Working Paper No. 108

CAPSACentre for Alleviation of Poverty through Sustainable Agriculture

Market Participation of Smallholder Agricultural Households in

Papua New Guinea

By Upali Wickramasinghe, Norah Omot, Arnold D. Patiken, and Joshua Ryan

ESCAP is the regional development arm of the United Nations and serves as the main economic and social development centre for the United Nations in Asia and the Pacific. Its mandate is to foster cooperation between its 53 members and 9 associate members. ESCAP provides the strategic link between global and country-level programmes and issues. It supports Governments of countries in the region in consolidating regional positions and advocates regional approaches to meeting the region’s unique socioeconomic challenges in a globalizing world. The ESCAP office is located in Bangkok, Thailand. Please visit the ESCAP website at www.unescap.org for further information.

CAPSA-ESCAPThe Centre for Alleviation of Poverty through Sustainable Agriculture (CAPSA) is a subsidiary body of UNESCAP. It was established as the Regional Coordination Centre for Research and Development of Coarse Grains, Pulses, Roots and Tuber Crops in the Humid Tropics of Asia and the Pacific (CGPRT Centre) in 1981 and was renamed CAPSA in 2004.

Objectives• Enhanced national capacity for socioeconomic and policy research on

sustainable agriculture for poverty reduction and food security

• Enhanced regional coordination and networking to successfully scale up and scale out research findings that have implications for policy design and implementation related to sustainable agriculture and rural development

• Enhanced capacity of policymakers and senior government officials to design and implement policies to achieve rural development, poverty reduction and food security through sustainable agriculture in Asia and the Pacific

The shaded areas of the map indicate ESCAP members and associate members.

Working Paper No. 108

CAPSACentre for Alleviation of Poverty through Sustainable Agriculture

Market Participation of Smallholder Agricultural Households in

Papua New Guinea

Upali Wickramasinghe

Regional Adviser, CAPSA-ESCAP

Norah Omot Director

Enabling Environment Programme, NARI

Arnold D. Patiken Junior Economist

Enabling Environment Programme, NARI

Joshua Ryan Senior Economist

Enabling Environment Programme, NARI

Market Participation of Smallholder Agricultural Households in Papua New Guinea

CAPSA Working Paper No. 108

CAPSA-ESCAPJalan Merdeka 145, Bogor 16111Indonesia

Copyright © CAPSA-ESCAP 2014All rights reservedPrinted in IndonesiaISBN 978-979-9317-78-0

Cover photo by: Upali Wickramasinghe

Cover design by: Fransisca A. Wijaya

The designations employed and the presentation of material in this publication do not imply the expression of any opinion whatsoever on the part of the Secretariat of the United Nations concerning the legal status of any country, territory, city or area of its authorities, or concerning the delimitation of its frontiers or boundaries. The content and views expressed in this publication are those of the authors and not necessarily reflects the views or policies, or carry the endorsement of the United

iii

Table of Contents

Page List of Tables ......................................................................................................... v

List of Figures ......................................................................................................... vii

List of Appendices ........................................................................................................ ix

List of Abbreviations ..................................................................................................... xi

Foreword ......................................................................................................... xiii

Executive Summary ..................................................................................................... xv

1. Introduction ......................................................................................................... 1

2. Agriculture in the Economy and Smallholder Agriculture ....................................... 5

2.1 Country and the economy ......................................................................... 5

2.2 Current state of agriculture ....................................................................... 6

2.2.1 Agriculture in the national economy ........................................ 6

2.2.2 Agricultural trade ..................................................................... 8

2.2.3 Land allocation ........................................................................ 9

2.2.4 Agricultural production ............................................................. 9

2.2.5 Farming practices and livelihoods ........................................... 12

2.2.6 Smallholder market participation ............................................. 13

2.3 Agricultural policy environment ................................................................. 14

2.4 Agriculture’s potential ............................................................................... 14

3. Explaining Market Participation of Smallholder Farm Households ........................ 17

3.1 Exchange economy and production specialization ................................... 17

3.2 Other explanations .................................................................................... 19

3.3 Analytical framework ................................................................................. 20

4. Data and Description of Production and Marketing Environments ........................ 23

4.1 Introduction ............................................................................................... 23

4.2 Data collection .......................................................................................... 23

4.3 Limitation and scope ................................................................................. 25

4.4 Description of the production environment ............................................... 26

iv

4.4.1 Land ownership, usage and within-farm land fragmentation ... 26

4.4.2 Demographic features ............................................................. 27

4.4.3 Agricultural inputs and services ............................................... 28

4.4.4 Agricultural capital, access to irrigation and improved land .... 29

4.4.5 Crop choice ............................................................................. 30

4.5 Marketing arrangements ........................................................................... 32

4.6 Nature of crop sales .................................................................................. 33

5. Determinants of Farm Sales .................................................................................. 35

5.1 Estimating the model with censored data ................................................. 35

5.2 Empirical model variables ......................................................................... 36

5.3 Regression results of farm household sales ............................................. 38

5.3.1 Land ownership and land fragmentation ................................. 39

5.3.2 Demographic characteristics ................................................... 41

5.3.3 Agricultural inputs and services ............................................... 41

5.3.4 Agricultural capital and equipment .......................................... 42

5.3.5 Distance to markets and market participation costs ................ 42

5.3.6 Crop diversification and market participation .......................... 44

5.3.7 Household consumption and market participation .................. 45

6. Summary, Conclusions and Recommendations .................................................... 47

7. References ......................................................................................................... 53

v

List of Tables

Page

Table 2.1 Area harvested and production ............................................................. 10

Table 4.1 Agricultural capital and equipment .......................................................... 30

Table 5.1 Summary regression results of farm sales of cash crops, staples,

vegetables, and total sales ..................................................................... 38

vii

List of Figures

Page

Figure 2.1 Agriculture in Papua New Guinea in the world context ............................ 7

Figure 2.2 Composition of agricultural exports - 2010 (value in 1000$) .................... 8

Figure 2.3 Composition of agricultural imports - 2010 (value in 1000$) .................. 8

Figure 2.4 Land use pattern - 2010 .......................................................................... 9

Figure 2.5 Agriculture production indices 1962-2010 (2004-2006=100) .................. 10

Figure 2.6 Composition of agricultural production and value (2010) ....................... 11

Figure 4.1 Conceptual framework for data analysis .................................................. 25

Figure 4.2 Household land ownership and crop allocation (Cumulative density) ...... 27

Figure 4.3 Distribution of land plots ......................................................................... 27

Figure 4.4 Demographic characteristics of farm households ................................... 28

Figure 4.5 Labour use in agriculture ........................................................................ 29

Figure 4.6 Inputs used and access to credit and information .................................. 29

Figure 4.7 Land ownership and crop diversification .................................................. 31

Figure 4.8 Household crop choice ............................................................................. 31

Figure 4.9 Crop marketing by main markets ............................................................ 32

Figure 4.10 Crop marketing by main buyers ............................................................. 33

Figure 4.11 Household sales income by crop type .................................................... 34

Figure 4.12 Household crop sales by type and district .............................................. 34

Figure 5.1 Land ownership and farm sales among smallholders .............................. 40

Figure 5.2 Within-farm land fragmentation ................................................................ 40

Figure 5.3 Farm-to-market distances and farm sales .............................................. 43

Figure 5.4 Farm-to-market distance, land and crop diversification and farm sales ... 45

Figure 5.5 Family consumption and sales ............................................................... 46

ix

List of Appendices

Page

Appendix 1 Map of the study areas ............................................................................. 57

Appendix 2 Summary statistics: all farms .................................................................... 58

Appendix 3 Summary statistics: Farms that own less than 2 hectares of land ............ 59

Appendix 4 Summary statistics: Farms that own more than 2 hectares of land .......... 60

Appendix 5 Regression results of farm sales of cash crops, staples, vegetables,

and total sales .......................................................................................... 61

xi

List of Abbreviations

ADB Asian Development Bank

CDF Cumulative Density Function

FPDA Fresh Produce Development Authority

GDP Gross Domestic Product

GNI Gross National Income

IFAD International Fund for Agricultural Development

LLG Local Level Government

MLE Maximum Likelihood Estimate

NARI National Agricultural Research Institute

OLS Ordinary Least Squares

PHR Poverty Headcount Ratio

PNG Papua New Guinea

WB World Bank

xiii

Foreword

Ernst F. Schumacher, in his much celebrated book “Small Is Beautiful: Economics

as if People Mattered”, first published in 1973, convincingly argued that there is virtue in

smallness, and that the appropriate scale of an activity must be the cornerstone of

economics. His viewpoint is more relevant than ever. Today, small-scale farming is both

vilified and praised, but it will certainly continue for a while. There are at least 500 million

small farmers in the world, who cultivate a large share of global agricultural land and

produce about 80 per cent of the world’s food. Paradoxically, they are also the poorest and

most food-insecure.

Small-scale farming has several advantages in land-scarce and labour-abundant

countries in Asia and the Pacific. Enhanced agricultural productivity triggers growth and

facilitates a broad-based structural transformation, reducing poverty among the rural poor

who depend on agriculture as their main livelihood. Food production by the rural poor can

be a powerful tool to address food insecurity and malnutrition among communities living

further away from markets. Incomes generated by smallholder farmers have a high

likelihood of being spent on locally produced goods and services, stimulating employment-

intensive growth in the local non-farm economy. Productivity growth in smallholder

agriculture encourages farmers to move into productive agriculture and slows migration out

of the sector, reducing the unsustainable growth of urban centres without commensurate

growth in productive employment.

But, how do we enhance market participation and growth in agricultural productivity

to break ‘poverty traps’? Building physical infrastructure such as roads and ports,

information and communications channels connecting small farms to markets, and

institutions to reduce transaction costs and minimize risks, are essential to enhance the

farmer’s access to the market. However, the precise nature of the infrastructure and

institutions enabling smallholders to participate effectively in local, regional and national

markets varies significantly across countries and even within the same country. Country-

specific studies are necessary to identify precise mechanisms and channels, design public

policy interventions and deepen our understanding of the economic, social and political

contexts of market participation by small farmers.

This working paper fills a lacuna with its study of Papua New Guinea. It is hoped this

will provide useful insights and guidance on fundamental public policy instruments to break

xiv

the ‘cycle of poverty, hunger and low agricultural productivity’ that traps smallholder farm

households in developing countries. Last, but not least, I hope this study will help identify

and implement policies targeting smallholder agriculture in Papua New Guinea, enabling

the country to move towards sustainable development and realizing national development

goals set out in its Vision 2050.

December 2014 Katinka Weinberger Director CAPSA-ESCAP

xv

Executive Summary

Recent international and national development dialogues on poverty, food insecurity

and sustainable development including the Rio+20 summit have emphasized the need to

integrate smallholder farmers, marginalized and vulnerable communities dependent on

agriculture, with local, national and regional markets as a welfare enhancing strategy. This

emphasis stems from the understanding that market participation allows farm households

to: enhance resource-use efficiency through the higher division of labour, comparative

advantage and larger markets; benefit from increasing returns to scale and increased

functional operations of agribusiness ventures; reduce unit costs of production, processing

and sale of new and value added products; benefit from dynamic technical change in

agricultural production and processing; and move into productive agriculture rather than

moving out of agriculture that limits capacity for realizing broad-based structural

transformation. Recent research has shown that farmers increase market participation

when the net benefits of participating in markets outweigh the costs.

This working paper presents the results of an analysis of lowland smallholder

agriculture in Papua New Guinea. The Vision 2050 document of the Government of Papua

New Guinea aims to reduce the current heavy dependency of the economy on the mining

industry and expand the contribution of the agriculture, forestry and fisheries sectors. The

Papua New Guinea Development Strategic Plan 2011-2030 calls for converting 70 per cent

of subsistence farmers into small- and medium-scale agricultural enterprises (DNPM, 2010).

The government policy emphasizes the introduction of efficient land administration by

allowing land owners to profit from their land along with the development of roads and

supply chains linking producers with markets and expanding extension services as key

strategic thrusts. This paper presents the results of a study based on a small household

survey conducted in the Morobe province of Papua New Guinea in 2012, which the authors

believe, offers useful insights into the nature of smallholder agriculture in the lowland

districts of the country. It identifies some channels that could contribute to integrating

smallholder agricultural households with local, national and regional markets.

The paper calls for public policies to enable farmers to absorb production and

marketing risks and reduce farm-to-market transaction costs which, in turn, will give farmers

incentives to invest in agriculture, enhancing agricultural productivity to produce marketable

xvi

surpluses and participate effectively in markets to enhance their welfare. The specific

results and recommendations of the study are summarized under the following four areas.

First, the study finds that there is high prevalence of within-farm land fragmentation

among smallholders and others alike and it is positively correlated with farm sales. This

deprives them of the opportunity of employing economies of scale in production. But the

arrangement is optimal within the given agroecological and socioeconomic conditions, and,

hence, merely increasing land rights through land alienation and other methods, is unlikely

to change agricultural practices and market participation. Establishing land rights should go

hand in hand with creating conducive conditions for farmers to invest in enhancing

agricultural productivity.

Second, farms were found engaged in extensive crop diversification, a practice that

does not allow farmers to produce a sufficient marketable surplus in any given commodity.

