Impact of Bank Lending Rate on Unemployment in Nigeria

Impact of Bank Lending Rate on Unemployment in Nigeria

Livinus Mmaduabuchi Okeke, Ebele Stella Nwokoye

Department of Economics, Nnamdi Azikiwe University, Awka Nigeria.

DOI: https://dx.doi.org/10.47772/IJRISS.2025.90600051

Received: 25 May 2025; Accepted: 29 May 2025; Published: 28 June 2025

ABSTRACT

Considering the high unemployment rate, available studies for Nigeria are one-sided as they focused only on aggregate unemployment without considering disaggregating unemployment which could provide areas of focus for the monetary policy authority towards tackling unemployment in Nigeria. This study intends to breach this gap by disaggregating unemployment into youth and adult unemployment. This study examines specifically, the impact of banks’ lending rate on youth and adult unemployment rates in Nigeria using annual data from 1981 to 2021. It employed the autoregressive distributed lag technique and estimation procedures under the framework of the Philips curve theory and Taylor’s rule. The data for this study is an annual time series data that covers from 1981 to 2021. The data for the variables were sourced from the Central Bank of Nigeria (CBN) statistical bulletin and the African Development Bank database. The Econometric software for estimation is STATA 17.  Results show that banks’ lending had negative and significant impacts on aggregate, and youth unemployment, and negative but insignificant impacts on adult unemployment in the long run. The study concludes that banks’ lending rate affected aggregate, youth and adult unemployment differently. To reduce unemployment in the Nigeria, the monetary authority should implement a tight monetary policy to increase the banks’ lending rate, however, with the youth category in exception.

INTRODUCTION

The macroeconomic objectives of an economy may be summarized as achieving sustainable economic growth, maintaining price stability, ensuring full employment, and achieving balance of payment equilibrium. Full employment, a macroeconomic target that has proven difficult for developing nations such as Nigeria to attain, has been a focal point of attention in recent years. It is often assessed by the measurement of the unemployment rate. Unemployment is a scenario whereby persons who are eager to work at the prevailing wage rate are not able to obtain jobs (Echem, Aduku & Ejiofor, 2022). Analysis of the unemployment rate is mainly on the demographic decomposition – youth, adult and aggregate groups. The concept “youth” covers persons that are between the ages of 15 to 24 years and “adult” denotes persons that are aged 25 years and above (International Labour Organization – ILO, 2016). 

Unemployment refers to the state in which individuals either cease their job search or labour under substandard circumstances (known as underemployment), resulting in negative consequences for the individuals, their families, society, and the economy. Unemployment has the potential to permanently harm the future job opportunities of an individual and repeatedly generates long-run patterns of inappropriate labor conduct. Unemployment can also generate a sense of vulnerability, idleness and uselessness among the labour force. An apparent gain in claiming the productive potentials of the unemployed is an economic one. The unemployed are a costly group. Unemployment dampens the economic well-being of a nation. The subsequent loss of income by the workers translates to less savings and less aggregate demand. Most of the unemployed, especially the youth, are compelled to live on family welfare, thus constraining household consumption and investment. Furthermore, social investment in education is lost when human capital remains idle. Governments also experience lower social security payments and generally must spend more on remedial services, like crime avoidance and anti-drug avoidance. In total, these impacts pose a concrete risk to the economic growth and development potential of a country (Remeikienė, Žufan, Gasparėnienė & Ginevičius, 2020). 

With the high rate of unemployment, the disposable income of individuals falls from a lack of stable income. This leads to a decline in goods and services. Businesses could be adversely affected by the decline in consumption, which is usually accompanied by a fall in GDP. Therefore, putting an end to unemployment as a macroeconomic problem is dominating the macroeconomic policy objectives of policymakers, especially in recent years in developing countries such as Nigeria. The policy authority responds to increasing unemployment by enhancing aggregate demand, household total spending, enterprises, and government in the economy. Expansion of the economy – expanding the production of goods and services – improving the demand for goods and services; enterprises could then start to employ more workers to increase the supply to meet the demand by consumers (Zhang & Davise, 2023). One of the policies that have been employed over the years to curb unemployment is monetary policy. 

The term “monetary policy” describes a collection of regulations intended to control the cost, value, and availability of money in an economy.  It aims to accomplish important macroeconomic goals and is directed by the anticipated level of macroeconomic activity over a given time period.  By changing the circumstances under which they meet the economy’s liquidity needs, monetary policy is implemented. The central bank provides liquidity to players in the money market by adjusting various components of its balance sheet or implementing actions that have a more direct impact on interest rates (Essien et al., 2016). Central banks are government institutions that issue currency and conduct monetary policy. The CBN is the institution in Nigeria that does this and is specifically under law to implement monetary policy, and has over the years used a lot of tools like interest rates, money supply management, and exchange rate management in advancing its goals (Okeke & Chukwu, 2021).

Interest rates are the returns on borrowed funds and come in different forms in the economy. They are the Monetary Policy Rate (MPR), previously referred to as the Minimum Rediscount Rate, the prime rate, and the savings rate. The MPR is the reference interest rate at which the CBN loans financially stable deposit banks. It has significant influence on the availability of credit, saving conduct, monetary aggregates, and reserve of the banking system. Changes in the MPR can potentially generate volatility in other interest rates within the economy, particularly consumer lending interest rates. Raising interest rates has the potential to deter consumer spending and investment by businesses and, as a consequence, reduce the number of hiring and increase unemployment. Reducing interest rates can encourage economic growth and employment. As Fix (2023) shows, increased borrowing costs have the potential to curtail capital accumulation and consequently the demand for labor. Swastika and Masih (2016) also show that unemployment is likely to be influenced by the lending rate since monetary policy operates to indirectly impact such rates in the long run. These interest rate adjustments, therefore, can have great effects on employment levels through an influence on investment, capital expenses, and the demand for labor. 

