THE DETERMINANTS OF URBAN POVERTY IN THE KANO METROPOLIS

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1 THE DETERMINANTS OF URBAN POVERTY IN THE KANO METROPOLIS Abstract Introduction Murtala Mohammed Ahmed Department of Economics Sa adatu Rimi College of Education, Kumbotso, Kano This paper investigates the determinants of urban poverty in the Kano Metropolis, using primary data from an administered questionnaire within the study area. A total of 360 copies were administered on the basis of the simple random sampling technique based on which forty-five (45) questionnaires were distributed to each of the eight (8) local government areas within the Kano Metropolis. The logit model was applied whereby per capita expenditure was used as a dependent variable, while education, age, sex, occupation, and household size were regarded as independent variables. The results revealed that all the five variables were statistically significant in determining poverty levels in the study area. Therefore, the study recommends that investment in education and the creation of more job opportunities would go a long way in enhancing higher standards of living, reducing poverty and raising the per capita income of the people in the Kano Metropolis. This paper investigates the determinants of urban poverty in the Kano Metropolis. Urban poverty is a multidimensional phenomenon. The urban poor live with many deprivations. Their daily challenges, according to Baharoglu and Kessides (2004), include limited access to employment opportunities and income, inadequate and insecure housing and services, violent and unhealthy environments, little or no social protection mechanisms and limited access to adequate health and education opportunities. They went further to state that urban poverty is not just a collection of characteristics but is also a dynamic condition of vulnerability or susceptibility to risks. The poor in the cities, according Baker and Schuler (2004), suffer from various deprivations, such as lack of access to employment, adequate housing and infrastructure and social protection and lack of access to health, education and personal security. Therefore, urban poverty is often characterized by cumulative deprivation; one dimension of poverty is often the cause of or contributor to another dimension. Poverty is found in the rural as well as urban areas of Nigeria. Up to the 1980s, poverty was largely associated with the rural areas in developing countries, but the situation has changed with the dramatic increase in the number and proportion of the population living in urban areas, and a corresponding increase in the level of urban poverty (Maconachie and Binns 2006). 75

2 The incidence of poverty in Nigeria has been on the increase. It rose from 28.1% in 1980 to 46.3% in 1985 and 65.6% in 1996 (National Bureau of Statistics (NBS), 2006). The share of Nigeria s population living below the poverty line has fallen from 70% in 1999 to 54.4% in 2004 but increased to 69% (or 112, 518, 505 Nigerians) in 2010, while 51.6% of Nigerians were living below US$1 per day in 2004, but this increased to 61.2% in Additionally, 75.5% of Nigerians considered themselves to be poor in 2004, and in 2010 the number went up to 93.9% (NBS, 2012). However, NBS (2012) also reports that the poverty level in Nigeria is the lowest in the South-West geo-political zone (59.1%) and that the North-West and North-East political zones recorded the highest poverty rates in the country with 77.7% and 76.3% respectively. Among the states from the North-West and North-East zones, Sokoto had the highest poverty rate with 86.4% in 2010 against 95% recorded by Jigawa in Additionally, Kano, Kaduna, and Sokoto, all states in the North West, are home to 1/3 of Nigeria s poor (Adegbola, 2008). Kano State being one of the most highly urbanized states in Nigeria is confronted with abject poverty, which resulted from low level of employment, illiteracy, food insecurity, inadequate infrastructural facilities, etc. (Utai, 2007). It has been reported by the Kano State Ministry of Social Welfare (2007) that 3.2million out of 9 million people (postulated by census 2006) in Kano State are living below $1dollar per day, which shows the extent to which the poverty level is rising in the state. It has also been reported by NBS (2012) that 72.3% of the population in Kano State was affected by poverty, while 66% lived on less than US$1 per day. Therefore, the focus of this paper is to find out the determinants of urban poverty in Kano Metropolis with the following specific objectives: to identify the effects of education on poverty and to examine the impact of household size on poverty in the Kano Metropolis. The scope of paper is restricted to all the eight (8) local government areas of the Kano Metropolis, viz: Kano Municipal, Dala, Gwale, Fagge, Nassarawa, Tarauni, Kumbotso and Ungogo. Thus, the paper is organized into five sections. Section one is the introduction and section two reviews the related literature and theoretical issues. The methodology of the paper is presented in section three, while section four contains data presentation and analysis of results. The last section contains a conclusion and recommendations. The Dimensions and Determinants of Urban Poverty As cited in Shewaye (2002), the World Bank sees urban poverty as a multi-dimensional phenomena characterized by cumulative deprivation where one form of deprivation leads to another. The various dimensions of urban poverty include: income, health, education, tenure insecurity, personal insecurity and lack of empowerment, among others. The multi-faceted nature of urban poverty is also noted in Tizeta (2001). Accordingly, the various features of poverty that characterize most of the urban poor are: unemployment, lack of wage employment, failure to send children to school, lack of access to health facilities, sanitation, potable water, electric services and good housing. Above all, lack of 76

