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July 31, 2014

The Rural Non-farm Economy, Livelihood Strategies and

Household Welfare in Rural Pakistan

Technical Paper for

2nd ADB-Asian Think Tank Development Forum

To be held on

November 20-21, 2014

Seoul, Republic of Korea

By: Dr. Shujaat Farooq

Research Economist

Pakistan Institute of Development Economics (PIDE), Islamabad

Cell no: +92-332-8306825, Email: shujaat@pide.org.pk

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The Rural Non-farm Economy, Livelihood Strategies and Household

Welfare in Rural Pakistan

Abstract

In this study, an attempt is made to analyze the role of rural non-farm enterprises in

employment provision and household welfare in rural Pakistan. The study has used

various micro datasets including PPHS-2010, HIES, 2010 and PSLM-2010. The results

reveal that majority of the non-farm enterprises are micro-enterprises and or informal with

limited representation from the production activities. Majority of the non-farm

employment accumulate in paid employment and self employment categories. Non-farm

economy is one of the major sources of employment and income for poorer households.

The higher non-farm income sources for the poorer reflects the equity enhancing in

Pakistan. Though there is more rural poverty among non-farm households; however, it is

not worst for paid non-farm worker than paid agricultural worker. Non-farm economy may

not have a significant impact to reduce rural poverty, but it has a significant positive

impact on child school enrollment in rural Pakistan.

Key words: Non-farm sector, rural development, employment, household welfare, poverty

reduction

JEL Codes: I32, J21, J43, O14, Q10, R11

I. Introduction

Traditionally, the structural movement of labor from farm to off-farm sector has

widely been acknowledged around the globe, especially in developing world for

economic growth and rapid industrialization (Lewis 1954). Rural non-farm economy has

mostly been remained unobserved in developing world; however, in recent it has gained

considerable attention due to rising risks of poverty and vulnerability. The developing

world is increasingly coming to realize the contribution of rural non-farm activities for

economic development directly because of its size and its responsiveness to growing

agricultural output, lowering pressure on urban migration and establishing export

markets, and indirectly through provision of financing, processing, marketing and other

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support services that accelerate agricultural and non-agricultural growth.1 Sitting in

overcrowded urban capitals, policymakers view the rural non-farm sector as a potential

breakwater to control rural-urban migration and reducing pressure on overstretched urban

service delivery systems. Sitting in rural compounds, households consider non-farm

activities as means of diversifying incomes and reducing seasonal and inter-annual

consumption risks. Amid growing landlessness, poor households increasingly depend on

non-farm earnings for their survival (Haggblade et al. 2007).

Traditional theories have linked up the rural development primarily with

agricultural growth, due to its predominance in rural life. During 1980s and onward

various socio-economic households surveys conducted in developing countries have

revealed the rising reliance of rural economy on non-farm sector (Malik 2008). This

emergence cropped up due to globalization and liberalization starting from the late 1980s

and early 90s in various developing countries which led new economic opportunities for

the private sector and foreign direct investment to expand domestic markets and access

new markets. As a result, large exporters, agribusiness firms, and supermarket chains

increasingly penetrate in the rural economies, altering the scale and structure of rural

supply chains as they do. The enormous increase in the availability of information and

communication technology greatly facilitated this potential boon (Haggblade et al. 2007).

The dominant growth-centric development paradigm in Pakistan has long been

looking to the farm sector for rural poverty alleviation while the non-agricultural

activities have widely been ignored. The importance of rural non-farm economy in

Pakistan cannot be ignored due to multiple reasons. First, around two-third of the total

population and three-fourth of the poor are residing in rural areas where half of the rural

households are non-agricultural (GoP, 2010a). Second, agriculture is the main livelihood

source in rural Pakistan, however, the 2000 Agricultural Census reveals that only 37

percent of the rural households owned land, and 61 percent of these owned fewer than 5

acres. The overall Gini coefficient of land ownership in Pakistan is 0.66; if rural landless

households are included, the Gini coefficient is 0.86. Third, growth in agricultural

production in Pakistan is not sustained over time and even higher agricultural growth has

minor impacts on the incomes of agricultural laborers and landless households. In the

1 Weersink et al. 1998, Escobal 2001, Lamb 2003, Joliffe 2004, Malik 2008.

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presence of skewed land distribution and fluctuated agricultural growth, rural non-farm

sector is an important pathway for poverty reduction and employment provision.

Presently more than half of the rural Pakistani laborers are employed in non-farm

activities.

The ‘non-farm’ sector includes all economic activities in rural areas except

agriculture, livestock, fishing and hunting. Like other developing countries, rural non-

farm sector in Pakistan is comprises of heterogeneous activities. However it is not

performing well due to its small manufacturing base. Non-farm enterprises are generally

quite small in terms of assets size, employment level, low human capital, lack of access

to finance, limited forward and backward linkages, un-experienced managers and higher

closure rates (Sur and Jian 2006).

A considerable body of literature has discussed the issues of agriculture and

poverty in Pakistan; however, majority of the studies have ignored the role of rural non-

farm economy in poverty alleviation and resources diversification. Few studies, however,

have analyzed but in limited range. For example, Adams and Janes (1995) examined

sources of non-farm income inequality, and Nasir (1999) analyzed the link of poverty

with employment. Arif et al. (2000) viewed the level of poverty among the various farm

and non-farm groups. Sur and Jian (2006), World Bank (2007) and Malik (2008) have

analyzed the structure of rural non farm economy; however, no comprehensive study has

been prepared to analyze its structure, employment provision, labor diversification and

contribution in household welfare. In view of the growing importance of rural non-farm

activities, this examination is essential for policy formulation to eradicate rural poverty.

The present study aims to fill this gap by examining the structure of rural non-farm

enterprises in terms of business and employment provision, household livelihood

strategies in terms of labor diversification and the impact of household livelihood

strategies on household welfare. Poverty status of household and child school enrollment

status has been taken as the measure of household welfare.

The paper is divided in 7 sections. Section 2 presents the theoretical

considerations of dynamics of rural non-farm economy, followed by data description and

methodology in section 3. Profile of Pakistani rural non-farm enterprises is given in

section 4 while the role of non-farm economy in employment provision and labor

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diversification has been detailed in section 5. The penultimate section discussed the role

of non-farm economy in equity enhancement and household welfare. Conclusions and

policy recommendation have been reported in last section.

II. Dynamics of Rural Non-Farm Economy and Household Welfare: Some

Theoretical Considerations

The absence of land or poor land endowments are the key push factors to work in

non-farm activities. On the other hand, higher wages in non-farm sector could be the

major pull factors. These pull factors would be more strong if farm income is not

sufficient to fulfill family needs (Barrett et al. 2001). The pull factors may also become

more powerful when higher agricultural productivity stimulates employment generation

in non-farm sector through its linkage effects (Haggblade and Hazell 1989).

Traditional rural insight, developed in colonial era has notion the non-farm sector

as a low productivity sector. Hymer and Resnick ( 1969) contended that peasants

produced only food and some basic non-food goods—called Z goods, to serve their

needs. With rising world economy, labor was induced to move from non-food items to

cash crops for exports. As a result, there was a decline of rural non-food goods and an

expansion of both exports and imports. The later studies not support this idea due to its

non-applicability to the post-colonial era. Ranis and Stewart (1993) revealed that both

the micro and macro policies will determine the future path of the economy whether it

should produce Z goods or balance sectoral growth or Z goods will be displaced by

imported goods or by subsidized urban domestic goods.

