Investment Indicators and Economic Growth in Nigeria: A Systematic Review

Abstract

Most developing countries, including Nigeria, are stuck in a vicious cycle of low investment caused by insufficient domestic savings, resulting in inadequate capital formation and a large savings-investment gap. Given the significance of investment in poverty alleviation and economic growth, the study conducted a disaggregated analysis of various measures of investment in economic growth in Nigeria from 1981 to 2020. Using the conventional and structural break stationarity tests, as well as the Autoregressive Distributed Lag (ARDL) approach, the epistemological findings confirm a compelling co-integrating relationship among the study variables and show that credit to the private sector, domestic investment, economic liberalization, foreign portfolio investment, and interest rate have a significant positive impact on long-term growth, whereas foreign direct investment, capital expenditure, and inflation have a significant negative impact. Furthermore, the short-run results revealed that economic liberalization, private-sector credit, and portfolio investment all correlate positively with growth. In contrast, foreign direct investment, infrastructure spending, and inflation rate are profoundly negative. The study, therefore, advocated for effective fiscal and monetary policy coordination to lower the cost of doing business, incentivize and open up opportunities for domestic and foreign investors, increase infrastructure spending to create jobs, reduce poverty and sustain growth.

1. Introduction

Investment entails the purchase or acquisition of new capital equipment such as machines, buildings, and other means of production that boost the economy’s productive capacity. It encourages long-run economic growth by producing new capital goods and expanding countries’ production efficiency. The level of growth in any economy is a reliable predictor of the country’s ability to invest and allocate resources effectively [1]. Countries see investment as a major element in increasing productivity by advancing technological progress and reducing unemployment as they advance toward economic growth. Investment is regarded as the engine of dynamic economic growth, which in turn contributes to the achievement of political and socioeconomic goals. Fluctuations in investment have a significant impact not only on aggregate demand, but also on an economy’s factor productivity and long-term economic growth [2]. This has prompted several countries to concentrate on improving favorable investment conditions.

Investment comprises both domestic and foreign components that can be broadly classified into four key components: the private domestic investment (private investment), public domestic investment (government investment), Foreign Direct Investment (FDI), and Foreign Portfolio Investment (FPI). Government and public corporations invest in social infrastructure, real estate, and tangible assets through public domestic investment [3]. Public investment is needed to construct the infrastructure and social capital necessary for private sector investments in sectors of the economy that provide higher returns on investments [4]. Because private initiative and resources are limited in developing countries, the government must create an enabling environment through fiscal stimulus sufficient to encourage the growth of private investment. Public investment in key sectors of the economy should thus serve as a catalyst for economic growth. However, public investment is usually made for political reasons and thus lacks economic justification [5].

Private investment may be complemented by public investment in goods that improve the productivity of private capital, such as infrastructure. Economic growth is boosted by public investment because it encourages new private investment to benefit from the higher productivity it generates. When the government invests in public goods like physical infrastructures or services, it offers companies a chance to invest, which creates an economic cycle of opportunity [2]. Public investment boosts output, attracts private investment, and reduces unemployment. Furthermore, governments can encourage private investment through public spending by creating an appropriate political and institutional setting that minimizes uncertainty. Governments can implement public incentives like tax cuts or subsidizing policies that motivate private firms to invest in projects [6].

According to the traditional Keynesian model, an increase in government spending will lead to an increase in total demand, which will lead to an increase in total investment and employment. Keynesians frequently cite the multiplier effect as proof that government spending can effectively boost private sector investment. Neoclassical economists, on the other hand, reject this notion and contend that government spending can suppress consumer spending and private investment through interest rates or the tax structure [6]. Crowding-out effects result from two investments competing for the same resources and from a decline in private sector productivity as a result of a larger public sector. As a result of competition for limited material and financial resources, a larger fiscal deficit brought on by increased public investment may also drive away private investment through high interest rates, credit restrictions, and increased current or future tax burdens on households. In other words, countries are unable to convert additional public investment into consistently high output growth when there is a lack of absorptive capacity [7].

According to Kumo [8], private investment refers to investments made in the private sector with the intention of making a profit. It is a fundamental economic operating principle in a market-based economy where physical and financial resources are frequently privately owned and production choices are motivated by the pursuit of profit. Private investment has the potential to increase the economy’s productivity and efficiency by leveraging resources and making smart investment decisions [9]. Thus, private investment is a crucial precondition for economic growth because it enables business owners to launch economic activity by effectively allocating resources to produce goods and services. According to conventional wisdom, private investment affects growth more significantly and positively than public investment. Productivity in the private sector is typically higher than that of the public sector due to the relative lower level of corruption there [6].

The ability of the private sector to allocate and use resources effectively, as well as its contributions to the volume of gross domestic investment, are both critical. The provision of infrastructure and social services, as well as the creation of jobs and income, have all been fueled by private sector investment. The private sector has the potential to produce equitable and sustainable growth in developing nations, according to the European Commission (EU) [10]. According to the International Finance Corporations (IFC) [11], the private sector plays a significant role in addressing the developmental challenges faced by emerging nations by providing contributions in a variety of areas, such as growth, employment, poverty reduction, service provision, food security, climate change mitigation, environmental sustainability, and tax contributions. This means that at the very least, the existence of the private sector can encourage economic growth and the eradication of poverty.

Private investment involves making wise investment decisions, whereas public investment is frequently perceived as being politically motivated and lacking in economic reason. Countries see increased private investment as a crucial factor in boosting productivity levels by expediting technological advancement and bringing down the unemployment rate as they move toward economic expansion. By producing new capital assets and boosting countries’ production efficiency, it encourages long-term capital accumulation [7]. Since the private sector has continued to be one of the primary drivers of growth in contemporary economies all over the world, developing countries have given it more attention [12]. As a result, there is currently a paradigm shift from government to private sector-led growth policies, emphasizing the dominance of market forces in the economy and the reduction of the role of the public sector in production. The public sector needs to re-evaluate its part in the growth process in light of the new paradigm. The rule states that public-sector spending must be focused on initiatives that complement rather than supplant private-sector investment [13].

Gitahi et al. [14] argued that developing countries should aim for and maintain a level of private investment of at least 25% of GDP in order to sustain growth and poverty reduction. They noted the significance of private investment in contributing to economic growth, including its capacity to efficiently allocate and employ resources. Bage [15] came to the conclusion that investment rates between 20% and 25% could lead to growth rates between 7% and 8% based on the experiences of Asian nations. Between 1999 and 2019, Nigeria’s average private investment as a percentage of GDP was less than 15% [16]. This percentage is lower than that obtainable in most Sub-Saharan African economies and that needed to achieve higher growth rates [17]. Despite a significant increase in government fiscal operations in recent years aimed at increasing private sector-led growth, the stylized reality in Nigeria demonstrates that private investment growth has been noticeably unacceptable and has continued to decline [18].

Foreign capital inflows are important mechanisms for moving resources from developed to developing nations, where they are typically more productive. To close the savings and foreign exchange gap, capital inflows are required from countries with low wealth creation. Given that those economies rely so heavily on foreign sources for their productivity expansion, these foreign capitals augment domestically sourced capitals [19]. The consistent rise in international financial trades is what has led to a considerable boost in capital inflows into economies around the world. The increased globalization of investors seeking greater rates of return on investment over time and the chance to diversify risk globally has also been a fundamental factor underlying this experience. As a result, many economies have removed restrictions that hinder capital inflows in order to promote the inflow of globally mobile capital.