3 employment is one of the greatest economic challenges that incapacitate poor people to meet their basic needs. Onibokun and Kumuyi (1996) in their study on urban poverty argue that poverty is linked to a shortage of vital resources and the endurance of a harsh and an inhospitable environment, including the breakdown of economics, demographic, ecological, cultural and social systems. Kedir and Mckay (2005) view urban poverty to exist as a result of low levels of physical and human capital, unequal distribution of productive assets, inadequate access to social services, high fertility, especially amongst the urban poor, and urban development strategies, which are biased against labor absorption. Poverty can also be as a result of the problems of urbanization. According to Ward (1999), the factors that cause poverty in most urban cities can be linked to the inner urban decay caused by poor public facilities that have been allowed to get run down due to lack of maintenance and investment. Insufficient resources and poor maintenance skills are often accountable for this. In addition, the available amenities are frequently inadequate to meet the increasing demands placed on them. The quality of social services, particularly education and health, falls, thus placing poor families at a disadvantage. According to Mitlin (2005), the following factors emerged as being important in determining urban poverty; a) The Cash economy: The urban poor enter the labour market because of the need for cash. Despite the widespread significance of urban agriculture, money is needed for basic services, land, food and clothes. Lack of cash forces families to borrow with consequent problems of debt management. The poor and those living in the informal sector face difficulties in acquiring and maintaining assets. Rising prices create difficulties in creating further wealth for households seeking to avoid poverty. b) The Labour market: Within the labour market, the poorest urban household faces low pay for unskilled and skilled labour. There is no provision for further training and no credit opportunities for micro-entrepreneurs due to insufficient collaterals. Besides, there is much job insecurity and long hours of poor working conditions. In the poorest household, children need to work (forced labour), thereby reducing their educational opportunities and their chances of moving out of poverty (the vicious circle). The poor salary collected makes saving and investment difficult, especially in securing assets that may offer new opportunities and greater security. Poor working conditions increase the likelihood of poor health. In many studies (such as in Simbowale 2003, Anyawu, 2011 e.t.c.), higher educational status has been shown to be correlated with a lower incidence of poverty. c) Lack of basic services (associated with the residential location): The poor often spend a considerable proportion of their income on basic needs such as water, transport and health services. This reduces the amount of income available for food and investments in human resources or enterprise development (Mitlin, 2005). d) Household composition: The incidence of poverty often increases with household size and with an increasing ratio of dependents to non-dependents. 77

