Is Macroeconomic Instability a Preventive Measure in Attaining Sustainable Development Goals in Nigeria?



Samson Aladejare1, Ishaku Nyiputen2, Festus Osagu3



Abstract: This study evaluates the impact of macroeconomic instability on Nigeria’s quest to attain the sustainable development goals set by the United Nations by 2030. The impact of macroeconomic instability was viewed from three different perspectives which are: as a source of macroeconomic outcome, domestic sources, and external sources of instability. The structural vector autoregressive model and the generalized forecast error variance decomposition were adopted to gauge the short-term and future impacts of attaining these goals. Findings from the study suggest that the impact of instability resulting from macroeconomic outcome have been moderate. Nevertheless, they are not to be taken for granted. While shocks from domestic sources have not been really preventive in attempts at meeting the set goals. Shocks from external sources, and specifically emanating from oil price, constitute a huge cause of concern if Nigeria is to achieve the set goals by 2030.

Keywords: Macroeconomic; Instability; Sustainable; Development; Goals

JEL Classification: E61; F53; O20



1. Introduction

It is no hidden fact that the Nigerian economy is regarded as one of the most unstable in the world. This instability can be linked to its heavy reliance on commodity export (crude oil) for revenue source. Hence, constituting a major challenge to the nation’s development planning, as well as increasing the cost of doing business.

In time past, various Nigerian governments have tried to ensure sustainability in development programs, with little or no success recorded. For instance, the national development plan from 1962 to 1985 failed to deliver on the targeted infrastructural development it was initiated for, due to structural changes in the Nigerian economy. Likewise, the structural adjustment program of 1986-1989 also did not help to smoothen the path of development as anticipated by its initiators. Since it created an exchange rate problem which also culminated in rising price levels. From 1990 to 1999, four rolling plans were launched by the government with the goal of reviving, as well as providing new infrastructural development for the country. However, this goal was hampered by macroeconomic constraints such as the rising cost of servicing the nation’s debt. Furthermore, the aspiration of becoming one of the twenty leading economies in the world drove policymakers into formulating the vision 20:2020. Given the abundant resource endowment of Nigeria, it was rational to think such vision was achievable ‘ceterisparibus’. From the economic point of view, the economy was anticipated to grow to at least $900 billion United States (US) Dollars by 2020, as against $212 billion US Dollars as at 2008 when the vision was conceived. Suggesting that the economy will have to grow at a constant annual average rate of 13.4 percent. However, as at 2017 (three years to the end of the vision), the economy only grew to $375.77 billion US Dollars, with an average annual growth rate of 6.27 percent from 2008 to 2017 (WDI, 2018). To further exacerbate the problem, the fall in the international price of oil experienced from late 2014 to 2016, saw the economy plunge into a recession in 2016.

Prior to the recession year, a summit of heads of state which includes Nigeria, in 2015, adopted 17 Sustainable Development Goals (SDGs) to be achieved by 2030. The goals are meant to chart out a global, holistic set of objectives to help set the world on a path towards sustainable development, by dealing with the economic development, social inclusion, and environmental sustainability of signatory nations. This study believes that having a stable macroeconomic atmosphere should be imperative in stimulating long-term planning for the purpose of achieving these set-out SDGs. Reason being that instability in the macroeconomic sphere of a country, has the tendency of derailing a nation’s quest for economic prosperity, as a result of the effect such volatility will exert on various economic activities such as production, investment, and financing (Chow et al. 2018). Hence, the need to assess the antecedence of macroeconomic instability in achieving some of the SDGs, and evaluate the prospects of Nigeria in meeting the 2030 target, given the presence of volatility in the country’s macroeconomic environment.

For the aim of elaborate measure of macroeconomic instability, this study views macroeconomic instability from three different perspectives. The first stem from the fact that instability can be the result of macroeconomic outcome. For instance, it is expected that macroeconomic policies such as controlling inflation, achieving growth in output, reducing unemployment of factors of production, etc., initiated in an economy should help to guarantee economic stability. On the contrary, macroeconomic policies in developing countries, have been known to rather exaggerate instability in these countries (Loayza et al., 2007). Secondly, macroeconomic instability can arise from self-inflicted domestic shocks, triggered by the very nature of the instability associated with a country’s development process and self-inflicted policy mistakes (Loayza et al., 2007). This intrinsic instability can be traced to the development of the country’s financial system. Thirdly, macroeconomic instability can arise from external sources. For example, an oil-dependent country such as Nigeria is susceptible to bigger exogenous shocks from volatile resource price, than a well-diversified economy. This is due to the weak “shock absorbing” feature of an oil-driven economy in the presence of volatile oil prices. For instance, it is a known fact that oil revenue constitutes about 75 percent of the total Federal government revenue in Nigeria (Aladejare, 2018). Furthermore, reduction in capital inflows due to changes in the international financial markets can stimulate external volatility. More so, that the financial market of a developing nation like Nigeria does not possess sufficient market instruments to neutralize effects from such external shocks (World Bank, 2000).

The rest of this study is structured as follows. Section 2 covers the study’s literature review. Section 3 captures the methodology and data description of the study. Section 4 covers the study’s empirical findings. While the concluding remarks of the study can be found in section 5.



2. Literature Review

Today, the concept of macroeconomic instability or volatility is gradually growing to become an area of independent study. Thus, graduating from a second-order area of interest to a focal spot in development economics (Aizenman & Pinto, 2005). Macroeconomic instability or volatility is usually characterized by frequent fluctuations in the general condition, and in the essential macroeconomic aggregates in the economy (Ukwu et al., 2003). It is the measure of the variation in the growth rate of economic variables, which includes the gross domestic product (GDP), inflation, money supply, real interest rate, lending rates, etc. It is normally measured by the standard deviation in the macroeconomic variable over some time period.

Notwithstanding, most prior studies on macroeconomic instability focussed on its effect on economic growth. Hence, there exist divergent views on the nature of impact economic instability exerts on long-run economic prosperity. For instance, there are studies that report a positive effect of economic volatility on long-run growth (Ghosh & Ostry 1997; Canton 2002). Specifically, studies such as Kormendi and Meguire (1985) and Grier and Tullock (1989) supports the evidence of a positive impact of output volatility on economic growth.