While crop diversification allows farmers to spread risks, they forgo considerable potential

profit from more specialized farming operations. The practice indicates the inability of

farmers to absorb risks and the high transaction costs of market participation. Thus,

addressing the fundamental issue of risk management and creating a system where

farmers can absorb more risks, will enable smallholders to move towards more

commercialized operations and enhance productivity to produce a marketable surplus.

Third, smallholder agricultural marketing arrangements are still informal and in a

rudimentary state of development. Because of high transportation costs, farmers tend not to

engage in markets and when they do, sell small quantities, mostly directly to consumers in

temporary roadside markets, earning meagre incomes. Nevertheless, this is an optimal

arrangement given their situation and farmers are unlikely to change their production and

marketing behaviour until the underlying environment is improved. Attempts to encourage

better market participation must be coordinated with improvements in their production and

risk environments so that farmers not only produce more but use their meagre resources

more efficiently, producing fewer but high-value crops and engage in markets.

Fourth, although not conclusive, the study finds a highly significant association

between the level of food and non-food consumption and farm sales. A key motivation for

farmer participation in markets is the need to enhance their consumption and access to

services, especially of commodities that cannot be produced at home and other essential

services, suggesting that farm households are trying their best to improve their living

standards. Improving the agricultural production and marketing environments is more likely

to enhance food security and farmer welfare than anything else.

1

1. Introduction

The outcome document titled “The Future we want” of the United Nations

Conference on Sustainable Development held in 2012, also known as Rio+20 (United

Nations, 2012) urged nations to enhance the welfare of smallholders1, subsistence farmers

and marginalized groups such as women and vulnerable communities dependent on

agriculture, by developing appropriate policies and strategies to integrate them with local,

regional and national markets. Several international conferences have issued declarations

and calls for urgent action (G8, 2009). The renewed interest in smallholder agriculture stems

from a realization that the participation of marginal communities in markets is a prerequisite

for enhancing their welfare. Enhanced market participation allows farmers, fishers and other

marginalized groups to realize their comparative advantages in agricultural production

activities (Timmer, 2005) and exchange marketable surplus for purchasing products and

services that cannot be produced at home. Dynamic technical change in agriculture and

production specialization is another factor contributing to enhancing opportunities for rural

communities to move from subsistence agriculture towards more specialized, market-

oriented commercial operations (Chenery et al., 1986). Market integration also encourages

rural agrarian households to move into productive agriculture rather than moving out of

agriculture, thereby contributing to agricultural and rural development (Mazumdar, 1987)

and broad-based structural transformation (ADB, 2013).

Although the benefits of smallholder market participation are widely accepted, there

is little consensus on strategies for facilitating their integration into local, regional or national

markets. This makes it imperative to identify plausible channels to facilitate their market

entry and participation, and offer incentives for them to seek improved market opportunities.

Enhanced understanding of such channels will also allow governments and international

agencies to implement strategies and programmes to remove bottlenecks in market

participation. Public policy that has been advanced over two centuries suggests that

improving market access opportunities for smallholders will require addressing two

fundamental factors: an incentives structure for smallholders to seek more opportunities for

specialization and enhancing their productivity, and mechanisms to reduce market

participation costs.

1 World Bank (2003) defines smallholders as farmers with a low asset base and operating less than two hectares of cropland. IFAD (2011) defines them as farmers with small landholdings and associated characteristics including dependence mostly on household labour for production and low use of technology.

Chapter 1

2

There are a number of studies on market participation of smallholders in Africa, but

country-level empirical studies in Asia and the Pacific are limited. Yet, the Asia-Pacific

region is home to 87 per cent of the world’s 500 million small farms with less than 2

hectares (Thapa and Guiha, 2011). While agrifood systems are evolving rapidly in Asia and

Latin America, and emerging in Africa, the diffusion of transformation is highly correlated

with connection to urban growth nodes (Reardon and Timmer, 2014). Farm households in

the ‘hinterlands’ continue to be in the stage of semi-subsistence agriculture, marked by a

slower process of transformation.

Identifying the binding constraints to integration of these marginal communities with

local, regional and national markets should be a policy priority within the post-2015

development agenda. This working paper presents the results of a study undertaken in

Papua New Guinea, which offers a unique case of a marginal community that is also ‘agro-

climatologically’ challenged. The paper summarizes current theoretical and empirical

understanding of smallholder market participation, presents a model that explains the nature

of smallholder market participation along with a procedure for estimating such a model

where data availability is limited, and empirical results of an analysis conducted on a data

set that CAPSA collected in 2012 in the Morobe province in PNG.

The paper hypothesizes that participation of smallholder farm families in agricultural

markets depends on its net benefits. If the cost of market participation rises (reduces) in

relation to benefits, measured in consumption, households will reduce (increase) the

quantity of commodities traded and increase (decrease) the range of commodities produced

within the farm to compensate for the lost consumption. The costs of market participation

are hypothesized to be dependent on distance to markets, transport and transaction costs

(searching, negotiation and organizing production and transportation). The benefits of

market participation originate from households’ capacity to enhance the bundle of

commodities available for consumption, made possible by exchanging surplus production

for non-home produced commodities and services. It also saves the time taken up by

unproductive agricultural activities which can, instead, be used for more productive and

income-generating, activities or for learning new production and processing technologies.

The paper also hypothesizes that a given household first decides whether or not to produce

for markets, and once in the market, how much to sell. Thus, the household decision-

making process is sequential, which some call a ‘double hurdle’ decision-making process.

Chapter 2 discusses the role of agriculture in the economy of PNG, the status of

smallholder agriculture and the national agricultural policy environment. Chapter 3 reviews

3

literature on market participation of smallholder farm households with particular focus on the

current state of knowledge and presents an analytical framework. Chapter 4 describes

smallholder production and marketing arrangements and the nature of crop sales in PNG

using data from the CAPSA survey 2012 carried out in Morobe province. Chapter 5 presents

the results of the data analysis and Chapter 6 summarizes a set of recommendations.

5

2. Agriculture in the Economy and Smallholder

Agriculture

2.1 Country and the economy

Papua New Guinea is a country with harsh and rugged terrain, and has a total land

area of 470,000 km2, made up of the mainland and some 600 large and small islands.

Nearly two-thirds of the land area comprises a vast chain of overlapping mountain ranges

located in the middle of the country, with peaks of over 4,000 m and steep mountain ranges

and valleys. Swamps are predominant in the south-western part of the country. Some areas

have excessive rainfall. As a result, 70 per cent of land has never been used for agriculture.

Only 7 per cent of the total land area is classed as high or of very high quality and a further

20 per cent is of moderate quality for growing crops. PNG was estimated to have a

population of 7.4 million people in 2013 (ADB, 2014) and the rate of population growth was

estimated at 2.13 per cent per annum (World Bank, 2014). The best agricultural lands in the

country are highly populated (Bourke et al., 2009).

By 2050, according to Vision 2050, PNG should realize its full economic potential,

reduced dependence on the mining industry and expanded the capacity of agriculture and

manufacturing industries. Moving in that direction will require credible strategies and

programmes to develop the economy and integrate the vast majority of people who depend

on agriculture, in particular subsistence agriculture. Understanding current economic

conditions will make it possible to devise strategies and programmes that would lead the

economy towards this noble goal.

In 2010, the per capita gross national income (GNI) in PNG was $1,300. The

relatively high per capita income is a reflection of the high income earned through royalties

from extraction industries. However, the fact that 35.8 per cent of the population was living

on less than $.25 a day in 1996 (World Bank, 2014), implies that the national per capita

income figure hides the true nature of poverty and destitution among the lower income

sections of society. In addition, to meet food security requirements of a population more

than twice the current size and probably affluent, the economy must provide suitable

employment opportunities and produce adequate food in a sustainable way. The dominance

of agriculture in livelihoods implies that PNG will need to evolve a strong agricultural sector

to meet the food security and employment generation requirements of the country.

Chapter 2

6

According to 2009 estimates, the Poverty Headcount Ratio (PHR) at national poverty

line is 39.9 per cent, whereas the PHR at rural poverty line is 41.6 per cent and the PHR at

urban poverty line is 29.3 per cent. This indicates that rural poverty is generally higher than

the national average and significantly higher than urban poverty. At the current rate of

population growth of 2.8 per cent during 2009-2011, PNG will have 20 million people by

2050. Most rural families are small and live in scattered communities or hamlets, dependent

on subsistence farming, supplemented by cash income earned from tree and food crops,

livestock and non-agricultural activities. Some estimates suggest that 18 per cent rural

people are extremely poor while 42 per cent are marginally poor. The majority of the cash

crop producers are marginally poor or not so poor.

2.2 Current state of agriculture

2.2.1 Agriculture in the national economy

The total size of the national economy of PNG in 2013 stood at Kina 34,611 million in

2013 (ADB, 2014) or $15,289 million. Agriculture contributes 27 per cent of the GDP,

industry 45 per cent and services 28 per cent. The contribution of the agricultural sector in

the national economy can be significantly higher if all products and services produced and

informally traded are considered in the national accounting system. Some estimates

suggest that agriculture’s contribution would be as high as 37 per cent if informal production

and trading were considered. Agriculture is the major source of cash income for most

villagers. Agriculture also provides employment to a large section of the population as

traders, transporters and retailers. Agriculture is estimated to provide Kina 200 million

annually to rural village households (Bourke et al., 2009).

According to the World Bank Development Indicators database 2014, the value

added as a percentage of GDP in agriculture2 was 37 per cent in 2014 whereas

employment in agriculture as a percentage of total employment in 20003 was 72 per cent.

Assuming that employment in agriculture as a percentage of total employment remains the

same4, agriculture currently employs over 2 million people directly. A large share of

employment in agriculture combined with a moderate share of agriculture in GDP implies a

low level of value addition per worker.

2 The ADB Key Indicators for Asia and the Pacific 2014 reports the share of agriculture in GDP at producers’ prices in 2013 as 27.1 per cent. 3 Latest data available for this indicator. 4 Given that the structure of the economy has not changed significantly within the last 10 years, it may not be inaccurate to assume that this rate remains close to the value for the year 2000.

7

Over 85 per cent of the PNG population lives in rural areas and depends on

subsistence agriculture, forests and rivers for their basic food needs. Fresh food crops sold

in informal open markets provide 21.7 per cent of income and involve 94 per cent of the

total rural population. Arabica coffee is the main income earner, providing 33 per cent of

rural income and involving 44.5 per cent of the rural population. This is followed by cocoa,

providing 10.9 per cent of income and employing 26.7 per cent of the population; betel nut

and betel pepper, providing 9.9 per cent of income and employing 35.2 per cent of the

population; and copra, providing 8.1 per cent of income and employing 16.6 per cent of the

population. All other crops, including oil palm, provide less than 3 per cent of income to rural

people (Allen et al., 2009).

The share of agriculture in GDP has been around 35 per cent of GDP, but the

employment share of agriculture has been even higher, estimated to be still between 75 and

80 per cent (see Figure 2.1). This dichotomy – small share of agriculture in GDP with a high

share of agricultural employment in total employment - means that agriculture continues to

offer employment and livelihoods to the great majority of the people but its share in GDP

does not, and cannot, support them. This results in the greater majority of the people in

agriculture being poorer even by national standards.

Figure 2.1 Agriculture in Papua New Guinea in the world context

Source: Authors based on World Bank (2014)

PapuaNewGuinea

AgricultureshareofGDP

Agriculturalemploymentintotal

employment

PapuaNewGuineay=759671x-5.551R²=0.73072

y=1E+07x-6.44R²=0.63832

0

10

20

30

40

50

60

70

80

90

100

4 5 6 7 8 9 10 11 12

Agriculturein

GDPandagriculturalemploym

entintotal(%)

PercapitaGDP(logarithmicscale)

Chapter 2

8

2.2.2 Agricultural trade

Large-scale corporations, often multinational companies, mostly carry out palm oil

cultivation in the country, much of the production being exported in bulk in raw form. This is

probably due to the fact that many plantations are owned by multinationals who may find it

economical to process the oil palm further in their overseas facilities. Palm oil has been the

main export commodity in the recent past (see Figure 2.2), but traditional agricultural

products, especially coffee and cocoa still earn almost the same export revenues. Rice,

meat and wheat products dominate imports (see Figure 2.3).

Figure 2.2 Composition of agricultural exports - 2010 (value in 1000$)

Palm oil

Coffee, green

Cocoa beans

Palm kernel oil

Coconut (copra) oil

Rubber Nat Dry

Exports (value in 1000$)

Figure 2.3 Composition of agricultural imports - 2010 (value in 1000$)

Rice

Sheep meat

Wheat

Food Prep Nes

Buckwheat

Beef & Veal

Feed prep.

Sugar Conf.

Beverage Non Alc.

MaltPig meat

Cow milk, whole, fresh

Imports (value in 1000$)

Source: Authors based on FAOSTAT (2004)

9

2.2.3 Land allocation

Out of a total land area of 452,860 km2 in PNG, agricultural land is estimated to be

115,500 km2 consisting of 2,600 km

2 of arable land and 7,000 km

2 of permanent crops

(FAOSTAT, 2014). Agricultural land area is 760 km2, which includes arable land, areas

under permanent crops, permanent pasture and land under market or kitchen gardens and

temporarily fallowed (see Figure 2.4). The area under permanent crops is approximately 60

per cent, which mostly consists of palm oil and tree crops. The arable land area is estimated

at 260,000 hectares, consisting of temporary crops. Arable land area per person is

estimated at 0.04 hectares in 2009.

