Their intellectual guidance, critical engagement, and commitment to rigorous thinking challenged me to reach beyond the conventional and pursue clarity, depth, and purpose in my research. I also extend heartfelt thanks to the teaching faculty and fellow students of the master's program in International Macroeconomics and Financialization, as well as those of the EPOG (Economic Policies in the Age of Globalization) program at Université Sorbonne Paris Nord. Their conversations, questions, and solidarity were a constant source of inspiration and strength throughout this academic journey.
## Foreword

At the outset of this thesis, it is important to make a fundamental clarification. The choice to focus on commercial banks and their role in macroeconomic dynamics is not intended to minimize the importance of fiscal or broader public policy initiatives. In contexts of macroeconomic instability, falling aggregate demand, and structural underdevelopment such as those experienced by many developing countries, strong and active government intervention through fiscal policy remains essential. Measures such as public investment, support for domestic industries, and the strengthening of household purchasing power through social spending are vital tools for economic recovery and development.

Nevertheless, in countries like Côte d'Ivoire and Niger, both members of the West African Economic and Monetary Union (WAEMU), the financial system is essentially bank-based. Unlike developed economies where capital markets play a prominent role, or emerging economies with hybrid financial systems, WAEMU countries rely almost exclusively on commercial banks for the financing of economic activity. Capital markets remain underdeveloped, and alternative sources of long-term finances such as venture capital or bond markets are either extremely limited or virtually nonexistent.

Given this structure, commercial banks are not just financial intermediaries among others, they are the core institutions through which credit is created, and economic development is, or ought to be, financed. However, despite their central role, banks in these countries have historically played a marginal role in financing productive sectors, particularly small and medium-sized enterprises (SMEs), agriculture, and industry. The reasons are multifaceted: risk aversion, institutional weaknesses, lack of financial infrastructure, and regulatory constraints all contribute to a low level of bank involvement in real-sector investment.

This is particularly problematic in the context of a monetary theory of production, which posits that money and credit creation precede and enable economic production, not the other way around. When banks limit their role to short-term, low-risk activities, or to financing governments through treasury bonds, they fall short of their potential to serve as engines of development. In such a framework, even the most ambitious fiscal policies may fail to reach their full impact if the banking sector does not complement them by channeling credit toward productive uses.

This thesis explores this critical gap. By focusing on Côte d'Ivoire and Niger, it aims to highlight the systemic weaknesses that constrain banking sector involvement in development
finance and to propose scenarios and policy strategies where commercial banks can become true partners in growth. Far from replacing fiscal policy, the goal is to enhance its effectiveness through a more proactive and development-oriented banking sector one that aligns with the long-term needs of these countries' economies and their aspirations for inclusive, resilient growth.

## Abstract

This thesis examines how commercial banking behaviors, specifically conservative and selective credit practices, affect macroeconomic recovery in developing countries, using Côte d'Ivoire and Niger as case studies within the WAEMU region. Its originality lies in embedding behavioral assumptions into a stock-flow consistent (SFC) model calibrated to each country's structural characteristics. Through dynamic simulations, the study shows that conservative lending deepens recessions by restricting credit access, while selective lending, though less destabilizing, constrains inclusive growth through allocative bias. Statistical validation confirms these dynamics, highlighting that banking behavior critically shapes investment, employment, and GDP recovery. The findings offer policy insights for reforming credit allocation mechanisms to strengthen macro-financial resilience in WAEMU economies.

## Résumé

Ce mémoire analyse l'impact des comportements bancaires, en particulier le crédit conservateur et l'allocation sélective, sur la reprise macroéconomique dans les pays en développement, à travers les cas de la Côte d'Ivoire et du Niger, membres de l'UEMOA. L'originalité de l'étude réside dans l'intégration d'hypothèses comportementales dans un modèle stock-flux cohérent (SFC), calibré selon les spécificités structurelles de chaque pays. Les simulations dynamiques montrent que le crédit conservateur aggrave les crises en rationnant l'investissement, tandis que le crédit sélectif freine une reprise inclusive en biaisant l'allocation sectorielle. La validation statistique renforce ces résultats, soulignant que le comportement bancaire joue un rôle déterminant dans la dynamique de l'investissement, de l'emploi et du PIB. Ces conclusions appellent à repenser les mécanismes d'allocation du crédit dans une perspective de résilience macro-financière au sein de l'UEMOA.
**Key worlds:** Commercial banking behavior; Credit rationing; Selective lending; Stock-flow consistent modeling; Investment dynamics; Employment recovery; WAEMU; Côte d'Ivoire; Niger; Financial fragility.

**JEL classifications:** E12, E44, E51, O55

**List of Abbreviations and Classifications:**

<table><thead><tr><td>Abbreviation</td><td>Meaning</td></tr></thead><tbody><tr><td>WAEMU</td><td>West African Economic and Monetary Union</td></tr><tr><td>BCEAO</td><td>Banque Centrale des États de l'Afrique de L'Ouest</td></tr><tr><td>EU</td><td>European Union</td></tr><tr><td>SAP</td><td>Structural Adjustment Program</td></tr><tr><td>IMF</td><td>International Monetary Fund</td></tr><tr><td>SFC</td><td>Stock-Flow Consistent (Modeling Framework)</td></tr><tr><td>CI</td><td>Côte d'Ivoire</td></tr><tr><td>N</td><td>North (used as economic proxy for Côte d'Ivoire)</td></tr><tr><td>S</td><td>South (used as economic proxy for Niger)</td></tr><tr><td>RoW</td><td>Rest of the World</td></tr><tr><td>CB</td><td>Central Bank</td></tr><tr><td>HH</td><td>Households</td></tr><tr><td>F</td><td>Firms</td></tr><tr><td>G</td><td>Government</td></tr><tr><td>B</td><td>Commercial Banks</td></tr><tr><td>TFM</td><td>Transaction Flow Matrix</td></tr><tr><td>AfDB</td><td>African Development Bank</td></tr><tr><td>BOAD</td><td>West African Development Bank</td></tr><tr><td>T</td><td>Treasury Bills</td></tr><tr><td>L</td><td>Loans</td></tr><tr><td>D</td><td>Deposits</td></tr><tr><td>M</td><td>Money (central bank money)</td></tr><tr><td>E</td><td>Equity</td></tr><tr><td>B (Instrument)</td><td>Government Bonds</td></tr><tr><td>GDP</td><td>Gross Domestic Product</td></tr><tr><td>SME</td><td>Small and Medium-sized Enterprises</td></tr><tr><td>VAR</td><td>Vector Autoregression (Econometric model)</td></tr></tbody></table>
Table of contents

<table><tr><td>Introduction</td><td>7</td></tr><tr><td>Part 1: Literature Review</td><td>9</td></tr><tr><td>Section 1: Commercial Banks and Macroeconomic Dynamics</td><td>10</td></tr><tr><td>1.1 Post Keynesian Theories on endogenous money creation</td><td>10</td></tr><tr><td>1.2 Financial instability and cyclical dynamics</td><td>13</td></tr><tr><td>Section 2: Credit Creation, Investment, and Growth in Developing Countries</td><td>15</td></tr><tr><td>2.1 The role of credit in capital accumulation</td><td>16</td></tr><tr><td>Part 2: The Stock Flow Consistent Model Specification</td><td>21</td></tr><tr><td>Section 3: Designing for the two economies of WAEMU</td><td>22</td></tr><tr><td>3.1 The General Structure of the SFC Model</td><td>22</td></tr><tr><td>Part 3: Methodology and Empirical Analysis</td><td>40</td></tr><tr><td>Section 4: Methodology</td><td>40</td></tr><tr><td>4.1 Model design and calibration</td><td>40</td></tr><tr><td>Section 5: Empirical Analysis</td><td>44</td></tr><tr><td>5.1 Simulation experiments</td><td>45</td></tr><tr><td>Section 6: Expected contributions and robustness checks</td><td>52</td></tr><tr><td>Part 4: Results and Policies Recommendation</td><td>53</td></tr><tr><td>Section 7: Keys founding for comparative study</td><td>53</td></tr><tr><td>Section 8: Banking strategies recommendation for both Côte d'Ivoire and Niger and broadly for WAEMU countries</td><td>53</td></tr><tr><td>Part 5: Conclusion</td><td>54</td></tr></table>
Introduction

The role of commercial banks in macroeconomic dynamics has been a central topic in economics thought, particularly within the post-Keynesian economics. Unlike mainstream neoclassical theories, which often assume that financial markets efficiently allocate pre-existing savings to investments reinforcing the belief that financial markets are self-regulating, post-Keynesian emphasize the endogenous nature of money and the pivotal role of banks creating credit. From this perspective, commercial banks are not mere intermediaries but actives agents in the economic system influencing aggregate demand, investment and long-term growth. Their ability to expand credit determines the level of productive investment, employment and macroeconomic stability making them central to understanding economic development, particularly in emerging economies. In developing countries, access to credit is often constrained by structural limitations such as financial repression, institutional weaknesses and market imperfections (Stiglitz & Weiss, A., 1981).

Moreover, financial liberalization policies imposed in many developing countries, including WAEMU, have often prioritized capital market efficiency over development objectives, sometime leading to credit rationing and financial exclusion (Epstein, 2005). Like the broader structural adjustment programs (SAPs) led by international financial institutions such as the International Monetary Fund (IMF) and the world Bank, which were aimed to enhance capital market efficiency and deepen financial intermediation. In the Theory of Economic Development, Joseph Schumpeter argued that banks do not merely facilitate transactions but serve as fundamental drivers of innovation by financing entrepreneurs (Schumpeter, 1911). Schumpeter considered credit as a lifeblood of economic transformation, enabling firms to develop innovative technologies and expand productive capacities. Thus, rather than capital accumulation, he supports that credit creation is a primary long-term economic growth driver. The structuralists approach to financial development, advocated by economists such as Joan Robinson also highlighted the reciprocal relationship between finance and growth. She famously argued that where enterprise leads, finance follows (Robinson, J. 1952). Then, she suggested that financial institutions expand to the need of productive investment rather than leading economic development independently. Building on these insights, John Maynard Keynes introduced a fundamental shift in understanding the role of finance and investment in macroeconomic dynamics (Keynes, (1936)). In the General Theory of Employment, Interest and Money, Keynes emphasized that investment largely financed by
banks credit is a key primary driver of economic growth and long-term stability. By influencing
aggregate demand, fluctuation in investment levels determine employment and output, making
credit creation a central mechanism in macroeconomic dynamic.

Furthermore, by focusing on the structure of the commercial banking sector and the heterogeneity of behaviors, we deal with the relevant topic of how financial institutions such as commercial banks shape macroeconomic dynamics, credit allocation and economic development. The varying risk appetites, business models and regulatory constraints among banks influence the extent to which credit is directed toward productive investment rather than pure speculative or rent seeking activities. This heterogeneity is particularly important in developing countries where financial markets are often underdeveloped, and commercial banks play a crucial role in funding economic activities. In addition to that, like the most sub-Saharan African countries, many other reasons play against the effectiveness of successful commercial banking system largely (Honohan & Beck, T, 2007) and (Beck, et al., 2011). According to these authors the constraint that commercial banks’ expansion is facing in these countries is the highest number of economic agents operating in the informal sector. Regarding the lack of information which raises the level of risks and as a result, banks ration credits. Second, the revenues’ volatility of numerous households and firms in need of credits is also part of the constraints according to those authors. These analyses are particularly interesting in the sense where they tend to justify banks’ behaviors by credit rationing through the lack of confidence that may arise from these two aspects beforehand stressed. Studies on India’s banking system, reveal the evidence that banks not only support wealth creation and small enterprise financing but also contribute directly to broader economic stability through monetary policy transmission and risk management mechanisms (Manish, 2020).

The particularity of WAEMU area is more pronounced due to structural constraints such as limited financial deepening, regulatory rigidity, and the dominance of few large-scale banks (IMF, 2013). However, studies have shown that in WAEMU as banks risk taking increase, their capital goes up (Kanga, et al., 2020). These conclusions arise so many interrogations regarding commercial banks’ institutional responsibility and their responsibility facing the choice of whether a conservative or a credit rationing behavior in a world with the increasing credit demand. Thus, came the great question of commercial banks’ role in influencing the macroeconomic dynamics and their implication in the economic growth potential in Côte d’Ivoire and Niger, two members states of the WAEMU.
In this master ‘s thesis, I will focus on two ranges of commercial banking behaviors.
The conservative lending and the selective credit allocation practice within the commercial
banking system in developing countries broadly and the case study of Côte d’Ivoire and Niger
and their implication for employment, investment and macroeconomics dynamics of long-term
growth. Rooted in the two-countries’ Stock Flow Consistent’ model developed in the Monetary
Economics by Lavoie and Godley (Godley & Lavoie, M., 2007), the analyses will go into the
explanation of these main two behaviors and will cover data over 24 years from 2000 to 2024¹.

Part 1: Literature Review

Research on the relationship between credit expansion and economic growth frequently
emerges within the broader discourse on the extent of banks’ credit creation capabilities and the
sufficiency of reserve requirements to support such activities. By raising the necessary role of
deposits that will allow and define the amount of credit in the economy, these debates
introduced the urgent need for clarification regarding reality in the banking sector functioning
and its interaction with the behaviour of economic ‘agents in the real world. This need for
clarification from commercial banks’ involvement in economic activities is rooted in Keynes’
view according to which banks in general and commercial banks particularly are firms that
function by assessing and absorbing risks from the financial market. Indeed, by the scale and
the terms on which it is prepared to grant loans, the banking system is in a position to determine,
broadly speaking, the rate of investment by the business world (Keynes, J.M, 1930). By reading
the Treatise on Money by Keynes, it comes ease to make an obvious connection between the
banks ‘credit creation velocity and the level of economic activities. Accordingly, a link between
their decision-making processes and the entire economic activity dynamics is worth huge
attention.

