Determination of credit programme participation and socioeconomic characteristics of beneficiaries: evidence from Sargodha.
Shah, Syeda Rizwana ; Bukhari, Aqsa Tabassam ; Hashmi, Amara Amjad 等
In Pakistan about 65 percent population live in rural areas. The
rural credit markets are segmented and imperfect. Micro-credit is
considered as a valuable technique to deal with imperfections of credit
markets. In this study, we analysed rural credit market of District
Sargodha, Pakistan to study the socio economic characteristics of the
beneficiaries and identify the determinants of participation in credit
programme in the year 2004-2005. To analyse characteristic the
descriptive analysis was used. For identifying the factors affecting
household access to credit and participation in programme, a binary
Logistic regression model was employed. Results of both analyses showed
that at household level, participation in credit programme was
significantly related to household characteristics, where participants
were indifferent to increase in rate of interest. More than 80 percent
loan came from informal sector but the probability of participation
significantly increased if there existed any formal financial
institution.
1. INTRODUCTION
International literature asserts that "micro-finance"
began alleviating poverty several decades ago when organisation in Latin
America, Bangladesh, and other developing nations started testing the
notions of lending small amounts to impoverished people (mostly women).
Professor Mohammad Younis (1) of Bangladesh and his Grameen Bank brought
it on to the world stage and showed how effectively it could be used to
change lives. Giving loans of as little as five dollars, Grameen brought
millions in to the micro credit net and in doing so lifted people,
particularly the rural poor, out of abject poverty [Ayesha (2007)]. By
1980, the success of such institutions prompted many NGO's and
International Organisations to provide micro-finance services.
Microfinance in Pakistan
In Pakistan, the First Microfinance Bank was instituted with the
conversion of the microfinance department of Agha Khan Rural Support
Programme. Later on "Khushhali Bank and Pakistan Poverty
Alleviation Fund (PPAF)" were establish. The First Women's
Bank was also active in lending microfinance. In NWFP, The Bank of
Khyber collaborated with NGO's and Rural Support Programmes (RSPs)
to serve lower income groups. Many other RSPs having largest coverage
represent the number of MFIs which function properly their due
responsibility in the development of microfinance institutions in
Pakistan. The Kashf Foundation, Taraqee and Damen have specialised in
microfinance. The Orangi Pilot Project (OPP) developed an individual
lending programme modified to urban slums, by targeting small
entrepreneurs in Karachi region. Irrespective of this fact that all
these institutions have made many achievements their contribution
remains not more by five out of hundred of the predicted needy
individuals [Montgomery (2005)].
The access to microfinance establishments and other countryside
associations offering monetary services was inadequate due to a tampered
institutional groundwork. (2) So the credit necessities of mass
underprivileged were pleased by unofficial credit markets.
The purpose of this study is to identify the factors that influence
the household access (3) to credit and determine the household to be a
programme participant. (4)
Objectives The underlying objectives of the study are to:
(1) Study the socioeconomic Characteristics of micro credit
beneficiaries.
(2) Determine the factors influencing household participation in
credit Programme.
2. REVIEW OF LITERATURE
The slogan "micro credit" was well known in third world
and modern world economies, in the end of 20th century. It is one of the
very important poverty reduction device for very poor in general and
particularly for women. The financial organisations were disable to
provide loan to the poorest possessing no "collateral". Due to
limited access of poor to the institutional credit, the impact of credit
on small farmers has been much below the expectations of policy makers
[Qureshi (1995)]. The lack of credit opportunities kept the poor in a
vicious circle of poverty. The availability of microfinance seems
imperative because financial markets were prone to neglect the
requirements of needy households, simply because of the existing
criteria in which financial worthiness requiring contacts, collateral
and accessibility [Kashf (1996)]. (5) MFIs challenged to defeat such
hurdles via inventive procedures like, to lend to more people together
and standard discounts proposals. In addition to this, it set up
stronger linkages among customers and officials [Montgomery (2005)].
