Constraint’s Analysis of Agricultural Credit Use ...

Constraint’s Analysis of Agricultural Credit Use: Implications for Poverty Reduction in Pakistan

By Waqar Akram Ph.D Scholar

A Dissertation

Submitted to the Department of Economics

In partial fulfillment of requirement for the degree of doctor of philosophy in Economics

2008

“In The Name of Allah Almighty and Most Merciful”

I thank Allah for His mercy, love, kindness

and the ray of light that shines in the darkness of

all ignorance to give me hope, trust, belief

and confidence to move on.

DEDICATION

I dedicate this dissertation to my kind, grand parents and my parents especially

my mother who encouraged me, my lovely brothers and sisters, whose

encouragement never let me quit and give up during the hardship.

ACKNOWLEDGEMENT If oceans turns into ink and all wood become pens, even then, the praise of

ALMIGHTY Allah cannot be expressed set my unfeigned and meek thanks

before him, who created the universe and bestowed the mankind with knowledge

and wisdom to search for its secrets, favored and invigorated me with the

fortitude and capability to apply complete my research work, and contribute a

drop to the existing ocean of scientific knowledge.

Trembling lips and wet eyes for prophet Hazrat Mohammad (SAW), who is

forever a torch of guidance for the entire humanity.

I would like to thank my professor Dr. Zakir Hussain for always providing me

help, support and patronage during my research work. I would also like to thank

him for his thoughtfulness, help and kindness during the hard times. I am highly

obliged and indebted to Dr. Sohail J. Malik for his kindness, willing cooperation,

support, and assistance throughout this research project. I thank Dr. Maqbool

Sial for his continuous encouragement, guidance and friendship. I also thank Dr.

Surayia Zakir for his moral support throughout my study program. I am grateful to

Khurram Shahzad Qureshi in the write-up of my dissertation. Thanks are also

due to Dr. Masood Sarwar Awan and Ms. Saima Ayaz those helped me to solve

the econometrics and data problems.

I am also extremely grateful to Dr. Ijaz Hussain who supported and encouraged

me during this whole period of study. My special thanks are also to my beloved

Uncle Zahid Iqbal and Puppho providing me the funding, whenever I needed.

Cordial wish and thanks are tendered to thank my classmates especially Samia

Muzaffar, Fozia Shaukat, Hadia Asghar, Niala Iram, Nisar Ahmad, Shabbir

Ahmad Gondal, and many of those who always prayed for my success. I am

highly thankful to Rizwan Ahmad who dedicatedly served me during the write up

of thesis till late night. I am thankful to my students who helped me a lot in data

collection. I am also thankful to my mother institute (UAF) friends; Dr.Khuda

Buksh, Waqas Chattha, Ali Imran Raja, Saeed Saleem, Rana Kashif, Rans Asif

and Zahid Sharif who always encouraged me to do Ph.D.

Special thanks to my lovely brothers; Shahzad Akram, Waqas Akram, and thanks

to Khurram Shahzad Qureshi for his great contribution in all computer work.

Waqar Akram

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TABLE OF CONTENTS Abstract .................................................................................................................... 1 Chapter 1 .................................................................................................................. 2 Introduction .............................................................................................................. 2 Chapter 2 .................................................................................................................. 9 Review of Literature ................................................................................................. 9

Supply Side of Credit .............................................................................................................. 13 Credit Markets in Developing Countries ............................................................................... 15 Methodological Issues ............................................................................................................ 20 Segmentation, Interest rate and the Collateral .................................................................... 25 Technical Efficiency ................................................................................................................ 27

Chapter 3 ................................................................................................................ 30 Rural Credit Perspective ....................................................................................... 30

Rural Credit Institutions ......................................................................................................... 31 The Formal Credit Market ....................................................................................................... 31 Informal Credit Market ............................................................................................................ 34 Produce Index Unit (PIU) ........................................................................................................ 35 Credit by Farm Size ................................................................................................................. 36 Divergence between Official Statistics and Survey Data .................................................... 38

Formal Credit by Purpose of Loan (Agricultural/Non-agricultural), Interest rates and Repayment ............................................................................................. 42

Purpose of Loan ...................................................................................................................... 42 Interest Rate Studies............................................................................................................... 43

Historical Trends .................................................................................................... 45 Trends in Household Borrowing Patterns Based on Available Surveys ........................... 45 Trends in Growth of Credit based on Secondary Data ....................................................... 50 Credit Requirements and Household Borrowing Behavior ................................................ 52 Term of Loans .......................................................................................................................... 56 Current Government Policies and Programs ....................................................................... 58 Political Economy of Credit Issues in Pakistan ................................................................... 61 Interest Rate and Transaction Costs of Formal Lending Institutions ............................... 62 Interaction with other Rural Factor Markets ......................................................................... 64

Chapter 4 ................................................................................................................ 66 Efficiency of Credit and Non-Credit Users .......................................................... 66 (Frontier Production Approach) ........................................................................... 66

Analytical Framework ............................................................................................................. 67 Allocative Efficiency of Credit users and Non-Credit Users............................................... 73 Derivation of Marginal Value Products and Opportunity Costs ......................................... 74 Technical Efficiency of Credit/Non Users ............................................................................. 76

Chapter 5 ................................................................................................................ 80 Credit Constraints and Borrowing Behavior ....................................................... 80

Methodology ............................................................................................................................ 82 Determination of Credit Constraints ..................................................................................... 87 Household Total Consumption Expenditures ...................................................................... 88 Perception and attitude to institutional credit ..................................................................... 88 Constraints from formal institutions ..................................................................................... 89 Collateral used by the respondents for the agricultural loan ............................................. 92 Purpose of the loan ................................................................................................................. 94 Formal institutions for crediting ............................................................................................ 96 Interest rate and the response of the farmers ...................................................................... 96 Time Lag in disbursement of loan ....................................................................................... 100

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Distance of the Bank ............................................................................................................. 100 Demand for Borrowing and Interest Rate Function ........................................................... 103 Rationale of selected variables ............................................................................................ 103 Determinants of Credit Constraints .................................................................................... 109 The credit constraint and household consumption Expenditure .................................... 112

Conclusions and Recommendation ................................................................... 114 Recommendations ................................................................................................................ 116

References ........................................................................................................... 118 Annexures ............................................................................................................ 128

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LIST OF TABLES Table 4.1: Marginal Value Product of Selected Inputs for Credit Users, Sargodha,

Punjab, 2007 ...................................................................................................... 80

Table 4. 2: Marginal Value Product of Selected Inputs for Non-Credit Users,

Sargodha, Punjab, 2007 ................................................................................... 80

Table 4. 3: Maximum Likelihood Estimates of the Cobb Douglas ...................... 82

Table 4. 4: Maximum Likelihood Estimates of the Cobb Douglas 82

Table 5. 1: Constraints for not applying for loan from formal institutions ............. 91

Table 5. 2: Collateral for loan .............................................................................. 93

Table 5. 3: Purpose of loan taken (Percent) ....................................................... 95

Table 5. 4: Institutional Sources .......................................................................... 98

Table 5. 5: Interest rate by the respondents ....................................................... 99

Table 5. 6: Time lagged between loan application and approval ...................... 102

Table 5. 7: Distance of the Bank ....................................................................... 102

Table 5. 8: Heckman selectivity model relating Interest rate (Dummy) with

independent variables in Sargodha Region ...................................................... 104

Table 5. 9: Heckman selectivity model relation interest rate with independent

variables in Sargodha Region ........................................................................... 106

Table 5. 10: Heckman selectivity model log of borrowing relating with

Independent variables, Sargodha Region ......................................................... 107

Table 5.11: Results of Logit Estimation (First Stage): Probability that Household

was Constrained, in Sargodha Region .............................................................. 111

Table 5. 12: Results of Heckman 2-Stage Procedure with Total Amount

borrowed, in Sargodha Region ......................................................................... 111

Table 5. 13: Results of Regression with total Consumption Expenditure, in

Sargodha Region .............................................................................................. 112

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LIST OF FIGURES Figure 1: Supply of Agriculture credit by institutions ........................................... 33

Figure 2: Agriculture Credit Advanced by ZTBL by Size of Holding .................... 36

Figure 3: Percentage Distribution of Agriculture Credit by Size of Holding by

Commercial Banks .............................................................................................. 37

Figure 4: Distribution of Agriculture credit by Commercial Banks between Farm

and Non-farm sector ........................................................................................... 38

Figure 5: Growth of Agricultural Credit and Production of Tractors in Pakistan . 51

Figure 6: Disbursement of Agricultural Credit per Hectare of Cropped Area ...... 53

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Abstract

The study analyzed the constraints faced by the farmers to rural credit by utilizing two household level data sets. The first survey Pakistan Rural Household Survey (PRHS) 2001 was utilized to study the purpose, source structure and utilization of rural credit and; the second which covered nearly 160 households from Sargodha District 2007 was used to calculate the demand and interest rate function by applying Heckman two stage procedures. The focus of this study was to find out the affect of credit constraints of institutional credit on consumption and production pattern of the rural farm households. After measuring the probability of being constrained used to study affect on consumption pattern of farmers who were credit constraint. The frontier production function was used to study the affect of credit constrained and un-constrained farmers. The analysis revealed that agricultural production loan was found as 45.8 percent. ZTBL was providing most of the loan to the farmers for their agricultural needs. The interest was ranging between 10 to 20 percent in all agro-climatic regions. The logit model was applied to determine the nominal interest rate and borrowing function of the farmers. The results showed that the transitory income, predicted interest rate, and farm size were significant. Credit constraints were determined by using Heckman’s two stage procedure. The results showed that the coefficient of education of male household was significant showing that education function as a facilitator to enter into credit market. The farmers faced many constraints namely: lower literacy rate, small and fragmented holdings, uneven access to agricultural extension and information and in ability to obtain adequate irrigation water, less access to agriculture credit institutions, and inequitable distribution of land and water. The results of the frontier production revealed that credit users and non credit users were allocatively inefficient, especially irrigation water. The mean technical efficiency of credit users was 90 and that of non-credit users was 79 percent, respectively. The high technical efficiency of credit users was attributed to better market access to the farmers to new technology through the availability of agricultural credit. The low level of technical efficiency of non-credit users as compared to credit users implied that potential for improvement exists. The high technical efficiency of credit users was safely attributed to credit availability through which farmers have an access to new technology. With respect to policy implication, the study suggested that development and dissemination of low cost and site-specific production technologies for the farmers. In this regard formation of Credit Assessment Bureaus for the risk assessment of the borrowers as it done in urban areas. Better dissemination of information and technology for improved decision making regarding use of credit.

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Chapter 1

Introduction

Agriculture credit played a pivotal role in the adoption of improves technologies in

the farming sector. The credit used as working capital to purchase the material

inputs as well as for consumption. Farmers need finances immediately after the

period of harvesting for the next cropping season. Modern agriculture is

comprised of high-yielding seeds, fertilizers, and pesticides most of which have

to be purchased against cash-coupled with the increased monetization of the

rural economy, more and more farm households turned towards dependence on

credit markets. Credit market used to provide cash reserves so as to smooth the

progress of production and consumption in the next cycle. Efficient credit market

provided an opportunity to the farmers meet the consumption requirements and

balanced input use which resulted in betterment of the farmers. (Feder, et. al.,

1990).

The development process of agriculture sector can be triggered through the easy

availability and access to credit. It provided ability to the farmers and

entrepreneurs to diversify agriculture sector by undertaking new investment or

adopt new technology. Rural credit market is comprised of formal and informal

sector, which were playing a significant and active role in rural economy (Adams

and Fitchett, 1992; Aleem, 1990). Though the informal is sector is charging high

interest rate but still its contribution is higher than the formal sector. This was

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mainly due some attractive features including collateral free lending, timely

delivery, and flexibility and loan transaction.

“Credit constraints of the farm households can be defined in four categories;

voluntary non-borrowers, non-rationed borrowers, rationed borrowers, and

involuntary non-borrowers. The first category is comprised of those who declined

to borrow at will either because they have strong risk aversion and fear of getting

into debt or because they were cautious and only would like to consume up to

what they earn. Second one, who wants to borrow less than their combined

available credit lines from all lenders. The third and fourth categories are those

who want to borrow more than their available credit limit at a particular point in

time and with no access to credit, or those who perceived that they were highly

unlikely to get credit respectively. So, that the perceived borrowing costs

outweighed the expected benefits of the loan”. (Zeller, et. al., 1997)

The rural credit programmes/schemes across the countries showed dismal

performance because poor administration and implementation as well as very

high social opportunity cost of the program funds. The only exception in this case

was Grameen Bank in Bangladesh, which showed positive economic outcome.

High covariate risk of agricultural production (Binswanger and Rosenzweig,

1986), the asymmetric information and lack of enforcement of loan contracts

(Hoff and Stiglitz, 1990) government imprudent interference in credit markets,

and rent seeking as a result of credit rationing were some of the factors alleged

for the poor performance of the public-directed credit schemes in many countries

and Pakistan is no exception.

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Pakistan is basically an agricultural country and at present agriculture accounts

for 22 percent of its GDP (GOP, 2006). Coupled with agro-based products, it

fetches 75 percent of the national merchandised exports earnings. Agriculture is,

therefore, a most important sector and backbone of the economy. With an

unabated annual population growth rate of 1.8 percent, the country's population

has reached 160 millions (GOP, 2006). To provide sufficient food, fiber and fuel

wood for the rapidly increasing population and raw materials for growing industry

posed a major challenge.

More than half of the population of Pakistan lives in rural areas, and agriculture is

a key contributor to employment, income generation, exports and Gross

Domestic Product (GDP). In spite of importance of agriculture, 70-80 percent of

the country's poor are rural inhabitants. As demonstrated by previous studies on

rural credit in Pakistan, one of the major factors hindering the adoption of modern

technologies and enhancement of productivity and development in the rural

sector was lack of credit or capital constraints faced by farmers in achieving their

full potential in production and marketing of farm products (Rana and Young,

1988; Taylor, et.al., 1986). “The lack of resource constraints was not only the

potential to realize productivity-enhancing opportunities but also the ability to

smooth consumption. Access to well functioning credit markets is, therefore,

crucial for sustainable rural poverty alleviation” (Malik, 1999).

The institutional agriculture credit was positively impacting the agriculture

productivity in Pakistan. The other variables like cropping intensity, labor, and

irrigation water availability were shown positive impact. Whereas floods,

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droughts, and cotton leaf curl virus (CLCV) were shown negative impact on

agriculture production (Iqbal et.al, 2003).

Prior to 1972, the main sources of institutional credit in Pakistan were the Zarai

Tarqaiti Bank (ZTBL; defunct Agricultural Development Bank of Pakistan (ADBP),

Credit Cooperatives and Taccavi loans. These institutions did not extend

significant formal credit in rural areas. The 1985 rural credit survey indicated that

89 percent of all rural borrowings were provided by non-institutional sources

(Qureshi, et.al., 1996). However, as the size of an average informal loan was

smaller than that of a formal loan, informal credit accounted for 68 percent of the

total volumes of loans outstanding (von Braun, et.al. 1993). The non-institutional

sources were generally comprised of market middlemen, input suppliers, friends

and relatives.

Operating under a maintained hypothesis of capital market inefficiency and

realizing that the informal sources of credit in the rural sector do not have the

capacity or willingness to supply loans to needy farmers at reasonable interest

rates, the government of Pakistan have initiated and implemented various

lending schemes since 1972 to increase the flow of credit to rural sector.

Following the enactment of these programs, institutional credit grew at 27.5

percent from Rs. 121 million in 1971-72 to 15.8 billion in 1986-87 (Scott and

David, 1988). Agricultural loans amounting to Rs. 104.8 billion were disbursed

during (July-March, 2006-07) as against Rs.91.2 billion during the corresponding

period last year, thereby registering an increase of 15 percent. The share of Zarai

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Tarqaiti Bank Limited (ZTBL) in supply of total agricultural credit by institutions

increased and was 32.9 percent during (July–March, 2006-07) while it was 31.8

percent during the same period last year. However, the share of Commercial

Banks has surpassed the share of ZTBL; it was 46.8 percent of the total

agricultural credit disbursed during July–March 2006-07. While the share of The

Punjab Provincial Cooperative Bank Limited (PPCBL) has also slightly increased

as it stood at 5 percent in supply of total agricultural credit by institutions. The

share of domestic private bank has increased; it was 15.3 percent as compared

to 12 percent in the corresponding period of last year (GOP, 2007).

Production efficiency of the credit user was found high but the resources were

utilized inefficiently. This was done by calculating the shadow price of capital

which indicated that interest rate have not done much to improve the access of

credit to rural poor. It implied that policies and procedures geared toward

reducing the cost of credit would help to improve the access to credit without

artificially lowering the interest rate (Sial and Carter, 1996).

There were conflicting views or opinions regarding the sufficiency, fairness and

the economic impact of the credit on the recipients. The main concerns related to

economic viability of the publicly funded rural credit institutions were huge

subsidy costs and high rates of defaults associated with these loans. The true

empirical estimation of the impact of credit was difficult due to the fungible nature

of credit and because it was not clear if the estimated credit effect reflects the

borrowing constraints or the intrinsic (unobservable) characteristics of a borrower

(David and Mayer, 1980).

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The present study developed an econometric framework to sort out the impact of

credit from other factors effecting agricultural growth, farm income and poverty.

The study also evaluated the extent of rural credit and determines the factor

affecting rural poor access to credit in Pakistan.

The principal beneficiaries of the findings of this research were the financially

constrained rural poor of Pakistan. The findings of the study will help agricultural

policy makers to devise better-targeted policies and programs for rural sector

agricultural lending. In addition to this, analysis and findings of this study create

awareness of the severity of the problem among the academia, international

donors and credit institutions.

This study is divided into six chapters. The second chapter is review of literature

about rural credit discussing different aspects of rural credit, while third chapter

presents historical rural credit situation in Pakistan. The fourth chapter

differentiates between the production efficiency credit user and non credit users.

The fifth chapter devoted to purpose, source, utilization, and pattern of rural

credit in Pakistan. The fifth chapter also focuses on the issue of credit constraint

and its impact on consumption pattern. The last chapter contains conclusions

and recommendations.

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Objectives

1. Evaluate and analyze the state of institutional credit in Pakistan and

provide historical perspective on the evolution of various credit

programs/policies and trends ;

2. Compare the technical efficiency of credit constrained and unconstrained

farmers in Punjab.

3. Identify credit constraints and estimate interest rate and borrowing

functions of respondents

4. Recommend measures for improvement and efficiency of credit policies

geared toward development and alleviation of poverty in the rural sector of

Pakistan.

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Chapter 2

Review of Literature

All the credit programmes in the developing countries’ governments and donors

during the past 40 years were designed with the aim to improve rural households’

access to credit. Among all these programmes, especially the” agricultural

development banks” that provided the credit with the objective to provide working

capital to rural farm households and for the sustainability of credit institutions at

subsidized interest rates. Unfortunately, these credit programmes were failed

both to achieve their defined objectives in the credit market. (Adams, et.al., 1984;

Braverman and Guasch, 1986; Adams and Vogel, 1985).

The other source of credit in the developing countries was informal credit market

for inter-temporal transfer of resources of the rural farm households. Poor farm

households relied heavily on the informal credit market to solve the problem of

capital shortage. In this regard they adopted complex strategies to increase their

productive capacity, share risk, and smooth consumption over their life cycles.

To meet these strategies households adopted different ways like; self-enforcing

informal contracts among friends, neighbors, and members of the extended

family, and are arranged within networks of informal institutions of diverse

natures (Fafchamps, 1992; Coate and Ravallion, 1993; Lund and Fafchamps,

1997).

Access to the credit and participation of the farm households in the credit market

are the main areas of concern and most of the credit market literature made

distinction between them. “Access to the credit is, if the farm household able to

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borrow from a particular source, whereas participation in the credit market is, if it

actually borrows from that source of credit. This implied that there are two types

of constraints impacting the credit market internal and external. The internal

factor is related to the decision made by the farm household to participate in the

credit or not, while access to credit can be a constraint externally imposed on the

farm households. Thus, there are two factors mainly related to participation of

the household in the credit market expected rate of return of the loan and/or risk

consideration in the presence of credit availability” (Diagne and Zeller, 2001).

The household non-participation behavior still benefiting him by providing him

knowledge and information about the risk and increases its ability to bear risk.

So the access to the household provides the opportunity to experiment with

riskier, but potentially high-yielding technology (Eswaran and Kotwal, 1990). The

need to accumulate the assets (precautionary savings), yielding poor or negative

returns negatively related to ability to borrow of the household (Deaton, 1991).

“A farm household can be credit constrained in two ways; when it would like to

borrow more than lenders allow or if its preferred demand for credit exceeds the

amount lenders are willing to supply” (Duca and Rosenthal, 1993).

“On the other hand, described credit constraints in two terms-redlining and credit

rationing. Redlining refers to excluding certain observationally distinct groups

from credit markets, rather than offering them a contract that require higher

interest payments and collateral guarantee. Credit rationing refers to a situation

in which, among observationally identical borrowers, some get loans and others

are denied” (Stiglitz and Weiss, 1992).

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The household welfare outcomes can be affected by access to credit by at least

two ways. First, capital constraints on farm households can be reduced. Planting

and the growth periods of the crops are the two main periods of capital shortage

because the expenditures on agricultural inputs are high, while returns are

received only after the harvest several months later. Therefore, to cope with

finance to purchase the inputs, the farm household adopted two strategies; either

to liquidify his savings or to obtain credit. Hence, the ability of poor household

with no or little savings to acquire needed agricultural inputs significantly affected

by access to credit. Access to credit also reduced the opportunity costs of

capital-intensive assets relative to family labor, thus encouraging labor-saving

technologies and raising labor productivity, a crucial factor for development.

(Delgado, 1995; Zeller, et al. 1997).

The second way, through which access to credit affects household welfare, was

by increasing its risk-bearing ability and altering its risk-coping strategy. Just

the knowledge that credit will be available to cushion consumption against an

income shortfall should a potentially profitable, but risky, investments turn out

badly induces the household to bear the additional risk. The household may

therefore be willing to adopt new, more risky technologies (Eswaran and

Kotwal, 1990).

