tooS A      39
POLRCY RESEARCH WORKING PAPER              2839
/ii< rketing Externalities
d :- .i4arket Development
M. Shahe Emran
Forhad Shilpi
The World Bank
Development Research Group
Rural Development
May 2002



I POLICY RESEARCH WORKING PAPER 2839
Abstract
Emran and Shilpi use survey data from Bangladesh to             commodity externality effects in the sale of farm
present empirical evidence on externalities at household        households. The vegetable markets in villages with low
level sales decisions resulting from increasing returns to      marketable surplus seem to be trapped in segmented
marketing. The increasing returns that arise from thick        local market equilibrium. The analysis points to the
market effects and fixed costs imply that a trader is able      coordination failure in farm sale decisions as a plausible
to offer higher prices to producers if the marketed             explanation for the lack of development of rural markets
surplus is higher in villages. The semi-parametric              even after market liberalization policies are implemented.
estimates identify highly nonlinear own and cross
This paper-a product of Rural Development, Development Research Group-is part of a larger effort in the group to
understand the process of development of rural markets. Copies of the paper are available free from the World Bank, 1818
H Street NW, Washington, DC 20433. Please contact Forhad Shilpi, room MC3-536, telephone 202-458-7476, fax 202-
522-1151, email address fshilpi@worldbank.org. Policy Research Working Papers are also posted on the Web at http://
econ.worldbank.org. Shahe Emran may be contacted at emran@stanford.edu. May 2002. (31 pages)
The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about
development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The
papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this
paper are entirely those of the authors. They do not necessarily represent the view of the World Bank, its Executive Directors, or the
countries they represent.
Produced by the Research Advisory Staff



Marketing Externalities and Market Development1 2
M. Shahe Emran
Stanford University and
George Washington University
Forhad Shilpi
World Bank
Key Words: Increasing Returns, Externalities, Marketing, Market Development,
Semiparametric Estimation
JEL Classification: D13; D43; 012; M31
'Emails: emranAstanford.edu, fshilpi@worldbank.org.
2 We would like to thank Joseph Stiglitz, Hanan Jacoby, Fahad Khalil, Marcel Fafchamps, Gershon Feder, Jose
Canals-Cerda, Takashi Kurosaki, Jishnu Das, Don Larson, Shabbir Jilany and the seminar participants at the
NEUDC, 2001 at Boston University, and at BIDS for useful comments. We would also like to thank Salman Zaidi
and Masako Hiraga for assistance with data, Misuzu Otsuka for superb research assistance.






Marketing Externalities and Market Development
Introduction
The importance of increasing returns, externalities, and multiple equilibria in explaining the
persistence of underdevelopment has been a perennial theme in the development literature. 1
That there can be multiple Pareto-ranked equilibria due to increasing returns and its associated
pecuniary externalities, and that an economy (or a region) can be trapped in a low-level equilib-
rium has gained wide acceptance in recent theoretical literature, ranging from endogenous growth
theory to new economic geography. Different sources of increasing returns and externalities have
been identified and analyzed (for example, fixed costs of production in urban manufacturing sector
as in Krugman (1991) and in subsequent literature on new economic geography, and endogenous
technology adoption in final goods sector in Ciccone and Matsuyama(1996)). 2      However, the
extant literature has not paid adequate attention to the role of marketing which is an important
source of increasing returns and externalities in a market economy, especially at an early stage
of development. The increasing returns to marketing arise from thick market effects and from
the existence of fixed costs, for example, in hiring a truck and in renting in a storage space.3
Understanding the implications of increasing returns and externalities associated with marketing
for development of markets is of paramount importance, as well-functioning markets are widely
viewed as the sine qua non of economic development. The objective of this paper is to address
this important but largely neglected issue; particularly to provide formal empirical evidence on
the existence and magnitudes of the externalities in farm level sales decisions that result from
increasing returns to marketing.
'The literature can be traced back at least to the presidential address of Allyn Young to the Royal Economic
Society (1928). The recent revival in interest owes much to the work of Murphy et. al. (1989). See also, Matsuyama
(1992), Eswaran and Kotwal (1996), Rodriguez-Clare (1996), Hoff and Stiglitz (2000) and Ray (2000), among others.
Hoff (2000) provides an excellent summary of the theoretical literature focusing on underdevelopment traps.
2There is a small (relative to the theoretical literature) but rapidly expanding empirical literature on increasing
returns, externalities and multiple equilibria. For instance, in Ciccone and Matsuyama(1996), private return to
investment increases with the aggregate level of investment. Productivity of firms increases with the size/density
of activities e.g. in Ciccone and Hall(1996).
3In their in-depth study on the peasant marketing in Indonesia, Hayami and Kawagoe (1993) report that there
are large economies of scale in long-distance trade. It costs about Rp. 1000- Rp. 2000 to charter a pony wagon
to Garut bazaar, carrying a load up to 200 kg. It costs Rp. 5000 to charter a mini truck for any load, up to the
maximum capacity of 2 tons. Therefore the cost of transportation is Rp. 5/kg or higher if a load of up to 200 kg
of soybean is carried from the study village to Garut Bazaar by pony wagon, but the cost declines to Rp. 2.5/kg if
a two ton load is carried by a mini truck. (P.54)



The increasing returns to marketing imply that a trader can offer better prices to a farm
household located in a village with high aggregate surplus, as she can achieve significant economies
of scale in marketing. This results in pecuniary externalities at the households level; the sales of
an individual household of any given commodity becomes a positive function of the village level
aggregate marketed surplus of (i) the commodity itself (called own externality), and (ii) all other
commodities produced in the village (called cross externality). The cross externality effect arises
from the fact that, when the marketable surplus of any given commodity is small, a trader needs
to pool multiple commodities for reaping economies of scale.
The existence of fixed costs and externalities also implies that there can be multiple equilibria
which can be identified with different levels of market development. We identify three distinct
stages of niarket development in the rural areas of developing countries. In the first and most
underdeveloped stage, the rural markets are isolated, and the market clearing occurs at the local
level. The marketable surplus is so low that it constrains the emergence and operation of a long-
range intermediary class that can connect the local market to the central or urban market(s),
as it is not possible to reap any significant economies of scale in marketing. At this stage of
market development, both the own and cross externality effects in the sales decisions discussed
earlier are absent or insignificant. Moreover, the price an individual farm household gets for the
output is likely to be negatively correlated with the aggregate supply brought to the local market
due to the standard supply shift effect. This, in turn, implies a negative own externality like
effect, as a farm household's sales, ceteris paribus, tend to be lower, when the neighbors bring in
more to the market, and thus depress the (local) market price. Observe that the existence of
fixed costs implies that there can be a coordination problem in the sales decisions of the farm
households and a village can be trapped in a segmented local market equilibrium. Given the
local market clearing, it might be individually optimal for the farm households not to produce
more surplus, but when everyone produces enough surplus, this might make long range marketing
intermediary profitable, and thus integrate the market with the urban centers. 4 In the second
stage of the market development, the long range marketing intermediaries are in operation, but
4Observe that the coordination failure can occur at two levels. First, the sales decisions of all the farm households
need to be coordinated. But that might not be sufficient to break out of the local equilibrium, as the increased
surplus will only glut the local market if it can not be coordinated with the entry of the prospective marketing
intermediary.
2



