DP2012-09

Agricu ltu ral Supply Response andSm allholders Ma rket Participation‒ the Case of Ca mbod ia*
Md. Shafiul AZAM
Katsushi S. IMAI
Raghav GAIHAMarch 16, 2012

* The Discussion Papers are a series of research papers in their draft form, circulated to encourage
discussion and comment. Citation and use of such a paper should take account of its provisional character.
In some cases, a written consent of the author may be required.

Agricultural Supply Response and Smallholders Market
Participation – the Case of Cambodia
Md Shafiul Azam*
Economics, School of Social Sciences, University of Manchester, UK,

Katsushi S. Imai
Economics, School of Social Sciences, University of Manchester, UK and
Research Institute for Economics & Business Administration (RIEB), Kobe University, Japan&

Raghav Gaiha
Faculty of Management Studies, University of Delhi, Delhi-110007, India
16th March 2012

Abstract
This paper explores the key causal factors behind agricultural supply response and farmers'
market participation decisions in Cambodia. A stylized farm household model with market
imperfections is considered and a two-step decision making process is outlined. Farmers
decide, first, whether or not to participate in the market and then they decide how much tosell. The model is estimated using a Heckman type regression model. We compute theunconditional marginal effects for the full sample as well as for the samples for the small andlarge holders separately. Non-price factors such as risk, technology and rural infrastructure
are important determinants of commercialization of agriculture in Cambodia. The marginal
effects for the small and large holders differ substantially both in quantitative and qualitative
terms. This suggests differential treatment in terms of intervention and incentives for small
and large holders would be more effective to promote market access.
Keywords: Agriculture, Supply response, Farm household model, Market participation,
Switching model, Cambodia
JEL classification: Q12, Q13, Q15, C24*Corresponding Author: Md Shafiul Azam (Dr) Economics, School of Social Sciences, University ofManchester, AL Building, Oxford Road, Manchester M13 9PL,UK ; Phone: +44-(0)161-283-0899;
Fax: +44-(0)161-275-4928; E-mail: Md.Azam@postgrad.manchester.ac.uk

Acknowledgements
We are grateful to T. Elhaut, Director, Development Studies and Statistics, IFAD, Dr. Ganesh
Thapa, Regional Economist, APR, and K. El-Harizi, CPM for Cambodia, APR, for theirencouragement and guidance at different stages. The views expressed are, however, personal
and any remaining deficiencies are our sole responsibility.
01. Introduction
A vast majority of rural smallholders in developing countries depend on traditional andsubsistence farming, the characteristic features of which are, among others, low productivity
and low marketed surplus. These smallholder subsistence farmers are most likely to beamong the poorest and the most vulnerable of all groups. They remain mostly outside themainstream exchange economy and unable to take advantage of the opportunities offered by
an exchange economy. On the other hand, subsistence farming is often considered to be theonly means of survival against adversaries caused by market failures of various kinds, unevenaccess to resources and the specific socio-economic and agro-climatic context under which
they operate. Hence, it is important to identify and address underlying factors leading to
subsistence farming and perpetuation of poverty and vulnerability of rural poor, because theintegration of smallholder subsistence farmers into the market mechanism through increased
participation and commercialization of agriculture would facilitate higher living standards
and reduce vulnerability.
The structure of and access to markets is critical in shaping the behavioural responses ofagricultural households in designing their livelihood strategy (Taylor and Adelman,
2003;Brooks, Dyer and Taylor, 2008). As long as markets are perfect for all goods (including
labour), households remain indifferent between consuming own-produced and marketpurchased goods. By consuming all or part of its own output, which could be sold at a given
market price, the household implicitly purchases goods from itself. By demanding leisure or
allocating its time to household production activities, it implicitly buys time, valued at market
wage from itself. Under these circumstances, the household effectively behaves as a profit
maximizing unit.
However, rural households in developing countries are often systematically exposed tomarket imperfections and constraints, referred to as “failures” (Thorbecke, 1993). In somecases, markets do not even exist, for example, in remote areas. In other cases where themarket exists, high transaction costs must be incurred in accessing markets. In yet others,
there are constraints on the quantities that can be exchanged (de Janvry and Sadoulet, 2006).
Moreover, as rural households are often unable to sell surplus labour at the going wage rate
on a year-round basis, they often have to pay more for foodstuffs than they can earn fromselling the same commodities, and risk is an inescapable fact of life for them all. Market
failures can therefore be pervasive and, coupled with uneven distribution and access to1

operational land and other productive assets, and risks, poor rural households may be forced
to devise countervailing strategies against these adversaries which are at best suboptimal.
These countervailing strategies are likely only to compensate for a small fraction of themarket failures under which households operate, in part because the effectiveness of thesestrategies is itself limited by poverty, and they are generally implemented at very high costs
in foregone expected incomes. In this fashion, poverty changes the set of options available tohouseholds, making poverty hard to escape (Duflo, 2003).
There have been important theoretical advances on household behaviour under market
failures since the beginning of the 1980s. These modelling frameworks have largely beensuccessful in explaining and identifying strategic variables and some of the binding
constraints such as transaction costs, risk and other factors affecting the marketing behaviourof subsistence/semi-subsistence farmers in developing countries. More importantly, theinsights derived from these efforts could be used for effective policy design to reduce poverty
and vulnerability. However, as noted by de Janvry and Sadoulet (2006 p177), more often thannot many theoretical derivatives and predictions emanating from these models are “blindly
accepted as truths when they remain to be empirically verified, their order of magnitude in
explaining observed behaviour remained to be ascertained, and their usefulness in the designof policies to be shown”.
In light of the above observations, this paper intends to contribute to the empirical literature
by investigating the supply response of farm households under market failures due totransactions costs and heterogeneous endowments. We take Cambodia as a case, anoverwhelmingly rural society characterized mainly by subsistence farming. In doing so, we
have taken account of the interrelationships among market participation, production and sales
decisions. We further investigate whether there are any systematic differences in behavioural
responses between small and large holders in terms of market participation and sales
decisions. This has strategic importance as this might call for differential policy interventions
for these two sub-sets of farming community and it is also important to focus attention on
policies to increase market participation of the smallholders (Sadoulet and de Janvry, 1995).

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1.1.

Background and Context1

Cambodia, a small Southeast Asian country, is an overwhelmingly agrarian society, where
agriculture outsizes rest of the economy. The country has just come out of a three-decade
long civil war and political conflict during which most of its social and physical infrastructure
was either destroyed or badly damaged. However, it has successfully managed its transition
to a market economy from a Soviet–style command-and–control economy during the 1990s.
Since then in a relatively stable macroeconomic environment, Cambodia registered a decadeof very high and sustained economic growth. In fact, it is one of only 46 countries to achieve
7 per cent average annual growth for 14 years in a row. As a result, the poverty head countfell from 47per cent of the population in 1994 to 35 per cent in 2004, and then to 30 per centin 2007. The poverty magnitude has also fallen, from about 4.3 million people in 2004, toabout 3.9 million in 2007. However, poverty methodology is conservative and the poverty
lines are very low2. The stellar growth that led to poverty reduction of just 1per cent a yearwith increasing inequality means that growth has not been particularly inclusive (ADB, 2011).
Agriculture remains a crucial part of Cambodia’s economy and accounts for 29 per cent of
the GDP in 2007. It employs around 59 per cent of the labour force. Agriculture has been
growing at 4.4 per cent over the past decade, against 4.0 per cent in Vietnam and 3.9 per centin Lao PDR. Growth in this sector is driven by mainly rice and, to a lesser extent, livestockand fisheries. Eighty per cent of farmers grow rice, 60 per cent of them for subsistence. Ricecovered 2.6 million hectare in 2007 (two thirds of arable land and 90 per cent of cultivatedland) and production grew from 3.4 to 6.7 million tons between 1997 and 2007. Yields
remain low, however, (at 2.6 tons/ha, against 3.5-4.0 tons/ha on average in the region).
Cassava is a promising crop, but only 3 per cent of cultivated land is used for it. Considering
the dominance of rice in the overall economy of Cambodia, a substantial productivity
increase of rice culture is imperative. In view of the performance of its immediateneighbours, a doubling of production per hectare of cultivable land is conceivable. With riceculture being mainly the domain of family-farms, improved sector-performance as a wholedepends on them.

1

These stylized facts are drawn from (ADB, 2011)
The average national poverty line for Cambodia in 2007 was KR 2,473 per capita per day, or $0.62 at the exchange rate of
KR4,000=$1. The regional poverty thresholds were KR3,092 per person per day for Phnom Penh ($0.77); KR2,704 forother urban areas ($0.68); and KR2,367 in rural areas ($0.59)).
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At the heart of the problem thus, lies the need for a transformation from a primarily
subsistence- oriented agriculture, characterized by low use of inputs and low returns to land,
to a more commercially-oriented, more intensive agriculture. Given that the agricultural
sector is dependent mainly on family-based rice production, converting ‘smallholder’
households into an ‘emerging’ class of commercial farmers is of strategic importance. The
scope for such a transformation remains large; yet the capacity to modernize and benefit fromthe opportunities of an evolving market economy seem limited. There are major obstacles,
ranging from the poor physical infrastructure resulting in high transport costs and poor
market integration, to low levels of education, training in new techniques and imperfect or
virtually non-existent rural credit and insurance market (Acker, 1999). In addition, thespecific topography of Cambodia may put a number of natural obstacles in the way of asustained intensification. Overcoming these bottlenecks will be the main key for achieving
sustained rapid economic growth based on expansion of increasingly productive employment
opportunities. In other words, the sustained productivity growth and intensification ofagriculture may lay ultimately with the limits of the rural development outreach itself. Given
the above context, an in depth analysis of market participation behaviour and its determinants
may help understand better appropriate policy choices for enhanced productivity and market
integration of small holders.