During 1990s, micro credit sector remained busy in providing
lending chance to individuals embarrassed by the formal financial
institutions [Black and Morgan (1999)]. Simultaneously, new economic
improvements lesser the overheads of generating funds. These give
confidence to credit providers to engage other individuals that were
marginal and offered additional loan. Yet the fraction of households
that were constrained did not alter as time goes on [Fissel and Jappelli
(1990)]. Atieno (2001) and Fredrick, et al. (2004) contradict the above
situation and argued that loan was available at rate of interest settled
by the market. Sustainable financial institutions offer credit not only
for agricultural production, but also for consumption smoothing and
income diversification. The rural financial services make available
their loan conveniences for "small and micro enterprises" and
households. However, there are upper edges which have to face by all
probable clients. It is due to the incomplete know-how among suppliers
and demanders. The availability of loans was directly affected by the
household characteristics [Okurut (2004); Diagne and Zeller (2001)].
Access to credit can considerably add to the capability of poor
household with fewer reserves to get required farm inputs. Access to
credit also lessen the prospective costs of capital-intensive
possessions relative to family labour, thus cheering labour-saving
equipments and lifting labour efficiency, a key issue for growth,
particularly in many African countries [Delgado (1995); Zeller, et al.
(1997)]. A non-participating household that having access to credit will
still beneficial on the grounds of awareness that increased its ability
to put up with risk, as it can be encouraged to experiment with riskier,
but potentially high-yielding technology [Eswaran and Kotwal (1990)].
However, it is not necessary that availability of loan to poor
individuals forever leads to effective measures taken to lesser poverty.
Small landholder farmers were too poor to take advantage of funds.
Although if they approach to credit, its restraints were so harsh that
increased yield would fail to undertake their feeding requirements. For
reducing market imperfections and malfunctions credit accessibility to
needy seemed to be a significant device. The reason behind this was that
if for sometime the credit restraints were made hassle free, the
concerned individuals may employ more labourers for getting increase in
produce [Simtowe (2006)].
There are two different facets of informal financial institutions.
Initially, private lending bodies and other unofficial commission agents
are present with official lenders, for example banks and NGO's, and
in recent times, MFI's. Next, probable loan takers apparently
experience substantial contract overheads on getting peripheral loan
[Gine (2002)].
There were two different steps in the course of action involved in
credit. First, people willing to borrow fix on their required amounts to
apply for, from a particular agency/lender at the existing rate of
interest, making demand side. In the next step lender decides to whom he
had to fund and how much? It was the supply side. The availability of
both institutional and non institutional credit was predicted through
correlations among suppliers and demanders. The access to formal credit
sector was constrained due to the institutional limitations. The
institutions sanctioned credit only for reproduction or manufacturing,
where as non institutional sources offerings were varied. The formal
lenders adopt severe collateral pre requisites to minimise evasion, thus
separating poor from the process. The low level of returns, asset growth
and limited formal lending for consumption smoothening, make the poor
household unattractive and render a high-risk contour for formal
lenders. So they move to the informal credit market to meet their credit
demands [Duong, et al. (2002); Pal (2002); Barslund and Tarp (2007)].
Credit demand is moderately modeled by unique characteristics of
borrowers. It was possible that most who seek credit would be able to
obtain it, but costs and conditions may be prohibitive for some high
risk borrowers [Atieno (2001): Okurut, et al. (2004)]. The demand for
credit increased if household's earnings were more, if it is the
owner of the house. In case of bigger families demand also raised.
Individual's socioeconomic aspects have an effect on its stipulated
employment for liquidity restrained "households". Where as
these socioeconomic factors exhibited no influence as for unconstrained
individuals are concerned. For reducing market imperfection and
malfunctions, credit accessibility to needy seemed to be a significant
device. The reason behind this was that if for sometime the credit
restraints were made hassle free. The concerned individual may employ
more labourers for getting increase in produce [Simtowe (2006)].
Household borrowing was determined in the long run by real spending,
gross wealth and the repayment term for outstanding credits, which had a
positive influence, and these were inversely related to the cost of
loans and the unemployment rate. Development in the short run was
influenced by changes in long-term interest rates and in employment
[Nieto (2007)]. The household aged between 20 to 30 are more passionate.
They continue taking on risk and hence experienced rapid increase in
earnings. These energetic households actively take part in borrowing
programmes than elder [Lehnert (2004)].
