Determinants of Exchange Rate Crisis in the Nigeria Economy

Determinants of Exchange Rate Crisis in the Nigeria Economy

Agada, Franklin Ayibatunimibofa1 and Benwari Pagaebinyo Clarence^2
1Department of Banking and Finance, Federal Polytechnics Ekowe, Bayelsa State, Nigeria.
²Department of Business Administration, Federal Polytechnics Ekowe, Bayelsa State, Nigeria.

DOI: https://dx.doi.org/10.47772/IJRISS.2023.7753

Received: 06 June 2023; Accepted: 24 June 2023; Published: 23 July 2023

ABSTRACT

The study examined the causes of exchange rate crises in Nigeria between 1986 and 2021. In particular, we investigate how the trade balance (LNBOP), oil price (COP), external debt (LNEXDS), and private sector credit (LNPSC) influence Nigeria’s exchange rate (EXR). The statistical bulletin of the Central Bank of Nigeria and the Nigeria Bureau of Statistics provided the study’s data. At the 5% level, descriptive statistics, the stationarity test, Johansen cointegration, VAR, and VAR Block Granger Causality were utilised. The variables were integrated at first difference for the stationarity test, yielding the Johansen cointegration test, which indicates a co-integrating relationship. LNEXDS and LNPSC are negative and significant in relation to EXR, whereas LNBOP and COP are negative but not significant. Only LNPSC and LNEXDS support REER for the VAR Block Granger Causality test, but all variables support EXR collectively. External debt and private sector credit are the primary determinants of the naira exchange crisis in Nigeria. The federal government of Nigeria should utilise less external debt since it weakens the country’s exchange rate position. In addition, the federal government of Nigeria should collaborate with producers to develop and integrate local content into their production processes in order to reduce the burden on EXR.

Keywords: Currency, Crisis, Choices, Naira, CBN, Influencing

INTRODUCTION

The term “exchange rate” is used to describe the value of one country’s currency in terms of other currencies. An origin country’s citizens must use a foreign currency in international transactions involving goods and services from citizens of an origin country’s economy. In return, their exports of goods and services or their investments in the economies of other countries bring in foreign currency (Tumala, 2017). A currency crisis may result from disruptions in the supply or demand chains (Tumala, 2017). Inappropriate and unrealistic exchange rate policies by some countries exacerbated the exchange rate and international debt crises of the late 1970s, according to many academic economists and policymakers. Financial crises, poor agricultural output, underground currency exchanges, a decline in international competitiveness, a widening trade deficit, and other economic imbalances have been blamed on Africa’s overvalued real exchange rates.

Keeping the real exchange rate above or below the long-run equilibrium level leads to exchange rate crises, which in turn severely hinders a country’s ability to grow and develop. Policymakers often use fiscal and monetary policies, including external sector policy arsenals, to correct the misalignment in the exchange rate and put it on the path of growth, along its fundamental equilibrium exchange rate, which is consistent with internal and external balance.

Currency crises are often precipitated by unpredictability in the foreign exchange market, the causes of which may differ from economy to economy. International economic contagion risks, causal links between different types of crises, and the high cost of economic downturns are all described in the literature of economic theory (Kaminsky & Reinhart, 1999; Reinhart & Rogoff, 2011). It is crucial to keep an eye on an appropriate set of early warning indicators in order to implement initiatives to lessen the impact of a recession or currency crisis.

Based on previous studies, Aghion, Bacchetta, and Ranciere (2008) examine the impact of real exchange rate volatility on a country’s economic growth; Umoru and Ose (2013) employ VECM methodology to probe the relationship between the trade deficit and the naira exchange rate; Ogundipe, Ojeaga, and Ogundipe (2013) examine the effect of devaluation on Nigeria’s trade balance utilising variance decomposition evaluation from 1970 to 2010; Currency devaluation is evaluated for its impact on the Nigerian economy by Osundina and Osundina (2014). For their study on the effects of devaluation on Nigeria’s government finances, Ezeh and Obi (2016) employ VECM, OLS, and Granger causality techniques. Over the time span of 1970-2014, Gordon (2017) investigated Nigeria’s export earnings in relation to the country’s exchange rate. These analyses did not find a correlation between the Nigerian naira exchange rate and other variables such as private sector credit, oil prices, balance of payments, or foreign debt stock. Therefore, it is posited that this study be conducted in order to fill this gap.