Figure 2.4 Land use pattern – 2010

Forests

64%

Arable land

1%

Permanent

crops1%

Permanent

meadows and pastures

0%

Other

34%

Source: Authors based on FAOSTAT (2014)

2.2.4 Agricultural production

FAO indices on overall agricultural production, cereals, crops, food crops, livestock

and non-food agricultural production show a boom in non-food agricultural commodity

production from 1970 till after the beginning of the new millennium, but no other sector has

experienced any dynamic change (see Figure 2.5). Production of cereals declined during

the same period, bouncing back at the turn of the century but has not grown fast enough to

generate employment or produce adequate food. Other sectors have stagnated during the

last five decades.

Chapter 2

10

Figure 2.5 Agriculture production indices 1962-2010 (2004-2006=100)

0

20

40

60

80

100

120

140

160

180

1962 1966 1970 1974 1978 1982 1986 1990 1994 1998 2002 2006 2010

Ind

ex v

alu

e (

2004-2

006

= 1

00)

Agriculture Cereals Crops Food Livestock Non Food

Source: Authors based on FAOSTAT (2014)

The palm industry’s contribution to the PNG economy is well recognized. It

contributes approximately 10 per cent to agricultural income; traditional industries such as

game meat, fresh fruits, bananas and berries still contribute 60 per cent. The unit price per

ton is the highest for game meat, followed by berries, indigenous pig meat, coffee and palm

oil, in that order. However, coffee still fetches more than twice the price of palm oil per ton.

Thus, traditional agricultural sectors continue to be attractive for investment. Oil crops, fruits,

roots and tubers have dominated agricultural production. While fruits and tubers are

cultivated mostly for domestic consumption, part of the produce is sold in local agricultural

markets, generating some income for households.

Table 2.1 Area harvested and production

Crop type 2000 2009 Output per hectare (tonne)

Area (ha) Production

(tonne)

Area

(ha)

Production

(tonne)

PNG

2000

PNG

2009

Malaysia

2009

Oil crops 332 829 509 560 336 300 684 010 1.5 2.0 4.56

Fruit excl. melon 168 280 1 666 300 210 900 2 076 000 9.9 9.8 11.99

Cereals 2 745 11 300 3 669 15 200 4.1 4.1 3.73

Pulses 5 000 2 500 5 000 2 750 0.5 0.6 NA

Coarse grains 2 466 10 600 3 300 14 400 4.3 4.4 4.9

Vegetables & melon 38 212 473 631 42 244 531 807 12.4 12.6 18.4

Roots and tubers 181 650 1 343 968 224 320 1 698 000 7.4 7.6 9.77

Source: FAOSTAT (2014)

11

The area under roots and tubers (staples in PNG) has increased significantly over

the last decade (see Table 2.1). The current land allocation for cereals, pulses and coarse

grains is relatively small, although the demand for cereals, especially rice, maize and wheat

is relatively large. The balance of the requirement is met through imports (see Figure 2.3).

Vegetable production is widespread throughout the country, especially in the central

highlands. The area under vegetable cultivation has increased by an estimated 4,000

hectares within the last decade. But, the output per hectare across different crops has

grown at a negligible rate within the last ten years. For example, the output per hectare of

palm oil – the best performing sector - has increased from 1.5 in 2000 to 2.0 tons in 2009,

whereas food crops have recorded very small or negligible growth. In comparison to

Malaysia – a country that has made considerable progress in agricultural production in

recent years - PNG has the potential to enhance output further in many sectors.

Figure 2.6 Composition of agricultural production and value (2010)

Source: Authors based on FAOSTAT (2014)

Chapter 2

12

Yam, taro, banana and sweet potato are the main staple food crops. In addition, farm

households cultivate vegetables, fruits and legumes, mostly for home consumption but a

limited quantity is sold for extra income. Bourke et al. (2009) estimate that 94 per cent of

rural villagers engage in such farming. Sago is an important staple, especially in seasonally

flooded areas where the soil and the environment are more suitable for the crop. Some

sago varieties grow in the wild, but other varieties are cultivated. Sweet potato is the most

important root crop in terms of the quantity of production with 2.9 million tons harvested per

year. This accounts for 64 per cent by weight of all staples produced and 34 per cent of all

staples consumed. Tropical and temperate vegetables are also grown, including green leafy

vegetables. Tropical fruits such as pineapple, pawpaw, guava and mango are grown

throughout the country. Many smallholder farmers cultivate cash crops such as coffee,

cocoa, copra, oil palm and betel nut. Smallholders dominate much of commercial crops

production.

Pigs, chicken and cattle are the main livestock, pigs being raised for food and

cultural reasons. An estimated 1.8 million pigs are raised in villages, with relatively smaller

numbers on commercial farms. While 1.5 million chickens are raised every year, production

is mainly by large commercial operations. The cattle population is estimated at 80,000 with

80 per cent commercially maintained and the remainder owned by villagers. Other, less

important livestock raised for meat are ducks, rabbits, sheep and goats (Bourke et al.,

2009).

Agriculture in PNG is of the subsistence or near-subsistence variety in the remote

regions, with a mixture of subsistence and commercial operations elsewhere. The costs of

market participation are often unique to each region, village or even farm household,

depending on social institutions, social norms, legal rules and the technological

environment. Farmers are vulnerable to external shocks caused by weather and market

volatility as well as farm failures. Smallholders are often marginalized, and unable to take

advantage of market opportunities even when market conditions are favourable.

2.2.5 Farming practices and livelihoods

Farming practices in PNG remain basic with limited use of chemical fertilizer and

pesticides, which are, however, applied in tree crop production and vegetables cultivation

for the market. Mechanization is extremely limited and farmers only use simple equipment

such as knives and spades. Labour markets are thin, family labour being primarily used for

farm operations.

13

The yield of staple crops and livestock productivity (feed efficiency and reproduction)

in rural areas is low. The sale of fresh produce is a vital part of smallholders’ livelihood and

cash earnings, especially for those located closer to markets. The fresh produce market is

estimated to be worth $95 million per year5 (Bourke et al., 2009) and demand is anticipated

to increase with rising incomes and the growth of the mining industry. Tubers and roots,

especially sweet potato, taro and yam, sago and banana, provide much of food energy.

Meat from pigs, fish and other animals, coconut and imported vegetable oil are the main

sources of fats.

2.2.6 Smallholder market participation

Smallholders sell their produce through open markets (local municipal outdoor

markets, roadside markets, government or church station markets), and purchase non-

home produced goods (‘store goods’) such as kerosene. Direct marketing is becoming

popular and some smallholders sell to supermarkets, hotels, schools and mines. Some

producers market over long distances through intermediaries, but this involves heavy risks

because of the difficulty of knowing in advance, prices in distant markets and occasional law

and order problems such as armed robberies.

Upland and lowland agriculture differ significantly. Smallholder producers in highland

provinces marketing over long distances, cultivate more temperate fruits and vegetables

such as cabbages, broccoli, carrots and Irish potato. They also ship large volumes of sweet

potato to coastal markets, particularly to Lae, Madang and Port Moresby. Smallholder

producers in the lowland areas mostly cultivate vegetables, tropical fruits, staple crops

(banana, taro and yam), cash crops and cereals. While some farms are located closer to

markets, those in the hinterland and mountainous regions travel longer distances to

markets. Post-harvest handling, processing, storage, transportation and sales are mostly

carried out by family members.

Some researchers have suggested that the marketing behaviour of smallholder

farmers is irrational. One such behaviour pattern discussed by Benediktsson (2002) is the

way farmers lose interest in marketing after long-distance travel, allegedly taking time off to

meet customary and social obligations first, resulting in a considerable loss of income.

Peanut farmers from Markham are also considered to have erratic marketing behaviour

reflected in their loss of interest in marketing at some stage of marketing. Farmers are also

considered to be engaging in marketing only when they need money urgently such as for

5 PGK 250 million per year according to FPDA estimates; converted to $ by using average currency bid rates for PGK as reported by http://www.oanda.com/currency/historical-rates/ for year 2009.

Chapter 2

14

paying school fees or meeting social obligations. Such behaviour cannot be simply

explained by behavioural attributes. The observed behaviour may be the result of an

agricultural production and marketing environment that limits net benefits farmers gain from

market participation. A lack of vertical coordination in marketing systems is a factor

contributing to high costs including the time it takes to organize marketing and waiting for

transporters to arrive to pick up their produce.

2.3 Agricultural policy environment

The Vision 2050 recognized that 80 per cent of PNG’s economy is dependent on

mining and energy. The strategic vision recognized the need to develop agriculture, forestry

and fisheries along with manufacturing, services and ecotourism to “shift an economy that is

currently dominated by the mining and energy sectors, to one that is dominated by

agriculture, forestry, fisheries, eco-tourism and manufacturing” (National Strategic Plan,

Papua New Guinea). The midterm development plan has a target to convert 70 per cent of

subsistence farmers into small- and medium-scale agricultural enterprises. The medium-

term plan proposed five core strategic thrusts to realize this objective: develop an efficient

land administration, allowing land owners to profit from their land; develop roads and supply

chains to link producers and markets; provide extension services to improve productivity;

utilize economic corridors, enabling to utilize niche markets; and enforce CODEX marketing

standards to improve agricultural exports.

The strategic plan of the National Agricultural Research Institute (NARI) proposes,

among others, enhancing the enabling environment; improving access to and utilization of

market information to enable smallholders to access different local and international

markets; identification and implementation of appropriate macro policies on trade, subsidies,

freight, taxation; and the development of infrastructure.

2.4 Agriculture’s potential

The availability of abundant land and water resources, geographic and climatic

variation across regions and biodiversity, mean that PNG has greater potential for

agricultural growth and development. Some researchers have noted a weak potential for

agriculture, mostly on account of geographic and climatic limitations. Heavy rains and cloud

cover are identified as factors contributing to weak agricultural growth. Yet, rainfall has been

fairly stable over the last several decades with occasional extremes. Diversity can be a

major advantage for increasing food production and incomes, as greater variation in climatic

15

conditions, topography and altitudes offer PNG a bigger opportunity for cultivating different

crop varieties and, hence, producing adequate food during the whole year.

PNG receives around 2,000 mm of rainfall annually. Although some changes in

weather patterns have been observed in recent years, the rainfall has been fairly stable.

Except for occasional droughts, the country has a fairly stable supply of water for

agricultural purposes. The soil is relatively good for agriculture, particularly in areas with

sedimentation or with volcanic ash deposits (Bourke and Harwood, 2009). The varying land

altitudes make it possible to grow a number of plants adapted to different climatic

conditions. Smallholders appear to have the capacity to raise agricultural production and,

hence, contribute to economic diversification and reduce dependence on food imports.

17

3. Explaining Market Participation of Smallholder

Farm Households

Recent theoretical and empirical studies have identified several factors contributing

to, or constraining market participation by agrarian households. This section summarizes

the current state of knowledge on smallholder market participation and presents a modelling

framework, which will be used in further analysis.

3.1 Exchange economy and production specialization

Perhaps the most widely used theoretical argument for the marginal participation of

smallholder agricultural households originates from the economic theory of the exchange

economy and forces shaping production specialization, going back to Adam Smith (1776).

The theory posits that larger markets allow for greater division of labour, which, in turn,

encourages economic agents to specialize in activities where they have comparative

advantage, effectively enlarging markets. According to the theory, the greatest

improvements in the productive powers of labour seem to have been the effects of the

division of labour (Book I, Chapter 1); the power of exchange gives rise to the division of

labour; and the extent of the division labour is limited by the extent of the market (Book I,

Chapter 3). Thus, a larger market allows a greater division of labour by generating adequate

demand for specialized products and skills. Specialization over skills improves labour

productivity, leading to greater production and supply, effectively enlarging the size of the

market.

Young (1928) identified the increasing returns to scale as a factor that plays a key

role in the process of specialization and market exchange. The capacity of an economy to

utilize increasing returns to scale - not larger operations - determines the growth and

development of that sector. Increasing returns to scale are enhanced by the functional

operation of firms. These include purchasing and storing materials, transforming these into

semi-finished and then into finished products, storing and selling the output and extending

credit to buyers (Stigler, 1951). An increase in the size of the market and lower average

fixed costs of new intermediate products, induce firms to specialize and increase the

number of products and transactions (Borland and Yang, 1992; and Yang, 2003). This

product specialization and expansion of the number of final and intermediate products leads

to further evolution in the division of labour.

Chapter 3

18

According to this theory, several factors can discourage market participation of some

communities and groups, such as: high transaction costs; limited opportunities for a greater

division of labour and specialization due to the limited market size; and a limited capacity to

utilize the increasing returns to scale in firm operations.

This theory of specialization is mostly referred to manufacturing industries. Both

Smith (1776) and Marshall (1920) viewed agriculture as a sector with limited opportunities

for specialization, economies of scale or division of labour due to the small size of

agricultural markets, the sharp seasonality of production and agricultural tasks not being

amenable to specialization (Yang et al., 2013). The limited opportunity for the division of

labour over tasks in agriculture makes it difficult for agricultural labourers to keep pace with

technological improvements as in the manufacturing sector.

Stigler (1951) noted that the division of labour was given high prominence in the

development of the manufacturing sector because this was observed to contribute to

technology improvement, whereas in agriculture, the increasingly intensive use of a

relatively fixed supply of land was widely observed to yield diminishing returns. Agriculture

has a lesser potential for division of labour compared to the manufacturing sector.

