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Financial Development and Economic Growth: Case of Liberia

John Flomo Delphin

Harvest Intercontinental American University, Monrovia, Montserrado, Liberia

DOI: https://dx.doi.org/10.47772/IJRISS.2025.910000161

Received: 03 February 2025; Accepted: 20 February 2025; Published: 06 November 2025

ABSTRACT

This study investigates the nexus between financial development and economic growth in Liberia, with a focus

on post-civil war reforms and their implications. Using quarterly data from 2000 to 2023, the research employs

the Vector Error Correction model and Granger causality tests to analyze the effects of various financial

development indicators—specifically M2 (both including and excluding currency outside the banking system),

domestic credit to the private sector, and mobile money—on real GDP growth. The results reveal a long-run

equilibrium relationship between financial development and economic growth, with domestic credit to the

private sector and the proportion of M2 within the formal banking system positively impacting growth. However,

M2 inclusive of currency outside the banking system and mobile money adoption show negative long-term

effects, potentially due to inefficiencies in financial intermediation and challenges in mobile money

implementation. Short-run dynamics indicate that financial development does not have immediate significant

effects on growth, and Liberia’s economy adjusts slowly to financial shocks. Based on these findings, this study

recommended enhancing financial inclusion, encouraging savings mobilization, and addressing the challenges

associated with mobile money adoption.

Keywords: Financial development, Economic Growth, Liberia, Vector Error Correction Model, Granger

causality

INTRODUCTION

The increasing demand for financial services has made the financial sector the focal point of economic activities

in many economies. For the economy to grow sustainably, a developed financial system is paramount. That is,

a robust financial system facilitates the flow of resources from savings to investments, hence, expand the

economy sustainably. In the early days, many prominent growth models did not directly acknowledge the

financial sector importance to growth. The exogenous growth model, as described by Solow (1956), identified

capital accumulation, the labor force, and land as critical drivers of growth, while the endogenous growth model,

as proposed by Romer (1990), emphasized the importance of technological advancements in enhancing

productivity and driving economic growth. However, over some decades now, the significance of a robust and

adequate financial system has also been acknowledged as a fundamental catalyst for economic growth (King &

Levine, 1993; Khan & Senhadji, 2000; Khan et al., 2005). This recognition is based on the premise that an

improved financial system offers enhanced financial services, thereby stimulating the economy to improve its

productivity.

While Solow (1956) and Romer (1990) did not directly acknowledged the pivotal role of the financial sectors

and implicitly indicated it important to economic growth. That is, the exogenous growth model recognizes the

importance of savings to economic growth, indicating that savings are equivalent to investment, which leads to

capital accumulation. This means capital accumulation, a significant component of the Solow (1956) growth

model, relies on savings. Also, the endogenous growth model underscores the role of financial sector services in

capital accumulation and technological innovation for growth. These services include mobilizing savings,

acquiring and allocating investment information, exerting corporate control, and mitigating risk.

The intermediating role of the financial sector to economic growth has been extensively explored in many

studies. Theoretically, this nexus dates back to Schumpeter (1911) and was later advanced by McKinnon (1973)

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and Shaw (1973). McKinnon and Shaw contended that government-imposed financial repression, including

interest rate ceilings and directed credit to preferential, non-productive sectors, hinders financial development.

They believed that financial liberation is pivotal to fostering economic growth. However, Robinson (1952) and

Kuznets (1955) argued that the growth of the financial system is contingent on the growth of the economy. This

perspective contrasts with the views of McKinnon, Shaw, and endogenous growth theorists, indicating that if

there is a causation, it is unidirectional from economic growth to financial development.

In view of this theoretically dilemma, King and Levine (1993) and Hassan et al. (2010) empirically analyzed the

impact of financial development on growth using longitudinal analysis. These studies, however, provided only

pooled estimates and did not account for the dynamic nature of the relationship between countries. Additionally,

in their findings, they mainly indicated that a significant coefficient for financial development in growth

regressions does not necessarily indicate causality from finance to growth or vice versa. In lieu of this finding,

many studies have pursued more dynamic time series analysis to clarify the causal relationship, with Granger

causality tests becoming a principal tool. Studies of selected countries by Adusei (2013), Wolde- Rufael (2009),

and Luintel and Khan (1999), Menyah et al. (2014) have shown that the pattern of causality differs significantly

among countries and generally weak evidence for a unidirectional connection from financial development to

economic growth. These findings indicated that understanding the causal relationship between financial

development and economic growth requires studies on individual countries using diverse financial factors.

This study follows this approach to the financial system's impacts on growth in Liberia. Like many Sub-Saharan

African countries, Liberia was encouraged to reform its financial systems in the 1980s to stabilize the

macroeconomy and boost economic growth. In the mid-1980s, the government of Liberia through the National

Bank of Liberia implemented several progressive reforms: eliminating subsidies to priority sectors, reducing

reserve requirements for a market- based refinancing allocation, updating stock market legislation, transferring

the management board to the association of brokerage houses, enacting a new banking law to increase the National

Bank's financial autonomy, and opening the banking sector to foreign participation to boost competition. These

reforms enhanced the financial system, but the fourteen years of civil war impeded its impact on growth.