A brief analysis of the trend showed that the unemployment rate rose from 23.1% in 2018 to 33.3% in 2020, with the youth unemployment rate and youth underemployment at roughly 42.5% and 21%, respectively. As of 2022, the unemployment rate increased to 37.7 per cent (Guardian, 2023) with an underemployment rate of 22.8 per cent. The employment condition of Nigeria has continued to deteriorate for over a fourth straight quarter. The employment conditions in Nigeria dropped to the lowest in about 3 years for the 3^rd quarter of 2023. Between July to September 2023, Nigeria’s employment condition degenerated below the benchmark point of 50 to 46.6 points compared to the 47.8 points recorded between April and June 2023. Nigeria’s current employment condition falls below the business condition – 58 and the level of production (59.8) (Bailey, 2023; Egole, 2023; Utomi, 2022). With a yearly new entrant of about 4 to 5 million into the Nigerian job market, the unemployment rate has continued to be challenging for the country. A study of this nature, therefore, should be conducted because to treat issues concerning the relevance of lending rate to the unemployment problem, an appropriate framework is a necessity to serve as a point of reference. The choice of policy variables and instruments is very crucial in dealing with the issue of unemployment. It is an issue confronting monetary policymakers. Studies such as this are capable of providing guidelines and lessons that could be relevant and can offer a good basis for policymaking. Also, considering the huge youth unemployment in particular, a study that could examine the effect of Banks lending rate on unemployment of different groups like the youth and adults should be a goal for the country. Thus, a study of this nature should be conducted. Also, given the fast-rising unemployment rate in the country, a study of this nature could endue and empower the policy authority to appraise and evaluate appropriately, different policy instruments and policy variables. It is safe to say that a study of this nature can set up the right framework and enable policymakers to identify and formulate policy that can reduce the unemployment of youths, adults and the aggregate unemployment level using the right policy variables and instruments. 

In recent times, slowing employment growth and increasing unemployment in Nigeria have hit both youth and adult populations hard. As a result, today’s unemployed are faced with an increasing deficit of decent work opportunities and high socio-economic uncertainties. Recently, the secretary general of the United Nations (UN) called upon countries to put an end to the vicious cycle of unemployment, stating that the unemployed – especially the youth are a valuable asset for our future. Unemployment is one of the major fundamental development challenges facing Nigeria now. Government from one time to the other have put in some policies and programmes to lessen unemployment in Nigeria. For example, the formation of Nigerian Directorate of Employment (NDE November, 1986) its propensity for acquisition programme for National Poverty Eradication Programme, (NAPEP, 2001), Poverty Alleviation Programme (PAD June 2009), Subsidy, Reinvestment and Employment Programme and the Youth Enterprise with Innovation in Nigeria (Yowin 2011) are a portion of the different interventions rich with job creation opportunities (Agang, 2010 & Ogun muck 2013). 

The interventions also include the establishment of exchange markets, removal of interest rates, foreign exchange markets unification and liberalization of bank licensing in 1987. In 1989, payment of interest on demand deposits was granted to banks, while interest rate administration was introduced in 1991 (Adeoye, Ojapinwa, & Odekunle, 2014). By keeping the benchmark lending rate, or MPR, at 14.0% and the corresponding asymmetric interest rate corridor at +200/-500 basis points, a strict monetary policy stance was maintained in 2017.  The liquidity ratio (LR) and cash reserve ratio (CRR) were set at 30.0% and 22.5%, respectively.  The Monetary Policy Committee (MPC) lowered the MPR from 50 basis points to 13.5% in March 2019 along with an asymmetric interest rate corridor of +200 and -500 basis points.  The CRR and the LR were maintained at 27.5% and 30.0%, respectively, while the asymmetric interest rate corridor was maintained at +100 and -700 basis points around the MPR. 

The benchmark lending rate, also known as the MPR, was raised from 11.5 percent to 18.5% in May 2022. In 2023, it was decreased to 18 percent, while the asymmetric corridor around the MPR and cash reserve ratio remained at +100 and -500 basis points (Adegboyega, 2023).The money supply was hiked in July 2023 by 16.53 per cent. Specifically, M2, comprising of narrow money, quasi money and currency outside banks increased to N64.35 trillion, which is 15.84 per cent higher than the N55.55 trillion reported for May 2023. In the same period, the exchange rate was adjusted upwards, which led to the naira depreciation (Business Day, 2023). 

The purpose was to boost credit flow to the productive sector in order to grow the economy, which would enhance the production of goods and services, boost aggregate demand, household spending, businesses, etc., and lead to more hiring and a lower unemployment rate.  The unemployment rate has remained high and may have continued to rise, according to the outlook. The unemployment crises in the country seem to become apparent as the years pass by. The unemployment issue affected not just the welfare of both the youth and the adults, but also the potential performance in the long run and the economic stability of the country. There is an increasing uncertainty of the people, especially the unemployed in their hopes of experiencing a satisfactory entry to the labour market, which has had unfortunate consequences on the economy and the society at large. The high unemployment rate in the country has limited several youths and adults from contributing effectively to national development. Especially with the current economic situation, they have less to spend as consumers, and as savers, they have less to invest and oftentimes do not have the “voice” to bring about transformation in society. The existing social unrest in the country and the rejection of the extant socio-economic system are also linked to the unemployment problem the country is facing. If no precedence is given to the unemployment problem among other economic problems, the situation could degenerate to preventing the country from innovating and developing competitive advantages in terms of human capital investment, and capable of eroding prospects. 

