Embed Size (px)
Citation preview
University of Zimbabwe
Graduate School of Management
DETERMINANTS OF LOAN DEFAULT IN AGRO-BASED CREDIT SCHEMES IN THE
TOBACCO INDUSTRY OF ZIMBABWE
A DISSERTATION SUBMITTED IN PARTIAL FULFILLMENT OF THE
REQUIREMENTS FOR A MASTER OF BUSINESS ADMINISTRATION DEGREE
TAFADZWA REGGIS DZINGAI
R015806K
ii
DEDICATION
This dissertation is dedicated to my late loving mother Winfreda Chanda Dzingai. May
her soul rest in eternal peace.
iii
DECLARATION
I, Tafadzwa Reggis Dzingai, do hereby declare that this dissertation is a result of my
own research and investigation except to the extent indicated in the acknowledgements,
references, and by comments included in the body of this document; and that it has not
been presented elsewhere in part or otherwise for the award of any academic
qualification in any other institution or publication.
Signed ___________________________ _______________
Tafadzwa Reggis Dzingai Date
The dissertation has been submitted with the knowledge of my Supervisor, Prof Claver
Pedzisai Bhunu
Signed ___________________________ _______________
Prof. Claver Pedzisai Bhunu Date
iv
ACKNOWLEDGEMENTS
I would like to thank my supervisor Prof Claver P.Bhunu for providing the necessary
support towards the accomplishment of this piece of work. From the University of
Zimbabwe’s Graduate School of Management, I wish to express my utmost gratitude to
Dr. David Madzikanda, the MBA Dissertations Coordinator and the whole GSM team for
a wonderful job in improving the quality of the University of Zimbabwe’s MBA.
I also acknowledge the willingness to share ideas and knowledge by the following
people: Mr John Hariye, fellow classmate and friend, Mrs Mavis Nyakachiranje, my
immediate boss and Mr Oswell Mharapara my boss and adviser. I am humbled by the
assistance and cooperation I received from Mr Stanford Banana and Mr Talent Dimingo
from the Tobacco Research Board’s Statistical Services Division. Let me also
acknowledge Mr Tichaziva Gwata, Amos Kambare, Pearson Siwelah and Marufu
Chimedza for assisting in the distribution of the questionnaires.
Special thanks also goes to all the tobacco growers and contracting firm employees
who responded to the survey questionnaire because of the value adding responses that
they gave. I am also indebted with the unprecedented support I received from the TIMB
team. Mr Meanwell Gudu, Mr Grant Matenda, Mr Kudakwashe Zinyama and Tinashe
Dhliwayo thank you very much.
I am grateful to my sisters Fadzai, Emelda and Angelica, my brother Richard and my
son Raphael, for their patience during the whole study period as I was not able to
actively play my role as a member of this blessed family during the course of this
programme.
Lastly, let me honour the love and endurance of my loving wife Nyasha Mambeu
Dzingai for if it was not for your patience and mutual support, this programme would
have been pointless and unsuccessful. You are a pillar to lean on and your love was
and will forever be my fountain of hope.
Tafadzwa Reggis Dzingai
2014
v
ABSTRACT
The tobacco industry is a major driver of Zimbabwe’s economic growth. It is currently on
the rebounds after years of massive collapse. The scarcity of credit on the financial
markets has seen the reintroduction of contract farming in the sector. However, this
noble arrangement is being threatened by the high levels of loan repayment default.
This study used a binary logistic regression model to analyse the major determinants of
loan default in agro-based credit scheme in Zimbabwe’s Tobacco Industry. Low Crop
yield, poor quality tobacco, low market prices, poor loan supervision, time spent by
farmer on farming activities, affiliation to farmer association and agro ecological
differences were found to be key determinants of loan default among tobacco farmers in
Zimbabwe. Data for the study was collected through two structured questionnaires. One
was administered to 138 tobacco contracted farmers while the other one was given to
16 contracting firms’ extension officers. Stratified random sampling was used to select
the farmers’ sample and purposive sampling was used for the extension officers. The
study recommends that policies to address default should focus on the empowerment of
the farmers with the skills rather than the knowhow alone. It was also found out that
persuasive measures were more effective than threat related measures and contracting
firms should aim to be as transparent as possible, while improving their communication
with farmers. Group lending was found to be inappropriate until the farming community
adopts relatively high levels of entrepreneurship and professionalism in addition to the
development of a specific legal framework. Government should consider supporting
contract farming by either addressing farmers’ social issues or providing incentives to
the contractors.
Keywords: loan repayment default; agro-based credit scheme; binary logistic regression
model
vi
Contents DEDICATION .............................................................................................................................. ii
DECLARATION ......................................................................................................................... iii
ACKNOWLEDGEMENTS .......................................................................................................... iv
ABSTRACT ................................................................................................................................. v
LIST OF TABLES ....................................................................................................................... ix
LIST OF FIGURES ..................................................................................................................... x
LIST OF ABBREVIATIONS ........................................................................................................ xi
CHAPTER 1 ............................................................................................................................... 1
INTRODUCTION AND BACKGROUND ..................................................................................... 1
1.1 INTRODUCTION ......................................................................................................... 1
1.2 BACKGROUND ........................................................................................................... 1
1.3 PROBLEM STATEMENT ............................................................................................. 3
1.4 RESEARCH OBJECTIVES .......................................................................................... 4
1.5 RESEARCH QUESTIONS ........................................................................................... 4
1.6 RESEARCH HYPOTHESES ........................................................................................ 4
1.7 JUSTIFICATION OF RESEARCH ................................................................................ 5
1.8 SCOPE OF RESEARCH.............................................................................................. 6
1.9 CHAPTER SUMMARY ................................................................................................ 6
CHAPTER 2 ............................................................................................................................... 7
LITERATURE REVIEW ON DETERMINANTS OF LOAN DEFAULT ......................................... 7
2.1 INTRODUCTION ......................................................................................................... 7
2.2 DEFINITION OF LOAN DEFAULT ............................................................................... 7
2.3 DEFINITION OF AN AGRO-BASED CREDIT SCHEME .............................................. 8
2.4 TYPES OF DEFAULTS IN AGRO BASED LOAN SCHEMES ...................................... 8
2.4.1 Farmer Default ...................................................................................................... 8
2.4.2 Company Default .................................................................................................. 9
2.5 UNDERPINNING THEORY ......................................................................................... 9
2.5.1 The Agency Theory............................................................................................... 9
2.6 FACTORS INFLUENCING LOAN DEFAULT ..............................................................11
2.6.1 Factors relating to the Borrower ...........................................................................11
2.6.2 Factors relating to the lender ...............................................................................12
2.6.3 Factors relating to the business operations ..........................................................13
vii
2.6.4 Extraneous Factors ..............................................................................................14
2.7 MEASURES TO CURB LOAN DEFAULT ...................................................................16
2.8 CONCEPTUAL FRAMEWORK ...................................................................................18
2.9 CHAPTER SUMMARY ...............................................................................................19
CHAPTER 3 ..............................................................................................................................20
RESEARCH METHODOLOGY .................................................................................................20
3 INTRODUCTION ...............................................................................................................20
3.1 RESEARCH PROBLEM RECAP ................................................................................20
3.2 RESEARCH PHILOSOPHY ........................................................................................20
3.3 RESEARCH APPROACH ...........................................................................................21
3.4 DATA COLLECTION ..................................................................................................21
3.5 DATA COLLECTION PROCEDURES.........................................................................21
3.6 SAMPLING PROCEDURES .......................................................................................22
3.7 DATA ANALYSIS PROCEDURES ..............................................................................25
3.7.1 Reliability Tests ....................................................................................................25
3.7.2 Factor Analysis ....................................................................................................26
3.7.3 Logistic Regression Analysis ...............................................................................26
3.7.4 Secondary Data Analysis .....................................................................................28
3.7.5 Significance Tests ................................................................................................28
3.8 ETHICAL CONSIDERATIONS ....................................................................................28
3.9 LIMITATIONS OF THE STUDY ..................................................................................29
3.10 CONCLUSION ............................................................................................................29
CHAPTER 4 ..............................................................................................................................30
RESULTS AND DISCUSSION ..................................................................................................30
4 INTRODUCTION ...............................................................................................................30
4.1 SAMPLE DESCRPTIVE STATISTICS ........................................................................30
4.2 EVALUATION OF LOAN DEFAULT DETERMINANTS ...............................................34
4.2.1 Farmer Related Factors .......................................................................................35
4.2.2 Lender Related Factors .......................................................................................37
4.2.3 Extraneous Factors ..............................................................................................39
4.2.4 Discussion of open ended Questions on determinants of loan default. ................41
4.3 MULTIVARIATE ANALYSIS .......................................................................................41
4.3.1 RELIABILITY TESTS ...........................................................................................41
viii
4.3.2 FACTOR ANALYSIS ............................................................................................43
4.3.3 LOGISTIC REGRESSION MODEL ......................................................................45
4.4 SIGNIFICANCE TESTS ..............................................................................................47
4.5 ANALYSIS OF SECONDARY DATA...........................................................................48
4.6 MEASURES TO MITIGATE LOAN DEFAULT ............................................................50
4.7 DISCUSSION OF FINDINGS ......................................................................................56
4.8 CHAPTER SUMMARY ...............................................................................................57
CHAPTER 5 ..............................................................................................................................58
SUMMARY, CONCLUSIONS AND RECOMMENDATIONS ......................................................58
5 INTRODUCTION ...............................................................................................................58
5.1 RESEARCH CONCLUSIONS .....................................................................................58
5.1.1 Research Objective Number 1 .............................................................................59
5.1.2 Research Objective 2 ...........................................................................................59
5.2 RECOMMENDATONS ................................................................................................60
5.2.1 Policy Recommendations.....................................................................................60
5.2.2 Managerial Recommendations ............................................................................60
5.3 CONTRIBUTION OF THE STUDY ..............................................................................61
5.4 AREAS FOR FURTHER RESEARCH .........................................................................62
6 REFERENCE .....................................................................................................................63
7 APPENDICES ....................................................................................................................71
Appendix A: Tobacco Growers’ Questionnaire ......................................................................71
Appendix B: Questionnaire for Tobacco Contracting Firms’ Employees ................................79
Appendix C1 : Correlation Matrices .......................................................................................86
Appendix C 2: Correlation Matrix for Logistic Regression ......................................................87
Appendix D: Extract from Secondary Data from TIMB ...........................................................88
Appendix E : Additional SPSS output for Factor Analysis ......................................................89
Appendix F: Evaluation of Contractors’ Credit Policy .............................................................90
ix
LIST OF TABLES
Table 1.1: TIMB Stop Order Repayments – A proxy for Tobacco Industry’s Default Rate… 2
Table 4.1: Survey Response Statistics…………………………………………… 30
Table 4.2: Marital Status Distribution……………………………………………... 31
Table 4.3: Contractors' Means Scores Across working experience…………… 40
Table 4.4: Summary of Reliability Test Results………………………………….. 42
Table 4.5: KMO and Bartlett's Test………………………………………………... 43
Table 4.6: Factor Analysis Result………………………………………………….. 44
Table 4.7: Communalities…………………………………………………………… 45
Table 4.8: Explanatory Variables in the Model……………………………………. 46
Table 4.9: Dependent Variable SPSS Encoding………………………………….. 47
Table 4.10: Model Summary………………………………………………………….. 48
Table 4.11: Multiple Regression Coefficients……………………………………….. 49
Table 4.12: Contractor's Response to Force and Persuasive Strategies………... 51
Table 4.13: Contractors' response on Set D Measures……………………………. 55
Table 4.14: Measures taken to reduce default……………………………………… 56
x
LIST OF FIGURES
Figure 2.1 Conceptual Framework………………………………………………… 18
Figure 4.1 Age Distribution of Farmers…………………………………………….. 31
Figure 4.2 Farm Size Distributions………………………………………………….. 32
Figure 4.3 Means of Farm Ownership……………………………………………… 33
Figure 4.4 Highest Educational Qualifications…………………………………….. 33
Figure 4.5 Five Point Likert Scale…………………………………………………… 35
Figure 4.6 Farmers’ Responses on Major Farmer Related Determinants of Loan
Default…………………………………………………………………….. 35
Figure 4.7 Mean Scores on Farmer Related Factors…………………………….. 36
Figure 4.8 Farmers’ Responses on Major Lender Related Determinants of Loan
Default……………………………………………………………………. 37
Figure 4.9 Mean Scores on Farmer Related Factors……………………………. 38
Figure 4.10 Farmers’ Responses on Major Extraneous Determinants of Loan Default
……………………………………………………………………………... 39
Figure 4.11 Mean Scores on Farmer Related Factors…………………………….. 40
Figure 4.12 Reponses to Default Mitigating Measures - Set A…………………… 50
Figure 4.13 Reponses to Default Mitigating Measures - Set B…………………… 52
Figure 4.14 Reponses to Default Mitigating Measures – Set C…………………… 53
Figure 4.15 Reponses to Default Mitigating Measures - Set D……………………. 54
xi
LIST OF ABBREVIATIONS
FAO Food and Agriculture Organisation
Ha Hectares
TIMB Tobacco Industry and Marketing Board
TRB Tobacco Research Board
ZTA Zimbabwe Tobacco Association
CHAPTER 1
INTRODUCTION AND BACKGROUND
1.1 INTRODUCTION
Zimbabwe was once “the largest producer of tobacco leaf in Africa and the world’s
fourth-largest producer of flue-cured tobacco, after China, Brazil and the United States
of America” (FAO 2003). The industry is on the rebound and requires more funding if
the sector is to sustainably achieve the necessary growth and development (TIMB
2011; TIMB 2013). Such funding was previously obtained from the banks, but this has
since spilled over to other players in the private sector, Non-Governmental
Organisations and the government (TIMB 2011). Due to the tight liquidity situation
prevailing in the Zimbabwe economy (Government of Zimbabwe 2013), it is necessary
for government and captains of the tobacco industry, to safeguard the sustainability of
available lines of credit. Contract farming has been re-introduced in the tobacco industry
since 2004, in a bid to increase funding of tobacco production as well as control the
quality of the crop (ZTA 2014). However, incidences of unpaid loans in the sector tend
to adversely affect the viability of this noble arrangement (TIMB 2011).This research
aims at developing an in-depth understanding of the factors that cause tobacco farmers
to default on their loans. A number of hypotheses will be tested to see if there exists any
relationship between factors such as experience in farming, level of education, affiliation
to any farmers association or group, climatic conditions and whether or not the
concerned farmer resides on the farm.
1.2 BACKGROUND
Despite theory suggesting that debt is a means of leveraging one’s business, and is
viewed as a means to create a fortune out of other people’s resources (Khandker 2003),
quite a number of tobacco farmers end up in a vicious cycle under which they are
trapped and may not be able to escape (Musara et al. 2011; Brehanu & Fufa 2008;
Akpan et al. 2014). Farmers fail to service their debt due to a number of factors which
this study seeks to investigate and obtain a deeper understanding in order to
recommend practical solutions for a better and sustainable tobacco industry in
Zimbabwe.
2
In a research by Gaisina (2011), debt in agriculture was found to constitute a pivotal role
through which the Agricultural Sector can develop. However since 2009, the Tobacco
Industry and Marketing Board has shown that in Zimbabwe most companies are hardly
able to recover more than 60% of the loans extended to farmers through the TIMB stop
order system as is shown in Table 1.1 below.
Table 1.1: TIMB Stop Order Repayments – A proxy for Tobacco Industry’s Default Rate
YEAR TOTAL DEBT DEDUCTED AMOUNT RECOVERY
RATE DEFAULT
2009 USD 86,729,001.70 USD 52,591,728.16 61% 39%
2010 USD 119,560,393.95 USD 73,592,932.71 62% 38%
2011 USD 204,980,499.21 USD 134,227,697.68 65% 35%
2012 USD 131,860,735.07 USD 120,188,684.52 91% 9%
2013 USD 368,136,114.71 USD 161,664,092.07 44% 56%
2014 USD 213,676,198.09 USD 130,693,659.54 61% 39%
Source: TIMB Stop Order Database (2014)
On another note, the Tobacco Research Board (TRB) is likely to write-off in excess of
USD700 000 due on unpaid loans given to farmers since the 2009-10 selling season
(TRB, 2013).This translates to about 40% default rate, a figure concurring with the
industrial average exhibited in Table 1.1. Whether or not this is because loan suppliers
are neglecting their role to scrutinize potential borrowers, thus giving loans without
proper risk analyses; it is much to be the aim of this study to delve into. Of particular
importance is also the nature of contracts that are entered into by tobacco farmers and
the respective contractors. According to Singh (2010), these contracts could be of three
types namely:
i. procurement contracts under which only produce sale and purchase conditions
are specified;
ii. resource provision contracts wherein some of the inputs are supplied by the
contracting firm and the produce is bought at pre-agreed prices; and
iii. total contracts under which the contracting firm supplies and manages all the
inputs on the farm and the farmer becomes just a supplier of land and labour
(Singh 2010).
3
Whereas the first type is generally referred to as marketing contracts, the other two are
types of production contracts (Singh, 2010). In the Tobacco industry, production
contracts tend to dominate. In essence, the most common is the resource provision
contract. The relevance and importance of each type varies from product to product and
over time and these types are not mutually exclusive (Gent 2005). But, there is a
systematic link between product and factor markets under the contract arrangement as
contracts require definite quality of produce and, therefore, specific inputs. Also,
different types of production contracts allocate production and market risks between the
producer and the processor in different ways (Musara et al. 2011).
With such an important role in the resuscitation of the Tobacco sector and the
Zimbabwe economy in general, credit or debt finance in Agriculture requires closer
monitoring to ensure that the benefits are maximized. It is upon this background that
this research aims to investigate the factors that influence failure to repay loans by
farmers.
1.3 PROBLEM STATEMENT
The current Financial Market is so much constrained that very limited lines of credit are
available for businesses to access (Government of Zimbabwe 2013). The situation is
even worse in the Agriculture sector which is now dominated by small scale farmers
with hardly any collateral for use in accessing formal credit lines. However the
reintroduction of contract farming has to a greater extent availed an essential substitute
for bank credit facilities. Contract farming and the limited credit facilities in the tobacco
sector are at verge of collapse due to high levels of poor performance on loans from the
tobacco sector. TIMB (2011) referred to side marketing as a major source of threat to
contract farming. The cumulative effects of continuous defaults have led to some
players in the industry downsizing their pool of beneficiaries while the cost of borrowing
has relatively increased as contractors endeavour to hedge against the high default risk
associated with farming. Government through its state owned bank, Agribank, has not
been as active as it was in the yesteryears. According to TIMB (2011) this is an
indicator of how unsustainable loan defaults can be in the long run. This study seeks to
investigate the determinants of loan default in Zimbabwe’s Tobacco industry with a view
4
to recommend practical solutions to the problems posed by loan defaults as well as
propose relevant tools to assist lenders in the sector to minimise their exposure to
default risk.