they need to pool together multiple commodities to cover the fixed costs in transportation and
storage and to reap the economies of scale. So this intermediate stage of market development
is characterized by the presence of both own and cross externality effects. In the last and most
developed stage of market development, the marketable surplus of each commodity is large enough
to permit commodity-wise specialization by the marketing intermediaries. As such there is no
cross commodity externalities at this stage, and it is characterized by the existence of only own
externality effect.
Using household level data from Bangladesh, we find significant evidence of both own and
cross commodity externality effects. While there are evidence of strong own externality effects in
rice markets, the cross externality effect seems to be absent. The evidence thus imply that the
rice markets in Bangladesh have attained the most developed stage of the three stages described
earlier. For a farm household located in a village in the first quartile (sorted by village surplus
of rice), the total sales of rice increases by 47 percent when the average sales of rice by all others
in the village doubles. In contrast, for vegetables, there is a negative own externality like effect
in villages with low marketable surplus, indicating the existence of segmented and local market
equilibrium. The sales of vegetables by a farm household in a village in the first quartile ( sorted
by surplus of vegetables) tends to decrease by 26 percent when the average sales by all others
doubles, ceteris paribus. 5 The vegetables markets in these villages seem to be trapped in the
low-level isolated market equilibrium. The analysis and evidence presented here thus point to a
plausible explanation of the apparently puzzling observation that, in many developing countries,
the deregulation and liberalization policies have largely failed to accelerate the development of
the rural markets (Kherallah et. al. (2000)). To the best of our knowledge, ours is the first
attempt at a formal econometric analysis of the externalities associated with increasing returns
to marketing in the rural markets of developing countries.6
The rest of the paper is organized as follows.  Section 1 discusses the different sources of
increasing returns to marketing and the many economic functions performed by a marketing
intermediary in developing countries. Section 2 describes a simple model of a marketing interme-
5The elasticities reported here are based on 2SLS estimates.
61t is interesting to compare our analysis to that of Demsetz (1968). Using data from a well developed market,
i.e. the New York Stock Exchange, Demsetz finds evidence of significant scale economies in securities trade. The
scale economies arise from thick market externalities from better matching of buyers and sellers when trade volume
of a given security increases. Thus only a subset of what we call own externality effects is considered.
3



diary that guides the empirical analysis. A discussion of the econometric issues involved in the
estimation of externality effects and of the specification of the estimating equation is provided in
the next section. Section 4 gives a brief discussion of the data. The main empirical results on
externalities, using a semi-parametric approach, are presented in section 5. Section 6 concludes
the paper with a summary of the results and a discussion on the implications of the results of
this paper for the broader theme of agricultural market development and poverty alleviation in
developing countries.
Section 1: The Economics of Marketing and Market Development
Several sources of increasing returns to marketing can be identified from existing theoretical
and empirical literature. First, the cost of transacting in a market can decline with the scale of
trading due to thick market externalities from better matching of buyers and sellers, and from bet-
ter generation and transmission of information (e.g. Demsetz (1968), Diamond (1982), Rubinstein
and Wolinsky (1987)). Second, as we mentioned above, two of the most important components
of marketing: transportation and storage give rise to natural increasing returns due to fixed costs
involved. These are known as increasing returns to bulking in the marketing literature. Third,
setting up of a marketing network frequently involves substantial sunk investments in "intangible
assets" like trust and reputation. The available empirical evidence from developing countries
indicate that markets in agricultural and many manufacturing inputs and outputs operate on the
basis of relational contracts. The relational contracts facilitate information sharing and informal
enforcement of contracts (Aoki and Hayami(1999)). The intermediaries traders need to incur
significant costs in developing these informal relationships, which can be termed as a trader's
"social capital". An important implication of the existence of increasing returns to marketing is
that, under plausible conditions, it generates pecuniary externalities at the level of individual farm
households. As the aggregate volume of trade increases allowing a trader to take advantage of
the many different sources of increasing returns, part of the gain is passed on to the producers in
the form of higher prices, especially when the entry into marketing is not restricted.7 Also, there
71n 1960s, there were extensive government interventions in the agricultural markets of developing countries
that often restricted entry by private traders. The markets have, however, been deregulated and liberalized starting
from late 1970s. Although stories of monopsonistic behavior in agricultural markets abound in popular accounts,
the prevailing consensus from more careful studies is that the markets, especially at the local level, are reasonably
4



are significant cross commodity externalities, especially at the early stages of development, as the
marketed surplus of a single commodity may not be large enough to recover the fixed costs, thus
providing incentives for pooling of different commodities.8  The lack of specialization in mar-
keting, both horizontally and vertically, at early stages of development is well-documented (see,
for example, Hirsch (1961), Hayami and Kawagoe (1993)). The wide prevalence of producers
and traders associations in developing countries probably reflects, at least in part, the outcome of
attempts to capture increasing returns to marketing at low levels of production and marketable
surplus. 9
While the existence of increasing returns to marketing can generate positive externalities, an
increase in the volume of trade, particularly of different commodities, can also increase transaction
costs, and thus eventually limit the number of commodities traded in a given market (Demsetz
(op cit)).  Market intermediaries play a critical role in inspecting and verifying quality of a
commodity thereby facilitating its exchange even without personal examination by the buyers.10
This is especially important in developing countries because of a lack of standardization. The
marketing intermediary can be looked upon as an expert in judging the quality of a product who
helps resolve the adverse selection problem d la Akerlof's lemon, and thus can stop a market
from unravelling completely (see, for example, Biglaiser (1993)). However, at early stages of
development, any given intermediary usually handles a number of commodities, and she can not
be expected to be equally expert in verifying quality of all of them. The diseconomies of scope
in performing quality verification is higher, the larger the number and volume of commodities in
competitive or at least "contestable" (see, for example, Chowdhury (1992) on Bangladesh, Kherallah et al (2000)
on Sub-Saharan Africa). That these markets are essentially contestable was forcefully argued early on by Bauer
and Yamey (1954).
8For instance, in smaller markets, once a truck is hired, traders are often seen to pool different commodities so as
to operate the truck near its capacity (to the extent possible). If a single trader's volume is not large enough, then
multiple traders, possibly dealing with different commodities, may hire a truck in partnership (see, for example,
Belshaw (1965) for evidence on Fiji, and Hayami and Kawagoe (op cit) for Indonesia.)
9There are evidence that the same produce gets better price when marketed through a marketing association in
comparison to individual marketing. For example, while per box of squash marketed through the Ha-ee vegetable
marketing group in Korea got $6.58, the price fetched by individual marketers was $5.92 in 1982 (P.118, Abbot,
1987). This gave an average $800 additional income to a member of the group. Although part of the price
differential might reflect better bargaining power of the group, economies of scale in marketing (transportation,
storage and packaging) arising from fixed costs is also an important factor.
10Even for relatively homogenous commodities like foodgrains, there are multiple dimensions of quality that can
be important: color, taste, appearance of kernels, moisture content, impurity, breakage of kernels, and baking
qualities (Gabre-Madhin, 1999).
5



the portfolio of a trader.11 With the increase in income and production, as marketed surplus
increases beyond a threshold, a trader can reap the economies of scale without pooling different
commodities together which leads to specialization in marketing and creates experts in quality
verification. The above discussion forms the basis for the three stages of market development
discussed earlier.
We utilize household level survey data from Bangladesh to test for the existence of the exter-
nality effects and to estimate them when they are present. The choice of Bangladesh as a case
study is motivated by two characteristics of its agriculture. The average farm size is very small
and economic activity is very densely distributed over geographic space, an ideal combination for
generating pecuniary externalities at the individual farm level.12 If the farm size is large enough
as is the case in much of Latin America, it is easy for individual farmers to reap the benefits of
increasing returns to marketing, and one should not observe any externality effects across different
producers. On the other hand, if the farms are small but geographically isolated, it would be
extremely difficult, if not impossible, to reap any economies of scale in marketing. The high
geographic density and small farm size coupled with the fact that most of the households produce
multiple commodities due to risk diversification reasons, imply that the forces of both own and
cross externalities are likely to be important in Bangladesh agriculture.
Section 2: Price formation in the presence of increasing returns to mar-
keting
At the heart of our analysis of externalities in farm level sales is the formation of producer
price of a commodity at a given market (or at the farm gate) in the presence of increasing returns
to marketing. In this section, we develop a simple model of price formation by considering
the optimization problem of an intermediary/trader. Let qi denote the amount of commodity i
shipped from the local market, and Q be the total volume of all commodities shipped from that
"1As pointed out before, even if traders are specialized commodity wise, but deal with relatively small volume, a
number of traders can pool together and hire a truck to transport to the nearest urban market. In this case, there
are no diseconomies from spreading thin the quality inspection skill, but there are costs associated with coalition
formation and collective action. Time and efforts need to be spent to establish such an arrangement and to hold
it together. Private marketing cooperatives, producers' associations fit into this category. The model developed in
the next section of the paper can be easily adapted to represent such an association where the quality verification
costs are replaced by the costs of coalition formation and collective action.
12With population density of more than 2000 per square mile, Bangladesh is considered as the most densely
populated non-city country in the world. The average farm size is about 1 hectare.
6