While on the other hand, there are studies that aligned with the conclusion of a negative effect of volatility on long-run growth (Kharroubi, 2007; Aysan, 2007). In this study category, cross country empirical findings suggest that economic volatility does not favour long-run growth (Ramey & Ramey 1995; Hnatkovska & Loayza 2004; Koren & Tenreyro 2007). These studies have been able to establish that the negative impact of economic volatility on long-run growth, is often being exacerbated in developing countries with an institutionally underdeveloped financial sector. Furthermore, the level of specialization and economic diversification adopted by a country, may aggravate more volatility and inverse effect on the long-run growth (Hausmann & Hidalgo, 2011).

Studies also abound that have investigated the cause of volatility in developing countries. Karras (2006) and Haddad et al. (2013) are examples of such, in which a negative effect of trade openness on economic volatility was found. For instance, Haddad et al. (2013) found out that countries with diversified export, possess the crucial ability to regulate the impact of trade openness on growth volatility. However, the study by Kim et al. (2016) showed that for economic growth to be achieved, there is the need for the trade volume to also grow. This, however, is not without a cost in terms of high volatility in the long-run, despite the potency of increase in foreign trade to lower economic instability in the short-run. In a slight contradiction, Mireku et al (2017) identified trade openness as one of the factors that give rise to long and short-run economic growth instability.

The impact of macroeconomic instability has also been examined on the use of natural resources in achieving sustainable development (Dauvergn 1999; Gaveau et al 2009; Huang 2011). Such studies also highlight the complementary role of the interaction between global financial markets, and the general economy, as a core stimulant in achieving sustainable development. Dauvergn (1999) for instance, revealed that in the presence of financial crisis, rising unemployment and declining income which are always accompanying phenomenon, are likely to activate greater natural resource extractions. This is because, people living in poorly and densely populated countries, have the tendency to fall back on economic activities such as fishing, wood fetching, and stone mining for survival; but with dire ecological consequences. Similarly, Huang (2011) showed that output volatility hinders sustainable development, especially in countries with poorly developed financial channels. The study revealed that countries with low-income level, trade-share level, as well as having poor energy-intensity, are more vulnerable to macroeconomic shocks. Hence, in the presence of output volatility, the impact is expected to be negative on the available natural resource (a vital ingredient of sustainability) since it will suffer depletion.

From the above review, prior studies are more concerned with the impact of macroeconomic instability on long-run economic growth. However, this study diverges from what these earlier studies have done; by examining the impact of macroeconomic volatility from three different sources on the ability of Nigeria in achieving the sustainable development goals, and the prospects of meeting the 2030 target.



3. Data Description and Study Methodology

3.1. Data Description

In this study, attaining the SDGs are conditioned to be responsive to macroeconomic shocks. Hence, this analysis examines the impact of macroeconomic shocks on three crucial SDGs which are: SDGs 7, 8, and 17. Since any success at fulfilling these three SDGs, will surely aid the actualization of most of the other goals, due to their close links. Hence, SDG 7 aims to “ensure access to affordable, reliable, sustainable, and modern energy for all”. The electric power consumption (EPC) is used to proxy this goal, with the intent of capturing the share of the population using reliable electricity in both urban and rural areas. SDG 8 aims to “promote sustained, inclusive and sustainable economic growth, full and productive employment and decent work for all”. To proxy this goal, we use the gross national income per capita (GNIPC) in current United States dollars (World Bank atlas method). SDG 17 is to “strengthen the means of implementation and revitalize the global partnership for sustainable development. To capture this goal, we use an indicator of debt sustainability which is external debt as a share of export (EDX). It should be noted that the indicators used to proxy these three SDGs also cross-reference with the SDGs 1, 3, 5, 9, 11, and 12 (SDSN 2015). Suggesting that our findings should also have significant impact on these other goals.

As stated earlier, this study dissects macroeconomic instability into three perspectives. The first which is instability as a macroeconomic outcome is being measured using the growth rates of the following variables: real GDP (denoted as GRGDP), CPI (denoted as GCPI), export (denoted as GX), and import (denoted as GM). While macroeconomic instability as a domestic source is gauged using the growth rates of broad money supply (denoted as GM2), the growth rate of nominal deposits (denoted as GDR), real interest rate (RINT), the growth rate of real broad effective exchange rate (denoted as GBEX), and the degree of openness (DOP). To measure the third source of macroeconomic instability, the net foreign direct investment inflows as a share of GDP (denoted as NFDIGDP), the Nigerian international price of oil (denoted as NOP) known as “Forcados”, and the growth rate of the external reserve (denoted as GXRE) were used.

Annual time series data spanning from 1980 to 2017 were used to gauge the impact of macroeconomic instability on the SDGs over time. While the generalized forecast error variance decomposition (GFEVD) was used to assess the potential of Nigeria in attaining these goals base on the 2030 target. All data were sourced from the World Bank Development Indicator (WDI).



3.2. Study Methodology

This study employs the Structural Vector Autoregressive (SVAR) model. Reason being that economic theories in many circumstances usually fall short in determining the nexus between certain variables. Whenever this is the case, the VAR model as established by Sims (1980) downplays the importance of theory in the nature of the relationship exhibited among variables in the model. Thereby, aiding researchers with better knowledge about the important interaction among macroeconomic variables beyond the limit.

The SVAR model imposes some identified restrictions on an ordinary VAR model in other to deduce structural shocks from it. Meanwhile, when restrictions are incorporated into a VAR model, essential information that could be derived from theory within the model can still be derived (Adedokun 2018). In addition, an SVAR model is multivariate in nature, exhibiting a linear representation of a vector of observables on the lags of the dependent variables and also on the explanatory variables. Furthermore, an SVAR model is used to generate precise identifying assumptions, with the goal of separating the impact of policy behaviour and the corresponding response of the economy; while ensuring that the model is free of any extra constrained assumptions required to give each parameter a behavioural meaning.

As a starting point, the SVAR framework on which this study is base is setup as follows.