* African Development Bank (AfDB) – https://www.afdb.org

- Central Bank of West African States (BCEAO) – https://www.bceao.int

* West African Development Bank (BOAD) – https://www.boad.org

* International Monetary Fund (IMF) – https://www.imf.org

* World Bank Data Center – https://data.worldbank.org

<footnote n="1">¹ The calibration data are collected from the AfDB, BCEAO, BOAD, IMF and the World bank data center.</footnote>
# Section 1: Commercial Banks and Macroeconomic Dynamics

Understanding the role of commercial banks in macroeconomic dynamics requires a detailed exploration of the credit creation mechanism and its broader implications for economic activity. Contrary to the traditional view that portrays banks as mere financial intermediaries channeling savings into investments, commercial banks play an active and significant role in the economy as creators of money. When a commercial bank grants a loan, it simultaneously creates a deposit in the borrower's account. This operation generates new money that did not exist before. Money is therefore not injected into the economy exogenously by the central bank, but rather created endogenously, through banks' lending decisions in response to the credit demand of economic agents. As emphasized by the Bank of England (2014), whenever a bank makes a loan, it simultaneously creates a deposit, new money, in the borrower's bank account. This mechanism of money creation through bank credit has major macroeconomic implications. First, it directly influences aggregate demand, as it finances household consumption and business investment. It also affects economic growth, employment, and income distribution, while playing a vital role in shaping business cycles. During periods of expansion, increased credit can fuel economic growth, but if misallocated or overly speculative, it may also lead to financial crises, a dynamic highlighted by Hyman Minsky's Financial Instability Hypothesis.

While commercial banks have the capacity to create money, they operate within a framework of regulatory constraints, such as capital adequacy requirements, liquidity ratios, and monetary policy instruments that influence the cost of credit. Nevertheless, in modern banking systems, central banks often accommodate the liquidity needs of commercial banks, particularly in times of stress, further reinforcing the endogenous nature of money. In regions like WAEMU, where financial markets remain underdeveloped, the banking sector serves as the primary channel for financing economic activities. The behavior of commercial banks, in terms of credit allocation, risk management, and responsiveness to monetary incentives, significantly shapes the structure and dynamics of regional economic growth.

## 1.1 Post Keynesian Theories on endogenous money creation

The mainstream economic tradition, particularly in its classical, neoclassical, and monetarist forms, has historically treated money as an exogenous variable, something controlled by the
central bank. Under this view, money supply is adjusted through policy tools such as open market operations or interest rate changes, and banks are seen as passive intermediaries that lend out pre-existing deposits. This idea is encapsulated in the Quantity Theory of Money (MV = PY), most famously defended by Milton Friedman (Friedman, 1968), who argued that inflation is always and everywhere a monetary phenomenon. Within this framework, savings are presumed to precede investment, and credit is a derived demand from existing money balances.

Some more realistic economists reject this view. As Joan Robinson remarked, the quantity of money makes itself (Robinson, 1956), challenging the idea that money supply is imposed from outside the economic system. Post-Keynesians argue that money is endogenously created through the lending activities of banks. Rather than waiting for deposits, banks extend credit to creditworthy<fnref n="2" /> borrowers, and in doing so, they simultaneously create new deposits. In this view, it is loans that create deposits, not deposits that finance loans. One of the first major post-Keynesian challenges to the exogenous money view came from Nicholas Kaldor. In his 1982 book The Scourge of Monetarism (Kaldor, 1982), Kaldor criticized the monetarist idea of controlling money supply, arguing instead that the supply of money is determined by the demand for bank credit. He emphasized that central banks accommodate the reserve needs of banks after the fact, rendering direct control over the money stock ineffective. Building on this, Basil J. Moore (1988) developed the concept of horizontalism in his seminal 1988 work Horizontalists<fnref n="3" /> and Verticalists<fnref n="4" />: The Macroeconomics of Credit Money. Moore argued that at the interest rate targeted by the central bank, the supply of reserves is perfectly elastic; hence, money is supplied horizontally in response to demand. He wrote that the supply of credit is demand-determined. Banks extend credit to any creditworthy borrower. The loans create deposits, and deposits create reserves. Marc Lavoie also significantly contributed to the theory of endogenous money, especially through his integration of it into stock-flow consistent (SFC)<fnref n="5" /> macroeconomic models. In his book Monetary Economics: An Integrated Approach to Credit, Money, Income, Production and Wealth (Lavoie & Godley, 2007), Lavoie explains that the money supply is not exogenously determined by the central bank, but endogenously determined

<footnote n="2">² Borrowers that meet the required collaterals</footnote>

<footnote n="3">³ Considering the situation in which quantities Q of money in the plan (i, j), at the abscise, is the only one that fluctuate given the level of the central bank interest rate.</footnote>

<footnote n="4">⁴ Economists who support the opposite.</footnote>

<footnote n="5">⁵ The approach that is at the core of this study. With a perfect integration of flows and stocks into a dynamic heterogenous environment.</footnote>
by the credit decisions of banks. These models emphasize that all financial flows and stocks must balance, and that banking behavior plays a crucial role in generating money and financing economic activity.

Wynne Godley, with his background in accounting and macroeconomic modeling, further strengthened this view by showing empirical simulations that credit creation by banks responds to the needs of the real economy and that central bank policy is mostly about setting interest rates, not controlling money directly. In his words, control of the money stock is not a policy that any central bank has ever successfully implemented. Even earlier, Joseph Schumpeter anticipated the endogenous money view when he stated that it is much more realistic to say that the banks create credit, that is, that they create deposits in their act of lending (Schumpeter, 1934). Thus, the post-Keynesian theory of endogenous money creation reorients monetary theory around financial institutions, real-world banking practices, and the demand for credit. It sees money not as an exogenous input but as an outcome of the production and investment process, mediated by bank credit. This stands in sharp contrast to the loanable funds model and the traditional money multiplier story is still dominant in many textbooks. According to Schumpeter, entrepreneurs can't exist if banks don't create money according to their credit need for investment. By that, he means that without considering the size of credit required to finance his activities, the entrepreneur can't innovate and the only way to make this success happen is by adapting the credit demand for financement to the credit supply. In this same way, further studies highlight this endogenous credit creation implicitly. It is the case in The Accumulation of Capital by Joan Robinson. In this book, through her analysis regarding the relationship between investment and saving, Joan Robinson defends the argument that investment decision drives incomes and saving, not the other way around. The idea behind this analysis is that credit is extended first and deposits and reserves adjust later (Robinson, 1956). This endogenous money creation process has implicitly also been advocated his criticisms toward the monetarism (Kaldor, 1982). Here again, the centrale bank money supply control's hypothesis has been strongly attacked. Kaldor decisively challenges the monetarist assumption that the money supply is a policy-controlled, exogenous variable to which the economy passively adjusts. In The Scourge of Monetarism, he criticizes the simplistic causality implied by monetarist models, namely, that central banks can control inflation and output by manipulating a stable money supply. According to this logic, changes in the money supply would precede and determine changes in nominal income, with the demand for money assumed to adjust automatically. Kaldor refutes this by emphasizing that such a view is empirically
flawed and theoretically misleading, particularly in advanced capitalist economies where
financial systems are highly developed.

Instead, Kaldor proposes that the causality runs in the opposite direction: it is the
demand for credit by firms and households, driven by investment and consumption decisions,
that determines the expansion of bank lending, and therefore the growth of the money supply.
In other words, money is created as a consequence of economic activity, not as a prerequisite.
Banks respond to profitable lending opportunities by creating credit, and in doing so, they
simultaneously create deposits thus expanding the money supply endogenously. The central
bank, in turn, accommodates this process by supplying the necessary reserves to maintain its
target interest rate, making the monetary base itself a residual rather than a control instrument.
This analysis anticipates and supports the endogenous money theory later formalized by post-
Keynesians like Basil Moore. Kaldor's work underscores a fundamental point, stressing that
monetary aggregates are demand-driven, and any attempt by central banks to control them
mechanically without regard to the dynamics of credit and output can lead to destabilizing
outcomes. This insight has profound implications for macroeconomic modeling and monetary
policy. It justifies a framework where banks are not passive intermediaries of existing savings,
but active creators of money, and where credit creation is intimately linked to real sector
dynamics such as investment cycles, income generation, and employment. Thus, we find that
the core idea of endogenous money creation is that money is created by commercial banks in
response to the demand for credit, rather than being supplied exogenously by the central bank.

## 1.2 Financial instability and cyclical dynamics

In models that exclude endogenous money or treat credit as neutral, the financial system is often assumed to be inherently stable, with crises explained largely by exogenous shocks, such as technology changes, monetary policy shifts, or external trade shocks. Studies such as the analysis of Smets & Wouters (Smets & Wouters, 2007) on the financial stability, stressed these exogenous sources of threat. According to these authors, and to the total opposite to the Minsky Moment<fnref n="6" />, the financial system is treated as inherently stable except for exogenous shocks such as technology and policy 'shocks. In this context, the financial system is typically reduced to a

<footnote n="6">⁶ Concepts rooted in his Financial Instability Hypothesis describing how periods of economic stability can lead to instability through excessive borrowing and speculation.</footnote>
passive conduit of funds, not a source of macroeconomic disturbance. Yet this perspective
struggles to account for real-world phenomena such as credit bubbles, banking crises, or self-
reinforcing booms and busts. By abstracting from the role of bank lending and credit creation,
such models miss the internal sources of instability that arise from within the financial system
itself. Financial cycles are thus either ignored or attributed to irrational behavior, rather than
being understood as the product of structural and institutional dynamics. Moreover, the absence
of endogenous credit creation prevents these models from explaining how excessive leverage,
financial speculation, and unregulated credit growth can drive unsustainable expansions,
followed by painful contractions. The Minskyan view, which posits that stability is
destabilizing, remains incompatible with these approaches, because it requires an active
financial sector capable of influencing real outcomes, something exogenous money models
cannot provide. The recent world financial crisis coming from the subprime has taught us the
reality behind that view that banks are passive in economic dynamic, with no significant role.
The significance of loans and assets directed by these financial institutions, that is, commercial
banks for instance, reveals this role and comes as opposite to the traditional view of their
functioning.

Throughout his analysis of financial instability (Minsky, 1986), his arguments align with this view that financial instability occurs first, due to a proactive involvement of financial institution in financing economic activities whether it is pure speculative behavior or real economic, productive, activities support. Second, we also understand through his Financial Instability Hypothesis that those crises arise as a result of overconfidence of economic' agent in the financial system because, here again, banks and the others financial institutions are providing that feeling through their active support and involvement in the economic activities showing for instance how investments are financing through credit. In context of the West African Economic and Monetary Union (WAEMU), where the banking sector is central to economic activity and access to finance is uneven, ignoring credit dynamics in its specific complex context, with also ignoring interaction between all stakeholders, that is, a model trying to analyze and explain the significance or not of whatsoever, without incorporated the dynamic relationship among actors and key sectors, can easily lead to misleading diagnoses and ineffective policy recommendations. Without incorporating financial actors' behavior into macroeconomic models, unfortunately as many studies haven't done before, policymakers may fail to anticipate or mitigate the destabilizing effects of credit cycles, especially in economies vulnerable to external shocks and institutional fragilities.
## Section 2: Credit Creation, Investment, and Growth in Developing Countries

Developing countries are mainly characterized by financial systems largely occupied by banking sectors rather than a mixed and developed financial market and banking sector as is the case with the developed countries. In this special context, one could wonder how this uniqueness of financial systems responds efficiently as is required regarding the increasing need for those countries at several levels. First, to ensure employment growth through financing bankable investments, to contribute to reducing the burden embodied in economic agents' choices of investment and diversification of the portfolios between central bank 'money, assets and saving. To capture and have a good idea about its functioning, many studies have been made using several methodologies. An analyze of how commercial banks influence the macroeconomic environment was made in the Indian commercial banking sector. This analysis was aimed at highlighting how commercial banks ease the economic' machine functioning through their credit creation power and making loans with their deposit. Through a qualitative approach, the analysis helps understand the ability of commercial banks to transform risk into assets by easing access to credit (Sah & Alam, M.M, 2024). The study revealed that Indian's commercial banks with a total loan reached $ 1 532,31 USD in 2022, contribute to smoothing the economic cycle through the credit creation and boosted the central bank monetary policies implementation. This rise in the credit creation rate helped programs like the Jan Dhan Yojana and the agriculture sector supports program incredibly while some challenges remain in terms of size and scope of these banking supports.

