




































American International Journal of Social Science Research; Vol. 2, No. 1; 2018 

ISSN 2576-103X  E-ISSN 2576-1048 

Published by Centre for Research on Islamic Banking & Finance and Business 

   

 

81 
 

Natural Disasters in Nigeria: An Econometric Model 
  

     Emmanuel Okokondem Okon
1 

 

 
1
Department of Economics,  Kogi State University, Anyigba, Kogi State, Nigeria 

Correspondence: Department of Economics, Kogi State University, Anyigba, Kogi State, Nigeria, E-mail: 

tonydom57@yahoo.com. Tel: +2348023275716  

 

 

 

Received:  January 12, 2018                       Accepted: January 17, 2018               Online Published: February 8, 2018   

 

 

Abstract 

This paper models and estimates the occurrence of natural disaster in Nigeria using the residual-based test for 

cointegration within an autoregressive distributed lag (ARDL) framework and error correction specification between 

the period 1970 and 2016, the results from the estimated static model shows that DLOG(TEM), LOG(GDPC) and 

LOG(URB) are long-run determinants of natural disasters in Nigeria. The short run error correction model results 

revealed that the coefficients of DLOG(CO2), DLOG(WIS), LOG(GDPC), LOG(URB), DLOG(GDPC(-2)) and 

LOG(URB(-1))seem to be significant and helpful in explaining  the occurrence of natural disaster (NAD)in Nigeria. 

The error correction term shows that speed of adjustment of disequilibrium in natural disaster (NAD) in the previous 

year which is corrected in the current year is about 44.3 percent. Therefore, Nigerian government should among 

other recommendations embark on reducing urbanization growth by making sure that industries which forge 

linkages with rural occupations should be promoted to mitigate a high rural-urban migration. Establishing of very 

effective early warning systems for meteorological, geophysical, biological, social and industrial hazards should be 

ensured.  

 

Key words: Natural Disaster, Autoregressive Distributed Lag Model.  

 

1. Introduction 

Economic growth, reflected in increases in national output per capita, makes possible an improved material standard 

of living. Sustainable development, popularly and concisely defined as ‘meeting the needs of the present generations 

without compromising the ability of future generations to meet their needs,' directly addresses the utilization of 

natural resources, the state of the environment, and intergenerational equity (Hess, 2013). UN organizations, various 

international institutions, and governments have placed importance on natural disasters and sustainable development 

(ADRC, 2003). 

During the past four decades, natural hazards such as earthquakes, volcanic activity, landslides, tsunamis, tropical 

cyclones and other severe storms, tornadoes and high winds, river floods and coastal flooding, wildfires and 

associated haze, drought, sand/dust storms, and insect infestations have caused major loss of human lives and 

livelihoods, the destruction of economic and social infrastructure, as well as environmental damage(ISDR, 2003). 

Economic losses have increased almost ten times during this period. In recent years, floods in Algeria, Bangladesh, 

Ethiopia, Guinea, India, Mozambique, Nigeria, Sudan, Thailand, Venezuela and Vietnam, volcanic eruptions in 

Ecuador, Democratic Republic of Congo, Indonesia, Montserrat, and the Philippines, and earthquakes in 

Afghanistan, El Salvador, India, Indonesia, Japan, Peru and Turkey, have created widespread social, economic and 

environmental destruction. In some cases, natural disasters can amplify man-made emergencies or vice versa, as 

epitomized by the drought, earthquakes and unfolding events in Afghanistan (ISDR, 2003). 

The escalation of severe disaster event striggered by natural hazards and related technological and environmental 

disasters is increasingly threatening both sustainable development and poverty-reduction initiatives. The loss of 

human lives and the rise in the cost of reconstruction efforts and loss of development assets has forced the issue of 

disaster reduction and risk management higher on the policy agenda of affected governments as well as multilateral 

and bilateral agencies and NGOs (ISDR, 2003). This trend led to the adoption of the International Strategy for 

Disaster Reduction (ISDR) by governments to succeed and promote implementation of the recommendations 



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emanating from the International Decade for Natural Disaster Reduction (1990-1999). The aim of the ISDR is to 

mobilize governments, UN agencies, regional bodies, the private sector and civil society to unite efforts in building 

resilient societies by developing a culture of prevention and preparedness(ISDR, 2003). 

In the light of this, the National Emergency Management Agency (NEMA) was established via Act 12 as amended 

by Act 50 of 1999, to manage natural and man-disasters in Nigeria. Basically it formulates policy on all activities 

relating to disaster management and coordinates the plans and programmes for efficient and effective response to 

disasters at national level (Shuaib, 2009). Indeed, the fallout of the recent flooding  in the year 2012 shows how far 

Nigeria is from meeting the demands of managing natural disasters (Tribune, 2012). Hundreds of villages were 

destroyed and thousands of people were affected by the floods, which occurred after heavy rainfall caused the Benue 

River to rise above its banks. The situation was further complicated by additional water that was released from a 

dam in neighboring Cameroon. In some places, houses were totally submerged, their residents forced to flee (MSF, 

2012). Out of Nigeria's 36 states, 32 were affected by the floods (OCHA, 15 Nov 2012). 

Nigeria is so blessed that the cases of natural disasters are few and far between;  and even the few cases of natural 

disasters that the country has experience are mild compared to what some developed countries of the world have had 

to contend with (Orhewere, 2012). The aim of this paper is to build an empirical model that will help to understand 

the mechanism of natural hazards of atmospherical, geological, hydrological, and biological origins and to analyze 

the transformation of these hazards into disasters. This paper is of importance because of the emphasis on disaster 

response and humanitarian assistance which have absorbed significant amounts of resources that could have been 

allocated for development efforts. If this trend were to persist, coping capacities of Nigeria will likely be 

overwhelmed. As such the paper offers proactive measures to prevent occurrence of natural disaster instead of 

reactionary measures to such crises. The intention is to increase the resistance of the society to natural disasters. The 

rest of the paper is therefore organized as follows. Following the introductory section, Section 2 reviews the 

literature. After examining natural disaster and sustainable Development in Nigeria, Section 3 also specification an 

estimation model.  An econometric analysis of natural disaster in Nigeria is presented in Section 4 while Section 5 

presents the summary, conclusions and policy implications. 

2. Review of Related Literature 

Sustainable development means encouraging economic growth while protecting the environment and improving our 

quality of life (Gov.UK, 2013). For  as  long  as  historical  records  of  extreme  events  in  the  natural  environment  

have  been  kept,  statistics  indicate  an  increasing  frequency of disasters (Zenklusen, 2007). In the second half of 

the 20
th

 century the number of  large  natural  catastrophes  doubled  and  yearly  damages  in monetary terms 

increased by more than a factor of 6 (Munich Re, 2006). Causes  and  effects  of  disasters  appear  to  be  closely  

related  to economic development. In rich countries, natural catastrophes involve physical damage and few 

casualties, whereas in the developing world the human cost  is  far  greater.  Globally, more  than  90%  of  total  

lives lost  to  natural  disasters  are  due  to  events  in  developing  countries  (Kreimer  and  Arnold,  2000;  Guha-

Sapir  et  al.,  2004).  In absolute monetary terms, natural disasters entail greater damages in industrialized countries 

while in relation to GDP, impacts appear primarily significant in the developing world (Mechler, 2003). A variety of 

explanations is put forward in the literature for both the increase in disaster impacts over time and their contrasting 

repercussions in developing and industrialized countries. Firstly, global demographic dynamics and economic 

growth expose more people and their assets to risk. Secondly, urbanization leads to a concentration of population, 

wealth and increasingly complex economic activities. A trend which may be accompanied by the growth of cities 

onto marginal lands. In combination, these processes can increase both the vulnerability of socio-economic systems 

and their exposure to natural hazards (Zenklusen, 2007). Thirdly, anthropogenic changes to the natural environment 

from local deforestation to global climate change are assumed in most of the literature to contribute to the rising 

frequency of extreme weather events and to their increasingly disastrous effects. Finally, development appears to 

explain differences in the vulnerability of physical structures and socio-economic systems to extreme events in the 

natural environment. In other words: disaster risk is a ramification of poverty - at the level of individuals, 

households, regions and countries (Zenklusen, 2007). 

If the distribution of catastrophic risk is influenced by the level of development, what are, in reversed causality, the 

effects of natural disasters on development (Zenklusen, 2007)? On this problem, there is considerable disagreement 

in catastrophe literature. Contrasting statements on the macroeconomic repercussions of disasters and their effects 

on growth and development indicate an intense debate. DHA (1994), for example, concludes that "disasters 

frequently wipe out years of development programming and set the slow course of improvement in the third world 

countries further behind, wasting precious resources." ECLAC (2003) finds natural catastrophes to "have a major 

impact on the living conditions, economic performance and environmental assets and services of affected countries 

or regions. Consequences may be long term and may even irreversibly affect economic and social structures and the 

environment." Such statements, however, are not undisputed as is illustrated by Albala-Bertrand (1993) objections,  

http://reliefweb.int/report/nigeria/floods-situation-report-no-2-15-november-2012


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which have become a classic in this debate: "... disasters are primarily a problem of development, they are not 

necessarily a problem for development." 