Foreign portfolio investments, foreign direct investments (or foreign aids and grants), foreign remittances, Official Development Assistance (ODA), and foreign loans are all examples of this inflow of foreign capital. The main benefit of the influx of foreign capital is that it promotes growth, especially in the nations that attracted it. For least developed countries (LDCs), FDI is regarded as a significant source of capital formation, technological know-how, employment creation, and trade opportunities [20]. The widely acknowledged benefits of FDI include filling the gap between domestically mobilized savings and desired investment, increasing tax revenues, and enhancing management, technology, and labor skills in host countries. These might assist the nation in breaking the cycle of underdevelopment [21]. The reduction of poverty in the host economies may be affected directly or indirectly by foreign direct investment.

By indirect impact, we mean that FDI may encourage the reduction of poverty through economic growth, which elevates living standards as a result of rising GDP, advances in technology and productivity, and changes in the business climate. On the other hand, FDI may directly contribute to the reduction of poverty through the creation of jobs and the generation of income as a result of the rise in the demand for jobs among foreign investors [22]. Because foreign investors typically have better investment arrangements and an understanding of the economic dynamics of the host country, FDI is also thought to be less vulnerable to crises. As a result, parties involved in developing economies typically assume that FDI inflow will bring the much-needed capital, new technologies, marketing strategies, and management skills.

Due to the overwhelming significance of FDI, governments in developing nations, including Nigeria, are paying more attention to the potential benefits of attracting significant FDI. Even though it seems reasonable to claim that FDI can increase knowledge spill overs to host economies, a host country’s ability to capitalize on and profit from these externalities might be constrained by its own domestic economic circumstances, which include an underdeveloped financial market and unregulated domestic industries. Additionally, since a sizable portion of FDI enters the country through mergers and acquisitions, a healthy stock market is better for the host economy. This has the potential to expand the sources of financing available to businesses, and consequently, play a significant role in fostering connections between domestic and international investors [20].

Foreign Portfolio Investment (FPI) refers to foreign investments in stocks, bonds, and other securities. FPI is a subset of global capital flows that include the transfer of financial assets like cash, stocks, and bonds across international borders in an effort to increase profits [23]. It includes transfers and financial assets like stocks or bonds that take place when investors buy non-controlling stakes in foreign corporations or government bonds, short-term securities, or notes, or buy foreign corporate or government securities. When foreigners deposit money in a nation’s banks or make purchases on its stock and bond markets, sometimes for speculative reasons in search of a profit, that nation’s financial system has received foreign funds. Debt and equity financial assets are included in portfolio investments in this way [19].

For foreign investors, Nigeria’s government has opened up a number of economic sectors and provided a number of incentives. Foreign portfolio flows have been promoted through liberalization with the primary goal of enhancing market activity and access to foreign capital [24]. Given that emerging markets have a low correlation to developed ones, foreign investors have been motivated to diversify their holdings, protect themselves against risk, and earn higher returns in these markets. By elevating foreign portfolio investment to a significant source of investible funds to support investment in both developed and developing nations, these developments have increased the variety of investment opportunities available. As trade flows result from people, businesses, and nations taking advantage of their own comparative advantages, capital and accumulated assets also move to the areas which are likely to be most productive [23].

Even a casual observer of Africa is likely to be aware that very few African nations have Nigeria’s level of economic potential. The nation is abundantly endowed with both natural and human resources. With a GDP of over USD 420 billion and an estimated population of over 211 million (National Population Commission, [25] estimate), Nigeria provides investors with a cheap labor pool, a wealth of natural resources, and perhaps the largest domestic market on the African continent. The nation has a large amount of arable land and favorable environmental factors that support agricultural activities. Nigeria has the second-largest oil reserves in Africa. Along with having abundant oil and gas reserves, the nation also has about 37 solid mineral types that are commercially available but have not yet been fully utilized. These minerals can be used in a variety of fields, including construction, pharmaceuticals, food processing, and other manufacturing processes [20]. Nigeria has a vast resource base, but the nation has not been able to grow its economy significantly or draw in foreign or portfolio investment on a par with its economic potential [19]. This is due to a lack of strong capital and money markets, poor infrastructure, corruption, an ineffective system for registering properties, an inconsistent regulatory environment, restructured trade policies, and slow and ineffective courts and dispute resolution procedures [12].

Nigeria has been categorized as an economy with low savings and even lower investment [7]. The government implemented a number of policies and reform initiatives, including the National Economic Empowerment and Development Strategy (NEEDS), privatization, and commercialization of federal and state-owned enterprises, to escape this low savings and low investment trap. Access to credit instruments offered by financial institutions has been made easier thanks to Nigeria’s foreign investment regime being liberalized by the Nigerian Investment Promotion Commission Decree of 1995. The lack of investable funds or their low availability, particularly in the productive sectors of the economy, has in turn been blamed for the unfavorable investment environment. Evidently, promoting balanced investment in physical and financial assets as well as human, natural, and environmental capital is necessary for the promotion of sustained economic growth [9].

The difference between the current rate of savings in LDCs and the required rate of investment is enormous. The Brussels Declaration set 30 global development objectives for LDCs, including a 25% investment to GDP ratio and a minimum 7% annual GDP growth rate for LDCs to achieve sustainable development and poverty reduction [26]. In order to achieve the desired level of economic growth, which is essential for achieving the Sustainable Development Goals (SDGs) and reducing poverty, the Nigerian government must figure out how to encourage both domestic and foreign investment. The current study, which takes into account the importance of time horizons in econometric modeling, offers insightful information to policymakers worried about the expansion of domestic and foreign investment by robustly evaluating the long- and short-term effects of disaggregated investment measures on Nigeria’s economic buoyancy and prosperity using annual data from 1981 to 2020 and the Autoregressive Distributed Lag (ARDL) methodology. Following is the format for the remaining section of the paper: The study’s theoretical foundation is covered in Section 2, and the methods and procedures that were used to conduct the study are covered in Section 3. Following section five, which wraps up the study and offers policy recommendations, section four discusses the empirical findings.

2. Theoretical background of the research

Theoretical research on the relationship between investment and economic growth is quite extensive and has produced a class of well-defined theories, including but not limited to the accelerator theory, Jorgensen’s neoclassical or user cost theory, and Tobin’s Q theory of investment, which provided the theoretical underpinning for this study.

2.1 The accelerator theory of investment

Although it gained popularity after Keynes, the accelerator theory can be traced at least as far back as Clark [27]. The theory is based on the investment philosophies of Keynes [28], Samuelson [29], and Chenery [30]. According to Clark [27], the demand for capital varies more with the acceleration of that demand than with the volume of demand for the finished good. The model is known as the simple accelerator model of investment and is thought to have Keynesian roots due to Clark’s emphasis on quantity rather than price. The basic accelerator model only considers output growth when explaining investment and presupposes that the desired level of capital is reached every time period. According to the accelerator principle, a company’s increased output rate will necessitate a corresponding increase in its capital stock.

According to the accelerator theory, investment will be impacted by changes in the variables affecting national income. The relationship between increases in investment and income growth is represented numerically by the accelerator. If national income rises, it would typically be positive; if national output or income stays the same, it might be zero [29]. Based on the acceleration theory, a change in output directly affects how much demand there is for capital goods. The relationship between the capital-output ratio and the change in output level determines how much the demand for capital goods has changed. Since changes in aggregate output are dependent on changes in aggregate expenditure or aggregate demand, which themselves equal changes in the level of equilibrium income, we can say that total investment in the economy is dependent on changes in aggregate demand at any given time period, which in equilibrium equals increases in the national income plus replacement investment, which is frequently assumed constant [14].