During late 1980s and early 1990s, the novel liberalization paradigm has launched

new opportunities for the private sector in rural areas by lowering government

involvement in the production sector and relaxing control on foreign exchange. As a

result, massive foreign investment was witnessed in Asia, Africa and Latin America with

rising rural non-farm activities. This new era endowed new economic opportunities for

some rural suppliers to access new markets in the non-farm sector. They were also

exposed others to new threats of competition from cheap manufactured imports and by

imposing quantity quality standards that risk excluding undercapitalized rural enterprises

on which the rural poor often depend (Haggblade et al. 2007).

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The empirical evidences from South East and some South Asian countries

signify that higher agricultural production spur the expansion of rural non-farm

economy, commencing usually near cities, spread eventually to include a broad

spectrum of rural economy (Yusuf and Kumar 1996). For example, the late 1970s

agricultural reforms in China gave much freedom to farmers to diversify their

production strategies. In parallel the restricted urban migration called “hukou”2 and

massive public investment led to establish Township and Village Enterprises (TVEs) and

specialized households (Ravallion 2009). It led two important consequences for the

Chinese labor market: first, it absorbed surplus labor and facilitated industrialization

without relying on migration, and second, the free entry of TVEs increased competition

in the market and created pressure for SOEs reform (ADB 2007).

Overseas remittances also stimulate rural economy by raising rural investment,

construction activities and agricultural inputs. In some cases, migrants contribute to

common funds for local public investments—thus creating jobs for local masons and

carpenters (Adams 1998, Ellis and Freeman 2004). In Pakistan, the return migrants from

Middle East have been establishing their small level businesses by utilizing the gained

experienced and saved money at abroad.

There are two major types of rural non-farm activities: high labor productivity and

lower labor productivity. A common view is that rural off-farm employment is a low

productivity sector producing low quality goods (Lanjouw and Lanjouw 2001). From a

social welfare perspective, employment creation is important even in lower labor

productivity sector, especially when fast growing rural labor force cannot be much

absorbed in over-crowded agriculture sector or agricultural employment is not an option

for certain sub-groups (Lanjouw 1999, Arif et al. 2000). The evidence suggests that non-

farm activities have the potential to improve rural well-being by absorbing surplus labor,

especially for the poor agrarian economies (Barrett et al. 2001). In Pakistan, high

agricultural growth has benefited mainly to large and medium farmers, therefore, the

incidences of poverty are much higher in pure agricultural zones i.e. Southern Punjab,

Rural Sindh (Malik 2005, Arif and Shujaat 2012). It is expected that rising landlessness

2 Under the hukou system, a rural migrant cannot avail urban services without obtaining registration there,

which can be difficult and costly—particularly for the poor (Ravallion 2009)

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along with bulk of potential working youth in rural Pakistan has generated low

productive rural non-farm activities; however, the low return may also enhance

household income and consequent rural welfare. In short, the contribution of rural non-

farm economy is highly appropriate for those agrarian economies that have high risk of

poverty, vulnerability and unfavorable labor-land-ratio (Admas and He 1995, Stifel

2010). Finding part-time local non-farm employment is vital for the welfare of small

farmers and their families.

The average share of non-farm economy in total rural household income is

varying across the continents; it is 37 percent in Africa, 47 percent in Latin America and

51 percent in Asia. Primary employment shares, the most widely available indicator of

rural non-farm activity, are about 30 percent in Asia and Latin America, 20 percent in

West Asia and North Africa, and 10 percent in Africa (see Appendix 1). Various studies

found negative correlation between the share of non-farm income and poverty in a

number of countries. In case of Ethiopia, farm and non-farm diversification not only

offers higher income but also better child nutrition (Barett et al. 2001). In case of

Tanzania, it has a positive association with per capita food consumptions (Lanjouw and

Feder 2001). Zhu and Luo (2006) found that non-farm income was an important source to

reduce income inequality in rural China.

A rising trend of rural economy including manufacturing, trade and services can

be seen in South Asian countries. It implies that not only the links between agriculture

and rural poverty should be examined, but also the rural non-farm sector should receive

attention. A dynamic labor-intensive agriculture combining with a modernizing non-

agricultural sector in Pakistan can provide diversified employment opportunities to the

rural households, resulting rapid growth, classless distribution, diminishing rural

unemployment and underemployment and lowering the pressure on rural-urban

migration. Special policy orientated attention is required to eradicate rural poverty and

hunger by promoting non-farm activities in rural Pakistan. The ongoing paper explores

the linkages between non-farm activities and rural welfare in Pakistan.

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III. Data Sources and Methodology

A. Data Sources

To analyze the structure of rural non-farm economy, employment diversification

and household welfare, the study has used multiple data sources as no single national

dataset details all the relevant information to fulfill these objectives. Various rounds of

Pakistan Labor Force Survey (LFS) and national statistics have been utilized; however,

the study has mostly used three cross-sectional micro datasets which are Pakistan Panel

Household Survey (PPHS)-2010, Pakistan Social and Living Measurement Survey

(PSLM)-2010 and Household Integrated Economic Survey (HIES)-2010.

PPHS 2010 is the third round of panel dataset carried out by the Pakistan Institute

of Development Economics (PIDE) where its first two rounds were conducted in 2001

and 2004. The 2010 round covers 4,142 households from all the four provinces with more

representation from poor and rural regions (for detail, see Arif and Shujaat (2012)). The

present study has used PPHS 2010 to examine the profile of non-farm enterprises as only

this dataset provides the latest information.

To analyze rural employment, labor diversification and its determinants, Pakistan

Social and Living Standards Measurement (PSLM)-2010 has been used. PSLM 2010 is

the district and provincial level representative dataset conducted by Pakistan Bureau of

Statistics (PBS). Various modules of PSLM collect a wide range of information on

household’s socio-demographic and economic characteristics including education, health,

employment, assets etc. The 2010 PSLM has covered 76,546 households from all the 4

provinces and 114 districts of Pakistan including the urban regions.

The study has used HIES 2010 for poverty estimates. HIES is the subset of PSLM

survey and representative at provincial level. It is used to estimate official headcount

poverty in Pakistan as it has detailed consumption modules covering all aspects of

consumption including food and non-food expenditures. The 2010 HIES covers 16,341

households from all the four provinces of Pakistan.

B. Methodology

It is worth to mention here that the present analysis is carried out only in rural

Pakistan. Before explaining methodology, clarification on three concepts is necessary

which are ‘non-farm’, ‘rural’ and ‘poverty’. Rural non-farm activities lie on or between

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the boundaries of usual rural-urban and agricultural and non-agricultural categories. The

ongoing study has followed the 2007 official industrial classification where agriculture

including the livestock has been considered as the farm activities while the non-farm

activities include all other employment except agriculture. Regarding ‘rural’ clarification,

both the PSLM and HIES follow rural-urban definition of 1998 census in which the ‘rural

towns’ fall under administrative status are treated as the urban areas, therefore, these

towns are not included in present analysis. Regarding ‘poverty’ measurement, the study

has followed the official methodology as defined by The Planning Commission of

Pakistan which can be called as Cost of Basic Needs approach. The basket of ‘basic

needs’ consists of food, education, clothing, health, housing, transportation and

recreation. The cost of this basket is poverty line (Rs. 723.4 for 2000-01 year), as defined

to impart 2,350 calorie intake per adult per equivalent per day with an adjustment of non-

food minimal requirement. The study has inflated the official poverty line for 2010 period

by using the Consumer Price Index and applied it on HIES 2010 dataset to measure

headcount poverty.