Recent research has significantly changed this view of agriculture. Roumasset and

Uy (1980), Roumasset and Smith (1981) and Roumasset et al. (1995) recognized that the

agricultural sector not only has the capacity for specialization, but also that division of labour

is a central driving force of the transformation from subsistence to commercial agriculture.

This transformation in agriculture is understood to co-evolve with labour institutions. The

latter evolve from the use of family labour for home production to the use of outside labour

for certain tasks, then to intermediate forms of specialization involving family labour and

hired labour, and to opportunities for hired labour to specialize in certain tasks and

eventually to farmers themselves specializing in certain tasks (Roumasset and Smith, 1981;

Eswaran and Kotwal, 1985; Kikuchi and Hayami, 1999). This process of change is

associated with underlying changes in marginal productivity of routing and managerial tasks

and widening wages of hired and owner-operators (Schaffner, 2001).

The process of commercialization of agriculture induces changes in the opportunity

cost of labour, which affects fertility choice and the composition of family labour (Evenson

and Roumasset, 1986). This further induces specialization of labour institutions and human

capital accumulation. Modernization and development is also associated with reduction of

the differential between the purchase and sales prices of commodities. This is known to be

associated with changes in the opportunity cost of food produced for home consumption,

19

which allow for the increased intensification of production and productivity, and greater

specialization of agricultural production (Roumasset and Lee, 2007).

3.2 Other explanations

Market failure as a reason for the marginal participation of agrarian households has

been studied extensively. Such studies have focused on various aspects of markets,

including market failures in insurance (Bromley and Chavas, 1989), food markets (de Janvry

et al. 1991; Fafchamps, 1993), credit markets (Eswaran and Kotwal, 1985; Rosenzweig and

Wolpin, 1993), and household-specific market failures (Kurosaki, 2003).

Several other factors and behavioural attributes have been shown to affect

smallholder market participation. The farm size per household worker, animal traction, mean

yield, age of household head and climate risk significantly affect market participation,

particularly with cash crop sales (Heltberg and Tarp, 2001). Similarly, the concentration of

crop acreage in districts has been shown to be associated with higher productivity

(Kurosaki, 2003). Land quality differentials have also been attributed an important role in

determining the process as well as the pace of agricultural modernization (Benjamin, 1995).

Access to productive technologies and adequate private and public goods to

produce marketable surpluses play a significant positive role in smallholder market

participation (Barrett, 2008). Significant investment is required to develop the institutional

and physical infrastructure necessary to ensure broad-based, low-cost access to

competitive and well-functioning markets. Public investment is essential, but not sufficient to

enable smallholders to enter markets; farmers need to invest on their lands and other assets

to fully benefit from public infrastructure. For this, smallholders need capital, which normally

comes from savings, and, in turn, their ability to generate a marketable surplus. This

suggests the existence of a vicious cycle among smallholders, which prevents them from

participating in markets. Thus, a trigger mechanism to break the cycle is required.

Traditional, personal connections and relationships, known as guanxi in China, which

can be used to secure resources or benefits in business and social life, have been shown to

reduce market transaction costs, helping farmers to enter into contracts and to access

supermarkets and international marketing channels (Hualing et al., 2008). Here, trust is

important in determining a farm family’s capacity to enter into contracts (Zhang and Hu,

2011). Even with limited technical and financial support, those entering into contracts have

developed opportunities to negotiate with buyers for better prices and flexible conditions.

Armed with their experiences, farmers attempt to move away from traditional ‘arm-length’

Chapter 3

20

business relationships and establish strong relationships with preferred buyers to reduce

costs, increase efficiency and enhance competitive advantage. Contracts can thus be a way

of breaking the vicious cycle, but such contracts also appear dependent on access to family

assets and personal connections.

3.3 Analytical framework

A simple model of household choice has been developed to capture the core issue

of specialization and market participation of smallholder agrarian households. In the case of

agrarian households, the bundle of consumption consists of home-produced staples,

market-bought staples, other market-purchased commodities and services (𝑐𝑖). The

household may earn income by selling staple food and cash crops it produces (𝑞𝑖), labour

and other services it owns such as renting out draft animals (𝑒𝑖). Production of staple and

cash crops is a function of assets held by the household (e.g. land, draft animals), flow of

services provided by resources held by the household (e.g. labour), the public sector (e.g.

irrigation and extension services) and the private sector (e.g. transport services, land tilling,

harvest collection). The household is assumed to maximize utility (U) by choosing how

much of each product or service to consume 𝑐𝑖, produce 𝑞𝑖, buy 𝑏𝑖 and sell 𝑠𝑖, subject to a

standard set of constraints: cash constraint, resources availability and the production

function. The cash constraint shows that the total value of purchases of the household must

be equal to or less than its income earned by selling staple or cash crops it produces and

the revenue it generates by supplying labour and other services. Household endowment

can incorporate borrowing and lending.

Given market-determined prices (𝑝𝑖∗), the household model can be written as

(1) 𝑀𝑎𝑥 𝑈(𝑐𝑖; 𝑧𝑢)

subject to

(2) ∑ 𝑝𝑖∗𝑐𝑖 ≤ ∑ 𝑝𝑖

∗(𝑞𝑖 + 𝑒𝑖)𝑵𝒊=𝟏

𝑵𝒊=𝟏

(3) 𝑐𝑖 ≤ 𝑞𝑖 − 𝑠𝑖 + 𝑏𝑖 + 𝑒𝑖 , 𝑖 = 1, … , 𝑁

(4) 𝑓(𝑞𝑖 ; 𝑧𝑞) ≥ 0.

where 𝑧𝑢 and 𝑧𝑞, respectively, are exogenously determined consumption and

production shifters and 𝑓 is the production technology.

Transaction costs can influence household decisions on whether or not to participate

in markets for goods or services. Three possibilities can be recognized depending on

whether the household is a net buyer, net seller or self-sufficient and hence neutral, which

can be represented by:

21

(5) 𝑝𝑖∗ = 𝑝𝑖 − 𝜏𝑖(𝑍, 𝐴, 𝐺, 𝑌) 𝑖𝑓 𝑠𝑖 > 𝑏𝑖 (𝑛𝑒𝑡 𝑠𝑒𝑙𝑙𝑒𝑟)

(6) 𝑝𝑖∗ = 𝑝𝑖 + 𝜏𝑖(𝑍, 𝐴, 𝐺 , 𝑌) 𝑖𝑓 𝑠𝑖 < 𝑏𝑖 (𝑛𝑒𝑡 𝑏𝑢𝑦𝑒𝑟)

(7) 𝑝𝑖∗ = 𝑝𝑖

𝑎 𝑖𝑓 𝑠𝑖 = 𝑏𝑖 = 0 (𝑠𝑒𝑙𝑓 𝑠𝑢𝑓𝑓𝑖𝑐𝑖𝑒𝑛𝑡)

where 𝑝𝑖 are market prices; 𝜏𝑖 are commodity-specific transaction costs, determined

by; (i) household characteristics (e.g. distance to markets, number of family members, age

and education of household head, social connections) Z; (ii) assets owned by the household

(land, draft animals) A; (iii) infrastructure provided by the government (e.g. irrigation and

extension services), G; and (iv) liquidity position of the household Y.

By rearranging the cash constraint (2) in terms of benefits and costs to market

participation, one can write:

(8) ∑ 𝑡𝑖(𝑏𝑖 + 𝑠𝑖)𝑵𝒊 = ∑ 𝑝𝑖

𝑵𝒊=𝟏 (𝑞𝑖 − 𝑐𝑖 + 𝑒𝑖).

The left side shows the costs of participating in markets for traded goods while the

right side shows the total revenue net of consumption plus endowments evaluated at the

market price. The equality of the equation implies that, for a given level of consumption and

endowment, an increase in the unit transaction cost (𝑡𝑖) will induce households to reduce

the quantity traded and increase the range of commodities produced within the farm to

compensate for lost consumption opportunities. Conversely, a reduction in unit transaction

costs is likely to stimulate commodity specialization and market exchange. This essentially

summarizes the tension between specialization and market participation on the one hand

and subsistence agriculture on the other (North, 1981).

The cost of market participation can thus explain the existence of an impressive

array of production and consumption arrangements. Smallholders may choose the level of

market participation depending on the underlying cost-benefit structure. At one end of the

spectrum there would be complete autarky while at the other end, there would be complete

commercialization. In reality, however, we hardly see the two extreme cases, but we often

observe an intermediate levels of market participation. Omamo (2007), using a numerical

simulation model with data from Kenya, shows that it is possible for households located far

from markets to allocate a larger share of productive resources to intercropping and less to

more specialized activities. The average area devoted to intercropping may increase as

market access falls, whereas that devoted to pure-stand cropping declines. He noted a

need for empirical analysis to determine whether high farm-to-market transaction costs are

associated with low market participation levels and a high degree of farm diversification.

Chapter 3

22

Based on evidence provided by Njehia (1994), the study suggests that the average area

devoted to intercropping rises as market access falls.

The optimization problem defined above would require evaluating the utility function

for each choice variable under the three possible market prices, namely at autarky, buyer

and seller prices. The model above suggests that a farmer faces a unique price for each

crop depending on a unique set of transaction costs, conditional on household

idiosyncrasies. These transaction costs are often unobservable, but can be inferred from

household and other conditions identified above. Followed by Goetz (1992) and Key et al.

(2000), the first order conditions for the maximization of the utility function will yield the

reduced form of the output marketed supply, conditional on market participation. Goetz

(1992) shows that transaction costs affect farmer decisions on whether or not to participate

in markets and, when in the market, how much to produce and sell. Their study confirms

that fixed transaction costs hinder market participation while better information stimulates it.

The study further identifies that changes in grain price have a differential impact on new

sellers compared to those already in the market. Key et al. (2000); and proportional

transaction costs affect the quantity sold whereas fixed transaction costs affect decisions on

whether to participate in markets.

Two questions are of particular interest to this exercise. One is how market

participation in crop sales would vary with distance to markets and, by extension,

transaction costs. Second, whether and to what extent transaction costs influence the

choice of farm diversification. We would also like to identify specific factors significantly

affecting household market participation in the conditions specific to PNG. This paper

hypothesizes that transaction costs, in combination with the production environment,

characterized by household assets - especially land, capital and labour availability -

determine farm decisions on market participation and how much to sell. Thus, households in

production and marketing environments with high transaction costs are expected to be less

market-oriented and, hence, choose agricultural products and crop varieties that do not

require frequent market interaction to earn cash income for purchasing essential non-farm

goods. If indeed, this is true, promoting smallholder market participation will require policy

tools to reduce transaction costs.

The exact procedure for estimating the model described above depends on the

nature of the available data and, hence, the approach taken in the study is discussed after

the data is fully described.

23

4. Data and Description of Production and

Marketing Environments

4.1 Introduction

This chapter describes the methodology adopted in the small Agriculture Household

Survey of Papua New Guinea conducted by the Centre for the Alleviation of Poverty through

Sustainable Agriculture (CAPSA), in collaboration with the National Agricultural Research

Institute (NARI) in Papua New Guinea (CAPSA, 2012) and a discussion of the nature of

agricultural production, marketing arrangements and crop sales.

4.2 Data collection

A questionnaire survey was conducted in the Morobe province of PNG to gather

information on smallholder market participation6. Morobe was selected because of the need

to study lowland agricultural systems in PNG which are less commercialized and oriented

towards home production. This area has less favourable climatic conditions and the farmers’

capacity to generate incomes is considered well below that in the highland. Hence, lowland

farmers can be considered as marginal players in fresh food production and their operating

environment was thus considered suitable for a study of factors limiting farm household

participation in local and national markets.

Logistic difficulties affecting the research team’s access to some remote villages

were a concern and a pragmatic approach was used for sample selection. The NARI staff’s

extensive knowledge of the area from their involvement in development projects there,

together with the intimate agricultural knowledge of the President of PNG Women in

Agriculture, a voluntary association, helped identify two regions within the province with

significant differences in agricultural practices as well as climatic and topological conditions.

Based on this, the research team selected Bulolo and Markham districts in Morobe as target

areas for the survey. Bulolo is a relatively mountainous region where farmers engage in

fresh food production on a small scale. It is located along a tributary of the Markham River

6 Defining smallholders rigidly as having less than two hectares of cropland (World Bank, 2003) is too restrictive in PNG because land ownership is not well defined and is often governed by customary practices. It is estimated that only 3 per cent land is alienated. For all practical purposes, the larger majority of farmers can be considered smallholders except those with over 6 to 7 hectares because many of them earn a small income from crop sales and operate with minimum agricultural equipment and capital.

Chapter 4

24

and has a road link to Lae district which is the main market town of the province. Markham

district is a vast plain surrounded by mountains. It has better soil but relatively drier with

pasture land and sugar plantations the dominant agricultural activity. However, small-scale

farm households cultivate roots and tubers, vegetables and cash crops. The road linking

Lae and Madang, running through the Ramu valley, serves the district.

The next step was selection of Local Level Governments (LLGs) from the district.

From further information on villages in the Bulolo area, it was confirmed that fresh food

production was generally along the Wau-Bulolo region, a long valley extending from Lae to

Bulolo. As much of the fresh food production takes place in the Mumeng LLG, which

stretches more than 10 km along the narrow valley, the research team decided to conduct

the survey in the Mumeng LLG area. Villages in the LLG, villages were selected at random.