Where   is a   matrix polynomial in the lag operator;   is a   matrix polynomial in the lag operator;   is a   vector of endogenous variables; and   is a k  vector of exogenous variables;   is a   vector of structural instabilities, with var  where   is a diagonal matrix.

Corresponding with this structural model is a reduced-form VAR:

Where   and   are matrices polynomial;   is a vector of reduced-form instabilities, with var .

If we let V be the contemporaneous coefficient matrix in the structural form, and letting p(L) be the parameter matrix in d(L) without contemporaneous coefficient. That is,

Thus, the structural and reduced-form equations can be linked through the following equation.

  and   =  

While the error terms are connected through:

  or  ; indicating that  

By estimating  , we can derive consistent estimates of F and  , which can be derived through the estimation of the maximum likelihood. Since the right-hand side is a composition of free coefficients of the order   to be estimated. While the left-hand side, is composed of coefficients of order  . Hence, to achieve identification, restrictions in the order of   is imposed. In addition, allowing the normalization of the diagonal elements of F to be unity will further yield   additional restrictions, which fundamentally should be propelled by economic theory.

Thus, the non-recursive SVAR model for this study is therefore as presented as follows.

Where; MI denotes macroeconomic instability as a source of macroeconomic outcome, domestic sources of macroeconomic instability, and external sources of macroeconomic instability. The first equation represents the weak response of MI to shocks from EDX, EPC and GNIPC. While equations two to four indicates that EDX (SDG 17), EPC (SDG 7) and GNIPC (SDG 8) are only responsive to shocks from MI.

In this specification, variations in the variables are as a result of the immediate and previous estimates of the structural instabilities.

The above SVAR matrix specification is used by this study in evaluating the specific impact the three perspective of macroeconomic instability, exerts on Nigeria’s potentials of attaining the SDGs.



4. Empirical Analysis

4.1. Unit Root Tests

This study adopts the Augmented Dickey Fuller (ADF) and Phillips-Perron (PP) unit root tests. The results are as presented in Tables 1.

Table 1. Unit Root Stationarity Test on Study Variables

Variable

ADF Test





PP Test


With Constant

With Constant & Trend

Without Constant & Trend

With Constant

With Constant & Trend

Without Constant & Trend

EDX

-4.701***a


-4.823***a


-4.775***a


-6.230***b


-6.433***b


-6.328***b


EPC

-7.986***a


-7.841***a


-7.629***a


-7.986***b


-7.845***b


-7.588***b


GNIPC

-3.137**a


-3.976**a


-2.989***a


-2.3261


-2.1579


-2.381**b


GRGDP

-4.611***a


-5.144***a


-4.043***a


-4.619***a


-5.165***a


-4.133**a


GCPI

-2.948**a


-3.599**a


-1.808*a


-2.818*a


-2.9054


-1.6662*a


GX

-8.022***a


-8.319***a


-7.025***a


-8.589***a


-19.786***a


-7.007***a


GM

-5.443***a


-5.399***a


-5.427***a


-5.437***a


-5.392***a


-5.420***a


GM2

-3.459**a


-3.459*a


-2.039**a


-3.179**a


-3.1344


-2.0061**a


GDR

-6.355***b


-6.275***b


-6.461***b


-7.182***a


-7.479***a


-6.895***a


RINT

-6.013***a


-6.510***a


-6.099***a


-6.013***a


-6.971***a


-6.099***a


GBEX

-10.82***a


-10.54***a


-10.77***a


-11.69***a


-12.048***a


-10.77***a


DOP

-8.197***b


-4.372***b


-8.268***b


-8.197***b


-8.384***b


-8.2676***b


NFDIGDP

-3.668***a


-3.619**a


-1.967***a


-3.636***b


-3.5274*a


-1.825*a


NOP

-5.479***b


-5.397***b


-5.553***b


-5.481***b


-5.399***b


-5.554***b


GXRE

-6.596***a


-6.597***a


-6.068***a


-6.596***a


-6.622***a


-6.080***a


Note: where a and b denotes stationarity at level and first difference respectively, *, **, *** denotes significance at 10%, 5%, and 1% respectively.

Source: Author’s Estimated Result.

From the output, it is evident that there is a mixture of level and first difference stationarity of series used in the study. Both the ADF and the PP unit root tests, attest to almost the same level of stationarity of each series. Hence, we can conclude that the series are stationary, and inferences reached base on the regression output can be relied upon for policy analysis. Furthermore, the presence of a combination of both level and first difference stationary variables, helps to further validate the VAR framework adopted in this study.



4.2. Variance Decomposition Analysis

Result for the variance decomposition analysis from the estimated SVAR models are as presented in Tables 2 to 4 in Appendix 1. While the GFEVD outputs are contained in Appendix 2 (Table 5-7). It was observed that the three SDGs responded more to shocks generated from within their self; even though the impacts also declined over the ten-year short-term. However, the shocks from the three sources of macroeconomic instability are as analysed as follows.

4.2.1. Instability as a Macroeconomic Outcome (Table 2)

For the ten years short-run period, SDG 17 response to shocks from the growth rates of GDP and CPI, are revealed to gradually increase to almost 10 percent by the 10th year. On the other hand, growth rates of export and import both have less than 1 percent shock impact on this goal.

Similarly, SDG 7 responded more to shocks from growth rates of GDP and CPI. Specifically, shocks from the growth rate of GDP grew gradually from 11.5 percent in the 2nd year to 15 percent by the 10th year. While shocks from the growth rate of CPI rose from 3 percent in the 3rd year, to about 15 percent in the 10th year. Shocks from the growth rate of export hovered around 2 percent from the 2nd year to the 10th year. While shocks from the growth rate of import were however higher, averaging about 8 percent.

SDG 8 responded less to shocks from macroeconomic outcomes when compared to the other goals. For instance, shocks from growth rates of GDP, CPI, and import were revealed to be less than 1 percent within the first six years. It later increased marginally above 1 percent in the remaining four years. However, shocks from the growth rate of export climbed from about 3 percent in the third year, to about 8.7 percent in the tenth year.