1.4 RESEARCH OBJECTIVES
The main aim of this study is to analyse factors influencing default rate in the Tobacco
Industry of Zimbabwe in an attempt to formulate pragmatic solutions to policy makers
and various stakeholders of the industry. This main objective will be addressed by
achieving the following sub objectives:
i. To identify the major determinants of loan default in Zimbabwe’s tobacco
industry.
ii. To develop a model upon which contracting companies can appraise the
creditworthiness of potential beneficiaries before accepting loan applications.
iii. To provide possible solutions as managerial recommendations to the tobacco
industry stakeholders.
1.5 RESEARCH QUESTIONS
i. What are the main factors influencing farmers’ default in the Tobacco Industry?
ii. To what extent do the identified factors influence loan default?
iii. How can contractors ascertain the creditworthiness of a farmer prior
commitment?
iv. What are the major strategies that the industry can use to deter default?
1.6 RESEARCH HYPOTHESES
H0: Xi affects a farmer’s loan repayment default
H1: Xi does not affect a farmer’s loan repayment default
Where Xi = a variable relating to either the farmer and / or the contractor as is shown
below:
X1: Level of farming experience
X2: Level of Education
X3: Affiliation to farmers association
X4: Off farm sources of income
5
X5: Level of indebtedness
X6: Time spent by farmer on farm activities
X7: Agro ecological Differences
X8: Loan Duration
X9: Social Contagion
X10: Means of farm ownership
X11: Average loan interest
X12: Poor Credit Appraisal
X13: Loan Supervision and technical back up
X14: Low Market Prices
X15: Poor Crop Quality
X16: Systems Failure
1.7 JUSTIFICATION OF RESEARCH
This study is meant to equip tobacco stakeholders especially contractors and financiers
alike with possible pre-contractual tools to evaluate the authenticity of a loan applicant
as well as assess the risk exposure prior to entering into a contract. It is also going to
provide the basis upon which an industry wide approach towards cultivating
professionalism in today’s farming community can be tackled. A lot of the existing
research is on evaluating viability of using contract farming as a model to develop the
Agriculture Sector in many countries especially the developing economies (Gent 2005;
Abwino & Rieks 2006; Shee & Turvey 2012; Melese 2012; Likulunga 2005; Musara et
al. 2011). This research will attempt to add onto the existing knowledge by taking
another dimension of searching for the knowledge of understanding the factors that are
detrimental to a model that has proven to be of high utility in as far as Agriculture
Development is concerned especially in Africa and many other developing economies.
Furthermore the research is particularly important to Zimbabwe’s Agricultural Sector,
post land redistribution and dollarization era. This is because, prior to the land
redistribution of 2000, tobacco was mainly produced by less than 2000 large scale
commercial farmers (ZTA 2014). This has since changed. Today the bulk of the crop is
produced by small scale farmers who constitute at least 80% of the nearly 100000
registered tobacco growers in Zimbabwe (ZTA 2014). Furthermore, the use of the
6
United States Dollar has a bearing on the manner in which debt management and
mismanagement can affect the sustenance of tobacco farming and the whole sector at
large. This can be hypothesised from the fact that during the Zimbabwe Dollar era,
borrowers could profitably walk out of default situations because of the inflationary
conditions that existed. However, post 2009, the economy was more stable and the
currency in use cannot easily be written off in the case of defaults. This has left
Agriculture lending being undertaken more by private players than government and
politicians. It is envisaged that the findings from this research will be more relevant to
Zimbabwe’s Tobacco industry today.
1.8 SCOPE OF RESEARCH
The research will target the tobacco industry in Zimbabwe. It is also expected to be
useful to stakeholders in non-tobacco farming sectors in Zimbabwe. Due to the
envisaged significance of the history behind the Agriculture sector in Zimbabwe and its
uniqueness relative to other countries, research results from this study may not be
generalized to other countries other than Zimbabwe.
1.9 CHAPTER SUMMARY
The chapter gave the background of the study, justifying the relevance of the research
through a discussion of the envisaged research gap. The main objective of the research
is to analyse the various factors behind the inability to repay loans by tobacco farmers in
Zimbabwe. This will be achieved through addressing five sub objectives and the
corresponding research questions.
7
CHAPTER 2
LITERATURE REVIEW ON DETERMINANTS OF LOAN DEFAULT
2.1 INTRODUCTION
The main aim of this study is to analyse factors influencing default rate in Zimbabwe’s
Tobacco industry, in an attempt to formulate pragmatic solutions to policy makers and
various stakeholders in the industry. This chapter critically reviews literature on factors
driving loan repayment defaults in different places and circumstances. The chapter
starts by giving definitions to the subject matter, default, contextualizing the concept to
the study by analysing the various forms of default in Agriculture. Furthermore, recent
literature on strategies to minimise the occurrence of default is also discussed. A
conceptual framework upon which farmers’ default can be studied comes before the
conclusion at the end.
2.2 DEFINITION OF LOAN DEFAULT
Default can be defined as the inability to repay a loan by either failing to fully pay up the
loan or neglecting the servicing of the loan (Agarwal 2001). Roark & Roark (2006) also
defined default as “any failure to fulfil the terms of an agreement”. In a loan
arrangement, the failure can either be payment of the interest component or the
principal when due (Nguta & Huka 2013). The term (default) is often used to describe
the failure of a borrower to meet the terms prescribed by the loan agreement. In this
case, the borrower is said to be “in default of the agreed upon repayment terms as
specified in the loan agreement” (Roark & Roark 2006). Similarly Namuyaga (2009)
gave reference to late payments and arrears as forms of default if ever such scenario
exist against the agreed schedule.
Some authors like Witzany (2009) believe that there is no universal definition for the
term default thus different organizations and situations alike, tend to demand peculiar
default definitions. Banks in particular use what Witzany (2009) referred to as soft and
hard definition of default. The two relate to the conditions of loan agreement which a
borrower fail to adhere to and consequently imply what should be treated as a default.
For example, a borrower is said to be in default if he or she fails to pay an obligation
within a 90 day period after the prescribed due date (Basel II cited in Witzany, 2009).
8
Default can be voluntary or involuntary (Awunyo-Vitor 2012). Voluntary default, also
known as strategic default is conscious and wilful decision made by a borrower to skip
payment of a loan obligation (Ojiako & Ogbukwa 2012). According to Fidrmuc & Hainz
(2010), this type of default is a result of misrepresentation of facts by the borrower such
as the project’s profitability in a bid to appear as if the project’s return could not meet the
loan obligation. In most cases, this is due to information asymmetry at the time of
entering into contract that leads to both adverse selection and moral hazard post
contract (Awunyo-Vitor 2012).
Involuntary default however, is where the debtor lacks the financial ability to service his
or her debt but would have done so if resources permitted (Awunyo-Vitor 2012). It is not
the scope of this study to classify the default as either voluntary or involuntary.
2.3 DEFINITION OF AN AGRO-BASED CREDIT SCHEME
Also known as Agricultural lending, an Agro-based credit scheme is a loan arrangement
in the Agriculture sector under which the lender (contractor), gives inputs in the form of
chemicals, seed and / or cash, to a borrower (the farmer) (Kohansal & Mansoori 2009).
The borrower, in most of the cases agrees to sell his produce to the contractor. This is
also referred to as contract farming. The tobacco industry in Zimbabwe has been seen
to promote the contract farming as evidenced by the realised benefits (TIMB 2011).
2.4 TYPES OF DEFAULTS IN AGRO BASED LOAN SCHEMES
2.4.1 Farmer Default
Farmer default is mainly caused by side-marketing (Melese 2012). Also referred to as
extra-contractual marketing, this is a situation in which a farmer “under or over supply a
contracting company” with produce (Dawes 2008). Contrary to Melese (2012); TIMB
(2011) and recently Mambondiani (2013), side-marketing can also be viewed not as a
cause but a means or kind of farmer default (Woodend 2003). This is because side
marketing is a typical example of the moral hazard, a paradigme of the Agency problem
(Morck 2009). Moral hazard is in this case due to the farmer’s act which was not part of
the deal at the time of entering the contract (Janda 2006). The farmer sells part of the
contracted produce to a third party in a bid to either abscond loan repayment or in some
9
cases to take advantage of higher prices being offered elsewhere in the market (Dawes
2008; TIMB 2011).
2.4.2 Company Default
According to Dawes (2008), this occurs when the contractor fails to supply the farmer
with the agreed inputs. Sometimes the inputs are supplied late such that the farmer
misses an operation at a critical stage of the crop cycle (Dawes 2008). Such delays can
significantly damage the whole farming programme resulting in farmer default as well.
Contractors can also default through late payment of delivered produce. This is a major
source of farmers’ discontent. According to Dawes (2008) farmer who experience a
delay in payment tend to side market in future as a means to hedge against the
distortions in the farm’s cash flow cycle. Such a delay can occur when the farmer is in
dire need of funds or the inputs in question, to run critical operations such as harvesting
on the farm (ZTA 2013). Some companies can manipulate quality parameters to short
change farmers in periods of higher or over supply (Melese 2012).
2.5 UNDERPINNING THEORY
2.5.1 The Agency Theory
The agency theory is a supposition that aims to explain the relationship between
principals and agents in a business set up (Morck 2009; Jensen & Meckling 1976). The
theory is meant to provide solutions towards the problems that accrue in such
relationships. This problem, referred to as the agency problem, can be defined as the
conflict of interest between the principal and agent (Janda 2006). In most cases, the
agent, who is supposed to make decisions in the best interest of the principal, is driven
by self-interest that may differ from the principal’s own interest (Morck 2009; Besley &
Ghatak 2014; Jensen & Meckling 1976). In the case of agro-based loans, the farmer
becomes the agent, whose principal, the contractor expects the farmer to act in a
manner that would guarantee sufficient return so as to be able to repay the loan (Besley
& Ghatak 2014). According to Morck (2009), agency problems “describe rational utility
maximising agents, whose self-interest leaves them insufficiently loyal to principals”. In
the case of agro-based credit schemes, it can be deduced therefore from Morck (2009),
10
that the borrowing farmer will operate his or her farm to maximise his or her own utility
rather than the contractor’s wealth. To this end, the agency problem can be simplified to
describe the situation in which an agent exhibits non optimal loyalty to the principal
(Morck 2009; Besley & Ghatak 2014).
Akerlof (1970) described the agency theory in an interesting manner in his famous
paper entitled, “The Markets for Lemons”. According to Akerlof (1970) potential buyers
are known to hold only the average knowledge of the used cars such that the market
price of used cars tend to be lower than what top quality car owners would willingly
accept. The top quality used car owners are therefore not willing to sell their cars at the
market price because they know their cars deserve a better price than what the market
offers. However, on the other side, owners of bad used car, the lemons, are happier
with the market price because it is over valuing their cars. Because of this situation, the
good used cars are eventually driven out of the market by the lemons.
In agro-based lending schemes, information asymmetry occurs because the farmer,
being the borrower, knows more about the expected crop yield and return than does the
contracting company, the lender. Deducing from Akerlof (1970), the riskier borrowers
tend to drive out the less risky borrowers, resulting in the market failing to efficiently
allocate the correct cost of borrowed funds to the various beneficiaries. If for example
the low risk borrowers are grouped together with the high risk borrowers, the former are
faced with an unfavourable interest rate that is higher than what they can efficiently
afford. This simultaneously leads to a reduction of the low risk clients taking loans from
the contractors while more and more of the high risk farmers continue to demand the
loans. In this case the more prudent farmers, whose probability of default is lower, tend
to demand less and less of the availed credit lines while the bulk of the beneficiaries
becomes that of the high risk farmers whose probability of default is very high (Akerlof
1970).
Information asymmetry can also lead to two important aspects of the agency problem
namely Adverse Selection and Moral Hazard. This is a tendency by the agent (or the
principal) to take advantage of information asymmetry by pretending to be what he or
she is not (Besley & Ghatak 2014). To this end, farmers provide information to the
11
contractor that would lure the loan officers to provide credit at a cost based on the
provided information and yet reality is to greater extent compromised in the availed
information (Melese 2012). In this case the contractor selects a borrower with
inadequate information to accurately evaluate the creditworthiness of the farmer (Salami
et al. 2010).
Moral Hazard on the other hand is post contractual (Janda 2006). This is a situation in
which the borrower acts in a way that was not part of the contract (Morck 2009). For
example a farmer decides to divert part or all of the given inputs towards another project
other than the agreed project (TIMB 2011).
2.6 FACTORS INFLUENCING LOAN DEFAULT
A lot of existing literature on loan default presented a number of factors which influence
the probability of default (Nawai & Shariff 2012; Magali 2013; Bichanga & Aseyo 2013;
Jouault & Featherstone 2011; Awunyo-Vitor 2012; Fidrmuc & Hainz 2010). In a study by
Nawai & Shariff (2010), these factors were classified into four categories namely,
borrower characteristics, firm characteristics, loan characteristics and lender
characteristics. However a more appropriate classification was done by Derban et al.
(2005) excluded firm characteristics while Addisu (2006) rephrased the four factors as
follows:
i. Borrower related factors
ii. Lender related factors
iii. Business operation related factors, and
iv. Extraneous factors
It is through this classification that this study will review the various factors that influence
loan default.
2.6.1 Factors relating to the Borrower
Borrower characteristics such as age, sex, marital status, experience of borrower in
farming and level of education are cited the most in literature relating to causes of
default (Kohansal & Mansoori 2009; Nawai & Shariff 2012; Fidrmuc & Hainz 2010;
Jouault & Featherstone 2011; Magali 2013; Akpan et al. 2014; Awunyo-Vitor 2012;
12
Brehanu & Fufa 2008). However most of the literature on these factors makes different
conclusions about the effects of these borrower related traits. Perhaps it is worth noting
that some of these factors (for example sex and age) do not directly have a bearing to
the independent variable, default. For instance male beneficiaries have high loan
repayment rates because they were generally found to own bigger pieces of land than
their female counterparts (Awunyo-Vitor 2012). Other aspects that have significant
literature coverage under borrower related factors include off farm income and level of
indebtedness (Awunyo-Vitor 2012; Brehanu & Fufa 2008; Magali 2013). According to
Ojiako & Ogbukwa (2012) if farmers can make extra sources of income elsewhere, then
loan default probability is reduced because the chances of loan diversion are lowered.
On the other hand, the higher the level of indebtedness, the more likely is the borrower
to default (Fidrmuc & Hainz 2010; Jouault & Featherstone 2011).
2.6.2 Factors relating to the lender
According to Sterns (1995) cited in Nawai & Shariff (2012), high default rate is caused
more by the lender than the borrower. Factors such as timely disbursement of inputs by
the contractor tend to affect the operations of the farmer resulting in lower yields and
returns (Bichanga & Aseyo 2013; Bwunyo-Wakuloba 2008; TIMB 2011). Akpan, Udoh,
& Akpan (2014), in their study of default in agro-based loan schemes in Nigeria
concluded that the time interval between loan application and disbursement was a
significant factor that influenced default amongst beneficiaries of agricultural loans.
Furthermore, default was found to be more likely when fewer visits are done by loan
officers (Akpan et al, 2014). Nawai and Shariff (2012) also added that loan monitoring
was an essential component in reducing default tendencies. They further argued that
financial rewards in the form of rebates to no defaulters motivated the borrowers to fulfil
their loan obligations. As a measure of loan monitoring and constant interaction
between the lender and the borrower, Brehanu & Fufa (2008) analysed the number of
days that borrower was in contact with extension officers over a three months period.
13
2.6.3 Factors relating to the business operations
A number of scholars on default rate argue that default is significantly affected by the
nature of the business in which the loan has been utilised (Sanjeev 1997; Magali 2013;
Wongnaa & Awunyo-vitor 2013; Awunyo-Vitor 2012). In particular, Awunyo-vitor (2012)
argued that the level of professionalism and formality of the business has a significant
and positive bearing on loan repayment behaviour. In particular, Agriculture credit is
viewed as riskier than other types loans such as salary loans and business loans
(Awunyo-Vitor 2012). This is because agriculture credit is usually offered as a terminal
loan. A terminal loan is one in which both the interest and the principal are paid right at
the end of the loan period(Parlour & Winton 2013). This poses a higher risk than if the
loan could be paid in instalments before the expiry date (Awunyo-Vitor 2012).
Furthermore, because agriculture is a sector dominated by a lot of politicking, against a
number of government interventions and vast climatic uncertainties, the business is thus
relatively risky as compared to other fields (Magali 2013). According to Osborne (2006),
different business operations have different business risks. Business risk is the
possibility that an enterprise fails to garner profits as per expectation because of factors
that are inherent to the nature of the business engaged by the enterprise (Schicks
2013). Such factors include sales volumes, input cost, competition, overall economic
climate and government regulation (Nawai & Shariff 2012; Nwachukwu 2013; Ojiako &
Ogbukwa 2012).
In addition, the business related factors can also delve into the nature of the loan. This
entails the contractual term engraved in the arrangement. According to Melese (2012)
many contracts in agriculture fail because farmers do not understand the contractual
specifications of the agreement. This can be due to the use of deep jargon beyond the
comprehension of the farmer (Dawes 2008). Another aspect frequently reviewed is the
loan repayment period. Although Jouault & Featherstone (2011) suggest that longer
loan repayment periods the higher the probability of default, Awunyo-Vitor (2012)
conclude the converse. The study found out that in farming, longer repayment periods
are better because farmers would have more time to recoup the investment especially
in capital expenditure related loans such as procurement of tractors (Awunyo-Vitor
2012; Mambondiani 2013).
14
2.6.4 Extraneous Factors
These are external factors of an environmental nature that are outside the control of
both the lender and the borrower (Nawai & Shariff 2012; Nawai 2010; Nawai & Shariff
2013). This includes the systematic risk due from business environmental aspects such
as politics, economic fundamentals, social factors, technology, environmental issues
and legislation.
2.6.4.1 Political Aspects
According to Bwunyo-Wakuloba (2008) political interference in loan schemes tends to
be associated with large default rates. In a study carried out in Tanzania, Magali (2013)
argued that the interference of politics in credit schemes was detrimental to the efficient
running of such programmes. The study recommended that politics should not be
entertained if recovery rate is matter of concern (Magali 2013).
2.6.4.2 Economic Aspects
Furthermore, an ailing economy as envisaged by aspects such as rising inflation and a
shrinking economy was seen to increase the propensity to default (Musara et al. 2011).
This is because borrowers in such circumstances, tend to increase their spending on
household consumption, at the expense of loan repayment (Bichanga & Aseyo 2013).
The current liquidity crisis in Zimbabwe has an adverse impact the viability of business
through problems such as the scarcity of long term debt financing on the financial
market (Government of Zimbabwe 2013; TIMB 2011). This undoubtedly impacts
negatively on the cost of debt that the contracting firms acquire from their lenders (TIMB
2011, p.5). Because of this, it can be argued that the extra cost is consequently
transferred to the final borrower, who in this case is the farmer. Some farmers shun
contract farming as oppressive and punitive to the farmer, because of the high interests
charges that are levied on the borrowed inputs (TIMB 2011). These aspects tend to
affect the viability of farming as a business, consequently resulting in loan beneficiaries
failing to service their debts (Jouault & Featherstone 2011; Fidrmuc & Hainz 2010) .