Since the establishment of the Central Bank of Liberia (CBL) in 1999 and the end of civil unrest in 2003, the

Liberian government, with the support of the international community, has implemented several strategic

initiatives to improve the financial sector. Initially, efforts were focused on restructuring the sector, which was

in disarray. This included introducing stricter banking supervision and regulatory frameworks to ensure financial

stability (Bartholomew, 2007). The CBL also established the Monetary Policy Committee (MPC) to guide policy

decisions based on comprehensive economic assessments. The national payment system was also overhauled

with a real-time gross settlement system and an automated clearing house to enhance the efficiency and security

of financial transactions within Liberia. Furthermore, the CBL has launched several initiatives to promote

financial inclusion, aiming to increase access to banking services for the unbanked population through

microfinance programs and support for mobile banking services in rural areas. These reforms have boosted

investment confidence and expanded the provision of financial services across the country.

These reforms and programs implemented by the CBL in the financial sector have led to significant growth in

the banking system. In 2023, total gross assets reached approximately L$319.26 billion (around 40.6% of GDP),

compared to just L$4.33 billion in 2003. Credits and advances to all sectors of the economy in 2023 amounted

to L$92,841.0 million (12.3% of GDP), reflecting an annual growth rate of 19.6% from L$77,620.5 million

(12.8% of GDP) in 2022 and a staggering 4,521.48% increase from L$2,008.9 million in 2003. Deposits surged

by about 16,516% from L$1.20 billion (US$26.6 million) in 2003 to L$198.712 billion (US$1,000.1 billion) in

2023. Meanwhile, Liberia's GDP growth was recorded at 4.6% in 2023, a significant improvement from the -

31.3% recorded in 2003 and higher than the 3.5% estimated for Sub- Saharan Africa in 2023 (ADB, 2023).

Despite the financial sector improvements and accelerated growth since 2003, it remains unclear whether this

development directly caused or contributed to the growth of the Liberian economy (CBL, 2003-2023)

A study by Prowd (2018) examined the finance growth in Liberia and knowledged long run nexus using the

ARDL model and annual data. However, Due to how well the financial sector has improved and the relatively

moderate economic growth the county has experienced since the end of the fourteen years civil war, this study

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re-examined the impact of the financial depth on economic growth using other financial factors and quarterly

data. While Prowd (2018) used only domestic credit to the private sector as a ratio of GDP as an indicator for

financial development and did not consider causality measures, this study measured the impact of financial

depth on economic growth using private domestic credit share of GDP, money supply (M2) as a ratio of GDP

and M2 less currency in circulation share of GDP. Additionally, this study analyzed the impact of mobile money

on growth in Liberia. Also, this study used the VEC model and Granger causality test to determine the direction

of causality between financial sector development and economic growth. Incorporating these financial metrics,

this study evaluated how financial development impacts economic growth through productivity, capital

accumulation, and accessible financial services deliveries. Furthermore, the study used the impulse response

function and forecast error variance decomposition to explain impulses from financial sector and growth

response and the explanatory power of each financial metrices on economic growth.

The paper proceeds as follows: Section 2 reviews Liberia's economic and financial developments over the past

two decades, focusing on the 1999 financial reforms, henceforth. Section 3 describes the variables and data

sources used. Section 4 outlines the econometric methodology and the procedures used. Section 5 presents the

empirical findings. Finally, Section 6 summarizes the main findings, conclusion, and policy implications.

Financial Development in Liberia

Since 2005, the Liberian government has implemented economic recovery and development programs to

expedite growth and improve living standards through easy access to essential services. Among other sectors,

the financial sector has seen substantial growth. By 2023, the banking industry's total assets reached

approximately LRD$ 319.26 billion, equivalent to about 39.79% of GDP, up from LRD$ 18.59 billion in 2008.

Commercial banks' credit increased by 22.5 percent, amounting to LRD$80.53 billion. Demand deposits rose

by 40.70 percent, from LRD

135.45 billion in 2022 to LRD 198.71 billion in October 2023. The banks' branches and networks climbed from

11 in 2006 to 90 in 2023. Meanwhile, Liberia recorded a 10 percent GDP growth, surpassing the average for

Sub-Saharan Africa during the same period.

The financial sector of Liberia comprises formal and informal components; however, since the end of the civil

war, there have been significant improvements in the formal sector. The number of licensed banks increased from

4 in 2003 to 9 in 2023, and bank branches expanded from 11 in 2007 to 90 in 2023, covering 9 out of 15 counties

compared to just 2 in 2003. By 2023, registered non-bank credit Microfinance Institutions (MFIs) grew to 21

from 19 in 2022, with 44 branches across nine counties. This sector included two licensed microfinance deposit-

taking institutions (MDIs) and 12 Rural Community Finance Institutions (RCFIs). Foreign exchange bureaus

rose to 211 in 2023 from 204 in 2022, and licensed Money Remittance Entities increased to 53 from 49 in

December 2022.