3. Natural Disaster and Sustainable Development in Nigeria  

The top 10 natural disasters in Nigeria between 1980-2011 show that they are mainly made up of drought, flood and 

epidemic. While drought affected 3,000,000 people, flood inundated areas that led to the evacuation of 3,014,265 

people and epidemic affected 80,000 people (see Table 1 in Appendix). In all, the ten top natural disasters affected 

6,094,265. Among the ten top natural disasters that affected Nigeria, flood has highest frequency and affected more 

people. All the major floods that led to the displacement of people in Nigeria occurred between 1980 and 2000 

decades (Odjugo, 2012).This period coincided with the period when rainfall becomes more erratic with higher 

intensity due to intensifying impacts of climate change (Odjugo, 2005, 2012).  

In 2009 and 2010, Nigeria ranked 12th and 11th amongst countries with the highest reported levels of displacement 

by sudden-onset disasters worldwide, according to IDMC's global data. Flooding and soil erosion in the states along 

the Niger River and its tributaries, River KatsinaAla and River Benue regularly cause internal displacement. About a 

million people living in the low-lying plains of the River Niger are considered at risk (Chinedu, 2008; U.S.DOS, 

2011).  

According to the National Emergency Management Agency, floods and storms displaced thousands of people in 

2011 and up to two million people in 2010, mainly in Jigawa, Sokoto and Kebbi States (IDMC, 2012a). The 2010 

floods where notably triggered by the opening of floodgates on the Challawa and Tiga dams by the authorities, 

following heavy rains (BBC, 24 Sept. 2010). Floods displaced another 140,000 people in 2009 (Dartmouth Floods 

Observatory) (IDMC, 2012b). Affected states included Zamfara, Kaduna, Niger, Benue, Adamawa, Nassarawa, 

Sokoto, Jigawa, and Abuja. Most recently, Nigeria witnessed a spate of floods that ravaged different parts of country 

in 2012, with the bursting of the banks of the Rivers Niger and Benue and their adjoining tributaries. From central 

Nigeria down to the south into the Niger Delta, unprecedented floods  destroyed lives and properties worth billions 

in states within the drainage basin of the two rivers. Several hectares of farms, livestock, homes and infrastructures 

were destroyed. The Red Cross reports put the figure at 137 people killed (IDMC, 2012). Thousands of people were 

displaced from their homes due to the swelling floodwaters. The worst hit states were Kogi, Benue, Taraba, 

Anambra, Delta and the Niger Delta communities (IDMC, 2012). 

The 10 deadliest natural disasters in Nigeria between 1980 and 2011 claimed 19,537 lives and they are epidemic in 

nature (see Table 1 in Appendix). The deadliest of the epidemic is the bacterial infectious disease (Cholera) which 

started 6th of May, 1991. It affected 10,000 people and killed 7,289 people in Bauchi and Kaduna States of Nigeria. 

The least among the 10 is also cholera that claimed 353 lives in July 2010. The 10 costliest disasters in Nigeria 

destroyed property worth $189.5bn (N30.3trillion) (seeTable1 in Appendix). Apart from the 1983 drought that 

destroyed property worth $71.7bn, all other destructions resulted from flood. It is therefore obvious that among the 

10 natural disasters in Nigeria, flood is the costliest and it affected more people while epidemic is the deadliest. 

Nigeria is vulnerable to many hazards, including, but not limited to, fires, flooding, transportation and industrial 

accidents, and political conflicts (Ibem, 2011). Nigeria is vulnerable to these and other hazards and disaster impacts 

because of high population densities in urban areas (50% of Nigerians live in urban areas) (Nwaka, 2005), an 

inability to integrate risk reduction measures into national development plans and programs (Abang, 2005), and 

poverty (NDMF 2010). In addition, scarcity of land, especially in urban centers like Lagos, has led to inordinate 

construction of structures in hazardous areas (NEMA, 2010; Ibem, 2011). Furthermore, the low level of disaster 

education is another reason why Nigeria is vulnerable to hazards (NEMA, 2010; Ibem, 2011).  

Variables of sustainable development, primarily the Human Development Index and other economic factors, 

especially in a country like Nigeria which is affected by natural disasters shows that Nigeria’s HDI value for 2012 is 

0.471—in the low human development category—positioning the country at 153 out of 187 countries and territories 

(UNDP, 2013). Between 2005 and 2012, Nigeria’s HDI value increased from0.434 to 0.471, an increase of 9 percent 

or average annual increase of about 1.2 percent (UNDP, 2013). The rank of Nigeria’s HDI for 2011 based on data 

available in 2012 and methods used in 2012 was– 154out of 187 countries (UNDP, 2013). In the 2011 HDR, Nigeria 

was ranked 156 out of 187 countries. However, it is misleading to compare values and rankings with those of 

previously published reports, because the underlying data and methods have changed (UNDP, 2013).  

Table 2 reviews Nigeria’s progress in each of the HDI indicators. Between 1980 and 2012, Nigeria’s life expectancy 

at birth increased by 6.8 years, mean years of schooling increased by 0.2 years and expected years of schooling 

increased by 2.4 years. Nigeria’s GNI per capita increased by about 34 percent between 1980 and 2012. 

 

 

 



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Table 2: Nigeria’s HDI trends based on consistent time series data, new component indicators and new   

methodology 

year Life 

expectancy 

at birth 

 

Expected 

years 

of 

schooling 

 

Mean 

years of 

schooling 

 

GNI per 

capita 

(2005 

PPP$) 

 

HDI value 

1980 45.5 6.6  1,571  

1985 45.9 8.4  1,202  

1990 45.6 6.5  1,274  

1995 45.1 6.5  1,303  

2000 46.3 7.9  1,285  

2005 49 9 5 1,540 0.434 

2010 51.4 9 5.2 1,928 0.462 

2011 51.9 9 5.2 2,017 0.467 

2012 52.3 9.0 5.2 2,102 0.471 

Source: UNDP (2013) 

 

Nigeria's economy is struggling to leverage the country's vast wealth in fossil fuels in order to displace the crushing 

poverty that affects about 57% of its population (Wikipedia, 2012). Economists refer to the coexistence of vast 

wealth in natural resources and extreme personal poverty in developing countries like Nigeria as the "resource 

curse". Although "resource curse" is more widely understood to mean an abundance of natural resources which fuels 

official corruption resulting in a violent competition for the resource by the citizens of the nation. Nigeria's exports 

of oil and natural gas—at a time of peak prices—have enabled the country to post merchandise trade and current 

account surpluses in recent years. Reportedly, 80% of Nigeria's energy revenues flow to the government, 16% 

covers operational costs, and the remaining 4% go to investors (Wikipedia, 2012). However, the World Bank has 

estimated that as a result of corruption 80% of energy revenues benefit only 1% of the population (Wikipedia, 

2012). In 2005, Nigeria achieved a milestone agreement with the Paris Club of lending nations to eliminate all of its 

bilateral external debt. Under the agreement, the lenders will forgive most of the debt, and Nigeria will pay off the 

remainder with a portion of its energy revenues. Outside of the energy sector, Nigeria's economy is highly 

inefficient. Moreover, human capital is underdeveloped— as earlier stated, Nigeria ranked 153 out of 187 countries 

in the United Nations Development Index in 2012 (UNDP, 2013)—and non-energy-related infrastructure is 

inadequate. 

4. Specification of Model 

Natural disasters have their root causes in the normal activities of the earth. In recent generations however, man's 

increased knowledge and technology has served to trigger some natural disasters. A natural disaster is the 

consequence of a natural hazard which affects human activities. Human vulnerability, exacerbated by the lack of 

planning or appropriate emergency management, leads to financial, environmental or human losses. The resulting 

loss depends on the capacity of the population to support or resist the disaster, and their resilience (NA, n.d.). This 

understanding is concentrated in the formulation: "disasters occur when hazards meet vulnerability". A natural 

hazard will hence never result in a natural disaster in areas without vulnerability.  

Since natural disasters are naturally occurring events, let’s start by assuming that the occurrence of natural disaster 

depends on factors such as hydrometeorological, geophysical, biological  and economic vulnerability: 

 

 NAD = ƒ(HYD, GPH, BIO, EVU)          …………………..(1) 

 

Where NAD is occurrence of natural disasters, HYD are hydrometeorological factors, i.e., natural processes or 

phenomena of atmospheric, hydrological or oceanographic nature that may cause loss of lives or injuries, property 

damage, social and economic disruption or environmental degradation. These include floods and wave surges, 

storms, landslides, avalanches, and droughts and related disasters (extreme temperatures and forest/scrub fires) 

(Odjugo, 2012).GPH are  geophysical factors, i.e.,  natural earth processes or phenomena that may cause loss of 

lives or injuries, property damage, social and economic disruption or environmental degradation. These include 

earthquakes, tsunamis and volcanic eruptions. BIO are biological factors, i.e., processes of organic origin or those 

conveyed by biological vectors, including exposure to pathogenic microorganisms, toxins and bioactive substances, 

http://en.wikipedia.org/wiki/Resource_curse
http://en.wikipedia.org/wiki/Resource_curse
http://en.wikipedia.org/wiki/Emergency_management


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which may cause loss of lives or injuries, property damage, social and economic disruption or environmental 

degradation. These include epidemics and insect infestations (Odjugo, 2012).EVU is economic vulnerability. 