market. We assume that there exists a variable cost per unit of the commodity i shipped to the
destination market u, denoted as di(tu, w), where tu is the distance between local market and the
destination market u, and w denotes the wage rate. 13 It is assumed that d (.) is a positive function
of both distance and wage rate. The wage rate reflects the costs of hiring labor for packaging,
and loading and unloading of commodities. The demand for commodity i in the destination
market is described by the inverse demand curve Pi = aiq,-' where 0 < cri < 1. We assume, for
simplicity, that supply in the local market can be described by an upward sloping supply curve
Pi = 77iqi6' where 6i > 0. 14  Both the demand and supply curves satisfy the conditions for an
interior solution. In order to capture the positive externality generated by an increased volume
of trade, we assume that, for a long-distance trader operating from the local market, there is a
set up cost r(qi, Q-i) = F + C(qi, Q-i), where Q-i = EkOi qk. Observe that the set up cost
equation r(qi, Q-i) is an affine function implying that there is a pure fixed cost component F
which includes costs incurred in building a trader's "social capital" along with any fixed costs
in transportation and storage. The other part of the set up cost function C(qi, Q-i) captures
the increasing returns due to factors like thick market effects, and possibly quantity discounts in
transportation and storage. The marginal quality verification cost for commodity i, denoted as
Mi(Q-i), is assumed to depend positively on the volume of all other goods that are being traded
by the intermediary. This is the diseconomies from spreading the quality verification skill over a
portfolio of multiple commodities. The optimization problem for a typical trader operating from
the local market can be described as:
n
Max{q,....q}fII =  E[Pi- Pi- rid,(t, W) - Mi(Q-i)]qi- r(qi, Q-i)         (1)
i=1
There are two important features of the profit equation as defined above.  First, instead
of defining the profit equation separately for each commodity, we allow the trader to combine
different commodities in her portfolio and to optimize the joint profit. Second, the function r(..)
"3Here u stands for urban.
14Although the specifications of demand and supply functions are simple, they allow for regional variations. The
demand for a commodity may depend on the region it is produced. This can be due to the fact that different aspects
of the quality of a commodity depend, among other things, on the agro-climatic characteristics of the geographical
region it is produced. For example, mango produced in Rajshahi usually command a premium in Bangladesh. The
supply of a commodity also is dependent on agro-climatic characteristics of a producing region.
7



captures the own and cross externalities that arise from different sources of increasing returns
discussed earlier. From equation (1), the first-order conditions for optimization can be derived
as:
(1 - o'1)P- (1 + 6i)Pi- ridi(t., w) - Mi(Q_i) - C(qi, Q_i) = 0         (2)
for all i = 1, 2, ....n, and where C' =  c 15 The marginal benefits from an increase in qi consist
of the marginal revenue in the destination market and the marginal benefit (-Ci()) generated
by an increase in trade volume. The marginal costs on the other hand comprise of the marginal
procurement, transportation and quality verification costs. The optimal qi is chosen so as to
balance the marginal benefits and costs.16
In order to detect the strength of the own and cross externality effects in the empirical analysis,
we solve equation (2) to derive an equation for the producer price at the local market:
Pi= piPi - vid,d+ Aigi(qi, Q_i)                        (3)
where pi = ____    vi =       Ai=        and gi(qi,Q-i) = -Mi(Qi) - C(qi, Q_i). So the
function gi(qi, Q_i) represents the effects of own and cross externalities on the producer price
received by the farmers in the local market, assuming that the intermediary can cover the fixed
cost F. From simple partial differentiation, we have:
L9gi =     _M'j- CS
9qjt
agi        ii
9qi
As defined before, Cij =@ 2  and C" =   .   We assume that Mi > 0 and Mi'I > 0. Note
that if C'j > 0, then  < 0. The intermediary can do better by specializing either in qi or qj as
the overall cross externality effect is negative. Similarly, if Cii > O, then the intermediary faces
"We use superscripts to a function for denoting partial derivatives.
16A zero profit condition characterizes the equilibrium due to free entry or contestability.  The zero profit
condition determines the equilibrium wage rate w'.
8



diseconomies of scale as  < 0. We focus on the more plausible case where both C"j < 0 and
Cii < 0 . In order to rule out the possibility of a complete specialization irrespective of the level
of market development, we further assume that gi(qi, Q_i) > 0 at Q-i = 0, qi O 0. Note that in
the case where the positive cross externality (-C'ij > 0) out weighs the rising quality verification
costs (Mij > 0), the net externality effect is positive, i.e., t  > 0. In order to allow for the
-possibility that the positive cross externality may wear out with an increase in trade volume at
higher level of market development, we assume that Ciii > 0.17 Under these assumptions, we
can show with simple differentiation that gi() is a concave function of Q_j. The decoupling of
trading of qi from all other commodities (Q-j) occurs if net externality generated by pooling
trade of these commodities is non-positive at qj = 0, Vj :A i, i.e., if -(Cii + Mi) < 0. If we define
marginal benefit from an increase in trade volume as (-Ci), the assumptions that C'j < 0 and
Ciii > 0 for all j 0 iim ply that the marginal benefit is increasing at a decreasing rate with an
increase in the volume of trade of all other commodities. We summarize the above discussion in
the following propositions.
Proposition 1: Given that (i) the marginal benefit from an increase in volume of trade
(-Ci) is increasing (-Cij> 0) but at a decreasing rate (-Ciij < 0) for all j ¢ i, (ii) the
cost of quality verification is a rising (Mij > 0) and convex (Mij > 0) function of volume of
all other commodities j 0 i, and (iii) the net externality function gi(qi, Q_) > 0 at Q-i = 0,
then gi(qi, Q_j) is a concave function of Q_j. Moreover, for any qi > qi , where di is such that
(Cii(qi, O) + Mij(0) 0 O, 'i # i), there is complete specialization in qi.
Compared with the cross externality effects, the relationship between own externality effect
and market development is different because such effects are likely to be present even in highly
developed markets (as in Demsetz (op cit)). Also, even with own externality effects, one can
expect diseconomies to set in eventually with an expansion in trade volume, as a trader needs
to delegate decision making and monitoring responsibility with the attendant incentive problems.
We present these insights in the form of a proposition below.
17We identify the level of market development with the volume of trade.
9



Proposition 2: Given that the marginal benefit from an increase in volume of trade (-Ci)
is increasing (-C" > 0) but at a decreasing rate (-C... < 0) for all i, and the net externality
function gi(qi, Q_i) > 0 at qi O 0, then gi(qi, Q_i) is a concave function of qi.
Observe that both own and cross externality effects are subject to important threshold effects,
especially at the low levels of marketable surplus, due to the pure fixed costs (F) involved in
marketing. Also, it follows from the concavity of gi() that the cross externality effects are stronger
at relatively low level of market development when the volume of trade is small.
Section 3: Econometric Specification
(3.1) An Econometric Model of Household's Sales Decision
In the preceding section, we developed the relationships between producer's price of a com-
modity and the volume of (i) trade of that commodity itself and (ii) trade of all other commodities.
In this section, we embed these relationships in a standard econometric model of the sales deci-
sion of a farm household. 18 The relevant optimization of a farm household involves a two stage
recursive process. In the first stage, land allocation and planting decisions are made based on
the expected prices which are primarily determined by the last period's actual prices. In the
second stage, sales decisions are made taking output as predetermined, and the volume of sales
depends on the current producer prices offered by the trader and on other household and village
characteristics. The sales decision of a household (h) for a given commodity i can be described
by the following equation:
Yih = I3iiXh + ,8i2Xr + 7riPi+ Eih                           (4)
Where i = 1, 2, ..., Yih is the sales of commodity i by household h, 3i and iri are parameters,
18From the discussion on the price formation in the preceding section, it seems natural to estimate a (producer)
price equation (equation (3)) to detect the externality effects. However, in our data set, it was not possible to match
the consumer and produce prices due to differences in units and quality, which renders this approach non-feasible.
The second option is to estimate an equation for the land price which reflects the expected producer prices because
of capitalization. But this approach is fraught with two important difficulties. First, the land price commands a
premium due to its collateral value in the credit market which dilutes the link between the producer prices and the
land price. Second, the land price may not reflect the producer price faithfully enough when the land market is
thin or virtually nonexistent. These problems seem to be important in case of Bangladesh. We focus on the sales
at individual household level which avoids the problems associated with a land price equation discussed above.
10