There are several empirical studies both in Nigeria and the rest of the world. Previous studies have examined the impact of Banks lending rate on unemployment. In Nigeria, studies such as Onyema, Ijeh and Obot (2023); Mwamuo (2022) and Goshit and Loremba (2020) are among the studies that have examined the impact of Banks lending rate on the unemployment rate. Several findings have been made in this area and have added great value to the related literature. However, to the best of our knowledge, all the previous studies focused on the aggregate unemployment rate. Therefore, an empirical study of the impact of Banks’ lending and specific demographic unemployment groups such as youth unemployment and adult unemployment has remained a gap to cover. The nature of youth unemployment is rather deferring from adult unemployment, and in most cases, are exposed to a higher risk of entering unemployment. The main objective of this study is therefore to determine the impact of banks’ lending rate on aggregate, youth and adult unemployment in Nigeria. There is a need to target policies on demographic unemployment groups. Empirical evidence in these areas will ensure a more specific and better way of addressing the issue of unemployment. Because there is need to target policy instrument that will be effective in reducing unemployment, this study can also play a vital role in our choice of monetary policy instruments in an attempt to influence the unemployment rate through monetary policy. It will also serve as a trajectory for efficient monetary policies and measures that can lead to inflation reduction in the country. 

To achieve the objective of this study, the paper is organized thus:

A review of empirical literature comes after the introduction section followed by the research method. The result is presented and discussed in section four while section five concludes this paper. 

LITERATURE REVIEW

Review of Basic Theories

Philips Curve Theory

Bill Philips first proposed the Philips Curve Theory in 1958 to explain the connection between unemployment and changes in money wages in the UK between 1861 and 1957.  According to the theory, most enterprises could affect some prices in marketplaces with imperfect competition.  The Philips curve theory explains why the unemployment rate and changes in wage rate are inversely related.   There may be a trade-off between inflation and unemployment as a result of this research.   It is thought that if the government attempts to reduce inflation, for example, by enacting contractionary monetary policy, unemployment will rise.   Consequently, one would anticipate a negative relationship between unemployment and inflation (Singh, 2018). 

The basics of the Philips curve is that inflation pressure could be week if the rate of unemployment is high. Also, the output gap is considered negative. With a low rate of unemployment, wages and prices may have upward pressure as firms compete for workers. Reducing unemployment and high economic growth rates could result to shortages of components that as well as other inputs resulting to an increase in price inflation. 

Phillips’ theory received criticism for failing to base his analysis of the inverse link between wage inflation and the unemployment rate on a theoretical framework.  On the other hand, Friedman contended that workers’ misunderstandings about whether real earnings have increased in tandem with nominal wages are the reason behind the short-term non-vertical Philips curve.  According to Friedman, Philips had committed three significant errors. He claims that these include (i) Philips’ inability to distinguish between nominal and real earnings  (ii) He neglected to account for both short-term and long-term trade-offs between the unemployment rate and wage inflation, and (iii) he neglected to give the predicted inflation a role.  As a result, a number of short-term Philips curves were produced, each based on the anticipated rate of price inflation (Singh, 2018).  The expectations-augmented Phillips curve is one example of this.

Nevertheless, from an operational standpoint, the expectations-augmented Phillips curve (4) requires more information regarding projected inflation and NAIRU, or the natural rate of unemployment (U*), which is determined by the trend rate of unemployment and is also referred to as the long-term unemployment rate. The adaptive expectations model and the logical expectations model are two approaches for modelling inflation rate expectations.

This theory is relevant to have study has it led to explanations of role of the government in attempting to reduce inflation, for example, through contractionary monetary policy, which is a basis of this study. In other words, the theory provides a foundation and the basis to augment monetary policy into the analysis of unemployment in an economy. 

The Taylor Rule

John Taylor established the eponymous rule in 1993. An assumption behind the Taylor rule is that policymakers possess knowledge of and can reach a consensus over the magnitude of the economy’s output gap. The Taylor rule is based on the assumption that the equilibrium federal funds rate, which is the interest rate targeted when inflation is at its desired level and the output gap is zero, remains constant at 2 percent in real terms and around 4 percent in nominal ones (Bernanke, 2015). John Taylor adopted the beliefs in the late 1970s and early 1980s that companies and households make decisions based on future expectations. Jonh Taylor is also of the assumption that firms and households are intelligent in forming their expectations. The Taylor rule also assumes that wages and prices do not adjust instantaneously to their market-clearing levels. Given the New Keynesian economics, Taylor stated that consumers would develop rational expectations concerning the future. But, because of nominal frictions – that is, sticky prices and sticky wages, it is not possible to reach market-clearing levels of wages and prices instantaneously. 

The gap between monetary theory and applied monetary policy is bridged by the Talor rule by revealing that the collection of activist feedback rules in line with a well-behaved equilibrium comprises certain rules of interest rate. With regard to the Taylor rule, only changes in the production gap and inflation have an impact on the instrument, which is the nominal short-term interest rate.  As stated by Taylor (1993), the regulation explained the monetary policy choices taken in the early years of Alan Greenspan’s Chairmanship of the Board of Governors of the U.S. Federal Reserve System, commonly referred to as “the Fed” (1987-1992).

The original Taylor rule, according to Carare and Tchaidze (2005) is defined as:

 (2.1)

Where:

 = short-term nominal interest rate

r* = real interest rate target

 = inflation gap – which is the difference between actual inflation  and inflation target .