The three groups of natural disaster are associated with distinct patterns and forms of economic vulnerability (EVU). 

This is in part because of differences in their frequency of occurrence and predictability. Vulnerability is the 

potential to suffer harm or loss, expressed in terms of sensitivity and resilience or of the magnitude of the 

consequences of the potential event.  Lets further assume that the vulnerability of an economy to natural 

hazards is determined by a complex, dynamic set of influences relating to factors such as the type of natural hazard 

(TNH); the overall structure of an economy (OSE), including natural resource endowments; the geographic size of a 

country (GSC); the country’s income level (GDP per capita)and stage of development (vulnerability is time-

dependent, the country’s stage of socioeconomic development matters, as does its state of technical and scientific 

advancement); prevailing socioeconomic conditions (PSC), including the policy environment and the state of the 

economy (ISDR, 2003, ODI, 2005). It is presented as follows: 

 

EVU = ƒ(TNH, GSC, OSE, GDP, PSC)          ………….(2) 

 

In terms of the estimation techniques, the scale and underlying determinants of natural disasters in a country  would 

be best understood using systems-of-equations approaches. This is because of the interdependence of  various 

factors. However, systems-of-equations approaches are also subject to statistical limitations, such as unavailability 

of data, the inability to correct for autocorrelation, a significant barrier to accuracy for most time-series studies. As 

such single equation models would be appropriate. Admittedly, they lack the ability to yield certain parameters, such 

as cross price elasticities that are calculable with a systems-of-equations approach. Nevertheless, despite its 

limitations, the single equation approach will be utilized because it allows for easy inclusion of various independent 

variables and is more statistically accurate. The linear- log version of it allows the estimated coefficients to be 

interpreted as elasticities or other magnitudes. 

Accordingly, a preliminary functional form for natural disaster in Nigeria involves the following variables: 

 

NAD = ƒ(RAF, TEM, WIS, CO2, GDPC, PGR, URB, POE)          …………………..(3) 

Where NAD is a dummy variable used to capture the occurrence of natural disasters. The dummy variable takes the 

value of one if disaster occurs in a year and zero otherwise; RAF is rainfall; TEMP is temperature; CO2 is emission 

of Carbon dioxide; WIS is wind speed; GDPC is gross domestic product per capita; URB is urbanization rate; PGR 

is population growth rate and POE is institutional/political environment (proxy by corruption index) 

Having defined the variables to include in the model, full specification of natural disaster function of Nigeria is now 

presented in a form that is estimable: 

 

NADt=  ɧ +ѦRAFt +ӃTEMt +ѰWISt + ӜCO2t+ѮGDPCt +ᾤPGRt + ₰URBt + ᴟPOEt + ᾨt         

…………………..(4) 

 

Where  ɧis constant term, Ѧ, Ӄ, Ѱ, Ӝ, Ѯ, ᾤ ₰ and  ᴟ are coefficients that will be estimated empirically, t  represents 

time index denoting annual observations(1970 – 2016);and ᾨ is the error term. 

Since the occurrence of natural disaster is a result of build-up of many factors (or continuous process), a dynamic 

specification of the natural disaster function with an autoregressive term that incorporates lagged values of the 

dependent and explanatory variables will be included in the set of regressors in order to capture the effect of 

previous factors. Equation 4 is now specified as a general dynamic equation called an autogressive distributed lag 

(ADL) of order k as follows: 

kkkkkkk 

 

NADt=  ɧ + jNADt-j + ѦjRAFt-j + ӃjTEMt-j + ѰjWISt-j + ӜjCO2t-j + ѮjGDPCt-j + ᾤjPGRt-j+  

 j=1 j=0                           j=0                             j=0                            j=0                             j=0      j=0  

 

kk 

₰URBt-j+ ᴟPOEt-j + Йt…………………..(5) 

j=0 j=0   

 



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All this does is to add lags 1 through k of the dependent variable and all independent variables to the original model. 

It can be seen that the lags of the dependent variable start at j=1, and lags of the independent variables start at j=0 

because of the need to include the contemporaneous values as in Equation 4. This equation can then be rewritten to 

obtain the error correction representation of the form:  

kkkkkkk 

∆NADt=  ɧ j NADt-j Ѧj∆RAFt-j Ӄj∆TEMt-j Ѱj∆WISt-j Ӝj∆CO2t-j Ѯj∆GDPCt-j ᾤj∆PGRt-j+  

 j=1                    j=0                             j=0                             j=0                            j=0                             j=0                                

j=0  

 

kk 

₰∆URBt-j ᴟ∆POEt-j-(NADt-j -1RAFt-j-2TEMt-j-3WISt-j-4CO2t-j-5GDPCt-j-6PGRt-j- 

j=0                       j=0   

 

7URBt-j-8POEt-j) + Йt……………………………………………………………………………………(6) 

 

The terms in parentheses in equation 6 represent the error correction term. The coefficients of this equation are 

functions of the coefficients of (5). 

4.1 Data Source and Description of Variables 

It must at the outset be recognized that estimation of natural disasters in the case of Nigeria (but also elsewhere) is 

beset with problems of data availability. The coverage for each of the variable spanned 1970 to 2016 (a period of 46 

years). This is to ensure enough data points for the econometric analysis. Also, the frequency and intensity of natural 

disasters have increased considerably in recent years, which informed the choice of this period. The nature of the 

data is economical and meteorological. The use of annual data is preferable in the literature in order to avoid 

seasonality problems. With the exception of the dummy variable for natural disaster that was constructed using 

binary based on the chronology of natural disasters in Nigeria, other data were obtained from websites, journals, 

newspapers, various issues of Central Bank of Nigeria (CBN) –Annual Reports, Statistical Bulletin and Annual 

Abstracts of statistics of National Bureau of Statistics (NBS) formerly Federal Office of Statistics (FOS). 

This paper does not try to quantify the economic and human loss of natural disasters, instead a dummy variable was 

used to capture the occurrence or infrequent of  natural disasters that affect vulnerable communities or geographic 

areas of Nigeria, causing substantial damage, disruption, and perhaps casualties and leaving the affected 

communities unable to function normally. The natural disasters that are prevalent in Nigeria include, but are not 

limited to, flooding and droughts. The dummy variable (NAD) takes the value of one if disaster occurs in a year and 

zero otherwise. The dummy variable was used as a dependent variable. In the dynamic estimation, the lagged values 

of the dummy variable was included in order to capture the quality of the institution and preparedness based on past 

experience of natural disasters in the country, which will also serve as an indicator of vulnerable. 

Other independent variables include rainfall, temperature, CO2 emission and vulnerability. The changes in 

temperature (TEM) and related local rainfall (RAF) variations affect the environment through accelerated 

desertification and land degradation. Floods are imminent if dams are full and need to be emptied quickly. In 

addition, the increased levels of carbon dioxide (CO2) emissions are regarded by many scientists as the main reason 

for the ongoing climate change problem. According to scientists climate change could reach catastrophic proportions 

in form of frequent flooding, drought, global sea level rise, etc(EP, 2011). Wind is air that moves quickly as a result 

of natural forces. Air has certain mass (kg) and mass density (Kg/m). Flow of air (wind) results in flow of mass 

(mass flow: Kg/s).The velocity attained by a mass of air traveling horizontally through the atmosphere is called 

wind speed (WIS). All these factors have a compound effect on the occurrence of natural disasters. On the one hand, 

they affect the intensity and frequency of extreme hydro meteorological events, and on the other hand, they increase 

the vulnerability of societies.  

The country is subject to high rate of population growth (PGR). Poverty and social and economic pressures, such as, 

unemployment and illegal land tenure practices, make people more vulnerable by forcing them to live in dangerous 

locations, often on unsafe land, in unsafe shelters or low-cost dwellings and coastal areas, because there is no other 

land available at reasonable cost sufficiently close to employment opportunities.    

Rapid urban growth (URB), particularly when it is accompanied by a large influx of people from rural areas, is one 

of the main factors contributing to increased vulnerability to natural disasters in many parts of Nigeria. The 

accelerated, and often uncontrolled, growth of cities has contributed to the ecological transformation of their 

immediate surroundings (pressure on scarce land, deforestation, etc.). In addition, the lack of appropriate drainage 

systems makes some cities susceptible to flash floods. 



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Political and institutional vulnerability (POE)is understood as institutional weakness as a whole, and more 

specifically the weakness of the democratic system –with its negative effects on the efficiency of public policies, the 

legitimacy  of government action, limited participation by citizens and the private sector in national  efforts, linkage 

with local government actions and civil organizations, the handling and management of emergencies, processing of 

citizens’ demands and needs, and the capacity to meet them (VRSD, n.d.). This has often been seen to be one of the 

prime causes of vulnerability to natural phenomena and in turn even a cause underlying other form of vulnerability. 