The researchers and policymakers supported the hypothesis that the government

and NGO-supported credit programs may crowd out the financial services offered

by these informal financial institutions. So, it is essential to understand how the

non-market informal institutions are serving households’ demand for financial

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services and how they are interacting with the formal credit institutions set up by

governments and NGOs. It is required to identify the policies and institutional

structure to provide the financial service as complement not as substitute for the

services offered by the informal credit. All the policy relevant information must be

quantify and analyze the determinants of households’ access to non-formal and

formal credit markets as well as the severity of their credit constraints.

Nguyen, 2007 empirically analyzed the Vietnam rural credit market during the

period from 1993 to 1998. The study distinguished between the formal and

informal sector of credit market and their interaction with each other. It also

highlighted the characteristics of borrowers; and its impacts on credit

participation and credit amount obtained. Heckman (1979) two stage

econometric model was used to view the probability of participation in credit

markets and credit amount received as a joint determination of the function of

household’s demand for credit and the function of lender’s decision on supply.

The education, health condition, fixed assets holding and distance from

household to formal bank branch were found the most important factors affecting

household’s credit activities. In addition, there was evidence of uniform

accessibility to formal sector across communes although level of credit rationing

was found as significant. Univariate probit model and bivariate probit model with

partial absorbability was employed to estimate the probability of demand and

supply of formal credit market showed a low participation probability. The high

demand for formal credit in 1993 was turned out as high level of credit rationing

due to limitation of formal credit access. The improved formal access in 1998

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however was anticipated by the reduction of formal demand. Given this result,

the role of formal credit in supporting rural development may be limited. This was

also suggested by other researchers (Bell, et. al., 1997 and Kochar, 1997).

Zeller, et. al., 1997 defined credit constraints farm households in four categories;

voluntary non-borrowers, non-rationed borrowers, rationed borrowers, and

involuntary non-borrowers. The first category is comprised of those who declined

to borrow at will either because they have strong risk aversion and fear of getting

into debt or because they were cautious and only would like to consume up to

what they earn. Second one, who wants to borrow less than their combined

available credit lines from all lenders. The third and fourth categories are those

who want to borrow more than their available credit limit at a particular point in

time and with no access to credit, or those who perceived that they were highly

unlikely to get credit respectively. So, that the perceived borrowing costs

outweighed the expected benefits of the loan.

Supply Side of Credit The quantity of credit, transaction costs and risks were identified as relevant

factors in the existing supply side credit market literature (Feder, 1985; Foltz,

2004). Binding supply constraint were related limitations imposed by lenders’

side on the first hand. Secondly, as lenders may pass on all the costs like;

transaction costs associated with screening, monitoring, and enforcing loan

contracts to borrowers, as in the case of group lending schemes. Finally, even for

households with access to credit, risk may reduce loan demand and hence

productivity (Besley and Coate, 1995). In the same perspective Boucher, et. al.,

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2005 analyzed that in the presence of moral hazard lenders required borrowers

to bear some contractual risk, and if this risk was sufficiently large, farmers

preferred not to borrow even though the loan would raise their productivity and

expected income. Lenders assess creditworthiness of their clients based on

observable characteristics (Bigsten, et. al., 2003), and extended loans at certain

interest rate. This implied that interest rate is the main factor by which borrowers

became credit-constrained. The borrower asked for mare credit than the lender

supplied at specific interest rate. In this case, the borrower exhausted the

available supply and then looked for alternative source. However, the fact that

this borrower exhausted its supply from one source, at specific interest rate,

made it a risky borrower for another lender.

1It is important to note, however, that credit constrain (of which rationing is a

special case) would arise even if there were no government intervention in credit

markets. There are three stylized facts about credit markets which make it quite

likely that even unregulated credit markets can easily be constrained in the

following ways i) Borrowers differ in the likelihood that they will default and it is

costly to determine the extent of that risk for each borrower. This is

conventionally known as the screening problem; ii) It is costly to ensure that

borrowers take those actions which make repayment most likely. This is the

incentives problem; and iii) It is difficult to compel repayment. This is the

enforcement problem. The upshot is that “interest rates may not equilibrate credit

supply and demand, there may be credit rationing, and in period of bad harvest,

lending may be unavailable at any price (Hoff and Stigliz, 1990) 1 Malik, S. J. (1999). Poverty and Rural Credit.

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Credit Markets in Developing Countries The inefficiency of the credit markets in developing countries is caused by market

imperfections such as interest rate ceilings imposed by governments, monopoly

power often exercised by informal lenders (Bell, et. al., 1997), high transaction

costs incurred by borrowers in loan acquisition, and moral hazard problems

(Carter, 1988; Carter and Weibe, 1990). Stiglitz and Weiss, (1981) argued that

information is a vital component in defining the lenders’ risk of transaction and

the benefits of the borrowers related to project. In the presence of asymmetry of

information in credit market the first-best credit allocation is not possible, and this

leads to the need for partial or full collateral. Then, inadequate collateral or lack

of it implied that some individuals will be denied credit, being otherwise identical

to those who have the collateral and obtain the credits. In this connection,

Banerjee, 2001 suggested that income or wealth level of borrowers has a direct

relationship with the amount of available credit and an inverse relationship with

cost of credit. This implied that high-income individuals borrowed large amounts

at low costs whereas low-income ones were able to borrow a small amount at

high cost.

Moreover, institutional lending sources or the formal lending sources may not be

allowed legally to charge above certain limits on loans, although informal lenders

in practice may do so. This was noted by Emana et. al. (2005) in Ethiopia, if

there is no interest rate allowed for the lender to charge at which the expected

return is positive, and then there will be credit rationing. Even if allowed to do so,

lenders may be affected by adverse selection and/or incentive problems so that

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the expected return on a loan may not monotonically increase with interest rate.

That is, lenders may try to avoid selection and incentive problems by rationing

credit.

The above discussion revealed that heterogeneity in resource allocation is mainly

caused by market failure and imperfections. Credit constraints were negatively

affecting productivity and welfare of the farm households. On the other hand,

improving the access to credit by different means brought positive outcome in

both production and welfare aspects.

Provision of rural finance also hindered by inadequate regulation and supervision

of financial intermediaries, limited lobbying power among the rural poor, weak

governance, corruption, and other political factors. (Yaron, et. al., 1997). The

finance frontier still can be move outward in the rural areas. This opportunity can

be availed, however, because of the high demand for financial services, the high

level of social capital and collateral substitutes can be used as proxies for

marketable physical collateral, and the informal mechanisms used to enforce

contracts (Von, 2003).

There were many studies which are supporting the hypothesis the access to

credit increased the productivity of the farm households and also support that

credit constraints could affect resource allocations, risk behavior and technology

choice and adoption in production, which may lead to lower output and

consumption of the constrained household. For example, higher income and

consumption were caused by better access to credit in Bangladesh (Diagne and

Zeller, 2001) and in higher farm profitability in Cote d’Voire (Adesina and Djato,

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1996), Malawi (Hazarika and Alwang, 2003) and in Tunisia (Foltz, 2004). In

examining sources of efficiency differentials among basmati rice producers in the

Punjab province of Pakistan, Ali and Flinn, (1989) found significant effect of

farmers’ access to credit. Parikh, et. al., (1995) also found that farmers with

greater loan uptake were less cost inefficient than those with smaller loan size.

Another study in Pakistan by, reported (Khandker and Faruqee, 2003) that formal

credit has positive impact on household welfare outcomes. It was also found that

formal credit increased rural income and productivity and that overall benefits

exceeded costs of the formal credit system by about 13 percent in India

(Binswanger and Khandker, 1995). Significant difference in productivities of

credit-constrained and unconstrained households was observed in China (Feder,

et. al., 1989, 1990). In Bangladesh, Pitt and Khandker (1996) examined the

impact of credit from the Grameen Bank and other two targeted credit programs

and found significant effects on household welfare, including education, labour

supply and asset holding. Freeman, et. al., 1998 found that the marginal

contribution of credit to milk productivity was different among credit-constrained

and non-constrained farmers in East Africa.

Guirkinger and Boucher (2005 & 2007) found that productivity of credit-

constrained households depended on their endowments of productive assets and

the credit they obtained from informal lenders. Similarly, 2Holden and Bekele

(2004) observed that households with access to credit compensated for

increasing risk of drought by reallocating their production in such a way that crop

2 Cited by Komicha and Öhlmer (2008) Influence of credit constraints on production efficiency: The case of farm households in Southeastern Ethiopia

- 18 -

sales were lower in good years to reduce the need to buy the crops in bad years,

and they argued that the households would be less able to do so without access

to credit.

A growing empirical literature analyzes the impacts of credit constraints both on

long term investments such as fixed farm assets (Carter and Olinto, 2003) and

short term profitability (Feder et. al., 1990; Folz, 2004). The present study added

to this literature by quantifying the impact of credit constraints on farm

productivity and consumption pattern of the farmers.

The studies on rural financial market imperfection and their implications for

poverty reduction mainly focused the inability of the poor to smooth his

consumption due to the inefficiency of financial markets. This inefficiency compel

the households to engage in potentially costly portfolio diversification such as:

investing in buffer stocks (Rozenzweig and Wolpin, 1993) and (Chaudhry and

Paxson, 2001), non-farm labor force participation (Rose, 1999) and migration

(Mesnard, 2000). Resultantly, they hesitate to adopt or unwilling to invest in

productivity increasing portfolio and indulge in poverty.

This led us to explore another relationship between rural poverty and resource

degradation. Poverty shortens households’ decision-making horizons leading, for

example, to the extension of the agricultural frontier to create income sources

(UNU/INRA, 1998). This view, however, defines the poverty and resource

problems too broadly since it does not take the multifaceted nature of both sides

of the question into account. Vosti and Reardon (1997) defined the conditioning

variables of the poverty environment relationship, such as the set of investment

- 19 -

technologies, economic, institutional and natural environments and emphasize

the importance of deep understanding of these site specific characteristics in

doing research in this area.

There is another issue which linked financial markets to resource degradation.

Alix (2002) studied and which demonstrated the specific ways in Peru; how the

incomplete credit markets distort the decision making procedure in agricultural

investment (Zwane, 2002). She concluded that there was no trade-off between

policies to decrease poverty and deforestation and indeed there may exist

policies, which will promote both of these goals via strengthening financial

markets.

Sial and Carter (1996) studied the financial market inefficiency in Pakistan using

primary data, found that credit users producing more than non credit user but

were using the resources inefficiently. This study also calculated the shadow

price of capital which indicated that interest rate have not done much to improve

the access of the rural poor’s to credit. The study suggested that policies and

procedures geared toward reducing the cost of credit would help to improve the

access to credit without artificially lowering the interest rate.

Burgess and Pande (2005) examined whether the bank branch expansion

program affected state output and poverty outcomes. The estimates of bank

expansion programme on state output can be biased because the banks were

lying face down to open more branches poor states as compared to richer states.

This problem was addressed by exploiting the fact that between 1977 and 1990

more bank branches were opened in financially less developed states. The

- 20 -

opposite was true outside this period. This change in the trend relationship

between a state’s financial development and branch openings enabled them to

isolate the policy-driven part of branch expansion and to use that to examine how

this expansion affected the Indian economy. From the analysis it was revealed

that branch expansion was associated with an increase in the shares of rural

credit and savings. In keeping with earlier studies, they also found that the

branch expansion increased non-agricultural, but not agricultural, output.

Similarly, Burgess, et.al. (2005) used household data from the National Sample

Survey to show that the simultaneous enforcement of directed bank lending

requirements was associated with increased bank borrowing among the poor, in

particular low caste and tribal groups.

Methodological Issues Diagne, et. al. (2001) reviewed the two main methodologies that were used up to

now to measure household access to credit and credit constraints and exposed

their shortcomings. This was done by testing violation of the life-cycle/permanent

income hypothesis by credit constrained households. Empirical evidence was

found inconclusive by using this methodology regarding presence or absence of

credit constraint. This implied that life cycle/permanent income hypothesis was

neither a sufficient nor necessary condition for being credit constrained. The

other method to measure the access and detection of credit constraint was

determined by collecting household-level credit market information directly from

household surveys, whether households were credit constrained. The reduced-

form regression equation was applied to determine the determinants of the

- 21 -

likelihood of a household being credit constrained and the effects of this

likelihood on various household outcomes. Despite representing a substantial

improvement in comparison to the first method, it is still incapable of providing

the framework that allows one to quantify the extent to which households are

credit constrained or to satisfactorily assess the impact of access to credit on

household welfare outcomes.

The credit market imperfection not only found in developing countries but also

found in developed countries. There are many studies which highlighted the

issue of imperfection in the developed world, where credit market imperfection is

considered to be significantly lower (Lee and Chambers, 1986; Tauer and Kaiser,

1988; Färe et. al., 1990; Jappelli, 1990; Blancard et. al., 2006). In this

perspective, Blancard, et al. (2006) studied short and long-run credit constraints

in French agriculture, where 67 percent of 178 sample farms were financially

constrained in the short-run and nearly all farms face investment constraints in

the long run, found that financially unconstrained farms are larger in size and

better in economic performance than financially constrained small farmers,

resulting in a difference of about 8.34 percent in profit. However, the nature and

extent of credit constraints in developed countries were significantly different

from those in developing countries, where the imperfection was also prevalent in

other factor markets.

Chloupkova and Bjørnskov (2003) revealed that “in the agriculture sector the

access to credit is necessary to invest in production structure and capacity. This

will provide the opportunity to access international market and reap the benefits

- 22 -

by competing at international market. Access to credit is the real problem among

the countries in Central European and East African, as well as a whole range of

other countries. The main reason for limited institutional lending to the farmers in

these due to the fact that farmers’ assets are not accepted as sufficient collateral,

because markets for such assets are thin. In addition, enforcing collateral rights

in developing countries is often impossible. Most of the Central Europe countries

providing the credit at subsidized interest rate relaxing collateral policy rather

than critically examining the issues and provide concrete measure. It was also

found that in the developing countries subsidized interest rate for agriculture

credit was expensive as well as socially distorting. The study concluded that the

problem can be alleviated by tapping into existing social structures, as for

example by relying on joint liability to supplement the traditional collateral. This

can be done by encouraging the provision of traditional micro-finance in an East

African context. This social capital will also create additional social and economic

benefits external to the original investment decisions”.

In the sustainable rural and social development perspective improved financial

services helped to improve the efficiency and profitability of the farmers who do

not leave rural area and marginal farms to seek off- farm employment

opportunities.

Formal and informal credit issues

Barslund and Tarp (2007) studied that “interaction between formal and informal

rural credit in Vietnam. The two data sets were basically used Vietnam

Household Living Standard Survey (VHLSS) 2002 and 2003. In Vietnam the

- 23 -

frontier of formal and informal credit market is expanding whole the share of

formal market is increasing. The overall interest rate has been falling suggesting

that market integration is in progress. In contrast, both the descriptive statistics

and the formal analysis in this study demonstrate that households actually

demand loans for other purposes, such as consumption smoothening and health

expenditures. Such loans are often obtained in response to temporary shocks

(i.e. having a person hospitalized) and thus work as a consumption smoothing

device. The formal loans were mainly provided for production and asset

accumulation. Because of the limited formal lending for consumption

smoothening, households direct this demand for credit at private money lenders.

On the other hand if, the formal sector entered the market for non-production

loans (on financially sustainable terms) this would provide borrowers with an

alternative to private money lenders. This could well be welfare increasing,

especially for marginalized low-income households. These type of policies brings

positive outcome in the social sector with high returns”.

In the analysis land was a statistically significant determinant of overall credit

demand. This implied that land (with a red book) is widely used as collateral and

plays a fundamental role in the operation of the credit market. The analysis also

revealed that households with a bad credit history are more likely to get rationed.

Ibrahim, et. al. (2007) revealed that “credit markets were slightly segmented and

that the informal sector was not only the major source of loans in rural areas in

Ethiopia as shown by Krishnan and Sciubba, 2004, but also dominates the urban

areas. It was found that access to the credit can be increased by removing the

- 24 -

barriers by restructuring the banking sector via reduced bureaucracy and

transaction costs. In addition, access to credit can be enhanced by strengthening

the linkages between formal and informal financial sectors. Micro-finance

institutions have been shown to be reaching vulnerable/relatively poorer groups

such as women”.

Zeller (1994) analyzed that “determinants of loan rationing by informal lenders

and by members of community-based groups that obtain credit from formal

lenders. The results showed that formal groups obtain and use information about

the creditworthiness of the credit applicant in a similar way as informal lenders

do. Land did not play significant role for both informal lenders and members of

the groups. The informant of the loan applicant was obtained about the wealth,

indebtedness, and income potential. It was revealed that community-based

groups have an information advantage over distant formal bank agents. Like

informal lenders, the group members have access to information that is only

available to insiders of the borrower's community. The leverage ratio was found

significant determinant of loan rationing is less regressive than the use of land as

collateral that has been identified as the overriding determinant for access to

formal credit contracted directly between the bank and the individual borrower. It

implied that social collateral through group liability can be a substitute of physical

collateral. Hence it increased participation of the poor in credit markets. The

wealth and leverage ratio were used as rationing criteria for both group members

and informal lenders”.

- 25 -

The study developed an understanding of the inter linkages between access to

credit and agricultural productions, particularly addressed the question of

whether greater access to financial services increase agricultural production.

The results showed that under certain conditions, only 18.1 percent of the

households were not credit constrained. Most households were credit

constrained due to a lack of collateral and because of the self-selection problem.

It explored the determinants for a household to be credit constrained, focusing on

the formal credit market by using a probit model. It also investigated the influence

of being credit constrained on the rice production by applying a switching

regression model. The results of the probit model showed that human capital

(i.e. education and age of the head of household) as well as wealth and risk-

bearing indicators are significant in determining whether a household is credit

constrained. In the profitability function the coefficient for family size has positive

sign but is insignificant for the non-credit constrained households. Meanwhile,

this coefficient for credit-constrained households is positive and significant

(Nuryartono, et. al. 2005).

Segmentation, Interest rate and the Collateral The research on the quantitative estimating demand side is limited. Furthermore,

some of the studies that try to estimate loan demand suffer from bias due to data

truncation 3(Hesser and Schuh, 1962; Pani, 1966; Long 1968; Ghatak, 1976) and

simultaneity bias arising from the endogeneity of interest rate. Most of the

3 Cited by RANJULA BALI SWAIN Demand, Segmentation and Rationing in the Rural Credit Markets of Puri.

- 26 -

studies in the analysis only take into account the only exception in this case was

the studies of (Nagarajan, et. al., 1995). Thus, an increase in the supply of funds

in one segment does not necessarily increase the availability or reduce the price

of credit in another, although some funds do flow between segments.” (Ghate, et.

al, 1992). The collateral offered by the household for the loan and the interest

rate at which it borrows the loan were systematically associated with each other.

Loan contracts were identified by the the type of collateral that the lender

required and the lack of suitable collateral that a borrower can offer for loan

(Binswanger and Sillers, 1983, Binswanger and Rosenzweig, 1986(a).

Furthermore, credit can also be rationed in terms of the ability to offer collateral

(Von et. al. 1983, Rudra 1982). The terms, on which credit was obtained, may

also depend on the type of collateral and the inelasticity of the loan, which is

further, reflected in the purpose of borrowing (marriage and medical purposes).

The formal credit institutions face a high cost in acquiring information about their

borrowers and an excess demand created by the subsidized interest rates.

These institutions lend mainly for productive purposes with the requirement of

collateral, thus excluding the group of borrowers who cannot meet these

conditions. The informal lenders, on the other hand, charge higher interest rates

and incur lower administrative costs. They have information on the borrower’s

capacity and willingness to repay, based on their geographic and social

immediacy to the borrower. Informal lenders sometimes lend credit even without

explicit collateral. These distinct characteristics of the formal and the informal

- 27 -

sector are consistent with the explanation for the existence of the segmented

rural credit markets (Yadav et.al., 1992).

There was a lot of literature existed which explains the segmentation in the rural

credit markets, and the high interest rates in terms of the quality or lack of

collateral that the borrower can offer to the lender. Bhaduri (1977 and 1983)

argued an extreme case where the lender transfers the entire risk of the default

on to the borrower through the mechanism of under pricing the collateral and

encouraging asset transfer. This is possible due to the personalized relations, the

restricted size of the credit market, the associated monopoly power accruing to

the lender and the inter linkages with the other markets.

Technical Efficiency Olagunju (2007) studied “the impact of credit on the resource productivity of the

sweet potato farmers in Nigeria. A sample of 140 farmers comprising of 60 credit

users and 80 non-credit users were not randomly selected. The objectives were

related to socio-economic differences and resource use efficiency between

farmers producing with credit and farmers producing without credit. Marginal

Value Product and Multiple Regression Technique were employed to analyze the

data. From the pooled data the variables farm size, labour, and planting

material were positive and significantly related to sweet potato. The results for

the farmers without credit were poor; that was an indication of inefficient use of

available resources and inability to acquire additional capital for expansion. The

results of credit user indicated that they were resourcefully efficient than their

counterparts producing without credit. The marginal value product (MVP) for

- 28 -

credit users was approximately unity suggested that they were using the

resources optimally. On the other hand, MVP was positive but less than unity for

the fertilizer, planting materials, labour and then capital indicated the

underutilization of the resources thereby resulting in lower output for farmers

without credit. It is therefore recommended that policies should be made to

ensure the availability of the credit to the farmers”.

Udayanganie et. al. (2006) assessed the technical efficiency of paddy production

in one of the major irrigation schemes in Sri Lanka with special emphasis on the

usage of agrochemical inputs and determinants of technical efficiency. The

presence of technical inefficiency and its causality is investigated using a

stochastic production frontier model. Data for the estimation was gathered from

a farm household survey covering 225 households across five administrative

units from 3 irrigation blocks for the cultivation season 2003/2004. The results of

the production function showed negative relationships between yield and the cost

of pesticides indicating an over use of pesticides. The average technical

efficiency was estimated to be 0.37. Among the determinants of inefficiency

estimates; the importance of credit and extension services on improving

efficiency of farmer played significant role in the area.