4 The vulnerabilities associated with old age and childhood are usually correlated with a higher incidence of poverty. The Measurement of Urban Poverty Falkingham and Namazie (2002) state that measuring urban poverty can be carried out using a number of approaches summarized below. Regardless of the methodology chosen, the data should ideally be comparable across cities and allow for disaggregation at the intra-city level. This will capture vast differences between the poor in small towns and mega cities or between urban slum areas within a given city. i. Income or Consumption Measures: Both are based on data that assess whether an individual or household can afford a basic basket of goods (typically food, housing, water, clothing, transport, etc.). Consumption is generally considered to be a better measure than income because incomes tend to fluctuate over time. There are problems of under-reporting (particularly income derived from the private and informal sectors). Money metric measures can be adjusted to account for the higher cost of living in urban areas when measuring poverty. ii. Unsatisfied Basic Needs Index: This approach defines a minimum threshold for several dimensions of poverty classifying those households who do not have access to these basic needs. They include characteristics such as literacy, school attendance, piped water, sewage, adequate housing, overcrowding and some kind of caloric and protein requirement. If a household is deficient in some of these categories, they are classified as having unsatisfied basic needs. iii. Asset Indicators: This has been used increasingly with the Demographic and Health Surveys (DHS), a standardized survey now administered in approximately 50 countries. A range of variables on the ownership of household assets are used to construct an indicator of household s socio-economic status. These assets include: a car, refrigerator, television, dwelling characteristics (type of roof, flooring, toilet) and access to basic services including clean water and electricity. iv. Vulnerability: This approach defines vulnerability as a dynamic concept referring to the risk that a household or individual will experience an episode of income or health poverty over time, and the probability of being exposed to a number of other risks (violence, crime, natural disasters, being pulled out of school). Vulnerability is measured by indicators that make it possible to assess a household s risk exposure over time. These indicators include measures of: physical assets, human capital, income diversification, links to networks, participation in the formal safety net, and access to credit markets. v. Participatory methods: This typically relies on qualitative approaches to capture aspects of urban poverty that may not be identified through pre-coded surveys. Through tools such as focus group discussions, case studies, and individual openended interviews, it is possible to determine the perceptions of poverty, identify priority needs and concerns, and gain insight on the effectiveness of programs and policies from the perspective of the beneficiaries. 78

5 A Review of Studies on Urban Poverty Olaniya (2000) investigated the role of households endowments in determining poverty in Nigeria. Based on panel data collected by National Consumer Survey (NSC) in 1985, 1992 and 1996, he adopted logit and probit models where the dependent variable was household per capita expenditure while the independent variables were human capital (education level, age, age 2 and household size), physical capital, share of income (wage, nonwage and other income), sex (female), region (NW, NE, SE and SW), occupation (farmer, formal and informal). His study reveals that both human and physical capital endowments determine poverty levels in Nigeria. This implies that education and physical capital have been powerful tools for escaping poverty by households in the study area (Nigeria). The results also show that being a farmer has a high probability of falling into poverty. Ghazouani and Goaied (2001) studied the determinants of urban and rural poverty in Tunisia, using survey on budget-consumption of the household carried out in They adopted the logit and probit model where household consumption expenditure was used as a dependent variable and the sex of the head of the family, the education of the head, family composition, age and region as independent variables. The finding showed that the main factors that determined poverty levels in the study area were the head of family s level of education, dependency ratio, the socio-professional category of the head, family residence and regional dummies. Serumaga-zake and Naude (2002) examined the determinants of urban and rural household poverty in the North West province of South Africa, using data gathered from a survey of 593 black households across the province. They employed the logit and probit model where the dependent variable was per capita consumption expenditure while the independent variables were human capital, region of residence and household characteristics. The results show that the major determinants of household poverty in both urban and rural areas are education and household size. This implies that higher levels of education are associated with lower levels of poverty, while larger households tend to face a higher probability of being poor. Simbowale (2003) examined the poverty level in the Agege area of Lagos, a metropolitan city in Nigeria, with the aid of a multiple regression model. The per capita expenditure on basic needs was used as a dependent variable, while the age of the head of the household, the sex of the head, educational status, per capita income and the size of the household were used as independent variables. The study reveals that education and income were the major determinants of poverty in the study area. Jamal (2005) investigated the determinants of poverty in the case of urban and rural areas of Pakistan, using the Pakistan Integrated Household Survey (PIHS) of 2001/2002.He employed a multiple regression model where total household expenditure was used as a dependent variable while demography (family size, proportion of children less than 5 years, proportion of members greater than 65 years), education, household assets, housing quality and services, transfers (households receiving overseas remittances, 79