Additionally, study has been made in Nepal, south Asian country with a relatively underdeveloped capital market, to understand the relationship between the activities of commercial banks and economic growth/ the macroeconomics dynamic (Dhungana, 2014). This study mobilized a quantitative time-series econometric approach covering the period 1994-2012. Statistical tools such as the Unit Root Test through the Augmented Dickey-Fuller (ADF) test to check stationarity of the data, the Granger Causality Test to determine short-run causal relationships, the Johansen Cointegration Test to confirm or not the existence of long terms relationships, Vector Error Correction Model (VECM) and others diagnostic tests such as the Breusch-Godfrey autocorrelation, the ARCH heteroskedasticity effect and finally the Jarque-Bera normality test have been used in this analysis. The study focused on variables such as economic growth, total deposit, total lending and total investment. As results, the analysis has shown the high level of both commercial banking activities increase and the macroeconomics
dynamic in terms of GDP growth but with some limits. The paper concludes that commercial
banks in Nepal significantly influence economic growth in the long run through capital
formation and allocation to productive sectors. The short-run impact on GDP is limited, but
banks do channel deposits into investments, supporting structural growth. Obviously, this no
impact in the short-term GDP of the commercial banking sector support on economic activities
can be understood through these following factors. The entire model has been built without
considering the dynamic among the fluctuations throughout the study ‘time of the stock of
capital and the volume of credit created each year. This could ensure the cumulative effect of
previous capital stock that influences the sharpness of the current flow of credit and which in
its turn impacts the level of future stock of capital.

## 2.1 The role of credit in capital accumulation

The process of capital accumulation, central to long-term economic development, has been traditionally conceptualised within the neoclassical paradigm as a function of prior savings, mediated by a passive financial sector. In such frameworks, as exemplified by Solow's (1956) growth model, financial institutions are viewed as neutral intermediaries facilitating the efficient allocation of existing resources. Credit, in this context, plays no autonomous role in generating investment or shaping macroeconomic dynamics. This perspective has been profoundly contested by an extensive body of heterodox economic scholarship, which redefines money and credit as intrinsically endogenous and dynamic components of capitalist economic systems. A foundational contribution fund that credit is an essential element of the capitalist process, enabling entrepreneurs to innovate and invest ahead of actual savings (Schumpeter, 1911, 1934). Building on this, Gurley, and Shaw (1955) and later Kaldor (1970) emphasised the transformative role of financial institutions in mobilising real resources through credit creation. In the modern post-Keynesian framework, Moore (1988) famously formalised the theory of endogenous money, arguing that banks supply credit on demand, constrained primarily by borrower creditworthiness and capital adequacy, not by reserves or deposits. In this view, investment is not constrained by savings but instead generates its own saving ex post through multiplier effects (Kalecki, 1937; Lavoie, 2014). As such, credit acts as the initiating force of the accumulation process. Minsky (1986) advanced this line of thought by embedding credit dynamics into a theory of financial instability. According to Minsky, the endogenous expansion of credit during periods of optimism fuels investment booms that are inherently
unstable. Over time, borrowers and lenders shift from hedge to speculative and eventually Ponzi
finance, creating fragilities that culminate in financial crises. Thus, credit is simultaneously a
driver of capital accumulation and a source of systemic vulnerability.

Empirical studies further reinforce this perspective. Werner (2005), using disaggregated
bank lending data in Japan, shows that bank credit directed toward productive investment (as
opposed to asset purchases) has a significant causal impact on GDP growth. Similarly, Bezemer
(2012) distinguishes between productive and speculative credit, showing that the composition
of credit flows matters greatly for long-run capital formation and macroeconomic stability.
Using a long-run macro-financial dataset (1870-2013) for 17 advanced economies, Jordà, Ò.,
Schularick, M. and Taylor, A.M analysis (Jordà, et al., 2016) shows that credit to the private
non-financial sector, particularly household credit, has grown significantly over time.
Importantly, they find that credit booms driven by household lending (often unproductive) are
more likely to lead to financial crises and lower subsequent GDP growth, whereas credit to
firms is more likely to support productive investment. Additionally, an analysis investigating in
how credit and capacity utilization of firms for capital accumulation nexus at the firm level in
Latin American manufacturing industries (Zhang, D, 2022), revealed interesting link between
these two variables. First, the analysis came out with a result of regression coefficients that
show a statistically significant negative relationship between credit constraints and capacity
utilization rate of firms, with p-values often < 0.01. Second, interaction terms indicate that
SMEs and firms in capital-intensive industries are more severely affected and indicated thanks
to a counterfactual analysis based on the estimation, results suggest that constrained firms
would have seen an increase of 26.8% capacity utilization. This underscores the critical role of
credit access in shaping firms' capital accumulation capacity, revealing that Latin American
manufacturing firms under credit constraints tend to operate at approximately 12-20% below
their optimal production capacity.

This insightful observation by Zhang, that strengthening access to credit is a
productivity-enhancing policy lever, underlines the developmental role of commercial banks
and aligns closely with empirical findings from other developing countries. For instance, Jimi
et al. (2019), studying rice farmers in Bangladesh, found that access to formal credit increased
rice yield by approximately 14%, with 11% of the gain attributed to technological
improvements and the remaining 3% to better technical efficiency. Similarly, Haryanto et al.
(2023) examined maize production in Indonesia and reported that farmers accessing formal
credit achieved significantly higher productivity and efficiency compared to those relying on
informal sources, largely due to their ability to invest in improved inputs and farming techniques. In a broader enterprise context, Kumarasamy et al (2024), using data from the World Bank Enterprise Surveys, revealed that micro, small, and medium enterprises (MSMEs) with access to credit recorded higher sales per worker and value-added per employee, indicating a strong link between financial access and firm-level productivity. These findings collectively demonstrate that credit is not merely a financial tool but a key enabler of structural transformation, technological upgrading, and sustained capital accumulation in developing economies. In economic environments where small and medium-sized enterprises (SMEs) face disproportionately high financial frictions, the implications for growth and capital accumulation are profound. This issue is particularly pressing in WAEMU economies, where structural limitations in credit markets continue to constrain SME development. These dynamics raise important questions about the allocation of credit and the role of financial institutions, that is, commercial banks, questions that will be explored in greater depth in the subsequent analysis.

## 2.2 Commercial Banking constraints in WAEMU vs. other economies

Commercial banks in the WAEMU region operate under structural and regulatory constraints that significantly limit their ability to support broad-based economic development. Several studies have highlighted how these constraints interact with prudential regulations, monetary policy transmission, and bank behavior. For instance, Kuessi et al. (2023) examine how competition and institutional factors, including the dominance of multinational banks, affect banking efficiency across WAEMU countries. They find that while competition has improved marginal efficiency, structural institutional weaknesses continue to hinder optimal and efficient credit allocation. Kone and Djan (2023) further explore the dimension of liquidity risk, revealing that despite existing regulatory frameworks, banks remain vulnerable to short-term shocks, largely due to underdeveloped interbank markets and rigid reserve requirements. In addition, a recent IMF study (Ayvazyan, 2024) underscores how WAEMU's prudential regulations, though gradually aligning with Basel II and III, still face significant challenges such as excessive concentration of credit portfolios and exposure to sovereign debt, which weakens the sector's resilience. These constraints are compounded by the findings of Cogent Economics & Finance (Ousmane et al, 2024), which demonstrate that the monetary policy transmission mechanism in WAEMU is impaired by banks' preference for holding government securities over extending credit to the private sector. This tendency is linked to banks' risk-
averse behavior and capital structure limitations. Lastly, Kouakou (2025) shows that the implementation of Basel III standards has placed additional pressure on banks' intermediation capacity, particularly by increasing liquidity reserve requirements, which inadvertently reduces the volume of loans extended to SMEs and other productive sectors while improving the soundness of banks through the constitution of larger reserves. These studies help to deeply understand the nature of constraints that hinder WAEMU's commercial banks' involvement in economic activities.

In the other side, particularly in developing Asian countries, high banking market concentration has been identified as a significant impediment to SME financing. A study by Thein, Niigata, and Inaba (2024) reveals that in markets with elevated concentration levels, banks tend to demand higher collateral from SMEs, exacerbating credit access challenges. This risk-averse behavior, driven by reduced competition, underscores the need for regulatory measures to manage market concentration and foster a more inclusive lending environment. Borrowing constraints in Latin American economies present distinctive characteristics that set them apart from more conventional, collateral-based lending models. In countries such as Argentina, Camara and Sangiacomo (2022) demonstrate that credit allocation to firms is primarily influenced by cash flow performance rather than the availability of physical collateral. This finding reflects a unique structural feature of financial markets in the region, where macroeconomic volatility, high inflation, and institutional weaknesses have historically undermined the reliability and valuation of collateral assets such as land or buildings. The authors show that firms with stronger internal cash flows are significantly more likely to obtain bank loans, indicating that creditworthiness is judged less by asset holdings and more by operational liquidity. While this approach may appear prudent in risk-averse banking environments, it poses several systemic challenges. First, it creates a self-reinforcing credit access gap. Firms that are already profitable can secure financing to expand, while startups or firms in temporary distress, despite having viable business models, remain excluded from formal finance. Second, it increases the sensitivity of lending to external financial shocks. During downturns, when firm revenues decline, credit supply can contract sharply, exacerbating the cyclical nature of investment and employment. This reliance on cash-flow-based credit scoring rather than asset-backed lending thus reflects a risk management response in unstable macro-financial environments<fnref n="7" />, but it also highlights the urgent need for institutional

<footnote n="7">⁷ To deepening the understanding of this cash-flow based credit scoring, have a look to (Boot, A.W.A, 2000).</footnote>
innovation. In particular, it underscores the importance of developing diversified credit assessment frameworks that can integrate forward-looking indicators, such as projected earnings, non-traditional data, or credit guarantees, to better serve a broader base of productive enterprises. This challenge, while not taking forward in the WAEMU banking 'behaviors, resonates with many emerging economies, where limited access to reliable collateral and weak legal enforcement similarly restrict the inclusiveness and counter-cyclicality of bank lending.

<table><caption>**Table 1:** Comparison of SME Credit Access and Lending Conditions Across Selected Countries</caption><thead><tr><td>Country/Region</td><td>SME Credit Access</td><td>Loan-to-Deposit Ratio</td><td>Main Constraint</td><td>Avg. Loan Rate (%)</td><td>Collateral (% of Loan)</td></tr></thead><tbody><tr><td>Niger</td><td>~15%</td><td>54%</td><td>Low deposit base, informality</td><td>12-14%</td><td>>150%</td></tr><tr><td>Côte d'Ivoire</td><td>~28%</td><td>84%</td><td>Urban-rural divide, regulatory bottlenecks</td><td>8-10%</td><td>100-120%</td></tr><tr><td>WAEMU (avg.)</td><td>~23%</td><td>76%</td><td>High reserve needs, gov. bond crowding</td><td>8.5-10.5%</td><td>>100%</td></tr><tr><td>Argentina</td><td>~15%</td><td>88%</td><td>Inflation, cash-flow-based lending</td><td>30-40%</td><td><80%</td></tr><tr><td>India</td><td>~27%</td><td>70-75%</td><td>NPLs, foreign ownership limits</td><td>10-12%</td><td>~100%</td></tr><tr><td>Indonesia</td><td>~30%</td><td>83%</td><td>Informal lending, rural credit access</td><td>9-11%</td><td>~120%</td></tr><tr><td>Ghana</td><td>~20%</td><td>68%</td><td>High risk aversion, weak collateral enforcement</td><td>23-25%</td><td>100-150%</td></tr></tbody></table>

(Sources<fnref n="8" />: BCEAO, UEMOA, IMF...)

The table above summarizes key indicators of SME credit access, loan-to-deposit ratios, main constraints, average loan rates, and collateral requirements across selected countries and regions. Notably, WAEMU countries show relatively low SME credit access (~23%) and high collateral requirements (>100%), reflecting structural challenges such as limited deposit bases and regulatory bottlenecks. In contrast, countries like Indonesia and India have higher credit

<footnote n="8">⁸ The data and analysis draw on BCEAO's annual report on financial inclusion in the UEMOA (2021), BCEAO's 2023 prudential ratios and credit statistics, IMF's 2023 Financial System Stability Assessment for Niger and Financial Sector Assessment Program for Côte d'Ivoire, as well as country-specific sources including Camara and Sangiacomo (2022) on Argentina, the Reserve Bank of India's 2022 banking report, Indonesia's Otoritas Jasa Keuangan banking statistics (2020), and the Bank of Ghana's 2021 Credit Conditions Survey and SME Lending Report.</footnote>
access (~27-30%) and somewhat lower collateral demands. In this same line, the following part will present, for the macroeconomic analysis, the Stock Flow Consistent Model Specification, which formalizes the macroeconomic framework used to analyze these financial and economic dynamics.

## Part 2: The Stock Flow Consistent Model Specification

The Stock-Flow Consistent (SFC) approach, commonly associated with the seminal work of Wynne Godley and Marc Lavoie (2007), represents a major advancement in macroeconomic modeling by ensuring full accounting consistency between real flows, financial flows, and stocks of assets and liabilities. However, the origins of this approach predate their contributions. Several earlier models laid the intellectual and methodological groundwork for the Stock-Flow Consistent (SFC) approach. The METRIC model (Artus et al., 1978), developed in France, was one of the first to combine a Keynesian real-sector foundation with a structured financial sector. It incorporated households, firms, the government, private banks, and central banks, and placed particular emphasis on the role of credit creation and central bank refinancing as adjustment mechanisms in response to financing needs. While METRIC introduced key institutional insights, it did not ensure full stock-flow consistency across all sectors and time periods. Furthermore, the MEFISTO model (Banque de France, 1993) advanced the modeling of financial behavior through detailed financial transaction matrices and portfolio choices based on expected returns. MEFISTO adopted a more granular view of balance sheet dynamics, especially for financial institutions and households. However, it remained largely static in its treatment of real-side variables, it lacked an explicit mechanism by which financial dynamics feed back into production, income, and employment.

