Welfare Costs of Risks and Management Options in Nigeria Olanrewaju OLANIYAN1, Omobowale A. ONI2, Abimbola O. ADEPOJU2, Foluso Y. OKUNMADEWA2, Ayodele FASHOGBON3 1Department of Economics, University of Ibadan 2Department of Agricultural Economics, University of Ibadan 3 World Bank Economics Research Group Abstract Most of Nigeria’s declining welfare indicators such as poverty, food insecurity and life expectancy at birth have been linked to high risk exposure. However, little empirical investigations have been made to uncover the dynamics of risks and their attendant welfare implications at the household level using aggregate national data. This study thus investigated risk prevalence, its welfare cost and management strategies within households in Nigeria. Data from the Harmonised National Living Standard Survey (NLSS) of households, collected between 2009 and 2010 by the National Bureau of Statistics, provided the data set for the study. The results revealed that predominant shocks within households in Nigeria are the death of the household head, conflicts in the community, death of a spouse, the household head being away, spouse being away from home, household head hospitalized and personal theft. Among variables that significantly reduce households’ welfare were death and absence of the household head, and community conflict. Further, the results showed that the level of wealth significantly mitigates the negative impact of some shocks. Also, findings from the study revealed that households make use of mixed strategies to cope, mitigate and reduce risk UNIV ERSIT Y O F IB ADAN L IB RARY 2 O. Olaniyan et al. AJSD Vol. 9 Num. 2 exposure and impact. The study concluded by recommending provision of well-managed and need responsive social infrastructure such as good health facilities, pipe-borne water, road network. In addition, people need to be sensitized to the need for life micro-assurance and government needs to subsidize it as this will help reduce the impact of the demise of the breadwinner on the household’s welfare. Keywords: Risks, Shocks, Welfare cost, poverty, Nigeria. Introduction It is becoming clear that Nigeria is one of the countries that will be unable to realise the objectives of the Millennium Development Goals as regards poverty reduction by the year 2015. This is based on the fact that progress towards halving the incidence of poverty in the country is slow. In 2004, the country’s poverty rate was estimated to be 54.4 percent but later increased to 69 percent (or 112,518,507 Nigerians) in 2010, with the North- West and North-East geo-political zones recording the highest poverty rates in the country at 77.7 percent and 76.3 percent respectively, while the South- West geo-political zone recorded the lowest at 59.1 percent. However, the ability to escape from poverty is dependent on the level and magnitude of risk exposure and the ability to manage these risks effectively (Deacon, 2002). Risks vary in nature and can range from macroeconomic shocks to natural disasters, health hazards, personal insecurity, and socially compulsive expenses such as dowry. Each of these categories contains a rich subset of varieties. Macroeconomic shocks can be caused by changes in the external trading environment and/or as a consequence of domestic policies. Natural disasters involve crop damage, housing and similar damages caused by weather. Risk, risk exposure, and vulnerability are closely related but not synonymous (see World Bank, 2001). Risks refer to uncertain events that are not well-being friendly. Risk exposure captures the probability that a certain risk will occur. As revealed by Olaniyan et al. (2003), there are different types of risk and shocks at the root of poverty in Nigeria. Risks and shocks are more prevalent among poor households that are less able to deal with them. If the UNIV ERSIT Y O F IB ADAN L IB RARY Welfare Costs of Risks and Management Options in Nigeria 3 poor must continue to survive under these vulnerable conditions, government should help to develop ways of reducing and mitigating and coping with the effects of the risk to their livelihoods. When people are not adequately protected (formally or informally) against risks, they lead to huge welfare losses. In the most extreme cases, they cause physical death. In many instances, they induce asset depletion (land, livestock, other physical assets, etc.) or delay investments (in capital assets, education, nutrition, etc.), which propel households into spiral irreversible losses and eventually poverty trap. A deeper understanding of the linkages between risk and the associated welfare costs along with household level management options would provide an empirical basis for social policy, thereby strengthening both the analytical and operational content of the country’s poverty reduction programmes. Moreover, it would provide needed empirical information on household risk situations that the country’s nascent micro- insurance policy can leverage on. The shocks and the losses they induce and the response are specific to different countries and also differ in parts of the same country – especially in a country as large and complex as Nigeria. An examination of the welfare position in any country must consider the (immediate) welfare effects of idiosyncratic health, covariant drought and more systemic commodity price shocks. Given the negative effects of these shocks it is important to examine the occurrence and effectiveness of self- and mutual insurance strategies as well as measures for mitigating the negative welfare effects of such shocks. This will include identifying the key determinants and costs of people’s management strategies. Hence, analysing the welfare costs of risks and risk management options is key to understanding the dynamics leading to, and perpetuating poverty. This study would, therefore, provide a dynamic approach to the differential and total welfare costs of risks and risk management options not just to individuals and households but also to the country in general. The following objectives are pursued in this paper: (1) to identify and describe prevalent risks to households in Nigeria; (2) estimate the welfare cost of risk exposure; and (3) identify households’ risk management strategies. UNIV ERSIT Y O F IB ADAN L IB RARY 4 O. Olaniyan et al. AJSD Vol. 9 Num. 2 Literature Review Risk and risk management options in Nigeria A large proportion of the African population lives below the poverty line (UNDP, 2008). Within this context, many households face hard times due to risks such as illness, disability, unemployment, natural disasters such as flood, famine, or drought, other disasters, and global financial crises. While some of these risks affect all population groups equally; others have more intense impacts on the poor. Since the poor are more vulnerable to risks, they are constantly preoccupied with risk-averse and coping strategies to avoid sinking further into poverty. These are done at a high cost to their future livelihoods. A plethora of efforts directed at analysis of poverty and poverty alleviation policy options in Nigeria have often focused on the analysis of risk and vulnerability (Alayande, 2003; Adubi et al., 2002, Okunmadewa and Elder, 2003; Olaniyan et al, 2003). While several studies describe the sources of risk, others have focused on the intent to capture various aspects of risks, vulnerability to shocks and poverty dynamics. The dynamics of poverty is often linked to the incidence of risks, shocks, and vulnerabilities of individuals and households. Okunmadewa and Elder (2003) argued that risk and shocks lead to volatility of welfare due to consumption reduction. However, economic theory holds that households would always prefer smooth instead of volatile consumption. Christiansen et al. (2007) submitted that given access to well-functioning credit or insurance markets, these preferences generate stable consumption paths, even when shocks occur. But many African countries are such that credit and insurance markets are imperfect leading to a situation where household consumption may be susceptible to shocks (Fafchamps and Lund, 2003; Kazianga and Udry, 2006). Risks are classified by the level at which they occur and by the nature of the event behind them. There are three sets of risks based on the level of occurrence; these are the micro, meso, and macro shocks (Fafschamp, 1999). Micro shocks are idiosyncratic in that they are individual or household level specific. Meso shocks affect groups of households that make up a community or village. Macro shocks occur at the national or international level. The other conventional approach to classifying shocks is the severity and frequency of shocks. The severity of shocks ranges from catastrophic (a UNIV ERSIT Y O F IB ADAN L IB RARY Welfare Costs of Risks and Management Options in Nigeria 5 natural disaster, death of the breadwinner) to minor ones like a slight illness, a few days without work for casual labourers among others (Deaton 1991). In the literature, shocks and risks are sometimes taken as synonymous. Exposure to risks usually results in shocks. The World Bank (2001) classified the different shocks into three categories: micro, meso and macro. While the micro is on the individual level, the meso and the macro correspond to the community and national/international levels respectively. Oni (2008) classified shocks in a way that is synonymous with the above as id io s y n c r a t i c ( in d iv id u a l - ba s e d ) , co mmu n i t y - ba s e d o r national/international-based. Some types of risks identified within the broad categories are gender risks, unemployment, death illnesses, pollution, environmental