6 households receiving domestic remittances), and location variables as independent variables. The results reveal that in the urban areas the main factors that determine poverty are household education, dependency ratio and oversea transfers. Mok et al (2006) investigated the determinants of urban household poverty in Malaysia, using the 2004/2005 Household Expenditure Survey (HES). They employed the Logit and probit model. Household expenditure was used as a dependent variable while socioeconomic and demographic status indicators and human capital and the dwelling endowment of the household were used as independent variables. The results show that human capital significantly reduced the chance of being poor while migrant workers are more prone to poverty. Household size, race and region are also important determinants of poverty outcome in the study area. Sarwar (2010) examined the determinants of urban poverty in Sargodha, a medium-size city of Pakistan based on the survey conducted in 2008 in which 330 households were studied. The binomial logit regression model was used in which the dependent variable was dichotomous: 0 when a household was above and 1 when below the poverty line. Predictor variables were education, family size, nature of occupation and public amenities. The results reveal that employment in the public sector, investment in human capital and access to public amenities reduce poverty, while employment in the informal sector, greater household size and female dominated households increase poverty in the study area. Akerele and Adewuyi (2011) examined the socio-economic determinants of welfare among the urban households of Ekiti State, Nigeria. Using a multistage sampling approach, a total of 80 households were studied between January and March Descriptive statistics, the poverty index and a multiple regression model were employed. The dependent variable was household per capita expenditure while the independent variables were the educational level of the household head, the educational level of spouse, the age of the household head, the sex of the household head, major occupation, remittance and dependency ratio. The results show that out of seven explanatory variables used in the regression model, only four are significant. They are the sex of the household head, the educational level of the household head, household size and dependency ratio. Methodology The Primary source of data collection was used, by taking a field survey through administration of questionnaires. A total number of three hundred and sixty (360) the questionnaires were distributed among the respondents of the Kano Metropolis that comprise of mainly household heads, but other household members were also allowed to provide relevant information, which was not adequately supplied by the household heads. An interview was also conducted where the respondents were found illiterate and could not give complete information as required. The survey method is believed useful in conducting this research work. This method is considered suitable as it provides the 80

7 modality for obtaining data from a sample size and generalizing the research finding obtained from as analysis of the sample to the entire population. The use of the survey method has been attested to by Adetoro (1993 in Mshelia 2003), as being a useful means of obtaining data, which are derived from questionnaire. The questionnaires were administered on the basis of the simple random sampling technique based on which forty five (45) questionnaires were distributed to each of the eight (8) local government areas within the Kano Metropolis (i.e. Kano Municipal, Dala, Gwale, Fagge, Nassarawa, Tarauni, Kumbotso and Ungogo). However, Kumbotso and Ungogo local governments both have some parts of them within the Metropolis and some parts outside it. This study considered only those parts that are within the Metropolis. The data collected from the administered questionnaires were presented by the use of tables and percentages for easy assimilation. However, the Logit Model was also used in determining the factors responsible for poverty in the Kano metropolis. The variables used include the educational status of the household head, the age of the household head, the gender of the household head, the household head s major occupation and the household size as explanatory variables while the mean per capita expenditure on basic needs is the dependent variable. Analysis of Data and Results As has been mentioned in the methodology, a total number of three hundred and sixty (360) questionnaires were distributed to various respondents within the Kano Metropolis. However, three hundred and forty questionnaires were retrieved, while twenty (20) were not returned. The socio-economic characteristics of the respondents that have a bearing on poverty in the Kano Metropolis are presented and analysed here under. The Gender Distribution of the Respondents Table 1 Sex Distribution of the Respondents Source: Field Survey Sex Frequency Percentage Male % Female 68 20% Total % Table 1 shows that 80% of the respondents are male while only 20% are female. This implies that the proportion of male respondents is far greater than the female respondents in the sample interviewed, which means that in Nigeria, or Northern Nigeria in particular, as well as the area of the research (the Kano Metropolis), males are considered the bread winners of the family more than their female counterparts. Therefore, this indicates that when more males are afflicted by poverty their female counterparts will also directly or indirectly be affected. 81