The study has defined labor diversification at individual and at household level.

At individual level, we construct five categories of workers by using their primary and

secondary occupations, as detailed in below table. At household level, the households

have been divided into three categories by using the primary nature of work activities of

their employed household members, which are; farm households, non-farm households

and mix households.

Activity Status Definition

Main Agriculture In which the primary sector of activity is agriculture

Main Non-farm In which the primary sector of employment is non-farm activity

Only Agriculture It which primary sector of employment is agriculture and have no

secondary sector of employment or the secondary is also agriculture

Only Non-farm It which primary sector of employment is non-farm and have no secondary

sector of employment or the secondary is also non-farm

Mix activity In which the primary sector of employment and secondary sector of

employment are varying

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The following equation has been estimated to find out the determinants of rural

labor diversification;

Act_cat i = α 0 + α1 I i + α2 Hhi + α3 Rg i + µ i (1)

The dependent variable Act_cat represents the employment status of rural worker

i with three outcomes; only agriculture, only non-agriculture and mix activities which has

been constructed by using the primary and secondary work activities. On right hand side,

Ii is vector of independent variables measuring individual characteristics for worker i:

gender, age and education, vector Hhi measures household characteristics including

household size, land ownership and irrigation status. Vector Rgi measures the community

and regional characteristics including the distance to middle school, distance to roads and

province. Since the dependent variable has three outcomes, therefore, the multinomial

logistic regression has been applied.

Headcount poverty rate and child school enrollment status (age 5-15 years) have

been taken as the household welfare indicators in this study. The following two equations

have been estimated to analyze the impact of labor diversification on household’s

welfare;

Povi = α 0 + α1 Hh_diversificationi + α2I i + α3 Hhi + α4 Rg i + µ i (2)

Eni = α 0 + α1 Hh_diversificationi + α2I i + α3 Hhi + α4 Rg i + µ i (3)

In equation 2, the dependent variable Povi is the headcount poverty status of rural

household i. On right hand side of equation 2, Ii is a vector of independent variables for

the head of household characteristics i.e. education, age and gender, vector Hhi is

comprises of household characteristics: land ownership and dependency ratio,3 vector Rgi

contains the provincial characteristics. In equation 3, vector Ii represents the individual

characteristics of children i: age and gender, vector Hhi is comprises of parental and

household characteristics including the education of mother, father, household size and

presence of TV, and vector Rgi contains the provincial dummies. The key independent

variable is the household diversification with three outcomes; farm households, non-farm

households and mix households. In equation 2, the dependent variable poverty status has

two outcomes; poor and non-poor while in equation 3 the school enrollment status has

3 Dependency ratio is number of dependent (below age 15 and above age 64) divide by number of

independent (age 15-64).

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two outcomes; whether child of age 5-15 years currently enrolled or not enrolled. Since

both the dependent variables have two outcomes, therefore, the Binary Logistic

Regression has been applied to estimate the impact of household labor diversification on

household’s welfare.

IV. Profile of Rural Non-Farm Economy in Pakistan

There is no precise number of rural non-farm enterprises in Pakistan but the

extrapolation from PSLM 2010 survey suggests that there are about 5 million rural non-

farm enterprises in Pakistan. The survey indicates that 23 percent of the Pakistani rural

households are the proprietor or partner of non-farm enterprises. Across the provinces,

rural households in Punjab own 26 percent, followed by 24 percent in Khyber

Pakhtunkah (KP), 18 percent in Balochistan and 15 percent in Sindh. Like other South

Asian countries, majority of the non-farm enterprises are micro-enterprises (96.6%), run

by only single person, while 2.8 percent of the enterprises are employing less than 10

persons and rest of are employing 10 and above workers. Rural enterprises are

predominantly related to trade and services activities. Production enterprises account for

only 12 percent of the enterprises, trade for 50 percent and services for the remaining 38

percent. The share of production enterprises is quietly small in Sindh and KP provinces

(Figure 1). This share is also less than the other South Asian countries; it is 27 percent in

Bangladesh and 40 percent in Sri Lanka (ADB and World Bank 2005).

Source: Authors’ estimation from PSLM 2010 micro dataset

138 9

15 12

46

6156

66

50

41

3135

19

38

0

10

20

30

40

50

60

70

Punjab sindh KP Balochistan Overall

Figure 1: Sectoral Distribution of Rural Nonfarm Enterprises (in %)

Production Trade Services

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PPHS 2010 survey discloses that most of the rural enterprises in Pakistan can be

considered to be informal businesses, not only do they have less than 10 workers, but

very few of them pay taxes (11%). Majority of the enterprises are fairly young as more

than half of the enterprises have been in operation for less than 10 years. Services

activities tend to be older (13.7 years) than the manufacturing/production (9.4 years) and

trading enterprises (10.5 years). Majority of the non-farm enterprises (94%) are owned

and operated by men. This number is not surprising as female labor force participation in

Pakistan for non-agricultural activities is only 14 percent, the lowest in South Asia.

About one-fifth of the managers have completed secondary education while more than

one-third had no schooling (Table 1).

Table 1: Province-wise Profile of Rural Non-Farm Enterprises in Pakistan

Overall Punjab Sindh KP Balochistan

Enterprise Profile Sole-proprietorships (%) 95.1 95.9 93.2 96.0 91.7

Pay any tax to govt. (%) 10.8 11.2 8.2 16.0 9.1

Average Age of enterprise (years) 11.3 12.0 7.4 16.8 12.7

Manager Profile Manager is male (%) 94.1 92.4 97.3 96 100.0

Manager Education (in grades) by category

Illiterate 35.8 38.8 29.7 20.0 58.3

1-5 grade 24.8 22.5 35.1 20.0 8.3

6-9 grade 20.9 25.5 10.8 20.0 8.3

10 and above 18.6 13.3 24.3 40.0 25.0 Source: Authors’ estimation from the PPHS 2010 micro dataset

Like other developing countries, rural non-farm enterprises primarily operate as

sole-proprietorships in Pakistan with more family workers captivation. On average, these

enterprises hire 1.3 full-time and 0.9 part-time workers including the paid and family

workers, thus totaling to 2.3 workers, on average. More than one-third of the rural

enterprises have hired family workers either full time or part time, while near to one-fifth

of rural enterprises have reported to hire paid workers. More than three-fourth of the

enterprises hire only one worker, either paid or unpaid, while a minor percentage of only

2.6 percent employee more than 5 workers. There is no representation of such enterprises

from KP and Balochistan provinces (Table 2).