Xh is a vector of control variables representing household and farm characteristics, X, is the vector
of village/region level characteristics, and Pi denotes the producer price in the local market that
the household has accesses to, and eih is the error term. Utilizing equation (3) to substitute for
Pi, we have:
Yih = f31lXh + I2Xr + qJPi+ Oidl(tu W) + Oigi(qi, Q-i) + eih           (5)
Where 7i =- 7riAi, j =irivi and Xi _ 7riAi. To allow for the differences in the own and cross
externality effects at different levels of market development, and for tractability in estimation, we
decompose gi( into two separable parts as:
gi(qi, Q-j) = si(qi) + ei(Q-j)                          (6)
Where si(qi) represents the own externality effects, and ei(Q-j) the cross externality effects.
Since there is no theoretical guidance in our framework about the functional specification of
d,(tU, w), we adopt a linear function, which gives us the following form for the sales equation:
Yih =/lXh + /i2Xr + 7i7Pi + iltu + i2w + Oi(Si(qi) + ei(Qi)) + eih          (7)
(3.2) Econometric Issues and Estimating Equation
If individual households are small producers, and hence are price takers in the market, then
village level total sales of commodity i and of all other commodities can be used as explanatory
variables in the household level sales regressions for commodity i to identify the own and cross
externality effects respectively.'9 However, several econometric issues including endogeneity and
omitted variables complicate the identification of these effects. For example, both the average
sales by households and the village level sales are typically higher in villages which are endowed
with better infrastructure.20 Hence omission of variables like infrastructure which may affect
19This is similar to the argument made by Rauch (1993) that since a worker is a price taker in the labor market,
average human capital in a city can be used to identify the positive human capital externality in individual wage
regressions.
20The existing evidence on Bangladesh show that both producer price and marketed surplus are higher in villages
11



both individual and village level sales positively can potentially cause serious upward bias in the
estimates of own and cross externality effects. In order to control for the level of development,
we include a number of village level variables such as village population, median household ex-
penditure, a number of dummies denoting whether there are markets within the village, whether
the village has electricity, telephone connection, paved roads and formal banks. We also included
percentage of operated land irrigated in the village so as to control for any supply shifts that
may affect both individual and village level sales due to adoption of HYV technology. This also
partially mitigates the effects of village level weather shocks (like rainfall).21 We include regional
dummies to control for regional production shocks, and use yield per acre at household level as
a control for unobserved heterogeneity, including factors like household-specific and village level
production shocks, and land quality differences for which data are not available. All regressions
also include a set of variables depicting household and farm characteristics that may influence a
household's sales decisions.
The village level sales variables (qi and Q-i) could also be spuriously correlated with sales by
the household (qih) if there are very few sellers in a village.22  Thus, instead of using aggregate
village level sales, we take out the sales by the household itself from the village level sales and
normalize by the number of producers. The per capita23 sales of a given commodity i and that
of all other commodities are denoted as qi and QC-i respectively. Using these adjusted variables,
we can rewrite the own and cross externality functions of equation (6) as si(4i) and ei(Q_i)
respectively. An important problem in estimation of the externality effects arise from the fact
that, even after controlling for all the effects discussed in the previous paragraph, the per capita
sales variables are likely to suffer from endogeneity due to simultaneity between own sales and
sales by others in the village. To address this problem we use instrumental variables and employ
a two-stage approach to the estimation. In addition to the other exogenous and predetermined
with relatively developed infrastructure. For example, Ahmed and Hossain (1990) report that paddy and rice prices
are 2 percent and 6 percent higher respectively in villages with developed infrastructure. The marketed surplus
depending on the landownership groups are as follows. For less than 0.5 acre land ownership group, the marketed
surplus is 52 percent (developed infrastructure) and is 14 percent (underdeveloped infrastructure).
21It is important to note here that the survey year 1995-96 was NOT subject to flood or drought in Bangladesh.
22 Moreover, there could be villages in the sample which do not sell (production for subsistence only) a given
commodity. If such cases are not insignificant in a data set, both qih and qi might be significantly censored resulting
in near-perfect fits.
23Throughout this paper per capita implies average over the producers.
12



variables, the following axe used in the first stage regressions as instruments for the average sales
by others in the village : (i) average household size and composition, (ii) average farm size and
input uses, and (iii) average per acre yield, where the average is taken over all other households
in the village, i.e., when estimating the sales equation for household h1, the average is over all
h #t h1.24 Note that (i) the average farm size, and (ii) the average household size and composition
of all others in the village are arguably exogenous with respect to the sales decision of a given
farm household and thus can be considered as identifying instruments. Also, observe that the
yield and input use qualify as instruments because production can be treated as predetermined,
given the two-stage sequential optimization by a farm household. In addition, the household
composition variables of any given household (like share of adult, average year of male education)
can also be treated as exogenous for the sales decision of that very household in a given year.
Although si(4,) and ei(O-i) functions are likely to be concave in their arguments, the existence
of threshold effects makes it difficult to approximate their curvature with a simple quadratic
specification of the functional form. Hence we utilize a semi-parametric approach to identify the
functional forms of both ei((_i) and si(4i). Notice that a fully non-parametric estimation in this
case is both computationally expensive and subject to the curse of dimensionality, as there exist a
large number of regressors besides Q-i or qi. The semi-parametric method suggested by Robinson
(1988) also involves running a large number of bivariate kernel regressions.25 Instead of using a
fully semi-parametric approach, we utilize a much simpler approach based on dummy variables
(see Cosslett, 1991). Taking discrete approximation, the function ei(Q(_) can be rewritten as:
ei (O-i)=     Ojwj                                    (8)
j=1
where wj is a dummy, Oj is the parameter to be estimated and I is the total number of dummies.
To approximate ej(O_i), the entire domain of observed Q-i is divided into intervals and each wj
24The first stage regressions also include a set of dummies indicating village level infrastructure and 7 regional
dummies.
25The semi-parametric approach d la Robinson(1988) Will require kernel estimations of E(YjQ-i) and E(Xj IQ-i)
for all j explanatory variables excluding Q-i at the first stage. In the second stage two more kernel regressions
are needed to determine the parametric and non-parametric parts of the model.. The estimation of the kernel
regressions in our case is especially complicated because most of the dependent variables are censored and the
explanatory variables include a large number of dummies. As the kernel estimation technique for discrete choice
models, censored and ordinary regression models are different, fully semi-parametric estimation of each equation
would require combination of different kernel regression techniques also.
13



