MIDDLEMEN: LINKING NEOCLASSICAL AND NEW INSTITUTIONAL
ECONOMICS
SIMONE CAROLINA BAUCH (1) ; ERIN O SILLS (2) ; SHANA SAMPAIO
SIEBER (3) .
1,2.NORTH CAROLINA STATE UNIVERSITY, RALEIGH, ESTADOS
UNIDOS; 3.UNIVERSIDADE FEDERAL DE PERNAMBUCO, RECIFE, PE,
BRASIL.
[email protected]
APRESENTAÇÃO ORAL
AGRICULTURA, MEIO AMBIENTE E DESENVOLVIMENTO
SUSTENTÁVEL
Middlemen of non-timber forest products: supply and market inefficiencies
ABSTRACT:
Non-timber forest products have been advocated as important tools to make conservation of
tropical forest feasible. However, there are innumerous costs (including transaction costs)
associated with the commercialization of these products and therefore the supply of their
market is of great importance to the income of innumerous households in the region that rely
on this as their only source of cash income. The importance of transaction costs in economic
exchange is an ingoing debate between Neoclassical and New Institutional Economics. While
the former argues that most transactions are made in perfectly competitive scenarios, the
latter dismisses this simplification of reality and proposes the inclusion of less measurable
aspects influencing markets, such as the institutional environment, the characteristics of the
actual commercial relations, and attributes and property rights pertaining to the good being
exchanged. Despite the fact that these alternative hypotheses seem more realistic, the analysis
of such costs has been mainly motivated by qualitative analysis, due to the difficulties in
measuring such variables. This paper contributes to the literature by estimating transaction
costs for non-timber forest product trade in Belem, Brazil, using data on 123 interviews with
vendors along the supply chain. A transaction cost function is estimated and the results show
that risk and information asymmetries have significant impacts on these costs.
Keywords: transaction costs, Amazon, non-timber forest products
1. Introduction
Although the neoclassical perfect competition model is restrictive, few alternatives have been
proposed to develop a real world model encompassing more realistic assumptions of market
behavior. Some assumptions of the neoclassical model, such as the large number of economic
agents or the inexistence of risk, have been relaxed but many are still to be discussed and
formalized in different ways. New Institutional Economics (NIE) discusses the existence of
transactions costs in most economic exchanges, especially in developing markets and
proposes the relaxation of the neoclassical assumptions about their absence. Albeit clearly
realistic, the main problem with this framework is the lack of rigorous data analysis, mostly
due to the often unobservable nature of the factors affecting transaction costs.
In the NIE framework, the most important factors determining the behavior (and therefore
revenue, costs and consequently profits) are not the prices and quantities of inputs and
outputs but also the more subjective characteristics of the market and products such as asset
specificity, contract forms, trust, experience, and other not so clearly defined or measurable
concepts. This is perhaps the main reason for which NIE literature bases its analysis on
descriptions of markets and other qualitative analysis rather than econometrics. The objective
of this paper is to test some of the NIE assumptions using econometric techniques similar to
those used in neoclassical economics analysis. The rest of this paper is organized as follows:
section 2 goes over the basic assumptions of the Neoclassical and NIE, section 3 highlights
some of the NIE hypotheses to be tested and proposes a theoretical model, section 4 discusses
the empirical application of the model as well as the data used in this application, section 5
presents and discusses the results of the empirical model, while section 6 presents the
conclusions of the paper.
2. Neoclassical and New Institutional Economics
Neoclassical economics is based on a set of assumptions that simplifies the real world to one
that can be modeled. The model of perfect competition makes the following assumptions
about the world in which business is conducted: perfect information, large number of firms
and buyers, homogenous commodities, no barriers to entry or exit, no economies of scale or
production externalities, profit maximization and complete set of markets. Neoclassical
economists might agree that this set of assumptions is too restrictive if compared to what
actually happens, however, they either consider that these assumptions are actually realistic
enough to describe most of the interactions in the market or not too restrictive to describe all
interactions.