degradation, natural disasters, conflict and wars as well as macroeconomic volatility. Heitzmann et al. (2002) categorized risks as natural risks (heavy rainfall, landslides, volcanic eruptions, earthquakes, floods, hurricanes, droughts, strong winds); health risks (illness, injury, accidents, disability, epidemics, malnutrition); economic risks (unemployment, harvest failure, output collapse, balance of payments shock, financial crisis, currency crisis, technological or trade-induced terms of trade shocks); life-cycle risks (birth, maternity, old-age, family break-up, death); social risks (crime, domestic violence, terrorism, gangs war, riots); political risks (discrimination, corruption, political unrest, coup d’état); and environmental risks (pollution, deforestation, land degradation, nuclear disaster). More specifically, there have been studies on risk involving price rigidity (Dorich, 2007), price increases (Wood et al., 2010), fluctuating economic cycles (Otrok, 1999), inflation (Fieding, 2004), conflict (Clemens and Moss, 2005), public policy reforms (Lucas, 2003; Suranovic, 2004), natural disasters/weather risks (Tatlock, 2006), and so on. Oyekale and Oyekale (2010) using the National Living Standard Survey (NLSS) data of 2004, identified the different shocks that make rural households in Nigeria vulnerable to poverty. Some of them include job loss, drought, low agricultural prices, no credit to expand business, no land, high commodity prices, low salaries and hard economic times. These factors are responsible for increasing the poverty of the already poor and increasing the incidence of the non-poor falling into poverty. Oni and Yusuf (2008) showed that both idiosyncratic and covariate shocks lead to variability in consumption (as a measure of their vulnerability to poverty) of rural Nigerians. In the study, the covariate risk factors that make rural people UNIV ERSIT Y O F IB ADAN L IB RARY 6 O. Olaniyan et al. AJSD Vol. 9 Num. 2 vulnerable include fluctuating weather conditions (since rural Nigeria is agrarian with dependence on rain-fed agriculture), volatility in government policies, unemployment, and AIDS and river blindness. The idiosyncratic factors include the low educational level/ no formal education, involvement in farming activities, large household size and having a female as the household head. Chiwaula and Waibel (2009), on their work on the role of shocks and risks in the livelihoods of small-scale fishing communities of Hadejia-Nguru wetlands in Nigeria, reported three major classes of risk-weather shocks, agriculture-related shocks, and conflict/crime shocks. Weather-related shocks (drought and flooding) seem to be more prominent in the area as a result of the dry weather conditions. Ecology-related shocks (crop pests and diseases, Typha grass, destruction of housing, and livestock pests and diseases) are also very important in the area. The study also found that security-related shocks (conflict with the nomadic Fulani, theft of livestock, theft of equipment, theft of cash and forced migration) have high prevalence rates. Literature abounds on the welfare consequences of the different risks that individuals, communities and the nation are exposed to. The welfare costs of exposure to risk are most often measured in terms of cost to consumption and or income. However, given the microeconomic theory assumption that all income is spent on consumption, the welfare cost can either be measured as a loss in consumption or income. Following Lucas’ approach (Lucas, 2003) in measuring the costs of economic fluctuations, the welfare cost of risks/shocks is given as that proportion of the present level of consumption that people are ready to permanently give up a proportion of in order to reduce the uncertainty of variability in consumption in future. The magnitude of these costs is however dependent on the consumer’s preference and risk aversion attitude. Otrok (1999) states that the welfare cost of consumption is the percentage of consumption that an agent would pay at every date and state to move from the fluctuating economy to a non- fluctuating economy. However, other non-economic welfare costs have been cited in the literature, including loss of lives, malnutrition, infant mortality, and low educational attainment (Tesliuc and Lindert, 2002; Clemens and Moss, 2005). UNIV ERSIT Y O F IB ADAN L IB RARY Welfare Costs of Risks and Management Options in Nigeria 7 With respect to the risks arising from macroeconomic variables in developing countries, Loayza et al. (2007) concluded that the volatility experienced in terms of large external shocks, macroeconomic rigidities, and weak institutions constitute direct welfare costs to risk averse individuals as well as indirect costs to others through its adverse effect on growth and development. High risk aversion could also be the cause of very stringent consumption smoothing in risk averse individuals as noticed in small welfare costs during the risk periods. Chetty and Looney (2004) suggest that many households in low-income countries resort to costly consumption smoothing measures such as subsistence constraints in response to risks, because of their high risk aversion rather than invest in insurance. With regard to another macroeconomic shock, in this case, an increase in import tariffs, welfare cost to the consumers in the importing countries is quite high resulting from the increased cost of the imported product, especially for a country largely dependent on imports. However, in the exporting country, the welfare loss will be borne by the producers as a result of the loss of revenue from the importing country and a decrease in the prices of their products in their own domestic economy (Suranovic, 2004). In estimating the welfare cost of a 17.5% increase in domestic fuel tax in the United Kingdom, which corresponds to sales taxes, Blundell (1996) found that losses were up to 2% of the total household consumption of the respondents studied. Olomola (2006) found that the welfare effects of foreign shocks and oil price shocks on the economic performance of Nigeria are, to a very large extent, based on the response of the domestic macroeconomic policies to such external shocks. Thus, output growth in Nigeria is affected by the domestic money supply and price shocks. In analysing the non-monetary cause of inflation volatility in Nigeria, Fielding (2004) found that infrastructure such as transportation, literacy, and communication are able to reduce inflation volatility while increased access to credit increased the inflation volatility. In assessing the welfare costs of conflicts, Hess (2004) states that war, at any level, leads to welfare losses both in economic and non-economic facets. In the non-economic facets are the loss of lives and loss of family/friends. Basing the measure of the economic welfare cost on Lucas’ (1987) approach, he found that people were ready to permanently give up 8% of their current consumption levels to avoid a war. In sub-Saharan Africa, Barry and Quentin (2007) reported that the cycle of conflicts and UNIV ERSIT Y O F IB ADAN L IB RARY 8 O. Olaniyan et al. AJSD Vol. 9 Num. 2 political instability in Guinea Bissau have led to loss of human lives, loss of investment growth as well as any attempt at poverty reduction. The study revealed that about 40% of GDP was lost as a result of the 11 months of civil war in 1998. Also, one in three persons living in poverty today in the country would not have been poor but for the conflict. Barro (2006) posits that the welfare cost of disasters, including wars, are large and could mean that people are willing to reduce 20% of their GDP to prevent the uncertainty of any such macroeconomic disaster. He, however, thinks that the welfare cost of economic fluctuation is lower with a reduction of GDP by 1.5% to reduce the uncertainty. Agriculture and weather-related shocks also carry welfare implications. Rainfall data, for instance, have been extensively used to identify the effect of weather shocks on agricultural yields and incomes (Porter, 2008). Other detrimental weather effects have been documented, such as long-term effects on school attendance and enrolment (Jacoby and Skou, 1997), the nutrition and height of children (Alderman, Hoddinott and Kinsey, 2006), and their ultimate educational attainment (Portner 2008). Reporting on the cost of water stress in sub-Saharan African countries, Tatlock (2006) reported that water stress is predominant and expected to increase as a result of the low-level infrastructural base to harness the available water bodies, unlike their counterparts in South Africa and Egypt. According to the report, water stress causes both a decline in economic development and some conflict in transboundary water bodies, both with negative welfare impacts. He also reported that in Kenya, the 1999-2000 droughts produced a 16% decline in the GDP. In a related issue, Moran (2006) gave the example of Niger as a country languishing in a food crisis as a result of cyclical floods and a non-responsive and corrupt political system. Alayande (2004) observed that Nigeria is bedevilled with major environmental hazards that have adversely affected the earning capacity of the people, especially in rural areas. The major environmental problems that have made Nigerians, especially rural dwellers, vulnerable to poverty are: drought and desertification; agricultural land degradation; and high incidence of gully, coastal and marine erosion. Large industrial pollution and urban waste are other environmental problems that have seriously affected the health of the people and hence their income-earning capacity. The fact that Nigeria is predominantly