 = output gap – which is the difference between potential and actual output. 

 and  = positive coefficients.

Following the formulation of Taylor (1993) originally,  and  were both 0.5. The inflation and real interest rate targets were both 2 per cent, while the constant, C was 1.

There are several modifications of the original Taylor rule because researchers have been making efforts to make the rule more realistic appropriate and applicable (Carare & Tchaidze, 2005). One way to overcome the seeming shortsightedness of policymakers is to incorporate forward-looking Behaviour into the original Taylor rule.  As a result, the central bank will determine the interest rate in the short term based on inflation and output gap expectations rather than current levels. Alternative modifications include the introduction of inflation and output gap lags.  The literature claims that when the interest rate is determined, it is impossible to determine the true inflation and output gaps.  Consequently, lags could guarantee more accurate timing.  Interest rate smoothing Behaviour: The most popular Talor rule modification among the basics is a lagged short-term interest rate. 

Theoretical and empirical papers have used the Taylor rule widely, both from prescriptive and descriptive points of view among others. Over the course of the last thirty years, the Taylor rule has consistently maintained its significance as a crucial tool for implementing monetary policy. The Taylor rule provides a concise and accurate explanation of the historical development of monetary policy (Net, 2014). 

The Taylor rule is subject to criticism due to its failure to include all the elements that influence the economy, despite its straightforwardness. Policymakers respond to several factors, including changes in the production gap, inflation, currency rates, political developments, and the stock market. Many writers and researchers advocate for the use of the unemployment gap instead of the output gap to enhance the accuracy of the statistics, as proposed by Taylor (1999). Okun’s law, developed in 1962, demonstrates the correlation between the output gap and the unemployment gap. Implementing such a regulation might be beneficial in mitigating economic volatility, particularly if the natural interest rates and unemployment rate are precisely assessed. Furthermore, lag-based rules do not always indicate a retrospective approach, since delays might serve as predictors of future values (Carare & Tchaidze, 2005). Furthermore, the Taylor rule fails to offer instructions on how to proceed when the projected rate is negative, a scenario commonly observed during moments of economic crisis in the majority of nations. The Taylor rule is subject to criticism due to the challenging nature of measuring the output gap, leading to varying assessments. Reaching a consensus on the magnitude of the production gap at any given time is both impractical and unadvisable (Bernanke, 2015). 

This theory or rule is related to this study as it provides a framework for analysis. Also, it is a valuable contribution to monetary policy. Since this study concerns monetary policy, it is appropriate to include the Taylor rule in this study. 

REVIEW OF EMPIRICAL LITERATURE

Banks’ Lending Rate and Aggregate, Youth, and Adult Unemployment Rates 

In a study conducted by Göçer (2013), the author investigated the influence of banks’ lending rates on unemployment in fourteen specifically chosen nations within the European Union. The study encompassed the time frame spanning from 1980 to 2012. The study used panel data analysis techniques that considered cross-section dependencies and structural fractures.  It has been demonstrated that as loan rates rise, unemployment rates in the countries under study decline. 

Furlanetto, Gelain, and Groshenny (2015) examined the effects of monetary policy on the US economy, focusing on its response to unemployment increases driven by structural factors, using a Taylor-type rule over the period 1981 to 2015. Their findings showed that reduced matching efficiency led to higher inflation and a positive output gap, resulting in increased interest rates.

In Nigeria, Essien et al. (2016) studied the relationship between unemployment and monetary policy from 1983Q1 to 2014Q1 using a vector autoregressive (VAR) framework. They found a positive association between the policy rate and unemployment. Similarly, Arigo et al. (2016), employing the VAR approach over 1983–2014, observed that increases in the policy rate caused a significant and sustained rise in unemployment over ten quarters.

Benazic and Rami (2016) investigated the link between monetary policy and unemployment in Croatia from 1980 to 2016 using the ARDL technique, concluding that the monetary policy rate (MPR) had a minimal impact on unemployment. Selim and Hassan (2018) compared interest-free and interest-based monetary policies across 23 developed countries, grouped into those following interest-free monetary policy (IFMP) and interest-based monetary policy (IBMP). Using t-tests, they found that countries implementing IFMP performed better, indicated by a lower Monetary Index (MI), than those adopting IBMP.

Goshit and Iorember (2020) analyzed the asymmetric transmission of the MPR to unemployment in Nigeria from 2000Q1 to 2018Q4 using an asymmetric ARDL model. They found that tightening the MPR had a significant, positive effect on unemployment, demonstrating full transmission, whereas reducing the MPR had a negligible and insignificant impact, showing incomplete transmission. Zhou (2021) studied the relationship between monetary policy and US unemployment from 1983 to 2018, finding that unemployment disparities significantly and positively influenced the federal interest rate.

Nwamuo (2022) assessed the impact of MPR on Nigeria’s unemployment rate from 1981 to 2020 using ARDL and ECM approaches. Results indicated that while the prime lending rate and minimum rediscount rate had insignificant short-term effects, the prime lending rate was strongly associated with increased unemployment in the long term, whereas the minimum rediscount rate was linked to decreased unemployment.

Enyoghasim, Ogwuru, Agu, and Igbinedion (2022) examined the influence of interest rate spread on unemployment in Nigeria from 1981 to 2011 using ARDL. Their findings showed that inflation and interest rates had insignificant effects on unemployment. Onyema, Ijeh, and Obot (2023) explored how commercial banks’ lending rates, bank credit to the private sector, total private sector deposits, and inflation impacted unemployment in Nigeria over 1991–2021 using ARDL. They identified a consistent relationship between unemployment and these financial variables but highlighted the inefficiency of monetary policy tools in managing unemployment.

