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\title{Micro Credit of Community Banks and Consumption of the Poor}
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             \author[1]{Mr. Roko Lumi  Peter}

             \author[2]{Dr. M S.  Isyaka}

             \author[3]{Isyaka Mohammed  Salisu}

             \affil[1]{  University of Abuja,Nigeria.}

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\date{\small \em Received: 16 December 2015 Accepted: 2 January 2016 Published: 15 January 2016}

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\begin{abstract}
        


This article investigates the influence of community banks' micro credit on consumption of the beneficiaries. A survey is conducted in tracing the loan beneficiaries and estimating the pattern of change in their consumption in Bauchi State. Various items for consumption of the poor were identified and logged side by side with the OLS equation with a view to see the credit influence over a period of time. The result shows that micro credit and consumption of the poor are positively related. However, the outcome defends on many socio- economic characteristics of the borrowers.

\end{abstract}


\keywords{socio- economic characteristics of the borrowers.}

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\let\tabcellsep& 	 	 		 
\section[{Introduction}]{Introduction}\par
icro-credit means small size of loan mainly for small-scale investment granted by the lender based on trust as opposed to collateral security. Many studies revealed that micro credit policy through financial institutions is capable of increasing the productive capacity of the poor, raising the return to assets, and promoting wage employment (World \hyperref[b36]{Bank, 1990;}\hyperref[b46]{Zaman, 1999;}\hyperref[b41]{Wydick, .1999};  {\ref Udry, 1994;}\hyperref[b24]{and Pitt and Khandker, 1998)}.  {\ref '} The subject of accessing-credit to the poor has constantly secured attention all over the World, and the World wide economic recession has brought attention on the poor as a potent means of revamping the economics of the developing nations in sub-Saharan Africa and Asian countries. Attention is particularly paid on micro credit of financial sector.\par
The self-help efforts by poor people to promote their welfare through self-employment are ignored by the Nigerian financial sector. This is because traditionally the very poor have not been recognised as credit-worthy since they do not have collateral securities. Thus using traditional commercial banking methods, lending to the poor is not believed to be cost-effective.\par
Since 1977, the Federal Government's attention was centered on developing the Nigerian financial sector as a whole in order to achieve free flow of fluids from the surplus sector to the deficit sector. To achieve this, the government identified the most poverty stricken areas, the rural areas, and embarked on rural banking programmes by directing the commercial banks to open rural branches. While this was achieved, the goal of poverty alleviation is yet to be achieved. This was because the accumulated savings particularly of rural areas were not used for rural investments due to difficulties involved in borrowing by the rural dwellers. In order to ensure access to credit by the poor rural dwellers and to achieve grassroots development a Community type of banking was introduced in 1990. Community Bank makes use of credit character as the prime principle of lending over collateral securities. The World Bank's World Development Report  {\ref (1998)} conducted extensive studies on micro credit institutionsacross the World. The study revealed the suitability of these institutions in providing credit for non-corporate business, small fanners, rural producers, trade groups and other micro enterprises across the World. \hyperref[b25]{Pulley (1989)} also made an extensive study of the strategy for making the poor credit-worthy as a major effort at alleviating poverty in the World. \hyperref[b42]{Yaron (1992)} reviewed some rural finance institutions in some developing countries. The institutions succeeded in achieving the aims and objectives for which they were established and the environment in which they operate. The Community banks have the same basic characteristics as the traditional commercial banks except that the institution and their operations are not strictly in line with orthodox banking principles. To achieve credit access to the poor in Nigeria the Community bank and peoples bank are designed to have access to land, credit and technological know how by the poor (Mabogunje 1993).\par
Although it has been proved that micro credit is a good instrument of poverty alleviation (Halin and Ganuzza 1996, and Odejide, 1997), even after the establishment of Community banks in 1990 the poverty level has been on the increase in Nigeria in general and in Bauchi State in particular \hyperref[b8]{(FOS 1999)}.\par
With classification of Nigeria being among the twenty five poorest countries of the World, Bauchi Stale is among the top two poorest states in Nigeria second only to Sokoto State \hyperref[b8]{(FOS, 1999)}. The poverty profile-of Bauchi State has been on the increase since 1980. For instance, the percentage of those below the poverty line was 46 in 1980 but tins percentage increased to 83.5 in 1999.\par
The question is are Community banks assisting in increasing consumption of community banks' loan beneficiaries in the Slate? And how can we improve their performance over time? This needs investigation and there for the object of this article is to provide answers to these questions. 