rural based and dependent on agriculture which relies on unpredictable weather conditions is a major risk UNIV ERSIT Y O F IB ADAN L IB RARY Welfare Costs of Risks and Management Options in Nigeria 9 factor (Oni and Yusuf, 2008; Oyekale and Oyekale, 2010), making the majority of Nigerians vulnerable to poverty and causing a decline in wellbeing. The mere existence of risk does not generate adverse outcomes as this depends on the risk management options that are utilised. Two sets of strategies are at the disposal of the household for the purpose of coping with risks to which they are prone; these are formal and informal strategies. Informal strategies are individual or household based, while formal strategies are market-based activities as well as publicly provided mechanisms for the poor to mitigate the attendant effects of risks. Both sets of strategies are supposed to be complementary, given the magnitude of the vulnerability of the poor to poverty-associated risks. For example, those working in the informal sector may be vulnerable, even when they are not poor, because they lack any sort of safety net to protect them if they lose their jobs. Holzmann and Jørgensen (2000) however identified that it is possible to separate risk management into ex ante and ex post strategies. Ex ante risk management can be classified into three types of strategies. First is risk prevention or reduction, which are actions taken to eliminate or reduce the occurrence of risky events. The second is the prevention or reduction of exposure to risk. This strategy focuses on the fact that even if the risks exist, the strategies must ensure actions take place to prevent or reduce exposure to such risks. The third is risk mitigation – actions that can be taken ex ante to provide compensation in the case of a risk-generated loss. Some of these risk management strategies, as implemented in Nigeria, have been provided in Olaniyan et al. (2003). Ex post risk management on its own includes response actions taken after a risk has occurred. It involves activities to deal with realized (or actual) losses, such as the selling of assets, seeking “emergency” loans (from relatives, friends, banks), removing children from school, migration, seeking temporary employment. Within the broad risk management options, the capacities of individuals, households, and communities to handle risks and their choice of risk management instruments depend on the characteristics of those risks (their sources, correlation, intensity, and frequency) and the portfolio of assets that the individuals, households, and communities control (Jalan and Ravallion, 2000). De Ferranti et al. (2000) further argued that efficient risk UNIV ERSIT Y O F IB ADAN L IB RARY 10 O. Olaniyan et al. AJSD Vol. 9 Num. 2 management must involve a combination of the three strategies for (i) risk prevention; (ii) risk mitigation by pooling uncorrelated risks or sources of income or by making use of formal and informal insurance; and (iii) coping strategies designed to relieve the impact of the risk once it has occurred. Within each of the risk management instruments, some of them will fall into informal arrangements while others fall into market-based arrangements and the rest are those that will be within public arrangements usually as some form of social protection. Oni (2008) identified three strategies in risk management in Nigeria: prevention, mitigation, and coping strategies. Risk prevention includes measures that reduce the probability of welfare reduction through risks; examples include immunization, irrigation, education, land agreement, and so on. Risk mitigation, on the one hand, involves strategies to reduce the impact of the welfare-reducing risk and it includes issues like insurance, price support, portfolio diversification, intercropping, mixed cropping and mixed farming. On the other hand, coping mechanisms are put in place to relieve the impact of a risk event that has occurred. Such measures include a reduction in consumption, sale of assets, school dropout and borrowing. However, as implied in Oni and Yusuf (2008), policy measures in risk and vulnerability management should be region-specific since there are differing levels of risk factors and risk exposure in the different regions of Nigeria. The level of intervention in risk management, as well as the level of formality, determines the composition of the actors within the management strategy. Heitzman et al. (2002) identified intervention levels as micro, meso, macro and global, while there are formal and informal intervention practices. In all, the major actors in any risk management strategy are the households, NGOs, the private sector, the public sector, donors or international organizations, that are either exposed to risks that have to be managed and/or provide the management strategies to manage the risks. Analytical Framework Prevalence of risks Risk profiles of the country were done using graphical analyses. Bar chats were used to describe the incidence of identified risks. Due to the high number of risk exposure observed in the population, the study took a UNIV ERSIT Y O F IB ADAN L IB RARY Welfare Costs of Risks and Management Options in Nigeria 11 further step to bunch them into groups. Factor analysis was used to achieve this task. Factor analysis was used to reduce some risk variables to a few manageable ones, especially those related to shocks. This method has the advantage of reducing multicollinearity among variables, reducing some dummy variables to single continuous and bringing down the number of variables to a level that can be reasonably handled. Factor analysis works by extracting variables (factors) that capture essential variability in the original variables thus making it a linear combination of all the variables. Given that death of the household head, conflicts in the community, death of a spouse, the household head being away, spouse away from home, household head hospitalized and personal theft were high-incidence reported risks, this however still makes the explanation of risks affecting households in Nigeria ambiguous. Therefore there is a need to explore a robust way of categorizing risks based on their correlation structure. Factor analysis will help bunch up risks that affect the population in a similar way into the same category. Therefore we were able to re-categorize risks based on their correlation structure to security risk, social risks, life-cycle risks, and health risks. Moreover, the key risks identified using factor analysis were further profiled graphically using relative incidence measures. To examine the set of people most affected by the isolated risks in the population, this study examined the incidence of reported shocks based on household characteristics as well as regional differences. The household characteristics used include: gender of household head, poverty status, and age and educational level of the household head. Location characteristics include sector (urban and rural dwelling) and region (North-east, North- west, North-central, South-east, South-west, and South-south). The relative incidence is the ratio of the incidence of the risks on a particular population relative to its country-wide incidence (Tesluic and Lindert, 2000). If the relative incidence of risks reported is lower than 1, this implies that the occurrence of the risk is lower than the national average, whereas if it is higher than 1, it implies that the occurrence is greater than the national average. Probability of reporting risks The probability of a household reporting a shock based on the household and location characteristics is examined in this section. UNIV ERSIT Y O F IB ADAN L IB RARY 12 O. Olaniyan et al. AJSD Vol. 9 Num. 2 Multivariate logistic regression was used in the study to identify significant predictors of exposure to risks in the country. The model also attempted to determine the effect of the set of explanatory variables on the probability of reporting identified risks while holding the other predictor variables constant. The predictors used in the model include: economic status (wealth factor - which is determined by assets owned by the household and poverty status), exogenous household characteristics (gender, age, educational level of the head) and location characteristic (sector- urban/rural and regions within the country). The dependent variables used were risks such as household experiencing the death of household head, household experiencing the death of a spouse, household head and spouse away from home, disability of household head, household head and spouse hospitalized, home burglary, physical harm, motorcycle and vehicle theft and community conflict. Estimation of the cost of welfare on risks The associated welfare loss due to exposure to shocks were quantified. In order to determine the monetary values of welfare losses associated with shock, the approach of Tesliuc and Lindert (2002) was adopted. It was noted that shocks lead to a reduction in income or expenditure, but the ability to mitigate their impacts will minimize their impacts. It was assumed that mitigation ability will be determined by a household’s wealth and varies from zone to zone. The equation to be estimated can be expressed as: (1) The level of consumption without shock can be estimated as: (2) Cost of welfare shock, i.e. the impact of shock, can be regarded as Cs – Cw where: Xh represents the households’ socio-economic characteristics such as household size (number of children and gender, age), human capital UNIV ERSIT Y O F IB ADAN L IB RARY Welfare Costs of Risks and Management Options in Nigeria 13 characteristics (educational level of the household