The CBL reform strategies have significantly bolstered financial institutions. The banking industry's balance

sheet indicators show growth through increased intermediation. The industry remained stable; however, key

balance sheet indicators improved in 2023 compared to 2022: total assets grew by 42.0% to L$293.71 billion,

total loans and advances by 19.6% to L$92.84 billion, total capital by 24.1% to L$39.01 billion, and deposits by

46.6% to L$198.71 billion. The liquidity ratio stood at 44.1%, exceeding the minimum requirement by 29.1

percentage points. Loans and overdrafts increased by 19.6% to L$92.84 billion, with no single sector accounting

for more than 50% of total loans. The trade sector, the largest, accounted for 31.54% of total industry loans.

Microfinance institutions saw a 28% increase in clients to 98,455 in 2023, with active borrowers rising by 38.0%

to 90,731. Outstanding loans grew to L$3.48 billion from L$3.01 billion in 2022, and total capital increased to

L$1.70 billion from L$1.36 billion. RCFI deposits surged by 96.0% to L$681.07 million, with loans and advances

rising by 28.0% to L$345.71 million. The Liberia Enterprise Development Finance Company (LEDFC) invested

US$40.0 million in over 700 SMEs, providing technical support and enhancing financial management skills.

LEDFC's total assets grew to L$6.11 billion, and its loan portfolio increased to L$2.93 billion in 2023. MDI's

total assets rose 30% to L$1.58 billion and deposits 33% to L$932.2 million, with total loans and advances at

L$889.6 million in 2023.

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𝟎

The mobile money network continues to expand, with rising numbers of registered institutions, agents, and

subscribers. Transaction volumes and values have increased, reflecting growing adoption and confidence in

mobile money. Registered subscribers rose to 9,329,179 in 2023 from 9,248,817 in 2022, and active subscribers

to 2,599,162 in 2023 from 2,531,871 in 2022. Registered institutions increased by 80.9% to 1,511 in 2023. The

volume of US dollar transactions grew by 72.4% to US$36.83 million, and Liberia dollar transactions by 16.0%

to L$549.13 million in 2023. The value of US dollar transactions rose by 57.3% to US$3,475.64 million, and

Liberian dollar transactions by 50.0% to L$421,969.09 million.

Variable Measurement and data sources

The financial sector development generally enhances financial intermediary services' quantity, quality, and

efficiency. This multifaceted process involves numerous activities and institutions, making it difficult to

understand its impact with a single measure. In this study, four widely recognized financial development

measures backed by empirical studies were used to investigate the impact of the financial sector on economic

growth in Liberia.

The first measure, M2Y, denotes the ratio of the M2 to GDP. M2Y has been utilized as a standard indicator of

financial development in various studies (Calderon & Liu, 2003; King & Levine, 1993). The second indicator,

M2CY, is the ratio of quasi-liquid assets to GDP. This measure has been arguably considered in many studies

(Gelb, 1989; Neal, 1988). In developing countries like Liberia, a significant portion of M2 is liquid, mostly in

the hands of individuals rather than the banking sector. This means that an M2 to GDP ratio increase may indicate

more significant currency usage rather than increased bank deposits, making this measure less reflective of

financial intermediation since currency in circulation does not indicate financial development. Based on this

ideology, this study incorporated Neal (1988) ideology: using the ratio of M2 less currency outside the bank to

GDP as a financial measure.

The third indicator, DC/Y, is the ratio of commercial bank credit to the private sector to GDP. This indicator

measures the allocation of financial assets beyond what M2C/Y can provide. Although higher M2C/Y ratios can

result from increased private savings, high reserve requirements may limit credit to the private sector, which is

crucial for investment and economic growth. While an increase in DC/Y does not certainly indicate productive

investments, it is an indication of the quantity and efficiency of investment due to commercial banks' stringent

screening measures on loans through which they identify entrepreneurs with higher chances of efficiency usage,

that is, better allocation of funds and possibilities to diversify risk which increase return on capital. This measure

is extensively used in literature (Prowd, 2018; Bader & Abu-Qarn, 2008).

The fourth indicator, DBM is a dummy variable to account for the introduction of mobile money in 2010, with

a value of zero before 2010 and one afterward. This study used real GDP to measure economic growth. The

sample spans from quarter one of 2000 to quarter four of 2023; this timeframe was chosen due to the lack of

access to data beyond 2000 in Liberia.

METHODOLOGY

Cointegration methodology has been instrumental in establishing linear combination of the series for

cointegration for long run (Granger, 1988). The Johnasen (1988) procedures for assessing the system for

cointegrating equations was used to test for long run relationship. The number of cointegrating equations in the

system was determined using the maximum likelihood from the maximum eigenvalue statistics introduced by

Johansen and Juselius (1990) as follow:

𝝀

𝒎𝒂𝒙

(

𝒓𝟎

)

= −𝒏 𝒍𝒐𝒈(𝟏 − 𝝀

𝒓

+𝟏

)

If the presence of cointegration exist, the VEC mode is use, otherwise the VAR is used. The VEC approach

identifies the direction of causality among variables and distinguishes between short- and long-run causality.