Another void that this research helps to fill is that of studies that concentrate on the years following the liberalisation of the Nigerian economy (between 1986 and 2021). As a result, the causes of the recent currency crisis in Nigeria are the subject of this investigation. Our paper is structured as follows: introduction; literature review; methodology; results and discussion; conclusion; and recommendations.

LITERATURE REVIEW

The term “exchange rate” is commonly used to describe the value of one foreign currency in relation to a domestic currency. One national economy’s worth is measured against that of others, in particular trading partners’ economies (Adesoye, 2012). Exchange rates allow the cost or price of an item to be expressed in a globally accepted currency. Changes in the value of a country’s currency are one example of an endogenous factor that is known to affect economic output. For instance, a stronger currency raises the cost of imported goods while lowering the cost of goods produced at home. Therefore, an exporting nation with a strong currency will have more foreign reserves, which may help stabilise the economy. Unless the country has a monopoly on the production of tradeable goods, a stronger currency causes importers to begin shopping elsewhere, driving up both export prices and the value of the currency. Currency depreciation, on the other hand, causes import prices to rise while export prices fall. The value of a country’s currency declines because its people are forced to pay inflated prices for imported goods and services. When a country’s currency falls in value, consumers can buy more of their desired goods for the same amount of money, which further reduces the locals’ purchasing power.

Kaminsky, Lizondo, and Reinhart (1998) state that the indicators used in crisis prediction models are influenced by the various theories that attempt to explain financial crises. Krugman’s (1979) seminar paper serves as the theoretical basis for the discussion of the causes and indicators of currency crises. The weakening of economic fundamentals is what led to the decline of fixed/pegged exchange rate regimes, as argued by Krugman (1979). Examples include inordinately expansionary monetary and fiscal policies and persistent balance of payments deficits, both of which lead to a sustained loss of foreign reserves and force monetary authorities to abandon currency parity. According to the Krugman (1979) model, also known as the conventional practice, an unsubstantiated attack on the domestic currency will occur if domestic credit growth is intense relative to the increase in money demand under a pegged exchange rate regime. the attack that ultimately led to the peg being abandoned by the central bank. Because of this expectation that the fixed exchange rate will ultimately fail, economic agents usually conclude the procedures by attacking the currency. Depending on the extent to which federal spending is backed by excessive money creation, indicators such as credit to the public service and fiscal inequities may point to an impending crisis. In contrast to the second-generation models that highlight the potentiality of myriad equilibria and personality attacks on a currency after the weakening of foundations, the first-generation additions of Krugman’s (1979) prototype highlighted the significance of fiscal and monetary disparity in shocks against distinctive currency rate regimes.

The second-generation models postulate that economic crises can occur even if there are no discernible shifts in the economy’s fundamentals. A variety of equilibria and self-reinforcing crises may emerge depending on the character of economic policies implemented in this context. The premise is that economic actors make decisions based on the insight that economic policies are adaptable and adaptable to market conditions. Economic agents’ actions have knock-on effects on a variety of other factors, and policies adjust accordingly.