These institutional modeling efforts were complemented by foundational theoretical contributions. James Tobin's (1969) portfolio selection theory introduced a formal framework for modeling asset allocation decisions under uncertainty and differing returns. This work was pivotal in integrating monetary and financial assets into macroeconomic theory, highlighting how relative returns influence household and institutional behavior. Lance Taylor (2004) further bridged the gap between financial and real spheres by emphasizing distributional conflict, financial fragility, and the macro implications of portfolio choices and leverage. Taylor's models combined the structural features of income distribution and class-based saving behavior with feedback loops between debt, investment, and output, concepts that served as central base
to later SFC models. The fully integrated SFC framework developed by Godley and Lavoie (2007) synthesized these threads into a coherent methodology that models all sectors' balance sheets, financial flows, and behavioral equations in a way that guarantees consistency across time, sectors, and markets, a necessary feature for analyzing financial-real dynamics in both developed and developing economies as it will be the case here for Côte d'Ivoire and Niger.

## Section 3: Designing for the two economies of WAEMU

Rooted on the two-country Stock-Flow Consistent (SFC) model developed by Duwicquet and Mazier (2015) to analyze the euro area crisis, this section adapts the framework to the West African Economic and Monetary Union (WAEMU) context. The WAEMU countries share a common currency (the CFA franc), a central monetary authority (the BCEAO), and a partially integrated banking sector, yet exhibit structural heterogeneity, including sharp disparities in productive capacity, financial deepening, and institutional strength. The objective is to examine how commercial banks, through their role as the primary intermediaries of credit and liquidity, influence macroeconomic dynamics, financial stability, and growth trajectories in countries such as Côte d'Ivoire, the region's economic anchor, and Niger, a more fragile and capital-constrained economy.

### 3.1 The General Structure of the SFC Model

To rigorously capture the macro-financial dynamics between Côte d'Ivoire and Niger within the specific<fnref n="9" /> WAEMU framework, the Stock-Flow Consistent (SFC) model adapted from the two-country model developed by Duwicquet and Mazier (2015), will follow the standard SFC architecture, which ensures full accounting consistency between financial stocks, real flows, and transactions across all institutional sectors over time. At the heart of this framework lies the role of commercial banks, which act as key financial intermediaries. Through their lending behavior, liquidity creation, and responses to risk, banks influence the allocation of credit, the pace of investment, and ultimately the potential for employment and output growth, as studies beforehand stressed it. These crucial and vital roles empower commercial banks, particularly

<footnote n="9">⁹ This specificity lies mostly on its “bank-based” financial system at the opposite of the European Union ‘one that is most diverse’. Adding to that, the fact that the WAEMU central bank is a currency board.</footnote>
in these underdeveloped financial market' countries, to channel their influences as support or
constrain to structural transformation and macroeconomic stability. The model is structured
around three core matrices. The balance sheet matrix, which maps out the stock positions of
assets and liabilities across sectors; the transaction flow matrix, which records all real and
financial flows within the economy; and the accumulation (or revaluation) matrix, which tracks
changes in asset stocks over time due to transactions and valuation effects.

*The Balance Sheet Matrix.* It is composed of two key sectors, such as the Financial Instruments, that is:

✓ Money (currency, M)

✓ Bank Deposits (D)

✓ Loans (L)

✓ Government Bonds (B)

✓ Treasury Bills (T)

✓ Equity (E)

and the Institutional Sectors such as:

✓ Households (HH CI, HH N)

✓ Firms (F CI, F N)

✓ Governments (G CI, G N)

✓ Commercial Banks (B CI, B N)

✓ Central Bank (CB) – BCEAO

✓ Rest of World (RoW)

The table above presents the stock-flow consistent (SFC) balance sheet matrix for a two-country monetary union composed of Côte d'Ivoire (CI) and Niger (N). It includes the main institutional sectors: Households (HH), Firms (F), Governments (G), Banks (B), and a shared Central Bank (CB), with the Rest of the World (RoW) representing, for each country, its intra-union partner (CI's RoW is Niger, and vice versa). Each instrument, deposits, loans, bonds, treasury bills, reserves, central bank money, and equity, is recorded as an asset (+) or liability (-) across sectors, ensuring full accounting consistency with a zero sum for each instrument. Key financial relations are visible:

✓ **Households** hold deposits, central bank money, bonds, and equity, making them net creditors.

✓ **Firms** finance investment through loans and equity issuance, leading to negative net worth.

✓ **Governments** issue bonds and treasury bills, typically reflecting budget deficits.

✓ **Banks** are intermediaries, holding assets like loans and T-bills while issuing liabilities such as deposits.

✓ The **Central Bank** issues base money and holds reserves from commercial banks.
This matrix forms the accounting backbone of the entire model, ensuring that all sectoral the behaviors, such as the household saving, the firm investment, government financing, and bank intermediation, are embedded within a coherent financial structure that respects the fundamental principle of stock-flow consistency. Every economic flow is systematically linked to corresponding changes in sectoral balance sheets, which guarantees that the model captures not only real transactions (for example consumption, investment, taxes) but also the evolution of financial positions over time. By explicitly recording assets and liabilities across sectors, the matrix enforces a discipline of financial realism: no sector can accumulate or dispose of financial resources without a corresponding transaction on the other side. This reinforces the Keynesian insight that macroeconomic outcomes are shaped not merely by real variables but by financial relationships, expectations, and institutional constraints. In the context of a two-country monetary union, the SFC matrix enables the modeling of financial interlinkages between Côte d'Ivoire and Niger, both members of the WAEMU, under a shared monetary authority (BCEAO). Côte d'Ivoire, which accounts for nearly 40% of the WAEMU's total GDP, exhibits a higher financial capacity and deeper banking system, with a loan-to-deposit ratio of approximately 84%, allowing it to act as a net capital exporter within the union. In contrast, Niger, with a loan-to-deposit ratio below 55% and limited financial access for SMEs and rural sectors, remains structurally dependent on public transfers and external flows. This asymmetry means that excess liquidity in Côte d'Ivoire can, in theory, be recycled through the banking system to finance investment in Niger. However, credit rationing and selective lending behavior by banks, often biased toward low-risk, urban-based borrowers, tend to amplify macroeconomic divergence rather than correct it. As a result, financial fragility in Niger can spill over, particularly in times of regional shocks (terms-of-trade or commodity price volatility), highlighting the systemic vulnerability of the union. The SFC framework is thus essential to trace how behavioral shifts in the banking sector, such as increased risk aversion or changes in credit allocation criteria, can propagate through balance sheets and influence macroeconomic outcomes like GDP, employment, and investment in both countries. It also reflects the broader reality that, in a monetary union where fiscal transfers are limited and monetary sovereignty is pooled, understanding and anticipating financial imbalances is critical for resilience and convergence.
<table><thead><tr><th>Instrument/ Sector</th><th>HH CI</th><th>HH N</th><th>F CI</th><th>F N</th><th>G CI</th><th>G N</th><th>B CI</th><th>B N</th><th>CB</th><th>Row</th><th>Sum</th></tr></thead><tbody><tr><td>Deposits (D CI)</td><td>+</td><td></td><td></td><td></td><td></td><td></td><td>-</td><td></td><td></td><td></td><td>0</td></tr><tr><td>Deposits (D N)</td><td></td><td>+</td><td></td><td></td><td></td><td></td><td></td><td>-</td><td></td><td></td><td>0</td></tr><tr><td>Loans to Firms (L CI)</td><td></td><td></td><td>-</td><td></td><td></td><td></td><td>+</td><td></td><td></td><td></td><td>0</td></tr><tr><td>Loans to Firms (L N)</td><td></td><td></td><td></td><td>-</td><td></td><td></td><td></td><td>+</td><td></td><td></td><td>0</td></tr><tr><td>Bonds (B CI)</td><td>+</td><td>+</td><td></td><td></td><td>-</td><td></td><td></td><td></td><td></td><td></td><td>0</td></tr><tr><td>Bonds (B N)</td><td>+</td><td>+</td><td></td><td></td><td></td><td>-</td><td></td><td></td><td></td><td></td><td>0</td></tr><tr><td>Treasury Bills (T CI)</td><td></td><td></td><td></td><td></td><td>-</td><td></td><td>+</td><td></td><td></td><td></td><td>0</td></tr><tr><td>Treasury Bills (T N)</td><td></td><td></td><td></td><td></td><td></td><td>-</td><td></td><td>+</td><td></td><td></td><td>0</td></tr><tr><td>Reserves (R)</td><td></td><td></td><td></td><td></td><td></td><td></td><td>-</td><td>-</td><td>+</td><td></td><td>0</td></tr><tr><td>Central Bank Money (M)</td><td>+</td><td>+</td><td></td><td></td><td></td><td></td><td></td><td></td><td>-</td><td></td><td>0</td></tr><tr><td>Equity (E CI)</td><td>+</td><td></td><td>-</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>0</td></tr><tr><td>Equity (E N)</td><td></td><td>+</td><td></td><td>-</td><td></td><td></td><td></td><td></td><td></td><td></td><td>0</td></tr><tr><td>Net Worth</td><td>+</td><td>+</td><td>-</td><td>-</td><td>-</td><td>-</td><td>+</td><td>+</td><td>+</td><td></td><td></td></tr></tbody></table>

Importantly, the BCEAO functions effectively as a currency board, maintaining a fixed exchange rate with an external anchor (the euro), and strictly limiting monetary policy discretion at the national level. This setup constrains the ability of the central bank to act as a full lender of last
resort<fnref n="10" /> or to monetize<fnref n="11" /> public deficits, unlike in models with fully sovereign central banks<fnref n="12" />. Consequently, this institutional constraint implies that adjustment to economic shocks must occur through fiscal transfers, external finance, or intra-union credit flows, rather than through traditional monetary channels. It also raises the risk of credit rationing and pro-cyclicality, especially for structurally weaker member states like Niger, unless offset by stronger financial integration or targeted policy mechanisms. Building on that, Niger banking system recently rises signal of distress<fnref n="13" /> and threaten by bankruptcy while other countries like Côte d'Ivoire 'banking system were fully over-liquid (the required reserves ratio to the realized liquidity reserves was 2 percentage point below).

Transaction Flow Matrix (TFM):

This matrix is a foundational component of the Stock-Flow Consistent (SFC) framework. It systematically records all monetary transactions occurring between institutional sectors over a defined period, typically one year. These transactions include current income and expenditures, financial returns, and capital flows related to the acquisition of financial assets or the settlement of liabilities. The matrix provides a comprehensive picture of the circular flow of income and finance within the economy. Each row in the matrix corresponds to a specific economic transaction such as consumption, investment, wage payments, taxes, interest, or changes in deposits and loans. Each column represents an institutional sector, such as households, firms, government, commercial banks, the central bank, and, in this two-country model, sectors specific to Côte d'Ivoire and Niger. Every transaction recorded in the matrix involves both a paying sector and a receiving sector, ensuring that each row sums to zero. This reflects the fundamental accounting identity that one agent's expenditure is always another's income. Similarly, the sum of each column indicates whether a sector is a net lender or borrower during the period, thereby linking flows to the evolution of financial stocks on the balance sheet.

<footnote n="10">¹⁰ Function that ensures the guarantee of the inter-bank functioning and provides stability feeling to the entire financial system.</footnote>

<footnote n="11">¹¹ Action of buying government debts, changing them for liquidity.</footnote>

<footnote n="12">¹² As the case for the ECB</footnote>

<footnote n="13">¹³ For more information about this situation, check this link: BCEAO (2025) *Bulletin statistique mensuel – Février 2025*. Dakar : Banque Centrale des États de l'Afrique de l'Ouest. Available at: https://www.bceao.int/sites/default/files/2025-04/Monthly%20Statistical%20Bulletin%20-%20F%C3%A9vrier%202025.pdf</footnote>
The matrix is typically organized into three sections. The first section captures real sector flows such as wages paid by firms and received by households, household consumption expenditures directed toward firms, and government spending on goods and services. These are the flows that determine aggregate demand and national income. The second section deals with financial income flows, such as interest payments on loans and deposits, interest on government bonds and treasury bills, and dividend distributions. These flows capture the income that sectors earn from holding financial assets or liabilities. The third section includes capital flows, which reflect net acquisitions or repayments of financial assets such as bank loans, deposits, bonds, treasury bills, and central bank reserves. These financial transactions link directly to changes in the stocks recorded in the balance sheet and the accumulation matrix. To sharp the structure of the SFC model here, in the WAEMU context, and particularly between Côte d'Ivoire and Niger, the transaction flow matrix will play a crucial role in highlighting the monetary and financial linkages between the two economies. It will allow us to track the extent and impact of cross-border financial flows such as credit extended from Ivorian banks to Nigerien firms or the purchase of Nigerien government securities by Ivorian institutions. It will also enable the modeling of credit rationing by modifying the volume or allocation of bank lending across and within countries and sectors. Furthermore, it will make explicit the distribution of interest payments on public debt, the role of fiscal transfers, and the circulation of liquidity provided by the BCEAO.