Long-run causality is tested by evaluating whether the coefficients of the ECT are significantly different from

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zero. In contrast, the VAR model tests short-run causality using standard F-tests on lagged variables. The VAR

model can be explicitly represented as:

𝑝−1

𝑝−1 𝑚

Δ𝑋1𝑡 = 𝜃1 + ∑ 𝜑11,𝑘Δ𝑋1,𝑡−𝑘 + ∑ 𝜑12,𝑘Δ𝑋2,𝑡−𝑘 + ∑ 𝜑13,𝑘Δ𝑋3,𝑡−𝑘 + ∑ 𝜆1,ℎECTℎ,𝑡−1 + 𝜇1𝑡

𝑘=1

𝑝−1

𝑘=1

𝑝−1

𝑘=1

𝑝−1

ℎ=1

𝑚

Δ𝑋2𝑡 = 𝜃2 + ∑ 𝜑21,𝑘Δ𝑋1,𝑡−𝑘 + ∑ 𝜑22,𝑘ΔX2,𝑡−𝑘 + ∑ 𝜑23,𝑘ΔX3,𝑡−𝑘 + ∑ 𝜆2,ℎECTℎ,𝑡−1 + 𝜇2𝑡

𝑘=1

𝑝−1

𝑘=1

𝑝−1

𝑘=1

𝑝−1

ℎ=1

𝑚

Δ𝑋3𝑡 = 𝜃3 + ∑ 𝜑31,𝑘Δ𝑋1,𝑡−𝑘 + ∑ 𝜑32,𝑘Δ𝑋2,𝑡−𝑘 + ∑ 𝜑33,𝑘Δ𝑋3,𝑡−𝑘 + ∑ 𝜆3,ℎECTℎ,𝑡−1 + 𝜇3𝑡

𝑘=1

ℎ=1

The ECT (error-correction term) captures the long-term equilibrium relationship. At the same time, φ_(ij,k)

represents the impact of the kth lagged value of variable j on the current value of variable i, for i, j = Y1 to Y4.

The VEC model identifies the direction of causality among variables and differentiates between short- and long-

run causality. Given cointegration, Long-run causality from variable Yi to Yj is tested by examining the null

hypothesis that λj,h = 0 for h = 1, ..., m. Using the standard F-test, the short-run causality is assessed by testing

the null hypothesis that φij,1 = ... = φij, P-1 = 0. Rejection of the null hypothesis indicates that variable Yi Granger

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causes variable Yj.

Due to the inclusion of many lags in the VEC model, the study used the Impulse Response Function (IRF) and

Forecast Error Variance Decomposition (FEVD) to explain financial development impacts on economic growth

in Liberia. Both IRFs and FEVDs computations are useful in analyzing how shocks to these financial factors

reverberate through the system on output. The IRFs show the effects of shocks on the adjustment path of the

variables in the model. The VAR equation above which is used to trace the impulse response is regarded as the

Vector Moving Average model. The IRFs can be determined by differentiating the VAR equation with respect

to each of the shocks and written in a more compact form as follow:

∞

𝑥

𝑡

𝑥



𝑡

∑

𝚵

𝑖

𝑤

𝑖

𝑥

𝑡

𝑥

 +

𝑤

𝜀

𝑡

+ +

𝑤

𝜀

𝑡

−1

𝑤

𝜀

𝑡

−2

+..

FEVD measures the contribution of each shock to the forecast error variance. It shows a change in a variable is

endogenously and exogenously. It is noted that, in the short run, most of the variation is independently

determined, however, as the lagged variables’ effect manifest, the percentage of the effect of exogenous variables

impact increases over time. From the IRF equation, n-period forecast error of 𝑥

𝑡

can bet written as follow:

𝐸

𝑡

𝑥

𝑡

𝑥

 +

𝑤

𝜀

𝑡

+ +

𝑤

𝜀

𝑡

−1

+..

1-period forecast error: 𝑥

𝑡+1

− 𝐸

𝑡

𝑥

𝑡+1

= 𝑤

𝜀

𝑡+1

2-period forecast error: 𝑥

𝑡+2

− 𝐸

𝑡

𝑥

𝑡+2

= 𝑤

𝜀

𝑡+2

+ 𝑤

𝜀

𝑡+1

n-period forecast error: 𝑥

𝑡+𝑛

− 𝐸

𝑡

𝑥

𝑡+𝑛

= 𝑤

𝜀

𝑡+𝑛

+ 𝑤

𝜀

𝑡+𝑛−1

+ 𝑤

𝜀

𝑡+𝑛−2

+ ⋯ + 𝑤

𝑛−1

𝜀

𝑡+1

Following the VEC estimation, a series of residual post-tests were conducted to validate the model's reliability

and stability. These examinations verified the credibility of estimated and findings. The tests included the model

stability, autocorrelation and normality.

