The Effects of Small and Medium Enterprises (SMEs) Performance on Poverty Alleviation in Nigeria

The Effects of Small and Medium Enterprises (SMEs) Performance on Poverty Alleviation in Nigeria

T. A Abari-Ogunsona, R. A Danmola, E. O Braimoh, M. O Bofele

Department of Economics, Faculty of Social Sciences, Lagos State University, OJO

DOI: https://dx.doi.org/10.47772/IJRISS.2025.907000385

Received: 14 July 2025; Accepted: 20 July 2025; Published: 19 August 2025

ABSTRACT

The study investigated the effects of small and medium enterprises (SMEs) performance on poverty alleviation in Nigeria using annual time-series data from 1990 to 2022. Secondary data were sourced from the Central Bank of Nigeria (CBN) Statistical Bulletin and World Bank Development Indicators. The variables used in the study were – commercial bank loan to SMEs (CBLSME), employment rates (EMPR), trade openness (TOP), interest rates (RINTR), inflation rates (INFLR) and poverty rate (PR). The independent variable (SMEs) was proxied by commercial bank loans to SMEs, employment rate and trade openness, while interest rate and inflation rate were used as the control variables. The dependent variable (poverty alleviation) was proxied as the poverty rate. A multiple linear regression model was built and estimated using the Auto-Regressive Distributed Lag Bound test (ARDL). It was found that there is no long-run relationship among the variables. Findings revealed that all the variables except trade openness had a significant relationship with poverty alleviation in Nigeria. It was therefore recommended that the government should prioritise strategies that foster job creation by implementing targeted programs to support skill development, entrepreneurship and job placement. It suggests that policymakers should carefully assess interest rate policies, as transparent and flexible interest rate policies encourage savings and investments and contribute to economic growth. Also, policymakers should assess trade policies in order to develop strategies that promote fair trade.

Keywords: SMEs, Poverty, Trade Openness, Interest rate, Inflation rate, Employment rate, commercial banks’ loan to SMEs

INTRODUCTION

Poverty is a persistent socioeconomic problem, particularly in Nigeria, where a significant portion of the population lives below the poverty line (Bello, 2025). Despite being rich in natural and human resources, the country continues to struggle with high levels of unemployment, low income, poor access to basic services, and wide income inequality. These challenges have hindered inclusive growth and development, especially in rural and underserved areas (Isiaka & Olayiwola, 2022; Alalade, 2019).

 According to Oparah et al (2023), the persistence of poverty in Nigeria is driven by structural factors such as weak governance, inadequate infrastructure, limited access to quality education and healthcare, and a fragile social safety net. These issues compound the effects of rapid population growth and economic instability, making poverty alleviation a complex and urgent national priority. Addressing this problem requires coordinated efforts across various sectors to promote equitable resource distribution, job creation, and sustainable economic opportunities.

 The United Nations Sustainable Development Goal (SDG) 1 aims to end poverty in all its forms everywhere by the year 2030. This global objective recognises that poverty is not only a humanitarian issue but also a major barrier to sustainable development. In countries like Nigeria, poverty undermines economic progress by limiting access to education, healthcare, and productive employment. When a large segment of the population is trapped in poverty, consumer spending declines, productivity weakens, and national growth potential is diminished. Eradicating poverty is therefore critical, not only for improving individual well-being but also for accelerating inclusive and resilient economic development, United Nations (2015).

Furthermore, Onah (2024) stated that People are impoverished when they are unable to enjoy life’s better things, achieve their desired level of well-being, or maintain a standard of living considered acceptable by societal norms. Poverty, therefore, is not limited to a lack of income but also includes deprivation in access to opportunities, dignity, and the capacity to live a fulfilling life (Adewale et al, 2024). People affected by poverty may experience a deep sense of hopelessness, often feeling that life is not worth living. This emotional toll can lead to persistent gloom, low self-esteem, and a loss of motivation, further entrenching them in the cycle of deprivation. Poverty not only limits material well-being but also erodes psychological resilience and social connection.