2. Review of Literature
The literature on agricultural supply response is vast and varied. Rather than going for an
exhaustive review of this enormous literature, this study sets out to review a subset of theliterature which is concerned with market participation and commercialisation of agriculture
in subsistence/ semi-subsistence agrarian economies. Even this subset of literature varies
considerably in several dimensions as it evolves over time – differences span frommotivational contexts to methodology and econometric methods employed; and thus they are
not directly comparable. The review is structured in the following chronologically distinct
phases: i) pre-farm household model era during the 1960s and 70s; ii) farm household modelera of late 1970s and 1980s; and iii) recent studies focusing on market failures.
Initial attempts to construct models of peasant production taking into account the fact thatpeasant households have a dual character as both production and consumption units traces
back to 1960s in the Indian subcontinent, particularly in India. The primary focus of theseearly modelling efforts was to explain the apparent lack of correlation between marketed4

surplus and production of food grains. This generated a fair amount of scepticism and debate
over the usefulness of price policy for increasing the marketed surplus of food grains(Krishnan, 1965; Nowshirvani, 1967; Askari and Cummings, 1974 and 1976). Some of thesestudies also attempted to show that, in the case of the marketed surplus from peasant farms,
inverse/perverse responses were not only theoretically credible but in practice quite probable.
Askari and Cummings’ (1976) contention that, given land reform, availability of fertilizers,
pesticides, and irrigation, farmers will in fact respond to economic incentives by producing
and marketing larger quantities has been questioned by many observers. The objection has
been that while farmers may be responsive to price changes, their planting and marketing
decisions are primarily governed by traditional behaviour and practices, thereby making priceresponses only of secondary importance in explaining output variation. Similarly, Mathur andEzekiel (1961), and Enke (1963) are of the opinion that the marketed surplus of subsistence
farmers may have fixed or relatively fixed monetary obligations and hence, only dispose of asmuch of their production as is necessary to obtain the desired money income. The subsistence
farmers are most likely to be in debt because of social obligations or an unforeseen drought,
and thus in order to meet commitments in such circumstances; they need to sell a portion of
their produce. The result is that an increase in the price of product will be followed by adecrease in the quantity disposed of, since a smaller quantity marketed can meet their cash
requirements. Olson (1960) and Krishnan (1965) have also suggested an inverse relationshipbetween the marketed volume of subsistence crop and price. They argue that an increase in
price for a subsistence crop may increase the producer’s real income sufficiently so that theincome effect on his demand for consumption of the crop outweighs the price effects on
production and consumption, and hence the marketed surplus may vary inversely with market
price.
It is also generally argued that the size of family in a household has a significant effect on themarketed surplus as evidenced by the findings of Sharma and Gupta (1970). Larger family
size disposes of lower marketed surplus than smaller one, since the larger the family size, thehigher will be the quantity consumed, and less will be available for disposal. In other words,
an increase in the marketed surplus is likely to be siphoned- off by an increase in the family
size. Many earlier studies have also indicated that there is a strong link between marketablesurplus and output. They further suggest that the marketable surplus for the rice increases
more than proportionally to the increase in output and that the elasticity of marketable surplus

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with respect to output is very high relative to the partial and total price elasticities. Suchfindings were reported by Bardhan (1970), and Haessel (1975) in their respective studies.
Haessel (1975) argues that the elasticity of marketed surplus with respect to output issubstantially greater than unity. From the policy standpoint this means that, as output
increases, farmers will retain a smaller proportion for consumption purposes and make alarger proportion available for off-farm consumption.
The development of farm household models in the late 1970s and early 1980s marked asignificant advancement in the theoretical as well as conceptual understanding of the issues
that hitherto remained unexplained or at best controversial. Building on the standard
production economics and the early 20th century analysis by Chayanov (1926) of peasantagriculture in Russia, these farm household models developed by Barnum and Squire (1979)
have been used to understand and analyse multitude of policy issues relating to rural
economies of developing countries. Particularly, these models have largely been able toexplain sometimes paradoxical – and even perverse – microeconomic responses of peasantsto changes in relative prices (Strauss, 1986; Lopez, 1984; de Janvry, Fafchamps, andSadoulet 1991). Several other theoretical and empirical studies have used similar modelling
approaches to analyse farm household responses under imperfect labour (Lopez 1984;
Benjamin 1992; Jacoby 1993; Sadoulet, de Janvry, and Benjamin 1998), or food markets (deJanvry, Fafchamps, and Sadoulet 1991; Goetz 1992; Skoufias 1994; Abdulai and Delgado
1999). The distinctive features of these models emanates from non-separability3 rather thanstandard neo-classical seperabiltiy assumption. The conceptual derivatives and thepredictions of these farm household models are, however, extremely sensitive to the set ofassumptions on which they are based (Brooks, Dyer and Taylor, 2008). As household’s
decision- making is assumed to be “recursive” in the sense that consumption and labour
supply decisions depend on its production decisions but not the other way round: production
decisions are independent of the other decisions (Singh, Squire and Strauss, 1986a). Thismeans that as far as production is concerned, the household acts as a profit-maximizing unit
as it would have done in a standard neoclassical set up. The on-farm production effect of anincrease, for example, in the price of staple unambiguously results in increases in labour input

3

A household model is said to be non-separable when the household’s production decisions are affected by its consumer
characteristics (consumer preferences, demographic characteristics, etc.); whereas, in a separable model, households behave
as a pure profit maximizing units. The profits, in turn, affect consumption, but without feedback on production decisions.

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(waged/ or family, or both) and total production 4 (Barnum and Squire, 1979, Chapter 3;
Singh, Squire and Strauss, 1986b, p.154). However, as a consumer, household now faces
higher staple price but at the same time experiences higher income due to higher profits fromfarm production leading to a positive income effect competing with a negative substitution
effect. The net effect becomes ambiguous depending on the slope of the household’s utility
function as well as the magnitude of the profit effect. Hence, as a result of the “recursive”
relationship between the household’s consumption and production decisions, the supply
response of marketed surplus, especially at the market level, may turn out to be negative(Barnum & Squire, 1979; Singh, Squire and Strauss, 1986b). However, non-separability
makes theoretical and, in particular, empirical analyses more difficult. Therefore, mostempirical analyses assume separable farm household models or use reduced forms of a nonseparable farm household model.
In contrast to early farm household model-based works, recent studies emphasize transaction
costs and institutional factors in determining households’ decisions on market participation(Goetz, 1992; Key, Sadoulet and de Janvry, 2000; Vakis, Sadoulet, and de Janvry, 2003;
Vance and Goeghegan, 2004; Carter and Yao, 2002; Carter and Olinto, 2003). Goetz (1992),
in his pioneering work, estimated a switching regression model of market participation andamount traded to grain market in Senegal – separating the decision of whether or not toparticipate in markets from the decision of how much to trade. He found that fixedtransactions costs significantly hindered, while better information stimulated, smallholder’smarket participation. He also decomposed the impact of a rise in the price of grains between
entries of new sellers and increase in the sale of producers already in the market. Elaborating
the works by Goetz (1992), Key, Sadoulet, and de Janvry (2000) develop a model of supply
response when transaction costs cause some producers to buy, others to sell, and others not toparticipate in markets. They consider fixed transaction costs (FTC) and proportionaltransaction costs (PTC). Fixed transaction costs are invariant to the quantity of the goodtraded, whereas proportional transaction costs increase proportionally in quantity. Thus, PTC
corresponds to constant marginal transaction costs. They estimated the model using dataconsisting of Mexican corn producers and the results indicate that both types of transactions

4

It is important to distinguish between the supply responses of agricultural output and the marketed surplus. The above

analysis, which rules out perverse supply response, only applies to the former.

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costs – fixed and variable – play a significant role in explaining household behaviour, withproportional transaction costs being more important in selling decisions.
Heltberg and Tarp (2002) use Goetz’s approach to estimate reduced form equations formarket participation and value of food crops (as a group), cash crops (as a group), and total
value of crops sales, using data from a 1996-97 Living Standard Measurement Survey(LSMS). Factors significantly affecting market participation included farm size perhousehold worker, animal traction, mean maize yield, age of household head, climate risk.
Explaining variation in the value of sales for food crops or cash crops was much lessconclusive, and the authors recognize that aggregation of sales into food or cash crop groups
may mask underlying causal mechanisms related to individual crop decisions. Henning andHenningsen (2007) estimated a non-separable behavioural household model by incorporating
non-proportional variable transactions costs as well as labour heterogeneity along with fixedand variable proportional transaction costs. The authors used household data from Mid-West
Poland. They confirmed that both transactions costs, including the non-proportional ones aswell as labour heterogeneity, significantly influence household behaviour, although in mostcases price elasticities remain the same.
Only a handful of such studies are available despite theoretical advances and most of them
are carried out in the sub-Saharan African context. To the best of our knowledge, there ishardly any such study undertaken in the Asian context. Cambodia with its remarkably
undiversified and largely subsistence agriculture, lends itself to a very interesting case in thisregard. An empirical investigation of agricultural supply response taking into account market
participation behaviour of households under transaction costs would be a notable contribution
to the literature.

3. Theoretical Considerations
3.1.

Transaction costs

Transaction costs are the embodiment of barriers to market participation by resource-poor
smallholders and have been used as a definitional characteristic of smallholders. It is also
considered to be one of the factors liable for market failures in developing countries (deJanvry, Fafchamps, and Sadoulet, 1991; Sadoulet and de Janvry, 1995). Farmers generally
face transaction costs relating to market and information search, screening, enforcement,
bargaining, transfer, or monitoring. These costs tend to be higher for farmers living in remote8areas with poor infrastructures of communication and transportation. The farmers lackinformation about the prices of products at the local level, and at final consumer’s level,
about quality requirements, about places and best periods for selling their products, about
potential buyers, about production in other areas; but also about their rights and thelegislative frameworks. Information about market demand is difficult and costly to obtain forsmallholders. Information may be obtained through contacts with other members of thecommunity but the accuracy of information is not guaranteed since those actors might have
‘opportunistic behaviour’. Thus, the distance to the market together with poor infrastructure
and poor access to assets and information may be manifested in high exchange costs, which
could be an impediment to enable many transactions to take place.
Transaction costs can explain why some farmers participate in markets while others are
simply self-sufficient. Differences in transactions costs as well as differential access to assets
and services to mitigate these transactions costs are possible factors underlying
heterogeneous market participation among smallholders. Transactions costs are broadly
categorized into fixed and variable (or proportional) transaction costs (Key, Sadoulet and de
Janvry, 2000). Fixed transactions costs (FTCs) may include the costs of: (a) search for abuyer with the best price, or search for a market; (b) negotiation and bargaining – these costs
may be important when there is imperfect information regarding prices; (c) screening,
enforcement, bribing, and supervision – farmers who sell their product on credit may have toscreen buyers to make sure they are reliable. Farmers may have to screen potential input
sellers when there is asymmetric information as to the quality and the price of the inputs.
FTCs are invariant to the volume of inputs and outputs traded and are often lumpy since, forexample, a farmer may incur the same search cost to sell either one ton or ten tons of a
product. Once the information about the market has been obtained and contacts made withthe buyer, a household can sell any amount without having to incur extra costs.
Variable transactions costs (VTCs), on the other hand, include costs of transferring the
product or inputs being traded, such as transportation costs and time spent to deliver the
products to (or inputs from the) market. VTCs thus include the per-unit costs of accessing
the markets, which raise the price effectively paid for inputs and lowers the price effectively
received for output, thereby creating a price band within which some households find it
unprofitable to sell output or buy inputs. In general, while information variables are expected
to determine FTCs, measures of distance and transport are expected to determine VTCs.

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3.2.

Modelling Market Participation and Amount Traded

Agricultural output marketing decisions can be modelled as a two-step decision making
process: first, households decide whether or not to participate in the market, and then, theydecide on how much to sell. As opposed to much of the earlier empirical works onagricultural supply response and market participation, recent developments in farm household
modelling under transactions costs (e.g., Key et al., 2000) allow interpretation of results bydistinguishing between fixed transactions costs, which influence only whether to participate
in the market or not, and variable transactions costs, which can influence both the decisions –market participation as well as the amount traded. In addition, this distinction is also
important from technical as well as policy points of view. Technically, it helps to improve theestimation procedure while yielding valuable insights into the effectiveness of particularpolicy interventions for reducing transactions costs.
The conceptual framework of this study thus rests on a stylized static farm household model
incorporating transaction costs in line with Key, Sadoulet and de Janvry (2000). In order tofocus more on transaction costs, following Key, Sadoulet and de Janvry (2000) the model
ignores some aspects of households’ decisions, such as risk (price) and credit constraints. In
addition, ‘market participation’ is treated as a choice variable in the model. Households are
assumed to maximize utility with respect to consumption (), sales (), and purchase () of each good), production (), input use (. Goods consumed include

self-produced agricultural goods, market commodities and leisure. Households produce
agricultural products () using labour, other variable inputs and land ().

