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THE FIRST INTERNATIONAL CONFERENCE ON SHARI’AH ORIENTED PUBLIC POLICY
IN ISLAMIC ECONOMIC SYSTEM (ICOSOPP 2015)
“Formulating Effective Public Policy in the Islamic Economic System under the Framework of Shari’ah”
30-31 March 2015, Ar-Raniry State Islamic University, Banda Aceh, Indonesia
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Promoting Maqāṣid al-Shari`ah and Achieving Sustainable
Economic Development: The Potential of Proposed Two Tier
Mudarabah Business Model on Cash Waqf
Dr. Abu Umar Faruq Ahmada
, Mohammad Ashraful Mobinb
Corresponding Email: faruq@isra.my, 1400028@student.inceif.org
a
International Shari`ah Research Academy for Islamic Finance (ISRA)
Associate Professor, INCEIF, Kuala Lumpur, Malaysia
b
Graduate Student, INCEIF, The Global University of Islamic Finance,
Kuala Lumpur, Malaysia
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Abstract
Islamic microfinance provides an alternative model for a significant number of underprivileged people
who are not served by conventional microfinance. In order to give access to sustainable services to a
greater extent, the Islamic microfinance is in paramount need of the adoption of innovative business
models and sound practices into the industry. To this end, the research seeks to propose two tier
mudarabah model based on cash waqf as an alternative to Islamic microfinance institutions. The
objectives of the study are: to a) help develop and implement an appropriate business model; b)
safeguard the maqasid al-Shari`ah so as to promote the well-being of the people through establishing
justice and eliminating hardship; and 2) help make a reform in the present institutional framework for
Islamic microfinance and waqf institutions. The study finds that cash waqf based microfinance
institutions can play an important role inter alia in financial inclusion and developing socio-economic
conditions of the poor and underprivileged people in society.
Keywords: Waqf, Maqasid al-Shari`ah, Microfinance, Poverty, Two Tier Mudarabah
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1. Introduction
According to a recent World Bank report (as at 8 October 2014), there were 2.2 billion people lived on
less than USD2.00 per day (average poverty line in developing countries) in 2011, compared with 2.59
billion people who lived on the same amount in 1981. Although the number of people living below the
average poverty line has reduced by 15% from 20 year ago, this number still makes up of an alarming
30.6% of the current world population of 7.2 billion. Out of those living below average poverty line, as
reported by the Islamic Development Bank (IDB), 650 million are Muslims.
As part of the initiatives of addressing the poverty problem, many international and national institutions
have undertaken programs to help countries provide better job opportunities to the unemployed, and to
promote microenterprises. The latter, which normally turned away by conventional profit-orientated
financial institutions due to lack of collaterals, high transaction costs, and fragile legal enforcement
mechanisms, have been served by microcredit and microfinance institutions (MFIs) since their
inception in the 1970s with phenomenal success. However, there is an increasing trend for the Muslim
microentrepreneurs turning to Islamic microfinance Institutions (IMFIs) for Shari`ah complaint
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financing taking into consideration of faithful Muslims‟ adherence to their religious practice.
According to IDB, the number of customers of IMFIs though quadrupled in recent years has reached to
only 10, 00000 Muslims compared to the 650 million Muslims who are below the average poverty line.
There is an urgency for the IMFIs to build capacity and capability in order to provide Shari`ah
compliant products to adherent Muslims who are in dire need of Islamic microfinancing.
In Islamic economics and finance, there is a near consensus at least in theory that out of all financial
products and instruments, profit sharing instruments are the backbone of Shari`ah compliant
transactions, which is justified by the way any risk involved is properly shared by the parties involved
in the underlying contracts. This risk sharing is highly consistent with the general spirit of Islamic
teachings. In this paper we will make a humble attempt to propose a two tier mudarabah model for
waqf based MFIs.
This research is designed to explore a cash waqf based Islamic microfinancing model for adoption by
the IMFIs to finance the Muslim microentrepreneurs with the ultimate objectives of poverty alleviation.
The following will present the findings of the previous study on microfinancing particularly in Islamic
microfinancing, the issues relating to the IMFIs, the proposed cash waqf based Islamic microfinancing
model, the analysis of data from other established models, and conclusion of the study on the effective
of the proposed model.
2. The Rationale for Promoting Maqasid Al-Shari`ah
The key objective of Shari`ah lies in the principle of eliminating agony, hardship and difficulty from
people and finding solutions to their problems. Emphasising this principle the Qur‟an declares that
Allah does not want to impose any hardship on His servants, although the context of the relevant text
illustrates the rituals of ablution for prayer. The application of the contents of the text is eternal. The
verse reads: “Allah does not want to impose any hardship on you, but wants to make you pure, and to
bestow upon you the full measure of His blessings, so that you might have cause to be grateful.” (5: 6).
The objectives of Shari`ah have either been directly mentioned in the Qur‟an and the Prophetic
traditions (Sunnah), or referred to them by some of the predecessors among the Muslim jurists. These
were recognised by almost all of them in order to serve the common interests of entire human beings
and to protect them from being harmed. For instance, the twelfth century Muslim philosopher Al-
Ghazali (d.1058AH/1111CE) classified the maqasid into five key categories. He writes: “The very
objective of the Shari`ah is to promote the well-being of the people, which lies in safeguarding their
faith (dīn), their self (nafs), their intellect („aql), their posterity (nasl), and their wealth (māl). Whatever
ensures the safeguard of these five serves public interest and is desirable.”
Al-Shatibi (d.790H/1388CE) endorses Al-Ghazali‟s five key categories of maqasid al-Shari`ah, and
point out that they are the most preferable in terms of their harmony with the fundamental nature
of Shari`ah. However, mostly late and contemporary scholars, have extended the scope of the
maqasid to embrace other vital objectives of Shari`ah. The Maliki jurist, Al-Qarafi (d.684
AH/1285CE) has added the protection of `ird (integrity) as the sixth objective of Shari`ah. Later, the
fourteenth century Ibn Taymiyyah (d.728AH/1327CE) was perhaps the first scholar to depart from the
perception of locking up the maqasid to a definite number.He provides a broader definition maqasid al-
Shari`ah by adding the preservation of the social order, promotion of the wellbeing and righteousness
of the community, preservation of the family, etc. to the previous list of his predecessors. He asserts:
“Both its general rules and specific proofs indicate that the all-purpose principle of Islamic legislation
is to preserve the social order of the community and insure its healthy progress by promoting the well-
being and righteousness of that which prevails in it, namely, the human species. The well-being and
virtue of human beings consist of the soundness of their intellect, the righteousness of their deeds as
well as the goodness of the things of the world where they live that are put at their disposal.”
The Muslim scholar Ibn al-Qayyim (d.751AH/1350CE) looks into the maqasid in a bit different way.
He observes: “Shari`ah is based on wisdom and achieving people‟s welfare in this life and the
afterlife. Shari‟ah is all about justice, mercy, wisdom, and good. Thus, any ruling that replaces justice
with injustice, mercy with its opposite, common good with mischief, or wisdom with nonsense, is a
ruling that does not belong to the Shari`ah, even if it is claimed to be so according to some
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interpretations.” However, the contemporary scholar Al-Qaradawi has further included in the list of the
maqasid human dignity, liberty, social welfare and human alliance branding them as the
higher maqasid of the Shari`ah.
From the above discussions it is evident that the objective of Shari`ah in general is to safeguard the
society and ensure the stability of its healthy growth and development. From these
perspectives, Shari`ah encourages individuals and societies to be in complete harmony and well
protected, with a sense of unity and trust where each member of the community renders help to other,
enjoins what is good, and refrains from doing mischief.
3. The Plan of the Study
The organisation of the paper is as follows:
Following the introduction and a related analysis on the rationale for promoting maqasid al-Shari`ah
the study reviews the relevant literature in search of finding out the gap in previous research and to fill
in this in present research. It then thoroughly discusses the waqf and its investment potentials, concepts
and comparative analysis of both Islamic and conventional microfinance as a background study of the
research. Then the discussions on the core issue of the research i.e., the cash waqf based Islamic
microfinance has followed, which included inter alia the alienation of the structure of the proposed
'two tier mudarabah cash waqf model', its salient features and the justification for proposing the model.
In order to test the feasibility of the proposed cash waqf based microfinance model, the study came up
with a hypothesis tom prove whether the MFIs can lead the economic growth of a country. The paper
concludes with the summary and suggestions.
4. The Review of Literature
Waqf in general have had a long record in culture and civilisation of the human and nations‟ history. Its
instance and history can be traced back Egypt's pharaohs. Pharaohs in old Egypt, allocated property and
lands for monks and even in the ancient Greek civilisation, there were actually signs of library waqf
and educational centers. Waqf can be seen to instances and charity activities during the lifetime of
Prophet Muhammad (pbuh) and in his holy traditions prior to the advent of Islam in the then Arabian
peninsula. Prophet Abraham (pbuh), the father of the Muslim ummah, during his lifetime had founded
charity and the relief efforts to assist Muslims with their property through introducing waqf, with the
aim of leaving the ownership of that property remain forever in their hands and spending it to public
welfares.
Given the above backdrop, waqf in Islam may have likely been influenced by earlier civilisations such
as the ancient Mesopotamia, Greece, Rome as well as the pre-Islamic Arabs whereby there were certain
knowledge of such endowments. However, the extent of how much influence of these ancient
institutions has had on waqf is still a matter of intense debate. Nevertheless, Romans‟, Byzantines‟,
even Mesopotamians‟, Sassanids‟, Jewish and Buddhists‟ influences have been accepted as plausible.
The latest research is more decisive and points to the Sassanid law as the most likely source. In
summary, it can be assumed that Muslims were urged strongly to endow their assets in the service of
mankind and they learned how to do it from the earlier civilisations, which had dominated the region in
which they had found themselves.
Since Islam is a religion of fitrah or inborn nature, some of the customs or practices were adopted by
other nations prior to the advent of Prophet Muhammad (pbuh) which did not contradict the teachings
of Islam were retained during the time of the Prophet including those adapted from other civilisations.
This ability may well have originated with a tradition attributed to the Prophet, which says: “A word of
wisdom is the lost property of a believer; he can take it wherever he finds it, because he is more entitled
to it.” (Al-Tirmidhi, 1992: 2687)
Although waqf is not specifically mentioned in the Qur‟an, the concept of wealth redistribution is
strongly emphasised (2:215, 264, 270 & 280). There is also definitive evidence that many great
personalities of Islam had endowed their properties for charitable purposes. A Prophetic tradition most
probably accounts for the origin of the institution of waqf in the world of Islam. It mentions: “When a
man dies, all his acts come to an end, except for three: ongoing charity, or knowledge (by which people
benefit), or a righteous offspring, who prays for him” (Muslim, 1992: ch. 3, no. 14).
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Although the classical sources have normally separates each one of these good deeds, it is always
preferred for the modern generation to combine them due to the argument that this combination is what
constitutes the very essence of the Islamic waqf. For this Muslims needed an institution that would
enable them to perform all three of these good deeds. The waqf fitted the criteria. It indeed, assures
ongoing charity for many years, even centuries, after the death of the founder; it can finance scholars
whose lasting works will benefit mankind for a long period and the good deeds, that accrue to them
would be shared by the waqf ‟s founder who had provided for their sustenance in the first place.
Finally, the management of waqf can be entrusted to the offspring of the founder so that while, careful
and loyal management is assured, on the other hand, the offspring would pray for the deceased since he
or she is not destitute.
Although Muslims may have been encouraged to adopt ideas from other civilisations, the actual
process of adoption is not as simple as stated in the aforementioned Prophetic tradition. Institutions that
were adopted need to be shaped and re-shaped to conform to the basic teachings of Islam. However,
there were substantial differences in the opinions of the early great jurists concerning the structure and
judicial framework of waqf.
While Imam Shafi`i had objections to certain aspects of waqf institution, Imam Abu Yusuf, the disciple
of Imam Abu Hanifah has differed from his mentors. It can be debated in general that Imam Shafi`i and
Abu Yusuf wanted to expand the waqf and therefore facilitated their foundation, but others preferred to
restrict this institution.
The basic problem lies in Islamic law of inheritance whereby the founder is allowed to entrust the
management of his waqf to any of his offspring which would initiate a defector primogeniture, and this
practice itself could violate the basic principles of Shari`ah, which promulgates a distribution of
property among all the inheritors. Due to this particular issue, most Muslim jurists found that it is
extremely difficult to sanction the waqf and ended up tolerating this practice.
In order to maintain justice, fairness and well-being for all, the Muslim society needed this institution
and the turning point emerged when Imam Abu Yusuf observed the importance of this institution had
become during his pilgrimage. He then introduced a set of new legislature, which facilitated the
establishment of the very foundation of waqf. With this, the institution thus emerged after the demise of
the Prophet and its legal structure was firmly established during the second half of the second century.
As the system itself did not originate from Islam, whereby it is not specifically mentioned in the Qur‟an
and objected to initially by many of the eminent jurists, was embraced so enthusiastically and
developed to such a phenomenal dimension that there is a need to explain the reasons for its existence.
There are mainly two explanations for this either historically or from the economic point of view.
For the historical explanation, the great Islamic conquests had enriched the Muslim world beyond any
imagination achieving the economic preconditions for the emergence of this institution due to the
emphasis attached in Islamic teachings on the importance of righteous actions and charitable deeds. As
wealth in Islam is considered an important source of test and trial, the natural tendency among the rich
Muslims to carry out good deeds as preparation for the hereafter can be easily understood. Thus, it is
due to these historical reasons that although not mentioned in the Qur‟an specifically, and objected to
initially, the waqf has been embraced so enthusiastically.
However, the economic theory also has its own explanation as to why the waqf system was needed and
implemented. According to the theory, there were compelling reasons for the waqf system to emerge.
Under the conditions of rational behavior (business transactions), public good would tend to be under
produced and this becomes a dilemma, and would push for the creation of public good which promotes
a demand for the creation of non-market institutions. This demand may also explain behind the
popularity of waqf and its increase in usage spreading across the Muslim world. The theory explains
the universality of the waqf or waqf-like non-market institutions. After all, endowments are known not
only in the Muslim world but also in other non-Muslim great civilisations.
Suggestions of establishing waqf-based financing institutions serving the poor have been made by
various scholars. Cizakca (2004) suggests a model in which the concept of cash waqf can be used in
contemporary times to serve the social objectives in the society. One use of cash waqf would be to
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provide microfinance to the poor. Similarly, Elgari (2004) proposes establishing a nonprofit financial
intermediary, what he named as qard hasan bank and which gives interest free loan to finance
consumer lending for the poor. According to him, the capital of the bank would come from monetary
(cash) waqf to be donated by wealthy Muslims. Kahf (2004) and Ahmed (2003) propose establishing
MFIs based on zakah, awqaf, and sadaqah. They suggest that the returns from waqf and funds from
sadaqah can be used to finance productive microenterprises at subsidised rates. In addition, zakah can
be given out to the poor for consumption purposes to avoid diversion of funds from productive heads.
Before the advent of the microcredit movement pioneered by the Grameen Bank (GB) of Bangladesh,
which was established by Muhammad Yunus in the 1970s, poor microentrepreneurs who needed
financing for their business and production needs would turn to family, friends, relatives and
moneylenders for loans. Many argued that the moneylenders charged very high interest rate and often
exploitative due to scarcity of capital and their monopolistic position. However, these moneylenders
served only a tiny portion of the community. As such, the emergence of microcredit institution like GB
has a bigger objective of reaching out to a nationwide of microentrepreneurs rather than to intervene in
the credit market of the moneylenders. (Armendariz & Morduch, 2010).
Microcredit, often used interchangeably with microfinance, is referred to small loan giving out by
institution like GB to microentrepreneurs usually for a short financing period. The objective of
microcredit institutions is to focus on providing small loans to the very poor to use as capital for
productions and subsequently to improve their income levels. It is also recognised that the economic
condition of the poor household, including microentrepreneurs, can be improved by accessing
microcredit and other financial services such as savings, microinsurance, product marketing and
distribution. This new recognition is pushing the microcredit movement one notch higher to a
commercially oriented and fully regulated microfinance entity which, with the extended market and
services, will be able to operate as a sustainable business model, and at the same time continue to play
its role to reduce poverty and promote positive social change. (Armendariz & Morduch, 2010).
Mudarabah as a profit sharing financial instrument constitutes a vital place in Islamic finance. Islam
has duly encouraged the use of profit sharing principle in business that will ensure fairness for all the
parties in the underlying contract. A variation of classical mudarabah, two-tier mudarabah model was
widely adopted by Islamic financial institutions.
Fairness is best ensured by profit sharing arrangement as rewards are a function of realised productivity
all along the line. The waqf fund owner, the financial intermediary and the ultimate user of fund in
productive enterprise each gets a share out of the value added. When the economic environment fails to
add any value, or when it erodes part of the value invested in the venture, all the three share in the
misfortune; the fund owner loses part of his funds and the intermediary as well as the ultimate user of
fund in productive enterprise go unrewarded for their services. Profit sharing is harmonious with the
nature of man's economic environment on both sides of the intermediating institution, with the fund
owners along with the fund users.
Unlike the previous papers, the present paper studies the economics of microfinancing and discusses
the sustainability and operational issues of a waqf based MFI in details. To do so, our study first
outlines the features of the conventional MFIs and then critically examines their strengths and
weaknesses. Drawing on the strengths of conventional MFIs, an alternative of Islamic microfinancing
based of waqf is then outlined.
5. Waqf and its Investment Potentials
The very word 'waqf' and its plural form 'awqaf' are derived from the Arabic root verb waqafa, which
means „causing a thing to stop and stand still‟, „to contain‟, „to restrain‟, and „to preserve‟. Literally, its
noun form also means philanthropic, pious (charitable) foundations or simply religious endowment
(Cizakca, 1998); Mohsin, 2009). The Prophet promotes and encourages waqf. Waqf has since been
widely used not just by Arabs, but also by Muslims from other parts of the world. This has resulted in
different transliterations in different jurisdictions, such as wakf, vakf, vakif, vaqf, wakaf (Islahi, 2003).
Waqf is also known with different nomenclatures, such as habs in North and South Africa and qadsh in
Hebrew (Osman, 2012).
As for the technical definition of waqf, Muslim scholars differ, depending on their positions on some
elements and conditions of waqf. However, they are agreed on the basic concept of waqf, which is the
permanent dedication of a portion of one‟s wealth for the pleasure of Allah (Hashim, 2007). A
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permanent dedication means that a portion of a person‟s property is alienated from him and
„transferred‟ to Allah. There is a near consensus among the Muslim jurists to define waqf as
confinement of the property from the ownership and dedication of its usufruct to charitable purposes
(Mohsin, 2009). Therefore, it should not be inherited, gifted, mortgaged, and so on. It is one of the
essential characteristics of waqf that the property remains intact while income derived there from or the
property itself, is used for certain philanthropic activities for the sake of Allah, and regarded ad
ongoing charity (Hashim, 2007).
However defined, this institution - whereby a privately owned property is endowed for a charitable
purpose in perpetuity and the revenue generated is spent for that purpose - stands out as one of the
major achievements of Islamic civilisation. Hashim (2007) also stated that the general aims of waqf
include assisting the needy, helping the oppressed, improving the lives of the downtrodden, regulation
of the economy, raising the standard of living of the people, dissemination of sciences and knowledge,
constructing and administering mosques, shrines, libraries, schools, clinics, hospitals, welfare centers,
etc. In addition, waqf elevates the thoughts and purifies the human beings from selfishness and love of
the world. It also cultivates the spirit of helping one another in the society, and joining in mutual care
and love.
Waqf properties can contribute to the development of the socioeconomic such as building commercial
projects such as hotels, business centers or in forms of social projects like orphanage, caring centers
and many others as long as it is Shari`ah compliant.
One of the Qur'anic expressions is worthy of mention in this connection. Allah reminds the faithful
Muslims by saying: "the likeness of those who spend their wealth in Allah's way is as the likeness of a
grain which grows seven branches, in every branch contains of hundred seeds, and (remembers) Allah
will give increase manifold to which he will and Allah is All Embracing and All Knowing".
Being a less famous concept in the Islamic World (compared to immovable waqf), the cash waqf is a
very important instrument to explain. In fact, it only started to have a considerable impact since the
15th century with the Ottoman Empire. Therefore, there is not enough ancient literature available,
which deals with the conceptualisation of this kind of waqf. However, more recently scholars have
dealt with this particular kind with more details and they even gave concrete application samples to be
followed. The cash
The cash waqf is defined as “a form of movable waqf property which is structured by money with the
main purpose to be in service for pious and charitable projects for the people for the sake of Allah”
(Adam & Lahsasna, 2013). The cash waqf has gone through a revolutionary stage, from being rejected
because its corpus in the form of cash cannot be perpetual and ironically based on this analogy too it
found its legality till date. Imam Shafi`i like Abu Yusuf, approves of the waqf of movables subject to
`urf or custom of the relevant society. Imam Malik has also approved it as it has been learned from his
famous treatise Al-Mudawwanah. The position of Hanbali school of thought (madhhab) is similar to
that of the Shafi`is. Finally, the Shiite‟s position is also positive as revealed by the Fatawa of Shaikh
‘Abdullah Al-Mazandari (Cizakca, 1998).