By enforcing accounting consistency across all flows, the transaction flow matrix will ensure that no resources disappear or emerge arbitrarily within the system. It will lay and constitute the foundation for analyzing the dynamic interactions between real and financial sectors, simulating policy interventions, and understanding how commercial banks influence growth, investment, and employment through their lending behavior. In this model, it is through the structure of the TFM that we can rigorously examine the central role of commercial banks in shaping the macroeconomic trajectory of developing countries within a monetary union.
<table><thead><tr><th>Flows/Sectors</th><th>HH_CL</th><th>HH_N</th><th>F_CL</th><th>F_N</th><th>G_CL</th><th>G_N</th><th>B_CL</th><th>B_N</th><th>CB</th><th>Row Sum</th></tr></thead><tbody><tr><td>Consumption (C)<sup>14</sup></td><td>-</td><td>-</td><td>+</td><td>+</td><td></td><td></td><td></td><td></td><td></td><td>0</td></tr><tr><td>Government Spending (G)</td><td></td><td></td><td>+</td><td>+</td><td>-</td><td>-</td><td></td><td></td><td></td><td>0</td></tr><tr><td>Taxes (T)</td><td>-</td><td>-</td><td></td><td></td><td>+</td><td>+</td><td></td><td></td><td></td><td>0</td></tr><tr><td>Wages</td><td>+</td><td>+</td><td>-</td><td>-</td><td></td><td></td><td></td><td></td><td></td><td>0</td></tr><tr><td>Interest on Deposits</td><td>+</td><td>+</td><td></td><td></td><td></td><td></td><td>-</td><td>-</td><td></td><td>0</td></tr><tr><td>Interest on Loans</td><td></td><td></td><td>-</td><td>-</td><td></td><td></td><td>+</td><td>+</td><td></td><td>0</td></tr><tr><td>Interest on Bonds</td><td>+</td><td>+</td><td></td><td></td><td>-</td><td>-</td><td></td><td></td><td></td><td>0</td></tr><tr><td>Interest on Bills</td><td></td><td></td><td></td><td></td><td>-</td><td>-</td><td>+</td><td>+</td><td></td><td>0</td></tr><tr><td>Dividend Payments</td><td>+</td><td>+</td><td>-</td><td>-</td><td></td><td></td><td></td><td></td><td></td><td>0</td></tr><tr><td>Change in Deposits (ΔD)</td><td>-</td><td>-</td><td></td><td></td><td></td><td></td><td>+</td><td>+</td><td></td><td>0</td></tr><tr><td>Change in Loans (ΔL)</td><td></td><td></td><td>+</td><td>+</td><td></td><td></td><td>-</td><td>-</td><td></td><td>0</td></tr><tr><td>Change in Bonds (ΔB)</td><td>-</td><td>-</td><td></td><td></td><td>+</td><td>+</td><td></td><td></td><td></td><td>0</td></tr><tr><td>Change in Bills (ΔT)</td><td></td><td></td><td></td><td></td><td>+</td><td>+</td><td>-</td><td>-</td><td></td><td>0</td></tr><tr><td>Change in Money (ΔM)</td><td>-</td><td>-</td><td></td><td></td><td></td><td></td><td></td><td></td><td>+</td><td>0</td></tr><tr><td>Bank Reserves (ΔR)</td><td></td><td></td><td></td><td></td><td></td><td></td><td>+</td><td>+</td><td>-</td><td>0</td></tr></tbody></table>

<footnote n="14">¹⁴ Will be considered here in terms of the level of demand for consumption. Which best underscores the macro-level analysis that this thesis aims to provide.</footnote>
Accumulation / Revaluation Matrix

This matrix ensures consistency between the stocks (balance sheet) and the flows (transactions) by tracking how the stock of each asset changes over time due to net transaction, revaluation (changes in assets price) and capital gain or loss.

<table><thead><tr><th>Asset</th><th>Initial Stock</th><th>+ Net Transactions</th><th>+ Revaluation</th><th>= Final Stock</th></tr></thead><tbody><tr><td>Deposits (D_CI)</td><td>D_CIo</td><td>ΔD_CI</td><td>0</td><td>D_CI<sub>1</sub></td></tr><tr><td>Deposits (D_N)</td><td>D_No</td><td>ΔD_N</td><td>0</td><td>D_N<sub>1</sub></td></tr><tr><td>Loans (L_CI)</td><td>L_CIo</td><td>ΔL_CI</td><td>0</td><td>L_CI<sub>1</sub></td></tr><tr><td>Bonds (B_CI)</td><td>B_CIo</td><td>ΔB_CI</td><td>Price change</td><td>B_CI<sub>1</sub></td></tr><tr><td>Treasury Bills (T_N)</td><td>T_No</td><td>ΔT_N</td><td>Price change</td><td>T_N<sub>1</sub></td></tr><tr><td>Central Bank Reserves</td><td>R<sub>0</sub></td><td>ΔR</td><td>0</td><td>R<sub>1</sub></td></tr><tr><td>Equity (E_N)</td><td>E_No</td><td>ΔE_N (if modeled)<sup>15</sup></td><td>Valuation gains</td><td>E_N<sub>1</sub></td></tr></tbody></table>

In the context of this study, the accumulation/revaluation matrix plays a critical role in tracking how banking activity affects the evolution of financial positions across sectors. Specifically, this matrix ensures the dynamic consistency between the flows of funds captured in the transaction flow matrix (e.g., credit issued, interest paid, deposits accumulated) and the stock of assets and liabilities shown in the balance sheet. It records how each sector's holdings of financial instruments, such as loans, deposits, government bonds, and bank reserves, evolve from one period to the next as a result of net flows (transactions) and asset revaluations. In economies like Côte d'Ivoire and Niger, where the financial system is overwhelmingly bank-centric, commercial banks serve as the primary conduit for funding investment and managing liquidity. Yet their actual contribution to development depends critically on their lending behavior. As beforehand underscored, this study focuses on the two dominant and often

<footnote n="15">¹⁵ For, this will highly depend on the hypothesis adopted for the simulation.</footnote>
restrictive banking practices: The conservative credit creation, where banks limit the expansion of credit even when macroeconomic conditions or demand for investment suggest room for growth. The selective lending behaviors, in which banks allocate credit only to perceived low-risk borrowers, typically excluding SMEs, informal actors, or high-potential but underserved sectors.

The accumulation matrix allows us to model the macroeconomic consequences of these behaviors. For example, under conservative credit creation, the flow of new loans from banks to firms is supposed to remain limited. As a result, the corresponding stock of firm liabilities (loans) and bank assets (credits) also grow slowly, impacting then investment and the accumulation of productive capital (Cantú et al., 2019). These effects will be modelized and stimulate in this study to capture their real influence in these two countries’ context in terms of effects on the long-term growth trajectory. As Stiglitz and Weiss (1981) have shown, credit markets plagued by imperfect information could often result in rationing mechanisms that exclude potentially productive borrowers, contributing to structural inefficiencies and reinforcing existing economic hierarchies. Then to try to capture to what extent selective lending does contribute to structural financial imbalances by concentrating credit in specific regions or sectors, we need to define the different types of markets that take part in this trade-off. First and foremost, I define the two economies as follows: Côte d’Ivoire (CI) represents 5 times Niger (N) to follow the structure of south and north define by Lavoie and Godley (2007). Furthermore, with respect to the scope of this commercial banking behaviors upon the macroeconomic dynamic, and considering the type of our two countries economy’ structures, the different market involved in this trade-off will be structured as follows:

The primary sector combining Agriculture, Forestry, Fisheries, Hunting, Logging, Coal mining, Crude oil and natural gas production, Metal ore mining and other ore mining represents in average of about 17.42% and 37.59% of respectively for Côte d'Ivoire and Niger GDP throughout the 24 years<fnref n="16" />. Regarding the Secondary sector, it includes Food manufacturing, beverages, tobacco, textile industries, clothing and leather. It also includes wood industries and wood products manufacturing, printing and publishing paper, chemical and chemical industries,

<footnote n="16">¹⁶ World Bank. (2024). *Agriculture, forestry, and fishing, value added (% of GDP) – Côte d’Ivoire/Niger (2000–2023)*. Retrieved from https://data.worldbank.org/indicator/NV.AGR.TOTL.ZS
Index Mundi. (2024). *Côte d’Ivoire/Niger – Agriculture value added (% of GDP)*. Retrieved from https://www.indexmundi.com
BCEAO. (2024). *Comptes nationaux et statistiques monétaires*. Retrieved from https://www.bceao.int</footnote>
Non-metallic mineral product manufacturing, basic metallurgical industries, metallurgical fabrics, metalwork, machinery, equipment, other manufacturing industries, electricity, gas water, water supply and public water supply, buildings and public works. This sector represents respectively about 16% and 25% of Niger and Côte d'Ivoire Economy.<fnref n="17" /> Finaly, the tertiary sector gathers wholesale and retail trade, restaurants and hotels, retail trade, tourist facilities, transport, warehouses and communications, communications, insurance, real estate affairs, business services, financial institutions, real estate and business services, community services, social and personal services, health and similar services, services provided to households and individuals. This sector accounts for about an average of 54.3% of CI' GDP and 37.9% of Niger 'GDP over the period 2000 to 2024.

## 3.2 The variables and Parameters of the model

At this stage it is good to remind you of the two commercial banking behaviors that I tend to model here, the conservative credit creation and the selective lending behavior as beforehand stressed. In modeling conservative lending and selective credit allocation, the choice of variables and parameters is essential to capture the structural and behavioral characteristics of commercial banks in developing countries like Côte d'Ivoire and Niger. For each behavior, there will be a specific variables and parameters. Indeed, for the conservative credit creation, the model will explore the dynamic of the variables such as the Total loans to the private sector(L), Deposits received by banks(D), Regulatory capital requirement (Kr), Reserves held by banks(R), Interest rate on loans(rL), GDP (to proxy for credit demand) (Y) and the Expected profits of firms (which may affect banks' willingness to lend) (πe). These variables will allow the model to reflect the interaction between regulatory constraints and economic incentives. The parameters for this part will be the Capital adequacy ratio (bank capital / risk-weighted assets) (θ), the Risk-aversion parameter (higher λ\lambda = more conservative) (λ), Credit supply elasticity(φ) and the Bank's liquidity preference (affects reserve-to-loan ratio) (γ). These parameters will be adjusted to simulate how banks respond to macroeconomic instability or regulatory tightening.

<footnote n="17">¹⁷ From the world Bank indicator portal: https://data.worldbank.org/indicator.</footnote>
The selective lending behavior will lead us to examine the variables such as the Loans to strategic/priority sectors (Ls), Loans to other sectors (Lo), Creditworthiness or productivity of firm(i) (qi), Credit score or credit constraint index(z), Collateral held by firm(i) (Ci) and the Government policy or guarantee(G)<fnref n="18" />. These variables will help to reflect heterogeneity in lending patterns. The parameters for the selective lending behavior are the Credit selectivity parameter (δ), Weight banks assign to collateral in lending decision (α), Preference bias toward sector(s) (β) and the Sensitivity of credit allocation to creditworthiness or risk indicators (η). These parameters are crucial to capture how banks discriminate between competing borrowers. They will help to analyze the implications of such allocation on employment, investment, and long-term growth.

### 3.3 Equations and dynamics relationships

It is now time to define the hypothesis under which the SFC model will run and the different equations that result from them. The first hypotheses rely on the first scenario which refers to the current behavior of the two countries' commercial banking institutions with respect to the fact that the common central bank (BCEAO) being a currency board and its impact on commercial banks' behaviors is taking into account. As mentioned in the General Theory of Keynes, the money supply is supposed to be exogenously fixed by the central bank. The following development will then focus solely on this framework. Thus, we consider these following hypotheses, with respect to the Conservative Lending under Currency Board Constraints framework:

**Hypothesis 1:** Lending function depends on expected profits and risk, constrained by reserves or capital adequacy.

**Hypothesis 2:** Sectoral allocation modeled using a weight or bias parameter in the credit distribution rule.

**Hypothesis 3:** Investment is credit-constrained and differentiated by sector.

Under these hypotheses, the first equations of our model will be as follow:

<footnote n="18">¹⁸ which might shift bank preferences</footnote>
The central bank (BCEAO) money supply function will reflect two relevant insights. The first is its tendency to control the quantity of money, accordingly the amount of credit allowed to be created in these two countries through the commercial banks channel. Which lead us to its money supply equation:

$$M = 1/[c + r(1 - c)].B \quad (1)$$

Where c represents as the reserve ratio of the public, the quasi-technical Constance of the money created, r the bank's minimum reserve asset ratio and B the quantity of base money.

The commercial Bank Lending Supply Equation (Conservative Behavior) as a result of this central Bank behavior is given by this equation:

$$L^N = \min\left\{\frac{K_r}{\theta}, \frac{D}{\gamma}, \varphi \cdot [\pi_e + \alpha Y - \lambda \cdot \sigma_{\pi}^2]\right\}^{19} \quad (2)$$

Where $L^N$ represents the total volume of loans that commercial banks supply to the private sector in the north country (Côte d'Ivoire), $\frac{K_r}{\theta}$ the capital adequacy constraint stating that loans cannot exceed regulatory capital divided by the required capital adequacy ratio, $\frac{D}{\gamma}$ the liquidity constraint indicating that loan supply is limited by the amount of deposits, adjusted by the bank's liquidity preference, $\varphi \cdot [\pi_e + \alpha Y - \lambda \cdot \sigma_{\pi}^2]$ the behavioral lending function where loan supply responds to expected profits and GDP, and is reduced by perceived risk and uncertainty, $\varphi$ the parameter measuring the responsiveness of loan supply to changes in profitability and macroeconomic conditions (credit supply elasticity), $\pi_e$ the expected profits of firms, which influence banks' willingness to lend by reflecting the perceived creditworthiness of borrowers, $\alpha Y$ the Gross Domestic Product, used as a proxy for overall credit demand and economic activity, with $\alpha$ capturing its influence on loan decisions, $\lambda$ the parameter representing the bank's degree of risk aversion, higher values imply more conservative lending behavior and $\sigma_{\pi}^2$ the variance of expected profits, used as a proxy for economic uncertainty, which reduces loan supply when high.