In absence of transactions costs, households’ problem is to maximize utility function (1)subject to constraints (2) – (4):
(1)
(2)

(3)
(4)

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wherestands for the market price,is the endowment of goodexogenous transfers and other incomes andand,represents

correspond to household andproduction characteristics, respectively. The cash constraint (2) states that all purchases of thehousehold must be less than or equal to sales and other exogenous income (A) such aspensions, and remittances. The resource balance equation (3) states that the consumed andsold quantity cannot exceed the production, endowment and purchased quantity of each good
. In the case of inputs, the resource balance states that sales, input use, and consumption

cannot exceed endowment and purchased quantity of each input. Equation (4) corresponds

to the production function that relates all inputs and outputs.
Now consider that market exchange involves transaction costs: proportional transaction costs() corresponds to the costs incurred for each unit of marketed output

transaction costs (sold and fixed) which by definition are independent of the amount being transacted.

Similarly, in the case of purchase, proportional () and fixed transaction costs () occur.

With transaction costs equation (2) is transformed in equation (5):(5)
takes the value 1 for the sellers and 0 for the autarkic households for each good

, whiletakes the value 1 for buyers and 0 for autarkic households. The equation suggests that in
the cases when sales involve transaction costs, the price received by the farmer will be themarket pricereduced by the amount of the proportional transaction costs, since thefarmer has to incur this amount for each unit of sold product as a proportional cost. In
addition, marketing of each productwill cost a fixed amountfor the household. The

fixed transaction costs include, for example, costs of search for buyers, costs of collecting
information about the prices and the monitoring costs of the fulfilment of the contractual
agreement. Inversely, when buying goods, the household has to pay an additional
proportional transaction cost

besides the market price for each unit bought.

household also incurs a fixed one-time cost

The

. The first order conditions of the

maximization problem of the utility function will yield the reduced form output marketed
supply, conditional on market participation (Goetz, 1992, Key et al., 2000).
11

Output market participation:(6)
Output marketed supply:(7)
This implies that for those who sell output or buy, the amount of output sold or bought isunaffected by the fixed transactions costs. Once the fixed cost of participating in the market ispaid, fixed transaction costs do not affect the sales volume. Participation in the market isdetermined by discrete comparisons of expected utility from the alternative marketing regime(i.e., participation vs. autarky) and hence it will be affected both by the fixed and variabletransactions costs.

3.3.

Reduced Form and Marketed Surplus

Based on the conceptual framework described above, following Strauss (1984) and later by
Goetz (1992), and Heltberg and Tarp (2006), the reduced form of the underlying modelling
framework could be explained as follows:
(8)
(9)
where

is the production of crop

goods prices,by household,

consumption of crop,arefixed factors pertaining to production, i.e., household characteristics andother factors affecting production,exogenous income sources andhousehold characteristics related to consumption,
farm profits, not accounting for the cost of family labour

inputs. Marketed surplus is then computed by taking the difference between production andconsumption:(10)
This is the reduced form for marketed surplus expressed as the function of all the exogenous
variables irrespective of whether they relate to household’s production or consumption
decisions. The theoretical restrictions of standard supply and demand functions do not apply
to marketed surplus. Also, as Sadoulet and de Janvry (1995) noted, it does not require
estimating the complete system of demand and supply of all products.

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Using the results of equation (6 and 7), we further assume that both fixed and variabletransactions costs impact on market participation while supply decisions, conditional onmarket participation, only depend on variable transactions costs. Technically, this implies that
we can use fixed transactions costs to identify market participation:(11)
Finally, we postulate that variables that explain marketed quantities also explain the selectionof marketing regime, whereas fixed transaction costs help determine market participation, but
do not affect the amount traded conditional on being already in the market.

3.4.

The Empirical Model and Estimation Strategy

While most empirical studies on output marketed supply or input demand have used
Heckman’s (1976) sample selection model or its variants of double hurdle and switching
regression models (e.g., Goetz, 1992; Winter-Nesson and Temu, 2005), some used the more
restrictive tobit model to analyse output marketed supply (e.g., Holloway et al., 2000). As
fixed transactions costs are expected to affect the decision to participate in a market, but not
the amount traded, the sample selection model has been considered more appropriate than the
restrictive tobit model. While tobit model assumes that “zero” values associated with nonparticipation are outcomes of a rational choice (i.e., corner solutions), the sample selection
model explains non-participation using prohibitive transactions costs and other factors.
We estimate the econometric model using the framework of the standard Heckman sample
selection model, where the values of sales of agricultural outputs as well as the choice
between autarky and selling regime were determined jointly. Three sets of regressions are runfor: (i) total sales of all crops, (ii) sales value of marketed food crops, and (iii) sales value ofmarketed cash crops, and each regression has both a selection and a value component. We
then split the sample into two parts: (a) a sample of small holders having operational land lessthan or equal to one hectare, and (b) a sample of large holders having operational land greaterthan one hectare and run each regression for them.
The econometric model is posited as follows:
(12)
(13)
(14)
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where,,is a vector of all the explanatory variables except fixed transaction costs ( ), and, andare parameters to be estimated. Subscript,

indexes households and crop

aggregation (total sales, sales of basic food crops, and sales of cash crops) is suppressed fornotational simplicity.
Sample selection model in marketed supply could be explained in two steps. In the first step,
selection into regimes i.e. selling, and autarky is modelled in separate probit type equation,i.e., an equation for selling versus autarchy including fixed and proportional transaction costs
as well as all other explanatory variables. In the second step, the determinants of marketed
value conditional on market participation are analysed. Hence, both regressors andparameters are allowed to vary across the two-steps and across regimes. In practice, however,
we estimate the model using maximum likelihood procedure which jointly estimates theparameters of the selection and marketing equation. Standard errors are based on the HuberWhite estimator of variance and are considered robust against many types of misspecificationof the model. Covariance between the probability of participation and the quantity traded (the’s) is captured by modelling the joint likelihood of market participation and marketed
values. However, interpretation of the coefficients is not straightforward as in the case of
OLS. Only those variables appear in the outcome equation (in this case, quantity equation)
but not in the selection equation, the coefficients of those could be interpreted as the marginal
effects of a unit change in the independent variables. If, on the other hand, the variables
appear in both the selection and outcome equations, the coefficients in the outcome equation
is affected by its presence in the selection equation as well. This is because both regressors aswell as the parameters are allowed to vary across the two-steps and across regimes (i.e.,
selling and autarky).
The other justification for employing Heckman sample selection model is to correct for
selectivity bias because selling households are non-random subsets of all sampled
households. Households might choose to participate in the market or not due to some
unobserved characteristics – risk aversion, farmer’s skill, or soil quality- and this is likely tobe the case where transactions cost barriers are important and a large segment of subsistence
farmers operate in autarky. Least squares without selectivity corrections would lead to invalid
estimates of the parameters for the full sample. Unconditional marginal effects (i.e., for thefull sample) cannot be derived from the least squares parameters and the possibility that14

regressors might influence market regime and traded volume differently would completely
escape least squares analysis (Heltberg and Tarp, 2002).

3.5.

Specification of the Model

Unlike some of the previous studies (Goetz, 1992; Key, Sadoulet and de Janvry, 2000), thisstudy uses aggregate value of sales as dependent variable. One of the major reasons for using
sales value rather than quantity marketed (i.e., in tons or kgs’) is to make most out of the data,that is, to use all available information in the data, including those who produce and sell crops
other than rice or food crops. Moreover, due to substitution between crops, some exogenous
variables may increase individual crop sales at the expense of other crops. It is, however, now
well established that single crop supply is more elastic than the aggregate output supply.
Arguably, aggregate supply is what ultimately matters to policy makers (Binswanger, 1990).
Secondly, the choice of aggregating over multiple crops, on the other hand, makes it
impractical to work with quantities, because quantities produced or sold of different crops
cannot be aggregated directly. Values resolve this by using market prices as implicit weights(Heltberg and Tarp, 2002). The greyer side of this kind of aggregation is that aggregation
conceals differences in the underlying causal mechanisms related to individual crop
decisions. Farmers may view differently in their portfolio of crop production, in which casesingle crop estimation is necessary to provide the full picture. However, agriculture in
Cambodia is remarkably undiversified and characterized by mono-cropping (largely due toits’ agro-climatic conditions), where paddy is being produced predominantly. Aggregation
over food crops is basically aggregation of paddy produced in dry and wet seasons.
Therefore, the aggregation bias discussed above is unlikely to pose serious problems in our
case.
Exogenous set of regressors include variables theoretically expected to affect quantities to be
sold as well as whether to participate in the market or not, that is, to select marketing regime.
Price of paddy is the most natural candidate to be included in the model. Paddy is the single
most important crop in relatively less diversified Cambodian agriculture. Over 90 per cent of
the cultivable land is devoted to paddy production. Hence, price of paddy is expected to be
the principal determinant of agricultural supply response in Cambodia.
Three variables are included in the model to capture the effect of household endowments:
land per worker, ownership of agricultural implements (plough, hand tractor, tractor or water
15

pump), and land title. Secured land ownership motivates farmers to invest in landdevelopment and maintain soil quality. Theoretically, all these are expected to have positiveeffects on marketed surplus and participation.
Ethnicity is included in the model to reflect the case that higher mutual trust and common
belief and understanding might affect the market participation through information sharing
thus reducing the fixed transaction costs. Theoretically, older and more experienced
household heads have greater contacts; allowing trading opportunities to be discovered atlower costs; and this may also reflect increased trust gained through repeated exchange withthe same party. Among the other background characteristics of a household, a dummy forhouseholds having any of its members employed in a paid job is included to take into accountnon-farm earning opportunities.
Village level median rice yield is included in the model with a three- pronged objective of
capturing state of technology use, climatic condition, and past investment. A dummy for risky
region seeks to capture production risks posed by natural disasters - excessive rain, flood ordrought.
With a Heckman two-step approach, we first estimate a probit model of participation in therelevant market as a function of both variables that are also likely to determine crop sales
volume, conditional on market participation as well as one or more variables that satisfy
exclusion restrictions (Wooldridge, 2006). Our exclusion restriction variables focus on thefactors affecting transactions costs. The exclusion restrictions we employ are similar to thoseused by Heltberg and Tarp (2006) in their analysis of the determinants of market participationin Mozambique.
Transaction costs are important determinants of market participation as well as the amounttraded, but they pose serious empirical challenges relating to measurement. First, whentransactions costs are too high to prevent exchanges to take place, then, by definition thesecosts cannot be observed because no transaction has taken place. Second, even when atransaction takes place, transaction costs cannot be easily recorded in a survey (Key, Sadouletand de Janvry, 2000). Transaction costs are thus unlikely to be observable in standard
household surveys. CSES-2004, 2007 data that we employ in our analysis is no exception inthis case. Hence, building on the past empirical works elsewhere (particularly Heltberg andTarp, 2002), this paper resorts to the observable exogenous variables that are theoretically16expected to capture or explain these transaction costs. Variables such as distance to market
outlets, ownership of transport equipments, and information/communication assets areexamples of exogenous determinants of transaction costs.
More specifically, variables used to capture transactions costs are: distance to nearest market,
distance to the nearest bus-stop, distance to the provincial capital, ownership of transport
equipments (cart, bi-cycle or motor-cycle), ownership of information/communication assets(radio, television or telephone), village population density, and education of the head of thehouseholds. By increasing travel time and transport cost, distance to market outlets (or busstop, provincial capital) is expected to have a negative effect not only on market participation
but also on the amount traded. It is thus related to VTCs. The other VTCs – for example,
ownership of transport equipments- are expected to have positive influence on market
participation as well as quantity sold. Access to communication/information networks
essentially mitigates the fixed transactions costs and is thus likely to facilitate market
participation only. Other information variables included to capture fixed transactions costs
are education of head of household, 5 and (log) of village population density. A better
educated head of household is assumed to be capable of higher level of information
processing and well- networked within the community. Similarly, in a densely populated
close-nit society information flow is assumed to be faster and better than in a sparsely
populated community. Both of these variables are expected to affect market participation
positively.
While ownership of transport equipments is also supposed to enhance market participation,
whether this is through its role in accessing information (FTCs) or in facilitatingtransportation to market outlets (VTCs) is a question of empirical validation. This variable
might have dual roles: first, gathering market information by going physically to the nearest
market place; second, transporting farm outputs to the market. The empirical approachproceeds by estimating and comparing the significance of two different versions of the
Heckman model, one with the variable used only in the selection (first stage) relating toparticipation and another with the variable used in both participation and sales equations. The
preferred model would suggest the dominant attribute of transport equipment ownership –information or transport attributes or both.

