INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN APPLIED SCIENCE (IJRIAS)

ISSN No. 2454-6194 | DOI: 10.51584/IJRIAS | Volume X Issue X October 2025

Diaspora Remittances and Economic Growth in Nigeria: An Auto

Regressive Distributed Lag (ARDL) Approach

Boniface Linus Akpan

Department of Economics Akwa Ibom State University, Nigeria

DOI: https://dx.doi.org/10.51584/IJRIAS.2025.10100000185

Received: 31 October 2025; Accepted: 07 November 2025; Published: 22 November 2025

ABSTRACT

This paper examines the impact of diaspora remittances on Nigeria’s economic growth between 1990 and

2024, emphasizing the role of remittances as a non-debt source of development finance. Using annual time

series data obtained from the Central Bank of Nigeria (CBN) and the National Bureau of Statistics (NBS), the

study employs the Autoregressive Distributed Lag (ARDL) approach to assess both the short-run and long-run

dynamics among remittance inflows, real gross domestic product (RGDP), gross fixed capital formation

(GFCF), government expenditure (GEX), trade openness (OPEN), and exchange rate (EXR). Empirical results

reveal a significant and positive long-run relationship between remittances and economic growth, suggesting

that diaspora inflows contribute meaningfully to Nigeria’s development trajectory. GFCF emerges as the most

substantial long-run driver of GDP growth, while the exchange rate exhibits an insignificant impact. The

findings underscore remittances’ stabilizing role in mitigating currency shortfalls and macroeconomic

fluctuations. However, the results also indicate that sustainable growth requires channeling remittance inflows

into productive investment rather than consumption. The study concludes that policymakers should promote

financial inclusion, formalize remittance transfer systems, and develop incentives that encourage diaspora

resources to support capital formation and enterprise development in Nigeria.

Keywords: Diaspora remittances, economic growth, ARDL model, capital formation, Nigeria, diaspora

finance, development economics.

INTRODUCTION

Over the past three decades, international migration has become an increasingly significant feature of global

economic relations, linking labour mobility, financial transfers, and development outcomes. For many

developing countries, remittances (the money sent home by Diaspora) have emerged as a vital and stable

source of external finance that often surpasses official development assistance and, in some cases, foreign

direct investment (World Bank, 2024). These transfers serve as a lifeline for millions of households, providing

income for consumption, education, health, and investment, while also contributing to macroeconomic stability

and poverty reduction (International Monetary Fund [IMF], 2019).

Nigeria, Africa’s most populous country, represents a particularly compelling case in the study of remittances

and development. The country is consistently among the top remittance recipients in Sub-Saharan Africa,

accounting for nearly 40 percent of the region’s total inflows (Osei-Gyebi, Okorie, & Adegboye, 2023).

According to the National Bureau of Statistics (NBS, 2025), Nigeria’s personal remittance inflows rose from

USD 17.21 billion in 2020 to USD 20.93 billion in 2024, an increase of roughly 9 percent despite persistent

domestic economic instability. The main sources of these transfers are diaspora populations in the United

States, the United Kingdom, and parts of Europe and the Middle East.

Remittances occupy a dual role in Nigeria’s economic landscape. At the micro level, they enhance household

welfare by funding basic consumption and investment in human capital (Adams & Cuecuecha, 2013). At the

macro level, they provide foreign-exchange inflows that can stabilize the balance of payments and stimulate

productive investment when properly harnessed (Ratha, 2013). The Harrod–Domar growth model supports this

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logic, emphasizing the importance of capital accumulation and savings in driving output expansion (Memon,

2007). When remittances are channelled into productive sectors such as manufacturing, housing, or small

enterprise development, they can close the savings–investment gap that constrains growth in many developing

economies.

Despite these theoretical benefits, the Nigerian experience presents a paradox. Large and growing remittance

inflows have not translated into commensurate improvements in macroeconomic performance.

Unemployment, inflation, and infrastructural deficits remain stubbornly high, and the economy continues to

depend heavily on oil revenues. A substantial portion of remittance inflows is spent on consumption and social

transfers rather than productive investment (Taylor, 1999; Ahlburg, 1991). Moreover, informal transfer

channels, high transaction costs, and weak institutional frameworks limit the development impact of

remittances (Odozi, Awoyemi, & Omonona, 2010). These issues raise a critical policy question: under what

conditions can diaspora remittances become a sustained engine of economic growth in Nigeria?