Economic development (GDPC) is widely understood as a process of increasing complexity that is reflected in the 

growing proportion of GDP accounted for by the secondary and tertiary sectors of the economy. The stage of 

development of an economy, measured in terms of the degree of sectoral, geographic, and financial integration, 

levels of economic specialization, and government revenue-raising capabilities, is likely to influence vulnerability to 

natural disasters (Benson and Clay, 1998). A developing country like Nigeria is typically perceived as most 

vulnerable, even though its absolute losses as a consequence of a particular disaster may be small relative to the 

levels reported in developed countries.  

 

4.2 The Modeling Process 

An important characteristic of the raw data being used for the regression is stationarity. When using time series 

modeling, various diagnostic tests and checks are employed as part of the estimation procedure. These tests are used 

to identify the most acceptable model and validate the data results (Walsh, 1997). 

4.2.1. Stationarity Tests 

4.2.1.1. The Augmented Dickey-Fuller (ADF) Test  

To check the stationarity of the data described above, the Augmented Dickey-Fuller (ADF) test is applied. The 

Augmented Dickey-Fuller (ADF) test for autoregressive unit root tests the null hypothesis H0: μ=0 against the one 

sided alternative H1: μ< 0 in the regression 

 

∆Yt = β0 + μYt-1 + δ1∆Yt-1 + δ2∆Yt-2 +  …+ δp∆Yt-p+ ut                  …………………………………….(7) 

 

Under the null hypothesis μ=0, Yt has a unit root; under the alternate hypothesis, Yt is stationary. The ADF statistic 

is the OLS t-statistic testing μ=0 in the equation above. If instead the alternate hypothesis is that Yt is stationary 

around a deterministic linear time trend, then this trend t (the period number), must be added as an additional 

regressor in which case the Dickey-Fuller regression becomes: 

∆Yt = β0 +αt+ μYt-1 + δ1∆Yt-1 + δ2∆Yt-2 +  …+ δp∆Yt-p+ ut            ……………………………………(8) 

 

Where α is an unknown coefficient and the ADF statistic is the OLS statistic testing μ=0 in the above equation.  

The lag length p can be chosen using the Akaike’s Information Criteria (AIC) because it known as the best 

information criteria to use. Burnham and Anderson (2004) argue that AIC has theoretical as well as practical 

advantage because it is derived from principles of information criteria. Yang (2005) also argues that the rate at 

which AIC converges to the optimum is the best possible. The general form for calculating AIC is: 

AIC =  -  …………………………………………………………………..………………………..(9) 

 

Where L is likelihood value, p is the number of parameters and T is number of observation. Given a set of candidate 

values for the data, the preferred value is the one with the minimum AIC value. 

 

The ADF test does not have a normal distribution under the null hypothesis, even in large samples (Girma, 

2012).Critical values for the one sided ADF test depends on equations 7 and 8 used above. The null hypothesis of 

non-stationarity is tested using the t-statistic with critical values calculated by MacKinnon. The null hypothesis that 

Yt is non-stationary time series is rejected if μ are less than zero and statistically significant for each. The ADF test 

is unable to distinguish well between stationary and non stationary series with a high degree of autoregression. 

Given the inherent weakness of this test to distinguish between the null and the alternative hypotheses, DF-GLS test 

is also used. Essentially the test is an augmented Dickey–Fuller test except that the time series is transformed via a 

generalized least squares (GLS) regression before performing the test. It has significantly greater power than the 

previous versions of the augmented Dickey–Fuller test (Girma, 2012). 

4.2.1.2 Residual-based Test for Cointegration 

Therefore, the detection of cointegration is very important in practice prior to estimation. One of most popular tests 

for (a single) cointegration has been suggested by Engle and Granger (1987). For instance, let’s consider the 

multiple regression: 



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88 
 

yt =β
’
xt + ut, t = 1, …, T          ……………………………………………………(10) 

 

wherext = (x1t; x2t, …, xKt;)
’
 is the k-dimensional I(1) regressors. Notice that for yt and xt to be cointegrated, ut must be 

I(0). Otherwise it is spurious. Thus, a basic idea behind is to test whether ut is I(0) or I(1). 

 

The Engle and Granger cointegration test is carried out in two steps: 

Firstly, the OLS regression of equation (10) is estimated and the residuals obtained by 

^ 

ut = yt - β
’
xt, t = 1, …, T                         …………………………………………..(11) 

where β are the OLS estimate of β. 

In the case of this paper, the OLS regression of equation (4) is run. 

                                                                                                                                     ^ 

Secondly, a unit root test is applied to ut by constructing an AR(1) regression for  ut: 

 

^          ^ 

ut = ut-1 + εt ……………………………………………………………………....(12) 

 

 

That is, the Dickey-Fuller (ADF) test of H0 : = 1 against H1 : < 1 in (12) is conducted. This is called the residual-

based EGDF cointegration test. Strictly, it is the test of no-cointegration, because the null of unit root in ^ut implies 

that there is no-cointegration between y and x. So if H0 : = 1 in (12) is rejected, the conclusion is that there is a 

cointegration and vice versa. 

4.3. Estimation Technique and Procedure 

Currently modern economic analysis involves the use of econometric methods where appropriate statistical and 

econometric test can be conducted to ensure the validity and reliability of the data and result, for accurate projection 

and prediction of the phenomenon in question (Articles, 2013). The multiple equation models is presented in 

equation  

The empirical viability of the model will be tested using the ordinary least square (OLS) analytical technique. The 

use of OLS method according to koutsoyians (2001), yields parameter estimates with optimal properties such as 

unbiased minimum variance and efficient, thereby making the parameter estimates best linear and unbiased (BLUE). 

However, equation 4 is a static model and does not take into account the dynamics of  natural disaster process. In 

order to capture the dynamics, the general specification of the ADLM is used. The exact lag length of the general 

ADLM is determined by the Akaike information criterion (AIC) and the Schwarz criterion (SC). The general-to-

specific modelling approach is applied to reduce the number of explanatory variables in the initial equation, keeping 

only the underlying influencing factors based on both statistical significance and the sensible economic 

interpretation of the estimated parameters associated with these factors. 

Within the general-to-specific framework, the specification starts from a general autoregressive distributed lag 

model (ADLM), which incorporates as many variables as possible supported by appropriate economic theory. The 

general ADLM is first estimated and the sum of squared residuals of the general model is calculated. Then the 

restricted (specific) model is estimated and the sum of the squared residuals of this model is calculated. The third 

step is to test the restrictions imposed by comparing the sums of squared residuals of the ADLM and the restricted 

model using the F-statistic. Since the restricted model is simple in structure and has more degrees of freedom than 

the general ADLM when it is estimated, the specific model is preferred to the complicated ADLM if the restrictions 

are accepted. 

4.4. Diagnostic Tests 

There are a number of diagnostic tests/checks which must be implemented in order to evaluate the estimated model 

and to identify the most ‘satisfactory’ or ‘acceptable’ estimation. If any of the assumptions are violated, problems 

can arise with regard to the validity and reliability of the estimated parameters and models. In order to assess 

whether the coefficients estimated are theoretically meaningful, they must first be examined in terms of both sign 

and magnitude. Economic theory imposes certain constraints on the signs of coefficients; parameters with ‘incorrect’ 

signs are rejected on thegrounds of being theoretically implausible. A priori expectations exist with regard to the 

signs of coefficients. In general, an unexpected parameter sign or size arises as a result of deficiencies in the model 

itself (Walsh, 1997).For example, the presence of multicollinearity; the omission of a relevant variable; the inclusion 

of an unimportant variable. 

Various statistical tests will be carried out so as to verify the acceptability, reliability, and robustness of the 

estimated regression result. For example, the Student t-Test will be used to test the statistical significance of the 

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individual parameter estimates in the regression models. Concerning, the standard error of the coefficients. The 

standard error test will give a general guide to the likely accuracy of a regression parameter. The F-Test will be used 

to test for the overall significance of the model. It tests the simultaneous null hypothesis of all the parameter to be 

equal to zero in the regression model. The R
2
-Coefficient of Determination will beused to measure the goodness of 

fit of a regression line. It measures the proportion of the total variation in the dependent variable explain by the 

repressors in the model. The R-squared (R
2
)value ranging from ‘0’ to ‘1’ or the ‘corrected R-squared’ (R

2
) which is 

adjusted for degrees of freedom indicates the explanatory power (goodness of fit) of the model. 

Similarly, various econometric tests will be carried out in order to verify whether the estimated regression results 

conform to the classical (normal) linear regression model assumption. For example, the test of normality will be 

used to verify whether the error term is normally distributed. Precisely, the Jacque-Beva (JB) test will be used to 

verify this assumption.  In the same vein, the test of heteroscedasticity will be used to verify the assumption of equal 

spread of the error variance (homoscedastic) between members of the same series of observations. The white’s 

heteroscedasticity test (with no cross term) will be employed in the test. In addition, the test of autocorrelation will 

beused to verify the randomness of the error term between members of the same series of observations. As a result 

of the numerous assumptions and problems associated with the conventional Durbin-Watson (DW) test, the 

Breusch-Godfrey (LM) test will be employed to verify this hypothesis. To a follow up, the test of specification error 

will be used to verify whether the econometric regression model being estimated is correctly specified. The 

Ramsey’s RESET (Regression Specification Error Test) will be employed.  To verify the reliability of the estimated 

regression model in forecasting future values, the Henry Theil’s inequality coefficient will be used. 