Iqbal et.al., (2003) studied how the institutional credit was impacting the

agriculture productivity in Pakistan and described the institutional credit situation

in recent past in Pakistan. In the analysis the formal credit was used an

explanatory variable in production function. The results showed that the impact of

institutional credit was positive. The other variables like cropping intensity, labor,

- 29 -

and irrigation water availability were also found positive. Natural vagaries in the

form of floods, droughts, and cotton leaf curl virus (CLCV) were shown negative

impact on agriculture production.

- 30 -

Chapter 3

Rural Credit Perspective

Agriculture credit is part and parcel of all agriculture activities to improve the rural

sector of Pakistan since the 1950s. The agriculture credit provision was included

as basic component to improve rural economies in Pakistan (Zubeiri, 1989; Malik,

et. al., 1991). There are many studies which reflected that the linkages of

agriculture credit to welfare enhancing and poverty reduction in Pakistan

(Qureshi, et al., 1996; Malik ,1999; Malik and Nazli,1999). These studies

highlighted that well functioning of rural credit market has significant impact on

poverty reduction. This will not only increase the production by judicious use of

inputs. Resultantly, this will help to improve and more efficient consumption

smoothing. The rural credit market efficiency to reduce poverty can be enhanced

by removing these bottlenecks; by reducing institutional high transaction costs

both explicit and implicit, the need for better flow of information, a rationalization

of the existing collateral requirements for institutional credit, and a simplification

of the loan application procedure.

Poverty reduction is basically linked to how rural factor markets function. Rural

incomes and employment level for poverty reduction is determined by these

factor market functioning. Credit (working capital) is extremely important to

combine all the rural factor markets. It provides the opportunity to combine all the

resources for better production, growth and development. In this perspective

credit is necessary condition for the well functioning of the other rural factor

markets.

- 31 -

Rural Credit Institutions

The rural financial market in Pakistan is comprised of two components, i.e., the

formal and informal. The formal component included; The Zarai Tarqaiti Bank

Limited (ZTBL), Commercial Banks, the Federal Banks for Cooperatives (FBC)

and other financial institutions engaged in rural lending. The informal sources

composed of commission agents, input dealers, professional moneylenders and

landlords. The informal sector extends loans in the form cash and kind for both

consumption and production. This was the basic reason of the dominance of

informal sector in rural credit market in Pakistan.

This is evident from the Rural Credit Survey 1996 only 22 percent of rural

households borrowed from formal sources. Borrowing from informal sources is

comparatively easy in terms of access, procedures and collateral requirements.

This translates into low transaction costs. Furthermore, unlike institutional credit;

where the range of purposes for which credit is available is limited non-

institutional credit is available for consumption, social ceremonies and other non-

productive purposes (Himayatullah, 1995).

The Formal Credit Market

Before the partition institutional credit in rural areas was mainly provided as

“Taccavi” loans by the government and as co-operative credit by the co-

operative societies. Agricultural Development Finance Corporation and

Agricultural Bank were established in 1950s to with objective reduce the burden

on informal sources. These were merged to form the Agriculture Development

Bank of Pakistan (ADBP) in 1961. ZTBL emerged as the largest institutional

- 32 -

source that is mandated to extend long term credit to farmers and co-operative

societies.

Commercial Banks were the second major source of lending. Commercial Banks

provided mark-up free loans to small farmers and poor households from 1979 to

1987. This was highly criticized and abused because the main beneficiaries

were the landlords instead of small farm households. The political power always

played a dominant role in rural credit market. Volume and disbursement of the

agriculture credit is increasing considerably day by day but still there is room of

improvement.

- 33 -

Figure 1: Supply of Agriculture credit by institutions

Supply of Agriculture Credit by Institutions

020000400006000080000

100000120000

1987

/88

1989

/90

1991

/92

1993

/94

1995

/96

1997

/98

1999

/00

2001

/02

2003

/04

Years

Rupe

ss( m

illio

ns)

ZTBL*Commercial banksTaccavi loansCooperativesTotal

The formal credit is comprised of another component named; The Federal Banks

for co-operatives (FBC) was founded in 1976. The philosophy behind the

establishment of this was to disbursement of credit to small farmers and to give a

new lease of life to the co-operative movement. The bank provided credit facilities

to the Provincial Co-operative Banks (PCBs) and regulates their operations. The

PCBs extended loans to co-operative societies which were resulted in disastrous

failure. This was due corruption and frauds in the co-operative. A study by the

Punjab Economic Research Council (1986) revealed that almost 70 percent of the

societies operating in Punjab comprised of bogus societies (Annex1 and 2). After

much legal deliberation, the Punjab Undesirable Co-operative Societies

(dissolution) Act 1992 was implemented by the Government of Punjab.

- 34 -

Informal Credit Market

The main sources in Informal credit market are; friends and relatives, professional

moneylenders, landowners, commission agents and merchants and factories.

The proportion of lending among these resources has been changing over time

but without disturbing the overall share of informal sources in the credit market.

It was depicted by the results of the Rural Credit Survey 1996 that 78 percent of

total credit was disbursed by non-institutional lending sources. In the absence of

well functioning formal credit market, the informal sources played key role to

provide credit rural population of Pakistan. Informal sector is very crucial in the

situation when access to rural credit is difficult. These informal sources are more

important than formal sources. According to the Rural Credit Survey, 1985 friends

and relatives constituted the largest source of non-institutional credit. This

behavior was also confirmed by the Pakistan Rural Household Survey (2001) that

the informal loans from friends and relatives 61 percent for more recent times.

The predominance of the informal sector in credit market because of unconditional

and diversity of purpose for which this sector lend. Informal sources not only

provide credit for non-farm activities but also for consumption purposes. The Rural

Financial Market Study 1996 indicated that 56 percent of informal loans were

used for consumption purposes. On the other hand formal lending sources lend

mainly to agriculture sector with emphasis on the cropping sector.

- 35 -

Produce Index Unit (PIU)

The inability of small and tenant farmers to provide acceptable collateral poses

serious hurdles to obtaining institutional credit. The assessment of the value of

the land for collateral which were till recently made on the basis of Produce Index

Unit (PIU) are now made on the basis of the three year averages of the value of

land mutations in the area according to the Government land record. This has

helped ease a major impediment to the proper assessment of the value of the

collateral and hence the value of the loan that was possible. Previously one PIU

was valued at Rs. 400. For irrigated agriculture this implied a maximum

availability of Rs. 20,000 to Rs. 32,000. In January 2003 ZTBL made a proposal

to the government to value one PIU at Rs. 2000. Although no decision was taken

on this as yet, the ZTBL started lending at market value assessed as described

above. The value for irrigated agriculture based on this record of mutations is

currently ranging, on average between Rs. 100,000 to Rs. 150,000 Farmers can

now get five to six times what they were getting previously against the same

collateral based on the produce index unit (PIU) assessment.

- 36 -

Credit by Farm Size In 2004-05, 74 percent of total agricultural credit by Commercial Banks was

reportedly lent to subsistence farmers; 18 percent to medium farmers and 8

percent to larger farmers. ZTBL has reportedly disbursed 99 percent of its loans

to small farmers during 2004-05. Similarly, commercial banks were disbursing a

larger share of credit to the small farmers.

Figure 2: Agriculture Credit Advanced by ZTBL by Size of Holding

Agriculture Credit Advanced by ZTBL by Size of Holding

05000000

10000000150000002000000025000000300000003500000040000000

1987

-88

1989

-90

1991

-92

1993

-94

1995

-96

1997

-98

1999

-00

2001

-02

2003

-04

Years

Rup

ees

(Mill

ion)

tenant upto 5 hectares 5 to 20 hectare over 20 to 40 above 40 total

- 37 -

Figure 3: Percentage Distribution of Agriculture Credit by Size of Holding by Commercial Banks

Percentage Distribution of Agriculture Credit by Size of Holding by Commercial Banks

02040

6080

100

1988

-89

1990

-91

1992

-93

1994

-95

1996

-97

1998

-99

2000

-01

2002

-03

2004

-05

Years

Perc

enta

ge

Subsistence Economic Abvove Economic

Commercial banks disbursed credit for farm as well as for non-farm purposes.

Farm credit fulfils production and development needs and non-farm credit was

given for livestock, poultry, etc. However, the share of farm credit in total

agriculture credit was increasing as shown in Fig.4

- 38 -

Figure 4: Distribution of Agriculture credit by Commercial Banks between Farm and Non-farm sector

Distribution of Agriculture Credit By Commercial Banks to Farm and Non Farm Sector

0100002000030000400005000060000

1988

-89

1990

-91

1992

-93

1994

-95

1996

-97

1998

-99

2000

-01

2002

-03

2004

-05

Years

Rupe

es (M

illio

n)

Farm Credit Non Farm Credit Totla Credit

Divergence between Official Statistics and Survey Data

An increasing gap between the richest and poorest section of the rural population

in access to formal credit was found (Khandker and Faruqee, 2001). In 1985,

poorest households received only 1 percent of the formal credit against 60

percent by the richest households. In 1996, this gap had widened as poorest

households received 2 percent and the richest households received 72 percent

of the formal credit. Informal lenders in this study also remained the major credit

providers for the poor households. The findings of this study are presented in

Table 3.1. The study found that households with large operational holding (more

than 25 acres), who are about 4 percent of total borrowers, obtained 42 percent

of total credit exclusively from formal sources. In contrast, households with no

operational holding, who constitute 34 percent of the borrowing households,

- 39 -

received only 5 percent of loan extended by formal sources. The subsistence

farmers, comprising 35 percent of borrowing households, got only 18 percent of

total formal loans in 1996. The highest proportions, of those who can be

categorized as the poorest households, depend exclusively on informal sources.

According to this study of those that rely exclusively on informal sources 31

percent were subsistence farmers and 25 percent were landless households.

Table 3. 1: Distribution of Borrower Households by Operational Holdings (1996)

Categories Borrower Household Category by Operational HoldingLandless Subsistence Small Medium Large

All households 34.2 35.2 18.3 8.2 4.1 Households borrowing exclusively from formal sources 5.2 17.6 21.3 14.3 41.6

Households borrowing exclusively from informal sources 24.6 30.8 19.9 15.3 9.4

Households borrowing from both Formal and informal sources 20.4 27.9 20.2 15.1 16.4

Source: Khandker and Faruqee (2001); Values are in Percent

It was confirmed by many studies that there was a discrepancy between the

official statistics and surveys data of the agriculture credit market. In spite of well

targeted subsidized government programs, small farmers and poor households

were failed to access to institutional sources of credit. Small and tenant farmers

rely predominantly on non-institutional sources for their credit needs (Punjab

Economic Research Institute 1986; Applied Economics Research Centre 1986;

Scott and Redding, 1988; Malik, 1989, 1990, 1992 and 1999; Qureshi and Shah,

1992). Access to institutional credit hindered by unacceptable of collateral for

institutional sources. As land was the most commonly accepted form of collateral

by all institutional sources, therefore, tenants and landless households are forced

- 40 -

to borrow from landlords or commission agents. In both cases the loan

arrangements generally also restrict the borrower’s ability to make independent

decisions regarding crop production.

The government has sponsored several subsidized credit schemes in the past to

facilitate the small farmers. However, the distortions introduced by these

schemes coupled with other weaknesses in the system further reinforced the

domination of the large farmers. Low recovery rates and high default were the

main features of formal credit. This problem was also coupled by the weighted

rate of return per year was low on subsidize credit (Qureshi, 1995).

According to the report of the Committee on Rural Finance, SBP 2002

Commercial banks have not only exhibited a lack of initiative in increasing their

client base but have also been biased towards urban areas in terms of credit

disbursement. A picture of the rural-urban credit disparity can be obtained from

the data on advances and deposits by rural urban branches of Commercial

banks (Table 3.2). The advance to deposit ratio was 13.44 percent for rural

areas as compared to 62.27 percent for urban regions. Ironically this situation

has persisted despite a much larger recovery to loan ratio for rural areas as

compared to urban regions (Annex 3).

- 41 -

Table 3. 2: Commercial Banks Urban-Rural Profile.

Commercial Banks Urban-Rural Profile CBs Rural Urban Total

Branches (Number) 3183 3513 6696 Deposits (Rs. Billion) 159.00 582.00 741.00 Advances (Rs. Billion) 21.50 362.00 383.50 Advances as a Proportion of Deposits (%) 13.44 62.27 51.77

Branch Profile Deposits/ Branch (Rs. Million) 50.00 165.00 111.00 Advances/ Branch 6.75 103.00 57.30

Source: CRF report, State Bank of Pakistan, 2002

Irfan, et. al., 1999 examined the structure of informal credit markets in Pakistan.

He observed that nearly 53 percent of the loans of informal lender were going to

small farmers and landless, 21 percent to medium farmers and only 9 percent to

large farmers. This was attributed by them to various factors. These included

geographical accessibility to these lending sources thus lower transaction cost;

the absence of long drawn out or extensive legal procedures; and generally no or

minimal collateral requirements. The minimum time lag between loan agreement

and loan disbursement was another factor. On the other hand formal sources are

usually located in towns away from the borrowers; have complex procedures and

exacting collateral requirements and generally involved delays in processing and

disbursements thus adding to transaction costs. These factors have been

established by several studies as being responsible for the lack of access of the

small farmers to institutional sources of credit. While the volume of lending by

commercial banks has increased over time, the same cannot be said about their

client base, which has stagnated at around 250,000 borrowers.

- 42 -

Formal Credit by Purpose of Loan (Agricultural/Non-agricultural), Interest rates and Repayment Purpose of Loan Qureshi, et. al. (1996) pointed out that the focus of rural financial markets was

mainly on agriculture and within agriculture, primarily on the crop sector. Very little

credit was disbursed for livestock, fishery, and forestry. The non-existence of any

formal credit for small business enterprises in rural areas prevents non-farm

employment generation and results in rural-urban migration. The lack of credit for

non-farm sector is a major factor in the lack of diversification of the non-farm

sector.

Irfan, et. al. (1999) reported that 90 percent of formal sector loans were

disbursed for production purposes. However, 33 percent of total loans extended

by the shopkeepers were used for consumption purposes. Similar results are

obtained by the Rural Financial Market Study 1996; only 5 percent of formal loan

compared with 56 percent of informal loan were utilized to meet the consumption

needs.

World Bank (2002) study noted that the lack of sufficient loanable funds for

landless and small farmers (even from informal lenders) was the major cause of

rising trends in the sharecropping arrangements of tenancy. Rural Credit Surveys

provide ample evidence of the unmet credit needs of the rural non-farm sector.

- 43 -

Interest Rate Studies

There was a large degree of heterogeneity found in the informal credit market of

Pakistan like other developing countries. The structure and operations differ from

region to region. Furthermore, cultural and religious values prohibit the public

admission of charging the explicit interest rate (Malik, 1999) examined at length

the phenomenon of zero explicit rates lending in the informal rural credit market.

This makes it difficult to measure the implicit interest rate.

Several studies have found rates on rural lending to be exorbitant (Qureshi, 1984;

Aleem, 1990; Irfan, et. al., 1999; World Bank, 2002). However, the computed

interest rates by Malik (1999) on the data of two Rural Credit Surveys 1973 and

1985 did not indicate an excessively overpriced picture of non-institutional interest

rates. He found lower interest rates were charged by the institutional sources as

compared to non-institutional sources. This was due largely to the subsidy on

capital that is available to institutional lending sources in rural areas from the State

Bank of Pakistan and from international institutions. He found that after excluding

interest free loans from the computation (these loans largely from friends and

relatives in the informal sector and those disbursed under a special mark-up free

credit scheme by formal sources accounted for over 80 percent of rural lending in

that year) institutional interest rates were 12 percent and non-institutional 22

percent.

- 44 -

Table 3. 3: Average Rate of Interest and Its variance by Source of Credit (1973 and 1985).

Interest rate excluding zero rate loans Interest rate including

zero rate loans 1973 Variance 1985 Variance 1985 Variance

Institutional Sources 8.2 1.8 12.2 2.3 5.7 6.2 Cooperative Societies 8.6 2.5 10.9 3.2 2.1 4.5 Cooperative Banks 8.7 2.3 ZTBL (defunct ADBP) 7.2 1.3 12.3 2.3 9.6 5.5 Commercial Banks 8.9 1.4 12.3 2.1 2.6 5.1 Taccavi Loans 6.6 3.2 11.2 2.7 6.3 6 Other Govt. and Semi-Govt. 6.4 5.3 11.8 2.4 2 5.3 Non-Institutional Sources 15.3 11.6 21.9 16.6 2.2 8.2 Friends and Relatives 9.3 9.7 15.5 12.1 0.2 1.9 Professional Money Lenders 18.5 13 33.4 20.4 31.5 21.3 Land owners 10.3 9.4 22.8 12.5 1.4 6.2 Commission Agents 13.5 11.3 18.6 14.4 5.2 11.1 Merchants - - - - - - Factories 8.9 5.3 18.6 14.4 8.3 10 Others 113 11.1 25 23.9 1.2 7.2 All Sources 10.3 7.1 18.1 13.9 2.6 8

Source: Malik (1999) Notes: (-) indicates not applicable

The cost of lending per volume of credit transaction for informal lender estimated

by Irfan, et. al., (1999) ranged between 3 to 5 percent whereas the same cost for

ZTBL was 14 percent in 1996. Contrary to the findings of (Aleem, 1990, Irfan,

et. al., 1999) indicate that because of the presence of more than one lender in a

village. Pakistan’s informal credit markets were competitive in nature. However,

the higher interest charges are not attributed to the nature of informal markets

but to the cost of generating funds by the informal lenders. This study indicated

that informal interest rates are determined not only by opportunity, administrative,

and risk costs but also by the cost of generating funds. This study found that

borrowing, especially from formal sources, is the main source of fund generation

for the informal lenders. Nearly one-third of the total funds utilized in the informal

- 45 -

credit transactions originate from the formal credit sources. Processing units

appeared as the most prominent source generating 70 percent of the funds from

institutional sources. The funds that are generated through borrowing incur

additional cost. This study found that on average informal lenders paid about 19

percent annual interest rate on their funds borrowed from institutional sources.

However, they found 23 percent (27 percent if source was processing unit and 28

percent if source was friends and relatives). Commission agents and input

dealers paid the highest interest rate because of their higher dependence on

professional moneylenders. The study found different rates of interest across

inputs. For example, highest interest was charged for pesticides (35 percent).

This study examined the operations of informal credit markets using the field

survey data on 1018 informal lenders in all Pakistan. Nearly 21 percent lenders

reported the interest rate charged by friends and relatives. Lowest interest

charges found for seeds 8 percent. However, on average the interest rate was

found to be 25 percent.

Historical Trends Trends in Household Borrowing Patterns Based on Available Surveys There were four major surveys were conducted in Pakistan.

1. Rural credit survey of Pakistan (1973), Agricultural Census Organization

(supervised by the State Bank of Pakistan). The sample was comprised

of 99,081 households from all over the country.

2. Rural credit survey (1985), Agricultural Census Organization. A total of

54,987 households were covered from all over the country.

- 46 -

3. A bench-mark credit survey (1990) was conducted on a sub-sample

(2300 households) of the 1985 Rural Credit Survey by the International

Food Policy Research Institute (IFPRI) between June-July, 1990 in the

districts of Punjab and NWFP and during September, 1990 in the

districts of Sindh.

4. Rural Financial Market Study (RFMS) (1996) collaboration of the

Applied Economic Research Center (AERC), Karachi, the Punjab

Economic Research Institute (PERI), Lahore and the Pakistan Institute

of Development Economics (PIDE), Islamabad. A sample of 6000

households was selected from 250 selected villages. However, because

of issues with the quality of the date a large number of observations were

dropped. The revised sample contains 4380 households in 217 villages.

Irfan et al. (1998) compared the borrowing from institutional and a non-

institutional source across these surveys is presented in Table 3.4. This table

showed a considerable rise in the share of borrowing from institutional sources

between 1973 and 1985 (10 percent to 40 percent). However, a comparison of

the sub-sample of 633 households if 1985 Rural Credit Survey with 1990 Rural

Credit Survey indicates a sharp decline in borrowing from institutional sources

(from 59 percent to 24 percent). Comparing a larger sample of 1996 Rural

Credit Survey with 1985, this table shows that institutional borrowings has

declined from 40 percent in 1985 to 22 percent in 1996. One reason for the

large proportion of borrowing from institutional sources in 1985 was the

scheme of mark-up free credit under which institutional sources were

- 47 -

encouraged to lend at zero rates for production purposes. This scheme

introduced in 1979 had led to the rapid increase of institutional lending. The

recovery ratio for past-due agriculture loans was 28 percent in FY02 compared

to 36 percent in the preceding year.

Table 3. 4: Percentage Distribution of Total Borrowing by Source as indicated by Rural Credit Surveys

Sources of funds 1973 1985a 1985b 1990 1996

Institutional sources 9.8 39.5 58.5 23.6 22.0 Non-Institutional sources 90.2 60.5 41.2 76.4 78.0 Total 100 100 100 100 100

Source: Irfan et al. (1998) Notes:

1. The first rural credit survey was conducted in 1973. This survey covered 94082 households. The second rural credit survey that covered 54987 households was conducted in 1985. In 1990, 2300 households were selected from the lists of 1985 rural credit survey. Out of 2300 households of 1990 Rural Credit Survey, a comparison on 633 households was presented by IFPRI. Column 4 and column 5 of 1990 presents the distribution of credit based on these 633 households.

2. Column 3 reports the results of the full sample that are comparable with the results of rural credit survey 1973.

Column 4 reports the results of the selected sample of the rural credit survey 1985 in order to compare

Among institutional sources, ZTBL appeared as the largest source of institutional

credit in these surveys, while friends and relatives turned up as major source of

non-institutional credit Table 3.5. This table showed that borrowings from friends

and relatives had declined from 61 percent in 1973 to 36 percent in 1985 and

increased to 45 percent in 1996. The borrowings from ZTBL during the period

1973 to 1985 had increased from 5 percent to 32 percent and declined to 19

percent in 1996. This may be due to the relatively slower growth in credit by ZTBL

in the early 1990s (Table 3.4) Malik (1999) and Khandker and Faruqee (2001).