2.6.4.3 Social Aspects
Social factors such as level of morality and the set ethical guidelines were also found to
separate low defaulting societies from very high defaulting ones (Guiso et al. 2013).
15
Guiso et al. (2013) concluded that when members of one societal setting learn from one
another about the means of defaulting, the social stigma attributed such default loses its
immorality level. Because more and more people are engaging into such behaviour,
society tends to reduce the stigma and resentment against the deed, a concept referred
to as social contagion (Guiso et al. 2013).
2.6.4.4 Technological Aspects
According to TIMB (2011, p.5) under the discussion of Tobacco Industry’s Financial
Issues, “financiers and insurance companies experienced high levels of poor
performance in loans / premiums” due to stop order system challenges among other
factors. Default rate for 2010/11 tobacco season was therefore aggravated by the
inconsistency and porosity of TIMB’s stop order system (TIMB 2011). In addition,
technological aspects emanate from the use of up to date technologies in farming. This
has an effect on the productivity and also profitability of farming (Hanyani-Mlambo
2006). According to TRB (2013) despite the efforts made to disseminated information
on best practices of tobacco production, there remains a lot of farmers failing to adopt
such practices and as such fail to produce the expected yields and quality for profits
(SNV 2009).
2.6.4.5 Environmental Aspects
According to Brehanu & Fufa (2008), agro-ecological differences of farming areas was
one of the significant determinants of default small scale farmers in Ethiopia. In
Zimbabwe, the TRB identified three broad categories that are suitable for tobacco
growing, namely fast growing, medium and slow growing areas (TRB 2013). On the
other hand, the TIMB’s periodic reports such as TIMB (2014b) present tobacco farming
regions based on provincial categories. There tends to be a constant and significant
trend that clearly shows differences in the quantity and yields that are produced from
these different regions (TIMB 2013; TIMB 2014b). This, to a certain extent, supports the
findings by studies such as Salami et al. (2010) and Brehanu & Fufa (2008) in which
agro ecology plays a significant role in the expected yields and quality of farm produce.
It is also known that different farming areas possess different rainfall patterns and
general soil textures that in turn affects the viability of different farming projects (Salami
et al. 2010). Perhaps one important environmental aspect peculiar to the current
16
Zimbabwe tobacco sector is deforestation; against very low rates of afforestation
(Mambondiani 2013; Government of Zimbabwe 2013). Deforestation has an effect of
reducing the availability of firewood for curing the harvested tobacco and yet this has a
significant effect on the quality of the sellable output (SNV 2009; TIMB 2011)
2.7 MEASURES TO CURB LOAN DEFAULT
Strategies aimed at mitigating default rate are to a large extent measures which try to
address the agency problem (Besley & Ghatak 2014). These strategies are usually
engaged by the principal(the lender) to try to inline the agent’s effort towards ensuring
that their interests are not compromised (Bastos & Garcia 2010).
To effectively address the issue of loan default most literature refers to the concept of
group lending especially in situations where collateral hardly exists (Namuyaga 2009;
Nguta & Huka 2013; Field & Pande 2010; Foster & Zurada 2013). Group lending is a
loan given to a group of farmers who subsequently become jointly liable to the servicing
of the debt (Namuyaga 2009). Group members in such an arrangement need to be
aware and willing to jointly own the liability (Gaisina 2011). In a group lending scheme,
the duty to screen, monitor and enforce loan repayment is significantly transferred to the
benefiting group (Brehanu & Fufa 2008). This arguably, results in lower default rates
(Paal & Wiseman 2011). In this case, physical collateral is substituted with social
collateral (Karlan et al. 2009).
Some lenders make use of the credit policy to mitigate credit risk (Shee & Turvey 2012).
A credit policy is a set of clear guidelines specifying the terms and conditions of a given
loan scheme (Shee & Turvey 2012). With such guidelines, the lending authorities
provide relevant parameters required by its loan officers to offer credit. This reduces the
risk of giving credit to bad borrowers (Besley & Ghatak 2014). To this end, lenders are
essentially aiming at dealing with adverse selection issues (Besley & Ghatak 2014).
Most lenders prefer collateral as a means of guarantee for loan repayment (Liebeskind
2003). However this is not always available especially in Agriculture let alone small
scale farmers (Kohansal & Mansoori 2009). Collateral is a form of repayment guarantee
in which the borrower pledges the sale of a valuable belonging in the event of default
17
(Agarwal 2001). This effectively reduces the exposure of the lender in any loan
arrangement (Agarwal 2001).
Another common tool used by lenders is the third party credit guarantee (Kohansal &
Mansoori 2009). This an agreement under which another person other than the
borrower, a third party, acts as a surety (Janda 2006). This implies that the third party
takes responsibility of the debt as soon as the borrower default (Fidrmuc & Hainz 2010;
White 2011). However Bwunyo-Wakuloba (2008) argues that for such arrangement to
work effectively, there should exist supporting legislation that is fully operational. This is
hardly the case in most developing economies such as Zimbabwe (Musara et al. 2011).
Shee & Turvey (2012) came up with another useful tool which they referred to as Risk
Contingent Credit. In general this refers to any credit instrument that imbeds within
structure, a contingent claim which when triggered, transfers part or all of the borrower’s
liability to the lender or a counterparty usually, an insurer (Shee & Turvey 2012). Most
authors of this concept, argue that this tool works in favour of lenders operating in an
environment under which collateral is very minimal (Shee & Turvey 2012). It is also
beneficial to a borrower because the embedded option can also be exercised to the
borrower’s favour (Shee & Turvey 2012).
Besley & Ghatak (2014)also posited that threats can be very useful to ensuring that
farmers pay their loans on time. In this case, the principal creates a relatively hostile
environment for the agent to ensure that his interest are optimally observed (Bwunyo-
Wakuloba 2008). However this is contrary to Akpan et al. (2014), who advocated for
moral persuasion as the best means of gaining the borrower’s willingness to repay a
given loan.
According to Nguta & Huka (2013), the best way to ensure that borrowers do not easily
default is by ensuring that they have access to adequate technical training peculiar to
their line of business. Contracting companies, in the case of tobacco contract farming in
Zimbabwe, have reportedly been seen to increase their training and extension
endeavours alongside government’s extension officers (TIMB 2011; TRB 2013).
18
However default still remains a threat to tobacco contract farming business
(Mambondiani 2013).
2.8 CONCEPTUAL FRAMEWORK
The study will be guided by a conceptual framework as is outlined in Figure 2.1:
Figure 2-1 Conceptual Framework
Off farm Income
Affiliation to Farmer
association
Farming Experience
Level of Education
DEFAULT
Credit appraisal
Loan supervision
and technical back
up Systems failure
Level of debt
Means of farm
acquisition
Average loan
interest
Time spent by
farmer on farm
activities
Agro-ecological
differences
Loan duration
Social Contagion
19
2.9 CHAPTER SUMMARY
The chapter reviewed literature on the determinants of loan repayment default. The
review was underpinned by the Agency Theory which is essentially the best means of
understanding the relationship between borrowers and lenders. A number of factors
influencing loan default were discussed. Furthermore, the chapter presented some
strategies that have been suggested in other researches. The discussion ended with a
conceptual framework which was used to determine the methodology of this study.
20
CHAPTER 3
RESEARCH METHODOLOGY
3 INTRODUCTION
This chapter outlines how the research was conducted. A discussion of the research
philosophy and design will be done. The main aim is to give the rationale behind
adoption of the chosen methodology and methods. However a recap of the research
problem and the envisaged study gap will be put at the start of the chapter to refresh the
focus of the study as well as reinforce the chosen methodology.
3.1 RESEARCH PROBLEM RECAP
Zimbabwe’s financial sector is constrained with limited resources to cater for the high
demand for credit especially in the Agriculture Sector (Hanyani-Mlambo 2006; Vitoria et
al. 2012). The onset of Contract Farming as a major source of credit for tobacco farmers
in Zimbabwe since 2004 has a potential for more benefits than the costs to be incurred
(Woodend 2003; Melese 2012; Musara et al. 2011; TIMB 2011). However, at an
average of about 40% default rate per annum (TIMB 2014a), the industry is at risk of
collapse. This is because of the cumulative adverse effects of default on the capital
base (Ojiako & Ogbukwa 2012). The main objective of this study is to investigate the
factors influencing loan default in Zimbabwe’s tobacco farming sector.
3.2 RESEARCH PHILOSOPHY
The research was predominantly positivist in nature. It was quantitatively designed and
undertaken. This was because it partly used secondary data available from the Tobacco
Industry and Marketing Board to assess relationships between selected explanatory
variables and the dependent variable, default. In addition, the study tested various
hypotheses outlined in Chapter 1. It is also worthy to note that, the quantitative
approach was found to be relevant for this study because the research findings are
expected to be inferred to the whole Zimbabwe Tobacco Industry (Kothari 2004).
Furthermore the research was done with the view to unearth facts rather than in-depth
analyses of subjective aspects of default such as attitudes and feelings of respondents
(Saunders et al. 2009). It was therefore more inclined to being objective rather
subjective. This implies that, the study was concerned with a rational explanation of a
particular problem of why tobacco growers in Zimbabwe fail to service their debts.
21
Studies of this nature are dominant in business and management researches
(Sulkowski 2010; Saunders et al. 2009; Burrel & Morgan 2005). They are usually
quantitative and positivist in nature (Saunders et al. 2009).
3.3 RESEARCH APPROACH
The study followed the deductive approach. This approach is concerned with the
development of a hypothesis from existing theory and then research is conducted to
confirm or reject the hypothesis (Snieder & Larner 2009). This approach is often
referred to as the top-down approach since the reasoning start at the top, where there is
an existing theory and ends at the bottom with specific conclusion from research
(Saunders et al. 2009; Snieder & Larner 2009).
3.4 DATA COLLECTION
The study made use of both secondary and primary data. Secondary data was
accessed from the Tobacco Industry and Marketing Board’s Tobacco Grower’s
database. This is data that was collected by the TIMB not for the purposes of this
research but was found to be relevant and useful in addressing the objectives of this
research (Saunders et al. 2009). Primary data on the other hand was obtained through
a survey conducted across the tobacco industry. Two questionnaires were administered
to the industry. One was responded to by contracting companies’ extension officers
while the other one was for the contracted tobacco growers. This approach was
adopted from a number of studies carried out on loan default (Magali 2013; Bichanga &
Aseyo 2013; Addisu 2006; Nguta & Huka 2013; Mambondiani 2013).
3.5 DATA COLLECTION PROCEDURES
Secondary data was sought from the Head offices of the TIMB. It was availed in the
form of excel tables. Primary data was obtained through the use of two different
structured questionnaires. One set of questionnaires was distributed to agro-based loan
recipients in the tobacco industry. This was basically administered to tobacco growers
who had benefited from contract farming input loans in 2013/14 tobacco season. To
facilitate in the smooth distribution of the questionnaires, extension officers from
selected tobacco contracting companies were used. These extension officers were
provided with the necessary information to ensure that they assist respondents to
22
accurately construe the contents of the questionnaire thereby reducing the levels of
communication noise between the researcher and the respondents. According to
Kothari (2004), this inclusion of more technically astute individuals in the administration
of a survey goes a long way in addressing issues relating to reliability and, arguably,
validity. These extension officers also ensured that respondents understood the
rationale of the study and provide the necessary reassurance about the confidentiality of
their responses, lest they fear prosecution and black listing in the case of defaulters.
The researcher was actively involved in the administration of the questionnaire. In the
process, this provided enough time with the extension officers as they acquire the
necessary skill and knowledge of the survey directly from the researcher. This was a
very essential step since most of the respondents required various levels of translations
into vernacular. The other questionnaire set was administered to contracting companies’
employees many of whom were the research aides.
3.6 SAMPLING PROCEDURES
The research used a stratified random sampling procedure for the first set of
questionnaires that was meant for the farmers. Stratified Random Sampling is a special
type of random sampling in which the population is divided into sub groups called strata
(Saunders et al. 2009; Kothari 2004). Sample items were randomly selected from each
stratum (Kothari 2004). Each stratum was proportionally represented in the final sample
(Saunders et al. 2009). Stratified sampling was considered the best for this survey
because the population under study did not constitute a homogenous group (Kothari
2004). The population was that of all tobacco growers in Zimbabwe. However not all
tobacco farmers made use of agro based loans during the 2013-4 tobacco season. The
sampling frame was therefore regarded as those tobacco farmers who accessed loans
of any form in their production line during this period. The study divided the sampling
frame into three distinct strata based on the three broad classifications of tobacco
growing regions namely fast growing, medium and slow growing regions (TRB 2013).
However, to ensure that the sample accurately captures the true nature of the
population under study, a proportionate representation of all the existing contracting
companies as at 27 June 2014 was followed based on the purchased quantities per
23
contracting company as at that date. The size of the sample was calculated using
Cochrane’s sample size formula as adopted from Bartlett et al. (2001) :
n= s2(x)(y)
(E)2
Where n= sample size
x = average recovery rate
y = average default rate
s = standard deviation for a chosen confidence level
E= the allowable Margin of Error
According to TIMB (2014), the average (mode) recovery and default rates since 2009
were found to be 61% and 39% respectively. The chosen confidence level was 95%
and the allowable margin of error was pegged at 7%. This Margin of error was adopted
as a result of trade-offs between the statistical significance of the sample as well as the
practicality of the research vis a vis the budget and time constraints of the study(Hair et
al. 2010). However other similar studies used a margin of error of 5% (Bartlett et al.
2001; Magali 2013; Awunyo-Vitor 2012). Therefore the sample size ‘n’ was calculated
as shown below:
n= 1.962(0.39)(0.61)
(0.07)2
=187
According to Hair et al. (2010, p101), a sample size of at least 100 respondents is an
acceptable sample size. In addition the minimum number of participants in a survey was
suggested to be five times the number of predictor variables (Brace et al. 2012). The
187respondents used in the research were satisfactorily justified. Table 3.1 below
shows how the stratification was done to incorporate the three tobacco growing regions
as well as the two major categories of tobacco farmers as adopted from TIMB (2014b).
According to TIMB (2013), at least 80% of the active tobacco farmers in the year 2013
24
were small scale. This was adopted in ensuring the sample resembles as much as
possible, the real population being represented. However, the three tobacco farming
regions were given equal weighting in the sample since data about the precise
proportion was not readily available.
Table 3.1: Sample Distribution
Number of Respondents in Sample
Contractor
Contract Purchases
as at 27.06.14
(million kg)
Growers proportion
Total
Slow Growing
Medium Growing
Fast Growing
small scale
Large Scale
small scale
Large Scale
small scale
Large Scale
Zimbabwe Leaf Tobacco
24.7 16% 30 8 2 8 2 8 2
Mashonaland Tobacco Company
25.7 17% 31 8 2 8 2 8 2
TianZe Tobacco
18.8 12% 23 6 2 6 2 6 2
Northern Tobacco
24.7 16% 30 8 2 8 2 8 2
Boost Africa 10.9 7% 13 4 1 4 1 4 1
Tribac 13.2 9% 16 4 1 4 1 4 1
Chidziva Tobacco
10.6 7% 13 3 1 3 1 3 1
Curverid 11.8 8% 14 4 1 4 1 4 1
Intercontinental Leaf Tobacco
0.7 0% 1 0 0 0 0 0 0
Golden Leaf 4.3 3% 5 1 0 1 0 1 0
Leaf Trade 0.1 0% 0 0 0 0 0 0 0
TSL Classic 3.0 2% 4 1 0 1 0 1 0
Shasha Tobacco
2.0 1% 2 1 0 1 0 1 0
KM 1.9 1% 2 1 0 1 0 1 0
Midriver 1.3 1% 2 1 0 1 0 1 0
TOTAL 153.7 100% 187 50 12 50 12 50 12
Source: Adopted from TIMB Weekly Tobacco Report – 2014 week 19
On the other hand, to administer the other set of questionnaires, purposive sampling
was conducted. According to Kothari (2004, p67), purposive sampling is ideal “when the
universe happens to be small and a known characteristic of it is to be studied
intensively”. In this study, only 15 contracting companies constituted the population of
all contracting companies. However, an additional respondent was selected from the
TIMB despite not currently being employee by any contracting company. His experience
25
in tobacco contract farming was very much reputable and the researcher was referred
by some of the respondents to invite him to take part in the survey. Perhaps it is worth
noting that purposive sampling was deemed fit for this sample since the researcher was
not interested in accessing the views of all the employees of these firms but instead
sought to explore the knowledge possessed by those employees. In addition the study
used respondents whose interaction with the farmers was more direct and constant.
Such employees were thus viewed to be the main source of information which
strategists in individual contracting firms would use to curb default problems. This
certainly addressed the quest by the research to seek pragmatic strategies to mitigate
loan default. It is upon this background that, the researcher adopted a pathway that was
relatively contrary to Mambondiani (2013) in which his sampling frame for the
contracting firm’s employees targeted the managers right up to the top.
Purposive sampling was also chosen because the sample size of 16 respondents was
viewed to be a very small sample size relative to the total number of contracting
companies’ employees (Saunders et al. 2009). According to Saunders et al (2009) non
probabilistic sampling methods of this nature tend to assist researches seeking to
represent the views of whole study population (in this case all employees of the
contracting firms) in a more accurate manner than if the sampling is done
probabilistically despite the use of a very small sample size.
3.7 DATA ANALYSIS PROCEDURES
The data was analysed using Statistical Package for Social Sciences (SPSS). The
following analyses were done:
3.7.1 Reliability Tests
According to Field (2005), reliability means consistency. It is the extent to which an
instrument yields the same results for the same population at different times (Saunders
et al. 2009; Field 2005; Kothari 2004; Tavakoli 2013). The study used Cronbach’s alpha
to estimate the reliability of the research instruments used. This was chosen mainly
because a significant part of the survey questionnaire had multiple Likert questions that
constituted a scale which in this case calls for a need to establish its consistency (Royal
2011; Tavakol & Dennick 2011). Reliability was also ensured through the use of a pilot
26
study that was done before the final survey was conducted. Cronbach’s alpha tests
were calculated using SPSS.
3.7.2 Factor Analysis
This is a method used to reduce data by compacting the available variables to fewer
factors (Burns & Burns 2008). This was done through the grouping together of all
correlated variables (Kothari 2004). Factor analysis procedure followed the steps
adopted from the work of (Williams et al. 2010):
ü Step 1: Selecting and Measuring a set of variables in a given domain
ü Step 2: Data screening in order to prepare the correlation matrix
ü Step 3: Factor Extraction
ü Step 4: Factor Rotation to increase interpretability
ü Step 5: Interpretation
ü Further Steps: Validation and Reliability of the measures
According to Williams et al. (2010), factor analysis also provides valid measurements for
yet another valuable test referred to as construct validity. Validity in this case, refers to
the extent to which an instrument measures what it is supposed to measure while
construct validity in particular evaluates the extent to which the given score in a
questionnaire (scale) conform to existing sound theory or relationships (Kothari 2004).