8 The Age Distribution of the Respondents The study reveals that majority of the sampled household heads were between 18 and 65 years old as shown by Table 2 below. Table 2 The Age Distribution of the Respondents Source: Field Survey Age Frequency Percentage % % % Total % Table 2 shows that the majority of the respondents fall between the ages 18 and 65 years, which constitute 83.24% of the total households interviewed. Therefore, the majority of the respondents are middle-aged people. This distribution has two implications on poverty. While the distributions still rank all the respondents on the average at their economically active age, it also shows that they are still at the child-bearing age. In the first reason cited, the ability of a family to go about their daily activities in order to earn an income with which they cater family basic needs is enhanced. Thus, these can result in the reduction of poverty. Alternatively, the fact that most are still child bearing leaves much to be desired. This is because the larger the family size in which there are more dependents that do not contribute to the generation of the income level of the family, the more thinly spread is the family s income on basic needs, thus, leading to poverty aggravation. The Marital Status of the Respondents Table 3 Marital Status of the Respondents Marital status Frequency Percentage Single % Married % Divorced/widow % Total % Source: Field Survey Table 3 indicates that 24.41% of the respondents are single, 69.71% are married while 5.88% are divorced/widow. Therefore, as the married and divorced/widowed constituted 75.59% of the sampled population more needs to be done to uplift their standard of living because they are the population that have to cater for the families under their care, if poverty is to be reduced to the barest minimum level. 82

9 The Household Size of the Respondents From Table 4, it is discovered that the household size of the respondents ranges between 1to 20. However, the average family size of the respondents is 7. The impact of large family size (if they are not working, i.e. dependents) is such that it reduces per capita expenditure of the family, thereby aggravating the poverty level. The distribution of the household by size is as shown below. Table 4 The Distribution of the Respondents by Household Size Household Size Frequency Percentage % % % % Total % Source: Field Survey Table 4 shows that 1-5, 6-10, and16-20 of the respondents have 45.59%, 34.12%, 17.06% and 3.24% of the household sizes, respectively. While the household sizes of get 0%. Even though household size tends to reduce per capita expenditure, it can also enhance it when the majority of the household are working and contributing to the expenditure of the family. The Educational Qualification of the Respondents The level of education of the respondents reveals that most of them are educated with about 88% of the respondents being formally educated. Table 5 Educational Qualification of the Respondents Educational Level Frequency Percentage Informal % Primary % Secondary % OND/NCE % B.Sc/HND % Others % Total % Source: Field Survey Table 5 shows that the percentage of those that are not educated or are informally educated is 11.47%, while the percentage of those that are formally educated are 3.53%, 22.35%, 30.29%, 32.06% and 0.29%, respectively, which varied from primary to 83