5

Alternatively, education could be included as an endowment variable as well.

17

Finally, the issue of endogeneity needs to be addressed. One might suspect potential
endogeneity of some of the variables used in the analysis; specifically ‘price of paddy’ and‘land per worker’. The data on ‘price of paddy’ in our sample show considerable variation
both across villages and through the harvest season. As price varies depending on the place
and time of sale, it is potentially endogenous. Moreover, it is observed only for those farmers
who actually sold paddy during the period of the survey. Therefore, village level medianpaddy price is derived and used in the analysis.
Cambodian land market is largely inactive and passing through a transitional phase from astate-owned one to a market- based one. Land is largely state allocated and market turnoverof land is very low. In our data, land turnover is in fact less than 7 percent, which is
negligible from an analytical point of view. Hence, the variable ‘land per worker’ (defined as
total arable land holding divided by the number of economically active members of thehousehold) in our case is highly unlikely to be endogenous. In fact, the absence of an active
land market is the rationale given for the treatment of landownership as an exogenous
regressor in almost all the empirical works involving household behaviour in similar settingsin Africa and South Asia (Khandker, 2005).

4. Data, Variables and Descriptive Statistics
Data used for this study come from Cambodian Socio-economic Survey -2004 (CSES) and
CSES - 2007. Details about the data sets and variables used in the analysis are given below:

4.1.

CSES - 2004

The CSES – 2004 is a standard LSMS type survey and is the first multi-objective household
survey undertaken in Cambodia. Data were collected over a 15 month period from November
2003 through January 2005. A total of 14842 households were interviewed in 900 villages
during a 15 month period The 2004 CSES is also the first household survey that covers theentire country.
The 2004 CSES collected data on household consumption using two different data collection
methodologies, i.e., recall questions similar to those used in previous surveys and a calendar
month diary in which all household economic transactions were recorded. Consequently, theCSES-2004 survey teams spent more than one month in each surveyed villages. In addition todata on household consumption and a wide range of social indicators, the CSES collected
data on the daily time use of all household members, data on sources of household income,18

village data on land use and access to community and social services (for examples, roads,
electricity, water, markets, school and health facilities), and data on up to three prices fromlocal markets for 93 food and non-food items.
2004 CSES sample was drawn from 45 strata (24 provinces, urban and rural) in three steps
using the 1998 population census as the sampling frame. First, 900 villages were selectedfrom the various strata using systematic random sampling. Second, one census enumeration
area was chosen randomly from each sample enumeration area, yielding a total sample of15000 households (of which 14984 were actually interviewed). One thousand households
were interviewed each month of the survey in a randomly selected (and therefore nationally
representative) sample of 60 villages. The 2004 CSES is not self-weighting. Two sets ofadjusted sample design weight are provided, one for use with the calendar year 2004 sample
of 12000 households (of which 11993 households were actually interviewed) and the otherfor use with the full sample of 15000 households (of which 14984 households were actuallyinterviewed). Estimates presented in this paper are based on the calendar year 2004 sample of
11993 households actually interviewed and are weighted to be representative of

theCambodian population. Calendar year 2004 data are used to avoid introducing seasonalbiases. Table 1 provides all the variables used in the estimation of the model.
Table 1 Variables used in the Model
Variables
Dependent
Log sales value food crops
Log sales value cash crops
Log sales value-all crops
Explanatory –Log price of paddy
Explanatory – households’ background Characteristics
Log age of hhh
Dependency ratio
Dummy for ethnicity
Dummy for paid job
Explanatory – households’ Endowments
Log land per worker
Dummy for land title
Dummy for ownership of ag. Equip
Explanatory – Geographical and technological
Characteristics
Log rice yield (village level)
Dummy for risky region
Explanatory – Variable Transaction Costs
Log distance to market
Dummy for ownership of transport equipments
Log distance to bus stop

Label
Natural log of Marketed Surplus of basic Food Crops
Natural log of Marketed Surplus of Cash Crops
Natural log of Marketed Surplus of all Crops
Natural log of village level median Price of Paddy
Log age of head of the household in years
Dependency ratio
Dummy for the Ethnicity (Khmer=1, 0 otherwise)
Dummy for whether any member of the household has apaid job or not
Natural log of farm size per worker
Dummy for whether household has land title or not
Dummy for ownership of agricultural Implement (Plough,hand tractor, tractor or water pump)Natural log of median village level rice yield
Dummy for risky region – region reported most crop
damage due to excessive rain, flood or draught last year
Natural log distance to nearest market
Dummy for ownership of transport equipment – cart, vanetc.
Natural log distance to bus stop

19

Log distance to provincial capital
Explanatory – Fixed Transaction Costs
Log village population density

Natural log distance to provincial capital
Natural log of village population density per square
kilometre
Dummy for ownership of information equipment – radio,TV, or Telephone
Educational level of the head of the household

Dummy for ownership of radio, tv, or telephone
Education of hhh

Table 2 Descriptive Statistics (CSES – 2004)
Variables
Log sales value-all crops
Log sales value food crops
Log sales value cash crops
Log price of paddy
Log land per worker
Log age of hhh
Dependency ratio
Dummy for land title
Dummy for paid job
Dummy for risky region
Log rice yield (village level)
Dummy for ownership of ag. Equip.
Dummy for ethnicity
Dummy for ownership of transport equipments
Log distance to market
Log distance to bus stop
Log distance to provincial capital
Dummy for ownership of radio, tv, or telephone
Log village population density
Education of hhh
Number of Observations

4.2.

Mean
12.74612.77912.3796.088-1.240
3.752
0.817
0.554
0.283
0.064
7.340
0.491
0.961
0.728
1.528
2.065
3.451
0.619
7.015
5.729
11862

Standard deviation
1.52
1.44
1.59
0.24
1.22
0.32
0.70
0.50
0.45
0.24
0.56
0.50
0.19
0.44
1.20
1.58
0.84
0.49
0.73
10.54

Minimum
5.7
5.7
7.1
3.9
-10.3
2.9
0.0
0.0
0.0
0.0
6.2
0.0
0.0
0.0
-2.3
-2.3
0.0
0.0
4.6
0.0

Maximum
18
18
18
7
4
5
6
1
1
1
12
1
1
1
5
6
6
1
10
19

CSES – 2007

The Cambodian Socio-economic Survey -2007 is similarly a standard LSMS type survey
comprising data from 3598 households from 360 villages. These villages are in factsubsample of the CSES-2004, but the households are not necessarily the same as in 2004.
There are various modules containing detailed households and village characteristics
including households’ socio-economic characteristics, economic activities – crop production,
consumption of food and non-food items, health and education status, access to social andcommunity services. Table 3 gives descriptive statistics for all the variables used in theestimation of the model for CSES-2007.

20

Table 3 Descriptive Statistics (CSES - 2007)
Variables
Log sales value-all crops
Log sales value food crops
Log sales value cash crops
Log rice yield (village level)
Log price of paddy (village level)
Dummy for risky region
Log land per worker
Dummy for ethnicity
Age of head of households in years
Dependency ratio
Education of hhh
Dummy for land title
Dummy for ownership of ag. Equip.
Dummy for ownership of transport equipments
Dummy for ownership of radio, tv, or telephone
Log distance to market
Log distance to bus stop
Log distance to provincial capital
Dummy for paid job
Log village population density

Mean
13.46
13.54
13.08
.93
4.38
.53
-.92
.98
44.86
.79
4.37
.58
.38
.25
.74
1.58
2.065
3.451
.41
7.015

Standard deviation
1.60
1.57
1.58
.22
2.93
.50
1.19
.16
13.80
.68
3.63
.49
.49
.43
.44
1.20
1.58
0.84
.49
0.73

Minimum
-3.47
8.51
-3.46
.39
0
0
6.50
0
16
0
0
0
0
0
0
-2.30
-2.3
0.0
0
4.6

Maximum
17.74
17.77
17.76
1.34
7.17
1
3.91
1
91
5
19
1
1
1
1
4.59
6
6
1
10

5. Results
We estimated the model using two different sets of household survey data namely CSES –2004 and CSES – 2007 for Cambodia as described in the previous section. CSES – 2004 is
the most comprehensive household data and more representative than the CSES – 2007 data
although they share same questionnaire and similar methodology in terms of sampling andother attributes. Hence they are comparable and the results from the CSES – 2007 data should
provide some sense of validation of our initial estimates. In what follows, we first present theresults of our initial estimates based on CSES – 2004 data and then the results based on CSES
– 2007 in the next subsection.

5.1.

Estimates Based on CSES - 2004

As noted earlier, the coefficients of the two stage selectivity models can not be interpreted as
marginal effects when the same set of exogenous variables appear in both the selection andoutcome equations. Accordingly, we have computed unconditional marginal effects(computational procedure is given in Appendix 1) for all the models. The marginal effects6
for all households, large holders (farmers who have more than one hectare of operationalland) and small holders (farmers with one hectare or less operational land) across crop types
are presented in figure 1, 2, and 3 respectively along with the regression results (Table 4, 5,
and 6). For each model, there are two columns: first column reports the log of annual sales
6

For details about marginal effects of selectivity models please see Huang, Raunikar, and Mistra 199;
Hoffmann, and Kassouf 2005; Sigelman and Zeng 1999.

21

given market participation and the second column shows the results of market participation
probability model. However, our principal focus would be to disentangle distinctive (if any)
features of agricultural supply responses between small and large holders emanating fromtheir

behavioural

and

other

attributes

andanalysewelfareimplicationsforsubsistence(mostly smallholders) farmers. Accordingly, we will touch upon some of the main
findings of the overall model in this section and then move on to the next section where wecompare the marginal effects as well as the regression results for the small and large holders.