takes the value of unity if it belongs to a certain interval and zero otherwise. We define 20 such
dummies, and include 19 of them in the regression as each regression also contains an intercept
term. Similarly, we defined 20 dummies (denoted as Vj, j = 1, .., 20) over the entire domain of di
and include 19 of them in the regression:
k
Si NO= E   jVj                                 (9)
j=1
Now substituting equations (8) and (9) into equation (7), we have the following estimating equa-
tion:
k           I
Yih = 3ilXh + 3i2Xr + m7Pii+ Oilt. + O i2W + Xi E  (3vj + /i E  03wj+ eih  (10)
j=2         j=2
which can be estimated consistently using appropriate estimation technique given that k and 1 are
fixed. Note that the finer are the dummies the better will be the approximation of ei(Q-i) and
si(qi). However, such finer approximation is achieved at the cost of roughness of the estimated
parameters 0j and (j. To obtain smoothed estimates 6^ (Q_i) and si(qi), we follow the two-
step procedure suggested by Jacoby (2000).  In the first step, an appropriate estimator (see
the discussion below) is used to estimate the parameter vector in equation (10). We calculate
O 6jwj for each observation utilizing the estimated parameters. In the second step, gi(Qti) is
estimated by running a simple kernel regression of Ej"= 9jwj against Q-i using locally weighted
scatterplot smoother (LOWESS). We follow a similar procedure to obtain the smoothed estimate
of Si(di)-
There are alternative econometric approaches one can follow to estimate the parameters of
the sales equation (10). Since there is significant censoring in the data due to non-participation
of the households in the market, and heteroscedasticity is an important problem in the cross-
section data, Tobit is likely to over-estimate the (slope) parameters, while OLS (with standard
errors corrected for clustering effects) is likely to under-estimate them ( for a lucid discussion
of alternative estimators, see Deaton, 1997).26 So when both of these estimation methods are
employed, one can provide with bounds for the estimated values of the parameters. Another
26It is important to emphasize that we use instruments and employ a two-stage procedure to correct for endo-
geneity biases in both Tobit and OLS regressions.
14



option is to use Censored Least Absolute Deviation (CLAD) (Powell, 1984) for estimation of the
parameters which is free of distributional assumptions and is expected to give better estimates,
compared to both Tobit and OLS. However, as we discussed above, endogeneity (simultaniety)
is likely to be a significant problem in estimating the sales equations, and thus an instrumental
variables approach is necessary to tackle the problem. Unfortunately, not much is known, to our
knowledge, about the properties of CLAD when instrumental variables are used, which renders
CLAD unsuitable for our purpose.27  Since our basic objective is to see if there is any evidence
of the externality effects in the data due to increasing returns to marketing, a bounds approach
seems a sensible route which we follow here.
Section 4: Data and Descriptive statistics
The data come from the 1995/96 Household Expenditure Survey of Bangladesh, a nation wide
representative households survey conducted periodically by the Bangladesh Bureau of Statistics.
A two-stage stratified random sample of 7420 households were drawn from 371 primary sampling
units (PSUs). A separate community questionnaire was administered in a subset of 252 rural
PSUs generating much of the community level data utilized in our study.
The household questionnaire collected detailed information on a household's food and non-
food expenditure, income, education and other household characteristics. The survey included
a module where information on agricultural production, home consumption and sales were col-
lected for 27 different crops. Of these 27 crops, we focus on only three broad groups: rice (both
high yielding varieties (HYV) and local varieties), vegetables, and fruits. The survey provides
information on input uses (expenditure on fertilizer) and size of the operational holdings. In
addition to the farm characteristics, we include household characteristics such as household size,
composition, average years of education for male and female adults as explanatory variables. As
the consumer prices at relevant destination markets are not available in the data set, we control
for them with a set of regional dummies.
[REFER TO TABLE 1]
27We note that when CLAD is employed for estimation using the instrumental variables, the parameter estimates
are, in general, very close to the estimates obtained by 2SLS. Moreover, the shapes of the own and cross externality
curves from nonpararnetric estimation remain essentially the same as the ones reported here. The results of CLAD
estimation are available on request.
15



Descriptive statistics are reported in Table 1. Nearly 81 percent of 4980 rural households are
engaged in crop production.28 The predominance of rice cultivation is evident as about 78 percent
of the farmers produce some rice. Market participation rate is fairly high with about 75 percent
of the farmers selling some output in the market. However, there exist wide variations in the
market participation rates among farmers producing different crops. The market participation
rates are higher for rice compared with vegetables and fruits. Average sales by a producer is
highest for rice, about taka 5400, and is less than tk.1000 for vegetables and fruits.
The village level summary statistics also point to the predominance of rice both in production
and sales. Although a smaller percentage of production (29 percent) of rice is sold in the market,
its market appears to be thicker than other crops. Both mean and median sales of rice exceed
that of any other crops. These variations in the market thickness of different crops along with
variations in the market participation of households even within a single crop category provide
an excellent opportunity to test the externality effects in trading of different crops.
Section 5: Empirical Results
In this section, we present the estimates of the household's sales equations for rice, vegetables
and fruits. The dependent variable is the logarithm of quantity of commodity i sold by house-
hold h.29 As noted before, we use instruments to tackle potential endogeneity problems in the
estimation of the own and cross externality effects. The first stage regressions for instrumental
variable estimates show that the instruments have high explanatory power, as the F tests for joint
significance of the instruments have a P-value of 0.00 in all of the first stage regressions.30
[REFER TO TABLE 21
The results of the semiparametric estimates of the sales equations are presented in Table 2.
The estimates of the parametric part suggest that, among the household and farm characteristics,
household size, farm size and yield per acre are the major determinants of household's sales
decisions. The wage rate has a consistently negative sign across the sales equations for all three
28About 5038 households are rural but IDs of three PSUs in the household data could not be matched with that
of PSUs reported in the community data forcing us to drop around 60 observations.
29Since there is censoring, and natural log of zero is undefined, we define the dependent variable as In(quantity
sold+1). In what follows, we use the phrase 'logarithm of quantity of is to denote ln(qi + 1).
30The results of first stage regressions are omitted for the sake of brevity and are available on request.
16



crop groups, which confirms the hypothesis that a higher wage increases the costs of a trader.
Consistent with the d priori expectations, the Tobit estimates are, in general, numerically larger
than those from TSLS.
The Wald tests of joint significance of the dummies capturing the own externality effects show
strong evidence of own externality effects across all three crop groups; the null hypothesis of no
own externality can be rejected in all cases with a P-value of 0.00, except for the TSLS regression
for rice where the null is rejected with a P-value of 0.04. The evidence on cross externality is
mixed. According to the Tobit estimates, cross externality effects are statistically significant for
all three crops. However, the TSLS estimates indicate that it is insignificant for rice (P-value
0.35), marginally significant for fruits (P-value 0.18) but significant for vegetables (P-value 0.09)
(see Table 2).
[REFER TO FIGURES 1 AND 2]
To analyze the pattern of non-linearity in the externality effects, we plot the non-parametric
2SLS estimates of the own externality (si(Qt)) and cross externality (ei(Q..-) ) functions along
with their confidence intervals in Figure 1 and Figure 2 respectively.31   The estimated own
externality curve, si(di), for rice show that it is monotonically upward sloping.32 Although there
is no evidence of significant non-linearity, the curve is mildly concave indicating that the strength
of own externality effect weakens at higher level of trade. For fruits, the own externality curve
is quasi concave, indicating that there is important threshold effects in operation. At very low
level of sales there is little or no evidence of any own externality effect, but the effect picks up
strongly after sales passes a threshold. Vegetables by far display the most non-linearity; the own
externality curve is convex up to a point and then it becomes concave. The own externality
effect is negative at lower levels of trade and then increases up to a point before tapering off to
some extent at higher levels of trade. As discussed before, such negative own externality effects
for vegetables is primarily due to the effects of supply shifts on the local market price. This
31The shapes of the curves remain essentially same when Tobit estimates are used instead.
32Note that each of the regression includes an intercept term and hence for normalization we dropped one dummy
(01 = 0 for dummies w3 capturing the spline of ei(.) and el = 0 for v; defining the spline of s.(.). Therefore the
estimated functions are in fact v  and v  respectively where j = 2.. .k. Even if we drop the intercept term,
identifications of both a, and C, are not possible. What matters for our analysis is the shape of each of the
functions. For a sense of numerical magnitude of the externality effects, we present the elasticity estimates in the
following sections.
17



