Global Uncertainty,Climate Change and the Unemployment-Economic Growth Relationship in Nigeria

Abstract

This study investigates global uncertainty, climate change and the unemployment-economic growth relationship in Nigeria. The study utilised the autoregressive distributed lag (ARDL) estimation technique using quarterly data, 1990–2020. Findings indicate that global uncertainty and unemployment impact negatively economic growth both in the short run and long run. Also, climatic change variables employed in this study such as temperature and level of rainfall have a negative impact on economic growth both in the long-run and short-run. The moderating effects of global uncertainty and climatic variables on the unemployment-economic growth relationship were positive, though insignificant. The policy implication underlying the finding is that tackling economic uncertainty and climate change is necessary for solving unemployment problem and the attainment of sustainable growth in Nigeria.

JEL Codes: C22, D80, Q25, E24, O40

Keywords

Global uncertainty climate change unemployment economic growth ARDL

Introduction

Nigeria has experienced several uncertainty-inducing events, particularly in the last two decades, such as the 2007–2009 Global Financial Crisis, the tension generated by the transition from one elected president to another in 2015, the escalating tensions from worsening insecurity of lives and property, the collapse in commodity prices like international oil prices that started in 2013, the 2016 and 2019 economic recessions, and the outbreak of COVID-19 Pandemic, among others. These events have had attendant consequences on the economy, in terms of decline in productivity, trade and capital market, employment generation, among others. For instance, during the GFC, Nigeria’s total market capitalisation dropped from N12.40 trillion to N4.69 trillion, a decline of 62.18% (Satti et al., 2016). Besides, the county’s foreign reserve dipped from $64 billion to $47 billion between 2008 and 2009. The shock in global oil prices in 2015 also led to a series of negative setbacks for Nigeria’s economy, such as decline in gross domestic product (GDP) growth from 5.1% to 1.4% in 2014 and 2016 (Ahmad & Odonkor, 2020; Oloni et al., 2017). Similarly, due to negative consequences of the COVID-19 pandemic Nigeria recorded a decline in exports, imports, and fixed capital formation by 27.0%, 23.3%, and 7.6%, respectively (Ozili, 2020). These declines add up to a 1.90% contraction of the economy (National Bureau of Statistics, (NBS) (2020). Figure 1 shows the trend in Nigeria’s GDP growth rate from 1990 to 2020, a period which covers the pre and post-global financial crises as well as the pre and after the height of the COVID-19 pandemic. As visualised, due to the collapse of the international oil prices in 2013, GDP growth rate declined from 6.67% in 2013 to 6.31% in 2014 before plummeting to 2.65% in 2015. The outbreak of COVID-19 pushed Nigeria into recession with negative GDP growth of -1.62%. Although Nigeria improved marginally in 2017, 2018 and 2019 with positive GDP growth rate of 0.81%, 1.92% and 2.21%, the country experienced a negative GDP growth rate of -1.79% in 2020 as a result of the 2019 global economic recession with the attendant consequence on Nigeria economic performance.

Figure 1.

Source: Authors based on Data from CBN Statistical Bulletin (2022).

However, beyond economic uncertainties, Nigeria is increasingly facing the challenge of climate change. In simple terms, climate change is the change in climatic and atmospheric conditions that alters weather patterns and frequency. Contributing further, Amobi and Onyishi (2015), Sequeira et al. (2018), Akuwudike and Mac-Ozigbo (2020) aver that climate change can have serious effects on the aggregate economy. According to Abidoye and Odusola (2015) the key sectors of the economy which climate change affects include agriculture, forestry, energy, tourism and water resources. Climate change also weighs on economic growth by depressing productivity, destroying housing and physical infrastructure, and dampening hydroelectric production. Other effects of climate change on the economy as enunciated by, Garcia-Verdu et al. (2019) include reduced productivity, slow investment and damage to capital, the environment, and biodiversity hurt manufacturing, wholesale and retail trade. As stated by, Dell et al. (2012) climate change in the form of both higher and lower levels of temperature has substantially reduced the level and rate of economic growth regardless of the category of the country, of which the poorest countries are severely affected than their richest counterparts. Similarly, Sangkhaphan and Shu (2020) stated that rainfall has reduced growth at the national level through its significant negative effect on agriculture and service sectors.