Empirical Results

This section analyzed and explained the findings of this study. It is laid out in the manner that consist of pre-

estimations, estimations, robust analysis and post-estimations.

Table 1 provides the summary statistics and correction metrics. The table indicates the means, standard

deviations and corrections among the variables.

Table 1: Descriptive and Correlation Statistics

Mean

Std. Dev.

LogGDP

DC/GDP

M2C/GDP

M2/GDP

DBM

LogGDP

2.956

0.358

1.000

DC/GDP

0.164

0.171

0.056

1.000

M2C/GDP

0.346

0.262

0.088

0.570

1.000

M2/GDP

0.422

0.301

0.134

0.681

0.697

1.000

DBM

0.546

0.500

0.738

0.550

0.459

0.418

1.000

The results in Table 1 indicate that the mean for LogGDP is 2.956 and deviation from the mean is 0.358. This

means that the quarterly change in outputs has been relatively low since the 2000. However, for DC/GDP the

average is 16.4% and the deviation from this average is 17%, indicating that credit to the private sector in Libera

has been changing significantly every quarter in Liberia. Also, for M2C/GDP and M2/GDP the average values

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are 34.6% and 42.2% and their deviations are 26.2% and 30.1% respectively, these show a moderately low

quarterly change in these indicators since the 2000 in Liberia. Additionally, the correlations among the exogenous

variables are all below 70%. Therefore, this indicates that multicollinearity is not an issue in this study.

A necessary but not sufficient condition for cointegration is that each of the variables is integrated of the same

order one. Contingent on this, this study first determined the degree of integration of each variable using

Augmented Dickey Fuller test (ADF). The results of the ADF test are presented in Table 2.

Table 2: Augmented Dickey Fuller Test Results

Test Stat

10%

P-value

LogGDP

-0.895

-3.517

-2.894

-2.582

0.78970

ΔLogGDP

-7.195

-3.518

2.895

-2.582

0.00000

M2C/GDP

-2.695

-3.517

-2.894

-2.582

0.07490

ΔM2C/GDP

-9.273

-3.518

-2.895

-2.582

0.00000

DBM

-1.09

-3.518

-2.895

-2.582

0.71900

ΔDBM

-6.856

-3.518

-2.895

-2.582

0.00000

DC/GDP

-2.806

-3.517

-2.894

-2.582

0.05750

ΔDC/GDP

-9.477

-3.517

-2.894

-2.582

0.00000

M2/GDP

-2.804

-3.517

-2.894

-2.582

0.05770

ΔM2/GDP

-9.434

-3.517

-2.894

-2.582

0.00000

Note: Δ indicates first difference

From Table 2, the study fails to reject the null hypothesis for LogGDP, M2C/GDP, DC/GDP, DBM and M2/GDP

variables at level for unit root at the 5 percent significant level, however, the study rejects the null hypothesis

for these variables at first difference at all level of significance. This indicates that all the variables were stationary

at first difference which is appropriate for using the VEC model.

The integration of all variables at their first differences indicates a long-run relationship. To further ascertain this

long-run relationship, the study used the Johansen trace statistics for cointegration (λtrace). The selection of lag

length was based on the Akaike Information Criterion (AIC), which indicated an optimal lag length of four for

the model (see Appendix 1). The results of the Johansen cointegration test are presented in Table 3.

Table 3: Johansen Test for Cointegration Results

Rank

Params

Eigenvalue

Tr. Stat

CV_5%

825.9473

81.2767

68.52

847.7182

0.37387

37.7347*

47.21

855.8111

0.15974

21.5489

29.68

101

862.1834

0.12806

8.8044

15.41

104

866.2755

0.08424

0.6201

3.76

105

866.5856

0.00665

Note: * indicates cointegration at the 5%. L*.

From the results in Table 3, at rank zero, 81.2767 > 68.52, the study rejects the null hypothesis of no cointegrating

relationships. This suggests that there is at least one cointegrating relationship among the variables. At rank one,

37.7347 < 47.21, therefore, the study fails to reject the null hypothesis that there is at most one cointegrating

relationship. This means that one cointegrating relationship is likely present. Moreover, for rank two, three and

four, in each case, the trace statistic is less than the corresponding critical value, indicating the failure to reject

the null hypothesis of at most two, three, or four cointegrating relationships.

Therefore, the test indicates that there is one cointegrating relationship among the variables in this study, as

indicated by the selected rank of 1. This means that the variables share a long-term equilibrium relationship.

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To further understand this long run relationship, the study estimated the VEC model.

T able 4: Long Run Estimates Results

Coefficient

Stnd. Er.

Prob.

[95% conf. Int.]