To this end, a discussion of the survey’s construct validity were done and presented in
the next chapter.
3.7.3 Logistic Regression Analysis
A logistic Regression Model was then done using the factors found from Factor Analysis
above. Only the significant factors and variables will be included in the analysis. The
logistic regression analysis is appropriate when the outcome of a model is dichotomous,
that is the value of the dependent variable takes either of two possible values (Wuensch
2014). In the study, the outcome was to predict between loan default and its absence.
The explanatory variables on the other hand are of any type, that is, nominal, ordinal,
27
and / or interval data (Burns & Burns 2008; Wuensch 2014). One important
characteristic of this regression analysis compared to the ordinary least squares
regression analysis is that it does not make any assumptions about the distributions of
the predictor variables (Burns & Burns 2008). In addition more statistically robust
analyses are obtainable in the case of using different data types of the independent
variables (Kothari 2004). However according to Burns and Burns (2008), the major
disadvantage of Logistic Regression Models is that they do not predict the numerical
values for the dependent variable.
See below the Logistic Regression Model
Equation 1 : Logistic Regression Model
Where the dependent variable Y = either 0 when there is default or 1 when there is no
default, is the y - intercept, = the Beta coefficients of the respective explanatory
factors Fn. Where n =1, 2, 3, ..., m factors found from factor analysis.
The Explanatory Variables are as shown below
X1: Level of farming experience
X2: Level of Education and Agriculture related skill
X3: Affiliation to farmers association
X4: Off-farm sources of income
X5: Level of indebtedness
X6: Time spent by farmer on farm activities
X7: Agro-ecological differences
X8: Loan duration
X9: Social contagion
X10: Means of farm acquisition
X11: Average loan interest
X12: Level of Credit appraisal
28
X13: Loan supervision and technical back-up
X14: Low Tobacco Prices on the Market
X15: Poor Quality Tobacco
X16: Systems Failure
εi is the error term
3.7.4 Secondary Data Analysis
One of the 15 contracting companies operating in the 2012/13 season was randomly
selected. Data regarding that company’s contracted growers was accessed from the
TIMB Stop Order data base. All the selected growers were coded, and grower identity
was not disclosed. The idea was to regress various attributes for each farmer against
the defaulted amount as the dependent variable. However, only four variables were
deemed to be reliable and consistent. The model was meant to complement the survey
research findings.
3.7.5 Significance Tests
Significance tests were done as the two Multivariate models were being established.
The various significance levels were a result of the SPSS analysis done. The β
coefficients found for each variable gave the explanatory power of the given
independent variable in relation to the dependent variable (Gujarati 2004). By so doing,
the hypotheses earlier mentioned were thus tested. Model parameters calculated by
SPSS will also be explained and discussed.
3.8 ETHICAL CONSIDERATIONS
According to Saunders et al. (2009, p183) research ethics “refers to the appropriateness
of your behaviour in relation to the rights of those who become the subject of your work
or are affected by it”. In line with this definition, the research was conducted with
maximum consciousness of the right for respondents to confidentiality and anonymity.
Since the study made use of secondary data obtained from the Tobacco Industry and
Marketing Board, the permission to access and use the data was sought from the
highest office of the organisation through a meeting set between the researcher and the
office. To abide to the general research ethics, no grower identity was revealed. Instead
of using TIMB registry growers’ numbers, the research was conducted with coded
identities of growers (see Appendix D). During primary data collection, no respondents
29
were forced to take part and the participants reserved the right to adjourn participation
at any time without even giving notice to the researcher. All the distributed
questionnaires had a cover letter which sought to clarify the objectives of the study and
emphasised the commitment to confidentiality and privacy (see Appendix A and B).Both
sets of questionnaires did not require the respondents to offer any form of identity. This
was meant to conform to the anonymity pledge vouched in the cover letter.
3.9 LIMITATIONS OF THE STUDY
During the research, valuable and more accurate secondary data for loan default was
not easily accessible because the contracting companies were not at ease to divulge
that information. They regarded the information as highly confidential. Even the tobacco
growers would not easily disclose the monetary value of their debt as well as the
respective repaid value. The researcher also attributed this discomfort to privacy
issues.During the pilot study, it was noted that most farmers could not easily remember
the value of their inputs and as such, the quality of the response to those questions was
very low and unnecessarily burdened the load for respondent. The questions to
measure these variables were therefore redesigned accordingly.
3.10 CONCLUSION
This chapter outlined how the research was done. It specifically denotes the research
philosophy as quantitative and follows the positivist approach. The use of secondary
data to validate the primary data obtained through the survey questionnaire was also a
means that the research undertook to increase reliability and validity. The chapter also
explained the rationale behind the use of stratified random sampling and purposive
sampling as the chosen sampling techniques for this study. The study had its own
limitations which explicitly stated. Of importance, the research was conducted with due
consideration to research ethics.
30
CHAPTER 4
RESULTS AND DISCUSSION
4 INTRODUCTION
In this chapter, the results of the study are discussed and analysed. The results are
summed up into tables, cross tabulations and graphs which will be fully explained and
discussed. The results from the statistical package SPSS are concisely compiled. The
chapter outlines the various analyses mentioned in chapter two namely reliability test,
factor analysis and significance tests. At the end, a Logistic Regression Model will be
discussed to unpack the predictive power of the model. To this end, various parameters
will be presented and discussed. A comprehensive analysis of the strategies to mitigate
loan default as suggested by both surveys will be discussed. To kick start the
discussion, the initial part of the chapter explores the descriptive statistics of the survey.
4.1 SAMPLE DESCRPTIVE STATISTICS
One hundred and eighty seven questionnaires were sent to tobacco contracted
growers. Of these, 138 responded. This implies that the survey response rate from the
tobacco growers was 73.8%. On the other hand, all the 15 questionnaires sent to the 15
contracting companies’ extension employees were successfully completed and
returned. The other one that was given to a referral expert was also completed and
returned. As is shown in Table 4.1, the study resulted in an overall response rate of
75.9%. This is satisfactorily well above the average response rates of 55.6% and 52.7%
that Baruch (1999) and Baruch & Holtom (2008), respectively concluded to be the mean
response rates for surveys conducted by a number of top studies. A guarantee of
validity for this study was therefore relatively justified.
Table 4.1: Survey Response Statistics
Questionnaires Response
Rate Sent Returned
Males Females Total
Contracted Growers 187 102 36 138 73.8%
Contracting Firms' Employees 16 10 6 16 100.0%
203 112 42 154 75.9%
Out of all the 154 respondents who participated in the survey, 112 were men
constituting 72.7% while the remaining 27.3% were women. This arguably depicts the
31
dominance of men in Zimbabwe’s tobacco industry, tallying with the findings in
Mambondiani (2013).
Table 4.2 Marital Status Distribution
Valid Frequency Valid Percent Cumulative Percent
Valid Single 20 15.2 15.2
Married 99 75.0 90.2
Widowed 9 6.8 97.0
Divorced 4 3.0 100.0
Total 132 100.0
Table 4.2 shows that 75% of the respondents indicated that they were married and only
15% said they were single. This can arguably be taken to suggest that, about 85% of
the tobacco growers have families to look after. There is therefore a high possibility of
consistent non compressible expenditure that these farmers are likely to meet
periodically, as they strive to fend for their families. Based on this finding, it can be
confirmed why some writers recommend that contracting firms should strongly consider
addressing the social welfare of input recipients so as to reduce incidences of default
through loan input diversion (Bichanga & Aseyo 2013; Magali 2013; Melese 2012;
Mambondiani 2013). According to Wongnaa & Awunyo-Vitor (2013), farmers with
families are more likely to default than those without.
Figure 4-1 Age Distribution of Farmers
21-30 years 31 to 40 Years 41 to 50 Years above 51 Years
Valid Percent 14.5 35.1 32.1 18.3
0
5
10
15
20
25
30
35
40
Valid
Perc
en
t
32
The mean age of the respondents was 41.52 years. The youngest respondent was 21
years while the oldest was 65 years. As shown in Figure 4.1, most of the respondents
fell under the 31-40 years category, constituting a valid percentage of 35.1. The age
distribution was relatively balanced contrary to what Ojiako & Ogbukwa (2012) found in
a study of farming cooperatives in Nigeria. The distribution shows a relatively normal
distribution, in which 49.6% of the farmers are at most 40 years old and 50.4% are
above 40 years.
Figure 4-2 Farm Size Distribution
Figure 4.2 shows that more small scale farmers responded to the survey than large
scale farmers. This is as per expectation (TIMB 2011). Sixty two per cent of the farmers
were small scale, while the remaining 38% constituted the large scale farmers. Most
farmers did an average of 2Ha of tobacco in the 2013/14 season. However, a mean
tobacco hacterage of 6.1Ha per farmer was grown during the season. This further
agrees with the reports that today’s tobacco industry is largely dominated by small scale
farmers (TIMB 2011; TIMB 2013; ZTA 2014).
.
Small scale 62%
Large scale 38%
33
Figure 4-3 Means of Farm Ownership
Figure 4.3, shows that the majority of the farmers who responded to the questionnaire
have offer letters while only 14% hold title deeds to their land. This confirms why banks
and most formal financiers in the financial markets are hesitant to offer lines of credit to
tobacco farmers due to lack of acceptable collateral (TIMB 2011; ZTA 2014). Eleven
percent of the farmers are leasing while 23% are in the communal lands. This evidence
that the structure of the farming community has significantly changed (ZTA 2014) and
as such requires new and tailor made strategies for the growth recently being seen to
continue sustainably (TIMB 2013).
Figure 4-4 Highest Educational Qualification
Title Deeds 14%
Offer Letter 51%
Communal / Village 23%
Leasing 11%
None of the above 1%
3%
10.40%
48.10%
22.20%
16.30%
No Formal Education
Primary Education
Secondary Education
Tertiary up to Diploma
Above Diploma
34
Figure 4.4 shows that most of the farmers who responded to the survey attended at
least primary school education. This implies a literacy rate of more than 90%. The
modal class is for those who attended school up to secondary education who
constituted nearly half of the sample surveyed. Nearly 40% of the farmers attended
tertiary education implying that the potential for training and extension services impact
on the tobacco industry is very encouraging. Concerns by stakeholders to leverage
farming through adoption of best farming practices and more cost efficient technologies
is thus eased since the high literacy rate envisaged in the sector shows a relatively
trainable sector (TRB 2013).
4.2 EVALUATION OF LOAN DEFAULT DETERMINANTS
In this section, factors influencing loan default are addressed. Data analysis
emphasised responses from the farmers. Responses from the contractors were thus
used for validation and critical comparisons’ sake. Where the same question was given
to both the farmer and the contractor, mean scores from both sets of responses were
graphically presented and compared. However, the analysis gave more weight to
farmer’s responses over the contractors’. This guideline was adopted from SSC (2001)
because the two sets of questionnaires could not be equally weighted due to the
differences in the sample sizes (16 contractors against 138 farmers) as well as the
heterogeneity that characterise the two populations surveyed. However, some
questions were purposely designed and targeted to either farmer’s or the contractor’s
view so as to address particular research objectives. Such responses would appear in
one set of the survey questionnaire. To interpret the mean scores, responses were
based on a five point Likert scale. The higher the score the more positive is the
response. Therefore a score of ‘1’ has been consistently associated with the highest
negativity, while 5 has the highest positive score throughout the survey (see Figure 4.5).
Mean scores which were at least 4.0 would imply greater (positive) importance rating
(Johns 2010).
35
Figure 4-5 Five Point Likert Scale Source: Johns (2010), bk.Survey Question Bank
4.2.1 Farmer Related Factors
During the survey, farmers were requested to identify the major factors they attributed to
loan default during the season. Figure 4.6 shows farmers’ perspective on farmer related
determinants of default.
Figure 4-6: Farmers’ Responses on Major Farmer Related Determinants of Loan Default
From Figure 4.6, the three most cited factors were Poor quality tobacco, Crop yield and
Farmer’s Farming Experience. About 26% of all the respondents attributed their failure /
success to repay their loans to the quality of their tobacco. Closer to this response rate
and significantly high, was crop yield at 21.5%. The least number of responses was for
“affiliation to farmer association”. Perhaps this can be explained by the concerns raised
by farmers, about the unnecessarily large numbers of tobacco unions that has left them
powerless and with fewer benefits to the farmer (TIMB 2011, p.4). The result was
consistent with the responses given by farmers on the extent to which they would rate
the identified factors in influencing loan default (see Figure 4.7).
9.9
17.2
2.2 4.4
9.1 8.8
21.5
25.9
0
5
10
15
20
25
30
Level ofFarmer
Education
FarmingExperience
Affiliationto Farmer
Association
Off FarmIncome
Level ofdebt
Time Spenton FarmActivities
Crop Yield PoorQuality
Tobacco
Perc
en
tag
e R
esp
on
se
36
Figure 4-7 Mean Scores on Farmer Related Factors
Figure 4.7 shows the mean scores based on how farmers and contractors rated the
extent to which listed farmer related factors would influence loan default. There was
consistency in farmers’ responses on Poor Quality tobacco and Crop Yield as was the
case in the overall responses found in Figure 4.6. Although Farming Experience was
rated the next best mean score after Poor Quality Tobacco and Crop yield; tallying with
the responses in Figure 4.5, the mean score of 3.85 falls short of the threshold of 4.0;
implying that farmers were somehow on the neutral side in rating Farming Experience
as a major determinant of default. Contractors’ responses have also been
superimposed to see how they compare with those of the farmer. The highest scores
still converge on Poor Quality and Crop Yield affirming the importance of these factors
as determinants of farmer default. The lowest mean score from the farmers (Affiliation to
Farmer Association) received a somewhat neutral response by the contractors. On this
note, the survey findings suggested that farmers placed the most importance on Yield
and Quality of one’s crop.
Level ofFarmer
Education
FarmingExperien
ce
Affiliationto
FarmerAssociati
on
Off FarmIncome
Level ofdebt
TimeSpent on
FarmActivities
CropYield
PoorQuality
Tobacco
Farmer 3.15 3.85 2.29 3.23 3.59 3.79 4.33 4.46
Contractor 3.07 3.5 3.14 3.57 4.14 4.43 4.93 4.86
0
1
2
3
4
5
6
Mean
Sco
res
37
4.2.2 Lender Related Factors
Figure 4-8: Farmers’ Responses on Major Lender Related Determinants of Loan Default
Figure 4.8 shows how the farmers responded to lender related factors as major
determinants of loan default. The highest response was on Poor Borrower’s Appraisal at
30.6%, followed by Loan supervision and technical back up at 28%. The lowest mention
was that referring to loan duration. At this point, perhaps it is accurate to conclude that
contractors are not short changing farmers through shortening repayment periods
especially in cases where capital expenditure is involved as was alleged by farmers’
representatives in TIMB (2011).
8.6
18.8
30.6
28
14
0
5
10
15
20
25
30
35
Loan Duration Average LoanInterest
Poor Borrowers'Appraisal
Loan Supervisionand technical back
up
Systems failure
Perc
en
tag
e R
esp
on
se
38
Figure 4-9 Mean Scores on Farmer Related Factors
Figure 4.9 goes further to show that farmers’ on loan duration remained lowest on mean
scores with a very negative score of 1.7. This confirms the earlier argument that farmers
are very much satisfied with the time they are given to repay their loans. To consolidate
this conclusion, contractors on the other side tend to air the same view, with loan
duration having the lowest score of 2.23. There seems to be no significant differences in
the scores that were aired by farmers as compared to the contractors save for
responses to ‘loan supervision and technical back up’ where contractors’ mean score
was 4.15 while farmers’ score remained below 4.0 at 3.94. This result implies a
somewhat differing position between the farmers and the contractors. The overall
conclusion however is that lender related factors were viewed to have insignificant
relevance in inducing loan default. This is contrary to Sterns (1995) cited in Nawai &
Shariff (2012) who claimed that high loan default is mainly due to lender related factors.
Responses on poor borrower appraisal also showed some inconsistency since it had
the highest response of 30.6% (see Table 4.8), but did not get mean scores above 4.0
and even from the contractors. To further delve into this finding, an additional analysis
was done on the responses given by the contractors on the extent to which some critical
traits of a potential borrower were being valued before a loan application was approved.
Results of this outcome indicated that most contractors were not easily permeable. The
responses indicated that contractors had very strict credit policies that they used to
Loan DurationAverage Loan
Interest
PoorBorrowers'Appraisal
LoanSupervision
and technicalback up
Systemsfailure
Farmer 1.7 3.15 3.72 3.94 3.41
Contractor 2.23 3 3.92 4.15 3.69
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
Mean
Sco
res
39
appraise potential borrowers. Perhaps the two diverging responses would imply that
farmers were very much aware that if contractors do not screen the loan recipients, they
are most likely to lose through default. On the other hand they were aware that this was
no longer much of an issue since the selection for borrowers was now very strict to
deter potential bad debts. Appendix F shows the responses tabulated against the
contractors’ working experience.
4.2.3 Extraneous Factors
Figure 4-10 Farmers’ Responses on Major Extraneous Determinants of Loan Default
Figure 4.10 shows that Low Market Prices were the major extraneous factors that
influenced default with 61.1% of the farmers stating this factor. Agro ecological
differences had the lowest percentage response of 15%. This shows that tobacco
farmers did not attribute much to the differences in the farming regions. Perhaps their
judgements on this factor are not as reliable as would be given by the contractors
whose inter regional exposure may relatively be higher than that of the farmers. Figure
4.11 shows that agro ecological differences were also lowly rated by the contractors.
The researcher was also interested seeing whether or not, there were any significant
differences in the mean scores by the contractors on the Agro ecological differences
across the different levels of experience within the contracting firm’s employees. Table
4.3 clearly indicated that those with more working experience had the lowest score of
2.00 while the ‘below five years’ cluster had a score of 3.88.
23.9
15
61.1
0
10
20
30
40
50
60
70
Social Contagion Agroecological Differences Low Market Prices
Resp
on
se P
erc
en
tag
e
40
Table 4.3 Contractors' Means Scores Across working experience
years been
working
Social Contagion
(Defaulters not being punished and peers
default because there is no harm)
Agro Ecological Differences
(Farming districts differences e.g
differences in weather and soils)
Low prices on
the market
Below 5years 3.25 3.88 3.38
over 5 years 4.00 2.00 5.00
Mean 3.54 3.25 4.00
In addition, Table 4.3 also reveals that the two clusters had diverging perspectives on
both Social Contagion and Low Market prices. On low market prices, the “over 5 years”
cluster gave it a 5 while the “less than five” scored 3.38. The Mean scores hereby stated
in Table 4.3 were thus combined with those of the farmers and graphically presented as
is illustrated in Figure 4.11.
