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Acknowledgements
Dr. Sohail J. Malik
This study was completed by a team comprising Hiba
Zaidi, Hassan Vaqar, Hina Nazli, and Sohail J. Malik
from Innovative Development Strategies (Pvt.) Ltd.
The study would not have been possible without the
financial and considerable technical support
provided by the Pakistan Microfinance Network
(PMN); Aban Haq and Zahra Khalid contributed
considerably to the overall direction, analysis, and
write-up of the report.
Thanks are also due to a group comprising Ibrar
Anjum of the National Rural Support Programme,
Ayesha Baig and Habib ur Rahman of the First
MicroFinanceBank Ltd., Khalid Mahmud of the World
Bank, and Mehr Shah of PMN who participated in a
workshop to provide comments and direction for the
eventual presentation of the data in this report.
Acronyms & Abbreviations
Exchange Rate
A2FS Access to Finance Survey
BRI Bank Rakyat Indonesia
BRI-UD Bank Rakyat Indonesia Unit Desas
DFID Department for International Development
FBS Federal Bureau of Statistics
FMFBL First MicroFinanceBank Ltd.
GDP Gross Domestic Product
HH Household
HIES Household Integrated Economic Survey
LGO Local Government Ordinance
MF Microfinance
MFD Microfinance Department
MFP Microfinance provider
NWFP Northwest Frontier Province
PL Poverty line
PKR/Rs. Pakistani Rupees
PMN Pakistan Microfinance Network
PPS Probability Proportional to Size
PSLM Pakistan Social and Living Standards Measurement (Survey)
PSU Primary sampling unit
RICS Rural Investment Climate Survey
SBP State Bank of Pakistan
SSU Secondary sampling unit
USD United States Dollars
(Nov 2009) PKR/USD = 83.3/1
Contents
1 Introduction 1
2 Methodology 4
3 Profiling Poverty in Different Agro-climatic Zones of Pakistan 8
4 The Aggregate Rural Economy by Agro-climatic Zone 13
5 Market Constraints and Limitations 32
6 Characteristics of Rural Financial Markets 39
7 Non-parametric Correlations between Variables 48
8 Conclusion and Ideas for Policymakers and Practitioners 52
ANNEXES
Annex A
Annex B
Annex C enclosed CD
3.1 Rural-Urban Poverty Distribution 8
3.2 Depth of Poverty 10
3.3 Implications of Regional Variation in Poverty 12
4.1 Rural Incomes: People, Sources, and Volumes 13
4.2 Expenditure Patterns 21
4.3 Savings in the Rural Economy 23
4.4 Debt and Repayment Behaviour 26
4.5 Asset Profiles 28
4.6 Rural Housing 28
5.1 Nature of Businesses 32
5.2 Constraints to Rural Business Development 32
5.3 Efficiency of the Legal System 36
5.4 Social Capital in Rural Markets 37
6.1 State of Access to Finance 39
6.2 Demand for Formal Financial Services 42
6.3 State of Financial Literacy 44
6.4 Sources of Information on Financial Matters 45
6.5 Perceptions and Preferences 47
Classification of Districts into Agro-climatic Zones 57
Design of the Four Data Sources 59
List of Tables in Volume II – Statistical Appendix (on ) 65
List of Tables
Table 1
Table 2
Table 3
Table 4a
Table 4b
Table 5
Table 6
Table 7
Table 8
Table 9
Table 10
List of Figures
Figure 1 Trend in Active Borrowers by Rural or Urban Status and Market Shares of MFPs in Rural Outreach
Figure 2 Data Classification Scheme
Figure 3 Trend in the Poverty Headcount Ratio
Figure 4 Rural Economic Status by Agro-climatic Zone 2005-06
Figure 5 Distribution of Rural Poor by Poverty Band
Figure 6 Distribution of Rural Households by Agro-climatic Zones within a Poverty Band
Figure 7a Rural Household Occupations – Non-poor
Figure 7b Rural Household Occupations – Poor
Figure 8 Sources of Rural Income – Non-poor vs. Poor
Figure 9 Average Annual Rural Income from Non-agricultural Sources
Figure 10 Average Annual Rural Income from Crop
Figure 11 Average Annual Rural Income from Livestock
Figure 12 Breakdown of Rural Expenditure into Major Categories
Figure 13 Average Rural Household Expenditure on Agriculture
Figure 14 Average Rural Household Expenditure on Non-agricultural Activities
8
9
15
19
20
24
25
28
29
48
50
1
6
8
9
11
12
13
14
16
17
18
19
21
22
22
List of Tables
Table 1
Table 2
Table 3
Table 4a
Table 4b
Table 5
Table 6
Table 7
Table 8
Table 9
Table 10
List of Figures
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7a
Figure 7b
Figure 8
Figure 9
Figure 10
Figure 11
Figure 12
Figure 13
Figure 14
Trend in Poverty Indicators
Rural-Urban Poverty by Agro-climatic Zone (2005-06)
Total Rural Income by Agro-climatic Zone
Percentage of Households Receiving Remittances
Average Annual Inflow of Remittances per Household
Total Rural Household Savings (Reported)
Rural Savings as Percentage of Total Rural Incomes
Borrowing and Repayment Profile of Rural Households
Value of Livestock and Land Owned by Households
Non-parametric Correlation between Variables – Non-poor
Non-parametric Correlation between Variables – Poor
Trend in Active Borrowers by Rural or Urban Status and Market Shares of MFPs in Rural Outreach
Data Classification Scheme
Trend in the Poverty Headcount Ratio
Rural Economic Status by Agro-climatic Zone 2005-06
Distribution of Rural Poor by Poverty Bands
Distribution of Rural Households by Agro-climatic Zones within a Poverty Band
Rural Household Occupations – Non-poor
Rural Household Occupations – Poor
Sources of Rural Income – Non-poor vs. Poor
Average Annual Rural Income from Non-agricultural Sources
Average Annual Rural Income from Crop
Average Annual Rural Income from Livestock
Breakdown of Rural Expenditure into Major Categories
Average Rural Household Expenditure on Agriculture
Average Rural Household Expenditure on Non-agricultural Activities
Figure 15
Figure 16
Figure 17
Figure 18a
Figure 18b
Figure 19a
Figure 19b
Figure 20a
Figure 20b
Figure 21
Figure 22
Figure 23
Figure 24a
Figure 24b
Figure 24c
Figure 25
Figure 26
Figure 27
Figure 28
Figure 29
Figure 30
Figure 31
Figure 32a
Figure 32b
Figure 33
Figure 34a
Figure 34b
Average Rural Household Expenditure for Consumption
Average Household Savings (Reported)
Rural Households Receiving Loans
Share of Loans Taken for Household and Other Purposes – Poor
Share of Loans Taken for Household and Other Purposes – Non-poor
House Occupancy Status – Poor
House Occupancy Status – Non-poor
Distribution of Number of Rooms – Poor
Distribution of Number of Rooms – Non-poor
Businesses Registered With a Formal Authority
Biggest Constraints to Rural Business Development
Key Constraints to Growth of Enterprises
Entrepreneurs that have applied for a Loan in the last five years(2000-05)
Entrepreneurs wanting to apply for a Loan in the last five years (2000-05)
Reasons for not taking out Loans from Banks
Respondents’ perceptions of the predictability of laws that affect business operations
Respondents’ perception of whether or not the legal system upholds contract and property rights in business disputes
Respondents’ perception of the reliance on counterpart’s reputation for business dealings
Respondents’ perception of whether or not a business contract is protection against cheating
Percentage of Banked Rural Households
Households that have never borrowed from a Bank
Households that would like assistance in opening Bank Accounts
Top reasons cited for opening a Bank Account – Poor
Top reasons cited for opening a Bank Account – Non-poor
Understanding of Financial Terms
Top Sources of Information on Financial Matters – Poor
Top Sources of Information on Financial Matters – Non-poor
23
25
26
27
27
29
30
31
31
32
33
34
34
35
35
36
37
38
38
39
40
43
43
44
44
46
46
Figure 15 Average Rural Household Expenditure for Consumption
Figure 16 Average Household Savings (Reported)
Figure 17 Rural Households Receiving Loans
Figure 18a Share of Loans Taken for Household and Other Purposes – Poor
Figure 18b Share of Loans Taken for Household and Other Purpose
Figure 19a House Occupancy Status – Poor
Figure 19b House Occupancy Status – Non-poor
Figure 20a Distribution of Number of Rooms – Poor
Figure 20b Distribution of Number of Rooms – Non-poor
Figure 21 Businesses Registered With a Formal Authority
Figure 22 Biggest Constraints to Rural Business Development
Figure 23 Key Constraints to Growth of Enterprises
Figure 24a Entrepreneurs that have applied for a Loan in the last five years (2000-05)
Figure 24b Entrepreneurs wanting to apply for a Loan in the last five years
Figure 24c Reasons for not taking out Loans from Banks
Figure 25 Respondents’ perceptions of the predictability of laws that affect business operations
Figure 26 Respondents’ perception of whether or not the legal system upholds contract and property rights in business disputes
Figure 27 Respondents’ perception of the reliance on counterpart’s reputation for business dealings
Figure 28 Respondents’ perception of whether or not a business contract is protection against cheating
Figure 29 Percentage of Banked Rural Households
Figure 30 Households that have never borrowed from a
Figure 31 Households t
Figure 32a Top rea
Figure 32b Top reasons cited f
Figure 33 Understanding of Financial Terms
Figure 34a Top Sources of Information on Financial
Figure 34b Top Sources of Information on F
1. Introduction
1
Jun 30(2006)
66%
34%
0
500
Act
ive
Bo
rro
we
rs (
00
0’s
)
Dec 31(2006)
Jun 30(2007)
Dec 31(2007)
Jun 30(2008)
Dec 31(2008)
Jun 30(2009)
Rural
Active Borrowers by Rural/Urban
1000
1500
2000
Urban
Source: Pakistan Microfinance Network
40%
41%45% 42% 45% 44%
60%
59%55%
58% 55% 56%
Market Share (Rural)
14%
32%
26%
15%
8%
5%NRSP
KB
FMFBL
PRSP
KASHF
Others
1. Haq and Montoya. 2008. Pakistan: Country Level Savings Assessment. Pakistan Microfinance Network.
The rapid growth of microfinance (MF) in Pakistan 87 percent of the entire rural outreach can be
in recent years has occurred without much formal accounted for by just four MFPs (see Figure 1). The
analysis of the potential market it seeks to serve, rural-urban divide in access to formal financial
especially in light of its much highlighted role as a services is further accentuated if one examines
serious form of intervention for increasing access the spread of the entire banking sector across
to finance to the currently marginalized. Although Pakistan – just 33 percent of all branches are in currently 56 percent of MF clients are rural, there the rural areas where 67 percent of the has been an increasing trend within microfinance population resides.providers (MFPs) to pursue urban clients. Almost
1
Figure 1: Rural Outreach of Microfinance in Pakistan
Introduction
2
Bank Rakyat Indonesia (BRI) is a state-owned bank in Indonesia that has gained international recognition as a
success story in MF. Its extensive branch network consists of more than 5,000 retail outlets, or “BRI Unit Desas (BRI-
UD)” across Indonesia at the sub-district level. Unit Desas operate predominantly in rural areas on a full
commercial basis with each unit acting as a semi-autonomous entity serving micro and small customers. It
maintains its small and midsized business loan levels at about 80 percent of its total lending portfolio. The bank’s
profit as of December 2008 was USD 5.25 billion, the highest of all banks in Indonesia. This profit comes mainly
from Unit Desas which have disbursed USD 4.4 billion in micro loans (Dec 2008) to 4.5 million customers.
The picture, however, has not always been so rosy. It became clear to Indonesian authorities in 1984 that BRI’s (an
ailing agricultural credit bank at the time) dependence on subsidies was too great for the Indonesian Government
to finance. With a final, one-time subsidy of USD 70 million, the bank was instructed to drastically change the “rules
of the game” or to shutdown all operations.
The bank then designed a rural finance system that has become the flagship of the world’s rural MF industry.
Profound changes were introduced across the board with the aim of achieving sustainability. Within two years, BRI
had achieved full coverage of its costs, and since then has generated profits unprecedented in rural finance.
The success of the Unit Desa stems from pricing and products designed with the double-bottom line approach,
flexible product features, broadened eligibility requirements, fully voluntary savings programmes, and incentive
schemes designed to encourage timely repayment.
Other examples of MF institutions that have had considerable success in reaching out to rural communities include
PRODEM in Bolivia and Equity Bank in Kenya.
2. Of that 88 percent, 32 percent are ‘informally banked’, i.e. adults without bank accounts or access to other services, but use one or
more informal financial products. Examples include borrowing from moneylenders, friends or family, shopkeepers, or participating in
savings committees. The remaining 56 percent are the ‘financially excluded’, i.e. those who are excluded from any kind of formal or
informal financial services. Source: A2FS 2006–07.
3. Akhtar, Shamshad. Governor SBP Speech: Building Inclusive Financial System in Pakistan. June 2007.
Sources: Yaron, Jacob. 2004. Rural Microfinance: The Challenge and Best Practices. Tanzania. Agriculture Investment Sourcebook. World Bank. 2005. Agriculture Investment Note: MFIs moving into rural finance for agriculture. p. 314–318.
In a country where 88 percent of the population is of the rural populace would like to have access to
unbanked and the average population per formal financial services (see for more
branch is 2,450 (one of the highest in the region) , on this). Given the socio-economic characteristics
it is easy to understand why the banking sector and relatively lower income levels of rural
would choose to focus its presence in large cities Pakistan, the MF sector can fill this gap and
and urban localities. This however, also means expand its outreach to the rural unbanked
that a large segment of the population is deprived segment of the population. International
of formal financial services by virtue of its experiences have shown that MF institutions are
geographic location. As the recent Access to especially positioned to do so (see ).
Finance Survey (A2FS) showed, a large percentage
Section 6
Box 1
2
3
Profiling Pakistan's Rural Economy for Microfinance
Box 1: International Experiences in Rural Microfinance
3
The Pakistan Microfinance Network (PMN) and its
members thus identified rural finance as a key
area for research in MF. This report, second in a
series of PMN’s work on rural finance , used four
nationally representative datasets to construct
poverty profiles by agro-climatic zones. It
analyzed the livelihood sources, income and
expenditure patterns, savings and asset profiles,
and existing state of access to finance across poor
and non-poor segments in the different zones.
The key characteristics of rural financial markets
and the overall attitudes and perceptions that
play an important role in how MF and formal
finance are perceived, were also analyzed for each
category. Finally, the major constraints to
enterprise development in each zone were
identified.
Given the large volume of data, this report
presents only aggregate descriptions – more
detailed tabulations are presented in a second
volume (see ).
The following section gives a brief overview of the
data and methodology for the classification of
areas into agro-climatic zones, rural and urban
categories, and poor and non-poor segments of
society. A target market for MF across the
different income levels is also defined. The
subsequent sections look at the different aspects
of the rural economy mentioned above. The last
section concludes the report with some highlights
and suggestions on using the information
presented here.
enclosed CD
4
4. The first study Rural Finance Policy in Pakistan: Its Scope, Sources and Implications for the Microfinance Sector was published earlier
this year.
Introduction
4
A number of surveys and studies have been used to identify constraints and limitations to
conducted by government agencies and enterprise growth and productivity in the rural
international financial institutions which focus on market. Both surveys concentrated on non-farm
different aspects of Pakistan’s economy. Some enterprises which was also a key area in this study
have even focused solely on the rural areas. Four given the comparative size and importance of this
such surveys were selected as data sources in light sector in the rural economy, and the relatively
of the objective and scope of this report, namely: little information currently available on it.
1. Household Integrated Economic Survey All data was combined using agro-climatic zones
(HIES)/Pakistan Social and Living Standards (explained below) as the common denominator,
Measurement (PSLM) Survey 2005-06 : which helped maintain data integrity while
conducted by the Federal Bureau of Statistics allowing for more meaningful disaggregation
(FBS) for the Government of Pakistan. than a provincial breakdown . The fact that all
surveys were carried out in overlapping periods 2. Access to Finance Survey (A2FS) 2006-07: between 2005 and 2007 also facilitated
commissioned by the State Bank of Pakistan comparability. (SBP) and managed by PMN.
3. Rural Investment Climate Survey (RICS) 2005:
commissioned by The World Bank and The analysis of data disaggregated into conducted by Innovative Development appropriate groups is more pragmatic from the Strategies (Pvt.) Ltd. policymakers’ and practitioners’ point of view as
there is considerable diversity in economic and 4. Domestic Commerce Survey 2006-07:
living conditions across Pakistan. For this study, commissioned by the Ministry of Commerce
the national level data was classified using and conducted by Innovative Development
Strategies (Pvt.) Ltd. a) agro-climatic zones;
The PSLM uses data from rural households and b) household poverty status; and was the main source for creating the expenditure,
income, savings, and poverty profiles of the rural c) rural and urban categories. economy. The A2FS served as the chief source of
information on the behaviour, practices, and These zones were first
preferences of consumers in relation to the use of classified in Pakistan by Thomas C. Pinckney in
financial services. Information obtained from 1989 and have been used extensively since then.
RICS and the Domestic Commerce Survey was Pinckney divided the country into nine zones
Data Classification
Agro-climatic zones:
2. Methodology
5. The HIES was started in 1963 and continued to be carried out with some breaks. The last round of surveys was conducted in 2004 05 as
a subsample of the PSLM 2004-05 district-level survey. The two names are therefore often mentioned together. From this point
onwards in the report, any reference to the PSLM should be interpreted as a reference to the merged surveys of HIES and PSLM.