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Table 2: Province-wise Employment Size of Rural Non-Farm Enterprises in Pakistan

Employment Distribution Overall Punjab Sindh KP Balochistan

Average number of workers 2.3 2.6 1.7 1.5 1.4

Average full time workers only

(in numbers) 1.3 1.3 1.6 1.3 0.8

Average part time workers only

(in numbers) 0.9 1.3 1.9 0.3 0.7

Employment size distribution of enterprise

Less than 2 workers 77.9 81.6 77.0 60.0 58.3

2-5 workers 19.5 15.3 20.3 40.0 41.7

More than 5 workers 2.6 3.1 2.7 0.0 0.0

Enterprise hiring full time paid

workers (%) 15.3 10.7 21.6 36.0 8.3

Enterprise hiring full time family

Paid workers (%) 26.1 18.9 32.4 52.0 50.0

Enterprise hiring part time paid

workers (%) 4.2 4.1 2.7 8.0 8.3

Enterprise hiring par time family

Paid workers (%) 13.4 14.3 10.8 4.0 33.3

Note: Manager is not included in employment calculation

Source: Authors’ estimation from the PPHS 2010 micro dataset

Labor productivity in these enterprises varies widely across activities and gender.

A comparison of rural non-agricultural daily wages in Table 3 reveals that services sector

has highest wages while manufacturing sector has the lowest wages in both Pakistan and

Sudan. Pakistani male, on average, earns 2 to 3 times more wages than the females. More

than half of the rural non-farm enterprises are located in homes either inside or outside

the residence, with a minor percentage at road side, main commercial area or industrial

sides (Figure 2). PPHS survey reveals that 87 percent of the enterprises sell their products

in the same village/town, followed by 6.9 percent to cities, 5.3 percent to other villages

and only 0.7 percent to other provinces and countries.

Table 3: Average Daily Wage in Rural Non-farm Sector by Type of Activity

Type of Industry Pakistan (in Rupees) Sudan

(in Sudani Pounds) Both sexes Male Female

Agriculture 327 357 90 -

Manufacturing 232 238 89 21 - 23

Trade/Commerce 321 323 177 75 - 80

Services 350 365 202 150 - 180

Source: Authors’ estimation from PSLM 2010 for Pakistan, Haggblade et al. (2007) for Sudan

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Source: Authors’ estimation from the PPHS 2010 micro dataset

Table 4 shows that rural non-farm enterprises in Pakistan are generally quite in

terms of asset size and business turnover. Annual median sale revenues range from Rs.

110 thousand to Rs. 201 thousand across the provinces with an average of Rs. 130

thousand at national level. Annual profit seems to be low with a median value of Rs. 60

thousand only. Median value of assets including the fixed assets and property is very low.

Lower asset values reflect the lower productivity of workers in these enterprises.

Table 4: Sale and Assets of Non-Farm Enterprises in Pakistan (in 000 Rs)

Overall Punjab Sindh KP Balochistan

Annual Sales revenues (mean) 443 343 500 113 278

Annual Sales revenues (median) 130 120 110 125 201

Annual net profit (median) 60 60 60 90 80

Value of property (Land/Building)

(median) 25 20 18 130 120

Value of assets (median) 2 2.5 - 4 -

Asset per worker (median) 1.2 1.5 - 3.2 - Source: Authors’ estimation from the PPHS 2010 micro dataset

Remote locations frequently mandate long and tortuous supply chains linking

rural producers with final markets. Rural infrastructure seems to be one of the major

obstacles for non-farm enterprises in Pakistan to their operation and growth including the

access to finance, human capital, physical capital and access to markets. The ongoing

research has plotted both the soft rural infrastructure and physical rural infrastructure at

30

26

1

21

2

2

7

2

9

0 10 20 30 40

home, inside residence

home, outside residence

industrial site

local market/ baazar

main commercial area

baazar (like weekly)/baazar mandi

roadside

other fixed place

mobile

Figure 2: Place of Business of Rural Non-farm Enterprises (%)

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district level in which the district literacy rate (%) and average distance to secondary

school (in km) has been taken for the former and percentage of villages who have access

to metallic road with less than 1 km and average distance to commercial bank (in km) has

been taken for the later. As shown in Figure 3, on y-axis percentage of rural households

who own non-farm enterprises has been plotted with the soft and physical infrastructure

indicators. The trend reveals that rural households in those districts, who have, on

average, higher literacy and less distance to secondary schools, own more non-farm

enterprises. Similarly, households with easy access to metallic road and commercial

banks own more non-farm businesses in their districts.

Figure 3: District level Rural Infrastructure & Households own Non-farm Enterprise (in %)

Source: District level rural infrastructure data has been taken from Mouza Statistics, GoP (2008)

Very few of them use the modern business services i.e. accounting, marketing,

insurance and information technology (see appendix 2). The profile of these enterprises

as presented above suggests that these rural enterprises are poorly equipped to provide

the catalytic higher rural growth, decent job generations and poverty reduction as

required by these enterprises to cope with the rising risks and competitions of associated

010

20

30

40

20 40 60 80literacy_rate

Fitted values non_farm

010

20

30

40

0 20 40 60distance_high_school

Fitted values non_farm

010

20

30

40

0 20 40 60 80 100access_metal_road

Fitted values non_farm

010

20

30

40

0 50 100 150 200distance_commerical_bank

Fitted values non_farm

15

with globalizations. Relatively fewer shares of production enterprises in Pakistan

highlight the missed potential for value addition. There seems absence of essential

agricultural support services and linkages, necessary to stimulate the growth of non-farm

sector. With poor equipment including the human, physical and financial margins along

with regional disparities, often restrict low income households to run low productivity

enterprises with higher labor intensity and lower financial returns.

V. Employment Provision and Labor Diversification in Rural Non-farm Economy

This section analyzes the role of non-farm sector in employment provision and

household’s livelihood strategies that how they individually and collectively allocate

labor supply between farm and off-farm activities. Functionally, rural non-farm economy

is supposed to play a central role in the process of structural transformation, during which

the agricultural share in national income declines and transfers to manufacturing and

services sector. Similar structural transformation can be perceived in Pakistan by

transferring agricultural share to services sector and stagnant share of industrial sector

over the last four decades (see appendix 3). In parallel, inter-temporal labor movement

also took place with declining share of agricultural employment, but it is still a dominant

source of rural employment.

An impressive rural employment growth in trade and construction activities can

be seen overtime with some marginal increase in services sector, especially the

professional services (see appendix 4). One major realization, however in Pakistan is that

the share of labor associated with agriculture has not declined at the same pace as the

share of agriculture has declined overtime. Within non-farm employment, four sub-

sectors manufacturing, construction, commerce and service are the more important for

employment provision in rural Pakistan (Figure 4).

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Note: ‘others’ include finance & insurance, housing, private and government services

Source: GoP, Government of Pakistan, 2014

There are several reasons for this structural shift. Overseas migration and return

migration seems to be one of the major factors in some rural regions, especially in north

and central Punjab and north KP. Arif and Irfan (1997) have examined occupational

shifts among migrants returned from the Middle East and found a clear move out of the

production sector into business activities, with a decline in production sector employment

by 40 percent. Unequal land distribution could be another reason as only 37 percent of

the rural households own land. Though three land reforms have so far been carried out in

Pakistan; however, they remained unsuccessful for right allocation. The 2000

Agricultural Census shows that near to two-third of land owner households are small

farmers (own less than 5 acres) who own only 15 percent of the cultivated land while

only 2 percent large farmers (own 50 acres or more) own 30 percent of the total

cultivated land. In Pakistan, land is distributed far more unevenly than income (Adams,

1995; Hirashima, 2009). Other reasons of establishing the non-farm activities could be

the education, public investment in rural areas, and higher agricultural income for

landholder households.