In order to comply with the rules and principles of Shari`ah and to achieve the desired economic goals
and objectives, the cash waqf trustee/manager has to follow the guidance mentioned by the scholars
regarding investments areas and methods, and to use the modern tools of successful management.
While early Muslim scholars have mentioned the mudarabah as an investment tool to fructify the
monetary waqf money, the International Fiqh Council of OIC, in its recent fatwa about monetary waqf -
mentioned above - approved Shari`ah compliant investment methods.
Due to its flexibility, as well as the important liquidity that could be raised by cash waqf, this institution
could be a real catalyst for economic developments. In fact, the monetary waqf provides more cash to
invest and to inject in the economy. This type of financing can be directed to any sector and any needs,
as long as they comply with the tenets of Shari`ah. It could considerably contribute to reducing
government social expenditures due to investing in welfare. Furthermore, it contributes to lowering
unemployment rates directly, by creating employment opportunities itself, or indirectly by participating
in projects creation. Knowing the economic reality of many Muslim countries and their dependence on
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aids of the wealthier countries, it is urgent for them, more than ever, to encourage the creation of such
institutions, which will definitely ensure an acceptable level of life for a large part of the society.
It is common to see the objectives set on waqf along the lines of community development. Creating
new waqf for financing the needs of the people is everyone‟s job and not one that is left to the trustee
who handles the waqf assets. Nevertheless, such trustee or institution holds a huge responsibility of
continuing to redevelop all of its waqf assets in its portfolio, ensure growth and diversification of its
assets, maximize the potentials of its investments and the potentials of the waqf properties, efficiently
and effectively manage its waqf fund, establishing that waqf is the way forward and the best form of
charitable spending and ensure that the proceeds to go towards community development. Sounds like a
mountain of jobs although usually it is entrusted to non-profit organisations.
The applications of waqf in meeting the needs of the people discussed in Islamic finance, being the
financial intermediaries. Socio-economic aspects can be fulfilled by introducing waqf-based
organisations such as those are seen in microfinance, takaful and for the purpose of guarantee. Next,
the study will discuss briefly some of examples of its application in Islamic finance. Cash waqf can be
used in waqf MFI in different ways: corpus of waqf invested and returns used for social purposes,
corpus of waqf given for financing as interest-free loans, corpus of waqf can be used as capital to create
waqf MFI.
6. Islamic and Conventional Microfinance: Concepts and Comparative Analysis
The term microfinance can be described as the provision of financial service to low income clients,
including self employed, low income entrepreneurs in both urban and rural areas. The microfinance
revolution which begun with independent initiatives in Latin America and South Asia starting in the
1970s, has so far allowed 65 million poor people around the world to receive small loans without
collateral, build up assets, and buy insurance. The idea of microfinance programs has come from
Muhammad Yunus who began a microfinance program among women in Bangladesh in 1976 through
the University of Chittagong; however the concept is relatively simple and enjoys a long tradition in
many developing countries. The basic idea of microfinance is to provide credit to working poor who
otherwise would not have access to credit services. This service has been provided in variety in
different countries by moneylenders in poor communities for a long time.
There are many definitions for microfinance, depending on which component one aims to highlight.
One of the most accurate definitions is the one given by the Organization for Economic Co-operation
and Development (OECD) as: "microfinance aims the access to finance small projects, led by
marginalised people who aspire to create their own jobs, often by default of other professional
opportunities and because access to traditional sources is denied". Besides microcredit and MFIs (such
as non-governmental organisations - NGOs, savings and credit cooperatives, public banks, commercial
banks or any other financial institutions) offer microinsurance, microsavings, microequity and
microtransfer for poor people. The main objective of such institutions, besides profitability for profit-
based ones, is to alleviate poverty by providing social and financial intermediation. This would be
realised by both offering dedicated financial services for the targeted persons and supporting them (by
offering training, education, health care, etc.), which allows them to increase their income and to be
less vulnerable. In order to ensure their sustainability and fulfill their main purpose, MFIs aim to find
the right balance between the pursuit of profit, the social support of microentrepreneurs and mentoring
them to succeed in their projects. For governmental institutions or development entities (like welfare
associations, waqf entities, etc.), as they may be perceived as philanthropic organisation, i.e., having
the function of making grants, the challenge is to toggle between social aspect and return on
investment. This ensures the widespread availability of their funds (minimise the call to donations as
much as they can) and will encourage project proponents to be rigorous.
There are many business models that could be used in microfinance. For the sake of relevance, in this
study we chose to present the most used ones.
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6.1. The Grameen Bank model
The most classic and common application of this model consists in targeting a group of poor people
after evaluating their resources, of which mostly are women. The whole group will be responsible for
any member's default. This technique replaces the classic collateral method used by commercial banks
and reduces the risk of default and negligence. The group members should be closely monitored and
supported when needed. This model is widely spread with slight different ways of application.
6.2. The Village Bank model
This model consists in creating small banks in poor villages with 30 to 50 individuals and providing a
capital to be used in providing weekly term loans. The implementing and funding entity recovers the
capital and the interests every 4 months (i.e., on quarterly basis). Only banks having repaid their full
engagement amount will be eligible to receive new funds. The role of intra-borrowers pressure is of a
high importance in ensuring the full reimbursement within the deadlines. Members are highly
encouraged to do savings; the amounts are used in enhancing the autonomy of the institution which
will enable it to be a dependent institution (generally over a three year period).
6.3. The Credit Union model
This model is based on the mutuality concept. It implies the implementation of a non-profit financial
cooperative both owned and controlled by its members. These members mobilise the savings, provide
the loans for productive and provident purposes and share the ownership namely through some
common bonds.
6.4. The Self-Help Groups model
The fourth model is called the Self-Help Groups, which was originated in India. Under this model,
groups of 10 to 15 members are built. The members should be relatively homogenous, at least, in terms
of income. The different members mobilise commonly their savings within the group and use them for
lending. They can also seek external funding for supplementary financing. The conditions and terms of
the loans are democratically fixed between the members of the group. Usually the Self-Help Groups
are supported by NGOs but generally, they have the objective to become independent and sustainable
institutions.
It is argued that despite the phenomenal success of many MFIs to alleviate poverty around the globe,
the high interest rate on the microloan has increased the disparity between the rich and the poor sectors
of the economy (Tahir, 2012). With more and more MFIs operating like commercial entities with the
aim of maximising profit and creating values for their shareholders, their social goal of alleviating
poverty and fair distribution of income has suddenly become unattainable.
In an interest free model of microfinance in Islamic finance, it is suggested that four key principles
must strictly be followed, such as 1) belief in divine guidance, 2) no dealings with riba or interest
based transactions, 3) no dealings with other Shari`ah non-compliant transactions, 3) preferably
dealings with risk sharing financial products and instruments, and 4) financing based on involving in
real assets (Abdullah & Chee, 2012). It is also suggested that the act of lending is to be treated more of
as a moral phenomenon than a purely economic activity (Zaman, 1991). Borrowing is not prohibited in
Islam though the Prophet (pbuh) discouraged taking loans unnecessarily. In this respect, the confluence
of Islamic finance and microfinance will solve the problem of income disparity, because the elements
of riba or interest must be avoided in Islamic financial instruments and will refocus the MFI to its
social course.
The relatively new practice of IMFIs has an outreach of only 1 million Muslims compared to 650
million Muslims who are living below the average poverty line of developing countries. The very low
outreach is due to Islamic microfinance is a new phenomenon, and still is in the state of its infancy. The
key is for the IMFIs to build capacity and capability to provide a diversified array of Shari`ah
compliant Islamic microfinance products to the Muslims in dire need of Islamic microfinancing.
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The IMFIs provide intermediary financial services by accepting funds from investors and other
stakeholders on one hand, and disburse the funds with or without profits to poor microentrepreneurs
and households on the other hand, via an Islamic financial instrument as shown in the basic Islamic
microfinance model below (Figure 1). The basic model consists of five essential components which are
needed to complete a full cycle of Islamic microfinancing process, namely funding, an Islamic
microfinance institution, an Islamic financial instrument for disbursement of fund, the borrower, and
the repayment. In the following paragraphs we will attempt to elaborate on the first two components:
funding and IMFIs, which are the most relevant components to our study.
Source: Authors' own
Armendariz and Morduch (2010: 5-8) argued that poorer enterprises should be able to pay banks higher
interest rates than richer enterprises. They also have the perception that money should flow from rich
depositors to poor entrepreneurs. The money did not flow easily to the poor countries as predicted due
to high credit risk resulting from lack of collaterals, high transaction costs, and weak legal enforcement
mechanisms. One thing is right about the principle though is that the borrowers are forced to pay higher
interest rate charged by the banks. In the case of Banco Compartamos, the largest Latin America
microfinance institution based in Mexico had charged its customers an average interest rate of roughly
100% per annum while the yearly inflation rate in Mexico was around 4%. The extraordinary high
returns had attracted more profit oriented capital when some MFIs like Bank Rakyat Indonesia,
Kenya‟s Equity Bank and Banco Comparators went public in 2003, 2006 and 2007 respectively for the
shareholders to realised the value of their investment, although at the same time, the funding from the
listing had allowed the banks to expand their outreach dramatically to more customers (Armendariz &
Morduch, 2010: 239-241).
Since giving and taking interest is categorically prohibited in IslamIMFIs cannot adopt the above
mentioned profit-oriented model using interest.. Instead, it should emerge as a donor funded institution
with clear mission of eradicating poverty and fulfilling just and equitable socio-economic development.
The funding for IMFIs will be derived mainly from donations in the form of cash waqf, sadaqah,
zakah, hibah and tabarru`, equity capital and the government's subsidies to partially finance the high
transaction costs. In deciding on the type of funding, the IMFIs have to consider the costs, benefits, and
nature of each fund.
The discussions went around whether the IMFIs should be run by non-governmental organisations
(NGOs), non-banking institutions, private banks, government banks, cooperatives or agencies, and also
methods to be followed to act as a best model. The GB which pioneered microcredit financing in the
1070s in Bangladesh adopted its classic „group lending‟ and „graduated financing‟ methodology with
great success. The Hodeidah Microfinance Programme of Yemen, began in 1997, was using the group
lending methodology but the financing was disbursed through murabahah contract. Dusuka (2008)
proposed a special purpose vehicle (SPV), using funds from banks for general and specific Islamic
microfinance purposes. Wilson (2007) argues that it should be run by a non-bank institution using a
proposed wakalah or mudharabah model similar to the ones used in takaful operations. Incidentally,
Funding
Investors ' capital
Donors ' cash waqf,
sadaqah, zakah,
hibah, tabarru`
Government subsidy
Islamic
microfinance
institution
NGO
Nonbank IMFI
Islamic banks
Cooperatives
Agencies Financial
instruments
Mudarabah
Musharakah
Ijarah
Murabahah
Qard
Salam
Istisna'a
Istijrar
Small
investors
Microentrepreneurs
Poor households
Microprojects
Figure 1: Basic Islamic Microfinance Model
Repayment
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the models proposed by both Dusuka and Wilson are suitable for funding by waqf and zakah according
to the terms and conditions set out by the Shari`ah.
In another developments in IMFIs' programs, the Deprived Families Economic Empowerment Program
(June 2006 - December 2015) meant for the economically deprived and socially marginalised families
in Palestine and donated by the Islamic Development Bank (IDB) include: (a) charity to take care of
their immediate consumption needs; (b) an institutional mechanism to improve their skill level required
to make them economically active; and (c) a financing mechanism that does not penalise the new
entrepreneurs with a cost, should there be an incidence of failure in their respective ventures
(Obaidullah, 2008). The program serves as a model for the institutions of zakah, waqf and qard hasan
to perform similar poverty reduction and microenterprise development programs.
7. Cash Waqf Based Islamic Microfinance
The previous studies have shown the cash waqf is an alternative way to promote entrepreneurship to
the poor and needy people but its function is limited by the availability of funds in the microfinancial
institution. Masyita (2003) states that micro finance program which uses mudarabah and musharakah
investment methods to generate funds, is important for poverty alleviation. This program particularly
aims to help poor people initiating their business and improving their quality of life.
As mentioned by Ahmed (2007), the creation of cash waqf has two forms. Firstly, cash is transformed
into waqf and used for interest-free lending to beneficiaries of the waqf. Secondly, the cash in the waqf
is invested and its net return is assigned to the beneficiaries of the waqf. In this regard, the second form
of cash waqf is appropriate for micro financing initiatives. Moreover, micro financing is facing the
problem of limited funds to promote entrepreneurship. In this respect, cash waqf could invest by
providing funding to microfinance and play its essential role to achieve socio economic relief which is
consistent with the objectives of waqf.
7.1. Structure of the Proposed Two Tier Mudarabah Cash Waqf Model
The above paragraphs have suggested the use of cash waqf to fund IMFIs by way of profit sharing
agreement. Siddiqi (1991) has lent his support by saying that the best, and the most efficient and the
fairest arrangement between fund owners and financial intermediaries is that of profit-sharing. The said
arrangement can be made by adopting a two-tier mudarabah structure which represents an ideal mode
of financial intermediation that replaces the interest based system. Interest based system, being
oppressive, does not assume any risk of providing capital regardless of the outcome of the venture
undertaken by the borrower.
The first tier of the mudarabah agreement is between the IMFIs and the waqf fund managers who agree
to invest the waqf money in the IMFIs and to share profit with them. In this case, the waqf fund
managers are the capital providers (rabb al-mal) and the IMFIs as managers (mudarib) of the capital.
The second tier of the mudarabah agreement is between the IMFIs and the micro entrepreneurs who
seek financing from the IMFIs. The profits accruing from the micro entrepreneurs' venture will be
shared between the entrepreneurs and the IMFIs in a pre agreed proportion and the loss will be borne
by the IMFIs only. In this instance, the IMFIs function as the providers of the capital and the
microentrepreneur work as managers (mudarib).
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The two-tier mudarabah structure is shown in the figure below.
Figure 2: Two Tier Mudarabah Cash Waqf Model
Source: Authors' own
7.1.1. The Salient Features of the Proposed Model
The proposed model consists of four components, namely 1) the Waqf fund, 2) the beneficiaries, 3) the
fund manager, and 4) the regulatory bodies.
Waqf fund
A cash waqf is established by cash donation from individual, family, company or other institutions for
specific purposes, such as education, charity, etc. The cash donation may be one off or may be
continuous in fixed intervals.
Beneficiaries
The beneficiaries of the waqf fund are entitled to receive part of the fund or part of the income
generated by the fund. The beneficiaries are usually named or described at the time of establishing the
fund.
Fund manager
An independent person is appointed to work as trustee or waqf fund manager. He will supervise and
monitor the collection of waqf fund, investment and distribution of profit.
Investment in microfinance will follow the two tiers mudarabah model. In this respect, the trustee will
enter into a mudarabah contract with the IMFI. The Trustee will be the fund provider (rabb al Mal)
and the IMFI will be the mudarib. With this fund, the IMFI will enter into another mudarabah contract
with its client (microentrepreneur). IMFI will provide the fund and the microentrepreneur will be the
skill or labor provider. The trustee shall invest the fund in different IMFIs to diversify the risks.
According to the underlying mudarabah contract, profits from the investment will be shared by both
parties and loss will be borne by the fund provider. In this proposed model, profit earned from
microentrepreneurs will be distributed between the IMFI and the trustee according to pre-agreed ratio.
The trustee will channel this profit for defraying management expenses, distribution to the
beneficiaries, and for building reserve.
Regulatory bodies
It is recommended that the administration of waqf should be managed by non-governmental
organisations with expertise in fund management. The government should act as supervisor/nazir only.
The religious council should provide the Shari`ah guidelines to the fund management, and the
Regulation
Regulatory
Body -Waqf
Waqf Fund
Provider
Trustee/Fund Manager
(Rabbul Mal)
Individuals
Islamic Microfinance Institutions
(Mudarib) / Partner (Musharik)
Regulatory
Body -
Microfinanc
e
Microentrepreneurs (Mudarib)
Organisations
Religious
Body
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supervisory power be given to intervene directly in case of negligence or mismanagement for any of
the waqf properties (MIA Mohsin, 2009).
7.1.2. Justifications for the Proposed Model
The numerous benefits can be driven from cash waqf for the IMFIs and the society, specifically when
the proposed model is based on two tier mudarabah. Some of the justifications are outlined below:
 Benefits of waqf founder: Every individual who contributes to cash waqf (donor) will be
benefited both in this life and the hereafter.
 Financial Inclusion: Extending financial facilities to the poor people who are unable to
provide securitisation for their borrowing is a big challenge in this world. The proposed model
will provide financing opportunity to the poor people.
 Sources of fund for IMFIs: Cash endowment offers the IMFIs another source of fund needed
to carry out their objectives.
 Creating more business opportunities: Cash endowment increases the accumulation of
liquidity and capital in the industry and creates more business opportunities.
 Achieving religious goal: Characterising partnership-based instruments in economic terms as
risk-sharing does reflect the ideals of Islamic teachings. So, it will be a great way to achieve
the religious goal for the donors as well as all parties involved in this proposed model.
 Employment generation: There is a near consensus that the conventional microcredit is not a
good job creation tool as risk-averse lenders tend to focus more on those who already have an
enterprise and are looking to expand it. Implementing the proposed model might change this
equation and the focus could be more on those who have human capital and have the skills, or
can acquire those skills which might lead to creating job/enterprise opportunities for them.
 Risk sharing: The two tier mudarabah model represents an ideal mode of financial
intermediation that could replace the interest based system. Fairness is best ensured by profit-
sharing arrangement as rewards are a function of realised productivity that channelled to all
the parties involved. In this connection, the IMFIs as fund receivers and providers are
facilitating micro entrepreneurs to share the risk.
7.1.3. The Empirical Study
To test the feasibility of cash waqf based microfinance model, we come up with the following
hypothesis that will test whether or not the MFIs lead economic growth. Bangladesh has been taken as
case for this empirical study from the premise that it has already got reputation for its 'minimalist'
Grameen program focusing mainly on livelihood enterprises that use a similar model involving group-
based and graduated financing schemes.
The GB has reversed conventional banking practice by removing the need for collateral and created a
banking system based on mutual trust, accountability, participation and creativity. The GB provides
credit to the poorest of the poor in rural areas of the country, without any collateral. Its founder
Muhammad Yunus reasoned that if financial resources can be made available to the poor people on
terms and conditions that are appropriate and reasonable, millions of small people (in Bangladesh) with
their millions of small pursuits can add up to create the biggest development wonder.
Available data from 1985 up to 2013 has been collected from its website and the DataStream; Data has
been taken on yearly basis. For this study, the source data is very important as the report contains the
background of the institutions and from the activities we can understand whether the institution is
operating on religious basis. The representativeness of the sample is always an important issue.
Although the website is one of the main reliable sources for studying the performance of a financial
institution, we did not cross check the data with any third party sources. Also, time-series approaches
have been adopted to test the hypothesis whether or not the microfinance industry has impact on
growth and development. However, to test the lead-lag relationship we would apply the following
procedures.
After examining the unit-root tests and the order of the VAR, the Johansen co integration tests will be
applied. The co integrating estimated vectors will then be subjected to exactly identifying and over
identifying restrictions. The test of co integration is designed to examine the long-run theoretical or
equilibrium relationship and to rule out spurious relationship among the variables. But the evidence of
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co integration cannot tell us which variable is leading and which variable is lagging. That can be done
by the test of vector error correction model (VECM) that can indicate the direction of Granger causality
both in the short and long run. In order to obtain robust estimates, this study would include real interest
rate and inflation as related control variables.
7.2. Testing Stationarity of Variables
We begin our empirical testing by determining the stationarity of the variables used. In order to
proceed with the testing of co integration later, ideally, our variables should be I(1), in that in their
original level form, they are non-stationary and in their first differenced form, they are stationary. The
differenced form for each variable used is created by taking the difference of their log forms. For
example, DGIA = LGIA – LGIAt-1. We then conducted the Augmented Dickey-Fuller (ADF) test on
each variable (in both level and differenced form). The table below summarizes the results. (See
Appendix 1 t for details.)
Variable ADF Test Statistics Critical Value Implication
LRGDP 2.3142 -3.6921 Non -Stationary
LMFLA -1.9343 -3.6921 Non -Stationary
RINTR -3.3459 -3.6921 Non -Stationary
INF -2.4947 -3.6921 Non -Stationary
Relying primarily on the AIC and SBC criteria, the conclusion that can be made from the above results
is that all the variables we are using for this analysis are I(1), and thus we may proceed with testing of
co integration. Note that in determining which test statistic to compare with the 95% critical value for
the ADF statistic, we have selected the ADF regression order based on the highest computed value for
AIC and SBC. In some instances, AIC and SBC give different orders and in that case, we have taken
different orders and compared both. This is not an issue as in all cases, the implications are consistent.
7.2.1. Determination of Order of the VAR Model
Before proceeding with test of co integration, we need to first determine the order of the vector auto
regression (VAR), that is, the number of lags to be used. As per the table below, results show that AIC
recommends order of 3 whereas SBC favours 1 lag.