6. It may have been even more useful to go a step further and present district-level numbers, but this would have compromised the
representative power of the data.
7. The Demand for Public Storage of Wheat in Pakistan – IFPRI Research Report 77. Dec 1989.
-
5
6
7
Profiling Pakistan's Rural Economy for Microfinance
5Methodology
based on local cropping patterns and rotations. As Balochistan and the Northwest Frontier Province
wheat is the rabi season’s largest crop in virtually (NWFP) have not been sub-divided as they show
all areas, the primary kharif season crops little or no variation in cropping patterns. The
(irrigated rice and cotton) were used as the basis listing of districts in each zone is given in .
for differentiating between these zones. This
methodology divided Pakistan into nine agro-
climatic zones (shown on the map below).
Annex A
8. The rabi crop (winter crop) is harvested in spring.
9. The kharif crop (summer or monsoon crop) is harvested in autumn.
8
9
Distribution of Agro-climatic Zones
nIra
THARPARKERBADIN
UMER KOT
SANGHAR
NAWABSHAH
KHAIRPUR
NAUSHAHRO FEROZE
SUKKUR
GHOTKI
JACOBABAD
SHIKARPUR
DADU
KHUZDAR
PANJGUR
AWARAN
KECH
GWADAR
KHARAN
KALAT
MASTUNG
BOLAN
NASIRABAD
JAFARABAD
DERA BUGTI
KOHLUSIBI
RAHIMYAR KHAN
BAHAWALPUR
BAHAWALNAGARLODHRAN
VIHARIMULTAN
MUZAFFARGARHBARKHAN
LORALAIZIARAT
QUETTA
PISHIN
QILA ABDULLAH QILA SAIFULLAH
ZHOB
MUSAKHEL
DERAISMAIL KHAN
TANK
MIANWALI
BANNU KARAK
ATTOCK
NOWSHERAPESHAWAR
SWABI
BATGRAM
DAGGAR
MARDANCHARSADDA
MALAKAND
CHITRAL
KOHISTAN
SWAT
RAWALPINDI
HARIPUR
CHAKWAL
KHUSHAB
JHELUMGUJRAT
SIALKOT
NAROWALMANDI
BAHAUDDINGUJRANWALA
HAFIZABADSARGODHA
KASUR
OKARA
PAKPATTAN
SAHIWAL
FAISALABADTOBA TEK SINGH
KHANEWAL
JHANG
BHAKKAR
LEYYAH
DERA GHAZI KHAN
RAJANPUR
LAHORESHEKHUPURA
CHAGAI
NUSHKI
KOHATHANGU
NANKHANA SAHIB
KARACHI
LOWER DIR
UPPER DIR
GILGIT AGENCY
THATTA
HYDERABAD
F
A
TA
LAKKI MARWAT
LASBELA
LARKANA
JHAL MAGSI
MIRPUR KHAS
ABBOTTABAD
MANSEHRA
ISLAMABAD
KASHMIR(DISPUTED TERRITORY)
Agro-climatic Zones
Mixed Punjab
Cotton-wheat Punjab
Low Intensity Punjab
Rice-wheat Punjab
Barani Punjab
Cotton-wheat Sindh
Rice-other Sindh
NWFP
Balochistan
N/A
Rural-Urban Segmentation:
Household Poverty Status:
Section 3
Box 2 Section 3
Annex B
Data from HIES/PSLM according to the LGO criteria.
and A2FS were further classified by rural-urban The original Domestic Commerce Survey and RICS status. This however, was not a straightforward datasets do not differentiate between the exercise. The Local Government Ordinance (LGO) economic status of respondents and are collected 2001 divides areas into zilas, tehsils, towns, from cities and rural areas only. unions, villages, and neighbourhoods based on
the levels at which local government councils For this study, data was were established. This diluted the rural-urban
segregated by poor vs. non-poor status for distinction for certain parts of the country. Rural different aspects of the rural economy and settings are defined under this Ordinance as households within each zone. The categories composed of villages where: were based on the official national poverty line
(PL) defined for 2005-06 by the Government of “‘Village’ means an integrated and Pakistan (see on poverty profile for
contiguous human habitation commonly details). in explains how the
identified by a name and includes a dhok, population is further disaggregated into six
chak, killi, goth, gaown, basti or any other poverty bands for more detailed poverty profiling
comparable habitation.” and defining the MF market niche. However, this
breakdown was not carried any further in order to This study used the demarcation of villages and
maintain the representativeness of the data. classification of data under rural and urban
categories as defined by the Population Census of Figure 2 summarizes how data has been classified the Government of Pakistan and adopted by the using rural areas as an example. Similar FBS for its own surveys (see ). Separate breakdowns are also presented for ‘Urban’ and zones were created for Punjab and Sindh, i.e., ‘Pakistan’ in the data tables. ‘Other Urban’, for all peri-urban localities that
cannot be neatly categorized as rural or urban
6
10. The complete text of the LGO 2001 is available at: http://www.nrb.gov.pk/publications/Punjab_Local_Government_Ordinance_2001_old.pdf
10
Agro-cZones
limatic Poverty StatusRegion
1. Rice-wheat Punjab
2. Mixed Punjab
3. etc.
1. Rice-wheat Punjab
2. Mixed Punjab
3. etc.
Number/% of households within each zone displaying a certain behavior or characteristic
Number/% of households within each zone displaying a certain behavior or characteristic
Rural
Poor
Non-poor
Profiling Pakistan's Rural Economy for Microfinance
Figure 2: Data Classification Scheme
7
The income and poverty status of the sample
households were also used to identify the MF
target market. The aim was to see whether or not
they differed in characteristics, and how this
affected their needs for financial services, so that
MFPs can design products and strategies
accordingly (see in the next section).
This report’s main focus is the segmentation of
markets in rural areas, and it therefore
concentrates mainly on rural data in its analytical
and descriptive section. The aggregate data for
urban areas across the different agro-climatic
zones is available in ‘
’ on the (the complete list
of tables on the CD is also given in ).
Box 2
Volume II – Statistical
Appendix enclosed CD
Annex C
Methodology
Poverty has generally remained high in Pakistan.
The officially reported declining trend of the early
and mid 2000s has reversed following the food The incidence of poverty has always been higher
price inflation of 2008. Based on the official PL , in the rural areas of Pakistan. Poverty measured at
the trends in the poverty headcount ratio (the national levels is not particularly useful for policy
percentage of the population below the PL) as purposes due to the wide disparity in economic
reported by the Government of Pakistan are given and social conditions across the country. It is
in Table 1 and presented graphically in Figure 3 therefore advisable to disaggregate the
population by agro-climatic zones to obtain a
more meaningful analysis. This allows one to draw
inferences about region-
specific development needs,
especial ly the need for
f i n a n c i a l s e r v i c e s , t h e
development of which is
widely considered to be one of
t h e m o s t i m p o r t a n t
interventions for poverty
alleviation and economic
development.
3.1 Rural-Urban Poverty Distribution
.
3. Profiling Poverty in Different Agro-Climatic Zones of Pakistan
1998-99 20.9 34.7 30.6
2000-01 22.7 39.3 34.5
2004-05 14.9 28.1 23.9
2005-06 13.1 27.0 22.3
2008-09 N/A N/A 30.0*
Year Urban
Table 1: Trend in Poverty Indicators
Source: Pakistan Economic Survey 2007-08.
HEADCOUNT RATIO
Pakistan
(%)
Rural
11
11. The PL used in this analysis was first defined and adopted in 1998-99 by the Planning Commission of the Government of Pakistan. It is
based on a caloric norm of 2,350 calories per adult-equivalent per day. This PL was approximated at PKR 748.56 per month per adult in
2000-01 prices and was subsequently revised to PKR 944.47 for 2005-06. No official PL has been released since then.
Figure 3: Trend in the Poverty Headcount Ratio
0%
5%
10%
15%
20%
25%
30%
35%
40%
45%
1998-99 2000-01 2004-05 2005-06 2008-09
Urban
Rural
Pakistan
Profiling Pakistan's Rural Economy for Microfinance8
*Estimate by the Panel of Economists appointed by the Prime Minister of Pakistan.
9
Table 2 presents estimates of the incidence of poverty not only between rural and urban sectors,
poverty by agro-climatic zone based on PSLM but also among agro-climatic zones (see Figure 4).
2005–06 data and the official PL. These data As expected, the percentage of poor was higher in
showed that there is variation in the incidence of the rural areas for all zones, which contributes
Rural Urban Total Agro-climatic zones
Poor Non-poor Poor Non-poor Poor Non-poor
Rice-wheat Punjab 22.2 77.8 11.4 88.6 18.4 81.6
Mixed Punjab 22.6 77.4 6.0 94.0 19.6 80.4
Cotton-wheat Punjab 22.6 77.4 13.3 86.7 21.8 78.2
Low Intensity Punjab 26.1 73.9 16.7 83.3 25.0 75.0
Barani Punjab 7.2 92.8 1.5 98.5 5.5 94.5
Cotton-wheat Sindh
Rice-other Sindh
NWFP
Balochistan
Other Punjab Urban
Other Sindh Urban
29.6 70.4 11.5 88.5 26.5 73.5
35.1 64.9 4.9 95.1 19.0 81.0
29.2 70.8 22.7 77.3 28.2 71.8
56.6 43.4 32.4 67.6 50.9 49.1
N/A N/A 15.9 84.1 15.9 84.1
N/A N/A 20.4 79.6 20.4 79.6
Total 27.0 73.0 13.1 86.9 22.3 77.7
Table 2: Rural-Urban Poverty by Agro-climatic Zone (2005-06)
Source: PSLM 2005-06
(% of population)
Profiling Poverty in Different Agro-climatic Zones of Pakistan
12. See Annex B for a description of the PSLM survey methodology.
12
Figure 4: Rural Economic Status by Agro-climatic Zone 2005-06
0% 20% 40% 60% 80% 100%
Rice-wheat Punjab
Mixed Punjab
Cotton-wheat Punjab
Low Intensity Punjab
Barani Punjab
Cotton-wheat Sindh
Rice-other Sindh
NWFP
Balochistan
Rural Poor Rural Non-poor
10% 30% 50% 70% 90%
towards the 27 percent poor in the rural areas as strong likelihood of an increase in the incidence of
compared with just 13 percent in the urban areas poverty in Pakistan. The Task Force on
overall. Balochistan has the highest percentage of Food Security 2008 – based on the World poor (56.6 percent and 32.4 percent for rural and Bank's estimates of the poverty headcount urban areas, respectively), while Barani Punjab ratio – estimated that the poverty headcount had has the lowest incidence of poverty (7.2 percent increased to 36.1 percent in 2008-09, which is and 1.5 percent for rural and urban areas, equivalent to 62 million people living below the PL respectively ). in Pakistan.
Unprecedented food inflation, steep rises in
international energy prices since 2007, low gross
domestic product (GDP), and a slow-down in All poor are not the same. Once the PL is
sectoral growth since the mid-2000s all indicate a established, households can be categorized into
3.2 Depth of Poverty
10 Profiling Pakistan's Rural Economy for Microfinance
13
The official inflation-adjusted PL given by the Planning Commission of Pakistan for 2005-06 is PKR 944.47 per adult-
equivalent per month. The population is further divided into six poverty bands based on the distance from this PL, as shown
below:
The population living just above and below the PL (poverty bands ‘poor’ and ‘vulnerable’) is very susceptible to even small
shocks such as food or fuel price inflation and agricultural performance. This means they tend to frequently move above or
below the PL.
The MF market traditionally excludes the ‘extremely poor’ (as they are seen as too poor to be helped by MF and instead need
safety nets such as direct income transfers) and the ‘non-poor’ (as they generally have access to commercial banks, so MF
products are not suitable for them). We therefore defined the MF market segment as comprising the middle four poverty
bands for the purposes of this report (highlighted dark grey in the figure above).
Thus,
MF poor = ultra poor + poor
MF non-poor = vulnerable + quasi non-poor
That said, we are of the view that this traditional definition should be used with caution when studying the rural economy.
The reason is that residents of the rural areas of Pakistan lack access to finance because of the absence of formal financial
institutions in their villages, unlike urban areas where income defines access to financial services. Microfinance institutions
should therefore opt for a broader view of their target market in rural Pakistan than the traditional segmentation mentioned
above.
Microfinance Target Market
·
·
13. The low incidence of poverty in Barani Punjab and in the country overall, indicates that the official PL has been set too low. However,
since we are interested only in variations in the key characteristics of the rural economy, the absolute levels are unimportant and are
taken here as given.
Box 2: Poverty Bands and the Microfinance Market
Poverty Band Distance from PL Level of Income (PKR)
Extremely Poor
< 50 %
472.23
Ultra Poor
> 50 % <
75 %
< 708.35
Poor
> 75 % <
100 %
< 944.47
Vulnerable
<
100 % <
125 %
< 1,180.59
Quasi Non-Poor < 125 % < 200 % < 1,888.94
Non-Poor > 200 % > 1,888.94
PL = PKR 944.47
Mic
rofi
nan
ce M
arke
t
Source: Economic Survey of Pakistan 2007-08
11
poverty bands based on their expenditure relative has the lowest percentage (16.4 percent) across
to the PL. This provides a measure of the depth of all zones.
poverty and is particularly useful for policymakers To show the flip side, the distribution of as it shows the diversity of economic conditions households by agro-climatic zones falling under a across zones. poverty band is shown in Figure 6. It is interesting
Figure 5 groups households by the poverty bands to note that households from the Cotton-wheat
of extremely poor, ultra poor, poor, vulnerable, Punjab zone contribute the most towards all
quasi non-poor and non-poor. Overall, poverty bands. Barani Punjab has a negligible
approximately 28 percent of rural households are contribution towards the lower end of the
on or below the PL. However, another 20.2 spectrum of poverty bands, while the share of
percent in the vulnerable group are sensitive to households from the Rice-other Sindh zone have
slight changes in the economy. Barani Punjab has low shares at the higher end of the spectrum
the largest percentage of households in the non- followed by Balochistan.
poor and quasi non-poor groups combined (83
percent), while the Rice-other Sindh zone has the
lowest (34 percent). On the other hand, the Rice-
other Sindh zone has the highest percentage of
households (50.3 percent) in the poor and
vulnerable groups combined, while Barani Punjab
Profiling Poverty in Different Agro-climatic Zones of Pakistan
7 15 18 38 23
7 18 20 35 20
9 18 19 34 18
9 18 23 36 14
1 4 12 48 35
10 22 24 33 11
14 27 24 25 9
6 21 23 33 18
17 26 19 29 8
0% 20% 40% 60% 80% 100%
Rice-wheat Punjab
Mixed Punjab
Cotton-wheat Punjab
Low Intensity Punjab
Barani Punjab
Cotton-wheat Sindh
Rice-other Sindh
NWFP
Balochistan
Extremely Poor
Ultra Poor
Poor
Vulnerable
Quasi Non-poor
Non-poor
Figure 5: Distribution of Rural Poor by Poverty Bands
10% 30% 50% 70% 90%
Profiling Pakistan's Rural Economy for Microfinance12
3.3 Implications of Regional Variation in
Poverty
market, being able to differentiate it from the
safety net, and designing specific types of MF
services that are appropriately aligned to the
socio-economic needs of a particular band in a Understanding regional variation in poverty is particular zone. extremely useful for policymakers defining
national strategies for its alleviation. Strategies
based on more aggregate underlying analysis
tend to be inefficient because they embody the
‘one-size-fits-all’ principle. Disaggregation of the
type shown in Figures 5 and 6 above shows in
greater detail where particular types of resources
need to be deployed and leads to more efficient
strategies. For example, resources deployed for
safety net interventions need to be allocated in
the proportion of the extremely poor in each
region. This provides the ability to target more
effectively, and once resources are allocated, to
monitor and refine the flow of development
resources. Such a categorization is also extremely
useful for assessing the size of the potential MF
11 12 21 9 0 12 13 12 11
11 14 18 7 1 11 11 18 8
13 14 17 9 4 11 9 17 5
16 14 18 8 9 9 6 15 5
19 16 19 6 12 6 4 16 2
6 10 52 17 3 62 3
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Extremely Poor
Ultra Poor
Poor
Vulnerable
Quasi Non-poor
Non-poor
Pove
rty
Ban
ds
Rice-wheat Punjab Mixed Punjab Cotton-wheat Punjab
Low Intensity Punjab Barani Punjab Cotton-wheat Sindh
Rice-other Sindh NWFP Balochistan
Figure 6: Distribution of Rural Households by Agro-climatic Zones within a Poverty Band
13
The data from the PSLM 2005-06 permits the as this helps gauge client needs and identify key
estimation of some aggregate measures of the areas for the provision of financial services within
rural economy. This information can provide the the rural economy. This sub-section provides
setting for an assessment of the potential size of information on the proportion of rural
the rural financial market and its segments across households engaged in different occupations, the
Pakistan's agro-climatic zones and consequently, distribution of employment across different
the demand for financial services. sectors, and aggregate and average incomes
across rural Pakistan. Only headline indicators are discussed in the
sections below. Further disaggregated data is
available in the accompanying tables. This section Figures 7a and 7b show the distribution of compares the sources of income, expenditure, occupations of rural households. As shown, there and savings as given for the full market and the are significant differences in the distribution of market defined for MF (see ). occupations across the poor and non-poor.
However, the distribution across different agro-
climatic zones is quite similar. The only
exception is Barani Punjab, where the
proportion of households engaged in skilled I t i s important for MFPs to possess
agriculture/fisheries is much lower than other comprehensive knowledge of the occupations
areas. and sources of livelihood of their potential clients
What Do Rural Households Do?