Commission agents and landlords were also found to be important sources of

- 48 -

credit in rural Pakistan. Their relative importance has increased over time. Recent

data also notes an increasing share of loans from landlord (World Bank, 2002).

Table 3. 5: Percentage Distribution of Total Borrowings by Source: All Households and Small Farm Households All Households Small Farm 1973 1985 1996 1973 1985 Institutional Sources 9.8 39.5 22.0 10.7 9.0 Cooperative Societies 1.0 2.3 0.40 0.8 0.4 Cooperative Banks 0.3 - - 0.3 - ADBP 5.0 31.5 19.03 3.6 4.0 Commercial Banks 2.6 5.4 0.70 5.3 4.3 Taccavi Loans 0.6 0.2 - 0.4 0.0 Other Govt. and Semi-Government. 0.3 0.2 1.87 0.2 0.2 Non-Institutional Sources 90.2 60.5 78.0 89.3 91.0 Friends and Relatives 60.7 35.5 44.62 57.8 71.0 Professional Money Lenders 2.1 2.1 - 2.4 1.2 Land owners 10.7 8.0 9.98 8.1 9.4 Commission Agents and Merchants 12.9 12.6 13.10 16.2 7.3 Factories 0.7 1.6 - 0.7 0.5 Others 3.1 0.7 10.29 4.1 1.7 Source: Malik (1999) and Khandker and Faruqee (2001) Notes: 0.0 denotes insignificant proportion

- indicates not applicable

Focusing on small farmers, Malik, 1999 reported a decline in borrowing from

institutional sources and an increasing reliance on loans from friends and relatives

during 1973 and 1985. He found an insignificant share of institutional lending (1.8

percent of total borrowing) in 1985 for tenant households who relied largely on non-

institutional sources. For these households landlords were the most significant

source of borrowing. World Bank, 2002 also confirmed that landlords were a

major source of borrowing for tenant households. This later study however, finds

that the bulk of the flow of loans from friends and relatives is towards large

farmers and the landless. The marginal and small farmers depend largely on the

informal lenders other than friends and relatives. For them landlords were found to

be a major source of borrowing in 2001.

- 49 -

Malik, (1999) separating credit constrained households with those who are not,

used the IFPRI 1990 data indicated that the unconstrained households have

higher land value, value of farm implements, gross farm earnings and fertilizer

expenditure. However, the ratio of gross farm earnings to both, the value of farm

implements and to land value is much higher for the credit constrained

households. The amount borrowed and debt outstanding from formal sources is

much smaller for the constrained households. Unconstrained households, on the

hand, have higher savings in financial institutions and total initial liquid assets.

It is surprising to note that no significant change has been observed over time in

the pattern of lending and borrowing over time. For example, the Pakistan Rural

Household Survey 2001 (which has questions on rural credit) conducted in

selected districts of all provinces of Pakistan supports the findings of previous

surveys, i.e., the dominant role of informal lenders in rural credit market; the high

share of ZTBL 88 percent in formal loans; and the dominance of friends and

relatives (61 percent ) in informal lending The share of shopkeepers and

landlords was also found to be significant (18 percent and 16 percent ,

respectively) and new categories of informal lenders are also emerging. “With

the intensification of agriculture, new types of informal lenders such as

middlemen traders have also become a critical presence in the rural economy,

both as marketing intermediaries and as financiers of cultivation” (World Bank

,2002).

- 50 -

Trends in Growth of Credit based on Secondary Data

Since 1972, institutional agricultural credit has grown at an annual rate of 15

percent (Table 3.6). ADBP the major source of formal credit has grown at over 17

per cent per annum during this period. Commercial Banks grew at 11 percent

and Cooperatives at 15 percent per annum. The share of lending by ZTBL in total

institutional credit was 56 percent in 2001-02, followed by commercial banks 33

percent and cooperatives 11 percent. Agricultural credit grew at relatively slower

rates 11.37 percent during the 1990s as compared to the growth during the

period to the end of the 1980s 20 percent.

Table 3. 6: Growth of Institutional Agricultural Credit 1972-2002. (Percent per annum)

ZTBL Cooperatives/FBCCommercial

Banks Total Agriculture

Credit 1971-72 to 2001-03 17.14 15.22 10.98 14.9 1971-72 to 1990-91 21.00 22.00 15.00 20.00 1990-91 to 2001-03 12.12 4.69 12.84 11.37

Source: Malik (1999) and Government of Pakistan (2001-02) Notes: All growth rates are computed using a semi-log regression model.

Commercial Bank credit in total agricultural credit has increased more rapidly in

recent years resulting from the significant changes in credit policy during the last

few years. The performance of cooperatives has however, remained

unsatisfactory. Government’s efforts to organize and broaden the role of

cooperatives in providing credit and inputs and marketing of output were not met

with any significant success. Mechanization in agriculture has not only helped to

enhance yield but also increased the acreage available for cultivation. Nearly 81

percent of total farms were using tractors in 1990 (Agriculture Census 1990).

Farm mechanization was a result of the farm sector’s responses to changing

- 51 -

conditions and constraints in agriculture. After the mid sixties, there was a rapid

increase in tractorization primarily because of rapid tube well development and

increased cropping intensity, which in turn introduced the need for tractorization

among farmers (Chaudhry 1986). In recent years bullock ploughing has become

even more expensive than ploughing by tractor. The data on credit and use of

farm machinery indicates that higher growth of subsidized institutional credit is

strongly correlated with increased mechanization in agriculture (Fig 5). This

figure shows a decline in tractor production in late 1980s when agricultural credit

was stagnant. After that tractor production shows an increase as does agricultural

credit by formal sources. Similar relationship exists between tube wells and

credit. These data lend support to the notion that subsidized credit has resulted

in the increased use of farm machinery.

Figure 5: Growth of Agricultural Credit and Production of Tractors in Pakistan

Growth of Agricultural Credit and Production of Tractors in Pakistan

0

20000

40000

60000

80000

100000

120000

1987

/88

1989

/90

1991

/92

1993

/94

1995

/96

1997

/98

1999

/00

2001

/02

2003

/04

Years

Rup

ees

(Mill

ion)

05000100001500020000250003000035000400004500050000

No

of tr

acto

rs

Total Agri-Credit No. of Tractors

Source: Computed from Agricultural Statistics of Pakistan (2004-05)

- 52 -

The available data indicate that institutional credit was unequally distributed

across provinces. In 2004-05, nearly 79 percent of the institutional credit was

disbursed in Punjab which accounts for only 59 percent of the total rural

population. The province of NWFP with 16 percent of the rural population

received only 7 percent of the institutional credit. Balochistan, which accounts for

6 percent of rural population and where agriculture is the major source of

livelihood, received only 0.5 percent of total institutional credit. Sindh is 19

percent of the rural population and receiving 14 percent of rural institutional

credit. A comparison of these proportions represents only one outcome that

requires more in depth examination.

This comparison changes when per hectare availability of credit was taken into

account. Interestingly, Sindh has the highest average disbursement of Rs 3,978

per hectare; followed by Punjab with Rs 2,890, NWFP Rs1458/hectare and

Balochistan Rs 937/hectare. This wide variation is mainly attributable to: (1) the

financial position of farmers in the respective province and their ability to provide

acceptable collateral, (2) variation in the distribution of land, (3) the track record

or repayments, (4) traditional reliance on non-institutional sources, and (5)

concentration of agricultural activities (SBP, 2002).

Credit Requirements and Household Borrowing Behavior

The National Credit Consultative Committee of the State Bank of Pakistan has a

methodology to compute credit demand (requirements) which was based on the

assumption that 75 percent of the total estimated input costs of the small farmer

would be met through credit. Credit disbursement takes place each year on the

- 53 -

basis of this calculation. The ratio of credit disbursement per cropped hectare

showed a considerable increase in the disbursement over time (Fig.6). However,

a comparison of provincial credit demand (requirements) indicated that the share

of Sindh in total credit demand is higher relative to its share in cropped area

whereas in NWFP and Balochistan, credit disbursement remained far low as

compared to the cropped area.

Figure 6: Disbursement of Agricultural Credit per Hectare of Cropped Area

Agricultural Credit per Hecter of Cropped Area

0

1000

2000

3000

4000

5000

6000

1987

/88

1989

/90

1991

/92

1993

/94

1995

/96

1997

/98

1999

/00

2001

/02

2003

/04

Years

Rup

ees

(Mill

ions

)

credit per hecter

Source: Computed from Agricultural Statistics of Pakistan (various issues)

The coverage of commercial banks in Balochistan was extremely low (Annex 4).

If credit was supplied on the basis of computed requirements, then the question

of credit constrained households would not arise. However, the survey data

(1973, 1985, 1990, 1996, and 2001) indicated a severe problem of access to

subsidized institutional credit by small farmers.

- 54 -

The data of Rural Credit Surveys (1973 and 1985) indicated a decline in the

proportion of institutional credit, for small farm households. This data shows an

increased dependency on landlords and decline in the share of credit from friends

and relatives for the tenant households. A reverse pattern has been observed for

the farm households other than tenant. The lack of formal credit to small farmers

and landless households leads to increasing dependence on the credit extended

by landlord. This, in most of the cases, results in sharecropping arrangement of

tenancy where inputs are mostly provided by the landlord and output is divided

on prior understanding between tenant and landlord. The Agriculture Census

Data indicates an increase in the proportion of sharecropped area in total tenant

operated area during 1990-2000. Table 3.7 showed that the proportion of loans to

landless in total loans declined from 5 percent in 1986-87 to 0.02 percent in

1992-93. Next three years exhibit a remarkable increase. Since then this

proportion has declined consistently. In 2004-05 only 0.04 percent of total loans

were going to the landless households. Similar trends can be seen for the

amount of loan. For example, in 2001-02, landless households received only

0.59 percent of total amount of loans advanced by the ZTBL. This indicates that

landless households are credit constrained and the lack of credit is reflected in

increasing sharecropping tenancy arrangements (World Bank, 2002).

- 55 -

Table 3. 7: Percentage Distribution of Loans Advanced through ZTBL by Size of Holding

Years Landless Owner-size of holding

Up to 5.06 Over 5.06 Over 20.23 to Over 40.47 No Amount No Amount No Amount No. Amount No Amount

1986-87 5.29 16.89 43.13 28.77 41.46 42.22 7.36 8.11 2.76 4.02 1987-88 4.32 14.37 48.32 35.27 38.07 37.78 6.92 8.67 2.37 3.911988-89 1.55 8.49 53.78 41.29 35.85 36.65 6.50 9.03 2.33 4.551989-90 0.57 8.67 54.49 37.74 35.84 38.10 6.41 9.28 2.68 6.211990-91 0.55 7.79 54.84 38.17 34.68 37.27 6.89 10.28 3.04 6.501991-92 0.10 7.32 50.78 28.78 38.27 43.55 7.49 11.48 3.37 8.871992-93 0.02 8.52 54.61 20.47 21.29 46.45 8.70 5.12 15.38 19.451993-94 25.00 28.79 21.96 11.73 22.81 27.54 9.47 10.95 20.77 20.981994-95 11.00 18.74 51.81 30.23 31.97 42.20 3.80 6.25 1.41 2.591995-96 10.01 10.67 52.28 36.79 32.85 43.33 3.62 6.58 1.25 2.621996-97 6.69 8.07 54.63 35.38 33.46 46.65 3.86 6.99 1.36 2.911997-98 4.74 9.88 62.53 38.59 27.87 41.66 4.29 8.51 0.58 1.371998-99 2.96 2.71 66.62 47.61 25.64 40.03 3.18 6.45 1.60 3.201999-00 1.85 1.15 69.72 52.86 24.27 38.03 2.95 5.64 1.20 2.322000-01 2.60 1.39 71.26 55.19 22.84 36.88 2.44 4.92 0.86 1.622001-02 1.73 0.68 74.96 61.19 20.54 31.91 2.06 3.75 0.70 2.472002-03 0.61 0.23 76.28 59.75 19.96 34.04 2.22 4.28 0.93 1.71 2003-04 1.06 0.50 78.81 63.23 17.65 31.33 1.83 3.73 0.64 1.21 2004-05 0.04 0.59 99.40 64.04 0.49 30.59 0.05 3.63 0.02 1.15

Source: Agriculture Statistics of Pakistan (2004-05) Note: Amount are in percentage share and No’s are in percentage of total no

The issue of bonded labor also arises because of lack of assets, extremely low

incomes, and lack of funds that compel the poor households, especially landless,

tenant or laborer, to depend on landlord or employer even to fulfill their basic

needs. Bonded labor is a highly exploitative form of relationship in which

borrower’s labor contract is tied with one landlord until they can pay back the

debt with interest. The provision of food and clothing are accumulated in their

debt along with interest payments. Hence, their debt increases on a daily basis

and renders numerous generations of the tenant indebted to the landlord. In

Pakistan, data on bonded labour was not readily available. The only sources of

data are sporadic surveys conducted by different NGOs or humanitarian

- 56 -

organizations. Based on these surveys, a few studies have projected the figures

of bonded labour that indicate a higher incidence of bonded labour in agricultural

sector of Pakistan (Ercelawn and Nauman, 2000 and Ercelawn and Ali, 2003.

The problem of bonded labor is deeply rooted in the lack of access to formal

credit that forces them to depend on informal credit sources for their credit

needs. Once they accumulate a large amount of debt that they are unable to pay

back, they are bounded to work for the landlord or employer without any wages.

In the absence of strict enforcement of laws against bonded labor in Pakistan,

credit can play a major role to reduce the incidence of bonded labour by meeting

the consumption and investment needs of poor people.

Term of Loans

Short-term loans from ZTBL have been increased over time. It is depicted from

the data in table 3.8 that in 1985-86, 55 percent short-term loans were extended.

With the introduction of schemes such as One Window Operation, Revolving

Credit Scheme and Loans for Newly Identified Priority Items by the ZTBL has

increased the proportion of short-term production loans. In 1999-00, among the

total disbursed loans by the ZTBL, 84 percent were short-term, 5 percent were

medium-term, and 11 percent were long-term Table 3.8. This proportion further

increased to 88.06 percent in 2004-05.The province of Punjab and Sindh exhibit

similar patterns (Annex 5).

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Table 3. 8: Percentage Distribution of Total ZTBL Loans with amount by Term Years Short-term loans (%) Medium-term loans Long-term loans (%) Number Amount Number Amount Number Amount 1985-86 55.49 24.87 19.98 15.54 24.53 59.60 1986-87 44.43 20.03 22.27 11.14 33.30 68.83 1987-88 37.69 22.08 34.78 16.59 27.54 61.33 1988-89 43.30 24.55 30.76 15.99 25.95 59.46 1989-90 43.82 25.28 29.21 14.87 26.97 59.85 1990-91 49.68 30.40 27.68 14.42 22.64 55.18 1991-92 60.33 44.72 23.54 12.34 16.01 58.84 1992-93 82.91 35.35 7.74 8.76 9.35 55.88 1993-94 50.23 24.60 25.87 19.01 23.90 56.40 1994-95 71.95 43.04 14.16 10.48 13.90 46.48 1995-96 80.09 60.12 6.76 5.05 13.15 34.83 1996-97 75.93 51.13 14.66 15.64 9.41 33.22 1997-98 84.43 63.36 11.22 10.31 4.35 26.33 1998-99 90.14 73.29 4.83 4.54 5.03 22.17 1999-00 86.97 66.05 5.73 5.16 7.29 28.79 2000-01 86.87 68.10 6.14 5.30 6.98 26.60 2001-02 88.74 75.80 5.91 5.10 5.35 19.10 2002-03 88.54 49.30 7.31 5.50 4.15 15.20 2003-04 88.53 82.30 8.18 6.20 3.29 11.50 2004-05 88.06 83.70 8.01 5.50 3.93 10.80 Source: Computed from Agricultural Statistics of Pakistan 2004-05.

Informal loans are mostly short-term in nature. In 1985, nearly 70 percent loans

were short-term and in 1996, this proportion has increased to 93 percent. Malik,

1999 found considerable variations in the term structure of loans across

provinces. The proportion of short term loans in total informal lending in Sindh

was 91 percent; in Punjab 67 percent, in Balochistan 61 percent and in NWFP 40

percent. The largest proportion of short-term loans in the lending portfolio was

from factories 93 percent, followed by professional money lenders 87 percent

and commission agents and merchants 81 percent.

- 58 -

Current Government Policies and Programs

Provision of rural credit in Pakistan has been an active policy consideration of the

government. During the initial years it provided “Taccavi” loans in the event of a

natural disaster. However these were insignificant in terms of volume. In 1972

the Banking Reforms were implemented prior to which commercial banks played

little role in agricultural credit to small farmers. The Bank Reforms made specific

targets mandatory for the CBs in order to promote their lending activities. Under

the same reforms the SBP was made responsible for expanding the credit base

for agriculture as well as improving accessibility for small farmers. A similar effort

was undertaken in 1979 with the introduction of the Supervised Agricultural

Credit System in the ZTBL. At present all ZTBL credit programs are conducted

under this system (SBP 2002).

In 1997 the ZTBL initiated the One Window Operation to make credit facilities

easily accessible particularly to small farmers so that they could fulfill procedural

requirements more efficiently. This operation was undertaken in collaboration with

the Provincial Governments, Revenue Officials and Postal Authorities. The clients

are provided with Agriculture Pass Books on the spot in which land records are

noted. The focal points are responsible for sanctioning loans, which are

disbursed the following day from the concerned bank branch. The maximum loan

limit in these instances was Rs. 50,000. (SBP 2002)

The ZTBL initiated a micro credit scheme in July 2000, which targeted drought-

affected farmers. This scheme encouraged the rural poor to engage in profitable

- 59 -

commercial activities. Under this scheme potential borrowers could obtain a

maximum of Rs. 25,000 against personal guarantee and viable security. The

program included 136 loanable items and funding was extended for 18 months

at 16 percent per annum. A sum of Rs. 149.6 millions was disbursed to 6430

clients during the first year of implementation. During FY01 loan recovery

exhibited a very encouraging figure of 90 percent. Another 1.6 billion were

provided to farmers for installation of 8991 tubewells so that they could

effectively deal with the serious water shortage that was a result of the drought.

This amount was double compared to the Rs. 830 million disbursed the previous

year for installation of 4375 tubewells (SBP 2002)

In order to enhance the access of small farmers to formal credit ZTBL altered

the requirement of minimum 12.5 acres of land to 5 acres of land. Moreover, in

view of small land holdings in NWFP the loan limit of Rs. 50,000 for production

loan was increased to Rs. 100,000 against personal security. ZTBL also

simplified the loan appraisal as well as the application forms.

The other programmes named as Loan under one window, Revolving finance

scheme/”Sada Bahar” scheme, Credit to Women Scheme, Micro Credit scheme,

and crop maximization project.(GOP 2006-07)

Some of the major steps taken by State Bank of Pakistan during recent years

are listed below:

• Agricultural credit scheme has been extended to encompass the

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complete value chain of agriculture ranging from inputs, production,

storage and marketing to transport, processing and distribution.

• Since October 2000 the SBP has stopped the provision of subsidized

credit facilities to ZTBL and FBC for the smooth functioning of the

financial system. Moreover the minimum average rate on T-bills has been

set as the pricing criteria for all new credit lines to the ZTBL to compel

these institutions to make lending and recovery process more efficient

and effective. Other closely related targets are (1) to encourage other

financial institutions to lend their funds to ZTBL (2) Agricultural lending

rates would be in consonance commercial lending rates through market

based cost of funding (3) eventually, interest rates would be forced down

in the wake of greater competition amongst banks.

• Previously SBP assigned certain territorial boundaries to commercial

banks for lending purposes. More often than not, commercial banks used

this restriction as an excuse for not extending loans at all. By repealing

this rule not only would banks lend on purely commercial grounds but the

farmers would also have a wider choice in terms of selecting the potential

lender.

• With a view to improved credit provision to farmers, SBP has provided the

facility of revolving credit thereby allowing farmers to obtain fresh credit

even if they have not been able to repay outstanding loans. Prior to this

rule banks could not sanction revolving credit facility beyond three years,

particularly not to those farmers who had been unable to repay prior debts.

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This step would enhance farmers’ welfare by meeting credit needs more

efficiently. Banks would also not demand fresh documents at each

renewal. It was expected that 48 percent of production loans would be

disbursed under this scheme during the year 2002-03. (SBP 2002-03)

Political Economy of Credit Issues in Pakistan

The main reason for the distortion and efficient functioning are politics and power

structure. This is mainly caused by the big landlords who are dominating the rural

sector. They always became the main beneficiaries of any credit programme by

distorting the small and tenant farmers. On the other hand the credit policies

governing the operation of formal institutions are intrinsically biased against small

and tenant farmers. This distortion led the small farmers towards informal credit

sources, which can be exploitative at times. There are many factors which are

causing inefficiency in the rural credit market. These are comprised of existence

of unjust and repressive power structures, unequal distribution of resources,

particularly land, inefficient performance of government sponsored credit

institutions and an inherently discriminatory credit policy.

Various studies have confirmed that small farmers and poor households rely

predominantly on non-institutional sources for their credit needs despite a

number of policies and subsidies designed to encourage their access to

institutional credit. (Punjab Economic Research Institute, Lahore, 1986; Applied

Economics Research Center, Karachi, 1986; Scott and Redding, 1988; Malik,

1989, 1990, 1992 and 1999; Qureshi and Shah, 1992). These studies concluded

- 62 -

that credit schemes for small farmers, especially those in the subsidized credit

fail to achieve their objectives.

In order to examine the access of small farmers to institutional loans, Malik

(1999) computed a measure of access to institutional credit by small farmers.