10 secondary and tertiary institutions. This shows that the average Kano Metropolis resident is educated. This could be that any urban dweller requires a bit of education before he/she can properly fit into the life style of the urbanites. The Occupational Distribution of the Respondents Even though the majorities of the respondents are educated, it is shown in Table 6 that 45.88% of the respondents are non-salary/wage earners (self-employed) while 54.12% of the respondents are salary/wage earners, which comprises respondents working in various government organizations, as well as private companies. Table 6 The Occupation of the Respondents Occupation Frequency Percentage Salary/Wage Earner % Non-Salary/Wage Earner Total % Source: Field Survey The relatively lower percentage of the non-salary/wage earners indicates the inability of the government to provide good and adequate jobs for the qualified masses and also from the facts that the high percentage of non-salary/wage earners must have been due to the educational status of the respondents. For instance, about 37.35% of the respondents have education below secondary school. This qualification, by Nigerian standards, is not enough to be employed by the government. On the other hand, it could be due to the geographical area because the area of the study (the Kano Metropolis) is titled center of commerce. So this can also influence the distribution of the sampled population to have higher Non-Salary/Wage Earners (45.88%). The Average Monthly Income of the Respondents The income of a household is a function of the number of persons working in the household and sometimes the level of educational attainment. Income is a determinant of household expenditure since it serves as the budget constraints to the amount that can be pent within a period. There is also bound to be a correlation between income and the poverty level of a household, all things being equal. Table 7 The Distribution of the Respondents by Monthly Income Monthly Income (N) Frequency Percentage % % % % % Total % Source: Field Survey

11 Table 7 shows that 19.20%, 33.24%, 22.35%, 17.06% and 8.24% of the respondents have monthly incomes of N5,000- N 9,000, N10,000- N39,000, N40,000- N69,000, N70,000- N99,000 and N100,000- N150,000, respectively. About 50.88% of the respondents earn a monthly income that is below N 38,435:30 (i.e. the mean per capita expenditure on the basic needs of the sampled households interviewed) in which they are categorized as poor or are afflicted by poverty, while 49.12% earn N 38,435:30 and above in which they are categorized as non-poor or are not affected by poverty. The Average Monthly Basic Needs Expenditure of the Respondents The Monthly basic needs expenditure distribution of the household is meant to show in more detail the pattern of household expenditure in the study area. The expenditure pattern of a household can provide an insight into the economic welfare or the living condition of the household. Table 8 The Monthly Basic Needs Expenditure of the Respondents Monthly expenditure(n) Frequency Percentage % % % % % Total % Source: Field Survey Table 8 shows that 19.12%, 31.76%, 25.59%, 15.29% and 8.24% of the respondents get monthly basic needs expenditure of N4,000- N6,000, N7,000- N29,000, N30,000- N59,000, N60,000- N89,000 and N90,000- N130,000, respectively. In this research, the mean per capita expenditure was used as a proxy for the standard of living (the poverty level) of the households interviewed. Households were classified as poor or non-poor in relation to the mean per capita expenditure on basic needs of the sampled households given as N 38, 435:30. Therefore, the households with less than the stated mean per capita expenditure on basic needs are classified as poor, while those with an amount greater or equal to N 38, 435:30 are classified as non-poor. This shows that about 51% of the households interviewed are poor, while around 49% are found to be non-poor. 85

12 Regression Results Table 9 Regression Output VARIABLES CO-EFFICIENTS PROB. z-stat PROB. (LR STAT) C EDUC AGE SEX 0CCUPATION SIZE * ( ) * ( ) ** ( ) * ( ) * ( ) * ( ) Source: computed from field survey data (2011), *(**) denote rejection of hypothesis at 1%, (5%) significant levels, Standard errors in parenthesis. LnY = x x x x x 5 The result from Table 9 reveals that education, age, occupation and the size of the household have positive effects on the household per capita expenditure, but only the sex of the household has a negative impact on that (see Appendix I). The four variables namely: education, occupation, sex and the size of the household are significant at one (1) per cent level, however, only the age of the household is significant at five (5) per cent level. The coefficient of LR stat. ( ) implies that all the explanatory variables jointly exact a significant influence at one (1) per cent level on the household per capita expenditure. Discussions/Policy Implications The result from Table 9 shows that there is a positive and significant relationship between education and household per capita expenditure (measured as a proxy for poverty). This implies that an additional year gained by the household in acquiring formal education leads to a rise in household income and, by implication, an increase in household per capita expenditure, as well as a reduction in household poverty level. Education attainment enhances human capital and participation in the labour market has been widely accepted as a veritable tool for poverty reduction and improving the standard of living. The coefficient suggests that an additional year spent in acquiring education will lead to, on average, an increase in the household per capita expenditure by 72.7%, which, on the other hand, leads to the reduction of the household poverty level by 72.7%. This result is similar to the findings of Simbowale (2003), Akerele (2011) and Anyanwu (2011). 86