means that the producer price is determined by market clearing at the local level, which in turn
implies that there is a meagre marketable surplus for the village as whole and the local markets
are not integrated with the urban markets. So a statistically and numerically significant negative
own externality indicates that the vegetables markets in villages with low levels of marketable
surplus might be caught in underdevelopment trap, as the marketing intermediaries are unable to
recover the fixed costs involved (the storage and transport costs are much higher for perishable
commodities like vegetables). The weakening of own externality at a high level of trade, on
the other hand, may be due to agency problems in trading as the intermediary has to delegate
decision making and decentralize operations when the scale of trade grows beyond a point.
The cross externality functions plotted in Figure 2 show that, for rice, there are no significant
evidence of any cross externality effects as the curve is nearly a horizontal line with few minor
bumps. The absence of any significant cross externality effect for rice, however, is not surprising.
The current consensus among the observers of rural markets in Bangladesh is that the rice market
is by far the most developed one, and thus it is only natural that a feature of relatively undeveloped
markets like cross externality will be absent from a developed market. The cross externality curve
is concave shaped for fruits; there seems to be significant positive cross externality effects at low
levels of trade which yields to the diseconomies of scope after a threshold is reached. In case
of vegetables, the cross externality curve seems to be flat up to a point after which it has a
negative slope indicating the onset of diseconomies of scope. An apparently plausible alternative
explanation of the negative cross externality effect for fruits and vegetables is that it is due to
crowding out in production that results from fixity of land. For example, as more land is allocated
to other crops (rice plus fruiits), less is available for planting vegetables. However, observe that
such crowding out effects are not likely to be important in our analysis, as we exclude a number
of crops and the adding up constraint in land allocation is not relevant for our case.
Estimates of Elasticities
While the graphs of the nonparametric functions of the own and cross externality effects are
excellent tools for identifying the shapes of the relationships, they do not provide us with any
sense of the numerical magnitudes of these effects. In this section, we report the estimates of
elasticities of own and cross externality effects for different quartiles of households sorted according
18



to the level of sales. The elasticities of household sales with respect to the per capita sales of the
commodity itself (own externality) and of all other commodities (cross externality) by all other
households in the village are calculated using the estimated ei(Q-i) and si(qi) functions. For the
elasticity estimation, we divide the entire domain of the explanatory variables, log of Q-i and log
of di, into five intervals each containing approximately 20 percent of the observations. Next we
estimated a straight line spline through the mean of two consecutive intervals. The elasticity is
then estimated as the slope of the spline. The estimated elasticities are presented in Table 3.
[REFER TO TABLE 3]
The pattern of the elasticity of own externality effect varies widely across different crop groups.
The magnitude of the elasticity is the highest in the first quartile for rice, and then it declines
monotonically (Tobit estimates) or remains constant (TSLS estimates). The elasticity is mono-
tonically increasing for both vegetables and fruits according to the estimates from TSLS, while the
Tobit estimates indicate that there are non-linearities in the relationships, particularly for fruits.
If we focus on the relatively conservative estimates of the TSLS regressions, the evidence indicate
that a doubling of per capita sales of rice by all other households in the village increases the total
sales of rice by a given household by 47 percent if it belongs to a village in the first quartile,
and by 38 percent if the household belongs to any of the other three quartiles.33 For vegetables,
the elasticities for the first two quartiles are negative in both TSLS and Tobit estimates (-0.26
(TSLS), -0.49 (Tobit) for the first quartile, and -0.18 (TSLS) and -0.35 (Tobit) for the second
quartile), indicating an initial phase of diseconomies. According to TSLS estimates, this initial
phase of diseconomies is eventually overcome in the third quartile (elasticities are 0.10 for third
quartile and 0.27 for fourth quartile). According to the Tobit estimates, this initial phase of
diseconomies weakens significantly in third and fourth quartiles but does not disappear entirely
(elasticities are -0.04 (third quartile) and -0.01 (fourth quartile)). For fruits, the own elasticity
is small in the first quartile (0.10 (TSLS)), but increases dramatically with the volume of sales
(0.49 for fourth quartile(TSLS)). Note that the elasticity estimates in case of fruits for different
quartiles obtained from the Tobit are consistently much higher than the TSLS estimates.
33It is interesting to note that, according to the Tobit estimates for rice, the own externality effect is very high
(0.89) in first quartile but dies down very fast at higher levels of trade; the elasticity for the last quartile being only
0.29.
19



The elasticity estimates for cross externality effects show a monotonically declining (alge-
braically) trend in case of fruits with a positive effect for the first one or two quartiles, depending
on the estimator used. The elasticity estimates for the last two quartiles are, however, signifi-
cantly negative, irrespective of the estimation method employed. Except for the second quartile
where elasticity estimates are rather small (-0.02 (TSLS) and 0.05 (Tobit)), the effects are large in
magnitude for all other quartiles. For example, concentrating on the TSLS estimates, a doubling
of per capita sales of all other commodities by all other households in the village tend to increase
the total sales of fruits by a typical household by 11 percent if the village level surplus is low
enough to be in the first quartile. In contrast, the total sales of fruits of a typical household
decreases by (i) 28 percent if it belongs to a village in the third quartile, and (ii) 49 percent if the
sales volume in the village is high enough to be in the fourth quartile. The elasticity estimates
for the cross externality effects for other two crop groups, i.e., rice and vegetables do not follow
any monotonic pattern. For rice, consistent with the flat shape of the nonparametric curve, the
magnitudes of the elasticity are very small across second and third quartiles and is large only
for first quartile. The important thing to note in the elasticity estimates for vegetables is that
the cross externality is negative for the first and fourth quartiles. In contrast, for the second and
third quartiles, the estimates are positive and numerically significant, especially according to the
Tobit estimates, providing strong evidence of pooling of other commodities to achieve economies
of scale in this range of overall commodity trade.
Are the Results due to unobserved heterogeneity?
In the above, we presented evidence of strong own externality effects for all three crop groups:
rice, vegetables, and fruits. The vegetables exhibit the strongest cross externality effects, espe-
cially at medium volumes of trade, among three crop groups and there are weak evidence of its
operation, only at low volume of trade, for fruits. The evidence on rice suggest a virtual absence
of cross externality effects.34 A more skeptical among us might still have reservations about the
results on the ground that we did not have adequate controls for heterogeneity at household (like
soil quality) or village (weather shocks, pest attacks) levels in our data set. A strong counter-
argument is that any heterogeneity in production, both at household and village levels, should be
34Recall that the overall cross externality effects are insignificant for rice according to the TSLS estimates.
20



adequately controlled for by the per acre yield variable. In this section, we provide additional
evidence to support the conclusion that the correlations between individual sales and the village
level sales are, in general, not driven by unobserved heterogeneity in the production side. We
concentrate on the sub-sample of households who produce a commodity but do not sell anything in
the market. If the increasing returns in marketing due to fixed costs and thick market effects are
the real driving forces behind the correlations between a given household's sales and the average
sales of its neighbors, then such correlations will be absent for the sub-sample of households who
do not sell anything in the market. Observe that the preceding argument encounters a difficulty
because the sales of a household is zero, by definition, if it does not participate in the market,
and we can not estimate a sales equation to test the validity of the argument. However, a related
argument can still be made focusing on the production of such self-sufficient households. Con-
sider the case where some unobserved household level production characteristics rather than the
economies of scale in marketing are the driving force behind the observed correlations between
the sales of an individual farmer and that of the neighbors in a village. Such correlations in
the production side implies that the production of even a self-sufficient farm household will be
correlated with the production of, and hence with the sales of, its neighbors in the village. So
the production of the non-participants in the market can be used as a dependent variable to test
the hypothesis that the correlations are not due to increasing returns to marketing, rather due to
some omitted correlated production characteristics or shocks. The results are reported in Table
4 where the dependent variable for both the sub-sample and the full sample is the logarithm of
production and the estimation is done using OLS with standard errors corrected for the clustering
effects.
The results, in general, support the conclusion that the correlations between the sales of an
individual household and the average sales of its neighbors are not driven by unobserved hetero-
geneity. The evidence is very strong in case of own externality effects for rice and vegetables
where the coefficients of the own externality variables are both numerically and statistically in-
significant for the sub-sample of non-market producers, but are markedly larger in magnitude
and statistically highly significant for the full sample. For fruits, however, the evidence indicate
that unobserved production heterogeneity might be important (the coefficient in the sub-sample
of non-market producers is numerically and statistically significant). But observe that the mag-
21