head), number of spouses, number of kids between 0-4 years, number of kids between 5-9 years, number of kids between 10-17 years, poverty status Rh is a vector of regional dummies such as geopolitical zones (North- East, North-West, South-East, South-South, South-West); S is the vector of the shock dummies (e.g., death of household head, death of spouse, community conflict); W is household wealth. If this specification is correct, then the level of consumption can be estimated in the absence of shocks simply by replacing all the indicator variables for shocks with zeroes. âi, ìi, ái and ëi are the respective parameters to be estimated e are error terms of the models. Results generated will provide information on the impact of shocks on income and consumption by the economic status of the household and overall impact of shocks on aggregate welfare (using per capita consumption expenditure as a proxy for aggregate welfare). OLS regression using semi-log function was used for the analysis. Risk management options A social risk management matrix was used, following Holzmann (2001), to describe the risk management options adopted by the population. Descriptive statistics of percentage and frequency were used to describe the relative usage of various risk management options. Responses to risks can be categorized as risk coping, risk mitigation, and risk reduction. Strategies in responses to risks can be classified into three: informal, market based and public arrangements. The informal arrangements involve responses that do not have a bearing on market forces or connection with government interventions. They are arrangements made at the idiosyncratic levels. Such include selling of real assets, informal social groups, migration, marriages, less-risky productions, personal hygiene, and shared tenancy. The market- based strategies include the sale of financial assets, insurance, labour standards and borrowing from financial institutions. The public strategies emanate from the government and include pension schemes, public works, subsidies, health interventions, macroeconomic policies, labour laws and protection of property rights. UNIV ERSIT Y O F IB ADAN L IB RARY 14 O. Olaniyan et al. AJSD Vol. 9 Num. 2 Data and descriptive statistics This study utilised data from the Harmonised Nigeria Living Standard Survey (HNLSS) of households that were collected between 2009 and 2010 by the National Bureau of Statistics (NBS). The sample design was two-stage stratified sampling. The survey covered all 36 states of the federation and the Federal Capital Territory (FCT). The sample studied for the Harmonised Nigeria Living Standard was designed to have the LGA as a reporting domain. However, the sample design for the survey also facilitated the provision of estimates at national and sub-national levels (national, zonal, and state). The sampling frame for all the 774 LGAs in the country used the enumeration areas (EAs) demarcated by the National Population Commission (NPopC) for the 2006 Housing and Population Census. The frame was constructed into replicates such that each LGA had 3 replicates and in each replicate there were 10 EAs serially numbered 01-10. A complete listing of housing units and households was carried out in each of the EAs just before the start of the main survey. A two-stage sample design was adopted in the survey of which selection of EAs constituted the first stage/Primary Sampling Units (PSUs), while a selection of households (HHs) formed the second stage/Secondary or Ultimate Sampling Units (USUs). The welfare approach, also known as Part A component, was conducted in 77,400 households, which gives an average of one hundred households per local government area, while the consumption approach (Part B) covered 50 households in each local government (NBS, 2010). In all, the empirical analysis made use of 27,747 households. The survey enquired about household expenditure, income sources, health, education, social capital, employment issues and experience of shocks and their coping methods. The descriptive statistics from Table 1 show that a typical household in Nigeria has an equivalent adult member size of 3.4, about 1 kid between 10-17 ages, and about 84 percent male head, 84 percent married and monogamous, with age around 48 years and the majority have no formal education. Based on the national moderate (N= 88,693.46) and core (N= 44,346.73 ) poverty lines per annum computed by the Nigerian Bureau of Statistics, using HNLSS 2009/2010 data, about 48.5 percent and 28.4 percent of Nigerian households could be described as UNIV ERSIT Y O F IB ADAN L IB RARY Welfare Costs of Risks and Management Options in Nigeria 15 Figure 1: Incidence of reported shocks in Nigeria, 2009-2010. extremely poor and moderately poor respectively, with about 71 percent residing in rural areas. Empirical Results Prevalent risks Findings reveal in Figure 1 that the prevalent shocks are the death of the household head, conflicts in the community, death of a spouse, the household head being away, spouse away from home, household head hospitalized and personal theft. The shocks with the lowest incidence according to findings are spouse disability, car vandalism, and vehicle theft at 0.8%, 1.2%, and 1.4% of the population respectively. These shocks are idiosyncratic in nature. The shocks are felt by a few of the sample units. Grouping of risks using factor analysis is shown in Table 1. The result shows that there are eight bunched risks affecting households in Nigeria. Six factors are uniquely identified through factor 1 to factor 6 based on their correlations with shocks. However, these six factors can be summed up into four categories: security risk, social risks, life-cycle risks, and health risks. As highlighted in Table 2, factor1 identifies the security risks (vehicle UNIV ERSIT Y O F IB ADAN L IB RARY 16 O. Olaniyan et al. AJSD Vol. 9 Num. 2 theft, car vandalism, motorcycle theft, bicycle theft, home burglary, robbery by force, personal theft, physical harm, and other violence), factor 2 and 3 depict life-cycle risks (death of household head, death of spouse, head away, and spouse away), factor 4 represents the social risk (community conflict), and factor 5 and 6 identify health risks (household head disabled, spouse disabled, and household head hospitalized and spouse hospitalized). Table 1: Summary statistics of rural households in Nigeria Variable Type Mean Std. Dev. Min Max Per capita expenditure Continuous 410692.5 1.80E+07 1573.189 2.01E+09 Poverty status (Non-poor as default) Moderate poor Dummy 0.284 0.000 1.000 Extreme poor Dummy 0.485 0.000 1.000 Household size Continuous 3.398 1.944 0.660 9.610 Household head’s age Continuous 47.865 16.200 15.000 95.000 Heads’ education (No formal as default) Head with primary education Dummy 0.241 0.000 1.000 Head with secondary education Dummy 0.176 0.000 1.000 Head with post sec. education Dummy 0.082 0.000 1.000 Head with tertiary education Dummy 0.043 0.000 1.000 Male head Dummy 0.845 0.000 1.000 Marital status (Never married as default) Head married monogamy Dummy 0.837 0.000 1.000 Head married polygamy Dummy 0.009 0.000 1.000 No. of kids between 05-09 yrs Continuous 0.501 0.803 0.000 8.000 No. of kids between 10-17 yrs Continuous 0.825 1.120 0.000 8.000 Wealth Index (PCA) Continuous 0.054 2.499 -2.481 15.802 Number of spouse Continuous 0.864 0.672 0.000 7.000 Rural Dummy 0.713 0.000 1.000 Geopolitical zones (North Central) North East Dummy 0.124 0.000 1.000 North West Dummy 0.228 0.000 1.000 South East Dummy 0.154 0.000 1.000 South South Dummy 0.101 0.000 1.000 South West Dummy 0.204 0.000 1.000 Source: HNLSS 2009/2010 data Sample size: 27,747 UNIV ERSIT Y O F IB ADAN L IB RARY 1 7 Table 2: Factor loadings (pattern matrix) and unique variances Variable Factor 1 Factor 2 Factor 3 Factor 4 Factor 5 Factor 6 Factor 7 Factor 8 Uniqueness Vehicle theft 0.8055 -0.0018 0.0049 -0.2276 0.1223 -0.02 -0.0377 0.0468 0.2803 Car vandalism 0.8561 -0.0067 0.0002 -0.192 0.1057 -0.017 -0.0147 0.0469 0.2163 Motorcycle theft 0.7965 0.01 -0.0081 -0.0949 0.0547 -0.0212 0.0718 -0.0675 0.3432 Bicycle theft 0.7232 -0.0072 -0.0015 -0.0542 0.0295 -0.0085 0.021 -0.0291 0.4717 Home burglary 0.6214 0.0109 -0.0016 0.0881 -0.0337 0.0237 -0.0025 -0.0862 0.5968 Robbery by force 0.642 0.0124 0.0065 0.1439 -0.1214 0.055 -0.1405 0.0141 0.5292 Personal theft 0.5785 -0.0011 0.0009 0.1526 -0.123 0.051 -0.1314 0.0333 0.6059 Physical harm 0.7315 -0.0032 0.0116 0.1625 -0.0934 0.0066 0.0559 0.0033 0.4265 Other violence 0.5934 0.0048 0.0068 0.1655 -0.0333 -0.0499 0.1534 0.029 0.5924 Conflict in-community 0.0472 0.0067 0.0078 0.1629 0.0093 -0.0583 0.1399 0.0564 0.9449 Household head dead -0.0084 0.7985 -0.0449 -0.0026 0.0037 -0.0004 -0.0019 -0.001 0.3603 Household head away -0.012 0.0252 0.7597 -0.0091 -0.0123 -0.0057 0.0011 0.0029 0.4219 Spouse dead -0.0039 0.7871 -0.0603 -0.0096 -0.0114 -0.0002 0.0028 0.0031 0.3766 Spouse away -0.0064 0.084 0.7591 -0.0054 0.0023 0.0023 -0.0014 -0.005 0.4167 Spouse disable 0.0116 0.0002 0.0139 0.043 0.1295 0.2413 0.0411 0.0089 0.9211 Household head disabled -0.0015 0.0094 0.0054 0.0332 0.1237 0.2448 0.0445 0.0041 0.9215 Household head hospitalised -0.0014 0.0076 0.0143 0.1954 0.2734 -0.0722 -0.0548 -0.006 0.8786 Spouse hospitalised 0.0102 0.0185 0.0098 0.1858 0.2692 -0.0854 -0.0686 -0.009 0.8804 Source: Authors’ computation from the HNLSS 2009 data. UNIV ERSIT Y O F IB ADAN L IB RARY 18 O. Olaniyan et al. AJSD Vol. 9 Num. 2 Relative incidence of reported shocks by household and location characteristics Figure 2 (a-f) illustrates the relative incidence of the risks. Following from results presented in Figure 1, the risk with the highest incidence of