The econometric software packages used for the analysis of this work are the Eviews 4.1 and SPSS 19 versions 

while the Microsoft Excel 2010 is used to enter the data. 

5. Presentation and Analysis of Empirical Results 

5.1 Descriptive Statistics and Correlation Results 

Table 6a and 6b (see Appendix) present the descriptive statistics and correlations matrix respectively for all the 

variables (dependent and independent variables). Generally, the result shows a low correlation between the 

variables, with the exception of POE and CO2, POE and URB, and POE and GDPC. Some of the explanatory 

variables are positively correlated with natural disaster (TEM, CO2, WIS, URB and POE) while some are negatively 

correlated (RAF, GDPC and PRG). The expectation is that RAF and PRG should be positively correlated with 

natural disaster (NAD) instead they showed negative correlation even though the degree of negative correlation is 

low. However, the simple correlation coefficient between the variables and natural disaster (NAD) show 

insignificance at the 0.01  and 0.05 levels in the 2-tailed test.  

5.2 Unit Root Test Results 

Using the Augmented Dickey-Fuller (ADF) test, the stationarity of the individual variables was tested. The null 

hypotheses of a random walk (H0: μ=0) against the alternate hypothesis of a stationary process (H1: μ<0) is tested 

by using Dickey and Fuller critical values. As depicted in Table 6a, the results of the stationarity test indicate that 

carbon dioxide (CO2) emissions, population growth rate (PRG) and Political/institutional vulnerability (POE) are 

non stationary at level in any of the conventional levels. On the other hand, at levels with intercept, economic 

development (GDPC) showed stationarityat 1% significant level but became insignificant with linear trend. Rapid 

urban growth (URB) failed to show stationarity with intercept but was stationary with linear trend in level at 10% 

significant level. So also is natural disaster occurrence (NAD) significant at 1%  level with linear trend. However, 

average temperature (TEM), rainfall (RAF)and wind speed(WIS) are stationary at level whether with intercept or 

linear trend.In order words, they were found to reject the null hypothesis “no stationary” at level. This indicates that 

these time series data are non-mean reverting, convergence toward their long-run equilibrium and variances are 

constant overtime. Since some variable are non stationary in level, the next task is to check if the variables are 

stationary in difference. This is in line with Granger (1969), Granger and Newbold (1974). As such, the same 

Augmented Dickey-Fuller (ADF) test is applied. 

 

Table 6a: Augmented Dickey-Fuller (ADF) Unit Root Test in Level 

Variables  

 

Intercept Linear Trend Order of 

Integration 

TEM -4.296893* (9)[-3.596616] -5.112504* (9) [-4.192337] 1(0) 

RAF 

 

-6.091051* (9) [-3.596616] -6.073567* (9) [-4.192337 1(0) 

CO2 -1.987759 (9) [-3.596616] 

 

-2.115374 (9) [-4.192337] 

 

- 



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WIS  -5.294050* (9) [-3.600987] 

 

-5.343122* (9) [-4.198503] 

 

1(0) 

GDPC  

 

-3.655511* (9) [-3.621023] -1.587990 (9) [-4.234972] 

 

1(0) 

URB -1.419016 (9) [-3.596616] -3.199481***(9) [-3.191277] 

 

1(0) 

PRG  

 

-1.672438 (9) [-3.596616] 

 

-2.229717 (9) [-4.192337] 

 

- 

POE 

 

-2.282445 (9) [-3.596616] 

 

-3.075318 (9) [-4.192337] 

 

- 

NAD -2.008288 (9) [-3.605593] -8.208143*(9) [-4.192337] 

 

1(0) 

 

Note: * significant at 1%; ** significant at 5%, ***  significant at 10% .ADF critical values  

are shown in parenthesis. The lag lengths shown in brackets are selected using the minimum 

Schwarz and Akaike Information criteria. 

Source: Stationarity test results from analysis using Eviews 4.0 

 

As can be seen in Table 6b, for all the variables, the Augmented Dickey-Fuller test statistics were less than the 

critical values at 1%, 5% and10%  levels of significance. The results of the stationarity test indicate that carbon 

dioxide (CO2) emissions, population growth rate (PRG) and Political/institutional vulnerability (POE) are now 

stationary at first difference.That is, they are integrated of order one I (1). This means that in the short run, the 

variables are stable. To verify the result of the above test, the DF-GLS test is thereby applied. 

 

Table 6b: Augmented Dickey-Fuller Unit Root Test in Difference 

Variables  

 

Intercept Linear Trend Order of 

Integration 

TEM -10.78070* (9) [-3.600987] -10.64100 * (9) [-4.198503] 1(1) 

RAF -10.09870* (9) [-3.600987] 

 

 

-9.981021* (9) [-4.198503] 1(1) 

CO2 -6.635362 * (9) [-3.600987] -6.568786* (9) [-4.198503] 1(1) 

WIS -5.992423* (9) [-3.610453] -5.918662* (9) [-4.211868] 

 

1(1) 

GDPC  -3.542123** (9) [-2.945842] -3.390824*** (9) [-3.202445] 

 

 

1(1) 

 

URB -7.181967* (9) [-3.600987] 

 

-7.225526* (9) [-4.198503] 

 

1(1) 

PRG -4.915900* (9) [-3.600987] -4.671687*(9) [-4.198503] 

 

1(1) 

POE 

 

-7.695598* (9) [-3.600987] 

 

-7.597783* (9) [-4.198503] 

 

1(1) 

NAD -8.491946*(9) [-3.605593] 

 

-8.430518*(9) [-4.205004] 

 

1(1) 

 

Note: * significant at 1%; ** significant at 5%, ***  significant at 10% . ADF critical values  

are shown in parenthesis. The lag lengths shown in brackets are selected using the minimum  

Schwarz and Akaike Information criteria. 

Source: Stationarity test results from analysis using Eviews 4.0 

 

Table 6c shows that most of the variables were stationary at level, since each reported t-statistic is not smaller than 

the critical test values. However, the results of the tests shows the presence of  unit roots in urbanization rate (URB) 



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and population growth rate (PRG) variables. First difference operation is all that is required to bring these variables 

to stationarity as depicted in Table 6c below. 

 

Table 6c: DF-GLS test Result on Variables 

Variables  

 

At level Order of 

integration 

First difference Order of Integration 

TEM -4.189232 

 

1(0) 

 

-10.63576 

 

1(1) 

RAF 

 

-6.163155 

 

1(0) 

 

-10.21979 

 

1(1) 

CO2 -1.654473 

 

 

1(0) -6.637115 

 

1(1) 

WIS -5.043018 

 

 

1(0) -7.070265 

 

1(1) 

GDPC  

 

-2.410385 1(0) 

 

-6.098260 

 

1(1) 

URB 0.121186 

 

- -7.029838 

 

1(1) 

PRG -1.636577 

 

- -3.443879 

 

1(1) 

POE  

 

-2.096869 

 

1(0) 

 

 

-7.783140 

 

1(1) 

NAD -8.324248 

 

1(0) 

 

-8.611630 

 

1(1) 

 

Note: 1%, 5% and 10%DF-GLS  critical test values at level are-2.621185, -1.948886 and -1.611932 respectively. 

While 1%, 5% and 10% DF-GLS critical test values at first difference are -2.622585,-1.949097 and -1.611824 

respectively. 

Source: Stationarity test results from analysis using Eviews 4.0 

 

5.3. Result of Co-Integration Test 

Since some of the variables are integrated at an order 1(0) and of the order 1(1). There is need to test for possible co-

integration among these variables. The Engle and Granger two-step method is adopted. The long run relations 

among the variables is estimated by OLS and stationarity of the residuals is tested. Again, ADF and DF tests were 

employed to test for cointegrated variables. The results of cointegrationtests are reported in Table 7a and 7b. 

Table 7a: ADF test Result on Residual 

Variable ADF statistic Level 

 

                                 Critical values  

Residual (μt) Intercept Lag Order of Integration 

-9.366773          (9)               1(0) 

1% 

-3.600987 

5% 

-2.935001 

10% 

-2.605836 

Linear trend Lag Order of Integration 

-9.293054          (9)               1(0) 

 

1% 

-4.198503 

 

5% 

-3.523623 

 

10% 

-3.192902 

 

None                Lag      Order of 

Integration 

-9.486447    (9)               1(0) 

 

1% 

-2.622585 

 

5% 

-1.949097 

 

10% 

-1.611824 

 

 

Source: Stationarity test results from analysis using Eviews 4.0 

 

 



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Table 7b: DF-GLS test Result on Residual 

Variable DF-GLS statistic Level                                  Critical values  

Residual (μt) Intercept Lag Order of Integration 

-2.387739         (9)               1(0) 

1% 

-2.625606 

 

5% 

-1.949609 

 

10% 

-1.611593 

 

 Linear trend Lag Order of Integration 

-8.383332         (9)     1(0) 

 

1% 

-3.770000 

 

5% 

-3.190000 

 

10% 

-2.890000 

 

Source: Stationarity test results from analysis using Eviews 4.0 

 

Given the ADF and DF 1%, 5% and 10% critical t-values, variables in the multivariate regression of equation 4  are 

said to be cointegrated. Therefore, Ho is rejected, i.e., there is a long run relationship among the variables of the 

model at the chosen critical level. The solved static long run equation is reported in Tables 8a and 8b(see 

Appendix).The results from the estimated static model shows that DLOG(TEM), LOG(GDPC) and LOG(URB) are 

long-run determinants of natural disasters in Nigeria. 