Box 2
4.1 Rural Incomes: People, Sources, and
Volumes
4. The Aggregate Rural Economy by Agro-climatic Zone
14. The market defined for MF excludes the bands ‘extremely poor’ and ‘non-poor’.
14
113 1 11 48 8 5 22
0221 9 55 5 6 20
02 21 14 55 5 4 17
2 21 10 58 9 3 16
6 4 4 4 24 22 5 15 17
0 4 6 2 11 37 3 6 31
2 5 3 3 9 40 2 5 29
1 5 5 3 17 41 7 5 16
12 12 3 8 38 3 4 29
0% 20% 40% 60% 80% 100%
Rice-wheat Punjab
Mixed Punjab
Cotton-wheat Punjab
Low Intensity Punjab
Barani Punjab
Cotton-wheat Sindh
Rice-other Sindh
NWFP
Balochistan
Senior Officials Professionals
Technician & Associated Professions Clerks
Services, Shop & Related Workers Skilled Agriculture/Fishery Workers
Craft & Trade Workers Plant/Machinary Operators
Elementary Occupation
Figure 7-A: Rural Household Occupations – Non-poor
10% 30% 50% 70% 90%
The Aggregate Rural Economy by Agro-climatic Zone
The majority of the poor in most regions are areas (the data for the employment status of
engaged in elementary occupations , while the households is reported in Tables B1-B4 on the
non-poor are working mostly in skilled ).
agriculture/fisheries. The third most important
occupation for both poor and non-poor groups
appears to be services/shops/retail.The data in Table 3 gives the aggregate value in
rupees of rural income from different sources for Variations were also seen in terms of the both the poor and non-poor, as well as the value employment status of households across zones generated by the MF market (shown as numbers and income profiles. Most poor are ‘paid in parenthesis). employees’ across all zones. The case is the same
in the non-poor category. The only exception is The ‘non-agricultural’ sector generates nearly 60 Punjab (minus Barani Punjab), where ‘own percent of total rural incomes. Although its cultivators’ is the largest employment group. importance varies across different agro-climatic More ‘own cultivators’ are non-poor than poor zones – from generating a share in aggregate irrespective of the zone. ‘Share croppers’ is the incomes as high as 83 percent in NWFP to as low second largest category, especially in Sindh, as 30 percent in the Rice-other Sindh zone – it irrespective of poverty band. A considerable emerges as the largest income generator in six of percentage of Sindh’s poor fall into this category. the nine zones. Although ‘livestock’ is often cited ‘Self employed in non-agriculture’ makes up the as an important and growing sector of the rural third largest category across the country economy, its aggregate contribution is still quite irrespective of poverty band, most likely the low – its highest share is ten percent in the Rice-services and retail sector since it emerged as the
other Sindh zone (this is also the zone where the third largest occupation group across all rural
enclosed CD
What is the Size of the Rural Economy?
Profiling Pakistan's Rural Economy for Microfinance14
21 13 28 10 7 40
11 13 31 7 7 39
10 10 36 10 5 37
1 12 45 8 3 31
5 8 10 13 24 41
00 3 4 34 2 5 52
1211 6 42 12 44
0111 18 36 9 7 28
02 5 2 4 31 1 5 50
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Rice-wheat Punjab
Mixed Punjab
Cotton-wheat Punjab
Low Intensity Punjab
Barani Punjab
Cotton-wheat Sindh
Rice-other Sindh
NWFP
Balochistan
Senior Officials Professionals
Technician & Associated Professions Clerks
Services, Shop & Related Workers Skilled Agriculture/Fishery Workers
Craft & Trade Workers Plant/Machinary Operators
Elementary Occupation
Figure 7-B: Rural Household Occupations – Poor
15
15. Elementary occupations include day labour in agriculture, construction, trade, and transport.
15
share of ‘crop income’ is the largest, standing at 60 Figure 8 shows that the largest portion of rural
percent). ‘Remittances’ shows considerable incomes is from ‘non-agricultural business
variation across zones; a significant share of activities,’ for both the poor and non-poor. This
Barani Punjab’s income (21 percent) comes from income is more important for the non-poor as the
this source. The Cotton-wheat Sindh zone (both share of income generated from the ‘non-
poor and non-poor households) and the Rice- agricultural sector’ is more than double that of
other Sindh zone (poor households) showed income from ‘agriculture.’ Nonetheless, it is also
negative net transfers implying that the resources significant for the poor with its share being ten
remitted by these households are greater than percent more than income from ‘agriculture.’ This
those received. A large number of households in confirms earlier findings in research literature
these zones are composed of Punjabis and that showed the importance of the non-farm
Pathans with familial responsibilities to their sector in the overall rural economy of Pakistan.
zones of origin, which could explain these The World Bank , for example, concluded:
negative net remittances.
Table 3: Total Rural Income by Agro-climatic Zone
Source: PSLM 2005-06
Note: Aggregates for the MF market are given in blue in parentheses.
Total Crop Income Total Non-agricultural
Income
Total Income from Livestock
Total Net Transfers
Rice-wheat Punjab
6,361
(6,361)
88,593
(52,344)
11,974
(11,953)
191,649
(75,624)
3,210
(3,210)
26,006
(16,601)
2,119
(2,118)
42,371
(14,124)
Mixed Punjab
Cotton-wheat Punjab
Low Intensity Punjab
Barani Punjab
Cotton-wheat Sindh
11,707
(11,649)
-Rice other Sindh
NWFP
Balochistan
Total
(81,133)
82,785
6,935
(6,691)
86,356 (37,329)
15,861 (15,202)
96,982 (62,754)
2,724 (2,717)
17,795 (10,626)
1,866
(1,869)
11,627
(9,496)
17,894
(17,731)
158,335
(85,147)
16,408
(16,346)
135,643
(89,317)
2,616
(2,641)
23,592
(13,402)
1,913
(1,909)
12,284
(5,403)
8,624
(8,569)
37,077
(27,561)
19,420
(19,096)
68,245
(43,310)
1,560
(1,557)
9,394
(6,454)
1,739
(1,739)
9,795
(8,417)
66
(66)
8,691
(5,905)
365
(365)
33,737
(15,435)
129
(129)
3,976
(2,630)
175
(175)
11,992
(7,138)
48,084
(42,305)
5,958
(5,958)
75,248
(26,213)
1,770
(1,748)
5,009
(4,603)
-101
(-100)
- 398
(-263)
19,884(18,857)
34,962
(28,207)
6,462
(6,462)
21,148
(17,417)
3,223
(3,186)
5,821
(5,066)
(-99)
210
(444)
(117,791)
6,995
(6,993)
25,307
(17,994)
23,559
(23,526)
419,356 3,743
(3,743)
12,284
(9,440)
7,561
(7,514)
37,847
(24,762)
4,321
(4,218)
15,158
(12,293)
5,070
(5,035)
14,454
(12,506)
590
(588)
1,049
(947)
233
(230)
1,291
(946)
502,562
(309,085)
105,078
(103,944)
1,056,461
(460,367)
19,564
(19,521)
104,926
(69,768)
15,402
(15,350)
127,018
(70,468)
Poor Non-poor Poor Non-poor Poor Non-poor Poor Non-poor
-101
16. World Bank. 2007. Pakistan Promoting Rural Growth and Poverty Reduction. Washington D.C. See also:
1. Cororaton, Caesar B. and David Orden. 2007. “Inter-sectoral and Poverty Implications of Changes in Cotton and Textile Policies in
Pakistan: A CGE Analysis”. Research Report, Washington D.C.: International Food Policy Research Institute.
2. Dorosh, Paul A., Muhammad Khan Niazi, and Hina Nazli. 2003. “Distributional Impacts of Agricultural Growth in Pakistan: A
Multiplier Analysis” The Pakistan Development Review. 42(3): 249–275.
3. Dorosh, Paul. and Sohail J. Malik. 2006. Transitions Out of Poverty: Drivers of Real Income Growth for the Poor in Rural Pakistan.
Background Paper. Washington, D.C.: World Bank.
4. Malik, Sohail J. 1999. Poverty and Rural Credit: The Case of Pakistan. Islamabad: Pakistan Institute of Development Economics.
16
The Aggregate Rural Economy by Agro-climatic Zone
(All figures in Rs.)
The absence of strong farm to non-farm linkages “Although agriculture is at the heart of the implies that any growth in, for example, the rural economy, the majority of Pakistan’s agricultural sector, does not lead to employment rural poor are now neither tenant farmers nor generation and growth in the non-farm sector and farm owners. Farmers (including both owners vice-versa. This means that any increase in
and tenants) comprised only 43 percent of incomes in the farm sector leads largely to an
households in the bottom 40 percent of the increase in the demand for goods and services
rural per capita expenditure distribution in produced either in the urban areas or abroad. The 2004–05. Non-farm households (excluding lack of processing and value-adding activities for agricultural labourer households) accounted agricultural produce in the non-farm sector is
for slightly more than half (52 percent) of the symptomatic of these weak linkages. It is
therefore the strengthening of farm to non-farm poor. Overall, agriculture (including both crop linkages that is at the heart of rural development and livestock production) accounts for only and poverty reduction in Pakistan. about 40 percent of rural household incomes;
the poorest 40 percent of rural households
derive only about 30 percent of their total
income from agriculture”.
[World Bank 2007]
Profiling Pakistan's Rural Economy for Microfinance16
Agriculture
Non-agriculture
Livestock
Net Transfers
28%
59%
6%
7%
Non-poor
37%
47%
9%
7%
Poor
Figure 8: Sources of Rural Income – Non-poor vs. Poor
17
How Do Incomes Vary Across Different Sectors?
enclosed
CD
extremely poor within the population below the
PL, averages of the MF poor are almost the same There are variations in income levels across agro- as those for total poor. climatic zones which need to be considered while
designing financial services and products to cater ‘Income from non-agricultural sources’ shows
to the specific needs of clients in these areas. The considerable variation across the agro-climatic
disaggregation of income by sector, therefore, is zones, and amongst the non-poor in particular.
useful in helping recognize where potential exists Overall, the average non-agricultural income that
and how the provision of financial services in key the non-poor earn is more than three times that
sectors can help boost these sectors and the rural of the poor. The sale of retail and wholesale goods
economy on the whole. Here, we discuss average contributes the most towards non-agricultural
income from the four major sources of rural income. High average incomes for the non-poor in
the non-farm sector in NWFP can be explained by income – crop income, income from non-the Province's proximity to the Afghan border and agricultural activities, livestock, and transfers, and the trade activities that traditionally take place how they vary across the different regions of across the Durand Line; the contribution of black Pakistan (note that Tables B7-B11 on the market trade to the economy cannot be ignored. show further disaggregated data within these As far as the MF target market households are four categories). Average incomes for MF market concerned, the numbers vary much less across segments are also shown in Figures 9 through 11. zones with a high of PKR 403,063 per annum in As discussed above, these are shown separately NWFP (non-poor) to a low of PKR 129,077 in the for the two bands in the poor category (MF poor) Cotton-wheat Punjab zone (poor). These figures and the two bands in the non-poor category (MF are also in line with how MFPs and regulators non-poor). Given the negligible share of the
17. This includes income from the sale of goods (retail and wholesale), work done on raw material, repair and maintenance,
transportation, commissions and fees, contractual work, the sale of food items, rent, services, construction, and others. See Table B6
on the enclosed CD for more detailed data.
17
0
100,000
200,000
300,000
400,000
500,000
600,000
700,000
800,000
900,000
1,000,000
1,100,000
Rs.
per
HH
Figure 9: Average Annual Rural Income from Non-agricultural sources
Rice Mixed
Punjab
Punjab
Cotton
Wheat
Punjab
Low
Intensity
Barani
Punjab
Cotton
Wheat
Sindh
NWFP BalochistanRice
Sindh
Wheat
Punjab
Other
All Poor All Non-poor MF Poor MF Non-poor
The Aggregate Rural Economy by Agro-climatic Zone
currently define the MF target client. On the whole, average incomes from farming are
much lower than average incomes from non-‘Average household income from agriculture’ agricultural activities – the maximum average comes mainly from crops and their by-products. income from farming is PKR 175,995 in the Although the incomes generated from this sector Cotton-wheat Punjab zone (non-poor), which is vary across agro-climatic zones as well as between close to the average non-agricultural income for the poor and non-poor, the scale of difference is the poor of PKR 170,419 (comparing Figure 10 much smaller compared to income differences in below and Figure 9 above). This has important the non-agricultural sector. For the MF target implications for MFPs in terms of the poverty households, considerable variation is seen in crop levels of their clients and the sustainability of rural income with a high of PKR 156,324 in the Cotton- outreach. A poverty focused programme would wheat Sindh zone (non-poor) and a low of PKR target more agri-based clients whereas a 10,268 in Barani Punjab. commercially focused provider would target non-
agricultural households, yet both would be Interestingly, average incomes for the entire non-
serving the MF niche. poor category and those for the MF non-poor are
less variable compared to the ‘non-agricultural
sector’. This shows that households in the highest
income band are engaged mainly in non-
agricultural professions.
Profiling Pakistan's Rural Economy for Microfinance18
18
18. The Microfinance Ordinance (2001) of the SBP considered people below taxable income eligible for MF. An income of PKR
100,000 and above was considered taxable at the time. Under SBP’s Microfinance Department (MFD) Circular No. 2 of 2009, this
income threshold was revised and a maximum loan (other than housing) of up to PKR 150,000 can be made to a single borrower
with an annual household income of (net of business expenses) up to PKR 300,000.
Figure 10: Average Annual Rural Income from Crop
0
20,000
40,000
60,000
80,000
100,000
120,000
140,000
160,000
180,000
Rs.
per
HH
Mixed
Punjab
Punjab
Cotton
Wheat
Punjab
Low
Intensity
Barani
Punjab
Cotton
Wheat
Sindh
NWFP BalochistanRice
Wheat
Punjab
Rice
Sindh
Other
All Poor All Non-poor MF Poor MF Non-poor
19
The ‘livestock sector’ is promising, and given capacity. There is a pressing need both for the
financial support may bring sustainable flows of provision of financial services by MFPs and
income and livelihood to the rural economy. expertise in how additional value-added products
Currently, income generated from the sale of milk can be produced and marketed successfully.
contributes the most towards income from this ‘ Transfer payments’ mostly comprising sector, followed by poultry products. Microcredit remittances, are a significant component offacilities and insurance for farm animals can help
income in some regions of the
country. Not surprisingly, only
a few poor households across
all zones receive any foreign remittances, and even in
the case of the non-poor,
proportions are quite low
except for NWFP and the Rice-
wheat Punjab zone (see Tables
4a and 4b). However, regions
vary considerably in terms
o f d o m e s t i c f l o w s . A
considerable percentage of enhance this sector's households in Punjab and
NWFP receive domestic
remittances. The NWFP
Figure 11: Average Annual Rural Income from Livestock
0
5,000
10,000
15,000
20,000
25,000
30,000
35,000
40,000
Rs.
per
HH
Rice Mixed
Punjab
Punjab
Cotton
Wheat
Punjab
Low
Intensity
Barani
Punjab
Cotton
Wheat
Sindh
NWFP BalochistanRice
Wheat
Punjab
Wheat
Sindh
All Poor All Non-poor MF Poor MF Non-poor
Domestic Foreign
Poor
Poor Non-Poor
Rice-wheat Punjab 9.4 19.4 1.2 13.6
Mixed Punjab 13.3 22.4 0.5 4.8
Cotton-wheat Punjab 11.1
20.8 0.4
2.9
Low Intensity Punjab 11.9
28.4
0.7
5.3
Barani Punjab 17.4
23.2
0.0
5.1
Cotton-wheat Sindh 0.0
1.0 0.0
0.1
Rice-other Sindh
0.4
0.6
0.7
1.7
NWFP
20.7
29.8
6.8
14.8
Balochistan
0.3
3.1
0.9
2.1
Total
9.4
19.1
1.7
6.7
Table 4a: Percentage of Households Receiving Remittances
Non-Poor
The Aggregate Rural Economy by Agro-climatic Zone
presents an interesting case:
in contrast to other agro-
climatic zones, both foreign
and domestic remittances
are higher irrespective of
being in the poor or non-poor
categories in NWFP (see
for more on trends in
remittances for Pakistan).
Box 3
Profiling Pakistan's Rural Economy for Microfinance20
33,772 45,857 60,562 124,530
Domestic Foreign
Poor Non-poor Poor
Rice-wheat Punjab
37,838
41,454
45,526 158,355
Mixed Punjab
29,652
42,208
54,000
98,808
Cotton-wheat Punjab
23,215
42,133
45,220
105,887
Low Intensity Punjab
31,992
37,736
47,626
81,570
Barani Punjab
32,280
50,564
0
88,380
Cotton-wheat Sindh
12,000
28,316
0
34,429
Rice-other Sindh
12,000
37,231
29,862
80,567
NWFP
42,690
56,446
70,778
119,128
Balochistan 42,000 66,709 73,129 114,354
Total
Table 4b: Average Annual Inflow of Remittances per Household
(PKR per household [HH])
Non-poor
In a country like Pakistan where there is significant migrant exodus both into and out of the country, remittances
make an important contribution to local incomes. According to the SBP, remittances to Pakistan have witnessed a
fivefold increase since 2001 with foreign remittances at USD 7.8 billion for 2008-09, a 22 percent increase over the
previous year. World Bank (2009) estimates place domestic remittances at approximately USD 7.0 billion. In terms
of their importance to the rural economy, remittances contribute about seven percent to total rural incomes with
more reliance on domestic than foreign transfers.