Evgenidis and Fasianos (2023) investigated monetary policy’s impact on US macroeconomic and labor market indicators, considering the Covid-19 pandemic, using a Bayesian VAR model. Their results showed that expansionary monetary policy through interest rate adjustments had a stronger and more lasting effect on unemployment during the pandemic compared to pre-pandemic periods.

Couto and Brenck (2023) studied how interest rate fluctuations affected female and Black employment in Brazil from 2012 to 2021 using panel data fixed effects. They found that real interest rates positively influenced the unemployment gap between Black and White males but had no significant effect on the gap between Black women and White males. Additionally, they observed a negative effect on the unemployment ratio between White women and men.

Bennani (2023) examined the effects of monetary policy shocks on unemployment across ethnic groups in the US from 1969Q2 to 2015Q4, using narrative identification and local projections. The study revealed that monetary tightening positively and significantly affected White workers, had an even greater effect on Black workers, but showed no statistically significant impact on Hispanic workers.

Abbas and Qadr (2023) analyzed the effectiveness of monetary policy instruments on Iraq’s unemployment rate from 1990 to 2020 using the ARDL model. Their findings demonstrated a strong and significant relationship both in the short and long term. 

A lot has been done empirically on the impact of monetary policy (interest rate) on unemployment both in Nigeria and the rest of the world. However, to the best of my knowledge, none of the previous studies disaggregated the unemployment rate. In developing countries such as Nigeria, unemployment is more prevalent among the youth than any other demographic group, perhaps as a result of the dominating youth population in the country. Thus, reducing youth unemployment has become a major area of focus for the monetary authority. Therefore, an empirical relationship between monetary policy (interest rate) and the specific demographic unemployment groups such as youth unemployment and adult unemployment has remained a gap to cover. This study tends to cover this gap and add value to the previous literature by providing empirical evidence on the impact of interest specifically on the youth unemployment and adult unemployment rate, which have not been established by the previous studies. In addition, the previous studies used monetary policy rates. But this study tends to use banks’ lending rates, therefore, may also differ from the previous studies in terms of the indicator. This is necessary because, in Nigeria, the banks’ lending rate may directly affect investment and economic activities and, therefore, employment level than any other rate. 

RESEARCH METHOD

Theoretical Framework

This analysis is grounded in the Phillips Curve theory and Taylor’s Rule. According to Friedman (1968) and Phelps (1968), the Phillips Curve becomes vertical at the natural rate of unemployment, also known as NAIRU. This implies that inflation rises when unemployment falls below its natural level and declines when unemployment exceeds it. The short-run Phillips Curve illustrates this inverse relationship between inflation and unemployment (Van der Ploeg, 2005). The model considers the accelerationist version of the Phillips Curve, where the inflation gap for the upcoming period (πₜ₋₁) is influenced by the output gap (yₜ), and expected inflation is assumed to be equal to the inflation rate in the previous period (Van der Ploeg, 2005). 

 with b > 0         (3.1)

Where  represents a cost-push disturbance. Systematic deviance from the NAIRU () induces inflation 

or deflation. In an alternative way, with unexpected inflation, aggregate supply increases. Aggregate demand is determined negatively by the real interest rate: 

  (3.2)

In this framework, the long-term real interest rate is denoted as *r*, ϑ represents the degree of persistence in the output gap, and *eyₜ* stands for a demand shock, such as that caused by fiscal expansion. The inflation forecast for the next period is expressed as: πₜ₊₁|ₜ = πₜ + b·yₜ, and the real interest rate is given by: iₜ – πₜ₊₁|ₜ = iₜ – πₜ – b·yₜ. Assuming that employment and output are at their efficient, long-run equilibrium levels, the target output gap is zero (Van der Ploeg, 2005). Solving this framework through optimization leads to the derivation of the Taylor Rule, which determines the optimal nominal interest rate (Zhou, 2021).: 

        (3.3)

 Where  represents the monetary authority’s (Central Bank) policy interest.  is the equilibrium interest rate, the current inflation rate is , while  represents the target rate of inflation the central bank sets.  represents the production gap. The expected value is influenced by currency loss, presented as:

       (3.4)

Where  is the production gap,  is the inflation rate at time t, while the target inflation rate is .

Macroeconomic policy primarily focuses on stabilizing key variables such as production levels, unemployment, and the volatility of past outcomes relative to expectations. However, since natural levels of production and employment are not directly controllable through monetary policy, they are not considered explicit policy targets (Zhou, 2021). Instead, a central objective is to select an inflation target that supports maximum aggregate output while allowing only minimal fluctuations in interest rates around the target.

A key element of the Taylor Rule is its emphasis on managing economic volatility rather than assessing overall production efficiency. The rule serves as a guideline for setting interest rates in a way that stabilizes inflation and output. It can be reformulated using forecasts for one-period-ahead inflation and output gaps. By substituting equation (3.3) into (3.2), the rule provides a functional expression for the optimal nominal interest rate.;

       (3.5)

Monetary disinflation—achieved by targeting a lower inflation rate (π\*)—typically leads to a temporary rise in nominal interest rates, which in turn reduces aggregate demand and output. A negative demand shock, such as a decline in *ey*, lowers output and increases unemployment, prompting a more accommodative monetary policy with lower interest rates and inflation. In contrast, a permanent shock that raises *e\_π* results in increased inflationary pressure, which must be addressed through tighter monetary policy. This often leads to higher unemployment (Van der Ploeg, 2005).