This measure indicates that small farmer’s access to institutional sources of

credit is not only limited but had deteriorated over the period that he studied. The

World Bank, (2002) study with very recent data confirms that the same patterns

still-hold. More that 44 percent of the formal loans went to large farmers and only

18 percent small and marginal farmers were successful in obtaining formal loan

in 2001. The proportion was only 1.5 percent for the landless. On the other hand,

the proportion of landless, marginal and small farmers in informal loans remained

more than 70 percent. Irfan, et. al., 1999 also confirmed this pattern. This was

resulted in large part due to the fact that land was the preferred collateral and

thus excluded the small and tenant farmers. Other implicit transaction cost

makes this lending relatively in attractive.

Interest Rate and Transaction Costs of Formal Lending Institutions

Government sponsored credit schemes are subsidized. Therefore institutional

credit market continues to be dominated by large farmers who can influence

access. In order to analyze the performance of formal credit institutions, the

Pakistan Institute of Development Economics (PIDE), Islamabad, conducted a

survey of Commercial Banks and ZTBL in 1996. Using this data, and the Bank's

income and expenditure accounts, recovery performance, and record of credit

disbursements and deposit mobilization Qureshi et al (1998) examined banking

- 63 -

operations of the ZTBL and commercial banks. They found that overall

operations of ZTBL depend to a large extent on economic subsidy. ZTBL’s

financial margin was low and that the Bank was unable to cover its costs within

those financial margins. The study estimated that the net subsidy to ZTBL had

increased from 1685 million rupees in 1991 to 3312 million rupees in 1995. The

average subsidy was 4.7 percent of the loans outstanding in 1991 and 7.5

percent in 1995. Therefore lending on such terms by formal lenders such as

ZTBL was not cost-effective. The nominal interest rate of ZYBL for these loans

rose very little in the period from 1991 to 1995, 12.5 percent to 13.5 percent. The

rate of inflation during this period ranged between 9.6 percent and 13.9 percent.

The real rate of interest on ZTBL loan was either close to zero or negative for

most of the time during the study period. Thus one way of reducing the subsidy

dependence of ZTBL is to increase the nominal interest rate. This study

computed a Subsidy Dependence Index (SDI) which measures the percentage

increase in the average on-lending interest rate required to eliminate all subsidies

in a given year while keeping the return on equity equal to the non-concession

borrowing cost. The SDI increased from 0.38 to 0.56 during 1991-1995. This

meant that in order to reduce subsidy, the ZTBL had to increase the on-lending

rate by 38 percent in 1991 and 56 percent in 1995. Using the nominal rate for

those years, it means, the nominal rate should have increased from 12.5 percent

to 17.2 percent in 1991 and from 13.5 percent to 21 percent in 1995. The high

value of SDI showed that the ZTBL had been a vehicle for distributing subsidized

loans rather than a financial institution operating under commercial principles.

- 64 -

The high dependence on subsidies erodes the economic viability of Banks.

Improvements in the loan recovery rate can help to reduce subsidy dependence.

The loan recovery rate of the ZTBL was 59 percent in 1991 for all past loans.

This declined further to 45 percent in 1996 the last year covered by the study.

The loan default cost was as much as 60 percent for ZTBL. What was most

interesting was that the majority of the ZTBL loans had gone to those who did not

require a subsidy? Given the skewed distribution of land holding and the socio-

political environment under which the economy benefits are most likely to accrue

to those who wield the power.

Interaction with other Rural Factor Markets

The land is most acceptable collateral for the lending institutions in the Pakistan.

Rural labor and land market are interconnected with credit market. But the land

distribution in Pakistan is of skewed nature. This fact is also providing the

opportunity to large farmers to distort small farmers and dominate the credit

market. This required much attention for the well functioning of the credit market.

Credit also has both direct and indirect linkages with the rural labor market. The

productivity of land can be enhanced by using better quality farm inputs such as

fertilizers and seeds by combining efficient farm labor. Jehangir and Sampath

(1999) found that fertilizer use had a significant and positive impact on the use of

permanent hired farm labor. Fertilizer use can be enhanced through the provision

of credit to small farmers. This would in turn have positive bearing on the use of

farm labor. Various studies indicate the welfare impact of farm credit on

- 65 -

productivity and farmer welfare (Qureshi, et .al., 1996, Malik, 1999, Malik and

Nazli, 1999).

The income inequality mitigating effect of employment in the rural non-farm

sector of Pakistan was established by Adams (1993) and Ercelawn (1984).

Access to credit for non-farm purposes through a well functioning credit market

will help private employment and mitigate poverty and inequality.

- 66 -

Chapter 4

Efficiency of Credit and Non-Credit Users (Frontier Production Approach)

Despite the fact Pakistan agriculture is producing food for the rural as well as for

urban population but still the position of these farmers is vulnerable. Capital

shortage is the main cause of their vulnerability and inability to invest on their

farms to increase productivity of their farms. This is the main cause of their

poverty and low incomes of the farmers. This led decline growth of agriculture

and its contribution to gross domestic product. Several efforts were to increase

production and productivity of these farmers so as to achieve the objective of

food security and poverty reduction with the sustainability of environment.

With the increase in population, there is need to produce more food to feed the

growing population. Population pressure is pushing the people to discover new

lands and marginal lands. These all challenges can only be met by raising the

productivity and efficiency of the farming sector.

Agriculture growth is the most efficient mean for the poverty reduction and

sustaining the environment. This can be done through different means; raising

productivity per unit of land and irrigation efficiency, use the cultivable land where

it is available, and improved technologies for the agriculture production. There

are many constraints socially and economically which comprised of: low literacy

rate, squeezed distribution of land and fragmentation of land, asymmetry of

information in extension services for crop production and marketing of the

produce. Lack of access to institutional agriculture credit is main constrain to

- 67 -

agriculture production (Rana and Young, 1988). In this chapter, the efficiency of

credit user and non-credit users was examined.

“Farmers are generally believed to maximize their profit. However, it should be

noted, efficiency (allocative and technical) and profit maximization are two sides

of the same coin in that at the level of individual production unit you cannot have

one without the other. The strict definition of economic efficiency also requires a

competitive market, since neither the individual production unit nor the sector can

attain efficiency if producers face different prices or if some economic agents can

influence the prices and returns of other economic agents” (Ellis, 1993).

Efficiency of a firm consists of two components: technical efficiency (TE), which

reflects the ability of a firm to obtain maximum output from a given set of inputs,

and allocative efficiency (AE), which reflects the ability of a firm to use the inputs

in optimal proportions, given their respective prices. These two measures are

then combined to estimate the total economic efficiency (EE). The concept of

technical, allocative and economic efficiency can be illustrated by using

input/input space (input-oriented measures) or output/output space (output-

oriented measures, Coelli, 1996) or input-output space (Ali and Chaudhry, 1990).

These concepts were explained by employing input-oriented measures.

Analytical Framework There are two main approaches (with a number of sub-options under each) to

measure TE, AE, and EE. These include stochastic frontier (parametric

approach) and Data Envelop Analysis (DEA), also named as non-parametric

approach. These two methods have a range of strength and weaknesses which

- 68 -

may influence the choice of methods in particular application and the constraints,

advantages and disadvantages of each approach has been discussed by Coelli,

1996; Coelli and Perelman,1999. 4The present study employed a Stochastic

Production Frontier approach introduced by Aigner, et. al. (1977); and Meeusen

and Broeck (1977). Following their specification, the stochastic production

frontier was written as,

( ) NiexFy iii

.............,2,1, == εβ (4.1)

Where: yi was output for the ith farm, xi was a vector of k inputs,β is a vector of

k unknown parameters, ε i was an error term. The stochastic frontier is also

called “composed error” model, because it postulates that the error term ε i was

decomposed into two components: a stochastic random error component and a

technical inefficiency component as follow,

uv iii −=ε (4.2)

Where: vi was a symmetrical two sided normally distributed random error that

captures the stochastic effects outside the farmer’s control (e.g. weather, natural

disaster, and luck), observation and measurement errors, and other statistical

noise. It was assumed to be independently and identically distributed ⎟⎠⎞⎜

⎝⎛ σ 2

,0v

N .

Thus, vi allowed the frontier to vary across farms, or over time for the same

farm, and therefore the frontier was stochastic. The termui , was one sided

4 Similar methodology used by I. L. Aburime et.al (2006): An Analysis of Technical Efficiency of Beekeeping Farms in Oyo State, Nigeria.

- 69 -

(ui ≥0) efficiency component that captures the technical efficiency of the i-th

farmer. This one sided error term can follow different distributions such as,

truncated-normal, half-normal, exponential, and gamma (Stevenson, 1980;

Aigner, et. al., 1977; Green, 2000, and Meeusen and Von den Broeck, 1977). In

this study it was assumed ui followed a half normal distribution ⎟⎠⎞⎜

⎝⎛ σ 2

,0u

N as

typical done in the applied stochastic frontier literature. The truncation-normal

distribution is a generalization of the half-normal distribution. It was obtained by

the truncation at zero of the normal distribution with mean µ, and variance,σ 2u . If

µ was pre-assigned to be zero, then the distribution is the half-normal. The two

error components (v andu ) were also assumed to be independent of each

other. The variance parameters of the model are parameterized as:

10; 2

2222 ≤≤=+= γγ

σσσσσ and

s

uuvs

(4.3)

The maximum likelihood estimation of equation (4.1) provided consistent

estimators for β , γ, and σ 2s parameters. Jondrow, et. al. (1982) have shown that

inferences about technical inefficiency of individual farmers can be made by

considering the conditional distribution of ui given the fitted values of ε i and the

respective parameters. Given the normal distributional assumption of vi and

half-normal distribution ofui , the conditional mean ofui , given ε i is shown to be

( ) ( )( ) ⎥

⎥

⎦

⎤

⎢⎢

⎣

⎡−

−= •

•

σε

σεσεσε

λ

λ

λ

s

i

si

siii F

fuE

/1o (4.4)

- 70 -

Where: f • and F • represent the standard normal density and distribution

functions, respectively andσσσσ 2

222

s

uv=o

. Hence, equation (4.1) and (4.2) provide

estimates for vi and ui after replacingε i , σ 2s and γ by their estimates.

Multiplying by e vi− of both sides of equation (1) and replacing β ’s with maximum

likelihood estimates yields stochastic production frontier as:

eyexFy v

i

uii

ii −−⊗•=⎟

⎠⎞⎜

⎝⎛= β, (4.5)

Where: yi

•

was the observed output of the i-th farm adjusted for the statistical

random noise captured by vi (Bravo-Ureta, and Rieger, 1991). All other

variables are as explained earlier and β⊗

is the vector of parameters estimated

by maximum likelihood estimation technique. The technical efficiency (TE)

relative to the stochastic production frontier is captured by the one-sided error

components ui ≥ 0, i.e.

⎥⎥⎥

⎦

⎤

⎢⎢⎢

⎣

⎡

⎟⎠⎞⎜

⎝⎛

=⊗

−

exy

e vi

iu

i

i

F β, (4.6)

The equation (4.5) was used to derive mathematically the dual cost frontier of

equation (4.1). This cost frontier is used to derive input demand functions (that

minimizes cost function) which in turn are used to get the technically and

allocatively efficient level of input vectors.

- 71 -

Assume that the functional form of the production technology is self-dual, the

corresponding dual cost frontier can be expressed in general form as:

⎟⎟⎠

⎞⎜⎜⎝

⎛=

⊗• β,,wycc i ii

(4.7)

Where: ci represents the minimum cost of the i-th farm associated with the

production of output vector yi

•

, wi the vector of input prices for the i-th farm and

β⊗

the vector of estimated parameters of production function as explained

earlier. Once we have level of output, input prices and parameters of production

function then by applying Shephard’s (1970) Lemma we can estimate a system

of cost-minimizing conditional input demand functions as;

w

wycwyxx

i

ii

ii

c

i

c

i

∂

∂ ⎟⎟⎠

⎞⎜⎜⎝

⎛

=⎟⎟⎠

⎞⎜⎜⎝

⎛=

⊗•

⊗•

ββ

,,,, (4.8)

Where: xci stands for cost minimizing level of the ith input and xc

i ( )⋅ represents

the functional relationship between xci and the input prices and output.

Substituting the output level of farms, input prices, and parameters of production

function into the system of cost-minimizing conditional input demand functions

(equation 4.8) provides the economically efficient (technically and allocatively

efficient) input vector xEi The cost of the observed operating input mix is xw ii

(observed level of input prices and quantity) while the economically efficient cost

of production of the farm are estimated as wi xEi , given the actual level of output.

- 72 -

These production costs are used as basis for calculating economic efficiency

(EE) as below,

⎥⎥

⎦

⎤

⎢⎢

⎣

⎡=

xwxwEE

ii

i

E

ii (4.9)

The allocative efficiency (AE) index was estimated from a relationship between

economic, technical and allocative efficiency developed by Farrell, 1957;

⎟⎟⎠

⎞⎜⎜⎝

⎛=

TEEEAE

i

ii (4.10)

The function determining the technical inefficiency effect was defined in general

farm as a linear function of socio factors as,

( )ZFIE ji = (4.11)

The more detail about dependent and independent variables is given in empirical

model.

Sample and Data

To generate information on farm management practices and income pattern of

the farmers, a detailed survey was conducted by the researcher. The Sargodha

region in Punjab was selected as the study area as majority of the farmers was

living in the rural areas of Punjab. The interviews were conducted at the farm

level with individual farmers. Only very few farmers were reluctant to reveal

information. A random sample of 160 respondents, from the mixed cropping zone

of Punjab was taken. Of the total sample, 82 respondents were non-credit users

and 78 were credit users. The cropping pattern was different, credit users were

growing cash crops and non-credit users were raising food security crops. The

- 73 -

size of farm for credit users was 19 acres and for non-credit farm size was 5

acres.

Allocative Efficiency of Credit users and Non-Credit Users Market forces seldom set the price to individuals for water; rather, publicly owned

water supply agency or regulated private water utility sets charges. Water prices

(rates, in public utility jargon) have both efficiency and equity impacts, as well as

influencing agency revenues. Several techniques are available by which value

productivity of irrigation water can be estimated. Residual analysis using farm

budgets or programming models is a common approach. These have the

advantages of relative simplicity and ease of data collection, and these can be

readily adapted for expected changes in technology and in input and output prices.

Production function approach relies on statistical analysis of the inputs and yields

from the agricultural production processes. Data can be obtained from either crop

production experiments or survey of farmers. While either experimental or survey

data are expensive, the production approach measures actual rather than

hypothesized resource allocation, and provides a useful check on the budget

approach.

The intent here was to show estimates of the marginal value product of irrigation

water derived from production function analysis of a farm survey data. Given the

farm survey data in the Sargodha region, following functional form in linear

logarithms is estimated:

Farm survey data were used to estimate the economic value of selected input and

to measure the allocative efficiency of these inputs, the Cobb-Douglas (C-D) form

- 74 -

provided the best fit to the survey data [as measured by the Coefficient of Trans log

and Transcendental production function, Determination (R2) and t-tests on the

regression coefficients. The C-D function is probably the most widely used form for

fitting agricultural production data, because of its parsimony in parameters, ease of

interpretation, and computational simplicity.

The C-D form is linear in logarithms and can be conveniently analyzed with

standard linear regression. The function form fitted to the data is as follows:

LnY = Ln a+ Ln W + LnNP + Ln C+ Ln L +e (4.12)

Where:

Ln: Logarithm;

Y: Gross value of the Crop products (Rupees)/acre

W: Irrigation water in acre inches/acre

NP: Nutrient Kg/acre

C: Cash inputs (Rs.)/acre (includes seed, plant protection and land tillage)

L: Labor Input in man days/acre; and

e: error term.

Derivation of Marginal Value Products and Opportunity Costs Marginal value product (MVP) of Xi = dY/dXi = bi (Y^/Xi )

Where Y represents farm revenue, Xi represents the level of input of the ith

resource, and bi is the regression coefficient of the ith input in a Cobb-Douglas

model, it can be shown that (Heady and Dillon, 1969):

Following customary practice, one can obtain a point estimation of MVP by

evaluating above equation at the mean value for each input, in this case irrigation

- 75 -

water: The example for derivation of MVP and its associated statistics is given

below:

The intent here is to show estimates of the marginal value product of major farm

inputs derived from production function analysis of a farm survey data. Given the

farm survey data for different crops and irrigation water in the Sargodha region, the

following functional form in linear logarithms was estimated: In this study, individual

farm budget of representative categories i.e. credit users and non-credit users were

developed for production function analysis.

The results of the analysis were obtained in Table 4.1 and Table 4.2. The results

revealed that credit users and non credit users were allocatively inefficient,

especially in water use and fertilizer. The ratio of MVP/OC was greater than one

showing scarcity of the most of the inputs. However, the ratio of cash inputs and

labor were low but the coefficients of these inputs were not significant: (except non

credit users) and magnitudes of the parameters were also low. The results were

consistent with Olagunju, 2007. The resource-use efficiency of fertilizer and capital

were higher than the non-credit users.

Table 4.1: Marginal Value Product of Selected Inputs for Credit Users, Sargodha, Punjab, 2007 Variables per Acre Unit Mean Pro.Elasticity MVP (Rs) OC (Rs) MVP/OC

Irrigation Water Acres Inches 74 0.318*** 1095 300 3.65

Fertilizer N/Kgs 682 0.342*** 20 20 1.03 Labor M/Days 39 0.057 35.3 150 0.24 Cash Inputs Rupees 27244 0.376 0.26 1 0.26 Gross Revenue Rupees 115845 - - - -

*** indicate that the coefficient is significantly different from zero 0.01 percent probability level; OC: Opportunity cost

- 76 -

Table 4. 2: Marginal Value Product of Selected Inputs for Non-Credit Users, Sargodha, Punjab, 2007

Variables per Acre Unit Mean Pro.Elasticity MVP (Rs) OC (Rs) MVP/OC

Irrigation Water Acres Inches 29 0.701*** 384 300 1.28 Fertilizer N/Kgs 227 0.122*** 53 20 2.63 Labor M/Days 36 0.012 54 150 0.37 Cash Inputs Rupees 6103 0.062*** 2.16 1 2.16 Gross Revenue Rupees 35045 - - - -

*** indicate that the coefficient is significantly different from zero 0.01 percent probability level; OC: Opportunity cost

Technical Efficiency of Credit/Non Users The maximum-likelihood estimates of Cobb-Douglas stochastic production

frontier and parameters explaining inefficiency of credit users were obtained in

Table 4.3 for the study area. The estimated coefficients of the input variables of

frontier production function have positive sign and consistent with economic

theory. All coefficients were highly significant (p=0.01) except labor input. In the

Cobb-Douglas production function, the parameters were the respective

elasticities of input which provides the important direction in production decision.

The elasticity for some inputs was smaller as their sum was equal to one

showing constant return to scale. The mean technical efficiency of credit users

was 90 and that of non-credit users was 79 percent, respectively (Table 4.3 and

4.4).The results were consistent with Desai and Mellor, 1993. The efficiency of

credit users was high then their counterparts.

The high technical efficiency of credit users was attributed to better market

access to the farmers to new technology through the availability of agricultural

credit. In order to test this hypothesis preferably we would have incorporated

the new technology (which is not available) in the inefficiency model to study the

- 77 -

impact of new technology on technical inefficiency. However, scale variables

farm size and education played major role in lowering the inefficiency. The low

level of technical efficiency of non-credit users as compared to credit users

implied that potential for improvement exists. The inefficiency parameters i.e.

farm size, experience and education could play a major role in improving the

efficiency of non-credit users.

The value of γ-estimate for credit users was significantly (p=0.10) different from

zero, indicates that random error was dominant and playing a significant role to

explain the variation in the dependent variable and this was normal especially in

case of agriculture where risk was assumed to be a main source of variation

(Table 4.3 and 4.4). However, the case for the non-credit users where the value

of γ-estimate was also significant. The results were consistent with Olomola,

1997. The credit was contributing significantly in reducing risk and access to all

the resources on which farmer is dependent in production process. The results

were consistent with (Sial and Carter, 1996).

- 78 -

Table 4. 3: Maximum Likelihood Estimates of the Cobb Douglas Stochastic Frontier Function of Credit Users in Sargodha Division Variables Coefficients t-statistics

Constant 1.945*** 3.045

Irrigation Water 0.233*** 2.502

Fertilizer cost 0.405*** 3.543

Cash Inputs 0.346*** 2.795

Labor Cost 0.058 0.736

Technical Inefficiency Model Constant 0.021 0.031

Farm Size -0.006 0.287 Experience 0.001 0.133

Education -0.008 -0.396 sigma-squared 0.043* 1.625 Gamma 0.557* 1.673

Mean Efficiency 0.90 1 * indicate that the coefficient is significantly different from zero 0.10 percent probability level; 2 ** indicate that the coefficient is significantly different from zero 0.05 percent probability level 3*** indicate that the coefficient is significantly different from zero 0.01 percent probability level; Table 4. 4: Maximum Likelihood Estimates of the Cobb Douglas Stochastic Frontier Function of Non-Credit Users in Sargodha Division Variables Coefficients t-statistics

Constant 3.781*** 15.7

Irrigation Water 0.668*** 7.259 Fertilizer cost 0.044 0.977 Cash Inputs 0.003 0.068 Labor Cost 0.003 0.055

Technical Inefficiency Model

Constant 0.400*** 5.915

Farm Size -0.017 0.842 Experience -0.0008 -0.724 Education -0.013* -1.857 sigma-squared 0.017*** 4.901 Gamma 0.999 0.269 Mean Efficiency 0.79

1 * indicate that the coefficient is significantly different from zero 0.10 percent probability level; 2 ** indicate that the coefficient is significantly different from zero 0.05 percent probability level 3*** indicate that the coefficient is significantly different from zero 0.01 percent probability level;

- 79 -

In summary, the farm enterprises were predominantly small and constitute

majority of the farming community in Sargodha Division. The Division represents

the mixed cropping zone where most of the crops are grown. The mean technical

efficiency in the region was 0.90 percent, and 0.79 percent, for credit and non-

credit users respectively. The high technical efficiency of credit users was safely

attributed to credit availability through which they have an access to new

technology. The results of the allocative efficiency showed that farmers were

inefficient (MVP/OC) >1) in their input use at the farm level. Thus both categories

of farm were economically inefficient. The results were consistent with

Udayanganie, et.al. (2006). Credit improved the economic efficiency of the credit

user especially with good extension services.