Choice Criteria
AIC SBC
Optimal order 3 1
Given this apparent conflict between recommendation of AIC and SBC, we address this in the
following manner. First we checked for serial correlation for each variable and as there was no serial
correlation we decided to go for SBC suggestion. (See Appendix 2)
7.2.2. Testing Cointegration
Once we have established that the variables are I(1) and determined the optimal VAR order as 2, we
are ready to test for co integration. As depicted in the table below, the maximal Eigen value suggests
one co integration. (See Appendix 3 )
Null Alternative Statistic 95% Critical Value
r = 0 r = 1 80.3794 31.7900
r<= 1 r = 2 25.0497 25.4200
We are inclined to believe that there is one co integrating vector as intuition as well as the past studies
has proven that each one of these three variables are related to other variables. Based on the above
statistical results as well as our insight, for the purpose of this study, we shall assume that there is one
co integrating vector, or relationship. Statistically, the above results indicate that the variables we have
chosen, in some combination, results in a stationary error term. The results show that the independent
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variables (MFLA, RINTR and INF) jointly explained variations or changes in economic growth.
More specifically, the result reveals that a microfinance bank operation (MFLA) has a positive impact
on economic growth. Furthermore, the result indicates that microfinance bank is statistically
significant in explaining economic in Bangladesh. That is, MFLA plays a vital role in the achievement
of economic growth.
By examining the error correction term, et-1, for each variable, and checking whether it is significant,
we found that there is only one exogenous variable, microfinance loan and advances and other
variables are endogenous. ( See Appendix 4 )
Variable ECM(-1) t-ratio p-value Implication
dLRGDP 0 Variable is endogenous
dINF 0.057 Variable is endogenous
dLMFLA 0.553 Variable is exogenous
dRINTR .075 Variable is endogenous
Cointegration, however, cannot tell us the direction of Granger causality as to which variable is leading
and which variable is lagging (i.e. which variable is exogenous and which variable is endogenous). For
discerning the endogeneity /exogeneity of the variables, we applied the vector error correction
modeling technique. The analysis result reveals that microfinance operations (captured by the loans and
advances microfinance banks offer to the members of the society) have statistically significant positive
impact or effect on the Bangladesh economy. That is, the more the activities of microfinance banks in
Bangladesh, the higher would be the growth of the economy. Furthermore, the vector error correction
model test result shows a unidirectional causality running from economic growth to microfinance
banks operations. Therefore, government and policy makers should pay serious attention to the
operations of microfinance banks in Bangladesh. At the same time, programs and policies that would
boost their activities should be implemented. This would further position microfinance banks to
effectively service the very poor of the society who engage in micro, small and medium scale
enterprises (MSMEs) which are the engine of economic growth.
8. Summary, Conclusion, and Suggestions
Islamic financial instruments provide enough room to fulfill financing needs in extreme cases. It is a
better alternative to conventional financing tools. The paper sheds new light on how cash waqf can be
an alternative source of financing the IMFIs. A model of cash waqf has been developed by using two
tier mudarabah contract. The major practical implication is the necessity of developing new sustainable
model as a means for survival and growth. It would help to reform the present institutional framework
of microfinance and waqf fund with a view to enhance their performance in the direction of achieving
the ultimate goals in religion and socio economic developments. Cash waqf based MFIs can play an
important role in financial inclusion, employment generation, risk diversification, creating more
business opportunities, and last but not least to improve the socio-economic conditions of the poor and
the whole society. Despite the fact that the proposed two tier mudarabah structure has been hit by
major barriers in Islamic banking industry, the benefits of practicing the proposed structure in cash
waqf based IMFIs outweigh its shortcomings. Study may help create incentives for researchers to
pursue further inquiries into an area that is extremely promising.
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Appendix
Appendix 1: Stationary Test
12/12/2014 11:08:22 AM
ADF tests for variable INF
The Dickey-Fuller regressions include no intercept and no trend
*******************************************************************************
18 observations used in the estimation of all ADF regressions.
Sample period from 1996 to 2013
*******************************************************************************
Test Statistic CV LL AIC SBC HQC
DF -1.2010 -1.8014 -44.4306 -45.4306 -45.8758 -45.4920
ADF(1) -.91251 -1.9141 -43.6714 -45.6714 -46.5617 -45.7941
ADF(2) .016893 -1.7848 -38.4798 -41.4798 -42.8153 -41.6639
ADF(3) .11886 -1.8167 -38.3791 -42.3791 -44.1599 -42.6247
ADF(4) .028931 -1.8280 -38.2670 -43.2670 -45.4930 -43.5740
ADF(5) -.066284 -1.9887 -38.0388 -44.0388 -46.7099 -44.4071
*******************************************************************************
CV = 95% simulated critical value using 18 obs. and 1000 replications.
The critical values are computed by stochastic simulations.
LL = Maximized log-likelihood AIC = Akaike Information Criterion
SBC = Schwarz Bayesian Criterion HQC = Hannan-Quinn Criterion
ADF tests for variable INF
The Dickey-Fuller regressions include an intercept but not a trend
*******************************************************************************
18 observations used in the estimation of all ADF regressions.
Sample period from 1996 to 2013
*******************************************************************************
Test Statistic CV LL AIC SBC HQC
DF -3.2546 -3.1380 -40.5704 -42.5704 -43.4607 -42.6931
ADF(1) -2.7181 -3.1154 -40.5006 -43.5006 -44.8362 -43.6848
ADF(2) -.95983 -3.0515 -37.8646 -41.8646 -43.6453 -42.1101
ADF(3) -.82549 -2.9688 -37.8596 -42.8596 -45.0855 -43.1665
ADF(4) -1.0262 -3.1176 -37.4611 -43.4611 -46.1322 -43.8294
ADF(5) -1.4620 -3.2046 -36.4066 -43.4066 -46.5229 -43.8363
*******************************************************************************
95% published asymptotic critical value corresponding to ADF(0) = -3.0401
CV = 95% simulated critical value using 18 obs. and 1000 replications.
The critical values are computed by stochastic simulations.
LL = Maximized log-likelihood AIC = Akaike Information Criterion
SBC = Schwarz Bayesian Criterion HQC = Hannan-Quinn Criterion
ADF tests for variable INF
The Dickey-Fuller regressions include an intercept and a linear trend
*******************************************************************************
18 observations used in the estimation of all ADF regressions.
Sample period from 1996 to 2013
*******************************************************************************
Test Statistic CV LL AIC SBC HQC
DF -4.3715 -3.7087 -37.4981 -40.4981 -41.8336 -40.6822
ADF(1) -4.1697 -3.7207 -36.5683 -40.5683 -42.3491 -40.8139
ADF(2) -2.0974 -3.5885 -35.0893 -40.0893 -42.3152 -40.3962
ADF(3) -2.0134 -3.7096 -34.9521 -40.9521 -43.6232 -41.3204
ADF(4) -2.2776 -3.7635 -34.0512 -41.0512 -44.1675 -41.4809
ADF(5) -2.4947 -4.2335 -33.1417 -41.1417 -44.7032 -41.6328
*******************************************************************************
95% published asymptotic critical value corresponding to ADF(0) = -3.6921
CV = 95% simulated critical value using 18 obs. and 1000 replications.
The critical values are computed by stochastic simulations.
LL = Maximized log-likelihood AIC = Akaike Information Criterion
SBC = Schwarz Bayesian Criterion HQC = Hannan-Quinn Criterion
Abu Umar Faruq Ahmad & Mohammad Ashraful Mobin / Proceeding ICOSOPP 2015
___________________________________________________________________________________
« 437 »
Page 1
12/12/2014 11:08:39 AM
ADF tests for variable DINF
The Dickey-Fuller regressions include no intercept and no trend
*******************************************************************************
17 observations used in the estimation of all ADF regressions.
Sample period from 1997 to 2013
*******************************************************************************
Test Statistic CV LL AIC SBC HQC
DF -5.8914 -1.9702 -38.9103 -39.9103 -40.3269 -39.9517
ADF(1) -6.8715 -2.0325 -34.5411 -36.5411 -37.3743 -36.6239
ADF(2) -3.1330 -1.8652 -34.5005 -37.5005 -38.7504 -37.6248
ADF(3) -2.2649 -1.8940 -34.4982 -38.4982 -40.1646 -38.6639
ADF(4) -1.8978 -1.8365 -34.4922 -39.4922 -41.5752 -39.6992
ADF(5) -2.0292 -2.0664 -34.0156 -40.0156 -42.5153 -40.2641
*******************************************************************************
CV = 95% simulated critical value using 17 obs. and 1000 replications.
The critical values are computed by stochastic simulations.
LL = Maximized log-likelihood AIC = Akaike Information Criterion
SBC = Schwarz Bayesian Criterion HQC = Hannan-Quinn Criterion
ADF tests for variable DINF
The Dickey-Fuller regressions include an intercept but not a trend
*******************************************************************************
17 observations used in the estimation of all ADF regressions.
Sample period from 1997 to 2013
*******************************************************************************
Test Statistic CV LL AIC SBC HQC
DF -5.6910 -3.0176 -38.8055 -40.8055 -41.6387 -40.8883
ADF(1) -6.7848 -3.1255 -34.1930 -37.1930 -38.4428 -37.3172
ADF(2) -3.1351 -3.0602 -34.1737 -38.1737 -39.8401 -38.3393
ADF(3) -2.3069 -3.1371 -34.1715 -39.1715 -41.2545 -39.3786
ADF(4) -1.9639 -3.1545 -34.1416 -40.1416 -42.6413 -40.3901
ADF(5) -2.0893 -3.1883 -33.6109 -40.6109 -43.5271 -40.9008
*******************************************************************************
95% published asymptotic critical value corresponding to ADF(0) = -3.0522
CV = 95% simulated critical value using 17 obs. and 1000 replications.
The critical values are computed by stochastic simulations.
LL = Maximized log-likelihood AIC = Akaike Information Criterion
SBC = Schwarz Bayesian Criterion HQC = Hannan-Quinn Criterion
ADF tests for variable DINF
The Dickey-Fuller regressions include an intercept and a linear trend
*******************************************************************************
17 observations used in the estimation of all ADF regressions.
Sample period from 1997 to 2013
*******************************************************************************
Test Statistic CV LL AIC SBC HQC
DF -5.4295 -3.7607 -38.7428 -41.7428 -42.9926 -41.8670
ADF(1) -6.4126 -3.9137 -34.1618 -38.1618 -39.8282 -38.3274
ADF(2) -2.9510 -3.6439 -34.1499 -39.1499 -41.2329 -39.3569
ADF(3) -2.1931 -3.8820 -34.1472 -40.1472 -42.6468 -40.3956
ADF(4) -1.8507 -3.9679 -34.1016 -41.1016 -44.0178 -41.3915
ADF(5) -2.0371 -4.2148 -33.3645 -41.3645 -44.6974 -41.6958
*******************************************************************************
95% published asymptotic critical value corresponding to ADF(0) = -3.7119
CV = 95% simulated critical value using 17 obs. and 1000 replications.
The critical values are computed by stochastic simulations.
LL = Maximized log-likelihood AIC = Akaike Information Criterion
SBC = Schwarz Bayesian Criterion HQC = Hannan-Quinn Criterion
Abu Umar Faruq Ahmad & Mohammad Ashraful Mobin / Proceeding ICOSOPP 2015
___________________________________________________________________________________
« 438 »
Page 2
12/12/2014 11:08:58 AM
ADF tests for variable RINTR
The Dickey-Fuller regressions include no intercept and no trend
*******************************************************************************
18 observations used in the estimation of all ADF regressions.
Sample period from 1996 to 2013
*******************************************************************************
Test Statistic CV LL AIC SBC HQC
DF -.44411 -1.8014 -12.1980 -13.1980 -13.6432 -13.2594
ADF(1) -.43587 -1.9141 -10.6562 -12.6562 -13.5466 -12.7790
ADF(2) -.43760 -1.7848 -10.6305 -13.6305 -14.9661 -13.8147
ADF(3) -1.2259 -1.8167 -5.2578 -9.2578 -11.0386 -9.5034
ADF(4) -1.6118 -1.8280 -4.0544 -9.0544 -11.2803 -9.3613
ADF(5) -1.3159 -1.9887 -3.9907 -9.9907 -12.6618 -10.3590
*******************************************************************************
CV = 95% simulated critical value using 18 obs. and 1000 replications.
The critical values are computed by stochastic simulations.
LL = Maximized log-likelihood AIC = Akaike Information Criterion
SBC = Schwarz Bayesian Criterion HQC = Hannan-Quinn Criterion
ADF tests for variable RINTR
The Dickey-Fuller regressions include an intercept but not a trend
*******************************************************************************
18 observations used in the estimation of all ADF regressions.
Sample period from 1996 to 2013
*******************************************************************************
Test Statistic CV LL AIC SBC HQC
DF -.76779 -3.1380 -11.9250 -13.9250 -14.8153 -14.0477
ADF(1) -1.3627 -3.1154 -9.6866 -12.6866 -14.0221 -12.8707
ADF(2) -1.3427 -3.0515 -9.6218 -13.6218 -15.4025 -13.8673
ADF(3) -.87267 -2.9688 -4.9293 -9.9293 -12.1552 -10.2362
ADF(4) -.65560 -3.1176 -3.9275 -9.9275 -12.5986 -10.2958
ADF(5) -.69051 -3.2046 -3.8035 -10.8035 -13.9198 -11.2332
*******************************************************************************
95% published asymptotic critical value corresponding to ADF(0) = -3.0401
CV = 95% simulated critical value using 18 obs. and 1000 replications.
The critical values are computed by stochastic simulations.
LL = Maximized log-likelihood AIC = Akaike Information Criterion
SBC = Schwarz Bayesian Criterion HQC = Hannan-Quinn Criterion
ADF tests for variable RINTR
The Dickey-Fuller regressions include an intercept and a linear trend
*******************************************************************************
18 observations used in the estimation of all ADF regressions.
Sample period from 1996 to 2013
*******************************************************************************
Test Statistic CV LL AIC SBC HQC
DF -2.7084 -3.7087 -8.2158 -11.2158 -12.5514 -11.4000
ADF(1) -3.3459 -3.7207 -5.2742 -9.2742 -11.0550 -9.5198
ADF(2) -4.5596 -3.5885 -1.9409 -6.9409 -9.1669 -7.2479
ADF(3) -2.3914 -3.7096 -1.9187 -7.9187 -10.5899 -8.2870
ADF(4) -1.7874 -3.7635 -1.9187 -8.9187 -12.0350 -9.3484
ADF(5) -2.3306 -4.2335 -.24603 -8.2460 -11.8075 -8.7371
*******************************************************************************
95% published asymptotic critical value corresponding to ADF(0) = -3.6921
CV = 95% simulated critical value using 18 obs. and 1000 replications.
The critical values are computed by stochastic simulations.
LL = Maximized log-likelihood AIC = Akaike Information Criterion
SBC = Schwarz Bayesian Criterion HQC = Hannan-Quinn Criterion
Abu Umar Faruq Ahmad & Mohammad Ashraful Mobin / Proceeding ICOSOPP 2015
___________________________________________________________________________________
« 439 »
Page 3
12/12/2014 11:08:58 AM
ADF tests for variable RINTR
The Dickey-Fuller regressions include no intercept and no trend
*******************************************************************************
18 observations used in the estimation of all ADF regressions.
Sample period from 1996 to 2013
*******************************************************************************
Test Statistic CV LL AIC SBC HQC
DF -.44411 -1.8014 -12.1980 -13.1980 -13.6432 -13.2594
ADF(1) -.43587 -1.9141 -10.6562 -12.6562 -13.5466 -12.7790
ADF(2) -.43760 -1.7848 -10.6305 -13.6305 -14.9661 -13.8147
ADF(3) -1.2259 -1.8167 -5.2578 -9.2578 -11.0386 -9.5034
ADF(4) -1.6118 -1.8280 -4.0544 -9.0544 -11.2803 -9.3613
ADF(5) -1.3159 -1.9887 -3.9907 -9.9907 -12.6618 -10.3590
*******************************************************************************
CV = 95% simulated critical value using 18 obs. and 1000 replications.
The critical values are computed by stochastic simulations.
LL = Maximized log-likelihood AIC = Akaike Information Criterion
SBC = Schwarz Bayesian Criterion HQC = Hannan-Quinn Criterion
ADF tests for variable RINTR
The Dickey-Fuller regressions include an intercept but not a trend
*******************************************************************************
18 observations used in the estimation of all ADF regressions.
Sample period from 1996 to 2013
*******************************************************************************
Test Statistic CV LL AIC SBC HQC
DF -.76779 -3.1380 -11.9250 -13.9250 -14.8153 -14.0477
ADF(1) -1.3627 -3.1154 -9.6866 -12.6866 -14.0221 -12.8707
ADF(2) -1.3427 -3.0515 -9.6218 -13.6218 -15.4025 -13.8673
ADF(3) -.87267 -2.9688 -4.9293 -9.9293 -12.1552 -10.2362
ADF(4) -.65560 -3.1176 -3.9275 -9.9275 -12.5986 -10.2958
ADF(5) -.69051 -3.2046 -3.8035 -10.8035 -13.9198 -11.2332
*******************************************************************************
95% published asymptotic critical value corresponding to ADF(0) = -3.0401
CV = 95% simulated critical value using 18 obs. and 1000 replications.
The critical values are computed by stochastic simulations.
LL = Maximized log-likelihood AIC = Akaike Information Criterion
SBC = Schwarz Bayesian Criterion HQC = Hannan-Quinn Criterion
ADF tests for variable RINTR
The Dickey-Fuller regressions include an intercept and a linear trend
*******************************************************************************
18 observations used in the estimation of all ADF regressions.
Sample period from 1996 to 2013
*******************************************************************************
Test Statistic CV LL AIC SBC HQC
DF -2.7084 -3.7087 -8.2158 -11.2158 -12.5514 -11.4000
ADF(1) -3.3459 -3.7207 -5.2742 -9.2742 -11.0550 -9.5198
ADF(2) -4.5596 -3.5885 -1.9409 -6.9409 -9.1669 -7.2479
ADF(3) -2.3914 -3.7096 -1.9187 -7.9187 -10.5899 -8.2870
ADF(4) -1.7874 -3.7635 -1.9187 -8.9187 -12.0350 -9.3484
ADF(5) -2.3306 -4.2335 -.24603 -8.2460 -11.8075 -8.7371
*******************************************************************************
95% published asymptotic critical value corresponding to ADF(0) = -3.6921
CV = 95% simulated critical value using 18 obs. and 1000 replications.
The critical values are computed by stochastic simulations.
LL = Maximized log-likelihood AIC = Akaike Information Criterion
SBC = Schwarz Bayesian Criterion HQC = Hannan-Quinn Criterion
Abu Umar Faruq Ahmad & Mohammad Ashraful Mobin / Proceeding ICOSOPP 2015
___________________________________________________________________________________
« 440 »
Page 4
12/12/2014 11:09:06 AM
ADF tests for variable RGDP
The Dickey-Fuller regressions include no intercept and no trend
*******************************************************************************
18 observations used in the estimation of all ADF regressions.
Sample period from 1996 to 2013
*******************************************************************************
Test Statistic CV LL AIC SBC HQC
DF 28.1666 -1.8014 -230.7847 -231.7847 -232.2299 -231.8461
ADF(1) 1.4538 -1.9141 -223.0962 -225.0962 -225.9865 -225.2189
ADF(2) 1.3789 -1.7848 -223.0620 -226.0620 -227.3976 -226.2462
ADF(3) .36700 -1.8167 -219.3594 -223.3594 -225.1401 -223.6049
ADF(4) .23244 -1.8280 -219.2198 -224.2198 -226.4457 -224.5267
ADF(5) .42511 -1.9887 -218.6040 -224.6040 -227.2751 -224.9723
*******************************************************************************
CV = 95% simulated critical value using 18 obs. and 1000 replications.
The critical values are computed by stochastic simulations.
LL = Maximized log-likelihood AIC = Akaike Information Criterion
SBC = Schwarz Bayesian Criterion HQC = Hannan-Quinn Criterion
ADF tests for variable RGDP
The Dickey-Fuller regressions include an intercept but not a trend
*******************************************************************************
18 observations used in the estimation of all ADF regressions.
Sample period from 1996 to 2013
*******************************************************************************
Test Statistic CV LL AIC SBC HQC
DF 28.6085 -3.1380 -220.6383 -222.6383 -223.5287 -222.7611
ADF(1) 3.0181 -3.1154 -219.9200 -222.9200 -224.2555 -223.1041
ADF(2) 3.4054 -3.0515 -218.6961 -222.6961 -224.4769 -222.9417
ADF(3) 1.8581 -2.9688 -217.2535 -222.2535 -224.4794 -222.5604
ADF(4) 1.6762 -3.1176 -217.2520 -223.2520 -225.9231 -223.6203
ADF(5) 2.0683 -3.2046 -215.7164 -222.7164 -225.8327 -223.1461
*******************************************************************************
95% published asymptotic critical value corresponding to ADF(0) = -3.0401
CV = 95% simulated critical value using 18 obs. and 1000 replications.
The critical values are computed by stochastic simulations.