\section[{II.}]{II.} 
\section[{Literature Review a) Conceptual issues}]{Literature Review a) Conceptual issues}\par
Poverty is a multidimensional phenomenon that affects the physical, moral and psychological conditions of man \hyperref[b39]{(World Bank. 2000)}. It is multifaceted and differently perceived from society to society, making it difficult to give a generally acceptable definition of it. Many scholars view poverty with respect to the extent of their exposure to and perception of the poor in their own society. On the basis of income and consumption, \hyperref[b27]{Rowntree (1901)}, whose approach is frequently used by World \hyperref[b39]{Bank (2000)}, use monetary income or consumption to identify poverty. Tins perception has become the workhouse of quantitative poverty analysis, which is emulated by different writers holding similar opinions. These include Czukas, et al (1998), Deacon (1998), and \hyperref[b18]{Morduch (1990)}, suggesting that the low levels of income force the poorer households to enter low return activities. On the basis of consumption, Glewwe and Hall (1998) specifically considered poverty on the vulnerability of households to declining consumption following economic shocks. \hyperref[b32]{Townsend (1996)}, Moser (1998), Attanasio and Rios Rull (1999), Behrman and Deolalikar (1990), \hyperref[b3]{Chaudhuri and Paxson (1994)}. \hyperref[b19]{Morduch (1995)}: and Rosenzwerg and Stark (1989) also hold this view. On broader basis, poverty is perceived from the point of deprivation. \hyperref[b29]{Sen (1999)} defined poor people as those who live without fundamental freedoms of action and choice that the better off take for granted. Mc Connell (1969) also holds this view.  {\ref Townsend (1997)} sees poverty as a situation where "individuals or families-are in command of resources which overtime, fall seriously short of the resources commanded by the average family". This defimtion shows the dynamism of poverty in such a way that as the average standards of the relevant communities increase, the relative poverty line also increases as well. Njoku (1998) defined poverty as a kind of deprivation from something, which every human being has a natural right to have. This includes poverty of loneliness, the unloved, and the unwanted. Salient as this definition is, it managed to summarise the psychological effect of poverty such as bearing children on the streets, lack of privacy, dignity, and security, and lack of opportunity to participate in the formulation of decisions that affect the life of the poor.\par
In the CBN's (1999) typology, although poverty is considered as one of the symptoms or manifestations of underdevelopment, its multidimensionality is better seen from its attributes'. According to UNDP Human Development Report  {\ref (1997)}, poverty means more than a lack of what is necessary for material well-being. It also means the denial of opportunities and choices basic to human development. Hence there is the concept of human poverty, or poverty of lives and opportunities which is also multidimensional. It includes denial of choice and opportunities to lead a long, healthy and creative life, and to enjoy a decent standard of living, freedom, dignity, self-esteem and the respect of others. 
\section[{b) Theoretical causes of poverty}]{b) Theoretical causes of poverty}\par
Johnson (1974), cited in \hyperref[b9]{Galadi (2000)}, developed two conceptual sources of poverty: the factors which make the number of individuals in the consuming unit large relative to the amount of productive services the unit is able to supply; and those which make the value of productive services the household can supply low relative to the household need. He mentioned excessive family size in relation to income under his first factor, while under the second factor he listed chronic obsolescence of acquired human skill, mental or physical incapacity, and discrimination in terms of age, colour, race or sex.\par
As observed by Abdullahi (1993), and analysed by Njoku (1998) poverty may be structural or conjectural. The structural poverty is long term or persistent and is normally caused by limited productive resources, lack of skills for gainful employment, location disadvantages, or endemic sociopolitical and cultural factors. On the other hand, the conjectural poverty is temporary or short term and possibly reversible. It is caused by natural disasters like drought, earthquake, volcanic eruption, and flood, or man-made disasters like wars, environmental degradation, structural adjustment program, and so on. However, conjectural poverty may lead to structural poverty where for example, the natural and man-made disasters are prolonged. \hyperref[b30]{Telia (1997)} ably postulated two theories namely, the corruption theory and the element of luck theory. The corruption theory is rooted in poverty itself. "It is a situation where everybody that seeks political power does so with the intention to acquire property that will not only last the lifetime of that individual, but also sustain the family after he is long dead"  {\ref (Telia 1997:76)}. The attribute of this theory is already emphasised in power theory. With regard to the element of luck theory, it hinges on the accident of birth. "Some people are born into societies that value and cherish general development; societies where the older generation have built an enviable structure based on hard work, honesty, self-service, good values, and pride which the younger generation cannot build upon for the benefit of present and future generations. In such societies, poverty exists mainly within the context of individual attributes".  {\ref (Telia, 1997:76}).\par
Telia's observation typically explained why Africa in general is poor relative to Western countries as a result of superior infrastructures the formers have long ago. \hyperref[b11]{Galbraith (1971)} making reference to Africa observes that for sub-Saharan Africa poverty is caused by absence of opportunity rather than absence of aptitude. He emphasised that the countries in this region have had only few years of independence to face the task of economic development. In his earlier work, \hyperref[b10]{Galbraith (1958)} identified two broad categories of poverty, namely, case poverty and insular poverty. According to him the case poverty is due to some characteristics of the individual poor or his family, such as mental deficiency, bad health, inability to adopt to the discipline of modern economic life, excessive procreation, alcohol, insufficient education or a combination of these handicaps. With regard to the insular poverty, it manifests itself as an island of poverty within a country. In the Poverty Island, everyone is, or at least majority are poor. 