13 The result also shows that the age of the household has a positive and significant impact on household per capita expenditure in the study area. The coefficient means that a unit increase in the household age leads to, on average, an increase of the household per capita expenditure by 2.76% and, by implication, reduces the poverty level of the household by the same amount. According to the life-cycle hypothesis, poverty is expected to be relatively high at a young age, decreases during the middle age and then increases again at old age because the productivity of the household decreases and there exist a few savings to compensate for this loss of productivity and income/expenditure at this latter stage. This result has been confirmed by the findings of Anyanwu (2011). Further, the result shows that the household size has a positive and significant impact on the household per capita expenditure. This means that the larger the number of individuals who contribute to the family income/expenditure the lower would be the poverty level in that family and vice versa. The coefficient indicates that a unit increase of the household which contributes to the family expenditure leads to an increase of household expenditure, on average, by 10.36% and, by implication, a reduction of poverty by the same amount. A similar result is obtained by the findings of Olaniyan (2000) and Serumaga-Zake (2002). Furthermore, the result indicates that the sex of the household has a negative but significant impact on the household per capita expenditure. This is related to the feminization of poverty, a phenomenon said to exist when poverty is more prevalent among female headed households than among male headed households. Some of the reasons advanced for this existence of feminized poverty include: the presence of discrimination against women in the labour market or that women tend to have lower education than men and hence they are paid lower wages/salaries. This result confirms the observation by Bastos et al (2009) and Anyanwu (2011) that poverty is not a gender neutral condition, as the number of poor women exceeds that concerning men and women and men experience poverty in distinctive ways. In addition, the result reveals that there is a positive and significant relationship between the occupation of the household and its per capita expenditure. It is hypothesized that occupation has a high correlation with poverty because occupations which require low amounts of capital, either human or physical, will be associated with low earnings and therefore with higher poverty rates. Finally, all the signs of the four variables (i.e. education, age, occupation and size) confirm the a priori expectation but only the sign of the sex of the family deviated from the a priori expectation. Conclusion and Recommendations The main findings of the study show that all the five variables (i.e. education, age, sex, occupation and household size) used in the research are significant in determining the poverty level in the Kano Metropolis. It is, therefore, concluded that education and household size are important determinants that affect poverty in the Kano Metropolis. 87

14 Hence, rejecting the null hypotheses I and II of the study. These results are similar to the findings of Simbowale (2003), Akerele (2011) and Anyawu (2011) with respect to education. Further, the study is also in conformity with the findings of Olaniyan (2000) and Serumaga-Zake (2002) with respect to household size. From the analysis of the findings, it is obvious that poverty exists in the Kano Metropolis where the study was carried out and it also found that education, age, sex, occupation and household size were important in determining the poverty level in the study area. Therefore, any attempt at improving these factors will be a good step in the right direction in alleviating poverty in the state, as well as in Nigeria as a whole. The research hence recommends, among others, improvement in human capital through investment in education. This should involve training in life skills and vocations, which would help to stimulate the innate entrepreneurial potentials of the household and expand the frontier of their income generating capacities and become more productive. Furthermore, policy efforts that would facilitate employment opportunities would help to reduce the number of dependents (who could be potentially employed) and enable them to contribute to the general household expenditure. This would be a welcome development towards reducing poverty in the study area. Finally, gender policy measures that would focus on building women capacity and skew more productive resources towards them would be a landmark achievement in reducing poverty and promoting household standard of living in the study area. 88

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