Small and medium-sized businesses (SMEs) are acknowledged on a global scale as the driving force behind the expansion of contemporary economies. However, SMEs in the case of Nigeria have performed below expectations, with a dismal result. Thus, adding to the high rate of unemployment, poverty, and the low standard of living in the country. Though SMEs provide seventy percent (70%) industrial employment and sixty percent (60%) of agricultural sector employment, and it only account for ten (10%) to fifteen percent (15%) of the total industrial output with a capacity utilization of over thirty percent (30%) (Abdullahi, Tahir, Aliyu and Abubakar, 2015). According to Mbuyisa and Leonard (2017), poverty seems to be a persistent problem. Several authors have examined the role of SMEs in reducing poverty; however, none of the studies reviewed considered the role of trade openness in evaluating this relationship, which this study considers.

In light of this, this study seeks to investigate empirically the effect of Small and Medium Scale Enterprises (SMEs) on poverty alleviation in Nigeria.

LITERATURE REVIEW

Conceptual Review

Concept of Small and Medium-Scale Enterprises

Central Bank of Nigeria (2010) describes Small Scale Enterprises as those enterprises that have a total asset base (excluding real estate) of less than one (1) million naira, and employing or recruiting less than fifty (50) full-time staffs, while Medium Scale Enterprises are those enterprises that have a total asset base (excluding real estate) of less than fifty (50) million naira, and employing or recruiting less than one hundred (100) full-time workers (Osemene, Salman, and Kolawole, 2017).

Concept of Poverty

Poverty can be categorised, based on different criteria, such as absolute poverty, relative poverty, rural poverty and urban poverty (Darsono and Donkwa, 2016). Absolute poverty is explained as lack of minimum physical requirements or provision for existence; relative poverty, on the other hand, is explained as a situation in which a person’s or household’s provision or requirement of goods and services is lower than that of others, while Rural poverty is characterize by poor material or resources condition, lack of infrastructures, low level of education, underemployment, low investment, poor health status, and high out- migration, and also urban poverty on the other hand is characterized and considered by environmental degradation, low per capital income, overcrowded accommodation, and other problems or constraints associated with urban areas such as slums, shanties and ghettos (Abdullahi, Abubakar, Aliyu, Umar, Umar, Sabiu and Abubakar, 2015).

Theoretical Review

The Vicious Cycle of Poverty theory (Ragnar Nurkse, 1953)

The Vicious Cycle of Poverty theory, popularised by economist Ragnar Nurkse (1953), explains how poverty sustains itself through a self-reinforcing loop of low income, low savings, low investment, and low productivity. In this cycle, poor individuals or countries are unable to save due to inadequate income, which limits investment in critical areas such as education, infrastructure, and business development. This, in turn, results in low productivity and continued poverty. The theory applies strongly to the Nigerian context, where many Small and Medium Scale Enterprises (SMEs) face structural challenges such as limited access to finance, poor infrastructure, and weak institutional support. These challenges hinder SME growth, limit job creation, and reduce income-generation opportunities, thereby reinforcing poverty. Breaking this cycle requires targeted interventions such as improved access to credit, investment in human capital, and supportive government policies to stimulate SME development and foster inclusive economic growth.

Empirical Review

Abari-Ogunsona et al (2025) examined the impact of microfinance banks on poverty reduction in Nigeria. The coverage period employed was from 1990 to 2024, investigating how financial indicators like credit, money supply, inflation, and interest rates affect poverty levels. Using OLS and ARDL econometric models, the study found that the broad money supply reduced short-term poverty, but inflation significantly worsened it long term. Unexpectedly, domestic credit to the private appeared to increase poverty. The study concluded that microfinance alone is insufficient for poverty alleviation, recommending coordinated monetary, fiscal, and social policies