LL = Maximized log-likelihood AIC = Akaike Information Criterion
SBC = Schwarz Bayesian Criterion HQC = Hannan-Quinn Criterion
ADF tests for variable RGDP
The Dickey-Fuller regressions include an intercept and a linear trend
*******************************************************************************
18 observations used in the estimation of all ADF regressions.
Sample period from 1996 to 2013
*******************************************************************************
Test Statistic CV LL AIC SBC HQC
DF 9.4671 -3.7087 -220.5889 -223.5889 -224.9245 -223.7731
ADF(1) 1.8211 -3.7207 -219.8668 -223.8668 -225.6475 -224.1123
ADF(2) 2.3142 -3.5885 -218.6752 -223.6752 -225.9012 -223.9822
ADF(3) .89606 -3.7096 -217.1616 -223.1616 -225.8327 -223.5299
ADF(4) .54860 -3.7635 -217.1085 -224.1085 -227.2248 -224.5382
ADF(5) 1.6978 -4.2335 -214.8257 -222.8257 -226.3871 -223.3167
*******************************************************************************
95% published asymptotic critical value corresponding to ADF(0) = -3.6921
CV = 95% simulated critical value using 18 obs. and 1000 replications.
The critical values are computed by stochastic simulations.
LL = Maximized log-likelihood AIC = Akaike Information Criterion
SBC = Schwarz Bayesian Criterion HQC = Hannan-Quinn Criterion
Abu Umar Faruq Ahmad & Mohammad Ashraful Mobin / Proceeding ICOSOPP 2015
___________________________________________________________________________________
« 441 »
Page 5
12/12/2014 11:09:16 AM
ADF tests for variable DRGDP
The Dickey-Fuller regressions include no intercept and no trend
*******************************************************************************
21 observations used in the estimation of all ADF regressions.
Sample period from 1993 to 2013
*******************************************************************************
Test Statistic CV LL AIC SBC HQC
DF .18518 -1.9301 53.4704 52.4704 51.9482 52.3571
ADF(1) .19665 -1.9171 53.4752 51.4752 50.4307 51.2485
*******************************************************************************
CV = 95% simulated critical value using 21 obs. and 1000 replications.
The critical values are computed by stochastic simulations.
LL = Maximized log-likelihood AIC = Akaike Information Criterion
SBC = Schwarz Bayesian Criterion HQC = Hannan-Quinn Criterion
ADF tests for variable DRGDP
The Dickey-Fuller regressions include an intercept but not a trend
*******************************************************************************
21 observations used in the estimation of all ADF regressions.
Sample period from 1993 to 2013
*******************************************************************************
Test Statistic CV LL AIC SBC HQC
DF -1.6368 -3.0261 55.0213 53.0213 51.9768 52.7946
ADF(1) -1.7512 -3.0817 55.2956 52.2956 50.7288 51.9556
*******************************************************************************
95% published asymptotic critical value corresponding to ADF(0) = -3.0115
CV = 95% simulated critical value using 21 obs. and 1000 replications.
The critical values are computed by stochastic simulations.
LL = Maximized log-likelihood AIC = Akaike Information Criterion
SBC = Schwarz Bayesian Criterion HQC = Hannan-Quinn Criterion
ADF tests for variable DRGDP
The Dickey-Fuller regressions include an intercept and a linear trend
*******************************************************************************
21 observations used in the estimation of all ADF regressions.
Sample period from 1993 to 2013
*******************************************************************************
Test Statistic CV LL AIC SBC HQC
DF -3.0805 -3.7280 58.0880 55.0880 53.5212 54.7479
ADF(1) -3.7337 -3.6876 59.9402 55.9402 53.8512 55.4869
*******************************************************************************
95% published asymptotic critical value corresponding to ADF(0) = -3.6454
CV = 95% simulated critical value using 21 obs. and 1000 replications.
The critical values are computed by stochastic simulations.
LL = Maximized log-likelihood AIC = Akaike Information Criterion
SBC = Schwarz Bayesian Criterion HQC = Hannan-Quinn Criterion
Abu Umar Faruq Ahmad & Mohammad Ashraful Mobin / Proceeding ICOSOPP 2015
___________________________________________________________________________________
« 442 »
Page 6
12/12/2014 11:09:27 AM
ADF tests for variable DRINTR
The Dickey-Fuller regressions include no intercept and no trend
*******************************************************************************
17 observations used in the estimation of all ADF regressions.
Sample period from 1997 to 2013
*******************************************************************************
Test Statistic CV LL AIC SBC HQC
DF -3.1968 -1.9702 23.2832 22.2832 21.8666 22.2417
ADF(1) -2.0182 -2.0325 24.0855 22.0855 21.2523 22.0027
ADF(2) -2.8460 -1.8652 26.1303 23.1303 21.8805 23.0061
ADF(3) -2.7919 -1.8940 26.6299 22.6299 20.9634 22.4642
ADF(4) -1.5471 -1.8365 27.6226 22.6226 20.5396 22.4155
ADF(5) -1.4740 -2.0664 27.6839 21.6839 19.1843 21.4354
*******************************************************************************
CV = 95% simulated critical value using 17 obs. and 1000 replications.
The critical values are computed by stochastic simulations.
LL = Maximized log-likelihood AIC = Akaike Information Criterion
SBC = Schwarz Bayesian Criterion HQC = Hannan-Quinn Criterion
ADF tests for variable DRINTR
The Dickey-Fuller regressions include an intercept but not a trend
*******************************************************************************
17 observations used in the estimation of all ADF regressions.
Sample period from 1997 to 2013
*******************************************************************************
Test Statistic CV LL AIC SBC HQC
DF -3.2423 -3.0176 23.7102 21.7102 20.8770 21.6274
ADF(1) -2.0737 -3.1255 24.4910 21.4910 20.2411 21.3667
ADF(2) -3.0831 -3.0602 27.1313 23.1313 21.4648 22.9656
ADF(3) -3.4916 -3.1371 28.7268 23.7268 21.6438 23.5198
ADF(4) -2.0996 -3.1545 29.0311 23.0311 20.5315 22.7827
ADF(5) -2.2017 -3.1883 29.5917 22.5917 19.6754 22.3018
*******************************************************************************
95% published asymptotic critical value corresponding to ADF(0) = -3.0522
CV = 95% simulated critical value using 17 obs. and 1000 replications.
The critical values are computed by stochastic simulations.
LL = Maximized log-likelihood AIC = Akaike Information Criterion
SBC = Schwarz Bayesian Criterion HQC = Hannan-Quinn Criterion
ADF tests for variable DRINTR
The Dickey-Fuller regressions include an intercept and a linear trend
*******************************************************************************
17 observations used in the estimation of all ADF regressions.
Sample period from 1997 to 2013
*******************************************************************************
Test Statistic CV LL AIC SBC HQC
DF -2.9599 -3.7607 23.7855 20.7855 19.5357 20.6613
ADF(1) -1.7236 -3.9137 24.7333 20.7333 19.0669 20.5676
ADF(2) -2.6912 -3.6439 27.4342 22.4342 20.3512 22.2271
ADF(3) -3.3105 -3.8820 29.3832 23.3832 20.8835 23.1347
ADF(4) -1.9210 -3.9679 29.7924 22.7924 19.8761 22.5025
ADF(5) -1.9650 -4.2148 30.2464 22.2464 18.9136 21.9151
*******************************************************************************
95% published asymptotic critical value corresponding to ADF(0) = -3.7119
CV = 95% simulated critical value using 17 obs. and 1000 replications.
The critical values are computed by stochastic simulations.
LL = Maximized log-likelihood AIC = Akaike Information Criterion
SBC = Schwarz Bayesian Criterion HQC = Hannan-Quinn Criterion
Abu Umar Faruq Ahmad & Mohammad Ashraful Mobin / Proceeding ICOSOPP 2015
___________________________________________________________________________________
« 443 »
Page 7
12/12/2014 11:09:33 AM
ADF tests for variable MFLA
The Dickey-Fuller regressions include no intercept and no trend
*******************************************************************************
18 observations used in the estimation of all ADF regressions.
Sample period from 1996 to 2013
*******************************************************************************
Test Statistic CV LL AIC SBC HQC
DF 3.1178 -1.8014 -105.8564 -106.8564 -107.3016 -106.9178
ADF(1) 2.4125 -1.9141 -105.8533 -107.8533 -108.7436 -107.9760
ADF(2) 1.3202 -1.7848 -105.5763 -108.5763 -109.9119 -108.7605
ADF(3) 1.5635 -1.8167 -105.0735 -109.0735 -110.8543 -109.3191
ADF(4) .88120 -1.8280 -104.0596 -109.0596 -111.2855 -109.3665
ADF(5) 1.5023 -1.9887 -101.3291 -107.3291 -110.0002 -107.6974
*******************************************************************************
CV = 95% simulated critical value using 18 obs. and 1000 replications.
The critical values are computed by stochastic simulations.
LL = Maximized log-likelihood AIC = Akaike Information Criterion
SBC = Schwarz Bayesian Criterion HQC = Hannan-Quinn Criterion
ADF tests for variable MFLA
The Dickey-Fuller regressions include an intercept but not a trend
*******************************************************************************
18 observations used in the estimation of all ADF regressions.
Sample period from 1996 to 2013
*******************************************************************************
Test Statistic CV LL AIC SBC HQC
DF 1.2673 -3.1380 -105.8188 -107.8188 -108.7092 -107.9416
ADF(1) 1.1276 -3.1154 -105.8174 -108.8174 -110.1529 -109.0015
ADF(2) .28723 -3.0515 -105.3909 -109.3909 -111.1716 -109.6364
ADF(3) .57142 -2.9688 -104.9883 -109.9883 -112.2142 -110.2952
ADF(4) .025329 -3.1176 -103.8081 -109.8081 -112.4792 -110.1764
ADF(5) .43429 -3.2046 -101.1033 -108.1033 -111.2196 -108.5330
*******************************************************************************
95% published asymptotic critical value corresponding to ADF(0) = -3.0401
CV = 95% simulated critical value using 18 obs. and 1000 replications.
The critical values are computed by stochastic simulations.
LL = Maximized log-likelihood AIC = Akaike Information Criterion
SBC = Schwarz Bayesian Criterion HQC = Hannan-Quinn Criterion
ADF tests for variable MFLA
The Dickey-Fuller regressions include an intercept and a linear trend
*******************************************************************************
18 observations used in the estimation of all ADF regressions.
Sample period from 1996 to 2013
*******************************************************************************
Test Statistic CV LL AIC SBC HQC
DF -.023637 -3.7087 -105.3468 -108.3468 -109.6823 -108.5309
ADF(1) -.076943 -3.7207 -105.3349 -109.3349 -111.1156 -109.5804
ADF(2) -1.0774 -3.5885 -103.9979 -108.9979 -111.2239 -109.3049
ADF(3) -.77977 -3.7096 -103.9965 -109.9965 -112.6676 -110.3648
ADF(4) -2.9502 -3.7635 -97.8867 -104.8867 -108.0030 -105.3164
ADF(5) -1.9343 -4.2335 -97.8010 -105.8010 -109.3625 -106.2921
*******************************************************************************
95% published asymptotic critical value corresponding to ADF(0) = -3.6921
CV = 95% simulated critical value using 18 obs. and 1000 replications.
The critical values are computed by stochastic simulations.
LL = Maximized log-likelihood AIC = Akaike Information Criterion
SBC = Schwarz Bayesian Criterion HQC = Hannan-Quinn Crite
Abu Umar Faruq Ahmad & Mohammad Ashraful Mobin / Proceeding ICOSOPP 2015
___________________________________________________________________________________
« 444 »
Page 8
12/12/2014 11:09:36 AM
ADF tests for variable LMFLA
The Dickey-Fuller regressions include no intercept and no trend
*******************************************************************************
18 observations used in the estimation of all ADF regressions.
Sample period from 1996 to 2013
*******************************************************************************
Test Statistic CV LL AIC SBC HQC
DF 2.3138 -1.8014 -.54959 -1.5496 -1.9948 -1.6110
ADF(1) 1.2407 -1.9141 1.0204 -.97962 -1.8700 -1.1024
ADF(2) 1.3182 -1.7848 1.1803 -1.8197 -3.1553 -2.0039
ADF(3) 1.1641 -1.8167 1.1826 -2.8174 -4.5981 -3.0629
ADF(4) .59985 -1.8280 2.6506 -2.3494 -4.5754 -2.6564
ADF(5) .65325 -1.9887 2.7675 -3.2325 -5.9036 -3.6008
*******************************************************************************
CV = 95% simulated critical value using 18 obs. and 1000 replications.
The critical values are computed by stochastic simulations.
LL = Maximized log-likelihood AIC = Akaike Information Criterion
SBC = Schwarz Bayesian Criterion HQC = Hannan-Quinn Criterion
ADF tests for variable LMFLA
The Dickey-Fuller regressions include an intercept but not a trend
*******************************************************************************
18 observations used in the estimation of all ADF regressions.
Sample period from 1996 to 2013
*******************************************************************************
Test Statistic CV LL AIC SBC HQC
DF -2.1054 -3.1380 2.1812 .18124 -.70913 .058470
ADF(1) -2.1289 -3.1154 3.7552 .75517 -.58039 .57101
ADF(2) -1.9801 -3.0515 3.8122 -.18785 -1.9686 -.43339
ADF(3) -1.9193 -2.9688 3.8367 -1.1633 -3.3892 -1.4702
ADF(4) -1.6880 -3.1176 4.7946 -1.2054 -3.8766 -1.5737
ADF(5) -1.7820 -3.2046 5.3291 -1.6709 -4.7872 -2.1006
*******************************************************************************
95% published asymptotic critical value corresponding to ADF(0) = -3.0401
CV = 95% simulated critical value using 18 obs. and 1000 replications.
The critical values are computed by stochastic simulations.
LL = Maximized log-likelihood AIC = Akaike Information Criterion
SBC = Schwarz Bayesian Criterion HQC = Hannan-Quinn Criterion
ADF tests for variable LMFLA
The Dickey-Fuller regressions include an intercept and a linear trend
*******************************************************************************
18 observations used in the estimation of all ADF regressions.
Sample period from 1996 to 2013
*******************************************************************************
Test Statistic CV LL AIC SBC HQC
DF -2.5449 -3.7087 3.5674 .56737 -.76819 .38321
ADF(1) -3.4877 -3.7207 7.1633 3.1633 1.3826 2.9178
ADF(2) -3.4412 -3.5885 7.6162 2.6162 .39023 2.3092
ADF(3) -3.4745 -3.7096 8.0769 2.0769 -.59425 1.7086
ADF(4) -4.9348 -3.7635 13.4053 6.4053 3.2890 5.9756
ADF(5) -4.9950 -4.2335 14.4244 6.4244 2.8630 5.9334
*******************************************************************************
95% published asymptotic critical value corresponding to ADF(0) = -3.6921
CV = 95% simulated critical value using 18 obs. and 1000 replications.
The critical values are computed by stochastic simulations.
LL = Maximized log-likelihood AIC = Akaike Information Criterion
SBC = Schwarz Bayesian Criterion HQC = Hannan-Quinn Criterion
Abu Umar Faruq Ahmad & Mohammad Ashraful Mobin / Proceeding ICOSOPP 2015
___________________________________________________________________________________
« 445 »
Page 9
12/12/2014 11:09:45 AM
ADF tests for variable DMFLA
The Dickey-Fuller regressions include no intercept and no trend
*******************************************************************************
17 observations used in the estimation of all ADF regressions.
Sample period from 1997 to 2013
*******************************************************************************
Test Statistic CV LL AIC SBC HQC
DF -2.6813 -1.9702 2.8882 1.8882 1.4716 1.8468
ADF(1) -2.5390 -2.0325 3.0179 1.0179 .18465 .93505
ADF(2) -2.1219 -1.8652 3.0362 .036222 -1.2136 -.088012
ADF(3) -1.5748 -1.8940 4.3762 .37620 -1.2902 .21056
ADF(4) -1.6117 -1.8365 4.6098 -.39016 -2.4732 -.59721
ADF(5) -1.5638 -2.0664 4.6506 -1.3494 -3.8490 -1.5979
*******************************************************************************
CV = 95% simulated critical value using 17 obs. and 1000 replications.
The critical values are computed by stochastic simulations.
LL = Maximized log-likelihood AIC = Akaike Information Criterion
SBC = Schwarz Bayesian Criterion HQC = Hannan-Quinn Criterion
ADF tests for variable DMFLA
The Dickey-Fuller regressions include an intercept but not a trend
*******************************************************************************
17 observations used in the estimation of all ADF regressions.
Sample period from 1997 to 2013
*******************************************************************************
Test Statistic CV LL AIC SBC HQC
DF -2.8808 -3.0176 3.5443 1.5443 .71112 1.4615
ADF(1) -2.8967 -3.1255 4.0468 1.0468 -.20304 .92254
ADF(2) -2.5106 -3.0602 4.1066 .10663 -1.5598 -.059015
ADF(3) -1.7028 -3.1371 4.8758 -.12424 -2.2073 -.33130
ADF(4) -1.8416 -3.1545 5.3659 -.63412 -3.1338 -.88259
ADF(5) -1.8530 -3.1883 5.5919 -1.4081 -4.3243 -1.6980
*******************************************************************************
95% published asymptotic critical value corresponding to ADF(0) = -3.0522
CV = 95% simulated critical value using 17 obs. and 1000 replications.
The critical values are computed by stochastic simulations.
LL = Maximized log-likelihood AIC = Akaike Information Criterion
SBC = Schwarz Bayesian Criterion HQC = Hannan-Quinn Criterion
ADF tests for variable DMFLA
The Dickey-Fuller regressions include an intercept and a linear trend
*******************************************************************************
17 observations used in the estimation of all ADF regressions.
Sample period from 1997 to 2013
*******************************************************************************
Test Statistic CV LL AIC SBC HQC
DF -2.6001 -3.7607 3.6582 .65816 -.59166 .53393
ADF(1) -2.6314 -3.9137 4.1414 .14142 -1.5250 -.024228
ADF(2) -2.1920 -3.6439 4.1740 -.82595 -2.9090 -1.0330
ADF(3) -1.2913 -3.8820 5.1344 -.86560 -3.3652 -1.1141
ADF(4) -1.3908 -3.9679 5.4378 -1.5622 -4.4784 -1.8521
ADF(5) -1.3624 -4.2148 5.5956 -2.4044 -5.7372 -2.7357
*******************************************************************************
95% published asymptotic critical value corresponding to ADF(0) = -3.7119
CV = 95% simulated critical value using 17 obs. and 1000 replications.
The critical values are computed by stochastic simulations.