The existing literature offers mixed evidence. Some studies find that remittances significantly enhance GDP

and reduce poverty in Sub-Saharan Africa (Gupta & Pattillo, 2009; Hassan et al., 2017), whereas others report

weak or negligible long-run effects (Chami, Fullenkamp, & Jahjah, 2005). Differences in methodology, data

coverage, and national contexts partly explain these inconsistencies. In Nigeria, relatively few empirical

studies have applied dynamic time-series models that distinguish between short- and long-run effects, limiting

understanding of the mechanisms through which remittances influence growth.

This paper contributes to the debate by empirically examining the long- and short-run effects of diaspora

remittances on Nigeria’s economic growth from 1990 to 2024. Using the Autoregressive Distributed Lag

(ARDL) model, the study analyzes the relationships among remittances, real GDP, gross fixed capital

formation, government expenditure, trade openness, and exchange rate. The research seeks to determine

whether remittances promote growth directly through capital accumulation or indirectly through

macroeconomic stabilization.

The findings aim to enrich both academic and policy discussions on how Nigeria can better leverage its

diaspora resources for sustainable development. By aligning remittance flows with productive investment and

formal financial systems, policymakers can enhance their contribution to growth, employment, and

diversification.

LITERATURE REVIEW AND THEORETICAL FRAMEWORK

Conceptualizing Remittances and Economic Growth

Diaspora remittances represent cross-border financial transfers made by individuals working abroad to support

households and communities in their countries of origin. The International Monetary Fund (IMF, 2019) defines

remittances as household income from foreign economies, primarily earned through diaspora labour, while the

World Bank (2024) classifies them into personal transfers, compensation of employees, and capital transfers.

These flows are recognized as one of the most stable and countercyclical sources of external finance for

developing countries, often surpassing foreign direct investment (FDI) and official development assistance

(ODA) (World Bank, 2024).

Remittances can affect economic growth through both microeconomic and macroeconomic mechanisms. At

the household level, they finance consumption, education, and healthcare, thereby improving human capital

and poverty outcomes (Adams & Cuecuecha, 2013; Gupta & Pattillo, 2009). At the macro level, they can

stimulate aggregate demand, enhance investment, and stabilize foreign exchange reserves (Ratha, 2013).

However, the growth impact of remittances depends largely on how they are utilized and the broader

institutional and policy environment (Fayissa & Nsiah, 2010).

In the Nigerian context, remittances have become increasingly significant as a share of gross domestic product

(GDP), foreign reserves, and capital inflows (Central Bank of Nigeria [CBN], 2024). Nonetheless, their

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developmental potential is constrained by factors such as high transaction costs, limited financial inclusion,

poor institutional capacity, and widespread use of informal transfer systems (Odozi, Awoyemi, & Omonona,

2010). These challenges underscore the need to examine empirically how remittances interact with other

macroeconomic variables to influence long-term growth.

Transmission Mechanisms

Remittances influence economic growth through several interrelated channels. These channels include among

others;

Capital Accumulation Channel

When remittances are invested in housing, business enterprises, or physical infrastructure, they expand the

productive capacity of the economy. Osili (2007) and Fonta et al. (2015) found that remittances in Nigeria

have financed housing construction, agricultural inputs, and small-scale business ventures, thereby

contributing to capital formation.

Human Capital Channel

Remittances can enhance human capital development by financing education and healthcare. Studies show that

households receiving remittances in Nigeria spend significantly more on education and health than non-

receiving households, improving labour productivity in the long term (Adams & Cuecuecha, 2013).

Financial Development Channel

As remittance inflows pass through formal financial institutions, they can deepen financial intermediation and

increase the supply of loanable funds. Giuliano and Ruiz-Arranz (2009) demonstrated that remittances promote

economic growth more effectively in countries with less developed financial systems, suggesting a

complementary relationship between remittances and financial development.