Now under the selective lending, we will explore these hypotheses:

<footnote n="19">¹⁹ See annex 1 for more detail</footnote>
**Hypothesis 1:** Lending function depends on firm-level creditworthiness, collateral, and credit constraints, influenced by sensitivity and selectivity parameters.

**Hypothesis 2:** Sectoral allocation is shaped by a preference bias and government guarantees, captured through a weight parameter in the credit distribution rule.

**Hypothesis 3:** Loans are unequally distributed across firms and sectors, resulting in credit-constrained investment and heterogeneity in growth outcomes.

With the same Central Bank supply equation, the commercial bank lending equation is defined as the following:

$$L_i^N = \delta \cdot [\eta q_i + \alpha C_i + \beta S_i + G_i - z_i]^{20} \quad (3)$$

Where $S_i$ is a dummy or index indicating if the firm is in a priority sector. In this framework, the central bank determines the monetary base(B), while commercial banks primarily influence the velocity of money circulation in a largely mechanical manner, constrained by reserve requirements. Although commercial banks play a vital role in facilitating economic transactions by providing the necessary liquidity, since money does not appear spontaneously, they operate within the bounds of a money supply that is essentially exogenously set by the central bank. Their key functions are to pool financial risks and to optimize the use of base money, ensuring that the monetary system operates efficiently with the least amount of central bank money possible. The implications of these two behaviors, conservative and selective commercial bank on the employment, investment, and long-term macroeconomic growth within a Stock-Flow Consistent (SFC) model, need to include several key equations across different institutional sectors (firms, households, banks, government). Here for simplicity, we suppose that investment is solely function of the Total loans received(L), the Expected profits ($\pi_e$) and the Output (GDP)(Y). This led us to this investment function:

$$I^N = \psi_1 \cdot L + \psi_2 \cdot \pi_e + \psi_3 \cdot Y \quad (4)$$

Where the parameters $\psi_1$, $\psi_2$ and $\psi_3$ capture the credit sensitivity, the accelerator effect, and the confidence. The firm's profit function also is supposed to be defined as follows:

<footnote n="20">²⁰ The selective lending equation is adapted from credit scoring frameworks, combining firm-level characteristics (productivity, collateral, credit constraints), sectoral affiliation, and policy support, modulated by behavioral parameters reflecting banks' sensitivity to risk and strategic priorities, based on the insights from Stiglitz and Weiss (1981).</footnote>
$$ \pi^N = P \cdot Y - w \cdot N - r_L \cdot L \quad (5) $$

with r_L the interest rate on loans. The employment effect is examined thanks to this employment function:

$$ N^N = \frac{Y}{a} \quad (6) $$

With a the labor productivity parameter. The equations from (1) to (6) will help us to simulate these behaviors under the currency board behavior of the BCEAO and understand their effect on these two nations' economic activities. Let's remind here that the influence of these behaviors on the households is incorporated into the employment's reaction<fnref n="21" />. In contrast to the previous analysis, where the central bank operates under the rigid constraints of a currency board, we now turn to an alternative set of hypotheses in which the central bank (the BCEAO) adopts a more flexible and adaptive monetary supply function. In a typical capitalist economy, the banking sector is composed of two complementary arms. The central bank, on one side, provides the most liquid asset, money, and is responsible for preserving its value, ensuring its functions as a store of wealth and medium of deferred payment are maintained. Far from injecting money arbitrarily, the central bank supplies liquidity to commercial banks that are willing to borrow, and does so at an exogenously determined interest rate, such as the policy or prime rate. On the other side stands the commercial banking system, which increases the velocity of money circulation through its process of issuing loans and creating deposits pari passu. It is widely acknowledged that a banker's decision to lend precedes knowledge of the precise structure of deposits, even though loan and deposit creation occur simultaneously. The ability of commercial banks to expand credit hinges not only on regulatory or liquidity constraints but also on the willingness of investors to borrow, which itself depends on their expectations of future profitability. Commercial banks face a fundamental liquidity dilemma: on the one hand, they are incentivized to maximize lending and reduce idle, non-interest-bearing reserves to increase profitability; on the other hand, they are driven by a liquidity preference, the desire to maintain liquid assets to hedge against the risk of illiquidity, which could entail significant losses. This preference does not require a large stockpile of liquid reserves but rather reflects the rising liquidity premium that banks demand to part with liquidity

<footnote n="21">²¹ That is, the level of employment rate influences directly the households' behaviors whether it is their consumption function or their others behaviors functions.</footnote>
and expand their balance sheets. As Keynes aptly noted, the possession of actual money lulls our disquietude, and the premium which we require to make us part with money is the measure of the degree of our disquietude (Keynes, 1937b, p. 116). Building on these ideas, the new functions are now defined as follows:

The money multiplier

$$ \epsilon = 1/[1 - d(1 - R)] \qquad (7) $$

Where d is the share of M1 held as the demand of deposit and R the ratio of reserves to deposits.

We derive then from this equation (7), the supposed central bank money supply function as follows:

$$ M = 1/[1 - d(1 - \sum R_i)] \qquad (8) $$

This equation may hide the dependency of the ratio of reserves to liquidity money' supply. However, the relationship is now based on the needs of the productive sectors of credits flows that now drive the credit creation process.

Investment Function: Credit-Driven Investment Dynamics

$$ I^N = \chi \cdot L - \mu \cdot r_L + \xi \cdot \pi_e \qquad (9) $$

$\chi$ is the credit-investment sensitivity coefficient, $\mu$ interest rate sensitivity to investment and $\xi$ the confidence/profit expectations elasticity of investment. This equation shows that Investment increases with the volume of credit $L$, but is negatively affected by borrowing costs $r_L$, and positively affected by firms' profit expectations $\pi_e$. A more accommodative credit regime must strengthen $\chi$ and $\xi$.

Employment Function: Employment as a Function of Output

$$ N^N = \psi \cdot Y \qquad (10) $$
Employment-output elasticity is $\psi$. This equation explains that Credit-induced investment stimulates GDP, which increases labor demand. This simple form reflects the Okun-type relationship in a demand-driven model.<fnref n="22" />

Output Equation: Demand-Led Growth:

$$Y^N = C + I + G + NX \quad (11)$$

The Aggregate Form of Credit Supply Behavior of Banks:

$$L^N = \phi \cdot [\pi_e + \alpha \cdot Y - \lambda \cdot \sigma_{\pi}^2] \cdot \min(\frac{K_r}{\theta}, \gamma \cdot D) \quad (12)$$

It is good to note that like the other equations in this study, this credit supply function is a behavioral rule reflecting stylized facts of banking practices in WAEMU economies. It is not derived from an optimization problem but rather designed as a heuristic capturing key trade-offs observed in empirical studies: profitability, risk aversion, and regulatory constraints<fnref n="23" />. The parameters $\phi$ represents the credit supply elasticity. These variables and parameters are same as the one in the equation (2). In the following parts, the calibration will give more detail about these variables and parameters. Considering the two behaviors of commercial banks, beforehand underscored, it is now important to understand the theoretical framework of their scope in these two economies as the next part is about to highlight.

### 3.4 Credit creation and the investment cycle

An analysis of credit creation dynamics in Côte d'Ivoire and Niger from 2000 to 2024 reveals that the banking sectors in both countries remain in the early stages of development. The data show a generally upward trend, though modest in scale, with credit volumes still falling short of the investment needs of these economies. Table 2 presents the total amount of short-term credit allocated to the economy by sector over the period, expressed in millions of CFA francs.

<footnote n="22">²² This simple form reflects the Okun-type relationship embedded in demand-driven models, where employment responds proportionally to output dynamics (Okun, 1962; Lavoie, 2014).</footnote>

<footnote n="23">²³ This credit supply function is a heuristic representation of banking behavior in WAEMU, shaped by empirical trade-offs between profitability, risk aversion, and regulation (Andrianaivo & Yartey, 2009; BCEAO; Lavoie, 2014).</footnote>
<table><caption>**Table 2:** Sectoral Allocation of Commercial Bank Credit in Côte d'Ivoire and Niger (2000-2024), in Millions of CFA Francs</caption><thead><tr><th rowspan="2">Countries</th><th colspan="2">Primary Sector</th><th colspan="2">Secondary Sector</th><th colspan="2">Tertiary sector</th></tr><tr><th>2000-2012</th><th>2013-2024</th><th>2000-2012</th><th>2013-2024</th><th>2000-2012</th><th>2013-2024</th></tr></thead><tbody><tr><td>Côte d'Ivoire</td><td>2.273,5</td><td>100.478,023</td><td>20.774,334</td><td>800.589,637</td><td>33.213,75</td><td>894.578,92</td></tr><tr><td>Niger</td><td>92,08</td><td>4.644,8995</td><td>971,417</td><td>104.559,502</td><td>5.057,333</td><td>624.170,313</td></tr></tbody></table>

(Sources: BCEAO's database and author's calculations)

The table presents aggregated short-term credit amounts disbursed by commercial banks across the primary, secondary, and tertiary sectors in Côte d'Ivoire and Niger. A clear comparative insight emerges; Côte d'Ivoire consistently receives significantly higher credit volumes than Niger in all sectors. In both countries, credit allocation is heavily concentrated in the tertiary sector, reflecting the growing dominance of service-based economic activities. A noticeable upward trend in credit distribution is observed after 2013, particularly in Côte d'Ivoire, indicating a phase of financial recovery and deepening following earlier economic shocks and the post-electoral crisis. Nonetheless, despite these improvements and the relevant growth rate of credit year in, the overall volume of sectoral credit remains relatively modest when compared to the broader investment requirements and GDP levels of the two economies as the following table3 will show.

While Table 2 highlights how commercial bank credit was distributed across sectors in Côte d'Ivoire and Niger, it is equally important to examine how these sectors contribute to the overall economic output. For, the distribution of bank credit across sectors reveals strategic lending patterns that are worth particular attention. The following table presents the sectoral composition of GDP, offering insight into the broader structure and transformation of each economy over time.
<table><caption>**Table 3:** Share of Economic Sectors in GDP (%)</caption><thead><tr><th rowspan="2">Countries</th><th colspan="2">Primary Sector</th><th colspan="2">Secondary Sector</th><th colspan="2">Tertiary sector</th></tr><tr><th>2000-2012</th><th>2013-2024</th><th>2000-2012</th><th>2013-2024</th><th>2000-2012</th><th>2013-2024</th></tr></thead><tbody><tr><td>Côte d'Ivoire</td><td>19.6%</td><td>15.3%</td><td>17.8%</td><td>22.9%</td><td>57.4%</td><td>53.6%</td></tr><tr><td>Niger</td><td>34.5%</td><td>34.8%</td><td>21.7%</td><td>22.4%</td><td>37.0%</td><td>38.0%</td></tr></tbody></table>

(Sources: IFM, World Bank and BCEAO's database)

From this table providing a comparative overview of the average contribution of the primary, secondary, and tertiary sectors to the GDP of Côte d'Ivoire and Niger, across 2000-2012 and 2013-2024, we can make an assessment of how each country's economic structure has evolved over time. In Côte d'Ivoire, the data reveals a gradual decline in the primary sector's share, falling from 19.6% to 15.3%, which reflects a shift away from agriculture and resource-based activities. Conversely, the secondary sector, which includes industry, construction, and energy, grew significantly, from 17.8% to 22.9%. This shift suggests ongoing industrialization efforts, supported by infrastructure investments and economic diversification strategies. Meanwhile, the tertiary sector remains dominant, albeit slightly declining, indicating the strong role of services such as finance, trade, and public administration. In contrast, Niger displays a more static economic structure, with the primary sector consistently accounting for over one-third of GDP across both periods (34.5% and 34.8%). This underscores the country's continued dependence on agriculture and extractive industries. The secondary sector's share remains modest but stable, while the tertiary sector shows a slight increase, reflecting gradual urbanization and service sector development.

The previous section offered a descriptive overview of how commercial bank credit is distributed across economic sectors and how these sectors contribute to GDP in Côte d'Ivoire and Niger over time. While these figures provide important macroeconomic insights, they do not fully account for the complex, dynamic interactions between financial institutions,
production sectors, and aggregate demand. To explore these interdependencies more rigorously, the next section adopts the Stock-Flow Consistent (SFC) modeling approach, as stressed at the beginning of the analysis. This framework enables a systematic analysis of the monetary and real linkages within the economy, ensuring that all financial flows and stocks are coherently tracked over time. It serves as the foundation for the empirical simulations that follow, aiming to assess how conservative credit creation and selective lending behavior contribute to sectoral balances/imbalances and institutional behavior that affect macroeconomic stability and growth.

# Part 3: Methodology and Empirical Analysis

Building on the insights from the previous section's data analysis, this part presents the methodological framework and empirical strategies employed to analyze the dynamics of credit allocation, sectoral transformation, and macroeconomic performance in Côte d'Ivoire and Niger. By integrating the Stock-Flow Consistent (SFC) modeling approach, the methodology aims to capture the interdependence between real and financial sectors, with a series of simulation-based and econometric analyses to assess the impact of commercial banks' behavior on economic dynamics and resilience.

# Section 4: Methodology

## 4.1 Model design and calibration

As previously indicated, the Stock-Flow Consistent (SFC) model developed in this thesis draws inspiration from the two-country structure proposed by Vincent Duwicquet and Jacques Mazier (2015). In this framework, Côte d'Ivoire is assumed to be five times larger than Niger in terms of economic weight (GDP, financial flows, and employment base). For simplicity, all baseline equations have been designed to reflect the structure and behavior of Côte d'Ivoire's economy. To simulate Niger's dynamics, we apply a proportional scaling by dividing the relevant variables and parameters by five. This allows us to maintain consistency across countries while
capturing relative asymmetries in size and banking behavior. These parameters are represented in the following table 4.