Aghion, Bacchetta, and Ranciere (2008) argue that actual currency volatility can have a major impact on a country’s potential for lengthy efficiency growth. It was pointed out, though, that the impact would vary considerably depending on a country’s level of economic development. Exchange rate depreciation customarily causes a devastating recession and elevated inflation while boosting the trade balance, as shown by Yildirim and Ivrendi (2016) in their study of four sampled emerging economies. As a result of their research, they concluded that depreciation has significant “stagflationary” effects that primarily affect the macroeconomy via supply-side channels, specifically the cost of imports. Pandey (2013) conducted an empirical test of the Marshall-Lerner condition with respect to India’s international trade and found that a depreciation of the currency led to a rise in the trade surplus. Birgul and Sevcan (2016) used the ARDL approach to look into the long-term co-integration relationship among the variables related to the devaluation of the Turkish lira and found evidence in support of the Marshal-Lerner condition, which states that the sum of export and import price elasticities is greater than unity. In the long run, the depreciation of the Turkish currency benefited the country’s trade balance. Frankel and Saravelos (2012) conducted a comprehensive literature review of early warning indicators and numerous experiments to determine if indicators useful for predicting one crisis episode were also useful for predicting the next. They used fifty annual macroeconomic and financial variables for a sample of roughly 122 countries to see if leading indicators could account for the cross-country incidence of the 2008-2009 crises. The study concludes that, across all dimensions of crises, foreign reserves and real currency overvaluation are the most informative indicators. Between 1986 and 2008, Loto (2011) analysed the effects of the appreciation and devaluation of the Nigerian naira on the country’s trade balance. Due to the fact that the combined import and export demand elasticities were found to be less than unity, the OLS analysis concluded that devaluation does not strengthen the trade balance. Therefore, it appears that Nigeria did not satisfy the Marshall-Lerner circumstance for a devaluation. Umoru and Ose (2013) utilised information from Nigeria to examine the J-curve effect by means of the VECM. Their research revealed a recurring relationship between the trade deficit and the real naira exchange rate. Currency devaluation and its impact on Nigeria’s trade balance were studied by Ogundipe, Ojeaga, and Ogundipe (2013) using Johansen’s Co-integration and variance decomposition evaluation from 1970 to 2010. The results demonstrated a long-run stationary connection between the trade surplus or deficit and its factors like the nominal rate of currency. In addition, the trade balance is significantly correlated with factors that affect it, albeit in a non-linear fashion. There is no brief connection between the currency rate and the trade balance, the variability of the money stock is a bigger factor in the trade balance’s volatility than the exchange rate’s, and the currency value initiates an inelastic but notable sway on the trade balance in the long run. Osundina and Osundina (2014) looked into the impact of devaluation on the Nigerian economy. The study concludes that a currency devaluation leads to a decline in imports, an increase in exports, and a spike in interest rates. Akindiyo and Olawole (2015) argue that a weaker naira is bad for Nigeria. They reasoned that decline shouldn’t be a possible response to the periodic worldwide economic downturns. Ezeh and Obi (2016) used vector error correction models (VECM), ordinary least squares (OLS), and granger causality to study the effects of currency devaluation on govt. spending, earnings, and fiscal modification in Nigeria between 1981 and 2014. Currency devaluation was found to have positive and causal relationships with a subset of fiscal variables. Gordon (2017) used quantitative tools to analyse statutory annual data from 1970-2014 and found that the Nigerian currency exchange rate (EXR) and export growth (EXP) are not co-integrated, finding no evidence of a causal connection between them. They are not in a state of equilibrium with one another because of this. However, there is only a direct line of reasoning from the currency rate to the expansion of exports.

METHODOLOGY

Researchers used an ex-post facto design because the data they used was secondary in nature and collected after the fact. From 1986 to 2021, each annual series was assured a spot on CBN. In order to estimate the finer points, we employ techniques such as co-integration, VAR, and VEC-Granger causation, as well as descriptive statistics, the unit root, and VEC-Granger causality. To accurately represent our goals, our model takes the form of:

EXR = f(BOP, EXDS, PSC, COP)                                                                     3.1

The mathematical and econometric equations are;

EXR_t = β_o + β₁LNBOP_t + β₂LNEXDS_t + β₃LNPSC_t+ β₄COP_t                                  3.2

EXR_t = β_o + β₁LNBOP_t + β₂LNEXDS_t + β₃LNPSC_t+ β₄COP_t+ µ_t             3.3

On apriori, β₁, β₃, and β₄ ˃ 0;β₂ ˂ 0.