Most of the formally sent foreign remittances are transferred through banks. However, this was not always the
case. Only ten years ago, a mere 15 percent of international remittances came through formal channels compared
to over 70 percent currently. This impressive performance can be credited to SBP’s efforts to bring transfers into
the formal net, and changes in international practices in light of new money laundering laws. Other channels for
remitting money across borders are exchange companies and the Pakistan Postal Service, with a 17 percent and a
2.5 percent share respectively, in foreign remittance flows.
By contrast, most people prefer to use the post office or friends and family for transferring money within the
country, according to the A2FS. This is followed by the use of bank services (through branches or electronically).
The First MicroFinanceBank Ltd. (FMFBL) is the only MF bank offering this service to date.
There is evidence from many countries that workers’ remittances play a major role in the transformation of
grassroots-level economies. Many NGOs evolved efficient models that pool the remittances for microcredit
initiatives at the village level. This new concept has been replicated worldwide, but is still not the focus of
policymakers and organizations working in the microcredit sector of Pakistan.
Source: Bringing Finance to Pakistan’s Poor. May 2009. The World Bank.
Box 3: The Remittances Market in Pakistan
21
4.2 Expenditure Patterns poor spend 66 percent of their incomes on
consumption compared to 46 percent spent by
the non-poor. Expenditure patterns present the other side of
the rural economy interlinked with income The average expenditure by households on generation. The breakup of aggregate agricultural inputs varies considerably across expenditures given in Figure 12 reiterates the different agro-climatic zones. Within agriculture dominance of non-agricultural activities in the inputs, the distribution of expenditure is very rural economy. The poor spend more than twice similar across the data with the highest share as much on non-agricultural activities (22 spent on fertilizers (22 percent). Low agricultural percent) than on agriculture (ten percent). By expenditure in Barani Punjab is expected, as from comparison, the share of non-farm expenditure a region with a predominant non-farm sector (see (41 percent) in total is almost four times as much Figure 13). There is considerable variation in how as agricultural expenditure (11 percent) for the much this segment spends on agriculture-related non-poor. The major difference in expenditure activities across different zones as was the case in patterns of the poor and non-poor is in terms of agriculture incomes for the MF target households. shares spent on household consumption – the
Non-poor Poor
Figure 12: Breakdown of Rural Expenditure into major categories
Agriculture
Non-agriculture
Livestock
Household Consumption
41%
2%
46%
11% 10%
22%
2%
66%
19
19. Inputs include seeds, fertilizers, pesticides, labour, land rent, electricity, and other utilities. A detailed breakup is available in
Tables G3 and G4 on the enclosed CD.
The Aggregate Rural Economy by Agro-climatic Zone
The average ‘non-agricultural expenditure’ assessing the needs of non-farm households.
varies little for the poor across different zones Most of this variation disappears when one
unlike between the corresponding non-poor. On examines non-farm expenditure in the defined
the whole, around 84 percent of this expenditure MF market. This is not unlike what is observed in
is the cost of purchasing goods solely for resale. the case of income from non-agriculture for this
This information is important for MFPs in segment (see Figure 14).
Profiling Pakistan's Rural Economy for Microfinance22
0
10,000
20,000
30,000
40,000
50,000
60,000
70,000
80,000
90,000
100,000
110,000
120,000
Rs.
per
HH
Figure 13: Average Rural Household Expenditure on Agriculture
Mixed
Punjab
Punjab
Cotton
Wheat
Punjab
Low
Intensity
Barani
Punjab
Cotton
Wheat
Sindh
NWFP BalochistanRice
Wheat
Punjab
Rice
Sindh
Other
All Poor All Non-poor MF Poor MF Non-poor
0
100,000
200,000
300,000
400,000
500,000
600,000
700,000
800,000
900,000
Rs.
per
HH
Figure 14: Rural Household Expenditure on Non-agriculture Activities
Mixed
Punjab
Punjab
Cotton
Wheat
Punjab
Low
Intensity
Barani
Punjab
Cotton
Wheat
Sindh
NWFP BalochistanRice
Wheat
Punjab
Rice
Sindh
Other
All Poor All Non-poor MF Poor MF Non-poor
20. This include expenditure on utilities, fuel, salaries and wages of employees, rent, repair and maintenance, expenditure on raw
material, packing, advertisements, the cost of goods purchased for resale, and others.
20
23
It is important to look at the trend in expenditure to tap into this resource base. Total reported
for consumption in households across zones as savings disaggregated by sources and value can
it constitutes the largest share of overall give insights into the existing savings potential
expenditure in the rural economy, especially for and preferences in the rural economy.
the poor. Figure 15 shows that household The most popular mode of savings is to convert it consumption expenditure varies little across into gold and silver, mostly in the shape of zones especially between the poor. This is jewellery. This is confirmed by the data. Table 5 understandable as these include basic necessities shows that the value of savings in gold and silver is like food and clothing for which a bare minimum larger than all other reported forms oflevel needs to be maintained.
saving. The next most popular means is to hold it Average household expenditure on livestock in cash or in a bank. These preferences are the shows little variation across zones and economic same across the poor and non-poor. There is huge status. Overall, the poor spend PKR 7,367 potential to leverage the savings currently held in annually as compared to PKR 11,355 spent by the such forms – gold in particular – by households non-poor. The corresponding households in the for more productive uses. This is especially defined MF market also have similar livestock-relevant in the present scenario where rising gold related expenditures. prices in the last few years have made purchasing
it unaffordable for many. It has created the need
to efficiently invest the resources that would have
gone into savings held as gold. Estimates of potential rural savings are extremely
important for designing appropriate MF products
4.3 Savings in the Rural Economy
Figure 15: Average Rural Household Expenditure for Consumption
0
20,000
40,000
60,000
80,000
100,000
120,000
140,000
Rs.
per
HH
Mixed
Punjab
Punjab
Cotton
Wheat
Punjab
Low
Intensity
Barani
Punjab
Cotton
Wheat
Sindh
NWFP BalochistanRice
Wheat
Punjab
Rice
Sindh
Other
All Poor All Non-poor MF Poor MF Non-poor
21
21. This includes expenditure on food, clothing and footwear, fuel and lighting, housing, health care, education, durables like electronics
and furniture, and others. See Tables G5-G6 on the enclosed CD for detailed data.
The Aggregate Rural Economy by Agro-climatic Zone
high as 31.7 percent for non-poor
households in Barani Punjab, and
as low as 2.2 percent for the
aggregate poor households in the
Rice-wheat Punjab zone.
It should be noted that this data
pertains to 'reported' savings,
and there is a great possibility
that total reported savings are
different from total actual
savings . It is l ikely that
households under-report their
savings for various reasons. Thus,
aggregate rural savings (and
r e s u l t a n t l y s a v i n g s a s a
percentage of income) are
actually higher than suggested by
the figures given.
Estimates of the ‘average savings’
of households across zones are
given in Figure 16. Unlike credit
where MFPs focus on a certain
market niche, the target market
for savings mobilization runs When examined in conjunction with data on across all income groups for MFPs. In fact, given aggregate incomes, the ‘aggregate savings’ data in the limited footprint of the financial sector in rural Table 5 above shows a very low propensity to Pakistan, MFPs will most likely face relatively little save. This is as much a consequence of high levels competition in leading in rural savings than in of poverty as it is of thin financial markets and urban deposits. overall attitudes and perceptions. Table 6 shows
that reported aggregate household savings provides interesting insights into rural
expressed as a percentage of aggregate rural perceptions of formal financial service providers
income is extremely low. It comes as no surprise and their [the rural populations’] preferences
that non-poor households save more than poor regarding products. Combined with this section's
households as a percentage of their incomes, i.e. savings potential estimates, these can be valuable
8.6 percent as compared to 4.9 percent in exploring the currently untapped rural savings
respectively. However, this savings ratio varies market.
considerably across agro-climatic zones, and is as
Section 6
Profiling Pakistan's Rural Economy for Microfinance24
Sources22 Total Savings in
Cash/Bank
Total Savings in Gold and Silver
Poor Non-poor Poor Non-poor
Rice-wheat Punjab 527
(527)
22,688
(8,776)
2,416
(2,384)
24,962
(14,084)
Mixed Punjab 2,591
(2,591)
28,551
(8,544)
3,743
(3,726)
32,557
(18,058)
Cotton-wheat Punjab
1,057
(1,057)
16,503
(6,294)
2,863
(2,805)
32,726
(16,079)
Low Intensity Punjab
1,540
(1,540)
11,397
(5,040)
2,371
(2,371)
14,276
(10,277)
Barani Punjab 145
(145)
18,514
(9,229)
258
(258)
19,148
(12,134)
Cotton-wheat Sindh 583
(583)
14,802
(8,670)
1,342
(1,342)
10,535
(6,898)
Rice-other Sindh 1,489
(1,489)
7,655
(4,208)
1,263
(1,253)
5,231
(3,734)
NWFP 1,464
(1,464)
29,690
(9,479)
5,297
(5,297)
46,320
(24,629)
Balochistan
1,029
(1,029)
4,806
(3,304)
2,759
(2,735)
7,938
(6,333)
Total
10,852
(10,852)
154,606
(63,573)
22,310
(22,170)
193,695
(112,226)
Table 5: Total Rural Household Savings (Reported) (Million PKR)
Source: PSLM 2005–06Note: Aggregates for the MF market are given in blue in parentheses.
22. The percentage of households that saved through other means (profits received on savings during the previous year, total value of
shares and stocks, amounts received from selling securities last year, and dividends received last year) is negligible.
23. Total actual savings = total income - total expenses.
23
25
Total Income
Total Savings
Savings as % of Income
Poor
(Million PKR)
Non-poor
(Million PKR) Poor
%
Non-poor
%
Rice-wheat Punjab 23,664
(23,642)
348,619
(158,693)
527
(527)
22,688
(8,776)
2.2
(2.2)
6.5
(5.5)
Mixed Punjab 27,386
(26,479)
212,759
(120,205)
2,591
(2,591)
28,551
(8,544)
9.5
(9.8)
13.4
(7.1)
Cotton-wheat Punjab
38,830
(38,624)
329,854
(193,269)
1,057
(1,057)
16,503
(6,294)
2.7
(2.7)
5.0
(3.3)
Low Intensity Punjab
31,343
(30,961)
124,510
(85,743)
1,540
(1,540)
11,397
(5,040)
4.9
(5.0)
9.2
(5.9)
Barani Punjab
735
(735) 58,396
(31,108) 145
(145) 18,514
(9,229) 19.8
(19.8) 31.7
(29.7)
Cotton-wheat Sindh
Rice-other Sindh
19,334
(19,225)
127,942
(72,858)
583
(583)
14,802
(8,670)
3.0
(3.0)
11.6
(11.9)
NWFP
29,468
(28,406)
62,141
(51,134)
1,489
(1,489)
7,655
(4,208)
5.1
(5.2)
12.3
(8.2)
Balochistan
41,858
(41,776)
494,795
(169,987)
1,464
(1,464)
29,690
(9,479)
3.5
(3.5)
6.0
(5.6)
Total
10,213
(10,071)
31,952
(26,692)
1,029
(1,029)
4,806
(3,304)
10.1
(10.2)
15.0
(12.4)
222,830
(219,948)
1,790,968
(909,688)
10,852
(10,852)
154,606
(63,573)
4.9
(4.9)
8.6
(7.0)
Poor
(Million PKR)
Non-poor
(Million PKR)
Table 6: Rural Savings as Percentage of Total Rural Incomes
Note: Aggregates for the MF market are given in blue in parentheses Source: PSLM 2005-06
Figure 16: Average Household Savings (Reported)
0
20,000
40,000
60,000
80,000
100,000
120,000
140,000
160,000
Rs.
per
HH
All Poor All Non-poor MF Poor MF Non-poor
Mixed
Punjab
Punjab
Cotton
Wheat
Punjab
Low
Intensity
Barani
Punjab
Cotton
Wheat
Sindh
NWFP BalochistanRice
Wheat
Punjab
Rice
Sindh
Other
The Aggregate Rural Economy by Agro-climatic Zone
2. Purpose of Loans: 4.4 Debt and Repayment Behaviour
3. Size of Loans:
1. Sources of Loans:
4. Repayment Behaviour:
In nearly all agro-climatic
zones irrespective of poor or non-poor status, a
larger percentage of households borrowed for It is useful to look at some indicators of the
household needs (such as marriage and funeral current debt profile of rural households in order
expenses) than any other needs (such as business to assess demand for credit services and ascertain
investments). Overall, a higher proportion of poor the potential market for financial products.
households borrowed for household needs than Overall, nearly a quarter of poor (21 percent) and the non-poor households. Interestingly, even 18 percent of non-poor rural households took out urban numbers show similar trends with more loans in one form or another in the 12 months households borrowing for household needs than prior to the PSLM survey. The highest proportion for other purposes (see Figures 18a and 18b). was seen in NWFP while the lowest was in
Balochistan (see Figure 17).
Some interesting patterns in rural debt behaviour The average size of the are:
outstanding debt per reporting household is quite
The largest sources of loans large and it is about twice as large for poor
are informal. Data from the A2FS showed that of households as it is for the non-poor. This is not
the 35 percent of the total population that is using surprising given that most households borrow for
a credit facility, only two percent borrowed from a household needs and that the poor are more
formal source while 33 percent borrowed from an vulnerable to income and consumption shocks;
informal source. Within informal sources, they would have a greater need to borrow money
shopkeepers and family and friends were the two to smoothen spending over a year compared to
leading sources. In fact, those that borrowed from the non-poor.
these informal sources often borrowed more than The data on average once during a period of twelve months.
repayments during the previous year presented in
Profiling Pakistan's Rural Economy for Microfinance26
Rice Mixed
Punjab
Punjab
Cotton
Wheat
Punjab
Low
Intensity
Barani
Punjab
Cotton
Wheat
Sindh
NWFP BalochistanRice
Figure 17: Rural Households Receiving Loans
0
5
10
15
20
25
30
35
40
45
50
% o
f H
H
Poor Non-poor
Wheat
Punjab
Other
Sindh
27
Table 7 shows that overall, the average repayment Balochistan which has the largest gap between
was almost equal to the average borrowing during loans borrowed and repaid). While such
the preceding year for the poor. The repayment behaviour also represents some element of
even exceeds borrowing in some zones (Sindh and seasonality with the above average repayments in
Low Intensity Punjab) where the borrowers paid good years, it is a respectable indication and
part of earlier debts accrued, as well. Similar confirms a healthy functioning of the rural
behaviour is also shown by non-poor households financial market.
in most agro-climatic zones (with the exception of
Figure 18-A: Share of Loans Taken for Household and Other Purposes – Poor
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
HH needs Other needs
Mixed
Punjab
Punjab
Cotton
Wheat
Punjab
Low
Intensity
Barani
Punjab
Cotton
Wheat
Sindh
NWFP BalochistanRice
Wheat
Punjab
Rice
Sindh
Other
Figure 18-B: Share of Loans Taken for Household and Other Purposes – Non-poor
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
HH needs Other needs
Mixed
Punjab
Punjab
Cotton
Wheat
Punjab
Low
Intensity
Barani
Punjab
Cotton
Wheat
Sindh
NWFP BalochistanRice
Wheat
Punjab
Rice
Sindh
Other
The Aggregate Rural Economy by Agro-climatic Zone
Profiling Pakistan's Rural Economy for Microfinance28
Outstanding Loans Loans Taken Out in the
Last One Year
Loans Repaid in the Last One Year
Poor Non-poor Poor Non-poor
Rice-wheat Punjab 33,694 60,896 18,655 46,044 12,776 25,535
Mixed Punjab 29,512
62,848
14,380
47,326
10,157
53,263
Cotton-wheat Punjab
17,051 66,927 13,902 45,788 10,212 48,078
Low Intensity Punjab
23,119 34,526 17,580 20,260 28,340 23,953
Barani Punjab
20,945
29,869
21,996
28,356
21,022
10,111
Cotton-wheat Sindh
46,337
27,323
25,953
26,829
27,430
21,013
Rice-other Sindh
14,832
29,868
13,015
24,007
15,475
33,191
NWFP
37,318 54,606 20,643 36,679 18,163 23,436
Balochistan
21,765
55,424
16,511
56,304
9,291
17,472
Total
28,907
53,925
17,771
39,345
16,611
33,161
Table 7: Borrowing and Repayment Profile of Rural Households(PKR per HH)
Source: PSLM 2005–06
Poor Non-poor
4.5 Asset Profiles
4.6 Rural Housing
purposes (for non-agricultural land). On the other
hand, there is considerably less variation in the
value of livestock owned by the households The value of assets that rural households own is a
across the zones. relevant piece of information for financial service
providers, particularly in the context of
forwarding loans to households. It provides an
estimate of the collateral that rural households
Housing MF is a relatively new product market hold.
and one that is greatly demanded by the low Land holdings and livestock are the main assets of income population. Recent revisions in the SBP’s rural households. The value of land and livestock loan limit for housing MF has created room for is the price these assets will sell for in the market MFPs to design products and price them at current rates. As shown in Table 8, there is some appropriately for this market. In this context, it is degree of variation in the average landholding of useful to be aware of rural ‘housing occupancy households across the agro-climatic zones, but status,’ i.e. if households rent accommodation or the variation in the value of land is more own their houses. Figures 19a and 19b show that pronounced. The value of land depends on many the overwhelming majority of the sample rural factors such as irrigation and soil quality (for households live in their own houses, i.e. 88 agricultural land), and location and ease with percent and 94 percent of poor and non-poor which land can be used for different commercial rural households, respectively. The second largest
24
24. See SBP Microfinance Department (MFD) Circular no. 2 of 2009, according to which the loan limit for housing MF loans is now PKR
500,000 to a single borrower with a household annual income of up to PKR. 600,000. However, at least 60 percent of the housing loan
portfolio of an MFB should be within the loan limit of PKR 250,000 or below.