Therefore, contractionary monetary measures—such as raising interest rates, reducing the money supply, or allowing currency depreciation (an increase in the exchange rate)—tend to increase unemployment. Conversely, expansionary policies have the opposite effect.”

The data for this study is an annual time series data that covers from 1981 to 2021. The data for the variables will be sourced from the Central Bank of Nigeria (CBN) statistical bulletin and the African Development Bank database. The Econometric software for estimation is STATA 17.

On the other hand, the ARDL model specified to capture the objectives would be estimated using the Ordinary Least Square (OLS) technique with lags to be determined by Akaike’s Information Criterion (AIC). The OLS technique is a linear estimator used to estimate linear fun random variables. It is unbiased in that its average or expected value is assumed to be equal to the true value. Also, it has a minimum variance in the class of all such linear unbiased estimators. An unbiased estimator with the least variance is known as an efficient estimator. Thus, the model can be saithe d to be the best linear unbiased estimator, hence, the study subjects to the OLS estimator.

The ARDL method as established by Pesaran, Shin, & Smith (2001), is used to investigate long-run relationships between variables based on the standards of F-tests or t-tests. The strength of the ARDL model has to do with its ability to handle relationships irrespective of whether the regressors are I(0), I(1) or a mix of I(0) and I(1) variables. Assuming there is a long-run relationship among the variables in the model, without having any prior information about the direction of the long-run relationship among the variables, the ARDL approach enables us to estimate an unrestricted conditional error-correction model (UECM) by taking each of the variables in turn as dependent variables.”

Model Specification

Our model is based on the theoretical framework discussed in section (3.1). For this study, we chose the CBN lending rate as our policy interest rate. Also, for this study, money supply and exchange rate are included as monetary policy variables. In the literature, the consumer price index (CPI) is commonly used as an indicator of inflation. In this study, CPI is used as the measurement of the degree of inflation, . As regards the target rate of inflation, we use the CBN target inflation rate. As regards the production gap – , we use the Hodrick-Prescott (HP) filtering technique, to decompose the real GDP variable by minimizing the variance of fluctuation. The function form of the unemployment equation is presented as:

     (3.6)

Equation (3.6) is respecified as;

 (3.7)

Where UNEMP is unemployment rate captured as aggregate unemployment rate AUNEMP, youth unemployment rate YUNEMP, and adult unemployment rate ADUNEMP, LENDR; banks’ lending rate; broad money supply M2; real exchange rate EXCH; inflation gap; INFGAP; (which captures the difference between actual inflation and target inflation).

Production gap GDPGAP, (the difference between actual GDP and potential GDP, this was generated empirically using the HP filtering technique); and β­₀ is the constant while β­₁­ to β­₅ are the coefficients of the respective explanatory variables.

Equation (3.7) is specified in an autoregressive-distributed-lag (ARDL) model as;

  (3.8)

In this model, *eᵢ* represents the error term, while *βᵢ* (for i = 1, 2, 3, … 6) and *ϕᵢ* (for i = 1, 2, 3, … 6) denote the long-run and short-run coefficients of the explanatory variables, respectively. The appropriate number of lags to include in the model is determined using the Akaike Information Criterion (AIC), which helps identify the optimal lag length based on model fit and complexity.

To achieve objective one, two and three, equation 3.8 was estimated by respectively substituting aggregate unemployment, youth unemployment and adult unemployment as dependent variables. 

The Autoregressive Distributed Lag model has small properties, which is an advantage over other similar models. The ARDL model provides reliable and unbiased estimates, along with valid *t*-statistics, for both short-run and long-run relationships, even in the presence of endogenous explanatory variables. This model remains applicable when the regressors are integrated at level I(0), first difference I(1), or a combination of both. If cointegration is found among the variables, it indicates a stable long-run relationship, meaning the variables can adjust toward equilibrium over time. This long-run adjustment is captured through an ECM, represented as:

   (3.9)

Where;  is the error correction term. All data analysed were done using STATA 17 Econometric package.

RESULT PRESENTATION AND DISCUSSION OF FINDINGS

This section presents the Pre-estimation tests, long-run estimates and the post-estimation tests.

Table 1: Descriptive statistics of the variables

From the result, the ADUNEMP, YUNEMP, AUNEMP, LENDR, EXCH, M2 and INFGAP centred around the mean values respectively, as shown by the standard deviation values that are close to the mean values respectively. On the other hand, the production gap has a value that is either far above or below its mean value. The minimum values fall below their corresponding mean values, while the maximum values exceed their respective means.

The result showed significant skewness probability values for the adult unemployment rate, youth unemployment rate, aggregate unemployment rate, M2, and real exchange rate respectively. On this basis, the null hypothesis of the normal distribution of the variables respectively is rejected at the 5 per cent level. This means that the series for the ADUNEMP, YUNEMP, AUNEMP, M2, and EXCH respectively are not normally distributed. The variables are either skewed to the right or left. On the other hand, banks’ lending rate and the production gap variables have insignificant skewness probability values. This means that the bank’s lending rate and the production gap variables are normally distributed. 

As regards the Kurtosis, significant probability values are recorded respectively for the ADUNEMP, YUNEMP, AUNEMP, and INFGAP. This means that at the 5 per cent level, the variables are significantly different from the kurtosis of normal distribution. However, banks’ lending rate, broad money supply, real exchange rate, and the production gap showed insignificant Kurtosis probability values. Therefore, the kurtosis of normal distribution is accepted. 