In contrast, villages in Markham district are spread across the vast plain over relatively long

distances from each other and, accordingly the team selected six villages randomly from –

the Leron-Wantoat, Onga-Waffa and Umi-Atzera LLGs.

Interviews were conducted from 27 October to 7 November 2012 in the two districts

with the help of experienced agricultural extension workers attached to NARI. The survey

questionnaire was administered to 210 farm households divided equally between the two

districts. Of these, 198 completed questionnaires were retained including 98 from Markham

and 100 from the Wau-Bulolo area. The remaining questionnaires were discarded as the

data collected was found inconsistent not verifiable. In each village the objective was to

administer the survey to every third farm household. However, in some cases, it was not

possible to adhere to this principle due to the non-availability of potential respondents as

well as the smaller size of the village. In such instances, enumerators selected the next

available household.

The small size of the sample survey was compensated for by the detailed

information on household demographic background, agricultural inputs (land, labour, seed,

agricultural capital, fertilizer, pesticides and extension services), outputs (types of production

and distribution), risk and crop damage experienced by farm households, marketing

arrangements, credit and family finance, and consumption. The data collected and its

relationship to the analysis are shown in Figure 4.1. The data were collected in September

2012. Crop production cycles were noted to be different across the two microregions and

crops and, hence, rigid calendar months could not be used for collecting data in the two

districts. Instead, the crop cycle before the survey period was used as the basis for

collecting data. This also made it easier to collect information about secondary harvests,

25

especially of vegetable. The data reported in the paper including production, sales and

income refer to the respective crop cycle values.

Figure 4.1 Conceptual framework for data analysis

4.3 Limitation and scope

As noted by previous surveys, obtaining accurate estimates of income, cash flow

and monetary controls was extremely difficult. No written records are available and income

and consumption data had to be collected using farmers’ recall of these. As selection of the

villages was determined by physical access, the sample may also be biased towards farm

households with relatively better market access against those relying completely on

subsistence farming. Therefore, the authors’ estimates of the proportion of farm households

participating in markets could have an upward bias. This weakness prevented the authors

from undertaking a complete analysis using the model described above where farmers first

decide on market participation and then, its extent. However, this does not affect the

analysis on farmer decisions on how much to sell.

Chapter 4

26

The results of the study do not allow extrapolation beyond the region under study.

However, the data closely mimic that collected elsewhere in similar conditions of agricultural

production, including agricultural inputs, marketing arrangements and consumption patterns.

Therefore, this analysis of the factors limiting market participation can be considered as

reasonably valid for similar farming environments. Appendix 2 provides a statistical

summary of the variables used in the study.

4.4 Description of the production environment

4.4.1 Land ownership, usage and within-farm land fragmentation

An agricultural household has7 on average 2.3 hectares of land. However, 80 per

cent households in the survey area own less than 3 hectares and 60 per cent, less than 2

hectares (see Figure 4.2). Of the area cultivated, 80 per cent farm households cultivate less

than 2.5 hectares and 60 per cent less than 1.5 hectares. Thus, farm households can be

characterized as smallholders even by the most rigid definition of smallholders

(ownership/cultivation of 2 hectares). The land area under cash crops and vegetables is

even smaller. Almost 40 per cent of households do not cultivate vegetables and about 75

per cent do not cultivate cash crops, suggesting that only a smaller percentage of

households actually engage in cash crops cultivation.

Farm households in Bulolo have more land plots on average than those in Markham,

but in both districts the majority of households hold more than 4 plots of land each and

some hold as many as 10 (see Figure 4.3). Thus, the actual operational land size is even

smaller than what is normally assumed based on land ownership data. The ownership of

multiple land plots is known to have emerged from the households’ need to ensure survival

during crop failures. The continuation of this practice implies limited capacity or a lack of

alternative income opportunities outside agriculture that limit consumption smoothing in

case of farm failure. This also implies farm households’ inability for commercial agriculture.

The production environment can be characterized by limited access to agricultural

technology (cultivation and processing technologies and agricultural capital) and public

services (irrigation facilities and extension services) and the limited availability of inputs

such as fertilizer and pesticides, markets and services such as transport, credit and

information.

7 Land is community-owned and only 3 per cent is estimated to have been alienated. Land ownership claims also overlap among clan members. The amount of land indicated owned by households may refer to household-cultivated land or cultivable without dispute.

27

Figure 4.2 Household land ownership and crop allocation

(Cumulative density)

Figure 4.3 Distribution of land plots

Source: Authors based on CAPSA-NARI survey (2012)

4.4.2 Demographic features

The age of the household head varies from between under 20 years to 75 years,

with a near normal distribution. The agricultural population of PNG can, therefore, be

considered as generally young (see Figure 4.4). This may be due to the relatively young

population and limited work opportunities for youth in the industrial and services sectors.

The average level of education of household heads is 5.5 years with a standard deviation of

3.8 years, but a large proportion of them do not have formal education. Households, on

average have three adult members, but the distribution is skewed towards the right.

0.1

.2.3

.4.5

Den

sity

0 2 4 6 8 10 12Number of plots

Bulolo Markham

Overall

Number of land plots per farm

Chapter 4

28

Figure 4.4 Demographic characteristics of farm households

Source: Authors based on CAPSA-NARI survey (2012)

4.4.3 Agricultural inputs and services

The use of important agricultural inputs is discussed in this section including labour,

seeds, fertilizer and pesticides. Hired labour use in a crop cycle is limited to 46 per cent of

farms, but their usage is quite limited. On average, labour is hired for three days, and paid

PGK 198.00 per day. The majority of households do not hire labour (see Figure 4.5) and

those that do, hire for specific tasks such as land preparation, seed sowing, applying

fertilizer, weeding, harvesting, cleaning and transporting.

Approximately 35 per cent farmers buy seeds from the market and about 25 per cent

also use high-yielding varieties for some crops. Only 5 per cent farmers use chemical or

organic fertilizer but over 35 per cent use pesticides (see Figure 4.6). Close to a quarter of

the farmers in Markham, and less than 5 per cent in Bulolo, have received extension

services. Access to credit and the use of price information is limited, but it was observed

that farmers in Markham use more credit facilities and advance price information for

marketing.

0

.01

.02

.03

.04

.05

Den

sity

20 40 60 80Years

Age distribution of household heads

0

.05

.1.1

5.2

.25

Den

sity

0 5 10 15Years

Schooling of household head0

.1.2

.3.4

.5

Den

sity

0 2 4 6 8 10Number

Distribution of adults in the family

Female Male

Gender composition of household heads

29

Figure 4.5 Labour use in agriculture

Source: Authors based on CAPSA-NARI survey (2012)

Figure 4.6 Inputs used and access to credit and information

Source: Authors based on CAPSA-NARI survey (2012)

4.4.4 Agricultural capital, access to irrigation and improved land

Farmers in the study area use very basic agricultural equipment. Table 4.1 shows

the approximate percentage of equipment for cultivation and farming operations. The

0.1

.2.3

.4

Den

sity

0 10 20 30Number of days

Number of days labour hired (per farm / crop cycle)

0

.00

2.0

04

.00

6.0

08

Den

sity

0 500 1000 1500 2000PGK

Payments for labour (per farm / cycle)0

10

20

30

40

50

Bulolo Markham

Seed purchased High-yielding varieties

Chemical fertiliser Pesticides

perc

ent

Graphs by dt

Nature of inputs used

Bulolo Markham

No Yes

Extension services

Bulolo Markham

No Yes

Access to credit

Bulolo Markham

No Yes

Use of price information for marketing

Chapter 4

30

majority of farms operate with bush knives and spades, and occasionally with forks and

wheelbarrows. Mobile phone penetration is 86 per cent of the population. Only 13 per cent

of cultivated land is irrigated. Approximately 40 per cent farmers have protected their land

from erosion by building rock and soil bunds, terraces, mulching8 and planting grass lines.

Table 4.1 Agricultural capital and equipment

Item(s) Households that own items (%)

Basic farm equipment

Bush knife, spade > 95 Fork 32 Wheelbarrow / cart 18 Knapsack 13 Axe 9 Iron bar, iron spade, Grass knife 1 -3 Secator, Sprinkler < 1

Motorized equipment

Generator 12 Water pump 2.5 Tractor 2 Motorized insecticide pump 1.5 Machine pulled plough 0.5

Processing equipment

Coffee pulper, mill < 1

Transport / ICT

Mobile phones 57 Radio / TV 30 Lorry 2.5 Motor cycle 1 Farm animals Cattle 38 Buffalo 5 Chicken 3 - 4 Pigs 2

Source: CAPSA-NARI survey, 2012

4.4.5 Crop choice

The survey recorded 40 varieties of food crops cultivated in Bulolo and Markam.

These can be broadly classified into vegetables (14 varieties), staples (8), fruits (8), cereals

(2) and cash crops (8)9. Of the total land claimed by households, 71 per cent is cultivated

and the rest is either left fallow or used for other purposes. Of the total land cultivated, the

largest area is allocated to roots and tubers (38 per cent), followed by vegetables (27 per

cent), cash crops (22 per cent), fruits (7 per cent) and cereals (6 per cent). A significant

difference is observed between the extent of land owned and cultivated. Farm household

8 Applying a protective layer to the soil. Farmers in PNG use grass clippings, straw, bark chips and similar material for mulching. 9 Some varieties can be placed in more than one group, depending on the plant variety and their use. For example, when sugarcane is cultivated in a home garden, it is consumed as a fruit, but when grown on a commercial scale, it becomes a cash crop. The paper classifies by immediate use.

31

land allocation decisions for crops in the two districts are significantly different. Farmers in

Bulolo have allocated more for vegetables and staples while in Markham, more staples and

cash crops are planted (see Figure 4.7).

Figure 4.7 Land ownership and crop diversification

Figure 4.8 Household crop choice

Source: Authors based on CAPSA-NARI survey (2012)

It was observed that the number of cultivated crops, compared to the pattern of land

allocation under each type, is similar in both districts (see Figure 4.8). For example, farmers

in both districts cultivate a higher number of cash crops, followed by staples, vegetables and

fruits. It was also observed that farmers allocate more land for staples but cultivate a larger

02

46

81

0

He

cta

res

Bulolo Markham

Land cultivated Vegetables

Staples Cash crops

Fruits Cereals

Land allocation by type of crop and district

Chapter 4

32

number of cash crops in a smaller area. It may indicate a higher intensity of cash crop rather

than staple cultivation.

4.5 Marketing arrangements

For each household and each commodity produced, the survey recorded whether

the commodity was sold within the village or in a town, a large city or the capital city. Figure

4.9 summarizes the marketing behaviour by type of commodity and destination. The authors

estimated the probability of selling a given commodity in a particular location and to whom it

was sold. It was observed that a farmer was 83 per cent likely to sell vegetables within the

village itself and 16 per cent likely to market in a nearby town, with a just 1-per cent

likelihood of selling in a large city. However, the marketing of cash crops is very different.

There is only a 1-per cent chance of selling cash crops within the village, a 28-per cent

likelihood of marketing in a nearby town, 56-per cent chance of selling in a large town and

16-per cent likelihood of selling in the capital city.

Figure 4.9 Crop marketing by main markets

Source: Authors based on CAPSA-NARI survey (2012)

This decision appears to depend on the type of commodity, especially its

perishability and bulk. For example, vegetables, staples and fruits are highly perishable and

there is, hence, a higher likelihood of spoilage during transportation as well as high

transaction costs. Transporting small volumes is not cost-effective. Therefore, it can be

concluded that farmers in such a marketing environment behave highly rationally, contrary

to the popular belief that farmers are ignorant.

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Cereal Vegetables Staples Fruits Cash crops

Perc

en

tag

e

Village Town Large city Capital city

33

4.6 Nature of crop sales

Similarly, the survey asked farm households whether they sold their produce to

consumers, traders, companies, government agencies or a category other than these. The

analysis shows that farmers mostly sell directly to consumers (see Figure 4.10). It can be

observed that a farmer is 93 per cent likely to sell vegetables and 66 per cent likely to sell

cash crops directly to consumers. As noted earlier about the greater likelihood of cash crops

being sold in large cities and the capital city, there is a slightly higher probability of farmers

selling cash crops, cereals and fruits to traders. Thus, it can be seen that the function of

intermediaries is not widespread in the agricultural sector, being limited to cultivations that

are more commercialized and to certain commodities such as cash crops, cereals and fruits,

which can be considered high-value crops.

Figure 4.10 Crop marketing by main buyers

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Cereal Vegetables Staples Fruits Cash crops

Perc

en

tag

e

Consumers Traders Company Govt Agency Others

Source: Authors based on CAPSA-NARI survey (2012)

Of the farmers surveyed, 84 per cent participated in markets, earning on average

PGK 1,632 during the crop cycle. It is observed that farm household market participation

differs significantly by type of commodity, ranging from over 60 per cent for cash crops, 40

per cent for vegetables, 30 per cent for staples and 25 per cent for fruits to less than 10 per

cent for cereals (see Figure 4.11). The contribution of these commodities to total sales

revenue is significantly different to the commodities’ market. Cash crops contribute 80 per

cent to sales revenue while vegetables, staples and fruits, each contribute 6 per cent and

cereals just about 1 per cent.