A. Rural Non-farm Labor Diversification in Pakistan

Both the micro and macro factors determine the livelihood strategies of

households to allocate labor in farm and off-farm activities. These factors vary across the

households and regions as according to the available opportunities and risks. Non-farm

2117

2

1913

2

27

44

14

7

14

51

15

0

10

20

30

40

50

Agriculture Manu. & mining Construction Wholesale &

Trade

Transport &

Communication

Electricity, gas

& water

others

Figure 4: Share of Major Sectors in GDP and in Employment, 2013-14

Share in GDP (%) Share in Eemployment (%)

17

employment activities can be classified into four major categories; employer, self-

employed, paid employed and unpaid family helper. Table 5 shows that non-agricultural

income accumulates to rural households primarily come through the paid and self

employment; both these categories constitute around 93 percent of all non-farm workers

while unpaid family worker and employer contribute only 6 percent and 0.8 percent of

their share. There is also marked difference in the nature of activities as near to two-third

of the self employed workers are engaged in trade and transport services. Service and

manufacturing are other important sectors for this category. Service sector and the

manufacturing sector are the other important sectors to provide the livelihood to rural

workers. For wage employees, services and construction activities account for as much as

two-thirds of rural non-farm employment. Transport and manufacturing were the other

important sectors of employment for wage employees. Government employees,

especially in educational and health activities account for significant proportion of rural

services sector.

Table 5: Rural Non-farm Employment in Pakistan by Employment Type and Status (in %)

Type of Industry

1996-97 2010-11

All non-

farm

workers

Self

employed

Paid

employees

All non-farm

workers

Self

employed

Paid

employees

Mining 0.5 0.4 2.9 1.7 0.4 1.7

Manufacturing 13.4 13.2 11.9 11.3 8.7 11.4

Electricity gas &

water 1.5 0.1 1.9 1.2 0.3 1.6

Construction 24.1 2.5 31.7 22.4 2.0 31.4

Whole sale & retail

trade 19.3 55.7 6.4 23.1 54.7 9.6

Transport

&communication 12.1 12.4 12.8 11.7 14.7 11.1

Professional services 0.9 0.5 1.0 1.03 1.2 1.0

Social & personal

services 28.3 15.2 31.4 27.6 18.2 32.2

% share 100 20.2 73.6 100 24.3 68.9

Total 100 100 100 100 100 100

Source: Authors’ estimation from the PSLM 2010 micro dataset, Arif et al. (2000) for 1996-97 numbers

Though results not given in table, the distribution of females in rural non-farm

activities is quite different with a significant share of unpaid family helper (18%), while

18

64 percent are the self-employed and 18 percent fall in paid category. Only the services

(social and personal) and manufacturing sector contributes to 83 percent in non-farm

employment with their shares of 58 percent and 25 percent, respectively. Both the

production (20%) and services (71%) only contributes to 91 percent employment for paid

employed females while production (26%), trade (20%) and services (53%) are the main

employment sources for self-employed females.

PSLM survey provides the information on both the primary and secondary nature

of work activities. Males have almost equal distribution in farm and off-farm activities on

primary activities than females, while both the sexes are mostly involved in farm

activities in their secondary occupations (appendix 5). As detailed in methodology, the

ongoing study has estimated the labor diversification at individual and household level,

where the former diversification has been classified into five categories on the basis of

primary and secondary nature of work activity while the later has three categories. As

given in table 5, the individual level employment diversification shows that majority of

the rural workers have only primary sector of employment, either agriculture (53%) or

non-farm (40%) while only 3 percent of the workers have also reported their secondary

occupations. Only 5 percent of the workers are differ over their primary and secondary

work activities—called mix activity. The female’s distribution is highly skewed toward

only the farm activities with a very lower percentage in off-farm and mix activities.

Table 6: Distribution of Rural Employed Worker by Employment Diversification (in %)

Activity Type Male Female Both Sexes

Only Agriculture 46.7 81.2 52.9

Only Non-farm 44.9 15.5 39.6

Both Agriculture 1.8 2.5 2.0

Both Non-farm 0.7 0.3 0.6

Mix activity 5.9 0.6 5.0

Total 100 100 100 Source: Author’s calculation from PSLM, 2010 micro dataset

The household level employment diversification presents an interesting picture as

given in table 7. There are more pure non-farm households than the farm households at

national level with a significant representation of mix households (20%) as well. Around

6 percent of the households are not involved in any employment activity. Heterogeneity

prevails across the provinces with more agricultural households in Punjab and Sindh,

19

while more mix households in Punjab and KPK, reflecting more resource diversification

in KP and Punjab than the other provinces. However, more households in KP are doing

‘nothing’ as compared to other provinces which might be due to the worse law and order

situation in this province.

Table 7: Distribution of Rural Households by Employment Diversification (in %)

Province Only Farm

Households

Only Non-farm

Households

Mix

Households

Doing

Nothing

Total

National 36.3 38.1 19.9 5.7 100

Punjab 35.0 34.0 24.0 7.0 100

Sindh 44.9 34.7 18.9 1.5 100

KPK 20.3 42.0 24.4 13.3 100

Baluchistan 44.2 46.5 8.5 0.7 100 Source: Author’s calculation from PSLM, 2010 micro dataset

B. The Determinants of Rural Employment Diversification

This section details results over the determinants of rural employment

diversification where the dependent variable has three outcomes on the basis of reported

primary and secondary nature of work activities of employed workers: only agriculture,

only non-agriculture and mix activities. Two models have been estimated: in first model,

all the households are included while in second model only land holder households have

been analyzed to observe the impact of irrigated land on employment diversification. The

results are given in table 8 with reported relative risk ratios (RRR) of multinomial logistic

model in which farm activity serve as the reference category.

Regarding individual characteristics of rural workers, male are more likely to be

employed in non-farm and mix activities as compared to females working in farm

activities. The positive impact of age on non-farm and mix activities suggests that with

gaining experience, rural workers switch their status of employment to off-farm

activities. Education, especially the higher education seems to have a strong impact on

workers to work in off-farm and mix activities (Table 8).

20

Table 8: The Determinants of Livelihood Strategies of Rural Workers--Multinomial Logistic Model

Correlates

Model 1 Model 2 (Only land owners)

Only Non-Farm/Farm Mix/Farm Only Non-farm/Farm Mix/Farm

RRR St. Error RRR St. Error RRR St. Error RRR St. Error

Sex (male=1) 4.541* 0.114 20.375* 2.099 3.136* 0.143 36.375* 6.268

Age (years) 1.096* 0.003 1.252* 0.009 1.062* 0.006 1.279* 0.012

Age square 0.999* 0.000 0.997* 0.000 0.999* 0.000 0.997* 0.000

Education (below primary grade as ref.)