As for exchanges in the neoclassical model, since goods are homogeneous, exchanges are
based purely on the price being charged for different goods. These exchanges are all of the all
of the “spot market” type, where an anonymous buyer and seller come together,
simultaneously completing all aspects of the exchange (transfer of the good or service in
question, accompanying property rights and payment) with no outstanding obligations (such
as warranties or guarantees) (Dorward, Kydd, and Poulton, 1998). As a consequence, “the
assumptions of the neo-classical economic model suggest that all parties to an exchange
process have the necessary information to be able to make rational choices. In particular, this
implies that all parties to an exchange are able to process the exchange at zero cost” (Loader,
1997).
New Institutional Economics (NIE) came about as an alternative to this view of market
interactions. Nabli and Nugent (1989) classify the ideas of NIE into two main (but
interrelated) schools: the transaction cost school and the collective action school. The latter
explores the conditions under which economic agents will achieve and sustain successful
cooperation in either the economic or political spheres. This paper focuses on the Transaction
Cost school of NIE, which shares the assumptions of rational, maximizing, self-interest
economic agents with neoclassical economics. However it relaxes some of the more
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restrictive assumptions of neoclassical perfect competition to reflect more common, real
world situations (Dorward, Kydd, and Poulton, 1998).
In contrast to the assumptions of perfect competition, common elements seen as central to
NIE, are (Dorward, Kydd, and Poulton, 1998):
- There are substantial transaction costs involved in most forms of economic activity.
Therefore profit-maximizing economic agents need to minimize the sum of
transformation (production) and transaction costs. By contrast, neoclassical
economics concerns itself exclusively with transformation costs.
- The most important source of transaction costs is the need to acquire information in
order to do business. Information needs encompass not just facts concerning available
technology and prevailing prices, but also information on the reputations of other
people and organizations.
- Risk is unavoidable and needs appropriate strategies to be devised to cope with
unforeseen, or undesirable, events and outcomes.
- In addition to political, climatic and market factors, an important source of risk is the
unpredictable and/or opportunistic behavior of other economic agents.
- Economic agents establish institutions to reduce the uncertainty inherent in human
interaction (social, economic and political) and/or to overcome market failures caused
by the presence of risk and imperfect information.
Thus, entrepreneurs and consumers do not automatically share common knowledge, nor is
there a fictitious auctioneer who cares that both sides of the market become aware of each
other (Mantzavinos, 2001) and therefore there are transaction costs inherent in the exchange
process. Based on these modifications and complications in the model, New Institutional
Economics consider that for trade to take place, the partners to the exchange have to spend
resources in three broad areas of contractual activity (Dorward, Kydd, and Poulton, 1998):
- Measuring the valuable attributes of what is being exchanged: an important
characteristic of NIE is that goods and services are not homogeneous, but have a
variety of attributes. In agricultural marketing the most important attributes relate to
the quality of the product being exchanged. Often, one or both parties to an exchange
possess incomplete information concerning the attributes of the good in question.
- Protecting (and capturing) rights to the goods being exchanged.
- Policing and enforcing agreements.
The relative importance of the different costs associated with a given transaction depends on
the nature of the transaction. In this regard Williamson (1995) identifies three key
characteristics of transactions: the degree of uncertainty surrounding the transaction (risk),
the frequency of the transaction (number of crop sales, seasonality), and the extent to which
the transaction involves one or both of the contracting parties in investment in specific assets.
As such, the amount of transaction costs in the market challenges its efficiency, in the sense
that more effort and resources will be need to be used to overcome these costs, therefore
most of the assumptions of perfect competitions do not hold.
3. Hypothesis and theoretical model
Under the NIE set of alternative assumptions different hypotheses arise concerning factors
defining how markets work. We review the literature in NIE to identify these hypotheses and
the context in which they are proposed and analyzed.
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The basic hypothesis in the NIE transaction costs literature is that trade requires cooperation,
which is costly. A very important name in this literature is that of North, who rejects the
assumptions of perfect competition and instead looks at the “transaction costs” of exchange.
He considers that the cost of information is the key to the costs of transacting (North, 1990).
Also, North considers that in Neoclassical economics institutions are imbedded in the
assumption of zero transaction costs and this would be the reason why the model of perfect
competition says nothing about them. However, once you accept the existence of transaction
costs you must consider the institutional environment in which exchange takes place.