Unit Root Tests

The stationarity of the variables was examined using the Augmented Dickey-Fuller (ADF) unit root tests. Prior to conducting the test, the optimal lag length was determined, and the results are displayed in Table 2. 

Table 2: Lag Order Selection

All the lag-order selection criteria are significant at lag two. This suggests the selection of lag two as the appropriate lag order. Therefore, lag 2 is used as the appropriate lag. With the identification of the lag length, we conducted a unit root test using the ADF and the Phillips-Perron (PP) unit root tests and the result is shown in Table 3.

Table 3: Unit root test results

The ADF unit test indicates that the ADUNEMP, YUNEMP, AUNEMP, LENDR, M2, EXCH, INFGAP and production gap variable are non-stationary at the level. This is indicated by the test statistics of the variables, which are below the 5 percent critical value at the given level. Thus, the null hypothesis regarding the presence of a unit root at the level is confirmed. In light of this, we calculate the first derivative of several economic indicators, such as ADUNEMP, YUNEMP, AUNEMP, LENDR, M2, EXCH, INF, and GDPGAP variable. Subsequently, we reevaluate the statistical test. The first difference result shows significant test statistics for the relevant variables. Thus, the null hypothesis regarding the existence of a unit root is rejected when considering the first difference. Consequently, the ADUNEMP, YUNEMP, AUNEMP, LENDR, M2, EXCH, INFGAP, and GDPGAP exhibit stationarity when measured at the first difference. In other words, it is the first-order integral, denoted as I(1). 

The outcome of the PP test closely resembles that of the ADF. The variables of ADUNEMP, YUNEMP, AUNEMP, LENDR, M2, EXCH, INFGAP and GDPGAP are stationary when differenced once. Given that none of the dependent variables exhibit integration or stationarity at the base level, the application of the ARDL approach in this study is justified. 

Bounds Test for Level Form Relationship 

This part focuses on examining the impact of broad money supply, bank lending rates, and currency rates on aggregate employment, youth employment, and adult employment rates, which align with goals one, two, and three respectively. We will present and analyse the findings in a comprehensive manner. In order to begin our analysis, we utilize the Pesaran, Shin & Smith(2001) Bounds test to determine if there is a cointegration, or level form connection, among the variables in our model. The findings of the Bounds test are displayed in Table 4.

Table 4: Bounds test result for level form relationship (level effect) of the variables in the model

The p-value is significant for the order 1, I(1) variables for all the estimated models – the estimations when the AUNEMP is taken as the dependent variable, when the YUNEMP is taken as the dependent variable, and when the ADUNEMP is taken as the dependent variable. Also, the F-value and the t-value are both significant. This means that the variables in the estimated models respectively for aggregated, youth and adult unemployment rates are cointegrated – and have a long-run relationship. Based on the test result, we could say that the aggregated unemployment rate, broad money supply, banks’ lending rate, exchange rate, inflation gap, and production gap have long-run relationships. Also, there is a cointegration between the YUNEMP, M2, LENDR, EXCH, the INFGAP, and the GDPGAP. Similarly, there is a level form relationship between adult unemployment rate, broad money supply, banks’ lending rate, exchange rate, the inflation gap, and the production gap. 

Table 5: Long-run estimates of the model for objectives one, two and three

The impact of banks’ lending rate on aggregate, youth and adult unemployment rates were examined. As shown in column (1), the coefficient for banks’ lending rate is negative and significant in column (1). Since the coefficient is statistically significant, the null hypothesis that banks’ lending rate has no meaningful effect on the aggregate unemployment rate in the long run is rejected at the 5% significance level. Specifically, a 1% increase in the banks’ lending rate corresponds to a 0.02% significant decrease in the aggregate unemployment rate. Therefore, banks’ lending rate negatively and significantly influences aggregate unemployment over the long term. A similar pattern appears in column (2), where the coefficient is -0.0634 with a t-value of -2.42, indicating that a 1% rise in the lending rate results in a 0.06% reduction in YUNEMP. Given the significance, the null hypothesis is also rejected at the 5% level here, confirming a negative and significant long-run effect of LENDR on YUNEMP. In column (3), representing adult unemployment, the coefficient is -0.0177 with a t-value of -1.65, suggesting that a 1% increase in LENDR leads to a 0.02% decrease in adult unemployment; however, this effect is not statistically significant at the 5% level. Thus, LENDR has a negative but insignificant influence on ADUNEMP in the long run.

The analysis further reveals that broad money supply positively and significantly affects aggregate and adult unemployment, while its impact on youth unemployment is positive but not significant. Additionally, the real exchange rate exerts a positive and significant influence on aggregate, youth, and adult unemployment. The INFGAP shows a positive but insignificant effect on aggregate and youth unemployment, whereas it has a negative and significant effect on ADUNEMP. Lastly, the GDPGAP is positively related to AUNEMP, though insignificantly, but negatively and significantly related to youth and ADUNEMP in the long run.   

Table 6: Diagnostics of the model 

Evaluation Based on Statistical Criteria

This concerns the evaluation of the statistical reliability of the estimated parameters of the model. The reliability criteria include the following statistical tests: F-statistics, t-statistics, and the coefficient of determination (R²).

Coefficient of Determination (R²) Test: The model in column (1) explains 88.04% of the variation in the aggregate unemployment rate, leaving 11.96% due to factors outside the study. For youth unemployment, the independent variables account for 87.41% of the variation, with the remainder attributed to other factors. Similarly, the model explains 88.69% of the variation in adult unemployment, with the rest influenced by variables not included in the analysis.