According to the accelerator theory, investment changed as a linear function of output changes. It highlights the importance of demand conditions as the primary factor influencing investment. The demand for factories and machinery is deduced from the demand for the company’s goods. So, as demand or profits increase in an economy, so do business investments. It suggests that businesses have two options for meeting demand when supply levels result in excess demand. To balance demand, they either increase investment or raise prices to reduce demand [30]. According to the theory, most companies choose to boost output and profits. In addition, the theory explains how this expansion draws in more investors, which invigorates growth. The model, however, has over time been retooled into several versions used in scientific inquiry, including the simple and flexible accelerator model, the partial adjustment model, and the crowding-out and crowding-in accelerator model, since it neglects the impact on investment of uncertainty, expectations, profits, financial factors, and capital cost [30, 31, 32, 33].

2.2 Jorgensen’s neo-classical/user cost of capital theory of investment

The user cost of capital theory, proposed by Jorgensen [34] and Jorgensen [35], states that a firm weighs the costs and benefits of investment and invests when the benefits outweigh the costs [36]. The theory is founded on the neoclassical theory of optimal capital accumulation, which is determined by the relative prices of production factors. The model attempts to assess the benefit and cost of capital ownership. The model, which is based on Keynes’ original approach, attempts to relate the level of investment to the Marginal Efficiency of Capital, the interest rate, and the tax rules that affect firms.

If a perfect capital market exists, the firm will maximize its present value by investing in all projects with positive total expected returns at the current market rate of interest. According to Irving Fisher [37], all productive projects can be ranked based on their rate of return over cost. Firms will continue to invest as long as the rate of return exceeds the market rate of interest because doing so increases capital accumulation or investment. The theory held that because consumption is a function of disposable income, and savings is income not spent, while investment is income spent. This means that savings and investment are also determined by disposable income. This theory contends that savings determine investment and is primarily concerned with market equilibrium and economic growth at full employment rather than resource underutilization [38].

2.3 Tobin’s Q theory of investment

According to Tobin’s [39] Q theory of investment, investment is made until the market value of assets equals the replacement cost of assets. The Tobin average q is calculated using stock market data on the value of the firm’s share price. The theory identified a link between investment fluctuations and stock market fluctuations. When firms have many opportunities, stock/share prices in the stock market tend to be high (as it entails high investment and high returns to shareholders). As a result, stock prices demonstrate the incentive to invest [40]. The theory is a positive function of q, which is defined as the ratio of the market value of existing capital to the cost of capital replacement. Q is a barometer for investors because it evaluates a company’s prospects. Q can be defined as.

2.4 Stock value of firms/replacement cost of investment

When q is greater than one, the firm will increase its investment because the profits generated will exceed the cost of the firm’s assets. If Q is less than one, the firm would be better off selling its assets rather than attempting to use them because the firm’s value is less than the cost of reproducing their capital. The ideal state is when Q is close to one, indicating that the firm is in equilibrium [41]. An unstable political climate may also discourage investment or lead to high levels of inflation, which would lower the rate of national growth. For investors, inflation volatility breeds uncertainty regarding the viability of potential projects. It is an established fact that fiscal deficits can discourage private investment. Political instability is also likely to reduce policymakers’ time horizons, which will result in less-than-ideal macroeconomic short-term policies. Additionally, it might cause policy changes to occur more frequently, which would increase volatility and harm economic growth [42].

The assumptions underlying the widespread optimizing investment models typically are not met in developing nations like Nigeria because of institutional and structural factors like the absence of well-functioning financial markets, the relative larger role of the government in capital formation, distortions brought on by foreign exchange restrictions, and other market imperfections [9]. Besides, when attempts are made to apply the standard models empirically, severe data limitations arise even if they could be directly adjusted to the Nigerian situation. For instance, most developing nations lack data on factors like the stock of capital, the labor force, and wages, and without knowledge of real financing rates (debt and equity), it is difficult to determine the service price or user cost of capital. In economies like Nigeria where independent public institutions play a relatively important role, there are also serious conceptual issues with defining private investment; it is frequently questionable whether such enterprises should be classified as part of the public sector or part of the private sector [43]. Overall, it is probably reasonable to assume that these various issues have historically tended to make it difficult to model investment for developing countries along conventional theoretical lines.

3. Data and methodology

In order to offer empirical solutions to the research problems, this study used the quantitative approach and a descriptive research design. Answering who, what, when, where, and how questions about a research problem is made easier by descriptive research designs [9]. Secondary data sourced from the Central Bank of Nigeria (CBN), the National Bureau of Statistics (NBS), and the statistical database for the World Development Indicators were used for this study. The subjects of the data collection included the Real Gross Domestic Product (RGDP), various components of investment disaggregated into Foreign Direct Investment Inflow as a percentage of GDP (FDI), Foreign Portfolio Investment (PINV), Gross Fixed Capital Formation as a percentage of GDP (GFCF) (proxy for private investment), and Government Capital Expenditure (CAPEX) (proxy for public investment), and control variables such as Domestic Credit to Private Sector (CPS), Inflation rate (INFR), Effective Interest rate (INTR) and Economic Liberalization (ELB) adopted as a dummy variable, respectively. Economic liberalization was included in the model as a dummy variable and measured with the value of 1 in years 1986–1993 and 2007–2020 when major attempt at economic liberalization started in Nigeria. All variables were taken on annual basis in nominal and percentage terms from 1981 to 2020. Data on RGDP, CAPEX, CPS and PINV taken in nominal forms were log-transformed to stabilize the variance of the series and make interpretation in proportionate terms while the GFCF, INFR and INTR retained their percentage form. E-views 12 statistical package was utilized for data analysis.

Following the lead of Omojolaibi et al. [44], Kengdo et al. [45] and Babu et al. [9], the study modeled economic growth proxy by RGDP as a function of disaggregated forms of investment and the afore-mentioned control variables. In accordance with the literature and the study’s objectives, an Autoregressive Distributed Lag (ARDL) model with modifications, presented in its general form as an unrestricted error-correction model (UECM) regression from which all tests and estimations are conducted was devised and specified as follows:

$$\begin{array}{l}∆\mathit{Ln}{\mathit{\text{RGDP}}}_{\mathrm{t}}=\propto {}_0+\propto {}_1{\text{LnRGDP}}_{\mathrm{t}}+\propto {}_2{\mathrm{ELB}}_{\mathrm{t}+}\propto {}_3{\mathrm{FDI}}_{\mathrm{t}}+\propto {}_4{\text{GFCF}}_{\mathrm{t}}+\propto {}_5{\text{INFR}}_{\mathrm{t}}\\ +\propto {}_6{\text{INTR}}_{\mathrm{t}}+\propto {}_7{\text{LnCAPEX}}_{\mathrm{t}}+\propto {}_8{\text{LnCPS}}_{\mathrm{t}}+\propto {}_8{\text{PINV}}_{\mathrm{t}}+\sum _{i=0}^n{\beta }_1∆{\text{LnRGDP}}_{\mathrm{t}}\\ +\sum _{i=0}^n{\beta }_2∆{\mathit{ELB}}_{\mathrm{t}}+\sum _{i=0}^n{\beta }_3∆{\mathit{FDI}}_{\mathrm{t}}+\sum _{i=0}^n{\beta }_4∆{\mathit{\text{GFCF}}}_{\mathrm{t}}+\sum _{i=0}^n{\beta }_5∆{\text{INFR}}_{\mathrm{t}}+\sum _{i=0}^n{\beta }_6∆{\text{INTR}}_{\mathrm{t}}\\ +\sum _{i=0}^n{\beta }_7∆\mathrm{Ln}{\text{CAPEX}}_{\mathrm{t}}+\sum _{i=0}^n{\beta }_8∆{\mathit{\text{LnCPS}}}_{\mathrm{t}}+\sum _{i=0}^n{\beta }_9∆\mathit{Ln}{\text{PINV}}_{\mathrm{t}}+{\mathit{ECT}}_{\mathrm{t}-1}+{\mathcal{E}}_{\mathrm{t}},\end{array}$$E1