LL = Maximized log-likelihood AIC = Akaike Information Criterion
SBC = Schwarz Bayesian Criterion HQC = Hannan-Quinn Criterion
35. abu umar
35. abu umar
35. abu umar
35. abu umar
35. abu umar
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35. abu umar

  • 1. THE FIRST INTERNATIONAL CONFERENCE ON SHARI’AH ORIENTED PUBLIC POLICY IN ISLAMIC ECONOMIC SYSTEM (ICOSOPP 2015) “Formulating Effective Public Policy in the Islamic Economic System under the Framework of Shari’ah” 30-31 March 2015, Ar-Raniry State Islamic University, Banda Aceh, Indonesia ___________________________________________________________________________ Promoting Maqāṣid al-Shari`ah and Achieving Sustainable Economic Development: The Potential of Proposed Two Tier Mudarabah Business Model on Cash Waqf Dr. Abu Umar Faruq Ahmada , Mohammad Ashraful Mobinb Corresponding Email: faruq@isra.my, 1400028@student.inceif.org a International Shari`ah Research Academy for Islamic Finance (ISRA) Associate Professor, INCEIF, Kuala Lumpur, Malaysia b Graduate Student, INCEIF, The Global University of Islamic Finance, Kuala Lumpur, Malaysia ___________________________________________________________________________________ Abstract Islamic microfinance provides an alternative model for a significant number of underprivileged people who are not served by conventional microfinance. In order to give access to sustainable services to a greater extent, the Islamic microfinance is in paramount need of the adoption of innovative business models and sound practices into the industry. To this end, the research seeks to propose two tier mudarabah model based on cash waqf as an alternative to Islamic microfinance institutions. The objectives of the study are: to a) help develop and implement an appropriate business model; b) safeguard the maqasid al-Shari`ah so as to promote the well-being of the people through establishing justice and eliminating hardship; and 2) help make a reform in the present institutional framework for Islamic microfinance and waqf institutions. The study finds that cash waqf based microfinance institutions can play an important role inter alia in financial inclusion and developing socio-economic conditions of the poor and underprivileged people in society. Keywords: Waqf, Maqasid al-Shari`ah, Microfinance, Poverty, Two Tier Mudarabah ___________________________________________________________________________________ 1. Introduction According to a recent World Bank report (as at 8 October 2014), there were 2.2 billion people lived on less than USD2.00 per day (average poverty line in developing countries) in 2011, compared with 2.59 billion people who lived on the same amount in 1981. Although the number of people living below the average poverty line has reduced by 15% from 20 year ago, this number still makes up of an alarming 30.6% of the current world population of 7.2 billion. Out of those living below average poverty line, as reported by the Islamic Development Bank (IDB), 650 million are Muslims. As part of the initiatives of addressing the poverty problem, many international and national institutions have undertaken programs to help countries provide better job opportunities to the unemployed, and to promote microenterprises. The latter, which normally turned away by conventional profit-orientated financial institutions due to lack of collaterals, high transaction costs, and fragile legal enforcement mechanisms, have been served by microcredit and microfinance institutions (MFIs) since their inception in the 1970s with phenomenal success. However, there is an increasing trend for the Muslim microentrepreneurs turning to Islamic microfinance Institutions (IMFIs) for Shari`ah complaint
  • 2. Abu Umar Faruq Ahmad & Mohammad Ashraful Mobin / Proceeding ICOSOPP 2015 ___________________________________________________________________________________ « 421 » financing taking into consideration of faithful Muslims‟ adherence to their religious practice. According to IDB, the number of customers of IMFIs though quadrupled in recent years has reached to only 10, 00000 Muslims compared to the 650 million Muslims who are below the average poverty line. There is an urgency for the IMFIs to build capacity and capability in order to provide Shari`ah compliant products to adherent Muslims who are in dire need of Islamic microfinancing. In Islamic economics and finance, there is a near consensus at least in theory that out of all financial products and instruments, profit sharing instruments are the backbone of Shari`ah compliant transactions, which is justified by the way any risk involved is properly shared by the parties involved in the underlying contracts. This risk sharing is highly consistent with the general spirit of Islamic teachings. In this paper we will make a humble attempt to propose a two tier mudarabah model for waqf based MFIs. This research is designed to explore a cash waqf based Islamic microfinancing model for adoption by the IMFIs to finance the Muslim microentrepreneurs with the ultimate objectives of poverty alleviation. The following will present the findings of the previous study on microfinancing particularly in Islamic microfinancing, the issues relating to the IMFIs, the proposed cash waqf based Islamic microfinancing model, the analysis of data from other established models, and conclusion of the study on the effective of the proposed model. 2. The Rationale for Promoting Maqasid Al-Shari`ah The key objective of Shari`ah lies in the principle of eliminating agony, hardship and difficulty from people and finding solutions to their problems. Emphasising this principle the Qur‟an declares that Allah does not want to impose any hardship on His servants, although the context of the relevant text illustrates the rituals of ablution for prayer. The application of the contents of the text is eternal. The verse reads: “Allah does not want to impose any hardship on you, but wants to make you pure, and to bestow upon you the full measure of His blessings, so that you might have cause to be grateful.” (5: 6). The objectives of Shari`ah have either been directly mentioned in the Qur‟an and the Prophetic traditions (Sunnah), or referred to them by some of the predecessors among the Muslim jurists. These were recognised by almost all of them in order to serve the common interests of entire human beings and to protect them from being harmed. For instance, the twelfth century Muslim philosopher Al- Ghazali (d.1058AH/1111CE) classified the maqasid into five key categories. He writes: “The very objective of the Shari`ah is to promote the well-being of the people, which lies in safeguarding their faith (dīn), their self (nafs), their intellect („aql), their posterity (nasl), and their wealth (māl). Whatever ensures the safeguard of these five serves public interest and is desirable.” Al-Shatibi (d.790H/1388CE) endorses Al-Ghazali‟s five key categories of maqasid al-Shari`ah, and point out that they are the most preferable in terms of their harmony with the fundamental nature of Shari`ah. However, mostly late and contemporary scholars, have extended the scope of the maqasid to embrace other vital objectives of Shari`ah. The Maliki jurist, Al-Qarafi (d.684 AH/1285CE) has added the protection of `ird (integrity) as the sixth objective of Shari`ah. Later, the fourteenth century Ibn Taymiyyah (d.728AH/1327CE) was perhaps the first scholar to depart from the perception of locking up the maqasid to a definite number.He provides a broader definition maqasid al- Shari`ah by adding the preservation of the social order, promotion of the wellbeing and righteousness of the community, preservation of the family, etc. to the previous list of his predecessors. He asserts: “Both its general rules and specific proofs indicate that the all-purpose principle of Islamic legislation is to preserve the social order of the community and insure its healthy progress by promoting the well- being and righteousness of that which prevails in it, namely, the human species. The well-being and virtue of human beings consist of the soundness of their intellect, the righteousness of their deeds as well as the goodness of the things of the world where they live that are put at their disposal.” The Muslim scholar Ibn al-Qayyim (d.751AH/1350CE) looks into the maqasid in a bit different way. He observes: “Shari`ah is based on wisdom and achieving people‟s welfare in this life and the afterlife. Shari‟ah is all about justice, mercy, wisdom, and good. Thus, any ruling that replaces justice with injustice, mercy with its opposite, common good with mischief, or wisdom with nonsense, is a ruling that does not belong to the Shari`ah, even if it is claimed to be so according to some
  • 3. Abu Umar Faruq Ahmad & Mohammad Ashraful Mobin / Proceeding ICOSOPP 2015 ___________________________________________________________________________________ « 422 » interpretations.” However, the contemporary scholar Al-Qaradawi has further included in the list of the maqasid human dignity, liberty, social welfare and human alliance branding them as the higher maqasid of the Shari`ah. From the above discussions it is evident that the objective of Shari`ah in general is to safeguard the society and ensure the stability of its healthy growth and development. From these perspectives, Shari`ah encourages individuals and societies to be in complete harmony and well protected, with a sense of unity and trust where each member of the community renders help to other, enjoins what is good, and refrains from doing mischief. 3. The Plan of the Study The organisation of the paper is as follows: Following the introduction and a related analysis on the rationale for promoting maqasid al-Shari`ah the study reviews the relevant literature in search of finding out the gap in previous research and to fill in this in present research. It then thoroughly discusses the waqf and its investment potentials, concepts and comparative analysis of both Islamic and conventional microfinance as a background study of the research. Then the discussions on the core issue of the research i.e., the cash waqf based Islamic microfinance has followed, which included inter alia the alienation of the structure of the proposed 'two tier mudarabah cash waqf model', its salient features and the justification for proposing the model. In order to test the feasibility of the proposed cash waqf based microfinance model, the study came up with a hypothesis tom prove whether the MFIs can lead the economic growth of a country. The paper concludes with the summary and suggestions. 4. The Review of Literature Waqf in general have had a long record in culture and civilisation of the human and nations‟ history. Its instance and history can be traced back Egypt's pharaohs. Pharaohs in old Egypt, allocated property and lands for monks and even in the ancient Greek civilisation, there were actually signs of library waqf and educational centers. Waqf can be seen to instances and charity activities during the lifetime of Prophet Muhammad (pbuh) and in his holy traditions prior to the advent of Islam in the then Arabian peninsula. Prophet Abraham (pbuh), the father of the Muslim ummah, during his lifetime had founded charity and the relief efforts to assist Muslims with their property through introducing waqf, with the aim of leaving the ownership of that property remain forever in their hands and spending it to public welfares. Given the above backdrop, waqf in Islam may have likely been influenced by earlier civilisations such as the ancient Mesopotamia, Greece, Rome as well as the pre-Islamic Arabs whereby there were certain knowledge of such endowments. However, the extent of how much influence of these ancient institutions has had on waqf is still a matter of intense debate. Nevertheless, Romans‟, Byzantines‟, even Mesopotamians‟, Sassanids‟, Jewish and Buddhists‟ influences have been accepted as plausible. The latest research is more decisive and points to the Sassanid law as the most likely source. In summary, it can be assumed that Muslims were urged strongly to endow their assets in the service of mankind and they learned how to do it from the earlier civilisations, which had dominated the region in which they had found themselves. Since Islam is a religion of fitrah or inborn nature, some of the customs or practices were adopted by other nations prior to the advent of Prophet Muhammad (pbuh) which did not contradict the teachings of Islam were retained during the time of the Prophet including those adapted from other civilisations. This ability may well have originated with a tradition attributed to the Prophet, which says: “A word of wisdom is the lost property of a believer; he can take it wherever he finds it, because he is more entitled to it.” (Al-Tirmidhi, 1992: 2687) Although waqf is not specifically mentioned in the Qur‟an, the concept of wealth redistribution is strongly emphasised (2:215, 264, 270 & 280). There is also definitive evidence that many great personalities of Islam had endowed their properties for charitable purposes. A Prophetic tradition most probably accounts for the origin of the institution of waqf in the world of Islam. It mentions: “When a man dies, all his acts come to an end, except for three: ongoing charity, or knowledge (by which people benefit), or a righteous offspring, who prays for him” (Muslim, 1992: ch. 3, no. 14).
  • 4. Abu Umar Faruq Ahmad & Mohammad Ashraful Mobin / Proceeding ICOSOPP 2015 ___________________________________________________________________________________ « 423 » Although the classical sources have normally separates each one of these good deeds, it is always preferred for the modern generation to combine them due to the argument that this combination is what constitutes the very essence of the Islamic waqf. For this Muslims needed an institution that would enable them to perform all three of these good deeds. The waqf fitted the criteria. It indeed, assures ongoing charity for many years, even centuries, after the death of the founder; it can finance scholars whose lasting works will benefit mankind for a long period and the good deeds, that accrue to them would be shared by the waqf ‟s founder who had provided for their sustenance in the first place. Finally, the management of waqf can be entrusted to the offspring of the founder so that while, careful and loyal management is assured, on the other hand, the offspring would pray for the deceased since he or she is not destitute. Although Muslims may have been encouraged to adopt ideas from other civilisations, the actual process of adoption is not as simple as stated in the aforementioned Prophetic tradition. Institutions that were adopted need to be shaped and re-shaped to conform to the basic teachings of Islam. However, there were substantial differences in the opinions of the early great jurists concerning the structure and judicial framework of waqf. While Imam Shafi`i had objections to certain aspects of waqf institution, Imam Abu Yusuf, the disciple of Imam Abu Hanifah has differed from his mentors. It can be debated in general that Imam Shafi`i and Abu Yusuf wanted to expand the waqf and therefore facilitated their foundation, but others preferred to restrict this institution. The basic problem lies in Islamic law of inheritance whereby the founder is allowed to entrust the management of his waqf to any of his offspring which would initiate a defector primogeniture, and this practice itself could violate the basic principles of Shari`ah, which promulgates a distribution of property among all the inheritors. Due to this particular issue, most Muslim jurists found that it is extremely difficult to sanction the waqf and ended up tolerating this practice. In order to maintain justice, fairness and well-being for all, the Muslim society needed this institution and the turning point emerged when Imam Abu Yusuf observed the importance of this institution had become during his pilgrimage. He then introduced a set of new legislature, which facilitated the establishment of the very foundation of waqf. With this, the institution thus emerged after the demise of the Prophet and its legal structure was firmly established during the second half of the second century. As the system itself did not originate from Islam, whereby it is not specifically mentioned in the Qur‟an and objected to initially by many of the eminent jurists, was embraced so enthusiastically and developed to such a phenomenal dimension that there is a need to explain the reasons for its existence. There are mainly two explanations for this either historically or from the economic point of view. For the historical explanation, the great Islamic conquests had enriched the Muslim world beyond any imagination achieving the economic preconditions for the emergence of this institution due to the emphasis attached in Islamic teachings on the importance of righteous actions and charitable deeds. As wealth in Islam is considered an important source of test and trial, the natural tendency among the rich Muslims to carry out good deeds as preparation for the hereafter can be easily understood. Thus, it is due to these historical reasons that although not mentioned in the Qur‟an specifically, and objected to initially, the waqf has been embraced so enthusiastically. However, the economic theory also has its own explanation as to why the waqf system was needed and implemented. According to the theory, there were compelling reasons for the waqf system to emerge. Under the conditions of rational behavior (business transactions), public good would tend to be under produced and this becomes a dilemma, and would push for the creation of public good which promotes a demand for the creation of non-market institutions. This demand may also explain behind the popularity of waqf and its increase in usage spreading across the Muslim world. The theory explains the universality of the waqf or waqf-like non-market institutions. After all, endowments are known not only in the Muslim world but also in other non-Muslim great civilisations. Suggestions of establishing waqf-based financing institutions serving the poor have been made by various scholars. Cizakca (2004) suggests a model in which the concept of cash waqf can be used in contemporary times to serve the social objectives in the society. One use of cash waqf would be to
  • 5. Abu Umar Faruq Ahmad & Mohammad Ashraful Mobin / Proceeding ICOSOPP 2015 ___________________________________________________________________________________ « 424 » provide microfinance to the poor. Similarly, Elgari (2004) proposes establishing a nonprofit financial intermediary, what he named as qard hasan bank and which gives interest free loan to finance consumer lending for the poor. According to him, the capital of the bank would come from monetary (cash) waqf to be donated by wealthy Muslims. Kahf (2004) and Ahmed (2003) propose establishing MFIs based on zakah, awqaf, and sadaqah. They suggest that the returns from waqf and funds from sadaqah can be used to finance productive microenterprises at subsidised rates. In addition, zakah can be given out to the poor for consumption purposes to avoid diversion of funds from productive heads. Before the advent of the microcredit movement pioneered by the Grameen Bank (GB) of Bangladesh, which was established by Muhammad Yunus in the 1970s, poor microentrepreneurs who needed financing for their business and production needs would turn to family, friends, relatives and moneylenders for loans. Many argued that the moneylenders charged very high interest rate and often exploitative due to scarcity of capital and their monopolistic position. However, these moneylenders served only a tiny portion of the community. As such, the emergence of microcredit institution like GB has a bigger objective of reaching out to a nationwide of microentrepreneurs rather than to intervene in the credit market of the moneylenders. (Armendariz & Morduch, 2010). Microcredit, often used interchangeably with microfinance, is referred to small loan giving out by institution like GB to microentrepreneurs usually for a short financing period. The objective of microcredit institutions is to focus on providing small loans to the very poor to use as capital for productions and subsequently to improve their income levels. It is also recognised that the economic condition of the poor household, including microentrepreneurs, can be improved by accessing microcredit and other financial services such as savings, microinsurance, product marketing and distribution. This new recognition is pushing the microcredit movement one notch higher to a commercially oriented and fully regulated microfinance entity which, with the extended market and services, will be able to operate as a sustainable business model, and at the same time continue to play its role to reduce poverty and promote positive social change. (Armendariz & Morduch, 2010). Mudarabah as a profit sharing financial instrument constitutes a vital place in Islamic finance. Islam has duly encouraged the use of profit sharing principle in business that will ensure fairness for all the parties in the underlying contract. A variation of classical mudarabah, two-tier mudarabah model was widely adopted by Islamic financial institutions. Fairness is best ensured by profit sharing arrangement as rewards are a function of realised productivity all along the line. The waqf fund owner, the financial intermediary and the ultimate user of fund in productive enterprise each gets a share out of the value added. When the economic environment fails to add any value, or when it erodes part of the value invested in the venture, all the three share in the misfortune; the fund owner loses part of his funds and the intermediary as well as the ultimate user of fund in productive enterprise go unrewarded for their services. Profit sharing is harmonious with the nature of man's economic environment on both sides of the intermediating institution, with the fund owners along with the fund users. Unlike the previous papers, the present paper studies the economics of microfinancing and discusses the sustainability and operational issues of a waqf based MFI in details. To do so, our study first outlines the features of the conventional MFIs and then critically examines their strengths and weaknesses. Drawing on the strengths of conventional MFIs, an alternative of Islamic microfinancing based of waqf is then outlined. 5. Waqf and its Investment Potentials The very word 'waqf' and its plural form 'awqaf' are derived from the Arabic root verb waqafa, which means „causing a thing to stop and stand still‟, „to contain‟, „to restrain‟, and „to preserve‟. Literally, its noun form also means philanthropic, pious (charitable) foundations or simply religious endowment (Cizakca, 1998); Mohsin, 2009). The Prophet promotes and encourages waqf. Waqf has since been widely used not just by Arabs, but also by Muslims from other parts of the world. This has resulted in different transliterations in different jurisdictions, such as wakf, vakf, vakif, vaqf, wakaf (Islahi, 2003). Waqf is also known with different nomenclatures, such as habs in North and South Africa and qadsh in Hebrew (Osman, 2012). As for the technical definition of waqf, Muslim scholars differ, depending on their positions on some elements and conditions of waqf. However, they are agreed on the basic concept of waqf, which is the permanent dedication of a portion of one‟s wealth for the pleasure of Allah (Hashim, 2007). A
  • 6. Abu Umar Faruq Ahmad & Mohammad Ashraful Mobin / Proceeding ICOSOPP 2015 ___________________________________________________________________________________ « 425 » permanent dedication means that a portion of a person‟s property is alienated from him and „transferred‟ to Allah. There is a near consensus among the Muslim jurists to define waqf as confinement of the property from the ownership and dedication of its usufruct to charitable purposes (Mohsin, 2009). Therefore, it should not be inherited, gifted, mortgaged, and so on. It is one of the essential characteristics of waqf that the property remains intact while income derived there from or the property itself, is used for certain philanthropic activities for the sake of Allah, and regarded ad ongoing charity (Hashim, 2007). However defined, this institution - whereby a privately owned property is endowed for a charitable purpose in perpetuity and the revenue generated is spent for that purpose - stands out as one of the major achievements of Islamic civilisation. Hashim (2007) also stated that the general aims of waqf include assisting the needy, helping the oppressed, improving the lives of the downtrodden, regulation of the economy, raising the standard of living of the people, dissemination of sciences and knowledge, constructing and administering mosques, shrines, libraries, schools, clinics, hospitals, welfare centers, etc. In addition, waqf elevates the thoughts and purifies the human beings from selfishness and love of the world. It also cultivates the spirit of helping one another in the society, and joining in mutual care and love. Waqf properties can contribute to the development of the socioeconomic such as building commercial projects such as hotels, business centers or in forms of social projects like orphanage, caring centers and many others as long as it is Shari`ah compliant. One of the Qur'anic expressions is worthy of mention in this connection. Allah reminds the faithful Muslims by saying: "the likeness of those who spend their wealth in Allah's way is as the likeness of a grain which grows seven branches, in every branch contains of hundred seeds, and (remembers) Allah will give increase manifold to which he will and Allah is All Embracing and All Knowing". Being a less famous concept in the Islamic World (compared to immovable waqf), the cash waqf is a very important instrument to explain. In fact, it only started to have a considerable impact since the 15th century with the Ottoman Empire. Therefore, there is not enough ancient literature available, which deals with the conceptualisation of this kind of waqf. However, more recently scholars have dealt with this particular kind with more details and they even gave concrete application samples to be followed. The cash The cash waqf is defined as “a form of movable waqf property which is structured by money with the main purpose to be in service for pious and charitable projects for the people for the sake of Allah” (Adam & Lahsasna, 2013). The cash waqf has gone through a revolutionary stage, from being rejected because its corpus in the form of cash cannot be perpetual and ironically based on this analogy too it found its legality till date. Imam Shafi`i like Abu Yusuf, approves of the waqf of movables subject to `urf or custom of the relevant society. Imam Malik has also approved it as it has been learned from his famous treatise Al-Mudawwanah. The position of Hanbali school of thought (madhhab) is similar to that of the Shafi`is. Finally, the Shiite‟s position is also positive as revealed by the Fatawa of Shaikh ‘Abdullah Al-Mazandari (Cizakca, 1998). In order to comply with the rules and principles of Shari`ah and to achieve the desired economic goals and objectives, the cash waqf trustee/manager has to follow the guidance mentioned by the scholars regarding investments areas and methods, and to use the modern tools of successful management. While early Muslim scholars have mentioned the mudarabah as an investment tool to fructify the monetary waqf money, the International Fiqh Council of OIC, in its recent fatwa about monetary waqf - mentioned above - approved Shari`ah compliant investment methods. Due to its flexibility, as well as the important liquidity that could be raised by cash waqf, this institution could be a real catalyst for economic developments. In fact, the monetary waqf provides more cash to invest and to inject in the economy. This type of financing can be directed to any sector and any needs, as long as they comply with the tenets of Shari`ah. It could considerably contribute to reducing government social expenditures due to investing in welfare. Furthermore, it contributes to lowering unemployment rates directly, by creating employment opportunities itself, or indirectly by participating in projects creation. Knowing the economic reality of many Muslim countries and their dependence on
  • 7. Abu Umar Faruq Ahmad & Mohammad Ashraful Mobin / Proceeding ICOSOPP 2015 ___________________________________________________________________________________ « 426 » aids of the wealthier countries, it is urgent for them, more than ever, to encourage the creation of such institutions, which will definitely ensure an acceptable level of life for a large part of the society. It is common to see the objectives set on waqf along the lines of community development. Creating new waqf for financing the needs of the people is everyone‟s job and not one that is left to the trustee who handles the waqf assets. Nevertheless, such trustee or institution holds a huge responsibility of continuing to redevelop all of its waqf assets in its portfolio, ensure growth and diversification of its assets, maximize the potentials of its investments and the potentials of the waqf properties, efficiently and effectively manage its waqf fund, establishing that waqf is the way forward and the best form of charitable spending and ensure that the proceeds to go towards community development. Sounds like a mountain of jobs although usually it is entrusted to non-profit organisations. The applications of waqf in meeting the needs of the people discussed in Islamic finance, being the financial intermediaries. Socio-economic aspects can be fulfilled by introducing waqf-based organisations such as those are seen in microfinance, takaful and for the purpose of guarantee. Next, the study will discuss briefly some of examples of its application in Islamic finance. Cash waqf can be used in waqf MFI in different ways: corpus of waqf invested and returns used for social purposes, corpus of waqf given for financing as interest-free loans, corpus of waqf can be used as capital to create waqf MFI. 6. Islamic and Conventional Microfinance: Concepts and Comparative Analysis The term microfinance can be described as the provision of financial service to low income clients, including self employed, low income entrepreneurs in both urban and rural areas. The microfinance revolution which begun with independent initiatives in Latin America and South Asia starting in the 1970s, has so far allowed 65 million poor people around the world to receive small loans without collateral, build up assets, and buy insurance. The idea of microfinance programs has come from Muhammad Yunus who began a microfinance program among women in Bangladesh in 1976 through the University of Chittagong; however the concept is relatively simple and enjoys a long tradition in many developing countries. The basic idea of microfinance is to provide credit to working poor who otherwise would not have access to credit services. This service has been provided in variety in different countries by moneylenders in poor communities for a long time. There are many definitions for microfinance, depending on which component one aims to highlight. One of the most accurate definitions is the one given by the Organization for Economic Co-operation and Development (OECD) as: "microfinance aims the access to finance small projects, led by marginalised people who aspire to create their own jobs, often by default of other professional opportunities and because access to traditional sources is denied". Besides microcredit and MFIs (such as non-governmental organisations - NGOs, savings and credit cooperatives, public banks, commercial banks or any other financial institutions) offer microinsurance, microsavings, microequity and microtransfer for poor people. The main objective of such institutions, besides profitability for profit- based ones, is to alleviate poverty by providing social and financial intermediation. This would be realised by both offering dedicated financial services for the targeted persons and supporting them (by offering training, education, health care, etc.), which allows them to increase their income and to be less vulnerable. In order to ensure their sustainability and fulfill their main purpose, MFIs aim to find the right balance between the pursuit of profit, the social support of microentrepreneurs and mentoring them to succeed in their projects. For governmental institutions or development entities (like welfare associations, waqf entities, etc.), as they may be perceived as philanthropic organisation, i.e., having the function of making grants, the challenge is to toggle between social aspect and return on investment. This ensures the widespread availability of their funds (minimise the call to donations as much as they can) and will encourage project proponents to be rigorous. There are many business models that could be used in microfinance. For the sake of relevance, in this study we chose to present the most used ones.