In economic discourse, economic growth refers to the ability of an economy to accelerate its productivity, for example more capable of producing more goods and services or raising the living standard of its people. Although economic growth is necessary but not sufficient condition for development, it has been recognised as a key variable in curtailing unemployment (Islam et al., 2021; Ouhibi, 2021). It is a medium through which unemployment can be reduced, inflation stabilised, innovation promoted and a panacea for poverty reduction (Wolde et al., 2022). In support of this, Lee (2000) and Conteh (2021) assert that economic growth provides a platform for entrepreneurs to emerge, who on the other hand reduces unemployment rate through the creation of jobs. Meanwhile, a macroeconomic variable such as unemployment has an effect on the overall economic performance, so also environmental variables like as Carbon dioxide (CO₂) emissions, rainfall, and temperature.

Global uncertainty and climate change affect economic growth and unemployment through several channels. Uncertainty and climate change have pervasive socio-economic consequences that affect not only the productive sectors of the economy like agriculture but other macroeconomic variables. More extreme weather has the potential to dampen economic growth through damage to the capital stock and labour productivity will weaken. Higher temperatures and other climatic changes manifestation will also impact other aspects of life that are related to economic activity, for example, health and well-being and environmental quality which has consequences on human labour productivity and economic growth (Carleton and Hsiang, 2016). Changes in rainfall, atmospheric carbon CO2 and ozone concentrations, and other extreme events spurred by climate change will likely affect agricultural activities. In addition, climate change might have negative consequences on agricultural output and hampers employment creation. Uncertainty reduces the ability of countries to attract foreign investment and therefore causes unemployment. It also prevents efficient climate-change-induced reallocation of capital, and other resources to maximise productivity and create employment (Bellon & Massetti, 2022). Economic damages due to climate change have a destructive effect on employment. In some cases, climate change may lead to environmental degradation which will have a negative effect on the world of work (Knotek and Khan, 2011; ILO, 2018).

Over the past decade, Nigeria has witnessed growth rate. However, the country experienced downward trajectory growth rate in 2014 and 2016, This was attributed to productivity challenges, high exchange rate and infrastructural challenges. In spite of the economic growth rate, one of the persistent economic problems that Nigeria is grappling with is unemployment. Available statistics from NBS (2021) reveal that unemployment rate increased from 23.1% in 2018 to 27.1% in 2020. With a labour force of 80.2 million, it therefore implies that about 21.7 million Nigerians (aged 15–64), who during the year 2020 were available for work and actively sought for work were without work. What is particularly worrisome presently is that the rising unemployment is not just a one-off event. It appears to have become intractable and persistent because, since 2015, the unemployment rate has been on the increase from one quarterly period to another. In 2015, it was 7.5% which further escalated to 23.1% in 2018. There was a sharp increase to 27.1% in 2020, by 2020 the unemployment rate in Nigeria has risen to 33.3% which has been generally attributed to the COVID-19 Pandemic (NBS, 2020). This is suggestive that uncertainty-inducing events, like the COVID-19 Pandemic, can significantly influence the unemployment-economic-growth relationships in Nigeria (Ozili, 2020).

Since climatic change can directly alter economic outcomes such as gross domestic product (GDP), investment, price level, quantity, pattern and composition of imports and exports, several programmes and policies have been put in place (Mehta et al., 2019), and their effect on economic growth and unemployment is worth examining. Thus, as different economies strive to promote sustainable growth, which is one of the Sustainable Development Goals, there is a need to understand the moderation effect of uncertainty and climate change in the unemployment-growth relationship.

A plethora of studies have examined unemployment and growth relationship with mixed outcomes, influenced majorly by methodology, the role of global economic uncertainties and climate change in this relationship remains unresolved and has generally neglected the moderation effect of uncertainty on unemployment and growth relationship. Contributing to literature, this study examines the effect of global uncertainty and climate change on the unemployment-growth relationship as well as the moderation effect of climate change on the unemployment-growth relationship. The main finding is that global uncertainty and unemployment impact negatively on economic growth both in the short-run and long-run. Also, climatic change variables employed in this study such as carbon dioxide emission and level of rainfall have a negative impact on economic growth both in the long-run and short-run. The result of the moderation effect of global uncertainty and climatic variables on the unemployment-economic growth relationship was positive, though insignificant.

The remaining part of the paper is structured as follows: the second section reviewed related literature, the third section is methodology, the fourth section presents empirical results and discussion while the fifth section concludes.