Ibi-Oluwatoba et al (2020) examined the effect of small and medium-scale enterprises on poverty reduction in Nigeria, using annual time series data sourced from the CBN Annual Report of various issues. SMEs were proxied by the ratio of commercial bank credit to SME’s to private sector credit (RCP), microfinance credit to SME’s (MCS), Inflation (INF) and interest rate (INT), while poverty was proxied by the incidence of poverty (PI). The model built was estimated using the autoregressive distributed lag bound test and Vector Error Correction Mechanism (VECM). It was found that there exists a long-run relationship between SME’s and poverty reduction. However, the error correction mechanism was not correctly signed and found to be insignificant, difficult to adjust in the long run. While DLRCP and LINF had a negative effect on DLPI, indicating PI reduction. DLMCS and LINT had a positive impact on DLPI. It found that DLMCS causes variation in DLPI and DLRCP, while LINT and LINF cause less change in poverty level in Nigeria. In conclusion, there is a significant relationship between SME’s and poverty reduction in Nigeria.

Kowo et al  (2019) examined the role of small and medium enterprises in poverty eradication in Nigeria. One hundred and forty-two (142) questionnaires were administered randomly to the entire employee population of the SME companies that are registered with SMEDAN in Lagos, Nigeria. The sample size was determined using the Yamane formula. The data was analysed using manual and electronic-based methods with the aid of a data preparation grid and the statistical package for the social sciences (SPSS) statistical package version 21.0. The study made use of statistical tools, which include: analysis of variance (ANOVA), correlation efficiency and regression analysis in testing hypotheses where applicable. Therefore, the study found out that SME development affects poverty alleviation and also Training organised by SMEDAN affects SMEs’ employment creation.

Osemene et al (2017) examined how SMEs contribute to poverty alleviation and reduction in Kwara State. The study employed primary data through the administration of a questionnaire to a sample of 100 small and medium-sized enterprises. A probit regression was used to shed light on the impact of income generated by owners of SMEs on poverty alleviation in Nigeria. The results of the regression analysis revealed that income generated by owners of SMEs has a strong impact on poverty alleviation in Kwara State. The study, therefore, recommended that small and medium-scale enterprise owners should seek and maintain sufficient funds at a lower interest rate for the smooth operation of their businesses.

Ajayi (2016) investigated the impact of the external business environment on the organisational performance of Micro, Small and Medium Scale Enterprises in Nigeria. It also reviewed literature on MSMEs, business environment and organisational performance. Secondary sources of data were utilised for the study. The findings from the reviewed literature showed that the external business environment (economic, political, legal, socio-cultural environment, demographic, natural, technological, global and financial environment) has an influence on MSME operators in Nigeria. It was also discovered that there exists a relationship between SMEs and the environment in which they occur. The study concluded that SME operators should understand all these types of external business environment and their implications on the organisational performance of their business activities to identify opportunities and threats to their businesses and update their knowledge, understanding and skills to meet the predicted changes in the realm of their enterprises

METHODOLOGY

Research Design

The design adopted for the study was an ex post facto research design. The study covers the period between 1990 to 2022, which implies a time frame of thirty-three (33) years. The variables deployed were employment rate, interest rate, inflation rate, trade openness, commercial bank loan to SMEs (SME financing) and poverty rates.

Sources of Data

Secondary data was collected from the Central Bank of Nigeria (CBN) statistical bulletin and the World Bank Development Indicator.

Model Specification

In order to examine the effects of small and medium enterprises (SMES) on poverty alleviation in Nigeria, a multiple linear regression model was built, which is a modified version of the model in the work of Edom, Inah and Emori (2015).

The research techniques postulate a functional model to capture the objective as follows

PR = f (SMEF, INFLR, REIR, TOP, EMPR)                                     (1)

The above equation can be defined econometrically as

 PR= β₀ + β₁SMEF + β₂INFLR + β₃ REIR + β₄TOP + β₅EMPR + e……… (2)

Where,

PR = Poverty Rate

SMEF = Small and Medium Enterprises Financing

INFLR = Inflation Rate

REIR = Real Interest Rate

TOP = Trade Openness (Trade percentage of GDP)

EMPR = Employment Rate

Β₀ is the Intercept

β₁, β₂, β₃ β₄ and β₅ are coefficient of the independents variables

µ _t is the stochastic term: This is because mathematicians believe that all the dependent variable in the model is explained by the independent variables. But econometricians believed that the independent variables do not explain all the variations in the dependent variables. Thus, they include an error term (stochastic disturbance term) that captures the other variables not included in the models.