  • 8. Abu Umar Faruq Ahmad & Mohammad Ashraful Mobin / Proceeding ICOSOPP 2015 ___________________________________________________________________________________ « 427 » 6.1. The Grameen Bank model The most classic and common application of this model consists in targeting a group of poor people after evaluating their resources, of which mostly are women. The whole group will be responsible for any member's default. This technique replaces the classic collateral method used by commercial banks and reduces the risk of default and negligence. The group members should be closely monitored and supported when needed. This model is widely spread with slight different ways of application. 6.2. The Village Bank model This model consists in creating small banks in poor villages with 30 to 50 individuals and providing a capital to be used in providing weekly term loans. The implementing and funding entity recovers the capital and the interests every 4 months (i.e., on quarterly basis). Only banks having repaid their full engagement amount will be eligible to receive new funds. The role of intra-borrowers pressure is of a high importance in ensuring the full reimbursement within the deadlines. Members are highly encouraged to do savings; the amounts are used in enhancing the autonomy of the institution which will enable it to be a dependent institution (generally over a three year period). 6.3. The Credit Union model This model is based on the mutuality concept. It implies the implementation of a non-profit financial cooperative both owned and controlled by its members. These members mobilise the savings, provide the loans for productive and provident purposes and share the ownership namely through some common bonds. 6.4. The Self-Help Groups model The fourth model is called the Self-Help Groups, which was originated in India. Under this model, groups of 10 to 15 members are built. The members should be relatively homogenous, at least, in terms of income. The different members mobilise commonly their savings within the group and use them for lending. They can also seek external funding for supplementary financing. The conditions and terms of the loans are democratically fixed between the members of the group. Usually the Self-Help Groups are supported by NGOs but generally, they have the objective to become independent and sustainable institutions. It is argued that despite the phenomenal success of many MFIs to alleviate poverty around the globe, the high interest rate on the microloan has increased the disparity between the rich and the poor sectors of the economy (Tahir, 2012). With more and more MFIs operating like commercial entities with the aim of maximising profit and creating values for their shareholders, their social goal of alleviating poverty and fair distribution of income has suddenly become unattainable. In an interest free model of microfinance in Islamic finance, it is suggested that four key principles must strictly be followed, such as 1) belief in divine guidance, 2) no dealings with riba or interest based transactions, 3) no dealings with other Shari`ah non-compliant transactions, 3) preferably dealings with risk sharing financial products and instruments, and 4) financing based on involving in real assets (Abdullah & Chee, 2012). It is also suggested that the act of lending is to be treated more of as a moral phenomenon than a purely economic activity (Zaman, 1991). Borrowing is not prohibited in Islam though the Prophet (pbuh) discouraged taking loans unnecessarily. In this respect, the confluence of Islamic finance and microfinance will solve the problem of income disparity, because the elements of riba or interest must be avoided in Islamic financial instruments and will refocus the MFI to its social course. The relatively new practice of IMFIs has an outreach of only 1 million Muslims compared to 650 million Muslims who are living below the average poverty line of developing countries. The very low outreach is due to Islamic microfinance is a new phenomenon, and still is in the state of its infancy. The key is for the IMFIs to build capacity and capability to provide a diversified array of Shari`ah compliant Islamic microfinance products to the Muslims in dire need of Islamic microfinancing.
  • 9. Abu Umar Faruq Ahmad & Mohammad Ashraful Mobin / Proceeding ICOSOPP 2015 ___________________________________________________________________________________ « 428 » The IMFIs provide intermediary financial services by accepting funds from investors and other stakeholders on one hand, and disburse the funds with or without profits to poor microentrepreneurs and households on the other hand, via an Islamic financial instrument as shown in the basic Islamic microfinance model below (Figure 1). The basic model consists of five essential components which are needed to complete a full cycle of Islamic microfinancing process, namely funding, an Islamic microfinance institution, an Islamic financial instrument for disbursement of fund, the borrower, and the repayment. In the following paragraphs we will attempt to elaborate on the first two components: funding and IMFIs, which are the most relevant components to our study. Source: Authors' own Armendariz and Morduch (2010: 5-8) argued that poorer enterprises should be able to pay banks higher interest rates than richer enterprises. They also have the perception that money should flow from rich depositors to poor entrepreneurs. The money did not flow easily to the poor countries as predicted due to high credit risk resulting from lack of collaterals, high transaction costs, and weak legal enforcement mechanisms. One thing is right about the principle though is that the borrowers are forced to pay higher interest rate charged by the banks. In the case of Banco Compartamos, the largest Latin America microfinance institution based in Mexico had charged its customers an average interest rate of roughly 100% per annum while the yearly inflation rate in Mexico was around 4%. The extraordinary high returns had attracted more profit oriented capital when some MFIs like Bank Rakyat Indonesia, Kenya‟s Equity Bank and Banco Comparators went public in 2003, 2006 and 2007 respectively for the shareholders to realised the value of their investment, although at the same time, the funding from the listing had allowed the banks to expand their outreach dramatically to more customers (Armendariz & Morduch, 2010: 239-241). Since giving and taking interest is categorically prohibited in IslamIMFIs cannot adopt the above mentioned profit-oriented model using interest.. Instead, it should emerge as a donor funded institution with clear mission of eradicating poverty and fulfilling just and equitable socio-economic development. The funding for IMFIs will be derived mainly from donations in the form of cash waqf, sadaqah, zakah, hibah and tabarru`, equity capital and the government's subsidies to partially finance the high transaction costs. In deciding on the type of funding, the IMFIs have to consider the costs, benefits, and nature of each fund. The discussions went around whether the IMFIs should be run by non-governmental organisations (NGOs), non-banking institutions, private banks, government banks, cooperatives or agencies, and also methods to be followed to act as a best model. The GB which pioneered microcredit financing in the 1070s in Bangladesh adopted its classic „group lending‟ and „graduated financing‟ methodology with great success. The Hodeidah Microfinance Programme of Yemen, began in 1997, was using the group lending methodology but the financing was disbursed through murabahah contract. Dusuka (2008) proposed a special purpose vehicle (SPV), using funds from banks for general and specific Islamic microfinance purposes. Wilson (2007) argues that it should be run by a non-bank institution using a proposed wakalah or mudharabah model similar to the ones used in takaful operations. Incidentally, Funding Investors ' capital Donors ' cash waqf, sadaqah, zakah, hibah, tabarru` Government subsidy Islamic microfinance institution NGO Nonbank IMFI Islamic banks Cooperatives Agencies Financial instruments Mudarabah Musharakah Ijarah Murabahah Qard Salam Istisna'a Istijrar Small investors Microentrepreneurs Poor households Microprojects Figure 1: Basic Islamic Microfinance Model Repayment
  • 10. Abu Umar Faruq Ahmad & Mohammad Ashraful Mobin / Proceeding ICOSOPP 2015 ___________________________________________________________________________________ « 429 » the models proposed by both Dusuka and Wilson are suitable for funding by waqf and zakah according to the terms and conditions set out by the Shari`ah. In another developments in IMFIs' programs, the Deprived Families Economic Empowerment Program (June 2006 - December 2015) meant for the economically deprived and socially marginalised families in Palestine and donated by the Islamic Development Bank (IDB) include: (a) charity to take care of their immediate consumption needs; (b) an institutional mechanism to improve their skill level required to make them economically active; and (c) a financing mechanism that does not penalise the new entrepreneurs with a cost, should there be an incidence of failure in their respective ventures (Obaidullah, 2008). The program serves as a model for the institutions of zakah, waqf and qard hasan to perform similar poverty reduction and microenterprise development programs. 7. Cash Waqf Based Islamic Microfinance The previous studies have shown the cash waqf is an alternative way to promote entrepreneurship to the poor and needy people but its function is limited by the availability of funds in the microfinancial institution. Masyita (2003) states that micro finance program which uses mudarabah and musharakah investment methods to generate funds, is important for poverty alleviation. This program particularly aims to help poor people initiating their business and improving their quality of life. As mentioned by Ahmed (2007), the creation of cash waqf has two forms. Firstly, cash is transformed into waqf and used for interest-free lending to beneficiaries of the waqf. Secondly, the cash in the waqf is invested and its net return is assigned to the beneficiaries of the waqf. In this regard, the second form of cash waqf is appropriate for micro financing initiatives. Moreover, micro financing is facing the problem of limited funds to promote entrepreneurship. In this respect, cash waqf could invest by providing funding to microfinance and play its essential role to achieve socio economic relief which is consistent with the objectives of waqf. 7.1. Structure of the Proposed Two Tier Mudarabah Cash Waqf Model The above paragraphs have suggested the use of cash waqf to fund IMFIs by way of profit sharing agreement. Siddiqi (1991) has lent his support by saying that the best, and the most efficient and the fairest arrangement between fund owners and financial intermediaries is that of profit-sharing. The said arrangement can be made by adopting a two-tier mudarabah structure which represents an ideal mode of financial intermediation that replaces the interest based system. Interest based system, being oppressive, does not assume any risk of providing capital regardless of the outcome of the venture undertaken by the borrower. The first tier of the mudarabah agreement is between the IMFIs and the waqf fund managers who agree to invest the waqf money in the IMFIs and to share profit with them. In this case, the waqf fund managers are the capital providers (rabb al-mal) and the IMFIs as managers (mudarib) of the capital. The second tier of the mudarabah agreement is between the IMFIs and the micro entrepreneurs who seek financing from the IMFIs. The profits accruing from the micro entrepreneurs' venture will be shared between the entrepreneurs and the IMFIs in a pre agreed proportion and the loss will be borne by the IMFIs only. In this instance, the IMFIs function as the providers of the capital and the microentrepreneur work as managers (mudarib).
  • 11. Abu Umar Faruq Ahmad & Mohammad Ashraful Mobin / Proceeding ICOSOPP 2015 ___________________________________________________________________________________ « 430 » The two-tier mudarabah structure is shown in the figure below. Figure 2: Two Tier Mudarabah Cash Waqf Model Source: Authors' own 7.1.1. The Salient Features of the Proposed Model The proposed model consists of four components, namely 1) the Waqf fund, 2) the beneficiaries, 3) the fund manager, and 4) the regulatory bodies. Waqf fund A cash waqf is established by cash donation from individual, family, company or other institutions for specific purposes, such as education, charity, etc. The cash donation may be one off or may be continuous in fixed intervals. Beneficiaries The beneficiaries of the waqf fund are entitled to receive part of the fund or part of the income generated by the fund. The beneficiaries are usually named or described at the time of establishing the fund. Fund manager An independent person is appointed to work as trustee or waqf fund manager. He will supervise and monitor the collection of waqf fund, investment and distribution of profit. Investment in microfinance will follow the two tiers mudarabah model. In this respect, the trustee will enter into a mudarabah contract with the IMFI. The Trustee will be the fund provider (rabb al Mal) and the IMFI will be the mudarib. With this fund, the IMFI will enter into another mudarabah contract with its client (microentrepreneur). IMFI will provide the fund and the microentrepreneur will be the skill or labor provider. The trustee shall invest the fund in different IMFIs to diversify the risks. According to the underlying mudarabah contract, profits from the investment will be shared by both parties and loss will be borne by the fund provider. In this proposed model, profit earned from microentrepreneurs will be distributed between the IMFI and the trustee according to pre-agreed ratio. The trustee will channel this profit for defraying management expenses, distribution to the beneficiaries, and for building reserve. Regulatory bodies It is recommended that the administration of waqf should be managed by non-governmental organisations with expertise in fund management. The government should act as supervisor/nazir only. The religious council should provide the Shari`ah guidelines to the fund management, and the Regulation Regulatory Body -Waqf Waqf Fund Provider Trustee/Fund Manager (Rabbul Mal) Individuals Islamic Microfinance Institutions (Mudarib) / Partner (Musharik) Regulatory Body - Microfinanc e Microentrepreneurs (Mudarib) Organisations Religious Body
  • 12. Abu Umar Faruq Ahmad & Mohammad Ashraful Mobin / Proceeding ICOSOPP 2015 ___________________________________________________________________________________ « 431 » supervisory power be given to intervene directly in case of negligence or mismanagement for any of the waqf properties (MIA Mohsin, 2009). 7.1.2. Justifications for the Proposed Model The numerous benefits can be driven from cash waqf for the IMFIs and the society, specifically when the proposed model is based on two tier mudarabah. Some of the justifications are outlined below:  Benefits of waqf founder: Every individual who contributes to cash waqf (donor) will be benefited both in this life and the hereafter.  Financial Inclusion: Extending financial facilities to the poor people who are unable to provide securitisation for their borrowing is a big challenge in this world. The proposed model will provide financing opportunity to the poor people.  Sources of fund for IMFIs: Cash endowment offers the IMFIs another source of fund needed to carry out their objectives.  Creating more business opportunities: Cash endowment increases the accumulation of liquidity and capital in the industry and creates more business opportunities.  Achieving religious goal: Characterising partnership-based instruments in economic terms as risk-sharing does reflect the ideals of Islamic teachings. So, it will be a great way to achieve the religious goal for the donors as well as all parties involved in this proposed model.  Employment generation: There is a near consensus that the conventional microcredit is not a good job creation tool as risk-averse lenders tend to focus more on those who already have an enterprise and are looking to expand it. Implementing the proposed model might change this equation and the focus could be more on those who have human capital and have the skills, or can acquire those skills which might lead to creating job/enterprise opportunities for them.  Risk sharing: The two tier mudarabah model represents an ideal mode of financial intermediation that could replace the interest based system. Fairness is best ensured by profit- sharing arrangement as rewards are a function of realised productivity that channelled to all the parties involved. In this connection, the IMFIs as fund receivers and providers are facilitating micro entrepreneurs to share the risk. 7.1.3. The Empirical Study To test the feasibility of cash waqf based microfinance model, we come up with the following hypothesis that will test whether or not the MFIs lead economic growth. Bangladesh has been taken as case for this empirical study from the premise that it has already got reputation for its 'minimalist' Grameen program focusing mainly on livelihood enterprises that use a similar model involving group- based and graduated financing schemes. The GB has reversed conventional banking practice by removing the need for collateral and created a banking system based on mutual trust, accountability, participation and creativity. The GB provides credit to the poorest of the poor in rural areas of the country, without any collateral. Its founder Muhammad Yunus reasoned that if financial resources can be made available to the poor people on terms and conditions that are appropriate and reasonable, millions of small people (in Bangladesh) with their millions of small pursuits can add up to create the biggest development wonder. Available data from 1985 up to 2013 has been collected from its website and the DataStream; Data has been taken on yearly basis. For this study, the source data is very important as the report contains the background of the institutions and from the activities we can understand whether the institution is operating on religious basis. The representativeness of the sample is always an important issue. Although the website is one of the main reliable sources for studying the performance of a financial institution, we did not cross check the data with any third party sources. Also, time-series approaches have been adopted to test the hypothesis whether or not the microfinance industry has impact on growth and development. However, to test the lead-lag relationship we would apply the following procedures. After examining the unit-root tests and the order of the VAR, the Johansen co integration tests will be applied. The co integrating estimated vectors will then be subjected to exactly identifying and over identifying restrictions. The test of co integration is designed to examine the long-run theoretical or equilibrium relationship and to rule out spurious relationship among the variables. But the evidence of
  • 13. Abu Umar Faruq Ahmad & Mohammad Ashraful Mobin / Proceeding ICOSOPP 2015 ___________________________________________________________________________________ « 432 » co integration cannot tell us which variable is leading and which variable is lagging. That can be done by the test of vector error correction model (VECM) that can indicate the direction of Granger causality both in the short and long run. In order to obtain robust estimates, this study would include real interest rate and inflation as related control variables. 7.2. Testing Stationarity of Variables We begin our empirical testing by determining the stationarity of the variables used. In order to proceed with the testing of co integration later, ideally, our variables should be I(1), in that in their original level form, they are non-stationary and in their first differenced form, they are stationary. The differenced form for each variable used is created by taking the difference of their log forms. For example, DGIA = LGIA – LGIAt-1. We then conducted the Augmented Dickey-Fuller (ADF) test on each variable (in both level and differenced form). The table below summarizes the results. (See Appendix 1 t for details.) Variable ADF Test Statistics Critical Value Implication LRGDP 2.3142 -3.6921 Non -Stationary LMFLA -1.9343 -3.6921 Non -Stationary RINTR -3.3459 -3.6921 Non -Stationary INF -2.4947 -3.6921 Non -Stationary Relying primarily on the AIC and SBC criteria, the conclusion that can be made from the above results is that all the variables we are using for this analysis are I(1), and thus we may proceed with testing of co integration. Note that in determining which test statistic to compare with the 95% critical value for the ADF statistic, we have selected the ADF regression order based on the highest computed value for AIC and SBC. In some instances, AIC and SBC give different orders and in that case, we have taken different orders and compared both. This is not an issue as in all cases, the implications are consistent. 7.2.1. Determination of Order of the VAR Model Before proceeding with test of co integration, we need to first determine the order of the vector auto regression (VAR), that is, the number of lags to be used. As per the table below, results show that AIC recommends order of 3 whereas SBC favours 1 lag. Choice Criteria AIC SBC Optimal order 3 1 Given this apparent conflict between recommendation of AIC and SBC, we address this in the following manner. First we checked for serial correlation for each variable and as there was no serial correlation we decided to go for SBC suggestion. (See Appendix 2) 7.2.2. Testing Cointegration Once we have established that the variables are I(1) and determined the optimal VAR order as 2, we are ready to test for co integration. As depicted in the table below, the maximal Eigen value suggests one co integration. (See Appendix 3 ) Null Alternative Statistic 95% Critical Value r = 0 r = 1 80.3794 31.7900 r<= 1 r = 2 25.0497 25.4200 We are inclined to believe that there is one co integrating vector as intuition as well as the past studies has proven that each one of these three variables are related to other variables. Based on the above statistical results as well as our insight, for the purpose of this study, we shall assume that there is one co integrating vector, or relationship. Statistically, the above results indicate that the variables we have chosen, in some combination, results in a stationary error term. The results show that the independent
  • 14. Abu Umar Faruq Ahmad & Mohammad Ashraful Mobin / Proceeding ICOSOPP 2015 ___________________________________________________________________________________ « 433 » variables (MFLA, RINTR and INF) jointly explained variations or changes in economic growth. More specifically, the result reveals that a microfinance bank operation (MFLA) has a positive impact on economic growth. Furthermore, the result indicates that microfinance bank is statistically significant in explaining economic in Bangladesh. That is, MFLA plays a vital role in the achievement of economic growth. By examining the error correction term, et-1, for each variable, and checking whether it is significant, we found that there is only one exogenous variable, microfinance loan and advances and other variables are endogenous. ( See Appendix 4 ) Variable ECM(-1) t-ratio p-value Implication dLRGDP 0 Variable is endogenous dINF 0.057 Variable is endogenous dLMFLA 0.553 Variable is exogenous dRINTR .075 Variable is endogenous Cointegration, however, cannot tell us the direction of Granger causality as to which variable is leading and which variable is lagging (i.e. which variable is exogenous and which variable is endogenous). For discerning the endogeneity /exogeneity of the variables, we applied the vector error correction modeling technique. The analysis result reveals that microfinance operations (captured by the loans and advances microfinance banks offer to the members of the society) have statistically significant positive impact or effect on the Bangladesh economy. That is, the more the activities of microfinance banks in Bangladesh, the higher would be the growth of the economy. Furthermore, the vector error correction model test result shows a unidirectional causality running from economic growth to microfinance banks operations. Therefore, government and policy makers should pay serious attention to the operations of microfinance banks in Bangladesh. At the same time, programs and policies that would boost their activities should be implemented. This would further position microfinance banks to effectively service the very poor of the society who engage in micro, small and medium scale enterprises (MSMEs) which are the engine of economic growth. 8. Summary, Conclusion, and Suggestions Islamic financial instruments provide enough room to fulfill financing needs in extreme cases. It is a better alternative to conventional financing tools. The paper sheds new light on how cash waqf can be an alternative source of financing the IMFIs. A model of cash waqf has been developed by using two tier mudarabah contract. The major practical implication is the necessity of developing new sustainable model as a means for survival and growth. It would help to reform the present institutional framework of microfinance and waqf fund with a view to enhance their performance in the direction of achieving the ultimate goals in religion and socio economic developments. Cash waqf based MFIs can play an important role in financial inclusion, employment generation, risk diversification, creating more business opportunities, and last but not least to improve the socio-economic conditions of the poor and the whole society. Despite the fact that the proposed two tier mudarabah structure has been hit by major barriers in Islamic banking industry, the benefits of practicing the proposed structure in cash waqf based IMFIs outweigh its shortcomings. Study may help create incentives for researchers to pursue further inquiries into an area that is extremely promising.