Figure 1 Marginal Effects across Crop

22

Table 4 Regression Results (Heckman Sample Selection Model), Dependent Variable = log Sales value ofmarketed Surplus
Food Crops
Quantity
SelectCash Crops
Quantity
SelectAll Crops
Quantity
SelectLog median price of paddy (vill)

0.347**(2.90)

0.784***(10.74)-0.369*(-2.09)0.366***(4.96)

0.349***(3.38)

0.737***(10.64)Log land per worker

0.772***(15.99)0.663***(33.28)0.257***(5.28)

0.129***(7.08)

0.734***(19.35)0.557***(30.21)

Log age of HH

0.420***(4.27)-0.0283(-0.43)0.239(1.45)

0.306***(4.47)

0.498***(5.72)

0.147*(2.34)

Dependency ratio-0.00217***(-4.59)0.00345***(-12.48)-0.000014(-0.02)0.000181(-0.67)-0.00209***(-5.43)0.00245***(-9.56)

Dummy for land title-0.0699(-1.32)-0.0122(-0.35)-0.293***(-3.62)0.0776*(2.17)-0.127**(-2.73)0.0472(1.41)

Dummy for paid job

0.0825(1.15)-0.0573(-1.23)0.0149(0.15)

0.00889(0.19)

0.0762(1.23)-0.0558(-1.27)

Dummy for risky region-0.638***(-5.01)-0.484***(-6.99)-0.442**(-2.99)0.345***(5.46)-0.426***(-4.81)-0.111(-1.81)

Log median rice yield (vill)

1.075***(15.87)0.787***(23.47)0.331***(4.80)

0.0964**(3.06)

0.932***(17.66)0.644***(20.58)Dummy for ownership of ag. Implements

0.417***(6.75)

0.220***(5.72)-0.139(-1.41)0.244***(6.15)

0.301***(5.58)

0.264***(7.33)

Dummy for ethnicity-0.171(-0.93)0.0837(0.76)-0.503(-1.91)0.202(1.79)-0.144(-0.93)0.150(1.48)

Dummy for ownership of transport
equipments

0.171*

0.06580.111-0.00574

0.180**

0.0383(2.56)(1.53)(1.13)(-0.13)(3.16)(0.95)

Log distance to market-0.0459(-1.87)-0.0484**(-2.92)-0.108**(-2.84)0.0663***(3.90)-0.0512*(-2.39)-0.00394(-0.25)

Log distance to bus stop

0.0613**(3.18)

0.0565***(4.55)-0.0554(-1.81)

0.0925***(-7.52)-0.0259(-1.64)0.00425(0.36)

Log distance to provincial capital-0.0236(-0.60)-0.00821(-0.33)-0.0772(-1.39)0.0843***(3.43)-0.0255(-0.78)0.0173(0.74)

Variables

0.0677

0.0919*0.103**(1.83)(2.54)(2.89)

Log village population density

0.00501(0.17)

0.179***(6.22)

0.0910**(3.17)

Educational level of HH

0.00337*(1.98)

0.00120(0.73)

0.00216(1.27)

Dummy for ownership of radio, TV, or
telephone

Constant
N

1.259(1.10)
6978-10.15(-15.84)

t statistics in parentheses * p < 0.05, ** p < 0.01, *** p < 0.001

23

13.86
(7.74)
6978-6.644(-10.34)

2.470(2.54)-9.981(-16.48)

5.2.

Summary of Results:

All Households
The price of paddy, the most important crop in Cambodian agriculture cultivated in
more than 80% of the arable land, has expected positive sign and significance inalmost all the equations. For all crops and food crops, price of paddy has significant
positive effect and for cash crops it has negative but significant effect. This istheoretically consistent as higher paddy price induces households to produce more of
the product rather than producing crops that are relatively less profitable. The
marginal effect of price of paddy accordingly comes out to be highest in magnitude.
An interesting finding is that higher price of paddy not only results in higher marketed
surplus of rice, it also motivates households to diversify their portfolio of crop
production. As price increases so does profit and income of the farmers and better- off
farmers can now afford to produce more cash crops resulting in increased market
participation and commercialisation of agriculture.



Based on the CSES – 2004 data, except land title (which is very important for small
holders that we will discuss later), the coefficients for other capital endowment
variables, namely - land per worker, ownership of agricultural implements – arepositive and highly significant for both quantity and market participation equations,indicating that relatively well-endowed farmers participate in the market andcommercialize. The marginal effects for these two variables are also among thehighest (3rd and 4th, respectively). This is even more important for the smallholders’group (fig.2).So interventions that build households’ agricultural assets and intensify
cropping pattern (expansion of new arable land is constrained by cost of demining,
and redistribution is politically not feasible) could pay higher dividends in terms ofpoverty reduction, through increased market participation and commercialisation ofagriculture by smallholders.



The dummy for risky region, as expected, has the largest (absolute) negative marginal
effect on food crop marketed surplus while the opposite is true for cash. This is aninteresting finding. Food crop is dominated overwhelmingly by paddy in Cambodia
which is susceptible to damage due to disasters such as excessive rain, flood, ordrought. Farmers in disaster prone region opt for safer but low return crops such as
mango/ or banana resulting in potential food insecurity/ persistent poverty andvulnerability (or possibly a geographic poverty trap). This is particularly true in a24subsistence economy characterised by imperfect credit and insurance markets. Henceliving and farming in risky regions clearly does not help market integration andcommercialization of agriculture.
Marginal effect of village level median rice yield, a variable capturing combined
effect of geographic features, state of technology use and the effect of other inputs isof considerable significance. Increased rice yield not only results in larger marketed
surplus of rice itself, it also helps generate larger marketed surplus of cash crops.
Improved rice/food productivity can drive commercialization of other crops as it
potentially could free up land and other resources tied up in subsistence farming. For
smallholders this has the highest marginal effect. This is true for both sets of estimates
based on CSES – 2004 and CSES – 2007.



Factors capturing variable transaction costs, except transport equipment ownership,
have in most cases (both in selection as well as sales/quantity equations) expected
signs and are significant. Thus variable transaction costs constitute one of the major
binding constraints to market participation and commercialisation of agriculture in
Cambodia which is an important finding with significant policy implications. This isparticularly important for smallholders. The coefficient of (log) distance to market forthis group is negative and highly significant in both participation and quantity
equations. So providing better access to markets is likely to induce smallholders tocommercialise.

Variables (i.e. ownership of radio, television or telephone, education level of the headof the household, and the village population density) capturing fixed transaction costs
and information processing have expected signs and are significant in all the
specifications, suggesting that better access to information networks is likely to resultinincreased

agricultural

outputs,enhancedmarketparticipation

andcommercialisation.

5.3.

Small Holders vs. Large Holders

Subsistence/semi-subsistence households might exhibit differential supply response for anumber of reasons: first, the assets, technologies and incentives available to the poor and thenon-poor may differ. This is, for example, the case if smallholders find themselves unable toshare in the market- based growth for lack of skill, labour or land. Or, second, the behavioural
responses (controlling for assets, technologies and incentives) may vary between small andlarge land holders. This is, for example, the case of risk aversion or lack of skill/or ability
25

preventing the poor from taking advantage of market opportunities (Heltberg and Tarp,
2002). If the poor/vulnerable are to be able to reap benefits out of larger economic growth
process, it is important that their degree of market integration is increased.
Table 5 Regression Results (CSES – 2004; Small holders), Dependent Variable = log Sales value ofmarketed Surplus
Variables
Log median price of paddy (vill)

Food Crops
Quantity
Select0.323
0.765***(1.49)(7.66)Cash Crops
Quantity
Select-0.3920.241*(-1.55)(2.29)All Crops
Quantity
Select0.231
0.634***(1.32)(6.94)

Log land per worker

0.248*(2.53)

0.474***(16.63)0.138*(2.02)

0.0426(1.61)

0.242***(3.50)

0.348***(13.96)

Log age of HH

0.0669(0.43)-0.130(-1.49)0.265(1.10)

0.180(1.88)

0.146(1.10)

0.0375(0.47)

Dependency ratio-0.00137(-1.62)-0.0028***(-7.49)-0.0000427(-0.05)-0.00034(-0.91)-0.00123(-1.92)-0.002***(-6.13)

Dummy for land title-0.103(-1.12)-0.0262(-0.54)-0.205(-1.60)0.151**(2.95)-0.0843(-1.12)0.0713(1.62)

Dummy for paid job

0.169(1.35)-0.115(-1.82)0.147(0.98)-0.0303(-0.47)0.152(1.56)-0.101(-1.78)Dummy for risky region-0.674**(-3.16)-0.338***(-3.50)-0.616**(-2.81)0.344***(3.90)-0.470***(-3.40)-0.00313(-0.04)

Log median rice yield (vill)

0.787***(5.26)

0.724***(16.51)

0.0997(0.83)

0.271***(6.20)

0.668***(5.51)

0.653***(16.21)

Dummy for ownership of ag. Implements

0.265**(2.73)

0.159**(3.24)-0.165(-1.27)0.230***(4.41)

0.207*(2.55)

0.199***(4.49)

Dummy for ethnicity

0.322(1.02)

0.0356(0.23)-0.551(-1.28)0.277(1.66)

0.200(0.76)

0.208(1.50)

Dummy for transport equipments

0.0691(0.64)

0.0419(0.74)

0.264(1.90)-0.0404(-0.68)0.119(1.38)

0.00553(0.11)

Log distance to market-0.139**(-3.10)-0.0768***(-3.30)-0.146*(-2.52)0.0208(0.84)-0.148***(-4.10)-0.0460*(-2.17)

Log distance to bus stop

0.0343(0.96)

0.0718***(4.13)

0.0312(0.73)-0.067***(-3.83)0.000900(0.04)

0.0118(0.76)

Log distance to provincial capital-0.0390(-0.61)-0.0517(-1.53)-0.117(-1.50)0.0143(0.42)-0.0517(-1.04)-0.0359(-1.19)Dummy for radio, TV, or telephone

3.746(1.62)-0.00274(-0.05)15.58***(5.94)

0.0771(1.54)

5.372**(2.75)

0.0393(0.83)

Log village population density-0.0354(-0.86)0.164***(4.07)

0.0510(1.36)

Educational level of HH

0.000450(0.17)-9.042(-10.31)-0.00232(-0.84)-6.661(-7.32)-0.00083(-0.34)-9.010(-11.31)

Constant
N

3982

3982

t statistics in parentheses * p < 0.05, ** p < 0.01, *** p < 0.001

26

Table 6 Regression Results (CSES 2004 – Large holders), Dependent Variable = log Sales value ofmarketed Surplus
Variables

Food Crops
Quantity
SelectCash Crops
Quantity
SelectAll Crops
Quantity

SelectLog median price of paddy (vill)-0.158(-1.05)0.868***(8.19)-0.332(-1.31)0.565***(5.25)

0.0525(0.39)

1.001***(9.15)

Log land per worker

0.448***(7.28)

0.614***(14.28)0.245**(2.73)-0.140**(-3.25)0.610***(11.42)0.358***(7.84)

Log age of HH

0.543***(4.08)-0.0396(-0.40)0.251(1.20)

0.221*(2.14)

0.581***(5.01)-0.00541(-0.05)

Dependency ratio

0.000801(1.37)-0.0036***(-8.53)0.0000552(0.07)

0.00104*(2.46)-0.00104*(-2.09)-0.002***(-4.14)