nitude of the coefficient in the full sample (0.09) is 50 percent higher than the estimate from the
sub-sample (0.06). So it is unlikely that the own externality effect in case of fruits is exclusively
due to unobserved heterogeneity.
The evidence on cross externality effects show that, in case of rice, there is no significant
difference between the coefficient estimates from the full- and sub-sample, thus confirming our
earlier result that cross externality effects are virtually absent from rice sales. For vegetables,
on the other hand, the evidence clearly indicate that the cross-externality effect is not driven by
unobserved production heterogeneity (the coefficients are: for sub-sample -0.003 (t =-0.12) and
for full sample -0.04 (t = -1.57)).  Similar results are obtained for cross-externality effect in
case of fruits, where the coefficient for the full sample -0.06 (t = -1.73) is much larger than the
corresponding coefficient for the sub-sample of non-market producers -0.01 (t = -0.35).
The above discussion leads us to conclude that, except for the case of own externality effect
for fruits, our results are not likely to be due to unobserved heterogeneity.
Conclusions:
Using a simple model of a marketing intermediary in the presence of increasing returns to
marketing due to thick market effects and fixed costs, we provide evidence on pecuniary exter-
nalities at the producer level sales decision in the rural markets of Bangladesh. The evidence,
first of its kind, show that there are strong own externalities stemming from the expansion of the
scale of trading of a commodity itself, and also cross externalities from trading of different com-
modities. The evidence on the rice market show that there is strong own externality effects, but
no significant cross externality effect, implying that rice marketing is specialized and the market
has attained a level of maturity. There are evidence of a negative own externality like effect in
the vegetables markets in villages with low or moderate marketable surplus. These vegetables
markets might be caught in an underdevelopment trap in that they are not served by long-range
marketing intermediaries, and hence the markets are essentially small and isolated local markets.
At medium volume of trade, there is also strong cross externality effects in the case of vegetables.
The significant cross externality effects suggest that the intermediaries dealing with vegetables,
particularly in moderately developed vegetables markets, still need to pool other commodities to
reap the increasing returns to marketing. Although the evidence in case of fruits indicate that
22



they are serviced by formal marketing intermediaries, the level of development of these markets
is also low. It seems that vegetables and to some extent, fruits markets are characterized by a
lack of specialization in marketing.
The results reported in this paper have important implications for the design and placement
of poverty alleviation projects and for policies to accelerate the development of rural markets.
An important implication of the findings reported in this paper is that an otherwise identical
farm household is expected to get, on an average, lower prices for its produce if located in a
relatively low income, low surplus, and hence less commercialized region . This might result
in geographic pockets of subsistence dominated economy with high incidence of poverty. The
extent of development of markets in a particular geographic region, from this perspective, is a
local public good for individual households located in that very region.35 Another important
implication of the results reported here is that market deregulation and liberalization may not
always generate spontaneous and cumulative forces of market development. For example, our
analysis and evidence indicate that certain markets might be trapped in a low level of development
due to factors like low marketable surplus, and a lack of storage and transportation facilities, as
they constrain the ability of a marketing intermediary to reap the economies of scale and might
create a coordination failure at the farm level sales decisions. More importantly, one would,
in general, observe uneven development of rural markets for different commodities and across
different geographic locations even if the same generic policies of deregulation and liberalization
are implemented across the country. Although necessary, deregulation and liberalization may not
be sufficient for development of rural markets. The recent evidence on the rural markets in Sub-
Saharan Africa show that the policies of deregulation and liberalization implemented during the
1980s have improved the efficiency of marketing, but have largely failed to generate a spontaneous
and sustained dynamics of market development ( see, for example, Kherallah et al. (2000), Jerome
and Ogunkola (2000)). Although development of a market is best viewed as a highly complex
path dependent evolutionary process, the results of this paper indicate that the increasing returns
to marketing and the resulting externalities at the farm level might be important in shaping that
process in the rural areas of developing countries.
35While free mobility of labor can, in principle, mitigate the effects of geographic poverty traps, the underdevel-
opment traps in our case can persist due to the area specificity of agricultural land.
23



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102, 581-593.
25



Table 1: Descriptive Statistics
Unit Median Mean    Standard  No. of
Deviation Observations
Rice      Sales             taka     575   5443    12190        3157
As % of production %        9      20       24        3157
Vegetables Sales            taka       0    929     4352        2803
As % of production %         0     22       30        2803
Fruits    Sales             taka       0    908     4506        2635
As %ofproduction %          0      26       31        2635
Total      Sales            taka    1500   7579    16008        4026
As % of production %        27     30       27        4026
Village Level
Rice      Sales             taka   42265  69011    76331         249
As % of production %        29     29       18         249
Vegetables Sales            taka    1890  10457    38194         249
As % of production %        32     34       26         249
Fruits    Sales             taka    2000   9609    25943         249
As % of production %       27      32       25         249
Total      Sales            taka   88398 122537    120657        249
As % of production %       40      39       17         249
26



Table 2:Non-Parametric Instrumental Variable Estimation of Household's Sales Decision
Dependent variable=log(quantity sold)         Rice                    Vegetables                   Fruits
Tobit        TSLS          Tobit         TSLS         Tobit        TSLS
R      t      R      t     B      t      B      t     B      t     B      t
Village level  Median expenditure   -0.66  -4.96  -0.43  -3.16  -0.01  -0.07  0.01  0.09   0.23   1.30  0.15   1.18
variables   Travel time to nearest mkt -4.E-03  -0.49 6.E-04  0.09  0.01  1.75  0.01  1.09  -0.01  -1.49  -0.01  -0.99
Population             3.E-05  0.82 5.E-05   1.37 -5.E-05  -1.72 -6.E-06  -0.25 1.E-05  0.29 5.E-06  0.15
Agricultural Wage Rate  -0.53  -1.93  -0.31  -1.30  -0.50  -1.91  -0.20  -0.82  -0.58  -1.67  -0.31  -0.86
Dummy for   Bank in the village     -0.64  -0.94  -0.31  -0.49  -1.31  -1.83  -0.62  -1.34  -0.66  -0.78  -0.41  -0.57
Market in the village   -0.24  -0.75  -0.25  -0.78  0.94  2.95   0.37   1.57  0.98   2.52   0.58   1.71
Telephone in the village  0.26  0.65  0.06   0.18   0.67   1.65  0.20   0.55  1.03   2.07   0.60   1.42
Electricity in the village  -0.35  -1.98  -0.11  -0.71  -0.29  -1.79  -0.07  -0.53  -0.03  -0.12  0.09  0.50
Paved road in the village  0.33  1.91  0.19  1.17  -0.54  -2.99  -0.17  -1.14  -0.28  -1.23  -0.17  -0.95
% of land irrigated    4.E-03  1.44 1.E-03   0.55   0.01  2.16 3.E-03   1.17  -0.01  -2.08 -4.E-03  -1.24
Household   Household size          -3.07 -11.61  -1.57  -9.88  -1.93  -7.44  -0.93  -7.57  -2.04  -6.32  -1.13  -6.22
Variables    share of adult male    -0.61  -0.97  -0.23  -0.58  -0.68  -1.11  -0.18  -0.56  -1.16  -1.57  -0.57  -1.20
share of adult female   -0.94  -1.43  -0.43  -1.10  -0.23  -0.38  -0.11  -0.42  -1.19  -1.56  -0.61  -1.44
share of children        2.50  5.09   1.08   3.92   1.00   2.12  0.32   1.28  -0.12  -0.19  0.03   0.09
Av. yr of male education  -0.33  -1.80  -0.18  -1.65  -0.52  -2.81  -0.23  -2.40  -0.24  -1.06  -0.14  -1.11
Av. yr of female education  -0.16  -0.72  -0.02  -0.14  -0.54  -2.38  -0.16  -1.69  -0.49  -1.79  -0.18  -1.18
Yield per acre           3.45  29.44  1.92  25.99   2.38  29.75  1.27  25.28   1.45  16.57  0.90  17.83
Farm size                3.79  39.29  2.26  38.80   2.22  26.72  1.27  22.83  1.59  15.88   1.07  16.70
Fertilizer use per acre  -0.01  -0.96 -2.E-03  -2.59  -0.09  -1.76  -0.02  -0.66  0.03  0.58  0.03  1.15
Share in total production  -0.34  -0.92  -0.10  -0.42  0.21  0.64  -0.24  -1.29  -0.93  -2.01  -1.01  -3.98
Constant term                       -10.94  -6.70  -4.34  -3.54  -1.48  -0.99  -0.04  -0.03  -0.06  -0.03  0.75  0.43
R2/Pseudo R2                         0.18          0.47         0.18          0.56         0.11         0.43
No. of Observations                  2852         2852          2505         2505         1943          1943
Tests of joint significance of         F P-value     F P-value     F P-value    F P-value    F P-value     F P-value
Own Extemality effect                2.09  0.004  1.71   0.04   5.54   0.00  3.14   0.00  3.14   0.00   4.07   0.00
Cross Commodity Extemality effect    209   0.004  1.10   0.35   2.81   0.00   1.49  0.09   1.82  0.02   1.31   0.18
All regressions included a set of regional dummies also (omitted for brevity)
Note: Standard errors for TSLS are corrected for within cluster correlations in residuals due to survey design.
27