On the issue of the importance of trust, Adams and Goldsmith (1999) focus on the formation
of new business arrangements, such as trust based strategic fuzzy alliances (SFAs). They
discuss four elements which create trust: knowledge, risk, free will and predictability. With
perfect knowledge (complete rationality), trust is not necessary because all actions and
reactions are known with certainty. The paper uses a multinomial logit model to explain the
governance choice decision made by each firm. The study is based on a survey of 49
horticultural and pork processing firms and three governance choices are considered: tangible
asset-based alliances (physical assets are jointly held), intangible asset based alliances (no
physical assets are jointly held, but some other assets such as knowledge are shared) and
mixed alliances (a mixture of the two previous types). The authors conclude that SFA’s were
reserved for lower risk, higher knowledge, lower specificity settings and argue that under
high transaction risks situation firms would choose to integrate rather than attempt to invest
in a private contract. This goes against the conclusions reached by Ring and Van de Ven
(1992) who argued that because risk is high in these transactions (high asset specificity, high
uncertainty and a high level of recurrence) high levels of trust are not only sufficient, they are
also necessary. According to Chiles and McMackin (1996), not only do individuals search
for governance structures that reduce direct costs, but also methods for protecting against
opportunistic behavior from their bilateral trading partners. Thus trust has a direct role in cost
reduction because, in the limit, with complete trust there is no opportunism.
Overa (2006) analyzes the change that a greater access to cell phones has brought to informal
trader’s business practice by analyzing case studies drawn from 80 interviews. The
qualitative analysis of these interviews draws the conclusions that due to the broader access
to information, there was a reduction in transportation costs and transaction costs
(information asymmetries) through the adaptation of information technology and
enhancement of trust among economic agents. These results indicate that transaction costs are
influenced by an agents’ access to information.
Loader (1997) notes that in fresh product markets, the application of transaction cost analysis
is relatively rare, although there are some notable applications - usually to notions of
integration and coordination. Loader’s paper applies transaction costs economics to analyze
the supply chain for Egyptian potatoes in the UK. His paper discusses ways in which the
implications of transaction costs can be assessed in a qualitative way. The result is a simple
scheme of classification, based on relatively limited data, which can identify some of the key
features of the contractual arrangements at different stages of the system. He concludes that
“the potential for investigations to isolate key features of systems and offer measures for
remedying problems is considerable, and thus the measurement aspects questioned above
require further attention” (p. 33).
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From a purely theoretical discussion, Dorward et al (1998) propose that the analysis of “nonstandard” governance structures should define the attributes of the transactions in question in
terms of asset specificity, uncertainty and frequency, description of the incentive and adaptive
attributes of alternative governance structures, and also decisions on whether the observed
structures are functioning to reduce transaction costs (and therefore to promote trade) or are
primarily exploitative or otherwise inefficient. From these ideas they point out the following
hypotheses:
- Due to risk mitigating factors arising from bilateral contracts, prices observed in a bilateral
contract might be higher that those justified purely by production costs alone (i.e. higher than
hypothetical “perfectly competitive” prices).
- The more complex and impersonal are trading links, though, the higher transaction costs
will be.
- In general, the greater the degree of asset specificity the less likely it is that contracts will be
of the spot market type.
The estimation of transaction costs is difficult and three different approaches have been used
to measure the level and effects of transaction costs (Loader, 1997):
- Assessing the extent of market failure (and using it as a proxy for the level of implied
transaction costs in the system).
- Wallis and North (1986) suggest that productive activities can be divided into transaction
and transformation functions, and therefore they divide different employee categories
within an organization identified with different functions. Then they argue that the costs
associated with employees that deal with transactions are costs not directly associated
with production, and can therefore be used as an estimate of transaction costs.
- Frank and Henderson (1992), assess whether or not the organizational relations in the
system fulfill the attributes of transactions predicted by transaction cost reasoning. They
use uncertainty (measured by variance of sales over time), concentration (measured by
concentration ratios), idiosyncratic investments (measured with advertising/sales ratios
and R&D expenditure), and costs of co-ordination (measured by specialisation and
capital/sales ratios). Although empirically ’successful’, Frank and Henderson
acknowledge continuing problems with transaction cost measurement.