Stabilization and Risk Mitigation Channel

Remittances are often countercyclical, rising during periods of economic downturn or crisis. This stabilizing

role was evident in Nigeria during the 2016 recession when remittance inflows provided critical foreign

exchange support amid declining oil revenues (CBN, 2020).

However, remittances may also have adverse effects if they lead to dependency, reduced labour supply, or

exchange rate appreciation (“Dutch disease”) that weakens export competitiveness (Chami, Fullenkamp, &

Jahjah, 2005). The net effect therefore depends on institutional quality, financial infrastructure, and

macroeconomic management (Olowa & Awoyemi, 2012).

Theoretical Foundations

Harrod–Domar Growth Model

The Harrod–Domar model (Harrod, 1939; Domar, 1946) provides a foundational framework linking savings,

investment, and economic growth. It posits that economic expansion depends on the rate of savings and the

productivity of capital, measured by the capital–output ratio. In the context of remittances, these inflows can

supplement domestic savings, thereby financing investment and promoting long-run growth (Memon, 2007).

For developing countries such as Nigeria, where savings and investment rates are low, remittances can play a

critical role in closing the savings–investment gap.

Neoclassical Migration Theory

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The Neoclassical Migration Theory (Todaro, 1969; Harris & Todaro, 1970) explains migration as a rational

economic decision driven by wage differentials between origin and destination countries. Diaspora move to

maximize expected income, and part of their earnings abroad is remitted home to improve family welfare (de

Haas, 2010). In this view, remittances are an outcome of labour mobility and income disparities and represent

a potential channel for redistributing global income toward developing economies.

Altruism Theory

The Altruism Theory (Stark, 1991; Chami et al., 2005) proposes that Diaspora remit money out of concern for

their families’ welfare. Such transfers are countercyclical, increasing during periods of economic hardship at

home. The theory emphasizes remittances as stabilizing inflows that protect household consumption during

downturns—consistent with Nigeria’s experience where remittances often rise during recessions and

inflationary shocks.

Together, these frameworks suggest that remittances can serve both consumptive and productive purposes,

influencing short-run welfare and long-run growth outcomes.

Empirical Evidence

Empirical research presents mixed findings on the remittance–growth nexus. In a cross-country study, Fayissa

and Nsiah (2010) found that remittances significantly promote economic growth in Sub-Saharan Africa when

accompanied by sound institutions and financial systems. Similarly, Olayungbo and Quadri (2019) using the

Pooled Mean Group (PMG) estimator for 20 African countries (2000–2015), concluded that both remittances

and financial development have positive and statistically significant effects on growth in the short and long

run. More so, Okeke and Chinanuife (2022) used an interactive approach to examine the role of financial

development on remittance-investment nexus in Nigeria. The study showed that financial deepening dampens

the effect of remittances on private domestic investment.

In contrast, Chami et al. (2005) argued that remittances may reduce labour market participation and

productivity if recipients rely excessively on external income, potentially offsetting macroeconomic benefits.

Adams and Page (2005), however, reported that a 10% increase in per capita remittances could reduce poverty

by up to 3.5% across developing economies, suggesting substantial welfare gains even if growth effects are

modest. Also, Okeke, Chinanuife and Muogbo (2021) showed a uni-directional causality between remittances

and private domestic investment. This shows that while remittances affect the volume of private domestic

investment, there was no evidence as to whether an increase in the level of domestic investment increases

international remittances.

In the Nigerian context, Akinpelu et al. (2013) and Olaniyan (2019) found long-run positive relationships

between remittances and GDP, although the magnitude of the effect was smaller compared to that of domestic

investment. Conversely, some studies (Odozi et al., 2010) suggest that institutional weaknesses and informal

transfer systems dilute remittances’ growth potential. The absence of coordinated policy frameworks and low

diaspora investment participation continue to limit Nigeria’s ability to leverage remittances for structural

transformation.

While consensus exists, that remittances influence economic performance, evidence remains inconclusive on

the magnitude and direction of this relationship in Nigeria. Previous research has often overlooked dynamic

linkages among remittances, capital formation, and exchange rate behaviour, or failed to distinguish between

short-run and long-run effects. Furthermore, few studies have examined how macroeconomic openness and

fiscal policy interact with remittance inflows to shape growth trajectories.