Figure 4-11 Mean Scores on Farmer Related Factors
In Figure 4.11, it is shown that the results remained consistent with the responses given
in Figure 4.10. Low Market Prices remained at the top with both farmers and contractors
viewing the factor as a key determinant of loan default. Figure 4.11 also concurs with
responses in Figure 4.10 that social contagion was not a major factor determining loan
default. This is against what Guiso et al. (2013) premised in their study of determinants
of attitude towards default. Although there is some evidence that social contagion has
an effect on loan default, the extent was found to lie relatively on the less extent to
neutral rating.
Social ContagionAgroecological
DifferencesLow Market
Prices
Farmer 3.56 3.04 4.39
Contrator 3.54 3.25 4
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
5
Mean
Sco
res
41
4.2.4 Discussion of open ended Questions on determinants of loan default.
There was a relatively consistent reference to factors such as side marketing, loan
diversion and inadequate loan inputs supplies, being stated as other factors that
influence default. The study acknowledged these responses, but instead felt that side
marketing and input diversion were not causes but types of default as posited by
Woodend (2003). The two are however prevalent in the tobacco industry. The objective
of this study was to unearth the causes of defaults as envisaged by either side
marketing, input diversion, or any other forms of default. However inadequacy of inputs
supplied, together with late disbursement of inputs, were considered as determinants of
default despite Dawes (2008) referring to these two as forms of contracting firms’
default. It was also found that some farmers stated greed as a factor that causes default
in tobacco farming. This is in tandem with the premises in Mambondiani (2013) and
SNV (2009), in which they concluded that farmers’ default through side marketing was
out of greediness.
4.3 MULTIVARIATE ANALYSIS
In this section, a logistic Regression model that would assist in the evaluation of a
borrower’s credit worthiness prior to contractual commitment is developed and
analysed.
4.3.1 RELIABILITY TESTS
4.3.1.1 Interpretation of Reliability Test Results
Most researchers advocate that a Cronbach’s α value of at least 0.6 is an acceptable
level of validity for any given study (Yusoff 2011; Yu 2001; Tavakol & Dennick 2011).
This research also managed to get such an acceptable value for all the identified factors
combined as is shown in Table 4.3 below. The study’s Cronbach’s alpha was found to
be 0.601. This implies that at least 60.01% of the result is due to the consistency of the
survey questionnaire to measure the intended variable (Field 2005; Yu 2001; Tavakol &
Dennick 2011).
4.3.1.2 Discussion of Reliability Test Results
Where there is a possibility to distinguish given scales or concepts, Tavakol & Dennick
(2011) suggested that different reliability tests should be conducted per each scale or
42
concept. This is because Cronbach’s α value tends to be inflated, sometimes
unnecessarily when the number of measurements increases (Tavakol & Dennick 2011).
It is against this background that the study also calculated the reliability levels for each
factor as identified from the literature reviewed. All the alpha values were below 0.6 (see
Table 4.3). Farmer related factors yielded the highest α of 0.501 while lender related
factors and extraneous factors were 0.218 and 0.200 respectively. On face value, the
individual reliability levels would imply that the questionnaire was not consistent and
thus results are questionable, but as explicitly suggested by Royal (2011), instruments
used to measure behavioural (latent) traits such as knowledge, attitudes and perhaps
ability, “do not possess the property of reliability” and as such he argues that there will
never be a reliable instrument in this field but rather reliable results. According to
Tavakoli (2013), low α can be taken to simply imply little similarities in the responses
given by the respondents. According to Tavakol & Dennick (2011), researchers should
not always rely on published α estimates but rather, a fair judgement for reliability
results should consider three aspects of the study, namely the characteristics of the
instrument, the conditions of administration and the characteristics of the respondents
(Royal 2011).
Table 4.4 Summary of Reliability Test Results
Number of items
Cronbach's
α
Valid Cases
Hotelling 's T-squared Test Sig
Farmer Related Factors 8 0.501 71.7% 0.000
Lender Related Factors 5 0.218 77.5% 0.000
Extraneous Factors 3 0.200 76.8% 0.000
All the Identified Factors 16 0.605 60.1% 0.000
Table 4.4 also shows that all the factors were significant with the Hotelling T-Squared
Significant Tests yielding a significant level of 0.000 (p<0.05). As is the case in most
surveys, missing responses on a number of cases are expected (Yu 2001; Field 2005;
Baruch & Holtom 2008). The column titled valid cases in Table 4.4 highlights the SPSS
outcome that shows the simulated percentages of the cases which were actually used
43
to calculate the reliability value after the missing responses were suppressed. The 16
items that constitute the 3 identified factors analysed and presented in Table 4.3 can be
seen in the Questionnaire as shown in Appendix A.
4.3.2 FACTOR ANALYSIS
Table 4.5 shows the Kaiser-Meyer-Olkin Measure on Sampling Adequacy of 0.56.
According to Kaiser (1974) cited by Field (2005), KMO values above 0.5 are acceptable.
However, the closer the value is to 1, the better. KMO measures the suitability of a
factor analysis to be undertaken. Therefore, the factor analysis for this survey was
feasible.
Bartlett’s Test of Sphericity was also done (see Table 4.5). It tests the null hypothesis
that the correlation Matrix is an identity matrix. The objective would be to reject this null
hypothesis because an identity matrix would show that the variables in question are not
related and as such would not be grouped into any factor or component. Table 4.5
shows a significance value of 0.000 which implies that we reject H0 and thus conclude
that the variables in question are related and can be grouped into distinct components.
Table 4.5 KMO and Bartlett's Test
Kaiser-Meyer-Olkin Measure of Sampling Adequacy. 0.56
Bartlett's Test of Sphericity
Approx. Chi-Square 242.742
df 120
Sig. 0.000
a Based on correlations
To ascertain the extent of Multicollinearity within the explanatory variables a correlation
matrix was analysed (See Appendix C1). The closer the correlation value is to zero, the
better (Hair et al. 2010). Most of the correlation values found in the matrix were close to
zero. The highest correlation was between Crop Yield and Crop Quality with a value of
0.586. According Hair et al (2006) cited in Saunders et al (2009), correlation values of
0.9 and above depict the existents of multicollearity problems. The correlation values
found for the study are therefore proving the absence of Multicollinearity problems.
The SPSS result for Factor analysis is shown in Table 4.6. Nomenclature of the
resultant three factors was based on the judgements of the researcher (Williams et al.
2010). Factor 1 was found to be dominated by variables either affecting or affected by
44
the farmer’s skills and knowledge thus the name Farming Skills and Knowhow. Factor 2
was relatively difficult to name. However it was found to have an inclination to financial
aspects of the borrowed inputs, intellectual capacity of the farmer to construe
contractual obligations as well as the natural endowments of the farm’s location. To this
end, the factor was thus given the name Financials and Capacity. Factor 3 was a mixed
bag but the common element in this cluster was predominantly exogenous to the farmer
thus the name Exogenous Miscellaneous.
Table 4.6 Factor Analysis Result
FACTOR 1
Farming skills and knowhow (sources and application)
FACTOR 2
Financials and
Capacity
FACTOR 3
Exogenous Miscellaneous
Loan Supervision and technical back up
0.636
Poor Quality Tobacco 0.631
Crop Yield 0.605
Low Market Prices 0.575
Systems failure 0.565
Farming Experience 0.515
Affiliation to Farmer Association
0.381
Time Spent on Farm Activities
0.36
Average Loan Interest 0.695
Level of Farmer Education 0.539
Level of debt 0.538
Poor Borrowers' Appraisal 0.533
Agro ecological Differences 0.436
Off Farm Income 0.606
Loan Duration 0.536
Social Contagion 0.502
During the analysis, it was noted that two variables namely “Affiliation to Farmer
association” and “time spent by farmer on farm activities”, struggled to perfectly fit in the
three groups as shown by those low values of factor loadings. According to Fletcher
(2007), the higher the factor loading the better because it shows weights and
correlations between each variable and the factor. However the selection criterion was
simply to classify each variable into the factor where it exhibits the highest value
(Williams et al. 2010). Table 4.7 shows the respective communalities for the extracted
45
variables. According to Torres-reyna (n.d.), communalities show the proportion of
variance in the variable that can be explained by the factors.
Table 4.7 Communalities
Initial Extraction
Level of Farmer Education 1.000 .450
Farming Experience 1.000 .436
Affiliation to Farmer Association 1.000 .281
Off Farm Income 1.000 .516
Level of debt 1.000 .349
Time Spent on Farm Activities 1.000 .189
Crop Yield 1.000 .553
Poor Quality Tobacco 1.000 .490
Loan Duration 1.000 .370
Average Loan Interest 1.000 .566
Poor Borrowers' Appraisal 1.000 .387
Loan Supervision and technical back up 1.000 .422
Systems failure 1.000 .402
Social Contagion 1.000 .303
Agro ecological Differences 1.000 .362
Low Market Prices 1.000 .445
Extraction Method: Principal Component Analysis.
The factors extracted were also found to yield a Cumulative Total variance of almost
41% (see Total Variance Explained Table in Appendix E). This value gives the
percentage of variance accounted for by the three components extracted (Fletcher
2007). This value indicates that the three factors account for 41% of the common
variance of all the sixteen original variables. This was the initial and necessary
consideration required prior to Logistic Regression Modelling (Kothari 2004). Also refer
to Appendix E for the other part of the output by the SPSS syntax for the Analysis done.
4.3.3 LOGISTIC REGRESSION MODEL
In this section, the factors identified in 4.3.2 were put into a Logistic Regression Model.
Due to the relatively low Total Cumulative Variance found for the three factors, the
researcher expected to find more individual variables that would be significant in the
final regression model. To this end, a number of iterations and combinations were tried,
46
and Table 4.8 summarises the results for the best Logistic Regression Model showing
the specific coefficients of the factors and additional individual variables whose beta (B)
and Exponential Beta (Exp (B)) made the best fit for the model.
Table 4.8 Explanatory Variables in the Model B S.E. Wald df Sig. Exp(B)
Factor1 -0.833 0.402 4.304 1 0.038 0.435
Factor2 0.032 0.303 0.011 1 0.916 1.032
Factor3 0.315 0.294 1.145 1 0.285 1.37
Slow Growing Region 6.407 2 0.041
Fast Growing Region -1.851 0.749 6.107 1 0.013 0.157
Medium Growing Region -0.55 0.84 0.428 1 0.513 0.577
Hecterage 0.015 0.025 0.339 1 0.56 1.015
Farming Experience 0.079 0.054 2.087 1 0.149 1.082
No Formal Education 1.277 4 0.865
Primary Education -21.693 22457.03 0 1 0.999 0
Secondary Education -0.06 1.182 0.003 1 0.959 0.941
Tertiary up to Diploma -0.796 0.976 0.665 1 0.415 0.451
Above Diploma -0.803 0.954 0.709 1 0.4 0.448
Affiliation to Association 0.96 0.628 2.338 1 0.126 2.612
Resident Farmer 0.019 0.905 0 1 0.984 1.019
Constant 1.149 0.872 1.737 1 0.188 3.154
a Variable(s) entered on step 1: FActor1, FACtor2, FACtor3, Growing Region, Size of Farm, Farming Experience, Highest Educational Qualification, Affiliation to farmer Association, Resident Farmer.
From Table 4.8, Factor 1 was statistically significant in the predicting loan default
among tobacco farmers in Zimbabwe. In addition, it was also found that being in the fast
growing region is statistically significant. From Table 4.8, the following Logistic
Regression Model was found:
Y = - (0.833 Factor 1+ 1.851 Fast Growing Region) + ε
Where Y is dichotomous and takes the values of 0 when there is no payment and 1
when the loan is repaid as is exhibited in Table 4.9. On the other side of the equation, ε
is a random error term. Please note that the variable stated, Affiliation to farmer
Association with no statistical significance in Table 4.9, was the one rated on the 5 point
scale while the one included in Factor 1 was the yes or no response in question 3 of
section B of the farmers’ questionnaire (See Appendix A). The later was more reliable to
be used in predicting loan default as depicted in the Logistic Regression results.
47
Table 4.9 Dependent Variable SPSS Encoding Original Value Internal Value
Defaulted 0
Repaid 1
It is worth noting that interpretation of the Logistic Regression Model Coefficients uses
the odd ratios. Beta values are best explained in the form of the Exp (B) which is
basically eB. For example, Table 4.8 shows the coefficient of Factor 1 is 0.833 thus Exp
(B) = e-0.833 = 0.435.
To interpret the odds ratios and coefficients in the model, Burns & Burns (2008) posit
that the odds ratio describes the impact of a unit increase in the explanatory variable on
the probability odd event occurring. In the case of factor 1 in the model, a unit increase
in factor 1 is associated with a 56.5% (that is 1- 0.435) decrease in the odds of our
dependent variable which in this case is the failure to repay a loan (default). In practice,
the model is advocating for an increase in all variables classified under factor 1
combined to reduce the chances of a farmer defaulting.
Though relatively controversial, the model also suggests that contractors and policy
makers alike, endeavour to positively adjust the farmer’s agro ecological conditions in
their regions resemble as much as possible, those found in fast tobacco growing region.
According to the model an increase in the variables that adjust a farm to resemble those
peculiar to the fast growing region, reduces default by 84.3%, that is 1- 0.157 = 0.843.
Unfortunately the agro ecological characteristics in these districts are beyond the scope
of this paper although it is known that this region is mainly in Mashonaland West
Province of Zimbabwe, and TIMB (2013) reported that this is where the bulk of the
tobacco sold in 2012/13 season came from. In addition weekly TIMB bulletins were also
showing the dominance of this region (TIMB 2014b).
4.4 SIGNIFICANCE TESTS
The following hypothesis tests were thus concluded based on the statistical significance
shown in Table 4.8:
I. H0: Factor 1 affects farmers’ loan repayment default
48
Since the significance level was 0.038 and less than 0.05, Factor 1 is statistically
significant. We accept H0 and conclude that the Farming skills and knowhow
factor affect farmer’s loan repayment default
II. H0: Factor 2 affects farmers’ loan repayment default
We therefore reject H0 since the significance level of 0.285>0.05 and thus
conclude that the Financials and Capacity Factor does not affect farmers’ loan
repayment default.
III. H0: Factor 3 affects farmers’ loan repayment default
We therefore reject H0 since the significance level of 0.916>0.05 and thus
conclude that the Exogenous Miscellaneous Factor does not affect farmers’ loan
repayment default
4.5 ANALYSIS OF SECONDARY DATA
A multiple Regression Model was done using data from the TIMB. As earlier discussed
section 3.9, the TIMB database was still under construction and the research could only
make use of a limited meaningful variables. The resultant model is shown in Table 4.10.
Table 4.10 Model Summary
Model R R
Square
Adjusted R
Square
Std. Error of
the Estimate
Change Statistics
Durbin-Watson
R Square Change
F Change
df1 df2 Sig. F
Change
1 .390a 0.152 0.149 28.92578 0.152 53.659 4 1196 0 0.29
a. Predictors: (Constant), Mass sold (kg), Level of Debt, Average Price, Amount Paid in Season
b. Dependent Variable: Default
Since the sig. F Change value was less than 0.05, the model was found to be
statistically significant. The R squared and Adjusted R squared values were 0.152 and
0.149. A value of R square indicates the predictive power of the model (Hair et al.
2010). In this case, the statistic suggests that the entire explanatory variable in the
model, can only explain 15.2% changes in the dependent variable. This very low R
square can be an indication that there are other independent variables that have been
left out of the model (Gujarati 2004). Table 4.11 shows the coefficients of the model:
49
Table 4.11 Multiple Regression Coefficients
Model
Unstandardized
Coefficients
Standardized
Coefficients
t Sig.
Collinearity Statistics
B Std. Error Beta Tolerance VIF
1 (Constant) 57.515 2.796 20.572 .000
Level of Debt .005 .000 .490 11.565 .000 .395 2.530
Average Price -3.616 1.128 -.090 -3.205 .001 .902 1.108
Amount Paid in
Season -.010 .001 -.548 -12.372 .000 .361 2.768
Mass sold (kg) -.001 .001 -.057 -1.872 .061 .766 1.305
a. Dependent Variable: Default
From Table 4.11, the four identified explanatory variable were:
i. Level of Debt – this was estimated by the amount put on the contract’s stop
order list for 2013/14 season. The variable was found to be statistically
significant since the significant value was less than 0.05
ii. Average Price per farmer – calculated by dividing the gross amount received by
the farmer’s gross mass sold. This was a proxy for the Quality of the crop. The
variable was found to be statistically significant since the significant value was
less than 0.05
iii. Amount Paid in Season – the stop deductions during the season as per TIMB
Stop Order Database. The variable was found to be statistically significant since
the significant value was less than 0.05
iv. Mass sold – this was an attempt to estimate the crop yield which could not be
easily calculated due to poorly captured hacterage data per grower. This is
probably the main reason why it was statistically insignificant as sig value > 0.05
As shown in Table 4.11, there were no problems of collinearity since all the values of
the VIF were between 2 and 10 (Hair et al. 2010).
The results also confirm the survey and Logistic Regression Model that the quality of
the crop is a significant factor in reducing the magnitude of farmer default. The B
coefficient of -3.616 shows that for a unit increase in the crop quality as measured by
50
the average price per kilogram of the sold crop, there is a corresponding 3.616 times
reduction in the amount of default. Furthermore, this suggests that the size of the loan
also plays a significant role in determining loan default. The other two significant
variables have lesser Beta values.
4.6 MEASURES TO MITIGATE LOAN DEFAULT
The research also sought to provide measures that could be used by the industry to
curb default incidences. This section suggests possible solutions as managerial
recommendations to tobacco industry stakeholders. Both farmers and the contractors
were requested to rate a number of suggested measures that would curb loan default.
The strategies were effectively targeting the contractors and the Government as
represented by its various departments. However, the questionnaire was purposely
designed in such a way that would sometimes ask the same question in different ways
under four categories which to a large were arbitrary.
Figure 4-12 Reponses to Default Mitigating Measures - Set A
Figure 4.12 shows the highest score was on the ‘on time incentives’, which had a mean
score of 4.21. Group lending had the lowest mean score with a value of 2.87. This
GroupLending
On timerepaymentincentives
Threat ofFarm
AssetsSeizure
Loanrepaymentrescheduli
ng
ThirdParty
GuaranteeSystem
Establishment of an
ActiveCentralCredit
Assessment Bureau
Threat ofBlackListing
Farmers 2.87 4.21 3.64 3.66 3.1 3.9 3.82
Contractors 3.14 3.86 3.85 3.5 2.93 3.86 3.71
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
Mean
Sco
res
51
implies that most the farmers were the negative side of this strategy. It is consistent with
the low scores given to affiliation to farmers’ association as a major determinant of
default as discussed earlier in this chapter. For all the suggested measures, contractors
were pessimistic about the listed strategies. The lowest of their score was however
given a 2.93, third party guarantee system. It seems both parties agree that threat
related measures yield less impact than the moral persuasion as is shown by low mean
scores on the measure of threatening to black list farmers. As a follow up to this
measure, contractors were asked to rate threat or moral persuasion in curbing default.