5-9 grades 1.738* 0.032 1.464* 0.058 1.728* 0.061 1.390* 0.072

10-12 grades 3.030* 0.071 2.317* 0.106 3.293* 0.126 2.056* 0.116

14 and above grades 14.621* 0.859 9.069* 0.783 16.956* 1.182 7.011* 0.679

Land ownership (acres) 0.920* 0.002 0.980* 0.002 - - - -

Irrigated land (yes=1) - - - - 0.572* 0.020 0.578* 0.029

Middle school (>44 mint) 0.617* 0.016 1.028 0.057 0.702* 0.034 0.874** 0.059

Public transport (>44 mint) 0.751* 0.031 1.143*** 0.087 1.064 0.070 1.236** 0.109

Province (North and Central Punjab as ref.)

South Punjab 0.586* 0.015 1.025 0.049 0.729* 0.031 1.117** 0.068

Sindh 0.500* 0.011 0.464* 0.022 0.669* 0.029 0.747* 0.049

KPK 1.465* 0.037 1.988* 0.092 1.658* 0.068 2.463* 0.143

Baluchistan 0.677* 0.016 0.096* 0.009 0.366* 0.019 0.098* 0.013

Constant 0.067* 0.004 0.000* 0.000 0.048* 0.005 0.000* 0.000

Pseudo R2 0.160 0.179

N 92,006 39,327

*significant at 1%, ** significant at 5%, *** significant at 10 %

Source: Author’s calculation from PSLM, 2010 micro dataset

21

Regarding household characteristics, landownership put away the workers to join

non-farm and mix activities. However, as shown in model 2, the quality of land also

matters for land owner households as un-irrigated (barani) land induce the households to

shift their supply of labor toward the non-farm and mix activities. The two community

variables related to soft and physical infrastructure which are access to middle school and

public transport show that an increase in distance to these facilities reduces the chances

for workers to be participating in off-farm and mix activities.

For deeper analysis, the province Punjab has been split into two sub-heads, the

southern Punjab and Punjab excluding south. The results show that except KP province,

rural employed workers residing in all other provinces including the southern Punjab are

less likely to be participating in non-farm and mix activities while comparing these

workers to those who are living/working in north and central Punjab (Table 8).

VI. Role of Non-farm Economy in Equity Enhancement and Household Welfare

As detailed earlier that 23 percent of the rural households own enterprises or some

sort of micro businesses in Pakistan. The possession of rural enterprises and share of

income sources in households by household expenditure quintiles are given in table 9 in

which the household’s sources of income has been given by farm-income (agricultural

wage and total farm income excluding agricultural wages) and off-farm income (business

income and non-agricultural wages). The results reveal that wealthier households are

more likely to own non-farm rural enterprises in Pakistan. Regional variation also

prevails with more ownership in province Punjab and KP than the other provinces. In all

the four provinces, enterprise ownership tends to increase monotonically with the per

capita household expenditures/quintile (see appendix 6). Though the richest households

own more enterprises and also earn their income from farm sources, the poorer

households are reliant for employment in these enterprises and earn significant share of

their income from non-farm sources as the share of non-farm wage in household income

is 46 percent with an aggregate of 57 percent for the lowest quintile.

Since majority of poorer households earn their income from non-farm sources,

especially the non-farm wages, therefore, the non-farm income sources for the poorer

reflects equity enhancing in Pakistan. In some developing countries, non-farm income

22

sources are inequitable as they have less contribution for the poorer households i.e.

Ecuador and Vietnam or neutral equitable i.e. India and Ethiopia. In absolute terms, the

poorest rural households in Pakistan earn near to four times as much income from non-

agricultural wage employment (Rs. 65,846.7 annually) as from agricultural wage

employment (Rs. 17,452.3 annually). The results suggests that even a low return from

non-farm enterprises may contribute to enhance household income and consequent

increase in the welfare of poorer rural households who are landless and are mostly

engaged in low productive agricultural activities.

Table 9: Percentage of Rural Households that Own Enterprise and Annual Household

Income Shares by Expenditure Quintile

Ownership and

income sources

Household’s Per capita Expenditure Quintile

Overall Poorest

quintile

Quintile

2

Quintile

3

Quintile

4

Richest

quintile

Households own

enterprise 18.2 22.3 23.4 27.6 31.8 23.0

Household’s source of income

Agricultural wages 11.3 8.4 5.3 4.3 1.2 7.3

Total Farm (excl.

agric. wages) 31.4 34.6 43.8 48.6 55.5 44.8

Net Business

Income 11.1 13.1 14.6 15.6 16.2 13.8

Non-agricultural

Wages 46.2 43.9 36.3 31.5 27.1 34.1

Total Non-farm 57.3 57 50.9 47.1 43.3 47.9

Source: Authors ‘estimation from HIES 2010 micro dataset

Poverty incidences in Pakistan are considerably higher in rural areas than in urban

areas throughout the history of Pakistan, with a gradual shift to rural areas. Except the

latest numbers, rural poverty fluctuates across the decades (see appendix 7). The question

is how do poverty levels differ across rural population groups engaged in farm and off-

farm activities? The present study has established various categories of farm and off-farm

activities at individual and at household level on the basis of primary occupation (Table

10). The individual classification shows that poverty incidences are much lower among

the workers who are employer in non-farm activities or own cultivator and livestock

holder in farm-activities. Paid employee category is challenging with highest incidences

of rural poverty, but interestingly paid employees in non-agricultural sector have more

than 6 percentage lower incidences of poverty than their counterparts getting wage in

23

agriculture sector. Female workers are facing the higher incidences of poverty among all

the categories except the own cultivators. The household level categories show that

incidences of poverty are lower among the farm households as compared to the off-farm

households; however, the results reveal that the diversified households (both the farm and

off-farm) are getting some benefits of diversification since with lower incidences of

poverty among these households than the non-farm households. Another interesting

finding is that both the pure farm households and mixed households headed by females

have the lower incidences of poverty than their relative households headed by males.

Table 10: Incidences of Poverty by Activity Status of Rural Employed Workers and

Households, 2010-11

Activity type Both sexes Male Female

At individual level

Employer (non-agriculture) 5.1 5.1 -

Self employed (non-agriculture) 13.6 13.1 22.8

Paid employee 20.2 19.2 29.3

- Non-agriculture 18.8 18.4 23.2

- Agriculture 25.3 22.3 39.4

Own cultivator 7.9 8.0 4.8

Share cropper/contract cultivator 15.6 15.7 -

Live stock 9.7 9.7 9.7

At household level (by sex of head of household)

Only farm households 14.1 14.1 11.6

Only non-farm households 19.3 19.2 22.0

Mix households 16.2 16.3 9.4

Overall poverty (%) 16.0 15.5 19.6 Source: Author’s calculation from HIES, 2010 micro dataset

Table 11 presents data on the nature of activities of rural non-farm workers by

their poverty status. Data on the nature of activities of females are also presented in this

table. Activities that are particularly important for the poor include manufacturing,

construction, transport and domestic services activities. Such activities might resemble

more closely the low productivity options, providing incomes to persons who lack an

alternative source of income. For example, wages of construction workers (semi-skilled

and unskilled) are very low in the country. In real terms they have declined overtime or

best are stagnant. While viewing the poverty incidences among self-employed and paid

workers where more than 94 percent of the rural males and females are employed, the

results reflect mark differences of poverty between the two groups. Poverty incidences

24

are comparatively smoother and are less among the self-employed workers as compared

to the paid employee category. The lower incidences of poverty in services activities for

the wage-employee category might be due to the employment of these workers in

educational and medical services run by the government. Female workers have higher

incidences of poverty than males except mining and transport sector which might be the

data representation issue as very few Pakistani females are engaged in these sectors.