Overview of the Literature

In economic literature, many explanations have been adduced for unemployment problems. While some attribute it to economic growth, others blame it on external sources and shocks or unpredictable events. Mouhammed (2012), contend that several unpredictable events such as climate change global uncertainties and economic policies have generated a high rate of structural unemployment which has contributed to the increase in the rate of unemployment The risks associated with climate change have specific implications on unemployment. Climate change may lead to job and work productivity losses because it increases the frequency of extreme weather events and threatens the provision of ecosystem (ILO, 2018). One of the famous theories linking unemployment and economic growth is Okun’s Law. The law provides the connection between employment rate and the growth rate of the economy. As further portrayed, output is linked to the amount of labour employed in the production process and for this, a positive relationship exists between output and employment. To reduce employment, therefore, the economy must grow at a rate above its potential. The Schumpeter (1934) theory demonstrates that unemployment can be reduced through innovation. Alehile (2018), Islam et al. (2021), Desbordes and Eberhardt (2022) aver that innovation creates different channels which create jobs relative to job destruction. Another theory worth discussing is the Wait-and-See Theory propounded by Bernanke (1983). It is a means through which uncertainty shock affects the economy is the Wait-and-See mechanism propounded by Bernanke (1983). It was explained that there is partial irreversibility between shocks and economic performance. Bloom (2009) quantifies this mechanism by showing a sharp drop and rebound in the way production and employment respond to uncertainty shocks.

Existing studies have examined the relationship between unemployment and economic growth as well as the impact of climate change on economic growth using different methodology. Kalu (2021) adopted the ARDL model in its baseline form to estimate the linkage between economic growth and unemployment. It was found that female unemployment has a positive and significant effect on growth while youth unemployment has negative and significantly effect on growth. Also, male unemployment has insignificant effect on growth. The finding further confirm that growth adjusts to unemployment dynamics. The study by Khan (2020) estimated the effect of unemployment on economic growth using the Ordinary Least Square (OLS) regression technique. Finding indicates that unemployment has a negative and insignificant impact on economic growth, an indication that unemployment depresses growth. Seth et al. (2018) examined the relationship between unemployment and economic growth using the ARDL Bound Testing and the Parsimonious Error Correction Model (ECM). The study found that there is no long-run relationship between unemployment and economic growth. Ademola and Badiru (2016) examined the effect of unemployment and inflation on economic performance with the result indicating that unemployment and inflation are positively related to economic growth. Resurreccion (2014) investigated the link between unemployment, inflation and economic growth. Findings indicate that unemployment is negatively related with inflation and economic growth. Enejoh and Tsauni (2017) uncovered that a long-run positive relationship exists between unemployment and output. Besides, unemployment rate has a positive impact on growth. However, Conteh (2021) found no long-run association between unemployment and economic growth in Liberia.

Wensheng et al. (2017) used FABVAR model to investigate the impulse responses of economic growth, inflation, interest rate and global uncertainty. Findings suggest that global uncertainty shocks are associated with a sharp decline in global inflation, global growth and interest rate. The reported result depicts that global uncertainty is associated with 18.26% and 14.95% variation in global growth and inflation respectively. Using individual survey data from the Consensus Forecasts over the period of 1989–2014, Ozturk and Sheng (2017) found that common global uncertainty shocks produce large and persistent negative responses in economic growth, whereas the contributions of idiosyncratic uncertainty shocks are negligible. Rossi and Sekhposyan (2017) investigated the heterogeneity of uncertainty across Euro Area countries as well as the spillover effects. Uncertainty was found to have a negative impact on macroeconomic variables. Lensink et al. (1999) investigate the effect of uncertainty on growth in a cross-section of 138 developing and developed economies. The study found a robust and negative effect of uncertainty on economic growth, which underlines the importance of export stability and policy credibility.