DCGDP

1.9096**

1.0329

1.85

0.064

-20.51038 -3.03705

M2CGDP

37.4237*

8.5439

4.38

0.000

-212.435 -88.90298

M2GDP

-30.7926*

7.4619

-4.13

0.000

78.322311 -87.8053

DBM

-2.1351*

0.3442

-6.20

0.000

2.426237 - 7.310358

Constant

-1.9656

Note: *, ** & *** indicates significant at the 1%, 5% and 10% respectively

Table 4 provides the long run estimates. From the results, DC/GDP coefficient is positive- statistically

significant. This positive coefficient means that a 1 percent increase in credit to the private sector relative to

GDP is associated with a 0.019 unit increase in growth, ceteris paribus. This result highlights the critical role of

financial intermediation in Liberia's economic development, where increased access to credit for economic

agents stimulates investment, consumption, and hence growth.

M2C/GDP coefficient is positive-statistically significant. This positive impact indicates that a 1 percent increase

in broad money that is controlled by commercial banks is associated with a 0.37 unit increase in growth. This

indicates that a higher proportion of money held within the banking system significantly boosts economic output.

This could be due to increase savings and investments facilitated by the banking sector for productive uses,

hence productivity and growth.

M2/GDP is negative-statistically significant. The negative-significant indicates that a 1 percent increase in the

M2, inclusive of currency in circulation, is associated with a 0.31 percent decrease in economic growth. This

inverse nexus indicates that a large share of currency held outside the banking system may undermine the

effectiveness of financial development. According to the McKinnon-Shaw hypothesis (1973), if M2 includes a

significant portion of cash outside the formal banking sector, it signals inefficiencies in the financial system's

ability to mobilize savings and allocate capital toward productive investments. Additionally, low interest rates

on deposits (see Appendix 2) reduce incentives for saving, resulting in inefficient capital allocation. In such

cases, an increase in the M2 does not contribute to productive investments but instead fuels inflation or

speculative activities, which can dampen economic growth.

DBM coefficient is negative-statistically significant. The negative impact means that the introduction of mobile

money in 2011 in Liberia has deepen economy overtime. While mobile money is generally seen as a tool for

enhancing financial inclusion and promoting economic growth, the negative coefficient in this study suggests

that the implementation of the service in Liberia may have faced a variety of challenges, from slow adoption and

infrastructure limitations to regulatory hurdles and unintended economic consequences. These factors may have

hindered its potential to drive economic activity in the long run, leading to the observed negative impact on

economic growth.

Table 5: Short Run Estimates Results

Coefficient

Stnd. Er.

Prob.

[95% conf. Int.]

ECT

-0.050**

0.008

-0.630

0.057

-0.02033-0.0104

DC/GDP

0.124

0.295

0.420

0.675

-0.45444-0.7022

M2C/GDP

1.414

1.323

1.070

0.285

-1.1789 - 4.0078

M2/GDP

-1.390

1.122

-1.240

0.215

-3.5893 - 0.8093

DBM

0.011

0.106

0.100

0.918

-0.1966- 0.2184

Constant

0.009

0.012

0.800

0.426

-0.0137- 0.0326

The results show that the ECT is negative, indicating that deviation from the previous period is corrected for in

the current period at a speed of 5 percent. However, the p-value is 0.057 is slightly above the conventional

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threshold of 0.05, indicates that it is weakly significant, implying a moderate speed of adjustment towards

equilibrium, that is, the adjustment speed indicates that an unanticipated changes in credit availability, M2, or

financial innovation—economic growth in Liberia would take a longer time to return to its long-run growth path.

That is, the Liberian economy may experience prolonged periods of suboptimal growth following financial

disturbances, ceteris paribus. Moreover, none of the financial development indicators show a statistically

significant short-run impact on economic growth.

To further understand the short run impact, the study conducted the granger causality test as indicated in Table

Table 6: Granger Causality Results

Chi-Squared (χ²)

P-Value

Conclusion

DC/GDP

2.2462

0.134

No Granger causality

M2C/GDP

0.05138

0.821

No Granger causality

M2/GDP

0.01893

0.891

No Granger causality

0.85169

0.356

No Granger causality

ALL

20.427*

0.000

Granger causality

Note: * means causality

From Table 6, none of the financial indicators Granger-cause economic growth independently. However, when

all these indicators are considered together, they collectively have a significant impact on economic growth. This

means that while no single financial development indicator can predict economic growth in the short run, the

combined influence of these indicators is substantial.

The study further analyzed the responsiveness of economic growth to shock from the financial sector. The results

are display in Figure 1.

Figure 1: Response to Non-factorized One Unit Innovations

Response of GDP to BM Innovation

-2

-4

-6

-.05

-8

-10

-.10

Response of GDP to M2/GDP Innovation

-2.0

-1.0

-1.5

-0.5

Response of GDP to M2C/GDP Innovation

Response of GDP to DCPS/GDP Innovation

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From Figure 1, a one standard deviation shock to DC/GDP negatively affects GDP from quarter 1 to quarter 8.