The regression results show that DLOG(TEM) has significant impact on the occurrence of natural disaster in 

Nigeria. It is estimated from the result that 1% increase in DLOG(TEM)will, on the average lead to decrease by 

6.4% in the occurrence of natural disaster. The sign borne by the parameter estimates is not in conformity with the 

economic a priori expectation. Changes in climate not only affect average temperatures in Nigeria, but also extreme 

temperatures, increasing the likelihood of weather-related natural disasters. However, effort is been made at 

reducing the emission of greenhouse gases which increase overall temperatures. 

However, the sign borne by the parameter estimate of LOG(GDPC)  does conform to the a priori expectation. The 

result shows a negative relationship between LOG(GDPC) and NAD. That is, a 1% increase in LOG(GDPC) 

decreases the occurrence of natural disaster by 0.41%.  This implies that economic development plays an important 

part in reducing the occurrence of  natural disasters in Nigeria. Although Nigerian economy has been growing, the 

economic growth is not accompanied by a structural change of the Nigerian economy. The economy lacks 

diversification and agricultural production lacks modernisation. To address this, the government is encouraging the 

diversification of the Nigerian economy away from the oil and gas sector. It is addressing the infrastructure deficit in 

the country and the development of the agricultural sector through modernisation and the establishment of staple-

crop processing zones, with the value chain model to provide linkages to the manufacturing sector. 

The coefficient of LOG(URB) is about 2.25, meaning that holding other variables constant, 1percent increase in the 

log of urbanization rate (URB) leads to an increase in the occurrence of natural disaster by about 2.25 percent. In 

Nigeria urban growth is informed by natural population increases in the urban areas, rural – urban migration, 

creation of administrative towns and population concentration in towns blessed with natural resources.  This high 

concentration of people beyond the facilities necessary to sustain the growth creates unhealthy competition for the 

available facilities and opportunities, especially housing. Poorly planned and managed houses increasingly occur in 

peripheral zones of marginal habitation, leaving hundreds of millions of people at the mercy of natural disasters. 

Vulnerability is acute along coastal areas where any land remaining available for urban growth is generally risk-

prone. Thus the rapid rate of urbanization in Nigeria is characteristic of economic growth without development. The 

variable TEM is important but it does not significantly affect the occurrence of natural disaster positively. Also, the 

variable RAF (rainfall) is necessary in the model but appear insignificant at any conventional levels. Nigeria has a 

rainy season and suffers from seasonal flash floods. These flash foods are sometimes lethal, especially in the rural 

areas or overcrowded slums, where drainage is poor or does not exist at all. Suffice to say that the ravaging global 

warming has massively contributed to the intensity of rainfall and the consequent flooding in parts of Nigeria. 

Nigeria suffered its worst floods in 40 years in 2012.  Hundreds of people were killed and millions more were 

displaced (Murdock,  2012). 

Table 8a (see Appendix) shows that the variance inflation factors (VIF's) for the explanatory variables in the 

regression model are  less suggesting the absence of  multicollinearity. This is also confirmed by the Durbin-Watson 

statistics of 2.654 implying absence of serial correlation (Table 8b see Appendix). 

The next move was to switch to a short run model with an error correction mechanism as shown in equation 6. The 

estimated form of equation 6 is shown in Table 9 (see Appendix). In the over-parameterized error correction model 

in Table 9, all the variables were lagged. Later, it was reduced to a parsimonious model through the elimination of 

insignificant variables. This led to the final estimation in Table 10 which is then used for further analysis. Moreover, 

the parsimonious model is preferred since it has lower Schwartz Criterion (SC) and standard deviation. 

file:///C:/Users/IBennett/AppData/Local/Microsoft/Windows/Temporary%20Internet%20Files/Content.Outlook/AppData/Local/Microsoft/Windows/Temporary%20Internet%20Files/Content.Outlook/3OL8NMHE/World%20Bank%20and%20the%20Global%20Facility%20for%20Disaster%20Risk%20Reduction,%20Cities%20and%20Flooding%20--%20A%20Guide%20to%20Integrated%20Urban%20Flood%20Risk%20Management%20for%20the%2021st%20Century


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Table 10 shows that the model gives a reasonable good fit. This is so because the explanatory variables accounted 

for 56 percent of the total variation in natural disasters (NAD). The remaining 44 percent are due to factors 

exogenous to the model but being taken care of by the error term. The F-ratio of 4.92 confirms the overall 

significance of the explanatory variables taken together while the Durbin–Watson statistics of 1.98 only corroborates 

findings that the residual of the model contains insignificant serial correlation, i.e., it suggests lesser degree of 

autocorrelation. The coefficient of the error correction term is statistically significant and carries the expected 

negative sign at both 5% and 1% level of significant. It shows 44.3 percent speed of adjustment of disequilibrium in 

natural disaster (NAD) in the previous year which is corrected in the current year.  

The results of the short run error correction model in Table 10 reveal that the coefficients of DLOG(CO2), 

DLOG(WIS), LOG(GDPC), LOG(URB), DLOG(GDPC(-2)) and LOG(URB(-1))seem to be significant and helpful 

in explaining  the occurrence of natural disaster (NAD)in Nigeria. Still from Table 10, a unit change in DLOG(CO2) 

brings about 0.8 increase in the occurrence of natural disaster thereby suggesting that there are other relevant 

variables apart from  DLOG(CO2)  that can lead to natural disaster (NAD). In Nigeria, carbon dioxide emissions 

from fossil fuels have increased significantly over the last century.  The transport sector accounts for a major share 

of fossil fuel consumption, hence it is very likely that the sector has a strong influence in this upward trend. The 

release of greenhouse gases (GHGs) into the atmosphere each year—mostly in the form of carbon dioxide (CO2)—

contributing to climate change which in turn leads to the occurrence of natural disasters. 

LOG(URB(-1)) shows that given other variables, 1percent increase in previous URB leads to about 7.51 percent 

decrease in current NAD. This is significant at 5 percent level of significance. Also, a unit change in the second 

period lag of  the first difference of  log GDPC leads to about 0.49 percent decrease in current NAD. It was also 

discovered that a unit increase in DLOG(WIS), other things being equal, will increase the occurrence of natural 

disaster (NAD) by 1.23 and vice-versa. Similarly, a unit increase in LOG(URB) will increase NAD by 10.60 and 

vice-versa. The magnitude of the increase is higher than it was in the long run static estimate. Finally, a unit increase 

in the value of LOG(GDPC), other variables held constant, will lead to 0.23 decrease in the occurrence of natural 

disaster (NAD). The size of the coefficient is less than it was in the long run estimates. The LOG(WIS) was shown 

to have a negative sign contrary to apriori expectation and it showed statistically insignificant coefficient but 

because of its importance a redundant variables test was carried out to know whether it should have been dropped 

from the parsimonious model. Despite its wrong sign and statistical insignificant coefficient, the result in Table 

11(see Appendix) shows that the variable should not be deleted from the short run error correction model  

The diagnostic tests results in Table 12 – 15 (see Appendix) show that the model for the occurrence of natural 

disaster (NAD) qualifies all the diagnostic tests. The model is free from the problems of serial correlation, 

heteroscedasticity and that the error terms are normally distributed as shown by the Jacque – Bera (JB) test of 

normality in Figure 1(see Appendix). The Ramsey Regression specification Error Test (RESET) with F-distribution 

of 0.281444 concludes that the estimated model is correctly specified; that is, there is no specification error. 

Stability of the model is checked through the graphs of CUSUM and CUSUM Squares tests in Figures 2 and 3 (see 

Appendix). The CUSUM and CUSUM Squares tests confirm that the model is stable as the calculated line lies 

inside the critical bounds at 5 percent level of significance. If the lines cross the critical bounds then the proposed 

model is unstable. The results in figures 2 and 3 show that the lines are within the critical bounds, so model is 

statistically stable. 

Both graphical and forecast evaluation output for the dependent variable NAD is given in Figure 4 (see Appendix). 

The actual values of NAD are within the forecast interval for most of the forecast period. To test whether the value 

of the dependent variable at t  time might have come from the model fitted to all the data up to that point, a plot of 

the recursive residuals and standard errors was done using the One-Step Forecast test. The plot helps to spot the 

periods when the equation is least successful. For the test equation, there is evidence of instability early in the 

sample period especially around 1990 (see Figure 5 in Appendix). 