A priori Expectation

On a priori grounds, all the specified independent variables are expected to have a positive influence on the dependent variable based on the theoretical expectation or presumption. They are stated below:

PR= β₀ + β₁SMEF + β₂INFLR + β₃ REIR + β₄TOP + β₅EMPR + e……… (2)

β₁< 0; β₂>0; β₃>0; β₄<0, β₅<0

Estimation Techniques

This study involves the use of the econometric method of analysis. The method used, as earlier stated, was a multiple regression model of the ordinary least squares (OLS) technique using the E-Views statistical package.

The estimation ensured that the variables in their behaviours conform to the assumptions of the classical regression model. Testing for stationarity without a constant or trend is the initial stage. The next step, if the variables are not stationary, is to compare the variables and see whether the differenced variables are stationary. The variables are said to be integrated of order one I (1) if they become stationary after the initial difference.

Then, the Autoregressive Distributed Lag model (ARDL) was used to conduct the cointegration test. Cointegration tests are used to determine whether two or more time series are cointegrated. Although the time series may not be stationary on their own, they may be stationary when combined linearly.

The ordinary least squares (OLS) method was employed for the analysis. This is due to the very straightforward OLS computational process, which is the best linear estimator available. It is the best linear unbiased estimator (BLUE) since it is efficient, has the minimal variance, and is unbiased in the conventional linear regression (CLR) model’s estimation. The basic assumptions of the OLS are related to the form of the relationship among the distribution of the random variance (Ui).

PRESENTATION AND ANALYSIS OF DATA

Descriptive Statistics

The section presented an analysis of descriptive statistics of all the variables under study. This helps in a better understanding of the distribution and trends in the variables. Furthermore, descriptive statistics were used to ascertain the statistical behaviour of the data in the model. Essentially, time series data spanning 1990 and 2022 was used. The table below summarises the mean, standard deviation, minimum and maximum values, as well as skewness and kurtosis of all the variables of interest.

Table 4.1 Descriptive Statistics Result

Source: Authors’ Computation (2023) using E-view

The dataset shows a mean poverty rate (PR) of 53.74, ranging from 40.00 to 80.80, with a slight right skew (0.59) and moderate kurtosis (2.08); the Jarque-Bera test (JB = 3.07, p = 0.2157) confirms normality. Real interest rate (RINTR) has a mean of 3.07, high variability (SD = 10.14), is left-skewed (-1.37), peaked (kurtosis = 5.54), and not normally distributed (JB = 19.21, p < 0.05). Employment rate (EMPR) averages 57.04, is slightly left-skewed (-0.63), moderately peaked (2.52), and normally distributed (JB = 2.46, p = 0.2917). Trade openness (TOP) averages 37.64, is slightly right-skewed (0.66), moderately peaked (2.96), and also normally distributed (JB = 2.41, p = 0.2993).

Lag Selection Criteria

Table 4.2 Optimal Lag Order Selection Result

Source: Authors’ Computation (2025) using E-view 10

To identify the most suitable lag length, the study employed various information criteria, namely the Akaike information criteria (AIC), Schwarz information criteria (SIC), Hannan-Quinn (HQ) criteria, and final predictor error (FPE) criteria.

The natural rule of thumb is the selection of the lag identified by the criterion which gives the lowest value. From the above table, the model indicates that lag 2 is the optimal lag and that AIC is the best criterion for the model since it has the lowest value, 31.75623.

Unit Root Test

The study adopts the Augmented Dickey-Fuller method of testing the unit root of data series, which by implication establishes the stationarity of the data series. The result of this test forms the basis of either rejecting or accepting the null hypothesis.