Future Forecast using GFEVD (Table 5)

SDG 17 will continue to respond more to shocks from itself until the goal date. Although, at a lower response rate compared to the past. Shocks from the growth rate of GDP is anticipated to impact more on this goal, ranging from 23 percent in the first year to 35 percent by the set date. The shock effect of growth rates of CPI and export are anticipated to be relatively stable at 2 percent within the period. While the growth rate of import will be relatively stable at a higher rate of about 8 percent, after declining from about 10.7 percent in the first year.

As Nigeria race against time to ensure the fulfilment of SDG 7, shocks from the growth rate of GDP will rise from less than one percent to 18 percent in the goal year. A shock from the growth rate of CPI is expected to rise from 3 percent in the third year to about 15 percent in the goal year. while shock from the growth rate of export is anticipated to rise from about 5 percent to 10 percent in the last six years of the goal period, before remaining relatively stable to the eleventh year. Similarly, shock from the growth of import is anticipated to rise from less than 1 percent in the first year, to a relatively steady 12 percent from the third year to the final target year.

The anticipated future shocks to SDG 8, will be higher from the growth rate of export. The shock is anticipated to rise from about 8 percent to 24 percent by the goal year. An anticipated shock from the growth rate of GDP will rise from about 6 percent to 11 percent by the end of the goal year. While growth rates of CPI and import will exert less than 1 percent shock, almost through-out the remaining goal period.

4.2.2. Domestic Sources of Macroeconomic Instability (Table 3)

The shock response of SDG 17, was highest from real interest rate aside response to shocks from itself. Growing from about 6 percent in the second year to about 17 percent in the tenth year. This is followed by a response to shocks from trade openness which grew from about 3 percent in the fourth year to 14 percent in the tenth year. On the contrary, the goal’s response to shocks from the growth of broad exchange rate grew from about 5 percent in the second year to 7 percent in the third year. It remained relatively steady to the sixth year, before declining marginally to about 6 percent in the remaining four years. Shocks from the growth rates of broad money supply and deposit rates fell below 1 percent in the ten-year period.

Observing the response of SDG 7 reveals less response to the domestic source of macroeconomic instability. For instance, the highest shock response is from the growth rates of GDP, which was steady at 3 percent on the average from year 2 to year 6. Before rising to about 4 percent in year 7 and further rising marginally to 4.6 percent in year 10.

Similarly, SDG 8 responded less to shocks from the growth rate of GDP, while rising to 2 percent from the fifth year and peaking at about 9 percent in the tenth year. Shocks from the growth rate of CPI rose from about 2 percent in year two, to about 4 percent in year 4 and remained relatively stable at this rate to the tenth year. Shocks from deposit rate also grew from 0.1 percent in year 2, to 10.4 percent in the tenth year. However, shocks from the growth rate of import are revealed to be less than 1 percent throughout the ten-year period. While shocks from the growth rate of export increased from less than 1 percent in year two, to about 6 percent by the tenth year.



Future Forecast using GFEVD (Table 6)

Future shocks from growth in broad money supply, to SDG 17, is anticipated to be higher for the remaining target period. Nevertheless, it is expected to decline steadily from 11 percent to 8 percent by the end of the goal year. Furthermore, an anticipated shock from the growth in the broad exchange rate is to rise from 0.2 percent in the first year to about 13 percent in the third year and stabilizing at such to the sixth year. From the seventh year, the impact of the shock is expected to be declining marginally to 11.6 percent by the target year. Lastly, the shock from the growth rate of nominal deposit is expected to remain less than 1 percent through the 11-year period.

The shock impact of the growth rate of GDP to SDG 7, is expected to be almost stable at 2 percent in the first five years of the left period for the goal target. However, it is expected to rise from almost 3 percent in the sixth year to about 6 percent in the eleventh year. Shocks from the growth rate of GDP will rise from less than one percent to 18 percent in the goal year. Shocks emanating from the growth rate of CPI is expected to rise from about 1 percent in the third year to about 3 percent in the final goal year. While shocks from the growth rate of nominal deposit rate is to rise from about 1 percent in the third year to about 9.6 percent in the final year target. The growth rate of import is expected to exert minimal shock ranging from less than a percentage from the second year to about 1.7 percent in the final target year. Shocks from the growth rate of export on the contrary is anticipated to fluctuate between 4.6 and 6.5 percent in the remaining eleven-year period.

Future shocks to the SDG 8, is anticipated to be highest from the growth rate of deposit rates. Rising from 11.4 percent in the first year, to about 32 percent by the eleventh year. Anticipated shocks from the growth in GDP is next to rise from about 2.7 percent to about 12 percent in the final target year of the goal. The shock effect from the growth rate of CPI, is expected to be lower than 1 percent after the first year of the last eleven-year period. On the other hand, it will be higher from the growth rate of import. As it is anticipated to rise from 7.6 percent to 9 percent between year 1 and year 5. It is, however, to decline from the sixth year to the eleventh year from 8.7 percent to 7.4 percent. For shock originating from the growth rate of export, it is anticipated to rise from 1.5 percent to 8.5 percent between the second and the eleventh year.



4.2.3. External Sources of Macroeconomic Instability (Table 4)

Shocks from the net foreign direct investment inflow as a share of the GDP to SDG 17, rose from 1.3 percent in the third year to about 8.4 percent in the tenth year. The shock impact from the Nigerian international oil price however grew to a significant amount of 20.4 percent in the tenth year, from about 1.7 percent in the second year. The shock from growth in the external reserve rose from 5.8 percent in the second year, to 14.9 in the tenth year.

Assessing the response of SDG 7 to external shock variables, suggest that NFDIGDP exerts less than 2 percent shock to the goal in the ten-year period. While shock from the Nigerian Forcados grew from about 3.4 percent in the second year, to about 40 percent in the tenth year. Shock from the growth in external reserve increased marginally from 1 percent in the second year, to 3.2 percent in the tenth year.

The shock impact of NFDIGDP on SDG 8 grew from 1.4 percent in the fifth year to about 4.2 percent in the tenth year. For the first five years, the shock impact is revealed to be less than one percent. On the other hand, shock from the Nigerian Forcados is much larger, rising from about 12 percent in the second year to about 57.2 percent in the tenth year. While shock from the growth rate of external reserve increased from 1.5 in year five to 4.1 percent in year 10. Similar to shocks from NFDIGDP, the earlier five years had shocks of less than a percentage.