where $\propto$₀ = Intercept and i is the lag indicator. $\propto$₂, $\propto$₃, $\propto$₄, $\propto$₅, $\propto$₆, $\propto$₇, $\propto$₈, and $\propto$₉ represent the long-run multipliers which show the long-run effects of the identified determinants of investment on economic growth to be calculated. $∆=$Denotes the first difference operator, t = deterministic time trend consisting of years from 1981 to 2020. β₂, β₃, β₄, β₅, β₆, β₇, β₈ and β₉ are the short-run dynamic coefficients which help to estimate the error correction mechanism and the model’s convergence to equilibrium. k is the number of explanatory variables, and ξ is the disturbance term that is uncorrelated with the x’s. while ECT_t−1 is the error correction term’s one-period lag value and the speed adjustment parameter that gauges how quickly the variables, in the event of a disturbance, returned from short-run disequilibrium to long-run equilibrium. The coefficient must be negative, less than one and statistically significant in order to achieve long-run equilibrium. The coefficient’s value provides the long-run annual path to equilibrium GDP. A test of the disturbance term, ℰ_t, can confirm that the regression includes an adequate lag length. If the error series is serially uncorrelated and indicative of a white noise process, the lag lengths included are sufficient.

3.1 Estimation procedure

The co-integration relationship between the variables, which must first be established before the long-run and short-run relationships are ascertained, was examined using the ARDL bounds test developed by Pesaran and Shin [46]. One of the most important characteristics of co-integrated variables, according to Pesaran and Shin [46], is that they have a propensity to react to any shock that could require a break from long-run predictability. As a result, the ARDL method’s error correction model illustrates the degree to which the variables are vulnerable to short-run shocks on the one hand, as well as the measure of such variations caused by the shocks that are adjusted within a year. The ARDL has been widely used in recent empirical analysis due to its robustness, reliability, and statistical properties, which are thought to be superior to other long-run analytical techniques in the literature. When compared to other previous and traditional co-integration methods of Engle and Granger [47] and the maximum likelihood method postulated by Johansen; Johansen and Juselius [48] which call for a lengthy sample period and all variables to be I (1), the ARDL methodology has a number of advantages.

The first is that the ARDL approach does not require that all variables under study be integrated in the same order and can be used whether the underlying variables are integrated in order one, order zero, or a combination of both. This model, however, cannot be used when the underlying variables are integrated of order I. (2). None of the variables in this study were integrated of order I. (2). As a result, the econometric methodology is eased of the burden of deciding the order of integration among variables and pre-testing for unit roots [49]. However, Pesaran and Shin [46] clarified that the dependent variable should be first difference stationary to ensure the validity of the co-integrating relationship.

According to Rahman and Islam [50], the presence of any I(2) variable(s) may cause the system to crash. As a result, performing some efficient unit root tests to ensure that no I(2) variable(s) is/are present in the model is desirable. Second, when the ARDL technique is used, unbiased estimates of the long-run and short-run parameters of the study variables can be estimated simultaneously, as can the speed of adjustment to long-run equilibrium precipitated by any short-run exogenous shocks. The ARDL approach to co-integration was chosen for its suitability, particularly in studies with relatively small samples, such as the present study. Pesaran and Shin [46] asserted that in the presence of small and finite sample data sizes, the short and long-run parameters calculated using the ARDL technique are relatively more efficient, robust, and better. Furthermore, in comparison to vector autoregressive (VAR) models, the ARDL model can accommodate a greater number of variables and is more adaptable in terms of lag structure because it can incorporate various optimal lag structures for distinct variables in the model. The use of lag variables protects against the conundrum of serial correlation. The ARDL technique is also more user friendly and simple than other multivariate co-integration methods since it allows the co-integration relationship to be estimated by OLS once the model’s lag order is determined. Another advantage of the ARDL method is that it accounts for potential endogeneity among the explanatory variables. The ARDL approach is expected to correct the endogeneity problem because the macroeconomic variables used in this study are disposed to have an endogenous effect on each other [13].

Despite the advantages of ARDL technique outlined above, the ARDL also suffers from some drawbacks including expressly believing that the relationship between various measures of investment and economic growth is linear and symmetrical. These two assumptions are too restrictive and impractical, particularly for economic variables that have become increasingly unstable in view of globalization where economies are now more interconnected. Similarly, the error correction term of this method is assumed to linearly respond to economic volatilities and at constant speed. However, this might not be the case if time series respond asymmetrically, implying that they react differently to economic forces pooling positively or negatively away from the equilibrium. Also, application of linear ARDL would result in inconsistent and non-robust estimates, resulting in the wrong policy conclusions [51]. The nonlinear ARDL method specified by Shin et al. [52] however, evades such an unrealistic assumption by decomposing the changes in the measures of investment into their partial sum of positive and negative deviations and estimating the nonlinear version of the ARDL model.

4. Results and discussions

4.1 Preliminary analysis of study variables

This preliminary analysis provided a general idea of the nominal data set by describing the main attributes of the study variables in our model to establish if the data series are normally distributed and suitable for running the OLS regression. The summary of descriptive statistics of the study variables in their nominal form are presented in Table 1.

Variables	RGDP	CAPEX	CPS	FDI	GFCF	INFR	INTR	PINV
Mean	36,182,298	509,505.1	6,245,879	1.50	35.72	19.14	17.64	315,207.5
Median	24,477,911	315,196.9	725,500.4	1.13	32.05	12.95	17.25	17,798.1
Maximum	84,064,364	2,289,000	29,890,460	5.79	89.39	72.84	31.66	2,687,233
Minimum	13,779,258	4100.1	96,705	0.20	14.17	5.39	8.92	151.60
Std. dev.	21,874,515	572,049.4	49,050,394	1.25	19.19	16.83	4.75	669,079.7
Skewness	0.723	1.334	1.232	1.72	1.064	1.81	0.281	2.677
Kurtosis	2.077	4.369	3.118	5.996	3.823	5.15	3.826	9.231
J. Bera	4.905	14.982	10.147	34.62	8.683	29.63	1.663	112.53
J.B Prob.	0.144	0.114	0.173	0.000	0.013	0.00	0.436	0.104
Obs.	40	40	40	40	40	40	40	40

Table 1.

Descriptive statistics of the examined variables in nominal form.