\section[{c) Micro Credit and Consumption: empirical evidence}]{c) Micro Credit and Consumption: empirical evidence}\par
The relationship between micro-credit and poverty has occupied an important place in the quest for understanding the influence of credit on major variables indexing poverty. Theoretical and empirical research on the relation between micro-credit and poverty is a recent phenomenon. The attention of World scholars on micro credit and poverty is perhaps after the establishment of some successful micro credit institutions in Asia. These include the Badan Kredit Kecamatan (Bkk) established 1970 in Indonesia and the Grameen Bank (GB) in Bangladesh established 1976. There are theoretical and empirical evidences on the influence of micro credit on the poor based on change in their income. However the results are-not unanimous. Earlier theories show that micro finance provides financial resources to the poor segment of the society, and leads to creation of selfemployment opportunity and improved economic condition \hyperref[b14]{(Hussain, 1988 and}\hyperref[b42]{Yaron, 1992}). These observations serve as a watershed in investigating the relationship between micro credit and poverty. Hence in the 1990s a number of researches were made based on the influence of credit on the major determinants of poverty. The works of \hyperref[b46]{Zaman (1999)}, Morduch (1998), Pitt and Khandker (1998), and Khandker (1998) investigate the impact of micro-credit on consumption. These evidences are discussed in turn. \hyperref[b46]{Zaman (1999)} explores the relationship between micro-credit and the reduction of poverty and vulnerability by focusing on Bangladesh Rural Advancement Committee (BRAC), one of the largest micro-credit providers in Bangladesh. Zaman analysed survey data collected in Bangladesh in 1995 from which he drew his consumption data of 1072 households. He argued that there are a number of ways by which micro-credit can reduce poverty and vulnerability, namely, consumption, crisis coping mechanisms, building assets, and empowering women using Ordinary Least Squares (OLS) regression.\par
Zaman's results (heteroscedasticity-corrected) for BRAC variables and Lamda term shows that the BRAC members who have more than ten decimals of land and have borrowed more than 10,000 taka is positive and significant at 10\% level. However, the interpretation of this coefficient is unambiguous. Given Zaman's mean loan size for the 10,000 plus category as 13,090 taka, the result shows that borrowing more than 10,000 taka varies a moderate poor households' consumption per adult equivalent by 18.8\% relative to an identical non-borrowing BRAC member. By using eligible non-member as against non-borrowing member, the result however, varies significantly. "For instance, BRVO coefficient is significantly negative and a household borrowing more than 10,000 taka is 48\% worse off compared to an eligible non BRAC member" on the other hand, the result of the OLS shows that a household with more than 10,000 taka loans is 13.8\% better off than an eligible non-member.\par
Zaman also assesses the other non-BRAC determinants of poverty He accepts that aside from BRAC variables age, education and occupation of the household, the dependency ratio, the wealth endowment of the household, and village condition significantly determine poverty. He concludes that the impact of micro-credit on income poverty is a function of borrowing a certain loan threshold, and to a certain extent contingent on how poor is the household. Perhaps Zaman regards income and consumption poverty to mean the same.\par
The results of  {\ref Morduch (1998)} and Khandker (1998) both cited in a review article of Morduch (1999) are however, not unanimous. \hyperref[b20]{Morduch (1999)} analysed a World Bank survey of 1,800 household conducted in 1992 in Bangladesh but found that access to credit programs yields no appreciable increase in average consumption level in the short run. For those with access however, the volatility of consumption over the three main cropping seasons in the area is roughly half that of control group. He found that variability is 47 percent lower for eligible Grameen households, 54 percent lower for eligible BRAC households, and 51 percent lower for eligible Bangladesh Rural Development Board (BRDB) households compared to a control group. Morduch concludes that program participation does not benefit in terms of greater consumption levels but they participate because they benefit from risk reduction.\par
Morduch's finding is a direct attack on Khandker's work in the sense that the same data analysed by Khandker was analysed by Morduch. Khandker analyses the impact of male and female borrowing on different outcome variables per capital spending, net worth, school enrolment, fertility, and height for age, and contraceptive. He found that moderate poverty falls by around 15 percent and mat micro finance has a positive effect on household consumption.\par
Although there is mixed result on the effect of micro credit on consumption, the argument that micro credit increases consumption of borrower carries more weight. 