Future Forecast using GFEVD (Table 7)

Assessing the future shocks to SDG 17, reveals that shocks from NFDIGDP will grow marginally from about 5.5 percent in the first year to about 5.9 percent in the eleventh year. As prior observed in the short-term analysis, the shock from the Nigerian oil price will continue to be larger to the goal target date. Its shock impact is anticipated to rise from about 13.1 percent in the first year to about 35.1 percent in the final SDG target date. While shocks from external reserve growth will grow from 2.3 in year one to 13.2 percent by year four 4; before declining to 12 percent in year eleven

For SDG 7, NFDIGDP is expected to have diminishing shock effect for the remaining years of the goals. Its impact is anticipated to decline marginally from 4 percent in the first year, to about 1.2 percent by the end of the SDG target year. A shock from the Nigerian international oil price is anticipated to rise from 17.7 percent in the first year, to about 56 percent by the end of the goal target period. Shock from external reserve growth is anticipated to be less than 2 percent for the remaining target period.

NFDIGDP is expected to exert less shock impact on SDG 8 for the remainder of the goal duration. Its impact is suggested to decline from about 3.8 percent in the first year to about 1.3 percent in the eleventh year. The Nigerian Forcados on the other hand is anticipated to continue to be a major source of shock to the actualization of this goal. Rising from about 19.8 percent in the first year to about 70.4 percent in the final year of the SDG lifespan. Exerted shock from growth in the external reserve is expected to be less than two percent for the remaining goal period.



4.3. Impulse Response Function (IRF) Analysis

Below is the analysis of the IRF of the three SDGs, to the three sources of macroeconomic instabilities evaluated in this study. It was observed that the generalized IRFs for the three dependent variables are of no significant variance from the normal IRFs. Hence, for the sake of brevity and clarity, they are not reported.

4.3.1. IRFs from Instability as a Macroeconomic Outcome

Aside from the positive response of SDG 17 to shocks emanating from itself (see Appendix 3), its response to the variables of interest used in gauging instability as a result of macroeconomic outcome reveals mix response (see Figure 1). Specifically, SDG 17 impulse response to shocks from the growth rates of GDP and CPI is negative. While its response to shocks from the growth rates of export, suggest an almost neutral effect with exception to the second and fourth year when the response is slightly negative. Furthermore, response to shocks from the growth rate of import is revealed to be positive in the first three years, before turning slightly negative to the tenth year.

In Figure 2 (see Appendix 3), the impulse response of SDG 7 to shocks from macroeconomic outcome, suggests positive impacts from the growth rates of GDP, export and import. While only the growth rate of CPI is shown to negatively impact on the SDG.

The impulse response of SDG 8 to instability as a macroeconomic outcome in Figure 3 (see Appendix 3), indicates that this goal responds positively to shocks from itself and the growth rate of export. Furthermore, its response to the growth rate of GDP in the first three years is shown to be neutral, and later positive for the rest of the period. However, SDG 8 impulse response to the growth rate of CPI and import is significantly negative within the short term.



4.3.2. IRFs from Domestic Sources of Macroeconomic Instability

The IRF function for SDG 17 in Figure 4 (see Appendix 3), reveals a mixture of positive and negative functions. While SDG 17 responded positively to shocks emanating from itself, the growth rate of deposit rate, and the real interest rate; the same cannot be said of its response to shocks from the growth rates of broad money supply, broad effective exchange rate, and the degree of openness, which are all negative.

Similarly, there is a mixture of the response of SDG 7 to shocks from domestic sources of macroeconomic instability. Figure 5 (see Appendix 3) shows SDG 7 responding positively to shocks originating from itself, although the impact declines with time. SDG 7 impulse response to shocks from the growth rate of broad money supply, suggests a negative pattern up to year 5 before turning positive. While its impulse response to shocks from the degree of openness is positive. On the other hand, the impulse response to shocks from the growth rate of deposit rate and the real interest rate is negative. While the impulse response to shocks from the broad money supply fluctuated slightly within the first four years before turning neutral.

The impulse response of SDG 8 to shocks from domestic sources of macroeconomic instability is contained in Figure 6 (see Appendix 3). The outcome shows a positive impulse response of SDG 8 to shocks emanating from itself. Similarly, shocks from the growth rate of broad money supply only appear positive from the fifth year, after being neutral in the preceding four years. Furthermore, the degree of openness also exerts a growing positive impact on SDG 8. However, impulse responses from the growth rate of nominal deposit rates, and the real interest rate to SDG 8 are negative. Likewise, the impulse response from the growth rate in the broad exchange rate is slightly negative all through the short-term.

4.3.3. IRFs from External Sources of Macroeconomic Instability

In Figure 7 (see Appendix 3), the impulse response of SDG 17 to the three external sources of macroeconomic instability as used in this study is captured. Empirical observation from the output indicates that though the response of this goal to self-generated shock is positive, the impact is however declining. Impulse responses from net foreign direct investment inflows, suggest a positive response in the first two years, before turning negative. The Nigerian international oil price, however, suggest a diminishing negative impact on SDG 17 in the short-term. Impact of growth rate of external reserve is positive, but steadily declining from the third year.

Figure 8 (see Appendix 3) contains the IRF response of SDG 7 to shocks originating from domestic sources of macroeconomic instability. The output shows SDG 7 responding positively to shocks from itself at a constant rate. Likewise, is its response to shocks emanating from the Nigerian international price of oil. While its response to shocks from net foreign direct investment inflows declined from being positive to neutral in the fourth and fifth year; before turning negative from the sixth year to the tenth year. Response to shocks from the growth in external reserve remained steadily negative from the third year to the tenth year.

Analysis of the impulse response of SDG 8 to external sources of macroeconomic instability in Figure 9 (see Appendix 3), indicates that SDG 8 responded positively to shocks originating from itself, but at a declining rate. While response to shock from the net foreign direct investment inflows, suggest a constant positive effect. Response to the Nigerian international oil price also indicate a positive response. However, the impact begins to decline from the sixth year. For the response to shocks from the growth in the external reserve, there is a steady growing negative effect from the third year to the tenth year.