25. Land assets include agricultural and non-agricultural land, commercial land, and residential land and properties.
29
Figure 19-A: House Occupancy Status Poor –
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Rice-wheat Punjab
Mixed Punjab
Cotton-wheat Punjab
Low Intensity Punjab
Barani Punjab
Cotton-wheat Sindh
Rice-other Sindh
NWFP
Balochistan
Owner occupied house On rent Rent free
79
93
6 15
7
87
89
86
97
97
97
90
4
1
1
1
9
11
14
2
3
2
9
The Aggregate Rural Economy by Agro-climatic Zone
25Value of Land
PKR per HH
Average Land Holdings (Acres)
Value of LivestockPKR per HH
Poor Non-poor Poor Non-poor Poor Non-poor
193,051 428,802 4 7 30,691 45,489
136,491 323,564 3 7 21,211 36,963
123,922 350,653 4 10 18,170 38,551
84,678
183,974
8
10
15,741
30,003
273,504
436,027
2
5
17,158
36,371
87,654
260,741
6
8
16,609
24,927
84,113
321,285
9
11
25,740
33,250
305,389 903,060 3 4
20,512
25,259
85,040 186,753 9 15 9,368 15,004
Total 148,480 418,169 6 8
20,311
34,683
Table 8: Value of Livestock and Land Owned by Households
Note: Land and livestock values are self assessed by reporting respondents. Source: PSLM 2005-06
Rice-wheat Punjab
Mixed Punjab
Cotton-wheat Punjab
Low Intensity Punjab
Barani Punjab
Cotton-wheat Sindh
Rice-other Sindh
NWFP
Balochistan
house occupancy status is of families living rent- non-poor (28 percent and 39 percent,
free with extended family, which is higher for respectively) figures. On the other hand, a larger
poor households (nine percent as compared with percentage of non-poor (33 percent) live in
four percent of non-poor families). It is also houses with three or more rooms than the poor
interesting to note that this varies little across (21 percent). Given the high dependency ratios
different regions. and large family size of the average rural poor
Even though household occupancy status is very household, these numbers highlight the need for similar for the poor and non-poor, this disguises products that can enable these households to the quality and type of housing across different add-on to existing accommodation and/or income strata. Figures 20a and 20b reflect the improve their existing property. variation in the number of rooms in an average
house between the poor and non-poor. This
difference is much more pronounced than
occupancy status. The percentage of poor living in
one-room (38 percent) and two-room (41
percent) houses is larger than the corresponding
Profiling Pakistan's Rural Economy for Microfinance30
26
Figure 19-B: House Occupancy Status Non-poor –
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Rice-wheat Punjab
Mixed Punjab
Cotton-wheat Punjab
Low Intensity Punjab
Barani Punjab
Cotton-wheat Sindh
Rice-other Sindh
NWFP
Balochistan
Owner occupied house On rent Rent free
93 7
95
93
96
91
97
96
89
2
6
1
2
4
5
5
4
3
2
2
7
96 2 2
26. The ‘rent-free’ category refers to the occupants living in houses owned by their parents or members of their extended families to
whom they pay no rent.
31
Figure 20-B: Distribution of Number of Rooms Non-poor –
Figure 20-A: Distribution of Number of Rooms Poor –
23
27
28
35
12
44
40
18
35
38
37
42
40
41
39
40
35
32
32
29
25
23
39
14
17
37
26
7
7
5
2
8
3
10
7
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Rice-wheat Punjab
Mixed Punjab
Cotton-wheat Punjab
Low Intensity Punjab
Barani Punjab
Cotton-wheat Sindh
Rice-other Sindh
NWFP
Balochistan
3
41
44
41
36
25
42
50
26
25
41
37
44
46
27
42
36
36
47
16
17
13
14
40
14
14
32
24
2
2
2
4
8
2
0
6
4
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Rice-wheat Punjab
Mixed Punjab
Cotton-wheat Punjab
Low Intensity Punjab
Barani Punjab
Cotton-wheat Sindh
Rice-other Sindh
NWFP
Balochistan
1 room 2 rooms 3-4 rooms 5 or more rooms
The Aggregate Rural Economy by Agro-climatic Zone
1 room 2 rooms 3-4 rooms 5 or more rooms
Profiling Pakistan's Rural Economy for Microfinance32
In addition to examining the characteristics of wholesale trading. This is followed by services
rural households and the size of the rural provision (40 percent), and manufacturing (12.9
economy, it is useful to understand the structure percent), and within this, the processing or
and limitations of markets in rural Pakistan. This manufacturing of agricultural and fishing
section looks at ownership structures of products (four percent) . Most of these
businesses, key constraints faced, and the enterprises operate in the informal sector, making
significance of social capital in the market. it difficult for them to develop credibility as it is
conventionally viewed by financial service The sub-sections below draw upon two surveys, providers. In addition, most rural businesses lack the Domestic Commerce Survey 2006-07 and the formal registration with government agencies or RICS 2005. The latter is based solely on data from business associations, which adds to the issue of rural areas, while the former only allows for establishing credit-worthiness in the eyes of consolidated results across the agro-climatic formal financial institutions (see Figure 21). zones. Interpreters of this data should bear this
distinction in mind.
The following tables present some of the most Over 90 percent of all businesses in rural Pakistan
common constraints reported by enterprise are sole proprietorships. Around 47 percent of all
owners to conducting and expanding their business enterprises are involved in retail and/or
businesses. These tables are based on the
5.2 Constraints to Rural Business
Development5.1 Nature of Businesses
5. Market Constraints and Limitations
Figure 21: Businesses Registered with a Formal Authority
0%
5%
10%
15%
20%
25%
30%
35%
Government agency City-wide association
Mixed
Punjab
Punjab
Cotton
Wheat
Barani
Punjab
Cotton
Wheat
Sindh
NWFP BalochistanRice
Wheat
Punjab
Rice
Sindh
Other
27
27. See Tables H20-H24 on the enclosed CD for a detailed breakup of different activities that businesses are involved in, in each agro-
climatic zone.
Source: Domestic Commerce Survey 2006-07
33
Domestic Commerce Survey 2007. A sizeable leading constraint, it is obvious that the existing
proportion (71.9 percent) of overall respondents sources are not sufficient to meet the needs of
in all zones cited financing as the biggest rural businesses. It is therefore interesting to see
constraint to business development (see Figure what percentage of these entrepreneurs:
22). The importance of access to finance is a) have explored the possibility of getting a loan reinforced as it also emerged as one of the biggest
from a bank in the past; constraints to the growth of enterprises – 48.9
percent of entrepreneurs felt this was the key b) are interested in applying for a bank loan; and
hindrance in the expansion of their businesses.
Other constraints included the quality of public c) are not interested in a bank loan and why. services and business laws and regulations (see
Figure 23). Figures 24a to 24c provide some answers to these
questions. Currently, most businesses use loans from friends
and relatives or their own resources to start-up
their businesses. Given that finance remains the
Figure 22: Biggest Constraints to Rural Business Development
62
73
69
58
82
79
82
58
17
6
9
12
10
8
4
10
13
17
16
21
5
10
9
10
3
5
1
0
7
8
4
3
5
3
2
4
16
0% 20% 40% 60% 80% 100%
Rice-wheat Punjab
Mixed Punjab
Cotton-wheat Punjab
Barani Punjab
Cotton-wheat Sindh
Rice-other Sindh
NWFP
Balochistan
Financing Do not have the means to assess market demand
Do not have a reliable network of partners at other placesGovernment regulations
Other
10% 30% 50% 70% 90%
Source: Domestic Commerce Survey 2007
28
28. Finance emerged as the biggest constraint in the RICS sample as well.
Market Constraints and Limitations
The majority of businessmen have not applied for that have wanted to apply for a loan is
a loan in the past five years across all zones. Only considerably higher – the lowest at 15.8 percent the Rice-other Sindh zone shows a relatively in NWFP, and the highest at 47.5 percent in the higher percentage of those that have applied for a Cotton-wheat Punjab zone (see Figures 24a and loan. Compared to this, the percentages of those 24b).
Profiling Pakistan's Rural Economy for Microfinance34
Figure 23: Key Constraints to Growth of Enterprises
0%
10%
20%
30%
40%
50%
60%
70%
80%
Taxation and regulation system (licensing, permits etc.)
Quality of Public services (electricity, roads, communications)
Lack of access to finance
Mixed
Punjab
Punjab
Cotton
Wheat
Barani
Punjab
Cotton
Wheat
Sindh
NWFP BalochistanRice
Wheat
Punjab
Rice
Sindh
Other
Figure 24-A: Entrepreneurs that have applied for a loan in the last five years (2000-05)
0% 20% 40% 60% 80% 100%
Rice-wheat Punjab
Mixed Punjab
Cotton-wheat Punjab
Low Intensity Punjab
Barani Punjab
Cotton-wheat Sindh
Rice-other Sindh
NWFP
Yes No
23
8
6
25
4
21
46
26
77
92
94
75
96
79
54
74
10% 30% 50% 70% 90%
Source: RICS 2005
Source: Domestic Commerce Survey 2007
35
The question is that if entrepreneurs need loans, variation across different regions, some reasons
what prevents them from approaching banks? stand out. Complicated procedures have been
Figure 24c shows the different reasons cited by cited as the most common reason for not
respondents for why they do not take out bank borrowing from banks. Keeping in mind low
loans despite the stated need. Although there is literacy levels and the scanty documentation
Figure 24-B: Entrepreneurs wanting to apply for a loan in the last five years (2000-05)
29
36
48
20
36
27
34
16
71
64
52
80
64
73
66
84
0% 20% 40% 60% 80% 100%
Rice-wheat Punjab
Mixed Punjab
Cotton-wheat Punjab
Low Intensity Punjab
Barani Punjab
Cotton-wheat Sindh
Rice-other Sindh
NWFP
Yes No
10% 30% 50% 70% 90%
Source: RICS 2005
Figure 24-C: Reasons for not taking out loans from Banks
Rice-wheat Punjab
Mixed Punjab
Cotton-wheat Punjab
Low Intensity Punjab
Barani Punjab
Cotton-wheat Sindh
Rice-other Sindh
NWFP
Total
Easier to borrow from friends/family High interest rate Duration too short
Insufficient collateral High cost of application Bank located too far
Complicated procedure Other
15
2
2
3
3
4
31
35
36
17
29
8
9
48
31
4
17
5
25
5
5
9
18
23
23
33
21
3
18
2
1
9
1
2
1
2
8
5
2
29
18
26
33
22
74
68
38
32
5
8
17
5
10
5
2
0% 20% 40% 60% 80% 100%10% 30% 50% 70% 90%
Source: RICS 2005
Market Constraints and Limitations
available with informal businesses, bank thus shatters the usual negative perceptions
procedures can easily deter those in need of about legal systems in Pakistan as the majority of
finance. In addition, about one-third of all rural entrepreneurs perceived laws that make up
respondents cited high interest rates as a reason the business environment as mostly predictable
for not taking out formal loans, especially in and supportive of the smooth functioning of
Punjab and NWFP. This is especially relevant for business activities.
MFPs, as they often lend at high interest rates. Figure 25 shows that according to a considerable Insufficient collateral also emerged as one of the majority (69.5 percent) the laws that affect major reasons in Punjab. Further insight into the business operations in the market are highly or preferences of potential rural clients is provided somewhat predictable. The responses were by the low percentage who did not borrow generally similar across agro-climatic zones. because of the ease of borrowing from friends Similar results were obtained when businessmen and relatives. Surprisingly, the absence of formal were asked about how predictable they perceived banking facilities or branches located too far was laws implemented in their community to be. not one of the main reasons cited.
In the context of the relationship between a
financial service provider and a business, it is
important to note peoples’ perceptions of The efficiency of the legal system within which whether or not the legal system helps resolve businesses operate is important for the resolution disputes that may arise during business of disputes and the effectiveness of available legal transactions. Around 70 percent of rural solutions. The following data from RICS shows entrepreneurs agreed that the legal system that on the whole, the legal system is perceived as reinforces and upholds business contracts and efficient and effective in establishing norms and protects property rights (see Figure 26). business values in rural markets. The evidence
5.3 Efficiency of the Legal System
Profiling Pakistan's Rural Economy for Microfinance36
Figure 25: Respondents' perceptions of the predictability of laws that affect business operations
7
8
7
22
8
7
8
35
61
64
61
42
69
62
66
31
26
28
27
24
23
11
9
27
6
1
5
12
0
20
17
7
Rice-wheat Punjab
Mixed Punjab
Cotton-wheat Punjab
Low Intensity Punjab
Barani Punjab
Cotton-wheat Sindh
Rice-other Sindh
NWFP
Highly predictable Somewhat predictable Unpredictable Highly unpredictable
0% 20% 40% 60% 80% 100%10% 30% 50% 70% 90%
Source: RICS 2005
37
5.4 Social Capital in Rural Markets that information flows must be strong and vibrant
within a business community (see Figure 27).
However, it would be difficult to engage in From the perspective of MF providers, existing
transactions outside the community due to social capital in rural markets is of particular
greater information asymmetries and the importance given the reliance of this sector on
absence of mechanisms to overcome them. reputations and the communal bonds of people in
an area. ‘Social capital’ refers to the relationships Around 72 percent of rural entrepreneurs agreed and general know-how between individuals and that a business contract is protection against entities in a community that can be economically cheating in market transactions. Once under valuable. The role of social capital is especially contract, the incidence of cheating is quite low, important for joint liability mechanisms used in according to a significant majority of rural microcredit. entrepreneurs (see Figure 28). The importance of
having a binding contract thus mitigates the risks Given the informal nature and single ownership
of moral hazards that may arise in MF due to the structure of rural businesses, it is not surprising to
relatively light documentation involved in see that a sizeable majority (72.5 percent) of rural
microcredit transactions. entrepreneurs base their business decisions on
the reputation of counterparties. This also shows
5
10
6
20
10
1
1
23
52
63
63
49
67
48
59
52
41
27
29
29
23
44
37
25
1
0
2
0
7
3
0
0% 20% 40% 60% 80% 100%
Rice-wheat Punjab
Mixed Punjab
Cotton-wheat Punjab
Low Intensity Punjab
Barani Punjab
Cotton-wheat Sindh
Rice-other Sindh
NWFP
Strongly agree Agree Disagree Strongly disagree
2
Figure 26:
Respondents’ perception of whether or not the legal system upholds contracts and property rights in business disputes
10% 30% 50% 70% 90%
Source: RICS 2005
Market Constraints and Limitations
Profiling Pakistan's Rural Economy for Microfinance38
Figure 27: Respondents’ perception of the reliance on counterpart’s reputation for business dealings
24
8
6
42
15
13
6
49
59
75
77
46
70
65
73
34
16
17
15
12
15
20
21
17
2
2
2
0% 20% 40% 60% 80% 100%
Rice-wheat Punjab
Mixed Punjab
Cotton-wheat Punjab
Low Intensity Punjab
Barani Punjab
Cotton-wheat Sindh
Rice-other Sindh
NWFP
Strongly agree Agree Disagree Strongly disagree
Figure 28: Respondents’ perception of whether or not a business contract is protection against cheating
13
2
4
32
5
7
3
34
57
69
68
39
70
58
64
45
30
29
27
29
25
34
33
21
0
2
1
0% 20% 40% 60% 80% 100%
Rice-wheat Punjab
Mixed Punjab
Cotton-wheat Punjab
Low Intensity Punjab
Barani Punjab
Cotton-wheat Sindh
Rice-other Sindh
NWFP
Strongly agree Agree Disagree Strongly disagree
10% 30% 50% 70% 90%
10% 30% 50% 70% 90%
Source: RICS 2005
Source: RICS 2005
39
The development of the rural financial market in Within the poor, the percentage of banked
households ranges from as low as 1.6 percent in Pakistan – particular the MF sector – is dependent the Rice-other Sindh zone to as high as 22.8 upon the existing conditions that prevail not only percent in Azad Jammu and Kashmir (AJK) and in the overall rural economy, but also on existing 21.9 percent in NWFP (see Figure 29). The high financial markets in these areas. In particular, any percentage of banked households in AJK can be policy designed to promote MF in the country explained by the presence of large diasporas from needs to take explicit cognizance of the current this district in the UK that regularly remits money state of access to finance in rural Pakistan. Data to relatives, and the requirement of a bank from the A2FS enabled us to describe some key account to receive compensation from the aspects of the current condition of rural financial government for damage after the October 2005 markets. earthquake. The latter also explains the high
percentage of banked households in NWFP.