\section[{III.}]{III.} 
\section[{Methodology}]{Methodology}\par
This research is conducted in Bauchi State covering four local government areas of Bauchi, Darazo, Misau, and Katagum with surviving Community banks. Ten settlements are selected from these local government areas. Namely, Akuyam, Azare, Bingi/ Bulamari Darazo, Yelvva, Kafin Kuka, Bakin Kura. Tashar Danasabe and Fadamar Mada. These local government areas are selected because agriculture is the main occupation in the area. Local government areas in the far north of the state are semi desert and therefore their inhabitants rely mainly on commerce. The far north area also has the problem of seasonal migration to the southern part of the country. As for southern part of the state, the rocky nature of the area and the availability of mineral resource mean that agriculture and mining are the two key occupations. To determine the influence of any credit on the main occupation agriculture is therefore a problem in southern part of the state.\par
According to Human Development Report (HDR) 1998, Bauchi State poverty profile (on the basis of Economic status index (ESI), nutritional Index (NI), Educational Index (EI), and Housing Quality Index (HQI) shows that the state has an aggregate of 0.53 meaning that more than half of the population are poverty stricken. The EI in particular shows that the state has weak index of 0.30. According to the same HDR (1998), the state has Income Poverty Index (IPI) of 0.25 and Human Poverty Index (HPI) of 0.30. Of this Bauchi Local Government exceed the state averages for both HPI. and IPI, but Katagum exceeds only HPI average, and Misau only IPI average while Darazo fail to meet both the state HPT and TPT averages. The report also shows that women are about 17 percent poorer than men in the area.\par
The study covered the period 1998 -2002. It was during this period that the bank became fully established and operational in the area. Our choice of Bauchi state is because it is one of the poverty state in Nigeria, second only to Sokoto (FOS, 1997) and we have good knowledge of the area in term of culture, geography, economy, and politics that will help us in tins research. The loan beneficiaries covering these periods were consulted in order to examine the influence of the loans on the borrowers consumption, income, assets etc.\par
The study is carried out by means of primary data. The data is obtained by means of questionnaire. The questionnaire is limited to straight dichotomy questions, which allow for 'Yes' or 'No' answer. However, fill the blank column is provided where necessary to allow the respondent to agree or disagree to a given statement or fill according to what is in his or her mind. This method of questioning is necessary because of the level of education of the people in the area is low. However, the primary method of data collection is supplemented with secondary one collected from the bank. These include the size of the loans disbursed, beneficiaries, and location.\par
The data is collected from the eligible credit users and the eligible non-credit users of the bank. The four Community banks -of Azare, Akuyam, Bauchi and Darazo are chosen because this banks are the only Community banks recognized as viable in Bauchi State as at 1998.\par
Our questionnaires were administered to a sample of 30 percent of the loan beneficiaries for the major occupation -agriculture in the study areas. This gives us 170 in Azare, 50 in Garu, 52 in Darazo and 60 in Akuyam. We applied a stratified random sampling technique through which every Kth borrower was selected. The population was stratified into borrowers for different agricultural practice, from which 30 percent sample was selected, 19 percent of the sample size are women because they constituted 19 percent of the population size. For each bank 30 percent of its borrowers are selected. And 30 percent is, believed, a fair representation. We administered a total of 313 questionnaires. According to CBN (1999), a poor is one who lacks physical necessities, assets, and income, or one whose condition includes social inferiority, isolation, physical weakness, vulnerability, seasonality, powerlessness and humiliation. In the approach of World \hyperref[b36]{Bank (1990)}, a poor is one who survives on less than one US dollar per day, and a moderate poor is one who live on less than two US dollar per day. A control group introduced in our work helps to indicate how poor are the loan beneficiaries by determining their average households consumption per adult equivalent. This enables to ascertain that the respondents were from poor family who were enjoying credit or would have been eligible to enjoy credit of the Community banks. This is further evidenced from the size of loans demanded by them ranging from 10,000 to 100,000.\par
A structured questionnaire was developed to collect detail information on social and economic activities of the sample households. This information is contained in our questionnaire in appendix 4 include sex, occupation, age, family size, educational qualification, credit, consumption, and so on.. 
