Rural Report Final WEB - Pakistan Microfinance Connect · BRI Bank Rakyat Indonesia ... PPS Probability Proportional to Size PSLM Pakistan Social and Living Standards Measurement

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


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


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


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


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%


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


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?


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


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


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


(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]


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


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.


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


Wheat

Punjab

Rice

Sindh

Other


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


Wheat

Punjab

Wheat

Sindh


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


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


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


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 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).


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


Wheat

Punjab

Rice

Sindh

Other


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


Wheat

Punjab

Rice

Sindh

Other


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


Wheat

Punjab

Rice

Sindh

Other


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.


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


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)


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)


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


Mixed

Punjab

Punjab

Cotton

Wheat

Punjab

Low

Intensity

Barani

Punjab

Cotton

Wheat

Sindh


Wheat

Punjab

Rice

Sindh

Other


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


Rice Mixed

Punjab

Punjab

Cotton

Wheat

Punjab

Low

Intensity

Barani

Punjab

Cotton

Wheat

Sindh


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


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


Wheat

Punjab

Rice

Sindh

Other



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


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


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


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


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


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


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


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


Barani Punjab

Cotton-wheat Sindh

Rice-other Sindh

NWFP

Balochistan

1 room 2 rooms 3-4 rooms 5 or more rooms


1 room 2 rooms 3-4 rooms 5 or more rooms


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


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).


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


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


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


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


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


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


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


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


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



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


Barani Punjab

Cotton-wheat Sindh

Rice-other Sindh

NWFP


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


Barani Punjab

Cotton-wheat Sindh

Rice-other Sindh

NWFP


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


Figure 30: Households that have never borrowed from a Bank

0

90

92

94

96

98

100

Poor Non-poor


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



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


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


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



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


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


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


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


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’,


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


Punjab

Punjab

Cotton

Wheat

Punjab

Low

Intensity

Barani

Punjab

Cotton

Wheat

Sindh

Rice

Punjab

Wheat

Rice

Sindh

Other


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.



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**


.867** .855** 1.0 .782** .915** .879**

Net Remittances


.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


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**



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


Net

Remittances


Non-agricultural

Income

.661* 1.0 .855** .830** .636* .988** Non-agricultural Income


.927** .903** 1.0 .855** .903**

Aggregate 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**).


Income from

Livestock


Table 10: Non-parametric Correlation between Variables Poor–

’S SPEARMANRHO

Crop

Income

Net

Remittances


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*


.636* .867** .636*

Net Savings in the Last One Year .721* .685*


.661* .758* .830**

Aggregate Income

.758*

.782**

.733*


.855**

.830**

.855**

HH ( %

Rural banked )

.697*

HH Receiving loans

.709* Average Loans

Repaid in the Last One Year

.685* .661*


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




Gold and Silver

Owned

Aggregate Income



1.0 .696* .696* .696* .696*


.696*

1.0

.770**


1.0 .721* .927**


.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


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.


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


Barani Punjab

Cotton-wheat Sindh

Annex A: Classification of Districts into Agro-climatic Zones

Annex A


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.


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:


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:


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


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