Where, EXR = Exchange rate, BOP = Trade balance, COP =Crude oil price, PSC = Private sector credit, EXDS = External debt stock, β_o = Intercept, β₁, β₂, β₃and β₄= Constant parameters, Ln = Natural logarithm, µ_t = Error term

The Johansen co-integration model is given as;

DATA ANALYSIS AND DISCUSSION OF FINDINGS

Table 4.1: Descriptive Statistics

Source: E-views Output

The mean values for EXR, LNBOP, LNEXDS, LNPSC, and COP as seen in table 4.1 are 117.4544, 1.330398,7.016357, 6.930630, and 45.82662 respectively. Similarly, their smallest and biggest values are 2.02 and 380.75,-2.617296 and 3.598106, 3.724546 and 9.449800, 2.753763 and 10.06727, and12.62 and 109.45 respectively. Their level of variabilities are 105.5452%, 1.525107%, 1.321844%, 2.436582%, and 30.60857% respectively. LNBOP, LNEXDS, and LNPSC are negative (-0.464162, -0.245234, and -0.342810 respectively) while EXR and COP are positive (0.813735 and 0.686025 respectively). LNPSC and COP are platykurtic as they are smaller than 3; while EXR, LNBOP, and LNEXDS are mesokurtic as they are approximately 3 (2.863568, 2.744856, and 2.771828). The Jarque-Bera p-values for EXR, LNBOP, LNEXDS, LNPSC, and COP (0.151175, 0.518642, 0.812797, 0.226200, and 0.169667 respectively) shows that the variables are normally distributed at 5% level.

Table 4.2: Stationarity Result

Source: E-views Output

According to Table 4.2, all of the parameters are stable after the first difference. This means that their t-statistics in the ADF are greater than their t-critical values at the first difference and that their p-values are less than the 5% threshold for significance. Therefore, the Johansen co-integration test is used to confirm the existence of lengthy form among the parameters.

Table 4.3: Johansen Co-integration Result

Source: E-views Output

The result of table 4.3 displays the absence of co-integrating nexus among the variables. This is necessitated as both the Trace and Max-Eigen p-values are above the 5% level. Thus, we estimate the VAR equation.

Table 4.4: VAR Result

Source: E-views Output

The lag 1 of EXR is positive (0.930888) and significant (9.21060) to previous period EXRusing the rule of the thumb ≥2. This means that EXR is autoregressive and as such previous period EXR can be used to predict the current period EXR. Hence, a unit increase in the previous period EXR will cause the current period EXR to increase by 0.930888 unit. LNBOP is negative (-5.302215) and insignificant (-1.16703) to EXR. This means that a unit increase in LNBOP will lead to a reduction in EXR by 5.302215 units. COP is negative (-0.314008) and insignificant (-1.15504) to EXR. This means that a unit increase in COP will lead to a reduction in EXR by 0.314008 units. Though LNEXDS is negative (-1.260278) but it is significant (-2.76170) to EXR. This implies that a unit increase in LNEXDS will lead to about 1.260278 units decrease in EXR. Similarly, LNPSC is negative (-0.973910) but significant (-3.84662) to EXR.This implies that a unit increase in LNPSC will lead to about 0.973910 unit decrease in EXR.

The Adjusted R-squ. indicates that LNBOP, LNPSC, LNEXDS, and COP explains about 95.3% variations in EXR; while the remaining are explained by other factors not included in this study. The F-stat. of 126.0618 shows that the model is significant and of good fit.

Table 4.5:VAR Block Exogeneity Wald Test

Source: E-views Output

Table 4.5 of the VAR Block Exogeneity Wald result shows that LNEXDS and LNPSC supports EXR; while COP and LNBOP do not support EXR. Collectively, there is the presents of joint support from the variables causing changes in EXR.