\section[{Motlel specification}]{Motlel specification}\par
To estimate the influence of credit on consumption, we have: Q=Po + PiWi + PaYi+Pau+Bi 1 Where >i=0 Cj=log of total consumption per adult equivalent. W;=a vector of individual household and village characteristics. Yj=P| + Pt X; + TJ; and is a latent (unobserved) continuous variable or the participation equation. x= Mill ratio. 
\section[{Global Journal of Management and Business Research}]{Global Journal of Management and Business Research}\par
Volume XVI Issue XI Version I Year ( ) A xi=the vector of individual household and village characteristics that affect the probability of participation. lgcade =f (agml760, agfl760, adeqhrs, aghhh, aghhhsq, rehhs, hhhlr, lglaqo, olbli, hhhps, hhhss, hhhsx, vill, vil2, vil3, vil4, vil5, vil6, vil7, vil8, vil9, villO, bmva, mloaduml, mloadum2, mloadum3, uloaduml, uloadum2, uloadum3, memlenl. memlen2, memlen3, memlen4). OLS with village dummies: corns = f (agml760, agfl760, adeqhrs, aghhh, aghhhsq, rehhs, hhhlr, lglaqo, otbli, hhhpr, hhhss, hhhsx, vill, vil2, vil3, vil4, vil5, vil6, vil7, vil8, vil9, villO, bmva, mloaduml, mloadum2, mloadum.3, uloaduml, uloadum2, uloadum3, memlenl, memlen2, memlen3, memlen4 ). The definition of our equation is given in Appendix \hyperref[fig_0]{1} IV. 
\section[{Data Presentation and Result}]{Data Presentation and Result}\par
Table \hyperref[tab_0]{1} shows the economic characteristics of the respondents according to the size of loan they have enjoyed. About 23 percent of the respondents have not enjoyed loan from the Community banks yet. This group serves as a control group. 23 percent falls within the N10, 000 loan tlireshold and about 20 percent enjoyed between Nl 1,000 to N20,000, while 14 percent enjoyed between N21,000-30,000.Those who obtained between W31,000 to M40,000 constitutes about 6 percent while tho.se who enjoyed N50.000 loan threshold are about 5 percent. The last groups of N60, 000 loan tliresholds have about 9 percent. Table \hyperref[tab_1]{2} shows the consumption in relation to the credit borrowed. It shows that households with access (o credits of £410. 000 thresholds spend the average of 2863.49 on nutrition per month for the household. The table shows that as credit increases up to \$430, 000 threshold consumption also increases. However, borrowing £440, 000 credit threshold leads to a reduction of household consumption relative to £430, 000 threshold borrowers. This might be as a result of relatively limited sample size of the respondents witliin the £440, 000 credit threshold. It might also be as a result of influence of other socio economic characteristics of the borrowers. These characteristics include education, health, period of exposure to the credit, etc. However, borrowing £450, 000 and £460, 000 Naira raises consumption significantly relative to £410, 000 credit threshold borrowers. This shows that credit raises income and an increase in income in turn increases consumption. The table also shows that about 42 percent of the Community bank's borrowers are core poor while about 58 percent are moderate poor. Consumotion N'000\par
Credit consumption Curve> Credit =£4'000  Our result shows that borrowing N10, 000 and using it for 2 -4 years raises consumption by about 17.9 percent (significance at 10 percent level) relative to identical non-borrowing members. The Ols results show increase in consumption by about 32.3 percent (significance at 5 percent level). Borrowing N40, 000 and using it for 2-4 years, raises consumption by about 57.65 percent (significance at 5 percent level) relative to an identical non borrowing member. And borrowing N50, 000 and N60, 000 raises consumption by about 56.78 percent (significance at 5 percent level) and about 59.36 percent (significance at 10 percent) respectively. The Ols estimates give 3.75 times rise in consumption for a borrower of N60, 000 (at 5 percent significance level) relation to an identical non-borrowing member.\par
V. 