occurrence countrywide is the death of the household head at 6.8%. With respect to the gender of the household head, it is observed that the relative incidence of experiencing the shock of the death of a household head is 0.6 for female-headed households and 0.8 for male-headed households. This reveals that when compared to the national incidence, the relative incidence of this shock was 20% and 40% lower than the country incidence for male and female-headed households respectively. The incidence of this shock was 1.3 for extremely poor households and 1.02 and 0.6 for the moderately poor and non-poor respectively. The implication of this result is that the extreme poor are the most exposed on the instance of the death of the head of the household relative to the national average followed by the moderately poor and then the non-poor. The sectoral difference in the relative incidence of experiencing shocks is presented in Figure 2e. The incidence of experiencing the death of the household head is 1.04 for rural households and 0.88 for urban households, with a relative incidence of 4 percent higher and 16 percent lower for the rural and urban sectors respectively. This implies that rural households are more exposed to the death of household heads relative to the national average than urban households. Female-headed households are less exposed to shocks compared to male-headed households. Therefore, the extremely poor are worse off in terms of security and life-cycle risks. As regards age, households with heads aged 60 and above are worse off in terms of shocks such as spouse and head disabilities. Households with heads aged between 25 and 59 are more vulnerable to shocks such as spouse deceased, head deceased, robbery by force and home burglary. Therefore the aged are more prone to health risks, while the active working age groups are prone to security and life-cycle shocks/risks. UNIV ERSIT Y O F IB ADAN L IB RARY Welfare Costs of Risks and Management Options in Nigeria 19 Figure 2d: Relative incidence by educational level of household head. Figure 2b: Relative incidence by poverty status Figure 2c: Relative incidence by age of household head. Figure 2a: Relative incidence by gender. UNIV ERSIT Y O F IB ADAN L IB RARY 20 O. Olaniyan et al. AJSD Vol. 9 Num. 2 Figure 2e: Relative incidence by sector. Figure 2f: Relative incidence by region. Figure 2: Relative incidence of risks by household and location characteristics. Rural households are worse off in terms of life cycle, health, and security risks than urban households. Further, the results show that the north western part of Nigeria is the worst in terms of security risk. The south eastern part of Nigeria experienced more health and life-cycle risks relative to national incidence. Therefore, households in the south eastern part of Nigeria are more vulnerable to health and life-cycle risks than those in any other geopolitical zones in Nigeria. The results further reveal that the south western and north central geopolitical zones are less exposed to risks compared to the North western, North eastern, South eastern and South- south geopolitical zones of Nigeria. Probability of reporting shocks The diagnostic statistics of the result in Table 3 show that the models fitted for the probability of reporting shocks are statistically significant. Therefore, holding other variables constant, moderately poor households are UNIV ERSIT Y O F IB ADAN L IB RARY Welfare Costs of Risks and Management Options in Nigeria 21 more likely to report a shock of death of household head or head away than non-poor households, while non-poor households are more likely to report the shock of household head hospitalized or spouse hospitalized. The level of wealth appears to significantly influence the probability of reporting disability of household head, home burglary, physical harm, vehicle theft, and community conflicts. Rural dwellers have a higher probability than urban dwellers of experiencing shocks related to death of spouse, hospitalization of household head or spouse(life-cycle and health risks), while the urban households have a higher tendency of reporting that the household head is away from home or that there is conflict in their community(social and life-cycle risks). Residents of the North western zone of Nigeria have a higher probability of experiencing security risk than any other region in the country. In explicit terms, the North western zone has the highest probability of reporting a home burglary, physical harm, motorcycle theft, vehicle theft and conflict, thus making it the least secure zone in the country. The South eastern part of Nigeria has the highest probability of reporting conflict, household head dead, head and spouse away from home and head and spouse hospitalized than any other geopolitical zone in Nigeria. The South-south geopolitical zone has the highest probability of experiencing shock such as head disability (health risk) more than any other geopolitical zone of Nigeria. Educational level attainment significantly influences shocks reported by households. Household heads who have primary education only have the highest likelihood of reporting shocks like home burglary, physical harm and vehicle theft than household heads who have higher educational attainment. This implies that the lower the educational attainment, the higher the likelihood of facing security risk. Large households have a higher likelihood of reporting hospitalization of head of household/spouse as well as home burglary, motorcycle theft, spouse death, household head disabled and conflict in the community than smaller households (security, health, social and life-cycle risks). Households with older household heads have a higher probability of reporting death of spouse, and absence of head/spouse (life-cycle risk). It is important to note that, the number of children and spouses in a household significantly influences life cycle shocks reported in households. UNIV ERSIT Y O F IB ADAN L IB RARY 2 2 Table 3: Probability of Reporting Shocks; Reporting Odd Ratios Head_de ad Spouse_d ead Head_a way Sp_Aw ay Head_Dis bld H_Hospt lsd Sp_Hospital ised Home_Burgl ary Physicalha rm Cycleth eft vehiclethe ft Conflict Wealth 1.012 1.029* 1.035* 1.03* 0.925** 0.998 1.026 1.044** 0.962* 0.984 1.061* 1.023* -0.013 -0.015 -0.016 -0.016 -0.023 -0.015 -0.174 -0.017 -0.021 -0.026 -0.028 -0.013 Moderate poor 0.955 0.875*** 0.935* 1.158 0.972 1.693*** 1.762*** 1.077 0.971 0.953 0.955 1.065 -0.078 -0.08 -0.092 -0.114 -0.152 -0.201 -0.204 -0.114 -0.127 -0.155 -0.16 -0.094 Non poor 0.618*** 0.585* 0.635*** 0.952 1.424** 3.775*** 2.392*** 1.004 0.979 1.149 0.88 1.157* -0.043 -0.046 -0.051 -0.075 -0.157 -0.324 -0.216 -0.838 -0.098 -0.136 -0.114 -0.079 North East 1.119 1.15 0.73* 0.907 1.381* 1.466* 1.175 1.351* 0.784 1.175 1.266 0.881 -0.1 -0.107 -0.102 -0.123 -0.259 -0.225 -0.193 -0.174 -0.13 -0.214 -0.248 -0.095 North West 0.771** 0.823* 1.051 1.19 1.373* 1.959*** 1.948*** 2.327*** 2.064*** 2.868*** 2.433*** 0.73** -0.066 -0.718 -0.164 -0.131 -0.226 -0.254 -0.253 -0.242 -0.241 -0.396 -0.378 -0.07 South East 1.38*** 1.071 1.884*** 2.572*** 1.856*** 3.677*** 3.308*** 1.355* 0.982 0.472*** 0.74 1.990*** -0.116 -0.104 -0.203 -0.28 -0.295 -0.451 -0.433 -0.161 -0.141 -0.981 -0.165 -0.175 South South 0.992 1.12 1.817*** 1.9*** 2.166*** 3.028*** 2.656*** 1.064 1.084 0.613* 0.71 1.663*** -0.099 -0.122 -0.219 -0.242 -0.376 -0.403 -0.378 -0.146 -0.166 -0.135 -0.181 -0.161 South West 0.896 0.809* 1.504*** 1.563*** 1.431* 2.034*** 1.272 0.714* 0.612** 0.6** 0.96 1.025 -0.082 -0.084 -0.164 -0.178 -0.234 -0.261 -0.187 -0.093 -0.095 -0.114 -0.184 0.955 Rural Comm. 1.114 1.156* 0.828* 0.913 0.999 1.203* 1.175* 0.952 1.008 1.05 0.889 0.888* -0.075 -0.088 -0.062 -0.07 -0.112 -0.948 -0.108 -0.08 -0.105 -0.132 -0.118 -0.059 Primary Education 1.097 0.992 1.049 0.911 1.034 0.893 1.645*** 1.438*** 1.522*** 1.715*** 0.911 1.131* -0.073 -0.753 -0.848 -0.074 -0.117 -0.072 -0.152 -0.124 -0.155 -0.207 -0.128 -0.816 Secondary Educatn 0.923 0.914 0.849 0.658*** 0.626* 0.726** 1.023 1.342** 1.427** 1.416* 0.835 1.031 -0.079 -0.085 -0.092 -0.074 -0.113 -0.078 -0.123 -0.142 -0.178 -0.218 -0.141 -0.898 PostSecondary Edu 0.919 0.901 0.948 0.844 0.946 0.655** 1.036 1.2 1.227 1.513* 0.919 1.128 UNIV ERSIT Y O F IB ADAN L IB RARY Head_de ad Spouse_d ead Head_a way Sp_Aw ay Head_Dis bld H_Hospt lsd Sp_Hospital ised Home_Burgl ary Physicalha rm Cycleth eft vehiclethe ft Conflict 2 3 -0.167 -0.108 -0.127 -0.113 -0.186 -0.091 -0.157 -0.162 -0.206 -0.286 -0.189 -0.12 College Degree 1.136 1.011 0.833 0.807 0.843 0.659* 0.648* 1.187 0.737 1.006 0.473* 0.999 -0.167 -0.162 -0.16 -0.147 -0.24 -0.116 -0.143 -0.217 -0.217 -0.327 -0.169 -0.142 Household Size 1.034 1.047* 0.510*** 0.536*** 1.034 1.135*** 1.106** 1.089** 1.034 1.093* 0.983 1.065** -0.024 -0.26 -0.024 -0.024 -0.038 -0.317 -0.353 -0.311 -0.391 -0.466 -0.05 -0.255 HH.Age (years) 0.997 1.008*** 1.03*** 1.039*** 1.038*** 1.001 0.995* 1.002 0.995* 1.002 1.001 0.996* -0.002 -0.002 -0.002 0 -0.003 -0.002 -0.003 -0.002 -0.003 0 -0.004 0 Male Headed HH 0.277*** 1.448*** 0.288*** 1.071 1.636*** 0.62*** 1.394** 0.657*** 0.634*** 0.452*** 1.102 0.983 -0.022 -0.151 -0.027 -0.103 -0.212 -0.055 -0.17 -0.07 -0.08 -0.071 -0.216 -0.084 No. of Spouse 0.853** 0.754*** 1.18* 0.949 0.696*** 0.82** 1.085 1.02 1.006 1.136 1.143 1.038 -0.05 -0.046 -0.995 -0.077 -0.069 -0.062 -0.839 -0.07 -0.087 -0.105 -0.12 -0.625 Children btw 1-4yr 1.125*** 1.08* 2.91*** 2.597*** 0.779** 0.764*** 0.961 0.943 0.961 0.864* 0.998 0.920* -0.039 -0.04 -0.166 -0.145 -0.061 -0.041 -0.049 -0.042 -0.054 -0.056 -0.072 -0.364 Children btw 5-9yr 0.979*** 0.971 2.728*** 2.392*** 0.860* 0.983 0.92 0.918* 0.870* 