Based on the framework set in section 2 and the above discussed literature we propose the
following theoretical model of a transaction cost function, which includes not only prices and
quantities, as suggested by the neoclassical model, but other less tangible factors as well:
[1] TC = f(p, q, w, x, product characteristics, firm characteristics, commercial characteristics)
where TC are the transactions costs; p is the output price; w are the input prices; q is the
quantity sold; x is the quantity of inputs used (which can be capital, labor, energy, materials,
and services); product characteristics encompass variables such as asset specificity,
seasonality, storage time, form of product sold (in natura, oil, peeled nuts) and should be
considered when estimating a multiproduct function; firm characteristics include years of
experience, number of products sold, number of suppliers, processing, scale of operations;
commercial characteristics are also firm characteristics but refer specifically to know the firm
deals with market uncertainties such as risk, vertical integration, scale of operations and
forms of commercial contracts).
This cost function can be estimated for any economic agent in the supply chain, which in the
neoclassical literature would be referred to as producers and consumers. However, the author
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of the present paper has admittedly never seen a cost function (nor, for that matter, a share or
production function) which has been estimated for middlemen in a given economy. As
discussed in the previous section, this would be due to the fact that in the neoclassical
economics framework there are no transaction costs and therefore middlemen are inexistent,
unless they provide some further processing or value adding to the product. However,
middlemen in an economy can be viewed as absorbing most of the information and risk costs
associated with the economic activity.
4. Empirical model
The data for the empirical model comes from 123 interviews with vendors of non-timber
forest products (NTFPs) in the city of Belem, in the Brazilian Amazon, carried out from
January through April 2006. This represents 17.3% of all vendors of these products in the
city, and data was collected using a random sample of markets and vendors. The respondents
are located in the same city but in different markets, varying from the ports (where most of
the produce arrives from rural areas in the islands surrounding the city) (28.6% of the
interviews), to the Ver o Peso (the largest market of NTFPs) (25.2% of interviews), to
neighborhood markets (32.7% of interviews), and finally grocery stores and specialty stores
(13.6% of interviews). These interviewers are basically middlemen who do not process the
product in any way since 99.3% of the interviewers said they buy the product in the same
form as it is sold. This is a very interesting aspect of the supply chain of NTFPs since there
are virtually no costs associated to the activity besides the cost of buying the input and fixed
costs associated with the stall owner’s labor and stall or store maintenance costs. A
consequence of this specific characteristic is that this simplifies the model used since we
consider output quantity to be the same as input quantity, and are both called “product”
throughout the text.
The NTFPs considered in this survey are brazil nuts (Bertholletia excelsa, nuts sold either
shelled or not - 14.3% of interviews), Andiroba (Carapa guianensis, a seed used for
medicinal purposes – 13.6% of interviews), Copaiba (Copaifera sp. an oleoresin used for
medicinal purposes – 9.5% of interviews), Açaí (Euterpe oleracea, a palm berry usually
consumed as pulp together with manioc flour and an important staple in the rural areas of the
Amazon – 20.4% of interviews), Piquia (Caryocar villosum, a fruit consumed cooked but
sold fresh – 19% of interviews) and Uxi (Endopleura uchi, a fruit consumed fresh – 23.1% of
interviews). The first 3 products can be stored for up to a year, while the last 3 are highly
perishable, having a shelf life less than a week when fresh. There are also some interviews
that refer to dried piquia as tea and these observations were included in the non-perishable
group. For the purpose of the transaction cost function estimation 3 different regressions were
considered: 1. the data for all products was pooled together and dummies were included in
the model to account for specificities due to the product; 2. data was divided into perishable
and non-perishable products and a separate regression was run for each sub-group.
The variables considered in our analysis were chosen to reflect those in equation [1] and also
some specific characteristics of agricultural products that influence either the level of
uncertainty or the asset specificity involved in production, processing and handling.
According to Dorward et al (1998) these variables include perishability, quality standards
required for the raw material or commodity, seasonal variability of raw material supply
technical sophistication and equipment specialization in post-harvest activities and/or in
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processing, and level of fixed costs and scope for economies of scale in post-harvest activities
and/or in processing. The variables I considered in the analysis (and the reasons for including
them) are:
- q is the amount of product sold per month; this variable is included in the model to
indicate scale operations for the one product the vendor was interviewed on.
- nacq is the number of product acquisitions from suppliers per month. Number of
acquisitions or number of marketings is used in the literature to show relation to risk since
the larger the number of transactions the lower the variation in price is expected to be,
however, the tradeoff comes in the form of transaction costs involved in each exchange
(Goodwin and Kastens, 1996).