\section[{CONCLUSION}]{CONCLUSION}\par
From our findings we can conclude that micro credit of the Community Bank is a good poverty vaccine, which improve the consumption the poor. The result confirm the works of many other scholars in the area of micro finance and poverty especially Zaman (1999), and Dunford (2001). It further justified the earlier theories that poverty is a result of self-perpetuating, social, cultural and economic deficiencies, which are beyond the capacity of individuals to remedy through their own efforts. The results also show that, once the poor people are opportune they can set themselves free from poverty by particularly improving their consumption. 
\section[{VI.}]{VI.} 
\section[{Recommendation}]{Recommendation}\par
Having seen the influence of Community banks micro credit on poverty alleviation, the coverage of Community, banks is grossly inadequate for the impact to be felt on the general populace. There is therefore, the need to ensure the spread of the banks to each of local government headquarters and District headquarters in the country.\par
A general reform needs to be made on the social responsibility of the Community Banks. The responsibility should not be in term of low cost of borrowing but in term of given entrepreneurial training especially for women as a condition of getting the loans. Credit with education is effective in poverty alleviation in Ghana and Bangladesh  {\ref (Dunford 2001}\hyperref[b46]{, Zaman 1999)}.\par
The Community banking system should be a member of the clearing system. The National Board for Community Banks (NBCB) can be empowered to open and equip state offices and be the agent in the clearing system for the Community banks in order to quicken service to there customers. The bank suffers from lack of patronage by many people as a result of delay in executing services. The Community banking micro credit should be seen a measure against for poverty and hence the attention of international institutions and bodies, the National Policies on Poverty Eradication and the Local Support Programs should invest in Community Banking. 
\section[{Global Journal of Management and Business Research}]{Global Journal of Management and Business Research}\par
Volume XVI Issue XI Version I Year ( )A 2016\par
Micro Credit of Community Banks and Consumption of the Poor bmva = 1 if household head is the bank's member, 0 if not Mloadum =1 if household has equivalent of 100 acre of land and has borrowed less than 5000 Naira, 0 if not. mloadum2" = 1 if household has more than equivalent of 100 acre of landand has borrowed between 5000 to 10,000, 0 if not. moladum3 = 1 if household has more than equivalent of 100 acre of landand has borrowed more than 10,000 Naira, 0 if not. uloaduml =1 if household has less than equivalent of 100 acre of land andhas borrowed less than 5000 Naira 0 if not. uloadum2 = 1 is household has less than equivalent of 100 acre of land andborrowed between 5000-10,000 Naira, 0 if not uloadum3: 1 if household has less than equivalent of 100 acre of land andhas borrowed more than 10,000 Naira, 0 if not. memlenl: 1 if membership length between 1-12 month, 0 if not. memlen2: 1 if membership length between 12-24 month, 0 if not. memlen3: 1 if membership length between \hyperref[b22]{[24]}\hyperref[b23]{[25]}\hyperref[b24]{[26]}\hyperref[b25]{[27]}\hyperref[b26]{[28]}\hyperref[b27]{[29]}\hyperref[b28]{[30]}\hyperref[b29]{[31]}\hyperref[b30]{[32]}\hyperref[b31]{[33]}\hyperref[b32]{[34]}\hyperref[b33]{[35]}\hyperref[b34]{[36]}  \begin{figure}[htbp]