Studies on climate change and economic growth include Kadanali and Yalcinkaya (2020) that examined the symmetric and asymmetric effects of climate change on economic growth in the top 20 economies in the world (WTE-20). The study concluded that climate change has a negative and significant effect on growth. Earlier, Kahn et al. (2019) found that real output growth per capita is adversely affected by permanent changes in temperature. This is in tandem with the findings of Taher (2019) and Berlemann and Wenzel (2018) who stated that industries are driven by climate conditions such as temperature and rainfall. Henseler & Schumacher (2019) investigated the impact of weather on economic growth and employment and found that the main effects of weather are caused by temperature which triggers growth. Odusola & Abidoye (2012) found that long term temperature change reduces economic growth. The evidence was based on a study involving 34 selected African countries based on annual data from 1961–2009. In a study on the interconnection among climatic change, human development and economic growth Akanbi et al. (2014) concluded that climate change has hampered investment, with its attendant consequence on growth. Ogbuabor and Egwuchukwu (2017) examined the impact of climate change on overall growth and found that forest depletion has a negative impact on growth in the short-run while carbon emissions have a negative effect on growth in both long-run and short-run. This supports Abidoye and Odusola (2015) that also found a negative impact of climate change on economic growth. Azam et al. (2016) used the panel fully modified ordinary least squares (FMOLS) method to examine the impact of environmental degradation proxied by CO₂ emissions per capita along with energy use, trade, and human capital on economic growth in China, the USA, India, and Japan. The study found a positive and significant impact of CO₂ emissions on economic growth in both the short- and long-term. This is, however, in sharp contrast with Borhan et al. (2012) who used two-stage least square method to show that CO₂ shows negative significant relationship with income and economic growth. Colacito et al. (2018) used Panel regressions analysis to show that an increase in the average summer temperature has a significant and robust negative effect on GDP growth.

Methodology

Methodological Framework

A standard framework to assess climate damages emanates by linking economic activities to emissions of greenhouse gases (GHG) and evaluate how human activities influences atmospheric GHG concentrations drive changes in climate such as changes in temperature (Bellon & Massetti, 2022). These climate changes will in turn result in physical and biogeochemical impacts which ultimately affect productivity of the various sectors of the economy and lead to economic losses.

Our objective in this study is to analyse the effect of global uncertainty and climate change on the unemployment-growth relationship in Nigeria. This study draws heavily from Alehile (2018), and Islam et al. (2021) in modelling the transmission channels of uncertainty and climate change on economic performance through a production function approach that captures the relationship between economic output and factor inputs and the efficiency of technology. The framework provides the link between inputs and output level. Adopting a modified version of the Cobb-Douglas production function, the technical relationship that exist is of the form:

Y_{t} = A_{t} \sum_{i = 1}^{j} K \begin{matrix} a j \\ j t \end{matrix} L_{j t}^{β j} X_{j t}^{j δ}

(1)

where

Y_t = Output at time t

A_t = Technical efficiency

K_j = capital

L_t = labour

X_t = Vector of other variables including climate variables, uncertainty variable and other control variables

β_j = measures the responsiveness of output to a change in the labour level

Taking the natural logarithm, the linear form of Equation (1) is specified as:

L o g Y_{t} = A_{t} + \partial_{J} L o g K_{j t} + β_{j} L o g L_{j} + δ_{j t} L o g X_{j t}

(2)

Model Specification

In line with the objective of the study, the empirical model is specified as:

G D P P C = φ_{i} U N E M_{t} + θ_{i} U N C_{t} + δ_{i} (U N E M * U N C) t + θ_{i} C A P t + ϑ_{i} L F P R_{t} + ϑ_{i} F D I_{t} + σ_{i} T E M P t + σ_{i} R A I N_{t} + τ_{i} (U N E M * A R F) t + γ i (U N E M * T E M P) t + μ_{t}

(3)

where GDPP = economic growth (measured by Gross Domestic Product Per Capita), UEMP = unemployment rate, UNC = World Uncertainty Index, CAP = Gross Fixed Capital Formation, LFPR = Labor Force Participation (working age population 15–64 years), FDI = (Net foreign direct investment inflows (% of GDP), TEMP ₌ Maximum temperature level, RAIN = Average Total Annual Rainfall, UNEM*UNC = Moderating effect of unemployment and World uncertainty Index, (UNEM*ARF) = Moderating effect of unemployment and Climate change, (UNEM*TEMP) = Moderating effect of unemployment and climate change.