- varx is the variation in input price (difference between maximum and minimum price
paid to supplier for product in the last year). Variation in price paid to suppliers has many
reasons to exist: seasonality of the products, differences in quality of product and also
differences in contracts among sellers and buyers (as discussed before, prices of trust
based agreements may be higher than in a competitive market).
- pack is a dummy referring to vendors that sell product with no package. This variable is
included since this would mean greater costs to vendors but also perception of a more
sophisticated product by the consumer which would change her willingness to pay.
- storage refers to the maximum number of days each vendor can store the product. This
variable is included since a longer storage time would mean that the vendor has more
time to sell the product and therefore less risk associated with loss.
- buysale is a dummy of whether the vendor sells the product in the same form as it was
bought. If so then there is no processing involved and in a spot market we’d expect the
price difference between the acquisition and the sale to be equal to the marginal cost.
- sup is the number of suppliers the vendor has for that specific product. A larger number
of suppliers might indicate that the vendor has more options of whom to buy from, but
also that there is less trust in these interactions.
- prod is a dummy with 1 if the vendor buys the product directly from the producer. If a
vendor buys the product straight from the producer the cost associated with the product is
expected to be lower since the producer generally does not have the same information as
the vendor.
- bplace is a dummy if the vendor buys the product at same location where he sells. If the
vendor buys the product at the same location where he sells it there are no transportation
costs associated with it and therefore transaction costs (as defined in the function to be
estimated) are expected to be higher to account for these additional costs.
- exp is the number of years of experience the vendor has selling these products.
Experience is considered in our model to account for human capital, since the more years
of experience a given vendor has the more he’d learn about how the market works.
- imp is a rating from 0 to 10 of how important the product is for the vendor in terms of
revenue. Importance in revenue is a perceived measure on how the vendor sees the
product as related to his income. If the product is very important to the vendor we’d
expect a more risk averse behavior.
- natura is a dummy referring to a product being sold in natura (not processed). This
variable is included to measure the effect of processing on value of a product.
- trust is a dummy variable that refers to vendors that stated the main reason for choosing a
supplier as trust. This is a motif that appears repeatedly in NIE literature, such as in
Fafchamps and Minten (2002): “trust enables agents to place and take orders, pay by
check, use invoicing, provide trade credit, and offer warranty… which are often
dramatically absent from liberalized markets in poor countries”.
7
-
price is a dummy that refers to vendors that choose suppliers based solely on price, which
would be the case in the perfect competition model.
cons is a dummy of vendors selling to final consumers. If a vendor sells to the final
consumer we’d expect the transaction costs to be higher, due to information asymmetries.
transa is a proxy for the distance of where the product was bought based on the number
of transactions the product might have gone through (if from port =1, if from Ver-o-Peso
=2, if from other markets = 3). As the number of transactions increases we’d expect the
transaction costs to increase.
The variables used and their descriptive statistics are given in table 1 (see appendix 1).
Seasonality was not included since all the products considered in this analysis have roughly
the same 3 months season. In the empirical analysis we first estimate the correlation matrix
between several variables that are suggested in both the Neoclassical and NIE literature. This
correlation matrix is presented in table 2 (see appendix 1). From this table we can that, for
example, as experience increases vendors tend to specialize in one product (as shown by the
higher importance of the product in their revenue), increase purchases from producers and
buy at the same location as they sell it. Another interesting set of correlations pertains to the
number of suppliers: the higher the importance of the product to a vendor’s revenue the
higher the number of suppliers. There is also a positive correlation for selling unprocessed
and unpackaged products and having more suppliers, maybe due to the fact that there are
more suppliers for these products. Also, larger scale operations usually deal with unprocessed
products, usually store the product less time, buy from producers and have more suppliers,
but do not buy at the same location as the product is sold.
Following we estimate the transaction cost function in [1] using proxies for some
unobservable characteristics. Also, the dependent variable, called transaction cost throughout
the text, was calculated from the data as being the difference between the input price and the
sale price. Considering that 99.2% sell the product just as bought, 70.7% don’t have any
packaging for their product, and 32.5% even buy the product in the same market (or place) as
they sell the product it seems a reasonable assumption to consider this difference as a
measure of transaction costs per se. This is a unique characteristic of the studied market,
since usually we would expect middlemen to have some value aggregation or otherwise
perceptible impact on the product, but this comes to show the importance of transaction costs
in developing markets, such as this one. In the estimated functions I include variables such as
packaging, place of acquisition and processing to verify if there is any significant impact of
these characteristics on the transaction costs. Other costs associated to the vendor’s labor and
store/stall are not included due to the assumption that these costs are fixed, since the vendor
always sells more than one product and therefore these costs are shared among all sales.