- 80 -

Chapter 5

Credit Constraints and Borrowing Behavior

Most countries including Pakistan motivated by consideration of the potential

gain and the increase in welfare of the farmers, especially the smaller farmers,

have undertaken widespread credit programmes. However, in most such cases,

the Government intervention takes the form of interest rate ceiling or subsidies

interest rates thereby necessitating rationing. When credit was rationed some

borrowers cannot obtain the amount of credit they desire at the prevailing interest

rates, nor can they secure more credit by offering to pay higher interest rate. In

such cases, liquidity can become a constraint and most binding where the

access to credit is limited by consideration inherent either in the design of credit

programmes or those that arise out of the skewed socio- economic and political

structure which diverted such credit away from those that need it. (Malik, 1999)

It was important to note, however, that credit constrain (of which rationing is a

special case would arise even if there were no government intervention in credit

markets. There are three stylized facts about credit markets which make it quite

likely that even unregulated credit markets can easily be constrained (Hoff and

Stiglitz, 1990)

1. “Screening problem: Borrowers differ in the likelihood that they will

default, and it is costly to determine the extent borrower’s risk.

2. Incentives problem: It is costly to ensure that borrowers take those

actions which make repayment most likely.

3. Enforcement problem: It is difficult to compel repaymen”t.

- 81 -

The upshot is that “interest rates may not equilibrate credit supply and demand;

there may be credit rationing, and in period of bad harvest, lending may be

unavailable at any price” (Hoff and Stiglitz,, 1990)

“There were several questions regarding agricultural credit which solicit answers.

The questions are: what is the nominal interest rate reported by respondents?

What was the borrowing behaviour of the respondents at this interest rate? Are

farmers really credit constrained? How the credit constraints are determined?

How the credit constraints affecting the consumption pattern of the farm

households? How the credit constraints affecting the farm production of the

farmers”? (Malik, 1999). In order to provide answer these and related

questions, some simplifying assumptions were made. First of all, if a household

was constrained in the formal market, then, credit being fungible, it must be

constrained overall. Therefore, the present study focused on the market of

institutional credit. Two data set were utilized for the analysis in this chapter. The

data from Pakistan Rural Household Survey (PRHS) 2001 comprising 2642

households from 16 districts of Pakistan was taken to identify the constraints. In

addition, household and farm survey of 160 farm households was conducted in

summer 2007 from Sargodha region.

In the Pakistan Rural Household Survey (PRHS 2001), a number of questions

were asked to determine the factors affecting the use of institutional credit in

Pakistan. These ranged from the reasons for not applying credit to a formal

institutions, distance of the bank, interest rates of the institutions, time lapsed

between application and loan disbursement, purpose of loan, formal institutions

- 82 -

and collateral used for the loan from the institutional sources. These were the

issues that covered the wide range of problems affecting the institutional credit

structure in Pakistan. The respondent’s response highlighted the relative

importance of each constraining factor. These perceptions were ascertained in

most cases from the experience of the rural credit and non-credit users. In the

present study was disaggregated according to agro climatic zones. The

classification of the districts of into agro-climatic zones was based on Pakistan

Agricultural Research Council, 1989. This classification highlighted the different

areas that would need to be addressed when designing policies of these regions.

Methodology The demand for borrowing suffers from two problems namely truncation bias

because of external borrowing; and implicit interest rate. In order to address

these problems, Iqbal (1986) showed a procedure for imputing interest rate for

non responsive respondents from an interest rate function regressed over

personal and binary variables. However, in estimating such functions the data on

households reporting interest rate might create problem. The econometric

literature provides an opportunity to adjust for selection bias which is well

reflected in the seminal work of Heckman (1979). 5In this study we assumed that

the credit market is dichotomous i.e., there are only two segments; formal and

informal; whereas in reality there are several classification in formal and informal

sectors that we explained in chapter 3. Moreover, the analysis does not explicitly

account for differential borrowing behaviour due to the presence of credit

5 See also Malik, S. J. (1999). Poverty and Rural Credit.

- 83 -

constraint. Thus, the present chapter was an application of Heckman’s

procedure.

The problem, simply stated, there was some underlying selection process that

warranted explicitly accounted for in the econometric framework when estimating

from the data on economic agents who already opted to behave one way or the

other. The data available for analysis were after the fact i.e. after some

underlying process of selection already took place. This implied that some

households decided to transact in the credit market for their borrowing and others

did not. The underlying selection process in this case postulated on the basis of

presence or absence of a credit constraint.

The elements of the Heckman approach were laid out as follows.

The model was:

0i i i i iy x if xβ ε β ε′ ′= + + >

= 0 0 (1 )i ii f xβ ε′ + ≤ − − − −

Where:

yi = the dependent variable and the xi were regressors. In addition, there was a

censoring indicator, zi which was as follows: zi = 1 if yi > 0 and : zi = 0 if yi ≤ 0.

It was expressed that:

- 84 -

1 (2)i i i iE y z xβ σλ′⎡ = ⎤ = + − − − − −⎣ ⎦

Where λ i is the inverse Mill’s Ratio defined as:

(3)

i

ii

x

x

α βφσλ

α βσ

′+⎛ ⎞⎜ ⎟⎝ ⎠= − − − − −

′+⎛ ⎞Φ⎜ ⎟⎝ ⎠

Where:

φ and Φ were, respectively, the probability density function (p.d.f) and

cumulative density function (c.d.f) of the standard normal distribution. Here value

of the λ i depends on β and σ . However, information was available about the

censoring indicator:

[ ] ( )

[ ] ( )

1

0 1 1 (4)

ii i

ii i

xP z x

xP z x

β γσβ γσ

′⎛ ⎞ ′= = Φ = Φ⎜ ⎟⎝ ⎠

′⎛ ⎞ ′= = −Φ = −Φ −− − − − − −⎜ ⎟⎝ ⎠

Where: Ii

βγσ

= . This was precisely the Logit model so γ can be estimated by

Logit procedure. The estimated value of γ , γ̂ can then be substituted into the

definition of λ :

( )( )ˆˆ (5)ˆ

ii

i

xx

φ γλ

γ′

= − − − − −′Φ

The second step was to estimate OLS on:

ˆ (6)i i iy xβ σλ ξ′= + + − − − − −

For non-limit observations, i.e. yi > 0. It should be noted, however, that the

estimator found from the Heckman procedure was consistent and asymptotically

- 85 -

normal, it was not efficient. Furthermore, “the replacement of λ i with λ̂ led to

hetroskedasticity and, therefore, to biased and inconsistent calculated standard

errors” (Kmenta, 1986,p 564).

In applying the Heckman approach to the problem, firstly define the censoring

indicator INT as follows:

INT = 1 if interest > 0

= 0 otherwise.

INT= the credit is not at zero interest rate (either formal or informal credit market)

From this indicator one can define a variable MAXINT as follows:

MAXINT = β ′Xi + Ui if INT = 1

= 0 otherwise

Where MAXINT was the highest nominal interest rate reported by a household

from among its current loans.

The following three step procedure was followed. First, a Logit regression of INT

was run on the following variables:

(i) Land owned: Acres of land owned by the household

(ii) Education of males: The maximum educational level among male members of

the household

(iii) Dependency ratio: The ratio of the household size to the number of adult

males

(iv) Source Dummy: A dummy variable for the source of the loan, which was

specified as one if the source was an official lending agency otherwise zero

- 86 -

(v) Transitory income: A measure of transitory income, defined as a difference

between estimated food expenditure and the value of farm production. This

definition is correct only for unconstrained households.

Secondly, the Inverse Mill’s Ratio was computed and added as the explanatory

variable in the regression of MAXINT on the explanatory variable listed above.

Thirdly, the demand for borrowing (BORR) was estimated by using the predicted

interest rate (the price of credit which was estimated from the interest rate

function, and this price was used to measure the effect on overall demand for

credit or in other words willingness to pay for credit.) from the second stage as an

additional regressors, together with the regressors given above, with the

exception of the dummy variable for source of loan.

1 2a

i iBORR Xα α= + (Predicted MAXINTi)+ εi

Where: includes regressors variables (i) to (v) with the exception of (iv) and

BORR was defined as external borrowing minus external lending plus or minus

change in assets which were obtained as the sum of net deposits in financial

institutions; expenditure on the furniture; appliances and other durables; less

receipt from the sale of property. In this case, imputing the interest rate

(predicted interest rate) where no explicit interest was reported, assuming

consistent behavior across households.

Ultimately there were two equations, the borrowing equation and the interest rate

equation, and two endogenous variables (BORR, MAXINT) and three sets of

exogenous variables (Xi,aiX ). Both equations were exactly identified.

- 87 -

Determination of Credit Constraints For the estimation of the constraint, Heckman (1979) technique was used to

estimate a Logit model for the probability that the households was constrained (An individual is credit constrained if his/her terms of access to the credit market

imply that he/she does not exploit (either because he/she is unable or unwilling)

a socially profitable (expected income enhancing) investment.)

As a first stage and then using the inverse of Mill’s Ratio from the first stage as a

right hand side variable in the regression to explain the borrowing behavior of

farm households as the adjustment factor to adjust for selectivity bias .The

dependent variable in the second stage was the total amount borrowed per farm

households.

The Logit model was used to estimate the probability that a household was

constrained includes the following variable:

(i) Land value: Value of land owned.

(ii) Value of farm implements: This includes value of owned farm tools,

livestock; tube well, tractor and tractor

drawn implements

(iii) Number of dependents: Number of family member under sixteen

years of age

(iv) Farm experience: Years of farm experience of head of

households.

- 88 -

(v) Initial liquid assets: This includes total value of grain in

store, value of assets and earning from

sale of assets and property.

(vi) Previous year’s income: Previous year’s income.

The inverse Mill’s ratio were estimated from the above analysis and introduced

as an explanatory variable to estimate borrowing behavior of farm households.

The dependent variable in this equation was the gross amount borrowed by a

household from all sources and for all purposes over the previous year i.e.

without deducting any repayments. The additional variables used in the second

stage were as follows:

(i) Operational holding: Total operational holding in acres.

(ii) Dependency ratio: Ratio of family under sixteen to total household size.

(iii) Education level: Maximum level of education of male family members.

(iv) Savings: Net deposits in financial institutional.

Household Total Consumption Expenditures The simple regression model was used to estimate the impact of credit

constraints on household total consumption by regressing on following variables;

Education Level, Value of Implements, Operational Holding (Acres),Dependency

Ratio, Tehsil Dummies, Predicted probability of being constrained

Perception and attitude to institutional credit A detailed examination of perception of and attitudes to institutional credit is

warranted to gain some understanding of how households can end up being

constrained in the market of institutional credit. This was defined as:

- 89 -

Quantity Rationing: Individual has a profitable project and wants a loan, but is

denied access. Transaction Cost Rationing: Individual has a profitable project

but does not apply because, once the transaction costs associated with loan

application (and monitoring) are factored in, the project is no longer profitable.

Risk Rationing: individuals are able to borrow, but only under high collateral

contracts. High contractual risks constrain credit.

Constraints from formal institutions The respondents were asked to state the most important reason for not applying

for loan from a formal institution. Of the eight reasons listed, five were classified

as reasons that affect the demand side, namely: i) do not need; ii) involves

paying bribe; iii) inadequate collateral; iv) private sources sufficient; and v) do not

want to pay interest (“Riba”)

The additional three reasons were categorized as affecting the supply side.

These were: i) cumbersome procedure; ii) lenders too far away; and iii)

expensive procedures. The third category was used to capture cases where the

respondent felt that there was more than one reason or the reason not listed in

seven discussed above. Table 5.1 revealed that on supply side, 9 percent of

respondents pointed out cumbersome and nearly 29 percent revealed that they

do not need credit. The results were consistent with Carter, 1988; Carter and

Weibe, 1990). Stiglitz and Weiss (1981). These are issues that can be quite

easily addressed by policy. For instance effective publicity coupled with further

simplification of method especially security would help remove such constraints.

- 90 -

This would increase wakefulness among the farmers and also reduce concealed

operational costs.

On the demand side, unacceptable collateral or inadequate collateral 28.4

percent, no need and disliked borrowing (religious reasons) as the most

important reasons 29.4 percent. Nearly five percent stated that private (non

institutional) sources were sufficient. The inadequate collateral also covered a

large number of cases of ‘unacceptable’ collateral. Land was the most readily

acceptable form of collateral and this prevents a large number of tenants and

land less people from participating in the formal credit markets. There were large

regional differences in the ordering of the various credit constraint reasons which

reflected the relative poverty of the different regions. In the same way it was

showing the level of development of rural credit markets in each region. The

results were consistent with Boucher et. al. (2005) and Zeller et. al. (1997).

The results further showed that in Cotton-Wheat Sindh, Low intensity Punjab;

and Cotton-Wheat Punjab zones, 52 percent, 44 percent and 42 percent

respondent cited inadequate collateral as the most constraining factor in

obtaining credit from the formal sources respectively. In NWFP province, 63

percent of the farmers indicated that they do not want to pay interest.

- 91 -

Table 5. 1: Constraints for not applying for loan from formal institutions

(Percent) Agro-Climatic Regions

Mixed Punjab

Barani Punjab

Rice/Wheat Punjab

Low Intensity Punjab

Cotton/ Wheat Punjab

Cotton/ Wheat Sindh

Rice and other Sindh N.W.F.P Baluchista

n Total

Lenders too far away - - - - 5.6 10.4 - 7.9 3.5 Don't need credit 19.3 31.8 29.6 31.9 23.6 17.1 31.5 20.6 59 29.4 Prefer informal lenders 8.5 7.8 7.4 1.4 4.1 7.8 8.8 2.9 2.5 5.5 Cumbersome procedure

13.1 15.5 8.0 12.3 12.5 3.9 12.4 4.1 10.1 9.0

Don't possess adequate collateral

31.3 17.1 34.6 44.9 42.1 52.4 21.9 6.2 6.6 28.4

Involves paying a bribe 5.1 7.8 8.0 4.3 2.6 1.9 1.2 0.3 - 2.5 Do not want to pay interest

21.6 14.7 12.4 5.2 14.7 8.0 10.8 63.7 12.8 19.7

Other................... 1.1 5.3 - - 0.4 3.3 3.0 2.2 1.1 2.0 Total 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0

- 92 -

Collateral used by the respondents for the agricultural loan Inadequate collateral or lack of it implied that some individuals were denied

credit. In this regard, the farmers were asked about the collateral used by them

for the credit. Among the all possible options for collateral include but not limited

to: i) none; ii) agricultural land; iii) residential or commercial property; iv) other

ornaments/utensils/consumer durables; v); Gold; vi) guarantee by landlord; vii)

guarantee by relative/friend/neighbor; viii) guarantee by employer; and ix) any

other source. It was evident from the Table 5.2 that about 77 percent farmers

used agricultural land as collateral for the loan which was acceptable by the all

institutional sources which showed how much farmers were bound to use land as

collateral and the others have negligible share. The results were consistent with

Banerjee, 2001. This can also be seen across the regions that farmers were

highly dependent upon land. It was obvious that alternative forms of collateral

were imperative to increase the access of the tenant and poor rural households

to institutional credit. One of the often-suggested ways to get around the problem

of adequate collateral was through group borrowing and lending. This also

enabled the enforcement repayment. Unfortunately, Pakistan experience with

cooperatives was dismal.

- 93 -

Table 5. 2: Collateral for loan (Percent)

Agro-Climatic Regions

Mixed Punjab

Barani Punjab

Rice/ Wheat Punjab

Low Intensity Punjab

Cotton Wheat Punjab

Cotton Wheat Sindh

Rice and other Sindh

N.W.F.P Baluchistan Total

None 33.3 29.4 - - 2.0 - 14.7 3.0 43.8 11.3 Agricultural land 60.0 64.7 77.8 100.0 89.8 88.0 76.5 78.8 25.0 76.9

Residential or commercial property - - - - - 4.0 - - 6.3 .9

Other ornaments/utensils/consumer durables

- - - - - 4.0 - - - .5

Gold - - 11.1 - - 4.0 - - - .9

Guarantee by landlord - - - - - - 2.9 3- - .9

Guarantee by relative/friend/neighbor

- - - - 2.0 - - 15.2 6.3 3.3

Guarantee by employer - 5.9 - - - - 5.9 - - 1.4

Others............ 6.7 - 11.1 - 6.1 - - - 18.8 3.8

Total 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0

- 94 -

Purpose of the loan Respondents were asked to cite the purpose of the loan taken. The purposes of

loan were: i) agricultural production; ii) purchase of agricultural land; iii) purchase

of tractor; iv) purchase of thresher; v) purchase of tube well; vi) purchase of other

livestock; vii) other agricultural costs; viii) food/clothing; ix) medical expenses; x)

marriage/death or other ceremonial expenses; xi) marriage/death or other

ceremonial expenses; xii) purchase of consumer durables; and xiii) purchase or

improvement of family dwelling; xiv) to pay off old loans; and xv) for non-

agricultural production. Among all the purposes, the agricultural production was

found as 45.8 percent given in Table 5.3. It was followed by purchase of tractor

8.5 percent and purchase of land 6.1 percent all other have share less than six

percent. Across the agro-climatic regions, most of the loans were for the

agricultural production only but in Balochistan 50 percent of the loan was for the

purchase of agricultural land. The results were consistent with Malik (1999), Irfan,

et.al. (1999) and Qureshi, et al.(1996)

- 95 -

Table 5. 3: Purpose of loan taken (Percent)

Agro-Climatic Regions

Mixed Punjab

Barani Punjab

Rice/ wheat Punjab

Low Intensit

y Punjab

Cotton Wheat Punjab

Cotton Wheat Sindh

Rice and other Sindh

N.W.F.P Baluchistan Total

Agricultural Production 46.7 29.4 66.7 85.7 71.4 16.0 50.0 30.3 6.3 45.8

Purchase of agricultural land - - - - - 8.0 8.8 - 50.0 6.1

Purchase of tractor 26.7 5.9 - - 8.2 20.0 2.9 6.1 6.3 8.5

purchase of thresher - - - - - - 2.9 - - .5

Purchase of tube well - 11.8 - 7.1 4.1 8.0 - 3.0 12.5 4.7

Purchase of other livestock - 17.6 - - 2.0 8.0 8.8 - - 4.2

Other agricultural Costs 13.3 5.9 - - - 4.0 5.9 3.0 - 3.3

Food/clothing - - 11.1 - - 4.0 5.9 9.1 - 3.3

Medical expenses - - 11.1 - 2.0 12.0 5.9 3.0 6.3 4.2

Marriage/death or other ceremonial expenses 13.3 5.9 - - 6.1 4.0 2.9 3.0 6.3 4.7

Purchase of consumer durables - 5.9 - - - - - - - .5

Purchase. Improvement of family dwelling - 5.9 - - 2.0 - 6.0 24.2 - 5.7

To pay off old loans - - 11.1 7.2 2.2 4.0 - 6.1 - 2.8

For non-agricultural production - 11.7 - - 2.0 12.0 - 12.2 12.3 5.7

Total 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0

- 96 -

Formal institutions for crediting

The respondents were asked about the formal institutions that they used

for formal loaning. The formal institutions were: i) Commercial Banks; ii)

Zarari Taraqiati Bank (ZTBL) (defunct Agricultural Development Bank); iii)

Women’s Bank; vi) Non-governmental organizations; v) House Building

and finance corporation vi) others (cooperatives). The entire share in this

regard was covered by ZTBL which was 86.3 percent given in Table 5.5

ZTBL was providing most of the loan to the farmers for their agricultural

needs. This was not only in one region, it was found more than 70 percent

loan provided by ZTBL. So there is a dire need to strengthen this

institution for the proper agricultural loaning.

Interest rate and the response of the farmers

The most important issue related to decision making of the farmers for the

loan was interest rate. It represented the cost of borrowing at margin of

the household from the PRHS (2001) data; the average interest rate was

calculated for all the agro-climatic regions. The interest was floating

between 10 to 20 percent. But amazingly it was found higher in Barani

Punjab 20 percent which was followed by Balochistan 17 percent in Table

5.6. The results were consistent with Malik (1999). In order to check the

response of the farmers and their ability to re-pay loan, following two

interesting questions were asked; i) if you were offered a higher interest

rate, would you borrow less? ii) if you were offered a larger loan at the

same interest rate, would you borrow more? For the first question more

- 97 -

than 50 percent respondents replied positively which showed that the

farmers have the ability to repay even at the higher rate of interest. Duca

and Rosenthal (1993) argued that a farm household was credit

constrained only when it would like to borrow more than lenders allow or if

its preferred demand for credit exceeds the amount lenders are willing to

supply. Stiglitz and Weiss (1992), on the other hand, described credit

constraints in two terms - redlining and credit rationing.

Therefore, they must be treated as customer not a beneficiary. For the

second question farmers’ response was negative that showed that the

farmers were not able to exhaust all their resources and they were not

diversifying agriculture.

- 98 -

Table 5. 4: Institutional Sources (Percent)

Agro-Climatic Regions

Mixed Punjab

Barani Punjab

Rice/ Wheat Punjab

Low Intensity Punjab

Cotton Wheat Punjab

Cotton Wheat Sindh

Rice and other Sindh

N.W.F.P

Baluchistan Total

Commercial Bank 26.7 11.8 11.1 - 4.1 8.0 2.9 6.1 6.3 7.1 Agricultural Development Bank

73.3 70.6 88.9 100.0 91.8 92.0 79.4 84.8 93.8 86.3

Women's Bank - - - - - - 2.9 - - .5

NGO - - - - - - 14.7 - - 2.4

HBFC - - - - - - - 3.0 - .5

Other - 17.6 - - 4.1 - - 6.1 - 3.3

Total 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0

- 99 -

Table 5. 5: Interest rate by the respondents

(Percent)

Agro-Climatic Regions

Interest rate Mixed Punjab

Barani Punjab

Rice Wheat

Low Intensity Punjab

Cotton Wheat Punjab

Cotton Wheat Sindh

Rice and other N.W.F.P Baluchista

n

12.73 20.29 9.11 12.07 10.71 14.96 16.29 13.67 17.19 If you were offered a higher interest rate, would you borrow less?