Estimation Technique

The estimation technique of the study is the ARDL framework. The ARDL has been adjudged as capable of capturing both short-run and long-run relationship among variables. The ARDL model suggests that once the order of the ARDL is determined, the relationship can be estimated using the ordinary least squares (OLS) technique. The OLS technique is the best linear and unbiased estimator (BLUE). The Philips Perron (P-P) Unit root test is used to verify the orders of integration of the variables to ensure that they are consistent with the framework. The procedure, is followed by a test for the long-run relationship among the used variables. According to Pesaran et al. (2001) when the calculated F-statistic lies above the upper level of the band, the null hypothesis of non-existence of long-run relationship among variables is rejected, implying that there is co-integration, whereas, in the case were it lies below the lower level of the band, the null hypothesis is not rejected, meaning there is no cointegration. But when the F-statistics fall within the upper and the lower bands, the result is inconclusive, meaning that the presence or absence of cointegration is not determined.

In line with ARDL approach, the long-run form of Equation (3) is specified as:

\begin{matrix} G D P P C_{t} = \partial + \sum_{\begin{matrix} i = 0 \\ q \end{matrix}}^{q} φ_{i} U N E M_{t - 1} + \sum_{i = 0}^{q} θ_{i} U N C_{t - 1} + \sum_{i = 0}^{q} δ_{i} {(U N E M * U N C)}_{t - 1} + \sum_{i = 0}^{q} ℵ_{i} C A P_{t - 1} \\ \begin{matrix} + \sum_{i = 0}^{q} c_{i} L F P R_{t - 1} + \sum_{i = 0}^{q} ϑ_{i} F D I_{t - 1} \sum_{i = 0}^{q} σ_{i} A R F_{t - 1} + \sum_{i = 0}^{q} ℵ_{i} T E M P_{t - 1} \\ \begin{matrix} + \sum_{i = 0}^{q} τ_{i} {(U N E M * A R F)}_{t - 1} + \sum_{i = 0}^{q} γ_{i} {(U N E M * T E M P)}_{t - 1} + \sum_{j = 1}^{p} τ_{i} G D P P C_{t - 1} + μ_{t} \end{matrix} \end{matrix} \end{matrix}

Similarly, the short-run error correction model can be specified as:

{G D P P C}_{t} = \partial + \sum_{i = 0}^{q} φ_{i} {∆ U N E M}_{t - 1} + \sum_{i = 0}^{q} θ_{i} {∆ U N I C}_{t - 1} + \sum_{i = 0}^{q} δ_{i} {∆ (U N E M * U N C)}_{t - 1} + \sum_{i = 0}^{q} ℵ_{i} {∆ C A P}_{t - 1} + \sum_{i = 0}^{q} C_{i} {∆ L F P R}_{t - 1} + \sum_{i = 0}^{q} ϑ_{i} {∆ F D I}_{t - 1} \sum_{i = 0}^{q} σ_{i} {∆ R A I N}_{t - 1} + \sum_{i = 0}^{q} ж_{i} {∆ T E M P}_{t - 1} + \sum_{i = 0}^{q} τ_{i} {∆ (U N E M * A R F)}_{t - 1} + \sum_{i = 0}^{q} γ_{i} {∆ (U N E M * T E M P)}_{t - 1} + \sum_{j = 1}^{p} τ_{i} {∆ G D P P C}_{t - 1} + {∆ E C M}_{t - 1} + μ_{t}

(5)

where ECM is the error correction term obtained from the long-run estimation of equation, Δ is first difference operator. All other variables remain as defined earlier.

Data Source, Description and Measurement

Data were for the study were sourced from the Central Bank of Nigeria (CBN) Statistical Bulletin, the global uncertainty index of Davis (2020), NBS and World Development Indicators (WDI). The study period covers 1990–2020. Variables employed for empirical estimation are economic growth (measured by gross domestic product per capita, constant 2010 US$) growth, climate change (measured by changes in rainfall and temperature), unemployment rate; labour force (measured by the working population aged, 15–64 years); FDI (Net inflows, percentage of GDP) and physical capital stock (measured by gross fixed capital formation).

Result and Discussions

Baseline Results

This section provides the descriptive summary statistics and correlation matrix. It gives crucial information regarding the behaviour of the series employed for the model estimation.

As depicted in Table 1, the average value for economic growth rate stood at 5.3 and standard deviation of 0.1039. Uncertainty has a mean value and standard deviation of 0.300 and 0.231 respectively, unemployment 4.55 and 0.137, temperature value stood at 4.511 and 0.004, rainfall 2.98 and 0.02, labour force participation 3.37 and 0.03, capital stock 1.16 and 0.17. on the other hand, the mean and standard deviation value for FDI stood at –1.62 and 0.34, respectively.

Table 1.