The estimated transaction cost function is shown below:
[2]
log(TCi) = f(qi, nacqi, varxi, packi, storagei, buysalei, supi, prodi, bplacei, expi, impi,
naturai, dpayi, revi, trusti, pricei, consi, transai)
The variables have been described above and the index i refers to the function estimated,
either the pooled data, data on perishable products or non-perishable products.
The results for the regressions are given in table 3 (see appendix 1). The three regressions
present significant F statistics (P<0.0001 for the first two and P=0.02 for the non-perishable
data) and high R2 and adjusted R2 (respectively 73.6% and 68.1% for the pooled data, 81.8%
and 76.1% for the perishable data, and 57.5% and 32.6% for the non-perishable data). The
8
significant variables vary from one regression to the other, but they have the same sign in all
regressions which indicates some robustness across different types of products. The
differences between the perishable and non-perishable regression estimates can be explained
by the fact that specific characteristics to each set of products are being identified. As I’ve
discussed above, the main difference between the perishable and non-perishable products is
the possibility of storing the product for a longer time. However, storage time was not
significant in any of our regressions. The variables referring to unprocessed products and the
case when vendors sell the product in the same form as bought were not significant in any of
the regressions, despite the indications that they would serve as proxies to other costs not
included in the model that could influence our measure of transaction costs. The product’s
importance in the vendor’s revenue, number of suppliers, price as determining the choice of
suppliers, and the number of product purchases from suppliers in a month were also not
significant in any regressions even though they have been discussed in literature as having
important influence on affecting transactions.
The regression on perishable products has the greatest number of significant variables. In this
regression the absence of packaging increases the transaction costs. This might seem
unexpected however, considering that the packaging in most of the products is very rustic, the
sign on the coefficient might be offsetting some of the effect of scale since there is a positive
correlation among absence of packaging and quantity. As predicted by our model, when the
vendor buys the product from producers transaction costs are higher, in the sense that the
vendor receives a product at a lower price but sells it at the market price. If the vendor buys
the product at the same location where he sells it the variation in prices are higher. This is an
unexpected result, but comes to show that transportation cost is not a significant cost in the
process. The larger the scale of operations the lower the price variation between the purchase
and sale of the product, which indicates that larger operations are more efficient when it
comes to minimizing transaction costs. This is also true for experience, as traders become
more experienced they tend to reduce transaction costs, making exchanges more efficient.
Another interesting result is the realization that risk management is indeed an important
factor influencing transaction costs. This can be assessed by analyzing the coefficient on the
variable referring to the price variation (maximum price minus minimum) paid to suppliers;
as expected a greater input price variation increases transaction costs. Finally, açaí is the
product with higher value in this data, while uxi is the least valuable; this is picked up by
their respective dummy variables.
In the regression with the non-perishable products there are less significant variables (only
two). In this case the products considered have a longer shelf life and therefore the risk
involved in this activity is lower, which might explain the reason that most variables relating
to risk were not significant. However it is interesting to note that trust is important in these
commercial relations, and an explanation for this would be the fact that the quality of these
products is not as straight forward to assess as in the case of perishable fruit. For example,
you cannot identify the quality of brazil nuts with their shell on, and the quality of the oils is
perceived over time on whether it curdles (bad quality) or not.
From the results in table 3 we can see that the market strives for efficiency, as indicated by
the significant coefficient on experience and scale. However, due to asymmetric information
and risk this is not always possible and thus other factors come into play to explain vendor’s
behaviour such as trust in the case of non-perishable products or risk minimization in the case
of perishable products.
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5. Conclusions
The importance of transaction costs in economic exchange is an ingoing debate between
Neoclassical and New Institutional Economics. While the former argues that most
transactions are made in perfectly competitive scenarios the latter dismisses this
simplification of reality and proposes the inclusion of less measurable aspects influencing
markets, such as the institutional environment, the characteristics of the actual commercial
relations, and attributes and property rights pertaining to the good being exchanged. Despite
the fact that these alternative hypotheses seem more realistic, the analysis of such costs has
been mainly motivated by qualitative analysis, due to the difficulties in measuring such
variables.