Table 4.12 shows that there was a general consensus on the use of persuasive
strategies in addressing farmers’ loan repayment issues. However, it was also noted
that contractors favoured the use of debt collectors in collecting debts. It can arguably
be concluded that the use of force is not completely out picture but rather left to be a
measure of last resort.
Table 4.12 Contractor's Response to Force and Persuasive Strategies
years been working
Duress/ Force: (through threats of
litigation and seizures of assets
Moral persuasion (the use of incentives and
continuous persuasive communication)
Confiscation of defaulters' properties
The use of debt collectors and the
action of law
Below 5years
3.78 3.78 3.78 4
over 5 years 1.8 4.6 2.4 4
Mean Scores
3.07 4.07 3.29 4
Interesting observations on Table 4.12 are the highly negative score of 1.8 given for
duress by the over 5 years category of contracting companies’ employees as well as the
same group’s very high positivity credited to Moral Persuasion.
52
Figure 4-13 Reponses to Default Mitigating Measures - Set B
Four of the measures were given mean scores of above 4.0 by the farmers. Figure 4.13
show that both farmers and contractors gave the highest positive score on Extension
and Training support services. Second to this measure was the improvement in
communication which was credited with 4.49 by the farmers and 4.57 by the contactors.
The fourth measure supported by the farmers received relatively lower relevance by the
contractor. There seemed to be a differing perspective on the risk sharing strategy as
farmers scored 4.06 while the contractors were relatively hesitant to support this
measure. Consistency in the way group lending was rated continued to be seen across
strategy sets. This is contrary to the suggestions by most researchers on this subject,
especially in low income dominated economies like Zimbabwe (Brehanu & Fufa 2008;
Paal & Wiseman 2011; Namuyaga 2009).
ClearContractual
Terms
ImprovedCommunicat
ion withFarmers
ExtensiveExtension
support andtraining
RiskSharing
AddressFarmers'
SocialIssues
GroupLending
Farmers 4.12 4.49 4.65 4.06 3.83 3.25
Contractors 4 4.57 4.86 3.93 3.93 3.43
0
1
2
3
4
5
6
Mean
Sco
res
53
Figure 4-14 Reponses to Default Mitigating Measures – Set C
Only one of the listed measures was scored below 4.0 by both the farmers and the
contractors as is shown in Figure 4.14. With farmers giving it a 3.84 and the contractors
scoring slightly higher at 3.93, Arbitration and Conciliation by the government was not
significantly supported by the both sets of respondents. The survey findings were that
provision of specific contract farming legislation and Incentives for contractors were
scored positively and with consensus between the farmers and the contractors. Perhaps
this is an indication of lack of proper legislation governing tobacco contract farming.
Perhaps the laws that are currently in place may not be as relevant as is required by the
current set up. This is also captures the why collateral was not mentioned in the survey,
because the current legal set is relatively not conducive to enforce such measures (ZTA
2014; TIMB 2011). It was also noted that farmers were consistent in highly rating
training and extension services.
Provision ofSpecificContract
Farming Laws
Arbitration andConciliation
Training andEducation of
Farmers
Incentives andSubsidies forContractors
AddressFarmer's
Social Issues
Farmers 4.11 3.84 4.66 4.13 4.13
Contractors 4.21 3.93 4.71 4.29 4
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
5
Mean
n S
co
res
54
Figure 4-15 Reponses to Default Mitigating Measures - Set D
Figure 4.15 shows that only the measure to involve farmers in the drafting of contracts
was rated below 4.0 by both farmers and contractors. In addition, both farmers and
contractors gave “flexibility and adequate input support”, the highest mean score.
Differing perspectives were however noted on offering above market prices. Farmers’
mean score was above the 4.0 threshold while the contractors were relatively negative
at 3.86. An analysis of the contractors’ responses as tabulated in Table 4.13 shows that
employees with more experience in the industry agreed to the measure while the below
5 years category was somewhat on the neutral position. This further analysis for offering
above market prices shows that farmers have the support of the more experienced
employees who are relatively aware of the feasibility and the resultant positive outcome
possibly through minimisation of loan default by side marketing (Mambondiani 2013;
TIMB 2011).
Offer abovemarket prices
Flexible andadequate
input support
FarmerInvolvement in
Drafting ofContracts
Supportfarmers'
Social Needs
Input PricingTransparency
Farmers 4.26 4.45 3.91 4.12 4.51
Contractors 3.86 4.29 3.14 4.07 4.36
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
5
Mean
Sco
res
55
Table 4.13 Contractors' response on Set D Measures
years been working
Offer above-market prices (fair prices)
Ensure flexibility
and adequate
input support
Farmers involvement in
drafting of contracts
Support farmers social needs (e.g input support for
food crops)
Transparency in the pricing
of inputs
Below 5years 3.78 4.22 2.56 3.89 4.22
over 5 years 4 4.4 4.2 4.4 4.6
Total 3.86 4.29 3.14 4.07 4.36
Another observation in Table 4.13 was that, the two employee classes were in disarray
on two measures namely farmers’ involvement in drafting contract and supporting
farmers’ social needs. On both incidences, the more experienced are in favour of the
idea while the less than five years groups is against the measures. It can be argued that
strategies of this nature can vary with the amount experience gained by the contractor
in the industry with an inclination towards farmer involvement and support as the
experience increases.
Inconsistent responses were noted on responses regarding the addressing of farmer’s
social issues. Contrary to set B in Figure 4.13, both contractors and farmers were on the
positive end when the same question was asked in set C and D as shown in Figure 4.14
and Figure 4.15. This inconsistency was critically analysed and it was noted that the
introduction of the state in the provision of these social services may have led to the
shift in scores from in Set C. Set B tends to imply an additional financial obligation on
the farmer as well as the contractor while set C has the government to cater. To this
end, it can be argued that this factor may be an issue to ponder on, as the sentiments
hereby shown support the idea of supplementing the socio economic welfare of the
farmer so that incidences of default through loan diversion are minimised.
The researcher was also interested in knowing the specific comments of the contracting
companies’ experiences towards loan default and the measures that they have so far
taken. This was sought in order to evaluate the strength of the contribution that the
study would give in as far as the mitigation of loan default was concerned. Table 4.14
summarises the responses given by the contractors as opened ended questions.
56
Table 4.14 Measures taken to reduce default
Strategy used by contractor N Percent
i. Intensify monitoring of growers during the selling season 2 12.50%
ii. Litigation through debt collectors 8 50.00%
iii. Asked for collateral 1 6.25%
iv. Stringent vaulting / Strict selection of farmers 2 12.50%
v. Encourage farmers to insure their tobacco 2 12.50%
vi. Persuasive communication and training in groups 2 12.50%
vii. Extensive agronomy services 1 6.25%
viii. Contract terms are clear and explained 1 6.25%
ix. Defaulters dropped out 1 6.25%
x. Requirements of surety for every loan beneficiary 1 6.25%
xi. Before contracting farmers TIMB and XDS to establish farmer indebtedness
1 6.25%
xii. Establish relationships with other contracting companies 1 6.25%
xiii. Select farmers with proper and enough curing space 1 6.25%
The findings in Table 4.14 conform to the earlier conclusion about force and persuasion.
Of the 16 respondents, 8 mentioned the use of debt collectors as means to recoup
unpaid funds. However, contractors tend to take this as a last resort and somewhat
trying to prevent defaulting rather deal with defaulters. This can be shown from the
dominance of pre-selling measures such as monitoring, training, strict selection criteria
and transparency in contractual terms. However, there was one respondent who
indicated the existence of a credit rating bureau in the industry. The TIMB’s role in
providing farmer’s credit worthiness was relatively limited and as such, establishing an
active credit bureau would be meaningful. Albeit, the survey found out that this was not
critical in causing default. Probably this is because of the existence of robust credit
policies as evidenced in the findings.
4.7 DISCUSSION OF FINDINGS
Through factor analysis, and the resultant logistic and regression analysis, it was found
out that factors relating to farmers’ skills and knowhow were significant predictors of
default in tobacco agro based credit schemes. The model showed that loan supervision
and technical support, crop quality, market prices and farming experience were the
factors that cause loan default in the tobacco industry. This was also found to hold by
Akpan et. al (2014). Issues to do with systems failure were also found to be in the
57
significant component. It was however noted that this variable may have been
construed to relate to failing farming systems and technologies. The researcher initially
wanted to confirm allegations in TIMB (2011) that default was due to inconsistent TIMB
stop order system, to which the researcher realised that it could be better evaluated by
the contractors rather than the farmers. Therefore this variable requires further
validation, but as a component of the skills and knowhow factors, taking the adopted
interpretation, systems failure was found to a relevant determinant of loan default.
Active loan supervision was found to reduce loan default as was the case in studies by
Nawai and Shariff (2012). Farming experience and affiliation to farmer association were
also part of the significant factor. It is also important to note that the two variables did
not fit well in the significant factor and as such should arguably get a relatively less
consideration in determining potential defaults.
4.8 CHAPTER SUMMARY
In this chapter, research findings were discussed based on the research objectives. All
the research questions were addressed using the findings from the research surveys
conducted. Major determinants of default in the tobacco industry were discussed based
on the survey conducted across the tobacco sector. A consolidation of 16 determinants
of loan default was done and three factors were identified through the use of Factor
Analysis. A logistic regression model was derived and factors relating to farming skills
and knowhow were statistically significant as determinants of loan default. A
complementary multiple regression model was also tested and confirmed that crop yield
was amongst the major determinants of loan default.
58
CHAPTER 5
SUMMARY, CONCLUSIONS AND RECOMMENDATIONS
5 INTRODUCTION
This is the final chapter of the study in which an overview of the study was outlined,
research conclusions discussed while each research objective is addressed
accordingly. Recommendations will also follow to mark the end of the research report.
These recommendations are limited to the findings emanating from this research only.
As a salutation, a list of areas for further research is provided.
5.1 RESEARCH CONCLUSIONS
The following research hypotheses were statistically based on the logistic regression
model thus the null hypotheses were accepted:
H0X1: Level of farming experience affects level of loan repayment default
H0X13: Loan Supervision and technical back up affects level of loan repayment default
H0X14: Market Prices affect level of loan repayment default
H0X15: Crop Quality affects level of loan repayment default
H0X16: Systems Failure affects level of loan repayment default
H0X7: Agro ecological Differences affects level of loan repayment default
H0X6: Time spent by farmer on farm activities
H0X3: Affiliation to farmers association
However the was no statistical evidence to validate the following null hypotheses, which
were consequently rejected:
H0X2: Level of Education affects level of loan repayment default
H0X4: Off farm sources of income
H0X5: Level of indebtedness
H0X8: Loan Duration
H0X9: Social Contagion
H0X10: Means of farm ownership
H0X11: Average loan interest
H0X12: Poor Credit Appraisal
59
5.1.1 Research Objective Number 1
To identify the major determinants of loan default in Zimbabwe’s tobacco
industry.
The following factors were found to be the major determinants of loan default in
Zimbabwe’s Tobacco industry:
i. Quality of the crop
ii. Crop Yields
iii. Market Prices
iv. Loan supervision and technical back up
v. Time spent by farmer on farming activities
vi. Farming Experience
vii. Affiliation to farmer association
viii. Systems Failure
ix. Agro ecological differences
5.1.2 Research Objective 2
To develop a model upon which contracting companies can appraise the
creditworthiness of potential beneficiaries before accepting loan application.
The following Logistic Regression Model was found to be handy in predicting whether or
not a farmer would default:
Y = - (0.833 Factor 1+ 1.851 Fast Growing Region) + ε
Where Y is dichotomous and takes the values of 0 when there is no payment and 1
when the loan is repaid and ε is a random error term
ü Factor 1 addresses variables that are linked to ensuring that the farmer has the
requisite skills and tobacco farming know how. Most importantly, the variables
depict the ability of the farmer to apply such skill and knowhow.
ü Fast Growing Region was found to be a proxy for Agro ecological differences for
which measures should be taken to artificially adjust farming regions to resemble
those found in the fast growing region so as to get the best quality and yields.
60
Fast tobacco growing region is mainly in the Mashonaland West Province of
Zimbabwe.
Both variables have negative beta coefficients implying an inverse relationship with
default.
5.2 RECOMMENDATONS
5.2.1 Policy Recommendations
i. Based on the response for training and extension services, contracting
companies and government should invest more in the training and education of
farmers. The research found out that there are already existing measures put in
place to ensure that farmers are trained regularly by the contractors and
government extension workers. On this note, the research recommends that
training be focused on ensuring that farmers are particularly empowered with the
skills and not only the knowledge to do tobacco. Acquisition of hands on skills
combined with the knowledge of why some of the critical operations are done
would improve the farmers’ ability to get better quality and yields. To this end,
farmer field schools with certification based on the output instead the
conventional class room training characterised by theory and less practicals,
after which certificates are issued based on passing exams.
ii. Some of the challenges being faced in the contracting farming were seen to be
emanating from lack of proper legal framework. The study recommended that
there be put in place tailor made laws that are expected to support both farmers
and contractors operationalise noble ideas such the sharing of risk, improved
pricing mechanisms, credit guarantee systems, the use of collateral and probably
group lending in the long run.
5.2.2 Managerial Recommendations
i. To address the issue of quality of the crop, increased technical back up should
be given to the farmers especially at critical stages such as at harvesting and
curing, where the crop’s quality is most affected.
61
ii. The study also recommends that contracting firms should consider investing in
proper farm infrastructural development to alleviate possible challenges being
faced by farmers due to Agro ecological differences across the farming regions.
iii. Group lending was not an option in the short to medium term until the viable
culture of professionalism and entrepreneurship is dominant in the farming
community. Farmers were seen to be resentful when asked about group lending
despite the remarkable benefits that are expected from this arrangement.
iv. The use of threats and force related measures should be avoided at all cost.
Contractors should aim at creating long term relationships with their farmers
through improved and constant communication. The use of persuasive strategies
such as the provision of incentives for on time loan repayments was also
recommended. Such communication should provide the requisite transparency in
the way loaned inputs are being priced. This also goes a long way in gaining the
trust of the farmer as well as build long lasting business relationships.
v. Contracting firms should be flexible in providing inputs to farmers so as to
respond and adjust to possible additions such as labour cash flows and
additional fertilisers say in the event of heavy leaching. Inputs such as fertilisers
are unlikely to be fixed at a certain quantity, since there exist significant agro
ecological differences across the different farming districts.
vi. To reduce loan default through input diversion, contracting firms and government
should cooperate to ensure that tobacco farmer’s social issues are well
addressed. At this point, it was noted that both the farmers and the contractors
were not willing to carry the whole cost burden probably due to viability concerns.
This is the reason why the study recommends the involvement of government
either directly providing the service to the farmers or indirectly through provision
of subsidies and incentives to contracting firms.
5.3 CONTRIBUTION OF THE STUDY
This study has contributed to the existing body of literature especially in Zimbabwe by
suggesting another paradigm upon which stakeholders in the industry can focus as they
endeavour to safeguard the sustainability of the industry through contract farming.
Previous researches would recommend establishment of training programme without
62
specifying the focus of such programmes. The contribution from this study was that
training should emphasise hands on training with a focus to empowering the tobacco
farmer with the skill to things right as opposed to mere knowledge about the processes.
In addition, quality and yields are a result of the requisite skill rather than the
knowledge. This contribution was anchored by arguably the first Logistic Regression
Model developed from this study to assist contracting firms predict default.
5.4 AREAS FOR FURTHER RESEARCH
i. Is Group lending viable for Tobacco Contract Farming in Zimbabwe? What are
the necessary and sufficient prerequisites for its sustainability?
ii. An analysis of the reliability of Agriculture related survey researches. The case of
Zimbabwe’s Tobacco Industry.
a. What would be the best measure of reliability scores?
b. Is Cronbach’s alpha value of 0.6 the best cut off point?
iii. Determinants of Loan Default in the tobacco industry, using a Multiple Linear
Regression Analysis using secondary data from the TIMB.
iv. Are prices being offered by the tobacco industry viable for today’s tobacco
farmers? An investigation into the profitability of tobacco production against rising
cost of production.
63
6 REFERENCE
Abwino, E.N. & Rieks, H., 2006. Out-grower System through contract farming - Zambia, Epopa.
Addisu, M., 2006. Micro-finance Repayment Problems in the Informal Sector in Addis Ababa. Ethopian Journal of Business & Development, 1(2), pp.29–50.
Agarwal, V., 2001. Negotiation and Drafting Clauses in Loan Agreements : Events of Default. In UNITAR Workshop on Negotiation Techniques for Senior Officials from Eastern and Southern Africa. Geneva: UNITAR, pp. 1–19.
Akerlof, G.A., 1970. The Market for “ Lemons ”: Quality Uncertainty and the Market Mechanism. The Quaterly Journal Of Economics, 84(3), pp.488–500.
Akpan, U.A., Udoh, E.J. & Akpan, S.B., 2014. Analysis of loan default among agricultural credit guarantee scheme ( ACGS ) loan beneficiaries in Akwa Ibom State , Nigeria. , 2(2), pp.121–128.
Awunyo-Vitor, D., 2012. Determinants of loan repayment default among farmers in Ghana. Journal of Development and Agricultural Economics, 4(November), pp.339–345. Available at: http://www.academicjournals.org/JDAE.
Bartlett, J.E., Kotrlik, J.W. & Higgins, C.C., 2001. Organizational Research : Determining Appropriate Sample Size in Survey Research. InformationTechnology, Learning and Performance Journal, 19(1), pp.43–50.
Baruch, Y., 1999. Response Rate in Academic Studies-A Comparative Analysis. Human Relations, 52(4), pp.421–438. Available at: http://hum.sagepub.com/cgi/doi/10.1177/001872679905200401 [Accessed July 21, 2014].
Baruch, Y. & Holtom, B.C., 2008. Survey response rate levels and trends in organizational research. Human Relations, 61(8), pp.1139–1160. Available at: http://hum.sagepub.com/cgi/doi/10.1177/0018726708094863 [Accessed July 9, 2014].
Bastos, R.R. & Garcia, J.P., 2010. A Trade Credit Explanation Based on the Agency Theory, Legal Factors and Credit Contagion. Universidad de Salamanca.
Besley, T.J. & Ghatak, M., 2014. Solving Agency Problems : Intrinsic Motivation, Incentives, and Productivity. Background paper for the 2014 World Development Report, London.
Bichanga, W.O. & Aseyo, L., 2013. Causes of Loan Default within Micro Finance Institutions in Kenya. , pp.316–335.
64
Brace, N., Snelgar, R. & Kemp, R., 2012. SPSS for Psychologists. In SPSS for Psychologists. New York: Plagrave Macmillan. Available at: http://erlanbakiev.weebly.com/uploads/1/0/8/3/10833829/multiple_regression_spss..pdf.
Brehanu, A. & Fufa, B., 2008. Repayment rate of loans from semi-formal financial institutions among small-scale farmers in Ethiopia: Two-limit Tobit analysis. The Journal of Socio-Economics, 37(6), pp.2221–2230. Available at: http://www.sciencedirect.com/science/article/pii/S1053535708000218 [Accessed April 6, 2014].