The F-Test: “The F-values of 6.08 (p = 0.0000), 12.52 (p = 0.0002), and 48.02 (p = 0.0000) for aggregate, youth, and adult unemployment rates respectively are all significant at the 5% level. Therefore, the null hypothesis that the independent variables have no joint effect on these unemployment rates is rejected. This confirms that the independent variables jointly have a significant impact on aggregate, youth, and adult unemployment rates.

Evaluation Based on Econometrics Criteria

Durbin-Watson Autocorrelation Test: The DW stat is 2.2405, 2.2811 and 2.4943 respectively for the models for aggregate, youth and adult unemployment rates in columns (1), (2) and (3). Since the DW statistic respectively is approximately 2, we accept the null hypothesis of no autocorrelation. This means that the models for aggregate, youth and adult unemployment rates in columns (1), (2) and (3) are free from the problem of autocorrelation.

Breusch-Godfrey Serially Correlation Test: The insignificant Chi-square Statistics of 1.376 (p = 0.2408),3.023 (p = 0.0821), and 1.708 (p = 0.4300) respectively for the models for aggregate, youth and adult unemployment rates in columns (1), (2) and (3) means that the independent variables are serially uncorrelated. Therefore, the null hypothesis of no serial correlation is accepted at the 5 per cent level.

Breusch–Pagan/Cook–Weisberg test: The Chi-square statistics of 1.01 (p = 0.4453), 1.16 (p = 0.2823), and 1.34 (p = 0.2467) for the models on aggregate, youth, and adult unemployment rates in columns (1), (2), and (3) respectively are not significant. Hence, the null hypothesis of constant variance among the independent variables is accepted at the 5% level, indicating the absence of heteroskedasticity in the models. 

Multicollinearity Test: A variance inflation factor (VIF) test was also performed to assess multicollinearity among the independent variables. The results are shown in Table 4.9.”

Table 7: Variance inflation test statistics

The VIF of the variables respectively is very low. The mean-variance inflation factor is also lower than the conventional 10. Therefore, the null hypothesis of no multicollinearity is accepted. This means that the independent variables do not have the problem of multicollinearity. This justifies the inclusion of the variables in the same model as explanatory variables. 

Cusum square test: The CUSUM square test was conducted to determine the stability of the model. 

DISCUSSION OF FINDINGS

The results demonstrate that, over time, LENDR had a negative and significant effect on both the aggregate and youth unemployment rates, as well as a negative and negligible effect on the ADUNEMP. Similarly, adjustments to the LENDR have a long-term positive impact on the economy’s youth and aggregate unemployment rates, but they have no effect in reducing ADUNEMP in the long term. This means that a higher bank lending rate could be implemented if there is a higher unemployment rate. However, based on our findings, tight monetary policy in terms of higher bank rates worsens the youth unemployment rate. Though it reduces the adult and aggregate unemployment environment, the youth unemployment environment gets wider with higher bank rates. This implies that the youth category or population could be given downward reviewed rates – to ease monetary policy, different from the general lending rate provisions in the economy. The findings are in agreement with most previous studies including Brazil, Couto and Brenck (2023), and Nwamuo (2022) who found a negative relationship between the lending rates and unemployment for a category of people – the relative unemployment of white women to white men, and a positive relationship between the lending rates and unemployment for another category – the relative unemployment of black men to white men. 

Other findings show that broad money supply had a positive and significant impact on aggregate and adult unemployment and positive but insignificant impact on youth unemployment rate. This implies that changes in quantity of money (expansionary monetary policy) have a detrimental impact on unemployment in the long-run. The higher the broad money supply in the long-run, the higher the unemployment environmental especially adult and aggregate unemployment. Exchange rate had a positive and significant impact on aggregate youth and adult unemployment in the long-run. This implies that exchange rate depreciation would instead result in a higher unemployment rate. However, depreciation of real exchange rate should enhance exports resulting in increase in GDP and environment. But based on our findings, a rise in exchange rate ultimately increases prices, which increases unemployment, majorly because the rise in prices reduces aggregate demand and prices. INFGAP had a positive and insignificant impact on aggregate and YUNEMP and a negative but significant impact on ADUNEMP in the long-run, GDPGAP had a positive and insignificant impact on the aggregate unemployment and a negative but significant impact on youth and adult unemployment in the long-run.     

The policy implications of our findings are that higher LENDR could be implemented if the unemployment environment is higher, and slows down the domestic economy. This means that the monetary authority could implement a tight monetary policy to increase the banks’ lending rate. However, a tight monetary policy in terms of higher LENDR may not be targeted to the youth population. In other words, a tight monetary policy in terms of higher banks’ lending rates could be targeted to groups, with the youth category an exception. With the ease of monetary policy (lower banks’ lending rate) for the youth category, and a tight monetary policy (higher banks’ lending rate) for other groups in the economy, both aggregate, youth and adult unemployment rates could be reduced.

CONCLUSION AND RECOMMENDATION

This study examined the impact bank lending rate on the unemployment rate in Nigeria, covering the periods from 1981 to 2021. Based on the findings, we therefore conclude that bank lending rate affected youths and adult unemployment differently. From our results, banks’ lending had negative and significant impacts on aggregate, and youth unemployment, and negative but insignificant impacts on adult unemployment in the long run. This indicates that high bank lending rate had a negative and significant impact on youth unemployment. The study therefore recommends that to reduce unemployment in Nigeria, the monetary authority should implement a tight monetary policy to increase banks’ lending rate. However, with the youth category in exception because bank lending rate had a negative significant impact on youth unemployment. 

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