In this paper I estimated a transaction cost function which included variables suggested both
by neoclassical and NIE models. The analysis of the coefficient estimates shows that
quantity, along with other variables such as experience, trust and price variation (measures of
social capital, information asymmetries, and risk) play a significant role in explaining
transaction costs in the market for NTFPs in Belem. This shows that middlemen play an
important role in developing markets by absorbing most of the information and risk
associated to this economic activity. The policy implications of such results are particularly
important for the design of institutions that support markets (Fafchamps and Minten, 2002).
Thus, by understanding the constraints for efficiency in a market, policy makers can
influence the outcomes and therefore achieve more competitive scenarios. “The challenge of
economic development is therefore to reduce the transaction costs of increasingly complex
forms of trade. This is achieved through the development of institutions that support trade,
making information available, protecting property rights and providing effective mechanisms
for enforcing agreements” (Dorward et al, 1998).
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10
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Changing Trade Practices in Ghana. World Development 34(7) p. 1301-1315.
Ring, P.S., Van de Ven, A.H., 1992. Structuring Cooperative Relationships Between
Organizations. Strategic Management Journal 13: 483-498.
Wallis, J.J. and North, D.C. 1986. Measuring the Transaction Sector in the American
Economy, 1870-1970, in Engerman, S.L. and R.E. Gallman (eds), Long Term Factors
in Economic Growth, University of Chicago Press.
Williamson, O.E. 1995. The Institutions of Governance of Economic Development and
Reform. Proceedings of the World Bank Conference on Development Economics 1994.
World Bank, Washington DC.
11
APPENDIX 1: Tables
12
Description
to castanha
to andiroba
to copaiba
to piquia
to uxi
to acai
If questionnaire refers
If questionnaire refers
If questionnaire refers
If questionnaire refers
If questionnaire refers
If questionnaire refers
Years of experience
Think market for NRFPs will increase in future
Think market for NRFPs will decrease in future
Importance of product in vendor's revenue
Sells unprocessed product (i.e. in natura )
Sells the product as bought
Has no packaging for product
Days the product is stored
Buys the product in the same place as sold
Buys product from producer
Number of suppliers
Chooses suppliers based on trust
Chooses suppliers based on quality
Chooses suppliers based on price
Sells to final consumers
Quantity sold per week
Units in average sale the vendor makes
Units in average acquisition the vendor makes
Number of acquisitions per month
Maximum price paid for supplier
Minimum price paid for supplier
Selling price
Consumer buys substitute when product is not available
Number of transactions up to this vendor
Pooled
Mean
Variable
brazilnut
0.163
andiroba
0.114
copaiba
0.081
piquia
0.220
uxi
0.252
acai
0.171
exp
17.984
inc
0.626
dec
0.203
imp
7.358
natura
0.748
buysale
0.992
pack
0.707
storage
67.455
bplace
0.325
bproducer
0.431
sup
6.313
trust
0.358
quality
0.439
price
0.187
cons
0.846
q
848.54
qav
12.911
sizeq
253.970
nacq
9.304
maxw
8.034
minw
4.998
outputp
11.879
subst
0.418
transa
1.959
n=123 Perishable
n=76
N
Std Dev
Mean
Std Dev
0.371
0
0
0.319
0
0
0.274
0
0
0.416
0.316
0.468
0.436
0.408
0.495
0.378
0.276
0.450
12.692
17.987
12.098
0.486
0.618
0.489
0.404
0.237
0.428
2.743
7.382
2.884
0.436
0.961
0.196
0.090
0.987
0.115
0.457
0.921
0.271
185.514
30.421
154.797
0.470
0.171
0.379
0.497
0.513
0.503
9.241
6.711
7.798
0.481
0.408
0.495
0.498
0.408
0.495
0.391
0.171
0.379
0.363
0.750
0.436
1487.60 1204.31 1418.06
46.637
20.320
58.380
393.297 319.388 346.122
10.830
13.467
11.851
12.135
6.294
12.925
6.687
3.159
5.472
21.847
6.764
12.788
0.495
0.560
0.500
0.413
1.895
0.419
Table 1: Descriptive statistics.