Descriptive Summary Statistics of the Variables.

	GDP	UNEMP	UNC	RAIN	TEMP	LABOUR	FDI	CAP
Mean	5.4415	0.6627	0.3040	3.0726	1.5220	1.4800	0.1083	1.4239
Median	5.4460	0.6017	0.2397	3.0740	1.5221	1.4885	0.1941	1.4375
Maximum	5.5885	0.9971	1.0711	3.1143	1.5304	1.5135	0.8041	1.7404
Minimum	5.3060	4.5532	0.3000	2.9894	4.5114	3.3721	–1.6276	1.1681
Std Dev.	0.1039	0.1368	0.2312	0.0259	0.0040	0.0308	0.3470	0.1723
Obs	124	124	124	124	124	124	124	124
Correlation Result
	GDP	UNEMP	UNC	RAIN	TEMP	LABOUR	FDI	CAP
GDP	1
UNEMP	0.525	1
UNC	0.397	0.238	1
RAIN	–0.098	0.193	–0.060	1
MAX	0.500	0.014	0.157	–0.122	1
LABOUR	–0.532	–0.143	–0.242	0.304	–0.340	1
FDI	–0.234	–0.582	–0.069	–0.139	–0.099	0.179	1
CAP	–0.904	–0.329	–0.349	0.131	–0.603	0.645	0.169	1

Unit Root Test

ADF and the Phillip Perron unit root tests result (Table 2).

Table 2.

ADF Unit Root Test Results.

With Constant & Trend
Variables	Level Statistics	Level p Values	1st Diff. Statistics	1st Diff. p Values	Remark
GDP	–	–	–4.004	.002	I(1)
UNEMP	–3.830	.018	–	–	I(0)
UNC	–4.639	.001	–	–	I(0)
RAIN	–	–	–3.298	.072	I(1)
TEMP	–	–	–4.304	.004	I(1)
LABOUR	–	–	–4.714	.001	I(1)
FDI	–3.596	.034	–	–	I(0)
CAP	–	–	–3.139	.001	I(1)
Phillips-Perron (PP) Unit Root Test Results
With Constant & Trend
Variables	Level Statistics	Level p Values	1st Diff. Statistics	1st Diff. p Values	Remark
GDP	–	–	–3.048	.124	I(1)
UNEMP	–	–	–5.104	.003	I(1)
UNC	–7.327	.000	–	–	I(0)
RAIN	–4.048	.009	–	–	I(0)
TEMP	–	–	–6.626	.000	I(1)
LABOUR	–	–	–4.775	.001	I(1)
FDI	–	–	–5.246	.002	I(1)
CAP	–	–	–5.057	.003	I(1)

The results in Table 2 indicate that all the variables are either I (0) or I (1), which is consistent with the ARDL framework. However, since some of the variables are I (1), the study also conducted the Pesaran et al. (2001) bounds test for cointegration.

Long-run Bounds Test

The cointegration result is presented in Table 3. The result shows that the variables are cointegrated since the calculated F-statistic (3.467) based on the optimum lags selected by Akaike Information Criterion exceed the upper bound of the 5% critical values. This indicates that the variables in the model have a long-run relationship, a justification for the estimation of the long-run model of the ARDL specification.

Table 3.

Result of Long-run Bounds Test.

ARDL Bound Test
Models	Lower Bound (I0)	Upper Bound (I1)	Significance	F-statistic	K
Panel 1	2.07	3.16	10%	3.467051	10
	1.98	3.04	5%
	2.18	3.28	2.5%
	2.84	4.1	1%
	2.41	3.61	1%

The estimated long-run result is presented in Table 4. The result shows that global uncertainty has a negative and significant effect on economic growth. As indicated, 1% increase in global uncertainty retards economic growth by 0.02%. In the same vein, unemployment has negative and significant effect on economic growth. The estimated coefficient suggestive that 1% increase in unemployment rate reduces economic growth by about –0.070% in the long run. The implication is that high unemployment rate reduces economic output and consequent decline in economic growth. The result is in tandem with Wensheng et al. (2017) and Ozturk and Sheng (2017) that global uncertainty produces large and persistent negative effect on growth. This result supports the finding of previous studies like Omitogun and Longe (2017), Akuthson et al. (2018), Dayıoğlu and Aydın (2020) and Danjuma et al. (2021) that unemployment remains one of the most challenges hindering the attainment sustainable growth in Nigeria. Moreover, climatic change variables used in this study like temperature and rainfall has negative and significant effect on economic growth. The estimated coefficient suggests that 1% increase in rainfall and temperature reduce economic growth by about 0.16% and 0.6%, respectively. This indicates that climate change impedes the growth process which is felt through extreme weather, such as floods heatwaves, drought, and storms which destroys landed properties and farm produces which result to decline in agricultural production. This finding is supported by Akanbi et al. (2014), Abidoye and Odusola (2015), Ogbuabor and Egwuchukwu (2017), Kadanali and Yalcinkaya (2020).