The level of volatility in the variables is depicted by the standard deviation in Table 1. It shows how far away from the mean each variable deviates. The data’s asymmetry is shown by the data’s skewness. All of the variables under investigation are positively skewed, which indicates a somewhat heavy-right tail, according to the data in Table 1. The sharpness of a normal distribution curve’s peak is assessed by kurtosis. A platykurtic distribution is evident in RGDP with kurtosis values under 3, suggesting that the variable produce outliers that are less frequent and less extreme than those found in the normal distribution. However, all the explanatory variables show evidence of leptokurtic distribution with kurtosis value greater than 3. This indicates that these variables are with higher-than-normal kurtosis and the weight in the tail of their population density function is larger than normal. The Jarque-Bera statistics measures how well sample data fit a normal distribution in terms of skewness and kurtosis. The null hypothesis is firmly accepted for these observations, as demonstrated by the probability values of the Jarque-Bera statistics for INTR, LOGRGDP, LOGCAPEX, LOGCPS, and LOGPINV series. As a result, these variables can be said to have a normal distribution since their corresponding Jarque-Bera probability values have a significance level of larger than 5%. The FDI, GFCF, and INFR series, on the other hand, display substantial Jarque-Bera probability values of less than 0.05, which clearly confirm a lack of normalcy in their residuals. This suggests that these variables are highly susceptible to shocks and other fluctuations in the economy which may have caused outliers, resulting in residual non-normality. The Jarque-Bera probability for all the logged variables showed that they are normally distributed for the purpose of our regression analysis. However, normality of data distribution is not required to apply the ARDL co-integration method used in this study [50].

Researchers often rely on correlation coefficient and Variance Inflation Factor (VIF) test among pairs of predictors to measure the severity of multi-collinearity on the accuracy of estimated regression coefficients [9, 53]. The study therefore evaluated the degree of linear dependency and level of multi-collinearity problems among the explanatory variables specified in the empirical model using the Pearson’s Product Moment correlation coefficient. The correlation analysis does not however infer any measure of causality or causal relationship between the examined variables. The Pearson’s Product Moment correlation coefficient of the explanatory variables in their nominal form are displayed in Table 2.

Correlation	CAPEX	CPS	FDI	GFCF	INFR	INTR	PINV
CAPEX	1.00
CPS	0.70	1.00
FDI	−0.16	−0.25	1.00
GFCF	−0.65	−0.61	−0.14	1.00
INFR	−0.32	−0.27	0.45	0.20	1.00
INTR	−0.07	−0.14	0.56	−0.32	0.37	1.00
PINV	0.41	−0.51	−0.11	−0.49	−0.20	−0.06	1.00

Table 2.

Pearson’s correlation coefficients for explanatory variables.

From Table 2, multi-collinearity among the study variables explicitly stated in the model does not pose a serious challenge since the correlation coefficients of these variables were detected to be within the acceptable threshold limit of $\pm$0.80 [13, 50]. Nevertheless, one should be cautious in interpreting high correlation coefficient or high VIF values of the variables as evidence of high degree of multi-collinearity. Robert [53] emphasize that it is important to compare VIF (and tolerance) threshold values to other factors affecting the variance of the regression coefficient. Values of the VIF of 10, 20, 40, or even greater do not, by themselves, discredit the conclusions of regression studies, suggest the use of ridge regression, or support the integration of two or more independent variables into one new variable to address multi-collinearity complications. Similarly, many recent studies have suggested that multi-collinearity may not necessarily be a problem and the frequently used method of resolving multi-collinearity issues in regression analysis may sometimes create bigger problems than the ones they seek to correct [9, 51, 53].

4.2 Test of stationarity of study variables

Time series analysis requires that the data be checked for stationarity. The literature on time series econometrics has made it clear that test results can be misleading if the estimated variables are non-stationary and/or not co-integrated. When time series data is not stationary, shocks in the data will disintegrate rather than be amplified, whereas shocks are neutralized and the data returns to its mean value when time series data is stationary. Because time series data are vulnerable to unit root problems, all study variables are pre-tested for them to ensure accurate estimation and effective time series appraisal. To ensure the validity of the co-integration bounds test, the time series’ stationarity must be checked to ensure that none of the variables are integrated beyond one (I (1)). The sequence of integration of the variables under investigation was checked for this purpose using both conventional—The Augmented Dickey-Fuller (ADF) and Phillips-Perron (PP)—tests and structural breakpoint methods. This is important because econometric analysis of non-stationary variables affects the accuracy and reliability of empirical results. While ADF tests use parametric autoregression to approximate the ARMA structure of the errors in the test regression, PP tests help to correct the bias caused by ADF tests due to omitted autocorrelation. Thus, the Phillips-Perron (PP) unit root tests differ from the ADF tests primarily in how they handle serial correlation and heteroscedasticity in the errors [54].

The Augmented Dickey and Fuller (ADF) and Phillips-Perron (PP) unit root tests of stationarity, which are the norm, are typically ineffective in the presence of structural break. A break is a recurring shock that affects a time series over an extended period of time. The ability to veto a false unit root null hypothesis will be limited if this break is not explicitly addressed during the unit root review process [55]. The Zivot-Andrews [56]) unit root test was used to fully account for unobserved heterogeneity in the variables under study and demonstrate how susceptible the estimated results are to structural changes. The Zivot and Andrews test uses an endogenous sequential test to discover each potential break in the entire sample by using several dummy variables. The results of the conventional and structural breakpoint unit root tests are presented in Table 3.

	ADF		PP Test			Zivot-Andrews
Variables	Level	1st diff.	Level	1st diff.	Remark	Break date	Level	1st diff	Rmk
RGDP	−1.319^n	−3.335^b	1.411^n	−2.695^a	I(1)	2002	−2.0653	11/2507^a	I(1)
CAPEX	4.007^n	−6.592^a	1.411^n	−6.030^a	I(1)	2016	−0.6117^n	−7.2387^a	I(1)
CPS	5.397^n	−5.751^a	4.991^n	−3.218^a	I(1)	2006	0.6076^n	−7.6604^a	I(1)
ELB	−2.038^n	−6.053^a	−2.079^n	−6.053^a	I(1)	2011	−6.6963^a	−7.5044^a	I(0)
FDI	−3.832^a	−5.451^a	−3.766^a	−14.34^a	I(0)	1995	−4.8842^b	−10.0321^a	I(0)
GFCF	−3.711^a	−4.584^a	−3.711^a	−4.610^a	I(0)	2017	−4.1190^n	−5.0928^a	I(1)
INFR	−2.978^b	−2.934^a	−2.847^c	−10.14^a	I(0)	2004	−6.3718^a	−11.1946^a	I(0)
INTR	−2.858^c	−2.707^a	−2.497^n	−6.973^a	I(1)	1995	−3.2059^n	−5.7041^a	I(1)
PINV	3.107^n	−3.210^a	−3.224^b	−10.28^a	I(0)	2018	−4.0011	−9.8706^A	I(1)

Table 3.

ADF, PP, and Zivot-Andrews stationarity tests results.

a, b, and c denote the rejection of the null hypothesis at 1%, 5%, and 10% significance levels, respectively, while n denotes not significant.

The conventional and structural breakpoint unit root tests yielded similar results, as shown in Table 3. The variables under consideration were stationary at levels and first difference, but not at second difference. The dependent variable (RGDP) was also stationary at first difference, indicating that the necessary and sufficient condition for implementing the ARDL method were fulfilled.

4.3 Bounds test to co-integration

After the order of integration of the variables is established by unit root testing and the optimal lag length is determined using the appropriate criteria, such as Akaike Information Criterion (AIC), Schwarz Bayesian Criterion (SBC), and Hannan-Quinn Criterion (HQC), the F-test at its ideal lag length was used to test the joint significance of the lagged level variables stipulated in the model. It tested the null hypothesis that there is no long run relationship between the variables against the alternative hypothesis that the variables have long run relationship. Using Narayan [57] upper and lower bounds F-statistics for a small sample size (30–80 observations), it is possible to determine whether the variables are co-integrated or not. The guideline is to reject the null hypothesis if the calculated F-Statistic exceeds the upper bound critical value at 5% level of significance confirming a long-run relationship among the variables and accept the null hypothesis if the computed F-statistic is below the lower bound critical value. The optimal lag length was determined using the Akaike Information Criterion (AIC) as the relevance of the F-test results is highly dependent on the choice of the correct lag length.