Once a household decides to utilise a particular source of loan,
the next question will be the determination of interest rates [Ho
(2004)]. The dominance of doubling of rates of interest in the non
institutional sector was confusing [Gill (2003)]. In the same area, on
superficially similar loan transactions, rates of interest may get
different standards [Udry (1991)], but were frequently to the upper edge
[Gill (2003)]. Usually, to reduce evasion and risk, higher than normal
interest rates were charged as prerequisites. In rural settings of
Pakistan, the standard deviation of interest rate charged by
moneylenders was 40 percent per annum [Aleem (1990)]. Evidence from
India showed that informal interest rate varied from 20 percent to 120
percent [Timberg and Aiyar (1984)]. Demand for credit was extremely
value responsive at higher interest rates. It was also observed that
amount of loan was very sensitive to alterations in credit developments
as compared to rate of interest. This is a common practice with
households having low earnings [Karlan and Zinman (2005)]. Along with
increase in rate of interest, poor's demand for finance increased
with increase in lending each year. However, the loan portfolio of
financial institutions was shifted towards comparatively wealthier
customers as compared to the composition of the portfolio that was
without an interest rate change. The results on one hand supported those
who argue that raising rates can improve the financial permanence of
microfinance organisations. On the other side results also supported
those who argue that the poor, and particularly the poorest, do consider
prices and reduced loan demand accordingly [Dehejia, et al. (2005)]. On
extra use of credit, clients experience an increasing trend in rate of
interest. After some time this trend become straight and household
experience the upper edge of credit. The interest rate assumed a wide
range of values in informal sector. Sometimes rate of interest was
charged in terms to under price the collateral [Bhaduri (1973)]. Being
able to provide marketable collateral was showed an inverse relationship with higher rates of interest [Sarap (1991) and Ho (2004)]. Conversely,
in economies where there is developed agriculture sectors and landlords
and middle man hold on informal credit markets, collateral was scarcely
under priced. Though the rates of interest charged were high. It is due
to the reason that households demanded much more than was offered by
official lenders. They had to move to informal supplier. The informal
money lenders give loans at very high rates. These landlords and
commission agents give loans not only for production but also for every
reason. They did not documented the contracts and do not involve
borrowers on paper work. These things make borrowing easy and gorgeous
[Gill (2003)].
3. DATA AND METHODOLOGY
The Data
The study uses a survey data of rural household collected by the
Rural Community Development Society (RCDS) (6) a Pakistani NGO in the
year 2004-05. A sample of 910 from district Sargodha (7) was taken from
low income category of population with average monthly income Rs 3872.
The data included but not limited to household characteristics including
size, composition, employment, expenditures and borrowing.
Methodology
The analytical procedures used for the present study can be divided
into two categories:
1. Descriptive Analysis.
2. Econometric Modelling.
Descriptive Analysis
The analysis under this category includes calculation and
comparison of average household characteristics using descriptive
statistics. The demographic as well socioeconomic profile of borrowers
including information about credit will be examined to assess the
general characteristic of borrowing households. The descriptive
statistics analysis will also help provide some insight about the
importance of various factors related to the use of credit, which in
turn will be useful for developing, and estimation of the econometric
modelling discussed in the following section.
Econometric Modelling
In this section, the household decision of borrowing and
determinants of participation were analysed. Following Ho (2004) and
Nguyen (2007) credit programme was specified by participation as a
function of household characteristics. A twofold logistic regression
technique was employed to predict the probability of participation. With
a particular reference of participation to credit, the dependent
variable assumed two values i.e. "1" if there was
participation to credit and "0" otherwise. Numerous mortgages
taken by individual were supposed as different dealings. The logistic
regression model then expressed as
[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]
Where "[rho](y = 1)" indicated the probability of
obtaining credit. HAGE narrated the age of household head. HEDU was the
years of schooling of head of household. HHS was the household size. EAR
were the number of household family members participating in economic
activities. INC was the monthly revenue earned by household. INT showed
the rate of interest charged on loan. The variable OWNH represented the
ownership of house. It was a categorical variable. Having a house owned
by the household equals "1" otherwise "0". SL was
the source of loan. It was divided into formal and informal financial
institutions. FFI showed the presence or absence of formal financial
institution or NGO operating near the village.