Table 4.

Estimated Long-run Coefficients.

Variables	Coefficient	Std Error	t-statistic	p Value
UNEMP	–0.070	0.014	–4.793	.000
UNC	–1.790	0.306	–5.834	.000
RAIN	–0.159	0.020	–7.751	.000
TEMP	–0.455	0.151	–3.002	.004
LABOUR	1.557	0.782	1.989	.049
FDI	0.111	0.063	–1.739	.086
CAP	0.024	0.019	1.267	.208
UNCUNEMP	0.123	0.303	0.407	.685
UNCTEMP	1.179	0.201	5.843	.000
UNCRAIN	0.055	0.030	1.850	.068
Constant	10.124	12.682	0.7982	.426
R-squaredDurbin–Watson	0.8331.959	Prob(F-statistic) 0.000

Similarly, uncertainty and climatic change has positive moderation effect on the unemployment-growth relationship, though not statistically significant. The finding has several implications. First, keeping the level of uncertainty and unemployment rate in the economy at the barest minimum will enhance economic growth. However, this will have dampening effect on investment and increase employment rate, demand and consumption. Second, by keeping the level of uncertainty in the economy at the barest minimum and with adoption of adequate climate changes adaptation policies and measures will accentuate growth. Furthermore, a reduction in the level of uncertainty in the economy with proper and adequate climate change adaptation mechanism will enhance growth through adequate utilisation of the growing labour force in the country and increase in investment level as caused by a decrease in uncertainty level.

Other control variables such as labour participation and foreign direct investment (FDI) has a positive and significant relationship with economic growth. The result depicts that 1% increase on labour force participation rate increase economic growth rate by about 1.56% in the long run. The magnitude of the FDI coefficient is about 0.11 which indicates that for Nigeria, 1% increase in FDI inflow leads to 0.11% in economic growth rate in the long run. Also, physical capital stock has a positive effect on economic growth in the long-run. Capital stock is a potential growth enhancing variable.

Thus, labour force participation and FDI inflows capital stock improves the competitiveness of the economy and enhance the provision of goods and services for the domestic economy. FDI inflow enhances the capacity of the domestic economy to compete in the global economic arena. These economic benefits from FDI inflow, in turn, lead to higher economic growth of the domestic economy. FDI also accelerates the speed of technology adoption to improve efficiency (Othman et al., 2018). This finding is consistent with Njangang et al. (2021) and Saadi (2020) who also demonstrated in a baseline estimation that FDI enhances growth and Yakubu et al. (2020) who found that labour force has negative effect on economic growth in both short-run and long-run.

The result of the short-run effect presented in Table 5. The result indicates that uncertainty has a significant negative effect on economic growth. This is indicative that both in the short and long-run that rising uncertainty impedes growth. In addition, unemployment has negative and significant effect on economic growth, though the effect is stronger in short-rum than in the long-run. Previous studies such as Resurreccion (2014), Elorhor (2019) had similar findings. However, Kalu (2021) has contrary result. The moderating effect of uncertainty levels and unemployment shows positive and insignificant effect. The, moderating effect of uncertainty and changes in climatic variables offer positive moderating effects on this relationship. Climate change variables (carbon dioxide emission and rainfall) have negative effect on economic growth. Similar result was obtained in the long-run. Labor force has a negative and insignificant effect on economic growth in the short run. This finding is similar to Yakubu et al. (2020) and contrary to Young (2018) and Soava et al. (2020). Moreover, contrary to Oyedokun (2018), capital formation was found to have negative effect on economic growth both in the short and long-run. Conversely, physical capital stock has negative effect on economic growth in the short-run. While capital formation is generally seen as potential growth enhancing variable, the negative and significant effect on economic growth in the long-run result of this study, can be attributed to the fact that the national capital capacity of the country to produce has no positive impact on the country’s economic growth. This supports the assertion by Onyinye et al. (2017) and Ajose and Oyedokun (2018) that gross capital formation accentuates growth. Given that Nigerian economy is labour abundant, it is expected that trade activities, would influence unemployment-economic growth relationship through the provision of job opportunities.