Dummy for land title-0.0744(-1.12)0.00969(0.20)-0.343***(-3.31)0.0406(0.79)-0.192**(-3.27)0.0398(0.76)

Dummy for paid job

0.0404(0.45)

0.0155(0.23)-0.124(-0.92)0.0161(0.23)

0.0270(0.34)-0.00188(-0.03)

Dummy for risky region

0.204(1.33)-0.553***(-5.68)-0.342(-1.72)0.420***(4.42)-0.273*(-2.40)-0.195*(-2.05)

Log median rice yield (vill)

0.568***(7.72)

0.771***(15.49)0.531***(6.04)-0.0328(-0.70)0.743***(11.65)0.588***(11.33)

Dummy for ownership of ag. Implements

0.118(1.39)

0.148*(2.38)-0.0874(-0.64)0.0929(1.41)

0.0504(0.67)

0.118(1.81)

Dummy for ethnicity-0.561*(-2.50)0.236(1.52)-0.468(-1.44)0.173(1.08)-0.362(-1.92)0.156(1.00)

Dummy for transport equipments

0.0539(0.61)-0.0217(-0.34)-0.0500(-0.37)-0.0562(-0.83)0.0874(1.15)-0.103(-1.52)

Log distance to market

0.0289(0.94)-0.0222(-0.96)-0.0635(-1.27)0.0796**(3.26)

0.00174(0.06)

0.0289(1.17)

Log distance to bus stop

0.0407(1.72)

0.0309(1.76)-0.129**(-3.04)-0.120***(-6.70)-0.0370(-1.82)0.00123(0.07)

Log distance to provincial capital-0.0913(-1.81)0.0247(0.67)-0.0512(-0.65)0.136***(3.65)-0.0780(-1.81)0.0370(0.98)

0.179***

0.08180.250***(4.05)(1.53)(4.96)

Log village population density

0.0665(1.82)

0.181***(4.19)

0.209***(4.96)

Educational level of HH

0.00470*(2.06)

0.00299(1.34)

0.00344(1.41)

Dummy for ownership of radio, TV, or
telephone

Constant

9.32(7.05)

N

2996-11.07(-11.91)

11.95(5.05)
2996

t statistics in parentheses* p < 0.05, ** p < 0.01, *** p < 0.001

27-6.613(-7.04)6.615(5.44)-11.36(-11.62)

Hence it is vital to understand the factors underlying systematic differences in market
integration for various crops across farm households.
As revealed by regression results in table 5 and 6 and the corresponding marginal
effects in fig. 2 and 3, while price of paddy, rice yield capturing the state oftechnology use, among other factors, farm size and ownership of agriculturalequipment have similar positive and significant marginal effects, there are notabledifferences between small and large holders’ responses. For smallholders it is the rice
yield rather than the price of paddy which has the highest marginal effect. Similarly,
ownership of agricultural implements is far more important to smallholders than tolarge holders. The implication is that interventions meant to build smallholders’agricultural assets, provide access to technology through better extension services,
irrigation during dry season, among others, are likely to payoff in terms of increased
production (both food and cash crops) and greater market integration of smallholders.



The dummy for risky region affects large holders’ supply response more severely thansmall holders’.



One of the major findings of this analysis is that for small/subsistence holders,transaction costs turn out to be one of the main barriers for generating marketed
surplus of food crops. Variables capturing variable transaction costs, that is, distanceto market, distance to bus stop, distance to provincial capital and ownership oftransport equipments all have expected sign and significant marginal effects on supply
response. But the same is not true for large holders. This has far reaching policy
implications – developing rural infrastructures such as road networks connecting
markets and storage facilities and access to information networks- would potentially
pay high dividends in terms of increased food production resulting in higher marketed
surplus and commercialisation. This could potentially ensure better nutritional status
and food security of the poor and reduce vulnerability of small and subsistence
farmers.



Similarly, secure land ownership facilitates market integration of subsistence farmers.
For large holders group land title variable does not have significant marginal effect.
Obviously, large holders are likely to be powerful rural elites and would feel moreassured about their possession than smallholders.



Subsistence households having alternative earning sources (these are mostly paid
domestic workers as suggested by data) to meet their cash requirements produce food28

crops only for their own consumption, not for sale. The intuition is that smallholders
do not need to commit forced sale of part of their produce to meet theiremergency/urgent obligation as they have alternative sources of cash.

Figure 2 Marginal Effects Across Crop types – Small Holders

Figure 3 Marginal Effects Across Crop Types – Large Holders

29

Figure 4 compares unconditional marginal effects of small and large holders for all crop
categories. Clearly, the differences between the marginal effects of small and large holders
are substantial.

Figure 4 Marginal Effects of Small vs Large Holders

5.4.

Estimates Based on CSES – 2007 Data

Table 7 gives the regression results based on CSES – 2007 data for all households and thecorresponding unconditional marginal effects are reported in figure 5 and in appendix 2. Asbefore, for each model there are two columns – the first column (quantity) reports the log of
annual sales given market participation and the second column (select) shows the results ofmarket participation probability model.

30

Table 7 Regression Results (CSES-2007), Dependent Variable = log Sales value of marketed Surplus
Food Crops
Quantity
SelectVariables

Cash Crops
Quantity
SelectAll Crops
Quantity
SelectLog median price of paddy (vill)

0.0148(0.42)

0.175***(8.04)-0.109*(-2.37)0.0782***(4.13)-0.0781**(-3.22)0.0640**(3.16)

Log land per worker

0.687***(10.15)0.584***(15.86)0.181**(2.61)

0.0404(1.33)

0.635***(13.09)0.436***(13.40)Log age of HH

0.00170(0.50)

0.000336(0.13)-0.00669(-1.08)0.00770**(3.03)

0.00194(0.64)

0.00267(1.03)

Dependency ratio-0.513***(-6.46)-0.421***(-8.00)-0.143(-1.22)-0.0392(-0.77)-0.461***(-6.86)-0.353***(-6.93)

Dummy for land title

0.238*(2.35)

0.0729(1.00)-0.170(-0.99)0.123(1.71)

0.148(1.69)

0.139(1.93)

Dummy for paid job

0.124(1.21)-0.142(-1.93)-0.269(-1.65)0.0622(0.87)

0.0116(0.13)-0.108(-1.48)Dummy for risky region-0.104(-0.97)-0.0971(-1.27)0.104(0.59)

0.137(1.83)

0.0728(0.80)

0.0644(0.85)

Log median rice yield (vill)

1.388***(10.49)0.983***(12.16)0.157(0.96)

0.0636(0.89)

1.194***(11.41)0.809***(10.75)

Dummy for ownership of ag. Implements

0.134(1.14)-0.0260(-0.30)-0.431*(-2.10)0.177*(2.06)

0.150(1.45)-0.0389(-0.45)

Dummy for ethnicity

0.589(1.52)

0.358(1.42)-1.519*(-2.34)0.160(0.62)-0.487(-1.36)

0.507*(2.15)

Dummy for transport equipments

0.0166(0.14)

0.326***(3.55)

0.162(0.77)

0.126(1.41)

0.0582(0.54)

0.352***(3.77)

Log distance to provincial capital-0.120*(-2.48)-0.111**(-3.17)0.0811(1.05)-0.0719(-1.37)-0.0565(-1.37)-0.0764(-1.41)

Log distance to bus stop

0.178*(2.38)

0.0586(1.08)-0.149(-1.21)-0.145**(-2.74)0.0633(0.98)-0.0397(-0.74)

Log distance to market-0.0457(-0.61)-0.0149(-0.27)0.420***(3.44)

0.0631(1.84)

0.0164(0.26)-0.0251(-0.72)0.273*(2.46)-0.0710(-0.81)0.155(1.39)

Log village population density-0.0956***(-3.77)

0.0610**(3.10)-0.0309(-1.24)

Educational level of HH

0.0236*(2.08)

0.0132(1.38)

0.0248*(2.21)

Dummy for radio, TV, or telephone

Constant
Number of Observations

12.16(20.15)1698-0.570(-1.32)

16.46(17.05)1698-0.904(-2.19)13.87(27.51)1698-0.0210(-0.05)t statistics in parentheses* p < 0.05, ** p < 0.01, *** p < 0.001

5.5.

Summary of Results

All households:
For market participation decision – the coefficients for log price of rice are positiveand highly significant in all three models. However, for all quantity equations they are31negative and significant for cash crops. For food crops it is still positive but nonsignificant. The marginal effects seem to be weaker compared to the estimates based on
CSES – 2004 data. This could be due to the fact that the sample size for CSES – 2007 ismuch smaller than that of CSES – 2004 and that there is very little price variation in thedata.


The coefficients for the capital endowment variable – land per worker – are positiveand significant for both quantity and market participation equations indicating that
relatively well-endowed farmers participate in the market and commercialize. This iscompletely in line with the initial estimates based on CSES – 2004 data. The marginal
effects for this variable are also the second largest, implying that for generating
marketable surpluses and commercialization of agriculture in Cambodia land
redistribution/allocation has significant policy implications.
Among the household background characteristic variables – dependency ratio has the

expected sign in all the models and is highly significant. The Cambodian population isrelatively young and the higher dependency ratio seems to be a deterrent for generating
marketable agricultural surpluses and commercialization as suggested by the size of themarginal effect for this variable (largest negative). Other background variables such as‘Log of age of head of household’ and ‘any member has paid job’ have the expected
signs, but are non-significant in most cases.
Geographical features such as median village rice yield per hectare, dummy for risky

region in most cases (in both market participation and quantity equations) have theexpected signs and are significant. So improved rice/food productivity can drivecommercialization of other crops as it potentially could free up land and other resourcestied up in subsistence farming.
Variables capturing transaction costs (both fixed and variable) have in most cases

similar estimates as with CSES – 2004

data in terms of the sign and size of thecoefficients and marginal effects. Ownership of transport equipment, and distancevariables have in most cases expected signs and are significant, indicating that measures
to reduce transactions costs could help farmers to produce beyond subsistence level.
Similarly, variables such as ownership of radio, TV or telephone, village population
density, and the educational level of head of household, which are expected to capture
fixed transaction costs and information processing, all have sizeable marginal effects.

32

Figure 5 Marginal Effects - All (CSES- 2007)

5.6.