4. RESULTS AND DISCUSSIONS
This section provides major characteristics of households that are
micro credit beneficiaries. Such characteristics include age of
household, household size, number of dependents and earners, household
marital status etc.
Age of Head of Borrower Household
In the sample, the frequency distribution of credit borrowers with
respect to their age showed that probability of getting initial external
financing had increased from the age of 28 years and decreased after 50
years. It was at its peak in the age of 35 years. The household members
below 18 and above 70 were credit rationed. So the financially deprived
people of these two segments of sample population were not given micro
loans. It affected mainly the youngest strata of the population, thus
depriving them of the financial support needed to establish their own
businesses (see Table 1).
Education
The correlation of distribution of education and borrowing
activities showed that households head's having no schooling,
primary or lower secondary school were the main borrowers. The
Households' head possessing university degree did not borrow much.
The underlying assumption is that higher education surely helped them to
find a paid job and they were not in need of micro finance. This
concludes that higher education did not determine credit participation.
Marital Status and Family Size
The households headed by male members have higher probability of
obtaining loan from either sector. A household leaded by a male family
member was expected to be professed even economically and consequently
lesser hazard, compared to a lady headed household. A high dependency
ratio tend to boost the danger of loans and accordingly left a positive
outcome on the interest rate.
Income and Expenditures of Household
The explanatory data from the household investigation demonstrates
that the curriculum mark the poor. The average Income of household Rs
3872.77 was too stumpy to carry out his needs. The per capita income Rs
646.2442 was below poverty line in the period of data collection
(2004-05).That insufficient income trapped the households in to a
vicious circle of poverty. To break that trap they required a big push,
which they obtained from external borrowing.
Household Occupation
Descriptive statistics for the occupation of borrowers showed that
labour class and people enjoying self-businesses were the main
borrowers. The percentage from private sector, agriculture sector and
from live stock comprised of a small proportion of borrowers.
Characteristics of Credit
Different household decided to apply to the offered sources of
credit. The applications depend on how these households meet their
distinctive and economic characteristics. These characteristics
determined the judgment to pertain for credit, and to which sector it
was applied for i.e. either formal or informal lenders.
The source of credit was divided into formal and informal sector.
The result showed that majority of loan was given by friends and
relatives. It was also observed that friends and relatives make
available loans for almost all kind of purposes. It ensures the reality
that informal institutions believe in a lower fudging possibility. So
the informal sector is the biggest source of loans not in the rural area
but also take over in the urban areas.
Based on the classification of informal markets in the study
region, it was seen that out of those who had used credit from informal
sources, 82 percent used family and friends and 5 percent took credit
from moneylenders and property owners. The proportion of loan from
formal sector was 12 percent. It was due to the reason that friends and
relatives offer loan for every reason and they were best aware of the
personal characteristics of households. They knew very much about the
borrower's credibility so the fudging rates were minimised. The
rate of interest was nominal on such loans. Perhaps this was the biggest
reason of taking loan from this sector.
[FIGURE 1 OMITTED]
Determinants of Participation in Credit Programme
In this section, the household participation in credit determined
by various factors was analysed with Logistic regression analysis. Rural
financial institutions provide credit both for consumption and
production purposes. It was supposed that, on demand of credit ,an
individual either apply to formal or informal institution for loan. The
applications were made on the chance of obtaining funds from either
sector. The probability of getting loan depends on the money demanded,
the previous loan records and also on the available supply. It was
assumed that for an individual household "i" for a certain
time period "t", the probability of obtaining loan from any
informal institution was greater than the formal institution. Following
Ho (2004) this assumption was also applied. The results were shown in
Table 2.
The change in dependent variable was explained by the magnitude of
odd ratios. The results demonstrated that there was a negative relation
ship between the age of household and access to credit (which is =1) for
a household. It mean, if age of household head increase there is a
significant decrease in the participation to credit programme. The odds
ratio for education of head of household showed that it had negative
effect on the access to credit, that if education increase there is a
significant decrease in participation to credit programme.