* 95% Confidence Interval  O LOWESS Estimate of si(qi)  95% Confidence Interval  O LOWESS Estimate of si(qi)
2.30413 -.998531-
95% Cofdneitna             OESEUmt ts(i
0                             /
.         ,
E                                     ~~~~~~~~~~~~~~~~~E
-1.83681  __ _ _ _ _ __1__ _ _ __ _ _ _ _ __ _ _5303__ __    _ __ _ _ _ _ _ __ _ _ _ _ __ _ _ _ _ _ __ _ _ _ _ _
3.7195                                 8.307f1     1.4d091                                 9.29705
Log(ql)                                             Log(qi)
Rice                                             Vegetables
*95% Confidence Interval  * LOWESS Estimfate of si(qi)
2.49506
0~
Fruits
Figure 1: Own Externa3lity
28



95% Confidence interval  O LOWESS Estimate of ei(Q-i)       95% Confidence interval  O LOWESS Estimate of ei(Q-i)
.988084 - '  _  _                                           .741074 -
o                                                           0                  _._______
c                                                           c
2                                                           2
-1.627 1            --1.91606
1.7&29                                        9.398il       6.d034                                 *      10.75d6
Log(Q-i)                                                    Lop(Q-i
Rice                                      ~~~~~~~~~~~~Vegetab~ies
95% Confidence interval   LOWESS Estimate of ei(Q-i)
.583956 -
T
C                                                        -
E
S
2~~~~~~~~~2



Table 3: Estimates of elasticities at different quartile of the cross and own externality variables
Rice          Vegetables             Fruits
TSLS     Tobit     TSLS     Tobit     TSLS     Tobit
Own Externality
Quartile        1       0.47    0.89      -0.26   -0.49       0.10    0.29
2       0.38     0.49     -0.18    -0.35      0.12     0.21
3       0.38     0.32      0.10    -0.04      0.38     0.44
4       0.38     0.29      0.27    -0.01      0.49     0.51
Average                 0.41    0.50      -0.02   -0.22       0.27    0.36
Cross Commodity Externality
Quartile        1       0.23    0.34      -0.20   -0.23       0.11    0.26
2       0.00     0.08      0.04     0.14      -0.02    0.05
3      -0.01     0.04      0.11     0.30     -0.28    -0.43
4       0.08     0.17      -0.14   -0.11     -0.49    -0.94
Average                 0.07    0.16     -0.048    0.02      -0.17   -0.27
30



Table 4: Parametric Estimation of Household's Production Decisions
Dependent variable                 Rice                   Vegetables                   Fruits
=log(production)          Full sample  Sub-Sample'  Full sample  Sub-Sample'  Full sample  Sub-Sample'
B      t     B      t     B      t     v      t     v      t     B      t
Village level Variables
Own extemality            0.05   2.42  -0.01  -0.44  0.10  6.44  0.002  0.10  0.09   5.02  0.06   2.91
Cross commodity externality  -0.03  -1.35  -0.04  -1.48  -0.04  -1.78 -0.003  -0.12  -0.06  -1.73  -0.01  -0.35
Median expenditure        0.13   3.49  0.17   3.92  0.06   1.61 -0.007  -0.17  0.10  2.12  0.06   1.14
Travel time to nearest mkt  0.00  1.84  0.002  1.41  0.00  2.33 4E-03   2.44  0.00   1.78 3E-03   1.05
Population                0.00  -2.27 -2E-05  -2.12  0.00  -0.45 2E-07  0.27  0.00  -0.49 -4E-07  -0.21
Agricultural Wage Rate    0.05   0.64  0.25   2.23  -0.06  -0.57  0.07  0.58  -0.06  -0.72  -0.04  -0.32
Dummy for
Bank in the village       0.13   0.67  0.12   0.56  0.12   0.57  0.19   0.80  0.34   1.22  0.16   0.39
Market in the village    -0.19  -2.01  -0.03  -0.26  -0.27  -2.45  -0.32  -2.54  -0.41  -2.84  -0.38  -2.76
Telephone in the village  0.08   0.88  -0.02  -0.14  0.17  1.73   0.09  0.96  0.22   1.69  0.02   0.14
Electricity in the village  -0.01  -0.13  -0.17  -2.73  -0.01  -0.22  -0.03  -0.53  -0.08  -1.28  -0.16  -2.21
Paved road in the village  -0.09  -1.70  -0.12  -1.9  -0.04  -0.69  0.09  -1.43  -0.06  -1.09  -0.03  -0.45
% of land irrigated       0.00  -3.66 -0.002  -1.94  0.00  -4.21 -0.004  -3.35  0.00  -4.65 -0.005  -4.9
Household Variables
Household size            0.73  13.49  0.76  10.02  0.64   9.85  0.65   7.57  0.63   9.45  0.57   6.09
share of adult male       0.14   2.81  0.07   0.94  0.03   0.46  -0.01  -0.13  0.11  1.54  0.09   0.85
share of adult female     0.11   1.87  0.14   1.61   0.24  3.70   0.28  3.42  0.18   2.31  0.26   2.49
share of children         -0.20  -7.08  -1.68  -4.36  -0.21  -5.94  -0.20  -4.20  -0.19  -4.85  -0.22  -4.16
Av. yrofmaleeducation     0.16   4.69  0.19   3.32   0.21  4.98   0.24  4.81   0.15  3.77   0.30  4.64
Av. yr of female education  0.16  3.30  0.12  1.57   0.02  0.37  -0.06  -0.98  0.17  2.99   0.08  1.01
Yield per acre            0.47  18.49  0.42   14.3   0.67  34.47  0.58  23.07  0.71  32.32  0.74  29.89
Hired labor (hours)       0.42  14.97  0.42   9.57  0.29   9.86  0.27   8.38  0.33  10.57  0.31   7.47
Seed Expenditure          0.23  12.90  0.22   11.3   0.28  15,16  0.23  12.29  0.28  15.40  0.27  11.94
Share in total production  -0.66  -8.45  -0.51  -5.28  0.11  0.81  0.29  1.66  -0.49  -3.18  0.09  0.49
Constant term             2.11   4.51    1.2  2.17   1.69  3.21   1.45  2.07  2.30   4.05   1.65  2.25
R2/Pseudo R2              0.70         0.61          0.76         0.65        0.80         0.78
No. of Observations       2763         1200         2239         1171         1719          776
Note: 1/: Sub-sample includes only those households which produce but do not sell the commodity.
All regressions are estimated using OLS. Standard errors are corrected for clustering effects.
31












Policy Research Working Paper Series
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