However, this impact turns relatively positive from quarter 9 to quarter 18, with a return to a negative effect

between quarters 18 and 20. Following this, the impact becomes positive again until quarter 22, after which it

remains relatively insignificant up to quarter 25. Similarly, a one standard deviation shock to M2C/GDP shows

a generally positive impact from quarter 1 to quarter 25, despite high fluctuations, with an overall upward trend.

A comparable trend is observed for BM, although with lower fluctuations. Conversely, a one standard deviation

shock to M2/GDP has a generally negative impact from quarter 1 to quarter 25, with fluctuations trending

downward. To quantify the effects of the shock, the study used a Cholesky one standard deviation forecast to

analyze the contribution of financial development indicators to economic growth. Although the forecast covered

25 quarters, Table 6 provides results at five-quarter intervals (see Appendix 3).

Table 7: Cholesky One Standard Deviation Shock Forecasts

Quarter

S. E

LogGDP

DC/GDP

M2C/GDP

M2/GDP

DBM

0.07418

100.00000

0.00000

0.13782

57.36324

2.17443

37.09889

1.80648

1.55690

0.20880

46.05772

6.27897

43.95046

2.07925

1.63360

0.24199

38.56236

5.47445

37.90716

16.47584

1.58019

0.27028

31.47455

5.66474

36.97558

24.27889

1.60625

0.29022

28.06221

5.69163

35.82776

27.81730

2.60110

From Table 7, in the first quarter, GDP is determined independently, with no contribution from the financial

indicators. However, as the forecast horizon extends, the contribution of financial development indicators to

explaining the variance in economic growth gradually increases. By the fifth quarter, the explanatory power of

GDP decreases to 57.36 percent, while M2C/GDP becomes more significant, accounting for approximately 37.1

percent of the variance compared to other indicators. Over time, the contribution of M2/GDP also rises

substantially, explaining 27.82% of the variance by the 25th quarter, though M2C/GDP remains the more

influential indicator in explaining economic growth. This means that in the long run, financial development

indicators play an increasingly important role in explaining variations in economic growth, particularly

M2C/GDP and M2/GDP. The results demonstrate that, over time, economic growth becomes endogenously

determined by these financial variables.

Post Analysis

To study the robustness of the findings, the study performed tests for heteroscedasticity, autocorrelation,

normality, and the stability of the residuals. The results indicate that the analysis is devoid of serial correlation

and heteroscedasticity, the residuals follow a normal distribution, and the model exhibits stability (see Appendix

4).

SUMMARY OF THE FINDINGS

This study analyzed the impacts of financial development on economic growth in Liberia. The ADF test

indicated that all variables were stationary at first difference, justifying the use of the VEC model. The

Johansen’s cointegration test confirmed the presence of a long-run equilibrium relationship between economic

growth and financial development.

The long-run estimates showed that increased credit to the private sector statistically impact growth and a

higher proportion of M2 within the banking system have positive impacts on economic growth, signifying

the importance of financial intermediation and savings mobilization to the long run growth path of Liberia.

Conversely, an increase in M2 inclusive of currency outside the banking system, negatively affect growth. This

supports the McKinnon-Shaw hypothesis, suggesting that inefficiencies in the financial system and financial

repression undermine the ability of financial development to spur growth. The introduction of mobile money also

showed a negative long-term impact, potentially due to adoption challenges and structural inefficiencies in

Liberia. In the short run, none of the financial development indicators showed significant effects on

economic growth. The ECT indicated a moderate speed of adjustment towards long-run equilibrium,

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hence, Liberia’s economy takes longer time to recover from financial shocks. Granger causality tests confirmed

that financial development indicators collectively influence economic growth, though no individual indicator

Granger-causes growth. The impulse response analysis showed that shocks to financial development variables

affect GDP growth in a fluctuating manner, with M2C/GDP showing sustained positive impacts over time.

CONCLUSION

The findings of this study indicate that the impact of financial development on economic growth is dependent

on the specific financial indicator used. M2, when adjusted to exclude currency outside the banking system,

emerges as a more effective measure of financial development in promoting growth compared to M2 inclusive

of currency in circulation. Additionally, the results showed that domestic credit to the private sector positively

influences economic growth but this impact is not statistically valid. However, the introduction of mobile money

does not appear to have a significant impact on growth in Liberia. Furthermore, while financial development

significantly contributes to long-term economic growth, its impact on growth is not evident in the short term.

RECOMMENDATIONS

Given the significant impact of private sector credit on economic growth, it is imperative that the CBL keenly

work with financial institutions to expand access to credit. Policymaking efforts aimed at promoting financial

inclusion, particularly for SMEs, hold the potential to stimulate investment and drive higher economic growth.

Strengthening the capacity of banks to extend credit, alongside improvements in credit infrastructure, could

further bolster financial development and contribute significantly to sustain economic growth.

The positive impact of M2 when adjusted to exclude currency outside banking system on growth underscores

the importance of funds being held within the formal banking system. The CBL should encourage commercial

banks to give high interest on deposit coupled with awareness to incentivize savings and deposit mobilization.