To fill these gaps, this study adopts the ARDL model to evaluate both the immediate and persistent effects of

remittances on economic growth in Nigeria, covering an extended dataset (1990–2024). By incorporating

additional control variables (gross fixed capital formation, government expenditure, trade openness, and

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exchange rate) it provides a more comprehensive understanding of how remittances integrate into Nigeria’s

macroeconomic system.

METHODOLOGY

Research Design and Data Sources

This study adopts a quantitative research design based on secondary time-series data covering the period 1990–

2024. The focus is on establishing both short-run and long-run relationships between diaspora remittances and

Nigeria’s economic growth. Annual data were obtained from credible national and international sources,

including the Central Bank of Nigeria (CBN) Statistical Bulletin, the National Bureau of Statistics (NBS), and

the World Development Indicators (WDI) database of the World Bank.

The variables used include:

Real Gross Domestic Product (RGDP): proxy for economic growth;

Remittances (REM): personal remittance inflows as a percentage of GDP;

Gross Fixed Capital Formation (GFCF): proxy for domestic investment;

Government Expenditure (GEX): total public spending as a percentage of GDP;

Trade Openness (OPEN): sum of exports and imports as a percentage of GDP;

Exchange Rate (EXR): annual average nominal exchange rate (naira per USD).

All variables were transformed into natural logarithms to stabilize variance and allow for elasticity

interpretation of coefficients.

Model Specification

To analyse the dynamic relationship between remittances and economic growth, the study employs the

Autoregressive Distributed Lag (ARDL) model developed by Pesaran, Shin, and Smith (2001). The ARDL

framework is particularly suitable because it accommodates variables integrated of different orders—either

I(0) or I(1)—and provides reliable estimates even with small sample sizes.

The model is expressed in functional form as:

RGDP = f(REM, GFCF, GEX, OPEN, EXR)

RGDP stands for real gross domestic product

REM stands for remittance

(1)

GFCF stands for gross fixed capital formation

GEX stands for government expenditure

OPEN stands for trade openness

EXR stands for exchange rate

Expanding Equation (1) into an estimable ARDL cointegrating and long run form gives:

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q

i=1

q

∑

∆logRGDP = β₀+ β₁

∆logRGDP + β_{2 i=0}∆logGFCF_t−i+ β_{3 i=0}∆logUNE_t−i

+

t

t−i

q

∑

q

∑

β_{4 i=0}∆logREM_t−i+ β_{5 i=0}∆logODA_t−i+ β_{6 i=0}∆logEXR_t−i+ β₇

∆logTOP

+

t−i

i=0

∑

β_{8 i=0}∆logGEX_t−i+ φECM_t−1+ α₁logGFCF_t+ α₂logUNE_t+ α₃logREM_t+ α₄logODA_t+

α₅logEXR_t+α₆logTOP + α₇logGEX_t+ μ_t

(2)

t

Where:

∆ denotes first difference;

β₀is the constant term;

μ_tis the white-noise error term;

α_icoefficients capture long-run relationships;

The summations capture short-run dynamics through lagged differences.

φ measures the speed of adjustment to long run equilibrium

Estimation Procedure

The study conducted unit root test using Augmented Dickey–Fuller (ADF), bound test cointegration. Other

postestimation tests include diagnostic and stability tests using Breusch–Godfrey Serial Correlation LM test,

Jarque–Bera normality test, Breusch–Pagan–Godfrey heteroskedasticity test, CUSUM and CUSUMSQ

Description of Variables

Variable

RGDP

REM

Definition

Expected

Sign

Rationale

Real Gross Domestic Product

Diaspora remittances (% of GDP)

Gross Fixed Capital Formation (% of GDP)

Government expenditure (% of GDP)

Trade openness (% of GDP)

Dependent variable measuring

economic growth

+

Expected to stimulate growth via

investment and consumption

GFCF

GEX

Indicator of domestic investment

and capital accumulation

Reflects fiscal contribution to

infrastructure and demand

OPEN

Expected to foster

competitiveness and technology

diffusion

EXR

Exchange rate (₦/USD)

±

Effect may vary depending on

competitiveness or import costs

Justification for the ARDL Approach

The ARDL model was preferred for several reasons. First, it is robust in small-sample contexts and can

accommodate regressors with mixed integration orders (I(0) and I(1)). Second, it provides distinct short-run

and long-run elasticity estimates, allowing for nuanced interpretation of remittances’ effects on growth. Third,

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the model efficiently corrects endogeneity by incorporating lags of dependent and independent variables, thus

reducing omitted-variable bias.