5. Conclusions and Recommendations

It appears the impact of instability as a macroeconomic outcome is not really significant on SDG 17 and SDG 8. However, for the remaining goal period, this source of instability will be moderately impactful on SDG 17; especially, shocks from growth in the size of the economy. Similarly, the SDG 8 will respond more to shocks from growth in the size of the economy, as well as export in the time left for the goal to be achieved. For SDG 7, instability as a macroeconomic outcome is revealed to exert more impact, while in the future, the impact is anticipated to be relatively stable. Nevertheless, despite the low impact of this perceived source of macroeconomic instability on the SDGs, the negative effect of growth in CPI on the SDGs should be controlled. As failure to do such could impact inversely on the attempt to achieve a sustainable consumption and production pattern for the country in the future.

Shocks emanating from the domestic sources of instability to the three SDGs have been moderately low within the time frame of this study. This trend is expected to continue into the remaining target period of the goal. The low impact of this perceived source of instability is not unconnected to the evolving stage of the Nigerian financial system. Being a developing country, the Nigeria financial market is still equipped with less developed financial channels, as well as having a low interaction with the global financial market. Thus, instruments within the financial system are not adequate to make the sector the main driver of sustainable growth.

On shocks from external sources, empirical evidence from this study suggest that there is a low impact of shocks from net foreign direct investment inflows, and the growth in the external reserve to the three SDGs. However, shocks from the fluctuating international price of the Nigerian oil has been the major source of external shocks to the actualization of these goals. This source of an external shock is also revealed to be growing to a worrisome level on the three SDGs; a trend which is anticipated to even be higher for the remaining target period of the goals. For example, the shock impact is anticipated to reach about 41 percent for SDG 17, 59 percent for SDG 7, and 71 percent for SDG 8 by the end of the goals lifespan. The shock implication of the fluctuating Nigerian international price of oil could be devastating. Reason being that the need to end poverty, and promote healthy living and well-being of all citizens by the government could be daunting to achieve. Also, the goal to ensure gender equality and women empowerment could be hampered. In addition, it would be difficult for the government to provide sustainable infrastructures, ensure comprehensive and sustainable industrialization, and promote the growth of innovation. All this is because oil revenue alone constitutes about 75 percent of all government revenue sources (Aladejare 2018). This shows the crucial position oil revenue occupy in Nigeria in the actualization of the SDGs. Hence, there is a need to diversify the country’s revenue base to help lower the shock effect of a declining oil price, on the country’s SDGs actualization. This is because, a diversified export base, will afford the country’s policymakers the crucial ability to regulate any emanating shock from the country’s expanding trade openness.



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Appendices

Appendix 1. Variance Decomposition Outputs

Table 2. Instability as a Macroeconomic Outcome


EDX

GRGDP

GCPI

GX

GM

Variance decomposition of EDX






Year 1

100.0000

0.000000

0.000000

0.000000

0.000000

Year 2

94.75079

1.925351

2.588457

0.003293

0.732114

Year 3

87.54376

6.005033

5.375747

0.559973

0.515488

Year 4

84.58520

6.513656

7.593058

0.656536

0.651551

Year 5

83.58757

7.141420

8.118119

0.573974

0.578918

Year 6

83.18467

7.196234

8.470375

0.544452

0.604274

Year 7

83.02756

7.295691

8.553150

0.533286

0.590313

Year 8

82.88126

7.378537

8.637252

0.514675

0.588280

Year 9

82.71780

7.473600

8.724579

0.507036

0.576985

Year 10

82.59761

7.517484

8.807811

0.500972

0.576119

Variance decomposition of EPC

EPC





Year 1

100.0000

0.000000

0.000000

0.000000

0.000000

Year 2

81.91614

11.45477

0.002034

1.629057

4.998002

Year 3

74.96492

9.781949

3.247193

2.557335

9.448602

Year 4

70.00021

10.15297

8.711384

2.139223

8.996210

Year 5

66.55984

11.92138

10.95907

2.066935

8.492773

Year 6

64.38301

12.57431

12.40856

2.190796

8.443318

Year 7

62.69903

13.75132

13.37610

1.984499

8.189046

Year 8

61.61907

14.48785

13.90819

1.928292

8.056593

Year 9

60.87198

14.79435

14.46610

1.887383

7.980194

Year 10

60.15256

15.17496

14.97343

1.815476

7.883576

Variance decomposition of GNIPC

GNIPC





Year 1

100.0000

0.000000

0.000000

0.000000

0.000000

Year 2

98.29759

0.211553

0.079380

0.869561

0.541918

Year 3

95.91454

0.093423

0.290336

3.417580

0.284125

Year 4

94.25166

0.183774

0.451189

4.836050

0.277323

Year 5

92.32063

0.559163

0.601719

5.937109

0.581381

Year 6

90.59035

0.961785

0.770798

6.880782

0.796280

Year 7

88.94481

1.441956

0.997637

7.562352

1.053244

Year 8

87.51108

1.928209

1.236480

8.057313

1.266919

Year 9

86.26464

2.395986

1.461987

8.420189

1.457194

Year 10

85.28396

2.802757

1.654468

8.658390

1.600425

Source: Authors Estimated Output.