  • 15. Abu Umar Faruq Ahmad & Mohammad Ashraful Mobin / Proceeding ICOSOPP 2015 ___________________________________________________________________________________ « 434 » References Abdullah, A. K. M., and Zaid Safdar, 2010. Linking up and reaching out in Bangladesh: information and communications technology for microfinance. World Bank Publications. Çizakça, M., 1998. Awqaf in History and Its Implications for Modern Islamic Economies. Islamic Economic Studies, 6(1), pp.43–70. Bagazonzya, H.K. et al., 2010. Linking up and reaching out in Bangladesh information and communications technology for microfinance. Adam, S. & Lahsasna, A., 2013. Cash Endowment As Source of Fund in Islamic Micro-Financing. 4th International Conference on Business and Economic Research (4th ICBER 2013) Proceeding, (March), pp.1362–1378. Ahmed, Habib, 2007.Waqf-based microfinance: realizing the social role of Islamic finance. World Bank Akhter, W., Akhtar, N. & Jaffri, S.K.A., 2009. Islamic Microfinance and Poverty Alleviation: A Case of Pakistan. Proceeding 2nd CBRC, pp.1–8. Berthold, S. Megan, 2015. Rights-Based Clinical Practice with Survivors of Human Trafficking. Human Rights-Based Approaches to Clinical Social Work. Springer International Publishing. 63-84. Çizakça, M., 1998. Awqaf in History and Its Implications for Modern Islamic Economies. Islamic Economic Studies, 6(1), pp.43–70. Cizakca, Murat, 1997. Towards a comparative economic history of the waqf system. Al-Shajarah 6 : 63-102. El-Gamal, M. et al., 2014. Bank-insured RoSCA for microfinance: Experimental evidence in poor Egyptian villages. Journal of Economic Behavior and Organization, 103, pp.S56–S73. Clarke, Matthew, and David Tittensor, 2014. Islam and development: exploring the invisible aid economy. Ashgate. Cull, Robert, Asli Demirgüç-Kunt, and Jonathan Morduch, 2010 "Microfinance meets the market." Contemporary Studies in Economic and Financial Analysis 92: 1-30. El-Gari, Mohamed A., 2004. "The Qard Hassan Bank." International Seminar on Nonbank Financial Institutions: Islamic Alternatives. Hashim, Ashraf bin Md., 2007 “The Collection of Waqf through Insurance Companies: A Critical Analysis of the Malaysian Experience." Review of Islamic Economics11.1: 63-74. Hulme, David, 2000. Impact assessment methodologies for microfinance: theory, experience and better practice. World development 28.1: 79-98. Kahf, Monzer,2003. The role of waqf in improving the ummah welfare. International Seminar on Waqf as a Private Legal Body organized by the Islamic University of North Sumatra, Medan, Indonesia. Ledgerwood, Joanna, 1999. Sustainable Banking with the Poor Microfinance Handbook. Masyita, D. & Ahmed, H., 2011. Why is Growth of Islamic Microfinance Lower than Conventional ? A Comparative Study of the Preferences and Perceptions of the Clients of Islamic and Conventional Microfinance Institutions‟ in Indonesia. 8th International Conference on Islamic Economics and Finance, pp.1–22.
  • 16. Abu Umar Faruq Ahmad & Mohammad Ashraful Mobin / Proceeding ICOSOPP 2015 ___________________________________________________________________________________ « 435 » Md. Shahedur Rahaman Chowdhury, 2011. Economics of Cash WAQF management in Malaysia: A proposed Cash WAQF model for practitioners and future researchers. African Journal of Business Management, 5(30), pp.12155–12163. Mohsin, Magda Ismail Abdel, 2013 "Financing through cash-waqf: a revitalization to finance different needs." International Journal of Islamic and Middle Eastern Finance and Management 6.4: 5- 5. Mohsin, Magda Ismail Abdel, 2009. Cash waqf: A new financial product. Pearson Malaysia. Salarzehi, Habibollah, Hamed Armesh, and Davoud Nikbin, 2010. Waqf as a social entrepreneurship model in Islam. International Journal of Business and Management 5.7: p179. Wilson, Rodney, 2007. Making development assistance sustainable through Islamic microfinance. International Journal of Economics, Management and Accounting 15.2 Zeller, M. & Meyer, R.L.E., 2002. The triangle of microfinance: Financial sustainability, outreach, and impact. Intl Food Policy Res Inst.
  • 17. Abu Umar Faruq Ahmad & Mohammad Ashraful Mobin / Proceeding ICOSOPP 2015 ___________________________________________________________________________________ « 436 » Appendix Appendix 1: Stationary Test 12/12/2014 11:08:22 AM ADF tests for variable INF The Dickey-Fuller regressions include no intercept and no trend ******************************************************************************* 18 observations used in the estimation of all ADF regressions. Sample period from 1996 to 2013 ******************************************************************************* Test Statistic CV LL AIC SBC HQC DF -1.2010 -1.8014 -44.4306 -45.4306 -45.8758 -45.4920 ADF(1) -.91251 -1.9141 -43.6714 -45.6714 -46.5617 -45.7941 ADF(2) .016893 -1.7848 -38.4798 -41.4798 -42.8153 -41.6639 ADF(3) .11886 -1.8167 -38.3791 -42.3791 -44.1599 -42.6247 ADF(4) .028931 -1.8280 -38.2670 -43.2670 -45.4930 -43.5740 ADF(5) -.066284 -1.9887 -38.0388 -44.0388 -46.7099 -44.4071 ******************************************************************************* CV = 95% simulated critical value using 18 obs. and 1000 replications. The critical values are computed by stochastic simulations. LL = Maximized log-likelihood AIC = Akaike Information Criterion SBC = Schwarz Bayesian Criterion HQC = Hannan-Quinn Criterion ADF tests for variable INF The Dickey-Fuller regressions include an intercept but not a trend ******************************************************************************* 18 observations used in the estimation of all ADF regressions. Sample period from 1996 to 2013 ******************************************************************************* Test Statistic CV LL AIC SBC HQC DF -3.2546 -3.1380 -40.5704 -42.5704 -43.4607 -42.6931 ADF(1) -2.7181 -3.1154 -40.5006 -43.5006 -44.8362 -43.6848 ADF(2) -.95983 -3.0515 -37.8646 -41.8646 -43.6453 -42.1101 ADF(3) -.82549 -2.9688 -37.8596 -42.8596 -45.0855 -43.1665 ADF(4) -1.0262 -3.1176 -37.4611 -43.4611 -46.1322 -43.8294 ADF(5) -1.4620 -3.2046 -36.4066 -43.4066 -46.5229 -43.8363 ******************************************************************************* 95% published asymptotic critical value corresponding to ADF(0) = -3.0401 CV = 95% simulated critical value using 18 obs. and 1000 replications. The critical values are computed by stochastic simulations. LL = Maximized log-likelihood AIC = Akaike Information Criterion SBC = Schwarz Bayesian Criterion HQC = Hannan-Quinn Criterion ADF tests for variable INF The Dickey-Fuller regressions include an intercept and a linear trend ******************************************************************************* 18 observations used in the estimation of all ADF regressions. Sample period from 1996 to 2013 ******************************************************************************* Test Statistic CV LL AIC SBC HQC DF -4.3715 -3.7087 -37.4981 -40.4981 -41.8336 -40.6822 ADF(1) -4.1697 -3.7207 -36.5683 -40.5683 -42.3491 -40.8139 ADF(2) -2.0974 -3.5885 -35.0893 -40.0893 -42.3152 -40.3962 ADF(3) -2.0134 -3.7096 -34.9521 -40.9521 -43.6232 -41.3204 ADF(4) -2.2776 -3.7635 -34.0512 -41.0512 -44.1675 -41.4809 ADF(5) -2.4947 -4.2335 -33.1417 -41.1417 -44.7032 -41.6328 ******************************************************************************* 95% published asymptotic critical value corresponding to ADF(0) = -3.6921 CV = 95% simulated critical value using 18 obs. and 1000 replications. The critical values are computed by stochastic simulations. LL = Maximized log-likelihood AIC = Akaike Information Criterion SBC = Schwarz Bayesian Criterion HQC = Hannan-Quinn Criterion
  • 18. Abu Umar Faruq Ahmad & Mohammad Ashraful Mobin / Proceeding ICOSOPP 2015 ___________________________________________________________________________________ « 437 » Page 1 12/12/2014 11:08:39 AM ADF tests for variable DINF The Dickey-Fuller regressions include no intercept and no trend ******************************************************************************* 17 observations used in the estimation of all ADF regressions. Sample period from 1997 to 2013 ******************************************************************************* Test Statistic CV LL AIC SBC HQC DF -5.8914 -1.9702 -38.9103 -39.9103 -40.3269 -39.9517 ADF(1) -6.8715 -2.0325 -34.5411 -36.5411 -37.3743 -36.6239 ADF(2) -3.1330 -1.8652 -34.5005 -37.5005 -38.7504 -37.6248 ADF(3) -2.2649 -1.8940 -34.4982 -38.4982 -40.1646 -38.6639 ADF(4) -1.8978 -1.8365 -34.4922 -39.4922 -41.5752 -39.6992 ADF(5) -2.0292 -2.0664 -34.0156 -40.0156 -42.5153 -40.2641 ******************************************************************************* CV = 95% simulated critical value using 17 obs. and 1000 replications. The critical values are computed by stochastic simulations. LL = Maximized log-likelihood AIC = Akaike Information Criterion SBC = Schwarz Bayesian Criterion HQC = Hannan-Quinn Criterion ADF tests for variable DINF The Dickey-Fuller regressions include an intercept but not a trend ******************************************************************************* 17 observations used in the estimation of all ADF regressions. Sample period from 1997 to 2013 ******************************************************************************* Test Statistic CV LL AIC SBC HQC DF -5.6910 -3.0176 -38.8055 -40.8055 -41.6387 -40.8883 ADF(1) -6.7848 -3.1255 -34.1930 -37.1930 -38.4428 -37.3172 ADF(2) -3.1351 -3.0602 -34.1737 -38.1737 -39.8401 -38.3393 ADF(3) -2.3069 -3.1371 -34.1715 -39.1715 -41.2545 -39.3786 ADF(4) -1.9639 -3.1545 -34.1416 -40.1416 -42.6413 -40.3901 ADF(5) -2.0893 -3.1883 -33.6109 -40.6109 -43.5271 -40.9008 ******************************************************************************* 95% published asymptotic critical value corresponding to ADF(0) = -3.0522 CV = 95% simulated critical value using 17 obs. and 1000 replications. The critical values are computed by stochastic simulations. LL = Maximized log-likelihood AIC = Akaike Information Criterion SBC = Schwarz Bayesian Criterion HQC = Hannan-Quinn Criterion ADF tests for variable DINF The Dickey-Fuller regressions include an intercept and a linear trend ******************************************************************************* 17 observations used in the estimation of all ADF regressions. Sample period from 1997 to 2013 ******************************************************************************* Test Statistic CV LL AIC SBC HQC DF -5.4295 -3.7607 -38.7428 -41.7428 -42.9926 -41.8670 ADF(1) -6.4126 -3.9137 -34.1618 -38.1618 -39.8282 -38.3274 ADF(2) -2.9510 -3.6439 -34.1499 -39.1499 -41.2329 -39.3569 ADF(3) -2.1931 -3.8820 -34.1472 -40.1472 -42.6468 -40.3956 ADF(4) -1.8507 -3.9679 -34.1016 -41.1016 -44.0178 -41.3915 ADF(5) -2.0371 -4.2148 -33.3645 -41.3645 -44.6974 -41.6958 ******************************************************************************* 95% published asymptotic critical value corresponding to ADF(0) = -3.7119 CV = 95% simulated critical value using 17 obs. and 1000 replications. The critical values are computed by stochastic simulations. LL = Maximized log-likelihood AIC = Akaike Information Criterion SBC = Schwarz Bayesian Criterion HQC = Hannan-Quinn Criterion
  • 19. Abu Umar Faruq Ahmad & Mohammad Ashraful Mobin / Proceeding ICOSOPP 2015 ___________________________________________________________________________________ « 438 » Page 2 12/12/2014 11:08:58 AM ADF tests for variable RINTR The Dickey-Fuller regressions include no intercept and no trend ******************************************************************************* 18 observations used in the estimation of all ADF regressions. Sample period from 1996 to 2013 ******************************************************************************* Test Statistic CV LL AIC SBC HQC DF -.44411 -1.8014 -12.1980 -13.1980 -13.6432 -13.2594 ADF(1) -.43587 -1.9141 -10.6562 -12.6562 -13.5466 -12.7790 ADF(2) -.43760 -1.7848 -10.6305 -13.6305 -14.9661 -13.8147 ADF(3) -1.2259 -1.8167 -5.2578 -9.2578 -11.0386 -9.5034 ADF(4) -1.6118 -1.8280 -4.0544 -9.0544 -11.2803 -9.3613 ADF(5) -1.3159 -1.9887 -3.9907 -9.9907 -12.6618 -10.3590 ******************************************************************************* CV = 95% simulated critical value using 18 obs. and 1000 replications. The critical values are computed by stochastic simulations. LL = Maximized log-likelihood AIC = Akaike Information Criterion SBC = Schwarz Bayesian Criterion HQC = Hannan-Quinn Criterion ADF tests for variable RINTR The Dickey-Fuller regressions include an intercept but not a trend ******************************************************************************* 18 observations used in the estimation of all ADF regressions. Sample period from 1996 to 2013 ******************************************************************************* Test Statistic CV LL AIC SBC HQC DF -.76779 -3.1380 -11.9250 -13.9250 -14.8153 -14.0477 ADF(1) -1.3627 -3.1154 -9.6866 -12.6866 -14.0221 -12.8707 ADF(2) -1.3427 -3.0515 -9.6218 -13.6218 -15.4025 -13.8673 ADF(3) -.87267 -2.9688 -4.9293 -9.9293 -12.1552 -10.2362 ADF(4) -.65560 -3.1176 -3.9275 -9.9275 -12.5986 -10.2958 ADF(5) -.69051 -3.2046 -3.8035 -10.8035 -13.9198 -11.2332 ******************************************************************************* 95% published asymptotic critical value corresponding to ADF(0) = -3.0401 CV = 95% simulated critical value using 18 obs. and 1000 replications. The critical values are computed by stochastic simulations. LL = Maximized log-likelihood AIC = Akaike Information Criterion SBC = Schwarz Bayesian Criterion HQC = Hannan-Quinn Criterion ADF tests for variable RINTR The Dickey-Fuller regressions include an intercept and a linear trend ******************************************************************************* 18 observations used in the estimation of all ADF regressions. Sample period from 1996 to 2013 ******************************************************************************* Test Statistic CV LL AIC SBC HQC DF -2.7084 -3.7087 -8.2158 -11.2158 -12.5514 -11.4000 ADF(1) -3.3459 -3.7207 -5.2742 -9.2742 -11.0550 -9.5198 ADF(2) -4.5596 -3.5885 -1.9409 -6.9409 -9.1669 -7.2479 ADF(3) -2.3914 -3.7096 -1.9187 -7.9187 -10.5899 -8.2870 ADF(4) -1.7874 -3.7635 -1.9187 -8.9187 -12.0350 -9.3484 ADF(5) -2.3306 -4.2335 -.24603 -8.2460 -11.8075 -8.7371 ******************************************************************************* 95% published asymptotic critical value corresponding to ADF(0) = -3.6921 CV = 95% simulated critical value using 18 obs. and 1000 replications. The critical values are computed by stochastic simulations. LL = Maximized log-likelihood AIC = Akaike Information Criterion SBC = Schwarz Bayesian Criterion HQC = Hannan-Quinn Criterion
  • 20. Abu Umar Faruq Ahmad & Mohammad Ashraful Mobin / Proceeding ICOSOPP 2015 ___________________________________________________________________________________ « 439 » Page 3 12/12/2014 11:08:58 AM ADF tests for variable RINTR The Dickey-Fuller regressions include no intercept and no trend ******************************************************************************* 18 observations used in the estimation of all ADF regressions. Sample period from 1996 to 2013 ******************************************************************************* Test Statistic CV LL AIC SBC HQC DF -.44411 -1.8014 -12.1980 -13.1980 -13.6432 -13.2594 ADF(1) -.43587 -1.9141 -10.6562 -12.6562 -13.5466 -12.7790 ADF(2) -.43760 -1.7848 -10.6305 -13.6305 -14.9661 -13.8147 ADF(3) -1.2259 -1.8167 -5.2578 -9.2578 -11.0386 -9.5034 ADF(4) -1.6118 -1.8280 -4.0544 -9.0544 -11.2803 -9.3613 ADF(5) -1.3159 -1.9887 -3.9907 -9.9907 -12.6618 -10.3590 ******************************************************************************* CV = 95% simulated critical value using 18 obs. and 1000 replications. The critical values are computed by stochastic simulations. LL = Maximized log-likelihood AIC = Akaike Information Criterion SBC = Schwarz Bayesian Criterion HQC = Hannan-Quinn Criterion ADF tests for variable RINTR The Dickey-Fuller regressions include an intercept but not a trend ******************************************************************************* 18 observations used in the estimation of all ADF regressions. Sample period from 1996 to 2013 ******************************************************************************* Test Statistic CV LL AIC SBC HQC DF -.76779 -3.1380 -11.9250 -13.9250 -14.8153 -14.0477 ADF(1) -1.3627 -3.1154 -9.6866 -12.6866 -14.0221 -12.8707 ADF(2) -1.3427 -3.0515 -9.6218 -13.6218 -15.4025 -13.8673 ADF(3) -.87267 -2.9688 -4.9293 -9.9293 -12.1552 -10.2362 ADF(4) -.65560 -3.1176 -3.9275 -9.9275 -12.5986 -10.2958 ADF(5) -.69051 -3.2046 -3.8035 -10.8035 -13.9198 -11.2332 ******************************************************************************* 95% published asymptotic critical value corresponding to ADF(0) = -3.0401 CV = 95% simulated critical value using 18 obs. and 1000 replications. The critical values are computed by stochastic simulations. LL = Maximized log-likelihood AIC = Akaike Information Criterion SBC = Schwarz Bayesian Criterion HQC = Hannan-Quinn Criterion ADF tests for variable RINTR The Dickey-Fuller regressions include an intercept and a linear trend ******************************************************************************* 18 observations used in the estimation of all ADF regressions. Sample period from 1996 to 2013 ******************************************************************************* Test Statistic CV LL AIC SBC HQC DF -2.7084 -3.7087 -8.2158 -11.2158 -12.5514 -11.4000 ADF(1) -3.3459 -3.7207 -5.2742 -9.2742 -11.0550 -9.5198 ADF(2) -4.5596 -3.5885 -1.9409 -6.9409 -9.1669 -7.2479 ADF(3) -2.3914 -3.7096 -1.9187 -7.9187 -10.5899 -8.2870 ADF(4) -1.7874 -3.7635 -1.9187 -8.9187 -12.0350 -9.3484 ADF(5) -2.3306 -4.2335 -.24603 -8.2460 -11.8075 -8.7371 ******************************************************************************* 95% published asymptotic critical value corresponding to ADF(0) = -3.6921 CV = 95% simulated critical value using 18 obs. and 1000 replications. The critical values are computed by stochastic simulations. LL = Maximized log-likelihood AIC = Akaike Information Criterion SBC = Schwarz Bayesian Criterion HQC = Hannan-Quinn Criterion