Table 11: Incidences of Poverty in Rural Non-farm Employment in Pakistan by Employment

Type and Status (in %)

Type of Industry

Overall Female only

All non-

farm

workers

Self

employed

Paid

employees

All non-farm

workers

Self

employed

Paid

employees

Mining 13.8 23.5 19.4 6.7 - -

Manufacturing 23.3 17.8 24.5 40.8 36.4 43.3

Electricity gas &

water 3.5 1.1 3.6 - - -

Construction 22.3 9.6 22.4 37.9 - 42.1

Whole sale & retail

trade 16.1 11.6 24.5 31.2 19.3 55.4

Transport &

communication 15.0 17.9 15.1 4.4 - 6.0

Professional services 10.7 10.8 13.0 17.1 - 17.1

Social & personal

services 13.8 15.4 13.5 15.2 16.4 15.0

Overall poverty (%) 17.3 13.7 18.8 23.3 22.8 23.2

Total 100 100 100 100 100 100

Source: Authors’ estimation from the HIES 2010 micro dataset

The above discussion concludes that rural non-farm economy may not have a

clear pattern to reduce rural poverty in Pakistan as poverty incidences are higher among

the non-farm households compared to the farm households. However, access to non-farm

jobs does lead to equity enhancement and to improve the absolute income levels of the

poorer laborer as the paid labor in non-farm activities has lower incidences of poverty

than the agricultural paid labor. Though majority of the labor in non-farm activities

especially in manufacturing sector are engaged in low productive activities and thus, are

facing higher incidences of poverty, but even lower productivity may contribute to

enhance household income as it is an important source of secondary employment for the

small and landless farmers. In figure 5, the district level rural poverty and rural multiple

25

deprivation has been plotted with the district level average total mix and non-farm

households.4 No clear trend of household labor diversification can be seen with poverty;

however, rural deprivation has a clear negative trend, suggesting that the districts with

larger share of mix and non-farm activities have the lower deprivation rates.

Figure 5: District level Rural Poverty and Deprivation by Household’s Labor Diversification

The logistic regression results over the impact of household’s labor diversification on

poverty and child school enrollment are given in table 12 and table 13, respectively. The

given results in table 12 are in the line of table 10 that households engaged in non-farm

activities and in mix activities are more likely to have higher incidences of rural poverty

while comparing them with farm households. Regarding the other control variables, male

headed rural households are more likely to face more poverty while the educational

attainment of head of household has a significant impact on poverty reduction. As

expected, assets, ownership of land, has a significant and negative association with the

poverty status while higher the dependency burden, more the poverty level. Across the

provinces, households in KP and Balochsitan provinces have lower incidences of poverty

as compared to the province Punjab.

4 The district level rural poverty and deprivation data has been taken from Jamal (2011), deprivation

includes education, health, housing quality, housing services and economic wellbeing.

20

40

60

80

10

0

10 20 30 40 50rural_poverty

Fitted values mix_nonfarm_hh

20

40

60

80

10

0

0 20 40 60 80deprivation

Fitted values mix_nonfarm_hh

26

Table 12: The Impact of Household’s Labor Diversification on Rural Poverty—Logit Model

Correlates Odd ratio Std. Error

Farm household as ref.

Non-farm. household 1.491* 0.144

Mix households 1.297** 0.142

Sex of head (male=1) 1.908* 0.392

Age of head (in years) 1.014 0.017

Age square of head 1.000 0.000

Education of head (below primary grade as ref.)

5-9 grades 0.563* 0.054

10-12 grades 0.293* 0.044

14 and above grades 0.120* 0.044

Dependency ratio 1.832* 0.074

Land ownership (acres) 0.920* 0.016

Province (Punjab as ref.)

Sindh 1.114 0.103

KPK 0.724* 0.081

Baluchistan 0.426* 0.057

Constant 0.039* 0.018

Pseudo R2 0.093

N 7,242 *significant at 1%, ** significant at 5%

Source: Authors’ estimation from the HIES 2010 micro dataset

Contrary to table 12, an interesting finding emerges from table 13 in which the

impact of household’s labor diversification on child school enrollment status has been

discussed. The results show that children of those households who are engaged in non-

farm activities and in mix activities are more likely to be currently enrolled while

comparing them with those farm households who are not sending their children at

present. These results might explain figure 5 that non-farm and mix households might not

have stronger impact on rural poverty but it can reduce the rural deprivation including the

education.

Regarding the other control variables, male children are more likely to be enrolled

and age has a negative association with enrollment. Educational attainment of both the

father and mother has a significant impact on child enrollment while the larger size

households, household size, are less likely to send their children in school. Across the

provinces, children belong to Sindh and Balochistan provinces are less likely to attend

school currently while comparing all these provinces with province Punjab.

27

Table 13: The Impact of Household’s Labor Diversification on Child Enrollment

(age 5-16) —Logit Model

Correlates Odd ratio Std. Error

Farm household as ref.

Non-farm. household 1.331* 0.056

Mix households 1.223* 0.058

Sex of child (male=1) 2.412* 0.085

Age of child (in years) 0.956* 0.005

Education of mother (below primary grade as ref.)

5-9 grades 0.988 0.242

10-12 grades 3.720* 0.318

14 and above grades 4.077* 0.760

Education of father (below primary grade as ref.)

5-9 grades 1.853* 0.082

10-12 grades 2.729* 0.162

14 and above grades 3.177* 0.315

Household size (in numbers) 0.990** 0.005

TV (yes=1) 1.984* 0.076

Province (Punjab as ref.)

Sindh 0.412* 0.020

KPK 1.115** 0.059

Baluchistan 0.456* 0.025

Constant 0.673* 0.053

Pseudo R2 0.121

N 15,804 *significant at 1%, ** significant at 5%

Source: Authors’ estimation from the HIES 2010 micro dataset

VII. Conclusion and Policy Implications

This paper has examined the role of non-farm economy in rural employment

provision and its linkages with household welfare, including the poverty status and

school enrollment of children (age 5-15 years) in Pakistan. At present, 23 percent of the

rural Pakistani households own some sorts of non-farm enterprises where 96 percent of

the non-farm enterprises are micro-enterprises. Most of the enterprises are informal and

they have poor asset endowments and are highly influenced by the available soft and

physical capital and infrastructure. Households, on average, own more enterprises in

those districts that have good physical and human capital.

Although agriculture still dominates in rural employment, its importance has

declined overtime. There is a shift away from farm to non-farm activities in rural areas.