It is possible to generate an index based on the Access to finance is cited as one of the major A2FS data which combines two different types of constraints in the setting up and growth of information into one indicator. A value of ‘one’ for business enterprises in rural Pakistan. The A2FS the index shows that a particular aspect has a indicates that only six percent of the overall poor distribution equal to the share of the populationhouseholds and 18.5 percent of the overall non-
in that agro-climatic zone, i.e. the aspect is poor households in rural areas are banked. This normally distributed according to the distribution explains, in part, why the deficiency of access to of the population. However, an index value finance is so often reported as a major constraint greater than one shows that the aspect has a by rural enterprises. higher concentration in that zone than indicated
6.1 State of Access to Finance
6. Characteristics of Rural Financial Markets
Figure 29: Percentage of Banked Rural Households
0
5
10
15
20
25
30
35
40
45
Poor Non-poor
NWFP Balochistan AJKMixed
Punjab
Punjab
Cotton
Wheat
Punjab
Low
Intensity
Barani
Punjab
Cotton
Wheat
Sindh
Rice
Punjab
Wheat
Rice
Sindh
Other
% o
f H
Hs
Source: A2FS 2006–07
Characteristics of Rural Financial Markets
by its share in the population, and a value less AJK have 3.8 times the banked households in all
than one shows that it is distributed more weakly regions. Similarly, NWFP has 3.6 times and the
than the population share in that zone. The index Rice-other Sindh zone has 0.3 times the overall
needs to be viewed in conjunction with the percentage for poor. The highest index for the
distribution information. It can be used to assess non-poor is in NWFP – 2.2 times the overall non-which region is the weakest and which is the poor banked percentage, followed by AJK and strongest in a particular aspect of access of Barani Punjab. This indicates that the use of finance. banking services as a percentage of the poor rural
households is relatively higher in AJK, followed by For example, if 30 percent of respondents indicate NWFP. It is quite low for the poor households in that they obtain their banking information from the Rice-other Sindh zone. If the policy is to the radio and the value of the index is 1.0 for that increase banking services for the poor in the least particular zone, it shows that the intensity of this intense regions, then the focus should be on the information is normally distributed as per Rice-other Sindh zone. If the policy is to use the population share. However, if the index is 1.5 for areas with the highest density, then the focus that zone, it shows that the intensity is much should be on AJK and NWFP. higher, and a value of less than 1.0 shows that the
intensity is much lower than the population share
would indicate. The further away the index value Nearly 98 percent of A2FS respondents in rural is from one, the more or less intense the Pakistan reported never having taken a formal prevalence. It can therefore be used to devise bank loan. This percentage was the highest for appropriate regionally disaggregated policy. poor households in the Cotton-wheat zone, the
Index values across the different zones for the Low Intensity zone, and Barani Punjab, for poor intensity of the banked populace (the ratio of the households where 100 percent reported not percentage banked in an agro-climatic zone to the having taken out a formal bank loan. Even for percentage of households in the total sample of NWFP – which had the highest percentage of the same zone) shows that poor households in households that had borrowed formally – the
Access to Credit
Profiling Pakistan's Rural Economy for Microfinance40
Figure 30: Households that have never borrowed from a Bank
0
90
92
94
96
98
100
Poor Non-poor
NWFP Balochistan AJKMixed
Punjab
Punjab
Cotton
Wheat
Punjab
Low
Intensity
Barani
Punjab
Cotton
Wheat
Sindh
RiceRice
Punjab
Wheat
Sindh
Other
% o
f H
Hs
Source: A2FS 2006–7
41
number was a mere 2.7 percent (see Figure 30). credit due to the availability of trusted agencies in
Although a higher proportion of the non-poor close proximity , and minor documentation
have accessed formal lenders, the numbers are requirements. However, high interest rates do
not so different from the poor. It goes to show that serve as deterrents for those not using this
irrespective of income levels, rural populations source. These various sources mentioned enjoy
rarely use formal financial services. This may be widespread popularity because the workings of
due to the non-availability of such services, or each is different (in addition to most being flexible
their unsuitability to the rural population. credit modes), and people often combine
different sources to meet their requirements. The proportion of households that reported
taking loans from informal sources is much higher
because the proportion of households that have Generally, more rural non-poor use formal savings never taken a loan from informal sources is quite mechanisms compared with the rural poor, low. The major sources of informal loans seem to although use is minimal for both groups. be friends and family and shopkeeper credit (SeePLS/Savings Accounts were the most popular at the end of for the mechanics products amongst those who saved formally with of how shopkeepers’ credit works in Pakistan). 4.7 percent of poor and 11.2 percent of non-poor
Overall, nearly half of the poor households reporting their use. Use is lower for
reported borrowing from friends and family Current/Cheque Accounts, and even lower for
compared to 40 percent of non-poor households Basic Banking Accounts. About 1.5 percent of the
doing the same. This shows how prevalent this poor and 4.5 percent of the non-poor have ever
practice is. The numbers vary little across regions. invested in Prize Bonds. The use of Islamic Saving
Shopkeeper’s credit is even more prevalent with accounts, Pensions in Annuity, and Government
over 52 percent of the poor and 46 percent of the Savings Certificates is virtually non-existent.
non-poor having availed this type of credit Specifically, MF bank/institution saving products
(mostly in-kind). have only been used by a negligible 0.3 percent of
the poor and 0.4 percent of the non-poor. People prefer to use various informal sources of
credit for a variety of reasons . They borrow from Informal methods of saving are more common
friends, neighbours, and family, as it offers among both the rural poor and non-poor. Higher
convenience in loan repayment and often bears percentages of the non-poor have used these
no interest. Shopkeepers’ credit is also considered methods to save which is similar to what was
favourable as goods themselves are available on observed for formal methods. The most popular is
credit and payments in-kind are also possible in saving at home, with 53 percent of the poor and
some instances. Committees are another 60 percent of the non-poor having saved this way
informal source that are popular because of the at some point in their lives. In order of popularity,
availability of lump sum amounts, instalment saving at home is followed by savings
convenience, and also because they effectively i n l i ve sto c k , co m m i tte e s , l a n d , w i t h
serve as a saving mechanism by preventing excess friends/family/neighbours, and in gold/jewellery,
expenditure. Borrowing from money lenders to and household items.
meet cash shortfalls is also a means of acquiring
Access to Savings and Other Financial Services
Box 4 Section 6.1
29. These reasons came forth in focus group discussions conducted in both rural and urban areas in Sindh, Punjab, NWFP, Balochistan,
and AJK for the A2FS 2006–07.
30. This was commonly cited as a reason by focus group participants in Karachi.
29
30
Characteristics of Rural Financial Markets
Profiling Pakistan's Rural Economy for Microfinance42
Insurance does not seem to be a popular product
in rural Pakistan. Life insurance is the most
popular with 1.3 percent of the poor and 5.5
percent of the non-poor having used it. For the Although the proportion of rural households non-poor, Postal Life, Group Provident and vehicle currently using formal financial institutions is insurance products are a little more popular, with pitifully low, this does not mean they are not about 0.2 percent to 0.3 percent of people having interested in banking or financial services. In fact, ever used them. The use of all remaining a fairly large percentage of poor households (30.3 insurance products is negligible. percent) and non-poor households (26.9 percent)
expressed a desire for assistance on opening bank Remittances are mostly sent via Post Office
accounts. And quite logically, the agro-climatic money orders, with 2.5 percent of the poor and
zones with the lowest percentage of banked 5.6 percent of the non-poor ever having used
households have the highest percentage them. The second most popular method of
expressing that desire. For poor households, this transferring money is through friends and family.
proportion was the highest in the Cotton-wheat The use of all remaining remittance channels is
Sindh zone and the Rice-other Sindh zone (see negligible (see in above for more
Figure 31). However, the percentage of such on domestic transfers).
households was the lowest in Balochistan. Similar
patterns are also evident for non-poor
households.
6.2 Demand for Formal Financial
Services
Box 3 section 4
31
In agricultural settings, suppliers of seed, fertilizers, and pesticides operate through numerous outlets in the main
agricultural markets in different regions. These outlets are mostly owned by ‘baniyas’ (dealers), who play an
important role in the agricultural supply chain, especially that of wheat. These baniyas purchase inputs directly
from outlets of fertilizer and agro-input companies and then sell them to farmers. However, farmers often do not
have enough cash flows to pay for these materials on the spot. The baniya extends credit to such farmers at interest
rates between six and eight percent per month.
For example, a bag of urea fertilizer would retail at PKR 530, but if taken on credit, the farmer would purchase the
fertilizer at a cost of PKR 700 to be repaid after six months. Repayment is accepted both in cash, and in-kind in most
cases when the farmer sells his produce six months later. Cash payments often receive discounts of PKR 40-50.
Apart from agricultural inputs and suppliers’ credit, baniyas also provide sales support often suggesting popular
seed grades or what would suit a specific farmer’s needs. They also provide market access to farmers who can then
sell their produce to the baniyas. This saves time and effort on the part of the farmer in terms of identifying and
accessing flour mills directly. Hence, baniyas serve as important building blocks of the rural agricultural system in
the country.
Source: FMFBL’s study on rural economic activities, Value Chain Dynamics and Models. February 2007.
Box 4: The Mechanics of Shopkeepers’ Credit
31. Insurance products available in the country include vehicle, household contents, property, electrical equipment, group accidental,
life, Postal life, personal accident, dreaded disease, endowment/investment saving plans, group provident funds, education for
children, Government's pension schemes and Islamic insurance.
43
Most of those interested in having bank accounts received minimal responses (see Figures 32a and
cited ‘to save money’ or ‘to access a loan’ as the 32b). There were some interesting variations
main reasons. These were followed by ‘the need across zones; accessing loans was the number one
to keep money in a safe place’, and ‘to withdraw reason for zones in Sindh whereas liquidity
money when needed’. Reasons such as payment preference ranked the highest in the zones of
of bills, to start a business, or build a home, Punjab.
Figure 31: Households that would like assistance in opening Bank Accounts
0
5
10
15
20
25
30
35
40
45
50
Poor Non-poor
NWFP Balochistan AJKMixed
Punjab
Punjab
Cotton
Wheat
Punjab
Low
Intensity
Barani
Punjab
Cotton
Wheat
Sindh
Rice
Punjab
Wheat
Rice
Sindh
Other
Figure 32-A: Top reasons cited for opening a Bank Account – Poor
0
10
20
30
40
50
60
70
80
90
% o
f H
Hs
To earn profit/earn an income To keep money in a safe place i.e. to guard against theft
To access a loan for your business To withdraw money when needed
To access a loan To save money
NWFP Balochistan AJKMixed
Punjab
Punjab
Cotton
Wheat
Punjab
Low
Intensity
Barani
Punjab
Cotton
Wheat
Sindh
Rice
Punjab
Wheat
Rice
Sindh
Other
% o
f H
Hs
Source: A2FS 2006-07
Characteristics of Rural Financial Markets
Source: A2FS 2006-07
6.3 State of Financial Literacy tremendously with Balochistan ranking the
lowest and Barani Punjab ranking the highest. As
would be expected, the non-poor have a relatively An understanding of financial terms is quite low in
better grasp of financial terms than the poor. the rural areas and does not usually extend
Figure 33 uses A2FS data to present the beyond basic terms. Understandably, financial
differences across zones by categorizing the literacy is better in urban areas. Within rural
understanding of a few key financial terms by Pakistan, the state of financial literacy varies
Profiling Pakistan's Rural Economy for Microfinance44
Figure 32-B: Top reasons cited for opening a Bank account – Non-poor
NWFP Balochistan Overall0
10
20
30
40
50
60
70
80
To keep money in a safe place i.e. to guard against theft
To withdraw money when you required
To access a loan
To save money
Rice Mixed
Punjab
Punjab
Cotton
Wheat
Punjab
Low
Intensity
Barani
Punjab
Cotton
Wheat
Sindh
Rice
Punjab
Wheat Other
Sindh
% o
f H
Hs
Level of UnderstandingHIGH
Level of UnderstandingAVERAGE
Level of UnderstandingLOW
?Bank (except in Balochistan where it is AVERAGE)
?Pension (except in Balochistan where it is AVERAGE)
?Interest
?Loan (formal & informal)
?Profit on Saving/Businesses etc.
?Money Lender
?Saving Committee
?Bank Account (except for Barani Areas and NWFP where it is HIGH)
?PLS/Current Account
?Cheque Book
?Insurance
?Money Order
?Collateral Mortgage
?Debit Card
?ATM
?Islamic Banking
?Shares
?Stock Exchange
?Investment
?SWIFT Transfers
Understanding decreases as terminology complexity increases
Figure 33: Understanding of Financial Terms
Source: A2FS 2006-07
45
6.4 Sources of Information on Financial
Matters
Print Media:
Electronic media:
Social networks:
Punjab. Radio, surprisingly, has little importance
except, perhaps, in the Cotton-wheat Sindh zone
where 20 percent of respondents cited it as a
source. Cable television has insignificant rural It is important to understand where rural penetration. households get information regarding financial
matters in order to promote MF and financial A small percentage of poor
literacy. According to the A2FS, the three major respondents across all zones reported
sources of financial information are the electronic newspapers as a source of information on
media, the print media, and social networks (see financial matters – the highest was 12 percent in Figures 34a and 34b). Barani Punjab which also has relatively higher
literacy levels than the rest of the country. By This medium includes contrast, nearly a quarter of the non-poor across television, radio, private cable television Sindh mentioned newspapers as sources of channels, and the internet. Television appears to financial information. Magazines did not show up be a more important source of financial as important sources anywhere in the country. information, although this importance varies
across zones (as little as 4.7 percent of poor Compared to both the electronic respondents cited it as a source of information in
and print media, social networks clearly emerged Low Intensity Punjab, whereas 32 percent of the as the dominant source of information on non-poor respondents in Barani Punjab cited it as financial matters for the rural population. These important). It is an important source for both the networks include fathers and older brothers, poor and non-poor of NWFP and the non-poor of
Characteristics of Rural Financial Markets
About 17.2 percent of respondent households in the A2FS had heard of MF and reported that they understood the
concept. This proportion was the lowest in Balochistan where only 1.2 percent of the poor and eight percent of the
non-poor knew what the term meant. On the other hand, some zones showed a much higher level of awareness
about MF, especially amongst the non-poor. For example, 40 percent of the non-poor in NWFP and between 24
percent and 33 percent of the non-poor in Punjab had heard of, and understood the term.
Box 5: Awareness of Microfinance
Households with Knowledge of Microfinance
0
5
10
15
20
25
NWFP Balochistan AJK TotalMixed
Punjab
Punjab
Cotton
Wheat
Punjab
Low
Intensity
Barani
Punjab
Cotton
Wheat
Sindh
Rice
Punjab
Wheat
Rice
Sindh
Other
% o
f H
Hs
Source: A2FS 2006-07
other family members, shopkeepers, places of depending on the region. For example,
work or worship, jirgas and autaqs, and others shopkeepers are relatively more important in the
such as banks, friends, and people. Of these ‘other Rice-other Sindh zone, whereas the jirga is more
family members’ was clearly the most common important in Low Intensity Punjab.
source of information on financial matters,
followed by the ‘jirga’ and ‘shopkeepers’,
Profiling Pakistan's Rural Economy for Microfinance46
Figure 34-A: Top sources of information on financial matters – Poor
0
10
20
30
40
50
60
70
80
90
100
Newspaper
Radio Shopkeepers
Television
Autaq/Jirga
Other family members
Figure 34-B: Top sources of information on financial matters – Non-poor
0
10
20
30
40
50
60
70
80
90
100
Newspaper Television Autaq/Jirga Other family members
NWFP Balochistan AJKMixed
Punjab
Punjab
Cotton
Wheat
Punjab
Low
Intensity
Barani
Punjab
Cotton
Wheat
Sindh
Rice
Punjab
Wheat
Rice
Sindh
Other
NWFP Balochistan AJKMixed
Punjab
Punjab
Cotton
Wheat
Punjab
Low
Intensity
Barani
Punjab
Cotton
Wheat
Sindh
Rice
Punjab
Wheat
Rice
Sindh
Other
% o
f H
Hs
% o
f H
Hs
47
6.5 Perceptions and Preferences ?People trust formal institutions (27 percent
trust banks) relatively more than informal
service providers (13 percent trust informal Current A2FS data shows both the low
moneylenders), but the overall percentages penetration of formal financial institutions and
expressing this confidence are low. the wide use of informal finance in rural Pakistan.
Therefore, any attempt to expand the outreach of ?Security is a major concern and banks are financial institutions in these areas implies
considered safe places to keep money. This also competition with existing financial mechanisms. means that the reliability of the institutions is The perceptions of different service providers more important than the product. (formal and informal) are important in this
context and would either need to be changed (if ?Borrowing often has negative connotations
negative) or capitalized upon (if positive). The whereas saving is perceived as a healthy and
A2FS provides some interesting insights of positive practice.
perceptions regarding different financial service
providers. For example: The A2FS data and findings from focus group
discussions also provide other interesting insights ?When thinking of financial service providers
into peoples’ aspirations, perceptions, and (formal and informal), people often think of the
opinions regarding matters of finance, society, prerequisites for transactions rather than the
and intra-household relationships. Microfinance services. In the case of formal financial
providers and other service providers would find institutions, respondents felt one required a
them useful in their client targeting and marketing permanent address and identity documents,
strategies.whereas no documentation or legal formalities
were needed for informal financial institutions.
Characteristics of Rural Financial Markets
Profiling Pakistan's Rural Economy for Microfinance48
Spearman’s rank correlation coefficient (rho) is a distribution and nature. This is a useful tool that
non-parametric measure of correlation that can be used to obtain an overall idea of the
illustrates the strength and direction of linkages and relationships in the rural economy
relationships between variables without making (see Table 9).
any assumption about their underlying
7. Non-parametric Correlations between Variables
Crop Income 1.0
.661*
.867**
.952**
.709*
.661* 1.0 .855** .830** .636* .988**
Table 9: Non-parametric Correlation between Variables Non-poor–
’S RHOSPEARMAN
Crop Income
Non-
agricultural Income
Income from Livestock
Net
Remittances
Agricultural Expenditure
Non-agricultural
Income
Non-agricultural Income
0.5
.830**
.782**
1.0
.794**
Income from Livestock
.867** .855** 1.0 .782** .915** .879**
Net Remittances
Agricultural Expenditure
.952**
.636*
.915**
1.0
.685*
.709* .988** .879** .794** .685* 1.0
Household Consumption Expenditure
Gold and Silver Owned
Aggregate Income
Aggregate Reported Savings
Savings as % of Income
HH Receiving loans
.709*
.952**
.867**
.782**
.661*
.927**
.903**
.733*
.697*
.879**
.903**
.794**
.879**
.855**
.709* .988** .879** .794** .685* 1.000**
.879**
.685*
.867**
.830**
-.636* -.709* -.782** -.685* -.758*
.878** .799** .646* .890**
Non-agricultural Income
Household Consumption Expenditure
1.0 .915** .927** .927** .842** -.685*
Net Savings in the Last One Year
.915** 1.0 .903** .879** .903** Net Savings in the Last One Year
Table 9 above presents the correlation between that saving in gold are the most popular mode of
the many variables in the rural economy for the savings among rural households.
non-poor. Some of the key relationships are as ?The aggregate reported savings and percentage follows:
of rural households banked are positively
?Incomes from agriculture and non-agricultural correlated (0.770**).
sources are almost perfectly correlated (1.0) ?Overall, there is a strong positive correlation with expenditure in the respective areas. This
between income, expenditure, and savings. means that increasing expenditure in these High incomes are associated with high activities will increase the income generated by expenditure and high household savings in the same proportion, and vice-versa. corresponding areas.