The results are presented in Table 4.3 below

Table 4. 3Augmented Dickey Fuller Test Statistics for all variables

Source: Authors’ Computation (2025) using E-view 10

The unit root test results, based on the Augmented Dickey-Fuller (ADF) test, indicate that all variables except real interest rate (RINTR) are stationary at first difference, i.e., integrated of order one (I(1)), while RINTR is stationary at level, i.e., integrated of order zero (I(0)). According to Engle and Granger (1987), a variable is I(d) if it becomes stationary after differencing d times. The decision rule applied is that the null hypothesis of a unit root is rejected when the p-value is less than the significance level. These results imply that the series are now suitable for further econometric analysis such as cointegration testing, policy inference, and forecasting.

Table 4.4 Co-integration Test using Auto-Regressive Distributed Lag Bound Co-integration Test

Source: Authors’ Computation (2023) using E-view 10

The results of the Autoregressive Distributed Lag (ARDL) bound test in Table 4.4 indicate that there is no long-run relationship between poverty rate, commercial banks loans to small and medium scale enterprises, employment rate, real interest rate, inflation rate and trade openness in Nigeria between 1990 and 2022. The F-statistic is less than the critical value for the upper bound at the 5% significance level, which means that we can reject the null hypothesis of no cointegration. This suggests that there is no long-run relationship between the variables.

Auto-Regressive Distributed Lag Estimation

Since there is no long-run relationship in the ARDL bound test, the ARDL model can be used for the estimation.

Table 4.5: Estimation Result

Source: Authors’ Computation (2025) using E-view 10

Interpretation

The table above shows that;

The regression analysis shows that most variables have statistically insignificant effects. Lagged poverty rates (PR (-1) and PR (-2)) have positive but insignificant coefficients (0.3679 and 0.2582). Employment rate (EMPR) shows a strong negative effect (-2.4610) on poverty, but it is also statistically insignificant, contrasting with Osemene et al. (2017), who found significant SME impacts in Kwara State, suggesting regional variations. Commercial bank loans to SMEs (CBLSME) have an unexpected positive, yet insignificant, coefficient (0.4609), which might imply issues with loan mismanagement or accessibility, as noted by Abari-Ogunsona et al. (2025). Inflation rate (INFLR) has insignificant effects, with a complex dynamic (negative immediate, positive lagged), partially aligning with Isiaka and Olayiwola (2022). Only Trade Openness (TOP) significantly reduces poverty (coefficient = -0.5609, p = 0.0320), supporting Ibi-Oluwatoba et al. (2020) on trade’s role in poverty reduction. The insignificance of the real interest rate (RINTR) may reflect inconsistent monetary policies.

CONCLUSION AND RECOMMENDATION

Conclusion

This study examines how Small and Medium Scale Enterprises (SMEs) impact poverty alleviation in Nigeria. Using data from the World Development Indicators and Central Bank of Nigeria publications, the research focuses on specific objectives: assessing the effects of SMEs financing, inflation rates, trade openness, interest rates, and employment rates on poverty alleviation. The aim is to gain insights into the comprehensive role of SMEs in influencing poverty levels in Nigeria.

In conclusion, there is a significant negative relationship between the employment rate and poverty alleviation. This implies that an increase in the employment rate corresponds to a decrease in poverty levels.  Also, there is a positive relationship between commercial bank loans to Small and Medium Enterprises (SMEs) and poverty alleviation. Contrary to expectations, an increase in these loans is associated with an increase in poverty levels. Furthermore, there is a negative relationship between the real interest rate and the poverty rate, suggesting that an increase in the real interest rate is linked to a decrease in poverty levels. Also, the study finds a negative relationship between trade openness and poverty alleviation, and there is also a negative relationship between the inflation rate and poverty alleviation.

Recommendations

Based on the findings of this study, the policy implications are discernible. It is therefore recommended that:

Given the significant negative relationship between employment and poverty, policymakers should prioritise job creation through entrepreneurship support, skill development, and targeted employment programs. The observed negative link between real interest rates and poverty also suggests the need for balanced monetary policies that encourage investment without harming low-income groups. Additionally, the significant negative effect of trade openness on poverty highlights the importance of inclusive trade policies that promote fair trade and equitable growth. Recognising the multifaceted nature of poverty, an integrated policy approach—addressing employment, SME financing, interest rates, and trade—will be most effective in achieving sustainable and inclusive economic development.

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