Chapter 4

34

The volume of sales and main sources of crop sales are significantly different in the

two districts. Farmers in Bulolo earn relatively less from sales but sell a greater variety of

vegetables; farmers in Markham earn relatively more from sales, specializing in cash crops

and staples (see Figure 4.12). Farmers in Markham earn, on average eight times more than

those in Bulolo. These facts suggest that farmers in Markham have a higher orientation

towards cash crop sales, whereas farmers in Bulolo sell more of different types of

commodities, but earn less.

Figure 4.11 Household sales income by crop type

Figure 4.12 Household crop sales by type and district

Source: Authors based on CAPSA-NARI survey (2012)

35

5. Determinants of Farm Sales

5.1 Estimating the model with censored data

As discussed above, around 14 per cent households in the sample do not participate

in markets10

. Thus, the dependent variable is not observed for some farm households, but

independent variables are observed for all. This means that farm households have latent or

unobserved sales and can enter markets as soon as a threshold of critical variables is

reached. In agrarian settings, the threshold could be a level of benefits for the family farm to

participate in markets. The authors are interested in explaining household decisions and the

degree of their market participation. Accordingly, sales values are considered a dependent

variable, which is not observed for some households. Therefore, the data set falls into the

category known as censored data as the full information set is available for all independent

variables, but some data are not observable for the dependent variable. When a data set is

censored this way, estimating the model using the Linear Model becomes untenable. The

Tobit model is a particularly useful model in this environment, which is used for estimating

the regression models.

The model can be specified as a linear combination of an unobserved latent variable,

y*,

𝑦𝑖∗ = Χ𝑖

′𝛽 + 𝜀𝑖 , 𝑖 = 1, … , 𝑁

where 𝜖𝑖 ∼ 𝑁(0, 𝜎2), 𝑎𝑛𝑑 Χ𝑖 denotes the (Kx1) vector of exogenous and fully

observed variables. If y* were to be observed, the model parameters can be estimated by

OLS in the usual way. The observed variable 𝑦𝑖 is related to the latent variable 𝑦𝑖∗ through

the observation rule

𝑦 = [𝑦∗ 𝑖𝑓 𝑦∗ > 𝐿𝐿 𝑖𝑓 𝑦∗ ≤ 𝐿

The probability of an observation being censored is Pr(𝑦∗ ≤ 𝐿) = Pr(Χ𝑖′𝛽 + 𝜀𝑖 ≤ 𝐿) =

Φ{(𝐿 − Χ𝑖′𝛽/𝜎}, where Φ is the standard normal cumulative distribution function. The

conditional mean of the non-censored observations differs from standard OLS because of

the censoring, which leads to OLS being inconsistent. Estimation of the conditional mean

10 As explained in the methodology, the authors suspect overestimation of the market participation rate due to sample bias towards farmers living closer to the road and suggest that it not be used for policy purposes.

Chapter 5

36

depends crucially on the normality assumption. The data set that we have in the case of

PNG is only left censored and L = 0.

As has been shown, the censoring point may be unknown (Carson and Sun, 2007)

and the unknown censoring point can be set to zero as a ‘normalization’ can lead to

inconsistent estimators. In such situations, it has become a standard practice to treat the

unknown threshold, 𝛾 = min(𝑢𝑛𝑐𝑒𝑛𝑠𝑜𝑟𝑒𝑑 𝑦) and proceed as if 𝛾 is known. Estimates of the

Tobit model based on this procedure have been shown to be consistent (Carson and Sun,

2007; Cameron and Trivedi, 2010). Following the suggestion made by Cameron and Trivedi

(2010), the unknown threshold was set to 𝛾 − Δ, where Δ = 10−6.

The standard practice is to use either the maximum likelihood estimator or the two-

step procedure for estimating models and in this case, the MLE was used. However, the

consistency of MLE crucially depends on the normality assumption. During the process of

estimation, it was found that the dependent variable in absolute terms shows high skewness

and heteroskedasticity, and therefore, the dependent variable, sales income, was

transferred by using natural logarithm. It was found that skewness and non-normal kurtosis

are reduced within acceptable levels, and hence the model can be thought to generate

consistent estimates. Thus, the model developed in section 3.3 was estimated by using the

Tobit model structure, implemented by using the MLE method. The model focuses on

estimating market participation of farm households, conditioned on household assets and

characteristics, access to publicly and privately provided services such as extension

services, credit facilities and information and communication, and other market-related

information such as distances and transaction costs.

5.2 Empirical model variables

The model developed in section 3.4 was estimated using the Tobit model structure

as this allows for estimating regression models with truncation. We regress farm sales as a

function of household assets, demographic characteristics, access to publicly and privately

provided services such as extension services, credit facilities, access to information and

communication, and factors that work as barriers for market entry, especially distance and

transportation costs. To further understand how these factors may affect smallholders and

other differently, the model with total sales was estimated separately for households with

less than 2 hectares and greater than 2 hectares.

Descriptive statistics (means, standard errors maximum and minimum values) are

given in Appendix 2 for the variables included in the regressions. The empirical models

37

used household demographic characteristics, access to assets and to input markets,

transaction costs and household food and non-food requirements that should be fulfilled

through markets. These are briefly described below.

Household characteristics include sex, age and years of schooling of household

head (HH), and the number of adults in the family. Household assets include land owned by

the family, number of land parcels, land area with irrigation and improvements, number of

cattle owned and agricultural equipment. Based on research elsewhere, it is assumed that

farm households tend to use mechanized equipment as commercialization progresses. To

account for this possibility, agricultural equipment was classified into basic and non-basic.

Non-basic agricultural equipment used by farm households consisted of ploughs, sprinklers,

motorized insecticide pumps and wheelbarrows, whereas most farmers used basic

equipment such as spades, bush knives, axes and forks. The total number of equipment

with each household under the two categories was used as an approximation for orientation

towards mechanization with commercialization.

The theoretical discussion above suggests that farm households tend to become

more specialized with agricultural development and commercialization. To account for the

possibility for greater commercialization and hence sales with greater commercialization, the

number of crops cultivated was used. This is expected to represent the extent of farm

diversification. Greater commercialization would imply a negative association between sales

and greater diversification. A positive and significant contribution of diversification on sales

would thus imply reliance of farm households on crop diversification, which is a form of

coping strategy in environments characterized by high risk or near-subsistence agriculture.

To capture access to information and communications technology, the authors used

a dummy representing whether the family had access to mobile phones or TV/radio.

Whether the farm received price information before selling was used to determine if farm

families actually used existing channels of communication. Farmers’ access to inputs is

represented by the use of high-yielding varieties, fertilizer, pesticides and labour.

Transaction costs are not measurable using available data and are approximated by

distance to markets, a common approach in empirical research, as these are generally

observed to be proportional to distance. Rural farm households also find it difficult to

participate in markets when distances increase due to rising transport and transaction costs

such as those related to negotiation and protection from pilferage and loss. To represent the

fact that household ability to participate in markets decreases with distances, the model

uses inverse distances.

Chapter 5

38

5.3 Regression results of farm household sales

Four models were estimated to identify factors that would lead to farm household

participation in agricultural markets in PNG. These include a model with total sales and

three commodity-specific models, namely cash crops, staples and vegetables. Table 5.1

shows the variables identified as significant and their associated signs. The following

sections explain the possible underpinnings for the observed results and factors that may

explain why variables, that generally explain farm sales, are not significant in the case

studied in this paper.

Table 5.1 Summary regression results of farm sales of cash crops, staples, vegetables, and

total sales

Variable

Total sales Cash

crops Staples Vegetables All

farms

Smallholders

(L< 2 ha)

Others

(L > 2 ha)

1=HH is female - *** - ***

Age of household head + *

No of adults - * + *

Land owned (ha) - * + *

Land owned (squared) + **

No of land parcels + ** + *** + ** + **

No of land parcels squared - * + ** - **

1=HH used improved land /

irrigation - *** - *** - *** - ***

No of improved agricultural

equipment + *** + ***

No of basic agricultural

equipment - *** - * + **

1=Farm used high-yielding

varieties + *** + ***

1=Farm used extension services + **

1=Farm used chemical fertilizer - ** - ***

1=Farm used seed purchased + **

No of crops cultivated + ** + * + ***

No of crops sustained damages + *** + **

1=Farm obtained credit - **

1= Farm used mobile phones,

TV/radio - **

1=farm received price

information + ** + *** + *** + *

District + *** + *** + *** - ***

Distance to markets (inverse) - *** - *** - *** - ***

Total food expenditure + ***

Total non-food expenditure + ** + ** + **

Family non-farm income + ***

Source: Authors based on Appendix 5

39

5.3.1 Land ownership and land fragmentation

The first thing to be noted is that access to land is not significant in the overall model

with all farm households considered. However, increasing land ownership is positively and

significantly associated with sales among those owning more than 2 hectares. Interestingly,

sales are negatively correlated with rising land ownership among smallholders, but the

secondary effect is positive. This implies that increasing land ownership induces farm

households to disengage from markets but increase sales when the land ownership rises.

This may be due to the existence of a sort of land threshold even within smallholder family

farms. It may be that families increase consumption of home-produced commodities when

their land ownership increases at lower levels, but find it profitable to sell when a small

surplus is generated with an increased amount of land. This result is similar to the

observation in other contexts (Wickramasinghe, 2014). To visualize this relationship, a

scatter diagram between the extent of land owned and sales for those who own less than 3

hectares was drawn along with Locally Weighted Scatterplot Smoothing, or Lowess

smoother11

(see Figure 5.1). It can be seen that with rising land ownership at lower levels,

farms hardly increase sales volumes but these begin to increase when the extent of land

rises above 2 hectares. It was also observed that sales continue to rise up to about 4

hectares of land and decline afterwards (not shown in the figure). The result suggests the

existence of a non-linear relationship between land ownership and sales. Thus land

ownership alone cannot explain the farmers’ market participation decisions.

Fragmentation of land within households is common in PNG as seen earlier.

Regression models show a positive association between the number of plots of land owned

and sales. Among smallholders with less than 2 hectares of land, the relationship is even

stronger as can be observed. In addition, the squared value of the number of plots is

significant and negative in the model with total sale and also in cash crop sales, but positive

for smallholders. Like the relationship observed above between sales and land ownership,

the authors observed a non-liner linear relationship between sales and the number of plots.

In particular, it was observed that an increase in the number of land parcels contributed to

farm sales but sales began to decline when the number of plots rose above six. A scatter

diagram of the number of land plots and sales along with a Lowess smoother shows this

relationship (see Figure 5.2). Land fragmentation is noted to be a particularly acute problem

among farm households owning between 5 and 7 hectares of land and the importance of

having many land plots begins to dissipate beyond 7 plots of land. As research shows,

11 The Lowess smoother uses locally-weighted polynomial regression.

Chapter 5

40

holding many plots is a sign that farm households are merely attempting to survive under

difficult conditions, which is a result of expected farm failures (Jodha, 1978) and transaction

costs (Wickramasinghe, 1995).

Figure 5.1 Land ownership and farm sales among smallholders

Figure 5.2 Within-farm land fragmentation

Source: Authors based on CAPSA-NARI survey (2012)

41

5.3.2 Demographic characteristics

The authors find three demographic characteristics affecting farm sales: the gender

of the household head, the age of the household head and the number of adults in the

family. Econometric results suggest that being a female household head decreases the

likelihood of market participation. This seems to suggest that female-headed households

face constraints to effective engagement in markets. This is somewhat puzzling given the

specific conditions in PNG where women appear to contribute significantly in the entire

agricultural value-chain including cultivation, transportation, negotiations with buyers and

vendors, and sales of the produce. Age was found to be a significant factor in explaining

sales only in the regression for farms that own less than 2 hectares, but the result confirms

the general observation that farming operations are increasingly manned by the elderly. The

number of adults in the family is a significant variable among vegetable growers.

5.3.3 Agricultural inputs and services

The models used several variables to gauge the impact of the availability of

agricultural inputs and services, including access to irrigation and improved land, high-

yielding varieties, chemical fertilizer, pesticides and market-purchased seeds. The variable

that represents access to irrigation and improved land, negatively affects sales. A scatter

diagram (not shown) along with Lowess smoother between farms with access to irrigation

and land improvements, showed that only a small proportion of farms had access to

irrigation or engaged in land improvements, but their sales volumes were found to be small,

regardless of the amount of land owned by the household.

The use of high-yielding varieties is positively and significantly correlated with sales.

The authors observed this was the case for regression models for vegetables and total

sales. The fact that the dummy variable on whether farms used high-yielding varieties is

significant only in the regression model on vegetables and the total sales, implies that it is

an important factor for vegetables but may not be the case for cash crops and staples.

The use of chemical fertilizer and pesticides was found to have a negative effect on

sales. This needs further investigation although the authors suspect that the result is due to

the fact that only a fraction of farmers use these and that pesticide use may also reflect

vulnerability to pest attacks and poor soil conditions and, hence, their inability to participate

effectively in markets. Seeds purchased from markets have only been used by farmers with

land over 2 hectares. Access to extension services was found to positively and significantly

contribute to sales only in the case of the sale of staple crops. However, this needs to be

Chapter 5

42

further verified given that only a small proportion of households received such assistance.

Farm households rely on family labour for farming and hired labour is rarely used. As

a result, variables on labour usage were not significant in the estimated models. The use of

hired labour is limited to specialized tasks, particularly among large-scale cash crop

farmers.