6. Summary of Findings, Conclusion and Policy Implications  

The empirical model is developed in the light of recent developments in the methodology of econometric modeling 

and the analysis of time series with stochastic non-stationary components. In modeling the natural disaster equation, 

the paper examined each series from 1970 -2015enteringthe model to ensure the stationary and the order of 

integration. The results of the Augmented Dickey-Fuller(ADF) unit root test show that  most of the variables were 

stationary at level while the other variables were stationary at first difference(integrated of order one).Using the 

residual-based test for cointegration within an autoregressive distributed lag (ARDL) framework and error 

correction specification. The findings indicated that all the variables had significant existence in co integrated 

vector. This means that all the variables had long term equilibrium existence. The results from the estimated static 

long run model shows that DLOG(TEM), LOG(GDPC) and LOG(URB) are  determinants of natural disasters in 

Nigeria. The short run error correction model results revealed that the coefficients of DLOG(CO2), DLOG(WIS), 



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LOG(GDPC), LOG(URB), DLOG(GDPC(-2)) and LOG(URB(-1)) have significant impact on the occurrence of 

natural disaster (NAD) in Nigeria. 

The error correction term (ECM) is of the expected negative sign and also significant. The result shows that the 

occurrence of natural disaster in Nigeria has an automatic mechanism and that natural disaster in Nigeria responds to 

deviation from equilibrium in a balancing manner. The absolute value of the coefficient of the error-correction term 

indicates that about 44.3 per cent of the disequilibrium in the occurrence of natural disasters is offset by short-run 

adjustment in each year. In order words, a value of -0.443047 for the ECM coefficient suggests a fast speed of 

adjustment strategy of  44.3per cent.  

In conclusion, even if it is not possible to prevent natural disasters, much can be done to build capacities to reduce 

the vulnerabilities that too often lead to a crisis situation. Therefore, Nigerian government must embark on reducing 

urbanization growth by making sure that industries which forge linkages with rural occupations should be promoted 

to mitigate a high rural-urban migration. In that light, the sitting of new industries and the location of infrastructure 

should not be very close to the sea in order for them to be free from sea level rise. 

The appropriate authority should prepare comprehensive hazard maps and vulnerability   analysis for the country by 

compiling historical data of disaster occurrence; analyzing of meteorological, seismological and environmental 

records; and employing satellite imagery and the GIS system to plot the hazard maps. In addition, establishing of 

very effective early warning systems for meteorological, geophysical, biological, social and industrial hazards is 

required. 

To achieve effective management of floods, compliance with planning and urban laws should be enforced; 

embankments and levies along rivers and coastline prone to flood should be build; establishing of  rainstorm early 

warning system; establishing and monitor weather stations, river and tidal gauges; ensuring appropriate management 

of dams; ensuring proper maintenance of existing urban drainage channels; enforcing environmental sanitation laws 

in towns and cities. 

Nigeria should focus more on economic activities that are tertiary in nature which generate little greenhouse gases. 

In that light, effort should be made to develop a technology that can capture at least 80% of carbon emitted by 

industries which are discharged into the atmosphere. Better still, a cleaner source of energy should be developed, 

instead of over dependence on fossil fuel energy that generates greenhouse gases. 

To control the velocity and direction of wind speed, tree planting should be encouraged in Nigeria. This is can be 

done by encouraging viable forestation and reforestation programmes using tested drought resistant and economic 

tree species. In the same vein, the development and adoption of efficient wood stoves and alternative sources of fuel 

wood should be encouraged. 

More effort should be made at translating Nigeria’s economic growth into job creation and poverty alleviation. 

Emphasis should be on improving the quality of public investment projects; enhancing budgeting, public financial 

management and procurement processes; prudent debt management practices and an enhanced domestic revenue 

base. 

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Business Administration, Economics, Law and Social Sciences (HSG). 

http://verdi.unisg.ch/www/edis.nsf/SysLkpByIdentifier/3321/$FILE/dis3321.pdf. 

 

Appendix 

 

Table 5: Top 10 Natural Disasters in Nigeria for the period of 1980-2011 sorted by numbers of people affected, 

killed and economic damage cost 

        Number of People Affected         Number of People killed           Economic Damage Cost 

Disasters  Date  Total No 

Affected  

Disaster

s  

Date  No 

Killed  

Disasters  Date  Damage 

(000 US$ 

) 

Drought  Jun-83  3,000,00

0  

Epidemic  6-May-

91  

7,289  Drought  Jun-83  71,103  

Flood  13-Sep-

10  

1,500,20

0  

Epidemic  Feb-96  4,346  Flood  11-Sep-

94  

66,500  

Flood  11-Sep-

94  

580,000  Epidemic  Oct-69  2,000  Flood  13-Sep-

10  

30,000  

Flood  Aug-88  300,000  Epidemic  1-Jan-

09  

1,701  Flood  23-Sep-

85  

8,000  

Flood  5-Sep-03  210,000  Epidemic  19-Feb-

96  

1,193  Flood  20-Sep-

00  

4,805  

Flood  10-Sep-

09  

150,000  Epidemic  Nov-86  1,000  Flood  27-Aug-

01  

3,000  

Flood  10-Oct-

98  

100,000  Epidemic  27-Jun-

11  

694  Flood  5-Sep-03  2,570  

Flood  15-Sep-

99  

90,000  Epidemic  28-Feb-

05  

561  Flood  15-Aug-

00  

1,900  

Flood  27-Aug-

01  

84,065  Epidemic  Apr-91  400  Flood  28-Aug-

11  

1,500  

Epidemic  Oct-69  80,000  Epidemic  23-Jul-

10  

353  Flood  7-Aug-

05  

147  

Total  6,094,26

5 

Total  19,537 Total  189.525 

Source: Odjugo (2012). 

 LOG(T

EM) 

LOG(R

AF) 

LOG(

CO2) 

LOG(W

IS) 

LOG(G

DPC) 

LOG(U

RB) 

LOG(P

RG) 

POE NAD 

 Mean  3.322620  

5.289438 

 

11.0227

1 

 1.382663  6.063567  1.185547  0.960060  

0.4418

60 

 

0.465116 

 Median  3.317816  

5.300315 

 

11.0913

8 

 1.354546  5.909549  1.193922  0.996949  

0.0000

00 

 

0.000000 

 Maximum  3.424263  

5.587249 

 

11.5525

7 

 1.621959  7.740664  1.335001  1.043804  

1.0000

00 

 

1.000000 

 Minimum  3.295837  

4.865995 

 

10.4607

3 

 1.139434  5.115716  0.993252  0.662688  

0.0000

00 

 

0.000000 

 Std. Dev.  0.021910  

0.137207 

 

0.29312

3 

 0.130730  0.612226  0.092049  0.105636  

0.5024

86 

 

0.504685 

Skewness  2.401359 -

0.969375 

 

0.03516

 0.148466  1.159258  0.016772 -1.608022  

0.2341

 

0.139876 

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97 
 

Table 6a: Descriptive Statistics 

Source: Author’s Estimation using Eviews 4.0. 

 

Table 6b: Pearson Correlation Matrix 

 TEM RAF CO2 WIS GDPC URB PRG POE NAD 

TEM 1         

RAF -0.222 

(0.152) 

1        

CO2 0.487
** 

(0.001) 

-0.196 

(0.209) 

1       

WIS 0.047 

(0.763) 

0.058 

(0.710) 

0.095 

(0.546) 

1      

GDPC 0.157 

(0.313) 

0.183 

(0.241) 

0.539
** 

(0.000) 

-0.046 

(0.767) 

1     

URB 0.427
** 

(0.004) 

0.107 

(0.494) 

0.612
**

 

(0.000) 

0.135 

(0.389) 

0.496
**

 

(0.001) 

1    

PRG -0.297 

(0.053) 

-0.046 

(0.771) 

-

0.496
**

 

(0.001) 

0.015 

(0.926) 

-

0.444
**

 

(0.003) 

-

0.555
**

 

(0.000) 

1   

POE 0.398
**

 

(0.008) 

-0.150  

(0.336) 

0.711
**

 

(0.000) 

0.119 

(0.447) 

0.465
**

 

(0.002) 

0.636
**

 

(0.000) 

-

0.505
**

 

(0.001) 

1  

NAD 0.054 

(0.733) 

-0.104 

(0.507) 

0.151 

(0.333) 

0.133 

(0.395) 

-0.125 

(0.423) 

0.371
*
 

(0.014) 

-0.245 

(0.113) 

0.203 

(0.192) 

1 

**. Correlation is significant at the 0.01 level (2-tailed). 

*. Correlation is significant at the 0.05 level (2-tailed). 

  Source: Author’s Estimation using SPSS 19 

Table 8a: Result of Static Long Run EstimationCoefficients 

Model Unstandardize

d Coefficients 

Stan

dardi

zed 

Coeff

icient

s 

t Sig. 95.0% 

Confidence 

Interval for B 

Correlations Collinearity 

Statistics 

B Std. 