Econometric Software

All econometric analyses were performed using EViews 12 statistical software, which provides efficient tools

for ARDL estimation, bounds testing, and diagnostic checking.

RESULTS AND DISCUSSION

Table 4.1 Descriptive Statistics

LEXR

4.7312

4.8812

7.2991

2.0001

1.3776

-0.3881

2.3857

1.4290

0.4894

165.593

64.5266

35

LGEX

3.3760

3.2015

6.8156

0.9122

1.4777

0.5156

2.8425

1.5874

0.4521

118.161

74.2489

35

LGFCF

2.4987

2.6012

2.9826

1.7850

0.4063

-0.4458

1.6984

3.6303

0.1628

87.4552

5.61430

35

LODA

6.9212

7.5010

9.3441

5.0238

1.3597

-0.0982

1.4515

3.5531

0.1692

242.245

62.8652

35

LOPEN

1.7053

1.7749

4.0000

-1.6094

1.1723

-0.1750

4.3074

2.6715

0.2629

59.6860

46.7310

35

LREM

8.3509

9.7533

10.1005

2.7725

2.0600

-1.1061

3.3410

7.3075

0.0258

292.282

144.290

35

LRGDP

4.5378

4.5890

4.9487

3.9569

0.3025

-0.3394

1.7711

2.8743

0.2376

158.823

3.11132

35

LUNE

1.5712

1.6014

1.8794

1.0952

0.2126

-0.4578

2.3334

1.8706

0.3924

54.9947

1.53774

35

Mean

Median

Maxi.

Mini.

Std. Dev.

Skewness

Kurtosis

J-B

Prob.

Sum

Sum Sq. Dev.

Obs.

Source: Author, 2025

Table 4.1 shows the mean, median, maximum, and minimum, standard deviation, skewness, kurtosis, and

Jaque-Bera test for the normality of the Model variables. The descriptive analysis is based on 35 annual

observations for each variable. Starting with measures of central tendency, Remittances (REM) and Official

Development Assistance (ODA) record the highest mean values of 8.35 and 6.92 respectively, showing that

these external financial inflows dominate in size compared to other variables. Government Expenditure (GEX)

and Exchange Rate (EXR) follow with mean values of 3.38 and 4.73. In contrast, Gross Fixed Capital

Formation (GFCF), Trade Openness (OPEN), and Unemployment Rate (UNE) have relatively low means of

2.50, 1.71, and 1.57 respectively. The medians for most variables are close to their means, such as EXR (mean

4.73, median 4.88) and GEX (mean 3.38, median 3.20), suggesting that the distributions are not heavily

influenced by extreme outliers.

Looking at the range and variability, Remittances (REM) has the highest standard deviation of 2.06, which

indicates large fluctuations in remittance inflows across the years. Similarly, Government Expenditure (GEX)

and Official Development Assistance (ODA) show moderate variability with standard deviations of 1.48 and

1.36. In contrast, Unemployment Rate (UNE) and Gross Fixed Capital Formation (GFCF) are much more

stable, with standard deviations of 0.21 and 0.41 respectively. The minimum and maximum values further

confirm these variations: REM ranges widely from 2.77 to 10.10, while GFCF is confined to a narrower band

between 1.79 and 2.98.

The skewness and kurtosis statistics highlight the shape of the distributions. Exchange Rate (EXR) and GFCF

are slightly negatively skewed, while Government Expenditure (GEX) is slightly positively skewed, all of

which imply mild asymmetry. Remittances (REM) stands out with strong negative skewness (-1.106), meaning

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most values are concentrated at the higher end with a long tail on the lower side. Trade Openness (OPEN) also

shows evidence of fat tails, with a kurtosis value above 4, indicating a tendency for more extreme values. More

so, the Jarque–Bera test results show that most variables, including EXR, GEX, GFCF, ODA, OPEN,

Remittances (REM) and UNE, are normally distributed since their probability values are greater than 0.05.