variable
exp
inc
imp
natura
buysale
pack
storage
bproducer
bplace
transa
sup
trust
quality
price
cons
q
nacq
pvar
exp
1
inc
imp
natura
0.020 0.228*** -0.005
1
0.329*** -0.062
1
0.007
1
buysale pack
storage bproducer bplace
transa
sup
trust
quality
price
-0.022 0.099
0.043
0.373*** 0.164** 0.158**
0.048
0.025
-0.020
-0.035
-0.070 -0.054
0.155
0.265***
-0.073
-0.036
0.120 0.191*** -0.163**
0.069
-0.088 0.084
-0.032
0.265*** -0.281*** 0.100 0.325*** 0.231*** -0.104
-0.040
0.156** 0.820*** -0.230***
0.051
-0.396*** -0.148* 0.202*** 0.082 -0.203*** 0.038
1
-0.058
0.033
-0.104
0.063
-0.009
0.013
0.068
-0.102
0.043
1
-0.191***
0.127
-0.316*** -0.064 0.174** -0.005 -0.151** -0.012
1
0.057
0.363*** 0.213*** -0.132* -0.002
-0.037
-0.117
1
-0.008
-0.074
0.046
0.138*
-0.075
-0.080
1
0.406*** -0.246*** -0.047
0.015
-0.155**
1
-0.029
0.074
-0.112
-0.054
1
-0.059
0.104
-0.032
1
-0.524*** -0.314***
1
-0.046
1
cons
-0.073
-0.098
-0.298***
-0.248***
-0.039
-0.275***
0.155**
-0.264***
0.297***
0.286***
-0.127
-0.103
0.061
0.205***
1
q
0.0
0.17
0.1
0.32
0.0
0.31
-0.19
0.21
-0.31
-0.0
0.17
0.0
-0.1
0.22
-0.1
1
*** - significant at 5% significance lev
Table 2: Correlation matrix.
13
Pooled data
Variable
Parameter
Intercept
1.510
exp
-1.846E-01**
imp
-3.92E-02
natura
-0.795
buysale
1.565
pack
0.573
storage
0.000
bproducer
0.173
bplace
0.324
transa
-0.380
sup
-6.58E-03
trust
0.109
price
-0.469
cons
0.537
q
-1.99E-04***
nacq
1.14E-02
varx
3.81E-02***
acai
-0.271
brazil nuts
-1.077*
andiroba
-0.592
uxi
-4.065***
piquia
-3.377***
tea
Perishable
Standard
Error
1.880
(0.087)
1.07E-02
6.24E-02
0.691
1.541
0.517
7.15E-04
0.286
0.361
0.350
1.44E-02
0.289
0.351
0.568
(0.036)
9.32E-05
1.77E-02
(0.028)
1.71E-02
0.896
(0.139)
0.722
0.545
(<0.0001) 0.763
(<0.0001) 0.776
Estimate
Nonperishable
Standard
Standard
Parameter
Estimate
Error
Parameter Estimate Error
-3.199*** (0.048)
1.587
2.887*** (0.043)
1.367
-6.39E-03
1.17E-02 -3.07E-02* (0.138) 2.02E-02
-5.15E-02
6.99E-02
2.22E-02
0.117
0.926
0.942
-0.472
1.266
0.531
1.410
1.052** (0.051)
0.529
-0.657
1.085
1.17E-03
9.89E-04
3.09E-04
1.07E-03
0.682*** (0.037)
0.320
-0.497
0.595
0.889** (0.100)
0.532
-0.125
0.512
-0.581
0.413
-9.08E-02
0.664
2.72E-03
1.84E-02 -2.08E-02
2.14E-02
-0.372
0.332
0.963** (0.083)
0.536
-0.471
0.419
-0.316
0.662
0.855** (0.091)
0.498
-3.17E-04*** (0.004)
1.06E-04 -7.24E-05
1.54E-04
1.95E-02
1.60E-02
6.19E-02
9.77E-02
4.82E-02*** (0.005)
1.63E-02
1.63E-02
4.12E-02
3.097*** (<0.0001) 0.617
-0.611
0.894
-0.255
0.598
-0.626** (0.045)
0.305
-2.044** (0.099)
1.199
Table 3: OLS results.
14