The study’s observations are annual, with a sample size of 40 and 8 parameters. Considering the small number of observations and the need to keep degrees of freedom, an ideal lag length of 1, 2 was chosen and imposed on the dependent variable and dynamic regressors. This ensured that the model dynamics were not constrained by too few lags and that the chosen model did not suffer from serial correlation. The study therefore estimated Eq. (1) with the lag structure (1, 1, 0, 2, 0, 2, 2, 2, 1) being the most efficient of the estimated models. Table 4 provides a summary of the results from the estimated F-test and the ARDL bounds testing approach.

Model	F-statistics	K	Critical values			Decision
LOGRGDP = f(ELB, FDI, GFCF, INFR, INTR, LOGCAPEX, LOGCPS, LOGPINV)		9.0222	8	%	Lower Bound I(0)	Upper Bound 1(1)	Reject H0 and accept HA. Series are co-integrated
				1%	2.79	4.10
				2.5%	2.48	3.70
				5%	2.22	3.39
				10%	1.95	3.06

Table 4.

ARDL bounds test for co-integration results.

Table 4 shows that the calculated F-statistic value of 9.0222 is greater than the upper bound critical value of 4.10 at the 1% significance level, indicating the existence of a long-run relationship between economic growth and the various investment determinants explicitly stated in the model. This means that in the long run, these variables move together, and any short-run divergence in their interplay will revert to equilibrium.

4.4 Long-run effects of investment determinants on economic growth in Nigeria

The study estimated the conditional ARDL long-run model for Eq. (1) to determine the long-run effects of determinants of investment on economic growth in Nigeria. Table 5 contains the long-run estimated results.

Variables	Coefficients	Std. error	t-statistic	P-value
ELB	0.1529	0.0379	4.0388	0.0008***
FDI	−0.0195	0.0129	−1.5139	0.1474^NS
GFCF	0.0073	0.0022	3.3134	0.0039***
INFR	−0.0025	0.0010	−2.4387	0.0254**
INTR	0.0141	0.0041	3.4045	0.0032***
LOGCAPEX	−0.1957	0.0481	−4.0709	0.0007***
LOGCPS	0.3451	0.0299	11.5161	0.0000***
LOGPINV	0.0396	0.0182	2.1811	0.0427**

Table 5.

ARDL long-run estimated results.

The long-run coefficient of Economic Liberalization (ELB) is positively correlated with economic growth and statistically significant at 1% level. According to the findings in Table 5, increased liberalization of the Nigerian economy is expected to boost economic growth by 15.29%. The result is consistent with the findings of Gitahi et al. [14] who reported a significant positive impact of economic liberalization on private investment growth in Kenya.

From Table 5, the long-run coefficient of Foreign Direct Investment Inflow (FDI) showed evidence of an inconsequential negative effect on economic growth in Nigeria during the review period. The negative sign of the coefficient indicates that FDI is displacing or crowding-out domestic investment and limiting growth in Nigeria during the review period. Nigeria needs significant inflows of foreign direct and portfolio investment to propel growth because it is a developing nation with weak domestic capital formation and a shortage of necessary infrastructure. Due to rising insecurity, a lack of essential infrastructure, and the displacement of multinational corporations to nearby nations, the country has lost its appeal to foreign investors.

The long run coefficient of Domestic Investment (GFCF) displays a positive effect on private investment that is significant at 1% level, suggesting that increased domestic capital formation is required to engender growth. The findings indicate that a percentage increase in domestic investment, all else being equal, stimulated an increase in economic growth of about 0.7%. Domestic investment, also known as gross fixed capital formation, is a critical determinant of economic growth. It is vital in growing the supply side of the economy’s production capacity, which results in higher output and exports. The result validates prior expectations, Solow’s [58] growth theory, which explains the relationship between savings, capital accumulation, investment, and economic growth, as well as current studies by Apanisile and Okunlola [59], Babu et al. [9], and Daniel [60], which reported a significant positive relationship between domestic capital formation and long-term economic growth in Nigeria. Sub-Saharan African countries, Kazakhstan, and Poland, in that order.

Table 5 indicated a negative influence of inflation rate (INFR) on economic growth that is significant at 5% level. Accordingly, a percentage increase in the inflation rate, while other explanatory variables remain constant, results in a 0.25% increase in economic growth. In order to maintain macroeconomic stability, inflation must be kept under control. High inflation, driven primarily by food and fuel prices, creates uncertainty for investors, reduces consumer welfare, and dampens growth. According to the findings, an unchecked sharp increase in the broad level of prices diminishes the worth of assets, disposable income, and pushes people into poverty. Consumers’ ability to demand goods and services deteriorates as their incomes are decimated by rising prices, resulting in a decline in their standard of living and a decline in economic growth. The findings are consistent with those of Akinlo and Oyeleke [18] and Nguyen [51], who reported a significant negative relationship between inflation rate and economic growth in Nigeria and Vietnam.

From the results in Table 5, the long-run coefficient of effective interest rate (INTR) is positively related with economic growth and statistically significant at 1% level, acknowledging that an efficient and relatively stable financial system is vital for investment and inclusive growth. Explicitly, a percentage increase in interest rate other things remaining equal, triggered an increase of approximately 1.41% in economic growth. The availability of financial services, including a productive interest rate, is a critical component for encouraging healthy and equitable economic growth. According to McKinnon and Shaw [61], financial repression caused by low real interest rates frequently results in the withdrawal of funds from the banking sector as well as a disincentive to save. It decreases the availability of bank credit, which reduces investment and growth. According to the McKinnon-Shaw financial liberalization hypothesis, high real deposit interest rates boost financial savings, which induces a rise in the size and quality of domestic investment, thereby encouraging growth. The result is consistent with the findings of Nonvida and Amegnaglo [62], Miftahu [63], and Babu et al. [9] who found significant evidence of a positive effect of interest rate on economic growth in Benin, Nigeria, and Sub-Saharan Africa, respectively.

According to Table 5, the long-run coefficient of government capital expenditure (LOGCAPEX) displayed a negative effect on economic growth that is significant at 1% level. From the results, a percentage increase in government infrastructure spending activated a decrease in economic growth of about 19.57%. Nigeria’s fiscal experience over the years explains the challenges of implementing effective fiscal policy responses in an environment with highly volatile revenue flows. In a dynamic situation, uncertain revenue flows tend to reduce the quality and efficiency of government expenditures in Nigeria, while private investments tend to be reduced. Revenue fluctuation leads to expenditure uncertainty, which frequently results in a large number of unfinished capital projects. Government spending in Nigeria has been largely unproductive due to spending volatility. The increase in capital spending may result in less thorough scrutiny of new projects, as many are premised on the belief that high revenue will continue unabated [64]. When revenue drops, many projects are unable to be sustained and must be abandoned, while those that do survive are either poorly executed or are only properly funded through borrowing. Ultimately, a procyclical spending pattern combined with poor oil earnings management caused low growth, persistent fiscal deficits, and debt accumulation. Sustainable growth and poverty reduction are unattainable without a significant reduction in volatility. The result agrees with the findings of Quashigah et al. [65] and Ogar et al. [66] who in their studies found evidence indicating a significant negative effect of capital expenditure on economic growth in Ghana and Nigeria, respectively, but in contrast with the views of Keynes [28], Easterly and Rebello [67] and Solow and Swan [68] who argued that government capital spending through its multiplier effects can stimulate investment and economic growth.