Small vs. Large Holders

As with the case of all households, regression results and the corresponding marginal effects
for small and large holders based on CSES – 2007 data are largely in line with our initial
estimates based on CSES – 2004 data. The main findings are highlighted below:


Results based on CSES – 2007 are largely similar to our initial estimates based on

CSES – 2004. As revealed by the regression results in table 8 and 9, and thecorresponding marginal effects in fig. 6 and 7, variables such as price of paddy, rice yield(capturing the state of technology use), land per worker, and land title continue to have
similar positive and significant marginal effects, with notable differences between small
and large holders’ responses. Price of paddy is no longer the dominant determinant of
supply response; however, it remains the case that for small holders it is far less importantfor commercial production of agricultural outputs than for large holders. Similarly, land
title, transaction costs variables, capital endowment and household backgroundcharacteristics such as land per worker and ethnicity are far more important tosmallholders than to large holders.
Transaction costs continue to be one of the main barriers for generating marketed

surplus of food crops for small/subsistence holders. Variables capturing variabletransaction costs, that is, distance to market, distance to bus stop, distance to provincial
capital and ownership of transport equipments all have the expected signs and significant
marginal effects on supply response. However, the same is not true for large holders.
33Similarly, the land title variable has sizeable and significant positive marginal effects,
consistent with our earlier findings with CSES – 2004 data. Thus, it is safe to argue that
secure land ownership facilitates market integration of subsistence farmers in Cambodia.
Table 8 Regression Results (CSES – 2007; Small holders), Dependent Variable = log Sales value ofmarketed Surplus
Food Crops
Quantity
SelectCash Crops
Quantity
SelectAll Crops
Quantity
SelectLog median price of paddy (vill)-0.237***(-3.40)0.128***(4.61)-0.150*(-2.44)-0.0899***(-3.64)-0.155***(-3.60)0.0153(0.66)

Log land per worker

0.623***(4.46)

0.390***(7.64)

0.227*(2.34)-0.0920(-1.91)0.0700(0.83)

0.19***(4.35)

Log age of HH

0.0113(1.50)-0.00114(-0.35)-0.0094(-1.27)0.00495(1.35)-0.000184(-0.03)0.0028(0.89)

Dependency ratio

0.227(1.29)-0.291***(-4.15)-0.179(-1.29)0.00823(0.11)-0.00415(-0.03)-0.20**(-3.13)

Dummy for land title

0.0757(0.32)

0.189(1.90)

0.0879(0.38)

0.213(1.96)

0.217(1.21)

0.150(1.63)

Dummy for paid job-0.0222(-0.10)-0.162(-1.69)-0.0351(-0.18)0.0940(0.91)-0.00477(-0.03)-0.0874(-0.97)

Dummy for risky region

0.185(0.74)-0.139(-1.33)-0.426(-1.64)0.195(1.68)-0.124(-0.64)0.0221(0.23)

Log median rice yield (vill)

0.549(1.69)

0.925***(7.68)

0.670*(2.19)

0.329**(2.69)

0.108(0.46)

0.84***(7.86)

Dummy for ownership of ag. Implements

0.350(1.22)-0.0671(-0.56)-0.125(-0.38)0.374**(2.87)

0.254(1.15)-0.0021(-0.02)

Dummy for ethnicity-0.125(-0.08)1.126*(2.02)-0.397(-0.52)-0.183(-0.48)-1.770*(-1.97)0.590(1.57)

Dummy for transport equipments-0.643*(-1.97)0.299*(2.20)

0.0533(0.19)-0.0582(-0.39)-0.442(-1.74)0.216(1.66)

Log distance to provincial capital

0.0900(0.68)-0.116*(-2.27)-0.0904(-0.78)-0.0967(-1.32)0.0291(0.30)-0.0621(-1.31)

Log distance to bus stop

0.367
(1.95)

0.0608(0.80)

0.167(0.88)-0.176*(-2.08)0.430**(3.07)-0.0619(-0.87)

Log distance to market-0.0981(-0.58)0.0111(0.16)

0.363*(2.52)

0.0996(1.75)

0.0725(0.59)-0.0082(-0.13)

Variables

Dummy for radio, TV, or telephone

0.349**(2.84)-0.202(-1.21)0.165(1.40)

Log village population density-0.0393(-1.39)0.0403(1.19)-0.0084(-0.34)

Educational level of HH

0.0203(1.79)

0.0266(1.79)

0.0259*(2.45)

Constant
Number of Observations

14.18(7.56)
917-1.936(-2.69)t statistics in parentheses * p < 0.05, ** p < 0.01, *** p < 0.001

34

13.71
(10.20)917-0.995(-1.60)15.32(13.59)917-0.879(-1.58)

Table 9 Regression Results (CSES 2007 – Large holders), Dependent Variable = log Sales value ofmarketed Surplus
Variables

Food Crops
Quantity
SelectCash Crops
Quantity
SelectAll Crops
Quantity
SelectLog median price of paddy (vill)

0.00844(0.15)

0.216***(6.32)-0.123(-1.67)-0.061*(-1.97)-0.0293(-0.91)0.178***(4.85)

Log land per worker

0.769***(6.50)

0.651***(7.21)

0.390(1.90)-0.128(-1.61)

0.782***(10.41)0.496***(5.06)

Log age of HH-0.00337(-0.92)-0.00128(-0.30)-0.00615(-0.65)0.011**(2.74)

0.00230(0.67)-0.00017(-0.03)

Dependency ratio-0.435***(-4.15)-0.434***(-5.19)-0.136(-0.75)0.0350(0.46)-0.471***(-6.20)-0.429***(-4.76)

Dummy for land title

0.141(1.40)-0.0241(-0.22)-0.195(-0.80)0.0460(0.45)-0.0185(-0.19)0.183(1.53)

Dummy for paid job

0.242*(2.33)-0.101(-0.90)-0.395(-1.60)-0.0509(-0.49)0.0664(0.67)-0.170(-1.36)

Dummy for risky region-0.00751(-0.07)-0.0419(-0.37)0.394(1.60)

0.0693(0.67)

0.240*(2.48)

0.0964(0.75)

Log median rice yield (vill)

1.512***(7.78)

0.969***(8.45)

0.265(1.14)-0.0847(-0.90)1.346***(13.64)0.812***(7.05)

Dummy for ag. Implements

0.0479(0.42)

0.0166(0.13)-0.244(-0.82)-0.0066(-0.05)-0.0158(-0.14)-0.243(-1.57)

Dummy for ethnicity

0.387(1.20)-0.0247(-0.07)-2.299*(-2.40)0.425(1.13)-0.648(-1.92)0.333(0.96)

Dummy for transport equipments

0.0352(0.29)

0.288*(2.26)

0.194(0.68)

0.0721(0.62)

0.109(1.01)

0.327*(2.28)

Log distance to provincial capital-0.0981*(-2.19)-0.0456(-0.52)0.203*(1.97)-0.0082(-0.10)-0.0508(-1.22)-0.101(-1.00)

Log distance to bus stop

0.00786(0.11)

0.0221(0.29)-0.327(-1.93)-0.181**(-2.58)-0.150*(-2.22)-0.141(-1.64)

Log distance to market

0.00261(0.03)-0.0933(-1.87)0.247(1.28)

0.0708(1.55)

0.0573(0.78)

0.0445(0.80)

Dummy for radio, TV, or telephone

0.218(1.40)-0.102(-0.93)0.00289(0.02)

Log village population density-0.120**(-2.78)0.098***(3.63)-0.0433(-0.95)

Educational level of HH

0.00625(0.35)

0.0110(0.78)

0.0129(0.63)

Constant
Number of Observations

12.93(19.26)781

0.175(0.27)

t statistics in parentheses * p < 0.05, ** p < 0.01, *** p < 0.001

35

18.15
(13.00)781-1.366(-2.31)

14.27(27.08)781

0.471(0.68)

Figure 6 Marginal Effects - Small Holders (CSES - 2007)

Figure 7 Marginal Effects - Large Holders (CSES - 2007)36

6. Conclusion
Using household survey data of Cambodia (CSES – 2004 and CSES – 2007), this study
assessed the effects of transactions costs as well as price and non-price factors on market
participation and marketed surplus. A switching regression model was used that accounts not
only for the effects of fixed and variable transactions costs but also for the role ofheterogeneous endowments, technology, and other institutional factors in promoting market
participation and generating marketable surpluses. In doing so, we estimated the model forthree crop types and the same regressions for smallholders - who are most likely to be among
the poorest and the most vulnerable – as well as large holders. We estimated theunconditional marginal effects, that is, the total change in quantities transacted unconditional
on market participation, for the full sample and for the samples of small and large holders.
Some of our findings we believe could be very useful for future policy design for poverty
alleviation and small/subsistence holder’s welfare.
Comparison between the marginal effects of the small and large holders reveals substantial
qualitative and quantitative differences between these two groups. By providing technicalsupport/ extension services and resolving land issues and land redistribution, targeted support
in the form of basic agricultural assets can have high payoffs in terms of poverty alleviation,
reduced vulnerability and increased food security. Furthermore, targeted support helps thepoor to participate in the growth process and to reap benefit out of the opportunities opened
up by the exchange economy. Having secure land title is important for poverty alleviation
and reducing vulnerability through increased participation of smallholders and subsistence
farmers in markets and commercial sales.

Integrating non-ethnic Khmers into the mainstream of economic activities is also likely to
payoff in terms of poverty alleviation and a vibrant rural economy. Another important findingis the transaction cost barrier for smallholders’ market participation. Easier access of
smallholders to better rural infrastructure (e.g. road networks and other physical facilities) is
important for producing outputs to meet market demand. Evidence points to geographic
poverty traps that are exacerbated by risk aversion and low-return crops, causing poverty topersist for generations. High yielding varieties, extension services along with insurance
would help in breaking out of the shackles of subsistence farming.

37

Overall, the findings suggest that policy options other than price policies are available topromote commercialization and generate agricultural surpluses, particularly by thesmallholders. This is important because, in the short run, higher prices are likely to benefit
sellers (mostly large holders who are already in the market) only, impose costs on buying
households unable to respond to price incentives, and bypass those failing to participate inmarkets because of high transactions costs and other barriers to market entry. Therefore, pricepolicies will have very different behavioural and welfare implications for different segmentsof the farming population. In the face of the food price dilemma facing many countries
recently, policies that reduce transactions costs and induce smallholders to commercialize
could thus be important alternatives to price policies to promote marketed surplus andcommercialization of agriculture by smallholders and thereby reduce poverty andvulnerability of this particular community of farmers.

38

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42

Appendix 1
Marginal Effects
There are four different responses of marketing behaviour to changes in explanatory variables
in selectivity models (Huang et al. 1991). These are: i) the change in probability of marketing
participation; ii) the change in desired quantities traded; iii) the change in actual quantities
traded conditional on market participation; and iv) the total change in quantities transacted
unconditional on market participation. The unconditional marginal effect, which is ofparticular interest for this study, on actual quantities transacted (variable) per unit change in thecan be calculated as (Huang et al. 1991) follows:(15)

where,

andcorrespond to the estimated parameters for variableselection equation of the model respectively; while,andin the quantity andstand for cumulative andprobability density functions of standard normal distribution respectively. The above
equation shows that the total unconditional marginal effect on quantities transacted equals thesum of (1) the marginal effect on quantities transacted by participants weighted by theprobability of participation, and (2) the marginal effect on the probability of participation
weighted by the expected quantities transacted by participants. While the changes in thequantities transacted by participants weighted by probability of participation represent the
total effects due to current participants, the changes in probability of participation weighted
by expected quantities transacted by participants represents total effects due to newparticipants.