0.97 1.043 0.914* -0.041 -0.428 -0.167 -0.145 -0.077 -0.054 -0.052 -0.045 -0.055 -0.067 -0.808 -0.04 Children_10- 17yr 1.545** 1.417*** 3.197*** 3.188*** 0.934 0.951 0.964 0.906* 0.93 0.887* 0.936 0.944 -0.049 -0.478 -0.18 -0.175 -0.057 -0.043 -0.046 -0.039 -0.052 -0.056 -0.068 -0.034 Prob > chi2 0 0 0 0 0 0 0 0 0 0 0 0 Observations 27747 27747 27747 27747 27747 27747 27747 27747 27747 27747 27747 27747 Pseudo-R2 9.5% 9.5% 15.5% 12.8% 8.5% 8.6% 4.8% 2% 2.3% 4.5% 2.6% 2.2% Standard errors in parentheses; *; ** , *** 10%, 5% and 1% level of significance respectively Wealth Index is a composite of the asset base of the responding households. UNIV ERSIT Y O F IB ADAN L IB RARY 24 O. Olaniyan et al. AJSD Vol. 9 Num. 2 Welfare costs of risk The result of the estimated models for the welfare cost of risks is depicted in Table 3. Based on the diagnostic statistics, the models have a good fit. Table 4 shows that the death and absence of the household head has a negative impact on the consumption level (welfare) of the affected rural household. Therefore, life-cycle risks are important factors reducing rural households' welfare in Nigeria. Similarly, the death of the head in an urban households has significant negative effect on the household’s consumption level. Table 4: Welfare costs of risks (Impacts of Risks) in Nigeria Dependent Variables (Per capita expenditure) Coefficient (Rural Households) Coefficient (Urban Households) Coefficients (All Households) Age of HH Head (years) -0.000 (0.000) 0.000(0.000) 0.000(0.000) Male head 0.058(0.020)** 0.035(0.013)** 0.035(0.011)** Primary Education of HH head 0.013(0.0192) 0.019(0.009)* 0.0200(0.009)* Secondary Education of HH head 0.016(0.0205) 0.009(0.012) 0.016(0.010) Post-secondary Education of HH head 0.053(0.0231)* 0.039(0.017)* 0.051(0.013)*** College degree of HH head 0.077(0.028)** 0.046(0.026)* 0.066(0.018)*** Moderately poor 0.531(0.021)*** 0.650(0.012)*** 0.618(0.010)*** Extremely poor 1.279(0.016)*** 1.356(0.009)*** 1.343(0.008)*** Household size -0.072(0.006)*** -0.603(0.004)*** -0.063(0.003)*** Number of spouses -0.069(0.015)*** -0.019(0.008) -0.032(0.007)*** Number of kids , 0-4 years 0.017(0.009)* 0.007(0.005) 0.009(0.005)* Number of kids , 5-9 years -0.104(0.011) 0.005(0.006) 0.001(0.005) Number of kids , 10-17 years 0.010(0.009) 0.002(0.005) 0.004(0.005) Dead of Household Head -0.909(0.043)* -0.055(0.024)* -0.057(0.019)** Wealth Index (WI) 0.023(0.006)*** 0.019(0.005)*** 0.017(0.003)*** Dead of Household Head* Wealth Index 0.0009(0.077) -0.008(0.012) -0.001(0.008) Dead of Household Spouse 0.046(0.051) 0.012(0.026) 0.017(0.021) Dead of Household Spouse* Wealth Index -0.008(0.016) -0.003(0.013) -0.004(0.009) Community Conflict 0.099(0.077) 0.009(0.039) 0.046(0.033) North East 0.145(0.035)*** -0.003(0.014) 0.024(0.012)* North West 0.109(0.026)*** -0.025(0.013)* 0.005(0.011) South East 0.103(0.033)** 0.003(0.013) 0.013(0.012) UNIV ERSIT Y O F IB ADAN L IB RARY Welfare Costs of Risks and Management Options in Nigeria 25 Dependent Variables (Per capita expenditure) Coefficient (Rural Households) Coefficient (Urban Households) Coefficients (All Households) South South 0.046(0.033) -0.453(0.015)** -0.036(0.013)** South West 0.049(0.023)* -0.028(0.015)* 0.008(0.011) Community Conflict*North East -0.176(0.153) -0.021(0.064) -0.056(0.056) Community Conflict*North West 0.021(0.126) 0.072(0.059) 0.029(0.050)) Community Conflict*SE -0.935(0.109) 0.040(0.049) -0.003(0.043) Community Conflict*SS -0.197(0.114)* -0.030(0.058) -0.076(0.050) Community Conflict*SW -0.664(0.091) -0.011(0.065) -0.042(0.047) Community Conflict*Wealth Index -0.016(0.022) -0.037(0.018)* -0.018(0.012) North East*Wealth Index 0.039(0.013)** 0.029(0.007)*** 0.037(0.006)*** North West*Wealth Index 0.011(0.009) 0.026(0.007)*** 0.028(0.005)*** South East*Wealth Index -0.015(0.009)* 0.005(0.006) 0.003(0.004) South South*Wealth Index -0.016(0.009)* 0.010(0.007) 0.005(0.005) South West*Wealth Index 0.007(0.007) 0.004(0.007) 0.013(0.004)** Community Conflict*North East*WI 0.016(0.055) 0.024(0.029) 0.010(0.023) Community Conflict*North West*WI -0.002(0.044) 0.066(0.029)* 0.035(0.023) Community Conflict*South East*WI -0.007(0.032) 0.031(0.022) 0.005(0.016) Community Conflict*South South*WI 0.036(0.031) 0.027(0.027) 0.022(0.018) Community Conflict* South West *WI 0.002(0.027) 0.065(0.028)* 0.019(0.017) Household head Away -0.095(0.045)* -0.009(0.026) -0.010(0.021) Household head away*Wealth Index 0.034(0.014)* -0.022(0.0130 0.005(0.009) Household Spouse away 0.069(0.048) 0.028(0.026) 0.026(0.021) Household Spouse away*Wealth Index -0.035(0.014)* -0.001(0.013) -0.019(0.008) Household Head Hopitalised 0.125(0.035)*** 0.119(0.019)*** 0.122(0.016)*** Head Hospitalised* Wealth Index -0.009(0.011) -0.019(0.008)* -0.016(0.006)* Personal Theft -0.032(0.046) 0.021(0.021) -0.001(0.019) Personal Theft*Wealth Index -0.006(0.013) 0.014(0.009) -0.000(0.007) Home Burglary 0.019(0.047) 0.005(0.023) 0.018(0.019) Home Burglary*Wealth Index 0.023(0.013)* -0.000(0.010) 0.012(0.007)* Robbery by force -0.033(0.056) -0.011(0.025) -0.019(0.022) Robbery by force*Wealth Index -0.008(0.014) -0.008(0.011) -0.012(0.008) Household Spouse Hospitalised -0.013(0.045) 0.003(0.021) -0.002(0.018) Household Spouse Hospitalised*Wealth Index 0.001(0.013) 0.003(0.009) 0.001(0.007) Other violence -0.027(0.048) -0.026(0.023) -0.013(0.019) UNIV ERSIT Y O F IB ADAN L IB RARY 26 O. Olaniyan et al. AJSD Vol. 9 Num. 2 Dependent Variables (Per capita expenditure) Coefficient (Rural Households) Coefficient (Urban Households) Coefficients (All Households) Other Violence*Wealth Index 0.022(0.014) -0.006(0.011) 0.009(0.008) Observations 19793 7954 27747 R-Square 0.68 0.63 0.68 Prob > F 0 0 0 Source: Computation from HNLSS 2009 data. Note: Values quoted in parentheses are standard errors of the respective coefficients. Community conflicts among rural households in the South-South zone had a negative effect on consumption levels of affected households. Interactions of shocks with the wealth index show that the absence of the household head and home burglary of wealthier households that reside in the rural North Eastern region had a positive impact on consumption. The implication of this result is that though the absence of the household head and burglary are supposed to reduce the consumption of affected rural households in the North eastern region of Nigeria, the wealthy status of affected households however more than compensated for the negative effects of the shocks. In the same vein, community conflicts among wealthy households in the North-West and South-West zones of Nigeria had a positive impact on consumption. This implies that the wealthy status of households in these regions more than compensated for the effect of community crises. Meanwhile, the absence of the household spouse from wealthy households in the rural South-East and South-South zones impacted consumption negatively. Likewise, in the urban sector, community conflicts, as well as illness and hospitalization of wealthy households, reduced consumption in the affected households. In addition, a number of socioeconomic characteristics significantly contribute to the welfare of both rural and urban households as observed from the models. Households’ responses to risk exposure Households’ responses to risk exposure were categorised into coping, mitigating and reduction mechanisms. The HNLSS (2009) data sets contain a few of these household risk management strategies which were profiled using wealth quartile, sex of household head, sector, and region. As observed from the data, most households use a combination of strategies. As UNIV ERSIT Y O F IB ADAN L IB RARY Welfare Costs of Risks and Management Options in Nigeria 27 presented in Table 5, four risk coping strategies (Self-help, Informal insurance, Public work, and Job loss/School absenteeism) were observed among the sampled households. Self-help is primarily from the sale of water owned by the household or fetched to sell to other households in order to cope with risk experienced. Informal insurance comes from membership in some form of social network such as cooperatives, religious groups, ethnic groups, and other such informal organizations. Table 5: Main coping strategies Self Help Informal Insurance Public Works Stop School/job Wealth Level Poorest (1) 0.296 38.449 1.094 0.355 2 0.259 85.06 1.057 0.689 3 0.448 87.024 1.193 0.647 Richest(4) 0.125 89.808 2.033 0.605 Sex Male 0.567 85.886 1.369 0.542 Female 0.552 86.331 1.209 0.744 Sector Urban 0.96 86.12 1.207 4.652 Rural 0.405 85.889 1.399 6.164 Region North Central 0.391 88.221 1.645 0.416 North East 0.429 74.199 0.99 0.408 North West 0.381 81.476 1.409 0.348 South East 0.756 96.036 2.064 0.985 South South 0.857 87.871 1.109 1.181 South West 0.724 87.434 0.779 0.46 A number of households were involved in public works set up by government which pays some form of stipend in order to cope with the shock. Likewise, job loss/school absenteeism is a form of disinvestment in human capital. Some households cope with the shock of sick or hospitalized household members by leaving school or jobs. Of course, this is a form of UNIV ERSIT Y O F IB ADAN L IB RARY 28 O. Olaniyan et al. AJSD Vol. 9 Num. 2 reduced consumption since there is a loss of revenue from the attendant job loss. Also, there is a loss in human capital from household members that have to leave school in order to give care. In all, the most adopted coping strategy is that of informal insurance which they derive from different social networks available to the households within their community. Risk mitigation strategies are ex post measures taken to decrease the impact of risk in the event that it