While the odds ratio for household size revelled that as the no of
headcount member of household increases there is a significant increase
in the participation to credit programme
The odds ratio for earners of household (no of headcount members
involved in economic activities) was depicting that participation to
credit programme for a household significantly increases as the earner
of household increases.
The odd ratio for the income (natural log) of household showed that
income increases the participation to credit programme decreases.
The odds ratio for the variable rate of interest (natural log)
indicated that participation to credit programme significantly increases
as rate of interest increases.
The odds ratio for the dichotomous variable of the ownership of
house (if household head owned the house = 1, zero otherwise) revealed
that odds of the event of participation to credit programme of a
household head possessing a house significantly increases significantly
more than odds of a household living in a rented house.
The odds ratio for the binary variable of the source of loan
(formal = 1 and 2 otherwise) was showing that odds of the event of
participation to credit programme to formal source decreases
significantly than odds of informal source.
The odds ratio for the dichotomous variable of the presence of
formal financial institution (if a formal financial institution or NGO
operating in the area = 1, zero otherwise) was predicting that odds of
the event of participation to credit programme of a household
significantly increases about 3.192 times more than odds of absence of
formal financial institution.
Above results demonstrated that a household demanded more debt when
its income was higher, when it owned its own home, when the family size
was larger and the head was working. The age of head of household showed
a negative affect on participation as younger households are more
energetic and motivated. These results are consistent with Lehnert
(2004) and Nugyen (2007) but it differs with swain (2001) who stated
that with the increase in age, accumulated experience, practical and
professional wisdom of the household increased its income generating
capability and he demanded more credit to explore his capabilities or to
spend on consumption.
Education level also showed a negative affect on credit
participation. It is also similar to Nugyen (2007). Households heads
possessing higher degrees were showing almost no participation. Because
higher education may help head of households easier to find a paid job.
The variable estimated for the earning members of a household have
a positive relationship with the access to credit. It was because as the
household had more than one earner it was easier for him to repay and to
fulfill collateral requirement. Households having more than one earning
members showed economically sound position.
The coefficient estimate for the household size variable was
positive and significantly consistent with the previous studies [Ho
(2004); Simtowe (2006) and Nguyen (2007)]. Having a bigger family,
ceteris paribus: increased the demand for loans, because per capita
income was smaller for big households. A large family was likely to
expect a large flow of income in the future as the children grow up and
begin to work, thus they were likely to demand more credit.
The rate of interest was reported as positive. It meant that
households were indifferent about the rate of interest. This was
consistent with Malik (1999), Gill (2003) and Dehejia, et al. (2005).
The results supported those who argued that raising rates could improve
the financial permanence of microfinance organisations. Other things
being constant, and assuming all borrowers have equally good credit, a
lender would prefer to lend out a big loan to one borrower rather than
several small loans to several borrowers had he the choice, because his
transaction cost would be lower.
The coefficient of income of household was negative and
significant. As income from both farm and off-farm activities enhance
farmers' confidence to not borrow. Because we can categorise the
observed loan as micro credit or micro financing loans which more than
80 percent comes from the informal sector and most often used for the
consumption purposes or to smooth the consumption patterns in the bad
yeas. Where the borrowers also belonged to the low income group. Their
per capita income was Rs 646 which was below poverty line in the year of
data collection 2004-05. Therefore such income reflects capacity to
finance their spending by themselves, hence as household income
increased, the probability to borrow is expected to decrease.
The existence of the any financial institute or NGO had strongly
positive effect on the participation activities in the credit programme.
The underlying fact is that, if there is any financial institute it
breaks the monopoly of the land lords and arthies in the credit market,
especially in the rural areas. It helps to overcome the market
imperfection in the credit market.
As most of the loan in our data was taken from informal sources,
therefore the dummy variable of formal sector showed a negative relation
ship with credit participation variable. It is showing the fact that
more risky and consumption purpose loans are not given by financial
institutes.
Over all we can say that the results of the research in hand are in
line with general established economic theory. It is very reasonable
approach to study the determinants of household participation in credit
programme as a function of household's socio economic
characteristics. [See Atieno (2001); Fredrick, et al. (2004); Malik
(1999); Dehejia, et al. (2005); Ho (2004); Nguyen (2007)].