Table 4.2: Correlation Matrix

RGDP

1

EXR

GEX

GFCF

ODA

OPEN

REM

UNE

RGDP

EXR

0.6538

0.5818

0.6897

0.6617

0.3722

0.6116

0.5032

1

0.7678

0.6422

0.7852

0.3413

0.5591

0.3923

1

GEX

0.7737

0.6763

0.3865

0.6182

0.3017

1

GFCF

ODA

OPEN

REM

UNE

0.6687

0.3231

0.6054

0.5417

1

0.2242

0.5215

0.4226

1

0.1640

-0.2744

1

0.5900

1

Source: Author, 2025

Table 4.2 shows the correlation matrix of the variables. It could be observed that the correlation matrix of all

the variables were less than 0.8 indicating moderate correlation.

Table 4.3: Unit Root and Co-integration Tests

Variables

Critical Values

ADF

Order of Integration

Statistics

1%

-4.1219

-4.4205

-3.6254

-2.3170

-4.2970

-2.5072

-2.3170

-2.9996

5%

-3.1449

10%

-2.7137

RGDP

EXR

-22.0159

-6.1927

-3.9552

-5.6962

-6.5744

-2.8213

I(1)

I(0)

-3.2598

-3.2126

-3.1753

-3.2126

-2.6143

-2.7711

-2.7476

-2.7289

-2.7476

-2.7711

-1.6104

GFCF

ODA

REM

OPEN

UNE

GEX

Source: Author, 2025

Table 4.3 shows the Augmented Dickey–Fuller (ADF) unit root test. It could be observed that the variables

were integrated of mixed orders—I(0) and I(1)—but none were I(2). This confirmed the appropriateness of the

ARDL methodology. The subsequent bounds test for co-integration yielded an F-statistic value exceeding the

upper critical bound, indicating a statistically significant long-run equilibrium relationship among remittances,

economic growth, and the control variables.

Table 4.4: ARDL BOUND Test for Co-Integration

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F-Bounds Test

Test Statistic

F-statistic

K

Null Hypothesis: No levels relationship

Value

40.14741

7

Signif.

10%

5%

I(0)

2.2

I(1)

3.09

3.49

3.87

4.37

2.56

2.88

3.29

2.5%

1%

Source: Author, 2025

It could be observed from table 4.4 that the F-statistic which is 40.14714 at absolute value is above the critical

values at 1%, 5%, and 10% levels of significance for both the lower bound and upper bound. This, therefore,

means that there is a co-integration (long-run relationship) between the variables.

Table 4.5: Short-run ARDL

Conditional Error Correction Regression

Variable

Coefficient

1.270292

-0.393827

0.005043

0.007817

0.155806

0.002271

0.006959

0.013295

-0.021113

-0.011363

-0.013514

0.003455

0.004446

0.008087

0.003553

-0.016438

-0.430223

Std. Error

0.222209

0.072839

0.006659

0.003084

0.028043

0.003469

0.002652

0.005116

0.013795

0.005759

0.005879

0.001904

0.004123

0.004407

0.004501

0.003980

0.097865

t-Statistic

5.716652

-5.406791

0.757281

2.534485

5.556054

0.654601

2.624422

2.598728

-1.530499

-1.973018

-2.298654

1.815066

1.078352

1.835073

0.789416

-4.129969

-4.396086

Prob.

C

0.0000

0.4593

0.0214

0.0000

0.5215

0.0178

0.0187

0.1443

0.0650

0.0345

0.0872

0.2959

0.0841

0.4407

0.0007

0.0006

LRGDP(-1)*

LEXR(-1)

LGEX(-1)

LGFCF**

LODA(-1)

LOPEN**

LREM(-1)

LUNE**

D(LEXR)

D(LEXR(-1))

D(LGEX)

D(LODA)

D(LODA(-1))

D(LREM)

D(LREM(-1))

ECM(-1)

Source: Author, 2025.