should occur. As shown in Table 6, the study identified the two major risk mitigation strategies used by households. These include health consultation and rehabilitation of physically challenged household members. Health consultation refers to the choice of different health care providers by households when faced with health risks. These include public health care services, professional healthcare services, community healthcare practices and traditional healthcare. Table 6: Risk mitigation strategies Public Health Professional Health Care Community Health care Traditional Health care Rehabilitation Wealth Level Poorest (1) 23.79 18.548 44.355 13.306 1.072 2 34.815 22.963 27.778 14.444 1.229 3 36.842 28.947 26.645 7.566 0.371 Richest ( 4) 35.689 34.275 22.261 7.774 0.797 Sex Male 34.974 25.518 28.368 11.14 0.8 Female 28.829 28.829 33.033 9.309 0.897 Sector Urban 37.168 29.204 23.304 10.324 0.575 Rural 31.332 25.326 32.637 10.705 0.924 Region North Central 46.316 25.264 15.789 12.632 0 North East 42.857 15.873 33.333 7.937 0 North West 46.199 11.111 30.994 11.696 0.568 South East 20.867 36.585 33.604 8.943 1.101 South South 29.012 21.605 37.654 11.728 1.515 South West 37.551 28.571 22.449 11.429 0.865 UNIV ERSIT Y O F IB ADAN L IB RARY Welfare Costs of Risks and Management Options in Nigeria 29 Risk reduction strategies are adopted to prevent or reduce the occurrence of a shock. As shown in Tables 7 and 8, health and security strategies are the two major risk reduction strategies utilized in Nigeria. Health risk reduction strategies involve protection against malaria using insecticide-treated nets, insecticides, window protection, prevention against HIV/AIDS as well as treatment of water. The security risk reduction measures identified were the use of police protection and neighbourhood watch in the community. Table 7: Health Risk Reduction Strategies Protect Windows Use Insecticides Treated Nets Use Insecticides HIV/AIDS prevention Treatment of Water Wealth Level Poorest (1) 4.903 27.029 75.146 76.084 12.226 2 9.407 25.888 76.519 81.406 13.845 3 18.753 28.036 76.879 83.791 15.518 Richest (4) 48.119 39.513 80.683 89.246 20.46 Sex Male 20.337 31.29 78.703 83.898 15.886 Female 20.014 23.756 69.665 75.616 13.565 Sector Urban 41.363 34.247 79.608 85.756 18.987 Rural 11.817 28.465 76.375 81.352 14.119 Region North Central 25.014 23.502 70.051 76.006 18.526 North East 11.473 39.953 80.256 86.488 8.885 North West 7.837 33.946 85.513 85.212 16.664 South East 14.239 23.763 72.226 88.084 10.958 South South 24.329 33.595 74.598 72.272 9.058 South West 37.699 29.15 78.283 84.531 21.919 Source: Computation from HNLSS 2009 Data.UNIV ERSIT Y O F IB ADAN L IB RARY 30 O. Olaniyan et al. AJSD Vol. 9 Num. 2 Table 8: Security Risk Reduction Strategies Police in the community Neighbourhood watch in community Wealth Level Poorest (1) 2 3 Richest (4) 33.381 39.419 48.570 56.235 17.337 19.797 20.477 19.634 Sex Male Female 43.216 50.767 19.305 19.293 Sector Urban Rural 57.531 39.105 19.261 19.320 Region North Central North East North West South East South South South West 43.108 33.751 39.677 60.732 37.388 48.442 21.535 12.667 20.440 15.764 20.072 22.265 Source: Computation from HNLSS 2009 data. Conclusion This study found that the prevalent idiosyncratic shocks in Nigeria are the death of the household head, conflicts in the community, death of a spouse, the household head being away, spouse away from home, household head hospitalized and personal theft. The shock with the highest incidence of occurrence country-wide was the death of the household head. Risks affecting households in the country can be categorized as security risk, social risks, life-cycle risks and health risks. The extreme poor were the most exposed to the death of the head of the household relative to the national average followed by the moderately poor and non-poor. Rural households are more exposed to the death of household heads relative to the national average than urban households. On welfare impact of risks, the study deduced that death and absence of the household head have a negative impact on the consumption level (welfare) of the affected rural households. In the same vein, the absence of the household head and burglary was expected to reduce consumption of affected rural households in the North UNIV ERSIT Y O F IB ADAN L IB RARY Welfare Costs of Risks and Management Options in Nigeria 31 eastern region of Nigeria, the wealthy status of affected households however more than compensated for the negative effects of the shocks. Further, the study observed that households adopt different risk management strategies which could either be coping, mitigation, reduction or combination of strategies. In general, most of the strategies adopted by households are health-related. Arising from the findings of the study, the following recommendations are pertinent. First, national development policy and programmes articulated towards investment in well-managed and needs- responsive infrastructures like good health facilities, pipe-borne water, and road network are needed to reduce the prevalence of risks as most risks are health and life-cycle related. Secondly, because of the high poverty rate in the country, there is a need to subsidize and sensitize people to the need for life micro-assurance as this will help reduce the impact of the demise of the breadwinner on the household’s welfare. References Adepoju, A. O, Yusuf, S.A., Omonona, B. T. and Okumadewa, F.Y., (2011): Vulnerability profile of rural households in Southwest Nigeria. Journal of Agricultural Science, 3 (1): 128-139, March 2011. Adubi, A., Ogwumike, F. and Agba, A. (2002). Major sources and levels of risks in Nigeria. Consultant Report for the World Bank/NPC Social Risk Assessment Exercise. Afonja, S. (2002) Social risk assessment for Nigeria: Risk prevention, mitigation and coping strategies. Consultant Report for the World Bank/NPC Social Risk Assessment Exercise. Alayande, C. and Alayande, O. (2004). A quantitative and qualitative assessment of vulnerability to poverty in Nigeria. Submitted for the CSAE Conference on Poverty Reduction, Growth, and Human Development in Africa, March 2004. Alayande, B. A. (2003). Determinants of vulnerability to poverty in Nigeria: A Probit analysis. Consultant Report for the World Bank/NPC Social Risk Assessment Exercise. Alwang, J., Siegel, P.B. and Jørgensen, S.L. (2001). Vulnerability: A view from different disciplines. Social Protection Discussion Paper, No. 0115. The World Bank: Washington, D.C. (see: www.worldbank.org/SP). UNIV ERSIT Y O F IB ADAN L IB RARY 32 O. Olaniyan et al. AJSD Vol. 9 Num. 2 Aseffa, Y. (2010): Microinsurance: A tool for growth and development of the African Insurance Industry. A paper presented at Nigerian Insurance Association, International Insurance Conference, held at ILO Regional Office for Africa Lagos, 5th May. Baeza, C. (2000 ). Community risk sharing arrangements in Latin America. Paper presented at World Bank Workshop on “Risks, Poverty and Insurance: Innovations for the Informal Economy” Workshop Proceedings. Barro, R. J. (2006). On the welfare cost of consumption uncertainty. NBER Working Paper 12763. Barry, B. and Wodon, Q. (2007). Conflict, growth and poverty in Guinea Bissau. World Bank Working Paper 79, pp111-122. Blundell, R. (1996). Tax policy reforms: Why we need microeconomics. Fiscal Studies, 16 (3): 106-25. Brown, W. and Churchill, C. (2000). Concept Paper on Documenting the Development of Risk Managing Financial Services. Calmeadow. Washington, D.C. see www.mip.org. Cafiero, C. and Vakis, R. (2006). Risk and vulnerability considerations in poverty analysis: Recent advances and future directions. SP Discussion Papers, 0610; World Bank, Washington DC. Chetty, R. and Looney, A., (2004). Consumption smoothing and the welfare consequences of social insurance in developing countries. Journal of Public Economics, 90 pp. 2351-2356. Chiwuala, L. and Waibel, H.(2009). The role of shocks and risks for the livelihood of small scale fishing communities of Hadejia-Nguru Wetlands in Nigeria. Paper for Presentation at the Conference of German Economic Association, Frankfurt, Germany, June 2009. Christiaensen, L., Hoffmann, V. and Sarris, A. (2007). Gauging the welfare effects of shocks in rural Tanzania, Policy Research Working Paper 4406, The World Bank, Washington DC. Clemens, M. and Moss, T.(2005). Costs and causes of Zimbabwe’s crisis. Centre for Global Development, July 2005, posted on www. Cgdev.org. De Ferranti, D., Perry, G., Gill, I. and Serven, L. (2000). Securing our future in a global economy. World Bank Latin American and Caribbean Studies, Washington, D.C. UNIV ERSIT Y O F IB ADAN L IB RARY Welfare Costs of Risks and Management Options in Nigeria 33 Del Ninno, C. and Marini, A., (2005). Household’s vulnerability to shocks in Zambia. World Bank Social Protection Discussion Paper, 0536, September 2005. Dercon, S. and Hoddinott, J., (2004). Health, shocks and poverty persistence. In Insurance against Poverty, (ed.) S. Dercon. Oxford University Press. Dercon, S. (2002). Income risks, coping strategies and safety nets. World Bank Research Observer, 17 (2): 141-66. Dercon, S. (2011). Social protection, efficiency and growth. CSAE Working Paper WPS/2011-17. Department for International Development (DFID).