Table 4.6: VAR Serial Correlation (SC) Test

Source: E-views Output

The p-values (0.7304 and 0.7389) and (0.7207 and 0.7294) for lag 1 and 2 for LRE and Rao F-stat. respectively are higher that 5% level. As a result, the no autocorrelation.

Table 4.7: Heteroskedasticity Test

Source: E-views Output

At the 5% level, the variance is equal because the p-value associated to it 0.0803 and it is higher than 5%. Thus, no form of heteroskedasticity.

DISCUSSION OF FINDINGS

The trade deficit hurts the value of the naira. Consequently, the EXR will worsen along with Nigeria’s trade balance. We blame Nigeria’s chronic trade deficit for this situation. This unfavourable standing is a result of the country’s heavy reliance on and preference for imported goods and services, as well as the exportation of primarily primary products with high demand elasticity. This is in line with the research of researchers like Pandey (2013), Yildirim and Ivrendi (2016), Frankel and Saravelos (2012), and Birgul and Sevcan (2007). (2016). This goes against the results found by Loto (2011), Osundina and Osundina (2014), Umoru and Ose (2013), Akindiyo and Olawole (2015), Ogundipe et al (2013).

Nigeria’s EXR is significantly slowed by the country’s large stock of external debt. This means that Nigeria’s EXR will increase in tandem with the value of her external debt. This is because the principal and interest on such debt must be repaid using foreign currency. This agrees with the findings of Yildirim and Ivrendi (2016) and Birgul and Sevcan (2016) but not with those of Ogundipe et al. (2013), Akindiyo and Olawole (2015), and Osundina and Osundina (2016). (2014).

The private credit market has a depressing effect on EXR. Therefore, the EXR falls as banks make more loans and advances overall. We contrast this with the fact that most bank advances and loans to the private sector are utilized to buy industrial equipment and materials, most of which come across the country boundaries, negatively impacting our EXR position. This is consistent with the findings of Yildirim and Ivrendi (2016), but contradicts those of Ogundipe et al (2013).

There is little to no impact on the EXR from the decline in oil prices. So, if the price of crude oil drops, we can anticipate an increase in EXR. This is due to the fact that OPEC sets the crude oil price, the foundation of the Nigerian economy, and that price fluctuates frequently. This is in line with the research of researchers like Pandey (2013), Yildirim and Ivrendi (2016), Frankel and Saravelos (2012), and Birgul and Sevcan (2007). (2016). This goes against the results found by Loto (2011), Osundina and Osundina (2014), Umoru and Ose (2013), Akindiyo and Olawole (2015), Ogundipe et al (2013).

CONCLUSION AND RECOMMENDATIONS

5.1       Conclusion

Our study analysed the Naira exchange predicament from 1986 to 2021 from the standpoint of responses and policy options. On the basis of Uwatt’s (2017) study, we analysed the interplay between Nigeria’s trade deficit, external debt, private sector credit, and the price of crude oil to determine the severity of the country’s exchange rate crisis. Our research shows that the external debt and private sector credit are two of the most significant contributors to the naira exchange rate crisis. We attribute this to the fact that that most bank advances and loans to the private sector are utilized to buy industrial equipment and materials, most of which come across the country boundaries, negatively impacting our EXR position, and to the use of foreign currencies in the repayment of principal and interest on such debt. Birgul and Sevcan(2016), Frankel and Saravelos (2012), and Pandey (2013) all agree with these results.

5.2       Recommendations

We recommend that Nigeria’s federal government pursue economic diversification in order to lessen the impact of rising crude oil prices on the country’s economy. Because of the negative impact on Nigeria’s EXR ranking. If the Nigerian government wants to improve the country’s exchange rate position, it needs to reduce its use of external debt stock. Additionally, the Nigerian federal government should work with producers to develop and incorporate local content into their production processes in order to reduce the demand for EXR.

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