This could encourage saving and enhance financial intermediation, hence channel more resources toward

productive investments. Additionally, financial literacy programs can raise awareness about the benefits of using

banking services especially in the hinterlands of Liberia. Also, the CBL should continue to implement policies

aimed at reducing cash dependency and encouraging electronic transactions. Enhancing mobile banking services

and ensuring proper regulation can improve financial inclusion and economic stability.

While mobile money has the potential to promote financial inclusion, its negative impact on growth observed in

this study suggests that there are significant challenges in its implementation. Therefore, the CBL should

collaborate with telecommunications companies to invest more in nationwide infrastructure and develop a more

accessible, efficient, user-friendly transaction platform. Ensuring mobile money services, such as purchasing

goods and paying bills are cost- free, coupled with instant payment notifications, could boost user confidence

and encourage broader adoption. Furthermore, a target should be set to integrate mobile money into all economic

activities, supported by continuous awareness campaigns to encourage businesses to prioritize mobile payments.

In addition, the CBL should also enhance regulatory frameworks to facilitate the expansion and effective

utilization of mobile money. Together with other strategic initiatives, these measures would promote wider

adoption and strengthen mobile money's positive contribution to economic growth.

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ISSN No. 2454-6186 | DOI: 10.47772/IJRISS | Volume IX Issue X October 2025

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APPENDIX

Appendix 1

Lag Order Selection

Lag

FPE

AIC

HQIC

SBIC

341.20

0.00

-7.55

-7.50

-7.42

708.68

734.97

25.00

0.00

-15.25

-14.91*

-14.41*

730.66

43.96

25.00

0.01

0.00

-15.18

-14.56

-13.65

768.24

75.15

25.00

0.00

-15.47

-14.56

-13.23

820.53

104.57

25.00

0.00

-16.08

-14.90

-13.14

836.12

31.20

25.00

0.18

0.00

-15.87

-14.40

-12.23

870.07

67.90

25.00

0.00

-16.07

-14.32

-11.73

893.28

46.41

25.00

0.01

0.00

-16.03

-14.00

-11.00

938.96

91.36*

25.00

0.00

6.9e-1*

-16.49*

-14.18

-10.76

Appendix 3

Period

S.E.

LogGDP

DCPS/GDP

M2C/GDP

M2/GDP

DBM

0.074181

100.0000

0.000000

0.092420

91.15313

0.235529

3.925697

3.292127

1.393519

0.110541

74.76312

0.197569

21.01395

2.434992

1.590370

0.128207

64.13646

0.939154

31.55126

2.002934

1.370193

0.137822

57.36324

2.174426

37.09889

1.806475

1.556964

0.150360

50.78445

7.387406

38.73395

1.668351

1.425849

0.164125

48.89041

7.470545

40.95643

1.485738

1.196874

0.185414

44.65672

7.272272

45.66214

1.329801

1.079060

0.199209

43.32584

6.826461

47.29390

1.161084

1.392724

0.208796

46.05772

6.278969

43.95046

2.079246

1.633600

0.215700

46.73486

5.965875

42.02641

3.649809

1.623051

0.221416

45.46239

5.667903

40.13756

7.190674

1.541478

0.229218

42.86151

5.436275

38.71286

11.50974

1.479612

0.237096

40.06055

5.535706

38.04612

14.85089

1.506731

0.241986

38.56236

5.474449

37.90716

16.47584

1.580185

0.246746

37.19507

5.308631

37.71137

18.15810

1.626825

0.251104

36.13901

5.405936

36.69652

20.18659

1.571944

0.260142

33.90710

5.784501

35.46859

23.35006

1.489751

0.265764

32.49668

5.826960

36.04001

24.11571

1.520644

0.270281

31.47455

5.664736

36.97558

24.27889

1.606248

0.273752

30.99060

5.555682

37.20227

24.36561

1.885838

0.277803

30.18993

5.587447

36.72897

25.38953

2.104120

0.281284

29.51974

5.710047

36.35999

26.05414

2.356077

0.285816

28.71572

5.727872

36.01117

26.99615

2.549097

0.290217

28.06221

5.691631

35.82776

27.81730

2.601099

Choles ky One S.D. (d.f. adjus ted)

Choles ky ordering: LogGDP DCPS/GDP M2C/GDP M2/GDP DBM

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Appendix 4

LM Test for Residual Serial Correlation

LRE* stat

Prob.

Rao F-stat

Prob.

17.81532

0.8501

0.6998694

(25, 139.0)

0.8515

30.10989

0.2202

1.231454

(25, 139.0)

0.2229

32.84841

0.1349

1.3456168

(25, 139.0)

0.137

Residual Heteroskedasticity

Chi-sq

Prob.

1176.332

1110

0.0815

Jarque-Bera Residual Normality

Equation

chi2

Prob > chi2

D_LoGDP

154.335

0.176

D_DCPSGDP

36.875

0.776

D_M2CGDP

3.586

0.125

D_M2GDP

14.245

0.0008

D_BM

3334.218

0.180

ALL

3543.259

0.251