As seen on table 4.5 below, based on the ARDL short-run conditional error correction regression. The speed of

adjustment of the short run dynamics to long run equilibrium shows that annually, about 43 percent of the

fluctuations in the short run get adjusted towards long run equilibrium. This is negative and significant. Also,

in the short term, remittances and exchange rate fluctuations exhibit minor and sometimes negative effects on

growth, but these are corrected over time, as indicated by a 43% speed of adjustment toward long-run

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equilibrium. This suggests that the short-run dynamics of these variables do not have a robust, immediate

influence on real economic output.

Table 4.6 Long-run ARDL Result

Levels Equation

Case2: Restricted Constant and No Trend

Variable

LEXR

Coefficient

0.012804

0.019848*

0.395619*

0.005766

0.017671*

0.033758*

-0.053609

3.225504

Std.Error

0.015891

0.006195

0.057343

0.008914

0.005180

0.009084

0.037740

0.057949

t-Statistic

0.805732

3.203895

6.899157

0.646863

3.411580

3.716103

-1.420467

55.66115

Prob.

0.4315

0.0052

0.0000

0.5264

0.0033

0.0017

0.1736

0.0000

LGEX

LGFCF

LODA

LOPEN

LREM

LUNE

C

Source: Author, 2025

* Prob. Significant at 5%

Based on the long-run ARDL estimates as seen on table 4.6, the analysis reveals a statistically significant and

positive long-run relationship between several key variables and economic growth. A positive and statistically

significant relationship exists between remittances and economic growth in Nigeria. A 1% increase in

remittances raises real GDP by approximately 0.03%, confirming that diaspora inflows serve as a sustainable,

non-debt source of external finance. Gross Fixed Capital Formation (GFCF) was observed to be the most

influential long-run variable, indicating that domestic investment is the key driver of sustained growth. Other

contributors include Government expenditure (GEX) and trade openness (OPEN) also have positive and

significant long-run effects on economic growth. Exchange rate (EXR) and official development assistance

(ODA) show positive but statistically insignificant relationships with GDP.

Diagnostic Tests

Normality Test

The model was examined for normal distribution. The Jarque-Bera (JB) statistics is used to test for the

normality of the model. The null hypothesis is that the model is normally distributed. The decision rule is to

reject the null hypothesis if the p-value is less than 0.05 level of significance. The result of the normality test is

presented on figure 4.1.

Figure 4.1: Normality test

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Source: Author, 2025

In figure 4.1, the Jaque-Bera statistic was used to test for the normality of the model. The probability value of

Jaque-Berap (Prob. JB = 0.634514), is greater than 0.05. Thus, the residual of the estimated ARDL model is

normally distributed. That is, the study, therefore, accepts the null hypothesis that the model is normally

distributed.

Table 4.7: Heteroskedasticity Test (Breusch-Pagan-Godfrey test)

Heteroskedasticity Test: Breusch-Pagan-Godfrey

F-statistic

2.214147

21.82742

10.68499

Prob. F(15,17)

0.0587

0.1124

0.7746

Obs*R-squared

Scaled explained SS

Prob. Chi-Square (15)

Source: Author, 2025

Based on the results of the Breusch-Pagan-Godfrey test as seen on table 4.7, the model's residual is

homoskedastic, meaning that it has a constant variance. The null hypothesis (H₀) of the test shows that the

residual ais homoskedastic, and the decision rule is to reject this hypothesis if the p-value is less than 0.05. The

Prob. F-statistic of 0.0587 is greater than 0.05 significance level, leading to the conclusion that we fail to reject

the null hypothesis. This outcome is favourable, as it confirms that the model's standard errors and the

statistical significance of its coefficients are reliable. The absence of heteroskedasticity ensures that the

inferences drawn from the regression results are valid.

Figure 4.2 (a and b) Stability Test

Figure-4.2a CUSUM test

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Figure-4.2b CUSUM of Square test

To determine the stability of the model, CUSUM and CUSUM of squares were used. The estimated model is

stable if its recursive residuals lie within the two critical bounds. On the other hand, if residuals fall outside the

two critical lines the model is said to be unstable. The results of the stability test are presented in Figures 4.2a

and 4.2b.

The analysis in figure 4.2a and 4.2b indicate that both the graph of CUSUM and CUSUM of square were

stable because the recursive residuals fall within the critical line, meaning that they are all within the 5%

critical bounds. This result implies that the estimated parameters for the study are stable for the period under

investigation.