Within the non-farm sector, the share of manufacturing has declined substantially with a

28

corresponding increase in the importance of service and commerce activities. The present

analysis shows that non-farm income accumulates to rural households primarily come

through the paid employment and self employment. Self employed workers are mostly

engaged in trade and transport services while services and construction activities account

for two-thirds of rural non-farm paid employees.

The individual level employment diversification shows that majority of the rural

workers have only primary sector of employment, either agriculture (53%) or non-farm

(40%) while only 5 percent of the workers are differ over their primary and secondary

work activities—called mix activity. Worker’s education, gender, availability of land and

access to infrastructure are the key determinants to influence livelihood strategy of rural

workers. Households possess more employment diversification as 20 percent of the

households are the mixed households by performing both the farm and off-farm

activities.

Our findings show that the poorer households are reliant for employment to non-

farm economy as the share of non-farm income for the poorest quintile is 57 percent. The

higher non-farm income sources for the poorer reflects the equity enhancing in Pakistan.

Though there is more rural poverty among non-farm households; however, non-farm

workers no longer appear to be the worst category of the poor. Wage workers in the non-

farm sector have been better off than at least agricultural laborers. The multivariate

analysis shows that non-farm economy may not have a significant impact on rural

poverty, however, households engaged in non-farm and mix activities are more likely to

send their children in school.

Despite non-farm sector’s large contribution in rural economy, data information

on non-farm enterprises is limited in Pakistan. Inefficiency of institutions is one of the

major barriers for the development of rural non-farm economy. The easy, smooth and

equitable functioning of a market can be facilitated through supporting institutional

mechanisms which could help to promote economic activity by reducing transaction costs

and other hurdles. Increasing competition requires institutions for quality control,

capacity building, research and development along with reducing disputes, defining

property rights and contracts, and increasing healthy competition in markets. It is

recommended that public investment along with technical training is required to improve

29

the productivity and size of this sector, especially to improve poor manufacturing base.

Targeted policies are required to overcome the regional disparities by diverting resources

toward the deprived and remote areas.

The lower participation of poor households in non-farm activities can be

improved through social and economic resource mobilization. For rural development, a

dynamic labor-intensive agriculture along with a modern non-agriculture sector can

provide better employment and income to rural households, with egalitarian income

distribution and elimination of rural poverty. Policy intervention to promote rural non-

farm employment is also justified to stop, to some extent, migration to cities. The design

of rural development pro-agricultural policies needs to be revisited to address to needs of

local non-farm activities. In particular, the growth and concentration of such activities in

rural towns and villages raises substantially the demands for physical and soft

infrastructure services.

30

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Appendix 1: Composition of Rural Non-farm Employment by Continent (in %)

Region

Nonfarm

Share of

Rural

Labor

Women

Share of

total Rural

Nonfarm

Labor

Share of Rural Non-farm Employment by Sector

Manufacturing Commerce

and

transport

Personal

Financial

and

Community

services

Construction,

utility and

mining

Africa 19 35 21 31 36 12

Asia 30 25 22 28 34 15

Latin

America 30 40 23 22 35 20

West Asia

and North

Africa

24 8 22 24 32 21

Note: the reported numbers are un-weighted

Source: Haggblade et al. 2007

Appendix 2: Enterprises Using Modern Practice/Services (in %)

Retail Wholesale Storage Transport Overall

Engineering 13.4 11.8 33.2 42.4 16.7

Management 3.9 7.2 21.4 8.1 7.0

Marketing 15.5 21.0 26.5 23.2 18.7

Accounting 6.7 8.2 25.5 6.1 9.1

Legal 5.4 9.6 21.9 25.3 9.5

Insurance 3.6 3.4 12.2 21.2 5.5

Information Technology 5.1 5.2 15.3 2.0 6.1

Source: Malik (2008, table 13)

Appendix3: Sectoral Share in Gross Domestic Product Overtime in Pakistan

Type of Industry 1950s 1960s 1970s 1980s 1990s 2000s

Agriculture and Livestock 48.8 40.7 33.7 28.6 26.0 23.0

Industry 12.9 19.1 22.6 23.3 24.6 22.5

Services 38.4 40.2 43.7 48.2 49.3 54.4

Total 100 100 100 100 100 100

Source: Various editions of Pakistan Economic Survey, Ministry of Finance, Islamabad

34

Appendix 4: Percentage Distribution of Employed Persons of 10 Years Age and Above By Major

Industry, 1974-2013 (Primary Activity only)

Type of Industry 1974-75 1982-83 1990-91 2001-02 2007-08 2012-13

Agriculture Forestry, Hunting and

Fishing 72.1 67.7 63.8 59.0 60.9 59.5

Mining and Quarrying 0.1 0.1 0.1 0.1 0.1 0.2

Manufacturing 9.3 9.4 8.1 8.7 8.4 8.8

Electricity, Gas and Water 0.2 1.0 0.5 0.6 0.4 0.4

Construction 3.4 4.1 6.6 6.2 6.1 7.7

Wholesale, Retail Trade, Restaurant

and Hotels 5.8 7.1 7.8 9.2 9.2 14.0

Transport, Storage and

Communication 2.9 3.1 3.7 4.8 4.4 2.6

Financing, Insurance Real Estate

and 0.1 0.3 0.3 0.3 0.4 0.5

Community, Social and Personal

Services 5.7 6.9 9.0 11.1 10.0 6.3

Activities Not adequately Defined 0.3 0.3 0.1 - - -

Total 100 100 100 100 100 100

Source: Various editions of Labour Force Survey of Pakistan

Appendix 5: Percentage Distribution of Rural Employed Worker by Gender in Primary and Secondary

Activities

Gender Primary Activity Secondary Activity

Agriculture Non-agriculture Agriculture Non-agriculture

Both Sexes 56.0 44.0 76.6 23.4

Male 49.9 50.1 76.1 23.9

Female 84.0 16.0 82.1 17.9

N 92,707 6,972

Source: Author’s calculation from PSLM, 2010 micro dataset

35

Appendix 6: Percentage of Rural Households in Pakistan by Expenditure Quintile that Own

Shops and/or other Businesses

Expenditure Quintile National Punjab Sindh KP Balochistan

Poorest quintile 18.2 19.5 13.1 19.1 13.4

Quintile 2 22.3 24.5 15.4 23.8 15.5

Quintile 3 23.4 24.6 15.3 24.2 15.9

Quintile 4 27.6 28.7 16.3 27.7 19.8

Richest quintile 31.8 33.6 23.5 35.6 26.5

Overall 23.0 25.5 15.2 24.0 18.4

Source: Authors ‘estimation from HIES 2010 micro dataset

Appendix7: Poverty Trends in Pakistan: Head Count Ratios

Years Overall Urban Rural

1963-64 40.2 44.5 38.9

1969-70 46.5 38.8 49.1

1979-80 30.6 25.9 32.5

1984-85 24.5 21.2 25.9

1987-88 22.1 18.6 23.6

1992-93 26.8 24.6 28.3

1996-97 29.8 22.6 33.1

1998-99 30.6 20.9 34.7

2000-01 34.5 22.7 39.3

2004-05 23.9 14.9 28.1

2005-06 22.3 13.1 27.0

2010-11 12.4 7.5 15.6

Source: Various editions of Pakistan Economic Survey, Ministry of Finance, Islamabad