?The incomes and expenditures across the ?Interestingly, the correlation between the agriculture and non-agricultural categories
aggregate income and percentage of income show a moderately strong positive correlation saved is negative (-.758*). This indicates that as (0.636*). income levels rise, the proportion saved
?Household consumption expenditure is strongly declines. Furthermore, households receiving
positively correlated (0.927**) with the sources loans and the proportion of savings as a
of income as well as with savings in various percentage of income have a strong negative
forms. correlation (-0.902**).
?There is a strong positive correlation between ?The percentage of households receiving loans is
savings in the preceding year and gold and silver strongly positively correlated with aggregate
owned by households (0.903**). It indicates income, consumption, and savings variables.
49
’S RHOSPEARMAN
Crop Income
Non-
agricultural Income
Income from Livestock
Net
Remittances
Agricultural Expenditure
Non-agricultural
Income
.661* 1.0 .855** .830** .636* .988** Non-agricultural Income
Gold and Silver Owned
.927** .903** 1.0 .855** .903**
Aggregate Income
Aggregate Reported Savings
Savings as % of Income
HH Receiving loans
.830**
-.758*
.879** .855** 1.0 .830** .927** -.758*
Rural Banked HH (%)
.903** .903** 1.0 .842**
-.685* 1.0
.835** .720* .774** .890** -.902**
.770**
* Correlation is significant at the 0.05 level (two-tailed)** Correlation is significant at the 0.01 level (two-tailed)
Table 9 : (continued) Non-parametric Correlation between Variables Non-poor–
Non-parametric Correlations between Variables
Borrowing from friends and relatives and Table 10 shows the overall relationships in the
borrowing from shopkeepers are positively rural economy for the poor.
correlated with each other (0.818**).
Profiling Pakistan's Rural Economy for Microfinance50
Income from
Livestock
Non-agricultural Income
Table 10: Non-parametric Correlation between Variables Poor–
’S SPEARMANRHO
Crop
Income
Net
Remittances
Agricultural Expenditure
Non-
agricultural Income
Non-agricultural
Income
.830** .855** .988**
Income from Livestock .830** 1.0 .782** .818**
Net Remittances .855** .782** 1.0
.879** Agricultural Expenditure .758*
.988**
.818**
.879**
1.0
Expenditure on Livestock
.696* .696* .696* .696*
Household Consumption Expenditure
.636* .867** .636*
Net Savings in the Last One Year .721* .685*
Gold and Silver Owned
.661* .758* .830**
Aggregate Income
.758*
.782**
.733*
Aggregate Reported Savings
.855**
.830**
.855**
HH ( %
Rural banked )
.697*
HH Receiving loans
.709* Average Loans
Repaid in the Last One Year
.685* .661*
Non-agricultural Income
51
Some of the key relationships for the poor in the ?Households taking loans from friends and those
rural economy shown by Table 10 are as follows: borrowing from shopkeepers have a correlation
of 0.830**, similar to the one for the rural non-?The correlation between the incomes and poor.
expenditure/consumption variables for the
rural poor are similar to the ones for the non- ?On the whole, fewer variables are correlated
poor. with each other in the rural economy for the
poor than the non-poor. ?Expenditure on livestock has a moderately
strong positive correlation with income from Overall, the relationships among the different
non-agricultural sources and net remittances variables present a bird’s eye view of the rural
(0.696*). economy, i.e. how different sectors link together
and the factors that can possibly have a direct or ?The average amounts of loans repaid by indirect impact on them. These relationships
households has a moderately strong negative quantify the size of the possible policy levers that relationship with households that take loans can be used to impact the desired key aspects of from friends and shopkeepers (-.685*). the rural economy.
’S SPEARMAN
RHO
Expenditure on Livestock
Household Consumption Expenditure
Net Savings in the Last One Year
Gold and Silver
Owned
Aggregate Income
Aggregate Reported Savings
Expenditure on Livestock
1.0 .696* .696* .696* .696*
Household Consumption Expenditure
.696*
1.0
.770**
Net Savings in the Last One Year
1.0 .721* .927**
Gold and Silver Owned
.696* .721* 1.0 .879**
Aggregate Income .696* .770** 1.0 .661*
Aggregate Reported Savings .696* .927** .879** .661* 1.0
LoansHH Receiving
(%)
.685*
* Correlation is significant at the 0.05 level (two-tailed)** Correlation is significant at the 0.01 level (two-tailed)
Table 10 (continued): Non-parametric Correlation between Variables Poor–
Non-parametric Correlations between Variables
Profiling Pakistan's Rural Economy for Microfinance52
The 2006 Department for International The information and analysis in this report
Development (DFID) financed Oxford Policy address the needs of both the policymakers and
Management Group’s Poverty and Social Impact the MF institutions. It presents poverty profiles
Assessment of Microfinance Policy in Pakistan and detailed analyses of livelihood sources,
concluded that ‘the MF sector has yet to income and expenditure patterns, savings and
demonstrate its potential in terms of its social and asset profiles, and the existing state of access to
poverty impact’. One major reason for this finance across the poor and non-poor segments in
conclusion is the lack of formal analysis of the different agro-climatic zones. The characteristics
market that MFPs wish to target. The study also of rural financial markets and how MF and formal
concluded that MFPs and clients in Pakistan share finance are perceived are analyzed for each
similar aspirations – for example, for more access category. The major constraints to enterprise
to savings and insurance products, and for more development in each zone are also discussed.
flexibility in microcredit. However, these The poverty profile shows a high incidence of aspirations have been slow in being realized. poverty in Pakistan, more so in the rural areas. The
Inadequate data and analysis have hampered this poverty headcount ratio of 30 percent for
process. This study was an attempt to use 2008-09 shows that the officially reported
comprehensive datasets and a descriptive declining trend of poverty in the early and mid
analysis at the aggregate and agro-climatic levels 2000s reversed after the food price inflation of
to assist policymakers in designing more efficient 2008. Overall, the poor stand at 27 percent in rural
MF policy, and the MFPs in designing their areas as compared to 13 percent in urban areas in
expansion strategy as well as improving 2005-06. The higher incidence of poverty in rural
effectiveness of their operations. areas is true for all zones. Dividing the distribution
of households into poverty zones shows that The role of the policymaker is increasingly seen as another 20.2 percent of rural households are one of an enabler and facilitator as the MF sector sensitive to slight changes in the economy, and moves from being a heavily subsidized social are thus categorized as ‘vulnerable.’ Barani safety net, to a market-based intervention that Punjab has the largest percentage of households allows the poor economic security in a sustainable in the non-poor and quasi non-poor groups manner. It is therefore supremely important for combined (83 percent) while the Rice-other Sindh policymaking to be based on sound data and zone has the lowest (34 percent). This implies that analysis to eliminate the subsidy element, and to the latter has the highest percentage of facilitate the MF sector to avoid the use of one- households (50.3 percent) in the sensitive-poor size-fits-all financial policies. and vulnerable groups combined amongst all
zones. These groups can be used to determine the Monitoring and evaluation based on good quality size of the potential MF market and the number of data and analysis are also required to ensure that households that it seeks to serve. MFPs perform in an efficient and sustainable
manner as financially viable entities, while In this regard, the data presented also enables a maintaining their poverty reduction focus. This is better understanding of what constitutes an especially important in Pakistan where MFPs eligible borrower, and how the characteristics of generally spend very little, if indeed, anything, on groups vary from zone to zone. The Microfinance monitoring and evaluation, which, in the long- Institutions Ordinance 2001 defines the ‘poor’ as term are good practices to adopt. persons who have the minimal means of
8. Conclusion and Ideas for Policymakers and Practitioners
53
subsistence, and whose total annual income is this report shows the remarkable disparity
less than the minimum taxable limit set by law. By between the aggregate savings of poor and non-
classifying the poor relative to national income poor households, resulting from the different
tax, this definition actually sets the bar much marginal propensities to save (of the two groups).
higher than that set by the PL. The use of poverty These differences need to be considered when
bands allows greater precision in categorization. defining the savings mobilization strategies of the
MF institutions. Overall, there is a low propensity It is interesting to note that households from the to save as is evidenced by the low reported Cotton-wheat Punjab zone contribute the most savings of poor (4.6 percent of income) and non-towards all poverty bands. Barani Punjab has the poor households (8.3 percent of income). smallest contribution towards the lower end of Insurance penetration is also pitifully low as is the the spectrum of poverty bands, while the Rice- understanding of insurance products. other Sindh zone has the highest at the higher end
of the poverty spectrum, followed by Balochistan. Average repayment was almost equal to average
The distribution of the poor by depth of poverty in borrowings during the previous year. About 21
each zone acts as a benchmark against which the percent of poor rural households borrow money
achievements of the MF sector can be evaluated. from formal and informal sources, as opposed to
In addition, the type of economic activity varies 18 percent of non-poor households. While poor
across these zones (categorized by distribution of households borrow twice as much (and have
households and depth of poverty); therefore debts twice as large) as non-poor households, the
planners can design more regionally appropriate close relationship between repayment and
MF interventions. borrowing in both categories – particularly poor
households – indicates a well functioning credit Dividing aggregate rural income into its
market. components highlights the large share of income
generated from non-agricultural business Data from the Domestic Commerce Survey and activities, especially those of the non-poor. RICS confirm the importance of finance for the Remittances and revenue from livestock continue setting up and growth of enterprises; nearly 72 to be important for rural incomes. The percent of respondents cited finance as the importance of these sources varies across zones biggest constraint in setting up an enterprise, and and this in turn determines the types of MF about 49 percent cited it as the biggest constraint products that are needed. The expenditure profile to business growth. The absence of contracts and of the rural economy shows that aggregate non- contract enforcement are generally cited as major agricultural expenditure by households is twice reasons for the lack of development of domestic total agricultural expenditure. Consumption commerce. However, there appears to be a expenditure data shows that, as a proportion of general awareness of the laws that govern their incomes, the poor spend almost one and a commerce and of the importance of contracts in half times more than the non-poor (see Figure 12 business transactions. Nearly 70 percent of on page 21). These aggregates provide a respondents stated that laws governing business quantification of the potential size of the operations in the market are predictable. About microcredit market in each zone. 72 percent agreed that business contracts act as
protection against cheating in transactions, while Although MF is generally associated with
70 percent said the legal system reinforces and microcredit only, resource mobilization (savings)
upholds business contracts and protects property and risk mitigation (insurance) are equally
rights. important components. To this end, the data in
Conclusion and Ideas for Policymakers and Practitioners
Data from the A2FS was used to describe some of While this report’s observations and analyses
the key characteristics of the rural financial underscore important insights into existing
market. It showed that only two percent of patterns of poverty and provide a baseline of
respondents reported having taken out a formal information, it is essential that follow-up surveys
bank loan. The proportion of households that be conducted to provide fresh data against which
reported taking loans from friends and relatives is MF interventions can be measured. Ideally, this
much higher i.e., 53.3 percent of poor households would involve the use of longitudinal surveys of
and 74.5 percent of non-poor households. In representative households where the same
addition, only 47.2 percent of poor and 53.6 homes are visited repeatedly. This would reduce
percent of non-poor households reported never the ‘noise’ inherent in comparing information
having obtained shopkeeper credit. from successive cross-sectional surveys. Survey
content should cover household-level socio-Several aspects of the information analyzed from economic characteristics – asset position, credit, the A2FS provide valuable insights on financial borrowing, savings, and repayments, etc. – and literacy. While knowledge and understanding of
perceptual information on key aspects of the complex financial terms is quite low, there is a
financial markets – investment climate and definite desire to learn as evidenced by the fact
domestic commerce – from which these that 30.3 percent of poor households and 26.9
households are drawn, to enable a more percent of non-poor households expressed a
complete picture of the MF environment and its desire for assistance to open bank accounts.
impact. Saving money or to obtain access to loans were
cited as the main reasons for opening a bank It should be noted that this report does not cover
account, followed by the need to keep money in a interest rates. This information is vital, and future
safe place and to withdraw money when needed. analyses would greatly benefit from doing so.
Understanding these variations across zones will Finally, it is important that such surveys and
aid the process of setting up better-informed analyses provide a basis for monitoring and
sales promotion programmes by MF institutions. evaluating the MF sector, and enabling greater
effectiveness in meeting their [the MF sector’s] There is a great need to spread awareness about
socio-economic objectives of improved incomes MF in rural areas. Available information indicates
and reduced vulnerability to risk for the poor.that only 17.2 percent of respondent households
surveyed in the A2FS had even heard of the
concept of MF. That said, it is important to note
that social networks are major sources of
information – especially for poorer households in
each zone – and must be capitalized upon to
spread awareness. Television is also a principle
source and significantly more important than
radio in nearly all zones except Balochistan and
the Rice-wheat Sindh zone. Newspapers, and to a
much lesser extent, magazines, are important
sources in Barani Punjab in both poor and non-
poor households.
Profiling Pakistan's Rural Economy for Microfinance54
57
Tando Mohammad Khan
Khairpur
Thatta
Karachi South
West Karachi
Larkana
Lower Dir
Buner
Nowshera
Kohat
Hangu
Mansehra
Haripur
Kohistan
Swabi
Lakki Marwat
Malakand
Districts
Gujranwala Sialkot
Mandi Bahauddin
Lahore
Sheikhupura
Sargodha
Faisalabad
Toba Tek Singh
Vihari
Multan
Pakpattan
Bahawalpur
Rahimyar Khan
Mianwali
Dera Ghazi Khan
Muzaffargarh
Islamabad
Rawalpindi
Chakwal
Nawabshah
Ghotki
Tharparkar Mirpur Khas
Jacobabad
Dadu
Karachi East
Central Karachi
Malir
Chitral
Charsadda
Peshawar
Karak
Tank
Abbottabad
Batgram
Mardan
Bannu
Upper Dir
Gujrat
Hafizabad
Narowal
Kasur
Khushab
Jhang
Okara
Sahiwal
Khanewal
Lodhran
Bahawalnagar
Bhakkar
Rajanpur
Dera Ismail Khan
Attock
Jhelum
Sukkar
Nowshero Feroze
Hyderabad
Sanghar
Shikarpur
Badin
Agro-climatic Zones
Cotton-wheat Punjab
Rice-other Sindh
NWFP
Rice-wheat Punjab
Mixed Punjab
Low Intensity Punjab
Barani Punjab
Cotton-wheat Sindh
Annex A: Classification of Districts into Agro-climatic Zones
Annex A
Profiling Pakistan's Rural Economy for Microfinance58
Quetta Kalat
Sibi Zhob
Makran
Pishan
Nasirabad
Ziarat
Qila Abdullah
Lasbela
Khuzdar
Punjgur
Mastung Kharan
Turbat
Districts
Balochistan
Agro-climatic Zones
Annex A (continued)
Source: The Demand for Public Storage of Wheat in Pakistan – IFPRI Research Report 77 (Dec 1989)
59
1. Survey Design of the PSLM/HIES
2005-06
Sample Size and its Allocation: Universe:
Sample Design:Sampling Frame:
Selection of Primary Sampling Units (PSUs):
Selection of Secondary Sampling Units (SSUs):
Stratification Plan
A. Urban Domain:
B. Rural Domain:
each administrative division constituted a
stratum.
The sample size for The universe of this survey consisted of
the four provinces was fixed at 15,453 households all urban and rural areas of the four provinces and
comprising 1,109 sample village/enumeration Islamabad, excluding the protected areas of
blocks. NWFP and military restricted areas.
A two-stage stratified sample The FBS developed its own
design was adopted in this survey. urban area frame which was updated in 2003.
Each city/town was divided into enumeration
blocks (E. blocks) consisting of 200-250 Villages and enumeration blocks in urban and
households identifiable on sketch maps. Each rural areas respectively, were taken as PSUs.
enumeration block was classified into three Sample PSUs from each ultimate stratum/sub-
categories of income groups: low, middle, and stratum were selected by the Probability
high, keeping in view the living standard of the Proportional to Size (PPS) Method of sampling
majority of the people. A list of villages published schemes.
by the Population Census Organization obtained
as a consequence of the Population Census of
1998 was taken as the rural frame. Households within sample PSUs were taken as
SSUs. A specified number of households, i.e. 16
and 12 from each sample PSU of rural and urban
areas were selected respectively, using a Is lamabad, Lahore,
systematic sampling technique with a random Gujranwala, Faisalabad, Rawalpindi, Multan,
start. Bahawalpur, Sargodha, Sialkot, Karachi,
Hyderabad, Sukkur, Peshawar, and Quetta, were The detailed breakup of the sample is given in the
considered as large-sized cities. Each of these tables below.
cities constituted a separate
stratum and was further sub-
stratified according to low,
middle, and high- income
groups. After excluding the
population of 14 large-sized
cities, the remaining urban
population in each defunct
division in all provinces was
grouped together to form a
stratum.