<table><caption>**Table 4:** Baseline Calibration Values (Scenario 0)</caption><thead><tr><th>Parameter</th><th>Description</th><th>(CI)</th><th>(N)</th></tr></thead><tbody><tr><td>theta</td><td>Capital adequacy ratio</td><td>0.08</td><td>0.08</td></tr><tr><td>lambda</td><td>Risk aversion coefficient</td><td>0.7</td><td>0.8</td></tr><tr><td>phi</td><td>Credit supply elasticity</td><td>0.9</td><td>0.7</td></tr><tr><td>gamma</td><td>Bank liquidity preference</td><td>0.1</td><td>0.15</td></tr><tr><td>psi1</td><td>Credit sensitivity of investment</td><td>0.4</td><td>0.3</td></tr><tr><td>psi2</td><td>Profit sensitivity of investment (accelerator)</td><td>0.3</td><td>0.2</td></tr><tr><td>psi3</td><td>Output (confidence) effect on investment</td><td>0.3</td><td>0.2</td></tr><tr><td>r<sub>L</sub></td><td>Interest rate on loans</td><td>0.10</td><td>0.10</td></tr><tr><td>a</td><td>Productivity of labor</td><td>1.5</td><td>1.5</td></tr><tr><td>delta</td><td>Selectivity of lending</td><td>0.8</td><td>0.8</td></tr><tr><td>eta</td><td>Sensitivity to firm solvency (pi_e/Y)</td><td>0.6</td><td>0.6</td></tr><tr><td>alpha</td><td>Weight given to collateral</td><td>0.5</td><td>0.5</td></tr><tr><td>beta</td><td>Sectoral preference bias</td><td>0.7</td><td>0.7</td></tr><tr><td>chi</td><td>Credit-investment sensitivity</td><td>0.6</td><td>0.6</td></tr><tr><td>mu</td><td>Interest rate sensitivity of investment</td><td>0.4</td><td>0.4</td></tr><tr><td>xi</td><td>Elasticity of investment to expected profits</td><td>0.5</td><td>0.5</td></tr></tbody></table>

This Scenario 0 reflects current structural differences: Côte d'Ivoire shows more credit dynamism (lower risk aversion, higher elasticity), while Niger remains more conservative. Common monetary rules (capital ratio, interest rate, for example) ensure comparability. These baseline values set a realistic ground for testing alternative banking behaviors. The capital adequacy ratio ($\theta$ = 0.08) and interest rate ($r_L$ = 10%) are harmonized across countries, reflecting the regionally coordinated prudential and monetary policy under BCEAO
supervision. The investment parameters ($\psi_1$, $\psi_2$ and $\psi_3$) reflect both credit responsiveness and macroeconomic confidence, with slightly lower values for Niger to account for structural fragilities and investment inertia.

<table><caption>**Table 5:** Initial State Variables</caption><thead><tr><th>Variable</th><th>(CI)</th><th>(N)</th></tr></thead><tbody><tr><td>Capital stock (Kr)</td><td>1000</td><td>200</td></tr><tr><td>Deposits (D)</td><td>5000</td><td>800</td></tr><tr><td>Output (Y)</td><td>10000</td><td>2000</td></tr><tr><td>Expected profits (pi_e)</td><td>500</td><td>80</td></tr><tr><td>Profit uncertainty (sigma_pi)</td><td>50</td><td>20</td></tr><tr><td>Investment (I)</td><td>800</td><td>160</td></tr><tr><td>Employment (N)</td><td>5000</td><td>1000</td></tr></tbody></table>

*(Sources: BCEAO's database and author's calculations)*

The model considers five key scenarios to capture how commercial banking behavior influences macroeconomic dynamics in these two countries. Scenario 0 (Baseline) assumes a neutral environment where credit is moderately responsive to profitability and liquidity conditions, serving as a reference point. In Scenario 1 (Conservative Lending), banks adopt a risk-averse and liquidity-constrained stance, which reduces credit availability and dampens investment and GDP growth, particularly during periods of uncertainty. Scenario 2 (Selective Lending) reflects a situation where banks prioritize certain sectors or firms, potentially overlooking dynamic or inclusive growth areas, thereby limiting broad-based employment gains. Scenario 3 (Conservative + Selective) illustrates the compounded restrictive effect of both credit rationing and targeting, significantly constraining access to finance and undermining long-term growth potential. Lastly, Scenario 4 (Developmental Lending) explores a more proactive and inclusive banking posture, where reduced constraints and broader credit eligibility support increased investment, job creation, and structural transformation. Together, these scenarios will help to
assess how banking practices can either constrain or enable sustainable economic growth in contexts like Côte d'Ivoire and Niger.

## 4.2 Data collection and sources

The data used in this study encompass banking sector indicators, macroeconomic variables, and institutional information relevant to analyzing commercial banking behaviors in Côte d'Ivoire and Niger from 2000 to 2024. Banking data, including credit volumes, sectoral loan distribution, liquidity, and capital adequacy ratios, are primarily sourced from the Central Bank of West African States (BCEAO) and complemented by national treasury, providing quarterly and annual figures necessary to calibrate scenarios of conservative and selective lending. Macroeconomic indicators such as GDP, investment, employment are obtained from the World Bank's World Development Indicators and national statistical offices (INS and INSTAT), while financial system development data are drawn from IMF's Financial Access Survey. Additionally, policy documents and reports from BCEAO, IMF, and Ministries of Finance supply context on regulatory frameworks and credit policies, especially regarding developmental lending initiatives. Despite the overall comprehensiveness of these sources, certain limitations arise from inconsistent sectoral data and reporting frequency differences, particularly in Niger, which are mitigated through the use of proxy variables and qualitative insights from expert interviews<fnref n="24" /> to ensure robustness in the analysis.

## 4.3 Model validation

This part aims to indicate the convergence between our model and the reality regarding the influence of conservative and selective lending behaviors on the economic dynamics of Côte d'Ivoire (CI) and Niger (N). Indeed, by analyzing data from Tables 2 and 3, it appears that some sectors were more financially constrained than others, revealing an underlying pattern of credit rationing and misalignment between financial allocation and sectoral performance. For example, during the first period (2000–2012), in Niger, the primary sector, including agriculture and fishery, received only 92.08 million CFA Francs in credit, while the secondary sector,

<footnote n="24">²⁴ Mainly through e-mail exchanges to confirm or reject some data.</footnote>
comprising mainly industrial activities, received 971.417 million CFA Francs. However, the sector that benefited from the largest share of financial resources (the secondary sector) contributed only 21.7% to GDP over the same period. In contrast, the primary sector, which was significantly underfinanced, contributed 34.5% to GDP. This empirical observation supports one of the model's central assumptions, that is, commercial banks do not always allocate credit efficiently based on sectoral productivity or potential output contribution. Instead, they follow selective lending criteria often based on perceived risk, sectoral volatility, or collateral availability, which can lead to suboptimal macroeconomic outcomes. Our model integrates this reality by embedding selective and conservative lending constraints into the credit supply functions for each country. Specifically, banks' loan supply is influenced not only by their own capital and deposit base, but also by a behavioral filter that accounts for risk aversion, profit expectations, and variance in sectoral returns. As the simulation results will show, such constraints generate differentiated investment paths and GDP growth trajectories for CI and N, reflecting real-world dynamics observed in the UEMOA zone. Furthermore, the model captures how credit rationing can limit investment in high-output sectors, reinforcing structural imbalances and potentially slowing down overall economic development. In this regard, our SFC model with behavioral credit constraints proves relevant for analyzing the long-term impact of monetary and financial institutional arrangements on growth and structural transformation. Thus, the model not only reproduces key stylized facts from the data but also offers a coherent analytical framework for exploring how changes in banking behavior, whether policy-induced or institutionally driven, may affect macroeconomic performance and resilience in the WAEMU region.

## Section 5: Empirical Analysis

This section provides a preliminary empirical exploration of the mechanisms proposed in the theoretical framework. The simulation results offer precise forecasts to illustrate how variations in banking behavior and sectoral credit distribution can shape macroeconomic outcomes. In particular, the analysis examines how lending decisions, conditioned by profitability expectations, risk constraints, and financial regulations, interact with structural biases in credit allocation across sectors. These dynamics help explain differences in investment levels, employment trends, and output growth, especially when access to credit is limited or unevenly
distributed. By simulating different scenarios<fnref n="25" />, this section sheds light on how financial frictions and institutional preferences can generate heterogeneous development paths across sectors and countries.

## 5.1 Simulation experiments

The following simulation results illustrate the macro-financial dynamics in a two-country monetary union composed of Côte d'Ivoire (N) and Niger (S). The experiments explore the interactions between GDP, credit supply, risk perception, and bank behavior under different institutional conditions.

<figure><img src="image_3.png" /><figcaption>**Figure 1:** Macroeconomic Dynamics of Côte d'Ivoire and Niger</figcaption></figure>

Figure 1 illustrates the simulated evolution of GDP and credit supply for Côte d'Ivoire (N) and

<footnote n="25">²⁵ See the hypotheses quoted above</footnote>
Niger (S) over 50 periods. The results reveal stark contrasts between the two countries. Côte d'Ivoire exhibits strong volatility in both GDP and credit, with notable downturns around periods t=20t = 20t=20 and t=35t = 35t=35, followed by sharp drops in credit supply. These fluctuations point to financial shocks and feedback loops likely fueled by pro-cyclical credit conditions. In contrast, Niger's GDP follows a smooth but steadily declining trajectory. This persistent contraction is not merely the result of structural weaknesses, but is also explained by a poorly calibrated selective lending behavior. While credit in Niger increases gradually, the allocation appears inefficient, failing to stimulate productive investment or offset declining demand. This suggests that although banks are not rationing credit in quantity, they are directing it to less productive uses or low-return sectors, undermining economic recovery. Consequently, the contrast between the two countries highlights how both financial asymmetries and suboptimal credit selection can exacerbate macroeconomic divergence within a monetary union.

<figure><img src="image_4.png" /><figcaption>**Figure 2:** Microfinancial Constraints and Risk Channel</figcaption></figure>
This figure 2 presents a focused simulation of how commercial banks adjust their credit supply in response to changes in risk perception, profitability expectations, and capital adequacy. This experiment is designed to capture the internal mechanisms that govern conservative and selective lending behavior in a stylized banking system. One of the most striking features in the simulation is the strong inverse relationship between perceived risk ($\sigma^2$) and credit supply. During the simulated crisis around 2009, a sharp spike in perceived risk, likely triggered by an external shock or a wave of default coincides with an immediate and severe contraction in credit. This reflects a nonlinear risk-aversion behavior: as uncertainty rises, banks do not reduce credit gradually but rather pull back sharply, exhibiting a threshold effect. This behavior confirms that banks tend to overreact to rising risk by withdrawing credit, even if fundamentals do not deteriorate proportionately. As a result, financial instability is amplified, particularly in economies where risk perception is highly volatile or poorly managed.

In parallel, the relationship between credit and the capital buffer (bottom-right panel) is more complex. Although banks experience a decline in capital during the crisis, the contraction in credit appears not to be directly proportional to changes in capital. Credit supply falls much more abruptly than the capital buffer itself, suggesting that capital adequacy alone does not constrain lending in normal conditions, but may act as a reinforcing mechanism during downturns. Once capital drops below a prudential threshold, banks may overcorrect, reducing credit beyond what is necessary to rebuild buffers. This highlights the role of capital not just as a regulatory requirement, but as a psychological or behavioral anchor in credit decision-making. Finally, the model reveals a clear and positive relationship between expected profits ($\pi_e$) and credit supply. When profitability expectations are high, banks expand lending consistently. This link confirms a forward-looking behavior in credit allocation: banks are willing to take on more exposure when they anticipate adequate returns, even if current conditions are unfavorable. Conversely, once profit expectations collapse, as they do during the simulated crisis, credit contracts sharply, regardless of actual capital or liquidity conditions. This reinforces the idea that credit is guided less by current balance sheet metrics than by forward-looking assessments of risk and return.

This simulation illustrates also that banks do not respond to financial variables in isolation. The risk-credit nexus is dominant and highly nonlinear, driving pro-cyclical credit patterns. The capital buffer serves as a secondary constraint, influencing credit supply more in crisis than in normal times. Meanwhile, expected profits function as a primary motivator for
expansion, making profitability expectations a key policy lever. These findings emphasize how micro-financial constraints, when poorly calibrated or misaligned, can generate systemic effects and fuel macroeconomic instability.

These findings highlight the importance of understanding how these banks adjust their credit supply in response to core financial signals. To further disentangle these relationships, we conduct a set of static sensitivity analyses, isolating the individual effects of key balance sheet variables on credit supply. The aim is to assess whether capital buffers, deposits, expected profits, or risk perceptions exert the greatest influence on credit decisions when all other factors are held constant.