(2005). Social transfer and chronic poverty: Emerging evidence and the challenge ahead. DFID Practice Paper. London: DFID. Ellis, F. (2008). We are all poor here, economic difference, social Divisiveness and targeting cash transfers in Sub-Saharan Africa, Paper Presented at Conference on Social Protection for the Poorest in Africa: Learning from Experience, Entebbe, Uganda. Fafchamps, M. and Lund, S. (2003). Risk-sharing networks in rural Philippines. Journal of Development Economics, 71-2: 261-287. Fielding, D. (2008). Inflation volatility and economic development: Evidence from Nigeria. University of Otago, Economic Discussion Paper, No 0807, September 2008. Heathcote, J., Storesletten K. and Violante, G. L. (2011). From wages to welfare: Gains and losses from rising inequality. Prepared for a talk at the Tenth World Congress of the Economic Society, Shanghai, China. Hauck, K. (1997). The social security program of the Self-Employed Women’s Association Ahmedabad, India. World Health Organization: Geneva Draft. Heitzman, K. Canagarajah, R. S., and Siegel, P. B. (2002). Guideline for assessing the sources of risk and vulnerability. Social Protection Discussion Paper, 0218, June, 2002; Social protection Unit, Human Development Network, The World Bank. Hoddinott, J. and Quisumbing, A. (2003). Methods for microeconometric risk and vulnerability assessments. Social Protection Discussion Paper, 0323, The World Bank, Washington, D.C. UNIV ERSIT Y O F IB ADAN L IB RARY 34 O. Olaniyan et al. AJSD Vol. 9 Num. 2 Hodges, A. (2008). Mission to Nigeria. Unpublished UNICEF Trip Report. Holzmann, R. and Jørgensen, S. (1999). Social Protection as Social Risk Management: Conceptual Underpinnings for the Social Protection Sector Strategy Paper. Journal of International Development 11: 1005- 1027. Holzmann, R. and Jørgensen, S. (2000). Social risk management: A new conceptual framework for social protection and beyond. Social Protection Discussion Paper, No. 0006. World Bank: Washington, D.C. see www.worldbank.org/sp. Holzmann, R. (2001). Risk and vulnerability: The forward looking role of social protection in a globalizing world. Social Protection Discussion Paper No. 0109. World Bank: Washington DC. March. www.worldbank.org/sp. Holzmann, R. and Jørgensen, S.L. (2000). Social risk management: A new conceptual framework for social protection and beyond. Social Protection Discussion Paper No. 0006. The World Bank: Washington, D.C. www.worldbank.org/SP). Ichoku, H. (2005). Income redistributive effects of the healthcare financing system in Nigeria. Retrieved December 14, 2007, from http://www.meetings&wokshops/DAKAR/restricted/PDFFile/ Hyacinthementa. Jacoby, H. G. (1994). Borrowing constraints and progress through school: Evidence from Peru. The Review of Economics and Statistics, 76(1): 151- 160. Jalan, J. and Ravallion, M. (2000b). Behavioral responses to risk in rural China. Journal of Development Economics, 66: 23-49. Juetting, J. and Tine, J. (2000). Microinsurance schemes and health care provision in developing countries: An empirical analysis of the impact of mutual health insurance schemes in rural Senegal, ZEF Project Report, No. 5, Center for Development Research, Bonn, Germany. Juetting, J. (1999). Strengthening social security systems in rural areas of developing countries. ZEF Discussion Papers on Development Policy, No. 9. Center for Development Research, Bonn, Germany. www.zef.de. UNIV ERSIT Y O F IB ADAN L IB RARY Welfare Costs of Risks and Management Options in Nigeria 35 Juetting, J. (2000). Determinants of viable insurance schemes for the rural poor in developing countries: Lessons from SEWA. Center for Development Research: Bonn, Germany. www.zef.de. Kazianga, H. and Udry, C. (2006). Consumption smoothing? Livestock, insurance and drought in rural Burkina Faso. Journal of Development Economics, 79 (2): 413-446. Ligon, E. and Schechter, L. (2004). Evaluating different approaches to estimating vulnerability. Social Protection Discussion Paper, 0210, The World Bank, Washington D.C. Loayza, N. V., Ranciere, R., Serven, L. and Ventura, J. (2007). Macroeconomic volatility and welfare in developing countries: An introduction. The World Bank Economic Review, 21 (3): 343-357. Lucas, R. E. (2003). Macroeconomic priorities. Department of Agricultural Economics, University of Chicago. January 10, 2003. McCord, M.J. (2000). AIG and NHHP partnering with FINCA Uganda: An example of the Partner/Agent Model. A paper from the Proceedings: World Bank Workshop on Risks, Poverty and Insurance: Innovations for the Informal Economy. Moghalu, C. (2004). Social protection profile of Nigeria. An ILO Working Paper. Mohammed, S., Sambo, M. S. and Dong, H. (2011). Understanding client satisfaction with a health insurance scheme in Nigeria: Factors and enrollees’ experiences. Health Research Policy and Systems, 9 (20). Moran, M. (2006). Dry and destitute, Niger languishes. Analysis Brief posted on website of the Council on Foreign Relations (www.cfr.org), copyright 2011. Okunmadewa, F. and Elder, J. (2003). Nigeria: risk and vulnerability Assessment. Human Development 3 (12). Olaniyan, O., Yusuf, S.A. and Oni, O. (2004). Social protection strategy for Nigeria: Policy note. Nigeria Social Protection Committee, National Planning Commission, Abuja. August, 2004. Olaniyan, O., Oyeranti, O., Bankole, A. and Oni, O. (2003). Evaluation of Risk management Agencies in Nigeria. Consultant Report for the World Bank/NPC Social Risk Assessment Exercise. UNIV ERSIT Y O F IB ADAN L IB RARY 36 O. Olaniyan et al. AJSD Vol. 9 Num. 2 Olomola P. A., (2006). The relative importance of domestic and external shocks to output and prices in Nigeria. Munich Personal RePEC Archive (MPRA) Paper 1025: Nigerian Economy: Essay on Economic Development. Compiled by Mete Feridun. Omoruan, A. I., Bamidele, A. P. and Phillips, O.F. (2009). Social health insurance and sustainable healthcare reform in Nigeria. Ethno-Med, 3(2): 105-110. Oni, O. A. and Yusuf, S. A. (2008). Determinants of expected poverty among rural households in Nigeria. African Economic Research Consortium (AERC) Research Paper 183. September, 2008. Oni, O. A. (2008). Farming households’ vulnerability to risk in the northern region of Oyo State, Nigeria. Journal of Social Science, 17 (3): 201-210. Otrok, C. (1999). On measuring the welfare cost of business cycles. First Version, 1998; Updated Version, 1999. Oyekale, A. S. and Oyekale, T. O. (2010). An assessment of income shocks and expected poverty dynamics in Nigeria. The Icfai University Journal of Agricultural Economics, 7 (3): 18-50. PenCom (National Pension Commission). (2010). Annual Report and Statement of Accounts. National Pension Commission, Wuse Abuja, Nigeria. Pryer, I. (1989). When breadwinners fall ill: Preliminary findings from a case study in Bangladesh. IDS Bulletin, 20 (2): 49-57. Rosenzweig, M. and Wolpin, K. (1993). Credit market constraints, consumption smoothing, and the accumulation of durable production assets in low-income countries: Investments in Bullocks in India. Journal of Political Economy, 1001(2): 223-244. Rosenzweig, M. and Binswanger, H. (1993). Wealth, weather risk and the composition and profitability of agricultural investments. Economic Journal, 103: 56-78. Rutherford, S. (2000). The Poor and Their Money. Department for International Development (DFID). Oxford University Press. Sango, D., Hoffman, V. and Christiaensen, L. (2007). Risks and coping strategies in L. Christiaensen and A. Sarris (eds). Rural Household Vulnerability and Insurance Against Commodity Risks. Evidence from the UNIV ERSIT Y O F IB ADAN L IB RARY Welfare Costs of Risks and Management Options in Nigeria 37 United Republic of Tanzania, Food and Agricultural Organization, Rome, Italy. Sanubi , F. A. (2011). Social protection as a residual safety net in democratic governance in Nigeria: A critical analysis of some current policy initiatives. A paper presented at the International Conference tagged, “Social Protection for Social Justice”. Institute of Development Studies, UK. April, 13-15. Siegel, P. and Alwang, J. (1999). An asset-based approach to social risk management: A conceptual framework. Social Protection Discussion Paper , No. 9926. World Bank, Washington, D.C. www.worldbank.org/sp. Suranovic, S. M. (2004). Welfare effects of a tariff. International Trade Theory and Policy; Chapter 90, pg 8. Last updated online, 20/8/2004. Tatlock, C. W. (2006). Water stress in sub-Saharan Africa. Posted on Website of Council on Foreign Relations. www.cfr.org, copyright 2011. Tesluic, E. D. and Lindert, K. (2002). Vulnerability: A quantitative and qualitative assessment. Guatemala Poverty Assessment Program. The World Bank. (2011). Measuring vulnerability. Poverty Reduction and Equity. The World Bank Group. Vennam, U., Crivello, G. and Kangara, H. (2010). Children’s perspectives on risk and vulnerability in contexts of poverty and change: Findings from Andhra Pradesh, India. Young Lives Working Paper, 69. Wood, B., Nelson, C. and Nogueria, L. (2010). Measuring the Welfare Impact of Food Price Increases on Mexican Households. University of Illinois at Urbana-Champaign. World Bank. (2000). Social Protection Sector Strategy: From Safety Net to Trampoline. Human Development Network, Social Protection Team, Washington, D.C. World Bank. (2000). Risks, poverty and insurance: Innovations for the informal economy. Workshop Proceedings, September 6, 2000. World Bank. (2000). World Development Report 2000/2001 – Attacking Poverty. Oxford University Press: New York. World Bank. (2001). World Development Repor t 2000/2001: Attacking Poverty. Oxford University Press, Washington, DC. UNIV ERSIT Y O F IB ADAN L IB RARY 38 O. Olaniyan et al. AJSD Vol. 9 Num. 2 World Bank. (2001). Social Protection Sector Strategy: From Safety Net to Springboard. The Human Development Network. Washington, D.C. Zeller, M., Schrieder, G., von Braun, J. and Heidhues, F. (1997). Rural Finance for Food Security for the Poor: Implications for Research and Poverty. Food Policy Review No. 4. IFPRI: Washington, D.C. UNIV ERSIT Y O F IB ADAN L IB RARY