Yes 66.7 44.4 50.0 36.7 64.0 70.6 64.7 66.7 43.8

No 33.3 44.4 50.0 63.3 36.0 29.4 32.4 33.3 56.2

indifferent - 11.2 - - - - 2.9 - -

Total

100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0

If you were offered a larger loan at the same interest rate, would you borrow more?

Yes 13.3 - 21.4 14.3 36.0 35.3 35.3 36.4 25.0

No 86.7 88.9 78.6 85.7 64.0 64.7 61.8 63.6 75.0

indifferent - 11.1 - - - - 2.9 - - Total

100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0

- 100 -

Time Lag in disbursement of loan

There was one probable problem that could arise even if a loan was

granted by an institution. Was the loan became available in time and what

was the time lag? This affected the eventual productivity of such loan

especially where short run loans were concerned. A fertilizer loan, for

example, if not made available on time would be useless to the farmer.

The main reason for the delayed disbursement of loans was as non-

cooperation and lengthy procedure. From the Table 5.6 it can be inferred

that most of loans were sanctioned and disbursed in a period of 1-3

months. Across the agro-climatic regions, more than 75 percent credit was

disbursed in one quarter except Balochistan. Though the figures were

acceptable but in Pakistan majority of loans were for short-term production

with a per acre upper limit.

Distance of the Bank

Education, health condition, fixed assets holding and distance from

household to formal bank branch were among the most important factors

affecting household’s credit activities. In the present study, the distance of

the formal bank was measured in the form of kilometers from the

community. It was found that the distance of the formal bank was more

than 20 Kms in 28.8 percent of area as shown in Table 5.7. The

percentage of more than 20 Kms was 81.3 percent in Balochistan which

was clear indication of backwardness and poverty stricken. The

- 101 -

government must develop policies for these remote areas to exploit the

resources and to increase productivity of agriculture sector.

- 102 -

Table 5. 6: Time lagged between loan application and approval (Percent)

Agro-Climatic Regions

Time lagged (months) Mixed Punjab

Barani Punjab

Rice / Wheat Punjab

Low Intensity Punjab

Cotton Wheat Punjab

Cotton Wheat Sindh

Rice and other Sindh

N.W.F.P Baluchistan Total

1-3 86.7 100.0 100.0 84.4 90.5 85.7 75.0 71.4 50.0 81.5

3-6 13.3 - - 6.7 9.5 14.3 25.0 28.6 50.0 16.4

6-9 - - - 6.7 - - - - - 1.6

More than 9 - - - 2.2 - - - - - 0.5

Total 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 Table 5. 7: Distance of the Bank

(Percent) Agro-Climate Regions

KM Mixed Punjab

Barani Punjab

Rice/ Wheat Punjab

Low Intensity Punjab

Cotton Wheat Punjab

Cotton Wheat Sindh

Rice and other Sindh

N.W.F.P Baluchistan Total

1-5 33.3 35.3 33.3 - 14.3 8.0 26.5 33.3 - 20.3

5-10 33.3 35.3 44.4 14.3 36.7 20.0 17.6 30.3 - 26.4

10-15 - - - 14.3 12.2 12.0 26.5 12.1 - 11.3

15-20 26.7 5.9 - 28.6 14.3 16.0 5.9 9.1 18.8 13.2

More than 20 6.7 23.5 22.2 42.9 22.4 44.0 23.5 15.2 81.3

28.8

Total 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0

- 103 -

Demand for Borrowing and Interest Rate Function Before discussing the results, first there was a need to explain the rationale for

using these explanatory variables as discussed above.

Rationale of selected variables Xi is a vector of all personal variables. The vector Xi includes variables that

influence the opportunity, administrative and risk costs of borrowing. These three

types of cost determine the normal interest rate. The variable associated with

opportunity cost was the presence of government regulated lending agencies in

villages. Such agencies provided loans at below market rates of interest. Their

presence in the village reduced the average interest rate. The dummy variable

for the source of a loan was used to capture this effect. Village population could

have been used as a proxy for the administrative cost of lending because the

larger the loan, the smaller the unit cost of administrating it, but unfortunately this

information was not available. The risk cost was explained by income earning

and loan repayment ability of a farm household. These were determined by, for

example, total land owned, maximum educational level of households males,

dependency ratio, transitory income. Land owned is a measure of the

household’s endowments; the educational level of male household is a measure

of the investment opportunities available to the household, as well as its ability to

cope with the cumbersome paperwork and administrative details required for

getting loans from official agencies; the dependency ratio is a measure of the

household’s stage in its life cycle, and therefore of its need for current

consumption; and finally (v) transitory income variable, was indented to capture

- 104 -

variations in the demand for credit arising out of windfall gains and losses. From

the permanent income hypotheses, if a household was not on its optimal

consumption path, perhaps because it was credit constrained, it treated such

windfall gains as a substitute for credit, where as household (e.g., of large

landlords) treated such gains as a complement to credit.

It is the time to discuss results which were quite interesting in view of some of the

limitations of data because of the assumed dichotomous nature of the credit

market.

Heckman’s selectivity model relating Interest rate (Dummy) with independent variables in Sargodha Region

1 2 3

4 5 6 7 8 9

( 1)log [ ( 1)] log ( ) ( ) ( )1 ( 1)

( ) ( ) ( ) ( ) ( ) ( )

p INTit p INT edu sizhld lwp INT

ltinc dpratio sorcdm bhldm shadm sildm

α β β β

β β β β β β

⎡ ⎤== = = + + + +⎢ ⎥− =⎣ ⎦

+ + + + +

Table 5. 8: Heckman selectivity model relating Interest rate (Dummy) with independent variables in Sargodha Region

Independent Variables Coefficients Standard Error p-value

Odd Ratio’s

Constant -6.631 4.989 0.184 -2.880 Educational Level (Years) -0.021 0.126 0.865 -0.009 Size of Holding (Acres) .0180 .0340 0.865 0.008 Log of Wage Rate (Rs) 0.316 0.645 0.624 0.137 Log of transitory Income (Rs) 0.138 0.285 0.628 0.060 Dependency Ratio -0.129 0.256 0.613 -0.056 Source Dummy 7.474*** 1.342 0.000 3.246 Bhalwal Dummy -1.577 2.594 0.543 -0.685 Shahpur Dummy 1.652 1.292 0.804 0.717 Sillanwali Dummy 0.336 1.358 0.184 0.146

Dependent variable: Interest rate dummy; *** showed that the coefficient is significantly different from zero at 0.01 probability level

- 105 -

Model Results

The results were obtained in Table 5.8 which revealed that most of the

coefficients were having expected signs but were not significant except source

dummy(p=0.01). This showed that institutional sources were active in loaning,

thus likely to pay interest. On the demand side it will reduce the transaction cost

of the farmer. The results were consistent with Carter (1988); Carter and Weibe,

(1990), Stiglitz and Weiss (1981), and Malik (1999).

After estimating the above Logit model, the inverse Mill Ratio’s were computed

and added as an independent variable in the above mentioned logit model. The

results were revealed in Table 5.9. Again most of the parameters showed

expected sign but not really significant. However, it was interesting to note that

source dummy was not significant in the interest rate equation. This showed that

borrowing from institutional sources was not important in lowering the overall

mark up. The respondents mostly banked on informal sources (i.e. commission

agents; money lenders, input suppliers, etc.). The Inverse Mill’s Ratio was

calculated to control the selection bias in the subsequent analysis and thus,

introduced in the model as an explanatory variable. The coefficient was having

expected sign but the coefficient was not significant. The results were consistent

with Malik, 1999 who used a panel data from International Food Policy Research

Institute to prove above assertions.

- 106 -

Heckman’s selectivity model relation interest rate with independent variables in Sargodha Region

1 2 3 4 5

6 7 8 9 10

( ) ( ) ( ) ( ) ( )( ) ( ) ( ) ( ) ( )i

MAXINT edu sizhld lw ltinc dpratiosorcdm bhldm shadm sildm

α β β β β ββ β β β β λ

= + + + + + ++ + + +

Table 5. 9: Heckman selectivity model relation interest rate with independent variables in Sargodha Region Independent Variables Coefficients Std. Error t-Statistics (Constant) 20.747*** 8.345 2.486 Dependency Ratio 0.153 0.264 0.579 Size of Holding (Acres) 0.027 0.022 1.241 Education (Years) -0.105 0.100 -1.050 Log of transitory Income (Rs) 0.076 0.217 0.352 Log of wage (Rs) -0.018 0.684 -0.026 Source Dummy -8.523 6.074 -1.403 Bhalwal dummy -0.002 2.952 -0.001 Shahpur dummy -1.366 1.091 -1.252 Sillanwali dummy -0.199 0.911 -0.218 Inverse Mill's Ratio -2.993 3.340 -0.896

Dependent variable: Interest rate

Notes: *** showed that the coefficient is significantly different from zero at 0.01 probability level

Heckman’s selectivity model log of borrowing relating with Independent variables, Sargodha Region

1 2 3 4 5

6 7 8 9

log ( ) ( ) ( ) ( ) ( )( int) ( ) ( ) ( )BORR edu sizhld lw ltinc dpratiopred bhldm shadm sildm

α β β β β ββ β β β

= + + + + + ++ + +

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Table 5. 10: Heckman selectivity model log of borrowing relating with Independent variables, Sargodha Region Independent Variables Coefficients Std. Error t-Statistics Constant 7.437*** 2.012 3.696 Dependency Ratio 0.016 0.114 0.144 Log of Transitory Income (Rs) 0.326*** 0.094 3.459 Education Level (Years) 0.115*** 0.044 2.627 Size of Holding (Acres) 0.004 0.009 0.509 Log of wage (Rs) 0.084 0.301 0.279 Bhalwal dummy -1.023 1.062 -0.963 Shahpur dummy -0.812** 0.467 -1.740 Sillanwali dummy -0.770** 0.405 -1.903 Predicted Interest Rate -1.103** 0.655 -1.684

Dependent Variable: Log of Borrowing R2 = 0.45

Notes: * showed that the coefficient is significantly different from zero at 0.10 probability level. ** showed that the coefficient is significantly different from zero at 0.05 probability level;

*** showed that the coefficient is significantly different from zero at 0.01 probability level Later on the borrowing function was estimated with Ordinary Least Square (OLS)

and the results were shown in Table 5.10. The results showed that the transitory

income, education level, and predicted interest rate was significant and with

expected sign. The transitory income was anticipated to capture variations in

the demand for credit arising out from the windfall gains and losses. As

envisaged from the permanent income hypothesis, if a household is not on its

optimal consumption path because of its credit constrained, it will treat such

gains as a substitute for credit, whereas households (e.g., of large landlords) will

treat such gains as complement to credit. The results were supporting the same

phenomenon in Pakistan credit market. The coefficient of maximum education of

household males, which measures the household’s ability to cope with the

procedure required for getting loans, has expected sign and significantly different

from zero which implied that education played a significant role in borrowing

decision and reducing the transaction cost of the credit. Most importantly and

encouraging was that predicted interest rate showed expected sign (negative)

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and was significant (p=0.10). However, the model can predict the price for the

credit which has an overall effect on the demand for credit. The land owned

found with expected sign but statistically non-significant. This was consistent with

the behavior of Pakistan credit market, where land was most important form of

collateral, but which was never seized in reality by the lending agency in the

event of default. The land title merely provided an entry into the credit market and

nothing else. The interest rate charged and the amount borrowed depended

upon other considerations. The coefficient of wage rate was as expected positive

but non significant. Intuitively with higher wage rate there was more ability to pay

high interest rate and more liberty in borrowing. But in Pakistan’s labour market,

the wage rate was too low to provide the liberty to households for additional

borrowing. Since wage rate was a good proxy for ability to repay. In these

circumstances it was why such households should be charged a higher interest

rate. This was mainly due to monopoly power of informal lender, who charged at

their own will. The coefficient of dependency ratio showed expected sign but

insignificant. If the ratio of the adult males to household size was greater, the

household will borrow more but in Pakistan the households mainly depend upon

sole earner which reduced the ability to pay interest rate and borrowing. The

tehsil dummies were found significant which implied that all the revenue units

have good quality of land and thus same pattern of borrowing was followed in all

the areas.

The important contribution of the estimated model was the confirmation that

household and loan characteristics and area effects can be used to predict the

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price of credit. Therefore, the results of the study must be carefully interpreted

because of underlying assumptions of the model. The assumptions were the

credit market was dichotomous, however in reality there are several segments of

the credit market. Furthermore, the analysis does not explicitly include differential

borrowing behavior of the respondents due to credit constraints. Similarly, the

imputed interest rate assumed consistent behavior across the respondents and

this may not hold true due to credit constraints.

Determinants of Credit Constraints The above description of credits constraints was augmented with recently

collected survey data (summer, 2007) from Sargodha region. In this section,

differential behavior of respondents due to credit constraints was analyzed

through the estimation a logit model. The results were obtained in Table 5.11.

The results revealed that the probability that household was constrained by land

value, value of farm implements, farm experience, dependency ratio, operational

holding, educational level and savings. As expected, the probability being

constrained was dependent negatively on value of land, farm experience,

savings, and educational level. Among all these variables, the value of land was

found significant which showed that the probability of being constrained reduced

as the value of land increased and this was a common characteristic of Pakistan

credit market. The land was acceptable collateral and credit amount assessed

on the basis of its value. The value of farm implements depicted positive sign and

significant which is counter-intuitive. All other variable were not significantly

different from zero. These were the results of Logit estimation (First Stage). This

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led us to estimate the Inverse Mill’s Ratio which was included as independent

variable in the borrowing function along with other independent variables. The

interest rate was not included among the independent variables. Thus Heckman

two stage procedures was applied to estimate the parameter. The dependent

variable was gross amount borrowed from all the sources over the previous year

without deducting any repayments. The results revealed that land value and

savings in the financial institutions was a negative function of total amount

borrowed. The results found counter intuitive. The results were consistent with

Malik, 1999. Total borrowing per household was positively and significantly

dependent upon initial liquid assets. One can expect that higher the initial liquid

assets, lower tends to be the borrowing. The results suggested that higher level

of initial liquid assets leads to higher borrowing. This could be the result of

characteristics of Pakistan credit market which was dominated by large farmers.

It was interesting to note that the educational level in the household, its

operational holding and the dependency ratio have seemingly no impact on its

aggregate borrowing behavior. Tehsil dummies were also found significant

showing the same borrowing pattern. Most importantly the Inverse Mill’s Ratio

found positive and significant which suggested that coefficients of the model

were reliable and unbiased. The results of the study were consistent with Malik,

(1999) and Nguyen (2007). However, some of the variables were at variance with

this study.

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Table 5.11: Results of Logit Estimation (First Stage): Probability that Household was Constrained, in Sargodha Region Independent Variables Coefficients Std.Error Sig. Odd Ratio’s Constant 12.627** 5.787 0.029 5.484 Log of Value of Land (Rs.) -1.189** 0.491 0.015 -0.520 Farm Experience (Years) -0.003 0.021 0.890 -0.001 Log of Value of farm implements (Rs) 0.529** 0.227 0.020 0.230 Operational Holding (Acres) 0.014 0.017 0.404 0.006 Log of Saving (Rs) -0.056 0.070 0.419 -0.024 Dependency Ratio 0.202 0.184 0.271 0.088 Educational Level (Years) -0.044 0.066 0.503 -0.019

Constraints dummy Constrained 1 unconstrained 0 Notes: * showed that the coefficient is significantly different from zero at 0.1 probability level; ** showed that the coefficient is significantly different from zero at 0.05 probability level; and *** showed that the coefficient is significantly different from zero at 0.01 probability level Table 5. 12: Results of Heckman 2-Stage Procedure with Total Amount borrowed, in Sargodha Region Independent Variables Coefficients Std.Error t-Statistics (Constant) 2.065 3.855 0.536 Log Value of Land (Rs) -0.241 0.290 -0.830 Log Value of Farm implements (Rs) 0.168 0.131 1.281 Log of Savings (Rs) -0.115*** 0.046 -2.502 log of Initial Liquid Assets (Rs) 0.792*** 0.173 4.584 Operational Holding 0.013 0.012 1.067 Dependency Ratio -0.062 0.108 -0.570 Education (Years) -0.012 0.053 -0.225 Bhalwal Dummy -0.289 0.698 -0.413 Shahpur Dummy 1.275*** 0.631 2.018 Sillanwali Dummy 1.122** 0.625 1.796 Inverse Mill's Ratio 0.357*** 0.077 4.667

Dependent Variable: Log of Borrowing

Notes: * showed that the coefficient is significantly different from zero at 0.1 probability level; ** showed that the coefficient is significantly different from zero at 0.05 probability level; and *** showed that the coefficient is significantly different from zero at 0.01 probability level

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Table 5. 13: Results of Regression with total Consumption Expenditure, in Sargodha Region Independent Variables Coefficients Std. Error t-Statistics (Constant) 12.371 0.899 13.759 Education Level (Years) -0.042 0.032 -1.301 Log of Value of Implements(Rs) 0.109* 0.071 1.537 Operational Holding (Acres) 0.022*** 0.006 3.646 Dependency Ratio -0.137** 0.071 -1.922 Bhalwal Dummy -0.270 0.432 -0.626 Shahpur Dummy -0.214 0.386 -0.556 Sillanwali Dummy -0.423 0.387 -1.094 Predicted probability of being constrained -0.101 0.349 -0.290

Dependent Variable: Log of Total Consumption Expenditure

Notes: * showed that the coefficient is significantly different from zero at 0.1 probability level; ** showed that the coefficient is significantly different from zero at 0.05 probability level; and *** showed that the coefficient is significantly different from zero at 0.01 probability level The credit constraint and household consumption Expenditure How does the credit constraint affect the total consumption expenditure of the

farm household? In order to aforementioned, total consumption expenditure was

regressed with educational level, value of implements, operational holdings,

dependency ratio, probability of being constrained, and tehsil dummies. The

results were presented in Table 5.13; the consumption expenditure of the farm

households was positively and significantly determined by operational holding

and value of implements. It was negatively determined by dependency ratio

which was unexpected. It was negatively determined by probability of being

constrained but not significant in the estimated results.

In summary, three step procedures was followed, first logit regression was

estimated to ascertain the probability that positive interest was charged.

Secondly the interest rate was regressed with several explanatory variables

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coupled with inverse mill’s ratio from the first stage. Thirdly, the demand for

borrowing was regressed on predicted interest rate coupled with second stage

while including some other explanatory variables. The land coefficient was not

significant. The coefficient of education of male household was significant

showing that education function as a facilitator to enter into credit market and

educing transaction cost. Transitory income also played a significant role in

borrowing behavior. In the borrowing model, initial liquid assets has significantly

impact on the amount of borrowing. Operational holding also showed significant

impact on the total household consumption expenditure.

.

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Chapter 6

Conclusions and Recommendation

The importance of credit in the agricultural production sequence was generally

recognized. Most countries including Pakistan motivated by consideration of the

potential gain and the increase in welfare of the farmers, especially the smaller

farmers, have undertaken widespread credit programmes. During the credit

rationing many borrowers could not obtain the required amount of credit at the

prevailing interest rates nor could they secure more credit by offering to pay

higher interest rate. There were several questions regarding agricultural credit

which solicit answers. Are farmers really credit constrained? How the credit

constraints are determined? A number of questions were asked to determine the

factors affecting the use of institutional credit in Pakistan. The issues ranged from

the reasons for not applying credit to a formal institutions, distance of the bank,

interest rates of the institutions, time lapsed between application and loan

disbursement, purpose of loan, formal institutions and collateral used for the loan

from the institutional sources. The objective of the study was to perform

constraint analysis and its impact on consumption pattern and farm production.

The data sources include cross section survey PRHS 2001 and, farm survey of

Sargodha regions.

The results of the frontier production revealed that credit users and non credit users

were allocatively inefficient, especially in water use. The mean technical efficiency

of credit users was 90 and that of non-credit users was 79 percent, respectively.

The high technical efficiency of credit users was attributed to better market

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access to the farmers to new technology through the availability of agricultural

credit. The low level of technical efficiency of non-credit users as compared to

credit users implied that potential for improvement exists. The high technical

efficiency of credit users was safely attributed to credit availability through which

they have an access to new technology.

The data revealed that agricultural production loan was found as 45.8 percent. In

agro-climatic regions, most of the loans were for the agricultural production only

but in Baluchistan 50 percent of the loan was for the purchase of agricultural

land.

ZTBL was providing most of the loan to the farmers for their agricultural needs.

The most important issue related to decision making of the farmers for the loan

was interest rate. The interest was ranging between 10 to 20 percent. The time

lag in disbursement of loan was tremendous in all the regions. This affected the

eventual productivity of such loan especially where short run loans were

concerned. Across the agro-climatic regions, more than 75 percent credit was

disbursed in three months except Baluchistan. This showed the backwardness

and inefficiency of the credit market.

The logit model was used to determine the nominal interest rate and borrowing

function of the farmers. The results showed that the transitory income, predicted

interest rate, and farm size were significant but barring transitory income, the

later two variables were not having expected sign. This was perhaps due to

underlying assumptions. This included dichotomy credit and differential

borrowing behavior of the respondents due to credit constraints.

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Credit constraints were determined by using Heckman’s two stage procedure.

The results revealed that the coefficient of education of male household was

significant showing that education function as a facilitator to enter into credit

market. Furthermore, farm experience also showed a positive response on

household borrowing.

The farmers faced many constraints namely: lower literacy rate, small and

fragmented holdings, uneven access to agricultural extension and information

and in ability to obtain adequate irrigation water, less access to agriculture credit

institutions, and inequitable distribution of land and water.

Recommendations

• Credit disbursement must be demand driven under the supervised credit

scheme.

• Encourage Credit Assessment Bureaus for the risk assessment of the

borrowers as it done in urban areas.

• Better dissemination of information and technology for improved decision

making regarding use of credit.