<figure><img src="image_5.png" /><figcaption>**Figure 3:** Sensitivity of Credit to Balance Sheet Variables</figcaption></figure>

After simulations, the results offer useful insights into the mechanics of conservative and selective lending. Firstly, credit appears relatively insensitive to changes in capital buffers (Kr) and deposits (D) within the examined range. This suggests that under normal conditions, liquidity and capitalization thresholds are not binding constraints on credit supply, which join the endogenous credit creation' ideas. Banks maintain comfortable margins above regulatory minimum. In contrast, the relationship between credit and expected profits ($\pi_e$) is clearly
positive and linear: as profitability expectations rise, banks expand credit correspondingly. More critically, the negative relationship between credit and perceived risk (σ²) is strongly nonlinear and convex. This indicates that as uncertainty increases, banks do not reduce credit supply gradually, but rather with increasing severity, a pattern consistent with a nonlinear risk aversion profile. These simulations validate the behavioral assumptions embedded in the model: banks lend more when profit prospects are high and risk is low, but contract sharply when risk rises, even in the absence of liquidity or capital constraints. The result is a selective credit environment, where misjudgments in risk or profitability can have substantial macroeconomic effects.

Let's now explore the different results resulting from our SFC hypotheses. To establish a benchmark for comparison, we first simulate a baseline scenario in which commercial banks operate in a neutral institutional and monetary context. In this configuration, the credit supply is more moderately responsive to profitability and liquidity conditions, without significant intervention from the central bank (BCEAO). There are no extraordinary constraints or incentives imposed on banks; instead, lending behavior is driven by standard prudential logic, with moderate sensitivity to risk and profitability. This scenario helps us assess how more restrictive or proactive credit strategies influence macroeconomic dynamics in subsequent simulations.

<figure><img src="image_6.png" /><figcaption>**Figure 4:** Macroeconomic Effects of Conservative vs. Flexible Bank Lending in a Neutral Monetary Environment (Scenario 0)</figcaption></figure>
The first graph displays the evolution of GDP under two lending behaviors: conservative and flexible. When banks follow a conservative approach, GDP exhibits a steady and uninterrupted decline over time. This outcome reflects the limited responsiveness of credit to economic conditions; banks restrict loan issuance, which dampens investment and reduces aggregate demand, then, reinforcing the economic slowdown. In contrast, flexible lending behavior, characterized by greater credit responsiveness to profitability, results in a temporary contraction followed by a gradual recovery and eventual growth. This demonstrates how a more accommodative stance from banks can mitigate downturns and support real sector resilience. The second graph illustrates investment dynamics under the same two lending behaviors. Conservative lending leads to a continuous drop in investment, as credit-constrained firms are unable to finance capital formation. This result is particularly significant in the context of WAEMU, where private investment is a key engine of growth. On the other hand, under flexible lending, investment initially declines but then rebounds steadily, supported by more responsive credit allocation. The recovery of investment precedes and supports GDP growth, highlighting its central role in the macroeconomic feedback loop within a stock-flow consistent framework. The third graph tracks changes in employment, which is modeled as a function of output. Employment declines in both cases initially; however, under flexible lending, it eventually recovers alongside GDP and investment. Conversely, under conservative lending, employment continues to shrink as the economy contracts. This divergence underscores the importance of credit conditions for labor market outcomes: when banks supply credit more readily in response to improving conditions, the positive spillovers to employment become evident.

We now turn to the second alternative scenario, in which the BCEAO adopts an accommodative stance. The figure below compares the macroeconomic effects of selective and conservative lending behaviors in this more supportive monetary environment. In this setting, GDP increases by more than 4% under selective lending, while it declines by around 7% under conservative behavior. Investment follows a similar path, rising by approximately 5% when lending is selectively allocated to productive sectors, but dropping by 8% under conservative constraints. Finally, employment increases by about 3.5% with selective credit, whereas it falls by nearly 6% under conservative lending. These results highlight the amplified positive effect of an accommodative central bank when paired with targeted, development-oriented credit allocation by commercial banks.
<figure><img src="image_7.png" /><figcaption>**Figure 5:** Impact of Lending Behavior under a Supportive Monetary Regime</figcaption></figure>

We now turn to the empirical analysis to statistically and econometrically validate the simulation results.

## 5.2 Statistical validation

The statistical validation of the simulated scenarios relies on historical macroeconomic data from Côte d'Ivoire and Niger between 2000 and 202', as compiled in the data tables. A review of trends reveals that during periods characterized by reduced credit to the private sector, interpreted as conservative banking behavior, both countries experienced simultaneous declines in GDP growth, investment, and employment. Notably, during the global financial crisis of 2008-2009 and the COVID-19 shock in 2020, Côte d'Ivoire and Niger recorded negative investment growth and stagnating employment, despite differing sectoral performances. These patterns confirm the simulation's prediction that conservative credit contraction magnifies macroeconomic slowdowns. Furthermore, the data show that selective lending practices, such
as disproportionate credit allocation to capital-intensive sectors, coincided with weak employment outcomes and uneven investment recovery, particularly in Niger. The statistical evidence therefore supports the model's finding that conservative credit behavior hinders recovery more severely than selective lending, but both introduce distortions that limit inclusive growth and delay the return to pre-shock trajectories.

## Section 6: Expected contributions and robustness checks

We found thanks to this analysis that increases in risk aversion ($\lambda$) under conservative lending sharply reduce credit elasticity ($\phi$), resulting in sustained declines in investment and delayed GDP recovery. In contrast, rising sectoral selectivity (reflected in a lower effective $\alpha$) leads to slower but targeted investment, moderately supporting GDP while constraining employment growth. The analysis highlights how credit allocation mechanisms directly influence the speed and inclusiveness of post-crisis recovery. The core contribution lies in showing, quantitatively, that variable interactions such as credit increasing investment which in its turn boost GDP are highly sensitive to behavioral parameters, with conservative behavior amplifying crises more than selective lending, particularly in employment outcomes. To ensure the robustness of the results, several validation steps are conducted. First, the model's baseline behavior is cross-checked against observed macroeconomic patterns using historical data. Second, key parameters, including the credit elasticity ($\phi$), risk aversion ($\lambda$), and selectivity bias ($\alpha$), are systematically varied to test the sensitivity of the results to behavioral intensities. Third, additional simulations are run under alternative shock scenarios, including a persistent productivity shock and a sudden drop in profit expectations, to assess the stability of recovery paths across different behavioral regimes. In all variants, the findings remain qualitatively robust: conservative lending consistently results in deeper and slower recoveries, while selective lending produces more moderate but still significant distortions. Together, these elements enhance the explanatory power of the model and offer concrete policy insights for central banks and development institutions aiming to strengthen counter-cyclical and inclusive credit allocation frameworks in low-income economies.
# Part 4: Results and Policies Recommendation

## Section 7: Keys founding for comparative study

The simulations reveal distinct macro-financial trajectories in response to commercial bank behaviors in the two economies studied. In the baseline stock-flow consistent model, calibrated separately for Côte d'Ivoire and Niger, conservative lending, characterized by high risk aversion ($λ$'↑) and lower credit elasticity ($φ$'↓), results in a more pronounced contraction of investment (I), which in turn depresses GDP (Y) and slows employment recovery (N = Y/a). This behavior produces prolonged output gaps and underutilization of labor, especially in Niger, where structural liquidity constraints and lower capital stock sensitivity ($Kr/θ$) amplify the effect of credit rationing. Selective lending, modeled via a reduction in the effective marginal efficiency of credit allocation ($α$'↓), induces less severe but more asymmetric impacts. While aggregate investment declines are less pronounced than in the conservative scenario, sectoral misallocation leads to suboptimal employment growth and delayed convergence to pre-shock GDP levels. The model also demonstrates that under both behaviors, the investment function's responsiveness to credit plays a crucial role in the transmission of financial shocks to the real economy. Comparatively, Côte d'Ivoire shows more resilience under both behaviors due to higher total factor productivity and stronger capital market depth, whereas Niger's recovery path remains fragile and highly sensitive to the structure of credit flows. These findings emphasize the importance of credit distribution mechanisms and their behavioral underpinnings in shaping and influencing the post-crisis macroeconomic dynamics across structurally differentiated WAEMU economies

## Section 8: Banking strategies recommendation for both Côte d'Ivoire and Niger and broadly for WAEMU countries

Considering the model's results, banking strategies in Côte d'Ivoire, Niger, and more broadly across WAEMU should move beyond procyclical credit behavior and adopt a developmentally oriented allocation logic to mitigate macro-financial fragility. The simulations demonstrate that conservative lending strategies, anchored in elevated risk aversion and liquidity preference,
amplify investment volatility and employment underutilization following real shocks. Thus, for
financial stability and macroeconomic resilience, national authorities should enhance counter-
cyclical credit capacity through both regulatory and institutional reforms. For Côte d'Ivoire,
where financial depth is relatively higher, the recommendation is to institutionalize a credit
guidance framework for commercial banks that conditions lending volumes on macro-
prudential indicators, particularly private investment multipliers. This involves dynamically
adjusting capital adequacy ratios and liquidity coverage rules during downturns to avoid
contractionary spirals. Furthermore, selective lending must be refined through sectoral credit
allocation benchmarks, particularly toward labor-intensive and export-generating sectors,
without introducing distortive rationing mechanisms.

For Niger, where capital formation is more vulnerable to exogenous shocks and
structural liquidity constraints, the focus must be on de-risking private credit. This entails state-
backed partial guarantee schemes, subsidized refinancing windows through BCEAO, and long-
term liquidity provisioning to banks conditioned on productive lending. Targeted fiscal-
monetary coordination is also required to crowd in credit for essential infrastructure and SME
development, particularly in under-banked regions. At the WAEMU level, the BCEAO should
establish a behavioral credit monitoring mechanism, capturing national-level credit rationing
indices (For example, λ-effective, φ-adjusted) and integrating them into its macro-financial
surveillance toolkit. Additionally, the zone would benefit from a regional development credit
facility, acting as a lender of last resort not only for liquidity crises but also to smooth lending
across countries during asymmetric real shocks. Regulatory harmonization should also be
aligned with credit diversification incentives to reduce allocative inefficiencies arising from
narrow sectoral exposure. Finally, the effective macroeconomic recovery and structural
transformation in WAEMU hinges on reorienting commercial banking behavior from risk-
averse preservation to dynamic intermediation, one that balances financial soundness with
inclusive growth imperatives

**Part 5: Conclusion**

This thesis has explored the critical influence of commercial bank behavior on macroeconomic
dynamics and long-term growth prospects in developing economies, with specific reference to
Côte d'Ivoire and Niger, two structurally differentiated but institutionally connected members
of the WAEMU zone. By integrating theoretical insights from post-Keynesian, structuralist, and Minskyan frameworks into a dynamic, stock-flow consistent (SFC) model, the analysis has captured how endogenous credit creation and behavioral asymmetries among banks translate into divergent economic trajectories, particularly under conditions of financial constraint and macroeconomic shock. Mainly two commercial banking behaviors, conservative credit creation and selective lending, were specified and calibrated based on empirically grounded parameters reflecting each country's structural characteristics. The simulations have demonstrated that under the conservative behavior, where banks respond defensively to uncertainty through heightened risk aversion and liquidity preference, credit allocation becomes excessively restrictive. This generates sharp declines in private investment and delays in GDP and employment recovery, validating the pro-cyclicality hypothesis of bank behavior in financially repressed environments. Selective lending, while more efficient in channeling credit toward high-productivity sectors, was shown to produce allocative distortions that particularly hinder employment-intensive sectors, thus limiting inclusive growth despite marginally better aggregate performance. Crucially, the empirical data and robustness tests confirm that these behaviors are not neutral technical choices, but active drivers of macro-financial dynamics. The statistical validation reinforces the model's results: real GDP, investment, and employment respond asymmetrically to variations in credit supply, with more acute effects observed when credit rationing or sectoral exclusion is intensified. The role of institutional constraints, notably the BCEAO's currency board arrangement and the underdevelopment of capital markets, further compounds the challenges posed by these credit behaviors.

This work contributes not only to the literature on endogenous money and financial fragility in developing economies but also provides operational tools for policymakers. The policy simulations suggest that more inclusive and development-oriented credit practices, supported by targeted and accommodated monetary policy, counter-cyclical regulatory buffers, and sectoral credit guidance, can significantly improve macroeconomic resilience and growth performance in WAEMU countries. The study thereby opens a pathway for rethinking banking strategy not merely as a matter of risk management, but as a central lever of structural transformation. The thesis reinforces thus a foundational insight from Keynesian and post-Keynesian thought, that is, in economies constrained by institutional fragility and capital scarcity, the quality and orientation of credit creation, more than its volume alone, define the trajectory of investment, employment, and long-run economic potential.
**Annex 1: Rationale Behind the Loan Supply Equation**

To model conservative commercial bank behavior in developing countries, the total supply of loans to the private sector is defined as the minimum of three key constraints:

**Capital Adequacy Constraint** defines as:

1. Regulators require banks to hold a certain amount of capital relative to their risky assets (mostly loans).

2. If $\theta$ is the required capital adequacy ratio, and $K_r$ is regulatory capital, then:

$$ \text{Maximum Loans} = \frac{K_r}{\theta} $$

Which gives us a ceiling on lending based on prudential regulation.

**Liquidity Constraint:**

1. Banks can only lend a fraction of their deposits, keeping part as reserves.

2. If $\gamma$ is the reserve-to-loan ratio (bank's liquidity preference), and $D$ is deposits, then:

$$ \text{Maximum Loans} = \frac{D}{\gamma} $$

This ensures banks retain sufficient liquidity to meet withdrawals and settle payments.

**Behavioral Lending Function**

Even if banks have enough capital and liquidity, they may lend less due to:

1. Poor macro-economic Outlook,

2. Low expected profits from borrowers,

3. High uncertainty or perceived credit risk.

This is captured by a behavioral function, such as:
$$L_b = \varphi \cdot [\pi_e + \alpha Y - \lambda \cdot \sigma_{\pi}^2]$$

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