The long-run coefficient of domestic credit to private sector (LOGCPS) in line with a priori expectation is positively related to growth and statistically significant at 1% level. According to the results in Table 5, a percentage increase in credit to private sector is associated with an increase in economic growth of approximately 34.51%. Credit is the main channel through which savings are transformed into investments. Access to credit at low-interest rate enables an individual to build up finances that can support productive investment in various enterprises and private sector inventiveness that is important for growth and poverty reduction. The result is in accord with extant studies of Vincent et al. [69] who reported a significant positive impact of credit to private sector on economic growth in Nigeria.

The long-run coefficient of Foreign Portfolio Investment (LOGPINV) generated a positive impact on economic growth that is statistically significant at 5% level. Based on Table 5, a percentage increase in foreign portfolio investment, while holding other explanatory variables constant, elicited a rise in economic growth of around 3.96%. In light of the growing gap between their domestic capital stock and capital requirements, emerging nations are feeling the need for foreign capital to augment domestic resources. An important method for boosting the amount of funds available for domestic investment is through foreign capital inflow. To close the savings and foreign exchange shortfalls related to the quick wealth generation and growth required to end the pervasive poverty in developing nations, foreign capital inflow is necessary. In addition, foreign investors favor developing nations over developed nations due to the higher rates of return on investment in these countries. The result is in harmony with existing studies of Ezeanyanji and Ifeako [23] and Ozigbo [70] who reported a significant positive impact of foreign portfolio investment on economic growth in Nigeria.

4.5 Short-run impact of investment determinants on economic growth in Nigeria

Utilizing the most effective lag length, the researchers estimated an unrestricted Error Correction Model (ECM) linked to the long-run relationship from equation (1) to scrutinize the model’s short-run dynamics. Table 6 presents a summary of the short-run properties of the model’s transition to equilibrium.

Variables	Coefficients	Std. error	t-statistic	P-value
D(ELB)	0.0792	0.0293	2.7043	0.0145**
D(FDI)	−0.0152	0.0063	−2.4140	0.0266**
D(GFCF)	0.0032	0.0019	1.6771	0.1108^NS
D(GFCF(−1))	−0.0038	0.0018	−2.1839	0.0424**
D(INFR)	−0.0022	0.0005	−4.9315	0.0001***
D(INTR)	0.0022	0.0021	1.0663	0.3004^NS
D(INTR(−1))	0.0064	0.0029	−2.2306	0.0387***
D(LOGCAPEX)	−0.0179	0.0215	−0.8341	0.4151^NS
D(LOGCAPEX(−1))	0.1042	0.0282	3.6906	0.0017***
D(LOGCPS)	0.1072	0.0435	2.4628	0.0241**
D(LOGCPS(−1))	−0.1046	0.0458	−2.2862	0.0346**
D(LOGPINV)	0.0109	0.0065	1.6748	0.1113^NS
C	12.2399	1.5619	7.8363	0.0000***
ECT(−1)	−0.8786	0.1123	−7.8228	0.0000***

Table 6.

ARDL short-run estimated results.

Cointeq = LOGRGDP − (0.1529***ELB − 0.0195*FDI + 0.0073***GFCF − 0.0024**INFR +0.0141***INTR − 0.1957***LOGCAPEX +0.3451***LOGCPS +0.0396**LOGPINV.

Table 6 lagged error term coefficient or adjustment speed (ECT(−1)) is, as predicted, negative, less than one and highly significant at the 1% level, further supporting the existence of a unique long-term relationship between the indicators of investment and economic growth in Nigeria. In the current year, approximately 87.86% of the short-run disequilibrium caused by shocks in previous years converges back to long-run equilibrium. Because the pace of adjustment is relatively quick, any short-run outliers should take about 1.14 years to recover from and return the economy to its long-run optimum growth path.

From Table 6, the short-run coefficient of the present level of Economic Liberalization D(ELB) in Nigeria in agreement with the long-run results motivated a positive impact on the current level of economic growth that is significant at 1% level. Clearly, an increase in the present rate of deregulation of the Nigerian economy is expected to produce an escalation of about 7.92% in the current level of economic growth. This variable’s positive sign indicates that increased deregulation of the Nigerian economy will open up more sectors of the economy to private sector participation and result in greater efficiency in resource allocation, thereby stimulating private investment and inclusive growth.

From Table 6, the current level of foreign direct investment inflow D(FDI) in covenant with the long-run result is inversely related to the current level of economic growth in Nigeria and statistically significant at 5% level. Clearly, a percentage increase in the present rate of foreign direct investment inflow is associated with about 1.52% decline in current level of economic growth. Foreign direct investment (FDI) is frequently asserted as a viable vehicle for economic growth because it is thought to be less vulnerable to crisis since investors typically have better investment arrangements and comprehend the host country’s economic complexities. While it appears that FDI can provide greater technology transfer to host economies, the results indicate that Nigeria has not successfully attracted FDI commensurate with its vast resources or accomplish a high growth rate because it failed to develop the absorptive capacity to benefit from foreign investment. The amount of FDI inflow has decreased as Nigeria’s level of insecurity has risen. The gradual exodus of foreign direct and portfolio investment from Nigeria to other West African nations, which has a negative impact on government revenue generation and economic growth, is caused by the government’s apparent inability to provide a safe and secure environment for people, property, and the performance of business and economic activities.

Gross fixed capital formation in the current period D(GFCF) had a negligible positive impact on the current level of growth, whereas its one-year lag value D(GFCF(−1)) was negatively related to the current level of economic growth and significant at the 5% level. As a result, a percentage increase in the variable’s one-year lag value reduces the current rate of economic growth by approximately 0.38%. Pervasive and structural security issues generate economic risks and uncertainties that change people’s saving, investing, and consumption habits, skewing the equilibrium resource allocation within a nation. Infrastructure damage brought on by terrorist actions can disrupt commerce and increase a nation’s cost of doing business. Reduced profits as a result of these higher costs translate into a lower return on investment. Because of its blatantly detrimental effects on inclusive growth, insecurity in Nigeria hinders and undermines domestic investment. All of these obstacles impede Nigeria’s growth by suppressing domestic capital formation and private investment.

The short-run coefficient of present inflation rate in Nigeria D(INFR) in concord with the long-run result initiated a negative influence on the current level of economic growth that is significant at 1% level. Evidently, a percentage increase in the present inflation rate will stimulate a decline of approximately 0.22% in the current rate of economic growth. Monetary policy, in contrast to fiscal policy, can address the problem of economic shocks very quickly. Monetary policy goals include managing a variety of monetary targets, such as fostering economic growth, achieving full employment, stabilizing prices, preventing economic crises, and maintaining real exchange rates and interest rates. However, the importance of securing price stability is typically highlighted because rising prices erode disposable income and plunge people into poverty. A decline in standard of living results from consumers’ declining purchasing power as a result of income erosion brought on by price increases.

The coefficient of present rate of effective Interest Rate D(INTR) initiated a marginal positive impact on the current level of economic growth, whereas the one period lag value of the variable D(INTR(−1)) initiated a significant negative impact on the current rate of economic growth, which is significant at the 1% probability level. According to Table 6, a percentage increase in the previous interest rate is associated with a 0.64% decrease in the current rate of economic growth. The interest rate has the potential to have an impact on the overall economy by determining the magnitude of macroeconomic indicators such as capital flows, investment, exchange rate, and credit demand. The McKinnon-Shaw hypothesis stipulates that a high deposit rate is a necessary policy tool for galvanizing financial savings for investment. It can, however, be self-defeating if executed during inflationary periods because the relatively high real lending rate impedes investment spending.