43

Appendix 2
Table 10 Marginal Effects: All Households (CSES-2004)
ME - Food Crops

ME - Cash Crops

ME - All Crops

3.486***(10.68)1.246***(4.90)

3.726***(10.79)Log land per worker

3.113***(35.48)0.497***(7.97)

3.034***(33.15)

Log age of HH

0.0912(0.31)

1.199***(5.03)

1.123***(3.56)

Dependency ratio-0.0159***(-12.85)-0.000693(-0.74)-0.0130***(-10.19)

Dummy for land title-0.0832(-0.53)0.216(1.76)

0.154(0.93)

Dummy for paid job-0.290(-1.40)-0.0181(-0.11)-0.342(-1.54)

Dummy for risky region-2.004***(-8.68)1.272***(4.84)-0.723*(-2.43)Log median rice yield (vill)

3.748***(25.06)

0.385***(3.52)

3.528***(22.59)Dummy for ownership of ag. Implements

1.052***(6.41)

0.805***(6.33)

1.390***(7.86)

Dummy for ethnicity

0.207(0.43)

0.514(1.51)

0.514(1.00)

Dummy for ownership of transport equipments

0.293(1.54)-0.0188(-0.12)0.210(1.04)

Log distance to market-0.218**(-2.94)0.213***(3.62)-0.0349(-0.44)

Log distance to bus stop

0.270***(4.85)-0.342***(-8.09)0.00955(0.16)

Log distance to provincial capital-0.0385(-0.34)0.290***(3.41)

0.0838(0.71)

Dummy for ownership of radio, TV, or telephone

0.291(1.79)

0.290*(2.26)

0.464**(2.68)

Log village population density

0.0109(0.08)

0.637***(6.14)

0.445**(3.19)

Educational level of HH

0.0475*(2.31)
6913

0.0295(1.79)
6913

0.0630**(2.90)

Variables
Log median price of paddy (vill)

Number of Observations

Marginal effects; t statistics in parentheses (d) for discrete change of dummy variable from 0 to 1*
p < 0.05, ** p < 0.01, *** p < 0.001

44

Table 11 Marginal Effects: Small Holders (CSES-2004)
ME - Food Crops

ME - Cash Crops

ME - All crops

Log median price of paddy (vill)2.596***(7.71)

0.614*(2.18)

2.832***(7.01)

Log land per worker

1.619***(17.15)0.139(1.96)

1.594***(14.51)

Log age of HH-0.417(-1.41)0.539*(2.10)

0.213(0.60)

Dependency ratio-0.00943***(-7.58)-0.000958(-0.95)-0.00912***(-6.30)

Dummy for land title-0.107(-0.66)

0.389**(2.88)

0.280(1.44)

Dummy for paid job-0.339(-1.67)-0.0629(-0.37)-0.382(-1.55)

Dummy for risky region-1.080***(-4.50)0.985**(3.28)-0.176(-0.51)

Log median rice yield (vill)2.554***(17.47)0.769***(6.58)

3.066***(17.26)

Dummy for ownership of ag. Implements

0.575***(3.52)

0.609***(4.49)

0.932***(4.79)

Dummy for ethnicity

0.178(0.36)

0.609(1.82)

0.920(1.67)

Dummy for ownership of transport equipments

0.151(0.80)-0.0746(-0.46)

0.0651(0.29)

Log distance to market-0.282***(-3.58)0.0368(0.56)-0.251**(-2.67)

Log distance to bus stop

0.244***(4.15)-0.182***(-3.91)

0.0514(0.75)

Log distance to provincial capital-0.179(-1.56)0.0229(0.25)-0.174(-1.30)

Dummy for ownership of radio, TV, or telephone-0.00907(-0.05)0.214(1.55)

0.170(0.82)

Log village population density-0.117(-0.86)0.457***(4.06)

0.221(1.36)

Educational level of HH

0.00149(0.17)
3982-0.00646(-0.84)3982-0.00360(-0.34)

Number of Observations
Marginal effects; t statistics in parentheses (d) for discrete change of dummy variable from 0 to 1*
p < 0.05, ** p < 0.01, *** p < 0.001

45

Table 12 Marginal Effects: Large Holders (CSES – 2004)
ME - Food Crops
4.697***(8.58)ME - Cash Crops
2.362***(5.18)ME - All Crops
4.683***(9.78)

Log land per worker

3.623***(16.09)-0.541**(-2.96)2.103***(10.43)Log age of HH

0.0716(0.14)

1.033*(2.36)

0.397(0.86)

Dependency ratio-0.0192***(-8.94)0.00456*(2.53)-0.00910***(-4.79)Dummy for land title

0.0137(0.05)

0.0786(0.36)

0.0456(0.20)

Dummy for paid job

0.107(0.31)

0.0341(0.12)

0.0109(0.03)

Dummy for risky region-2.854***(-6.27)1.852***(4.04)-1.126*(-2.54)

Log median rice yield (vill)4.552***(17.52)0.00944(0.05)

3.270***(14.37)

Dummy for ownership of ag. Implements

0.875**(2.75)

0.373(1.38)

0.591*(2.00)

Dummy for ethnicity

1.039(1.28)

0.604(0.96)

0.505(0.67)

Dummy for ownership of transport
equipments-0.0906-0.262-0.405(-0.27)(-0.90)(-1.42)

Log distance to market-0.107(-0.89)0.328**(3.17)

0.135(1.25)

Log distance to bus stop

0.192*(2.12)-0.561***(-7.36)-0.0212(-0.26)

Log distance to provincial capital

0.0874(0.46)

0.578***(3.66)

0.115(0.69)

Dummy for ownership of radio, TV, or
telephone

0.983***

0.353

1.179***(4.07)(1.54)(4.90)

0.366(1.82)

0.789***(4.14)

0.969***(4.96)

Log median price of paddy (vill)

Log village population density

0.0259*0.0130(2.06)(1.34)
Number of Observations
2996
2996
Marginal effects; t statistics in parentheses (d) for discrete change of dummy variable from 0 to 1*
p < 0.05, ** p < 0.01, *** p < 0.001
Educational level of HH

46

0.0160(1.41)

Table 13 Marginal Effects: All Households (CSES-2007)
ME - Food Crops

ME- Cash Crops

ME- All
Crops

Variables
Log median price of paddy (vill)

0.911***(8.03)-0.371***(-4.73)0.249*(2.55)

Log land per worker

3.356***(17.58)0.223(1.77)

2.484***(16.07)Log age of HH

0.00257(0.19)

0.0322**(3.07)

0.0139(1.11)

Dependency ratio-2.427***(-8.86)-0.209(-0.99)-1.972***(-8.07)

Dummy for land title

0.492(1.30)

0.496(1.69)

0.757*(2.17)

Dummy for paid job-0.672(-1.76)0.206(0.69)-0.502(-1.42)

Dummy for risky region-0.553(-1.38)0.627*(2.05)

0.353(0.96)

Log median rice yield (vill)

5.762***(13.70)0.319(1.08)

4.616***(12.88)Dummy for ownership of ag. Implements-0.0691(-0.15)0.667(1.90)-0.0819(-0.20)

Dummy for ethnicity

2.014(1.72)

0.345(0.33)

2.331(1.80)

Dummy for ownership of transport equipments

1.686***(3.57)

0.607(1.59)

1.644***(3.92)

Log distance to provincial capital-0.0995(-0.34)-0.210(-0.97)-0.349(-1.33)Log distance to bus stop

0.390
(1.38)-0.673**(-3.09)-0.144(-0.56)

Log distance to market-0.635***(-3.47)0.298*(2.11)-0.157(-0.93)

Dummy for ownership of radio, TV, or telephone

1.403*(2.48)-0.305(-0.83)0.710(1.43)

Log village population density-0.494***(-3.75)0.268**(3.14)-0.145(-1.24)0.122*
0.0582(2.07)(1.39)
Number of Observations
1698
1698
Marginal effects; t statistics in parentheses (d) for discrete change of dummy variable from 0 to 1*
p < 0.05, ** p < 0.01, *** p < 0.001
Educational level of HH

47

0.117*(2.20)
1698

Table 14 Marginal Effects: Small Holders (CSES-2007)
ME - Food Crops

ME- Cash
Crops

ME - All Crops

0.535***(4.50)-0.345***(-4.00)0.0112(0.09)

Log land per worker

1.669***(7.63)-0.280(-1.68)1.016***(4.62)

Log age of HH-0.00207(-0.15)0.0156(1.23)

0.0156(0.97)

Dependency ratio-1.313***(-4.32)-0.00534(-0.02)-1.126***(-3.43)

Dummy for land title

0.907*(2.16)

0.754*(2.06)

0.938*(2.02)

Dummy for paid job-0.768(-1.88)0.326(0.90)-0.490(-1.07)

Dummy for risky region-0.607(-1.33)0.596(1.53)

0.0645(0.13)

Log median rice yield (vill)

4.223***(8.20)

1.288**(3.03)

4.657***(8.54)

Dummy for ownership of ag. Implements-0.216(-0.42)

1.343**(2.78)

0.110(0.19)

Dummy for ethnicity

3.219***(4.99)-0.790(-0.51)2.503(1.47)

Dummy for ownership of transport equipments

1.266*(2.03)-0.192(-0.38)0.982(1.52)

Log distance to provincial capital

0.0238(0.08)-0.271(-1.06)-0.0112(-0.03)Log distance to bus stop

0.396
(1.19)-0.588*(-2.00)-0.140(-0.38)Log distance to market-0.525*(-2.35)0.333(1.69)-0.333(-1.38)Dummy for ownership of radio, TV, or telephone

1.795**(2.66)-0.660(-1.33)0.931(1.39)

Log village population density-0.186(-1.39)0.142(1.20)-0.0471(-0.34)

Variables
Log median price of paddy (vill)

0.0962
0.09350.145*(1.79)(1.80)(2.46)
Number of Observations
917
917
917
Marginal effects; t statistics in parentheses (d) for discrete change of dummy variable from 0 to 1 * p < 0.05, ** p
< 0.01, *** p < 0.001
Educational level of HH

48

Table 15 Marginal Effects: Large Holders (CSES-2007)
ME - Food Crops
Variables
Log median price of paddy (vill)
0.999***(6.40)ME - Cash Crops

ME- All Crops-0.344*(-2.40)0.487***(4.45)

Log land per worker

3.547***(8.58)-0.521(-1.40)2.110***(7.30)

Log age of HH-0.00834(-0.42)0.0509**(2.87)

0.00152(0.11)

Dependency ratio-2.310***(-6.09)0.134(0.38)-1.647***(-6.10)

Dummy for land title-0.00792(-0.02)0.170(0.36)

0.520(1.41)

Dummy for paid job-0.299(-0.56)-0.376(-0.80)-0.451(-1.12)

Dummy for risky region-0.198(-0.38)0.472(0.99)

0.486(1.26)

Log median rice yield (vill)

5.548***(10.87)-0.343(-0.78)3.512***(10.53)

Dummy for ownership of ag. Implements

0.111(0.18)-0.110(-0.19)-0.687(-1.60)

Dummy for ethnicity

0.171(0.11)

1.412(0.97)

0.627(0.42)

Dummy for ownership of transport
equipments

1.334*

0.425

1.025*(2.33)(0.79)(2.44)

Log distance to provincial capital-0.208(-0.51)0.0360(0.10)-0.242(-0.80)

Log distance to bus stop

0.107
(0.30)-1.015**(-3.15)-0.538*(-2.07)

Log distance to market-0.499*(-2.16)0.420*(2.00)

0.0842(0.51)

Dummy for ownership of radio, TV, or
telephone

0.956-0.5010.00832(1.47)(-0.95)(0.02)-0.553**(-2.66)

0.493***(3.71)-0.125(-0.96)Log village population density

Educational level of HH

0.02870.0556(0.35)(0.78)
Number of Observations
781
781
Marginal effects; t statistics in parentheses (d) for discrete change of dummy variable from 0 to 1*
p < 0.05, ** p < 0.01, *** p < 0.001

49

0.0374(0.63)
781