\noindent\textbf{1}\includegraphics[]{image-2.png}
\caption{\label{fig_0}Figure 1 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{1} \par 
\begin{longtable}{P{0.2932748538011696\textwidth}P{0.18888888888888888\textwidth}P{0.3678362573099415\textwidth}}
Loan threshold in Naira\tabcellsep Number of respondents\tabcellsep Percentage\\
0\tabcellsep 71\tabcellsep 22.61\\
10000\tabcellsep 73\tabcellsep 23.25\\
20000\tabcellsep 63\tabcellsep 20.06\\
30000\tabcellsep 45\tabcellsep 14.33\\
40000\tabcellsep 18\tabcellsep 5.73\\
50000\tabcellsep 15\tabcellsep 4.78\\
60000\tabcellsep 29\tabcellsep 9.24\\
Total\tabcellsep 314\tabcellsep 100\\
\tabcellsep \tabcellsep Source: Author's survey 2002.\end{longtable} \par
 
\caption{\label{tab_0}Table 1 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{2} \par 
\begin{longtable}{P{0.7352373887240355\textwidth}P{0.04540059347181009\textwidth}P{0.0693620178041543\textwidth}}
Observation\tabcellsep Credit threshold £4' 000\tabcellsep Consumption W\\
70\tabcellsep 10\tabcellsep 2863.49\\
64\tabcellsep 20\tabcellsep 4047.92\\
43\tabcellsep 30\tabcellsep 5142.64\\
19\tabcellsep 40\tabcellsep 4861.00\\
16\tabcellsep 50\tabcellsep 7060.67\\
29\tabcellsep 60\tabcellsep 8325.93\\
\multicolumn{3}{l}{Source: Author's sun>ey 2002. By looking at figure 4.4 we can see that the credit consumption curve is not linear. It is rather an}\\
\multicolumn{3}{l}{inverse S -shape. To linearise it we take the log of consumption of households. In addition, many factors affect consumption, even}\\
\multicolumn{3}{l}{though credit is our reference variable. Hence a number of coefficients of other socio economic variables are needed in the}\\
\multicolumn{3}{l}{consumption function. Appendix 1 shows a matrix for poverty model with consumption as dependent variables and from the matrix}\\
we can estimate the coefficients of other variables.\tabcellsep \tabcellsep \end{longtable} \par
 
\caption{\label{tab_1}Table 2 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{3} \par 
\begin{longtable}{P{0.14647707979626484\textwidth}P{0.21791171477079793\textwidth}P{0.183276740237691\textwidth}P{0.1630730050933786\textwidth}P{0.13926146010186757\textwidth}}
Variable\tabcellsep Coefficient eqn 3.41\tabcellsep Standard deviation 3.41\tabcellsep Coefficient Equ 3.61\tabcellsep Standard deviation eqn 3.61\\
Ahhh\tabcellsep 0.02444*\tabcellsep 0.01348\tabcellsep 345.6**\tabcellsep 133.3\\
Aghhsq\tabcellsep -.00023\tabcellsep 0.0001703\tabcellsep -3.703**\tabcellsep 1.684\\
Agml760\tabcellsep 0.01918\tabcellsep 0.01672\tabcellsep 42.0\tabcellsep 164.6\\
Agfl760\tabcellsep 0.02196\tabcellsep 0.03097\tabcellsep 192.6\tabcellsep 306.5\\
Otbli\tabcellsep 0.13992**\tabcellsep 0.06681\tabcellsep 947.7\tabcellsep 668.7\\
Hhht\tabcellsep 0.11967*\tabcellsep 0.07258\tabcellsep 501.9\tabcellsep 728.0\\
Hhhps\tabcellsep -0.07411*\tabcellsep 0.04312\tabcellsep -585.6\tabcellsep 424.1\\
Hhhss\tabcellsep -0.05719\tabcellsep 0.03928\tabcellsep -670.1*\tabcellsep 388.2\\
IlMlSX\tabcellsep -0.07014\tabcellsep 0.04486\tabcellsep -507.0\tabcellsep 447.8\\
Hhlr\tabcellsep -0.00474\tabcellsep 0.03705\tabcellsep -214.4\tabcellsep 364.3\\
Rehhs\tabcellsep -0.05545\tabcellsep 0.06729\tabcellsep 81.3\tabcellsep 665.0\\
Loglag\tabcellsep 0.03056\tabcellsep 0.02052\tabcellsep 0.5974***\tabcellsep 0.1366\\
Bmra\tabcellsep 0.41002***\tabcellsep 0.06222\tabcellsep 2141.3***\tabcellsep 617,0\\
Mload 1\tabcellsep -0.09296*\tabcellsep 0.05314\tabcellsep -1489.1***\tabcellsep 527.5\\
Mload 2\tabcellsep 0.03543\tabcellsep 0.05221\tabcellsep 22.0\tabcellsep 519.8\\