Error 

Beta Lower 

Bound 

Upp

er 

Bou

nd 

Zero

-

orde

r 

Parti

al 

Part Tolera

nce 

VI

F 

0 46 

 Kurtosis  11.74144  

5.166725 

 

1.94432

8 

 1.949658  4.215383  2.333972  4.744990  

1.0548

25 

 

1.019565 

Jarque-Bera  178.2331  

15.14576 

 

2.00557

2 

 2.134569  12.27771  0.796787  23.98671  

7.1720

52 

 

7.167353 

 Probability  0.000000  

0.000514 

 

0.36685

6 

 0.343941  0.002157  0.671398  0.000006  

0.0277

08 

 

0.027773 

 Sum  142.8727  

227.4458 

 

473.976

5 

 59.45452  260.7334  50.97852  41.28258  

19.000

00 

 

20.00000 

 Sum Sq. 

Dev. 

 0.020161  

0.790679 

 

3.60868

0 

 0.717791  15.74245  0.355869  0.468679  

10.604

65 

 

10.69767 

 

Observations 

 43  43  43  43  43  43  43  43  43 



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98 
 

1 (Constan

t) 

.955 1.709  .559 .580 -2.519 4.428      

dlog(tem

) 

-

6.254 

2.795 -.302 -2.238 .032 -11.933 -.574 -.247 -.358 -.289 .915 1.09

3 

dlog(raf) -.366 .354 -.139 -1.035 .308 -1.086 .353 -.042 -.175 -.134 .925 1.08

1 

dlog(wis

) 

.601 .397 .201 1.513 .139 -.206 1.408 .137 .251 .195 .943 1.06

1 

log(prg) -

1.104 

.779 -.231 -1.418 .165 -2.686 .478 -.247 -.236 -.183 .628 1.59

1 

log(urb) 2.521 .993 .460 2.538 .016 .503 4.540 .372 .399 .328 .508 1.96

7 

log(gdpc

) 

-.403 .133 -.489 -3.028 .005 -.674 -.133 -.134 -.461 -.391 .639 1.56

6 

dlog(co2

) 

.666 .410 .225 1.626 .113 -.167 1.499 .093 .269 .210 .871 1.14

8 

poe .047 .191 .047 .248 .805 -.340 .435 .203 .043 .032 .463 2.16

0 

  Source: Author’s Estimation using SPSS 19 

 

Table 9: Over-Parameterized Error Correction Model 

Variable Coefficien

t 

Std. Error t-Statistic Prob. 

C 14.54585 15.20703 0.956522 0.3492 

LOG(TEM) -3.160734 4.162950 -0.759253 0.4558 

LOG(RAF) -0.363992 0.627564 -0.580008 0.5678 

DLOG(CO2) 0.968087 0.478086 2.024925 0.0552 

DLOG(WIS) 1.311114 0.673748 1.946001 0.0645 

LOG(GDPC) -0.373245 0.275818 -1.353230 0.1897 

LOG(URB) 7.363983 4.344310 1.695087 0.1042 

DLOG(PRG) 1.076444 1.594759 0.674989 0.5067 

POE -0.087065 0.262085 -0.332199 0.7429 

ECM(-1) -0.403471 0.626984 -0.643511 0.5265 

DLOG(TEM(-1)) 3.426916 6.194118 0.553253 0.5857 

DLOG(RAF (-1)) 0.375765 0.438020 0.857872 0.4002 

DLOG(CO2(-1)) 0.032105 0.574586 0.055874 0.9559 

LOG(WIS) -0.890754 0.943498 -0.944096 0.3554 

DLOG(GDPC(-1)) 0.001699 0.295554 0.005749 0.9955 

LOG(URB (-1)) -4.866011 3.976078 -1.223822 0.2340 

LOG(PRG) -1.241566 1.242472 -0.999272 0.3285 

POE (-1) 0.177780 0.246100 0.722389 0.4777 

NDIS(-1) -0.004560 0.611617 -0.007456 0.9941 

R
2
 = 0.60; F-statistic = 1.89; D.W = 2.1 

Source: Author’s Estimation using Eviews 4.0. 

Table 8b: Model Summary ( Static Long Run) 

Mod

el 

R R 

Squar

e 

Adjust

ed R 

Squar

e 

Std. 

Error 

of the 

Estimat

e 

Change Statistics Durbin-

Watson R 

Square 

Change 

F 

Change 

df1 df2 Sig. F 

Change 

1 .658
a
 .433 .299 .42253 .433 3.240 8 34 .008 2.654 

  Source: Author’s Estimation using SPSS 19 

 

 



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99 
 

 

Table 10: Parsimonious Model 

Variable Coefficien

t 

Std. Error t-Statistic Prob.   

C -0.316394 1.281522 -0.246889 0.8066 

DLOG(CO2) 0.785255 0.376684 2.084651 0.0454 

DLOG(WIS) 1.233072 0.507744 2.428531 0.0212 

LOG(GDPC) -0.229464 0.119147 -1.925898 0.0633 

LOG(URB) 10.59663 3.628343 2.920516 0.0065 

ECM(-1) -0.443047 0.160161 -2.766265 0.0095 

DLOG(GDPC(-2)) -0.487135 0.190127 -2.562154 0.0155 

LOG(URB(-1)) -7.507716 3.591246 -2.090560 0.0449 

LOG(WIS) -1.114064 0.676738 -1.646228 0.1098 

R
2
 = 0.56; F-statistic = 4.92; D.W = 1.98 

Source: Author’s Estimation using Eviews 4.0. 

 

Table 11: Redundant Variables: LOG(WINS) 

F-statistic 2.710066     Probability 0.109819 

Log likelihood ratio 3.352371     Probability 0.067108 

Source: Author’s Estimation using Eviews 4.0. 

 

Table 12: Breusch-Godfrey Serial Correlation LM Test 

F-statistic 0.591069     Probability 0.560268 

Obs*R-squared 1.566673     Probability 0.456879 

Source: Author’s Estimation using Eviews 4.0. 

 

Table 13: White Heteroskedasticity Test 

F-statistic 0.996940     Probability 0.491553 

Obs*R-squared 16.38060     Probability 0.426728 

Source: Author’s Estimation using Eviews 4.0. 

 

Table 14: ARCH Test 

F-statistic 0.504247     Probability 0.482092 

Obs*R-squared 0.524358     Probability 0.468989 

Source: Author’s Estimation using Eviews 4.0. 

 

Table 15: Ramsey RESET Test 

F-statistic 0.281444     Probability 0.599662 

Log likelihood ratio 0.373510     Probability 0.541097 

Source: Author’s Estimation using Eviews 4.0. 

 

 

 

 



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100 
 

0

1

2

3

4

5

6

7

-0.5 0.0 0.5

Series: Residuals

Sample 1973 2012

Observations 40

Mean      -3.59E-15

Median   0.011152

Maximum  0.633268

Minimum -0.865459

Std. Dev.   0.335722

Skewness  -0.454435

Kurtosis   3.073417

Jarque-Bera  1.385725

Probability  0.500142

 
Figure 1: Normality Test Result 

-20

-15

-10

-5

0

5

10

15

20

1985 1990 1995 2000 2005 2010

CUSUM 5% Significance

 
Figure 2: CUSUM Test Result 

-0.4

0.0

0.4

0.8

1.2

1.6

1985 1990 1995 2000 2005 2010

CUSUM of Squares 5% Significance

 
Figure 3: CUSUM of Squares Test Result 

 
Figure 4: Forecast Evaluation Output for NAD Variable 

-1.2 
-0.8 
-0.4 
0.0 
0.4 
0.8 
1.2 
1.6 
2.0 
2.4 

75 80 85 90 95 00 05 10 

N AD I  F 

F o r e c a s t :  N D I S F 
A c t u a l :  N D I S 
F o r e c a s t  s a m p l e :  1 9 7 0  2 0 1 2 A d j u s t e d  s a m p l e :  1 9 7 3  2 0 1 2 I n c l u d e d  o b s e r v a t i o n s :  4 0 

Root Mean Squared Error  0.331499 
Mean Absolute Error       0.263066 
Mean Abs. Percent Error  12.98143 
Theil Inequality Coefficient  0.256291 
      Bias Proportion         0.000000 
      Variance Proportion  0.144240 
      Covariance Proportion  0.855760 



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101 
 

.00

.05

.10

.15

-.8

-.4

.0

.4

.8

1985 1990 1995 2000 2005 2010

One-Step Probability Recursive Residuals

 
Figure 5: One-Step Forecast Test Result 

 

Variables Excluded 

There are other factors hypothesized to affect tourism demand levels but which have been omitted from estimation 

in this study. The inclusion or exclusion of certain other variables from the study means that the subsequent results 

are subject to biases entailed in mis-specification and omitted variables, particularly, if the variable excluded is 

correlated with the dependent variable. Essentially, it would prove impractical to attempt to include all possible 

variables in a regression model. Nonetheless, certain variables are excluded purely on grounds of inadequate data. 

Indeed, loss of degrees of freedom means that only the most important variables remain. Some of the most obvious 

omissions in addition to reasons for their omission are as follows; access transport costs, marketing expenditure 

abroad and sociological factors. 

 

 

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This is an open-access article distributed under the terms and conditions of the Creative Commons Attribution 

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