5. CONCLUSION
The paper is an attempt to analyse the factors affecting household
participation in credit programmes. It utilised a cross-sectional
household data set from the period 2004-2005. The results drawn from the
Logistic regression concluded that credit was accessible at market
determined rates. At household level, the participation to credit was
influenced by age of the head of household, years of schooling of
household head, earners in a household and household size. The ownership
of house increased the probability of obtaining loan. One surprising
result was that household was indifferent about rate of interest. They
prefer liquidity even at high rate of interest. The presence of formal
financial institution increased the access to credit by household. It
was observed that major source of external financing was the informal
credit market.
6. SUGGESTIONS
To perk up the rural credit markets the following actions are
proposed.
* The procedure of getting loan should be simplified. So that
illiterate can easily understand terms and conditions of agreement.
* For the purpose of reimbursement, MFIs should create such
incentives that would influence borrowers to repay their loans at the
given time.
* MFIs should devise such policies that credit should reach to the
low income group and women for smoothing consumption and fox"
carrying on income-generating and income diversifying activities.
Operating these recommendations can improve the efficiency of the
lending methods and increase household participation in credit
programmes and helped them to get out of poverty.
REFERENCES
Aleem, I. (1990) Imperfect Information, Screening, and the Costs of
Informal Lending. A Study of a Rural Credit Market in Pakistan. World
Bank Economic Review 4:3, 329-49.
Atieno, R. (2001) Formal and Informal Institutions, Lending
Policies and Access to Credit by Small-scale Enterprises in Kenya.
University of Nairobi, African Economic Research Consortium, Nairobi
(AERC, Research Paper 111).
Ayesha, T. H. (2007) Banking The Poor. The News.
Barslund, M. and F. Tarp (2007) Formal and Informal Rural Credit in
Four Provinces of Vietnam. Department of Economics, University of
Copenhagen, Denmark. (Discussion Papers).
Bhaduri, A. (1973) A Study in Agricultural Backwardness under
Semi-eudalism. Economic Journal 83:1, 120-37.
Black, S. E. and D. P. Morgan (1999) Meet the New Borrowers. In
Economics and Finance, Federal Reserve Bank of New York 5:3.
Dehejia, R., H. Montgomery, and J. Morduch (2005) Do Interest Rates
Matter? Credit Demand in the Dhaka Slums. Preliminary draft Columbia
University, Asian Development Bank Institute New York University, March
9.
Delgado, C. (1995) Africa's Changing Agricultural Development
Strategies: Past and Present Paradigms as a Guide to the Future. 2020
Vision Food, Agriculture, and the Environment. Washington, DC.:
International Food Policy Research Institute. (Discussion Paper 3).
Diagne, A., and M. Zeller (2001) Access to Credit and Its Impact on
Welfare in Malawi International Food Policy Research Institute,
Washington, DC. (Research Report 116).
Duong, Pham, and Y. Izumida (2002) Rural Development Finance in
Vietnam. A Micro-econometric Analysis of Household Surveys. World
Development 30: 2, 319-35.
Eswarn and A. Kotwal (1990) Implications of Credit Constraints for
Risk Behaviour in Less Developed Economies. Oxford Economic Papers 42,
473-482.
Fissel, G. S. and T. Jappelli (1990) Do Liquidity Constraints Vary
Over Time? Evidence from Survey and Panel Data. Journal of Money,
Credit, and Banking 22:2, 253-62.
Fredrick, F. and K. Bokosi (2004) Determinants and Characteristics
of Household Demand for Smallholder Credit in Malawi. http//econ
papers.repc.org/scripts?
Gill, A. (2003) Interlinked Agrarian Credit Markets in a Developing
Economy: A Case Study of Indian Punjab. Department of Correspondence
Courses, Punjabi University, Patiala, India, Paper presented in the
International Conference on Globalisation and Development organised by
the Development Studies Association, U.K hosted by the University of
Strathclyde, Glasgow 10-12.
Gin'e, X. and The World Bank (2002) The Impact of Credit on
Income Poverty in Urban Mexico. An Endogeneity-Corrected Estimation.