Mload 3\tabcellsep 0.02448\tabcellsep 0.05980\tabcellsep -427.9\tabcellsep 596.5\\
Mload 4\tabcellsep 0.17041**\tabcellsep 0.08192\tabcellsep 152.1\tabcellsep 822.1\\
Mload 5\tabcellsep 0.L4867**\tabcellsep 0.07074\tabcellsep 1052.2\tabcellsep 705.9\\
Mload 6\tabcellsep 0.21371\tabcellsep 0.05920\tabcellsep 1827,3***\tabcellsep 585.7 ?\\
Mien 1\tabcellsep 0.00638*\tabcellsep 0.05025\tabcellsep -300.3\tabcellsep 505.9\\
Mien 2\tabcellsep 0.J 338/**\tabcellsep 0.05441\tabcellsep 680.9\tabcellsep 544.5\\
Mien 3\tabcellsep 0.05813\tabcellsep 0.05056\tabcellsep 417.9\tabcellsep 510.4\\
Mien 4\tabcellsep 0.12394**\tabcellsep 0.05278\tabcellsep 1509.6***\tabcellsep 522.7\\
Vil 1\tabcellsep -0.00181\tabcellsep 0.07052\tabcellsep 288.17\tabcellsep 755.4\\
Vil2\tabcellsep 0.08843*\tabcellsep 0.04738\tabcellsep 588.7\tabcellsep 574.6\\
Vil 3\tabcellsep 0.0484\tabcellsep 0.1024\tabcellsep 1158\tabcellsep 1101\\
Vil 4\tabcellsep 0.21263***\tabcellsep 0.05934\tabcellsep 3076.8***\tabcellsep 677.4\\
Vil 5\tabcellsep -0.00246**\tabcellsep 0.07036\tabcellsep -565.1\tabcellsep 728.4\\
Vil 6\tabcellsep 0.10690*\tabcellsep 0.06140\tabcellsep 780\tabcellsep 647.9\\
Vil 7\tabcellsep -0.07110\tabcellsep 0.1008\tabcellsep -1355\tabcellsep 1005\\
Vil 8\tabcellsep 0.08432***\tabcellsep 0.07761\tabcellsep 309.3\tabcellsep 806.8\\
Vil 9\tabcellsep 0.00927\tabcellsep 0.7965\tabcellsep -334.9\tabcellsep 834.2\\
Vil 10\tabcellsep 0.004282\tabcellsep 0.002944\tabcellsep 31.19\tabcellsep 29.52\\
Lamda\tabcellsep 0.4800.\tabcellsep \tabcellsep \tabcellsep \\
Square\tabcellsep 0.4540\tabcellsep \tabcellsep 0.4230\tabcellsep \end{longtable} \par
  {\small\itshape [Note: *** Significant at 1 percent ** significant at 5 percent, * significant at 1 percent Sources: Basic statistics of the regression result (appendix 5.\hyperref[b12]{13)} ]} 
\caption{\label{tab_2}Table 3 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{} \par 
\begin{longtable}{P{0.85\textwidth}}
Akuyam\\
Azare\\
Bingi/Bulamari\\
Darazo\\
Yelwa\\
Kafm kuka\\
Bakin kura\\
Tashar danasable\\
Fadamar mada\\
Sarma\end{longtable} \par
 
\caption{\label{tab_3}}\end{figure}
 			\footnote{© 2016 Global Journals Inc. (US) 1Micro Credit of Community Banks and Consumption of the Poor} 			\footnote{© 2016 Global Journals Inc. (US)} 			\footnote{Micro Credit of Community Banks and Consumption of the Poor} 			\footnote{© 2016 Global Journals Inc. (US) 1} 		 		\backmatter  			 
\subsection[{Appendix 1}]{Appendix 1}\par
variable definition for poverty model lgcade = log of total consumption per adult equivalent lglaqo = log of quantity of land owed ahhh = age of the household head in years aghhhsq = age of the household head squared agml760 = number of adult males in the household aged 17 -60 agml760 = number of adult females in the household aged 17-60 otbli = 1 if household member of other lending institutions, o if nothhhlr = 1 if household head is a manual labourer, o if not adeqhsr = ratio of the number of adult equivalents to household size depend = number aged under 17 plus those over 60 rehhs = ratio of earners to household size lihliht = 1 if household head is in good health, o if not hhhps = 1 if house hold head attend primary school, 0 if not hhhss = 1 if household head attend secondary school, 0 if not hhhas =1 if household head attend advance secondary school, 0 if not hhhuni =1 if household head attend university, 0 if not hhhsx = I if household head is male, 0 if not This page is intentionally left blank 			 			  				\begin{bibitemlist}{1}
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\end{document}