Access to information and communication, and in particular to price information, is

known to positively affect market participation. While the authors found evidence that having

advance price information positively and significantly contributed to sales across the four

models estimated, access to channels that receive information, namely mobile phones and

TV/radio, had no positive impact. This is surprising because it was observed that mobile

phone penetration was quite high even in rural areas. It appears that farmers rely on friends

who visit market centres frequently to obtain price information. This shows the need to focus

on mechanisms of information transmission rather than trying to increase access to mobile

phones for enhancing market participation. Finding out how farm households receive market

information needs further investigation.

5.3.4 Agricultural capital and equipment

Ownership or access to improved agricultural equipment affects sales among farms

with less than 2 hectares of land, but having more basic agricultural equipment appears to

have a negative impact on market participation. Those with more basic agricultural

equipment tend not to engage in markets compared to those with improved equipment. This

simply reflects limited access to agricultural equipment among farm households. Given the

importance of agricultural capital formation in agrarian transformation and development as

has been well established in the literature, the authors investigated an additional potential

link with market participation, namely that between land ownership and capital

accumulation. However, a strong and systematic association between agricultural

equipment and sales or with land cultivated was not found. It is thus concluded that

agricultural capital formation is at a rudimentary level in PNG.

5.3.5 Distance to markets and market participation costs

Smallholder market participation is hindered by transaction costs (e.g. of information

gathering, organizing transportation and ensuring goods are not damaged or pilfered during

transport). Transaction costs, especially those of organizing farm operations, managing

labour and controlling shirking, are rarely observable and measurable in practice. Although

the transportation cost was considered as a measurable variable, even that was not

43

observable when farmers were not participating in markets (e.g. from remote rural

communities). Non-participation of farm households in markets is often a result of high

marketing and transportation costs. Thus, the quantity that can be transported for any given

unit of value diminishes with increasing distance. The use of average transport costs in

regression models would have been ideal but it was extremely difficult to measure the

quantities transported due to the non-availability of standard measures to convert various

units used by households to identify the quantities. The distance could only be estimated in

time units and, hence, the authors used the inverse distance as an approximation for the

average transportation cost. To put this in perspective, a farmer located farther away from

the market will be able to transport a smaller quantity of produce with a given amount of

money, compared to a farmer located closer to the market. Thus, distance is a barrier to

market participation. As shown in the theoretical section, farm households located farther

from the market, rely on home-produced goods over market-purchased goods and, hence,

their degree of market participation is lower.

Figure 5.3 Farm-to-market distances and farm sales

Source: Authors based on CAPSA-NARI survey (2012)

Distance from the market is highly significant in sales in four out of five regression

models. Distance does not affect sales only in the case of staples. It is found that most

smallholder farms are less than 300 minutes of travel time away from main markets, but a

Chapter 5

44

few are located away from markets farther away (see Figure 5.3). Sales were observed to

rise with distance until about 400 minutes of travel time. Beyond this distance, sales

remained the same. This shows the possibility that distance itself may not be a

disadvantage and that there is an optimum distance at which farms tend to be profitable. In

the two districts surveyed, it can be observed that most vegetable farms are close to roads

and towns whereas farmers in mountainous regions without access to transport, tend to

practise subsistence agriculture, occasionally travelling to markets with their produce, which

is usually non-perishable.

5.3.6 Crop diversification and market participation

It was shown that the choice between subsistence agriculture and market

participation depends on the net benefits and costs of participating in markets. A study of

crop choice, diversification and how households use markets to meet their basic

consumption requirements, makes it possible to understand household preferences.

There is strong evidence to support the idea that farm households diversify

cultivation and that there is a positive and significant contribution of crop diversification to

farm sales. The number of crops cultivated is significant and has positive impact on sales in

three regression models, namely total sales, sales of smallholders and cash crops. Crop

diversification is widely practised in PNG. It is not surprising to find farm households

engaged in crop diversification in an agrarian environment broadly characterized as

subsistence or near-subsistence in PNG. Similarly, a positive correlation is observed

between distance to markets and the number of crops cultivated (see Figure 5.4). Thus,

farms located away from markets tend to cultivate more crops, selling the surplus in the

market to generate income to purchase food and non-food commodities.

While farm households facing larger risks would be expected to avoid market

participation, the study results suggest that households with higher crop damage tend to sell

more. While further analysis is needed to ascertain the possible causes of this link, it is likely

that farm households adjust crop mix to keep earnings constant in the face of risks. For

example, farms may increase sales of staples and vegetables when cash crops and other

commodities are damaged, reducing the marketable surplus.

45

Figure 5.4 Farm-to-market distance, land and crop

diversification and farm sales

Source: Authors based on CAPSA-NARI survey (2012)

5.3.7 Household consumption and market participation

It was also observed that farm sales were associated with the consumption level of

food and non-food commodities. Food expenditure is significant in the cash crop regression

whereas non-food expenditure is significant in three regressions, namely total sales,

smallholders (l<2 hectares) and cash crop. It may be noted that non-food expenditure is

smaller in percentage terms for every PGK a farm earns through sales, compared to food

expenditure. Food expenditure rises with sales income and becomes constant after sales

reach PGK 7,000 whereas non-food expenditures reaches a maximum slightly earlier, but

05

10

15

20

Num

ber

0 200 400 600 800Distance to markets (minutes)

No of plots Lowess - distance & no. of plots

No of crops cultivated Lowess - distance & no. of crops

Chapter 5

46

declines with an additional rise in sales income (see Figure 5.5). While it cannot be known

from the relationship whether consumption requirements induce farmers to engage in sales

or higher incomes contribute to higher consumption, the positive correlation makes it likely

that market participation may be driven by the need to generate income to fulfil the

household’s basic food and non-food requirements. Conversations with farmers during the

survey confirmed that a motive for market participation was to enable them to buy goods not

produced at home, such as kerosene, tinned fish, clothes and other essentials. The scatter

diagram between consumption expenditure and sales as well as regression results confirm

the positive association between consumption and market sales.

Figure 5.5 Family consumption and sales

Source: Authors based on CAPSA-NARI survey (2012)

47

6. Summary, Conclusions and Recommendations

The Rio+20 Outcome Document urged nations to enhance the welfare of

smallholders and subsistence farmers, and marginalized groups including women and

vulnerable communities dependent on agriculture, through strategies and programmes to

integrate them with local, regional and national markets. The Asia-Pacific region is home to

87 per cent of an estimated 500 million smallholders with less than 2 hectares of land and

some countries in the region have large smallholder agricultural communities that still

depend on subsistence agriculture.

This study is part of a series of investigations in selected Asia-Pacific countries to

identify the main constraints to smallholder market participation in order to integrate such

marginal communities into local, regional and national markets. It summarizes current

theoretical and empirical understanding of smallholder market participation with a modelling

approach and estimation procedure; synthesizes recent studies on smallholder agriculture

in PNG; and presents the results of a mini-household survey in PNG that attempted to

identify factors promoting or limiting smallholder market participation along with a set of

recommendations.

There are several factors behind the international community, coming together at the

United Nations to urge governments to develop strategies and programmes to integrate

agricultural smallholders and marginal communities with local, regional and national

markets. A key reason is the understanding that market participation by marginal agricultural

communities enhances household welfare as it allows households to utilize resources

efficiently, using their comparative advantage to produce food and agricultural commodities

and exchange these for commodities and services that cannot be produced at home.

Dynamic technical change in agriculture and production specialization is also known to

allow rural communities to move from subsistence to more specialized, market-oriented

commercial agricultural operations, thereby promoting their welfare. Market integration also

encourages rural households to move into productive agriculture rather than out of

agriculture, leading to unsustainable urbanization.

Recent theoretical developments and empirical studies have identified several

factors that contribute to, or limit the participation of agrarian households in markets. Market

failure as a reason for marginal participation of agrarian households in markets has been

extensively studied. Such studies have focused on various aspects of markets including

Chapter 6

48

market failures in insurance, food and credit markets as well as household-specific market

failures. The second and, perhaps, the most widely used theoretical argument for the

marginal participation of smallholder agricultural households originates from the economic

theory of the exchange economy and forces shaping production specialization, which goes

back to Adam Smith. The theory posits that larger markets allow for a greater division of

labour, which, in turn, enables economic agents to specialize in activities where they have

comparative advantage, effectively enlarging markets. Increasing returns to scale play a key

role in the process, which is further enhanced by the functional operations of firms and the

lowering of average fixed costs required for producing and selling new and intermediate

products. Recent research has shown that agriculture too offers the potential to specialize in

commodities and increase the division of labour in farming activities. This process enables

agricultural households to participate in markets and move from subsistence to commercial

agriculture.

Recent models have explained market participation, or its lack, in the presence of

transaction costs and that how such costs affect farmers’ decisions on whether and when to

participate in markets, how much to produce and sell, confirming that fixed transaction costs

are a hurdle to market participation while better information promotes it. As shown, market

participation decisions are a result of the underlying net costs and benefits to market

participation, in the sense of the tension between specialization and home production, as

recognized by Douglas North in 1981. Thus, a reduction in transaction costs induces farm

households to increase the quantity traded and reduce the range of commodities produced

within the farm, because market exchange allows farm households to consume a range of

commodities bought from the market.

Recent literature also shows the importance of several additional variables. Some of

these include farm size per household worker, animal traction, mean yield and age of the

household head. A number of other factors have also been recognized, including climate

risk, land quality differentials, access to productive technologies, public goods and services,

and traditional trust and personal connections in contract farming. Recent findings suggest

that the choice of farm specialization over mixed cultivation and enhanced market

participation is a result of the relatively better capacity of households to produce agricultural

commodities, in turn made possible by better access to land and irrigation, and favourable

environments for transaction costs.

PNG has a population of 7.4 million people and a land area of 470,000 km². The

current per capita GNI is $1,300, but poverty measured by the PHR is almost 40 per cent of

49

the population. Rural poverty is endemic primarily because of a heavy dichotomy in the

economy, characterized by extremely large agricultural employment (80 per cent) and the

significantly low value added from agriculture (27 per cent). Mining and construction

contribute 35 per cent to the GDP. The agricultural sector has been stagnant during the last

five decades except for some swings in the production of cereals and non-food agricultural

commodities. Almost 80 per cent of the people who depend on agriculture, practise

subsistence farming. The government’s Vision 2050 aims to reduce the dependency on the

mining industry and expand the contribution of the agriculture, forestry and fisheries sectors

to realize the country’s full economic potential. The midterm development plan of the

government aims to turn 70 per cent of subsistence farmers into small- and medium-scale

agricultural enterprises. Efficient land administration, which allows land owners to profit from

their land, along with the development of roads and supply chains linking producers with

markets, and expanding extension services, are the key strategies towards realizing these

goals. This study intends to deepen understanding of the factors contributing to or limiting

incentives for farmers to invest in agriculture and enhance their capacity to profit from land

and engage in markets productively.

This paper uses a simple model of household choice to capture core issues affecting

the volume of farm sales. The model assumes that a household maximizes utility by

choosing how much of each product or service to consume, produce, buy and sell, subject

to a cash constraint, a resource availability constraint and the production function. The cash

constraint shows that the total value of household purchases must be equal to or less than

the income it earns by selling staple/cash crops it produces and by selling its labour and

other services. It is assumed that borrowing or lending are equal in a given crop cycle and

that the household endowment consists of household savings from previous years or

cropping seasons.

PNG offers a case for investigating factors that may improve understanding of

household choices between market participation and home production for consumption.

This paper investigated factors that led some farm families in PNG to participate, and others

to shun markets. For this purpose, it conducted a mini-household survey in Bulolo and

Markham districts in Morobe province where lowland agriculture is practised. Covering 200

households, the survey included modules on household background, agricultural land,

labour and other services, farm capital and livestock, input markets, crop production,

marketing, access to credit, family income and transfers, and consumption.

Chapter 6

50

This section summarizes the findings of the study. First, as to the nature of

agriculture, lowland farm households operate very small pieces of land, not necessarily

because of lack of ownership but due to economic conditions. Within-family land

fragmentation is widespread across the region and farms are operated with very little

agricultural equipment, limited access to input markets including labour, high-yielding crop

varieties, fertilizer and pesticides, and extension services, in particular. Enhancing land

ownership, including through land alienation is, therefore, unlikely to significantly change the

nature of household agriculture and, hence, will have insignificant impact on integrating

smallholders with local and national markets.

Second, in addition to land fragmentation within the farm family, farmers are

extensively practising crop diversification. Research elsewhere and current theoretical

knowledge suggest that this is primarily due to their inability to cope with the potential risks

of farm failure. Smaller land sizes and extensive crop diversification mean that farmers can

produce only a small surplus of any given commodity for the market. If harvesting periods

also differ for different types of crops, a farmer would have only a small amount of surplus

produce for the market. Transporting small quantities over long distances is also not

economical. Farmers can come together into farmers’ organizations and appoint some of

themselves as agents for transporting and selling in markets. However, this depends on

trust relationships and the costs of organizing and keeping such coalitions together over

time. Public policy can facilitate farmer market participation either through incentives for

establishing farmers’ organizations, addressing the risk of farm failure that is responsible for

extensive crop diversification in the first place, or a combination of the two.

Third, smallholder agricultural marketing arrangements remain informal, with

producers selling small quantities directly to consumers at temporary roadside markets,

earning perhaps just enough to purchase their most basic food and non-food needs. Indeed,

this marketing arrangement may perhaps be the best option given the smaller size of their

marketable surplus. Therefore, any attempt to encourage greater market participation

through better marketing arrangements must be coordinated with attempts to enhance their

production and reduce their risks so that farmers not only produce more but also use their

meagre resources more efficiently, producing a fewer number of high-value crops.