Burns, R. & Burns, R., 2008. Logistic Regression. In Business Research Methods and Statistics using SPSS. Sage Publications Ltd, pp. 568–575. Available at: http://www.uk.sagepub.com/burns/website material/Chapter 24 - Logistic regression.pdf.
Burrel, G. & Morgan, G., 2005. Sociological Paradigms and Organisational Analysis - Elements of the Sociology of Corporate Life, Burlington: Ashgate Publishing Limited.
Bwunyo-Wakuloba, R.A., 2008. Causes of Default in Government Micro Credit Programmes : A case of UASIN Gishu District Trade Development Joint Loan Board. Joint Japan / World Bank Graduate Scholarship Program.
Dawes, M.A., 2008. Contracting smallholder farmers – experiences from the field. In Support Capacity for Enhanced Market Access and Knowledge Management. Harare: SVN.
Derban, W., Binner, J. & Mullineux, A., 2005. Loan Repayment Performance in Community Development Finance Institutions in the UK. Small Business Economics, 25(4), pp.319–332. Available at: http://econpapers.repec.org/RePEc:kap:sbusec:v:25:y:2005:i:4:p:319-332 [Accessed July 20, 2014].
FAO, 2003. tobacco case studies.pdf - y4997e00.pdf, Available at: ftp://ftp.fao.org/docrep/fao/006/y4997e/y4997e00.pdf [Accessed June 9, 2014].
Fidrmuc, J. & Hainz, C., 2010. Default rates in the loan market for SMEs: Evidence from Slovakia. Economic Systems, 34(2), pp.133–147. Available at: http://www.sciencedirect.com/science/article/pii/S0939362509000739 [Accessed April 8, 2014].
Field, A.P., 2005. Chapter 15 - Reliability Analysis. In Discovering Statistics Using SPSS. London: Sage, pp. 1–7.
65
Field, E. & Pande, R., 2010. Repayment Frequency and Default in Micro-Finance : Evidence from India. Journal Of the European Economic Association, 6(2), pp.501–509.
Fletcher, T.D., 2007. Factor Analysis, Available at: https://www.princeton.edu/~otorres/Factor.pdf.
Foster, B.P. & Zurada, J., 2013. Loan defaults and hazard models for bankruptcy prediction. Managerial Auditing Journal, 28(6), pp.516–541. Available at: http://www.emeraldinsight.com/10.1108/02686901311329900 [Accessed April 1, 2014].
Gaisina, S., 2011. Credit policies for Kazakhstani agriculture. Central Asian Survey, 30(2), pp.257–274. Available at: http://www.tandfonline.com/doi/abs/10.1080/02634937.2011.565228 [Accessed May 5, 2014].
Gent, R. Van, 2005. Contract Farming, Available at: http://www.fao.org/fileadmin/user_upload/contract_farming/presentations/Contract_farming_van_Gent.pdf.
Government of Zimbabwe, 2013. Zimbabwe Agenda for Sustainable Socio-Economic Transformation ( Zim Asset ) “ Towards an Empowered Society and a Growing Economy ” OCTOBER 2013- DECEMBER 2018, Harare, Zimbabwe.
Guiso, L., Sapienza, P. & Zingales, L., 2013. The Determinants of Attitudes toward Strategic Default on Mortgages. The Journal of Finance, 68(4), pp.1473–1515. Available at: http://doi.wiley.com/10.1111/jofi.12044 [Accessed May 13, 2014].
Gujarati, D.N., 2004. Basic Econometrics Fourth., McGraw-Hill Companies. Available at: http://www.ncbi.nlm.nih.gov/pubmed/24908661.
Hair, J.F. et al., 2010. Multivariate Data Analysis Seventh Ed., New Jersey: Pearson Prentice Hall.
Hanyani-Mlambo, B.T., 2006. Agricultural Extension and Farmer Support Systems in Zimbabwe ’ s Resettlement Areas,
Janda, K., 2006. Lender and Borrower as Principal and Agent, Prague. Available at: http://ies.fsv.cuni.cz.
Jensen, M.C. & Meckling, W.H., 1976. Theory of the Firm : Managerial Behavior , Agency Costs and Ownership Structure. Journal of Financial Economics, 3(4), pp.305–360.
66
Johns, R., 2010. Survey Question Bank Methods Fact Sheet 1: Likert Items and Scales, Available at: http://surveynet.ac.uk/sqb/datacollection/likertfactsheet.pdf.
Jouault, A. & Featherstone, A.M., 2011. Determining the Probability of Default of Agricultural Loans in a French Bank. Journal of Applied Finance aand Banking, 1(1), pp.1–30.
Karlan, D. et al., 2009. Trust and social collateral. Quarterly Journal of Economics, 124, pp.1307–1361. Available at: http://qje.oxfordjournals.org/content/124/3/1307.short.
Khandker, S., 2003. The impact of farm credit in Pakistan. Agricultural Economics, 28(3), pp.197–213. Available at: http://www.sciencedirect.com/science/article/pii/S0169515003000173 [Accessed April 8, 2014].
Kohansal, M.R. & Mansoori, H., 2009. Factors Affecting on loan Repayment Performance of Farmers in Khorasan-Razavi Province of Iran. In Conference on International Research on Food Security, Natural ResourcesManagement and Rural Development. Trpentag: University of Hamburg. Available at: http://www.tropentag.de/2009/abstracts/full/264.pdf.
Kothari, C.R., 2004. Research Methodology - Methods and Techniques Second Rev., New Delhi: New Age International (P) Limited.
Liebeskind, D.S., 2003. Collateral circulation. Stroke; a journal of cerebral circulation, 34, pp.2279–2284.
Likulunga, M.L., 2005. The Status of Contract Farming and Contractual Arrangements in Zambian Agriculture and Agribusiness,
Magali, J.J., 2013. Factors Affecting Credit Default Risks For Rural Savings and Credits Cooperative Societies ( SACCOS ) in Tanzania. , 5(32).
Mambondiani, R., 2013. An investigation into the causes and impact of side-marketing in contract farming in the tobacco industry in Zimbabwe : Implications for contract farming business strategies (March 2008 - June 2013). University of Zimbabwe.
Melese, A.T., 2012. Contract Farming : Business Models that Maximise the Inclusion of and Benefits for Smallholder Farmers in the Value Chain. , (November 2011), pp.8–10.
Morck, R., 2009. Generalized Agency Problems, Cambridge. Available at: http://nber.org.papers/w15051.
67
Musara, J. et al., 2011. Determinats of Smallholder Cotton Contract Farming Participation in a Recovering Economy : Emperical REsults From Patchway District , Zimbabwe. Journal of Sustainable Development in Africa, 13(4), pp.1–12.
Namuyaga, J., 2009. Client Perception of Loan Default Management in Group Lending : A Comparative Study of FINCA(U) Ltd and MIDCSCS in Gombe Subcounty, Wakiso District. , (January).
Nawai, N., 2010. Determinants of Repayment Performance in Microcredit Programs : A Review of Literature. International Journal of Business and Social Science, 1(2), pp.152–161. Available at: http://ijbssnet.com/journals/Vol._1_No._2_November_2010/12.pdf.
Nawai, N. & Shariff, M.N.M., 2012. Factors Affecting Repayment Performance in Microfinance Programs in Malaysia. Procedia - Social and Behavioral Sciences, 62, pp.806–811. Available at: http://www.sciencedirect.com/science/article/pii/S187704281203577X [Accessed April 8, 2014].
Nawai, N. & Shariff, M.N.M., 2013. Loan Repayment Problems in Microfinance Programs that use Individual Lending Approach: A Qualitative Analysis. Journal of Transformative Entrepreneurship, 1(2), pp.93–99. Available at: http://cps.umk.edu.my/journals/download/(3)JTE.Vol.1.Issue2.Page 92-99 - JTE082013020.pdf [Accessed May 20, 2014].
Nguta, M.H. & Huka, G.S., 2013. Factors Influencing Loan Repayment Default in Micro-Finance Institutions : The Experience of Imenti North District , Kenya. International Journal For Applied Science and Technology, 3(3), pp.80–84.
Nwachukwu, J., 2013. Default in a government-sponsored agricultural loan programme in South-Eastern Nigeria. International Journal of Social Economics, 40(10), pp.898–922. Available at: http://www.emeraldinsight.com/10.1108/IJSE-2012-0105 [Accessed April 1, 2014].
Ojiako, I.A. & Ogbukwa, B.C., 2012. Economic analysis of loan repayment capacity of small-holder cooperative farmers in Yewa North Local Government Area of Ogun State, Nigeria. African Journal of Agricultural Reseearch, 7(13), pp.2051–2062. Available at: http://www.academicjournals.org/ajar/abstracts/abstracts/abstract2012/5 Apr/Ojiako and Ogbukwa.htm [Accessed April 22, 2014].
Osborne, T., 2006. Credit and risk in rural developing economies. Journal of Economic Dynamics and Control, 30(4), pp.541–568. Available at: http://www.sciencedirect.com/science/article/pii/S0165188905000424 [Accessed April 8, 2014].
68
Paal, B. & Wiseman, T., 2011. Group insurance and lending with endogenous social collateral. Journal of Development Economics, 94, pp.30–40.
Parlour, C.A. & Winton, A., 2013. Laying off credit risk: Loan sales versus credit default swaps. Journal of Financial Economics, 107(1), pp.25–45.
Roark, B. & Roark, R., 2006. DEFAULT. Concise Encyclopedia of Real Estate Business Terms. 2006.
Royal, K., 2011. Understanding Reliability in Higher Education Student Learning Outcomes Assessments. Quality Approaches in Higher Education, 2(2), pp.8–15. Available at: www.asq/edu/index.html.
Salami, A. et al., 2010. Smallholder Agriculture in East Africa : Trends , Constraints and Opportunities, Tunis, Tunisia.
Sanjeev, G.M., 1997. Bankers ’ Perceptions on Causes of Bad Loans in Banks.
Saunders, M., Lewis, P. & Thornhill, A., 2009. Research Methods for business students Fifth., Harlow: Pearson Education.
Schicks, J., 2013. The Definition and Causes of Microfinance Over-Indebtedness: A Customer Protection Point of View. Oxford Development Studies, 41(sup1), pp.S95–S116. Available at: http://www.tandfonline.com/doi/abs/10.1080/13600818.2013.778237 [Accessed April 2, 2014].
Shee, A. & Turvey, C.G., 2012. Collateral-free lending with risk-contingent credit for agricultural development: indemnifying loans against pulse crop price risk in India. Agricultural Economics, 43(5), pp.561–574. Available at: http://doi.wiley.com/10.1111/j.1574-0862.2012.00603.x [Accessed May 5, 2014].
Snieder, R. & Larner, K., 2009. The Art of Being a Scientist: A Guide for Graduate Students and their Mentors, , Cambridge : Cambridge University Press.
SNV, 2009. Inventory of Smallholder Contract Farming Practices in Zimbabwe, Harare.
SSC, 2001. Approaches to the Analysis of Survey Data, Reading UK. Available at: www.reading.ac.uk/ssc.
Sulkowski, L., 2010. Two Paradigms in Management Epistemology. Journal of Intercultural Management, 2(1), pp.109–119.
Tavakol, M. & Dennick, R., 2011. Making sense of Cronbach’s alpha. International Journal of Medical Education, 2, pp.53–55. Available at: http://www.ijme.net/archive/2/cronbachs-alpha/ [Accessed July 10, 2014].
69
Tavakoli, H., 2013. A Dictionary of Research Methodology and Statistics in Applied Linguistics, Rahnama Press. Available at: http://books.google.com/books?id=sVb9AAAAQBAJ&pgis=1 [Accessed July 26, 2014].
TIMB, 2013. Annual Statistical Report, Available at: www.timb.co.zw.
TIMB, 2014a. Growers’ Stop Order Database, Harare.
TIMB, 2011. Reviewing The Industry For Sustatainable Growth. In National Tobacco Workshop. Harare: Tobacco Industry and Marketing Board.
TIMB, 2014b. Weekly Tobacco Report - Bulletin 26 for Week Ending 27 June 2014, Harare.
Torres-reyna, O., Getting Started in Factor Analysis ( using Stata 10 ),
TRB, 2013. Tobacco Research Board Annual Report, Harare.
Vitoria, B., Mudimu, G. & Moyo, T., 2012. Status of Agricultural and Rural Finance in Zimbabwe,
White, B.T., 2011. Preventing Strategic Default : Lessons frm Japan. Arizona Journal of International and Comparative Law. Available at: http://www.ajicl.org/AJICL2012/documents/04_29_2_WhiteV2.pdf.
Williams, B., Brown, T. & Onsman, A., 2010. Exploratory factor analysis : A five-step guide for novices. Australasian Journal of Pramedicine, 8(3). Available at: http://ro.ecu.edu.au/cgi/viewcontent.cgi?article=1373&context=jephc.
Witzany, J., 2009. Basel II Capital Requirement Sensitivity to the Definition of Default. ICFAI Journal Of Financial Risk Management, pp.55–76.
Wongnaa, C.A. & Awunyo-vitor, D., 2013. Factors Affecting Loan Repayment Performance Among Yam Farmers in the Sene District , Ghana Key words. Agris on-line Papers in Economics and Informatics, 5(2), pp.111–122.
Woodend, J.J., 2003. POTENTIAL OF CONTRACT FARMING AS A MECHANISM FOR THE COMMERCIALISATION OF SMALLHOLDER AGRICULTURE THE ZIMBABWE CASE STUDY REPORT PREPARED FOR : , (September).
Wuensch, K.L., 2014. Binary Logistic Regression with SPSS. , pp.1–24. Available at: http://core.ecu.edu/psyc/wuenschk/MV/Multreg/Logistic-SPSS.PDF [Accessed June 25, 2014].
70
Yu, C.H., 2001. An introduction to computing and interpreting Cronbach Coefficient Alpha in SAS. Arizona State University.
Yusoff, M.S.B., 2011. Reliability & Validity of the Adult Learning Inventory among Medical Students. Education in Medicine Journal, 3(1). Available at: http://www.saifulbahri.com/ejournal/eimj/2011/vol3issue1/e22-e31.pdf [Accessed July 10, 2014].
ZTA, 2014. History of Tobacco - Zimbabwe Tobacco Association. Available at: http://www.fctobacco.com/index.php/about/history-of-tobacco [Accessed June 9, 2014].
71
7 APPENDICES
Appendix A: Tobacco Growers’ Questionnaire
University of Zimbabwe Graduate School of Management
Dear Tobacco Grower
RE: REQUEST TO PARTICIPATE IN A RESEARCH SURVEY
I am a final year Master of Business Administration student at the University of
Zimbabwe’s Graduate School of Management. As required by the program, I am
undertaking a research on the determinants of loan default in agro-based credit
schemes in the tobacco industry of Zimbabwe. I am hereby requesting you to
complete the attached questionnaire to your best ability and return it to me preferably
before 12 July 2014.
All the information that you shall provide in the survey will remain anonymous and shall
be treated with high confidentiality. The research is not conducted for any commercial
purposes but for academic use only. For any further details concerning this
questionnaire and the research, please do not hesitate to contact me on +263 772 843
200.
Yours faithfully
TafadzwaReggisDzingai
72
SECTION A: Background Information
Tick box applicable
1. Sex : Male Female
2. Age : ______________years
3. Marital Status: Single Married Widowed Divorced
4. Where is your farm? (Tick the most Appropriate)
Doma/ Chinhoyi Rusape / Nyazura / Odzi
Trelawney / Darwendale Karoi
Banket / Ayshire Tengwe
Headlands / Macheke Bromely
Mvurwi / Concession Centenary / Mt Darwin
Bindura / Shamva Matebeleland
Chegutu / Selous Masvingo
Beatrice / Norton Midlands
Marondera / Wedza
5. Means of Farm ownership
Title deeds
Offer letter
Communal / Village
Leasing
None of the above
6. How many hectares of Tobacco did you do this season (2013/14) __________
7. How many hectares do you have at your farm? (How big is your farm?)
__________________________
SECTION B: EVALUATION OF FACTORS INFLUENCING LOAN DEFAULT
1. How many years have you been into Tobacco Farming? ___________________
2. Highest Qualification achieved (tick most appropriate).
No Formal Education
Primary Education
Secondary Education
73
Tertiary up to Diploma
Above Diploma
3. Are you affiliated to any Farmers’ Association or Club? Yes No
4. Do you have another source of income besides farming? Yes No
5. If yes, please tick the most appropriate source(s) that you also have:
o Formal Full time Employment
o Formal Part-time Employment
o Informal Employment
o Other Income Generating Projects
6. Do you stay at your farm? Yes No
7. How would you rate your participation in your farm field activities?
Not involved
Partly Involved
Somewhat involved
Very Much involved
Full time
8. Did you manage to fully repay your contract loan this year? Yes No
9. What would you say were the major reasons of your success or failure to repay
your loan? (May tick more than one factor)
Level of farmer education
Farming Experience
Affiliation to farmer association
Off farm Income
Level of Debt
Time spend by farmer on farm activities
Crop yield
Quality of your tobacco
10. State any other factors if any …………………………………………………………...
11. How would you rate the time that you are given to repay your loan?
Very Short
Short
Fair and Enough
Long
Very long
74
12. What would you say are the major lender related factors causing loan
repayment default in Tobacco Contract Farming? (May tick more than one factor)
Loan Duration
Average Loan Interest
Inability by contractors to select good borrowers
Inadequate follow-up and technical backup
Systems failure
13. What would you say are the major extraneous factors causing loan repayment
default in Tobacco Contract Farming? (May tick more than one factor
Defaulters not being punished and peers are defaulting because there is no harm.
Farming Districts differences e.g. differences in weather and soils
Low Prices on the market
14. State any other factors if any …………………………………………………………...
15. Of the factors identified to what extent does each factors contribute to loan default?
Farmer Related Factors Not at all
Limited extent
Not Sure
Certain extent
Large extent
Level of farmer education
Farming Experience
Affiliation to farmer association
Off farm Income
Level of Debt
Time spend by farmer on farm activities
Crop yield
Poor Quality tobacco
Lender Related factors Not at all
Limited extent
Not Sure
Certain extent
Large extent
Loan Duration
Average Loan Interest
Inability by contractors to select good borrowers
75
Inadequate follow-up and technical backup
Systems failure
Extraneous Factors Not at all
Limited extent
Not Sure
Certain extent
Large extent
Defaulters not being punished and peers are defaulting because there is no harm.
Farming Districts differences e.g. differences in weather and soils
Low Prices on the market
SECTION C: MEASURES TO MITIGATE LOAN DEFAULT
16. To what extent would you rate the effectiveness of the following strategies as
means of reducing loan default in the tobacco industry?