Table 3. Domestic Sources of Macroeconomic Instability


EDX

GM2

GDR

RINT

GBEX

DOP

Variance decomposition of EDX







Year 1

100.0000

0.000000

0.000000

0.000000

0.000000

0.000000

Year 2

88.11256

1.030890

0.432886

5.759834

4.663160

0.000669

Year 3

82.64811

1.762109

1.064415

6.267785

7.292014

0.965563

Year 4

79.22990

1.246701

0.858979

8.911821

7.103536

2.649059

Year 5

75.07242

0.968004

0.796654

11.22477

6.926029

5.012116

Year 6

71.85922

0.859100

0.834912

12.70032

6.785014

6.961439

Year 7

68.99027

0.856627

0.756516

14.01215

6.452697

8.931737

Year 8

66.26001

0.895702

0.724130

15.14553

6.195164

10.77946

Year 9

63.93158

0.986497

0.701416

16.06699

5.945397

12.36811

Year 10

61.87156

1.094448

0.666324

16.85632

5.702691

13.80866








Variance decomposition of EPC

EPC






Year 1

100.0000

0.000000

0.000000

0.000000

0.000000

0.000000

Year 2

95.65882

3.730222

0.212897

0.008396

0.343293

0.046367

Year 3

94.33675

3.301011

1.106785

0.544378

0.366483

0.344596

Year 4

93.05917

3.298855

1.455580

1.467405

0.306327

0.412659

Year 5

91.98869

2.888776

1.560443

2.330049

0.274149

0.957887

Year 6

89.67976

3.266718

1.843076

3.071873

0.245055

1.893514

Year 7

87.17318

3.941063

2.077615

3.500356

0.225077

3.082715

Year 8

84.89606

4.330062

2.245355

3.848677

0.211040

4.468807

Year 9

82.71366

4.518795

2.378826

4.273132

0.198237

5.917355

Year 10

80.60947

4.637094

2.467982

4.792757

0.187594

7.305107








Variance decomposition of GNIPC

GNIPC






Year 1

100.0000

0.000000

0.000000

0.000000

0.000000

0.000000

Year 2

97.38542

0.001071

1.675317

0.116770

0.167153

0.654266

Year 3

92.25060

0.323056

3.893836

2.478761

0.189945

0.863798

Year 4

88.48856

0.780500

4.296638

5.171483

0.135786

1.127030

Year 5

84.71128

2.043706

4.306679

7.183746

0.085252

1.669336

Year 6

80.59800

4.169424

4.322257

8.407687

0.059681

2.442955

Year 7

76.93752

6.223843

4.331767

9.092566

0.050488

3.363811

Year 8

74.00645

7.702662

4.331930

9.548405

0.047085

4.363473

Year 9

71.65603

8.661134

4.310838

9.963882

0.044030

5.364088

Year 10

69.71067

9.286752

4.264523

10.39085

0.039893

6.307307

Source: Authors estimated output.

Appendix 2. Generalized Forecast Error Variance Decomposition (GFEVD)

Table 5. Instability as a Macroeconomic Outcome


EDX

GRGDP

GCPI

GX

GM

Variance decomposition of EDX






Year 1

60.57418

23.15607

4.216996

1.331178

10.72157

Year 2

59.24115

27.08889

3.447088

1.375876

8.846995

Year 3

54.97237

32.61204

2.510618

2.408914

7.496065

Year 4

53.35562

33.36766

2.528171

2.608616

8.139929

Year 5

52.73348

34.37946

2.324868

2.462714

8.099480

Year 6

52.42527

34.58850

2.209174

2.432879

8.344181

Year 7

52.23266

34.81801

2.095005

2.433964

8.420370

Year 8

52.08309

34.99084

2.019533

2.409517

8.497017

Year 9

51.95580

35.15423

1.971124

2.406385

8.512459

Year 10

51.86452

35.24203

1.939515

2.402659

8.551278

Year 11

51.80042

35.32017

1.914147

2.396931

8.568335







Variance decomposition of EPC

EPC





Year 1

94.85298

0.118297

0.066320

4.737238

0.225163

Year 2

69.00688

13.73542

0.069232

9.200442

7.988023

Year 3

62.32879

11.40460

3.009436

11.20205

12.05512

Year 4

56.49818

13.26553

7.963198

9.411973

12.86111

Year 5

52.83273

14.38371

10.39785

9.833130

12.55258

Year 6

50.08914

15.23266

11.90078

10.17518

12.60224

Year 7

48.22200

16.34011

12.92622

10.13153

12.38015

Year 8

46.94232

17.04608

13.52131

10.23687

12.25341

Year 9

45.96616

17.49301

14.03491

10.33683

12.16909

Year 10

45.19135

17.86863

14.48882

10.35083

12.10036

Year 11

44.57857

18.17678

14.82927

10.38746

12.02792







Variance decomposition of GNIPC

GNIPC





Year 1

85.88800

5.845723

0.152548

7.944350

0.169377

Year 2

83.38397

4.163889

0.052788

12.26472

0.134631

Year 3

77.48602

4.849222

0.053454

17.42972

0.181580

Year 4

74.17873

5.786951

0.104063

19.70893

0.221332

Year 5

71.26691

7.001595

0.186385

21.14090

0.404205

Year 6

69.03499

7.933987

0.293396

22.24538

0.492247

Year 7

67.20482

8.793356

0.453035

22.96678

0.582015

Year 8

65.75705

9.523428

0.632608

23.44610

0.640814

Year 9

64.60741

10.12817

0.812584

23.76495

0.686894

Year 10

63.76941

10.58975

0.974539

23.95080

0.715497

Year 11

63.20359

10.91477

1.108029

24.03918

0.734433

Source: Authors estimated output.