Ho, G. (2004) Rural Credit Markets in Vietnam, Macalester College Theory and Practice. Grand Prize Thesis. National Undergraduate Research
Contest in Agriculture, Environmental and Development Economics.
Karlan, D. and J. Zinman (2005) Elasticities of Demand for Consumer
Credit. Yale University, MIT. Poverty Action Lab, Dartmouth College Economic Growth Center Yale University, New Haven, CT 06520-8269, Centre
(Discussion Paper No. 926).
Lehnert, A. (2004) Housing, Consumption, and Credit Constraints
Board of Governors of the Federal Reserve--Household and Real Estate
Finance Section September 2004 (Feds Working Paper No. 2004-63.)
Malik, S. J. and Hina Nazli (1999) Rural Poverty and Credit Use:
Evidence from Pakistan. The Pakistan Development Review 38:4, 699-716.
Montgomery, H. (2005) Serving the Poorest of the Poor: The Poverty
Impact of the Khushhali Bank's Microfinance Lending in Pakistan.
Online available at: http//www.adbi.org/
files/2005.09.28.book.khushali.microfinance.study.pd
Nguyen, C. H. (2007) Determinants of Credit Participation and its
Impact on Household Consumption. Centre for Economic Reforms and
Transformation, School of Management and Languages, Heriot-Watt
University, Edinburgh.
Nieto, F. (2007) Determinants of Household in Spain. Bank of Spain.
(Research Paper No. WP-07162007).
Okurut, N., A. Schoombee, and S. Berg (2004) Demand and Credit
Rationing in the Informal Financial Sector in Uganda. Makerere
University and University of Stellenbosch, African Development and
Poverty Reduction. (The Macro Micro Linkage, Forum Paper 2004).
Pal, S. (2002) Household Sectoral Choice and Effective Demand for
Rural Credit in India Cardi. Business School, Cardi. University,
Aberconway Building, Colum Drive, Cardi. UK. Applied Economics 14,
1743-1755.
Qureshi, S. K. (1995) Institutional Credit And Group Based Lending
for Rural Poor in Pakistan. The Pakistan Development Review 35:4,
769-778.
Sarap, K. (1991) Interlinked Agrarian Markets in Rural India. New
Delhi: Sage Publications.
Scott, A. (1996) Consumption, 'Credit Crunches' and
Financial Deregulation London Business School--Department of Economics;
Centre for Economic Policy Research (CEPR) May. (CEPR Discussion Paper
No. 1389).
Simtowe, F. P. (2006) To What Extent are Credit Constraints
Responsible for the Non-Separable Behaviour at Household Level? Evidence
from Tobacco Growing Households in Rural Malawi University of
Malawi--Centre for Agricultural Research and Development. Journal of
Applied Sciences.
Swain, B. R. (1999) The Demand and Supply of Credit for Households:
An Analysis of Purl District Credit Markets. (Working Paper).
Timberg, T. A., and C. V. Aiyar (1984) Informal Credit Markets in
India. Economic Development and Cultural Change 33:1, 43-59.
Udry, C. (1991) Rural Credit in Northern Nigeria. New Haven:
Department of Economics, Yale University.
Zeller, M., G. Schrieder, J. yon Braun, and F. Heidhues (1997)
Rural Finance for Food Security for the Poor: Implications for Research
and Policy. Washington, DC, International Food Policy Research
Institute. (Food Policy Review No. 4.)
Comments
The paper analyses the determination of Credit Programme
Participation and Socio-Economic Characteristics of Beneficiaries by
using a survey data that was collected from 910 rural households in
Sargodha by a Pakistani NGO in the year 2004-05. The paper needs some
revisions. First of all, the introduction and review of literature
section need to be improved. Review of literature needs to be written
with continuity of ideas. Furthermore, it is not clear from the review
whether the review is reflecting evidence from Pakistan or other
countries.
The review suggests that household borrowing is determined in the
long run by real spending, gross wealth and the repayment terms for
outstanding credits. On the other hand household borrowing is inversely
related to the cost of loans and the unemployment rate. The inverse
relationship with unemployment rate is not expected. I anticipate a
positive relationship the higher the unemployment rate, the higher will
be the borrowing need of a person. There is also need to review recent
literature on microcredit in Pakistan. For example, Access to Finance
Study carried out by State Bank in collaboration with DFID and GTZ. The
finding of the study suggests that only 2 percent of the population get
credit form the formal sector.