Strategy Not at all
Limited extent
Not Sure
Certain extent
Large extent
Providing inputs to farmers in groups
Incentives for on time repayments
Threat of seizure of farm assets
Loan repayment rescheduling
Third Party guarantee system
Establishment of an active Central Credit Assessment Bureau
Threat of Black listing
76
17. Do you think the following contractual arrangements can help to reduce loan
default? (You may tick more than one box)
Strongly
Disagree Disagree
Not
Sure Agree
Strongly
Agree
Clear contractual terms
Improved communication with
farmers
Extensive extension support
and training services
Sharing of risks associated with
farming
Address farmers’ social issues
(food crops, fees etc.)
Providing inputs to farmers in
groups
77
18. Do you think the following government initiatives would protect contracting
companies from the dangers of farmers’ failure to repay their input loans?
Strongly
Disagree Disagree
Not
Sure Agree
Strongly
Agree
Provide specific legislation on
contract farming
Arbitration and conciliation of
disputes
Training and education of
farmers
Incentives and subsidies for
companies (e.g. tax breaks)
Address farmers’ social issues
(food crops, fees etc.)
78
19. Do you think the following measure would help contracting companies reduce
farmers’ loan repayment default?
THANK YOU VERY MUCH FOR YOURTIME.
END OF QUESTIONNAIRE
Strongly
Disagree Disagree
Not
Sure Agree
Strongly
Agree
Offer above-market prices (fair
prices
Ensure flexibility and adequate
input support
Farmer involvement in drafting
of contracts
Support farmers’ social needs
(e.g. input support for food
crops)
Transparency in the pricing of
inputs
79
Appendix B: Questionnaire for Tobacco Contracting Firms’ Employees
University of Zimbabwe
Graduate School of Management
Dear Sir or Madam
RE: REQUEST TO PARTICIPATE IN A RESEARCH SURVEY
I am a final year Master of Business Administration student at the University of
Zimbabwe’s Graduate School of Management. As required by the program, I am
undertaking a research on the determinants of loan default in agro-based credit
schemes in the tobacco industry of Zimbabwe. I am hereby requesting you to
complete the attached questionnaire to your best ability and return it to me preferably
before 18 July 2014.
All the information that you shall provide in the survey will remain anonymous and shall
be treated with high confidentiality. The research is not conducted for any commercial
purposes but for academic use only. For any further details concerning this
questionnaire and the research, please do not hesitate to contact me on +263 772 843
200.
Yours faithfully
Tafadzwa Reggis Dzingai
SECTION A: BACKGROUND INFORMATION
80
Tick box applicable
1. Sex : Male Female
2. How many years have you been working for your company? _____________
3. Has your company experienced any incidence of loan default?
4. Comment on the severity of farmers’ loan repayment default within your
organisation?
5. What strategies /efforts have you made to prevent farmers from defaulting?
______________________________________________________________________
______________________________________________________________________
______________________________________________________________________
______________________________________________________________________
___________________________________________________________________
SECTION B: EVALUATION OF CONTRACTOR’S CREDIT POLICY
6. Does your company possess a clear credit policy? Yes No
Q: To what extent do you consider the following aspects before giving credit to a
farmer? (Tick box most applicable)
Aspect Not
at all
Limited
Extent
Not
Sure
Certain
Extent
Large
Extent
7. Character (The customer’s willingness to meet the credit obligations)
8. Capacity (the customer’s ability to meet credit obligations out of operating cash flow)
9. Capital (the customer’s financial reserves)
10. Collateral (An asset pledged in case of default)
11. Conditions (General economic conditions in the tobacco industry)
Yes No
Not an issue
Very Limited extent
Sustainable levels
Very bad
Extremely Bad
81
SECTION C: EVALUATION OF FACTORS INFLUENCING LOAN DEFAULT
For each of the following statements, please tick the most applicable box.
Strongly
Disagree Disagree
Not
Sure Agree
Strongly
Agree
12. The company’s contract system
does not buy tobacco from non-
contracted farmers.
13. The company supports the farmer
100% with all the required inputs.
14. Inputs pricing is transparently
communicated to farmers.
15. Poor monitoring by the contractor
increases the default rate.
16. Most farmers default through side-
marketing.
17. Farmers who engage in side-
marketing are greedy.
18. Farmers engage in side-marketing
to avoid input loan repayment.
To what extent does each of the following factors contribute to loan default?
To what extent does each of the following factors contribute to loan default?
Farmer Related Factors Not at all
Limited extent
Not Sure
Certain extent
Large extent
19. Level of farmer education
20. Farming Experience
21. Affiliation to farmer association
22. Off farm Income
23. Level of Debt
24. Time spend by farmer on farm activities
25. Low Crop yield
26. Poor Quality tobacco
82
Lender Related factors Not at all
Limited extent
Not Sure
Certain extent
Large extent
27. Loan Duration
28. Average Loan Interest
29. Inability by contractors to select good
borrowers
30. Inadequate follow-up and technical backup
31. Systems failure
Extraneous Factors Not at all
Limited extent
Not Sure
Certain extent
Large extent
32. Defaulters not being punished and peers
default because there is no harm.
33. Farming Districts differences e.g.
differences in weather and soils
34. Low Prices on the market
35. In your own view what would you say are the major factors influencing loan
repayment default within your contracted growers?
______________________________________________________________________
______________________________________________________________________
______________________________________________________________________
______________________________________________________________________
______________________________________________________________________
______________________________________________________________________
______________________________________________________________________
______________________________________________________________________
______________________________________________________________________
______________________________________________________________________
SECTION D: MEASURES TO MITIGATE LOAN DEFAULT
83
To what extent would you rate the effectiveness of the following strategies as
means of reducing loan default in the tobacco industry?
Strategy Not at all
Limited extent
Not Sure
Certain extent
Large extent
36. Providing inputs to farmers in groups
37. Incentives for on time repayments
38. Threat of seizure of farm assets
39. Loan repayment rescheduling
40. Third Party guarantee system
41. Establishment of an active Central
Credit Assessment Bureau
42. Threat of Black listing
Do you think the following contractual arrangements can help to reduce loan
default?
Do you think the following government initiatives would protect contracting
companies from the dangers of farmers’ failure to repay their input loans?
Strongly
Disagree Disagree
Not
Sure Agree
Strongly
Agree
43. Clear contractual terms
44. Improved communication with farmers
45. Extensive extension support and training services
46. Sharing of risks associated with farming
47. Address farmers’ social issues (food crops, fees etc.)
48. Providing inputs to farmers in groups
Strongly
Disagree Disagree
Not
Sure Agree
Strongly
84
Do you think the following measures would help contracting companies reduce
farmers’ loan repayment default?
To what extent would you recommend the following strategies as means of
reducing default rate in the Tobacco Industry?
Not at Limited Not Certain Large
Agree
49. Provide specific legislation on contract farming
50. Arbitration and conciliation of disputes
51. Training and education of farmers
52. Incentives and subsidies for companies (e.g. tax breaks)
53. Address farmers’ social issues (food crops, fees etc.)
Strongly
Disagree Disagree
Not
Sure Agree
Strongly
Agree
54. Offer above-market prices (fair prices
55. Ensure flexibility and adequate input support
56. Farmer involvement in drafting of contracts
57. Support farmers’ social needs (e.g. input support for food crops)
58. Transparency in the pricing of inputs
85
All Extent Sure Extent Extent
59. Duress / Force: ( through threats of
litigation and seizure of Assets)
60. Moral Persuasion ( The use of
incentives and continuous persuasive
communication)
61. Confiscation of defaulters’ properties
62. The use Debt collectors and the action
of law.
63. State any additional comments on the determinants of loan default in
Zimbabwe’s Tobacco
Industry.___________________________________________________________
___________________________________________________________________
___________________________________________________________________
___________________________________________________________________
___________________________________________________________________
________
64. State any other strategies that you would recommend to reduce default.
___________________________________________________________________
___________________________________________________________________
___________________________________________________________________
___________________________________________________________________
___________________________________________________________________
___________________________________________________________________
THANK YOU VERY MUCH FOR YOUR TIME.
END OF QUESTIONNAIRE
86
Appendix C1 : Correlation Matrices
Lev
el
of
Fa
rme
r
Ed
uca
tio
n
Fa
rmin
g E
xp
eri
en
ce
Aff
ilia
tio
n t
o F
arm
er
Ass
oci
ati
on
Off
Fa
rm I
nco
me
Lev
el
of
de
bt
Tim
e S
pe
nt
on
Fa
rm
Act
ivit
ies
Cro
p Y
ield
Po
or
Qu
ali
ty T
ob
acc
o
Loa
n D
ura
tio
n
Av
era
ge
Lo
an
In
tere
st
Po
or
Bo
rro
we
rs'
Ap
pra
isa
l
Loa
n S
up
erv
isio
n a
nd
tech
nic
al
ba
ck u
p
Sy
ste
ms
fail
ure
So
cia
l C
on
tag
ion
Ag
ro e
colo
gic
al
Dif
fere
nce
s
Low
Ma
rke
t P
rice
s
Level of
Farmer
Education
1 0.209 0.234 -
0.054 0.097 0.143
-
0.015 0.021 0.209 0.229 0.417 0.006 0.094 0.02 0.13 0.015
Farming
Experience 0.209 1 0.285 0.26
-
0.021 0.154 0.224 0.145 0.06
-
0.078 0.004 0.3 0.185 0.125 0.095 0.076
Affiliation to
Farmer
Association
0.234 0.285 1 0.262 0.119 -
0.083 0.043 0.134
-
0.083 0.133 0.091 0.162 0.201
-
0.004 0.165 0.018
Off Farm
Income
-
0.054 0.26 0.262 1
-
0.132 0.163 0.221 0.062 0.068
-
0.225
-
0.044
-
0.059 -0.01 0.233
-
0.099
-
0.045
Level of debt 0.097 -
0.021 0.119
-
0.132 1 0.031
-
0.049 0.063 0.039 0.429 0.195 0.021 0.069
-
0.107 0.117
-
0.037
Time Spent on
Farm
Activities
0.143 0.154 -
0.083 0.163 0.031 1 0.208 0.195 0.044
-
0.169
-
0.133 0.166 0.109 0.004 0.065 0.15
Crop Yield -
0.015 0.224 0.043 0.221
-
0.049 0.208 1 0.586
-
0.179
-
0.195
-
0.021 0.195 0.106
-
0.038 -0.03 0.367
Poor Quality
Tobacco 0.021 0.145 0.134 0.062 0.063 0.195 0.586 1
-
0.214
-
0.094 0.081 0.262 0.092 0.003 0.126 0.317
Loan
repayment 0.209 0.06
-
0.083 0.068 0.039 0.044
-
0.179
-
0.214 1
-
0.008 0.108
-
0.132
-
0.133 0.131
-
0.063
-
0.133
Average Loan
Interest 0.229
-
0.078 0.133
-
0.225 0.429
-
0.169
-
0.195
-
0.094
-
0.008 1 0.11 0.029 0.094
-
0.086 0.209
-
0.083
Poor
Borrowers'
Appraisal
0.417 0.004 0.091 -
0.044 0.195
-
0.133
-
0.021 0.081 0.108 0.11 1 0.051 0.104 0.279 0.154
-
0.012
Loan
Supervision
and technical
back up
0.006 0.3 0.162 -
0.059 0.021 0.166 0.195 0.262
-
0.132 0.029 0.051 1 0.343 0.204 0.207 0.34
Systems
failure 0.094 0.185 0.201 -0.01 0.069 0.109 0.106 0.092
-
0.133 0.094 0.104 0.343 1 0.167 0.365 0.323
Social
Contagion 0.02 0.125
-
0.004 0.233
-
0.107 0.004
-
0.038 0.003 0.131
-
0.086 0.279 0.204 0.167 1 0.026 0.043
Agro
ecological
Differences
0.13 0.095 0.165 -
0.099 0.117 0.065 -0.03 0.126
-
0.063 0.209 0.154 0.207 0.365 0.026 1 0.104
Low Market
Prices 0.015 0.076 0.018
-
0.045
-
0.037 0.15 0.367 0.317
-
0.133
-
0.083
-
0.012 0.34 0.323 0.043 0.104 1
87
Appendix C 2: Correlation Matrix for Logistic Regression
C
on
sta
nt
FA
cto
r1
FA
Cto
r2
FA
Cto
r3
FA
RM
(1)
FA
RM
(2)
He
cte
rag
e
EX
PE
RIE
NC
E
No
Fo
rma
l
Ed
uca
tio
n
Pri
ma
ry
Ed
uca
tio
n
Se
con
da
ry
Ed
uca
tio
n
Te
rtia
ry u
p t
o
Dip
lom
a
Ab
ov
e
Dip
lom
a
Re
sid
en
t(1
)
Constant 1 -0.108 -0.003 0.061 -0.572 -0.422 -0.129 -0.07 0 -0.345 -0.409 -0.531 0.015 -0.195
FActor1 -0.108 1 -0.126 0.075 0.21 0.067 -0.152 0.037 0 0.013 0.233 0.347 -0.079 -0.35
FACtor2 -0.003 -0.126 1 0.038 -0.121 -0.12 -0.061 -0.228 0 0.057 0.025 -0.061 -0.029 0.276
FACtor3 0.061 0.075 0.038 1 -0.155 0.063 -0.05 -0.069 0 0.07 -0.031 0.066 0.119 -0.01
Fast Growing
Region -0.572 0.21 -0.121 -0.155 1 0.461 -0.013 -0.188 0 0.193 0.231 0.348 -0.395 0.006
Medium
Growing
Region
-0.422 0.067 -0.12 0.063 0.461 1 0.065 -0.119 0 0.109 -0.068 0.056 -0.086 0.128
Hecterage -0.129 -0.152 -0.061 -0.05 -0.013 0.065 1 -0.117 0 0.247 0.221 0.018 0.044 -0.207
EXPERIENCE -0.07 0.037 -0.228 -0.069 -0.188 -0.119 -0.117 1 0 -0.214 -0.108 -0.064 0.166 -0.18
No Formal
Education 0 0 0 0 0 0 0 0 1 0 0 0 0 0
Primary
Education -0.345 0.013 0.057 0.07 0.193 0.109 0.247 -0.214 0 1 0.636 0.481 -0.071 -0.392
Secondary
Education -0.409 0.233 0.025 -0.031 0.231 -0.068 0.221 -0.108 0 0.636 1 0.64 -0.088 -0.556
Tertiary up to
Diploma -0.531 0.347 -0.061 0.066 0.348 0.056 0.018 -0.064 0 0.481 0.64 1 -0.225 -0.287
Above
Diploma 0.015 -0.079 -0.029 0.119 -0.395 -0.086 0.044 0.166 0 -0.071 -0.088 -0.225 1 -0.098
Resident
Farmer -0.195 -0.35 0.276 -0.01 0.006 0.128 -0.207 -0.18 0 -0.392 -0.556 -0.287 -0.098 1
88
Appendix D: Extract from Secondary Data from TIMB
Coded Grower's Number
Stop Order Amount ($)
Amount Paid in Season ($)
Mass sold (kg)
Gross Value of Mass sold ($)
Average Price ($)
Default (%)
108 2648.6 15.29 2668 9117.33 3.41729 99.42271
142 1324.3 0.03 611 2037.4 3.334534 99.99773
202 1324.3 31.19 3922 11434.98 2.915599 97.64479
208 617.3 15.04 755 2300.4 3.046887 97.56358
306 18900 11902.79 4516 13702.22 3.03415 37.02228
322 1324.3 15.2 3557 10828.03 3.044147 98.85222
485 2264.17 1435.73 1816 6549.9 3.606773 36.58913
628 388.3 155.49 720 2683.01 3.726403 59.95622
682 1324.3 31.12 2110 7362.56 3.489365 97.65008
871 3972.9 2483.5 2738 7605.27 2.777673 37.48899
972 388.3 15.05 701 1208.1 1.723395 96.12413
1092 577.35 26.24 54 118.8 2.2 95.4551
1131 1324.3 534.71 172 528.8 3.074419 59.6232
1170 388.3 247.79 252 415 1.646825 36.18594
1184 2648.6 116.91 86 116.1 1.35 95.58597
1215 1324.3 31.11 829 2513.6 3.032087 97.65083
1286 2648.6 1637.84 928 1632.4 1.759052 38.16205
1349 436.39 276.24 218 703.8 3.22844 36.69882
1419 1324.3 825.7 382 824.8 2.159162 37.65008
1451 1128.07 720.9 2410 6568.6 2.72556 36.09439
1933 718.47 437.9 350 1104.7 3.156286 39.05104
1961 34068 854.89 589 2009.35 3.41146 97.49064
2115 1324.3 540.17 215 531.65 2.472791 59.2109
2154 199.39 124.91 103 123.8 1.201942 37.35393
2276 388.3 15.09 743 1718.1 2.312382 96.11383
2384 2648.6 1686.43 752 1654.85 2.200598 36.32749
2427 617.3 394.13 1955 6772.73 3.464312 36.1526
2508 617.3 15.15 888 3340.15 3.76143 97.54576
2672 617.3 378.1 277 375.5 1.355596 38.74939
2698 199.39 0.52 232 825.15 3.556681 99.7392
2811 199.39 127.29 4875 15376.21 3.154094 36.16029
2918 436.39 272.49 271 913.81 3.371993 37.55815
2977 436.39 274.24 844 2037.4 2.413981 37.15713
3156 1324.3 807.28 378 806.3 2.133069 39.041
89
Appendix E : Additional SPSS output for Factor Analysis
Total Variance Explained
Component
Initial Eigenvalues Extraction Sums of Squared
Loadings
Rotation Sums of Squared
Loadings
Total % of
Variance
Cumulative
% Total
% of
Variance
Cumulative
% Total
% of
Variance
Cumulative
%
1 2.723 17.020 17.020 2.723 17.020 17.020 2.568 16.050 16.050
2 2.132 13.324 30.344 2.132 13.324 30.344 2.067 12.917 28.967
3 1.668 10.424 40.768 1.668 10.424 40.768 1.888 11.801 40.768
4 1.297 8.104 48.872
5 1.223 7.642 56.514
6 1.107 6.920 63.434
7 .910 5.688 69.121
8 .835 5.219 74.341
9 .738 4.615 78.956
10 .698 4.366 83.321
11 .592 3.698 87.019
12 .583 3.643 90.662
13 .472 2.952 93.614
14 .429 2.682 96.296
15 .315 1.969 98.264
16 .278 1.736 100.000
Extraction Method: Principal Component Analysis.
Component Matrix(a)
Component Transformation Matrix
Component 1 2 3
1 .906 .123 .404
2 -.267 .908 .322
3 -.327 -.400 .856
Extraction Method: Principal Component Analysis.
Rotation Method: Varimax with Kaiser Normalization.
90
Appendix F: Evaluation of Contractors’ Credit Policy
years been working
The customer's willingness to meet credit obligations out of operating
cash flow
The customer's ability to meet
credit obligations out of operating
cash flow
The customer’s
financial reserves
An asset pledged in
case of default
General economic
conditions in the tobacco
industry
Below 5years 4.67 4.67 3.67 3.33 4.44
over 5 years 4.2 5 4.6 3.4 3.4
Total 4.5 4.79 4 3.36 4.07