DISCUSSION OF FINDING

The findings for Nigeria align with broader empirical patterns observed across West Africa, though with

notable distinctions in magnitude and policy context. For instance, in Ghana, Adams & Cuecuecha (2013) and

Adams & Page (2005) found that remittances significantly reduce poverty and support investment but have

modest direct effects on GDP growth. Similar to Nigeria remittances improve welfare and human capital but

need to be more investment-oriented to drive long-run growth. The study of Adarkwa (2015) on Senegal

economy showed that remittances contribute positively to GDP, with effects stronger than in Nigeria due to

better formal remittance systems and diaspora-targeted investment policies. Nigeria’s weaker institutional

framework limits comparable benefits; Senegal’s “Plan Sénégal Émergent” integrates remittances into national

development strategies. Similarly, Cape Verde showed highly remittance-dependent (over 10% of GDP), with

positive macroeconomic stability effects (Fayissa & Nsiah, 2010).

Also, remittances in the Gambia was observed to drive household welfare and small business formation

(Anyanwu & Erhijakpor, 2010). However, like Nigeria, informal channels and high transfer costs limit

macroeconomic gains. Both face similar structural barriers, informality, limited financial inclusion, and low

productive investment. Remittances effect in Sierra Leone showed Mixed evidence. Eggoh, Bangake and

Semedo (2019) observed that remittances support consumption but show weak links to output growth of Sierra

Leone.

Therefore, across West Africa, remittances consistently act as countercyclical and stabilizing flows but only

translate into significant long-run growth when supported by strong financial systems, institutional quality, and

diaspora investment frameworks. Nigeria’s case shows potential given its scale of inflows but underscores the

need for improved remittance formalization, lower transaction costs, and diaspora-linked investment

instruments such as diaspora bonds and SME financing programs.

CONCLUSION AND RECOMMENDATION

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This study investigated the complex relationship between remittance and the economic growth of Nigeria from

1990 to 2024. Using the Autoregressive Distributed Lag (ARDL) model, the analysis sought to determine if

there are significant short-run and long-run impacts of key economic variables on economic growth. Based on

the findings, the positive relationship between remittances and economic growth is a long-run phenomenon

that holds significant implications for Nigeria's present reality. As a nation heavily reliant on foreign exchange

from oil, Nigeria's economy is highly vulnerable to global commodity price shocks. The study's findings

demonstrate that remittances serve as a stable and reliable source of non-debt-creating foreign exchange,

crucial for cushioning the economy against external shocks and supporting an import-dependent structure.

While remittances provide a crucial economic cushion, the analysis reveals that Gross Fixed Capital Formation

(GFCF) stands as the most potent long-run driver of economic expansion, a result consistent with established

growth theories. The positive impact of GFCF, alongside the significant contributions of Government

Expenditure (GEX) and Trade Openness (OPEN), provides empirical backing for the nation's infrastructural

push and diversification agenda. Conversely, the finding that the Exchange Rate (EXR) maintains an

insignificant long-run relationship underscores the complexity of currency management in a dual-market

system and suggests that policy attention should strategically pivot towards structural fundamentals

Based on the empirical results of this study, the following recommendations were made:

1. Given the proven long-run positive relationship, the government should implement policies to encourage

a shift in remittance usage from consumption to productive investment. This can be achieved by

providing attractive investment opportunities for the Nigerian diaspora, such as diaspora bonds or

targeted real estate and infrastructure projects. Additionally, financial inclusion initiatives should be

strengthened to ensure that remittance recipients have access to banking and credit facilities to facilitate

savings and investment.

2. The finding that government expenditure is the primary adjusting variable highlights its crucial role in

macroeconomic management. The government should utilize fiscal policy in a counter-cyclical manner,

increasing targeted spending on critical sectors during economic downturns and consolidating during

periods of growth. This proactive use of government expenditure will help stabilize the economy and

ensure it remains on a sustainable growth trajectory.

3. To better track and manage remittance inflows, the Central Bank of Nigeria should continue to promote

the use of formal channels for sending and receiving funds. Lowering transfer fees and improving the

efficiency of these channels can reduce reliance on informal networks, thereby providing more accurate

data for economic forecasting and policy formulation.

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