Fourth, although not conclusive and unsure about the causative direction, we find a

highly significant association between the level of food and non-food consumption and

smallholder market participation. Food and non-food expenditure initially rises with rising

farm sales but food expenditure begins to stabilize and non-food expenditure declines

51

slightly when farm incomes rise further. It appears that a key motive for farmer market

participation is the need to enhance the bundle of consumption and services, especially of

commodities not produced at home and other essential services. This suggests that farm

households are making their best effort to improve their living standard. The best that the

government can do is to promote conditions conducive for farm households to enhance their

agricultural production and marketing in order to move out of poverty and food insecurity.

Further analysis of the other key requirements of smallholder households, namely

education, health, social obligations and recreation activities is required to understand how

these affect their market participation. Because such aspects are primary factors

contributing to human welfare and poverty reduction, increasing market participation is likely

to yield greater dividends in terms of enhanced food security and poverty reduction, thus

contributing to sustainable development.

53

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Appendices

Appendix 1. Map of the study areas

Appendices

58

Appendix 2. Summary statistics: all farms

Variable Obs Mean Std. Dev. Min Max

Total sales (PGK) 198 1 632 4 974 0 65 300

Cash crop sales (PGK) 198 1 292 4 865 0 65 300

Staple sales (PGK) 198 108 310 0 2 300

Vegetable sales (PGK) 198 110 444 0 4 800

Fruits sales (PGK) 198 110 383 0 3 550

Cereal sales (PGK) 198 13 84 0 1 000

1=household head is female 198 0.16 0.37 0 1

Age of household head 198 41 11 16 77

Years of schooling 198 6 4 0 16

Number of adults in the family 198 3 2 0 10

Extent of land owned (ha) 198 2.18 2.20 0 18.9

Number of land parcels 198 4 2 0 11

1=Farms with irrigation and land improvement 198 0.45 0.50 0 1

1=Farms with cattle ownership 198 0.06 0.24 0 1

Value of farm equipment (PGK) 198 1 535 8 817 0 100 000

1=Farm hired labour 198 0.47 0.50 0 1

1=Farm used seed purchased 198 0.27 0.44 0 1

1=Farm used high-yielding varieties 198 0.17 0.38 0 1

1=Farm received extension services 198 0.13 0.34 0 1

1=Farm used fertilizer or pesticides 198 0.29 0.45 0 1

Number of crops cultivated 198 6.68 3.87 0 19

Number of crops sustained damages 198 2.70 2.70 0 13

1=Farm household received credit 198 0.14 0.34 0 1

1=Farm family has TV/Radio 198 0.28 0.45 0 1

1=Farm received advance price information 198 0.25 0.44 0 1

Average distance to markets (minutes) 198 119 191 0 1 560

Food expenditure (PGK) 198 151 299 0 3 101

Non-food expenditure (PGK) 198 46 134 0 860

1=Farm family had non-farm income 198 0.20 0.40 0 1

1=Farm family received assistance 198 0.14 0.34 0 1

Source: CAPSA survey (2012)

59

Appendix 3. Summary statistics: Farms that own less than 2 hectares of land

Variable Obs Mean Std. Dev. Min Max

Total sales (PGK) 115 893 1 506 0 9 690

Cash crop sales (PGK) 115 608 1 200 0 6 000

Staple sales (PGK) 115 79 267 0 2 300

Vegetable sales (PGK) 115 120 495 0 4 800

Fruits sales (PGK) 115 76 253 0 1 730

Cereal sales (PGK) 115 9 48 0 400

1=household head is female 115 0.15 0.36 0 1

Age of household head 115 42 11 25 72

Years of schooling 115 6 4 0 14

Number of adults in the family 115 3 2 0 10

Extent of land owned (ha) 115 1.02 0.60 0 2

Number of land parcels 115 4 2 0 11

1=Farms with irrigation and land improvement 115 0.50 0.50 0 1

1=Farms with cattle ownership 115 0.06 0.24 0 1

Value of farm equipment (PGK) 115 1 370 9 503 0 100 000

1=Farm hired labour 115 0.37 0.48 0 1

1=Farm used seed purchased 115 0.23 0.43 0 1

1=Farm used high-yielding varieties 115 0.15 0.36 0 1

1=Farm received extension services 115 0.07 0.26 0 1

1=Farm used fertilizer or pesticides 115 0.34 0.48 0 1

Number of crops cultivated 115 6.08 3.67 0 18

Number of crops sustained damages 115 2.61 2.74 0 12

1=Farm household received credit 115 0.17 0.37 0 1

1=Farm family has TV/Radio 115 0.19 0.40 0 1

1=Farm received advance price information 115 0.24 0.43 0 1

Average distance to markets (minutes) 115 103 181 0 1 560

Food expenditure (PGK) 115 131 308 0 3 101

Non-food expenditure (PGK) 115 34 104 0 600

1=Farm family had non-farm income 115 0.21 0.41 0 1

1=Farm family received assistance 115 0.12 0.33 0 1

Source: CAPSA survey (2012)

Appendices

60

Appendix 4. Summary statistics: Farms that own more than 2 hectares of land

Variable Obs Mean Std. Dev. Min Max

Total sales (PGK) 83 2 656 7 380 0 65 300

Cash crop sales (PGK) 83 2 239 7 301 0 65 300

Staple sales (PGK) 83 148 359 0 1 900

Vegetable sales (PGK) 83 96 365 0 3 077

Fruits sales (PGK) 83 156 509 0 3 550

Cereal sales (PGK) 83 18 118 0 1 000

1=household head is female 83 0.18 0.39 0 1

Age of household head 83 39 12 16 77

Years of schooling 83 5 4 0 16

Number of adults in the family 83 3 2 0 10

Extent of land owned (ha) 83 3.79 2.58 2.03 18.9

Number of land parcels 83 5 2 1 11

1=Farms with irrigation and land improvement 83 0.40 0.49 0 1

1=Farms with cattle ownership 83 0.06 0.24 0 1

Value of farm equipment (PGK) 83 1 764 7 818 0 54 220

1=Farm hired labour 83 0.61 0.49 0 1

1=Farm used seed purchased 83 0.31 0.47 0 1

1=Farm used high-yielding varieties 83 0.20 0.41 0 1

1=Farm received extension services 83 0.22 0.41 0 1

1=Farm used fertilizer or pesticides 83 0.22 0.41 0 1

Number of crops cultivated 83 7.51 4.00 0 19

Number of crops sustained damages 83 2.82 2.66 0 13

1=Farm household received credit 83 0.10 0.30 0 1

1=Farm family has TV/Radio 83 0.40 0.49 0 1

1=Farm received advance price information 83 0.27 0.44 0 1

Average distance to markets (minutes) 83 140 203 0.1 1 440

Food expenditure (PGK) 83 178 286 0 2 148

Non-food expenditure (PGK) 83 62 167 0 860

1=Farm family had non-farm income 83 0.18 0.39 0 1

1=Farm family received assistance 83 0.16 0.37 0 1

Source: CAPSA survey (2012)

61

Appendix 5. Regression results of farm sales of cash crops, staples, vegetables and total sales

Variables

Total sales

Cash crops

Staples Vegetables All land holders

Smallholders (L<2 ha)

Others (L>2)

1=HH is a female -0.731*** -0.257 -1.762*** -0.546 0.618 -1.072

(0.281) (0.436) (0.362) (0.460) (0.841) (0.897)

Age of HH 0.00615 0.0266* -0.0101 -0.00908 -0.00855 -0.0324

(0.010) (0.014) (0.015) (0.017) (0.031) (0.032)

No of years of schooling of HH

0.0302 0.0398 0.0652 -0.0299 -0.0674 0.013

(0.028) (0.038) (0.043) (0.046) (0.082) (0.086)

No of adults 0.0171 0.0388 -0.153* -0.164 0.307 0.335*

(0.066) (0.097) (0.084) (0.108) (0.192) (0.202)

Land owned (ha) 0.0851 -1.830* 0.343* 0.231 -0.465 -0.0457

(0.114) (0.932) (0.205) (0.182) (0.395) (0.417)

land owned (squared) -0.000996 0.943** -0.0154 -0.00687 0.00918 -0.0208

(0.007) (0.410) (0.011) (0.012) (0.038) (0.039)

No of land parcels 0.478** 0.724*** 0.482 0.813** 1.189** 0.159

(0.184) (0.249) (0.401) (0.321) (0.571) (0.543)

No of land parcels (Sq.) -0.0311* -0.0638** -0.0302 -0.0683** -0.0518 0.0434

(0.016) (0.026) (0.031) (0.027) (0.048) (0.047)

1=farm used irrigation or improved land

-0.746*** -0.252 -1.385*** -0.449 -2.173*** -1.342*

(0.244) (0.332) (0.362) (0.393) (0.751) (0.746)

1=farm owned cattle 0.675 0.606 0.218 1.657** -2.406 -2.603

(0.461) (0.782) (0.717) (0.713) (1.576) (1.838)

Ln(equipment value-PGK) 0.644*** 0.596*** 0.264 0.2 -0.0599 0.429

(0.152) (0.212) (0.228) (0.243) (0.487) (0.492)

Ln(equipment value-PGK) (squared)

-0.387*** -0.920* -0.467 -0.0934 0.00304 -0.689**

(0.098) (0.491) (0.349) (0.157) (0.300) (0.316)

1=farm hired labour -0.0175 -0.56 -0.36 0.101 0.261 -0.272

(0.223) (0.389) (0.295) (0.353) (0.665) (0.708)

1=farm used seed purchased

0.118 -0.0736 0.847** 0.654 -1.306 -0.886

(0.284) (0.493) (0.340) (0.441) (0.930) (0.975)

1=farm used high-yielding varieties

1.116*** 0.937 0.446 0.083 1.433 3.113***

(0.359) (0.605) (0.432) (0.554) (1.124) (1.170)

Appendices

62

Appendix 5. Regression results of farm sales of cash crops, staples, vegetables and total sales

Variables

Total sales

Cash crops

Staples Vegetables All land holders

Smallholders (L<2 ha)

Others (L>2)

1=farm received extension services

0.38 1.006 -0.289 0.481 1.893** 1.301

(0.301) (0.618) (0.351) (0.459) (0.889) (0.967)

1= farm used chemical fertilizer or pesticide

-0.545** -1.201*** -0.308 -0.0194 -0.945 -0.12

(0.245) (0.330) (0.367) (0.400) (0.745) (0.766)

No of crops cultivated 0.0685** 0.0768* 0.00118 0.168*** -0.0379 -0.0792

(0.029) (0.042) (0.040) (0.045) (0.085) (0.092)

No of crops damaged 0.011 0.0653 -0.0198 -0.0947 0.459*** 0.244**

(0.039) (0.054) (0.053) (0.063) (0.115) (0.121)

1=family obtained credit 0.00752 0.29 -1.088** 0.0378 0.721 0.84

(0.313) (0.402) (0.486) (0.494) (0.975) (0.959)

1= family owned mobile phones, TV or radio

0.0258 0.159 -0.234 -0.998** 0.325 0.791

(0.270) (0.398) (0.333) (0.439) (0.824) (0.878)

1=family received advance price information

0.619** 1.247*** 0.72 1.311*** -1.632* 0.222

(0.269) (0.390) (0.433) (0.418) (0.838) (0.850)

District dummy 0.895*** 1.197*** 0.519 1.734*** 0.399 -1.711*

(0.297) (0.405) (0.430) (0.476) (0.865) (0.919)

Distance to markets (inverse)

-0.521*** -0.474*** -0.617*** -0.819*** -0.139 -0.192

(0.063) (0.087) (0.092) (0.129) (0.176) (0.194)

Family food expenditure 0.000203 0.000365 0.000355 0.00142*** -0.00505* -0.00453*

(0.000) (0.000) (0.000) (0.001) (0.003) (0.002)

Family non-food expenditure

0.00192** 0.00222 0.00209** 0.00413*** 0.00141 -0.00205

(0.001) (0.002) (0.001) (0.001) (0.002) (0.003)

Family non-farm income 0.137 -0.386 0.516 -0.554 2.502*** 0.269

(0.280) (0.365) (0.469) (0.464) (0.852) (0.854)

Family assistance received -0.27 -0.299 -0.588 0.177 0.174 0.799

(0.305) (0.413) (0.446) (0.487) (0.875) (0.943)

Constant 1.679** -0.225 2.123 -2.471* -3.28 2.233

(0.739) (0.904) (1.368) (1.330) (2.256) (2.232)

Sigma 1.330*** 1.228*** 1.015*** 1.793*** 2.893*** 3.191***

(0.075) (0.093) (0.084) (0.119) (0.296) (0.318)

Observations 198 115 83 198 198 198

63

Appendix 5. Regression results of farm sales of cash crops, staples, vegetables and total sales

Variables

Total sales

Cash crops

Staples Vegetables All land holders

Smallholders (L<2 ha)

Others (L>2)

Left censored observations 27 20 7 129 135 130

Log likelihood -307.81 -173.25 -116.14 -305.45 -209.99 -234.67

LR chi^2 239.99 142.35 116.39 213.38 77.27 48.37

Prob>chi^2 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000

Pseudo R2 0.2805 0.2912 0.3338 0.2589 0.1554 0.0934

Standard errors in parentheses: *** p<0.01, ** p<0.05, * p<0.1