Table 6. Domestic Sources of Macroeconomic Instability


EDX

GM2

GDR

RINT

GBEX

DOP

Variance decomposition of EDX







Year 1

80.11765

11.17796

0.257114

2.674355

0.221359

5.551557

Year 2

71.50651

7.106697

0.173010

7.886720

9.248138

4.078921

Year 3

67.50231

5.315811

0.344699

8.181328

13.15901

5.496851

Year 4

60.89495

6.239964

0.231165

11.45202

13.01459

8.167313

Year 5

55.45818

6.648746

0.188950

13.76057

13.01960

10.92395

Year 6

51.65226

7.021136

0.197064

15.23381

12.97898

12.91676

Year 7

48.56364

7.377462

0.166389

16.44204

12.62138

14.82908

Year 8

45.91926

7.632950

0.154741

17.43745

12.38886

16.46674

Year 9

43.74774

7.890626

0.148256

18.25739

12.14232

17.81366

Year 10

41.91998

8.112822

0.137938

18.94181

11.88701

19.00044

Year 11

40.35679

8.306248

0.132424

19.51376

11.67096

20.01982








Variance decomposition of EPC

EPC






Year 1

93.38001

1.682958

0.029871

0.220957

0.066813

4.619388

Year 2

90.74654

2.504148

0.183052

0.529418

0.752389

5.284456

Year 3

90.92431

1.941779

1.109976

0.914910

0.709178

4.399846

Year 4

90.27516

1.676681

1.464908

1.461146

1.060634

4.061474

Year 5

89.23373

1.685199

1.625023

2.580614

1.317917

3.557515

Year 6

85.78168

2.870326

2.013907

4.457698

1.469928

3.406462

Year 7

82.40636

4.182916

2.338564

5.916840

1.477045

3.678277

Year 8

79.78713

4.977567

2.565911

6.946049

1.487896

4.235446

Year 9

77.50293

5.428252

2.739283

7.848351

1.540292

4.940895

Year 10

75.36300

5.727916

2.855135

8.731983

1.625616

5.696355

Year 11

73.31341

5.991517

2.931501

9.583814

1.724551

6.455207








Variance decomposition of GNIPC

GNIPC






Year 1

76.08376

2.745419

1.929466

11.37468

7.572865

0.293808

Year 2

73.95793

2.746790

0.538678

13.62746

7.627965

1.501180

Year 3

65.35411

3.691083

0.508097

19.97899

8.468312

1.999403

Year 4

59.52713

4.452466

0.434827

24.16196

9.001310

2.422305

Year 5

54.37576

5.956983

0.396038

27.04654

9.089921

3.134766

Year 6

49.82968

7.971810

0.414548

28.98407

8.726306

4.073587

Year 7

46.39526

9.684141

0.446827

30.12988

8.264941

5.078955

Year 8

43.94064

10.82363

0.474324

30.79887

7.904931

6.057607

Year 9

42.12396

11.51342

0.490976

31.24895

7.662922

6.959774

Year 10

40.70076

11.92964

0.496917

31.60096

7.508446

7.763281

Year 11

39.52708

12.20960

0.496271

31.89416

7.405859

8.467022

Source: Authors estimated output.

Table 7. External Sources of Macroeconomic Instability


EDX

NFDIGDP

NOP

GXRE

Variance decomposition of EDX





Year 1

79.03761

5.529226

13.14402

2.289138

Year 2

68.86621

5.719562

16.45354

8.960689

Year 3

62.78882

4.428143

19.82901

12.95403

Year 4

58.46968

3.973529

24.34964

13.20715

Year 5

55.00262

4.164034

27.91753

12.91582

Year 6

52.31071

4.497900

30.50335

12.68804

Year 7

50.38747

4.861310

32.29184

12.45938

Year 8

49.03231

5.213087

33.48787

12.26673

Year 9

48.09564

5.506128

34.27177

12.12646

Year 10

47.46331

5.735555

34.77353

12.02760

Year 11

47.04415

5.908904

35.08692

11.96002






Variance decomposition of EPC

EPC




Year 1

77.91485

3.955562

17.72847

0.401111

Year 2

72.65391

3.147099

23.72907

0.469921

Year 3

66.40342

2.480660

30.59327

0.522643

Year 4

60.69443

2.054324

36.55009

0.701157

Year 5

55.98868

1.751222

41.40511

0.854980

Year 6

52.12243

1.540056

45.33473

1.002788

Year 7

48.99464

1.399373

48.46956

1.136431

Year 8

46.45978

1.311531

50.97525

1.253442

Year 9

44.39257

1.261082

52.99172

1.354623

Year 10

42.69161

1.235741

54.63096

1.441692

Year 11

41.27781

1.226612

55.97904

1.516536






Variance decomposition of GNIPC

GNIPC




Year 1

76.32851

3.826802

19.75666

0.088030

Year 2

60.60834

1.821561

37.52361

0.046494

Year 3

49.17243

1.079667

49.71856

0.029341

Year 4

41.96964

0.664420

57.21149

0.154445

Year 5

37.21364

0.462355

61.95263

0.371378

Year 6

33.94167

0.423903

65.04915

0.585278

Year 7

31.60636

0.509002

67.10837

0.776267

Year 8

29.89924

0.674536

68.48616

0.940070

Year 9

28.63321

0.885068

69.40662

1.075102

Year 10

27.68669

1.113133

70.01633

1.183852

Year 11

26.97735

1.338567

70.41413

1.269951

Source: Authors estimated output.

Appendix 3. Impulse Response Functions (IRF)

Figure 1. Impulse Response of SDG 17 to Instability as a Macroeconomic Outcome.

Source: Authors estimated output.

Figure 2. Impulse Response of SDG 7 to Instability as a Macroeconomic Outcome

Source: Authors estimated output.

Figure 3. Impulse Response of SDG 8 to Instability as a Macroeconomic Outcome.

Source: Authors Estimated Output.


Figure 4. Impulse Response of SDG 17 to Domestic Sources of Macroeconomic Instability

Source: Authors estimated output.

Figure 5. Impulse Response of SDG 7 to Domestic Sources of Macroeconomic Instability.

Source: Authors estimated output.

Figure 6. Impulse response of SDG 8 to domestic sources of macroeconomic instability.

Source: Authors estimated output.

Figure 7. Impulse Response of SDG 17 to External Sources of Macroeconomic Instability

Source: Authors estimated output.

Figure 8. Impulse Response of SDG 7 to External Sources of Macroeconomic Instability.

Source: Authors estimated output.

Figure 9. Impulse Response of SDG 8 to External Sources of Macroeconomic Instability.

Source: Authors Estimated Output.



1 Federal University Wukari, Nigeria, Address: PMB 1020, Katsina Ala Rd, Wukari, Nigeria, Corresponding author: aladejare@fuwukari.edu.ng.

2 Federal University Wukari, Nigeria, Address: PMB 1020, Katsina Ala Rd, Wukari, Nigeria, E-mail: ishakurimamtanung@gmail.com.

3 University of Ibadan, Nigeria, Address: Oduduwa Road, Ibadan, Nigeria, E-mail: festusosagu@gmail.com.

AUDŒ, Vol. 16, no. 5/2020, pp. 144-170