Each district in
Punjab, Sindh, and NWFP was
considered as an independent
stratum, whereas in Balochistan,
Annex B: Design of the Four Data Sources
Province/Area Number of E. Blocks Number of Villages
Punjab
14,549
25,875
Sindh 9,025 5,871
NWFP 1,913 7,337
Balochistan
613
6,557
AJK/Kashmir
210
1,654
Northern Areas
64
566
FATA -
25,96
Islamabad
324
132
Total 26,698
50,588
Annex B
Table B-1: Number of Enumeration Blocks and Villages as Per Sampling Frame
Selection of SSUs:
Selection of Respondents:
2. Survey Design of the A2FS
Stratification of Urban and Rural Areas:
Selection of PSUs:
Households within each sample
PSU were considered as SSUs. Fifteen households
were selected from each sample village and
enumeration block by a random systematic
scheme.
A Kish Grid was used to
select respondents if a household had more than
one valid respondent.
A total sample size of 10,700 interviews was
designed to be conducted throughout Pakistan
using this sample methodology. The detailed
breakdown is given in the tables below.
The A2FS adopted a multi-stage stratified area-
based probability sampling technique. This
technique called for various stages starting from
the stratification of cities to the selection of the
ultimate target respondents. The sample design
was provided by the FBS and is comparable to
their official publications.
The sample universe comprised all sane adult
Pakistanis 18 years and older. The sample design
had four stages:
The urban
and rural domains were identical to those defined
by the FBS for the PSLM/HIES.
These were selected in the
same manner as for the FBS data for the
PSLM/HIES.
Profiling Pakistan's Rural Economy for Microfinance60
Province/Area Urban Rural TotalPSUs: Punjab
240
244 484
Sindh 140 132 272
NWFP 88
119 207
Balochistan
63
83 146
Overall
531
578 1,109
SSUs/Households:
Punjab
2,790
3,892
6,682
Sindh 1,666
2,107
3,773
NWFP 1,049
1,901
2,950
Balochistan
735 1,313 2,048
Overall
6,240 9,214 15,453
Table B-2: Profile of PSLM Sample 2005-06
Table B-3: Distribution of Enumeration Blocks and Villages (PSU)
Province/Area Urban Rural Total
Punjab 130 200 330
Sindh 80 78 158
NWFP
38
66
104
Balochistan
30
48
78
AJK/Kashmir
12
18
30
Total
290
410
700
Table B-4: Distribution of Households (SSU)
Province/Area Urban Rural Total
Punjab
Sindh
NWFP
Balochistan
AJK/Kashmir
Total
1,950 3,000 4,950
1,200 1,170 2,370
570 990 1,560
480 720 1,170
180 270 450
4,350 6,150 10,700
A complete description of the sample design and estimation procedure can be downloaded from the FBS website athttp://www.statpak.gov.pk/depts/fbs/statistics/pslm2005_06/appendix_a.pdf
3. Sampling Methodology of Domestic
Commerce Survey 2007
Sample Design:
Sample Size:
domestic commerce had a minimum value of 100
(transport). For this sample size, a population
proportion can be estimated by the sample
proportion within about eight percent with a The survey was conducted in a probability of at least 0.90. The sample sizes for selected number of cities (large, medium, and the surveys of the other sectors of domestic small) which represented all strata of population. commerce were multiples of that of transport as Since organized markets do not generally exist in indicated in Table B-5.rural areas, and small/medium towns are
considered as feeding areas to the rural
population, markets in small towns were covered
as proxies for rural markets.
The survey was integrated at the city-level for the
four sectors, retail and wholesale markets,
storage and warehousing, transport, and real
estate. A specified number of retail and wholesale
markets were selected in each city and a specified
number of establishments were randomly
selected from within these. Real estate agents
were similarly randomly selected from within
markets while key property developers in cities The distribution of the samples within cities is
were identified, and a sample was selected from given in Table B-6.
amongst the known groups. For storage and
warehousing areas, storage locations in cities
were identified, and then the requisite
sample was randomly selected in each
such area by sampling every second or
third establishment depending on the
required sample size. For transport, the
companies operating in each city were
identified and all major players were
interviewed. In some cases where the
sample size could not be covered by
interviewing major operators, smaller
operations were identified, and then a
random sample was drawn from
amongst them. This approach was
necessitated by the fact that there are
relatively few inter-city transport
operators in the country, and the
objectives of the study were to acquire
information on how countrywide
transport systems operate.
The overall sample size
was 2,000 establishments. The sample
sizes for different sectors within
61
Sector Sample Size
Retail 1,000
Wholesale
500
Real Estate
200
Storage and Warehouses
200
Transport
100
Table B-5: Sample Size by Sector
City Retail Wholesale
Real Estate
Storage
Transport
Faisalabad 90
45
15
15
10
Gujranwala
60
30
10
15
5
Lahore
140
70
30
25
15
Rawalpindi 60
30
10
10
5
Multan
60
30
10
15
5
Okara
40
20
10
15
5
Hyderabad
60
10
15
5
Nawabshah
40
20
10
5
5
Karachi
180
90
40
35
20
Sukkur
50
25
10
10
5
Peshawar
60
30
10
10
5
Abbottabad
40
20
10
5
5
Quetta
60
30
10
15
5
Islamabad
60 30 15 10 5
Table B-6: Sample Sizes by City
30
Annex B
The retail markets to be covered in each city are were selected in the first stage. Villages and town
shown in Table B-7. committees were selected in the second stage,
and enterprises and households
were selected in the third stage
on the basis of the listing data.
The
PPS Systematic Method of
selection was use to select
districts in the first stage after
sorting the districts in descending
order of size, where district
population was taken as a
measure of size.
Ten districts were
selected using this procedure.
These were Attock, Bahawalpur,
Faisalabad, Jhelum, Kasur,
Khanewal, Pakpattan, Sargodha,
Sialkot, and Vihari.
The Karachi Division in the
Sindh Province is highly urbanized standing at 95 For the percent. The districts in the division were retail and wholesale markets, equal numbers of therefore excluded from the selection frame. sample establishments were obtained in each Instead, one additional district from Sukkur market based on the desired sample size if there Division was selected . Five districts were was more than one sample market in a city. In selected from Sindh in this manner. These were each market, its central point was selected as a Khairpur, Mirpur Khas, Jacobabad, Nawabshah, starting point, and every tenth establishment was and Badin. included in the sample. This process was
continued until the required sample size was The selected districts in NWFP were Dera
achieved. All establishments from the starting Ismail Khan, Lakki Marwat, Swat, Lower Dir,
point to the last selected establishment were Haripur, Swabi, and Peshawar. Two tehsils were
listed on sheets of paper along with the names of selected from each district using the PPS
the owners of the businesses and the nature of Methodology.
the activities being carried out.
Villages and town committees were selected in
each tehsil/taluka. A three-stage stratified sampling approach was
adopted to select the sample of enterprises and Villages had been arranged in households in Punjab, NWFP, and Sindh. Districts descending order of population size in each tehsil
Stage 1 – District Selection:
Punjab:
Sindh:
Selection Procedure of Establishments:
NWFP:
Stage 2 – Villages/Town Committees Selection: 4. Sampling Methodology of RICS 2005
Rural Sample:
Profiling Pakistan's Rural Economy for Microfinance62
City Market
Faisalabad Ghanta Ghar, Satyana Road and Ghulam M. Abad
Gujranwala Gujranwala City
Lahore
Anarkali, Shah Alam, Ichra, Baghanpura, Gulberg
Rawalpindi
Saddar Market, Satellite Town, Muslim Town
Multan
Bohar Gate, Haram Gate, Cantt.
Okara
Okara City
Hyderabad
Shahi Bazar, Latifabad No. 7, Phuleli
Nawabshah
Shahi Bazar
Karachi
Saddar, Landhi, Liaquatabad, Shah Faisal
Sukkur
New Sukkar, Old Sukkur
Peshawar
Cantt, City, University Town
Abbotabad
Main Saddar
Quetta
City, Satellite Town
Islamabad
Aabpara Market, Karachi Company, Super Market
Table B-7: Retail Market by City
32. This division has the highest number of districts followed by the Karachi Division. However, the population of the Hyderabad Division
is higher than that of the Sukkur Division, but the proportion of the rural population is higher in the Sukkur Division.
32
63
and the PPS Method was applied to select them. It The listing exercise was conducted in 58 areas of
was decided to list all households and enterprises 12 districts in Sindh and NWFP. Thirty-one tehsils
in a village, however, for very large villages where in these districts were covered. Out of these 58
the total population exceeded 250 households, areas, 50 percent were rural and urban each. The
blocks of 250 households were formed and one same exercise was conducted in 100 areas of ten
block was randomly selected. For villages that districts in Punjab. Thirty-four tehsils in these
made up one complete and one incomplete block, districts were covered. Again, 50 percent were
the complete block was selected. rural and 50 percent were urban.
All town committees with The listing exercise enumerated households and
populations of 100,000 and less within a stratum non-farm establishments. The exercise covered
were defined to constitute the sampling frame. 11,565 households and non-farm establishments,
The requisite number of samples of town 40 percent in Sindh and 60 percent in NWFP. The
committees was selected from this frame using presence of collective living, i.e., establishments
the PPS Systematic Method of Selection. These within households were found to be minimal in
town committees were chosen as part of RICS both provinces. Part of the reason for this is
based on a prior decision that all such urban cultural in nature as most household
localities are feeding areas for the rural establishments are related to traditional crafts
population, and all investments in these areas are and are usually run by females. People of
directly linked to the rural population. conservative society often do not allow the
reporting of any business activities running within At the second stage, all villages/town committees the household. in a selected district were arranged in descending
order by population size, and the final selection The distribution of housing and non-housing units
was carried out using the PPS Systematic Method across rural and urban areas also showed some
of Selection. Four areas (two rural and two urban) interesting patterns. Sixty-eight percent of the
in each district of NWFP and six areas (three rural listed units were housing units in the rural areas in
and three urban) in each district of Sindh were both provinces. This proportion was 49 percent in
selected in this manner. Eight to ten villages/town the urban areas. In NWFP, the hujra and bhatek
committees within each selected district were are very common. These are the meeting places
selected in Punjab. of males, usually situated next to housing units.
Such institutions constituted nine percent of the The listing of enterprises and total units covered in the NWFP listing because of
households in the selected areas of Sindh and the presence of these units. Overall, 7.5 percent NWFP began on August 24, 2005, while the same of the listed units were excluded from the sample listing was initiated in Punjab on April 6, 2005. The selection exercise as they were either empty at listing was undertaken by teams recruited from the time of listing, or were found to be institutions the enumerators of the FBS and others who spoke such as government or semi-government offices, the local languages. These teams were trained schools, hospitals, mosques, batheks or hujras. extensively before being sent into the field. They With 10,690 households and non-farm units were supplied with bound copies of the listing (4,517 in Sindh and 6,173 in NWFP) remaining for sheets permitting up to 360 entries each, for each sample selection, the distribution of the number cluster. Indelible ink markers were also supplied of households, collective living, and non-housing so that listing numbers could be marked on each units in Sindh and NWFP showed that the listed establishment or house as it was listed. establishments numbered less than 20 in seven
Town Committees:
Listing Exercise:
Annex B
areas of Sindh and one area of NWFP. sample of 348 households in 58 areas was
selected. The type of business activity across districts
showed that trading enterprises are the most Six households in each area were randomly
common in Khairpur, Jacobabad, and Nawabshah selected from the list of households without
in Sindh. However, service enterprises were found enterprises in Punjab. Thus, a sample of 600
to be more common in Mirpur Khas and Badin. households in 100 areas was selected.
Trading is the predominant activity in all districts
in NWFP. Trading enterprises were found to be
more common in the urban areas whereas rural
areas had more service enterprises. The highest
number of production activities in all districts was
found in Badin.
Enterprises were divided into two main categories
in each area, small and large. Small enterprises
consisted of two or fewer workers. Those with
three or more workers were identified as large
enterprises. Seven enterprises were randomly
selected from the group of large enterprises and
three from the group of small enterprises in each
area. All available large enterprises were selected
for interview and the remaining picked from the
group of small enterprises if an area did not have
seven large enterprises. Data was sorted
according to the type of enterprises (trade,
production, services) in each area before making
this random selection, thereby allowing implicit
stratification by type. If the total number of
enterprises was less than ten in any area, the
balance number required was selected from the
closest village.
The selected enterprises were either standalone
or household-based. Standalone enterprises
w e r e a d m i n i s t e r e d j u s t e n t e r p r i s e
questionnaires, whereas household-based
enterprises were administered both enterprise
and household enterprise questionnaires.
Six households in each area were
randomly selected from the list of households
without enterprises in Sindh and NWFP. Thus, a
Stage 3(I) – Sample Selection of Enterprises:
Stage 3(ii) – Sample of Households without
Enterprises:
Profiling Pakistan's Rural Economy for Microfinance64
65
A Demographics
B Income and Assets
C Savings Patterns
1. Earner Ratio
2. Literacy Rate
3. Dependency Ratio
4. Child Ratio
1. Total Number of Household Heads in Each Occupational Group
2. Percentage Distribution of Occupation of Household Head in Each Occupational Group
3. Total Number of Employed Household Heads in Each Employment Group
4. Percentage Distribution of Employed Household Heads in Each Employment Status Group
5. Average Gross Income, Expenditure, and Net Income From Crops, Livestock, and Non-farm
Enterprise by Agro-climatic Zone and Poverty Band
6. Total Gross Income, Expenditure, and Net Income From Crops, Livestock, and Non-farm Enterprise
by Agro-climatic Zone and Poverty Band
7. Average Income Received (Non-agriculture)
8. Income Received from Crop Production
9. Average of Transfers Received and Paid
10. Total Transfers Received and Paid and Net Transfer Income
11. Total Transfers by Agro-climatic Zone and Poverty Band
12. Percentage of Households Receiving Remittances from Abroad from Difference Sources
13. Value of Land
1. Mean Savings and Borrowings by Agro-climatic Zone and Poverty Band
2. Total Savings and Borrowings by Agro-climatic Zone and Poverty Band
3. Loans: Amount Currently Owed by a Household
4. Loans: Amount Borrowed in the Last One Year
5. Loans: Amount Repaid in the Last One Year
Annex C: List of Tables in Volume II – Statistical Appendix (on enclosed CD)
Annex C
6. Money Received from Group Insurance
7. Percentage Distribution of Loan-receiving Households and Purpose of Loan
1. Current Occupancy Status of Houses
2. Average Number of Rooms Occupied by Households (including bedrooms and living rooms)
3. Percentage Distribution of Occupancy of Household Rooms
4. Main Source of Drinking Water for Households (% of households)
5. Types of Toilets Used by Households (% of Households)
1. Sources of Information on Financial Matters
2. Understanding of Financial Terms
3. Self-assessed Need for Financial Education
4. Availability of Basic Documents
5. Experience with Various Products and Services – Detailed
6. Experience with Various Products and Services – Summary
7. Reasons for Having Bank Accounts
1. Poverty Profiles of the Agro-climatic Zones
2. Distribution of Poor by Poverty Band
3. Distribution of Households by Agro-climatic Zone
1. Average Non-agricultural Expenditure by Item, Agro-climatic Zone, and Poverty Band
2. Total Non-agricultural Expenditure by Item, Agro-climatic Zone, and Poverty Band
3. Average Expenditure on Agricultural Inputs by Agro-climatic Zone and Poverty Band
4. Total Expenditure on Agricultural Inputs by Agro-climatic Zone and Poverty Band
5. Average Household Consumption Expenditure by Agro-climatic Zone and Poverty Band
6. Total Household Consumption Expenditure by Agro-climatic Zone and Poverty Band
D Housing Structures
E Preference for Financial Services – Access to Finance
F Poverty Profile
G Expenditure
Profiling Pakistan's Rural Economy for Microfinance66
67
H Domestic Commerce, RICS Legal, RICS Financial Services, and Types of Businesses
Domestic Commerce
RICS Legal
RICS Financial Services
Types of Businesses
1. Problems Facing Businessmen (%)
2. Impediments to Business Expansion (%)
3. Businesses Registered with Government Agencies (%)
4. Key Constraints to Growth of Enterprises (%)
5. Main Constraints to Business Enterprise Development (%)
6. Bazaar Association Membership (%)
7. City-wide Association Membership (%)
8. Trade by Location (%)
9. Nature of Business Ownership (2005)
10. Nature of Laws Affecting Business Operations (2005)
11. Nature of Laws Being Implemented in Communities (2005)
12. Reliance on Peoples’ Reputations for Business Dealings and Contracts (2005)
13. Business Contracts as Protection Against Cheating (2005)
14. Legal System Upholding Contracts and Legal Rights in Business Disputes (2005)
15. Entrepreneurs Wanting to Apply for Loans in the Last Five Years (2000–05)
16. Entrepreneurs Applying for Loans in the Last Five Years (2000–05)
17. Reasons for Not Taking Out Loans Despite Need
18. Entrepreneurs with PLS Accounts
19. Entrepreneurs with Current Accounts
20. Businesses Involved in Retail and/or Wholesale Trading
21. Businesses Involved in Services
22. Businesses Involved in Manufacturing Non-agricultural Goods
23. Businesses Involved in the Processing of Agricultural, Hunting, and Fishing Products
24. Businesses Involved in Construction
Annex C