Table 5.

Estimated Short-run coefficients (Dependent Variable: GDPPC).

Variables	Coefficient	Std Error	t-statistic	p Value
GDP	1.418	0.065	21.64	.000
UNEMP	–0.070	0.021	–3.359	.001
UNC	–0.997	0.273	3.647	.001
RAIN	–0.159	0.049	–3.201	.002
TEMP	–0.455	0.214	–2.127	.036
LABOUR	0.026	0.011	2.190	.031
FDI	0.002	0.000	2.659	.009
CAP	–0.071	0.035	–2.021	.046
UNCUNEMP	0.002	0.005	0.394	.694
UNCTEMP	0.239	0.205	1.163	.248
UNCRAIN	0.055	0.043	1.285	.202
ECM(-1)*	–0.016	0.002	-6.845	.000

The result of the error correction term coefficient of -0.016 further substantiates the existence of long-run relationship among the variables and it also denotes that the speed of adjustment of the variables’ convergence to equilibrium is 1.6%.

Model Diagnostic Tests

Some of the diagnostic tests conducted to measure the consistency and reliability of the ARDL model and the results of this study includes; Breusch-Godfrey Serial Correlation LM Test has been conducted to check the model’s autocorrelation problem.

The test result in Table 6 produced Chi-Square statistics of 0.000 with a significant probability. This is an indication that there is auto-correlation problem in the estimated ARDL model. In the same process, Heteroskedasticity Test of Breusch-Pagan-Godfrey conducted produced an insignificant Chi-square values of 0.5219, indicating that there is no heteroskedasticity problem in the estimated model. However, the autocorrelation problem of the model is corrected by the Newey-West HAC method. The method produced a significant Chi-square value of 3.184 and an insignificant p value of .0776.

Table 6.

Model Diagnostic Tests Results.

Tests	Chi-square	Probability
Breusch–Godfrey serial correlation LM test	0.000	.000
Heteroskedasticity test: Breusch–Pagan–Godfrey	0.522	.205
Newey–West HAC method	3.184	.078

Stability Test Result

As suggested by Pesaran et al. (2001), stability test is conducted for the model based on CUSUM tests. It is suggested that for a model to be stable along the sampled period, the residuals must be within the straight lines of the critical bounds at 5% significance level. The stability result is presented in Figures 2 and 3. It shows that the model is stable over the study period. The residual lies within the straight-line critical bounds at 5% level of significance.

Figure 2.

CUSUM Test Result.

Figure 3.

Recursive Estimates.

Conclusion

This study examined global uncertainties, climate change and the unemployment-economic growth relationship in Nigeria between 1990 and 2020 using quarterly data. The study utilised the ARDL and found that global uncertainty has negative effect on economic growth in the long run and short run. In the same vein, unemployment has negative and significant effect on economic growth in the long run. Moreover, climatic change variables used in this study like temperature and rainfall has negative and significant effect on economic growth. in the long run as well as short run. Similarly, uncertainty and climatic change has positive moderation effect on the unemployment-growth relationship, though not statistically significant. Other control variables such as labour participation. FDI and capital stock has a positive and significant relationship with economic growth.

We conclude that uncertainty and climate change dampens economic growth which in turn promote unemployment in Nigeria in the long run. However, labour force participation, FDI inflows and capital stock improves the economic growth rate and enhance employment for the domestic economy. This paper also suggest that government action should be directed towards productive economics activates coupled with FDI inflows into the country to stimulate economic activities in the long run and contribute to higher employment generation. Government should mobilise revenue to invest in the productive sectors of the economy and introduce policies that make climate adaptation programs more efficient. Ensuring efficient adaptation can greatly reduce the impacts caused by climate on economic growth and employment.

Footnotes

Declaration of Conflict of Interests

The authors declared no potential conflicts of interest with respect to the research, authorship and/or publication of this article.

Funding

The authors received no financial support for the research, authorship and/or publication of this article.

ORCID iDs

Richardson Kojo Edeme

Ekene ThankGod Emeka

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