• Revision .of Produce Index Units keeping in view the prices and

productivity of Agriculture

• Enhance the productive use of agriculture credit through higher input use

leading to higher productivity. It will also help the farmers to smooth their

consumption.

• There is a dire need to review the existing collateral requirements and

loan application procedure for institutional credit. The crops and livestock

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can also used as collateral especially for tenants and land less poor.

Introduction of group lending schemes in the rural areas.

• Well functioning credit market not only solves the financial problems of the

farmer’s especially small farmers but also help to reduce the poverty.

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Annexures Annex 1: Distribution of Society Loans According To Categories

Zones Actual Loans Family Loans Bogus Loans Total Loans Number Amount Number Amount Number Amount Number Amount

Barani 3 8320 - - 5 15584 8 23904

Rice 19 77594 17 102359 95 358079 131 538032

Mixed 29 95142 13 56551 95 390228 137 541921

Cotton 3 12453 16 84382 78 360087 97 456922

Overall 54 193509 46 243292 273 1123978 373 1560779

Source: Punjab Economic Research Institute (PERI) August, 1986.

Annex 2: Extent of Genuine Loans

Actual Plus Family Loans Zones Genuine Loans Loans with below Loans with above Loans below Total Actual Bogus Loans All Loans

Number Percent Number Percent Number Percent Number Percent Number Percent Number Percent Number PercentBarani 3 38 - - - - - - 3 38 5 62 8 100 Rice 12 9 3 2 12 9 9 1 36 27 95 73 131 100 Mixed 17 13 2 1 10 7 13 10 42 31 95 69 137 100 Cotton 3 3 1 1 5 5 10 10 19 19 78 79 97 100 Overall 35 9 6 2 27 7 32 9 100 27 273 73 373 100

Source: Punjab Economic Research Institute (PERI) August, 1986.

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Annex 3: Number of Branches, Deposits, Loans, Advances and Recovery of Banks

Number of Branches, Deposits, Loans, Advance and Recovery of Banks During 1999

Bank Name ABL HBL MCB NBP UBL CBs ADBP Total BRANCHES

Urban 714.000 844.000 653.000 656.000 646.000 3,513.000 3,513.000 Rural 215.000 861.000 562.000 752.000 793.000 3,183.000 346.000 3,529.000 Total 929,000 1,705.000 1,215.000 1,408.000 1,439.000 6,696.000 346.000 7,042.000

BANK DEPOSITS Urban 78,218.000 155,053.000 101,170.000 175,413.838 716,673.000 581,527.838 581,527.000 Rural 14,889.000 41,251.361 29,166.000 44,690.031 29,245.000 159,241.392 1,587.725 160,829.117 Total 93,107.000 196,304.361 130,336.000 220,103.869 100,918.000 740,769.230 1,587.725 742,356.955

LOANS AND ADVANCES Urban 52.576 143,912.000 64,790.000 110,225.305 43,141.000 362,121.629 362,121.629 Rural 2.688 7,463.173 2,643.000 8,924.913 2,376.000 21,409.774 24,423.889 45,833.663 Total 55.264 151,375.921 67,433.000 119,150.218 45,517.000 383,531.403 24,423.889 407,955.292

RECOVERY Urban 40.264 3,898.800 1,040.000 20,616.582 4,896.000 30,491.646 30,491.646 Rural 2.059 205.500 260.000 7,088.994 104.000 7,660.553 30,129.165 37,789.718 Total 42.323 4,104.300 1,300.000 27,705.576 5,000.000 38,152.199 30,129.165 68,281.364

Advances as a proportion of Deposits % Urban 0.07 92.82 64.04 62.84 60.19 62.27 62.27 Rural 0.02 18.09 9.06 19.97 8.12 13.44 1538.29 28.50 Total 0.06 77.11 51.74 54.13 45.10 51.77 1538.29 54.95

Recovery as a proportion of Advances % Urban 76.58 2.71 1.61 18.70 11.35 8.42 8.42 Rural 76.60 2.75 9.84 79.43 4.38 35.78 123.36 82.45 Total 76.58 2.71 1.93 23.25 10.98 9.95 123.36 16.74

Source: CRF, State Bank of Pakistan, 2002.

Note: The table provides a comparison of rural urban bank branches of commercial banks with respect to recovery, loans, advances and bank deposits. The figures are obviously tilted in favor of urban regions. One can see that the advance to deposit ratio is clearly biased in favor of urban areas. In terms of individual banks this ratio is quite low for ABL, both for urban (0.07%) as well as rural (0.02%) regions. However for other banks such as HBL, MCB, NBP and UBL this ratio is roughly 60% for urban areas and between 8% to 18% for urban areas.

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Annex 4: Institutional Agricultural Credit by Province

Provinces ADBP

Prov. As

% Of Total ADBP Lendin

Commerc

ial Banks

Prov. As% Of Total NCBs

Lending

Cooperative

Societies

Prov. As

% of Total

Cooperati

Total Inst.

Lending

Prov. As % of Tot.

Inst. Lending

Rural pop in

(‘000’)*

Prov. Rural

pop. As % of total rural pop.

1988-89 Punjab 5334.7 62.56 1366.94 44.79 1836.13 71.73 8537.7760.39 42082.1 58.96Sindh 1988.0 23.32 1433.4 46.97 224.85 8.78 3646.2925.79 13255.9 18.57NWFP 819.85 9.62 188.08 6.16 498.69 19.48 1506.6210.66 11547.6 16.18Balochistan 384.17 4.51 63.53 2.08 - 447.7 3.17 4491.99 6.29

All 8526.7 3051.95 2559.67 14138.3100.00% 71377.8 100.00%

1989-90 Punjab 6224.6 73.00 1734.13 47.78% - 7958.7959.36% 42375.0 58.96%Sindh 1904.2 22.33 1695.59 46.72% 56.53 11.16% 3656.3427.27% 13348.2 18.57%NWFP 722.59 8.47% 138.32 3.81% 450 88.84% 1310.919.78% 11628.0 16.18%Balochistan 419.97 4.93% 61.54 1.70% - 481.51 3.59% 4523.25 6.29%

All 9271.4 3629.58 506.53 13407.5100.00% 71874.5 100.00%

1990-91 Punjab 5624.4 68.44 1798.9 46.58 2831.96 100.00 10255.368.77 42388.3 58.96Sindh 1679.0 20.43 1799.1 46.59 - 3478.1123.32 13352.4 18.57NWFP 424.09 5.16 156.6 4.06 - 580.69 3.89 11631.6 16.18Balochistan 490.8 5.97 107.2 2.78 - 598 4.01 4524.67 6.29

All 8218.3 3861.8 2831.96 14912.1100.00 71897.0 100.00

1991-92 Punjab 4981.6 72.02 2009.6 48.17 2979.26 100.00 9970.5170.87 44260.8 62.92Sindh 1443.9 20.88 1974.9 47.33 - 3418.8924.30 13942.2 19.82NWFP 292.09 4.22 128.3 3.08 - 420.39 2.99 12145.5 17.26Balochistan 199.56 2.88 59.4 1.42 - 258.96 1.84 4724.55 6.72

All 6917.2 4172.2 2979.26 14068.7100.00 70348.7 100.00

1992-93 Punjab 6377.9 74.74 2502.46 55.38 2721.81 100.00 11602.273.55 45411.6 58.96Sindh 1629.8 19.10 1823.18 40.34 - 3453.0121.89 14304.7 18.57NWFP 298.83 3.50 129.84 2.87 - 428.67 2.72 12461.3 16.18Balochistan 226.89 2.66 63.61 1.41 - 290.5 1.84 4847.39 6.29

All 8533.5 4519.09 2721.81 15774.4100.00 77025.1 100.00

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1993-94 Punjab 6282.4 70.76 2618.6 64.62 2381.8 97.91 11282.873.44 46592.3 58.96Sindh 1964.6 22.13 1305.5 32.22 - 3270.1221.29 14676.71 18.57NWFP 376.25 4.24 97.2 2.40 50 2.06523.45 3.41 12785.31 16.18Balochistan 254.62 2.87 30.7 0.76 0.72 0.03286.04 1.86 4973.427 6.29

All 8877.9 4052 2432.52 15362.4100.00 79027.82 100.00

1994-95 Punjab 10014. 69.55 1967.8 48.97 3302.7 92.95 15285.269.57 47201.6 58.96Sindh 3048.6 21.17 1860.1 46.29 - 4908.7822.34 14868.62 18.57NWFP 712.95 4.95 147.7 3.68 241.6 6.80 1102.255.02 12952.49 16.18Balochistan 623.2 4.33 42.5 1.06 9.04 0.25674.74 3.07 5038.458 6.29

All 14399. 4018.1 3553.34 21971.0100.00 80061.16 100.00

1995-96 Punjab 8143.5 79.37 2155.6 43.13 4603.94 77.72 14903.070.36 48381.64 58.96Sindh 1439.3 14.03 2504.7 50.11 1189.46 20.08 5133.4724.23 15240.34 18.57NWFP 499.49 4.87 337.8 6.76 125.46 2.12962.75 4.55 13276.3 16.18Balochistan 178.16 1.74 33.7 0.67 4.6 0.08216.46 1.02 5164.419 6.29

All 10260. 4998.1 5923.46 21182.0100.00% 82062.69 100.00%

1996-97 Punjab 8648.4 74.79 1791.8 40.62% 3725.44 75.72% 14165.667.80% 48870.39 58.96%Sindh 2109.2 18.24 2269.4 51.45% 1189.46 24.18% 5568.1126.65% 15394.29 18.57%NWFP 522.77 4.52% 336.1 7.62% - 858.87 4.11% 13410.42 16.18%Balochistan 282.56 2.44% 13.4 0.30% 4.91 0.10%300.87 1.44% 5216.59 6.29%

All 11563. 4410.7 4919.81 20893.5100.00% 82891.69 100.00%

1997-98 Punjab 16877. 76.27 3143.53 55.61 4722.93 100.00 24743.976.13 50043.28 62.92Sindh 3624.9 16.38 2136.84 37.80 - 5761.7417.73 15763.76 19.82NWFP 1095.2 4.95 343.36 6.07 - 1438.634.43 13732.27 17.26Balochistan 530.31 2.40 29.44 0.52 - 559.75 1.72 5341.788 6.72

All 22127. 5653.17 4722.93 32504.0100.00 79539.3 100.00

1998-99 Punjab 22176. 74.20 4249.09 59.23 4967.66 100.00 31392.974.69 50400.19 58.96Sindh 4981.3 16.67 2449.5 34.14 - 7430.8317.68 15876.19 18.57NWFP 1404.2 4.70 419.18 5.84 - 1823.464.34 13830.21 16.18Balochistan 1324.9 4.43 56.62 0.79 - 1381.573.29 5379.887 6.29

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All 29886. 7174.39 4967.66 42028.8100.00 85486.48 100.00

1999-00 Punjab 17690. 73.19 6022.63 65.54 4301.61 100.00 28014.374.38 51559.4 58.96Sindh 4790.7 19.82 2574.65 28.02 - 7365.44 19.56 16241.34 18.57 NWFP 1183.1 4.89 532.29 5.79 - 1715.4 4.55 14148.31 16.18 Balochistan 507 2.10 59.41 0.65 - 566.41 1.50 5503.624 6.29

All 24171. 9188.98 4301.61 37661.6 100.00 87452.67 100.00

2000-01

Punjab 19589. 71.78 7423.03 62.19 4829.53 100.00 31842.3 72.27

Sindh 5663.0 20.75 3843.97 32.20 - - 9507.05 21.58

NWFP 1324.6 4.85 607.25 5.09 - - 1931.88 4.38

Balochistan 715.71 2.62 62.03 0.52 - - 777.746 1.77

All Pakistan 27293. 100.00 11936.2 100.00 4829.53 100.00 44058.9 100.00

2001-02

Punjab 21123. 73.22 11395.0 65.59 5126.24 100.00 37644.5 73.31

Sindh 5864.9 20.33 4272.84 24.59 - - 10137.7 19.74

NWFP 1367.3 4.74 1417.36 8.16 - - 2784.73 5.42

Balochistan 492.83 1.71 287.94 1.66 - - 780.776 1.52

All Pakistan 28848. 100.00 17373.1 100.00 5126.24 100.00 51347.8 100.00

2002-03

Punjab 22489. 77.27 15247.2 67.48 5494.71 100.00 43231.9 75.58

Sindh 4731.7 16.26 5360.33 23.72 - - 10092.1 17.64

NWFP 1504.3 5.17 1775.88 7.86 - - 3280.23 5.73

Balochistan 381.42 1.31 212.95 0.94 - - 594.374 1.04

All Pakistan 29107. 22596.4 5494.71 100.00 57198.6 100.00

2003-04

Punjab 23320. 78.54 24278.7 73.59 7563.54 100.00 55162.6 78.53

Sindh 4247.8 14.31 5829.97 17.67 - - 10077.7 14.35

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NWFP 1769.1 5.96 2645.05 8.02 - - 4414.19 6.28

Balochistan 356.21 1.20 237.21 0.72 - - 593.422 0.84

All Pakistan 29693. 32991.0 7563.54 70248.0 100.00

2004-05

Punjab 29085. 78.15 39098.3 76.78 7607.46 100.00 75790.8 79.16

Sindh 5206.1 13.99 7589.44 14.90 - - 12795.5 13.36

NWFP 2751.2 7.39 3886.94 7.63 - - 6638.22 6.93

Balochistan 174.47 0.47 349.39 0.69 - - 523.864 0.55

All Pakistan 37216. 50924.1 7607.46 100.00 95748.5 100.00

Source: Agricultural Statistics of Pakistan 2004-05.

* Province wise rural population has been computed keeping the 1981 population as base year.

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Annex 5: Term Wise Position of Agricultural Credit

Advanced by ADBP s. in Million) (No. of Cases in ‘00’).

(Province Short term Mediu Long t

Total credit # of

cases Amount Share(%)

# of cases Amount Share

(%) # of

cases Amount Share(%)

# of cases Amount

1985-86 Punjab 258 626.14 19.8 114 356.06 11.2 195 2187.54 69 567 3169.74Sindh 302 629.34 44.3 45 261.11 18.4 28 531.62 37.3 375 1422.07NWFP 13 11.77 2.5 47 165.51 35.3 26 291.61 62.6 86 468.89Balochistan 13 30.09 19.2 5 27.81 17.8 10 98.51 63 28 156.411986-87 Punjab 199 571.7 15.3 82 238.64 6.4 230 2923.26 78.3 511 3733.6Sindh 201 589.95 39.3 64 229.26 15.3 44 683.12 45.4 309 1502.33NWFP 10 6.85 1.3 55 145.68 28.3 29 361.72 70.4 94 514.25Balochistan 9 21.31 11.2 9 48.02 25.3 11 120.32 63.5 29 189.651987-88 Punjab 227 862.42 18.4 199 596.71 12.8 236 3215.68 68.8 662 4674.81Sindh 201 776.64 40.2 122 375.46 19.5 48 778.84 40.3 371 1930.94NWFP 16 11.52 1.7 83 214.42 31.4 32 456.64 66.9 131 682.58Balochistan 9 27.34 8.8 14 73.76 23.8 15 209.05 67.4 38 310.151988-89 Punjab 312 1223.62 22.9 187 648.64 12.1 236 3462.44 65 735 5334.7Sindh 206 819.87 41.3 99 356.46 17.9 42 811.71 40.8 347 1988.04NWFP 23 17.52 2.1 87 275.54 33.6 33 526.79 64.3 143 819.85Balochistan 8 32.13 8.4 17 82.62 21.5 18 269.42 70.1 43 384.171989-90 Punjab 283 1471.09 23.6 172 762.2 12.2 224 3991.37 64.1 679 6224.66Sindh 182 829.26 43.5 65 301.04 15.8 34 773.91 40.6 281 1904.22NWFP 16 17.04 2.4 75 240.84 33.3 23 464.71 64.3 114 722.59Balochistan 8 26.47 6.3 14 74.44 17.7 20 319.06 76 42 419.971990-91 Punjab 286 1615.08 28.7 145 625.57 11.1 160 3383.84 60.2 591 5624.49Sindh 155 814.87 48.5 49 307.51 18.3 22 556.63 33.2 226 1679.01NWFP 15 31.01 7.3 53 177.14 41.8 9 215.94 50.9 77 424.09Balochistan 7 37.41 7.6 11 74.68 15.2 20 378.71 77.2 38 490.8 1991-92 Punjab 331 2152.9 43.2 110 439.45 8.8 108 2389.3 48 550 4981.65Sindh 146 867.88 60.1 45 261.59 18.1 13 314.52 21.8 204 1443.99NWFP 23 39.87 13.6 38 127.8 43.8 5 1224.42 42.6 66 292.09Balochistan 5 32.67 16.4 4 24.95 12.5 8 141.94 71.1 17 199.561992-93 Punjab 1079 2130.7 33.4 77 344.59 5.4 153 3902.68 61.2 1309 6377.97Sindh 530 817.89 50.2 40 270.93 16.6 16 541.01 33.2 586 1629.83

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NWFP 26 29.33 9.8 30 95.54 32 8 173.96 58.2 64 298.83Balochistan 5 39.08 17.2 6 36.82 16.2 8 150.99 66.6 19 226.891993-94 Punjab 344 1431.51 22.8 143 795.5 12.7 161 4055.41 64.6 648 6282.42Sindh 163 694.76 35.4 70 649.7 33.1 23 620.16 31.6 256 1964.62NWFP 23 31.11 8.3 57 204.69 54.4 61 140.45 37.3 141 376.25Balochistan 6 26.17 10.3 6 37.77 14.8 10 190.68 74.9 22 254.621994-95 Punjab 809 4142.34 41.4 131 705.84 7 197 5166.6 51.6 1137 10014.7Sindh 389 1665.27 54.6 53 454.36 14.9 40 929.05 30.5 482 3048.68NWFP 153 306.55 43 76 279.55 39.2 8 126.85 17.8 237 712.95Balochistan 21 83.36 13.4 10 69.35 11.1 20 470.49 75.5 51 623.2 1995-96 Punjab 748 4775.9 58.6 47 297.83 3.7 146 3069.81 37.7 941 8143.54Sindh 207 1042.09 72.4 10 92.82 6.4 18 304.4 21.2 235 1439.31NWFP 99 243.42 48.7 32 118.75 23.8 9 137.32 27.5 140 499.49Balochistan 1996-97 Punjab 666 4354.7 50.4 103 1010.18 11.7 101 3283.57 37.9 870 8648.45Sindh 245 1330.33 63.1 32 517.13 24.5 9 261.79 12.4 286 2109.25NWFP 65 169.33 32.4 53 250.04 47.8 5 103.4 19.8 123 522.77Balochistan 8 58.31 20.7 2 31.49 11.1 7 192.76 68.2 17 282.561997-98 Punjab 2152 10878.2 64.5 198 1251.24 7.4 110 4748.05 28.1 2460 16877.4Sindh 390 2521.38 69.6 70 554.66 15.3 13 548.86 15.1 473 3624.9NWFP 155 491.12 44.8 89 437.6 40 6 166.55 15.2 250 1095.27Balochistan 19 129.85 24.5 4 37.79 7.1 11 362.67 68.4 34 530.311998-99 Punjab 3242 1702.05 76.8 87 530.22 2.4 155 4619.95 20.8 3484 2217622Sindh 537 3910.74 78.5 43 406.87 8.2 19 663.72 13.3 599 4981.33NWFP 184 714.82 50.9 79 368.62 26.2 14 320.84 22.8 277 1404.28Balochistan 34 251.42 19 5 53.34 4 35 1019.94 77 74 1324.71999-00 Punjab 2539 11291.22 63.8 90 473.75 2.7 225 5925.15 33.5 2854 17690.1Sindh 491 3998.82 83.5 43 411.27 8.6 15 380.7 7.9 549 4790.79NWFP 140 553.89 46.8 76 351.38 29.7 11 277.84 23.5 227 1183.11Balochistan 15 122.06 24.1 1 10.78 2.1 16 374.11 73.8 32 506.952000-01 Punjab 2883 13143.65 67.1 115 567.33 2.9 232 5878.76 30.0 323 19589.Sindh 528 4531.10 80.0 51 467.31 8.3 32 664.67 11.7 611 5663.0 NWFP 177 699.09 52.8 87 390.19 29.5 9 235.35 17.8 273 1324.6Balochistan 32 215.61 30.2 3 20.42 2.8 18 479.69 67 53 715.722001-02

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Punjab 3045 16030.87 75.9 123 609.11 2.9 184 4483.34 21.2 335 21123.Sindh 507 4739.47 80.8 44 467.55 8.0 28 657.88 11.2 579 5864.9NWFP 179 830.19 60.7 82 380.35 27.8 7 156.84 11.5 268 1367.3Balochistan 36 269.99 54.8 2 21.72 4.4 8 201.13 40.8 46 492.842002-03 Punjab 3136 17949.01 35.3 173 845.55 3.8 148 3695.44 16.4 345 22490.Sindh 437 3956.11 83.6 39 311.95 6.6 16 463.74 9.8 492 4731.8NWFP 156 939 62.4 94 429.03 28.5 6 136.32 9.1 256 1504.3Balochistan 25 234.19 61.4 3 18.33 4.8 6 128.90 33.8 34 381.422003-04 Punjab 3188 19414.85 83.3 233 1165.41 5.0 117 2740.05 11.7 353 23320.Sindh 374 3597.21 84.7 34 264.09 6.2 13 386.51 9.1 421 4247.8NWFP 207 1247.45 70.5 82 407.39 23 5 114.31 6.5 294 1769.1Balochistan 6 181.30 50.9 1 7.16 2 6 167.75 47.1 28 356.212004-05 Punjab 3082 24255.42 83.4 232 1319.68 4.5 150 3509.93 12.1 346 29085.Sindh 409 4677.98 89.9 29 240.07 4.6 10 288.08 5.5 448 5206.1NWFP 256 2107.66 76.6 80 487.32 17.7 6 156.31 5.7 342 2751.2Balochistan 13 109.67 62.9 1 7.24 4.1 2 57.56 33.00 16 174.47Source: Agricultural Statistics of Pakistan 2004-05.