The authors have analysed the data by using the descriptive
analysis and econometric modeling techniques. The paper needs more
editing as the authors have not referred tables of results in the
discussion and reported in an appendix. They should report the results
in the main body of the text. In results section, they also discuss
evidence from Malawi and Kenya confusing them with Pakistan. The result
that participation to credit program significantly increases as rate of
interest increases is contrary to the perception and findings from other
countries. It should be further explained. In the conclusion sections
general suggestions have been given. These suggestions should be linked
with empirical findings of the paper.
There are too many references in the reference list but a number of
references referred in the text have not been written. Unnecessary
references should be curtailed and only relevant references should be
included. Comparison of the results with other authors should come in a
sub section of result. In the end, author should discuss the theme of
the conference, the economic sustainability and try to link the paper
with theme.
Talat Anwar
Canadian International Development Agency, Islamabad.
Rizwana Shah <rizwanashahzad208@yahoo.com>, Aqsa Tabassam
Bukhari <imaqsa_bukhari@yahoo.com>, Amara Amjad Hashmi
<ammara_hsa@hotmail.com> are Graduate Students, and Sofia Anwer
<sofia_ageconomist@yahoo.com> is Professor, Department of
Economics, University of Sargodha.
(1) The pioneer of Grameen Bank, Bangladesh.
(2) www.bwtp.orj/arcm/pakistan.
(3) A household had access to credit from a particular source if it
was able to borrow from that source.
(4) Participation in the credit programs referred to the situation
in which household actually borrowed from that source of credit.
(5) Annual report Kashif Foundation (1996).
(6) RCDS, was registered in 1998, a member organisation of Pakistan
Poverty Alleviation Fund (PPAF). The World Bank funded PPAF has been
designed to reduce poverty and empower the rural and urban poor in
Pakistan. RCDS has an exclusive focus on poor farmers and low-income
entrepreneurs.
(7) The population estimate for the study region, Sargodha Town
(2007) is 686,312, for the tehsil 10, 81,459 and for the district 26,
65,979. The economy of the region is pre-dominantly based on agriculture
[Pakistan (2007) "Ministry of local Government and Rural
Development". http//www.pap.org.pk].
Table 1
General Characteristics of Household Borrowers
Variables Minimum Maximum
Age (Years) 18 70
Education (Years of Schooling) 0 14
Martial Status 0 1
Family Size 2 12
Earners 1 6
School Going Members 0 6
Learners 1 3
Income (Rs) 1500 12000
Expenditures (Rs) 1000 12000
Per Capita Income (Rs) 200.00 1666.67
Amount Borrowed 2000 40000
Days of Process 1 45
Variables Mean Md. Deviation
Age (Years) 40.97 11.142
Education (Years of Schooling) 4.10 3.878
Martial Status 0.98 0.129
Family Size 6.52 2.167
Earners 1.40 0.960
School Going Members 1.70 1.442
Learners 1.27 0.578
Income (Rs) 3872.77 2346.683
Expenditures (Rs) 3625.00 1941.136
Per Capita Income (Rs) 646.2442 400.83380
Amount Borrowed 21350.00 53089.252
Days of Process 3.65 9.279
Table 2
Determinants of Households Participation in the Credit Programme
Variable Coefficients Sig. Odd Ratio's
HACE -0.51 0.012 0.950
(0.020)
HEDU -0.28 0.387 0.972
(.033)
HHS 0.242 0.001 1.274
(0.075)
EAR 0.390 0.000 1.477
(0.037)
INC -1.160 0.000 0.314
(0.290)
INT 1.319 0.000 3.740
(0.248)
OWNH(cat) 0.145 0.376 1.156
(0.164)
SL(cat) -1.631 0.000 0.196
(0.361)
FFI(cat) 1.161 0.000 3.192
(0.246)
constant 5.013 0.000 150.339
(0.685)
Dependent Variable = participation in credit programme (yes = 1, 0
otherwise).