  • 21. Abu Umar Faruq Ahmad & Mohammad Ashraful Mobin / Proceeding ICOSOPP 2015 ___________________________________________________________________________________ « 440 » Page 4 12/12/2014 11:09:06 AM ADF tests for variable RGDP The Dickey-Fuller regressions include no intercept and no trend ******************************************************************************* 18 observations used in the estimation of all ADF regressions. Sample period from 1996 to 2013 ******************************************************************************* Test Statistic CV LL AIC SBC HQC DF 28.1666 -1.8014 -230.7847 -231.7847 -232.2299 -231.8461 ADF(1) 1.4538 -1.9141 -223.0962 -225.0962 -225.9865 -225.2189 ADF(2) 1.3789 -1.7848 -223.0620 -226.0620 -227.3976 -226.2462 ADF(3) .36700 -1.8167 -219.3594 -223.3594 -225.1401 -223.6049 ADF(4) .23244 -1.8280 -219.2198 -224.2198 -226.4457 -224.5267 ADF(5) .42511 -1.9887 -218.6040 -224.6040 -227.2751 -224.9723 ******************************************************************************* CV = 95% simulated critical value using 18 obs. and 1000 replications. The critical values are computed by stochastic simulations. LL = Maximized log-likelihood AIC = Akaike Information Criterion SBC = Schwarz Bayesian Criterion HQC = Hannan-Quinn Criterion ADF tests for variable RGDP The Dickey-Fuller regressions include an intercept but not a trend ******************************************************************************* 18 observations used in the estimation of all ADF regressions. Sample period from 1996 to 2013 ******************************************************************************* Test Statistic CV LL AIC SBC HQC DF 28.6085 -3.1380 -220.6383 -222.6383 -223.5287 -222.7611 ADF(1) 3.0181 -3.1154 -219.9200 -222.9200 -224.2555 -223.1041 ADF(2) 3.4054 -3.0515 -218.6961 -222.6961 -224.4769 -222.9417 ADF(3) 1.8581 -2.9688 -217.2535 -222.2535 -224.4794 -222.5604 ADF(4) 1.6762 -3.1176 -217.2520 -223.2520 -225.9231 -223.6203 ADF(5) 2.0683 -3.2046 -215.7164 -222.7164 -225.8327 -223.1461 ******************************************************************************* 95% published asymptotic critical value corresponding to ADF(0) = -3.0401 CV = 95% simulated critical value using 18 obs. and 1000 replications. The critical values are computed by stochastic simulations. LL = Maximized log-likelihood AIC = Akaike Information Criterion SBC = Schwarz Bayesian Criterion HQC = Hannan-Quinn Criterion ADF tests for variable RGDP The Dickey-Fuller regressions include an intercept and a linear trend ******************************************************************************* 18 observations used in the estimation of all ADF regressions. Sample period from 1996 to 2013 ******************************************************************************* Test Statistic CV LL AIC SBC HQC DF 9.4671 -3.7087 -220.5889 -223.5889 -224.9245 -223.7731 ADF(1) 1.8211 -3.7207 -219.8668 -223.8668 -225.6475 -224.1123 ADF(2) 2.3142 -3.5885 -218.6752 -223.6752 -225.9012 -223.9822 ADF(3) .89606 -3.7096 -217.1616 -223.1616 -225.8327 -223.5299 ADF(4) .54860 -3.7635 -217.1085 -224.1085 -227.2248 -224.5382 ADF(5) 1.6978 -4.2335 -214.8257 -222.8257 -226.3871 -223.3167 ******************************************************************************* 95% published asymptotic critical value corresponding to ADF(0) = -3.6921 CV = 95% simulated critical value using 18 obs. and 1000 replications. The critical values are computed by stochastic simulations. LL = Maximized log-likelihood AIC = Akaike Information Criterion SBC = Schwarz Bayesian Criterion HQC = Hannan-Quinn Criterion
  • 22. Abu Umar Faruq Ahmad & Mohammad Ashraful Mobin / Proceeding ICOSOPP 2015 ___________________________________________________________________________________ « 441 » Page 5 12/12/2014 11:09:16 AM ADF tests for variable DRGDP The Dickey-Fuller regressions include no intercept and no trend ******************************************************************************* 21 observations used in the estimation of all ADF regressions. Sample period from 1993 to 2013 ******************************************************************************* Test Statistic CV LL AIC SBC HQC DF .18518 -1.9301 53.4704 52.4704 51.9482 52.3571 ADF(1) .19665 -1.9171 53.4752 51.4752 50.4307 51.2485 ******************************************************************************* CV = 95% simulated critical value using 21 obs. and 1000 replications. The critical values are computed by stochastic simulations. LL = Maximized log-likelihood AIC = Akaike Information Criterion SBC = Schwarz Bayesian Criterion HQC = Hannan-Quinn Criterion ADF tests for variable DRGDP The Dickey-Fuller regressions include an intercept but not a trend ******************************************************************************* 21 observations used in the estimation of all ADF regressions. Sample period from 1993 to 2013 ******************************************************************************* Test Statistic CV LL AIC SBC HQC DF -1.6368 -3.0261 55.0213 53.0213 51.9768 52.7946 ADF(1) -1.7512 -3.0817 55.2956 52.2956 50.7288 51.9556 ******************************************************************************* 95% published asymptotic critical value corresponding to ADF(0) = -3.0115 CV = 95% simulated critical value using 21 obs. and 1000 replications. The critical values are computed by stochastic simulations. LL = Maximized log-likelihood AIC = Akaike Information Criterion SBC = Schwarz Bayesian Criterion HQC = Hannan-Quinn Criterion ADF tests for variable DRGDP The Dickey-Fuller regressions include an intercept and a linear trend ******************************************************************************* 21 observations used in the estimation of all ADF regressions. Sample period from 1993 to 2013 ******************************************************************************* Test Statistic CV LL AIC SBC HQC DF -3.0805 -3.7280 58.0880 55.0880 53.5212 54.7479 ADF(1) -3.7337 -3.6876 59.9402 55.9402 53.8512 55.4869 ******************************************************************************* 95% published asymptotic critical value corresponding to ADF(0) = -3.6454 CV = 95% simulated critical value using 21 obs. and 1000 replications. The critical values are computed by stochastic simulations. LL = Maximized log-likelihood AIC = Akaike Information Criterion SBC = Schwarz Bayesian Criterion HQC = Hannan-Quinn Criterion
  • 23. Abu Umar Faruq Ahmad & Mohammad Ashraful Mobin / Proceeding ICOSOPP 2015 ___________________________________________________________________________________ « 442 » Page 6 12/12/2014 11:09:27 AM ADF tests for variable DRINTR The Dickey-Fuller regressions include no intercept and no trend ******************************************************************************* 17 observations used in the estimation of all ADF regressions. Sample period from 1997 to 2013 ******************************************************************************* Test Statistic CV LL AIC SBC HQC DF -3.1968 -1.9702 23.2832 22.2832 21.8666 22.2417 ADF(1) -2.0182 -2.0325 24.0855 22.0855 21.2523 22.0027 ADF(2) -2.8460 -1.8652 26.1303 23.1303 21.8805 23.0061 ADF(3) -2.7919 -1.8940 26.6299 22.6299 20.9634 22.4642 ADF(4) -1.5471 -1.8365 27.6226 22.6226 20.5396 22.4155 ADF(5) -1.4740 -2.0664 27.6839 21.6839 19.1843 21.4354 ******************************************************************************* CV = 95% simulated critical value using 17 obs. and 1000 replications. The critical values are computed by stochastic simulations. LL = Maximized log-likelihood AIC = Akaike Information Criterion SBC = Schwarz Bayesian Criterion HQC = Hannan-Quinn Criterion ADF tests for variable DRINTR The Dickey-Fuller regressions include an intercept but not a trend ******************************************************************************* 17 observations used in the estimation of all ADF regressions. Sample period from 1997 to 2013 ******************************************************************************* Test Statistic CV LL AIC SBC HQC DF -3.2423 -3.0176 23.7102 21.7102 20.8770 21.6274 ADF(1) -2.0737 -3.1255 24.4910 21.4910 20.2411 21.3667 ADF(2) -3.0831 -3.0602 27.1313 23.1313 21.4648 22.9656 ADF(3) -3.4916 -3.1371 28.7268 23.7268 21.6438 23.5198 ADF(4) -2.0996 -3.1545 29.0311 23.0311 20.5315 22.7827 ADF(5) -2.2017 -3.1883 29.5917 22.5917 19.6754 22.3018 ******************************************************************************* 95% published asymptotic critical value corresponding to ADF(0) = -3.0522 CV = 95% simulated critical value using 17 obs. and 1000 replications. The critical values are computed by stochastic simulations. LL = Maximized log-likelihood AIC = Akaike Information Criterion SBC = Schwarz Bayesian Criterion HQC = Hannan-Quinn Criterion ADF tests for variable DRINTR The Dickey-Fuller regressions include an intercept and a linear trend ******************************************************************************* 17 observations used in the estimation of all ADF regressions. Sample period from 1997 to 2013 ******************************************************************************* Test Statistic CV LL AIC SBC HQC DF -2.9599 -3.7607 23.7855 20.7855 19.5357 20.6613 ADF(1) -1.7236 -3.9137 24.7333 20.7333 19.0669 20.5676 ADF(2) -2.6912 -3.6439 27.4342 22.4342 20.3512 22.2271 ADF(3) -3.3105 -3.8820 29.3832 23.3832 20.8835 23.1347 ADF(4) -1.9210 -3.9679 29.7924 22.7924 19.8761 22.5025 ADF(5) -1.9650 -4.2148 30.2464 22.2464 18.9136 21.9151 ******************************************************************************* 95% published asymptotic critical value corresponding to ADF(0) = -3.7119 CV = 95% simulated critical value using 17 obs. and 1000 replications. The critical values are computed by stochastic simulations. LL = Maximized log-likelihood AIC = Akaike Information Criterion SBC = Schwarz Bayesian Criterion HQC = Hannan-Quinn Criterion
  • 24. Abu Umar Faruq Ahmad & Mohammad Ashraful Mobin / Proceeding ICOSOPP 2015 ___________________________________________________________________________________ « 443 » Page 7 12/12/2014 11:09:33 AM ADF tests for variable MFLA The Dickey-Fuller regressions include no intercept and no trend ******************************************************************************* 18 observations used in the estimation of all ADF regressions. Sample period from 1996 to 2013 ******************************************************************************* Test Statistic CV LL AIC SBC HQC DF 3.1178 -1.8014 -105.8564 -106.8564 -107.3016 -106.9178 ADF(1) 2.4125 -1.9141 -105.8533 -107.8533 -108.7436 -107.9760 ADF(2) 1.3202 -1.7848 -105.5763 -108.5763 -109.9119 -108.7605 ADF(3) 1.5635 -1.8167 -105.0735 -109.0735 -110.8543 -109.3191 ADF(4) .88120 -1.8280 -104.0596 -109.0596 -111.2855 -109.3665 ADF(5) 1.5023 -1.9887 -101.3291 -107.3291 -110.0002 -107.6974 ******************************************************************************* CV = 95% simulated critical value using 18 obs. and 1000 replications. The critical values are computed by stochastic simulations. LL = Maximized log-likelihood AIC = Akaike Information Criterion SBC = Schwarz Bayesian Criterion HQC = Hannan-Quinn Criterion ADF tests for variable MFLA The Dickey-Fuller regressions include an intercept but not a trend ******************************************************************************* 18 observations used in the estimation of all ADF regressions. Sample period from 1996 to 2013 ******************************************************************************* Test Statistic CV LL AIC SBC HQC DF 1.2673 -3.1380 -105.8188 -107.8188 -108.7092 -107.9416 ADF(1) 1.1276 -3.1154 -105.8174 -108.8174 -110.1529 -109.0015 ADF(2) .28723 -3.0515 -105.3909 -109.3909 -111.1716 -109.6364 ADF(3) .57142 -2.9688 -104.9883 -109.9883 -112.2142 -110.2952 ADF(4) .025329 -3.1176 -103.8081 -109.8081 -112.4792 -110.1764 ADF(5) .43429 -3.2046 -101.1033 -108.1033 -111.2196 -108.5330 ******************************************************************************* 95% published asymptotic critical value corresponding to ADF(0) = -3.0401 CV = 95% simulated critical value using 18 obs. and 1000 replications. The critical values are computed by stochastic simulations. LL = Maximized log-likelihood AIC = Akaike Information Criterion SBC = Schwarz Bayesian Criterion HQC = Hannan-Quinn Criterion ADF tests for variable MFLA The Dickey-Fuller regressions include an intercept and a linear trend ******************************************************************************* 18 observations used in the estimation of all ADF regressions. Sample period from 1996 to 2013 ******************************************************************************* Test Statistic CV LL AIC SBC HQC DF -.023637 -3.7087 -105.3468 -108.3468 -109.6823 -108.5309 ADF(1) -.076943 -3.7207 -105.3349 -109.3349 -111.1156 -109.5804 ADF(2) -1.0774 -3.5885 -103.9979 -108.9979 -111.2239 -109.3049 ADF(3) -.77977 -3.7096 -103.9965 -109.9965 -112.6676 -110.3648 ADF(4) -2.9502 -3.7635 -97.8867 -104.8867 -108.0030 -105.3164 ADF(5) -1.9343 -4.2335 -97.8010 -105.8010 -109.3625 -106.2921 ******************************************************************************* 95% published asymptotic critical value corresponding to ADF(0) = -3.6921 CV = 95% simulated critical value using 18 obs. and 1000 replications. The critical values are computed by stochastic simulations. LL = Maximized log-likelihood AIC = Akaike Information Criterion SBC = Schwarz Bayesian Criterion HQC = Hannan-Quinn Crite
  • 25. Abu Umar Faruq Ahmad & Mohammad Ashraful Mobin / Proceeding ICOSOPP 2015 ___________________________________________________________________________________ « 444 » Page 8 12/12/2014 11:09:36 AM ADF tests for variable LMFLA The Dickey-Fuller regressions include no intercept and no trend ******************************************************************************* 18 observations used in the estimation of all ADF regressions. Sample period from 1996 to 2013 ******************************************************************************* Test Statistic CV LL AIC SBC HQC DF 2.3138 -1.8014 -.54959 -1.5496 -1.9948 -1.6110 ADF(1) 1.2407 -1.9141 1.0204 -.97962 -1.8700 -1.1024 ADF(2) 1.3182 -1.7848 1.1803 -1.8197 -3.1553 -2.0039 ADF(3) 1.1641 -1.8167 1.1826 -2.8174 -4.5981 -3.0629 ADF(4) .59985 -1.8280 2.6506 -2.3494 -4.5754 -2.6564 ADF(5) .65325 -1.9887 2.7675 -3.2325 -5.9036 -3.6008 ******************************************************************************* CV = 95% simulated critical value using 18 obs. and 1000 replications. The critical values are computed by stochastic simulations. LL = Maximized log-likelihood AIC = Akaike Information Criterion SBC = Schwarz Bayesian Criterion HQC = Hannan-Quinn Criterion ADF tests for variable LMFLA The Dickey-Fuller regressions include an intercept but not a trend ******************************************************************************* 18 observations used in the estimation of all ADF regressions. Sample period from 1996 to 2013 ******************************************************************************* Test Statistic CV LL AIC SBC HQC DF -2.1054 -3.1380 2.1812 .18124 -.70913 .058470 ADF(1) -2.1289 -3.1154 3.7552 .75517 -.58039 .57101 ADF(2) -1.9801 -3.0515 3.8122 -.18785 -1.9686 -.43339 ADF(3) -1.9193 -2.9688 3.8367 -1.1633 -3.3892 -1.4702 ADF(4) -1.6880 -3.1176 4.7946 -1.2054 -3.8766 -1.5737 ADF(5) -1.7820 -3.2046 5.3291 -1.6709 -4.7872 -2.1006 ******************************************************************************* 95% published asymptotic critical value corresponding to ADF(0) = -3.0401 CV = 95% simulated critical value using 18 obs. and 1000 replications. The critical values are computed by stochastic simulations. LL = Maximized log-likelihood AIC = Akaike Information Criterion SBC = Schwarz Bayesian Criterion HQC = Hannan-Quinn Criterion ADF tests for variable LMFLA The Dickey-Fuller regressions include an intercept and a linear trend ******************************************************************************* 18 observations used in the estimation of all ADF regressions. Sample period from 1996 to 2013 ******************************************************************************* Test Statistic CV LL AIC SBC HQC DF -2.5449 -3.7087 3.5674 .56737 -.76819 .38321 ADF(1) -3.4877 -3.7207 7.1633 3.1633 1.3826 2.9178 ADF(2) -3.4412 -3.5885 7.6162 2.6162 .39023 2.3092 ADF(3) -3.4745 -3.7096 8.0769 2.0769 -.59425 1.7086 ADF(4) -4.9348 -3.7635 13.4053 6.4053 3.2890 5.9756 ADF(5) -4.9950 -4.2335 14.4244 6.4244 2.8630 5.9334 ******************************************************************************* 95% published asymptotic critical value corresponding to ADF(0) = -3.6921 CV = 95% simulated critical value using 18 obs. and 1000 replications. The critical values are computed by stochastic simulations. LL = Maximized log-likelihood AIC = Akaike Information Criterion SBC = Schwarz Bayesian Criterion HQC = Hannan-Quinn Criterion
  • 26. Abu Umar Faruq Ahmad & Mohammad Ashraful Mobin / Proceeding ICOSOPP 2015 ___________________________________________________________________________________ « 445 » Page 9 12/12/2014 11:09:45 AM ADF tests for variable DMFLA The Dickey-Fuller regressions include no intercept and no trend ******************************************************************************* 17 observations used in the estimation of all ADF regressions. Sample period from 1997 to 2013 ******************************************************************************* Test Statistic CV LL AIC SBC HQC DF -2.6813 -1.9702 2.8882 1.8882 1.4716 1.8468 ADF(1) -2.5390 -2.0325 3.0179 1.0179 .18465 .93505 ADF(2) -2.1219 -1.8652 3.0362 .036222 -1.2136 -.088012 ADF(3) -1.5748 -1.8940 4.3762 .37620 -1.2902 .21056 ADF(4) -1.6117 -1.8365 4.6098 -.39016 -2.4732 -.59721 ADF(5) -1.5638 -2.0664 4.6506 -1.3494 -3.8490 -1.5979 ******************************************************************************* CV = 95% simulated critical value using 17 obs. and 1000 replications. The critical values are computed by stochastic simulations. LL = Maximized log-likelihood AIC = Akaike Information Criterion SBC = Schwarz Bayesian Criterion HQC = Hannan-Quinn Criterion ADF tests for variable DMFLA The Dickey-Fuller regressions include an intercept but not a trend ******************************************************************************* 17 observations used in the estimation of all ADF regressions. Sample period from 1997 to 2013 ******************************************************************************* Test Statistic CV LL AIC SBC HQC DF -2.8808 -3.0176 3.5443 1.5443 .71112 1.4615 ADF(1) -2.8967 -3.1255 4.0468 1.0468 -.20304 .92254 ADF(2) -2.5106 -3.0602 4.1066 .10663 -1.5598 -.059015 ADF(3) -1.7028 -3.1371 4.8758 -.12424 -2.2073 -.33130 ADF(4) -1.8416 -3.1545 5.3659 -.63412 -3.1338 -.88259 ADF(5) -1.8530 -3.1883 5.5919 -1.4081 -4.3243 -1.6980 ******************************************************************************* 95% published asymptotic critical value corresponding to ADF(0) = -3.0522 CV = 95% simulated critical value using 17 obs. and 1000 replications. The critical values are computed by stochastic simulations. LL = Maximized log-likelihood AIC = Akaike Information Criterion SBC = Schwarz Bayesian Criterion HQC = Hannan-Quinn Criterion ADF tests for variable DMFLA The Dickey-Fuller regressions include an intercept and a linear trend ******************************************************************************* 17 observations used in the estimation of all ADF regressions. Sample period from 1997 to 2013 ******************************************************************************* Test Statistic CV LL AIC SBC HQC DF -2.6001 -3.7607 3.6582 .65816 -.59166 .53393 ADF(1) -2.6314 -3.9137 4.1414 .14142 -1.5250 -.024228 ADF(2) -2.1920 -3.6439 4.1740 -.82595 -2.9090 -1.0330 ADF(3) -1.2913 -3.8820 5.1344 -.86560 -3.3652 -1.1141 ADF(4) -1.3908 -3.9679 5.4378 -1.5622 -4.4784 -1.8521 ADF(5) -1.3624 -4.2148 5.5956 -2.4044 -5.7372 -2.7357 ******************************************************************************* 95% published asymptotic critical value corresponding to ADF(0) = -3.7119 CV = 95% simulated critical value using 17 obs. and 1000 replications. The critical values are computed by stochastic simulations. LL = Maximized log-likelihood AIC = Akaike Information Criterion SBC = Schwarz Bayesian Criterion HQC = Hannan-Quinn Criterion