An effective and largely stable financial system is essential for investment and growth, as is widely acknowledged in the empirical literature. Low interest rates are thought to facilitate capital accumulation or encourage investment spending. Contrary to the McKinnon-Shaw hypothesis, the Neo-Keynesians hold that financial liberalization is bad for investment and growth. They stress that saving is determined by investment, not the other way around, and that a high interest rate may lower saving by suppressing investment. Besides, financing working capital with borrowed money at high interest rates may result in cost-push inflation. They place more value on the possibility of profit and a plentiful and flexible supply of credit for the private sector than on prior saving. According to the study’s findings, high interest rates caused short-term financial hardship and economic stagnation even though they seem to have improved economic performance over the long term.

The current level of Government Capital Expenditure D(LOGCAPEX) in consonance with the long-run results, is inversely related to the current level of economic growth and statistically significant at 5% probability level. A percentage increase in current capital spending provoked a 1.79% increase in the current rate of economic growth. In contrast to the long-run result, the coefficient of the one-period lag of government infrastructure spending D(LOGCAPEX (−)) reinforced a positive and significant impact on the current level of growth at the 1% probability level. As a result, a percentage increase in previous levels of infrastructure spending resulted in a 10.42% increase in current economic growth. Increased government spending on infrastructure facilities lowers businesses’ overhead and per unit cost of production, stimulating private investment growth and lowering poverty levels. Improved government spending is frequently designed to support growth and its use must be both economically and politically efficient and effective. As a result, effective management of social regulations is required so that government expenditure policies remain optimal, efficient, and effective in stimulating economic growth. However, the study result indicated that government infrastructural spending in Nigeria has been inefficient and retarding growth during the review period.

The current level of domestic Credit to Private Sector D(LOGCPS) has a significant positive influence on economic growth at the 5% probability level, in line with the long-run result. Specifically, a percentage point increase in current private sector credit stimulated a 10.72% increase in current economic growth. The coefficient of the one-year lag of private sector credit D(LOGCPS(−)) variance with the long-run result, on the other hand, is associated with a negative and significant impact on the current level of economic growth, which is significant at 5%. As a result, a percentage increase in previous levels of private sector lending motivated a drop in current economic growth of around 10.46%. The private sector may seek credit to improve labor productivity in both existing and new businesses. This is because lower labor productivity reduces the value of a company’s assets. Credit supply influences labor productivity through investments in human capital, research and development for modern technology, and physical capital to improve the capital-labor ratio. Nigeria has insufficient domestic savings, which may make it difficult for financial institutions and markets to meet domestic credit demand solely through savings. These gaps imply that additional funding sources, such as foreign capital inflows, may be needed to supplement domestic savings in order to meet the private sector’s credit demand. However, the risks associated with the financial system’s information asymmetry could limit the amount of money available to the financial intermediaries and credit demanders. Due to this restriction, both funds from domestic and international sources are uncertain. Potential credit supply restrictions for investments in human and physical capital—the catalysts for higher labor productivity—are brought on by the uncertainty.

According to Table 6, the current rate of foreign portfolio investment D(LOGPINV), in tandem with the long-run results, caused a positive but statistically insignificant impact on the current rate of economic growth in Nigeria during the evaluation period. Foreign capital enhances domestic resources by advancing capital stock, technology, managerial aptitude, entrepreneurial prowess, brands, and market access. Consequently, increasing the inflow of foreign capital should improve the economic well-being of the country’s economic agents.

4.6 Econometric diagnostics checks

The validity, stability, and fulfillment of the desirable statistical assumptions of an effective ARDL model were evaluated by a number of diagnostic tests such as normality, serial correlation, heteroscedasticity, and specification error. The regressors were tested for serial correlation to confirm they did not share a serial correlation, the residuals were tested for normality to guarantee they are normally distributed, and lastly, the model was tested for heteroscedasticity to certify there is no ARCH effect. Additionally, the stability of the ARDL model was assessed using CUSUM (Cumulative Sum of Recursive Residuals) and CUSUMsq (Cumulative Sum of Squares of Recursive Residuals). The results of the diagnostics tests are presented in Table 7.

Problem	Test	Probability	Conclusion
Normality	Jarque Bera	0.3892	Normality exists
Serial correlation	Breusch-Godfrey LM Test	0.1146	No serial correlation
Heteroscedasticity	Breusch-Pagan-Godfrey	0.2385	No heteroscedasticity
Specification error	Ramsey RESET	0.7746	Correctly specified

Table 7.

ARDL diagnostics tests results.

Diagnostic tests statistics of Table 7 indicate that the model’s residuals are normally distributed and do not exhibit any problems with multi-collinearity, serial correlation, heteroscedasticity, or model misspecification error. The estimated model is regarded as being properly defined because the foregoing qualities are desired ones for OLS models. The CUSUM and CUSUM of squares tests, which are shown in Figures 1 and 2, also verified that the model’s parameter estimates are relatively robust over a variety of structural modifications and that the estimated outcomes are reliable and adequate for economic forecasting and decision-making.

Figure 1.

CUSUM plot.

Figure 2.

CUSUM of squares plot.

5. Conclusion and recommendations

Due to low levels of income and domestic savings, Nigeria has a very large gap between investment and saving. In order to increase job creation, combat poverty, and foster growth, the nation has opened up several economic sectors to foreign investors and provided a number of investment incentives after realizing the ineptitude of domestic capital. The study therefore, conducted a disaggregated analysis of the impact of various investment indicators on economic growth of Nigeria over a 40-year period from 1981 to 2020. Domestic investment was disaggregated into private and public investment while foreign investment was disaggregated into foreign direct and portfolio investment. Credit to private sector, inflation, interest rates and economic liberalization were adopted as control variables and their relative effects on economic growth were tested using the ARDL methodology.

The empirical findings confirm co-integration among the study variables and demonstrate a significant positive impact of economic liberalization on growth both in the long and short-term while credit to private sector, domestic investment, foreign portfolio investment, and interest rate were efficient and associated with a significant improvement in economic growth only in the long-run. Furthermore, inflation rate significantly inhibited growth in the long and short-run, government infrastructure spending was ineffective and significantly suppressed growth in the long-run while foreign direct investment crowded-out growth but significant only in the short-run. The error correction mechanism revealed a high adjustment process of the selected macroeconomic indicators of investment in Nigeria, as the speed of adjustment to long-run equilibrium was 87.86% approximately while the diagnostics and stability tests showed that the model is appropriately specified and dynamically stable over time.

As a result, the study advocated for effective fiscal and monetary policy coordination to lower the cost of doing business, incentivize and create an enabling environment for domestic and foreign investors, and improved infrastructure spending to generate jobs, alleviate poverty, sustain growth and achieve overall macroeconomic stability. This study has important policy implications, including the need to liberalize the financial industry. According to McKinnon and Shaw, such liberalization entails getting rid of excessive reserve requirements, interest rate ceilings, and enforced credit distributions while also using the proper macroeconomic tools to secure price levels. Improved savings and investment are anticipated, along with a decrease in the profitability of investing in various economic sectors. The study therefore suggests limiting the growth of the money supply, lowering the cost of governance, and deficit financing in order to reduce the negative effects of inflation, particularly in the long run.

JEL classification codes:

A22, E62, F16, G18, H26